Abstract
BACKGROUND: Schizophrenia is a heterogeneous disorder, and treatment-resistant schizophrenia (TRS) affects 20-30% of patients, yet objective biomarkers for its identification remain limited. Routine electroencephalography (EEG) offers a non-invasive window into cortical network dynamics, with previous studies reporting paroxysmal epileptiform activity and background slowing in a subset of patients. However, the biological significance of these findings-whether purely pathological or potentially compensatory-remains unclear. This study aimed to compare EEG abnormalities between TRS patients and those in clinical remission and to propose an integrative neurobiological interpretation. METHODS: In a cross-sectional design, 89 patients with schizophrenia (39 TRS, 50 in remission) underwent routine EEG recordings using the international 10-20 system. TRS was defined according to TRRIP consensus criteria, requiring <20% symptom reduction after adequate antipsychotic trials. EEG analysis focused on the prevalence of interictal epileptiform discharges (IEDs) and the severity of background slowing, assessed on a 4-point ordinal scale. RESULTS: IEDs were more than twice as prevalent in TRS patients compared to those in remission. Background slowing was significantly more severe in the TRS group, with the majority showing moderate-to-severe abnormalities versus predominantly normal-to-mild patterns in remission patients. Focal EEG abnormalities also followed this pattern. Multivariate analysis confirmed that both IEDs and background severity were independent predictors of TRS. CONCLUSIONS: EEG abnormalities, particularly IEDs and background slowing, are potential neurophysiological signatures associated with treatment resistance. We propose an integrative hypothesis suggesting that IEDs may originate as a failed compensatory mechanism-the brain's attempt to restore network homeostasis. In chronic TRS these discharges become maladaptive, contributing to excitotoxicity and network dysfunction. This framework opens avenues for EEG-based stratification and novel therapeutic strategies targeting cortical excitability.