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Pyramidal tract lesions determine the clinical syndrome of Hereditary Spastic Paraplegia (HSP). The clinical impairments of HSP are typically exemplified by their deficits in mobility, leading to falls and injuries. The first aim of this study was to identify the cause for postural abnormalities caused by pyramidal tract lesions in HSP. The second aim was to specify the effect of treadmill training for postural abnormalities. We examined nine HSP patients before and after treadmill training, as well as nine healthy control subjects during perturbed and unperturbed stance. We found that HSP was associated with larger sway amplitudes and velocities. Body excursions following platform tilts were larger, and upper body excursions showed a phase lead. Model-based analysis detected a greater time delay and a reduced long-term error correction of postural reactions in the center of mass. HSP patients performed significantly better in clinical assessments after treadmill training. In addition, treadmill training reduced sway amplitudes and body excursions, most likely by increasing positional and velocity error correction gain as a compensatorymechanism, while the time delay and long-term error correction gain remained largely unaffected. Moreover, the upper body's phase lead was reduced.We conclude that HSP leads to very specific postural impairments. While postural control generally benefits from treadmill training, the effect seems to mainly rely on compensatory mechanisms, whereas the original deficits are not affected significantly. [ABSTRACT FROM AUTHOR]

Pyramidal tract lesions determine the clinical syndrome of Hereditary Spastic Paraplegia (HSP). The clinical impairments of HSP are typically exemplified by their deficits in mobility, leading to falls and injuries. The first aim of this study was to identify the cause for postural abnormalities caused by pyramidal tract lesions in HSP. The second aim was to specify the effect of treadmill training for postural abnormalities. We examined nine HSP patients before and after treadmill training, as well as nine healthy control subjects during perturbed and unperturbed stance. We found that HSP was associated with larger sway amplitudes and velocities. Body excursions following platform tilts were larger, and upper body excursions showed a phase lead. Model-based analysis detected a greater time delay and a reduced long-term error correction of postural reactions in the center of mass. HSP patients performed significantly better in clinical assessments after treadmill training. In addition, treadmill training reduced sway amplitudes and body excursions, most likely by increasing positional and velocity error correction gain as a compensatory mechanism, while the time delay and long-term error correction gain remained largely unaffected. Moreover, the upper body’s phase lead was reduced. We conclude that HSP leads to very specific postural impairments. While postural control generally benefits from treadmill training, the effect seems to mainly rely on compensatory mechanisms, whereas the original deficits are not affected significantly.

There is experimental evidence that the brain systems involved in action execution also play a role in action observation and understanding. Recently, it has been suggested that the sensorimotor system is also involved in language processing. Supporting results are slower response times and weaker motorrelated MEG Beta band power suppression in semantic decision tasks on single action verbs labels when the stimulus and the motor response involve the same effector. Attenuated power suppression indicates decreased cortical excitability and consequent decreased readiness to act. The embodied approach forwards that the simultaneous involvement of the sensorimotor system in the processing of the linguistic content and in the planning of the response determines this language-motor interference effect. Here, in a combined behavioral and MEG study we investigated to what extent the processing of actions visually presented (i.e., pictures of actions) and verbally described (i.e., verbs in written words) share common neural mechanisms. The findings demonstrated that, whether an action is experienced visually or verbally, its processing engages the sensorimotor system in a comparable way. These results provide further support to the embodied view of semantic processing, suggesting that this process is independent from the modality of presentation of the stimulus, including language. [ABSTRACT FROM AUTHOR]

A broad sketch for a model of speech production is outlined which describes developmental aspects of its cognitive-linguistic and sensorimotor components. A description of the emergence of phonological knowledge is a central point in our model sketch. It will be shown that the phonological form level emerges during speech acquisition and becomes an important representation at the interface between cognitive-linguistic and sensorimotor processes. Motor planning as well as motor programming are defined as separate processes in our model sketch and it will be shown that both processes revert to the phonological information. Two computational simulation experiments based on quantitative implementations (simulation models) are undertaken to show proof of principle of key ideas of the model sketch: (i) the emergence of phonological information over developmental stages, (ii) the adaptation process for generating new motor programs, and (iii) the importance of various forms of phonological representation in that process. Based on the ideas developed within our sketch of a production model and its quantitative spell-out within the simulation models, motor planning can be defined here as the process of identifying a succession of executable chunks from a currently activated phoneme sequence and of coding them as raw gesture scores. Motor programming can be defined as the process of building up the complete set of motor commands by specifying all gestures in detail (fully specified gesture score including temporal relations). This full specification of gesture scores is achieved in our model by adapting motor information from phonologically similar syllables (adapting approach) or by assembling motor programs from sub-syllabic units (assembling approach). [ABSTRACT FROM AUTHOR]

