TY - GEN
T1 - A Joint Model for Predicting Structural and Functional Brain Health in Elderly Individuals
AU - Varatharajah, Yogatheesan
AU - Saboo, Krishnakant
AU - Iyer, Ravishankar
AU - Przybelski, Scott
AU - Schwarz, Christopher
AU - Petersen, Ronald
AU - Jack, Clifford
AU - Vemuri, Prashanthi
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - This paper presents a machine-learning-based 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 individual's health, brain-age ground-truth can be defined only for healthy individuals; and 2) while brain-age 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 multitask-learning model to facilitate learning from population-level 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 Mini-Mental 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.
AB - This paper presents a machine-learning-based 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 individual's health, brain-age ground-truth can be defined only for healthy individuals; and 2) while brain-age 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 multitask-learning model to facilitate learning from population-level 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 Mini-Mental 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.
KW - Brain age
KW - Brain health
KW - Cognition
KW - Machine learning
KW - Neuroimaging
UR - http://www.scopus.com/inward/record.url?scp=85084341945&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85084341945&partnerID=8YFLogxK
U2 - 10.1109/BIBM47256.2019.8983291
DO - 10.1109/BIBM47256.2019.8983291
M3 - Conference contribution
AN - SCOPUS:85084341945
T3 - Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
SP - 1657
EP - 1664
BT - Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
A2 - Yoo, Illhoi
A2 - Bi, Jinbo
A2 - Hu, Xiaohua Tony
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Y2 - 18 November 2019 through 21 November 2019
ER -