ing. O.P.P. (Hugo) Staak

ing. O.P.P. (Hugo) Staak

Assistant Professor
ing. O.P.P. (Hugo) Staak
  • Psychiatry

Research Programs




As a physicist in a multidisciplinary environment, I enjoy creating mathematical models to understand the relationships between human brain and behavior in health and disease. Starting as postdoc in the UMCU neuroimaging group, I set up an image-processing pipeline for quantitative analysis of thousands of MRI brain images. As assistant professor, I implemented advanced statistical analyses to study dynamic changes in brain morphology. Central in my research have always been how (image) data represents information and how knowledge of data quality can be used to perform optimal analyses. Applied to multicenter imaging studies, my work resulted in a method to determine reliability of, for example, (twin) heritability or longitudinal studies. At that time the first machine learning steps in this field were made and I realized that multivariate modeling much better uses all the information available in brain images. My expertise on reliability and individual variation enabled me to shift my focus, from group-level analyses to making predictions about individuals, based on their data. My team performed the first large-scale study to classify persons with and without schizophrenia based on MRI brain images. In recent years, we have developed and applied machine learning methods to further investigate the heterogeneity of brain diseases. For multicenter designs, we recently have developed meta-modeling. I have expanded the use of pattern recognition analyses to applications in other domains, including clinical data and vocabulary data. The latter work is in collaboration with the Faculty of Humanities, Dept. of Languages, within the strategic theme Dynamics of Youth. Since 2016 I have been appointed part-time Assistant professor in that Department. In the coming years, I plan to do research on models to predict people’s development of (psychiatric) disorders in the first twenty years of their lives. This requires further development of machine learning in multicenter, multimodal and cross-diagnostic settings. Since the development of such models will be a multi-disciplinary effort, it is essential to introduce the next generation of researchers to this field in my lectures on topics with integrated state-of-the-art machine learning, to engage them in future innovation.

Research line

Patterns in Psychiatry

Most recent key publications

1. Dluhoš P, Schwarz D, Cahn W, van Haren N, Kahn R, Španiel F, Horáček J, Kašpárek T, Schnack H. Multi-center machine learning in imaging psychiatry: A meta-model approach. Neuroimage. 2017 Apr 17;155:10-24.

2. 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.

3. Schnack, H. G. and Kahn, R. S. (2016). "Detecting neuroimaging biomarkers for psychiatric disorders: sample size matters." Frontiers in Psychiatry 7: 50.

4. 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).

5. 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)

6. 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).

7. Brouwer RM, Hulshoff Pol HE, Schnack HG, Segmentation of MRI Brain Scans Using Non-Uniform Partial Volume Densities. NeuroImage 49:467-477 (2010).

Fellowship and Awards

1: Seed money Dynamics of Youth, University Utrecht, 2013 (PODIUM project)

Research Output (167)

Genetic variants associated with longitudinal changes in brain structure across the lifespan

Brouwer Rachel M, Klein Marieke, Grasby Katrina L, Schnack Hugo G, Jahanshad Neda, Teeuw Jalmar, Thomopoulos Sophia I, Sprooten Emma, Franz Carol E, Gogtay Nitin, Kremen William S, Panizzon Matthew S, Olde Loohuis Loes M, Whelan Christopher D, Aghajani Moji, Alloza Clara, Alnæs Dag, Artiges Eric, Ayesa-Arriola Rosa, Barker Gareth J, Bastin Mark E, Blok Elisabet, Bøen Erlend, Breukelaar Isabella A, Bright Joanna K, Buimer Elizabeth E L, Bülow Robin, Cannon Dara M, Ciufolini Simone, Crossley Nicolas A, Damatac Christienne G, Dazzan Paola, de Mol Casper L, de Zwarte Sonja M C, Desrivières Sylvane, Díaz-Caneja Covadonga M, Janssen Joost, Koevoets Martijn G J C, Mandl René C W, Setiaman Nikita, van Haren Neeltje E M, Westeneng Henk-Jan, van Eijk Kristel R, Cahn Wiepke, Hillegers Manon, Kahn Rene S, Ophoff Roel A, van den Berg Leonard H, Veldink Jan H, Hulshoff Pol Hilleke E, Apr 2022, In: Nature Neuroscience. 25 , p. 421-432 12 p.

