EEG-fMRI Studies Research Articles

High-quality multimodal MRI with simultaneous EEG using conductive ink and polymer-thick film nets.

Objective
Combining magnetic resonance imaging (MRI) and electroencephalography (EEG) provides a powerful tool for investigating brain function at varying spatial and temporal scales. Simultaneous acquisition of both modalities can provide unique information that a single modality alone cannot reveal. However, current simultaneous EEG-fMRI studies are limited to a small set of MRI sequences due to the image quality and safety limitations of commercially available MR-conditional EEG nets. We tested whether the Inknet2, a high-resistance polymer thick film (PTF) based EEG net that uses conductive ink, could enable the acquisition of a variety of MR image modalities with minimal artifacts by reducing the RF-shielding caused by traditional MR-conditional nets.

Approach
We first performed simulations to model the effect of the EEG nets on the magnetic field and image quality. We then performed phantom scans to test image quality with a conventional copper EEG net, with the new Inknet2, and without any EEG net. Finally, we scanned five human subjects at 3 Tesla (3T) and three human subjects at 7 Tesla (7T) with and without the Inknet2 to assess structural and functional MRI image quality.

Main results
Across these simulations, phantom scans, and human studies, the Inknet2 induced fewer artifacts than the conventional net and produced image quality similar to scans with no net present.

Significance
Our results demonstrate that high-quality structural and functional multimodal imaging across a variety of MRI pulse sequences at both 3T and 7T is achievable with an EEG net made with conductive ink and polymer thick film technology.&#xD.

EEG as a potential ground truth for the assessment of cognitive state in software development activities: A multimodal imaging study.

Cognitive human error and recent cognitive taxonomy on human error causes of software defects support the intuitive idea that, for instance, mental overload, attention slips, and working memory overload are important human causes for software bugs. In this paper, we approach the EEG as a reliable surrogate to MRI-based reference of the programmer's cognitive state to be used in situations where heavy imaging techniques are infeasible. The idea is to use EEG biomarkers to validate other less intrusive physiological measures, that can be easily recorded by wearable devices and useful in the assessment of the developer's cognitive state during software development tasks. Herein, our EEG study, with the support of fMRI, presents an extensive and systematic analysis by inspecting metrics and extracting relevant information about the most robust features, best EEG channels and the best hemodynamic time delay in the context of software development tasks. From the EEG-fMRI similarity analysis performed, we found significant correlations between a subset of EEG features and the Insula region of the brain, which has been reported as a region highly related to high cognitive tasks, such as software development tasks. We concluded that despite a clear inter-subject variability of the best EEG features and hemodynamic time delay used, the most robust and predominant EEG features, across all the subjects, are related to the Hjorth parameter Activity and Total Power features, from the EEG channels F4, FC4 and C4, and considering in most of the cases a hemodynamic time delay of 4 seconds used on the hemodynamic response function. These findings should be taken into account in future EEG-fMRI studies in the context of software debugging.

NeuXus open-source tool for real-time artifact reduction in simultaneous EEG-fMRI

The simultaneous acquisition of electroencephalography and functional magnetic resonance imaging (EEG-fMRI) allows the complementary study of the brain's electrophysiology and hemodynamics with high temporal and spatial resolution. One application with great potential is neurofeedback training of targeted brain activity, based on the real-time analysis of the EEG and/or fMRI signals. This depends on the ability to reduce in real time the severe artifacts affecting the EEG signal acquired with fMRI, mainly the gradient and pulse artifacts. A few methods have been proposed for this purpose, but they are either slow, hardware-dependent, publicly unavailable, or proprietary software. Here, we present a fully open-source and publicly available tool for real-time EEG artifact reduction in simultaneous EEG-fMRI recordings that is fast and applicable to any hardware. Our tool is integrated in the Python toolbox NeuXus for real-time EEG processing and adapts to a real-time scenario well-established artifact average subtraction methods combined with a long short-term memory network for R peak detection. We benchmarked NeuXus on three different datasets, in terms of artifact power reduction and background signal preservation in resting state, alpha-band power reactivity to eyes closure, and event-related desynchronization during motor imagery. We showed that NeuXus performed at least as well as the only available real-time tool for conventional hardware setups (BrainVision's RecView) and a well-established offline tool (EEGLAB's FMRIB plugin). We also demonstrated NeuXus’ real-time ability by reporting execution times under 250 ms. In conclusion, we present and validate the first fully open-source and hardware-independent solution for real-time artifact reduction in simultaneous EEG-fMRI studies.

