2023
- Opt Express 31(6): 10918-10935, 2023.
Authors:
Stockhausen A, Rodriguez-Gatica JE, Schweihoff J, Schwarz MK and Kubitscheck U
ABSTRACT:
Common light sheet microscopy comes with a trade-off between light sheet width defining the optical sectioning and the usable field of view arising from the divergence of the illuminating Gaussian beam. To overcome this, low-diverging Airy beams have been introduced. Airy beams, however, exhibit side lobes degrading image contrast. Here, we constructed an Airy beam light sheet microscope, and developed a deep learning image deconvolution to remove the effects of the side lobes without knowledge of the point spread function. Using a generative adversarial network and high-quality training data, we significantly enhanced image contrast and improved the performance of a bicubic upscaling. We evaluated the performance with fluorescently labeled neurons in mouse brain tissue samples. We found that deep learning-based deconvolution was about 20-fold faster than the standard approach. The combination of Airy beam light sheet microscopy and deep learning deconvolution allows imaging large volumes rapidly and with high quality.
Link
- Cerebral Cortex 33:16 9439–9449, 2023.
Authors:
Morales-Gregorio A, van Meegen A, van Albada SJ
ABSTRACT:
Numbers of neurons and their spatial variation are fundamental organizational features of the brain. Despite the large corpus of cytoarchitectonic data available in the literature, the statistical distributions of neuron densities within and across brain areas remain largely uncharacterized. Here, we show that neuron densities are compatible with a lognormal distribution across cortical areas in several mammalian species, and find that this also holds true within cortical areas. A minimal model of noisy cell division, in combination with distributed proliferation times, can account for the coexistence of lognormal distributions within and across cortical areas. Our findings uncover a new organizational principle of cortical cytoarchitecture: the ubiquitous lognormal distribution of neuron densities, which adds to a long list of lognormal variables in the brain.
Link
- Molecular and Cellular Neuroscience, 2023.
Authors:
Anne-Sophie Hafner, Jochen Triesch
ABSTRACT:
High turnover rates of synaptic proteins imply that synapses constantly need to replace their constituent building blocks. This requires sophisticated supply chains and potentially exposes synapses to shortages as they compete for limited resources. Interestingly, competition in neurons has been observed at different scales. Whether it is competition of receptors for binding sites inside a single synapse or synapses fighting for resources to grow. Here we review the implications of such competition for synaptic function and plasticity. We identify multiple mechanisms that synapses use to safeguard themselves against supply shortages and identify a fundamental neurologistic trade-off governing the sizes of reserve pools of essential synaptic building blocks.
Link
- IEEE Transactions on Visualization and Computer Graphics, 2023.
Authors:
Sumit Kumar Vohra, Philipp Harth, Yasuko Isoe, Armin Bahl, Haleh Fotowat, Florian Engert, Hans-Christian Hege, Daniel Baum
ABSTRACT:
One of the fundamental problems in neurobiological research is to understand how neural circuits generate behaviors in response to sensory stimuli. Elucidating such neural circuits requires anatomical and functional information about the neurons that are active during the processing of the sensory information and generation of the respective response, as well as an identification of the connections between these neurons. With modern imaging techniques, both morphological properties of individual neurons as well as functional information related to sensory processing, information integration and behavior can be obtained. Given the resulting information, neurobiologists are faced with the task of identifying the anatomical structures down to individual neurons that are linked to the studied behavior and the processing of the respective sensory stimuli. Here, we present a novel interactive tool that assists neurobiologists in the aforementioned task by allowing them to extract hypothetical neural circuits constrained by anatomical and functional data. Our approach is based on two types of structural data: brain regions that are anatomically or functionally defined, and morphologies of individual neurons. Both types of structural data are interlinked and augmented with additional information. The presented tool allows the expert user to identify neurons using Boolean queries. The interactive formulation of these queries is supported by linked views, using, among other things, two novel 2D abstractions of neural circuits. The approach was validated in two case studies investigating the neural basis of vision-based behavioral responses in zebrafish larvae. Despite this particular application, we believe that the presented tool will be of general interest for exploring hypotheses about neural circuits in other species, genera and taxa.
Link
2022
- Magnetic Resonance in Medicine Volume 89, Issue 2, 2022
Authors:
Jan Malte Oeschger, Karsten Tabelow, Siawoosh Mohammadi
ABSTRACT:
Purpose
To compare the estimation accuracy of axisymmetric diffusion kurtosis imaging (DKI) and standard DKI in combination with Rician bias correction (RBC).
Methods
Axisymmetric DKI is more robust against noise-induced variation in the measured signal than standard DKI because of its reduced parameter space. However, its susceptibility to Rician noise bias at low signal-to-noise ratios (SNR) is unknown. Here, we investigate two main questions: first, does RBC improve estimation accuracy of axisymmetric DKI; second, is estimation accuracy of axisymmetric DKI increased compared to standard DKI? Estimation accuracy was investigated on the five axisymmetric DKI tensor metrics (AxTM): the parallel and perpendicular diffusivity and kurtosis and mean of the kurtosis tensor, using a noise simulation study based on synthetic data of tissues with varying fiber alignment and in-vivo data focusing on white matter.
Results
RBC mainly increased accuracy for the parallel AxTM in tissues with highly to moderately aligned fibers. For the perpendicular AxTM, axisymmetric DKI without RBC performed slightly better than with RBC. However, the combination of axisymmetric DKI with RBC was the overall best performing algorithm across all five AxTM in white matter and axisymmetric DKI itself substantially improved accuracy in axisymmetric tissues with low fiber alignment.
Conclusion
Combining axisymmetric DKI with RBC facilitates accurate DKI parameter estimation at unprecedented low SNRs in white matter, possibly making it a valuable tool for neuroscience and clinical research studies where scan time is a limited resource. The tools used here are available in the open-source ACID toolbox for SPM.
Link
- NeuroImage Volume 262, 2022, 119529
Authors:
Siawoosh Mohammadi, Tobias Streubel, Leonie Klock, Luke J. Edwards, Antoine Lutti, Kerrin J. Pine, Sandra Weber, Patrick Scheibe, Gabriel Ziegler, Jürgen Gallinat, Simone Kühn, Martina F. Callaghan, Nikolaus Weiskopf, Karsten Tabelow
ABSTRACT:
Multi-Parameter Mapping (MPM) is a comprehensive quantitative neuroimaging protocol that enables estimation of four physical parameters (longitudinal and effective transverse relaxation rates R1 and R2*, proton density PD, and magnetization transfer saturation MTsat) that are sensitive to microstructural tissue properties such as iron and myelin content. Their capability to reveal microstructural brain differences, however, is tightly bound to controlling random noise and artefacts (e.g. caused by head motion) in the signal. Here, we introduced a method to estimate the local error of PD, R1, and MTsat maps that captures both noise and artefacts on a routine basis without requiring additional data. To investigate the method’s sensitivity to random noise, we calculated the model-based signal-to-noise ratio (mSNR) and showed in measurements and simulations that it correlated linearly with an experimental raw-image-based SNR map. We found that the mSNR varied with MPM protocols, magnetic field strength (3T vs. 7T) and MPM parameters: it halved from PD to R1 and decreased from PD to MTsat by a factor of 3-4. Exploring the artefact-sensitivity of the error maps, we generated robust MPM parameters using two successive acquisitions of each contrast and the acquisition-specific errors to down-weight erroneous regions. The resulting robust MPM parameters showed reduced variability at the group level as compared to their single-repeat or averaged counterparts. The error and mSNR maps may better inform power-calculations by accounting for local data quality variations across measurements. Code to compute the mSNR maps and robustly combined MPM maps is available in the open-source hMRI toolbox.
Link
Towards a representative reference for MRI-based human axon radius assessment using light microscopy
NeuroImage Volume 249, 2022, 118906Authors:
Laurin Mordhorst, Maria Morozova, Sebastian Papazoglou, Björn Fricke, Jan Malte Oeschger, Thibault Tabarin, Henriette Rusch, Carsten Jäger, Stefan Geyer, Nikolaus Weiskopf, Markus Morawski, Siawoosh Mohammadi
ABSTRACT:
Non-invasive assessment of axon radii via MRI bears great potential for clinical and neuroscience research as it is a main determinant of the neuronal conduction velocity. However, there is a lack of representative histological reference data at the scale of the cross-section of MRI voxels for validating the MRI-visible, effective radius (𝑟eff).
Because the current gold standard stems from neuroanatomical studies designed to estimate the bulk-determined arithmetic mean radius (𝑟arith) on small ensembles of axons, it is unsuited to estimate the tail-weighted 𝑟eff. We propose CNN-based segmentation on high-resolution, large-scale light microscopy (lsLM) data to generate a representative reference for 𝑟eff. In a human corpus callosum, we assessed estimation accuracy and bias of 𝑟arith and 𝑟eff. Furthermore, we investigated whether mapping anatomy-related variation of 𝑟arith and 𝑟eff is confounded by low-frequency variation of the image intensity, e.g., due to staining heterogeneity. Finally, we analyzed the error due to outstandingly large axons in 𝑟eff. Compared to 𝑟arith, 𝑟eff was estimated with higher accuracy (maximum normalized-root-mean-square-error of 𝑟eff: 8.5 %; 𝑟arith: 19.5 %) and lower bias (maximum absolute normalized-mean-bias-error of 𝑟eff: 4.8 %; 𝑟arith: 13.4 %). While 𝑟arith was confounded by variation of the image intensity, variation of 𝑟eff seemed anatomy-related. The largest axons contributed between 0.8 % and 2.9 % to 𝑟eff. In conclusion, the proposed method is a step towards representatively estimating 𝑟eff at MRI voxel resolution. Further investigations are required to assess generalization to other brains and brain areas with different axon radii distributions.
Link
- Elife 11:e79303, 2022
Authors:
Ian A. Clark, Siawoosh Mohammadi, Martina F. Callaghan, Eleanor A. Maguire
ABSTRACT:
Conduction velocity is the speed at which electrical signals travel along axons and is a crucial determinant of neural communication. Inferences about conduction velocity can now be made in vivo in humans using a measure called the magnetic resonance (MR) g-ratio. This is the ratio of the inner axon diameter relative to that of the axon plus the myelin sheath that encases it. Here, in the first application to cognition, we found that variations in MR g-ratio, and by inference conduction velocity, of the parahippocampal cingulum bundle were associated with autobiographical memory recall ability in 217 healthy adults. This tract connects the hippocampus with a range of other brain areas. We further observed that the association seemed to be with inner axon diameter rather than myelin content. The extent to which neurites were coherently organised within the parahippocampal cingulum bundle was also linked with autobiographical memory recall ability. Moreover, these findings were specific to autobiographical memory recall and were not apparent for laboratory-based memory tests. Our results offer a new perspective on individual differences in autobiographical memory recall ability, highlighting the possible influence of specific white matter microstructure features on conduction velocity when recalling detailed memories of real-life past experiences.
Link
- Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM), 2022.
Authors:
Philipp Harth, Sumit Vohra, Daniel Udvary, Marcel Oberlaender, Hans-Christian Hege, Daniel Baum
ABSTRACT:
The analysis of brain networks is central to neurobiological research. In this context the following tasks often arise: (1) understand the cellular composition of a reconstructed neural tissue volume to determine the nodes of the brain network; (2) quantify connectivity features statistically; and (3) compare these to predictions of mathematical models. We present a framework for interactive, visually supported accomplishment of these tasks. Its central component, the stratification matrix viewer, allows users to visualize the distribution of cellular and/or connectional properties of neurons at different levels of aggregation. We demonstrate its use in four case studies analyzing neural network data from the rat barrel cortex and human temporal cortex.
Link
- Cell Reports, 39(2), 2022.
Authors:
Daniel Udvary, Philipp Harth, Jakob H. Macke, Hans-Christian Hege, Christiaan P. J. de Kock, Bert Sakmann, Marcel Oberlaender.
ABSTRACT:
The neurons in the cerebral cortex are not randomly interconnected. This specificity in wiring can result from synapse formation mechanisms that connect neurons, depending on their electrical activity and genetically defined identity. Here, we report that the morphological properties of the neurons provide an additional prominent source by which wiring specificity emerges in cortical networks. This morphologically determined wiring specificity reflects similarities between the neurons’ axo-dendritic projections patterns, the packing density, and the cellular diversity of the neuropil. The higher these three factors are, the more recurrent is the topology of the network. Conversely, the lower these factors are, the more feedforward is the network’s topology. These principles predict the empirically observed occurrences of clusters of synapses, cell type-specific connectivity patterns, and nonrandom network motifs. Thus, we demonstrate that wiring specificity emerges in the cerebral cortex at subcellular, cellular, and network scales from the specific morphological properties of its neuronal constituents.
Link
- J Physiol. 2022
Authors:
Bernáez Timón, L.; Ekelmans, P.; Kraynyukova, N.; Rose, T.; Busse, L.; Tchumatchenko, T.
ABSTRACT:
Due to the staggering complexity of the brain and its neural circuitry, neuroscientists rely on the analysis of mathematical models to elucidate its function. From Hodgkin and Huxley’s detailed description of the action potential in 1952 to today, new theories and increasing computational power have opened up novel avenues to study how neural circuits implement the computations that underlie behaviour. Computational neuroscientists have developed many models of neural circuits that differ in complexity, biological realism or emergent network properties. With recent advances in experimental techniques for detailed anatomical reconstructions or large-scale activity recordings, rich biological data have become more available. The challenge when building network models is to reflect experimental results, either through a high level of detail or by finding an appropriate level of abstraction. Meanwhile, machine learning has facilitated the development of artificial neural networks, which are trained to perform specific tasks. While they have proven successful at achieving task-oriented behaviour, they are often abstract constructs that differ in many features from the physiology of brain circuits. Thus, it is unclear whether the mechanisms underlying computation in biological circuits can be investigated by analysing artificial networks that accomplish the same function but differ in their mechanisms. Here, we argue that building biologically realistic network models is crucial to establishing causal relationships between neurons, synapses, circuits and behaviour. More specifically, we advocate for network models that consider the connectivity structure and the recorded activity dynamics while evaluating task performance.
Link
- Proc. Natl. Acad. Sci. U.S.A. 119, e2207032119
Authors:
Kraynyukova, N.; Renner, S.; Born, G.; Bauer, Y.; Spacek, M.A.; Tushev, G.; Busse, L., Tchumatchenko, T
ABSTRACT:
The brain’s connectome provides the scaffold for canonical neural computations. However, a comparison of connectivity studies in the mouse primary visual cortex (V1) reveals that the average number and strength of connections between specific neuron types can vary. Can variability in V1 connectivity measurements coexist with canonical neural computations? We developed a theory-driven approach to deduce V1 network connectivity from visual responses in mouse V1 and visual thalamus (dLGN). Our method revealed that the same recorded visual responses were captured by multiple connectivity configurations. Remarkably, the magnitude and selectivity of connectivity weights followed a specific order across most of the inferred connectivity configurations. We argue that this order stems from the specific shapes of the recorded contrast response functions and contrast invariance of orientation tuning. Remarkably, despite variability across connectivity studies, connectivity weights computed from individual published connectivity reports followed the order we identified with our method, suggesting that the relations between the weights, rather than their magnitudes, represent a connectivity motif supporting canonical V1 computations.
Link
- eNeuro 1 February 2022, 9 (1)
Authors:
Good, T; Schirner, M; Shen, K; Ritter, P; Mukherjee, P; Levine, B; McIntosh, AR
ABSTRACT:
Following traumatic brain injury (TBI), cognitive impairments manifest through interactions between microscopic and macroscopic changes. On the microscale, a neurometabolic cascade alters neurotransmission, while on the macroscale diffuse axonal injury impacts the integrity of long-range connections. Large-scale brain network modeling allows us to make predictions across these spatial scales by integrating neuroimaging data with biophysically based models to investigate how microscale changes invisible to conventional neuroimaging influence large-scale brain dynamics. To this end, we analyzed structural and functional neuroimaging data from a well characterized sample of 44 adult TBI patients recruited from a regional trauma center, scanned at 1–2 weeks postinjury, and with follow-up behavioral outcome assessed 6 months later. Thirty-six age-matched healthy adults served as comparison participants. Using The Virtual Brain, we fit simulations of whole-brain resting-state functional MRI to the empirical static and dynamic functional connectivity of each participant. Multivariate partial least squares (PLS) analysis showed that patients with acute traumatic intracranial lesions had lower cortical regional inhibitory connection strengths than comparison participants, while patients without acute lesions did not differ from the comparison group. Further multivariate PLS analyses found correlations between lower semiacute regional inhibitory connection strengths and more symptoms and lower cognitive performance at a 6 month follow-up. Critically, patients without acute lesions drove this relationship, suggesting clinical relevance of regional inhibitory connection strengths even when traumatic intracranial lesions were not present. Our results suggest that large-scale connectome-based models may be sensitive to pathophysiological changes in semi-acute phase TBI patients and predictive of their chronic outcomes.
