Poster No:
1984
Submission Type:
Abstract Submission
Authors:
Summer Edwards1, Jesse Farrand1, Quinn Smith1, Jayce Doose2, Corbin Ping2, Linbi Hong3, Noam Schneck3, Robin Goldman4, Mark George2, Lisa McTeague2, Paul Sajda3, Lei Ding1,5, Han Yuan1,5
Institutions:
1University of Oklahoma, Norman, OK, 2Medical University of South Carolina, Charleston, SC, 3Columbia University, New York City, NY, 4University of Wisconsin, Madison, WI, 5Institute of Biomedical Engineering, Science, and Technology, Norman, OK
First Author:
Co-Author(s):
Corbin Ping
Medical University of South Carolina
Charleston, SC
Mark George
Medical University of South Carolina
Charleston, SC
Lisa McTeague
Medical University of South Carolina
Charleston, SC
Lei Ding
University of Oklahoma|Institute of Biomedical Engineering, Science, and Technology
Norman, OK|Norman, OK
Han Yuan
University of Oklahoma|Institute of Biomedical Engineering, Science, and Technology
Norman, OK|Norman, OK
Introduction:
Emotion regulation (ER) is the ability to manage emotional experiences (Ochsner et al., 2012). In persons with depression, these processes are usually altered (Joorman et al., 2010, Schneck et al., 2023). Functional imaging studies of ER especially using fMRI have outlined the processes and neural systems involved in emotion generation and regulation. Recently, functional near-infrared spectroscopy (fNIRS) is increasingly being used in functional imaging studies, especially when it provides whole-head measurement concurrently with electroencephalogram (EEG) and transcranial magnetic stimulation (TMS). The purpose of this study was to evaluate fNIRS in measuring the ER network engagement in healthy subjects, using cap-based, whole-head fNIRS recording and brain-wide diffuse optical tomography (DOT).
Methods:
A pilot group of four healthy subjects' data have been collected so far at the Medical University of South Carolina. Simultaneous fNIRS and peripheral measurements were recorded by a NIRScout NSXP-CORE (NIRX, NY, USA) system and actiCHamp 64-channel (Brain Products, Munich, Germnay) system. 32 fNIRS laser source, 32 detectors, and 8 short-separation channels were arranged evenly over the whole head totaling 116 channels. fNIRS data was sampled at 7.81Hz via a customized parallel sequence. The peripheral measurements included pulse oximetry, accelerometry, and respiration. All subjects completed three repeated sessions of the ER task (Waugh et al. 2016), with cues of looking at neutral and negative pictures, as well as reappraising the negative pictures. fNIRS recordings were automatically preprocessed by adapting an automatic denoising procedure, namely principal-component-analysis-based general linear model (PCA-GLM) (Zhang et al., 2021). The preprocessing steps included rejecting bad channels that showed no heartbeat frequency peak, identifying and rejecting bad time segments with excessive head movements, band-pass filtering with 0.008–0.2 Hz, and removing the physiological noises of superficial contribution, respiration, cardiac pulsation and motion acceleration via general linear model regression. After preprocessing, DOT were computed (Khan et al., 2021, 2022). Participant-specific structural MRI data were acquired and used to create individual finite element method (FEM)-based forward model linking hemodynamic responses and scalp-based light measurements. The volumetric inverse source space was constructed with nodes inside the brain (enclosed by the pial surface) and no deeper than 45 mm from the scalp. The DOT results of inverse calculations were smoothed with a 6 mm spherical kernel and then projected to the fsaverage5 smoothed white matter surface from FREESURFER. Based on individuals' DOT results, group analysis was performed using a mixed-effect model.
Results:
In the contrast of reappraising negative pictures compared with simply viewing negative pictures, subjects showed significant oxygenated hemoglobin activation (p < 0.01) in the dorsolateral prefrontal cortex, ventrolateral prefrontal cortex, and middle temporal gyrus. The fNIRS DOT results from our pilot sample are comparable to those seen in fMRI (Ochsner et al., 2002, 2004). These regions are all associated with cognitive control of emotions, which may be an informative biomarker in the treatment of medication-resistant depression using repetitive TMS.
