Browsing by Keyword "Neurology (clinical)"
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Item Brain connectivity and cognitive functioning in individuals six months after multiorgan failure(2020) Jimenez-Marin, Antonio; Rivera, Diego; Boado, Victoria; Diez, Ibai; Labayen, Fermin; Garrido, Irati; Ramos-Usuga, Daniela; Benito-Sánchez, Itziar; Rasero, Javier; Cabrera-Zubizarreta, Alberto; Gabilondo, Iñigo; Stramaglia, Sebastiano; Arango-Lasprilla, Juan Carlos; Cortes, Jesus M.; Tecnalia Research & InnovationMultiorgan failure (MOF) is a life-threating condition that affects two or more systems of organs not involved in the disorder that motivates admission to an Intensive Care Unit (ICU). Patients who survive MOF frequently present long-term functional, neurological, cognitive, and psychiatric sequelae. However, the changes to the brain that explain such symptoms remain unclear. Objective: To determine brain connectivity and cognitive functioning differences between a group of MOF patients six months after ICU discharge and healthy controls (HC). Methods: 22 MOF patients and 22 HC matched by age, sex, and years of education were recruited. Both groups were administered a 3T magnetic resonance imaging (MRI), including structural T1 and functional BOLD, as well as a comprehensive neuropsychological evaluation that included tests of learning and memory, speed of information processing and attention, executive function, visual constructional abilities, and language. Voxel-based morphometry was used to analyses T1 images. For the functional data at rest, functional connectivity (FC) analyses were performed. Results: There were no significant differences in structural imaging and neuropsychological performance between groups, even though patients with MOF performed worse in all the cognitive tests. Functional neuroimaging in the default mode network (DMN) showed hyper-connectivity towards sensory-motor, cerebellum, and visual networks. DMN connectivity had a significant association with the severity of MOF during ICU stay and with the neuropsychological scores in tests of attention and visual constructional abilities. Conclusions: In MOF patients without structural brain injury, DMN connectivity six months after ICU discharge is associated with MOF severity and neuropsychological impairment, which supports the use of resting-state functional MRI as a potential tool to predict the onset of long-term cognitive deficits in these patients. Similar to what occurs at the onset of other pathologies, the observed hyper-connectivity might suggest network re-adaptation following MOF.Item Brain oscillatory activity as a biomarker of motor recovery in chronic stroke(2020-04-01) Ray, Andreas M.; Figueiredo, Thiago D. C.; López‐Larraz, Eduardo; Birbaumer, Niels; Ramos‐Murguialday, Ander; López-Larraz, Eduardo; Ramos-Murguialday, Ander; Medical TechnologiesIn the present work, we investigated the relationship of oscillatory sensorimotor brain activity to motor recovery. The neurophysiological data of 30 chronic stroke patients with severe upper‐limb paralysis are the basis of the observational study presented here. These patients underwent an intervention including movement training based on combined brain–machine interfaces and physiotherapy of several weeks recorded in a double‐blinded randomized clinical trial. We analyzed the alpha oscillations over the motor cortex of 22 of these patients employing multilevel linear predictive modeling. We identified a significant correlation between the evolution of the alpha desynchronization during rehabilitative intervention and clinical improvement. Moreover, we observed that the initial alpha desynchronization conditions its modulation during intervention: Patients showing a strong alpha desynchronization at the beginning of the training improved if they increased their alpha desynchronization. Patients showing a small alpha desynchronization at initial training stages improved if they decreased it further on both hemispheres. In all patients, a progressive shift of desynchronization toward the ipsilesional hemisphere correlates significantly with clinical improvement regardless of lesion location. The results indicate that initial alpha desynchronization might be key for stratification of patients undergoing BMI interventions and that its interhemispheric balance plays an important role in motor recovery.Item