dr. H.G. (Hugo) Schnack

dr. H.G. (Hugo) Schnack

Assistant Professor
dr. H.G. (Hugo) Schnack
  • 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 (159)

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 mei 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.

Brain age prediction in schizophrenia:Does the choice of machine learning algorithm matter?

Lee Won Hee, Antoniades Mathilde, Schnack Hugo G, Kahn Rene S, Frangou Sophia 5 mrt 2021, In: Psychiatry Research - Neuroimaging. 310 8 p.

The Speed of Development of Adolescent Brain Age Depends on Sex and Is Genetically Determined

Brouwer Rachel M, Schutte Jelle, Janssen Ronald, Boomsma Dorret I, Hulshoff Pol Hilleke E, Schnack Hugo G 19 okt 2020, In: Cerebral Cortex. 31 , p. 1296-1306 11 p.

The YOUth cohort study:MRI protocol and test-retest reliability in adults

Buimer Elizabeth E.L., Pas Pascal, Brouwer Rachel M., Froeling Martijn, Hoogduin Hans, Leemans Alexander, Luijten Peter, van Nierop Bastiaan J., Raemaekers Mathijs, Schnack Hugo G., Teeuw Jalmar, Vink Matthijs, Visser Fredy, Hulshoff Pol Hilleke E., Mandl René C.W. okt 2020, In: Developmental Cognitive Neuroscience. 45 , p. 1-15

Dissimilarity in Sulcal Width Patterns in the Cortex can be Used to Identify Patients With Schizophrenia With Extreme Deficits in Cognitive Performance

Janssen Joost, Díaz-Caneja Covadonga M, Alloza Clara, Schippers Anouck, de Hoyos Lucía, Santonja Javier, Gordaliza Pedro M, Buimer Elizabeth E L, van Haren Neeltje E M, Cahn Wiepke, Arango Celso, Kahn René S, Hulshoff Pol Hilleke E, Schnack Hugo G 23 sep 2020, In: Schizophrenia bulletin. 47 , p. 552-561 10 p.

Predicting future suicidal behaviour in young adults, with different machine learning techniques:A population-based longitudinal study

van Mens Kasper, de Schepper C. W.M., Wijnen Ben, Koldijk Saskia J., Schnack Hugo, de Looff Peter, Lokkerbol Joran, Wetherall Karen, Cleare Seonaid, C O'Connor Rory, de Beurs Derek 15 jun 2020, In: Journal of Affective Disorders. 271 , p. 169-177 9 p.

Changes in the intracranial volume from early adulthood to the sixth decade of life:A longitudinal study

Caspi Yaron, Brouwer Rachel M, Schnack Hugo G, van de Nieuwenhuijzen Marieke E, Cahn Wiepke, Kahn René S, Niessen Wiro J, van der Lugt Aad, Pol Hilleke Hulshoff 24 apr 2020, In: NeuroImage. 220 , p. 1-16

Two distinc neuroanatomica subtypes of schizophrenia revealed using machine learning

Chand Ganesh B., Dwyer Dominic B., Erus Guray, Sotiras Aristeidis, Varol Erdem, Srinivasan Dhivya, Doshi Jimit, Pomponio Raymond, Pigoni Alessandro, Dazzan Paola, Kahn Rene S., Schnack Hugo G., Zanetti Marcus V., Meisenzahl Eva, Busatto Geraldo F., Crespo-Facorro Benedicto, Pantelis Christos, Wood Stephen J., Zhuo Chuanjun, Shinohara Russell T., Shou Haochang, Fan Yong, Gur Ruben C., Gur Raquel E., Satterthwaite Theodore D., Koutsouleris Nikolaos, Wolf Daniel H., Davatzikos Christos 27 feb 2020, In: Brain : a journal of neurology. 143 , p. 1027-1038 12 p.

Structural methods in gray matter

Mandl René C.W., Schnack Hugo G., Brouwer Rachel M., Hulshoff Pol Hilleke E. 1 jan 2020, p. 3-26 24 p.

All research output

Thank you for your review!

Has this information helped you?

Please tell us why, so that we can improve our website.

Working at UMC Utrecht





Practical maakt gebruik van cookies

Deze website maakt gebruik van cookies Deze website toont video’s van o.a. YouTube. Dergelijke partijen plaatsen cookies (third party cookies). Als u deze cookies niet wilt kunt u dat hier aangeven. Wij plaatsen zelf ook cookies om onze site te verbeteren.

Lees meer over het cookiebeleid

Akkoord Nee, liever niet