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10.1016/j.neuroimage.2021.118048

Based on the ADNI cohort, we validate the effectiveness of our method by presenting the superior performance of our network in eMCI identification.

A unified framework for personalized regions selection and functional relation modeling for early MCI identification

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10.3389/fnins.2021.696639

Through experiments, we have the following findings: (1) with the increase of n, the discriminative ability of PCn-based BFNs tends to decrease; (2) fusing the PCn-based BFNs (n>1) with the PC1-based BFN can generally improve the sensitivity for MCI identification, but fail to help the classification accuracy.

Brain Function Network: Higher Order vs. More Discrimination

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10.3389/fnins.2021.720909

Experimental results demonstrate that our method is superior to previous methods in both tasks of AD/MCI identification and MCI conversion prediction, and also helps discover discriminative brain regions and functional connectivities associated with AD.

Modularity-Guided Functional Brain Network Analysis for Early-Stage Dementia Identification

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10.3233/JAD-190715

Additionally, gyrification index and cortical thickness derived from surface-based morphometry outperformed other features in MCI identification, suggesting they were some key morphological biomarkers for early MCI diagnosis.

Identifying Mild Cognitive Impairment with Random Forest by Integrating Multiple MRI Morphological Metrics.

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10.1109/CBD.2019.00062

To address this challenge, we propose a new MCI identification method based on multi-view graph convolutional networks (MVGCNs).

Mild Cognitive Impairment Identification Based on Multi-View Graph Convolutional Networks

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10.1007/978-3-030-32692-0_58

Besides, most of the current studies that utilize the dFC for MCI identification mainly relied on the local clustering coefficient for extracting dynamic features and the support vector machine (SVM) as a classifier.

Adaptive Functional Connectivity Network Using Parallel Hierarchical BiLSTM for MCI Diagnosis

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10.1109/TMI.2018.2882189

In this paper, we proposed a novel sparse constrained effective connectivity inference method and an elastic multilayer perceptron classifier for MCI identification.

Novel Effective Connectivity Inference Using Ultra-Group Constrained Orthogonal Forward Regression and Elastic Multilayer Perceptron Classifier for MCI Identification

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10.1016/J.PATCOG.2018.12.001

Individualized MCI identification results show that our method could achieve a balance between the individually consistent brain functional network construction and the adequately maintained inter-group brain functional network distinctions, thus leading to a more accurate classification result.

Strength and similarity guided group-level brain functional network construction for MCI diagnosis

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Mci Identification
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What is/are Mci Identification?

Mci Identification - Based on the ADNI cohort, we validate the effectiveness of our method by presenting the superior performance of our network in eMCI identification. [1] Through experiments, we have the following findings: (1) with the increase of n, the discriminative ability of PCn-based BFNs tends to decrease; (2) fusing the PCn-based BFNs (n>1) with the PC1-based BFN can generally improve the sensitivity for MCI identification, but fail to help the classification accuracy. [2] Experimental results demonstrate that our method is superior to previous methods in both tasks of AD/MCI identification and MCI conversion prediction, and also helps discover discriminative brain regions and functional connectivities associated with AD. [3] Additionally, gyrification index and cortical thickness derived from surface-based morphometry outperformed other features in MCI identification, suggesting they were some key morphological biomarkers for early MCI diagnosis. [4] To address this challenge, we propose a new MCI identification method based on multi-view graph convolutional networks (MVGCNs). [5] Besides, most of the current studies that utilize the dFC for MCI identification mainly relied on the local clustering coefficient for extracting dynamic features and the support vector machine (SVM) as a classifier. [6] In this paper, we proposed a novel sparse constrained effective connectivity inference method and an elastic multilayer perceptron classifier for MCI identification. [7] Individualized MCI identification results show that our method could achieve a balance between the individually consistent brain functional network construction and the adequately maintained inter-group brain functional network distinctions, thus leading to a more accurate classification result. [8]