ENAQT Boundary Conditions on the Allen Mouse Cortex Connectome: Code, Data, and Figures

Zenodo Description ENAQT Boundary Conditions on the Allen Mouse Cortex Connectome This dataset accompanies the study testing whether environment-assisted noise-augmented transport (ENAQT) — a hallmark signature of the Connectome Routing Networks (CRN) framework — arises on the directed weighted mouse cortex connectome from the Allen Brain Atlas (34 cortical regions, 17 per hemisphere). Background. Previous work demonstrated ENAQT (inverted-U selectivity over dephasing rate κ) on human connectome subcortical motifs (basal ganglia → thalamus, 8/8 subjects). The present study asks whether this signature generalizes to dense cortical association networks. Main result. Zero inverted-U profiles were detected across 37 conditions: 6 fMRI co-activation pattern (CAP) derived target sets × 2 weight normalizations, 1 DMN-vs-LCN biologically motivated task, 8 sparsity thresholds (density 0.30–1.0, diameter 1–4), and 16 single-hemisphere reduced-target configurations. All κ-curves showed monotonic decay — coherent transport was always optimal at minimal dephasing, with no room for dephasing-assisted improvement. Predictor analysis. Systematic comparison of topological descriptors across 35 graphs (8 HCP positive, 20 Erdős-Rényi, 7 mouse cortex variants) identified an empirical two-dimensional boundary: ENAQT on weighted biological graphs requires algebraic connectivity λ₂ > 1.0 AND weight coefficient of variation CV ∈ (0.2, 0.5). The Allen mouse cortex violates both (λ₂ < 0.7, CV > 0.9). This rule achieves 100% specificity (zero false positives) on all tested weighted graphs. Mechanistic finding. ENAQT on HCP survives at zero diagonal disorder (ε = 0), confirming it is purely topology-driven. Spectral overlap η₀ and dark-state indicators were evaluated but do not predict ENAQT across graph types. Conclusion. ENAQT is a property of hierarchical bottleneck circuits (basal ganglia, mushroom body), not of dense cortical sheets. This defines quantitative applicability limits for the CRN framework. Contents. 4 Python scripts (GKSL κ-sweep, sparsity sweep, predictor hunt, dark-state diagnostic) 8 output CSVs covering all 37 conditions + 35-graph predictor table 7 publication-quality figures (300 dpi) 4 execution logs Manuscript draft (Markdown) SHA256 manifest for reproducibility Source data. Mouse cortex connectivity and fMRI from Fasoli et al. (2026, PLoS Comput Biol, doi:10.1371/journal.pcbi.1013995), Mendeley doi:10.17632/xscxtshgfx.2. HCP reference data from Dolgikh (2025), Zenodo doi:10.5281/zenodo.18519173. Environment. Python 3.11, conda environment crn_clean (numpy, scipy, pandas, matplotlib, networkx, openpyxl). Related works. CRN FCN Paper (rate-matched wave vs classical transport): doi:10.5281/zenodo.18960172 CRN HCP ENAQT Core Dataset: doi:10.5281/zenodo.18519173 CRN Drosophila DES Dataset: doi:10.5281/zenodo.18697116 CRN Energy Analysis (C5c): doi:10.5281/zenodo.18785551