We have performed a study of emotion regulation training in veterans with combat-related PTSD using real-time fMRI neurofeedback (rtfMRI-nf) with simultaneous EEG. Eighteen PTSD patients learned to upregulate their left amygdala activity using rtfMRI-nf during a positive emotion induction task based on retrieval of happy autobiographical memories. Enhancement in the amygdala-orbitofrontal functional connectivity during the rtfMRI-nf task showed positive correlation with severity of PTSD symptoms. Enhancement in left-lateralized upper alpha EEG coherence also positively correlated with PTSD severity. These results suggest that the rtfMRI-nf of the amygdala has the potential to correct the functional connectivity deficiencies specific to PTSD.
Twenty male patients with a primary diagnosis of PTSD related to combat trauma participated in the study and completed the first training session (Fig. 1) involving rtfMRI-nf with simultaneous EEG. Clinical assessment included the Clinician-Administered PTSD Scale for DSM-IV (CAPS), the Hamilton Depression Rating Scale (HDRS), and other instruments. Data from 18 patients were included in the group analyses.
The experiments were performed on a GE Discovery MR750 3T MRI scanner with an 8-channel receive-only head coil. A gradient echo EPI sequence with FOV/slice=240/2.9 mm, TR/TE=2000/30 ms, SENSE R=2, image matrix 96×96, flip=90°, 34 axial slices, was employed for fMRI. Simultaneous EEG recordings were performed using a 32-channel MR-compatible EEG system (Brain Products GmbH) in 0.016−250 Hz band with 0.1 µV resolution and 5 kS/s sampling. The rtfMRI-nf was implemented using a custom real-time system with a neurofeedback GUI (Fig. 1A)1. The nf signal was based on fMRI activation in the LA target ROI (Fig. 1C)1. The rtfMRI-nf session protocol (Fig. 1B) included seven runs, and each run (except Rest) consisted of 40-s blocks of Rest, Happy Memories, and Count conditions. For each Happy Memories condition, the participant was asked to feel happy by evoking happy autobiographical memories, while trying to raise the level of the red bar to that of the blue target bar (Fig. 1A). The height of the target bar was increased linearly from run to run (Fig. 1A) to introduce a linear trend across the four rtfMRI-nf runs1.
fMRI data analysis was performed in AFNI6. GLM functional connectivity analysis was conducted for Happy Memories conditions in each run, using the LA target ROI (Fig. 1C) as the seed. The LA connectivity maps for the four rtfMRI-nf runs were concatenated. Linear trend across the runs was evaluated using the 3dTfitter AFNI program, and the functional connectivity slope (FCS) was determined for each voxel (Fig. 2B). Group analysis was performed on the FCS data using the 3dttest++ AFNI program with the patients’ CAPS and HDRS ratings included as two covariates.
EEG data analysis was conducted in BrainVision Analyzer 2. Artifacts were removed using average artifact subtraction and ICA. Upper alpha EEG band was defined as [IAF…IAF+2] Hz, where IAF is the individual alpha peak frequency1. EEG coherence was computed for each pair of channels as the ratio of cross-spectrum and auto-spectrum, separately for Happy Memories and Rest conditions in each run1. The EEG coherence slope (ECS) for the upper alpha band was determined for each channel pair (Fig. 3B).
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