Fiedler Lambda2 Algebraic Connectivity Differentiates Extra-Axial Compression from Intra-Axial Infiltration in Brain Tumors: A Complete 33-Subject rs-fMRI Analysis (ds001226)

We present a complete analysis of all 33 available subjects from the ds001226 brain tumor rs-fMRI dataset (CON=11, Meningioma=13, Glioma/other=9) using Fiedler lambda2 algebraic connectivity computed from Schaefer-100 parcellation with a unified abs(corr) Laplacian pipeline. The central finding is that lambda2 discriminates the MODE of network perturbation: Meningioma (extra-axial, compressive) shows elevated lambda2 (mean=11.46 vs CON=9.08, AUC=0.706), reflecting compensatory hyperconnectivity driven by mass effect, while diffuse glioma (intra-axial, infiltrative) shows lambda2 indistinguishable from controls (mean=9.85, AUC=0.556). Large high-grade astrocytomas with significant mass effect show paradoxically elevated lambda2, bridging both mechanisms. Meningioma size correlates with lambda2 (r=0.45). Lo-Shu SE-palace zone energy provides complementary spatial discrimination (AUC=0.738). Lambda2 is reframed as a 'compression index' for non-invasive tumor type stratification.