Professor University of California, San Francisco, California
Rationale: PNES are thought to be mediated by psychological stress factors but the biological mechanisms underlying them are currently unknown. The overall goal of this study is to demonstrate that PNES is associated with transient phases or states characterized by an enhanced synchronization between brain regions normally involved in emotion control or “overshooting” that can be detected by resting state fMRI (rsfMRI) when analyzed with an dynamic approach. Ongoing stress enhances the “overshooting” until it is “released” by a seizure. Methods: Eleven patients diagnosed with PNES without concomitant epileptic seizures underwent rsfMRI and structural MRI (sMRI) on a 3T magnet. COVID-19 prohibited the recruitment of a matching control population and thus 3T rsfMRI and sMRI data with similar acquisition parameters from 54 healthy controls (Con) from a publicly available data repository were used as reference. rsfMRI was processed using Conn (AICHA parcellation) and sliding windows (size 60 sec) combined with graph analysis and hierarchical cluster analysis were used to eliminate motion affected windows and to identify seven activity states (AS) characterized by different regional positive strength profiles. Individual gray matter connectivity patterns (gm conn) were characterized by calculating the profile similarity index (1). Pearson correlations were used to determine the amount of variation in each AS explained by gm conn. Group differences were investigated with Bonferroni corrected (Bc) Wilcoxon-tests. Results: PNES spent more time in AS5 than Con (14.2 vs. 6.5 % scan acquisition time, p=0.003) and Con more time in AS6 than PNES (30.9 vs. 2.9 %scan acquisition time p=0.0001). This difference was not caused by a dwell time shift between AS5 and AS6 please Figure 1 for an overview of the transitions between AS. AS5 was characterized by a higher global positive strength than AS6 (71.9±1.2 vs 63.1±1.3, p< 0.0001), global negative strength was not different between the two (51.9±5.8 vs 52.9±3.2). Amygdala connectivity was characterized by calculating the community structure of AS5 and AS6. The amygdala module in AS5 and six encompassed precuneus, anterior cingulate, hippocampus, parts of the medial and lateral temporal lobe and angular gyrus and in AS5 but not AS6 also large parts of the orbitofrontal and dorso-lateral frontal cortex, i.e., regions involved in bottom-up and top-down emotion control. In PNES gm conn explained 16.5% of the variability of the functional connectivity of AS5 but only 10.7% of that of AS6 (p=0.01). Con showed the opposite pattern, i.e., gm conn explained 16.3% of the variability of the functional connectivity of AS6 but only10.6% of that of AS5 (p = 0.0001). Conclusions: The findings of this study indicate that the interictal state of PNES is characterized by the more frequent occurrence of states with increased connectivity strength (AS6, AS2) compared to controls (AS7) and that gray matter connectivity is facilitating this shift. Although very preliminary, these findings are consistent with the hypothesized “overshooting”.
Neuroimage Clin 2019;23:101888
Funding: Please list any funding that was received in support of this abstract.: DoD Award W81XWH-17-1-0336