Search result: Catalogue data in Spring Semester 2021

Health Sciences and Technology Master Information
Major in Neurosciences
Elective Courses
NumberTitleTypeECTSHoursLecturers
227-0390-00LElements of MicroscopyW4 credits3GM. Stampanoni, G. Csúcs, A. Sologubenko
AbstractThe lecture reviews the basics of microscopy by discussing wave propagation, diffraction phenomena and aberrations. It gives the basics of light microscopy, introducing fluorescence, wide-field, confocal and multiphoton imaging. It further covers 3D electron microscopy and 3D X-ray tomographic micro and nanoimaging.
ObjectiveSolid introduction to the basics of microscopy, either with visible light, electrons or X-rays.
ContentIt would be impossible to imagine any scientific activities without the help of microscopy. Nowadays, scientists can count on very powerful instruments that allow investigating sample down to the atomic level.
The lecture includes a general introduction to the principles of microscopy, from wave physics to image formation. It provides the physical and engineering basics to understand visible light, electron and X-ray microscopy.
During selected exercises in the lab, several sophisticated instrument will be explained and their capabilities demonstrated.
LiteratureAvailable Online.
227-0395-00LNeural SystemsW6 credits2V + 1U + 1AR. Hahnloser, M. F. Yanik, B. Grewe
AbstractThis course introduces principles of information processing in neural systems. It covers basic neuroscience for engineering students, experiment techniques used in animal research and methods for inferring neural mechanisms. Students learn about neural information processing and basic principles of natural intelligence and their impact on artificially intelligent systems.
ObjectiveThis course introduces
- Basic neurophysiology and mathematical descriptions of neurons
- Methods for dissecting animal behavior
- Neural recordings in intact nervous systems and information decoding principles
- Methods for manipulating the state and activity in selective neuron types
- Neuromodulatory systems and their computational roles
- Reward circuits and reinforcement learning
- Imaging methods for reconstructing the synaptic networks among neurons
- Birdsong and language
- Neurobiological principles for machine learning.
ContentFrom active membranes to propagation of action potentials. From synaptic physiology to synaptic learning rules. From receptive fields to neural population decoding. From fluorescence imaging to connectomics. Methods for reading and manipulation neural ensembles. From classical conditioning to reinforcement learning. From the visual system to deep convolutional networks. Brain architectures for learning and memory. From birdsong to computational linguistics.
Prerequisites / NoticeBefore taking this course, students are encouraged to complete "Bioelectronics and Biosensors" (227-0393-10L).

As part of the exercises for this class, students are expected to complete a programming or literature review project to be defined at the beginning of the semester.
227-1034-00LComputational Vision (University of Zurich)
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: INI402

Mind the enrolment deadlines at UZH:
Link
W6 credits2V + 1UD. Kiper
AbstractThis course focuses on neural computations that underlie visual perception. We study how visual signals are processed in the retina, LGN and visual cortex. We study the morpholgy and functional architecture of cortical circuits responsible for pattern, motion, color, and three-dimensional vision.
ObjectiveThis course considers the operation of circuits in the process of neural computations. The evolution of neural systems will be considered to demonstrate how neural structures and mechanisms are optimised for energy capture, transduction, transmission and representation of information. Canonical brain circuits will be described as models for the analysis of sensory information. The concept of receptive fields will be introduced and their role in coding spatial and temporal information will be considered. The constraints of the bandwidth of neural channels and the mechanisms of normalization by neural circuits will be discussed.
The visual system will form the basis of case studies in the computation of form, depth, and motion. The role of multiple channels and collective computations for object recognition will
be considered. Coordinate transformations of space and time by cortical and subcortical mechanisms will be analysed. The means by which sensory and motor systems are integrated to allow for adaptive behaviour will be considered.
ContentThis course considers the operation of circuits in the process of neural computations. The evolution of neural systems will be considered to demonstrate how neural structures and mechanisms are optimised for energy capture, transduction, transmission and representation of information. Canonical brain circuits will be described as models for the analysis of sensory information. The concept of receptive fields will be introduced and their role in coding spatial and temporal information will be considered. The constraints of the bandwidth of neural channels and the mechanisms of normalization by neural circuits will be discussed.
The visual system will form the basis of case studies in the computation of form, depth, and motion. The role of multiple channels and collective computations for object recognition will
be considered. Coordinate transformations of space and time by cortical and subcortical mechanisms will be analysed. The means by which sensory and motor systems are integrated to allow for adaptive behaviour will be considered.
LiteratureBooks: (recommended references, not required)
1. An Introduction to Natural Computation, D. Ballard (Bradford Books, MIT Press) 1997.
2. The Handbook of Brain Theorie and Neural Networks, M. Arbib (editor), (MIT Press) 1995.
227-1046-00LComputer Simulations of Sensory Systems Information W3 credits3GT. Haslwanter
AbstractThis course deals with computer simulations of the human auditory, visual, and balance system. The lecture will cover the physiological and mechanical mechanisms of these sensory systems. And in the exercises, the simulations will be implemented with Python. The simulations will be such that their output could be used as input for actual neuro-sensory prostheses.
ObjectiveOur sensory systems provide us with information about what is happening in the world surrounding us. Thereby they transform incoming mechanical, electromagnetic, and chemical signals into “action potentials”, the language of the central nervous system.
The main goal of this lecture is to describe how our sensors achieve these transformations, how they can be reproduced with computational tools. For example, our auditory system performs approximately a “Fourier transformation” of the incoming sound waves; our early visual system is optimized for finding edges in images that are projected onto our retina; and our balance system can be well described with a “control system” that transforms linear and rotational movements into nerve impulses.
In the exercises that go with this lecture, we will use Python to reproduce the transformations achieved by our sensory systems. The goal is to write programs whose output could be used as input for actual neurosensory prostheses: such prostheses have become commonplace for the auditory system, and are under development for the visual and the balance system. For the corresponding exercises, at least some basic programing experience is required!!
ContentThe following topics will be covered:
• Introduction into the signal processing in nerve cells.
• Introduction into Python.
• Simplified simulation of nerve cells (Hodgkins-Huxley model).
• Description of the auditory system, including the application of Fourier transforms on recorded sounds.
• Description of the visual system, including the retina and the information processing in the visual cortex. The corresponding exercises will provide an introduction to digital image processing.
• Description of the mechanics of our balance system, and the “Control System”-language that can be used for an efficient description of the corresponding signal processing (essentially Laplace transforms and control systems).
Lecture notesFor each module additional material will be provided on the e-learning platform "moodle". The main content of the lecture is also available as a wikibook, under Link
LiteratureOpen source information is available as wikibook Link

