Impact of cognition-related single nucleotide polymorphisms on brain imaging phenotype in Parkinson's disease.

Multiple single nucleotide polymorphisms may contribute to cognitive decline in Parkinson's disease. However, the mechanism by which these single nucleotide polymorphisms modify brain imaging phenotype remains unclear. The aim of this study was to investigate the potential effects of multiple single nucleotide polymorphisms on brain imaging phenotype in Parkinson's disease. Forty-eight Parkinson's disease patients and 39 matched healthy controls underwent genotyping and 7T magnetic resonance imaging. A cognitive-weighted polygenic risk score model was designed, in which the effect sizes were determined individually for 36 single nucleotide polymorphisms. The correlations between polygenic risk score, neuroimaging features, and clinical data were analyzed. Furthermore, individual single nucleotide polymorphism analysis was performed to explore the main effects of genotypes and their interactive effects with Parkinson's disease diagnosis. We found that, in Parkinson's disease, the polygenic risk score was correlated with the neural activity of the hippocampus, parahippocampus, and fusiform gyrus, and with hippocampal-prefrontal and fusiform-temporal connectivity, as well as with gray matter alterations in the orbitofrontal cortex. In addition, we found that single nucleotide polymorphisms in α-synuclein (SNCA) were associated with white matter microstructural changes in the superior corona radiata, corpus callosum, and external capsule. A single nucleotide polymorphism in catechol-O-methyltransferase was associated with the neural activities of the lingual, fusiform, and occipital gyri, which are involved in visual cognitive dysfunction. Furthermore, DRD3 was associated with frontal and temporal lobe function and structure. In conclusion, imaging genetics is useful for providing a better understanding of the genetic pathways involved in the pathophysiologic processes underlying Parkinson's disease. This study provides evidence of an association between genetic factors, cognitive functions, and multi-modality neuroimaging biomarkers in Parkinson's disease.

Neuro-Inspired Reinforcement Learning to Improve Trajectory Prediction in Reward-Guided Behavior.

Hippocampal pyramidal cells and interneurons play a key role in spatial navigation. In goal-directed behavior associated with rewards, the spatial firing pattern of pyramidal cells is modulated by the animal's moving direction toward a reward, with a dependence on auditory, olfactory, and somatosensory stimuli for head orientation. Additionally, interneurons in the CA1 region of the hippocampus monosynaptically connected to CA1 pyramidal cells are modulated by a complex set of interacting brain regions related to reward and recall. The computational method of reinforcement learning (RL) has been widely used to investigate spatial navigation, which in turn has been increasingly used to study rodent learning associated with the reward. The rewards in RL are used for discovering a desired behavior through the integration of two streams of neural activity: trial-and-error interactions with the external environment to achieve a goal, and the intrinsic motivation primarily driven by brain reward system to accelerate learning. Recognizing the potential benefit of the neural representation of this reward design for novel RL architectures, we propose a RL algorithm based on [Formula: see text]-learning with a perspective on biomimetics (neuro-inspired RL) to decode rodent movement trajectories. The reward function, inspired by the neuronal information processing uncovered in the hippocampus, combines the preferred direction of pyramidal cell firing as the extrinsic reward signal with the coupling between pyramidal cell-interneuron pairs as the intrinsic reward signal. Our experimental results demonstrate that the neuro-inspired RL, with a combined use of extrinsic and intrinsic rewards, outperforms other spatial decoding algorithms, including RL methods that use a single reward function. The new RL algorithm could help accelerate learning convergence rates and improve the prediction accuracy for moving trajectories.

The Association Between the Gut Microbiota and Parkinson's Disease, a Meta-Analysis.

Patients with Parkinson's disease (PD) were often observed with gastrointestinal symptoms, which preceded the onset of motor symptoms. Neuropathology of PD has also been found in the enteric nervous system (ENS). Many studies have reported significant PD-related alterations of gut microbiota. This meta-analysis was performed to evaluate the differences of gut microbiota between patients with PD and healthy controls (HCs) across different geographical regions. We conducted a systematic online search for case-control studies detecting gut microbiota in patients with PD and HCs. Mean difference (MD) and 95% confidence interval (CI) were calculated to access alterations in the abundance of certain microbiota families in PD. Fifteen case-control studies were included in this meta-analysis study. Our results showed significant lower abundance levels of Prevotellaceae (MD = -0.37, 95% CI = -0.62 to -0.11), Faecalibacterium (MD = -0.41, 95% CI: -0.57 to -0.24), and Lachnospiraceae (MD = -0.34, 95% CI = -0.59 to -0.09) in patients with PD compared to HCs. Significant higher abundance level of Bifidobacteriaceae (MD = 0.38, 95%; CI = 0.12 to 0.63), Ruminococcaceae (MD = 0.58, 95% CI = 0.07 to 1.10), Verrucomicrobiaceae (MD = 0.45, 95% CI = 0.21 to 0.69), and Christensenellaceae (MD = 0.20, 95% CI = 0.07 to 0.34) was also found in patients with PD. Thus, shared alterations of certain gut microbiota were detected in patients with PD across different geographical regions. These PD-related gut microbiota dysbiosis might lead to the impairment of short-chain fatty acids (SCFAs) producing process, lipid metabolism, immunoregulatory function, and intestinal permeability, which contribute to the pathogenesis of PD.

