Singing training predicts increased insula connectivity with speech and respiratory sensorimotor areas at rest.

The insula contributes to the detection of salient events during goal-directed behavior and participates in the coordination of motor, multisensory, and cognitive systems. Recent task-fMRI studies with trained singers suggest that singing experience can enhance the access to these resources. However, the long-term effects of vocal training on insula-based networks are still unknown. In this study, we employed resting-state fMRI to assess experience-dependent differences in insula co-activation patterns between conservatory-trained singers and non-singers. Results indicate enhanced bilateral anterior insula connectivity in singers relative to non-singers with constituents of the speech sensorimotor network. Specifically, with the cerebellum (lobule V-VI) and the superior parietal lobes. The reversed comparison showed no effects. The amount of accumulated singing training predicted enhanced bilateral insula co-activation with primary sensorimotor areas representing the diaphragm and the larynx/phonation area-crucial regions for cortico-motor control of complex vocalizations-as well as the bilateral thalamus and the left putamen. Together, these findings highlight the neuroplastic effect of expert singing training on insula-based networks, as evidenced by the association between enhanced insula co-activation profiles in singers and the brain's speech motor system components.

Volitional modification of brain activity in adolescents with Autism Spectrum Disorder: A Bayesian analysis of Slow Cortical Potential neurofeedback.

Autism spectrum disorder is (ASD) characterized by a persisting triad of impairments of social interaction, language as well as inflexible, stereotyped and ritualistic behaviors. Increasingly, scientific evidence suggests a neurobiological basis of these emotional, social and cognitive deficits in individuals with ASD. The aim of this randomized controlled brain self-regulation intervention study was to investigate whether the core symptomatology of ASD could be reduced via an electroencephalography (EEG) based brain self-regulation training of Slow Cortical Potentials (SCP). 41 male adolescents with ASD were recruited and allocated to a) an experimental group undergoing 24 sessions of EEG-based brain training (n1 = 21), or to b) an active control group undergoing conventional treatment (n2 = 20), that is, clinical counseling during a 3-months intervention period. We employed real-time neurofeedback training recorded from a fronto-central electrode intended to enable participants to volitionally regulate their brain activity. Core autistic symptomatology was measured at six time points during the intervention and analyzed with Bayesian multilevel approach to characterize changes in core symptomatology. Additional Bayesian models were formulated to describe the neural dynamics of the training process as indexed by SCP (time-domain) and power density (PSD, frequency-domain) measures. The analysis revealed a substantial improvement in the core symptomatology of ASD in the experimental group (reduction of 21.38 points on the Social Responsiveness Scale, SD = 5.29), which was slightly superior to that observed in the control group (evidence Ratio = 5.79). Changes in SCP manifested themselves as different trajectories depending on the different feedback conditions and tasks. Further, the model of PSD revealed a continuous decrease in delta power, parallel to an increase in alpha power. Most notably, a non-linear (quadratic) model turned out to be better at predicting the data than a linear model across all analyses. Taken together, our analyses suggest that behavioral and neural processes of change related to neurofeedback training are complex and non-linear. Moreover, they have implications for the design of future trials and training protocols.

Brain-machine interfaces for rehabilitation in stroke: A review.

BACKGROUND: 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.

Brain-computer interfaces in the completely locked-in state and chronic stroke.

Brain-computer interfaces (BCIs) use brain activity to control external devices, facilitating paralyzed patients to interact with the environment. In this chapter, we discuss the historical perspective of development of BCIs and the current advances of noninvasive BCIs for communication in patients with amyotrophic lateral sclerosis and for restoration of motor impairment after severe stroke. Distinct techniques have been explored to control a BCI in patient population especially electroencephalography (EEG) and more recently near-infrared spectroscopy (NIRS) because of their noninvasive nature and low cost. Previous studies demonstrated successful communication of patients with locked-in state (LIS) using EEG- and invasive electrocorticography-BCI and intracortical recordings when patients still showed residual eye control, but not with patients with complete LIS (ie, complete paralysis). Recently, a NIRS-BCI and classical conditioning procedure was introduced, allowing communication in patients in the complete locked-in state (CLIS). In severe chronic stroke without residual hand function first results indicate a possible superior motor rehabilitation to available treatment using BCI training. Here we present an overview of the available studies and recent results, which open new doors for communication, in the completely paralyzed and rehabilitation in severely affected stroke patients. We also reflect on and describe possible neuronal and learning mechanisms responsible for BCI control and perspective for future BMI research for communication in CLIS and stroke motor recovery.

