Efectividad del tratamiento con neurofeedback en trastornos del estado del ánimo, ansiedad y fibromialgia: una revisión de literatura

El neurofeedback es una técnica no invasiva en la que se pretende corregir, mediante condicionamiento operante, ondas cerebrales que se encuentren alteradas en el electroencefalograma. Desde 1967, se han conducido numerosas investigaciones relacionadas con los efectos de la técnica en el tratamiento de alteraciones psicológicas. Sin embargo, a la fecha no existen revisiones sistemáticas que reúnan los temas que serán aquí tratados. El aporte de este trabajo es la revisión de 56 artículos, publicados entre los años 1995 y 2013 y la evaluación metodológica de 29 estudios incluidos en la revisión. La búsqueda fue acotada a la efectividad del neurofeedback en el tratamiento de depresión, ansiedad, trastorno obsesivo compulsivo (TOC), ira y fibromialgia. Los hallazgos demuestran que el neurofeedback ha tenido resultados positivos en el tratamiento de estos trastornos, sin embargo, es una técnica que aún está en desarrollo, con unas bases teóricas no muy bien establecidas y cuyos resultados necesitan de diseños metodológicamente más sólidos que ratifiquen su validez.

Evidence-Based Information on the Clinical Use of Neurofeedback for ADHD

Neurofeedback (NF) is a training to enhance self-regulatory capacity over brain activity patterns and consequently over brain mental states. Recent findings suggest that NF is a promising alternative for the treatment of attention-deficit/hyperactivity disorder (ADHD). We comprehensively reviewed literature searching for studies on the effectiveness and specificity of NF for the treatment of ADHD. In addition, clinically informative evidence-based data are discussed. We found 3 systematic review on the use of NF for ADHD and 6 randomized controlled trials that have not been included in these reviews. Most nonrandomized controlled trials found positive results with medium-to-large effect sizes, but the evidence for effectiveness are less robust when only randomized controlled studies are considered. The direct comparison of NF and sham-NF in 3 published studies have found no group differences, nevertheless methodological caveats, such as the quality of the training protocol used, sample size, and sample selection may have contributed to the negative results. Further data on specificity comes from electrophysiological studies reporting that NF effectively changes brain activity patterns. No safety issues have emerged from clinical trials and NF seems to be well tolerated and accepted. Follow-up studies support long-term effects of NF. Currently there is no available data to guide clinicians on the predictors of response to NF and on optimal treatment protocol. In conclusion, NF is a valid option for the treatment for ADHD, but further evidence is required to guide its use.

Biofeedback : retroalimentación electroencefalográfica : estudio de la efectividad de la adopción de patrones neuronales por entrenamiento para paliar el dolor

S’ha demostrat que la pràctica d'estats de relaxació profunda redueix el dolor i incrementa el benestar i la qualitat de vida. Els estats de relaxació profunda i meditació estan al seu torn associats a patrons electroencefalogràfics (EEG) característics. En el present projecte proposem un procediment pal·liatiu del dolor basat en l 'entrenament autodidàctic per aconseguir aquests estats de relaxació. Per a això es proposa retro-alimentar al subjecte un senyal auditiu reflex de la seva activitat EEG que l'ajudi a modificar voluntàriament la seva activitat cerebral, la qual cosa li permet aproximar-la progressivament al patró EEG corresponent al de relaxació profunda desitjat. S’ha dissenyat un protocol d'entrenament d'estats mentals de relaxació i meditació (entre altres) i implementat un programa que processa el senyal EEG i genera un senyal auditiu reflex de l'activitat cerebral de l'individu. També registra les lectures EEG del progrés de l'individu, genera les gràfiques corresponents i genera dades estadístiques per a futur anàlisi.

