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Discriminative functional connectivity signature of cocaine use disorder links to rTMS treatment response

Nature Mental Health volume 2, pages 388–400 (2024)Cite this article

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Abstract

Cocaine use disorder (CUD) is prevalent, and repetitive transcranial magnetic stimulation (rTMS) shows promise in reducing cravings. However, the association between a consistent CUD-specific functional connectivity signature and treatment response remains unclear. Here we identify a validated functional connectivity signature from functional magnetic resonance imaging to discriminate CUD, with successful independent replication. We found increased connectivity within the visual and dorsal attention networks and between the frontoparietal control and ventral attention networks, alongside reduced connectivity between the default mode and limbic networks in patients with CUD. These connections were associated with drug use history and cognitive impairments. Using data from a randomized clinical trial, we also established the prognostic value of these functional connectivities for rTMS treatment outcomes in CUD, especially involving the frontoparietal control and default mode networks. Our findings reveal insights into the neurobiological mechanisms of CUD and link functional connectivity biomarkers with rTMS treatment response, offering potential targets for future therapeutic development.

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Fig. 1: FC discriminative pattern of patients with CUD versus healthy controls.
Fig. 2: Statistical difference in the classifier-identified discriminative FCs between CUD patients and healthy controls, examined by two-sample t-tests.
Fig. 3: Replication of the discriminative FC signature in the independent cohort.
Fig. 4: Prediction of the craving VAS score changes specific to active rTMS in ten fivefold cross-validation.

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Data availability

The data supporting the results in this study are available within the paper and its Supplementary Information. Part of this study analyzed data from a clinical trial and did not generate new clinical trial data. The datasets used in this study are publicly available. The SUDMEX-CONN dataset is available from Zenodo (https://zenodo.org/record/5123331). The SUDMEX-TMS dataset is also available from Zenodo (https://zenodo.org/record/7126853). The UCLA-CNP dataset is available from OpenNeuro (https://openneuro.org/datasets/ds000030/versions/1.0.0). The NYU dataset is available from the International Neuroimaging Data-sharing Initiative (https://fcon_1000.projects.nitrc.org/indi/retro/nyuCocaine.html).

Code availability

All statistical analyses and machine learning models were implemented in Python 3.7. The packages used in this study are publicly available. The XGBoost classification analysis was implemented using the xgboost 1.6.2 package (https://xgboost.readthedocs.io/en/stable/python/python_intro.html). The RVM regression was done using the sklearn 0.24.2 package (https://scikit-learn.org/stable/index.html). All other statistical analysis methods were implemented using the scipy 1.10.1 package (https://docs.scipy.org/doc/scipy/reference/index.html). Main code for analysis is provided at https://github.com/zhangyubrain/FCN-CUD-Phenotyping-rTMS. Resting-state functional MRI data were processed with fMRIPrep 20.2.3 (https://hub.docker.com/r/nipreps/fmriprep/tags). Internal operations of fMRIPrep 20.2.3 use the following software: Advanced Normalization Tools 2.3.3, Nipype 1.6.1, FSL 5.0.9, FreeSurfer 6.0.1, AFNI 20160207.

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Acknowledgements

This work was supported in part by National Institutes of Health grants R01MH129694, R21MH130956 and R21AG080425 to Y.Z.; R01MH132784 and K23MH114023 to G.A.F.; DP1MH116506 and R44MH123373 to A.E.; it was also supported by an Alzheimer’s Association grant (AARG-22-972541) as well as Lehigh University Faculty Innovation Grant (FIGAWD35), CORE and Accelerator grants to Y.Z. Portions of this research were conducted on Lehigh University’s Research Computing infrastructure partially supported by National Science Foundation Award 2019035. This work was also supported in part by philanthropic funding and grants from the One Mind Baszucki Brain Research Fund, the SEAL Future Foundation and the Brain and Behavior Research Foundation to G.A.F.

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Authors and Affiliations

  1. Department of Bioengineering, Lehigh University, Bethlehem, PA, USA

    Kanhao Zhao & Yu Zhang

  2. Center for Psychedelic Research and Therapy, Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, Austin, TX, USA

    Gregory A. Fonzo

  3. Center for Neuroscience Research, Children’s National Hospital, Washington DC, USA

    Hua Xie

  4. George Washington University School of Medicine, Washington DC, USA

    Hua Xie

  5. Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Desmond J. Oathes

  6. Wu Tsai Neuroscience Institute, Stanford University, Stanford, CA, USA

    Corey J. Keller

  7. Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA

    Corey J. Keller & Amit Etkin

  8. Department of Psychology, Lehigh University, Bethlehem, PA, USA

    Nancy B. Carlisle

  9. Alto Neuroscience, Los Altos, CA, USA

    Amit Etkin

  10. Instituto de Neurobiología, Universidad Nacional Autónoma de México campus Juriquilla, Querétaro, Mexico

    Eduardo A. Garza-Villarreal

  11. Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, USA

    Yu Zhang

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Contributions

K.Z. conceptualized and designed the work, performed data analysis and result interpretation and drafted and revised the manuscript. H.X., G.A.F., N.B.C. and A.E. interpreted the data, refined the design of the work and revised the manuscript. D.J.O. and C.J.K. interpreted the data and revised the manuscript. E.A.G.-V. collected the clinical trial data, interpreted the data, refined the design of the work and revised the manuscript. Y.Z. conceptualized and designed the work, oversaw the analysis and interpretation of data and revised the manuscript.