Frontoparietal 18F-FDG-PET hypo-metabolism in Lennox-Gastaut syndrome: Further evidence highlighting the key network

IntroductionLennox Gastaut syndrome (LGS) can be conceptualised as a “secondary network epilepsy”, in which the shared electroclinical manifestations reflect epileptic recruitment of a common brain network, despite a range of underlying aetiologies. We aimed to identify the key networks recruited by the epileptic process of LGS using interictal 2-deoxy-2-(18F)fluoro-D-glucose positron emission tomography (18F-FDG-PET). MethodsGroup analysis of cerebral 18F-FDG-PET, comparing 21 patients with LGS (mean age = 15 years) and 18 pseudo-controls (mean age = 19 years), studied at Austin Health Melbourne, between 2004 and 2015. To minimise the influence of individual patient lesions in the LGS group, we only studied brain hemispheres without structural MRI abnormalities. The pseudo-control group consisted of age- and sex-matched patients with unilateral temporal lobe epilepsy, using only the hemispheres contralateral to the side of epilepsy. Voxel-wise permutation testing compared 18F-FDG-PET uptake between groups. Associations were explored between areas of altered metabolism and clinical variables (age of seizure onset, proportion of life with epilepsy, and verbal/nonverbal ability). Penetrance maps were calculated to explore spatial consistency of altered metabolic patterns across individual patients with LGS. ResultsAlthough not always readily apparent on visual inspection of individual patient scans, group analysis revealed hypometabolism in a network of regions including prefrontal and premotor cortex, anterior and posterior cingulate, inferior parietal lobule, and precuneus (p < 0.05, corrected for family-wise error). These brain regions tended to show a greater reduction in metabolism in non-verbal compared to verbal LGS patients, although this difference was not statistically significant. No areas of hypermetabolism were detected on group analysis, although ∼25 % of individual patients showed increased metabolism (relative to pseudo-controls) in the brainstem, putamen, thalamus, cerebellum, and pericentral cortex. DiscussionInterictal hypometabolism in frontoparietal cortex in LGS is compatible with our previous EEG-fMRI and SPECT studies showing that interictal bursts of generalised paroxysmal fast activity and tonic seizures recruit similar cortical regions. This study provides further evidence that these regions are central to the electroclinical expression of LGS.

A-PASS: an automated pipeline to analyze simultaneously acquired EEG-fMRI data for studying brain activities during sleep

Objective. Concurrent electroencephalography and functional magnetic resonance imaging (EEG-fMRI) signals can be used to uncover the nature of brain activities during sleep. However, analyzing simultaneously acquired EEG-fMRI data is extremely time consuming and experience dependent. Thus, we developed a pipeline, which we named A-PASS, to automatically analyze simultaneously acquired EEG-fMRI data for studying brain activities during sleep. Approach. A deep learning model was trained on a sleep EEG-fMRI dataset from 45 subjects and used to perform sleep stage scoring. Various fMRI indices can be calculated with A-PASS to depict the neurophysiological characteristics across different sleep stages. We tested the performance of A-PASS on an independent sleep EEG-fMRI dataset from 28 subjects. Statistical maps regarding the main effect of sleep stages and differences between each pair of stages of fMRI indices were generated and compared using both A-PASS and manual processing methods. Main results. The deep learning model implemented in A-PASS achieved both an accuracy and F1-score higher than 70% for sleep stage classification on EEG data acquired during fMRI scanning. The statistical maps generated from A-PASS largely resembled those produced from manually scored stages plus a combination of multiple software programs. Significance. A-PASS allowed efficient EEG-fMRI data processing without manual operation and could serve as a reliable and powerful tool for simultaneous EEG-fMRI studies on sleep.

