EditorialControl of MovementHighlights from the 30th Annual Meeting of the Society for the Neural Control of MovementMarta Russo, Nofar Ozeri-Engelhard, Kathleen Hupfeld, Caroline Nettekoven, Simon Thibault, Ehsan Sedaghat-Nejad, Daniela Buchwald, David Xing, Omid Zobeiri, Konstantina Kilteni, Scott T. Albert, and Giacomo ArianiMarta RussoDepartment of Neurology, Tor Vergata Polyclinic, Rome, ItalyDepartment of Biology, Northeastern University, Boston, Massachusetts, Nofar Ozeri-EngelhardWM Keck Center for Collaborative Neuroscience, Rutgers, The State University of New Jersey, Piscataway, New Jersey, Kathleen HupfeldDepartment of Applied Physiology and Kinesiology, University of Florida, Gainesville, Florida, Caroline NettekovenWellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UKDepartment of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom, Simon ThibaultImpAct team, Lyon Neuroscience Research Center, Inserm U1028, CNRS UMR5292, University of Lyon 1, Lyon, France, Ehsan Sedaghat-NejadLaboratory for Computational Motor Control, Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, Maryland, Daniela BuchwaldOttobock SE & Co. KGaA, Software & Electronics Engineering, Duderstadt, Germany, David XingDepartment of Neurobiology, Northwestern University, Evanston, Illinois, Omid ZobeiriDepartment of Biomedical Engineering, McGill University, Montreal, Quebec, Canada, Konstantina KilteniDepartment of Neurology, Tor Vergata Polyclinic, Rome, Italy, Scott T. AlbertLaboratory for Computational Motor Control, Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, Maryland, and Giacomo ArianiThe Brain and Mind Institute, Western University, London, Ontario, CanadaDepartment of Computer Science, Western University, London, Ontario, CanadaPublished Online:23 Sep 2021https://doi.org/10.1152/jn.00334.2021This is the final version - click for previous versionMoreSectionsPDF (1 MB)Download PDFDownload PDFPlus ToolsExport citationAdd to favoritesGet permissionsTrack citations ShareShare onFacebookTwitterLinkedInEmail INTRODUCTIONThe 30th meeting of the Society for the Neural Control of Movement (NCM) was originally scheduled to take place in Dubrovnik, Croatia, in April of 2020. Due to the COVID-19 pandemic, the in-person meeting was canceled and replaced by a virtual symposium showcasing the work of the society’s 2020 scholarship winners (https://ncm-society.org/symposium/). By the spring of 2021 (April 20th-22nd), the annual meeting was ready to return, although moving for the first time to a completely virtual setting on the online platforms Pheedloop (https://pheedloop.com) and Gathertown (https://gather.town) (1).More than ever before, this virtual format facilitated participation and social media engagement (e.g., the popular hashtag #NCM2021) from the research community all around the world (Fig. 1). Indeed, the forced choice of moving online had several positive side effects. First, rebounding from a concerning dip in academic participation from female researchers during the COVID-19 pandemic (2), with respect to previous in-person meetings, NCM 2021 showed an uptick in the percentage of female panelists (34% female attendees, 44% female speakers, and 30% female submissions). Second, early career researchers had more opportunities to present their work thanks to the addition of “data blitz” sessions on top of the traditional posters, individual talks, and panel discussions. This novel category allowed 5 min for presentation and 1 min for questions, followed by separate breakout rooms. Third, to enhance discussions during the meeting, panels, and posters were prerecorded and posted online in advance. Live recordings of every session allowed delegates to catch up on missed talks or rewatch talks later. These were welcome additions, as indicated by many respondents to the meeting’s feedback survey.Figure 1.World countries color-coded by the number of people attending Neural Control of Movement (NCM) 2021 affiliated with a university located in that country. Colors are represented on a log10 scale. The number insets in each country show the exact number of attendees.Download figureDownload PowerPointAs done in previous years (3–5), here we present highlights from the meeting. These highlights revolve around four central themes: 1) neuroplasticity, 2) complex motor skills, 3) multimodal sensory integration, and 4) the role of descending spinal tracts in motor control.NEUROPLASTICITY FOLLOWING ALTERED SENSORY INPUTA particular focus of NCM 2021 was on studies using multimodal neuroimaging to understand brain plasticity resultant from exposure to body augmentation technologies or abnormal sensory input. Tamar Makin, the 2020 Early Career Award winner, was commended for their innovative work examining the drivers and limitations of plasticity in the human brain as well as their contributions to the Society—including the organization of the first all-female NCM panel in 2018. Investigating embodiment of prostheses using fMRI, Makin and colleagues found that two-handed participants represented cosmetic prostheses more like hands and functional prostheses more like tools. However, among prosthesis users, both functional and cosmetic prostheses were not represented