Altered structural connectivity and functional brain dynamics in individuals with heavy alcohol use
Poster No:
2020
Submission Type:
Abstract Submission
Authors:
Parker Singleton1, Puneet Velidi2, Louisa Schilling3, Andrea Luppi4, Keith Jamison3, Linden Parkes5, Amy Kuceyeski3
Institutions:
1Weill Cornell Medicine, New York, NY, 2Cornell University, Ithaca, NY, 3Weill Cornell Medicine, New York City, NY, 4McGill University, Montreal, NY, 5Rutgers University, Philadelphia, PA
First Author:
Co-Author(s):
Introduction:
Alcohol use disorder (AUD) is a long-term and recurring neurological condition that can continue unabated despite significant adverse effects on the person, their family, and community. In 2021, over 11% of adults in the United States had AUD. Despite this impact and prevalence, the root neurobiological causes of AUD remain unidentified, and there are limited effective treatment methods available. Significantly, it's been observed that only a fraction of individuals who regularly consume alcohol eventually develop AUD. This emphasizes the urgent need to uncover biological elements that predispose a person to develop AUD, and to improve treatment paradigms. Neuroimaging studies provide evidence pointing toward neurobiological mechanisms of AUD, which likely involve differences in receptor concentration/function, brain activity patterns, and anatomy (gray and white matter) (Kuceyeski 2013). However, a unifying computational model integrating multi-modal observations into a single framework has not been proposed, hampering our ability to understand the neurobiological mechanisms of AUD.
Methods:
We used publicly available high resolution, preprocessed MRI data from the Human Connectome Project – Young Adult S1200 (van Essen 2013) in this study. Regional time-series and structural connectomes (via deterministic tractography) for HCP subjects (N = 130 AUD; N = 308 control; see Figure 1 caption for details) were extracted using the 268-region Shen atlas. Brain-states were defined via k-means clustering and network control theory (NCT)-based transition energy (TE) between every pair of brain states was calculated as described previously (Singleton 2022; Cornblath 2020). TE is the minimum energy input into a network-here, the structural connectome-required to move from one brain activity state to another (Gu 2015). We calculated average TE as the average amount of energy required to transition between all four brain-states. We also counted the number of state transitions in each scan from the k-means partition of fMRI volumes. Meta-state complexity (MSC; the Lempel-Ziv compressibility of the brain-state time-series) was used to measure the information content of fMRI scans (Singleton 2022). Average TE, state-transitions, and MSC were compared across groups via ANOVA with covariates for age, sex, mean framewise displacement, and age:sex interaction. A dopamine depletion simulation was performed on a PET-derived D2 receptor (D2R) map wherein average TE for control subjects was compared using the true D2R map versus a 'depleted' D2R map using two-sided, paired t-tests. P-values were corrected for multiple comparisons via False Discovery Rate.
Results:
Individuals with AUD have higher average TE and lower MSC as well as a lower number of state transitions compared to those without. On an individual level, TE is inversely correlated with both MSC and state transitions. Finally, we present an in silico evaluation linking decreases in D2 receptor levels with increases in transition energy in the brain.
Conclusions:
The brain activity of individuals with AUD reflects a less dynamic system with less complexity and higher energetic barriers compared to individuals without and SUD. More broadly, this work demonstrates that whole-brain, multimodal imaging information can be combined under a network control framework to identify and evaluate neurobiological correlates and mechanisms of AUD.
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Diffusion MRI Modeling and Analysis 2
Task-Independent and Resting-State Analysis 1
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Transmitter Receptors
Keywords:
CHEMOARCHITECTURE
Computational Neuroscience
Dopamine
FUNCTIONAL MRI
Other - Alcohol Use Disorder; Network Control Theory
1|2Indicates the priority used for review
·Figure 1
·Figure 2
Provide references using author date format
Gu S (2015), ‘Controllability of structural brain networks’, Nature Communications, 1;6:8414.
Kuceyeski A (2013), ‘Loss in connectivity among regions of the brain reward system in alcohol dependence’, Human Brain Mapping, 34(12):3129-42.
Singleton, S.P (2022), ‘Receptor-informed network control theory links LSD and psilocybin to a flattening of the brain’s control energy landscape’, Nature Communications, 13, 5812
Van Essen DC (2013), ‘The WU-Minn Human Connectome Project: an overview’, Neuroimage. 15;80:62-79.