1,739 research outputs found

    IN VIVO EVALUATION OF FAST SENSORIMOTOR INTEGRATION IN THE HUMAN MOTOR HAND AREA: FROM PHYSIOLOGY TO PATHOLOGY

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    The studies included in this thesis mainly evaluated in vivo the fast sensorimotor integration in the human sensorimotor area, by using a well-known TMS (transcranial magnetic stimulation) technique, called short-latency afferent inhibition (SAI). Section 1 will review current knowledge on the biological and physiological basis of fast sensorimotor integration and its role in mild cognitive impairment and dementia. Section 2 will report two studies. The first one is focused on using an innovative central sulcus-based mapping technique of SAI. We showed for the first time a centre-surround organization of fast sensorimotor integration in human motor hand area (Dubbioso et al., under review). The second study is mainly focused on the role of cerebellum in the modulation of somatosensory afferent pathway (Dubbioso et al. 2015). Indeed, we demonstrated that patients with pure cerebellar atrophy had an altered capability of cerebellar filtering or processing of time specific incoming sensory volleys, influencing the sensorimotor integration and plasticity of primary motor cortex (M1). Section 3 will report two studies where SAI has been used as a tool to investigate functional involvement of central cholinergic circuits in two different types of cognitive impairment. In the first study we showed that patients with the adult form of Niemann Pick type C (NPC) are characterized by abnormal SAI (Dubbioso et al. 2014) whereas in the second one we found that SAI is normal in Parkinson disease (PD) patients with Freezing of Gait (FOG) (Dubbioso et al. 2015). Such results indicate that cognitive decline in NPC resembles from physiologically and clinical point of view primary form of cholinergic dementia such as Alzheimer disease. On the contrary, cognitive impairment in PD patients with FOG is mainly due to the involvement of non-cholinergic circuits, resembling forms of cognitive impairment dominated mainly by executive dysfunctions such as Fronto-temporal dementia

    Change blindness: eradication of gestalt strategies

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    Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

    Motor imagery and motor illusion: from plasticity to a translational approach

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    Motor imagery e illusione motoria: dalla plasticit\ue0 ad un approccio traslazional

    Seven properties of self-organization in the human brain

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    The principle of self-organization has acquired a fundamental significance in the newly emerging field of computational philosophy. Self-organizing systems have been described in various domains in science and philosophy including physics, neuroscience, biology and medicine, ecology, and sociology. While system architecture and their general purpose may depend on domain-specific concepts and definitions, there are (at least) seven key properties of self-organization clearly identified in brain systems: 1) modular connectivity, 2) unsupervised learning, 3) adaptive ability, 4) functional resiliency, 5) functional plasticity, 6) from-local-to-global functional organization, and 7) dynamic system growth. These are defined here in the light of insight from neurobiology, cognitive neuroscience and Adaptive Resonance Theory (ART), and physics to show that self-organization achieves stability and functional plasticity while minimizing structural system complexity. A specific example informed by empirical research is discussed to illustrate how modularity, adaptive learning, and dynamic network growth enable stable yet plastic somatosensory representation for human grip force control. Implications for the design of “strong” artificial intelligence in robotics are brought forward

    Assessment of motor recovery and decline

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    Assessment of motor disorders forms an important ingredient of neurology, rehabilitation medicine and orthopaedics. Until now, however, many of the employed assessment tools are derived from empirical knowledge. Almost no relation exists with modern theoretical notions about motor control. In the present article, motor control theory is reviewed in the light of its potential contribution to understanding motor recovery. An attempt is made to present a theoretical framework for the assessment of motor disorders related to recent insights in motor control. The framework emphasizes the dynamical character of recovery. The principle of output optimization is discussed and it is stressed that compensation plays a permanent role in adapting to damage of the body or to changes in the environment. An assessment procedure is introduced to measure the (mental) costs of this compensation. It is argued that changes in the costs of compensation across time reflect recovery

    Computational neurorehabilitation: modeling plasticity and learning to predict recovery

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    Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling – regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity
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