For good overviews of the neuroscience, I recommend:

• Principles of Neural Science (5th Ed, 2012), by Eric Kandel, James Schwartz, Thomas Jessell, Steven Siegelbaum, A.J. Hudspeth
ISBN 0071390111 / 9780071390118
THE standard textbook on neuroscience.
NOTE: The 6th edition will be released on February 5, 2021!
• L. R. Squire, D. Berg, F. E. Bloom, Lac S. du, A. Ghosh, and N. C. Spitzer. Fundamental Neuroscience, Academic Press - Elsevier, 2012 [ISBN: 9780123858702].
This book covers the biological components, from the functioning of an individual ion channels through the various senses, all the way to consciousness. And while it does not cover the computational aspects, it nevertheless provides an excellent overview of the underlying neural processes of sensory systems.

• G. Mather. Foundations of Sensation and Perception, 2nd Ed Psychology Press, 2009 [ISBN: 978-1-84169-698-0 (hardcover), oder 978-1-84169-699-7 (paperback)]
A coherent, up-to-date introduction to the basic facts and theories concerning human sensory perception.

• The best place to get started with Python programming are the Link

On signal processing with Python, my upcoming book
• Hands-on Signal Analysis with Python (Due: January 13, 2021
ISBN 978-3-030-57902-9, Link)
will contain an explanation to all the required programming tools and packages.
Prerequisites / Notice• Since I have to gravel from Linz, Austria, to Zurich to give this lecture, I plan to hold this lecture in blocks (every 2nd week).
• In addition to the lectures, this course includes external lab visits to institutes actively involved in research on the relevant sensory systems.
252-0312-00LUbiquitous Computing Information W6 credits2V + 3AC. Holz
AbstractUbiquitous Computing means interacting with information and with each other anywhere, mediated through miniature technology everywhere. We will investigate the technical aspects of Ubicomp, particularly sensing, processing, and sense making: input (touch & gesture), activity, monitoring cardiovascular health and neurological conditions, context & location sensing, affective computing.
ObjectiveThe course will combine high-level concepts with low-level technical methods needed to sense, detect, and understand them.

