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Updated: Jan 7, 2026

Basics of Multivariate Analysis in Neuroimaging Data
Published on: July 24, 2010
Multimodal Canonical Correlation Analysis with Joint Independent Component Analysis (mCCA+jICA) of IVIM and ASL MRI
Maurizio Bergamino1, Lauren R Ott1, Molly M McElvogue1
1Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, AZ 85013, USA.
Abstract:
Mild traumatic brain injury (mTBI) frequently causes subtle brain changes that are difficult to detect with conventional diagnostic approaches. In this exploratory pilot study, we combined tri-exponential intravoxel incoherent motion (IVIM) and pseudocontinuous arterial spin labeling (pCASL) MRI with Multimodal Canonical Correlation Analysis and joint independent component analysis (mCCA+jICA) to identify imaging signatures distinguishing mTBI patients from healthy controls (HCs) and their associations with clinical function. Cerebral blood flow (CBF) and IVIM-derived metrics were extracted from 90 brain regions in 19 mTBI patients and 24 HCs, and multivariate components were identified using mCCA+jICA. Two independent components (IC2, IC15) showed group differences at the uncorrected level (p < 0.05) but did not survive false discovery rate (FDR) correction. IC2 correlated positively with CBF and perfusion fraction (Fp) and negatively with tissue diffusion fraction (Fs), consistent with reduced vascular integrity in mTBI, while IC15 showed similar trends. One component correlated with Glasgow Outcome Scale-Extended (GOS-E) scores (uncorrected p = 0.046). Although this study is preliminary and limited by a small sample size, our findings suggest that mTBI is associated with perfusion and microstructural alterations, particularly in subcortical regions, and demonstrate the potential value of combining IVIM and ASL within multivariate fusion frameworks to reveal patterns not captured by single-modality approaches.
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