Personal Page | Dr. Dirk Smit

My research field are on the crossroads of psychiatry, genetics and neuroscience (viz., electrophysiology). I am part of the Psychiatric Genomics Consortium as one of the main analysts of the OCD working group, and co-lead of the OC symptom and Hoarding GWAS groups [1-3]. I am also involved in the (genetic factor) analysis of substance use disorders [4-6] and misophonia [7].

I also lead the ENIGMA-EEG “genetics of electrophysiology” working group of the ENIGMA consortium. An overview of the goals and methods of this project can be found in our overview article [8,9]. Our main aim is to find genetic variants for well-known electrophysiological markers of brain activity, such as power in the relevant frequency bands and functional brain connectivity. Next, we use these measures to find links between behavioral phenotypes and neurological / psychiatric disorders [10].

Further interests are in the effects of DBS treatment on the brain as measured with electrophysiological measures, a highly invasive, relatively successful, but still poorly understood treatment of last resort in OCD and other disorders [11]. Machine learning in prediction of disorders and brain age estimation are other side projects. Finally, I am interested in the variability and noisy nature of human behavior, trying to explain how this come about by correlating it to noisy measures of the brain [12].

I have been a teacher of many courses on statistics (neuroscience) and methodology (general psychology). Currently I only teach in the Rhythms of the Brain course of the Master of Neuroscience at the VU University where I teach the brain connectivity and machine learning lectures.

Dr. Dirk Smit

Assistant Professor , Psychiatry Department, Amsterdam UMC, The Netherlands Compulsion Impulsiveness Attention (CIA), Amsterdam Neuroscience, The Netherlands

#Psychiatric Genetics #Electrophysiology (EEG) #behavior genetics #OCD #Machine Learning

Key publications

  1. Strom, N. I. et al. Genome-wide association study identifies new locus associated with OCD. 2021.10.13.21261078
    Preprint (2021).
  2. Strom, N. I. et al. Genome-Wide Association Study of Obsessive-Compulsive Symptoms including 33 943 individuals from the general population. 2022.11.30.22282898 Preprint (2022).
  3. Strom, N. I. et al. Meta-analysis of genome-wide association studies of hoarding symptoms in 27,651 individuals. Transl Psychiatry 12, 1–8 (2022).
  4. Abdellaoui, A., Smit, D. J. A., van den Brink, W., Denys, D. & Verweij, K. J. H. Genomic relationships across psychiatric disorders including substance use disorders. Drug and Alcohol Dependence 220, 108535 (2021).
  5. Marees, A. T. et al. Potential influence of socioeconomic status on genetic correlations between alcohol consumption measures and mental health. Psychological Medicine 50, 484–498 (2020).
  6. Marees, A. T. et al. Genetic correlates of socio-economic status influence the pattern of shared heritability across mental health traits. Nature Human Behaviour 1–9 (2021) doi:10.1038/s41562-021-01053-4.
  7. Smit, D. J. A. et al. Genetic evidence for the link of misophonia with psychiatric disorders and personality. 2022.09.04.22279567 Preprint (2022).
  8. Smit, D. J. A. et al. Genome-wide association analysis links multiple psychiatric liability genes to oscillatory brain activity. Human Brain Mapping 39, 4183–4195 (2018).
  9. Smit, D. J. A. et al. Large-scale collaboration in ENIGMA-EEG: A perspective on the meta-analytic approach to link neurological and psychiatric liability genes to electrophysiological brain activity. Brain and Behavior 11, e02188 (2021).
  10. Stevelink, R. et al. Shared genetic basis between genetic generalized epilepsy and background electroencephalographic oscillations. Epilepsia n/a,.
  11. Eijsker, N., Schroder, A., Smit, D. J., van Wingen, G. & Denys, D. Structural and Functional Brain Abnormalities in Misophonia. Biological Psychiatry 87, S225–S226 (2020).
  12. Smit, D. J. A., Linkenkaer-Hansen, K. & de Geus, E. J. Long-range temporal correlations in resting-state alpha oscillations predict human timing-error dynamics. The Journal of Neuroscience 33, 11212–11220 (2013).




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