Browsing by Subject "Autism spectrum disorder"

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    Open Access
    Multivariate data analysis identifies natural clusters of Tuberous Sclerosis Complex Associated Neuropsychiatric Disorders (TAND)
    (2021-10-24) de Vries, Petrus J.; Leclezio, Loren; Gardner-Lubbe, Sugnet; Krueger, Darcy; Sahin, Mustafa; Sparagana, Steven; De Waele, Liesbeth; Jansen, Anna
    Background Tuberous Sclerosis Complex (TSC), a multi-system genetic disorder, is associated with a wide range of TSC-Associated Neuropsychiatric Disorders (TAND). Individuals have apparently unique TAND profiles, challenging diagnosis, psycho-education, and intervention planning. We proposed that identification of natural TAND clusters could lead to personalized identification and treatment of TAND. Two small-scale studies showed cluster and factor analysis could identify clinically meaningful natural TAND clusters. Here we set out to identify definitive natural TAND clusters in a large, international dataset. Method Cross-sectional, anonymized TAND Checklist data of 453 individuals with TSC were collected from six international sites. Data-driven methods were used to identify natural TAND clusters. Mean squared contingency coefficients were calculated to produce a correlation matrix, and various cluster analyses and exploratory factor analysis were examined. Statistical robustness of clusters was evaluated with 1000-fold bootstrapping, and internal consistency calculated with Cronbach’s alpha. Results Ward’s method rendered seven natural TAND clusters with good robustness on bootstrapping. Cluster analysis showed significant convergence with an exploratory factor analysis solution, and, with the exception of one cluster, internal consistency of the emerging clusters was good to excellent. Clusters showed good clinical face validity. Conclusions Our findings identified a data-driven set of natural TAND clusters from within highly variable TAND Checklist data. The seven natural TAND clusters could be used to train families and professionals and to develop tailored approaches to identification and treatment of TAND. Natural TAND clusters may also have differential aetiological underpinnings and responses to molecular and other treatments.
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    Open Access
    De novo variants in CACNA1E found in patients with intellectual disability, developmental regression and social cognition deficit but no seizures
    (2021-10-26) Royer-Bertrand, Beryl; Jequier Gygax, Marine; Cisarova, Katarina; Rosenfeld, Jill A.; Bassetti, Jennifer A.; Moldovan, Oana; O’Heir, Emily; Burrage, Lindsay C.; Allen, Jake; Emrick, Lisa T.; Eastman, Emma; Kumps, Camille; Abbas, Safdar; Van Winckel, Geraldine; Chabane, Nadia; Zackai, Elaine H.; Lebon, Sebastien; Keena, Beth; Bhoj, Elizabeth J.; Umair, Muhammad; Li, Dong; Donald, Kirsten A.; Superti-Furga, Andrea
    Background De novo variants in the voltage-gated calcium channel subunit α1 E gene (CACNA1E) have been described as causative of epileptic encephalopathy with contractures, macrocephaly and dyskinesias. Methods Following the observation of an index patient with developmental delay and autism spectrum disorder (ASD) without seizures who had a de novo deleterious CACNA1E variant, we screened GeneMatcher for other individuals with CACNA1E variants and neurodevelopmental phenotypes without epilepsy. The spectrum of pathogenic CACNA1E variants was compared to the mutational landscape of variants in the gnomAD control population database. Results We identified seven unrelated individuals with intellectual disability, developmental regression and ASD-like behavioral profile, and notably without epilepsy, who had de novo heterozygous putatively pathogenic variants in CACNA1E. Age of onset of clinical manifestation, presence or absence of regression and degree of severity were variable, and no clear-cut genotype–phenotype association could be recognized. The analysis of disease-associated variants and their comparison to benign variants from the control population allowed for the identification of regions in the CACNA1E protein that seem to be intolerant to substitutions and thus more likely to harbor pathogenic variants. As in a few reported cases with CACNA1E variants and epilepsy, one patient showed a positive clinical behavioral response to topiramate, a specific calcium channel modulator. Limitations The significance of our study is limited by the absence of functional experiments of the effect of identified variants, the small sample size and the lack of systematic ASD assessment in all participants. Moreover, topiramate was given to one patient only and for a short period of time. Conclusions Our results indicate that CACNA1E variants may result in neurodevelopmental disorders without epilepsy and expand the mutational and phenotypic spectrum of this gene. CACNA1E deserves to be included in gene panels for non-specific developmental disorders, including ASD, and not limited to patients with seizures, to improve diagnostic recognition and explore the possible efficacy of topiramate.
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    Recurrence quantification analysis of resting state EEG signals in autism spectrum disorder – a systematic methodological exploration of technical and demographic confounders in the search for biomarkers
    (BioMed Central, 2018-07-02) Heunis, T; Aldrich, C; Peters, J M; Jeste, S S; Sahin, M; Scheffer, C; de Vries, P J
    Background Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a worldwide prevalence of 1–2%. In low-resource environments, in particular, early identification and diagnosis is a significant challenge. Therefore, there is a great demand for ‘language-free, culturally fair’ low-cost screening tools for ASD that do not require highly trained professionals. Electroencephalography (EEG) has seen growing interest as an investigational tool for biomarker development in ASD and neurodevelopmental disorders. One of the key challenges is the identification of appropriate multivariate, next-generation analytical methodologies that can characterise the complex, nonlinear dynamics of neural networks in the brain, mindful of technical and demographic confounders that may influence biomarker findings. The aim of this study was to evaluate the robustness of recurrence quantification analysis (RQA) as a potential biomarker for ASD using a systematic methodological exploration of a range of potential technical and demographic confounders. Methods RQA feature extraction was performed on continuous 5-second segments of resting state EEG (rsEEG) data and linear and nonlinear classifiers were tested. Data analysis progressed from a full sample of 16 ASD and 46 typically developing (TD) individuals (age 0–18 years, 4802 EEG segments), to a subsample of 16 ASD and 19 TD children (age 0–6 years, 1874 segments), to an age-matched sample of 7 ASD and 7 TD children (age 2–6 years, 666 segments) to prevent sample bias and to avoid misinterpretation of the classification results attributable to technical and demographic confounders. A clinical scenario of diagnosing an unseen subject was simulated using a leave-one-subject-out classification approach. Results In the age-matched sample, leave-one-subject-out classification with a nonlinear support vector machine classifier showed 92.9% accuracy, 100% sensitivity and 85.7% specificity in differentiating ASD from TD. Age, sex, intellectual ability and the number of training and test segments per group were identified as possible demographic and technical confounders. Consistent repeatability, i.e. the correct identification of all segments per subject, was found to be a challenge. Conclusions RQA of rsEEG was an accurate classifier of ASD in an age-matched sample, suggesting the potential of this approach for global screening in ASD. However, this study also showed experimentally how a range of technical challenges and demographic confounders can skew results, and highlights the importance of probing for these in future studies. We recommend validation of this methodology in a large and well-matched sample of infants and children, preferably in a low- and middle-income setting.