Brain-computer interface based on high frequency steady-state visual evoked potentials: A feasibility study(2009) Hoffmann, Ulrich; Fimbel, Eric J.; Keller, Thierry; Tecnalia Research & InnovationBrain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs) are systems in which virtual or physical objects are tagged with flicker of different frequencies. When a user focuses on one of the objects its flicker frequency becomes visible in the electroencephalogram (EEG) and so the object on which the user focuses can be determined from brain activity alone. A significant problem inherent to such systems is that typically flicker with frequencies in the range 5 - 30 Hz is used. Flicker in this frequency range is known to elicit easily detectable SSVEPs but is very tiring and annoying for users and can possibly trigger epileptic seizures. In this paper we study the feasibility of using higher frequencies for which the perceived flicker is less intensive. We compare the classification accuracy that can be achieved for stimuli flickering with low frequencies (15 - 20 Hz), medium frequencies (30 - 45 Hz), and high frequencies (50 - 85 Hz). The classification of the data is done with a Bayesian algorithm that learns classification rules and selects optimal electrode pairs. The results show that the medium frequency range can be used to build a high-performance BCI for which the flicker is hardly visible. We also found that for some subjects even high frequency flicker evokes reliably detectable SSVEPs.Item Brain-computer interface in paralysis(2008-12) Birbaumer, Niels; Murguialday, Ander Ramos; Cohen, Leonardo; Medical TechnologiesPurpose of review: Communication with patients suffering from locked-in syndrome and other forms of paralysis is an unsolved challenge. Movement restoration for patients with chronic stroke or other brain damage also remains a therapeutic problem and available treatments do not offer significant improvements. This review considers recent research in brain-computer interfaces (BCIs) as promising solutions to these challenges. Recent findings: Experimentation with nonhuman primates suggests that intentional goal directed movements of the upper limbs can be reconstructed and transmitted to external manipulandum or robotic devices controlled from a relatively small number of microelectrodes implanted into movement-relevant brain areas after some training, opening the door for the development of BCI or brain-machine interfaces in humans. Although noninvasive BCIs using electroencephalographic recordings or event-related-brain-potentials in healthy individuals and patients with amyotrophic lateral sclerosis or stroke can transmit up to 80 bits/min of information, the use of BCIs - invasive or noninvasive - in severely or totally paralyzed patients has met some unforeseen difficulties. Summary: Invasive and noninvasive BCIs using recordings from nerve cells, large neuronal pools such as electrocorticogram and electroencephalography, or blood flow based measures such as functional magnetic resonance imaging and near-infrared spectroscopy show potential for communication in locked-in syndrome and movement restoration in chronic stroke, but controlled phase III clinical trials with larger populations of severely disturbed patients are urgently needed.Item Brain-computer interface in stroke: A review of progress(2011-10) Silvoni, Stefano; Ramos-Murguialday, Ander; Cavinato, Marianna; Volpato, Chiara; Cisotto, Giulia; Turolla, Andrea; Piccione, Francesco; Birbaumer, Niels; Medical TechnologiesBrain-computer interface (BCI) technology has been used for rehabilitation after stroke and there are a number of reports involving stroke patients in BCI-feedback training. Most publications have demonstrated the efficacy of BCI technology in post-stroke rehabilitation using output devices such as Functional Electrical Stimulation, robot, and orthosis. The aim of this review is to focus on the progress of BCI-based rehabilitation strategies and to underline future challenges. A brief history of clinical BCI-approaches is presented focusing on stroke motor rehabilitation. A context for three approaches of a BCI-based motor rehabilitation program is outlined: the substitutive strategy, classical conditioning and operant conditioning. Furthermore, we include an overview of a pilot study concerning a new neuro-forcefeedback strategy. This pilot study involved