Posts Tagged POOR

[Abstract+References] Longitudinal Structural and Functional Differences Between Proportional and Poor Motor Recovery After Stroke

Background. Evolution of motor function during the first months after stroke is stereotypically bifurcated, consisting of either recovery to about 70% of maximum possible improvement (“proportional recovery, PROP”) or in little to no improvement (“poor recovery, POOR”). There is currently no evidence that any rehabilitation treatment will prevent POOR and favor PROP. Objective. To perform a longitudinal and multimodal assessment of functional and structural changes in brain organization associated with PROP. Methods. Fugl-Meyer Assessments of the upper extremity and high-density electroencephalography (EEG) were obtained from 63 patients, diffusion tensor imaging from 46 patients, at 2 and 4 weeks (T0) and at 3 months (T1) after stroke onset. Results. We confirmed the presence of 2 distinct recovery patterns (PROP and POOR) in our sample. At T0, PROP patients had greater integrity of the corticospinal tract (CST) and greater EEG functional connectivity (FC) between the affected hemisphere and rest of the brain, in particular between the ventral premotor and the primary motor cortex. POOR patients suffered from degradation of corticocortical and corticofugal fiber tracts in the affected hemisphere between T0 and T1, which was not observed in PROP patients. Better initial CST integrity correlated with greater initial global FC, which was in turn associated with less white matter degradation between T0 and T1. Conclusions. These findings suggest links between initial CST integrity, systems-level cortical network plasticity, reduction of white matter atrophy, and clinical motor recovery after stroke. This identifies candidate treatment targets.

