1. Benevides TW, Cook B, Klinger LG, McLean KJ, Wallace GL, Carey ME, Lee WL, Ventimiglia J, Schiff LD, Shea L. Brief Report: Under-Identification of Symptomatic Menopause in Publicly-Insured Autistic People. J Autism Dev Disord;2024 (Aug 29)

Menopause is a normal part of aging and in the general population is associated with chronic conditions that impact health, mortality, and well-being. Menopause is experienced differently by autistic individuals, although no studies have investigated this topic in a large sample. The purpose of this study was to investigate rates of, and factors associated with symptomatic menopause among autistic individuals and to identify the prevalence of co-occurring conditions in symptomatic individuals. We included autistic females aged 35-70 years enrolled for 10 + months in 2014-2016 Medicare and/or Medicaid (n = 26,904), excluding those with gender dysphoria. Those with symptomatic menopause were compared to a non-symptomatic reference group on demographic, enrollment characteristics, and co-occurring conditions through logistic regression. Approximately 4% of publicly-insured autistic females aged 46-70 years had symptomatic menopause in their medical records. Intellectual disability was associated with a lower likelihood of symptomatic menopause, and being Medicare-enrolled or dual-enrolled was associated with higher likelihood of having symptomatic menopause recorded. In adjusted models, rates of ADHD, anxiety and depressive disorders, headache/migraine, altered sensory experiences, altered sexual function, and sleep disturbance were significantly higher in the symptomatic menopause sample compared to the reference group. More work to better support autistic women in discussing menopausal symptoms and co-occurring conditions with primary care providers is needed, particularly among those for whom self-report of symptoms are more challenging to ascertain. Factors associated with specific types of health care coverage warrant greater investigation to support better identification.

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2. Bright MA, Ortega DP, Bodi CB, Walsh K. School-Based Victimization Prevention Education Programs for Children and Youth With Intellectual and Developmental Disabilities: A Scoping Review. Child Maltreat;2024 (Aug 29):10775595241276412.

Youth with intellectual and developmental disabilities (IDD) are at significantly higher risk of experiencing multiple types of interpersonal victimization across their lifespan compared to their peers without IDD. Despite the extensive literature on efficacy of prevention education programs for children without IDD, very little is known about comparable programs for children with IDD. In this scoping review, we synthesized the literature on existing programs for children with IDD. We identified thirteen programs which we critically assessed against established best practice criteria for prevention and special education and evaluation. The current literature on prevention education programs for children with IDD exhibits significant limitations, such as weak research designs and poor measurement of outcomes.

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3. Corey J, Tsai JM, Mhadeshwar A, Srinivasan S, Bhat A. Digital motor intervention effects on motor performance of individuals with developmental disabilities: a systematic review. J Intellect Disabil Res;2024 (Aug 29)

BACKGROUND: Individuals (i.e. children/young adults) with developmental disabilities (DDs) and intellectual disabilities (IDs) often display a variety of physical and motor impairments. It is well known that participation in motor activities can positively impact the development of children’s cognitive and social skills. Recently, virtual and digital technologies (e.g. video conferencing applications, virtual reality and video gaming) have been increasingly used to promote better physical/motor outcomes. The efficacy of digital technologies in improving motor outcomes for those with DD/ID varies depending on the technology and population, and the comparative effects of various technologies are unknown. The aim of our study is to conduct a systematic review to comprehensively examine the quantitative and qualitative results of current studies reporting the efficacy of digitally based motor interventions on motor outcomes in individuals with DD/ID. METHODS: Literature published from 1900 to 2024 was searched in four health sciences databases: PubMed, PsycINFO, Scopus and CINAHL. Articles that examined the effects of gross motor/physical activity training using technologies such as exergaming (i.e. exercise through video gaming such as the Wii and Xbox Kinect), virtual reality or telehealth video conferencing applications (i.e. Zoom, Webex or mobile health apps) on the standardised or game-specific gross motor performance of individuals with DD/ID diagnoses that do not typically experience significant walking challenges using experimental or quasi-experimental study designs were included. Thirty relevant articles were retrieved from a search of the databases PubMed (914), PsycINFO (1201), Scopus (1910) and CINAHL (948). RESULTS: Our quantitative synthesis of this published literature suggests strong and consistent evidence of small-to-large improvements in motor skill performance following digital movement interventions. CONCLUSIONS: Our review supports the use of digital motor interventions to support motor skill performance in individuals with DD without ID. Digital technologies can provide a more engaging option for therapists to promote motor skill development in individuals with DD or for caregivers to use as an adjunct to skilled therapy.

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4. Jahani A, Jahani I, Khadem A, Braden BB, Delrobaei M, MacIntosh BJ. Twinned neuroimaging analysis contributes to improving the classification of young people with autism spectrum disorder. Sci Rep;2024 (Aug 29);14(1):20120.

