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B.A. Coffman

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YNICL Journal 2025 Journal Article

Auditory sensory processing measures using EEG and MEG predict symptom recovery in first-episode psychosis with a single-tone paradigm

  • F. López-Caballero
  • B.A. Coffman
  • M. Curtis
  • A.L. Sklar
  • S. Yi
  • D.F. Salisbury

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.

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YNIMG Journal 2013 Journal Article

Using joint ICA to link function and structure using MEG and DTI in schizophrenia

  • J.M. Stephen
  • B.A. Coffman
  • R.E. Jung
  • J.R. Bustillo
  • C.J. Aine
  • V.D. Calhoun

In this study we employed joint independent component analysis (jICA) to perform a novel multivariate integration of magnetoencephalography (MEG) and diffusion tensor imaging (DTI) data to investigate the link between function and structure. This model-free approach allows one to identify covariation across modalities with different temporal and spatial scales [temporal variation in MEG and spatial variation in fractional anisotropy (FA) maps]. Healthy controls (HC) and patients with schizophrenia (SP) participated in an auditory/visual multisensory integration paradigm to probe cortical connectivity in schizophrenia. To allow direct comparisons across participants and groups, the MEG data were registered to an average head position and regional waveforms were obtained by calculating the local field power of the planar gradiometers. Diffusion tensor images obtained in the same individuals were preprocessed to provide FA maps for each participant. The MEG/FA data were then integrated using the jICA software (http: //mialab. mrn. org/software/fit). We identified MEG/FA components that demonstrated significantly different (p <0. 05) covariation in MEG/FA data between diagnostic groups (SP vs. HC) and three components that captured the predominant sensory responses in the MEG data. Lower FA values in bilateral posterior parietal regions, which include anterior/posterior association tracts, were associated with reduced MEG amplitude (120–170ms) of the visual response in occipital sensors in SP relative to HC. Additionally, increased FA in a right medial frontal region was linked with larger amplitude late MEG activity (300–400ms) in bilateral central channels for SP relative to HC. Step-wise linear regression provided evidence that right temporal, occipital and late central components were significant predictors of reaction time and cognitive performance based on the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) cognitive assessment battery. These results point to dysfunction in a posterior visual processing network in schizophrenia, with reduced MEG amplitude, reduced FA and poorer overall performance on the MATRICS. Interestingly, the spatial location of the MEG activity and the associated FA regions are spatially consistent with white matter regions that subserve these brain areas. This novel approach provides evidence for significant pairing between function (neurophysiology) and structure (white matter integrity) and demonstrates that this multivariate, multimodal integration technique is sensitive to group differences in function and structure.

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