Biology subjects

Weiskopf, N.

Publications and source records attributed to Weiskopf, N..

48 records · Page 3Linked to original sources

bioRxiv2021

A representative reference for MRI-based human axon radius assessment using light microscopy

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 on 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 frequency-weighted 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 potential error due to outstandingly large axons in reff. Compared to rarith, reff was estimated with higher accuracy (normalized-root-mean-square-error of reff: 7.2 %; rarith: 21.5 %) and lower bias (normalized-mean-bias-error of reff: -1.7 %; rarith: 16 %). While rarith was confounded by variation of the image intensity, variation of reff seemed anatomy-related. The largest axons contributed between 0.9 % and 3 % to reff. In conclusion, the proposed method accurately estimates reff at MRI voxel resolution across a human corpus callosum sample. Further investigations are required to assess generalization to brain areas with different axon radii ensembles.

neuroscience↗

bioRxiv2021

Perceived and mentally rotated contents are differentially represented in cortical depth of V1

Primary visual cortex (V1) in humans is known to represent both veridically perceived external input and internally-generated contents underlying imagery and mental rotation. However, it is unknown how the brain keeps these contents separate thus avoiding a mixture of the perceived and the imagined which could lead to potentially detrimental consequences. Inspired by neuroanatomical studies showing that feedforward and feedback connections in V1 terminate in different cortical layers, we hypothesized that this anatomical compartmentalization underlies functional segregation of external and internally-generated visual contents, respectively. We used high-resolution layer-specific fMRI to test this hypothesis in a mental rotation task. We found that rotated contents were predominant at outer cortical depth bins (i.e. superficial and deep). At the same time perceived contents were represented stronger at the middle cortical bin. These results identify how through cortical depth compartmentalization V1 functionally segregates rather than confuses external from internally-generated visual contents. These results indicate that feedforward and feedback manifest in distinct subdivisions of the early visual cortex, thereby reflecting a general strategy for implementing multiple cognitive functions within a single brain region.

neuroscience↗

bioRxiv2021

Coherent waves of myelin plasticity during motor-skill learning

Motor skill learning relies on neural plasticity in the motor and limbic systems. However, the spatial and temporal dependencies of these changes--and their microstructural underpinnings--remain unclear. Eighteen healthy males received training in a computer-controlled motion game 4 times a week, for 4 weeks. Performance improvements were observed in all trained participants. Serial myelin-sensitive multiparametric mapping at 3T during this period of intensive motor skill acquisition revealed temporally and spatially distributed, performance-related myelin-sensitive microstructural changes in the grey and white matter across the corticospinal system and hippocampus. Interestingly, analysis of the trajectory of these transient changes revealed a time-shifted choreography across white and grey matter of the corticospinal system as well as with changes in the hippocampus. Crucially, in the cranial corticospinal tracts, myelin-sensitive changes during training in the posterior part of the limb of the internal capsule were of greater magnitude in lower-limb trainees compared to upper limb trainees. Motor skill learning is depended on coherent waves of plasticity within a corticospinal-hippocampal loop.

neuroscience↗

bioRxiv2020

Hippocampal grey matter tissue microstructure does not explain individual differences in hippocampal-dependent task performance

Individual differences in scene imagination, autobiographical memory recall, future thinking and spatial navigation have long been linked with hippocampal structure in healthy people, although evidence for such relationships is, in fact, mixed. Extant studies have predominantly concentrated on hippocampal volume. However, it is now possible to use quantitative neuroimaging techniques to model different properties of tissue microstructure in vivo such as myelination and iron. Previous work has linked such measures with cognitive task performance, particularly in older adults. Here we investigated whether performance on scene imagination, autobiographical memory, future thinking and spatial navigation tasks was associated with hippocampal grey matter myelination or iron content in young, healthy adult participants. MRI data were collected using a multi-parameter mapping protocol (0.8mm isotropic voxels) from a large sample of 217 people with widely-varying cognitive task scores. We found little evidence that hippocampal grey matter myelination or iron content was related to task performance. This was the case using different analysis methods (voxel-based quantification, partial correlations), when whole brain, hippocampal regions of interest, and posterior:anterior hippocampal ratios were examined, and across different participant sub-groups (divided by gender, task performance). Variations in hippocampal grey matter myelin and iron levels may not, therefore, help to explain individual differences in performance on hippocampal-dependent tasks, at least in young, healthy individuals.

neuroscience↗

bioRxiv2020

Measuring the iron content of dopaminergic neurons in substantia nigra with MRI relaxometry

