Early Microstructure Changes of White Matter Fiber Bundles in Patients with Amnestic Mild Cognitive Impairment Predicts Progression of Mild Cognitive Impairment to Alzheimer's Disease.

BACKGROUND: Amnestic mild cognitive impairment (aMCI) is the transitional stage between normal aging and Alzheimer's disease (AD). Some aMCI patients will progress into AD eventually, whereas others will not. If the trajectory of aMCI can be predicted, it would enable early diagnosis and early therapy of AD. OBJECTIVE: To explore the development trajectory of aMCI patients, we used diffusion tensor imaging to analyze the white matter microstructure changes of patients with different trajectories of aMCI. METHODS: We included three groups of subjects:1) aMCI patients who convert to AD (MCI-P); 2) aMCI patients who remain in MCI status (MCI-S); 3) normal controls (NC). We analyzed the fractional anisotropy and mean diffusion rate of brain regions, and we adopted logistic binomial regression model to predicate the development trajectory of aMCI. RESULTS: The fraction anisotropy value is significantly reduced, the mean diffusivity value is significantly increased in the two aMCI patient groups, and the MCI-P patients presented greater changes. Significant changes are mainly located in the cingulum, fornix, hippocampus, and uncinate fasciculus. These changed brain regions significantly correlated with the patient's Mini-Mental State Examination scores. CONCLUSION: The study predicted the disease trajectory of different types of aMCI patients based on the characteristic values of the above-mentioned brain regions. The prediction accuracy rate can reach 90.2%, and the microstructure characteristics of the right cingulate band and the right hippocampus may have potential clinical application value to predict the disease trajectory.

Repeated anodal high-definition transcranial direct current stimulation over the left dorsolateral prefrontal cortex in mild cognitive impairment patients increased regional homogeneity in multiple brain regions.

Transcranial direct current stimulation (tDCS) can improve cognitive function. However, it is not clear how high-definition tDCS (HD-tDCS) regulates the cognitive function and its neural mechanism, especially in individuals with mild cognitive impairment (MCI). This study aimed to examine whether HD-tDCS can modulate cognitive function in individuals with MCI and to determine whether the potential variety is related to spontaneous brain activity changes recorded by resting-state functional magnetic resonance imaging (rs-fMRI). Forty-three individuals with MCI were randomly assigned to receive either 10 HD-tDCS sessions or 10 sham sessions to the left dorsolateral prefrontal cortex (L-DLPFC). The fractional amplitude of low-frequency fluctuation (fALFF) and the regional homogeneity (ReHo) was computed using rs-fMRI data from all participants. The results showed that the fALFF and ReHo values changed in multiple areas following HD-tDCS. Brain regions with significant decreases in fALFF values include the Insula R, Precuneus R, Thalamus L, and Parietal Sup R, while the Temporal Inf R, Fusiform L, Occipital Sup L, Calcarine R, and Angular R showed significantly increased in their fALFF values. The brain regions with significant increases in ReHo values include the Temporal Inf R, Putamen L, Frontal Mid L, Precentral R, Frontal Sup Medial L, Frontal Sup R, and Precentral L. We found that HD-tDCS can alter the intensity and synchrony of brain activity, and our results indicate that fALFF and ReHo analysis are sensitive indicators for the detection of HD-tDCS during spontaneous brain activity. Interestingly, HD-tDCS increases the ReHo values of multiple brain regions, which may be related to the underlying mechanism of its clinical effects, these may also be related to a potential compensation mechanism involving the mobilization of more regions to complete a function following a functional decline.

Changes in the connection network of whole-brain fiber tracts in patients with Alzheimer's disease have a tendency of lateralization.

Alzheimer's disease is a common progressive neurodegenerative disorder in the elderly. Diffusion tensor imaging (DTI) has been widely used to explore structural integrity and to describe white matter degeneration in Alzheimer's disease. Previous research has indicated that the change of connections between white matter fiber tracts is very important for investigating the brain function of Alzheimer's disease patients. However, whether white matter features can be used as potential biomarkers for predicting Alzheimer's disease tendency requires more in-depth research. In this study, we investigated the relationship between the damage in white matter tracts and the decline of cognitive function in Alzheimer's disease. DTI data were collected from 38 Alzheimer's disease patients and 30 normal controls. Fiber assignment by continuous tracking approach was used to establish connections between different brain regions of the whole brain, network-based statistical analysis and support vector machine classification analysis were used to explore the connection of whole-brain fiber bundles between the two groups. Most importantly, our results showed that the connections between brain regions of Alzheimer's disease patients were damaged, and the damage were mainly located in the right hemisphere, there was a certain degree of lateralization effect. Using whole-brain fiber bundle connection network as a feature for classification, we found it helped to improve the classification accuracy in Alzheimer's disease patients, which is useful for early clinical diagnosis of Alzheimer's disease. These findings further suggested that we can use the whole-brain fiber bundle connection network of Alzheimer's disease patients as a potential diagnostic indicator of Alzheimer's disease in the future.