Winter School on Imaging Genetics

November 26-29, 2019

Imaging genetics is an emerging research field that embraces the neuroimaging and genetics communities and aims at blending the respective know-how and methodologies within a unified framework pursuing a holistic view of the human being. Concretely, Imaging Genetics refers to the use of anatomical or physiological imaging technologies as phenotypic assays to evaluate genetic variation. Genetic information and neuroimaging data (structural and functional) are integrated within a unified model enabling the assessment of the link between genes and brain structure and function in health and disease, paving the way to multi-modal multi-scale precision medicine. This summer school is devoted to construct new professional figures and researchers to work at the frontiers of directed at researchers who wish to develop their knowledge and skills on state-of-the- art developments in the field of neuroimaging genetics.

The School on Imaging genetics aims at gathering the knowledge in the different fields that are touched by these topics providing the students a comprehensive view of this research area as well as awareness about the cutting-edge methodological, experimental and clinical aspects that are involved. The students will acquire cutting-edge knowledge in the fields of Signal modeling in structural, microstructural and functional imaging, Omics data analysis, Biostatistics, Integrative data representations for multiple genomic experiments, Programming in R/Bioconductor.

The school provides theoretical and practical sections in each topic covered by the speakers. Moreover 2 hands-on sections are devoted to combine signals from images and genetics. Students must bring their laptops. They will receive by email, weeks before starting the school, the list of all software to be installed in their laptops before school starts. Moreover, organizers will meet students on November 25th from 3:00 to 6:00 pm at the location of the school to help on installation problems that could not be solved by remote tutoring.


Andre Altmann
André Altmann
University College London (UK)

Genome-wide association studies (GWAS) are still the major work horse for identifying novel genes that are associated with disease risk or certain traits. In this talk I will cover the basics of genomics (genome organization, genetic variation, etc.) and introduce how genetic variation is measured at low cost today (sanger sequencing, genotyping chips, next generation sequencing, …). Next, I will introduce the underlying principles of genome-wide association studies, basic tests, genetic model (assumptions), power analysis and the rationale of ever growing studies. I will introduce the motivation behind ‘imaging genetics’, i.e., investigating the genetic basis of phenotypes that we can drive from images. Further, I will cover the practical aspects of actually conducting GWAS, this will entail basic steps like how the data actually looks like (popular file forms), cleaning data, quality control, imputation of missing SNPs (and why this is possible), confounding factors such as population structure (and how to address this), and checking your GWAS results for ‘correctness’.

Dr. Andre Altmann studied Computer Science at the RWTH Aachen and graduated with a work in the field of spoken language recognition at the Chair for Computer Science 6. After that he pursued a PhD in the Computational Biology group of the Max Planck Institute for Informatics in Saarbrücken, Germany. Following his PhD, he was a first a postdoctoral researcher in the Statistical Genetics Group the Max Planck Institute of Psychiatry in Munich, Germany and then at the FIND lab of the Stanford University (USA). In August 2015 Andre joined UCL as a MRC Senior Fellow, where he started the COMputational Biology in Imaging and geNEtics (COMBINE) lab as part of UCL's Centre for Medical Image Computing (CMIC).

Marco Lorenzi
Marco Lorenzi
Université Côte d'Azur, Inria (FR)

This talk aims at covering the statistical background required to perform association analysis in typical imaging-genetics studies. We will introduce the notion of statistical association, and highlight the standard analysis paradigm in univariate modeling. We will then explore multivariate association models, generalizing to high-dimensional data the notion of statistical association. In particular, we will focus on standard paradigms such as Canonical Correlation Analysis (CCA), Partial Least Squares (PLS), and Reduced Rank Regression (RRR). We will finally introduce more advanced analysis frameworks, such as Bayesian and deep association methods. Within this context we will present the Multi-Channel Variational Autoencoder, recently developed by our group.

Dr Marco Lorenzi is a permanent Research Scientist at Inria, and a member of the Epione Research Group of Inria Sophia Antipolis, France. Prior to this, he was Research Associate in the Centre for Medical Image Computing (CMIC) at University College London (UCL), and completed his PhD in 2012 at the Asclepios Research Group of Inria Sophia Antipolis. His research focus is in biomedical applications of statistical learning, particularly in the problem of analyzing heterogeneous and high-dimensional biomedical data.

