  Many supervised machine learning frameworks have been proposed for disease
classification using functional magnetic resonance imaging (fMRI) data,
producing important biomarkers. More recently, data pooling has flourished,
making the result generalizable across a large population. But, this success
depends on the population diversity and variability introduced due to the
pooling of the data that is not a primary research interest. Here, we look at
hierarchical Sparse Connectivity Patterns (hSCPs) as biomarkers for major
depressive disorder (MDD). We propose a novel model based on hSCPs to predict
MDD patients from functional connectivity matrices extracted from resting-state
fMRI data. Our model consists of three coupled terms. The first term decomposes
connectivity matrices into hierarchical low-rank sparse components
corresponding to synchronous patterns across the human brain. These components
are then combined via patient-specific weights capturing heterogeneity in the
data. The second term is a classification loss that uses the patient-specific
weights to classify MDD patients from healthy ones. Both of these terms are
combined with the third term, a robustness loss function to improve the
reproducibility of hSCPs. This reduces the variability introduced due to site
and population diversity (age and sex) on the predictive accuracy and pattern
stability in a large dataset pooled from five different sites. Our results show
the impact of diversity on prediction performance. Our model can reduce
diversity and improve the predictive and generalizing capability of the
components. Finally, our results show that our proposed model can robustly
identify clinically relevant patterns characteristic of MDD with high
reproducibility.
