Print Close

Dynamic Coactivation Pattern Analysis Reveals Altered Brain State Dynamics in Cocaine use disorder

Poster No:

580

Submission Type:

Abstract Submission

Authors:

Benjamin Klugah-Brown¹, Xing Yao¹, Pan Wang¹, Bharat Biswal²

Institutions:

¹University of Electronic Science and Technology of China, Chengdu, Sichuan, ²New Jersey Institute of Technology, Newark, NJ

First Author:

Benjamin Klugah-Brown
University of Electronic Science and Technology of China
Chengdu, Sichuan

Co-Author(s):

Xing Yao
University of Electronic Science and Technology of China
Chengdu, Sichuan

Pan Wang
University of Electronic Science and Technology of China
Chengdu, Sichuan

Bharat Biswal
New Jersey Institute of Technology
Newark, NJ

Introduction:

Illicit substance use poses a pervasive global health challenge, affecting millions worldwide (SAMSHA, 2019). Cocaine addiction, characterized by severe neurobiological and neuropsychiatric consequences, necessitates a deeper understanding of its neural underpinnings (Volkow et al., 2019). Resting-state functional magnetic resonance images (rsfMRI) studies have highlighted the importance of investigating functional network connectivity characteristics (Kelly et al., 2011; Ma et al., 2015) to gain a more comprehensive understanding of the dynamic nature of brain function in cocaine used disorder (CUD).

Methods:

This study employed dynamic coactivation pattern (CAP) analysis (Liu & Duyn, 2013), a data-driven approach, to explore the spatial and temporal dynamics of brain states in CUD. Using rsfMRI data from 56 CUD and 57 healthy control (HC) subjects, we identified six CAP states and compared their temporal dynamics between the two groups. Additionally, we integrated dynamic findings with stationary functional network connectivity (FNC), revealing nuanced changes in activation and connectivity within functional networks linked to CAP states.

Results:

Five CAP states exhibited spatial similarity between CUD and HC, while state 6 showed opposing spatial patterns. Specifically, state 6 in the CUD group displayed stronger activation in the frontoparietal and visual networks. This heightened activation in the frontoparietal network, commonly associated with higher-order cognitive functions, suggests that cocaine may have induced neural excitation in the brain's resting state.
Temporal dynamics analysis revealed significant differences in CUD. The fraction of time and counts spent in the default mode network (DMN) and frontoparietal network (FPN) were reduced, indicating potential dysfunction within executive and decision-making processes. Conversely, increased activation in the ventral attention network (VAN) suggests heightened attention to drug-related cues.
Clinical correlations uncovered associations between CAP states and the duration of cocaine use. A significant negative association was observed with state 5, while a positive association was found with state 6. These findings underscore the intricate interplay between neural adaptations and cocaine use patterns in CUD.
Integration with stationary FNC revealed network-specific changes. Notably, state 1 exhibited alterations in the control network, state 3 in the DMN, state 4 in the dorsal attention network, and state 5 in the visual network. These findings highlight the complexity of neural adaptations in CUD.

·Figure 1. A) The spatial patterns for the six CAP states in cocaine addiction disease (CUD) and health control (HC) group. The first row shows the six states of the HC group, representing the activat

·Figure 2. Functional connectivity between highest peaks in CAP state and the whole brain.

Conclusions:

This research highlights the dynamic nature of neural alterations in CUD, providing a foundation for understanding the disease's neurobiology. Dynamic CAP analysis proved instrumental in unraveling the temporal intricacies of brain states linked to cocaine addiction. The findings contribute to advancing diagnostic strategies for CUD, emphasizing the need for a comprehensive approach to address the dynamic interplay of neural changes in addiction.

Disorders of the Nervous System:

Psychiatric (eg. Depression, Anxiety, Schizophrenia) ¹

Emotion, Motivation and Social Neuroscience:

Reward and Punishment

Modeling and Analysis Methods:

Connectivity (eg. functional, effective, structural)

fMRI Connectivity and Network Modeling ²

Novel Imaging Acquisition Methods:

BOLD fMRI

Keywords:

Addictions

FUNCTIONAL MRI

^1|2Indicates the priority used for review

Provide references using author date format

Kelly, C., Zuo, X. N., Gotimer, K., Cox, C. L., Lynch, L., Brock, D., Imperati, D., Garavan, H., Rotrosen, J., Castellanos, F. X., & Milham, M. P. (2011). Reduced interhemispheric resting state functional connectivity in cocaine addiction. Biological Psychiatry, 69(7), 684–692. https://doi.org/10.1016/j.biopsych.2010.11.022
Liu, X., & Duyn, J. H. (2013). Time-varying functional network information extracted from brief instances of spontaneous brain activity. Proceedings of the National Academy of Sciences of the United States of America, 110(11). https://doi.org/10.1073/pnas.1216856110
Ma, L., Steinberg, J. L., Cunningham, K. A., Lane, S. D., Bjork, J. M., Neelakantan, H., Price, A. E., Narayana, P. A., Kosten, T. R., Bechara, A., & Moeller, F. G. (2015). Inhibitory behavioral control: A stochastic dynamic causal modeling study comparing cocaine dependent subjects and controls. NeuroImage: Clinical, 7, 837–847. https://doi.org/10.1016/j.nicl.2015.03.015
SAMSHA. (2019). Key substance use and mental health indicators in the United States: Results from the 2019 National Survey on Drug Use and Health. HHS Publication No. PEP19-5068, NSDUH Series H-54, 170.
Volkow, N. D., Michaelides, M., & Baler, R. (2019). The neuroscience of drug reward and addiction. Physiological Reviews, 99(4). https://doi.org/10.1152/physrev.00014.2018