Decoding diagnosis and lifetime consumption in alcohol dependence from grey-matter pattern information.

OBJECTIVE: We investigated the potential of computer-based models to decode diagnosis and lifetime consumption in alcohol dependence (AD) from grey-matter pattern information. As machine-learning approaches to psychiatric neuroimaging have recently come under scrutiny due to unclear generalization and the opacity of algorithms, our investigation aimed to address a number of methodological criticisms.

METHOD: Participants were adult individuals diagnosed with AD (N = 119) and substance-naïve controls (N = 97) ages 20-65 who underwent structural MRI. Machine-learning models were applied to predict diagnosis and lifetime alcohol consumption.

RESULTS: A classification scheme based on regional grey matter attained 74% diagnostic accuracy and predicted lifetime consumption with high accuracy (r = 0.56, P < 10-10 ). A key advantage of the classification scheme was its algorithmic transparency, revealing cingulate, insular and inferior frontal cortices as important brain areas underlying classification. Validation of the classification scheme on data of an independent trial was successful with nearly identical accuracy, addressing the concern of generalization. Finally, compared to a blinded radiologist, computer-based classification showed higher accuracy and sensitivity, reduced age and gender biases, but lower specificity.

CONCLUSION: Computer-based models applied to whole-brain grey-matter predicted diagnosis and lifetime consumption in AD with good accuracy. Computer-based classification may be particularly suited as a screening tool with high sensitivity.