Overview for Overview for DummiesDummies

Outline Outline

Getting started with an experimentGetting started with an experiment Things you need to know for scanningThings you need to know for scanning SPM & your dataSPM & your data AcronymsAcronyms

Getting startedGetting started

Cogent scripts….Cogent scripts….– Allow you to present Allow you to present scanner-synchronized visual scanner-synchronized visual

stimuli, auditory stimuli, mechanical stimuli, and stimuli, auditory stimuli, mechanical stimuli, and taste and smell stimuli. Details at: taste and smell stimuli. Details at: http://www.vislab.ucl.ac.uk/Cogent/http://www.vislab.ucl.ac.uk/Cogent/

– It is also used in monitoring key presses and other It is also used in monitoring key presses and other physiological recordings from the subject, as well physiological recordings from the subject, as well as logging stimulus and scan onset times. as logging stimulus and scan onset times.

– Try and get hold of one to modify rather than Try and get hold of one to modify rather than starting from scratch! People are more than happy starting from scratch! People are more than happy to share scripts around…to share scripts around…

– If you need help, talk to Eric Featherstone…If you need help, talk to Eric Featherstone…– ……and if you need to use any special equipment and if you need to use any special equipment

then Peter Aston is the man to seethen Peter Aston is the man to see

Getting started…Getting started…scanning decisions to be madescanning decisions to be made

What are your scanning parameters What are your scanning parameters – how many conditions/sessions/blocks?how many conditions/sessions/blocks?– how much brain coverage do you needhow much brain coverage do you need– what ISI do you wantwhat ISI do you want– how many measurements & at what anglehow many measurements & at what angle

TRs TRs

It takes 44 horizontal slices to get whole brain It takes 44 horizontal slices to get whole brain coverage. coverage.

Each slice takes about 90s to acquire, with a standard Each slice takes about 90s to acquire, with a standard slice thickness of 3mmslice thickness of 3mm

So, acquiring the whole brain may take too long…So, acquiring the whole brain may take too long… Factors you need to take into account are:Factors you need to take into account are:

– TR lengthTR length– ISI lengthISI length– TR should not equal the ISI…TR should not equal the ISI…

And if you want to do a DCM (dynamic causal model) And if you want to do a DCM (dynamic causal model) then you need a TR ideally of less than 3sthen you need a TR ideally of less than 3s

TR decisions – an exampleTR decisions – an example

I needed to present stimuli for 1s and also give the I needed to present stimuli for 1s and also give the subject time to respondsubject time to respond

I wanted 6 stimulus presentations per block I wanted 6 stimulus presentations per block Interleaved between blocks I needed a fixation time for Interleaved between blocks I needed a fixation time for

the baseline, and ideally I want to use DCM toothe baseline, and ideally I want to use DCM too Also, having the TR & ISI both divisible by the number of Also, having the TR & ISI both divisible by the number of

stimuli in a block, stimuli are evenly presented stimuli in a block, stimuli are evenly presented throughout the blockthroughout the block

So the choices were…So the choices were…– TR of 30 slices (2.7s) with an ISI of 36 slices (3.24s), TR of 30 slices (2.7s) with an ISI of 36 slices (3.24s),

or a TR of 36 slices (3.24s) with an ISI of 30 slices or a TR of 36 slices (3.24s) with an ISI of 30 slices (2.7s)(2.7s)

– Fixation between trails was equivalent to 5*TRFixation between trails was equivalent to 5*TR– The first choice gave plenty of time for the subject to The first choice gave plenty of time for the subject to

respond and also allows me to carry out a DCM respond and also allows me to carry out a DCM analysisanalysis

Summary for scanningSummary for scanning

Get you script ready & working with the scannerGet you script ready & working with the scanner Make sure it logs all the data you need for your Make sure it logs all the data you need for your

analysisanalysis Back up your data from the stimulus PC! You can Back up your data from the stimulus PC! You can

transfer it via the network after each scanning transfer it via the network after each scanning session…session…

Provide the radiographers with tea, biscuits, chocolate Provide the radiographers with tea, biscuits, chocolate etc. etc.

Hurrah! I have brain data!Hurrah! I have brain data!

