What if rest is not best? Considering brain state for individualized FC-based rTMS
Poster No:
74
Submission Type:
Abstract Submission
Authors:
Hallee Shearer1, Jeffrey Eilbott2, Fidel Vila Rodriguez3, Tamara Vanderwal2
Institutions:
1Northeastern University, Somerville, MA, 2University of British Columbia, Vancouver, British Columbia, 3UBC Department of Psychiatry, Division of Neuroscience and Translational Psychiatry, Vancouver, British Columbia
First Author:
Co-Author(s):
Fidel Vila Rodriguez
UBC Department of Psychiatry, Division of Neuroscience and Translational Psychiatry
Vancouver, British Columbia
UBC Department of Psychiatry, Division of Neuroscience and Translational Psychiatry
Vancouver, British Columbia
Introduction:
Repetitive Transcranial Magnetic Stimulation (rTMS) offers potential as a psychiatric treatment for multiple disorders, and is a first-line option for treatment-resistant depression (TRD). Recent endeavours to optimize rTMS response have focused on the individualization of stimulation locations, often determined from individual fMRI scans (Cash et al., 2021; Fox et al., 2013). Currently, target localization uses resting-state fMRI. Alternative acquisition conditions, such as movie-watching, offer potential advantages that may warrant further investigation. For example, movie-watching improves the reliability of FC (Shearer et al., 2024; Wang et al., 2017) and constrains brain state across individuals and repeat scans (van der Meer et al., 2020). A fundamental question is whether using different acquisition conditions for individualized rTMS would change the location and reliability of individualized targets.
Methods:
Data. We used minimally preprocessed data from the HCP 7T data release (Van Essen et al., 2013). Data were from 4 sessions across 2 days. Rest and movie runs were ~15 minutes long (TR=1000ms). Each movie scan contained several clips from different movies. We used one rest and one movie scan from each day: Rest1 and Movie2 from day 1, and Rest4 and Movie4 from day 2. Subjects with mean framewise displacement >0.2mm in any run were excluded, and two subjects were removed based on quality control checks, leaving N=109 (healthy adults, 64 F, mean age 29.4 ± 3.4).
Individualized target localization. The left subgenual cingulate cortex (SGC) was defined as the Glasser parcel number 164 (Glasser et al., 2016), and the left DLPFC was defined according to Cash et al. (2021). DLPFC-SGC seedmaps were obtained following a dual regression approach (Cash et al., 2021). Resulting seedmaps were then thresholded to retain the top 5% of anti-correlated vertices. The center of gravity of the largest cluster of contiguous vertices was identified as the target. The analysis was repeated at a range of cluster thresholds from 0.5% to 95%.
Analysis. Within-subject distances between targets were calculated for each pair of cross-day scans and for each cluster threshold, as geodesic surface distance. For each subject, condition, and cluster threshold, the distance between the subject and every other subject was calculated and averaged. To obtain a measure of between-subject distance per scan, these distances were further averaged across subjects. Distances (from the 5% cluster threshold targets) were compared statistically using the Friedman test and Dunn's post test. For each scan and cluster threshold, the average between-subject distance was divided by the average within-subject distance to obtain a ratio influenced by both the reliability and individual variation of the individualized targets.
Individualized target localization. The left subgenual cingulate cortex (SGC) was defined as the Glasser parcel number 164 (Glasser et al., 2016), and the left DLPFC was defined according to Cash et al. (2021). DLPFC-SGC seedmaps were obtained following a dual regression approach (Cash et al., 2021). Resulting seedmaps were then thresholded to retain the top 5% of anti-correlated vertices. The center of gravity of the largest cluster of contiguous vertices was identified as the target. The analysis was repeated at a range of cluster thresholds from 0.5% to 95%.
Analysis. Within-subject distances between targets were calculated for each pair of cross-day scans and for each cluster threshold, as geodesic surface distance. For each subject, condition, and cluster threshold, the distance between the subject and every other subject was calculated and averaged. To obtain a measure of between-subject distance per scan, these distances were further averaged across subjects. Distances (from the 5% cluster threshold targets) were compared statistically using the Friedman test and Dunn's post test. For each scan and cluster threshold, the average between-subject distance was divided by the average within-subject distance to obtain a ratio influenced by both the reliability and individual variation of the individualized targets.
Results:
The locations, reliability, and intersubject variability of individualized targets varied significantly across acquisition conditions (Figure 1A-F). Between-/within-subject distance of targets, of which a high value is optimal, also appeared dependent upon condition, with resting state numerically highest and movie-watching numerically lowest across cluster thresholds (Figure 1G).
Conclusions:
This exploratory initial investigation in healthy adults suggests that acquisition condition affects the localization of individualized rTMS targets. This data may underestimate the utility of movie-watching data as both movie runs contain clips of different movies and thus are not ideal test-retest conditions, but for now, it appears that acquisition condition matters when it comes to identifying small, individualized patterns of anti-correlation in the DLPFC of healthy adults.
Brain Stimulation:
TMS 1
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia)
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
Novel Imaging Acquisition Methods:
Imaging Methods Other 2
Keywords:
FUNCTIONAL MRI
Psychiatric Disorders
Transcranial Magnetic Stimulation (TMS)
Other - naturalistic neuroimaging; brain state; precision psychiatry
1|2Indicates the priority used for review
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
Fox, M. D. (2013). Identification of reproducible individualized targets for treatment of depression with TMS based on intrinsic connectivity. NeuroImage, 0, 151–160. https://doi.org/10.1016/j.neuroimage.2012.10.082
Glasser, M. F. (2016). A multi-modal parcellation of human cerebral cortex. Nature, 536(7615), 171–178. https://doi.org/10.1038/nature18933
Shearer, H. (2024). Comparing reliability-based measures of functional connectivity between movie and rest: An ROI-based approach. Imaging Neuroscience. https://doi.org/10.1162/imag_a_00411
van der Meer, J. N. (2020). Movie viewing elicits rich and reliable brain state dynamics. Nature Communications, 11(1), 5004. https://doi.org/10.1038/s41467-020-18717-w
Van Essen, D. C. (2013). The WU-Minn Human Connectome Project: An overview. NeuroImage, 80, 62–79. https://doi.org/10.1016/j.neuroimage.2013.05.041
Wang, J. (2017). Test–retest reliability of functional connectivity networks during naturalistic fMRI paradigms. Human Brain Mapping, 38(4), 2226–2241. https://doi.org/10.1002/hbm.23517