Oscillatory Coupling Between Neural and Cardiac Rhythms.

Oscillations serve a critical role in organizing biological systems. In the brain, oscillatory coupling is a fundamental mechanism of communication. The possibility that neural oscillations interact directly with slower physiological rhythms (e.g., heart rate, respiration) is largely unexplored and may have important implications for psychological functioning. Oscillations in heart rate, an aspect of heart rate variability (HRV), show remarkably robust associations with psychological health. Mather and Thayer proposed coupling between high-frequency HRV (HF-HRV) and neural oscillations as a mechanism that partially accounts for such relationships. We tested this hypothesis by measuring phase-amplitude coupling between HF-HRV and neural oscillations in 37 healthy adults at rest. Robust coupling was detected in all frequency bands. Granger causality analyses indicated stronger heart-to-brain than brain-to-heart effects in all frequency bands except gamma. These findings suggest that cardiac rhythms play a causal role in modulating neural oscillations, which may have important implications for mental health.

Enhancing precision in human neuroscience.

Human neuroscience has always been pushing the boundary of what is measurable. During the last decade, concerns about statistical power and replicability - in science in general, but also specifically in human neuroscience - have fueled an extensive debate. One important insight from this discourse is the need for larger samples, which naturally increases statistical power. An alternative is to increase the precision of measurements, which is the focus of this review. This option is often overlooked, even though statistical power benefits from increasing precision as much as from increasing sample size. Nonetheless, precision has always been at the heart of good scientific practice in human neuroscience, with researchers relying on lab traditions or rules of thumb to ensure sufficient precision for their studies. In this review, we encourage a more systematic approach to precision. We start by introducing measurement precision and its importance for well-powered studies in human neuroscience. Then, determinants for precision in a range of neuroscientific methods (MRI, M/EEG, EDA, Eye-Tracking, and Endocrinology) are elaborated. We end by discussing how a more systematic evaluation of precision and the application of respective insights can lead to an increase in reproducibility in human neuroscience.

Cortico-ocular coupling in the service of episodic memory formation.

Encoding of visual information is a necessary requirement for most types of episodic memories. In search for a neural signature of memory formation, amplitude modulation of neural activity has been repeatedly shown to correlate with and suggested to be functionally involved in successful memory encoding. We here report a complementary view on why and how brain activity relates to memory, indicating a functional role of cortico-ocular interactions for episodic memory formation. Recording simultaneous magnetoencephalography and eye tracking in 35 human participants, we demonstrate that gaze variability and amplitude modulations of alpha/beta oscillations (10-20 Hz) in visual cortex covary and predict subsequent memory performance between and within participants. Amplitude variation during pre-stimulus baseline was associated with gaze direction variability, echoing the co-variation observed during scene encoding. We conclude that encoding of visual information engages unison coupling between oculomotor and visual areas in the service of memory formation.

Decomposing age effects in EEG alpha power.

Increasing life expectancy is prompting the need to understand how the brain changes during healthy aging. Research utilizing electroencephalography (EEG) has found that the power of alpha oscillations decrease from adulthood on. However, non-oscillatory (aperiodic) components in the data may confound results and thus require re-investigation of these findings. Thus, the present report analyzed a pilot and two additional independent samples (total N = 533) of resting-state EEG from healthy young and elderly individuals. A newly developed algorithm was utilized that allows the decomposition of the measured signal into periodic and aperiodic signal components. By using multivariate sequential Bayesian updating of the age effect in each signal component, evidence across the datasets was accumulated. It was hypothesized that previously reported age-related alpha power differences will largely diminish when total power is adjusted for the aperiodic signal component. First, the age-related decrease in total alpha power was replicated. Concurrently, decreases of the intercept and slope (i.e. exponent) of the aperiodic signal component were observed. Findings on aperiodic-adjusted alpha power indicated that this general shift of the power spectrum leads to an overestimation of the true age effects in conventional analyses of total alpha power. Thus, the importance of separating neural power spectra into periodic and aperiodic signal components is highlighted. However, also after accounting for these confounding factors, the sequential Bayesian updating analysis provided robust evidence that aging is associated with decreased aperiodic-adjusted alpha power. While the relation of the aperiodic component and aperiodic-adjusted alpha power to cognitive decline demands further investigation, the consistent findings on age effects across independent datasets and high test-retest reliabilities support that these newly emerging measures are reliable markers of the aging brain. Hence, previous interpretations of age-related decreases in alpha power are reevaluated, incorporating changes in the aperiodic signal.