Simultaneous electroencephalography-functional magnetic resonance imaging for assessment of human brain function.

Electroencephalography (EEG) and functional Magnetic Resonance Imaging (MRI) have long been used as tools to examine brain activity. Since both methods are very sensitive to changes of synaptic activity, simultaneous recording of EEG and fMRI can provide both high temporal and spatial resolution. Therefore, the two modalities are now integrated into a hybrid tool, EEG-fMRI, which encapsulates the useful properties of the two. Among other benefits, EEG-fMRI can contribute to a better understanding of brain connectivity and networks. This review lays its focus on the methodologies applied in performing EEG-fMRI studies, namely techniques used for the recording of EEG inside the scanner, artifact removal, and statistical analysis of the fMRI signal. We will investigate simultaneous resting-state and task-based EEG-fMRI studies and discuss their clinical and technological perspectives. Moreover, it is established that the brain regions affected by a task-based neural activity might not be limited to the regions in which they have been initiated. Advanced methods can help reveal the regions responsible for or affected by a developed neural network. Therefore, we have also looked into studies related to characterization of structure and dynamics of brain networks. The reviewed literature suggests that EEG-fMRI can provide valuable complementary information about brain neural networks and functions.

Electroencephalography-functional magnetic resonance imaging of epileptiform discharges: Noninvasive investigation of the whole brain.

Simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI) is a unique and noninvasive method for investigating epileptic activity. Interictal epileptiform discharge-related EEG-fMRI provides cortical and subcortical blood oxygen level-dependent (BOLD) signal changes specific to epileptic discharges. As a result, EEG-fMRI has revealed insights into generators and networks involved in epileptic activity in different types of epilepsy, demonstrating-for instance-the implication of the thalamus in human generalized spike and wave discharges and the role of the default mode network in absences and focal epilepsy, and has suggested a mechanism for the cortico-subcortical interactions in Lennox-Gastaut syndrome discharges. EEG-fMRI can find deep sources of epileptic activity not available to scalp EEG or magnetoencephalography, and provides critical new information to delineate the epileptic focus when considering surgical treatment or electrode implantation. In recent years, methodological advances, such as artifact removal and automatic detection of events, have rendered this method easier to implement, and its clinical potential has since been established by evidence of the impact of BOLD response on clinical decision-making and of the relationship between concordance of BOLD responses with extent of resection and surgical outcome. This review presents the recent developments in EEG-fMRI methodology and EEG-fMRI studies in different types of epileptic disorders as follows: EEG-fMRI acquisition, gradient and pulse artifact removal, statistical analysis, clinical applications, presurgical evaluation, altered physiological state in generalized genetic epilepsy, and pediatric EEG-fMRI studies.

SY6.4. EEG and fMRI recordings: Established tool or research topic?

EEG with functional MRI combines the spatial resolution of MRI with the temporal resolution of EEG, to reveal an epileptic focus or network. It images blood flow changes in the brain in response to the epileptic discharges of interest, providing a temporally smoothed, average response to a series of epileptic discharges, displayed as a statistical ‘activation map’. Key advantages of EEG-fMRI include that it is non-invasive and offers whole brain coverage, allowing visualisation of deep epileptic foci, and distributed epilepsy networks. Group EEG- fMRI studies have been particularly useful in revealing the brain networks responsible for epileptic syndromes, such as Benign Epilepsy with Centro-Temporal spikes, Genetic Generalised Epilepsy, and Lennox-Gastaut Syndrome, providing important mechanistic insights. When used as a tool for presurgical localisation in individuals, results have been mixed, with around 50% of studies providing helpful localisation. Practical limitations include difficulty detecting discharges due to the harsh recording environment of the MRI, sensitivity of fMRI to patient motion and insufficient epileptic discharges during the fMRI session. Even with optimal recordings some studies show no significant activation, possibly due to frequent epileptic discharges that are too low in amplitude to penetrate the scalp, contaminating the ‘reference’ periods used in analysis. Many EEG-fMRI studies show networks of brain regions are involved, rather than a single region, frustrating interpretation when seeking a surgical target. However, these patterns likely reflect real network recruitment by interictal discharges, and hence indicate the network through which epilepsy is being expressed. There is some evidence that when there is a network of spike related activation, seizure freedom rates are affected by the extent of removal of the region of greatest fMRI activation. Even when not providing definitive localisation, EEG- fMRI activation maps can provide a starting point for subsequent investigations such as additional structural imaging, or invasive studies.