Tactile event-related potentials in amyotrophic lateral sclerosis (ALS): Implications for brain-computer interface.

OBJECTIVE: We investigated neurophysiological brain responses elicited by a tactile event-related potential paradigm in a sample of ALS patients. Underlying cognitive processes and neurophysiological signatures for brain-computer interface (BCI) are addressed. METHODS: We stimulated the palm of the hand in a group of fourteen ALS patients and a control group of ten healthy participants and recorded electroencephalographic signals in eyes-closed condition. Target and non-target brain responses were analyzed and classified offline. Classification errors served as the basis for neurophysiological brain response sub-grouping. RESULTS: A combined behavioral and quantitative neurophysiological analysis of sub-grouped data showed neither significant between-group differences, nor significant correlations between classification performance and the ALS patients' clinical state. Taking sequential effects of stimuli presentation into account, analyses revealed mean classification errors of 19.4% and 24.3% in healthy participants and ALS patients respectively. CONCLUSIONS: Neurophysiological correlates of tactile stimuli presentation are not altered by ALS. Tactile event-related potentials can be used to monitor attention level and task performance in ALS and may constitute a viable basis for future BCIs. SIGNIFICANCE: Implications for brain-computer interface implementation of the proposed method for patients in critical conditions, such as the late stage of ALS and the (completely) locked-in state, are discussed.

Hybrid EEG/EOG-based brain/neural hand exoskeleton restores fully independent daily living activities after quadriplegia.

Direct brain control of advanced robotic systems promises substantial improvements in health care, for example, to restore intuitive control of hand movements required for activities of daily living in quadriplegics, like holding a cup and drinking, eating with cutlery, or manipulating different objects. However, such integrated, brain- or neural-controlled robotic systems have yet to enter broader clinical use or daily life environments. We demonstrate full restoration of independent daily living activities, such as eating and drinking, in an everyday life scenario across six paraplegic individuals (five males, 30 ± 14 years) who used a noninvasive, hybrid brain/neural hand exoskeleton (B/NHE) to open and close their paralyzed hand. The results broadly suggest that brain/neural-assistive technology can restore autonomy and independence in quadriplegic individuals' everyday life.

Emotional Dimensions of Music and Painting and their Interaction.

Usually it is accepted that human manifestations such as music or painting share a common artistic trait. However, very little is known about the genetic, behavioral, developmental and neurobiological basis of such a musical-pictorial "universal". In an attempt to approach commonalities and differences between the psychology of music and pictorial art in Experiment 1 we investigated the emotional dimensions valence and arousal in a large sample (N =156, M age = 21,44 years, SD = 3,89 years, range = 16-35 years) using a representative selection of musical and pictorial artistic stimuli. We found a stronger variability of valence and arousal with paintings and stronger effects of music on valence. In Experiment 2 (N =202, M age = 21,35 years, SD = 3,57 years, range = 16-35 years) we present first quantitative data on the interaction between the two artistic categories of stimuli on a behavioral level, again observing effects of pictorial art and music on valence and arousal. Furthermore in Experiment 2 we replicated a more pronounced effect of music on the valence of pictures, particularly on positive valence the results of the ANOVA showed an increase in group A2: F(1, 120) = 6.23, p < .05, in group C2: F(1, 120) = 89.03, p < .001, and a surprisingly emotionally negative influence of pleasant paintings on the positive valence of music, group A1: F(1, 127) = 19.69, p < .001. Despite the unresolved problem of non-representativeness of the stimuli and the sample selected these results may suggest superior emotional "power" of music over painting.

