Mesoscale insights in Epileptic Networks: A Multimodal Intracranial Dataset

Area of Science:

• Neuroscience
• Epilepsy Research
• Data Science

Background:

• Understanding mesoscale epileptic network dynamics is key to epilepsy pathophysiology and treatment.
• Existing research often lacks multi-modal neural recordings at the mesoscale.
• Focal epilepsy presents complex spatiotemporal dynamics that require detailed neural signal analysis.

Purpose of the Study:

• To present a comprehensive dataset of multi-modal neural recordings from epilepsy patients.
• To enable investigation of mesoscale epileptic networks using intracranial electroencephalography (iEEG), Local Field Potentials (LFP), and Multiunit Activity (MUA).
• To facilitate research into the relationship between High-Frequency Oscillations (HFOs) and neural activity in focal epilepsy.

Main Methods:

• Acquisition of intracranial electroencephalography (iEEG), Local Field Potentials (LFP), and Multiunit Activity (MUA) data.
• Utilized Microelectrode Arrays (MEAs; Utah array; Blackrock) for neural recordings.
• Recorded 12 seizures from 5 epilepsy patients.

Main Results:

• A comprehensive dataset containing synchronized iEEG, LFP, and MUA recordings is now available.
• The dataset allows for the study of complex interactions between diverse neural signals.
• High temporal resolution facilitates the computation of High-Frequency Oscillations (HFOs) in iEEG and LFP signals.

Conclusions:

• This dataset provides a valuable resource for studying mesoscale epileptic networks in focal epilepsy.
• It enables exploration of the spatiotemporal dynamics of epileptic networks by integrating iEEG, LFP, and MUA.
• The data supports research into the relationship between High-Frequency Oscillations (HFOs) and Multiunit Activity (MUA).