Modelling large motion events in fMRI studies of patients with epilepsy

Stochastic dynamic causal modelling of fMRI data: should we care about neural noise?

Dynamic causal modelling (DCM) was introduced to study the effective connectivity among brain regions using neuroimaging data. Until recently, DCM relied on deterministic models of distributed neuronal responses to external perturbation (e.g., sensory stimulation or task demands). However, accounting for stochastic fluctuations in neuronal activity and their interaction with task-specific processes may be of particular importance for studying state-dependent interactions. Furthermore, allowing for random neuronal fluctuations may render DCM more robust to model misspecification and finesse problems with network identification. In this article, we examine stochastic dynamic causal models (sDCM) in relation to their deterministic counterparts (dDCM) and highlight questions that can only be addressed with sDCM. We also compare the network identification performance of deterministic and stochastic DCM, using Monte Carlo simulations and an empirical case study of absence epilepsy. For example, our results demonstrate that stochastic DCM can exploit the modelling of neural noise to discriminate between direct and mediated connections. We conclude with a discussion of the added value and limitations of sDCM, in relation to its deterministic homologue.

Direction-sensitive codes for observed head turns in human superior temporal sulcus

Humans and other primates are adept at using the direction of another's gaze or head turn to infer where that individual is attending. Research in macaque neurophysiology suggests that anterior superior temporal sulcus (STS) contains a direction-sensitive code for such social attention cues. By contrast, most human functional Magnetic resonance imaging (fMRI) studies report that posterior STS is responsive to social attention cues. It is unclear whether this functional discrepancy is caused by a species difference or by experimental design differences. Furthermore, social attention cues are dynamic in naturalistic social interaction, but most studies to date have been restricted to static displays. In order to address these issues, we used multivariate pattern analysis of fMRI data to test whether response patterns in human right STS distinguish between leftward and rightward dynamic head turns. Such head turn discrimination was observed in right anterior STS/superior temporal gyrus (STG). Response patterns in this region were also significantly more discriminable for head turn direction than for rotation direction in physically matched ellipsoid control stimuli. Our findings suggest a role for right anterior STS/STG in coding the direction of motion in dynamic social attention cues.

Representation and propagation of epileptic activity in absences and generalized photoparoxysmal responses

Although functional imaging studies described networks associated with generalized epileptic activity, propagation patterns within these networks are not clear. In this study, electroencephalogram (EEG)-based coherent source imaging dynamic imaging of coherent sources (DICS) was applied to different types of generalized epileptiform discharges, namely absence seizures (10 patients) and photoparoxysmal responses (PPR) (eight patients) to describe the representation and propagation of these discharges in the brain. The results of electrical source imaging were compared to EEG-functional magnetic resonance imaging (fMRI) which had been obtained from the same data sets of simultaneous EEG and fMRI recordings. Similar networks were described by DICS and fMRI: (1) absence seizures were associated with thalamic involvement in all patients. Concordant results were also found for brain areas of the default mode network and the occipital cortex. (2) Both DICS and fMRI identified the occipital, parietal, and the frontal cortex in a network associated with PPR. (3) However, only when PPR preceded a generalized tonic-clonic seizure, the thalamus was involved in the generation of PPR as shown by both imaging techniques. Partial directed coherence suggested that during absences, the thalamus acts as a pacemaker while PPR could be explained by a cortical propagation from the occipital cortex via the parietal cortex to the frontal cortex. In conclusion, the electrical source imaging is not only able to describe similar neuronal networks as revealed by fMRI, including deep sources of neuronal activity such as the thalamus, but also demonstrates interactions interactions within these networks and sheds light on pathogenetic mechanisms of absence seizures and PPR.

Improving the sensitivity of EEG-fMRI studies of epileptic activity by modelling eye blinks, swallowing and other video-EEG detected physiological confounds

RATIONALE

To improve the sensitivity and specificity of simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) it is prudent to devise modelling strategies explaining the residual variance. The purpose of this study is to investigate the potential value of including additional regressors for physiological activities, derived from video-EEG, in the modelling of haemodynamic patterns linked to interictal epileptiform discharges (IEDs) using simultaneously recorded video-EEG-fMRI.

