General Overview

The IMPRS on Cognitive NeuroImaging pursues an innovative, flexible, individualized, and digital teaching program in order to facilitate the participation of all students. This includes those doing their PhD with faculty at associated partner institutions, including the international partner institution. It also aims to be resilient against exceptional circumstances, such as the current Covid-19 pandemic, with its restrictions and physical distancing. This necessitates hybrid teaching formats, which means that our courses are recorded and broadcast for interested students from our main and associated partners. Lectures are made available through an online learning platform.

To encourage  active involvement of our students in course activities, we supplement classical frontal lecture series with flipped classroom teaching (for courses where theoretical knowledge is taught) and integrate blended learning activties for courses where practical knowledge (such as data analysis methodology) is taught.

The IMPRS on Cognitive NeuroImaging applies a credit point system, which requires students to collect at least 26 ECTS CPs. This credit point system is supported by all participating faculties and ensures that no additional curricular work at the University needs to be carried out by students of the IMPRS on Cognitive NeuroImaging. This avoids unnecessary bureaucracy and ensures efficiency.

Syllabus Teaching Currciulum

1. Core Course

1.1 Foundations of Neuroscience (spanning all modules):

This course consists of a theoretical part (Days 1-3) and a practical part (Days 4 and 5) and successful participation is mandatory for all students. The course takes place annually in the context of the introductory month. The main aim is to teach students the basics of neuroscience with regard to signal transduction, neuroanatomy, and systems neuroscience.

For the practical part we aim to combine the latest virtual mixed-reality neuroanatomical training with hands-on exercises in the neuroanatomical section hall. The VR-system will allow teaching faculty and students to display information on the different brain systems and pathways, providing life-like interaction. This will allow students to gain a deeper understanding of human neuroanatomy and how different brain areas work together and communicate. Users can twist, rotate, and scale the human brain to accommodate their visual needs. Flipped classroom activities are integrated as opportunity to receive course credits.
Course credits and conditions: 2 ECTS; Contribution to flipped classroom activities in the context of the practical part.

2. Basic and Advanced Courses

2.1 Basic Courses

Cognitive Neuroscience (Module I)

The aims of the course are to provide students with a deep understanding of Cognitive Neuroscience, including theories of the neural underpinnings of a variety of cognitive processes and the research that has led to those theories. Students learn how the cognitive system develops and why neuroplasticity is crucial for cognitive processes. Topics include: Volition and Cognitive Control, Attention and Perception, Memory, Cognitive Spaces, Language, Development of Cognition, and Learning.

Course credits and conditions: 2 ECTS; Active participation and submission (and positive evaluation) of a critical review on a research article related to the lecture’s scope.

Clinical Neuroscience (Module II)

This course provides students with a basic knowledge of the correlates of the most common neurological and psychiatric diseases and how they can be differentiated and diagnosed. This requires a basic understanding of the human brain, both regarding neuroanatomy and neurotransmission. The importance of brain plasticity is emphasized in the context of stroke recovery. The course consists of a practical part where participants, with the help of VR-tools, dive into the disordered human brain. Topics include: Basics of Neurology, Stroke, Recovery after Stroke, Aphasia, Parkinson’s Disease and other Disorders of Basal Ganglia, Dementia, Schizophrenia, Depression, and Pain.

Course credits and conditions: 2 ECTS; Active participation and submission (and positive evaluation) of a critical review on a research article related to the lecture’s scope.

Basic (f)MRI (Module III)

This course provides students with the necessary background knowledge of MR image acquisition and processing. We introduce the basic concepts in theory and practice. Students learn to develop an intuitive understanding of the most important technical parameters of a neuroimaging study. This course also equips students with an overview of the ecosystem of computational tools for processing and analysis and a set of best practices as a starting point for their own MRI studies.

Course credits and conditions: 2 ECTS for successfully presenting and discussing a paper relevant to the content of the lecture series in a separate half-day session after the lecture series.

Basic Electroencephalography (EEG) and Magnetoencephalography (MEG) (Module III)

Students learn the basics of EEG & MEG including signal generation and measurement and are introduced to time-series analysis, time-frequency analysis, source localization, spatio-temporal modeling, and fusion with other modalities (e.g., local field potentials, DTI, fMRI). Based on this overview of available experimental techniques, students will be able to assess the pros and cons and specific prerequisites of each as well as perform various kinds of data analysis methods. The latter are trained in practical sessions.