In Parkinsons disease, the depletion of iron-rich dopaminergic neurons in substantia nigras nigrosome 1 precedes first motor symptoms by two decades. Monitoring this neuronal depletion at an early disease stage is needed for diagnosis and treatment monitoring. Magnetic resonance imaging (MRI) is particularly suitable for this task due to its sensitivity to tissue iron. However, the mechanisms of MRI contrast in substantia nigra are not well understood, hindering the development of specific biomarkers. We showed that the dominant contribution to the effective transverse MRI relaxation rate [Formula] in nigrosome 1 originates from iron accumulated in the neuromelanin of dopaminergic neurons. We linked [Formula] quantitatively to the product of cell density and local iron concentration in dopaminergic neurons, combining quantitative 3D iron histology, biophysical modeling, and quantitative MRI on post mortem brain tissue. This knowledge opens an avenue for monitoring neuronal iron and density in vivo and may be applied to detect early neurodegeneration in Parkinsons disease.

neuroscience↗

bioRxiv2020

Relating quantitative 7T MRI across cortical depths to cytoarchitectonics, gene expression and connectomics: a framework for tracking neurodegenerative disease

Cortical layer-specific ultra-high field MRI has the potential to provide anatomically precise biomarkers and mechanistic insights into neurodegenerative disease. Here we compare cortical layer-specificity for a 7T multi-parametric mapping (MPM) 500m whole brain acquisition to the von Economo and Big Brain post-mortem histology atlases. We also investigate the relationship between 7T MPMs, layer-specific gene expression and Huntingtons disease related genes, using the Allen Human Brain atlas. Finally we link MPM cortical depth measures with white matter connections using high-fidelity diffusion tractography from a 300mT/m Connectom MRI system. We show that R2* across cortical depths is highly correlated with layer-specific cell number, cell staining intensity and gene expression. Furthermore white matter connections were highly correlated with grey matter R1 and R2* across cortical depths. These findings demonstrate the potential of combining 7T MPMs, gene expression and white matter connections to provide an anatomically precise framework for tracking neurodegenerative disease.

neuroscience↗

bioRxiv2020

Can we predict real-time fMRI neurofeedback learning success from pre-training brain activity?

Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large interindividual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pre-training functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pre-training activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.

neuroscience↗

bioRxiv2019

PyRates - A Python Framework for Rate-Based Neural Simulations

In neuroscience, computational modeling has become an important source of insight into brain states and dynamics. A basic requirement for computational modeling studies is the availability of efficient software for setting up models and performing numerical simulations. While many such tools exist for different families of neural models, there is a lack of tools allowing for both a generic model definition and efficiently parallelized simulations. In this work, we present PyRates, a Python framework that provides the means to build a large variety of neural models as a graph. PyRates provides intuitive access to and modification of all mathematical operators in a graph, thus allowing for a highly generic model definition. For computational efficiency and parallelization, the model graph is translated into a tensorflow-based compute graph. Using the example of two different neural models belonging to the family of rate-based population models, we explain the mathematical formalism, software structure and user interfaces of PyRates. We then show via numerical simulations that the behavior shown by the model implementations in PyRates is consistent with the literature. Finally, we demonstrate the computational capacities and scalability of PyRates via a number of benchmark simulations of neural networks differing in size and connectivity.

neuroscience↗

bioRxiv2018

Activity-is better than connectivity-neurofeedback training in Huntington’s disease

Non-invasive methods, such as neurofeedback training (NFT), could support cognitive symptom management in Huntingtons disease (HD) by targeting brain regions whose function is impaired. The aim of our single-blind, sham-controlled study was to collect rigorous evidence regarding the feasibility of NFT in HD by examining two different methods, activity and connectivity real-time fMRI NFT. Thirty-two HD gene-carriers completed 16 runs of NFT training, using an optimized real-time fMRI protocol. Participants were randomized into four groups, two treatment groups, one receiving neurofeedback derived from the activity of the Supplementary Motor Area (SMA), and another receiving neurofeedback based on the correlation of SMA and left striatum activity (connectivity NFT), and two sham control groups, matched to each of the treatment groups. We examined differences between the groups during NFT training sessions and after training at follow-up sessions. Transfer of training was measured by measuring the participants ability to upregulate NFT target levels without feedback (near transfer), as well as by examining change in objective, a-priori defined, behavioural measures of cognitive and psychomotor function (far transfer) before and at 2 months after training. We found that the treatment group had significantly higher NFT target levels during the training sessions compared to the control group. However, we did not find robust evidence of better transfer in the treatment group compared to controls, or a difference between the two NFT methods. We also did not find evidence in support of a relationship between change in cognitive and psychomotor function and NFT learning success. We conclude that although there is evidence that NFT can be used to guide participants to regulate the activity and connectivity of specific regions in the brain, evidence regarding transfer of learning and clinical benefit was not robust. Although the intervention is non-invasive, given the costs and absence of reliable evidence of clinical benefit, we cannot recommend real-time fMRI NFT as a potential intervention in HD.

