High-resolution fMRI maps of cortical activation in nonhuman primates: correlation with intrinsic signal optical images

One of the most widely used functional brain mapping tools is blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI). This method has contributed to new understandings of the functional roles of different areas in the human brain. However, its ability to map cerebral cortex at high spatial (submillimeter) resolution is still unknown. Other methods such as single- and multiunit electrophysiology and intrinsic signal optical imaging have revealed submillimeter resolution of sensory topography and cortical columnar activations. However, they are limited either by spatial scale (electrophysiology characterizes only local groups of neurons) or by the inability to monitor deep structures in the brain (i.e., cortical regions buried in sulci or subcortical structures). A method that could monitor all regions of the brain at high spatial resolution would be ideal. This capacity would open the doors to investigating, for example, how networks of cerebral cortical columns relate to or produce behavior. In this article we demonstrate that, without benefit of contrast agents, at a magnetic field strength of 9.4 tesla, BOLD fMRI can reveal millimeter-sized topographic maps of digit representation in the somatosensory cortex of the anesthetized squirrel monkey. Furthermore, by mapping the "funneling illusion," it is possible to detect even submillimeter shifts in activation in the cortex. Our data suggest that at high magnetic field strength, the positive BOLD signal can be used to reveal high spatial resolution maps of brain activity, a finding that weakens previous notions about the ultimate spatial specificity of the positive BOLD signal.     

Negative BOLD fMRI response in the visual cortex carries precise stimulus-specific information

Sustained positive BOLD (blood oxygen level-dependent) activity is employed extensively in functional magnetic resonance imaging (fMRI) studies as evidence for task or stimulus-specific neural responses. However, the presence of sustained negative BOLD activity (i.e., sustained responses that are lower than the fixation baseline) has remained more difficult to interpret. Some studies suggest that it results from local "blood stealing" wherein blood is diverted to neurally active regions without a concomitant change of neural activity in the negative BOLD regions. However, other evidence suggests that negative BOLD is a result of local neural suppression. In both cases, regions of negative BOLD response are usually interpreted as carrying relatively little, if any, stimulus-specific information (hence the predominant reliance on positive BOLD activity in fMRI). Here we show that the negative BOLD response resulting from visual stimulation can carry high information content that is stimulus-specific. Using a general linear model (GLM), we contrasted standard flickering stimuli to a fixation baseline and found regions of the visual cortex that displayed a sustained negative BOLD response, consistent with several previous studies. Within these negative BOLD regions, we compared patterns of fMRI activity generated by flickering Gabors that were systematically shifted in position. As the Gabors were shifted further from each other, the correlation in the spatial pattern of activity across a population of voxels (such as the population of V1 voxels that displayed a negative BOLD response) decreased significantly. Despite the fact that the BOLD signal was significantly negative (lower than fixation baseline), these regions were able to discriminate objects separated by less than 0.5 deg (at approximately 10 deg eccentricity). The results suggest that meaningful, stimulus-specific processing occurs even in regions that display a strong negative BOLD response.     

Resting State Brain Function Analysis Using Concurrent BOLD in ASL Perfusion fMRI

The past decade has seen astounding discoveries about resting-state brain activity patterns in normal brain as well as their alterations in brain diseases. While the vast majority of resting-state studies are based on the blood-oxygen-level-dependent (BOLD) functional MRI (fMRI), arterial spin labeling (ASL) perfusion fMRI can simultaneously capture BOLD and cerebral blood flow (CBF) signals, providing a unique opportunity for assessing resting brain functions with concurrent BOLD (ccBOLD) and CBF signals. Before taking that benefit, it is necessary to validate the utility of ccBOLD signal for resting-state analysis using conventional BOLD (cvBOLD) signal acquired without ASL modulations. To address this technical issue, resting cvBOLD and ASL perfusion MRI were acquired from a large cohort (n = 89) of healthy subjects. Four widely used resting-state brain function analyses were conducted and compared between the two types of BOLD signal, including the posterior cingulate cortex (PCC) seed-based functional connectivity (FC) analysis, independent component analysis (ICA), analysis of amplitude of low frequency fluctuation (ALFF), and analysis of regional homogeneity (ReHo). Consistent default mode network (DMN) as well as other resting-state networks (RSNs) were observed from cvBOLD and ccBOLD using PCC-FC analysis and ICA. ALFF from both modalities were the same for most of brain regions but were different in peripheral regions suffering from the susceptibility gradients induced signal drop. ReHo showed difference in many brain regions, likely reflecting the SNR and resolution differences between the two BOLD modalities. The DMN and auditory networks showed highest CBF values among all RSNs. These results demonstrated the feasibility of ASL perfusion MRI for assessing resting brain functions using its concurrent BOLD in addition to CBF signal, which provides a potentially useful way to maximize the utility of ASL perfusion MRI.     

