Is There a Bias Towards Males in the Diagnosis of Autism? A Systematic Review and Meta-Analysis.

Autism is more frequently diagnosed in males, with evidence suggesting that females are more likely to be misdiagnosed or underdiagnosed. Possibly, the male/female ratio imbalance relates to phenotypic and camouflaging differences between genders. Here, we performed a comprehensive approach to phenotypic and camouflaging research in autism addressed in two studies. First (Study 1 - Phenotypic Differences in Autism), we conducted a systematic review and meta-analysis of gender differences in autism phenotype. The electronic datasets Pubmed, Scopus, Web of Science, and PsychInfo were searched. We included 67 articles that compared females and males in autism core symptoms, and in cognitive, socioemotional, and behavioural phenotypes. Autistic males exhibited more severe symptoms and social interaction difficulties on standard clinical measures than females, who, in turn, exhibited more cognitive and behavioural difficulties. Considering the hypothesis of camouflaging possibly underlying these differences, we then conducted a meta-analysis of gender differences in camouflaging (Study 2 - Camouflaging Differences in Autism). The same datasets as the first study were searched. Ten studies were included. Females used more compensation and masking camouflage strategies than males. The results support the argument of a bias in clinical procedures towards males and the importance of considering a 'female autism phenotype'-potentially involving camouflaging-in the diagnostic process.

Developmental epileptic encephalopathy in DLG4-related synaptopathy.

OBJECTIVE: The postsynaptic density protein of excitatory neurons PSD-95 is encoded by discs large MAGUK scaffold protein 4 (DLG4), de novo pathogenic variants of which lead to DLG4-related synaptopathy. The major clinical features are developmental delay, intellectual disability (ID), hypotonia, sleep disturbances, movement disorders, and epilepsy. Even though epilepsy is present in 50% of the individuals, it has not been investigated in detail. We describe here the phenotypic spectrum of epilepsy and associated comorbidities in patients with DLG4-related synaptopathy. METHODS: We included 35 individuals with a DLG4 variant and epilepsy as part of a multicenter study. The DLG4 variants were detected by the referring laboratories. The degree of ID, hypotonia, developmental delay, and motor disturbances were evaluated by the referring clinician. Data on awake and sleep electroencephalography (EEG) and/or video-polygraphy and brain magnetic resonance imaging were collected. Antiseizure medication response was retrospectively assessed by the referring clinician. RESULTS: A large variety of seizure types was reported, although focal seizures were the most common. Encephalopathy related to status epilepticus during slow-wave sleep (ESES)/developmental epileptic encephalopathy with spike-wave activation during sleep (DEE-SWAS) was diagnosed in >25% of the individuals. All but one individual presented with neurodevelopmental delay. Regression in verbal and/or motor domains was observed in all individuals who suffered from ESES/DEE-SWAS, as well as some who did not. We could not identify a clear genotype-phenotype relationship even between individuals with the same DLG4 variants. SIGNIFICANCE: Our study shows that a subgroup of individuals with DLG4-related synaptopathy have DEE, and approximately one fourth of them have ESES/DEE-SWAS. Our study confirms DEE as part of the DLG4-related phenotypic spectrum. Occurrence of ESES/DEE-SWAS in DLG4-related synaptopathy requires proper investigation with sleep EEG.

Expanding the clinical and molecular spectrum of FOXG1- and ZBTB18-associated neurodevelopmental disorders.

Introduction The zinc finger BTB domain-containing protein ZBTB18 binds to FOXG1 to form a transcriptional repressive complex involved in neuronal differentiation. Disruption of the components of this complex results in chromosome 1q43-q44 deletion syndrome/intellectual developmental disorder 22 or in FOXG1 syndrome. Case presentation This study reports on five patients with cognitive and behavioral impairment, seizures, microcephaly, and/or congenital brain abnormalities. Whole exome sequencing identified deleterious ZBTB18 variants in three patients and deleterious FOXG1 variants in the remaining patients. We have detected a missense variant within the BTB domain of ZBTB18 in two affected monozygotic twins. In addition, we observed agenesis of the septum pellucidum in a missense FOXG1 carrier with a severe FOXG1 syndrome. Conclusion Although the ZBTB18 zinc finger domains harbor the majority of known deleterious variants, we report a novel de novo rare missense variant within the BTB domain. The agenesis of the septum pellucidum observed in a missense FOXG1 carrier could be considered as a novel clinical feature associated with FOXG1 syndrome. The severe FOXG1 syndrome in this patient contrasts with the milder phenotype expected for a missense. Genetic or environmental factors may explain this phenotypic variability in FOXG1 syndrome.

PET and MRI detection of early and progressive neurodegeneration in spinocerebellar ataxia type 36.

