DCM for evoked responses Ryszard Auksztulewicz SPM for

DCM for evoked responses Ryszard Auksztulewicz SPM for M/EEG course, 2017

? 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 Collect data Pick the best model Make an inference (conclusion)

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

Phillips et al. , 2016

Data for DCM for ERPs / ERFs 1. 2. 3. 4. 5. Downsample Filter (e. g. 1 -40 Hz) Epoch Remove artefacts Average • • Per subject Grand average 6. Plausible sources • • Literature / a priori Dipole fitting / 3 D source reconstruction

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

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

Models

Neuronal (source) model Kiebel et al. , 2008

NEURAL MASS MODEL L 2/3 Inhib Inter L 4 Spiny Stell L 5/6 Pyr spm_fx_erp

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

NEURAL MASS MODEL L 2/3 L 4 Inhib Inter CANONICAL MICROCIRCUIT x v Pyr Spiny mv Stell Spiny Stell Inhib Inter L 5/6 Pyr spm_fx_erp spm_fx_cmc

Canonical Microcircuit Model (‘CMC’) Supragranular Layer Granular Layer Infragranular Layer

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

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

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

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

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

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

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

Canonical Microcircuit Model (‘CMC’) Inhibitory Interneurons Superficial Pyramidal Cells Supragranular Layer Granular Layer Spiny Stellate Cells Infragranular 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, τ S Kernels: pre-synaptic inputs -> post-synaptic membrane potentials [ H: max PSP; τ: rate constant ] Sigmoid operator: PSP -> firing rate David et al. , 2006; Pinotsis et al. , 2012

Canonical Microcircuit Model (‘CMC’) Supragranular Layer Granular Layer Infragranular Layer Pinotsis et al. , 2012

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

5 4 3 2 1

5 4 3 2 1 Input

5 4 3 2 1 Input

5 4 3 2 1 Input

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 Collect data Pick the best model Make an inference (conclusion)

Fitting DCMs to data

Fitting DCMs to data H. Brown

Fitting DCMs to data H. Brown

Fitting DCMs to data 1. Check your data H. Brown

Fitting DCMs to data 1. Check your data 2. Check your sources H. Brown

Fitting DCMs to data 1. Check your data 2. Check your sources OFC A 19 OFC IPL A 19 V 4 IPL V 4 Model 1 3. Check your model IPL V 4 Model 2 H. Brown

Fitting DCMs to data 1. Check your data 2. Check your sources 3. Check your model 4. Re-run model fitting H. Brown

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

Phillips et al. , 2016

? 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

Example #3: Group differences Boly et al. , 2011

Example #4: Factorial design & CMC Attention cf. Feldman & Friston, 2010 FORWARD PREDICTION ERROR L 2/3 p x xx x L 4 s A 1 mx m ST G L 5/6 BACKWARD PREDICTIONS Bastos et al. , Neuron 2012 Auksztulewicz & Friston, 2015

2 x 2 design: Attended vs unattended Standard vs deviant (Only trials with 2 tones) N=20 Auksztulewicz & Friston, 2015

Expectation Flexible factorial design Thresholded at p<. 005 peak-level Corrected at a cluster-level p. FWE<. 05 stimate Attention Auksztulewicz & Friston, 2015

Contrast estimate Flexible factorial design Thresholded at p<. 005 peak-level Corrected at a cluster-level p. FWE<. 05 A 1 E 1 A 1 E 0 A 0 E 1 A 0 E 0 Auksztulewicz & Friston, 2015

Connectivity structure input Extrinsic modulation Inh Int SP input

Superficial pyramidal cells: signal prediction errors to higher areas Brown & Friston, 2010 Feldman & Friston, 2013 Inhibitory interneurons: xv mv prediction errors of hidden states Bastos et al. , 2012 xx attentional effects of ACh -> depression of I. I. activity Xiang et al. , 1998 Buia and Tiesinga, 2006 attentional gamma spike-LFP phase locking mx Vinck et al. , 2013 SPM CMC

Auksztulewicz & Friston, 2015

Fardo et al. (2017) Neuroimage

Motivate your assumptions! Rubbish data Perfect model Rubbish results Perfect data Rubbish model Rubbish results

Thank you! Karl Friston Gareth Barnes Andre Bastos Harriet Brown Jean Daunizeau Francesca Fardo Marta Garrido Stefan Kiebel Vladimir Litvak Rosalyn Moran Will Penny Dimitris Pinotsis Bernadette van Wijk