Ann Rehabil Med.  2025 Feb;49(1):15-22. 10.5535/arm.240073.

Associations Between Stroke Outcome Assessments and Automated Tractography Fractional Anisotropy Incorporating Age

• Mochizuki M
• Uchiyama Y
• Domen K
• Koyama T

• KMID: 2565455
• DOI: http://doi.org/10.5535/arm.240073

Abstract

Objective
To evaluate the association between outcomes, including affected extremity functions and activities of daily living (ADL), and fractional anisotropy (FA) derived from automated tractography incorporating age among patients after stroke.
Methods
This study enrolled stroke patients, and diffusion-tensor imaging was conducted during the second week. Standardized automated tractography was utilized to compute FA values in the corticospinal tract (CST), the inferior fronto-occipital fasciculus (IFOF), and the superior longitudinal fasciculus (SLF). Outcome evaluations were performed at discharge from our affiliated rehabilitation facility. Extremity functions were assessed using the total scores of the motor component of the Stroke Impairment Assessment Set (SIAS-motor). Independence levels in ADL were appraised through the motor and cognition components of the Functional Independence Measure (FIM). For each outcome measure, multivariate regression analysis incorporated the FA values of the CST, the IFOF, and the SLF, along with age.
Results
Forty-two patients were enrolled in the final analytical database. Among the four explanatory variables, the CST emerged as the most influential factor for SIAS-motor scores. Conversely, age proved to be the primary determinant for both the motor and cognition components of FIM, surpassing the impact of FA metrics, including the CST and the IFOF.
Conclusion
The key influencing factors exhibited significant variations based on the targeted outcome assessments. Clinicians should be aware of these differences when utilizing neuroimaging techniques to predict stroke outcomes.

Keyword

Neuroimaging; Prognosis; Recovery; Tract

Figure

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  Fig. 1. An example (patient 42 in Table 1) of computed tomography (CT, upper panels) and diffusion-tensor imaging (DTI, lower panels). CST, corticospinal tract; SLF, superior longitudinal fasciculus; IFOF, inferior fronto-occipital fasciculus; R, right; L, left.

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Reference

1. Boyd LA, Hayward KS, Ward NS, Stinear CM, Rosso C, Fisher RJ, et al. Biomarkers of stroke recovery: consensus-based core recommendations from the stroke recovery and rehabilitation roundtable. Neurorehabil Neural Repair. 2017; 31:864–76. DOI: 10.1177/1545968317732680. PMID: 29233071.

2. Heiss WD. Contribution of neuro-imaging for prediction of functional recovery after ischemic stroke. Cerebrovasc Dis. 2017; 44:266–76. DOI: 10.1159/000479594. PMID: 28869961.

3. Kim B, Winstein C. Can neurological biomarkers of brain impairment be used to predict poststroke motor recovery? A systematic review. Neurorehabil Neural Repair. 2017; 31:3–24. DOI: 10.1177/1545968316662708. PMID: 27503908.

4. Rosso C, Lamy JC. Prediction of motor recovery after stroke: being pragmatic or innovative? Curr Opin Neurol. 2020; 33:482–7. DOI: 10.1097/wco.0000000000000843. PMID: 32657889.

5. Zhang JJ, Sánchez Vidaña DI, Chan JN, Hui ESK, Lau KK, et al. Biomarkers for prognostic functional recovery poststroke: a narrative review. Front Cell Dev Biol. 2023; 10:1062807. DOI: 10.3389/fcell.2022.1062807. PMID: 36699006.

6. Mukherjee P. Diffusion tensor imaging and fiber tractography in acute stroke. Neuroimaging Clin N Am. 2005; 15:655–65, xii. DOI: 10.1016/j.nic.2005.08.010. PMID: 16360595.

7. Stinear CM, Smith MC, Byblow WD. Prediction tools for stroke rehabilitation. Stroke. 2019; 50:3314–22. DOI: 10.1161/strokeaha.119.025696. PMID: 31610763.

