Scan-associated anxiety (scanxiety): the enigma of emotional breathing oscillations at 0.32 Hz (19 bpm).

MRI-related anxiety in healthy participants is often characterized by a dominant breathing frequency at around 0.32 Hz (19 breaths per minute, bpm) at the beginning but in a few cases also at the end of scanning. Breathing waves at 19 bpm are also observed in patients with anxiety independently of the scanned body part. In patients with medically intractable epilepsy and intracranial electroencephalography (iEEG), spontaneous breathing through the nose varied between 0.24 and 0.37 Hz (~19 bpm). Remarkable is the similarity of the observed breathing rates at around 0.32 Hz during different types of anxiety states (e.g., epilepsy, cancer, claustrophobia) with the preferred breathing frequency of 0.32 Hz (19 bpm), which is predicted by the binary hierarchy model of Klimesch. This elevated breathing frequency most likely reflects an emotional processing state, in which energy demands are minimized due to a harmonic coupling ratio with other brain-body oscillations.

Respiration-entrained brain oscillations in healthy fMRI participants with high anxiety.

Brain-body interactions can be studied by using directed coupling measurements of fMRI oscillations in the low (0.1-0.2 Hz) and high frequency bands (HF; 0.2-0.4 Hz). Recently, a preponderance of oscillations in the information flow between the brainstem and the prefrontal cortex at around 0.15/0.16 Hz was shown. The goal of this study was to investigate the information flow between BOLD-, respiratory-, and heart beat-to-beat interval (RRI) signals in the HF band in healthy subjects with high anxiety during fMRI examinations. A multivariate autoregressive model was concurrently applied to the BOLD signals from the middle frontal gyrus (MFG), precentral gyrus and the brainstem, as well as to respiratory and RRI signals. Causal coupling between all signals was determined using the Directed Transfer Function (DTF). We found a salience of fast respiratory waves with a period of 3.1 s (corresponding to ~ 0.32 Hz) and a highly significant (p < 0.001) top-down information-flow from BOLD oscillations in the MFG to the brainstem. Additionally, there was a significant (p < 0.01) information flow from RRI to respiratory oscillations. We speculate that brain oscillations around 0.32 Hz, triggered by nasal breathing, are projected downwards to the brainstem. Particularly interesting is the driving force of cardiac to respiratory waves with a ratio of 1:1 or 1:2. These results support the binary hierarchy model with preferred respiratory frequencies at 0.32 Hz and 0.16 Hz.

Processing of fMRI-related anxiety and information flow between brain and body revealed a preponderance of oscillations at 0.15/0.16 Hz.

Slow oscillations of different center frequencies and their coupling play an important role in brain-body interactions. The crucial question analyzed by us is, whether the low frequency (LF) band (0.05-0.15 Hz) or the intermediate frequency (IMF) band (0.1-0.2 Hz) is more eminent in respect of the information flow between body (heart rate and respiration) and BOLD signals in cortex and brainstem. A recently published study with the LF band in fMRI-naïve subjects revealed an intensive information flow from the cortex to the brainstem and a weaker flow from the brainstem to the cortex. The comparison of both bands revealed a significant information flow from the middle frontal gyrus (MFG) to the precentral gyrus (PCG) and from brainstem to PCG only in the IMF band. This pattern of directed coupling between slow oscillations in the cortex and brainstem not only supports the existence of a pacemaker-like structure in brainstem, but provides first evidence that oscillations centered at 0.15/0.16 Hz can also emerge in brain networks. BOLD oscillations in resting states are dominating at ~ 0.08 Hz and respiratory rates at ~ 0.32 Hz. Therefore, the frequency component at ~ 0.16 Hz (doubling-halving 0.08 Hz or 0.32 Hz) is of special interest, because phase coupled oscillations can reduce the energy demand.

Sleep-Specific Processing of Auditory Stimuli Is Reflected by Alpha and Sigma Oscillations.

