# The study's entry recipe: `tvbo run JR_tvboptim.yaml`. Its directory is the study root. tvbo_class: tvbo:SimulationStudy citekey: JR_tvboptim title: "Reproducing MEG resting-state frequency gradients with network dynamics" label: "Jansen-Rit MEG frequency gradient: fitting region-specific time constants to a spatial frequency gradient" description: >- Fits the region-specific Jansen-Rit time constants a and b so that each region's simulated peak frequency matches a target gradient running from visual cortex (11 Hz) to the most distant association areas (7 Hz). The target is not supplied to the fit: it is derived from the connectome's own tract lengths to bilateral lateral occipital cortex, so the whole experiment is reproducible from this recipe and the curated database alone. experiments: - tvbo:experiment/JR_MEG_FrequencyGradient_Optimization # The same curated experiment without its settle: the approach from the initial state is what the fitted run deliberately discards. - iri: tvbo:experiment/JR_MEG_FrequencyGradient_Optimization id: 2 label: "Startup transient" description: "The network's approach to its limit cycle, before the settle is cut away." integration: transient_time: 0.0 duration: 1000.0 optimizations: null explorations: null figures: - name: jr_frequency_gradient label: "Jansen-Rit MEG frequency gradient optimization" description: >- Region-specific time constants fitted so the network reproduces a posterior-to-anterior peak-frequency gradient, with the target derived from the connectome's own tract lengths. layout: | aabbccdd aabbccdd eeeeffgg eeeeffgg width: 260.0 height: 130.0 font_size: 7.0 panel_numbers: true panels: a: kind: grid label: "Network dynamics" description: >- The y0 trajectory of every region, shaded by node: the approach from the initial state, and the settled activity the spectra are computed from. grid: ncols: 1 cell: kind: cartesian ylabel: "y0 [a.u.]" cells: - kind: cartesian layers: - used: {experiment: 2, output: integration__y0_trace} mark: line encoding: {color: integration__y0_mean} style: {colormap: cividis, opts: {linewidth: 0.4}} annotations: - text: Transient loc: "upper right" ylabel: "y0 [a.u.]" hide_xticklabels: true - kind: cartesian layers: - used: {experiment: 1, output: integration__y0_trace} mark: line encoding: {color: integration__y0_mean} style: {colormap: cividis, opts: {linewidth: 0.4}} annotations: - text: Stabilized loc: "upper right" xlabel: "Sample" ylabel: "y0 [a.u.]" b: kind: cartesian label: "Power spectral density" description: "Mean spectrum across regions; the alpha peak the fit moves region by region." layers: - used: {experiment: 1, output: integration__simulated_psd} mark: line encoding: {x: integration__psd_frequencies, color: node} style: {colormap: cividis, opacity: 0.18, opts: {linewidth: 0.4}} - used: {experiment: 1, output: integration__avg_spectrum} mark: line encoding: {x: integration__psd_frequencies} style: {color: "#000000", opts: {linewidth: 1.4}} annotations: - text: "Peak @ {:.1f} Hz" used: {experiment: 1, output: integration__peak_frequency} loc: "upper right" xlabel: "Frequency [Hz]" ylabel: "Power" yscale: log xlim: [0, 20] c: kind: heatmap label: "Parameter landscape and fitted values" description: "Peak frequency over the (a, b) plane, with each region's fitted pair marked." layers: - used: {experiment: 1, output: results} encoding: {x: JansenRit.a, y: JansenRit.b} style: {colormap: cividis} - used: {experiment: 1, output: optimization__spectral_gradient_fit__fitted__dynamics__b} mark: scatter encoding: {x: optimization__spectral_gradient_fit__fitted__dynamics__a} style: {color: white, opts: {s: 14, edgecolors: black, linewidths: 0.4, zorder: 5}} xlabel: "a [ms^-1]" ylabel: "b [ms^-1]" colorbar: show: true label: "Peak frequency [Hz]" d: kind: cartesian label: "Target versus simulated" description: >- Every region before and after the fit; the dashed line is agreement, and fitting collapses the cloud onto it. layers: - used: {experiment: 1, output: integration__peak_frequencies} mark: scatter label: Before encoding: {x: integration__target_peak_frequencies} style: {color: "#b0b0b0", opts: {s: 12, alpha: 0.7, edgecolors: k, linewidths: 0.3}} - used: {experiment: 1, output: optimization__spectral_gradient_fit__observation__peak_frequencies} mark: scatter label: After encoding: {x: optimization__spectral_gradient_fit__observation__target_peak_frequencies} style: {color: royalblue, opts: {s: 12, alpha: 0.7, edgecolors: k, linewidths: 0.3}} xlabel: "Target [Hz]" ylabel: "Simulated [Hz]" xlim: [6, 12] ylim: [6, 12] aspect: equal legend: "upper left" rules: - {orientation: diagonal, at: [1.0, 0.0]} e: kind: grid label: "Brain frequency maps" description: "Initial, target and fitted peak frequency painted into the Desikan-Killiany volume." grid: col_labels: [Initial, Target, Fitted] cell: kind: volume volume: atlas: tvbo:atlas/DesikanKilliany view: sagittal colormap: cividis_r vmin: 6.5 vmax: 11.5 layers: - used: {experiment: 1, output: integration__peak_frequencies} - used: {experiment: 1, output: integration__target_peak_frequencies} - used: {experiment: 1, output: optimization__spectral_gradient_fit__observation__peak_frequencies} f: kind: surface label: "Target gradient" description: "The target gradient on the fsaverage cortical surface." layers: - used: {experiment: 1, output: integration__target_peak_frequencies} surface: atlas: tvbo:atlas/DesikanKilliany template: fsaverage hemi: both view: dorsal colormap: cividis_r symmetric: false vmin: 6.5 vmax: 11.5 g: kind: surface label: "Fitted gradient" description: "The fitted gradient on the same surface and colour scale." layers: - used: {experiment: 1, output: optimization__spectral_gradient_fit__observation__peak_frequencies} surface: atlas: tvbo:atlas/DesikanKilliany template: fsaverage hemi: both view: dorsal colormap: cividis_r symmetric: false vmin: 6.5 vmax: 11.5