An example of an evoked potential signal from our lab


An example of an evoked potential signal from our lab


Our work in EEG

I have been working on EEG projects for several years now, both at ANU and now at UNE. I maintain an active EEG and postural sway lab at The Canberra Hospital, where we are looking at ERP and resting state measures in both Parkinson’s disease and multiple sclerosis. We have a BioSemi 64-channel active-electrode system (at TCH), a Compumedics Neuroscan NuAmps 32-channel passive electrode system (at UNE), and a wireless active-electrode DSI Wearable Sensing 24-electrode system.

Deborah Apthorp
Associate Professor in Psychology

My research interests include visual perception, Parkinson’s disease, postural sway, and EEG.


A biomarker of cognition in Multiple Sclerosis (MS) that is independent from the response of people with MS (PwMS) to test questions would provide a more holistic assessment of cognitive decline. One suggested method involves event-related potentials (ERPs). This systematic review tried to answer five questions about the use of ERPs in distinguishing PwMS from controls: which stimulus modality, which experimental paradigm, which electrodes, and which ERP components are most discriminatory, and whether amplitude or latency is a better measure. Our results show larger pooled effect sizes for visual stimuli than auditory stimuli, and larger pooled effect sizes for latency measurements than amplitude measurements. We observed great heterogeneity in methods and suggest that future research would benefit from more uniformity in methods and that results should be reported for the individual subtypes of PwMS. With more standardised methods, ERPs have the potential to be developed into a clinical tool in MS.

Resting-state electroencephalography (RSEEG) is a method under consideration as a potential biomarker that could support early and accurate diagnosis of Parkinson’s disease (PD). RSEEG data is often contaminated by signals arising from other electrophysiological sources and the environment, necessitating pre-processing of the data prior to applying machine learning methods for classification. Importantly, using differing degrees of pre-processing will lead to different classification results. This study aimed to examine this by evaluating the difference in experimental results when using re-referenced data, data that had undergone filtering and artefact rejection, and data without muscle artefact. The results demonstrated that, using a Random Forest Classifier for feature selection and a Support Vector Machine for disease classification, different levels of pre-processing led to markedly different classification results. In particular, the presence of muscle artefact was associated with inflated classification accuracy, emphasising the importance of its removal as part of pre-processing.

Current tests of disease status in Parkinson’s disease suffer from high variability, limiting their ability to determine disease severity and prognosis. Event-related potentials, in conjunction with machine learning, may provide a more objective assessment. In this study, we will use event-related potentials to develop machine learning models, aiming to provide an objective way to assess disease status and predict disease progression in Parkinson’s disease.

Sleep restriction affects attention in different ways. Performance on an attentional blink task was unaffected by sleep restriction in two studies, but performance on a vigilance task was affected in both. In the second study, we looked at resting state EEG and found alpha was reduced after sleep restriction, which may have balanced out performance on the attentional blink task.

Background: Huntington’s disease (HD) causes progressive atrophy to the striatum, a critical node in frontostriatal circuitry. Maintenance of motor function is dependent on functional connectivity of these premotor, motor, and dorsolateral frontostriatal circuits, and structural integrity of the striatum itself. We aimed to investigate whether size and shape of the striatum as a measure of frontostriatal circuit structural integrity was correlated with functional frontostriatal electrophysiological neural premotor processing (contingent negative variation, CNV), to better understand motoric structure–function relationships in early HD.

Methods: Magnetic resonance imaging (MRI) scans and electrophysiological (EEG) measures of premotor processing were obtained from a combined HD group (12 presymptomatic, 7 symptomatic). Manual segmentation of caudate and putamen was conducted with subsequent shape analysis. Separate correlational analyses (volume and shape) included covariates of age, gender, intracranial volume, and time between EEG and MRI.

Results: Right caudate volume correlated with early CNV latency over frontocentral regions and late CNV frontally, whereas right caudate shape correlated with early CNV latency centrally. Left caudate volume correlated with early CNV latency over centroparietal regions and late CNV frontally. Right and left putamen volumes correlated with early CNV latency frontally, and right and left putamen shape/volume correlated with parietal CNV slope.