either as hands or as tools, but instead constituted a separate category (6, 7). Makin and collaborators explored a similar effect in another group of expert tool users, London litter pickers (7). Similar to amputees, litter pickers viewed their tool as a separate entity and not as an extension of their hand. Together, these results suggest that the human brain is plastic enough to create representations that are distinct from those shaped by evolution.A PhD student in Makin’s laboratory, Elena Amoruso, explicitly tested this hypothesis in their “third thumb” project (8). Participants wore an artificial thumb controlled by their toes. Coordination rapidly improved with practice and led to successful learning transfer when the controllers were switched (e.g., the toe that controlled flexion/extension now controlled abduction/adduction). When using local anesthesia to block proprioceptive and somatosensory input of the toes controlling the artificial thumb, early training was not affected, but retention of learning on the second day was smaller compared to a sham anesthesia control group, suggesting that sensory input played a critical role in learning to control the additional finger. The third thumb was also tested in a real-world context (9), where participants wore the device for ∼4.5 h a day for 5 days. Participants rapidly improved their performance and reported increasing sensations that the thumb was part of their body. Participants thus were able to develop a sense of proprioception of the thumb’s position relative to their biological fingers. Together, Makin and colleagues’ work demonstrates the remarkable ability of the human nervous system to undergo plastic changes—an ability Makin believes we should leverage in the development of prostheses. Similarly, Robert Nickl characterized the stability of neuronal responses to movement intention in a patient with incomplete tetraplegia who was bilaterally implanted with multi-unit electrode arrays in the primary motor (M1) and primary somatosensory (S1) cortices (10). Across 12 sessions, the number of active units was more stable in S1 than M1. However, in both areas, the number of overall active units showed a nearly exponential decline. With respect to recorded neuronal activity, in a single channel stability declined within minutes to hours. Interestingly, contralateral activity was more stable than activity in ipsilateral units. This characterization of the stability of neural activity is important for the development of brain-machine interface devices capable of decoding this representation into motor output.The brain’s ability to form new representations can also be probed by studying the neuroplastic changes driven by exposure to a completely novel environment. For example, Grant Tays investigated the impact of microgravity on fifteen astronauts who spent ∼6 months onboard the International Space Station (11–13). Participants completed MRI and behavioral testing multiple times pre- and postflight. Their data suggested little cognitive change from pre- to postflight but pronounced postflight impairments to mobility, balance, and bimanual coordination (14). These performance declines extended beyond only peripheral changes such as disuse muscle atrophy, suggesting that centrally mediated processes might also contribute to these effects. Further probing central nervous system changes with spaceflight, Kathleen Hupfeld discussed their work on vestibular processing in astronauts. Hupfeld and colleagues applied vestibular stimulation (pneumatic cheekbone taps; 15) to measure brain activity during vestibular processing at multiple times pre- and postflight. As previously demonstrated on Earth (12, 16), preflight, vestibular stimulation elicited activation of the parietal opercular area (i.e., the so-called “vestibular cortex”) and deactivation of somatosensory and visual cortices. Postflight, astronauts showed widespread reductions in somatosensory and visual cortical deactivation. In addition, greater reductions in the deactivation of visual brain regions were associated with smaller declines in standing balance. These findings suggest that microgravity exposure results in cortical plasticity in the form of sensory reweighting, i.e., down-weighting of vestibular inputs (due to this system’s altered signaling in the absence of gravity), and concurrent up-weighting of other sensory processing regions, such as the somatosensory and visual cortices. This reweighting may facilitate more adaptable postflight standing balance when crewmembers readjust to normal vestibular inputs on Earth.To better characterize the mechanisms underlying cortical plasticity, Caroline Nettekoven presented their data on the relation between motor cortical γ-aminobutyric acid (GABA), previously shown to play a role in motor learning and visuomotor adaptation (17–19). Although participants adapted to a stepwise increasing rotation (or performed a control task) in the MRI scanner, Nettekoven and colleagues measured GABA concentration in the left M1 hand area. GABA concentration before adapting predicted retention of the adapted movement but not the extent of adaptation, suggesting a role for M1 GABA in maintaining but not acquiring the