{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T10:57:27Z","timestamp":1785841047865,"version":"3.56.0"},"posted":{"date-parts":[[2023,4,3]]},"group-title":"Addiction Medicine","reference-count":95,"publisher":"openRxiv","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2023,4,12]]},"abstract":"<jats:title>Full abstract<\/jats:title>\n                <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Previous studies in people with substance use disorders (SUDs) have implicated both the frontopolar cortex and amygdala in drug cue reactivity and craving, and amygdala-frontopolar coupling is considered a marker of early relapse risk. Accumulating data highlight that the frontopolar cortex can be considered a promising therapeutic target for transcranial magnetic stimulation (TMS) in SUDs. However, one-size-fits-all approaches to TMS targets resulted in substantial variation in both physiological and behavioral outcomes. Individualized TMS approaches to target cortico-subcortical circuits like amygdala-frontopolar have not yet been investigated in SUDs.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>Here, we (1) defined individualized TMS target location based on functional connectivity of the amygdala-frontopolar circuit while people were exposed to drug-related cues, (2) optimized coil orientation based on maximizing electric field (EF) perpendicular to the individualized target, and (3) harmonized EF strength in targeted brain regions across a population.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Method<\/jats:title>\n                  <jats:p>MRI data including structural, resting-state, and task-based fMRI data were collected from 60 participants with methamphetamine use disorders (MUDs). Craving scores based on a visual analog scale were collected immediately before and after the MRI session. We analyzed inter-subject variability in the location of TMS targets based on the maximum task-based connectivity between the left medial amygdala (with the highest functional activity among subcortical areas during drug cue exposure) and frontopolar cortex using psychophysiological interaction (PPI) analysis. Computational head models were generated for all participants and EF simulations were calculated for fixed vs. optimized coil location (Fp1\/Fp2 vs. individualized maximal PPI location), orientation (AF7\/AF8 vs. orientation optimization algorithm), and stimulation intensity (constant vs. adjusted intensity across the population).<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>\n                    Left medial amygdala with the highest (mean \u00b1 SD: 0.31\u00b10.29) functional activity during drug cue exposure was selected as the subcortical seed region. Amygdala-to-whole brain PPI analysis showed a significant cluster in the prefrontal cortex (cluster size: 2462 voxels, cluster peak in MNI space: [25 39 35]) that confirms cortico-subcortical connections. The location of the voxel with the most positive amygdala-frontopolar PPI connectivity in each participant was considered as the individualized TMS target (mean \u00b1 SD of the MNI coordinates: [12.6 64.23 -0.8] \u00b1 [13.64 3.50 11.01]). Individual amygdala-frontopolar PPI connectivity in each participant showed a significant correlation with VAS scores after cue exposure (\n                    <jats:italic>R<\/jats:italic>\n                    =0.27,\n                    <jats:italic>p<\/jats:italic>\n                    =0.03). Averaged EF strength in a sphere with r = 5mm around the individualized target location was significantly higher in the optimized (mean \u00b1 SD: 0.99 \u00b1 0.21) compared to the fixed approach (Fp1: 0.56 \u00b1 0.22, Fp2: 0.78 \u00b1 0.25) with large effect sizes (Fp1:\n                    <jats:italic>p<\/jats:italic>\n                    = 1.1e-13, Hedges\u2019g = 1.5, Fp2:\n                    <jats:italic>p<\/jats:italic>\n                    = 1.7e-5, Hedges\u2019g = 1.26). Adjustment factor to have identical 1 V\/m EF strength in a 5mm sphere around the individualized targets ranged from 0.72 to 2.3 (mean \u00b1 SD: 1.07 \u00b1 0.29).\n                  <\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>Our results show that optimizing coil orientation and stimulation intensity based on individualized TMS targets led to stronger electric fields in the targeted brain regions compared to a one-size-fits-all approach. These findings provide valuable insights for refining TMS therapy for SUDs by optimizing the modulation of cortico-subcortical circuits.