Longitudinal Disconnection Tractograms to Investigate the Functional Consequences of White Matter Damage: An Automated Pipeline.
High Angular Resolution Diffusion Imaging
Neurosurgery
open source software
tractography
white matter
Journal
Journal of neuroimaging : official journal of the American Society of Neuroimaging
ISSN: 1552-6569
Titre abrégé: J Neuroimaging
Pays: United States
ID NLM: 9102705
Informations de publication
Date de publication:
07 2020
07 2020
Historique:
received:
16
12
2019
accepted:
27
03
2020
pubmed:
22
5
2020
medline:
16
2
2021
entrez:
22
5
2020
Statut:
ppublish
Résumé
Neurosurgical resection is one of the few opportunities researchers have to image the human brain pre- and postfocal damage. A major challenge associated with brains undergoing surgical resection is that they often do not fit brain templates most image-processing methodologies are based on. Manual intervention is required to reconcile the pathology, requiring time investment and introducing reproducibility concerns, and extreme cases must be excluded. 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, which aligns images using an approach sensitive to white matter microstructure. 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.
Sections du résumé
BACKGROUND AND PURPOSE
Neurosurgical resection is one of the few opportunities researchers have to image the human brain pre- and postfocal damage. A major challenge associated with brains undergoing surgical resection is that they often do not fit brain templates most image-processing methodologies are based on. Manual intervention is required to reconcile the pathology, requiring time investment and introducing reproducibility concerns, and extreme cases must be excluded.
METHODS
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.
RESULTS
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, which aligns images using an approach sensitive to white matter microstructure.
CONCLUSIONS
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.
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
443-457Subventions
Organisme : NIH HHS
ID : 5R01NS066654-05
Pays : United States
Informations de copyright
© 2020 American Society of Neuroimaging.
Références
Berti A, Garbarini F, Neppi-Modona M. Disorders of higher cortical function. In Zigmond MJ, Rowland LP, Coyle JT, eds. Neurobiology of Brain Disorders. Amsterdam, Netherlands: Elsevier BV, 2015:525-41.
Geschwind N. Disconnexion syndromes in animals and man I. Brain 1965;88:237-94.
Bates E, Wilson SM, Saygin AP, et al. Voxel-based lesion-symptom mapping. Nat Neurosci 2003;6:448-50.
Geschwind, N. Disconnexion syndromes in animals and man II. Brain 1965;88:585-644.
Basser PJ, Mattiello J, LeBihan D. MR diffusion tensor spectroscopy and imaging. Biophys J 1994;66:259-67.
Le Bihan D, Mangin JF, Poupon C, et al. Diffusion tensor imaging: concepts and applications. J Magn Reson Imaging 2001;13:534-46.
Pierpaoli C, Jezzard P, Basser PJ, et al. Diffusion tensor MR imaging of the human brain. Radiology 1996;201:637-48.
Pajevic S, Pierpaoli C. Color schemes to represent the orientation of anisotropic tissues from diffusion tensor data: application to white matter fiber tract mapping in the human brain. Magn Reson Med 1999;42:526-40.
Tuch DS. Q-ball imaging. Magn Reson Med 2004;52:1358-72.
Tournier JD, Calamante F, Gadian DG, et al. Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. Neuroimage 2004;23:1176-85.
Jensen JH, Helpern JA, Ramani A, et al. Diffusional kurtosis imaging: the quantification of non-Gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med 2005;53:1432-40.
Assaf Y, Basser PJ. Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain. Neuroimage 2005;27:48-58.
Jeurissen B, Tournier JD, Dhollander T, et al. Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data. Neuroimage 2014;103:411-26.
Tuch DS, Reese TG, Wiegell MR, et al. High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity. Magn Reson Med 2002;48:577-82.
Hess CP, Mukherjee P, Han ET, et al. Q-ball reconstruction of multimodal fiber orientations using the spherical harmonic basis. Magn Reson Med 2006;56:104-17.