Characterizing the seizure onset zone and epileptic network using EEG-fMRI in a rat seizure model

Accurate epileptogenic zone (EZ) or seizure onset zone (SOZ) localization is crucial for epilepsy surgery optimization. Previous animal and human studies on epilepsy have reported that changes in blood oxygen level-dependent (BOLD) signals induced by epileptic events could be used as diagnostic markers for EZ or SOZ localization. Simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) recording is gaining interest as a non-invasive tool for preoperative epilepsy evaluation. However, EEG-fMRI studies have reported inconsistent and ambiguous findings. Therefore, it remains unclear whether BOLD responses can be used for accurate EZ or SOZ localization. In this study, we used simultaneous EEG-fMRI recording in a rat model of 4-aminopyridine-induced acute focal seizures to assess the spatial concordance between individual BOLD responses and the SOZ. This was to determine the optimal use of simultaneous EEG-fMRI recording in the SOZ localization. We observed a high spatial consistency between BOLD responses and the SOZ. Further, dynamic BOLD responses were consistent with the regions where the seizures were propagated. These results suggested that simultaneous EEG-fMRI recording could be used as a noninvasive clinical diagnostic technique for localizing the EZ or SOZ and could be an effective tool for mapping epileptic networks.

The relationship between EEG and fMRI connectomes is reproducible across simultaneous EEG-fMRI studies from 1.5T to 7T

Both electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are non-invasive methods that show complementary aspects of human brain activity. Despite measuring different proxies of brain activity, both the measured blood-oxygenation (fMRI) and neurophysiological recordings (EEG) are indirectly coupled. The electrophysiological and BOLD signal can map the underlying functional connectivity structure at the whole brain scale at different timescales. Previous work demonstrated a moderate but significant correlation between resting-state functional connectivity of both modalities, however there is a wide range of technical setups to measure simultaneous EEG-fMRI and the reliability of those measures between different setups remains unknown. This is true notably with respect to different magnetic field strengths (low and high field) and different spatial sampling of EEG (medium to high-density electrode coverage). Here, we investigated the reproducibility of the bimodal EEG-fMRI functional connectome in the most comprehensive resting-state simultaneous EEG-fMRI dataset compiled to date including a total of 72 subjects from four different imaging centers. Data was acquired from 1.5T, 3T and 7T scanners with simultaneously recorded EEG using 64 or 256 electrodes. We demonstrate that the whole-brain monomodal connectivity reproducibly correlates across different datasets and that a moderate crossmodal correlation between EEG and fMRI connectivity of r ≈ 0.3 can be reproducibly extracted in low- and high-field scanners. The crossmodal correlation was strongest in the EEG-β frequency band but exists across all frequency bands. Both homotopic and within intrinsic connectivity network (ICN) connections contributed the most to the crossmodal relationship. This study confirms, using a considerably diverse range of recording setups, that simultaneous EEG-fMRI offers a consistent estimate of multimodal functional connectomes in healthy subjects that are dominantly linked through a functional core of ICNs across spanning across the different timescales measured by EEG and fMRI. This opens new avenues for estimating the dynamics of brain function and provides a better understanding of interactions between EEG and fMRI measures. This observed level of reproducibility also defines a baseline for the study of alterations of this coupling in pathological conditions and their role as potential clinical markers.

