Descending Command Neurons in the Brainstem that Halt Locomotion.

The episodic nature of locomotion is thought to be controlled by descending inputs from the brainstem. Most studies have largely attributed this control to initiating excitatory signals, but little is known about putative commands that may specifically determine locomotor offset. To link identifiable brainstem populations to a potential locomotor stop signal, we used developmental genetics and considered a discrete neuronal population in the reticular formation: the V2a neurons. We find that those neurons constitute a major excitatory pathway to locomotor areas of the ventral spinal cord. Selective activation of V2a neurons of the rostral medulla stops ongoing locomotor activity, owing to an inhibition of premotor locomotor networks in the spinal cord. Moreover, inactivation of such neurons decreases spontaneous stopping in vivo. Therefore, the V2a "stop neurons" represent a glutamatergic descending pathway that favors immobility and may thus help control the episodic nature of locomotion.

Repetition suppression for visual actions in the macaque superior temporal sulcus.

In many brain areas, repetition of a stimulus usually weakens the neural response. This "adaptation" or repetition suppression effect has been observed with mass potential measures such as event-related potentials (ERPs), in fMRI BOLD responses, and locally with local field potentials (LFPs) and spiking activity. Recently, it has been reported that macaque F5 mirror neurons do not show repetition suppression of their spiking activity for single repetitions of hand actions, which disagrees with human fMRI adaptation studies. This finding also contrasts with numerous studies showing repetition suppression in macaque inferior temporal cortex, including the rostral superior temporal sulcus (STS). Since the latter studies employed static stimuli, we assessed here whether the use of dynamic action stimuli abolishes repetition suppression in the awake macaque STS. To assess adaptation effects in the STS, we employed the same hand action movies as used when examining adaptation in F5. The upper bank STS neurons showed repetition suppression during the approaching phase of the hand action, which corresponded to the phase of the action for which these neurons responded overall the strongest. The repetition suppression was present for the spiking activity measured in independent single-unit and multiunit recordings as well as for the LFP power at frequencies > 50 Hz. Together with previous data in F5, these findings suggest that adaptation effects differ between F5 mirror neurons and the STS neurons.

Encoding of point of view during action observation in the local field potentials of macaque area F5.

The discovery of mirror neurons compellingly shows that the monkey premotor area F5 is active not only during the execution but also during the observation of goal-directed motor acts. Previous studies have addressed the functioning of the mirror-neuron system at the single-unit level. Here, we tackled this research question at the network level by analysing local field potentials in area F5 while the monkey was presented with goal-directed actions executed by a human or monkey actor and observed either from a first-person or third-person perspective. Our analysis showed that rhythmic responses are not only present in area F5 during action observation, but are also modulated by the point of view. Observing an action from a subjective point of view produced significantly higher power in the low-frequency band (2-10 Hz) than observing the same action from a frontal view. Interestingly, an increase in power in the 2-10 Hz band was also produced by the execution of goal-directed motor acts. Independently of the point of view, action observation also produced a significant decrease in power in the 15-40 Hz band and an increase in the 60-100 Hz band. These results suggest that, depending on the point of view, action observation might activate different processes in area F5. Furthermore, they may provide information about the functional architecture of action perception in primates.

Mirror neurons encode the subjective value of an observed action.

Objects grasped by an agent have a value not only for the acting agent, but also for an individual observing the grasping act. The value that the observer attributes to the object that is grasped can be pivotal for selecting a possible behavioral response. Mirror neurons in area F5 of the monkey premotor cortex have been suggested to play a crucial role in the understanding of action goals. However, it has not been addressed if these neurons are also involved in representing the value of the grasped object. Here we report that observation-related neuronal responses of F5 mirror neurons are indeed modulated by the value that the monkey associates with the grasped object. These findings suggest that during action observation F5 mirror neurons have access to key information needed to shape the behavioral responses of the observer.

Physiologically inspired model for the visual recognition of transitive hand actions.

