@[toc](fMRI) We use: - [Siemens TIM Trio 3T][1] as a member of [CUBIC][2] - situated at [Royal Holloway, University of London][3] - MRI simulator (at University of Surrey.) This page will detail BOLD fMRI, while Structural MRI, DTI, MRS and ASL are located on the [MRI methods][4] page. ---------- Training & SOP ============== Training must take place before conducting MRI experiments in the lab (see [Access & Training][5] section for details). - [SOP][6], [TRA][7] & appendices - You cannot watch the MRI Safety Video (CUBIC MRI Video.mp4) in OSF. You will either need to use [this dropbox link][8] to view in browser, or download it (645 MB). There is a DVD copy available on request - Useful forms/manuals folder e.g. [Directions to MRI Scanner][9]) - Details on [MRI-specific setup][10] **NB: SOP versions are correct as of 11/03/2021, and are for reference only as may well be out of date, download the latest version directly from Q-Pulse to ensure correct procedure** ---------- General Background ================== fMRI is a very versatile method, and can be used in concert with other MRI measures (ASL, MRS, DTI, Cortical Thickness) as well as other techniques such as EEG and Neurostimulation. ***NB: ASL can be used for functional MRI as it measures Cerebral Blood Flow, see [MRI Methods][11] page for information.*** **MRI Physics & Basics** - FSL Course (2018) [Introduction to MRI Physics][12] - OHBM (2017): [MR Physics - Basics to State of the art][13] - **FMRIB** MRI Introduction Lecture ([Part 1][14], [Part 2][15], [Slides][16]) - In-Depth [MRI Lectures][17] (inc. [fMRI][18]) - [MRI Basics][19] Book - [MRI Glossary][20] **Neuroanatomy** - [Fundamental Neuroscience for Neuroimaging][21] (Coursera, inc. functional neuroanatomy & experimental design) - [Neuroanatomy Tutorial][22] (also [Useful Links][23]) - [Neuroanatomy for Dummies][24] **fMRI Background & Resources** - **Principles of fMRI** [Part 1][25] and [Part 2][26] (Coursera, also on YouTube [Pt1][27], [Pt2][28]) - [MRI Lectures][29] (inc. [Intro to fMRI][30]) - [**CUBIC**][31] has a [Wiki Page][32] - Andrew Jahn ["Brain Book"][33] project - **[DartBrains][34]** Introduction to fMRI analysis - Soares et al (2016): [Hitchikers guide to fMRI][35] - **OHBM** (2017) Full Day [Advanced fMRI Course][36], also other [Resources][37] (e.g. [Introduction to Network Neuroscience][38] & [functional connectivity in ageing][39] - **NIMH** [fMRI Course][40] - **MIT** CBBM [fMRI Bootcamp][41], lectures on [fMRI][42], [MVPA][43] & [Neural Decoding][44] - Rorden Lab [fMRI Course][45] - Friston (2003) [Introduction to SPM and Design][46] - Fee-paying Courses: [Utrecht fMRI][47] (inc rsfMRI, MVPA) - [**MRC CBU**][48] fMRI [Imaging Wiki][49] & [Handbook][50] ([Wiki Overview][51]) - [Neurostars][52] community for fMRI help ---------- Design & Acquisition ==================== - Harvard Centre for Brain Science [FAQ on **Acquisition**][53] ---------- Data Structure -------------- - [**BIDS**][54] (Brain Imaging Data Structure): Best practice organisation of neuroimaging data ([Starter Kit][55], [Wiki][56] & [Tutorials][57]) - **COBIDAS**: [Best Practices in Data Analysis and Sharing in Neuroimaging using MRI][58] ---------- Design & Statistics ------------------- **Design** - MRC CBU [Efficient fMRI Design][59] - [**Neurodesign**][60] (optimise fMRI experimental design[ \[Durnez et al 2018\]][61]) from [NeuroPowertools][62] - Chris Rorden's [fMRI Simulator][63] - [PractiCal fMRI][64] Blog **Statistics** - [Statistical Bias in fMRI][65] - Cremers et al (2017): [Power & Inference in fMRI][66] (and [commentary piece][67]) - Eklund et al (2016): [False Positives in fMRI][68] - Lindquist 