Brain networks modeling for studying the mechanism underlying the development of Alzheimer's disease

Anodal HD-tDCS on the dominant anterior temporal lobe and dorsolateral prefrontal cortex: clinical results in patients with mild cognitive impairment

OBJECTIVES

Mild cognitive impairment (MCI) is a neurocognitive disorder in which the cognitive and mental abilities of humans are declined. Transcranial direct-current stimulation (tDCS) is an emerging noninvasive brain stimulation technique aimed at neuromodulation. In this study, we investigate whether high-definition anodal tDCS stimulation (anodal HD-tDCS) in MCI patients in two different brain regions will be effective in improving cognitive function.

METHODS

This study was done as a randomized, double-blind clinical trial. Sixty MCI patients (clinically diagnosed by expert neurologists) were randomly divided into three groups. Two groups received 2-mA anodal HD-tDCS for 20 min for 2 weeks (5 consecutive days in each week, 10 days in total). In the first group (twenty patients), the left dorsolateral prefrontal cortex (left DLPFC) was targeted. In the second group (twenty patients), the target zone was the dominant anterior temporal lobe (DATL). The third group (twenty patients) formed the Sham group. The Montreal Cognitive Assessment (MoCA) and Quality of Life in Alzheimer's Disease (QoLAD) were considered as the outcome measures.

RESULTS

MCI patients obtained the highest MoCA mean scores in both left DLPFC and DATL groups versus the study baseline 2 weeks after the intervention. In addition, the MoCA mean scores of MCI patients were greater in both intervention groups compared to the Sham group up to 3 months post-stimulation (p-value ≤ 0.05). However, as we moved away from the first stimulation day, a decreasing trend in the MoCA mean scores was observed. Moreover, in the left DLPFC and DATL groups, higher QoLAD mean scores were observed 3-month post-stimulation, highlighting the effectiveness of anodal HD-tDCS in improving the quality of life in MCI patients.

CONCLUSION

In this research, it was shown that applying anodal HD-tDCS at left DLPFC and DATL brain regains for two successive weeks improves cognitive function in MCI patients (by obtaining higher values of MoCA scores) up to 3 months after the intervention compared to the Sham group. This illustrates the positive effects of HD-tDCS, as a non-pharmacological intervention, for improving cognitive function and quality of life in MCI patients.

SIGNIFICANCE

Two weeks after anodal HD-tDCS of the DLPFC and DATL brain regions, the MCI patients achieved the highest MoCA mean scores compared to the Sham group across all measurement intervals.

Crosstalk Between Gut Microflora and Vitamin D Receptor SNPs Are Associated with the Risk of Amnestic Mild Cognitive Impairment in a Chinese Elderly Population

Background: The interactions between environmental factors and genetic variants have been implicated in the pathogenesis of Alzheimer’s disease (AD). The altered gut microbiota (GM) and vitamin D deficiency are closely associated with the higher risk of AD. Objective: This study was performed to evaluate whether the crosstalk between GM and single nucleotide polymorphisms (SNPs) of vitamin D receptor (VDR) or vitamin D binding protein (VDBP) have a link with the risk of amnestic mild cognitive impairment (aMCI) in the Chinese elderly population. Methods: A total of 171 aMCI patients and 261 cognitive normal controls (NC) were enrolled in this study. Six tag SNPs of VDR and VDBP were genotyped by PCR-RFLP. The serum levels of vitamin D, Aβ1-42, and p-tau (181P) were determined by using of ELISA kits. The alterations in the GM were analyzed by full-length 16S ribosomal RNA (rRNA) gene sequencing. Results: The frequencies of AG genotype and A allele of VDR rs1544410 in aMCI group were significantly higher than that in NC group (genotype: p = 0.002, allele: p = 0.003). Patients with aMCI showed an abnormal GM composition compared with NC group. Interestingly, significant differences in GM composition were found between aMCI and NC group among individuals with AG genotype, as well as between individuals with AG and GG genotype of VDR rs1544410 among patients with aMCI. Conclusion: These results implicated that the crosstalk between gut microflora and vitamin D receptor variants are associated with the risk of aMCI in Chinese elderly population.

