Mechanisms Of Mild Cognitive Impairment Research Articles

Topological properties analysis and identification of mild cognitive impairment based on individual morphological brain network connectome.

Mild cognitive impairment is considered the prodromal stage of Alzheimer's disease. Accurate diagnosis and the exploration of the pathological mechanism of mild cognitive impairment are extremely valuable for targeted Alzheimer's disease prevention and early intervention. In all, 100 mild cognitive impairment patients and 86 normal controls were recruited in this study. We innovatively constructed the individual morphological brain networks and derived multiple brain connectome features based on 3D-T1 structural magnetic resonance imaging with the Jensen-Shannon divergence similarity estimation method. Our results showed that the most distinguishing morphological brain connectome features in mild cognitive impairment patients were consensus connections and nodal graph metrics, mainly located in the frontal, occipital, limbic lobes, and subcortical gray matter nuclei, corresponding to the default mode network. Topological properties analysis revealed that mild cognitive impairment patients exhibited compensatory changes in the frontal lobe, while abnormal cortical-subcortical circuits associated with cognition were present. Moreover, the combination of multidimensional brain connectome features using multiple kernel-support vector machine achieved the best classification performance in distinguishing mild cognitive impairment patients and normal controls, with an accuracy of 84.21%. Therefore, our findings are of significant importance for developing potential brain imaging biomarkers for early detection of Alzheimer's disease and understanding the neuroimaging mechanisms of the disease.

White matter alterations in mild cognitive impairment revealed by meta-analysis of diffusion tensor imaging using tract-based spatial statistics.

The neuropathological mechanism of mild cognitive impairment (MCI) remains unclarified. Diffusion tensor imaging (DTI) studies revealed white matter (WM) microarchitecture alterations in MCI, but consistent findings and conclusions have not yet been drawn. The present coordinate-based meta-analysis (CBMA) of tract-based spatial statistics (TBSS) studies aimed to identify the most prominent and robust WM abnormalities in patients with MCI. A systematic search of relevant studies was conducted through January 2022 to identify TBSS studies comparing fractional anisotropy (FA) between MCI patients and healthy controls (HC). We used the seed-based d mapping (SDM) software to achieve the CBMA and analyze regional FA alterations in MCI. Meta-regression analysis was subsequently applied to explore the potential associations between clinical variables and FA changes. MCI patients demonstrated significantly decreased FA in widely distributed areas in the corpus callosum (CC), including the genu, body, and splenium of the CC, as well as one cluster in the left striatum. FA in the body of the CC and in three clusters in the splenium of the CC was negatively associated with the mean age. Additionally, FA in the genu of the CC and in three clusters in the splenium of the CC had negative correlations with the MMSE scores. Disrupted integrities of the CC and left striatum might play vital roles in the process of cognitive decline. These findings enhanced our understanding of the neural mechanism underlying WM neurodegeneration in MCI and provided perspectives for the early detection and intervention of dementia.Registration number: CRD42022235716.

Multiomics Analysis of Structural Magnetic Resonance Imaging of the Brain and Cerebrospinal Fluid Metabolomics in Cognitively Normal and Impaired Adults.

IntroductionIntegrating brain imaging with large scale omics data may identify novel mechanisms of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD). We integrated and analyzed brain magnetic resonance imaging (MRI) with cerebrospinal fluid (CSF) metabolomics to elucidate metabolic mechanisms and create a “metabolic map” of the brain in prodromal AD.MethodsIn 145 subjects (85 cognitively normal controls and 60 with MCI), we derived voxel-wise gray matter volume via whole-brain structural MRI and conducted high-resolution untargeted metabolomics on CSF. Using a data-driven approach consisting of partial least squares discriminant analysis, a multiomics network clustering algorithm, and metabolic pathway analysis, we described dysregulated metabolic pathways in CSF mapped to brain regions associated with MCI in our cohort.ResultsThe multiomics network algorithm clustered metabolites with contiguous imaging voxels into seven distinct communities corresponding to the following brain regions: hippocampus/parahippocampal gyrus (three distinct clusters), thalamus, posterior thalamus, parietal cortex, and occipital lobe. Metabolic pathway analysis indicated dysregulated metabolic activity in the urea cycle, and many amino acids (arginine, histidine, lysine, glycine, tryptophan, methionine, valine, glutamate, beta-alanine, and purine) was significantly associated with those regions (P < 0.05).ConclusionBy integrating CSF metabolomics data with structural MRI data, we linked specific AD-susceptible brain regions to disrupted metabolic pathways involving nitrogen excretion and amino acid metabolism critical for cognitive function. Our findings and analytical approach may extend drug and biomarker research toward more multiomics approaches.

