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Cerebellocerebral connectivity predicts body mass index: a new open-source Python-based framework for connectome-based predictive modeling
T. Bachmann, K. Mueller, SNA. Kusnezow, ML. Schroeter, P. Piaggi, CM. Weise
Language English Country United States
Document type Journal Article
Grant support
NIH
NLK
BioMedCentral Open Access
from 2012
Directory of Open Access Journals
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PubMed Central
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- MeSH
- Adult MeSH
- Body Mass Index * MeSH
- Connectome * methods MeSH
- Humans MeSH
- Magnetic Resonance Imaging * methods MeSH
- Young Adult MeSH
- Cerebellum * diagnostic imaging physiology MeSH
- Nerve Net diagnostic imaging physiology MeSH
- Obesity diagnostic imaging MeSH
- Check Tag
- Adult MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
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'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.
Department of Information Engineering University of Pisa Pisa 56122 Italy
Department of Neurology University of Halle Medical Center Halle 06102 Germany
Department of Neurology University of Leipzig Medical Center Leipzig 04103 Germany
Max Planck Institute for Human Cognitive and Brain Sciences Leipzig 04103 Germany
References provided by Crossref.org
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