Kumar P, Kathuria P, Nair P, et al. Prediction of upper limb motor recovery after subacute ischemic stroke using diffusion tensor imaging: a systematic review and meta-analysis. J Stroke 2016;18:50-9.
Kumar P, Yadav AK, Misra S, et al. Prediction of upper extremity motor recovery after subacute intracerebral hemorrhage through diffusion tensor imaging: a systematic review and meta-analysis. Neuroradiology 2016;58:1043-50.
Yogarajah M, Focke NK, Bonelli S, et al. Defining meyers loop-temporal lobe resections visual field deficits and diffusion tensor tractography. Brain 2009;132:1656-68.
Chen X, Weigel D, Ganslandt O, et al. Prediction of visual field deficits by diffusion tensor imaging in temporal lobe epilepsy surgery. Neuroimage 2009;45:286-97.
Glenn OA, Henry RG, Berman JI, et al. DTI-based three-dimensional tractography detects differences in the pyramidal tracts of infants and children with congenital hemiparesis. J Magn Reson Imaging 2003;18:641-8.
Mandelli ML, Caverzasi E, Binney RJ, et al. Frontal white matter tracts sustaining speech production in primary progressive aphasia. J Neurosci 2014;34: 9754-67.
Jang, SH. Diffusion tensor imaging studies on arcuate fasciculus in stroke patients: a review. Front Hum Neurosci 2013;7:749.
Mori S, van Zijl P. Fiber tracking: principles and strategies-a technical review. NMR Biomed 2002;15:468-80.
Caverzasi E, Hervey-Jumper SL, Jordan KM, et al. Identifying preoperative language tracts and predicting postoperative functional recovery using HARDI q-ball fiber tractography in patients with gliomas. J Neurosurg 2016;125:33-45.
Duffau H. The anatomo-functional connectivity of language revisited. New insights provided by electrostimulation and tractography. Neuropsychologia 2008;46: 927-34.
Kim SH, Jang SH. Prediction of aphasia outcome using diffusion tensor tractography for arcuate fasciculus in stroke. AJNR Am J Neuroradiol 2012;34:785-90.
Wu JS, Zhou LF, Tang WJ, et al. Clinical evaluation and follow-up outcome of diffusion tensor imaging-based functional neuronavigation: a prospective, controlled study in patients with gliomas involving pyramidal tracts. Neurosurgery 2007;61:935-49.
Leclercq D, Duffau H, Delmaire C. Comparison of diffusion tensor imaging tractography of language tracts and intraoperative subcortical stimulations. J Neurosurg 2010;112:503-11.
Henry RG, Berman JI, Nagarajan SS, et al. Subcortical pathways serving cortical language sites: initial experience with diffusion tensor imaging fiber tracking combined with intraoperative language mapping. Neuroimage 2004;21:616-22.
Lehéricy S, Duffau H, Van de Moortele PF, et al. Validity of presurgical functional localization. In Stippich C, ed. Clinical Functional MRI. Berlin, Heidelberg: Springer, 2007:167-87.
Lehéricy S, Leclercq D, Duffau H, et al. Presurgical functional localization possibilities limitations, and validity. In Stippich C, ed. Clinical Functional MRI. Berlin, Heidelberg: Springer, 2014:247-67.
Berman JI, Berger MS, Chung S, Nagarajan SS, and Henry RG. Accuracy of diffusion tensor magnetic resonance imaging tractography assessed using intraoperative subcortical stimulation mapping and magnetic source imaging. J Neurosurg 2007;107:488-94.
Bucci M, Mandelli ML, Berman JI, et al. Quantifying diffusion MRI tractography of the corticospinal tract in brain tumors with deterministic and probabilistic methods. Neuroimage Clin 2013;3:361-8.
Jordan KM, Amirbekian B, Keshavan A, et al. Cluster confidence index: a streamline-wise pathway reproducibility metric for diffusion-weighted MRI tractography. J Neuroimaging 2017;28:64-9.
Mandelli, ML, Berger MS, Bucci M, Berman JI, et al. Quantifying accuracy and precision of diffusion MR tractography of the corticospinal tract in brain tumors. J Neurosurg 2014;121:349-58.