METHODS

Ten patients with IED (focal epilepsy: 6, idiopathic generalized epilepsy (IGE):4) were studied. BOLD-sensitive fMRI images were acquired on a 3T MRI scanner. 64-channel EEG was recorded using MR-compatible system. A custom made, dual-video-camera system synchronised with EEG was used to record video simultaneously. IEDs and physiological activities were identified and labelled on video-EEG using Brain Analyzer2. fMRI time-series data were pre-processed and analysed using SPM5 software. Two general linear models (GLM) were created; GLM1: IEDs were convolved with the canonical haemodynamic response function and its derivatives. Realignment parameters and pulse regressors were included in the design matrix as confounds, GLM2: GLM1 and additional regressors identified on video-EEG including: eye blinks, hand or foot movement, chewing and swallowing were also included in the design matrix. SPM [F] maps (p<0.05, corrected for family wise error and p<0.001, uncorrected) were generated for both models. We compared the resulting blood oxygen level dependent (BOLD) maps for cluster size, statistical significance and degree of concordance with the irritative zone.

RESULTS

BOLD changes relating to physiological activities were generally seen in expected brain areas. In patients with focal epilepsy, the extent and Z-score of the IED-related global maximum BOLD clusters increased in 4/6 patients and additional IED-related BOLD clusters were observed in 3/6 patients for GLM2. Also, the degree of concordance of IED-related maps with irritative zone improved for one patient for GLM2 and was unchanged for the other cases. In patients with IGE, the size and statistical significance for global maximum and other BOLD clusters increased in 2/4 patients. We conclude that the inclusion of additional regressors, derived from video based information, in the design matrix explains a greater amount of variance and can reveal additional IED-related BOLD clusters which may be part of the epileptic networks.

Association between CHRNA5 genetic variation at rs16969968 and brain reactivity to smoking images in nicotine dependent women

BACKGROUND

Tobacco smoking is the leading preventable cause of death in the developed world. Identifying risk factors for smoking may lead to more effective treatments. Genome wide association studies revealed a relationship between development of nicotine dependence and a single-nucleotide polymorphism (SNP, rs16969968) of the nicotine acetylcholine receptor (nAChR) alpha-5 subunit gene (CHRNA5). The relationship between this SNP and other factors contributing to smoking behavior such as smoking cue reactivity is unclear.

METHODS

We assessed the role of rs16969968 on brain functional MRI (fMRI) reactivity to smoking cues by studying nicotine dependent women with the nicotine dependence 'risk' allele (A allele, N=14) and without the 'risk' allele (G/G smokers, N=10). Nicotine dependence severity, as assessed with the Fagerstrom test for nicotine dependence, smoking pack-years, and expired carbon monoxide levels, were equivalent in these groups.

RESULTS

We observed a group difference in fMRI reactivity; women without the A allele (G/G smokers) showed greater fMRI reactivity to smoking images in brain areas related to memory and habitual behavior such as the hippocampus and dorsal striatum.

CONCLUSIONS

Our finding suggests that nicotine-dependent smokers lacking the rs16969968 A allele are more likely to recall smoking-related memories and engage in habitual responding to smoking cues than A allele smokers. Although more studies are necessary to determine the mechanism underlying and significance of this cue reactivity difference, these data suggest that smokers may develop and remain nicotine dependent due to different factors including genetics and cue reactivity. This finding may have implications for personalizing smoking treatment.

Mapping hemodynamic correlates of seizures using fMRI: A review

Functional magnetic resonance imaging (fMRI) is able to detect changes in blood oxygenation level associated with neuronal activity throughout the brain. For more than a decade, fMRI alone or in combination with simultaneous EEG recording (EEG-fMRI) has been used to investigate the hemodynamic changes associated with interictal and ictal epileptic discharges. This is the first literature review to focus on the various fMRI acquisition and data analysis methods applied to map epileptic seizure-related hemodynamic changes from the first report of an fMRI scan of a seizure to the present day. Two types of data analysis approaches, based on temporal correlation and data driven, are explained and contrasted. The spatial and temporal relationship between the observed hemodynamic changes using fMRI and other non-invasive and invasive electrophysiological and imaging data is considered. We then describe the role of fMRI in localizing and exploring the networks involved in spontaneous and triggered seizure onset and propagation. We also discuss that fMRI alone and combined with EEG hold great promise in the investigation of seizure-related hemodynamic changes non-invasively in humans. We think that this will lead to significant improvements in our understanding of seizures with important consequences for the treatment of epilepsy.

