Neurofeedback and the Neural Representation of Self: Lessons From Awake State and Sleep

Using spectral continuity to extract breathing rate from heart rate and its applications in sleep physiology

Introduction: ECG Derived Respiration (EDR) are a set of methods used for extracting the breathing rate from the Electrocardiogram (ECG). Recent studies revealed a tight connection between breathing rate and more specifically the breathing patterns during sleep and several related pathologies. Yet, while breathing rate and more specifically the breathing pattern is recognised as a vital sign it is less employed than Electroencephalography (EEG) and heart rate in sleep and polysomnography studies. Methods: This study utilised open-access data from the ISRUC sleep database to test a novel spectral-based EDR technique (scEDR). In contrast to previous approaches, the novel method emphasizes spectral continuity and not only the power of the different spectral peaks. scEDR is then compared against a more widely used spectral EDR method that selects the frequency with the highest power as the respiratory frequency (Max Power EDR). Results: scEDR yielded improved performance against the more widely used Max Power EDR in terms of accuracy across all sleep stages and the whole sleep. This study further explores the breathing rate across sleep stages, providing evidence in support of a putative sleep stage "REM0" which was previously proposed based on analysis of the Heart Rate Variability (HRV) but not yet widely discussed. Most importantly, this study observes that the frequency distribution of the heart rate during REM0 is closer to REM than other NREM periods even though most of REM0 was previously classified as NREM sleep by sleep experts following either the original or revised sleep staging criteria. Discussion: Based on the results of the analysis, this study proposes scEDR as a potential low-cost and non-invasive method for extracting the breathing rate using the heart rate during sleep with further studies required to validate its accuracy in awake subjects. In this study, the autonomic balance across different sleep stages, including REM0, was examined using HRV as a metric. The results suggest that sympathetic activity decreases as sleep progresses to NREM3 until it reaches a level similar to the awake state in REM through a transition from REM0.

Spatiotemporal profile of altered neural reactivity to food images in obesity: Reward system is altered automatically and predicts efficacy of weight loss intervention

Introduction

Obesity presents a significant public health problem. Brain plays a central role in etiology and maintenance of obesity. Prior neuroimaging studies have found that individuals with obesity exhibit altered neural responses to images of food within the brain reward system and related brain networks. However, little is known about the dynamics of these neural responses or their relationship to later weight change. In particular, it is unknown if in obesity, the altered reward response to food images emerges early and automatically, or later, in the controlled stage of processing. It also remains unclear if the pretreatment reward system reactivity to food images is predictive of subsequent weight loss intervention outcome.

Methods

In this study, we presented high-calorie and low-calorie food, and nonfood images to individuals with obesity, who were then prescribed lifestyle changes, and matched normal-weight controls, and examined neural reactivity using magnetoencephalography (MEG). We performed whole-brain analysis to explore and characterize large-scale dynamics of brain systems affected in obesity, and tested two specific hypotheses: (1) in obese individuals, the altered reward system reactivity to food images occurs early and automatically, and (2) pretreatment reward system reactivity predicts the outcome of lifestyle weight loss intervention, with reduced activity associated with successful weight loss.

Results

We identified a distributed set of brain regions and their precise temporal dynamics that showed altered response patterns in obesity. Specifically, we found reduced neural reactivity to food images in brain networks of reward and cognitive control, and elevated reactivity in regions of attentional control and visual processing. The hypoactivity in reward system emerged early, in the automatic stage of processing (< 150 ms post-stimulus). Reduced reward and attention responsivity, and elevated neural cognitive control were predictive of weight loss after six months in treatment.

Discussion

In summary, we have identified, for the first time with high temporal resolution, the large-scale dynamics of brain reactivity to food images in obese versus normal-weight individuals, and have confirmed both our hypotheses. These findings have important implications for our understanding of neurocognition and eating behavior in obesity, and can facilitate development of novel integrated treatment strategies, including tailored cognitive-behavioral and pharmacological therapies.

