Mapping Epileptic Networks: IED-Triggered Hemodynamic Changes Identified via Simultaneous EEG-fMRI Recordings

Interictal epileptiform discharges (IEDs) are brief neuronal discharges occurring between seizures in patients with epilepsy. The characterization of the hemodynamic response function (HRF) specific to IEDs could increase the accuracy of other functional imaging techniques to localize epileptiform activity, including functional near-infrared spectroscopy and functional magnetic resonance imaging. This study evaluated the possibility of using an intraoperative multispectral imaging system combined with electrocorticography (ECoG) to measure the average HRF associated with IEDs in eight patients. Inter-patient variability of the HRF is illustrated in terms of oxygenated hemoglobin peak latency, oxygenated hemoglobin increase/decrease following IEDs, and signal-to-noise ratio. A sub-region was identified using an unsupervised clustering algorithm in three patients that corresponded to the most active area identified by ECoG.

Analysis of EEG–fMRI data in focal epilepsy based on automated spike classification and Signal Space Projection

Outcome of epilepsy surgery remains suboptimal, calling for the identification of new, complementary biomarkers of the epileptogenic zone (EZ). Recently, we identified local wake slow waves (LoWS) as a potential regulator of network excitability that interacts with interictal epileptiform discharges (IEDs). In this study, we tested whether this interaction is associated with surgical outcome. In this retrospective study, we analyzed intracranial recordings from patients with intractable focal epilepsy who underwent surgery at the Hospital of the University of Pennsylvania. We used surgical success as an indicator that most or all of the EZ had been resected. We used linear mixed models to test whether the incidence of IEDs and LoWS, as well as their interaction, can accurately delineate the EZ in patients with successful vs poor outcome. Across 55 patients (30 women, mean age ±SD: 34 ± 10 years), we found that, although IEDs were more frequent in the seizure-onset zone, their rate in resected and nonresected areas was not associated with surgical success. Indeed, neither the rate of IEDs (F [1, 52.57] = 0.070, p = 0.793) nor that of LoWS (F [1, 48.81] = 1.1032, p = 0.299) in resected vs nonresected areas differed across surgical outcomes. Next, we examined their interaction, validating our previous findings in this larger, independent cohort, by confirming that the closer the LoWS are to an IED, the lower the network excitability during the IED. Furthermore, we found that the delay from IED to the subsequent LoWS-but not the reverse-is associated with surgical outcomes (IED to LoWS: F [1, 52.17] = 5.344, p = 0.025; LoWS to IED: F [1, 52.56] = 1.038, p = 0.313), with shorter delays observed within the EZ. We confirm this using classification analyses that yielded a significant accuracy of 63% (interquartile range: 57%-69%, p < 0.0001), underscoring its potential utility as an additional biomarker of the EZ. The temporal proximity of LoWS to a preceding IED in the resected cortex is associated with surgical outcome. This may reflect changes in the regulation of network excitability in the EZ as a form of homeostatic regulation. It raises the possibility to use this index as an additional prognostic biomarker in epilepsy surgery.

Using patient-specific hemodynamic response function in epileptic spike analysis of human epilepsy: a study based on EEG–fNIRS

We aimed to investigate patterns of electroencephalography-correlated functional MRI (EEG-fMRI) and subtle structural abnormalities in patients with mesial temporal lobe epilepsy (MTLE) with hippocampal sclerosis (MTLE-HS) or normal MRI (MTLE-NL). We evaluated EEG-fMRI acquisition of the 25 patients with diagnosis of MTLE who had interictal epileptiform discharges (IEDs) in the intra-MRI EEG: 13 MTLE-HS and 12 MTLE-NL. fMRI was performed using echo-planar images in a 3T MRI coupled with EEG acquired with 64 MRI-compatible electrodes. In the first level analyses, the time of the IEDs ipsilateral to the epileptogenic zone was used as the paradigm, and four contrasts maps were built according to the variation of the hemodynamic response function (HRF) peaks (0, +3, +5, and +7 s). Second level group analyses were performed combining the contrast maps of MTLE-HS or MTLE-NL patients with each different HRF obtained at the first level. Areas of gray matter atrophy were evaluated with voxel-based morphometry (VBM) in both groups. MTLE-HS and MTLE-NL had IED-related positive BOLD (posBOLD) detected in the ipsilateral anterior temporal lobe and insula. However, only MTLE-HS had significant posBOLD on contralateral hippocampus and anterior cingulate, whereas MTLE-NL had areas of posBOLD on ipsilateral frontal lobe. Both groups had significant IED-related negBOLD responses in areas of the default mode network (DMN), such as posterior cingulate and precuneus. There was no overlap of both posBOLD and negBOLD and areas of atrophy detected by VBM. Similar IEDs have different patterns of hemodynamic responses in sub-groups of MTLE. In both MTLE-HS and MTLE-NL, there is a possible suppression of the DMN related to IEDs, as demonstrated by the negBOLD in these areas. The brain areas involved in the interictal related hemodynamic network are not the regions with the most significant gray matter atrophy in MTLE with or without MRI signs of HS.