Enhancement of Hippocampal Spatial Decoding Using a Dynamic Q-Learning Method With a Relative Reward Using Theta Phase Precession.

Hippocampal place cells and interneurons in mammals have stable place fields and theta phase precession profiles that encode spatial environmental information. Hippocampal CA1 neurons can represent the animal's location and prospective information about the goal location. Reinforcement learning (RL) algorithms such as Q-learning have been used to build the navigation models. However, the traditional Q-learning ([Formula: see text]Q-learning) limits the reward function once the animals arrive at the goal location, leading to unsatisfactory location accuracy and convergence rates. Therefore, we proposed a revised version of the Q-learning algorithm, dynamical Q-learning ([Formula: see text]Q-learning), which assigns the reward function adaptively to improve the decoding performance. Firing rate was the input of the neural network of [Formula: see text]Q-learning and was used to predict the movement direction. On the other hand, phase precession was the input of the reward function to update the weights of [Formula: see text]Q-learning. Trajectory predictions using [Formula: see text]Q- and [Formula: see text]Q-learning were compared by the root mean squared error (RMSE) between the actual and predicted rat trajectories. Using [Formula: see text]Q-learning, significantly higher prediction accuracy and faster convergence rate were obtained compared with [Formula: see text]Q-learning in all cell types. Moreover, combining place cells and interneurons with theta phase precession improved the convergence rate and prediction accuracy. The proposed [Formula: see text]Q-learning algorithm is a quick and more accurate method to perform trajectory reconstruction and prediction.

Design, synthesis and biological evaluation of brain targeting l-ascorbic acid prodrugs of ibuprofen with "lock-in" function.

A novel brain targeting l-ascorbic acid derivatives with "lock-in" function were designed and synthesized as prodrugs to achieve the effective delivery of ibuprofen to brain by glucose transporter 1 (GLUT1) and the Na(+)-dependent vitamin C transporter SVCT2. Ibuprofen-loaded four prodrugs were tested in the animals. Results from the in vivo distribution study after i.v. administration of these four prodrugs and naked ibuprofen indicated that four prodrugs exhibited excellent transport ability across the BBB and significantly increased the level of ibuprofen in brain. Among them, prodrugs 4 showed higher brain concentration. Both biodistribution data and pharmacokinetic parameters suggested that l-ascorbic acid thiamine disulfide delivery system was a promising carrier to enhance CNS drug's delivery ability into brain.

Design, synthesis and biological evaluation of multivalent glucosides with high affinity as ligands for brain targeting liposomes.

The new bifunctional cluster glucosides were designed and synthesized as liposome ligands for preparing novel liposome to achieve the effective delivery of drug formulations to brain by GLUT1. Docetaxel-loaded five liposomes were prepared successfully and tested in the animals. Results from the in vivo distribution study after i.v. administration of these five liposomes and blank-docetaxel indicated that the coupled liposomes Lip-1, Lip-2, Lip-3, Lip-5 exhibited excellent transport ability across the BBB. In particular, they significantly increased the level of docetaxel in brain compared to blank-docetaxel and Lip. Among them, Lip-5 showed higher brain concentration. Both pharmacokinetics and distribution study in mice confirmed that this novel brain targeting drug delivery system was a promising carrier to enhance brain delivery capacity for CNS drugs.

Design, synthesis and biological evaluation for docetaxel-loaded brain targeting liposome with "lock-in" function.

BACKGROUND: Glucose-modified liposome showed a good brain-targeting ability. However, bidirectional transport of glucose transporter-1 (GLUT1) might reversely pump drugs out of the brain before releasing from the liposomes. PURPOSE: To overcome the bidirectional delivery of GLUT1, the thiamine disulfide system (TDS), with ability of "lock-in", was introduced and a new ligand, L-TDS-G, was designed and synthesized. METHODS: The liposome was prepared and characterized for particle size, zeta potential, surface morphological property, encapsulation efficiency and release profile. C6 glioma cells were used as an in vitro model to access the cellular uptake abilities and cytotoxicity of the liposomes. Competition assay was performed to validate the GLUT1-mediated transport mechanism. Furthermore, the brain targeting abilities of the liposomes were evaluated through in vivo. RESULTS: The preliminary evaluation in vivo demonstrated that L-TDS-G-coated liposome has an improved targeting ability and significantly increased the area under the concentration-time of docetaxel in brain as compared to naked docetaxel, non-coated and L-G coated liposomes. The relative uptake efficiency and concentration efficiency were enhanced by 3.82- and 4.99-fold compared to that of naked docetaxel, respectively. CONCLUSION: The results of this study indicated that L-TDS-G-coated liposome is a promising drug delivery system to enhance the brain concentrations of chemotherapeutic agents.

N-(2,3,4-Trifluoro-phen-yl)pyrrolidine-1-carboxamide.

In the title compound, C(11)H(11)F(3)N(2)O, a urea derivative, the best plane through the pyrrole ring makes a dihedral angle of 9.69 (13)° with the benzene ring. The amino H atom is shielded, so that it is not involved in any hydrogen-bonding inter-actions.