Abstract
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.
Highlights
Modern techniques to diagnose mental disorders were first established in the late 19th century (Laffey, 2003) but its genesis can be traced back to 4th century BCE (Elkes and Thorpe, 1967)
Gold standard for diagnosing most mental-disorders rely primarily on information collected from various informants regarding the onset, course, and duration of various behavioral descriptors that are considered by providers when conferring a diagnostic decision based on Diagnostic and Statistical Manual (DSM)-5/International Classification of Diseases-10th Edition (ICD-10) criteria (World Health Organization, 2004; Pelham et al, 2005; American Psychiatric Association, 2013)
We provide a comprehensive report on ML methods used for diagnosis of Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD) in recent years using magnetic resonance imaging (MRI) data sets
Summary
Machine-Learning (ML) is a subset of artificial intelligence that gives the machine the ability to learn from data without providing specific instructions (Alpaydin, 2016). In unsupervised learning (Hinton et al, 1999), there is no corresponding output for the input data. The simplest type of deep neural network is a deep feed forward network in which the nodes in each layer are connected to the nodes in the layer (Glorot and Bengio, 2010). There is no cycle and no connection between nodes in the same layer and as the name implies, information flows forward from the input layer to the output layer of the network. Multi-layer-perceptron (MLP) (Hornik et al, 1989; Gardner and Dorling, 1998) is a specific type of feed-forward network in which each node is connected to all the nodes in the layer. The information is propagated (Rumelhart et al, 1986) through the network over the weighted links that connect nodes of consecutive layers. We focus on description of deep-learning models, methods, and techniques to make it more accessible to neuroscientists
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