暂无摘要。

Previous studies using EEG (electroencephalography) as biomarker for dementia have attempted to research, but results have been inconsistent. Most of the studies have extremely small number of samples (average N = 15) and studies with large number of data do not have control group. We identified EEG features that may be biomarkers for dementia with 120 subjects (dementia 10, MCI 33, against control 77).
We recorded EEG from 120 patients with dementia as they stayed in relaxed state using a single-channel EEG device while conducting real-time noise reduction and compared them to healthy subjects. Differences in EEG between patients and controls, as well as differences in patients\' severity, were examined using the ratio of power spectrum at each frequency.
In comparing healthy controls and dementia patients, significant power spectrum differences were observed at 3 Hz, 4 Hz, and 10 Hz and higher frequencies. In patient group, differences in the power spectrum were observed between asymptomatic patients and healthy individuals, and between patients of each respective severity level and healthy individuals.
A study with a larger sample size should be conducted to gauge reproducibility, but the results implied the effectiveness of EEG in clinical practice as a biomarker of MCI (mild cognitive impairment) and/or dementia.

Recent studies deal with disorders and deficits caused by vascular syndrome in efforts for prediction and prevention. Cardiovascular health declines with age due to vascular risk factors, and this leads to an increasing risk of cognitive decline. Mild cognitive impairment (MCI) is defined as the negative cognitive changes beyond what is expected in normal aging. The purpose of the study was to compare older adults with vascular risk factors (VRF), MCI patients, and healthy controls (HC) in social cognition and especially in theory of mind ability (ToM). The sample comprised a total of 109 adults, aged 50 to 85 years (M = 66.09, SD = 9.02). They were divided into three groups: (a) older adults with VRF, (b) MCI patients, and (c) healthy controls (HC). VRF and MCI did not differ significantly in age, educational level or gender as was the case with HC. Specifically, for assessing ToM, a social inference test was used, which was designed to measure sarcasm comprehension. Results showed that the performance of the VRF group and MCI patients is not differentiated, while HC performed higher compared to the other two groups. The findings may imply that the development of a vascular disorder affecting vessels of the brain is associated from its \"first steps\" to ToM decline, at least regarding specific aspects of it, such as paradoxical sarcasm understanding.

One of the current challenges in the field of Alzheimer\'s disease (AD) is to identify patients with mild cognitive impairment (MCI) that will convert to AD. Artificial intelligence, in particular machine learning (ML), has established as one of more powerful approach to extract reliable predictors and to automatically classify different AD phenotypes. It is time to accelerate the translation of this knowledge in clinical practice, mainly by using low-cost features originating from the neuropsychological assessment. We performed a meta-analysis to assess the contribution of ML and neuropsychological measures for the automated classification of MCI patients and the prediction of their conversion to AD. The pooled sensitivity and specificity of patients\' classifications was obtained by means of a quantitative bivariate random-effect meta-analytic approach. Although a high heterogeneity was observed, the results of meta-analysis show that ML applied to neuropsychological measures can lead to a successful automatic classification, being more specific as screening rather than prognosis tool. Relevant categories of neuropsychological tests can be extracted by ML that maximize the classification accuracy.

BACKGROUND: Mild cognitive impairment (MCI) is a common disorder affecting as much as 15% of the elderly population. Transcranial direct current stimulation (tDCS) is a non-invasive technique of neuromodulation that has proven to influence performance in different cognitive domains.
OBJECTIVE: We investigated the effects on cognition of 20-day anodal tDCS in 17 MCI patients compared with 17 matched MCI patients.
METHODS: Patients underwent neuropsychological evaluation at baseline and then were randomly assigned to the anodal or sham group. The tDCS protocol consisted in 20 min, 5 days per week (up to a total of 20 days), of 2-mA anodal stimulation over the left dorsolateral prefrontal cortex (DLPFC). The location of anodal electrode was chosen in accordance with previous reports which relate anodal stimulation of this site with cognitive enhancement. At the end of the last day of stimulation, a second neuropsychological evaluation was performed. We compared baseline and post-stimulation neuropsychological results in the anodal vs sham group using repeated measures ANOVA as a statistical analysis test.
RESULTS: At follow-up, patients exposed to anodal stimulation showed improvement in episodic verbal memory (p < 0.001) and figure naming test (p < 0.01), in a general index of cognitive function (Brief Mental Deterioration Battery) (p < 0.0001) and in a mood measurement test (Beck Depression Inventory) (p < 0.01).
CONCLUSIONS: Anodal tDCS could be a useful tool to improve cognitive symptoms in MCI although more evidence is needed to understand the exact underlying mechanisms. Confirmation of its potential benefits in MCI would be significant.

