Distinct neurocomputational mechanisms support informational and socially normative conformity.

A change of mind in response to social influence could be driven by informational conformity to increase accuracy, or by normative conformity to comply with social norms such as reciprocity. Disentangling the behavioural, cognitive, and neurobiological underpinnings of informational and normative conformity have proven elusive. Here, participants underwent fMRI while performing a perceptual task that involved both advice-taking and advice-giving to human and computer partners. The concurrent inclusion of 2 different social roles and 2 different social partners revealed distinct behavioural and neural markers for informational and normative conformity. Dorsal anterior cingulate cortex (dACC) BOLD response tracked informational conformity towards both human and computer but tracked normative conformity only when interacting with humans. A network of brain areas (dorsomedial prefrontal cortex (dmPFC) and temporoparietal junction (TPJ)) that tracked normative conformity increased their functional coupling with the dACC when interacting with humans. These findings enable differentiating the neural mechanisms by which different types of conformity shape social changes of mind.

Morphology, Connectivity, and Encoding Features of Tactile and Motor Representations of the Fingers in the Human Precentral and Postcentral Gyrus.

Despite the tight coupling between sensory and motor processing for fine manipulation in humans, it is not yet totally clear which specific properties of the fingers are mapped in the precentral and postcentral gyrus. We used fMRI to compare the morphology, connectivity, and encoding of the motor and tactile finger representations (FRs) in the precentral and postcentral gyrus of 25 5-fingered participants (8 females). Multivoxel pattern and structural and functional connectivity analyses demonstrated the existence of distinct motor and tactile FRs within both the precentral and postcentral gyrus, integrating finger-specific motor and tactile information. Using representational similarity analysis, we found that the motor and tactile FRs in the sensorimotor cortex were described by the perceived structure of the hand better than by the actual hand anatomy or other functional models (finger kinematics, muscles synergies). We then studied a polydactyly individual (i.e., with a congenital 6-fingered hand) showing superior manipulation abilities and divergent anatomic-functional hand properties. The perceived hand model was still the best model for tactile representations in the precentral and postcentral gyrus, while finger kinematics better described motor representations in the precentral gyrus. We suggest that, under normal conditions (i.e., in subjects with a standard hand anatomy), the sensorimotor representations of the 5 fingers in humans converge toward a model of perceived hand anatomy, deviating from the real hand structure, as the best synthesis between functional and structural features of the hand.SIGNIFICANCE STATEMENT Distinct motor and tactile finger representations exist in both the precentral and postcentral gyrus, supported by a finger-specific pattern of anatomic and functional connectivity across modalities. At the representational level, finger representations reflect the perceived structure of the hand, which might result from an adapting process harmonizing (i.e., uniformizing) the encoding of hand function and structure in the precentral and postcentral gyrus. The same analyses performed in an extremely rare polydactyly subject showed that the emergence of such representational geometry is also found in neuromechanical variants with different hand anatomy and function. However, the harmonization process across the precentral and postcentral gyrus might not be possible because of divergent functional-structural properties of the hand and associated superior manipulation abilities.

Motor skill learning decreases movement variability and increases planning horizon.

We investigated motor skill learning using a path tracking task, where human subjects had to track various curved paths at a constant speed while maintaining the cursor within the path width. Subjects' accuracy increased with practice, even when tracking novel untrained paths. Using a "searchlight" paradigm, where only a short segment of the path ahead of the cursor was shown, we found that subjects with a higher tracking skill differed from the novice subjects in two respects. First, they had lower movement variability, in agreement with previous findings. Second, they took a longer section of the future path into account when performing the task, i.e., had a longer planning horizon. We estimate that between one-third and one-half of the performance increase in the expert group was due to the longer planning horizon. An optimal control model with a fixed horizon (receding horizon control) that increases with tracking skill quantitatively captured the subjects' movement behavior. These findings demonstrate that human subjects not only increase their motor acuity but also their planning horizon when acquiring a motor skill.NEW & NOTEWORTHY We show that when learning a motor skill humans are using information about the environment from an increasingly longer amount of the movement path ahead to improve performance. Crucial features of the behavioral performance can be captured by modeling the behavioral data with a receding horizon optimal control model.

Concurrent stable and unstable cortical correlates of human wrist movements.

