Pubmed du 08/04/19

lundi 8 avril 2019

1. Aghdam MA, Sharifi A, Pedram MM. Diagnosis of Autism Spectrum Disorders in Young Children Based on Resting-State Functional Magnetic Resonance Imaging Data Using Convolutional Neural Networks. Journal of digital imaging. 2019.

Statistics show that the risk of autism spectrum disorder (ASD) is increasing in the world. Early diagnosis is most important factor in treatment of ASD. Thus far, the childhood diagnosis of ASD has been done based on clinical interviews and behavioral observations. There is a significant need to reduce the use of traditional diagnostic techniques and to diagnose this disorder in the right time and before the manifestation of behavioral symptoms. The purpose of this study is to present the intelligent model to diagnose ASD in young children based on resting-state functional magnetic resonance imaging (rs-fMRI) data using convolutional neural networks (CNNs). CNNs, which are by far one of the most powerful deep learning algorithms, are mainly trained using datasets with large numbers of samples. However, obtaining comprehensive datasets such as ImageNet and achieving acceptable results in medical imaging domain have become challenges. In order to overcome these two challenges, the two methods of "combining classifiers," both dynamic (mixture of experts) and static (simple Bayes) approaches, and "transfer learning" were used in this analysis. In addition, since diagnosis of ASD will be much more effective at an early age, samples ranging in age from 5 to 10 years from global Autism Brain Imaging Data Exchange I and II (ABIDE I and ABIDE II) datasets were used in this research. The accuracy, sensitivity, and specificity of presented model outperform the results of previous studies conducted on ABIDE I dataset (the best results obtained from Adamax optimization technique : accuracy = 0.7273, sensitivity = 0.712, specificity = 0.7348). Furthermore, acceptable classification results were obtained from ABIDE II dataset (the best results obtained from Adamax optimization technique : accuracy = 0.7, sensitivity = 0.582, specificity = 0.804) and the combination of ABIDE I and ABIDE II datasets (the best results obtained from Adam optimization technique : accuracy = 0.7045, sensitivity = 0.679, specificity = 0.7421). We can conclude that the proposed architecture can be considered as an efficient tool for diagnosis of ASD in young children. From another perspective, this proposed method can be applied to analyzing rs-fMRI data related to brain dysfunctions.

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2. Campos C, Duck M, McQuillan R, Brazill L, Malik S, Hartman L, McPherson AC, Gibson BE, Jachyra P. Exploring the Role of Physiotherapists in the Care of Children with Autism Spectrum Disorder. Phys Occup Ther Pediatr. 2019 : 1-15.

AIMS : Children with autism spectrum disorder (ASD) are less likely to participate in physical activity than their age related peers, and it has been suggested that physiotherapists (PT) could potentially facilitate their participation. Currently, no research has examined PTs’ potential role in enhancing physical activity (PA) participation. The purpose of this qualitative study was to examine PTs experiences and perspectives of working with children with ASD, and to explore potential directions for PTs to potentially increase PA. METHODS : Ten pediatric PTs in Canada were interviewed, and data were analyzed using thematic analysis. RESULTS : Three themes were identified : the role of PT, perceived lack of expertise, confidence and training, and structural and systemic barriers. The accounts highlight the social and institutional complexity and constraints in PTs potential promotion of PA for children with ASD. Participants supported a primarily consultative role whereby PTs could educate and partner with parents, teachers, and community service providers to enhance gross motor development and individualize PA needs. CONCLUSIONS : These findings indicate how PTs might be involved in enhancing PA among children with ASD.

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3. Klebanoff SM, Rosenau KA, Wood JJ. The therapeutic alliance in cognitive-behavioral therapy for school-aged children with autism and clinical anxiety. Autism. 2019 : 1362361319841197.

Little is known about the alliance between therapists and children with autism spectrum disorder who are receiving psychological therapies in outpatient treatment settings. This study examined the therapeutic alliance in children with autism spectrum disorder and clinical anxiety, who were receiving cognitive behavioral therapy in a randomized, controlled trial. The Therapeutic Alliance Scale for Children was administered to a sample of children and youth with autism spectrum disorder and anxiety ( N = 64 ; aged 7-14) as well as to their parents and therapists. A comparison sample of typically developing youth with clinical anxiety ( N = 36 ; aged 5-12) was included. The child-therapist alliance was more positive among typically developing children than among children with autism spectrum disorder ; correspondingly, the parent-therapist alliance was also more positive among parents of typically developing children. Therapist reports of positive child-therapist alliance predicted post-treatment reductions in anxiety among children with autism spectrum disorder, although child reports of this alliance did not. Parent reports of positive parent-therapist alliance also predicted post-treatment reductions in the child’s anxiety in the group with autism spectrum disorder. A strong therapeutic alliance appears to be associated with better treatment outcomes in children with autism spectrum disorder receiving cognitive behavioral therapy, although a thoughtful and diagnostically sensitive approach is advisable to promote a positive alliance with children with autism spectrum disorder.

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4. Lempriere S. Autism mutation produces hyper-connected neurons. Nature reviews Neurology. 2019.

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5. Moskalyuk A, Van De Vijver S, Verstraelen P, De Vos WH, Kooy RF, Giugliano M. Single-Cell and Neuronal Network Alterations in an In Vitro Model of Fragile X Syndrome. Cereb Cortex. 2019.

