The relationship between the prefrontal cortex and limb motor function in stroke: A study based on resting-state functional near-infrared spectroscopy

Wang, Dan; Wang, Jie; Zhao, Hongbo; Liang, Yahui; Zhang, Wenyue; Li, Mingxi; Liu, Hua; Hu, Di; Zhang, Sibin; Xing, Enlong; Su, Ying; Yu, Wanchen; Sun, Jinyan; Yang, Aoran

doi:10.1016/j.brainres.2023.148269

Cited by 7 publications

(3 citation statements)

References 48 publications

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“…This study also demonstrated the compensatory role of contralesional PFC in motor rehabilitation, which substitutes or strengthens the functions of stroke-affected regions [ 36 , 37 ]. The ability of contralesional PFC to form new neural pathways and assume roles for damaged areas highlights a key adaptation mechanism in poststroke recovery [ 38 , 39 ].…”

Section: Discussionmentioning

confidence: 82%

Use of cortical hemodynamic responses in digital therapeutics for upper limb rehabilitation in patients with stroke

Kim,

Lee

et al. 2024

J NeuroEngineering Rehabil

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Background Stroke causes long-term disabilities, highlighting the need for innovative rehabilitation strategies for reducing residual impairments. This study explored the potential of functional near-infrared spectroscopy (fNIRS) for monitoring cortical activation during rehabilitation using digital therapeutics. Methods This cross-sectional study included 18 patients with chronic stroke, of whom 13 were men. The mean age of the patients was 67.0 ± 7.1 years. Motor function was evaluated through various tests, including the Fugl–Meyer assessment for upper extremity (FMA-UE), grip and pinch strength test, and box and block test. All the patients completed the digital rehabilitation program (MotoCog®, Cybermedic Co., Ltd., Republic of Korea) while being monitored using fNIRS (NIRScout®, NIRx Inc., Germany). Statistical parametric mapping (SPM) was employed to analyze the cortical activation patterns from the fNIRS data. Furthermore, the K-nearest neighbor (K-NN) algorithm was used to analyze task performance and fNIRS data to classify the severity of motor impairment. Results The participants showed diverse task performances in the digital rehabilitation program, demonstrating distinct patterns of cortical activation that correlated with different motor function levels. Significant activation was observed in the ipsilesional primary motor area (M1), primary somatosensory area (S1), and contralateral prefrontal cortex. The activation patterns varied according to the FMA-UE scores. Positive correlations were observed between the FMA-UE scores and SPM t-values in the ipsilesional M1, whereas negative correlations were observed in the ipsilesional S1, frontal lobe, and parietal lobe. The incorporation of cortical hemodynamic responses with task scores in a digital rehabilitation program substantially improves the accuracy of the K-NN algorithm in classifying upper limb functional levels in patients with stroke. The accuracy for tasks, such as the gas stove-operation task, increased from 44.4% using only task scores to 83.3% when these scores were combined with oxy-Hb t-values from the ipsilesional M1. Conclusions The results advocated the development of tailored digital rehabilitation strategies by combining the behavioral and cerebral hemodynamic data of patients with stroke. This approach aligns with the evolving paradigm of personalized rehabilitation in stroke recovery, highlighting the need for further extensive research to optimize rehabilitation outcomes.

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Section: Discussionmentioning

confidence: 82%

Use of cortical hemodynamic responses in digital therapeutics for upper limb rehabilitation in patients with stroke

Kim,

Lee

et al. 2024

J NeuroEngineering Rehabil

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“…As referenced from previous studies ( Wang et al, 2023 ), we also computed the laterality index (LI) of HbO2 as an alternative feature using Equation (7) . Here, ΔoxyR and ΔoxyL represent HbO2 concentration changes in the right (four right channels were averaged) and left (four left channels were averaged) forehead, respectively.…”

Section: Methodsmentioning

confidence: 99%

Automatic depression diagnosis through hybrid EEG and near-infrared spectroscopy features using support vector machine

Yi,

Xie,

et al. 2023

Front. Neurosci.

