Abstract
Cognitive reserve (CR) has been introduced to explain individual differences in susceptibility to cognitive or functional impairment in the presence of age or pathology. We developed a deep learning model to quantify the CR as residual variance in memory performance using the structural MRI data from a lifespan healthy cohort. The generalizability of the sMRI-based deep learning model was tested in two independent healthy and Alzheimer’s cohorts using transfer learning framework.
Structural MRIs were collected from three cohorts: 495 healthy adults (initially aged 20-80) from RANN, 620 healthy participants (age 36-100) from lifespan Human Connectome Project Aging (HCPA), and 941 subjects from Alzheimer’s Disease Neuroimaging Initiative (ADNI). Region of interest (ROI)-specific cortical thickness and volume measures were extracted using the Desikan-Killiany Atlas. Cognitive reserve was quantified by residuals which subtract the predicted memory from the true memory. Cascade neural network (CNN) models were used to train RANN dataset for memory prediction. Transfer learning was applied to transfer the T1 imaging-based model from source domain (using RANN) to the target domain (HCPA or ADNI).
The CNN model trained on the RANN dataset exhibited strong linear correlation between true and predicted memory based on the chosen T1 cortical thickness and volume predictors. In addition, the model generated from healthy lifespan data (RANN) was able to generalize to an independent healthy lifespan data (HCPA) and older demented participants (ADNI) across different scanner types. The estimated CR was correlated with CR proxies such education and IQ across all three datasets.
The current findings suggest that the transfer learning approach is an effective way to generalize the residual-based CR estimation. It is applicable to various diseases and may flexibly incorporate different imaging modalities such as fMRI and PET, making it a promising tool for scientific and clinical purposes.
Highlight
Quantification of cognitive reserve using brain measures for pre-symptomatic Alzheimer’s patients can be estimated by leveraging lifespan data.
Multi-center, multi-scanner, multi-sequence can affect the performance of the quantification.
Leveraging lifespan data from a single site can improve the performance.
Transfer learning allows the pre-trained network to successfully reconstruct the dataset acquired from different domains or age groups.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Xi Zhu, e-mail: xi.zhu{at}nyspi.columbia.edu, Yi Liu, email: yl4358{at}cumc.columbia.edu, Christian G. Habeck, email: ch629{at}cumc.columbia.edu, Yaakov Stern, email: ys11{at}cumc.columbia.edu
↵* Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
