Neuroinformatics challenges to the structural, connectomic, functional, and electrophysiological multimodal imaging of human traumatic brain injury

Frontiers in Neuroinformatics

Volumn 8, Issue FEB, 2014, Pages

  Goh, S Y Matthew ^a   Irimia, Andrei ^a   Torgerson, Carinna M ^a   Van Horn, John D ^a  

^a UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

Author keywords

Connectomics; DTI; MRI; Neuroanatomy; Neuroinformatics; Rehabilitation; Traumatic brain injury

Indexed keywords

ALGORITHM; ALZHEIMER DISEASE; ARTICLE; BIOINFORMATICS; CONNECTOME; DATA ANALYSIS; ELECTROENCEPHALOGRAPHY; ELECTROPHYSIOLOGY; FRACTIONAL ANISOTROPY; FUNCTIONAL MAGNETIC RESONANCE IMAGING; GENETIC HETEROGENEITY; HUMAN; MAGNETOENCEPHALOGRAPHY; NEUROIMAGING; SPATIOTEMPORAL ANALYSIS; TRACTOGRAPHY; TRAUMATIC BRAIN INJURY;

EID: 84923264859     PISSN: None     EISSN: 16625196     Source Type: Journal    
DOI: 10.3389/fninf.2014.00019     Document Type: Article

References (77)

