Browsing by Keyword "General Biochemistry,Genetics and Molecular Biology"
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Item ArmAssist Robotic System versus Matched Conventional Therapy for Poststroke Upper Limb Rehabilitation: A Randomized Clinical Trial: A randomized clinical trial(2017) Tomić, Tijana J. Dimkić; Savić, Andrej M.; Vidaković, Aleksandra S.; Rodić, Sindi Z.; Isaković, Milica S.; Rodríguez-de-Pablo, Cristina; Keller, Thierry; Konstantinović, Ljubica M.; Tecnalia Research & InnovationThe ArmAssist is a simple low-cost robotic system for upper limb motor training that combines known benefits of repetitive task-oriented training, greater intensity of practice, and less dependence on therapist assistance. The aim of this preliminary study was to compare the efficacy of ArmAssist (AA) robotic training against matched conventional arm training in subacute stroke subjects with moderate-to-severe upper limb impairment. Twenty-six subjects were enrolled within 3 months of stroke and randomly assigned to the AA group or Control group (n = 13 each). Both groups were trained 5 days per week for 3 weeks. The primary outcome measure was Fugl-Meyer Assessment-Upper Extremity (FMA-UE) motor score, and the secondary outcomes were Wolf Motor Function Test-Functional Ability Scale (WMFT-FAS) and Barthel index (BI). The AA group, in comparison to the Control group, showed significantly greater increases in FMA-UE score (18.0 +/- 9.4 versus 7.5 +/- 5.5, p = 0.002) and WMFT-FAS score (14.1 +/- 7.9 versus 6.7 +/- 7.8, p = 0.025) after 3 weeks of treatment, whereas the increase in BI was not significant (21.2 +/- 24.8 versus 13.1 +/- 10.7, p = 0.292). There were no adverse events. We conclude that arm training using the AA robotic device is safe and able to reduce motor deficits more effectively than matched conventional arm training in subacute phase of stroke.Item Neurogenetic profiles delineate large-scale connectivity dynamics of the human brain(2018-12-01) Diez, Ibai; Sepulcre, Jorge; Tecnalia Research & InnovationExperimental and modeling work of neural activity has described recurrent and attractor dynamic patterns in cerebral microcircuits. However, it is still poorly understood whether similar dynamic principles exist or can be generalizable to the large-scale level. Here, we applied dynamic graph theory-based analyses to evaluate the dynamic streams of whole-brain functional connectivity over time across cognitive states. Dynamic connectivity in local networks is located in attentional areas during tasks and primary sensory areas during rest states, and dynamic connectivity in distributed networks converges in the default mode network (DMN) in both task and rest states. Importantly, we find that distinctive dynamic connectivity patterns are spatially associated with Allen Human Brain Atlas genetic transcription levels of synaptic long-term potentiation and long-term depression-related genes. Our findings support the neurobiological basis of large-scale attractor-like dynamics in the heteromodal cortex within the DMN, irrespective of cognitive state.