Cortical activity has been shown to correlate with different parameters of movement. However, the dynamic properties of cortico-motor mappings still remain unexplored in humans. Here, we show that during the repetition of simple stereotyped wrist movements both stable and unstable correlates simultaneously emerge in human sensorimotor cortex. Using visual feedback of wrist movement target inferred online from MEG, we assessed the dynamics of the tuning properties of two neuronal signals: the MEG signal below 1.6 Hz and within the 4 to 6 Hz range. We found that both components are modulated by wrist movement allowing for closed-loop inference of movement targets. Interestingly, while tuning of 4 to 6 Hz signals remained stable over time leading to stable inference of movement target using a static classifier, the tuning of cortical signals below 1.6 Hz significantly changed resulting in steadily decreasing inference accuracy. Our findings demonstrate that non-invasive neuronal population signals in human sensorimotor cortex can reflect a stable correlate of voluntary movements. Hence, we provide first evidence for a stable control signal in non-invasive human brain-machine interface research. However, as not all neuronal signals initially tuned to movement were stable across days, a careful selection of features for real-life applications seems to be mandatory.

Adaptation paths to novel motor tasks are shaped by prior structure learning.

After extensive practice with motor tasks sharing structural similarities (e.g., different dancing movements, or different sword techniques), new tasks of the same type can be learned faster. According to the recent "structure learning" hypothesis (Braun et al., 2009a), such rapid generalization of related motor skills relies on learning the dynamic and kinematic relationships shared by this set of skills. As a consequence, motor adaptation becomes constrained, effectively leading to a dimensionality reduction of the learning problem; at the same time, adaptation to tasks lying outside the structure becomes biased toward the structure. We tested these predictions by investigating how previously learned structures influence subsequent motor adaptation. Human subjects were making reaching movements in 3D virtual reality, experiencing perturbations either in the vertical or in the horizontal plane. Perturbations were either visuomotor rotations of varying angle or velocity-dependent forces of varying strength. We found that, after extensive training with both kinematic or dynamic perturbations, adaptation to unpracticed, diagonal, perturbations happened along the previously learned structure (vertical or horizontal), and resulting adaptation trajectories were curved. This effect is robust, can be observed on the single-subject level, and occurs during adaptation both within and across trials. Additionally, we demonstrate that structure learning changes involuntary visuomotor reflexes and therefore is not exclusively a high-level cognitive phenomenon.

Mental Tasks Modulate Motor-Units Above 10 Hz and are a Potential Control Signal for Movement Augmentation: a Preliminary Study.

Spinal motor neurons receive a wide range of input frequencies. However, only frequencies below ca. 10 Hz are directly translated into motor output. Frequency components above 10 Hz are filtered out by neural pathways and muscle dynamics. These higher frequency components may have an indirect effect on motor output, or may simply represent movement-independent oscillations that leak down from supraspinal areas such as the motor cortex. If movement-independent oscillations leak down from supraspinal areas, they could provide a potential control signal in movement augmentation applications. We analysed high-density electromyography (HD-EMG) signals from the tibialis anterior muscle while human subjects performed various mental tasks. The subjects performed an isometric dorsiflexion of the right foot at a low level of force while simultaneously (1) imagining a movement of the right foot, (2) imagining a movement of both hands, (3) performing a mathematical task, or (4) performing no additional task. We classified the channel-averaged HD-EMG signals and the cumulative spike train (CST) of motor-units using a filter bank and a linear classifier. We found that in some subjects, the mental task can be classified from the channel-averaged HD-EMG signals and the CST in oscillations above 10 Hz. Furthermore, we found that these oscillation modulations are incompatible with a systematic and task-dependent change in force level. Our preliminary findings from a limited number of subjects suggest that some mental task-induced oscillations from supraspinal areas leak down to spinal motor neurons and are discriminable via EMG or CST signals at the innervated muscle.

Discrimination of finger movements by magnetomyography with optically pumped magnetometers.

Optically pumped magnetometers (OPM) are quantum sensors that offer new possibilities to measure biomagnetic signals. Compared to the current standard surface electromyography (EMG), in magnetomyography (MMG), OPM sensors offer the advantage of contactless measurements of muscle activity. However, little is known about the relative performance of OPM-MMG and EMG, e.g. in their ability to detect and classify finger movements. To address this in a proof-of-principle study, we recorded simultaneous OPM-MMG and EMG of finger flexor muscles for the discrimination of individual finger movements on a single human participant. Using a deep learning model for movement classification, we found that both sensor modalities were able to discriminate finger movements with above 89% accuracy. Furthermore, model predictions for the two sensor modalities showed high agreement in movement detection (85% agreement; Cohen's kappa: 0.45). Our findings show that OPM sensors can be employed for contactless discrimination of finger movements and incentivize future applications of OPM in magnetomyography.