Alterations of grey matter volumes and network-level functions in patients with stable chronic obstructive pulmonary disease

ObjectiveThe purpose of this study was to investigate structural and functional alterations of the brain in the patients with stable chronic obstructive pulmonary disorder (COPD) and further investigate how these alterations correlated to parameters of pulmonary function test, cognitive function and disease duration in patients with COPD. MethodForty-five patients with stable COPD and forty age- and gender-matched healthy controls were enrolled into this study. Both resting-state fMRI and structural MRI were acquired for each participant. Voxel-based morphology was utilized to analyze alterations of the grey matter volume (GMV), and the seed-based resting-state functional connectivity (FC) was used to evaluate the network-level functional alterations. ResultsCompared to healthy controls, patients with stable COPD showed decreased GMV in the left supramarginal gyrus/precentral gyrus (SMG/PreCG), bilateral posterior midcingulate cortex (pMCC), right middle occipital gyrus (MOG) and right SMG. Furthermore, COPD patients mainly showed decreased FC within the visual network, frontoparietal network and other brain regions. Subsequent correlational analyses revealed that the decreased FC within visual network, frontoparietal network were positively correlated with the Montreal Cognitive Assessment score, language-domain score, attention-domain score and disease duration in patients with COPD. ConclusionOur findings provided evidence that COPD patients showed decreased GMV and regional and network-level functional alterations within the visual network, frontoparietal network and other networks. We speculated that atrophic GMV and FC of visual network and frontoparietal network are involved in the neural mechanism of mild cognitive impairment in stable COPD patients.

Aberrant Connectivity in Mild Cognitive Impairment and Alzheimer Disease Revealed by Multimodal Neuroimaging Data

Background: Making use of multimodal data simultaneously to understand the neural mechanism of mild cognitive impairment (MCI) has been in the focus nowadays. The simultaneous use of multimodal data can take advantage of each modality which may only provide the view of one specific aspect of the brain. Objective: To this end, the present study used structural magnetic resonance imaging (sMRI), fluorodeoxyglucose positron emission tomography (FDG-PET) and florbetapir PET to reveal the integrated brain network between MCI and normal controls (NCs). Methods: In this study, 116 MCI, 116 NC and 116 Alzheimer disease (AD) subjects from the Alzheimer's Disease Neuroimaging Initiative were included for the evaluation of the brain covariance graphic model. Sparse inverse covariance estimation was utilized to get the graphic model. Results: The connections among different brain regions were quite different between NC and MCI or between MCI and AD subjects (p < 0.01). The number of connections, which were represented by the covariance among different brain regions in the graphic model, decreased from NC to MCI and then AD, especially in the temporal lobe, occipital-parietal lobe and parietal-temporal lobe. Conclusion: These findings are good evidence to reveal the difference between MCI or AD and NC, and enhance the understanding of MCI.

The baseline and longitudinal changes of PCC connectivity in mild cognitive impairment: a combined structure and resting-state fMRI study.

The baseline and longitudinal changes of the posterior cingulate cortex (PCC) connectivity were assessed in order to clarify the neural mechanism of mild cognitive impairment (MCI). Twenty-eight right-handed subjects (14 MCI patients and 14 healthy elders) participated in this study. Clinical and neuropsychological examinations were performed on all the subjects. PCC functional connectivity was studied by examining the correlation between low frequency fMRI signal fluctuations in the PCC and those in all the other brain regions. Additionally, we traced all the MCI patients and compared their PCC connectivity in the initial stage and that in 3 years later. We also explored the relationship between the PCC functional connectivity strength and cognitive performances. Our results are as follows: Functional connectivity between the PCC and a set of regions is decreased in MCI patients. Most of these regions are within the default mode network (DMN). Three years later, the regions of superior frontal gyrus (SFG) and middle frontal gyrus (MFG) presented further decreased connectivity to the PCC in MCI. In addition, we also find enhanced functional connectivity between PCC and medial prefrontal cortex (MPFC), PCC and anterior cingulate cortex (ACC) in MCI patients. At last, our research also shows that the PCC connectivity with some regions significantly correlates with the cognitive performances of patients as measured by mini-mental state examination (MMSE), and California verbal learning test (CVLT) scores. The baseline and longitudinal changes of the PCC connectivity in our study suggest that impairment and compensation coexist in the disease progress of MCI patients.

Heterogenous mechanisms of mild cognitive impairment in Parkinson’s disease

Mild cognitive impairment in Parkinson disease (PD-MCI) shows heterogeneity in the clinical presentation, neuropsychology, neuroimaging, and neuropathology, suggesting abnormal metabolic network activities involving several cortical and subcortical systems. Prospective studies using specific biomarkers, including amyloid imaging and CSF biomarkers are important for the diagnosis and prognostic assessment of early cognitive deficits in PD patients.