Related Experiment Video
Updated: Nov 18, 2025
07:12
Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method
Published on: August 2, 2021
3.9K
Personalized connectivity-guided DLPFC-TMS for depression: Advancing computational feasibility, precision and
Robin F H Cash1,2, Luca Cocchi3, Jinglei Lv1,2,4
1Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Victoria, Australia.
Human Brain Mapping
|February 5, 2021
Summary
Personalized repetitive transcranial magnetic stimulation (rTMS) targets for depression, guided by brain connectivity, are now reliably achievable with high precision. This method ensures stable and individualized treatment, overcoming previous limitations in target accuracy.
Area of Science:
- Neuroscience
- Medical Imaging
- Psychiatry
Background:
- Repetitive transcranial magnetic stimulation (rTMS) is used for depression but outcomes vary.
- Clinical response to rTMS correlates with functional connectivity between the dorsolateral prefrontal cortex (DLPFC) and subgenual cingulate cortex (SGC).
- Interindividual variability in SGC-related networks across the DLPFC necessitates personalized stimulation targets.
Purpose of the Study:
- To develop reliable and accurate methods for computing individualized, connectivity-guided rTMS targets.
- To assess the precision, reproducibility, and stability of these personalized targets.
Main Methods:
- Utilized resting-state functional MRI scans from 1,000 healthy adults.
- Developed computational methodologies to pinpoint individualized connectivity-guided stimulation targets.
- Validated target accuracy and stability across separate days and over a 1-year period.
Main Results:
- Personalized targets were pinpointed with a median accuracy of approximately 2 mm between scans.
- Targets demonstrated high intraindividual stability, with a median distance of 2.7 mm after 1 year.
- Interindividual variation in targets was significantly greater than intraindividual variation, indicating true personalization.
- Personalized targets exhibited heritability, suggesting long-term stability and genetic influence.
Conclusions:
- A robust computational framework enables precise and reliable computation of personalized, connectivity-guided TMS targets.
- This approach overcomes limitations of previous methods and offers potential for improved rTMS treatment outcomes.
- The methodology has broad applicability for advancing research in basic neuroscience and clinical applications.
