Neuromelanin levels in individuals with substance use disorders: A systematic review and meta-analysis.

Dopamine's role in addiction has been extensively studied, revealing disruptions in its functioning throughout all addiction stages. Neuromelanin in the substantia nigra (SN) may reflect dopamine auto-oxidation, and can be quantified using neuromelaninsensitive magnetic resonance imaging (neuromelanin-MRI) in a non-invasive manner.In this pre-registered systematic review, we assess the current body of evidence related to neuromelanin levels in substance use disorders, using both post-mortem and MRI examinations. The systematic search identified 10 relevant articles, primarily focusing on the substantia nigra. An early-stage meta-analysis (n = 6) revealed varied observations ranging from standardized mean differences of -3.55 to +0.62, with a pooled estimate of -0.44 (95 % CI = -1.52, 0.65), but there was insufficient power to detect differences in neuromelanin content among individuals with substance use disorders. Our gap analysis highlights the lack of sufficient replication studies, with existing studies lacking the power to detect a true difference, and a complete lack of neuromelanin studies on certain substances of clinical interest. We provide recommendations for future studies of dopaminergic neurobiology in addictions and related psychiatric comorbidities.

Speech markers to predict and prevent recurrent episodes of psychosis: A narrative overview and emerging opportunities.

Preventing relapse in schizophrenia improves long-term health outcomes. Repeated episodes of psychotic symptoms shape the trajectory of this illness and can be a detriment to functional recovery. Despite early intervention programs, high relapse rates persist, calling for alternative approaches in relapse prevention. Predicting imminent relapse at an individual level is critical for effective intervention. While clinical profiles are often used to foresee relapse, they lack the specificity and sensitivity needed for timely prediction. Here, we review the use of speech through Natural Language Processing (NLP) to predict a recurrent psychotic episode. Recent advancements in NLP of speech have shown the ability to detect linguistic markers related to thought disorder and other language disruptions within 2-4 weeks preceding a relapse. This approach has shown to be able to capture individual speech patterns, showing promise in its use as a prediction tool. We outline current developments in remote monitoring for psychotic relapses, discuss the challenges and limitations and present the speech-NLP based approach as an alternative to detect relapses with sufficient accuracy, construct validity and lead time to generate clinical actions towards prevention.