Conclusions:
Conclusions: These preliminary results from a small pilot sample of healthy subjects in an ongoing study show that fNIRS can measure brain network engagement during emotion regulation. These significant oxygenated hemoglobin changes during ER are consistent with findings from previous fMRI reports. fNIRS' may be a useful tool to study depression at baseline as well as over time with treatments like rTMS.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia)
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI) 2
Motion Correction and Preprocessing
Novel Imaging Acquisition Methods:
NIRS 1
Keywords:
Data analysis
Emotions
Modeling
Near Infra-Red Spectroscopy (NIRS)
Psychiatric Disorders
Other - Diffuse optical tomography
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Task-activation
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
Yes
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
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Please indicate which methods were used in your research:
Structural MRI
Optical Imaging
For human MRI, what field strength scanner do you use?
3.0T
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Free Surfer
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Provide references using APA citation style.
1. Joormann, J., & Gotlib, I. H. (2010). Emotion regulation in depression: relation to cognitive inhibition. Cognition & emotion, 24(2), 281–298. https://doi.org/10.1080/02699930903407948
2. Khan, A., Zhang, F., Yuan, H., & Ding, L. (2021). Brain-wide functional diffuse optical tomography of resting state networks. Journal of neural engineering. 18. 10.1088/1741-2552/abfdf9.
3. Khan, A. F., Zhang, F., Shou, G., Yuan, H., & Ding, L. (2022). Transient brain-wide coactivations and structured transitions revealed in hemodynamic imaging data. NeuroImage, 260, 119460. https://doi.org/10.1016/j.neuroimage.2022.119460
4. Ochsner, K. N., Bunge, S. A., Gross, J. J., & Gabrieli, J. D. (2002). Rethinking feelings: an FMRI study of the cognitive regulation of emotion. Journal of cognitive neuroscience, 14(8), 1215–1229. https://doi.org/10.1162/089892902760807212
5. Ochsner, K. N., Ray, R. D., Cooper, J. C., Robertson, E. R., Chopra, S., Gabrieli, J. D., & Gross, J. J. (2004). For better or for worse: neural systems supporting the cognitive down- and up-regulation of negative emotion. NeuroImage, 23(2), 483–499. https://doi.org/10.1016/j.neuroimage.2004.06.030
6. Ochsner, K. N., Silvers, J. A., & Buhle, J. T. (2012). Functional imaging studies of emotion regulation: a synthetic review and evolving model of the cognitive control of emotion. Annals of the New York Academy of Sciences, 1251, E1–E24. https://doi.org/10.1111/j.1749-6632.2012.06751.x
7. Schneck, N., Herzog, S., Lu, J., Yttredahl, A., Ogden, R. T., Galfalvy, H., Burke, A., Stanley, B., Mann, J. J., & Ochsner, K. N. (2023). The Temporal Dynamics of Emotion Regulation in Subjects With Major Depression and Healthy Control Subjects. Biological psychiatry, 93(3), 260–267. https://doi.org/10.1016/j.biopsych.2022.09.002
8. Waugh, C. E., Zarolia, P., Mauss, I. B., Lumian, D. S., Ford, B. Q., Davis, T. S., Ciesielski, B. G., Sams, K. V., & McRae, K. (2016). Emotion regulation changes the duration of the BOLD response to emotional stimuli. Social cognitive and affective neuroscience, 11(10), 1550–1559. https://doi.org/10.1093/scan/nsw067
9. Zhang, F., Cheong, D., Khan, A. F., Chen, Y., Ding, L., & Yuan, H. (2021). Correcting physiological noise in whole-head functional near-infrared spectroscopy. Journal of neuroscience methods, 360, 109262. https://doi.org/10.1016/j.jneumeth.2021.109262
No