Link
- Cell Reports, 38:110340
Authors:
Triebkorn, P; Stefanovski, L; Dhindsa, K; Diaz-Cortes, MA; Bey, P; Bülau, K; Pai, R; Spiegler, A; Solodkin, A; Jirsa, Viktor; McIntosh, AR; Ritter, P
ABSTRACT:
Introduction
Computational brain network modeling using The Virtual Brain (TVB) simulation platform acts synergistically with machine learning (ML) and multi-modal neuroimaging to reveal mechanisms and improve diagnostics in Alzheimer’s disease (AD).Methods
We enhance large-scale whole-brain simulation in TVB with a cause-and-effect model linking local amyloid beta (Aβ) positron emission tomography (PET) with altered excitability. We use PET and magnetic resonance imaging (MRI) data from 33 participants of the Alzheimer’s Disease Neuroimaging Initiative (ADNI3) combined with frequency compositions of TVB-simulated local field potentials (LFP) for ML classification.Results
The combination of empirical neuroimaging features and simulated LFPs significantly outperformed the classification accuracy of empirical data alone by about 10% (weighted F1-score empirical 64.34% vs. combined 74.28%). Informative features showed high biological plausibility regarding the AD-typical spatial distribution.Discussion
The cause-and-effect implementation of local hyperexcitation caused by Aβ can improve the ML–driven classification of AD and demonstrates TVB’s ability to decode information in empirical data using connectivity-based brain simulation.
Link
- Front. Neuroinform., 26 May 2022
Authors:
Gulín-González, J; Bringas-Vega, ML; Martínez-Montes, E; Ritter, P; Solodkin, A; Valdes-Sosa, MJ; Valdes-Sosa, PA
- Cell Reports, 38:110340
Authors:
Aschauer, DF; Eppler, JB; Ewig, L; Chambers, AR; Pokorny, C; Kaschube, M; Rumpel, S
ABSTRACT:
Sensory stimuli have long been thought to be represented in the brain as activity patterns of specific neuronal assemblies. However, we still know relatively little about the long-term dynamics of sensory representations. Using chronic in vivo calcium imaging in the mouse auditory cortex, we find that sensory representations undergo continuous recombination, even under behaviorally stable conditions. Auditory cued fear conditioning introduces a bias into these ongoing dynamics, resulting in a long-lasting increase in the number of stimuli activating the same subset of neurons. This plasticity is specific for stimuli sharing representational similarity to the conditioned sound prior to conditioning and predicts behaviorally observed stimulus generalization. Our findings demonstrate that learning-induced plasticity leading to a representational linkage between the conditioned stimulus and non-conditioned stimuli weaves into ongoing dynamics of the brain rather than acting on an otherwise static substrate.
Link
- Development (2022) 149 (20): dev200439.
Authors:
Rodriguez-Gatica, JE; Iefremova, V; Sokhranyaeva, L; Yeung, SWCA; Breitkreuz, Y; Brüstle, O; Schwarz, M; Kubitscheck, U
Abstract:
Organoids are stem cell-derived three-dimensional cultures offering a new avenue to model human development and disease. Brain organoids allow the study of various aspects of human brain development in the finest details in vitro in a tissue-like context. However, spatial relationships of subcellular structures, such as synaptic contacts between distant neurons, are hardly accessible by conventional light microscopy. This limitation can be overcome by systems that quickly image the entire organoid in three dimensions and in super-resolution. To that end we have developed a system combining tissue expansion and light-sheet fluorescence microscopy for imaging and quantifying diverse spatial parameters during organoid development. This technique enables zooming from a mesoscopic perspective into super-resolution within a single imaging session, thus revealing cellular and subcellular structural details in three spatial dimensions, including unequivocal delineation of mitotic cleavage planes as well as the alignment of pre- and postsynaptic proteins. We expect light-sheet fluorescence expansion microscopy to facilitate qualitative and quantitative assessment of organoids in developmental and disease-related studies.
Link
- Scientific reports, 12:3162
Authors:
Seiler, JPH; Dan, O; Tüscher, O; Loewenstein, Y; Rumpel, S
ABSTRACT:
Boredom has been defined as an aversive mental state that is induced by the disability to engage in satisfying activity, most often experienced in monotonous environments. However, current understanding of the situational factors inducing boredom and driving subsequent behavior remains incomplete. Here, we introduce a two-alternative forced-choice task coupled with sensory stimulation of different degrees of monotony. We find that human subjects develop a bias in decision-making, avoiding the more monotonous alternative that is correlated with self-reported state boredom. This finding was replicated in independent laboratory and online experiments and proved to be specific for the induction of boredom rather than curiosity. Furthermore, using theoretical modeling we show that the entropy in the sequence of individually experienced stimuli, a measure of information gain, serves as a major determinant to predict choice behavior in the task. With this, we underline the relevance of boredom for driving behavioral responses that ensure a lasting stream of information to the brain.
Link
- Nature Communication 13, 5574
Authors:
Strauss S*, Korympidou MM*, Ran Y, Franke K, Schubert T, Baden T, Berens P, Euler T#, Vlasits AL#
ABSTRACT:
Motion sensing is a critical aspect of vision. We studied the representation of motion in mouse retinal bipolar cells and found that some bipolar cells are radially direction selective, preferring the origin of small object motion trajectories. Using a glutamate sensor, we directly observed bipolar cells synaptic output and found that there are radial direction selective and non-selective bipolar cell types, the majority being selective, and that radial direction selectivity relies on properties of the center-surround receptive field. We used these bipolar cell receptive fields along with connectomics to design biophysical models of downstream cells. The models and additional experiments demonstrated that bipolar cells pass radial direction selective excitation to starburst amacrine cells, which contributes to their directional tuning. As bipolar cells provide excitation to most amacrine and ganglion cells, their radial direction selectivity may contribute to motion processing throughout the visual system.
Link
- Exp Neurol. 2022 Aug;354:114111
Authors:
Meier JM, Perdikis D, Blickensdörfer A, Stefanovski L, Liu Q, Maith O, Dinkelbach HÜ, Baladron J, Hamker FH, Ritter P
ABSTRACT:
Deep brain stimulation (DBS) has been successfully applied in various neurodegenerative diseases as an effective symptomatic treatment. However, its mechanisms of action within the brain network are still poorly understood. Many virtual DBS models analyze a subnetwork around the basal ganglia and its dynamics as a spiking network with their details validated by experimental data. However, connectomic evidence shows widespread effects of DBS affecting many different cortical and subcortical areas. From a clinical perspective, various effects of DBS besides the motoric impact have been demonstrated. The neuroinformatics platform The Virtual Brain (TVB) offers a modeling framework allowing us to virtually perform stimulation, including DBS, and forecast the outcome from a dynamic systems perspective prior to invasive surgery with DBS lead placement. For an accurate prediction of the effects of DBS, we implement a detailed spiking model of the basal ganglia, which we combine with TVB via our previously developed co-simulation environment. This multiscale co-simulation approach builds on the extensive previous literature of spiking models of the basal ganglia while simultaneously offering a whole-brain perspective on widespread effects of the stimulation going beyond the motor circuit. In the first demonstration of our model, we show that virtual DBS can move the firing rates of a Parkinson’s disease patient’s thalamus – basal ganglia network towards the healthy regime while, at the same time, altering the activity in distributed cortical regions with a pronounced effect in frontal regions. Thus, we provide proof of concept for virtual DBS in a co-simulation environment with TVB. The developed modeling approach has the potential to optimize DBS lead placement and configuration and forecast the success of DBS treatment for individual patients.
Link
- Radiology August 2022
Authors:
Brammerloh, M; Kirilina, E; Alkemade, A; Bazin, P; Jantzen, C; Jäger, C; Herrler, A; Pine, KJ; Gowland, PA; Moarawski, M; Forstmann, BU; Weiskopf, N
ABSTRACT:
The swallow tail sign on MRI scans and histologically defined nigrosome 1 differ with respect to gemeometry; thus, further investigation is warranted.
Link
- NeuroImage February 2022
Authors:
Rusch, H; Brammerloh, M; Stieler, J; Sonntag, M; Mohammadi, S; Weiskopf, N; Arendt, T; Kirilina, E; Morawski, M
ABSTRACT:
The accessibility of new wide-scale multimodal imaging techniques led to numerous clearing techniques emerging over the last decade. However, clearing mesoscopic-sized blocks of aged human brain tissue remains an extremely challenging task. Homogenizing refractive indices and reducing light absorption and scattering are the foundation of tissue clearing. Due to its dense and highly myelinated nature, especially in white matter, the human brain poses particular challenges to clearing techniques.
Here, we present a comparative study of seven tissue clearing approaches and their impact on aged human brain tissue blocks (> 5 mm). The goal was to identify the most practical and efficient method in regards to macroscopic transparency, brief clearing time, compatibility with immunohistochemical processing and wide-scale multimodal microscopic imaging. We successfully cleared 26 × 26 × 5 mm3-sized human brain samples with two hydrophilic and two hydrophobic clearing techniques. Optical properties as well as light and antibody penetration depths highly vary between these methods. In addition to finding the best clearing approach, we compared three microscopic imaging setups (the Zeiss Laser Scanning Microscope (LSM) 880 , the Miltenyi Biotec Ultramicroscope ll (UM ll) and the 3i Marianas LightSheet microscope) regarding optimal imaging of large-scale tissue samples.
We demonstrate that combining the CLARITY technique (Clear Lipid-exchanged Acrylamide-hybridized Rigid Imaging compatible Tissue hYdrogel) with the Zeiss LSM 880 and combining the iDISCO technique (immunolabeling-enabled three-dimensional imaging of solvent-cleared organs) with the Miltenyi Biotec UM ll are the most practical and efficient approaches to sufficiently clear aged human brain tissue and generate 3D microscopic images. Our results point out challenges that arise from seven clearing and three imaging techniques applied to non-standardized tissue samples such as aged human brain tissue.
Link
- NeuroImage December 2022
Authors:
Petersen, M; Nägele, FL; Mayer, C; Schell, M; Rimmele, DL; Petersen, E; Kühn, S; Gallinat, J; Hanning, U; Fiehler, J; Twerenbold, R; Gerloff, C; Thomalla, G; Cheng, B
ABSTRACT:
Age-related cortical atrophy, approximated by cortical thickness measurements from magnetic resonance imaging, follows a characteristic pattern over the lifespan. Although its determinants remain unknown, mounting evidence demonstrates correspondence between the connectivity profiles of structural and functional brain networks and cortical atrophy in health and neurological disease. Here, we performed a cross-sectional multimodal neuroimaging analysis of 2633 individuals from a large population-based cohort to characterize the association between age-related differences in cortical thickness and functional as well as structural brain network topology. We identified a widespread pattern of age-related cortical thickness differences including “hotspots” of pronounced age effects in sensorimotor areas. Regional age-related differences were strongly correlated within the structurally defined node neighborhood. The overall pattern of thickness differences was found to be anchored in the functional network hierarchy as encoded by macroscale functional connectivity gradients. Lastly, the identified difference pattern covaried significantly with cognitive and motor performance. Our findings indicate that connectivity profiles of functional and structural brain networks act as organizing principles behind age-related cortical thinning as an imaging surrogate of cortical atrophy.
Link
- PLOS Computational Biology (2022)
Authors:
Senk J, Kriener B, Djurfeldt M, Voges N, Jiang HJ, Schüttler L, Gramelsberger G, Diesmann M, Plesser HE, van Albada SJ
ABSTRACT:
Sustainable research on computational models of neuronal networks requires published models to be understandable, reproducible, and extendable. Missing details or ambiguities about mathematical concepts and assumptions, algorithmic implementations, or parameterizations hinder progress. Such flaws are unfortunately frequent and one reason is a lack of readily applicable standards and tools for model description. Our work aims to advance complete and concise descriptions of network connectivity but also to guide the implementation of connection routines in simulation software and neuromorphic hardware systems. We first review models made available by the computational neuroscience community in the repositories ModelDB and Open Source Brain, and investigate the corresponding connectivity structures and their descriptions in both manuscript and code. The review comprises the connectivity of networks with diverse levels of neuroanatomical detail and exposes how connectivity is abstracted in existing description languages and simulator interfaces. We find that a substantial proportion of the published descriptions of connectivity is ambiguous. Based on this review, we derive a set of connectivity concepts for deterministically and probabilistically connected networks and also address networks embedded in metric space. Beside these mathematical and textual guidelines, we propose a unified graphical notation for network diagrams to facilitate an intuitive understanding of network properties. Examples of representative network models demonstrate the practical use of the ideas. We hope that the proposed standardizations will contribute to unambiguous descriptions and reproducible implementations of neuronal network connectivity in computational neuroscience.
Link
- Frontiers in Neuroinformatics (2022)
Authors:
Tiddia, G; Golosio, B; Albers, J; Senk, J; Francesco, S; Pronold, J; Fanti, V; Pastorelli, E; Paolucci, PS; van Albada, SJ
ABSTRACT:
Spiking neural network models are increasingly establishing themselves as an effective tool for simulating the dynamics of neuronal populations and for understanding the relationship between these dynamics and brain function. Furthermore, the continuous development of parallel computing technologies and the growing availability of computational resources are leading to an era of large-scale simulations capable of describing regions of the brain of ever larger dimensions at increasing detail. Recently, the possibility to use MPI-based parallel codes on GPU-equipped clusters to run such complex simulations has emerged, opening up novel paths to further speed-ups. NEST GPU is a GPU library written in CUDA-C/C++ for large-scale simulations of spiking neural networks, which was recently extended with a novel algorithm for remote spike communication through MPI on a GPU cluster. In this work we evaluate its performance on the simulation of a multi-area model of macaque vision-related cortex, made up of about 4 million neurons and 24 billion synapses and representing 32 mm2 surface area of the macaque cortex. The outcome of the simulations is compared against that obtained using the well-known CPU-based spiking neural network simulator NEST on a high-performance computing cluster. The results show not only an optimal match with the NEST statistical measures of the neural activity in terms of three informative distributions, but also remarkable achievements in terms of simulation time per second of biological activity. Indeed, NEST GPU was able to simulate a second of biological time of the full-scale macaque cortex model in its metastable state 3.1× faster than NEST using 32 compute nodes equipped with an NVIDIA V100 GPU each. Using the same configuration, the ground state of the full-scale macaque cortex model was simulated 2.4× faster than NEST.
Link
- eLife 11e77220 (2022)
Authors:
Boelts, J.; Lueckmann, J.; Gao, Richard; Macke, Jackob
ABSTRACT:
Inferring parameters of computational models that capture experimental data is a central task in cognitive neuroscience. Bayesian statistical inference methods usually require the ability to evaluate the likelihood of the model—however, for many models of interest in cognitive neuroscience, the associated likelihoods cannot be computed efficiently. Simulation-based inference (SBI) offers a solution to this problem by only requiring access to simulations produced by the model. Previously, Fengler et al. introduced Likelihood Approximation Networks (LAN, Fengler et al., 2021) which make it possible to apply SBI to models of decision-making, but require billions of simulations for training. Here, we provide a new SBI method that is substantially more simulation-efficient. Our approach, Mixed Neural Likelihood Estimation (MNLE), trains neural density estimators on model simulations to emulate the simulator, and is designed to capture both the continuous (e.g., reaction times) and discrete (choices) data of decision-making models. The likelihoods of the emulator can then be used to perform Bayesian parameter inference on experimental data using standard approximate inference methods like Markov Chain Monte Carlo sampling. We demonstrate MNLE on two variants of the drift-diffusion model (DDM) and show that it is substantially more efficient than LANs: MNLE achieves similar likelihood accuracy with six orders of magnitude fewer training simulations, and is significantly more accurate than LANs when both are trained with the same budget. This enables researchers to perform SBI on custom-tailored models of decision-making, leading to fast iteration of model design for scientific discovery.
Link
- Front. Neuroinform. 16:790966 (2022)
Authors:
Maith, O; Dinkelbach, M.; Baladron, J; Vitay, J; Hamker, FH
ABSTRACT:
Multi-scale network models that simultaneously simulate different measurable signals at different spatial and temporal scales, such as membrane potentials of single neurons, population firing rates, local field potentials, and blood-oxygen-level-dependent (BOLD) signals, are becoming increasingly popular in computational neuroscience. The transformation of the underlying simulated neuronal activity of these models to simulated non-invasive measurements, such as BOLD signals, is particularly relevant. The present work describes the implementation of a BOLD monitor within the neural simulator ANNarchy to allow an on-line computation of simulated BOLD signals from neural network models. An active research topic regarding the simulation of BOLD signals is the coupling of neural processes to cerebral blood flow (CBF) and cerebral metabolic rate of oxygen (CMRO2). The flexibility of ANNarchy allows users to define this coupling with a high degree of freedom and thus, not only allows to relate mesoscopic network models of populations of spiking neurons to experimental BOLD data, but also to investigate different hypotheses regarding the coupling between neural processes, CBF and CMRO2 with these models. In this study, we demonstrate how simulated BOLD signals can be obtained from a network model consisting of multiple spiking neuron populations. We first demonstrate the use of the Balloon model, the predominant model for simulating BOLD signals, as well as the possibility of using novel user-defined models, such as a variant of the Balloon model with separately driven CBF and CMRO2 signals. We emphasize how different hypotheses about the coupling between neural processes, CBF and CMRO2 can be implemented and how these different couplings affect the simulated BOLD signals. With the BOLD monitor presented here, ANNarchy provides a tool for modelers who want to relate their network models to experimental MRI data and for scientists who want to extend their studies of the coupling between neural processes and the BOLD signal by using modeling approaches. This facilitates the investigation and model-based analysis of experimental BOLD data and thus improves multi-scale understanding of neural processes in humans.
