Estimating multivariate similarity between neuroimaging datasets with sparse canonical correlation analysis: an application to perfusion imaging

An increasing number of neuroimaging studies are based on either combining more than one data modality (inter-modal) or combining more than one measurement from the same modality (intra-modal). To date, most intra-modal studies using multivariate statistics have focused on differences between datase...

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Main Authors:	Rosa, Maria J., Mehta, Mitul A., Pich, Emilio M., Risterucci, Celine, Zelaya, Fernando, Reinders, Antje A. T. S., Williams, Steve C. R., Dazzan, Paola, Doyle, Orla M., Marquand, Andre F.
Format:	Online
Language:	English
Published:	Frontiers Media S.A. 2015
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4603249/

id	pubmed-4603249
recordtype	oai_dc
spelling	pubmed-46032492015-11-02 Estimating multivariate similarity between neuroimaging datasets with sparse canonical correlation analysis: an application to perfusion imaging Rosa, Maria J. Mehta, Mitul A. Pich, Emilio M. Risterucci, Celine Zelaya, Fernando Reinders, Antje A. T. S. Williams, Steve C. R. Dazzan, Paola Doyle, Orla M. Marquand, Andre F. Neuroscience An increasing number of neuroimaging studies are based on either combining more than one data modality (inter-modal) or combining more than one measurement from the same modality (intra-modal). To date, most intra-modal studies using multivariate statistics have focused on differences between datasets, for instance relying on classifiers to differentiate between effects in the data. However, to fully characterize these effects, multivariate methods able to measure similarities between datasets are needed. One classical technique for estimating the relationship between two datasets is canonical correlation analysis (CCA). However, in the context of high-dimensional data the application of CCA is extremely challenging. A recent extension of CCA, sparse CCA (SCCA), overcomes this limitation, by regularizing the model parameters while yielding a sparse solution. In this work, we modify SCCA with the aim of facilitating its application to high-dimensional neuroimaging data and finding meaningful multivariate image-to-image correspondences in intra-modal studies. In particular, we show how the optimal subset of variables can be estimated independently and we look at the information encoded in more than one set of SCCA transformations. We illustrate our framework using Arterial Spin Labeling data to investigate multivariate similarities between the effects of two antipsychotic drugs on cerebral blood flow. Frontiers Media S.A. 2015-10-13 /pmc/articles/PMC4603249/ /pubmed/26528117 http://dx.doi.org/10.3389/fnins.2015.00366 Text en Copyright © 2015 Rosa, Mehta, Pich, Risterucci, Zelaya, Reinders, Williams, Dazzan, Doyle and Marquand. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Rosa, Maria J. Mehta, Mitul A. Pich, Emilio M. Risterucci, Celine Zelaya, Fernando Reinders, Antje A. T. S. Williams, Steve C. R. Dazzan, Paola Doyle, Orla M. Marquand, Andre F.
spellingShingle	Rosa, Maria J. Mehta, Mitul A. Pich, Emilio M. Risterucci, Celine Zelaya, Fernando Reinders, Antje A. T. S. Williams, Steve C. R. Dazzan, Paola Doyle, Orla M. Marquand, Andre F. Estimating multivariate similarity between neuroimaging datasets with sparse canonical correlation analysis: an application to perfusion imaging
author_facet	Rosa, Maria J. Mehta, Mitul A. Pich, Emilio M. Risterucci, Celine Zelaya, Fernando Reinders, Antje A. T. S. Williams, Steve C. R. Dazzan, Paola Doyle, Orla M. Marquand, Andre F.
author_sort	Rosa, Maria J.
title	Estimating multivariate similarity between neuroimaging datasets with sparse canonical correlation analysis: an application to perfusion imaging
title_short	Estimating multivariate similarity between neuroimaging datasets with sparse canonical correlation analysis: an application to perfusion imaging
title_full	Estimating multivariate similarity between neuroimaging datasets with sparse canonical correlation analysis: an application to perfusion imaging
title_fullStr	Estimating multivariate similarity between neuroimaging datasets with sparse canonical correlation analysis: an application to perfusion imaging
title_full_unstemmed	Estimating multivariate similarity between neuroimaging datasets with sparse canonical correlation analysis: an application to perfusion imaging
title_sort	estimating multivariate similarity between neuroimaging datasets with sparse canonical correlation analysis: an application to perfusion imaging
description	An increasing number of neuroimaging studies are based on either combining more than one data modality (inter-modal) or combining more than one measurement from the same modality (intra-modal). To date, most intra-modal studies using multivariate statistics have focused on differences between datasets, for instance relying on classifiers to differentiate between effects in the data. However, to fully characterize these effects, multivariate methods able to measure similarities between datasets are needed. One classical technique for estimating the relationship between two datasets is canonical correlation analysis (CCA). However, in the context of high-dimensional data the application of CCA is extremely challenging. A recent extension of CCA, sparse CCA (SCCA), overcomes this limitation, by regularizing the model parameters while yielding a sparse solution. In this work, we modify SCCA with the aim of facilitating its application to high-dimensional neuroimaging data and finding meaningful multivariate image-to-image correspondences in intra-modal studies. In particular, we show how the optimal subset of variables can be estimated independently and we look at the information encoded in more than one set of SCCA transformations. We illustrate our framework using Arterial Spin Labeling data to investigate multivariate similarities between the effects of two antipsychotic drugs on cerebral blood flow.
publisher	Frontiers Media S.A.
publishDate	2015
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4603249/
_version_	1613487288074895360

Estimating multivariate similarity between neuroimaging datasets with sparse canonical correlation analysis: an application to perfusion imaging

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