Decoding natural grasp types from human ECoG.

Electrocorticographic (ECoG) signals have been successfully used to provide information about arm movement direction, individual finger movements and even continuous arm movement trajectories. Thus, ECoG has been proposed as a potential control signal for implantable brain-machine interfaces (BMIs) in paralyzed patients. For the neuronal control of a prosthesis with versatile hand/arm functions, it is also necessary to successfully decode different types of grasping movements, such as precision grip and whole-hand grip. Although grasping is one of the most frequent and important hand movements performed in everyday life, until now, the decoding of ECoG activity related to different grasp types has not been systematically investigated. Here, we show that two different grasp types (precision vs. whole-hand grip) can be reliably distinguished in natural reach-to-grasp movements in single-trial ECoG recordings from the human motor cortex. Self-paced movement execution in a paradigm accounting for variability in grasped object position and weight was chosen to create a situation similar to everyday settings. We identified three informative signal components (low-pass-filtered component, low-frequency and high-frequency amplitude modulations), which allowed for accurate decoding of precision and whole-hand grips. Importantly, grasp type decoding generalized over different object positions and weights. Within the frontal lobe, informative signals predominated in the precentral motor cortex and could also be found in the right hemisphere's homologue of Broca's area. We conclude that ECoG signals are promising candidates for BMIs that include the restoration of grasping movements.

The control and training of single motor units in isometric tasks are constrained by a common input signal.

Recent developments in neural interfaces enable the real-time and non-invasive tracking of motor neuron spiking activity. Such novel interfaces could provide a promising basis for human motor augmentation by extracting potentially high-dimensional control signals directly from the human nervous system. However, it is unclear how flexibly humans can control the activity of individual motor neurons to effectively increase the number of degrees of freedom available to coordinate multiple effectors simultaneously. Here, we provided human subjects (N = 7) with real-time feedback on the discharge patterns of pairs of motor units (MUs) innervating a single muscle (tibialis anterior) and encouraged them to independently control the MUs by tracking targets in a 2D space. Subjects learned control strategies to achieve the target-tracking task for various combinations of MUs. These strategies rarely corresponded to a volitional control of independent input signals to individual MUs during the onset of neural activity. Conversely, MU activation was consistent with a common input to the MU pair, while individual activation of the MUs in the pair was predominantly achieved by alterations in de-recruitment order that could be explained by history-dependent changes in motor neuron excitability. These results suggest that flexible MU recruitment based on independent synaptic inputs to single MUs is unlikely, although de-recruitment might reflect varying inputs or modulations in the neuron's intrinsic excitability.

Principles of human movement augmentation and the challenges in making it a reality.

Augmenting the body with artificial limbs controlled concurrently to one's natural limbs has long appeared in science fiction, but recent technological and neuroscientific advances have begun to make this possible. By allowing individuals to achieve otherwise impossible actions, movement augmentation could revolutionize medical and industrial applications and profoundly change the way humans interact with the environment. Here, we construct a movement augmentation taxonomy through what is augmented and how it is achieved. With this framework, we analyze augmentation that extends the number of degrees-of-freedom, discuss critical features of effective augmentation such as physiological control signals, sensory feedback and learning as well as application scenarios, and propose a vision for the field.

Learning optimal adaptation strategies in unpredictable motor tasks.

Picking up an empty milk carton that we believe to be full is a familiar example of adaptive control, because the adaptation process of estimating the carton's weight must proceed simultaneously with the control process of moving the carton to a desired location. Here we show that the motor system initially generates highly variable behavior in such unpredictable tasks but eventually converges to stereotyped patterns of adaptive responses predicted by a simple optimality principle. These results suggest that adaptation can become specifically tuned to identify task-specific parameters in an optimal manner.

Short-latency activation of striatal spiny neurons via subcortical visual pathways.