The Fragile X mental retardation protein (FMRP) is involved in many cellular processes and it regulates synaptic and network development in neurons. Its absence is known to lead to intellectual disability, with a wide range of comorbidities including autism. Over the past decades, FMRP research focused on abnormalities both in glutamatergic and GABAergic signaling, and an altered balance between excitation and inhibition has been hypothesized to underlie the clinical consequences of absence of the protein. Using Fmrp knockout mice, we studied an in vitro model of cortical microcircuitry and observed that the loss of FMRP largely affected the electrophysiological correlates of network development and maturation but caused less alterations in single-cell phenotypes. The loss of FMRP also caused a structural increase in the number of excitatory synaptic terminals. Using a mathematical model, we demonstrated that the combination of an increased excitation and reduced inhibition describes best our experimental observations during the ex vivo formation of the network connections.

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6. Neuhaus E, Webb SJ, Bernier RA. Linking social motivation with social skill : The role of emotion dysregulation in autism spectrum disorder. Development and psychopathology. 2019 : 1-13.

Autism spectrum disorder (ASD) is associated with pervasive social deficits as well as marked emotion dysregulation across the life span. Decreased social motivation accounts in part for social difficulties, but factors moderating its influence are not fully understood. In this paper, we (a) characterize social and emotional functioning among children and adolescents with ASD, (b) explore contributions of social motivation and emotion dysregulation to social skill, and (c) consider biological sex and intellectual functioning as moderators of these associations. In a sample of 2,079 children and adolescents with ASD from the Simons Simplex Collection, we document direct effects of social motivation, internalizing symptoms, aggression, attention problems, irritability, and self-injurious behavior on children’s social skills. Furthermore, dysregulation in several domains moderated the association between social motivation and social skill, suggesting a blunting effect on social motivation in the context of emotional difficulties. Moreover, when considering only individuals with intellectual skills in the average range or higher, biological sex further moderated these associations. Findings add to our understanding of social-emotional processes in ASD, suggest emotion dysregulation as a target of intervention in the service of social skill improvements, and build on efforts to understand sources of individual difference that contribute to heterogeneity among individuals with ASD.

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7. Sachdeva A, Jain P, Gunasekaran V, Mahay SB, Mukherjee S, Hagerman R, Shankar S, Kapoor S, Kedia SN. Consensus Statement of the Indian Academy of Pediatrics on Diagnosis and Management of Fragile X Syndrome in India. Indian pediatrics. 2019 ; 56(3) : 221-8.

JUSTIFICATION : Fragile X Syndrome (FXS) is the most common genetic cause of inherited intellectual disability and autism spectrum disorder (ASD). Early identification results in appropriate management and improvement in functioning. Risk assessment in other family members can lead to prevention of the disorder. This necessitated the formulation of IAP recommendations for the diagnosis and management of FXS in Indian children and adolescents. PROCESS : The meeting on formulation of national consensus guidelines on Fragile X syndrome was organized by the Indian Academy of Pediatrics in New Delhi on 25th February, 2017. The invited experts included Pediatricians, Developmental Pediatricians, Psychiatrists, Pediatric Neurologists, Gynecologists, Geneticists, Clinical Psychologists and Remedial Educators, and representatives of Parent Organizations. Guidelines were framed after extensive discussions. A writing committee was formed that drafted the manuscript, which was circulated among members for critical appraisal, and finalized. RECOMMENDATIONS : The committee recommended that early diagnosis of FXS is crucial for early, timely and appropriate management. The interventions including timely occupational therapy, speech therapy and behavioral modifications help to improve the developmental potential and reduce the maladaptive behavior. Pharmacotherapy may be needed to control and improve behavioral symptoms. In addition, the emergence of targeted treatments such as low dose sertraline, metformin and /or minocycline may also be helpful for behavior, and perhaps cognition. Genetic counselling is helpful to communicate the risk for future children with FXS or permutation involvement.

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8. Shephard E, Tye C, Ashwood KL, Azadi B, Johnson MH, Charman T, Asherson P, McLoughlin G, Bolton PF. Oscillatory neural networks underlying resting-state, attentional control and social cognition task conditions in children with ASD, ADHD and ASD+ADHD. Cortex. 2019 ; 117 : 96-110.

Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are common and impairing neurodevelopmental disorders that frequently co-occur. The neurobiological mechanisms involved in ASD and ADHD are not fully understood. However, alterations in large-scale neural networks have been proposed as core deficits in both ASD and ADHD and may help to disentangle the neurobiological basis of these disorders and their co-occurrence. In this study, we examined similarities and differences in large-scale oscillatory neural networks between boys aged 8-13 years with ASD (n = 19), ADHD (n = 18), ASD + ADHD (n = 29) and typical development (Controls, n = 26). Oscillatory neural networks were computed using graph-theoretical methods from electroencephalographic (EEG) data collected during an eyes-open resting-state and attentional control and social cognition tasks in which we previously reported disorder-specific atypicalities in oscillatory power and event-related potentials (ERPs). We found that children with ASD showed significant hypoconnectivity in large-scale networks during all three task conditions compared to children without ASD. In contrast, children with ADHD showed significant hyperconnectivity in large-scale networks during the attentional control and social cognition tasks, but not during the resting-state, compared to children without ADHD. Children with co-occurring ASD + ADHD did not differ from children with ASD when paired with this group and vice versa when paired with the ADHD group, indicating that these children showed both ASD-like hypoconnectivity and ADHD-like hyperconnectivity. Our findings suggest that ASD and ADHD are associated with distinct alterations in large-scale oscillatory networks, and these atypicalities present together in children with both disorders. These alterations appear to be task-independent in ASD but task-related in ADHD, and may underlie other neurocognitive atypicalities in these disorders.

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