Faculty Opinions recommendation of Joint assessment of structural, perfusion, and diffusion MRI in Alzheimer's disease and frontotemporal dementia.

2011 ◽  
Author(s):  
Brandy Matthews
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Frontotemporal Dementia ◽  
Diffusion Mri ◽  
And Diffusion ◽  
Joint Assessment

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.

Histochemical visualization and diffusion MRI at 7 Tesla in the TgCRND8 transgenic model of Alzheimer’s disease

2010 ◽  
Vol 215 (1) ◽  
pp. 29-36 ◽  
Author(s):  
Jonathan D. Thiessen ◽  
Kathryn A. C. Glazner ◽  
Solmaz Nafez ◽  
Angela E. Schellenberg ◽  
Richard Buist ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Diffusion Mri ◽  
7 Tesla ◽  
Transgenic Model ◽  
Model Of Alzheimer’S Disease ◽  
And Diffusion

scholarly journals White matter damage in frontotemporal dementia and Alzheimer's disease measured by diffusion MRI

Brain ◽  
2009 ◽  
Vol 132 (9) ◽  
pp. 2579-2592 ◽  
Author(s):  
Y. Zhang ◽  
N. Schuff ◽  
A.-T. Du ◽  
H. J. Rosen ◽  
J. H. Kramer ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
White Matter ◽  
Frontotemporal Dementia ◽  
Diffusion Mri ◽  
White Matter Damage

scholarly journals Differential Diagnosis of Alzheimer's Disease and Mild Cognitive Impairment Using the Structural and Diffusion MRI Features of the Brain

2021 ◽  
Author(s):  
Seyed Amir Zamanpour ◽  
Bahare Bigham ◽  
Mohamad-Hoseyn Sigari ◽  
Hoda Zare
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Diffusion Mri ◽  
Support Vector ◽  
Treatment And Prevention ◽  
Mri Features ◽  
The Brain ◽  
And Diffusion

Abstract Introduction: Accurate, fast, and reliable diagnosis of Alzheimer's Disease (AD) from Mild Cognitive Impairment (MCI) is crucial for prescribing proper treatment and prevention of disease progression. At first glance, structural and diffusion MRI images, are affected by neurodegenerative proceedings in AD and MCI. In this study, we are looking for the most effective features to detect and differentiate between healthy normal control (NC), AD, and MCI groups by non-invasive Magnetic Resonance Imaging (MRI) method and propose the automatic multi-class classification using the structural and diffusion MRI Features of the brain. Methods: The structural and diffusion MRI data were downloaded from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database on three groups including AD, MCI, and NC subjects. Four famous classification models of machine learning were used to discover the best classification as a diagnostic tool for separation of the NC, AD and MCI groups. Results: Taken together, our results from this study lead to classify three groups for differentiation between the NC group and patients with MCI and AD, with average accuracy factor 89.9% for Support Vector Machine (SVM) and 91.9% for Artificial Neural Network (ANN) using selected features. Conclusions: Top 9 regions repetitive of WM based on four types of features are the caudate nucleus, corpus callosum, hippocampus, para hippocampus, temporal gyrus, putamen nucleus, cingulate gyrus, the region of 36 and 3 Brodmann. Therefore, these regions could be considered for identifying, monitoring, and future drug trials that could target this brain region to AD and MCI Management.

scholarly journals Diagnosis and Prognosis of Alzheimer’s Disease Using Brain Morphometry and White Matter Connectomes

2018 ◽  
Author(s):  
Yun Wang ◽  
Chenxiao Xu ◽  
Ji-Hwan Park ◽  
Seonjoo Lee ◽  
Yaakov Stern ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
White Matter ◽  
Diffusion Mri ◽  
Structural Mri ◽  
List Type ◽  
Csf Biomarker ◽  
Multimodal Mri ◽  
And Diffusion