Test-retest reliability of resting-state EEG in young and older adults.

The quantification of resting-state electroencephalography (EEG) is associated with a variety of measures. These include power estimates at different frequencies, microstate analysis, and frequency-resolved source power and connectivity analyses. Resting-state EEG metrics have been widely used to delineate the manifestation of cognition and to identify psychophysiological indicators of age-related cognitive decline. The reliability of the utilized metrics is a prerequisite for establishing robust brain-behavior relationships and clinically relevant indicators of cognitive decline. To date, however, test-retest reliability examination of measures derived from resting human EEG, comparing different resting-state measures between young and older participants, within the same adequately powered dataset, is lacking. The present registered report examined test-retest reliability in a sample of 95 young (age range: 20-35 years) and 93 older (age range: 60-80 years) participants. A good-to-excellent test-retest reliability was confirmed in both age groups for power estimates on both scalp and source levels as well as for the individual alpha peak power and frequency. Partial confirmation was observed for hypotheses stating good-to-excellent reliability of microstates measures and connectivity. Equal levels of reliability between the age groups were confirmed for scalp-level power estimates and partially so for source-level power and connectivity. In total, five out of the nine postulated hypotheses were empirically supported and confirmed good-to-excellent reliability of the most commonly reported resting-state EEG metrics.

Saccades are locked to the phase of alpha oscillations during natural reading.

We saccade 3 to 5 times per second when reading. However, little is known about the neuronal mechanisms coordinating the oculomotor and visual systems during such rapid processing. Here, we ask if brain oscillations play a role in the temporal coordination of the visuomotor integration. We simultaneously acquired MEG and eye-tracking data while participants read sentences silently. Every sentence was embedded with a target word of either high or low lexical frequency. Our key finding demonstrated that saccade onsets were locked to the phase of alpha oscillations (8 to 13 Hz), and in particular, for saccades towards low frequency words. Source modelling demonstrated that the alpha oscillations to which the saccades were locked, were generated in the right-visual motor cortex (BA 7). Our findings suggest that the alpha oscillations serve to time the processing between the oculomotor and visual systems during natural reading, and that this coordination becomes more pronounced for demanding words.

Brain areas associated with visual spatial attention display topographic organization during auditory spatial attention.

Spatially selective modulation of alpha power (8-14 Hz) is a robust finding in electrophysiological studies of visual attention, and has been recently generalized to auditory spatial attention. This modulation pattern is interpreted as reflecting a top-down mechanism for suppressing distracting input from unattended directions of sound origin. The present study on auditory spatial attention extends this interpretation by demonstrating that alpha power modulation is closely linked to oculomotor action. We designed an auditory paradigm in which participants were required to attend to upcoming sounds from one of 24 loudspeakers arranged in a circular array around the head. Maintaining the location of an auditory cue was associated with a topographically modulated distribution of posterior alpha power resembling the findings known from visual attention. Multivariate analyses allowed the prediction of the sound location in the horizontal plane. Importantly, this prediction was also possible, when derived from signals capturing saccadic activity. A control experiment on auditory spatial attention confirmed that, in absence of any visual/auditory input, lateralization of alpha power is linked to the lateralized direction of gaze. Attending to an auditory target engages oculomotor and visual cortical areas in a topographic manner akin to the retinotopic organization associated with visual attention.

Decomposing the role of alpha oscillations during brain maturation.

