Predicting symptom progression in first-episode psychosis (FEP) is crucial for tailoring treatment and improving outcomes. Temporal lobe function, indicated by neurophysiological biomarkers like N100, predicts symptom progression and correlates with untreated psychosis. Our recent report showed that source-localized magnetoencephalography (MEG) M100 responses to tones in an oddball paradigm predicted recovery in FEP positive symptoms. This study expands these results with a simpler single-tone paradigm, with both MEG and EEG, and measuring associations across symptom dimensions. We recorded MEG (M100) and EEG (N100) in 29 FEP individuals and assessed symptom severity at baseline and after ∼ 7 months using the Positive and Negative Syndrome Scale (PANSS). Sequential regression analyses predicted symptom change (ΔPANSS) from Duration of untreated Active Psychosis (DAP) and baseline M100, controlling for baseline symptoms. Identical regressions were conducted in a subsample measuring N100 with EEG (n = 24). Smaller baseline M100 predicted worse symptom recovery at follow-up, independent of baseline symptom severity. Longer DAP showed a similar predictive effect, but this relationship was accounted for by M100. Regressions revealed M100 predictions were mostly related to general psychopathology. Identical results were found for N100 measured with EEG. Temporal lobe dysfunction in FEP, especially poor auditory sensory processing, indicates a worse recovery trajectory in general psychopathology. Longer untreated psychosis worsens temporal lobe function, predicting poorer progression. N100 measured with EEG and a single-tone task could be a cost-effective tool for informing clinicians about overall symptom progression, guiding treatment resource allocation and interventions.
Pubmed ID: 39756309 RIS Download
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Software as collaborative, open source application dedicated to analysis of brain recordings: MEG, EEG, fNIRS, ECoG, depth electrodes and animal invasive neurophysiology. User-Friendly Application for MEG/EEG Analysis.
View all literature mentionsInteractive Matlab toolbox for processing continuous and event-related EEG, MEG and other electrophysiological data incorporating independent component analysis (ICA), time/frequency analysis, artifact rejection, event-related statistics, and several useful modes of visualization of the averaged and single-trial data. First developed on Matlab 5.3 under Linux, EEGLAB runs on Matlab v5 and higher under Linux, Unix, Windows, and Mac OS X (Matlab 7+ recommended). EEGLAB provides an interactive graphic user interface (GUI) allowing users to flexibly and interactively process their high-density EEG and other dynamic brain data using independent component analysis (ICA) and/or time/frequency analysis (TFA), as well as standard averaging methods. EEGLAB also incorporates extensive tutorial and help windows, plus a command history function that eases users'' transition from GUI-based data exploration to building and running batch or custom data analysis scripts. EEGLAB offers a wealth of methods for visualizing and modeling event-related brain dynamics, both at the level of individual EEGLAB ''datasets'' and/or across a collection of datasets brought together in an EEGLAB ''studyset.'' For experienced Matlab users, EEGLAB offers a structured programming environment for storing, accessing, measuring, manipulating and visualizing event-related EEG data. For creative research programmers and methods developers, EEGLAB offers an extensible, open-source platform through which they can share new methods with the world research community by publishing EEGLAB ''plug-in'' functions that appear automatically in the EEGLAB menu of users who download them. For example, novel EEGLAB plug-ins might be built and released to ''pick peaks'' in ERP or time/frequency results, or to perform specialized import/export, data visualization, or inverse source modeling of EEG, MEG, and/or ECOG data. EEGLAB Features * Graphic user interface * Multiformat data importing * High-density data scrolling * Defined EEG data structure * Open source plug-in facility * Interactive plotting functions * Semi-automated artifact removal * ICA & time/frequency transforms * Many advanced plug-in toolboxes * Event & channel location handling * Forward/inverse head/source modeling
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