Preview

Neurology, Neuropsychiatry, Psychosomatics

Advanced search

Role of the intact hemisphere in determining the rehabilitation potential in the acute period of ischemic stroke: a diffusion and perfusion model

https://doi.org/10.14412/2074-2711-2019-1-28-35

Abstract

The promising approaches to determining the rehabilitation potential of ischemic stroke (IS) patients include an assessment of the microstructural integrity of the brain matter by diffusion tensor imaging (DTI), the main indicator of which is fractional anisotropy (FA). The role of the intact hemisphere in the rehabilitation process after IS remains a controversial subject. The hypothesis for the investigation is that the development of a diffusion and perfusion model (DPM) based on the assessment of FA in combination with data on cerebral blood flow velocity (CBFV) and the impact of the focus will be able to predict the patients' neurological status by the end of the acute period of IS.

Objective: to investigate the role of diffusion and perfusion characteristics of the intact hemisphere in determining the rehabilitation potential in the acute period of IS and to develop a prognostic DPM.

Patients and methods. The investigation enrolled 100 patients with IS and 10 individuals in the control group. All the examinees underwent brain MRI. Perfusion-weighted sequence without bolus injection of a contrast agent was used to quantify CBFV in 10 areas according to the Alberta stroke program early CT score (ASPECTS). Values for FA in 10 areas of both hemispheres were calculated using DTI findings. Neurological and functional statuses were evaluated over time with the National Institute of Health Stroke Scale (NIHSS) and the modified Rankin scale.

Results. The NIHSS score at discharge was associated with FA and CBFV in 4 and 6 of the 10 areas of the intact hemisphere, respectively. DPM for predicting the rehabilitation potential included the key parameters correlating with a discharge NIHSS score (in order of decreasing the significance): admission NIHSS value (r = 0.55; p < 0.001), the size of a focus (r = 0.42; p < 0.001), FA in the contralateral cingulum bundle FA (r = -0.28; p = 0.007), and CBFV in M2 white matter [r = -0.24; p = 0.025; R2 = 0.642; p(F) <0.001].

Conclusion. In addition to the NIHSS score at admission, the size of a focus, DPM values (FA in the contralateral cingulum bundle and CBFV in the white matter) allow prediction of the rehabilitation potential in IS.

About the Authors

N. A. Kaileva
Acad. E.A. Vagner Perm State Medical University, Ministry of Health of Russia; Perm City Clinical Hospital Four
Russian Federation

26, Petropavlovskaya St., Perm 614990, 

2, Kim St., Perm 614107



A. A. Kulesh
Acad. E.A. Vagner Perm State Medical University, Ministry of Health of Russia; Perm City Clinical Hospital Four
Russian Federation

Aleksey Aleksandrovich Kulesh

26, Petropavlovskaya St., Perm 614990, 

2, Kim St., Perm 614107



N. Kh. Gorst
Acad. E.A. Vagner Perm State Medical University, Ministry of Health of Russia
Russian Federation

26, Petropavlovskaya St., Perm 614990



A. Yu. Bykova
Acad. E.A. Vagner Perm State Medical University, Ministry of Health of Russia; Perm City Clinical Hospital Four
Russian Federation

26, Petropavlovskaya St., Perm 614990, 

2, Kim St., Perm 614107



V. E. Drobakha
Acad. E.A. Vagner Perm State Medical University, Ministry of Health of Russia; Perm City Clinical Hospital Four
Russian Federation

26, Petropavlovskaya St., Perm 614990, 

2, Kim St., Perm 614107



K. V. Sobyanin
Institute of Continuum Mechanics, Ural Branch, Russian Academy of Sciences
Russian Federation

1, Academician Korolev St., Perm 614013



I. N. Shardakov
Institute of Continuum Mechanics, Ural Branch, Russian Academy of Sciences
Russian Federation

1, Academician Korolev St., Perm 614013



V. V. Shestakov
Acad. E.A. Vagner Perm State Medical University, Ministry of Health of Russia
Russian Federation

26, Petropavlovskaya St., Perm 614990



References

1. Veerbeek JM, Kwakkel G, van Wegen EE, et al. Early prediction of outcome of activities of daily living after stroke: a systematic review. Stroke. 2011 May;42(5):1482-8. doi: 10.1161/STROKEAHA.110.604090.