Brain-machine interface (BMI) in paralysis.

INTRODUCTION: Brain-machine interfaces (BMIs) use brain activity to control external devices, facilitating paralyzed patients to interact with the environment. In this review, we focus on the current advances of non-invasive BMIs for communication in patients with amyotrophic lateral sclerosis (ALS) and for restoration of motor impairment after severe stroke. BMI FOR ALS PATIENTS: BMI represents a promising strategy to establish communication with paralyzed ALS patients as it does not need muscle engagement for its use. Distinct techniques have been explored to assess brain neurophysiology to control BMI for patients' communication, especially electroencephalography (EEG) and more recently near-infrared spectroscopy (NIRS). Previous studies demonstrated successful communication with ALS patients using EEG-BMI when patients still showed residual eye control, but patients with complete paralysis were unable to communicate with this system. We recently introduced functional NIRS (fNIRS)-BMI for communication in ALS patients in the complete locked-in syndrome (i.e., when ALS patients are unable to engage any muscle), opening new doors for communication in ALS patients after complete paralysis. BMI FOR STROKE MOTOR RECOVERY: In addition to assisted communication, BMI is also being extensively studied for motor recovery after stroke. BMI for stroke motor recovery includes intensive BMI training linking brain activity related to patient's intention to move the paretic limb with the contingent sensory feedback of the paretic limb movement guided by assistive devices. BMI studies in this area are mainly focused on EEG- or magnetoencephalography (MEG)-BMI systems due to their high temporal resolution, which facilitates online contingency between intention to move and sensory feedback of the intended movement. EEG-BMI training was recently demonstrated in a controlled study to significantly improve motor performance in stroke patients with severe paresis. Neural basis for BMI-induced restoration of motor function and perspectives for future BMI research for stroke motor recovery are discussed.

Decoding of motor intentions from epidural ECoG recordings in severely paralyzed chronic stroke patients.

OBJECTIVE: Recently, there have been several approaches to utilize a brain-computer interface (BCI) for rehabilitation with stroke patients or as an assistive device for the paralyzed. In this study we investigated whether up to seven different hand movement intentions can be decoded from epidural electrocorticography (ECoG) in chronic stroke patients. APPROACH: In a screening session we recorded epidural ECoG data over the ipsilesional motor cortex from four chronic stroke patients who had no residual hand movement. Data was analyzed offline using a support vector machine (SVM) to decode different movement intentions. MAIN RESULTS: We showed that up to seven hand movement intentions can be decoded with an average accuracy of 61% (chance level 15.6%). When reducing the number of classes, average accuracies up to 88% can be achieved for decoding three different movement intentions. SIGNIFICANCE: The findings suggest that ipsilesional epidural ECoG can be used as a viable control signal for BCI-driven neuroprosthesis. Although patients showed no sign of residual hand movement, brain activity at the ipsilesional motor cortex still shows enough intention-related activity to decode different movement intentions with sufficient accuracy.

Altered brain activity in severely obese women may recover after Roux-en Y gastric bypass surgery.

OBJECTIVE: Neuroimaging studies have demonstrated alterations in brain activity in obese (OB) subjects that might be causally linked to their disorder. Roux-en Y gastric bypass (RYGB) surgery induces a marked and sustained weight loss and may affect brain activity. The aim of this study was to compare brain activity pattern between severely OB women (n=11), normal-weight women (NW, n=11) and previously severely OB women who had undergone RYGB surgery (RYGB, n=9) on average 3.4±0.8 years (all >1 year) before the experiment. DESIGN: Brain activity was assessed by functional magnetic resonance imaging during a one-back task containing food- and non-food-related pictures and during resting state. Hunger and satiety were repeatedly rated on a visual analog scale during the experiment. RESULTS: As compared with NW and also with RYGB women, OB women showed (1) a higher cerebellar and a lower fusiform gyrus activity during the visual stimulation independently of the picture category, (2) a higher hypothalamic activation during the presentation of low- vs high-caloric food pictures, (3) a higher hippocampal and cerebellar activity during the working memory task and (4) a stronger functional connectivity in frontal regions of the default mode network during resting state. There were no differences in brain activity between the NW and RYGB women, both during picture presentation and during resting state. RYGB women generally rated lower on hunger and higher on satiety, whereas there were no differences in these ratings between the OB and NW women. CONCLUSION: Data provide evidence for an altered brain activity pattern in severely OB women and suggest that RYGB surgery and/or the surgically induced weight loss reverses the obesity-associated alterations.