Berman, JI. Advanced diffusion MR tractography for surgical planning. In Pillai JJ, ed. Functional Brain Tumor Imaging. New York, NY: Springer, 2013:183-94.
Chamberland M, Whittingstall K, Fortin D, et al. Real-time multi-peak tractography for instantaneous connectivity display. Front Neuroinform 2014;8:59.
Fillard P, Descoteaux M, Goh A, et al. Quantitative evaluation of 10 tractography algorithms on a realistic diffusion MR phantom. Neuroimage 2011;56:220-34.
Crettenand S, Meredith SD, Hoptman MJ, Reilly RB. Quantitative analysis and comparison of diffusion tensor imaging tractography algorithms (abstract). ISSC 2006;105-10.
Neher PF, Descoteaux M, Houde JC, Stieltjes B, Maier-Hein KH. Strengths and weaknesses of state of the art fiber tractography pipelines-a comprehensive in-vivo and phantom evaluation study using Tractometer. Med Image Anal 2015;26:287-305.
Maier-Hein KH, Neher PF, Houde JC, et al. The challenge of mapping the human connectome based on diffusion tractography. Nat Commun 2017;8:1349.
Wakana S, Caprihan A, Panzenboeck MM, et al. Reproducibility of quantitative tractography methods applied to cerebral white matter. Neuroimage 2007;36:630-44.
Kinoshita M, Yamada K, Hashimoto N, et al. Fiber-tracking does not accurately estimate size of fiber bundle in pathological condition: initial neurosurgical experience using neuronavigation and subcortical white matter stimulation. Neuroimage 2005;25:424-9.
Duffau H. The dangers of magnetic resonance imaging diffusion tensor tractography in brain surgery. World Neurosurg 2014;81:56-8.
Golby AJ, Kindlmann G, Norton I, et al. Interactive diffusion tensor tractography visualization for neurosurgical planning. Neurosurgery 2011;68:496-505.
Ius T, Angelini E, de Schotten MT, et al. Evidence for potentials and limitations of brain plasticity using an atlas of functional resectability of WHO grade II gliomas: towards a “minimal common brain.” Neuroimage 2011;56:992-1000.
Meyer S, Kessner SS, Cheng B, et al. Voxel-based lesion-symptom mapping of stroke lesions underlying somatosensory deficits. Neuroimage Clin 2016;10:257-66.
Kinoshita M, Nakajima R, Shinohara H, et al. Chronic spatial working memory deficit associated with the superior longitudinal fasciculus: a study using voxel-based lesion-symptom mapping and intraoperative direct stimulation in right prefrontal glioma surgery. J Neurosurg 2016;125:1024-32.
Campana S, Caltagirone C, Marangolo P. Combining voxel-based lesion-symptom mapping (VLSM) combining voxel-based lesion-symptom mapping (VLSM) with A-tDCS language treatment: predicting outcome of recovery in nonfluent chronic aphasia. Brain Stimul 2015;8:769-76.
Almairac F, Herbet G, Moritz-Gasser S, et al. The left inferior fronto-occipital fasciculus subserves language semantics: a multilevel lesion study. Brain Struct Funct 2014;220:1983-95.
Brett M, Leff A, Ashburner J. Automated nonlinear coregistration of damaged brains to a normal template using cost function masking. Neuroimage 2000;11:S566.
Ashburner J, Friston KJ. Unified segmentation. Neuroimage 2005;26:839-51.
Andersen SM, Rapcsak SZ, Beeson PM. Cost function masking during normalization of brains with focal lesions: still a necessity? Neuroimage 2010;53:78-84.
Fiez JA, Damasio H, Grabowski TJ. Lesion segmentation and manual warping to a reference brain: intra- and interobserver reliability. Hum Brain Mapp 2000;9:192-211.
Werner R, Wilms M, Cheng B, Forkert ND. Beyond cost function masking: RPCA-based non-linear registration in the context of VLSM. PRNI 2016;1-4.
