scholarly journals Population Graph-Based Multi-Model Ensemble Method for Diagnosing Autism Spectrum Disorder

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6001
Author(s):  
Zarina Rakhimberdina ◽  
Xin Liu ◽  
Tsuyoshi Murata
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Brain Imaging ◽  
Network Architecture ◽  
Data Exchange ◽  
Imaging Techniques ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Imaging Data ◽  
Model Ensemble

With the advancement of brain imaging techniques and a variety of machine learning methods, significant progress has been made in brain disorder diagnosis, in particular Autism Spectrum Disorder. The development of machine learning models that can differentiate between healthy subjects and patients is of great importance. Recently, graph neural networks have found increasing application in domains where the population’s structure is modeled as a graph. The application of graphs for analyzing brain imaging datasets helps to discover clusters of individuals with a specific diagnosis. However, the choice of the appropriate population graph becomes a challenge in practice, as no systematic way exists for defining it. To solve this problem, we propose a population graph-based multi-model ensemble, which improves the prediction, regardless of the choice of the underlying graph. First, we construct a set of population graphs using different combinations of imaging and phenotypic features and evaluate them using Graph Signal Processing tools. Subsequently, we utilize a neural network architecture to combine multiple graph-based models. The results demonstrate that the proposed model outperforms the state-of-the-art methods on Autism Brain Imaging Data Exchange (ABIDE) dataset.

scholarly journals The functional brain organization of an individual predicts measures of social abilities in autism spectrum disorder: Predicting symptoms in autism with brain imaging

2018 ◽  
Author(s):  
Evelyn MR Lake ◽  
Emily S Finn ◽  
Stephanie M Noble ◽  
Tamara Vanderwal ◽  
Xilin Shen ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Brain Imaging ◽  
Data Exchange ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Imaging Data ◽  
Brain Organization ◽  
Functional Brain ◽  
Functional Brain Organization

ABSTRACTAutism Spectrum Disorder (ASD) is associated with multiple complex abnormalities in functional brain connectivity measured with functional magnetic resonance imaging (fMRI). Despite much research in this area, to date, neuroimaging-based models are not able to characterize individuals with ASD with sufficient sensitivity and specificity; this is likely due to the heterogeneity and complexity of this disorder. Here we apply a data-driven subject-level approach, connectome-based predictive modeling, to resting-state fMRI data from a set of individuals from the Autism Brain Imaging Data Exchange. Using leave-one-subject-out and split-half analyses, we define two functional connectivity networks that predict continuous scores on the Social Responsiveness Scale (SRS) and Autism Diagnostic Observation Schedule (ADOS) and confirm that these networks generalize to novel subjects. Notably, these networks were found to share minimal anatomical overlap. Further, our results generalize to individuals for whom SRS/ADOS scores are unavailable, predicting worse scores for ASD than typically developing individuals. In addition, predicted SRS scores for individuals with attention-deficit/hyperactivity disorder (ADHD) from the ADHD-200 Consortium are linked to ADHD symptoms, supporting the hypothesis that the functional brain organization changes relevant to ASD severity share a component associated with attention. Finally, we explore the membership of predictive connections within conventional (atlas-based) functional networks. In summary, our results suggest that an individual’s functional connectivity profile contains information that supports dimensional, non-binary classification in ASD, aligning with the goals of precision medicine and individual-level diagnosis.

scholarly journals The Role of Diffusion Tensor MR Imaging (DTI) of the Brain in Diagnosing Autism Spectrum Disorder: Promising Results

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8171
Author(s):  
Yaser ElNakieb ◽  
Mohamed T. Ali ◽  
Ahmed Elnakib ◽  
Ahmed Shalaby ◽  
Ahmed Soliman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Brain Imaging ◽  
Gold Standard ◽  
Data Exchange ◽  
Diffusion Tensor ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Machine Learning Techniques ◽  
Feature Engineering

