SU-E-I-106: Differential Diagnosis of Mild Cognitive Impairment and Alzheimerˈs Disease with VOI Based Diffusion Tensor Analysis

Objective: Application of diffusion tensor imaging (DTI) to explore the changes of FA value in patients with Parkinson's disease (PD) with mild cognitive impairment. Methods: 27 patients with PD were divided into PD with mild cognitive impairment (PD-MCI) group (n = 7) and PD group (n = 20). The original images were processed using voxel-based analysis (VBA) and tract-based spatial statistics (TBSS). Results: The average age of pd-mci group was longer than that of PD group, and the course of disease was longer than that of PD group. Compared with PD group, the voxel based analysis-fractional anisotropy (VBA-FA) values of PD-MCI group decreased in the following areas: bilateral frontal lobe, bilateral temporal lobe, bilateral parietal lobe, bilateral subthalamic nucleus, corpus callosum, and gyrus cingula. Tract-based spatial statistics-fractional anisotropy (TBSS-FA) values in PD-MCI group decreased in bilateral corticospinal tract, anterior cingulum, posterior cingulum, fornix tract, bilateral superior thalamic radiation, corpus callosum(genu, body and splenium), bilateral uncinate fasciculus, bilateral inferior longitudinal fasciculus, bilateral superior longitudinal fasciculus, bilateral superior fronto-occipital fasciculus, bilateral inferior fronto-occipital fasciculus, and bilateral parietal-occipital tracts. The mean age of onset in the PD-MCI group was greater than that in the PD group, and the disease course was longer than that in the PD group. Conclusion: DTI-based VBA and TBSS post-processing methods can detect abnormalities in multiple brain areas and white matter fiber tracts in PD-MCI patients. Impairment of multiple cerebral cortex and white matter fiber pathways may be an important causes of cognitive dysfunction in PD-MCI.

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Olfactory Function and Diffusion Tensor Imaging as Markers of Mild Cognitive Impairment in Early Stages of Parkinson's Disease

Clinical EEG and Neuroscience ◽

10.1177/15500594211058263 ◽

2021 ◽

pp. 155005942110582

Author(s):

Sophie A. Stewart ◽

Laura Pimer ◽

John D. Fisk ◽

Benjamin Rusak ◽

Ron A. Leslie ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Cognitive Impairment ◽

Diffusion Tensor Imaging ◽

Mild Cognitive Impairment ◽

White Matter ◽

Neurodegenerative Disorder ◽

Diffusion Tensor ◽

Early Stage ◽

White Matter Integrity

Parkinson's disease (PD) is a neurodegenerative disorder that is typified by motor signs and symptoms but can also lead to significant cognitive impairment and dementia Parkinson's Disease Dementia (PDD). While dementia is considered a nonmotor feature of PD that typically occurs later, individuals with PD may experience mild cognitive impairment (PD-MCI) earlier in the disease course. Olfactory deficit (OD) is considered another nonmotor symptom of PD and often presents even before the motor signs and diagnosis of PD. We examined potential links among cognitive impairment, olfactory functioning, and white matter integrity of olfactory brain regions in persons with early-stage PD. Cognitive tests were used to established groups with PD-MCI and with normal cognition (PD-NC). Olfactory functioning was examined using the University of Pennsylvania Smell Identification Test (UPSIT) while the white matter integrity of the anterior olfactory structures (AOS) was examined using magnetic resonance imaging (MRI) diffusion tensor imaging (DTI) analysis. Those with PD-MCI demonstrated poorer olfactory functioning and abnormalities based on all DTI parameters in the AOS, relative to PD-NC individuals. OD and microstructural changes in the AOS of individuals with PD may serve as additional biological markers of PD-MCI.

