Deep learning for risk-based stratification of cognitively impaired individuals

Michael F Romano; Xiao Zhou; Akshara R Balachandra; Michalina F Jadick; Shangran Qiu; Diya A Nijhawan; Prajakta S Joshi; Shariq Mohammad; Peter H Lee; Maximilian J Smith; Aaron B Paul; Asim Z Mian; Juan E Small; Sang P Chin; Rhoda Au; Vijaya B Kolachalama

doi:10.1016/j.isci.2023.107522

Deep learning for risk-based stratification of cognitively impaired individuals

iScience. 2023 Aug 2;26(9):107522. doi: 10.1016/j.isci.2023.107522. eCollection 2023 Sep 15.

Authors

Michael F Romano^{1

2}, Xiao Zhou^{1

3}, Akshara R Balachandra^{1

4}, Michalina F Jadick¹, Shangran Qiu¹, Diya A Nijhawan¹, Prajakta S Joshi^{5

6

7}, Shariq Mohammad⁸, Peter H Lee⁹, Maximilian J Smith⁹, Aaron B Paul¹⁰, Asim Z Mian¹¹, Juan E Small⁹, Sang P Chin^{3

12

13}, Rhoda Au^{5

7

14

15

16}, Vijaya B Kolachalama^{1

3

14

17}

Affiliations

¹ Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
² Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.
³ Department of Computer Science, Boston University, Boston, MA, USA.
⁴ Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
⁵ Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
⁶ Department of General Dentistry, Boston University School of Dental Medicine, Boston, MA, USA.
⁷ The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
⁸ Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
⁹ Department of Radiology, Lahey Hospital & Medical Center, Burlington, MA, USA.
¹⁰ Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.
¹¹ Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
¹² Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
¹³ Center of Mathematical Sciences & Applications, Harvard University, Cambridge, MA, USA.
¹⁴ Boston University Alzheimer's Disease Research Center, Boston, MA, USA.
¹⁵ Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA.
¹⁶ Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
¹⁷ Faculty of Computing & Data Sciences, Boston University, Boston, MA, USA.

Abstract

Quantifying the risk of progression to Alzheimer's disease (AD) could help identify persons who could benefit from early interventions. We used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 544, discovery cohort) and the National Alzheimer's Coordinating Center (NACC, n = 508, validation cohort), subdividing individuals with mild cognitive impairment (MCI) into risk groups based on cerebrospinal fluid amyloid-β levels and identifying differential gray matter patterns. We then created models that fused neural networks with survival analysis, trained using non-parcellated T1-weighted brain MRIs from ADNI data, to predict the trajectories of MCI to AD conversion within the NACC cohort (integrated Brier score: 0.192 [discovery], and 0.108 [validation]). Using modern interpretability techniques, we verified that regions important for model prediction are classically associated with AD. We confirmed AD diagnosis labels using postmortem data. We conclude that our framework provides a strategy for risk-based stratification of individuals with MCI and for identifying regions key for disease prognosis.

Keywords: Health sciences; Illness behavior.