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

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.

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

Journal of Alzheimer s Disease ◽

10.3233/jad-210579 ◽

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.

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Grey-matter volume as a potential feature for the classification of Alzheimer’s disease and mild cognitive impairment: an exploratory study

Neuroscience Bulletin ◽

10.1007/s12264-013-1432-x ◽

2014 ◽

Vol 30 (3) ◽

pp. 477-489 ◽

Cited By ~ 22

Author(s):

Yane Guo ◽

Zengqiang Zhang ◽

Bo Zhou ◽

Pan Wang ◽

Hongxiang Yao ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Exploratory Study ◽

Grey Matter ◽

Grey Matter Volume ◽

Matter Volume

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Deterioration of abstract reasoning ability in mild cognitive impairment and Alzheimer’s disease: correlation with regional grey matter volume loss revealed by diffeomorphic anatomical registration through exponentiated lie algebra analysis

European Radiology ◽

10.1007/s00330-010-1939-8 ◽

2010 ◽

Vol 21 (2) ◽

pp. 419-425 ◽

Cited By ~ 4

Author(s):

Takashi Yoshiura ◽

Akio Hiwatashi ◽

Koji Yamashita ◽

Yasumasa Ohyagi ◽

Akira Monji ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Lie Algebra ◽

Grey Matter ◽

Grey Matter Volume ◽

Abstract Reasoning ◽

Reasoning Ability ◽

Volume Loss ◽

Matter Volume

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

BMC Bioinformatics ◽

10.1186/s12859-020-3437-6 ◽

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

Journal of Alzheimer s Disease ◽

10.3233/jad-150047 ◽

2015 ◽

Vol 46 (1) ◽

pp. 167-178 ◽

Cited By ~ 13

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

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Deficits in episodic memory retrieval reveal impaired default mode network connectivity in amnestic mild cognitive impairment

NeuroImage Clinical ◽

10.1016/j.nicl.2014.02.010 ◽

2014 ◽

Vol 4 ◽

pp. 473-480 ◽

Cited By ~ 38

Author(s):

Cameron J. Dunn ◽

Shantel L Duffy ◽

Ian B Hickie ◽

Jim Lagopoulos ◽

Simon J.G. Lewis ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Episodic Memory ◽

Default Mode Network ◽

Memory Retrieval ◽

Network Connectivity ◽

Amnestic Mild Cognitive Impairment ◽

Default Mode

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Hippocampal atrophy and verbal episodic memory performance in amnestic mild cognitive impairment and mild Alzheimer's disease: A preliminary study

Dementia & Neuropsychologia ◽

10.1590/s1980-57642009dn20100008 ◽

2008 ◽

Vol 2 (1) ◽

pp. 37-41 ◽

Cited By ~ 4

Author(s):

Nathalia Carollina Peruzza Marchiani ◽

Marcio Luiz Figueredo Balthazar ◽

Fernando Cendes ◽

Benito Pereira Damasceno

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Episodic Memory ◽

Memory Performance ◽

Verbal Learning ◽

Amnestic Mild Cognitive Impairment ◽

Significant Difference ◽

Verbal Episodic Memory

Abstract To evaluate hippocampal volume in patients with AD and aMCI, and correlate its atrophy with verbal episodic memory performance. Methods: We studied 42 individuals older than 50 years, including 14 with amnestic mild cognitive impairment (aMCI), 14 with mild Alzheimer's disease (AD) and 14 normal controls. All individuals were submitted to the Rey auditory verbal learning test (RAVLT) to evaluate episodic memory. They were also submitted to the forward (FDS) and backward digit span (BDS) subtest of WAIS-R to evaluate working memory and attention, and to the Mini Mental State Examination (MMSE). Hippocampal volumetric measurements were performed according to anatomic guidelines from a standard protocol using high-resolution T1-inversion recovery 3-mm coronal MRI slices. Hippocampal volumes (HV) were corrected for the variation in total intracranial volume. There was no significant difference between the three groups concerning age and education. Results: On RAVLT, there was a continuum between the three groups, with AD recalling less words, controls more, and aMCI subjects showing an intermediate performance on all sub-items. We found an asymmetry between HVs, with smaller mean left HV for all groups. ANOVA and post hoc Tukey's test for comparisons of HV showed a significant difference among groups, with difference between controls and both AD and aMCI, although there was no significant difference between AD and aMCI groups. Conclusions: There was a significant correlation between hippocampal volumes and scores on RAVLT, confirming that medial temporal structures are closely associated with memory performance in normal ageing as well as in aMCI and AD.

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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

Journal of Alzheimer s Disease ◽

10.3233/jad-200520 ◽

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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