| Autor: |
Wang Z; McConnell Brain Imaging Centre, Department of Biomedical Engineering, Faculty of Medicine, School of Computer Science, The Neuro-Montreal Neurological Institute (MNI), McGill University, Montreal, Quebec, Canada.; Mila-Quebec Artificial Intelligence Institute, Montreal, Quebec, Canada., Diedrichsen J; Western Institute for Neuroscience, Western University, London, Ontario, Canada.; Department of Computer Science, Western University, London, Ontario, Canada.; Department of Statistical and Actuarial Sciences, Western University, London, Ontario, Canada., Saltoun K; McConnell Brain Imaging Centre, Department of Biomedical Engineering, Faculty of Medicine, School of Computer Science, The Neuro-Montreal Neurological Institute (MNI), McGill University, Montreal, Quebec, Canada.; Mila-Quebec Artificial Intelligence Institute, Montreal, Quebec, Canada., Steele C; Department of Psychology, Concordia University, Montreal, Quebec, Canada.; Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany., Arnold-Anteraper SR; Advanced Imaging Research Center, UTSW, Dallas, Texas, United States.; Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois, United States., Yeo BTT; Department of Electrical & Computer Engineering, Centre for Translational MR Research, Centre for Sleep & Cognition, N.1 Institute for Health and Institute for Digital Medicine, National University of Singapore, Singapore, Singapore., Schmahmann JD; Ataxia Center, Cognitive Behavioral Neurology Unit, Laboratory for Neuroanatomy and Cerebellar Neurobiology, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States., Bzdok D; McConnell Brain Imaging Centre, Department of Biomedical Engineering, Faculty of Medicine, School of Computer Science, The Neuro-Montreal Neurological Institute (MNI), McGill University, Montreal, Quebec, Canada.; Mila-Quebec Artificial Intelligence Institute, Montreal, Quebec, Canada. |
| Abstrakt: |
The human cerebellum is increasingly recognized to be involved in nonmotor and higher-order cognitive functions. Yet, its ties with the entire cerebral cortex have not been holistically studied in a whole brain exploration with a unified analytical framework. Here, we characterized dissociable cortical-cerebellar structural covariation patterns based on regional gray matter volume (GMV) across the brain in n = 38,527 UK Biobank participants. Our results invigorate previous observations in that important shares of cortical-cerebellar structural covariation are described as 1 ) a dissociation between the higher-level cognitive system and lower-level sensorimotor system and 2 ) an anticorrelation between the visual-attention system and advanced associative networks within the cerebellum. We also discovered a novel pattern of ipsilateral, rather than contralateral, cerebral-cerebellar associations. Furthermore, phenome-wide association assays revealed key phenotypes, including cognitive phenotypes, lifestyle, physical properties, and blood assays, associated with each decomposed covariation pattern, helping to understand their real-world implications. This systems neuroscience view paves the way for future studies to explore the implications of these structural covariations, potentially illuminating new pathways in our understanding of neurological and cognitive disorders. NEW & NOTEWORTHY Cerebellum's association with the entire cerebral cortex has not been holistically studied in a unified way. Here, we conjointly characterize the population-level cortical-cerebellar structural covariation patterns leveraging ∼40,000 UK Biobank participants whole brain structural scans and ∼1,000 phenotypes. We revitalize the previous hypothesis of an anticorrelation between the visual-attention system and advanced associative networks within the cerebellum. We also discovered a novel ipsilateral cerebral-cerebellar associations. Phenome-wide association (PheWAS) revealed real-world implications of the structural covariation patterns. |