Autism spectrum disorder (ASD) is a combination of developmental anomalies that causes social and behavioral impairments, affecting around 2% of US children. Common symptoms include difficulties in communications, interactions, and behavioral disabilities. The onset of symptoms can start in early childhood, yet repeated visits to a pediatric specialist are needed before reaching a diagnosis. Still, this diagnosis is usually subjective, and scores can vary from one specialist to another. Previous literature suggests differences in brain development, environmental, and/or genetic factors play a role in developing autism, yet scientists still do not know exactly the pathology of this disorder. Currently, the gold standard diagnosis of ASD is a set of diagnostic evaluations, such as the Autism Diagnostic Observation Schedule (ADOS) or Autism Diagnostic Interview–Revised (ADI-R) report. These gold standard diagnostic instruments are an intensive, lengthy, and subjective process that involves a set of behavioral and communications tests and clinical history information conducted by a team of qualified clinicians. Emerging advancements in neuroimaging and machine learning techniques can provide a fast and objective alternative to conventional repetitive observational assessments. This paper provides a thorough study of implementing feature engineering tools to find discriminant insights from brain imaging of white matter connectivity and using a machine learning framework for an accurate classification of autistic individuals. This work highlights important findings of impacted brain areas that contribute to an autism diagnosis and presents promising accuracy results. We verified our proposed framework on a large publicly available DTI dataset of 225 subjects from the Autism Brain Imaging Data Exchange-II (ABIDE-II) initiative, achieving a high global balanced accuracy over the 5 sites of up to 99% with 5-fold cross validation. The data used was slightly unbalanced, including 125 autistic subjects and 100 typically developed (TD) ones. The achieved balanced accuracy of the proposed technique is the highest in the literature, which elucidates the importance of feature engineering steps involved in extracting useful knowledge and the promising potentials of adopting neuroimaging for the diagnosis of autism.

scholarly journals Sex-dependent and sex-independent brain resting-state functional connectivity in children with autism spectrum disorder

2016 ◽  
Author(s):  
Xin Di ◽  
Bharat B Biswal
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Resting State ◽  
Data Exchange ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Imaging Data ◽  
Resting State Functional Connectivity ◽  
Main Effects

Background: Males are more likely to suffer from autism spectrum disorder (ASD) than females. As to whether females with ASD have similar brain alterations remain an open question. The current study aimed to examine sex-dependent as well as sex-independent alterations in resting-state functional connectivity in individuals with ASD compared with typically developing (TD) individuals. Method: Resting-state functional MRI data were acquired from the Autism Brain Imaging Data Exchange (ABIDE). Subjects between 6 to 20 years of age were included for analysis. After matching the intelligence quotient between groups for each dataset, and removing subjects due to excessive head motion, the resulting effective sample contained 28 females with ASD, 49 TD females, 129 males with ASD, and 141 TD males, with a two (diagnosis) by two (sex) design. Functional connectivity among 153 regions of interest (ROIs) comprising the whole brain was computed. Two by two analysis of variance was used to identify connectivity that showed diagnosis by sex interaction or main effects of diagnosis. Results: The main effects of diagnosis were found mainly between visual cortex and other brain regions, indicating sex-independent connectivity alterations. We also observed two connections whose connectivity showed diagnosis by sex interaction between the precuneus and medial cerebellum as well as the precunes and dorsal frontal cortex. While males with ASD showed higher connectivity in these connections compared with TD males, females with ASD had lower connectivity than their counterparts. Conclusions: Both sex-dependent and sex-independent functional connectivity alterations are present in ASD.

scholarly journals An Autism Spectrum Disorder Adaptive Identification Based on the Elimination of Brain Connections: A Proof of Long-Range Underconnectivity

2021 ◽  
Author(s):  
Fatima zahra Benabdallah ◽  
Ahmed Drissi El Maliani ◽  
Dounia Lotfi ◽  
Rachid Jennane ◽  
Mohammed El hassouni
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Long Range ◽  
Data Exchange ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Imaging Data ◽  
Brain Imaging Data

Abstract Autism spectrum disorder (ASD) is theoretically characterized by alterations in functional connectivity between brain regions. Many works presented approaches to determine informative patterns that help to predict autism from typical development. However, most of the proposed pipelines are not specifically designed for the autism problem, i.e they do not corroborate with autism theories about functional connectivity. In this paper, we propose a framework that takes into account the properties of local connectivity and long range under-connectivity in the autistic brain. The originality of the proposed approach is to adopt elimination as a technique in order to well emerge the autistic brain connectivity alterations, and show how they contribute to differentiate ASD from controls. Experimental results conducted on the large multi-site Autism Brain Imaging Data Exchange (ABIDE) show that our approach provides accurate prediction up to 70% and succeeds to prove the existence of deficits in the long-range connectivity in the ASD subjects brains.

