RT Journal Article
SR Electronic
T1 Employing connectome-based models to predict working memory in multiple sclerosis
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.03.01.432930
DO 10.1101/2021.03.01.432930
A1 Heena R. Manglani
A1 Stephanie Fountain-Zaragoza
A1 Anita Shankar
A1 Jacqueline A. Nicholas
A1 Ruchika Shaurya Prakash
YR 2021
UL http://biorxiv.org/content/early/2021/03/02/2021.03.01.432930.abstract
AB Background Individuals with multiple sclerosis (MS) are vulnerable to deficits in working memory, and the search for neural correlates of working memory in circumscribed areas has yielded inconclusive findings. Given the widespread neural alterations observed in MS, predictive modeling approaches that capitalize on whole-brain connectivity may better capture individual-level working memory in this population.Methods Here, we applied connectome-based predictive modeling to functional MRI data from working memory tasks in two independent samples with relapsing-remitting MS. In the internal validation sample (ninternal = 36), functional connectivity data were used to train a model through cross-validation to predict accuracy on the Paced Visual Serial Addition Test, a gold-standard measure of working memory in MS. We then tested its ability to predict performance on the N-back working memory task in the external validation sample (nexternal = 36).Results The resulting model successfully predicted working memory in the internal validation sample but did not extend to the external sample. We also tested the generalizability of an existing model of working memory derived in healthy young adults to people with MS. It showed successful prediction in both MS samples, demonstrating its translational potential. We qualitatively explored differences between the healthy and MS models in intra- and inter-network connectivity amongst canonical networks.Discussion These findings suggest that connectome-based predictive models derived in people with MS may have limited generalizability. Instead, models identified in healthy individuals may offer superior generalizability to clinical samples, such as MS, and may serve as more useful targets for intervention.Impact Statement Working memory deficits in people with multiple sclerosis have important consequence for employment, leisure, and daily living activities. Identifying a functional connectivity-based marker that accurately captures individual differences in working memory may offer a useful target for cognitive rehabilitation. Manglani et al. demonstrate machine learning can be applied to whole-brain functional connectivity data to identify networks that predict individual-level working memory in people with multiple sclerosis. However, existing network-based models of working memory derived in healthy adults outperform those identified in multiple sclerosis, suggesting translational potential of brain networks derived in large, healthy samples for predicting cognition in multiple sclerosis.Competing Interest StatementH.R.M., S.FZ., and A.S. report no competing interests. J.A.N. has received research grants from Biogen Idec, Genzyme, Novartis, PCORI, ADAMAS and Alexion. She has received consulting fees and honoraria from Biogen, Genentech, GW Pharmaceuticals, EMD Serono, Bristol Myers Squib, Novartis, Alexion, Viela Bio and the American Academy of Neurology. R.S.P. receives speaking honoraria from Sanofi Genzyme.