scholarly journals Rationale of an Advanced Integrative Approach Applied to Autism Spectrum Disorder: Review, Discussion and Proposal

2021 ◽  
Vol 11 (6) ◽  
pp. 514
Author(s):  
María Luján Ferreira ◽  
Nicolás Loyacono
Keyword(s):  
Autism Spectrum Disorder ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Diagnosis And Treatment ◽  
Integrative Model ◽  
Integrative Approach ◽  
Allopathic Medicine ◽  
Medical Problems ◽  
Static Encephalopathy ◽  
The Brain

The rationale of an Advanced Integrative Model and an Advanced Integrative Approach is presented. In the context of Allopathic Medicine, this model introduces the evaluation, clinical exploration, diagnosis, and treatment of concomitant medical problems to the diagnosis of Autism Spectrum Disorder. These may be outside or inside the brain. The concepts of static or chronic, dynamic encephalopathy and condition for Autism Spectrum Disorder are defined in this model, which looks at the response to the treatments of concomitant medical problemsto the diagnosis of Autism Spectrum Disorder. (1) Background: Antecedents and rationale of an Advanced Integrative Model and of an Advanced Integrative Approach are presented; (2) Methods: Concomitant medical problems to the diagnosis of Autism Spectrum Disorder and a discussion of the known responses of their treatments are presented; (3) Results: Groups in Autism are defined and explained, related to the responses of the treatments of the concomitant medical problems to ASD and (4) Conclusions: The analysis in the framework of an Advanced Integrative Model of three groups including the concepts of static encephalopathy; chronic, dynamic encephalopathy and condition for Autism Spectrum Disorder explains findings in the field, previously not understood.

Possibility that the Onset of Autism Spectrum Disorder is Induced by Failure of the Glutamine-Glutamate Cycle

2020 ◽  
Vol 14 (2) ◽  
pp. 170-174
Author(s):  
Koichi Kawada ◽  
Nobuyuki Kuramoto ◽  
Seisuke Mimori
Keyword(s):  
Autism Spectrum Disorder ◽  
Endoplasmic Reticulum ◽  
Environmental Factors ◽  
Endoplasmic Reticulum Stress ◽  
Synapse Formation ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Neurodevelopmental Disease ◽  
Number Of Patients ◽  
The Brain

: Autism spectrum disorder (ASD) is a neurodevelopmental disease, and the number of patients has increased rapidly in recent years. The causes of ASD involve both genetic and environmental factors, but the details of causation have not yet been fully elucidated. Many reports have investigated genetic factors related to synapse formation, and alcohol and tobacco have been reported as environmental factors. This review focuses on endoplasmic reticulum stress and amino acid cycle abnormalities (particularly glutamine and glutamate) induced by many environmental factors. In the ASD model, since endoplasmic reticulum stress is high in the brain from before birth, it is clear that endoplasmic reticulum stress is involved in the development of ASD. On the other hand, one report states that excessive excitation of neurons is caused by the onset of ASD. The glutamine-glutamate cycle is performed between neurons and glial cells and controls the concentration of glutamate and GABA in the brain. These neurotransmitters are also known to control synapse formation and are important in constructing neural circuits. Theanine is a derivative of glutamine and a natural component of green tea. Theanine inhibits glutamine uptake in the glutamine-glutamate cycle via slc38a1 without affecting glutamate; therefore, we believe that theanine may prevent the onset of ASD by changing the balance of glutamine and glutamate in the brain.

scholarly journals Graph Ricci Curvatures Reveal Disease-related Changes in Autism Spectrum Disorder

2021 ◽  
Author(s):  
Pavithra Elumalai ◽  
Yasharth Yadav ◽  
Nitin Williams ◽  
Emil Saucan ◽  
Jürgen Jost ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Resting State ◽  
Neurodevelopmental Disorders ◽  
Data Exchange ◽  
Meta Analysis ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
The Brain

