OpenMAP-BrainAge: Generalizable and Interpretable Brain Age Predictor
Pengyu Kan, Craig Jones, Kenichi Oishi

TL;DR
This paper introduces OpenMAP-BrainAge, a transformer-based, interpretable brain age prediction model that demonstrates high accuracy, robustness across datasets, and meaningful neuroanatomical insights related to aging and cognitive decline.
Contribution
It presents a scalable, interpretable transformer architecture with self-supervised pre-training for accurate brain age prediction across diverse datasets.
Findings
Achieved MAE of 3.65 years on test datasets.
High generalizability with MAE of 3.54 years on external dataset.
Identified brain regions linked to aging and cognitive decline.
Abstract
Purpose: To develop an age prediction model which is interpretable and robust to demographic and technological variances in brain MRI scans. Materials and Methods: We propose a transformer-based architecture that leverages self-supervised pre-training on large-scale datasets. Our model processes pseudo-3D T1-weighted MRI scans from three anatomical views and incorporates brain volumetric information. By introducing a stem architecture, we reduce the conventional quadratic complexity of transformer models to linear complexity, enabling scalability for high-dimensional MRI data. We trained our model on ADNI2 3 (N=1348) and OASIS3 (N=716) datasets (age range: 42 - 95) from the North America, with an 8:1:1 split for train, validation and test. Then, we validated it on the AIBL dataset (N=768, age range: 60 - 92) from Australia. Results: We achieved an MAE of 3.65 years on ADNI2 3…
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Taxonomy
TopicsFunctional Brain Connectivity Studies · Dementia and Cognitive Impairment Research · EEG and Brain-Computer Interfaces
