Real-time monitoring and regulating auditory cortex alpha activity in patients with chronic tinnitus.

OBJECTIVE: Low levels of alpha activity (8-13Hz) mirror a state of enhanced responsiveness, whereas high levels of alpha are a state of reduced responsiveness. Tinnitus is accompanied by reduction of alpha activity in the perisylvian regions compared to normal hearing controls. This reduction might be a key mechanism in the chain of reactions leading to tinnitus. We devised a novel spatial filter as an on-line source monitoring method, which can be used to control alpha activity in the primary auditory cortex. In addition, we designed an innovative experimental procedure to enable suppression of visual and somatosensory alpha, facilitating auditory alpha control during alpha neurofeedback. APPROACH: An amplitude-modulated auditory stimulation with 40 Hz modulation frequency and 1000 Hz carrier frequency specifically activates the primary auditory cortex. The topography of 40 Hz oscillation depicts the activity of the auditory cortices. We used this map as a spatial filter, which passes the activity originating from the auditory cortex. To suppress superposition of auditory alpha by somatosensory and visual alpha, we used a continuous tactile jaw-stimulation and visual stimulation protocol to suppress somatosensory alpha of regions adjacent to the auditory cortex and visual alpha for local regulation of auditory alpha activity only. MAIN RESULTS: This novel spatial filter for online detection of auditory alpha activity and the usage of multi-sensory stimulation facilitate the appearance of alpha activity from the auditory cortex at the sensor level. SIGNIFICANCE: The proposed procedure can be used in an EEG-neurofeedback-treatment approach allowing online auditory alpha self-regulation training in patients with chronic tinnitus.

Volitional regulation of brain responses to food stimuli in overweight and obese subjects: A real-time fMRI feedback study.

Obese subjects who achieve weight loss show increased functional connectivity between dorsolateral prefrontal cortex (dlPFC) and ventromedial prefrontal cortex (vmPFC), key areas of executive control and reward processing. We investigated the potential of real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback training to achieve healthier food choices by enhancing self-control of the interplay between these brain areas. We trained eight male individuals with overweight or obesity (age: 31.8 ± 4.4 years, BMI: 29.4 ± 1.4 kg/m2) to up-regulate functional connectivity between the dlPFC and the vmPFC by means of a four-day rt-fMRI neurofeedback protocol including, on each day, three training runs comprised of six up-regulation and six passive viewing trials. During the up-regulation runs of the four training days, participants successfully learned to increase functional connectivity between dlPFC and vmPFC. In addition, a trend towards less high-calorie food choices emerged from before to after training, which however was associated with a trend towards increased covertly assessed snack intake. Findings of this proof-of-concept study indicate that overweight and obese participants can increase functional connectivity between brain areas that orchestrate the top-down control of appetite for high-calorie foods. Neurofeedback training might therefore be a useful tool in achieving and maintaining weight loss.

Differential neural mechanisms for early and late prediction error detection.

Emerging evidence indicates that prediction, instantiated at different perceptual levels, facilitate visual processing and enable prompt and appropriate reactions. Until now, the mechanisms underlying the effect of predictive coding at different stages of visual processing have still remained unclear. Here, we aimed to investigate early and late processing of spatial prediction violation by performing combined recordings of saccadic eye movements and fast event-related fMRI during a continuous visual detection task. Psychophysical reverse correlation analysis revealed that the degree of mismatch between current perceptual input and prior expectations is mainly processed at late rather than early stage, which is instead responsible for fast but general prediction error detection. Furthermore, our results suggest that conscious late detection of deviant stimuli is elicited by the assessment of prediction error's extent more than by prediction error per se. Functional MRI and functional connectivity data analyses indicated that higher-level brain systems interactions modulate conscious detection of prediction error through top-down processes for the analysis of its representational content, and possibly regulate subsequent adaptation of predictive models. Overall, our experimental paradigm allowed to dissect explicit from implicit behavioral and neural responses to deviant stimuli in terms of their reliance on predictive models.

Self-Regulation of Anterior Insula with Real-Time fMRI and Its Behavioral Effects in Obsessive-Compulsive Disorder: A Feasibility Study.

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. METHODS: 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: 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. CONCLUSION: 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.

Fast mental states decoding in mixed reality.

The combination of Brain-Computer Interface (BCI) technology, allowing online monitoring and decoding of brain activity, with virtual and mixed reality (MR) systems may help to shape and guide implicit and explicit learning using ecological scenarios. Real-time information of ongoing brain states acquired through BCI might be exploited for controlling data presentation in virtual environments. Brain states discrimination during mixed reality experience is thus critical for adapting specific data features to contingent brain activity. In this study we recorded electroencephalographic (EEG) data while participants experienced MR scenarios implemented through the eXperience Induction Machine (XIM). The XIM is a novel framework modeling the integration of a sensing system that evaluates and measures physiological and psychological states with a number of actuators and effectors that coherently reacts to the user's actions. We then assessed continuous EEG-based discrimination of spatial navigation, reading and calculation performed in MR, using linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Dynamic single trial classification showed high accuracy of LDA and SVM classifiers in detecting multiple brain states as well as in differentiating between high and low mental workload, using a 5 s time-window shifting every 200 ms. Our results indicate overall better performance of LDA with respect to SVM and suggest applicability of our approach in a BCI-controlled MR scenario. Ultimately, successful prediction of brain states might be used to drive adaptation of data representation in order to boost information processing in MR.