ABSTRACT
ObjectiveTo explore low-frequency repetitive transcranial magnetic stimulation (rTMS) combined with bilateral isokinematic training (BIT) on upper limb motor function and activities of daily living of stroke patients. MethodsFrom September, 2021 to September, 2022, 60 stroke inpatients in Zhejiang Provincial People's Hospital were randomly divided into rTMS group (n = 20), BIT group (n = 20) and combination group (n = 20). All the patients accepted routine rehabilitation, moreover, rTMS group accepted 1 Hz rTMS on healthy side, BIT group accepted BIT, and the combination group accepted the combination of 1 Hz rTMS on healthy side and BIT, for four weeks. They were evaluated with Fugl-Meyer Assessment-Upper Extremities (FMA-UE), Wolf Motor Function Test (WMFT), Carroll Upper Extremities Function Test (UEFT) and modified Barthel Index (MBI) before and after treatment. ResultsThe scores of FMA-UE, WMFT, UEFT and MBI significantly improved in all the groups after treatment (|t| > 5.052, P < 0.001), and improved the most in the combination group (F > 9.834, P < 0.001). ConclusionBoth low-frequency rTMS and BIT can effectively improve upper limb motor function and activities of daily living of stroke patients, and the combination of them is more effective.
ABSTRACT
ObjectiveTo investigate the effect of high-definition transcranial direct current stimulation (HD-tDCS) combined with rehabilitation robot on upper limb and hand dysfunction in patients with subacute stroke. MethodsFrom December, 2019 to December, 2021, 50 inpatients with subacute stroke in Zhejiang Provincial People's Hospital were randomly divided into control group (n = 25) and experimental group (n = 25). Both groups received routine rehabilitation therapy, while the control group added sham HD-tDCS combined with rehabilitation robot, and the experimental group added HD-tDCS combined with rehabilitation robot, for four weeks. The upper limb and hand function was assessed with Action Research Arm Test (ARAT), Fugl-Meyer Assessment-Upper Extremities (FMA-UE) and Motor Assessment Scale (MAS) before and after treatment. ResultsAfter treatment, the scores of ARAT, FMA-UE and MAS increased in the two groups (∣Z∣ > 3.320, t > 6.379, P < 0.01), while the scores of FMA-UE and MAS were higher in the experimental group than in the control group (Z = -2.379, t = 3.181, P < 0.05), as well as the scores of grasping and gross motor of ARAT (∣Z∣ > 2.033, P < 0.05). ConclusionThe combination of HD-tDCS and rehabilitation robot can be more effective on upper limb and hand function in patients with subacute stroke than rehabilitation robot alone.
ABSTRACT
Brain functional network changes over time along with the process of brain development, disease, and aging. However, most of the available measurements for evaluation of the difference (or similarity) between the individual brain functional networks are for charactering static networks, which do not work with the dynamic characteristics of the brain networks that typically involve a long-span and large-scale evolution over the time. The current study proposes an index for measuring the similarity of dynamic brain networks, named as dynamic network similarity (DNS). It measures the similarity by combining the "evolutional" and "structural" properties of the dynamic network. Four sets of simulated dynamic networks with different evolutional and structural properties (varying amplitude of changes, trend of changes, distribution of connectivity strength, range of connectivity strength) were generated to validate the performance of DNS. In addition, real world imaging datasets, acquired from 13 stroke patients who were treated by transcranial direct current stimulation (tDCS), were used to further validate the proposed method and compared with the traditional similarity measurements that were developed for static network similarity. The results showed that DNS was significantly correlated with the varying amplitude of changes, trend of changes, distribution of connectivity strength and range of connectivity strength of the dynamic networks. DNS was able to appropriately measure the significant similarity of the dynamics of network changes over the time for the patients before and after the tDCS treatments. However, the traditional methods failed, which showed significantly differences between the data before and after the tDCS treatments. The experiment results demonstrate that DNS may robustly measure the similarity of evolutional and structural properties of dynamic networks. The new method appears to be superior to the traditional methods in that the new one is capable of assessing the temporal similarity of dynamic functional imaging data.