The Brain as a Distributed Intelligent Processing System: An EEG Study

Background: Various neuroimaging studies, both structural and functional, have provided support for the proposal that a distributed brain network is likely to be the neural basis of intelligence. The theory of Distributed Intelligent Processing Systems (DIPS), first developed in the field of Artificial Intelligence, was proposed to adequately model distributed neural intelligent processing. In addition, the neural efficiency hypothesis suggests that individuals with higher intelligence display more focused cortical activation during cognitive performance, resulting in lower total brain activation when compared with individuals who have lower intelligence. This may be understood as a property of the DIPS. Methodology and Principal Findings: In our study, a new EEG brain mapping technique, based on the neural efficiency hypothesis and the notion of the brain as a Distributed Intelligence Processing System, was used to investigate the correlations between IQ evaluated with WAIS (Whechsler Adult Intelligence Scale) and WISC (Wechsler Intelligence Scale for Children), and the brain activity associated with visual and verbal processing, in order to test the validity of a distributed neural basis for intelligence. Conclusion: The present results support these claims and the neural efficiency hypothesis.

Kernel machines for epilepsy diagnosis via EEG signal classification: A comparative study

Objective: We carry out a systematic assessment on a suite of kernel-based learning machines while coping with the task of epilepsy diagnosis through automatic electroencephalogram (EEG) signal classification. Methods and materials: The kernel machines investigated include the standard support vector machine (SVM), the least squares SVM, the Lagrangian SVM, the smooth SVM, the proximal SVM, and the relevance vector machine. An extensive series of experiments was conducted on publicly available data, whose clinical EEG recordings were obtained from five normal subjects and five epileptic patients. The performance levels delivered by the different kernel machines are contrasted in terms of the criteria of predictive accuracy, sensitivity to the kernel function/parameter value, and sensitivity to the type of features extracted from the signal. For this purpose, 26 values for the kernel parameter (radius) of two well-known kernel functions (namely. Gaussian and exponential radial basis functions) were considered as well as 21 types of features extracted from the EEG signal, including statistical values derived from the discrete wavelet transform, Lyapunov exponents, and combinations thereof. Results: We first quantitatively assess the impact of the choice of the wavelet basis on the quality of the features extracted. Four wavelet basis functions were considered in this study. Then, we provide the average accuracy (i.e., cross-validation error) values delivered by 252 kernel machine configurations; in particular, 40%/35% of the best-calibrated models of the standard and least squares SVMs reached 100% accuracy rate for the two kernel functions considered. Moreover, we show the sensitivity profiles exhibited by a large sample of the configurations whereby one can visually inspect their levels of sensitiveness to the type of feature and to the kernel function/parameter value. Conclusions: Overall, the results evidence that all kernel machines are competitive in terms of accuracy, with the standard and least squares SVMs prevailing more consistently. Moreover, the choice of the kernel function and parameter value as well as the choice of the feature extractor are critical decisions to be taken, albeit the choice of the wavelet family seems not to be so relevant. Also, the statistical values calculated over the Lyapunov exponents were good sources of signal representation, but not as informative as their wavelet counterparts. Finally, a typical sensitivity profile has emerged among all types of machines, involving some regions of stability separated by zones of sharp variation, with some kernel parameter values clearly associated with better accuracy rates (zones of optimality). (C) 2011 Elsevier B.V. All rights reserved.

A Nonstationary Model of Newborn EEG

The detection of seizure in the newborn is a critical aspect of neurological research. Current automatic detection techniques are difficult to assess due to the problems associated with acquiring and labelling newborn electroencephalogram (EEG) data. A realistic model for newborn EEG would allow confident development, assessment and comparison of these detection techniques. This paper presents a model for newborn EEG that accounts for its self-similar and non-stationary nature. The model consists of background and seizure sub-models. The newborn EEG background model is based on the short-time power spectrum with a time-varying power law. The relationship between the fractal dimension and the power law of a power spectrum is utilized for accurate estimation of the short-time power law exponent. The newborn EEG seizure model is based on a well-known time-frequency signal model. This model addresses all significant time-frequency characteristics of newborn EEG seizure which include; multiple components or harmonics, piecewise linear instantaneous frequency laws and harmonic amplitude modulation. Estimates of the parameters of both models are shown to be random and are modelled using the data from a total of 500 background epochs and 204 seizure epochs. The newborn EEG background and seizure models are validated against real newborn EEG data using the correlation coefficient. The results show that the output of the proposed models has a higher correlation with real newborn EEG than currently accepted models (a 10% and 38% improvement for background and seizure models, respectively).

