Pre-treatment structural brain biomarkers predict response to repetitive transcranial magnetic stimulation in subjective tinnitus - Scorecard - MDSpire
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Structural Brain Biomarkers Prior to Treatment as Predictors of Response to Repetitive Transcranial Magnetic Stimulation in Subjective Tinnitus

  • By

  • Zhongling Ding

  • Bo Peng

  • Mengfang Gong

  • Hongxuan Qiu

  • Qian He

  • Xiaoting Zhu

  • Shiyu Kang

  • Xiaoliang Sheng

  • Jisheng Liu

  • Yakang Dai

  • Duo-Duo Tao

  • June 24, 2026

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Clinical Scorecard: Structural Brain Biomarkers Prior to Treatment as Predictors of Response to Repetitive Transcranial Magnetic Stimulation in Subjective Tinnitus

At a Glance

CategoryDetail
ConditionSubjective Tinnitus
Key MechanismsStructural brain alterations and neuroplastic changes induced by rTMS.
Target PopulationPatients with subjective tinnitus.
Care SettingClinical neuromodulation and neuroimaging.

Key Highlights

  • 56.25% of patients classified as responders to rTMS.
  • Right pars triangularis of the inferior frontal gyrus identified as a key predictor.
  • Predictive model achieved AUC of 0.85 and accuracy of 0.77.
  • Responders showed larger gray matter volume in specific brain regions compared to non-responders and healthy controls.
  • No robust correlation found between structural features and baseline clinical measures.

Guideline-Based Recommendations

Diagnosis

  • Utilize high-resolution T1-weighted structural MRI to assess brain features.

Management

  • Consider pre-treatment brain structural biomarkers for rTMS efficacy prediction.

Monitoring & Follow-up

  • Evaluate changes in brain structure post-rTMS treatment.

Risks

  • Potential variability in rTMS response among patients.

Patient & Prescribing Data

64 patients with subjective tinnitus and 18 healthy controls.

Pre-treatment assessment of brain structure may guide individualized rTMS treatment.

Clinical Best Practices

  • Incorporate neuroimaging findings into treatment planning for tinnitus.
  • Use machine learning models to enhance predictive accuracy for rTMS outcomes.

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