Optimizing differential identifiability improves connectome predictive modeling of cognitive deficits from functional connectivity in Alzheimer's disease.

Autor:	Svaldi DO; Indiana University School of Medicine, Indianapolis, Indiana, USA., Goñi J; School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA.; Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana, USA.; Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA., Abbas K; School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA.; Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana, USA., Amico E; School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA.; Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana, USA., Clark DG; Indiana University School of Medicine, Indianapolis, Indiana, USA., Muralidharan C; Indiana University School of Medicine, Indianapolis, Indiana, USA., Dzemidzic M; Indiana University School of Medicine, Indianapolis, Indiana, USA., West JD; Indiana University School of Medicine, Indianapolis, Indiana, USA., Risacher SL; Indiana University School of Medicine, Indianapolis, Indiana, USA., Saykin AJ; Indiana University School of Medicine, Indianapolis, Indiana, USA., Apostolova LG; Indiana University School of Medicine, Indianapolis, Indiana, USA.
Jazyk:	angličtina
Zdroj:	Human brain mapping [Hum Brain Mapp] 2021 Aug 01; Vol. 42 (11), pp. 3500-3516. Date of Electronic Publication: 2021 May 05.
DOI:	10.1002/hbm.25448
Abstrakt:	Functional connectivity, as estimated using resting state functional MRI, has shown potential in bridging the gap between pathophysiology and cognition. However, clinical use of functional connectivity biomarkers is impeded by unreliable estimates of individual functional connectomes and lack of generalizability of models predicting cognitive outcomes from connectivity. To address these issues, we combine the frameworks of connectome predictive modeling and differential identifiability. Using the combined framework, we show that enhancing the individual fingerprint of resting state functional connectomes leads to robust identification of functional networks associated to cognitive outcomes and also improves prediction of cognitive outcomes from functional connectomes. Using a comprehensive spectrum of cognitive outcomes associated to Alzheimer's disease (AD), we identify and characterize functional networks associated to specific cognitive deficits exhibited in AD. This combined framework is an important step in making individual level predictions of cognition from resting state functional connectomes and in understanding the relationship between cognition and connectivity. (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)
Databáze:	MEDLINE
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