Autism spectrum disorder (ASD) is diagnosed using comprehensive behavioral information. Neuroimaging offers additional information but lacks clinical utility for diagnosis. This study investigates whether multi-forms of magnetic resonance imaging (MRI) contrast can be used individually and in combination to produce a categorical classification of young individuals with ASD. MRI data were accessed from the Autism Brain Imaging Data Exchange (ABIDE). Young participants (ages 2-30) were selected, and two group cohorts consisted of 702 participants: 351 ASD and 351 controls. Image-based classification was performed using one-channel and two-channel inputs to 3D-DenseNet deep learning networks. The models were trained and tested using tenfold cross-validation. Two-channel models were twinned with combinations of structural MRI (sMRI) maps and amplitude of low-frequency fluctuations (ALFF) or fractional ALFF (fALFF) maps from resting-state functional MRI (rs-fMRI). All models produced classification accuracy that exceeded 65.1%. The two-channel ALFF-sMRI model achieved the highest mean accuracy of 76.9% ± 2.34. The one-channel ALFF-based model alone had mean accuracy of 72% ± 3.1. This study leveraged the ABIDE dataset to produce ASD classification results that are comparable and/or exceed literature values. The deep learning approach was conducive to diverse neuroimaging inputs. Findings reveal that the ALFF-sMRI two-channel model outperformed all others.

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5. Kliemann D, Galdi P, Van De Water AL, Egger B, Jarecka D, Adolphs R, Ghosh SS. Resting-State Functional Connectivity of the Amygdala in Autism: A Preregistered Large-Scale Study. Am J Psychiatry;2024 (Aug 29):appiajp20230249.

OBJECTIVE: Three leading neurobiological hypotheses about autism spectrum disorder (ASD) propose underconnectivity between brain regions, atypical function of the amygdala, and generally higher variability between individuals with ASD than between neurotypical individuals. Past work has often failed to generalize, because of small sample sizes, unquantified data quality, and analytic flexibility. This study addressed these limitations while testing the above three hypotheses, applied to amygdala functional connectivity. METHODS: In a comprehensive preregistered study, the three hypotheses were tested in a subset (N=488 after exclusions; N=212 with ASD) of the Autism Brain Imaging Data Exchange data sets. The authors analyzed resting-state functional connectivity (FC) from functional MRI data from two anatomically defined amygdala subdivisions, in three hypotheses with respect to magnitude, pattern similarity, and variability, across different anatomical scales ranging from whole brain to specific regions and networks. RESULTS: A Bayesian approach to hypothesis evaluation produced inconsistent evidence in ASD for atypical amygdala FC magnitude, strong evidence that the multivariate pattern of FC was typical, and no consistent evidence of increased interindividual variability in FC. The results strongly depended on analytic choices, including preprocessing pipeline for the neuroimaging data, anatomical specificity, and subject exclusions. CONCLUSIONS: A preregistered set of analyses found no reliable evidence for atypical functional connectivity of the amygdala in autism, contrary to leading hypotheses. Future studies should test an expanded set of hypotheses across multiple processing pipelines, collect deeper data per individual, and include a greater diversity of participants to ensure robust generalizability of findings on amygdala FC in ASD.

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6. Özel F, Stratmann M, Papadopoulos FC, Rüegg J, Bornehag CG. Gender-specific play behavior in relation to autistic traits and behavioral difficulties at the age of seven in the SELMA study. PLoS One;2024;19(8):e0308605.

BACKGROUND: Childhood gender nonconformity is related to psychological distress and behavioral difficulties. Similarly, there is evidence for a link between gender nonconformity, or gender dysphoria in some studies, and autism spectrum disorder and related traits. Our knowledge on those associations mostly originates from clinical populations, which might lead to overestimation. Thus, this study aimed to assess associations between gender nonconformity and behavioral difficulties in a population-based study. METHODS: In the Swedish Environmental Longitudinal, Mother and Child, Asthma and Allergy (SELMA) study, cross-sectional associations between gender-specific play behavior and behavioral outcomes and autistic traits were investigated among 718 children at 7-years of age. Play behavior was measured using the Preschool Activities Inventory; behavioral outcomes and autistic traits were measured with the Strengths and Difficulties Questionnaire and the Social Responsiveness Scale, respectively. Linear and logistic regression analyses were performed. RESULTS: Higher composite play behavior scores (indicating either increased masculine or decreased feminine play behavior) were associated with increased autistic trait scores in girls (β = 0.13; 95% confidence interval [CI] = 0.00, 0.26). Furthermore, higher composite scores were shown to be associated with behavioral difficulties in both girls (β = 0.11; 95% CI = 0.04, 0.18) and boys (β = 0.10; 95% CI = 0.02, 0.19). Additionally, higher feminine scores were related with increased problems in peer relationships in boys (β = 0.04; 95% CI = 0.00, 0.07). CONCLUSIONS: This study suggests a link between gender nonconforming play behavior and autistic traits as well as behavioral difficulties among children in a non-clinical population, which calls attention to the necessity of supporting children with gender nonconformity from early ages.

