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Armin Duff

Bio: Armin Duff is an academic researcher from Pompeu Fabra University. The author has contributed to research in topics: Hemiparesis & Rehabilitation. The author has an hindex of 16, co-authored 35 publications receiving 660 citations.

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Journal ArticleDOI
TL;DR: The results suggest that SVR systems are more beneficial than CT for upper-limb recovery, whereas NSVR systems were not, and 6 principles of neurorehabilitation are identified that may disambiguate the contradictory results found in the current literature.
Abstract: Background. Despite the rise of virtual reality (VR)-based interventions in stroke rehabilitation over the past decade, no consensus has been reached on its efficacy. This ostensibly puzzling outco...

113 citations

Journal ArticleDOI
TL;DR: The temporal structure of recovery in patients with hemiparesis is analyzed and a precise gradient of enhanced sensitivity to treatment that expands far beyond the limits of the so-called critical window is uncovered.
Abstract: Previous studies in humans suggest that there is a 3- to 6-mo “critical window” of heightened neuroplasticity post-stroke. We analyze the temporal structure of recovery in patients with hemiparesis...

84 citations

Journal ArticleDOI
TL;DR: Implicitly reinforcing arm-use by augmenting visuomotor feedback as proposed by RIMT seems beneficial for inducing significant improvement in chronic stroke patients.
Abstract: After stroke, patients who suffer from hemiparesis tend to suppress the use of the affected extremity, a condition called learned non-use. Consequently, the lack of training may lead to the progressive deterioration of motor function. Although Constraint-Induced Movement Therapies (CIMT) have shown to be effective in treating this condition, the method presents several limitations, and the high intensity of its protocols severely compromises its adherence. We propose a novel rehabilitation approach called Reinforcement-Induced Movement Therapy (RIMT), which proposes to restore motor function through maximizing arm use. This is achieved by exposing the patient to amplified goal-oriented movements in VR that match the intended actions of the patient. We hypothesize that through this method we can increase the patients self-efficacy, reverse learned non-use, and induce long-term motor improvements. We conducted a randomized, double-blind, longitudinal clinical study with 18 chronic stroke patients. Patients performed 30 minutes of daily VR-based training during six weeks. During training, the experimental group experienced goal-oriented movement amplification in VR. The control group followed the same training protocol but without movement amplification. Evaluators blinded to group designation performed clinical measurements at the beginning, at the end of the training and at 12-weeks follow-up. We used the Fugl-Meyer Assessment for the upper extremities (UE-FM) (Sanford et al., Phys Ther 73:447–454, 1993) as a primary outcome measurement of motor recovery. Secondary outcome measurements included the Chedoke Arm and Hand Activity Inventory (CAHAI-7) (Barreca et al., Arch Phys Med Rehabil 6:1616–1622, 2005) for measuring functional motor gains in the performance of Activities of Daily Living (ADLs), the Barthel Index (BI) for the evaluation of the patient’s perceived independence (Collin et al., Int Disabil Stud 10:61–63, 1988), and the Hamilton scale (Knesevich et al., Br J Psychiatr J Mental Sci 131:49–52, 1977) for the identification of improvements in mood disorders that could be induced by the reinforcement-based intervention. In order to study and predict the effects of this intervention we implemented a computational model of recovery after stroke. While both groups showed significant motor gains at 6-weeks post-treatment, only the experimental group continued to exhibit further gains in UE-FM at 12-weeks follow-up (p<.05). This improvement was accompanied by a significant increase in arm-use during training in the experimental group. Implicitly reinforcing arm-use by augmenting visuomotor feedback as proposed by RIMT seems beneficial for inducing significant improvement in chronic stroke patients. By challenging the patients’ self-limiting believe system and perceived low self-efficacy this approach might counteract learned non-use. Clinical Trials NCT02657070 .

58 citations

Journal ArticleDOI
TL;DR: Preliminary support is provided for the hypothesis underlying RGS that this novel neurorehabilitation approach engages human mirror mechanisms that can be employed for visuomotor training by identifying the brain areas involved in controlling the catching of approaching colored balls in the virtual environment of the RGS.
Abstract: The Rehabilitation Gaming System (RGS) has been designed as a flexible, virtual-reality (VR)-based device for rehabilitation of neurological patients. Recently, training of visuomotor processing with the RGS was shown to effectively improve arm function in acute and chronic stroke patients. It is assumed that the VR-based training protocol related to RGS creates conditions that aid recovery by virtue of the human mirror neuron system. Here, we provide evidence for this assumption by identifying the brain areas involved in controlling the catching of approaching colored balls in the virtual environment of the RGS. We used functional magnetic resonance imaging of 18 right-handed healthy subjects (24 ± 3 years) in both active and imagination conditions. We observed that the imagery of target catching was related to activation of frontal, parietal, temporal, cingulate and cerebellar regions. We interpret these activations in relation to object processing, attention, mirror mechanisms, and motor intention. Active catching followed an anticipatory mode, and resulted in significantly less activity in the motor control areas. Our results provide preliminary support for the hypothesis underlying RGS that this novel neurorehabilitation approach engages human mirror mechanisms that can be employed for visuomotor training.

58 citations


Cited by
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TL;DR: In this article, the authors propose that the brain produces an internal representation of the world, and the activation of this internal representation is assumed to give rise to the experience of seeing, but it leaves unexplained how the existence of such a detailed internal representation might produce visual consciousness.
Abstract: Many current neurophysiological, psychophysical, and psychological approaches to vision rest on the idea that when we see, the brain produces an internal representation of the world. The activation of this internal representation is assumed to give rise to the experience of seeing. The problem with this kind of approach is that it leaves unexplained how the existence of such a detailed internal representation might produce visual consciousness. An alternative proposal is made here. We propose that seeing is a way of acting. It is a particular way of exploring the environment. Activity in internal representations does not generate the experience of seeing. The outside world serves as its own, external, representation. The experience of seeing occurs when the organism masters what we call the governing laws of sensorimotor contingency. The advantage of this approach is that it provides a natural and principled way of accounting for visual consciousness, and for the differences in the perceived quality of sensory experience in the different sensory modalities. Several lines of empirical evidence are brought forward in support of the theory, in particular: evidence from experiments in sensorimotor adaptation, visual \"filling in,\" visual stability despite eye movements, change blindness, sensory substitution, and color perception.

2,271 citations

Journal ArticleDOI
TL;DR: In this paper, the recent progress of synaptic electronics is reviewed, with a focus on the use of synaptic devices for neuromorphic or brain-inspired computing.
Abstract: In this paper, the recent progress of synaptic electronics is reviewed. The basics of biological synaptic plasticity and learning are described. The material properties and electrical switching characteristics of a variety of synaptic devices are discussed, with a focus on the use of synaptic devices for neuromorphic or brain-inspired computing. Performance metrics desirable for large-scale implementations of synaptic devices are illustrated. A review of recent work on targeted computing applications with synaptic devices is presented.

993 citations

01 Jan 2016

760 citations