A broad sketch for a model of speech production is outlined which describes developmental aspects of its cognitive-linguistic and sensorimotor components. A description of the emergence of phonological knowledge is a central point in our model sketch. It will be shown that the phonological form level emerges during speech acquisition and becomes an important representation at the interface between cognitive-linguistic and sensorimotor processes. Motor planning as well as motor programming are defined as separate processes in our model sketch and it will be shown that both processes revert to the phonological information. Two computational simulation experiments based on quantitative implementations (simulation models) are undertaken to show proof of principle of key ideas of the model sketch: (i) the emergence of phonological information over developmental stages, (ii) the adaptation process for generating new motor programs, and (iii) the importance of various forms of phonological representation in that process. Based on the ideas developed within our sketch of a production model and its quantitative spell-out within the simulation models, motor planning can be defined here as the process of identifying a succession of executable chunks from a currently activated phoneme sequence and of coding them as raw gesture scores. Motor programming can be defined as the process of building up the complete set of motor commands by specifying all gestures in detail (fully specified gesture score including temporal relations). This full specification of gesture scores is achieved in our model by adapting motor information from phonologically similar syllables (adapting approach) or by assembling motor programs from sub-syllabic units (assembling approach).

In this article, we review recent advances in research on rhythm and musical beat perception, focusing on the role of predictive processes in auditory motor interactions. We suggest that experimental evidence of the motor system’s role in beat perception, including in passive listening, may be explained by the generation and maintenance of internal predictive models, concordant with the Active Inference framework of sensory processing. We highlight two complementary hypotheses for the neural underpinnings of rhythm perception: The Action Simulation for Auditory Prediction hypothesis (Patel and Iversen, 2014) and the Gradual Audiomotor Evolution hypothesis (Merchant and Honing, 2014) and review recent experimental progress supporting each of these hypotheses. While initial formulations of ASAP and GAE explain different aspects of beat-based timing–the involvement of motor structures in the absence of movement, and physical entrainment to an auditory beat respectively–we suggest that work under both hypotheses provide converging evidence toward understanding the predictive role of the motor system in the perception of rhythm, and the specific neural mechanisms involved. We discuss future experimental work necessary to further evaluate the causal neural mechanisms underlying beat and rhythm perception.

In this article, we review recent advances in research on rhythm and musical beat perception, focusing on the role of predictive processes in auditory motor interactions. We suggest that experimental evidence of the motor system's role in beat perception, including in passive listening, may be explained by the generation and maintenance of internal predictive models, concordant with the Active Inference framework of sensory processing. We highlight two complementary hypotheses for the neural underpinnings of rhythm perception: The Action Simulation for Auditory Prediction hypothesis (Patel and Iversen, 2014) and the Gradual Audiomotor Evolution hypothesis (Merchant and Honing, 2014) and review recent experimental progress supporting each of these hypotheses. While initial formulations of ASAP and GAE explain different aspects of beat-based timing–the involvement of motor structures in the absence of movement, and physical entrainment to an auditory beat respectively–we suggest that work under both hypotheses provide converging evidence toward understanding the predictive role of the motor system in the perception of rhythm, and the specific neural mechanisms involved. We discuss future experimental work necessary to further evaluate the causal neural mechanisms underlying beat and rhythm perception. [ABSTRACT FROM AUTHOR]

For vision, audition and tactile sense, the optimal stimulus frequency for fMRI is somewhat known. For proprioception, i.e., the “movement sense”, however, the optimal frequency is unknown. We studied the effect of passive-finger-movement frequency on proprioceptive fMRI responses using a novel pneumatic-movement actuator. Eleven healthy right-handed volunteers participated in the study. The movement actuator passively moved the participant’s right index finger at frequencies of 0.3, 1, 3, 6, 9, or 12 Hz in a blocked design. A functional localizer was used to define regions-of-interest in SI and SII cortices. In addition, effect of movement range on the fMRI responses was tested in a separate session with 1, 3, 5, and 7 mm movement ranges at a fixed 2 Hz frequency. In primary somatosensory (SI) cortex, the responses were stronger at 3 Hz than at 0.3 Hz (p < 0.001) or 1 Hz (p < 0.05), and at ≥6 Hz than 0.3 Hz (p < 0.001 for frequencies ≥ 6 Hz). In secondary somatosensory (SII) cortex, all movements, except at 0.3 Hz, elicited significant responses of similar strength. In addition, 6, 9, and 12-Hz movements elicited a significant offset response in both SI and SII cortices (p < 0.001–0.05). SI cortex required a total stimulation duration of 4 min to elicit significant activations at the group-level whereas for SII cortex 1 min 20 s was sufficient. Increase in the movement range led to stronger responses in SI cortex, but not in SII cortex. Movements above 3 Hz elicited the strongest SI cortex responses, and increase in the movement range enhanced the response strength. We thus recommend that movements at 3–6 Hz with a movement range of 5 mm or higher to be used in future studies of proprioception. Our results are in-line with previous fMRI and PET studies using tactile or median nerve stimulation at different stimulation frequencies.