Recent research revealed a surprisingly large range of cognitive operations to be preserved during sleep in humans. The new challenge is therefore to understand functions and mechanisms of processes, which so far have been mainly investigated in awake subjects. The current study focuses on dynamic changes of brain oscillations and connectivity patterns in response to environmental stimulation during non-REM sleep. Our results indicate that aurally presented names were processed and neuronally differentiated across the wake-sleep spectrum. Simultaneously recorded EEG and MEG signals revealed two distinct clusters of oscillatory power increase in response to the stimuli: (1) vigilance state-independent θ synchronization occurring immediately after stimulus onset, followed by (2) sleep-specific α/σ synchronization peaking after stimulus offset. We discuss the possible role of θ, α, and σ oscillations during non-REM sleep, and work toward a unified theory of brain rhythms and their functions during sleep.SIGNIFICANCE STATEMENT Previous research has revealed (residual) capacity of the sleeping human brain to interact with the environment. How sensory processing is realized by the neural assemblies in different stages of sleep is however unclear. To tackle this question, we examined simultaneously recorded MEG and EEG data. We discuss the possible role of θ, α, and σ oscillations during non-REM sleep. In contrast to versatile θ band response that reflected early stimulus processing step, succeeding α and σ band activity was sensitive to the saliency of the incoming information, and contingent on the sleep stage. Our findings suggest that the specific reorganization of mechanisms involved in later stages of sensory processing takes place upon falling asleep.

Processing of fMRI-related anxiety and bi-directional information flow between prefrontal cortex and brain stem.

Brain-heart synchronization is fundamental for emotional-well-being and brain-heart desynchronization is characteristic for anxiety disorders including specific phobias. Recording BOLD signals with functional magnetic resonance imaging (fMRI) is an important noninvasive diagnostic tool; however, 1-2% of fMRI examinations have to be aborted due to claustrophobia. In the present study, we investigated the information flow between regions of interest (ROI's) in the cortex and brain stem by using a frequency band close to 0.1 Hz. Causal coupling between signals important in brain-heart interaction (cardiac intervals, respiration, and BOLD signals) was studied by means of Directed Transfer Function based on the Granger causality principle. Compared were initial resting states with elevated anxiety and final resting states with low or no anxiety in a group of fMRI-naïve young subjects. During initial high anxiety the results showed an increased information flow from the middle frontal gyrus (MFG) to the pre-central gyrus (PCG) and to the brainstem. There also was an increased flow from the brainstem to the PCG. While the top-down flow during increased anxiety was predominant, the weaker ascending flow from brainstem structures may characterize a rhythmic pacemaker-like activity that (at least in part) drives respiration. We assume that these changes in information flow reflect successful anxiety processing.

Verification of a Central Pacemaker in Brain Stem by Phase-Coupling Analysis Between HR Interval- and BOLD-Oscillations in the 0.10-0.15 Hz Frequency Band.

The origin of slow intrinsic oscillations in resting states of functional magnetic resonance imaging (fMRI) signals is still a matter of debate. The present study aims to test the hypothesis that slow blood oxygenation level-dependent (BOLD) oscillations with frequency components greater than 0.10 Hz result from a central neural pacemaker located in the brain stem. We predict that a central oscillator modulates cardiac beat-to-beat interval (RRI) fluctuations rapidly, with only a short neural lag around 0.3 s. Spontaneous BOLD fluctuations in the brain stem, however, are considerably delayed due to the hemodynamic response time of about ∼2-3 s. In order to test these predictions, we analyzed the time delay between slow RRI oscillations from thorax and BOLD oscillations in the brain stem by calculating the phase locking value (PLV). Our findings show a significant time delay of 2.2 ± 0.2 s between RRI and BOLD signals in 12 out of 23 (50%) participants in axial slices of the pons/brain stem. Adding the neural lag of 0.3 s to the observed lag of 2.2 s we obtain 2.5 s, which is the time between neural activity increase and BOLD increase, termed neuro-BOLD coupling. Note, this time window for neuro-BOLD coupling in awake humans is surprisingly of similar size as in awake head-fixed adult mice (Mateo et al., 2017).

International Federation of Clinical Neurophysiology (IFCN) - EEG research workgroup: Recommendations on frequency and topographic analysis of resting state EEG rhythms. Part 1: Applications in clinical research studies.

In 1999, the International Federation of Clinical Neurophysiology (IFCN) published "IFCN Guidelines for topographic and frequency analysis of EEGs and EPs" (Nuwer et al., 1999). Here a Workgroup of IFCN experts presents unanimous recommendations on the following procedures relevant for the topographic and frequency analysis of resting state EEGs (rsEEGs) in clinical research defined as neurophysiological experimental studies carried out in neurological and psychiatric patients: (1) recording of rsEEGs (environmental conditions and instructions to participants; montage of the EEG electrodes; recording settings); (2) digital storage of rsEEG and control data; (3) computerized visualization of rsEEGs and control data (identification of artifacts and neuropathological rsEEG waveforms); (4) extraction of "synchronization" features based on frequency analysis (band-pass filtering and computation of rsEEG amplitude/power density spectrum); (5) extraction of "connectivity" features based on frequency analysis (linear and nonlinear measures); (6) extraction of "topographic" features (topographic mapping; cortical source mapping; estimation of scalp current density and dura surface potential; cortical connectivity mapping), and (7) statistical analysis and neurophysiological interpretation of those rsEEG features. As core outcomes, the IFCN Workgroup endorsed the use of the most promising "synchronization" and "connectivity" features for clinical research, carefully considering the limitations discussed in this paper. The Workgroup also encourages more experimental (i.e. simulation studies) and clinical research within international initiatives (i.e., shared software platforms and databases) facing the open controversies about electrode montages and linear vs. nonlinear and electrode vs. source levels of those analyses.

