{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T16:29:01Z","timestamp":1759940941519},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319555232"},{"type":"electronic","value":"9783319555249"}],"license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016]]},"DOI":"10.1007\/978-3-319-55524-9_24","type":"book-chapter","created":{"date-parts":[[2017,4,11]],"date-time":"2017-04-11T00:18:56Z","timestamp":1491869936000},"page":"259-270","source":"Crossref","is-referenced-by-count":2,"title":["Combining Deep Learning Networks with Permutation Tests to Predict Traumatic Brain Injury Outcome"],"prefix":"10.1007","author":[{"given":"Y.","family":"Cai","sequence":"first","affiliation":[]},{"given":"S.","family":"Ji","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,4,12]]},"reference":[{"key":"24_CR1","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.neuroimage.2016.01.056","volume":"129","author":"J Mitra","year":"2016","unstructured":"Mitra, J., Shen, K., Ghose, S., Bourgeat, P., Fripp, J., Salvado, O., Pannek, K., Taylor, D.J., Mathias, J.L., Rose, S.: Statistical machine learning to identify traumatic brain injury (TBI) from structural disconnections of white matter networks. Neuroimage 129, 247\u2013259 (2016)","journal-title":"Neuroimage"},{"key":"24_CR2","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1089\/neu.2012.2627","volume":"30","author":"HS Levin","year":"2013","unstructured":"Levin, H.S., Li, X., McCauley, S.R., Hanten, G., Wilde, E.A., Swank, P.: Neuropsychological outcome of mTBI: a principal component analysis approach. J. Neurotrauma 30, 625\u2013632 (2013)","journal-title":"J. Neurotrauma"},{"key":"24_CR3","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1002\/mrm.10209","volume":"48","author":"DC Alexander","year":"2002","unstructured":"Alexander, D.C., Barker, G.J., Arridge, S.R.: Detection and modeling of non-Gaussian apparent diffusion coefficient profiles in human brain data. Magn. Reson. Med. 48, 331\u2013340 (2002)","journal-title":"Magn. Reson. Med."},{"key":"24_CR4","doi-asserted-by":"crossref","first-page":"1487","DOI":"10.1016\/j.neuroimage.2006.02.024","volume":"31","author":"SM Smith","year":"2006","unstructured":"Smith, S.M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T.E., Mackay, C.E., Watkins, K.E., Ciccarelli, O., Cader, M.Z., Matthews, P.M., Behrens, T.E.J.: Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 31, 1487\u20131505 (2006)","journal-title":"Neuroimage"},{"key":"24_CR5","doi-asserted-by":"crossref","unstructured":"Nichols, T., Holmes, A.: Nonparametric permutation tests for functional neuroimaging. In: Human Brain Function, 2nd edn., pp. 887\u2013910 (2003)","DOI":"10.1016\/B978-012264841-0\/50048-2"},{"key":"24_CR6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/hbm.1058","volume":"15","author":"TE Nichols","year":"2002","unstructured":"Nichols, T.E., Holmes, A.P.: Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum. Brain Mapp. 15, 1\u201325 (2002)","journal-title":"Hum. Brain Mapp."},{"key":"24_CR7","first-page":"794","volume":"23","author":"K Arfanakis","year":"2002","unstructured":"Arfanakis, K., Haughton, V.M., Carew, J.D., Rogers, B.P., Dempsey, R.J., Meyerand, M.E.: Diffusion tensor MR imaging in diffuse axonal injury. AJNR Am. J. Neuroradiol. 23, 794\u2013802 (2002)","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"24_CR8","doi-asserted-by":"crossref","first-page":"514","DOI":"10.3174\/ajnr.A0856","volume":"29","author":"DR Rutgers","year":"2008","unstructured":"Rutgers, D.R., Toulgoat, F., Cazejust, J., Fillard, P., Lasjaunias, P., Ducreux, D.: White matter abnormalities in mild traumatic brain injury: a diffusion tensor imaging study. AJNR Am. J. Neuroradiol. 29, 514\u2013519 (2008)","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"24_CR9","doi-asserted-by":"crossref","first-page":"2508","DOI":"10.1093\/brain\/awm216","volume":"130","author":"MF Kraus","year":"2007","unstructured":"Kraus, M.F., Susmaras, T., Caughlin, B.P., Walker, C.J., Sweeney, J.A., Little, D.M.: White matter integrity and cognition in chronic traumatic brain injury: a diffusion tensor imaging study. Brain 130, 2508\u20132519 (2007)","journal-title":"Brain"},{"key":"24_CR10","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1212\/WNL.0b013e3181d0ccdd","volume":"74","author":"AR Mayer","year":"2010","unstructured":"Mayer, A.R., Ling, J., Mannell, M.V., Gasparovic, C., Phillips, J.P., Doezema, D., Reichard, R., Yeo, R.A.: A prospective diffusion tensor imaging study in mild traumatic brain injury. Neurology 74, 643\u2013650 (2010)","journal-title":"Neurology"},{"key":"24_CR11","doi-asserted-by":"crossref","first-page":"1876","DOI":"10.1093\/brain\/awu095","volume":"137","author":"T Ilvesm\u00e4ki","year":"2014","unstructured":"Ilvesm\u00e4ki, T., Luoto, T.M., Hakulinen, U., Brander, A., Ryymin, P., Eskola, H., Iverson, G.L., \u00d6hman, J.: Acute mild traumatic brain injury is not associated with white matter change on diffusion tensor imaging. Brain 137, 1876\u20131882 (2014)","journal-title":"Brain"},{"key":"24_CR12","doi-asserted-by":"crossref","first-page":"3209","DOI":"10.1093\/brain\/awn247","volume":"131","author":"SN Niogi","year":"2008","unstructured":"Niogi, S.N., Mukherjee, P., Ghajar, J., Johnson, C.E., Kolster, R., Lee, H., Suh, M., Zimmerman, R.D., Manley, G.T., McCandliss, B.D.: Structural dissociation of attentional control and memory in adults with and without mild traumatic brain injury. Brain 131, 3209\u20133221 (2008)","journal-title":"Brain"},{"key":"24_CR13","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1016\/j.neuroimage.2011.12.062","volume":"60","author":"JY Wang","year":"2012","unstructured":"Wang, J.Y., Abdi, H., Bakhadirov, K., Diaz-Arrastia, R., Devous, M.D.: A comprehensive reliability assessment of quantitative diffusion tensor tractography. Neuroimage 60, 1127\u20131138 (2012)","journal-title":"Neuroimage"},{"key":"24_CR14","doi-asserted-by":"crossref","first-page":"1367","DOI":"10.3171\/2013.2.JNS121294","volume":"118","author":"S Farquharson","year":"2013","unstructured":"Farquharson, S., Tournier, J.-D., Calamante, F., Fabinyi, G., Schneider-Kolsky, M., Jackson, G.D., Connelly, A.: White matter fiber tractography: why we need to move beyond DTI. J. Neurosurg. 118, 1367\u20131377 (2013)","journal-title":"J. Neurosurg."},{"key":"24_CR15","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1016\/j.neuroimage.2011.09.015","volume":"62","author":"M Jenkinson","year":"2012","unstructured":"Jenkinson, M., Beckmann, C.F., Behrens, T.E.J., Woolrich, M.W., Smith, S.M.: FSL. Neuroimage 62, 782\u2013790 (2012)","journal-title":"Neuroimage"},{"key":"24_CR16","unstructured":"Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., Ng, A.Y.: Multimodal deep learning. In: Proceedings of 28th International Conference on Machine Learning, pp. 689\u2013696 (2011)"},{"key":"24_CR17","doi-asserted-by":"crossref","first-page":"948","DOI":"10.1212\/01.wnl.0000305961.68029.54","volume":"70","author":"EA Wilde","year":"2008","unstructured":"Wilde, E.A., McCauley, S.R., Hunter, J.V., Bigler, E.D., Chu, Z., Wang, Z.J., Hanten, G.R., Troyanskaya, M., Yallampalli, R., Li, X., Chia, J., Levin, H.S.: Diffusion tensor imaging of acute mild traumatic brain injury in adolescents. Neurology 70, 948\u2013955 (2008)","journal-title":"Neurology"},{"key":"24_CR18","unstructured":"Andersson, J.L.R., Jenkinson, M., Smith, S.: Non-linear registration aka Spatial normalisation FMRIB Technical report TR07JA2. In Pract. 22 (2007)"},{"key":"24_CR19","unstructured":"Nowlan, S.J.: Maximum likelihood competitive learning. In: Advances in Neural Information Processing Systems, vol. 2, pp. 574\u2013582 (1990)"},{"key":"24_CR20","unstructured":"Cotter, A., Shamir, O., Srebro, N., Sridharan, K.: Better mini-batch algorithms via accelerated gradient methods. In: NIPS, pp. 1\u20139 (2011)"},{"key":"24_CR21","first-page":"153","volume":"19","author":"Y Bengio","year":"2007","unstructured":"Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H.: Greedy layer-wise training of deep networks. Adv. Neural Inf. Process. Syst. 19, 153 (2007)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"24_CR22","doi-asserted-by":"crossref","unstructured":"Kao, P., Rojas, E., Chen, J., Zhang, A., Manjunath, B.S.: Unsupervised 3-D feature learning for mild traumatic brain injury. In: MICCAI Workshop:mTOP Grand Challenge (2016)","DOI":"10.1007\/978-3-319-55524-9_26"},{"key":"24_CR23","unstructured":"Bellotti, R., Lombardi, A., Amoroso, N., Tateo, A., Tangaro, S.: Semi-unsupervised prediction for mild TBI based on both graph and K-nn methods. In: MICCAI Workshop:mTOP Grand Challenge (2016)"}],"container-title":["Lecture Notes in Computer Science","Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-55524-9_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,20]],"date-time":"2019-09-20T18:10:15Z","timestamp":1569003015000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-55524-9_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"ISBN":["9783319555232","9783319555249"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-55524-9_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2016]]}}}