Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion

Cerebral Cortex, Journal Year: 2018, Volume and Issue: 29(6), P. 2533 - 2551

Published: May 10, 2018

Resting-state functional magnetic resonance imaging (rs-fMRI) offers the opportunity to delineate individual-specific brain networks. A major question is whether network topography (i.e., location and spatial arrangement) behaviorally relevant. Here, we propose a multi-session hierarchical Bayesian model (MS-HBM) for estimating cortical networks investigate can predict human behavior. The multiple layers of MS-HBM explicitly differentiate intra-subject (within-subject) from inter-subject (between-subject) variability. By ignoring variability, previous mappings might confuse variability differences. Compared with other approaches, parcellations generalized better new rs-fMRI task-fMRI data same subjects. More specifically, estimated single session (10 min) showed comparable generalizability as by 2 state-of-the-art methods using 5 sessions (50 min). We also that behavioral phenotypes across cognition, personality, emotion could be predicted modest accuracy, reports predicting based on connectivity strength. Network was more effective prediction than size, well parcellation approaches. Thus, similar strength, serve fingerprint

Language: Английский

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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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Imaging-based parcellations of the human brain

Nature reviews. Neuroscience, Journal Year: 2018, Volume and Issue: 19(11), P. 672 - 686

Published: Oct. 9, 2018

Language: Английский

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Dump the “dimorphism”: Comprehensive synthesis of human brain studies reveals few male-female differences beyond size

Neuroscience & Biobehavioral Reviews, Journal Year: 2021, Volume and Issue: 125, P. 667 - 697

Published: Feb. 21, 2021

With the explosion of neuroimaging, differences between male and female brains have been exhaustively analyzed. Here we synthesize three decades human MRI postmortem data, emphasizing meta-analyses other large studies, which collectively reveal few reliable sex/gender a history unreplicated claims. Males' are larger than females' from birth, stabilizing around 11 % in adults. This size difference accounts for reproducible findings: higher white/gray matter ratio, intra- versus interhemispheric connectivity, regional cortical subcortical volumes males. But when structural lateralization present independent size, explains only about 1% total variance. Connectome multivariate prediction largely based on brain perform poorly across diverse populations. Task-based fMRI has especially failed to find activation men women verbal, spatial or emotion processing due high rates false discovery. Overall, male/female appear trivial population-specific. The is not "sexually dimorphic."

Language: Английский

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Individual Variation in Functional Topography of Association Networks in Youth

Neuron, Journal Year: 2020, Volume and Issue: 106(2), P. 340 - 353.e8

Published: Feb. 19, 2020

Language: Английский

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Optimising network modelling methods for fMRI

NeuroImage, Journal Year: 2020, Volume and Issue: 211, P. 116604 - 116604

Published: Feb. 13, 2020

A major goal of neuroimaging studies is to develop predictive models analyze the relationship between whole brain functional connectivity patterns and behavioural traits. However, there no single widely-accepted standard pipeline for analyzing connectivity. The common procedure designing based entails three main steps: parcellating brain, estimating interaction defined parcels, lastly, using these integrated associations parcels as features fed a classifier predicting non-imaging variables e.g., traits, demographics, emotional measures, etc. There are also additional considerations when correlation-based measures connectivity, resulting in supplementary utilising Riemannian geometry tangent space parameterization preserve connectivity; penalizing estimates with shrinkage approaches handle challenges related short time-series (and noisy) data; removing confounding from brain-behaviour data. These six steps contingent on each-other, optimise general framework one should ideally examine various methods simultaneously. In this paper, we investigated strengths short-comings, both independently jointly, following measures: parcellation techniques four kinds (categorized further depending upon number parcels), five decision staying ambient matrices or space, choice applying estimators, alternative handling confounds finally novel classifiers/predictors. For performance evaluation, have selected two largest datasets, UK Biobank Human Connectome Project resting state fMRI data, run more than 9000 different variants total ∼14000 individuals determine optimum pipeline. independent validation, some best-performing ABIDE ACPI datasets (∼1000 subjects) evaluate generalisability proposed network modelling methods.

Language: Английский

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Trait-like variants in human functional brain networks

Proceedings of the National Academy of Sciences, Journal Year: 2019, Volume and Issue: 116(45), P. 22851 - 22861

Published: Oct. 14, 2019

Resting-state functional magnetic resonance imaging (fMRI) has provided converging descriptions of group-level brain organization. Recent work revealed that networks identified in individuals contain local features differ from the description. We define these as network variants. Building on studies, we ask whether distributions variants reflect stable, trait-like differences Across several datasets highly-sampled show 1) are highly stable within individuals, 2) found characteristic locations and associate with across large groups, 3) task-evoked signals demonstrate a link to variation, 4) cluster into subgroups basis variant characteristics related behavior. These results suggest may trait-like, functionally relevant individual

Language: Английский

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Movie-watching outperforms rest for functional connectivity-based prediction of behavior

NeuroImage, Journal Year: 2021, Volume and Issue: 235, P. 117963 - 117963

Published: April 2, 2021

A major goal of human neuroscience is to relate differences in brain function behavior across people. Recent work has established that whole-brain functional connectivity patterns are relatively stable within individuals and unique individuals, features these predict various traits. However, while most often measured at rest, certain tasks may enhance individual signals improve sensitivity differences. Here, we show compared the resting state, during naturalistic viewing—i.e., movie watching—yields more accurate predictions trait-like phenotypes domains both cognition emotion. Traits could be predicted using less than three minutes data from single video clips, clips with highly social content gave predictions. Results suggest stimuli amplify behaviorally relevant networks.

Language: Английский

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There is no single functional atlas even for a single individual: Functional parcel definitions change with task

NeuroImage, Journal Year: 2019, Volume and Issue: 208, P. 116366 - 116366

Published: Nov. 15, 2019

The goal of human brain mapping has long been to delineate the functional subunits in and elucidate role each these regions. Recent work focused on whole-brain parcellation Magnetic Resonance Imaging (fMRI) data identify create a atlas. Functional connectivity approaches understand at network level require such an atlas assess connections between parcels extract properties. While no single emerged as dominant date, there remains underlying assumption that exists. Using fMRI from highly sampled subject well two independent replication sets, we demonstrate parcellations based reconfigure substantially meaningful manner, according state.

Language: Английский

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The individual functional connectome is unique and stable over months to years

NeuroImage, Journal Year: 2019, Volume and Issue: 189, P. 676 - 687

Published: Feb. 2, 2019

Functional connectomes computed from fMRI provide a means to characterize individual differences in the patterns of BOLD synchronization across regions entire brain. Using four resting-state datasets with wide range ages, we show that functional connectome are stable 3 months 1-2 years (and even detectable at above-chance levels years). Medial frontal and frontoparietal networks appear be both unique stable, resulting high ID rates, as did combination these two networks. We conduct analyses demonstrating results not driven by head motion. also edges contributing most successful tend connect nodes parietal cortices, while least cross-hemispheric homologs. Our demonstrate is rates an idiosyncratic aspect specific dataset, but rather reflect connectivity

Language: Английский

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