Daniel M. Goldenholz

Mapping the Signal-To-Noise-Ratios of Cortical Sources in Magnetoencephalography and Electroencephalography

Although magnetoencephalography (MEG) and electroencephalography (EEG) have been available for decades, their relative merits are still debated. We examined regional differences in signal‐to‐noise‐ratios (SNRs) of cortical sources in MEG and EEG. Data from four subjects were used to simulate focal and extended sources located on the cortical surface reconstructed from high‐resolution magnetic resonance images. The SNR maps for MEG and EEG were found to be complementary. The SNR of deep sources was larger in EEG than in MEG, whereas the opposite was typically the case for superficial sources. Overall, the SNR maps were more uniform for EEG than for MEG. When using a noise model based on uniformly distributed random sources on the cortex, the SNR in MEG was found to be underestimated, compared with the maps obtained with noise estimated from actual recorded MEG and EEG data. With extended sources, the total area of cortex in which the SNR was higher in EEG than in MEG was larger than with focal sources. Clinically, SNR maps in a patient explained differential sensitivity of MEG and EEG in detecting epileptic activity. Our results emphasize the benefits of recording MEG and EEG simultaneously. Hum Brain Mapp 2009.

Improved recovery of the hemodynamic response in diffuse optical imaging using short optode separations and state-space modeling.

Diffuse optical imaging (DOI) allows the recovery of the hemodynamic response associated with evoked brain activity. The signal is contaminated with systemic physiological interference which occurs in the superficial layers of the head as well as in the brain tissue. The back-reflection geometry of the measurement makes the DOI signal strongly contaminated by systemic interference occurring in the superficial layers. A recent development has been the use of signals from small source-detector separation (1cm) optodes as regressors. Since those additional measurements are mainly sensitive to superficial layers in adult humans, they help in removing the systemic interference present in longer separation measurements (3 cm). Encouraged by those findings, we developed a dynamic estimation procedure to remove global interference using small optode separations and to estimate simultaneously the hemodynamic response. The algorithm was tested by recovering a simulated synthetic hemodynamic response added over baseline DOI data acquired from 6 human subjects at rest. The performance of the algorithm was quantified by the Pearson R(2) coefficient and the mean square error (MSE) between the recovered and the simulated hemodynamic responses. Our dynamic estimator was also compared with a static estimator and the traditional adaptive filtering method. We observed a significant improvement (two-tailed paired t-test, p<0.05) in both HbO and HbR recovery using our Kalman filter dynamic estimator compared to the traditional adaptive filter, the static estimator and the standard GLM technique.

The utility of near-infrared spectroscopy in the regression of low-frequency physiological noise from functional magnetic resonance imaging data.

Near-infrared spectroscopy (NIRS) signals have been shown to correlate with resting-state BOLD-fMRI data across the whole brain volume, particularly at frequencies below 0.1Hz. While the physiological origins of this correlation remain unclear, its existence may have a practical application in minimizing the background physiological noise present in BOLD-fMRI recordings. We performed simultaneous, resting-state fMRI and 28-channel NIRS in seven adult subjects in order to assess the utility of NIRS signals in the regression of physiological noise from fMRI data. We calculated the variance of the residual error in a general linear model of the baseline fMRI signal, and the reduction of this variance achieved by including NIRS signals in the model. In addition, we introduced a sequence of simulated hemodynamic response functions (HRFs) into the resting-state fMRI data of each subject in order to quantify the effectiveness of NIRS signals in optimizing the recovery of that HRF. For comparison, these calculations were also performed using a pulse and respiration RETROICOR model. Our results show that the use of 10 or more NIRS channels can reduce variance in the residual error by as much as 36% on average across the whole cortex. However the same number of low-pass filtered white noise regressors is shown to produce a reduction of 19%. The RETROICOR model obtained a variance reduction of 6.4%. Our HRF simulation showed that the mean-squared error (MSE) between the recovered and true HRFs is reduced by 21% on average when 10 NIRS channels are applied and by introducing an optimized time lag between the NIRS and fMRI time series, a single NIRS channel can provide an average MSE reduction of 14%. The RETROICOR model did not provide a significant change in MSE. By each of the metrics calculated, NIRS recording is shown to be of significant benefit to the regression of low-frequency physiological noise from fMRI data.