Infra-Low Frequency Neurofeedback in Tension-Type Headache: A Cross-Over Sham-Controlled Study

Primary headaches are highly prevalent and represent a major cause of disability in young adults. Neurofeedback is increasingly used in the treatment of chronic pain; however, there are few studies investigating its efficacy in patients with headaches. We report the results of a cross-over sham-controlled study on the efficacy of neurofeedback in the prophylactic treatment of tension-type headache (TTH). Participants received ten sessions of infra-low frequency electroencephalographic neurofeedback and ten sessions of sham-neurofeedback, with the order of treatments being randomized. The study also included a basic psychotherapeutic intervention — a psychoeducational session performed before the main study phases and emotional support provided throughout the study period. The headache probability was modeled as a function of the neurofeedback and sham-neurofeedback sessions performed to date. As a result, we revealed a strong beneficial effect of neurofeedback and no influence of the sham sessions. The study supports the prophylactic use of infra-low frequency neurofeedback in patients with TTH. From a methodological point of view, we advocate for the explicit inclusion of psychotherapeutic components in neurofeedback study protocols.

Training the Conductor of the Brainwave Symphony: In Search of a Common Mechanism of Action for All Methods of Neurofeedback

There are several different methods of neurofeedback, most of which presume an operant conditioning model whereby the subject learns to control their brain activity in particular regions of the brain and/or at particular brainwave frequencies based on reinforcement. One method, however, called infra-low frequency [ILF] neurofeedback cannot be explained through this paradigm, yet it has profound effects on brain function. Like a conductor of a symphony, recent evidence demonstrates that the primary ILF (typically between 0.01–0.1 Hz), which correlates with the fluctuation of oxygenated and deoxygenated blood in the brain, regulates all of the classic brainwave bands (i.e. alpha, theta, delta, beta, gamma). The success of ILF neurofeedback suggests that all forms of neurofeedback may work through a similar mechanism that does not fit the operant conditioning paradigm. This chapter focuses on the possible mechanisms of action for ILF neurofeedback, which may be generalized, based on current evidence.

Rhythmicity in heart rate and its surges usher a special period of sleep, a likely home for PGO waves

High amplitude electroencephalogram (EEG) events, like unitary K-complex (KC), are used to partition sleep into stages and hence define the hypnogram, a key instrument of sleep medicine. Throughout sleep the heart rate (HR) changes, often as a steady HR increase leading to a peak, what is known as a heart rate surge (HRS). The hypnogram is often unavailable when most needed, when sleep is disturbed and the graphoelements lose their identity. The hypnogram is also difficult to define during normal sleep, particularly at the start of sleep and the periods that precede and follow rapid eye movement (REM) sleep. Here, we use objective quantitative criteria that group together periods that cannot be assigned to a conventional sleep stage into what we call REM0 periods, with the presence of a HRS one of their defining properties. Extended REM0 periods are characterized by highly regular sequences of HRS that generate an infra-low oscillation around 0.05 Hz. During these regular sequence of HRS, and just before each HRS event, we find avalanches of high amplitude events for each one of the mass electrophysiological signals, i.e. related to eye movement, the motor system and the general neural activity. The most prominent features of long REM0 periods are sequences of three to five KCs which we label multiple K-complexes (KCm). Regarding HRS, a clear dissociation is demonstrated between the presence or absence of high gamma band spectral power (55-95 Hz) of the two types of KCm events: KCm events with strong high frequencies (KCmWSHF) cluster just before the peak of HRS, while KCm between HRS show no increase in high gamma band (KCmNOHF). Tomographic estimates of activity from magnetoencephalography (MEG) in pre-KC periods (single and multiple) showed common increases in the cholinergic Nucleus Basalis of Meynert in the alpha band. The direct contrast of KCmWSHF with KCmNOHF showed increases in all subjects in the high sigma band in the base of the pons and in three subjects in both the delta and high gamma bands in the medial Pontine Reticular Formation (mPRF), the putative Long Lead Initial pulse (LLIP) for Ponto-Geniculo-Occipital (PGO) waves.

The Interventions for Sleep Disorders in Children with Attention Deficit and Hyperactivity Disorder: A Narrative Review

BACKGROUND: Attention deficit and hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder in childhood. Children with ADHD have more frequent comorbid sleep disorders than healthy children. The current methods of treating sleep disorders in ADHD children are still focused on pharmacological interventions which in fact only provide little benefit. AIM: This study aims to identify existing studies regarding the types of nonpharmacological and complementary interventions for sleep disorders in children with ADHD using a narrative review approach. METHODS: This was a narrative review. Inclusion criteria included articles in English and Bahasa, full-text articles, primary studies, and children with ADHD aged 18 years with or without comorbidities as the research sample, and the article having been published in the last 10 years. Searches were performed in several databases: PubMed, Springer, Web of Science, and Google Scholar. RESULTS: The result showed that programs for managing sleep disorder in ADHD are behavioral therapy, behavioral parent training (BPT), behavioral sleep intervention, neurofeedback, mind-body therapy, L-theanine supplementation, horse milk supplementation, and play therapy. The programs are found to be effective in improving sleep. CONCLUSION: The program most comprehensive with a high level of ease of implementation was BPT.

