Post-stimulus fMRI and EEG responses: Evidence for a neuronal origin hypothesised to be inhibitory

Investigating the Consistency of Negative BOLD Responses to Combinations of Visual, Auditory, and Somatosensory Stimuli and Their Modulation by the Level of Task Demand

Negative BOLD fMRI responses (NBR) occur commonly in sensory cortex and default mode network regions but remain poorly utilized as a marker of brain function due to an incomplete understanding. To better understand how NBR manifest across the brain, compare between different sensory stimuli and how they are modulated by changes in task demand, we recorded fMRI during trials of visual, auditory, or somatosensory stimulation, delivered either alone or in concurrent pairs. Twenty young-adult participants were cued to attend to a single modality and detect targets in each trial. We found that NBR were consistently induced in all non-task-relevant primary sensory cortices and default mode regions during all stimuli. NBR were observed within the stimulated modality, in the cortex ipsilateral to the stimulus; as well as cross-modal responses bilaterally within the cortex of an unstimulated sensory modality. The NBR regions showed high spatial overlap with the primary sensory positive BOLD response (PBR) of the stimulated modality. The NBR occurred in spatially comparable regions across different modality stimuli such that the peak voxel location and spatial extent were comparable between within and cross-modal NBRs. Some specific differences were seen, such as stronger magnitude sensorimotor NBR to somatosensory stimuli than to visual or auditory. No significant relationships were found between subjects' PBR and NBR magnitude, but significant linear correlations were observed between NBRs indicating that subjects with high magnitude NBR within one sensory modality also displayed high magnitude cross-modal NBR in a different modality. These findings suggest that cortical NBR are largely consistent between different sensory stimuli but also contain stimulus-specific variability in magnitude and spatial extent. Finally, positive BOLD responses were stronger to dual stimuli in all contralateral primary sensory regions, whilst NBR were slightly increased in specific regions of ipsilateral visual and sensorimotor cortex. This finding suggests a strong contribution to NBR from bottom-up stimulus input that was further modulated by attention during dual conditions and that NBR is driven by a combination of bottom-up and top-down influences whereby contributions to its generation arise from both feed-forward signals from subcortical or activated sensory regions and feedback mechanisms such as higher-level attentional control.

Increased alpha power in autistic adults: Relation to sensory behaviors and cortical volume

Alpha-band (~10 Hz) neural oscillations, crucial for gating sensory information, may offer insights into the atypical sensory experiences characteristic of autism spectrum disorder (ASD). We investigated alpha-band EEG activity in autistic adults (n = 29) compared with a nonautistic group (n = 23) under various stimulus-driven and resting-state conditions. The autistic group showed consistently higher alpha amplitude across all time points. In addition, there was proportionally more suppression of alpha at stimulus onset in the autistic group, and alpha amplitude in this stimulus-onset period correlated with sensory behaviors. Recent research suggests a link between subcortical structures' volume and cortical alpha magnitude. Prompted by this, we explored the association between alpha power and the volume of subcortical structures and total cortical volume in ASD. Our findings indicate a significant correlation with total cortical volume and a group by hippocampal volume interaction, pointing to the potential role of anatomical structural characteristics as potential modulators of cortical alpha oscillations in ASD. Overall, the results highlight altered alpha in autistic individuals as potentially contributing to the heightened sensory symptoms in autistic compared with nonautistic adults.

Whole brain functional connectivity: Insights from next generation neural mass modelling incorporating electrical synapses

The ready availability of brain connectome data has both inspired and facilitated the modelling of whole brain activity using networks of phenomenological neural mass models that can incorporate both interaction strength and tract length between brain regions. Recently, a new class of neural mass model has been developed from an exact mean field reduction of a network of spiking cortical cell models with a biophysically realistic model of the chemical synapse. Moreover, this new population dynamics model can naturally incorporate electrical synapses. Here we demonstrate the ability of this new modelling framework, when combined with data from the Human Connectome Project, to generate patterns of functional connectivity (FC) of the type observed in both magnetoencephalography and functional magnetic resonance neuroimaging. Some limited explanatory power is obtained via an eigenmode description of frequency-specific FC patterns, obtained via a linear stability analysis of the network steady state in the neigbourhood of a Hopf bifurcation. However, direct numerical simulations show that empirical data is more faithfully recapitulated in the nonlinear regime, and exposes a key role of gap junction coupling strength in generating empirically-observed neural activity, and associated FC patterns and their evolution. Thereby, we emphasise the importance of maintaining known links with biological reality when developing multi-scale models of brain dynamics. As a tool for the study of dynamic whole brain models of the type presented here we further provide a suite of C++ codes for the efficient, and user friendly, simulation of neural mass networks with multiple delayed interactions.

Negative BOLD Responses Surpass Positive Responses in Task Specificity, Reflecting Neural Reconfigurations Better Than Functional Connectivity

Objective

To investigate whether the Negative BOLD Response (NBR) is more task-specific than the Positive BOLD Response (PBR) during cognitive tasks and to determine whether task-evoked activity reflects brain reconfigurations during different tasks better than functional connectivity.

Methods

Functional Magnetic Resonance Imaging (fMRI) data were collected from 214 participants under 50 years old (152 in Dataset 1 and 62 in Dataset 2) performing twelve cognitive tasks spanning vocabulary, speed of processing, fluid reasoning, and memory domains. Data analysis included subject-level and group-level analyses, focusing on comparing the spatial patterns and task specificity of NBR and PBR through similarity measures using Dice coefficients. Additionally, functional connectivity was assessed using the Multi-session Hierarchical Bayesian Model (MS-HBM) to evaluate its sensitivity to task-induced brain reconfigurations compared to task-evoked activity.

Results

NBR demonstrated significantly greater task specificity compared to PBR across all cognitive tasks, with lower mean Dice coefficients for NBR maps (mean: 0.44, SD: 0.13) than for PBR maps (mean: 0.67, SD: 0.09; t(65) = 18.38, p < 0.001). Functional connectivity analyses indicated that the default mode network (DMN) remained stable across tasks, suggesting that task-evoked activity reflects task-specific brain reconfigurations better than functional connectivity.