High-level:
– input modalities for interactive systems (touch, gesture)
– "activities" and "events" (exercises and other mechanical activities such as movements and resulting vibrations)
– health monitoring (basic cardiovascular physiology)
– location (GPS, urban simulations, smart cities and development)
– affective computing (emotions, mood, personality)

Low-level:
– sampling (Shannon Nyquist) and filtering (FIR, IIR), time and frequency domains (Fourier transforms)
– cross-modal sensor systems, signal synchronization and correlation
– event detection, classification, prediction using basic signal processing as well as learning-based methods
– sensor types: optical, mechanical/acoustic, electromagnetic

– signals modalities and processing of: application (modalities/methods)
* touch detection (resistive sensing, capacitive sensing, diffuse illumination/DI, spectral reflections, frustrated total internal reflection/FTIR, fingerprint scanning, surface-acoustic waves)
* gesture recognition (inertial sensing through accelerometers, gyroscopes)
* activity detection and tracking (inertial, acoustic, vibrotactile for classification, counting, vibrometry)
* occupation and use (electricity monitoring, water consumption, single-point sensing)
* cardiovascular (electrocardioagraphy, photoplethysmography, pulse oximetry, ballistocardiography, blood pressure, pulse transit time, bio impedance)
* affective computing (heart rate variability, R-R intervals, electrodermal activity, sympathetic tone, facial expressions)
* neurological (fatigue, fatigability)
* location (GPS, BLE, Wifi)
Content"The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it" — Mark Weiser, 1991.

This is the premise of Ubiquitous Computing, a vision that is slowly becoming reality as everything is a device and we can interact with information and with each other anywhere, mediated through miniature technology. Along with this change, interaction modalities have changed, too, from explicit input on keyboards and mice to implicit and passively observed input through sensors in the environment (e.g., speakers, cameras, temperature/occupancy detectors) and those we now wear on our bodies (e.g., health sensors, activity sensors, miniature computers we call smartwatches).

In this course, we will look at the technical side of Ubicomp, particularly
– sensing (incl. 'signals', sampling, data acquisition methods, controlled user studies, uncontrolled studies in-the-wild),
– processing (incl. frequencies, feature extraction, detection), and
– sense making: input sensing (touch & gesture), activity sensing (motion), monitoring cardiovascular health, affective state, neurological conditions (with basics on cardiovascular physiology + PPG, PulseOx, ECG, EDA, BCG, SCG, HRV, BioZ, IPG, PAT, PTT), context & location sensing (GPS/Wifi, motion).

Lectures will be accompanied by practical sessions that focus on sensor modalities and signal processing. Here, we will work on existing data sets and devise methods to record our own data for processing and prediction purposes.

A series of reading assignments, covering both well-established publications in Ubicomp as well as emerging results and methods, will bridge the fundamentals and topics taught in class to academic research and real-world problems.

More information on the course site: Link
Lecture notesCopies of slides will be made available. Lectures will be recorded and made available online.

More information on the course site: Link
LiteratureWill be provided in the lecture. To put you in the mood:
Mark Weiser: The Computer for the 21st Century. Scientific American, September 1991, pp. 94-104
327-2125-00LMicroscopy Training SEM I - Introduction to SEM Restricted registration - show details
Limited number of participants.

Master students will have priority over PhD students. PhD students may still enroll, but will be asked for a fee. (Link).

Registration form: (Link)
W2 credits3PP. Zeng, A. G. Bittermann, S. Gerstl, L. Grafulha Morales, K. Kunze, J. Reuteler
AbstractThe introductory course on Scanning Electron Microscopy (SEM) emphasizes hands-on learning. Using 2 SEM instruments, students have the opportunity to study their own samples, or standard test samples, as well as solving exercises provided by ScopeM scientists.
Objective- Set-up, align and operate a SEM successfully and safely.
- Accomplish imaging tasks successfully and optimize microscope performances.
- Master the operation of a low-vacuum and field-emission SEM and EDX instrument.
- Perform sample preparation with corresponding techniques and equipment for imaging and analysis
- Acquire techniques in obtaining secondary electron and backscatter electron micrographs
- Perform EDX qualitative and semi-quantitative analysis
ContentDuring the course, students learn through lectures, demonstrations, and hands-on sessions how to setup and operate SEM instruments, including low-vacuum and low-voltage applications.
This course gives basic skills for students new to SEM. At the end of the course, students with no prior experience are able to align a SEM, to obtain secondary electron (SE) and backscatter electron (BSE) micrographs and to perform energy dispersive X-ray spectroscopy (EDX) qualitative and semi-quantitative analysis. The procedures to better utilize SEM to solve practical problems and to optimize SEM analysis for a wide range of materials will be emphasized.

