Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: A critical review

Standard univariate analysis of neuroimaging data has revealed a host of neuroanatomical and functional differences between healthy individuals and patients suffering a wide range of neurological and psychiatric disorders. Significant only at group level however these findings have had limited clini...

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Bibliographic Details
Published in:	Neuroscience and biobehavioral reviews Vol. 36; no. 4; pp. 1140 - 1152
Main Authors:	Orrù, Graziella, Pettersson-Yeo, William, Marquand, Andre F., Sartori, Giuseppe, Mechelli, Andrea
Format:	Journal Article
Language:	English
Published:	Kidlington Elsevier 01.04.2012
Subjects:	Adult and adolescent clinical studies Alzheimer Disease - diagnosis Alzheimer Disease - genetics Alzheimer Disease - metabolism Artificial Intelligence Autistic Disorder - diagnosis Autistic Disorder - genetics Autistic Disorder - metabolism Behavioral psychophysiology Biological and medical sciences Biomarkers Bipolar Disorder - diagnosis Bipolar Disorder - genetics Bipolar Disorder - metabolism Classification Degenerative and inherited degenerative diseases of the nervous system. Leukodystrophies. Prion diseases Depressive Disorder, Major - diagnosis Depressive Disorder, Major - genetics Depressive Disorder, Major - metabolism Fundamental and applied biological sciences. Psychology Humans Medical sciences Mental Disorders - diagnosis Mental Disorders - genetics Mental Disorders - metabolism Nervous System Diseases - diagnosis Nervous System Diseases - genetics Nervous System Diseases - metabolism Neurology Organic mental disorders. Neuropsychology Psychology. Psychoanalysis. Psychiatry Psychology. Psychophysiology Psychopathology. Psychiatry Psychoses Schizophrenia Schizophrenia - diagnosis Schizophrenia - genetics Schizophrenia - metabolism Support Vector Machine Neuroimaging Alzheimer disease Biological marker Schizophrenia Bipolar disorder Developmental disorder Review Support Vector Machine Psychosis Learning Acquisition process Imaging Degenerative disease Diagnosis Alzheimer's disease Mood disorder Nervous system diseases Prediction Depression Cerebral disorder Autism Pervasive developmental disorder Major depression Central nervous system disease Machine learning Mental disorder Critical study Autistic spectrum disorder
ISSN:	0149-7634, 1873-7528, 1873-7528
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Summary:	Standard univariate analysis of neuroimaging data has revealed a host of neuroanatomical and functional differences between healthy individuals and patients suffering a wide range of neurological and psychiatric disorders. Significant only at group level however these findings have had limited clinical translation, and recent attention has turned toward alternative forms of analysis, including Support-Vector-Machine (SVM). A type of machine learning, SVM allows categorisation of an individual's previously unseen data into a predefined group using a classification algorithm, developed on a training data set. In recent years, SVM has been successfully applied in the context of disease diagnosis, transition prediction and treatment prognosis, using both structural and functional neuroimaging data. Here we provide a brief overview of the method and review those studies that applied it to the investigation of Alzheimer's disease, schizophrenia, major depression, bipolar disorder, presymptomatic Huntington's disease, Parkinson's disease and autistic spectrum disorder. We conclude by discussing the main theoretical and practical challenges associated with the implementation of this method into the clinic and possible future directions.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 ObjectType-Review-3 content type line 23 ObjectType-Article-2 ObjectType-Feature-1
ISSN:	0149-7634 1873-7528 1873-7528
DOI:	10.1016/j.neubiorev.2012.01.004