HA-HI: Synergising fMRI and DTI through Hierarchical Alignments and
  Hierarchical Interactions for Mild Cognitive Impairment Diagnosis
Xiongri Shen Zhenxi Song Linling Li Min Zhang Lingyan Liang Honghai Liu Demao Deng Zhiguo Zhang
Early diagnosis of mild cognitive impairment (MCI) and subjective cognitive
decline (SCD) utilizing multi-modal magnetic resonance imaging (MRI) is a
pivotal area of research. While various regional and connectivity features from
functional MRI (fMRI) and diffusion tensor imaging (DTI) have been employed to
develop diagnosis models, most studies integrate these features without
adequately addressing their alignment and interactions. This limits the
potential to fully exploit the synergistic contributions of combined features
and modalities. To solve this gap, our study introduces a novel Hierarchical
Alignments and Hierarchical Interactions (HA-HI) method for MCI and SCD
classification, leveraging the combined strengths of fMRI and DTI. HA-HI
efficiently learns significant MCI- or SCD- related regional and connectivity
features by aligning various feature types and hierarchically maximizing their
interactions. Furthermore, to enhance the interpretability of our approach, we
have developed the Synergistic Activation Map (SAM) technique, revealing the
critical brain regions and connections that are indicative of MCI/SCD.
Comprehensive evaluations on the ADNI dataset and our self-collected data
demonstrate that HA-HI outperforms other existing methods in diagnosing MCI and
SCD, making it a potentially vital and interpretable tool for early detection.
The implementation of this method is publicly accessible at
https://github.com/ICI-BCI/Dual-MRI-HA-HI.git.