Childhood and adolescence are critical stages of the human lifespan, in which fundamental neural reorganizational processes take place. A substantial body of literature investigated accompanying neurophysiological changes, focusing on the most dominant feature of the human EEG signal: the alpha oscillation. Recent developments in EEG signal-processing show that conventional measures of alpha power are confounded by various factors and need to be decomposed into periodic and aperiodic components, which represent distinct underlying brain mechanisms. It is therefore unclear how each part of the signal changes during brain maturation. Using multivariate Bayesian generalized linear models, we examined aperiodic and periodic parameters of alpha activity in the largest openly available pediatric dataset (N=2529, age 5-22 years) and replicated these findings in a preregistered analysis of an independent validation sample (N=369, age 6-22 years). First, the welldocumented age-related decrease in total alpha power was replicated. However, when controlling for the aperiodic signal component, our findings provided strong evidence for an age-related increase in the aperiodic-adjusted alpha power. As reported in previous studies, also relative alpha power revealed a maturational increase, yet indicating an underestimation of the underlying relationship between periodic alpha power and brain maturation. The aperiodic intercept and slope decreased with increasing age and were highly correlated with total alpha power. Consequently, earlier interpretations on age-related changes of total alpha power need to be reconsidered, as elimination of active synapses rather links to decreases in the aperiodic intercept. Instead, analyses of diffusion tensor imaging data indicate that the maturational increase in aperiodic-adjusted alpha power is related to increased thalamocortical connectivity. Functionally, our results suggest that increased thalamic control of cortical alpha power is linked to improved attentional performance during brain maturation.

A benchmark for prediction of psychiatric multimorbidity from resting EEG data in a large pediatric sample.

Psychiatric disorders are among the most common and debilitating illnesses across the lifespan and begin usually during childhood and adolescence, which emphasizes the importance of studying the developing brain. Most of the previous pediatric neuroimaging studies employed traditional univariate statistics on relatively small samples. Multivariate machine learning approaches have a great potential to overcome the limitations of these approaches. On the other hand, the vast majority of existing multivariate machine learning studies have focused on differentiating between children with an isolated psychiatric disorder and typically developing children. However, this line of research does not reflect the real-life situation as the majority of children with a clinical diagnosis have multiple psychiatric disorders (multimorbidity), and consequently, a clinician has the task to choose between different diagnoses and/or the combination of multiple diagnoses. Thus, the goal of the present benchmark is to predict psychiatric multimorbidity in children and adolescents. For this purpose, we implemented two kinds of machine learning benchmark challenges: The first challenge targets the prediction of the seven most prevalent DSM-V psychiatric diagnoses for the available data set, of which each individual can exhibit multiple ones concurrently (i.e. multi-task multi-label classification). Based on behavioral and cognitive measures, a second challenge focuses on predicting psychiatric symptom severity on a dimensional level (i.e. multiple regression task). For the present benchmark challenges, we will leverage existing and future data from the biobank of the Healthy Brain Network (HBN) initiative, which offers a unique large-sample dataset (N = 2042) that provides a wide array of different psychiatric developmental disorders and true hidden data sets. Due to limited real-world practicability and economic viability of MRI measurements, the present challenge will permit only resting state EEG data and demographic information to derive predictive models. We believe that a community driven effort to derive predictive markers from these data using advanced machine learning algorithms can help to improve the diagnosis of psychiatric developmental disorders.

Oscillatory connectivity as a mechanism of auditory sensory gating and its disruption in schizophrenia.

