Trent Henderson

Canberra, Australian Capital Territory, Australia
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About

Trent is an Accredited Statistician, lead data scientist, and final year statistics PhD…

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Experience & Education

  • Nous Group

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Volunteer Experience

  • Volunteer Research Assistant

    Northside Family and Counselling Services

    - 2 months

    Health

    I engaged in data collection and analysis of scores on various road rage questionnaires that were completed by Australian Army Veterans. My analysis and writeup was used by a Social Worker to obtain PhD Candidature.

Publications

  • An Empirical Evaluation of Time-Series Feature Sets

    2021 International Conference on Data Mining Workshops (ICDMW)

    Solving time-series problems using informative features has been rising in popularity due to the availability of numerous software packages for time-series feature extraction. Feature-based time-series analysis can now be performed using any one of a range of time-series feature sets, including hctsa (7730 features), feasts (42 features), tsfeatures (63 features), Kats (40 features), tsfresh (up to 1558 features), TSFEL (390 features), and the C-coded catch22 (22 features). There is substantial…

    Solving time-series problems using informative features has been rising in popularity due to the availability of numerous software packages for time-series feature extraction. Feature-based time-series analysis can now be performed using any one of a range of time-series feature sets, including hctsa (7730 features), feasts (42 features), tsfeatures (63 features), Kats (40 features), tsfresh (up to 1558 features), TSFEL (390 features), and the C-coded catch22 (22 features). There is substantial overlap in the types of time-series analysis methods included in these feature sets (including properties of the autocorrelation function and Fourier power spectrum, and distributional shape statistics), but they are yet to be systematically compared. Here we compare these seven feature sets on their computational speed, assess the redundancy of features contained in each set, and evaluate the overlap and redundancy across different feature sets. We take an empirical approach to measuring feature similarity, based on the similarity of their outputs across a diverse set of real-world and model-simulated time series. We find that feature sets vary across approximately three orders of magnitude in their computation time per feature on a laptop for a 1000-sample time series, from the fastest feature sets catch22 and TSFEL to tsfeatures. Using PCA to evaluate feature redundancy within each set, we find the highest within-set redundancy for TSFEL and tsfresh. For example, in TSFEL, 90% of the variance across 390 features can be captured with just four principal components. Finally, we introduce a metric for quantifying overlap between pairs of feature sets, which indicates substantial overlap between the feature sets. We found that the largest feature set, hctsa, is the most comprehensive, and that tsfresh is the most distinctive, due to its incorporation of large numbers of Fourier coefficients that are summarized at higher levels in the other sets.

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  • Neural and self-report markers of reassurance: A generalized additive modelling approach

    Frontiers in psychiatry

    Research has shown that engaging in self-reassurance, a compassionately motivated cognitive relating style, can down-regulate neural markers of threat and pain. Whilst important, the relationship between neural and self-report markers of reassurance are largely unknown. Here we analyzed previously published fMRI data which measured neural responses when participants engaged in self-reassurance toward a mistake, setback or failure. Within the present paper, we identified correlations between…

    Research has shown that engaging in self-reassurance, a compassionately motivated cognitive relating style, can down-regulate neural markers of threat and pain. Whilst important, the relationship between neural and self-report markers of reassurance are largely unknown. Here we analyzed previously published fMRI data which measured neural responses when participants engaged in self-reassurance toward a mistake, setback or failure. Within the present paper, we identified correlations between regions of interest extracted during self-reassurance with fMRI and self-report data. Using generalized additive modelling, we show that participants with greater inadequate forms of self-criticism exhibited greater neural activation within the medial prefrontal cortex (MPFC) and anterior insula (AI). Furthermore, a relationship between greater fears of expressing compassion to the self and neural activation within the MPFC returned non-significant after correction for multiple comparisons. No significant relationships were observed between brain activation and hated and reassuring forms of self-criticism. Our results identify preliminary evidence for neural activity during self-reassurance as correlated with self-report markers, and we outline a method for modelling neural and self-report data which can be applied to future studies in compassion science, particularly with a clinical sample.

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  • Physiological fractals: visual and statistical evidence across timescales and experimental states

    Journal of the Royal Society Interface

    A marker of engaging in compassion meditation and related processes is an increase in heart-rate variability (HRV), typically interpreted as a marker of parasympathetic nervous system response. While insightful, open questions remain. For example, which timescale is best to examine the effects of meditation and related practices on HRV? Furthermore, how might advanced time-series analyses––such as stationarity––be able to examine dynamic changes in the mean and variance of the HRV signal across…

    A marker of engaging in compassion meditation and related processes is an increase in heart-rate variability (HRV), typically interpreted as a marker of parasympathetic nervous system response. While insightful, open questions remain. For example, which timescale is best to examine the effects of meditation and related practices on HRV? Furthermore, how might advanced time-series analyses––such as stationarity––be able to examine dynamic changes in the mean and variance of the HRV signal across time? Here we apply such methods to previously published data, which measured HRV pre- and post- a two-week compassionate mind training (CMT) intervention. Inspection of these data reveals that a visualization of HRV correlations across resting and compassion meditation states, pre- and post-two-week training, is retained across numerous recording timescales. Here, the fractal-like nature of our data indicates that the accuracy of representing HRV data can exist across timescales, albeit with greater or lesser granularity. Interestingly, inspection of the HRV signal at Time 2 compassion meditation versus Time 1 revealed a more highly correlated (i.e. potentially more stable) signal. We followed up these results with tests of stationarity, which revealed Time 2 had a less stochastic (variable) signal than Time 1, and a measure of distance in the time series, which showed that Time 2 had less of an average difference between rest and meditation than at Time 1. Our results provide novel assessment of visual and statistical markers of HRV change across distinct experimental states.

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  • The core self-evaluation scale: A replication of bi-factor dimensionality, reliability, and criterion validity

    Journal of Personality and Individual Differences

    The core self-evaluation scale (CSES) is the most popularly used measure of core self-evaluations. However, recent research has highlighted the potential existence of a negative wording factor (NWF) associated with the negatively-worded items of the CSES. Discounting the effects of a NWF may lead to biased estimates of the reliability and criterion-related validity of the CSES. The current research investigated whether the NWF found in previous research could be replicated in three Australian…

    The core self-evaluation scale (CSES) is the most popularly used measure of core self-evaluations. However, recent research has highlighted the potential existence of a negative wording factor (NWF) associated with the negatively-worded items of the CSES. Discounting the effects of a NWF may lead to biased estimates of the reliability and criterion-related validity of the CSES. The current research investigated whether the NWF found in previous research could be replicated in three Australian samples, which consisted of both students and adult community volunteers. Results revealed that the NWF provided a better model fit than the unifactor model but was inferior to an alternative model which measured a positive wording factor (PWF). However, the NWF model possessed equivalent composite reliability to the unifactor model in Study 1, but slightly superior composite reliability in Study 2 and Study 3. In addition, the NWF possessed poorer criterion validity estimates for both life satisfaction and career satisfaction. Contrasts to previous research which has substantiated the claim of a NWF in the CSES, and recommendations for research and practice are discussed.

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Honors & Awards

  • 2018 Postgraduate Student Award

    Griffith University

    Received the 2018 Postgraduate Student Award for being the top performing student of my graduating cohort.

  • The Griffith Award for Academic Excellence

    Griffith University

    Awards for Academic Excellence are bestowed each year on undergraduate and postgraduate students who have achieved a GPA of 6.0 or greater in the course of their studies. My performance also placed me in the top 5% of students across the University.

  • Dean's Commendation for Academic Excellence

    The University of Queensland

    I received the Dean's Commendation for Academic Excellence in Semester 2, 2016, for achieving a semester GPA of 7 on a 7-point scale.

  • Dean's Commendation for Academic Excellence

    University of Queensland

    I received the Dean's Commendation for Academic Excellence in Semester 1, 2016 for achieving a GPA of 7, on a 7-point scale.

  • Public Speaker of the Year

    Southern Cross Catholic College

  • Academic Excellence Award

    Southern Cross Catholic College

    I was recognised for achieving in the top 5% of my cohort at the College, where I received an award for my academic ability.

  • Dux Award for Physical Education

    Southern Cross Catholic College

    I was awarded the Class Dux award for Physical Education for being the overall highest achieving student in both theoretical and practical work.

  • Living the Mission Award

    Southern Cross Catholic College

    I was recognised for contributing significantly to the Redcliffe and Scarborough communities through my actions at the College as a Student Leader.

  • School Captain for Communications

    Southern Cross Catholic College

    I was elected by my fellow peers and teachers for this position. My role involved facilitating interactions between the College and external parties, including news outlets and radio broadcasters. I also was actively a member of the Student Council, where decisions regarding events and College matters where debated and decided upon. I independently conceived, planned, and organised a Flood Benefit Concert, which raised funds for flood victims through various performances of music, dance, and…

    I was elected by my fellow peers and teachers for this position. My role involved facilitating interactions between the College and external parties, including news outlets and radio broadcasters. I also was actively a member of the Student Council, where decisions regarding events and College matters where debated and decided upon. I independently conceived, planned, and organised a Flood Benefit Concert, which raised funds for flood victims through various performances of music, dance, and art.

Languages

  • English

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