scholarly journals Cross-species machine learning improves diagnostic classification of human psychiatric disorders

2019 ◽  
Author(s):  
Yafeng Zhan ◽  
Jianze Wei ◽  
Jian Liang ◽  
Xiu Xu ◽  
Ran He ◽  
...  
Keyword(s):  
Machine Learning ◽  
Psychiatric Disorders ◽  
Brain Imaging ◽  
Data Exchange ◽  
Autism Spectrum ◽  
Diagnostic Classification ◽  
Imaging Data ◽  
Primate Model ◽  
Compulsive Disorder ◽  
Brain Imaging Data

AbstractPsychiatric disorders often exhibit shared (co-morbid) symptoms, raising controversies over accurate diagnosis and the overlap of their neural underpinnings. Because the complexity of data generated by clinical studies poses a formidable challenge, we have pursued a reductionist framework using brain imaging data of a transgenic primate model of autism spectrum disorder (ASD). Here we report an interpretable cross-species machine learning approach which extracts transgene-related core regions in the monkey brain to construct the classifier for diagnostic classification in humans. The cross-species classifier based on core regions, mainly distributed in frontal and temporal cortex, identified from the transgenic primate model, achieved an accuracy of 82.14% in one clinical ASD cohort obtained from Autism Brain Imaging Data Exchange (ABIDE-I), significantly higher than the human-based classifier (61.31%, p < 0.001), which was validated in another independent ASD cohort obtained from ABIDE-II. Such monkey-based classifier generalized to achieve a better classification in obsessive-compulsive disorder (OCD) cohorts, and enabled parsing of differential connections to right ventrolateral prefrontal cortex being attributable to distinct traits in patients with ASD and OCD. These findings underscore the importance of investigating biologically homogeneous samples, particularly in the absence of real-world data adequate for deconstructing heterogeneity inherited in the clinical cohorts.One Sentence SummaryFeatures learned from transgenic monkeys enable improved diagnosis of autism-related disorders and dissection of their underlying circuits.

scholarly journals Autism Spectrum Disorder Studies Using fMRI Data and Machine Learning: A Review

2021 ◽  
Vol 15 ◽  
Author(s):  
Meijie Liu ◽  
Baojuan Li ◽  
Dewen Hu
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Feature Selection ◽  
Data Exchange ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Fmri Data ◽  
Selection Methods ◽  
Learning Methods ◽  
Machine Learning Methods

Machine learning methods have been frequently applied in the field of cognitive neuroscience in the last decade. A great deal of attention has been attracted to introduce machine learning methods to study the autism spectrum disorder (ASD) in order to find out its neurophysiological underpinnings. In this paper, we presented a comprehensive review about the previous studies since 2011, which applied machine learning methods to analyze the functional magnetic resonance imaging (fMRI) data of autistic individuals and the typical controls (TCs). The all-round process was covered, including feature construction from raw fMRI data, feature selection methods, machine learning methods, factors for high classification accuracy, and critical conclusions. Applying different machine learning methods and fMRI data acquired from different sites, classification accuracies were obtained ranging from 48.3% up to 97%, and informative brain regions and networks were located. Through thorough analysis, high classification accuracies were found to usually occur in the studies which involved task-based fMRI data, single dataset for some selection principle, effective feature selection methods, or advanced machine learning methods. Advanced deep learning together with the multi-site Autism Brain Imaging Data Exchange (ABIDE) dataset became research trends especially in the recent 4 years. In the future, advanced feature selection and machine learning methods combined with multi-site dataset or easily operated task-based fMRI data may appear to have the potentiality to serve as a promising diagnostic tool for ASD.

scholarly journals A-24 Metacognition and behavioral regulation associated with distinct connectivity patterns in autism spectrum disorder

2019 ◽  
Vol 34 (6) ◽  
pp. 883-883
Author(s):  
H Bednarz ◽  
R Kana
Keyword(s):  
Autism Spectrum Disorder ◽  
Executive Function ◽  
Data Exchange ◽  
Brain Connectivity ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Parent Ratings ◽  
Behavioral Regulation ◽  
Imaging Data ◽  
Frontal Pole

