Intracranial Electroencephalography Reveals Selective Responses to Cognitive Stimuli in the Periventricular Heterotopias

Surgical treatment of epilepsy in a patient with bilateral periventricular nodular heterotopia: A case report

Background

Periventricular nodular heterotopia (PNH) is a rare pathological condition characterized by the presence of nodules of gray matter located along the lateral ventricles of the brain. The condition typically presents with seizures and other neurological symptoms, and various methods of surgical treatment and postoperative outcomes have been described in the literature.

Case Description

We present a case study of a 17-year-old patient who has been experiencing seizures since the age of 13. The patient reported episodes of loss of consciousness and periodic freezing with preservation of posture. Two years later, the patient experienced his first generalized tonic-clonic seizure during nocturnal sleep and was subsequently admitted to a neurological department. A magnetic resonance imaging scan of the brain with an epilepsy protocol (3 Tesla) confirmed the presence of an extended bilateral subependymal nodular heterotopy at the level of the temporal and occipital horns of the lateral ventricles, which was larger on the left side, and a focal subcortical heterotopy of the right cerebellar hemisphere. The patient underwent a posterior quadrant disconnection surgery, which aimed to isolate the extensive epileptogenic zone in the left temporal, parietal, and occipital lobes using standard techniques. As of today, 6 months have passed since the surgery and there have been no registered epileptic seizures during this period following the surgical treatment.

Conclusion

Although PNHs can be extensive and located bilaterally, surgical intervention may still be an effective way to achieve seizure control in selected cases.

Periventricular nodular heterotopia is coupled with the neocortex during resting and task states

Periventricular nodular heterotopia (PVNH) is a well-defined developmental disorder characterized by failed neuronal migration, which forms ectopic neuronal nodules along the ventricular walls. Previous studies mainly focus on clinical symptoms caused by the PVNH tissue, such as seizures. However, little is known about whether and how neurons in the PVNH tissue functionally communicate with neurons in the neocortex. To probe this, we applied magnetoencephalography (MEG) and stereo-electroencephalography (sEEG) recordings to patients with PVNH during resting and task states. By estimating frequency-resolved phase coupling strength of the source-reconstructed neural activities, we found that the PVNH tissue was spontaneously coupled with the neocortex in the α-β frequency range, which was consistent with the synchronization pattern within the neocortical network. Furthermore, the coupling strength between PVNH and sensory areas effectively modulated the local neural activity in sensory areas. In both MEG and sEEG visual experiments, the PVNH tissue exhibited visual-evoked responses, with a similar pattern and latency as the ipsilateral visual cortex. These findings demonstrate that PVNH is functionally integrated into cognition-related cortical circuits, suggesting a co-development perspective of ectopic neurons after their migration failure.

BrainQuake: An Open-Source Python Toolbox for the Stereoelectroencephalography Spatiotemporal Analysis

Intracranial stereoelectroencephalography (SEEG) is broadly used in the presurgical evaluation of intractable epilepsy, due to its high temporal resolution in neural activity recording and high spatial resolution within suspected epileptogenic zones. Neurosurgeons or technicians face the challenge of conducting a workflow of post-processing operations with the multimodal data (e.g., MRI, CT, and EEG) after the implantation surgery, such as brain surface reconstruction, electrode contact localization, and SEEG data analysis. Several software or toolboxes have been developed to take one or more steps in the workflow but without an end-to-end solution. In this study, we introduced BrainQuake, an open-source Python software for the SEEG spatiotemporal analysis, integrating modules and pipelines in surface reconstruction, electrode localization, seizure onset zone (SOZ) prediction based on ictal and interictal SEEG analysis, and final visualizations, each of which is highly automated with a user-friendly graphical user interface (GUI). BrainQuake also supports remote communications with a public server, which is facilitated with automated and standardized preprocessing pipelines, high-performance computing power, and data curation management to provide a time-saving and compatible platform for neurosurgeons and researchers.