1. Prabhakaran, S, Zarahn, E, Riley, C. Inter-individual variability in the capacity for motor recovery after ischemic stroke. Neurorehabil Neural Repair. 2008;22:6471Google ScholarLink
2. Winters, C, van Wegen, EE, Daffertshofer, A, Kwakkel, G. Generalizability of the proportional recovery model for the upper extremity after an ischemic stroke. Neurorehabil Neural Repair. 2015;29:614622Google ScholarLink
3. Buch, ER, Rizk, S, Nicolo, P, Cohen, LG, Schnider, A, Guggisberg, AG. Predicting motor improvement after stroke with clinical assessment and diffusion tensor imaging. Neurology. 2016;86:19241925Google ScholarCrossrefMedline
4. Feng, W, Wang, J, Chhatbar, PY. Corticospinal tract lesion load: An imaging biomarker for stroke motor outcomes. Ann Neurol. 2015;78:860870Google ScholarCrossrefMedline
5. Byblow, WD, Stinear, CM, Barber, PA, Petoe, MA, Ackerley, SJ. Proportional recovery after stroke depends on corticomotor integrity. Ann Neurol. 2015;78:848859Google ScholarCrossrefMedline
6. Ma, C, Liu, A, Li, Z, Zhou, X, Zhou, S. Longitudinal study of diffusion tensor imaging properties of affected cortical spinal tracts in acute and chronic hemorrhagic stroke. J Clin Neurosci. 2014;21:13881392Google ScholarCrossrefMedline
7. Thomalla, G, Glauche, V, Koch, MA, Beaulieu, C, Weiller, C, Rother, J. Diffusion tensor imaging detects early Wallerian degeneration of the pyramidal tract after ischemic stroke. Neuroimage. 2004;22:17671774Google ScholarCrossrefMedline
8. Thomalla, G, Glauche, V, Weiller, C, Röther, J. Time course of Wallerian degeneration after ischaemic stroke revealed by diffusion tensor imaging. J Neurol Neurosurg Psychiatry. 2005;76:266268Google ScholarCrossrefMedline
9. Zarahn, E, Alon, L, Ryan, SL. Prediction of motor recovery using initial impairment and fMRI 48 h poststroke. Cereb Cortex. 2011;21:27122721Google ScholarCrossrefMedline
10. Nudo, RJ. Postinfarct cortical plasticity and behavioral recovery. Stroke. 2007;38(2 suppl):840845Google ScholarCrossrefMedline
11. Nudo, RJ, Wise, BM, SiFuentes, F, Milliken, GW. Neural substrates for the effects of rehabilitative training on motor recovery after ischemic infarct. Science. 1996;272:17911794Google ScholarCrossrefMedline
12. Carmichael, ST. Cellular and molecular mechanisms of neural repair after stroke: making waves. Ann Neurol. 2006;59:735742Google ScholarCrossrefMedline
13. Murphy, TH, Corbett, D. Plasticity during stroke recovery: from synapse to behaviour. Nat Rev Neurosci. 2009;10:861872Google ScholarCrossrefMedline
14. Carmichael, ST, Chesselet, MF. Synchronous neuronal activity is a signal for axonal sprouting after cortical lesions in the adult. J Neurosci. 2002;22:60626070Google ScholarMedline
15. Buch, ER, Liew, SL, Cohen, LG. Plasticity of sensorimotor networks: multiple overlapping mechanisms. Neuroscientist. 2017;23:185196Google ScholarLink
16. Smith, SM, Jenkinson, M, Johansen-Berg, H. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage. 2006;31:14871505Google ScholarCrossrefMedline
17. Keihaninejad, S, Zhang, H, Ryan, NS. An unbiased longitudinal analysis framework for tracking white matter changes using diffusion tensor imaging with application to Alzheimer’s disease. Neuroimage. 2013;72:153163Google ScholarCrossrefMedline
18. Nicolo, P, Rizk, S, Magnin, C, Pietro, MD, Schnider, A, Guggisberg, AG. Coherent neural oscillations predict future motor and language improvement after stroke. Brain. 2015;138(pt 10):30483060Google ScholarCrossrefMedline
19. Grefkes, C, Nowak, DA, Eickhoff, SB. Cortical connectivity after subcortical stroke assessed with functional magnetic resonance imaging. Ann Neurol. 2008;63:236246Google ScholarCrossrefMedline
20. Rehme, AK, Eickhoff, SB, Wang, LE, Fink, GR, Grefkes, C. Dynamic causal modeling of cortical activity from the acute to the chronic stage after stroke. Neuroimage. 2011;55:11471158Google ScholarCrossrefMedline
21. Fugl-Meyer, AR, Jääskö, L, Leyman, I, Olsson, S, Steglind, S. The post-stroke hemiplegic patient. 1. a method for evaluation of physical performance. Scand J Rehabil Med. 1975;7:1331Google ScholarMedline
22. Gladstone, DJ, Danells, CJ, Black, SE. The Fugl-Meyer assessment of motor recovery after stroke: a critical review of its measurement properties. Neurorehabil Neural Repair. 2002;16:232240Google ScholarLink
