How brain structure–function decoupling supports individual cognition and its molecular mechanism DOI Creative Commons
Xiaoxi Dong,

Qiongling Li,

Xuetong Wang

et al.

Human Brain Mapping, Journal Year: 2024, Volume and Issue: 45(2)

Published: Jan. 30, 2024

Abstract Functional signals emerge from the structural network, supporting multiple cognitive processes through underlying molecular mechanism. The link between human brain structure and function is region‐specific hierarchical across neocortex. However, relationship structure–function decoupling manifestation of individual behavior cognition, along with significance functional systems involved, specific mechanism remain incompletely characterized. Here, we used structural‐decoupling index (SDI) to quantify dependency on connectome using a significantly larger cohort healthy subjects. Canonical correlation analysis (CCA) was utilized assess general multivariate pattern SDIs whole traits. Then, predicted five composite scores resulting in primary networks, association all respectively. Finally, explored related SDI by investigating its genetic factors neurotransmitter receptors/transporters. We demonstrated that neocortex, spanning networks networks. revealed better performance cognition prediction achieved high‐level SDIs, varying different regions predicting processes. found were associated gene expression level several receptor‐related terms, also spatial distributions four receptors/transporters correlated namely D2, NET, MOR, mGluR5, which play an important role flexibility neuronal function. Collectively, our findings corroborate macroscale provide implications for comprehending decoupling. Practitioner Points Structure–function High‐level contributes much more than low‐level cognition. could be regulated genes pivotal receptors are crucial flexibility.

Language: Английский

Neurodevelopment of the association cortices: Patterns, mechanisms, and implications for psychopathology DOI Creative Commons
Valerie J. Sydnor,

Bart Larsen,

Danielle S. Bassett

et al.

Neuron, Journal Year: 2021, Volume and Issue: 109(18), P. 2820 - 2846

Published: July 15, 2021

Language: Английский

Citations

494
Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior DOI
Ru Kong, Qing Yang, Evan M. Gordon

et al.

Cerebral Cortex, Journal Year: 2021, Volume and Issue: 31(10), P. 4477 - 4500

Published: March 31, 2021

Resting-state functional magnetic resonance imaging (rs-fMRI) allows estimation of individual-specific cortical parcellations. We have previously developed a multi-session hierarchical Bayesian model (MS-HBM) for estimating high-quality network-level Here, we extend the to estimate areal-level While parcellations comprise spatially distributed networks spanning cortex, consensus is that parcels should be localized, is, not span multiple lobes. There disagreement about whether strictly contiguous or noncontiguous components; therefore, considered three MS-HBM variants these range possibilities. Individual-specific estimated using 10 min data generalized better than other approaches 150 out-of-sample rs-fMRI and task-fMRI from same individuals. connectivity derived also achieved best behavioral prediction performance. Among variants, exhibited resting-state homogeneity most uniform within-parcel task activation. In terms prediction, gradient-infused was numerically best, but differences among were statistically significant. Overall, results suggest MS-HBMs can capture behaviorally meaningful parcellation features beyond group-level Multi-resolution trained models are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Kong2022_ArealMSHBM).

Language: Английский

Citations

182
A comprehensive macaque fMRI pipeline and hierarchical atlas DOI Creative Commons
Benjamin Jung, Paul A. Taylor, Jakob Seidlitz

et al.

NeuroImage, Journal Year: 2021, Volume and Issue: 235, P. 117997 - 117997

Published: March 28, 2021

Functional neuroimaging research in the non-human primate (NHP) has been advancing at a remarkable rate. The increase available data establishes need for robust analysis pipelines designed NHP and accompanying template spaces to standardize localization of results. Our group recently developed NIMH Macaque Template (NMT), high-resolution population average anatomical associated resources, providing researchers with standard space macaque . Here, we release NMT v2, which includes both symmetric asymmetric templates stereotaxic orientation, improvements spatial contrast, processing efficiency, segmentation. We also introduce Cortical Hierarchy Atlas Rhesus (CHARM), hierarchical parcellation cerebral cortex varying degrees detail. These tools have integrated into software AFNI provide comprehensive pipeline fMRI processing, visualization data. AFNI's new @animal_warper program can be used efficiently align scans v2 space, afni_proc.py integrates these results full using macaque-specific parameters: from motion correction through regression modeling. Taken together, represent an all-in-one package functional analysis, as demonstrated demos task resting state fMRI.

