Mental disorders such as Autism Spectrum Disorders (ASD) are heterogeneous disorders that are notoriously difficult to diagnose, especially in children. The current psychiatric diagnostic process is based purely on the behavioural observation of symptomology (DSM-5/ICD-10) and may be prone to over-prescribing of drugs due to misdiagnosis. In order to move the field towards more quantitative fashion, we need advanced and scalable machine learning infrastructure that will allow us to identify reliable biomarkers of mental health disorders. In this paper, we propose a framework called ASD-DiagNet for classifying subjects with ASD from healthy subjects by using only fMRI data. We designed and implemented a joint learning procedure using an autoencoder and a single layer perceptron which results in improved quality of extracted features and optimized parameters for the model. Further, we designed and implemented a data augmentation strategy, based on linear interpolation on available feature vectors, that allows us to produce synthetic datasets needed for training of machine learning models. The proposed approach is evaluated on a public dataset provided by Autism Brain Imaging Data Exchange including 1035 subjects coming from 17 different brain imaging centers. Our machine learning model outperforms other existing methods from 10 imaging centers with increase in classification accuracy up to 28% with maximum accuracy of 82%. The machine learning technique presented in this paper, in addition to yielding better quality, gives enormous advantages in terms of execution time (40 minutes vs. 7 hours on other methods). The implemented code is available as GPL license on GitHub portal of our lab (https://github.com/pcdslab/ASD-DiagNet).

/pdf/asd-diagnet-a-hybrid-learning-approach-for-detection-of-4zlg3a1x9y.pdf

ASD-DiagNet: A Hybrid Learning Approach for Detection of Autism Spectrum Disorder Using fMRI Data.

Deep learning for neuroimaging-based diagnosis and rehabilitation of Autism Spectrum Disorder: A review.

Accurate diagnosis of Autism Spectrum Disorder (ASD) is essential for its management and rehabilitation Neuroimaging techniques that are non-invasive are disease markers and may be leveraged to aid ASD diagnosis Structural and functional neuroimaging techniques provide physicians substantial information about the structure (anatomy and structural connectivity) and function (activity and functional connectivity) of the brain Due to the intricate structure and function of the brain, diagnosing ASD with neuroimaging data without exploiting artificial intelligence (AI) techniques is extremely challenging AI techniques comprise traditional machine learning (ML) approaches and deep learning (DL) techniques Conventional ML methods employ various feature extraction and classification techniques, but in DL, the process of feature extraction and classification is accomplished intelligently and integrally In this paper, studies conducted with the aid of DL networks to distinguish ASD were investigated Rehabilitation tools provided by supporting ASD patients utilizing DL networks were also assessed Finally, we presented important challenges in this automated detection and rehabilitation of ASD

pdf/deep-learning-for-neuroimaging-based-diagnosis-and-1st7bfo8c5.pdf

Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review

Autism Spectrum Disorder (ASD), according to DSM-5 in the American Psychiatric Association, is a neurodevelopmental disorder that includes deficits of social communication and social interaction with the presence of restricted and repetitive behaviors. Children with ASD have difficulties in joint attention and social reciprocity, using non-verbal and verbal behavior for communication. Due to these deficits, children with autism are often socially isolated. Researchers have emphasized the importance of early identification and early intervention to improve the level of functioning in language, communication, and well-being of children with autism. However, due to limited local assessment tools to diagnose these children, limited speech-language therapy services in rural areas, etc., these children do not get the rehabilitation they need until they get into compulsory schooling at the age of seven years old. Hence, efficient approaches towards early identification and intervention through speedy diagnostic procedures for ASD are required. In recent years, advanced technologies like machine learning have been used to analyze and investigate ASD to improve diagnostic accuracy, time, and quality without complexity. These machine learning methods include artificial neural networks, support vector machines, a priori algorithms, and decision trees, most of which have been applied to datasets connected with autism to construct predictive models. Meanwhile, the selection of features remains an essential task before developing a predictive model for ASD classification. This review mainly investigates and analyzes up-to-date studies on machine learning methods for feature selection and classification of ASD. We recommend methods to enhance machine learning’s speedy execution for processing complex data for conceptualization and implementation in ASD diagnostic research. This study can significantly benefit future research in autism using a machine learning approach for feature selection, classification, and processing imbalanced data.

A Review of Machine Learning Methods of Feature Selection and Classification for Autism Spectrum Disorder.

In the recent 5 years (2014–2018), there has been growing interest in the use of machine learning (ML) techniques to explore image diagnosis and prognosis of therapeutic lesion changes within the area of neuroradiology. However, to date, the majority of research trend and current status have not been clearly illuminated in the neuroradiology field. More than 1000 papers have been published during the past 5 years on subject classification and prediction focused on multiple brain disorders. We provide a survey of 209 papers in this field with a focus on top ten active areas of research; i.e., Alzheimer’s disease/mild cognitive impairment, brain tumor; schizophrenia, depressive disorders, Parkinson’s disease, attention-deficit hyperactivity disorder, autism spectrum disease, epilepsy, multiple sclerosis, stroke, and traumatic brain injury. Detailed information of these studies, such as ML methods, sample size, type of inputted features and reported accuracy, are summarized. This paper reviews the evidences, current limitations and status of studies using ML to assess brain disorders in neuroimaging data. The main bottleneck of this research field is still the limited sample size, which could be potentially addressed by modern data sharing models, such as ADNI.