A head view-invariant representation of gaze direction in anterior superior temporal sulcus

Humans show a remarkable ability to discriminate others' gaze direction, even though a given direction can be conveyed by many physically dissimilar configurations of different eye positions and head views. For example, eye contact can be signaled by a rightward glance in a left-turned head or by direct gaze in a front-facing head. Such acute gaze discrimination implies considerable perceptual invariance. Previous human research found that superior temporal sulcus (STS) responds preferentially to gaze shifts [1], but the underlying representation that supports such general responsiveness remains poorly understood. Using multivariate pattern analysis (MVPA) of human functional magnetic resonance imaging (fMRI) data, we tested whether STS contains a higher-order, head view-invariant code for gaze direction. The results revealed a finely graded gaze direction code in right anterior STS that was invariant to head view and physical image features. Further analyses revealed similar gaze effects in left anterior STS and precuneus. Our results suggest that anterior STS codes the direction of another's attention regardless of how this information is conveyed and demonstrate how high-level face areas carry out fine-grained, perceptually relevant discrimination through invariance to other face features.

Improving sensitivity of EEG-fMRI studies in epilepsy: the role of sleep-specific activity

Using simultaneous recordings of EEG and functional MRI (EEG-fMRI) in patients with focal epilepsy, recent studies have revealed insufficient sensitivity and a lack of correspondence between epileptic EEG foci and activation patterns in some patients. In this study of children with focal epilepsy, we explore whether sleep-specific activity (sleep spindles, k-complexes and vertex sharp waves) may increase the sensitivity of EEG-fMRI of interictal epileptiform discharges (IED). When considering the sleep-specific activity in a statistical model, it was possible to increase the statistical significance of the activated voxels inside of the expected source of the IED and to reduce the number of activated voxels outside of it. According to this study, it could be worthwhile to include sleep-specific activity into the model by analyzing EEG-fMRI data in epilepsy.

Perception and conception: temporal lobe activity during complex discriminations of familiar and novel faces and objects

Recent studies indicate that medial-temporal lobe (MTL) damage, either from focal lesions or neurodegenerative disease (e.g., semantic dementia), impairs perception as well as long-term declarative memory. Notably, however, these two patient groups show different performance for meaningful versus unfamiliar stimuli. In amnesics with nonprogressive MTL lesions, the use of meaningful stimuli, compared with unfamiliar items, boosted discrimination performance. In semantic dementia, a condition characterized by progressive deterioration of conceptual knowledge in the context of anterolateral temporal lobe damage, performance for meaningful stimuli was equivalent to that for unfamiliar items. To further investigate these findings, we scanned healthy volunteers while they performed odd-one-out discriminations involving familiar (i.e., meaningful/famous) and unfamiliar (i.e., novel) objects and faces and a baseline task of size oddity. Outside the scanner, volunteers' recognition memory was assessed. We found above baseline activity in the perirhinal cortex and hippocampus for all object and face discriminations and above baseline activity in the temporal pole for all face discriminations. The comparison of meaningful, relative to novel, faces and objects, revealed increased activity in the perirhinal cortex and hippocampus. In the temporal pole, we also found activity related to meaningfulness for faces but not for objects. Importantly, these meaningfulness effects were evident even for discriminations that were not subsequently well remembered, suggesting that the difference between meaningful and novel stimuli reflects perceptual or conceptual processes rather than solely incidental encoding into long-term memory. The results provide further evidence that the MTL is recruited during complex perceptual discrimination and additionally suggest that these structures are recruited in semantic processing of objects and faces.

A network approach to assessing cognition in disorders of consciousness

OBJECTIVE

Conventional assessments of consciousness rely on motor responses to indicate awareness. However, overt behaviors may be absent or ambiguous in patients with disorders of consciousness (DOC) resulting in underrating capacity for cognition. fMRI during a silent picture-naming task was evaluated as an indicator of command following when conventional methods are not sufficient.

