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

• Published in: Frontiers in neuroscience Vol. 9; p. 366
• 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: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 13.10.2015; Frontiers Media S.A
• Subjects: Antipsychotics; Blood flow; Brain research; Cerebral blood flow; Cognition & reasoning; Correlation analysis; Datasets; Medical imaging; Multivariate analysis; Neuroimaging; Neuroscience; Neurosciences; NMR; Nuclear magnetic resonance; Perfusion; R&D; Research & development; Semantic web; Sparsity; Spin labeling; Statistical analysis; Variables; Within-subjects design; United Kingdom > UK; Arterial Spin Labeling; pharmacological MRI; antipsychotics; sparse canonical correlation analysis; intra-modal data; multivariate analysis; resting cerebral blood flow; repeated measures
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2015.00366

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.