Transfer Function Models for the Localization of Seizure Onset Zone From Cortico-Cortical Evoked Potentials

Abstract

Surgical resection of the seizure onset zone (SOZ) could potentially lead to seizure-freedom in medically refractory epilepsy patients. However, localizing the SOZ can be a time consuming and tedious process involving visual inspection of intracranial electroencephalographic (iEEG) recordings captured during passive patient monitoring. Cortical stimulation is currently performed on patients undergoing invasive EEG monitoring for the main purpose of mapping functional brain networks such as language and motor networks. We hypothesized that evoked responses from single pulse electrical stimulation (SPES) can also be used to localize the SOZ as they may express the natural frequencies and connectivity of the iEEG network. To test our hypothesis, we constructed patient specific transfer function models from the evoked responses recorded from 22 epilepsy patients that underwent SPES evaluation and iEEG monitoring. We then computed the frequency and connectivity dependent “peak gain” of the system as measured by the norm from systems theory. We found that in cases for which clinicians had high confidence in localizing the SOZ, the highest peak gain transfer functions with the smallest “floor gain” (gain at which the dipped 3dB below DC gain) corresponded to when the clinically annotated SOZ and early spread regions were stimulated. In more complex cases, there was a large spread of the peak-to-floor (PF) ratios when the clinically annotated SOZ was stimulated. Interestingly for patients who had successful surgeries, our ratio of gains, agreed with clinical localization, no matter the complexity of the case. For patients with failed surgeries, the PF ratio did not match clinical annotations. Our findings suggest that transfer function gains and their corresponding frequency responses computed from SPES evoked responses may improve SOZ localization and thus surgical outcomes.

Passive and active markers of cortical excitability in epilepsy

“…87 Sources and sinks are quantified by dynamic model parameters. Transfer functions and directed transfer functions 64,[88][89][90][91] (to name a few) and linear time varying models 87,92 are two classes of dynamic models proposed to assist in localizing the EZ. 6 | EPILEPSY BIOMARKERS:…”

Stimulation to probe, excite, and inhibit the epileptic brain

“…Dynamic network and transfer function models utilizing CCEPs have been particularly useful in localizing seizure onset regions, because they mathematically describe the complex relationships between input stimuli and the remote output responses. [21][22][23] The models have been used to identify "resonant" regions in the epileptogenic network that corresponded to focal epileptogenic areas, and then were used to predict triggering of native seizures using periodic stimulation at the resonant frequency (Figure 1D-F). 16 This trend of increased crossover of CCEPs with novel techniques in computational neuroscience will continue to unveil how stimulation-evoked potentials can enable epileptogenic network discrimination and cortical hyperexcitability measurement in epilepsy.…”

Quantifying trial‐by‐trial variability during cortico‐cortical evoked potential mapping of epileptogenic tissue

“…Studying CCEP responses may lead to improvements in functional mapping as well as in therapies utilizing open loop or responsive neurostimulation. 11 A fundamental assumption made in many applications of CCEPs 6,[12][13][14][15][16][17][18] is that the average response over many trials reflects underlying, static effective connectivity. A complementary hypothesis is that inter-trial variability is not random noise that averaging ought to remove, but rather contains important information about brain function and epileptogenicity.…”

“…A fundamental assumption made in many applications of CCEPs 6,12–18 is that the average response over many trials reflects underlying, static effective connectivity. A complementary hypothesis is that inter‐trial variability is not random noise that averaging ought to remove, but rather contains important information about brain function and epileptogenicity.…”