Increased Aperiodic Exponents Track Depression Symptom Severity

Abstract

Roughly one-third of patients with major depressive disorder (MDD) fail to respond to standard treatments and develop treatment-resistant MDD. For these patients, alternative therapies ofer additional options but yield inconsistent outcomes. Progress has been limited by the absence of objective, brain-based biomarkers to guide target selection or track therapeutic response in real time. Instead, clinicians rely on behavioral assessments that evolve slowly over weeks to months, obscuring the underlying neural dynamics of symptom changes. Here, we test whether the aperiodic exponent of intracranial EEG (iEEG) local field potentials can serve as a neurophysiological marker of depression symptom severity. We leveraged a large iEEG cohort (N = 20) undergoing invasive monitoring for refractory epilepsy, yielding over 1,800 contacts spanning cortical and subcortical zones. For each contact, we estimated the aperiodic exponent (thought to reflect aspects of cortical excitability) of the power spectrum across 10-100 Hz within local brain regions and across distributed cortical association networks. Depressive symptoms were assessed with the Beck Depression Inventory-II (BDI-II) immediately before intracranial resting state recordings. With respect to the BDI-II scale, participants were identified as experiencing minimal (BDI-II [≤] 13) or elevated depression symptoms (BDI-II [≥] 14). Associations between symptom severity (BDI-II total score and Somatic-Afective, Cognitive, and Anhedonia subscales) and region- or network-level exponents were modeled with ordinary least squares (OLS) regression. The whole-brain, mean aperiodic exponent for each participant discriminated symptom status (AUC = 0.82). At the regional level, the orbitofrontal cortex, anterior cingulate cortex, insula, and amygdala showed higher exponents in the elevated depression symptom group (d = 1.18-1.71; p = 0.032-0.004). A post-hoc classification analysis across these four regions misclassified one participant per group (AUC = 0.86; 95% CI 0.64-1.00). In continuous analyses, BDI-II scores correlated positively with exponents in these same four regions (pFDR = 0.019-0.027; partial r=0.61-0.70) and at the network level in the Salience network (pFDR = 0.024; partial r = 0.63) and Default (pFDR = 0.046; partial r = 0.55) network. The Salience network significantly tracked Anhedonia symptoms (p = 0.004; partial r = 0.62). Here we report that intracranial aperiodic exponents within fronto-limbic and insular circuits, overlapping with networks implicated in contemporary accounts of depression pathophysiology, diferentiate depressive symptom status and scale with severity. These findings support the aperiodic exponent as a candidate neurophysiological marker of current depression symptom burden, with potential relevance for individualized neuromodulation in MDD.