American journal of Alzheimer's disease and other dementias最新文献

Dementia is attributable to 12 known risk factors in 40% cases. This study aimed to assess the prevalence of defined risk factors among people living with dementia. 174 patients with dementia and caregivers were interviewed using semi-structured pro forma, risk factors provided by the Lancet Commission on Dementia (2020), and Dementia Severity Rating Scale (DSRS). The prevalence of 11 known risk factors and associations between the risk factors and dementia severity were assessed. The mean age of the participants was 73.9 years (SD = 8.34 years). The education below intermediate level was 83.3%, 17.8% had hearing loss, 37.9% had hypertension, 24.1% had diabetes, 25.9% and 55.2% had alcohol and nicotine harmful use respectively and 8% had a history of traumatic brain injury and obesity each. There is a substantial prevalence of risk factors among people living with dementia in Nepal but no associations between any of the risk factors and dementia severity.

White matter hyperintensity (WMH) is associated with cognitive impairment. In this study, 79 patients with WMH from hospital 1 were randomly divided into a training set (62 patients) and an internal validation set (17 patients). In addition, 29 WMH patients from hospital 2 were used as an external validation set. Cognitive status was determined based on neuropsychological assessment results. A deep learning convolutional neural network of VB-Nets was used to automatically identify and segment whole-brain subregions and WMH. The PyRadiomics package in Python was used to automatically extract radiomic features from the WMH and bilateral hippocampi. Delong tests revealed that the random forest model based on combined features had the best performance for the detection of cognitive impairment in WMH patients, with an AUC of 0.900 in the external validation set. Our results provide clinical doctors with a reliable tool for the early diagnosis of cognitive impairment in WMH patients.

Background: The role of parity in predicting dementia risk in women is debated. This study examines how birth rate affects global dementia incidence.

Methods: Country-specific data on birth rate and dementia incidence rate were analyzed using bivariate analysis, partial correlation, and multiple linear regression. Confounding factors such as aging, affluence, genetic predisposition (I_bs), and urbanization were considered.

Results: Pearson's r and nonparametric analyzes showed a significant inverse correlation between birth rate and dementia incidence. This relationship remained significant after controlling for aging, affluence, I_bs, and urbanization. Multiple linear regression identified birth rate as a significant predictor of dementia incidence, and as the strongest predictor. Affluence and urbanization were not significant predictors. The correlation was stronger in developing countries.

Conclusions: Lower birth rate is an independent risk factor for dementia, particularly in developed countries. These findings highlight the importance of considering birth rate in dementia studies.

Previous research has shown that rTMS improves visual working memory (VWM) performance in older people with subjective cognitive decline (SCD). However, the influence of stimulation parameters on the effect is unclear. We focus on the total number of stimulus pulses and aim to investigate whether 10 Hz rTMS with different total pulses could have different effects on VWM in SCD subjects. 10 Hz rTMS with different total pulses targeting the left dorsolateral prefrontal cortex (DLPFC)was applied to 34 SCD subjects who completed both neuropsychological tests and EEG for the VWM task. Different EEG techniques were used simultaneously to investigate the effect of different numbers of rTMS pulses. Our study found that an increased number of 10 Hz rTMS pulses targeting the left DLPFC with increased cortical excitability, higher power of gamma oscillations and optimized allocation of attentional resources can achieve greater improvement in VWM in SCD subjects.

Objective: To assess the role of Machine Learning (ML) in identification critical factors of dementia and mild cognitive impairment.

Methods: 371 elderly individuals were ultimately included in the ML analysis. Demographic information (including gender, age, parity, visual acuity, auditory function, mobility, and medication history) and 35 features from 10 assessment scales were used for modeling. Five machine learning classifiers were used for evaluation, employing a procedure involving feature extraction, selection, model training, and performance assessment to identify key indicative factors.

Results: The Random Forest model, after data preprocessing, Information Gain, and Meta-analysis, utilized three training features and four meta-features, achieving an area under the curve of 0.961 and a accuracy of 0.894, showcasing exceptional accuracy for the identification of dementia and mild cognitive impairment.

Conclusions: ML serves as a identification tool for dementia and mild cognitive impairment. Using Information Gain and Meta-feature analysis, Clinical Dementia Rating (CDR) and Neuropsychiatric Inventory (NPI) scale information emerged as crucial for training the Random Forest model.

Objectives: Neuropsychological test batteries, which accurately and comprehensively assess cognitive functions, are a crucial approach in the early detection of and interventions for cognitive impairments. However, these tests have yet to gain wide clinical application in China owing to their complexity and time-consuming nature. This study aimed to develop the Computerized Neurocognitive Battery for Chinese-Speaking participants (CNBC), an autorun and autoscoring cognitive assessment tool to provide efficient and accurate cognitive evaluations for Chinese-Speaking individuals.