METHODS

A total of 10 patients with and without conventional evidence of awareness, who met diagnostic criteria for the minimally conscious state (MCS) (n = 5), vegetative state (VS) (n = 3), emerged from MCS (EMCS) (n = 1), and locked-in syndrome (LIS) (n = 1), participated in this observational fMRI study.

RESULTS

The LIS and EMCS patients engaged a complete network of essential language-related regions during the object-naming task. The MCS and 2 of the VS patients demonstrated both complete and partial preservation of the object-naming system. Patients who engaged a complete network scored highest on the Coma Recovery Scale-Revised.

CONCLUSIONS

This study supports the view that fMRI during object naming can elicit brain activations in patients with DOC similar to those observed in healthy subjects during command following, and patients can be stratified by completeness of the engaged neural system. These results suggest that activity of the language network may serve as an indicator of high-level cognition and possibly volitional processes that cannot be discerned through conventional behavioral assessment alone.

Brain reactivity to smoking cues prior to smoking cessation predicts ability to maintain tobacco abstinence

BACKGROUND

Developing the means to identify smokers at high risk for relapse could advance relapse prevention therapy. We hypothesized that functional magnetic resonance imaging (fMRI) reactivity to smoking-related cues, measured before a quit attempt, could identify smokers with heightened relapse vulnerability.

METHODS

Before quitting smoking, 21 nicotine-dependent women underwent fMRI during which smoking-related and neutral images were shown. These smokers also were tested for possible attentional biases to smoking-related words using a computerized emotional Stroop (ES) task previously found to predict relapse. Smokers then made a quit attempt and were grouped based on outcomes (abstinence vs. slip: smoking > or = 1 cigarette after attaining abstinence). Prequit fMRI and ES measurements in these groups were compared.

RESULTS

Slip subjects had heightened fMRI reactivity to smoking-related images in brain regions implicated in emotion, interoceptive awareness, and motor planning and execution. Insula and dorsal anterior cingulate cortex (dACC) reactivity induced by smoking images correlated with an attentional bias to smoking-related words. A discriminant analysis of ES and fMRI data predicted outcomes with 79% accuracy. Additionally, smokers who slipped had decreased fMRI functional connectivity between an insula-containing network and brain regions involved in cognitive control, including the dACC and dorsal lateral prefrontal cortex, possibly reflecting reduced top-down control of cue-induced emotions.

CONCLUSIONS

These findings suggest that the insula and dACC are important substrates of smoking relapse vulnerability. The data also suggest that relapse-vulnerable smokers can be identified before quit attempts, which could enable personalized treatment, improve tobacco-dependence treatment outcomes, and reduce smoking-related morbidity and mortality.

Dynamic causal modelling of effective connectivity from fMRI: are results reproducible and sensitive to Parkinson's disease and its treatment?

Dynamic causal modelling (DCM) of functional magnetic resonance imaging (fMRI) data offers new insights into the pathophysiology of neurological disease and mechanisms of effective therapies. Current applications can be used both to identify the most likely functional brain network underlying observed data and estimate the networks' connectivity parameters. We examined the reproducibility of DCM in healthy subjects (young 18–48 years, n = 27; old 50–80 years, n = 15) in the context of action selection. We then examined the effects of Parkinson's disease (50–78 years, Hoehn and Yahr stage 1–2.5, n = 16) and dopaminergic therapy. Forty-eight models were compared, for each of 90 sessions from 58 subjects. Model-evidences clustered according to sets of structurally similar models, with high correlations over two sessions in healthy older subjects. The same model was identified as most likely in healthy controls on both sessions and in medicated patients. In this most likely network model, the selection of action was associated with enhanced coupling between prefrontal cortex and the pre-supplementary motor area. However, the parameters for intrinsic connectivity and contextual modulation in this model were poorly correlated across sessions. A different model was identified in patients with Parkinson's disease after medication withdrawal. In “off” patients, action selection was associated with enhanced connectivity from prefrontal to lateral premotor cortex. This accords with independent evidence of a dopamine-dependent functional disconnection of the SMA in Parkinson's disease. Together, these results suggest that DCM model selection is robust and sensitive enough to study clinical populations and their pharmacological treatment. For critical inferences, model selection may be sufficient. However, caution is required when comparing groups or drug effects in terms of the connectivity parameter estimates, if there are significant posterior covariances among parameters.

