title: Estimating multivariate similarity between neuroimaging datasets with sparse canonical correlation analysis: an application to perfusion imaging.
abstract: 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.
authors.affiliation: Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London London, UK.
authors.forename: Maria J
authors.initials: MJ
authors.lastname: Rosa
authors.affiliation: Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London London, UK.
authors.forename: Mitul A
authors.initials: MA
authors.lastname: Mehta
authors.affiliation: F. Hoffmann-La Roche Ltd. Basel, Switzerland.
authors.forename: Emilio M
authors.initials: EM
authors.lastname: Pich
authors.affiliation: F. Hoffmann-La Roche Ltd. Basel, Switzerland.
authors.forename: Celine
authors.initials: C
authors.lastname: Risterucci
authors.affiliation: Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London London, UK.
authors.forename: Fernando
authors.initials: F
authors.lastname: Zelaya
authors.affiliation: Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London London, UK.
authors.forename: Antje A T S
authors.initials: AA
authors.lastname: Reinders
authors.affiliation: Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London London, UK.
authors.forename: Steve C R
authors.initials: SC
authors.lastname: Williams
authors.affiliation: Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London London, UK ; National Institute for Health Research Mental Health Biomedical Research Centre, South London and Maudsley National Health Service Foundation Trust, King's College London London, UK.
authors.forename: Paola
authors.initials: P
authors.lastname: Dazzan
authors.affiliation: Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London London, UK.
authors.forename: Orla M
authors.initials: OM
authors.lastname: Doyle
authors.affiliation: Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London London, UK ; Department of Cognitive Neuroscience, Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Netherlands.
authors.forename: Andre F
authors.initials: AF
authors.lastname: Marquand
keywords: Arterial Spin Labeling; antipsychotics; intra-modal data; multivariate analysis; pharmacological MRI; repeated measures; resting cerebral blood flow; sparse canonical correlation analysis