1. Achard, S., Delon-Martin, C., Vertes, P. E., Renard, F., Schenck, M., Schneider, F., et al. (2012). Hubs of brain functional networks are radically reorganized in comatose patients. Proc. Natl. Acad. Sci. U.S.A. 109, 20608-20613. doi: 10.1073/pnas.1208933109.
2. Akil, H., Martone, M. E., andVan Essen, D. C. (2011). Challenges and opportunities in miningneuroscience data. Science 331, 708-712. doi: 10.1126/science.1199305.
3. Arfanakis, K., Haughton, V. M., Carew, J. D., Rogers, B. P., Dempsey, R. J., and Meyerand, M. E. (2002). Diffusion tensor MR imaging in diffuse axonal injury. Am. J. Neuroradiol. 23, 794-802.
4. Ashburner, J., and Friston, K. J. (2000). Voxel-based morphometry-the methods. Neuroimage 11, 805-821. doi: 10.1006/nimg.2000.0582.
5. Bug, W. J., Ascoli, G. A., Grethe, J. S., Gupta, A., Fennema-Notestine, C., Laird, A. R., et al. (2008). The NIFSTD and BIRNLex vocabularies: building comprehensive ontologies for neuroscience. Neuroinformatics 6, 175-194. doi: 10.1007/s12021-008-9032-z
6. Bullmore, E. T., and Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and fucntional systems. Nat. Rev. Neurosci. 10, 186-198. doi: 10.1038/nrn2575.
7. Chen, A. J., and D'Esposito, M. (2010). Traumatic brain injury: from bench to bedside to society. Neuron 66,11-14. doi: 10.1016/j.neuron.2010.04.004.
8. Corrigan, J. D., Harrison-Felix, C., Bogner, J., Dijkers, M., Terrill, M. S., and Whiteneck, G. (2003). Systematic bias in traumatic brain injury outcome studies because of loss to follow-up. Arch. Phys. Med. Rehabil. 84, 153-160. doi: 10.1053/apmr.2003.50093.
9. Dale, A. M., Fischl, B., and Sereno, M. I. (1999). Cortical surface-based analysis-I. Segmentation and surface reconstruction. Neuroimage 9, 179-194. doi: 10.1006/nimg.1998.0395.
10. Dauguet, J., Peled, S., Berezovskii, V., Delzescaux, T., Warfield, S. K., Born, R., et al. (2007). Comparison of fiber tracts derived from in-vivo DTI tractogra-phy with 3D histological neural tract tracer reconstruction on a macaque brain. Neuroimage 37, 530-538. doi: 10.1016/j.neuroimage.2007.04.067.
11. Du, A. T., Schuff, N., Kramer, J. H., Rosen, H. J., Gorno-Tempini, M. L., Rankin, K., et al. (2007). Different regional patterns of cortical thinning in Alzheimer's disease and frontotemporal dementia. Brain 130, 1159-1166. doi: 10.1093/brain/ awm016.
12. Faul, M., Xu, L., Wald, M. M., and Coronado, V. G. (2010). Traumatic Brain Injury in the United States: Emergency Department Visits, Hospitalizations and Deaths 2002-2006. Atlanta, GA: Centers for Disease Control and Prevention, National Center for Injury Prevention and Control.
13. Filippi, M., Gawne-Cain, M. L., Gasperini, C., Vanwaesberghe, J. H., Grimaud, J., Barkhof, F., et al. (1998). Effect of training and different measurement strategies on the reproducibility of brain MRI lesion load measurements in multiple sclerosis. Neurology 50, 238-244. doi: 10.1212/WNL.50.1.238.
14. Gencer, N. G., and Acar, C. E. (2004). Sensitivity of EEG and MEG measurements to tissue conductivity. Phys. Med. Biol. 49, 701-717. doi: 10.1088/00319155/49/5/004.
15. Ghajar, J. (2000). Traumatic brain injury. Lancet 356, 923-929. doi: 10.1016/S0140-6736(00)02689-1.
16. Goh, S. Y. M., Irimia, A., Torgerson, C. M., Kikinis, R., Vespa, P. M., and Van Horn, J. D. (2013). "High-resolution electroencephalographic forward modeling in traumatic brain injury using the finite element method, in 2013. IEEE 10th International Symposium on Biomedical Imaging, San Francisco, CA, USA.