Corresponding author

Correspondence to Yu Zhang.

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Competing interests

G.A.F. received monetary compensation for consulting work for SynapseBio AI and owns equity in Alto Neuroscience. A.E. reports salary and equity from Alto Neuroscience and holds equity in Akili Interactive and Mindstrong Health. The other authors declare no competing interests.

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Nature Mental Health thanks James Mahoney, Abraham Zangen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Statistical difference in the classifier-identified discriminative FCs between CUD patients and healthy controls, examined by two-sample t-tests.

a, All significant hyperconnections (CUD > healthy controls). b, All significant hypoconnections (CUD < healthy controls). Histogram indicated the node strength calculated from the sum of the linked FC importance. VIS, visual network; SMN, somatomotor network; DAN, dorsal attention network; VAN, ventral attention network; LIM, limbic network; FPC, frontoparietal control network; DMN, default mode network. Only the significant t values that survived FDR were shown.

Extended Data Fig. 2 Visualization of the CUD-discriminative FCs involved in active repetitive transcranial magnetic stimulation treatment response specific prediction.

a, The rTMS predictive FC signature. b, We grouped the importance of predictive FCs into the seven typical networks including visual network (VIS), somatomotor network (SMN), dorsal attention network (DAN), ventral attention network (VAN), limbic network (LIM), frontoparietal control network (FPC), and default mode network (DMN).

Extended Data Fig. 3 Association between the discriminative FCs and treatment-outcome-predictive FCs.

a,b, Correlation between the top 2 active rTMS treatment response predictive FCs and active rTMS VAS score change. These two FCs were between the orbitofrontalcortex and anterior cingulate cortex, and between the middle temporal cortex and superior orbitofrontalcortex. Error bars = s.e.m. represent the Pearson’s correlation between FCs and VAS score changes based on the two-sided test against the alternative hypothesis that r ≠ 0. c,d, Correlation between these two FCs and sham rTMS VAS score change. e,f, These two FCs distribution between CUD and HC in the discovery and independent cohorts. The difference of FCs were comfirmed by independent samples t-test (two-sided test against the alternative hypothesis that t ≠ 0). The data in discovery cohort was augmented twice. These two FCs were significantly and specifically correlated to the VAS score change and significantly different between CUD and HC. The boxplots show the interquartile range (IQR; first quartile, 25th percentile; third quartile, 75th percentile), and the whiskers indicate Q1 − (1.5 × IQR) or Q3 + (1.5 × IQR). The line within the boxplot represents the median. The sample sizes for all panels were n = 213 (augmented FC from 71 subjects) for the CUD in discovery cohort, n = 174 (augmented FC from 58 subjects) for the HC in discovery cohort, n = 82 for the CUD in independent cohort, n = 81 for the HC in independent cohort. g, Venn diagram indicating the association between discriminative and abnormal FCs (551) with active rTMS treatment outcome. Discriminative atypical FCs were defined as the discriminative FCs identified by our classification models and the significantly atypical FCs detected by two-sample t-tests (two-sided test against the alternative hypothesis that t ≠ 0) comparing CUD and HC subjects, with those surviving FDR correction (pfdr < 0.05). The number of discriminative atypical FCs was equal to the sum of hyperconnections and hypoconnections. Deeper bluer shading indicates larger treatment predictive weights. The red numbers in the red rectangle represent the overlapping numbers between the top 100 treatment predictive FCs and all discriminative atypical FCs in descending order.

Extended Data Fig. 4 Illustration of our proposed analytical framework.

a, Region of interests (ROIs) level time series were extracted from fMRI BOLD signals based on the Schaefer atlas. Functional connectivity was calculated by Pearson’s correlation in time series between any pair of ROIs. b, The functional connectivity features were used to train the XGBoost model to classify the subjects into CUD patients or healthy controls on discovery cohort. The performance was cross-validated. Obtained diagnostic (discriminative) pattern was applied directly on the independent cohort to demonstrate the generalizability of its diagnostic power. c, Utilizing discriminative pattern as a mask to select the discriminative functional connectivity (FC) features from rTMS dataset, a relevance vector machine (RVM) model was employed to predict changes in visual analog scale (VAS) scores for patients undergoing repetitive transcranial magnetic stimulation (rTMS) treatment.

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Supplementary Figs. 1–12 and Tables 1–8.

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Zhao, K., Fonzo, G.A., Xie, H. et al. Discriminative functional connectivity signature of cocaine use disorder links to rTMS treatment response. Nat. Mental Health 2, 388–400 (2024). https://doi.org/10.1038/s44220-024-00209-1

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