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.

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.

Task-based default mode network connectivity predicts cognitive impairment and negative symptoms in first-episode schizophrenia.

Individuals diagnosed with schizophrenia (SZ) demonstrate difficulty distinguishing between internally and externally generated stimuli. These aberrations in "source monitoring" have been theorized as contributing to symptoms of the disorder, including hallucinations and delusions. Altered connectivity within the default mode network (DMN) of the brain has been proposed as a mechanism through which discrimination between self-generated and externally generated events is disrupted. Source monitoring abnormalities in SZ have additionally been linked to impairments in selective attention and inhibitory processing, which are reliably observed via the N100 component of the event-related brain potential elicited during an auditory paired-stimulus paradigm. Given overlapping constructs associated with DMN connectivity and N100 in SZ, the present investigation evaluated relationships between these measures of disorder-related dysfunction and sought to clarify the nature of task-based DMN function in SZ. DMN connectivity and N100 measures were assessed using EEG recorded from SZ during their first episode of illness (N = 52) and demographically matched healthy comparison participants (N = 25). SZ demonstrated less evoked theta-band connectivity within DMN following presentation of pairs of identical auditory stimuli than HC. Greater DMN connectivity among SZ was associated with better performance on measures of sustained attention (p = .03) and working memory (p = .09), as well as lower severity of negative symptoms, though it was not predictive of N100 measures. Together, present findings provide EEG evidence of lower task-based connectivity among first-episode SZ, reflecting disruptions of DMN functions that support cognitive processes. Attentional processes captured by N100 appear to be supported by different neural mechanisms.

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.

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.

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.

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.

Time Course of Brain Network Reconfiguration Supporting Inhibitory Control.

Hemodynamic research has recently clarified key nodes and links in brain networks implementing inhibitory control. Although fMRI methods are optimized for identifying the structure of brain networks, the relatively slow temporal course of fMRI limits the ability to characterize network operation. The latter is crucial for developing a mechanistic understanding of how brain networks shift dynamically to support inhibitory control. To address this critical gap, we applied spectrally resolved Granger causality (GC) and random forest machine learning tools to human EEG data in two large samples of adults (test sample n = 96, replication sample n = 237, total N = 333, both sexes) who performed a color-word Stroop task. Time-frequency analysis confirmed that recruitment of inhibitory control accompanied by slower behavioral responses was related to changes in theta and alpha/beta power. GC analyses revealed directionally asymmetric exchanges within frontal and between frontal and parietal brain areas: top-down influence of superior frontal gyrus (SFG) over both dorsal ACC (dACC) and inferior frontal gyrus (IFG), dACC control over middle frontal gyrus (MFG), and frontal-parietal exchanges (IFG, precuneus, MFG). Predictive analytics confirmed a combination of behavioral and brain-derived variables as the best set of predictors of inhibitory control demands, with SFG theta bearing higher classification importance than dACC theta and posterior beta tracking the onset of behavioral response. The present results provide mechanistic insight into the biological implementation of a psychological phenomenon: inhibitory control is implemented by dynamic routing processes during which the target response is upregulated via theta-mediated effective connectivity within key PFC nodes and via beta-mediated motor preparation.SIGNIFICANCE STATEMENT Hemodynamic neuroimaging research has recently clarified regional structures in brain networks supporting inhibitory control. However, due to inherent methodological constraints, much of this research has been unable to characterize the temporal dynamics of such networks (e.g., direction of information flow between nodes). Guided by fMRI research identifying the structure of brain networks supporting inhibitory control, results of EEG source analysis in a test sample (n = 96) and replication sample (n = 237) using effective connectivity and predictive analytics strategies advance a model of inhibitory control by characterizing the precise temporal dynamics by which this network operates and exemplify an approach by which mechanistic models can be developed for other key psychological processes.

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.

FEF-Controlled Alpha Delay Activity Precedes Stimulus-Induced Gamma-Band Activity in Visual Cortex.

Recent findings in the visual system of nonhuman primates have demonstrated an important role of gamma-band activity (40-100 Hz) in the feedforward flow of sensory information, whereas feedback control appears to be established dynamically by oscillations in the alpha (8-13 Hz) and beta (13-18 Hz) bands (van Kerkoerle et al., 2014; Bastos et al., 2015). It is not clear, however, how alpha oscillations are controlled and how they interact with the flow of visual information mediated by gamma-band activity. Using noninvasive human MEG recordings in subjects performing a visuospatial attention task, we show that fluctuations in alpha power during a delay period in a spatial attention task preceded subsequent stimulus-driven gamma-band activity. Importantly, these interactions correlated with behavioral performance. Using Granger analysis, we further show that the right frontal-eye field (rFEF) exerted feedback control of the visual alpha oscillations. Our findings suggest that alpha oscillations controlled by the FEF route cortical information flow by modulating gamma-band activity.SIGNIFICANCE STATEMENT Visual perception relies on a feedforward flow of information from sensory regions, which is modulated by a feedback drive. We have identified the neuronal dynamics supporting integration of the feedforward and feedback information. Alpha oscillations in early visual regions reflect feedback control when spatial attention is allocated and this control is exercised by the right frontal eye field. Importantly, the alpha-band activity predicted both performance and activity in the gamma band. In particular, gamma activity was modulated by the phase of the alpha oscillations. These findings provide novel insight into how the brain operates as a network and suggest that the integration of feedforward and feedback information is implemented by cross-frequency interactions between slow and fast neuronal oscillations.