Abstract Objective Executive function (EF) deficits are well documented in children with autism spectrum disorder (ASD)1-2 and are commonly measured clinically using parent ratings3. No studies have investigated whether parent ratings of EF predict brain connectivity in ASD. Aim Examine whether the Behavior Rating Inventory of Executive Function (BRIEF) predicts functional brain connectivity in ASD. Method Resting-state fMRI and behavioral data were obtained from the Autism Brain Imaging Data Exchange (ABIDE-II) database6 (n = 106 ASD, ages 5-13). ROI-to-ROI (Region of Interest) connectivity was computed for 132 ROIs spanning the whole brain, defined using Conn Toolbox. Multiple regression analyses examined the effect of BRIEF metacognition on connectivity while controlling for BRIEF behavioral regulation, and vice versa. Age, sex, and full-scale IQ were included as covariates. FDR correction was used (p < 0.05). Results More severe deficits in metacognition were associated with stronger connectivity between the left hippocampus and several ROIs, including the cerebellum and planum temporale. More severe deficits in metacognition were associated with weaker right hippocampus – right frontal pole connectivity. More severe deficits in behavioral regulation were associated with stronger connectivity between subcortical regions (i.e., thalamus, putamen, and caudate) and regions involved in motor (superior frontal gyrus) and limbic systems (cingulate gyrus). More severe behavioral regulation deficits were associated with weaker cerebellar-cerebellar connectivity. Conclusions Findings suggest that parent-ratings of metacognitive abilities in children with ASD are associated with hippocampal connectivity, while behavioral regulation abilities are associated with thalamic and striatum connections. These results build upon previous studies of metacognition and behavioral regulation 5,6.

scholarly journals Machine Learning Methods for Diagnosing Autism Spectrum Disorder and Attention- Deficit/Hyperactivity Disorder Using Functional and Structural MRI: A Survey

2021 ◽  
Vol 14 ◽  
Author(s):  
Taban Eslami ◽  
Fahad Almuqhim ◽  
Joseph S. Raiker ◽  
Fahad Saeed
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Imaging Techniques ◽  
Structural Mri ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Hyperactivity Disorder ◽  
Machine Learning Model

Here we summarize recent progress in machine learning model for diagnosis of Autism Spectrum Disorder (ASD) and Attention-deficit/Hyperactivity Disorder (ADHD). We outline and describe the machine-learning, especially deep-learning, techniques that are suitable for addressing research questions in this domain, pitfalls of the available methods, as well as future directions for the field. We envision a future where the diagnosis of ASD, ADHD, and other mental disorders is accomplished, and quantified using imaging techniques, such as MRI, and machine-learning models.

scholarly journals Predicting symptom severity in autism spectrum disorder based on cortical thickness measures in agglomerative data

2016 ◽  
Author(s):  
Elaheh Moradi ◽  
Budhachandra Khundrakpam ◽  
John D. Lewis ◽  
Alan C. Evans ◽  
Jussi Tohka
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Cortical Thickness ◽  
Symptom Severity ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Support Vector ◽  
Imaging Data ◽  
Learning Stage ◽  
Severity Prediction

AbstractMachine learning approaches have been widely used for the identification of neuropathology from neuroimaging data. However, these approaches require large samples and suffer from the challenges associated with multi-site, multi-protocol data. We propose a novel approach to address these challenges, and demonstrate its usefulness with the Autism Brain Imaging Data Exchange (ABIDE) database. We predict symptom severity based on cortical thickness measurements from 156 individuals with autism spectrum disorder (ASD) from four different sites. The proposed approach consists of two main stages: a domain adaptation stage using partial least squares regression to maximize the consistency of imaging data across sites; and a learning stage combining support vector regression for regional prediction of severity with elastic-net penalized linear regression for integrating regional predictions into a whole-brain severity prediction. The proposed method performed markedly better than simpler alternatives, better with multi-site than single-site data, and resulted in a considerably higher cross-validated correlation score than has previously been reported in the literature for multi-site data. This demonstration of the utility of the proposed approach for detecting structural brain abnormalities in ASD from the multi-site, multi-protocol ABIDE dataset indicates the potential of designing machine learning methods to meet the challenges of agglomerative data.

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