Fiber Connections between the Cerebral Cortex and the Corpus Callosum in Alzheimer's Disease: A Diffusion Tensor Imaging and Voxel-Based Morphometry Study

Background: Imaging biomarkers are increasingly used in Alzheimer’s disease (AD), and the identification of sex differences using neuroimaging may provide insight into disease heterogeneity, progression, and therapeutic targets. Objective: The purpose of this study was to investigate differences in grey matter (GM) volume and white matter (WM) microstructural disorganization between males and females with AD using voxel-based morphometry (VBM) and free-water-corrected diffusion tensor imaging (FW-DTI). Methods: Data were downloaded from the OASIS-3 database, including 158 healthy control (HC; 86 females) and 46 mild AD subjects (24 females). VBM and FW-DTI metrics (fractional anisotropy (FA), axial and radial diffusivities (AxD and RD, respectively), and FW index) were compared using effect size for the main effects of group, sex, and their interaction. Results: Significant group and sex differences were observed, with no significant interaction. Post-hoc comparisons showed that AD is associated with reduced GM volume, reduced FW-FA, and higher FW-RD/FW-index, consistent with neurodegeneration. Females in both groups exhibited higher GM volume than males, while FW-DTI metrics showed sex differences only in the AD group. Lower FW, lower FW-FA and higher FW-RD were observed in females relative to males in the AD group. Conclusion: The combination of VBM and DTI may reveal complementary sex-specific changes in GM and WM associated with AD and aging. Sex differences in GM volume were observed for both groups, while FW-DTI metrics only showed significant sex differences in the AD group, suggesting that WM tract disorganization may play a differential role in AD pathophysiology between females and males.

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Surface-based vertexwise analysis of morphometry and microstructural integrity for white matter tracts in diffusion tensor imaging: With application to the corpus callosum in Alzheimer's disease

Human Brain Mapping ◽

10.1002/hbm.23491 ◽

2017 ◽

Vol 38 (4) ◽

pp. 1875-1893 ◽

Cited By ~ 8

Author(s):

Xiaoying Tang ◽

Yuanyuan Qin ◽

Wenzhen Zhu ◽

Michael I. Miller

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Diffusion Tensor Imaging ◽

White Matter ◽

Corpus Callosum ◽

Diffusion Tensor ◽

White Matter Tracts ◽

Microstructural Integrity

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Machine Learning for the Classification of Alzheimer’s Disease and Its Prodromal Stage Using Brain Diffusion Tensor Imaging Data: A Systematic Review

Processes ◽

10.3390/pr8091071 ◽

2020 ◽

Vol 8 (9) ◽

pp. 1071

Author(s):

Lucia Billeci ◽

Asia Badolato ◽

Lorenzo Bachi ◽

Alessandro Tonacci

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Diffusion Tensor Imaging ◽

Magnetic Resonance ◽

Diffusion Tensor ◽

Classification Problem ◽

Computer Algorithms ◽

Imaging Data

Alzheimer’s disease is notoriously the most common cause of dementia in the elderly, affecting an increasing number of people. Although widespread, its causes and progression modalities are complex and still not fully understood. Through neuroimaging techniques, such as diffusion Magnetic Resonance (MR), more sophisticated and specific studies of the disease can be performed, offering a valuable tool for both its diagnosis and early detection. However, processing large quantities of medical images is not an easy task, and researchers have turned their attention towards machine learning, a set of computer algorithms that automatically adapt their output towards the intended goal. In this paper, a systematic review of recent machine learning applications on diffusion tensor imaging studies of Alzheimer’s disease is presented, highlighting the fundamental aspects of each work and reporting their performance score. A few examined studies also include mild cognitive impairment in the classification problem, while others combine diffusion data with other sources, like structural magnetic resonance imaging (MRI) (multimodal analysis). The findings of the retrieved works suggest a promising role for machine learning in evaluating effective classification features, like fractional anisotropy, and in possibly performing on different image modalities with higher accuracy.

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