2. Stinear CM, Byblow WD, Ackerley SJ, et al. Predicting Recovery Potential for Individual Stroke Patients Increases Rehabilitation Efficiency. Stroke. 2017 Apr;48(4):1011-1019. doi: 10.1161/STROKEAHA.116.015790

3. Lindenberg R, Renga V, Zhu LL, et al. Bihemispheric brain stimulation facilitates motor recovery in chronic stroke patients. Neurology. 2010 Dec 14;75(24):2176-84. doi: 10.1212/WNL.0b013e318202013a

4. Puig J, Blasco G, Daunis-I-Estadella J, et al. Decreased corticospinal tract fractional anisotropy predicts long-term motor outcome after stroke. Stroke. 2013 Jul;44(7):2016-8. doi: 10.1161/STROKEAHA.111.000382.

5. Song J, Nair VA, Young BM, et al. DTI measures track and predict motor function outcomes in stroke rehabilitation utilizing BCI technology. Front Hum Neurosci. 2015 Apr 27; 9:195. doi: 10.3389/fnhum.2015.00195. eCollection 2015.

6. Koyama T, Marumoto K, Miyake H, Domen K. Relationship between diffusion tensor fractional anisotropy and motor outcome in patients with hemiparesis after corona radiata infarct. J Stroke Cerebrovasc Dis. 2013 Nov; 22(8):1355-60. doi: 10.1016/j.jstrokecerebrovasdis.2013.02.017

7. Puig J, Blasco G, Schlaug G, et al. Diffusion tensor imaging as a prognostic biomarker for motor recovery and rehabilitation after stroke. Neuroradiology. 2017 Apr;59(4):343-351. doi: 10.1007/s00234-017-1816-0. Epub 2017 Mar 14.

8. Johansen-Berg H, Dawes H, Guy C, et al. Correlation between motor improvements and altered fMRI activity after rehabilitative therapy. Brain. 2002 Dec;125(Pt 12):2731-42.

9. Lindenberg R, Zhu LL, Ruber T, Schlaug G. Predicting functional motor potential in chronic stroke patients using diffusion tensor imaging. Hum Brain Mapp. 2012 May;33(5):1040-51. doi: 10.1002/hbm.21266. Epub 2011 Apr 29.

10. Young BM, Nigogosyan Z, Walton LM, et al. Changes in functional brain organization and behavioral correlations after rehabilitative therapy using a braincomputer interface. Front Neuroeng. 2014 Jul 15;7:26. doi: 10.3389/fneng.2014.00026. eCollection 2014.

11. Granziera C, Ay H, Koniak SP, et al. Diffusion tensor imaging shows structural remodeling of stroke mirror region: results from a pilot study. Eur Neurol. 2012;67(6):370-6. doi: 10.1159/000336062. Epub 2012 May 17

12. Schaechter JD, Fricker ZP, Perdue KL, et al. Microstructural status of ipsilesional and contralesional corticospinal tract correlates with motor skill in chronic stroke patients. Hum Brain Mapp. 2009 Nov;30(11):3461-74. doi: 10.1002/hbm.20770.

13. Kulesh A, Drobakha V, Kuklina E, et al. Cytokine Response, Tract-Specific Fractional Anisotropy, and Brain Morphometry in PostStroke Cognitive Impairment. J Stroke Cerebrovasc Dis. 2018 Jul;27(7):1752-1759. doi: 10.1016/j.jstrokecerebrovasdis.2018.02.004. Epub 2018 Mar 30.

14. Dacosta-Aguayo R, Grana M, FernandezAndujar M, et al. Structural integrity of the contralesional hemisphere predicts cognitive impairment in ischemic stroke at three months. PLoS One. 2014 Jan 24;9(1):e86119. doi: 10.1371/journal.pone.0086119.

15. Yu S, Liebeskind DS, Dua S, et al; UCLA Stroke Investigators. Postischemic hyperperfusion on arterial spin labeled perfusion MRI is linked to hemorrhagic transformation in stroke. J Cereb Blood Flow Metab. 2015 Mar 31;35(4): 630-7. doi: 10.1038/jcbfm.2014.238.