The striatum is a site of integration of neural pathways involved in reinforcement learning. Traditionally, inputs from cerebral cortex are thought to be reinforced by dopaminergic afferents signaling the occurrence of biologically salient sensory events. Here, we detail an alternative route for short-latency sensory-evoked input to the striatum requiring neither dopamine nor the cortex. Using intracellular recording techniques, we measured subthreshold inputs to spiny projection neurons (SPNs) in urethane-anesthetized rats. Contralateral whole-field light flashes evoked weak membrane potential responses in approximately two-thirds of neurons. However, after local disinhibitory injections of the GABA(A) antagonist bicuculline into the deep layers of the superior colliculus (SC), but not the overlying visual cortex, strong, light-evoked, depolarizations to the up state emerged at short latency (115 +/- 14 ms) in all neurons tested. Dopamine depletion using alpha-methyl-para-tyrosine had no detectable effect on striatal visual responses during SC disinhibition. In contrast, local inhibitory injections of GABA agonists, muscimol and baclofen, into the parafascicular nucleus of the thalamus blocked the early, visual-evoked up-state transitions in SPNs. Comparable muscimol-induced inhibition of the visual cortex failed to suppress the visual responsiveness of SPNs induced by SC disinhibition. Together, these results suggest that short-latency, preattentive visual input can reach the striatum not only via the tecto-nigro-striatal route but also through tecto-thalamo-striatal projections. Thus, after the onset of a biologically significant visual event, closely timed short-latency thalamostriatal (glutamate) and nigrostriatal (dopamine) inputs are likely to converge on striatal SPNs, providing depolarizing and neuromodulator signals necessary for synaptic plasticity mechanisms.

Hand movement direction decoded from MEG and EEG.

Brain activity can be used as a control signal for brain-machine interfaces (BMIs). A powerful and widely acknowledged BMI approach, so far only applied in invasive recording techniques, uses neuronal signals related to limb movements for equivalent, multidimensional control of an external effector. Here, we investigated whether this approach is also applicable for noninvasive recording techniques. To this end, we recorded whole-head MEG during center-out movements with the hand and found significant power modulation of MEG activity between rest and movement in three frequency bands: an increase for < or = 7 Hz (low-frequency band) and 62-87 Hz (high-gamma band) and a decrease for 10-30 Hz (beta band) during movement. Movement directions could be inferred on a single-trial basis from the low-pass filtered MEG activity as well as from power modulations in the low-frequency band, but not from the beta and high-gamma bands. Using sensors above the motor area, we obtained a surprisingly high decoding accuracy of 67% on average across subjects. Decoding accuracy started to rise significantly above chance level before movement onset. Based on simultaneous MEG and EEG recordings, we show that the inference of movement direction works equally well for both recording techniques. In summary, our results show that neuronal activity associated with different movements of the same effector can be distinguished by means of noninvasive recordings and might, thus, be used to drive a noninvasive BMI.

Differential representation of arm movement direction in relation to cortical anatomy and function.

Information about arm movement direction in neuronal activity of the cerebral cortex can be used for movement control mediated by a brain-machine interface (BMI). Here we provide a topographic analysis of the information related to arm movement direction that can be extracted from single trials of electrocorticographic (ECoG) signals recorded from the human frontal and parietal cortex based on a precise assignment of ECoG recording channels to the subjects' individual cortical anatomy and function. To this aim, each electrode contact was identified on structural MRI scans acquired while the electrodes were implanted and was thus related to the brain anatomy of each patient. Cortical function was assessed by direct cortical electrical stimulation. We show that activity from the primary motor cortex, in particular from the region showing hand and arm motor responses upon electrical stimulation, carries most directional information. The premotor, posterior parietal and lateral prefrontal cortex contributed gradually less, but still significant information. This gradient was observed for decoding from movement-related potentials, and from spectral amplitude modulations in low frequencies and in the high gamma band. Our findings thus demonstrate a close topographic correlation between cortical functional anatomy and direction-related information in humans that might be used for brain-machine interfacing.

Prediction of arm movement trajectories from ECoG-recordings in humans.

Electrocorticographic (ECoG) signals have been shown to contain reliable information about the direction of arm movements and can be used for on-line cursor control. These findings indicate that the ECoG is a potential basis for a brain-machine interface (BMI) for application in paralyzed patients. However, previous approaches to ECoG-BMIs were either based on classification of different movement patterns or on a voluntary modulation of spectral features. For a continuous multi-dimensional BMI control, the prediction of complete movement trajectories, as it has already been shown for spike data and local field potentials (LFPs), would be a desirable addition for the ECoG, too. Here, we examined ECoG signals from six subjects with subdurally implanted ECoG-electrodes during continuous two-dimensional arm movements between random target positions. Our results show that continuous trajectories of 2D hand position can be approximately predicted from the ECoG recorded from hand/arm motor cortex. This indicates that ECoG signals, related to body movements, can directly be transferred to equivalent controls of an external effector for continuous BMI control.