Gordillo N, Montseny E, Sobrevilla P. State of the art survey on MRI brain tumor segmentation. Magn Reson Imaging 2013;31:1426-38.
Menze BH, Jakab A, Bauer S, et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 2015;34:1993-2024.
Sanjuán A, Price CJ, Mancini L, et al. Automated identification of brain tumors from single MR images based on segmentation with refined patient-specific priors. Front Neurosci 2013;7:241.
Brennan NMP, Holodny AI. Use of advanced neuroimaging (FMRI DTI/tractography) in the treatment of malignant gliomas. In Gunel JM, Piepmeier JM Baehring JM, eds. Malignant Brain Tumors. Cham: Springer, 2016:3-13.
Field AS, Alexander AL, Wu YC, et al. Diffusion tensor eigenvector directional color imaging patterns in the evaluation of cerebral white matter tracts altered by tumor. J Magn Reson Imaging 2004;20:555-62.
Nimsky C, Ganslandt O, Hastreiter P, et al. Preoperative and intraoperative diffusion tensor imaging-based fiber tracking in glioma surgery. Neurosurgery 2005;56:130-8.
Jenkinson M, Beckmann CJ, Behrens TEJ, et al. FSL. Neuroimage 2012;62:782-90.
Leemans A, Jones DK. The B-matrix must be rotated when correcting for subject motion in DTI data. Magn Reson Med 2009;61:1336-49.
Garyfallidis E, Brett M, Amirbekian B, et al. Dipy, a library for the analysis of diffusion MRI data. Front Neuroinform 2014;8:8.
Dell'Acqua F, Lacerda L, Catani M, Simmons A. Anisotropic power maps: a diffusion contrast to reveal low anisotropy tissues from HARDI data (abstract). ISMRM-ESMRMB, 2014.
Descoteaux M, Angelino E, Fitzgibbons S, et al. Regularized, fast, and robust analytical Q-ball imaging. Magn Reson Med 2007;58:497-510.
Smith SM. Fast robust automated brain extraction. Hum Brain Mapp 2002;17:143-55.
Zhang H, Yushkevich P, Alexander D, et al. Deformable registration of diffusion tensor MR images with explicit orientation optimization. Med Image Anal 2006;10:764-85.
Smith S, Nichols T. Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage 2009;44:83-98.
Woolrich MW, Jbabdi S, Patenaude B, et al. Bayesian analysis of neuroimaging data in FSL. Neuroimage 2009;45:S173-86.
Smith SM, Jenkinson M, Woolrich MW, et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 2004;23:S208-19.
Keshavan A, Datta E, McDonough I, et al. Mindcontrol: a web application for brain segmentation quality control. Neuroimage 2017;170:365-72.
Berman JI, Chung S, Mukherjee P, et al. Probabilistic streamline q-ball tractography using the residual bootstrap. Neuroimage 2008;39:215-22.
Tristán-Vega A, Westin CF, Aja-Fernández S. Estimation of fiber orientation probability density functions in high angular resolution diffusion imaging. Neuroimage 2009;47:638-50.
Tristán-Vega, Antonio, and Santiago Aja-FernándezDWI. Filtering using joint information for DTI and HARDI. Med Image Anal 2010;14:205-18.
Tristán-Vega A, Westin CF. Probabilistic ODF estimation from reduced HARDI data with sparse regularization. Med Image Comput Comput Assist Interv 2011;14:82-90.
Gorgolewski K, Burns CD, Madison C, et al. Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python. Front Neuroinform 2011;5:13.
Catani M, Jones DK, ffytche DH. Perisylvian language networks of the human brain. Ann Neurol 2004;57:8-16.
Caverzasi E, Papinutto N, Amirbekian B, et al. Q-ball of inferior fronto-occipital fasciculus and beyond. PLoS One 2014;9:e100274.
Makris N, Preti MG, Asami T, et al. Human middle longitudinal fascicle: variations in patterns of anatomical connections. Brain Struct Funct 2013;218:951-68.