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Depression is a common mental disorder that seriously affects patients’ social function and daily life. Its accurate diagnosis remains a big challenge in depression treatment. In this study, we used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) and measured the whole brain EEG signals and forehead hemodynamic signals from 25 depression patients and 30 healthy subjects during the resting state. On one hand, we explored the EEG brain functional network properties, and found that the clustering coefficient and local efficiency of the delta and theta bands in patients were significantly higher than those in normal subjects. On the other hand, we extracted brain network properties, asymmetry, and brain oxygen entropy as alternative features, used a data-driven automated method to select features, and established a support vector machine model for automatic depression classification. The results showed the classification accuracy was 81.8% when using EEG features alone and increased to 92.7% when using hybrid EEG and fNIRS features. The brain network local efficiency in the delta band, hemispheric asymmetry in the theta band and brain oxygen sample entropy features differed significantly between the two groups (p < 0.05) and showed high depression distinguishing ability indicating that they may be effective biological markers for identifying depression. EEG, fNIRS and machine learning constitute an effective method for classifying depression at the individual level.

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“…fNIRS can detect activation patterns in the cerebral motor cortex, reflecting changes in neural remodeling during the recovery of motor function after stroke ( Huo et al, 2019 ). It has been shown that cerebral hemodynamic activity reflected by fNIRS can be a reliable neurobiomarker for the assessment of limb motor dysfunction in stroke patients ( Wang et al, 2023 ). It is well known that stroke-induced motor deficits are associated with an interhemispheric imbalance of motor activation ( Cunningham et al, 2015 ; Tang et al, 2015 ; Kinoshita et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

The assessment of interhemispheric imbalance using functional near-infrared spectroscopic and transcranial magnetic stimulation for predicting motor outcome after stroke

Chen,

Zhang,

Chen

et al. 2023

Front. Neurosci.

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ObjectiveTo investigate changes in interhemispheric imbalance of cortical excitability during motor recovery after stroke and to clarify the relationship between motor function recovery and alterations in interhemispheric imbalance, with the aim to establish more effective neuromodulation strategies.MethodsThirty-one patients underwent assessments of resting motor threshold (RMT) using transcranial magnetic stimulation (TMS); the cortical activity of the primary motor cortex (M1), premotor cortex (PMC), and supplementary motor area (SMA) using functional near-infrared spectroscopy (fNIRS); as well as motor function using upper extremity Fugl-Meyer (FMA-UE). The laterality index (LI) of RMT and fNIRS were also calculated. All indicators were measured at baseline(T1) and 1 month later(T2). Correlations between motor function outcome and TMS and fNIRS metrics at baseline were analyzed using bivariate correlation.ResultsAll the motor function (FMA-UE1, FMA-UE2, FMA-d2) and LI-RMT (LI-RMT1 and LI-RMT2) had a moderate negative correlation. The higher the corticospinal excitability of the affected hemisphere, the better the motor outcome of the upper extremity, especially in the distal upper extremity (r = −0.366, p = 0.043; r = −0.393, p = 0.029). The greater the activation of the SMA of the unaffected hemisphere, the better the motor outcome, especially in the distal upper extremity (r = −0.356, p = 0.049; r = −0.367, p = 0.042). There was a significant moderate positive correlation observed between LI-RMT2 and LI-SMA1 (r = 0.422, p = 0.018). The improvement in motor function was most significant when both LI-RMT1 and LI-SMA1 were lower. Besides, in patients dominated by unaffected hemisphere corticospinal excitability during motor recovery, LI-(M1 + SMA + PMC)2 exhibited a significant moderate positive association with the proximal upper extremity function 1 month later (r = 0.642, p = 0.007).ConclusionThe combination of both TMS and fNIRS can infer the prognosis of motor function to some extent. Which can infer the role of both hemispheres in recovery and may contribute to the development of effective individualized neuromodulation strategies.

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The relationship between the prefrontal cortex and limb motor function in stroke: A study based on resting-state functional near-infrared spectroscopy

Cited by 7 publications

References 48 publications

Use of cortical hemodynamic responses in digital therapeutics for upper limb rehabilitation in patients with stroke

Use of cortical hemodynamic responses in digital therapeutics for upper limb rehabilitation in patients with stroke

Automatic depression diagnosis through hybrid EEG and near-infrared spectroscopy features using support vector machine

The assessment of interhemispheric imbalance using functional near-infrared spectroscopic and transcranial magnetic stimulation for predicting motor outcome after stroke

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