Identifiability of Spectral Graph Model Parameters in Clinical MEG: Implications for Biophysical Interpretation and...

We evaluate the practical identifiability and clinical utility of local spectral graph model (SGM) parameters estimated from resting-state magnetoencephalography (MEG) in drug-resistant epilepsy.

A coupled excitatory-inhibitory SGM was fitted to MEG power spectra across 159 brain regions in 20 patients with temporal lobe epilepsy who achieved seizure freedom following surgery. Identifiability was assessed via boundary saturation analysis and inter-parameter correlations across four frequency bands. Identifiable parameters were tested for seizure onset zone (SOZ) discrimination.

Model fit was excellent (mean $r = 0.976$) and significantly exceeded a $1/f^\beta$ baseline ($p < 10^{-10}$). Gain parameters ($g_{ei}$, $g_{ii}$) were robustly estimable, whereas the excitatory time constant ($\tau _{e}$) showed 74% boundary saturation in broadband fits, reduced to ${\sim }3\%$ when restricted to 1-50 Hz. SOZ regions exhibited elevated $g_{ei}$ (Cohen's $d = +0.67$, FDR-corrected $p = 0.024$) and reduced $g_{ii}$ ($d = -0.55$, $p = 0.003$), with a composite biomarker achieving 2.6-fold improvement over chance.

Gain parameters are robustly identifiable from clinical MEG and capture excitatory-inhibitory imbalance in the SOZ, whereas time constants require band-limited fitting. These findings motivate an identifiability-aware framework in which only parameters demonstrably constrained by data are interpreted.

This is the first systematic assessment of neural mass model parameter identifiability in clinical epilepsy MEG, establishing practical guidelines for biophysical parameter interpretation.