Biology subjects

Sen-Gupta, I.

Publications and source records attributed to Sen-Gupta, I..

5 recordsLinked to original sources

bioRxiv2023

Theta mediated dynamics of human hippocampal-neocortical learning systems in memory formation and retrieval

Episodic memory arises as a function of dynamic interactions between the hippocampus and the neocortex, yet the mechanisms have remained elusive. Here, using human intracranial recordings during a mnemonic discrimination task, we report that 4-5 Hz (theta) power is differentially recruited during discrimination vs. overgeneralization, and its phase supports hippocampal-neocortical when memories are being formed and correctly retrieved. Interactions were largely bidirectional, with small but significant net directional biases; a hippocampus-to-neocortex bias during acquisition of new information that was subsequently correctly discriminated, and a neocortex-to-hippocampus bias during accurate discrimination of new stimuli from similar previously learned stimuli. The 4-5 Hz rhythm may facilitate the initial stages of information acquisition by neocortex during learning and the recall of stored information from cortex during retrieval. Future work should further probe these dynamics across different types of tasks and stimuli and computational models may need to be expanded accordingly to accommodate these findings.

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

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

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↗