Background: Structural magnetic resonance imaging (sMRI) can reflect structural abnormalities of the brain. Due to its high tissue contrast and spatial resolution, it is considered as an MRI sequence in diagnostic tasks related to Alzheimer's disease (AD). Thus far, most studies based on sMRI have only focused on pathological changes in disease-related brain regions in Euclidean space, ignoring the association and interaction between brain regions represented in non-Euclidean space. This non-Euclidean spatial information can provide valuable information for brain disease research. However, few studies have combined Euclidean spatial information in images and graph spatial information in brain networks for the early diagnosis of AD. The purpose of this study is to explore how to effectively combine multispatial information for enhancing AD diagnostic performance.
Methods: A multispatial information representation model (MSRNet) was constructed for the diagnosis of AD using sMRI. Specifically, the MSRNet included a Euclidean representation channel integrating a multiscale module and a feature enhancement module, in addition to a graph (non-Euclidean) representation channel integrating a node feature aggregation mechanism. This was accomplished through the adoption of a multilayer graph convolutional neural network and a node connectivity aggregation mechanism with fully connected layers. Each participants' gray-matter volume map and preconstructed radiomics-based morphology brain network (radMBN) were used as MSRNet inputs for the learning of multispatial information. Other than the multispatial information representation in MSRNet, an interactive mechanism was proposed to connect the Euclidean and graph representation channels by five disease-related brain regions which were identified based on a classifier operated on with two feature strategies of voxel intensities and radiomics features. MSRNet focused on disease-related brain regions while integrating multispatial information to effectively enhance disease discrimination.
Results: The MSRNet was validated on four publicly available datasets, achieving accuracies 92.8% and 90.6% for AD in intra-database and inter-database cross-validation, respectively. The accuracy of MSRNet in distinguishing between late mild cognitive impairment (MCI) and early MCI, and between progressive MCI and stable MCI, reached 79.8% and 73.4%, respectively. The experiments demonstrated that the model's decision scores exhibited good detection capability for MCI progression. Furthermore, the potential of decision scores for improving diagnostic performance was exhibited by combining decision scores with other clinical indicators for AD identification.
Conclusions: The MSRNet model could conduct an effective multispatial information representation in the sMRI-based diagnosis of AD. The proposed interaction mechanism in the MSRNet could help the model focus on AD-related brain regions, thus further improving the diagnostic ability.
Keywords: Alzheimer’s disease (AD); Key brain regions; multispatial information.
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