Dataset Information

Morphological Biomarker Differentiating MCI Converters from Nonconverters: Longitudinal Evidence Based on Hemispheric Asymmetry.

ABSTRACT: Identifying subjects with mild cognitive impairment (MCI) who may probably progress to Alzheimer's disease (AD) is important for better understanding the disease mechanisms and facilitating early treatments. In addition to the direct volumetric and thickness measurement based on high-resolution magnetic resonance imaging (MRI), hemispheric asymmetry could be a potential index to detect morphological variations in MCI patients with a high risk of conversion to AD. The present study collected a set of longitudinal MRI data from 53 MCI converters and nonconverters and investigated the asymmetry differences between groups. Asymmetry variation was observed in the medial temporal lobe, especially in the entorhinal cortex, between converters and nonconverters 3 years before the former developed AD. The proposed asymmetry analysis was observed to be sensitive to detect morphological changes between groups as compared to the methods of voxel-based morphometry (VBM) and thickness measurement. Hemispheric asymmetry in specific brain regions as a neuroimaging biomarker can provide helpful information for prediction of MCI conversion.

SUBMITTER: Long X

PROVIDER: S-EPMC5884406 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Morphological Biomarker Differentiating MCI Converters from Nonconverters: Longitudinal Evidence Based on Hemispheric Asymmetry.

Long Xiaojing X Jiang Chunxiang C Zhang Lijuan L

Behavioural neurology 20180319

Identifying subjects with mild cognitive impairment (MCI) who may probably progress to Alzheimer's disease (AD) is important for better understanding the disease mechanisms and facilitating early treatments. In addition to the direct volumetric and thickness measurement based on high-resolution magnetic resonance imaging (MRI), hemispheric asymmetry could be a potential index to detect morphological variations in MCI patients with a high risk of conversion to AD. The present study collected a se ...[more]

PMID: 29755611

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Project description:ObjectiveThe current study examined gender-related differences in hemispheric asymmetries of graph metrics, calculated from a cortical thickness-based brain structural covariance network named hemispheric morphological network.MethodsUsing the T1-weighted magnetic resonance imaging scans of 285 participants (150 females, 135 males) retrieved from the Human Connectome Project (HCP), hemispheric morphological networks were constructed per participant. In these hemispheric morphologic networks, the degree of similarity between two different brain regions in terms of the distributed patterns of cortical thickness values (the Jensen-Shannon divergence) was defined as weight of network edge that connects two different brain regions. After the calculation and summation of global and local graph metrics (across the network sparsity levels K = 0.10-0.36), asymmetry indexes of these graph metrics were derived.ResultsHemispheric morphological networks satisfied small-worldness and global efficiency for the network sparsity ranges of K = 0.10-0.36. Between-group comparisons (female versus male) of asymmetry indexes revealed opposite directionality of asymmetries (leftward versus rightward) for global metrics of normalized clustering coefficient, normalized characteristic path length, and global efficiency (all p < 0.05). For the local graph metrics, larger rightward asymmetries of cingulate-superior parietal gyri for nodal efficiency in male compared to female, larger leftward asymmetry of temporal pole for degree centrality in female compared to male, and opposite directionality of interhemispheric asymmetry of rectal gyrus for degree centrality between female (rightward) and male (leftward) were shown (all p < 0.05).ConclusionPatterns of interhemispheric asymmetries for cingulate, superior parietal gyrus, temporal pole, and rectal gyrus are different between male and female for the similarities of the cortical thickness distribution with other brain regions. Accordingly, possible effect of gender-by-hemispheric interaction has to be considered in future studies of brain morphology and brain structural covariance networks.

Project description:IntroductionThe present study aims to assess the differences between major depressive disorder (MDD) and mild cognitive impairment (MCI) in terms of verbal learning profile together with structural changes in the brain on magnetic resonance imaging (MRI) and to reveal predictive factors for MCI.MethodsFifty-six patients with MDD and 31 MCI subjects were assessed using the Turkish Verbal Memory Processes Test (VMPT). Brain MRI was used to evaluate sulcal atrophy (SA), ventricular atrophy, periventricular white matter hyperintensity (WMH), subcortical WMH, basal ganglia infarct, medial temporal lobe atrophy, and infratentorial infarct scores based on the Modified Visual MRI Rating Scale (MVMRS). The symptoms of depression were evaluated with the Beck Depression Inventory in both groups. Demographic factors, VMPT scores, and MVMRS scores between MDD and MCI groups were compared. Also, potential predictors of MCI were analyzed by binary logistic regression analyses.ResultsThe total scores of VMPT and the scores of VMPT subgroups, including immediate memory, highest learning, total learning, and delayed recall, were significantly higher in the MDD groups compared to MCI patients (Mann-Whitney U, Student's t-test, p < 0.05), indicating that higher scores were associated with better memory. The total MVMRS score and a subgroup of MVMRS, the SA score, were significantly higher in MCI patients compared to the MDD group, suggesting more atrophic changes and a higher burden of infarction in MCI patients. In our statistical analyses, impaired immediate memory (p < 0.001; OR = 6.002; 95% CI: 1.996-18.042), increased SA (p = 0.008; OR = 1.522; 95% CI: 1.118-2.073), and education (p = 0.028; OR = 0.84; 95% CI: 0.719-0.981) were significant predictive values obtained through backward Wald elimination in the binary logistic regression model for detecting MCI.ConclusionOur findings suggest that VMPT may potentially represent a novel neuropsychiatric test that might be combined with MRI-based morphometric evaluation methods, such as MVMRS.

Dataset Information

Morphological Biomarker Differentiating MCI Converters from Nonconverters: Longitudinal Evidence Based on Hemispheric Asymmetry.

Publications

Morphological Biomarker Differentiating MCI Converters from Nonconverters: Longitudinal Evidence Based on Hemispheric Asymmetry.

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