Experimental design of fMRI studies Methods & models for fMRI data analysis 12 November 2008 Klaas Enno Stephan Laboratory for Social and Neural Systems Research Institute for Empirical Research in Economics University of Zurich Functional Imaging Laboratory (FIL) Wellcome Trust Centre for Neuroimaging University College London With many thanks for slides & images to: FIL Methods group, particularly Christian Ruff

Overview of SPM RealignmentSmoothing Normalisation General linear model Statistical parametric map (SPM) Image time-series Parameter estimates Design matrix Template Kernel Gaussian field theory p <0.05 Statisticalinference

Categorical designs Subtraction - Pure insertion, evoked / differential responses Conjunction - Testing multiple hypotheses Parametric designs Linear - Adaptation, cognitive dimensions Nonlinear- Polynomial expansions, neurometric functions Factorial designs Categorical- Interactions and pure insertion Parametric- Linear and nonlinear interactions - Psychophysiological Interactions Overview

Aim: –Neuronal structures underlying a single process P? Procedure: – Contrast: [Task with P] – [control task without P ] = P  the critical assumption of „pure insertion“ -  Neuronal structures computing face recognition? Example: Cognitive subtraction

-  P implicit in control task ? „Queen!“ „Aunt Jenny?“ „Queen!“ „Aunt Jenny?“ „Related“ stimuli „Related“ stimuli -  Several components differ! „Distant“ stimuli „Distant“ stimuli Name Person! Name Gender! -  Interaction of process and task ? -  Interaction of process and task ? Same stimuli, different task Same stimuli, different task Cognitive subtraction: Baseline problems

Parahippocampal responses to words BOLD EPI fMRI at 2T, TR 3.2sec. Words presented every 16 secs; (i) studied words or (ii) new words SPM{F} testing for evoked responses “Baseline” here corresponds to session mean “Baseline” here corresponds to session mean Estimation depends crucially on inclusion of null events or long SOAs Estimation depends crucially on inclusion of null events or long SOAs “Cognitive” interpretation difficult, but useful to define regions generally involved in the task “Cognitive” interpretation difficult, but useful to define regions generally involved in the task Evoked responses

Differential responses Parahippocampal responses to words BOLD EPI fMRI at 2T, TR 3.2sec. Words presented every 16 secs; (i) studied words or (ii) new words SPM{F} testing for evoked responses Peri-stimulus time {secs} SPM{F} testing for differences studied words new words

Experimental design Word generationG Word repetitionR R G R G R G R G R G R G G - R = Intrinsic word generation …under assumption of pure insertion A categorical analysis

Categorical designs Subtraction - Pure insertion, evoked / differential responses Conjunction - Testing multiple hypotheses Parametric designs Linear - Adaptation, cognitive dimensions Nonlinear- Polynomial expansions, neurometric functions Factorial designs Categorical- Interactions and pure insertion Parametric- Linear and nonlinear interactions - Psychophysiological Interactions Overview

One way to minimise the baseline/pure insertion problem is to isolate the same process by two or more separate comparisons, and inspect the resulting simple effects for commonalities A test for such activation common to several independent contrasts is called “conjunction” Conjunctions can be conducted across a whole variety of different contexts: tasks stimuli senses (vision, audition) etc. Note: the contrasts entering a conjunction must be orthogonal ! Conjunctions

Example: Which neural structures support object recognition, independent of task (naming vs viewing)? A1A2 B2B1 Task (1/2) Viewing Naming Stimuli (A/B) Objects Colours Visual ProcessingV Object Recognition R Phonological RetrievalP Object viewingV,R Colour viewingV Object namingP,V,R Colour namingP,V (Object - Colour viewing) [ ] & (Object - Colour naming) [ ] [ V,R - V ] & [ P,V,R - P,V ] = R & R = R Price et al Common object recognition response (R )

Conjunctions

Test of global null hypothesis: Significant set of consistent effectsTest of global null hypothesis: Significant set of consistent effects  “Which voxels show effects of similar direction (but not necessarily individual significance) across contrasts?”  Null hypothesis: No contrast is significant: k = 0  does not correspond to a logical AND ! Test of conjunction null hypothesis: Set of consistently significant effectsTest of conjunction null hypothesis: Set of consistently significant effects  “Which voxels show, for each specified contrast, significant effects?”  Null hypothesis: Not all contrasts are significant: k < n  corresponds to a logical AND A1-A2 B1-B2 p(A1-A2) <  + p(B1-B2) <  + Friston et al.Neuroimage, 25: Friston et al. (2005). Neuroimage, 25: Nichols et al. (2005). Neuroimage, 25: Two types of conjunctions

SPM offers both conjunctions Friston et al.Neuroimage, 25: Friston et al. 2005, Neuroimage, 25: specificity Global null: k = 0 Conjunction null: k < n

F-test vs. conjunction based on global null Friston et al.Neuroimage, 25: Friston et al. 2005, Neuroimage, 25:

