1、Experimental Design,Christian Ruff With thanks to: Rik Henson Daniel Glaser,Realignment,Smoothing,Normalisation,General linear model,Statistical parametric map (SPM),Image time-series,Parameter estimates,Design matrix,Template,Kernel,Gaussian field theory,p 0.05,Statistical inference,Overview,Catego

2、rical 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 Pa

3、rametric- Linear and nonlinear interactions - Psychophysiological Interactions,Aim: Brain structures underlying process P? Procedure: Contrast: Task with P control task without P = P the critical assumption of pure insertion“,Cognitive Subtraction,Cognitive Subtraction: Baseline-problems,- Several c

4、omponents differ !,Distant“ stimuli,Differential event-related fMRI,Inferotemporal response to faces,SPMF testing for evoked responses,Evoked responses: Rest“ baseline,“Baseline” here corresponds to session mean (and thus processing during “rest”) Null events or long SOAs essential for estimation “C

5、ognitive” interpretation hardly possible, but useful to define regions generally involved in the task,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,Overview,Categorical designs

6、 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- Lin

7、ear and nonlinear interactions - Psychophysiological Interactions,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 indepe

8、ndent contrasts is called “Conjunction” Conjunctions can be conducted across a whole variety of different contexts: tasks stimuli senses (vision, audition) etc. But the contrasts entering a conjunction have to be truly independent!,Conjunctions,Example: Which neural structures support object recogni

9、tion, independent of task (naming vs viewing)?,Visual Processing V Object Recognition R Phonological Retrieval P (Object - Colour viewing) see later),Conjunctions,Price et al, 1997,Common object recognition response (R),Conjunctions,SPM8 offers two general ways to test the significance of conjunctio

10、ns: Test of global null hypothesis: Significant set of consistent effects “which voxels show effects of similar direction (but not necessarily individual significance) across contrasts?” Test of conjunction null hypothesis: Set of consistently significant effects “which voxels show, for each specifi

11、ed contrast, effects threshold?” Choice of test depends on hypothesis and congruence of contrasts; the global null test is more sensitive (i.e., when direction of effects hypothesised),Two flavours of inference about conjunctions,Friston et al. (2005). Neuroimage, 25:661-7. Nichols et al. (2005). Ne

12、uroimage, 25:653-60.,Overview,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

13、- Interactions and pure insertion Parametric- Linear and nonlinear interactions - Psychophysiological Interactions,Parametric Designs: General Approach,Parametric designs approach the baseline problem by: Varying a stimulus-parameter of interest on a continuum, in multiple (n2) steps. . and relating

14、 blood-flow to this parameter Flexible choice of tests for such relations : Linear Nonlinear: Quadratic/cubic/etc. Data-driven“ (e.g., neurometric functions) Model-based,Adaptation: Linear effects of time?,A linear parametric contrast,Adaptation: Nonlinear effects of time?,A nonlinear parametric con

15、trast,Inverted U response to increasing word presentation rate in the DLPFC,SPMF,Polynomial expansion: f(x) b1 x + b2 x2 + . up to (N-1)th order for N levels,E.g, F-contrast 0 1 0 on Quadratic Parameter =,Nonlinear parametric design matrix,(SPM8 GUI offers polynomial expansion as option during creat

16、ion of parametric modulation regressors),Linear change,Quadratic change,Mean response,Parametric Designs: Neurometric functions,Rees, G., et al. (1997). Neuroimage, 6: 27-78,versus,Inverted U response to increasing word presentation rate in the DLPFC,Rees, G., et al. (1997). Neuroimage, 6: 27-78,Par

17、ametric Designs: Neurometric functions,Coding of tactile stimuli in Anterior Cingulate Cortex: Stimulus (a) presence, (b) intensity, and (c) pain intensity Variation of intensity of a heat stimulus applied to the right hand (300, 400, 500, and 600 mJ),Bchel et al. (2002). The Journal of Neuroscience

18、, 22: 970-6,Assumptions:,Parametric Designs: Neurometric functions,Bchel et al. (2002). The Journal of Neuroscience, 22: 970-6, Stimulus presence, Pain intensity, Stimulus intensity,Parametric Designs: Model-based regressors,Seymour, ODoherty, et al. (2004). Nature.,Overview,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 i