ArticleCerebral cortex (New York, N.Y. : 1991)2021
A Connectivity-Based Psychometric Prediction Framework for Brain-Behavior Relationship Studies.
Article in Cerebral cortex (New York, N.Y. : 1991), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Multimodal neuroimaging data boosts the prediction of multifaceted cognition.Research square · 2025Article
- Dynamic Functional Connectivity Markers in Anorexia Nervosa and Their Association With Clinical Symptoms: A Cross-Sectional Study.European eating disorders review : the journal of the Eating Disorders Association · 2025Article
- Transdiagnostic Connectome-Based Prediction of Response Inhibition.Human brain mapping · 2025Article
- How measurement noise limits the accuracy of brain-behaviour predictions.Nature communications · 2024Article
- The challenges and prospects of brain-based prediction of behaviour.Nature human behaviour · 2023Review
- Asymmetric generalizability of multimodal brain-behavior associations across age-groups.Human brain mapping · 2022Article
- Cross-cohort replicability and generalizability of connectivity-based psychometric prediction patterns.NeuroImage · 2022Article
- Differences between multimodal brain-age and chronological-age are linked to telomere shortening.Neurobiology of aging · 2022Article
- Linking interindividual variability in brain structure to behaviour.Nature reviews. Neuroscience · 2022Review
- Cross-ethnicity/race generalization failure of behavioral prediction from resting-state functional connectivity.Science advances · 2022Article
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7 authors.
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Abstract
The recent availability of population-based studies with neuroimaging and behavioral measurements opens promising perspectives to investigate the relationships between interindividual variability in brain regions' connectivity and behavioral phenotypes. However, the multivariate nature of connectivity-based prediction model severely limits the insight into brain-behavior patterns for neuroscience. To address this issue, we propose a connectivity-based psychometric prediction framework based on individual regions' connectivity profiles. We first illustrate two main applications: 1) single brain region's predictive power for a range of psychometric variables and 2) single psychometric variable's predictive power variation across brain region. We compare the patterns of brain-behavior provided by these approaches to the brain-behavior relationships from activation approaches. Then, capitalizing on the increased transparency of our approach, we demonstrate how the influence of various data processing and analyses can directly influence the patterns of brain-behavior relationships, as well as the unique insight into brain-behavior relationships offered by this approach.
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