23. Pierpaoli, C, Walker, L, Irfanoglu, MO. TORTOISE: an integrated software package for processing of diffusion MRI dataPaper presented at: ISMRM–ESMRMB Joint Annual MeetingMay 1-7, 2010Stockholm, SwedenGoogle Scholar
24. Rohde, GK, Barnett, AS, Basser, PJ, Pierpaoli, C. Estimating intensity variance due to noise in registered images: applications to diffusion tensor MRI. Neuroimage. 2005;26:673684Google ScholarCrossrefMedline
25. Leemans, A, Jones, DK. The B-matrix must be rotated when correcting for subject motion in DTI data. Magn Reson Med. 2009;61:13361349Google ScholarCrossrefMedline
26. Wu, M, Chang, LC, Walker, L. Comparison of EPI distortion correction methods in diffusion tensor MRI using a novel framework. Med Image Comput Comput Assist Interv. 2008;11(pt 2):321329Google ScholarMedline
27. Chang, LC, Jones, DK, Pierpaoli, C. RESTORE: robust estimation of tensors by outlier rejection. Magn Reson Med. 2005;53:10881095Google ScholarCrossrefMedline
28. Zhang, H, Yushkevich, PA, Alexander, DC, Gee, JC. Deformable registration of diffusion tensor MR images with explicit orientation optimization. Med Image Anal. 2006;10:764785Google ScholarCrossrefMedline
29. Zhang, H, Yushkevich, PA, Rueckert, D, Gee, JC. A computational white matter atlas for aging with surface-based representation of fasciculi. In: Fischer, B, Dawant, B, Lorenz, C, eds. Proceedings of the 4th Workshop on Biomedical Image RegistrationBerlin, GermanySpringer2010:8390Google ScholarCrossref
30. Winkler, AM, Ridgway, GR, Webster, MA, Smith, SM, Nichols, TE. Permutation inference for the general linear model. Neuroimage. 2014;92:381397Google ScholarCrossrefMedline
31. Mori, S, Wakana, S, van Zijl, PCM, Nagae-Poetscher, LM. MRI Atlas of Human White Matter. 1st ed.Amsterdam, NetherlandsElsevier2005Google Scholar
32. Stinear, CM, Barber, PA, Smale, PR, Coxon, JP, Fleming, MK, Byblow, WD. Functional potential in chronic stroke patients depends on corticospinal tract integrity. Brain. 2007;130(pt 1):170180Google ScholarCrossrefMedline
33. Dalal, SS, Zumer, JM, Guggisberg, AG. MEG/EEG source reconstruction, statistical evaluation, and visualization with NUTMEG. Comput Intell Neurosci. 2011;2011:758973Google ScholarCrossrefMedline
34. Guggisberg, AG, Dalal, SS, Zumer, JM. Localization of cortico-peripheral coherence with electroencephalography. Neuroimage. 2011;57:13481357Google ScholarCrossrefMedline
35. Stenroos, M, Mäntynen, V, Nenonen, J. A Matlab library for solving quasi-static volume conduction problems using the boundary element method. Comput Methods Programs Biomed. 2007;88:256263Google ScholarCrossrefMedline
36. Sekihara, K, Nagarajan, SS, Poeppel, D, Marantz, A. Asymptotic SNR of scalar and vector minimum-variance beamformers for neuromagnetic source reconstruction. IEEE Trans Biomed Eng. 2004;51:17261734Google ScholarCrossrefMedline
37. Nolte, G, Bai, O, Wheaton, L, Mari, Z, Vorbach, S, Hallett, M. Identifying true brain interaction from EEG data using the imaginary part of coherency. Clin Neurophysiol. 2004;115:22922307Google ScholarCrossrefMedline
38. Sekihara, K, Owen, JP, Trisno, S, Nagarajan, SS. Removal of spurious coherence in MEG source-space coherence analysis. IEEE Trans Biomed Eng. 2011;58:31213129Google ScholarCrossrefMedline
39. Newman, ME. Analysis of weighted networks. Phys Rev E Stat Nonlin Soft Matter Phys. 2004;70(5 pt 2):056131Google ScholarCrossrefMedline
40. Stam, CJ, van Straaten, EC. The organization of physiological brain networks. Clin Neurophysiol. 2012;123:10671087Google ScholarCrossrefMedline
41. De Vico Fallani, F, Richiardi, J, Chavez, M, Achard, S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philos Trans R Soc Lond B Biol Sci. 2014;369:20130521. doi:10.1098/rstb.2013.0521. Google ScholarCrossrefMedline
42. Mayka, MA, Corcos, DM, Leurgans, SE, Vaillancourt, DE. Three-dimensional locations and boundaries of motor and premotor cortices as defined by functional brain imaging: a meta-analysis. Neuroimage. 2006;31:14531474Google ScholarCrossrefMedline
43. Dubovik, S, Pignat, JM, Ptak, R. The behavioral significance of coherent resting-state oscillations after stroke. Neuroimage. 2012;61:249257Google ScholarCrossrefMedline
44. Guggisberg, AG, Rizk, S, Ptak, R. Two intrinsic coupling types for resting-state integration in the human brain. Brain Topogr. 2015;28:318329Google ScholarCrossrefMedline