2008: [The Statistical Analysis of fMRI Data][69] & [2011 NonParametric Statistics in Neuroimaging][70] - Webb-Vargas (2017): [Big Data & Neuroimaging][71] - Gregory Ashby [Statistics of fMRI][72] (with [MATLAB code][73] & [GitHub][74]) - [**Jeanette Mumford**][75] has [Mumford Brain Stats][76] and [YouTube][77] - **Thomas Nichol's** NISOx [Presentations][78] & [Blog][79] - MIT [P Threshold FAQ][80] **Power** - Mumford 2012: [A power calculation guide for fMRI studies][81] - [**Neuropower]**[71] (help decide sample size [[Durnez et al 2016][82]]) from [NeuroPowertools][83] - [fMRIPower][84] (in MATLAB [Mumford & Nichols 2008][85]) ---------- Resting State fMRI ------------------ - OHBM Blog: How to: [Resting State Design & Analysis][86] - OHBM (2018) Alpine: [Resting State: State of the art][87] - Smith et al (2013): [Resting State connectivity review][88] - Friston et al (2014): [Resting State connectivity Review][89] - SPM [Dataset and Overview of DCM Analysis][90] ---------- Neurofeedback ------------- - Watanabe et al (2017): [Real-Time fMRI Neurofeedback][91] - Thibault et al (2018): [fMRI Neurofeedback: Systematic Review][92] - Lorenz et al (2016): [The Automatic Neuroscientist][93] - [Neurofeedback using Turbo Brain Voyager][94] (see [product webpage][95]) ---------- Combined Acquisitions --------------------- **tES-fMRI** See [Neurostimulation section][96] - Bergmann et al (2016): [Combined Brain Stimulation (TMS/tES) and Neuroimaging (EEG/MRI)][97] - Lorenz et al (2017): [Automatic Neuroscientist - tACS & fMRI][98] **EEG-fMRI** See [EEG Section][99] - Huster et al (2012): [Methods for EEG-fMRI][100] - Dong et al (2018): [Neuroscience Information Toolbox for EEG-fMRI][101] ---------- Analytic Approaches =================== ---------- Meta-Analysis ------------- - **OHBM** (2017) Half Day [Meta-Analysis Course][102] - Mega- vs Meta-Analysis ([Salimi-Khorsidi et al 2009][103]; [Costafreda 2009][104]) **Analysis Software** - [Neurosynth][105] (see [Yarkoni et al 2011][106]) - [Multi-level kernel density analysis][107] (MKDA, see [this walkthrough][108]) - [GingerALE][109] (and [Tutorial][110]) ---------- Connectivity ------------ This can be broadly divided into Functional and Effective connectivity (see [Friston (2011) Review][111]). - [Overview of Brain Connectivity][112] (2014 book) with *Methods for Connectivity Analysis in fMRI* chapter available on request. - Martinos Course on [Structural & Functional Connectivity][113] - 2019 Course on [Brain Connectivity][114] in GitHub - [Learn Brain Network Analysis][115] on GitHub ***Functional Connectivity***: looks at un-directed associations between time series, and makes no assumptions about underlying biology. Includes Seed Analysis (Pearson's Correlation, PPI), Multivariate decomposition (ICA/PCA). - Mantini (2014) [Introduction to Functional Connectivity][116] - Pernet [Introduction to fMRI Connectivity][117] (& [other resources][118]) - **REST** & **CONN** Toolboxes - see SPM section. - **PPI**: See [2011 Methods for Dummies][119] (includes SEM), [FSL Wiki][120], and [Andrew Jahn Blog][121]. - PPI is a type of Mediation Analysis (see [Mediation Toolbox][122] & [Walkthrough][123]) - **ICA**: See FSL [Lecture][124] & [Tutorials][125] ***Effective Connectivity*** looks at directed influence, claims to make statements about causal effects, and usually has some anatomical basis (and restriction to pre-defined areas). Includes SEM, Granger Causality & Multivariate Autoregressive Models, DCM and Bayes Net. - Friston et al (2017): [DCM revisited][126] - Rogers et al (2010) [Multivariate Autoregressive Models][127] ***Network Analysis*** and ***Graph Theory*** are concepts commonly used in connectivity analysis. - OHBM 2017 Half Day [Brain Graphs & Network Analysis][128] - Sporns [(2013) Complex Brain Networks][129] & [(2018) Graph Theory Methods][130] - Van Den Heuvel & Pol (2010): [Resting State Networks][131] ---------- Classification, Machine Learning & MVPA --------------------------------------- **Multi-Voxel Pattern Analysis (MVPA)** - Anzelotti & Coutanche (2018) [Beyond Connectivity: Multivariate Representations][132] - Krigeskorte et al (2008): [Representational Similarity Analysis][133] - **OHBM** (2017) Full Day [Pattern Recognition Course][134] *Toolboxes* - MRC CBU [MVPA][135] & [Representational Similarity Analysis][136] toolbox - [PyMVPA][137]: Python MPVA toolbox - [Princeton MVPA][138] (MATLAB) - [Simitar][139] (RSI, Searchlight analysis toolbox in MATLAB) **Machine Learning** For example Support Vector Machines (SVM) and Maximum uncertainty Linear Discriminant Analysis (MLDA). Can be used to categorise data and tell which parts are most discriminatory. Uses fMRI contrast maps, or structural image data. - Pereira et al (2009) [Machine Learning Classifiers & fMRI Overview][140] - OHBM (2017) [Multivariate Models of Brain Ageing][141] - [Machine Learning for Neuroimagers][142] from [Mohan Gupta][143] - [Example SVM/MLDA in R][144] *Toolboxes* - [NiLearn][145] (Neuroimaging Machine Learning in Python) - [SciKit][146] (Machine learning in Python) **Neural Networks & Deep Learning** - Vieira et al (2018) [Deep Learning & Neuroimaging Methods review][147] - [Neural Networks & Deep Learning][148] Book - A [Neural Network Playground][149] (on [GitHub][150]) ---------- Analysis Software ================= There are a number of different analysis approaches in fMRI (e.g GLM, PPI, DCM, MVPA, ICA) and a number of different analysis programs and toolboxes (e.g [SPM][151], [FSL][152], [AFNI][153], [Brain Voyager][154]). - MRC CBU [Principle of Analysis][155] - OHBM (2015) Half Day [Arts & Pitfalls of fMRI Preprocessing (FSL & SPM)][156] - [Neuroimaging Data Processing Wiki][157] (intro to fMRI analyses) - Rorden Lab [MRI Analysis Tutorials][158] - Boly (2009) [Temporal Basis Functions fMRI][159] - Eloyan et al 2014: [fMRI analysis using R, Quick-Start guide][160] a quick overview of fMRI analysis, with some links at end. ---------- Processing pipelines -------------------- There are a number of pipelines which attempt to standardise analysis, and use the optimal software for each processing stage. **Background** - [Standardized Imaging Pipelines: BIDS, fmriprep, mriqc, and other tools][161] from [Bjorn Schiffler][162] - [Docker][163] is sometimes used to create one interface with which to efficiently interact with a variety of different softwares (e.g. FSL, SPM, AFNI). - It also enables use on an HPC or cloud, see [how to Run BIDS app MRIQC and fMRIPREP on a cluster][164] **Pipelines** - [**BIDS App**][165] (see [GitHub][166] & [Gorgolewski et al 2017][167]) - [**fMRIprep**][168] (see [GitHub][169] & [Esteban et al 2019][170]) - [**MRIQC**][171] (quality control of MRI and fMRI data, [GitHub][172]) - [**Neuropipe**][173] (& [GitHub][174], with [Tutorial][175]) - [**NiPype**][176] from [NiPy][177], Pipeline in Python ([Lectures][178], [Tutorials][179], [Documentation][180]) - DartBrains [Lab Classes on fMRI analysis][181] with Python (using Nipype and fmriprep) - [LONI Pipeline][182] & other [software][183] - [NeuroElf][184] (& [GitHub][185]) - [CANLAB][186] also have a collection of [neuroimaging tools][187], [walkthroughs][188], and a [batch system][189] **Related toolboxes** As stated above, the pipelines use the best software for each