healthy participants. Finally we address some challenges for future BCI-based rehabilitation.Item Brain-computer interfaces for communication and rehabilitation(2016-09-01) Chaudhary, Ujwal; Birbaumer, Niels; Ramos-Murguialday, Ander; Medical TechnologiesBrain-computer interfaces (BCIs) use brain activity to control external devices, thereby enabling severely disabled patients to interact with the environment. A variety of invasive and noninvasive techniques for controlling BCIs have been explored, most notably EEG, and more recently, near-infrared spectroscopy. Assistive BCIs are designed to enable paralyzed patients to communicate or control external robotic devices, such as prosthetics; rehabilitative BCIs are designed to facilitate recovery of neural function. In this Review, we provide an overview of the development of BCIs and the current technology available before discussing experimental and clinical studies of BCIs. We first consider the use of BCIs for communication in patients who are paralyzed, particularly those with locked-in syndrome or complete locked-in syndrome as a result of amyotrophic lateral sclerosis. We then discuss the use of BCIs for motor rehabilitation after severe stroke and spinal cord injury. We also describe the possible neurophysiological and learning mechanisms that underlie the clinical efficacy of BCIs.Item Brain-machine interface in chronic stroke rehabilitation: A controlled study(2013-07) Ramos-Murguialday, Ander; Broetz, Doris; Rea, Massimiliano; Läer, Leonhard; Yilmaz, Özge; Brasil, Fabricio L.; Liberati, Giulia; Curado, Marco R.; Garcia-Cossio, Eliana; Vyziotis, Alexandros; Cho, Woosang; Agostini, Manuel; Soares, Ernesto; Soekadar, Surjo; Caria, Andrea; Cohen, Leonardo G.; Birbaumer, Niels; Medical TechnologiesObjective Chronic stroke patients with severe hand weakness respond poorly to rehabilitation efforts. Here, we evaluated efficacy of daily brain-machine interface (BMI) training to increase the hypothesized beneficial effects of physiotherapy alone in patients with severe paresis in a double-blind sham-controlled design proof of concept study. Methods Thirty-two chronic stroke patients with severe hand weakness were randomly assigned to 2 matched groups and participated in 17.8 ± 1.4 days of training rewarding desynchronization of ipsilesional oscillatory sensorimotor rhythms with contingent online movements of hand and arm orthoses (experimental group, n = 16). In the control group (sham group, n = 16), movements of the orthoses occurred randomly. Both groups received identical behavioral physiotherapy immediately following BMI training or the control intervention. Upper limb motor function scores, electromyography from arm and hand muscles, placebo-expectancy effects, and functional magnetic resonance imaging (fMRI) blood oxygenation level-dependent activity were assessed before and after intervention. Results A significant group × time interaction in upper limb (combined hand and modified arm) Fugl-Meyer assessment (cFMA) motor scores was found. cFMA scores improved more in the experimental than in the control group, presenting a significant improvement of cFMA scores (3.41 ± 0.563-point difference, p = 0.018) reflecting a clinically meaningful change from no activity to some in paretic muscles. cFMA improvements in the experimental group correlated with changes in fMRI laterality index and with paretic hand electromyography activity. Placebo-expectancy scores were comparable for both groups. Interpretation The addition of BMI training to behaviorally oriented physiotherapy can be used to induce functional improvements in motor function in chronic stroke patients without residual finger movements and may open a new door in stroke neurorehabilitation. NEUROL 2013;74:100-108Item Brain-Machine Interface in Chronic Stroke: Randomized Trial Long-Term Follow-up(2019-03) Ramos-Murguialday, Ander; Curado, Marco R.; Broetz, Doris; Yilmaz, Özge; Brasil, Fabricio L.; Liberati, Giulia; Garcia-Cossio, Eliana; Cho, Woosang; Caria, Andrea; Cohen, Leonardo G.; Birbaumer, Niels; Medical TechnologiesBackground. Brain-machine interfaces (BMIs) have been recently proposed as a new tool to induce functional recovery in stroke patients. Objective. Here we evaluated long-term effects of BMI training and physiotherapy in motor function of severely paralyzed chronic stroke patients 6 months after intervention. Methods. A