Conclusion

The findings confirm that NBR is inherently more task-specific than PBR and that task-evoked activity provides a more sensitive measure of task-specific neural reconfigurations than functional connectivity. This enhances our understanding of the neural mechanisms underlying cognitive processes and highlights the importance of considering NBR in cognitive neuroscience research.

Neurophysiological avenues to better conceptualizing adaptive cognition

We delve into the human brain's remarkable capacity for adaptability and sustained cognitive functioning, phenomena traditionally encompassed as executive functions or cognitive control. The neural underpinnings that enable the seamless navigation between transient thoughts without detracting from overarching goals form the core of our article. We discuss the concept of "metacontrol," which builds upon conventional cognitive control theories by proposing a dynamic balancing of processes depending on situational demands. We critically discuss the role of oscillatory processes in electrophysiological activity at different scales and the importance of desynchronization and partial phase synchronization in supporting adaptive behavior including neural noise accounts, transient dynamics, phase-based measures (coordination dynamics) and neural mass modelling. The cognitive processes focused and neurophysiological avenues outlined are integral to understanding diverse psychiatric disorders thereby contributing to a more nuanced comprehension of cognitive control and its neural bases in both health and disease.

Post-task responses following working memory and movement are driven by transient spectral bursts with similar characteristics

The post-movement beta rebound has been studied extensively using magnetoencephalography (MEG) and is reliably modulated by various task parameters as well as illness. Our recent study showed that rebounds, which we generalise as "post-task responses" (PTRs), are a ubiquitous phenomenon in the brain, occurring across the cortex in theta, alpha, and beta bands. Currently, it is unknown whether PTRs following working memory are driven by transient bursts, which are moments of short-lived high amplitude activity, similar to those that drive the post-movement beta rebound. Here, we use three-state univariate hidden Markov models (HMMs), which can identify bursts without a priori knowledge of frequency content or response timings, to compare bursts that drive PTRs in working memory and visuomotor MEG datasets. Our results show that PTRs across working memory and visuomotor tasks are driven by pan-spectral transient bursts. These bursts have very similar spectral content variation over the cortex, correlating strongly between the two tasks in the alpha (R2 = .89) and beta (R2 = .53) bands. Bursts also have similar variation in duration over the cortex (e.g., long duration bursts occur in the motor cortex for both tasks), strongly correlating over cortical regions between tasks (R2 = .56), with a mean over all regions of around 300 ms in both datasets. Finally, we demonstrate the ability of HMMs to isolate signals of interest in MEG data, such that the HMM probability timecourse correlates more strongly with reaction times than frequency filtered power envelopes from the same brain regions. Overall, we show that induced PTRs across different tasks are driven by bursts with similar characteristics, which can be identified using HMMs. Given the similarity between bursts across tasks, we suggest that PTRs across the cortex may be driven by a common underlying neural phenomenon.

Distractor inhibition by alpha oscillations is controlled by an indirect mechanism governed by goal-relevant information

The role of alpha oscillations (8-13 Hz) in cognition is intensively investigated. While intracranial animal recordings demonstrate that alpha oscillations are associated with decreased neuronal excitability, it is been questioned whether alpha oscillations are under direct control from frontoparietal areas to suppress visual distractors. We here point to a revised mechanism in which alpha oscillations are controlled by an indirect mechanism governed by the load of goal-relevant information - a view compatible with perceptual load theory. We will outline how this framework can be further tested and discuss the consequences for network dynamics and resource allocation in the working brain.

BOLD Response is more than just magnitude: Improving detection sensitivity through capturing hemodynamic profiles

Typical fMRI analyses often assume a canonical hemodynamic response function (HRF) that primarily focuses on the peak height of the overshoot, neglecting other morphological aspects. Consequently, reported analyses often reduce the overall response curve to a single scalar value. In this study, we take a data-driven approach to HRF estimation at the whole-brain voxel level, without assuming a response profile at the individual level. We then employ a roughness penalty at the population level to estimate the response curve, aiming to enhance predictive accuracy, inferential efficiency, and cross-study reproducibility. By examining a fast event-related FMRI dataset, we demonstrate the shortcomings and information loss associated with adopting the canonical approach. Furthermore, we address the following key questions: 1) To what extent does the HRF shape vary across different regions, conditions, and participant groups? 2) Does the data-driven approach improve detection sensitivity compared to the canonical approach? 3) Can analyzing the HRF shape help validate the presence of an effect in conjunction with statistical evidence? 4) Does analyzing the HRF shape offer evidence for whole-brain response during a simple task?

Physiological modeling of the BOLD signal and implications for effective connectivity: A primer

In this primer, I provide an overview of the physiological processes that contribute to the observed BOLD signal (i.e., the generative biophysical model), including their time course properties within the framework of the physiologically-informed dynamic causal modeling (P-DCM). The BOLD signal is primarily determined by the change in paramagnetic deoxygenated hemoglobin, which results from combination of changes in oxygen metabolism, and cerebral blood flow and volume. Specifically, the physiological origin of the so-called BOLD signal "transients" will be discussed, including the initial overshoot, steady-state activation and the post-stimulus undershoot. I argue that incorrect physiological assumptions in the generative model of the BOLD signal can lead to incorrect inferences pertaining to both local neuronal activity and effective connectivity between brain regions. In addition, I introduce the recent laminar BOLD signal model, which extends P-DCM to cortical depths-resolved BOLD signals, allowing for laminar neuronal activity to be determined using high-resolution fMRI data.