Dorsal and ventral cortices are coupled by cross-frequency interactions during working memory.

Oscillatory activity in the alpha and gamma bands is considered key in shaping functional brain architecture. Power increases in the high-frequency gamma band are typically reported in parallel to decreases in the low-frequency alpha band. However, their functional significance and in particular their interactions are not well understood. The present study shows that, in the context of an N-back working memory task, alpha power decreases in the dorsal visual stream are related to gamma power increases in early visual areas. Granger causality analysis revealed directed interregional interactions from dorsal to ventral stream areas, in accordance with task demands. Present results reveal a robust, behaviorally relevant, and architectonically decisive power-to-power relationship between alpha and gamma activity. This relationship suggests that anatomically distant power fluctuations in oscillatory activity can link cerebral network dynamics on trial-by-trial basis during cognitive operations such as working memory.

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.

Prestimulus oscillatory power and connectivity patterns predispose conscious somatosensory perception.

Which aspects of our sensory environment enter conscious awareness does not only depend on physical features of the stimulus, but also critically on the so-called current brain state. Results from magnetoencephalography/EEG studies using near-threshold stimuli have consistently pointed to reduced levels of α- (8-12 Hz) power in relevant sensory areas to predict whether a stimulus will be consciously perceived or not. These findings have been mainly interpreted in strictly "local" terms of enhanced excitability of neuronal ensembles in respective cortical regions. The present study aims to introduce a framework that complements this rather local perspective, by stating that the functional connectivity architecture before stimulation will predetermine information flow. Thus, information computed at a local level will be distributed throughout a network, thereby becoming consciously accessible. Data from a previously published experiment on conscious somatosensory near-threshold perception was reanalyzed focusing on the prestimulus period. Analysis of spectral power showed reduced α-power mainly in the contralateral S2 and middle frontal gyrus to precede hits, thus overall supporting the current literature. Furthermore, differences between hits and misses were obtained on global network (graph theoretical) features in the same interval. Most importantly, in accordance with our framework, we could show that the somatosensory cortex is "more efficiently" integrated into a distributed network in the prestimulus period. This finding means that when a relevant sensory stimulus impinges upon the system, it will encounter preestablished pathways for information flow. In this sense, prestimulus functional connectivity patterns form "windows" to conscious perception.

Same clock, different time read-out: Spontaneous brain oscillations and their relationship to deficient coding of cognitive content.

Neuronal oscillations provide an efficient means of communication, fostering functional neural states supporting action and reaction. High in the action hierarchy, cognitive abilities are severely compromised in neuropsychiatric disease such as schizophrenia. Current thinking highlights a clocking mechanism provided by the phase of an ongoing slow oscillation that offers a temporal frame for coding of perceptual and computational elements. Yet unclear is whether and how a dysregulated clocking mechanism accounts for diminished cognitive performance. Neuromagnetic oscillatory activity was related to cognitive performance assessed by the MATRICS Consensus Cognitive Battery in 58 healthy individuals (HC) and 46 schizophrenia patients (SZ). HC showed a correlation between gamma-band oscillations (>40 Hz) and working memory performance. This relationship was disrupted in several ways in SZ. First, patients evidenced lower gamma power, poorer working memory performance, and no relationship between these measures. Second, the power spectra were dominated by ~10 Hz alpha oscillations with no group differences in amplitude. However, analysis of phase-to-amplitude coupling (PAC) revealed exaggerated clocking of gamma activity by alpha phase in SZ, associated with poor working memory performance. Third, despite entrainment by the same 10 Hz clock, gamma amplitude was abnormally distributed across the duty cycle in SZ, a potential consequence of compromised interneuron inhibition. Fourth, SZ showed over-engagement of a fronto-parietal network measured by gamma phase coherence, suggesting a brain state hindering cognitive output. Such an endogenous temporal organization may be a core dysfunction in SZ: a segregation/integration input imbalance fostering reduced cognitive performance and compromised behavioral output.

Modulation of α power and functional connectivity during facial affect recognition.

