Functional Magnetic Resonance Imaging (fMRI) as a Predictive Tool for Rehabilitation Outcomes in Stroke Patients

Main Article Content

Dr Atiqa Ijaz
Dr Abdul Mannan
Dr Rameel Ur Rehman Cheema

Abstract

BACKGROUND: Functional magnetic resonance imaging (fMRI) is a non-invasive technique providing valuable insights into brain activity. Its application in stroke rehabilitation is emerging as a potential tool for predicting recovery outcomes.


OBJECTIVE: This study aimed to explore the predictive capacity of baseline fMRI brain activation patterns on rehabilitation outcomes in stroke patients.


METHODS: With 150 stroke patients, we conducted a prospective cohort research to examine the correlation between baseline fMRI results and 6-month rehabilitation outcomes. The modified Rankin Scale (mRS) and the National Institutes of Health Stroke Scale (NIHSS) were used as outcome indicators.


RESULTS: Our findings revealed that the degree of brain activation within motor-related regions at baseline significantly correlated with functional improvement after six months. Both NIHSS score (R^2 = 0.45, p<0.001) and mRS score (R^2 = 0.38, p<0.001) were strongly associated with baseline fMRI data.


CONCLUSION: Our results suggest that baseline fMRI brain activation patterns can predict rehabilitation outcomes in stroke patients, providing a promising direction for individualizing stroke rehabilitation strategies.

Article Details

How to Cite
Atiqa Ijaz, Abdul Mannan, & Rameel Ur Rehman Cheema. (2023). Functional Magnetic Resonance Imaging (fMRI) as a Predictive Tool for Rehabilitation Outcomes in Stroke Patients. Journal of Health and Rehabilitation Research, 3(1). Retrieved from https://jhrlmc.com/index.php/home/article/view/34
Section
Articles
Author Biographies

Dr Atiqa Ijaz, Medical Officer

Doctors Hospital Lahore

Dr Abdul Mannan, Lecturer

Avicenna Medical College

Dr Rameel Ur Rehman Cheema, Lecturer

Hussain College of Health Sciences

References

Olafson E. Understanding and Predicting Recovery After Stroke Using Structural and Functional Magnetic Resonance Imaging: Weill Medical College of Cornell University; 2023.

Lu M, Du Z, Zhao J, Jiang L, Liu R, Zhang M, et al. Neuroimaging mechanisms of acupuncture on functional reorganization for post-stroke motor improvement: a machine learning-based functional magnetic resonance imaging study. Frontiers in Neuroscience. 2023;17:1143239.

Włodarczyk L, Cichon N, Saluk-Bijak J, Bijak M, Majos A, Miller E. Neuroimaging techniques as potential tools for assessment of angiogenesis and neuroplasticity processes after stroke and their clinical implications for rehabilitation and stroke recovery prognosis. Journal of Clinical Medicine. 2022;11(9):2473.

Tao J, Zhang S, Kong L, Zhu Q, Yao C, Guo Q, et al. Effectiveness and functional magnetic resonance imaging outcomes of Tuina therapy in patients with post-stroke depression: A randomized controlled trial. Frontiers in Psychiatry. 2022;13:923721.

Tang J, Xiang X, Cheng X. The Progress of Functional Magnetic Resonance Imaging in Patients with Poststroke Aphasia. Journal of Healthcare Engineering. 2022;2022.

Tahmi M, Kane VA, Pavol MA, Naqvi IA. Neuroimaging biomarkers of cognitive recovery after ischemic stroke. Frontiers in Neurology. 2022;13:923942.

Shin H, Kim JK, Choo YJ, Choi GS, Chang MC. Prediction of motor outcome of stroke patients using a deep learning algorithm with brain MRI as input data. European Neurology. 2022;85(6):460-6.

Ma Z-Z, Wu J-J, Hua X-Y, Zheng M-X, Xing X-X, Ma J, et al. Brain function and upper limb deficit in stroke with motor execution and imagery: A cross-sectional functional magnetic resonance imaging study. Frontiers in Neuroscience. 2022;16:806406.

Ma S, Huang H, Zhong Z, Zheng H, Li M, Yao L, et al. Effect of acupuncture on brain regions modulation of mild cognitive impairment: A meta-analysis of functional magnetic resonance imaging studies. Frontiers in Aging Neuroscience. 2022;14:914049.

Li Y, Yan S, Zhang G, Shen N, Wu D, Lu J, et al. Tractometry‐Based Estimation of Corticospinal Tract Injury to Assess Initial Impairment and Predict Functional Outcomes in Ischemic Stroke Patients. Journal of Magnetic Resonance Imaging. 2022;55(4):1171-80.

Li Y, Li K, Feng R, Li Y, Li Y, Luo H, et al. Mechanisms of repetitive transcranial magnetic stimulation on post-stroke depression: a resting-state functional magnetic resonance imaging study. Brain Topography. 2022;35(3):363-74.

