Poster No:
54
Submission Type:
Abstract Submission
Authors:
Zamfira Parincu1, Anna Peterson1, Naomi Gaggi2, Kari Siu3, Katherine Collins3, Dan Iosifescu1
Institutions:
1New York University Grossman School of Medicine, New York, NY, 2New York University Grossman School of Medicine, Rockaway Park, NY, 3Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY
First Author:
Zamfira Parincu
New York University Grossman School of Medicine
New York, NY
Co-Author(s):
Anna Peterson
New York University Grossman School of Medicine
New York, NY
Naomi Gaggi, PhD
New York University Grossman School of Medicine
Rockaway Park, NY
Kari Siu
Nathan S. Kline Institute for Psychiatric Research
Orangeburg, NY
Introduction:
Previous research has shown that individuals with major depressive disorder have aberrant neuronal activity, or fractional amplitude of low frequency fluctuations (fALFF), in the frontal regions of the brain (Shu et al., 2020). fALFF is a noninvasive neuroimaging measure of regional, voxel-wise, spontaneous fluctuations of the fMRI BOLD signal, which reflects variations in intrinsic brain activity (Zuo et al., 2008). Transcranial photobiomodulation (tPBM) is a novel, noninvasive, and non-pharmacological treatment that uses red and/or near infrared light to penetrate the brain and to alter cerebral blood flow (Hamblin, 2016). In this preliminary study, we aimed to explore the effects of tPBM on fALFF, and elucidate whether depression severity and history impact these effects.
Methods:
We examined the change in fALFF in frontal regions directly irradiated by tPBM and the associations with depression severity, age of first onset, length of current depression episode, and number of lifetime depressive episodes. Depression severity was measured using the Montgomery–Åsberg Depression Rating Scale (MADRS) total score (Montgomery & Asbery, 1979). We included 10 participants diagnosed with MDD (mean age: 36 years, 70 % female, 90% non-Hispanic/Latino), who underwent sequential resting state MRI scans (3T Siemens Trio & 12 channel head coil). Multi-echo echo planar imaging (EPI) was acquired pre-tPBM, during tPBM, and immediately post-tPBM. EPI parameters were: TR=2.5s; TE=12.8,32.33,51.04 ms; slice thickness 2.5 mm. Continuous wave tPBM was delivered via laser probes (808 nm) placed over the forehead bilaterally. Specific standard EEG electrode positions were directly irradiated (F4, F3, Fp2, Fp1, Fz, Fpz). fMRI data were pre-processed using afni_proc.py (Cox, 1996) customized for multi-echo EPI and FreeSurfer (Fischl, 2012) was used to pre-process the structural T1. fALFF was calculated using afni 3dRSFC (Taylor & Saad, 2013) and was extracted from regions of interest (ROI) using Marsbar (Brett et al., 2002). The ROIs were created as 5 mm spheres centered on the cortical MRI coordinates of the bilateral irradiated regions. All statistical tests were performed in SPSS (IBM Corp).
Results:
When controlling for number psychiatric medications, we found that change in fALFF ('target engagement') in F4 from pre-tPBM to during tPBM was significantly associated with age of onset of first depressive episode (p=.02) and the length of current depressive episode was trending towards significance (p=.052). We also found that target engagement in F3 from pre-tPBM to during tPBM was significantly associated with the length of current depressive episode (p=.025). The age of onset of first depressive episode was trending towards significance (p=.074). Depression severity, the age of onset of current depressive episode, and the number of lifetime episodes were not significant.
Conclusions:
These preliminary results suggest that the age of onset of first depressive episode and length of current depressive episode may be useful for predicting tPBM treatment engagement. Understanding the relationship between these factors and treatment engagement may inform treatment personalization and may allow for more effective relief of depressive symptoms. These results also suggest that fALFF could be useful in exploring tPBM parameters to further enhance target engagement in depressed populations.
Brain Stimulation:
Non-Invasive Stimulation Methods Other 1
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
MRI
Other - Brain Stimulation; Depression; Transcranial Photobiomodulation
1|2Indicates the priority used for review
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Was this research conducted in the United States?
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Clinical history
For human MRI, what field strength scanner do you use?
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Provide references using APA citation style.
Brett, M.,et al. (2002). Region of interest analysis using the MarsBar toolbox for SPM 99. Neuroimage, 16(2), S497.
Cox, R. W. (1996). AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical research, 29(3), 162-173.
Fischl, B. (2012). FreeSurfer. Neuroimage, 62(2), 774-781.
Hamblin, M. R. (2016). Shining light on the head: photobiomodulation for brain disorders. BBA clinical, 6, 113-124.
IBM Corp. Released 2023. IBM SPSS Statistics for Windows, Version 29.0.2.0 Armonk, NY: IBM Corp
Montgomery, S. A., et al. (1979). A new depression scale designed to be sensitive to change. The British journal of psychiatry, 134(4), 382-389.
Shu, Y., et al (2020). Fractional amplitude of low-frequency fluctuation (fALFF) alterations in young depressed patients with suicide attempts after cognitive behavioral therapy and antidepressant medication cotherapy: a resting-state fMRI study. Journal of affective disorders, 276, 822-828.
Taylor, P. A., et al. (2013). FATCAT:(an efficient) functional and tractographic connectivity analysis toolbox. Brain connectivity, 3(5), 523-535.
Zou, Q. H., et al. (2008). An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF. Journal of neurosc
No