scholarly journals White Matter Hyperintensity Load Drives Differential Grey Matter Changes in Mild Cognitive Impairment

2020 ◽  
Author(s):  
Ashwati Vipin ◽  
Benjamin Yi Xin Wong ◽  
Dilip Kumar ◽  
Audrey Low ◽  
Kok Pin Ng ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
White Matter ◽  
Executive Control ◽  
Grey Matter ◽  
White Matter Hyperintensities ◽  
White Matter Hyperintensity ◽  
Grey Matter Volume ◽  
Executive Control Network ◽  
Matter Volume

Abstract Background: Small-vessel cerebrovascular disease often represented as white matter hyperintensities on magnetic resonance imaging, is considered an important risk factor for progression to dementia. Grey matter volume alterations in Alzheimer’s disease-specific regions comprising the default mode network and executive control network are also key features of early Alzheimer’s disease. However, the relationship between increasing white matter hyperintensity load and grey matter volume needs further examination in the cognitively normal and mild cognitive impairment. Here, we examined the load-dependent influence of white matter hyperintensities on grey matter volume and cognition in the cognitively normal and mild cognitive impairment stages.Methods: Magnetic resonance imaging data from 93 mild cognitive impairment and 90 cognitively normal subjects were studied and white matter hyperintensity load was categorized into low, medium and high terciles. We examined how differing loads of white matter hyperintensities related to whole-brain voxel-wise and regional grey matter volume in the default mode network and executive control network. We further investigated how regional grey matter volume moderated the relationship between white matter hyperintensities and cognition at differing white matter hyperintensity loads.Results: We found differential load-dependent effects of white matter hyperintensity burden on voxel-wise and regional grey matter atrophy in only mild cognitive impairment subjects. At low load, white matter hyperintensity load was positively related to grey matter volume in the executive control network but at high load, white matter hyperintensity load was negatively related to grey matter volume across both the executive control and default mode networks and no relationship was observed at medium white matter hyperintensity load. Additionally, negative associations between white matter hyperintensities and domains of memory and executive function were moderated by regional grey matter volume. Conclusions: Our results demonstrate dynamic relationships between white matter hyperintensity load, grey matter volume and cognition in the mild cognitive impairment stage. Interventions to slow the progression of white matter hyperintensities, instituted when white matter hyperintensity load is low could potentially prevent further cognitive decline.

Regional White Matter Hyperintensity Influences Grey Matter Atrophy in Mild Cognitive Impairment

2018 ◽  
Vol 66 (2) ◽  
pp. 533-549 ◽  
Author(s):  
Ashwati Vipin ◽  
Heidi Jing Ling Foo ◽  
Joseph Kai Wei Lim ◽  
Russell Jude Chander ◽  
Ting Ting Yong ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
White Matter ◽  
Grey Matter ◽  
White Matter Hyperintensity ◽  
Grey Matter Atrophy

scholarly journals Identification of early mild cognitive impairment using multi-modal data and graph convolutional networks

2020 ◽  
Vol 21 (S6) ◽  
Author(s):  
Jin Liu ◽  
Guanxin Tan ◽  
Wei Lan ◽  
Jianxin Wang
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Path Length ◽  
Grey Matter ◽  
Fmri Data ◽  
Grey Matter Volume ◽  
Modal Data ◽  
Convolutional Networks ◽  
Matter Volume ◽  
Early Mild Cognitive Impairment

