scholarly journals The Development of a Practical Artificial Intelligence Tool for Diagnosing and Evaluating Autism Spectrum Disorder: Multicenter Study

10.2196/15767 ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. e15767 ◽  
Author(s):  
Tao Chen ◽  
Ye Chen ◽  
Mengxue Yuan ◽  
Mark Gerstein ◽  
Tingyu Li ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Autism Spectrum Disorder ◽  
Psychiatric Disorders ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Unknown Etiology ◽  
Healthy Controls ◽  
Anatomical Changes ◽  
The Brain

Background Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with an unknown etiology. Early diagnosis and intervention are key to improving outcomes for patients with ASD. Structural magnetic resonance imaging (sMRI) has been widely used in clinics to facilitate the diagnosis of brain diseases such as brain tumors. However, sMRI is less frequently used to investigate neurological and psychiatric disorders, such as ASD, owing to the subtle, if any, anatomical changes of the brain. Objective This study aimed to investigate the possibility of identifying structural patterns in the brain of patients with ASD as potential biomarkers in the diagnosis and evaluation of ASD in clinics. Methods We developed a novel 2-level histogram-based morphometry (HBM) classification framework in which an algorithm based on a 3D version of the histogram of oriented gradients (HOG) was used to extract features from sMRI data. We applied this framework to distinguish patients with ASD from healthy controls using 4 datasets from the second edition of the Autism Brain Imaging Data Exchange, including the ETH Zürich (ETH), NYU Langone Medical Center: Sample 1, Oregon Health and Science University, and Stanford University (SU) sites. We used a stratified 10-fold cross-validation method to evaluate the model performance, and we applied the Naive Bayes approach to identify the predictive ASD-related brain regions based on classification contributions of each HOG feature. Results On the basis of the 3D HOG feature extraction method, our proposed HBM framework achieved an area under the curve (AUC) of >0.75 in each dataset, with the highest AUC of 0.849 in the ETH site. We compared the 3D HOG algorithm with the original 2D HOG algorithm, which showed an accuracy improvement of >4% in each dataset, with the highest improvement of 14% (6/42) in the SU site. A comparison of the 3D HOG algorithm with the scale-invariant feature transform algorithm showed an AUC improvement of >18% in each dataset. Furthermore, we identified ASD-related brain regions based on the sMRI images. Some of these regions (eg, frontal gyrus, temporal gyrus, cingulate gyrus, postcentral gyrus, precuneus, caudate, and hippocampus) are known to be implicated in ASD in prior neuroimaging literature. We also identified less well-known regions that may play unrecognized roles in ASD and be worth further investigation. Conclusions Our research suggested that it is possible to identify neuroimaging biomarkers that can distinguish patients with ASD from healthy controls based on the more cost-effective sMRI images of the brain. We also demonstrated the potential of applying data-driven artificial intelligence technology in the clinical setting of neurological and psychiatric disorders, which usually harbor subtle anatomical changes in the brain that are often invisible to the human eye.

scholarly journals The Development of a Practical Artificial Intelligence Tool for Diagnosing and Evaluating Autism Spectrum Disorder: Multicenter Study (Preprint)

2019 ◽  
Author(s):  
Tao Chen ◽  
Ye Chen ◽  
Mengxue Yuan ◽  
Mark Gerstein ◽  
Tingyu Li ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Autism Spectrum Disorder ◽  
Psychiatric Disorders ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Unknown Etiology ◽  
Healthy Controls ◽  
Anatomical Changes ◽  
The Brain

