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Resting State tutorial #23

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Resting State tutorial #23

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1 change: 0 additions & 1 deletion docs/Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,6 @@ Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
HypothesisTests = "09f84164-cd44-5f33-b23f-e6b0d136a0d5"
MetaGraphs = "626554b9-1ddb-594c-aa3c-2596fe9399a5"
Neuroblox = "769b91e5-4c60-41ee-bfae-153c84203cb2"
Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"

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23 changes: 18 additions & 5 deletions docs/src/tutorials/resting_state_wb.md
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Expand Up @@ -22,12 +22,12 @@ using DataFrames
using MetaGraphs
using DifferentialEquations
using Random
using Plots
using CairoMakie
using Statistics
using HypothesisTests

# read connection matrix from file
weights = CSV.read("../data/weights.csv",DataFrame)
weights = CSV.read("data/weights.csv",DataFrame)
region_names = names(weights)

wm = Array(weights)
Expand Down Expand Up @@ -55,7 +55,14 @@ To solve the system, we first create an Stochastic Differential Equation Problem
```@example resting-state-circuit
prob = SDEProblem(sys,rand(-2:0.1:4,76*2), (0.0, 6e5), [])
sol = solve(prob, EulerHeun(), dt=0.5, saveat=5)
plot(sol.t,sol[5,:],xlims=(1000,10000))

# show time series of one of the neural mass models
fig1 = Figure()
ax1 = Axis(fig1[1,1], xlabel="time in ms", ylabel="FH NMM #5")
xlims!(5000,10000)
ylims!(-0.5,0.2)
lines!(sol.t,sol[5,:])
fig1
```
To evaluate the connectivity of our simulated resting state network, we calculate the statistically significant correlations

Expand All @@ -73,10 +80,16 @@ for i in 1:76
p[i,j] = pvalue(OneSampleTTest(css[i,j,:]))
end
end
heatmap(log10.(p) .* (p .< 0.05),aspect_ratio = :equal)
fig2 = Figure()
ax2 = Axis(fig2[1,1], xlabel="regions", ylabel="regions",title="Simulated correlations")
heatmap!(log10.(p) .* (p .< 0.05),aspect_ratio = :equal)
fig2
```
Fig.: log10(p value) displaying statistally significant correlation between time series
```@example resting-state-circuit
heatmap(wm,aspect_ratio = :equal)
fig3 = Figure()
ax3 = Axis(fig3[1,1], xlabel="regions", ylabel="regions",title="HCP connection strength")
heatmap!(wm,aspect_ratio = :equal)
fig3
```
Fig.: Connection Adjacency Matrix that was used to connect the neural mass models
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