"Switch-Off" of Respiratory Sinus Arrhythmia May Be Associated With the Activation of an Oscillatory Source (Pacemaker) in the Brain Stem.

Recently, we reported on the unusual "switch-off" of respiratory sinus arrhythmia (RSA) by analyzing heart rate (HR) beat-to-beat interval (RRI) signals and respiration in five subjects during a potentially anxiety-provoking first-time functional magnetic resonance imaging (fMRI) scanning with slow spontaneous breathing waves (Rassler et al., 2018). This deviation from a fundamental physiological phenomenon is of interest and merits further research. Therefore, in this study, the interplay between blood-oxygen level-dependent (BOLD) activity in the cerebellum/brain stem, RRI, and respiration was probed. Both the cardiovascular and the respiratory centers are located in the medulla oblongata and pons, indicating that dominant slow rhythmic activity is present in the brain stem. The recording of BOLD signals provides a way to investigate associated neural activity fluctuation in the brain stem. We found slow spontaneous breathing waves associated with two types of slow BOLD oscillations with dominant frequencies at 0.10 and 0.15 Hz in the brain stem. Both BOLD oscillations were recorded simultaneously. One is hypothesized as vessel motion-based phenomenon (BOLDv) associated with the start of expiration; the other one as pattern associated with neural activity (BOLDn) acting as a driving force for spontaneous inspiration and RRI increase (unusual cessation of RSA) about 2-3 s after BOLDv. This time delay of 2-3 s corresponds to the neurovascular coupling time.

Coupling and Decoupling between Brain and Body Oscillations.

Cross frequency coupling is used to study the cross talk between brain oscillations. In this paper we focus on a special type of frequency coupling between brain and body oscillations, which is reflected by the numerical ratio (r) between two frequencies (m and n; n > m). This approach is motivated by theoretical considerations, indicating that during alert wakefulness brain-body oscillations form a coupled hierarchy of frequencies with integer relationships that are binary multiples (r = n:m = 1:2, 1:4, 1:8…..). During sleep we expect an irrational relationship (r = n/m = irrational number) between brain and body oscillations that reflects decoupling. We analyzed alpha frequency, heart rate, breathing frequency during performance of a memory tasks and in addition spindle frequency from data collected by the SIESTA sleep research group. As predicted, our results show a binary multiple frequency relationship between alpha, heart rate and breathing frequency during task performance but an irrational relationship between spindle frequency, heart rate and breathing frequency during sleep.

The frequency architecture of brain and brain body oscillations: an analysis.

Research on brain oscillations has brought up a picture of coupled oscillators. Some of the most important questions that will be analyzed are, how many frequencies are there, what are the coupling principles, what their functional meaning is, and whether body oscillations follow similar coupling principles. It is argued that physiologically, two basic coupling principles govern brain as well as body oscillations: (i) amplitude (envelope) modulation between any frequencies m and n, where the phase of the slower frequency m modulates the envelope of the faster frequency n, and (ii) phase coupling between m and n, where the frequency of n is a harmonic multiple of m. An analysis of the center frequency of traditional frequency bands and their coupling principles suggest a binary hierarchy of frequencies. This principle leads to the foundation of the binary hierarchy brain body oscillation theory. Its central hypotheses are that the frequencies of body oscillations can be predicted from brain oscillations and that brain and body oscillations are aligned to each other. The empirical evaluation of the predicted frequencies for body oscillations is discussed on the basis of findings for heart rate, heart rate variability, breathing frequencies, fluctuations in the BOLD signal, and other body oscillations. The conclusion is that brain and many body oscillations can be described by a single system, where the cross talk - reflecting communication - within and between brain and body oscillations is governed by m : n phase to envelope and phase to phase coupling.

State-dependent alpha peak frequency shifts: Experimental evidence, potential mechanisms and functional implications.