17. He, B., Musha, T., Okamoto, Y., Homma, S., Nakajima, Y., and Sato, T. (1987). Electric dipole tracing in the brain by means of the boundary element method and its accuracy. IEEE Trans. Biomed. Eng. 34,406-414. doi: 10.1109/TBME.1987. 326056.
18. Hillary, F. G., Steffener, J., Biswal, B. B., Lange, G., Deluca, J., and Ashburner, J. (2002). Functional magnetic resonance imaging technology and traumatic brain injuryrehabilitation: guidelines for methodological and conceptual pitfalls. J. HeadTraumaRehabil. 17,411-430. doi: 10.1097/00001199-200210000-00004.
19. Hofmann, M., Bezrukov, I., Mantlik, F., Aschoff, P., Steinke, F., Beyer, T., et al. (2011). MRI-based attenuation correction for whole-bodyPET/MRI: quantitative evaluation of segmentation-and atlas-based methods. J. Nucl. Med. 52, 1392-1399. doi: 10.2967/jnumed.110.078949.
20. Holshouser, B. A., Hinshaw, D. B. Jr., and Shellock, F. G. (1993). Sedation, anesthesia, and physiologic monitoring during MR imaging: evaluation of procedures and equipment. J. Magn. Reson. Imaging 3, 553-558. doi: 10.1002/jmri.1880030320.
21. Hoofien, D., Gilboa, A., Vakil, E., and Donovick, P. J. (2001). Traumatic brain injury (TBI) 10-20 years later: a comprehensive outcome study of psychiatric symptomatology, cognitive abilities and psychosocial functioning. Brain Inj. 15, 189-209. doi: 10.1080/026990501300005659.
22. Irimia, A., Chambers, M. C., Alger, J. R., Filippou, M., Prastawa, M. W., Wang, B., et al. (2011). Comparison of acute and chronic traumatic brain injury using semi-automatic multimodal segmentation of MR volumes. J. Neurotrauma 28, 2287-2306. doi: 10.1089/neu.2011.1920.
23. Irimia, A., Chambers, M. C., Torgerson, C. M., Filippou, M., Hovda, D. A., Alger, J. R., et al. (2012a). Patient-tailored connectomics visualization for the assessment of white matter atrophy in traumatic brain injury. Front. Neurol. 3:10. doi: 10.3389/fneur.2012.00010.
24. Irimia, A., Van Horn, J. D., and Halgren, E. (2012b). Source cancellation profiles of electroencephalography and magnetoencephalography. Neuroimage 59, 2464-2474. doi: 10.1016/j.neuroimage.2011.08.104.
25. Irimia, A., Wang, B., Aylward, S. R., Prastawa, M., Pace, D. F., Gerig, G., et al. (2012c). Neuroimaging of structural pathology and connectomics in traumatic brain injury: toward personalized outcome prediction. NeuroimageClin. 1, 1-17. doi: 10.1016/j.nicl.2012.08.002.
26. Irimia, A., Goh, S. Y., Torgerson, C. M., Chambers, M. C., Kikinis, R., and Van Horn, J. D. (2013a). Forward and inverse electroencephalographic modeling in health and in acute traumatic brain injury. Clin. Neurophysiol. 124, 2129-2145. doi: 10.1016/j.clinph.2013.04.336.
27. Irimia, A., Goh, S. Y. M., Torgerson, C. M., Stein, N. R., Chambers, M. C., Vespa, P. M., et al. (2013b). Electroencephalographic inverse localization of brain activity in acute traumatic brain injury as a guide to surgery, monitoring and treatment. Clin. Neurol. Neurosurg. 115, 2159-2165. doi: 10.1016/j.clineuro.2013. 08.003.
28. Iturria-Medina, Y., Canales-Rodriguez, E. J., Melie-Garcia, L., Valdes-Hernandez, P. A., Martinez-Montes, E., Aleman-Gomez, Y., et al. (2007). Characterizing brain anatomical connections using diffusion weighted MRI and graph theory. Neuroimage 36, 645-660. doi: 10.1016/j.neuroimage.2007.02.012.
29. Jack, C. R., Bernstein, M. A., Fox, N. C., Thompson, P. M., Alexander, G., Harvey, D., et al. (2008). The Alzheimer's Disease Neuroimaging Initiative (ADNI): the MR imaging protocol. J. Magn. Reson. Imaging 27, 685-691. doi: 10.1002/jmri.21049.
