Poster No:
1212
Submission Type:
Abstract Submission
Authors:
Xiaotian Wu1, Rongwei Zhai2, Chengjie Tang1, wenlei zhang1, Haifeng Jiang3
Institutions:
1Shanghai Mental Health Center, Shanghai, China, 2Lingang Laboratory, Shanghai, China, 3Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, Shanghai
First Author:
Xiaotian Wu
Shanghai Mental Health Center
Shanghai, China
Co-Author(s):
Haifeng Jiang
Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine
Shanghai, Shanghai
Introduction:
Dysfunction between cortical and subcortical circuits is thought to be central to the emergence of psychotic disorders(Fornito et al., 2013; Sabaroedin et al., 2022). Treatments targeting the cortex via transcranial magnetic stimulation (TMS) or subcortical regions via deep brain stimulation (DBS) have been used to treat various psychiatric disorders(Mayberg et al., 2005; Sydnor et al., 2022). However, the efficacy of these interventions varies and might be attributed to differences in neuronal activity patterns driven by heterogeneity in the directional flow of electrical signals between cortical and subcortical regions during stimulation(Mohan et al., 2020). Therefore, this study aims to use single-pulse electrical stimulation (SPES) to analyze the directional flow of signals between cortical and subcortical regions, construct effective connectivity networks, and explore their relationship with other connectivity metrics in two adult male rhesus monkeys.
Methods:
6-7 depth electrodes (48-56 contacts) were surgically implanted in cortical/subcortical targets of the right hemisphere in each of the two rhesus monkeys. These targets included orbitofrontal cortex (OFC), dorsolateral prefrontal cortex (DLPFC), ventromedial prefrontal cortex (VMPFC), anterior cingulate cortex (ACC), middle cingulate cortex (MCC), putamen, caudate, ventral striatum (VS), anterior hippocampus (AH), amygdala and posterior insula. We employed cathode leading biphasic bipolar stimulation (pulse width:0.2ms, frequency: 1 Hz) to deliver SPES. It was applied to sites located in the gray matter, while electrophysiologic data from other sites were simultaneously recorded. More than 30 trials were performed at intensities of 1mA, 2mA, 4mA and 6mA. A 10-minute resting-state recording was collected before each stimulation paradigm began. The maximum absolute amplitude within 10–100ms post-stimulation was z-scored against the pre-stimulation baseline [-200 -10ms] to quantify the response. Effective connectivity probability was estimated by setting a significance threshold of z = 4 and analyzing each channel response using the Cumulative Distribution Function across multiple trials. When stimulation channels were in the cortex and receiving channels in subcortex, the evoked potential was defined as Cortical-Subcortical Evoked Potential (CSEP). Conversely, when stimulation channels were in subcortex and receiving channels in the cortex, it was defined as Subcortical-Cortical Evoked Potential (SCEP). Magnitude-squared coherence was employed to estimate resting-state functional connectivity in the frequency domain, while resting-state recordings were utilized for Granger causality analysis as well. Spearman's rank correlation was used to compare the similarity between the different network connectivity metrics, and p-values were FDR-corrected for multiple comparisons (corrected p < 0.05) across different frequency bands.
Results:
We recorded intracranial SPES data from 63 electrode sites in two rhesus monkeys. First, we analyzed the similarity between effective connectivity and functional connectivity. CSEP demonstrated a higher similarity with functional connectivity (peak correlation: M5 R=0.61, M7 R=0.50) compared to SCEP (peak correlation: M5 R=0.34, M7 R=0.29). Significant differences between CSEP and SCEP were observed in low-frequency bands (theta: M5 p=0.0339, M7 p=0.0324; alpha and beta: both p<0.001). Further analysis of granger causality and effective connectivity revealed that CSEP also showed stronger similarity with granger causality (peak correlation: M5 R=0.54, M7 R=0.44) than SCEP (peak correlation: M5 R=0.31, M7 R=0.15) across 3-100 Hz (p<0.001).
Conclusions:
These results showed that, compared to SCEP, CSEP exhibited stronger consistency with different forms of connectivity. These findings may contribute to the development of innovative neuromodulation technologies by offering deeper insights into the mechanisms of directional signal transmission in brain circuits.
Brain Stimulation:
Direct Electrical/Optogenetic Stimulation 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
EEG/MEG Modeling and Analysis
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Subcortical Structures
Keywords:
Cortex
Electroencephaolography (EEG)
Sub-Cortical
Other - Single-Pulse Electrical Stimulation(SPES)
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.
Resting state
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?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
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Not applicable
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Yes
Please indicate which methods were used in your research:
EEG/ERP
Neurophysiology
Other, Please specify
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single-pulse electrical stimulation (SPES)
Provide references using APA citation style.
Fornito, A., Harrison, B. J., Goodby, E., Dean, A., Ooi, C., Nathan, P. J., Lennox, B. R., Jones, P. B., Suckling, J., & Bullmore, E. T. (2013). Functional Dysconnectivity of Corticostriatal Circuitry as a Risk Phenotype for Psychosis. JAMA Psychiatry, 70(11), 1143–1151. https://doi.org/10.1001/jamapsychiatry.2013.1976
Mayberg, H. S., Lozano, A. M., Voon, V., McNeely, H. E., Seminowicz, D., Hamani, C., Schwalb, J. M., & Kennedy, S. H. (2005). Deep Brain Stimulation for Treatment-Resistant Depression. Neuron, 45(5), 651–660. https://doi.org/10.1016/j.neuron.2005.02.014
Mohan, U. R., Watrous, A. J., Miller, J. F., Lega, B. C., Sperling, M. R., Worrell, G. A., Gross, R. E., Zaghloul, K. A., Jobst, B. C., Davis, K. A., Sheth, S. A., Stein, J. M., Das, S. R., Gorniak, R., Wanda, P. A., Rizzuto, D. S., Kahana, M. J., & Jacobs, J. (2020). The effects of direct brain stimulation in humans depend on frequency, amplitude, and white-matter proximity. Brain Stimulation, 13(5), 1183–1195. https://doi.org/10.1016/j.brs.2020.05.009
Sabaroedin, K., Razi, A., Chopra, S., Tran, N., Pozaruk, A., Chen, Z., Finlay, A., Nelson, B., Allott, K., Alvarez-Jimenez, M., Graham, J., Yuen, H. P., Harrigan, S., Cropley, V., Sharma, S., Saluja, B., Williams, R., Pantelis, C., Wood, S. J., … Fornito, A. (2022). Frontostriatothalamic effective connectivity and dopaminergic function in the psychosis continuum. Brain, 146(1), 372–386. https://doi.org/10.1093/brain/awac018
Sydnor, V. J., Cieslak, M., Duprat, R., Deluisi, J., Flounders, M. W., Long, H., Scully, M., Balderston, N. L., Sheline, Y. I., Bassett, D. S., Satterthwaite, T. D., & Oathes, D. J. (2022). Cortical-subcortical structural connections support transcranial magnetic stimulation engagement of the amygdala. Science Advances, 8(25), eabn5803. https://doi.org/10.1126/sciadv.abn5803
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