Diverse mutations in autism-related genes and their expression in the developing brain
Nature Genetics, August 18, 2022
Across >150,000 individuals, we identified hundreds of genes associated with autism spectrum disorder (ASD) and atypical neurodevelopment. Most ASD-related genes were also associated with developmental delay. However, increased mutation rates in ASD and shared genetic risk with schizophrenia was observed for some genes, many of which are enriched in developing neurons.
Ultrarare Coding Variants and Cognitive Function in Schizophrenia—Unraveling the Enduring Mysteries of Neuropsychiatric Genetics
DL Braff et al, JAMA Psychiatry, August 17, 2022
For neuropsychiatric genomics the big picture is that we are looking at a spectrum of common and rare (and ultrarare) variations to understand a whole-brain disorder that seems to involve a complex tapestry encompassing both cortical and subcortical dysfunctions. Synthesizing these findings into a coherent functional neurobiological model of schizophrenia will be our formative challenge.
Exploring Links Between Psychosis and Frontotemporal Dementia Using Multimodal Machine Learning: Dementia Praecox Revisited.
Koutsouleris Nikolaos et al. JAMA psychiatry 2022 8
In this diagnostic/prognostic study including 1870 patients, patients with schizophrenia expressed the neuroanatomical pattern of behavioral-variant frontotemporal dementia more strongly (41%) than that of Alzheimer disease (17%), and at lower levels, this difference was also encountered in those with major depression (22% vs 3%). Already in clinical high-risk states for psychosis the high expression of the behavioral-variant frontotemporal dementia pattern was linked to severe phenotypes, unfavorable courses, and elevated polygenic risks for schizophrenia and dementia, with further pattern progression being present in those patients who did not recover over time.
The performance of artificial intelligence-driven technologies in diagnosing mental disorders: an umbrella review
A Abd-Alrazak et al, NPJ Digital Medicine, July 7, 2022
We included 15 systematic reviews of 852 citations identified. The included reviews assessed the performance of AI models in diagnosing Alzheimer’s disease (n?=?7), mild cognitive impairment (n?=?6), schizophrenia (n?=?3), bipolar disease (n?=?2), autism spectrum disorder (n?=?1), obsessive-compulsive disorder (n?=?1), post-traumatic stress disorder (n?=?1), and psychotic disorders (n?=?1). The performance of the AI models in diagnosing these mental disorders ranged between 21% and 100%. AI technologies offer great promise in diagnosing mental health disorders. The reported performance metrics paint a vivid picture of a bright future for AI in this field.