Overview for Dummies

Outline Getting started with an experiment Getting started with an experiment Things you need to know for scanning Things you need to know for scanning SPM & your data SPM & your data Acronyms Acronyms

Getting started Cogent scripts…. Cogent scripts…. –Allow you to present scanner-synchronized visual stimuli, auditory stimuli, mechanical stimuli, and taste and smell stimuli. Details at: –It is also used in monitoring key presses and other physiological recordings from the subject, as well as logging stimulus and scan onset times. –Try and get hold of one to modify rather than starting from scratch! People are more than happy to share scripts around… –If you need help, talk to Eric Featherstone… –…and if you need to use any special equipment then Peter Aston is the man to see

Getting started… scanning decisions to be made What are your scanning parameters What are your scanning parameters – how many conditions/sessions/blocks? – how much brain coverage do you need – what ISI do you want – how many measurements & at what angle

TRs It takes 44 horizontal slices to get whole brain coverage. It takes 44 horizontal slices to get whole brain coverage. Each slice takes about 90s to acquire, with a standard slice thickness of 3mm Each slice takes about 90s to acquire, with a standard slice 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 length –ISI length –TR should not equal the ISI… And if you want to do a DCM (dynamic causal model) then you need a TR ideally of less than 3s And if you want to do a DCM (dynamic causal model) then you need a TR ideally of less than 3s

TR decisions – an example I needed to present stimuli for 1s and also give the subject time to respond I needed to present stimuli for 1s and also give the subject 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 the baseline, and ideally I want to use DCM too Interleaved between blocks I needed a fixation time for the baseline, and ideally I want to use DCM too Also, having the TR & ISI both divisible by the number of stimuli in a block, stimuli are evenly presented throughout the block Also, having the TR & ISI both divisible by the number of stimuli in a block, stimuli are evenly presented throughout the block So the choices were… So the choices were… –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 (2.7s) –Fixation between trails was equivalent to 5*TR –The first choice gave plenty of time for the subject to respond and also allows me to carry out a DCM analysis

Summary for scanning Get you script ready & working with the scanner Get you script ready & working with the scanner Make sure it logs all the data you need for your analysis Make sure it logs all the data you need for your analysis Back up your data from the stimulus PC! You can transfer it via the network after each scanning session… Back up your data from the stimulus PC! You can transfer it via the network after each scanning session… Provide the radiographers with tea, biscuits, chocolate etc. Provide the radiographers with tea, biscuits, chocolate etc.

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 & preprocessing… This is where we get into SPM & preprocessing… …and more decision-making! …and more decision-making! It can take a long time to process at this stage, so make sure you have decided in advance! It can take a long time to process at this stage, so make sure you have decided in advance!

Statistical Parametric Mapping SPM software (SPM99, SPM2) has been designed for the analysis of brain imaging data in fMRI, PET, SPECT, and future releases incorporate EEG & MEG (SPM5, soon to be available). SPM software (SPM99, SPM2) has been designed for the analysis of brain imaging data in fMRI, PET, SPECT, and future releases incorporate EEG & MEG (SPM5, soon to be available). It runs in Matlab…just type SPM at the prompt and all will be revealed. It runs in Matlab…just type SPM at the prompt and all will be revealed. There are sample data sets available on the SPM website to play with There are sample data sets available on the SPM website to play with

Preprocessing Possiblities… These steps basically get your imaging data to a state where you can start your analysis These steps basically get your imaging data to a state where you can start your analysis –Slice timing –Realignment –Coregistration –Normalisation –Smoothing –Segmentation Once you have carried out your pre-processing you can specify your design and data Once you have carried out your pre-processing you can specify your design and data

Contrasts, inference & basis functions Contrasts allow us to test hypotheses about our data, using t & f tests Contrasts allow us to test hypotheses about our data, using t & f tests Our fMRI data is a time series based on the haemodynamic response. The basis functions used in SPM are curves used to ‘describe’ or fit the haemodynamic response in relation to our model. Our fMRI data is a time series based on the haemodynamic response. The basis functions used in SPM are curves used to ‘describe’ or fit the haemodynamic response in relation to our model.

Linear hierarchical models 1 st level analysis: activation over scans (within subject) 1 st level analysis: activation over scans (within subject) 2 nd level analysis: activation over subjects 2 nd level analysis: activation over subjects Hierarchical models are central to: Hierarchical models are central to: –random effects analyses –Bayseian modelling –DCM

Connectivity Functional segregation – responses to an input giving a regionally specific effect Functional segregation – responses to an input giving a regionally specific effect Functional integration – how one region influences another…subdivided into: Functional integration – how one region influences another…subdivided into: –Functional connectivity: correlations among brain systems (e.g. principal component analysis) –Effective connectivity: the influence of one region over another (e.g. psycho-physiological interactions, or DCM)

Voxel-based Morphometry Contrasts differences in structural data on a voxel-by-voxel basis Contrasts differences in structural data on a voxel-by-voxel basis Used to compare size or shape of brain region, for instance Used to compare size or shape of brain region, for instance –Comparing brain-damaged patients with a control group –mapping changes in grey (or white) matter within subjects over time

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

Good Luck!