EEG signals respond differently to idea generation, idea evolution and evaluation in a loosely controlled creativity experiment

Many neurocognitive studies endeavor to understand neural mechanisms of basic creative activities in strictly controlled experiments. However, little evidence is available regarding the neural mechanisms of interactions between basic activities underlying creativity in such experiments. Moreover, strictly controlled experiments might limit flexibility/freedom needed for creative exploration. Thus, this study investigated the whole-brain neuronal networks’ interactions between three modes of thinking: idea generation, idea evolution, and evaluation in a loosely controlled creativity experiment. The loosely controlled creativity experiment will provide a degree of flexibility/freedom for participants to incubate creative ideas through extending response time from a few seconds to 3 min. In the experiment, participants accomplished a modified figural Torrance Test of Creative Thinking (TTCT-F) while their EEG signals were recorded. During idea generation, a participant was instructed to complete a sketch that was immediately triggered by a sketch stimulus at first sight. During idea evolution, a participant was instructed to complete a sketch that is radically distinctive from what was immediately triggered by the sketch stimulus. During the evaluation, a participant was instructed to evaluate difficulties of thinking and drawing during idea generation and evolution. It is expected that participants would use their experience to intuitively complete a sketch during idea generation while they could use more divergent and imaginative thinking to complete a possible creative sketch during idea evolution. Such an experimental design is named as a loosely controlled creativity experiment, which offers an approach to studying creativity in an ecologically valid manner. The validity of the loosely controlled creativity experiment could be verified through comparing its findings on phenomena that have been effectively studied by validated experimental research. It was found from our experiment that alpha power decreased significantly from rest to the three modes of thinking. These findings are consistent with that from visual creativity research based on event-related (de)synchronization (ERD/ERS) and task-related power changes (TRP). Specifically, in the lower alpha band (8–10 Hz), the decreases of alpha power were significantly lower over almost the entire scalp during idea evolution compared to the other modes of thinking. This finding indicated that idea evolution requires less general attention demands than the other two modes of thinking since the lower alpha ERD has been reported as being more likely to reflect general task demands such as attentional processes. In the upper alpha band (10–12 Hz), the decreases of alpha power were significantly higher over central sites during the evaluation compared to idea evolution. This finding indicated that evaluation involves more task-specific demands since the upper alpha ERD has been found as being more likely to reflect task-specific demands such as memory and intelligence, as was defined in the literature. In addition, new findings were obtained since the loosely controlled creativity experiment could activate multiple brain networks to accomplish the tasks involving the three modes of thinking. EEG microstate analysis was used to structure the unstructured EEG data to detect the activation of multiple brain networks. Combined EEG-fMRI and EEG source localization studies have indicated that EEG microstate classes are closely associated with the resting-state network as identified using fMRI. It was found that the default mode network was more active during idea evolution compared to the other two modes of thinking, while the cognitive control network was more active during the evaluation compared to the other two modes of thinking. This finding indicated that idea evolution might be more associated with unconscious and internal directed attention processes. Taken together, the loosely controlled creativity experiment with the support of EEG microstate analysis appears to offer an effective approach to investigating the real-world complex creativity activity.

Sleep Spindle-dependent Functional Connectivity Correlates with Cognitive Abilities.

EEG studies have shown that interindividual differences in the electrophysiological properties of sleep spindles (e.g., density, amplitude, duration) are highly correlated with trait-like "reasoning" abilities (i.e., "fluid intelligence"; problem-solving skills; the ability to employ logic or identify complex patterns), but not interindividual differences in STM or "verbal" intellectual abilities. Previous simultaneous EEG-fMRI studies revealed brain activations time-locked to spindles. Our group has recently demonstrated that the extent of activation in a subset of these regions was related to interindividual differences in reasoning intellectual abilities, specifically. However, spindles reflect communication between spatially distant and functionally distinct brain areas. The functional communication among brain regions related to spindles and their relationship to reasoning abilities have yet to be investigated. Using simultaneous EEG-fMRI sleep recordings and psychophysiological interaction analysis, we identified spindle-related functional communication among brain regions in the thalamo-cortical-BG system, the salience network, and the default mode network. Furthermore, the extent of the functional connectivity of the cortical-striatal circuitry and the thalamo-cortical circuitry was specifically related to reasoning abilities but was unrelated to STM or verbal abilities, thus suggesting that individuals with higher fluid intelligence have stronger functional coupling among these brain areas during spontaneous spindle events. This may serve as a first step in further understanding the function of sleep spindles and the brain network functional communication, which support the capacity for fluid intelligence.