Cognition is entangled with metabolism: relevance for resting-state EEG-fMRI

The brain is a living organ with distinct metabolic constraints. However, these constraints are typically considered as secondary or supportive of information processing which is primarily performed by neurons. The default operational definition of neural information processing is that (1) it is ultimately encoded as a change in individual neuronal firing rate as this correlates with the presentation of a peripheral stimulus, motor action or cognitive task. Two additional assumptions are associated with this default interpretation: (2) that the incessant background firing activity against which changes in activity are measured plays no role in assigning significance to the extrinsically evoked change in neural firing, and (3) that the metabolic energy that sustains this background activity and which correlates with differences in neuronal firing rate is merely a response to an evoked change in neuronal activity. These assumptions underlie the design, implementation, and interpretation of neuroimaging studies, particularly fMRI, which relies on changes in blood oxygen as an indirect measure of neural activity. In this article we reconsider all three of these assumptions in light of recent evidence. We suggest that by combining EEG with fMRI, new experimental work can reconcile emerging controversies in neurovascular coupling and the significance of ongoing, background activity during resting-state paradigms. A new conceptual framework for neuroimaging paradigms is developed to investigate how ongoing neural activity is "entangled" with metabolism. That is, in addition to being recruited to support locally evoked neuronal activity (the traditional hemodynamic response), changes in metabolic support may be independently "invoked" by non-local brain regions, yielding flexible neurovascular coupling dynamics that inform the cognitive context. This framework demonstrates how multimodal neuroimaging is necessary to probe the neurometabolic foundations of cognition, with implications for the study of neuropsychiatric disorders.

Beyond the Beta Rebound: Post-Task Responses in Oscillatory Activity follow Cessation of Working Memory Processes

Post-task responses (PTRs) are transitionary responses occurring for several seconds between the end of a stimulus/task and a period of rest. The most well-studied of these are beta band (13 - 30 Hz) PTRs in motor networks following movement, often called post-movement beta rebounds, which have been shown to differ in patients with schizophrenia and autism. Previous studies have proposed that beta PTRs reflect inhibition of task-positive networks to enable a return to resting brain activity, scaling with cognitive demand and reflecting cortical self-regulation. It is unknown whether PTRs are a phenomenon of the motor system, or whether they are a more general self-modulatory property of cortex that occur following cessation of higher cognitive processes as well as movement. To test this, we recorded magnetoencephalography (MEG) responses in 20 healthy participants to a working-memory task, known to recruit cortical networks associated with higher cognition. Our results revealed PTRs in the theta, alpha and beta bands across many regions of the brain, including the dorsal attention network (DAN) and lateral visual regions. These PTRs increased significantly (p < 0.05) in magnitude with working-memory load, an effect which is independent of oscillatory modulations occurring over the task period as well as those following individual stimuli. Furthermore, we showed that PTRs are functionally related to reaction times in left lateral visual (p < 0.05) and left parietal (p < 0.1) regions, while the oscillatory responses measured during the task period are not. Importantly, motor PTRs following button presses did not modulate with task condition, suggesting that PTRs in different networks are driven by different aspects of cognition. Our findings show that PTRs are not limited to motor networks but are widespread in regions which are recruited during the task. We provide evidence that PTRs have unique properties, scaling with cognitive load and correlating significantly with behaviour. Based on the evidence, we suggest that PTRs inhibit task-positive network activity to enable a transition to rest, however, further investigation is required to uncover their role in neuroscience and pathology.

Neuropsychological and Neurophysiological Mechanisms behind Flickering Light Stimulus Processing

The aim of this review is to summarise current knowledge about flickering light and the underlying processes that occur during its processing in the brain. Despite the growing interest in the topic of flickering light, its clinical applications are still not well understood. Studies using EEG indicate an appearing synchronisation of brain wave frequencies with the frequency of flickering light, and hopefully, it could be used in memory therapy, among other applications. Some researchers have focused on using the flicker test as an indicator of arousal, which may be useful in clinical studies if the background for such a relationship is described. Since flicker testing has a risk of inducing epileptic seizures, however, every effort must be made to avoid high-risk combinations, which include, for example, red-blue light flashing at 15 Hz. Future research should focus on the usage of neuroimaging methods to describe the specific neuropsychological and neurophysiological processes occurring in the brain during the processing of flickering light so that its clinical utility can be preliminarily determined and randomised clinical trials can be initiated to test existing reports.

Multi-Echo Investigations of Positive and Negative CBF and Concomitant BOLD Changes: Positive and negative CBF and BOLD changes

Unlike the positive blood oxygenation level-dependent (BOLD) response (PBR), commonly taken as an indication of an 'activated' brain region, the physiological origin of negative BOLD signal changes (i.e. a negative BOLD response, NBR), also referred to as 'deactivation' is still being debated. In this work, an attempt was made to gain a better understanding of the underlying mechanism by obtaining a comprehensive measure of the contributing cerebral blood flow (CBF) and its relationship to the NBR in the human visual cortex, in comparison to a simultaneously induced PBR in surrounding visual regions. To overcome the low signal-to-noise ratio (SNR) of CBF measurements, a newly developed multi-echo version of a center-out echo planar-imaging (EPI) readout was employed with pseudo-continuous arterial spin labeling (pCASL). It achieved very short echo and inter-echo times and facilitated a simultaneous detection of functional CBF and BOLD changes at 3 T with improved sensitivity. Evaluations of the absolute and relative changes of CBF and the effective transverse relaxation rate,R₂^* the coupling ratios, and their dependence on CBF at rest, CBF_rest indicated differences between activated and deactivated regions. Analysis of the shape of the respective functional responses also revealed faster negative responses with more pronounced post-stimulus transients. Resulting differences in the flow-metabolism coupling ratios were further examined for potential distinctions in the underlying neuronal contributions.