- Discussion of students' sample/interest
- Introduction and discussion on Electron Microscopy and instrumentation
- Lectures on electron sources, electron lenses and probe formation
- Lectures on beam/specimen interaction, image formation, image contrast and imaging modes.
- Lectures on sample preparation techniques for EM
- Brief description and demonstration of the SEM microscope
- Practice on beam/specimen interaction, image formation, image contrast (and image processing)
- Student participation on sample preparation techniques
- Scanning Electron Microscopy lab exercises: setup and operate the instrument under various imaging modalities
- Lecture and demonstrations on X-ray micro-analysis (theory and detection), qualitative and semi-quantitative EDX and point analysis, linescans and spectral mapping
- Practice on real-world samples and report results
Literature- Detailed course manual
- Williams, Carter: Transmission Electron Microscopy, Plenum Press, 1996
- Hawkes, Valdre: Biophysical Electron Microscopy, Academic Press, 1990
- Egerton: Physical Principles of Electron Microscopy: an introduction to TEM, SEM and AEM, Springer Verlag, 2007
Prerequisites / NoticeNo mandatory prerequisites. Please consider the prior attendance to EM Basic lectures (551- 1618-00V; 227-0390-00L; 327-0703-00L) as suggested prerequisite.
327-2126-00LMicroscopy Training TEM I - Introduction to TEM Restricted registration - show details
Number of participants limited to 6.
Master students will have priority over PhD students. PhD students may still enroll, but will be asked for a fee (Link).

TEM 1 registration form: (Link)
W2 credits3PP. Zeng, E. J. Barthazy Meier, A. G. Bittermann, F. Gramm, A. Sologubenko, M. Willinger
AbstractThe introductory course on Transmission Electron Microscopy (TEM) provides theoretical and hands-on learning for new operators, utilizing lectures, demonstrations, and hands-on sessions.
Objective- Overview of TEM theory, instrumentation, operation and applications.
- Alignment and operation of a TEM, as well as acquisition and interpretation of images, diffraction patterns, accomplishing basic tasks successfully.
- Knowledge of electron imaging modes (including Scanning Transmission Electron Microscopy), magnification calibration, and image acquisition using CCD cameras.
- To set up the TEM to acquire diffraction patterns, perform camera length calibration, as well as measure and interpret diffraction patterns.
- Overview of techniques for specimen preparation.
ContentUsing two Transmission Electron Microscopes the students learn how to align a TEM, select parameters for acquisition of images in bright field (BF) and dark field (DF), perform scanning transmission electron microscopy (STEM) imaging, phase contrast imaging, and acquire electron diffraction patterns. The participants will also learn basic and advanced use of digital cameras and digital imaging methods.

- Introduction and discussion on Electron Microscopy and instrumentation.
- Lectures on electron sources, electron lenses and probe formation.
- Lectures on beam/specimen interaction, image formation, image contrast and imaging modes.
- Lectures on sample preparation techniques for EM.
- Brief description and demonstration of the TEM microscope.
- Practice on beam/specimen interaction, image formation, Image contrast (and image processing).
- Demonstration of Transmission Electron Microscopes and imaging modes (Phase contrast, BF, DF, STEM).
- Student participation on sample preparation techniques.
- Transmission Electron Microscopy lab exercises: setup and operate the instrument under various imaging modalities.
- TEM alignment, calibration, correction to improve image contrast and quality.
- Electron diffraction.
- Practice on real-world samples and report results.
Literature- Detailed course manual
- Williams, Carter: Transmission Electron Microscopy, Plenum Press, 1996
- Hawkes, Valdre: Biophysical Electron Microscopy, Academic Press, 1990
- Egerton: Physical Principles of Electron Microscopy: an introduction to TEM, SEM and AEM, Springer Verlag, 2007
Prerequisites / NoticeNo mandatory prerequisites. Please consider the prior attendance to EM Basic lectures (551- 1618-00V; 227-0390-00L; 327-0703-00L) as suggested prerequisite.
327-2225-00LMaP Distinguished Lecture Series on Soft Robotics
This course is primarily designed for MSc and doctoral students. Guests are welcome.
W1 credit2SR. Katzschmann, L. Schefer
AbstractThis course is an interdisciplinary colloquium on Soft Robotics involving different internationally renowned speakers from academia and industry giving lectures about their cutting-edge research, which highlights the state-of-the-art and frontiers in the Soft Robotics field.
ObjectiveParticipants become acquainted with the state-of-the-art and frontiers in Soft Robotics, which is a topic of global and future relevance from the field of materials and process engineering. The self-study of relevant literature and active participation in discussions following presentations by internationally renowned speakers stimulate critical thinking and allow participants to deliberately discuss challenges and opportunities with leading academics and industrial experts and to exchange ideas within an interdisciplinary community.
ContentThis course is a colloquium involving a selected mix of internationally renowned speaker from academia and industry who present their cutting-edge research in the field of Soft Robotics. The self-study of relevant pre-read literature provided in advance to each lecture serves as a basis for active participation in the critical discussions following each presentation.
Lecture notesSelected scientific pre-read literature (max. three articles per lecture) relevant for and discussed during the lectures is posted in advance on the course web page.
Prerequisites / NoticeParticipants should have a solid background in materials science and/or engineering.
363-1130-00LDigital Health Restricted registration - show details W3 credits2VT. Kowatsch
AbstractToday, we face the challenge of chronic conditions. Personal coaching approaches are neither scalable nor financially sustainable. The question arises therefore to which degree Digital Health applications are appropriate to address this challenge. In this lecture, students will learn about the need, design and assessment of digital health interventions.
ObjectiveThe promise of more personalized, patient-centered, and outcomes-based healthcare is real, worthy, and within reach (Harvard Business Review, October 2019), NHS teams up with Amazon to bring Alexa to patients (The Guardian, July 2019), Apple Heart Study demonstrates the ability of wearable technology to detect atrial fibrillation (Stanford Medicine News, March 2019). In the midst of a global pandemic and a US recession, US digital health companies raised $5.4B in venture funding across the first six months of 2020. The sector is on track to have its largest funding year ever. (Rocket Health, 2020)