Sex differences in the cerebral BOLD signal response to painful heat stimuli

There are limited data addressing the question of sex differences in pain-related cerebral processing. This study examined whether pain-related blood oxygenation level-dependent (BOLD) signal change measured with functional magnetic resonance imaging (fMRI) demonstrated sex differences, under conditions of equivalent pain perception. Twenty-eight healthy volunteers (17 women, 11 men) were subject to a fMRI scan while noxious heat stimuli were applied to the dorsum of the left foot. Significant BOLD signal modulation was observed in several nociceptive processing regions of interest (ROIs) in all subjects. There were no sex differences in the spatial extent of BOLD signal change for any ROI, but the signal amplitude was lower for women in most ROIs and significantly so for the primary somatosensory cortex (S1), the midanterior cingulate cortex, and the dorsolateral prefrontal cortex (DLPFC). The BOLD signal response could be positive or negative, and frequently, both polarities were observed within a single ROI. In most ROIs, women show proportionately more voxels with negative signal change than men, and this difference was statistically significant for the S1 and the DLPFC. The time course of the negative signal change was very similar to that of the positive signal change, suggesting that the latter was not "driving" the former. The location of negative and positive clusters formed distinct patterns in several of the ROIs, and these patterns suggest something other than a local "steal" phenomenon as an explanation for the negative signal changes. Sex differences in baseline cerebral blood flow may contribute to the BOLD signal differences observed in this study.     

Resting-State Temporal Synchronization Networks Emerge from Connectivity Topology and Heterogeneity

Spatial patterns of coherent activity across different brain areas have been identified during the resting-state fluctuations of the brain. However, recent studies indicate that resting-state activity is not stationary, but shows complex temporal dynamics. We were interested in the spatiotemporal dynamics of the phase interactions among resting-state fMRI BOLD signals from human subjects. We found that the global phase synchrony of the BOLD signals evolves on a characteristic ultra-slow (<0.01Hz) time scale, and that its temporal variations reflect the transient formation and dissolution of multiple communities of synchronized brain regions. Synchronized communities reoccurred intermittently in time and across scanning sessions. We found that the synchronization communities relate to previously defined functional networks known to be engaged in sensory-motor or cognitive function, called resting-state networks (RSNs), including the default mode network, the somato-motor network, the visual network, the auditory network, the cognitive control networks, the self-referential network, and combinations of these and other RSNs. We studied the mechanism originating the observed spatiotemporal synchronization dynamics by using a network model of phase oscillators connected through the brain’s anatomical connectivity estimated using diffusion imaging human data. The model consistently approximates the temporal and spatial synchronization patterns of the empirical data, and reveals that multiple clusters that transiently synchronize and desynchronize emerge from the complex topology of anatomical connections, provided that oscillators are heterogeneous.     

