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, non-fluent/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.

Keywords: Alzheimer disease; Frontotemporal lobar degeneration; MRI; PBAC; Philadelphia Brief Assessment of Cognition; Sparse canonical correlation analysis.

Fig. 1

Sparse canonical correlation analysis solution vectors are overlaid on a slice of the brain where the brightness of the red-hued overlay is related to the solution’s weighting at the local voxel. A traditional canonical correlation analysis produces component vectors with global extent (to reader’s far left). Sparse solutions (increasingly sparse to the reader’s right) seek to extract controllably focal information thereby, in the context of this paper, isolating “networks” of voxels that collectively relate to cognition. This enables component vectors to be more easily interpreted in terms of traditional neuroscientific coordinate systems.

Fig. 2

Diagram of the study. The data splitting in step 1 happens only once. We perform stages 2 and 3 for each of the five PBAC sub-scales.

Fig. 3

We visualize, with a heatmap, the correlations between the different PBAC individual scales which are clustered together to form the sub-scales studied here. The total PBAC is an average of the 5 sub-scale scores. The sub-scales provide a reasonable separation of measurements.

Fig. 4

A one parameter search over sparseness, in the training dataset, allows us to identify the optimal sparseness parameter for each cognitive domain. The network variables x* and y* that arise from SCCAN computed at the optimal sparseness level will be evaluated in the test dataset for reproducibility.

Fig. 5

All of the x* solution vectors are combined in axial and sagittal views of the brain. Red is behavior, blue executive, green language, magenta episodic memory and yellow visuospatial. The left hemisphere of the brain is on the reader’s left in the axial view.

Fig. 6

We visualize the correlation between X_testx* and Y_testy* for each of the five PBAC sub-scales. We also show the PBAC sub-scales and their corresponding putative support regions in the cortex, as identified by SCCAN and verified in testing data. Each row, from the top, contains the results for the behavioral/social comportment scale, the executive/working memory scale, the language scale, the episodic memory scale and the visuospatial scale.