Machine learning studies on major brain diseases: 5-year trends of 2014–2018

Autism spectrum disorder (ASD) is mainly reflected in the communication and language barriers, difficulties in social communication, and it is a kind of neurological developmental disorder. Most researches have used the machine learning method to classify patients and normal controls, among which support vector machines (SVM) are widely employed. But the classification accuracy of SVM is usually low, due to the usage of a single SVM as classifier. Thus we used multiple SVMs to classify ASD patients and typical controls (TC). Resting-state functional magnetic resonance imaging (fMRI) data of 46 TC and 61 ASD patients were obtained from the Autism Brain Imaging Data Exchange database. Only 84 of 107 subjects are utilized in experiments because the translation or rotation of 7 TC and 16 ASD patients has surpassed ±2mm or ±2degrees. Then the random support vector machine cluster was proposed to distinguish TC and ASD. The results show that this method has an excellent classification performance based on all the features. Furthermore, the accuracy based on the optimal feature set could reach to 96.15%. Abnormal brain regions could also be found, such as inferior frontal gyrus (orbital and opercula part), hippocampus and precuneus. It is indicated that the method of random support vector machine cluster may apply to the auxiliary diagnosis of ASD.

/pdf/classification-of-autism-spectrum-disorder-using-random-3mruf7f1o6.pdf

Classification of Autism Spectrum Disorder Using Random Support Vector Machine Cluster.

Alzheimer's disease (AD) is a complex neurodegenerative disease involving a variety of pathogenic factors, and the etiology detection of this disease has been a major concern of researchers. Neuroimaging is a basic and important means to explore the problem. It is the main current scientific research direction for combining neuroimaging with other modal data to dig deep into the potential information of AD through the complementarities among multiple data points. Machine learning methods possess great potentiality and have reached some achievements in this research area. A few studies have proposed some solutions to the effects of multimodal data fusion, however, the overall analytical framework for data fusion and fusion result analysis has thus far been ignored. In this paper, we first put forward a novel multimodal data fusion method, and further present a new machine learning framework of data fusion, classification, feature selection, and disease-causing factor extraction. The real dataset of 37 AD patients and 35 normal controls (NC) with functional magnetic resonance imaging (fMRI) and genetic data was used to verify the effectiveness of the framework, which was more accurate in classification and optimal feature extraction than other methods. Furthermore, we revealed disease-causing brain regions and genes, such as the olfactory cortex, insula, posterior cingulate gyrus, lingual gyrus, CNTNAP2, LRP1B, FRMD4A, and DAB1. The results show that the machine learning framework could effectively perform multimodal data fusion analysis, providing new insights and perspectives for the diagnosis of Alzheimer's disease.

/pdf/effective-diagnosis-of-alzheimer-s-disease-via-multimodal-39wp6w54cx.pdf

Effective Diagnosis of Alzheimer's Disease via Multimodal Fusion Analysis Framework.

Asperger syndrome (AS) is subtype of autism spectrum disorder (ASD). Patients with AS retain normal language function but lack of social communication ability. Diagnosis and pathological analysis of AS through fMRI data, especially resting state fMRI data, is one of the hot topics in brain science. We employed a new model called the genetic-evolutionary random Support Vector Machine cluster (GE-RSVMC) to classify AS and normal people, and search for lesions. The model innovatively integrates the methods of cluster and genetic evolution to improve the performance of the model. Functional connectivity is used as a sample feature of this study. We randomly selected samples and sample features to construct GE-RSVMC, and then used the cluster to classify and extract lesions according to classification results. The model was validated by data of 157 participants (86 AS and 71 health controls) in the autistic brain imaging data exchange (ABIDE) database. the classification accuracy of the model reached to 97.5% and we discovered the brain regions with significant differences between AS and normal people, such as the Angular gyrus (ANG.R), Precuneus (PCUN.R), Caudate nucleus (CAU.R), Cuneus (CUN.R) and so on. Our method provides a new perspective for the diagnosis and treatment of AS. Meanwhile, because the model has excellent generalization performance, our model may provide a universal framework for other brain science research.

/pdf/analysis-of-asperger-syndrome-using-genetic-evolutionary-98bixurhhf.pdf

Analysis of Asperger Syndrome Using Genetic-Evolutionary Random Support Vector Machine Cluster.

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Papers

Classification of Autism Spectrum Disorder Using Random Support Vector Machine Cluster.

Effective Diagnosis of Alzheimer's Disease via Multimodal Fusion Analysis Framework.

Analysis of Asperger Syndrome Using Genetic-Evolutionary Random Support Vector Machine Cluster.