Movement related cortical potentials in severe chronic stroke.

Movement related cortical potentials (MRCPs) have been studied for many years and controlled using brain computer interfaces (BCIs). Furthermore, MRCPs have been proposed as reliable and immediate indicators of cortical reorganizations in motor learning and after stroke. In this study MRCPs preceding and during hand movements in severe chronic stroke were investigated. Eight severely impaired (no residual finger extension) chronic stoke patients underwent EEG and EMG recordings during a cue triggered hand movement paradigm. Four patients presented subcortical lesions only while the other four presented mixed (cortical and subcortical) lesions. MRCPs were measured before (slow cortical potentials SCPs) and at movement onset (motor potentials MPs). SCPs were observed during paretic hand movements only. Latencies were longer and reached their negativity peak earlier during paretic hand movement. When dividing the patients in subcortical only and mixed lesion patients, we observed significantly bigger MP peak amplitudes over the lesioned hemisphere during paretic and healthy hand movements in subcortical stroke patients. Furthermore, we observed a significant difference in MP peak latency between subcortical and mixed stroke patients during paretic hand movements. We demonstrated for the first time significant differences between subcortical only and mixed (cortical and subcortical) stroke patients' MRCPs during motor preparation and execution. Furthermore, we demonstrated how stroke produces a longer MRCP and that lesion location affects MP peak amplitude and latency. Finally, we propose the use MRCP based BCIs to reduce their duration (towards normal) and induce motor function recovery.

Neuromuscular electrical stimulation induced brain patterns to decode motor imagery.

OBJECTIVE: Regardless of the paradigm used to implement a brain-computer interface (BCI), all systems suffer from BCI-inefficiency. In the case of patients the inefficiency can be high. Some solutions have been proposed to overcome this problem, however they have not been completely successful yet. METHODS: EEG from 10 healthy users was recorded during neuromuscular electrical stimulation (NMES) of hands and feet and during motor imagery (MI) of the same limbs. Features and classifiers were computed using part of these data to decode MI. RESULTS: Offline analyses showed that it was possible to decode MI using a classifier based on afferent patterns induced by NMES and even infer a better model than with MI data. CONCLUSION: Afferent NMES motor patterns can support the calibration of BCI systems and be used to decode MI. SIGNIFICANCE: This finding might be a new way to train sensorimotor rhythm (SMR) based BCI systems for healthy users having difficulties to attain BCI control. It might also be an alternative to train MI-based BCIs for users who cannot perform real movements but have remaining afferents (ALS, stroke patients).

Prediction of brain-computer interface aptitude from individual brain structure.

OBJECTIVE: Brain-computer interface (BCI) provide a non-muscular communication channel for patients with impairments of the motor system. A significant number of BCI users is unable to obtain voluntary control of a BCI-system in proper time. This makes methods that can be used to determine the aptitude of a user necessary. METHODS: We hypothesized that integrity and connectivity of involved white matter connections may serve as a predictor of individual BCI-performance. Therefore, we analyzed structural data from anatomical scans and DTI of motor imagery BCI-users differentiated into high and low BCI-aptitude groups based on their overall performance. RESULTS: Using a machine learning classification method we identified discriminating structural brain trait features and correlated the best features with a continuous measure of individual BCI-performance. Prediction of the aptitude group of each participant was possible with near perfect accuracy (one error). CONCLUSIONS: Tissue volumetric analysis yielded only poor classification results. In contrast, the structural integrity and myelination quality of deep white matter structures such as the Corpus Callosum, Cingulum, and Superior Fronto-Occipital Fascicle were positively correlated with individual BCI-performance. SIGNIFICANCE: This confirms that structural brain traits contribute to individual performance in BCI use.