adapted state. This relationship between GABA and retention of the adapted movement was mediated by the change in functional connectivity between the left M1 hand area and the right cerebellar hand area. Participants with higher M1 GABA concentration before adapting showed a decrease in functional connectivity between M1 and the cerebellum during adaptation, and they better retained the adaptive movement. These findings imply a link between motor performance, motor network connectivity, and cortical inhibition, and shed light on the neurochemical bases of human motor adaptation (20). Chris Horton and colleagues provided additional evidence that region-specific cortical GABA concentrations predict aspects of human motor performance. Measuring GABA concentrations in S1 and thalamus during “go” and “stop” tasks, they found no association between GABA and “stopping” performance. However, in the “go” task, higher ipsilateral thalamic GABA correlated with faster reaction time. These data suggest that thalamic GABA concentration supports speeded selection and execution of cued choice responses.Together, these lines of research contribute to our understanding of how the brain adapts to perturbations or novel sensory conditions (e.g., prolonged prosthesis or tool use, microgravity, and visuomotor perturbations) and have numerous applications for improving human health, such as designing more effective prostheses and maintaining astronaut health during future missions to Mars.EXPLORING COMPLEX MOTOR SKILLS BEYOND UNIMANUAL REACHINGAnother emergent theme of NCM 2021 was a renewed push to study more complex motor skills. Although the definition of a “complex motor skill” may seem arbitrary, here we consider studies that examined naturalistic multijoint movements that tend to involve interactions with tools and/or the control of many degrees of freedom (DOFs).One example is sophisticated finger control. As Tamar Makin’s work shows, besides prompting questions about artificial limb embodiment and neural plasticity, augmentation technologies can open new avenues for studying complex behaviors by enabling previously impossible actions. In addition, within the clinical setting, we can leverage assistive technologies to measure hand function and monitor the progress of rehabilitation. For example, Jing Xu introduced a novel device, the HAND (Hand Actuation Neural-training Device), that is equipped with force sensors capable of detecting even the smallest isometric forces from a near-plegic hand in 3-D (21). They used this device to characterize finger coactivation patterns in healthy participants and stroke patients. The importance of such research is most evident in manual activities that demand finger individuation, such as producing a chord when playing the piano. By comparing naïve participants and expert musicians using a foot-controlled supernumerary robotic thumb to play the piano, Aldo Faisal asked what determines our ability to learn and use augmentation in skilled tasks. They showed that foot dexterity (and not task-relevant piano expertise) is the best predictor of future performance (22). In addition, the observation of highly idiosyncratic learning curves prompted new questions for future research: can everyone be augmented equally? Or should we design personalized training? Regardless, it is clear that we need real-world complex tasks to improve the training of real-world sensorimotor skills. According to Ilana Nisky, 2021 Early Career Award winner, robot-assisted surgery can bridge the gap between laboratory-based research and real-life applications. Currently, surgical training is not optimized, partly due to a lack of haptic feedback and partly due to limited knowledge on how to measure surgical skills (23–27). Nisky’s research used a teleoperated needle driving task and integrated data measuring the dynamics of the robotic manipulandum and modeling human kinematics to describe the quality of surgical skill throughout the learning process. A key aspect of surgical skill is that it requires extremely precise control of external tools via the coordination of multiple effectors. Similarly, watchmaking is a complex craft that includes bimanual control of 44 DOFs. To understand and model such dexterity, Aude Billard and colleagues examined cohorts of apprentices and expert watchmakers using a combination of motion capture and tactile sensing systems. They found that experts consistently used distinctive hand poses that optimized manipulability and made use of longer preparation times to reduce possible mistakes during execution time (28, 29). Moreover, their research revealed that the two hands can work together distributing control of different variables to achieve better precision than a single hand.Combining tool use and whole body movements is another way to increase realism and complexity in the study of motor skills. For example, Antonella Maselli presented their work on ball throwing in which they applied spatiotemporal principal component analysis and Hessian-based decomposition to whole body kinematics to obtain compact descriptions of unconstrained throwing. They used these descriptions to quantitatively characterize performance, individual strategies, gender