This study seeks to explore the early signs of cognitive impairment in patients with obstructive sleep apnea hypopnea syndrome (OSAHS). According to polysomnography, twenty patients diagnosed with OSAHS and twenty normal controls underwent event-related potential (ERP) examination including mismatch negativity (MMN) and P300. Compared with normal controls, OSAHS patients showed significantly prolonged latency of MMN and P300 at Cz. After controlling age and body mass index (BMI), MMN latency positively correlated with apnea hypopnea index (AHI), oxygen reduction index, stage N1 sleep and arousal index, while MMN latency negatively correlated with stage N3 sleep and mean blood oxygen saturation; and P300 latency positively related to AHI and oxygen reduction index; no relationships were found among MMN latency, MMN amplitude, P300 latency and P300 amplitude. These results suggest that the brain function of automatic processing and controlled processing aere impaired in OSAHS patients, and these dysfunction are correlated with nocturnal repeatedly hypoxemia and sleep structure disturbance.
ObjectiveTo explore the quality of life (QOL) of rural cognitive function impaired elderly in Guangyuan city and analysis the influencing factors, in order to provide evidence for improving the QOL of rural cognitive function impaired elderly. MethodsBy stratified cluster sampling method, Mini-Mental State Examination (MMSE) was adopted in the cognitive function impaired screening in Guangyuan rural area of Sichuan province in 2012, then we used SF-12 questionnaire to evaluate the QOL of those rural elderly (more than 60 years old) whose cognitive function was impaired. ResultsA total of 270 rural cognitive function impaired elderly were selected from 735 old people. The results of QOL assessment showed that:the mean of physical component summary (PCS) was 37.93±11.55, and the mean of mental component summary (MCS) was 44.07±13.14. Gender, age, education levels, economic situation of the selfassessment, chronic disease, being engaging in physical labour and daily life care were correlated with the score of QOL. ConclusionIn order to improve their QOL, we should help the elderly with cognitive function impaired and focus on prevention and individual treatment; their special difficulties should be fully considered when making the policy of health care and social security.
ObjectiveTo systematically review the efficacy of repetitive transcranial magnetic stimulation (rTMS) on patients with mild cognitive impairment (MCI). MethodsWe searched databases including PubMed, The Cochrane Library (Issue 10, 2015), EMbase, PsycINF, EBSCO, CBM, CNKI, WanFang Data and VIP from inception to October 2015 to collect randomized controlled trials (RCTs) about rTMS for patients with MCI. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. Then, meta-analysis was performed by using RevMan 5.3 software. ResultsA total of 5 RCTs involving 180 MCI patients were included. The results of meta-analysis showed that, compared with the control group, rTMS treatment could significantly improve the overall cognitive abilities of MCI patients (SMD=2.53, 95% CI 0.91 to 4.16, P=0.002), as well as the single-domain cognitive performances, including tests for episodic memory (MD=0.98, 95% CI 0.24 to 1.72, P=0.01) and verbal fluency (MD=2.08, 95% CI 0.46 to 3.69, P=0.01). rTMS was a well-tolerated therapy, with slightly more adverse events observed than the control group (RD=0.09, 95% CI 0.00 to 0.18, P=0.04), but cases were mainly transient headache, dizziness and scalp pain. ConclusionrTMS may benefit the cognitive abilities of MCI patients. Nevertheless, due to the limited quantity and quality of included studies, large-scale, multicenter, and high quality RCTs are required to verify the conclusion.
Cognitive impairment is one of the three primary symptoms of schizophrenic patients and shows important value in early detection and warning for high-risk individuals. To study the specifics of electroencephalogram (EEG) in patients with schizophrenia under the cognitive load, we collected EEG signals from 17 schizophrenic patients and 19 healthy controls, extracted signals of each band based on wavelet transform, calculated the characteristics of nonlinear dynamic and functional brain networks, and automatically classified the two groups of people by using a machine learning algorithm. Experimental results indicated that the correlation dimension and sample entropy showed significant differences in α, β, θ, and γ rhythm of the Fp1 and Fp2 electrodes between groups under the cognitive load. These results implied that the functional disruptions in the frontal lobe might be the important factors of cognitive impairments in schizophrenic patients. Further results of the automatic classification analysis indicated that the combination of nonlinear dynamics and functional brain network properties as the input characteristics of the classifier showed the best performance, with the accuracy of 76.77%, sensitivity of 72.09%, and specificity of 80.36%. The results of this study demonstrated that the combination of nonlinear dynamics and function brain network properties may be potential biomarkers for early screening and auxiliary diagnosis of schizophrenia.