Ranking diffusion tensor measures of brain aging and Alzheimer’s disease

2018 ◽  
Author(s):  
Bret Borowski ◽  
Clifford R. Jack ◽  
Michael W. Weiner ◽  
Paul M. Thompson ◽  
Artemis Zavaliangos-Petropulu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Aging ◽  
Diffusion Tensor

Longitudinal Changes in Individual BrainAGE in Healthy Aging, Mild Cognitive Impairment, and Alzheimer’s Disease

GeroPsych ◽  
2012 ◽  
Vol 25 (4) ◽  
pp. 235-245 ◽  
Author(s):  
Katja Franke ◽  
Christian Gaser
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Healthy Aging ◽  
Brain Aging ◽  
Elderly Subjects ◽  
Additional Increase ◽  
Longitudinal Changes ◽  
Novel Method ◽  
The Brain

We recently proposed a novel method that aggregates the multidimensional aging pattern across the brain to a single value. This method proved to provide stable and reliable estimates of brain aging – even across different scanners. While investigating longitudinal changes in BrainAGE in about 400 elderly subjects, we discovered that patients with Alzheimer’s disease and subjects who had converted to AD within 3 years showed accelerated brain atrophy by +6 years at baseline. An additional increase in BrainAGE accumulated to a score of about +9 years during follow-up. Accelerated brain aging was related to prospective cognitive decline and disease severity. In conclusion, the BrainAGE framework indicates discrepancies in brain aging and could thus serve as an indicator for cognitive functioning in the future.

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.

scholarly journals Novel Insights on Systemic and Brain Aging, Stroke, Amyotrophic Lateral Sclerosis, and Alzheimer’s Disease

2019 ◽  
Vol 10 (2) ◽  
pp. 470 ◽  
Author(s):  
Ashok K. Shetty ◽  
Raghavendra Upadhya ◽  
Leelavathi N. Madhu ◽  
Maheedhar Kodali
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Amyotrophic Lateral Sclerosis ◽  
Brain Aging ◽  
Lateral Sclerosis

scholarly journals Joint Assessment of Structural, Perfusion, and Diffusion MRI in Alzheimer's Disease and Frontotemporal Dementia

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Yu Zhang ◽  
Norbert Schuff ◽  
Christopher Ching ◽  
Duygu Tosun ◽  
Wang Zhan ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Frontotemporal Dementia ◽  
Diffusion Tensor ◽  
Healthy Control ◽  
Multimodal Mri ◽  
Imaging Markers ◽  
And Diffusion ◽  
Joint Assessment ◽  
Diffusion Tensor Imaging Dti

Most MRI studies of Alzheimer's disease (AD) and frontotemporal dementia (FTD) have assessed structural, perfusion and diffusion abnormalities separately while ignoring the relationships across imaging modalities. This paper aimed to assess brain gray (GM) and white matter (WM) abnormalities jointly to elucidate differences in abnormal MRI patterns between the diseases. Twenty AD, 20 FTD patients, and 21 healthy control subjects were imaged using a 4 Tesla MRI. GM loss and GM hypoperfusion were measured using high-resolution T1 and arterial spin labeling MRI (ASL-MRI). WM degradation was measured with diffusion tensor imaging (DTI). Using a new analytical approach, the study found greater WM degenerations in FTD than AD at mild abnormality levels. Furthermore, the GM loss and WM degeneration exceeded the reduced perfusion in FTD whereas, in AD, structural and functional damages were similar. Joint assessments of multimodal MRI have potential value to provide new imaging markers for improved differential diagnoses between FTD and AD.

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

Using Fractional Anisotropy Imaging to Detect Mild Cognitive Impairment and Alzheimer’s Disease among Mexican Americans and Non-Hispanic Whites: A HABLE Study

2021 ◽  
pp. 1-8
Author(s):  
James R. Hall ◽  
Leigh A. Johnson ◽  
Fan Zhang ◽  
Melissa Petersen ◽  
Arthur W. Toga ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Fractional Anisotropy ◽  
Mexican Americans ◽  
Diffusion Tensor ◽  
Characteristic Curve ◽  
Aging Brain ◽  
Community Based

<b><i>Introduction:</i></b> Alzheimer’s disease (AD) is the most frequently occurring neurodegenerative disease; however, little work has been conducted examining biomarkers of AD among Mexican Americans. Here, we examined diffusion tensor MRI marker profiles for detecting mild cognitive impairment (MCI) and dementia in a multi-ethnic cohort. <b><i>Methods:</i></b> 3T MRI measures of fractional anisotropy (FA) were examined among 1,636 participants of the ongoing community-based Health &amp; Aging Brain among Latino Elders (HABLE) community-based study (Mexican American <i>n</i> = 851; non-Hispanic white <i>n</i> = 785). <b><i>Results:</i></b> The FA profile was highly accurate in detecting both MCI (area under the receiver operating characteristic curve [AUC] = 0.99) and dementia (AUC = 0.98). However, the FA profile varied significantly not only between diagnostic groups but also between Mexican Americans and non-Hispanic whites. <b><i>Conclusion:</i></b> Findings suggest that diffusion tensor imaging markers may have a role in the neurodiagnostic process for detecting MCI and dementia among diverse populations.

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