How ongoing neuronal oscillations account for evoked fMRI variability

Correlations between Visual Temporal Resolution and Individual Alpha Peak Frequency: Evidence that Internal and Measurement Noise Drive Null Findings

The brain organizes the continuous flow of sensory input by parsing it into discrete events. In the case of two flashes separated by a brief ISI, for example, perception may be of a single flash or two distinct flashes, depending on the ISI but also on the speed of processing. A number of studies have reported evidence that participants with a higher EEG peak alpha frequency are able to detect the presence of two flashes separated by short intervals, whereas those with slower alpha report only one flash. Other studies have not found this correlation. We investigated potential factors that might mask the relationship between individual alpha frequency and visual perception. We recorded resting-state EEG from a large sample of participants (n = 50) and measured the temporal resolution of visual perception with the two-flash fusion task. We found that individual alpha frequency over posterior channels predicted the two-flash fusion threshold, in line with previous studies, but this correlation was significant only when taking into account the steepness of the psychophysical curve of the two-flash task. Participants with a relatively shallow psychophysical curve, likely reflecting high sensory and/or decision noise, failed to show this relationship. These findings replicate previous reports of a correlation between alpha frequency and visual temporal resolution, while also suggesting that an explanation of two-flash fusion performance that neglects the role of internal noise might be insufficient to account for all individual differences.

Transcranial stimulation of alpha oscillations modulates brain state dynamics in sustained attention

The brain operates an advanced complex system to support mental activities. Cognition is thought to emerge from dynamic states of the complex brain system, which are organized spatially through large-scale neural networks and temporally via neural synchrony. However, specific mechanisms underlying these processes remain obscure. Applying high-definition alpha-frequency transcranial alternating-current stimulation (HD α-tACS) in a continuous performance task (CPT) during functional resonance imaging (fMRI), we causally elucidate these major organizational architectures in a key cognitive operation-sustained attention. We demonstrated that α-tACS enhanced both electroencephalogram (EEG) alpha power and sustained attention, in a correlated fashion. Akin to temporal fluctuations inherent in sustained attention, our hidden Markov modeling (HMM) of fMRI timeseries uncovered several recurrent, dynamic brain states, which were organized through a few major neural networks and regulated by the alpha oscillation. Specifically, during sustain attention, α-tACS regulated the temporal dynamics of the brain states by suppressing a Task-Negative state (characterized by activation of the default mode network/DMN) and Distraction state (with activation of the ventral attention and visual networks). These findings thus linked dynamic states of major neural networks and alpha oscillations, providing important insights into systems-level mechanisms of attention. They also highlight the efficacy of non-invasive oscillatory neuromodulation in probing the functioning of the complex brain system and encourage future clinical applications to improve neural systems health and cognitive performance.

Targeted rhythmic visual stimulation at individual participants' intrinsic alpha frequency causes selective increase of occipitoparietal BOLD-fMRI and EEG functional connectivity

Neural oscillations in distinct frequency bands are ubiquitous in the brain and play a role in many cognitive processes. The "communication by coherence" hypothesis, poses that the synchronization through phase coupling of frequency-specific neural oscillations regulate information flow across distribute brain regions. Specifically, the posterior alpha frequency band (7-12 Hz) is thought to gate bottom-up visual information flow by inhibition during visual processing. Evidence shows that increased alpha phase coherency positively correlates with functional connectivity in resting state connectivity networks, supporting alpha mediates neural communication through coherency. However, these findings have mainly been derived from spontaneous changes in the ongoing alpha rhythm. In this study, we experimentally modulate the alpha rhythm by targeting individuals' intrinsic alpha frequency with sustained rhythmic light to investigate alpha-mediated synchronous cortical activity in both EEG and fMRI. We hypothesize increased alpha coherency and fMRI connectivity should arise from modulation of the intrinsic alpha frequency (IAF) as opposed to control frequencies in the alpha range. Sustained rhythmic and arrhythmic stimulation at the IAF and at neighboring frequencies within the alpha band range (7-12 Hz) was implemented and assessed in a separate EEG and fMRI study. We observed increased cortical alpha phase coherency in the visual cortex during rhythmic stimulation at the IAF as in comparison to rhythmic stimulation of control frequencies. In the fMRI, we found increased functional connectivity for stimulation at the IAF in visual and parietal areas as compared to other rhythmic control frequencies by correlating time courses from a set of regions of interest for the different stimulation conditions and applying network-based statistics. This suggests that rhythmic stimulation at the IAF frequency induces a higher degree of synchronicity of neural activity across the occipital and parietal cortex, which supports the role of the alpha oscillation in gating information flow during visual processing.

Contralateral delay activity, but not alpha lateralization, indexes prioritization of information for working memory storage

Working memory is inherently limited, which makes it important to select and maintain only task-relevant information and to protect it from distraction. Previous research has suggested the contralateral delay activity (CDA) and lateralized alpha oscillations as neural candidates for such a prioritization process. While most of this work focused on distraction during encoding, we examined the effect of external distraction presented during memory maintenance. Participants memorized the orientations of three lateralized objects. After an initial distraction-free maintenance interval, distractors appeared in the same location as the targets or in the opposite hemifield. This distraction was followed by another distraction-free interval. Our results show that CDA amplitudes were stronger in the interval before compared with the interval after the distraction (i.e., CDA amplitudes were stronger in response to targets compared with distractors). This amplitude reduction in response to distractors was more pronounced in participants with higher memory accuracy, indicating prioritization and maintenance of relevant over irrelevant information. In contrast, alpha lateralization did not change from the interval before distraction compared with the interval after distraction, and we found no correlation between alpha lateralization and memory accuracy. These results suggest that alpha lateralization plays no direct role in either selective maintenance of task-relevant information or inhibition of distractors. Instead, alpha lateralization reflects the current allocation of spatial attention to the most salient information regardless of task-relevance. In contrast, CDA indicates flexible allocation of working memory resources depending on task-relevance.

Cortex-wide spontaneous activity non-linearly steers propagating sensory-evoked activity in awake mice

The brain responds highly variably to identical sensory inputs, but there is no consensus on the nature of this variability. We explore this question using cortex-wide optical voltage imaging and whisker stimulation in awake mice. Clustering analysis reveals that the sensory-evoked activity propagates over the cortex via distinct pathways associated with distinct behavioral states. The pathway taken by each trial is independent of the level of primary sensory-evoked activation but is partially predictable by the spatiotemporal features of the preceding cortical spontaneous activity patterns. The sensory inputs reduce trial-to-trial variability in brain activity and alter temporal autocorrelation in spatial activity pattern evolutions, suggesting non-linear interactions between evoked activities and spontaneous activities. Further, evoked activities and spontaneous activities occupy different positions in the state space, suggesting that sensory inputs can intricately interact with the internal state to generate large-scale evoked activity patterns not frequented by spontaneous brain states.

Slow fluctuations in ongoing brain activity decrease in amplitude with ageing yet their impact on task-related evoked responses is dissociable from behavior

In humans, ageing is characterized by decreased brain signal variability and increased behavioral variability. To understand how reduced brain variability segregates with increased behavioral variability, we investigated the association between reaction time variability, evoked brain responses and ongoing brain signal dynamics, in young (N=36) and older adults (N=39). We studied the electroencephalogram (EEG) and pupil size fluctuations to characterize the cortical and arousal responses elicited by a cued go/no-go task. Evoked responses were strongly modulated by slow (<2 Hz) fluctuations of the ongoing signals, which presented reduced power in the older participants. Although variability of the evoked responses was lower in the older participants, once we adjusted for the effect of the ongoing signal fluctuations, evoked responses were equally variable in both groups. Moreover, the modulation of the evoked responses caused by the ongoing signal fluctuations had no impact on reaction time, thereby explaining why although ongoing brain signal variability is decreased in older individuals, behavioral variability is not. Finally, we showed that adjusting for the effect of the ongoing signal was critical to unmask the link between neural responses and behavior as well as the link between task-related evoked EEG and pupil responses.

