Quantification of changes in language-related brain areas in autism spectrum disorders using large-scale network analysis

2014 ◽  
Vol 9 (3) ◽  
pp. 357-365 ◽  
Author(s):  
Caspar J. Goch ◽  
Bram Stieltjes ◽  
Romy Henze ◽  
Jan Hering ◽  
Luise Poustka ◽  
...  
Keyword(s):  
Autism Spectrum Disorders ◽  
Network Analysis ◽  
Large Scale ◽  
Autism Spectrum ◽  
Brain Areas ◽  
Large Scale Network ◽  
Scale Network ◽  
Spectrum Disorders

Large-Scale Network Analysis for Online Social Brand Advertising

MIS Quarterly ◽  
2016 ◽  
Vol 40 (4) ◽  
pp. 849-868 ◽  
Author(s):  
Kunpeng Zhang ◽  
◽  
Siddhartha Bhattacharyya ◽  
Sudha Ram ◽  
◽  
...  
Keyword(s):  
Network Analysis ◽  
Large Scale ◽  
Large Scale Network ◽  
Brand Advertising ◽  
Scale Network

scholarly journals Dopaminergic Gene Dosage in Autism versus Developmental Delay: From Complex Networks to Machine Learning approaches

2020 ◽  
Author(s):  
André Santos ◽  
Francisco Caramelo ◽  
Joana Barbosa de Melo ◽  
Miguel Castelo-Branco
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorders ◽  
Network Analysis ◽  
Developmental Delay ◽  
Gene Dosage ◽  
Genetic Alterations ◽  
Autism Spectrum ◽  
Compact Set ◽  
Neural Basis ◽  
Spectrum Disorders

AbstractThe neural basis of behavioural changes in Autism Spectrum Disorders (ASD) remains a controversial issue. One factor contributing to this challenge is the phenotypic heterogeneity observed in ASD, which suggests that several different system disruptions may contribute to diverse patterns of impairment between and within study samples. Here, we took a retrospective approach, using SFARI data to study ASD by focusing on participants with genetic imbalances targeting the dopaminergic system. Using complex network analysis, we investigated the relations between participants, Gene Ontology (GO) and gene dosage related to dopaminergic neurotransmission from a polygenic point of view. We converted network analysis into a machine learning binary classification problem to differentiate ASD diagnosed participants from DD (developmental delay) diagnosed participants. Using 1846 participants to train a Random Forest algorithm, our best classifier achieved on average a diagnosis predicting accuracy of 85.18% (sd 1.11%) on a test sample of 790 participants using gene dosage features. In addition, we observed that if the classifier uses GO features it was also able to infer a correct response based on the previous examples because it is tied to a set of biological process, molecular functions and cellular components relevant to the problem. This yields a less variable and more compact set of features when comparing with gene dosage classifiers. Other facets of knowledge-based systems approaches addressing ASD through network analysis and machine learning, providing an interesting avenue of research for the future, are presented through the study.Lay SummaryThere are important issues in the differential diagnosis of Autism Spectrum Disorders. Gene dosage effects may be important in this context. In this work, we studied genetic alterations related to dopamine processes that could impact brain development and function of 2636 participants. On average, from a genetic sample we were able to correctly separate autism from developmental delay with an accuracy of 85%.

scholarly journals Current state of evidence of cannabis utilization for treatment of autism spectrum disorders

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Rumi Agarwal ◽  
Shanna L. Burke ◽  
Marlaina Maddux
Keyword(s):  
Autism Spectrum Disorders ◽  
Large Scale ◽  
Autism Spectrum ◽  
Adverse Outcomes ◽  
Cannabis Use ◽  
Care Providers ◽  
Risks And Benefits ◽  
Current State ◽  
Spectrum Disorders ◽  
The Impact

