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MSc Cognitive Neuroscience and Human Neuro-imaging

Programme code: PSYT23

This course is for students who want to pursue a research career in cognitive neuroscience and includes a 17 week long research project.

You will collect and analyse data, or analyse pre-existing data sets, in areas including functional MRI and neurophysiological recording (skin conductance response, transcranial magnetic stimulation or transcranial direct current stimulation). Applicants from life science backgrounds will be able to learn neuroimaging and neurophysiological data collection and analysis techniques (eg, statistical parametric mapping), and students from physical sciences, mathematics or engineering will be able to apply their technical knowledge to "real-life" cognitive brain science data.

Applying

To apply for this course, complete the University of Sheffield's postgraduate online application form.

Online application form

You can find more information about the application process on the University's postgraduate webpages.

How to apply: applying essentials

Contact

Course Director: Dr Hannes Saal

If you have any questions about this program or the application process, please contact: psypgt@sheffield.ac.uk | +44 (0)114 222 6534

You can also visit us throughout the year:
Postgraduate open days, visit afternoons and online chats

International students
Don't meet our entry requirements? Pre-Masters at our International College

About the course

This course has two pathways for you to choose from. Lectures and seminars on both pathways teach you about the relationship between brain function and the cognitive, perceptual and motor mechanisms that underpin behaviour. There is also a focus on how cognitive neuroscience relates cognitive and behavioural function to its underlying neural substrate.

Teaching is based in the Department of Psychology, where we have a strong track record in both computational and cognitive neuroscience. Your research project will be based either in Psychology or in the Department of Neuroscience, where there is significant expertise in using neuroimaging and neurophysiological recording techniques in the biological investigation of neurological and psychiatric disorders. Research in both departments also includes modulation of neural systems by novel treatments such as transcranial magnetic stimulation and transcranial direct current stimulation.

Investigative techniques covered: Functional MRI, structural MRI, skin conductance response recording, neuropsychology, transcranial magnetic stimulation, transcranial direct current stimulation

Areas of interest covered: Alzheimer's disease, epilepsy, motor neuron disease, perception, social cognition and communication (particularly theory of mind, empathy and emotion regulation)m executive functioning, EEG and autism, eye-tracking and visual attention, decision-making, development of cognition, emotional influences on behaviour

Depending on the pathway chosen, this degree will give you:

  • A broad and critical understanding of the latest cognitive and computational neuroscience
  • An appreciation of different approaches to understanding brain function
  • An in-depth knowledge of practical neuroanatomy
  • A range of computational and analytic skills relevant to the modelling of brain function
  • The ability to generate and test specific experimental hypotheses which incorporate constraints derived from psychophysics, cognitive neuroscience, and behavioural studies.
  • An appreciation of an academic scientific environment that rewards innovation, fosters a sense of community, and encourages students to direct their own learning.
  • The opportunity to conduct an extended research project involving neuroimaging / neurophysiological data which will put you in an excellent position to pursue an research career in a wide range of neuroscience fields
Entry requirements

For this course, we usually ask for an upper second class (2:1) degree or equivalent in either a life science, a physical science or mathematics.

If your background is in life sciences, we will teach you any relevant mathematics and basic coding skills.

If you have a qualification in mathematics, engineering, or the physical sciences, there is an introductory neuroscience course to ensure that all students have the same key knowledge.

We can also accept qualifications from other countries. You can find out which qualifications we accept from your country on the University's webpages for international students.

Prospective international students: Your country

International students

If you don't meet our entry requirements, our International College offers a Pre-Masters in Science and Engineering. The programme is designed to develop your academic level in your chosen subject, introduce you to the study skills that will be vital to success and help with language if you need it.

Pre-Masters at the University of Sheffield International College

English Language Requirements

If you have not already studied in a country where English is the majority language, it is likely that you will need to have an English language qualification.

  • For 2018 entry, we are asking for International English Language Testing Service (IELTS): Overall grade of 7.0 with no less than 7.0 in writing and no less than 6.0 in reading, speaking and listening
  • For 2019 entry, we are asking for International English Language Testing Service (IELTS): Overall grade of 6.5 with 6.0 in each component

You can find out whether you need to have an english language qualification, and which other English language qualifications we accept, on the University's webpages for international students.

English language requirements

The English Language Teaching Centre offers English language courses for students who are preparing to study at the University of Sheffield.

English Language Teaching Centre

Fees and funding

Up-to-date fees and funding opportunities can be found on the University of Sheffield's webpages for postgraduate students. These may include scholarships for home and international students and a 10% discount for University of Sheffield graduates.

Tuition fees

Funding your postgraduate course

Departmental Taught Postgraduate Bursaries

Each year we offer two bursaries to students on this program. Students who are awarded a bursary get a £1,500 reduction in their tuition fees. These bursaries are awarded on a competitive basis, based on:

  • Academic performance as indicated by GPA and transcript.
  • Other relevant skills and knowledge (for example, programming courses outside the degree, or relevant work experience).
  • Research activity (co-authoring papers, conference presentations, etc).
  • Personal statement, which should include information on why you want to do the course you have applied for, and how it fits with your aspirations.

To be considered for a bursary for a course you intend to start in 2018, submit your application by 31 May 2018. All applications received before this deadline will automatically be considered for a bursary.