Lee J, Kim H, Kim J, Chang WH, Kim Y-H. Multimodal imaging biomarker-based model using stratification strategies for predicting upper extremity motor recovery in severe stroke patients. Neurorehabilitation and Neural Repair. 2022;36(3):217-26.

Imura T, Mitsutake T, Hori T, Tanaka R. Predicting the prognosis of unilateral spatial neglect using magnetic resonance imaging in patients with stroke: A systematic review. Brain Research. 2022;1789:147954.

Ghaleh RA, Rahimibarghani S, Shirzad N, Ahangari A, Nazem-Zadeh M-R, Abadi AMA, et al. The Role of Baseline Functional MRI as a Predictor of Post-Stroke Rehabilitation Efficacy in Patients with Moderate to Severe Upper Extremity Dysfunction. Journal of Behavioral and Brain Science. 2022;12(12):658-69.

Billot A, Lai S, Varkanitsa M, Braun EJ, Rapp B, Parrish TB, et al. Multimodal neural and behavioral data predict response to rehabilitation in chronic poststroke aphasia. Stroke. 2022;53(5):1606-14.

Yang HE, Kyeong S, Kang H, Kim DH. Multimodal magnetic resonance imaging correlates of motor outcome after stroke using machine learning. Neuroscience Letters. 2021;741:135451.

Lee J, Kim H, Kim J, Lee HJ, Chang W, Kim YH. Differential early predictive factors for upper and lower extremity motor recovery after ischaemic stroke. European Journal of Neurology. 2021;28(1):132-40.

Lai S, Billot A, Varkanitsa M, Braun E, Rapp B, Parrish T, et al., editors. An exploration of machine learning methods for predicting post-stroke aphasia recovery. The 14th PErvasive Technologies Related to Assistive Environments Conference; 2021.

Imura T, Mitsutake T, Iwamoto Y, Tanaka R. A systematic review of the usefulness of magnetic resonance imaging in predicting the gait ability of stroke patients. Scientific Reports. 2021;11(1):14338.

da Silva MAS, Cook C, Stinear CM, Wolf SL, Borich MR. Paretic upper extremity strength at acute rehabilitation evaluation predicts motor function outcome after stroke. medRxiv. 2021:2021.10. 05.21264572.

Blaschke SJ, Hensel L, Minassian A, Vlachakis S, Tscherpel C, Vay SU, et al. Translating functional connectivity after stroke: functional magnetic resonance imaging detects comparable network changes in mice and humans. Stroke. 2021;52(9):2948-60.

Ansado J, Chasen C, Bouchard S, Northoff G. How brain imaging provides predictive biomarkers for therapeutic success in the context of virtual reality cognitive training. Neuroscience & Biobehavioral Reviews. 2021;120:583-94.

Puig J, Blasco G, Alberich-Bayarri A, Schlaug G, Deco G, Biarnes C, et al. Resting-state functional connectivity magnetic resonance imaging and outcome after acute stroke. Stroke. 2018;49(10):2353-60.

Berginström N, Nordström P, Ekman U, Eriksson J, Andersson M, Nyberg L, et al. Using functional magnetic resonance imaging to detect chronic fatigue in patients with previous traumatic brain injury: changes linked to altered striato-thalamic-cortical functioning. The Journal of head trauma rehabilitation. 2018;33(4):266-74.

Zhang Y, Hua Y, Bai Y. Applications of functional magnetic resonance imaging in determining the pathophysiological mechanisms and rehabilitation of spatial neglect. Frontiers in neurology. 2020;11:548568.

Ramage AE, Aytur S, Ballard KJ. Resting-state functional magnetic resonance imaging connectivity between semantic and phonological regions of interest may inform language targets in aphasia. Journal of Speech, Language, and Hearing Research. 2020;63(9):3051-67.

Min Y-S, Park JW, Park E, Kim A-R, Cha H, Gwak D-W, et al. Interhemispheric functional connectivity in the primary motor cortex assessed by resting-state functional magnetic resonance imaging aids long-term recovery prediction among subacute stroke patients with severe hand weakness. Journal of clinical medicine. 2020;9(4):975.

Gu Y, Bahrani M, Billot A, Lai S, Braun EJ, Varkanitsa M, et al., editors. A machine learning approach for predicting post-stroke aphasia recovery: A pilot study. Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments; 2020.

Farrens A, Vatinno A, Seo NJ, Sergi F. Predicting individual treatment response using functional magnetic resonance imaging (FMRI). The American Journal of Occupational Therapy. 2020;74(4_Supplement_1):7411500006p1-p1.

Shea-Shumsky NB, Schoeneberger S, Grigsby J. Executive functioning as a predictor of stroke rehabilitation outcomes. The Clinical Neuropsychologist. 2019;33(5):854-72.