differences, and common patterns from a heterogeneous sample of nontrained throwers (30–32). Zhaoran Zhang also investigated ball throwing but compared movements in a virtual and a real set-up. Tolerance-Noise-Covariation decomposition revealed distinct stages of learning, indicating that subjects reached the stage of fine-tuning throwing variability in the real but not in the virtual task. These findings resonate with the reported problems in transferring therapeutic benefits from virtual to real environments (33). Expanding the research on tool use to the more exotic example of manipulating a bullwhip, Marta Russo investigated how humans can achieve dexterity in manipulating the wave dynamics of the whip's infinite DOFs. Their experimental and simulation results suggested that humans may represent control of this prodigiously complex dynamic object in terms of low-dimensional dynamic primitives. Thus, in the same set of studies, Moses Nah tested whether a distant target could be reached with a whip using a controller composed of only motor primitives. This approach was able to manage 54 DOFs by means of a single submovement in joint space. A detailed model of the whip dynamics was not needed for this approach. This may be a key simplification that humans leverage to learn complex motor skills, avoiding the need to internalize the detailed dynamic properties of the object being manipulated (34, 58).If details regarding objects’ properties do not need to be internalized, then what are the neural mechanisms underlying tool use? Simon Thibault showed that there is a functional overlap between tool-use planning and complex syntactic processing in the basal ganglia. Behaviorally, this is reflected by bidirectional cross-domain learning transfer, where tool use benefits syntax and vice-versa (35). In addition, Raeed Chowdhury studied the neural underpinnings of highly feedback-driven tasks, such as balancing a stick on a palm. During the task, monkeys displayed multiple control schemes within each trial, suggesting that they might have had multiple goals, and thus corresponding neural strategies, in different phases of the arm motion.Primates are not the only species that use strategies to control objects. Indeed, it is possible to perform complex tool manipulations without the benefit of specialized hands. New Caledonian crows are an excellent nonprimate animal model for studying complex object manipulation, as shown by Christian Rutz’s work. These animals exhibit a striking degree of dexterity with their beak when manufacturing tools from raw plant materials, using these tools to extract insect prey from hiding places in deadwood, and storing tools for future use in holes or behind tree bark (36).Such sophisticated control of our bodies extends beyond hand and upper-limb dexterity and is also exemplified by walking. Jacqueline Palmer presented their results on the relationship between motor cortical activity and circuit-specific cortico-cortical interactions during a whole body dual-task involving balance and cognition in older adults. Consistent with findings in younger adults, their results support motor cortical β activity as a potential biomarker for individual levels of balance challenge in older adults (37). To capture individual differences in gait dynamics, Taniel Winner used a data-driven dynamical model: a recurrent neural network (RNN) with long short-term memory. Measuring how the internal parameters of the model discriminated individuals with or without stroke, their work showed that using advanced models over discrete summary variables increased the accuracy of group classification. The ability to discriminate between different individuals may lead to the development of individually tailored rehabilitation to improve balance and gait in elderly or impaired individuals.Finally, speech is another example of a complex motor skill that requires the fine control of several muscles to produce a sound. To better understand this complexity, two talks focused on the effects of different perturbations during speech production. First, Zoe Swann assessed how a startling acoustic stimulus might affect word repetition in individuals with poststroke aphasia and apraxia. Startle exposure resulted in faster and louder speech. These results were analogous to the finding that a startle during upper extremity movement produced a higher probability of muscle activity onset in severe poststroke subjects who were unable to activate their arm muscles on their own (38). Second, Ding-lan Tang examined movement variability during speech production perturbed by auditory feedback. Motor variability increased with auditory perturbation, and this higher variability persisted even after removing the perturbation.The breadth of research highlighted in this section confirms a renewed interest in understanding the neural control of complex motor skills across species, body parts, interacting tools, and artificial limbs. Future work should look into transferring these abilities to robotic devices, as part of a continuing effort to close the loop between biological capability in humans and technical capability in robots.MULTIMODAL SENSORIMOTOR INTEGRATION IN HEALTH AND INJURYIn