.SignificanceAdvances in electronics have allowed the recent development of compact, high channel count time domain functional near-infrared spectroscopy (TD-fNIRS) systems. Temporal moment analysis has been proposed for increased brain sensitivity due to the depth selectivity of higher order temporal moments. We propose a general linear model (GLM) incorporating TD moment data and auxiliary physiological measurements, such as short separation channels, to improve the recovery of the HRF.AimsWe compare the performance of previously reported multi-distance TD moment techniques to commonly used techniques for continuous wave (CW) fNIRS hemodynamic response function (HRF) recovery, namely block averaging and CW GLM. Additionally, we compare the multi-distance TD moment technique to TD moment GLM.ApproachWe augmented resting TD-fNIRS moment data (six subjects) with known synthetic HRFs. We then employed block averaging and GLM techniques with “short-separation regression” designed both for CW and TD to recover the HRFs. We calculated the root mean square error (RMSE) and the correlation of the recovered HRF to the ground truth. We compared the performance of equivalent CW and TD techniques with paired t-tests.ResultsWe found that, on average, TD moment HRF recovery improves correlations by 98% and 48% for HbO and HbR respectively, over CW GLM. The improvement on the correlation for TD GLM over TD moment is 12% (HbO) and 27% (HbR). RMSE decreases 56% and 52% (HbO and HbR) for TD moment compared to CW GLM. We found no statistically significant improvement in the RMSE for TD GLM compared to TD moment.ConclusionsProperly covariance-scaled TD moment techniques outperform their CW equivalents in both RMSE and correlation in the recovery of the synthetic HRFs. Furthermore, our proposed TD GLM based on moments outperforms regular TD moment analysis, while allowing the incorporation of auxiliary measurements of the confounding physiological signals from the scalp.

Functional magnetic resonance imaging (fMRI) has, since its de scription fifteen years ago, become the most common in-vivo neuroimaging technique. FMRI allows the identification of brain areas which are related to specific tasks, by statistical analysis of the BOLD (blood oxigenation level dependent) signal. Classically, the observed BOLD signal is compared to an expected haemodynamic response function (HRF) using a general linear model (GLM). However, the results of GLM rely on the HRF specification, which is usually determined in an ad hoc fashion. For periodic experimental designs, we propose a multisubject frequency domain brain mapping, which requires only the stimulation frequency, and consequently avoids subjective choices of HRF. We present some computational simulations, which demon strate a good performance of the proposed approach in short length time series. In addition, an application to real fMRI datasets is also presented.

Acute effects of Mozart K.448 on interictal epileptiform discharges in adult patients with drug-resistant focal epilepsy: A crossover randomized controlled trial

.SignificanceDeaf and hard-of-hearing infants’ hemodynamic response function (HRF) has not yet been characterized. However, without an appropriate estimate of their HRF, neuroimaging modalities relying on hemodynamic responses, e.g., functional magnetic resonance imaging (fMRI) or functional near-infrared spectroscopy (fNIRS), cannot be used reliably in this population, e.g., prior to and following cochlear implantation. We contribute to better theoretical models of and more suitable therapeutic interventions for the neural changes induced by auditory and language deprivation in deaf infants.AimWe aim to characterize the parameters of the HRF of deaf and hard-of-hearing infants in response to Italian.ApproachWe measured 2- to 20-month-old Italian-exposed infants’ HRF to Italian using fNIRS in the bilateral temporal, i.e., auditory, cortices. We characterized the HRF for all infants and for three clinically relevant subsets: (i) monolingual Italian infants, (ii) genetically deaf infants, and (iii) infants aged 5 to 12 months. We computed the following parameters: peak amplitude, time-to-peak, full width at half maximum, and where present, the amplitude and latency of the initial dip and/or the final undershoot, using a model-based parameter fitting approach. We statistically compared these HRF parameters to those of typically hearing infants.ResultsDeaf and typically hearing infants showed largely similar HRFs, with both groups reaching comparable main peak amplitudes. Minor differences have been found in the latencies of some response components.ConclusionsOur results provide the first detailed characterization of the hemodynamic response to native-language speech in deaf and hard-of-hearing infants, improving clinical and therapeutic approaches through more accurate analysis of fMRI and fNIRS data.