Although innumerable studies using an auditory sensory gating paradigm have confirmed that individuals with schizophrenia (SZ) show less reduction in brain response to the second in a pair of clicks, this large literature has not yielded consensus on the circuit(s) responsible for gating nor for the gating difference in SZ. Clinically stable adult inpatients (N = 157) and matched community participants (N = 90) participated in a standard auditory sensory gating protocol. Responses to paired clicks were quantified as peak-to-peak amplitude from a response at approximately 50 ms to a response at approximately 100 ms in MEG-derived source waveforms. For bilateral sources in each of four regions near Heschl's gyrus, the gating ratio was computed as the response to the second stimulus divided by the response to the first stimulus. Spectrally resolved Granger causality quantified effective connectivity among regions manifested in alpha-band oscillatory coupling before and during stimulation. Poorer sensory gating localized to A1 in SZ than in controls confirmed previous results, here found in adjacent brain regions as well. Spontaneous, stimulus-independent effective connectivity within the hemisphere from angular gyrus to portions of the superior temporal gyrus was lower in SZ and correlated with gating ratio. Significant involvement of frontal and subcortical brain regions previously proposed as contributing to the auditory gating abnormality was not found. Findings point to endogenous connectivity evident in a sequence of activity from angular gyrus to portions of superior temporal gyrus as a mechanism contributing to normal and abnormal gating in SZ and potentially to sensory and cognitive symptoms.

Effective connectivity between Broca's area and amygdala as a mechanism of top-down control in worry.

Individuals higher in trait worry exhibit increased activation in Broca's area during inhibitory processing tasks. To identify whether such activity represents an adaptive mechanism supporting top-down control, functional and effective connectivity of Broca's area were investigated during a task of inhibitory control. fMRI data obtained from 106 participants performing an emotion-word Stroop task were examined using psychophysiological interaction and Granger Causality (GC) analyses. Findings revealed greater directed connectivity from Broca's to amygdala in the presence of emotional distraction. Furthermore, a predictive relationship was observed between worry and the asymmetry in effective connectivity, with worriers exhibiting greater directed connectivity from Broca's to amygdala. When performing the task, worriers with greater GC directional asymmetry were more accurate than worriers with less asymmetry. Present findings indicate that individuals with elevated trait worry employ a mechanism of top-down control in which communication from Broca's to amygdala fosters successful compensation for interference effects.

Alpha oscillations govern interhemispheric spike timing coordination in the honey bee brain.

In 1929 Hans Berger discovered the alpha oscillations: prominent, ongoing oscillations around 10 Hz in the electroencephalogram of the human brain. These alpha oscillations are among the most widely studied brain signals, related to cognitive phenomena such as attention, memory and consciousness. However, the mechanisms by which alpha oscillations affect human cognition await demonstration. Here, we suggest the honey bee brain as an experimentally more accessible model system for investigating the functional role of alpha oscillations. We found a prominent spontaneous oscillation around 18 Hz that is reduced in amplitude upon olfactory stimulation. Similar to alpha oscillations in primates, the phase of this oscillation biased both timing of neuronal spikes and amplitude of high-frequency gamma activity (40-450 Hz). These results suggest a common role of alpha oscillations across phyla and provide an unprecedented new venue for causal studies on the relationship between neuronal spikes, brain oscillations and cognition.

Interpreting neural decoding models using grouped model reliance.

Machine learning algorithms are becoming increasingly popular for decoding psychological constructs based on neural data. However, as a step towards bridging the gap between theory-driven cognitive neuroscience and data-driven decoding approaches, there is a need for methods that allow to interpret trained decoding models. The present study demonstrates grouped model reliance as a model-agnostic permutation-based approach to this problem. Grouped model reliance indicates the extent to which a trained model relies on conceptually related groups of variables, such as frequency bands or regions of interest in electroencephalographic (EEG) data. As a case study to demonstrate the method, random forest and support vector machine models were trained on within-participant single-trial EEG data from a Sternberg working memory task. Participants were asked to memorize a sequence of digits (0-9), varying randomly in length between one, four and seven digits, where EEG recordings for working memory load estimation were taken from a 3-second retention interval. The present results confirm previous findings insofar as both random forest and support vector machine models relied on alpha-band activity in most subjects. However, as revealed by further analyses, patterns in frequency and topography varied considerably between individuals, pointing to more pronounced inter-individual differences than previously reported.

Rapid brain responses to affective pictures indicate dimensions of trauma-related psychopathology in adolescents.