motor tasks, sensory and motor circuits interact to adjust motor commands to changes in the environment and to modulate the sensory experience to optimize task performance. Here we discuss talks from this year’s meeting that investigated sensory processing and sensorimotor interactions in both healthy and injured systems.Sliman Bensmaia and colleagues tackled the question of how multimodal sensory information is represented in the cuneate nucleus, a brainstem structure that receives sensory input from primary afferents in the forelimbs. They recorded single-unit activity in the S1, cuneate, and cutaneous primary afferents in response to skin stimulation and found that responses of cuneate neurons resembled those of S1, more so than those of primary afferents. Moreover, by using their novel simulation model (39), Bensmaia’s group was able to use the activity of 5–9 primary afferents from different cutaneous modalities to faithfully predict the response of single cells in the cuneate to skin stimulation. This study demonstrates that integration of multimodal sensory information occurs at the level of the cuneate, well before sensory input reaches the brain. A study led by Nofar Ozeri-Engelhard also demonstrated that multimodal integration of sensory inputs happens in the spinal cord. Using intersectional genetics, they isolated the parvalbumin-expressing interneurons located in the deep dorsal horn of the spinal cord (dPVs) to study their role in sensory processing and motor performance. With functional and histological assays, they provided evidence that dPVs form a circuit that integrates multimodal sensory information to directly communicate with motor neurons. To test whether this circuit plays a role in motor performance, they ablated dPVs and showed that mouse locomotion was perturbed. These results suggest that peripheral sensory circuits directly modulate motor output in the spinal cord to adjust motor performance to changes in the sensory environment.As we have seen, sensory processing affects movement. However, the opposite is also true: motor circuits interact with sensory pathways to modify sensory input, and consequently, the sensory experience. Kazuhiko Seki tested the idea that a copy of the motor command (i.e., efference copy) modulates sensory-evoked potentials (SEPs) recorded in the cuneate nucleus of monkeys, in response to sensory stimulation of primary afferents innervating the forelimb (40). SEPs were recorded during active, passive, and no movement (i.e., hold) conditions. SEP amplitude was attenuated during active movement compared to hold, suggesting that the motor command’s efference copy modulated cuneate sensory responses. Although to a lesser degree, attenuation was also observed during passive movements, demonstrating that other descending sensory inputs are involved in modulating the sensory response. In support of these conclusions, Seki presented anatomical evidence that the cuneate nuclei receive top-down projections from both the somatosensory and motor (new M1) cortices, proposing these descending projections as the source of attenuation.Eiman Azim and colleagues studied the role of descending cortico-cuneate pathways in the execution of tactile-guided movements. Using genetic tools in mice, they identified local inhibitory neurons in the cuneate that bidirectionally regulate the activity of cuneolemniscal neurons (which project from the cuneate to the thalamus). Optogenetic manipulation of these neurons altered the gain of the activity of the cuneoleminiscal neurons, and accordingly, the performance of dexterous movements (41). Anatomical experiments showed that both the inhibitory neurons and the cuneolemniscal neurons receive input from the sensory cortex. This supports Azim’s hypothesis that the cortex indirectly disinhibits, and directly inhibits cuneolemniscal neurons to augment sensory information necessary for optimal task performance while attenuating unnecessary information. In contrast to Seki’s findings, Azim’s work showed that descending pathways mostly originated from sensory areas. This discrepancy might be due to differences in species since new M1, which projects to the cuneate in non-human primates, does not exist in mice.In the cerebellum, the efference copy of motor commands is believed to be used by internal models to predict the sensory consequences of active movements. Sensory prediction can be used to distinguish between a sensory state arising from active (i.e., self-generated) versus passive (i.e., externally generated) movement. For example, although vestibular-spinal reflexes are essential for maintaining balance in response to a passive perturbation, these are counterproductive during active movements. Indeed, Kathleen Cullen’s group showed that the responses of vestibulospinal neurons in deep cerebellar nuclei (DCN) are suppressed during active compared to passive movement (42). Omid Zobeiri investigated whether Purkinje cells in the vestibular cerebellum, which inhibit DCN, can predict the sensory consequences of efference copy and in turn suppress DCN responses during active head movements. Although single Purkinje cells did not encode sensory prediction, they showed heterogeneous responses to