Hofer S, Karaus A, Frahm J. Reconstruction and dissection of the entire human visual pathway using diffusion tensor MRI. Front Neuroanat 2010;4:15.
Fernández-Miranda JC, Wang Y, Pathak S, et al. Asymmetry, connectivity, and segmentation of the arcuate fascicle in the human brain. Brain Struct Funct 2014;220:1665-80.
Heide RJ, Von Der LM, Skipper E, et al. Dissecting the uncinate fasciculus: disorders, controversies and a hypothesis. Brain 2013;136:1692-707.
Herbet G, Maheu M, Costi E, et al. Mapping neuroplastic potential in brain-damaged patients. Brain 2016;139:829-44.
Feigl GC, Hiergeist W, Fellner C, et al. Magnetic resonance imaging diffusion tensor tractography: evaluation of anatomic accuracy of different fiber tracking software packages. World Neurosurg 2014;81:144-50.
Bernal B, Ardila A. The role of the arcuate fasciculus in conduction aphasia. Brain 2009;132:2309-16.
Dixit P, Liu GR. A review on recent development of finite element models for head injury simulations. Arch Comput Methods in Eng 2016;24:979-1031.
Tse KM, Tan LB, Lee SJ, et al. Development and validation of two subject-specific finite element models of human head against three cadaveric experiments. Int J Numer Method Biomed Eng 2013;30:397-415.
Garlapati RR, Roy A, Joldes GR, et al. More accurate neuronavigation data provided by biomechanical modeling instead of rigid registration. J Neurosurg 2014;120:1477-83.
Jezzard P, Balaban RS. Correction for geometric distortion in echo planar images from B0 field variations. Magn Reson Med 1995;34:65-73.
Andersson JLR, Skare S, Ashburner J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage 2003;20:870-88.
Forkel SJ, Catani M. Lesion mapping in acute stroke aphasia and its implications for recovery. Neuropsychologia 2018;115:88-100.
Chen DQ, Dell'Acqua F, Rokem A, et al. Diffusion weighted image co-registration: investigation of best practices. bioRxiv. https://doi.org/10.1101/864108
Yeatman JD, Dougherty RF, Myall NJ, et al. Tract profiles of white matter properties: automating fiber-tract quantification. PLoS One 2012;7:e49790.
O'Donnell LJ, Suter Y, Rigolo L, et al. Automated white matter fiber tract identification in patients with brain tumors. Neuroimage Clin 2017;13:138-53.
Catani M, ffytche DH. The rises and falls of disconnection syndromes. Brain 2005;128:2224-39.
Duffau H. Stimulation mapping of white matter tracts to study brain functional connectivity. Nat Rev Neurol 2015;11:255-65.
He BJ, Snyder AZ, Vincent JL, et al. Breakdown of functional connectivity in frontoparietal networks underlies behavioral deficits in spatial neglect. Neuron 2007;53:905-18.
Genova HM, Rajagopalan V, Chiaravalloti N, et al. Facial affect recognition linked to damage in specific white matter tracts in traumatic brain injury. Soc Neurosci 2014;10:27-34.
Cristofori I, Zhong W, Chau A, et al. White and gray matter contributions to executive function recovery after traumatic brain injury. Neurology 2015;84:1394-401.
Duffau H. The huge plastic potential of adult brain and the role of connectomics: new insights provided by serial mappings in glioma surgery. Cortex 2014;58:325-37.
Benzagmout M, Gatignol P, Duffau H. Resection of World Health Organization Grade II gliomas involving Broca's area: methodological and functional considerations. Neurosurgery 2007;61:741-53.
Papagno C, Gallucci M, Casarotti A, et al. Connectivity constraints on cortical reorganization of neural circuits involved in object naming. Neuroimage 2011;55:1306-13.
Duffau H. Interactions between diffuse low-grade glioma (DLGG) and brain plasticity. In Duffau H, ed. Diffuse Low-Grade Gliomas in Adults. Cham: Springer, 2013:337-56.