Link
- In: Giugliano M, Negrello M, Linaro D (Eds.), Computational Modelling of the Brain. Advances in Experimental Medicine and Biology, vol 1359 (pp. 201–234), Springer, Cham. (2022)
Authors:
Van Albada, SJ; Morales-Gregorio, A; Bakker, R; Palm, G; Goulas, A; Bludau, S; Dickscheid, T; Hilgetag, CC; Diesmann, M
ABSTRACT:
For constructing neuronal network models computational neuroscientists have access to wide-ranging anatomical data that nevertheless tend to cover only a fraction of the parameters to be determined. Finding and interpreting the most relevant data, estimating missing values, and combining the data and estimates from various sources into a coherent whole is a daunting task. With this chapter we aim to provide guidance to modelers by describing the main types of anatomical data that may be useful for informing neuronal network models. We further discuss aspects of the underlying experimental techniques relevant to the interpretation of the data, list particularly comprehensive data sets, and describe methods for filling in the gaps in the experimental data. Such methods of “predictive connectomics” estimate connectivity where the data are lacking based on statistical relationships with known quantities. Exploiting organizational principles that link the plethora of data in a unifying framework can be useful for informing computational models. Besides overarching principles, we touch upon the most prominent features of brain organization that are likely to influence predicted neuronal network dynamics, with a focus on the mammalian cerebral cortex. Given the still existing need for modelers to navigate a complex data landscape full of holes and stumbling blocks, it is vital that the field of neuroanatomy is moving toward increasingly systematic data collection, representation, and publication.
Link
2021
- Neuroforum, Volume 27 Issue 1
Authors:
Klein, PC; Ettinger, U; Schirner, M; Ritter, P; Rujescu, D; Falkai, P; Koutsouleris, N; Kambeitz-Ilankovic, N; Kambeitz-Ilankovic, L; Kambeitz, J
ABSTRACT:
Neuregulin-1 (NRG1) represents an important factor for multiple processes including neurodevelopment, brain functioning or cognitive functions. Evidence from animal research suggests an effect of NRG1 on the excitation-inhibition (E/I) balance in cortical circuits. However, direct evidence for the importance of NRG1 in E/I balance in humans is still lacking. In this work, we demonstrate the application of computational, biophysical network models to advance our understanding of the interaction between cortical activity observed in neuroimaging and the underlying neurobiology. We employed a biophysical neuronal model to simulate large-scale brain dynamics and to investigate the role of polymorphisms in the NRG1 gene (rs35753505, rs3924999) in n = 96 healthy adults. Our results show that G/G-carriers (rs3924999) exhibit a significant difference in global coupling (P = 0.048) and multiple parameters determining E/I-balance such as excitatory synaptic coupling (P = 0.047), local excitatory recurrence (P = 0.032) and inhibitory synaptic coupling (P = 0.028). This indicates that NRG1 may be related to excitatory recurrence or excitatory synaptic coupling potentially resulting in altered E/I-balance. Moreover, we suggest that computational modeling is a suitable tool to investigate specific biological mechanisms in health and disease.
Link
- Neuroforum, Volume 27 Issue 1
Authors:
Wachtler, T; Bauer, P; Denker, M; Grün, S; Hanke, M; Klein, J; Oeltze-Jafra, S; Ritter, P; Rotter, S; Scherberger, H; Stein, A; Witte, OW
ABSTRACT:
Increasing complexity and volume of research data pose increasing challenges for scientists to manage their data efficiently. At the same time, availability and reuse of research data are becoming more and more important in modern science. The German government has established an initiative to develop research data management (RDM) and to increase accessibility and reusability of research data at the national level, the Nationale Forschungsdateninfrastruktur (NFDI). The NFDI Neuroscience (NFDI-Neuro) consortium aims to represent the neuroscience community in this initiative. Here, we review the needs and challenges in RDM faced by researchers as well as existing and emerging solutions and benefits, and how the NFDI in general and NFDI-Neuro specifically can support a process for making these solutions better available to researchers. To ensure development of sustainable research data management practices, both technical solutions and engagement of the scientific community are essential. NFDI-Neuro is therefore focusing on community building just as much as on improving the accessibility of technical solutions.
Link
- Neuroforum, Volume 27 Issue 1
Authors:
Klingner, CM; Ritter, P; Brodoehl, S; Gaser, C; Scherag, A; Güllmar, D; Rosenow, F; Ziemann, U; Witte, OW
ABSTRACT:
In clinical neuroscience, there are considerable difficulties in translating basic research into clinical applications such as diagnostic tools or therapeutic interventions. This gap, known as the “valley of death,” was mainly attributed to the problem of “small numbers” in clinical neuroscience research, i.e. sample sizes that are too small (Hutson et al., 2017). As a possible solution, it has been repeatedly suggested to systematically manage research data to provide long-term storage, accessibility, and federate data. This goal is supported by a current call of the DFG for a national research data infrastructure (NFDI). This article will review current challenges and possible solutions specific to clinical neuroscience and discuss them in the context of other national and international health data initiatives. A successful NFDI consortium will help to overcome not only the “valley of death” but also promises a path to individualized medicine by enabling big data to produce generalizable results based on artificial intelligence and other methods.
Link
- arXiv (2021)
Authors:
Schirner, M; Domide, L; Perdikis, D; Triebkorn, P; Stefanovski, L; Pai, R; Popa, P; Valean, B; Palmer, J; Langford, C; Blickensdörfer, A; van der Vlag, M; Diaz-Pier, S; Peyser, A; Klijn, W; Pleiter, D; Nahm, Anne; Schmid, O; Marmaduke, W; Zehl, L; Fousek, J; Petkoski, S; Kusch, L; Hashemi, M; Marinazzo, D; Mangin, F; Flöel, A; Akintoye, S; Stahl, BC; Cepic, M; Johnson, E; Deco, G; McIntosh, AR; Hilgetag, CC; Morgan, M; Schuller, B; Upton, A; McMurtrie, C; Dickscheid, T; Bjaalie, JG; Amunts, K; Mersmann, J; Viktor, J; Ritter, P
ABSTRACT:
The Virtual Brain (TVB) is now available as open-source cloud ecosystem on EBRAINS, a shared digital research platform for brain science. It offers services for constructing, simulating and analysing brain network models (BNMs) including the TVB network simulator; magnetic resonance imaging (MRI) processing pipelines to extract structural and functional connectomes; multiscale co-simulation of spiking and large-scale networks; a domain specific language for automatic high-performance code generation from user-specified models; simulation-ready BNMs of patients and healthy volunteers; Bayesian inference of epilepsy spread; data and code for mouse brain simulation; and extensive educational material. TVB cloud services facilitate reproducible online collaboration and discovery of data assets, models, and software embedded in scalable and secure workflows, a precondition for research on large cohort data sets, better generalizability and clinical translation.
Link
- bioRxiv (2021)
Authors:
Chettouf, S; Triebkorn, P; Daffertshofer, A; Ritter, P
ABSTRACT:
Sensorimotor coordination requires orchestrated network activity mediated by inter- and intra-hemispheric, excitatory and inhibitory neuronal interactions. Aging-related structural changes may alter these interactions. Disbalancing strength and timing of excitation and inhibition may limit motor performance. This is particularly true during motor coordination tasks that have to be learned through practice. To investigate this, we simultaneously acquired electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) in two groups of healthy adults (young N=13: 20-25y and elderly N=14: 59-70y), while they were practicing a unimanual motor task. Both groups learned the task during brain scanning, which was confirmed by a 24h follow-up retention test. On average, quality of performance of older participants stayed significantly below that of the younger ones. Accompanying decreases in motor-event-related EEG-source beta band power (β, 15-30 Hz) were lateralized in both groups towards the contralateral side, albeit more so in younger participants. In the latter, the mean β-power during motor learning in bilateral pre-motor cortex (PM1) was significantly higher than in the older group. Combined EEG/fMRI analysis revealed positive correlations between fMRI signals and source-reconstructed β-amplitude time courses in contralateral and ipsilateral M1, and negative correlations in bilateral PM1 for both groups. The β-positive fMRI response in bilateral M1 might be explained by an increased cross-talk between hemispheres during periods of pronounced β-activity. During learning, the Rolandic β-power relative to rest was higher in bilateral PM1 in younger participants, suggesting less task-related beta band desynchronization in this (better performing) group. We also found positive correlations between Rolandic β-amplitude and fMRI-BOLD in bilateral M1 and negative correlations bilateral in PM1. This indicates that increased β-amplitudes are associated with increased M1 “activity” (positive BOLD response) and decreased PM1 “activity” (negative BOLD response). Our results point at decreased pre-motor inhibitory inputs to M1 as possible source for increased interhemispheric crosstalk and an aging-related decline in motor performance.
Link
- eNeuro 27 May 2021, 8 (4)
Authors:
Babiloni, C; Arakaki, X; Bonanni, L; Bujan, A; Carrillo, MC; Del Percio, C; Edelmayer, RM; Egan, G; Elahh, FM; Evans, A; Ferri, R; Frisoni, GB; Güntekin, B; Hainsworth, A; Hampel, H; Jelic, V; Jeong, J; Kim, DK; Kramberger, M; Kumar, S; Lizio, R; Nobili, F; Noce, G; Puce, A; Ritter, P; Smit, DJA; Soricelli, A; Teipel, S; Tucci, F; Sachdev, P; Valdes-Sosa, M; Valdes-Sosa, P; Vergallo, A; Yener, G
ABSTRACT:
Vascular contribution to cognitive impairment (VCI) and dementia is related to etiologies that may affect the neurophysiological mechanisms regulating brain arousal and generating electroencephalographic (EEG) activity. A multidisciplinary expert panel reviewed the clinical literature and reached consensus about the EEG measures consistently found as abnormal in VCI patients with dementia. As compared to cognitively unimpaired individuals, those VCI patients showed (1) smaller amplitude of resting state alpha (8–12 Hz) rhythms dominant in posterior regions; (2) widespread increases in amplitude of delta (< 4 Hz) and theta (4–8 Hz) rhythms; and (3) delayed N200/P300 peak latencies in averaged event-related potentials, especially during the detection of auditory rare target stimuli requiring participants’ responses in “oddball” paradigms. The expert panel formulated the following recommendations: (1) the above EEG measures are not specific for VCI and should not be used for its diagnosis; (2) they may be considered as “neural synchronization” biomarkers to enlighten the relationships between features of the VCI-related cerebrovascular lesions and abnormalities in neurophysiological brain mechanisms; and (3) they may be tested in future clinical trials as prognostic biomarkers and endpoints of interventions aimed at normalizing background brain excitability and vigilance in wakefulness.
Link
- eNeuro 27 May 2021, 8 (4)
Authors:
Arbabyazd, L; Shen, K; Wang, Z; Hoffman-Aptitius, M; Ritter, P; McIntosh, AR; Battaglia, D; Jirsa, V
ABSTRACT:
Large neuroimaging datasets, including information about structural connectivity (SC) and functional connectivity (FC), play an increasingly important role in clinical research, where they guide the design of algorithms for automated stratification, diagnosis or prediction. A major obstacle is, however, the problem of missing features [e.g., lack of concurrent DTI SC and resting-state functional magnetic resonance imaging (rsfMRI) FC measurements for many of the subjects]. We propose here to address the missing connectivity features problem by introducing strategies based on computational whole-brain network modeling. Using two datasets, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and a healthy aging dataset, for proof-of-concept, we demonstrate the feasibility of virtual data completion (i.e., inferring “virtual FC” from empirical SC or “virtual SC” from empirical FC), by using self-consistent simulations of linear and nonlinear brain network models. Furthermore, by performing machine learning classification (to separate age classes or control from patient subjects), we show that algorithms trained on virtual connectomes achieve discrimination performance comparable to when trained on actual empirical data; similarly, algorithms trained on virtual connectomes can be used to successfully classify novel empirical connectomes. Completion algorithms can be combined and reiterated to generate realistic surrogate connectivity matrices in arbitrarily large number, opening the way to the generation of virtual connectomic datasets with network connectivity information comparable to the one of the original data.
Link
- Front. Neuroinform., 01 April 2021
Authors:
Stefanovski, L; Meier, JM, Pai, RK; Triebkorn, P; Lett, T; Martin, L; Bülau, K; Hofmann-Aptitius, M; Solodkin, A; McIntosh, AR; Ritter, P
ABSTRACT:
Despite the acceleration of knowledge and data accumulation in neuroscience over the last years, the highly prevalent neurodegenerative disease of AD remains a growing problem. Alzheimer’s Disease (AD) is the most common cause of dementia and represents the most prevalent neurodegenerative disease. For AD, disease-modifying treatments are presently lacking, and the understanding of disease mechanisms continues to be incomplete. In the present review, we discuss candidate contributing factors leading to AD, and evaluate novel computational brain simulation methods to further disentangle their potential roles. We first present an overview of existing computational models for AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (www.thevirtualbrain.org), an open-source, multiscale, whole-brain simulation neuroinformatics platform. Finally, we discuss how this methodological approach may contribute to the understanding, improved diagnostics, and treatment optimization of AD.
Link
- Alzheimer’s & Dementia April 2021
Authors:
Babiloni, C; Arakaki, X; Azami, H; Bennys, K; Blinowska, K; Bonanni, L; Bujan, A; Carrillo, MC; Cichocki, A; Frutos-Lucas, J; Del Percio, C; Dubois, B; Edelmayer, R; Egan, G; Epelbaum, S; Escudero, J; Evans, A; Farina, F; Fargo, K; Fernández, A; Ferri, R; Frisoni, G; Hampel, H; Harrington, MG; Jelic, V; Jeong, J; Jiang, Y; Kaminski, M; Kavcic, V; Kilborn, K; Kumar, S; Lam, A; Lim, L; Lizio, R; Lopez, D; Lopez, S; Lucey, B; Maestú, F; McGeown, WJ; McKeith, I; Moretti, DV; Nobili, F; Noce, G; Olichney, J; Onofrj, M; Osorio, R; Parra-Rodriguez, M; Rajji, T; Ritter, P; Soricelli, A; Fabrizio, S; Tarnanas, I; Taylor, JP; Teipel, S; Tucci, F; Valdes-Sosa, M; Valdes-Soa, P; Weiergräber, M; Yener, G; Guntekin, B
ABSTRACT:
The Electrophysiology Professional Interest Area (EPIA) and Global Brain Consortium endorsed recommendations on candidate electroencephalography (EEG) measures for Alzheimer’s disease (AD) clinical trials. The Panel reviewed the field literature. As most consistent findings, AD patients with mild cognitive impairment and dementia showed abnormalities in peak frequency, power, and “interrelatedness” at posterior alpha (8-12 Hz) and widespread delta (< 4 Hz) and theta (4-8 Hz) rhythms in relation to disease progression and interventions. The following consensus statements were subscribed: (1) Standardization of instructions to patients, resting state EEG (rsEEG) recording methods, and selection of artifact-free rsEEG periods are needed; (2) power density and “interrelatedness” rsEEG measures (e.g., directed transfer function, phase lag index, linear lagged connectivity, etc.) at delta, theta, and alpha frequency bands may be use for stratification of AD patients and monitoring of disease progression and intervention; and (3) international multisectoral initiatives are mandatory for regulatory purposes.
Link
- International Conference on Artificial Intelligence and Statistics (pp. 343-351) (2021)
Authors:
Lueckmann, J. M., Boelts, J., Greenberg, D., Goncalves, P., & Macke, J.
ABSTRACT:
Recent advances in probabilistic modelling have led to a large number of simulation-based inference algorithms which do not require numerical evaluation of likelihoods. However, a public benchmark with appropriate performance metrics for such ’likelihood-free’ algorithms has been lacking. This has made it difficult to compare algorithms and identify their strengths and weaknesses. We set out to fill this gap: We provide a benchmark with inference tasks and suitable performance metrics, with an initial selection of algorithms including recent approaches employing neural networks and classical Approximate Bayesian Computation methods. We found that the choice of performance metric is critical, that even state-of-the-art algorithms have substantial room for improvement, and that sequential estimation improves sample efficiency. Neural network-based approaches generally exhibit better performance, but there is no uniformly best algorithm. We provide practical advice and highlight the potential of the benchmark to diagnose problems and improve algorithms. The results can be explored interactively on a companion website. All code is open source, making it possible to contribute further benchmark tasks and inference algorithms.
Link
- NeuroImage 244, 118559 (2021)
Authors:
Müller-Axt, C.; Eichner, C.; Rusch, H.; Kauffmann, L.; Bazin, P.-L.; Anwander, A.; Morawski, M.; von Kriegstein, K.