Methods: The CNBC was developed through collaboration between clinical neurologists and software engineers. Qualified volunteers were recruited to complete CNBC and traditional neurocognitive batteries. The reliability and validity of the CNBC were evaluated by analyzing the correlations between the measurements obtained from the computerized and the paper-based assessment and those between software-based scoring and manual scoring.

Results: The CNBC included 4 subtests and an autorun version. Eighty-six volunteers aged 51-82 years with 7-22 years of education were included. Significant correlations (0.256-0.666) were observed between paired measures associated with attention, executive function, and episodic memory from the CNBC and the traditional paper-based neurocognitive batteries. This suggests a strong construct validity of the CNBC in assessing these cognitive domains. Furthermore, the correlation coefficients between manual scoring and system scoring ranged from 0.904-1.0, indicating excellent inter-rater reliability for the CNBC.

Interpretation: A novel CNBC equipped with automated testing and scoring features was developed in this study. The preliminary results confirm its strong reliability and validity, indicating its promising potential for clinical utilization.

Introduction: To evaluate whether both acute and chronic low-intensity pulsed ultrasound (LIPUS) affect brain functions of healthy male and female mice. Methods: Ultrasound (frequency: 1.5 MHz; pulse: 1.0 kHz; spatial average temporal average (SATA) intensity: 25 mW/cm²; and pulse duty cycle: 20%) was applied at mouse head in acute test for 20 minutes, and in chronic experiment for consecutive 10 days, respectively. Behaviors were then evaluated. Results: Both acute and chronic LIPUS at 25 mW/cm² exposure did not affect the abilities of movements, mating, social interaction, and anxiety-like behaviors in the male and female mice. However, physical restraint caused struggle-like behaviors and short-time memory deficits in chronic LIPUS groups in the male mice. Conclusion: LIPUS at 25 mW/cm² itself does not affect brain functions, while physical restraint for LIPUS therapy elicits struggle-like behaviors in the male mice. An unbound helmet targeted with ultrasound intensity at 25-50 mW/cm² is proposed for clinical brain disease therapy.

Objectives: This study examined the acceptability and usefulness of Positive Behaviour Support (PBS) training in enhancing the capabilities of support staff and family members providing behaviour support to residents with dementia in residential aged care (RAC).

Methods: A mixed-methods pilot study was conducted across 3 RAC organisations, involving pre- and post-training questionnaire assessments for clinical leaders (n = 8), support staff (n = 37) and family members (n = 18).

Results: Findings indicated increased confidence among support staff and family members in providing behaviour support, with 96% indicating it would support their practices across settings. Key training benefits included identifying and addressing underlying causes of challenging behaviours. A majority (89%) expressed the need for further behaviour support training.

Conclusion: Recommendations focus on developing systems to enable effective and collaborative behaviour support practices. Further research is needed to examine application of PBS principles and planning for residents living with dementia.

Several research studies have demonstrated the potential use of cerebrospinal fluid biomarkers such as amyloid beta 1-42, T-tau, and P-tau, in early diagnosis of Alzheimer's disease stages. The levels of these biomarkers in conjunction with the dementia rating scores are used to empirically differentiate the dementia patients from normal controls. In this work, we evaluated the performance of standard machine learning classifiers using cerebrospinal fluid biomarker levels as the features to differentiate dementia patients from normal controls. We employed various types of machine learning models, that includes Discriminant, Logistic Regression, Tree, K-Nearest Neighbor, Support Vector Machine, and Naïve Bayes classifiers. The results demonstrate that these models can distinguish cognitively impaired subjects from normal controls with an accuracy ranging from 64% to 69% and an area under the curve of the receiver operating characteristics between 0.64 and 0.73. In addition, we found that the levels of 2 biomarkers, amyloid beta 1-42 and T-tau, provide a modest improvement in accuracy when distinguishing dementia patients from healthy controls.

Timely diagnosis of young-onset dementia (YOD) is critical. This study aimed to identify factors that increased time to diagnosis at each stage of the diagnostic pathway. Participants were patients diagnosed with YOD (n = 40) and their care partners (n = 39). Information was obtained from questionnaires, and review of medical records. Mean time from symptom onset to YOD diagnosis was 3.6 ± 2 years. Suspicion of depression/anxiety at presentation was associated with significantly increased time from presentation to specialist referral. Neurologist-diagnosed YOD was the fastest route to a diagnosis, whereas diagnoses made by other specialists significantly increased the time from first specialist visit to diagnosis. By investigating multiple stages of the diagnostic pathway, we identified two factors that increased time to diagnosis: suspicion of depression/anxiety at presentation delayed specialist referral from primary care, and diagnosis by a specialist other than a neurologist delayed diagnosis of YOD.