What are the rationale and implications behind the recent developments in the field of digital health?

Digital Health is the use of information and communication technology for the prevention and treatment of diseases in the everyday life of individuals. It is thus linked to topics such as digital health interventions, digital biomarker, digital coaches and healthcare chatbots, telemedicine, mobile and wearable computing, self-tracking, personalized medicine, connected health, smart homes or smart cars.

In the 20th century, healthcare systems specialized in acute care. In the 21st century, we now face the challenge of dealing with the specific characteristics of chronic conditions. These are now responsible for around 70% of all deaths worldwide and 85% of all deaths in Europe and are associated with an estimated economic loss of $7 trillion between 2011 and 2025. Chronic diseases are characterized in particular by the fact that they require an intervention paradigm that focuses on prevention and lifestyle change. Lifestyle (e.g., diet, physical activity, tobacco or alcohol consumption) can reduce the risk of suffering from a chronic condition or, if already present, can reduce its burden. A corresponding change in lifestyle is, however, only implemented by a fraction of those affected, partly because of missing or inadequate interventions or health literacy, partly due to socio-cultural influences. Individual personal coaching of these individuals is neither scalable nor financially sustainable.

Against this background, the question arises on how to develop evidence-based digital health interventions (DHIs) that allow medical doctors and other caregivers to scale and tailor long-term treatments to individuals in need at sustainable costs. At the intersection of health economics, information systems research, computer science, and behavioural medicine, this lecture has the objective to help students and upcoming healthcare executives interested in the multi-disciplinary field of digital health to better understand the need, design and assessment of DHIs.

After the course, students will be able to...

1. understand the importance of DHIs for the management of chronic conditions
2. understand the anatomy of DHIs
3. know frameworks for the design of DHIs
4. know evaluation criteria for DHIs
5. know technologies for DHIs
6. assess DHIs
7. discuss the advantages and disadvantages of DHIs
ContentTo reach these learning objectives, the following topics are covered in the lecture and will be discussed based on concrete national and international examples including DHIs from the Center for Digital Health Interventions (Link), a joint initiative of the Department of Management, Technology and Economics at ETH Zurich and the Institute of Technology Management at the University of St.Gallen:

1. Motivation for Digital Health
- The rise of chronic diseases in developed countries
- The discrepancy of acute care and care of chronic diseases
- Lifestyle as medicine and prevention
- From excellence of care in healthcare institutions to excellence of care in everyday life

2. Anatomy of Digital Health Interventions
- Just-in-time adaptive interventions
- Digital biomarker for predicting states of vulnerability
- Digital biomarker for predicting states of receptivity
- Digital coaching and healthcare chatbots

3. Design & Evaluation of Digital Health Interventions
- Overview of design frameworks
- Preparation of DHIs
- Optimization of DHIs
- Evaluation of DHIs
- Implementation of DHIs

4. Digital Health Technologies
- Technologies for telemedicine
- Mobile medical devices
- Virtual, augmented and mixed reality applications incl. live demonstrations
- Privacy and regulatory considerations

The Digital Health lecture is structured in two parts and follows the concept of a hybrid therapy consisting of live sessions and complementary online lessons. In the first part, students will learn and discuss the topics of the four learning modules in weekly online sessions. Complementary learning material (e.g., video and audio clips), multiple-choice questions and exercises are provided online.

