Differential Formation of Motor Cortical Dynamics during Movement Preparation According to the Predictability of Go Timing.

The motor cortex not only executes but also prepares movement, as motor cortical neurons exhibit preparatory activity that predicts upcoming movements. In movement preparation, animals adopt different strategies in response to uncertainties existing in nature such as the unknown timing of when a predator will attack-an environmental cue informing "go." However, how motor cortical neurons cope with such uncertainties is less understood. In this study, we aim to investigate whether and how preparatory activity is altered depending on the predictability of "go" timing. We analyze firing activities of the anterior lateral motor cortex in male mice during two auditory delayed-response tasks each with predictable or unpredictable go timing. When go timing is unpredictable, preparatory activities immediately reach and stay in a neural state capable of producing movement anytime to a sudden go cue. When go timing is predictable, preparation activity reaches the movement-producible state more gradually, to secure more accurate decisions. Surprisingly, this preparation process entails a longer reaction time. We find that as preparatory activity increases in accuracy, it takes longer for a neural state to transition from the end of preparation to the start of movement. Our results suggest that the motor cortex fine-tunes preparatory activity for more accurate movement using the predictability of go timing.

Robust unenhanced peripheral magnetic resonance angiography using single-slab 3D chemical-shift-encoded GRASE.

Objective.To develop a novel, unenhanced magnetic resonance angiography (MRA) exploiting cardiac-gated, single-slab 3D chemical-shift-encoded gradient- and spin-echo (GRASE) imaging for robust background suppression.Approach.The proposed single-slab 3D GRASE employs variable-flip-angles (VFA) in the refocusing radio-frequency (RF) pulse train to promote sensitivity to blood flow as well as imaging encoding efficiency. Phase encoding blips are inserted between adjacent lobes of the switching readout gradients such that chemical shift-induced phase information is encoded into different locations in k-space. Based on the assumption that most background signals in the angiogram come from the fatty tissues, the proposed method directly separates angiograms from fatty background tissue signals from highly incomplete measurements by solving a constrained optimization problem with sparsity prior. Numerical simulations and experiments were performed to validate the effectiveness of the proposed method in healthy volunteers as compared with conventional fresh blood imaging (FBI).Main results.Compared with conventional FBI, the proposed method yields clearer delineation of small branching arteries and robust fatty background suppression without apparent loss of signals.Significance.We have successfully demonstrated the feasibility of the proposed, single-slab 3D VFA GRASE with chemical-shift-encoded reconstruction for the generation of robust unenhanced peripheral MRA.

Modeling Long-term Spike Frequency Adaptation in SA-I Afferent Neurons Using an Izhikevich-based Biological Neuron Model.

To develop a biomimetic artificial tactile sensing system capable of detecting sustained mechanical touch, we propose a novel biological neuron model (BNM) for slowly adapting type I (SA-I) afferent neurons. The proposed BNM is designed by modifying the Izhikevich model to incorporate long-term spike frequency adaptation. Adjusting the parameters renders the Izhikevich model describing various neuronal firing patterns. We also search for optimal parameter values for the proposed BNM to describe firing patterns of biological SA-I afferent neurons in response to sustained pressure longer than 1-second. We obtain the firing data of SA-I afferent neurons for six different mechanical pressure ranging from 0.1 mN to 300 mN from the ex-vivo experiment on SA-I afferent neurons in rodents. Upon finding the optimal parameters, we generate spike trains using the proposed BNM and compare the resulting spike trains to those of biological SA-I afferent neurons using the spike distance metrics. We verify that the proposed BNM can generate spike trains showing long-term adaptation, which is not achievable by other conventional models. Our new model may offer an essential function to artificial tactile sensing technology to perceive sustained mechanical touch.

Development of Korea Neuroethics Guidelines.