Confusing placebo effect with natural history in epilepsy: A big data approach.

For unknown reasons, placebos reduce seizures in clinical trials in many patients. It is also unclear why some drugs showing statistical superiority to placebo in one trial may fail to do so in another. Using Seizuretracker.com, a patient‐centered database of 684,825 seizures, we simulated “placebo” and “drug” trials. These simulations were employed to clarify the sources of placebo effects in epilepsy, and to identify methods of diminishing placebo effects. Simulation 1 included 9 trials with a 6‐week baseline and 6‐week test period, starting at time 0, 3, 6…24 months. Here, “placebo” reduced seizures regardless of study start time. Regression‐to‐the‐mean persisted only for 3 to 6 months. Simulation 2 comprised a 6‐week baseline and then 2 years of follow‐up. Seizure frequencies continued to improve throughout follow‐up. Although the group improved, individuals switched from improvement to worsening and back. Simulation 3 involved a placebo‐controlled “drug” trial, to explore methods of placebo response reduction. An efficacious “drug” failed to demonstrate a significant effect compared with “placebo” (p = 0.12), although modifications either in study start time (p = 0.025) or baseline population reduction (p = 0.0028) allowed the drug to achieve a statistically significant effect compared with placebo. In epilepsy clinical trials, some seizure reduction traditionally attributed to placebo effect may reflect the natural course of the disease itself. Understanding these dynamics will allow future investigations into optimal clinical trial design and may lead to identification of more effective therapies. Ann Neurol 2015;78:329–336

Long-term monitoring of cardiorespiratory patterns in drug-resistant epilepsy.

Sudden unexplained death in epilepsy (SUDEP) during inpatient electroencephalography (EEG) monitoring has been a rare but potentially preventable event, with associated cardiopulmonary markers. To date, no systematic evaluation of alarm settings for a continuous pulse oximeter (SpO2) has been performed. In addition, evaluation of the interrelationship between the ictal and interictal states for cardiopulmonary measures has not been reported.

A big data approach to the development of mixed-effects models for seizure count data

Our objective was to develop a generalized linear mixed model for predicting seizure count that is useful in the design and analysis of clinical trials. This model also may benefit the design and interpretation of seizure‐recording paradigms. Most existing seizure count models do not include children, and there is currently no consensus regarding the most suitable model that can be applied to children and adults. Therefore, an additional objective was to develop a model that accounts for both adult and pediatric epilepsy.

Simulating clinical trials with and without intracranial EEG data

It is currently unknown whether knowledge of clinically silent (electrographic) seizures improves the statistical efficiency of clinical trials.

Preoperative prediction of temporal lobe epilepsy surgery outcome.

PURPOSE There is controversy about relative contributions of ictal scalp video EEG recording (vEEG), routine scalp outpatient interictal EEG (rEEG), intracranial EEG (iEEG) and MRI for predicting seizure-free outcomes after temporal lobectomy. We reviewed NIH experience to determine contributions at specific time points as well as long-term predictive value of standard pre-surgical investigations. METHODS Raw data was obtained via retrospective chart review of 151 patients. After exclusions, 118 remained (median 5 years follow-up). MRI-proven mesial temporal sclerosis (MTSr) was considered a separate category for analysis. Logistic regression estimated odds ratios at 6-months, 1-year, and 2 years; proportional hazard models estimated long-term comparisons. Subset analysis of the proportional hazard model was performed including only patients with commonly encountered situations in each of the modalities, to maximize statistical inference. RESULTS Any MRI finding, MRI proven MTS, rEEG, vEEG and iEEG did not predict two-year seizure-free outcome. MTSr was predictive at six months (OR=2.894, p=0. 0466), as were MRI and MTSr at one year (OR=10.4231, p=0. 0144 and OR=3.576, p=0. 0091). Correcting for rEEG and MRI, vEEG failed to predict outcome at 6 months, 1year and 2 years. Proportional hazard analysis including all available follow-up failed to achieve significance for any modality. In the subset analysis of 83 patients with commonly encountered results, vEEG modestly predicted long-term seizure-free outcomes with a proportional hazard ratio of 1.936 (p=0.0304). CONCLUSIONS In this study, presurgical tools did not provide unambiguous long-term outcome predictions. Multicenter prospective studies are needed to determine optimal presurgical epilepsy evaluation.

