The future of MRI biomarkers for TMS treatment may depend not only on identifying brain changes, but also on determining whether those changes are truly meaningful. As interest in precision psychiatry continues to grow, new research is exploring how neuroimaging can become a more reliable tool for guiding individualized transcranial magnetic stimulation (TMS). This latest preprint contributes to the growing body of interventional psychiatry research by proposing a practical framework for distinguishing genuine treatment-related brain changes from normal measurement variability.
Current MRI Challenges In Personalized TMS
Magnetic resonance imaging has become an important research tool for understanding structural and functional brain changes associated with psychiatric illness. Researchers have increasingly explored MRI to personalize TMS targeting, monitor treatment response, and identify biomarkers that predict clinical outcomes.
However, one persistent limitation has been uncertainty. Small differences observed between scans may reflect scanner variability, biological fluctuations, or image processing rather than actual brain changes. Without understanding this measurement error, clinicians cannot confidently determine whether an observed MRI change represents a true response to treatment.
The authors argue that improving confidence in MRI measurements is essential before imaging biomarkers can routinely influence clinical decision making.
How MRI Biomarkers For TMS Treatment Were Evaluated
To address this problem, investigators conducted a test and retest study involving 20 healthy volunteers who underwent structural MRI and resting-state functional MRI twice within a 30 minute period. Using these repeated scans, researchers calculated a statistical measure called the repeatability coefficient, which defines the amount of normal measurement variation expected for each MRI feature.
The team then applied these thresholds to 33 psychiatric patients undergoing individualized TMS, with 24 completing the full study. Patients received 20 TMS sessions over five weeks using MRI-informed targeting strategies based on their structural and functional brain characteristics.
By comparing MRI scans obtained before and after treatment against the repeatability thresholds, researchers could determine whether individual brain changes exceeded expected measurement uncertainty.
Structural MRI Showed Strong Reliability
Several MRI measures demonstrated excellent reproducibility.
Subcortical brain volumes and cortical thickness consistently produced reliable measurements across repeated scans, suggesting they may serve as dependable biomarkers in longitudinal studies. Functional connectivity measurements were somewhat more variable, reflecting the well-known challenges of resting-state fMRI.
One particularly interesting finding involved diffusion MRI measurements of fractional anisotropy, which reflects white matter integrity. Although some regions showed greater variability than others, fractional anisotropy ultimately proved to be the MRI measure most sensitive to detecting treatment-related neural changes following TMS.
White Matter Changes Emerged After TMS
When researchers examined individual patient outcomes, most structural brain measurements remained within expected ranges of measurement variability following treatment.
Fractional anisotropy told a different story.
Changes within the posterior cingulum exceeded repeatability thresholds in the majority of patients, suggesting that this white matter pathway may undergo measurable microstructural remodeling after TMS. Overall, 17 of 23 evaluable patients demonstrated meaningful changes in this region beyond expected measurement error.
Importantly, the study also reported significant reductions in depression and anxiety symptoms following treatment. Researchers further identified associations between anxiety improvement and changes within the default mode network, as well as structural alterations involving the frontal pole among patients with more severe baseline anxiety.
These findings suggest that MRI biomarkers may eventually help identify which neural circuits are responding to treatment while also providing insight into individual differences in clinical improvement.
Why Repeatability Matters For Precision Psychiatry
Many neuroimaging studies focus primarily on group averages. This investigation instead emphasizes individual patients.
By establishing repeatability thresholds before evaluating treatment effects, clinicians may gain greater confidence that observed MRI changes represent genuine biological adaptations rather than technical noise. That distinction becomes increasingly important as psychiatry moves toward personalized treatment planning and imaging-guided neuromodulation.
The approach could also support future efforts to identify early responders and nonresponders, allowing clinicians to refine treatment strategies using objective biological evidence.
Looking Ahead
Although these findings remain preliminary because the study has not yet undergone peer review, they highlight an important direction for precision psychiatry. Reliable MRI biomarkers could eventually strengthen individualized TMS targeting, improve interpretation of treatment-related brain changes, and accelerate the development of imaging-guided neuromodulation.
Larger prospective studies will be needed to validate these repeatability thresholds across broader patient populations. If confirmed, MRI biomarkers may become an increasingly valuable component of personalized psychiatric care by helping clinicians distinguish meaningful neuroplasticity from ordinary measurement variation.
Citations
- Tavakoli H, Rostami R, Fallahi A, et al. Assessing MRI Biomarker Repeatability to Guide Individualized TMS Treatment in Psychiatry. medRxiv. 2026. https://doi.org/10.64898/2026.07.25.26358908
- Barnhart HX, Barboriak DP. Applications of the Repeatability of Quantitative Imaging Biomarkers: A Review of Statistical Analysis of Repeat Data Sets. Translational Oncology. 2009;2(4):231-235. doi:10.1593/tlo.09268. PubMed: https://pubmed.ncbi.nlm.nih.gov/19956383/
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