Conclusions: Timing (latency) and pattern (slope) of frontostriatal circuit‐mediated premotor functional activation across scalp regions were correlated with abnormalities in structural integrity of the key frontostriatal circuit component, the striatum (size and shape). This was accompanied by normal reaction times, suggesting it may be undetected in regular tasks due to preserved motor “performance.” Such differences in functional activation may reflect atrophy‐based frontostriatal circuitry despecialization and/or compensatory recruitment of additional brain regions.

Background Huntington’s disease (HD) causes progressive motor dysfunction through characteristic atrophy. Changes to neural structure begin in premanifest stages yet individuals are able to maintain a high degree of function, suggesting involvement of supportive processing during motor performance. Electroencephalography (EEG) enables the investigation of subtle impairments at the neuronal level, and possible compensatory strategies, by examining differential activation patterns. We aimed to use EEG to investigate neural motor processing (via the Readiness Potential; RP), premotor processing and sensorimotor integration (Contingent Negative Variation; CNV) during simple motor performance in HD. Methods We assessed neural activity associated with motor preparation and processing in 20 premanifest (pre-HD), 14 symptomatic HD (symp-HD), and 17 healthy controls. Participants performed sequential tapping within two experimental paradigms (simple tapping; Go/No-Go). RP and CNV potentials were calculated separately for each group. Results Motor components and behavioural measures did not distinguish pre-HD from controls. Compared to controls and pre-HD, symp-HD demonstrated significantly reduced relative amplitude and latency of the RP, whereas controls and pre-HD did not differ. However, early CNV was found to significantly differ between control and pre-HD groups, due to enhanced early CNV in pre-HD. Conclusions For the first time, we provide evidence of atypical activation during preparatory processing in pre-HD. The increased activation during this early stage of the disease may reflect ancillary processing in the form of recruitment of additional neural resources for adequate motor preparation, despite atrophic disruption to structure and circuitry. We propose an early adaptive compensation mechanism in pre-HD during motor preparation.


Huntington’s Disease causes progressive motor dysfunction through atrophic disruption to the frontal cortical motor circuitry and basal ganglia regions. Symptom onset typically begins at age 40; subsequent neurodegeneration is incurable and terminal. Motor Response Potential (MRP) studies examining symptomatic patients have identified reduced peak neural amplitude and pre- and post-slope components compared with controls during simple movements such as finger tapping. However, little is known about the profile of MRPs in presymptomatic patients, who show no significant behavioural differences in response time from healthy controls. Atypical MRPs in presymptomatic patients may be a potential biomarker for the disease that could be useful for pharmaceutical trials.


10 genetically confirmed symptomatic HD participants, 17 genetically confirmed presymptomatic participants (PHD), and 17 healthy controls were recruited for this study. Participants completed a sequential tapping task using their right index finger, alternating key taps (left and right) at a rate of one every four seconds. In a cued tapping task, visual cues indicated the appropriate response times, and in self-paced tapping, the participant continued alternating taps at the same rate in the absence of cues. Conditions alternated in 24-second segments, and continued for a total task period of 8 minutes. Experimental variables included Contingent Negative Variation (CNV) magnitude (peak and area under the curve), slope and intercept, and amplitude (peak to peak) and latency of the motor response (-50 to +100 ms, and 100 to 300 ms relative to the response).


There was no amplitude difference between groups for peak or area under the curve CNV, or CNV slope or intercept. A significant group difference was found in the motor response peak to peak amplitude (F = 8.844, p = .001) and latency (F = 4.195, p = .022). Post hoc analyses revealed significantly larger amplitudes for controls than HD (p < .001), and PHD than HD (p =.041), and significantly later latencies for controls than HD (p = .020).


Presymptomatic patients show no significant difference in behavioural performance tasks using measures such as reaction time. This study found that MRP s of PHD participants varied markedly in profile, but were not significantly different from controls. Observed variability may indicate functional differences in the neural generation of motor responses during early stages of the disease. Additional analysis will be conducted to explore the relationship between PHD MRP profiles and other variables such as predicted time to onset, age, and number of CAG repeats.


Since moving to a regional, largely online university in 2018, my lab has faced the challenge of carrying research without a …