Biology subjects

Shrey, D. W.

Publications and source records attributed to Shrey, D. W..

6 recordsLinked to original sources

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

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↗

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

neuroscience↗