Repeat Surgery for Recurrent or Refractory Trigeminal Neuralgia: A Systematic Review and Meta-Analysis.

OBJECTIVE: Surgery can effectively treat Trigeminal neuralgia (TN), but postoperative pain recurrence or nonresponse are common. Repeat surgery is frequently offered but limited data exist to guide the selection of salvage surgical procedures. We aimed to compare pain relief outcomes after repeat microvascular decompression (MVD), percutaneous rhizotomy (PR), or stereotactic radiosurgery (SRS) to determine which modality was most efficacious for surgically refractory TN. METHODS: A PRISMA systematic review and meta-analysis was performed, including studies of adults with classical or idiopathic TN undergoing repeat surgery. Primary outcomes included complete (CPR) and adequate (APR) pain relief at last follow-up, analyzed in a multivariate mixed-effect meta-regression of proportions. Secondary outcomes were initial pain relief and facial numbness. RESULTS: Of 1299 records screened, 61 studies with 68 treatment arms (29 MVD, 14 PR, and 25 SRS) comprising 2165 patients were included. Combining MVD, PR, and SRS study data, 68.8% achieved initial CPR after a repeat TN procedure. On average, 49.6% of the combined sample of MVD, PR, and SRS had CPR at final follow-up, which was on average 2.99 years postoperatively. The proportion (with 95% CI) achieving CPR at final follow-up was 0.57 (0.51-0.62) for MVD, 0.60 (0.52-0.68) for PR, and 0.35 (0.30-0.41) for SRS, with a significantly lower proportion of pain relief with SRS. Estimates of initial CPR for MVD were 0.82 (0.78-0.85), 0.68 for PR (0.6-0.76), and 0.41 for SRS (0.35-0.48). CONCLUSIONS: Across MVD, PR, and SRS, about half of TN patients maintain complete CPR at an average follow-up time of 3 years after repeat surgery. In treating refractory or recurrent TN, MVD and PR were superior to SRS in both initial pain relief and long-term pain relief at final follow-up. These findings can inform surgical decision-making in this challenging population.

Stable Anatomy Detection in Multimodal Imaging Through Sparse Group Regularization: A Comparative Study of Iron Accumulation in the Aging Brain.

Multimodal neuroimaging provides a rich source of data for identifying brain regions associated with disease progression and aging. However, present studies still typically analyze modalities separately or aggregate voxel-wise measurements and analyses to the structural level, thus reducing statistical power. As a central example, previous works have used two quantitative MRI parameters-R2* and quantitative susceptibility (QS)-to study changes in iron associated with aging in healthy and multiple sclerosis subjects, but failed to simultaneously account for both. In this article, we propose a unified framework that combines information from multiple imaging modalities and regularizes estimates for increased interpretability, generalizability, and stability. Our work focuses on joint region detection problems where overlap between effect supports across modalities is encouraged but not strictly enforced. To achieve this, we combine L 1 (lasso), total variation (TV), and L 2 group lasso penalties. While the TV penalty encourages geometric regularization by controlling estimate variability and support boundary geometry, the group lasso penalty accounts for similarities in the support between imaging modalities. We address the computational difficulty in this regularization scheme with an alternating direction method of multipliers (ADMM) optimizer. In a neuroimaging application, we compare our method against independent sparse and joint sparse models using a dataset of R2* and QS maps derived from MRI scans of 113 healthy controls: our method produces clinically-interpretable regions where specific iron changes are associated with healthy aging. Together with results across multiple simulation studies, we conclude that our approach identifies regions that are more strongly associated with the variable of interest (e.g., age), more accurate, and more stable with respect to training data variability. This work makes progress toward a stable and interpretable multimodal imaging analysis framework for studying disease-related changes in brain structure and can be extended for classification and disease prediction tasks.