Striatal dopamine mediates hallucination-like perception in mice.

Hallucinations, a central symptom of psychotic disorders, are attributed to excessive dopamine in the brain. However, the neural circuit mechanisms by which dopamine produces hallucinations remain elusive, largely because hallucinations have been challenging to study in model organisms. We developed a task to quantify hallucination-like perception in mice. Hallucination-like percepts, defined as high-confidence false detections, increased after hallucination-related manipulations in mice and correlated with self-reported hallucinations in humans. Hallucination-like percepts were preceded by elevated striatal dopamine levels, could be induced by optogenetic stimulation of mesostriatal dopamine neurons, and could be reversed by the antipsychotic drug haloperidol. These findings reveal a causal role for dopamine-dependent striatal circuits in hallucination-like perception and open new avenues to develop circuit-based treatments for psychotic disorders.

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.