Human Connectome Project · Subject 100206

CRN Dynamics on Basal Ganglia Pathways

Interactive GKSL open-system transport on DTI-derived connectome subgraphs. Explore the inverted-U selectivity window predicted by CRN theory across two functionally distinct neural pathways.

T2 / T3 Dissociation

Both ports show the inverted-U transport profile (ENAQT), but respond differently to weight perturbation. In T2 (gating), shuffling edge weights destroys selectivity (G > 0) — the specific DTI weight pattern matters. In T3 (relay), surrogates perform equally well (G ≈ 0) — topology alone suffices. This double dissociation is a core prediction of CRN.

Subgraph Topology

ROI nodes20

DTI edges152

Density0.80

HemisphereLeft

CRN Architecture

Sources (BG input)3

Targets (gating out)2

Intermediate ROIs15

Disorder ε2.0

ENAQT Result

Peak R₀ selectivity7.13

κ* (optimal)1.0

Inverted-U window✓ confirmed

Topology-specific✓ G > 0

Connectome — Live GKSL Dynamics

κ (dephasing)

Layout

Scale

t = 0.00

Sources (Striatum)

Targets (Thal / Pall)

Intermediate (Cortical)

Node size ~ ρ_ii(t)

R₀(t) — Selectivity Dynamics

Inverted-U — R₀(T_end) vs κ

P_T(t) — Target Absorption

P_D(t) — Distractor Leakage

Subgraph Topology

ROI nodes~22

DTI edges~170

HemisphereLeft

PathwayThal → Motor Ctx

CRN Architecture

SourcesThalamus

TargetsPrecentral / Paracentral

Transport typeStructure-driven

Weight sensitivityLow (G ≈ 0)

Key Finding

ENAQT present✓

Topology-specific✗ G ≈ 0

InterpretationRelay, not gating

Weight shuffleNo effect

T3 — Motor Relay Pathway

T3 shows an inverted-U transport profile (ENAQT exists), but unlike T2, weight-shuffled surrogates perform equally well → graph geometry alone drives transport. CRN theory predicts this: gating circuits (T2) require weight-tuned interference, relay circuits (T3) broadcast via structure.

⚠ T3 trajectory data not loaded. Run: python crn_dump_trajectories.py --port T3 --outfile trajectories_100206_T3.json

Data source: Human Connectome Project (HCP), Subject 100206, DTI tractography. Van Essen et al. (2013). NeuroImage. doi:10.1016/j.neuroimage.2013.05.041
CRN framework: Dolgikh (2026). doi:10.5281/zenodo.18249250
GKSL solver: RK2, dt=0.05, T_end=10, γ_T=1.0, γ_L=5×10⁻⁴, ε=2.0