BACKGROUND: The spinocerebellar ataxias (SCAs) form a clinically, genetically, and pathological heterogeneous group of autosomal-dominant degenerative diseases. In particular, SCA36 is characterized by a late-onset, slowly progressive cerebellar syndrome typically associated with sensorineural hearing loss. This study was aimed at analyzing the neurodegenerative process underlying SCA36 through fluorodeoxyglucose positron emission tomography (FDG-PET) and MRI scans. METHODS: Twenty SCA36 patients underwent a study consisting of FDG-PET and MRI scans. Clinical motor evaluation was performed through the Scale for the Assessment and Rating of Ataxia (SARA). FDG-PET was carried out using a voxel-by-voxel and region-of-interest analysis. MRI evaluation was based on visual inspection and volumetric analysis. RESULTS: SARA ranged from 0 to 24.5 (4 patients asymptomatic, 3 with unspecific symptoms, and 13 with cerebellar signs). FDG-PET revealed hypometabolism in the asymptomatic stage in the vermis and right cerebellar hemisphere. In the ataxic stage, hypometabolism spread to both cerebellar hemispheres and the brain stem. MRI was normal in asymptomatic and preataxic individuals and showed superior cerebellar vermis atrophy early in the ataxic stage, diffuse cerebellar atrophy some years into the disease course, and a pattern of olivopontocerebellar atrophy in the oldest patients. There was no significant cerebellar atrophy in patients younger than 50 years. CONCLUSIONS: We present the first FDG-PET study of SCA36 and one of the largest neuroimaging study of SCAs. Our results revealed neuronal dysfunctions in the vermis and right cerebellar hemisphere as soon as a decade before the onset of motor symptoms. In the ataxic stage, dysfunctions spread to both hemispheres and the brain stem. © 2016 International Parkinson and Movement Disorder Society.

Female patient with autistic disorder, intellectual disability, and co-morbid anxiety disorder: Expanding the phenotype associated with the recurrent 3q13.2-q13.31 microdeletion.

In recent years, the advent of comparative genomic hybridization (CGH) and single nucleotide polymorphism (SNP) arrays and its use as a first genetic test for the diagnosis of patients with neurodevelopmental phenotypes has allowed the identification of novel submicroscopic chromosomal abnormalities (namely, copy number variants or CNVs), imperceptible by conventional cytogenetic techniques. The 3q13.31 microdeletion syndrome (OMIM #615433) has been defined as a genomic disorder mainly characterized by developmental delay, postnatal overgrowth, hypotonia, genital abnormalities in males, and characteristic craniofacial features. Although the 3q13.31 CNVs are variable in size, a 3.4 Mb recurrently altered region at 3q13.2-q13.31 has been recently described and non-allelic homologous recombination (NAHR) mediated by flanking human endogenous retrovirus (HERV-H) elements has been suggested as the mechanism of deletion formation. We expand the phenotypic spectrum associated with this recurrent deletion performing the clinical description of a 9-year-old female patient with autistic disorder, total absence of language, intellectual disability, anxiety disorder and disruptive, and compulsive eating behaviors. The array-based molecular karyotyping allowed the identification of a de novo recurrent 3q13.2-q13.31 deletion encompassing 25 genes. In addition, we compare her clinical phenotype with previous reports of patients with neurodevelopmental and behavioral disorders and proximal 3q microdeletions. Finally, we also review the candidate genes proposed so far for these phenotypes.

Interstitial microdeletions including the chromosome band 4q13.2 and the UBA6 gene as possible causes of intellectual disability and behavior disorder.

The few proximal 4q chromosomal aberrations identified in patients with neurodevelopmental phenotypes that have been published to date are variable in type, size and breakpoints and, therefore, encompass different chromosome bands and genes, making the establishment of genotype-phenotype correlations a challenging task. Here, microarray-based copy number analysis allowed us the detection of two novel and partially overlapping deletions in two unrelated families. In Family 1, a 4q13.1-q13.2 deletion of 3.84 Mb was identified in a mother with mild intellectual disability and in her two children, both with mild intellectual disability and attention deficit hyperactivity disorder. In Family 2, a de novo 4q13.2-q13.3 deletion of 6.81 Mb was detected in a female patient, born to unaffected parents, with a diagnosis of mild intellectual disability, behavioral disorder and facial dysmorphism. The shortest region of overlap between these two aberrations is located at chromosome 4q13.2 and includes 17 genes amongst of which we suggest UBA6 (ubiquitin-like modifier-activating enzyme 6) as a strong candidate gene for these phenotypes.

A Machine Learning Approach in Autism Spectrum Disorders: From Sensory Processing to Behavior Problems.

