Classification and Graphical Analysis of Alzheimer’s Disease and Its Prodromal Stage Using Multimodal Features From Structural, Diffusion, and Functional Neuroimaging Data and the APOE Genotype

Abstract

Classification of AD Using Six-Multi-Modalities on the basis of the 2-mm JHU-ICBM-labeled template atlas. To integrate the different modalities and different complementary information into one form, and to optimize the classifier, we used the multiple kernel learning (MKL) framework. The obtained results indicated that our multimodal approach yields a significant improvement in accuracy over any single modality alone. The areas under the curve obtained by the proposed method were 97.78, 96.94, 95.56, 96.25, 96.67, and 96.59% for AD vs. HC, MCIs vs. MCIc, AD vs. MCIc, AD vs. MCIs, HC vs. MCIc, and HC vs. MCIs binary classification, respectively. Our proposed multimodal method improved the classification result for MCIs vs. MCIc groups compared with the unimodal classification results. Our study found that the (left/right) precentral region was present in all six binary classification groups (this region can be considered the most significant region). Furthermore, using nodal network topology, we found that FDG, AV45-PET, and rs-fMRI were the most important neuroimages, and showed many affected regions relative to other modalities. We also compared our results with recently published results.

Predicting MCI to AD Conversation Using Integrated sMRI and rs-fMRI: Machine Learning and Graph Theory Approach

“…Meanwhile, we also compared the classification performances with other studies. Most previous methods that constructed brain networks only considered structural or functional features (Suk and Shen, 2014; Hu et al, 2015;Moradi et al, 2015;Raamana et al, 2015;Ardekani et al, 2016;Suk et al, 2016;Beheshti et al, 2017;Hojjati et al, 2017Hojjati et al, , 2018Zheng et al, 2019;Gupta et al, 2020;Zhu et al, 2021), and obtained an accuracy lower than that of the present study. Only Hojjatia's study (Hojjati et al, 2017) used graph theory and machine learning approach (mRMR, FS) to classify rs-fMRI and obtained a classification accuracy of 91.4%.…”

“…For the study of the topological properties of the brain, Power-264 brain regions might be considered as a template for constructing brain networks. In addition, other well-known prognostic information (DTI, ApoE status, Tau/Amyloid/FDG-PET) will be considered for classification ( Gupta et al, 2020 ; Fan et al, 2021 ). In terms of subject design, we believe that the follow-up data within the subject can better reveal the brain area where the sensitive characteristics of the transformed biomarker are located.…”

Altered Frequency-Dependent Brain Activation and White Matter Integrity Associated With Cognition in Characterizing Preclinical Alzheimer’s Disease Stages

“…Furthermore, white matter alterations were also revealed to be correlated with progressed cognitive decline ( Power et al, 2019 ). Through MRI modalities, which included combined ALFF and DTI analyses, Gupta et al created a classification tool to distinguish converting MCI (which progresses to AD) from non-converting MCI ( Gupta et al, 2020 ). However, the mentioned research lacked consideration of the entire AD preclinical stages (i.e., SCD, naMCI, and aMCI) and analyses of the relationship with cognition.…”

Multimodal Classification: Current Landscape, Taxonomy and Future Directions

“…Based on the their respective predictive power, each modality was given a weight used at the final multimodal classification step. [32] created a predictive model for distinguishing healthy patients from those with Alzheimer's disease or mild cognitive impairment where transformed image and APOE genotype based features were shaped into kernel space before classification with the multiple kernel learning (MKL) algorithm [5]. [89] also used the k-Nearest Neighbor (KNN) classifier with manually extracted features from EEG, ECG, and skin temperature data to predict emotional response to videos.…”