title: An empirical comparison of different approaches for combining multimodal neuroimaging data with support vector machine.
abstract: In the pursuit of clinical utility, neuroimaging researchers of psychiatric and neurological illness are increasingly using analyses, such as support vector machine, that allow inference at the single-subject level. Recent studies employing single-modality data, however, suggest that classification accuracies must be improved for such utility to be realized. One possible solution is to integrate different data types to provide a single combined output classification; either by generating a single decision function based on an integrated kernel matrix, or, by creating an ensemble of multiple single modality classifiers and integrating their predictions. Here, we describe four integrative approaches: (1) an un-weighted sum of kernels, (2) multi-kernel learning, (3) prediction averaging, and (4) majority voting, and compare their ability to enhance classification accuracy relative to the best single-modality classification accuracy. We achieve this by integrating structural, functional, and diffusion tensor magnetic resonance imaging data, in order to compare ultra-high risk (n = 19), first episode psychosis (n = 19) and healthy control subjects (n = 23). Our results show that (i) whilst integration can enhance classification accuracy by up to 13%, the frequency of such instances may be limited, (ii) where classification can be enhanced, simple methods may yield greater increases relative to more computationally complex alternatives, and, (iii) the potential for classification enhancement is highly influenced by the specific diagnostic comparison under consideration. In conclusion, our findings suggest that for moderately sized clinical neuroimaging datasets, combining different imaging modalities in a data-driven manner is no "magic bullet" for increasing classification accuracy. However, it remains possible that this conclusion is dependent on the use of neuroimaging modalities that had little, or no, complementary information to offer one another, and that the integration of more diverse types of data would have produced greater classification enhancement. We suggest that future studies ideally examine a greater variety of data types (e.g., genetic, cognitive, and neuroimaging) in order to identify the data types and combinations optimally suited to the classification of early stage psychosis.
authors: Department of Psychosis Studies, Institute of Psychiatry, King's College London London, UK.
authors: William
authors: W
authors: Pettersson-Yeo
authors: Department of Psychosis Studies, Institute of Psychiatry, King's College London London, UK ; Center for Mind/Brain Studies (CIMeC), University of Trento Trento, Italy.
authors: Stefania
authors: S
authors: Benetti
authors: Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, King's College London London, UK.
authors: Andre F
authors: AF
authors: Marquand
authors: Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, King's College London London, UK.
authors: Richard
authors: R
authors: Joules
authors: Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, King's College London London, UK.
authors: Marco
authors: M
authors: Catani
authors: Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, King's College London London, UK.
authors: Steve C R
authors: SC
authors: Williams
authors: Department of Psychosis Studies, Institute of Psychiatry, King's College London London, UK.
authors: Paul
authors: P
authors: Allen
authors: Department of Psychosis Studies, Institute of Psychiatry, King's College London London, UK.
authors: Philip
authors: P
authors: McGuire
authors: Department of Psychosis Studies, Institute of Psychiatry, King's College London London, UK.
authors: Andrea
authors: A
authors: Mechelli
keywords: MRI; integration; label fusion; multi-kernel learning; psychosis; support vector machine