Abstract Background The identification of early mild cognitive impairment (EMCI), which is an early stage of Alzheimer’s disease (AD) and is associated with brain structural and functional changes, is still a challenging task. Recent studies show great promises for improving the performance of EMCI identification by combining multiple structural and functional features, such as grey matter volume and shortest path length. However, extracting which features and how to combine multiple features to improve the performance of EMCI identification have always been a challenging problem. To address this problem, in this study we propose a new EMCI identification framework using multi-modal data and graph convolutional networks (GCNs). Firstly, we extract grey matter volume and shortest path length of each brain region based on automated anatomical labeling (AAL) atlas as feature representation from T1w MRI and rs-fMRI data of each subject, respectively. Then, in order to obtain features that are more helpful in identifying EMCI, a common multi-task feature selection method is applied. Afterwards, we construct a non-fully labelled subject graph using imaging and non-imaging phenotypic measures of each subject. Finally, a GCN model is adopted to perform the EMCI identification task. Results Our proposed EMCI identification method is evaluated on 210 subjects, including 105 subjects with EMCI and 105 normal controls (NCs), with both T1w MRI and rs-fMRI data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results show that our proposed framework achieves an accuracy of 84.1% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.856 for EMCI/NC classification. In addition, by comparison, the accuracy and AUC values of our proposed framework are better than those of some existing methods in EMCI identification. Conclusion Our proposed EMCI identification framework is effective and promising for automatic diagnosis of EMCI in clinical practice.

scholarly journals Voxel Level Survival Analysis of Grey Matter Volume and Incident Mild Cognitive Impairment or Alzheimer’s Disease

2015 ◽  
Vol 46 (1) ◽  
pp. 167-178 ◽  
Author(s):  
Lubov E. Zeifman ◽  
William F. Eddy ◽  
Oscar L. Lopez ◽  
Lewis H. Kuller ◽  
Cyrus Raji ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Survival Analysis ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Grey Matter ◽  
Grey Matter Volume ◽  
Matter Volume

scholarly journals Predictions of Current and Future Episodic Memory Using Grey Matter Volume and Functional Connectome: A Longitudinal Study in Amnestic Mild Cognitive Impairment Patients

2020 ◽  
Author(s):  
Zan Wang ◽  
Hao Shu ◽  
Duan Liu ◽  
Fan Su ◽  
Chunming Xie ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Episodic Memory ◽  
Grey Matter ◽  
Verbal Learning ◽  
Amnestic Mild Cognitive Impairment ◽  
Grey Matter Volume ◽  
Default Mode ◽  
Matter Volume ◽  
Limbic Systems

Abstract Background: Amnestic mild cognitive impairment (aMCI) patients are considered an at-risk group for progression to Alzheimer’s dementia and accurate prediction of aMCI progression could facilitate the optimal decision-making for both clinicians and patients. Based on the baseline whole-brain grey-matter volume (GMV) and resting-state functional connectivity (FC), we used relevance vector regression to predict the baseline and longitudinal Rey’s Auditory Verbal Learning Test Delayed Recall (AVLT-DR) scores of individual aMCI patients.Methods: Fifty aMCI patients completed baseline and 3-year follow-up visits. All patients underwent comprehensive neuropsychological assessments and multimodal brain MRI scans.Results: We found that the GMV pattern predicted the baseline AVLT-DR score, while the pattern of FC predicted the longitudinal AVLT-DR score. In particular, GMV predicted the baseline AVLT-DR score with an accuracy of r = 0.54 (P < 0.001); the regions that contributed the most were within the default mode (e.g., the posterior cingulate gyrus, angular gyrus and middle temporal gyrus) and limbic systems (e.g., the hippocampus and parahippocampal gyrus). The FC predicted the longitudinal AVLT-DR score with an accuracy of r = 0.50 (P < 0.001), and the connections that contributed the most were the within- and between-system connectivity of the default mode and limbic systems. As a complement, we demonstrated that the GMV and FC patterns could also effectively predict the baseline and longitudinal composite episodic memory scores (calculated by averaging three well-known episodic memory test scores).Conclusions: Our results demonstrated the multimodal brain features in the individualized prediction of aMCI patients’ current and future episodic memory performance. These “neural fingerprints” have the potential to be biomarkers for aMCI patients and can help medical professionals optimize individual patient management and longitudinal evaluation.

scholarly journals Threshold of white matter hyperintensity volume determines negative and positive associations between grey matter volume and hypertension