The visual recognition of actions is an important visual function that is critical for motor learning and social communication. Action-selective neurons have been found in different cortical regions, including the superior temporal sulcus, parietal and premotor cortex. Among those are mirror neurons, which link visual and motor representations of body movements. While numerous theoretical models for the mirror neuron system have been proposed, the computational basis of the visual processing of goal-directed actions remains largely unclear. While most existing models focus on the possible role of motor representations in action recognition, we propose a model showing that many critical properties of action-selective visual neurons can be accounted for by well-established visual mechanisms. Our model accomplishes the recognition of hand actions from real video stimuli, exploiting exclusively mechanisms that can be implemented in a biologically plausible way by cortical neurons. We show that the model provides a unifying quantitatively consistent account of a variety of electrophysiological results from action-selective visual neurons. In addition, it makes a number of predictions, some of which could be confirmed in recent electrophysiological experiments.

Pedunculopontine Chx10+ neurons control global motor arrest in mice.

Arrest of ongoing movements is an integral part of executing motor programs. Behavioral arrest may happen upon termination of a variety of goal-directed movements or as a global motor arrest either in the context of fear or in response to salient environmental cues. The neuronal circuits that bridge with the executive motor circuits to implement a global motor arrest are poorly understood. We report the discovery that the activation of glutamatergic Chx10-derived neurons in the pedunculopontine nucleus (PPN) in mice arrests all ongoing movements while simultaneously causing apnea and bradycardia. This global motor arrest has a pause-and-play pattern with an instantaneous interruption of movement followed by a short-latency continuation from where it was paused. Mice naturally perform arrest bouts with the same combination of motor and autonomic features. The Chx10-PPN-evoked arrest is different to ventrolateral periaqueductal gray-induced freezing. Our study defines a motor command that induces a global motor arrest, which may be recruited in response to salient environmental cues to allow for a preparatory or arousal state, and identifies a locomotor-opposing role for rostrally biased glutamatergic neurons in the PPN.

Neural theory for the perception of causal actions.

The efficient prediction of the behavior of others requires the recognition of their actions and an understanding of their action goals. In humans, this process is fast and extremely robust, as demonstrated by classical experiments showing that human observers reliably judge causal relationships and attribute interactive social behavior to strongly simplified stimuli consisting of simple moving geometrical shapes. While psychophysical experiments have identified critical visual features that determine the perception of causality and agency from such stimuli, the underlying detailed neural mechanisms remain largely unclear, and it is an open question why humans developed this advanced visual capability at all. We created pairs of naturalistic and abstract stimuli of hand actions that were exactly matched in terms of their motion parameters. We show that varying critical stimulus parameters for both stimulus types leads to very similar modulations of the perception of causality. However, the additional form information about the hand shape and its relationship with the object supports more fine-grained distinctions for the naturalistic stimuli. Moreover, we show that a physiologically plausible model for the recognition of goal-directed hand actions reproduces the observed dependencies of causality perception on critical stimulus parameters. These results support the hypothesis that selectivity for abstract action stimuli might emerge from the same neural mechanisms that underlie the visual processing of natural goal-directed action stimuli. Furthermore, the model proposes specific detailed neural circuits underlying this visual function, which can be evaluated in future experiments.

Neuronal encoding of human kinematic invariants during action observation.

Human movements, besides entailing the presence of a body shape, comply with characteristic kinematic laws of motion. Psychophysical studies show that low-level motion perception is biased toward stimuli complying with these laws. However, the neuronal structures that are sensitive to the kinematic laws of observed bodily movements are still largely unknown. We investigated this issue by dissociating, by means of computer-generated characters, form and motion information during the observation of human movements. In a functional imaging experiment, we compared the levels of blood oxygen level-dependent activity elicited by human actions complying with or violating the kinematic laws of human movements. Actions complying with normal kinematic laws of motion differentially activated the left dorsal premotor and dorsolateral prefrontal cortex as well as the medial frontal cortex. These findings suggest that the kinematic laws of human movements specifically modulate the responses of neuronal circuits also involved in action recognition and that are predominantly located in the left frontal lobe.