Neural correlates of visual working memory: fMRI amplitude predicts task performance

We used fMRI to investigate how moment-to-moment neural activity contributes to success or failure on individual trials of a visual working memory (WM) task. We found that different nodes of a distributed cortical network were activated to a greater extent for correct compared to incorrect trials during stimulus encoding, memory maintenance during delays, and at test. A logistic regression analysis revealed that the fMRI signal amplitude during the delay interval in a network of frontoparietal regions predicted successful performance on a trial-by-trial basis. Differential delay activity occurred even for only those trials in which BOLD activity during encoding was strong, demonstrating that it was not a simple consequence of effective versus ineffective encoding. Our results indicate that accurate memory depends on strong sustained signals that span the delay interval of WM tasks.     

Measuring Granger Causality between cortical regions from voxelwise fMRI BOLD signals with LASSO

Functional brain network studies using the Blood Oxygen-Level Dependent (BOLD) signal from functional Magnetic Resonance Imaging (fMRI) are becoming increasingly prevalent in research on the neural basis of human cognition. An important problem in functional brain network analysis is to understand directed functional interactions between brain regions during cognitive performance. This problem has important implications for understanding top-down influences from frontal and parietal control regions to visual occipital cortex in visuospatial attention, the goal motivating the present study. A common approach to measuring directed functional interactions between two brain regions is to first create nodal signals by averaging the BOLD signals of all the voxels in each region, and to then measure directed functional interactions between the nodal signals. Another approach, that avoids averaging, is to measure directed functional interactions between all pairwise combinations of voxels in the two regions. Here we employ an alternative approach that avoids the drawbacks of both averaging and pairwise voxel measures. In this approach, we first use the Least Absolute Shrinkage Selection Operator (LASSO) to pre-select voxels for analysis, then compute a Multivariate Vector AutoRegressive (MVAR) model from the time series of the selected voxels, and finally compute summary Granger Causality (GC) statistics from the model to represent directed interregional interactions. We demonstrate the effectiveness of this approach on both simulated and empirical fMRI data. We also show that averaging regional BOLD activity to create a nodal signal may lead to biased GC estimation of directed interregional interactions. The approach presented here makes it feasible to compute GC between brain regions without the need for averaging. Our results suggest that in the analysis of functional brain networks, careful consideration must be given to the way that network nodes and edges are defined because those definitions may have important implications for the validity of the analysis.     

Cerebral cortical representation of reflexive and volitional swallowing in humans

The purpose of this study was to compare cerebral cortical representation of experimentally induced reflexive swallow with that of volitional swallow. Eight asymptomatic adults (24-27 yr) were studied by a single-trial functional magnetic resonance imaging technique. Reflexive swallowing showed bilateral activity concentrated to the primary sensory/motor regions. Volitional swallowing was represented bilaterally in the insula, prefrontal, cingulate, and parietooccipital regions in addition to the primary sensory/motor cortex. Intrasubject comparison showed that the total volume of activity during volitional swallowing was significantly larger than that activated during reflexive swallows in either hemisphere (P < 0.001). For volitional swallowing, the primary sensory/motor region contained the largest and the insular region the smallest volumes of activation in both hemispheres, and the total activated volume in the right hemisphere was significantly larger compared with the left (P < 0.05). Intersubject comparison showed significant variability in the volume of activity in each of the four volitional swallowing cortical regions. We conclude that reflexive swallow is represented in the primary sensory/motor cortex and that volitional swallow is represented in multiple regions, including the primary sensory/motor cortex, insular, prefrontal/cingulate gyrus, and cuneus and precuneus region. Non-sensory/motor regions activated during volitional swallow may represent swallow-related intent and planning and possibly urge.     

Altered small-world brain networks in schizophrenia patients during working memory performance

