Biology subjects

Lopour, B. A.

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

3 recordsLinked to original sources

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.

neuroscience↗

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

neuroscience↗