What our graduates say

After finishing my undergraduate studies in biomedical engineering, my next step was to pursue a master’s degree focusing towards my passion for cognitive neuroscience. The course provided me with a broad range of modules helping me to gain a deep insight into the different areas of the cognitive neuroscience and brain imaging. The intensive master’s project helped me to develop my technical and critical thinking skills whilst increasing my knowledge and understanding of the field. The combination of lab-based and theoretical modules provided during the course qualified me for pursuing my ambition in research and land my desired PhD position at UCL. Studying the CNHN course was the best choice I made during my studies. I would strongly recommend this course to anyone interested in pursuing their interest in cognitive neuroscience research!

Mahtab Farahbakhsh

Current modules

All students will study:

Fundamentals of Cognition

This module provides an overview of the fundamental issues in cognitive neuroscience and its contributory disciplines. Topics include: fundamental issues in cognition (memory, attention, learning, perception, affect), developmental processes from neuroscience, psychology and dynamic systems perspectives, and theoretical approaches including cognitive neuropsychology, symbolic and sub-symbolic modelling, and methodological issues.

Assessment: Examination

Fundamentals of Neuroscience

This module provides an introduction to core aspects of contemporary neuroscience, and describes the current state of knowledge in the field, central theoretical issues and key practical approaches. Topics that are discussed include: neural signalling, sensation and sensory processing, movement and its central control, the changing brain (development and plasticity in the nervous system) and complex brain functions.

Assessment: Examination

Neuroimaging 1

This module provides an overview of neuroimaging techniques and fundamental data analysis methodologies employed, specifically those based around functional magnetic resonance imaging (fMRI). The two aspects of neuroimaging (techniques and data analysis) will be taught over the semester. For neuroimaging techniques, after introducing the physical principles underlying fMRI, a description of fMRI-based methods for mapping brain structure and function will follow. For neuroimaging data analysis, the general linear model methodology will be introduced based on the software SPM (Statistical Parametric Mapping), which is one of the most widely used packages for fMRI data analysis. Issues concerning fMRI experimental design and efficiency will also be discussed and taught in depth.

Neuroimaging 2

This module further develops on the foundational material in Neuroimaging 1 and provides an overview of neuroimaging techniques and fundamental data analysis methodologies. Specifically, it will focus on the techniques of electrophysiology, EEG, and MEG, optical methods and calcium imaging, each of which will be introduced in the lecture component of the module. In the associated lab classes, students will gain first-hand experience of analysing and processing data sets arising from these techniques.

Data Analysis and Visualization

This module will train students in basic skills in computational data analysis. Students will learn how to import/export scientific data sets in different formats, how to process and transform them, and how to visualise results. Teaching will be hands-on and computer lab-based and will focus on the programming language Python and associated scientific software. No prior programming experience will be necessary.

Research Project in Cognitive Neuroscience

The module allows students to work on an extended (17 week) research project within computational neuroscience and/or cognitive neuroscience and/or systems neuroscience and/or analysis of brain imaging data. Students will learn and apply appropriate research techniques, analyse and interpret the results, and write up the research findings using recognised journal frameworks. Students will receive guidance and regular feedback from their supervisors. The project culminates in an oral presentation and a written dissertation.

Students will also study two modules in either pathway 1 or pathway 2.

Pathway 1:

Applied Neuroanatomy and Clinical Neuroscience

This module comprises studying practical neuroanatomy (human brain dissection) and an overview of neuroradiology (mainly magnetic resonance imaging [MRI] and computed tomography [CT]). The two complementary components of neuroanatomy and neuroradiology will be taught in parallel over the semester. For neuroanatomy, a five-session programme involving practical brain dissection and detailed coverage of the structural and functional units of the human brain will be undertaken. For neuroradiology a series of lectures will be given. Both components will be supported by tutorials.

Assessment: Practical spotter exam

Systems Neuroscience

The module provides an advanced understanding of the brain's major computational systems and how they have been modelled. Major processing units of the brain (e.g, cerebellum and basal ganglia) will be described and, where appropriate, emphasis will be placed on understanding each of these structures as a series of repeating micro- or macrocircuits. The various strategies adopted for modelling these circuits and their interactions with other brain systems will be presented and their predictions for biology considered.

Pathway 2:

Computational Neuroscience 1

This module provides an introduction to methods in computational neuroscience from two different, but complementary perspectives. First, a high-level or ‘top-down’ view explores how neurons encode and decode sensory information. Second a ‘bottom-up’ or mechanistic approach looking at single neuron models at different levels of abstraction – from a simple ‘integrate-and-fire’ approximations to full conductance-based compartmental models. Throughout this module, the emphasis is on the use of mathematical and computational models of single neuronal function.

Computational Neuroscience 2

This module is based on the themes of information theory, Bayes' theorem, and learning algorithms. Information theory places limits on how much Shannon information can be transmitted/received by any communication channel, Bayes' theorem provides a method for interpreting incomplete or noisy information, and learning algorithms provide a mechanism for acquiring/storing/retrieving information about the environment. These three related ideas will be explored in the context of neuronal information processing.

The modules listed below are examples from the current academic year. Students choose to follow one of two pathways. There may be some changes before you start your course.

The content of our courses is reviewed annually to make sure it's up-to-date and relevant. Individual modules are occasionally updated or withdrawn. This is in response to discoveries through our world-leading research, funding changes, professional accreditation requirements, student or employer feedback, outcomes of reviews, and variations in staff or student numbers.