https://studiegids.vu.nl/en/courses/2025-2026/P_MBRIMGStudents learn how brain imaging results can be obtained and combined with genetics to detect genetic variations that significant influence nervous system functions related to behavioural traits, psychiatric and neurological disorders.Human behaviour shows substantial individual variation which can be explained to a large extent by differences in the genetic makeup of individuals. It is therefore of crucial importance to gain knowledge of the genetic underpinnings of normal and, in particular, abnormal behaviour. For example, knowledge about the genetic variants that underlie psychiatric conditions such as ADHD, depression and schizophrenia provides keys to obtain more in-depth understanding of the underlying pathophysiology. In addition identification of relevant genes affords the ability to predict at risk people for abnormal behaviour due to neurological disorder such as Alzheimer’s disease and can provide novel insights to improve care and pave the way to the application of personalised interventions. To date the search for genetic variants that influence normal and abnormal behaviour and elucidation of the biological pathways through which they do so remains a tremendous scientific challenge. This of course reflects the complexity of behavioural traits as well as their highly polygenetic background. An added problem is that for psychiatric and neurological symptoms, detailed quantitative descriptions of abnormal behaviours are generally lacking. Current health diagnostic systems generally entail only qualitative appraisals of behaviour symptoms or disorders, i.e., "one has a symptom or disorder or not", instead of behaviour itself. Quantitative measures of specific nervous system functions that underlie behaviour are more closely linked to genetic variation and therefore help elucidate the pathways by which these genetic variants influence behaviour. Measuring these biomarkers, or "endophenotypes", can furthermore help us understand how these genes exert their effect by highlighting the associated neurobiological changes. In this course we will concentrate on biomarkers from the central nervous system (brain), as obtained by neuroimaging. Students are introduced to the basic methodology required to obtain raw Magnetic Resonance Imaging (MRI) scans of the brain, as well as the subsequent analysis steps needed to arrive at final quantitative measures. This includes learning how to obtain measures of global and local brain structure from T1 weighted images, and parameters indicating brain connectivity from Diffusion Tensor Images (DTI), and measures of brain function and indicators of functional networks from Echo Planar Images (EPI). Based on published work from the scientific literature it will then be demonstrated how this brain imaging information can be combined with basic molecular information on the individual’s genomes in the context of Genome Wide Association, and candidate gene and polygenic score designs. Important findings and their implications in the field of brain imaging genetics will be highlighted and discussed. A specific focus of the course will be on pinpointing relevant genetic variation and understanding the functional consequences of specific risk genes on brain system level changes in Alzheimer’s disease.Tuition consists of lectures, homework assignments, and computer practicals.A final grade based on the average grade of 2 separate assessments. One assessment consist of an oral presentation on a recent brain imaging genetics paper (30%) and the other of a final written exam (70%) with open ended questions (understanding).Lecture slides and practical instructions provided by the course section accompanying the FMRIB software library that will be used for the MRI practicals in: https://fsl.fmrib.ox.ac.uk/fslcourse/.Thompson PM et al (2010). Imaging genomics. Curr Opin Neurology 23 (4):368-73.Thompson PM, et al (2014). The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behavior 8(2):153-82.Medland SE, et al (2014). Whole-genome analyses of whole-brain data: working within an expanded search space. Nat Neuroscience 17(6):791-800.Hashimoto R, et al (2015). Imaging genetics and psychiatric disorders. Curr Mol Medicine 15(2):168-75.Scelsi et al (2018). Genetic study of multimodal imaging Alzheimer’s disease progression score implicates novel loci. Brain 141 (7): 2167–2180.Soheili-Nezhad et al (2020). Imaging genomics discovery of a new risk variant for Alzheimer's disease in the postsynaptic SHARPIN gene. Hum Brain Mapp 41(13):3737-3748.Homann et al (2022). Genome-Wide Association Study of Alzheimer’s Disease Brain Imaging Biomarkers and Neuropsychological Phenotypes in the European Medical Information Framework for Alzheimer’s Disease Multimodal Biomarker Discovery Dataset. Front. Aging Neurosci. 14: 840651.Hibar et al (2017) Novel genetic loci associated with hippocampal volume. Nature Communications. 18 (8):13624During the course, additional reading material based on the latest developments in the field will be posted on Canvas or distributed in class.Entry only for students with an interest in the application of genetics in the behavioural or health sciences, with sufficient background in statistics, biology and psychology.