Cerebellar volume in substance use disorders: a mega-analysis by the ENIGMA addiction working group
Poster No:
692
Submission Type:
Abstract Submission
Authors:
Jalil Rasgado Toledo1, Alfonso Fajardo-Valdez2, Ian Harding3, Scott Mackey4, Hugh Garavan5, Eduardo Garza-Villarreal1
Institutions:
1Universidad Nacional Autónoma de México, Queretaro, Mexico, 2McGill University, Montreal, Quebec, 3Monash University, Melbourne, Australia, 4The University of Vermont, Burlington, VT, 5University of Vermont, Burlington, VT
First Author:
Co-Author(s):
Introduction:
The cerebellum contributes in among a wide variety of higher-order processes, including rewarding and emotional functions (Zhang et al. 2023) and evidence suggests the involvement of the cerebellum in substance use disorders (SUDs) and addiction. A recent meta-analysis showed addiction to any substance was related to low brain volume in cerebellar white matter, while long-term use was related to high brain volume in cerebellar gray matter (Pando-Naude et al. 2021). In this study, we wanted to determine the brain volume of the cerebellum and subdivisions in different SUDs using a mega-analysis.
Methods:
We used MRI T1w sequences from 3,172 individuals with SUDs (AUD n = 914, ATS n = 111, CANN n = 49, COC n = 405, COH = 30, NIC n = 577, OPI n = 58, Controls n = 1,028) across 60 sites from the ENIGMA-addiction working group. After QC, we tested 2 methods: 1) a deep-learning-based approach for automatic cerebellar parcellation (ACAPULCO) using an anatomical atlas (Han et al. 2020) and 2) the spatially unbiased infratentorial template (SUIT) toolbox (Diedrichsen 2006) for voxel-based morphometry (VBM). To study functionally defined ROIs we also obtained the mean volume for 10 cerebellar regions from the Multi-Domain Task Battery atlas (MDTB) (King et al. 2019) using the SUIT toolbox. We then independently compared the ACAPULCO and MDTB volumes and the SUIT-VBM between groups (patients vs controls) in general and with Subtance subgroups with a permutation Welch Two Sample t-tests analysis. Each test was controlled for age, sex, intracranial volume, and site. The Benjamini-Hochberg procedure was used to control for multiple comparisons (p-FDR < 0.05).
Results:
We found significant volume differences between SUDs and controls, which varied between types of substances. We found that SUD subjects displayed differences mainly in subregions 1, 4, 6, 9 and 10. In particular, the greatest effects appeared to be related to nicotine use disorder, with volume differences in almost all subregions, followed by amphetamine and alcohol. Subregions with the most group differences were 1 and 2. All group comparisons showed low to medium effect sizes. A set of affected regions were related depending on the substance, but some subregions of regions 1 and 4 according to the MDTB atlas, were found to have higher changes.
·Voxel-base morphometry results of the comparison between each substance with controls. Color bars indicate t-values.
·Significant volumes differences of each region of Multi-Domain Task Battery atlas (MDTB) between substance use disorder groups compared with controls. Abbreviations = healthy controls (HC); Substance
Conclusions:
Our results suggest that specific cerebellum regions are affected in SUDs, and some regions are affected in all SUDS, while other regions are only affected by substance. Regional variations may be associated with interference resolution, motor planning, active maintenance, and verbal comprehension, according to the MDTB atlas results.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Emotion, Motivation and Social Neuroscience:
Reward and Punishment 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Neuroanatomy Other
Keywords:
Cerebellum
Morphometrics
MRI
Psychiatric
STRUCTURAL MRI
1|2Indicates the priority used for review
Provide references using author date format
Han, S., Carass, A., He, Y., & Prince, J. L. (2020). Automatic cerebellum anatomical parcellation using U-Net with locally constrained optimization. NeuroImage, 218, 116819.
King, M., Hernandez-Castillo, C. R., Poldrack, R. A., Ivry, R. B., & Diedrichsen, J. (2019). Functional boundaries in the human cerebellum revealed by a multi-domain task battery. Nature Neuroscience, 22(8), 1371–1378.
Pando-Naude, V., Toxto, S., Fernandez-Lozano, S., Parsons, C. E., Alcauter, S., & Garza-Villarreal, E. A. (2021). Gray and white matter morphology in substance use disorders: a neuroimaging systematic review and meta-analysis. Translational Psychiatry, 11(1), 29.
Zhang, P., Duan, L., Ou, Y., Ling, Q., Cao, L., Qian, H., Zhang, J., Wang, J., & Yuan, X. (2023). The cerebellum and cognitive neural networks. Frontiers in Human Neuroscience, 17, 1197459.