16. Majer M, Mejdoubi M, Schertz M, et al. Raw arterial spin labeling data can help identify arterial occlusion in acute ischemic stroke. Stroke J Cereb Circ. 2015;46(6):e141–e144. doi:10.1161/STROKEAHA.114.008496

17. Bokkers RP, Hernandez DA, Merino JG, et al; National Institutes of Health Stroke Natural History Investigators. Whole-brain arterial spin labeling perfusion MRI in patients with acute stroke. Stroke. 2012 May;43(5): 1290-4. doi: 10.1161/STROKEAHA.110.589234. Epub 2012 Mar 15.

18. Grade M, Hernandez Tamames JA, Pizzini FB, et al. A neuroradiologist’s guide to arterial spin labeling MRI in clinical practice. Neuroradiology. 2015 Dec;57(12):1181-202. doi: 10.1007/s00234-015-1571-z. Epub 2015 Sep 9.

19. Barber PA, Demchuk AM, Zhang J, Buchan AM. Validity and reliability of a quantitative computed tomography score in predicting outcome of hyperacute stroke before thrombolytic therapy. Lancet. 2000 May 13;355(9216): 1670-4.

20. Woo-Suk T, Byung-Joo H, Sung-Bom P, et al. Current Clinical Applications of Diffusion-Tensor Imaging in Neurological Disorders. J Clin Neurol. 2018 Apr;14(2): 129–140. doi: 10.3988/jcn.2018.14.2.129.

21. Wardlaw JM, Doubal FN, Eadie E, et al. Little association between intracranial arterial stenosis and lacunar stroke. Cerebrovasc Dis. 2011;31(1):12-8. doi: 10.1159/000319773. Epub 2010 Oct 28.

22. Reijmer YD, Freeze WM, Leemans A, Biessels GJ; Utrecht Vascular Cognitive Impairment Study Group. The effect of lacunar infarcts on white matter tract integrity. Stroke. 2013 Jul;44(7):2019-21. doi: 10.1161/STROKEAHA.113.001321.

23. Werring DJ, Toosy AT, Clark CA, et al. Diffusion tensor imaging can detect and quantify corticospinal tract degeneration after stroke. J Neurol Neurosurg Psychiatry. 2000 Aug;69(2): 269-72.

24. Tatu L, Moulin T, Vuillier F, Bogousslavsky J. Arterial territories of the human brain. Front Neurol Neurosci. 2012;30:99-110. doi: 10.1159/000333602. Epub 2012 Feb 14.

25. Bubb EJ, Metzler-Baddeley C, Aggleton JP. The cingulum bundle: Anatomy, function, and dysfunction. Neurosci Biobehav Rev. 2018 Sep; 92:104-127. doi: 10.1016/j.neubiorev.2018.05.008. Epub 2018 May 16.

26. Lebel C, Gee M, Camicioli R, et al. Diffusion tensor imaging of white matter tract evolution over the lifespan. Neuroimage. 2012 Mar;60(1):340-52. doi: 10.1016/j.neuroimage.2011.11.094. Epub 2011 Dec 8.

27. Stern Y. Cognitive reserve in ageing and Alzheimer's disease. Lancet Neurol. 2012 Nov; 11(11):1006-12. doi: 10.1016/S1474-4422(12)70191-6.

28. Ray NJ, Metzler-Baddeley C, Khondoker MR, et al. Cholinergic basal forebrain structure influences the reconfiguration of white matter connections to support residual memory in mild cognitive impairment. J Neurosci. 2015 Jan 14;35(2):739-47. doi: 10.1523/JNEUROSCI.3617-14.2015.

29. Reil JC. Untersuchungen ü ber den Bau des grossen Gehirns im Menschen. Arch Physiol. 1809;9:136–208.

30. Fan YT, Lin KC, Liu HL, et al. Changes in structural integrity are correlated with motor and functional recovery after post-stroke rehabilitation. Restor Neurol Neurosci. 2015;33(6): 835-44. doi: 10.3233/RNN-150523.

31. Liu Z, Li Y, Zhang X, et al. Contralesional axonal remodeling of the corticospinal system in adult rats after stroke and bone marrow stromal cell treatment. Stroke. 2008 Sep;39(9): 2571-7. doi: 10.1161/STROKEAHA.107.511659. Epub 2008 Jul 10.

32. Fregni F, Pascual-Leone A. Hand motor recovery after stroke: tuning the orchestra to improve hand motor function. Cogn Behav Neurol. 2006 Mar;19(1):21-33.

