Cerebellocerebral connectivity predicts body mass index: a new open-source Python-based framework for connectome-based predictive modeling

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11110%2F25%3A10498024" target="_blank" >RIV/00216208:11110/25:10498024 - isvavai.cz</a>

Result on the web

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=KUh86LwZ1N" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=KUh86LwZ1N</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1093/gigascience/giaf010" target="_blank" >10.1093/gigascience/giaf010</a>

Result language

angličtina

Original language name

Cerebellocerebral connectivity predicts body mass index: a new open-source Python-based framework for connectome-based predictive modeling

Original language description

Background: The cerebellum is one of the major central nervous structures consistently altered in obesity. Its role in higher cognitive function, parts of which are affected by obesity, is mediated through projections to and from the cerebral cortex. We therefore investigated the relationship between body mass index (BMI) and cerebellocerebral connectivity. Methods: We utilized the Human Connectome Project&apos;s Young Adults dataset, including functional magnetic resonance imaging (fMRI) and behavioral data, to perform connectome-based predictive modeling (CPM) restricted to cerebellocerebral connectivity of resting-state fMRI and task-based fMRI. We developed a Python-based open-source framework to perform CPM, a data-driven technique with built-in cross-validation to establish brain-behavior relationships. Significance was assessed with permutation analysis. Results: We found that (i) cerebellocerebral connectivity predicted BMI, (ii) task-general cerebellocerebral connectivity predicted BMI more reliably than resting-state fMRI and individual task-based fMRI separately, (iii) predictive networks derived this way overlapped with established functional brain networks (namely, frontoparietal networks, the somatomotor network, the salience network, and the default mode network), and (iv) we found there was an inverse overlap between networks predictive of BMI and networks predictive of cognitive measures adversely affected by overweight/obesity. Conclusions: Our results suggest obesity-specific alterations in cerebellocerebral connectivity, specifically with regard to task execution. With brain areas and brain networks relevant to task performance implicated, these alterations seem to reflect a neurobiological substrate for task performance adversely affected by obesity.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

30103 - Neurosciences (including psychophysiology)

Project

—

Continuities

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

GigaScience

ISSN

2047-217X

e-ISSN

2047-217X

Volume of the periodical

14

Issue of the periodical within the volume

March

Country of publishing house

GB - UNITED KINGDOM

Number of pages

22

Pages from-to

giaf010

UT code for WoS article

001447281700001

EID of the result in the Scopus database

2-s2.0-105000153964