A Learnable Counter-condition Analysis Framework for Functional
  Connectivity-based Neurological Disorder Diagnosis
Eunsong Kang Da-woon Heo Jiwon Lee Heung-Il Suk
To understand the biological characteristics of neurological disorders with
functional connectivity (FC), recent studies have widely utilized deep
learning-based models to identify the disease and conducted post-hoc analyses
via explainable models to discover disease-related biomarkers. Most existing
frameworks consist of three stages, namely, feature selection, feature
extraction for classification, and analysis, where each stage is implemented
separately. However, if the results at each stage lack reliability, it can
cause misdiagnosis and incorrect analysis in afterward stages. In this study,
we propose a novel unified framework that systemically integrates diagnoses
(i.e., feature selection and feature extraction) and explanations. Notably, we
devised an adaptive attention network as a feature selection approach to
identify individual-specific disease-related connections. We also propose a
functional network relational encoder that summarizes the global topological
properties of FC by learning the inter-network relations without pre-defined
edges between functional networks. Last but not least, our framework provides a
novel explanatory power for neuroscientific interpretation, also termed
counter-condition analysis. We simulated the FC that reverses the diagnostic
information (i.e., counter-condition FC): converting a normal brain to be
abnormal and vice versa. We validated the effectiveness of our framework by
using two large resting-state functional magnetic resonance imaging (fMRI)
datasets, Autism Brain Imaging Data Exchange (ABIDE) and REST-meta-MDD, and
demonstrated that our framework outperforms other competing methods for disease
identification. Furthermore, we analyzed the disease-related neurological
patterns based on counter-condition analysis.