Is it time to put rest to rest?

Trends in Cognitive Sciences, Journal Year: 2021, Volume and Issue: 25(12), P. 1021 - 1032

Published: Oct. 6, 2021

Language: Английский

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Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study

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

Published: April 25, 2022

Abstract How individual differences in brain network organization track behavioral variability is a fundamental question systems neuroscience. Recent work suggests that resting-state and task-state functional connectivity can predict specific traits at the level. However, most studies focus on single traits, thus not capturing broader relationships across behaviors. In large sample of 1858 typically developing children from Adolescent Brain Cognitive Development (ABCD) study, we show predictive features are distinct domains cognitive performance, personality scores mental health assessments. On other hand, within each domain predicted by similar features. Predictive models generalize to measures same domain. Although tasks known modulate connectome, between resting task states. Overall, our findings reveal shared account for variation broad behavior childhood.

Language: Английский

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Brain–phenotype models fail for individuals who defy sample stereotypes

Nature, Journal Year: 2022, Volume and Issue: 609(7925), P. 109 - 118

Published: Aug. 24, 2022

Abstract Individual differences in brain functional organization track a range of traits, symptoms and behaviours 1–12 . So far, work modelling linear brain–phenotype relationships has assumed that single such relationship generalizes across all individuals, but models do not equally well participants 13,14 A better understanding whom fail why is crucial to revealing robust, useful unbiased relationships. To this end, here we related activity phenotype using predictive models—trained tested on independent data ensure generalizability 15 —and examined model failure. We applied data-driven approach neurocognitive measures new, clinically demographically heterogeneous dataset, with the results replicated two independent, publicly available datasets 16,17 Across three datasets, find reflect unitary cognitive constructs, rather scores intertwined sociodemographic clinical covariates; is, stereotypical profiles, when individuals who defy them. Model failure reliable, specific generalizable datasets. Together, these highlight pitfalls one-size-fits-all effect biased phenotypic 18–20 interpretation utility resulting models. present framework address issues so may reveal neural circuits underlie phenotypes ultimately identify individualized targets for intervention.

Language: Английский

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Task fMRI paradigms may capture more behaviorally relevant information than resting-state functional connectivity

NeuroImage, Journal Year: 2023, Volume and Issue: 270, P. 119946 - 119946

Published: Feb. 17, 2023

Characterizing the optimal fMRI paradigms for detecting behaviorally relevant functional connectivity (FC) patterns is a critical step to furthering our knowledge of neural basis behavior. Previous studies suggested that FC derived from task paradigms, which we refer as task-based FC, are better correlated with individual differences in behavior than resting-state but consistency and generalizability this advantage across conditions was not fully explored. Using data three tasks Adolescent Brain Cognitive Development Study ® (ABCD), tested whether observed improvement behavioral prediction power can be attributed changes brain activity induced by design. We decomposed time course each into model fit (the fitted condition regressors single-subject general linear model) residuals, calculated their respective compared performance these estimates original FC. The residual at predicting measure cognitive ability or two measures on tasks. superior content-specific insofar it only probed similar constructs predicted interest. To surprise, parameters, beta regressors, were equally if more predictive all measures. These results showed afforded largely driven associated Together previous studies, findings highlighted importance design eliciting meaningful activation patterns.

Language: Английский

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Improving power in functional magnetic resonance imaging by moving beyond cluster-level inference

Proceedings of the National Academy of Sciences, Journal Year: 2022, Volume and Issue: 119(32)

Published: Aug. 4, 2022

Inference in neuroimaging typically occurs at the level of focal brain areas or circuits. Yet, increasingly, well-powered studies paint a much richer picture broad-scale effects distributed throughout brain, suggesting that many reports may only reflect tip iceberg underlying effects. How versus perspectives influence inferences we make has not yet been comprehensively evaluated using real data. Here, compare sensitivity and specificity across procedures representing multiple levels inference an empirical benchmarking procedure resamples task-based connectomes from Human Connectome Project dataset (∼1,000 subjects, 7 tasks, 3 resampling group sizes, inferential procedures). Only (network whole brain) obtained traditional 80% statistical power to detect average effect, reflecting >20% more than (edge cluster) procedures. Power also increased substantially for false discovery rate- compared with familywise error rate-controlling The downsides are fairly limited; loss FDR was relatively modest gains power. Furthermore, methods introduce simple, fast, easy use, providing straightforward starting point researchers. This points promise sophisticated functional connectivity but related fields, including activation. Altogether, this work demonstrates shifting scale choosing control both immediately attainable can help remedy issues plaguing typical field.

