Magnetic resonance imaging (MRI) brain scans are currently not used for clinical diagnosis or prognosis of psychiatric disorders in individual patients. The reason is the large biological variability in (healthy) brain anatomy, in combination with the subtle and heterogeneous brain abnormalities involved in psychiatric disorders.We aim to make individual predictions from MRI brain scans, using multivariate pattern recognition techniques. This research involves the study of image analysis, (longitudinal) brain data analysis and modeling, group-level statistics, machine learning, healthy brain development and psychiatric disorders including schizophrenia and bipolar disorder. We also investigate the predictive potential of other sources of “Big Data”, such as genetic and clinical data.


  • Joost Janssen (Psychiatry, Madrid)
  • Tomas Kasparek (Psychiatry, Brno)
  • Frank Wijnen (UiL OTS – Linguistics, Faculty of Humanities, Utrecht Univeristy)


Key Publications:

  • Petr Dluhoš, Daniel Schwarz, Wiepke Cahn, Neeltje van Haren, René Kahn, Filip Španiel, Jiří Horáček, Tomáš Kašpárek, Hugo Schnack. Multi-center Machine Learning in Imaging Psychiatry: A Meta-Model Approach. NeuroImage (in press).
  • Nieuwenhuis, M., Schnack, H. G., van Haren, N. E., Lappin, J., Morgan, C., Reinders, A. A., . . . Dazzan, P. (2017). “Multi-center MRI prediction models: Predicting sex and illness course in first episode psychosis patients.” NeuroImage 145: 246-253.
  • Chen, A., Wijnen, F., Koster, C., Schnack, H. (2017). “Individualized Early Prediction of Familial Risk of Dyslexia: A Study of Infant Vocabulary Development.” Frontiers in Psychology 8: 156.
  • de Wit, S., Ziermans, T. B., Nieuwenhuis, M., Schothorst, P. F., van Engeland, H., Kahn, R. S., . . . Schnack, H. G. (2017). “Individual prediction of long‐term outcome in adolescents at ultra‐high risk for psychosis: Applying machine learning techniques to brain imaging data.” Human Brain Mapping 38: 704-714.
  • Schnack HG, Kahn RS, Detecting Neuroimaging Biomarkers for Psychiatric Disorders: Sample Size Matters. Front Psychiatry. 2016 Mar 31;7:50. doi: 10.3389/fpsyt.2016.00050. eCollection 2016.
  • Schnack HG, van Haren NE, Nieuwenhuis M, Hulshoff Pol HE, Cahn W, Kahn RS, Accelerated brain-aging in schizophrenia: a longitudinal pattern recognition study. Am J Psychiatry. 2016 Jun 1; 173(6):607-16
  • Schnack HG, van Haren NE, Brouwer RM, Evans A, Durston S, Boomsma DI, Kahn RS, Hulshoff Pol HE, Changes in thickness and surface area of the human cortex and their relationship with intelligence. Cereb Cortex 25:1608-1617 (2015).
  • Schnack HG, Nieuwenhuis M, van Haren NE, Abramovic L, Scheewe TW, Brouwer RM, Hulshoff Pol HE, Kahn RS, Can structural MRI aid in clinical classification? A machine learning study in two independent samples of patients with schizophrenia, bipolar disorder and healthy subjects. Neuroimage 84:299-306 (2014)
  • Mandl RC, Schnack HG, Zwiers MP, Kahn RS, Hulshoff Pol HE, Functional diffusion tensor imaging at 3 Tesla. Front Hum Neurosci 7:817 (2013).
  • Nieuwenhuis M, van Haren NE, Hulshoff Pol HE, Cahn W, Kahn RS, Schnack HG, Classification of schizophrenia patients and healthy controls from structural MRI scans in two large independent samples. NeuroImage 61:606-612 (2012).
  • Brouwer RM, Hulshoff Pol HE, Schnack HG, Segmentation of MRI Brain Scans Using Non-Uniform Partial Volume Densities. NeuroImage 49:467-477 (2010).


Current Lab Members:

Hugo Schnack – Assistant Professor

Ronald Janssen- Post-Doc

Jessica de Nijs – PhD student

Julia Binnewies- Master student

Taylor Portner – Master student

Suzan Stempher – Bachelor student

Laura Han – Guest PhD student (VU- Amsterdam)



Ao Chen – Post-doc (2014-2016)

Mireille Nieuwenhuis – PhD student (2011-2016)

Daniel van Opstal, Renee Clausing en Olivier de Vries (2016)

Woutje Berdowski (2015)

Petr Dluhos – visiting PhD student (2014-2015) -Berna Isik (2014) -Januschka Veldstra (2013) -Tim Mulder (2011-2012)