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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Time-varying functional connectivity predicts fluctuations in sustained attention in a serial tapping task

Cognitive Affective & Behavioral Neuroscience, Journal Year: 2024, Volume and Issue: 24(1), P. 111 - 125

Published: Jan. 22, 2024

Language: Английский

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Connectome-based models can predict processing speed in older adults

NeuroImage, Journal Year: 2020, Volume and Issue: 223, P. 117290 - 117290

Published: Aug. 29, 2020

Decrement in processing speed (PS) is a primary cognitive morbidity clinical populations and could significantly influence other functions, such as attention memory. Verifying the usefulness of connectome-based models for predicting neurocognitive abilities has significant translational implications on aging research. In this study, we verified that resting-state functional connectivity be used to predict PS 99 older adults by using predictive modeling (CPM). We identified two distinct connectome patterns across whole brain: fast-PS slow-PS networks. Relative network, network showed more within-network motor visual networks less between-network motor-visual, motor-subcortical/cerebellum motor-frontoparietal further prediction were also useful memory same sample. To test generalizability specificity models, applied these an independent sample three age groups (101 younger adults, 103 middle-aged 91 adults) confirmed specifically generalized but not adults. Taking all findings together, are strong The application CPM can complement conventional assessments, bring benefits patient management aid diagnoses, prognoses people undergoing process.

Language: Английский

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Functional connectivity during frustration: a preliminary study of predictive modeling of irritability in youth

Neuropsychopharmacology, Journal Year: 2021, Volume and Issue: 46(7), P. 1300 - 1306

Published: Jan. 21, 2021

Irritability cuts across many pediatric disorders and is a common presenting complaint in child psychiatry; however, its neural mechanisms remain unclear. One core pathophysiological deficit of irritability aberrant responses to frustrative nonreward. Here, we conducted preliminary fMRI study examine the ability functional connectivity during nonreward predict transdiagnostic sample. This included 69 youths (mean age = 14.55 years) with varying levels diagnostic groups: disruptive mood dysregulation disorder (n 20), attention-deficit/hyperactivity 14), anxiety 12), controls 23). During fMRI, participants completed frustrating cognitive flexibility task. Frustration was evoked by manipulating task difficulty such that, on trials requiring flexibility, "frustration" blocks had 50% error rate some rigged feedback, while "nonfrustration" 10% rate. nonfrustration were randomly interspersed. Child parent reports affective reactivity index used as dimensional measures irritability. Connectome-based predictive modeling, machine learning approach, tenfold cross-validation identify networks predicting Connectivity frustration (but not nonfrustration) predicted child-reported (ρ 0.24, root mean square 2.02, p 0.03, permutation testing, 1000 iterations, one-tailed). Results adjusted for age, sex, medications, motion, ADHD, symptoms. The primarily within motor-sensory networks; among motor-sensory, subcortical, salience between these frontoparietal medial frontal networks. provides evidence that individual differences may be associated frustration, phenotype-relevant state.

Language: Английский

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Functional Connectome–Based Predictive Modeling in Autism

Biological Psychiatry, Journal Year: 2022, Volume and Issue: 92(8), P. 626 - 642

Published: April 26, 2022

Language: Английский

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