ABSTRACT:
The human lateral geniculate nucleus (LGN) of the visual thalamus is a key subcortical processing site for visual information analysis. Due to its small size and deep location within the brain, a non-invasive characterization of the LGN and its microstructurally distinct magnocellular (M) and parvocellular (P) subdivisions in humans is challenging. Here, we investigated whether structural quantitative MRI (qMRI) methods that are sensitive to underlying microstructural tissue features enable MR-based mapping of human LGN M and P subdivisions. We employed high-resolution 7 Tesla in-vivo qMRI in N = 27 participants and ultra-high resolution 7 Tesla qMRI of a post-mortem human LGN specimen. We found that a quantitative assessment of the LGN and its subdivisions is possible based on microstructure-informed qMRI contrast alone. In both the in-vivo and post-mortem qMRI data, we identified two components of shorter and longer longitudinal relaxation time (T1) within the LGN that coincided with the known anatomical locations of a dorsal P and a ventral M subdivision, respectively. Through ground-truth histological validation, we further showed that the microstructural MRI contrast within the LGN pertains to cyto- and myeloarchitectonic tissue differences between its subdivisions. These differences were based on cell and myelin density, but not on iron content. Our qMRI-based mapping strategy paves the way for an in-depth understanding of LGN function and microstructure in humans. It further enables investigations into the selective contributions of LGN subdivisions to human behavior in health and disease.
Link
- bioRxiv
Authors:
M Paquette, C Eichner, TR Knösche, A Anwander
ABSTRACT:
The feasibility of non-invasive axonal diameter quantification with diffusion MRI is a strongly debated topic due to the neuroscientific potential of such information and its relevance for the axonal signal transmission speed. It has been shown that under ideal conditions, the minimal diameter producing detectable signal decay is bigger than most human axons in the brain, even using the strongest currently available MRI systems. We show that resolving the simplest situations including multiple diameters is unfeasible even with diameters much bigger than the diameter limit. Additionally, the recently proposed effective diameter resulting from fitting a single value over a distribution is almost exclusively influenced by the biggest axons. We show how impractical this metric is for comparing different distributions. Overall, axon diameters currently cannot be quantified by diffusion MRI in any relevant way.
Link
- Frontiers in Neuroinformatics 15:715131 (2021)
Authors:
Vieth, M., Stöber, T., Triesch, J.
ABSTRACT:
The Python Modular Neural Network Toolbox (PymoNNto) provides a versatile and adaptable Python-based framework to develop and investigate brain-inspired neural networks. In contrast to other commonly used simulators such as Brian2 and NEST, PymoNNto imposes only minimal restrictions for implementation and execution. The basic structure of PymoNNto consists of one network class with several neuron- and synapse-groups. The behaviour of each group can be flexibly defined by exchangeable modules. The implementation of these modules is up to the user and only limited by Python itself. Behaviours can be implemented in Python, Numpy, Tensorflow, and other libraries to perform computations on CPUs and GPUs. PymoNNto comes with convenient high level behaviour modules, allowing differential equation-based implementations similar to Brian2, and an adaptable modular Graphical User Interface for real-time observation and modification of the simulated network and its parameters
Link
- arXiv (2021)
Authors:
Upschulte, E., Harmeling, S., Amunts, K., Dickscheid, T.
Abstract:
We present a conceptually simple framework for object instance segmentation called Contour Proposal Network (CPN), which detects possibly overlapping objects in an image while simultaneously fitting closed object contours using an interpretable, fixed-sized representation based on Fourier Descriptors. The CPN can incorporate state of the art object detection architectures as backbone networks into a single-stage instance segmentation model that can be trained end-to-end. We construct CPN models with different backbone networks, and apply them to instance segmentation of cells in datasets from different modalities. In our experiments, we show CPNs that outperform U-Nets and Mask R-CNNs in instance segmentation accuracy, and present variants with execution times suitable for real-time applications. The trained models generalize well across different domains of cell types. Since the main assumption of the framework are closed object contours, it is applicable to a wide range of detection problems also outside the biomedical domain. An implementation of the model architecture in PyTorch is freely available.
Link
- NeuroImage (2021)
Authors:
Schiffer, C., Spitzer, H., Kiwitz, K. Unger, N., Wagstyl, K., Evans, A., Harmeling, S., Amunts, K., Dickscheid, T.
Abstract:
Human brain atlases provide spatial reference systems for data characterizing brain organization at different levels, coming from different brains. Cytoarchitecture is a basic principle of the microstructural organization of the brain, as regional differences in the arrangement and composition of neuronal cells are indicators of changes in connectivity and function. Automated scanning procedures and observer-independent methods are prerequisites to reliably identify cytoarchitectonic areas, and to achieve reproducible models of brain segregation. Time becomes a key factor when moving from the analysis of single regions of interest towards high-throughput scanning of large series of whole-brain sections. Here we present a new workflow for mapping cytoarchitectonic areas in large series of cell-body stained histological sections of human postmortem brains. It is based on a Deep Convolutional Neural Network (CNN), which is trained on a pair of section images with annotations, with a large number of un-annotated sections in between. The model learns to create all missing annotations in between with high accuracy, and faster than our previous workflow based on observer-independent mapping. The new workflow does not require preceding 3D-reconstruction of sections, and is robust against histological artefacts. It processes large data sets with sizes in the order of multiple Terabytes efficiently. The workflow was integrated into a web interface, to allow access without expertise in deep learning and batch computing. Applying deep neural networks for cytoarchitectonic mapping opens new perspectives to enable high-resolution models of brain areas, introducing CNNs to identify borders of brain areas.
Link
- Progress in Biophysics and Molecular Biology 0079-6107 (2021)
Authors:
Schwarz, M., Kubitscheck, U.
- Phys. Rev. Research 3, 043077 (2021)
Authors:
van Meegen, A., van Albada, S.
ABSTRACT:
A complex interplay of single-neuron properties and the recurrent network structure shapes the activity of cortical neurons. The single-neuron activity statistics differ in general from the respective population statistics, including spectra and, correspondingly, autocorrelation times. We develop a theory for self-consistent second-order single-neuron statistics in block-structured sparse random networks of spiking neurons. In particular, the theory predicts the neuron-level autocorrelation times, also known as intrinsic timescales, of the neuronal activity. The theory is based on an extension of dynamic mean-field theory from rate networks to spiking networks, which is validated via simulations. It accounts for both static variability, e.g., due to a distributed number of incoming synapses per neuron, and temporal fluctuations of the input. We apply the theory to balanced random networks of generalized linear model neurons, balanced random networks of leaky integrate-and-fire neurons, and a biologically constrained network of leaky integrate-and-fire neurons. For the generalized linear model network with an error function nonlinearity, a novel analytical solution of the colored noise problem allows us to obtain self-consistent firing rate distributions, single-neuron power spectra, and intrinsic timescales. For the leaky integrate-and-fire networks, we derive an approximate analytical solution of the colored noise problem, based on the Stratonovich approximation of the Wiener-Rice series and a novel analytical solution for the free upcrossing statistics. Again closing the system self-consistently, in the fluctuation-driven regime, this approximation yields reliable estimates of the mean firing rate and its variance across neurons, the interspike-interval distribution, the single-neuron power spectra, and intrinsic timescales. With the help of our theory, we find parameter regimes where the intrinsic timescale significantly exceeds the membrane time constant, which indicates the influence of the recurrent dynamics. Although the resulting intrinsic timescales are on the same order for generalized linear model neurons and leaky integrate-and-fire neurons, the two systems differ fundamentally: for the former, the longer intrinsic timescale arises from an increased firing probability after a spike; for the latter, it is a consequence of a prolonged effective refractory period with a decreased firing probability. Furthermore, the intrinsic timescale attains a maximum at a critical synaptic strength for generalized linear model networks, in contrast to the minimum found for leaky integrate-and-fire networks.
Link
- Journal of Microscopy (2021)
Authors:
Lindow, N., Brünig, F., Dercksen, V., Fabig, G., Kiewsiz, R., Redemann, S., Müller-Reichert, T., Prohaska, S., Baum, D.
Abstract:
We present a software-assisted workflow for the alignment and matching of filamentous structures across a three-dimensional (3D) stack of serial images. This is achieved by combining automatic methods, visual validation, and interactive correction. After the computation of an initial automatic matching, the user can continuously improve the result by interactively correcting landmarks or matches of filaments. Supported by a visual quality assessment of regions that have been already inspected, this allows a trade-off between quality and manual labour. The software tool was developed in an interdisciplinary collaboration between computer scientists and cell biologists to investigate cell division by quantitative 3D analysis of microtubules (MTs) in both mitotic and meiotic spindles. For this, each spindle is cut into a series of semi-thick physical sections, of which electron tomograms are acquired. The serial tomograms are then stitched and non-rigidly aligned to allow tracing and connecting of MTs across tomogram boundaries. In practice, automatic stitching alone provides only an incomplete solution, because large physical distortions and a low signal-to-noise ratio often cause experimental difficulties. To derive 3D models of spindles despite dealing with imperfect data related to sample preparation and subsequent data collection, semi-automatic validation and correction is required to remove stitching mistakes. However, due to the large number of MTs in spindles (up to 30k) and their resulting dense spatial arrangement, a naive inspection of each MT is too time-consuming. Furthermore, an interactive visualisation of the full image stack is hampered by the size of the data (up to 100 GB). Here, we present a specialised, interactive, semi-automatic solution that considers all requirements for large-scale stitching of filamentous structures in serial-section image stacks. To the best of our knowledge, it is the only currently available tool which is able to process data of the type and size presented here. The key to our solution is a careful design of the visualisation and interaction tools for each processing step to guarantee real-time response, and an optimised workflow that efficiently guides the user through datasets. The final solution presented here is the result of an iterative process with tight feedback loops between the involved computer scientists and cell biologists.
Link
- International Workshop on Brain-Inspired Computing, Lecture Notes in Computer Science book series (LNCS), volume 12339 (2021)
Authors:
van Albada, S., Pronold, J., van Meegen, A., Diesmann, M.
ABSTRACT:
We are entering an age of ‘big’ computational neuroscience, in which neural network models are increasing in size and in numbers of underlying data sets. Consolidating the zoo of models into large-scale models simultaneously consistent with a wide range of data is only possible through the effort of large teams, which can be spread across multiple research institutions. To ensure that computational neuroscientists can build on each other’s work, it is important to make models publicly available as well-documented code. This chapter describes such an open-source model, which relates the connectivity structure of all vision-related cortical areas of the macaque monkey with their resting-state dynamics. We give a brief overview of how to use the executable model specification, which employs NEST as simulation engine, and show its runtime scaling. The solutions found serve as an example for organizing the workflow of future models from the raw experimental data to the visualization of the results, expose the challenges, and give guidance for the construction of an ICT infrastructure for neuroscience.
Link
2020
- NeuroImage 221, 117172 (2020)
Authors:
Eichner, C.; Paquette, M.; Mildner, T.; Schlumm, T.; Pléh, K.; Samuni, L.; Crockford, C.; Wittig, R. M.; Jäger, C.; Möller, H. E. Friederici, A.D.; Anwander, A.
ABSTRACT:
Post-mortem diffusion MRI (dMRI) enables acquisitions of structural imaging data with otherwise unreachable resolutions – at the expense of longer scanning times. These data are typically acquired using highly segmented image acquisition strategies, thereby resulting in an incomplete signal decay before the MRI encoding continues. Especially in dMRI, with low signal intensities and lengthy contrast encoding, such temporal inefficiency translates into reduced image quality and longer scanning times. This study introduces Multi Echo (ME) acquisitions to dMRI on a human MRI system – a time-efficient approach, which increases SNR (Signal-to-Noise Ratio) and reduces noise bias for dMRI images. The benefit of the introduced ME-dMRI method was validated using numerical Monte Carlo simulations and showcased on a post-mortem brain of a wild chimpanzee. The proposed Maximum Likelihood Estimation echo combination results in an optimal SNR without detectable signal bias. The combined strategy comes at a small price in scanning time (here 30% additional) and leads to a substantial SNR increase (here white matter: ~ 1.6x, equivalent to 2.6 averages, grey matter: ~ 1.9x, equivalent to 3.6 averages) and a general reduction of the noise bias.
Link
- Journal of Molecular Biology 432:3722-3737 (2020)
Authors:
Abstract:
Mathematical models of varying complexity have helped shed light on different aspects of circadian clock function. In this work, we question whether minimal clock models (Goodwin models) are sufficient to reproduce essential phenotypes of the clock: a small phase response curve (PRC), fast jet lag, and seasonal phase shifts. Instead of building a single best model, we take an approach where we study the properties of a set of models satisfying certain constraints; here, a 1h-pulse PRC with a range of 3h and clock periods between 22h and 26h is designed. Surprisingly, almost all these randomly parameterized models showed a 4h change in phase of entrainment between long and short days and jet lag durations of three to seven days in advance and delay. Moreover, intrinsic clock period influenced jet lag duration and entrainment amplitude and phase. Fast jet lag was realized in this model by means of an interesting amplitude effect: the association between clock amplitude and clock period termed “twist.” This twist allows amplitude changes to speed up and slow down clocks enabling faster shifts. These findings were robust to the addition of positive feedback to the model. In summary, the known design principles of rhythm generation – negative feedback, long delay, and switch-like inhibition (we review these in detail) – are sufficient to reproduce the essential clock phenotypes. Furthermore, amplitudes play a role in determining clock properties and must be always considered, although they are difficult to measure.
- Journal of Comparative Neurology, 528(10), 1754-1774 (2020)
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Circadian clocks control rhythms in physiology and behavior entrained to 24 h light – dark cycles. Despite of conserved general schemes, molecular circadian clockworks differ between insect species. With RNA interference (RNAi) we examined an ancient circadian clockwork in a basic insect, the hemimetabolous Madeira cockroach Rhyparobia maderae. With injections of double-stranded RNA (dsRNA) of cockroach period (Rm´per), timeless 1 (Rm´tim1), or cryptochrome 2 (Rm´cry2) we searched for essential components of the clock´s core feedback loop. A single injection of dsRNA of each clock gene into adult cockroaches successfully and permanently knocked down respective mRNA levels within ~two weeks, deleting daytime-dependent mRNA rhythms for all three clock genes tested. Rm´perRNAi or Rm´cry2RNAi affected both genes, while Rm´tim1 was independent of both. Unexpectedly, circadian locomotor rhythms always remained. They expressed normal periods for Rm´perRNAi and shorter periods for Rm´tim1RNAi and Rm´cry2RNAi. As a hypothesis of the cockroach´s molecular clockwork, a network of switched differential equations was developed to model the oscillatory behavior of clock cells expressing the clock genes. Data were consistent with coupled oscillator cells expressing different periods based on core feedback loops with either PER, TIM1, or CRY2/PER complexes as negative feedback of the clockwork.
- bioRxiv (2020)
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We present a software-assisted workflow for the alignment and matching of filamentous structures across a 3D stack of serial images. This is achieved by combining automatic methods, visual validation, and interactive correction. After an initial alignment, the user can continuously improve the result by interactively correcting landmarks or matches of filaments. Supported by a visual quality assessment of regions that have been already inspected, this allows a trade-off between quality and manual labor. The software tool was developed to investigate cell division by quantitative 3D analysis of microtubules (MTs) in both mitotic and meiotic spindles. For this, each spindle is cut into a series of semi-thick physical sections, of which electron tomograms are acquired. The serial tomograms are then stitched and non-rigidly aligned to allow tracing and connecting of MTs across tomogram boundaries. In practice, automatic stitching alone provides only an incomplete solution, because large physical distortions and a low signal-to-noise ratio often cause experimental difficulties. To derive 3D models of spindles despite the problems related to sample preparation and subsequent data collection, semi-automatic validation and correction is required to remove stitching mistakes. However, due to the large number of MTs in spindles (up to 30k) and their resulting dense spatial arrangement, a naive inspection of each MT is too time consuming. Furthermore, an interactive visualization of the full image stack is hampered by the size of the data (up to 100 GB). Here, we present a specialized, interactive, semi-automatic solution that considers all requirements for large-scale stitching of filamentous structures in serial-section image stacks. The key to our solution is careful design of the visualization and interaction tools for each processing step to guarantee real-time response, and an optimized workflow that efficiently guides the user through datasets.
- eLIFE 9:e53148 (2020)
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Feedback inhibitory motifs are thought to be important for pattern separation across species. How feedback circuits may implement pattern separation of biologically plausible, temporally structured input in mammals is, however, poorly understood. We have quantitatively determined key properties of netfeedback inhibition in the mouse dentate gyrus, a region critically involved in pattern separation. Feedback inhibition is recruited steeply with a low dynamic range (0% to 4% of active GCs), and with a non-uniform spatial profile. Additionally, net feedback inhibition shows frequency-dependent facilitation, driven by strongly facilitating mossy fiber inputs. Computational analyses show a significant contribution of the feedback circuit to pattern separation of theta modulated inputs, even within individual theta cycles. Moreover, pattern separation was selectively boosted at gamma frequencies, in particular for highly similar inputs. This effect was highly robust, suggesting that frequency-dependent pattern separation is a key feature of the feedback inhibitory microcircuit.