Adapted cabling of an EEG cap improves simultaneous measurement of EEG and fMRI at 7T

BackgroundThe combination of EEG and ultra-high-field (7 T and above) fMRI holds the promise to relate electrophysiology and hemodynamics with greater signal to noise level and at higher spatial resolutions than conventional field strengths. Technical and safety restrictions have so far resulted in compromises in terms of MRI coil selection, resulting in reduced, signal quality, spatial coverage and resolution in EEG-fMRI studies at 7 T. New methodWe adapted a 64-channel MRI-compatible EEG cap so that it could be used with a closed 32-channel MRI head coil thus avoiding several of these compromises. We compare functional and anatomical as well as the EEG quality recorded with this adapted setup with those recorded with a setup that uses an open-ended 8-channel head-coil. ResultsOur set-up with the adapted EEG cap inside the closed 32 channel coil resulted in the recording of good quality EEG and (f)MRI data. Both functional and anatomical MRI images show no major effects of the adapted EEG cap on MR signal quality. We demonstrate the ability to compute ERPs and changes in alpha and gamma oscillations from the recorded EEG data. Comparison with existing methodsCompared to MRI recordings with an 8-channel open-ended head-coil, the loss in signal quality of the MRI images related to the adapted EEG cap is considerably reduced. ConclusionsThe adaptation of the EEG cap permits the simultaneous recording of good quality whole brain (f)MRI data using a 32 channel receiver coil, while maintaining the quality of the EEG data.

Multi-band SWIFT enables quiet and artefact-free EEG-fMRI and awake fMRI studies in rat

Functional magnetic resonance imaging (fMRI) studies in animal models provide invaluable information regarding normal and abnormal brain function, especially when combined with complementary stimulation and recording techniques. The echo planar imaging (EPI) pulse sequence is the most common choice for fMRI investigations, but it has several shortcomings. EPI is one of the loudest sequences and very prone to movement and susceptibility-induced artefacts, making it suboptimal for awake imaging. Additionally, the fast gradient-switching of EPI induces disrupting currents in simultaneous electrophysiological recordings. Therefore, we investigated whether the unique features of Multi-Band SWeep Imaging with Fourier Transformation (MB-SWIFT) overcome these issues at a high 9.4 T magnetic field, making it a potential alternative to EPI. MB-SWIFT had 32-dB and 20-dB lower peak and average sound pressure levels, respectively, than EPI with typical fMRI parameters. Body movements had little to no effect on MB-SWIFT images or functional connectivity analyses, whereas they severely affected EPI data. The minimal gradient steps of MB-SWIFT induced significantly lower currents in simultaneous electrophysiological recordings than EPI, and there were no electrode-induced distortions in MB-SWIFT images. An independent component analysis of the awake rat functional connectivity data obtained with MB-SWIFT resulted in near whole-brain level functional parcellation, and simultaneous electrophysiological and fMRI measurements in isoflurane-anesthetized rats indicated that MB-SWIFT signal is tightly linked to neuronal resting-state activity. Therefore, we conclude that the MB-SWIFT sequence is a robust preclinical brain mapping tool that can overcome many of the drawbacks of conventional EPI fMRI at high magnetic fields.

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.