Parkinson's disease medication state and severity assessment based on coordination during walking.

Walking is a complex motor function requiring coordination of all body parts. Parkinson's disease (PD) motor signs such as rigidity, bradykinesia, and impaired balance affect movements including walking. Here, we propose a computational method to objectively assess the effects of Parkinson's disease pathology on coordination between trunk, shoulder and limbs during the gait cycle to assess medication state and disease severity. Movements during a scripted walking task were extracted from wearable devices placed at six different body locations in participants with PD and healthy participants. Three-axis accelerometer data from each device was synchronized at the beginning of either left or right steps. Canonical templates of movements were then extracted from each body location. Movements projected on those templates created a reduced dimensionality space, where complex movements are represented as discrete values. These projections enabled us to relate the body coordination in people with PD to disease severity. Our results show that the velocity profile of the right wrist and right foot during right steps correlated with the participant's total score on the gold standard Unified Parkinson's Disease Rating Scale (UPRDS) with an r2 up to 0.46. Left-right symmetry of feet, trunk and wrists also correlated with the total UPDRS score with an r2 up to 0.3. In addition, we demonstrate that binary dopamine replacement therapy medication states (self-reported 'ON' or 'OFF') can be discriminated in PD participants. In conclusion, we showed that during walking, the movement of body parts individually and in coordination with one another changes in predictable ways that vary with disease severity and medication state.

Combining Multimodal Behavioral Data of Gait, Speech, and Drawing for Classification of Alzheimer's Disease and Mild Cognitive Impairment.

BACKGROUND: Gait, speech, and drawing behaviors have been shown to be sensitive to the diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, previous studies focused on only analyzing individual behavioral modalities, although these studies suggested that each of these modalities may capture different profiles of cognitive impairments associated with AD. OBJECTIVE: We aimed to investigate if combining behavioral data of gait, speech, and drawing can improve classification performance compared with the use of individual modality and if each of these behavioral data can be associated with different cognitive and clinical measures for the diagnosis of AD and MCI. METHODS: Behavioral data of gait, speech, and drawing were acquired from 118 AD, MCI, and cognitively normal (CN) participants. RESULTS: Combining all three behavioral modalities achieved 93.0% accuracy for classifying AD, MCI, and CN, and only 81.9% when using the best individual behavioral modality. Each of these behavioral modalities was statistically significantly associated with different cognitive and clinical measures for diagnosing AD and MCI. CONCLUSION: Our findings indicate that these behaviors provide different and complementary information about cognitive impairments such that classification of AD and MCI is superior to using either in isolation.

A Guide to the Generation of a 6-Hydroxydopamine Mouse Model of Parkinson's Disease for the Study of Non-Motor Symptoms.

In Parkinson's disease (PD), a large number of symptoms affecting the peripheral and central nervous system precede, develop in parallel to, the cardinal motor symptoms of the disease. The study of these conditions, which are often refractory to and may even be exacerbated by standard dopamine replacement therapies, relies on the availability of appropriate animal models. Previous work in rodents showed that injection of the neurotoxin 6-hydroxydopamine (6-OHDA) in discrete brain regions reproduces several non-motor comorbidities commonly associated with PD, including cognitive deficits, depression, anxiety, as well as disruption of olfactory discrimination and circadian rhythm. However, the use of 6-OHDA is frequently associated with significant post-surgical mortality. Here, we describe the generation of a mouse model of PD based on bilateral injection of 6-OHDA in the dorsal striatum. We show that the survival rates of males and females subjected to this lesion differ significantly, with a much higher mortality among males, and provide a protocol of enhanced pre- and post-operative care, which nearly eliminates animal loss. We also briefly discuss the utility of this model for the study of non-motor comorbidities of PD.

