Maladaptive brain organization at 1 month into abstinence as an indicator for future relapse in patients with alcohol use disorder.
Angela M MullerDieter J MeyerhoffPublished in: The European journal of neuroscience (2021)
Abstinence is a lifelong endeavor, and the risk of a relapse is always present for patients with Alcohol Use Disorder (AUD). The aim of the study was to better understand specific characteristics of the intrinsic whole-brain-network architecture of 34 AUD patients that may support abstinence or relapse. We used Graph Theory Analysis (GTA) of resting-state fMRI data from treatment seekers at 1 month of abstinence and their follow-up data as abstainers or relapsers 3 months later, together with data from 30 light/non-drinking controls scanned at the same interval. We determined the group-specific intrinsic community configurations at both timepoints as well as the corresponding modularity Q, a GTA measure that quantifies how well individual network communities are separated from each other. Both AUD groups at both timepoints had community configurations significantly different from those of controls, but the three groups did not significantly differ in their Q values. However, relapsers showed a maladaptive community configuration at baseline, which became more similar to the controls' community organization after the relapsers had started consuming alcohol again during the study interval. Additionally, successful recovery from AUD was not associated with re-gaining the intrinsic brain organization found in light/non-drinkers, but with a re-configuration resulting in a new brain organization distinctly different from that of healthy controls. Resting-state fMRI provides useful measures reflecting neuroplastic adaptations related to AUD treatment outcome.
Keyphrases
- resting state
- alcohol use disorder
- functional connectivity
- mental health
- healthcare
- smoking cessation
- electronic health record
- big data
- free survival
- ejection fraction
- end stage renal disease
- alcohol consumption
- newly diagnosed
- replacement therapy
- machine learning
- artificial intelligence
- blood brain barrier
- convolutional neural network