Abnormal Thalamic Functional Connectivity During Light Non–Rapid Eye Movement Sleep in Children With Primary Nocturnal Enuresis

To investigate abnormalities of thalamocortical and intrathalamic functional connectivity (FC) in children with primary nocturnal enuresis (PNE) during light non-rapid eye movement (NREM) sleep using a simultaneous electroencephalography (EEG)-functional magnetic resonance imaging (fMRI) method. Polysomnographic and EEG-fMRI data were obtained during sleep from 61 children with PNE (age 10.2 ± 1.7 years, 59% boys) and 61 age-matched controls (age 10.1 ± 1.4 years, 54% boys). All subjects first participated in one overnight video-polysomnographic study. Total sleep time, percentage of total sleep time in each sleep stage, arousal index, and awakening index were calculated. Simultaneous EEG-fMRI studies were then performed using a 3T MRI system with a 32-channel MRI-compatible EEG system. Visual scoring of EEG data permitted sleep staging. Thalamocortical and intrathalamic FCs in the waking state and at different stages of light sleep were calculated and compared. Children with PNE had a higher percentage of total sleep time in light sleep and a higher arousal index compared with controls. Abnormal thalamocortical FCs were detected in the lateral prefrontal cortex, medial prefrontal cortex, and inferior parietal lobule during light NREM sleep. Abnormal intrathalamic FCs were also detected during light NREM sleep among the motor, occipital, prefrontal, and temporal subdivisions of the thalamus. Abnormal prefrontal and parietal thalamocortical FCs, accompanied by abnormal intrathalamic FCs among the motor, occipital, prefrontal, and temporal subdivision of thalamus during light NREM sleep, may be related to abnormal sleep and enuresis in children with PNE.

Quantitative determination of concordance in localizing epileptic focus by component-based EEG-fMRI

Background and objectiveAccurate seizure onset zone (SOZ) localization is an essential step in pre-surgical assessment of patients with refractory focal epilepsy. Complex pathophysiology of epileptic cerebral structures, seizure types and frequencies have not been considered as influential features for accurate identification of SOZ using EEG-fMRI. There is a crucial need to quantitatively measure concordance between presumed SOZ and IED-related BOLD response in different brain regions to improve SOZ delineation. MethodsA novel component-based EEG-fMRI approach is proposed to measure physical distance between BOLD clusters and selected component dipole location using patient-specific high resolution anatomical images. The method is applied on 18 patients with refractory focal epilepsy to localize epileptic focus and determine concordance quantitatively and compare between maximum BOLD cluster with identified component dipole. To measure concordance, distance from a voxel with maximal z-score of maximum BOLD to center of extracted component dipole is measured. ResultsBOLD clusters to spikes distances for concordant (<25 mm), partially concordant (25–50 mm), and discordant (>50 mm) groups were significantly different (p < 0.0001). The results showed full concordance in 17 IED types (17.85 ± 4.69 mm), partial concordance in 4 (36.47 ± 8.84 mm), and nodiscordance, which is a significant rise compared to the existing literature. The proposed method is premised on the cross-correlation between the spike template outside the scanner and the highly-ranked extracted components. It successfully surpasses the limitations of conventional EEG-fMRI studies which are largely dependent on inside-scanner spikes. More significantly, the proposed method improves localization accuracy to 97% which marks a dramatic rise compared to conventional works. ConclusionsThis study demonstrated that BOLD changes were related to epileptic spikes in different brain regions in patients with refractory focal epilepsy. In a systematic quantitative approach, concordance levels based on the distance between center of maximum BOLD cluster and dipole were determined by component-based EEG-fMRI method. Therefore, component-based EEG-fMRI can be considered as a reliable predictor of SOZ in patients with focal epilepsy and included as part of clinical evaluation for patients with medically resistant epilepsy.