BACKGROUND: Advances in neuroscience and neurotechnology provide great benefits to humans though unknown challenges may arise. We should address these challenges using new standards as well as existing ones. Novel standards should include ethical, legal, and social aspects which would be appropriate for advancing neuroscience and technology. Therefore, the Korea Neuroethics Guidelines were developed by stakeholders related to neuroscience and neurotechnology, including experts, policy makers, and the public in the Republic of Korea. METHOD: The guidelines were drafted by neuroethics experts, were disclosed at a public hearing, and were subsequently revised by opinions of various stakeholders. RESULTS: The guidelines are composed of twelve issues; humanity or human dignity, individual personality and identity, social justice, safety, sociocultural prejudice and public communication, misuse of technology, responsibility for the use of neuroscience and technology, specificity according to the purpose of using neurotechnology, autonomy, privacy and personal information, research, and enhancement. CONCLUSION: Although the guidelines may require a more detailed discussion after future advances in neuroscience and technology or changes in socio-cultural milieu, the development of the Korea Neuroethics Guidelines is a milestone for the scientific community and society in general for the ongoing development in neuroscience and neurotechnology.

Investigation of Neural Substrates of Erroneous Behavior in a Delayed-Response Task.

Motor cortical neurons exhibit persistent selective activities (selectivity) during motor planning. Experimental perturbation of selectivity results in the failure of short-term memory retention and consequent behavioral biases, demonstrating selectivity as a neural characteristic of encoding previous sensory input or future action. However, even without experimental manipulation, animals occasionally fail to maintain short-term memory leading to erroneous choice. Here, we investigated neural substrates that lead to the incorrect formation of selectivity during short-term memory. We analyzed neuronal activities in anterior lateral motor cortex (ALM) of mice, a region known to be engaged in motor planning while mice performed the tactile delayed-response task. We found that highly selective neurons lost their selectivity while originally nonselective neurons showed selectivity during the error trials where mice licked toward incorrect direction. We assumed that those alternations would reflect changes in intrinsic properties of population activity. Thus, we estimated an intrinsic manifold shared by neuronal population (shared space), using factor analysis (FA) and measured the association of individual neurons with the shared space by communality, the variance of neuronal activity accounted for by the shared space. We found a positive correlation between selectivity and communality over ALM neurons, which disappeared in erroneous behavior. Notably, neurons showing selectivity alternations between correct and incorrect licking also underwent proportional changes in communality. Our results demonstrated that the extent to which an ALM neuron is associated with the intrinsic manifolds of population activity may elucidate its selectivity and that disruption of this association may alter selectivity, likely leading to erroneous behavior.

Finding Kinematics-Driven Latent Neural States From Neuronal Population Activity for Motor Decoding.

While intracortical brain-machine interfaces (BMIs) demonstrate feasibility to restore mobility to people with paralysis, it is still challenging to maintain high-performance decoding in clinical BMIs. One of the main obstacles for high-performance BMI is the noise-prone nature of traditional decoding methods that connect neural response explicitly with physical quantity, such as velocity. In contrast, the recent development of latent neural state model enables a robust readout of large-scale neuronal population activity contents. However, these latent neural states do not necessarily contain kinematic information useful for decoding. Therefore, this study proposes a new approach to finding kinematics-dependent latent factors by extracting latent factors' kinematics-dependent components using linear regression. We estimated these components from the population activity through nonlinear mapping. The proposed kinematics-dependent latent factors generate neural trajectories that discriminate latent neural states before and after the motion onset. We compared the decoding performance of the proposed analysis model with the results from other popular models. They are factor analysis (FA), Gaussian process factor analysis (GPFA), latent factor analysis via dynamical systems (LFADS), preferential subspace identification (PSID), and neuronal population firing rates. The proposed analysis model results in higher decoding accuracy than do the others ( % improvement on average). Our approach may pave a new way to extract latent neural states specific to kinematic information from motor cortices, potentially improving decoding performance for online intracortical BMIs.

Extracting single-trial neural interaction using latent dynamical systems model.