total of 30 chronic stroke patients with severe hand paresis from our previous study were invited, and 28 underwent follow-up assessments. BMI training included voluntary desynchronization of ipsilesional EEG-sensorimotor rhythms triggering paretic upper-limb movements via robotic orthoses (experimental group, n = 16) or random orthoses movements (sham group, n = 12). Both groups received identical physiotherapy following BMI sessions and a home-based training program after intervention. Upper-limb motor assessment scores, electromyography (EMG), and functional magnetic resonance imaging (fMRI) were assessed before (Pre), immediately after (Post1), and 6 months after intervention (Post2). Results. The experimental group presented with upper-limb Fugl-Meyer assessment (cFMA) scores significantly higher in Post2 (13.44 ± 1.96) as compared with the Pre session (11.16 ± 1.73; P =.015) and no significant changes between Post1 and Post2 sessions. The Sham group showed no significant changes on cFMA scores. Ashworth scores and EMG activity in both groups increased from Post1 to Post2. Moreover, fMRI-BOLD laterality index showed no significant difference from Pre or Post1 to Post2 sessions. Conclusions. BMI-based rehabilitation promotes long-lasting improvements in motor function of chronic stroke patients with severe paresis and represents a promising strategy in severe stroke neurorehabilitation.Item Brain-machine interfaces for rehabilitation in stroke: A review: A review(2018-07) López-Larraz, E.; Sarasola-Sanz, A.; Irastorza-Landa, N.; Birbaumer, N.; Ramos-Murguialday, A.; Medical TechnologiesBACKGROUND: Motor paralysis after stroke has devastating consequences for the patients, families and caregivers. Although therapies have improved in the recent years, traditional rehabilitation still fails in patients with severe paralysis. Brain-machine interfaces (BMI) have emerged as a promising tool to guide motor rehabilitation interventions as they can be applied to patients with no residual movement. OBJECTIVE: This paper reviews the efficiency of BMI technologies to facilitate neuroplasticity and motor recovery after stroke. METHODS: We provide an overview of the existing rehabilitation therapies for stroke, the rationale behind the use of BMIs for motor rehabilitation, the current state of the art and the results achieved so far with BMI-based interventions, as well as the future perspectives of neural-machine interfaces. RESULTS: Since the first pilot study by Buch and colleagues in 2008, several controlled clinical studies have been conducted, demonstrating the efficacy of BMIs to facilitate functional recovery in completely paralyzed stroke patients with noninvasive technologies such as the electroencephalogram (EEG). CONCLUSIONS: Despite encouraging results, motor rehabilitation based on BMIs is still in a preliminary stage, and further improvements are required to boost its efficacy. Invasive and hybrid approaches are promising and might set the stage for the next generation of stroke rehabilitation therapies.Item Brain–Machine Interface Induced Morpho-Functional Remodeling of the Neural Motor System in Severe Chronic Stroke(2020-04-01) Caria, Andrea; da Rocha, Josué Luiz Dalboni; Gallitto, Giuseppe; Birbaumer, Niels; Sitaram, Ranganatha; Murguialday, Ander Ramos; Medical TechnologiesBrain–machine interfaces (BMI) permit bypass motor system disruption by coupling contingent neuroelectric signals related to motor activity with prosthetic devices that enhance afferent and proprioceptive feedback to the somatosensory cortex. In this study, we investigated neural plasticity in the motor network of severely impaired chronic stroke patients after an EEG-BMI-based treatment reinforcing sensorimotor contingency of ipsilesional motor commands. Our structural connectivity analysis revealed decreased fractional anisotropy in the splenium and body of the corpus callosum, and in the contralesional hemisphere in the posterior limb of the internal capsule, the posterior thalamic radiation, and the superior corona radiata. Functional connectivity analysis showed decreased negative interhemispheric coupling between contralesional and ipsilesional sensorimotor regions, and decreased positive intrahemispheric coupling among contralesional sensorimotor regions. These findings indicate that BMI reinforcing ipsilesional brain activity and enhancing proprioceptive