Sparse canonical correlation analysis relates network-level atrophy to multivariate cognitive measures in a neurodegenerative population

Abstract

This study establishes that sparse canonical correlation analysis (SCCAN) identifies generalizable, structural MRI-derived cortical networks that relate to five distinct categories of cognition. We obtain multivariate psychometrics from the domain-specific sub-scales of the Philadelphia Brief Assessment of Cognition (PBAC). By using a training and separate testing stage, we find that PBAC-defined cognitive domains of language, visuospatial functioning, episodic memory, executive control, and social functioning correlate with unique and distributed areas of gray matter (GM). In contrast, a parallel univariate framework fails to identify, from the training data, regions that are also significant in the left-out test dataset. The cohort includes164 patients with Alzheimer’s disease, behavioral-variant frontotemporal dementia, semantic variant primary progressive aphasia, nonfluent/agrammatic primary progressive aphasia, or corticobasal syndrome. The analysis is implemented with open-source software for which we provide examples in the text. In conclusion, we show that multivariate techniques identify biologically-plausible brain regions supporting specific cognitive domains. The findings are identified in training data and confirmed in test data.

Finding imaging patterns of structural covariance via Non-Negative Matrix Factorization

“…This is a significant property in signal analysis and brain modeling (Daubechies et al, 2009) and has been associated with improved generalizability (Avants et al, 2014). Second, the components provide now a grouping of the data variables.…”

“…MVA methods can be separated into two sub-classes: i) confirmatory MVA techniques, such as structural equation modeling (McIntosh and Gonzalez-Lima, 1994) and dynamic causal modeling (Friston et al, 2003), that aim to assess the fitness of an explicitly formulated model of interactions between brain regions; and ii) exploratory techniques, such as Principal Component Analysis (PCA) (Friston et al, 1993; Strother et al, 1995; Hansen et al, 1999) and Independent Component Analysis (ICA) (McKeown et al, 1998; Calhoun et al, 2001; Beckmann and Smith, 2004), that aim to recover linear or non-linear relationships across brain regions and characterize patterns of common behavior. One may additionally aim to relate the extracted components to demographic, cognitive and/or clinical variables by either employing techniques like Partial Least Squares (McIntosh et al, 1996; McIntosh and Lobaugh, 2004; Krishnan et al, 2011) and Canonical Correlation Analysis (Hotelling, 1936; Friman et al, 2001; Witten et al, 2009; Avants et al, 2014), or by using the PCA and ICA components as features in supervised discriminative settings towards identifying abnormal brain regions (Duchesne et al, 2008), or patterns of brain activity (Mourão Miranda et al, 2005, 2007). …”

C9orf72 promoter hypermethylation is neuroprotective

“…[16][17][18] All C9orf72 patients completed a neuroimaging study, venipuncture study, and a brief neuropsychological screening assessment during a routine clinical examination using a subset of materials from the Philadelphia Brief Assessment of Cognition. 19,20 Baseline neuropsychological performance was assessed approximately 1 month (mean 5 0.8 months, SEM 5 1.0 months) from the baseline MRI acquisition, including (1) Mini-Mental State Examination (MMSE) (maximum score 30, prorated for motor weakness), (2) verbal recall following a delay after 3 trials of a 6-word list, and (3) category naming fluency for number of words beginning with "F" in 1 minute. One patient only had MMSE available for neuropsychological testing.…”

Cortical parcellation for neonatal brains

“…The third validation was to extract multivariate relationships between imaging variables and cortical volumes using sparse canonical correlation analysis for neuroimaging (SCCAN) [15]. We included the mean fractional anisotropy from DTI and the brain volume as control variables accounting for scaling effects.…”