8. Sagnier S, Sibon I. The new insights into human brain imaging after stroke. J Neurosci Res. 2022; 100:1171–81. DOI: 10.1002/jnr.24525. PMID: 31498491.

9. Koyama T, Domen K. Diffusion tensor fractional anisotropy in the superior longitudinal fasciculus correlates with functional independence measure cognition scores in patients with cerebral infarction. J Stroke Cerebrovasc Dis. 2017; 26:1704–11. DOI: 10.1016/j.jstrokecerebrovasdis.2017.03.034. PMID: 28478977.

10. Koyama T, Uchiyama Y, Domen K. Associations of diffusion-tensor fractional anisotropy and fim outcome assessments after intracerebral hemorrhage. J Stroke Cerebrovasc Dis. 2018; 27:2869–76. DOI: 10.1016/j.jstrokecerebrovasdis.2018.06.012. PMID: 30072174.

11. Koyama T, Uchiyama Y, Domen K. Outcome in stroke patients is associated with age and fractional anisotropy in the cerebral peduncles: a multivariate regression study. Prog Rehabil Med. 2020; 5:20200006. DOI: 10.2490/prm.20200006. PMID: 32789274.

12. Warrington S, Bryant KL, Khrapitchev AA, Sallet J, Charquero-Ballester M, Douaud G, et al. XTRACT - Standardised protocols for automated tractography in the human and macaque brain. Neuroimage. 2020; 217:116923. DOI: 10.1016/j.neuroimage.2020.116923. PMID: 32407993.

13. Koyama T, Mochizuki M, Uchiyama Y, Domen K. Applicability of fractional anisotropy from standardized automated tractography for outcome prediction of patients after stroke. J Phys Ther Sci. 2023; 35:838–44. DOI: 10.1589/jpts.35.838. PMID: 38075519.

14. Mochizuki M, Uchiyama Y, Domen K, Koyama T. Applicability of automated tractography during acute care stroke rehabilitation. J Phys Ther Sci. 2023; 35:156–62. DOI: 10.1589/jpts.35.156. PMID: 36744203.

15. Mochizuki M, Uchiyama Y, Domen K, Koyama T. Automated tractography for the assessment of aphasia in acute care stroke rehabilitation: a case series. Prog Rehabil Med. 2023; 8:20230041. DOI: 10.2490/prm.20230041. PMID: 38024960.

16. 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; 22:1355–60. DOI: 10.1016/j.jstrokecerebrovasdis.2013.02.017. PMID: 23510690.

17. Uchiyama Y, Domen K, Koyama T. Outcome prediction of patients with intracerebral hemorrhage by measurement of lesion volume in the corticospinal tract on computed tomography. Prog Rehabil Med. 2021; 6:20210050. DOI: 10.2490/prm.20210050. PMID: 34963905.

18. Miyamoto S, Ogasawara K, Kuroda S, Itabashi R, Toyoda K, Itoh Y, Committee for Stroke Guideline 2021, the Japan Stroke Society, et al. Japan Stroke Society Guideline 2021 for the treatment of stroke. Int J Stroke. 2022; 17:1039–49. DOI: 10.1177/17474930221090347. PMID: 35443847.

19. Koyama T, Marumoto K, Uchiyama Y, Miyake H, Domen K. Outcome assessment of hemiparesis due to intracerebral hemorrhage using diffusion tensor fractional anisotropy. J Stroke Cerebrovasc Dis. 2015; 24:881–9. DOI: 10.1016/j.jstrokecerebrovasdis.2014.12.011. PMID: 25724241.

20. Yu C, Zhu C, Zhang Y, Chen H, Qin W, Wang M, et al. A longitudinal diffusion tensor imaging study on Wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage. 2009; 47:451–8. DOI: 10.1016/j.neuroimage.2009.04.066. PMID: 19409500.

21. Li X, Morgan PS, Ashburner J, Smith J, Rorden C. The first step for neuroimaging data analysis: DICOM to NIfTI conversion. J Neurosci Methods. 2016; 264:47–56. DOI: 10.1016/j.jneumeth.2016.03.001. PMID: 26945974.

22. Tournier JD, Smith R, Raffelt D, Tabbara R, Dhollander T, Pietsch M, et al. MRtrix3: a fast, flexible and open software framework for medical image processing and visualisation. Neuroimage. 2019; 202:116137. DOI: 10.1016/j.neuroimage.2019.116137. PMID: 31473352.

23. Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. FSL. Neuroimage. 2012; 62:782–90. DOI: 10.1016/j.neuroimage.2011.09.015. PMID: 21979382.

24. Uchiyama Y, Domen K, Koyama T. Brain regions associated with Brunnstrom and functional independence measure scores in patients after a stroke: a tract-based spatial statistics study. J Phys Ther Sci. 2023; 35:211–6. DOI: 10.1589/jpts.35.211. PMID: 36866011.

25. Liu M, Chino N, Tuji T, Masakado Y, Hase K, Kimura A. Psychometric properties of the Stroke Impairment Assessment Set (SIAS). Neurorehabil Neural Repair. 2002; 16:339–51. DOI: 10.1177/0888439002239279. PMID: 12462765.

26. Linacre JM, Heinemann AW, Wright BD, Granger CV, Hamilton BB. The structure and stability of the Functional Independence Measure. Arch Phys Med Rehabil. 1994; 75:127–32. DOI: 10.1016/0003-9993(94)90384-0. PMID: 8311667.

27. Sanford J, Moreland J, Swanson LR, Stratford PW, Gowland C. Reliability of the Fugl-Meyer assessment for testing motor performance in patients following stroke. Phys Ther. 1993; 73:447–54. DOI: 10.1093/ptj/73.7.447. PMID: 8316578.

28. Wu Y, Sun D, Wang Y, Wang Y. Subcomponents and connectivity of the inferior fronto-occipital fasciculus revealed by diffusion spectrum imaging fiber tracking. Front Neuroanat. 2016; 10:88. DOI: 10.3389/fnana.2016.00088. PMID: 27721745.

29. Schwarz G, Kanber B, Prados F, Browning S, Simister R, Jäger R, et al. Acute corticospinal tract diffusion tensor imaging predicts 6-month functional outcome after intracerebral haemorrhage. J Neurol. 2022; 269:6058–66. DOI: 10.1007/s00415-022-11245-1. PMID: 35861854.

30. Tao J, Li Z, Liu Y, Li J, Bai R. Performance comparison of different neuroimaging methods for predicting upper limb motor outcomes in patients after stroke. Neural Plast. 2022; 2022:4203698. DOI: 10.1155/2022/4203698. PMID: 35707519.

31. Koyama T, Koumo M, Uchiyama Y, Domen K. Utility of fractional anisotropy in cerebral peduncle for stroke outcome prediction: comparison of hemorrhagic and ischemic strokes. J Stroke Cerebrovasc Dis. 2018; 27:878–85. DOI: 10.1016/j.jstrokecerebrovasdis.2017.10.022. PMID: 29174878.

32. Ivanova MV, Isaev DY, Dragoy OV, Akinina YS, Petrushevskiy AG, Fedina ON, et al. Diffusion-tensor imaging of major white matter tracts and their role in language processing in aphasia. Cortex. 2016; 85:165–81. DOI: 10.1016/j.cortex.2016.04.019. PMID: 27289586.

33. Miyai I, Sonoda S, Nagai S, Takayama Y, Inoue Y, Kakehi A, et al. Results of new policies for inpatient rehabilitation coverage in Japan. Neurorehabil Neural Repair. 2011; 25:540–7. DOI: 10.1177/1545968311402696. PMID: 21451116.