Gustatory Cortex Is Involved in Evidence Accumulation during Food Choice

Food choice is one of the most fundamental and most frequent value-based decisions for all animals including humans. However, the neural circuitry involved in food-based decisions is only recently being addressed. Given the relatively fast dynamics of decision formation, electroencephalography (EEG)-informed fMRI analysis is highly beneficial for localizing this circuitry in humans. Here, by using the EEG correlates of evidence accumulation in a simultaneously recorded EEG-fMRI dataset, we found a significant role for the right temporal-parietal operculum (PO) and medial insula including gustatory cortex (GC) in binary choice between food items. These activations were uncovered by using the “EEG energy” (power 2 of EEG) as the BOLD regressor and were missed if conventional analysis with the EEG signal itself were to be used, in agreement with theoretical predictions for EEG and BOLD relations. No significant positive correlations were found with higher powers of EEG (powers 3 or 4) pointing to specificity and sufficiency of EEG energy as the main correlate of the BOLD response. This finding extends the role of cortical areas traditionally involved in palatability processing to value-based decision-making and offers the “EEG energy” as a key regressor of BOLD response in simultaneous EEG-fMRI designs.

Resting-state functional magnetic resonance imaging signal variations in aging: The role of neural activity

Resting‐state functional magnetic resonance imaging (rs‐fMRI) has been extensively used to study brain aging, but the age effect on the frequency content of the rs‐fMRI signal has scarcely been examined. Moreover, the neuronal implications of such age effects and age–sex interaction remain unclear. In this study, we examined the effects of age and sex on the rs‐fMRI signal frequency using the Leipzig mind–brain–body data set. Over a frequency band of up to 0.3 Hz, we found that the rs‐fMRI fluctuation frequency is higher in the older adults, although the fluctuation amplitude is lower. The rs‐fMRI signal frequency is also higher in men than in women. Both age and sex effects on fMRI frequency vary with the frequency band examined but are not found in the frequency of physiological‐noise components. This higher rs‐fMRI frequency in older adults is not mediated by the electroencephalograph (EEG)‐frequency increase but a likely link between fMRI signal frequency and EEG entropy, which vary with age and sex. Additionally, in different rs‐fMRI frequency bands, the fMRI‐EEG amplitude ratio is higher in young adults. This is the first study to investigate the neuronal contribution to age and sex effects in the frequency dimension of the rs‐fMRI signal and may lead to the development of new, frequency‐based rs‐fMRI metrics. Our study demonstrates that Fourier analysis of the fMRI signal can reveal novel information about aging. Furthermore, fMRI and EEG signals reflect different aspects of age‐ and sex‐related brain differences, but the signal frequency and complexity, instead of amplitude, may hold their link.

Across the adult lifespan the ipsilateral sensorimotor cortex negative BOLD response exhibits decreases in magnitude and spatial extent suggesting declining inhibitory control

Ipsilateral sensorimotor (iSM1) cortex negative BOLD responses (NBR) are observed to unilateral tasks and are thought to reflect a functionally relevant component of sensorimotor inhibition. Evidence suggests that sensorimotor inhibitory mechanisms degrade with age, along with aspects of motor ability and dexterity. However, understanding of age-related changes to NBR is restricted by limited comparisons between young vs old adults groups with relatively small samples sizes. Here we analysed a BOLD fMRI dataset (obtained from the CamCAN repository) of 581 healthy subjects, gender-balanced, sampled from the whole adult lifespan performing a motor response task to an audio-visual stimulus. We aimed to investigate how sensorimotor and default-mode NBR characteristics of magnitude, spatial extent and response shape alter at every decade of the aging process. A linear decrease in iSM1 NBR magnitude was observed across the whole lifespan whereas the contralateral sensorimotor (cSM1) PBR magnitude was unchanged. An age-related decrease in the spatial extent of NBR and an increase in the ipsilateral positive BOLD response (PBR) was observed. This occurred alongside an increasing negative correlation between subject's iSM1 NBR and cSM1 PBR magnitude, reflecting a change in the balance between cortical excitation and inhibition. Conventional GLM analysis, using a canonical haemodynamic response (HR) function, showed disappearance of iSM1 NBR in subjects over 50 years of age. However, a deconvolution analysis showed that the shape of the iSM1 HR altered throughout the lifespan, with delayed time-to-peak and decreased magnitude. The most significant decreases in iSM1 HR magnitude occurred in older age (>60 years) but the first changes in shape and timing occurred as early as 30 years, suggesting possibility of separate mechanisms underlying these alterations. Reanalysis using data-driven HRs for each decade detected significant sensorimotor NBR into late older age, showing the importance of taking changes in HR morphology into account in fMRI aging studies. These results may reflect fMRI measures of the age-related decreases in transcollosal inhibition exerted upon ipsilateral sensorimotor cortex and alterations to the excitatory-inhibitory balance in the sensorimotor network.

Imaging faster neural dynamics with fast fMRI: A need for updated models of the hemodynamic response

Fast fMRI enables the detection of neural dynamics over timescales of hundreds of milliseconds, suggesting it may provide a new avenue for studying subsecond neural processes in the human brain. The magnitudes of these fast fMRI dynamics are far greater than predicted by canonical models of the hemodynamic response. Several studies have established nonlinear properties of the hemodynamic response that have significant implications for fast fMRI. We first review nonlinear properties of the hemodynamic response function that may underlie fast fMRI signals. We then illustrate the breakdown of canonical hemodynamic response models in the context of fast neural dynamics. We will then argue that the canonical hemodynamic response function is not likely to reflect the BOLD response to neuronal activity driven by sparse or naturalistic stimuli or perhaps to spontaneous neuronal fluctuations in the resting state. These properties suggest that fast fMRI is capable of tracking surprisingly fast neuronal dynamics, and we discuss the neuroscientific questions that could be addressed using this approach.

Investigating mechanisms of fast BOLD responses: The effects of stimulus intensity and of spatial heterogeneity of hemodynamics

Recent studies have demonstrated that fast fMRI can track neural activity well above the temporal limit predicted by the canonical hemodynamic response model. While these findings are promising, the biophysical mechanisms underlying these fast fMRI phenomena remain underexplored. In this study, we discuss two aspects of the hemodynamic response, complementary to several existing hypotheses, that can accommodate faster fMRI dynamics beyond those predicted by the canonical model. First, we demonstrate, using both visual and somatosensory paradigms, that the timing and shape of hemodynamic response functions (HRFs) vary across graded levels of stimulus intensity-with lower-intensity stimulation eliciting faster and narrower HRFs. Second, we show that as the spatial resolution of fMRI increases, voxel-wise HRFs begin to deviate from the canonical model, with a considerable portion of voxels exhibiting faster temporal dynamics than predicted by the canonical HRF. Collectively, both stimulus/task intensity and image resolution can affect the sensitivity of fMRI to fast brain activity, which may partly explain recent observations of fast fMRI signals. It is further noteworthy that, while the present investigations focus on fast neural responses, our findings suggest that a revised hemodynamic model may benefit the many fMRI studies using paradigms with wide ranges of contrast levels (e.g., resting or naturalistic conditions) or with modern, high-resolution MR acquisitions.

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.

Quantitative relations between BOLD responses, cortical energetics and impulse firing across cortical depth

The blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signal arises as a consequence of changes in cerebral blood flow (CBF) and cerebral metabolic rate of oxygen ( CMR O 2 ) that in turn are modulated by changes in neural activity. Recent advances in imaging have achieved sub-millimetre resolution and allowed investigation of the BOLD response as a function of cortical depth. Here, we adapt our previous theory relating the BOLD signal to neural activity to produce a quantitative model that incorporates venous blood draining between cortical layers. The adjustable inputs to the model are the neural activity and a parameter governing this blood draining. A three-layer version for transient neural inputs and a multi-layer version for constant or tonic neural inputs are able to account for a variety of experimental results, including negative BOLD signals.

Contrasting Measures of Cerebrovascular Reactivity Between MRI and Doppler: A Cross-Sectional Study of Younger and Older Healthy Individuals

Cerebrovascular reactivity (CVR) is used as an outcome measure of brain health. Traditionally, lower CVR is associated with ageing, poor fitness and brain-related conditions (e.g. stroke, dementia). Indeed, CVR is suggested as a biomarker for disease risk. However, recent findings report conflicting associations between ageing or fitness and CVR measures. Inconsistent findings may relate to different neuroimaging modalities used, which include transcranial Doppler (TCD) and blood-oxygen-level-dependant (BOLD) contrast magnetic resonance imaging (MRI). We assessed the relationship between CVR metrics derived from two common imaging modalities, TCD and BOLD MRI, within the same individuals and with expected significant differences (i.e., younger vs. older) to maximise the expected spread in measures. We conducted two serial studies using TCD- and MRI-derived measures of CVR (via inspired 5% CO2 in air). Study 1 compared 20 younger (24 ± 7 years) with 15 older (66 ± 7 years) participants, Study 2 compared 10 younger (22 ± 2 years) with 10 older (72 ± 4 years) participants. Combining the main measures across studies, no significant correlation (r = 0.15, p = 0.36) was observed between individual participant TCD- and BOLD-CVR measures. Further, these measures showed differential effects between age groups; with TCD-CVR higher in the older compared to younger group (4 ± 1 vs. 3 ± 1 %MCAv/mmHg P ET CO2; p < 0.05, Hedges' g = 0.75), whereas BOLD-CVR showed no difference (p = 0.104, Hedges' g = 0.38). In Study 2 additional measures were obtained to understand the origin of the discrepancy: phase contrast angiography (PCA) MRI of the middle cerebral artery, showed a significantly lower blood flow (but not velocity) CVR response in older compared with younger participants (p > 0.05, Hedges' g = 1.08). The PCA CVR metrics did not significantly correlate with the BOLD- or TCD-CVR measures. The differing CVR observations between imaging modalities were despite expected, correlated (r = 0.62-0.82), age-related differences in resting CBF measures across modalities. Taken together, findings across both studies show no clear relationship between TCD- and BOLD-CVR measures. We hypothesize that CVR differences between imaging modalities are in part due to the aspects of the vascular tree that are assessed (TCD:arteries; BOLD:venules/veins). Further work is needed to understand the between-modality CVR response differences, but caution is needed when comparing CVR metrics derived from different imaging modalities.

Multimodal EEG-fMRI: advancing insight into large-scale human brain dynamics

Advances in the acquisition and analysis of functional magnetic resonance imaging (fMRI) data are revealing increasingly rich spatiotemporal structure across the human brain. Nonetheless, uncertainty surrounding the origins of fMRI hemodynamic signals, and in the link between large-scale fMRI patterns and ongoing functional states, presently limits the neurobiological conclusions one can draw from fMRI alone. Electroencephalography (EEG) provides complementary information about neural electrical activity and state change, and simultaneously acquiring EEG together with fMRI presents unique opportunities for studying large-scale brain activity and gaining more information from fMRI itself. Here, we discuss recent progress in the use of concurrent EEG-fMRI to enrich the investigation of neural and physiological states and clarify the origins of fMRI hemodynamic signals. Throughout, we outline perspectives on future directions and open challenges.

Decoding Neural Responses to Motion-in-Depth Using EEG

Two stereoscopic cues that underlie the perception of motion-in-depth (MID) are changes in retinal disparity over time (CD) and interocular velocity differences (IOVD). These cues have independent spatiotemporal sensitivity profiles, depend upon different low-level stimulus properties, and are potentially processed along separate cortical pathways. Here, we ask whether these MID cues code for different motion directions: do they give rise to discriminable patterns of neural signals, and is there evidence for their convergence onto a single "motion-in-depth" pathway? To answer this, we use a decoding algorithm to test whether, and when, patterns of electroencephalogram (EEG) signals measured from across the full scalp, generated in response to CD- and IOVD-isolating stimuli moving toward or away in depth can be distinguished. We find that both MID cue type and 3D-motion direction can be decoded at different points in the EEG timecourse and that direction decoding cannot be accounted for by static disparity information. Remarkably, we find evidence for late processing convergence: IOVD motion direction can be decoded relatively late in the timecourse based on a decoder trained on CD stimuli, and vice versa. We conclude that early CD and IOVD direction decoding performance is dependent upon fundamentally different low-level stimulus features, but that later stages of decoding performance may be driven by a central, shared pathway that is agnostic to these features. Overall, these data are the first to show that neural responses to CD and IOVD cues that move toward and away in depth can be decoded from EEG signals, and that different aspects of MID-cues contribute to decoding performance at different points along the EEG timecourse.

The thermodynamics of thinking: connections between neural activity, energy metabolism and blood flow

Several current functional neuroimaging methods are sensitive to cerebral metabolism and cerebral blood flow (CBF) rather than the underlying neural activity itself. Empirically, the connections between metabolism, flow and neural activity are complex and somewhat counterintuitive: CBF and glycolysis increase more than seems to be needed to provide oxygen and pyruvate for oxidative metabolism, and the oxygen extraction fraction is relatively low in the brain and decreases when oxygen metabolism increases. This work lays a foundation for the idea that this unexpected pattern of physiological changes is consistent with basic thermodynamic considerations related to metabolism. In the context of this thermodynamic framework, the apparent mismatches in metabolic rates and CBF are related to preserving the entropy change of oxidative metabolism, specifically the O₂/CO₂ ratio in the mitochondria. However, the mechanism supporting this CBF response is likely not owing to feedback from a hypothetical O₂ sensor in tissue, but rather is consistent with feed-forward control by signals from both excitatory and inhibitory neural activity. Quantitative predictions of the thermodynamic framework, based on models of O₂ and CO₂ transport and possible neural drivers of CBF control, are in good agreement with a wide range of experimental data, including responses to neural activation, hypercapnia, hypoxia and high-altitude acclimatization. This article is part of the theme issue 'Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity'.

Late fMRI Response Components Are Altered in Autism Spectrum Disorder

Disrupted cortical neural inhibition has been hypothesized to be a primary contributor to the pathophysiology of autism spectrum disorder (ASD). This hypothesis predicts that ASD will be associated with an increase in neural responses. We tested this prediction by comparing fMRI response magnitudes to simultaneous visual, auditory, and motor stimulation in ASD and neurotypical (NT) individuals. No increases in the initial transient response in any brain region were observed in ASD, suggesting that there is no increase in overall cortical neural excitability. Most notably, there were widespread fMRI magnitude increases in the ASD response following stimulation offset, approximately 6–8 s after the termination of sensory and motor stimulation. In some regions, the higher fMRI offset response in ASD could be attributed to a lack of an “undershoot”—an often observed feature of fMRI responses believed to reflect inhibitory processing. Offset response magnitude was associated with reaction times (RT) in the NT group and may explain an overall reduced RT in the ASD group. Overall, our results suggest that increases in neural responsiveness are present in ASD but are confined to specific components of the neural response, are particularly strong following stimulation offset, and are linked to differences in RT.

Post-stimulus beta responses are modulated by task duration

Modulation of beta-band neural oscillations during and following movement is a robust marker of brain function. In particular, the post-movement beta rebound (PMBR), which occurs on movement cessation, has been related to inhibition and connectivity in the healthy brain, and is perturbed in disease. However, to realise the potential of the PMBR as a biomarker, its modulation by task parameters must be characterised and its functional role determined. Here, we used MEG to image brain electrophysiology during and after a grip-force task, with the aim to characterise how task duration, in the form of an isometric contraction, modulates beta responses. Fourteen participants exerted a 30% maximum voluntary grip-force for 2, 5 and 10 s. Our results showed that the amplitude of the PMBR is modulated by task duration, with increasing duration significantly reducing PMBR amplitude and increasing its time-to-peak. No variation in the amplitude of the movement related beta decrease (MRBD) with task duration was observed. To gain insight into what may underlie these trial-averaged results, we used a Hidden Markov Model to identify the individual trial dynamics of a brain network encompassing bilateral sensorimotor areas. The rapidly evolving dynamics of this network demonstrated similar variation with task parameters to the ‘classical’ rebound, and we show that the modulation of the PMBR can be well-described in terms of increased frequency of beta events on a millisecond timescale rather than modulation of beta amplitude during this time period. Our results add to the emerging picture that, in the case of a carefully controlled paradigm, beta modulation can be systematically controlled by task parameters and such control can reveal new information as to the processes that generate the average beta timecourse. These findings will support design of clinically relevant paradigms and analysis pipelines in future use of the PMBR as a marker of neuropathology.

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The post-movement beta rebound is modulated by task duration.

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Increasing task duration reduces amplitude of the post-movement beta rebound.

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The modulation is explained by increased frequency of short-timescale beta events.

Quantitative relations between transient BOLD responses, cortical energetics, and impulse firing in different cortical regions

The blood oxygen level-dependent (BOLD) functional magnetic resonance imaging signal arises as a consequence of changes in blood flow (cerebral blood flow) and oxygen usage (cerebral metabolic rate of oxygen) that in turn are modulated by changes in neuronal activity. Much attention has been given to both theoretical and experimental aspects of the energetics but not to the neuronal activity. Here we use our previous theory relating the steady-state BOLD signal to neuronal activity and amalgamate it with the standard dynamic causal model (DCM, Friston) theory to produce a quantitative model relating the time-dependent BOLD signal to the underlying neuronal activity. Unlike existing treatments, this new theory incorporates a nonzero baseline activity in a completely consistent way and is thus able to account for both positive and negative BOLD signals. It can reproduce a wide variety of experimental BOLD signals reported in the literature solely by adjusting the neuronal input activity. In this way it provides support for the claim that the main features of the signals, including poststimulus undershoot and overshoot, are principally a result of changes in neuronal activity.NEW & NOTEWORTHY A previous model relating the steady-state blood oxygen level-dependent (BOLD) signal to neuronal activity, both above and below baseline, is extended to account for transient BOLD signals. This allows for a detailed investigation of the role neuronal activity can play in such signals and also encompasses poststimulus undershoot and overshoot. A wide variety of experimental BOLD signals are reproduced solely by adjusting the neuronal input activity, including recent results regarding the BOLD signal in patients with schizophrenia.

