Purpose: Tract-speciˆc analysis (TSA) measures diŠusion parameters along a speciˆĉ ber that has been extracted byˆber tracking using manual regions of interest (ROIs), but TSA is limited by its requirement for manual operation, poor reproducibility, and high time consumption. We aimed to develop a fully automated extraction method for the cingulum bundle (CB) and to apply the method to TSA in neurobehavioral disorders such as Parkinson's disease (PD).Materials and Methods: We introduce the voxel classiˆcation (VC) and auto diŠusion tensorˆber-tracking (AFT) methods of extraction. The VC method directly extracts the CB, skipping theˆber-tracking step, whereas the AFT method usesˆber tracking from automatically selected ROIs. We compared the results of VC and AFT to those obtained by manual diŠusion tensorˆber tracking (MFT) performed by 3 operators. We quantiˆed the Jaccard similarity index among the 3 methods in data from 20 subjects (10 normal controls [NC] and 10 patients with Parkinson's disease dementia [PDD]). We used all 3 extraction methods (VC, AFT, and MFT) to calculate the fractional anisotropy (FA) values of the anterior and posterior CB for 15 NC subjects, 15 with PD, and 15 with PDD.Results: The Jaccard index between results of AFT and MFT, 0.72, was similar to the inter-operator Jaccard index of MFT. However, the Jaccard indices between VC and MFT and between VC and AFT were lower. Consequently, the VC method classiˆed among 3 diŠerent groups (NC, PD, and PDD), whereas the others classiˆed only 2 diŠerent groups (NC, PD or PDD).Conclusion: For TSA in Parkinson's disease, the VC method can be more useful than the AFT and MFT methods for extracting the CB. In addition, the results of patient data analysis suggest that a reduction of FA in the posterior CB may represent a useful biological index for monitoring PD and PDD.