TY  - JOUR
AU  - Wu, Jianxiao
AU  - Eickhoff, Simon B
AU  - Hoffstaedter, Felix
AU  - Patil, Kaustubh R
AU  - Schwender, Holger
AU  - Yeo, B T Thomas
AU  - Genon, Sarah
TI  - A Connectivity-Based Psychometric Prediction Framework for Brain–Behavior Relationship Studies
JO  - Cerebral cortex
VL  - 31
IS  - 8
SN  - 1460-2199
CY  - Oxford
PB  - Oxford Univ. Press
M1  - FZJ-2021-01921
SP  - 3732–3751
PY  - 2021
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - 33884421
UR  - <Go to ISI:>//WOS:000741348300001
DO  - DOI:10.1093/cercor/bhab044
UR  - https://juser.fz-juelich.de/record/892070
ER  -