Biology subjects

Lopour, B. A.

Publications and source records attributed to Lopour, B. A..

12 recordsLinked to original sources

bioRxiv2025

Prediction of treatment response in infantile epileptic spasms syndrome using EEG phase-amplitude coupling

ObjectiveTreatment selection for infantile epileptic spasms syndrome (IESS) is complex and multifaceted, and currently no EEG biomarkers can guide this decision by predicting treatment response. We tested the predictive value of phase-amplitude coupling (PAC), as IESS patients are known to have elevated PAC. MethodsWe analyzed retrospective EEG recordings from 40 IESS patients, before and after treatment, and 20 healthy controls. Patients were classified as responders (n=25) or non-responders (n=15) based on short-term treatment outcomes. We measured PAC in each EEG using modulation index (MI) and mean vector length (MVL) and analyzed the relationship between pre- and post-treatment values and the ability of pre-treatment values to predict response. ResultsMI and MVL values decreased with treatment in almost all subjects. However, non-responders had significantly higher pre-treatment MI than responders (P<0.05), suggesting utility for predicting treatment response. Logistic regression modeling suggested that a 0.5 unit decrease in log(MI), which is approximately one IQR of the pre-treatment log(MI) values, results in a 6-fold increase in odds of positive treatment response. SignificanceMI reflects short-term treatment response and is a candidate predictive EEG biomarker for IESS. MI may offer individualized insights for treatment selection and management strategies for IESS. Key Points BoxO_LIInfantile epileptic spasms syndrome (IESS) is associated with high phase-amplitude coupling (PAC) that decreases with treatment C_LIO_LILow pre-treatment values of EEG PAC are predictive of positive short-term treatment response for IESS C_LIO_LIThe results were strongest when PAC was measured using the modulation index, as this method is known to be robust to noise C_LI

neuroscience↗

bioRxiv2025

EEG Functional Connectivity as a Marker of Evolution from Infantile Epileptic Spasms Syndrome to Lennox-Gastaut Syndrome

Background and ObjectivesTimely diagnosis and effective treatment of Lennox-Gastaut Syndrome (LGS) improve prognosis and lower healthcare costs, but the transition from infantile epileptic spasms syndrome (IESS) to LGS is highly variable and insidious. Objective biomarkers are needed to monitor this progression and guide clinical decision making. MethodsWe retrospectively collected longitudinal EEG data at the Childrens Hospital of Orange County from fifteen children who were diagnosed with IESS and later with LGS between 2012 and 2021. EEGs were from IESS and LGS diagnoses, between the two diagnoses, and following LGS diagnosis. Functional connectivity networks were calculated using a cross-correlation-based method and assessed relative to diagnostic timepoint, treatment response, presence of clinical markers of disease, age, and amplitude of interictal spikes. ResultsConnectivity strength was high at LGS diagnosis and decreased after favorable response to treatment, but it remained stable or increased when response was unfavorable. In all subjects, connectivity strength was higher at the time of LGS diagnosis than at the preceding timepoint. Presence of clinical markers of LGS were associated with high connectivity strength, but no single marker predicted connectivity strength. DiscussionComputational EEG analysis can be used to map the evolution from IESS to LGS. Changes in connectivity may enable prediction of impending LGS and treatment response monitoring, thus facilitating earlier LGS treatment and guiding medical management. Key pointsO_LIEEG functional connectivity analysis can track progression from infantile epileptic spasms syndrome (IESS) to Lennox-Gastaut Syndrome (LGS). C_LIO_LIHigh connectivity strength at LGS diagnosis decreases with favorable treatment response but remains high with poor response. C_LIO_LIClinical LGS markers correlate with high connectivity, but no single marker predicts connectivity strength. C_LIO_LIEEG functional connectivity analysis may help predict LGS onset, enabling early intervention and improving prognosis. C_LI

neuroscience↗

bioRxiv2022

A novel method for multi-scale recording of intracranial EEG through dynamic alteration of electrode surface area

