title: Individualized Map of White Matter Pathways: Connectivity-Based Paradigm for Neurosurgical Planning.
abstract: Advances in white matter tractography enhance neurosurgical planning and glioma resection, but white matter tractography is limited by biological variables such as edema, mass effect, and tract infiltration or selection biases related to regions of interest or fractional anisotropy values.
  
  
  
  To provide an automated tract identification paradigm that corrects for artifacts created by tumor edema and infiltration and provides a consistent, accurate method of fiber bundle identification.
  
  
  
  An automated tract identification paradigm was developed and evaluated for glioma surgery. A fiber bundle atlas was generated from 6 healthy participants. Fibers of a test set (including 3 healthy participants and 10 patients with brain tumors) were clustered adaptively with this atlas. Reliability of the identified tracts in both groups was assessed by comparison with 2 experts with the Cohen κ used to quantify concurrence. We evaluated 6 major fiber bundles: cingulum bundle, fornix, uncinate fasciculus, arcuate fasciculus, inferior fronto-occipital fasciculus, and inferior longitudinal fasciculus, the last 3 tracts mediating language function.
  
  
  
  The automated paradigm demonstrated a reliable and practical method to identify white mater tracts, despite mass effect, edema, and tract infiltration. When the tumor demonstrated significant mass effect or shift, the automated approach was useful for providing an initialization to guide the expert with identification of the specific tract of interest.
  
  
  
  We report a reliable paradigm for the automated identification of white matter pathways in patients with gliomas. This approach should enhance the neurosurgical objective of maximal safe resections.
  
  
  
  AF, arcuate fasciculusDTI, diffusion tensor imagingIFOF, inferior fronto-occipital fasciculusILF, inferior longitudinal fasciculusROI, region of interestWM, white matter.
authors.affiliation: *Center for Biomedical Image Computing and Analytics, ‡Department of Neurosurgery, Perelman School of Medicine, and §Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
authors.forename: Birkan
authors.initials: B
authors.lastname: Tunç
authors.forename: Madhura
authors.initials: M
authors.lastname: Ingalhalikar
authors.forename: Drew
authors.initials: D
authors.lastname: Parker
authors.forename: Jérémy
authors.initials: J
authors.lastname: Lecoeur
authors.forename: Nickpreet
authors.initials: N
authors.lastname: Singh
authors.forename: Ronald L
authors.initials: RL
authors.lastname: Wolf
authors.forename: Luke
authors.initials: L
authors.lastname: Macyszyn
authors.forename: Steven
authors.initials: S
authors.lastname: Brem
authors.forename: Ragini
authors.initials: R
authors.lastname: Verma
mesh_terms: D001921:Brain / Q000000981:diagnostic imaging / Q000473:pathology; D001932:Brain Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D056324:Diffusion Tensor Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009413:Nerve Fibers, Myelinated / Q000473:pathology; D015203:Reproducibility of Results; D066127:White Matter / Q000000981:diagnostic imaging*