Neural populations produce complex oscillatory patterns thought to implement brain function. The dominant rhythm in the healthy adult human brain is formed by alpha oscillations with a typical power peak most commonly found between 8 and 12Hz. This alpha peak frequency has been repeatedly discussed as a highly heritable and stable neurophysiological "trait" marker reflecting anatomical properties of the brain, and individuals' general cognitive capacity. However, growing evidence suggests that the alpha peak frequency is highly volatile at shorter time scales, dependent on the individuals' "state". Based on the converging experimental and theoretical results from numerous recent studies, here we propose that alpha frequency variability forms the basis of an adaptive mechanism mirroring the activation level of neural populations which has important functional implications. We here integrate experimental and computational perspectives to shed new light on the potential role played by shifts in alpha peak frequency and discuss resulting implications. We further propose a potential mechanism by which alpha oscillations are regulated in a noisy network of spiking neurons in presence of delayed feedback.

Social Pavlovian conditioning: Short- and long-term effects and the role of anxiety and depressive symptoms.

Today's stressors largely arise from social interactions rather than from physical threat. However, the dominant laboratory model of emotional learning relies on physical stimuli (e.g. electric shock) whereas adequate models of social conditioning are missing, possibly due to more subtle and multilayered biobehavioral responses to such stimuli. To fill this gap, we acquired a broad set of measures during conditioning to negative social unconditioned stimuli, also taking into account long-term maintenance of conditioning and inter-individual differences. Fifty-nine healthy participants underwent a classical conditioning task with videos of actors expressing disapproving (US-neg) or neutral (US-neu) statements. Static images of the corresponding actors with a neutral facial expression served as CS+ and CS-, predicting US-neg and US-neu, respectively. Autonomic and facial-muscular measures confirmed differential unconditioned responding whereas experiential CS ratings, event-related potentials, and evoked theta oscillations confirmed differential conditioned responding. Conditioning was maintained at 1 month and 1 year follow-ups on experiential ratings, especially in individuals with elevated anxiety and depressive symptoms, documenting the efficiency of social conditioning and its clinical relevance. This novel, ecologically improved conditioning paradigm uncovered a remarkably efficient multi-layered social learning mechanism that may represent a risk factor for anxiety and depression.

Heartbeat-related EEG amplitude and phase modulations from wakefulness to deep sleep: Interactions with sleep spindles and slow oscillations.

Based on physiological models of neurovisceral integration, different studies have shown how cognitive processes modulate heart rate and how the heartbeat, on the other hand, modulates brain activity. We tried to further determine interactions between cardiac and electrical brain activity by means of EEG. We investigated how the heartbeat modulates EEG in 23 healthy controls from wakefulness to deep sleep and showed that frontocentral heartbeat evoked EEG amplitude and phase locking (as measured by intertrial phase locking), at about 300-400 ms after the R peak, decreased with increasing sleep depth with a renewed increase during REM sleep, which underpins the assumption that the heartbeat evoked positivity constitutes an active frontocortical response to the heartbeat. Additionally, we found that individual heart rate was correlated with the frequency of the EEG's spectral peak (i.e., alpha peak frequency during wakefulness). This correlation was strongest during wakefulness and declined linearly with increasing sleep depth. Furthermore, we show that the QRS complex modulates spindle phase possibly related to the correspondence between the frequency of the QRS complex and the spindle frequency of about 12-15 Hz. Finally, during deep sleep stages, a loose temporal coupling between heartbeats and slow oscillation (0.8 Hz) could be observed. These findings indicate that cardiac activity such as heart rate or individual heartbeats can modulate or be modulated by ongoing oscillatory brain activity.

Prestimulus amplitudes modulate P1 latencies and evoked traveling alpha waves.

Traveling waves have been well documented in the ongoing, and more recently also in the evoked EEG. In the present study we investigate what kind of physiological process might be responsible for inducing an evoked traveling wave. We used a semantic judgment task which already proved useful to study evoked traveling alpha waves that coincide with the appearance of the P1 component. We found that the P1 latency of the leading electrode is significantly correlated with prestimulus amplitude size and that this event is associated with a transient change in alpha frequency. We assume that cortical background excitability, as reflected by an increase in prestimulus amplitude, is responsible for the observed change in alpha frequency and the initiation of an evoked traveling trajectory.

Progesterone-associated increase in ERP amplitude correlates with an improvement in performance in a spatial attention paradigm.