Intracranial EEG (iEEG) plays a critical role in the treatment of neurological diseases, such as epilepsy and Parkinsons disease, as well as the development of neural prostheses and brain computer interfaces. While electrode geometries vary widely across these applications, the impact of electrode size on iEEG features and morphology is not well understood. Some insight has been gained from computer simulation studies and experiments in which signals are recorded using electrodes of different sizes concurrently in different brain regions. Here, we introduce a novel method to record from electrodes of different sizes in the exact same location by changing the size of iEEG electrodes after implantation in the brain. We first present a theoretical model and an in vitro validation of the method. We then report the results of an in vivo implementation in three human subjects with refractory epilepsy. We recorded iEEG data from three different electrode sizes and compared the amplitudes, power spectra, inter-channel correlations, and signal-to-noise ratio (SNR) of interictal epileptiform discharges, i.e., epileptic spikes. We found that iEEG amplitude and power decreased as electrode size increased, while inter-channel correlation increased with electrode size. The SNR of epileptic spikes was generally highest in the smallest electrodes, but 39% of spikes had maximal SNR in medium or large electrodes. This likely depends on the precise location and spatial spread of each spike. Overall, this new method enables multi-scale measurements of electrical activity in the human brain that facilitate our understanding of neurophysiology, treatment of neurological disease, and development of novel technologies.

neuroscience↗

bioRxiv2020

Computational characteristics of interictal EEG as objective markers of epileptic spasms

ObjectiveFavorable neurodevelopmental outcomes in epileptic spasms (ES) are tied to early diagnosis and prompt treatment, but uncertainty in the identification of the disease can delay this process. Therefore, we investigated five computational electroencephalographic (EEG) measures as markers of ES. MethodsWe measured 1) amplitude, 2) power spectra, 3) entropy, 4) long-range temporal correlations, via detrended fluctuation analysis (DFA) and 5) functional connectivity of EEG data from ES patients (n=40 patients) and healthy controls (n=20 subjects), with multiple blinded measurements during wakefulness and sleep for each patient. ResultsIn ES patients, EEG amplitude was significantly higher in all electrodes. Shannon and permutation entropy were lower in ES patients than control subjects, while DFA intercept values in ES patients were significantly higher than control subjects. DFA exponent values were not significantly different between the groups. EEG functional connectivity networks in ES patients were significantly stronger than controls. Using logistic regression, a multi-attribute classifier was derived that accurately distinguished cases from controls (area under curve of 0.96). ConclusionsComputational EEG features successfully distinguish ES patients from controls in a large, blinded study. SignificanceThese objective EEG markers, in combination with other clinical factors, may speed the diagnosis and treatment of the disease, thereby improving long-term outcomes. HighlightsO_LIObjective computational EEG features may aid diagnosis of epileptic spasms (ES) C_LIO_LIES EEG has increased delta and theta power and decreased entropy relative to controls C_LIO_LIStronger functional connectivity networks differentiate ES patients from controls C_LI

neuroscience↗

bioRxiv2020

Temporal dynamics of functional networks in long-term infant scalp EEG

Human functional connectivity networks are modulated on time scales ranging from milliseconds to days. Rapid changes in connectivity over short time scales are a feature of healthy cognitive function, and variability over long time scales can impact the likelihood of seizure occurrence. However, relatively little is known about modulation of healthy functional networks over long time scales. To address this, we analyzed functional connectivity networks calculated from long-term EEG recordings from 19 healthy infants. Networks were subject-specific, as inter-subject correlations between weighted adjacency matrices were low. However, within individual subjects, both sleep and wake networks were stable over time, with stronger functional connectivity during sleep than wakefulness. This enabled automatic separation of wakefulness and sleep states via principle components analysis of the functional network time series, with median classification accuracy of 91%. Lastly, we found that network strength, degree, clustering coefficient, and path length significantly varied with time of day, when measured in both wakefulness and sleep. Together, these results suggest that modulation of healthy functional networks occurs over long timescales and is robust and repeatable. Accounting for such temporal periodicities may improve the physiological interpretation and use of functional connectivity analysis to investigate brain function in health and disease.

