Multimodal Neuroimaging: Basic Concepts and Classification of Neuropsychiatric Diseases

Neuroimaging techniques are widely used in neuroscience to visualize neural activity, to improve our understanding of brain mechanisms, and to identify biomarkers—especially for psychiatric diseases; however, each neuroimaging technique has several limitations. These limitations led to the developme...

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Veröffentlicht in:Clinical EEG and Neuroscience 2019-01, Vol.50 (1), p.20-33
Hauptverfasser: Tulay, Emine Elif, Metin, Barış, Tarhan, Nevzat, Arıkan, Mehmet Kemal
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creator Tulay, Emine Elif
Metin, Barış
Tarhan, Nevzat
Arıkan, Mehmet Kemal
description Neuroimaging techniques are widely used in neuroscience to visualize neural activity, to improve our understanding of brain mechanisms, and to identify biomarkers—especially for psychiatric diseases; however, each neuroimaging technique has several limitations. These limitations led to the development of multimodal neuroimaging (MN), which combines data obtained from multiple neuroimaging techniques, such as electroencephalography, functional magnetic resonance imaging, and yields more detailed information about brain dynamics. There are several types of MN, including visual inspection, data integration, and data fusion. This literature review aimed to provide a brief summary and basic information about MN techniques (data fusion approaches in particular) and classification approaches. Data fusion approaches are generally categorized as asymmetric and symmetric. The present review focused exclusively on studies based on symmetric data fusion methods (data-driven methods), such as independent component analysis and principal component analysis. Machine learning techniques have recently been introduced for use in identifying diseases and biomarkers of disease. The machine learning technique most widely used by neuroscientists is classification—especially support vector machine classification. Several studies differentiated patients with psychiatric diseases and healthy controls with using combined datasets. The common conclusion among these studies is that the prediction of diseases increases when combining data via MN techniques; however, there remain a few challenges associated with MN, such as sample size. Perhaps in the future N-way fusion can be used to combine multiple neuroimaging techniques or nonimaging predictors (eg, cognitive ability) to overcome the limitations of MN.
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source SAGE Complete A-Z List
subjects Artificial intelligence
Biomarkers
Brain
Brain mapping
Classification
Cognitive ability
Data integration
Diseases
EEG
Functional magnetic resonance imaging
Inspection
Learning algorithms
Literature reviews
Medical imaging
Mental disorders
Nervous system
Neuroimaging
Neurology
Principal components analysis
title Multimodal Neuroimaging: Basic Concepts and Classification of Neuropsychiatric Diseases
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