Periodic Reporting for period 1 - WAVESCOPE (Charting the sea of brain waves: Investigation of cortical traveling waves during speech processing)
Período documentado: 2022-06-01 hasta 2024-05-31
The WAVESCOPE project aims to enhance the detection and understanding of traveling waves in neural electrophysiology data, recorded from the brain, with a focus on their role in speech and auditory processing. This research addresses the critical need to understand how neural oscillations contribute to cognitive functions. By developing advanced methodologies for detecting and analyzing traveling waves, the project seeks to provide better descriptions for the spatiotemporal dynamics of oscillations in these data, ultimately providing new insights into brain function that could inform both basic neuroscience and clinical applications. Given the increasing importance of neuroscience in addressing mental health and neurological disorders, foundational work investigating key aspects of neural data is necessary, for instance for arriving at useful biomarkers for differentiating between clinical diagnoses.
The project made progress through two primary work packages (WPs), focusing on advancing the detection and understanding of traveling waves in electrophysiology. Work Package 1 (WP1) was dedicated to developing methods for detecting and tracking cortical traveling waves. The main activities included creating a Python-based workflow that utilized innovative algorithms for wave detection. These methods were validated on simulations, demonstrating their effectiveness. Additionally, the workflow was adapted to different neurophysiological data types, successfully applied to invasive electrophysiology (iEEG) data, and partially applied to magnetoencephalography (MEG) data.
Work Package 2 (WP2) investigated the computational role of traveling waves in auditory and speech processing. This involved conceptualizing and implementing an MEG experiment, which included pilot data acquisition. However, a six-month delay caused by a cyberattack on the university hospital’s IT infrastructure necessitated a pivot to using iEEG data. This shift allowed the project to continue making progress by leveraging the enhanced spatiotemporal resolution of iEEG data. Extensive analysis of this data provided new insights into the spatiotemporal dynamics of mesoscale oscillatory networks.
Work Package 2 (WP2) investigated the computational role of traveling waves in auditory and speech processing. This involved conceptualizing and implementing an MEG experiment, which included pilot data acquisition. However, a six-month delay caused by a cyberattack on the university hospital’s IT infrastructure necessitated a pivot to using iEEG data. This shift allowed the project to continue making progress by leveraging the enhanced spatiotemporal resolution of iEEG data. Extensive analysis of this data provided new insights into the spatiotemporal dynamics of mesoscale oscillatory networks.
The project has produced several results that extend beyond current knowledge and methodologies for application in electrophysiological data. Key achievements include:
- Developing a novel python-based workflow for the detection and tracking of cortical traveling waves, which offers a more accurate and noise-robust tool for researchers.
- Gaining new insights into the spatiotemporal structure of mesoscale oscillatory networks during auditory and speech processing.
Continued research and validation to refine the developed tools, especially in the context of constraining generative models for traveling waves with the computed metrics.
- Developing a novel python-based workflow for the detection and tracking of cortical traveling waves, which offers a more accurate and noise-robust tool for researchers.
- Gaining new insights into the spatiotemporal structure of mesoscale oscillatory networks during auditory and speech processing.
Continued research and validation to refine the developed tools, especially in the context of constraining generative models for traveling waves with the computed metrics.