Identifying a taskinvariant cognitive reserve network using task potency Abstract   Cognitive reserve (CR) is thought to protect against the consequence of age or diseaserelated structural brain changes across multiple cognitive domains. The neural basis of CR may therefore comprise a functional network that is actively involved in many different cognitive processes. To investigate the existence of such a xe2x80x9ctaskinvariantxe2x80x9d CR network, we measured functional connectivity in a cognitively normal sample between 20 and 80 years old (Nxe2x80xafxe2x80xaf265), both at rest and during the performance of 11 separate tasks that aim to capture four latent cognitive abilities (i.e. vocabulary, episodic memory, processing speed, and fluid reasoning). For each individual, we determined the change in functional connectivity from the resting state to each task state, which is referred to as xe2x80x9ctask potencyxe2x80x9d (Chauvin et al., 2018, 2019). Task potency was calculated for each pair among 264 nodes (Power et al., 2012) and then summarized across tasks reflecting the same cognitive ability. Subsequently, we established the correlation between task potency and IQ or education (i.e. CR factors). We identified a set of 57 pairs in which task potency showed significant correlations with IQ, but not education, across all four cognitive abilities. These pairs were included in a principal component analysis, from which we extracted the first component to obtain a latent variable reflecting task potency in this taskinvariant CR network. This task potency variable was associated with better episodic memory (xcexb2xe2x80xafxe2x80xaf0.19, p

A Joint Model for Predicting Structural and Functional Brain Health in Elderly Individuals This paper presents a machinelearningbased joint model of brain age and cognitive performance, and demonstrates its superior performance relative to isolated models. Previous studies have chosen to study those two measures of brain health separately for two reasons: 1) although cognition can be measured regardless of an individualu0027s health, brainage groundtruth can be defined only for healthy individuals; and 2) while brainage models are developed using neuroimaging data alone, modeling of cognitive performance additionally requires measures of cognitive reserve and biomarkers of cognitive disorders. However, those two measures are biologically related to each other, because they both depend on brain structure. Hence, we developed a joint model by 1) explicitly defining the commonalities and differences between them in a graph, and 2) converting that graph into a multitasklearning model to facilitate learning from populationlevel data. Our model took as inputs structural neuroimaging data and information related to cognitive reserve and disorders, and predicted brain age and cognitive performance in terms of a MiniMental State Examination (MMSE) score. We implemented linear and nonlinear joint models and compared them against isolated models. Our results indicate that joint modeling substantially improves the accuracy of the modeling of individual measures, relative to isolated models.