neuroscience↗

bioRxiv2020

Patient-specific optimization of automated detection improves seizure onset zone localization based on high frequency oscillations

ObjectiveHigh frequency oscillations (HFOs) are a biomarker of the seizure onset zone (SOZ) and can be visually or automatically detected. In theory, one can optimize an automated algorithms parameters to maximize SOZ localization accuracy; however, there is no consensus on whether or how this should be done. Therefore, we optimized an automated detector using visually identified HFOs and evaluated the impact on SOZ localization accuracy. MethodsWe detected HFOs in intracranial EEG from 20 patients with refractory epilepsy from two centers using (1) unoptimized automated detection, (2) visual identification, and (3) automated detection optimized to match visually detected HFOs. ResultsSOZ localization accuracy based on HFO rate was not significantly different between the three methods. Across patients, visually optimized detector settings varied, and no single set of settings produced universally accurate SOZ localization. Exploratory analysis suggests that, for many patients, detection settings exist that would improve SOZ localization. ConclusionsSOZ localization accuracy was similar for all three methods, was not improved by visually optimizing detector settings, and may benefit from patient-specific parameter optimization. SignificanceVisual HFO marking is laborious, and optimizing automated detection using visual markings does not improve localization accuracy. New patient-specific detector optimization methods are needed.

neuroscience↗

bioRxiv2020

Amplitude of high frequency oscillations as a biomarker of the seizure onset zone

ObjectiveStudies of high frequency oscillations (HFOs) in epilepsy have primarily tested the HFO rate as a biomarker of the seizure onset zone (SOZ), but the rate varies over time and is not robust for all individual subjects. As an alternative, we tested the performance of HFO amplitude as a potential SOZ biomarker using two automated detection algorithms. MethodHFOs were detected in intracranial electroencephalogram (iEEG) from 11 patients using a machine learning algorithm and a standard amplitude-based algorithm. For each detector, SOZ and non-SOZ channels were classified using the rate and amplitude of high frequency events, and performance was compared using receiver operating characteristic curves. ResultsThe amplitude of detected events was significantly higher in SOZ. Across subjects, amplitude more accurately classified SOZ/non-SOZ than rate (higher values of area under the ROC curve and sensitivity, and lower false positive rates). Moreover, amplitude was more consistent across segments of data, indicated by lower coefficient of variation. ConclusionAs an SOZ biomarker, HFO amplitude offers advantages over HFO rate: it exhibits higher classification accuracy, more consistency over time, and robustness to parameter changes. SignificanceThis biomarker has the potential to increase the generalizability of HFOs and facilitate clinical implementation as a tool for SOZ localization.

neuroscience↗

bioRxiv2020

Automated Detection of Ripple Oscillations in Long-Term Scalp EEG from Patients with Infantile Spasms

ObjectiveScalp high frequency oscillations (HFOs) are a promising biomarker of epileptogenicity in infantile spasms (IS) and many other epilepsy syndromes, but prior studies have relied on visual analysis of short segments of data due to the prevalence of artifacts in EEG. Therefore, we set out to develop a fully automated method of HFO detection that can be applied to large datasets, and we sought to robustly characterize the rate and spatial distribution of HFOs in IS. MethodsWe prospectively collected long-term scalp EEG data from 13 subjects with IS and 18 healthy controls. For patients with IS, recording began prior to diagnosis and continued through initiation of treatment with adenocorticotropic hormone (ACTH). The median analyzable EEG duration was 18.2 hours for controls and 83.9 hours for IS subjects ([~]1300 hours total). Ripples (80-250 Hz) were detected in all EEG data using an automated algorithm. ResultsHFO rates were substantially higher in patients with IS compared to controls. In IS patients, HFO rates were higher during sleep compared to wakefulness (median 5.5/min and 2.9/min, respectively; p =0.002); controls did not exhibit a difference in HFO rate between sleep and wakefulness (median 0.98/min and 0.82/min, respectively). Spatially, the difference between IS patients and controls was most salient in the central/posterior parasaggital region, where very few HFOs were detected in controls. In IS subjects, ACTH therapy significantly decreased the rate of HFOs. DiscussionHere we show for the first time that a fully automated algorithm can be used to detect HFOs in long-term scalp EEG, and the results are accurate enough to clearly discriminate healthy subjects from those with IS. We also provide a detailed characterization of the spatial distribution and rates of HFOs associated with infantile spasms, which may have relevance for diagnosis and assessment of treatment response.