Impairment of working memory (WM) performance in schizophrenia patients (SZ) is well-established. Compared to healthy controls (HC), SZ patients show aberrant blood oxygen level dependent (BOLD) activations and disrupted functional connectivity during WM performance. In this study, we examined the small-world network metrics computed from functional magnetic resonance imaging (fMRI) data collected as 35 HC and 35 SZ performed a Sternberg Item Recognition Paradigm (SIRP) at three WM load levels. Functional connectivity networks were built by calculating the partial correlation on preprocessed time courses of BOLD signal between task-related brain regions of interest (ROIs) defined by group independent component analysis (ICA). The networks were then thresholded within the small-world regime, resulting in undirected binarized small-world networks at different working memory loads. Our results showed: 1) at the medium WM load level, the networks in SZ showed a lower clustering coefficient and less local efficiency compared with HC; 2) in SZ, most network measures altered significantly as the WM load level increased from low to medium and from medium to high, while the network metrics were relatively stable in HC at different WM loads; and 3) the altered structure at medium WM load in SZ was related to their performance during the task, with longer reaction time related to lower clustering coefficient and lower local efficiency. These findings suggest brain connectivity in patients with SZ was more diffuse and less strongly linked locally in functional network at intermediate level of WM when compared to HC. SZ show distinctly inefficient and variable network structures in response to WM load increase, comparing to stable highly clustered network topologies in HC.     

Hemodynamic traveling waves in human visual cortex

Functional MRI (fMRI) experiments rely on precise characterization of the blood oxygen level dependent (BOLD) signal. As the spatial resolution of fMRI reaches the sub-millimeter range, the need for quantitative modelling of spatiotemporal properties of this hemodynamic signal has become pressing. Here, we find that a detailed physiologically-based model of spatiotemporal BOLD responses predicts traveling waves with velocities and spatial ranges in empirically observable ranges. Two measurable parameters, related to physiology, characterize these waves: wave velocity and damping rate. To test these predictions, high-resolution fMRI data are acquired from subjects viewing discrete visual stimuli. Predictions and experiment show strong agreement, in particular confirming BOLD waves propagating for at least 5-10 mm across the cortical surface at speeds of 2-12 mm s-1. These observations enable fundamentally new approaches to fMRI analysis, crucial for fMRI data acquired at high spatial resolution.     

Changes in community structure of resting state functional connectivity in unipolar depression

Major depression is a prevalent disorder that imposes a significant burden on society, yet objective laboratory-style tests to assist in diagnosis are lacking. We employed network-based analyses of "resting state" functional neuroimaging data to ascertain group differences in the endogenous cortical activity between healthy and depressed subjects.We additionally sought to use machine learning techniques to explore the ability of these network-based measures of resting state activity to provide diagnostic information for depression. Resting state fMRI data were acquired from twenty two depressed outpatients and twenty two healthy subjects matched for age and gender. These data were anatomically parcellated and functional connectivity matrices were then derived using the linear correlations between the BOLD signal fluctuations of all pairs of cortical and subcortical regions.We characterised the hierarchical organization of these matrices using network-based matrics, with an emphasis on their mid-scale "modularity" arrangement. Whilst whole brain measures of organization did not differ between groups, a significant rearrangement of their community structure was observed. Furthermore we were able to classify individuals with a high level of accuracy using a support vector machine, primarily through the use of a modularity-based metric known as the participation index.In conclusion, the application of machine learning techniques to features of resting state fMRI network activity shows promising potential to assist in the diagnosis of major depression, now suggesting the need for validation in independent data sets.     

Swallow-related cerebral cortical activity maps are not specific to deglutition

Cortical representation of swallow-related motor tasks has not been systematically investigated. In this study, we elucidated and compared these cortical representations to those of volitional swallow using block-trial and single-trial methods. Fourteen volunteers were studied by functional magnetic resonance imaging. Cortical activation during both swallowing and swallow-related motor tasks that can be performed independent of swallowing, such as jaw clenching, lip pursing, and tongue rolling, was found in four general areas: the anterior cingulate, motor/premotor cortex, insula, and occipital/parietal region corresponding to Brodmann's areas 7, 19, and 31. Regions of activity, volume of activated voxels, and increases in signal intensity were found to be similar between volitional swallow and swallow-related motor tasks. These findings, using both block-trial and single-trial techniques, suggest that cerebral cortical regions activated during swallowing may not be specific to deglutitive function.     