A matching pursuit-based signal complexity measure for the analysis of newborn EEG

This paper presents a new relative measure of signal complexity, referred to here as relative structural complexity, which is based on the matching pursuit (MP) decomposition. By relative, we refer to the fact that this new measure is highly dependent on the decomposition dictionary used by MP. The structural part of the definition points to the fact that this new measure is related to the structure, or composition, of the signal under analysis. After a formal definition, the proposed relative structural complexity measure is used in the analysis of newborn EEG. To do this, firstly, a time-frequency (TF) decomposition dictionary is specifically designed to compactly represent the newborn EEG seizure state using MP. We then show, through the analysis of synthetic and real newborn EEG data, that the relative structural complexity measure can indicate changes in EEG structure as it transitions between the two EEG states; namely seizure and background (non-seizure).

EEG-based Brain-Computer Interfaces: An Overview of Basic Concepts and Clinical Applications in Neurorehabilitation

Some patients are no longer able to communicate effectively or even interact with the outside world in ways that most of us take for granted. In the most severe cases, tetraplegic or post-stroke patients are literally `locked in` their bodies, unable to exert any motor control after, for example, a spinal cord injury or a brainstem stroke, requiring alternative methods of communication and control. But we suggest that, in the near future, their brains may offer them a way out. Non-invasive electroencephalogram (EEG)-based brain-computer interfaces (BCD can be characterized by the technique used to measure brain activity and by the way that different brain signals are translated into commands that control an effector (e.g., controlling a computer cursor for word processing and accessing the internet). This review focuses on the basic concepts of EEG-based BC!, the main advances in communication, motor control restoration and the down-regulation of cortical activity, and the mirror neuron system (MNS) in the context of BCI. The latter appears to be relevant for clinical applications in the coming years, particularly for severely limited patients. Hypothetically, MNS could provide a robust way to map neural activity to behavior, representing the high-level information about goals and intentions of these patients. Non-invasive EEG-based BCIs allow brain-derived communication in patients with amyotrophic lateral sclerosis and motor control restoration in patients after spinal cord injury and stroke. Epilepsy and attention deficit and hyperactive disorder patients were able to down-regulate their cortical activity. Given the rapid progression of EEG-based BCI research over the last few years and the swift ascent of computer processing speeds and signal analysis techniques, we suggest that emerging ideas (e.g., MNS in the context of BC!) related to clinical neuro-rehabilitation of severely limited patients will generate viable clinical applications in the near future.

Long-Term Effect of Early Postoperative Pelvic Floor Biofeedback on Continence in Men Undergoing Radical Prostatectomy: A Prospective, Randomized, Controlled Trial

Purpose: The impact of pelvic floor muscle training on the recovery of urinary continence after radical prostatectomy is still controversial. We tested the effectiveness of biofeedback-pelvic floor muscle training in improving urinary incontinence in the 12 months following radical prostatectomy. Materials and Methods: A total of 73 patients who underwent radical prostatectomy were randomized to a treatment group (36) receiving biofeedback-pelvic floor muscle training once a week for 3 months as well as home exercises or a control group (37). Patients were evaluated 1, 3, 6 and 12 months postoperatively. Continence was defined as the use of 1 pad or less daily and incontinence severity was measured by the 24-hour pad test. Incontinence symptoms and quality of life were assessed with the International Continence Society male Short Form questionnaire and the Incontinence Impact Questionnaire. Pelvic floor muscle strength was evaluated with the Oxford score. Results: A total of 54 patients (26 pelvic floor muscle training and 28 controls) completed the trial. Duration of incontinence was shorter in the treatment group. At postoperative month 12, 25 (96.15%) patients in the treatment group and 21 (75.0%) in the control group were continent (p = 0.028). The absolute risk reduction was 21.2% (95% CI 3.45-38.81) and the relative risk of recovering continence was 1.28 (95% CI 1.02-1.69). The number needed to treat was 5 (95% CI 2.6-28.6). Overall there were significant changes in both groups in terms of incontinence symptoms, lower urinary tract symptoms, quality of life and pelvic floor muscle strength (p <0.0001). Conclusions: Early biofeedback-pelvic floor muscle training not only hastens the recovery of urinary continence after radical prostatectomy but allows for significant improvements in the severity of incontinence, voiding symptoms and pelvic floor muscle strength 12 months postoperatively.

