Multi-Scale Attention-Based Deep Neural Network for Brain Disease Diagnosis

Abstract

Whole brain functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used in the diagnosis of brain disorders such as autism spectrum disorder (ASD). Recently, an increasing number of studies have focused on employing deep learning techniques to analyze FC patterns for brain disease classification. However, the high dimensionality of the FC features and the interpretation of deep learning results are issues that need to be addressed in the FC-based brain disease classification. In this paper, we proposed a multi-scale attention-based deep neural network (MSA-DNN) model to classify FC patterns for the ASD diagnosis. The model was implemented by adding a flexible multi-scale attention (MSA) module to the auto-encoder based backbone DNN, which can extract multi-scale features of the FC patterns and change the level of attention for different FCs by continuous learning. Our model will reinforce the weights of important FC features while suppress the unimportant FCs to ensure the sparsity of the model weights and enhance the model interpretability. We performed systematic experiments on the large multi-sites ASD dataset with both ten-fold and leaveone-site-out cross-validations. Results showed that our model outperformed classical methods in brain disease classification and revealed robust intersite prediction performance. We also localized important FC features and brain regions associated with ASD classification. Overall, our study further promotes the biomarker detection and computer-aided classification for ASD diagnosis, and the proposed MSA module is flexible and easy to implement in other classification networks.

Brain disease research based on functional magnetic resonance imaging data and machine learning: a review

“…The widely utilized atlas is the automatic anatomical labeling (AAL) atlas (Tzourio-Mazoyer et al, 2002 ; Guo and Zhang, 2020 ; Ji and Yao, 2021 ; Pang et al, 2021 ; Wang et al, 2021 , 2022b ; Alorf and Khan, 2022 ; Cai et al, 2022 ; Hu et al, 2022 ; Lu et al, 2022 ; Wang T. et al, 2022 ; Chen et al, 2023 ). Besides, FC can be constructed by other atlases, such as the Power atlas (Power et al, 2011 ; Xu et al, 2020 ), Craddock 200 atlas (Craddock et al, 2011 ; Huang et al, 2021 ; Liang et al, 2021 , 2022 ), Bootstrap Analysis of Stable Clusters (Bellec et al, 2010 ; Subah et al, 2021 ; Wang N. et al, 2022 ), Brainnetome atlas (Fan et al, 2016 ; Jin et al, 2020 ), Yeo atlas (Yeo et al, 2011 ; Gullett et al, 2021 ), Harvard-Oxford atlas (Desikan et al, 2006 ; Cao et al, 2020 ), and Dosenbach atlas (Dosenbach et al, 2010 ; Zhao et al, 2022 ). In particular, Zhang et al ( 2022 ) constructed multiple FCNs based on the selected set of the atlas from generated multiple personalized atlases from the AAL atlas to improve the diagnosis effect of MCI.…”

Xception-Fractalnet: Hybrid Deep Learning Based Multi-Class Classification of Alzheimer’s Disease

“…Chemicals, head injuries, and genetic environmental factors are some of most important causes of AD. Behavior and mood instability, communication and recognition problems, learning issues, and memory loss are common signs of AD [8][9][10][11]. It triggers brain cell death, resulting in thinking, memory, and cognitive impairment.…”