The goal of the proposed grant proposal is to foster international collaboration between two laboratories studying the neuronal basis of perception and cognition.
Specific aims of the grant is the analysis of a unique data set obtained from patients with brain implants, the additional collection and analysis of complimentary scalp EEG data and the creation and public dissemination of an analysis toolbox to the wider scientific community. In addition, a publication will be prepared on the analysis of the experimental data sets. The grant will support student and postdoc salaries and medium-term exchange visits. Cognitive neuroscience research benefits vastly from the rapid progression of development in methodological approaches and analysis techniques. Recordings of electrocorticographic (ECoG) signals from neurosurgical patients with subdural electrodes represent a powerful approach for examining the neural mechanisms underlying sensorimotor and cognitive processes. While ECoG recordings provide an opportunity to study these processes with a unique combination of high spatial and temporal resolution, this approach also has its limitations. Electrode recordings sites rarely overlap across patients and ECoG signals are usually monitored only from a few cortical regions. Non-invasive recordings of neural activity in humans using electroencephalography (EEG) or magnetoencephalography (MEG) have a much lower spatial resolution and measure brain signals with a reduced signal-to-noise ratio compared to ECoG, but have the advantage of covering the entire head simultaneously. In addition, state-of-the-art source analysis techniques, especially the analysis of neural oscillations, may allow a relatively adequate estimation of the cortical sources underlying EEG/MEG signals, however, validating their source solutions requires invasive electrode placement. Therefore, translational studies combing ECoG and EEG/MEG signals from the same experimental paradigm are particularly promising. To date, however, there is no analysis and visualization toolbox for the comparison of ECoG and EEG/MEG signals that specifically focuses on neural oscillations. The proposed project will fuse the expertise of the laboratories of the two PIs by applying the oscillatory data analysis expertise of the Senkowski Lab to the unique multisensory ECoG data sets previously collected from invasive human intracranial recordings at the Thesen Lab. Specifically, available ECoG data from an intersensory attention paradigm will be analyzed and compared with EEG data recorded from the same paradigm. The central goals of the current proposal are (i) to develop a toolbox for the analysis and visualization of neural oscillations in ECoG data, and (ii) to test this ECoG-toolbox by analyzing intracranial and EEG data from an intersensory attention paradigm through collaborative data sharing. The toolbox, which will include functions enabling the comparison of ECoG signals with source-localized EEG/MEG signals, will be shared with the wider scientific community. Due to its functionality and its compatibility with the popular FieldTrip toolbox (http://fieldtrip.fcdonders.nl), the ECoG-toolbox will be of particular interest to the large numbe of researchers in this growing field of cognitive neuroscience. Overall, the development of the ECoG-toolbox could facilitate the translational research on neural oscillations. Since impairments in neural oscillations have often been linked to symptomatology in clinical populations, such as in patients with autism or schizophrenia, this research has significant clinical implications. A better understanding of the role of neural oscillations in cognitive functions could contribute to a better understanding of the symptomatology in these populations. Taken together, the current proposal will lead to empirical output based on collaborative sharing of data from an intersensory attention study. Furthermore, by developing a user-friendly toolbox for the analysis and visualization of neural synchrony in ECoG and combined ECoG-EEG data, there will be a direct benefit to the wider scientific community.
|Ahmed, Bilal; Brodley, Carla E; Blackmon, Karen E et al. (2015) Cortical feature analysis and machine learning improves detection of ""MRI-negative"" focal cortical dysplasia. Epilepsy Behav 48:21-8|