[Show abstract][Hide abstract] ABSTRACT: Background:
Abnormal upper arm-forearm muscle synergies after stroke are poorly understood. We investigated whether upper arm function primes paralyzed forearm muscles in chronic stroke patients after Brain-Machine Interface (BMI)-based rehabilitation. Shaping upper arm-forearm muscle synergies may support individualized motor rehabilitation strategies.
Thirty-two chronic stroke patients with no active finger extensions were randomly assigned to experimental or sham groups and underwent daily BMI training followed by physiotherapy during four weeks. BMI sessions included desynchronization of ipsilesional brain activity and a robotic orthosis to move the paretic limb (experimental group, n = 16). In the sham group (n = 16) orthosis movements were random. Motor function was evaluated with electromyography (EMG) of forearm extensors, and upper arm and hand Fugl-Meyer assessment (FMA) scores. Patients performed distinct upper arm (e.g., shoulder flexion) and hand movements (finger extensions). Forearm EMG activity significantly higher during upper arm movements as compared to finger extensions was considered facilitation of forearm EMG activity. Intraclass correlation coefficient (ICC) was used to test inter-session reliability of facilitation of forearm EMG activity.
Facilitation of forearm EMG activity ICC ranges from 0.52 to 0.83, indicating fair to high reliability before intervention in both limbs. Facilitation of forearm muscles is higher in the paretic as compared to the healthy limb (p<0.001). Upper arm FMA scores predict facilitation of forearm muscles after intervention in both groups (significant correlations ranged from R = 0.752, p = 0.002 to R = 0.779, p = 0.001), but only in the experimental group upper arm FMA scores predict changes in facilitation of forearm muscles after intervention (R = 0.709, p = 0.002; R = 0.827, p<0.001).
Residual upper arm motor function primes recruitment of paralyzed forearm muscles in chronic stroke patients and predicts changes in their recruitment after BMI training. This study suggests that changes in upper arm-forearm synergies contribute to stroke motor recovery, and provides candidacy guidelines for similar BMI-based clinical practice.
PLoS ONE 10/2015; 10(10):e0140161. DOI:10.1371/journal.pone.0140161 · 3.23 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Transcranial alternating current stimulation (tACS), a non-invasive and well-tolerated form of electric brain stimulation, can influence perception, memory, as well as motor and cognitive function. While the exact underlying neurophysiological mechanisms are unknown, the effects of tACS are mainly attributed to frequency-specific entrainment of endogenous brain oscillations in brain areas close to the stimulation electrodes, and modulation of spike timing dependent plasticity reflected in gamma band oscillatory responses. tACS-related electromagnetic stimulator artifacts, however, impede investigation of these neurophysiological mechanisms. Here we introduce a novel approach combining amplitude-modulated tACS during whole-head magnetoencephalography (MEG) allowing for artifact-free source reconstruction and precise mapping of entrained brain oscillations underneath the stimulator electrodes. Using this approach, we show that reliable reconstruction of neuromagnetic low- and high-frequency oscillations including high gamma band activity in stimulated cortical areas is feasible opening a new window to unveil the mechanisms underlying the effects of stimulation protocols that entrain brain oscillatory activity.
[Show abstract][Hide abstract] ABSTRACT: While earlier Brain-Computer Interface (BCI) studies have mostly focused on modulating specific brain regions or signals, new developments in pattern classification of brain states are enabling real-time decoding and modulation of an entire functional network. The present study proposes a new method for real-time pattern classification and neurofeedback of brain states from electroencephalographic (EEG) signals. It involves the creation of a fused classification model based on the method of Common Spatial Patterns (CSPs) from data of several healthy individuals. The subject-independent model is then used to classify EEG data in real-time and provide feedback to new individuals. In a series of offline experiments involving training and testing of the classifier with individual data from 27 healthy subjects, a mean classification accuracy of 75.30% was achieved, demonstrating that the classification system at hand can reliably decode two types of imagery used in our experiments, i.e., happy emotional imagery and motor imagery. In a subsequent experiment it is shown that the classifier can be used to provide neurofeedback to new subjects, and that these subjects learn to " match " their brain pattern to that of the fused classification model in a few days of neurofeedback training. This finding can have important implications for future studies on neurofeedback and its clinical applications on neuropsychiatric disorders.
[Show abstract][Hide abstract] ABSTRACT: Transcranial direct current stimulation (tDCS) can influence cognitive, affective or motor brain functions. Whereas previous imaging studies demonstrated widespread tDCS effects on brain metabolism, direct impact of tDCS on electric or magnetic source activity in task-related brain areas could not be confirmed due to the difficulty to record such activity simultaneously during tDCS. The aim of this proof-of-principal study was to demonstrate the feasibility of whole-head source localization and reconstruction of neuromagnetic brain activity during tDCS and to confirm the direct effect of tDCS on ongoing neuromagnetic activity in task-related brain areas. Here we show for the first time that tDCS has an immediate impact on slow cortical magnetic fields (SCF, 0-4Hz) of task-related areas that are identical with brain regions previously described in metabolic neuroimaging studies. 14 healthy volunteers performed a choice reaction time (RT) task while whole-head magnetoencephalography (MEG) was recorded. Task-related source-activity of SCFs was calculated using synthetic aperture magnetometry (SAM) in absence of stimulation and while anodal, cathodal or sham tDCS was delivered over the right primary motor cortex (M1). Source reconstruction revealed task-related SCF modulations in brain regions that precisely matched prior metabolic neuroimaging studies. Anodal and cathodal tDCS had a polarity-dependent impact on RT and SCF in primary sensorimotor and medial centro-parietal cortices. Combining tDCS and whole-head MEG is a powerful approach to investigate the direct effects of transcranial electric currents on ongoing neuromagnetic source activity, brain function and behavior.
