Test–Retest Reliability of Magnetoencephalography Resting-State Functional Connectivity in Schizophrenia

Abstract

The reliability of magnetoencephalography (MEG) resting-state functional connectivity in schizophrenia (SZ) is unknown as previous research has focused on healthy controls (HC). Here, we examined reliability in 26 participants (13-SZ, 13-HC). Eyes opened and eyes closed resting-state data were collected on 4 separate occasions during 2 visits, 1 week apart. For source modeling, we used minimum norm software to apply dynamic statistical parametric mapping. Source analyses compared the following functional connectivity metrics from each data run: coherence (coh), imaginary coherence (imcoh), pairwise phase consistency (ppc), phase-locking value (plv), phase lag index (pli), weighted phase lag index (wpli), and weighted phase lag index debiased (wpli2). Intraclass correlation coefficients (ICCs) were calculated for whole brain, network, and network pair averages. For reliability, ICCs above 0.75 = excellent, above 0.60 = good, above 0.40 = fair, and below 0.40 = poor reliability. We found the reliability of these metrics varied greatly depending on frequency band, network, network pair, and participant group examined. Broadband (1–58 Hz) whole brain averages in both HC and SZ showed excellent reliability for wpli2, and good to fair reliability for ppc, plv, and coh. Broadband network averages showed excellent to good reliability across 1 hour and 1 week for coh, imcoh, ppc, plv, wpli within default mode, cognitive control, and visual networks in HC, while the same metrics had excellent to fair reliability in SZ. Regional network pair averages showed good to fair reliability for coh, ppc, plv within default mode, cognitive control and visual network pairs in HC and SZ. In general, HC had higher reliability compared to SZ, and the default mode, cognitive control, and visual networks had higher reliability compared to somatosensory and auditory networks. Similar reliability levels occurred for both eyes opened and eyes closed resting-states for most metrics. The functional connectivity metrics of coh, ppc, and plv performed best across 1 hour and 1 week in HC and SZ. We also found that SZ had reduced coh, plv, and ppc in the dmn average and pair values indicating dysconnectivity in SZ. These findings encourage collecting both eyes opened and eyes closed resting-state MEG, while demonstrating that clinical populations may differ in reliability.

SEEG Functional Connectivity Measures to Identify Epileptogenic Zones

“…Our results demonstrate that between ImCoh and PDC inward consistently exhibit significant differences in EZs vs non-EZs when DLS is stable over 3 consecutive days. Similarly, other studies using MEG and resting-state scalp EEG have demonstrated that ImCoh has varied test-retest reliability depending on the brain network tested in patients with schizophrenia 22 and controls. 23 Also, in a study measuring FC in controls using MEG, PDC was the most robust directed measure.…”

“…Evaluation of SEEG FC stability over time or as a function of ASM dosage is limited, 20,21 although ImCoh and PDC metrics have demonstrated stability in magnetoencephalography (MEG) and scalp EEG studies. [22][23][24] Time and spectral domain features of intracranial EEG (iEEG) fluctuate, but previous studies demonstrated no significant difference in prediction of surgical outcomes with 1-hour time segments collected >4 hours apart or a difference between 1-hour segments and shorter segments as brief as 10 seconds. 21,25 Whereas one study demonstrated decreased cortical activity after ASM weaning in iEEG, 26 effects of ASMs on SEEG FC measures have not been formally studied.…”

Disrupted dynamic functional network connectivity in fetal alcohol spectrum disorders

“…The sFNC approach ascertains connectivity patterns based on a single connectivity or correlation value across the time analyzed which can often be as long as 5–10 min. While this approach has yielded reliable resting‐state networks (Candelaria‐Cook & Stephen, 2020; Dinis Fernandes et al, 2020; Franco et al, 2013; Li et al, 2018), it is more likely that functional connectivity changes over short segments of time due to dynamic (time‐varying) brain oscillations and that network correlations are not static (Iraji et al, 2021; Sakoğlu et al, 2010). The “dynamic” connectivity or dFNC approach utilizes smaller time windows (~2–10 s) to examine temporal scale changes, evaluates how the interactions of functional sources change over time, and tracks how brain networks vary over time producing different brain states.…”

Presurgical Evaluation of Epilepsy Using Resting-State MEG Functional Connectivity

“…The metrics, including coherence (Coh), imaginary coherence (imCoh), pairwise phase consistency (PPC), phase-locking value (PLV), PLI, weighted phase lag index (wPLI), and weighted phase lag index debiased (wPLI2), were compared in a recent study to test the reliability of resting-state MEG functional connectivity in schizophrenia (SZ). The article indicated that the reliability of these metrics varied greatly depending on the frequency band, network, and participant group examined (Candelaria-Cook and Stephen, 2020). Although there is no uniform standard for MEG resting-state functional connectivity, some identified factors should be considered: metrics (Colclough et al, 2016), frequency band (Meng and Xiang, 2016;Marquetand et al, 2019), and measurement duration (Marquetand et al, 2019).…”