DCM for evoked responses Ryszard Auksztulewicz SPM for
- Slides: 58
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
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