Sex Differences in Alzheimer’s Disease Revealed by Free-Water Diffusion Tensor Imaging and Voxel-Based Morphometry

2021 ◽  
pp. 1-20
Author(s):  
Maurizio Bergamino ◽  
Elizabeth G. Keeling ◽  
Leslie C. Baxter ◽  
Nicholas J. Sisco ◽  
Ryan R. Walsh ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Sex Differences ◽  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Free Water ◽  
Imaging Biomarkers ◽  
Voxel Based Morphometry ◽  
Disease Heterogeneity ◽  
Significant Group

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.

scholarly journals Free-water diffusion tensor imaging improves the accuracy and sensitivity of white matter analysis in Alzheimer’s disease

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Maurizio Bergamino ◽  
Ryan R. Walsh ◽  
Ashley M. Stokes
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Diffusion Tensor ◽  
Free Water ◽  
Potential Impact ◽  
Magnetic Resonance Imaging Mri ◽  
Underlying Pathology ◽  
Diffusion Tensor Imaging Dti

AbstractMagnetic resonance imaging (MRI) based diffusion tensor imaging (DTI) can assess white matter (WM) integrity through several metrics, such as fractional anisotropy (FA), axial/radial diffusivities (AxD/RD), and mode of anisotropy (MA). Standard DTI is susceptible to the effects of extracellular free water (FW), which can be removed using an advanced free-water DTI (FW-DTI) model. The purpose of this study was to compare standard and FW-DTI metrics in the context of Alzheimer’s disease (AD). Data were obtained from the Open Access Series of Imaging Studies (OASIS-3) database and included both healthy controls (HC) and mild-to-moderate AD. With both standard and FW-DTI, decreased FA was found in AD, mainly in the corpus callosum and fornix, consistent with neurodegenerative mechanisms. Widespread higher AxD and RD were observed with standard DTI; however, the FW index, indicative of AD-associated neurodegeneration, was significantly elevated in these regions in AD, highlighting the potential impact of free water contributions on standard DTI in neurodegenerative pathologies. Using FW-DTI, improved consistency was observed in FA, AxD, and RD, and the complementary FW index was higher in the AD group as expected. With both standard and FW-DTI, higher values of MA coupled with higher values of FA in AD were found in the anterior thalamic radiation and cortico-spinal tract, most likely arising from a loss of crossing fibers. In conclusion, FW-DTI better reflects the underlying pathology of AD and improves the accuracy of DTI metrics related to WM integrity in Alzheimer’s disease.

scholarly journals Microstructural white matter alterations in preclinical Alzheimer’s disease detected using free water elimination diffusion tensor imaging

PLoS ONE ◽  
2017 ◽  
Vol 12 (3) ◽  
pp. e0173982 ◽  
Author(s):  
Andrew R. Hoy ◽  
Martina Ly ◽  
Cynthia M. Carlsson ◽  
Ozioma C. Okonkwo ◽  
Henrik Zetterberg ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Diffusion Tensor ◽  
Free Water ◽  
Preclinical Alzheimer’S Disease ◽  
Water Elimination

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

Processes ◽  
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.

Voxel-Based Diffusion Tensor Imaging of an APP/PS1 Mouse Model of Alzheimer’s Disease

2013 ◽  
Vol 48 (1) ◽  
pp. 78-83 ◽  
Author(s):  
Xiaogang Shu ◽  
Yuan-Yuan Qin ◽  
Shun Zhang ◽  
Jing-Jing Jiang ◽  
Yan Zhang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Diffusion Tensor Imaging ◽  
Mouse Model ◽  
Diffusion Tensor ◽  
Model Of Alzheimer’S Disease

scholarly journals Amyloid and tau imaging biomarkers explain cognitive decline from late middle-age

Brain ◽  
2019 ◽  
Vol 143 (1) ◽  
pp. 320-335 ◽  
Author(s):  
Tobey J Betthauser ◽  
Rebecca L Koscik ◽  
Erin M Jonaitis ◽  
Samantha L Allison ◽  
Karly A Cody ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Decline ◽  
Middle Age ◽  
Neurofibrillary Tangle ◽  
Imaging Biomarkers ◽  
Cognitive Tests ◽  
Preclinical Alzheimer’S Disease ◽  
Significant Group ◽  
Health Factors

Abstract This study investigated differences in retrospective cognitive trajectories between amyloid and tau PET biomarker stratified groups in initially cognitively unimpaired participants sampled from the Wisconsin Registry for Alzheimer’s Prevention. One hundred and sixty-seven initially unimpaired individuals (baseline age 59 ± 6 years; 115 females) were stratified by elevated amyloid-β and tau status based on 11C-Pittsburgh compound B (PiB) and 18F-MK-6240 PET imaging. Mixed effects models were used to determine if longitudinal cognitive trajectories based on a composite of cognitive tests including memory and executive function differed between biomarker groups. Secondary analyses investigated group differences for a variety of cross-sectional health and cognitive tests, and associations between 18F-MK-6240, 11C-PiB, and age. A significant group × age interaction was observed with post hoc comparisons indicating that the group with both elevated amyloid and tau pathophysiology were declining approximately three times faster in retrospective cognition compared to those with just one or no elevated biomarkers. This result was robust against various thresholds and medial temporal lobe regions defining elevated tau. Participants were relatively healthy and mostly did not differ between biomarker groups in health factors at the beginning or end of study, or most cognitive measures at study entry. Analyses investigating association between age, MK-6240 and PiB indicated weak associations between age and 18F-MK-6240 in tangle-associated regions, which were negligible after adjusting for 11C-PiB. Strong associations, particularly in entorhinal cortex, hippocampus and amygdala, were observed between 18F-MK-6240 and global 11C-PiB in regions associated with Braak neurofibrillary tangle stages I–VI. These results suggest that the combination of pathological amyloid and tau is detrimental to cognitive decline in preclinical Alzheimer’s disease during late middle-age. Within the Alzheimer’s disease continuum, middle-age health factors likely do not greatly influence preclinical cognitive decline. Future studies in a larger preclinical sample are needed to determine if and to what extent individual contributions of amyloid and tau affect cognitive decline. 18F-MK-6240 shows promise as a sensitive biomarker for detecting neurofibrillary tangles in preclinical Alzheimer’s disease.

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