Language: Английский

Citations

123
Genetic and phylogenetic uncoupling of structure and function in human transmodal cortex DOI Creative Commons
Sofie L. Valk, Ting Xu, Casey Paquola

et al.

Nature Communications, Journal Year: 2022, Volume and Issue: 13(1)

Published: May 9, 2022

Abstract Brain structure scaffolds intrinsic function, supporting cognition and ultimately behavioral flexibility. However, it remains unclear how a static, genetically controlled architecture supports flexible behavior. Here, we synthesize genetic, phylogenetic cognitive analyses to understand the macroscale organization of structure-function coupling across cortex can inform its role in cognition. In humans, was highest regions unimodal lowest transmodal cortex, pattern that mirrored by reduced alignment with heritable connectivity profiles. Structure-function uncoupling macaques had similar spatial distribution, but observed an increased between function association cortices relative humans. Meta-analysis suggested least genetic control (low correspondence different primates) are linked social-cognition autobiographical memory. Our findings suggest evolutionary systems may support emergence complex forms

Language: Английский

Citations

99
Gradients in brain organization DOI Creative Commons
Boris C. Bernhardt, Jonathan Smallwood, Shella Keilholz

et al.

NeuroImage, Journal Year: 2022, Volume and Issue: 251, P. 118987 - 118987

Published: Feb. 10, 2022

Language: Английский

Citations

90
BrainStat: A toolbox for brain-wide statistics and multimodal feature associations DOI Creative Commons
Sara Larivière, Şeyma Bayrak, Reinder Vos de Wael

et al.

NeuroImage, Journal Year: 2022, Volume and Issue: 266, P. 119807 - 119807

Published: Dec. 10, 2022

Analysis and interpretation of neuroimaging datasets has become a multidisciplinary endeavor, relying not only on statistical methods, but increasingly associations with respect to other brain-derived features such as gene expression, histological data, functional well cognitive architectures. Here, we introduce BrainStat - toolbox for (i) univariate multivariate linear models in volumetric surface-based brain imaging datasets, (ii) multidomain feature association results spatial maps post-mortem expression histology, task-based fMRI meta-analysis, resting-state motifs across several common surface templates. The combination statistics into turnkey streamlines analytical processes accelerates cross-modal research. is implemented both Python MATLAB, two widely used programming languages the neuroinformatics communities. openly available complemented by an expandable documentation.

Language: Английский

Citations

84
Coordinated cortical thickness alterations across six neurodevelopmental and psychiatric disorders DOI Creative Commons
Meike D. Hettwer, Sara Larivière, Bo‐yong Park

et al.

Nature Communications, Journal Year: 2022, Volume and Issue: 13(1)

Published: Nov. 11, 2022

Abstract Neuropsychiatric disorders are increasingly conceptualized as overlapping spectra sharing multi-level neurobiological alterations. However, whether transdiagnostic cortical alterations covary in a biologically meaningful way is currently unknown. Here, we studied co-alteration networks across six neurodevelopmental and psychiatric disorders, reflecting pathological structural covariance. In 12,024 patients 18,969 controls from the ENIGMA consortium, observed that patterns followed normative connectome organization were anchored to prefrontal temporal disease epicenters. Manifold learning revealed frontal-to-temporal sensory/limbic-to-occipitoparietal gradients, differentiating shared illness effects on thickness along these axes. The principal gradient aligned with covariance established transcriptomic link cortico-cerebello-thalamic circuits. Moreover, gradients segregated functional involved basic sensory, attentional/perceptual, domain-general cognitive processes, distinguished between regional cytoarchitectonic profiles. Together, our findings indicate occur synchronized fashion multiple levels of hierarchical organization.

