Multimodality Alzheimer's Disease Analysis in Deep Riemannian Manifold

Ma, Junbo; Zhang, Jilian; Wang, Zeyu

doi:10.1016/j.ipm.2022.102965

Cited by 11 publications

(4 citation statements)

References 35 publications

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“…The regions of considerable interest in neuroscience include the cingulate cortex, hippocampal structure, precuneus, inferior temporal gyrus, angular gyrus, and pivotal brain areas involved in default mode networks. Additionally, the supraoccipital gyrus within visual networks, the anterior and posterior central gyrus within sensorimotor networks, as well as the thalamus within the limbic system and other brain regions have garnered considerable attention within the field (Yao et al, 2010;Lei et al, 2020;Ma et al, 2022;Han et al, 2023;Zhou et al, 2023). These regions are crucial for understanding various neural processes and their interconnectivity.…”

Section: Discussionmentioning

confidence: 99%

Brain age prediction using the graph neural network based on resting-state functional MRI in Alzheimer's disease

Gao

Liu

et al. 2023

Front. Neurosci.

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IntroductionAlzheimer's disease (AD) is a neurodegenerative disease that significantly impacts the quality of life of patients and their families. Neuroimaging-driven brain age prediction has been proposed as a potential biomarker to detect mental disorders, such as AD, aiding in studying its effects on functional brain networks. Previous studies have shown that individuals with AD display impaired resting-state functional connections. However, most studies on brain age prediction have used structural magnetic resonance imaging (MRI), with limited studies based on resting-state functional MRI (rs-fMRI).MethodsIn this study, we applied a graph neural network (GNN) model on controls to predict brain ages using rs-fMRI in patients with AD. We compared the performance of the GNN model with traditional machine learning models. Finally, the post hoc model was also used to identify the critical brain regions in AD.ResultsThe experimental results demonstrate that our GNN model can predict brain ages of normal controls using rs-fMRI data from the ADNI database. Moreover the differences between brain ages and chronological ages were more significant in AD patients than in normal controls. Our results also suggest that AD is associated with accelerated brain aging and that the GNN model based on resting-state functional connectivity is an effective tool for predicting brain age.DiscussionOur study provides evidence that rs-fMRI is a promising modality for brain age prediction in AD research, and the GNN model proves to be effective in predicting brain age. Furthermore, the effects of the hippocampus, parahippocampal gyrus, and amygdala on brain age prediction are verified.

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Section: Discussionmentioning

confidence: 99%

Brain age prediction using the graph neural network based on resting-state functional MRI in Alzheimer's disease

Gao

Liu

et al. 2023

Front. Neurosci.

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“…A brief introduction to these latest technologies is as follows. Ma et al [54] introduced the Riemannian manifold model to extract global features for the diagnosis of ADd. Hu et al [55] proposed a multi-band fusion model to estimate brain networks.…”

Section: G Compared With State-of-the-art Methodsmentioning

confidence: 99%

Estimating Functional Brain Networks by Low-Rank Representation With Local Constraint

Li,

Zheng,

Liu

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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The functional architecture undergoes alterations during the preclinical phase of Alzheimer's disease. Consequently, the primary research focus has shifted towards identifying Alzheimer's disease and its early stages by constructing a functional connectivity network based on resting-state fMRI data. Recent investigations show that as Alzheimer's Disease (AD) progresses, modular tissue and connections in the core brain areas of AD patients diminish. Sparse learning methods are powerful tools for understanding Functional Brain Networks (FBNs) with Regions of Interest (ROIs) and a connectivity matrix measuring functional coherence between them. However, these tools often focus exclusively on functional connectivity measures, neglecting the brain network's modularity. Modularity orchestrates dynamic activities within the FBN to execute intricate cognitive tasks. To provide a comprehensive delineation of the FBN, we propose a local similarity-constrained low-rank sparse representation (LSLRSR) method that encodes modularity information under a manifoldregularized network learning framework and further formulate it as a low-rank sparse graph learning problem, which can be solved by an efficient optimization algorithm. Specifically, for each modularity structure, the Schatten p-norm regularizer reduces the reconstruction error and provides a better approximation of the low-rank constraint. Furthermore, we adopt a manifold-regularized local similarity prior to infer the intricate relationship between subnetwork similarity and modularity, guiding the modeling of FBN. Additionally, the proximal average method approximates the joint solution's proximal map, and the resulting nonconvex optimization problems are solved using the alternating direction multiplier method (ADMM). Compared to state-of-the-art methods for constructing FBNs, our algorithm generates a more modular FBN. This lays the groundwork for further research into alterations in brain network modularity resulting from diseases.