Looking for neuronal currents using MRI: an EEG-fMRI investigation of fast MR signal changes time-locked to frequent focal epileptic discharges

RATIONALE

Reproducible direct measurement of neuronal electrical activity using MRI signal changes due to local magnetic field perturbations would represent a step change in neuroimaging methods. While some previous studies using experiments based on evoked and spontaneous activity provided encouraging results no clear demonstration of neuronal current-related MR changes in the human brain has emerged to date. The availability of simultaneously acquired EEG-fMRI in patients with frequent interictal epileptic discharges (IED), which have significantly greater amplitude than evoked potentials, offers the opportunity to further investigate the phenomenon.

METHODS

We re-analysed simultaneously acquired EEG-fMRI data in 6 epilepsy patients with very frequent focal IED and a well-localised generator. A model of MRI signal changes due to fast activity and BOLD signal changes was used to identify fast MR signal changes, potentially directly reflecting neuronal activity. Simultaneously-acquired EEG allowed the comparison of electrical source localisation (ESI), clinical epilepsy localisation and BOLD signal changes with the fast MR signal changes.

RESULTS

Clusters of IED-related fast MR signal change were observed in all cases. Spatial correspondence between the IED-related fast MR, BOLD, ESI clusters and irritative zone (IZ) was observed in one slice of a single dataset. The other IED-related fast MR clusters were remote from electro-clinically determined generators of interictal activity. The sign and magnitude of the fast MR signal changes varied across regions and subjects.

CONCLUSION

The observed fast MR changes cannot be confidently attributed to the direct effect of neuronal currents due to lack of spatial concordance with generators of interictal activity, IED-related BOLD clusters and ESI estimates.

Language lateralization correlates with verbal memory performance in children with focal epilepsy

PURPOSE

Assessment of language dominance with functional magnetic resonance imaging (fMRI) and neuropsychological evaluation is often used prior to epilepsy surgery. This study explores whether language lateralization and cognitive performance are systematically related in young patients with focal epilepsy.

METHODS

Language fMRI and neuropsychological data (language, visuospatial functions, and memory) of 40 patients (7-18 years of age) with unilateral, refractory focal epilepsy in temporal and/or frontal areas of the left (n = 23) or right hemisphere (n = 17) were analyzed. fMRI data of 18 healthy controls (7-18 years) served as a normative sample. A laterality index was computed to determine the lateralization of activation in three regions of interest (frontal, parietal, and temporal).

RESULTS

Atypical language lateralization was demonstrated in 12 (30%) of 40 patients. A correlation between language lateralization and verbal memory performance occurred in patients with left-sided epilepsy over all three regions of interest, with bilateral or right-sided language lateralization being correlated with better verbal memory performance (Word Pairs Recall: frontal r = -0.4, p = 0.016; parietal r = -0.4, p = 0.043; temporal r = -0.4, p = 0.041). Verbal memory performance made the largest contribution to language lateralization, whereas handedness and side of seizures did not contribute to the variance in language lateralization.

DISCUSSION

This finding reflects the association between neocortical language and hippocampal memory regions in patients with left-sided epilepsy. Atypical language lateralization is advantageous for verbal memory performance, presumably a result of transfer of verbal memory function. In children with focal epilepsy, verbal memory performance provides a better idea of language lateralization than handedness and side of epilepsy and lesion.