vestibular and proprioceptive sensory inputs and motor efference copy. Simulation data suggested that combining the responses of ∼40 Purkinje cells is sufficient to generate sensory predictions that suppress DCN responses during active movements, providing evidence that cerebellar internal models are encoded by Purkinje cell sub-populations.During voluntary movements, when sensory prediction matches motor output (i.e., no sensory prediction error, SPE), DCN downregulate their responses to externally applied perturbations. But how does DCN sensitivity change in the presence of an SPE? Robyn Mildren studied how varying degrees of SPE impact the suppressed DCN response during active head movements. DCN responses were recorded from the rostral fastigial nucleus of one rhesus monkey. Different magnitudes of assistive and resistive torques were externally applied to introduce SPEs. As SPE increased, suppression of DCN responses during active movements gradually decreased, suggesting a gradual shift in encoding from self-generated to externally applied motion.In addition to head movements, primates use eye movements to sample the visual environment. Ehsan Sedaghat-Nejad investigated the role of the oculomotor cerebellum in encoding SPEs by recording Purkinje cell activity while marmosets performed saccades to a target presented in random locations. Occasionally, the target location was quickly changed, to introduce a prediction error. They showed that Purkinje cells can be divided into subpopulations, according to their encoding of SPEs, resulting in a population activity that was predictive of the saccade termination (43).Our brain’s ability to perform two sequential saccades has been long studied by Michael Goldberg, commended for his lifelong contributions to science and to the NCM community, and selected by NCM for the distinguished career award lecture. Although the first saccadic eye movement is an “easy” task for the brain, the second requires an updated representation of the initial position from which the movement starts. This position could be estimated from proprioceptive information about the eye, or from the efference copy of the first movement. To distinguish between these possibilities, the Goldberg laboratory investigated the lateral intraparietal area (LIP), a region that evokes saccades. In a series of experiments, they showed that although LIP receives proprioceptive information regarding eye position, this information arrives too late to alter second saccade planning (44). However, they found that LIP neurons fired before the second saccade, even though the second movement was not in their initial receptive field (45). This observation implies that the efference copy of the first saccade re-maps LIP receptive fields to generate the second saccade. What then is the role of proprioception in saccadic eye movements? Interestingly, with an increase in the number of sequential saccades, the brain shifts from an efference copy to proprioceptive-based encoding (46, 47).Modulation of bottom-up sensory input by top-down signals is important to maintain movement accuracy. But what role do these interactions serve in functional recovery? Corinna Darian-Smith and their laboratory investigated this question by performing incomplete lesions of dorsal roots innervating forelimb fingers. Although these lesions produced severe deficits in prehension tasks, monkeys exhibited an impressive recovery over 1 to 3 mo. To uncover the mechanism underlying recovery, the Darian-Smith laboratory

Movements are defining characteristics of all behaviors. Animals walk around, move their eyes to explore the world or touch structures to learn more about them. So far we only have some basic understanding of how the brain generates movements, especially when we want to understand how different areas of the brain interact with each other. In this study we investigated the influence of sensory object information on grasp planning in four different brain areas involved in vision, touch, movement planning, and movement generation in the parietal, somatosensory, premotor and motor cortex. We trained one monkey to grasp objects that he either saw or touched beforehand while continuously recording neural spiking activity with chronically implanted floating multi-electrode arrays. The animal was instructed to sit in the dark and either look at a shortly illuminated object or reach out and explore the object with his hand in the dark before lifting it up. In a first analysis we confirmed that the animal not only memorizes the object in both tasks, but also applies an object-specific grip type, independent of the sensory modality. In the neuronal population, we found a significant difference in the number of tuned units for sensory modalities during grasp planning that persisted into grasp execution. These differences were sufficient to enable a classifier to decode the object and sensory modality in a single trial exclusively from neural population activity. These results give valuable insights in how different brain areas contribute to the preparation of grasp movement and how different sensory streams can lead to distinct neural activity while still resulting in the same action execution.