- abstract submitted (2020)
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Recently, the use of network-based methods on brain imaging data has received a lot of traction in the neuroscience community. Collaboration between brain regions in this paradigm to perform every cognitive task has been studied using a wide variance of methods. While some of these methods allow elegant mathematical interpretations of our data, others can easily become computationally expensive or difficult to interpret and compare between subjects or groups. Here, we suggest an intuitive and computationally efficient method to measure the reconfiguration of brain regions in relation to a template modular structure over time. This method uses a single a-priori modular structure as a reference and therefore does not rely on a stochastic data-driven estimation of modules. We calculate the deformation of a-prior modules over time and use the outcome as a source of tracking the activity patterns. Our proposed method thus has the benefit of using the same (biologically relevant) modular framework across samples and studies, which also allows for better interpretation of brain regions switching their modular alliance. In the Application section, we demonstrate that our newly proposed method yields highly similar results of whole-brain flexibility as in another study that uses one of the current well-established methods of calculating dynamic network reconfiguration.
- Science 368(6495):1057-1058 (2020)
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Many cases of blindness result from progressive loss of photoreceptors, which are the light-sensing cells in the eye. For individuals with such progressive blindness, potential therapies aim at restoring vision by making the retina light-sensitive again while minimally interfering with any healthy photoreceptors—goals that are usually contradictory. Many current therapeutic strategies interfere with remaining vision, making them primarily suitable for patients who have lost all light sensitivity. On page 1108 of this issue, Nelidova et al. (1) present a potential solution to this conundrum: making the retina sensitive to infrared light, which is largely undetectable by human photoreceptors. They use engineered nanoparticle sensors and gene therapy to induce infrared light sensitivity in mice with inherited degenerative blindness and in postmortem human retinas. This approach might avoid damage to functional photoreceptors by preventing saturation or hyperactivation while inducing light sensitivity in patients with partial retinal degeneration.
- bioRxiv (2020)
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Despite recent advances in tractography, the gap remains wide between the descriptions of white-matter pathways in the literature and the methods to reconstruct and study them from dMRI images. Here, we tackle this challenge by proposing a language to define white matter tracts, namely WMQLT, and a tool to automatically reconstruct pathways from their WMQLT queries. Our method is performant, flexible enough to allow defining tracts using multiple modalities, and allows to extend ROI-based reconstruction methods. Leveraging our language, we define 19 major brain tracts, alongside their subdivisions, and reconstruct them in a large population. We show that the shape of the reconstructed pathways, as well as their connectivity and lateralizations are in accordance with the current neuroanatomical literature. Finally, we showcase our technique in two scenarios: computing the functional subdivisions of a tract, and assessing the role of handedness and gender in the lateralization of language-related tracts.
- bioRxiv, 838383 (2020)
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Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators – trained using model simulations – to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features, and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin–Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics.
- Philosophical Transactions of the Royal Society B, 375(1796), 20190319 (2020)
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Concepts shape the interpretation of facts. One of the most popular concepts in systems neuroscience is that of ‘hierarchy’. However, this concept has been interpreted in many different ways, which are not well aligned. This observation suggests that the concept is ill defined. Using the example of the organization of the primate visual cortical system, we explore several contexts in which ‘hierarchy’ is currently used in the description of brain networks. We distinguish at least four different uses, specifically, ‘hierarchy’ as a topological sequence of projections, as a gradient of features, as a progression of scales, or as a sorting of laminar projection patterns. We discuss the interpretation and functional implications of the different notions of ‘hierarchy’ in these contexts and suggest that more specific terms than ‘hierarchy’ should be used for a deeper understanding of the different dimensions of the organization of brain networks.
- Annals of Neurology, 87(6), 962-975 (2020)
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Objective: Subthalamic nucleus deep brain stimulation (STN‐DBS) in Parkinson’s disease (PD) not only stimulates focal target structures but also affects distributed brain networks. The impact this network modulation has on non‐motor DBS effects is not well‐characterized. By focusing on the affective domain, we systematically investigate the impact of electrode placement and associated structural connectivity on changes in depressive symptoms following STN‐DBS, which have been reported to improve, worsen, or remain unchanged.
Methods: Depressive symptoms before and after STN‐DBS surgery were documented in 116 patients with PD from 3 DBS centers (Berlin, Queensland, and Cologne). Based on individual electrode reconstructions, the volumes of tissue activated (VTAs) were estimated and combined with normative connectome data to identify structural connections passing through VTAs. Berlin and Queensland cohorts formed a training and cross‐validation dataset used to identify structural connectivity explaining change in depressive symptoms. The Cologne data served as the test‐set for which depressive symptom change was predicted.
Results: Structural connectivity was linked to depressive symptom change under STN‐DBS. An optimal connectivity map trained on the Berlin cohort could predict changes in depressive symptoms in Queensland patients and vice versa. Furthermore, the joint training‐set map predicted changes in depressive symptoms in the independent test‐set. Worsening of depressive symptoms was associated with left prefrontal connectivity.
Interpretation: Fibers connecting the electrode with left prefrontal areas were associated with worsening of depressive symptoms. Our results suggest that for the left STN‐DBS lead, placement impacting fibers to left prefrontal areas should be avoided to maximize improvement of depressive symptoms. - Science Advances, 6(41), eaaz9281 (2020)
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Superficial white matter (SWM) contains the most cortico-cortical white matter connections in the human brain encompassing the short U-shaped association fibers. Despite its importance for brain connectivity, very little is known about SWM in humans, mainly due to the lack of noninvasive imaging methods. Here, we lay the groundwork for systematic in vivo SWM mapping using ultrahigh resolution 7 T magnetic resonance imaging. Using biophysical modeling informed by quantitative ion beam microscopy on postmortem brain tissue, we demonstrate that MR contrast in SWM is driven by iron and can be linked to the microscopic iron distribution. Higher SWM iron concentrations were observed in U-fiber–rich frontal, temporal, and parietal areas, potentially reflecting high fiber density or late myelination in these areas. Our SWM mapping approach provides the foundation for systematic studies of interindividual differences, plasticity, and pathologies of this crucial structure for cortico-cortical connectivity in humans.
- European Journal of Neuroscience (2020)
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Previous computational model‐based approaches for understanding the dynamic changes related to Parkinson’s disease made particular assumptions about Parkinson’s disease related activity changes or specified dopamine‐dependent activation or learning rules. Inspired by recent model‐based analysis of resting‐state fMRI, we have taken a data‐driven approach. We fit the free parameters of a spiking neuro‐computational model to match correlations of blood‐oxygen‐level‐dependent signals between different basal ganglia nuclei and obtain subject‐specific neurocomputational models of two subject groups: Parkinson patients and matched controls. When comparing mean firing rates at rest and connectivity strengths between the control and Parkinsonian model groups, several significant differences were found that are consistent with previous experimental observations. We discuss the implications of our approach and compare its results also with the popular “rate model” of the basal ganglia. Our study suggests that a model‐based analysis of imaging data from healthy and Parkinsonian subjects is a promising approach for the future to better understand Parkinson related changes in the basal ganglia and corresponding treatments.
- Frontiers in Neuroscience 14: 16 (2020)
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Transgenic Cre-recombinase expressing mouse lines are widely used to express fluorescent proteins and opto-/chemogenetic actuators, making them a cornerstone of modern neuroscience. The investigation of interneurons in particular has benefitted from the ability to genetically target specific cell types. However, the specificity of some Cre driver lines has been called into question. Here, we show that nonspecific expression in a subset of hippocampal neurons can have substantial nonspecific functional effects in a somatostatin-Cre (SST-Cre) mouse line. Nonspecific targeting of CA3 pyramidal cells caused large optogenetically evoked excitatory currents in remote brain regions. Similar, but less severe patterns of nonspecific expression were observed in a widely used SST-IRES-Cre line, when crossed with a reporter mouse line. Viral transduction on the other hand yielded more specific expression but still resulted in nonspecific expression in a minority of pyramidal layer cells. These results suggest that a careful analysis of specificity is mandatory before the use of Cre driver lines for opto- or chemogenetic manipulation approaches.
- Biorxiv (2020)
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All vertebrates are capable of generating dissimilar patterns of neuronal activity from similar sensory-driven input patterns, a phenomenon called pattern separation. It is unclear, however, how these separated patterns are transformed into lasting memories that retain the initial discrimination. Using dual-color in-vivo two-photon Ca2+ imaging, we show that the dentate gyrus, a region implicated in pattern separation, generates in immobile mice sparse, synchronized activity patterns driven by entorhinal cortex activity. These population events are structured and modified by changes in the environment; they incorporate place- and speed cells and are similar to population patterns evoked during self-motion. Inhibiting only granule cells in immobile mice impairs formation of pattern-separated memories. These patterns, thus, support the creation of precise memories by replaying the population codes of the current environment on a short time scale.
- Frontiers Physiol. 11, 272 (2020)
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Entrainment denotes a process of coordinating the internal circadian clock to external rhythmic time-cues (Zeitgeber), mainly light. It is facilitated by stronger Zeitgeber signals and smaller period differences between the internal clock and the external Zeitgeber. The phase of entrainment ψ is a result of this process on the side of the circadian clock. On Earth, the period of the day-night cycle is fixed to 24 h, while the periods of circadian clocks distribute widely due to natural variation within and between species. The strength and duration of light depend locally on season and geographic latitude. Therefore, entrainment characteristics of a circadian clock vary under a local light environment and distribute along geoecological settings. Using conceptual models of circadian clocks, we investigate how local conditions of natural light shape global patterning of entrainment through seasons. This clock-side entrainment paradigm enables us to predict systematic changes in the global distribution of chronotypes.
- bioRxiv (2020)
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Visual processing in the retina has been studied in great detail at all levels such that a comprehensive picture of the retina’s cell types and the many neural circuits they form is emerging. However, the currently best performing models of retinal function are black-box CNN models which are agnostic to such biological knowledge. In particular, these models typically neglect the role of the many inhibitory circuits involving amacrine cells and the biophysical mechanisms underlying synaptic release. Here, we present a computational model of temporal processing in the inner retina, including inhibitory feedback circuits and realistic synaptic release mechanisms. Fit to the responses of bipolar cells, the model generalized well to new stimuli including natural movie sequences, performing on par with or better than a benchmark black-box model. In pharmacology experiments, the model replicated in silico the effect of blocking specific amacrine cell populations with high fidelity, indicating that it had learned key circuit functions. Also, more in depth comparisons showed that connectivity patterns learned by the model were well matched to connectivity patterns extracted from connectomics data. Thus, our model provides a biologically interpretable data-driven account of temporal processing in the inner retina, filling the gap between purely black-box and detailed biophysical modeling.
- Optics Express, 28(10), 15587-15600 (2020)
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Light-sheet fluorescence microscopy (LSFM) helps investigate small structures in developing cells and tissue for three-dimensional localization microscopy and large-field brain imaging in neuroscience. Lattice light-sheet microscopy is a recent development with great potential to improve axial resolution and usable field sizes, thus improving imaging speed. In contrast to the commonly employed Gaussian beams for light-sheet generation in conventional LSFM, in lattice light-sheet microscopy an array of low diverging Bessel beams with a suppressed side lobe structure is used. We developed a facile elementary lattice light-sheet microscope using a micro-fabricated fixed ring mask for lattice light-sheet generation. In our setup, optical hardware elements enable a stable and simple illumination path without the need for spatial light modulators. This setup, in combination with long-working distance objectives and the possibility for simultaneous dual-color imaging, provides optimal conditions for imaging extended optically cleared tissue samples. We here present experimental data of fluorescently stained neurons and neurites from mouse hippocampus following tissue expansion and demonstrate the high homogeneous resolution throughout the entire imaged volume. Utilizing our purpose-built lattice light-sheet microscope, we reached a homogeneous excitation and an axial resolution of 1.2 µm over a field of view of (333 µm)^2.
- Frontiers in Physiology, 11, 334 (2020)
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Understanding entrainment of circadian rhythms is a central goal of chronobiology. Many factors, such as period, amplitude, Zeitgeber strength, and daylength, govern entrainment ranges and phases of entrainment. We have tested whether simple amplitude-phase models can provide insight into the control of entrainment phases. Using global optimization, we derived conceptual models with just three free parameters (period, amplitude, and relaxation rate) that reproduce known phenotypic features of vertebrate clocks: phase response curves (PRCs) with relatively small phase shifts, fast re-entrainment after jet lag, and seasonal variability to track light onset or offset. Since optimization found multiple sets of model parameters, we could study this model ensemble to gain insight into the underlying design principles. We found complex associations between model parameters and entrainment features. Arnold onions of representative models visualize strong dependencies of entrainment on periods, relative Zeitgeber strength, and photoperiods. Our results support the use of oscillator theory as a framework for understanding the entrainment of circadian clocks.
- PLOS ONE, prel. accepted (2020)
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Circadian clocks control rhythms in physiology and behavior entrained to 24 h light – dark cycles. Despite of conserved general schemes, molecular circadian clockworks differ between insect species. With RNA interference (RNAi) we examined an ancient circadian clockwork in a basic insect, the hemimetabolous Madeira cockroach Rhyparobia maderae. With injections of double-stranded RNA (dsRNA) of cockroach period (Rm´per), timeless 1 (Rm´tim1), or cryptochrome 2 (Rm´cry2) we searched for essential components of the clock´s core negative feedback loop. Single injections of dsRNA of each clock gene into adult cockroaches successfully and permanently knocked down respective mRNA levels within ~two weeks deleting daytime-dependent mRNA rhythms for Rm´per and Rm´cry2. Rm´perRNAi or Rm´cry2RNAi affected total mRNA levels of both genes, while Rm´tim1 transcription was independent of both, also keeping rhythmic expression. Unexpectedly, circadian locomotor activity of most cockroaches remained rhythmic for each clock gene knockdown employed. It expressed weakened rhythms and unchanged periods for Rm´perRNAi and shorter periods for Rm´tim1RNAi and Rm´cry2RNAi. As a hypothesis of the cockroach´s molecular clockwork, a basic network of switched differential equations was developed to model the oscillatory behavior of clock cells expressing respective clock genes. Data were consistent with two synchronized main groups of coupled oscillator cells, a leading (morning) oscillator, or a lagging (evening) oscillator that couple via mutual inhibition. The morning oscillators express shorter, the evening oscillators longer endogenous periods based on core feedback loops with either PER, TIM1, or CRY2/PER complexes as dominant negative feedback of the clockwork. We hypothesize that dominant morning oscillator cells with shorter periods express PER, but not CRY2, or TIM1 as suppressor of clock gene expression, while two groups of evening oscillator cells with longer periods either comprise TIM1 or CRY2/PER suppressing complexes. Modelling suggests that there is an additional negative feedback next to Rm´PER in cockroach morning oscillator cells.
- Scientific Reports volume 10, Article number: 4399 (2020)
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The retina decomposes visual stimuli into parallel channels that encode different features of the visual environment. Central to this computation is the synaptic processing in a dense layer of neuropil, the so-called inner plexiform layer (IPL). Here, different types of bipolar cells stratifying at distinct depths relay the excitatory feedforward drive from photoreceptors to amacrine and ganglion cells. Current experimental techniques for studying processing in the IPL do not allow imaging the entire IPL simultaneously in the intact tissue. Here, we extend a two-photon microscope with an electrically tunable lens allowing us to obtain optical vertical slices of the IPL, which provide a complete picture of the response diversity of bipolar cells at a “single glance”. The nature of these axial recordings additionally allowed us to isolate and investigate batch effects, i.e. inter-experimental variations resulting in systematic differences in response speed. As a proof of principle, we developed a simple model that disentangles biological from experimental causes of variability and allowed us to recover the characteristic gradient of response speeds across the IPL with higher precision than before. Our new framework will make it possible to study the computations performed in the central synaptic layer of the retina more efficiently.
- BioRxiv, 780031 (2019)
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In the outer plexiform layer (OPL) of the mouse retina, two types of cone photoreceptors (cones) provide input to more than a dozen types of cone bipolar cells (CBCs). This transmission is modulated by a single horizontal cell (HC) type, the only interneuron in the outer retina. Horizontal cells form feedback synapses with cones and feedforward synapses with CBCs. However, the exact computational role of HCs is still debated. Along with performing global signaling within their laterally coupled network, HCs also provide local, cone-specific feedback. Specifically, it has not been clear which synaptic structures HCs use to provide local feedback to cones and global forward signaling to CBCs.
Here, we reconstructed in a serial block-face electron microscopy volume the dendritic trees of five HCs as well as cone axon terminals and CBC dendrites to quantitatively analyze their connectivity. In addition to the fine HC dendritic tips invaginating cone axon terminals, we also identified “bulbs”, short segments of increased dendritic diameter on the primary dendrites of HCs. These bulbs are located well below the cone axon terminal base and make contact to other cells mostly identified as other HCs or CBCs. Using immunolabeling we show that HC bulbs express vesicular gamma-aminobutyric acid transporters and co-localize with GABA receptor γ2 subunits. Together, this suggests the existence of two synaptic strata in the mouse OPL, spatially separating cone-specific feedback and feedforward signaling to CBCs. A biophysics-based computational model of a HC dendritic branch supports the hypothesis that the spatial arrangement of synaptic contacts allows simultaneous local feedback and global feedforward signaling.
