Decoding post-stroke motor function from structural brain imaging

NeuroImage: Clinical

Volumn 12, Issue , 2016, Pages 372-380

  Rondina, Jane M ^a   Filippone, Maurizio ^b   Girolami, Mark ^c   Ward, Nick S ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b EURECOM   (France)

^c UNIVERSITY OF WARWICK   (United Kingdom)

Author keywords

Features extraction; Gaussian processes; Lesion load; Lesion patterns; Machine learning; Motor impairment; Multiple kernel learning; Patterns of lesion probability; Stroke

Indexed keywords

ADULT; ARTICLE; CEREBROVASCULAR ACCIDENT; CLINICAL ARTICLE; CONTROLLED STUDY; FEMALE; FUNCTIONAL NEUROIMAGING; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; KERNEL METHOD; MACHINE LEARNING; MALE; MIDDLE AGED; MOTOR PERFORMANCE; NUCLEAR MAGNETIC RESONANCE SCANNER; PRIORITY JOURNAL; PYRAMIDAL TRACT; BRAIN; COMPLICATION; DIAGNOSTIC IMAGING; MOTOR DISORDERS; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY;

BRAIN; FEMALE; HUMANS; MACHINE LEARNING; MAGNETIC RESONANCE IMAGING; MALE; MIDDLE AGED; MOTOR DISORDERS; STROKE;

EID: 84984706589     PISSN: None     EISSN: 22131582     Source Type: Journal    
DOI: 10.1016/j.nicl.2016.07.014     Document Type: Article

References (47)

