Diffusion tensor imaging (DTI) Analysis Based on Tract-based spatial statistics (TBSS) and Classification Using Multi-Metric in Alzheimer's Disease

Abstract

Background: Alzheimer’s disease (AD) is a brain disorder characterized by atrophy of cerebral cortex and neurofibrillary tangles. Accurate identification of individuals at high risk of developing AD is key to early intervention. Combining neuroimaging markers derived from diffusion tensor images with machine learning techniques, unique anatomical patterns can be identified and further distinguished between AD and healthy control (HC). Methods: In this study, 37 AD patients (ADs) and 36 healthy controls (HCs) from the Alzheimer’s Disease Neuroimaging Initiative were applied to tract-based spatial statistics (TBSS) analysis and multi-metric classification research. Results: The TBSS results showed that the corona radiata, corpus callosum and superior longitudinal fasciculus were the white matter fiber tracts which mainly suffered the severe damage in ADs. Using support vector machine recursive feature elimination (SVM-RFE) method, the classification performance received a decent improvement. In addition, the integration of fractional anisotropy (FA) + mean diffusivity (MD) + radial diffusivity (RD) into multi-metric could effectively separate ADs from HCs. The rank of significance of diffusion metrics was FA > axial diffusivity (DA) > MD > RD in our research. Conclusions: Our findings suggested that the TBSS and machine learning method could play a guidance role on clinical diagnosis.

Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing

“…‣ AD (e.g., Nir et al, 2015 – multicenter (FSL, TBSS); Douaud et al, 2011 ; Bourbon-Teles et al, 2023 ; Zhang and Zhan, 2023 – monocenter (TBSS); Chen Y. et al, 2023 – review; Takahashi et al, 2024 – NODDI).…”

Sparse Deep Neural Network for Encoding and Decoding the Structural Connectome

“…However, there are similar approaches reported in the literature, which we can compare to gain further insights into our proposed approach. Zhang and Zhan [18] , utilized DTI data from 37 AD patients and 36 healthy controls to identify white matter fiber tracts that are damaged in AD employing SVM-RFE. 78.8% accuracy was shown using FA+DA with 75 features selected by RFE.…”

“…al. [18], utilized DTI data from 37 AD patients and 36 healthy controls to identify white matter fiber tracts that are damaged in AD employing SVM-RFE. 78.8% accuracy was shown using FA+DA with 75 features selected by RFE.…”

“…Several recent approaches have been proposed to assess AD and PD. For example, support vector machine recursive feature elimination (SVM-RFE) [18] , CT-GAN [19] , SVM-RBF [20] , SVM [21] , [22] CNN [23] , 3D-CNN [23] . These approaches achieve state-of-the-art classification accuracy on similar datasets.…”