30. Jack, C. R. Jr., Bernstein, M. A., Borowski, B. J., Gunter, J. L., Fox, N. C., Thompson, PM, et al. (2010). Update on the magnetic resonance imaging core of the Alzheimer's disease neuroimaging initiative. AlzheimersDement. 6,212-220. doi: 10.1016/j.jalz.2010.03.004.
31. Jovicich, J., Czanner, S., Han, X., Salat, D., Van Der Kouwe, A., Quinn, B., et al. (2009). MRI-derived measurements of human subcortical, ventricular and intracranial brain volumes: reliability effects of scan sessions, acquisition sequences, data analyses, scanner upgrade, scanner vendors and field strengths. Neuroimage 46, 177-192. doi: 10.1016/j.neuroimage.2009.02.010.
32. Karlik, S. J., Heatherley, T., Pavan, F., Stein, J., Lebron, F., Rutt, B., et al. (1988). Patient anesthesia and monitoring at a 1.5-T MRI installation. Magn. Reson. Med. 7,210-221. doi: 10.1002/mrm.1910070209.
33. Kasahara, M., Menon, D. K., Salmond, C. H., Outtrim, J. G., Tavares, J. V., Carpenter, T. A., et al. (2011). Traumatic brain injury alters the functional brain network mediating working memory. Brain Inj. 25, 1170-1187. doi: 10.3109/02699052.2011.608210.
34. Keator, D. B., Helmer, K., Steffener, J., Turner, J. A., Van Erp, T. G., Gadde, S., et al. (2013). Towards structured sharing of raw and derived neuroimaging data across existing resources. Neuroimage 82, 647-661. doi: 10.1016/j.neuroimage.2013.05.094.
35. Kempton, M. J., Underwood, T. S., Brunton, S., Stylios, F., Schmechtig, A., Ettinger, U., et al. (2011). A comprehensive testing protocol for MRI neuroanatomical segmentation techniques: evaluation of a novel lateral ventricle segmentation method. Neuroimage 58, 1051-1059. doi: 10.1016/j.neuroimage.2011. 06.080.
36. Kinnunen, K. M., Greenwood, R., Powell, J. H., Leech, R., Hawkins, P. C., Bonnelle, V., et al. (2011). White matter damage and cognitive impairment after traumatic brain injury. Brain 134, 449-463. doi: 10.1093/brain/awq347.
37. Kraus, M. F., Susmaras, T., Caughlin, B. P., Walker, C. J., Sweeney, J. A., and Little, D. M. (2007). White matter integrity and cognition in chronic traumatic brain injury: a diffusion tensor imaging study. Brain 130, 2508-2519. doi: 10.1093/brain/awm216.
38. Kuceyeski, A., Maruta, J., Niogi, S. N., Ghajar, J., and Raj, A. (2011). The generation and validation of white matter connectivity importance maps. Neuroimage 58, 109-121. doi: 10.1016/j.neuroimage.2011.05.087.
39. Lehmann, M., Douiri, A., Kim, L. G., Modat, M., Chan, D., Ourselin, S., et al. (2010). Atrophy patterns in Alzheimer's disease and semantic dementia: a comparison of FreeSurfer and manual volumetric measurements. Neuroimage 49, 2264-2274. doi: 10.1016/j.neuroimage.2009.10.056.
40. Lima, E. A., Irimia, A., and Wikswo, J. P. (2006). "The magnetic inverse problem, in The SQUID Handbook, Vol. 2, eds J. Clarke, and A. I. Braginski (Weinheim, Germany: Wiley-VCH), 139-268. doi: 10.1002/9783527609956.ch10.
41. Ling, J., Merideth, F., Caprihan, A., Pena, A., Teshiba, T., and Mayer, A. R. (2012). Head injury or head motion? Assessment and quantification of motion artifacts in diffusion tensor imaging studies. Hum. Brain Mapp. 33, 50-62. doi: 10.1002/hbm.21192.
42. MacDonald, C. L., Dikranian, K., Bayly, P., Holtzman, D., and Brody, D. (2007). Diffusion tensor imaging reliably detects experimental traumatic axonal injury and indicates approximate time of injury. J. Neurosci. 27, 11869-11876. doi: 10.1523/JNEUROSCI.3647-07.2007.
43. Manley, G. T., and Maas, A. I. (2013). Traumatic brain injury: an international knowledge-based approach. J. Am. Med. Assoc. 310, 473-474. doi: 10.1001/jama.2013.169158.