In the second part, students work in teams and will use their knowledge from the first part of the lecture to critically assess DHIs. Each team will then present and discuss the findings of the assessment with their fellow students who will provide peer-reviews. Additional online coaching sessions are offered to support the teams with the preparation of their presentations.
Literature1. Cohen, A.B., Dorsey, E.R., Mathews, S.C. et al. (2020) A digital health industry cohort across the health continuum Nature Digital Medicine 3(68)
2. Collins, LM (2018) Optimization of Behavioral, Biobehavioral, and Biomedical Interventions: The Multiphase Optimization Strategy (MOST) New York: Springer.
3. Corneta, VP, and Holden, RJ (2018) Systematic Review of Smartphone-Based Passive Sensing for Health and Wellbeing Journal of Biomedical Informatics (77:January), 120-132.
4. Coravos, A., Khozin, S., and K. D. Mandl (2019) Developing and Adopting Safe and Effective Digital Biomarkers to Improve Patient Outcomes Nature Digital Medicine 2 Paper 14.
5. Katz, D. L., E. P. Frates, J. P. Bonnet, S. K. Gupta, E. Vartiainen and R. H. Carmona (2018) Lifestyle as Medicine: The Case for a True Health Initiative American Journal of Health Promotion 32(6), 1452-1458.
6. Kvedar, JC, Fogel AL, Elenko E and Zohar D (2016) Digital medicine’s march on chronic disease Nature Biotechnology 34(3), 239-246
7. Kowatsch, T., L. Otto, S. Harperink, A. Cotti and H. Schlieter (2019) A Design and Evaluation Framework for Digital Health Interventions it ‐ Information Technology 61(5‐6), 253‐263.
8. Mathews, SC, McShea, MJ, Hanley, CL et al. (2019) Digital health: a path to validation. npj Digital Medicine 2(38)
9. Nahum-Shani, I, Smith SN, Spring BJ, Collins LM, Witkiewitz K, Tewari A and Murphy SA (2018) Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support Annals of Behavioral Medicine 52 (6), 446-462.
10. Powell AC, Torous JB, Firth J, Kaufman KR (2020) Generating value with mental health apps BJPsych Open 6(2):e16. Published 2020 Feb 5. doi:10.1192/bjo.2019.98
11. Safavi K, Mathews SC, Bates DW, Dorsey ER, Cohen AB (2019) Top-Funded Digital Health Companies And Their Impact On High-Burden, High-Cost Conditions Health Affairs 38(1):115-12
376-0202-00LNeural Control of Movement and Motor LearningW4 credits3GN. Wenderoth, M. Altermatt, S. Gerritzen, C. Lustenberger
AbstractThis course extends the students' knowledge regarding the neural control of movement and motor learning. Particular emphasis will be put on those methods and experimental findings that have shaped current knowledge of this area including fMRI, EEG, TMS, electrical brain stimulation and classical behavioural experiments.
ObjectiveKnowledge of the neurophysiological basis underlying the neural control of movement and motor learning. One central element is that students have first hands-on experience in the lab where small experiments are independently executed, analysed and interpreted.
376-1150-00LClinical Challenges in Musculoskeletal Disorders Restricted registration - show details W2 credits2GM. Leunig, S. J. Ferguson, Z.‑M. Manjaly
AbstractThis course reviews musculoskeletal disorders focusing on the clinical presentation, current treatment approaches and future challenges and opportunities to overcome failures.
ObjectiveAppreciation of the surgical and technical challenges, and future perspectives offered through advances in surgical technique, new biomaterials and advanced medical device construction methods.
ContentFoot deformities, knee injuries, knee OA, hip disorders in the child and adolescent, hip OA, spine deformities, degenerative spine disease, shoulder in-stability, hand, rheumatoid diseases, neuromuscular diseases, sport injuries and prevention
376-1178-00LHuman Factors IIW3 credits2VM. Menozzi Jäckli, R. Huang, M. Siegrist
AbstractStrategies, abilities and needs of human at work as well as properties of products and systems are factors controlling quality and performance in everyday interactions. In Human Factors II (HF II), cognitive aspects are in focus therefore complementing the more physical oriented approach in HF I. A basic scientific approach is adopted and relevant links to practice are illustrated.
ObjectiveThe goal of the lecture is to empower students in designing products and systems enabling an efficient and qualitatively high standing interaction between human and the environment, considering costs, benefits, health, well-being, and safety as well. The goal is achieved in addressing a broad variety of topics and embedding the discussion in macroscopic factors such as the behavior of consumers and objectives of economy.
ContentCognitive factors in perception, information processing and action. Experimental techniques in assessing human performance and well-being, human factors and ergonomics in development of products and complex systems, innovation, decision taking, consumer behavior and user experience.
Literature- Salvendy G. (ed), Handbook of Human Factors, Wiley & Sons, 2012
- Stanton N.A. et al., Cognitive Work Analysis, CRC Press, 2017
- Further textbooks are introduced in the lecture
376-1306-00LClinical Neuroscience (University of Zurich) Information
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: BIO389