Fabrizio Pizzagalli
Fabrizio Pizzagalli
University of Southern California,
Los Angeles (US)

Understanding the mechanisms underlying brain structural and functional variation is essential for advancing neuroscience. Magnetic resonance imaging (MRI) can be used to derive metrics of brain structure and function and offer a powerful method to assess disease burden in the brain.
Genetic drivers of brain differences are important to identify as potential risk factors for heritable brain diseases, and targets for their treatment. Imaging genetic studies have found that, as with other complex traits, a single common variant explains less than 1% of the population variance, despite accounting for a large fraction of the variance in aggregate. Therefore, successful studies require tens of thousands of scans, as well as an independent sample for replication. Large-scale consortia in the field of neuroimaging genetics, including the Enhancing Neuro Imaging Genetics through Meta Analysis (ENIGMA) consortium, have identified common genetic variants that have small but significant associations with variations in brain structural morphometry. Large-scale biobanks have been amassed tens of thousands of MRI scans of individuals from a single scanner for genomic discoveries, yet, replicating effects and ensuring generalizability of findings to current scanned populations require assurance that the brain measures being studied are reliably extracted across a variety of possible MRI scanning paradigms.
We will show the most common techniques and tools used by the neuroimagers community to extract anatomical and functional features from MRI data. Using examples, we will provide instructions for quality control and statistical methods to assess the robustness of the extracted features that will be used as phenotype for the genetic studies.


Blaz Zupan
Blaž Zupan
University of Ljubljana (SI)

Clustering is a crucial procedure in exploratory data analysis. Given some data, clustering, combined with some visualization, is probably where start to fish for any useful data patterns. I will carry out a hands-on workshop where we will dive into some of the most famous clustering approaches. These will include hierarchical clustering, k-means, DBSCAN, and network-based clustering. We will also combine clustering algorithms with dimensionality reduction and embedding approaches, and learn about principal component analysis, multidimensional scaling, and t-SNE. We will learn how to apply these techniques to images that we will profile with deep learning models. During the workshop, we will use Orange, a data mining framework, and participants are welcome to download and install it from http://orange.biolab.si to follow along.

Dr. Blaž Zupan heads the bioinformatics lab at the University of Ljubljana and has a joint appointment at Baylor College of Medicine in Houston. In his work, he explores the combinations of machine learning and interactive visualizations. His research has focused on constructive induction, machine learning and epistasis approaches to the reconstruction of gene networks, large-scale data fusion, and algorithms to propose informative data visualizations. He believes that crafting simple tools that anybody can use to understand data is essential to advancements of humanity and democracy. His lab developed Orange (http://orange.biolab.si), a fully open-source, ever-evolving data mining suite with a visual programming environment. He also enjoys writing scripts for YouTube videos to explain data science (check out http://youtube.com/orangedatamining), and preparing courses that introduce data science.

Francesca Cordero
Francesca Cordero
University of Turin (IT)

Marco Beccuti
Marco Beccuti
University of Turin (IT)

This course aims to facilitate the use of computing demanding applications in the field of NGS data analysis. The main feature to perform a correct experimental design will be explained. Then, the tools for RNA-seq data analysis will be detailed considering the following steps: quality control, normalisation and data reformatting, selection differentially regulated genes/microRNA, multiple testing and biological interpretation. All these steps will be explained from the theoretical point of view followed by a set of computational exercises to analysis a set of RNASeq data.
The analysis will be performed based on Docker4seq package. This package uses docker containers that embed demanding computing tasks (e.g. short reads mapping) into isolated containers. This approach provides multiple advantages:

  • user does not need to install all the software on its local server;
  • results generated by different containers can be organized in pipelines;
  • reproducible research is guarantee by the possibility of sharing the docker images used for the analysis.