Interacting brains coming in sync through their minds: an interbrain neurofeedback study

Neurophysiological evidence shows that interpersonal action coordination is accompanied by interbrain synchronization (IBS). However, the functional significance of this association remains unclear. Using two experimental designs, we explored whether IBS is amenable to neurofeedback (NFB). Feedback was provided either as two balls approaching each other (so-called ball design), or as two pendula, each reflecting the oscillatory activity of one of the two participants (so-called pendulum design). The NFB was provided at delta (i.e., 2.5 Hz) and theta (i.e., 5 Hz) electroencephalography frequencies, and manipulated by enhanced and inverse feedback. We showed that the participants were able to increase IBS by using NFB, especially when it was fed back at the theta frequency. Apart from intra- and interbrain coupling, other oscillatory activities (e.g., power spectral density, peak amplitude, and peak frequency) also changed during the task compared with the rest. Moreover, all the measures showed specific correlations with the subjective postsurvey item scores, reflecting subjective feeling and appraisal. We conclude that the use of IBS for NFB might help in specifying the contribution of IBS to interpersonal action coordination and in providing important information about the neural mechanisms of social interaction and the causal dimension of IBS.

Vector Phase Analysis Approach for Sleep Stage Classification: A Functional Near-Infrared Spectroscopy-Based Passive Brain-Computer Interface

A passive brain-computer interface (BCI) based upon functional near-infrared spectroscopy (fNIRS) brain signals is used for earlier detection of human drowsiness during driving tasks. This BCI modality acquired hemodynamic signals of 13 healthy subjects from the right dorsolateral prefrontal cortex (DPFC) of the brain. Drowsiness activity is recorded using a continuous-wave fNIRS system and eight channels over the right DPFC. During the experiment, sleep-deprived subjects drove a vehicle in a driving simulator while their cerebral oxygen regulation (CORE) state was continuously measured. Vector phase analysis (VPA) was used as a classifier to detect drowsiness state along with sleep stage-based threshold criteria. Extensive training and testing with various feature sets and classifiers are done to justify the adaptation of threshold criteria for any subject without requiring recalibration. Three statistical features (mean oxyhemoglobin, signal peak, and the sum of peaks) along with six VPA features (trajectory slopes of VPA indices) were used. The average accuracies for the five classifiers are 90.9% for discriminant analysis, 92.5% for support vector machines, 92.3% for nearest neighbors, 92.4% for both decision trees, and ensembles over all subjects' data. Trajectory slopes of CORE vector magnitude and angle: m(|R|) and m(∠R) are the best-performing features, along with ensemble classifier with the highest accuracy of 95.3% and minimum computation time of 40 ms. The statistical significance of the results is validated with a p-value of less than 0.05. The proposed passive BCI scheme demonstrates a promising technique for online drowsiness detection using VPA along with sleep stage classification.

Modulation of Intrinsic Brain Connectivity by Implicit Electroencephalographic Neurofeedback

Despite the increasing popularity of neurofeedback, its mechanisms of action are still poorly understood. This study aims to describe the processes underlying implicit electroencephalographic neurofeedback. Fifty-two healthy volunteers were randomly assigned to a single session of infra-low frequency neurofeedback or sham neurofeedback, with electrodes over the right middle temporal gyrus and the right inferior parietal lobule. They observed a moving rocket, the speed of which was modulated by the waveform derived from a band-limited infra-low frequency filter. Immediately before and after the session, the participants underwent a resting-state fMRI. Network-based statistical analysis was applied, comparing post- vs. pre-session and real vs. sham neurofeedback conditions. As a result, two phenomena were observed. First, we described a brain circuit related to the implicit neurofeedback process itself, consisting of the lateral occipital cortex, right dorsolateral prefrontal cortex, left orbitofrontal cortex, right ventral striatum, and bilateral dorsal striatum. Second, we found increased connectivity between key regions of the salience, language, and visual networks, which is indicative of integration in sensory processing. Thus, it appears that a single session of implicit infra-low frequency electroencephalographic neurofeedback leads to significant changes in intrinsic brain connectivity.