SO WHAT DO I DO WITH IT NOW?SO WHAT DO I DO WITH IT NOW? This is where we get into SPM & This is where we get into SPM &

preprocessing…preprocessing… ……and more decision-making!and more decision-making! It can take a long time to process at this It can take a long time to process at this

stage, so make sure you have decided in stage, so make sure you have decided in advance!advance!

Preprocessing Possiblities…Preprocessing Possiblities…

These steps basically get your imaging data to a state These steps basically get your imaging data to a state where you can start your analysiswhere you can start your analysis– Slice timingSlice timing– RealignmentRealignment– CoregistrationCoregistration– NormalisationNormalisation– SmoothingSmoothing– Segmentation Segmentation

Once you have carried out your pre-processing you Once you have carried out your pre-processing you can specify your design and datacan specify your design and data

Contrasts, inference & basis Contrasts, inference & basis functionsfunctions

Contrasts allow us to test Contrasts allow us to test hypotheses about our hypotheses about our data, using t & f testsdata, using t & f tests

Our fMRI data is a time Our fMRI data is a time series based on the series based on the haemodynamic response. haemodynamic response. The basis functions used in The basis functions used in SPM are curves used to SPM are curves used to ‘describe’ or fit the ‘describe’ or fit the haemodynamic response haemodynamic response in relation to our model.in relation to our model.

Linear hierarchical modelsLinear hierarchical models

11stst level analysis: activation over scans (within level analysis: activation over scans (within subject)subject)

22ndnd level analysis: activation over subjects level analysis: activation over subjects Hierarchical models are central to:Hierarchical models are central to:

– random effects analysesrandom effects analyses– Bayseian modellingBayseian modelling– DCMDCM

Connectivity Connectivity

Functional segregation – responses to an input giving Functional segregation – responses to an input giving a regionally specific effecta regionally specific effect

Functional integration – how one region influences Functional integration – how one region influences another…subdivided into: another…subdivided into: – Functional connectivity: correlations among brain Functional connectivity: correlations among brain

systems (e.g. principal component analysis)systems (e.g. principal component analysis)– Effective connectivity: the influence of one region Effective connectivity: the influence of one region

over another (e.g. psycho-physiological over another (e.g. psycho-physiological interactions, or DCM)interactions, or DCM)

Voxel-based MorphometryVoxel-based Morphometry

Contrasts differences in structural data on a voxel-by-Contrasts differences in structural data on a voxel-by-voxel basisvoxel basis

Used to compare size or shape of brain region, for Used to compare size or shape of brain region, for instance instance – Comparing brain-damaged patients with a control Comparing brain-damaged patients with a control

groupgroup– mapping changes in grey (or white) matter within mapping changes in grey (or white) matter within

subjects over timesubjects over time

AcronymsAcronyms

SPM – statistical parametric SPM – statistical parametric mappingmapping

SnPM – statistical non-SnPM – statistical non-parametric mappingparametric mapping

DCM – dynamic causal modelDCM – dynamic causal model PPI – psychophysiological PPI – psychophysiological

interactioninteraction VBM – voxel-based VBM – voxel-based

morphometrymorphometry ROI – region of interestROI – region of interest ReML – restricted maximum ReML – restricted maximum

likelihoodlikelihood HRF – haemodynamic response HRF – haemodynamic response

functionfunction GLM – general linear modelGLM – general linear model RFT– random field theory RFT– random field theory

– (also GRF – gaussian)(also GRF – gaussian)

SOA – stimulus onset asynchronySOA – stimulus onset asynchrony ISI – interstimulus intervalISI – interstimulus interval PCA – principal component analysisPCA – principal component analysis ICA – independent component ICA – independent component

analysisanalysis FFX – fixed effects analysisFFX – fixed effects analysis RFX – random effects analysisRFX – random effects analysis PEB – parametric empirical bayesPEB – parametric empirical bayes FWE – family wise errorFWE – family wise error FDR – false discovery rateFDR – false discovery rate FWHM – full width half maximumFWHM – full width half maximum PPM – posterior probability mapPPM – posterior probability map FIR – finite impulse responseFIR – finite impulse response

Good Luck!Good Luck!