- Brain. 142(9):2558-2571 (2019)
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Pathological alterations to the locus coeruleus, the major source of noradrenaline in the brain, are histologically evident in early stages of neurodegenerative diseases. Novel MRI approaches now provide an opportunity to quantify structural features of the locus coeruleus in vivo during disease progression. In combination with neuropathological biomarkers, in vivo locus coeruleus imaging could help to understand the contribution of locus coeruleus neurodegeneration to clinical and pathological manifestations in Alzheimer’s disease, atypical neurodegenerative dementias and Parkinson’s disease. Moreover, as the functional sensitivity of the noradrenergic system is likely to change with disease progression, in vivo measures of locus coeruleus integrity could provide new pathophysiological insights into cognitive and behavioural symptoms. Locus coeruleus imaging also holds the promise to stratify patients into clinical trials according to noradrenergic dysfunction. In this article, we present a consensus on how non-invasive in vivo assessment of locus coeruleus integrity can be used for clinical research in neurodegenerative diseases. We outline the next steps for in vivo, post-mortem and clinical studies that can lay the groundwork to evaluate the potential of locus coeruleus imaging as a biomarker for neurodegenerative diseases.
Neuron density fundamentally relates to architecture and connectivity of the primate cerebral cortex
NeuroImage, 189, 777-792 (2019)Authors:
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Studies of structural brain connectivity have revealed many intriguing features of complex cortical networks. To advance integrative theories of cortical organization, an understanding is required of how connectivity interrelates with other aspects of brain structure. Recent studies have suggested that interareal connectivity may be related to a variety of macroscopic as well as microscopic architectonic features of cortical areas. However, it is unclear how these features are inter-dependent and which of them most strongly and fundamentally relate to structural corticocortical connectivity. Here, we systematically investigated the relation of a range of microscopic and macroscopic architectonic features of cortical organization, namely layer III pyramidal cell soma cross section, dendritic synapse count, dendritic synapse density and dendritic tree size as well as area neuron density, to multiple properties of cortical connectivity, using a comprehensive, up-to-date structural connectome of the primate brain. Importantly, relationships were investigated by multi-variate analyses to account for the interrelations of features. Of all considered factors, the classical architectonic parameter of neuron density most strongly and consistently related to essential features of cortical connectivity (existence and laminar patterns of projections, area degree), and in conjoint analyses largely abolished effects of cellular morphological features. These results confirm neuron density as a central architectonic indicator of the primate cerebral cortex that is closely related to essential aspects of brain connectivity and is also highly indicative of further features of the architectonic organization of cortical areas, such as the considered cellular morphological measures. Our findings integrate several aspects of cortical micro- and macroscopic organization, with implications for cortical development and function.
- Neurophotonics, 6(1), 015005 (2019)
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The goal of understanding the architecture of neural circuits at the synapse level with a brain-wide perspective has powered the interest in high-speed and large field-of view volumetric imaging at subcellular resolution. Here we developed a method combining tissue expansion and light sheet fluorescence microscopy to allow extended volumetric super resolution high-speed imaging of large mouse brain samples. We demonstrate the capabilities of this method by performing two color fast volumetric super resolution imaging of mouse CA1 and dentate gyrus molecular-, granule cell- and polymorphic layers. Our method enables an exact evaluation of granule cell and neurite morphology within the context of large cell ensembles spanning several orders of magnitude in resolution. We found that imaging a brain region of 1 mm3 in super resolution using light sheet fluorescence expansion microscopy is about 17-fold faster than imaging the same region by a current state of the art high resolution confocal laser scanning microscope.
- Data in Brief 25:104132 (2019)
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The hMRI toolbox is an open-source toolbox for the calculation of quantitative MRI parameter maps from a series of weighted imaging data, and optionally additional calibration data. The multi- parameter mapping (MPM) protocol, incorporating calibration data to correct for spatial variation in the scanner’s transmit and receive fields, is the most complete protocol that can be handled by the toolbox. Here we present a dataset acquired with such a full MPM protocol, which is made freely available to be used as a tutorial by following instructions provided on the associated toolbox wiki pages, which can be found at http://hMRI.info, and following the theory described in: hMRI e A toolbox for quantitative MRI in neuroscience and clinical research.
- Biol Cybern Dec;113(5-6):475-494 (2019)
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Neural responses to sudden changes can be observed in many parts of the sensory pathways at different organizational levels. For example, deviants that violate regularity at various levels of abstraction can be observed as simple On/Off responses of individual neurons or as cumulative responses of neural populations. The cortical deviance-related responses supporting different functionalities (e.g., gap detection, chunking, etc.) seem unlikely to arise from different function-specific neural circuits, given the relatively uniform and self-similar wiring patterns across cortical areas and spatial scales. Additionally, reciprocal wiring patterns (with heterogeneous combinations of excitatory and inhibitory connections) in the cortex naturally speak in favor of a generic deviance detection principle. Based on this concept, we propose a network model consisting of reciprocally coupled neural masses as a blueprint of a universal change detector. Simulation examples reproduce properties of cortical deviance-related responses including the On/Off responses, the omitted-stimulus response (OSR), and the mismatch negativity (MMN). We propose that the emergence of change detectors relies on the involvement of disinhibition. An analysis of network connection settings further suggests a supportive effect of synaptic adaptation and a destructive effect of N-methyl-D-aspartate receptor (NMDA-r) antagonists on change detection. We conclude that the nature of cortical reciprocal wiring gives rise to a whole range of local change detectors supporting the notion of a generic deviance detection principle. Several testable predictions are provided based on the network model. Notably, we predict that the NMDA-r antagonists would generally dampen the cortical Off response, the cortical OSR, and the MMN.
- Network Neuroscience, 3(4), 1038-1050 (2019)
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The anatomical wiring of the brain is a central focus in network neuroscience. Diffusion MRI tractography offers the unique opportunity to investigate the brain fiber architecture in vivo and noninvasively. However, its reliability is still highly debated. Here, we explored the ability of diffusion MRI tractography to match invasive anatomical tract-tracing connectivity data of the ferret brain. We also investigated the influence of several state-of-the-art tractography algorithms on this match to ground truth connectivity data. Tract-tracing connectivity data were obtained from retrograde tracer injections into the occipital, parietal, and temporal cortices of adult ferrets. We found that the relative densities of projections identified from the anatomical experiments were highly correlated with the estimates from all the studied diffusion tractography algorithms (Spearman’s rho ranging from 0.67 to 0.91), while only small, nonsignificant variations appeared across the tractography algorithms. These results are comparable to findings reported in mouse and monkey, increasing the confidence in diffusion MRI tractography results. Moreover, our results provide insights into the variations of sensitivity and specificity of the tractography algorithms, and hence into the influence of choosing one algorithm over another.
2019
- Journal of Comparative Neurology, 527(8), 1293-1314 (2019)
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The present study describes the ipsilateral and contralateral corticocortical and corticothalamic connectivity of the occipital visual areas 17, 18, 19, and 21 in the ferret using standard anatomical tract-tracing methods. In line with previous studies of mammalian visual cortex connectivity, substantially more anterograde and retrograde label was present in the hemisphere ipsilateral to the injection site compared to the contralateral hemisphere. Ipsilateral reciprocal connectivity was the strongest within the occipital visual areas, while weaker connectivity strength was observed in the temporal, suprasylvian, and parietal visual areas. Callosal connectivity tended to be strongest in the homotopic cortical areas, and revealed a similar areal distribution to that observed in the ipsilateral hemisphere, although often less widespread across cortical areas. Ipsilateral reciprocal connectivity was observed throughout the visual nuclei of the dorsal thalamus, with no contralateral connections to the visual thalamus being observed. The current study, along with previous studies of connectivity in the cat, identified the posteromedial lateral suprasylvian visual area (PMLS) as a distinct network hub external to the occipital visual areas in carnivores, implicating PMLS as a potential gateway to the parietal cortex for dorsal stream processing. These data will also contribute to a macro connectome database of the ferret brain, providing essential data for connectomics analyses and cross-species analyses of connectomes and brain connectivity matrices, as well as providing data relevant to additional studies of cortical connectivity across mammals and the evolution of cortical connectivity variation.
- Journal of Comparative Neurology, 527(8), 1315-1332 (2019)
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The present study describes the ipsilateral and contralateral cortico‐cortical and cortico‐thalamic connectivity of the parietal visual areas, posterior parietal caudal cortical area (PPc) and posterior parietal rostral cortical area (PPr), in the ferret using standard anatomical tract‐tracing methods. The two divisions of posterior parietal cortex of the ferret are strongly interconnected, however area PPc shows stronger connectivity with the occipital and suprasylvian visual cortex, while area PPr shows stronger connectivity with the somatomotor cortex, reflecting the functional specificity of these two areas. This pattern of connectivity is mirrored in the contralateral callosal connections. In addition, PPc and PPr are connected with the visual and somatomotor nuclei of the dorsal thalamus. Numerous connectional similarities exist between the posterior parietal cortex of the ferret (PPc and PPr) and the cat (area 7 and 5), indicative of the homology of these areas within the Carnivora. These findings highlight the existence of a frontoparietal network as a shared feature of the organization of parietal cortex across Euarchontoglires and Laurasiatherians, with the degree of expression varying in relation to the expansion and areal complexity of the posterior parietal cortex. This observation indicates that the ferret is a potentially valuable experimental model animal for understanding the evolution and function of the posterior parietal cortex and the frontoparietal network across mammals. The data generated will also contribute to a connectomics database, to further cross‐species analyses of connectomes and illuminate wiring principles of cortical connectivity across mammals.
- Journal of Comparative Neurology, 527(8), 1333-1347 (2019)
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The present study describes the ipsilateral and contralateral corticocortical and corticothalamic connectivity of the temporal visual areas 20a and 20b in the ferret using standard anatomical tract‐tracing methods. The two temporal visual areas are strongly interconnected, but area 20a is primarily connected to the occipital visual areas, whereas area 20b maintains more widespread connections with the occipital, parietal and suprasylvian visual areas and the secondary auditory cortex. The callosal connectivity, although homotopic, consists mainly of very weak anterograde labeling which was more widespread in area 20a than area 20b. Although areas 20a and 20b are well connected with the visual dorsal thalamus, the injection into area 20a resulted in more anterograde label, whereas more retrograde label was observed in the visual thalamus following the injection into area 20b. Most interestingly, comparisons to previous connectional studies of cat areas 20a and 20b reveal a common pattern of connectivity of the temporal visual cortex in carnivores, where the posterior parietal cortex and the central temporal region (PMLS) provide network points required for dorsal and ventral stream interaction enroute to integration in the prefrontal cortex. This pattern of network connectivity is not dissimilar to that observed in primates, which highlights the ferret as a useful animal model to understand visual sensory integration between the dorsal and ventral streams. The data generated will also contribute to a connectomics database, to facilitate cross species analysis of brain connectomes and wiring principles of the brain.
- Future Trends of HPC in a Disruptive Scenario, 34, 223 (2019)
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Mapping the microscopical organization of the human cerebral cortex provides a basis for multimodal brain atlases, and is indispensable for allocating functional imaging, physiological, connectivity, molecular, or genetic data to anatomically well specified structural entities of human brain organization at micrometer resolution. The analysis of histological sections is still considered a “gold standard” in brain mapping, and compared with other maps, e.g. from neuroimaging studies [1]. But while the spatial patterns of neuronal cells are inherently three-dimensional, such microscopic analysis is usually performed in individual 2D sections. Here we propose an HPC-based workflow that aims to recover the three-dimensional context from a stack of histological sections stained for neuronal cell bodies, imaged under a light microscope. Our aim is to align image data in consecutive sections at the micrometer resolution, where the texture is dominated by small objects like cell bodies, that often do not extend across sections. Therefore we cannot apply classical intensity-based image registration, where similarity of neighboring images is optimized at the pixel level. Our main contribution is a procedure to explicitly detect and match vessel-like structures in the brain tissue, guiding a feature-based image registration algorithm to 3D reconstruct regions of interest in the brain and recover the distribution of neuronal cells. To replace erroneous information in corrupted tissue areas, we further propose a simple predictive algorithm which generates realistic cell detections by learning from intact tissue parts in the local surroundings.
- Science advances, 5(4), eaar7633. (2019)
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Intrinsically generated patterns of coupled neuronal activity are associated with the dynamics of specific brain states. Sensory inputs are extrinsic factors that can perturb these intrinsic coupling modes, creating a complex scenario in which forthcoming stimuli are processed. Studying this intrinsic-extrinsic interplay is necessary to better understand perceptual integration and selection. Here, we show that this interplay leads to a reconfiguration of functional cortical connectivity that acts as a mechanism to facilitate stimulus processing. Using audiovisual stimulation in anesthetized ferrets, we found that this reconfiguration of coupling modes is context specific, depending on long-term modulation by repetitive sensory inputs. These reconfigured coupling modes lead to changes in latencies and power of local field potential responses that support multisensory integration. Our study demonstrates that this interplay extends across multiple time scales and involves different types of intrinsic coupling. These results suggest a previously unknown large-scale mechanism that facilitates multisensory integration.
- Magn Reson Med. 9 81:1265-1279 (2019)
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Abstract
Purpose
Water mobility in tissues is related to the microstructure that modulates diffusion and spin relaxation. Previous work has shown that the extracellular matrix (ECM) impacts water diffusion in cartilage. To investigate if similar contributions to image contrast exist for brain, which is characterized by a substantially lower ECM content, diffusion and relaxation were studied in fixed samples from goat and human thalamus before and after enzymatic digestion of ECM compounds. Selected experiments in human corpus callosum were included for comparing subcortical gray matter and white matter.Methods
Digestion of matrix components was achieved by treatment with hyaluronidase. Nonlocalized pulsed field gradient measurements were performed with urn:x-wiley:07403194:media:mrm27459:mrm27459-math-0001 values between 0.6 and 18,000 s/mm2 at 3T and temperatures between 0°C and 20°C, in addition to T1 and T2 relaxation measurements. The data were fitted to multiexponential models to account for different water compartments. After the measurements, the samples were sliced and stained for ECM‐sensitive markers to verify efficient digestion.Results
Microstructural alterations associated with hyaluronan digestion did not lead to measurable effects on water diffusion or urn:x-wiley:07403194:media:mrm27459:mrm27459-math-0002. However, T1 of the main relaxographic component, attributed to intra‐/extracellular water, decreased by 7%.Conclusion
Investigations with very strong gradients did not reveal a detectable effect on water diffusion or urn:x-wiley:07403194:media:mrm27459:mrm27459-math-0003 after hyaluronan removal, indicating that the brain ECM content is too low to produce a detectable effect. The subtle alteration of T1 upon hyaluronidase treatment might reflect a modulation of intercompartmental water exchange properties. - PLoS biology, 17(3), e2005346 (2019)
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The cerebral cortex of mammals exhibits intricate interareal wiring. Moreover, mammalian cortices differ vastly in size, cytological composition, and phylogenetic distance. Given such complexity and pronounced species differences, it is a considerable challenge to decipher organizational principles of mammalian connectomes. Here, we demonstrate species-specific and species-general unifying principles linking the physical, cytological, and connectional dimensions of architecture in the mouse, cat, marmoset, and macaque monkey. The existence of connections is related to the cytology of cortical areas, in addition to the role of physical distance, but this relation is attenuated in mice and marmoset monkeys. The cytoarchitectonic cortical gradients, and not the rostrocaudal axis of the cortex, are closely linked to the laminar origin of connections, a principle that allows the extrapolation of this connectional feature to humans. Lastly, a network core, with a central role under different modes of network communication, characterizes all cortical connectomes. We observe a displacement of the network core in mammals, with a shift of the core of cats and macaque monkeys toward the less neuronally dense areas of the cerebral cortex. This displacement has functional ramifications but also entails a potential increased degree of vulnerability to pathology. In sum, our results sketch out a blueprint of mammalian connectomes consisting of species-specific and species-general links between the connectional, physical, and cytological dimensions of the cerebral cortex, possibly reflecting variations and persistence of evolutionarily conserved mechanisms and cellular phenomena. Our framework elucidates organizational principles that encompass but also extend beyond the wiring economy principle imposed by the physical embedding of the cerebral cortex.
- Science advances, 5(6), eaav9694 (2019)
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The wiring of vertebrate and invertebrate brains provides the anatomical skeleton for cognition and behavior. Connections among brain regions are characterized by heterogeneous strength that is parsimoniously described by the wiring cost and homophily principles. Moreover, brains exhibit a characteristic global network topology, including modules and hubs. However, the mechanisms resulting in the observed interregional wiring principles and network topology of brains are unknown. Here, with the aid of computational modeling, we demonstrate that a mechanism based on heterochronous and spatially ordered neurodevelopmental gradients, without the involvement of activity-dependent plasticity or axonal guidance cues, can reconstruct a large part of the wiring principles (on average, 83%) and global network topology (on average, 80%) of diverse adult brain connectomes, including fly and human connectomes. In sum, space and time are key components of a parsimonious, plausible neurodevelopmental mechanism of brain wiring with a potential universal scope, encompassing vertebrate and invertebrate brains.
