Contribution of Brain Regions Asymmetry Scores Combined with Random Forest Classifier in the Diagnosis of Alzheimer’s Disease in His Earlier Stage

Mabrouk, Besma; BenHamida, Ahmed; Drissi, Nidhal; Bouzidi, Nouha; Mhiri, Chokri

doi:10.1007/s40846-023-00775-2

Cited by 8 publications

(7 citation statements)

References 28 publications

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“…Meanwhile, our approach incorporated recent techniques for MCI screening. First, we separately measured the volumes of the left and right hemispheres to account for hemisphere asymmetry, as suggested by Mabrouk et al [ 62 ]. Subsequently, we used the hippocampus and entorhinal cortex as MRI biomarkers, which are well recognized for their sensitivity in MCI classification, based on the findings of Park et al [ 63 ].…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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“…The images utilized in this study were gathered from the ADNI database (adni.loni.usc.edu), established with the primary aim of supporting Alzheimer's disease (AD) research. Automated image preprocessing was conducted in alignment with recent advancements in the field [6], [7]. The key preprocessing steps included normalization, denoising, and artifact correction (addressing Eddy currents and bias fields), as well as skull stripping.…”

Section: A Materialsmentioning

confidence: 99%

A Comparative Study of PCA and LDA for Dimensionality Reduction in a 4-Way Classification Framework

Mabrouk,

Jazzar,

Sallemi

et al. 2024

Preprint

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Alzheimer’s disease (AD), recognized as the secondmost impactful neurological disorder and currently incurable, stands as the leading cause of dementia. An imperative research focus is efficiently diagnosing the stages of patients, distinguishing early or late Mild Cognitive Impairment and AD from those with normal cognitive function. Advancements in anatomical and diffusion-weighted imaging, coupled with machine learning techniques, have significantly progressed in this predictive domain. However, in real-world trials, datasets often contain numerous features, and the curse of dimensionality can introduce challenges such as increased computational complexity, overfitting, and diminished model interpretability. To address these issues, the present study explores the efficacy of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) as dimensionality reduction techniques. LDA, a supervised technique emphasizing class separability, surpasses PCA, particularly in selecting features that significantly contribute to discriminating between classes. The 3D-LDA features obtained were subsequently assessed across various machine learning algorithms, leading to the establishment of a 4-way classification framework that utilized the K-Nearest Neighbors model. The outcome of this evaluation yielded an impressive accuracy rate of 87% in predicting the four different classes.

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“…Diffusion and anatomical images are automatically preprocessed within the MRTRIX3 software package (http://www.mrtrix.org), the FreeSurfer software package (http://surfer.nmr.mgh.harvard.edu/), the Advanced Normalization Tools ( http://stnava.github.io/ANTs/) and the FMRIB Software Library (FSL) (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/). According to recent works in the field [5,14], multiple steps have been carried out:…”

Section: Image Pre-processingmentioning

confidence: 99%

“…The connectivity matrix is a description of the weighted graph [5]. In particular, each element W i,j of the connectome represents the normalized weight between two nodes, where 0≤ W i,j ≤ 1.…”

Section: Features Selectionmentioning

confidence: 99%

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A novel approach to perform linear discriminant analyses for a 4-way alzheimer’s disease diagnosis based on an integration of pearson’s correlation coefficients and empirical cumulative distribution function

Mabrouk,

Hamida,

Mabrouki

et al. 2024

Multimed Tools Appl

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The diagnosis of Alzheimer's disease (AD) in his prodromal stage is a major topic. About 50% of the well-known Mild Cognitive Impairment (MCI) cohort are estimated to develop AD, and the reasons for conversion or not are still unknown. An efficient diagnosis of the earlier (EMCI) and later (LMCI) cohorts among normal control subjects (CN) is still a big research challenge. Encouraged by the available data provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, we aim to develop a 4-way framework for

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Contribution of Brain Regions Asymmetry Scores Combined with Random Forest Classifier in the Diagnosis of Alzheimer’s Disease in His Earlier Stage

Cited by 8 publications

References 28 publications

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

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

A Comparative Study of PCA and LDA for Dimensionality Reduction in a 4-Way Classification Framework

A novel approach to perform linear discriminant analyses for a 4-way alzheimer’s disease diagnosis based on an integration of pearson’s correlation coefficients and empirical cumulative distribution function

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