scholarly journals Quantitative Susceptibility Mapping of the Thalamus: Relationships with Thalamic Volume, Total Gray Matter Volume, and T2 Lesion Burden

2018 ◽  
Vol 39 (3) ◽  
pp. 467-472
Author(s):  
G.C. Chiang ◽  
J. Hu ◽  
E. Morris ◽  
Y. Wang ◽  
S.A. Gauthier
Keyword(s):  
Gray Matter ◽  
Gray Matter Volume ◽  
Susceptibility Mapping ◽  
Quantitative Susceptibility Mapping ◽  
Matter Volume

Evaluation and Prediction of Early Alzheimer’s Disease Using a Machine Learning-based Optimized Combination-Feature Set on Gray Matter Volume and Quantitative Susceptibility Mapping

2020 ◽  
Vol 17 (5) ◽  
pp. 428-437
Author(s):  
Hyug-Gi Kim ◽  
Soonchan Park ◽  
Hak Y. Rhee ◽  
Kyung M. Lee ◽  
Chang-Woo Ryu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Entorhinal Cortex ◽  
Clinical Data ◽  
Gray Matter ◽  
Gray Matter Volume ◽  
Posterior Cingulate Cortex ◽  
Group Classification ◽  
Svm Classifier ◽  
Quantitative Susceptibility Mapping ◽  
Matter Volume

Background: Because Alzheimer’s Disease (AD) has very complicated pattern changes, it is difficult to evaluate it with a specific factor. Recently, novel machine learning methods have been applied to solve limitations. Objective: The objective of this study was to investigate the approach of classification and prediction methods using the Machine Learning (ML)-based Optimized Combination-Feature (OCF) set on Gray Matter Volume (GMV) and Quantitative Susceptibility Mapping (QSM) in the subjects of Cognitive Normal (CN) elderly, Amnestic Mild Cognitive Impairment (aMCI), and mild and moderate AD. Materials and Methods: 57 subjects were included: 19 CN, 19 aMCI, and 19 AD with GMV and QSM. Regions-of-Interest (ROIs) were defined at the well-known regions for rich iron contents and amyloid accumulation areas in the AD brain. To differentiate the three subject groups, the Support Vector Machine (SVM) with the three different kernels and with the OCF set was conducted with GMV and QSM values. To predict the aMCI stage, regression-based ML models were performed with the OCF set. The result of prediction was compared with the accuracy of clinical data. Results: In the group classification between CN and aMCI, the highest accuracy was shown using the combination of GMVs (hippocampus and entorhinal cortex) and QSMs (hippocampus and pulvinar) data using the 2nd SVM classifier (AUC = 0.94). In the group classification between aMCI and AD, the highest accuracy was shown using the combination of GMVs (amygdala, entorhinal cortex, and posterior cingulate cortex) and QSMs (hippocampus and pulvinar) data using the 2nd SVM classifier (AUC = 0.93). In the group classification between CN and AD, the highest accuracy was shown using the combination of GMVs (amygdala, entorhinal cortex, and posterior cingulate cortex) and QSMs (hippocampus and pulvinar) data using the 2nd SVM classifier (AUC = 0.99). To predict aMCI from CN, the exponential Gaussian process regression model with the OCF set using GMV and QSM data was shown the most similar result (RMSE = 0.371) to clinical data (RMSE = 0.319). Conclusion: The proposed OCF based ML approach with GMV and QSM was shown the effective performance of the subject group classification and prediction for aMCI stage. Therefore, it can be used as personalized analysis or diagnostic aid program for diagnosis.

Gray matter volume reduction reflects chronic pain in trigeminal neuralgia

2012 ◽  
Vol 43 (01) ◽  
Author(s):  
M Obermann ◽  
R Rodriguez-Raecke ◽  
S Nägel ◽  
D Holle ◽  
N Theysohn ◽  
...  
Keyword(s):  
Chronic Pain ◽  
Trigeminal Neuralgia ◽  
Gray Matter ◽  
Gray Matter Volume ◽  
Volume Reduction ◽  
Matter Volume

scholarly journals Topographic patterns of white matter hyperintensities are associated with multimodal neuroimaging biomarkers of Alzheimer’s disease

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Malo Gaubert ◽  
Catharina Lange ◽  
Antoine Garnier-Crussard ◽  
Theresa Köbe ◽  
Salma Bougacha ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
White Matter ◽  
Glucose Metabolism ◽  
Corpus Callosum ◽  
Gray Matter ◽  
Fdg Pet ◽  
White Matter Hyperintensities ◽  
Gray Matter Volume ◽  
Matter Volume

