A Synergistic Examination of Excitatory-Inhibitory Balance in First-Episode Schizophrenia Merging 7-Tesla Magnetic Resonance Spectroscopic Imaging and Magnetoencephalography.

INTRODUCTION

Excitatory/inhibitory (E/I) imbalance is a proposed neural disruption in schizophrenia supported by magnetic resonance spectroscopic imaging (MRSI) evidence of altered gamma-aminobutyric acid (GABA) and glutamate (Glu) levels. However, there exists a paucity of data linking these abnormalities to impaired in vivo brain function putatively reflecting E/I imbalance. Here, associations between GABA/Glu and the evoked early auditory gamma-band response (EAGBR) were examined in first-episode schizophrenia (FESz).

METHODS

Twelve FESz underwent resting-state 7T-MRSI and magnetoencephalography (MEG) recorded during an auditory oddball task. MRSI spectra and source-localized MEG data were extracted from overlapping regions of left (LH) and right (RH) superior temporal sulcus. Associations between evoked EAGBR power and GABA/Glu, GABA/Cre, and Glu/Cre ratios were assessed. The Brief Psychiatric Rating Scale (BPRS) and Global Functioning: Role/Social (GF: Role/Social) scales were collected.

RESULTS

GABA/Glu ratios were inversely correlated with EAGBR power (LH: ρ = -0.87; RH: ρ = -0.85). LH GABA/Glu ratios were also inversely correlated with BPRS scores (ρ = -0.64) and positively correlated with GF: Social (ρ = 0.64) scores while RH EAGBR power was positively correlated with BPRS scores (ρ = 0.70). Stepwise linear regressions suggest these relationships were driven primarily by GABA concentrations.

CONCLUSION

Associations in FESz between GABA/Glu ratios, clinical ratings, and EAGBR power suggests this evoked gamma-band response reflects cortical hyperexcitability within the auditory system that is closely tied to disease debility at this early illness stage. These data highlight the potential of merging high-precision, in vivo neuro-chemical assays via 7-Tesla MRSI and physiological measures from MEG to validate established cellular models of disease.