Course credits and conditions: 2 ECTS for successfully presenting and discussing a paper relevant to the content of the lecture series in a separate half-day session after the lecture series.

Basic Statistics for Brain and Cognitive Sciences (spanning all modules)

This course introduces statistical concepts and tools useful for applications in the brain and cognitive sciences and covers the essential tools from exploratory data analysis and statistical modeling. A main focus is on practical, computational approaches: Students learn to implement and discuss data analyses in interactive sessions.

Course credits and conditions: 2 ECTS; Active participation (successful implementation of blended-learning statistical analysis) in the context of the interactive session.

2.2 Advanced Courses

Cognitive Coding (Module I)

This course gives students an overview of current neuroscientific developments on cognitive coding mechanisms and focuses on memory, spatial navigation, concept learning, and decision making as core cognitive model systems. Showcase examples of studies using advanced representation-based analyses of neuroimaging and electrophysiological data are discussed. Research results are embedded in current general frameworks of cognition and brain function.

Course credits and conditions: 1 ECTS, active participation is required.

Topics in Neural Signal Processing (Module III)

In this course, we will explore methods for multi-dimensional neural signal processing across modalities such as EEG/MEG or (rs)fMRI. In interactive sessions, we will introduce and discuss concepts and computational tools to visualize, analyse, and integrate spatiotemporal data. We will discuss advanced topics such as Machine Learning on generalizable graph representations.

Course credits and conditions: 2 ECTS for successfully presenting and discussing a paper relevant to the content of the lecture series in a separate half-day session after the lecture series.

Advanced Topics in Magnetic Resonance Imaging (Module III)

The lecture series focuses on topics related to the relaxation of water protons in biological tissues, defining a fundamental contrast mechanism of MRI. Besides theoretical aspects related to proton relaxation, experimental techniques for mapping relaxation times along with potential limitations as well as the biophysics underlying relaxation contrast will be discussed.
Topics include: Classical description of NMR based on the Bloch equations, Dipolar relaxation in the context of the “BPP theory”, Transverse relaxation and longitudinal relaxation, Magnetization transfer between water and macromolecules and membranes, Effects from local magnetic field inhomogeneities on effective transverse relaxation and the signal phase, Considerations of intercompartmental exchange, Discussion of potential relaxation sites at brain tissue components. Whenever possible, flipped classroom activities are integrated to ensure active participation of doctoral students.

Course credits and conditions:  2 ECTS CPs; Active participation during flipped classroom activities.

Neurostimulation (spanning all modules)

The course introduces students to the entire range of non-invasive brain stimulation methods, including important safety aspects. The integration of practical hands-on training allows participants to gain first experience with technical issues, device set-up, and motor threshold determination. The practical part includes an introduction to state-of-the-art neuro-navigation systems to guide precise coil placement outside the primary motor cortex. How non-invasive brain stimulation can be combined with other neuroimaging techniques is discussed and potential research fields are identified where this might be of particular benefit for scientific outcomes. Finally, in a blended-learning interactive session, students analyze a sample dataset, ensuring that those who finish the course will be well suited to conduct their own neurostimulation study.

Course credits and conditions: 2 ECTS; Active participation (successful implementation of TMS analysis in the blended learning session).

Advanced Neuroimaging Data Analysis (spanning all modules)

Students learn about essential, cutting-edge analysis tools in neuroimaging that are beyond standard analyses. Combining lectures and hands-on sessions, the course covers advanced topics such as large-scale integrative analysis of genetic and neuroimaging data to map gradients in the brain, analyses of learned representations using multivariate methods (e.g. grid cells), and advanced computational models of cognition (such as Deep Neural Networks or Reinforcement Learning).

Course credits and conditions: 2 ECTS CPs; Active participation during flipped classroom activities.


3. Scientific Courses (spanning all modules)

Scientific courses represent an additional offer for students to acquire statistical and programming skills necessary to complete their scientific analyses. As the need for such courses varies individually, they are not mandatory but are recognized as elective credits for the IMPRS curriculum. To date, the following courses are planned but may vary and be flexibly adapted based on students’ requirements.