Spontaneous alpha-band amplitude predicts subjective visibility but not discrimination accuracy during high-level perception

Near-threshold perception is a paradigm case of awareness diverging from reality - the perception of an unchanging stimulus can vacillate from undetected to clearly perceived. The amplitude of low-frequency brain oscillations - particularly in the alpha-band (8-13 Hz) - has emerged as a reliable predictor of trial-to-trial variability in perceptual decisions based on simple, low-level stimuli. Here, we addressed the question of how spontaneous oscillatory amplitude impacts subjective and objective aspects of perception using high-level visual stimuli. Human observers completed a near-threshold face/house discrimination task with subjective visibility ratings while electroencephalograms (EEG) were recorded. Using single-trial multiple regression analysis, we found that spontaneous fluctuations in prestimulus alpha-band amplitude were negatively related to visibility judgments but did not predict trial-by-trial accuracy. These results extend previous findings that indicate that strong prestimulus alpha diminishes subjective perception without affecting the accuracy or sensitivity (d') of perceptual decisions into the domain of high-level perception.

Non-additive activity modulation during a decision making task involving tactic selection

Human brain imaging has revealed that stimulus-induced activity does generally not simply add to the pre-stimulus activity, but rather builds in a non-additive way on this activity. Here we investigate this subject at the single neuron level and address the question whether and to what extent a strong form of non-additivity where activity drops post-cue is present in different areas of monkey cortex, including prefrontal and agranular frontal areas, during a perceptual decision making task involving action and tactic selection. Specifically we analyze spike train data recorded in vivo from the posterior dorsomedial prefrontal cortex (pmPFC), the supplementary motor area (SMA) and the presupplementary motor area (pre-SMA). For each neuron, we compute the ratio of the trial-averaged pre-stimulus spike count to the trial-averaged post-stimulus count. We also perform the ratio and averaging procedures in reverse order. We find that the statistics of these quantities behave differently across areas. pmPFC involved in tactic selection shows stronger non-additivity compared to the two other areas which more generically just increase their firing rate pos-stimulus. pmPFC behaved more similarly to pre-SMA, a likely consequence of the reciprocal connections between these areas. The trial-averaged ratio statistic was reproduced by a surrogate inhomogeneous Poisson process in which the measured trial-averaged firing rate for a given neuron is used as its time-dependent rate. Principal component analysis (PCA) of the trial-averaged firing rates of neuronal ensembles further reveals area-specific time courses of response to the stimulus, including latency to peak neural response, for the typical population activity. Our work demonstrates subtle forms of area-specific non-additivity based on the fine variability structure of pre- and post-stimulus spiking activity on the single neuron level. It also reveals significant differences between areas for PCA and surrogate analysis, complementing previous observations of regional differences based solely on post-stimulus responses. Moreover, we observe regional differences in non-additivity which are related to the monkey's successful tactic selection and decision making.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-021-09702-0.

EEG functional brain connectivity strengthens with age during attentional processing to faces in children

Studying functional connectivity may generate clues to the maturational changes that occur in children, expressed by the dynamical organization of the functional network assessed by electroencephalographic recordings (EEG). In the present study, we compared the EEG functional connectivity pattern estimated by linear cross-correlations of the electrical brain activity of three groups of children (6, 8, and 10 years of age) while performing odd-ball tasks containing facial stimuli that are chosen considering their importance in socioemotional contexts in everyday life. On the first task, the children were asked to identify the sex of faces, on the second, the instruction was to identify the happy expressions of the faces. We estimated the stable correlation pattern (SCP) by the average cross-correlation matrix obtained separately for the resting state and the task conditions and quantified the similarity of these average matrices comparing the different conditions. The accuracy improved with higher age. Although the topology of the SCPs showed high similarity across all ages, the two older groups showed a higher correlation between regions associated with the attentional and face processing networks compared to the youngest group. Only in the youngest group, the similarity metric decreased during the sex condition. In general, correlation values strengthened with age and during task performance compared to rest. Our findings indicate that there is a spatially extended stable brain network organization in children like that reported in adults. Lower similarity scores between several regions in the youngest children might indicate a lesser ability to cope with tasks. The brain regions associated with the attention and face networks presented higher synchronization across regions with increasing age, modulated by task demands.

Respiration aligns perception with neural excitability

Recent studies from the field of interoception have highlighted the link between bodily and neural rhythms during action, perception, and cognition. The mechanisms underlying functional body-brain coupling, however, are poorly understood, as are the ways in which they modulate behavior. We acquired respiration and human magnetoencephalography data from a near-threshold spatial detection task to investigate the trivariate relationship between respiration, neural excitability, and performance. Respiration was found to significantly modulate perceptual sensitivity as well as posterior alpha power (8-13 Hz), a well-established proxy of cortical excitability. In turn, alpha suppression prior to detected versus undetected targets underscored the behavioral benefits of heightened excitability. Notably, respiration-locked excitability changes were maximized at a respiration phase lag of around -30° and thus temporally preceded performance changes. In line with interoceptive inference accounts, these results suggest that respiration actively aligns sampling of sensory information with transient cycles of heightened excitability to facilitate performance.

Ongoing neural oscillations influence behavior and sensory representations by suppressing neuronal excitability

The ability to process and respond to external input is critical for adaptive behavior. Why, then, do neural and behavioral responses vary across repeated presentations of the same sensory input? Ongoing fluctuations of neuronal excitability are currently hypothesized to underlie the trial-by-trial variability in sensory processing. To test this, we capitalized on intracranial electrophysiology in neurosurgical patients performing an auditory discrimination task with visual cues: specifically, we examined the interaction between prestimulus alpha oscillations, excitability, task performance, and decoded neural stimulus representations. We found that strong prestimulus oscillations in the alpha+ band (i.e., alpha and neighboring frequencies), rather than the aperiodic signal, correlated with a low excitability state, indexed by reduced broadband high-frequency activity. This state was related to slower reaction times and reduced neural stimulus encoding strength. We propose that the alpha+ rhythm modulates excitability, thereby resulting in variability in behavior and sensory representations despite identical input.

EEG measures for clinical research in major vascular cognitive impairment: recommendations by an expert panel

Vascular contribution to cognitive impairment (VCI) and dementia is related to etiologies that may affect the neurophysiological mechanisms regulating brain arousal and generating electroencephalographic (EEG) activity. A multidisciplinary expert panel reviewed the clinical literature and reached consensus about the EEG measures consistently found as abnormal in VCI patients with dementia. As compared to cognitively unimpaired individuals, those VCI patients showed (1) smaller amplitude of resting state alpha (8-12 Hz) rhythms dominant in posterior regions; (2) widespread increases in amplitude of delta (< 4 Hz) and theta (4-8 Hz) rhythms; and (3) delayed N200/P300 peak latencies in averaged event-related potentials, especially during the detection of auditory rare target stimuli requiring participants' responses in "oddball" paradigms. The expert panel formulated the following recommendations: (1) the above EEG measures are not specific for VCI and should not be used for its diagnosis; (2) they may be considered as "neural synchronization" biomarkers to enlighten the relationships between features of the VCI-related cerebrovascular lesions and abnormalities in neurophysiological brain mechanisms; and (3) they may be tested in future clinical trials as prognostic biomarkers and endpoints of interventions aimed at normalizing background brain excitability and vigilance in wakefulness.

Predictive visuo-motor communication through neural oscillations

The mechanisms coordinating action and perception over time are poorly understood. The sensory cortex needs to prepare for upcoming changes contingent on action, and this requires temporally precise communication that takes into account the variable delays between sensory and motor processing. Several theorists^1,2 have proposed synchronization of the endogenous oscillatory activity observed in most regions of the brain³ as the basis for an efficient and flexible communication protocol between distal brain areas,^2,4 a concept known as "communication through coherence." Synchronization of endogenous oscillations^5,6 occurs after a salient sensory stimulus, such as a flash or a sound,^7-11 and after a voluntary action,^12-18 and this directly impacts perception, causing performance to oscillate rhythmically over time. Here we introduce a novel fMRI paradigm to probe the neural sources of oscillations, based on the concept of perturbative signals, which overcomes the low temporal resolution of BOLD signals. The assumption is that a synchronized endogenous rhythm will modulate cortical excitability rhythmically, which should be reflected in the BOLD responses to brief stimuli presented at different phases of the oscillation cycle. We record rhythmic oscillations of V1 BOLD synchronized by a simple voluntary action, in phase with behaviorally measured oscillations in visual sensitivity in the theta range. The functional connectivity between V1 and M1 also oscillates at the same rhythm. By demonstrating oscillatory temporal coupling between primary motor and sensory cortices, our results strongly implicate communication through coherence to achieve precise coordination and to encode sensory-motor timing.

Data-driven beamforming technique to attenuate ballistocardiogram artefacts in electroencephalography-functional magnetic resonance imaging without detecting cardiac pulses in electrocardiography recordings

Simultaneous recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a very promising non‐invasive neuroimaging technique. However, EEG data obtained from the simultaneous EEG–fMRI are strongly influenced by MRI‐related artefacts, namely gradient artefacts (GA) and ballistocardiogram (BCG) artefacts. When compared to the GA correction, the BCG correction is more challenging to remove due to its inherent variabilities and dynamic changes over time. The standard BCG correction (i.e., average artefact subtraction [AAS]), require detecting cardiac pulses from simultaneous electrocardiography (ECG) recording. However, ECG signals are also distorted and will become problematic for detecting reliable cardiac peaks. In this study, we focused on a beamforming spatial filtering technique to attenuate all unwanted source activities outside of the brain. Specifically, we applied the beamforming technique to attenuate the BCG artefact in EEG–fMRI, and also to recover meaningful task‐based neural signals during an attentional network task (ANT) which required participants to identify visual cues and respond accurately. We analysed EEG–fMRI data in 20 healthy participants during the ANT, and compared four different BCG corrections (non‐BCG corrected, AAS BCG corrected, beamforming + AAS BCG corrected, beamforming BCG corrected). We demonstrated that the beamforming approach did not only significantly reduce the BCG artefacts, but also significantly recovered the expected task‐based brain activity when compared to the standard AAS correction. This data‐driven beamforming technique appears promising especially for longer data acquisition of sleep and resting EEG–fMRI. Our findings extend previous work regarding the recovery of meaningful EEG signals by an optimized suppression of MRI‐related artefacts.

Pre-stimulus alpha predicts inattentional blindness

Pre- and post-stimulus oscillatory activity between 8 and 12 hertz, referred to as the alpha-band, correlates with conscious visual awareness of stimuli across a variety of psychophysical tasks. Within an EEG-adapted inattentional blindness task, the current study sought to examine whether this relationship holds for conscious awareness of stimuli under conditions of inattentional blindness. Noticing rates of the task-irrelevant unexpected stimulus were correlated with a significant decrease in alpha power over bilateral parietal-occipital areas during the pre-stimulus interval, and a significant decrease in alpha power over parietal-occipital regions in the right hemisphere during the post-stimulus interval. Findings are taken to imply alpha-band neural activity represents a valid correlate of consciousness that is not confounded by task relevancy or the need for report.

Imperceptible Somatosensory Single Pulse and Pulse Train Stimulation Oppositely Modulate Mu Rhythm Activity and Perceptual Performance

Subliminal stimulation alters conscious perception – a potential mechanism is the modulation of cortical background rhythms especially in the alpha range. Here, in the human somatosensory domain, we assessed effects of subthreshold (imperceptible) electrical finger nerve stimulation – either presented as single pulses or as brief (1 s) 7 Hz pulse trains—on mu-alpha rhythm and perceptual performance. In electroencephalography, subthreshold single pulses transiently (~150–350 ms poststimulus) increased mu activity (event-related synchronization), while, interestingly, subthreshold trains led to prolonged (>1 s) mu desynchronization. In psychophysics, detection of near-threshold target stimuli was consistently reduced when presented together with subthreshold trains (at three delays), whereas for targets paired with subthreshold single pulses detection remained unaffected (30 and 180 ms) or was even elevated (60 ms). Though both imperceptible, single pulses and pulse trains exerted opposite effects on neural signaling and perception. We suggest that the common neural basis is preferential activation of cortical inhibitory interneurons. While the inhibitory impact of a subthreshold single pulse (reflected by mu synchronization) is not psychophysically detectable—rather perception may be facilitated—repetition of the same subthreshold pulse shifts the excitation-inhibition balance toward an inhibitory cortical state (reflected by perceptual impediment) accompanied by mu desynchronization. These differential findings provide novel insights on the notion of alpha activity mediating functional inhibition.

How Alpha Rhythm Spatiotemporally Acts Upon the Thalamus-Default Mode Circuit in Idiopathic Generalized Epilepsy

GOAL

Idiopathic generalized epilepsy (IGE) represents generalized spike-wave discharges (GSWD) and distributed changes in thalamocortical circuit. The purpose of this study is to investigate how the ongoing alpha oscillation acts upon the local temporal dynamics and spatial hyperconnectivity in epilepsy.

METHODS

We evaluated the spatiotemporal regulation of alpha oscillations in epileptic state based on simultaneous EEG-fMRI recordings in 45 IGE patients. The alpha-BOLD temporal consistency, as well as the effect of alpha power windows on dynamic functional connectivity strength (dFCS) was analyzed. Then, stable synchronization networks during GSWD were constructed, and the spatial covariation with alpha-based network integration was investigated.

RESULTS

Increased temporal covariation was demonstrated between alpha power and BOLD fluctuations in thalamus and distributed cortical regions in IGE. High alpha power had inhibition effect on dFCS in healthy controls, while in epilepsy, high alpha windows arose along with the enhancement of dFCS in thalamus, caudate and some default mode network (DMN) regions. Moreover, synchronization networks in GSWD-before, GSWD-onset and GSWD-after stages were constructed, and the connectivity strength in prominent hub nodes (precuneus, thalamus) was associated with the spatially disturbed alpha-based network integration.

CONCLUSION

The results indicated spatiotemporal regulation of alpha in epilepsy by means of the increased power and decreased coherence communication. It provided links between alpha rhythm and the altered temporal dynamics, as well as the hyperconnectivity in thalamus-default mode circuit.

SIGNIFICANCE

The combination between neural oscillations and epileptic representations may be of clinical importance in terms of seizure prediction and non-invasive interventions.

A multimodal magnetoencephalography 7 T fMRI and 7 T proton MR spectroscopy study in first episode psychosis

We combined magnetoencephalography (MEG), 7 T proton magnetic resonance spectroscopy (MRS), and 7 T fMRI during performance of a task in a group of 23 first episode psychosis (FEP) patients and 26 matched healthy controls (HC). We recorded both the auditory evoked response to 40 Hz tone clicks and the resting state in MEG. Neurometabolite levels were obtained from the anterior cingulate cortex (ACC). The fMRI BOLD response was obtained during the Stroop inhibitory control task. FEP showed a significant increase in resting state low frequency theta activity (p < 0.05; Cohen d = 0.69), but no significant difference in the 40 Hz auditory evoked response compared to HC. An across-groups whole brain analysis of the fMRI BOLD response identified eight regions that were significantly activated during task performance (p < 0.01, FDR-corrected); the mean signal extracted from those regions was significantly different between the groups (p = 0.0006; d = 1.19). In the combined FEP and HC group, there was a significant correlation between the BOLD signal during task performance and MEG resting state low frequency activity (p < 0.05). In FEP, we report significant alteration in resting state low frequency MEG activity, but no alterations in auditory evoked gamma band response, suggesting that the former is a more robust biomarker of early psychosis. There were no correlations between gamma oscillations and GABA levels in either HC or FEP. Finally, in this study, each of the three imaging modalities differentiated FEP from HC; fMRI with good and MEG and MRS with moderate effect size.

Resolving the Connectome, Spectrally-Specific Functional Connectivity Networks and Their Distinct Contributions to Behavior

The resting human brain exhibits spontaneous patterns of activity that reflect features of the underlying neural substrate. Examination of interareal coupling of resting-state oscillatory activity has revealed that the brain’s resting activity is composed of functional networks, whose topographies differ depending on oscillatory frequency, suggesting a role for carrier frequency as a means of creating multiplexed, or functionally segregated, communication channels between brain areas. Using canonical correlation analysis (CCA), we examined spectrally resolved resting-state connectivity patterns derived from magnetoencephalography (MEG) recordings to determine the relationship between connectivity intrinsic to different frequency channels and a battery of over a hundred behavioral and demographic indicators, in a group of 89 young healthy participants. We demonstrate that each of the classical frequency bands in the range 1–40 Hz (δ, θ, α, β, and γ) delineates a subnetwork that is behaviorally relevant, spatially distinct, and whose expression is either negatively or positively predictive of individual traits, with the strongest link in the α-band being negative and networks oscillating at different frequencies, such as θ, β, and γ carrying positive function.

Temporal Signatures of Criticality in Human Cortical Excitability as Probed by Early Somatosensory Responses

Brain responses vary considerably from moment to moment, even to identical sensory stimuli. This has been attributed to changes in instantaneous neuronal states determining the system's excitability. Yet the spatiotemporal organization of these dynamics remains poorly understood. Here we test whether variability in stimulus-evoked activity can be interpreted within the framework of criticality, which postulates dynamics of neural systems to be tuned toward the phase transition between stability and instability as is reflected in scale-free fluctuations in spontaneous neural activity. Using a novel noninvasive approach in 33 male human participants, we tracked instantaneous cortical excitability by inferring the magnitude of excitatory postsynaptic currents from the N20 component of the somatosensory evoked potential. Fluctuations of cortical excitability demonstrated long-range temporal dependencies decaying according to a power law across trials, a hallmark of systems at critical states. As these dynamics covaried with changes in prestimulus oscillatory activity in the alpha band (8-13 Hz), we establish a mechanistic link between ongoing and evoked activity through cortical excitability and argue that the co-emergence of common temporal power laws may indeed originate from neural networks poised close to a critical state. In contrast, no signatures of criticality were found in subcortical or peripheral nerve activity. Thus, criticality may represent a parsimonious organizing principle of variability in stimulus-related brain processes on a cortical level, possibly reflecting a delicate equilibrium between robustness and flexibility of neural responses to external stimuli.SIGNIFICANCE STATEMENT Variability of neural responses in primary sensory areas is puzzling, as it is detrimental to the exact mapping between stimulus features and neural activity. However, such variability can be beneficial for information processing in neural networks if it is of a specific nature, namely, if dynamics are poised at a so-called critical state characterized by a scale-free spatiotemporal structure. Here, we demonstrate the existence of a link between signatures of criticality in ongoing and evoked activity through cortical excitability, which fills the long-standing gap between two major directions of research on neural variability: the impact of instantaneous brain states on stimulus processing on the one hand and the scale-free organization of spatiotemporal network dynamics of spontaneous activity on the other.

Intraindividual Characterization of the Sleep Spindle Variability in Healthy Subjects

Spatial and frequency characterization of sleep spindles have been extensively addressed using M/EEG or fMRI recordings. However, its intraindividual variability across time has not been addressed. Here we propose to assess the intraindividual variability of sleep spindles in a time-resolved way by means of a trial-to-trial-variability (TTV) measure. For that purpose, the EEG of 26 healthy subjects were recorded overnight. After an exhaustive preprocessing pipeline to remove artifacts, spindles were automatically detected using a complex demodulation-based method. Then, the Wavelet Scalogram was estimated to validate it. Spindle TTV of each participant was also computed for all the conventional EEG frequency bands. Root mean square (RMS) of each TTV signal was calculated as a measure of the total variability of each spindle. Results showed significant differences in the variability between frequencies. Specifically, RMS in the beta-1 frequency band showed higher values as compared to all the other frequency bands (p<0.001). TTV curves showed a dichotomic trend, with lower frequencies showing an increase in the variability before the spindle onset, and higher frequencies showing such increase after the onset. The dependence of the spindle variability with the frequency could be explained by the influence of the multiple cortical generators involved.Clinical Relevance- Sleep spindles are similarly affected in different cognitive-related disorders, which supports the relevance of assessing abnormal sleep patterns as a possible cause for such cognitive deficits.

Spontaneous Brain Oscillations and Perceptual Decision-Making

Making rapid decisions on the basis of sensory information is essential to everyday behaviors. Why, then, are perceptual decisions so variable despite unchanging inputs? Spontaneous neural oscillations have emerged as a key predictor of trial-to-trial perceptual variability. New work casting these effects in the framework of models of perceptual decision-making has driven novel insight into how the amplitude of spontaneous oscillations impact decision-making. This synthesis reveals that the amplitude of ongoing low-frequency oscillations (<30 Hz), particularly in the alpha-band (8-13 Hz), bias sensory responses and change conscious perception but not, surprisingly, the underlying sensitivity of perception. A key model-based insight is that various decision thresholds do not adapt to alpha-related changes in sensory activity, demonstrating a seeming suboptimality of decision mechanisms in tracking endogenous changes in sensory responses.

Model-based whole-brain effective connectivity to study distributed cognition in health and disease

Neuroimaging techniques are now widely used to study human cognition. The functional associations between brain areas have become a standard proxy to describe how cognitive processes are distributed across the brain network. Among the many analysis tools available, dynamic models of brain activity have been developed to overcome the limitations of original connectivity measures such as functional connectivity. This goes in line with the many efforts devoted to the assessment of directional interactions between brain areas from the observed neuroimaging activity. This opinion article provides an overview of our model-based whole-brain effective connectivity to analyze fMRI data, while discussing the pros and cons of our approach with respect to other established approaches. Our framework relies on the multivariate Ornstein-Uhlenbeck (MOU) process and is thus referred to as MOU-EC. Once tuned, the model provides a directed connectivity estimate that reflects the dynamical state of BOLD activity, which can be used to explore cognition. We illustrate this approach using two applications on task-evoked fMRI data. First, as a connectivity measure, MOU-EC can be used to extract biomarkers for task-specific brain coordination, understood as the patterns of areas exchanging information. The multivariate nature of connectivity measures raises several challenges for whole-brain analysis, for which machine-learning tools present some advantages over statistical testing. Second, we show how to interpret changes in MOU-EC connections in a collective and model-based manner, bridging with network analysis. Our framework provides a comprehensive set of tools that open exciting perspectives to study distributed cognition, as well as neuropathologies.

Interactions between stimuli-evoked cortical activity and spontaneous low frequency oscillations measured with neuronal calcium

Spontaneous brain activity has been widely used to map brain connectivity. The interactions between task-evoked brain responses and the spontaneous cortical oscillations, especially within the low frequency range of ~0.1 ​Hz, are not fully understood. Trial-to-trial variabilities in brain's response to sensory stimuli and the ability for brain to detect under noisy conditions suggest an appreciable impact of the brain state. Using a multimodality imaging platform, we simultaneously imaged neuronal Ca²⁺ and cerebral hemodynamics at baseline and in response to single-pulse forepaw stimuli in rat's somatosensory cortex. The high sensitivity of this system enables detection of responses to very weak and strong stimuli and real time determination of low frequency oscillations without averaging. Results show that the ongoing neuronal oscillations inversely modulate Ca²⁺ transients evoked by sensory stimuli. High intensity stimuli reset the spontaneous neuronal oscillations to an unpreferable excitability following the stimulus. Cerebral hemodynamic responses also inversely interact with the spontaneous hemodynamic oscillations, correlating with the neuronal Ca²⁺ transient changes. The results reveal competing interactions between spontaneous oscillations and stimulation-evoked brain activities in somatosensory cortex and the resultant hemodynamics.

The Relationship between Trial-by-Trial Variability and Oscillations of Cortical Population Activity

Neural activity fluctuates over time, creating considerable variability across trials. This trial-by-trial neural variability is dramatically reduced (“quenched”) after the presentation of sensory stimuli. Likewise, the power of neural oscillations, primarily in the alpha-beta band, is also reduced after stimulus onset. Despite their similarity, these phenomena have so far been studied and discussed independently. We hypothesized that the two phenomena are tightly coupled in electrophysiological recordings of large cortical neural populations. To test this, we examined magnetoencephalography (MEG) recordings of healthy subjects viewing repeated presentations of a visual stimulus. The timing, amplitude, and spatial topography of variability-quenching and power-suppression were remarkably similar. Neural variability quenching was eliminated by excluding the alpha-beta band from the recordings, but not by excluding other frequency-bands. Moreover, individual magnitudes of alpha-beta band-power explained 86% of between-subject differences in variability quenching. An alternative mechanism that may generate variability quenching is increased phase alignment across trials. However, changes in inter-trial-phase-coherence (ITPC) exhibited distinct timing and no correlations with the magnitude of variability quenching in individual participants. These results reveal that neural variability quenching is tightly coupled with stimulus-induced changes in the power of alpha-beta band oscillations, associating two phenomena that have so far been studied in isolation.

A New Modular Brain Organization of the BOLD Signal during Natural Vision

The resting blood oxygen level-dependent (BOLD) signal is synchronized in large-scale brain networks (resting-state networks, RSNs) defined by interregional temporal correlations (functional connectivity, FC). RSNs are thought to place strong constraints on task-evoked processing since they largely match the networks observed during task performance. However, this result may simply reflect the presence of spontaneous activity during both rest and task. Here, we examined the BOLD network structure of natural vision, as simulated by viewing of movies, using procedures that minimized the contribution of spontaneous activity. We found that the correlation between resting and movie-evoked FC (ρ = 0.60) was lower than previously reported. Hierarchical clustering and graph-theory analyses indicated a well-defined network structure during natural vision that differed from the resting structure, and emphasized functional groupings adaptive for natural vision. The visual network merged with a network for navigation, scene analysis, and scene memory. Conversely, the dorsal attention network was split and reintegrated into 2 groupings likely related to vision/scene and sound/action processing. Finally, higher order groupings from the clustering analysis combined internally directed and externally directed RSNs violating the large-scale distinction that governs resting-state organization. We conclude that the BOLD FC evoked by natural vision is only partly constrained by the resting network structure.

Task-evoked Negative BOLD Response and Functional Connectivity in the Default Mode Network are Representative of Two Overlapping but Separate Neurophysiological Processes

The topography of the default mode network (DMN) can be obtained with one of two different functional magnetic resonance imaging (fMRI) methods: either from the spontaneous but organized synchrony of the low-frequency fluctuations in resting-state fMRI (rs-fMRI), known as "functional connectivity", or from the consistent and robust deactivations in task-based fMRI (tb-fMRI), here referred to as the "negative BOLD response" (NBR). These two methods are fundamentally different, but their results are often used interchangeably to describe the brain's resting-state, baseline, or intrinsic activity. While the DMN was initially defined by consistent task-based decreases in blood flow in a set of specific brain regions using PET imaging, recently nearly all studies on the DMN employ functional connectivity in rs-fMRI. In this study, we first show the high level of spatial overlap between NBR and functional connectivity of the DMN extracted from the same tb-fMRI scan; then, we demonstrate that the NBR in putative DMN regions can be significantly altered without causing any change in their overlapping functional connectivity. Furthermore, we present evidence that in the DMN, the NBR is more closely related to task performance than the functional connectivity. We conclude that the NBR and functional connectivity of the DMN reflect two separate but overlapping neurophysiological processes, and thus should be differentiated in studies investigating brain-behavior relationships in both healthy and diseased populations. Our findings further raise the possibility that the macro-scale networks of the human brain might internally exhibit a hierarchical functional architecture.

Multiple mechanisms link prestimulus neural oscillations to sensory responses

Spontaneous fluctuations of neural activity may explain why sensory responses vary across repeated presentations of the same physical stimulus. To test this hypothesis, we recorded electroencephalography in humans during stimulation with identical visual stimuli and analyzed how prestimulus neural oscillations modulate different stages of sensory processing reflected by distinct components of the event-related potential (ERP). We found that strong prestimulus alpha- and beta-band power resulted in a suppression of early ERP components (C1 and N150) and in an amplification of late components (after 0.4 s), even after controlling for fluctuations in 1/f aperiodic signal and sleepiness. Whereas functional inhibition of sensory processing underlies the reduction of early ERP responses, we found that the modulation of non-zero-mean oscillations (baseline shift) accounted for the amplification of late responses. Distinguishing between these two mechanisms is crucial for understanding how internal brain states modulate the processing of incoming sensory information.

Mechanisms Underlying Visuospatial Working Memory Impairments in Schizophrenia

Working memory deficits are observed in the vast majority of individuals diagnosed with schizophrenia and those at risk for the disorder. Working memory impairments are present during the prodromal stage and persist throughout the course of schizophrenia. Given the importance of cognition in functional outcome, working memory deficits are an important therapeutic target for schizophrenia. This chapter examines mechanisms underlying working memory deficits in schizophrenia, focusing on the roles of perception and attention in the encoding process. Lastly, we present a comprehensive discussion of neural oscillation and internal noise in the context of the etiology of working memory deficits in schizophrenia and introduce noninvasive treatment strategies that could improve encoding processes.