Autism Spectrum Disorder (ASD) is a set of neurodevelopmental disorders that pose a significant global health burden. Measures from graph theory have been used to characterise ASD-related changes in resting-state fMRI functional connectivity networks (FCNs), but recently developed geometry-inspired measures have not been applied so far. In this study, we applied geometry-inspired graph Ricci curvatures to investigate ASD-related changes in resting-state fMRI FCNs. To do this, we applied Forman-Ricci and Ollivier-Ricci curvatures to compare networks of ASD and healthy controls (N = 1112) from the Autism Brain Imaging Data Exchange I (ABIDE-I) dataset. We performed these comparisons at the brain-wide level as well as at the level of individual brain regions, and further, determined the behavioral relevance of region-specific differences with Neurosynth meta-analysis decoding. We found brain-wide ASD-related differences for both Forman-Ricci and Ollivier-Ricci curvatures. For Forman-Ricci curvature, these differences were distributed across 83 of the 200 brain regions studied, and concentrated within the Default Mode, Somatomotor and Ventral Attention Network. Meta-analysis decoding identified the brain regions showing curvature differences as involved in social cognition, memory, language and movement. Notably, comparison with results from previous non-invasive stimulation (TMS/tDCS) experiments revealed that the set of brain regions showing curvature differences overlapped with the set of brain regions whose stimulation resulted in positive cognitive or behavioural outcomes in ASD patients. These results underscore the utility of geometry-inspired graph Ricci curvatures in characterising disease-related changes in ASD, and possibly, other neurodevelopmental disorders.

Executive Functions in Adolescents with Autism Spectrum Disorder

2017 ◽  
pp. 67-90
Author(s):  
Yael Dai ◽  
Inge-Marie Eigsti
Keyword(s):  
Autism Spectrum Disorder ◽  
Working Memory ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Clinical Implications ◽  
Future Directions ◽  
Adolescents With Autism ◽  
Memory Flexibility ◽  
The Brain

This chapter reviews strengths and weaknesses in executive function (EF) domains, including inhibition, working memory, flexibility, fluency, and planning, in adolescents (age 13–19) with autism spectrum disorder (ASD). Given the dramatic developmental changes in the brain regions that support EF during the period of adolescence, it is critical to evaluate which EF abilities show a distinct profile during this period. As this chapter will demonstrate, youth with ASD show deficits across all domains of EF, particularly in complex tasks that include arbitrary instructions. We describe the fundamental measures for assessing skills in each domain and discuss limitations and future directions for research, as well as clinical implications of these findings for working with youth with ASD.

scholarly journals Early white matter development is abnormal in tuberous sclerosis complex patients who develop autism spectrum disorder

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Anna K. Prohl ◽  
◽  
Benoit Scherrer ◽  
Xavier Tomas-Fernandez ◽  
Peter E. Davis ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
White Matter ◽  
Tuberous Sclerosis ◽  
Tuberous Sclerosis Complex ◽  
Neural Circuits ◽  
Diffusion Tensor ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Fiber Bundles ◽  
The Brain

Abstract Background Autism spectrum disorder (ASD) is prevalent in tuberous sclerosis complex (TSC), occurring in approximately 50% of patients, and is hypothesized to be caused by disruption of neural circuits early in life. Tubers, or benign hamartomas distributed stochastically throughout the brain, are the most conspicuous of TSC neuropathology, but have not been consistently associated with ASD. Widespread neuropathology of the white matter, including deficits in myelination, neuronal migration, and axon formation, exist and may underlie ASD in TSC. We sought to identify the neural circuits associated with ASD in TSC by identifying white matter microstructural deficits in a prospectively recruited, longitudinally studied cohort of TSC infants. Methods TSC infants were recruited within their first year of life and longitudinally imaged at time of recruitment, 12 months of age, and at 24 months of age. Autism was diagnosed at 24 months of age with the ADOS-2. There were 108 subjects (62 TSC-ASD, 55% male; 46 TSC+ASD, 52% male) with at least one MRI and a 24-month ADOS, for a total of 187 MRI scans analyzed (109 TSC-ASD; 78 TSC+ASD). Diffusion tensor imaging properties of multiple white matter fiber bundles were sampled using a region of interest approach. Linear mixed effects modeling was performed to test the hypothesis that infants who develop ASD exhibit poor white matter microstructural integrity over the first 2 years of life compared to those who do not develop ASD. Results Subjects with TSC and ASD exhibited reduced fractional anisotropy in 9 of 17 white matter regions, sampled from the arcuate fasciculus, cingulum, corpus callosum, anterior limbs of the internal capsule, and the sagittal stratum, over the first 2 years of life compared to TSC subjects without ASD. Mean diffusivity trajectories did not differ between groups. Conclusions Underconnectivity across multiple white matter fiber bundles develops over the first 2 years of life in subjects with TSC and ASD. Future studies examining brain-behavior relationships are needed to determine how variation in the brain structure is associated with ASD symptoms.