- arXiv:1905.07488 (2019)
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How can one perform Bayesian inference on stochastic simulators with intractable likelihoods? A recent approach is to learn the posterior from adaptively proposed simulations using neural network-based conditional density estimators. However, existing methods are limited to a narrow range of proposal distributions or require importance weighting that can limit performance in practice. Here we present automatic posterior transformation (APT), a new sequential neural posterior estimation method for simulation-based inference. APT can modify the posterior estimate using arbitrary, dynamically updated proposals, and is compatible with powerful flow-based density estimators. It is more flexible, scalable and efficient than previous simulation-based inference techniques. APT can operate directly on high-dimensional time series and image data, opening up new applications for likelihood-free inference.
- Network Neuroscience, 3(4), 905-923 (2019)
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The connections linking neurons within and between cerebral cortical areas form a multiscale network for communication. We review recent work relating essential features of cortico-cortical connections, such as their existence and laminar origins and terminations, to fundamental structural parameters of cortical areas, such as their distance, similarity in cytoarchitecture, defined by lamination or neuronal density, and other macroscopic and microscopic structural features. These analyses demonstrate the presence of an architectonic type principle. Across species and cortices, the essential features of cortico-cortical connections vary consistently and strongly with the cytoarchitectonic similarity of cortical areas. By contrast, in multivariate analyses such relations were not found consistently for distance, similarity of cortical thickness, or cellular morphology. Gradients of laminar cortical differentiation, as reflected in overall neuronal density, also correspond to regional variations of cellular features, forming a spatially ordered natural axis of concerted architectonic and connectional changes across the cortical sheet. The robustness of findings across mammalian brains allows cross-species predictions of the existence and laminar patterns of projections, including estimates for the human brain that are not yet available experimentally. The architectonic type principle integrates cortical connectivity and architecture across scales, with implications for computational explorations of cortical physiology and developmental mechanisms.
- Brain, 142(10), 3129–3143 (2019)
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Neuroimaging has seen a paradigm shift away from a formal description of local activity patterns towards studying distributed brain networks. The recently defined framework of the ‘human connectome’ enables global analysis of parts of the brain and their interconnections. Deep brain stimulation (DBS) is an invasive therapy for patients with severe movement disorders aiming to retune abnormal brain network activity by local high frequency stimulation of the basal ganglia. Beyond clinical utility, DBS represents a powerful research platform to study functional connectomics and the modulation of distributed brain networks in the human brain. We acquired resting-state functional MRI in 20 patients with Parkinson’s disease with subthalamic DBS switched on and off. An age-matched control cohort of 15 subjects was acquired from an open data repository. DBS lead placement in the subthalamic nucleus was localized using a state-of-the art pipeline that involved brain shift correction, multispectral image registration and use of a precise subcortical atlas. Based on a realistic 3D model of the electrode and surrounding anatomy, the amount of local impact of DBS was estimated using a finite element method approach. On a global level, average connectivity increases and decreases throughout the brain were estimated by contrasting on and off DBS scans on a voxel-wise graph comprising eight thousand nodes. Local impact of DBS on the motor subthalamic nucleus explained half the variance in global connectivity increases within the motor network (R = 0.711, P < 0.001). Moreover, local impact of DBS on the motor subthalamic nucleus could explain the degree to how much voxel-wise average brain connectivity normalized towards healthy controls (R = 0.713, P < 0.001). Finally, a network-based statistics analysis revealed that DBS attenuated specific couplings known to be pathological in Parkinson’s disease. Namely, coupling between motor thalamus and motor cortex was increased while striatal coupling with cerebellum, external pallidum and subthalamic nucleus was decreased by DBS. Our results show that resting state functional MRI may be acquired in DBS on and off conditions on clinical MRI hardware and that data are useful to gain additional insight into how DBS modulates the functional connectome of the human brain. We demonstrate that effective DBS increases overall connectivity in the motor network, normalizes the network profile towards healthy controls and specifically strengthens thalamo-cortical connectivity while reducing striatal control over basal ganglia and cerebellar structures.
- Neuron 103, 118-132 (2019)
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Animals use global image motion cues to actively stabilize their position by compensatory movements. Neurons in the zebrafish pretectum distinguish different optic flow patterns, e.g., rotation and translation, to drive appropriate behaviors. Combining functional imaging and morphological reconstruction of single cells, we revealed critical neuroanatomical features of this sensorimotor transformation. Terminals of direction-selective retinal ganglion cells (DS-RGCs) are located within the pretectal retinal arborization field 5 (AF5), where they meet dendrites of pretectal neurons with simple tuning to monocular optic flow. Translation-selective neurons, which respond selectively to optic flow in the same direction for both eyes, are intermingled with these simple cells but do not receive inputs from DS-RGCs. Mutually exclusive populations of pretectal projection neurons innervate either the reticular formation or the cerebellum, which in turn control motor responses. We posit that local computations in a defined pretectal circuit transform optic flow signals into neural commands driving optomotor behavior.
- Scientific reports 9.1 (2019): 1-16 (2019)
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The laminar organization of the cerebral cortex is a fundamental characteristic of the brain, with essential implications for cortical function. Due to the rapidly growing amount of high-resolution brain imaging data, a great demand arises for automated and flexible methods for discriminating the laminar texture of the cortex. Here, we propose a combined approach of unsupervised and supervised machine learning to discriminate the hierarchical cortical laminar organization in high-resolution 2-photon microscopic neural image data of mouse brain without observer bias, that is, without the prerequisite of manually labeled training data. For local cortical foci, we modify an unsupervised clustering approach to identify and represent the laminar cortical structure. Subsequently, supervised machine learning is applied to transfer the resulting layer labels across different locations and image data, to ensure the existence of a consistent layer label system. By using neurobiologically meaningful features, the discrimination results are shown to be consistent with the layer classification of the classical Brodmann scheme, and provide additional insight into the structure of the cerebral cortex and its hierarchical organization. Thus, our work paves a new way for studying the anatomical organization of the cerebral cortex, and potentially its functional organization.
- Frontiers in Integrative Neuroscience, 13, 54 (2019)
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Activity patterns of cerebral cortical regions represent the current environment in which animals receive multi-modal inputs. These patterns are also shaped by the history of activity that reflects learned information on past multimodal exposures. We studied the long-term dynamics of cortical activity patterns during the formation of multimodal memories by analyzing in vivo high-resolution 2-photon mouse brain imaging data of Immediate Early Gene (IEG) expression, resolved by cortical layers. Strikingly, in superficial layers II/III, the patterns showed similar dynamics across structurally and functionally distinct cortical areas and the consistency of dynamic patterns lasted for one to several days. By contrast, in deep layer V, the activity dynamics varied across different areas, and the current activities were sensitive to the previous activities at different time points, depending on the cortical locations, indicating that the information stored in the cortex at different time points was distributed across different cortical areas. These results suggest different roles of superficial and deep layer neurons in the long-term multimodal representation of the environment.
- In Symposium on Advances in Approximate Bayesian Inference (pp. 32-53) (2019)
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Approximate Bayesian Computation (ABC) provides methods for Bayesian inference in simulation-based models which do not permit tractable likelihoods. We present a new ABC method which uses probabilistic neural emulator networks to learn synthetic likelihoods on simulated data – both ’local’ emulators which approximate the likelihood for specific observed data, as well as ’global’ ones which are applicable to a range of data. Simulations are chosen adaptively using an acquisition function which takes into account uncertainty about either the posterior distribution of interest, or the parameters of the emulator. Our approach does not rely on user-defined rejection thresholds or distance functions. We illustrate inference with emulator networks on synthetic examples and on a biophysical neuron model, and show that emulators allow accurate and efficient inference even on problems which are challenging for conventional ABC approaches.
- arXiv preprint arXiv:1909.01908 (2019)
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A complex interplay of single-neuron properties and the recurrent network structure shapes the activity of individual cortical neurons, which differs in general from the respective population activity. We develop a theory that makes it possible to investigate the influence of both network structure and single-neuron properties on the single-neuron statistics in block-structured sparse random networks of spiking neurons. In particular, the theory predicts the neuron-level autocorrelation times, also known as intrinsic timescales, of the neuronal activity. The theory is based on a postulated extension of dynamic mean-field theory from rate networks to spiking networks, which is validated via simulations. It accounts for both static variability, e.g. due to a distributed number of incoming synapses per neuron, and dynamical fluctuations of the input. To illustrate the theory, we apply it to a balanced random network of leaky integrate-and-fire neurons, a balanced random network of generalized linear model neurons, and a biologically constrained network of leaky integrate-and-fire neurons. For the generalized linear model network, an analytical solution to the colored noise problem allows us to obtain self-consistent firing rate distributions, single-neuron power spectra, and intrinsic timescales. For the leaky integrate-and-fire networks, we obtain the same quantities by means of a novel analytical approximation of the colored noise problem that is valid in the fluctuation-driven regime. Our results provide a further step towards an understanding of the dynamics in recurrent spiking cortical networks.
- Magnetic resonance in medicine, 82(5), 1804-1811 (2019)
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Purpose: To propose and validate an efficient method, based on a biophysically motivated signal model, for removing the orientation‐dependent part of R*2 using a single gradient‐recalled echo (GRE) measurement.
Methods: The proposed method utilized a temporal second‐order approximation of the hollow‐cylinder‐fiber model, in which the parameter describing the linear signal decay corresponded to the orientation‐independent part of R*2. The estimated parameters were compared to the classical, mono‐exponential decay model for R*2 in a sample of an ex vivo human optic chiasm (OC). The OC was measured at 16 distinct orientations relative to the external magnetic field using GRE at 7T. To show that the proposed signal model can remove the orientation dependence of R*2, it was compared to the established phenomenological method for separating R*2 into orientation-dependent and independent parts.
Results: Using the phenomenological method on the classical signal model, the well‐known separation of R*2 into orientation‐dependent and ‐independent parts was verified. For the proposed model, no significant orientation dependence in the linear signal decay parameter was observed.
Conclusions: Since the proposed second‐order model features orientation‐dependent and ‐independent components at distinct temporal orders, it can be used to remove the orientation dependence of R*2 using only a single GRE measurement.
- Computational Brain & Behavior volume 2, pages229–232 (2019)
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The Target Article by Lee et al. (2019) highlights the ways in which ongoing concerns about research reproducibility extend to model-based approaches in cognitive science. Whereas Lee et al. focus primarily on the importance of research practices to improve model robustness, we propose that the transparent sharing of model specifications, including their inputs and outputs, is also essential to improving the reproducibility of model-based analyses. We outline an ongoing effort (within the context of the Brain Imaging Data Structure community) to develop standards for the sharing of the structure of computational models and their outputs.
- Network Neuroscience, 3(4), 944-968 (2019)
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The circadian clock of the nocturnal Madeira cockroach is located in the accessory medulla, a small non-retinotopic neuropil in the brain’s visual system. The clock comprises about 240 neurons that control rhythms in physiology and behaviour such as sleep-wake cycles. The clock neurons contain an abundant number of partly co-localized neuropeptides, among them pigment-dispersing factor (PDF), the insects’ most important circadian coupling signal that controls sleep-wake rhythms. We performed long-term loose patch clamp recordings under 12:12 h light-dark cycles in the cockroach clock in vivo. A wide range of timescales, from milliseconds to seconds, was found in spike and field potential patterns. We developed a framework of wavelet transform based methods to detect these multiscale electrical events. We analysed frequencies and patterns of events with interesting dynamic features, such as mixed-mode oscillations reminiscent of sharp-wave ripples. Oscillations in the beta/gamma frequency range (20 – 40 Hz) were observed to rise at dawn, when PDF is released, peaking just before the onset of locomotor activity of the nocturnal cockroach. We expect, that in vivo electrophysiological recordings combined with neuropeptide/antagonist applications and behavioural analysis will determine whether specific patterns of electrical activity recorded in the network of the cockroach circadian clock are causally related to with neuropeptide-dependent control of behaviour.
- PLOS Computational Biology, 15(10), e1007004 (2019)
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With the advent of advanced MRI techniques it has become possible to study axonal white matter non-invasively and in great detail. Measuring the various parameters of the long-range connections of the brain opens up the possibility to build and refine detailed models of large-scale neuronal activity. One particular challenge is to find a mathematical description of action potential propagation that is sufficiently simple, yet still biologically plausible to model signal transmission across entire axonal fibre bundles. We develop a mathematical framework in which we replace the Hodgkin-Huxley dynamics by a spike-diffuse-spike model with passive sub-threshold dynamics and explicit, threshold-activated ion channel currents. This allows us to study in detail the influence of the various model parameters on the action potential velocity and on the entrainment of action potentials between ephaptically coupled fibres without having to recur to numerical simulations. Specifically, we recover known results regarding the influence of axon diameter, node of Ranvier length and internode length on the velocity of action potentials. Additionally, we find that the velocity depends more strongly on the thickness of the myelin sheath than was suggested by previous theoretical studies. We further explain the slowing down and synchronisation of action potentials in ephaptically coupled fibres by their dynamic interaction. In summary, this study presents a solution to incorporate detailed axonal parameters into a whole-brain modelling framework.
- Scientific data, 6(1), 1-12 (2019)
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Models of large-scale brain networks that are informed by the underlying anatomical connectivity contribute to our understanding of the mapping between the structure of the brain and its dynamical function. Connectome-based modelling is a promising approach to a more comprehensive understanding of brain function across spatial and temporal scales, but it must be constrained by multi-scale empirical data from animal models. Here we describe the construction of a macaque (Macaca mulatta and Macaca fascicularis) connectome for whole-cortex simulations in TheVirtualBrain, an open-source simulation platform. We take advantage of available axonal tract-tracing datasets and enhance the existing connectome data using diffusion-based tractography in macaques. We illustrate the utility of the connectome as an extension of TheVirtualBrain by simulating resting-state BOLD-fMRI data and fitting it to empirical resting-state data.
- Frontiers in computational neuroscience, 13, 54 (2019)
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Introduction: While the prevalence of neurodegenerative diseases associated with dementia such as Alzheimer’s disease (AD) increases, our knowledge on the underlying mechanisms, outcome predictors, or therapeutic targets is limited. In this work, we demonstrate how computational multi-scale brain modeling links phenomena of different scales and therefore identifies potential disease mechanisms leading the way to improved diagnostics and treatment. Methods: The Virtual Brain (TVB; thevirtualbrain.org) neuroinformatics platform allows standardized large-scale structural connectivity-based simulations of whole brain dynamics. We provide proof of concept for a novel approach that quantitatively links the effects of altered molecular pathways onto neuronal population dynamics. As a novelty, we connect chemical compounds measured with positron emission tomography (PET) with neural function in TVB addressing the phenomenon of hyperexcitability in AD related to the protein amyloid beta (Abeta). We construct personalized virtual brains based on an averaged healthy connectome and individual PET derived distributions of Abeta in patients with mild cognitive impairment (MCI, N = 8) and Alzheimer’s Disease (AD, N = 10) and in age-matched healthy controls (HC, N = 15) using data from ADNI-3 data base (http://adni.loni.usc.edu). In the personalized virtual brains, individual Abeta burden modulates regional Excitation-Inhibition balance, leading to local hyperexcitation with high Abeta loads. We analyze simulated regional neural activity and electroencephalograms (EEG). Results: Known empirical alterations of EEG in patients with AD compared to HCs were reproduced by simulations. The virtual AD group showed slower frequencies in simulated local field potentials and EEG compared to MCI and HC groups. The heterogeneity Stefanovski et al. The Virtual Neurodegenerative Brain of the Abeta load is crucial for the virtual EEG slowing which is absent for control models with homogeneous Abeta distributions. Slowing phenomena primarily affect the network hubs, independent of the spatial distribution of Abeta. Modeling the N-methyl-D-aspartate (NMDA) receptor antagonism of memantine in local population models, reveals potential functional reversibility of the observed large-scale alterations (reflected by EEG slowing) in virtual AD brains. Discussion: We demonstrate how TVB enables the simulation of systems effects caused by pathogenetic molecular candidate mechanisms in human virtual brains.
- Neuroimage, 194, 191-210 (2019)
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Neuroscience and clinical researchers are increasingly interested in quantitative magnetic resonance imaging (qMRI) due to its sensitivity to micro-structural properties of brain tissue such as axon, myelin, iron and water concentration. We introduce the hMRI-toolbox, an open-source, easy-to-use tool available on GitHub, for qMRI data handling and processing, presented together with a tutorial and example dataset. This toolbox allows the estimation of high-quality multi-parameter qMRI maps (longitudinal and effective transverse relaxation rates R1 and R⋆2 , proton density PD and magnetisation transfer MT saturation) that can be used for quantitative parameter analysis and accurate delineation of subcortical brain structures. The qMRI maps generated by the toolbox are key input parameters for biophysical models designed to estimate tissue microstructure properties such as the MR g- ratio and to derive standard and novel MRI biomarkers. Thus, the current version of the toolbox is a first step towards in vivo histology using MRI (hMRI) and is being extended further in this direction. Embedded in the Statistical Parametric Mapping (SPM) framework, it benefits from the extensive range of established SPM tools for high-accuracy spatial registration and statistical inferences and can be readily combined with existing SPM toolboxes for estimating diffusion MRI parameter maps. From a user’s perspective, the hMRI-toolbox is an effi- cient, robust and simple framework for investigating qMRI data in neuroscience and clinical research.