Disrupted neural variability during propofol-induced sedation and unconsciousness

Variability quenching is a widespread neural phenomenon in which trial-to-trial variability (TTV) of neural activity is reduced by repeated presentations of a sensory stimulus. However, its neural mechanism and functional significance remain poorly understood. Recurrent network dynamics are suggested as a candidate mechanism of TTV, and they play a key role in consciousness. We thus asked whether the variability-quenching phenomenon is related to the level of consciousness. We hypothesized that TTV reduction would be compromised during reduced level of consciousness by propofol anesthetics. We recorded functional magnetic resonance imaging signals of resting-state and stimulus-induced activities in three conditions: wakefulness, sedation, and unconsciousness (i.e., deep anesthesia). We measured the average (trial-to-trial mean, TTM) and variability (TTV) of auditory stimulus-induced activity under the three conditions. We also examined another form of neural variability (temporal variability, TV), which quantifies the overall dynamic range of ongoing neural activity across time, during both the resting-state and the task. We found that (a) TTM deceased gradually from wakefulness through sedation to anesthesia, (b) stimulus-induced TTV reduction normally seen during wakefulness was abolished during both sedation and anesthesia, and (c) TV increased in the task state as compared to resting-state during both wakefulness and sedation, but not anesthesia. Together, our results reveal distinct effects of propofol on the two forms of neural variability (TTV and TV). They imply that the anesthetic disrupts recurrent network dynamics, thus prevents the stabilization of cortical activity states. These findings shed new light on the temporal dynamics of neuronal variability and its alteration during anesthetic-induced unconsciousness.

Task-evoked functional connectivity does not explain functional connectivity differences between rest and task conditions

During complex tasks, patterns of functional connectivity differ from those in the resting state. However, what accounts for such differences remains unclear. Brain activity during a task reflects an unknown mixture of spontaneous and task-evoked activities. The difference in functional connectivity between a task state and the resting state may reflect not only task-evoked functional connectivity, but also changes in spontaneously emerging networks. Here, we characterized the differences in apparent functional connectivity between the resting state and when human subjects were watching a naturalistic movie. Such differences were marginally explained by the task-evoked functional connectivity involved in processing the movie content. Instead, they were mostly attributable to changes in spontaneous networks driven by ongoing activity during the task. The execution of the task reduced the correlations in ongoing activity among different cortical networks, especially between the visual and non-visual sensory or motor cortices. Our results suggest that task-evoked activity is not independent from spontaneous activity, and that engaging in a task may suppress spontaneous activity and its inter-regional correlation.

Moment-to-Moment Fluctuations in Neuronal Excitability Bias Subjective Perception Rather than Strategic Decision-Making

Perceiving an external stimulus depends not only on the physical features of the stimulus, but also fundamentally on the current state of neuronal excitability, indexed by the power of ongoing alpha-band and beta-band oscillations (8-30 Hz). Recent studies suggest that heightened excitability does not improve perceptual precision, but biases observers to report the presence of a stimulus regardless of its physical presence. It is unknown whether this bias is due to changes in observers' subjective perceptual experience (perceptual bias) or their perception-independent decision-making strategy (decision bias). We tested these alternative interpretations in an EEG experiment in which male and female human participants performed two-interval forced choice (2IFC) detection and discrimination. According to signal detection theory, perceptual bias only affects 2IFC detection, but not discrimination, while interval decision bias should be task independent. We found that correct detection was more likely when excitability before the stimulus-present interval exceeded that before the stimulus-absent interval (i.e., 8-17 Hz power was weaker before the stimulus-present interval), consistent with an effect of excitability on perceptual bias. By contrast, discrimination accuracy was unaffected by excitability fluctuations between intervals, ruling out an effect on interval decision bias. We conclude that the current state of neuronal excitability biases the perceptual experience itself, rather than the decision process.

Beyond the evoked/intrinsic neural process dichotomy

Contemporary functional neuroimaging research has increasingly focused on characterization of intrinsic or "spontaneous" brain activity. Analysis of intrinsic activity is often contrasted with analysis of task-evoked activity that has traditionally been the focus of cognitive neuroscience. But does this evoked/intrinsic dichotomy adequately characterize human brain function? Based on empirical data demonstrating a close functional interdependence between intrinsic and task-evoked activity, we argue that the dichotomy between intrinsic and task-evoked activity as unobserved contributions to brain activity is artificial. We present an alternative picture of brain function in which the brain's spatiotemporal dynamics do not consist of separable intrinsic and task-evoked components, but reflect the enaction of a system of mutual constraints to move the brain into and out of task-appropriate functional configurations. According to this alternative picture, cognitive neuroscientists are tasked with describing both the temporal trajectory of brain activity patterns across time, and the modulation of this trajectory by task states, without separating this process into intrinsic and task-evoked components. We argue that this alternative picture of brain function is best captured in a novel explanatory framework called enabling constraint. Overall, these insights call for a reconceptualization of functional brain activity, and should drive future methodological and empirical efforts.

Is There a Nonadditive Interaction Between Spontaneous and Evoked Activity? Phase-Dependence and Its Relation to the Temporal Structure of Scale-Free Brain Activity

The aim of our study was to use functional magnetic resonance imaging to investigate how spontaneous activity interacts with evoked activity, as well as how the temporal structure of spontaneous activity, that is, long-range temporal correlations, relate to this interaction. Using an extremely sparse event-related design (intertrial intervals: 52-60 s), a novel blood oxygen level-dependent signal correction approach (accounting for spontaneous fluctuations using pseudotrials) and phase analysis, we provided direct evidence for a nonadditive interaction between spontaneous and evoked activity. We demonstrated the discrepancy between the present and previous observations on why a linear superposition between spontaneous and evoked activity can be seen by using co-occurring signals from homologous brain regions. Importantly, we further demonstrated that the nonadditive interaction can be characterized by phase-dependent effects of spontaneous activity, which is closely related to the degree of long-range temporal correlations in spontaneous activity as indexed by both power-law exponent and phase-amplitude coupling. Our findings not only contribute to the understanding of spontaneous brain activity and its scale-free properties, but also bear important implications for our understanding of neural activity in general.

Exploring the origins of EEG motion artefacts during simultaneous fMRI acquisition: Implications for motion artefact correction

Motion artefacts (MAs) are induced within EEG data collected simultaneously with fMRI when the subject's head rotates relative to the magnetic field. The effects of these artefacts have generally been ameliorated by removing periods of data during which large artefact voltages appear in the EEG traces. However, even when combined with other standard post-processing methods, this strategy does not remove smaller MAs which can dominate the neuronal signals of interest. A number of methods are therefore being developed to characterise the MA by measuring reference signals and then using these in artefact correction. These methods generally assume that the head and EEG cap, plus any attached sensors, form a rigid body which can be characterised by a standard set of six motion parameters. Here we investigate the motion of the head/EEG cap system to provide a better understanding of MAs. We focus on the reference layer artefact subtraction (RLAS) approach, as this allows measurement of a separate reference signal for each electrode that is being used to measure brain activity.

Through a series of experiments on phantoms and subjects, we find that movement of the EEG cap relative to the phantom and skin on the forehead is relatively small and that this non-rigid body movement does not appear to cause considerable discrepancy in artefacts between the scalp and reference signals. However, differences in the amplitude of these signals is observed which may be due to differences in geometry of the system from which the reference signals are measured compared with the brain signals. In addition, we find that there is non-rigid body movement of the skull and skin which produces an additional MA component for a head shake, which is not present for a head nod. This results in a large discrepancy in the amplitude and temporal profile of the MA measured on the scalp and reference layer, reducing the efficacy of MA correction based on the reference signals.

Together our data suggest that the efficacy of the correction of MA using any reference-based system is likely to differ for different types of head movement with head shake being the hardest to correct. This provides new information to inform the development of hardware and post-processing methods for removing MAs from EEG data acquired simultaneously with fMRI data.