In systems neuroscience, advances in simultaneous recording technology have helped reveal the population dynamics that underlie the complex neural correlates of animal behavior and cognitive processes. To investigate these correlates, neural interactions are typically abstracted from spike trains of pairs of neurons accumulated over the course of many trials. However, the resultant averaged values do not lead to understanding of neural computation in which the responses of populations are highly variable even under identical external conditions. Accordingly, neural interactions within the population also show strong fluctuations. In the present study, we introduce an analysis method reflecting the temporal variation of neural interactions, in which cross-correlograms on rate estimates are applied via a latent dynamical systems model. Using this method, we were able to predict time-varying neural interactions within a single trial. In addition, the pairwise connections estimated in our analysis increased along behavioral epochs among neurons categorized within similar functional groups. Thus, our analysis method revealed that neurons in the same groups communicate more as the population gets involved in the assigned task. We also showed that the characteristics of neural interaction from our model differ from the results of a typical model employing cross-correlation coefficients. This suggests that our model can extract nonoverlapping information about network topology, unlike the typical model.

Decoding Kinematic Information From Primary Motor Cortex Ensemble Activities Using a Deep Canonical Correlation Analysis.

The control of arm movements through intracortical brain-machine interfaces (BMIs) mainly relies on the activities of the primary motor cortex (M1) neurons and mathematical models that decode their activities. Recent research on decoding process attempts to not only improve the performance but also simultaneously understand neural and behavioral relationships. In this study, we propose an efficient decoding algorithm using a deep canonical correlation analysis (DCCA), which maximizes correlations between canonical variables with the non-linear approximation of mappings from neuronal to canonical variables via deep learning. We investigate the effectiveness of using DCCA for finding a relationship between M1 activities and kinematic information when non-human primates performed a reaching task with one arm. Then, we examine whether using neural activity representations from DCCA improves the decoding performance through linear and non-linear decoders: a linear Kalman filter (LKF) and a long short-term memory in recurrent neural networks (LSTM-RNN). We found that neural representations of M1 activities estimated by DCCA resulted in more accurate decoding of velocity than those estimated by linear canonical correlation analysis, principal component analysis, factor analysis, and linear dynamical system. Decoding with DCCA yielded better performance than decoding the original FRs using LSTM-RNN (6.6 and 16.0% improvement on average for each velocity and position, respectively; Wilcoxon rank sum test, p < 0.05). Thus, DCCA can identify the kinematics-related canonical variables of M1 activities, thus improving the decoding performance. Our results may help advance the design of decoding models for intracortical BMIs.

A Computational Modeling Reveals That Strength of Inhibitory Input, E/I Balance, and Distance of Excitatory Input Modulate Thalamocortical Bursting Properties.

The thalamus is a brain structure known to modulate sensory information before relaying to the cortex. The unique ability of a thalamocortical (TC) neuron to switch between the high frequency burst firing and single spike tonic firing has been implicated to have a key role in sensory modulation including pain. Of the two firing modes, burst firing, especially maintaining certain burst firing properties, was suggested to be critical in controlling nociceptive behaviors. Therefore, understanding the factors that influence burst firing properties would offer important insight into understanding sensory modulation. Using computational modeling, we investigated how the balance of excitatory and inhibitory inputs into a TC neuron influence TC bursting properties. We found that intensity of inhibitory inputs and the timing of excitatory input delivery control the dynamics of bursting properties. Then, to reflect a more realistic model, excitatory inputs delivered at different dendritic locations-proximal, intermediate, or distal-of a TC neuron were also investigated. Interestingly, excitatory input delivered into a distal dendrite, despite the furthest distance, had the strongest influence in shaping burst firing properties, suggesting that not all inputs equally contribute to modulating TC bursting properties. Overall, the results provide computational insights in understanding the detailed mechanism of the factors influencing temporal pattern of thalamic bursts.

Korea Brain Initiative: Emerging Issues and Institutionalization of Neuroethics.