Language: Английский

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Brain health in diverse settings: How age, demographics and cognition shape brain function

NeuroImage, Journal Year: 2024, Volume and Issue: 295, P. 120636 - 120636

Published: May 21, 2024

Diversity in brain health is influenced by individual differences demographics and cognition. However, most studies on diseases have typically controlled for these factors rather than explored their potential to predict signals. Here, we assessed the role of (age, sex, education; n = 1,298) cognition (n 725) as predictors different metrics usually used case-control studies. These included power spectrum aperiodic (1/f slope, knee, offset) metrics, well complexity (fractal dimension estimation, permutation entropy, Wiener spectral structure variability) connectivity (graph-theoretic mutual information, conditional organizational information) from source space resting-state EEG activity a diverse sample global south north populations. Brain-phenotype models were computed using reflecting local (power components) dynamics interactions (complexity graph-theoretic measures). Electrophysiological modulated despite varied methods data acquisition assessments across multiple centers, indicating that results unlikely be accounted methodological discrepancies. Variations signals mainly age cognition, while education sex exhibited less importance. Power measures sensitive capturing differences. Older age, poorer being male associated with reduced alpha power, whereas older network integration segregation. Findings suggest basic impact core function are standard Considering variability diversity settings would contribute more tailored understanding function.

Language: Английский

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Toward a functional future for the cognitive neuroscience of human aging

Neuron, Journal Year: 2025, Volume and Issue: 113(1), P. 154 - 183

Published: Jan. 1, 2025

Language: Английский

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What is the best brain state to predict autistic traits?

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 17, 2025

Abstract Autism is a heterogeneous condition, and functional magnetic resonance imaging-based studies have advanced understanding of neurobiological correlates autistic features. Nevertheless, little work has focused on the optimal brain states to reveal brain-phenotype relationships. In addition, there need better understand relevance attentional abilities in mediating Using connectome-based predictive modelling, we interrogate three datasets determine scanning conditions that can boost prediction clinically relevant phenotypes assess generalizability. dataset one, sample youth with autism neurotypical participants, find sustained attention task (the gradual onset continuous performance task) results high traits compared free-viewing social resting-state condition. two, observe network model generated from generalizes predict measures adults. three, show same one further responsiveness data Brain Imaging Data Exchange. sum, our suggest an in-scanner challenge help delineate robust markers support continued investigation under which psychiatric conditions.

Language: Английский

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Impact of concatenating fMRI data on reliability for functional connectomics

NeuroImage, Journal Year: 2020, Volume and Issue: 226, P. 117549 - 117549

Published: Nov. 26, 2020

Compelling evidence suggests the need for more data per individual to reliably map functional organization of human connectome. As notion that 'more is better' emerges as a golden rule connectomics, researchers find themselves grappling with challenges how obtain desired amounts participant in practical manner, particularly retrospective aggregation. Increasingly, aggregation across all fMRI scans available an being viewed solution, regardless scan condition (e.g., rest, task, movie). A number open questions exist regarding process and impact different decisions on reliability resultant aggregate data. We leveraged availability highly sampled test-retest datasets systematically examine strategies cortical connectomics. Specifically, we compared connectivity estimates derived after concatenating from: 1) multiple under same state, 2) states (i.e. hybrid or general connectivity), 3) subsets one long scan. also varied processing global signal regression, ICA-FIX, task regression) estimation procedures. When total time points equal, state held constant, shorter had clear advantage over single However, this was not necessarily true when conditions), where from states. Concatenating fewer numbers are reliable tends yield higher reliability. Our findings provide overview dependencies concatenation should be considered optimize analysis

Language: Английский

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Scan Once, Analyse Many: Using Large Open-Access Neuroimaging Datasets to Understand the Brain

Neuroinformatics, Journal Year: 2021, Volume and Issue: 20(1), P. 109 - 137

Published: May 11, 2021

We are now in a time of readily available brain imaging data. Not only researchers sharing data more than ever before, but additionally large-scale collecting initiatives underway with the vision that many future will use for secondary analyses. Here I provide an overview datasets and some example cases. Example cases include examining individual differences, robust findings, reproducibility-both public input availability as replication sample, methods development. further discuss variety considerations associated using existing opportunities large datasets. Suggestions readings on general neuroimaging topic-specific discussions also provided.

Language: Английский

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