Inference of hand movements from local field potentials in monkey motor cortex.

The spiking of neuronal populations in motor cortex provides accurate information about movement parameters. Here we show that hand movement target and velocity can be inferred from multiple local field potentials (LFPs) in single trials approximately as efficiently as from multiple single-unit activity (SUA) recorded from the same electrodes. Our results indicate that LFPs can be used as an additional signal for decoding brain activity, particularly for new neuroprosthetic applications.

Reciprocity of social influence.

Humans seek advice, via social interaction, to improve their decisions. While social interaction is often reciprocal, the role of reciprocity in social influence is unknown. Here, we tested the hypothesis that our influence on others affects how much we are influenced by them. Participants first made a visual perceptual estimate and then shared their estimate with an alleged partner. Then, in alternating trials, the participant either revised their decisions or observed how the partner revised theirs. We systematically manipulated the partner's susceptibility to influence from the participant. We show that participants reciprocated influence with their partner by gravitating toward the susceptible (but not insusceptible) partner's opinion. In further experiments, we showed that reciprocity is both a dynamic process and is abolished when people believed that they interacted with a computer. Reciprocal social influence is a signaling medium for human-to-human communication that goes beyond aggregation of evidence for decision improvement.

Modifying response times in the Simon task with transcranial random noise stimulation.

Perceptual decisions pervade our every-day lives, and can align or conflict with inbuilt biases. We investigated these conflicting biases by applying transcranial random noise stimulation (tRNS) while subjects took part in a visual Simon task - a paradigm where irrelevant spatial cues influence the response times of subjects to relevant colour cues. We found that tRNS reduces the response time of subjects independent of the congruence between spatial and colour cues, but dependent on the baseline response time, both between subjects and across conditions within subjects. We consider the reduction in response time to be non-specific to the Simon task, and cast our interpretations in terms of drift-diffusion models, which have been previously used as mechanistic explanations for decision-making processes. However, there have been few extensions of the drift-diffusion model to the Simon effect, and so we first elaborate on this interpretation, and further extend it by incorporating the potential action of tRNS.

Encoding of movement direction in different frequency ranges of motor cortical local field potentials.

Recent studies showed that the low-frequency component of local field potentials (LFPs) in monkey motor cortex carries information about parameters of voluntary arm movements. Here, we studied how different signal components of the LFP in the time and frequency domains are modulated during center-out arm movements. Analysis of LFPs in the time domain showed that the amplitude of a slow complex waveform beginning shortly before the onset of arm movement is modulated with the direction of the movement. Examining LFPs in the frequency domain, we found that direction-dependent modulations occur in three frequency ranges, which typically increased their amplitudes before and during movement execution: < or =4, 6-13, and 63-200 Hz. Cosine-like tuning was prominent in all signal components analyzed. In contrast, activity in a frequency band approximately 30 Hz was not modulated with the direction of movement and typically decreased its amplitude during the task. This suggests that high-frequency oscillations have to be divided into at least two functionally different regimes: one approximately 30 Hz and one >60 Hz. Furthermore, using multiple LFPs, we could show that LFP amplitude spectra can be used to decode movement direction, with the best performance achieved by the combination of different frequency ranges. These results suggest that using the different frequency components in the LFP is useful in improving inference of movement parameters from local field potentials.

Ownership and Agency of an Independent Supernumerary Hand Induced by an Imitation Brain-Computer Interface.

To study body ownership and control, illusions that elicit these feelings in non-body objects are widely used. Classically introduced with the Rubber Hand Illusion, these illusions have been replicated more recently in virtual reality and by using brain-computer interfaces. Traditionally these illusions investigate the replacement of a body part by an artificial counterpart, however as brain-computer interface research develops it offers us the possibility to explore the case where non-body objects are controlled in addition to movements of our own limbs. Therefore we propose a new illusion designed to test the feeling of ownership and control of an independent supernumerary hand. Subjects are under the impression they control a virtual reality hand via a brain-computer interface, but in reality there is no causal connection between brain activity and virtual hand movement but correct movements are observed with 80% probability. These imitation brain-computer interface trials are interspersed with movements in both the subjects' real hands, which are in view throughout the experiment. We show that subjects develop strong feelings of ownership and control over the third hand, despite only receiving visual feedback with no causal link to the actual brain signals. Our illusion is crucially different from previously reported studies as we demonstrate independent ownership and control of the third hand without loss of ownership in the real hands.