T1 values of right insula cortex and T2 values of left hippocampus and right insula cortex were significantly higher in AD patients than in NCs, but T1 values of left caudate showed a reverse trend (p less then 0.05). As the MMSE score decreased in AD patients, the BPV and BPV/ICV decreased, while the volume of CSF and T1 values of bilateral insula cortex and bilateral hippocampus as well as T2 values of bilateral hippocampus increased (p less then 0.05). Conclusion Synthetic MRI not only provides more information to differentiate AD patients from normal controls, but also reflects the disease severity of AD.Objective Olfactory impairment (OI) refers to decreased (hyposmia) or absent (anosmia) ability to smell. We sought to estimate the prevalence and correlates of OI among rural-dwelling Chinese older adults. Methods This population-based cross-sectional analysis included 4,514 participants (age ≥65 years; 56.7% women) from the Multidomain Interventions to Delay Dementia and Disability in Rural China (MIND-China). The 16-item Sniffin' Sticks identification test (SSIT) was used to assess olfactory function. Olfactory impairment was defined as the SSIT score ≤10, hyposmia as SSIT score of 8-10, and anosmia as SSIT score less then 8. Multivariable logistic regression models were used to examine factors associated with OI. Results The overall prevalence was 67.7% for OI, 35.3% for hyposmia, and 32.5% for anosmia. The prevalence increased with age for OI and anosmia, but not for hyposmia. The multivariable-adjusted odds ratio (OR) of OI was 2.10 (95% CI 1.69-2.61) for illiteracy and 1.41 (1.18-1.70) for elementary school (vs. middle school or above), 1.30 (1.01-1.67) for current smoking (vs. never smoking), 0.86 (0.74-0.99) for overweight and 0.73 (0.61-0.87) for obesity (vs. normal weight), 4.21 (2.23-7.94) for dementia, 1.68 (1.23-2.30) for head injury, and 1.44 (1.14-1.83) for sinonasal disease. Illiteracy in combination with either male sex or diabetes was significantly associated with an over two-fold increased OR of OI (p for interactions less then 0.05). Conclusion Olfactory impairment is highly prevalent that affects over two-thirds of rural-dwelling older adults in China. OI is correlated with illiteracy, current smoking, dementia, head injury, and sinonasal disease, but negatively associated with overweight or obesity. Olfactory impairment as a potential clinical marker of neurodegenerative disorders among older adults deserves further investigation.Experimental studies support the notion of spike-based neuronal information processing in the brain, with neural circuits exhibiting a wide range of temporally-based coding strategies to rapidly and efficiently represent sensory stimuli. Accordingly, it would be desirable to apply spike-based computation to tackling real-world challenges, and in particular transferring such theory to neuromorphic systems for low-power embedded applications. Motivated by this, we propose a new supervised learning method that can train multilayer spiking neural networks to solve classification problems based on a rapid, first-to-spike decoding strategy. The proposed learning rule supports multiple spikes fired by stochastic hidden neurons, and yet is stable by relying on first-spike responses generated by a deterministic output layer. In addition to this, we also explore several distinct, spike-based encoding strategies in order to form compact representations of presented input data. We demonstrate the classification performance of the learning rule as applied to several benchmark datasets, including MNIST. The learning rule is capable of generalizing from the data, and is successful even when used with constrained network architectures containing few input and hidden layer neurons. Furthermore, we highlight a novel encoding strategy, termed "scanline encoding," that can transform image data into compact spatiotemporal patterns for subsequent network processing. Designing constrained, but optimized, network structures and performing input dimensionality reduction has strong implications for neuromorphic applications. To evaluate the prevalence and treatment patterns of speech and language disorders in Germany. A retrospective analysis of data collected from 32% of the German population, insured by the statutory German health insurance (AOK, Local Health Care Funds). We used The International Statistical Classification of Diseases and Related Health Problems, 10th revision, German Modification (ICD-10 GM) codes for stuttering (F98.5), cluttering (F98.6), and developmental disorders of speech and language (F80) to identify prevalent and newly diagnosed cases each year. Prescription and speech therapy reimbursement data were used to evaluate treatment patterns. In 2017, 27,977 patients of all ages were diagnosed with stuttering (21,045 males, 75% and 6,932 females, 25%). Stuttering prevalence peaks at age 5 years (boys, 0.89% and girls, 0.40%). Cluttering was diagnosed in 1,800 patients of all ages (1,287 males, 71.5% and 513 females, 28.5%). LY2606368 Developmental disorders of speech and language were identified in 555,774 AOK entrance year, and is later than the prevalence peak of stuttering.This is the first nationwide study using health insurance data to analyze the prevalence and newly diagnosed cases of a speech and language disorder. Prevalence and gender ratio data were consistent with the international literature. The crude prevalence of developmental disorders of speech and language increased from 2015 to 2018, whereas the crude prevalence for stuttering remained stable. For cluttering, the numbers were too low to draw reliable conclusions. Proportional treatment allocation for stuttering peaked at 6 years of age, which is the school entrance year, and is later than the prevalence peak of stuttering.Purpose The cognitive effects of total sleep deprivation (TSD) on the brain remain poorly understood. Electroencephalography (EEG) is a very useful tool for detecting spontaneous brain activity in the resting state. Quasi-stable electrical distributions, known as microstates, carry useful information about the dynamics of large-scale brain networks. In this study, microstate analysis was used to study changes in brain activity after 24 h of total sleep deprivation. Participants and Methods Twenty-seven healthy volunteers were recruited and underwent EEG scans before and after 24 h of TSD. Microstate analysis was applied, and six microstate classes (A-F) were identified. Topographies and temporal parameters of the microstates were compared between the rested wakefulness (RW) and TSD conditions. Results Microstate class A (a right-anterior to left-posterior orientation of the mapped field) showed lower global explained variance (GEV), frequency of occurrence, and time coverage in TSD than RW, whereas microstate class D (a fronto-central extreme location of the mapped field) displayed higher GEV, frequency of occurrence, and time coverage in TSD compared to RW.