Pathophysiology of encephalopathy related to continuous spike and waves during sleep: the contribution of neuroimaging

In the last three decades, studies on functional neuroimaging have helped us to understand pathophysiological mechanisms responsible for electro-clinical patterns associated with epileptic encephalopathies with continuous spikes and waves during slow sleep (ECSWS). MEG and EEG source reconstruction have revealed sources of pathological brain activity associated with epileptiform discharges in the perisylvian region pointing to the significance of this brain area for ECSWS. PET studies have revealed areas of focal hypermetabolism in perisylvian, superior temporal and inferior parietal regions as well as central cortices which were related to epileptic activity. The widespread hypometabolism in regions that belong to the default network (prefrontal and posterior cingulate cortices, parahippocampal gyrus and precuneus) was interpreted as remote inhibition following epileptic activity, which could contribute to cognitive deficits in affected individuals. Note that the described metabolic changes were functional and disappeared after successful treatment and recovery of ECSWS and were found in both sleep and wakefulness which may account for cognitive deficits in patients during the day. EEG-fMRI studies have revealed a functional fingerprint of epileptic encephalopathy: significant positive BOLD signal changes were identified in the perisylvian regions, prefrontal cortex and anterior cingulate as well as thalamus and negative BOLD signal changes in the regions of the default mode network. The pattern of activation represents a propagation of epileptic activity specific to encephalopathy, which is independent of etiology and type of seizure associated with ECSWS. In summary, methods of neuroimaging have shed light on pathogenic mechanisms of ECSWS which may account for a number of clinical phenomena associated with this condition.

Component-related BOLD response to localize epileptic focus using simultaneous EEG-fMRI recordings at 3T

BackgroundSimultaneous EEG-fMRI experiments record spatiotemporal dynamics of epileptic activity. A shortcoming of spike-based EEG-fMRI studies is their inability to provide information about behavior of epileptic generators when no spikes are visible. New methodWe extract time series of epileptic components identified on EEG and fit them with Generalized Linear Model (GLM) model. This allows a precise and reliable localization of epileptic foci in addition to predicting generator's behavior. The proposed method works in the source domain and delineates generators considering spatial correlation between spike template and candidate components in addition to patient's medical records. ResultsThe proposed method was applied on 20 patients with refractory epilepsy and 20 age- and gender-matched healthy controls. The identified components were examined statistically and threshold of localization accuracy was determined as 86% based on Receiver Operating Characteristic (ROC) curve analysis. Accuracy, sensitivity, and specificity were found to be 88%, 85%, and 95%, respectively. Contribution of EEG-fMRI and concordance between EEG and fMRI were also evaluated. Concordance was found in 19 patients and contribution in 17. Comparison with existing methodsWe compared the proposed method with conventional methods. Our comparisons showed superiority of the proposed method. In particular, when epileptogenic zone was located deep in the brain, the method outperformed existing methods. ConclusionsThis study contributes substantially to increasing the yield of EEG-fMRI and presents a realistic estimate of the neural behavior of epileptic generators, to the best of our knowledge, for the first time in the literature.

Widespread interictal epileptic discharge more likely than focal discharges to unveil the seizure onset zone in EEG-fMRI

ObjectiveWe hypothesized that the number of interictal epileptic discharges (IEDs) during scan and their spatial extent are contributing factors in obtaining appropriate activations that reveal the seizure onset zone (SOZ) in EEG-fMRI. Methods157 IED types, each corresponding to one EEG scalp distribution, in 64 consecutive EEG-fMRI studies from 64 patients with refractory localization-related epilepsy were reviewed. To determine reliable activation, we used the threshold corresponding to corrected whole-brain topological false discovery rate (FDR). The location with maximum activation was compared to the presumed SOZ as defined by a comprehensive evaluation for each patient. ResultsThe number of IEDs was significantly higher in the types with t-value above FDR than with t-value below FDR. The presumed SOZ could be delineated in 30 of the 64 patients. Among these patients, the types of IED concordant with the SOZ had significantly larger extent on scalp EEG than the IED types discordant with the SOZ. ConclusionsThe number of IEDs is important factor in obtaining reliable activations in EEG-fMRI. IEDs with larger spatial extent are more likely to reveal, on maximum BOLD, accurate location of the SOZ. SignificanceWidespread discharges are more likely to yield a reliable activation for SOZ in EEG-fMRI.