DCM for evoked responses Harriet Brown SPM for M/EEG course, 2013.

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Presentation transcript:

DCM for evoked responses Harriet Brown SPM for M/EEG course, 2013

The DCM analysis pathway

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

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

Data for DCM for ERPs 1.Downsample 2.Filter (1-40Hz) 3.Epoch 4.Remove artefacts 5.Average

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

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

Models

Standard 3-population model (‘ERP’)

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

Canonical Microcircuit Model (‘CMC’)

Granular Layer Supra- granular Layer Infra- granular Layer

Superficial Pyramidal Cells Superficial Pyramidal Cells Canonical Microcircuit Model (‘CMC’) Granular Layer Supra- granular Layer Infra- granular Layer Spiny Stellate Cells Deep Pyramidal Cells Deep Pyramidal Cells Inhibitory Interneurons

Canonical Microcircuit Model (‘CMC’) Granular Layer Supra- granular Layer Infra- granular Layer Superficial Pyramidal Cells Superficial Pyramidal Cells Spiny Stellate Cells Deep Pyramidal Cells Deep Pyramidal Cells Inhibitory Interneurons

Canonical Microcircuit Model (‘CMC’) Granular Layer Supra- granular Layer Infra- granular Layer Superficial Pyramidal Cells Superficial Pyramidal Cells Spiny Stellate Cells Deep Pyramidal Cells Deep Pyramidal Cells Inhibitory Interneurons

Canonical Microcircuit Model (‘CMC’) Granular Layer Supra- granular Layer Infra- granular Layer Superficial Pyramidal Cells Superficial Pyramidal Cells Spiny Stellate Cells Deep Pyramidal Cells Deep Pyramidal Cells Inhibitory Interneurons

Canonical Microcircuit Model (‘CMC’) Granular Layer Supra- granular Layer Infra- granular Layer Superficial Pyramidal Cells Superficial Pyramidal Cells Spiny Stellate Cells Deep Pyramidal Cells Deep Pyramidal Cells Inhibitory Interneurons

Canonical Microcircuit Model (‘CMC’) Granular Layer Supra- granular Layer Infra- granular Layer Superficial Pyramidal Cells Superficial Pyramidal Cells Spiny Stellate Cells Deep Pyramidal Cells Deep Pyramidal Cells Inhibitory Interneurons

Canonical Microcircuit Model (‘CMC’) Granular Layer Supra- granular Layer Infra- granular Layer Superficial Pyramidal Cells Superficial Pyramidal Cells Spiny Stellate Cells Deep Pyramidal Cells Deep Pyramidal Cells Inhibitory Interneurons

Canonical Microcircuit Model (‘CMC’) Granular Layer Supra- granular Layer Infra- granular Layer Superficial Pyramidal Cells Superficial Pyramidal Cells Spiny Stellate Cells Deep Pyramidal Cells Deep Pyramidal Cells Inhibitory Interneurons

Canonical Microcircuit Model (‘CMC’)

Granular Layer Supra- granular Layer Infra- granular Layer

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

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

Designing your model Area 1Area 2 Area 3 Area 4

Designing your model time (ms) input input (1) Area 1Area 2 Area 3 Area 4

Designing your model time (ms) input input (1) Area 1Area 2 Area 3 Area 4

Designing your model time (ms) input input (1) Area 1Area 2 Area 3 Area 4

Designing your model time (ms) input input (1) Area 1Area 2 Area 3 Area 4

Designing your model time (ms) input input (1) Area 1Area 2 Area 3 Area 4

Designing your model time (ms) input input (1) Area 1Area 2 Area 3 Area 4

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

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

Fitting DCMs to data

1.Check your data

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

1.Check your data 2.Check your sources 3.Check your model Model 1 V4 IPL A19 OFC V4 IPL A19 OFC V4 IPL Model 2 V4 IPL Fitting DCMs to data

1.Check your data 2.Check your sources 3.Check your model 4.Re-run model fitting

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

What questions can I ask with DCM for ERPs? Questions about functional networks causing ERPs Garrido et al. (2008)

What questions can I ask with DCM for ERPs? Questions about connectivity changes in different conditions or groups Boly et al. (2011)

What questions can I ask with DCM for ERPs? Questions about the neurobiological processes underlying ERPs mode x mode 1 peri-stimulus time (ms) x mode 2 peri-stimulus time (ms) x 10 mode 1 peri-stimulus time (ms) x 10 peri-stimulus time (ms) -3 Deep Pyramidal Cell gain changed Superficial Pyramidal Cell gain changed Parameter value V4IPLArea 18SOG Area

How to use DCM for ERPs well A DCM study is only as good as its hypotheses…