A Simulation Model of Neural Activity During Hand Reaching Movement.

INTRODUCTION: The neural response is a noisy random process. The neural response to a sensory stimulus is completely equivalent to a list of spike times in the spike train. In previous studies, decreased neuronal response variability was observed in the cortex's various areas during motor preparatory in reaching tasks. The reasons for the reduction in Neural Variability (NV) are unclear. It could be influenced by an increased firing rate, or it could result from the intrinsic characteristic of cells during the Reaction Time (RT). METHODS: A neural response function with an underlying deterministic instantaneous firing rate signal and a random Poisson process spike generator was simulated in this research. Neural stimulation could help us understand the relationships between the complex data structures of cortical activities and their stability in detail during motor intention in arm-reaching tasks. RESULTS: Our measurements indicated a similar pattern of results to the cortex, a sharp reduction of the normalized variance of simulated spike trains across all trials. We also observed a reverse relationship between activity and normalized variance. CONCLUSION: The present study findings could be applied to neural engineering and brain-machine interfaces for controlling external devices, like the movement of a robot arm.

An Algorithmic Model of Decision Making in the Human Brain.

INTRODUCTION: One of the interesting topics in neuroscience is problem solving and decision-making. In this area, everything gets more complicated when events occur sequentially. One of the practical methods for handling the complexity of brain function is to create an empirical model. Model Predictive Control (MPC) is known as a powerful mathematical-based tool often used in industrial environments. We proposed an MPC and its algorithm as a part of the functionalities of the brain to improve the performance of the decision-making process. METHODS: We used a hybrid methodology whereby combining a powerful nonlinear control system tools and a modular fashion approach in computer science. Our hybrid approach employed the MPC and the Object-Oriented Modeling (OOM) respectively. Therefore, we could model the interaction between most important regions within the brain to simulate the decision-making process. RESULTS: The employed methodology provided the capability to design an algorithm based on the cognitive functionalities of the PFC and Hippocampus. The developed algorithm applied for modulation of neural circuits between cortex and sub-cortex during a decision making process. CONCLUSION: It is well known that the decision-making process results from communication between the prefrontal cortex (working memory) and hippocampus (long-term memory). However, there are other regions of the brain that play essential roles in making decisions, but their exact mechanisms of action still are unknown. In this study, we modeled those mechanisms with MPC. We showed that MPC controls the stream of data between prefrontal cortex and hippocampus in a closed-loop system to correct actions.

In vitro Cortical Network Firing is Homeostatically Regulated: A Model for Sleep Regulation.

Prolonged wakefulness leads to a homeostatic response manifested in increased amplitude and number of electroencephalogram (EEG) slow waves during recovery sleep. Cortical networks show a slow oscillation when the excitatory inputs are reduced (during slow wave sleep, anesthesia), or absent (in vitro preparations). It was recently shown that a homeostatic response to electrical stimulation can be induced in cortical cultures. Here we used cortical cultures grown on microelectrode arrays and stimulated them with a cocktail of waking neuromodulators. We found that recovery from stimulation resulted in a dose-dependent homeostatic response. Specifically, the inter-burst intervals decreased, the burst duration increased, the network showed higher cross-correlation and strong phasic synchronized burst activity. Spectral power below <1.75 Hz significantly increased and the increase was related to steeper slopes of bursts. Computer simulation suggested that a small number of clustered neurons could potently drive the behavior of the network both at baseline and during recovery. Thus, this in vitro model appears valuable for dissecting network mechanisms of sleep homeostasis.

Cerebral mGluR5 availability contributes to elevated sleep need and behavioral adjustment after sleep deprivation.

Increased sleep time and intensity quantified as low-frequency brain electrical activity after sleep loss demonstrate that sleep need is homeostatically regulated, yet the underlying molecular mechanisms remain elusive. We here demonstrate that metabotropic glutamate receptors of subtype 5 (mGluR5) contribute to the molecular machinery governing sleep-wake homeostasis. Using positron emission tomography, magnetic resonance spectroscopy, and electroencephalography in humans, we find that increased mGluR5 availability after sleep loss tightly correlates with behavioral and electroencephalographic biomarkers of elevated sleep need. These changes are associated with altered cortical myo-inositol and glycine levels, suggesting sleep loss-induced modifications downstream of mGluR5 signaling. Knock-out mice without functional mGluR5 exhibit severe dysregulation of sleep-wake homeostasis, including lack of recovery sleep and impaired behavioral adjustment to a novel task after sleep deprivation. The data suggest that mGluR5 contribute to the brain's coping mechanisms with sleep deprivation and point to a novel target to improve disturbed wakefulness and sleep.

Modulation of Neural Variability in Premotor, Motor, and Posterior Parietal Cortex during Change of Motor Intention.

The time course of neural variability was studied in three nodes of the parieto-frontal system: the dorsal premotor cortex (PMd, area 6), primary motor cortex (MI, area 4), and posterior parietal cortex (PPC, area 5) while monkeys made either direct reaches to visual targets or changed reach direction in response to an unexpected change of target location. These areas are crucial nodes in the distributed control of reaching and their lesion impairs trajectory formation and correction under different circumstances. During unperturbed reaches, neural variability declined before the onset of hand movement in both frontal and parietal cortex. When the original motor intention suddenly changed, neural variability displayed a complex and area-specific modulation because the perturbation of the motor state was signaled earlier in PMd than in MI and PPC. The comparison of perturbed versus unperturbed reaches revealed that, in the time between the onset of correction signal and trajectory change, identical hand movements were associated with different, therefore context-dependent, patterns of neural variability induced by the instruction to change hand movement direction. In PMd, neural variability was higher before the initiation of hand reach than before its correction, thus providing a neural underpinning to the phenomenon that it takes less time to correct than to initiate hand movement. Furthermore, neural variability was an excellent predictor of slow and fast reach corrections because it was lower during the latter than the former. We conclude that the analysis of neural variability can be an important tool for the study of complex forms of motor cognition. SIGNIFICANCE STATEMENT: No single study has been performed on neural variability during update of motor intention across monkey premotor, motor, and posterior parietal cortex. In perturbed reaches, target location changed unexpectedly during reaction time and the correction of hand trajectory required updating the original motor plan. Comparing unperturbed versus perturbed reaches revealed that neural variability displayed a complex context- and area-dependent pattern of modulation because, before trajectory correction, similar initial hand movements were associated with different patterns of variability depending on the instruction signal, and therefore on the future hand path and final destination. Furthermore, neural variability predicted both slow and fast hand movement corrections, also offering a neural underpinning to the phenomenon that it takes less time to correct than to initiate hand movement.

Impairment of online control of hand and eye movements in a monkey model of optic ataxia.

The parietal mechanisms for online control of hand trajectory were studied by combining single-cell recording and reversible inactivation of superior parietal area 5 (PE/PEc; SPL) of monkeys while these made reaches and saccades to visual targets, when the target position changed unexpectedly. Neural activity was modulated by hand position, speed, and movement direction, and by pre- and/or postsaccadic signals. After bilateral muscimol injection, an increase in the hand reaction- and movement-time toward both the first and second targets was observed. This caused an increase in the time necessary for the trajectory correction, and therefore an elongation of the hand-path toward the first target location. Furthermore, hand trajectories were different in shape than control ones. An elongation of the eye reaction time to both first and second targets was also observed, which could partially explain the deficit of planning and correction of hand movement. These results identify the superior parietal lobule as a crucial node in the online control of hand and eye movement and highlight the role of the eye impairment in the emergence of the reaching disorder so far regarded as the hallmark of optic ataxia.