Task effects on functional connectivity measures after stroke

Can motion capture improve task-based fMRI studies of motor function post-stroke? A systematic review

BACKGROUND

Variability in motor recovery after stroke represents a major challenge in its understanding and management. While functional MRI has been used to unravel interactions between stroke motor function and clinical outcome, fMRI alone cannot clarify any relation between brain activation and movement characteristics.

OBJECTIVES

We aimed to identify fMRI and kinematic coupling approaches and to evaluate their potential contribution to the understanding of motor function post-stroke.

METHOD

A systematic literature review was performed according to PRISMA guidelines on studies using fMRI and kinematics in post-stroke individuals. We assessed the internal, external, statistical, and technological validity of each study. Data extraction included study design and analysis procedures used to couple brain activity with movement characteristics.

RESULTS

Of the 404 studies found, 23 were included in the final review. The overall study quality was moderate (0.6/1). Thirteen studies used kinematic information either parallel to the fMRI results, or as a real-time input to external devices, for instance to provide feedback to the patient. Ten studies performed a statistical analysis between movement and brain activity by either using kinematics as variables during group or individual level regression or correlation. This permitted establishing links between movement characteristics and brain activity, unraveling cortico-kinematic relationships. For instance, increased activity in the ipsilesional Premotor Cortex was related to less smooth movements, whereas trunk compensation was expressed by increased activity in the contralesional Primary Motor Cortex.

CONCLUSION

Our review suggests that the coupling of fMRI and kinematics may provide valuable insight into cortico-kinematic relationships. The optimization and standardization of both data measurement and treatment procedures may help the field to move forward and to fully use the potential of multimodal cortico-kinematic integration to unravel the complexity of post-stroke motor function and recovery.

Predicting upper limb motor recovery in subacute stroke patients via fNIRS-measured cerebral functional responses induced by robotic training

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.

Large-scale brain networks and intra-axial tumor surgery: a narrative review of functional mapping techniques, critical needs, and scientific opportunities

In recent years, a paradigm shift in neuroscience has been occurring from "localizationism," or the idea that the brain is organized into separately functioning modules, toward "connectomics," or the idea that interconnected nodes form networks as the underlying substrates of behavior and thought. Accordingly, our understanding of mechanisms of neurological function, dysfunction, and recovery has evolved to include connections, disconnections, and reconnections. Brain tumors provide a unique opportunity to probe large-scale neural networks with focal and sometimes reversible lesions, allowing neuroscientists the unique opportunity to directly test newly formed hypotheses about underlying brain structural-functional relationships and network properties. Moreover, if a more complete model of neurological dysfunction is to be defined as a "disconnectome," potential avenues for recovery might be mapped through a "reconnectome." Such insight may open the door to novel therapeutic approaches where previous attempts have failed. In this review, we briefly delve into the most clinically relevant neural networks and brain mapping techniques, and we examine how they are being applied to modern neurosurgical brain tumor practices. We then explore how brain tumors might teach us more about mechanisms of global brain dysfunction and recovery through pre- and postoperative longitudinal connectomic and behavioral analyses.

Contralaterally controlled neuromuscular electrical stimulation-induced changes in functional connectivity in patients with stroke assessed using functional near-infrared spectroscopy

Contralaterally controlled neuromuscular electrical stimulation (CCNMES) is an innovative therapy in stroke rehabilitation which has been verified in clinical studies. However, the underlying mechanism of CCNMES are yet to be comprehensively revealed. The main purpose of this study was to apply functional near-infrared spectroscopy (fNIRS) to compare CCNMES-related changes in functional connectivity (FC) within a cortical network after stroke with those induced by neuromuscular electrical stimulation (NMES) when performing wrist extension with hemiplegic upper extremity. Thirty-one stroke patients with right hemisphere lesion were randomly assigned to CCNMES (n = 16) or NMES (n = 15) groups. Patients in both groups received two tasks: 10-min rest and 10-min electrical stimulation task. In each task, the cerebral oxygenation signals in the prefrontal cortex (PFC), bilateral primary motor cortex (M1), and primary sensory cortex (S1) were measured by a 35-channel fNIRS. Compared with NMES, FC between ipsilesional M1 and contralesional M1/S1 were significantly strengthened during CCNMES. Additionally, significantly higher coupling strengths between ipsilesional PFC and contralesional M1/S1 were observed in the CCNMES group. Our findings suggest that CCNMES promotes the regulatory functions of ipsilesional prefrontal and motor areas as well as contralesional sensorimotor areas within the functional network in patients with stroke.

Tracking the Effect of Therapy With Single-Trial Based Classification After Stroke

Stroke is a debilitating disease that leads, in the 50% of cases, to permanent motor or cognitive impairments. The effectiveness of therapies that promote recovery after stroke depends on indicators of the disease state that can measure the degree of recovery or predict treatment response or both. Here, we propose to use single-trial classification of task dependent neural activity to assess the disease state and track recovery after stroke. We tested this idea on calcium imaging data of the dorsal cortex of healthy, spontaneously recovered and rehabilitated mice while performing a forelimb retraction task. Results show that, at a single-trial level for the three experimental groups, neural activation during the reward pull can be detected with high accuracy with respect to the background activity in all cortical areas of the field of view and this activation is quite stable across trials and subjects of the same group. Moreover, single-trial responses during the reward pull can be used to discriminate between healthy and stroke subjects with areas closer to the injury site displaying higher discrimination capability than areas closer to this site. Finally, a classifier built to discriminate between controls and stroke at the single-trial level can be used to generate an index of the disease state, the therapeutic score, which is validated on the group of rehabilitated mice. In conclusion, task-related neural activity can be used as an indicator of disease state and track recovery without selecting a peculiar feature of the neural responses. This novel method can be used in both the development and assessment of different therapeutic strategies.