Neuroscience research has become a national priority for the Korean government. Korean scholars have dedicated interest in the societal ramifications of neurotechnologies; neuroethics is an integral component of the Korea Brain Initiative and to the formation of its growing neuroscience community.

A simulation study on the effects of neuronal ensemble properties on decoding algorithms for intracortical brain-machine interfaces.

BACKGROUND: Intracortical brain-machine interfaces (BMIs) harness movement information by sensing neuronal activities using chronic microelectrode implants to restore lost functions to patients with paralysis. However, neuronal signals often vary over time, even within a day, forcing one to rebuild a BMI every time they operate it. The term "rebuild" means overall procedures for operating a BMI, such as decoder selection, decoder training, and decoder testing. It gives rise to a practical issue of what decoder should be built for a given neuronal ensemble. This study aims to address it by exploring how decoders' performance varies with the neuronal properties. To extensively explore a range of neuronal properties, we conduct a simulation study. METHODS: Focusing on movement direction, we examine several basic neuronal properties, including the signal-to-noise ratio of neurons, the proportion of well-tuned neurons, the uniformity of their preferred directions (PDs), and the non-stationarity of PDs. We investigate the performance of three popular BMI decoders: Kalman filter, optimal linear estimator, and population vector algorithm. RESULTS: Our simulation results showed that decoding performance of all the decoders was affected more by the proportion of well-tuned neurons that their uniformity. CONCLUSIONS: Our study suggests a simulated scenario of how to choose a decoder for intracortical BMIs in various neuronal conditions.

EEG Beta Oscillations in the Temporoparietal Area Related to the Accuracy in Estimating Others' Preference.

Humans often attempt to predict what others prefer based on a narrow slice of experience, called thin-slicing. According to the theoretical bases for how humans can predict the preference of others, one tends to estimate the other's preference using a perceived difference between the other and self. Previous neuroimaging studies have revealed that the network of dorsal medial prefrontal cortex (dmPFC) and right temporoparietal junction (rTPJ) is related to the ability of predicting others' preference. However, it still remains unknown about the temporal patterns of neural activities for others' preference prediction through thin-slicing. To investigate such temporal aspects of neural activities, we investigated human electroencephalography (EEG) recorded during the task of predicting the preference of others while only a facial picture of others was provided. Twenty participants (all female, average age: 21.86) participated in the study. In each trial of the task, participants were shown a picture of either a target person or self for 3 s, followed by the presentation of a movie poster over which participants predicted the target person's preference as liking or disliking. The time-frequency EEG analysis was employed to analyze temporal changes in the amplitudes of brain oscillations. Participants could predict others' preference for movies with accuracy of 56.89 ± 3.16% and 10 out of 20 participants exhibited prediction accuracy higher than a chance level (95% interval). There was a significant difference in the power of the parietal alpha (10~13 Hz) oscillation 0.6~0.8 s after the onset of poster presentation between the cases when participants predicted others' preference and when they reported self-preference (p < 0.05). The power of brain oscillations at any frequency band and time period during the trial did not show a significant correlation with individual prediction accuracy. However, when we measured differences of the power between the trials of predicting other's preference and reporting self-preference, the right temporal beta oscillations 1.6~1.8 s after the onset of facial picture presentation exhibited a significant correlation with individual accuracy. Our results suggest that right temporoparietal beta oscillations may be correlated with one's ability to predict what others prefer with minimal information.

Implantable Neural Probes for Brain-Machine Interfaces - Current Developments and Future Prospects.

A Brain-Machine interface (BMI) allows for direct communication between the brain and machines. Neural probes for recording neural signals are among the essential components of a BMI system. In this report, we review research regarding implantable neural probes and their applications to BMIs. We first discuss conventional neural probes such as the tetrode, Utah array, Michigan probe, and electroencephalography (ECoG), following which we cover advancements in next-generation neural probes. These next-generation probes are associated with improvements in electrical properties, mechanical durability, biocompatibility, and offer a high degree of freedom in practical settings. Specifically, we focus on three key topics: (1) novel implantable neural probes that decrease the level of invasiveness without sacrificing performance, (2) multi-modal neural probes that measure both electrical and optical signals, (3) and neural probes developed using advanced materials. Because safety and precision are critical for practical applications of BMI systems, future studies should aim to enhance these properties when developing next-generation neural probes.

