Enhancing TMS outcome prediction: the added value of clinical complexity to functional connectivity
Presented During:
Wednesday, June 25, 2025: 5:45 PM - 5:57 PM
Brisbane Convention & Exhibition Centre
Room:
Great Hall (Mezzanine Level) Doors 6, 7 & 8
Poster No:
75
Submission Type:
Abstract Submission
Authors:
Nga Yan Tse1, Aswin Ratheesh2,3,4, Luke Hearne5,6,7, Conor Robinson5,6,7, Bjorn Burgher5,7, Luca Cocchi5,6,7, Robin Cash1,8, Andrew Zalesky1,8
Institutions:
1Systems Lab, Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Australia, 2Orygen, Melbourne, Australia, 3Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia, 4Discipline of Psychiatry and Mental Health, University of New South Wales, Randwick, New South Wales, Australia, 5QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia, 6University of Queensland, School of Biomedical Sciences, Faculty of Medicine, St Lucia, QLD, Australia, 7Queensland Neurostimulation Centre, Brisbane, QLD, Australia, 8Department of Biomedical Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, Melbourne, Australia
First Author:
Nga Yan Tse
Systems Lab, Department of Psychiatry, Melbourne Medical School, The University of Melbourne
Melbourne, Australia
Systems Lab, Department of Psychiatry, Melbourne Medical School, The University of Melbourne
Melbourne, Australia
Co-Author(s):
Aswin Ratheesh, A/Prof
Orygen|Centre for Youth Mental Health, The University of Melbourne|Discipline of Psychiatry and Mental Health, University of New South Wales
Melbourne, Australia|Melbourne, Australia|Randwick, New South Wales, Australia
Orygen|Centre for Youth Mental Health, The University of Melbourne|Discipline of Psychiatry and Mental Health, University of New South Wales
Melbourne, Australia|Melbourne, Australia|Randwick, New South Wales, Australia
Luke Hearne
QIMR Berghofer Medical Research Institute|University of Queensland, School of Biomedical Sciences, Faculty of Medicine|Queensland Neurostimulation Centre
Brisbane, QLD, Australia|St Lucia, QLD, Australia|Brisbane, QLD, Australia
QIMR Berghofer Medical Research Institute|University of Queensland, School of Biomedical Sciences, Faculty of Medicine|Queensland Neurostimulation Centre
Brisbane, QLD, Australia|St Lucia, QLD, Australia|Brisbane, QLD, Australia
Conor Robinson
QIMR Berghofer Medical Research Institute|University of Queensland, School of Biomedical Sciences, Faculty of Medicine|Queensland Neurostimulation Centre
Brisbane, QLD, Australia|St Lucia, QLD, Australia|Brisbane, QLD, Australia
QIMR Berghofer Medical Research Institute|University of Queensland, School of Biomedical Sciences, Faculty of Medicine|Queensland Neurostimulation Centre
Brisbane, QLD, Australia|St Lucia, QLD, Australia|Brisbane, QLD, Australia
Luca Cocchi
QIMR Berghofer Medical Research Institute|University of Queensland, School of Biomedical Sciences, Faculty of Medicine|Queensland Neurostimulation Centre
Brisbane, QLD, Australia|St Lucia, QLD, Australia|Brisbane, QLD, Australia
QIMR Berghofer Medical Research Institute|University of Queensland, School of Biomedical Sciences, Faculty of Medicine|Queensland Neurostimulation Centre
Brisbane, QLD, Australia|St Lucia, QLD, Australia|Brisbane, QLD, Australia
Robin Cash, PhD
Systems Lab, Department of Psychiatry, Melbourne Medical School, The University of Melbourne|Department of Biomedical Engineering, Faculty of Engineering and Information Technology, The University of Melbourne
Melbourne, Australia|Melbourne, Australia
Systems Lab, Department of Psychiatry, Melbourne Medical School, The University of Melbourne|Department of Biomedical Engineering, Faculty of Engineering and Information Technology, The University of Melbourne
Melbourne, Australia|Melbourne, Australia
Andrew Zalesky
Systems Lab, Department of Psychiatry, Melbourne Medical School, The University of Melbourne|Department of Biomedical Engineering, Faculty of Engineering and Information Technology, The University of Melbourne
Melbourne, Australia|Melbourne, Australia
Systems Lab, Department of Psychiatry, Melbourne Medical School, The University of Melbourne|Department of Biomedical Engineering, Faculty of Engineering and Information Technology, The University of Melbourne
Melbourne, Australia|Melbourne, Australia
Introduction:
Growing knowledge of the mechanism mediating transcranial magnetic stimulation (TMS) treatment success has driven substantial advances in target refinement for treatment-resistant depression (TRD). However, outcome variability remains, and clinical factors known to be robust prognostic markers of TMS outcomes are often overlooked in past studies aimed at identifying connectivity predictors of treatment response.
