Towards quantifying psychiatric diagnosis using machine learning algorithms and big fMRI data

Abstract

Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common brain disorders among children and is very difficult to diagnose using current methods. Similarly other mental disorders are subject to the same systematic errors with sufficient evidence of diagnostic errors as well as over-prescribing of drugs due to misdiagnosis . For most mental health disorders there is no quantitative method that will inform the presence or absence of a given mental disorder. We argue that definitive and quantitative diagnostic tests are necessary for ADHD and other mental disorders. To this end, big data Functional Magnetic Resonance Imaging (fMRI) and machine learning algorithms can be instrumental in changing the way psychiatric disorders are diagnosed and treated. We briefly discuss our recent research efforts and future directions for a quantitative gold standard tests for psychiatric diagnosis.Keywords: Machine learning and data mining, ADHD, fMRI, Big data, High performance computing Current and future research directionsIt is widely known that defining and diagnosing mental disorders is a difficult process due to overlapping nature of symptoms, and lack of a biological test that can serve as a definite and quantified gold standard [1]. To this end, Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common brain disorders among children and relies on the identification of abnormal mental characteristics. ADHD is notoriously difficult to diagnose, especially in children, with sufficient evidence of diagnostic errors as well as over-prescribing of drugs due to misdiagnosis [2]. The current psychiatric diagnosis is based purely on behavioural observation (DSM-5/ICD-10) but lacks biological and/or genetic validity and is prone to errors [1]. For most mental health disorders definitive and quantitative diagnostic tests which can detect the presence or the absence of a specific psychiatric disorder(s) are non-existent [1,3].Functional Magnetic Resonance Imaging (fMRI) is a non-invasive technique for studying the brain functional activities and is based on Blood Oxygen Level Dependent (BOLD) contrast [4]. During the fMRI scan, a series of images are taken using a scanner while the subject to a specific task such as resting, or doing various pre-determined tasks. The Saeed Big Data Analytics (2018) 3:7 Page 2 of 3 result of brain scanning is a series of low resolution images over time which shows the activity of the brain and can allow us to develop a highly informative brain connectome. There is evidence that ADHD, Bipolar Disorder (BD) and Schizophrenia have characteristics that differ in the regional and global connectivity of the brain when studied under resting state Functional Magnetic Resonance Imaging or fMRI [1,3,5].As data-scientists, along with our colleagues from psychiatry and neuroscience, the overarching question that we have been posing is as follows: Can we diagnose a person with ADHD (or other mental disorders) using fMRI scans using techniques from machine learning and novel ...

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