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Improving spDCM tutorial #500

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Improving spDCM tutorial #500

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8571333
updated CMC code (#474)
david-hofmann Nov 13, 2024
cac1cfb
removed some wrong lines that resulted from a rebase
david-hofmann Nov 13, 2024
94f9528
Changing edges to standard nomenclature.
david-hofmann Dec 10, 2024
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14 changes: 7 additions & 7 deletions docs/src/tutorials/spectralDCM.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -51,19 +51,19 @@ for i = 1:nr

## add Ornstein-Uhlenbeck block as noisy input to the current region
input = OUBlox(;name=Symbol("r$(i)₊ou"), σ=0.1)
add_edge!(g, input => region; :weight => 1/16) # Note that 1/16 is taken from SPM12, this stabilizes the balloon model simulation. Alternatively the noise of the Ornstein-Uhlenbeck block or the weight of the edge connecting neuronal activity and balloon model could be reduced to guarantee numerical stability.
add_edge!(g, input => region, weight=1/16) # Note that 1/16 is taken from SPM12, this stabilizes the balloon model simulation. Alternatively the noise of the Ornstein-Uhlenbeck block or the weight of the edge connecting neuronal activity and balloon model could be reduced to guarantee numerical stability.

## simulate fMRI signal with BalloonModel which includes the BOLD signal on top of the balloon model dynamics
measurement = BalloonModel(;name=Symbol("r$(i)₊bm"))
add_edge!(g, region => measurement; :weight => 1.0)
add_edge!(g, region => measurement, weight=1.0)
end
# Next we define the between-region connectivity matrix and make sure that it is diagonally dominant to guarantee numerical stability (see Gershgorin theorem).
A_true = 0.1*randn(nr, nr)
A_true -= diagm(map(a -> sum(abs, a), eachrow(A_true))) # ensure diagonal dominance of matrix
# Instead of a random matrix use the same matrix as is defined in [3]
A_true = [[-0.5 -2 0]; [0.4 -0.5 -0.3]; [0 0.2 -0.5]]
for idx in CartesianIndices(A_true)
add_edge!(g, regions[idx[1]] => regions[idx[2]]; :weight => A_true[idx[1], idx[2]])
add_edge!(g, regions[idx[1]] => regions[idx[2]], weight=A_true[idx[1], idx[2]])
end

# finally we compose the simulation model
Expand Down Expand Up @@ -125,11 +125,11 @@ for i = 1:nr
region = LinearNeuralMass(;name=Symbol("r$(i)₊lm"))
push!(regions, region)
input = ExternalInput(;name=Symbol("r$(i)₊ei"))
add_edge!(g, input => region; :weight => C)
add_edge!(g, input => region, weight=C)

## we assume fMRI signal and model them with a BalloonModel
measurement = BalloonModel(;name=Symbol("r$(i)₊bm"), lnτ=lnτ, lnκ=lnκ, lnϵ=lnϵ)
add_edge!(g, region => measurement; :weight => 1.0)
add_edge!(g, region => measurement, weight=1.0)
end

A_prior = 0.01*randn(nr, nr)
Expand All @@ -146,9 +146,9 @@ end
untune!(A, [])
for (i, idx) in enumerate(CartesianIndices(A_prior))
if idx[1] == idx[2]
add_edge!(g, regions[idx[1]] => regions[idx[2]]; :weight => -exp(A[i])/2) # -exp(A[i])/2: treatement of diagonal elements in SPM12 to make diagonal dominance (see Gershgorin Theorem) more likely but it is not guaranteed
add_edge!(g, regions[idx[1]] => regions[idx[2]], weight=-exp(A[i])/2) # -exp(A[i])/2: treatement of diagonal elements in SPM12 to make diagonal dominance (see Gershgorin Theorem) more likely but it is not guaranteed
else
add_edge!(g, regions[idx[2]] => regions[idx[1]]; :weight => A[i])
add_edge!(g, regions[idx[2]] => regions[idx[1]], weight=A[i])
end
end

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