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

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

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

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.

The DCM cycle Design a study that allows to investigate that system Extraction of time series from SPMs Parameter estimation for all DCMs considered Bayesian model selection of optimal DCM Statistical test on parameters of optimal model Hypothesis about a neural system Definition of DCMs as system models Data acquisition

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

Timing problems at long TRs Two potential timing problems in DCM: 1.wrong timing of inputs 2.temporal shift between regional time series because of multi-slice acquisition DCM is robust against timing errors up to approx. ± 1 s –compensatory changes of σ and θ h 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) 1 2 slice acquisition visual input

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

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

Practical steps of a DCM study - I 1.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? 2.Defining criteria for inference: single-subject analysis:stat. threshold? contrast? group analysis:which 2 nd -level model? 3.Conventional SPM analysis (subject-specific) DCMs are fitted separately for each session → for multi-session experiments, consider concatenation of sessions or adequate 2 nd level analysis

Practical steps of a DCM study - II 4.Extraction of time series, e.g. via VOI tool in SPM cave: anatomical & functional standardisation important for group analyses! 5.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. 6.Definition of model via DCM-GUI or directly in MATLAB

7.DCM parameter estimation cave: models with many regions & scans can crash MATLAB! 8.Model comparison and selection: Which of all models considered is the optimal one?  Bayesian model selection 9.Testing the hypothesis Statistical test on the relevant parameters of the optimal model Practical steps of a DCM study - III

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 Büchel & Friston 1997, Cereb. Cortex Büchel et al. 1998, Brain V5+ PPC V3A Attention – No attention Attention to motion in the visual system

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

Defining VOIs Single subject: choose co-ordinates from appropriate contrast. e.g. V5 from motion vs. no motion RFX: DCM performed at 1 st 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).

DCM button ‘specify’ NB: in order!

Can select: -effects of each condition -intrinsic connections -contrast of connections

Input (C)

Output Latent (intrinsic) connectivity (A)

Modulation of connections (B) Photic Attention Motion

V1IFGV5 SPC Motion Photic Attention Visual inputs drive V1, activity then spreads to hierarchically arranged visual areas.Visual inputs drive V1, activity then spreads to hierarchically arranged visual areas. Motion modulates the strength of the V1→V5 forward connection.Motion modulates the strength of the V1→V5 forward connection. The intrinsic connection V1→V5 is insignificant in the absence of motion (a 21 =- 0.05).The intrinsic connection V1→V5 is insignificant in the absence of motion (a 21 =- 0.05). Attention increases the backward-connections IFG→SPC and SPC→V5.Attention increases the backward-connections IFG→SPC and SPC→V5. A simple DCM of the visual system Re-analysis of data from Friston et al., NeuroImage 2003

V1 V5 SPC Motion Photic Attention V1V5SPC Motion Photic Attention V1 V5SPC Motion Photic Attention Attention 0.23 Model 1: attentional modulation of V1→V5 Model 2: attentional modulation of SPC→V5 Model 3: attentional modulation of V1→V5 and SPC→V5 Comparison of three simple models 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

Penny et al. 2004, NeuroImage 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)

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

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