Identification of the Neural Circuit Underlying Episodic Memory Deficit in Amnestic Mild Cognitive Impairment via Machine Learning on Gray Matter Volume

2021 ◽  
pp. 1-6
Author(s):  
Zan Wang ◽  
Zhengsheng Zhang ◽  
Chunming Xie ◽  
Hao Shu ◽  
Duan Liu ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Episodic Memory ◽  
Gray Matter ◽  
Gray Matter Volume ◽  
Memory Deficit ◽  
Amnestic Mild Cognitive Impairment ◽  
Default Mode ◽  
Matter Volume ◽  
Limbic Systems

Based on whole-brain gray matter volume (GMV), we used relevance vector regression to predict the Rey’s Auditory Verbal Learning Test Delayed Recall (AVLT-DR) scores of individual amnestic mild cognitive impairment (aMCI) patient. The whole-brain GMV pattern could significantly predict the AVLT-DR scores (r = 0.54, p < 0.001). The most important GMV features mainly involved default-mode (e.g., posterior cingulate gyrus, angular gyrus, and middle temporal gyrus) and limbic systems (e.g., hippocampus and parahippocampal gyrus). Therefore, our results provide evidence supporting the idea that the episodic memory deficit in aMCI patients is associated with disruption of the default-mode and limbic systems.

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 Predictions of Current and Future Episodic Memory Using Gay 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 ◽  
Verbal Learning ◽  
Amnestic Mild Cognitive Impairment ◽  
Angular Gyrus ◽  
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.

P300 responses are associated with subcortical gray matter volume in amnestic mild cognitive impairment and normal aging

2016 ◽  
Vol 108 ◽  
pp. 100
Author(s):  
Derya Durusu Emek-Sava ◽  
Berrin Çavuşoğlu ◽  
Duygu Hünerli ◽  
Deniz Yerlikaya ◽  
Arife Gökçeoğlu ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Gray Matter ◽  
Gray Matter Volume ◽  
Normal Aging ◽  
Amnestic Mild Cognitive Impairment ◽  
Matter Volume

scholarly journals MRI Markers for Mild Cognitive Impairment: Comparisons between White Matter Integrity and Gray Matter Volume Measurements

PLoS ONE ◽  
2013 ◽  
Vol 8 (6) ◽  
pp. e66367 ◽  
Author(s):  
Yu Zhang ◽  
Norbert Schuff ◽  
Monica Camacho ◽  
Linda L. Chao ◽  
Thomas P. Fletcher ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
White Matter ◽  
Gray Matter ◽  
Gray Matter Volume ◽  
White Matter Integrity ◽  
Matter Volume ◽  
Volume Measurements
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