Virtual Lesion Simulator: Connectome Lesion-Deficit Mapping

Lesion a Region

Lesion severity 70%

Cognitive Task

Live Readouts

Task performance

100%

Global efficiency

1.00

Lesioned regions

0 / 14

How it works

Each of the 14 nodes is a schematic brain region wired into a small connectome (thalamo-cortical hubs, the fronto-parietal attention network, the hippocampal memory loop, and the Broca–Wernicke language pathway). Lesioning a region sets its health h ∈ [0,1] to 1 − severity; every edge touching it loses strength accordingly:

w_eff(i,j) = w(i,j) · h(i) · h(j)
d(i,j) = 1 / w_eff(i,j)   (edge removed if w_eff < 0.05)

Distances are then the shortest weighted paths (Floyd–Warshall) through the whole graph — an intact detour can partially route around damage, exactly as collateral white-matter pathways do. Task performance uses global efficiency (Latora & Marchiori, 1994) over the task's critical node set S:

E(S) = 1/(|S|(|S|−1)) · Σ_{i≠j∈S} 1/d(i,j)
Performance = 100 · E(S) / E₀(S)   (E₀ = healthy baseline)

Click a region to lesion/restore it; the severity slider sets how damaged every lesioned region is.
Attention / Memory / Language switches which node set S drives the performance readout — highlighted in the scene.
Stimulate fires a signal pulse from that task's seed region and lets it propagate hop-by-hop; pulses die out at low-health nodes or missing edges, showing the lesion visually, not just numerically.

This is the same lesion-deficit logic used in real network neuroscience: ablate a node in a connectome model, recompute graph efficiency, and read off the predicted behavioral deficit — the approach behind "virtual lesion" studies that estimate cognitive impact of stroke or trauma before it can be observed clinically.

Healthy region Lesioned region Task-critical node

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