2020 ◽  
Vol 16 (S5) ◽  
Author(s):  
Ashwati Vipin ◽  
Benjamin Wong ◽  
Dilip Kumar ◽  
Audrey Low ◽  
Kok Pin Ng ◽  
...  
Keyword(s):  
White Matter ◽  
Grey Matter ◽  
White Matter Hyperintensity ◽  
Grey Matter Volume ◽  
Matter Volume

Cognitive impairment in paediatric multiple sclerosis patients is not related to cortical lesions

2014 ◽  
Vol 21 (7) ◽  
pp. 956-959 ◽  
Author(s):  
Maria A Rocca ◽  
Ermelinda De Meo ◽  
Maria P Amato ◽  
Massimiliano Copetti ◽  
Lucia Moiola ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
White Matter ◽  
Grey Matter ◽  
Cognitively Impaired ◽  
Grey Matter Volume ◽  
Cortical Lesions ◽  
White Matter Volume ◽  
White Matter Damage ◽  
Matter Volume ◽  
Multiple Sclerosis Patients

We investigated the contribution of cortical lesions to cognitive impairment in 41 paediatric MS patients. Thirteen (32%) paediatric MS patients were considered as cognitively impaired. T2-hyperintense and T1-hypointense white matter lesion volumes did not differ between cognitively impaired and cognitively preserved MS patients. Cortical lesions number, cortical lesions volume and grey matter volume did not differ between cognitively impaired and cognitively preserved patients, whereas white matter volume was significantly lower in cognitively impaired versus cognitively preserved MS patients ( p=0.01). Contrary to adult MS, cortical lesions do not seem to contribute to cognitive impairment in paediatric MS patients, which is likely driven by white matter damage.

Impairment of Everyday Spatial Navigation Abilities in Mild Cognitive Impairment Is Weakly Associated with Reduced Grey Matter Volume in the Medial Part of the Entorhinal Cortex

2020 ◽  
Vol 78 (3) ◽  
pp. 1149-1159
Author(s):  
Asma Hallab ◽  
Catharina Lange ◽  
Ivayla Apostolova ◽  
Cansu Özden ◽  
Gabriel Gonzalez-Escamilla ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Entorhinal Cortex ◽  
Metabolic Activity ◽  
Grey Matter ◽  
Spatial Navigation ◽  
Grey Matter Volume ◽  
Medial Part ◽  
Matter Volume ◽  
Sum Score

Background: Research in rodents identified specific neuron populations encoding information for spatial navigation with particularly high density in the medial part of the entorhinal cortex (ERC), which may be homologous with Brodmann area 34 (BA34) in the human brain. Objective: The aim of this study was to test whether impaired spatial navigation frequently occurring in mild cognitive impairment (MCI) is specifically associated with neurodegeneration in BA34. Methods: The study included baseline data of MCI patients enrolled in the Alzheimer’s Disease Neuroimaging Initiative with high-resolution structural MRI, brain FDG PET, and complete visuospatial ability scores of the Everyday Cognition test (VS-ECog) within 30 days of PET. A standard mask of BA34 predefined in MNI space was mapped to individual native space to determine grey matter volume and metabolic activity in BA34 on MRI and on (partial volume corrected) FDG PET, respectively. The association of the VS-ECog sum score with grey matter volume and metabolic activity in BA34, APOE4 carrier status, age, education, and global cognition (ADAS-cog-13 score) was tested by linear regression. BA28, which constitutes the lateral part of the ERC, was used as control region. Results: The eligibility criteria led to inclusion of 379 MCI subjects. The VS-ECog sum score was negatively correlated with grey matter volume in BA34 (β= –0.229, p = 0.022) and age (β= –0.124, p = 0.036), and was positively correlated with ADAS-cog-13 (β= 0.175, p = 0.003). None of the other predictor variables contributed significantly. Conclusion: Impairment of spatial navigation in MCI is weakly associated with BA34 atrophy.

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