scholarly journals Analysis of Grey Matter in Thalamus and Basal Ganglia Based on EEG α3/α2 Frequency Ratio Reveals Specific Changes in Subjects with Mild Cognitive Impairment

ASN NEURO ◽  
2012 ◽  
Vol 4 (7) ◽  
pp. AN20120058 ◽  
Author(s):  
Davide V Moretti ◽  
Donata PaternicoG ◽  
Giuliano Binetti ◽  
Orazio Zanetti ◽  
Giovanni B Frisoni
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Basal Ganglia ◽  
Frequency Ratio ◽  
Grey Matter

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 Predicting Future Cognitive Impairment in De Novo Parkinson's Disease Using Clinical Data and Structural MRI

2021 ◽  
Author(s):  
Nicola Smith ◽  
Owen A Williams ◽  
Lucia Ricciardi ◽  
Francesca Morgante ◽  
Thomas R Barrick ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Grey Matter ◽  
Temporal Cortex ◽  
Cognitive Status ◽  
Support Vector ◽  
Parkinson’S Disease Dementia ◽  
Lingual Gyrus

BACKGROUND Parkinson's disease is the second most common neurodegenerative condition and associated with increasing cognitive dysfunction as the disease progresses. However, subtle cognitive deficits can be detected at diagnosis in 42% of individuals, suggesting that damage may already be present. Our aim was to determine clinical and structural differences in those recently diagnosed with PD who later develop cognitive impairment, and whether these changes predict future cognitive decline. METHODS Clinical and imaging data was acquired from the Parkinson's Progression Markers Initiative for 318 individuals with a diagnosis of Parkinson's disease and baseline 3T T1-weighted MRI. The cohort was divided according to cognitive status over follow-up, with 9 individuals developing Parkinson's disease dementia, 102 developing mild cognitive impairment and 207 remaining cognitively unaffected. FINDINGS At baseline, those who went on to develop cognitive impairment (mild cognitive impairment or dementia) were older with more severe motor and non-motor symptoms (anosmia, rapid eye movement sleep behaviour disorder, depression). Grey matter loss was present in those destined for Parkinson's disease dementia in the precuneus, hippocampi, primary olfactory cortex, lingual gyrus, temporal cortex and cerebellum. Those who later developed mild cognitive impairment had an attenuated but similar pattern of grey matter loss in the temporal lobe, lingual gyrus and cerebellum. Using support vector machines with a feature selection step, future cognitive impairment could be predicted using 11 clinical variables (AUC = 0.81), structural imaging (AUC = 0.72) or a combination of these two modalities (AUC = 0.85). These models more accurately predicted those who developed dementia (subgroup sensitivity 100%). INTERPRETATION Significant abnormalities in cortical structure is present at least three years before dementia manifests in Parkinson's disease, with associated differences in clinical profiles. Combining this data provides a technique to accurately identify future cognitive impairment, providing a non-invasive way to stratify individuals early on.

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.

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