Introduction / Overview 2 nd November 2011 Rumana Chowdhury & Peter Smittenaar & Suz Prejawa Wellcome Trust Centre for Neuroimaging, UCL.

Slides:



Advertisements
Similar presentations
The General Linear Model (GLM)
Advertisements

Wellcome Dept. of Imaging Neuroscience University College London
Hierarchical Models and
1st level analysis - Design matrix, contrasts & inference
SPM Software & Resources Wellcome Trust Centre for Neuroimaging University College London SPM Course London, May 2011.
SPM Software & Resources Wellcome Trust Centre for Neuroimaging University College London SPM Course London, October 2008.
SPM for EEG/MEG Guillaume Flandin
Introduction / Overview 8th October 2008 Hanneke den Ouden, Justin Chumbley, Maria Joao Rosa Wellcome Trust Centre for Neuroimaging, UCL.
Group analysis Kherif Ferath Wellcome Trust Centre for Neuroimaging University College London SPM Course London, Oct 2010.
1st level analysis: basis functions, parametric modulation and correlated regressors. 1 st of February 2012 Sylvia Kreutzer Max-Philipp Stenner Methods.
Experimental design of fMRI studies Methods & models for fMRI data analysis in neuroeconomics April 2010 Klaas Enno Stephan Laboratory for Social and Neural.
Bayesian models for fMRI data
Group analyses of fMRI data Methods & models for fMRI data analysis in neuroeconomics November 2010 Klaas Enno Stephan Laboratory for Social and Neural.
Topological Inference Guillaume Flandin Wellcome Trust Centre for Neuroimaging University College London SPM Course London, May 2014 Many thanks to Justin.
Multiple testing Justin Chumbley Laboratory for Social and Neural Systems Research Institute for Empirical Research in Economics University of Zurich With.
07/01/15 MfD 2014 Xin You Tai & Misun Kim
The General Linear Model (GLM)
Group analyses of fMRI data Methods & models for fMRI data analysis 28 April 2009 Klaas Enno Stephan Laboratory for Social and Neural Systems Research.
Multiple comparison correction Methods & models for fMRI data analysis 29 October 2008 Klaas Enno Stephan Branco Weiss Laboratory (BWL) Institute for Empirical.
Group analyses of fMRI data Methods & models for fMRI data analysis 26 November 2008 Klaas Enno Stephan Laboratory for Social and Neural Systems Research.
Introduction to SPM SPM fMRI Course London, May 2012 Guillaume Flandin Wellcome Trust Centre for Neuroimaging University College London.
General Linear Model & Classical Inference Guillaume Flandin Wellcome Trust Centre for Neuroimaging University College London SPM M/EEGCourse London, May.
Methods for Dummies Second level analysis
With many thanks for slides & images to: FIL Methods group, Virginia Flanagin and Klaas Enno Stephan Dr. Frederike Petzschner Translational Neuromodeling.
Overview for Dummies Outline Getting started with an experiment Getting started with an experiment Things you need to know for scanning Things you need.
Introduction / Overview 23th October 2013 Archy de Berker & Marion Oberhuber Wellcome Trust Centre for Neuroimaging, UCL 2013 Methods for Dummies.
Methods for Dummies Overview Practical info –Topics to be covered in MfD 2007 – How to prepare your presentation – Where to find information and.
Random field theory Rumana Chowdhury and Nagako Murase Methods for Dummies November 2010.
7/16/2014Wednesday Yingying Wang
SPM Course Zurich, February 2015 Group Analyses Guillaume Flandin Wellcome Trust Centre for Neuroimaging University College London With many thanks to.
Introduction / Overview 15th October 2009 Maria Joao Rosa and Antoinette Nicolle Wellcome Trust Centre for Neuroimaging, UCL 2009.
Group analyses of fMRI data Methods & models for fMRI data analysis November 2012 With many thanks for slides & images to: FIL Methods group, particularly.
Wellcome Dept. of Imaging Neuroscience University College London
Methods for Dummies Overview and Introduction
Event-related fMRI SPM course May 2015 Helen Barron Wellcome Trust Centre for Neuroimaging 12 Queen Square.
Methods for Dummies Second level Analysis (for fMRI) Chris Hardy, Alex Fellows Expert: Guillaume Flandin.
Statistical Analysis An Introduction to MRI Physics and Analysis Michael Jay Schillaci, PhD Monday, April 7 th, 2007.
FMRI Modelling & Statistical Inference Guillaume Flandin Wellcome Trust Centre for Neuroimaging University College London SPM Course Chicago, Oct.
Introduction / Overview 6th October 2010 Suz Prejawa & Chris Lambert Wellcome Trust Centre for Neuroimaging, UCL 2010.
The General Linear Model
The General Linear Model Guillaume Flandin Wellcome Trust Centre for Neuroimaging University College London SPM fMRI Course London, May 2012.
SPM short – Mai 2008 Linear Models and Contrasts Stefan Kiebel Wellcome Trust Centre for Neuroimaging.
1 st level analysis: Design matrix, contrasts, and inference Stephane De Brito & Fiona McNabe.
Bayesian Inference in SPM2 Will Penny K. Friston, J. Ashburner, J.-B. Poline, R. Henson, S. Kiebel, D. Glaser Wellcome Department of Imaging Neuroscience,
The General Linear Model Christophe Phillips SPM Short Course London, May 2013.
The General Linear Model Guillaume Flandin Wellcome Trust Centre for Neuroimaging University College London SPM fMRI Course London, October 2012.
Group Analyses Guillaume Flandin SPM Course London, October 2016
The General Linear Model (GLM)
Topological Inference
SPM Software & Resources
The general linear model and Statistical Parametric Mapping
The General Linear Model
Wellcome Trust Centre for Neuroimaging University College London
M/EEG Statistical Analysis & Source Localization
Keith Worsley Keith Worsley
The General Linear Model (GLM)
The General Linear Model
Statistical Parametric Mapping
The general linear model and Statistical Parametric Mapping
SPM2: Modelling and Inference
The General Linear Model
Hierarchical Models and
The General Linear Model (GLM)
Wellcome Centre for Neuroimaging at UCL
MfD 04/12/18 Alice Accorroni – Elena Amoruso
Bayesian Inference in SPM2
The General Linear Model
The General Linear Model (GLM)
The General Linear Model
The General Linear Model
Presentation transcript:

Introduction / Overview 2 nd November 2011 Rumana Chowdhury & Peter Smittenaar & Suz Prejawa Wellcome Trust Centre for Neuroimaging, UCL

Methods for Dummies 2011 Basic Statistics fMRI (BOLD) EEG / MEG Connectivity VBM & DTI Introduction to MfD 2011 Areas covered in MfD Wednesdays / 13h00 – 14h00 / FIL Seminar Room Aim: to give a basic introduction to human brain imaging analysis methods, focusing on fMRI and M/EEG

Update to this ppt: please note that names assigned to talks have changed!

I. Basic Statistics 9 th Nov – 7 th Dec Linear Algebra & Matrices (Othman Al-Helli & Narges Bazargani) T-tests, ANOVA’s & Regression (Alexander Moscicki & Andrea Banino) General Linear Model (Anne Urai & Michael Trimble) Bayes for beginners (Luzia Troebinger & Ashwini Oswal) Random Field Theory (Stefania Kaninia & Laurel Morris) Introduction to MfD 2011

II. What are we measuring? 14 th Dec Basis of the BOLD signal (Laura Wolf & Peter Smittenaar) Introduction to MfD 2011 Christmas break, no talks on 21 st and 28 th Dec

III. fMRI Analysis 4 th & 11 th Jan Preprocessing: –Realigning and un-warping (Punit Shah & Emma Davis) –Co-registration & spatial normalisation (Eleanor Loh & … ) Introduction to MfD 2011

Study design and efficiency (Arman Eshaghi & Kristina DeDuck) 1 st level analysis – Design matrix contrasts and inference (Hsuan-Chen Wu & …) 1 st level analysis – Basis functions, parametric modulation and correlated regressors (Max-Philipp Stenner & …) 2 nd level analysis – between-subject analysis (Meghan Morley & …) III. fMRI Analysis (cont.) 18 th Jan – 8 th Feb

II. What are we measuring? 15 th Feb Basis of the M/EEG signal (Spas Getov & …) Introduction to MfD 2011

IV. EEG & MEG 22 nd & 29 th Feb Pre-processing and experimental design (Sarah Jensen & …) Contrasts, inference and source localisation (Budnik & …) Introduction to MfD 2011

V. Connectivity 7 th, 14, 21 st March Intro to connectivity - PPI & SEM (Leona Enke & Emma Jayne Kilford) DCM for fMRI – theory & practice (… & …) DCM for ERP / ERF – theory & practice (Elizabeth Mallia & …) Introduction to MfD 2011

VI. Structural MRI Analysis 28 th March & 4 th April Voxel Based Morphometry (Fahid Rasul & …) Basic DTI (Silvia Kreutzer & …)

Introduction to MfD 2011 Prize alert! As a way of saying thank you for taking part, this year the ‘best’ presenters will be awarded a very exciting prize… or depending on budget:

How to prepare your presentation Remember your audience are not experts… The aim of the sessions is to –introduce the concepts and explain why they are important to imaging analysis –familiarise people with the basic theory and standard methods Time: 45min. + 15min. questions – 2 presenters per session Don’t just copy last year’s slides, improve them! Talk to the allocated expert 1 week in advance Introduction to MfD 2011 Read the Presenter’s guide ( )

What if I can’t make my presentation? try and find someone else to swap with…. …if you still can’t find a solution, then get in touch with Peter or Rumana at least 3 weeks before the talk. Introduction to MfD 2011

Acronyms DCM – dynamic causal model DTI – diffusion tensor imaging FDR – false discovery rate FFX – fixed effects analysis FIR – finite impulse response FWE – family wise error FWHM – full width half maximum GLM – general linear model GRF – gaussian random field theory HRF – haemodynamic response function ICA – independent component analysis ISI – interstimulus interval PCA – principal component analysis PEB – parametric empirical bayes PPI – psychophysiological interaction PPM – posterior probability map ReML – restricted maximum likelihood RFT– random field theory RFX – random effects analysis ROI – region of interest SOA – stimulus onset asynchrony SPM – statistical parametric mapping VBM – voxel-based morphometry

Where to find help Key papers Previous years’ slides Human Brain Function Textbook (online) SPM course slides Cambridge CBU homepage (Rik Henson’s slides)Cambridge CBU homepage Methods Group Experts Monday Methods Meetings (4 th floor FIL, 12.30) SPM List Friday Project presentations (4 th floor FIL, 3pm) Introduction to MfD 2011 MfD HomeResources