Behavioral preference in sequential decision-making and its association with anxiety

In daily life, people often make consecutive decisions before the ultimate goal is reached (i.e., sequential decision-making). However, this kind of decision-making has been largely overlooked in the literature. The current study investigated whether behavioral preference would change during sequential decisions, and the neural processes underlying the potential changes. For this purpose, we revised the classic balloon analogue risk task and recorded the electroencephalograph (EEG) signals associated with each step of decision-making. Independent component analysis performed on EEG data revealed that four EEG components elicited by periodic feedback in the current step predicted participants' decisions (gamble vs. no gamble) in the next step. In order of time sequence, these components were: bilateral occipital alpha rhythm, bilateral frontal theta rhythm, middle frontal theta rhythm, and bilateral sensorimotor mu rhythm. According to the information flows between these EEG oscillations, we proposed a brain model that describes the temporal dynamics of sequential decision-making. Finally, we found that the tendency to gamble (as well as the power intensity of bilateral frontal theta rhythms) was sensitive to the individual level of trait anxiety in certain steps, which may help understand the role of emotion in decision-making.

Individual Differences in Reading Skill Are Related to Trial-by-Trial Neural Activation Variability in the Reading Network

Recent work has suggested that variability in levels of neural activation may be related to behavioral and cognitive performance across a number of domains and may offer information that is not captured by more traditional measures that use the average level of brain activation. We examined the relationship between reading skill in school-aged children and neural activation variability during a functional MRI reading task after taking into account average levels of activity. The reading task involved matching printed and spoken words to pictures of items. Single trial activation estimates were used to calculate the mean and standard deviation of children's responses to print and speech stimuli; multiple regression analyses evaluated the relationship between reading skill and trial-by-trial activation variability. The reliability of observed findings from the discovery sample (n = 44; ages 8-11; 18 female) was then confirmed in an independent sample of children (n = 32; ages 8-11; 14 female). Across the two samples, reading skill was positively related to trial-by-trial variability in the activation response to print in the left inferior frontal gyrus pars triangularis. This relationship held even when accounting for mean levels of activation. This finding suggests that intrasubject variability in trial-by-trial fMRI activation responses to printed words accounts for individual differences in human reading ability that are not fully captured by traditional mean levels of brain activity. Furthermore, this positive relationship between trial-by-trial activation variability and reading skill may provide evidence that neural variability plays a beneficial role during early reading development.SIGNIFICANCE STATEMENT Recent work has suggested that neural activation variability, or moment-to-moment changes in the engagement of brain regions, is related to individual differences in behavioral and cognitive performance across multiple domains. However, differences in neural activation variability have not yet been evaluated in relation to reading skill. In the current study, we analyzed data from two independent groups of children who performed an fMRI task involving reading and listening to words. Across both samples, reading skill was positively related to trial-by-trial variability in activation to print stimuli in the left inferior frontal gyrus pars triangularis, even when accounting for the more conventional measure of mean levels of brain activity. This finding suggests that neural variability could be beneficial in developing readers.

Exploring the advantages of multiband fMRI with simultaneous EEG to investigate coupling between gamma frequency neural activity and the BOLD response in humans

We established an optimal combination of EEG recording during sparse multiband (MB) fMRI that preserves high-resolution, whole-brain fMRI coverage while enabling broad-band EEG recordings which are uncorrupted by MRI gradient artefacts (GAs). We first determined the safety of simultaneous EEG recording during MB fMRI. Application of MB factor = 4 produced <1°C peak heating of electrode/hardware during 20 min of GE-EPI data acquisition. However, higher SAR sequences require specific safety testing, with greater heating observed using PCASL with MB factor = 4. Heating was greatest in the electrocardiogram channel, likely due to it possessing longest lead length. We investigated the effect of MB factor on the temporal signal-to-noise ratio for a range of GE-EPI sequences (varying MB factor and temporal interval between slice acquisitions). We found that, for our experimental purpose, the optimal acquisition was achieved with MB factor = 3, 3mm isotropic voxels, and 33 slices providing whole head coverage. This sequence afforded a 2.25 s duration quiet period (without GAs) in every 3 s TR. Using this sequence, we demonstrated the ability to record gamma frequency (55-80 Hz) EEG oscillations, in response to right index finger abduction, that are usually obscured by GAs during continuous fMRI data acquisition. In this novel application of EEG-MB fMRI to a motor task, we observed a positive correlation between gamma and BOLD responses in bilateral motor regions. These findings support and extend previous work regarding coupling between neural and hemodynamic measures of brain activity in humans and showcase the utility of EEG-MB fMRI for future investigations.

Inferring multi-scale neural mechanisms with brain network modelling

The neurophysiological processes underlying non-invasive brain activity measurements are incompletely understood. Here, we developed a connectome-based brain network model that integrates individual structural and functional data with neural population dynamics to support multi-scale neurophysiological inference. Simulated populations were linked by structural connectivity and, as a novelty, driven by electroencephalography (EEG) source activity. Simulations not only predicted subjects' individual resting-state functional magnetic resonance imaging (fMRI) time series and spatial network topologies over 20 minutes of activity, but more importantly, they also revealed precise neurophysiological mechanisms that underlie and link six empirical observations from different scales and modalities: (1) resting-state fMRI oscillations, (2) functional connectivity networks, (3) excitation-inhibition balance, (4, 5) inverse relationships between α-rhythms, spike-firing and fMRI on short and long time scales, and (6) fMRI power-law scaling. These findings underscore the potential of this new modelling framework for general inference and integration of neurophysiological knowledge to complement empirical studies.

Neuroscientists can use various techniques to measure activity within the brain without opening up the skull. One of the most common is electroencephalography, or EEG for short. A net of electrodes is attached to the scalp and reveals the patterns of electrical activity occurring in brain tissue. But while EEG is good at revealing electrical activity across the surface of the scalp, it is less effective at linking the observed activity to specific locations in the brain.

Another widely used technique is functional magnetic resonance imaging, or fMRI. A patient, or healthy volunteer, lies inside a scanner containing a large magnet. The scanner tracks changes in the level of oxygen at different regions of the brain to provide a measure of how the activity of these regions changes over time. In contrast to EEG, fMRI is good at pinpointing the location of brain activity, but it is an indirect measure of brain activity as it depends on blood flow and several other factors. In terms of understanding how the brain works, EEG and fMRI thus provide different pieces of the puzzle. But there is no easy way to fit these pieces together.

Other areas of science have used computer models to merge different sources of data to obtain new insights into complex processes. Schirner et al. now adopt this approach to reveal the workings of the brain that underly signals like EEG and fMRI.

After recording structural MRI data from healthy volunteers, Schirner et al. built a computer model of each person’s brain. They then ran simulations with each individual model stimulating it with the person’s EEG to predict the fMRI activity of the same individual. Comparing these predictions with real fMRI data collected at the same time as the EEG confirmed that the predictions were accurate. Importantly, the brain models also displayed many features of neural activity that previously could only be measured by implanting electrodes into the brain.

This new approach provides a way of combining experimental data with theories about how the nervous system works. The resulting models can help generate and test ideas about the mechanisms underlying brain activity. Building models of different brains based on data from individual people could also help reveal the biological basis of differences between individuals. This could in turn provide insights into why some individuals are more vulnerable to certain brain diseases and open up new ways to treat these diseases.