function of the affected hand elicits reorganization of contralesional and ipsilesional somatosensory and motor-assemblies as well as afferent and efferent connection–related motor circuits that support the partial re-establishment of the original neurophysiology of the motor system even in severe chronic stroke.Item Chapter 8 Neurofeedback and Brain-Computer Interface. Clinical Applications(2009) Birbaumer, Niels; Ramos Murguialday, Ander; Weber, Cornelia; Montoya, Pedro; Rossini, Luca; Izzo, Dario; Summerer, Leopold; Medical TechnologiesMost of the research devoted to BMI development consists of methodological studies comparing different online mathematical algorithms, ranging from simple linear discriminant analysis (LDA) (Dornhege et al., 2007) to nonlinear artificial neural networks (ANNs) or support vector machine (SVM) classification. Single cell spiking for the reconstruction of hand movements requires different statistical solutions than electroencephalography (EEG)-rhythm classification for communication. In general, the algorithm for BMI applications is computationally simple and differences in classification accuracy between algorithms used for a particular purpose are small. Only a very limited number of clinical studies with neurological patients are available, most of them single case studies. The clinical target populations for BMI-treatment consist primarily of patients with amyotrophic lateral sclerosis (ALS) and severe CNS damage including spinal cord injuries and stroke resulting in substantial deficits in communication and motor function. However, an extensive body of literature started in the 1970s using neurofeedback training. Such training implemented to control various EEG-measures provided solid evidence of positive effects in patients with otherwise pharmacologically intractable epilepsy, attention deficit disorder, and hyperactivity ADHD. More recently, the successful introduction and testing of real-time fMRI and a NIRS-BMI opened an exciting field of interest in patients with psychopathological conditions.Item Consensus-Based Core Set of Outcome Measures for Clinical Motor Rehabilitation After Stroke—A Delphi Study(2020-09-02) Pohl, Johannes; Held, Jeremia Philipp Oskar; Verheyden, Geert; Alt Murphy, Margit; Engelter, Stefan; Flöel, Agnes; Keller, Thierry; Kwakkel, Gert; Nef, Tobias; Ward, Nick; Luft, Andreas Rüdiger; Veerbeek, Janne Marieke; Tecnalia Research & InnovationIntroduction: Outcome measures are key to tailor rehabilitation goals to the stroke patient’s individual needs and to monitor poststroke recovery. The large number of available outcome measures leads to high variability in clinical use. Currently, an internationally agreed core set of motor outcome measures for clinical application is lacking. Therefore, the goal was to develop such a set to serve as a quality standard in clinical motor rehabilitation poststroke. Methods: Outcome measures for the upper and lower extremities, and activities of daily living (ADL)/stroke-specific outcomes were identified and presented to stroke rehabilitation experts in an electronic Delphi study. In round 1, clinical feasibility and relevance of the outcome measures were rated on a 7-point Likert scale. In round 2, those rated at least as “relevant” and “feasible” were ranked within the body functions, activities, and participation domains of the International Classification of Functioning, Disability, and Health (ICF). Furthermore, measurement time points poststroke were indicated. In round 3, answers were reviewed in reference to overall results to reach final consensus.Item Corrigendum: Brain-computer interfaces for communication and rehabilitation(2017-02-17) Chaudhary, Ujwal; Birbaumer, Niels; Ramos-Murguialday, Ander; Medical TechnologiesItem Corrigendum: Consensus-Based Core Set of Outcome Measures for Clinical Motor Rehabilitation After Stroke—A Delphi Study (Frontiers in Neurology, (2020), 11, (875), 10.3389/fneur.2020.00875)(2021-05-27) Pohl, Johannes; Held, Jeremia Philipp Oskar; Verheyden, Geert; Alt Murphy, Margit; Engelter, Stefan; Flöel, Agnes; Keller, Thierry; Kwakkel, Gert; Nef, Tobias; Ward, Nick; Luft, Andreas Rüdiger; Veerbeek, Janne Marieke; Tecnalia Research & InnovationIn the original article, there was a mistake in Table 2 as published. Important asterisks that were explained in the tables caption were not inserted in the table (second