[Show abstract][Hide abstract] ABSTRACT: For the current study the Lazarian stress-coping theory and the appendant model of psychosocial adjustment to chronic illness and disabilities (Pakenham, 1999) has shaped the foundation for identifying determinants of adjustment to ALS. We aimed to investigate the evolution of psychosocial adjustment to ALS and to determine its long-term predictors. A longitudinal study design with four measurement time points was therefore, used to assess patients' quality of life, depression, and stress-coping model related aspects, such as illness characteristics, social support, cognitive appraisals, and coping strategies during a period of 2 years. Regression analyses revealed that 55% of the variance of severity of depressive symptoms and 47% of the variance in quality of life at T2 was accounted for by all the T1 predictor variables taken together. On the level of individual contributions, protective buffering, and appraisal of own coping potential accounted for a significant percentage in the variance in severity of depressive symptoms, whereas problem management coping strategies explained variance in quality of life scores. Illness characteristics at T2 did not explain any variance of both adjustment outcomes. Overall, the pattern of the longitudinal results indicated stable depressive symptoms and quality of life indices reflecting a successful adjustment to the disease across four measurement time points during a period of about two years. Empirical evidence is provided for the predictive value of social support, cognitive appraisals, and coping strategies, but not illness parameters such as severity and duration for adaptation to ALS. The current study contributes to a better conceptualization of adjustment, allowing us to provide evidence-based support beyond medical and physical intervention for people with ALS.
Frontiers in Psychology 09/2015; 6. DOI:10.3389/fpsyg.2015.01197 · 2.80 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Introduction:
Obsessive-compulsive disorder (OCD) is a common and chronic condition that can have disabling effects throughout the patient's lifespan. Frequent symptoms among OCD patients include fear of contamination and washing compulsions. Several studies have shown a link between contamination fears, disgust over-reactivity, and insula activation in OCD. In concordance with the role of insula in disgust processing, new neural models based on neuroimaging studies suggest that abnormally high activations of insula could be implicated in OCD psychopathology, at least in the subgroup of patients with contamination fears and washing compulsions.
In the current study, we used a Brain Computer Interface (BCI) based on real-time functional magnetic resonance imaging (rtfMRI) to aid OCD patients to achieve down-regulation of the Blood Oxygenation Level Dependent (BOLD) signal in anterior insula. Our first aim was to investigate whether patients with contamination obsessions and washing compulsions can learn to volitionally decrease (down-regulate) activity in the insula in the presence of disgust/anxiety provoking stimuli. Our second aim was to evaluate the effect of down-regulation on clinical, behavioural and physiological changes pertaining to OCD symptoms. Hence, several pre- and post-training measures were performed, i.e., confronting the patient with a disgust/anxiety inducing real-world object (Ecological Disgust Test), and subjective rating and physiological responses (heart rate, skin conductance level) of disgust towards provoking pictures.
Results of this pilot study, performed in 3 patients (2 females), show that OCD patients can gain self-control of the BOLD activity of insula, albeit to different degrees. In two patients positive changes in behaviour in the EDT were observed following the rtfMRI trainings. Behavioural changes were also confirmed by reductions in the negative valence and in the subjective perception of disgust towards symptom provoking images.
Although preliminary, results of this study confirmed that insula down-regulation is possible in patients suffering from OCD, and that volitional decreases of insula activation could be used for symptom alleviation in this disorder.
PLoS ONE 08/2015; 10(8):e0135872. DOI:10.1371/journal.pone.0135872 · 3.23 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: In recent years, a significant effort has been invested in the development of kinematics-decoding models from electromyographic (EMG) signals to achieve more natural control interfaces for rehabilitation therapies. However, the development of a dexterous EMG-based control interface including multiple degrees of freedom (DOFs) of the upper limb still remains a challenge. Another persistent issue in surface myoelectric control is the non-stationarity of EMG signals across sessions. In this work, the decoding of 7 distal and proximal DOFs' kinematics during coordinated upper-arm, fore-arm and hand movements was performed. The influence of the EMG non-stationarity was tested by training a continuous EMG decoder in three different scenarios. Moreover, the generalization characteristics of two algorithms (ridge regression and Kalman filter) were compared in the aforementioned scenarios. Eight healthy participants underwent EMG and kinematics recordings while performing three functional tasks. We demonstrated that ridge regression significantly outperformed the Kalman filter, indicating a superior generalization ability. Furthermore, we proved that the performance drop caused by the session-to-session non-stationarities could be significantly mitigated by including a short re-calibration phase. Although further tests should be performed, these preliminary findings constitute a step forward towards the non-invasive control of the next generation of upper limb rehabilitation robotics.
2015 IEEE International Conference on Rehabilitation Robotics (ICORR), Singapore; 08/2015
[Show abstract][Hide abstract] ABSTRACT: The use of regression methods for decoding of neural signals has become popular, with its main applications in the field of Brain-Machine Interfaces (BMIs) for control of prosthetic devices or in the area of Brain-Computer Interfaces (BCIs) for cursor control. When new methods for decoding are being developed or the parameters for existing methods should be optimized to increase performance, a metric is needed that gives an accurate estimate of the prediction error. In this paper, we evaluate different performance metrics regarding their robustness for assessing prediction errors. Using simulated data, we show that different kinds of prediction error (noise, scaling error, bias) have different effects on the different metrics and evaluate which methods are best to assess the overall prediction error, as well as the individual types of error. Based on the obtained results we can conclude that the most commonly used metrics correlation coefficient (CC) and normalized root-mean-squared error (NRMSE) are well suited for evaluation of cross-validated results, but should not be used as sole criterion for cross-subject or cross-session evaluations.
37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’15); 08/2015