BACKGROUND Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with an unknown etiology. Early diagnosis and intervention are key to improving outcomes for patients with ASD. Structural magnetic resonance imaging (sMRI) has been widely used in clinics to facilitate the diagnosis of brain diseases such as brain tumors. However, sMRI is less frequently used to investigate neurological and psychiatric disorders, such as ASD, owing to the subtle, if any, anatomical changes of the brain. OBJECTIVE This study aimed to investigate the possibility of identifying structural patterns in the brain of patients with ASD as potential biomarkers in the diagnosis and evaluation of ASD in clinics. METHODS We developed a novel 2-level histogram-based morphometry (HBM) classification framework in which an algorithm based on a 3D version of the histogram of oriented gradients (HOG) was used to extract features from sMRI data. We applied this framework to distinguish patients with ASD from healthy controls using 4 datasets from the second edition of the Autism Brain Imaging Data Exchange, including the ETH Zürich (ETH), NYU Langone Medical Center: Sample 1, Oregon Health and Science University, and Stanford University (SU) sites. We used a stratified 10-fold cross-validation method to evaluate the model performance, and we applied the Naive Bayes approach to identify the predictive ASD-related brain regions based on classification contributions of each HOG feature. RESULTS On the basis of the 3D HOG feature extraction method, our proposed HBM framework achieved an area under the curve (AUC) of >0.75 in each dataset, with the highest AUC of 0.849 in the ETH site. We compared the 3D HOG algorithm with the original 2D HOG algorithm, which showed an accuracy improvement of >4% in each dataset, with the highest improvement of 14% (6/42) in the SU site. A comparison of the 3D HOG algorithm with the scale-invariant feature transform algorithm showed an AUC improvement of >18% in each dataset. Furthermore, we identified ASD-related brain regions based on the sMRI images. Some of these regions (eg, frontal gyrus, temporal gyrus, cingulate gyrus, postcentral gyrus, precuneus, caudate, and hippocampus) are known to be implicated in ASD in prior neuroimaging literature. We also identified less well-known regions that may play unrecognized roles in ASD and be worth further investigation. CONCLUSIONS Our research suggested that it is possible to identify neuroimaging biomarkers that can distinguish patients with ASD from healthy controls based on the more cost-effective sMRI images of the brain. We also demonstrated the potential of applying data-driven artificial intelligence technology in the clinical setting of neurological and psychiatric disorders, which usually harbor subtle anatomical changes in the brain that are often invisible to the human eye.

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.

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 Prediction of response to drug therapy in psychiatric disorders

Open Biology ◽  
2018 ◽  
Vol 8 (5) ◽  
pp. 180031 ◽  
Author(s):  
Shani Stern ◽  
Sara Linker ◽  
Krishna C. Vadodaria ◽  
Maria C. Marchetto ◽  
Fred H. Gage
Keyword(s):  
Autism Spectrum Disorder ◽  
Bipolar Disorder ◽  
Major Depression ◽  
Personalized Medicine ◽  
Psychiatric Disorders ◽  
Association Studies ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Genome Wide Association Studies ◽  
Patient Reported

Personalized medicine has become increasingly relevant to many medical fields, promising more efficient drug therapies and earlier intervention. The development of personalized medicine is coupled with the identification of biomarkers and classification algorithms that help predict the responses of different patients to different drugs. In the last 10 years, the Food and Drug Administration (FDA) has approved several genetically pre-screened drugs labelled as pharmacogenomics in the fields of oncology, pulmonary medicine, gastroenterology, haematology, neurology, rheumatology and even psychiatry. Clinicians have long cautioned that what may appear to be similar patient-reported symptoms may actually arise from different biological causes. With growing populations being diagnosed with different psychiatric conditions, it is critical for scientists and clinicians to develop precision medication tailored to individual conditions. Genome-wide association studies have highlighted the complicated nature of psychiatric disorders such as schizophrenia, bipolar disorder, major depression and autism spectrum disorder. Following these studies, association studies are needed to look for genomic markers of responsiveness to available drugs of individual patients within the population of a specific disorder. In addition to GWAS, the advent of new technologies such as brain imaging, cell reprogramming, sequencing and gene editing has given us the opportunity to look for more biomarkers that characterize a therapeutic response to a drug and to use all these biomarkers for determining treatment options. In this review, we discuss studies that were performed to find biomarkers of responsiveness to different available drugs for four brain disorders: bipolar disorder, schizophrenia, major depression and autism spectrum disorder. We provide recommendations for using an integrated method that will use available techniques for a better prediction of the most suitable drug.

scholarly journals Quantitation of plasma thiamine, related metabolites and plasma protein oxidative damage markers in children with autism spectrum disorder and healthy controls