Interictal Scalp Fast Oscillations as a Marker of the Seizure Onset ZoneAuthor Response

# {#article-title-2} Andrade-Valenca et al.1 propose a promising method for localizing the seizure onset zone (SOZ) in scalp EEG recordings. The methods are accessible for implementation in most EEG laboratories. Critically, when the SOZ is ill-defined on scalp recordings, ripples may help guide intracranial electrode placement; in the future they may sometimes circumvent the need for such electrodes. Because certain filter parameters can sometimes result in spurious high frequency oscillations,2 laboratories trying to …

Circadian and circaseptan rhythms in human epilepsy: a retrospective cohort study

BACKGROUND Epilepsy has long been suspected to be governed by cyclic rhythms, with seizure rates rising and falling periodically over weeks, months, or even years. The very long scales of seizure patterns seem to defy natural explanation and have sometimes been attributed to hormonal cycles or environmental factors. This study aimed to quantify the strength and prevalence of seizure cycles at multiple temporal scales across a large cohort of people with epilepsy. METHODS This retrospective cohort study used the two most comprehensive databases of human seizures (SeizureTracker [USA] and NeuroVista [Melbourne, VIC, Australia]) and analytic techniques from circular statistics to analyse patients with epilepsy for the presence and frequency of multitemporal cycles of seizure activity. NeuroVista patients were selected on the basis of having intractable focal epilepsy; data from patients with at least 30 clinical seizures were used. SeizureTracker participants are self selected and data do not adhere to any specific criteria; we used patients with a minimum of 100 seizures. The presence of seizure cycles over multiple time scales was measured using the mean resultant length (R value). The Rayleigh test and Hodges-Ajne test were used to test for circular uniformity. Monte-Carlo simulations were used to confirm the results of the Rayleigh test for seizure phase. FINDINGS We used data from 12 people from the NeuroVista study (data recorded from June 10, 2010, to Aug 22, 2012) and 1118 patients from the SeizureTracker database (data recorded from Jan 1, 2007, to Oct 19, 2015). At least 891 (80%) of 1118 patients in the SeizureTracker cohort and 11 (92%) of 12 patients in the NeuroVista cohort showed circadian (24 h) modulation of their seizure rates. In the NeuroVista cohort, patient 8 had a significant cycle at precisely 1 week. Two others (patients 1 and 7) also had approximately 1-week cycles. Patients 1 and 4 had 2-week cycles. In the SeizureTracker cohort, between 77 (7%) and 233 (21%) of the 1118 patients showed strong circaseptan (weekly) rhythms, with a clear 7-day period. Between 151 (14%) and 247 (22%) patients had significant seizure cycles that were longer than 3 weeks. Seizure cycles were equally prevalent in men and women, and peak seizure rates were evenly distributed across all days of the week. INTERPRETATION Our results suggest that seizure cycles are robust, patient specific, and more widespread than previously understood. They align with the accepted consensus that most epilepsies have some diurnal influence. Variations in seizure rate have important clinical implications. Detection and tracking of seizure cycles on a patient-specific basis should be standard in epilepsy management practices. FUNDING Australian National Health and Medical Research Council.