A versatile robotic platform for the design of natural, three-dimensional reaching and grasping tasks in monkeys.

OBJECTIVE: Translational studies on motor control and neurological disorders require detailed monitoring of sensorimotor components of natural limb movements in relevant animal models. However, available experimental tools do not provide a sufficiently rich repertoire of behavioral signals. Here, we developed a robotic platform that enables the monitoring of kinematics, interaction forces, and neurophysiological signals during user-defined upper limb tasks for monkeys. APPROACH: We configured the platform to position instrumented objects in a three-dimensional workspace and provide an interactive dynamic force-field. MAIN RESULTS: We show the relevance of our platform for fundamental and translational studies with three example applications. First, we study the kinematics of natural grasp in response to variable interaction forces. We then show simultaneous and independent encoding of kinematic and forces in single unit intra-cortical recordings from sensorimotor cortical areas. Lastly, we demonstrate the relevance of our platform to develop clinically relevant brain computer interfaces in a kinematically unconstrained motor task. SIGNIFICANCE: Our versatile control structure does not depend on the specific robotic arm used and allows for the design and implementation of a variety of tasks that can support both fundamental and translational studies of motor control.

EMG-based learning approach for estimating wrist motion.

This paper proposes an EMG based learning approach for estimating the displacement along the 2-axes (abduction/adduction and flexion/extension) of the human wrist in real-time. The algorithm extracts features from the EMG electrodes on the upper and forearm and uses Support Vector Regression to estimate the intended displacement of the wrist. Using data recorded with the arm outstretched in various locations in space, we train the algorithm so as to allow robust prediction even when the subject moves his/her arm across several positions in space. The proposed approach was tested on five healthy subjects and showed that a R(2) index of 63.6% is obtained for generalization across different arm positions and wrist joint angles.

The allele-specific probe and primer amplification assay, a new real-time PCR method for fine quantification of single-nucleotide polymorphisms in pooled DNA.

The evolution of fungicide resistance within populations of plant pathogens must be monitored to develop management strategies. Such monitoring often is based on microbiological tests, such as microtiter plate assays. Molecular monitoring methods can be considered if the mutations responsible for resistance have been identified. Allele-specific real-time PCR approaches, such as amplification refractory mutation system (ARMS) PCR and mismatch amplification mutation assay (MAMA) PCR, are, despite their moderate efficacy, among the most precise methods for refining SNP quantification. We describe here a new real-time PCR method, the allele-specific probe and primer amplification assay (ASPPAA PCR). This method makes use of mixtures of allele-specific minor groove binder (MGB) TaqMan probes and allele-specific primers for the fine quantification of SNPs from a pool of DNA extracted from a mixture of conidia. It was developed for a single-nucleotide polymorphism (SNP) that is responsible for resistance to the sterol biosynthesis inhibitor fungicide fenhexamid, resulting in the replacement of the phenylalanine residue (encoded by the TTC codon) in position 412 of the enzymatic target (3-ketoreductase) by a serine (TCC), valine (GTC), or isoleucine (ATC) residue. The levels of nonspecific amplification with the ASPPAA PCR were reduced at least four times below the level of currently available allele-specific real-time PCR approaches due to strong allele specificity in amplification cycles, including two allele selectors. This new method can be used to quantify a complex quadriallelic SNP in a DNA pool with a false discovery rate of less than 1%.

Auditory motion affects visual biological motion processing.

The processing of biological motion is a critical, everyday task performed with remarkable efficiency by human sensory systems. Interest in this ability has focused to a large extent on biological motion processing in the visual modality (see, for example, Cutting, J. E., Moore, C., & Morrison, R. (1988). Masking the motions of human gait. Perception and Psychophysics, 44(4), 339-347). In naturalistic settings, however, it is often the case that biological motion is defined by input to more than one sensory modality. For this reason, here in a series of experiments we investigate behavioural correlates of multisensory, in particular audiovisual, integration in the processing of biological motion cues. More specifically, using a new psychophysical paradigm we investigate the effect of suprathreshold auditory motion on perceptions of visually defined biological motion. Unlike data from previous studies investigating audiovisual integration in linear motion processing [Meyer, G. F. & Wuerger, S. M. (2001). Cross-modal integration of auditory and visual motion signals. Neuroreport, 12(11), 2557-2560; Wuerger, S. M., Hofbauer, M., & Meyer, G. F. (2003). The integration of auditory and motion signals at threshold. Perception and Psychophysics, 65(8), 1188-1196; Alais, D. & Burr, D. (2004). No direction-specific bimodal facilitation for audiovisual motion detection. Cognitive Brain Research, 19, 185-194], we report the existence of direction-selective effects: relative to control (stationary) auditory conditions, auditory motion in the same direction as the visually defined biological motion target increased its detectability, whereas auditory motion in the opposite direction had the inverse effect. Our data suggest these effects do not arise through general shifts in visuo-spatial attention, but instead are a consequence of motion-sensitive, direction-tuned integration mechanisms that are, if not unique to biological visual motion, at least not common to all types of visual motion. Based on these data and evidence from neurophysiological and neuroimaging studies we discuss the neural mechanisms likely to underlie this effect.

Synthetic brain imaging: grasping, mirror neurons and imitation.

The article contributes to the quest to relate global data on brain and behavior (e.g. from PET, Positron Emission Tomography, and fMRI. functional Magnetic Resonance Imaging) to the underpinning neural networks. Models tied to human brain imaging data often focus on a few "boxes" based on brain regions associated with exceptionally high blood flow, rather than analyzing the cooperative computation of multiple brain regions. For analysis directly at the level of such data, a schema-based model may be most appropriate. To further address neurophysiological data, the Synthetic PET imaging method uses computational models of biological neural circuitry based on animal data to predict and analyze the results of human PET studies. This technique makes use of the hypothesis that rCBF (regional cerebral blood flow) is correlated with the integrated synaptic activity in a localized brain region. We also describe the possible extension of the Synthetic PET method to fMRI. The second half of the paper then exemplifies this general research program with two case studies, one on visuo-motor processing for control of grasping (Section 3 in which the focus is on Synthetic PET) and the imitation of motor skills (Sections 4 and 5, with a focus on Synthetic fMRI). Our discussion of imitation pays particular attention to data on the mirror system in monkey (neural circuitry which allows the brain to recognize actions as well as execute them). Finally, Section 6 outlines the immense challenges in integrating models of different portions of the nervous system which address detailed neurophysiological data from studies of primates and other species; summarizes key issues for developing the methodology of Synthetic Brain Imaging; and shows how comparative neuroscience and evolutionary arguments will allow us to extend Synthetic Brain Imaging even to language and other cognitive functions for which few or no animal data are available.