Abstract Background White matter hyperintensities (WMH) are frequently found in Alzheimer’s disease (AD). Commonly considered as a marker of cerebrovascular disease, regional WMH may be related to pathological hallmarks of AD, including beta-amyloid (Aβ) plaques and neurodegeneration. The aim of this study was to examine the regional distribution of WMH associated with Aβ burden, glucose hypometabolism, and gray matter volume reduction. Methods In a total of 155 participants (IMAP+ cohort) across the cognitive continuum from normal cognition to AD dementia, FLAIR MRI, AV45-PET, FDG-PET, and T1 MRI were acquired. WMH were automatically segmented from FLAIR images. Mean levels of neocortical Aβ deposition (AV45-PET), temporo-parietal glucose metabolism (FDG-PET), and medial-temporal gray matter volume (GMV) were extracted from processed images using established AD meta-signature templates. Associations between AD brain biomarkers and WMH, as assessed in region-of-interest and voxel-wise, were examined, adjusting for age, sex, education, and systolic blood pressure. Results There were no significant associations between global Aβ burden and region-specific WMH. Voxel-wise WMH in the splenium of the corpus callosum correlated with greater Aβ deposition at a more liberal threshold. Region- and voxel-based WMH in the posterior corpus callosum, along with parietal, occipital, and frontal areas, were associated with lower temporo-parietal glucose metabolism. Similarly, lower medial-temporal GMV correlated with WMH in the posterior corpus callosum in addition to parietal, occipital, and fontal areas. Conclusions This study demonstrates that local white matter damage is correlated with multimodal brain biomarkers of AD. Our results highlight modality-specific topographic patterns of WMH, which converged in the posterior white matter. Overall, these cross-sectional findings corroborate associations of regional WMH with AD-typical Aß deposition and neurodegeneration.

scholarly journals Voxel-based correlation of 18F-THK5351 accumulation and gray matter volume in the brain of cognitively normal older adults

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Yoko Shigemoto ◽  
Daichi Sone ◽  
Miho Ota ◽  
Norihide Maikusa ◽  
Masayo Ogawa ◽  
...  
Keyword(s):  
Older Adults ◽  
Gray Matter ◽  
Gray Matter Volume ◽  
Cognitively Normal ◽  
Matter Volume ◽  
The Brain

CHARACTERIZATION OF COGNITIVE PERFORMANCE, GRAY MATTER VOLUME AND WHITE MATTER MICROSTRUCTURE IN COGNITIVELY UNIMPAIRED ADULTS WITH INSOMNIA SYMPTOMS

2019 ◽  
Vol 15 (7) ◽  
pp. P207-P209
Author(s):  
Oriol Grau-Rivera ◽  
Grégory Operto ◽  
Carles Falcon ◽  
Raffaele Cacciaglia ◽  
Gonzalo Sánchez-Benavides ◽  
...  
Keyword(s):  
White Matter ◽  
Cognitive Performance ◽  
Gray Matter ◽  
Gray Matter Volume ◽  
Matter Volume ◽  
White Matter Microstructure ◽  
Insomnia Symptoms

scholarly journals Housing quality and behavior affect brain health and anxiety in healthy Japanese adults

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Juan Cesar D. Pineda ◽  
Keisuke Kokubun ◽  
Toshiharu Ikaga ◽  
Yoshinori Yamakawa
Keyword(s):  
Sedentary Behavior ◽  
Fractional Anisotropy ◽  
Gray Matter ◽  
Gray Matter Volume ◽  
Housing Quality ◽  
Valuable Insight ◽  
Brain Health ◽  
Matter Volume ◽  
Japanese Adults ◽  
The Relationship

AbstractCountless studies in animals have shown how housing environments and behaviors can significantly affect anxiety and brain health, giving valuable insight as to whether this is applicable in the human context. The relationship between housing, behavior, brain health, and mental wellbeing in humans remains poorly understood. We therefore explored the interaction of housing quality, weekend/holiday sedentary behavior, brain structure, and anxiety in healthy Japanese adults. Whole-brain structural magnetic resonance imaging (MRI) methods based on gray matter volume and fractional anisotropy were used as markers for brain health. Correlation tests were conducted, and then adjusted for multiple comparisons using the False Discovery Rate method. Housing quality and weekend/holiday sedentary behavior were associated with fractional anisotropy, but not with gray matter volume. Fractional anisotropy showed significant associations with anxiety. Lastly, both weekend/holiday sedentary behavior and housing quality were indirectly associated with anxiety through fractional anisotropy. These results add to the limited evidence surrounding the relationship among housing, behavior, and the brain. Furthermore, these results show that behavior and housing qualities can have an indirect impact on anxiety through neurobiological markers such as fractional anisotropy.

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