Grasping movements are some of the most common movements primates do every day. They are important for social interactions as well as picking up objects or food. Usually, these grasping movements are guided by vision but proprioceptive and haptic inputs contribute greatly. Since grasping behaviors are common and easy to motivate, they represent an ideal task for understanding the role of different brain areas during planning and execution of complex voluntary movements in primates. For experimental purposes, a stable and repeatable presentation of the same object as well as the variation of objects is important in order to understand the neural control of movement generation. This is even more the case when investigating the role of different senses for movement planning, where objects need to be presented in specific sensory modalities. We developed a turntable setup for non-human primates (macaque monkeys) to investigate visually and tactually guided grasping movements with an option to easily exchange objects. The setup consists of a turntable that can fit six different objects and can be exchanged easily during the experiment to increase the number of presented objects. The object turntable is connected to a stepper motor through a belt system to automate rotation and hence object presentation. By increasing the distance between the turntable and the stepper motor, metallic components of the stepper motor are kept at a distance to the actual recording setup, which allows using a magnetic-based data glove to track hand kinematics. During task execution, the animal sits in the dark and is instructed to grasp the object in front of it. Options to turn on a light above the object allow for visual presentation of the objects, while the object can also remain in the dark for exclusive tactile exploration. A red LED is projected onto the object by a one-way mirror that serves as a grasp cue instruction for the animal to start grasping the object. By comparing kinematic data from the magnetic-based data glove with simultaneously recorded neural signals, this setup enables the systematic investigation of neural population activity involved in the neural control of hand grasping movements.

A major challenge in gene library generation is to guarantee a large functional size and diversity that significantly increases the chances of selecting different functional protein variants. The use of trinucleotides mixtures for controlled randomization results in superior library diversity and offers the ability to specify the type and distribution of the amino acids at each position. Here we describe the generation of a high diversity gene library using tHisF of the hyperthermophile Thermotoga maritima as a scaffold. Combining various rational criteria with contingency, we targeted 26 selected codons of the thisF gene sequence for randomization at a controlled level. We have developed a novel method of creating full-length gene libraries by combinatorial assembly of smaller sub-libraries. Full-length libraries of high diversity can easily be assembled on demand from smaller and much less diverse sub-libraries, which circumvent the notoriously troublesome long-term archivation and repeated proliferation of high diversity ensembles of phages or plasmids. We developed a generally applicable software tool for sequence analysis of mutated gene sequences that provides efficient assistance for analysis of library diversity. Finally, practical utility of the library was demonstrated in principle by assessment of the conformational stability of library members and isolating protein variants with HisF activity from it. Our approach integrates a number of features of nucleic acids synthetic chemistry, biochemistry and molecular genetics to a coherent, flexible and robust method of combinatorial gene synthesis.

Prediction of subcellular protein localization is essential to correctly assign unknown proteins to cell organelle-specific protein networks and to ultimately determine protein function. For metazoa, several computational approaches have been developed in the past decade to predict peroxisomal proteins carrying the peroxisome targeting signal type 1 (PTS1). However, plant-specific PTS1 protein prediction methods have been lacking up to now, and pre-existing methods generally were incapable of correctly predicting low-abundance plant proteins possessing non-canonical PTS1 patterns. Recently, we presented a machine learning approach that is able to predict PTS1 proteins for higher plants (spermatophytes) with high accuracy and which can correctly identify unknown targeting patterns, i.e. novel PTS1 tripeptides and tripeptide residues. Here we describe the first plant-specific web server PredPlantPTS1 for the prediction of plant PTS1 proteins using the above-mentioned underlying models. The server allows the submission of protein sequences from diverse spermatophytes and also performs well for mosses and algae. The easy-to-use web interface provides detailed output in terms of (i) the peroxisomal targeting probability of the given sequence, (ii) information whether a particular non-canonical PTS1 tripeptide has already been experimentally verified, and (iii) the prediction scores for the single C-terminal 14 amino acid residues. The latter allows identification of predicted residues that inhibit peroxisome targeting and which can be optimized using site-directed mutagenesis to raise the peroxisome targeting efficiency. The prediction server will be instrumental in identifying low-abundance and stress-inducible peroxisomal proteins and defining the entire peroxisomal proteome of Arabidopsis and agronomically important crop plants. PredPlantPTS1 is freely accessible at ppp.gobics.de.