Using the conjunction null is easy to interpret, but can be very conservative Friston et al.Neuroimage, 25: Friston et al. 2005, Neuroimage, 25:

Categorical designs Subtraction - Pure insertion, evoked / differential responses Conjunction - Testing multiple hypotheses Parametric designs Linear - Adaptation, cognitive dimensions Nonlinear- Polynomial expansions, neurometric functions Factorial designs Categorical- Interactions and pure insertion Parametric- Linear and nonlinear interactions - Psychophysiological Interactions Overview

Parametric designs Parametric designs approach the baseline problem by: –Varying the stimulus-parameter of interest on a continuum, in multiple (n>2) steps... –... and relating measured BOLD signal to this parameter Possible tests for such relations are manifold: Linear Nonlinear: Quadratic/cubic/etc. (polynomial expansion) Model-based (e.g. predictions from learning models)

Parametric modulation of regressors → inverted ‘U’ response to increasing word presentation rate in the DLPFC SPM{F} Polynomial expansion: f(x) ~ f(x) ~ b 1 x + b 2 x 2 + b 3 x 3... Linear Quadratic F-contrast [0 1 0] on quadratic parameter SPM5 offers polynomial expansion as option for parametric modulation of regressors Büchel et al. 1996, NeuroImage 4:60-66

Parametric modulation of regressors by time Büchel et al. 1998, NeuroImage 8:

“User-specified” parametric modulation of regressors Polynomial expansion & orthogonalisation Büchel et al. 1998, NeuroImage 8:

Investigating neurometric functions (= relation between a stimulus property and the neuronal response) Stimulus awareness Stimulus intensity Pain intensity Pain threshold: 410 mJ P1 P2 P3 P4 P0-P4: Variation of intensity of a laser stimulus applied to the right hand (0, 300, 400, 500, and 600 mJ) Büchel et al. 2002, J. Neurosci. 22:

Neurometric functions  Stimulus presence  Pain intensity  Stimulus intensity Büchel et al. 2002, J. Neurosci. 22:

Model-based regressors general idea: generate predictions from a computational model, e.g. of learning or decision-making Commonly used models: –Rescorla-Wagner learning model –temporal difference (TD) learning model use these predictions to define regressors include these regressors in a GLM and test for significant correlations with voxel-wise BOLD responses

TD model of reinforcement learning appetitive conditing with pleasant taste reward activity in ventral striatum and OFC correlated with TD prediction error at the time of the CS O‘Doherty et al. 2003, Neuron

Categorical designs Subtraction - Pure insertion, evoked / differential responses Conjunction - Testing multiple hypotheses Parametric designs Linear - Adaptation, cognitive dimensions Nonlinear- Polynomial expansions, neurometric functions Factorial designs Categorical- Interactions and pure insertion Parametric- Linear and nonlinear interactions - Psychophysiological Interactions Overview

Main effects and interactions A1A2 B2B1 Task (1/2) Viewing Naming Stimuli (A/B) Objects Colours Colours Objects Colours Objects Colours Objects Colours Objects interaction effect (Stimuli x Task) (Stimuli x Task) ViewingNaming Main effect of task:(A1 + B1) – (A2 + B2)Main effect of task:(A1 + B1) – (A2 + B2) Main effect of stimuli: (A1 + A2) – (B1 + B2)Main effect of stimuli: (A1 + A2) – (B1 + B2) Interaction of task and stimuli: Can show a failure of pure insertionInteraction of task and stimuli: Can show a failure of pure insertion (A1 – B1) – (A2 – B2)

Parametric interactions p < 0.05, corrected random effects, n=16 during learning, predictive tones -increasingly activate V1 & PUT in the absence of predicted visual stimuli, -increasingly deactivate V1 & PUT in the presence of predicted vis. stimuli Significant four-way interaction in V1 and putamen: V1 PUT A+A- V+V-V+V-V- V-V+V- A+A- Primary visual cortex Rescorla-Wagner learning curves (  =0.075): den Ouden et al. 2009, Cereb. Cortex

Psycho-physiological interactions (PPI) We can replace one main effect in the GLM by the time series of an area that shows this main effect. E.g. let's replace the main effect of stimulus type by the time series of area V1: Task factor Task A Task B Stim 1 Stim 2 Stimulus factor T A /S 1 T B /S 1 T A /S 2 T B /S 2 GLM of a 2x2 factorial design: main effect of task main effect of stim. type interaction main effect of task V1 time series  main effect of stim. type psycho- physiological interaction

PPI example: attentional modulation of V1→V5 attention no attention V1 activity V5 activity SPM{Z} time V5 activity Friston et al. 1997, NeuroImage 6: Büchel & Friston 1997, Cereb. Cortex 7: V1 V1 x Att. = V5 V5 Attention

PPI: interpretation Two possible interpretations of the PPI term: V1 Modulation of V1  V5 by attention Modulation of the impact of attention on V5 by V1. V1V5 V1 V5 attention V1 attention

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