Motor difficulties in 16p11.2 copy number variation.

The rare genetic variants 16p11.2 duplication and 16p11.2 deletion have opposing effects on brain structure and function, yet are associated with broadly similar clinical phenotypes that include autism, intellectual impairment, psychiatric illness, and motor difficulties. In recent years, studies have identified subtle distinctions between the phenotypic effects of 16p11.2 duplication and 16p11.2 deletion with respect to patterns of autism, intellectual impairment, and psychiatric illness. However, although divergent phenotypic findings in some motor domains have been reported, no study has yet made a comprehensive comparison of motor difficulties between 16p11.2 deletion and 16p11.2 duplication carriers to elucidate points of convergence and divergence. We sought to make such a comparison in a group of 133 16p11.2 deletion carriers, 122 duplication carriers, and 388 familial controls, hypothesizing that motor impairment would overall be greater in deletion than duplication carriers. In a series of regression models, we found that 16p11.2 deletion status tended to predict greater impairment along indices of gross motor function, but less impairment along indices of fine motor function. These findings point to a potential pattern of performance difficulties that could be investigated in future studies. Elucidating motor differences between 16p11.2 duplication and 16p11.2 deletion carriers may help in understanding the complex effect of 16p11.2 copy number variation and other rare genetic causes of autism.

Candidate biomarkers in psychiatric disorders: state of the field.

The field of psychiatry is hampered by a lack of robust, reliable and valid biomarkers that can aid in objectively diagnosing patients and providing individualized treatment recommendations. Here we review and critically evaluate the evidence for the most promising biomarkers in the psychiatric neuroscience literature for autism spectrum disorder, schizophrenia, anxiety disorders and post-traumatic stress disorder, major depression and bipolar disorder, and substance use disorders. Candidate biomarkers reviewed include various neuroimaging, genetic, molecular and peripheral assays, for the purposes of determining susceptibility or presence of illness, and predicting treatment response or safety. This review highlights a critical gap in the biomarker validation process. An enormous societal investment over the past 50 years has identified numerous candidate biomarkers. However, to date, the overwhelming majority of these measures have not been proven sufficiently reliable, valid and useful to be adopted clinically. It is time to consider whether strategic investments might break this impasse, focusing on a limited number of promising candidates to advance through a process of definitive testing for a specific indication. Some promising candidates for definitive testing include the N170 signal, an event-related brain potential measured using electroencephalography, for subgroup identification within autism spectrum disorder; striatal resting-state functional magnetic resonance imaging (fMRI) measures, such as the striatal connectivity index (SCI) and the functional striatal abnormalities (FSA) index, for prediction of treatment response in schizophrenia; error-related negativity (ERN), an electrophysiological index, for prediction of first onset of generalized anxiety disorder, and resting-state and structural brain connectomic measures for prediction of treatment response in social anxiety disorder. Alternate forms of classification may be useful for conceptualizing and testing potential biomarkers. Collaborative efforts allowing the inclusion of biosystems beyond genetics and neuroimaging are needed, and online remote acquisition of selected measures in a naturalistic setting using mobile health tools may significantly advance the field. Setting specific benchmarks for well-defined target application, along with development of appropriate funding and partnership mechanisms, would also be crucial. Finally, it should never be forgotten that, for a biomarker to be actionable, it will need to be clinically predictive at the individual level and viable in clinical settings.

Autism spectrum disorder and schizophrenia: An updated conceptual review.

Autism spectrum disorder (ASD) and schizophrenia (SCZ) are separate disorders, with distinct clinical profiles and natural histories. ASD, typically diagnosed in childhood, is characterized by restricted or repetitive interests or behaviors and impaired social communication, and it tends to have a stable course. SCZ, typically diagnosed in adolescence or adulthood, is characterized by hallucinations and delusions, and tends to be associated with declining function. However, youth with ASD are three to six times more likely to develop SCZ than their neurotypical counterparts, and increasingly, research has shown that ASD and SCZ converge at several levels. We conducted a systematic review of studies since 2013 relevant to understanding this convergence, and present here a narrative synthesis of key findings, which we have organized into four broad categories: symptoms and behavior, perception and cognition, biomarkers, and genetic and environmental risk. We then discuss opportunities for future research into the phenomenology and neurobiology of overlap between ASD and SCZ. Understanding this overlap will allow for researchers, and eventually clinicians, to understand the factors that may make a child with ASD vulnerable to developing SCZ. LAY SUMMARY: Autism spectrum disorder and schizophrenia are distinct diagnoses, but people with autism and people with schizophrena share several characteristics. We review recent studies that have examined these areas of overlap, and discuss the kinds of studies we will need to better understand how these disorders are related. Understanding this will be important to help us identify which autistic children are at risk of developing schizophrenia.

