Introduction to Neuroimaging Analysis
Run by School of Psychology
10.000 Credits or 5.000 ECTS Credits
Organiser: Prof Paul Mullins
Overall aims and purpose
Introduction to Neuroimaging Analysis focuses on teaching students the practicalities of modern neuroimaging techniques focusing on MRI, the processing and analysis of imaging data, and the design of neuroimaging research studies. The course aims to teach students the skills to design and analyse their own MRI and fMRI studies. For 2020-21 academic year, due to COVID-19 restrictions and to ensure student and staff safety, the module will be run as an on-line "virtual lab", where students will learn basic analysis, data visualisation and programming skills in Matlab, as well as in a number of other software tools (FSL, SPM, MRS and DTI packages).
1) Physical and practical principles of MRI imaging, Methods for image pre-processing (e.g. image registration etc.) and basic handling of imaging data; 2) Practicalities associated with the physiology of MRI contrast in the brain, with a focus on measures of cerebral metabolism and blood flow; 3) Principles and practical methodologies for measurement of changes in cerebral metabolism; 5) Practicalities of statistical analyses of functional imaging time-series; 6) Experimental design for functional imaging; 7) The safety and ethical issues that arise in regards to neuroimaging research and how they apply to the researcher, participants and the wider community.
Links to C grades Knowledge of key areas/principles only • Limited evidence of background study • Answer only poorly focused on ques3on & with some irrelevant material & poor structure • AQempts to present relevant and coherent arguments • Has several factual/computa3onal errors • No original interpreta3on • Only major links between topic are described • Limited problem solving • Many weaknesses in presenta3on & accuracy
Links to A grades Comprehensive knowledge • Detailed understanding of the subject area • Extensive background study • Highly focused answer & well-structured • Logically presented & defended arguments • No factual/computa3onal errors • Original interpreta3on • New links to topic are presented • New approach to a problem • Excellent presenta3on with very accurate communica3on
Links to B grades Strong Knowledge • Understands most but not all of subject area • Evidence of background study • Focused answer with good structure • Arguments presented coherently • Mostly free of factual/computa3onal errors • Some limited original interpreta3on • Well known links described between topics • Problems addressed by exis3ng methods/approaches • Good presenta3on, accurate communica3on
Have a solid understanding of practical application of the general linear model (GLM) to analysis of neuroimaging data
- A thorough understanding of MRI safety
- Understand modern neuroimaging data formats and solid data management and hygiene practices
- Be able to report and interpret the results of fMRI studies.
- An understanding of the ethical issues associated with handling modern neuroimaging data.
- Use this knowledge of the GLM to design a good fMRI study
- Be able to pre-process and analyze MRI and fMRI data.
|Lab Book Week 1||5.00|
|Lab Book Week 2||5.00|
|Lab Book Week 3||5.00|
|Lab Book Week 4||5.00|
|Lab Book Week 5||5.00|
|Lab Book Week 7||5.00|
|Lab Book Week 8||5.00|
|Lab Book Week 9||5.00|
|Lab Book Week 10||5.00|
|Lab Book Week 11||5.00|
Teaching and Learning Strategy
|Practical classes and workshops||
The course aims to teach students the skills to design and analyse their own MRI and fMRI studies. For 2020-21 academic year, due to COVID-19 restrictions and to ensure student and staff safety, the module will be run as an on-line "virtual lab", where students will learn basic analysis, data visualisation and programming skills in Matlab, as well as in a number of other software tools (FSL, SPM, MRS and DTI packages).
- Literacy - Proficiency in reading and writing through a variety of media
- Numeracy - Proficiency in using numbers at appropriate levels of accuracy
- Computer Literacy - Proficiency in using a varied range of computer software
- Self-Management - Able to work unsupervised in an efficient, punctual and structured manner. To examine the outcomes of tasks and events, and judge levels of quality and importance
- Exploring - Able to investigate, research and consider alternatives
- Information retrieval - Able to access different and multiple sources of information
- Inter-personal - Able to question, actively listen, examine given answers and interact sensitevely with others
- Critical analysis & Problem Solving - Able to deconstruct and analyse problems or complex situations. To find solutions to problems through analyses and exploration of all possibilities using appropriate methods, rescources and creativity.
- Safety-Consciousness - Having an awareness of your immediate environment, and confidence in adhering to health and safety regulations
- Presentation - Able to clearly present information and explanations to an audience. Through the written or oral mode of communication accurately and concisely.
- Teamwork - Able to constructively cooperate with others on a common task, and/or be part of a day-to-day working team
- Argument - Able to put forward, debate and justify an opinion or a course of action, with an individual or in a wider group setting
Subject specific skills
- Understand the scientific underpinnings of psychology as a discipline.
- Communicate psychological concepts effectively in written form.
- Communicate psychological concepts effectively in oral form.
- Be computer literate for the purpose of processing and disseminating psychological data and information.
- Retrieve and organise information effectively.
- Engage in effective teamwork for the purpose of collaborating on psychological projects.
- Work effectively under pressure (time pressure, limited resources, etc) as independent and pragmatic learners.
- Problem-solve by clarifying questions, considering alternative solutions, making critical judgements, and evaluating outcomes.
- Reason scientifically and demonstrate the relationship between theory and evidence.
- Understand and investigate the role of brain function in all human behaviour and experience.
- Comprehend and use psychological data effectively, demonstrating a systematic knowledge of the application and limitations of various research paradigms and techniques.
- Use a range of statistical methods with confidence.
- Use a variety of psychological tools, including specialist software, laboratory equipment and psychometric instruments.
- Be aware of ethical principles and approval procedures.
Resource implications for students
Computer access is required
Pre- and Co-requisite Modules
Courses including this module
Compulsory in courses:
- C8CU: MSc Neuroimaging year 1 (MSC/N)
Optional in courses:
- 6S26: BSc Neuropsychology year 3 (BSC/NI)
- C8BZ: MRes Psychology year 1 (MRES/PSYCH)
- C8EG: MSc Principles of Clinical Neuropsychology year 1 (MSC/PCNP)
- C8DU: MSc Psychology year 1 (MSC/PSY)
- C8EX: MSc Psychology (with Incorporated Pre-Masters) year 1 (MSC/PSY1)
- C8AL: MSc Psychological Research year 1 (MSC/PSYRES)
- C808: MSci Psychology with Clinical & Health Psychology year 4 (MSCI/PHS)
- C807: MSci Psychology year 4 (MSCI/PS)