Method

Ten right-handed patients with subcortical ischemic stroke in the left hemisphere motor pathway (Fig 1), and twelve age-matched healthy controls were investigated to undergo imaging and behavioral tasks. All patients were first-onset stroke patients who showed motor deficits in both the upper and lower extremities. MR images were acquired using a 3.0 Tesla GE Discovery 750 MR scanner. Functional MRI data were obtained using a single-shot GRE-EPI sequence (TR/TE = 2000/41 ms; field of view = 220×220mm²; matrix = 64×64; flip angle = 90°; slice thickness = 4mm; 0.5mm gap; 32 slices; 190 time points). Anatomical images were acquired using a T1-weighted 3D SPGR sequence (TR/TE = 8.2/3.2 ms; FOV = 256×256 mm²; matrix = 256×256; slice thickness = 1.0 mm, no gap; 188 slices). Firstly, data were analyzed with standard preprocessed using SPM8 including removing the first 10 volumes, slice timing, head motion estimation, spatially normalization and spatial smoothing. Secondly, ICA was performed using the GICA program of the fMRI toolbox¹ including three stages: (i) data reduction, (ii) application of the ICA algorithm, and (iii) back-reconstruction for each individual subject. Note that GICA was performed 100 times and obtained 25 independent components, 11 meaningful components of which were identified as RSNs via visual inspection. Then, each ICA component was acquired in a random-effect one-sample t-test using a FWE correction (P < 0.05) and a cluster size of 100 voxels to generate a sample-specific spatial map for each component. Finally, The group differences within the networks were compared using one way ANOVA-within subject manner. Multiple comparisons were corrected using a Monte Carlo simulation, resulting in a corrected threshold of P < 0.05 and a cluster size of at least 20 voxels. The intra-network functional connectivity of each region of interest that showed significant group differences were extracted and compared between groups using a general linear model with age and sex as covariates of no interest.

Result

Eleven RSNs² were identified via the Subject Order Independent Group ICA method. The RSNs were the ventral (vSMN) and dorsal (dSMN) SMNs, the medial (mVN) and lateral (lVN) visual networks, the auditory network (AN), the dorsal attention network (DAN), the anterior (aDMN) and posterior (pDMN) DMNs, the left (lFPN) and right (rFPN) FPNs, and Salience network (SN) (Fig2). Stroke patients exhibited significantly dynamic functional connectivity changes in the right superior parietal lobule of the DMN (Fig 3A) and the cuneus of mVN (Fig 3B). First, compared the healthy subjects, the stroke patients displayed significantly decreased intra-network FCs within one week, and on first month the functional connectivity was significantly increased, then the functional connectivity of stroke patients were gradually decreasing from first month to sixth month, and there were no significantly group differences between stroke patients and healthy controls on sixth month.

Discussion

In the current study, we used ICA to investigate the altered network reorganization during stroke recovery. We observed the dynamic evolution model of network reorganization beyond the motor system, which in multiple functional networks over time during stroke recovery process. To our knowledge, the brain is a complex network consisting of multiple subnetworks serving different functions. The subcortical lesion may impair a certain subnetwork, thus affecting the functional organization of the subnetworks. In the study, the results may reflect the adaptive reorganization of the functional network in stroke patients.

Conclusion

In conclusion, this study suggests that multiple function networks underwent dynamic reorganization during stroke recovery after motor pathway subcortical stroke. The stroke patients displayed that increased functional connectivity within one week after stroke and then gradually decreased, which may be predictive of the impairment and restoration evolution model of function over time. Taken together, the study expands our understanding of the lesion-induced network plasticity model occurring in the brain.

Acknowledgements

This study was supported by the Natural Science Foundation of China (Nos. 81601467)