Diagnosis of Mild Cognitive Impairment with Ordinal Pattern Kernel

Mild cognitive impairment (MCI) belongs to the prodromal stage of Alzheimer's disease (AD). Accurate diagnosis of MCI is very important for possibly deferring AD progression. Graph kernels, which measure the similarity between paired brain connectivity networks, have been widely used to diagnose brain diseases (e.g., MCI) and yielded promising classification performance. However, most of the existing graph kernels are based on unweighted graphs, and neglect the valuable weighted information of the edges in brain connectivity networks where edge weights convey the strengths of fiber connection or temporal correlation between paired brain regions. Accordingly, in this paper, we propose a new graph kernel called ordinal pattern kernel for measuring brain connectivity network similarity and apply it to brain disease classification tasks. Different from the existing graph kernels which measure the topological similarity of the unweighted graphs, our proposed ordinal pattern kernel can not only calculate the similarity of paired brain connectivity networks, but also capture the ordinal pattern relationship of edge weights in brain connectivity networks. To appraise the effectiveness of our proposed method, we perform extensive experiments in functional magnetic resonance imaging data of brain disease from Alzheimer's Disease Neuroimaging Initiative database. The experimental results show that our proposed ordinal pattern kernel outperforms the state-of-the-art graph kernels in the classification tasks of MCI.

Acupuncture enhances brain function in patients with mild cognitive impairment: evidence from a functional-near infrared spectroscopy study

Mild cognitive impairment (MCI) is a precursor to Alzheimer’s disease. It is imperative to develop a proper treatment for this neurological disease in the aging society. This observational study investigated the effects of acupuncture therapy on MCI patients. Eleven healthy individuals and eleven MCI patients were recruited for this study. Oxy- and deoxy-hemoglobin signals in the prefrontal cortex during working-memory tasks were monitored using functional near-infrared spectroscopy. Before acupuncture treatment, working-memory experiments were conducted for healthy control (HC) and MCI groups (MCI-0), followed by 24 sessions of acupuncture for the MCI group. The acupuncture sessions were initially carried out for 6 weeks (two sessions per week), after which experiments were performed again on the MCI group (MCI-1). This was followed by another set of acupuncture sessions that also lasted for 6 weeks, after which the experiments were repeated on the MCI group (MCI-2). Statistical analyses of the signals and classifications based on activation maps as well as temporal features were performed. The highest classification accuracies obtained using binary connectivity maps were 85.7% HC vs. MCI-0, 69.5% HC vs. MCI-1, and 61.69% HC vs. MCI-2. The classification accuracies using the temporal features mean from 5 seconds to 28 seconds and maximum (i.e, max(5:28 seconds)) values were 60.6% HC vs. MCI-0, 56.9% HC vs. MCI-1, and 56.4% HC vs. MCI-2. The results reveal that there was a change in the temporal characteristics of the hemodynamic response of MCI patients due to acupuncture. This was reflected by a reduction in the classification accuracy after the therapy, indicating that the patients’ brain responses improved and became comparable to those of healthy subjects. A similar trend was reflected in the classification using the image feature. These results indicate that acupuncture can be used for the treatment of MCI patients.

Brain network construction and analysis for patients with mild cognitive impairment and Alzheimer's disease based on a highly-available nodes approach

INTRODUCTION

Using brain network and graph theory methods to analyze the Alzheimer's disease (AD) and mild cognitive impairment (MCI) abnormal brain function is more and more popular. Plenty of potential methods have been proposed, but the representative signal of each brain region in these methods remains poor performance.

METHODS

We propose a highly-available nodes approach for constructing brain network of patients with MCI and AD. With resting-state functional magnetic resonance imaging (rs-fMRI) data of 84 AD subjects, 81 MCI subjects, and 82 normal control (NC) subjects from the Alzheimer's Disease Neuroimaging Initiative Database, we construct connected weighted brain networks based on the different sparsity and minimum spanning tree. Support Vector Machine of Radial Basis Function kernel was selected as classifier.

RESULTS

Accuracies of 74.09% and 77.58% in classification of MCI and AD from NC, respectively. We also performed a hub node analysis and found 18 significant brain regions were identified as hub nodes.

CONCLUSIONS

The findings of this study provide insights for helping understanding the progress of the AD. The proposed method highlights the effectively representative time series of brain regions of rs-fMRI data for construction and topology analysis brain network.