Insights into brain development from neurogenetic syndromes: evidence from fragile X syndrome, Williams syndrome, Turner syndrome and velocardiofacial syndrome

Over the past few decades, behavioral, neuroimaging and molecular studies of neurogenetic conditions, such as Williams, fragile X, Turner and velocardiofacial (22q11.2 deletion) syndromes, have led to important insights regarding brain development. These investigations allow researchers to examine "experiments of nature" in which the deletion or alteration of one gene or a contiguous set of genes can be linked to aberrant brain structure or function. Converging evidence across multiple imaging modalities has now begun to highlight the abnormal neural circuitry characterizing many individual neurogenetic syndromes. Furthermore, there has been renewed interest in combining analyses across neurogenetic conditions in order to search for common organizing principles in development. In this review, we highlight converging evidence across syndromes from multiple neuroimaging modalities, with a particular emphasis on functional imaging. In addition, we discuss the commonalities and differences pertaining to selective deficits in visuospatial processing that occur across four neurogenetic syndromes. We suggest avenues for future exploration, with the goal of achieving a deeper understanding of the neural abnormalities in these affected populations.

Independent component analysis reveals dynamic ictal BOLD responses in EEG-fMRI data from focal epilepsy patients

INTRODUCTION

Seizures occur rarely during EEG-fMRI acquisitions of epilepsy patients, but can potentially offer a better estimation of the epileptogenic zone than interictal activity. Independent component analysis (ICA) is a data-driven method that imposes minimal constraints on the hemodynamic response function (HRF). In particular, the investigation of HRFs with clear peaks, but varying latency, may be used to differentiate the ictal focus from propagated activity.

METHODS

ICA was applied on ictal EEG-fMRI data from 15 patients. Components related to seizures were identified by fitting an HRF to the component time courses at the time of the ictal EEG events. HRFs with a clear peak were used to derive maps of significant BOLD responses and their associated peak delay. The results were then compared with those obtained from a general linear model (GLM) method. Concordance with the presumed epileptogenic focus was also assessed.

RESULTS

The ICA maps were significantly correlated with the GLM maps for each patient (Spearman's test, p<0.05). The ictal BOLD responses identified by ICA always included the presumed epileptogenic zone, but were also more widespread, accounting for 20.3% of the brain volume on average. The method provided a classification of the components as a function of peak delay. BOLD response clusters associated with early HRF peaks were concordant with the suspected epileptogenic focus, while subsequent HRF peaks may correspond to ictal propagation.

CONCLUSION

ICA applied to EEG-fMRI can detect areas of significant BOLD response to ictal events without having to predefine an HRF. By estimating the HRF peak time in each identified region, the method could also potentially provide a dynamic analysis of ictal BOLD responses, distinguishing onset from propagated activity.

Causal hierarchy within the thalamo-cortical network in spike and wave discharges

BACKGROUND

Generalised spike wave (GSW) discharges are the electroencephalographic (EEG) hallmark of absence seizures, clinically characterised by a transitory interruption of ongoing activities and impaired consciousness, occurring during states of reduced awareness. Several theories have been proposed to explain the pathophysiology of GSW discharges and the role of thalamus and cortex as generators. In this work we extend the existing theories by hypothesizing a role for the precuneus, a brain region neglected in previous works on GSW generation but already known to be linked to consciousness and awareness. We analysed fMRI data using dynamic causal modelling (DCM) to investigate the effective connectivity between precuneus, thalamus and prefrontal cortex in patients with GSW discharges.

METHODOLOGY AND PRINCIPAL FINDINGS

We analysed fMRI data from seven patients affected by Idiopathic Generalized Epilepsy (IGE) with frequent GSW discharges and significant GSW-correlated haemodynamic signal changes in the thalamus, the prefrontal cortex and the precuneus. Using DCM we assessed their effective connectivity, i.e. which region drives another region. Three dynamic causal models were constructed: GSW was modelled as autonomous input to the thalamus (model A), ventromedial prefrontal cortex (model B), and precuneus (model C). Bayesian model comparison revealed Model C (GSW as autonomous input to precuneus), to be the best in 5 patients while model A prevailed in two cases. At the group level model C dominated and at the population-level the p value of model C was approximately 1.

CONCLUSION

Our results provide strong evidence that activity in the precuneus gates GSW discharges in the thalamo-(fronto) cortical network. This study is the first demonstration of a causal link between haemodynamic changes in the precuneus -- an index of awareness -- and the occurrence of pathological discharges in epilepsy.

