Towards a Universal Taxonomy of Macro-scale Functional Human Brain Networks

Brain Topography, Journal Year: 2019, Volume and Issue: 32(6), P. 926 - 942

Published: Nov. 1, 2019

Language: Английский

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Topographic organization of the human subcortex unveiled with functional connectivity gradients

Nature Neuroscience, Journal Year: 2020, Volume and Issue: 23(11), P. 1421 - 1432

Published: Sept. 28, 2020

Language: Английский

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BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets

Communications Biology, Journal Year: 2020, Volume and Issue: 3(1)

Published: March 5, 2020

Abstract Understanding how cognitive functions emerge from brain structure depends on quantifying discrete regions are integrated within the broader cortical landscape. Recent work established that macroscale organization and function can be described in a compact manner with multivariate machine learning approaches identify manifolds often as gradients. By topographic principles of organization, gradients lend an analytical framework to study structural functional across species, throughout development aging, its perturbations disease. Here, we present BrainSpace, Python/Matlab toolbox for (i) identification gradients, (ii) their alignment, (iii) visualization. Our furthermore allows controlled association studies between other brain-level features, adjusted respect null models account spatial autocorrelation. Validation experiments demonstrate usage consistency our tools analysis microstructural different scales.

Language: Английский

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Julich-Brain: A 3D probabilistic atlas of the human brain’s cytoarchitecture

Science, Journal Year: 2020, Volume and Issue: 369(6506), P. 988 - 992

Published: Aug. 21, 2020

Cytoarchitecture is a basic principle of microstructural brain parcellation. We introduce Julich-Brain, three-dimensional atlas containing cytoarchitectonic maps cortical areas and subcortical nuclei. The probabilistic, which enables it to account for variations between individual brains. Building such an was highly data- labor-intensive required the development nested, interdependent workflows detecting borders areas, data processing, provenance tracking, flexible execution processing chains handle large amounts at different spatial scales. Full coverage achieved by inclusion gap complement maps. dynamic will be adapted as mapping progresses; openly available support neuroimaging studies well modeling simulation; interoperable, enabling connection other atlases resources.

Language: Английский

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Global signal regression strengthens association between resting-state functional connectivity and behavior

NeuroImage, Journal Year: 2019, Volume and Issue: 196, P. 126 - 141

Published: April 9, 2019

Language: Английский

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Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics

NeuroImage, Journal Year: 2019, Volume and Issue: 206, P. 116276 - 116276

Published: Oct. 12, 2019

There is significant interest in the development and application of deep neural networks (DNNs) to neuroimaging data. A growing literature suggests that DNNs outperform their classical counterparts a variety applications, yet there are few direct comparisons relative utility. Here, we compared performance three DNN architectures machine learning algorithm (kernel regression) predicting individual phenotypes from whole-brain resting-state functional connectivity (RSFC) patterns. One was generic fully-connected feedforward network, while other two were recently published approaches specifically designed exploit structure connectome By using combined sample almost 10,000 participants Human Connectome Project (HCP) UK Biobank, showed kernel regression achieved similar across wide range behavioral demographic measures. Furthermore, network exhibited state-of-the-art connectome-specific DNNs. When fluid intelligence all algorithms dramatically improved when size increased 100 1000 subjects. Improvement smaller, but still significant, 5000 Importantly, competitive sizes. Overall, our study as effective for RSFC-based prediction, incurring significantly lower computational costs. Therefore, might serve useful baseline future studies.

Language: Английский

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Comparing spatial null models for brain maps

NeuroImage, Journal Year: 2021, Volume and Issue: 236, P. 118052 - 118052

Published: April 19, 2021

Technological and data sharing advances have led to a proliferation of high-resolution structural functional maps the brain. Modern neuroimaging research increasingly depends on identifying correspondences between topographies these maps; however, most standard methods for statistical inference fail account their spatial properties. Recently, multiple been developed generate null distributions that preserve autocorrelation brain yield more accurate estimates. Here, we comprehensively assess performance ten published frameworks in analyses data. To test efficacy situations with known ground truth, first apply them series controlled simulations examine impact resolution family-wise error rates. Next, use each framework two empirical datasets, investigating when testing (1) correspondence (e.g., correlating activation maps) (2) distribution feature within partition quantifying specificity an map intrinsic network). Finally, investigate how differences implementation models may performance. In agreement previous reports, find naive do not consistently elevated false positive rates unrealistically liberal While spatially-constrained yielded realistic, conservative estimates, even suffer from inflated variable across analyses. Throughout our results, observe minimal parcellation model Altogether, findings highlight need continued development statistically-rigorous comparing maps. The present report provides harmonised benchmarking future advancements.

Language: Английский

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Macroscopic gradients of synaptic excitation and inhibition in the neocortex

Nature reviews. Neuroscience, Journal Year: 2020, Volume and Issue: 21(3), P. 169 - 178

Published: Feb. 6, 2020

Language: Английский

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Geometric constraints on human brain function

Nature, Journal Year: 2023, Volume and Issue: 618(7965), P. 566 - 574

Published: May 31, 2023

The anatomy of the brain necessarily constrains its function, but precisely how remains unclear. classical and dominant paradigm in neuroscience is that neuronal dynamics are driven by interactions between discrete, functionally specialized cell populations connected a complex array axonal fibres

Language: Английский

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Brain States and Transitions: Insights from Computational Neuroscience

Cell Reports, Journal Year: 2020, Volume and Issue: 32(10), P. 108128 - 108128

Published: Sept. 1, 2020

Within the field of computational neuroscience there are great expectations finding new ways to rebalance complex dynamic system human brain through controlled pharmacological or electromagnetic perturbation. Yet many obstacles remain between ability accurately predict how and where best perturb force a transition from one state another. The foremost challenge is commonly agreed definition given state. Recent progress in has made it possible robustly define states transitions them. Here, we review art propose framework for determining functional hierarchical organization describing any We describe latest advances creating sophisticated whole-brain models with interacting neuronal neurotransmitter systems that can be studied fully silico design novel interventions them disease.

Language: Английский

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