ABSTRACTAccurate, reliable prediction of risk for Alzheimer’s disease (AD) is essential for early, disease-modifying therapeutics. Multimodal MRI, such as structural and diffusion MRI, is likely to contain complementary information of neurodegenerative processes in AD. Here we tested the utility of the multimodal MRI (T1-weighted structure and diffusion MRI), combined with high-throughput brain phenotyping—morphometry and structural connectomics—and machine learning, as a diagnostic tool for AD. We used, firstly, a clinical cohort at a dementia clinic (National Health Insurance Service-Ilsan Hospital [NHIS-IH]; N=211; 110 AD, 64 mild cognitive impairment [MCI], and 37 cognitively normal with subjective memory complaints [SMC]) to test the diagnostic models; and, secondly, Alzheimer’s Disease Neuroimaging Initiative (ADNI)-2 to test the generalizability. Our machine learning models trained on the morphometric and connectome estimates (number of features=34,646) showed optimal classification accuracy (AD/SMC: 97% accuracy, MCI/SMC: 83% accuracy; AD/MCI: 97% accuracy) in NHIS-IH cohort, outperforming a benchmark model (FLAIR-based white matter hyperintensity volumes). In ADNI-2 data, the combined connectome and morphometry model showed similar or superior accuracies (AD/HC: 96%; MCI/HC: 70%; AD/MCI: 75% accuracy) compared with the CSF biomarker model (t-tau, p-tau, and Amyloid β, and ratios). In predicting MCI to AD progression in a smaller cohort of ADNI-2 (n=60), the morphometry model showed similar performance with 69% accuracy compared with CSF biomarker model with 70% accuracy. Our comparison of classifiers trained on structural MRI, diffusion MRI, FLAIR, and CSF biomarkers show the promising utility of the white matter structural connectomes in classifying AD and MCI in addition to the widely used structural MRI-based morphometry, when combined with machine learning.HighlightsWe showed the utility of multimodal MRI, combining morphometry and white matter connectomes, to classify the diagnosis of AD and MCI using machine learning.In predicting the progression from MCI to AD, the morphometry model showed the best performance.Two independent clinical datasets were used in this study: one for model building, the other for generalizability testing.

Distinct social-behavioral changes in frontotemporal dementia and early onset Alzheimer's disease

2014 ◽  
Author(s):  
Joseph P. Barsuglia ◽  
Michelle J. Mather ◽  
Hemali V. Panchal ◽  
Aditi Joshi ◽  
Elvira Jimenez ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Frontotemporal Dementia ◽  
Early Onset ◽  
Behavioral Changes ◽  
Early Onset Alzheimer’S Disease

scholarly journals Death Rate Due to COVID-19 in Alzheimer’s Disease and Frontotemporal Dementia

2020 ◽  
Vol 78 (2) ◽  
pp. 537-541
Author(s):  
Jordi A. Matias-Guiu ◽  
Vanesa Pytel ◽  
Jorge Matías-Guiu
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Frontotemporal Dementia ◽  
Death Rate ◽  
Case Series ◽  
Care Homes ◽  
Relevant Factor ◽  
Increased Risk ◽  
Care Facilities ◽  
Probability Of Death

We aimed to evaluate the frequency and mortality of COVID-19 in patients with Alzheimer’s disease (AD) and frontotemporal dementia (FTD). We conducted an observational case series. We enrolled 204 patients, 15.2% of whom were diagnosed with COVID-19, and 41.9% of patients with the infection died. Patients with AD were older than patients with FTD (80.36±8.77 versus 72.00±8.35 years old) and had a higher prevalence of arterial hypertension (55.8% versus 26.3%). COVID-19 occurred in 7.3% of patients living at home, but 72.0% of those living at care homes. Living in care facilities and diagnosis of AD were independently associated with a higher probability of death. We found that living in care homes is the most relevant factor for an increased risk of COVID-19 infection and death, with AD patients exhibiting a higher risk than those with FTD.

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