Montgomerynicolaisen2019

Background The study aimed to investigate the relationship between transcription factor EB (TFEB) gene polymorphisms, including their haplotypes, and the cognitive functions of a selected population in Gongcheng County, Guangxi. Methods A case-control study approach was used. The case group comprised 339 individuals with cognitive impairment, as assessed by their Mini-Mental State Examination scores; the control population also comprised 339 individuals who were matched by sex and age (± 5 years) in a 11 ratio. TFEB gene polymorphisms were genotyped in 678 participants (190 men and 488 women, aged 30-91 years) by using the Sequenom MassARRAY platform. Results Multifactorial logistic regression analysis showed that in the dominant model, the risk of developing cognitive impairment was 1.547 times higher in cases with the TFEB rs14063A allele (AG + AA) than in those with the GG genotype (adjusted odds ratio [OR] = 1.547, Bonferroni correction confidence interval = 1.021-2.345). Meanwhile, the presence of the TFype of the TFEB gene increased the risk of cognitive impairment (P less then 0.05) and that the rs14063G-rs1062966T-rs2278068C-rs1015149C haplotype was associated with a reduced risk of cognitive impairment (P less then 0.05). Conclusion TFEB rs1062966 polymorphisms and their rs14063A-rs1062966C-rs2278068C-rs1015149T and rs14063G-rs1062966T-rs2278068C-rs1015149C haplotypes are genetic factors that may affect cognitive function among the rural Chinese population.The amygdala is known to be related to cognitive function. In this study, we used an automated approach to segment the amygdala into nine nuclei and evaluated amygdala and nuclei volumetric changes across the adult lifespan in subjects carrying the apolipoprotein E (ApoE) ε3/ε3 allele, and we related those changes to memory function alteration. We found that except the left medial nucleus (Me), whose volume decreased in the old group compared with the middle-early group, all other nuclei volumes presented a significant decline in the old group compared with the young group. Left accessory basal nucleus (AB) and left cortico-amygdaloid transition area (CAT) volumes were also diminished in the middle-late group. In addition, immediate memory recall is impaired by the process of aging, whereas delayed recall and delayed recognition memory functions were not significantly changed. We found significant positive correlations between immediate recall scores and volumes of the bilateral basal nucleus (Ba), AB, anterior amygdaloid area (AAA), CAT, whole amygdala, left lateral nucleus (La), left paralaminar nucleus (PL), and right cortical nucleus (Co). The results suggest that immediate recall memory decline might be associated with volumetric reduction of the amygdala and its nuclei, and the left AB and left CAT might be considered as potential imaging biomarkers of memory decline in aging.Drug addiction is defined as a compulsive pattern of drug-seeking- and taking- behavior, with recurrent episodes of abstinence and relapse, and a loss of control despite negative consequences. Addictive drugs promote reinforcement by increasing dopamine in the mesocorticolimbic system, which alters excitatory glutamate transmission within the reward circuitry, thereby hijacking reward processing. Within the reward circuitry, the striatum is a key target structure of drugs of abuse since it is at the crossroad of converging glutamate inputs from limbic, thalamic and cortical regions, encoding components of drug-associated stimuli and environment, and dopamine that mediates reward prediction error and incentive values. These signals are integrated by medium-sized spiny neurons (MSN), which receive glutamate and dopamine axons converging onto their dendritic spines. MSN primarily form two mostly distinct populations based on the expression of either DA-D1 (D1R) or DA-D2 (D2R) receptors. While a classical view ismon circuits could explain the co-occurrence of addiction and depression. We will therefore conclude this review by examining how the nucleus accumbens (NAc) could constitute a key interface between addiction and depression.We present a neurocomputational controller for robotic manipulation based on the recently developed "neural virtual machine" (NVM). The NVM is a purely neural recurrent architecture that emulates a Turing-complete, purely symbolic virtual machine. We program the NVM with a symbolic algorithm that solves blocks-world restacking problems, and execute it in a robotic simulation environment. Our results show that the NVM-based controller can faithfully replicate the execution traces and performance levels of a traditional non-neural program executing the same restacking procedure. Moreover, after programming the NVM, the neurocomputational encodings of symbolic block stacking knowledge can be fine-tuned to further improve performance, by applying reinforcement learning to the underlying neural architecture.The constant growth of the population with mobility impairments, such as older adults and people suffering from neurological pathologies like Parkinson's disease (PD), has encouraged the development of multiple devices for gait assistance. Robotic walkers have emerged, improving physical stability and balance and providing cognitive aid in rehabilitation scenarios. Different studies evaluated human gait behavior with passive and active walkers to understand such rehabilitation processes. However, there is no evidence in the literature of studies with robotic walkers in daily living scenarios with older adults with Parkinson's disease. This study presents the assessment of the AGoRA Smart Walker using Ramps Tests and Timed Up and Go Test (TUGT). Ten older adults participated in the study, four had PD, and the remaining six had underlying conditions and fractures. Each of them underwent a physical assessment (i.e., Senior Fitness, hip, and knee strength tests) and then interacted with the AGoRA SW. Kinematic anuggested that the walker, represents a valuable tool for assisting people with gait motor deficits in tasks that demanded more physical effort adapting its behavior to the specific needs of each user.In this study, we focused on the verification of suitable aggregation operators enabling accurate differentiation of selected neurophysiological features extracted from resting-state electroencephalographic recordings of patients who were diagnosed with schizophrenia (SZ) or healthy controls (HC). We built the Choquet integral-based operators using traditional classification results as an input to the procedure of establishing the fuzzy measure densities. The dataset applied in the study was a collection of variables characterizing the organization of the neural networks computed using the minimum spanning tree (MST) algorithms obtained from signal-spaced functional connectivity indicators and calculated separately for predefined frequency bands using classical linear Granger causality (GC) measure. In the series of numerical experiments, we reported the results of classification obtained using numerous generalizations of the Choquet integral and other aggregation functions, which were tested to find the most appropriate ones. The obtained results demonstrate that the classification accuracy can be increased by 1.81% using the extended versions of the Choquet integral called in the literature, namely, generalized Choquet integral or pre-aggregation operators.Biological as well as advanced artificial intelligences (AIs) need to decide which goals to pursue. We review nature's solution to the time allocation problem, which is based on a continuously readjusted categorical weighting mechanism we experience introspectively as emotions. One observes phylogenetically that the available number of emotional states increases hand in hand with the cognitive capabilities of animals and that raising levels of intelligence entail ever larger sets of behavioral options. Our ability to experience a multitude of potentially conflicting feelings is in this view not a leftover of a more primitive heritage, but a generic mechanism for attributing values to behavioral options that can not be specified at birth. In this view, emotions are essential for understanding the mind. For concreteness, we propose and discuss a framework which mimics emotions on a functional level. Based on time allocation via emotional stationarity (TAES), emotions are implemented as abstract criteria, such as satisfaction, challenge and boredom, which serve to evaluate activities that have been carried out. The resulting timeline of experienced emotions is compared with the "character" of the agent, which is defined in terms of a preferred distribution of emotional states. The long-term goal of the agent, to align experience with character, is achieved by optimizing the frequency for selecting individual tasks. Upon optimization, the statistics of emotion experience becomes stationary.General Linear Modeling (GLM) is the most commonly used method for signal detection in Functional Magnetic Resonance Imaging (fMRI) experiments, despite its main limitation of not taking into consideration common spatial dependencies between voxels. Multivariate analysis methods, such as Generalized Canonical Correlation Analysis (gCCA), have been increasingly employed in fMRI data analysis, due to their ability to overcome this limitation. This study, evaluates the improvement of sensitivity of the GLM, by applying gCCA to fMRI data after standard preprocessing steps. Data from a block-design fMRI experiment was used, where 25 healthy volunteers completed two action observation tasks at 1.5T. Whole brain analysis results indicated that the application of gCCA resulted in significantly higher intensity of activation in several regions in both tasks and helped reveal activation in the primary somatosensory and ventral premotor area, theoretically known to become engaged during action observation. In subject-level ROI analyses, gCCA improved the signal to noise ratio in the averaged timeseries in each preselected ROI, and resulted in increased extent of activation, although peak intensity was considerably higher in just two of them. In conclusion, gCCA is a promising method for improving the sensitivity of conventional statistical modeling in task related fMRI experiments.Background and Objectives Neurodegeneration and vascular burden are the two most common causes of post-stroke cognitive impairment. However, the interrelationship between the plasma beta-amyloid (Aβ) and tau protein, cortical atrophy and brain amyloid accumulation on PET imaging in stroke patients is undetermined. We aimed to explore (1) the relationships of cortical thickness and amyloid burden on PET with plasma Aβ40, Aβ42, tau protein and their composite scores in stroke patients; and (2) the associations of post-stroke cognitive presentations with these plasma and neuroimaging biomarkers. Methods The prospective project recruited first-ever ischemic stroke patients around 3 months after stroke onset. The plasma Aβ40, Aβ42, and total tau protein were measured with the immunomagnetic reduction method. Cortical thickness was evaluated on MRI, and cortical amyloid plaque deposition was evaluated by 18F-florbetapir PET. Cognition was evaluated with Mini-Mental State Examination (MMSE), Geriatric Depression Scale (GDS), Dementia Rating Scale-2 (DRS-2).