Edge-centric effective connection network based on muti-modal MRI for the diagnosis of Alzheimer’s disease

Zhang, S.; Zhao, Hang; Wang, Weiping; Wang, Zhen; Luo, Xiong; Hramov, Alexander E.; Kurths, Jürgen

doi:10.1016/j.neucom.2023.126512

Cited by 9 publications

(1 citation statement)

References 38 publications

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“…Fluid biomarkers found in blood and CSF have now become standard methods of diagnosing early AD patients [7][8][9], even showing great potential for subtyping AD [10]. Similarly, recent imaging-based approaches such as brain connectivity analysis in the form of connectomes have shown promising results for early diagnosis [11]. AD subtypes have been previously identified based on hallmark AD biomarkers obtained from brain imaging such as beta-amyloid [12] and tau accumulation [13,14].…”

Section: Introductionmentioning

confidence: 99%

Identifying Progression-Specific Alzheimer’s Subtypes Using Multimodal Transformer

Machado Reyes,

Chao,

Hahn

et al. 2024

JPM

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Alzheimer’s disease (AD) is the most prevalent neurodegenerative disease, yet its current treatments are limited to stopping disease progression. Moreover, the effectiveness of these treatments remains uncertain due to the heterogeneity of the disease. Therefore, it is essential to identify disease subtypes at a very early stage. Current data-driven approaches can be used to classify subtypes during later stages of AD or related disorders, but making predictions in the asymptomatic or prodromal stage is challenging. Furthermore, the classifications of most existing models lack explainability, and these models rely solely on a single modality for assessment, limiting the scope of their analysis. Thus, we propose a multimodal framework that utilizes early-stage indicators, including imaging, genetics, and clinical assessments, to classify AD patients into progression-specific subtypes at an early stage. In our framework, we introduce a tri-modal co-attention mechanism (Tri-COAT) to explicitly capture cross-modal feature associations. Data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (slow progressing = 177, intermediate = 302, and fast = 15) were used to train and evaluate Tri-COAT using a 10-fold stratified cross-testing approach. Our proposed model outperforms baseline models and sheds light on essential associations across multimodal features supported by known biological mechanisms. The multimodal design behind Tri-COAT allows it to achieve the highest classification area under the receiver operating characteristic curve while simultaneously providing interpretability to the model predictions through the co-attention mechanism.

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Section: Introductionmentioning

confidence: 99%

Identifying Progression-Specific Alzheimer’s Subtypes Using Multimodal Transformer

Machado Reyes,

Chao,

Hahn

et al. 2024

JPM

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Exploring intricate connectivity patterns for cognitive functioning and neurological disorders: incorporating frequency-domain NC method into fMRI analysis

Wang

2024

Cerebral Cortex

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This study extends the application of the frequency-domain new causality method to functional magnetic resonance imaging analysis. Strong causality, weak causality, balanced causality, cyclic causality, and transitivity causality were constructed to simulate varying degrees of causal associations among multivariate functional-magnetic-resonance-imaging blood-oxygen-level-dependent signals. Data from 1,252 groups of individuals with different degrees of cognitive impairment were collected. The frequency-domain new causality method was employed to construct directed efficient connectivity networks of the brain, analyze the statistical characteristics of topological variations in brain regions related to cognitive impairment, and utilize these characteristics as features for training a deep learning model. The results demonstrated that the frequency-domain new causality method accurately detected causal associations among simulated signals of different degrees. The deep learning tests also confirmed the superior performance of new causality, surpassing the other three methods in terms of accuracy, precision, and recall rates. Furthermore, consistent significant differences were observed in the brain efficiency networks, where several subregions defined by the multimodal parcellation method of Human Connectome Project simultaneously appeared in the topological statistical results of different patient groups. This suggests a significant association between these fine-grained cortical subregions, driven by multimodal data segmentation, and human cognitive function, making them potential biomarkers for further analysis of Alzheimer’s disease.

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Features of the resting-state functional network in patients with major depressive disorder: mutual information analysis in fMRI data

Khorev,

Kurkin,

Paunova

et al. 2023

2023 7th Scientific School Dynamics of Complex Networks and Their Applications (DCNA)

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Edge-centric effective connection network based on muti-modal MRI for the diagnosis of Alzheimer’s disease

Cited by 9 publications

References 38 publications

Identifying Progression-Specific Alzheimer’s Subtypes Using Multimodal Transformer

Identifying Progression-Specific Alzheimer’s Subtypes Using Multimodal Transformer

Exploring intricate connectivity patterns for cognitive functioning and neurological disorders: incorporating frequency-domain NC method into fMRI analysis

Features of the resting-state functional network in patients with major depressive disorder: mutual information analysis in fMRI data

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