Personalized functional brain network topography is associated with individual differences in youth cognition

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: Dec. 18, 2023

Abstract Individual differences in cognition during childhood are associated with important social, physical, and mental health outcomes adolescence adulthood. Given that cortical surface arealization development reflects the brain’s functional prioritization, quantifying variation topography of brain networks across developing cortex may provide insight regarding individual cognition. We test this idea by defining personalized (PFNs) account for interindividual heterogeneity network 9–10 year olds from Adolescent Brain Cognitive Development℠ Study. Across matched discovery ( n = 3525) replication 3447) samples, total representation fronto-parietal PFNs positively correlates general Cross-validated ridge regressions trained on PFN predict unseen data domains, prediction accuracy increasing along cortex’s sensorimotor-association organizational axis. These results establish is before critical transition into adolescence.

Language: Английский

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Parcellation of individual brains: From group level atlas to precise mapping

Neuroscience & Biobehavioral Reviews, Journal Year: 2025, Volume and Issue: unknown, P. 106172 - 106172

Published: April 1, 2025

Language: Английский

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TS-AI: A deep learning pipeline for multimodal subject-specific parcellation with task contrasts synthesis

Medical Image Analysis, Journal Year: 2024, Volume and Issue: 97, P. 103297 - 103297

Published: Aug. 8, 2024

Language: Английский

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Polygenic Risk Underlies Youth Psychopathology and Personalized Functional Brain Network Topography

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 26, 2024

Abstract Importance Functional brain networks are associated with both behavior and genetic factors. To uncover clinically translatable mechanisms of psychopathology, it is critical to define how the spatial organization these relates risk during development. Objective determine relationship between transdiagnostic polygenic scores (PRSs), personalized functional (PFNs), overall psychopathology (p-factor) early adolescence. Design The Adolescent Brain Cognitive Development (ABCD) Study⍰ an ongoing longitudinal cohort study 21 collection sites across United States. Here, we conduct a cross-sectional analysis ABCD baseline data, collected 2017-2018. Setting Study ® multi-site community-based study. Participants sample largely recruited through school systems. Exclusion criteria included severe sensory, intellectual, medical, or neurological issues that interfere protocol scanner contraindications. Split-half subsets were used for cross-validation, matched on age, ethnicity, family structure, handedness, parental education, site, sex, anesthesia exposure. Exposures Polygenic factors F1 (PRS-F1) F2 (PRS-F2) derived from adults in Psychiatric Genomic Consortium UK Biobanks datasets. PRS-F1 indexes liability common psychiatric symptoms disorders related mood disturbance; PRS-F2 rarer forms mental illness characterized by mania psychosis. Main Outcomes Measures (1) P-factor bifactor models youth- parent-reported health assessments. (2) Person-specific network topography magnetic resonance imaging (fMRI) scans. Results Total participants 11,873 youths ages 9-10 years old; 5,678 (47.8%) female, mean (SD) age was 9.92 (0.62) years. PFN found be heritable ( N =7,459, 57.06% vertices h 2 p FDR <0.05, =0.35). p-factor =5,815, r =0.12, 95% CI [0.09–0.15], p<0.001). Interindividual differences =0.12), =3,982, =0.05), =0.08). Cortical maps regression coefficients highly correlated =0.7, =0.003). Conclusions Relevance adulthood These results advance our understanding developmental drivers psychopathology. Key Points Question What adolescence? Findings In this =11,873, 9-10), PRS p-factor, PRS-F1, (capturing adulthood) all robustly topography. Meaning

Language: Английский

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pNet: A toolbox for personalized functional networks modeling

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: April 29, 2024

Abstract Personalized functional networks (FNs) derived from magnetic resonance imaging (fMRI) data are useful for characterizing individual variations in the brain topography associated with development, aging, and disorders. To facilitate applications of personalized FNs enhanced reliability reproducibility, we develop an open-source toolbox that is user-friendly, extendable, includes rigorous quality control (QC), featuring multiple user interfaces (graphics, command line, a step-by-step guideline) job-scheduling high performance computing (HPC) clusters. Particularly, toolbox, named network modeling (pNet), takes fMRI inputs either volumetric or surface type, ensuring compatibility formats, computes using two distinct methods: one method optimizes coherence FNs, while other enhances their independence. Additionally, provides HTML-based reports QC visualization FNs. The developed both MATLAB Python platforms modular design to extension modification by users familiar programming language. We have evaluated on datasets demonstrated its effectiveness user-friendliness interactive scripting examples. pNet publicly available at https://github.com/MLDataAnalytics/pNet .

Language: Английский

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TS-AI: A deep learning pipeline for multimodal subject-specific parcellation with task contrasts synthesis

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: June 14, 2024

Abstract Accurate mapping of brain functional subregions at an individual level is crucial. Task-based MRI (tfMRI) captures subject-specific activation patterns during various functions and behaviors, facilitating the localization functionally distinct subregions. However, acquiring high-quality tfMRI time-consuming resource-intensive in both scientific clinical settings. The present study proposes a two-stage network model, TS-AI, to individualize atlas on cortical surfaces through prediction data. TS-AI first synthesizes battery task contrast maps for each by leveraging tract-wise anatomical connectivity resting-state networks. These synthesized maps, along with feature networks, are then fed into end-to-end deep neural atlas. enables be used parcellation without acquisition actual fMRI scans. In addition, novel consistency loss designed assign vertices similar features same parcel, which increases specificity mitigates overfitting risks caused absence ground truth. individualized parcellations were validated assessing test-retest reliability, homogeneity, cognitive behavior using diverse reference atlases datasets, demonstrating superior performance generalizability TS-AI. Sensitivity analysis yielded insights region-specific influencing variation regionalization. identified accelerated shrinkage medial temporal cingulate parcels progression Alzheimer’s disease, suggesting its potential research applications.

Language: Английский

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Reproducible Sex Differences in Personalized Functional Network Topography in Youth

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 29, 2024

A key step towards understanding psychiatric disorders that disproportionately impact female mental health is delineating the emergence of sex-specific patterns brain organization at critical transition from childhood to adolescence. Prior work suggests individual differences in spatial functional networks across cortex are associated with psychopathology and differ systematically by sex.

Language: Английский

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Hierarchical individual variation and socioeconomic impact on personalized functional network topography in children

BMC Medicine, Journal Year: 2024, Volume and Issue: 22(1)

Published: Nov. 25, 2024

Language: Английский

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