Research has linked oscillatory activity in the α frequency range, particularly in sensorimotor cortex, to processing of social actions. Results further suggest involvement of sensorimotor α in the processing of facial expressions, including affect. The sensorimotor face area may be critical for perception of emotional face expression, but the role it plays is unclear. The present study sought to clarify how oscillatory brain activity contributes to or reflects processing of facial affect during changes in facial expression. Neuromagnetic oscillatory brain activity was monitored while 30 volunteers viewed videos of human faces that changed their expression from neutral to fearful, neutral, or happy expressions. Induced changes in α power during the different morphs, source analysis, and graph-theoretic metrics served to identify the role of α power modulation and cross-regional coupling by means of phase synchrony during facial affect recognition. Changes from neutral to emotional faces were associated with a 10-15 Hz power increase localized in bilateral sensorimotor areas, together with occipital power decrease, preceding reported emotional expression recognition. Graph-theoretic analysis revealed that, in the course of a trial, the balance between sensorimotor power increase and decrease was associated with decreased and increased transregional connectedness as measured by node degree. Results suggest that modulations in α power facilitate early registration, with sensorimotor cortex including the sensorimotor face area largely functionally decoupled and thereby protected from additional, disruptive input and that subsequent α power decrease together with increased connectedness of sensorimotor areas facilitates successful facial affect recognition.

Dynamics of alpha oscillations elucidate facial affect recognition in schizophrenia.

Impaired facial affect recognition is characteristic of schizophrenia and has been related to impaired social function, but the relevant neural mechanisms have not been fully identified. The present study sought to identify the role of oscillatory alpha activity in that deficit during the process of facial emotion recognition. Neuromagnetic brain activity was monitored while 44 schizophrenia patients and 44 healthy controls viewed 5-s videos showing human faces gradually changing from neutral to fearful or happy expressions or from the neutral face of one poser to the neutral face of another. Recognition performance was determined separately by self-report. Relative to prestimulus baseline, controls exhibited a 10- to 15-Hz power increase prior to full recognition and a 10- to 15-Hz power decrease during the postrecognition phase. These results support recent proposals about the function of alpha-band oscillations in normal stimulus evaluation. The patients failed to show this sequence of alpha power increase and decrease and also showed low 10- to 15-Hz power and high 10- to 15-Hz connectivity during the prestimulus baseline. In light of the proposal that a combination of alpha power increase and functional disconnection facilitates information intake and processing, the finding of an abnormal association of low baseline alpha power and high connectivity in schizophrenia suggests a state of impaired readiness that fosters abnormal dynamics during facial affect recognition.

Eye movements track prioritized auditory features in selective attention to natural speech.

Over the last decades, cognitive neuroscience has identified a distributed set of brain regions that are critical for attention. Strong anatomical overlap with brain regions critical for oculomotor processes suggests a joint network for attention and eye movements. However, the role of this shared network in complex, naturalistic environments remains understudied. Here, we investigated eye movements in relation to (un)attended sentences of natural speech. Combining simultaneously recorded eye tracking and magnetoencephalographic data with temporal response functions, we show that gaze tracks attended speech, a phenomenon we termed ocular speech tracking. Ocular speech tracking even differentiates a target from a distractor in a multi-speaker context and is further related to intelligibility. Moreover, we provide evidence for its contribution to neural differences in speech processing, emphasizing the necessity to consider oculomotor activity in future research and in the interpretation of neural differences in auditory cognition.

Enhanced resting-state oscillations in schizophrenia are associated with decreased synchronization during inattentional blindness.

Patients suffering from schizophrenia have been characterized by an apparent lack of theta (around 6 Hz) and gamma (>40 Hz) brain oscillatory activity during task execution. The neurocognitive reasons for these abnormal synchronization patterns, however, remain elusive. Recording the electroencephalogramm (EEG) during a selective visual attention task, the current study investigates whether abnormal brain oscillatory resting-state activity in the theta band might account for a lack of task-related brain oscillatory activity in schizophrenia. EEGs were recorded from 26 patients with schizophrenia and 26 healthy matched controls during rest and during the execution of a selective visual attention task, in which an unexpected object (monkey) appeared on the screen. On a behavioral level, patients were less likely to report perceiving the unexpected event than controls. Controls showed a stronger increase in task-related theta power than patients in prefrontal, parietal, and occipital brain regions. Task-related theta power change differed between patients who perceived, and patients who did not perceive the unexpected event. Moreover, patients showed higher levels of theta power during rest than controls, whereas the absolute theta power values during the selective attention task did not differ between groups. These results suggest that the failure to increase oscillatory activity during a cognitive task can be accounted for by abnormally high oscillatory activity in a resting state. This finding has important implications for future studies examining abnormal brain oscillatory activity in schizophrenia, which usually treat resting-state activity as a baseline for task-related activity.

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.