Any type of recovery prediction for stroke is absolutely useless to survivors. DELIVER 100% RECOVERY PROTOCOLS! Which is what survivors want! WHY THE FUCK AREN'T YOU DOING THAT?
Laziness? Incompetence? Or just don't care? NO leadership? NO strategy? Not my job? Not my Problem?
I'd have you all fired.
Predicting upper limb motor recovery in subacute stroke patients via fNIRS-measured cerebral functional responses induced by robotic training
Journal of NeuroEngineering and Rehabilitation volume 21, Article number: 226 (2024)
Abstract
Background
Neural activation induced by upper extremity robot-assisted training (UE-RAT) helps characterize adaptive changes in the brains of poststroke patients, revealing differences in recovery potential among patients. However, it remains unclear whether these task-related neural activities can effectively predict rehabilitation outcomes. In this study, we utilized functional near-infrared spectroscopy (fNIRS) to measure participants’ neural activity profiles during resting and UE-RAT tasks and developed models via machine learning to verify whether task-related functional brain responses can predict the recovery of upper limb motor function.
Methods
Cortical activation and brain network functional connectivity (FC) in brain regions such as the superior frontal cortex, premotor cortex, and primary motor cortex were measured using fNIRS in 82 subacute stroke patients in the resting state and during UE-RAT. The Fugl-Meyer Upper Extremity Assessment Scale (FMA-UE) was chosen as the index for assessing upper extremity motor function, and clinical information such as demographic and neurophysiological data was also collected. Robust features were screened in 100 randomly divided training sets using the least absolute shrinkage and selection operator (LASSO) method. Based on the selected robust features, machine learning algorithms were used to develop clinical models, fNIRS models, and combined models that integrated both clinical and fNIRS features. Finally, Shapley Additive Explanations (SHAP) was applied to interpret the prediction process and analyze key predictive factors.
Results
Compared to the resting state, task-related FC is a more robust feature for modeling, with screening frequencies above 90%. The combined models built using artificial neural networks (ANNs) and support vector machines (SVMs) significantly outperformed the other algorithms, with an average AUC of 0.861 (± 0.087) for the ANN and an average correlation coefficient (r) of 0.860 (± 0.069) for the SVM. Furthermore, predictive factor analysis of the models revealed that FC measured during tasks is the most important factor for predicting upper limb motor function.
Conclusion
This study confirmed that UE-RAT-induced FC can serve as an important predictor of rehabilitation, especially when combined with clinical information, further enhancing the accuracy of model predictions. These findings provide new insights for the early prediction of patients’ recovery potential, which may contribute to personalized rehabilitation decisions.
Background
More than two-thirds of stroke survivors have upper limb dyskinesia, which seriously affects their quality of life and social interactions [1]. Improving upper limb motor function has therefore become a key goal of rehabilitation therapy [2]. However, the heterogeneity among patients makes it challenging to assess rehabilitation potential in the early stages and may hinder the selection of optimal rehabilitation strategies. Therefore, it is particularly critical to identify predictors of rehabilitation, which are not only central to assessing rehabilitation potential, but also provide the basis for individualized treatment plans.
In recent years, machine learning algorithms such as support vector machines (SVMs) have been used to identify predictors of the potential for rehabilitation, such as time since stroke and initial FMA-UE scores [3,4,5]. These indicators provide a basis for predicting rehabilitation, but they lack sufficient sensitivity to distinguish subtle differences in patients’ functional status. After a stroke, the brain compensates for damaged areas by reorganizing neural networks. Therefore, functional connectivity (FC) between neural networks becomes a powerful indicator to characterize brain damage [6,7,8]. Studies based on functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) have shown that FC at rest correlates with functional impairment of the upper limb and is a valid biomarker for predicting motor recovery [9,10,11]. However, recent studies have shown that FC in the task state (task-FC) more directly reflects differences in functional compensation within the impaired region, whereas these differences are not apparent in the resting state [12, 13]. This may be because the brain allocates more neural resources to task-related processing, reducing noise and nonsmoothness, thereby revealing individual differences more effectively [14, 15].
In this context, effectively utilizing external tasks to elicit functional responses in patients is key to assessing rehabilitation potential. Upper Extremity Robot-Assisted Rehabilitation Training (UE-RAT) simulates real movement tasks by precisely controlling parameters, making the neural responses it evokes more compatible with the demands of daily life. Furthermore, incorporating visual and auditory stimuli in UE-RAT enhances interregional brain interactions and elicits more comprehensive brain functional response patterns [16, 17]. These properties allow UE-RAT-induced neural activity to effectively characterize adaptive changes in the brain following a stroke and reveal subtle differences in rehabilitation potential [18, 19]. However, it is important to note that the application of fMRI and EEG during UE-RAT tasks may be limited due to environmental constraints and sensitivity to motion.
fNIRS is an emerging neuroimaging tool that provides information on regional neural activity by noninvasively monitoring changes in oxygenation and deoxyhemoglobin concentrations [20]. Due to its resistance to motion artifacts and its ability to adapt to freely moving subjects, it has become an ideal method for assessing neural activation during rehabilitation training [21]. Research using fNIRS has demonstrated that patients with varying degrees of upper limb impairment exhibit differences in FC patterns at rest [22]. Further research, such as the studies by Huo [23] and Xie [24], indicates that these differences are more pronounced among patients during UE-RAT tasks. These results suggest that using fNIRS to measure changes in FC in the UE-RAT task using fNIRS can amplify individual differences, which provides an effective tool for accurately assessing rehabilitation potential.
The functional status of stroke patients is critical to rehabilitation prognosis, and the completion of UE-RAT requires activation of the nervous system that controls the patient’s upper limb movements. Therefore, we hypothesize that the neural activity of stroke patients during UE-RAT reflects their functional status. By analyzing these task-related neural activities, it is possible to quantify the patient’s functional status and use it as a quantitative indicator of rehabilitation prognosis. Therefore, the aim of our study is to utilize fNIRS to compute features such as cortical activation and FC in subacute stroke patients both at rest and during UE-RAT training. Subsequently, we will employ the LASSO algorithm to identify robust features and use machine learning algorithms to construct a predictive model for upper limb motor function recovery. Through this approach, we aim to explore the value of the neural activity characteristics induced by UE-RAT in relation to rehabilitation prognosis.
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