Mind the enrolment deadlines at UZH:
Link
W3 credits3VG. Schratt, University lecturers
AbstractThe lecture series "Clinical Neuroscience" presents a comprehensive, condensed overview of the most important neurological diseases, their clinical presentation, diagnosis, therapy options and possible causes. Patient demonstrations (Übungen) follow every lecture that is dedicated to a particular disease.
ObjectiveBy the end of this module students should be able to:
- demonstrate their understanding and deep knowledge concerning the main neurological diseases
- identify and explain the different clinical presentation of these diseases, the methodology of diagnosis and the current therapies available
- summarize and critically review scientific literature efficiently and effectively
376-1400-00LTransfer of Technologies into Neurorehabilitation Restricted registration - show details W3 credits2VL. Lünenburger, M. Altermatt, R. Gassert, H. Van Hedel, P. Wolf
AbstractThe course focuses on clinical as well as industrial aspects of advanced technologies and their transfer into neurorehabilitation from both theoretical and practical perspectives. The students will learn the basics of neurorehabilitation and the linkage to technologies, gain insight into the development within the medtech field and learn applications of technologies in clinical settings.
ObjectiveThe students will:
- Learn basics and principles of clinical neuroscience and neurorehabilitation.
- Gain insight into the technical basics of advanced technologies and the transfer into product development processes.
- Gain insight into the application, the development and integration of advanced technologies in clinical settings. This includes the advantages and limitations according to different pathologies and therapy goals.
- Get the opportunity to test advanced technologies in practical settings.
- Learn how to transfer theoretical concepts to actual settings in different working fields.
ContentMain focus:
- Neurobiological principles applied to the field of neurorehabilitation.
- Clinical applications of advanced rehabilitation technologies.
- Visit medical technology companies, rehabilitation centers and labs to gain deeper insight into the development, application and evaluation of advanced technologie
Lecture notesTeaching materials will be provided for the individual events and lectures.
- Slides (pdf files)
- Information sheets and flyers of the visited companies, labs and clinics
376-1414-01LCurrent Topics in Brain Research (FS)W1 credit1.5KI. Mansuy, F. Helmchen, further lecturers
AbstractDifferent national and international scientific guests are invited to present and discuss their most recent scientific results.
ObjectiveThe aim is to exchange scientific knowledge and data as well as to promote communication and collaboration among researchers. Students taking the course participate in all seminars within one semester and write a critical report about one seminar of their choice. Prof. Isabelle / Dr. Alberto Corcoba will send instructions for this report to students who have registered for the course one week before the start of the semester.
ContentVarious scientific guests from the fields of neuroepigenetics, neurochemistry, neuromorphology and neurophysiology will report on their latest scientific findings.
Lecture notesno handout
Literatureno literature
376-1430-00LModeling and Methods in Human Behavioural NeuroscienceW3 credits2GG. Bertolini
AbstractThe course presents models in human behavioral neuroscience and methods to:
1) Adapt the models to embed hypotheses;
2) Make model-based predictions;
3) Use models when designing data collections that verify/disprove predictions
ObjectiveAt the end of this module students should know:
• different types of models used in human behavioral neuroscience, their features and their limits
• how to use models to estimate expected human behavioural outcomes or to interpret behavioural data
• how to implement models and methods via software (Matlab)
Content1. Linear time-invariant model and their practical applications on neuroscience systems (e.g. sensory input, motor control). From equations to block diagram representation.

2. Psychophysical methods to test human perceptual response and statistical models of behaviour (e.g. Bayesian model). Examples from tasks probing perceptual responses.

3. How the brain controls our body through internal models (feedforward and feedback). Examples from motor and balance tasks. The optimal observer as a model of how the human brain interprets inputs, plans and compares actions and finally executes them.

The course will combine theoretical and practical knowledge on how to implement models and techniques via software (Matlab)
Lecture notesLectures slides will be handed out ahead of lectures via Moodle
Prerequisites / NoticeBasic knowledge of Matlab is important to follow the Matlab lectures. Material for self-evaluation and some pre-readings/refresh ppt slides are available at this link:
Link
376-1624-00LPractical Methods in Biofabrication Restricted registration - show details
Number of participants limited to 12.
W5 credits4PM. Zenobi-Wong, S. J. Ferguson, S. Schürle-Finke
AbstractBiofabrication involves the assembly of materials, cells, and biological building blocks into grafts for tissue engineering and in vitro models. The student learns techniques involving the fabrication and characterization of tissue engineered scaffolds and the design of 3D models based on medical imaging data. They apply this knowledge to design, manufacture and evaluate a biofabricated graft.
ObjectiveThe objective of this course is to give students hands-on experience with the tools required to fabricate tissue engineered grafts. During the first part of this course, students will gain practical knowledge in hydrogel synthesis and characterization, fuse deposition modelling and stereolithography, bioprinting and bioink design, electrospinning, and cell culture and viability testing. They will also learn the properties of common biocompatible materials used in fabrication and how to select materials based on the application requirements. The students learn principles for design of 3D models. Finally the students will apply their knowledge to a problem-based Project in the second half of the Semester. The Project requires significant time outside of class Hours, strong commitment and ability to work independently.
Prerequisites / NoticeNot recommended if passed 376-1622-00 Practical Methods in Tissue Engineering
376-1660-00LScientific Writing, Reporting and Communication Restricted registration - show details
Number of participants limited to 30.