2016 ◽  
Vol 50 (sup1) ◽  
pp. S85-S90 ◽  
Author(s):  
Attia Anwar ◽  
Marina Marini ◽  
Provvidenza Maria Abruzzo ◽  
Alessandra Bolotta ◽  
Alessandro Ghezzo ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Oxidative Damage ◽  
Plasma Protein ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Healthy Controls

scholarly journals An exploration of concomitant psychiatric disorders in children with autism spectrum disorder

2019 ◽  
Vol 88 ◽  
pp. 57-64 ◽  
Author(s):  
Luc Lecavalier ◽  
Courtney E. McCracken ◽  
Michael G. Aman ◽  
Christopher J. McDougle ◽  
James T. McCracken ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Psychiatric Disorders ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Spectrum Disorder

scholarly journals Autism Spectrum Disorder and International Travel

2018 ◽  
Vol 7 (1) ◽  
pp. 1-3 ◽  
Author(s):  
Wee Xuan Neo ◽  
Gerard Thomas Flaherty
Keyword(s):  
Autism Spectrum Disorder ◽  
Psychiatric Disorders ◽  
Travel Medicine ◽  
Autism Spectrum ◽  
International Travel ◽  
Spectrum Disorder ◽  
Health Counseling ◽  
Travel Health ◽  
Travel Experience ◽  
Travel Industry

The literature on international travellers with psychiatric disorders is limited. This perspective article highlights various travel-related aspects of autism spectrum disorder (ASD), including its aetiological association with maternal migration, the difficulties faced by longterm travelers with autistic children, and the facilitation of international travel for autistic individuals by the travel industry. Depending on the severity of their condition, autistic individuals may find specific aspects of the travel experience particularly distressing. Travel medicine practitioners should be aware of the unique needs of autistic travelers when providing pre-travel health counseling. There is also an onus on the travel industry to facilitate safe and enjoyable travel and remove barriers faced by autistic travellers.

scholarly journals 2288 Neural correlates of face processing in autism spectrum disorder: A quantitative meta-analysis of current literature and future directions

2018 ◽  
Vol 2 (S1) ◽  
pp. 21-22
Author(s):  
Carla J. Ammons ◽  
Mary-Elizabeth Winslett ◽  
Rajesh K. Kana
Keyword(s):  
Autism Spectrum Disorder ◽  
Face Processing ◽  
Repetitive Behavior ◽  
Autism Spectrum ◽  
Small Sample ◽  
Spectrum Disorder ◽  
Future Research ◽  
Healthy Controls ◽  
Specific Level

OBJECTIVES/SPECIFIC AIMS: Autism spectrum disorder (ASD) affects 1 in 68 people and includes restricted, repetitive behavior, and social communication deficits. Aspects of face processing (i.e., identity, emotion perception) are impaired in some with ASD. Neuroimaging studies have shown aberrant patterns of brain activation and connectivity of face processing regions. However, small sample sizes and inconsistent results have hindered clinical utility of these findings. The study aims to establish consistent patterns of brain responses to faces in ASD and provide directions for future research. METHODS/STUDY POPULATION: Neuroimaging studies were identified through a multi-database search according to PRISMA guidelines. In total, 23 studies were retained for a sample size of 383 healthy controls and 345 ASD. Peak coordinates were extracted for activation likelihood estimation (ALE) in GingerALE v2.3.6. Follow-up ALE analyses investigated directed Versus undirected gaze, static Versus dynamic, emotional Versus neutral, and familiar Versus unfamiliar faces. RESULTS/ANTICIPATED RESULTS: Faces produced bilateral activation of the fusiform gyrus (FG) in healthy controls (−42 −52 −20; 22 −74 −12, p<0.05, FDR) and left FG activation in ASD (−42 −54 −16, p<0.05, FDR). Activation in both groups was lateral to the mid-fusiform sulcus. Follow-up results pending. DISCUSSION/SIGNIFICANCE OF IMPACT: Reduced right FG activation to faces may inform biomarker or response to intervention studies. Mid-fusiform sulcus proved a reliable predictor of functional divides should be investigated on a subject-specific level.

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