Real-time fMRI neurofeedback: progress and challenges.

In February of 2012, the first international conference on real time functional magnetic resonance imaging (rtfMRI) neurofeedback was held at the Swiss Federal Institute of Technology Zurich (ETHZ), Switzerland. This review summarizes progress in the field, introduces current debates, elucidates open questions, and offers viewpoints derived from the conference. The review offers perspectives on study design, scientific and clinical applications, rtfMRI learning mechanisms and future outlook.

A new (semantic) reflexive brain-computer interface: in search for a suitable classifier.

The goal of the current study is to find a suitable classifier for electroencephalogram (EEG) data derived from a new learning paradigm which aims at communication in paralysis. A reflexive semantic classical (Pavlovian) conditioning paradigm is explored as an alternative to the operant learning paradigms, currently used in most brain-computer interfaces (BCIs). Comparable with a lie-detection experiment, subjects are presented with true and false statements. The EEG activity following true and false statements was classified with the aim to separate covert 'yes' from covert 'no' responses. Four classification algorithms are compared for classifying off-line data collected from a group of 14 healthy participants: (i) stepwise linear discriminant analysis (SWLDA), (ii) shrinkage linear discriminant analysis (SLDA), (iii) linear support vector machine (LIN-SVM) and (iv) radial basis function kernel support vector machine (RBF-SVM). The results indicate that all classifiers perform at chance level when separating conditioned 'yes' from conditioned 'no' responses. However, single conditioned reactions could be successfully classified on a single-trial basis (single conditioned reaction against a baseline interval). All of the four investigated classification methods achieve comparable performance, however results with RBF-SVM show the highest single-trial classification accuracy of 68.8%. The results suggest that the proposed paradigm may allow affirmative and negative (disapproving negative) communication in a BCI experiment.

Neural mechanisms of brain-computer interface control.

Brain-computer interfaces (BCIs) enable people with paralysis to communicate with their environment. Motor imagery can be used to generate distinct patterns of cortical activation in the electroencephalogram (EEG) and thus control a BCI. To elucidate the cortical correlates of BCI control, users of a sensory motor rhythm (SMR)-BCI were classified according to their BCI control performance. In a second session these participants performed a motor imagery, motor observation and motor execution task in a functional magnetic resonance imaging (fMRI) scanner. Group difference analysis between high and low aptitude BCI users revealed significantly higher activation of the supplementary motor areas (SMA) for the motor imagery and the motor observation tasks in high aptitude users. Low aptitude users showed no activation when observing movement. The number of activated voxels during motor observation was significantly correlated with accuracy in the EEG-BCI task (r=0.53). Furthermore, the number of activated voxels in the right middle frontal gyrus, an area responsible for processing of movement observation, correlated (r=0.72) with BCI-performance. This strong correlation highlights the importance of these areas for task monitoring and working memory as task goals have to be activated throughout the BCI session. The ability to regulate behavior and the brain through learning mechanisms involving imagery such as required to control a BCI constitutes the consequence of ideo-motor co-activation of motor brain systems during observation of movements. The results demonstrate that acquisition of a sensorimotor program reflected in SMR-BCI-control is tightly related to the recall of such sensorimotor programs during observation of movements and unrelated to the actual execution of these movement sequences.

Transition from the locked in to the completely locked-in state: a physiological analysis.