Ovarian sex hormones modulate neuronal circuits not directly involved in reproductive functions. In the present study, we investigated whether endogenous fluctuations of estradiol and progesterone during the menstrual cycle are associated with early cortical processing stages in a cued spatial attention paradigm. EEG was monitored while young women responded to acoustically cued visual stimuli. Women with large mean amplitude of the event-related potential (ERP) (80-120 ms following visual stimuli) responded faster to visual stimuli. In luteal women, mean amplitude of the ERP as well as alpha amplitude, an indicator of attentional modulation, correlated positively with progesterone. Further, cerebral asymmetry in ERP amplitude in the alpha frequency band following target presentation was restricted to luteal women. Critically, early follicular women responded slower to right hemifield compared to left hemifield targets. In late follicular or luteal women, we did not detect a right hemifield disadvantage. Progesterone correlated negatively with RTs in luteal women. Therefore, whereas our behavioral data indicate a functional cerebral asymmetry in early follicular women, EEG recording reveal a physiological cerebral hemisphere asymmetry in the alpha frequency band in luteal women. We assume that a progesterone-associated enhancement in synchronization of synaptic activity in the alpha frequency band in luteal women improves early categorization of visual targets in a cued spatial attention paradigm.

Alpha phase, temporal attention, and the generation of early event related potentials.

In the present study, we have investigated the influence of ongoing alpha phase on the generation of the P1 component of the visual ERP, recorded in a target detection task. Our hypothesis is that in trials where pre- or peristimulus alpha phase is already aligned in a way that voltage positive alpha peaks develop seamlessly into the P1, detection performance will be enhanced as compared to trials where alpha is not aligned. The findings supported our hypothesis and showed that target detection times for the subset of seamless alpha trials was significantly shorter than for trials that are not seamless. Our findings contradict the evoked model for the generation of early ERP components, which rests on the assumption of fixed latency, fixed polarity components. We found that in the non-seamless trials the 'candidate' component of the single trial P1 was at the opposite polarity. Despite this fact, alpha phase locking was at the same high level as was observed for the seamless trials. Finally, we found that prestimulus alpha phase was aligned already in a time window preceding the P1 by 400ms.

Resting state alpha frequency is associated with menstrual cycle phase, estradiol and use of oral contraceptives.

Ongoing intrinsic brain activity in resting, but awake humans is dominated by alpha oscillations. In human, individual alpha frequency (IAF) is associated with cognitive performance. Noticeable, performance in cognitive and emotional tasks in women is associated with menstrual cycle phase and sex hormone levels, respectively. In the present study, we correlated frequency of alpha oscillation in resting women with menstrual cycle phase, sex hormone level, or use of oral contraceptives. Electroencephalogram (EEG) was recorded from 57 women (aged 24.07 ± 3.67 years) having a natural menstrual cycle as well as from 57 women (aged 22.37 ± 2.20 years) using oral contraceptives while they sat in an armchair with eyes closed. Alpha frequency was related to the menstrual cycle phase. Luteal women showed highest and late follicular women showed lowest IAF or center frequency. Furthermore, IAF as well as center frequency correlated negatively with endogenous estradiol level, but did not reveal an association with endogenous progesterone. Women using oral contraceptives showed an alpha frequency similar to women in the early follicular phase. We suggest that endogenous estradiol modulate resting alpha frequency.

Lexical access and evoked traveling alpha waves.

Retrieval from semantic memory is usually considered within a time window around 300-600ms. Here we suggest that lexical access already occurs at around 100ms. This interpretation is based on the finding that semantically rich and frequent words exhibit a significantly shorter topographical latency difference between the site with the shortest P1 latency (leading site) and that with the longest P1 latency (trailing site). This latency difference can be described in terms of an evoked traveling alpha wave as was already shown in earlier studies.

Enhancing sleep quality and memory in insomnia using instrumental sensorimotor rhythm conditioning.

EEG recordings over the sensorimotor cortex show a prominent oscillatory pattern in a frequency range between 12 and 15 Hz (sensorimotor rhythm, SMR) under quiet but alert wakefulness. This frequency range is also abundant during sleep, and overlaps with the sleep spindle frequency band. In the present pilot study we tested whether instrumental conditioning of SMR during wakefulness can enhance sleep and cognitive performance in insomnia. Twenty-four subjects with clinical symptoms of primary insomnia were tested in a counterbalanced within-subjects-design. Each patient participated in a SMR- as well as a sham-conditioning training block. Polysomnographic sleep recordings were scheduled before and after the training blocks. Results indicate a significant increase of 12-15 Hz activity over the course of ten SMR training sessions. Concomitantly, the number of awakenings decreased and slow-wave sleep as well as subjective sleep quality increased. Interestingly, SMR-training enhancement was also found to be associated with overnight memory consolidation and sleep spindle changes indicating a beneficial cognitive effect of the SMR training protocol for SMR "responders" (16 out of 24 participants). Although results are promising it has to be concluded that current results are of a preliminary nature and await further proof before SMR-training can be promoted as a non-pharmacological approach for improving sleep quality and memory performance.