A variety of mental disorders are related to deviant brain activity, but these neural alterations do not validate psychiatric diagnostic categories. High symptom overlap and variable symptom patterns encourage a dimensional approach. Following the logic of the Research Domain Criteria (RDoC), we investigated trauma survivors for symptom clusters that might be associated with characteristics of ERPs, in particular with the early posterior negativity (EPN) elicited during affective picture processing. In rapid serial visual presentation, 90 adolescents (40 male/50 female, age M = 15.0 ± 2.5 years) who had been exposed to varying amounts of traumatic stress passively viewed a stream of high-arousing positive and low-arousing neutral pictures taken from the International Affective Picture System (IAPS). Using standardized interviews, symptoms of trauma-related mental disorders were assessed (including those for PTSD, depression, borderline personality disorder, and behavioral problems). A principal component analysis was performed to derive potential dimensions of psychopathology. Multiple regression analysis confirmed a factor comprising problems concentrating, sleeping difficulties, and mistrust as a predictor of a larger EPN difference between high-arousing positive and low-arousing neutral IAPS pictures (ß = 0.19, p < 0.05). Sex predicted the magnitude of the EPN (ß = 0.45, p < 0.001). Male adolescents displayed a stronger EPN suppression than female adolescents. The result suggests that problems concentrating, sleeping difficulties, and mistrust seem to be trans-diagnostic elements related to diminished early emotional discrimination represented by the EPN. Furthermore, our findings indicate that the EPN in response to emotional processing is modulated by sex.

Spectral fingerprints of facial affect processing bias in major depression disorder.

In major depressive disorder (MDD), processing of facial affect is thought to reflect a perceptual bias (toward negative emotion, away from positive emotion, and interpretation of neutral as emotional). However, it is unclear to what extent and which specific perceptual bias is represented in MDD at the behavior and neuronal level. The present report examined 48 medication naive MDD patients and 41 healthy controls (HCs) performing a facial affect judgment task while magnetoencephalography was recorded. MDD patients were characterized by overall slower response times and lower perceptual judgment accuracies. In comparison with HC, MDD patients exhibited less somatosensory beta activity (20-30 Hz) suppression, more visual gamma activity (40-80 Hz) modulation and somatosensory beta and visual gamma interaction deficit. Moreover, frontal gamma activity during positive facial expression judgment was found to be negatively correlated with depression severity. Present findings suggest that perceptual bias in MDD is associated with distinct spatio-spectral manifestations on the neural level, which potentially establishes aberrant pathways during facial emotion processing and contributes to MDD pathology.

Spatial specificity of alpha oscillations in the human visual system.

Alpha oscillations are strongly modulated by spatial attention. To what extent, the generators of cortical alpha oscillations are spatially distributed and have selectivity that can be related to retinotopic organization is a matter of continuous scientific debate. In the present report, neuromagnetic activity was quantified by means of spatial location tuning functions from 30 participants engaged in a visuospatial attention task. A cue presented briefly in one of 16 locations directing covert spatial attention resulted in a robust modulation of posterior alpha oscillations. The distribution of the alpha sources approximated the retinotopic organization of the human visual system known from hemodynamic studies. Better performance in terms of target identification was associated with a more spatially constrained alpha modulation. The present findings demonstrate that the generators of posterior alpha oscillations are retinotopically organized when modulated by spatial attention.

Oscillatory brain dynamics supporting impaired Stroop task performance in schizophrenia-spectrum disorder.