The spatio-temporal mapping of epileptic networks: combination of EEG-fMRI and EEG source imaging

Simultaneous EEG-fMRI acquisitions in patients with epilepsy often reveal distributed patterns of Blood Oxygen Level Dependant (BOLD) change correlated with epileptiform discharges. We investigated if electrical source imaging (ESI) performed on the interictal epileptiform discharges (IED) acquired during fMRI acquisition could be used to study the dynamics of the networks identified by the BOLD effect, thereby avoiding the limitations of combining results from separate recordings. Nine selected patients (13 IED types identified) with focal epilepsy underwent EEG-fMRI. Statistical analysis was performed using SPM5 to create BOLD maps. ESI was performed on the IED recorded during fMRI acquisition using a realistic head model (SMAC) and a distributed linear inverse solution (LAURA). ESI could not be performed in one case. In 10/12 remaining studies, ESI at IED onset (ESIo) was anatomically close to one BOLD cluster. Interestingly, ESIo was closest to the positive BOLD cluster with maximal statistical significance in only 4/12 cases and closest to negative BOLD responses in 4/12 cases. Very small BOLD clusters could also have clinical relevance in some cases. ESI at later time frame (ESIp) showed propagation to remote sources co-localised with other BOLD clusters in half of cases. In concordant cases, the distance between maxima of ESI and the closest EEG-fMRI cluster was less than 33 mm, in agreement with previous studies. We conclude that simultaneous ESI and EEG-fMRI analysis may be able to distinguish areas of BOLD response related to initiation of IED from propagation areas. This combination provides new opportunities for investigating epileptic networks.

Endogenous brain oscillations and related networks detected by surface EEG-combined fMRI

It is difficult to study the brain "at rest" with an approach generally pursued in science when external manipulation (independent variable) is used to obtain informative measurements (dependent variable) about the object of interest. External manipulation in its classic sense may suspend the resting state, and hence the object of interest will evade. Naturally, unless in a final and irreversible state, biological rest will always be an endogenously dynamic process. Combining two modalities, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), to simultaneously measure the brain's activity from two angles, one can be chosen to be interpreted as the independent variable and the other as the dependent variable, and without external manipulation the brain's spontaneous dynamics can be studied. The EEG, for example, observes endogenous modulations of vigilance and detects spontaneous events such as sleep spindles or epileptic discharges and can be used as the independent variable, i.e., to form a regressor to interrogate the fMRI data (dependent variable). The opposite is possible as well, and data fusion attempts try using all data both as dependent and independent variables at the same time. This review limits itself to an exemplary discussion of simultaneous EEG/fMRI studies in humans, and among a variety of proposed resting state networks only discusses a few, especially those for which non-resting state literature has proposed a functional meaning: the "default mode" network and an attentional network. It will be shown that one EEG feature can correlate with different fMRI activation maps and that a single resting state network may be associated with a variety of EEG patterns giving insight into the function of different resting states and the relationship between the two modalities in themselves.

Independent component analysis of interictal fMRI in focal epilepsy: Comparison with general linear model-based EEG-correlated fMRI

The general linear model (GLM) has been used to analyze simultaneous EEG-fMRI to reveal BOLD changes linked to interictal epileptic discharges (IED) identified on scalp EEG. This approach is ineffective when IED are not evident in the EEG. Data-driven fMRI analysis techniques that do not require an EEG derived model may offer a solution in these circumstances. We compared the findings of independent components analysis (ICA) and EEG-based GLM analyses of fMRI data from eight patients with focal epilepsy. Spatial ICA was used to extract independent components (IC) which were automatically classified as either BOLD-related, motion artefacts, EPI-susceptibility artefacts, large blood vessels, noise at high spatial or temporal frequency. The classifier reduced the number of candidate IC by 78%, with an average of 16 BOLD-related IC. Concordance between the ICA and GLM-derived results was assessed based on spatio-temporal criteria. In each patient, one of the IC satisfied the criteria to correspond to IED-based GLM result. The remaining IC were consistent with BOLD patterns of spontaneous brain activity and may include epileptic activity that was not evident on the scalp EEG. In conclusion, ICA of fMRI is capable of revealing areas of epileptic activity in patients with focal epilepsy and may be useful for the analysis of EEG-fMRI data in which abnormalities are not apparent on scalp EEG.