Simultaneous EEG-fMRI during a Working Memory Task: Modulations in Low and High Frequency Bands

Background

EEG studies of working memory (WM) have demonstrated load dependent frequency band modulations. FMRI studies have localized load modulated activity to the dorsolateral prefrontal cortex (DLPFC), medial prefrontal cortex (MPFC), and posterior parietal cortex (PPC). Recently, an EEG-fMRI study found that low frequency band (theta and alpha) activity negatively correlated with the BOLD signal during the retention phase of a WM task. However, the coupling of higher (beta and gamma) frequencies with the BOLD signal during WM is unknown.

Methodology

In 16 healthy adult subjects, we first investigated EEG-BOLD signal correlations for theta (5–7 Hz), alpha1 (8–10), alpha2 (10–12 Hz), beta1 (13–20), beta2 (20–30 Hz), and gamma (30–40 Hz) during the retention period of a WM task with set size 2 and 5. Secondly, we investigated whether load sensitive brain regions are characterised by effects that relate frequency bands to BOLD signals effects.

Principal Findings

We found negative theta-BOLD signal correlations in the MPFC, PPC, and cingulate cortex (ACC and PCC). For alpha1 positive correlations with the BOLD signal were found in ACC, MPFC, and PCC; negative correlations were observed in DLPFC, PPC, and inferior frontal gyrus (IFG). Negative alpha2-BOLD signal correlations were observed in parieto-occipital regions. Beta1-BOLD signal correlations were positive in ACC and negative in precentral and superior temporal gyrus. Beta2 and gamma showed only positive correlations with BOLD, e.g., in DLPFC, MPFC (gamma) and IFG (beta2/gamma). The load analysis revealed that theta and—with one exception—beta and gamma demonstrated exclusively positive load effects, while alpha1 showed only negative effects.

Conclusions

We conclude that the directions of EEG-BOLD signal correlations vary across brain regions and EEG frequency bands. In addition, some brain regions show both load sensitive BOLD and frequency band effects. Our data indicate that lower as well as higher frequency brain oscillations are linked to neurovascular processes during WM.     

Using High Spatial Resolution to Improve BOLD fMRI Detection at 3T

For different functional magnetic resonance imaging experiments using blood oxygenation level-dependent (BOLD) contrast, the acquisition of T ₂*-weighted scans at a high spatial resolution may be advantageous in terms of time-course signal-to-noise ratio and of BOLD sensitivity when the regions are prone to susceptibility artifacts. In this study, we explore this solution by examining how spatial resolution influences activations elicited when appetizing food pictures are viewed. Twenty subjects were imaged at 3 T with two different voxel volumes, 3.4 μl and 27 μl. Despite the diminution of brain coverage, we found that high-resolution acquisition led to a better detection of activations. Though known to suffer to different degrees from susceptibility artifacts, the activations detected by high spatial resolution were notably consistent with those reported in published activation likelihood estimation meta-analyses, corresponding to taste-responsive regions. Furthermore, these regions were found activated bilaterally, in contrast with previous findings. Both the reduction of partial volume effect, which improves BOLD contrast, and the mitigation of susceptibility artifact, which boosts the signal to noise ratio in certain regions, explained the better detection noted with high resolution. The present study provides further evidences that high spatial resolution is a valuable solution for human BOLD fMRI, especially for studying food-related stimuli.     

The neural basis of centre-surround interactions in visual motion processing

Perception of a moving visual stimulus can be suppressed or enhanced by surrounding context in adjacent parts of the visual field. We studied the neural processes underlying such contextual modulation with fMRI. We selected motion selective regions of interest (ROI) in the occipital and parietal lobes with sufficiently well defined topography to preclude direct activation by the surround. BOLD signal in the ROIs was suppressed when surround motion direction matched central stimulus direction, and increased when it was opposite. With the exception of hMT+/V5, inserting a gap between the stimulus and the surround abolished surround modulation. This dissociation between hMT+/V5 and other motion selective regions prompted us to ask whether motion perception is closely linked to processing in hMT+/V5, or reflects the net activity across all motion selective cortex. The motion aftereffect (MAE) provided a measure of motion perception, and the same stimulus configurations that were used in the fMRI experiments served as adapters. Using a linear model, we found that the MAE was predicted more accurately by the BOLD signal in hMT+/V5 than it was by the BOLD signal in other motion selective regions. However, a substantial improvement in prediction accuracy could be achieved by using the net activity across all motion selective cortex as a predictor, suggesting the overall conclusion that visual motion perception depends upon the integration of activity across different areas of visual cortex.     