row, second and third columns). There is also an incorrect abbreviation (FMMA instead of FMA, in the second row, second column). The corrected Table 2 appears below. In the original article, there was a mistake in Table 3 as published. Important footnotes (1) and (2), that were further explained in the table’s caption, were not inserted in the table. The corrected Table 3 appears below. The authors apologize for these errors and state that they do not change the scientific conclusions of the article in any way. The original article has been updated. (Table presented.).Item Decoding upper limb residual muscle activity in severe chronic stroke(2015-01-01) Ramos-Murguialday, Ander; García-Cossio, Eliana; Walter, Armin; Cho, Woosang; Broetz, Doris; Bogdan, Martin; Cohen, Leonardo G.; Birbaumer, Niels; Medical TechnologiesObjective: Stroke is a leading cause of long-term motor disability. Stroke patients with severe hand weakness do not profit from rehabilitative treatments. Recently, brain-controlled robotics and sequential functional electrical stimulation allowed some improvement. However, for such therapies to succeed, it is required to decode patients' intentions for different arm movements. Here, we evaluated whether residual muscle activity could be used to predict movements from paralyzed joints in severely impaired chronic stroke patients. Methods: Muscle activity was recorded with surface-electromyography (EMG) in 41 patients, with severe hand weakness (Fugl-Meyer Assessment [FMA] hand subscores of 2.93 ± 2.7), in order to decode their intention to perform six different motions of the affected arm, required for voluntary muscle activity and to control neuroprostheses. Decoding of paretic and nonparetic muscle activity was performed using a feed-forward neural network classifier. The contribution of each muscle to the intended movement was determined. Results: Decoding of up to six arm movements was accurate (>65%) in more than 97% of nonparetic and 46% of paretic muscles. Interpretation: These results demonstrate that some level of neuronal innervation to the paretic muscle remains preserved and can be used to implement neurorehabilitative treatments in 46% of patients with severe paralysis and extensive cortical and/or subcortical lesions. Such decoding may allow these patients for the first time after stroke to control different motions of arm prostheses through muscle-triggered rehabilitative treatments.Item The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain–behavior relationships after stroke: Big data neuroimaging to study brain–behavior relationships after stroke(2020) Liew, Sook‐Lei; Zavaliangos‐Petropulu, Artemis; Jahanshad, Neda; Lang, Catherine E.; Hayward, Kathryn S.; Lohse, Keith R.; Juliano, Julia M.; Assogna, Francesca; Baugh, Lee A.; Bhattacharya, Anup K.; Bigjahan, Bavrina; Borich, Michael R.; Boyd, Lara A.; Brodtmann, Amy; Buetefisch, Cathrin M.; Byblow, Winston D.; Cassidy, Jessica M.; Conforto, Adriana B.; Craddock, R. Cameron; Dimyan, Michael A.; Dula, Adrienne N.; Ermer, Elsa; Etherton, Mark R.; Fercho, Kelene A.; Gregory, Chris M.; Hadidchi, Shahram; Holguin, Jess A.; Hwang, Darryl H.; Jung, Simon; Kautz, Steven A.; Khlif, Mohamed Salah; Khoshab, Nima; Kim, Bokkyu; Kim, Hosung; Kuceyeski, Amy; Lotze, Martin; MacIntosh, Bradley J.; Margetis, John L.; Mohamed, Feroze B.; Piras, Fabrizio; Ramos‐Murguialday, Ander; Richard, Geneviève; Roberts, Pamela; Robertson, Andrew D.; Rondina, Jane M.; Rost, Natalia S.; Sanossian, Nerses; Schweighofer, Nicolas; Seo, Na Jin; Shiroishi, Mark S.; Soekadar, Surjo R.; Spalletta, Gianfranco; Stinear, Cathy M.; Suri, Anisha; Tang, Wai Kwong W.; Thielman, Gregory T.; Vecchio, Daniela; Villringer, Arno; Ward, Nick S.; Werden, Emilio; Westlye, Lars T.; Winstein, Carolee; Wittenberg, George F.; Wong, Kristin A.; Yu, Chunshui; Cramer, Steven C.; Thompson, Paul M.; Zavaliangos-Petropulu, Artemis; Ramos-Murguialday, Ander; Medical TechnologiesThe goal of the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well‐powered meta‐ and mega‐analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large‐scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided.Item Event-related desynchronization during movement attempt and execution in severely paralyzed stroke patients: An artifact removal relevance analysis: An artifact removal relevance analysis(2018) López-Larraz, Eduardo; Figueiredo, Thiago