Abstract
Estimation of individuals’ cognitive, behavioral and demographic (CBD) variables based on MRI has attracted much research interest in the past decade, and effective machine learning techniques are of great importance for these estimations. Partial least squares regression (PLSR) is an attractive machine learning technique that can accommodate both single- and multi-label learning in a simple framework, while its potential for MRI-based estimations of CBD variables remains to be explored. In this study, we systemically investigated the performance of PLSR in MRI-based estimations of individuals’ CBD variables, especially its performance in simultaneous estimation of multiple CBD variables (multi-label learning). We performed the study on the dataset included in the HCP S1200 release. Resting state functional connections (RSFCs) were used as features, and a total of 10 CBD variables (e.g., age, gender, grip strength, and picture vocabulary) were estimated. The results showed that PLSR performed well in both single- and multi-label learning. In fact, the present estimations were better than those reported in literatures, as indicated by stronger correlations between the estimated and actual CBD variables, as well as high gender classification accuracy (97.8% in this study). Moreover, the RSFCs that contributed to the estimations exhibited strong correlations with the CBD variable estimated, that is, PLSR algorithm automatically selected the RSFCs closely related to one CBD variable to establish predictive models for the variable. Besides, the estimation accuracies based on RSFCs among 100, 200, and 300 regions of interest (ROIs) were higher than those based on RSFCs among 15, 25, and 50 ROIs; the estimation accuracies based on RSFCs evaluated using partial correlation were higher than those based on RSFCs evaluated using full correlation. In addition to the aforementioned virtues, PLSR is efficient in model training and testing, and it is simple and easy to use. Therefore, PLSR can be a favorable choice for future MRI-based estimations of CBD variables.
Highlights
Individual differences in brain structure and function exist even among persons with no diagnosable neurological or psychiatric diseases
The largest R-values in the 5,000 permutations were by far smaller than those based on actual CBD variables, which were 0.157, 0.146, 0.161, 0.142, and 0.161 for age, education, composite scores of fluid cognition (CSFC), composite score of crystallized cognition (CSCC), and composite score of overall cognition (CSOC), respectively
It is valuable to estimate individuals’ CBD variables based on neuroimaging data, as these estimations may eventually lead to a better understanding of the neural basis that gives rise to individual differences in these variables, and may potentially assist in the clinical diagnosis of neuropsychiatric diseases
Summary
Individual differences in brain structure and function exist even among persons with no diagnosable neurological or psychiatric diseases. Numerous studies have been performed to relate these differences to variability in CBD variables (for reviews, see Kanai and Rees, 2011; Parasuraman and Jiang, 2012) Besides these studies on the neural basis of individual differences in CBD variables using statistical techniques, there is a surge of interest in estimating individuals’ CBD variables using machine learning techniques based on MRI-derived brain structural and functional measures (for reviews, see Arbabshirani et al, 2017; Rathore et al, 2017). The extensive use of these techniques in MRI-based estimations benefit from three of their advantages: (1) being simple and easy to use; (2) offering high estimation accuracies; and (3) enabling later inferences of the biological significance underlying the estimations
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