Atypical sensory processing described in autism spectrum disorders (ASDs) frequently cascade into behavioral alterations: isolation, aggression, indifference, anxious/depressed states, or attention problems. Predictive machine learning models might refine the statistical explorations of the associations between them by finding out how these dimensions are related. This study investigates whether behavior problems can be predicted using sensory processing abilities. Participants were 72 children and adolescents (21 females) diagnosed with ASD, aged between 6 and 14 years (M = 7.83 years; SD = 2.80 years). Parents of the participants were invited to answer the Sensory Profile 2 (SP2) and the Child Behavior Checklist (CBCL) questionnaires. A collection of 26 supervised machine learning regression models of different families was developed to predict the CBCL outcomes using the SP2 scores. The most reliable predictions were for the following outcomes: total problems (using the items in the SP2 touch scale as inputs), anxiety/depression (using avoiding quadrant), social problems (registration), and externalizing scales, revealing interesting relations between CBCL outcomes and SP2 scales. The prediction reliability on the remaining outcomes was "moderate to good" except somatic complaints and rule-breaking, where it was "bad to moderate." Linear and ridge regression achieved the best prediction for a single outcome and globally, respectively, and gradient boosting machine achieved the best prediction in three outcomes. Results highlight the utility of several machine learning models in studying the predictive value of sensory processing impairments (with an early onset) on specific behavior alterations, providing evidences of relationship between sensory processing impairments and behavior problems in ASD.

Viability Study of Machine Learning-Based Prediction of COVID-19 Pandemic Impact in Obsessive-Compulsive Disorder Patients.

BACKGROUND: Machine learning modeling can provide valuable support in different areas of mental health, because it enables to make rapid predictions and therefore support the decision making, based on valuable data. However, few studies have applied this method to predict symptoms' worsening, based on sociodemographic, contextual, and clinical data. Thus, we applied machine learning techniques to identify predictors of symptomatologic changes in a Spanish cohort of OCD patients during the initial phase of the COVID-19 pandemic. METHODS: 127 OCD patients were assessed using the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) and a structured clinical interview during the COVID-19 pandemic. Machine learning models for classification (LDA and SVM) and regression (linear regression and SVR) were constructed to predict each symptom based on patient's sociodemographic, clinical and contextual information. RESULTS: A Y-BOCS score prediction model was generated with 100% reliability at a score threshold of ± 6. Reliability of 100% was reached for obsessions and/or compulsions related to COVID-19. Symptoms of anxiety and depression were predicted with less reliability (correlation R of 0.58 and 0.68, respectively). The suicidal thoughts are predicted with a sensitivity of 79% and specificity of 88%. The best results are achieved by SVM and SVR. CONCLUSION: Our findings reveal that sociodemographic and clinical data can be used to predict changes in OCD symptomatology. Machine learning may be valuable tool for helping clinicians to rapidly identify patients at higher risk and therefore provide optimized care, especially in future pandemics. However, further validation of these models is required to ensure greater reliability of the algorithms for clinical implementation to specific objectives of interest.

Social Camouflaging in Females with Autism Spectrum Disorder: A Systematic Review.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with increasing prevalence, and a male-to-female ratio of 4:1. Research has been suggesting that discrepancy in prevalence may be due to the fact that females camouflage their symptoms. In this study, we aimed to systematically review evidence on the camouflage effect in females with ASD. Following the PRISMA guidelines, we reviewed empirical research published from January 2009 to September 2019 on PubMed, Web of Science, PsychInfo and Scopus databases. Thirteen empirical articles were included in this review. Overall, evidence supports that camouflaging seems to be an adaptive mechanism for females with ASD, despite the negative implications of these behaviours in their daily life.

Working Memory Performance, Pain and Associated Clinical Variables in Women With Fibromyalgia.

Working memory (WM) is a critical process for cognitive functioning in which fibromyalgia (FM) patients could show cognitive disturbances. Dyscognition in FM has been explained by interference from pain processing, which shares the neural substrates involved in cognition and may capture neural resources required to perform cognitive tasks. However, there is not yet data about how pain is related to WM performance, neither the role that other clinical variables could have. The objectives of this study were (1) to clarify the WM status of patients with FM and its relationship with nociception, and (2) to determine the clinical variables associated to FM that best predict WM performance. To this end, 132 women with FM undertook a neuropsychological assessment of WM functioning (Digit span, Spatial span, ACT tests and a 2-Back task) and a complete clinical assessment (FSQ, FIQ-R, BDI-1A, HADS, PSQI, MFE-30 questionnaires), including determination of pain thresholds and tolerance by pressure algometry. Patients with FM seem to preserve their WM span and ability to maintain and manipulate information online for both visuospatial and verbal domains. However, up to one-third of patients showed impairment in tasks requiring more short-term memory load, divided attention, and information processing ability (measured by the ACT task). Cognitive performance was spuriously related to the level of pain experienced, finding only that pain measures are related to the ACT task. The results of the linear regression analyses suggest that sleep problems and fatigue were the variables that best predicted WM performance in FM patients. Future research should take these variables into account when evaluating dyscognition in FM and should include dynamic measures of pain modulation.