C.; Insausti-Delgado, Ainhoa; Ziemann, Ulf; Birbaumer, Niels; Ramos-Murguialday, Ander; Tecnalia Research & Innovation; Medical TechnologiesThe electroencephalogram (EEG) constitutes a relevant tool to study neural dynamics and to develop brain-machine interfaces (BMI) for rehabilitation of patients with paralysis due to stroke. However, the EEG is easily contaminated by artifacts of physiological origin, which can pollute the measured cortical activity and bias the interpretations of such data. This is especially relevant when recording EEG of stroke patients while they try to move their paretic limbs, since they generate more artifacts due to compensatory activity. In this paper, we study how physiological artifacts (i.e., eye movements, motion artifacts, muscle artifacts and compensatory movements with the other limb) can affect EEG activity of stroke patients. Data from 31 severely paralyzed stroke patients performing/attempting grasping movements with their healthy/paralyzed hand were analyzed offline. We estimated the cortical activation as the event-related desynchronization (ERD) of sensorimotor rhythms and used it to detect the movements with a pseudo-online simulated BMI. Automated state-of-the-art methods (linear regression to remove ocular contaminations and statistical thresholding to reject the other types of artifacts) were used to minimize the influence of artifacts. The effect of artifact reduction was quantified in terms of ERD and BMI performance. The results reveal a significant contamination affecting the EEG, being involuntary muscle activity the main source of artifacts. Artifact reduction helped extracting the oscillatory signatures of motor tasks, isolating relevant information from noise and revealing a more prominent ERD activity. Lower BMI performances were obtained when artifacts were eliminated from the training datasets. This suggests that artifacts produce an optimistic bias that improves theoretical accuracy but may result in a poor link between task-related oscillatory activity and BMI peripheral feedback. With a clinically relevant dataset of stroke patients, we evidence the need of appropriate methodologies to remove artifacts from EEG datasets to obtain accurate estimations of the motor brain activity.Item Kinematic and neurophysiological consequences of an assisted-force-feedback brain-machine interface training: A case study(2013) Silvoni, Stefano; Cavinato, Marianna; Volpato, Chiara; Cisotto, Giulia; Genna, Clara; Agostini, Michela; Turolla, Andrea; Ramos-Murguialday, Ander; Piccione, Francesco; Medical TechnologiesIn a proof-of-principle prototypical demonstration we describe a new type of brain-machine interface (BMI) paradigm for upper limb motor-training. The proposed technique allows a fast contingent and proportionally modulated stimulation of afferent proprioceptive and motor output neural pathways using operant learning. Continuous and immediate assisted-feedback of force proportional to rolandic rhythm oscillations during actual movements was employed and illustrated with a single case experiment. One hemiplegic patient was trained for 2 weeks coupling somatosensory brain oscillations with force-field control during a robot-mediated center-out motor-task whose execution approaches movements of everyday life. The robot facilitated actual movements adding a modulated force directed to the target, thus providing a non-delayed proprioceptive feedback. Neuro-electric, kinematic, and motor-behavioral measures were recorded in pre- and post-assessments without force assistance. Patient's healthy arm was used as control since neither a placebo control was possible nor other control conditions. We observed a generalized and significant kinematic improvement in the affected arm and a spatial accuracy improvement in both arms, together with an increase and focalization of the somatosensory rhythm changes used to provide assisted-force-feedback. The interpretation of the neurophysiological and kinematic evidences reported here is strictly related to the repetition of the motor-task and the presence of the assisted-force-feedback. Results are described as systematic observations only, without firm conclusions about the effectiveness of the methodology. In this prototypical view, the design of appropriate control conditions is discussed. This study presents a novel operant-learning-based BMI-application