Foundation models for discovering robust biomarkers of neurological disorders from dynamic functional connectivity

Deepank Girish, Yi Hao Chan, Sukrit Gupta, Jing Xia, Jagath C. Rajapakse

Abstract:

Several brain foundation models (FM) have recently
been proposed to predict brain disorders by modelling dynamic
functional connectivity (FC). While they demonstrate remarkable
model performance and zero- or few-shot generalization, the
salient features identified as potential biomarkers are yet to be
thoroughly evaluated. We propose RE-CONFIRM, a framework
for evaluating the robustness of potential biomarker candidates
elucidated by deep learning (DL) models including FMs. From
experiments on five large datasets of Autism Spectrum Disorder
(ASD), Attention-deficit Hyperactivity Disorder (ADHD), and
Alzheimer’s Disease (AD), we found that although commonly
used performance metrics provide an intuitive assessment of
model predictions, they are insufficient for evaluating the robustness of biomarkers identified by these models. RE-CONFIRM
metrics revealed that simply finetuning FMs leads to models that
fail to capture regional hubs effectively, even in disorders where
hubs are known to be implicated, such as ASD and ADHD. In
view of this, we propose Hub-LoRA (Low-Rank Adaptation) as
a fine-tuning technique that enables FMs to not only outperform
customised DL models but also produce neurobiologically faithful
biomarkers supported by meta-analyses. RE-CONFIRM is generalizable and can be easily applied to ascertain the robustness
of DL models trained on functional MRI datasets. Code is available at: https://github.com/SCSE-Biomedical-Computing-Group/
RE-CONFIRM