Concordance of Epileptic Networks Associated with Epileptic Spikes Measured by High-Density EEG and Fast fMRI

Objective

The present study aims to investigate whether a newly developed fast fMRI called MREG (magnetic resonance encephalography) measures metabolic changes related to interictal epileptic discharges (IED). For this purpose BOLD changes are correlated with the IED distribution and variability.

Methods

Patients with focal epilepsy underwent EEG-MREG using a 64 channel cap. IED voltage maps were generated using 32 and 64 channels and compared regarding their correspondence to the BOLD response. The extents of IEDs (defined as number of channels with >50% of maximum IED negativity) were correlated with the extents of positive and negative BOLD responses. Differences in inter-spike variability were investigated between interictal epileptic discharges (IED) sets with and without concordant positive or negative BOLD responses.

Results

17 patients showed 32 separate IED types. In 50% of IED types the BOLD changes could be confirmed by another independent imaging method. The IED extent significantly correlated with the positive BOLD extent (p = 0.04). In 6 patients the 64-channel EEG voltage maps better reflected the positive or negative BOLD response than the 32-channel EEG; in all others no difference was seen. Inter-spike variability was significantly lower in IED sets with than without concordant positive or negative BOLD responses (with p = 0.04).

Significance

Higher density EEG and fast fMRI seem to improve the value of EEG-fMRI in epilepsy. The correlation of positive BOLD and IED extent could suggest that widespread BOLD responses reflect the IED network. Inter-spike variability influences the likelihood to find IED concordant positive or negative BOLD responses, which is why single IED analysis may be promising.     

The neural basis of the blood-oxygen-level-dependent functional magnetic resonance imaging signal

Magnetic resonance imaging (MRI) has rapidly become an important tool in clinical medicine and biological research. Its functional variant (functional magnetic resonance imaging; fMRI) is currently the most widely used method for brain mapping and studying the neural basis of human cognition. While the method is widespread, there is insufficient knowledge of the physiological basis of the fMRI signal to interpret the data confidently with respect to neural activity. This paper reviews the basic principles of MRI and fMRI, and subsequently discusses in some detail the relationship between the blood-oxygen-level-dependent (BOLD) fMRI signal and the neural activity elicited during sensory stimulation. To examine this relationship, we conducted the first simultaneous intracortical recordings of neural signals and BOLD responses. Depending on the temporal characteristics of the stimulus, a moderate to strong correlation was found between the neural activity measured with microelectrodes and the BOLD signal averaged over a small area around the microelectrode tips. However, the BOLD signal had significantly higher variability than the neural activity, indicating that human fMRI combined with traditional statistical methods underestimates the reliability of the neuronal activity. To understand the relative contribution of several types of neuronal signals to the haemodynamic response, we compared local field potentials (LFPs), single- and multi-unit activity (MUA) with high spatio-temporal fMRI responses recorded simultaneously in monkey visual cortex. At recording sites characterized by transient responses, only the LFP signal was significantly correlated with the haemodynamic response. Furthermore, the LFPs had the largest magnitude signal and linear systems analysis showed that the LFPs were better than the MUAs at predicting the fMRI responses. These findings, together with an analysis of the neural signals, indicate that the BOLD signal primarily measures the input and processing of neuronal information within a region and not the output signal transmitted to other brain regions.     