Reported autism diagnosis is associated with psychotic-like symptoms in the Adolescent Brain Cognitive Development cohort.

Although the schizophrenia (SCZ) rate is increased in autism spectrum disorder (ASD), it is difficult to identify which ASD youth will develop psychosis. We explored the relationship between ASD and emerging psychotic-like experiences (PLS) in a sample of 9127 youth aged 9-11 from the Adolescent Brain Cognitive Development (ABCD) cohort. We predicted that parent-reported ASD would be associated with PLS severity, and that ASD youth with PLS (ASD+/PLS+) would differ from ASD youth without PLS (ASD+/PLS-) and youth with PLS but not ASD (ASD-/PLS+) in cognitive function. We fit regression models that included parent-reported ASD, family history of psychosis, lifetime trauma, executive function, processing speed, working memory, age, sex, race, ethnicity, and income-to-needs ratio as predictors of Prodromal Questionnaire-Brief Child (PQ-BC) distress score, a continuous index of PLS severity. We assessed cognitive differences using regression models with ASD/PLS status and relevant covariates as predictors of NIH Toolbox measures. ASD increased raw PQ-BC distress scores by 2.47 points (95% CI 1.33-3.61), an effect at least as large as Black race (1.27 points, 95% CI 0.75-1.78), family history of psychosis (1.05 points, 95% CI 0.56-1.54), and Latinx ethnicity (0.99 points, 95% CI 0.53-1.45. We did not identify differences in cognition for ASD+/PLS+ youth relative to other groups. Our finding of association between ASD and PLS in youth is consistent with previous literature and adds new information in suggesting that ASD may be a strong risk factor for PLS even compared to established SCZ risk factors.

Psychotic symptoms in 16p11.2 copy-number variant carriers.

16p11.2 copy-number variation (CNV) is implicated in neurodevelopmental disorders, with the duplication and deletion associated with autism spectrum disorder (ASD) and the duplication associated with schizophrenia (SCZ). The 16p11.2 CNV may therefore provide insight into the relationship between ASD and SCZ, distinct disorders that co-occur at an elevated rate, and are difficult to distinguish from each other and from common co-occurring diagnoses such as obsessive compulsive disorder (OCD), itself a potential risk factor for SCZ. As psychotic symptoms are core to SCZ but distinct from ASD, we sought to examine their predictors in a population (n = 546) of 16p11.2 CNV carriers and their noncarrier siblings recruited by the Simons Variation in Individuals Project. We hypothesized that psychotic symptoms would be most common in duplication carriers followed by deletion carriers and noncarriers, that an ASD diagnosis would predict psychotic symptoms among CNV carriers, and that OCD symptoms would predict psychotic symptoms among all participants. Using data collected across multiple measures, we identified 19 participants with psychotic symptoms. Logistic regression models adjusting for biological sex, age, and IQ found that 16p11.2 duplication and ASD diagnosis predicted psychotic symptom presence. Our findings suggest that the association between 16p11.2 duplication and psychotic symptoms is independent of ASD diagnosis and that ASD diagnosis and psychotic symptoms may be associated in 16p11.2 CNV carriers. Autism Res 2020, 13: 187-198. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Either deletion or duplication at chromosome 16p11.2 raises the risk of autism spectrum disorder, and duplication, but not deletion, has been reported in schizophrenia (SCZ). In a sample of 16p11.2 deletion and duplication carriers, we found that having the duplication or having an autism diagnosis may increase the risk of psychosis, a key feature of SCZ.