Ilaria Boscolo Galazzo
Ilaria Boscolo Galazzo
University of Verona (IT)

In the last years, the study of brain functional connectivity (FC) has become an increasingly active field of research providing novel and crucial insights in resting-state as well as during tasks. FC can be derived from different non-invasive magnetic resonance imaging (MRI) modalities, such as fMRI based on the BOLD contrast or Arterial Spin Labeling (ASL). Classical connectivity analyses allow detecting regions with similar behaviours or coherent signals, indicating their membership to the same functional network. In particular, FC can be computed in both time and frequency domains exploring different approaches (e.g., seed-based correlation, independent component analysis, spectral coherence). More recently, new approaches based on graph theory have been proposed for extracting significant aspects of the network and quantitatively characterising its global organization. In this lecture, I will present an overview of the main non-invasive functional MRI modalities and their applications for task and resting-state paradigms. Moreover, I will illustrate the associated methods of analysis for both brain localization and connectivity estimation, giving a glimpse of their main advantages/disadvantages.



Tuesday 26 NovWednesday 27 NovThursday 28 NovFriday 29 Nov
10:00-11:00Boscolo GalazzoAltmannZupanAltmann
12:30-14:00Coffee BreakDeparture
11:30-12:30Boscolo GalazzoLorenziZupan
12:30-14:00Lunch Break
14:00-15:00Beccuti/CorderoLorenzi Imaging Genetics Lab
15:00-16:00Beccuti/Cordero Imaging Genetics Lab
17:00-18:00Social Event

During the social event, participants will be guided through a walking tour of the city center, with stops in the most important points of interests, such as: Casa di Giulietta, Arche Scaligere, Piazza dei Signori, Piazza delle Erbe e Piazza Bra with the magnificent Arena.


The school will be held at the Dept. of Computer Science of the University of Verona (Verona, Italy).
Partecipants are required to bring their laptop for hands-on laboratory sessions.


By plane: The airport of Verona is connected to the main European and national cities. From the airport you can reach the city by taxi or by bus. A shuttle bus connects the airport to Verona Porta Nuova train station.

By train: The train station of Verona Porta Nuova is connected with all the main Italian cities by fast and local trains. For the train schedule, please check the Italian railway company. From the station you can reach the Department of Computer Science by bus or by taxi.

By bus: Bus line 21 (towards S. Giovanni Lupatoto) get off at the first bus stop after Borgo Roma hospital; from the bus stop, you can see the department’s buildings. You can also catch bus line 22 (towards Policlinico/San Giovanni Lupatoto) and line 93 (during the night and on Sundays), towards Cadidavid. For these last two bus lines, get off at Borgo Roma hospital, then follow the map above. You can find timetables and line maps at the ATV website.

By car: Take the A4 Milano-Venezia highway, exit Verona Sud then follow the direction "Ospedale Borgo Roma" (hospital), on the right. At the hospital, go straight, cross the small bridge on a river and take the second right. From here you can see the department buildings on your left.


School directors

Rosalba Giugno, Associate Professor at the Department of Computer Science, University of Verona
Gloria Menegaz, Full Professor at the Department of Computer Science, University of Verona, Senior IEEE
Carlo Combi, Full Professor at the Department of Computer Science, University of Verona

Organizing committee

Rosalba Giugno, Associate Professor at the Department of Computer Science, University of Verona
Gloria Menegaz, Full Professor at the Department of Computer Science, University of Verona, Senior IEEE
Carlo Combi, Full Professor at the Department of Computer Science, University of Verona
Vincenzo Bonnici, Temporary Assistant Professor, Department of Computer Science, University of Verona
Luciano Cascione, Head of Bioinformatics, Institute of Oncology Research, Swiss Institute of Bioinformatics
Ilaria Boscolo Galazzo, Post-doc research associate, Department of Computer Science, University of Verona
Matteo Mantovani, Ph.D. candidate, Department of Computer Science, University of Verona
Antonino Aparo, Ph.D. student, Department of Computer Science, University of Verona
Samuele Cancellieri, Ph.D. student, Department of Computer Science, University of Verona
Beatrice Amico, Ph.D. student, Department of Computer Science, University of Verona

Web chair

Vincenzo Bonnici, Temporary Assistant Professor, Department of Computer Science, University of Verona
Matteo Mantovani, Ph.D. candidate, Department of Computer Science, University of Verona


The number of participants is limited to 20. Admission to the school is possible only if there are positions available.

Application requirements:

Applicants should send the necessary documentation via email at the following address: imagenschool2019@gmail.com

Deadline for applications: November 1, 2019

AFTER the notification of acceptance, payments can be made via credit card at the following link: payment.

Registration fees:

Payments must be made within November 7, 2019. An additive cost of € 100 is applied to late registrations.


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