Using an unbiased symbolic movement representation to characterize Parkinson's disease states.

Unconstrained human movement can be broken down into a series of stereotyped motifs or 'syllables' in an unsupervised fashion. Sequences of these syllables can be represented by symbols and characterized by a statistical grammar which varies with external situational context and internal neurological state. By first constructing a Markov chain from the transitions between these syllables then calculating the stationary distribution of this chain, we estimate the overall severity of Parkinson's symptoms by capturing the increasingly disorganized transitions between syllables as motor impairment increases. Comparing stationary distributions of movement syllables has several advantages over traditional neurologist administered in-clinic assessments. This technique can be used on unconstrained at-home behavior as well as scripted in-clinic exercises, it avoids differences across human evaluators, and can be used continuously without requiring scripted tasks be performed. We demonstrate the effectiveness of this technique using movement data captured with commercially available wrist worn sensors in 35 participants with Parkinson's disease in-clinic and 25 participants monitored at home.

Classification of TMS evoked potentials using ERP time signatures and SVM versus deep learning.

Modeling transcranial magnetic stimulation (TMS) evoked potentials (TEP) begins with classification of stereotypical single-pulse TMS responses in order to select validation targets for generative dynamical models. Several dimensionality reduction techniques are commonly in use to extract statistically independent features from experimental data for regression against model parameters. Here, we first designed a 3-dimensional feature space based on commonly described event-related potentials (ERP) from the literature. We then compared classification schemes which take as inputs either the 3D projection space or the original full rank input space. Their ability to discriminate TEP recorded from different brain regions given a stimulus site were evaluated. We show that a deep learning architecture, employing Convolutional Neural Network (CNN) and Multi-Layer Perceptron (MLP), yields better accuracy than the 3D projection and raw TEP input combined with Support Vector Machines. Such supervised feature extraction models may therefore be useful for scoring neural circuit simulations based on their ability to reproduce the underlying dynamical processes responsible for differential TEP responses.

Computational modeling of brainstem circuits controlling locomotor frequency and gait.

A series of recent studies identified key structures in the mesencephalic locomotor region and the caudal brainstem of mice involved in the initiation and control of slow (exploratory) and fast (escape-type) locomotion and gait. However, the interactions of these brainstem centers with each other and with the spinal locomotor circuits are poorly understood. Previously we suggested that commissural and long propriospinal interneurons are the main targets for brainstem inputs adjusting gait (Danner et al., 2017). Here, by extending our previous model, we propose a connectome of the brainstem-spinal circuitry and suggest a mechanistic explanation of the operation of brainstem structures and their roles in controlling speed and gait. We suggest that brainstem control of locomotion is mediated by two pathways, one controlling locomotor speed via connections to rhythm generating circuits in the spinal cord and the other providing gait control by targeting commissural and long propriospinal interneurons.

Wearable Nail Deformation Sensing for Behavioral and Biomechanical Monitoring and Human-Computer Interaction.

The dynamics of the human fingertip enable haptic sensing and the ability to manipulate objects in the environment. Here we describe a wearable strain sensor, associated electronics, and software to detect and interpret the kinematics of deformation in human fingernails. Differential forces exerted by fingertip pulp, rugged connections to the musculoskeletal system and physical contact with the free edge of the nail plate itself cause fingernail deformation. We quantify nail warpage on the order of microns in the longitudinal and lateral axes with a set of strain gauges attached to the nail. The wearable device transmits raw deformation data to an off-finger device for interpretation. Simple motions, gestures, finger-writing, grip strength, and activation time, as well as more complex idioms consisting of multiple grips, are identified and quantified. We demonstrate the use of this technology as a human-computer interface, clinical feature generator, and means to characterize workplace tasks.

A multiresolution diffused expectation-maximization algorithm for medical image segmentation.