Influence of respiratory biofeedback associated to re-expansive ventilation patterns in individuals with functional mouth breathing

Objectives: Assess the effect of re-expansive respiratory patterns associated to respiratory biofeedback (RBF) on pulmonary function, respiratory muscle strength and habits in individuals with functional mouth breathing (FMB). Methods: Sixty children with FMB were divided into experimental and control groups. The experimental group was submitted to 15 sessions of re-expansive respiratory patterns associated to RBF (biofeedback pletsmovent; MICROHARD (R) V1.0), which provided biofeedback of the thoracic and abdominal movements. The control group was submitted to 15 sessions using biofeedback alone. Spirometry, maximum static respiratory pressure measurements and questions regarding habits (answered by parents/guardians) were carried out before and after therapy. The Student`s t-test for paired data and non-parametric tests were employed for statistical analysis at a 5% Level of significance. Results: Significant changes were found in forced vital. capacity, Tiffeneau index scores, maximum expiratory pressure, maximum inspiratory pressure and habits assessed in FMB with the use of RBF associated to the re-expansive patterns. No significant differences were found comparing the experimental and control groups. Conclusions: The results allow the conclusion that RBF associated to re-expansive patterns improves forced vital capacity, Tiffeneau index scores, respiratory muscle strength and habits in FMB and can therefore be used as a form of therapy for such individuals. (C) 2008 Elsevier Ireland Ltd. All rights reserved.

From EEG to BOLD: Brain mapping and estimating transfer functions in simultaneous EEG-fMRI acquisitions

Simultaneous acquisition of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) aims to disentangle the description of brain processes by exploiting the advantages of each technique. Most studies in this field focus on exploring the relationships between fMRI signals and the power spectrum at some specific frequency bands (alpha, beta, etc.). On the other hand, brain mapping of EEG signals (e.g., interictal spikes in epileptic patients) usually assumes an haemodynamic response function for a parametric analysis applying the GLM, as a rough approximation. The integration of the information provided by the high spatial resolution of MR images and the high temporal resolution of EEG may be improved by referencing them by transfer functions, which allows the identification of neural driven areas without strong assumptions about haemodynamic response shapes or brain haemodynamic`s homogeneity. The difference on sampling rate is the first obstacle for a full integration of EEG and fMRI information. Moreover, a parametric specification of a function representing the commonalities of both signals is not established. In this study, we introduce a new data-driven method for estimating the transfer function from EEG signal to fMRI signal at EEG sampling rate. This approach avoids EEG subsampling to fMRI time resolution and naturally provides a test for EEG predictive power over BOLD signal fluctuations, in a well-established statistical framework. We illustrate this concept in resting state (eyes closed) and visual simultaneous fMRI-EEG experiments. The results point out that it is possible to predict the BOLD fluctuations in occipital cortex by using EEG measurements. (C) 2010 Elsevier Inc. All rights reserved.

Increased EMG response following electromyographic biofeedback treatment of rectus femoris muscle after spinal cord injury