BOLD fMRI has become a prevalent method to study cerebral sensory processing in rodent disease models, including pain and mechanical hypersensitivity. fMRI data analysis is frequently combined with a general-linear-model (GLM) -based analysis, which uses the convolution of a hemodynamic response function (HRF) with the stimulus paradigm. However, several studies indicated that the HRF differs across species, sexes, brain structures, and experimental factors, including stimulation modalities or anesthesia, and hence might strongly affect the outcome of BOLD analyzes. While considerable work has been done in humans and rats to understand the HRF, much less is known in mice. As a prerequisite to investigate mechano-sensory processing and BOLD fMRI data in male and female mice, we (1) designed a rotating stimulator that allows application of two different mechanical modalities, including innocuous von Frey and noxious pinprick stimuli and (2) determined and statistically compared HRFs across 30 brain structures and experimental conditions, including sex and, stimulus modalities. We found that mechanical stimulation lead to brain-wide BOLD signal changes thereby allowing extraction of HRFs from multiple brain structures. However, we did not find differences in HRFs across all brain structures and experimental conditions. Hence, we computed a whole-brain mouse HRF, which is based on 88 functional scans from 30 mice. A comparison of this mouse-specific HRF with our previously reported rat-derived HRF showed significantly slower kinetics in mice. Finally, we detected pronounced differences in cerebral BOLD activation between male and female mice with mechanical stimulation, thereby exposing divergent processing of noxious and innocuous stimuli in both sexes.

The impact of temporal regularization on estimates of the BOLD hemodynamic response function: A comparative analysis

Electroencephalogram(EEG)signals are mainly generated by postsynaptic potentials(EPSP)in the apical dendrites of pyramidal cells in the cortex. The mechanism of generation of interictal epileptiform discharges(IED)is paroxysmal depolarization shift(PDS). IEDs are negative towards the cortical surface and positive towards the white matter. To interpret EEG, readers should know normal EEG waveforms of wakefulness and sleep, normal variants such as wicket spikes or small sharp spikes, and artifacts such as eye movements or electromyography. Overinterpretation of EEG is a major cause of misdiagnosis; therefore, the accurate identification of IED is essential. The International Federation of Clinical Neurophysiology has proposed a criteria for the identification of IED. Long-term video-EEG is useful for correct diagnosis and localization of the epileptogenic zone in the setting of presurgical evaluation of intractable epilepsy and is also useful for the differential diagnosis of epileptic seizures and non-epileptic events. Clinical history and symptomatology are of utmost importance for correct diagnosis. Diagnosis should not be made using only EEG findings.

Reliability of the depth-dependent high-resolution BOLD hemodynamic response in human visual cortex and vicinity

Electroencephalogram (EEG) is one of most effective clinical diagnosis modalities for the localization of epileptic focus. Most current AI solutions use this modality to analyze the EEG signals in an automated manner to identify the epileptic seizure focus. To develop AI system for identifying the epileptic focus, there are many recently-published AI solutions based on biomarkers or statistic features that utilize interictal EEGs. In this review, we survey these solutions and find that they can be divided into three main categories: (i) those that use of biomarkers in EEG signals, including high-frequency oscillation, phase-amplitude coupling, and interictal epileptiform discharges, (ii) others that utilize feature-extraction methods, and (iii) solutions based upon neural networks (an end-to-end approach). We provide a detailed description of seizure focus with clinical diagnosis methods, a summary of the public datasets that seek to reduce the research gap in epilepsy, recent novel performance evaluation criteria used to evaluate the AI systems, and guidelines on when and how to use them. This review also suggests a number of future research challenges that must be overcome in order to design more efficient computer-aided solutions to epilepsy focus detection.

Identification of the hemodynamic response function (HRF) in functional magnetic resonance imaging (fMRI) is essential for a better understanding of cerebral activity. In a given region-of-interest (ROI) of the brain, several works have demonstrated that neighbor voxels have close shape responses, but with potentially different magnitude. This property of similarity also holds between HRFs corresponding to different stimuli in the paradigm. In this paper, we account for these features in a given ROI and estimate a single HRF shape but several magnitude coefficients per voxel and condition, which may be thought of representing the neural response level. Identified as a semiblind deconvolution inverse problem, we use a Gibbs sampling approach to estimate the unknown quantities. Our method, validated both on synthetic and real fMRI data provides spatial activation maps in a given ROI with no assumptions on the exact shape of the HRF.