Here, we developed a clinical complexity weight index reflecting the accumulative influence of psychiatric comorbidity, concurrent antidepressant status, and treatment resistance. Leveraging two independent TMS cohorts (Brisbane and Melbourne cohorts; n = 96), we sought to investigate the way in which connectivity predictors may be modulated by these clinical factors and identify generalizable connectivity features central to depressive symptom improvement as a function of clinical complexity across the whole brain.
Here, we developed a clinical complexity weight index reflecting the accumulative influence of psychiatric comorbidity, concurrent antidepressant status, and treatment resistance. Leveraging two independent TMS cohorts (Brisbane and Melbourne cohorts; n = 96), we sought to investigate the way in which connectivity predictors may be modulated by these clinical factors and identify generalizable connectivity features central to depressive symptom improvement as a function of clinical complexity across the whole brain.
Methods:
The Brisbane and Melbourne cohorts consisted of 59 (26 F, 33 M) and 37 (15F and 22M) adult MDD patients, respectively. T1 and resting-state-fMRI brain scans were acquired in each individual prior to connectivity-guided or conventional scalp-based TMS treatment to the left dorsal lateral prefrontal cortex (DLPFC). Treatment outcome was defined as the percentage improvement in the clinician-rated Montgomery–Åsberg Depression Rating Scale (MADRS) pre- to post- treatment. We first computed whole-brain connectivity maps from the DLPFC stimulation site and correlated every connection with MADRS percentage change with age, sex, and site as covariates, yielding a whole-brain map of optimal connectivity for symptom improvement (R-map). Using leave-one-out cross validation, predictive models based on the R-map alone and combined with clinical complexity index were evaluated.
Results:
Higher overall clinical complexity was associated with lower total depressive symptom reduction in the combined cohort (r = -.30; p = .003), though this effect was attenuated when adjusting for site (r = -.15; p =.144).
Using leave-one-out cross validation, the correlation between predicted outcome based on the group-average R-map generated in the training N-1 cohort and the empirical outcome across the testing subjects did not reach statistical significance (r = .18; p =.087). The performance however significantly improved after integrating the clinical complexity index into the predictive model (r =.35, p <.001). While the R-map was generated with age, sex, and site as covariates, we stringently tested whether the observed superior prediction would survive additional adjustment for site. This revealed relative robustness of the combined R-map and clinical complexity predictive model against inter-site variability (r = .20, p =.056).
Finally, a significant MADRS reduction by clinical complexity interaction was evident. Across the whole brain, negative connectivity between the DLPFC stimulation site and select regions including the sgACC, caudate and the medial prefrontal cortex (mPFC) was significantly associated with better outcomes in participants demonstrating higher complexity (FWE-corrected p <.05; cluster-based threshold of t >2.5).
Using leave-one-out cross validation, the correlation between predicted outcome based on the group-average R-map generated in the training N-1 cohort and the empirical outcome across the testing subjects did not reach statistical significance (r = .18; p =.087). The performance however significantly improved after integrating the clinical complexity index into the predictive model (r =.35, p <.001). While the R-map was generated with age, sex, and site as covariates, we stringently tested whether the observed superior prediction would survive additional adjustment for site. This revealed relative robustness of the combined R-map and clinical complexity predictive model against inter-site variability (r = .20, p =.056).
Finally, a significant MADRS reduction by clinical complexity interaction was evident. Across the whole brain, negative connectivity between the DLPFC stimulation site and select regions including the sgACC, caudate and the medial prefrontal cortex (mPFC) was significantly associated with better outcomes in participants demonstrating higher complexity (FWE-corrected p <.05; cluster-based threshold of t >2.5).
Conclusions:
Our work highlights that integrating clinical complexity could enhance functional connectivity-based prediction of TMS outcomes, supporting further refinement of personalization tailored to individual clinical profiles. Our findings also underscores the prognostic significance of DLPFC-sgACC connectivity, along with core limbic-striatal regions including the mPFC and caudate, in clinically complex presentations.
Brain Stimulation:
TMS 1
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling
Task-Independent and Resting-State Analysis
Keywords:
Psychiatric
Psychiatric Disorders
Transcranial Magnetic Stimulation (TMS)
Other - Major depression
1|2Indicates the priority used for review
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8. Cash RFH, Cocchi L, Lv J, Wu Y, Fitzgerald PB, Zalesky A. Personalized connectivity-guided DLPFC-TMS for depression: Advancing computational feasibility, precision and reproducibility. Hum Brain Mapp. 2021;(December 2020):1-18. doi:10.1002/hbm.25330