Abstract The core symptoms and co-morbidities associated with autism spectrum disorders (ASD) affect daily living and quality of life. Existing pharmacological interventions are only able to attenuate some related symptoms but are unable to address the underlying etiologies associated with ASD. Anecdotal evidence, which claims benefit from the use of cannabis to treat symptoms among this population, has been gaining popularity as families seek solutions. This paper analyzed recent peer-reviewed literature to identify the current state of evidence regarding cannabis use for the ASD population. Systematic reviews, reports, and experimental studies were assessed to understand the current extent and nature of the evidence on the risks and benefits of cannabis use for ASD. At this time, three large-scale clinical trials are currently at varying stages of progress and publication of results. Only five small studies were identified that have specifically examined cannabis use in ASD. Given the sparse state of evidence directly assessed in this population, studies which examined effects of cannabis on shared pathological symptoms of ASD such as hyperactivity, sleep disorders, self-injury, anxiety, behavioral problems, and communication were also reviewed. Studies revealed mixed and inconclusive findings of cannabis effects for all conditions, except epilepsy. Adverse outcomes were also reported, which included severe psychosis, increased agitation, somnolence, decreased appetite, and irritability. In addition, a wide range of cannabis compositions and dosage were identified within the studies, which impact generalizability. There is currently insufficient evidence for cannabis use in ASD, which creates an urgent need for additional large-scale controlled studies to increase understanding of risks and benefits and also to examine the impact of “entourage effects.” This will support discussions of treatment options between health care providers and ASD patients and their families. Evidence may lead to a desired new line of treatment or prevent adverse outcomes from unsubstantiated use amongst families aiming for symptom reduction.

scholarly journals Excess of non-verbal cases of autism spectrum disorders (ASDs) presenting to orthodox clinical practice in Africa - a trend possibly resulting from late diagnosis and intervention

2011 ◽  
Vol 17 (4) ◽  
pp. 3 ◽  
Author(s):  
Muideen O Bakare ◽  
Kerim M Munir
Keyword(s):  
Autism Spectrum Disorders ◽  
Clinical Practice ◽  
Large Scale ◽  
Expressive Language ◽  
Case Reports ◽  
Case Series ◽  
Autism Spectrum ◽  
Language Ability ◽  
Common Denominator ◽  
Spectrum Disorders

<p><strong>Objectives.</strong> Characteristics of children with autism spectrum disorders (ASDs) in Africa are not known because of unavailability of large-scale epidemiological studies in this region. This review explored the age at first presentation to orthodox clinical practice of African children with ASDs and their expressive language ability at presentation.</p><p><strong>Methods.</strong> A literature search of case series and case reports of ASDs from Africa was done through PubMed/MEDLINE, Google Scholar, African Journals Online (AJOL), and archives of the Nigerian Journal of Psychiatry. Six articles included content relating to age of the child at first presentation to orthodox clinical practice and symptoms at presentation related to expressive language ability and therefore fulfilled the inclusion criteria. Suggestions are made to explain the observations emanating from the review.<strong> </strong></p><p><strong>Results.</strong> An excess of non-verbal over verbal cases of ASDs have been presenting to orthodox clinical practice and there is a common denominator of late presentation/diagnosis and in turn late intervention, with most cases presenting for the first time well above 8 years of age. Attempts to explain these observations included low levels of knowledge and awareness about ASDs in Africa; problems with help-seeking behaviour; and lack of mental healthcare facilities and trained personnel.</p><p><strong>Conclusions.</strong> Enhancement of processes directed at ensuring early diagnosis and interventions, especially interventions aimed at improving speech and language development well and sufficiently early, may bring about a shift in the trend of excess non-verbal cases of ASDs over verbal cases presenting to orthodox clinical practice.</p>

scholarly journals A large-scale screen for coding variants in SERT/SLC6A4in autism spectrum disorders

2008 ◽  
Vol 1 (4) ◽  
pp. 251-257 ◽  
Author(s):  
Takeshi Sakurai ◽  
Jennifer Reichert ◽  
Ellen J. Hoffman ◽  
Guiqing Cai ◽  
Hywel B. Jones ◽  
...  
Keyword(s):  
Autism Spectrum Disorders ◽  
Large Scale ◽  
Autism Spectrum ◽  
Spectrum Disorders ◽  
Coding Variants
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