scholarly journals 60 Precision Medicine in Developmental Pediatrics: Image-based Classification of Children with Autism Spectrum Disorder using Deep Learning

2020 ◽  
Vol 25 (Supplement_2) ◽  
pp. e25-e25
Author(s):  
Sarah MacEachern ◽  
Deepthi Rajashekar ◽  
Pauline Mouches ◽  
Nathan Rowe ◽  
Emily Mckenna ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Deep Learning ◽  
Gray Matter ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Classification Model ◽  
Children With Asd ◽  
Adc Values ◽  
Deep Gray Matter ◽  
The Brain

Abstract Introduction/Background Autism spectrum disorder (ASD) is a neurodevelopmental disorder resulting in challenges with social communication, sensory differences, and repetitive and restricted patterns of behavior. ASD affects approximately 1 in 66 children in North America, with boys being affected four times more frequently than girls. Currently, diagnosis is made primarily based on clinical features and no robust biomarker for ASD diagnosis has been identified. Potential image-based biomarkers to aid ASD diagnosis may include structural properties of deep gray matter regions in the brain. Objectives The primary objective of this work was to investigate if children with ASD show micro- and macrostructural alterations in deep gray matter structures compared to neurotypical children, and if these biomarkers can be used for an automatic ASD classification using deep learning. Design/Methods Quantitative apparent diffusion coefficient (ADC) magnetic resonance imaging data was obtained from 23 boys with ASD ages 0.8 – 19.6 years (mean 7.6 years) and 39 neurotypical boys ages 0.3 – 17.75 years (mean 7.6 years). An atlas-based method was used for volumetric analysis and extraction of median ADC values for each subject within the cerebral cortex, hippocampus, thalamus, caudate, putamen, globus pallidus, amygdala, and nucleus accumbens. The extracted quantitative regional volumetric and median ADC values were then used for the development and evaluation of an automatic classification method using an artificial neural network. Results The classification model was evaluated using 10-fold cross validation resulting in an overall accuracy of 76%, which is considerably better than chance level (62%). Specifically, 33 neurotypical boys were correctly classified, whereas 6 neurotypical boys were incorrectly classified. For the ASD group, 14 boys were correctly classified, while 9 boys were incorrectly classified. This translates to a precision of 70% for the children with ASD and 79% for neurotypical boys. Conclusion To the best of our knowledge, this is the first method to classify children with ASD using micro- and macrostructural properties of deep gray matter structures in the brain. The first results of the proposed deep learning method to identify children with ASD using image-based biomarkers are promising and could serve as the platform to create a more accurate and robust deep learning model for clinical application.

scholarly journals Gut Microbiota and Fecal Metabolome Perturbation in Children with Autism Spectrum Disorder

2018 ◽  
Vol 10 (4) ◽  
pp. 205-212 ◽  
Author(s):  
Ashraf Mohamadkhani
Keyword(s):  
Autism Spectrum Disorder ◽  
Children With Autism ◽  
Fecal Microbiota ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
High Yield ◽  
Human Gut ◽  
Children With Asd ◽  
Metabolic Products ◽  
The Brain