Online Reduction of Artifacts in EEG of Simultaneous EEG-fMRI Using Reference Layer Adaptive Filtering (RLAF)

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) allow us to study the active human brain from two perspectives concurrently. Signal processing based artifact reduction techniques are mandatory for this, however, to obtain reasonable EEG quality in simultaneous EEG-fMRI. Current artifact reduction techniques like average artifact subtraction (AAS), typically become less effective when artifact reduction has to be performed on-the-fly. We thus present and evaluate a new technique to improve EEG quality online. This technique adds up with online AAS and combines a prototype EEG-cap for reference recordings of artifacts, with online adaptive filtering and is named reference layer adaptive filtering (RLAF). We found online AAS + RLAF to be highly effective in improving EEG quality. Online AAS + RLAF outperformed online AAS and did so in particular online in terms of the chosen performance metrics, these being specifically alpha rhythm amplitude ratio between closed and opened eyes (3-45% improvement), signal-to-noise-ratio of visual evoked potentials (VEP) (25-63% improvement), and VEPs variability (16-44% improvement). Further, we found that EEG quality after online AAS + RLAF is occasionally even comparable with the offline variant of AAS at a 3T MRI scanner. In conclusion RLAF is a very effective add-on tool to enable high quality EEG in simultaneous EEG-fMRI experiments, even when online artifact reduction is necessary.

Prestimulus EEG Power Predicts Conscious Awareness But Not Objective Visual Performance

Prestimulus oscillatory neural activity has been linked to perceptual outcomes during performance of psychophysical detection and discrimination tasks. Specifically, the power and phase of low frequency oscillations have been found to predict whether an upcoming weak visual target will be detected or not. However, the mechanisms by which baseline oscillatory activity influences perception remain unclear. Recent studies suggest that the frequently reported negative relationship between α power and stimulus detection may be explained by changes in detection criterion (i.e., increased target present responses regardless of whether the target was present/absent) driven by the state of neural excitability, rather than changes in visual sensitivity (i.e., more veridical percepts). Here, we recorded EEG while human participants performed a luminance discrimination task on perithreshold stimuli in combination with single-trial ratings of perceptual awareness. Our aim was to investigate whether the power and/or phase of prestimulus oscillatory activity predict discrimination accuracy and/or perceptual awareness on a trial-by-trial basis. Prestimulus power (3-28 Hz) was inversely related to perceptual awareness ratings (i.e., higher ratings in states of low prestimulus power/high excitability) but did not predict discrimination accuracy. In contrast, prestimulus oscillatory phase did not predict awareness ratings or accuracy in any frequency band. These results provide evidence that prestimulus α power influences the level of subjective awareness of threshold visual stimuli but does not influence visual sensitivity when a decision has to be made regarding stimulus features. Hence, we find a clear dissociation between the influence of ongoing neural activity on conscious awareness and objective performance.

Alpha Oscillations Reduce Temporal Long-Range Dependence in Spontaneous Human Brain Activity

Ongoing neural dynamics comprise both frequency-specific oscillations and broadband-features, such as long-range dependence (LRD). Despite both being behaviorally relevant, little is known about their potential interactions. In humans, 8-12 Hz α oscillations constitute the strongest deviation from 1/f power-law scaling, the signature of LRD. We postulated that α oscillations, believed to exert active inhibitory gating, downmodulate the temporal width of LRD in slower ongoing brain activity. In two independent "resting-state" datasets (electroencephalography surface recordings and magnetoencephalography source reconstructions), both across space and dynamically over time, power of α activity covaried with the power slope <5 Hz (i.e., greater α activity shortened LRD). Causality of α activity dynamics was implied by its temporal precedence over changes of slope. A model where power-law fluctuations of the α envelope inhibit baseline activity closely replicated our results. Thus, α oscillations may provide an active control mechanism to adaptively regulate LRD of brain activity at slow temporal scales, thereby shaping internal states and cognitive processes.SIGNIFICANCE STATEMENT The two prominent features of ongoing brain activity are oscillations and temporal long-range dependence. Both shape behavioral performance, but little is known about their interaction. Here, we demonstrate such an interaction in EEG and MEG recordings of task-free human brain activity. Specifically, we show that spontaneous dynamics in alpha activity explain ensuing variations of dependence in the low and ultra-low-frequency range. In modeling, two features of alpha oscillations are critical to account for the observed effects on long-range dependence, scale-free properties of alpha oscillations themselves, and a modulation of baseline levels, presumably inhibitory. Both these properties have been observed empirically, and our study hence establishes alpha oscillations as a regulatory mechanism governing long-range dependence or "memory" in slow ongoing brain activity.

Inter-subject Functional Correlation Reveal a Hierarchical Organization of Extrinsic and Intrinsic Systems in the Brain

The brain is constantly monitoring and integrating both cues from the external world and signals generated intrinsically. These extrinsically and intrinsically-driven neural processes are thought to engage anatomically distinct regions, which are thought to constitute the extrinsic and intrinsic systems of the brain. While the specialization of extrinsic and intrinsic system is evident in primary and secondary sensory cortices, a systematic mapping of the whole brain remains elusive. Here, we characterized the extrinsic and intrinsic functional activities in the brain during naturalistic movie-viewing. Using a novel inter-subject functional correlation (ISFC) analysis, we found that the strength of ISFC shifts along the hierarchical organization of the brain. Primary sensory cortices appear to have strong inter-subject functional correlation, consistent with their role in processing exogenous information, while heteromodal regions that attend to endogenous processes have low inter-subject functional correlation. Those brain systems with higher intrinsic tendency show greater inter-individual variability, likely reflecting the aspects of brain connectivity architecture unique to individuals. Our study presents a novel framework for dissecting extrinsically- and intrinsically-driven processes, as well as examining individual differences in brain function during naturalistic stimulation.

Spontaneous Neural Oscillations Bias Perception by Modulating Baseline Excitability

The brain exhibits organized fluctuations of neural activity, even in the absence of tasks or sensory input. A prominent type of such spontaneous activity is the alpha rhythm, which influences perception and interacts with other ongoing neural activity. It is currently hypothesized that states of decreased prestimulus α oscillations indicate enhanced neural excitability, resulting in improved perceptual acuity. Nevertheless, it remains debated how changes in excitability manifest at the behavioral level in perceptual tasks. We addressed this issue by comparing two alternative models describing the effect of spontaneous α power on signal detection. The first model assumes that decreased α power increases baseline excitability, amplifying the response to both signal and noise, predicting a liberal detection criterion with no effect on sensitivity. The second model predicts that decreased α power increases the trial-by-trial precision of the sensory response, resulting in improved sensitivity. We tested these models in two EEG experiments in humans where we analyzed the effects of prestimulus α power on visual detection and discrimination using a signal detection framework. Both experiments provide strong evidence that decreased α power reflects a more liberal detection criterion, rather than improved sensitivity, consistent with the baseline model. In other words, when the task requires detecting stimulus presence versus absence, reduced α oscillations make observers more likely to report the stimulus regardless of actual stimulus presence. Contrary to previous interpretations, these results suggest that states of decreased α oscillations increase the global baseline excitability of sensory systems without affecting perceptual acuity.

SIGNIFICANCE STATEMENT

Spontaneous fluctuations of brain activity explain why a faint sensory stimulus is sometimes perceived and sometimes not. The prevailing view is that heightened neural excitability, indexed by decreased α oscillations, promotes better perceptual performance. Here, we provide evidence that heightened neural excitability instead reflects a state of biased perception, during which a person is more likely to see a stimulus, whether or not it is actually present. Therefore, we propose that changes in neural excitability leave the precision of sensory processing unaffected. These results establish the link between spontaneous brain activity and the variability in human perception.