Lateral ventricle volume trajectories predict response inhibition in older age - a longitudinal brain imaging and machine learning approach

Abstract

Objective: In a three-wave ∼6 yrs longitudinal study we investigated if expansion of lateral ventricle (LV) volumes (regarded as a proxy for brain parenchyma loss) predicts performance on a test of response inhibition. Participants and Methods: Anatomical trajectories of left (LH) and right (RH) lateral ventricle volumes across the three study-waves were quantified using the Longitudinal Stream in Freesurfer 5.3 and modelled using a linear mixed-effects (LME) algorithm. All participants (N = 74, mean age 60.7 yrs at inclusion, 48 females) performed the Color-Word Interference Test (CWIT). Response time on the third condition was used as a measure of response inhibition (RI) and divided into three classes (fast, medium and slow). The Extreme Gradient Boosting (XGBoost) algorithm was used for calculating the relative importance of selected LV volume features from the LME model in predicting RI class. Finally, the two most important extracted features were fed into a 10-fold cross-validation framework, estimating the accuracy, precision and recall of the RI class prediction. Results: Four LME based features were selected to characterize LV volume trajectories: steepness of LV volume change and the LV volume at the time of inclusion, each from the right and left hemisphere. The XGBoost procedure selected the steepness measure from the right and the volume at inclusion from the left hemisphere as the two most important features to predict RI performance. The 10-fold cross validation procedure showed a recall, precision and accuracy score (.40 -.50) that were clearly above chance level. Conclusion: Measures of the LV volume trajectories gave a fairly good prediction of response inhibition performance, confirming the role of LV volume as a biomarker of cognitive function in older adults. Future studies should investigate the value of the lateral ventricle volume trajectories as predictors of cognitive preservation or decline into older age.[*]Corresponding authorNormal aging is associated with morphometric changes in several brain regions and a 1 corresponding decline in cognitive function, with trajectories of age-related changes that 2 are characterized by individual differences [1]. Major events, biological and genetic 3 factors through the lifespan obviously contribute to the heterogeneity observed in 4 samples of older individuals [2, 3, 12], both regarding the rate of structural brain 5 changes [5], the rate and extent of cognitive changes [6] and in brain-cognition 6 relations [7, 12]. In the severe end of the distribution, the most extensive parenchyma 7 loss is associated with dementia, a syndrome defined by a severe decline in cognitive 8 function [8]. On the other end of the scale we find so-called "superagers" [9]. They show 9 maintained cognitive function into old age [10], with a corresponding preservation of 10 brain structure over time [11, 12]. These findings support that age-related structural 11 brain changes can act as strong predictors of cognitive abilities in old age, and 12 emphasize the importan...