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Multimodal imaging in mild cognitive impairment: Metabolism, morphometry and diffusion of the temporal-parietal
K B Walhovd1, A M Fjell, I Amlien
1Center for the Study of Human Cognition, Department of Psychology, University of Oslo, Norway. k.b.walhovd@psykologi.uio.no
Abstract:
This study compared sensitivity of FDG-PET, MR morphometry, and diffusion tensor imaging (DTI) derived fractional anisotropy (FA) measures to diagnosis and memory function in mild cognitive impairment (MCI). Patients (n=44) and normal controls (NC, n=22) underwent FDG-PET and MRI scanning yielding measures of metabolism, morphometry and FA in nine temporal and parietal areas affected by Alzheimer's disease and involved in the episodic memory network. Patients also underwent memory testing (RAVLT). Logistic regression analysis yielded 100% diagnostic accuracy when all methods and ROIs were combined, but none of the variables then served as unique predictors. Within separate ROIs, diagnostic accuracy for the methods combined ranged from 65.6% (parahippocampal gyrus) to 73.4 (inferior parietal cortex). Morphometry predicted diagnostic group for most ROIs. PET and FA did not uniquely predict group, but a trend was seen for the precuneus metabolism. For the MCI group, stepwise regression analyses predicting memory scores were performed with the same methods and ROIs. Hippocampal volume and FA of the retrosplenial WM predicted learning, and hippocampal metabolism and parahippocampal cortical thickness predicted 5 minute recall. No variable predicted 30 minute recall independently of learning. In conclusion, higher diagnostic accuracy was achieved when multiple methods and ROIs were combined, but morphometry showed superior diagnostic sensitivity. Metabolism, morphometry and FA all uniquely explained memory performance, making a multi-modal approach superior. Memory variation in MCI is likely related to conversion risk, and the results indicate potential for improved predictive power by the use of multimodal imaging.
Insights
Combining multiple imaging techniques like FDG-PET, MR morphometry, and diffusion tensor imaging (DTI) improves diagnosis and memory function prediction in mild cognitive impairment (MCI). Morphometry alone showed superior diagnostic sensitivity for MCI.
Area of Science:
- Neuroimaging
- Cognitive Neuroscience
- Alzheimer's Disease Research
Background:
- Mild cognitive impairment (MCI) is a transitional stage between normal aging and Alzheimer's disease.
- Early detection and prediction of memory decline in MCI are crucial for timely intervention.
- Current diagnostic tools have limitations in sensitively detecting early pathological changes.
Purpose of the Study:
- To compare the diagnostic sensitivity of FDG-PET, MR morphometry, and DTI-derived fractional anisotropy (FA) in mild cognitive impairment (MCI).
- To assess the predictive power of these imaging modalities for memory function in MCI patients.
- To evaluate the utility of a multimodal imaging approach for MCI diagnosis and prognosis.
Main Methods:
- 44 MCI patients and 22 normal controls (NC) underwent FDG-PET and MRI scans.
- Measures included metabolism, morphometry, and FA in nine temporal and parietal regions.
- Memory function was assessed using the Rey Auditory Verbal Learning Test (RAVLT).
Main Results:
- Combined multimodal imaging achieved 100% diagnostic accuracy, though individual predictors were not unique.
- MR morphometry demonstrated superior diagnostic sensitivity across most regions of interest (ROIs).
- Metabolism, morphometry, and FA each independently predicted memory performance in MCI patients.
Conclusions:
- Multimodal neuroimaging approaches offer superior diagnostic and predictive power for MCI compared to single modalities.
- MR morphometry shows significant potential for identifying individuals with MCI.
- These findings suggest that combining imaging techniques can enhance the understanding of memory decline and conversion risk in MCI.
