DecoMP_ECoG · Decoding memory processing from experimental and spontaneous human brain activity using intracranial electrophysiological recordings and machine learning based methods.

Decoding memory processing from experimental and spontaneous human brain activity using intracranial electrophysiological recordings and machine learning based methods.

My project's name is DecoMP-ECoG. It stands for Decoding Memory Processing using ElectroCorticoGraphy. The aim of this project is to understand how memories are formed in the human brain, based on electrical brain signals. There are three main components in this work, based on the keywords in the title: Memory Our brain displays a specific pattern of brain activity when learning new information. There is considerable evidence that this pattern is then spontaneously replayed by the brain during rest and/or sleep. However, little is known about these spontaneous replays of brain activity. My project aims at identifying such replays and characterizing where and when they occur. To this end, I taught subjects a sequence of 12 images and had them rest afterwards (Figure 1). Decoding In this project, decoding refers to the use of machine learning methods to model brain activity. Machine learning is a set of techniques that teach a computer the relationship between some measured values (called data points) and labels. An example of data point would be the brain activity of a subject when shown an image. The corresponding label could then be whether this person has 'learned' or 'forgotten' the corresponding image. In this work, I used such models to identify which features of brain activity predict when images are learned. I also aimed to pinpoint times when the brain spontaneously replays the learned images in rest or sleep. Electrocorticography To measure brain activity, I recorded the electrical activity on the surface of the brain (Figure 2) in three structures involved in image processing and memory, namely the occipital cortex, the temporal cortex and the hippocampus. This kind of recording is called 'ElectroCorticoGraphy' (a.k.a. 'ECoG') or intracranial electroencephalography (EEG). Expected outcomes This research can have impact both in terms of neuroscience and for developing new methods for analyzing brain signals. • Neuroscience: This project could provide insights on how our brain creates and stores a permanent record of information and accesses it as long as years later. In the long-term, a better understanding of human memory could guide investigations of memory dysfunctions involved in degenerative diseases such as age-related dementia. Preliminary results suggested that the encoding of information starts during short rest periods interspersed in the learning task. These results will however need to be confirmed in a larger and healthy population. • Methods: Machine learning modelling of anatomical and functional brain data is a promising research, computer-aided diagnosis and brain-computer interface tool. However, few works have been applied to electrical brain signals. Therefore, adapting these methods to such recordings is crucial to provide state-of-the-art research and clinical tools. In the current research, we have applied an algorithm that can automatically select the important features for the problem at hand. However, relating this feature selection to brain signals is not straightforward, so we simulated ECoG data to investigate this relationship. I expect to make the results of my work fully accessible so that other groups can test the novel methods developed in this project. As part of this effort, I implemented the methods I developed in the open-source software PRoNTo. Version 2.1 of this software was recently released and version 3 (including ECoG modelling) will soon be available. The simulated data and corresponding code, as well as a preprocessing pipeline for ECoG data are also open source. Implementation I have conducted this research in the USA for the first two years (Laboratory of Behavioral and Cognitive Neuroscience, Stanford University) under the supervision of Dr. Parvizi and then in the UK (Department of Computer Science, University College London) for one year under the supervision of Dr. Mourão-Miranda.

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