neuroscience↗

bioRxiv2020

Towards automatic classification of pathological epileptic tissue with interictal high frequency oscillations

ObjectiveHigh frequency oscillations (HFOs) recorded by intracranial electrodes have generated excitement for their potential to help localize epileptic tissue for surgical resection. However, the number of HFOs per minute (i.e. the HFO "rate") is not stable over the duration of intracranial recordings; for example, the rate of HFOs increases during periods of slow-wave sleep. Moreover, HFOs that are predictive of epileptic tissue may occur in oscillatory patterns due to phase coupling with lower frequencies. Therefore, we sought to further characterize between-seizure (i.e. "interictal") HFO dynamics both within and outside the seizure onset zone (SOZ). ApproachUsing long-term intracranial EEG (mean duration 10.3 hours) from 16 patients, we automatically detected HFOs using a new algorithm. We then fit a hierarchical Negative Binomial model to the HFO counts. To account for differences in HFO dynamics and rates between sleep and wakefulness, we also fit a mixture model to the same data that included the ability to switch between two discrete brain states that were automatically determined during the fitting process. The ability to predict the SOZ by model parameters describing HFO dynamics (i.e. clumping coefficients and coefficients of variation) was assessed using receiver operating characteristic curves. Main resultsParameters that described HFO dynamics were predictive of SOZ. In fact, these parameters were found to be more consistently predictive than HFO rate. Using concurrent scalp EEG in two patients, we show that the model-found brain states corresponded to (1) non-REM sleep and (2) awake and rapid eye movement sleep. However the brain state most likely corresponding to slowwave sleep in the second model improved SOZ prediction compared to the first model for only some patients. SignificanceThis work suggests that delineation of seizure onset zone with interictal data can be improved by the inclusion of time-varying HFO dynamics. 1. Novelty & SignificanceThe rate of high frequency oscillations (HFOs), measured as number per minute, is a biomarker of the seizure onset zone (SOZ) in epilepsy patients. However, the rate changes over time and HFO occurrence can be phase-coupled to slow oscillations. Here we show, through novel application of negative binomial models to HFO count data, that HFO temporal dynamics are a biomarker of the SOZ and are superior to HFO rate. Specifically, more random occurrence of HFOs predicted SOZ, as opposed to events clustered in time. This suggests that consideration of HFO temporal dynamics can improve SOZ localization for epilepsy surgery.

neuroscience↗

bioRxiv2019

Spreading depolarization and repolarization during cardiac arrest as an ultra-early marker of neurological recovery in a preclinical model

Spreading depolarization (SD) accompanies numerous neurological conditions, including migraine, stroke, and traumatic brain injury. There is significant interest in understanding the relationship between SD and neuronal injury. However, characteristics underlying SD and repolarization (RP) induced by global cerebral ischemia (e.g., cardiac arrest (CA)) and reperfusion are not well understood. Quantifying features of SD and RP during CA and cardiopulmonary resuscitation (CPR) may provide important metrics for diagnosis and prognosis of neurological injury from hypoxia-ischemia. We characterized SD and RP in a rodent model of asphyxial CA+CPR using a multimodal platform including electrocorticography (ECoG) and optical imaging. We detected SD and RP by (1) alternating current (AC), (2) direct current (DC), and (3) optical imaging of spreading ischemia, spreading edema, and vasoconstriction. Earlier SD (r=-0.80; p<0.001) and earlier RP (r=-0.71, p<0.001) were associated with better neurological recovery after 24hrs. SD+RP onset times predicted good vs poor neurological recovery with 82% sensitivity and 91% specificity. To our knowledge, this is the first preclinical study to link SD and RP characteristics with neurological recovery post-CA. These data suggest that SD and RP may be ultra-early, real-time prognostic markers of post-CA outcome, meriting further investigation into translational implications during global cerebral ischemia.