Objective To study increases in electromyographic (EMG) response from the right and left rectus femoris muscles of individuals with long-term cervical spinal cord injuries after EMG biofeedback treatment. Design Repeated measure trials compared EMG responses before and after biofeedback treatment in patients with spinal cord injuries. Main outcome measures The Neuroeducator was used to analyse and provide feedback of the EMG signal and to measure EMG response. Setting Department of Traumatic Orthopaedics, School of Medicine, University of Sao Paulo, Brazil. Participants Twenty subjects (three men and 17 women), between 21 and 49 years of age, with incomplete spinal cord injury at level C6 or higher (range C2 to C6). Of these subjects, 10 received their spinal cord injuries from motor vehicle accidents, one from a gunshot, five from diving, three from falls and one from spinal disc herniation. Results Significant differences were found in the EMG response of the right rectus femoris muscle between pre-initial (T1), post-initial (T2) and additional (T3) biofeedback treatment with the subjects in a sitting position [mean (standard deviation) T1: 26 mu V (29); T2: 67 mu V (50); T3: 77 mu V (62)]. The mean differences and 95% confidence intervals for these comparisons were as follows: T1 to T2, -40.7 (-53.1 to -29.4); T2 to T3, -9.6 (-26.1 to 2.3). Similar differences were found for the left leg in a sitting position and for both legs in the sit-to-stand condition. Conclusions The EMG responses obtained in this study showed that treatment involving EMG biofeedback significantly increased voluntary EMG responses from right and left rectus femoris muscles in individuals with spinal cord injuries. (C) 2010 Chartered Society of Physiotherapy. Published by Elsevier Ltd. All rights reserved.

Gamma band oscillations under influence of bromazepam during a sensorimotor integration task: An EEG coherence study

The goal of the present study was to explore the dynamics of the gamma band using the coherence of the quantitative electroencephalography (qEEG) in a sensorimotor integration task and the influence of the neuromodulator bromazepam on the band behavior. Our hypothesis is that the needs of the typewriting task will demand the coupling of different brain areas, and that the gamma band will promote the binding of information. It is also expected that the neuromodulator will modify this coupling. The sample was composed of 39 healthy subjects. We used a randomized double-blind design and divided subjects into three groups: placebo (n = 13), bromazepam 3 mg (n = 13) and bromazepam 6 mg (n = 13). The two-way ANOVA analysis demonstrated a main effect for the factors condition (i.e., C4-CZ electrode pair) and moment (i.e., C3-CZ, C3-C4 and C4-CZ pairs of electrodes). We propose that the gamma band plays an important role in the binding among several brain areas in complex motor tasks and that each hemisphere is influenced in a different manner by the neuromodulator. (C) 2009 Elsevier Ireland Ltd. All rights reserved.

Good surgical outcome in discordant ictal EEG-MRI unilateral mesial temporal sclerosis patients

Purpose:Video electroencephalography (vEEG) monitoring of patients with unilateral mesial temporal sclerosis (uMTS) may show concordant or discordant seizure onset in relation to magnetic resonance imaging (MRI) evidence of MTS. Contralateral seizure usually leads to an indication of invasive monitoring. Contralateral seizure onset on invasive monitoring may contraindicate surgery. We evaluated long-term outcome after anteromesial temporal lobectomy (AMTL) in a consecutive series of uMTS patients with concordant and discordant vEEG findings, uniformly submitted to AMTL on the MRI evidence of MTS side without invasive monitoring. Methods:We compared surgical outcome of all uMTS patients undergoing vEEG monitoring between January 1999 and April 2005 in our service. Discordant cases were defined by at least one seizure onset contralateral to the MRI evidence of MTS. Good surgical outcome was considered as Engel`s class I. We also evaluated ictal SPECT concordance to ictal EEG and surgical outcome. Results:Fifty-four patients had concordant (C) and 22 had discordant (D) scalp EEG and MRI. Surgical outcome was similar in both groups (C = 74% versus D = 86%). Duration of follow-up was comparable in both groups: C = 56.1 +/- 20.7 months versus D = 59.8 +/- 21.2 months (p = 0.83, nonsignificant). Discordant single-photon emission computed tomography (SPECT) results did not influence surgical outcome. Discussion:Surgical outcome was not influenced by contralateral vEEG seizure onset or contralateral increased flow on ictal SPECT. Although vEEG monitoring should still be performed in these patients, to rule out psychogenic seizures and extratemporal seizure onset, a potentially risky procedure such as invasive monitoring may not only not be indicated in this patient population, but may also lead to patients erroneously being denied surgery.