Publications

Objectives: The cerebellum is known to play a strong functional role in both motor control and motor learning. Hence, the benefit of physiotherapeutic training remains controversial for patients with cerebellar degeneration. In this study, we examined the effectiveness of a 4-week intensive coordinative training for 16 patients with progressive ataxia due to cerebellar degeneration (n
10) or degeneration of afferent pathways (n
6). Methods: Effects were assessed by clinical ataxia rating scales, individual goal attainment scores, and quantitative movement analysis. Four assessments were performed: 8 weeks before, immediately before, directly after, and 8 weeks after training. To control for variability in disease progression, we used an intraindividual control design, where performance changes with and without training were compared. Results: Significant improvements in motor performance and reduction of ataxia symptoms were observed in clinical scores after training and were sustained at follow-up assessment. Patients with predominant cerebellar ataxia revealed more distinct improvement than patients with afferent ataxia in several aspects of gait like velocity, lateral sway, and intralimb coordination. Consistently, in patients with cerebellar but without afferent ataxia, the regulation of balance in static and dynamic balance tasks improved significantly. Conclusion: In patients with cerebellar ataxia, coordinative training improves motor performance and reduces ataxia symptoms, enabling them to achieve personally meaningful goals in everyday life. Training effects were more distinct for patients whose afferent pathways were not affected. For both groups, continuous training seems crucial for stabilizing improvements and should become standard of care. Level of evidence: This study provides Class III evidence that coordinative training improves motor performance and reduces ataxia symptoms in patients with progressive cerebellar ataxia

Neural model for the visual tuning properties of action-selective neurons in premotor cortex The visual recognition of goal-directed movements is crucial for the learning of actions, and possibly for the understanding of the intentions and goals of others. The discovery of mirror neurons has stimulated a vast amount of research investigating possible links between action perception and action execution [1,2]. However, it remains largely unknown what the precise nature of this putative visuo-motor interaction is, and which relevant computational functions can be accomplished by purely visual processing. Here, we present a neurophysiologically inspired model for the visual recognition of grasping movements from videos. The model shows that the recognition of functional actions can be accounted for to a substantial degree by the analysis of spatio-temporal visual features using well-established simple neural circuits. The model integrates a hierarchical neural architecture that extracts form information in a view-dependent way accomplishing partial position and scale invariance [3,4,5]. It includes physiologically plausible recurrent neural circuits that result in temporal sequence selectivity [6,7,8]. As a novel computational step, the model proposes a simple neural mechanism that accounts for the selective matching between the spatial properties of goal objects and the specific posture, position and orientation of the effector (hand). Opposed to other models that assume a complete reconstruction of the 3D effector and object shape our model is consistent with the fact that almost 90 % of mirror neurons in premotor cortex show view-tuning. We demonstrate that the model is sufficiently powerful for recognizing goal-directed actions from real video sequences. In addition, it correctly predicts several key properties of the visual tuning of neurons in premotor cortex. We conclude that the recognition of functional actions can be accomplished by simple physiologically plausible mechanisms, without the explicit reconstruction of the 3D structures of objects and effector. Instead, prediction over time can be accomplished by the learning of spatio-temporal visual pattern sequences. This ‘bottom-up’ view of action recognition complements existing models for the mirror neuron system [9] and motivates a more detailed analysis of the complementary contributions of visual pattern analysis and motor representations on the visual recognition of imitable actions. References [1] Di Pellegrino, G. et al. (1992): Exp. Brain Res. 91, 176-180. [2] Rizzolatti, G. and Craighero, L. (2004): Annu. Rev. Neurosci. 27, 169-192. [3] Riesenhuber, M. and Poggio, T. (1999): Nat. Neurosci. 2, 1019-1025. [4] Giese, A.M. and Poggio, T. (2003): Nat. Rev. Neurosci. 4, 179-192. [5] Serre, T. et al. (2007): IEEE Pattern Anal. Mach. Int. 29, 411-426. [6] Zhang, K. (1996): J. Neurosci. 16, 2112-2126. [7] Hopfield, J. and Brody, D. (2000): Proc Natl Acad Sci USA 97, 13919-13924. [8] Xie, X. and Giese, M. (2002): Phys Rev E Stat Nonlin Soft Matter Phys 65, 051904. [9] Oztop, E. et al. (2006): Neural Netw. 19, 254-271.

Neural model of action-selective neurons in STS and area F5 The visual recognition of goal-directed movements is crucial for the understanding of intentions and goals of others as well as for imitation learning. So far, it is largely unknown how visual information about effectors and goal objects of actions is integrated in the brain. Specifically, it is unclear whether a robust recognition of goal-directed actions can be accomplished by purely visual processing or if it requires a reconstruction of the three-dimensional structure of object and effector geometry. We present a neurophysiologically inspired model for the recognition of goal-directed grasping movements from real video sequences. The model integrates several physiologically plausible mechanisms in order to realize the integration of information about goal objects and the effector and its movement: (1) A hierarchical neural architecture for the recognition of hand and object shapes, which realizes position and scale-invariant recognition by subsequent increase of feature complexity and invariance along the hierarchy based on learned example views [1,2,3]. However, in contrast to standard models for visual object recognition this invariance is incomplete, so that the retinal positions of goal object and effector can be extracted by a population code. (2) Simple recurrent neural circuits for the realization of temporal sequence selectivity [4,5,6]. (3) A novel mechanism combines information about object shape and affordance and about effector (hand) posture and position in an object-centered frame of reference. This mechanism exploits gain fields in order to implement the relevant coordinate transformation [7,8]. The model shows that a robust integration of effector and object information can be accomplished by well-established physiologically plausible principles. Specifically, the proposed model does not contain explicit 3D representations of objects and the effector movement. Instead, it realizes predictions over time based on learned view-dependent representation of the visual input. Our results complement those of existing models of action recognition [8] and motivate a more detailed analysis of the complementary contributions of visual pattern analysis and motor representations on the visual recognition of imitable actions. References [1] Riesenhuber, M. and Poggio, T. (1999): Nat. Neurosci. 2, 1019-1025. [2] Giese, A.M. and Poggio, T. (2003): Nat. Rev. Neurosci. 4, 179-192. [3] Serre, T. et al. (2007): IEEE Pattern Anal. Mach. Int. 29, 411-426. [4] Zhang, K. (1996): J. Neurosci. 16, 2112-2126. [5] Hopfield, J. and Brody, D. (2000): Proc Natl Acad Sci USA 97, 13919-13924. [6] Xie, X. and Giese, M. (2002): Phys Rev E Stat Nonlin Soft Matter Phys 65, 051904. [7] Salinas, E. and Abbott, L. (1995): J. Neurosci. 75, 6461-6474. [8] Pouget, A. and Sejnowski, T. (1997): J. Cogn. Neurosci. 9, 222-237. [9] Oztop, E. et al. (2006): Neural Netw. 19, 254-271.