Understanding Functional Specialization in the Brain through DCM Analysis

1. The… U M D S’ GUIDE to M I E DCM Velia Cardin

2. Functional Specialization is a question of Where? • Wherein the brain is a certain cognitive/perceptual attribute processed? • What are the Regionally specific effects • your normal SPM analysis (GLM)

3. Functional Integration is a question of HOW How does the system work? What are the inter-regional effects? How do the components of the system interact with each other? Experimentally designed input

4. DCM overview • DCM allows you model brain activity at the neuronal level (which is not directly accessible in fMRI) taking into account the anatomical architecture of the system and the interactions within that architecture under different conditions of stimulus input and context. • The modelled neuronal dynamics (z) are transformed into area-specific BOLD signals (y) by a hemodynamic forward model (λ). The aim of DCM is to estimate parameters at the neuronal level so that the modelled BOLD signals are most similar to the experimentally measured BOLD signals.

5. Hypothesis abouta neural system The DCM cycle Statistical test on parameters of optimal model Definition of DCMs as systemmodels Bayesian modelselection of optimal DCM Design a study thatallows to investigatethat system Parameter estimationfor all DCMs considered Data acquisition Extraction of time seriesfrom SPMs

6. Planning a DCM-compatible study • Suitable experimental design: • preferably multi-factorial (e.g. 2 x 2) • e.g. one factor that varies the driving (sensory) input • and one factor that varies the contextual input • Hypothesis and model: • define specific a priori hypothesis • which parameters are relevant to test this hypothesis? • ensure that intended model is suitable to test this hypothesis → simulations before experiment • define criteria for inference

7. Timing problems at long TRs • Two potential timing problems in DCM: • wrong timing of inputs • temporal shift between regional time series because of multi-slice acquisition 2 slice acquisition 1 visualinput • DCM is robust against timing errors up to approx. ± 1 s • compensatory changes of σ and θh • Possible corrections: • slice-timing (not for long TRs) • restriction of the model to neighbouring regions • in both cases: adjust temporal reference bin in SPM defaults (defaults.stats.fmri.t0)

8. Defining your hypothesis Hypothesis A attention modulates V5 directly When attending to motion……. + Parietal areas + V5 Hypothesis B Attention modulates effective connectivity between PPC to V5 V1

9. Indirect influence Pulvinar • Evaluate whether DCM can answer your question • Can DCM distinguish between your hypotheses? Parietal areas V5 Direct influence V1 DCM cannot distinguish between direct and indirect! Hypotheses of this nature cannot be tested In case of

10. Practical steps of a DCM study - I • Definition of the hypothesis & the model (on paper!) • Structure: which areas, connections and inputs? • Which parameters represent my hypothesis? • How can I demonstrate the specificity of my results? • What are the alternative models to test? • Defining criteria for inference: • single-subject analysis: stat. threshold? contrast? • group analysis: which 2nd-level model? • Conventional SPM analysis (subject-specific) • DCMs are fitted separately for each session → for multi-session experiments, consider concatenation of sessions or adequate 2nd level analysis

11. Practical steps of a DCM study - II • Extraction of time series, e.g. via VOI tool in SPM • cave: anatomical & functional standardisation important for group analyses! • Possibly definition of a new design matrix, if the “normal” design matrix does not represent the inputs appropriately. • NB: DCM only reads timing information of each input from the design matrix, no parameter estimation necessary. • Definition of model • via DCM-GUI or directlyin MATLAB

12. Practical steps of a DCM study - III • DCM parameter estimation • cave: models with many regions & scans can crash MATLAB! • Model comparison and selection: • Which of all models considered is the optimal one? Bayesian model selection • Testing the hypothesisStatistical test onthe relevant parametersof the optimal model

13. Attention to motion in the visual system Stimuli 250 radially moving dots at 4.7 degrees/s Pre-Scanning 5 x 30s trials with 5 speed changes (reducing to 1%) Task - detect change in radial velocity Scanning(no speed changes) 6 normal subjects, 4 x 100 scan sessions; each session comprising 10 scans of 4 different conditions F A F N F A F N S ................. F - fixation point only A - motion stimuli with attention (detect changes) N - motion stimuli without attention S - no motion PPC V3A V5+ Attention – No attention Büchel & Friston 1997, Cereb. Cortex Büchel et al.1998, Brain

14. Specify design matrix • Normal SPM regressors • -no motion, no attention • -motion, no attention • -no motion, attention • -motion, attention • DCM analysis regressors • -no motion (photic) • -motion • -attention

15. Defining VOIs • Single subject: choose co-ordinates from appropriate contrast. • e.g. V5 from motion vs. no motion • RFX: DCM performed at 1st level, but define group maximum for area of interest, then in single subject find nearest local maximum to this using the same contrast and a liberal threshold (e.g. P<0.05, uncorrected).

16. DCM button ‘specify’ NB: in order!

17. Can select: • effects of each condition • intrinsic connections • contrast of connections

18. Input (C)

19. Output Latent (intrinsic) connectivity (A)

20. Photic Motion Attention Modulation of connections (B)

21. SPC V1 IFG V5 A simple DCM of the visual system Attention • Visual inputs drive V1, activity then spreads to hierarchically arranged visual areas. • Motion modulates the strength of the V1→V5 forward connection. • The intrinsic connection V1→V5 is insignificant in the absence of motion (a21=-0.05). • Attention increases the backward-connections IFG→SPC and SPC→V5. 0.55 0.26 0.72 0.37 0.56 0.42 Motion 0.66 0.88 -0.05 Photic 0.48 Re-analysis of data fromFriston et al., NeuroImage 2003

22. SPC SPC V1 V1 V5 V5 Comparison of three simple models Model 1:attentional modulationof V1→V5 Model 2:attentional modulationof SPC→V5 Model 3:attentional modulationof V1→V5 and SPC→V5 Attention Attention Photic Photic Photic SPC 0.55 0.03 0.85 0.86 0.85 0.70 0.75 0.70 0.84 1.36 1.42 1.36 0.89 0.85 V1 -0.02 -0.02 -0.02 0.56 0.57 0.57 V5 Motion Motion Motion 0.23 0.23 Attention Attention Bayesian model selection: Model 1 better than model 2, model 1 and model 3 equal → Decision for model 1: in this experiment, attention primarily modulates V1→V5

23. Bayes Information Criterion (BIC) and Akaike’s Information Criterion (AIC) BIC is biased towards simple models AIC is biased towards complex ones Make a decision if both factors are in agreement, in particular if Both provide factors of at least e (2.7183) Penny et al. 2004, NeuroImage

24. DCM button ‘compare’ The read-out in MatLab indicates which model is most likely

25. DCM is not exploratory! • DCM is tricky….. ASK the experts!!! Thanks to Klaas, Ollie and Barrie 