In this paper a new method for segmenting medical images is presented, the multiresolution diffused expectation-maximization (MDEM) algorithm. The algorithm operates within a multiscale framework, thus taking advantage of the fact that objects/regions to be segmented usually reside at different scales. At each scale segmentation is carried out via the expectation-maximization algorithm, coupled with anisotropic diffusion on classes, in order to account for the spatial dependencies among pixels. This new approach is validated via experiments on a variety of medical images and its performance is compared with more standard methods.

Spatiotemporal correlation of spinal network dynamics underlying spasms in chronic spinalized mice.

Spasms after spinal cord injury (SCI) are debilitating involuntary muscle contractions that have been associated with increased motor neuron excitability and decreased inhibition. However, whether spasms involve activation of premotor spinal excitatory neuronal circuits is unknown. Here we use mouse genetics, electrophysiology, imaging and optogenetics to directly target major classes of spinal interneurons as well as motor neurons during spasms in a mouse model of chronic SCI. We find that assemblies of excitatory spinal interneurons are recruited by sensory input into functional circuits to generate persistent neural activity, which interacts with both the graded expression of plateau potentials in motor neurons to generate spasms, and inhibitory interneurons to curtail them. Our study reveals hitherto unrecognized neuronal mechanisms for the generation of persistent neural activity under pathophysiological conditions, opening up new targets for treatment of muscle spasms after SCI.

An Optogenetic Demonstration of Motor Modularity in the Mammalian Spinal Cord.

Motor modules are neural entities hypothesized to be building blocks of movement construction. How motor modules are underpinned by neural circuits has remained obscured. As a first step towards dissecting these circuits, we optogenetically evoked motor outputs from the lumbosacral spinal cord of two strains of transgenic mice - the Chat, with channelrhodopsin (ChR2) expressed in motoneurons, and the Thy1, expressed in putatively excitatory neurons. Motor output was represented as a spatial field of isometric ankle force. We found that Thy1 force fields were more complex and diverse in structure than Chat fields: the Thy1 fields comprised mostly non-parallel vectors while the Chat fields, mostly parallel vectors. In both, most fields elicited by co-stimulation of two laser beams were well explained by linear combination of the separately-evoked fields. We interpreted the Thy1 force fields as representations of spinal motor modules. Our comparison of the Chat and Thy1 fields allowed us to conclude, with reasonable certainty, that the structure of neuromotor modules originates from excitatory spinal interneurons. Our results not only demonstrate, for the first time using optogenetics, how the spinal modules follow linearity in their combinations, but also provide a reference against which future optogenetic studies of modularity can be compared.

Mirror Neurons in Monkey Premotor Area F5 Show Tuning for Critical Features of Visual Causality Perception.

Humans derive causality judgments reliably from highly abstract stimuli, such as moving discs that bump into each other [1]. This fascinating visual capability emerges gradually during human development [2], perhaps as consequence of sensorimotor experience [3]. Human functional imaging studies suggest an involvement of the "action observation network" in the processing of such stimuli [4, 5]. In addition, theoretical studies suggest a link between the computational mechanisms of action and causality perception [6, 7], consistent with the fact that both functions require an analysis of sequences of spatiotemporal relationships between interacting stimulus elements. Single-cell correlates of the perception of causality are completely unknown. In order to find such neural correlates, we investigated the responses of "mirror neurons" in macaque premotor area F5 [8, 9]. These neurons respond during the observation as well as during the execution of actions and show interesting invariances, e.g., with respect to the stimulus view [10], occlusions [11], or whether an action is really executed or suppressed [12]. We investigated the spatiotemporal properties of the visual responses of mirror neurons to naturalistic hand action stimuli and to abstract stimuli, which specified the same causal relationships. We found a high degree of generalization between these two stimulus classes. In addition, many features that strongly reduced the similarity of the response patterns coincided with the ones that also destroy the perception of causality in humans. This implies an overlap of neural structures involved in the processing of actions and the visual perception of causality at the single-cell level.