Cerebral cortical representation of external anal sphincter contraction: effect of effort

The external anal sphincter (EAS) plays a critical role in maintaining fecal continence; however, cerebral cortical control of voluntary EAS contraction is not completely understood. Our aims were to determine the cortical areas associated with voluntary EAS contraction and to determine the effect of two levels of sphincter contraction effort on brain activity. Seventeen asymptomatic adults (ages 21-48, 9 male) were studied using functional magnetic resonance imaging (fMRI) to detect brain activity. Studies were done in two stages. In stage 1 (10 subjects, 5 male), anal sphincter pressure was monitored from a catheter-affixed bag. Subjects performed maximal and submaximal EAS contractions during two fMRI scanning sessions consisting of alternating 10-s intervals of sustained contraction and rest. In stage 2 studies, seven subjects (4 male) performed only maximum effort sphincter contractions without a catheter. EAS contraction was associated with multifocal fMRI activity in sensory/motor, anterior cingulate, prefrontal, parietal, occipital, and insular regions. Total cortical activity volume was significantly larger (P < 0.05) for maximal (5,175 +/- 720 microl) compared with submaximal effort contractions (2,558 +/- 306 microl). Similarly, percent fMRI signal change was significantly higher (P < 0.05) for maximal (4.8 +/- 0.1%) compared with submaximal effort contractions (2.2 +/- 0.1%). Cortical region-of-interest analysis showed the incidence of insular activation to be more common in women compared with men. Other cortical regions showed no such gender differences. fMRI activity detected in stage 2 showed similar regions of cortical activation to those of the stage 1 study. Willful contraction of the EAS is associated with multifocal cerebral cortical activity. The volume and intensity of cerebral cortical activation is commensurate with the level of contractile effort.     

Relating Alpha Power and Phase to Population Firing and Hemodynamic Activity Using a Thalamo-cortical Neural Mass Model

Oscillations are ubiquitous phenomena in the animal and human brain. Among them, the alpha rhythm in human EEG is one of the most prominent examples. However, its precise mechanisms of generation are still poorly understood. It was mainly this lack of knowledge that motivated a number of simultaneous electroencephalography (EEG) – functional magnetic resonance imaging (fMRI) studies. This approach revealed how oscillatory neuronal signatures such as the alpha rhythm are paralleled by changes of the blood oxygenation level dependent (BOLD) signal. Several such studies revealed a negative correlation between the alpha rhythm and the hemodynamic BOLD signal in visual cortex and a positive correlation in the thalamus. In this study we explore the potential generative mechanisms that lead to those observations. We use a bursting capable Stefanescu-Jirsa 3D (SJ3D) neural-mass model that reproduces a wide repertoire of prominent features of local neuronal-population dynamics. We construct a thalamo-cortical network of coupled SJ3D nodes considering excitatory and inhibitory directed connections. The model suggests that an inverse correlation between cortical multi-unit activity, i.e. the firing of neuronal populations, and narrow band local field potential oscillations in the alpha band underlies the empirically observed negative correlation between alpha-rhythm power and fMRI signal in visual cortex. Furthermore the model suggests that the interplay between tonic and bursting mode in thalamus and cortex is critical for this relation. This demonstrates how biophysically meaningful modelling can generate precise and testable hypotheses about the underpinnings of large-scale neuroimaging signals.     

A core system for the implementation of task sets

When performing tasks, humans are thought to adopt task sets that configure moment-to-moment data processing. Recently developed mixed blocked/event-related designs allow task set-related signals to be extracted in fMRI experiments, including activity related to cues that signal the beginning of a task block, "set-maintenance" activity sustained for the duration of a task block, and event-related signals for different trial types. Data were conjointly analyzed from mixed design experiments using ten different tasks and 183 subjects. Dorsal anterior cingulate cortex/medial superior frontal cortex (dACC/msFC) and bilateral anterior insula/frontal operculum (aI/fO) showed reliable start-cue and sustained activations across all or nearly all tasks. These regions also carried the most reliable error-related signals in a subset of tasks, suggesting that the regions form a "core" task-set system. Prefrontal regions commonly related to task control carried task-set signals in a smaller subset of tasks and lacked convergence across signal types.