33. Murphy TH, Corbett D. Plasticity during stroke recovery: from synapse to behaviour. Nat Rev Neurosci. 2009 Dec;10(12):861-72. doi: 10.1038/nrn2735. Epub 2009 Nov 4.

34. Stinear CM, Barber PA, Smale PR, et al. Functional potential in chronic stroke patients depends on corticospinal tract integrity. Brain. 2007 Jan;130(Pt 1):170-80.

35. Young BM, Stamm JM, Song J, et al. Brain-Computer Interface Training after Stroke Affects Patterns of Brain Behavior Relationships in Corticospinal Motor Fibers. Front Hum Neurosci. 2016 Sep 16;10:457. eCollection 2016.

36. Chen JL, Schlaug G. Resting state interhemispheric motor connectivity and white matter integrity correlate with motor impairment in chronic stroke. Front Neurol. 2013 Nov 7;4:178. doi: 10.3389/fneur.2013.00178. eCollection 2013.

37. Cunningham DA, Machado A, Janini D, et al. Assessment of inter-hemispheric imbalance using imaging and noninvasive brain stimulation in patients with chronic stroke. Arch Phys Med Rehabil. 2015 Apr;96(4 Suppl):S94-103. doi: 10.1016/j.apmr.2014.07.419. Epub 2014 Sep 3

38. Wake R, Miyaoka T, Kawakami K, et al. Characteristic brain hypoperfusion by 99mTcECD single photon emission computed tomography (SPECT) in patients with the firstepisode schizophrenia. Eur Psychiatry. 2010 Oct; 25(6):361-5. doi: 10.1016/j.eurpsy.2009.12.005. Epub 2010 Jul 7

39. Huang CW, Hsu SW, Chang YT, et al. Cerebral Perfusion Insufficiency and Relationships with Cognitive Deficits in Alzheimer's Disease: A Multiparametric Neuroimaging Study. Sci Rep. 2018 Jan 24;8(1): 1541. doi: 10.1038/s41598-018-19387-x

40. Shirani P, Thorn J, Davis C, et al. Severity of Hypoperfusion in Distinct Brain Regions Predicts Severity of Hemispatial Neglect in Different Reference Frames. Stroke. 2009 Nov; 40(11):3563–3566. doi: 10.1161/STROKEAHA.109.561969

41. Kulesh AA, Kaileva NA, Gorst NKh, et al. Svyaz' A relationship between the integrated assessment of magnetic resonance imaging markers for cerebral small vessel disease and the clinical and functional status in the acute period of ischemic stroke. Nevrologiya, neiropsikhiatriya, psikhosomatika = Neurology, Neuropsychiatry, Psychosomatics. 2018;10(1): 24-31. (In Russ.). doi: 10.14412/2074-2711- 2018-1-24-31

42. Parfenov VA, Ostroumova TM, Ostroumova OD, et al. Diffusion tensor magnetic resonance imaging in the diagnosis of white matter lesion in middle-aged patients with uncomplicated essential hypertension. Nevrologiya, neiropsikhiatriya, psikhosomatika = Neurology, Neuropsychiatry, Psychosomatics. 2018;10(2):20-6. (In Russ.). doi: 10.14412/2074-2711-2018-2-20-26

43. Ostroumova TM, Parfenov VA, Ostroumova OD, et al. Possibilities of contrast-free magnetic resonance perfusion imaging for the detection of early brain damage in essential hypertension. Nevrologiya, neiropsikhiatriya, psikhosomatika = Neurology, Neuropsychiatry, Psychosomatics. 2018;10(1):17-23. (In Russ.). doi: 10.14412/2074-2711-2018-1-17-23


Review

For citations:


Kaileva NA, Kulesh AA, Gorst NK, Bykova AY, Drobakha VE, Sobyanin KV, Shardakov IN, Shestakov VV. Role of the intact hemisphere in determining the rehabilitation potential in the acute period of ischemic stroke: a diffusion and perfusion model. Nevrologiya, neiropsikhiatriya, psikhosomatika = Neurology, Neuropsychiatry, Psychosomatics. 2019;11(1):28-35. (In Russ.) https://doi.org/10.14412/2074-2711-2019-1-28-35

Views: 821


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2074-2711 (Print)
ISSN 2310-1342 (Online)