Invited talk: Seydanur Tikir, Albert Einstein College of Medicine, Bronx, NY, USA

2019-09-18 at 11:00

Conference Room UCL R+0


Probing Flexibility of the Predictive Brain Using Computation-Intensive Approaches and EEG

The brain actively produces predictions of upcoming events in everyday life and update these predictions according to new information. We test the hypothesis that individuals with autism spectrum disorders (ASD) do not flexibly adjust their predictions following changes in environmental statistics, with the use of electroencephalography (EEG), behavior, and modeling. We will present our recent data showing how the amplitudes of certain evoked potentials (i.e., P3, CNV) are gradually modulated by environmental statistics in the neurotypical group, and how this pattern is different in the ASD group. This work required significant computational resources due to the use of high-density EEG (160 scalp electrodes), a high sampling rate (every two milliseconds), and several hours of collected data. We leveraged key advances in NSG to facilitate EEG analysis while employing computation-intensive algorithms. The talk will incorporate tactics for managing complexity in EEG analysis, modular processing of data, and automating the pipeline while ensuring methodological quality.