- Current Biology, 29(19), R933-R935 (2019)
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In animal eyes, the detection of slow global image motion is crucial to preventing blurry vision. A new study reveals how a mammalian global motion detector achieves this through ‘space–time wiring’ at its dendrites.
- Network Neuroscience Volume 3 | Issue 1 | 2019 (2019)
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Structural connectivity (SC), the physical pathways connecting regions in the brain, and functional connectivity (FC), the temporal coactivations, are known to be tightly linked. However, the nature of this relationship is still not understood. In the present study, we examined this relation more closely in six separate human neuroimaging datasets with different acquisition and preprocessing methods. We show that using simple linear associations, the relation between an individual’s SC and FC is not subject specific for five of the datasets. Subject specificity of SC-FC fit is achieved only for one of the six datasets, the multimodal Glasser Human Connectome Project (HCP) parcellated dataset. We show that subject specificity of SC-FC correspondence is limited across datasets due to relatively small variability between subjects in SC compared with the larger variability in FC.
2018
- European Journal of Neuroscience, 51(1), 282-299 (2018b)
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GABA is the most abundant neurotransmitter in the circadian pacemaker circuits of mammals and insects. In the Madeira cockroach the accessory medulla (AME) in the brain´s optic lobes is the circadian clock that orchestrates rest-activity rhythms in synchrony with light dark cycles. Three prominent GABAergic tracts connect the AME to termination sites of compound eye photoreceptors in the lamina and medulla. Parallel GABAergic light entrainment pathways were suggested to either advance or delay the clock for adjustment to changing photoperiods. In agreement with this hypothesis GABA activated or inhibited AME clock neurons, allowing for distinction of three different GABA response types. Here, we examined which GABA receptors are responsible for these response types. We found that both ionotropic GABAA receptors and metabotropic GABAB receptors were expressed in AME clock cells. Via different signaling pathways, either one of them could account for all three GABA response types. The muscimol-dependently activated GABAA receptor formed a chloride channel, while the SKF 97541-dependently activated GABAB receptor signaled via G-proteins, apparently targeting potassium channels. Expression of chloride exporters or –importers determined whether GABAA receptor activation hyper- or depolarized AME neurons. For GABAB receptor responses second messenger gated channels present in the clock cells appeared to decide about the polarity of the GABA response. In summary, circadian clock neurons co-expressed inhibitory and/or excitatory GABAA and GABAB receptors in various combinations, while cotransporter expression and the set of second messenger gated ion channels present allowed for distinct signaling in different clock neurons.
- Eneuro, 5(3) (2018)
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Presurgical planning for brain tumor resection aims at delineating eloquent tissue in the vicinity of the lesion to spare during surgery. To this end, noninvasive neuroimaging techniques such as functional MRI and diffusion-weighted imaging fiber tracking are currently employed. However, taking into account this information is often still insufficient, as the complex nonlinear dynamics of the brain impede straightforward prediction of functional outcome after surgical intervention. Large-scale brain network modeling carries the potential to bridge this gap by integrating neuroimaging data with biophysically based models to predict collective brain dynamics. As a first step in this direction, an appropriate computational model has to be selected, after which suitable model parameter values have to be determined. To this end, we simulated large-scale brain dynamics in 25 human brain tumor patients and 11 human control participants using The Virtual Brain, an open-source neuroinformatics platform. Local and global model parameters of the Reduced Wong–Wang model were individually optimized and compared between brain tumor patients and control subjects. In addition, the relationship between model parameters and structural network topology and cognitive performance was assessed. Results showed (1) significantly improved prediction accuracy of individual functional connectivity when using individually optimized model parameters; (2) local model parameters that can differentiate between regions directly affected by a tumor, regions distant from a tumor, and regions in a healthy brain; and (3) interesting associations between individually optimized model parameters and structural network topology and cognitive performance.
- PLoS computational biology, 14(11), e1006550 (2018)
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The architectonic type principle relates patterns of cortico-cortical connectivity to the relative architectonic differentiation of cortical regions. One mechanism through which the observed close relation between cortical architecture and connectivity may be established is the joint development of cortical areas and their connections in developmental time windows. Here, we describe a theoretical exploration of the possible mechanistic underpinnings of the architectonic type principle, by performing systematic computational simulations of cortical development. The main component of our in silico model was a developing two-dimensional cortical sheet, which was gradually populated by neurons that formed cortico-cortical connections. To assess different explanatory mechanisms, we varied the spatiotemporal trajectory of the simulated neurogenesis. By keeping the rules governing axon outgrowth and connection formation constant across all variants of simulated development, we were able to create model variants which differed exclusively by the specifics of when and where neurons were generated. Thus, all differences in the resulting connectivity were due to the variations in spatiotemporal growth trajectories. Our results demonstrated that a prescribed targeting of interareal connection sites was not necessary for obtaining a realistic replication of the experimentally observed relation between connection patterns and architectonic differentiation. Instead, we found that spatiotemporal interactions within the forming cortical sheet were sufficient if a small number of empirically well-grounded assumptions were met, namely planar, expansive growth of the cortical sheet around two points of origin as neurogenesis progressed, stronger architectonic differentiation of cortical areas for later neurogenetic time windows, and stochastic connection formation. Thus, our study highlights a potential mechanism of how relative architectonic differentiation and cortical connectivity become linked during development. We successfully predicted connectivity in two species, cat and macaque, from simulated cortico-cortical connection networks, which further underscored the general applicability of mechanisms through which the architectonic type principle can explain cortical connectivity in terms of the relative architectonic differentiation of cortical regions.
- European Journal of Neuroscience, 48(12), 3583-3596 (2018)
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Synchronous spiking of multiple neurons is a key phenomenon in normal brain function and pathologies. Recently, approaches to record spikes from the intact cortical surface using small high-density arrays of microelectrodes have been reported. It remained unaddressed how epicortical spiking relates to intracortical unit activity. We introduced a mesoscale approach using an array of 64 electrodes with intermediate diameter (250 μm) and combined large-coverage epicortical recordings in ferrets with intracortical recordings via laminar probes. Empirical data and modelling strongly suggest that our epicortical electrodes selectively captured synchronized spiking of neurons in the cortex beneath. As a result, responses to sensory stimulation were more robust and less noisy compared to intracortical activity, and receptive field properties were well preserved in epicortical recordings. This should promote insights into assembly-coding beyond the informative value of subdural EEG or single-unit spiking, and be advantageous to real-time applications in brain-machine interfacing.
- Cerebral Cortex, 28(8), 2991-3003 (2018)
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Cortical single neuron activity and local field potential patterns change at different depths of general anesthesia. Here, we investigate the associated network level changes of functional connectivity. We recorded ongoing electrocorticographic (ECoG) activity from temporo-parieto-occipital cortex of 6 ferrets at various levels of isoflurane/nitrous oxide anesthesia and determined functional connectivity by computing amplitude envelope correlations. Through hierarchical clustering, we derived typical connectivity patterns corresponding to light, intermediate and deep anesthesia. Generally, amplitude correlation strength increased strongly with depth of anesthesia across all cortical areas and frequency bands. This was accompanied, at the deepest level, by the emergence of burst-suppression activity in the ECoG signal and a change of the spectrum of the amplitude envelope. Normalization of functional connectivity to the distribution of correlation coefficients showed that the topographical patterns remained similar across depths of anesthesia, reflecting the functional association of the underlying cortical areas. Thus, while strength and temporal properties of amplitude co-modulation vary depending on the activity of local neural circuits, their network-level interaction pattern is presumably most strongly determined by the underlying structural connectivity.
- Methods, 50, 42-48 (2018)
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All-optical methods enable the control and monitoring of neuronal activity with minimal perturbation of the system. Although imaging and optogenetic manipulations can be performed at cellular resolution, the morphology of single cells in a dense neuronal population has often remained unresolvable. Here we describe in detail two recently established optogenetic protocols for systematic description of function and morphology of single neurons in zebrafish. First, the Optobow toolbox allows unbiased mapping of excitatory functional connectivity. Second, the FuGIMA technique enables selective labeling and anatomical tracing of neurons that are responsive to a given sensory stimulus or correlated with a specific behavior. Both strategies can be genetically targeted to a neuronal population of choice using the Gal4/UAS system. As these in vivo approaches are non-invasive, we envision useful applications for the study of neuronal structure, function and connectivity during development and behavior.
- Journal of biological rhythms, 33(1), 35-51 (2018)
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In the Madeira cockroach lesion and transplantation studies located the circadian clock in the accessory medulla (AME). The AME is innervated by ~240 adjacent neuropeptidergic pacemaker neurons that are spontaneously active in the circadian- as well as in the gamma frequency range of 20-70 Hz. Best studied are the pigment-dispersing factor neurons anterior to the AME (aPDFMEs) that control locomotor activity rhythms and couple both clocks. Here, we analyzed responses of AME neurons to PDF, GABA, and acetylcholine. Using calcium imaging and immunocytochemistry with primary cell cultures, we characterized for the first time PDF autoreceptors in the cockroach and found cell type-specific PDF signaling. While contralaterally projecting AME cells such as medium-sized aPDFMEs were inhibited by PDF, ipsilaterally remaining AME cells such as small aPDFMEs were activated. Only the largest aPDFME did not express PDF autoreceptors. Using in vivo intracellular recordings we demonstrated that the largest aPDFME generates regular action potential bursts, which were not light-dependent during the day. Furthermore, all PDF-sensitive neurons recorded received cholinergic excitatory- and GABAergic inhibitory synaptic inputs. We hypothesize that PDF-signaling activates ipsilateral clock outputs while suppressing outputs of the contralateral clock. Furthermore, we suggest that the largest aPDFME controls rest-arousal rhythms of the cockroach.
- European Journal of Neuroscience, 47(9), 1067-1080 (2018)
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In the Madeira cockroach pigment-dispersing factor-immunoreactive (PDF-ir) neurons innervating the circadian clock, the accessory medulla (AME) in the brain´s optic lobes, control circadian behavior. Circadian activity rhythms are entrained to daily light-dark cycles only by compound eye photoreceptors terminating in the lamina and medulla. Still, it is unknown which neurons connect the photoreceptors to the clock to allow for light-entrainment. Here, we characterized by multiple-label immunocytochemistry the serotonin (5-HT)-ir anterior fiber fan and GABA-ir pathways connecting the AME- and optic lobe neuropils. Colocalization of 5-HT with PDF was confirmed in PDF-ir lamina neurons (PDFLAs). Double-labeled fibers were traced to the AME originating from colabeled PDFLAs branching in accessory laminae and proximal lamina. The newly discovered GABA-ir medial layer fiber tract connected the AME to the medulla´s medial layer fiber system and the distal tract fibers connected the AME to the medulla. With Ca2+-imaging on primary cell cultures of the AME and with loose patch clamp recordings in vivo we showed that both neurotransmitters either excite or inhibit AME clock neurons. Because we found no colocalization of GABA and 5-HT in any optic lobe neuron, GABA- and 5-HT-neurons form separate clock input circuits. Among others, both pathways converged also on AME neurons that coexpressed mostly inhibitory GABA- and excitatory 5-HT-receptors. Our physiological and immunocytochemical studies demonstrate that GABA- and 5-HT-immunoreactive neurons constitute parallel excitatory or inhibitory pathways connecting the circadian clock either to the lamina and/or medulla where photic information from the compound eye is processed.
- PLoS computational biology, 14(4), e1006084 (2018)
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The relationship between the structural connectivity (SC) and functional connectivity (FC) of neural systems is of central importance in brain network science. It is an open question, however, how the SC-FC relationship depends on specific topological features of brain networks or the models used for describing neural dynamics. Using a basic but general model of discrete excitable units that follow a susceptible—excited—refractory activity cycle (SER model), we here analyze how the network activity patterns underlying functional connectivity are shaped by the characteristic topological features of the network. We develop an analytical framework for describing the contribution of essential topological elements, such as common inputs and pacemakers, to the coactivation of nodes, and demonstrate the validity of the approach by comparison of the analytical predictions with numerical simulations of various exemplar networks. The present analytic framework may serve as an initial step for the mechanistic understanding of the contributions of brain network topology to brain dynamics.
- Neuroimage 182:417-428 (2018)
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Recent breakthroughs in magnetic resonance imaging (MRI) enabled quantitative relaxometry and diffusion-weighted imaging with sub-millimeter resolution. Combined with biophysical models of MR contrast the emerging methods promise in vivo mapping of cyto- and myelo-architectonics, i.e., in vivo histology using MRI (hMRI) in humans. The hMRI methods require histological reference data for model building and validation. This is currently provided by MRI on post mortem human brain tissue in combination with classical histology on sections. However, this well established approach is limited to qualitative 2D information, while a systematic validation of hMRI requires quantitative 3D information on macroscopic voxels. We present a promising histological method based on optical 3D imaging combined with a tissue clearing method, Clear Lipid-exchanged Acrylamide-hybridized Rigid Imaging compatible Tissue hYdrogel (CLARITY), adapted for hMRI validation. Adapting CLARITY to the needs of hMRI is challenging due to poor antibody penetration into large sample volumes and high opacity of aged post mortem human brain tissue. In a pilot experiment we achieved transparency of up to 8 mm-thick and immunohistochemical staining of up to 5 mm-thick post mortem brain tissue by a combination of active and passive clearing, prolonged clearing and staining times. We combined 3D optical imaging of the cleared samples with tailored image processing methods. We demonstrated the feasibility for quantification of neuron density, fiber orientation distribution and cell type classification within a volume with size similar to a typical MRI voxel. The presented combination of MRI, 3D optical microscopy and image processing is a promising tool for validation of MRI-based microstructure estimates.
- eLife 7:e28927 (2018)
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The neurophysiological processes underlying non-invasive brain activity measurements are incompletely understood. Here, we developed a connectome-based brain network model that integrates individual structural and functional data with neural population dynamics to support multi-scale neurophysiological inference. Simulated populations were linked by structural connectivity and, as a novelty, driven by electroencephalography (EEG) source activity. Simulations not only predicted subjects’ individual resting-state functional magnetic resonance imaging (fMRI) time series and spatial network topologies over 20 minutes of activity, but more importantly, they also revealed precise neurophysiological mechanisms that underlie and link six empirical observations from different scales and modalities: (1) resting-state fMRI oscillations, (2) functional connectivity networks, (3) excitation-inhibition balance, (4, 5) inverse relationships between α-rhythms, spike-firing and fMRI on short and long time scales, and (6) fMRI power-law scaling. These findings underscore the potential of this new modelling framework for general inference and integration of neurophysiological knowledge to complement empirical studies.
- PLoS computational biology, 14(10), e1006359 (2018)
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Cortical activity has distinct features across scales, from the spiking statistics of individual cells to global resting-state networks. We here describe the first full-density multi-area spiking network model of cortex, using macaque visual cortex as a test system. The model represents each area by a microcircuit with area-specific architecture and features layer- and population-resolved connectivity between areas. Simulations reveal a structured asynchronous irregular ground state. In a metastable regime, the network reproduces spiking statistics from electrophysiological recordings and cortico-cortical interaction patterns in fMRI functional connectivity under resting-state conditions. Stable inter-area propagation is supported by cortico-cortical synapses that are moderately strong onto excitatory neurons and stronger onto inhibitory neurons. Causal interactions depend on both cortical structure and the dynamical state of populations. Activity propagates mainly in the feedback direction, similar to experimental results associated with visual imagery and sleep. The model unifies local and large-scale accounts of cortex, and clarifies how the detailed connectivity of cortex shapes its dynamics on multiple scales. Based on our simulations, we hypothesize that in the spontaneous condition the brain operates in a metastable regime where cortico-cortical projections target excitatory and inhibitory populations in a balanced manner that produces substantial inter-area interactions while maintaining global stability.
- Current Opinion in Neurobiology, Volume 56 (2018)
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Cell type classification has been a major part of retina research for over one hundred years. In recent years, the ability to sample large populations of retinal cells has accelerated cell type classification based on different criteria like genetics, morphology, function, and circuitry. For example, recent work includes bipolar and retinal ganglion cell classifications based on single-cell transcriptomes, large-scale electron microscopy reconstruction, and population-level functional imaging. With comprehensive descriptions of several retinal cell classes now within reach, it is important to reflect on the priority of these different criteria to create an accurate and useful classification. Here, we argue that functional information about retinal cells should be prioritized over other criteria when addressing questions of visual function because this criterion provides the most meaningful information about how the retina works.
- NeuroImage: Clinical, Volume 19, Pages 240-251 (2018)
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Alzheimer’s disease (AD) is marked by cognitive dysfunction emerging from neuropathological processes impacting brain function. AD affects brain dynamics at the local level, such as changes in the balance of inhibitory and excitatory neuronal populations, as well as long-range changes to the global network. Individual differences in these changes as they relate to behaviour are poorly understood. Here, we use a multi-scale neurophysiological model, “The Virtual Brain (TVB)”, based on empirical multi-modal neuroimaging data, to study how local and global dynamics correlate with individual differences in cognition. In particular, we modeled individual resting-state functional activity of 124 individuals across the behavioural spectrum from healthy aging, to amnesic Mild Cognitive Impairment (MCI), to AD. The model parameters required to accurately simulate empirical functional brain imaging data correlated significantly with cognition, and exceeded the predictive capacity of empirical connectomes.