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Short Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Prior research on drug addiction has linked the frontopolar cortex and amygdala coupling to drug cue reactivity\/craving. However, one-size-fits-all approaches for transcranial magnetic stimulation (TMS) over frontopolar-amygdala have led to inconsistent results.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Here, we (1) defined individualized TMS target location based on functional connectivity of the amygdala-frontopolar circuit while people were exposed to drug-related cues, (2) optimized coil orientation for maximum electric field (EF) perpendicular to the individualized target, and (3) harmonized EF strength in targeted brain regions across a population.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>MRI data were collected from 60 participants with methamphetamine use disorders (MUDs). and examined the variability in TMS target location based on task-based connectivity between the frontopolar cortex and amygdala. using psychophysiological interaction (PPI) analysis. EF simulations were calculated for fixed vs. optimized coil location (Fp1\/Fp2 vs. individualized maximal PPI), orientation (AF7\/AF8 vs. optimization algorithm), and stimulation intensity (constant vs. adjusted intensity across the population).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Left medial amygdala with the highest (0.31\u00b10.29) fMRI drug cue reactivity was selected as the subcortical seed region. The location of the voxel with the most positive amygdala-frontopolar PPI connectivity in each participant was considered as the individualized TMS target (MNI coordinates: [12.6,64.23,-0.8]\u00b1[13.64,3.50,11.01]). Individualized frontopolar-amygdala connectivity showed a significant correlation with VAS craving scores after cue exposure (\n                      <jats:italic>R<\/jats:italic>\n                      =0.27,\n                      <jats:italic>p<\/jats:italic>\n                      =0.03). Averaged EF strength in a sphere with r=5mm around the individualized target location was significantly higher in the optimized (0.99\u00b10.21V\/m) compared to the fixed approach (Fp1:0.56\u00b10.22V\/m, Fp2:0.78\u00b10.25V\/m) with large effect sizes (Fp1\n                      <jats:italic>:p<\/jats:italic>\n                      =1.1e-13,Hedges\u2019g=1.5, Fp2:\n                      <jats:italic>p<\/jats:italic>\n                      =1.7e-5,Hedges\u2019g=1.26). Adjustment factor to have identical 1V\/m EF strength in a 5mm sphere around the individualized targets ranged from 0.72-to-2.3 (1.07\u00b10.29).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our results show that optimizing coil orientation and stimulation intensity based on individualized TMS targets led to stronger harmonized electric fields in the targeted brain regions compared to a one-size-fits-all method that hopefully helps to refine future TMS therapy for MUDs.<\/jats:p>\n                  <\/jats:sec>\n                <\/jats:sec>","DOI":"10.1101\/2023.04.02.23288047","type":"posted-content","created":{"date-parts":[[2023,4,4]],"date-time":"2023-04-04T00:05:43Z","timestamp":1680566743000},"source":"Crossref","is-referenced-by-count":5,"title":["Optimizing Individual Targeting of Fronto-Amygdala Network with Transcranial Magnetic Stimulation (TMS): Biophysical, Physiological and Behavioral Variations in People with Methamphetamine Use Disorder"],"prefix":"10.64898","author":[{"given":"Ghazaleh","family":"Soleimani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christine A.","family":"Conelea","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rayus","family":"Kuplicki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Opitz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kelvin O","family":"Lim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0825-3606","authenticated-orcid":false,"given":"Martin P.","family":"Paulus","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamed","family":"Ekhtiari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2023041622501119000_2023.04.02.23288047v2.1","doi-asserted-by":"publisher","DOI":"10.1176\/appi.ajp.2013.12060782"},{"key":"2023041622501119000_2023.04.02.23288047v2.2","doi-asserted-by":"publisher","DOI":"10.1038\/s41398-022-01792-6"},{"key":"2023041622501119000_2023.04.02.23288047v2.3","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.1143-22.2022"},{"key":"2023041622501119000_2023.04.02.23288047v2.4","doi-asserted-by":"crossref","unstructured":"M. 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