Simultaneous EEG-fMRI: Evaluating the Effect of the EEG Cap-Cabling Configuration on the Gradient Artifact

Electroencephalography (EEG) data recorded during simultaneous EEG-fMRI experiments are contaminated by large gradient artifacts (GA). The amplitude of the GA depends on the area of the wire loops formed by the EEG leads, as well as on the rate of switching of the magnetic field gradients, which are essential for MR imaging. Average artifact subtraction (AAS), the most commonly used method for GA correction, relies on the EEG amplifier having a large enough dynamic range to characterize the artifact voltages. Low-pass filtering (250 Hz cut-off) is generally used to attenuate the high-frequency voltage fluctuations of the GA, but even with this precaution channel saturation can occur, particularly during acquisition of high spatial resolution MRI data. Previous work has shown that the ribbon cable, used to connect the EEG cap and amplifier, makes a significant contribution to the GA, since the cable geometry produces large effective wire-loop areas. However, by appropriately connecting the wires of the ribbon cable to the EEG cap it should be possible to minimize the overall range and root mean square (RMS) amplitude of the GA by producing partial cancelation of the cap and cable contributions. Here by modifying the connections of the EEG cap to a 1 m ribbon cable we were able to reduce the range of the GA for a high-resolution coronal echo planar Imaging (EPI) acquisition by a factor of ∼ 1.6 and by a factor of ∼ 1.15 for a standard axial EPI acquisition. These changes could potentially be translated into a reduction in the required dynamic range, an increase in the EEG bandwidth or an increase in the achievable image resolution without saturation, all of which could be beneficially exploited in EEG-fMRI studies. The re-wiring could also prevent the system from saturating when small subject movements occur using the standard recording bandwidth.

The relationship between negative BOLD responses and ERS and ERD of alpha/beta oscillations in visual and motor cortex

Previous work has investigated the electrophysiological origins of the intra-modal (within the stimulated sensory cortex) negative BOLD fMRI response (NBR, decrease from baseline) but little attention has been paid to the origin of cross-modal NBRs, those in a different sensory cortex. In the current study we use simultaneous EEG-fMRI recordings to assess the neural correlates of both intra- and cross-modal responses to left-hemifield visual stimuli and right-hand motor tasks, and evaluate the balance of activation and deactivation between the visual and motor systems. Within- and between-subject covariations of EEG and fMRI responses to both tasks are assessed to determine how patterns of event-related desynchronization/synchronisation (ERD/ERS) of alpha/beta frequency oscillations relate to the NBR in the two sensory cortices. We show that both visual and motor tasks induce intra-modal NBR and cross-modal NBR (e.g. visual stimuli evoked NBRs in both visual and motor cortices). In the EEG data, bilateral intra-modal alpha/beta ERD were consistently observed to both tasks, whilst the cross-modal EEG response varied across subjects between alpha/beta ERD and ERS. Both the mean cross-modal EEG and fMRI response amplitudes showed a small increase in magnitude with increasing task intensity. In response to the visual stimuli, subjects displaying cross-modal ERS of motor beta power displayed a significantly larger magnitude of cross-modal NBR in motor cortex. However, in contrast to the motor stimuli, larger cross-modal ERD of visual alpha power was associated with larger cross-modal visual NBR. Single-trial correlation analysis provided further evidence of relationship between EEG signals and the NBR, motor cortex beta responses to motor tasks were significantly negatively correlated with cross-modal visual cortex NBR amplitude, and positively correlated with intra-modal motor cortex PBR. This study provides a new body of evidence that the coupling between BOLD and low-frequency (alpha/beta) sensory cortex EEG responses extends to cross-modal NBR.

Cerebral blood volume changes during the BOLD post-stimulus undershoot measured with a combined normoxia/hyperoxia method

Cerebral blood flow (CBF) and blood oxygenation level dependent (BOLD) signal measurements make it possible to estimate steady-state changes in the cerebral metabolic rate of oxygen (CMRO2) with a calibrated BOLD method. However, extending this approach to measure the dynamics of CMRO2 requires an additional assumption: that deoxygenated cerebral blood volume (CBVdHb) follows CBF in a predictable way. A test-case for this assumption is the BOLD post-stimulus undershoot, for which one proposed explanation is a strong uncoupling of flow and blood volume with an elevated level of CBVdHb during the post-stimulus period compared to baseline due to slow blood volume recovery (Balloon Model). A challenge in testing this model is that CBVdHb differs from total blood volume, which can be measured with other techniques. In this study, the basic hypothesis of elevated CBVdHb during the undershoot was tested, based on the idea that the BOLD signal change when a subject switches from breathing a normoxic gas to breathing a hyperoxic gas is proportional to the absolute CBVdHb. In 19 subjects (8F), dual-echo BOLD responses were measured in primary visual cortex during a flickering radial checkerboard stimulus in normoxia, and the identical experiment was repeated in hyperoxia (50% O2/balance N2). The BOLD signal differences between normoxia and hyperoxia for the pre-stimulus baseline, stimulus, and post-stimulus periods were compared using an equivalent BOLD signal calculated from measured R2* changes to eliminate signal drifts. Relative to the pre-stimulus baseline, the average BOLD signal change from normoxia to hyperoxia was negative during the undershoot period (p = 0.0251), consistent with a reduction of CBVdHb and contrary to the prediction of the Balloon Model. Based on these results, the BOLD post-stimulus undershoot does not represent a case of strong uncoupling of CBVdHb and CBF, supporting the extension of current calibrated BOLD methods to estimate the dynamics of CMRO2.