The brain-intestinal axis concept describes the communication between the intestinal microbiota as an ecosystem of a number of dynamic microorganisms and the brain. The composition of the microbial community of the human gut is important for human health by influencing the total metabolomic profile. In children with autism spectrum disorder (ASD), the composition of the fecal microbiota and their metabolic products has a different configuration of the healthy child. An imbalance in the metabolite derived from the microbiota in children with ASD affect brain development and social behavior. In this article, we review recent discoveries about intestinal metabolites derived from microbiota based on high-yield molecular studies in children with ASD as part of the "intestinal brain axis".

scholarly journals Beta-adrenergic antagonism alters functional connectivity during associative processing in a preliminary study of individuals with and without autism

Autism ◽  
2019 ◽  
Vol 24 (3) ◽  
pp. 795-801
Author(s):  
John P Hegarty ◽  
Rachel M Zamzow ◽  
Bradley J Ferguson ◽  
Shawn E Christ ◽  
Eric C Porges ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Nervous System ◽  
Autonomic Nervous System ◽  
Functional Connectivity ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Semantic Fluency ◽  
Beta Adrenergic ◽  
Associative Processing ◽  
The Brain

Beta-adrenergic antagonism (e.g. propranolol) has been associated with cognitive/behavioral benefits following stress-induced impairments and for some cognitive/behavioral domains in individuals with autism spectrum disorder. In this preliminary investigation, we examined whether the benefits of propranolol are associated with functional properties in the brain. Adolescents/adults (mean age = 22.54 years) with (n = 13) and without autism spectrum disorder (n = 13) attended three sessions in which propranolol, nadolol ( beta-adrenergic antagonist that does not cross the blood–brain barrier), or placebo was administered before a semantic fluency task during functional magnetic resonance imaging. Autonomic nervous system measures and functional connectivity between language/associative processing regions and within the fronto-parietal control, dorsal attention, and default mode networks were examined. Propranolol was associated with improved semantic fluency performance, which was correlated with the baseline resting heart rate. Propranolol also altered network efficiency of regions associated with semantic processing and in an exploratory analysis reduced functional differences in the fronto-parietal control network in individuals with autism spectrum disorder. Thus, the cognitive benefits from beta-adrenergic antagonism may be generally associated with improved information processing in the brain in domain-specific networks, but individuals with autism spectrum disorder may also benefit from additional improvements in domain-general networks. The benefits from propranolol may also be able to be predicted from baseline autonomic nervous system measures, which warrants further investigation.

scholarly journals The Co-Occurrence of Autism Spectrum Disorder in Children With ADHD

2017 ◽  
Vol 24 (1) ◽  
pp. 94-103 ◽  
Author(s):  
Benjamin Zablotsky ◽  
Matthew D. Bramlett ◽  
Stephen J. Blumberg
Keyword(s):  
Autism Spectrum Disorder ◽  
Tourette Syndrome ◽  
National Survey ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Diagnosis And Treatment ◽  
Adhd Symptoms ◽  
Treatment Needs ◽  
Children With Adhd ◽  
Adhd Subtype

Objective: Children with ADHD frequently present with autism spectrum disorder (ASD) symptomatology, yet there is a notable gap in the treatment needs of this subpopulation, including whether the presence of ASD may be associated with more severe ADHD symptoms. Method: Data from the 2014 National Survey of the Diagnosis and Treatment of ADHD and Tourette Syndrome ( n = 2,464) were used to compare children diagnosed with ADHD and ASD with children with ADHD, but not ASD. Children were classified as needing treatment if it was received or their parents reported it was needed, but not received. Results: Approximately one in eight children currently diagnosed with ADHD was also diagnosed with ASD. Children diagnosed with both disorders had greater treatment needs, more co-occurring conditions, and were more likely to have a combined hyperactive/impulsive and inattentive ADHD subtype. Conclusion: These findings highlight the complexity of children diagnosed with both ADHD and ASD.

Sign in / Sign up

Export Citation Format

Share Document