Presentation 1: Prejudice towards people with borderline personality disorder: Integrating a theoretical framework, measurement, and lived experience perspectives

Presenter: Hannah Sheppard is a PhD Candidate in the School of Medicine and Psychology. Hannah’s PhD aims to develop a theoretical framework and a measure to investigate the prejudicial attitudes people hold towards people living with borderline personality disorder, and explores the lived experience perspective of these attitudes and their impact. 

Abstract: Evidence suggests that understanding and targeting prejudice towards people with mental illness needs a disorder-specific approach. Although people living with borderline personality disorder (BPD) face some of the highest levels of public and healthcare provider prejudice and discrimination, there is a lack of specific theory and related prejudice scales. To address this, the Prejudice towards People with BPD (PPBPD) scale was adapted from the Prejudice towards People with Mental Illness (PPMI) scale and its associated theoretical framework. The new PPBPD scale was validated across three samples (N = 834), primarily consisting of medical and psychology students. Subsequently, I conducted interviews with Australians living with BPD to determine if the framework  accurately represents their lived experiences and concerns. Preliminary thematic analyses indicate that the framework is largely reflected in their lived experiences. I will discuss the implications of this research for expanding and strengthening the framework, which can help us reduce prejudice and improve well-being of people living with BPD.

Presentation 2: Uncovering a stability signature of brain dynamics associated with meditation experience using massive time-series feature extraction

Presenter: Dr Neil Bailey is a senior research fellow at ANU, as well as the Head of Data Science at the Monarch Mental Health group. His research program explores how mental health can be improved, with 75 publications in a range of mental health treatment approaches including brain stimulation and mindfulness meditation. In particular, his research has examined the neural mechanisms underpinning the practice of mindfulness meditation. His research has also used machine learning techniques to show which measures of brain activity are optimal for predicting who will respond to a brain stimulation treatment for depression, and whether online mindfulness is effective at improving mental health.

Abstract: Previous research has examined resting electroencephalographic (EEG) data to explore brain activity related to meditation. However, this research has mostly examined power in different frequency bands. We compared >7000 time-series features of the EEG signal to comprehensively characterize brain activity differences between meditators and non-meditators. Eyes-closed resting-state EEG data from 49 meditators and 46 non-meditators was decomposed into the top eight principal components (PCs). We extracted 7,381 time-series features from each PC and each participant and used them to train classification algorithms to identify meditators. Highly differentiating individual features from successful classifiers were analysed in detail. Only the third PC showed above-chance classification accuracy (67%, p_FDR = 0.007), for which 405 features significantly distinguished meditators (all p_FDR < 0.05). Top-performing features indicated that meditators exhibited more consistent statistical properties across shorter subsegments of their EEG time-series (higher stationarity) and displayed an altered distributional shape of values about the mean. By contrast, classifiers trained with traditional band-power measures did not distinguish the groups (p_FDR > 0.05). Our novel analysis approach suggests the key signatures of meditators’ brain activity are higher temporal stability and a distribution of time-series values suggestive of longer, larger, or more frequent non-outlying voltage deviations from the mean within the third PC of their EEG data, a component which is likely to be generated by the bilateral medial prefrontal cortex, including the dorsal anterior cingulate cortex, superior frontal gyrus, bilateral middle prefrontal cortex and bilateral insula, regions responsible for attentional control and the processing of sensory information. The higher temporal stability observed in this EEG component might underpin the higher attentional stability associated with meditation. The novel time-series properties identified here have considerable potential for future exploration in meditation research and the analysis of neural dynamics more broadly.