Natural Oscillatory Frequency Slowing in the Premotor Cortex of Early-Course Schizophrenia Patients: A TMS-EEG Study.
Francesco Luciano DonatiAhmad MayeliKamakashi SharmaSabine A JanssenAlice D LagoyAdenauer G CasaliFabio FerrarelliPublished in: Brain sciences (2023)
Despite the heavy burden of schizophrenia, research on biomarkers associated with its early course is still ongoing. Single-pulse Transcranial Magnetic Stimulation coupled with electroencephalography (TMS-EEG) has revealed that the main oscillatory frequency (or "natural frequency") is reduced in several frontal brain areas, including the premotor cortex, of chronic patients with schizophrenia. However, no study has explored the natural frequency at the beginning of illness. Here, we used TMS-EEG to probe the intrinsic oscillatory properties of the left premotor cortex in early-course schizophrenia patients (<2 years from onset) and age/gender-matched healthy comparison subjects (HCs). State-of-the-art real-time monitoring of EEG responses to TMS and noise-masking procedures were employed to ensure data quality. We found that the natural frequency of the premotor cortex was significantly reduced in early-course schizophrenia compared to HCs. No correlation was found between the natural frequency and age, clinical symptom severity, or dose of antipsychotic medications at the time of TMS-EEG. This finding extends to early-course schizophrenia previous evidence in chronic patients and supports the hypothesis of a deficit in frontal cortical synchronization as a core mechanism underlying this disorder. Future work should further explore the putative role of frontal natural frequencies as early pathophysiological biomarkers for schizophrenia.
Keyphrases
- transcranial magnetic stimulation
- functional connectivity
- high frequency
- resting state
- bipolar disorder
- end stage renal disease
- working memory
- newly diagnosed
- chronic kidney disease
- ejection fraction
- peritoneal dialysis
- mental health
- prognostic factors
- blood pressure
- machine learning
- multiple sclerosis
- subarachnoid hemorrhage
- white matter
- patient reported
- data analysis
- artificial intelligence
- single molecule
- deep learning
- cerebral ischemia