Language: Английский

Citations

79
Micapipe: A pipeline for multimodal neuroimaging and connectome analysis DOI Creative Commons
Raúl Rodríguez‐Cruces, Jessica Royer, Peer Herholz

et al.

NeuroImage, Journal Year: 2022, Volume and Issue: 263, P. 119612 - 119612

Published: Sept. 6, 2022

Multimodal magnetic resonance imaging (MRI) has accelerated human neuroscience by fostering the analysis of brain microstructure, geometry, function, and connectivity across multiple scales in living brains. The richness complexity multimodal neuroimaging, however, demands processing methods to integrate information modalities consolidate findings different spatial scales. Here, we present micapipe, an open pipeline for MRI datasets. Based on BIDS-conform input data, micapipe can generate i) structural connectomes derived from diffusion tractography, ii) functional resting-state signal correlations, iii) geodesic distance matrices that quantify cortico-cortical proximity, iv) microstructural profile covariance assess inter-regional similarity cortical myelin proxies. above be automatically generated established 18 parcellations (100-1000 parcels), addition subcortical cerebellar parcellations, allowing researchers replicate easily Results are represented three surface spaces (native, conte69, fsaverage5), outputs BIDS-conform. Processed quality controlled at individual group level. was tested several datasets is available https://github.com/MICA-MNI/micapipe, documented https://micapipe.readthedocs.io/, containerized as a BIDS App http://bids-apps.neuroimaging.io/apps/. We hope will foster robust integrative studies morphology, cand connectivity.

Language: Английский

Citations

73
An Open MRI Dataset For Multiscale Neuroscience DOI Creative Commons
Jessica Royer, Raúl Rodríguez‐Cruces, Shahin Tavakol

et al.

Scientific Data, Journal Year: 2022, Volume and Issue: 9(1)

Published: Sept. 15, 2022

Multimodal neuroimaging grants a powerful window into the structure and function of human brain at multiple scales. Recent methodological conceptual advances have enabled investigations interplay between large-scale spatial trends (also referred to as gradients) in microstructure connectivity, offering an integrative framework study multiscale organization. Here, we share multimodal MRI dataset for Microstructure-Informed Connectomics (MICA-MICs) acquired 50 healthy adults (23 women; 29.54 ± 5.62 years) who underwent high-resolution T1-weighted MRI, myelin-sensitive quantitative T1 relaxometry, diffusion-weighted resting-state functional 3 Tesla. In addition raw anonymized data, this release includes brain-wide connectomes derived from (i) imaging, (ii) diffusion tractography, (iii) covariance analysis, (iv) geodesic cortical distance, gathered across parcellation Alongside, gradients estimated each modality scale. Our will facilitate future research examining coupling microstructure, function. MICA-MICs is available on Canadian Open Neuroscience Platform data portal ( https://portal.conp.ca ) Science Framework https://osf.io/j532r/ ).

Language: Английский

Citations

70
The frequency gradient of human resting-state brain oscillations follows cortical hierarchies DOI Creative Commons

Keyvan Mahjoory,

Jan‐Mathijs Schoffelen, Anne Keitel

et al.

eLife, Journal Year: 2020, Volume and Issue: 9

Published: Aug. 21, 2020

The human cortex is characterized by local morphological features such as cortical thickness, myelin content, and gene expression that change along the posterior-anterior axis. We investigated if some of these structural gradients are associated with a similar gradient in prominent feature brain activity - namely frequency oscillations. In resting-state MEG recordings from healthy participants (N = 187) using mixed effect models, we found dominant peak area decreases significantly axis following global hierarchy early sensory to higher order areas. This spatial was anticorrelated representing proxy hierarchical level. result indicates changes systematically globally establishes new structure-function relationship pertaining oscillations core organization may underlie specialization brain.

Language: Английский

Citations

124