44. Margulies, D. S., Bottger, J., Watanabe, A., and Gorgolewski, K. J. (2013). Visualizing the human connectome. Neuroimage 80, 445-461. doi: 10.1016/j.neuroimage.2013.04.111.
45. McDowell, S., Whyte, J., and D'Esposito, M. (1997). Working memory impairments in traumatic brain injury: evidence from a dual-task paradigm. Neuropsychologia 35, 1341-1353. doi: 10.1016/S0028-3932(97)00082-1.
46. Meythaler, J. M., Peduzzi, J. D., Eleftheriou, E., and Novack, T. A. (2001). Current concepts: diffuse axonal injury-associated traumatic brain injury. Arch. Phys. Med. Rehabil. 82, 1461-1471. doi: 10.1053/apmr.2001.25137.
47. Moeller, S., Yacoub, E., Olman, C. A., Auerbach, E., Strupp, J., Harel, N., et al. (2010). Multiband multislice GE-EPI at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI. Magn. Reson. Med. 63, 1144-1153. doi: 10.1002/mrm.22361.
48. Mori, S., and van Zijl, P. C. (2002). Fiber tracking: principles and strategies-a technical review. NMR Biomed. 15, 468-480. doi: 10.1002/nbm.781.
49. Niethammer, M., Hart, G. L., Pace, D. F., Vespa, P. M., Irimia, A., Van Horn, J. D., et al. (2011). Geometric metamorphosis. MedImage Comput. Comput. Assist. Interv. 14, 639-646.
50. Palacios, E. M., Sala-Llonch, R., Junque, C., Roig, T., Tormos, J. M., Bargallo, N., et al. (2013). Resting-state functional magnetic resonance imaging activity and connectivity and cognitive outcome in traumatic brain injury. J. Am. Med. Assoc. Neurol. 70, 845-851. doi: 10.1001/jamaneurol.2013.38.
51. Pandit, A. S., Expert, P., Lambiotte, R., Bonnelle, V., Leech, R., Turkheimer, F. E., et al. (2013). Traumatic brain injury impairs small-world topology. Neurology 80, 1826-1833. doi: 10.1212/WNL.0b013e3182929f38.
52. Powell, S., Magnotta, V. A., Johnson, H., Jammalamadaka, V. K., Pierson, R., and Andreasen, N. C. (2008). Registration and machine learning-based automated segmentation of subcortical and cerebellar brain structures. Neuroimage 39,238-247. doi: 10.1016/j.neuroimage.2007.05.063.
53. Prastawa, M., Bullitt, E., and Gerig, G. (2004). A brain tumor segmentation framework based on outlier detection. Med. Image Anal. 8, 275-283. doi: 10.1016/j.media.2004.06.007.
54. Prastawa, M., Bullitt, E., Moon, N., Van Leemput, K., and Gerig, G. (2003). Automatic brain tumor segmentation by subject specific modification of atlas priors. Acad. Radiol. 10,1341-1348. doi: 10.1016/S1076-6332(03)00506-3.
55. Rubinov, M., and Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059-1069. doi: 10.1016/j.neuroimage.2009.10.003.
56. Scannell, J. W., Blakemore, C., andYoung, M. P. (1995). Analysis of connectivityin the cat cerebral cortex. J. Neurosci. 15, 1463-1483.
57. Scannell, J. W., and Young, M. P. (1993). The connectional organization of neural systems in the cat cerebral cortex. Curr. Biol. 3, 191-200. doi: 10.1016/0960-9822(93)90331-H
58. Sharon, D., Hamalainen, M. S., Tootell, R., and Halgren, E. (2009). The advantage of combining MEG and EEG: comparison to fMRI in focally-stimulated visual cortex. Neuroimage 36, 1225-1235. doi: 10.1016/j.neuroimage.2007.03.066.
59. Skudlarski, P., Jagannathan, K., Calhoun, V. D., Hampson, M., Skudlarska, B. A., and Pearlson, G. (2008). Measuring brain connectivity: diffusion tensor imaging validates resting state temporal correlations. Neuroimage 43, 554-561. doi: 10.1016/j.neuroimage.2008.07.063.
60. Stanley, M. L., Moussa, M. N., Paolini, B. M., Lyday, R. G., Burdette, J. H., and Laurienti,P.J. (2013).Definingnodesincomplexbrainnetworks. Front. Comput. Neurosci. 7:169. doi: 10.3389/fncom.2013.00169.
