Hippocampus-centred grey matter covariance networks predict the development and reversion of mild cognitive
Mingxi Dang1, Caishui Yang1,2, Kewei Chen3
1State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, Beijing, 100875, China.
Background:
Mild cognitive impairment (MCI) has been thought of as the transitional stage between normal ageing and Alzheimer's disease, involving substantial changes in brain grey matter structures. As most previous studies have focused on single regions (e.g. the hippocampus) and their changes during MCI development and reversion, the relationship between grey matter covariance among distributed brain regions and clinical development and reversion of MCI remains unclear.
Methods:
With samples from two independent studies (155 from the Beijing Aging Brain Rejuvenation Initiative and 286 from the Alzheimer's Disease Neuroimaging Initiative), grey matter covariance of default, frontoparietal, and hippocampal networks were identified by seed-based partial least square analyses, and random forest models were applied to predict the progression from normal cognition to MCI (N-t-M) and the reversion from MCI to normal cognition (M-t-N).
Results:
With varying degrees, the grey matter covariance in the three networks could predict N-t-M progression (AUC = 0.692-0.792) and M-t-N reversion (AUC = 0.701-0.809). Further analyses indicated that the hippocampus has emerged as an important region in reversion prediction within all three brain networks, and even though the hippocampus itself could predict the clinical reversion of M-t-N, the grey matter covariance showed higher prediction accuracy for early progression of N-t-M.
Conclusions:
Our findings are the first to report grey matter covariance changes in MCI development and reversion and highlight the necessity of including grey matter covariance changes along with hippocampal degeneration in the early detection of MCI and Alzheimer's disease.
Insights
Grey matter covariance in brain networks predicts progression to mild cognitive impairment (MCI) and reversion to normal cognition. The hippocampus is key for reversion, while covariance aids early MCI detection.
Area of Science:
- Neuroscience
- Medical Imaging
- Gerontology
Background:
- Mild cognitive impairment (MCI) is a transitional stage between normal aging and Alzheimer's disease.
- MCI involves significant changes in brain grey matter structures.
- Previous research focused on single brain regions, leaving the role of distributed grey matter covariance in MCI development and reversion unclear.
Purpose of the Study:
- To investigate the relationship between grey matter covariance in default, frontoparietal, and hippocampal networks and MCI development/reversion.
- To assess the predictive power of grey matter covariance for progression from normal cognition to MCI (N-t-M) and reversion from MCI to normal cognition (M-t-N).
Main Methods:
- Utilized data from two independent studies (Beijing Aging Brain Rejuvenation Initiative and Alzheimer's Disease Neuroimaging Initiative).
- Employed seed-based partial least square analyses to identify grey matter covariance in key brain networks.
- Applied random forest models to predict clinical progression and reversion.
Main Results:
- Grey matter covariance in the studied networks demonstrated predictive ability for both N-t-M progression (AUC = 0.692-0.792) and M-t-N reversion (AUC = 0.701-0.809).
- The hippocampus was identified as a crucial region for predicting reversion across all networks.
- Grey matter covariance showed higher predictive accuracy for early N-t-M progression compared to the hippocampus alone for M-t-N reversion.
Conclusions:
- This study is the first to report grey matter covariance changes associated with MCI development and reversion.
- Findings underscore the importance of considering grey matter covariance alongside hippocampal changes for early MCI and Alzheimer's disease detection.
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