45. Singh, KD, Barnes, GR, Hillebrand, A. Group imaging of task-related changes in cortical synchronisation using nonparametric permutation testing. Neuroimage. 2003;19:15891601Google ScholarCrossrefMedline
46. Gunn, SR. Support vector machines for classification and regression (Technical report)University of Southamptonhttp://users.ecs.soton.ac.uk/srg/publications/pdf/SVM.pdf. Published May 101998. Accessed October 17, 2017. Google Scholar
47. Kim, KH, Kim, YH, Kim, MS, Park, CH, Lee, A, Chang, WH. Prediction of motor recovery using diffusion tensor tractography in supratentorial stroke patients with severe motor involvement. Ann Rehabil Med. 2015;39:570576Google ScholarCrossrefMedline
48. Rondina, JM, Park, CH, Ward, NS. Brain regions important for recovery after severe post-stroke upper limb paresis. J Neurol Neurosurg Psychiatry. 2017;88:737743Google ScholarCrossrefMedline
49. Winters, C, van Wegen, EE, Daffertshofer, A, Kwakkel, G. Generalizability of the maximum proportional recovery rule to visuospatial neglect early poststroke. Neurorehabil Neural Repair. 2017;31:334342Google ScholarLink
50. Marchi, NA, Ptak, R, Di Pietro, M, Schnider, A, Guggisberg, AG. Principles of proportional recovery after stroke generalize to neglect and aphasia. Eur J Neurol. 2017;24:10841087Google ScholarCrossrefMedline
51. Fields, RD, Woo, DH, Basser, PJ. Glial regulation of the neuronal connectome through local and long-distant communication. Neuron. 2015;86:374386Google ScholarCrossrefMedline
52. Volz, LJ, Sarfeld, AS, Diekhoff, S. Motor cortex excitability and connectivity in chronic stroke: a multimodal model of functional reorganization. Brain Struct Funct. 2015;220:10931107Google ScholarCrossrefMedline
53. Ward, NS, Newton, JM, Swayne, OB. Motor system activation after subcortical stroke depends on corticospinal system integrity. Brain. 2006;129(pt 3):809819Google ScholarCrossrefMedline
54. Cunningham, DA, Machado, A, Janini, D. Assessment of inter-hemispheric imbalance using imaging and noninvasive brain stimulation in patients with chronic stroke. Arch Phys Med Rehabil. 2015;96(4 suppl):S94S103Google ScholarCrossrefMedline
55. Carter, AR, Patel, KR, Astafiev, SV. Upstream dysfunction of somatomotor functional connectivity after corticospinal damage in stroke. Neurorehabil Neural Repair. 2012;26:719Google ScholarLink
56. Rizk, S, Ptak, R, Nyffeler, T, Schnider, A, Guggisberg, AG. Network mechanisms of responsiveness to continuous theta-burst stimulation. Eur J Neurosci. 2013;38:32303238Google ScholarCrossrefMedline
57. Amadi, U, Ilie, A, Johansen-Berg, H, Stagg, CJ. Polarity-specific effects of motor transcranial direct current stimulation on fMRI resting state networks. Neuroimage. 2014;88:155161Google ScholarCrossrefMedline
58. Grefkes, C, Nowak, DA, Wang, LE, Dafotakis, M, Eickhoff, SB, Fink, GR. Modulating cortical connectivity in stroke patients by rTMS assessed with fMRI and dynamic causal modeling. Neuroimage. 2010;50:233242Google ScholarCrossrefMedline
59. Volz, LJ, Rehme, AK, Michely, J. Shaping early reorganization of neural networks promotes motor function after stroke. Cereb Cortex. 2016;26:28822894Google ScholarCrossrefMedline
60. Sehm, B, Schäfer, A, Kipping, J. Dynamic modulation of intrinsic functional connectivity by transcranial direct current stimulation. J Neurophysiol. 2012;108:32533263Google ScholarCrossrefMedline
61. Mottaz, A, Solcà, M, Magnin, C, Corbet, T, Schnider, A, Guggisberg, AG. Neurofeedback training of alpha-band coherence enhances motor performance. Clin Neurophysiol. 2015;126:17541760Google ScholarCrossrefMedline
62. Vukelic, M, Gharabaghi, A. Self-regulation of circumscribed brain activity modulates spatially selective and frequency specific connectivity of distributed resting state networks. Front Behav Neurosci. 2015;9:181Google ScholarCrossrefMedline
63. Liew, SL, Rana, M, Cornelsen, S. Improving motor corticothalamic communication after stroke using real-time fMRI connectivity-based neurofeedback. Neurorehabil Neural Repair. 2016;30:671675Google ScholarLink

via Longitudinal Structural and Functional Differences Between Proportional and Poor Motor Recovery After StrokeNeurorehabilitation and Neural Repair – Adrian G. Guggisberg, Pierre Nicolo, Leonardo G. Cohen, Armin Schnider, Ethan R. Buch, 2017

, , , , , , , ,

Leave a comment

%d bloggers like this: