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Robert Whelan 

Associate Professor of Psychology

I am an Associate Professor of Psychology in Trinity College Dublin. The majority of my research is directed towards answering clinically relevant questions, using both structural and functional magnetic resonance imaging, high-density electroencephalography and behavioural assays. I am a strong advocate of the development of sophisticated methods to better understand brain systems in disease and in health, and many of my projects can be placed under the rubric of ‘Big Data’ approaches. For example, I am a principal investigator on the IMAGEN study, a longitudinal study of >2,000 adolescents that combines several different kinds of data (neural, personality, cognitive, life history and genetics). I have several ongoing collaborations across a range of disciplines, often in a data-analytic capacity. For example, I collaborate on projects related to adolescent psychiatry, both using neuroimaging and blood-based biomarkers and with the Irish Longitudinal Study on Ageing, in which we have used machine learning to interrogate brain and cardiovascular data.

Background

My research has a number of different strands. One goal is to relate the neurocognitive constructs of impulsivity and reward processing to real-world outcomes. To this end, we use structural and functional magnetic resonance imaging (MRI) and electroencephalography (EEG), typically utilizing a combination of theory- and data-driven approaches in large samples. Earlier research used primarily data-driven methods applied to MRI data: to show that the impulsivity construct could be decomposed into specific brain networks and that some of these networks were associated with substance misuse whereas others were associated with attention deficit hyperactivity disorder symptoms (paper here). A prospective, longitudinal study used machine leaning to show that individual differences in brain activity during assays of impulsivity and reward processing in substance-use naïve participants predicted future binge drinking (paper here). We have extended these data-driven methods to EEG, predicting alcohol use in young adults (see here). Recently, we have applied computational models of reward processing to MRI and EEG data in order to characterise sensation-seeking behaviour and alcohol use, respectively (see here and here). A second strand aim to better understand the neurocognitive basis of ADHD symptoms. Using machine learning, we showed EEG connectivity was a sensitive marker of ADHD symptoms, and that EEG spectral frequency could classify people with ADHD from controls (paper here). Another study showed that MRI functional connectivity was associated with response variability – a known characteristic of ADHD – in healthy adolescents. The large sample size allowed us characterise widespread brain networks and we showed that these networks were also differentially active in a separate sample of those with elevated ADHD symptoms (paper here). A third strand is focused on improving methods of interrogating brain data. For example, we have recently published in a Special Issue on Reproducibility in NeuroImage (paper here) that systematically quantified, for the first time, that the performance of various machines on different types of neuroimaging data was highly variable (see Trends in Neurosciences commentary here).

Key Publications

  • Jollans, L., … Whelan, R. (2019). Quantifying performance of machine learning methods for neuroimaging data, Neuroimage. doi: 10.1016/j.neuroimage.2019.05.082.

  • Enz, N., Ruddy, K. L., Rueda-Delgado, L. M., & Whelan, R. (2021). Volume of β-Bursts, But Not Their Rate, Predicts Successful Response Inhibition. Journal of Neuroscience, 41(23), 5069-5079.

  • Cao, Z., Bennett, M., O'Halloran, L., Pragulbickaite, G., Flanagan, L., McHugh, L., & Whelan, R. (2020). Aberrant reward prediction errors in young adult at‐risk alcohol users. Addiction Biology, e12873.

  • Farina, F. R., Emek-Savaş, D. D., Rueda-Delgado, L., Boyle, R., Kiiski, H., Yener, G., & Whelan, R. (2020). A comparison of resting state EEG and structural MRI for classifying Alzheimer’s disease and mild cognitive impairment. NeuroImage, 215, 116795.

  • Boyle, R., Jollans, L., Rueda-Delgado, L. M., Rizzo, R., Yener, G. G., McMorrow, J. P., ... & Whelan, R. (2021). Brain-predicted age difference score is related to specific cognitive functions: a multi-site replication analysis. Brain imaging and behavior, 15(1), 327-345.

You can find more of Rob's publications here.

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