Current Research

Stay tuned... 

Postdoctoral Work

My postdoctoral research used simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data to investigate how large scale functional brain networks are abnormal in focal epilepsy patients. Specifically, it focused on the dynamics of this disordered network activity at multiple timescales. 

Epileptic spike events are a common biomarker of focal epilepsy that are visible in EEG data. They occur on the timescale of milliseconds and are used to aid diagnosis and surgical planning. When we record EEG simultaneously with fMRI data, we can find all the brain regions that are activated or deactivated during these pathological spikes, but we can't tease apart these brain networks in time with the standard general linear model approach, which uses only the timing of the spike events and ignores all the rich information contained in the EEG signals. This standard modeling procedure provides information about neural activity only on the timescale of seconds, due to the slow nature of the blood flow response that fMRI measures. In my work, I'm using an EEG-fMRI data fusion approach that incorporates machine learning techniques on narrow windows of the EEG data, to ultimately determine the order in which these different regions are involved, providing information on the sub-second timescale at which spikes occur. You can hear me talk about this project (at a layperson level) on the 3RRR 102.7FM Melbourne public radio live science show called Einstein A Go Go by clicking HERE or in the mp3 file attached to the bottom of this page (starting around 14:20). If you prefer more technical details, check out our journal publication in Brain

My other major postdoctoral project focused on fMRI brain connectivity changes preceding, during, and following focal seizures on the timescale of minutes. The goal of this work is to provide insight into the transient brain states that permit seizures to occur, i.e. "pro-ictal" states. The manuscript is currently under peer review. 

Ph.D. Dissertation

Exposing Internal Attentional Brain States using Single-Trial EEG Analysis with Combined Imaging Modalities


The goal of this dissertation is to explore the neural correlates of endogenous task-related attentional modulations. Natural fluctuations in task engagement are challenging to study, primarily because they are by nature not event related and thus cannot be controlled experimentally. Here we exploit well-accepted links between attention and various measures of neural activity while subjects perform simple target detection tasks that leave their minds free to wander. We use multimodal neuroimaging, specifically simultaneous electroencephalograpy and functional magnetic resonance imaging (EEG-fMRI) and EEG-pupillometry, with data-driven machine learning methods and study activity across the whole brain. We investigate BOLD fMRI correlates of EEG variability spanning each trial, enabling us to unravel a cascade of attention-related activations and determine their temporal ordering. We study activity during auditory and visual paradigms independently, and we also combine data to investigate supra modal attention systems. Without aiming to study known attention-related functional brain networks, we found correlates of attentional modulations in areas representative of the default mode network (DMN), ventral attention network (VAN), locus coeruleus norepinephrine (LC-NE) system, and regions implicated in generation of the extensively-studied P300 EEG response to target stimuli. Our results reveal complex interactions between known attentional systems, and do so non-invasively to study normal fluctuations of task engagement in the human brain.
Subpages (2): Coursework PhD photos
Einstein A Go Go - 19 June 2016.mp3
Jennifer Walz,
May 11, 2017, 2:07 AM