Einige der häufigsten Bewegungen die wir täglich ausführen sind Greifbewegungen. Normalerweise greifen wir Objekte, nachdem wir sie gesehen haben. Doch auch andere Sinne, wie unser Tastsinn, können uns bei der Vorbereitung und Ausführung von Greifbewegungen behilflich sein. Tatsächlich ist ständiges Feedback im Umgang mit Objekten essentiell: Es hilft uns dabei, uns auf wechselnde Situationen einzustellen, zum Beispiel wenn ein Objekt aus unserer Hand zu rutschen droht. Obwohl generell angenommen wird, dass verschiedene Gehirnbereiche für verschiedene Aufgaben oder Sinne zuständig sind (wobei es keine komplette Trennung gibt, da die meisten Bereiche verschiedene Arten von Informationen verarbeiten) ist jede Bewegung ein Ergebnis des Zusammenspiels verschiedener Gehirnbereiche. Diese Zusammenarbeit zwischen Gehirnbereichen um Bewegungen entstehen zu lassen, wurde noch nicht umfassend erforscht, insbesondere nicht, wenn Objekte mit verschiedenen Sinnen wahrgenommen werden. In dieser Doktorarbeit habe ich untersucht, wie Informationen, die von unserem Tastsinn (so genannte taktile Informationen) stammen in verschiedenen Gehirnbereichen verarbeitet werden und wie diese Informationen anschließend genutzt wird, um Greifbewegungen zu planen und auszuführen. Desweiteren habe ich untersucht, ob die Art und Weise wie diese Bewegungen vom Gehirn geplant und ausgeführt werden sich verändert, wenn die Planung auf visuellen oder taktilen Informationen basiert. Hierfür wurden Mehrfachelektrodenarrays in den primären Motorkortex (M1), den primären somatosensorischen Kortex (S1), das anteriore intraparietale Areal AIP und den Handbereich des ventralen Premotorkortexes (Areal F5) eines Rhesusaffen (Macaca mulatta) implantiert. Der Affe wurde darauf trainiert, Objekte die er entweder gesehen oder berührt hat zu greifen. Das erlaubte es mir zu vergleichen, wie Informationen beider Bedingungen vom Gehirn verarbeitet werden. Bei einem Vergleich von Feuerraten wurden bereits Unterschiede in der Gehirnaktivität zwischen beiden Bedingungen sichtbar. Wenn man die Anzahl der signifikant modulierten Neurone vergleicht, ist es offensichtlich, dass Unterschiede in der Planung von Greifbewegungen basierend auf visuellen und taktilen Informationen vorliegen, die während der Ausführung der Bewegung nicht mehr vorhanden sind. Anschließend wurde ein Dekodierer auf den vorliegenden Daten trainiert, um vorhersagen zu können, welche Bewegungen auf der visuellen oder der taktilen Information basierten. Die Genauigkeit dieses Dekodierer ist während der frühen Memory-Period am höchsten. Trotzdem können auch zu einem späteren Zeitpunkt, kurz bevor die Greifbewegung beginnt, noch beide Bedingungen voneinander unterschieden werden. In einem zweiten Versuch wurde der Mittelfinger des Affen passiv stimuliert. Dies gab mir Einblicke wie taktile Information in den vier Gehirnbereichen verarbeitet wird. Zusammen ergeben die Ergebnisse einen Eindruck, wie taktile Informationen die fronto-parietalen Greifareale beeinflussen. AIP, ein Bereich, der dafür bekannt ist visuelle Objektinformationen zu verarbeiten, und F5, bekannt als Bereich für die Greifplanung, zeigen keine Reaktion zu taktilen Informationen, wenn keine Greifbewegung ausgeführt werden soll. Wichtiger noch, alle vier Bereiche zeigen signifikante Unterschiede in der neuronalen Aktivität während der Vorbereitung von Bewegungen, wenn visuell- und taktil-geleitete Bewegungen verglichen werden. Die Unterschiede sind stark genug, um die verschiedenen Bedingungen mit Hilfe eines Dekodierer unterscheiden zu können. Dies zeigt, dass die Art und Weise wie Objektinformation erworben wird berücksichtigt werden muss, wenn versucht wird, Aufnahmen der Gehirnaktivität in den fronto-parietalen Greifarealen zu machen, um damit beispielsweise eine Prothese oder einen Roboterarm zu steuern.