OBJECTIVE: To clarify the physiological and behavioral boundaries between locked-in (LIS) and the completely locked-in state (CLIS) (no voluntary eye movements, no communication possible) through electrophysiological data and to secure brain-computer-interface (BCI) communication. METHODS: Electromyography from facial muscles, external anal sphincter (EAS), electrooculography and electrocorticographic data during different psychophysiological tests were acquired to define electrophysiological differences in an amyotrophic lateral sclerosis (ALS) patient with an intracranially implanted grid of 112 electrodes for nine months while the patient passed from the LIS to the CLIS. RESULTS: At the very end of the LIS there was no facial muscle activity, nor external anal sphincter but eye control. Eye movements were slow and lasted for short periods only. During CLIS event related brain potentials (ERP) to passive limb movements and auditory stimuli were recorded, vibrotactile stimulation of different body parts resulted in no ERP response. CONCLUSIONS: The results presented contradict the commonly accepted assumption that the EAS is the last remaining muscle under voluntary control and demonstrate complete loss of eye movements in CLIS. The eye muscle was shown to be the last muscle group under voluntary control. The findings suggest ALS as a multisystem disorder, even affecting afferent sensory pathways. SIGNIFICANCE: Auditory and proprioceptive brain-computer-interface (BCI) systems are the only remaining communication channels in CLIS.

NMDA-receptor antagonist and morphine decrease CRPS-pain and cerebral pain representation.

A combination therapy of morphine with an NMDA-receptor antagonist might be more effective than morphine without a NMDA-receptor antagonist for the relief of neuropathic pain in patients with complex regional pain syndrome (CRPS). In order to test the efficacy of this combination therapy we performed a double-blind randomized placebo-controlled study on patients suffering from CRPS of the upper extremity. We used functional magnetic resonance imaging during movement of the affected and unaffected upper hand before and after a treatment regimen of 49 days that contrasted morphine and an NMDA-receptor antagonist with morphine and placebo. We postulated superior pain relief for the combination therapy and concomitant changes in brain areas associated with nociceptive processing. Only the combination therapy reduced pain at rest and during movement, and disability. After treatment, activation in the contralateral primary somatosensory (cS1) and anterior cingulate cortex was significantly reduced when the affected hand was moved. Pain relief during therapy was related to decreased activation in cS1 and secondary somatosensory cortex (S2). Our data suggest that the combination of morphine with an NMDA-receptor antagonist significantly affects the cerebral processing of nociceptive information in CRPS. The correlation of pain relief and decrease in cortical activity in cS1 and S2 is in accordance with the expected impact of the NMDA-receptor antagonist on cerebral pain processing with emphasis on sensory-discriminative aspects of pain.

Quantitative sensory testing in the German Research Network on Neuropathic Pain (DFNS): somatosensory abnormalities in 1236 patients with different neuropathic pain syndromes.

Neuropathic pain is accompanied by both positive and negative sensory signs. To explore the spectrum of sensory abnormalities, 1236 patients with a clinical diagnosis of neuropathic pain were assessed by quantitative sensory testing (QST) following the protocol of DFNS (German Research Network on Neuropathic Pain), using both thermal and mechanical nociceptive as well as non-nociceptive stimuli. Data distributions showed a systematic shift to hyperalgesia for nociceptive, and to hypoesthesia for non-nociceptive parameters. Across all parameters, 92% of the patients presented at least one abnormality. Thermosensory or mechanical hypoesthesia (up to 41%) was more frequent than hypoalgesia (up to 18% for mechanical stimuli). Mechanical hyperalgesias occurred more often (blunt pressure: 36%, pinprick: 29%) than thermal hyperalgesias (cold: 19%, heat: 24%), dynamic mechanical allodynia (20%), paradoxical heat sensations (18%) or enhanced wind-up (13%). Hyperesthesia was less than 5%. Every single sensory abnormality occurred in each neurological syndrome, but with different frequencies: thermal and mechanical hyperalgesias were most frequent in complex regional pain syndrome and peripheral nerve injury, allodynia in postherpetic neuralgia. In postherpetic neuralgia and in central pain, subgroups showed either mechanical hyperalgesia or mechanical hypoalgesia. The most frequent combinations of gain and loss were mixed thermal/mechanical loss without hyperalgesia (central pain and polyneuropathy), mixed loss with mechanical hyperalgesia in peripheral neuropathies, mechanical hyperalgesia without any loss in trigeminal neuralgia. Thus, somatosensory profiles with different combinations of loss and gain are shared across the major neuropathic pain syndromes. The characterization of underlying mechanisms will be needed to make a mechanism-based classification feasible.