stage, so may require installation of a number of auxiliary toolboxes. For instance, **fMRIPREP** [Manual Installation][190] requires FSL, AFNI and Freesurfer, but also: - [**ANTs**][191] (Advanced Normalisation Tools) (with [Video][192], [Wiki][193], and [Tutorial][194]) - [**ICA-AROMA**][195] (ICA-based Automatic Removal Of Motion Artifacts, [GitHub][196]) - [**C3D**][197] (Image Manipulation tool, [Documentation][198], [Videos][199]) ---------- SPM --- - **FIL** SPM [Courses][200] (inc, [2018 Course slides][201] & [2011 Videos][202]); SPM12 [Manual][203]; [WikiBook][204] & [Discussion Lists][205] (JISCMail) - SPM [Datasets/Tutorials][206] & [Extensions][207] - Lars Kasper (2015) [Arts & Pitfalls of fMRI Preprocessing (**SPM 12**)][208] - Andrew Jahn SPM [Tutorials][209] and [Videos][210] - Illustrated Example SPM12 [Structure & Pipelines][211] - SPM Code [Cheat Sheet][212] **Useful Toolboxes** - [**MARSBAR**][213] Region of Interest Toolbox ([Website][214], and Jessican Grahn [Tutorials][215] and [YouTube][216]) - [**WFU Pick Atlas**][217] for generating ROI - Rorden Lab [**Clinical Toolbox**][218] (for normalisation of lesioned brains, on [NITRC][219]) - **HMAT**: [Human Motor Area Template][220] (& **BGHAT**: Basal Ganglia Human Area Template) from [University of Florida][221] ([Mayka et al 2006][222]; [Prodoehl et al 2008][223]). *Also S-MATT: Sensorimotor Area Tract Template* - [**SnPM**][224] (non parametric permutation analysis, available at [NISOx][225]) - Rorden Lab [**MRIcron**][226] & [MRICroS][227] - MemoLab [fMRI Quality Assurance Toolbox][228] - [**REST**][229] Resting State Toolbox On [Resting State Forum][230] - [**CONN**][231] Functional connectivity toolbox (with rsfMRI & PPI). See [website][232] for e.g. [tutorials][233]; Andrew Jahn 2018 [Course][234] and [Videos][235] and Martinos Centre [Course][236]. - **[GraphVar][237]** Toolbox for MATLAB: Graph analysis of functional connectivity data ***Example Code*** - [Example SPM12 fMRI Analysis Pipeline][238], including example [behavioural file reader][239] - [MRI Quality Assurance scripts][240] - Rorden Lab [SPM Scripts][241] (inc. [pre-processing][242]) and [Tools][243] - MemoLab [SPM scripts][244] (inc [Batch System][245]) - Rik Henson [Useful Scripts][246] (& his [GitHub][247], includes SEM, ICA, Connectivity, ANOVA) - John Ashburner [Tips & Tricks][248] ---------- FSL --- - **FMRIB** FSL [Courses][249] (& [WIN Courses][250]); [Wiki][251] (& [Overview][252]); [FEAT][253] (fMRI Expert Analysis Tool, inc. [User Guide][254]) & [FSLEyes][255] (new FSLView). - Andrew Jahn 2018 [FSL Course][256], [Tutorials][257] (inc. [Automating FEAT][258]) & [Intro to FSLEyes][259] - OHBM Education Resources (2015): [FSL Pre-Processing Pipeline][260] - [FSL Quick Introduction][261] - Useful [Command-Line FSL functions][262] **Useful Toolboxes** - FSL [Other Software][263] - Tom Nichol's [FSL Scripts][264] - [ICA in FSL][265] - [FIX][266]: Automatic de-noising fMRI using ICA *[NB: requires R & MATLAB]* - [R for FSL][267] - Permutation Analysis in FSL ([Randomise][268], [PALM][269], see [Winkler et al 2014][270] ---------- AFNI ---- - NIMH AFNI [Documentation][271], [Bootcamp][272] (with [Videos][273]), [Handouts][274] & [Help Files][275] - Andrew Jahn 2018 [AFNI Course][276], [Tutorials][277] and [Videos][278] (including [rs-fMRI][279]) ---------- Brain Voyager ------------- NB: [Brain Voyager][280] and [Turbo Brain Voyager][281] are not free, but Brain Innovations does have [some free products][282] - Brain Innovations [Wiki][283], [Blog][284] and [Courses][285] (see also [Documentation][286]) - [YouTube 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