Vadim Zotev1, Raquel Phillips1, Masaya Misaki1, Chung Ki Wong1, Brent Wurfel1, Matthew Meyer1,2, Frank Krueger1,3, Matthew Feldner1,4, and Jerzy Bodurka1,5
1Laureate Institute for Brain Research, Tulsa, OK, United States, 2Laureate Psychiatric Clinic and Hospital, Tulsa, OK, United States, 3Neuroscience Dept., George Mason University, Fairfax, VA, United States, 4Dept. of Psychological Science, University of Arkansas, Fayetteville, AR, United States, 5College of Engineering, University of Oklahoma, Tulsa, OK, United States
Synopsis
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. Fifteen 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. Individual session-to-session variations in frontal EEG asymmetry (FEA)
changes during the rtfMRI-nf task significantly correlated with variations in PTSD
severity (CAPS) and co-morbid depression severity (HDRS). These results suggest
that variations in task-specific FEA changes during rtfMRI-nf training provide
a sensitive measure of individual response to treatment in PTSD patients.Purpose
Real-time fMRI neurofeedback (rtfMRI-nf)
1,2 is
a promising technique for studies and researching novel treatments of neuropsychiatric
disorders
3-5. EEG performed simultaneously with rtfMRI-nf allows
investigation of electrophysiological correlates of the rtfMRI-nf training
5.
Frontal EEG asymmetry (FEA) at rest has been shown to inversely correlate with PTSD
severity
6. FEA changes during emotional stimuli have been shown to reflect
PTSD patients’ response to CBT treatment
7. Here we describe the first study using rtfMRI-nf with simultaneous EEG in PTSD patients. We show that FEA changes during a happy emotion induction task
with rtfMRI-nf targeting the left amygdala (LA)
8 provide information about the
PTSD patients’ individual response to the emotion regulation training.
Methods
Fifteen male patients with a primary diagnosis
of PTSD related to combat trauma have completed the study in the experimental
group. The study included eight sessions (visits): an initial psychological
assessment, an initial Clinician-Administered PTSD Scale for DSM-IV (CAPS)
evaluation, an MRI session (structural MRI, resting fMRI, emotional Stroop task),
three rtfMRI-nf training sessions with simultaneous EEG (Fig. 1A-D), a repeat MRI
session, and a final CAPS evaluation (Fig. 1F). Severity of co-morbid
depression was evaluated using the Hamilton Depression Rating Scale (HDRS).
The experiments were performed on a GE Discovery MR750 3T MRI scanner with an 8-channel receive-only head coil. A single-shot 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 (Fig. 1B) 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). The nf signal was based on fMRI activation in the LA target ROI8 (Fig. 1C). The rtfMRI-nf session
protocol (Fig. 1D) 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 on the screen.
EEG data analysis was
performed using BrainVision Analyzer 2 as described previously5. Artifacts were removed using the average
artifact subtraction and ICA. Time-frequency analysis was conducted with 8 ms
temporal and 0.25 Hz frequency resolution using a continuous wavelet transform.
The upper alpha EEG band was defined individually for each subject as
[IAF…IAF+2] Hz, where IAF is the individual alpha peak frequency. Signals from frontal
EEG channels F3 and F4 with Cz reference (Fig. 1E) were used to define FEA = ln(P(F4))−ln(P(F3)), where P is EEG power in the upper alpha band. Average FEA changes
between Happy Memories and Rest conditions across four rtfMRI-nf runs (Practice,
Runs 1-3) in each rtfMRI-nf session were computed.
Results
Session-to-session variations in the average
individual Happy vs Rest FEA changes significantly correlated with the
corresponding variations in CAPS ratings (Fig. 2A) and HDRS ratings (Fig. 2B). Our interpretation of these results is illustrated
in Fig. 2C. A multiple regression analysis for ∆FEA vs ∆CAPS and ∆HDRS showed a
significant main effect (
F(2,12)=14.7,
p=0.001,
R2=0.711). The partial correlation
∆FEA vs ∆CAPS when controlling for ∆HDRS:
r(12)=0.71,
p=0.004. The partial correlation ∆FEA
vs ∆HDRS when controlling for ∆CAPS:
r(12)=0.58,
p=0.031. These partial correlations
are close to the corresponding zero-order correlations in Figs. 2A,B.
Discussion
The average
individual FEA changes during the rtfMRI-nf task are sensitive to severity of
PTSD symptoms. This effect is similar to the one observed in patients with
depression
5. The session-to-session variations in the FEA changes provide a
measure of an individual response to emotion regulation training, reflecting reduction
in both PTSD severity (CAPS) and co-morbid depression severity (HDRS). The
partial correlation analyses suggest that variations in CAPS ratings and
variations in HDRS ratings have essentially independent effects on the observed
variations in the FEA changes. Our results indicate that the FEA variations
associated with the rtfMRI-nf training can serve as a measure of treatment
response in PTSD.
Acknowledgements
This research was supported
by W81XWH-12-1-0697 grant from the US Department of Defense.References
1. deCharms RC. Applications of real-time fMRI. Nature Rev. Neurosci. 2008; 9:721.
2. Weiskopf N. Real-time fMRI and its application to neurofeedback. NeuroImage 2012; 62:682.
3. Ruiz S, et al. Acquired self-control of insula cortex modulates emotion recognition and brain network connectivity in schizophrenia. Hum. Brain Mapping 2013; 34:200.
4. Young KD, et al. Real-time fMRI neurofeedback training of amygdala activity in patients with major depressive disorder. PLoS ONE 2014; 9:e88785.
5. Zotev V, et al. Correlation between amygdala BOLD activity and frontal EEG asymmetry during real-time fMRI neurofeedback training in patients with depression. ArXiv:1409.2046v2.
6. Kemp AH, et al. Disorder specificity despite comorbidity: resting EEG alpha asymmetry in major depressive disorder and post-traumatic stress disorder. Biol. Psychol. 2010; 85:350.
7. Rabe S, et al. Changes in brain electrical activity after cognitive behavioral therapy for posttraumatic stress disorder in patients injured in motor vehicle accidents. Psychosom. Med. 2008; 70:13.
8. Zotev V, et al. Self-regulation of amygdala activation using real-time fMRI neurofeedback. PLoS ONE 2011; 6:e24522.