Abstract
Neurosurgical resection is one of the few opportunities researchers have to image the human brain both prior to and following focal damage. One of the challenges associated with studying brains undergoing surgical resection is that they often do not fit the brain templates most image-processing methodologies are based on, so manual intervention is required to reconcile the pathology and the most extreme cases must be excluded. Manual intervention requires significant time investment and introduces reproducibility concerns. We propose an automatic longitudinal pipeline based on High Angular Resolution Diffusion Imaging acquisitions to facilitate a Pathway Lesion Symptom Mapping analysis relating focal white matter injury to functional deficits. This two-part approach includes (i) automatic segmentation of focal white matter injury from anisotropic power differences, and (ii) modeling disconnection using tractography on the single-subject level, which specifically identifies the disconnections associated with focal white matter damage. The advantages of this approach stem from (1) objective and automatic lesion segmentation and tractogram generation, (2) objective and precise segmentation of affected tissue likely to be associated with damage to long-range white matter pathways (defined by anisotropic power), (3) good performance even in the cases of anatomical distortions by use of nonlinear tensor-based registration in the patient space, which aligns images using white matter contrast. Mapping a system as variable and complex as the human brain requires sample sizes much larger than the current technology can support. This pipeline can be used to execute large-scale, sufficiently powered analyses by meeting the need for an automatic approach to objectively quantify white matter disconnection.
- DTI
- Diffusion Tensor Imaging
- IOS
- Intra-Operative Stimulation
- VLSM
- Voxel-Based Lesion-Symptom Mapping
- MD
- mean diffusivity
- FA
- fractional anisotropy
- B0
- minimally diffusion-weighted image
- AP
- anisotropic power
- ASAP
- automatic segmentation of anisotropic power changes
- HARDI
- High Angular Resolution Diffusion Imaging
- MRI
- Magnetic Resonance Imaging
- FSL
- FMRIB Software Library
- Dipy
- Diffusion Imaging in Python
- APM
- Anisotropic Power Map was calculated
- DTI-TK
- Diffusion Tensor Imaging ToolKit
- TFCE
- Threshold-Free-Cluster-Enhancement
- ROI
- Region of Interest
- CCI
- Cluster Confidence Index
- AF
- arcuate Fascicle
- SLF II and SLF III
- components 2 and 3 of the SLF
- SLF-tp
- temporo-parietal component of the SLF
- IFOF
- inferior fronto-occipital Fascicle
- UF
- uncinate Fascicle
- ILF
- inferior longitudinal Fascicle
- Md-LF
- middle longitudinal Fascicle
- CST
- corticospinal tract
- OR
- optic radiation
- QC
- quality-control
Funding This work was supported by the National Institutes of Health [5R01NS066654-05]; KJ was supported by the Department of Defense (DoD) [National Defense Science & Engineering Graduate Fellowship (NDSEG) Program].
- DTI
- Diffusion Tensor Imaging
- IOS
- Intra-Operative Stimulation
- VLSM
- Voxel-Based Lesion-Symptom Mapping
- MD
- mean diffusivity
- FA
- fractional anisotropy
- B0
- minimally diffusion-weighted image
- AP
- anisotropic power
- ASAP
- automatic segmentation of anisotropic power changes
- HARDI
- High Angular Resolution Diffusion Imaging
- MRI
- Magnetic Resonance Imaging
- FSL
- FMRIB Software Library
- Dipy
- Diffusion Imaging in Python
- APM
- Anisotropic Power Map was calculated
- DTI-TK
- Diffusion Tensor Imaging ToolKit
- TFCE
- Threshold-Free-Cluster-Enhancement
- ROI
- Region of Interest
- CCI
- Cluster Confidence Index
- AF
- arcuate Fascicle
- SLF II and SLF III
- components 2 and 3 of the SLF
- SLF-tp
- temporo-parietal component of the SLF
- IFOF
- inferior fronto-occipital Fascicle
- UF
- uncinate Fascicle
- ILF
- inferior longitudinal Fascicle
- Md-LF
- middle longitudinal Fascicle
- CST
- corticospinal tract
- OR
- optic radiation
- QC
- quality-control