Exploring the relative efficacy of motion artefact correction techniques for EEG data acquired during simultaneous fMRI

Simultaneous EEG‐fMRI allows multiparametric characterisation of brain function, in principle enabling a more complete understanding of brain responses; unfortunately the hostile MRI environment severely reduces EEG data quality. Simply eliminating data segments containing gross motion artefacts [MAs] (generated by movement of the EEG system and head in the MRI scanner's static magnetic field) was previously believed sufficient. However recently the importance of removal of all MAs has been highlighted and new methods developed. A systematic comparison of the ability to remove MAs and retain underlying neuronal activity using different methods of MA detection and post‐processing algorithms is needed to guide the neuroscience community. Using a head phantom, we recorded MAs while simultaneously monitoring the motion using three different approaches: Reference Layer Artefact Subtraction (RLAS), Moiré Phase Tracker (MPT) markers and Wire Loop Motion Sensors (WLMS). These EEG recordings were combined with EEG responses to simple visual tasks acquired on a subject outside the MRI environment. MAs were then corrected using the motion information collected with each of the methods combined with different analysis pipelines. All tested methods retained the neuronal signal. However, often the MA was not removed sufficiently to allow accurate detection of the underlying neuronal signal. We show that the MA is best corrected using the RLAS combined with post‐processing using a multichannel, recursive least squares (M‐RLS) algorithm. This method needs to be developed further to enable practical utility; thus, WLMS combined with M‐RLS currently provides the best compromise between EEG data quality and practicalities of motion detection.

Effects of the Phantom Shape on the Gradient Artefact of Electroencephalography (EEG) Data in Simultaneous EEG–fMRI

Electroencephalography (EEG) signals greatly suffer from gradient artefacts (GAs) due to the time-varying field gradients in the magnetic resonance (MR) scanner during the simultaneous acquisition of EEG and functional magnetic resonance imaging (fMRI) data. The GAs are the principal contributors of artefacts while recording EEG inside an MR scanner, and most of them come from the interaction of the EEG cap and the subject’s head. Many researchers have been using a spherical phantom to characterize the GA in EEG data in combined EEG–fMRI studies. In this study, we investigated how the phantom shape could affect the characterization of the GA. EEG data were recorded with a spherical phantom, a head-shaped phantom, and six human subjects, individually, during the execution of customized and standard echo-planar imaging (EPI) sequences. The spatial potential maps of the root-mean-square (RMS) voltage of the GA over EEG channels for the trials with a head-shaped phantom closely mimicked those related to the human head rather than those obtained for the spherical phantom. This was confirmed by measuring the average similarity index (0.85/0.68). Moreover, a paired t-test showed that the head-shaped phantom’s and the spherical phantom’s data were significantly different (p < 0.005) from the subjects’ data, whereas the difference between the head-shaped phantom’s and the spherical phantom’s data was not significant (p = 0.07). The results of this study strongly suggest that a head-shaped phantom should be used for GA characterization studies in concurrent EEG–fMRI.

Reduced Dynamic Coupling Between Spontaneous BOLD-CBF Fluctuations in Older Adults: A Dual-Echo pCASL Study

Measurement of the dynamic coupling between spontaneous Blood Oxygenation Level Dependent (BOLD) and cerebral blood flow (CBF) fluctuations has been recently proposed as a method to probe resting-state brain physiology. Here we investigated how the dynamic BOLD-CBF coupling during resting-state is affected by aging. Fifteen young subjects and 17 healthy elderlies were studied using a dual-echo pCASL sequence. We found that the dynamic BOLD-CBF coupling was markedly reduced in elderlies, in particular in the left supramarginal gyrus, an area known to be involved in verbal working memory and episodic memory. Moreover, correcting for temporal shift between BOLD and CBF timecourses resulted in an increased correlation of the two signals for both groups, but with a larger increase for elderlies. However, even after temporal shift correction, a significantly decreased correlation was still observed for elderlies in the left supramarginal gyrus, indicating that the age-related dynamic BOLD-CBF uncoupling in this region is more pronounced and can be only partially explained with a simple time-shift between the two signals. Interestingly, these results were observed in a group of elderlies with normal cognitive functions, suggesting that the study of dynamic BOLD-CBF coupling during resting-state is a promising technique, potentially able to provide early biomarkers of functional changes in the aging brain.

Determining Excitatory and Inhibitory Neuronal Activity from Multimodal fMRI Data Using a Generative Hemodynamic Model

Hemodynamic responses, in general, and the blood oxygenation level-dependent (BOLD) fMRI signal, in particular, provide an indirect measure of neuronal activity. There is strong evidence that the BOLD response correlates well with post-synaptic changes, induced by changes in the excitatory and inhibitory (E-I) balance between active neuronal populations. Typical BOLD responses exhibit transients, such as the early-overshoot and post-stimulus undershoot, that can be linked to transients in neuronal activity, but they can also result from vascular uncoupling between cerebral blood flow (CBF) and venous cerebral blood volume (venous CBV). Recently, we have proposed a novel generative hemodynamic model of the BOLD signal within the dynamic causal modeling framework, inspired by physiological observations, called P-DCM (Havlicek et al., 2015). We demonstrated the generative model's ability to more accurately model commonly observed neuronal and vascular transients in single regions but also effective connectivity between multiple brain areas (Havlicek et al., 2017b). In this paper, we additionally demonstrate the versatility of the generative model to jointly explain dynamic relationships between neuronal and hemodynamic physiological variables underlying the BOLD signal using multi-modal data. For this purpose, we utilized three distinct data-sets of experimentally induced responses in the primary visual areas measured in human, cat, and monkey brain, respectively: (1) CBF and BOLD responses; (2) CBF, total CBV, and BOLD responses (Jin and Kim, 2008); and (3) positive and negative neuronal and BOLD responses (Shmuel et al., 2006). By fitting the generative model to the three multi-modal experimental data-sets, we showed that the presence or absence of dynamic features in the BOLD signal is not an unambiguous indication of presence or absence of those features on the neuronal level. Nevertheless, the generative model that takes into account the dynamics of the physiological mechanisms underlying the BOLD response allowed dissociating neuronal from vascular transients and deducing excitatory and inhibitory neuronal activity time-courses from BOLD data alone and from multi-modal data.