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bioRxiv2019

Detection of anomalous high frequency events in human intracranial EEG

ObjectiveHigh-frequency oscillations (HFOs) are a promising biomarker for the epileptogenic zone. However, no physiological definition of an HFO has been established, so detection relies on the empirical definition of an HFO derived from visual observation. This can bias estimates of HFO features such as amplitude and duration, thereby hindering their utility as biomarkers. Therefore, we set out to develop an algorithm that detects high frequency events in the intracranial EEG that stand out from the background and does not require assumptions about event amplitude or morphology.\n\nMethodWe propose the anomaly detection algorithm (ADA), which integrates several unsupervised machine learning techniques to identify segments of data that are distinct from the background. We apply ADA and a standard HFO detector using a root-mean-square amplitude threshold to intracranial EEG from 11 patients undergoing evaluation for epilepsy surgery. The rate, amplitude, and duration of the detected events and the percent overlap between the two detectors are compared.\n\nResultIn the seizure onset zone (SOZ), ADA detected a subset of conventional HFOs. In non-SOZ channels, ADA detected at least twice as many events as the standard approach, including some conventional HFOs; however, ADA also identified many low and intermediate amplitude events missed by the standard amplitude-based method. The rate of ADA events was similar across all channels; however, the amplitude of ADA events was significantly higher in SOZ channels, and the threshold between SOZ and non-SOZ channels was relatively consistent across patients.\n\nSignificanceADA does not require human supervision, parameter optimization, or prior assumptions about event shape, amplitude, or duration. It provides an unbiased estimate of HFO features, and our results suggest that amplitude may differentiate SOZ and non-SOZ channels. Further studies will examine the utility of HFO amplitude as a biomarker for epilepsy surgical outcome.

neuroscience↗

bioRxiv2019

Effect of interictal epileptiform discharges on EEG-based functional connectivity networks

ObjectiveFunctional connectivity networks (FCNs) based on interictal electroencephalography (EEG) can identify pathological brain networks associated with epilepsy. FCNs are altered by interictal epileptiform discharges (IEDs), but it is unknown whether this is due to the morphology of the IED or the underlying pathological activity. Therefore, we characterized the impact of IEDs on the FCN through simulations and EEG analysis.\n\nMethodsWe introduced simulated IEDs to sleep EEG recordings of eight healthy controls and analyzed the effect of IED amplitude and rate on the FCN. We then generated FCNs based on epochs with and without IEDs and compared them to the analogous FCNs from eight subjects with infantile spasms (IS), based on 1,340 visually marked IEDs. Differences in network structure and strength were assessed.\n\nResultsIEDs in IS subjects caused increased connectivity strength but no change in network structure. In controls, simulated IEDs with physiological amplitudes and rates did not alter network strength or structure.\n\nConclusionsIncreases in connectivity strength in IS subjects are not artifacts caused by the interictal spike waveform and may be related to the underlying pathophysiology of IS.\n\nSignificanceDynamic changes in EEG-based FCNs during IEDs may be valuable for identification of pathological networks associated with epilepsy.\n\nHighlightsO_LIInfantile spasms subjects exhibit broadly increased connectivity strength during interictal spikes\nC_LIO_LIFunctional connectivity network structure is unaltered by interictal spikes in infantile spasms\nC_LIO_LISimulated spikes in healthy control EEG did not alter network strength or structure\nC_LI

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