1. Alexander, G.E., Crutcher, M.D., DeLong, M.R., Basal ganglia-thalamocortical circuits: parallel substrates for motor, oculomotor, “prefrontal” and “limbic” functions. Prog. Brain Res. 85 (1990), 119–146, 10.1016/S0079-6123(08)62678-3.
2. Ashburner, J., Friston, K.J., Unified segmentation. NeuroImage 26 (2005), 839–851, 10.1016/j.neuroimage.2005.02.018.
3. Bates, E., Wilson, S.M., Saygin, A.P., Dick, F., Sereno, M.I., Knight, R.T., Dronkers, N.F., Voxel-based lesion-symptom mapping. Nat. Neurosci. 6 (2003), 448–450, 10.1038/nn1050.
4. Bayona, N.a., Bitensky, J., Foley, N., Teasell, R., Intrinsic factors influencing post stroke brain reorganization. Top. Stroke Rehabil. 12 (2005), 27–36, 10.1310/3BXL-18W0-FPJ4-F1GY.
5. Bohannon, Q.R., Motricity index scores are valid indicators of paretic upper extremity strength following stroke. J. Phys. Ther. Sci. 11 (1999), 59–61, 10.1589/jpts.11.59.
6. Bonita, R., Beaglehole, R., Recovery of motor function after stroke. Stroke 19 (1988), 1497–1500, 10.1161/01.STR.19.12.1497.
7. Burke Quinlan, E., Dodakian, L., See, J., McKenzie, A., Le, V., Wojnowicz, M., Shahbaba, B., Cramer, S.C., Neural function, injury, and stroke subtype predict treatment gains after stroke. Ann. Neurol. 77 (2015), 132–145, 10.1002/ana.24309.
8. Byblow, W.D., Stinear, C.M., Barber, P.A., Petoe, M.A., Ackerley, S.J., Proportional recovery after stroke depends on corticomotor integrity. Ann. Neurol., 2015, 10.1002/ana.24472.
9. Cao, Y., D'Olhaberriague, L., Vikingstad, E.M., Levine, S.R., Welch, K.M., Pilot study of functional MRI to assess cerebral activation of motor function after poststroke hemiparesis. Stroke 29 (1998), 112–122, 10.1161/01.STR.29.1.112.
10. Cohen, J.R., Asarnow, R.F., Sabb, F.W., Bilder, R.M., Bookheimer, S.Y., Knowlton, B.J., Poldrack, R.A., Decoding continuous variables from neuroimaging data: basic and clinical applications. Front. Neurosci., 5, 2011, 75, 10.3389/fnins.2011.00075.
11. Davatzikos, C., Shen, D., Gur, R.C., Wu, X., Liu, D., Fan, Y., Hughett, P., Turetsky, B.I., Gur, R.E., Whole-brain morphometric study of schizophrenia revealing a spatially complex set of focal abnormalities. Arch. Gen. Psychiatry 62 (2005), 1218–1227, 10.1001/archpsyc.62.11.1218.
12. Drucker, H., Burges, C.J.C., Kaufman, L., Smola, A., Vapnik, V., Support vector regression machines. Adv. Neural Inf. Proces. Syst. 9 (1997), 155–161 doi:10.1.1.10.4845.
13. Filippone, M., Marquand, a.F., Blain, C.R.V., Williams, S.C.R., Mourão-Miranda, J., Girolami, M., Probabilistic prediction of neurological disorders with a statistical assessment of neuroimaging data modalities. Ann. Appl. Stat. 6 (2012), 1883–1905, 10.1214/12-AOAS562.
14. Forkert, N.D., Verleger, T., Cheng, B., Thomalla, G., Hilgetag, C.C., Fiehler, J., Multiclass support vector machine-based lesion mapping predicts functional outcome in ischemic stroke patients. PLoS One, 10, 2015, e0129569, 10.1371/journal.pone.0129569.
15. Fu, C.H.Y., Mourao-Miranda, J., Costafreda, S.G., Khanna, A., Marquand, A.F., Williams, S.C.R., Brammer, M.J., Pattern classification of sad facial processing: toward the development of neurobiological markers in depression. Biol. Psychiatry 63 (2008), 656–662, 10.1016/j.biopsych.2007.08.020.
16. Geva, S., Jones, P.S., Crinion, J.T., Price, C.J., Baron, J.C., Warburton, E.a., The neural correlates of inner speech defined by voxel-based lesion-symptom mapping. Brain 134 (2011), 3071–3082, 10.1093/brain/awr232.
17. Gönen, M., Alpaydın, E., Multiple kernel learning algorithms. J. Mach. Learn. Res. 12 (2011), 2211–2268.
18. Hauk, O., Johnsrude, I., Pulvermüller, F., Somatotopic representation of action words in human motor and premotor cortex. Neuron 41 (2004), 301–307, 10.1016/S0896-6273(03)00838-9.
19. Hope, T.M.H., Seghier, M.L., Leff, A.P., Price, C.J., Predicting outcome and recovery after stroke with lesions extracted from MRI images. NeuroImage Clin. 2 (2013), 424–433, 10.1016/j.nicl.2013.03.005.
20. Hope, T.M.H., Parker Jones, I., Grogan, A., Crinion, J., Rae, J., Ruffle, L., Leff, a.P., Seghier, M.L., Price, C.J., Green, D.W., Comparing language outcomes in monolingual and bilingual stroke patients. Brain, 2015, 1–14, 10.1093/brain/awv020.
21. Kim, S.G., Ashe, J., Hendrich, K., Ellermann, J.M., Merkle, H., Uğurbil, K., Georgopoulos, A.P., Functional magnetic resonance imaging of motor cortex: hemispheric asymmetry and handedness. Science 261:80-. (1993), 615–617, 10.1126/science.8342027.
22. Kim, B.J., Kim, Y., Kim, N., Kwon, S.U., Kim, S.J., Kim, J.S., Kang, D., Disclosure, F., Lesion location-based prediction of visual field recovery after cerebral infarction 1–15. PLoS One, 2014, 10.1371/journal.pone.0143882.
23. Klöppel, S., Stonnington, C.M., Barnes, J., Chen, F., Chu, C., Good, C.D., Mader, I., Mitchell, L.A., Patel, A.C., Roberts, C.C., Fox, N.C., Jack, C.R., Ashburner, J., Frackowiak, R.S.J., Accuracy of dementia diagnosis - a direct comparison between radiologists and a computerized method. Brain 131 (2008), 2969–2974, 10.1093/brain/awn239.
24. Lindenberg, R., Renga, V., Zhu, L.L., Betzler, F., Alsop, D., Schlaug, G., Structural integrity of corticospinal motor fibers predicts motor impairment in chronic stroke. Neurology 74 (2010), 280–287, 10.1212/WNL.0b013e3181ccc6d9.