The Effects of Acupuncture Stimulation for Brain Activation and Alcohol Abstinence Self-Efficacy: Functional MRI Study.

We attempted to investigate whether acupuncture stimulation at HT7 can have an effect on brain activation patterns and alcohol abstinence self-efficacy. Thirty-four right-handed healthy subjects were recruited for this study. They were randomly assigned into two groups: the HT7 (Shenmen) group and the LI5 (Yangxi) group. Acupuncture stimulation was performed using a block paradigm during fMRI scanning. Additionally, the Korean version of Alcohol Abstinence Self-Efficacy Scale (AASES) was used to determine the effect of acupuncture stimulation on self-efficacy to abstain from alcohol use. According to the result of fMRI group analysis, the activation induced by HT7 stimulation was found on the bilateral postcentral gyrus, inferior parietal lobule, inferior frontal gyrus, claustrum, insula, and anterior lobe of the cerebellum, as well as on the left posterior lobe of the cerebellum (p < 0.001, uncorrected). According to the AASES analysis, the interaction effect for gender and treatment was marginally significant (F(1, 30) = 4.152, p = 0.050). For female group, the simple main effect of treatment was significant (F(1, 11) = 8.040, p = 0.016), indicating that the mean change score was higher in the HT7 stimulation than in the LI5 stimulation. Therefore, our study has provided evidence to support that HT7 stimulation has a positive therapeutic effect on the alcohol-related diseases.

Thermoelectric Signal Enhancement by Reconciling the Spin Seebeck and Anomalous Nernst Effects in Ferromagnet/Non-magnet Multilayers.

The utilization of ferromagnetic (FM) materials in thermoelectric devices allows one to have a simpler structure and/or independent control of electric and thermal conductivities, which may further remove obstacles for this technology to be realized. The thermoelectricity in FM/non-magnet (NM) heterostructures using an optical heating source is studied as a function of NM materials and a number of multilayers. It is observed that the overall thermoelectric signal in those structures which is contributed by spin Seebeck effect and anomalous Nernst effect (ANE) is enhanced by a proper selection of NM materials with a spin Hall angle that matches to the sign of the ANE. Moreover, by an increase of the number of multilayer, the thermoelectric voltage is enlarged further and the device resistance is reduced, simultaneously. The experimental observation of the improvement of thermoelectric properties may pave the way for the realization of magnetic-(or spin-) based thermoelectric devices.

Preparation of Microstructure Molds of Montmorillonite/Polyethylene Glycol Diacrylate and Multi-Walled Carbon Nanotube/Polyethylene Glycol Diacrylate Nanocomposites for Miniaturized Device Applications.

Environmentally friendly microstructure molds with montmorillonite (MMT) or multi-walled carbon nanotube (MWCNT) reinforced polyethylene glycol diacrylate (PEGDA) nanocomposites have been prepared for miniaturized device applications. The micropatterning of MMT/PEGDA and MWCNT/PEGDA with 0.5 to 2.0 wt% of MMTs and MWCNTs was achieved through a UV curing process with micro-patterned masks. Hexagonal dot arrays and complex patterns for microstructures of the nanocomposites were produced and characterized with an optical microscope; their thermal properties were studied by thermogravimetric analysis (TGA). The TGA results showed that these nanocomposites were thermally stable up to 350 °C. Polydimethylsiloxane thin replicas with different microstructures were prepared by a casting method using the microstructured nanocomposites as molds. It is considered that these microstructure molds of the nanocomposites can be used as microchip molds to fabricate nanobio-chips and medical diagnostic chip devices.

Motor cortical correlates of arm resting in the context of a reaching task and implications for prosthetic control.