for motor-training coupling brain oscillations and force feedback during an actual movement.Item Measuring resistance to externally induced movement of the wrist joint in chronic stroke patients using an objective hand-held dynamometer(2023-01) Mahmoud, Wala'; Haugland, Morten; Ramos-Murguialday, Ander; Hultborn, Hans; Ziemann, Ulf; Medical TechnologiesObjective: We evaluated the resistance to externally induced wrist extension in chronic stroke patients. We aimed to objectively measure and distinguish passive (muscle and soft tissue stiffness) and active (spasticity and spastic dystonia) components of the resistance. Methods: We used a hand-held dynamometer, which measures torque, joint movement and electromyography (EMG) simultaneously, to assess the resistance to externally induced wrist extension. Slow and fast stretches were applied to the affected and unaffected wrists in 57 chronic stroke patients (57 ± 11 years). We extracted from the data parameters that represent passive and muscle activity components and assessed the validity, test–retest reliability and the clinical utility of the measurement. Results: The analysis showed (1) a significant difference in the passive and muscle activity components between the affected and unaffected sides; (2) a significant correlation between passive and muscle activity components and the modified Ashworth scale (MAS); (3) a significant difference between the subgroups of patients stratified by the MAS; (4) an excellent intra-rater reliability on each of the passive and muscle activity components with intra-class coefficients between 0.92 and 0.99; (5) and small measurement error. Conclusions: Using a hand-held dynamometer, we were able to objectively measure the resistance to muscle stretch in the wrist joint in chronic stroke patients and discriminate muscle overactivity components from muscle and soft tissue stiffness. We demonstrated validity, test–retest reliability and the clinical utility of the measurement. Significance: Quantification of the different components of resistance to externally induced movement enables the objective evaluation of neurorehabilitation effects in chronic stroke patients.Item Neurodegeneration trajectory in pediatric and adult/late DM1: A follow‐up MRI study across a decade: A follow-up MRI study across a decade(2020-10-01) Labayru, Garazi; Jimenez‐Marin, Antonio; Fernández, Esther; Villanua, Jorge; Zulaica, Miren; Cortes, Jesus M.; Díez, Ibai; Sepulcre, Jorge; López de Munain, Adolfo; Sistiaga, Andone; Jimenez-Marin, Antonio; Tecnalia Research & InnovationObjective: To characterize the progression of brain structural abnormalities in adults with pediatric and adult/late onset DM1, as well as to examine the potential predictive markers of such progression. Methods: 21 DM1 patients (pediatric onset: N = 9; adult/late onset: N = 12) and 18 healthy controls (HC) were assessed longitudinally over 9.17 years through brain MRI. Additionally, patients underwent neuropsychological, genetic, and muscular impairment assessment. Inter-group comparisons of total and voxel-level regional brain volume were conducted through Voxel Based Morphometry (VBM); cross-sectionally and longitudinally, analyzing the associations between brain changes and demographic, clinical, and cognitive outcomes. Results: The percentage of GM loss did not significantly differ in any of the groups compared with HC and when assessed independently, adult/late DM1 patients and their HC group suffered a significant loss in WM volume. Regional VBM analyses revealed subcortical GM damage in both DM1 groups, evolving to frontal regions in the pediatric onset patients. Muscular impairment and the outcomes of certain neuropsychological tests were significantly associated with follow-up GM damage, while visuoconstruction, attention, and executive function tests showed sensitivity to WM degeneration over time. Interpretation: Distinct patterns of brain atrophy and its progression over time in pediatric and adult/late onset DM1 patients are suggested. Results indicate a possible neurodevelopmental origin of the brain abnormalities in DM1, along with the possible existence of an additional neurodegenerative process. Fronto-subcortical networks appear to be involved in the disease progression at young adulthood in pediatric onset DM1 patients. The involvement of a multimodal integration network in DM1 is discussed.