Search Results (4)

This study aimed to elucidate the long-term progression of mild cognitive impairment (MCI) within a comprehensive longitudinal dataset, distinguish it from healthy aging, explore the influence of a dementia subtype on this progression, and identify potential contributing factors. Patients with prodromal and preclinical cases underwent regular neuropsychological assessments utilizing various tools. The study included a total of 140 participants with MCI, categorized into Alzheimer’s disease (AD) and non-AD subtypes. Our dataset revealed an overall progression rate of 92.8% from MCI to the clinical stage of dementia during the follow-up period, with an annual rate of 15.7%. Notably, all prodromal cases of Lewy body dementia/Parkinson’s disease (LBD/PDD) and frontotemporal dementia (FTD) advanced to clinical stages, whereas 7% of vascular dementia (VaD) cases and 8.4% of AD cases remained in the prodromal stage throughout follow-up. Furthermore, we observed a faster progression rate in MCI-AD cases compared to non-AD sufferers (53.9% vs. 35.5%, Entropy: 0.850). This study revealed significant cognitive changes in individuals with MCI over time. The mini-mental state examination (MMSE), global deterioration scale (GDS), and calculation tests were the most effective tests for evaluation of MCI. These findings may offer valuable insights for the development of personalized interventions and management strategies for individuals with MCI. Full article

As Malaysia undergoes a demographic transformation of population aging, the prevalence of dementia is expected to rise, posing a major public health threat issue. Early screening to detect cognitive impairment is important to implement appropriate clinical interventions. The Visual Cognitive Assessment Test (VCAT) is a language-neutral cognitive assessment screening tool suitable for multilingual populations. This study was aimed to validate the VCAT screening tool for the detection of cognitive impairment amongst the population of Malaysia. A total of 184 participants were recruited, comprising 79 cognitively healthy participants (CHP), 46 mild cognitive impairment (MCI) patients, and 59 mild dementia (Alzheimer’s disease and Vascular Dementia) patients from five hospitals between May 2018 and December 2019 to determine the usefulness of VCAT. Diagnostic performance was assessed using area under the curve (AUC), and receiver operating characteristic (ROC) analysies was performed to determine the recommended cutoff scores. ROC analyses for the VCAT was comparable with that of MoCA (Montreal Cognitive Assessment) in differentiating between CHP, MCI, and mild dementia (AD and VaD) participants. The findings of this study suggest the following optimal cutoff score for VCAT: Dementia 0–19, MCI 20–23, Normal 24–30. The mean ± SD time to complete the VCAT was 10.0 ± 2.75 min in the CHP group and 15.4 ± 4.52 min in the CI group. Results showed that 76.0% of subjects thought that the instructions in VCAT were similar or easier to understand compared with MoCA. This study showed that the VCAT is a valid and useful screening tool for patients with cognitive impairment in Malaysia and is feasible to be used in the clinical settings. Full article

This study aimed to investigate whole-brain spontaneous activities changes in patients with vascular mild cognitive impairment (VaMCI), and to evaluate the relationships between these brain alterations and their neuropsychological assessments. Thirty-one patients with VaMCI and thirty-one healthy controls (HCs) underwent structural MRI and resting-state functional MRI (rs-fMRI) and neuropsychological assessments. The functional alterations were determined by the amplitude of low-frequency fluctuation (ALFF) and degree centrality (DC). The gray matter volume (GMV) changes were analyzed using voxel-based morphometry (VBM). Linear regression analysis was used to evaluate the relationships between the structural and functional changes of brain regions and neuropsychological assessments. The VaMCI group had significantly lower scores in the Montreal Cognitive Assessment (MoCA), and higher scores on the Hamilton Anxiety Rating Scale (HAMA) and Hamilton Depression Rating Scale (HAMD). Compared to the HCs, the VaMCI group exhibited GM atrophy in the right precentral gyrus (PreCG) and right inferior temporal gyrus (ITG). VaMCI patients further exhibited significantly decreased brain activity within the default mode network (DMN), including the bilateral precuneus (PCu), angular gyrus (AG), and medial frontal gyrus (medFG). Linear regression analysis revealed that the decreased ALFF was independently associated with lower MoCA scores, and the GM atrophy was independently associated with higher HAMD scores. The current finding suggested that aberrant spontaneous brain activity in the DMN might subserve as a potential biomarker of VaMCI, which may highlight the underlying mechanism of cognitive decline in cerebral small vessel disease. Full article

Characterizing dementia is a global challenge in supporting personalized health care. The electroencephalogram (EEG) is a promising tool to support the diagnosis and evaluation of abnormalities in the human brain. The EEG sensors record the brain activity directly with excellent time resolution. In this study, EEG sensor with 19 electrodes were used to test the background activities of the brains of five vascular dementia (VaD), 15 stroke-related patients with mild cognitive impairment (MCI), and 15 healthy subjects during a working memory (WM) task. The objective of this study is twofold. First, it aims to enhance the recorded EEG signals using a novel technique that combines automatic independent component analysis (AICA) and wavelet transform (WT), that is, the AICA–WT technique; second, it aims to extract and investigate the spectral features that characterize the post-stroke dementia patients compared to the control subjects. The proposed AICA–WT technique is a four-stage approach. In the first stage, the independent components (ICs) were estimated. In the second stage, three-step artifact identification metrics were applied to detect the artifactual components. The components identified as artifacts were marked as critical and denoised through DWT in the third stage. In the fourth stage, the corrected ICs were reconstructed to obtain artifact-free EEG signals. The performance of the proposed AICA–WT technique was compared with those of two other techniques based on AICA and WT denoising methods using cross-correlation $XCorr$ and peak signal to noise ratio $\left(PSNR\right)$ (ANOVA, $p$ ˂ 0.05). The AICA–WT technique exhibited the best artifact removal performance. The assumption that there would be a deceleration of EEG dominant frequencies in VaD and MCI patients compared with control subjects was assessed with AICA–WT (ANOVA, $p$ ˂ 0.05). Therefore, this study may provide information on post-stroke dementia particularly VaD and stroke-related MCI patients through spectral analysis of EEG background activities that can help to provide useful diagnostic indexes by using EEG signal processing. Full article