In the rapidly evolving landscape of neuroscience, understanding the enigmatic brain activity patterns associated with psychiatric disorders remains paramount. Among these, schizophrenia stands out as a disorder with complex neurophysiological underpinnings that have challenged researchers for decades. A recent comprehensive study spearheaded by De Pieri, Sabe, Rochas, and colleagues, as corrected and published in Schizophrenia (2025), offers insightful revelations into the resting-state gamma frequencies of schizophrenia patients, investigated through advanced electroencephalographic (EEG) and magnetoencephalographic (MEG) analyses. This systematic review and exploratory power-spectrum meta-analysis not only consolidates previous findings but also provides a fresh interpretative framework for how high-frequency oscillations may shed light on altered neural dynamics in schizophrenia.
Gamma oscillations, typically ranging from 30 to 100 Hz, are integral for various cognitive processes including perception, attention, and memory encoding. Aberrations in these oscillations have long been implicated in the pathophysiology of schizophrenia, yet the heterogeneity of outcomes across studies has obfuscated clear conclusions. The meta-analytical approach taken in this paper collates resting-state EEG and MEG data, deploying systematic methodologies that mitigate previous inconsistencies inherent in disparate sample sizes, recording modalities, and analytical strategies. Through rigorous spectral analysis, the authors aimed to discern whether gamma power alterations represent consistent biomarkers of the disorder or are influenced by clinical and methodological variability.
The study forefronts the use of both EEG and MEG, modalities offering complementary insights into brain function. While EEG measures electrical potentials directly related to neuronal activity, MEG captures magnetic fields generated by post-synaptic currents, enabling enhanced spatial resolution and source localization. This dual approach addresses previous limitations whereby single-modality investigations could not unambiguously attribute oscillatory changes to precise cortical regions or differentiate signal origin from noise. By combining datasets from an array of cohorts across multiple studies, De Pieri and team elegantly navigate heterogeneity by applying harmonized preprocessing and power-spectrum estimation protocols.
Intriguingly, the meta-analysis reveals a nuanced profile of gamma-band activity in schizophrenia. Rather than a unidirectional alteration, the authors report region-specific modulations with some cortical areas exhibiting increased gamma power, while others demonstrate reductions compared to control groups. These findings complicate earlier narratives that predominantly suggested global deficits in synchronization. Instead, they point toward a dysregulated balance, possibly reflecting aberrant excitatory-inhibitory mechanisms at the neuronal microcircuit level. Such imbalances may underlie the fragmented cognitive and perceptual experiences characteristic of schizophrenia, including hallucinations and impaired working memory.
This recharacterization aligns with current conceptual models emphasizing pathological disruption of gamma oscillations as a core feature of neural dysconnectivity. Notably, gamma rhythms are generated through the interplay of excitatory pyramidal neurons and inhibitory interneurons, particularly parvalbumin-positive fast-spiking cells. Dysfunction in these inhibitory circuits, potentially related to NMDA receptor hypofunction and oxidative stress, has emerged as a central hypothesis in schizophrenia research. The observed resting-state gamma alterations thus provide electrophysiological evidence supporting these molecular and cellular frameworks.
Moreover, the authors highlight the importance of resting-state networks in the schizophrenia pathology narrative. Resting-state brain dynamics, increasingly recognized for their role in maintaining baseline neural readiness and facilitating task-related activations, show abnormal gamma band synchrony patterns in patients. The meta-analytic results elucidate how these resting oscillatory discrepancies may underpin deficits in large-scale network connectivity, particularly within the default mode network and frontoparietal circuits. This potentially links the microscale disruptions of inhibitory interneurons with macroscale network-level dysfunction and clinical symptomatology.
The methodological rigor applied in this study is commendable. By carefully accounting for confounding variables such as medication status, illness duration, and comorbidities, the authors ensure that gamma power modulations are more confidently attributed to disease processes rather than external influences. Additionally, the exploratory nature of the power-spectrum meta-analysis permits an unbiased investigation of frequency bands rather than presupposing effects in specific subranges. Such an approach is crucial because gamma oscillations are not monolithic but encompass functionally distinct sub-bands that might differentially relate to psychopathology.
Another striking feature of this research is its potential to propel biomarker discovery for schizophrenia. The identification of reproducible electrophysiological signatures at rest could revolutionize diagnostic and prognostic frameworks, supplementing clinical observations with objective neurophysiological data. This might aid early detection, patient stratification, and the monitoring of treatment efficacy. Furthermore, understanding the oscillatory landscape of schizophrenia could inform neuromodulatory interventions such as transcranial magnetic stimulation or neurofeedback, which aim to restore normal rhythmicity and ameliorate symptoms.
The authors also discuss limitations inherent in the extant literature and their analysis. Variations in EEG and MEG hardware, differences in preprocessing pipelines, and the intrinsic variability of psychiatric populations introduce complexities that challenge absolute conclusions. Nevertheless, the systematic review provides a vital synthesis that underscores consistent trends and opens avenues for standardizing future research protocols. The correction published alongside the original article strengthens the validity and reliability of these findings by addressing minor inconsistencies or errors, reinforcing the authors’ commitment to scientific rigor.
From a translational perspective, the elucidation of resting-state gamma oscillations in schizophrenia dovetails with emerging pharmacological strategies targeting glutamatergic and GABAergic neurotransmission. As abnormal gamma patterns may mirror synaptic and circuit-level dysfunctions, pharmacotherapies restoring inhibitory control or enhancing synaptic plasticity hold promise. In addition, personalized medicine approaches could leverage electrophysiological phenotyping to tailor treatments based on individual neural signatures.
In conclusion, the work by De Pieri, Sabe, Rochas, et al. represents a significant advance in the quest to disentangle the neurophysiological correlates of schizophrenia. By leveraging systematic review and meta-analytical tools, this study refines our understanding of gamma oscillatory abnormalities in resting-state brain activity, highlighting their complex regional specificity and mechanistic implications. Such insights deepen our grasp of schizophrenia as a disorder of neural synchrony and set a foundation for innovative diagnostic and therapeutic strategies rooted in brain rhythms.
The field awaits further longitudinal and multimodal studies to validate and extend these findings, particularly exploring how resting-state gamma alterations evolve with disease progression and treatment. Meanwhile, this authoritative synthesis serves as a benchmark, galvanizing neuroscientists and clinicians to harness electrophysiological markers in unraveling the intricate tapestry of schizophrenia.
Subject of Research: Neurophysiological alterations in resting-state gamma frequencies in patients with schizophrenia.
Article Title: Author Correction: Resting-state EEG and MEG gamma frequencies in schizophrenia: a systematic review and exploratory power-spectrum meta-analysis.
Article References:
De Pieri, M., Sabe, M., Rochas, V. et al. Author Correction: Resting-state EEG and MEG gamma frequencies in schizophrenia: a systematic review and exploratory power-spectrum meta-analysis. Schizophr 11, 59 (2025). https://doi.org/10.1038/s41537-025-00611-3
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