Enhancing Prediction of Forelimb Movement Trajectory through a Calibrating-Feedback Paradigm Incorporating RAT Primary Motor and Agranular Cortical Ensemble Activity in the Goal-Directed Reaching Task.

Complete reaching movements involve target sensing, motor planning, and arm movement execution, and this process requires the integration and communication of various brain regions. Previously, reaching movements have been decoded successfully from the motor cortex (M1) and applied to prosthetic control. However, most studies attempted to decode neural activities from a single brain region, resulting in reduced decoding accuracy during visually guided reaching motions. To enhance the decoding accuracy of visually guided forelimb reaching movements, we propose a parallel computing neural network using both M1 and medial agranular cortex (AGm) neural activities of rats to predict forelimb-reaching movements. The proposed network decodes M1 neural activities into the primary components of the forelimb movement and decodes AGm neural activities into internal feedforward information to calibrate the forelimb movement in a goal-reaching movement. We demonstrate that using AGm neural activity to calibrate M1 predicted forelimb movement can improve decoding performance significantly compared to neural decoders without calibration. We also show that the M1 and AGm neural activities contribute to controlling forelimb movement during goal-reaching movements, and we report an increase in the power of the local field potential (LFP) in beta and gamma bands over AGm in response to a change in the target distance, which may involve sensorimotor transformation and communication between the visual cortex and AGm when preparing for an upcoming reaching movement. The proposed parallel computing neural network with the internal feedback model improves prediction accuracy for goal-reaching movements.

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.

Multimodal Optical Imaging to Investigate Spatiotemporal Changes in Cerebrovascular Function in AUDA Treatment of Acute Ischemic Stroke.

Administration of 12-(3-adamantan-1-yl-ureido)-dodecanoic acid (AUDA) has been demonstrated to alleviate infarction following ischemic stroke. Reportedly, the main effect of AUDA is exerting anti-inflammation and neovascularization via the inhibition of soluble epoxide hydrolase. However, the major contribution of this anti-inflammation and neovascularization effect in the acute phase of stroke is not completely elucidated. To investigate the neuroprotective effects of AUDA in acute ischemic stroke, we combined laser speckle contrast imaging and optical intrinsic signal imaging techniques with the implantation of a lab-designed cranial window. Forepaw stimulation was applied to assess the functional changes via measuring cerebral metabolic rate of oxygen (CMRO2) that accompany neural activity. The rats that received AUDA in the acute phase of photothrombotic ischemia stroke showed a 30.5 ± 8.1% reduction in the ischemic core, 42.3 ± 15.1% reduction in the ischemic penumbra (p < 0.05), and 42.1 ± 4.6% increase of CMRO2 in response to forepaw stimulation at post-stroke day 1 (p < 0.05) compared with the control group (N = 10 for each group). Moreover, at post-stroke day 3, increased functional vascular density was observed in AUDA-treated rats (35.9 ± 1.9% higher than that in the control group, p < 0.05). At post-stroke day 7, a 105.4% ± 16.4% increase of astrocytes (p < 0.01), 30.0 ± 10.9% increase of neurons (p < 0.01), and 65.5 ± 15.0% decrease of microglia (p < 0.01) were observed in the penumbra region in AUDA-treated rats (N = 5 for each group). These results suggested that AUDA affects the anti-inflammation at the beginning of ischemic injury and restores neuronal metabolic rate of O2 and tissue viability. The neovascularization triggered by AUDA restored CBF and may contribute to ischemic infarction reduction at post-stroke day 3. Moreover, for long-term neuroprotection, astrocytes in the penumbra region may play an important role in protecting neurons from apoptotic injury.

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.

Treatment experience of Taiwanese patients with anti-3-hydroxy-3-methylglutaryl-coenzyme A reductase myopathy.

Immune-mediated necrotizing myopathy (IMNM) has emerged as a new subgroup of idiopathic inflammatory myopathy in the past decade, associated with the presence of two autoantibodies against signal recognition particle and 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR). We aim to analyze the clinical, pathological, and imaging phenotypes of the patients with anti-HMGCR myopathy in our cohort. Five patients with anti-HMGCR myopathy have been enrolled who were all female; three were pediatric and two were adult patients. The muscle pathology of patients met the diagnostic criteria of IMNM. On muscle magnetic resonance imaging, adductors were earliest affected while lower legs were relatively preserved with highest degree of involvement in medial head of gastrocnemius. In upper extremities, biceps brachii was the most severely involved, followed by triceps. All patients were refractory to steroid mono-therapy. For pediatric patients, all three patients eventually became responsive to steroid with either intravenous immunoglobulin or rituximab despite variable motor function recovered at present due to different intervention timing. For adult patients, one with statin exposure responded well to steroid and azathioprine use and the motor function returned to the baseline. The other adult patient finally got stabilized and slowly improved with steroid and methotrexate 13 years after the start of therapy. The creatine kinase (CK) levels of all patients were decreased along with clinical severity. In conclusion, muscle imaging might be of help for the diagnosis. Treatment with immuno-suppressants could be considered together with steroid from the beginning.