neuroscience↗

bioRxiv2018

Apparent thinning of visual cortex during childhood is associated with myelination, not pruning

Microstructural mechanisms underlying apparent cortical thinning during childhood development are unknown. Using functional, quantitative, and diffusion magnetic resonance imaging in children and adults, we tested if tissue growth (lower T1 relaxation time and mean diffusivity (MD)) or pruning (higher T1 and MD) underlies cortical thinning in ventral temporal cortex (VTC). After age 5, T1 and MD decreased in mid and deep cortex of functionally-defined regions in lateral VTC, and in their adjacent white matter. T1 and MD decreases were (i) consistent with tissue growth related to myelin proliferation, which we verified with adult postmortem histology and (ii) correlated with apparent cortical thinning. Thus, contrary to prevailing theories, cortical tissue does not thin during childhood, it becomes more myelinated, shifting the gray-white matter boundary deeper into cortex. As tissue growth is prominent in regions with protracted functional development, our data suggest an intriguing hypothesis that functional development and myelination are interlinked.

neuroscience↗

bioRxiv2018

Deep Learning meets Topology-preserving Active Contours: towards scalable quantitative histology of cortical cytoarchitecture.

Deep learning has thoroughly changed the field of image analysis yielding impressive results whenever enough annotated data can be gathered. While partial annotation can be very fast, manual segmentation of 3D biological structures is tedious and error-prone. Additionally, high-level shape concepts such as topology or boundary smoothness are hard if not impossible to encode in Feedforward Neural Networks. Here we present a modular strategy for the accurate segmentation of neural cell bodies from light-sheet microscopy combining mixed-scale convolutional neural networks and topology-preserving geometric deformable models. We show that the network can be trained efficiently from simple cell centroid annotations, and that the final segmentation provides accurate cell detection and smooth segmentations that do not introduce further cell splitting or merging.

bioinformatics↗

bioRxiv2017

Quantitative MRI Provides Markers Of Intra-, Inter-Regional, And Age-Related Differences In Young Adult Cortical Microstructure

Measuring the structural composition of the cortex is critical to understanding typical development, yet few investigations in humans have charted markers in vivo that are sensitive to tissue microstructural attributes. Here, we used a well-validated quantitative MR protocol to measure four parameters (R1, MT, R2*, PD*) that differ in their sensitivity to facets of the tissue microstructural environment (R1, MT: myelin, macromolecular content; R2*: paramagnetic ions, i.e., iron; PD*: free water content). Mapping these parameters across cortical regions in a young adult cohort (18-30 years, N=93) revealed expected patterns of increased macromolecular content as well as reduced tissue water content in primary and primary adjacent cortical regions. Mapping across cortical depth within regions showed decreased expression of myelin and related processes - but increased tissue water content - when progressing from the grey/white to the grey/pial boundary, in all regions. Charting developmental change in cortical microstructure, we found that parameters with the greatest sensitivity to tissue myelin (R1 & MT) showed linear increases with age across frontal and parietal cortex (change 0.5-1.0% per year). Overlap of robust age effects for both parameters emerged in left inferior frontal, right parietal and bilateral pre-central regions. Our findings afford an improved understanding of ontogeny in early adulthood and offer normative quantitative MR data for inter- and intra-cortical composition, which may be used as benchmarks in further studies.\n\nHighlights O_LIWe mapped multi-parameter maps (MPMs) across and within cortical regions\nC_LIO_LIWe charted age effects on myelin and related processes at mid-cortical depth\nC_LIO_LIInter- and intra-regional differences in MPMs emerged at primary and association cortex\nC_LIO_LIIron-sensitive R2* map foci tended to overlap MPMs sensitive to myelin (R1, MT)\nC_LIO_LIR1 and MT increased with age (0.5-1.0% per year) in frontal and parietal cortex\nC_LI

neuroscience↗