DCM for evoked responses

DCM for evoked responses Ryszard Auksztulewicz SPM for M/EEG course, 2014

? Does network XYZ explain my data better than network XY? input input

? Does network XYZ explain my data better than network XY?Which XYZ connectivity structure best explains my data? input input

? Does network XYZ explain my data better than network XY?Which XYZ connectivity structure best explains my data?Are X & Y linked in a bottom-up, top-down or recurrent fashion? input input

? Does network XYZ explain my data better than network XY?Which XYZ connectivity structure best explains my data?Are X & Y linked in a bottom-up, top-down or recurrent fashion?Is my effect driven by extrinsic or intrinsic connections? context input

? Does network XYZ explain my data better than network XY?Which XYZ connectivity structure best explains my data?Are X & Y linked in a bottom-up, top-down or recurrent fashion?Is my effect driven by extrinsic or intrinsic connections?Which neural populations are affected by contextual factors? context input

? Does network XYZ explain my data better than network XY?Which XYZ connectivity structure best explains my data?Are X & Y linked in a bottom-up, top-down or recurrent fashion?Is my effect driven by extrinsic or intrinsic connections?Which neural populations are affected by contextual factors?Which connections determine observed frequency coupling? context input

? Does network XYZ explain my data better than network XY?Which XYZ connectivity structure best explains my data?Are X & Y linked in a bottom-up, top-down or recurrent fashion?Is my effect driven by extrinsic or intrinsic connections?Which neural populations are affected by contextual factors?Which connections determine observed frequency coupling?How changing a connection/parameter would influence data? context input

The DCM analysis pathway Build model(s) Fit your model parameters to the data Pick the best model Make an inference (conclusion) Collect data

Data for DCM for ERPs / ERFs • Downsample • Filter (e.g. 1-40Hz) • Epoch • Remove artefacts • Average • Per subject • Grand average • Plausible sources • Literature / a priori • Dipole fitting / 3D source reconstruction

The DCM analysis pathway Build model(s) Fit your model parameters to the data Pick the best model Make an inference (conclusion) Collect data

The DCM analysis pathway ‘Hardwired’ model features Build model(s) Fit your model parameters to the data Pick the best model Make an inference (conclusion) Collect data

Models

Neuronal (source) model Kiebel et al., 2008

NEURAL MASS MODEL CANONICAL MICROCIRCUIT Pyr Inhib Inter L2/3 xv Spiny Stell Spiny Stell mv L4 Inhib Inter Pyr Pyr L5/6 spm_fx_erp spm_fx_cmc

Canonical Microcircuit Model (‘CMC’) Output equation: Supra-granular Layer Granular Layer Infra-granular Layer Pinotsis et al., 2012

Canonical Microcircuit Model (‘CMC’) Bastos et al. (2012) Pinotsis et al. (2012)

Canonical Microcircuit Model (‘CMC’) Supra- granular Layer Granular Layer Infra-granular Layer

Canonical Microcircuit Model (‘CMC’) Inhibitory Interneurons Superficial Pyramidal Cells Supra-granular Layer Spiny Stellate Cells Granular Layer Infra-granular Layer Deep Pyramidal Cells Pinotsis et al., 2012

Canonical Microcircuit Model (‘CMC’) Voltage change rate: f(current) Current change rate: f(voltage,current) Pinotsis et al., 2012

Canonical Microcircuit Model (‘CMC’) Voltage change rate: f(current) Current change rate: f(voltage,current) H, τKernels: pre-synaptic inputs -> post-synaptic membrane potentials [ H: max PSP; τ: rate constant ] S Sigmoid operator: PSP -> firing rate David et al., 2006; Pinotsis et al., 2012

Canonical Microcircuit Model (‘CMC’) Supra-granular Layer Granular Layer Infra-granular Layer Pinotsis et al., 2012

The DCM analysis pathway ‘Hardwired’ model features Build model(s) Fit your model parameters to the data Pick the best model Make an inference (conclusion) Collect data

5 4 3 2 1

5 4 3 2 1 Input

Factor 1 5 4 3 2 1 Input

Factor 1 Factor 2 5 4 3 2 1 Input

The DCM analysis pathway Fixed parameters Build model(s) Fit your model parameters to the data Pick the best model Make an inference (conclusion) Collect data

Fitting DCMs to data

Fitting DCMs to data H. Brown

Fitting DCMs to data • Check your data H. Brown

Fitting DCMs to data • Check your data • Check your sources H. Brown

OFC OFC A19 IPL A19 IPL V4 V4 Model 1 Fitting DCMs to data • Check your data • Check your sources • Check your model IPL IPL V4 V4 Model 2 H. Brown

Fitting DCMs to data • Check your data • Check your sources • Check your model • Re-run model fitting H. Brown

The DCM analysis pathway Fixed parameters Build model(s) Fit your model parameters to the data Pick the best model Make an inference (conclusion) Collect data

? Does network XYZ explain my data better than network XY?Which XYZ connectivity structure best explains my data?Are X & Y linked in a bottom-up, top-down or recurrent fashion?Is my effect driven by extrinsic or intrinsic connections?Which connections/populations are affected by contextual factors? context input

Example #1: Architecture of MMN Garrido et al., 2008

Example #2: Role of feedback connections Garrido et al., 2007

DCM for evoked responses