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

Dementia and Geriatric Cognitive Disorders ◽

10.1159/000518102 ◽

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

Introduction: 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. Methods: 3T MRI measures of fractional anisotropy (FA) were examined among 1,636 participants of the ongoing community-based Health & Aging Brain among Latino Elders (HABLE) community-based study (Mexican American n = 851; non-Hispanic white n = 785). Results: 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. Conclusion: 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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Diffusion Tensor Imaging Surpasses Cerebrospinal Fluid as Predictor of Cognitive Decline and Medial Temporal Lobe Atrophy in Subjective Cognitive Impairment and Mild Cognitive Impairment

Journal of Alzheimer s Disease ◽

10.3233/jad-2012-121603 ◽

2013 ◽

Vol 33 (3) ◽

pp. 723-736 ◽

Cited By ~ 63

Author(s):

Per Selnes ◽

Dag Aarsland ◽

Atle Bjørnerud ◽

Leif Gjerstad ◽

Anders Wallin ◽

...

Keyword(s):

Cerebrospinal Fluid ◽

Cognitive Impairment ◽

Diffusion Tensor Imaging ◽

Mild Cognitive Impairment ◽

Cognitive Decline ◽

Medial Temporal Lobe ◽

Diffusion Tensor ◽

Medial Temporal Lobe Atrophy ◽

Subjective Cognitive Impairment ◽

Lobe Atrophy

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P2-265: Comparison of Diagnostic Accuracy of Volumetry or Diffusion Tensor Imaging in Mild Cognitive Impairment

Alzheimer s & Dementia ◽

10.1016/j.jalz.2016.06.1525 ◽

2016 ◽

Vol 12 ◽

pp. P728-P729 ◽

Cited By ~ 2

Author(s):

Eun Ye Lim

Keyword(s):

Cognitive Impairment ◽

Diffusion Tensor Imaging ◽

Mild Cognitive Impairment ◽

Diagnostic Accuracy ◽

Diffusion Tensor

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Group-level progressive alterations in brain connectivity patterns revealed by diffusion-tensor brain networks across severity stages in Alzheimer’s disease

10.1101/105270 ◽

2017 ◽

Author(s):

J. Rasero ◽

C. Alonso-Montes ◽

I. Diez ◽

L. Olabarrieta-Landa ◽

L. Remaki ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Disease Severity ◽

Diffusion Tensor ◽

Brain Networks ◽

Stage Ii ◽

Group Level ◽

The Brain

AbstractAlzheimer’s disease (AD) is a chronically progressive neurodegenerative disease highly correlated to aging. Whether AD originates by targeting a localized brain area and propagates to the rest of the brain across disease-severity progression is a question with an unknown answer. Here, we aim to provide an answer to this question at the group-level by looking at differences in diffusion-tensor brain networks. In particular, making use of data from Alzheimer's Disease Neuroimaging Initiative (ADNI), four different groups were defined (all of them matched by age, sex and education level): G1 (N1=36, healthy control subjects, Control), G2 (N2=36, early mild cognitive impairment, EMCI), G3 (N3=36, late mild cognitive impairment, LMCI) and G4 (N4=36, AD). Diffusion-tensor brain networks were compared across three disease stages: stage I 3(Control vs EMCI), stage II (Control vs LMCI) and stage III (Control vs AD). The group comparison was performed using the multivariate distance matrix regression analysis, a technique that was born in genomics and was recently proposed to handle brain functional networks, but here applied to diffusion-tensor data. The results were three-fold: First, no significant differences were found in stage I. Second, significant differences were found in stage II in the connectivity pattern of a subnetwork strongly associated to memory function (including part of the hippocampus, amygdala, entorhinal cortex, fusiform gyrus, inferior and middle temporal gyrus, parahippocampal gyrus and temporal pole). Third, a widespread disconnection across the entire AD brain was found in stage III, affecting more strongly the same memory subnetwork appearing in stage II, plus the other new subnetworks,including the default mode network, medial visual network, frontoparietal regions and striatum. Our results are consistent with a scenario where progressive alterations of connectivity arise as the disease severity increases and provide the brain areas possibly involved in such a degenerative process. Further studies applying the same strategy to longitudinal data are needed to fully confirm this scenario.

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