Data Processing Techniques Impact Quantification of Cortico-Cortical Evoked Potentials

BACKGROUND

Cortico-cortical evoked potentials (CCEPs) are a common tool for probing effective connectivity in intracranial human electrophysiology. As with all human electrophysiology data, CCEP data are highly susceptible to noise. To address noise, filters and re-referencing are often applied to CCEP data, but different processing strategies are used from study to study.

NEW METHOD

We systematically compare how common average re-referencing and filtering CCEP data impacts quantification.

RESULTS

We show that common average re-referencing and filters, particularly filters that cut out more frequencies, can significantly impact the quantification of CCEP magnitude and morphology. We identify that high cutoff high pass filters (> 0.5Hz), low cutoff low pass filters (< 200Hz), and common average re-referencing impact quantification across subjects. However, we also demonstrate that the presence of noise may impact CCEP quantification, and preprocessing is necessary to mitigate this. We show that filtering is more effective than re-referencing or averaging across trials for reducing most common types of noise.

COMPARISON WITH EXISTING METHODS

These results suggest that existing CCEP processing methods must be applied with care to maximize noise reduction and minimize changes to the data. We do not test every available processing strategy; rather we demonstrate that processing can influence the results of CCEP studies. We emphasize the importance of reporting all processing methods, particularly re-referencing methods.

CONCLUSIONS

We propose a general framework for choosing an appropriate processing pipeline for CCEP data, taking into consideration the noise levels of a specific dataset. We suggest that minimal gentle filtering is preferable.

HCN Channel Phosphorylation Sites Mapped by Mass Spectrometry in Human Epilepsy Patients and in an Animal Model of Temporal Lobe Epilepsy

Because hyperpolarization-activated cyclic nucleotide-gated (HCN) ion channels modulate the excitability of cortical and hippocampal principal neurons, these channels play a key role in the hyperexcitability that occurs during the development of epilepsy after a brain insult, or epileptogenesis. In epileptic rats generated by pilocarpine-induced status epilepticus, HCN channel activity is downregulated by two main mechanisms: a hyperpolarizing shift in gating and a decrease in amplitude of the current mediated by HCN channels, I_h. Because these mechanisms are modulated by various phosphorylation signaling pathways, we hypothesized that phosphorylation changes occur at individual HCN channel amino acid residues (phosphosites) during epileptogenesis. We collected CA1 hippocampal tissue from male Sprague Dawley rats made epileptic by pilocarpine-induced status epilepticus, and age-matched naïve controls. We also included resected human brain tissue containing epileptogenic zones (EZs) where seizures arise for comparison to our chronically epileptic rats. After enrichment for HCN1 and HCN2 isoforms by immunoprecipitation and trypsin in-gel digestion, the samples were analyzed by mass spectrometry. We identified numerous phosphosites from HCN1 and HCN2 channels, representing a novel survey of phosphorylation sites within HCN channels. We found high levels of HCN channel phosphosite homology between humans and rats. We also identified a novel HCN1 channel phosphosite S791, which underwent significantly increased phosphorylation during the chronic epilepsy stage. Heterologous expression of a phosphomimetic mutant, S791D, replicated a hyperpolarizing shift in I_h gating seen in neurons from chronically epileptic rats. These results show that HCN1 channel phosphorylation is altered in epilepsy and may be of pathogenic importance.

Signal recovery from stimulation artifacts in intracranial recordings with dictionary learning

OBJECTIVE

Electrical stimulation of the human brain is commonly used for eliciting and inhibiting neural activity for clinical diagnostics, modifying abnormal neural circuit function for therapeutics, and interrogating cortical connectivity. However, recording electrical signals with concurrent stimulation results in dominant electrical artifacts that mask the neural signals of interest. Here we develop a method to reproducibly and robustly recover neural activity during concurrent stimulation. We concentrate on signal recovery across an array of electrodes without channel-wise fine-tuning of the algorithm. Our goal includes signal recovery with trains of stimulation pulses, since repeated, high-frequency pulses are often required to induce desired effects in both therapeutic and research domains. We have made all of our code and data publicly available.

APPROACH

We developed an algorithm that automatically detects templates of artifacts across many channels of recording, creating a dictionary of learned templates using unsupervised clustering. The artifact template that best matches each individual artifact pulse is subtracted to recover the underlying activity. To assess the success of our method, we focus on whether it extracts physiologically interpretable signals from real recordings.

MAIN RESULTS

We demonstrate our signal recovery approach on invasive electrophysiologic recordings from human subjects during stimulation. We show the recovery of meaningful neural signatures in both electrocorticographic (ECoG) arrays and deep brain stimulation (DBS) recordings. In addition, we compared cortical responses induced by the stimulation of primary somatosensory (S1) by natural peripheral touch, as well as motor cortex activity with and without concurrent S1 stimulation.

SIGNIFICANCE

Our work will enable future advances in neural engineering with simultaneous stimulation and recording.