Prosthetic devices are being developed to restore movement for motor-impaired individuals. A robotic arm can be controlled based on models that relate motor-cortical ensemble activity to kinematic parameters. The models are typically built and validated on data from structured trial periods during which a subject actively performs specific movements, but real-world prosthetic devices will need to operate correctly during rest periods as well. To develop a model of motor cortical modulation during rest, we trained monkeys (Macaca mulatta) to perform a reaching task with their own arm while recording motor-cortical single-unit activity. When a monkey spontaneously put its arm down to rest between trials, our traditional movement decoder produced a nonzero velocity prediction, which would cause undesired motion when applied to a prosthetic arm. During these rest periods, a marked shift was found in individual units' tuning functions. The activity pattern of the whole population during rest (Idle state) was highly distinct from that during reaching movements (Active state), allowing us to predict arm resting from instantaneous firing rates with 98% accuracy using a simple classifier. By cascading this state classifier and the movement decoder, we were able to predict zero velocity correctly, which would avoid undesired motion in a prosthetic application. Interestingly, firing rates during hold periods followed the Active pattern even though hold kinematics were similar to those during rest with near-zero velocity. These findings expand our concept of motor-cortical function by showing that population activity reflects behavioral context in addition to the direct parameters of the movement itself.

Order-dependent modulation of directional signals in the supplementary and presupplementary motor areas.

To maximize reward and minimize effort, animals must often execute multiple movements in a timely and orderly manner. Such movement sequences must be usually discovered through experience, and during this process, signals related to the animal's action, its ordinal position in the sequence, and subsequent reward need to be properly integrated. To investigate the role of the primate medial frontal cortex in planning and controlling multiple movements, monkeys were trained to produce a series of hand movements instructed by visual stimuli. We manipulated the number of movements in a sequence across trials, making it possible to dissociate the effects of the ordinal position of a given movement and the number of remaining movements necessary to obtain reward. Neurons in the supplementary and presupplementary motor areas modulated their activity according to the number of remaining movements, more often than in relation to the ordinal position, suggesting that they might encode signals related to the timing of reward or its temporally discounted value. In both cortical areas, signals related to the number of remaining movements and those related to movement direction were often combined multiplicatively, suggesting that the gain of the signals related to movements might be modulated by motivational factors. Finally, compared with the supplementary motor area, neurons in the presupplementary motor area were more likely to increase their activity when the number of remaining movements is large. These results suggest that these two areas might play complementary roles in controlling movement sequences.

Effects of reward expectancy on sequential eye movements in monkeys.

Desirability of an action, often referred to as utility or value, is determined by various factors, such as the probability and timing of expected reward. We investigated how performance of monkeys in an oculomotor serial reaction time task is influenced by multiple motivational factors. The animals produced a series of visually-guided eye movements, while the sequence of target locations and the location of the rewarded target were systematically manipulated. The results show that error rates as well as saccade latencies were consistently influenced by the number of remaining movements necessary to obtain a reward. In addition, when the animal produced multiple saccades before fixating a given target, the first saccade tended to be directed towards the rewarded location, suggesting that saccades to rewarded location and visual target might be programmed concurrently. These results show that monkeys can utilize information about the required sequence of movements to update their subjective values.

Activity in prefrontal cortex during dynamic selection of action sequences.

Completing everyday tasks often requires the execution of action sequences matched to a particular problem. To study the neural processes underlying these behaviors, we trained monkeys to produce a series of eye movements according to a sequence that changed unpredictably from one block of trials to the next. We then applied a decoding algorithm to estimate which sequence was being represented by the ensemble activity in prefrontal cortex. We found that the sequence predicted by this analysis changed gradually from the sequence that had been correct in the previous block to the sequence that was correct in the current block, closely following the fraction of executed movements that were consistent with the corresponding sequence. Thus, the neural activity dynamically tracked the monkeys' uncertainty about the correct sequence of actions. These results are consistent with prefrontal involvement in representing subjective knowledge of the correct action sequence.