The course starts with a general overview of Matlab, looks at the various ways of how to get help in Matlab, and uses it as a simple pocket calculator, introducing variables along the way. Participants delve into basic operations on vectors and matrices and briefly look at non-numeric data. Once the basics are established, the course covers conditional statements (if-else) and iterative statements (loops) and participants work on writing simple scripts. We look at how to get data into and out of Matlab and how to plot data in various ways. Thereafter, the course spends a large amount of time on how to write (and debug) more complex pieces of code, using functions. The course addresses topics that can be helpful during typical research projects. Finally, participants spend a lot of time on data processing and analysis. The course intensively integrates blended learning activities.

Course credits and conditions: 1 ECTS, active participation and completion of exercises.

Neural Data Science with Python

In this practical course, basic computational skills (e.g. shell and git) and best practices to get started with computational data analysis projects are introduced first. Then Python and relevant libraries as a flexible tool for computational data analysis in cognitive neuroimaging are introduced .

Course credits and conditions: 1 ECTS, active participation and submission of exercises.

R for Neuroimaging Data Analysis

This course starts with an introduction to R. At the beginning students learn to write small scripts and to deal with different data types in R. As a next step, students learn how to use control flow statements and how to expand basic R capabilities by making use of packages. Participants learn how to write and debug functions and receive instruction on R’s basic plotting capabilities and basic statistical tools. They learn about reproducible analyses with RStudio, creating publication-ready graphics with ggplot2, how to simulate data in a very simple way, and start with the basic building blocks of statistical inference. The largest part of the workshop tackles the following topics: i) assumptions of parametric tests, ii) correlation, iii) regression, iv) comparing means, v) bootstrapping, and vi) exploratory data analysis. For each topic, students move from the theory and the most basic implementation to more complex approaches and integrate blended learning activities.

Course credits and conditions: 1 ECTS, active participation and completion of exercises.

Probabilistic Machine Learning

This course gives students an overview of current machine learning methods, focusing on the required practical skills to start their own projects in cognitive neuroimaging. Combining short impulse lectures and practical sessions (flipped classroom), we will cover parametric, non-parametric, and deep models across applications in supervised and unsupervised learning.

Course credits and conditions: 1 ECTS, active participation in flipped classroom activities.

Soft Skills Courses

A wide range of soft skill courses are offered. Participation in three courses is mandatory:

  • Critical thinking and logic (1-day course)
  • Time and project management (2-day course)
  • Good scientific practice (1-day course)

All mandatory courses will take place in the introductory weeks.

Depending on students’ needs (either as identified during the IDP or TAC meetings) further soft skill courses are planned, if a minimal number of students is reached (≥8 students). If the minimal number of participants is not achieved, IMPRS will support students in enrolling in an equivalent course offered by Research Academy Leipzig or by the Planck Academy. Potential topics for additional soft skills courses include:

  • Scientific writing
  • Grant proposal writing
  • Scientific presentation
  • Conflict management
  • Supervision of master students
  • Career planning and development

Other Activities Related to the Course Program

Summer School

A three-day international summer school will be organized every other year, the location alternates between MPI CBS in Leipzig and UCL in London. The main target groups are the doctoral students from all partners, but participation is also possible for an external audience. Speakers at the summer school will be both internal speakers from faculty of the IMPRS on Cognitive NeuroImaging, but also highly renowned external scientists. Student participants have the opportunity to present and discuss their work in poster sessions. Also, several horizontal program points (such as Open Science, Career Building, Research Ethics) are included and participants have the opportunity to attend small group workshops.


Alternating with the summer school, a student organized 3-day retreat will take place every other year. Students will receive the opportunity to invite 2-3 keynote speakers of their choice and are asked to organize one session dedicated to each IMPRS Module. In the context of this event it is also expected that students organize, small peer-to-peer teaching workshops or a hackathon on scientific topics of their choice. The retreat will bring students across institutions and modules together and encourage/strengthen exchange about PhD topics. The aim is to promote future collaborations and mutual support between students.

Research Stays 

In order to widen the research opportunities for doctoral students and in order to build up and intensify the collaboration between all participating institutions, doctoral students will be supported in conducting research visits of up to three months at one of the partner institutions, provided there is consent from the TAC.

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