25. Lyle, R.C., A performance test for assessment of upper limb function in physical rehabilitation treatment and research. Int. J. Rehabil. Res. 4 (1981), 483–492.
26. Marquand, A., Howard, M., Brammer, M., Chu, C., Coen, S., Mourão-Miranda, J., Quantitative prediction of subjective pain intensity from whole-brain fMRI data using Gaussian processes. NeuroImage 49 (2010), 2178–2189, 10.1016/j.neuroimage.2009.10.072.
27. Mathiowetz, V., Weber, K., Kashman, N., Volland, G., Adult norms for the Nine Hole Peg Test of finger dexterity. Occup. Ther. J. Res. 5 (1985), 24–38, 10.1177/153944928500500102.
28. Munsch, F., Sagnier, S., Asselineau, J., Bigourdan, A., Guttmann, C.R., Debruxelles, S., Poli, M., Renou, P., Perez, P., Dousset, V., Sibon, I., Stroke Location is an Independent Predictor of Cognitive Outcome. 2015, 10.1161/STROKEAHA.115.011242.
29. Park, C.H., Kou, N., Ward, N.S., The contribution of lesion location to upper limb deficit after stroke. J. Neurol. Neurosurg. Psychiatry, 2016 (Epub ahead of print).
30. Payabvash, S., Kamalian, S., Fung, S., Wang, Y., Passanese, J., Kamalian, S., Souza, L.C.S., Kemmling, a., Harris, G.J., Halpern, E.F., González, R.G., Furie, K.L., Lev, M.H., Predicting language improvement in acute stroke patients presenting with aphasia: a multivariate logistic model using location-weighted atlas-based analysis of admission CT perfusion scans. Am. J. Neuroradiol. 31 (2010), 1661–1668, 10.3174/ajnr.A2125.
31. Rasmussen, C., Gaussian processes for machine learning. Int. J. Neural Syst. 14 (2006), 69–106, 10.1142/S0129065704001899.
32. Särkämö, T., Tervaniemi, M., Soinila, S., Autti, T., Silvennoinen, H.M., Laine, M., Hietanen, M., Cognitive deficits associated with acquired amusia after stroke: a neuropsychological follow-up study. Neuropsychologia 47 (2009), 2642–2651, 10.1016/j.neuropsychologia.2009.05.015.
33. Schiemanck, S.K., Post, M.W.M., Kwakkel, G., Witkamp, T.D., Kappelle, L.J., Prevo, A.J.H., Ischemic lesion volume correlates with long-term functional outcome and quality of life of middle cerebral artery stroke survivors. Restor. Neurol. Neurosci. 23 (2005), 257–263.
34. Schulz, R., Park, C.-H., Boudrias, M.-H., Gerloff, C., Hummel, F.C., Ward, N.S., Assessing the integrity of corticospinal pathways from primary and secondary cortical motor areas after stroke. Stroke 43 (2012), 2248–2251, 10.1161/STROKEAHA.112.662619.
35. Seghier, M.L., Ramlackhansingh, A., Crinion, J., Leff, A.P., Price, C.J., Lesion identification using unified segmentation-normalisation models and fuzzy clustering. NeuroImage 41 (2008), 1253–1266, 10.1016/j.neuroimage.2008.03.028.
36. Smola, A.J., Sch, B., Schölkopf, B., A tutorial on support vector regression. Stat. Comput. 14 (2004), 199–222, 10.1023/B:STCO.0000035301.49549.88.
37. Stinear, C., Prediction of recovery of motor function after stroke. Lancet Neurol., 2010, 10.1016/S1474-4422(10)70247-7.
38. Sunderland, A., Tinson, D., Bradley, L., Hewer, R.L., Arm function after stroke. An evaluation of grip strength as a measure of recovery and a prognostic indicator. J. Neurol. Neurosurg. Psychiatry 52 (1989), 1267–1272, 10.1136/jnnp.52.11.1267.
39. Teipel, S.J., Born, C., Ewers, M., Bokde, A.L., Reiser, M.F., Moller, H.J., Hampel, H., Multivariate deformation-based analysis of brain atrophy to predict Alzheimer's disease in mild cognitive impairment. NeuroImage 38 (2007), 13–24, 10.1016/j.neuroimage.2007.07.008.
40. Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., Joliot, M., Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 15 (2002), 273–289, 10.1006/nimg.2001.0978.
41. Vemuri, P., Wiste, H.J., Weigand, S.D., Knopman, D.S., Trojanowski, J.Q., Shaw, L.M., Bernstein, M.A., Aisen, P.S., Weiner, M., Petersen, R.C., Jack, C.R., Serial MRI and CSF biomarkers in normal aging, MCI, and AD. Neurology 75 (2010), 143–151, 10.1212/WNL.0b013e3181e7ca82.
42. Wang, Y., Fan, Y., Bhatt, P., Davatzikos, C., High-dimensional pattern regression using machine learning: from medical images to continuous clinical variables. NeuroImage 50 (2010), 1519–1535, 10.1016/j.neuroimage.2009.12.092.
43. Ward, N.S., Does neuroimaging help to deliver better recovery of movement after stroke?. Curr. Opin. Neurol. 44 (2015), 323–329, 10.1097/WCO.0000000000000223.
44. Ward, N.S., Swayne, O.B.C., Newton, J.M., Age-dependent changes in the neural correlates of force modulation: an fMRI study. Neurobiol. Aging 29 (2008), 1434–1446, 10.1016/j.neurobiolaging.2007.04.017.
45. Yang, Z.-H., Zhao, X.-Q., Wang, C.-X., Chen, H.-Y., Zhang, Y.-M., Neuroanatomic correlation of the post-stroke aphasias studied with imaging. Neurol. Res. 30 (2008), 356–360, 10.1179/174313208X300332.
46. Zarahn, E., Alon, L., Ryan, S.L., Lazar, R.M., Vry, M.S., Weiller, C., Marshall, R.S., Krakauer, J.W., Prediction of motor recovery using initial impairment and fMRI 48 h poststroke. Cereb. Cortex 21 (2011), 2712–2721, 10.1093/cercor/bhr047.
47. Zhu, L.L., Lindenberg, R., Alexander, M.P., Schlaug, G., Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41 (2010), 910–915, 10.1161/STROKEAHA.109.577023.