Objectives: The aims of the current study are to (1) report the frequency of specific sleep disturbance symptoms in Mild Cognitive Impairment (MCI) and cognitive healthy older persons; (2) examine whether overall poor sleep and specific sleep disturbance symptoms are more common in persons with MCI compared to cognitive healthy older controls and; (3) examine the association between sleep disturbances and performance in general and specific cognitive domains in persons with MCI and separately in cognitive healthy older persons. Methods: Data were collected at the Fondazione Ospedale San Camillo Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Venice, Italy as part of the European VPH-DARE@IT project. We included 69 persons with MCI (mean age 75.7; SD = 7.7) and 72 sex-matched cognitively healthy controls (mean age 71.8; SD = 7.0). Participants underwent extensive neuropsychological assessment and evaluation of subjective sleep performance with the Sleep Continuity in Alzheimer\'s Disease Scale(SCADS). Results: A fifth of MCI patients (21.7%, n = 15) had poor sleep compared to 15.3% (n = 11) of cognitively healthy controls. MCI patients had a 3.2 higher odds of having poor sleep compared to cognitively healthy controls after adjustment for age, education, sex, and general cognitive functioning (Odds Ratio (OR) = 3.2; 95% Confidence Interval (CI) = 1.1-9.2). Persons who reported waking up twice or more during the night had higher odds of being MCI compared to those who never wake or wake only once (OR = 2.6; 95% CI = 1.1-6.1). In MCI patients, poor sleep was associated with better general cognitive functioning and short-term working memory, whereas in cognitive healthy older persons poor sleep was associated with impairment in episodic memory performance and executive functioning. Discussion: Our results confirm previous studies showing that sleep disturbances are common in MCI, and this may be due to an ongoing neurodegenerative process rather than a symptom of cognitive impairment. Future research with objective sleep measurements are needed in MCI as well as interventions to improve sleep with the aim of preventing cognitive decline.

This report presents the neuropsychological profile of an older gentleman diagnosed with single-domain mild cognitive impairment (MCI) at baseline who reverted to normal cognitive functioning at 1-year follow-up. The case highlights important considerations for assessing and diagnosing MCI in clinical settings in the context of sizeable reversion rates that have been reported extensively in the literature. A 72-year-old gentleman presented to our Neuropsychology Clinic with subjective memory complaints. Per recommendation, the patient returned for follow-up testing 1-year later. A clinical interview, comprehensive battery of neuropsychological tests, and mood questionnaires were administered during both evaluations. At baseline, DSM-5 Mild Neurocognitive Disorder consistent with single-domain amnestic MCI was diagnosed based on several impaired scores on the California Verbal Learning Test, 2nd version (CVLT-II) and collateral report of subtle decline in functioning. At follow-up, all cognitive performances fell within normal limits. The patient no longer met criteria for Mild Neurocognitive Disorder. The present case study highlights important considerations when assessing and diagnosing MCI in the clinical setting. Repeat testing in clinical settings is underscored by the sizeable rate of MCI reversion reported in the literature. Important diagnostic and feedback considerations are discussed.

暂无摘要。

The Montreal Cognitive Assessment (MoCA) is a widely used screening test for evaluation of mild cognitive impairment (MCI), with a single cutoff for all ages. We examined whether it is associated with age in a sample of cognitively-intact elderly (CIE). The average MoCA score was negatively correlated with age and was significantly higher for younger than older CIE. Additionally, 42% of the older elderly fell below the proposed MCI cutoff score, although all subjects were CIE. Thus, cognitive abilities captured by the MoCA test decrease with age, even in CIE. Therefore, cutoff scores by age for the MoCA are needed.