Brain connectomics markers for response prediction to transcranial magnetic stimulation in cocaine use disorder

Cocaine use disorder (CUD) is a worldwide health problem with limited effective treatment options. The therapeutic potential of repetitive transcranial magnetic stimulation (rTMS) is gaining more attention following evidence of its role on craving reduction in CUD. However, the heterogeneity of results underscores a pressing need for biomarkers of treatment outcome. We asked whether brain connectomics together with clinical assessments can predict response to add-on rTMS therapy for CUD better than solely conventional clinical assessments. Forty-four randomly assigned CUD patients underwent the 2-week double-blind acute phase [Sham (n = 20, 2f./18m.) and Active (n = 24, 4f./20m.)], in which they received 2 daily sessions of rTMS on the left dorsolateral prefrontal cortex. Subsequently, 19 and 14 patients continued to an open-label maintenance phase of two weekly rTMS sessions for 3 and 6 months, respectively. Pre and post treatment resting-state brain functional connectivity as well as two clinical scores of craving were measured to predict the subsequent response to rTMS therapy. Two conventional clinical scores, namely Cocaine Craving Questionnaires (CCQ) and Visual Analogue Scale (VAS) were used as craving level assessments. We used a priori seed-driven connectivity of Left Dorsolateral Prefrontal Cortex (LDLPFC) and Anterior Cingulate Cortex (ACC) together with the connectivity from a whole-brain multi-voxel pattern analysis at each time point to predict the reduction in craving after rTMS. The combination of connectivity changes and baseline craving severity improved the prediction of individual craving compared to the prediction with only the initial craving severity. The predictive model from the combination of neuromarkers could explain 45 to 97 percent of variance in craving changes assessed by two different clinical scores. We used leave-one-subject-out cross-validation to support the generalizability of our findings. Our results indicate that employing neuromarkers from resting-state functional connectivity of pre and post condition of CUD patients receiving add-on rTMS therapy increases the power of predicting craving changes and support the idea that neuromarkers may offer improvements in precision medicine approaches.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-05-02
DOI
https://doi.org/10.1038/s41598-025-99113-6
Citations
1
Primary Topic
Transcranial Magnetic Stimulation Studies
Type
article
Field-Weighted Citation Impact
0.84

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Brain connectomics markers for response prediction to transcranial magnetic stimulation in cocaine use disorder

Eduardo A. Garza‐Villarreal, Nayereh Ghazi, Hamid Soltanian‐Zadeh
1 citations
Scientific Reports
Transcranial Magnetic Stimulation Studies
0.84
article

Brain connectomics markers for response prediction to transcranial magnetic stimulation in cocaine use disorder

Eduardo A. Garza‐Villarreal, Nayereh Ghazi, Hamid Soltanian‐Zadeh
article en
1 citations

Abstract

Cocaine use disorder (CUD) is a worldwide health problem with limited effective treatment options. The therapeutic potential of repetitive transcranial magnetic stimulation (rTMS) is gaining more attention following evidence of its role on craving reduction in CUD. However, the heterogeneity of results underscores a pressing need for biomarkers of treatment outcome. We asked whether brain connectomics together with clinical assessments can predict response to add-on rTMS therapy for CUD better than solely conventional clinical assessments. Forty-four randomly assigned CUD patients underwent the 2-week double-blind acute phase [Sham (n = 20, 2f./18m.) and Active (n = 24, 4f./20m.)], in which they received 2 daily sessions of rTMS on the left dorsolateral prefrontal cortex. Subsequently, 19 and 14 patients continued to an open-label maintenance phase of two weekly rTMS sessions for 3 and 6 months, respectively. Pre and post treatment resting-state brain functional connectivity as well as two clinical scores of craving were measured to predict the subsequent response to rTMS therapy. Two conventional clinical scores, namely Cocaine Craving Questionnaires (CCQ) and Visual Analogue Scale (VAS) were used as craving level assessments. We used a priori seed-driven connectivity of Left Dorsolateral Prefrontal Cortex (LDLPFC) and Anterior Cingulate Cortex (ACC) together with the connectivity from a whole-brain multi-voxel pattern analysis at each time point to predict the reduction in craving after rTMS. The combination of connectivity changes and baseline craving severity improved the prediction of individual craving compared to the prediction with only the initial craving severity. The predictive model from the combination of neuromarkers could explain 45 to 97 percent of variance in craving changes assessed by two different clinical scores. We used leave-one-subject-out cross-validation to support the generalizability of our findings. Our results indicate that employing neuromarkers from resting-state functional connectivity of pre and post condition of CUD patients receiving add-on rTMS therapy increases the power of predicting craving changes and support the idea that neuromarkers may offer improvements in precision medicine approaches.

Scientific ReportsVol. 15(1)
Autonomous University of Queretaro (MX), University of Tehran (IR), Universidad Nacional Autónoma de México (MX)
National Science Foundation, Iran National Science Foundation
Zero hunger
Openalex Percentile: Top 27%
Transcranial Magnetic Stimulation Studies
0.84
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.