Only for Health Sciences and Technology MSc
W3 credits2VW. R. Taylor, S. H. Hosseini Nasab
AbstractThis course aims to teach students many of the unwritten rules on how to communicate effectively, from writing reports or manuscripts (or indeed their Master thesis!) through to improving skills in oral presentations, and presenting themselves at interview.
ObjectiveThis course will teach students to communicate effectively in official environments, including:
- writing manuscripts, theses, CVs, reports etc
- presenting posters
- oral presentations
- critical reviews of literature
376-1986-00LBayesian Data Analysis and Models of Behavior (University of Zurich)
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: DOEC0829

Mind the enrolment deadlines at UZH:
Link
W3 credits2SR. Polania, University lecturers
AbstractMaking sense of the data acquired via experiments is fundamental in many fields of sciences. This course is designed for students/researchers who want to gain practical experience with data analysis based on Bayesian inference. Coursework involves practical demonstrations and discussion of solutions for data analysis problems. No advanced knowledge of statistics and probability is required.
ObjectiveThe overall goal of this course it that the students are able to develop both analytic and problem-solving skills that will serve to draw reasonable inferences from observations. The first objective is to make the participants familiar with the conceptual framework of Bayesian data analysis. The second goal is to introduce the ideas of modern Bayesian data analysis, including techniques such as Markov chain Monte Carlo (MCMC) techniques, alongside the introduction of programming tools that facilitate the creation of any Bayesian inference model. Throughout the course, this will involve practical demonstrations with example datasets, homework, and discussions that should convince the participants of this course that it is possible to make inference and understand the data acquired from the experiments that they usually obtain in their own research (starting from simple linear regressions all the way up to more complex models with hierarchical structures and dependencies). After working through this course, the participants should be able to build their own inference models in order to interpret meaningfully their own data.
Prerequisites / NoticeThe very basics (or at least intuition) of programming in either Matlab or R
535-0534-00LDrug, Society and Public HealthW1 credit1VJ. Steurer, R. Heusser
AbstractIntroduction of basic concepts and methods in Public Health, epidemiology, and Evidence Based Medicine. An overview on concepts and principles of clinical trials on efficacy of drugs
ObjectiveStudents know the concepts and principles of epidemiological and clinical research, they are informed about the principles of evidence based medicine and know how and where to search for evidence.
ContentEinführung in Epidemiologie / Pharmakoepidemiologie / Evidence-based Medicine: Grundbegriffe, Studiendesigns, object-design, statistische Grundlagen, Kausalität in der Pharmako-Epidemiologie, Methoden und Konzepte, Fallbeispiele.
Lecture notesWird abgegeben
Literature- F. Gutzwiller/ F. Paccaud (Hrsg.): Sozial- und Präventivmedizin - Public Health. 4. Aufl. 2011, Verlag Hans Huber, Bern
- R. Beaglehole, R. Bonita, T. Kjellström: Einführung in die Epidemiologie. 1997, Verlag Hans Huber, Bern
- L. Gordis: Epidemiology, 4 th Ed. 2009, W.B. Saunders Comp.
- K.J. Rothman, S. Greenland: Modern Epidemiology, 2. Ed. 1998, Lippincott Williams & Wilkins
- A.G. Hartzema, M. Porta, H.H. Tilson (Eds.): Pharmacoepidemiology - An Introduction. 3. Ed. Harvey Whitney Comp., Cincinnati
- R. Bonita, R. Beaglehole. Einführung in die Epidemiologie, 2. überarbeitete Auflage, 2008 Huber Verlag.
- B.L. Strom (Eds.): Pharmacoepidemiology. 3. Ed. 2000, Wiley & Sons Ltd., Chichester
- S.E. Straus, W.S. Richardson, P.Glasziou, R.B. Haynes: Evidence-based Medicine. 2005, Churchill Livingstone, London
- U. Jaehde, R.Radziwill, S. Mühlebach, W. Schnack (Hrsg): Lehrbuch der Klinischen Pharmazie
- L.M. Bachmann, M.A. Puhan, J.Steurer (Eds.): Patientenorientierte Forschung. EInführung in die Planung und Durchführung einer Studie. Verlag Hans Huber, 2008
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