Rihui Li1, Sheng Li2, Jinsook Roh1, Chushan Wang3, Yingchun Zhang1. 1. University of Houston, Houston, TX, USA. 2. University of Texas Health Science Center, Houston, TX, USA. 3. Guangdong Provincial Work Injury Rehabilitation Hospital, Guangzhou, Guangdong, China.
Abstract
BACKGROUND: Persistent motor deficits are very common in poststroke survivors and often lead to disability. Current clinical measures for profiling motor impairment and assessing poststroke recovery are largely subjective and lack precision. OBJECTIVE: A multimodal neuroimaging approach was developed based on concurrent functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) to identify biomarkers associated with motor function recovery and document the poststroke cortical reorganization. METHODS: EEG and fNIRS data were simultaneously recorded from 9 healthy controls and 18 stroke patients during a hand-clenching task. A novel fNIRS-informed EEG source imaging approach was developed to estimate cortical activity and functional connectivity. Subsequently, graph theory analysis was performed to identify network features for monitoring and predicting motor function recovery during a 4-week intervention. RESULTS: The task-evoked strength at ipsilesional primary somatosensory cortex was significantly lower in stroke patients compared with healthy controls (P < .001). In addition, across the 4-week rehabilitation intervention, the strength at ipsilesional premotor cortex (PMC) (R = 0.895, P = .006) and the connectivity between bilateral primary motor cortices (M1) (R = 0.9, P = .007) increased in parallel with the improvement of motor function. Furthermore, a higher baseline strength at ipsilesional PMC was associated with a better motor function recovery (R = 0.768, P = .007), while a higher baseline connectivity between ipsilesional supplementary motor cortex (SMA)-M1 implied a worse motor function recovery (R = -0.745, P = .009). CONCLUSION: The proposed multimodal EEG/fNIRS technique demonstrates a preliminary potential for monitoring and predicting poststroke motor recovery. We expect such findings can be further validated in future study.
BACKGROUND: Persistent motor deficits are very common in poststroke survivors and often lead to disability. Current clinical measures for profiling motor impairment and assessing poststroke recovery are largely subjective and lack precision. OBJECTIVE: A multimodal neuroimaging approach was developed based on concurrent functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) to identify biomarkers associated with motor function recovery and document the poststroke cortical reorganization. METHODS: EEG and fNIRS data were simultaneously recorded from 9 healthy controls and 18 strokepatients during a hand-clenching task. A novel fNIRS-informed EEG source imaging approach was developed to estimate cortical activity and functional connectivity. Subsequently, graph theory analysis was performed to identify network features for monitoring and predicting motor function recovery during a 4-week intervention. RESULTS: The task-evoked strength at ipsilesional primary somatosensory cortex was significantly lower in strokepatients compared with healthy controls (P < .001). In addition, across the 4-week rehabilitation intervention, the strength at ipsilesional premotor cortex (PMC) (R = 0.895, P = .006) and the connectivity between bilateral primary motor cortices (M1) (R = 0.9, P = .007) increased in parallel with the improvement of motor function. Furthermore, a higher baseline strength at ipsilesional PMC was associated with a better motor function recovery (R = 0.768, P = .007), while a higher baseline connectivity between ipsilesional supplementary motor cortex (SMA)-M1 implied a worse motor function recovery (R = -0.745, P = .009). CONCLUSION: The proposed multimodal EEG/fNIRS technique demonstrates a preliminary potential for monitoring and predicting poststroke motor recovery. We expect such findings can be further validated in future study.
Authors: Amanda A Vatinno; Annie Simpson; Viswanathan Ramakrishnan; Heather S Bonilha; Leonardo Bonilha; Na Jin Seo Journal: Neurorehabil Neural Repair Date: 2022-03-20 Impact factor: 3.919