The Stroop color-word interference task, prompting slower response to color-incongruent than to congruent items, is often used to study neural mechanisms of inhibitory control and dysfunction in schizophrenia-spectrum disorders. Inconsistent findings of an augmented Stroop effect limit identification of relevant dysfunctional mechanism(s) in schizophrenia. The present study sought to advance understanding of normal and impaired neural oscillatory dynamics by distinguishing interference detection and response preparation during the Stroop task in schizophrenia-spectrum disorders via analysis of behavioral performance and 4-7 Hz (theta) and 10-30 Hz (alpha/beta) EEG oscillations in 40 patients (SZ) and 27 healthy comparison participants (HC). SZ responded more slowly and showed less dorsal anterior cingulate (dACC) theta enhancement during INC trials, less enhancement of dACC-sensorimotor cortex connectivity (theta phase synchrony) during INC trials, more alpha/beta suppression though less enhancement of that suppression during INC trials, and slower post-response alpha/beta rebound than did HC. Reaction time distributions showed larger group and Stroop effects during the 25% of trials with the slowest responses. Poorer theta phase coherence in patients indicates impaired communication between regions associated with interference processing (dACC) and response preparation (sensorimotor cortex). Results suggest a failure cascade in which compromised behavioral Stroop effects are driven at least in part by dysfunctional interference processing (less theta power increase) prompting dysfunctional motor response preparation (less alpha/beta power suppression). Inconsistent Stroop effects in past studies of schizophrenia may result from differing task parameters sampling different degrees of Stroop task difficulty.

FieldTrip Made Easy: An Analysis Protocol for Group Analysis of the Auditory Steady State Brain Response in Time, Frequency, and Space.

The auditory steady state evoked response (ASSR) is a robust and frequently utilized phenomenon in psychophysiological research. It reflects the auditory cortical response to an amplitude-modulated constant carrier frequency signal. The present report provides a concrete example of a group analysis of the EEG data from 29 healthy human participants, recorded during an ASSR paradigm, using the FieldTrip toolbox. First, we demonstrate sensor-level analysis in the time domain, allowing for a description of the event-related potentials (ERPs), as well as their statistical evaluation. Second, frequency analysis is applied to describe the spectral characteristics of the ASSR, followed by group level statistical analysis in the frequency domain. Third, we show how time- and frequency-domain analysis approaches can be combined in order to describe the temporal and spectral development of the ASSR. Finally, we demonstrate source reconstruction techniques to characterize the primary neural generators of the ASSR. Throughout, we pay special attention to explaining the design of the analysis pipeline for single subjects and for the group level analysis. The pipeline presented here can be adjusted to accommodate other experimental paradigms and may serve as a template for similar analyses.

Cross-frequency interactions between frontal theta and posterior alpha control mechanisms foster working memory.

Neural oscillatory activity in the theta (4-8 Hz) and alpha (8-14 Hz) bands has been associated with the implementation of executive function, with theta in midline frontal cortex and alpha in posterior parietal cortex related to working memory (WM) load. To identify how these spatially and spectrally distinct neural phenomena interact within a large-scale fronto-parietal network organized in service of WM, EEG was recorded while subjects performed an N-back WM task. Frontal theta power increase, paralleled by posterior alpha decrease, tracked participants' successful WM performance. These power fluctuations were inversely related both across and within trials and predicted reaction time, suggesting a functionally important communication channel within the fronto-parietal network. Granger causality analysis revealed directed parietal to frontal communication via alpha and frontal to parietal communication via theta. Results encourage consideration of these bidirectional, power-to-power, cross-frequency control mechanisms as an important feature of cerebral network organization supporting executive function.

Neural network communication facilitates verbal working memory.

Oscillatory brain activity in the theta, alpha, and gamma frequency ranges has been associated with working memory (WM). In addition to alpha and theta activity associated with WM retention, and gamma band activity with item encoding, activity in the alpha band is related to the deployment of attention resources and information. The present study sought to specify distinct roles of neuromagnetic 4-7 Hz theta, 9-13 Hz alpha, and 50-70 Hz gamma power modulation and communication in fronto-parietal networks during cued, hemifield-specific item presentation in a modified Sternberg verbal WM task in 14 student volunteers. Lateralized posterior alpha and gamma power during encoding suggest a preparatory role of alpha oscillations. Bilateral alpha power increases during maintenance reflect information retention for the non-lateralized probe response. Lateralized alpha power increase during encoding was apparently driven by a monotonic increase in fronto-parietal 6 Hz phase, suggesting a mechanism facilitating WM encoding and successful performance.