Brain activity mediators of PTSD symptom reduction during real-time fMRI amygdala neurofeedback emotional training

Self-regulation of brain activation with real-time functional magnetic resonance imaging neurofeedback (rtfMRI-nf) is emerging as a promising treatment for psychiatric disorders. The association between the regulation and symptom reduction, however, has not been consistent, and the mechanisms underlying the symptom reduction remain poorly understood. The present study investigated brain activity mediators of the amygdala rtfMRI-nf training effect on combat veterans' PTSD symptom reduction. The training was designed to increase a neurofeedback signal either from the left amygdala (experimental group; EG) or from a control region not implicated in emotion regulation (control group; CG) during positive autobiographical memory recall. We employed a structural equation model mapping analysis to identify brain regions that mediated the effects of the rtfMRI-nf training on PTSD symptoms. Symptom reduction was mediated by low activation in the dorsomedial prefrontal cortex (DMPFC) and the middle cingulate cortex. There was a trend toward less activation in these regions for the EG compared to the CG. Low activation in the precuneus, the right superior parietal, the right insula, and the right cerebellum also mediated symptom reduction while their effects were moderated by the neurofeedback signal; a higher signal was linked to less effect on symptom reduction. This moderation was not specific to the EG. MDD comorbidity was associated with high DMPFC activation, which resulted in less effective regulation of the feedback signal. These results indicated that symptom reduction due to the neurofeedback training was not specifically mediated by the neurofeedback target activity, but broad regions were involved in the process.

The Emergence of Spindles and K-Complexes and the Role of the Dorsal Caudal Part of the Anterior Cingulate as the Generator of K-Complexes

The large multicomponent K-complex (KC) and the rhythmic spindle are the hallmarks of non-rapid eye movement (NREM)-2 sleep stage. We studied with magnetoencephalography (MEG) the progress of light sleep (NREM-1 and NREM-2) and emergence of KCs and spindles. Seven periods of interest (POI) were analyzed: wakefulness, the two quiet "core" periods of light sleep (periods free from any prominent phasic or oscillatory events) and four periods before and during spindles and KCs. For each POI, eight 2-s (1250 time slices) segments were used. We employed magnetic field tomography (MFT) to extract an independent tomographic estimate of brain activity from each MEG data sample. The spectral power was then computed for each voxel in the brain for each segment of each POI. The sets of eight maps from two POIs were contrasted using a voxel-by-voxel t-test. Only increased spectral power was identified in the four key contrasts between POIs before and during spindles and KCs versus the NREM2 core. Common increases were identified for all four subjects, especially within and close to the anterior cingulate cortex (ACC). These common increases were widespread for low frequencies, while for higher frequencies they were focal, confined to specific brain areas. For the pre-KC POI, only one prominent increase was identified, confined to the theta/alpha bands in a small area in the dorsal caudal part of ACC (dcACC). During KCs, the activity in this area grows in intensity and extent (in space and frequency), filling the space between the areas that expanded their low frequency activity (in the delta band) during NREM2 compared to NREM1. Our main finding is that prominent spectral power increases before NREM2 graphoelements are confined to the dcACC, and only for KCs, sharing common features with changes of activity in dcACC of the well-studied error related negativity (ERN). ERN is seen in awake state, in perceptual conflict and situations where there is a difference between expected and actual environmental or internal events. These results suggest that a KC is the sleep side of the awake state ERN, both serving their putative sentinel roles in the frame of the saliency network.

Connectivity and complex systems: learning from a multi-disciplinary perspective

In recent years, parallel developments in disparate disciplines have focused on what has come to be termed connectivity; a concept used in understanding and describing complex systems. Conceptualisations and operationalisations of connectivity have evolved largely within their disciplinary boundaries, yet similarities in this concept and its application among disciplines are evident. However, any implementation of the concept of connectivity carries with it both ontological and epistemological constraints, which leads us to ask if there is one type or set of approach(es) to connectivity that might be applied to all disciplines. In this review we explore four ontological and epistemological challenges in using connectivity to understand complex systems from the standpoint of widely different disciplines. These are: (i) defining the fundamental unit for the study of connectivity; (ii) separating structural connectivity from functional connectivity; (iii) understanding emergent behaviour; and (iv) measuring connectivity. We draw upon discipline-specific insights from Computational Neuroscience, Ecology, Geomorphology, Neuroscience, Social Network Science and Systems Biology to explore the use of connectivity among these disciplines. We evaluate how a connectivity-based approach has generated new understanding of structural-functional relationships that characterise complex systems and propose a 'common toolbox' underpinned by network-based approaches that can advance connectivity studies by overcoming existing constraints.