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“…This stage is marked by challenges such as memory loss and difficulties in executing complex daily activities [1][2][3]. Once MCI deteriorates into AD, cognitive function cannot be restored to normal levels [4,5], leading to a significant reduction in the ability of patients with AD to independently perform daily activities [6]. This makes MCI a critical intervention point to potentially slow down cognitive decline.…”

Section: Introductionmentioning

confidence: 99%

Integrating Biomarkers From Virtual Reality and Magnetic Resonance Imaging for the Early Detection of Mild Cognitive Impairment Using a Multimodal Learning Approach: Validation Study

Park,

Kim,

Park

et al. 2024

J Med Internet Res

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Background Early detection of mild cognitive impairment (MCI), a transitional stage between normal aging and Alzheimer disease, is crucial for preventing the progression of dementia. Virtual reality (VR) biomarkers have proven to be effective in capturing behaviors associated with subtle deficits in instrumental activities of daily living, such as challenges in using a food-ordering kiosk, for early detection of MCI. On the other hand, magnetic resonance imaging (MRI) biomarkers have demonstrated their efficacy in quantifying observable structural brain changes that can aid in early MCI detection. Nevertheless, the relationship between VR-derived and MRI biomarkers remains an open question. In this context, we explored the integration of VR-derived and MRI biomarkers to enhance early MCI detection through a multimodal learning approach. Objective We aimed to evaluate and compare the efficacy of VR-derived and MRI biomarkers in the classification of MCI while also examining the strengths and weaknesses of each approach. Furthermore, we focused on improving early MCI detection by leveraging multimodal learning to integrate VR-derived and MRI biomarkers. Methods The study encompassed a total of 54 participants, comprising 22 (41%) healthy controls and 32 (59%) patients with MCI. Participants completed a virtual kiosk test to collect 4 VR-derived biomarkers (hand movement speed, scanpath length, time to completion, and the number of errors), and T1-weighted MRI scans were performed to collect 22 MRI biomarkers from both hemispheres. Analyses of covariance were used to compare these biomarkers between healthy controls and patients with MCI, with age considered as a covariate. Subsequently, the biomarkers that exhibited significant differences between the 2 groups were used to train and validate a multimodal learning model aimed at early screening for patients with MCI among healthy controls. Results The support vector machine (SVM) using only VR-derived biomarkers achieved a sensitivity of 87.5% and specificity of 90%, whereas the MRI biomarkers showed a sensitivity of 90.9% and specificity of 71.4%. Moreover, a correlation analysis revealed a significant association between MRI-observed brain atrophy and impaired performance in instrumental activities of daily living in the VR environment. Notably, the integration of both VR-derived and MRI biomarkers into a multimodal SVM model yielded superior results compared to unimodal SVM models, achieving higher accuracy (94.4%), sensitivity (100%), specificity (90.9%), precision (87.5%), and F1-score (93.3%). Conclusions The results indicate that VR-derived biomarkers, characterized by their high specificity, can be valuable as a robust, early screening tool for MCI in a broader older adult population. On the other hand, MRI biomarkers, known for their high sensitivity, excel at confirming the presence of MCI. Moreover, the multimodal learning approach introduced in our study provides valuable insights into the improvement of early MCI detection by integrating a diverse set of biomarkers.

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Multimodality Alzheimer's Disease Analysis in Deep Riemannian Manifold

Cited by 11 publications

References 35 publications

Brain age prediction using the graph neural network based on resting-state functional MRI in Alzheimer's disease

Brain age prediction using the graph neural network based on resting-state functional MRI in Alzheimer's disease

Estimating Functional Brain Networks by Low-Rank Representation With Local Constraint

Integrating Biomarkers From Virtual Reality and Magnetic Resonance Imaging for the Early Detection of Mild Cognitive Impairment Using a Multimodal Learning Approach: Validation Study

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