Contributing factors to advanced brain aging in depression and anxiety disorders

Han Laura K.M., Schnack Hugo G., Brouwer Rachel M., Veltman Dick J., van der Wee Nic J.A., van Tol Marie José, Aghajani Moji, Penninx Brenda W.J.H. Dec 2021, In: Translational Psychiatry. 11 , p. 1-11

Multi-scale semi-supervised clustering of brain images:Deriving disease subtypes

Wen Junhao, Varol Erdem, Sotiras Aristeidis, Yang Zhijian, Chand Ganesh B, Erus Guray, Shou Haochang, Abdulkadir Ahmed, Hwang Gyujoon, Dwyer Dominic B, Pigoni Alessandro, Dazzan Paola, Kahn Rene S, Schnack Hugo G, Zanetti Marcus V, Meisenzahl Eva, Busatto Geraldo F, Crespo-Facorro Benedicto, Rafael Romero-Garcia, Pantelis Christos, Wood Stephen J, Zhuo Chuanjun, Shinohara Russell T, Fan Yong, Gur Ruben C, Gur Raquel E, Satterthwaite Theodore D, Koutsouleris Nikolaos, Wolf Daniel H, Davatzikos Christos, 11 Nov 2021, In: Medical Image Analysis. 75 , p. 1-18

Quantified language connectedness in schizophrenia-spectrum disorders

Voppel A. E., de Boer J. N., Brederoo S. G., Schnack H. G., Sommer I. E.C. Oct 2021, In: Psychiatry Research. 304 , p. 1-8

Implementation of and experimental software for active selection of classification features[Formula presented]

Kok Thomas T., Krempl Georg, Schnack Hugo G. Aug 2021, In: Software Impacts. 9

Individualized prediction of three- and six-year outcomes of psychosis in a longitudinal multicenter study:a machine learning approach

de Nijs Jessica, Burger Thijs J, Janssen Ronald J, Kia Seyed Mostafa, van Opstal Daniël P J, de Koning Mariken B, de Haan Lieuwe, Cahn Wiepke, Schnack Hugo G, 2 Jul 2021, In: npj Schizophrenia. 7 11 p.

Sex Differences in Lifespan Trajectories and Variability of Human Sulcal and Gyral Morphology

Díaz-Caneja Covadonga M, Alloza Clara, Gordaliza Pedro M, Fernández-Pena Alberto, de Hoyos Lucía, Santonja Javier, Buimer Elizabeth E L, van Haren Neeltje E M, Cahn Wiepke, Arango Celso, Kahn René S, Hulshoff Pol Hilleke E, Schnack Hugo G, Janssen Joost 26 Jun 2021, In: Cerebral Cortex. 31 , p. 5107-5120 14 p.

De‐identification procedures for magnetic resonance images and the impact on structural brain measures at different ages

Buimer Elizabeth, Schnack Hugo, Caspi Yaron, van Haren Neeltje, Milchenko Mikhail, Pas P, ADNI , Hulshoff Pol Hilleke, Brouwer Rachel 11 May 2021, In: Human Brain Mapping. 42 , p. 3643-3655

Accelerated aging in the brain, epigenetic aging in blood, and polygenic risk for schizophrenia

Teeuw Jalmar, Ori Anil P S, Brouwer Rachel M, de Zwarte Sonja M C, Schnack Hugo G, Hulshoff Pol Hilleke E, Ophoff Roel A 17 Apr 2021, In: Schizophrenia Research. 231 , p. 189-197 9 p.

Active Selection of Classification Features

Kok Thomas T., Brouwer Rachel M., Mandl Rene M., Schnack Hugo G., Krempl Georg 13 Apr 2021, p. 184-195 12 p.

All research output

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