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7. Peristeri E, Drakoulaki K, Boznou A, Nerantzini M, Gena A, Lengeris A, Varlokosta S. What Silent Pauses Can ‘Tell’ Us About the Storytelling Skills of Autistic Children: Relations Between Pausing, Language Skills and Executive Functions. J Autism Dev Disord;2024 (Aug 29)

Silent pauses may serve communicative purposes such as demarcating boundaries between discourse units in language production. Previous research has shown that autistic children differ in their pausing behavior from typically-developing (TD) peers, however, the factors behind this difference remain underexplored. The current study was aimed at comparing the use of silent pauses in the narrative production of autistic children and age-matched TD children, and also to identify possible relations between pausing behavior and the children’s language and executive function abilities. According to the study’s findings, the autistic children did not differ from their TD peers in the use of grammatical pauses, however, the former tended to produce significantly less syntactically complex narratives than the TD group, which increased the likelihood that the autistic group would pause appropriately at phrasal boundaries. Though we have found low rates of ungrammatical silent pauses and omitted pauses in obligatory discourse contexts across both groups, autistic children with lower cognitive flexibility tended to use more ungrammatical pauses than their peers with higher cognitive flexibility scores. Also, the autistic group tended to omit obligatory silent pauses more often as their narration became more complex. The results demonstrate that syntactic complexity in narrative production modulated autistic children’s pausing behavior, and that structurally simple narrations boosted the autistic group’s appropriate use of grammatical pauses. The overall findings also demonstrate the importance of studying silent pauses in the narrative discourse of autistic children, and also highlight the links between silent pauses and the children’s syntactic and cognitive skills.

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8. Rahaie Z, Rabiee HR, Alinejad-Rokny H. CNVDeep: deep association of copy number variants with neurocognitive disorders. BMC Bioinformatics;2024 (Aug 29);25(1):283.

BACKGROUND: Copy number variants (CNVs) have become increasingly instrumental in understanding the etiology of all diseases and phenotypes, including Neurocognitive Disorders (NDs). Among the well-established regions associated with ND are small parts of chromosome 16 deletions (16p11.2) and chromosome 15 duplications (15q3). Various methods have been developed to identify associations between CNVs and diseases of interest. The majority of methods are based on statistical inference techniques. However, due to the multi-dimensional nature of the features of the CNVs, these methods are still immature. The other aspect is that regions discovered by different methods are large, while the causative regions may be much smaller. RESULTS: In this study, we propose a regularized deep learning model to select causal regions for the target disease. With the help of the proximal [20] gradient descent algorithm, the model utilizes the group LASSO concept and embraces a deep learning model in a sparsity framework. We perform the CNV analysis for 74,811 individuals with three types of brain disorders, autism spectrum disorder (ASD), schizophrenia (SCZ), and developmental delay (DD), and also perform cumulative analysis to discover the regions that are common among the NDs. The brain expression of genes associated with diseases has increased by an average of 20 percent, and genes with homologs in mice that cause nervous system phenotypes have increased by 18 percent (on average). The DECIPHER data source also seeks other phenotypes connected to the detected regions alongside gene ontology analysis. The target diseases are correlated with some unexplored regions, such as deletions on 1q21.1 and 1q21.2 (for ASD), deletions on 20q12 (for SCZ), and duplications on 8p23.3 (for DD). Furthermore, our method is compared with other machine learning algorithms. CONCLUSIONS: Our model effectively identifies regions associated with phenotypic traits using regularized deep learning. Rather than attempting to analyze the whole genome, CNVDeep allows us to focus only on the causative regions of disease.

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9. Vacas J, Antolí A, Sánchez-Raya A, Pérez-Dueñas C, Cuadrado F. Eye-Tracking Methodology to Detect Differences in Attention to Faces Between Developmental Language Disorder and Autism. J Speech Lang Hear Res;2024 (Aug 28):1-15.

PURPOSE: Developmental language disorder (DLD) and autism sometimes appear as overlapping conditions in behavioral tests. There is much literature on the visual scanning pattern (VSP) of faces in autistic children, but this is scarce regarding those with DLD. The purpose of this study was to compare the VSP of faces in young children with DLD, those with autism, and typically developing peers, assessing the effect of three variables. METHOD: Two eye-tracking experiments were designed to assess the effect of the emotion and the poser’s gender (Experiment 1) and the poser’s age (Experiment 2) on the VSP of participants (Experiment 1: N = 59, age range: 32-74 months; Experiment 2: N = 58, age range: 32-74 months). We operationalized the VSP in terms of attentional orientation, visual preference, and depth of processing of each sort of face. We developed two paired preference tasks in which pairs of images of faces showing different emotions were displayed simultaneously to compete for children’s attention. RESULTS: Data analysis revealed two VSP markers common to both disorders: (a) superficial processing of faces and (b) late orientation to angry and child faces. Moreover, one specific marker for each condition was also found: typical preference for child faces in children with DLD versus diminished preference for them in autistic children. CONCLUSIONS: Considering the similarities found between children with DLD and those with autism, difficulties of children with DLD in attention to faces have been systematically underestimated. Thus, more effort must be made to identify and respond to the needs of this population. Clinical practice may benefit from the potential of eye-tracking methodology and the analysis of the VSP to assess attention to faces in both conditions. This would also contribute to the improvement of early differential diagnosis in the long run.

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