61. Storti, S. F., Formaggio, E., Franchini, E., Bongiovanni, L. G., Cerini, R., Fiaschi, A., et al. (2012).Amultimodal imagingapproach to the evaluation of post-traumatic epilepsy. MAGMA 25, 345-360. doi: 10.1007/s10334-012-0316-9.
62. Strangman, G. E., O'Neil-Pirozzi, T. M., Supelana, C., Goldstein, R., Katz, D. I., and Glenn, M. B. (2010). Regional brain morphometry predicts memory rehabilitation outcome after traumatic brain injury. Front. Hum. Neurosci. 4:182. doi: 10.3389/fnhum.2010.00182.
63. Strogatz, S. H. (2001). Exploring complex networks. Nature 410, 268-276. doi: 10.1038/35065725.
64. Thirion, B., Tucholka, A., and Poline, J. (2010). Parcellation schemes and statistical tests to detect active regions on the cortical surface, in Proceedings of the Nineteenth International Conference on Computational Statistics, Paris.
65. Thompson, P. M., Hayashi, K. M., De Zubicaray, G., Janke, A. L., Rose, S. E., Semple, J., et al. (2003). Dynamics of gray matter loss in Alzheimer's disease. J. Neurosci. 23, 994-1005.
66. Tijssen, R. H., Jansen, J. F., and Backes, W. H. (2009). Assessing and minimizing the effects of noise and motion in clinical DTI at 3. T. Hum. Brain Mapp. 30, 2641-2655. doi: 10.1002/hbm.20695.
67. Ugurbil, K. (2012). The road to functional imaging and ultrahigh fields. Neuroimage 62, 726-735. doi: 10.1016/j.neuroimage.2012.01.134.
68. Van Dijk, K. R., Sabuncu, M. R., and Buckner, R. L. (2012). The influence of head motion on intrinsic functional connectivity MRI. Neuroimage 59, 431-438. doi: 10.1016/j.neuroimage.2011.07.044.
69. Van Horn, J. D., Irimia, A., Torgerson, C. M., Chambers, M. C., Kikinis, R., and Toga, A. W. (2012). Mapping connectivity damage in the case of phineas gage. PLoS ONE 7:e37454. doi: 10.1371/journal.pone.0037454.
70. Van Horn, J. D., and Toga, A. W. (2009). Is it time to re-prioritize neuroimaging databases and digital repositories? Neuroimage 47, 1720-1734. doi: 10.1016/j.neuroimage.2009.03.086.
71. Van Horn, J. D., and Toga, A. W. (2013). Human neuroimaging as a "Big Data science. Brain Imaging Behav. doi: 10.1007/s11682-013-9255-y [Epub ahead of print].
72. Van Leemput, K., Maes, F., Vandermeulen, D., Colchester, A., and Suetens, P. (2001). Automated segmentation of multiple sclerosis lesions by model outlier detection. IEEE Trans. Med. Imaging 20, 677-688. doi: 10.1109/42.938237.
73. Vincent, J. L., and Moreno, R. (2010). Clinical review: scoring systems in the critically ill. Crit. Care 14,207. doi: 10.1186/cc8204.
74. Wang, B., Prastawa, M. W., Awate, S. P., Irimia, A., Chambers, M. C., Vespa, P. M., et al. (2012). A patient-specific segmentation framework for longitudinal MR images of traumatic brain injury. Proc. SPIE 8314, 7. doi: 10.1117/12. 911043.
75. Weiner, M. W., Veitch, D. P., Aisen, P. S., Beckett, L. A., Cairns, N. J., Green, R. C., et al. (2012). The Alzheimer's disease neuroimaging initiative: a review of papers published since its inception. Alzheimers Dement. 8, S1-S68. doi: 10.1016/j.jalz.2011.09.172.
76. Wu, Y., Warfield, S. K., Tan, I. L., Wells Iii, W. M., Meier, D. S., Van Schijndel, R. A., et al. (2006). Automated segmentation of multiple sclerosis lesion subtypes with multichannel MRI. Neuroimage 32, 1205-1215. doi: 10.1016/j.neuroimage.2006.04.211.
77. Zalesky, A., Fornito, A., Egan, G. F., Pantelis, C., and Bullmore, E. T. (2012). The relationship between regional and inter-regional functional connectivity deficits in schizophrenia. Hum. Brain Mapp. 33, 2535-2549. doi: 10.1002/hbm.21379