scholarly journals Alterations of Functional Connectivity in Stroke Patients With Basal Ganglia Damage and Cognitive Impairment

2020 ◽  
Vol 11 ◽  
Author(s):  
Guanqun Yao ◽  
Jing Li ◽  
Sha Liu ◽  
Jiaojian Wang ◽  
Xiaohua Cao ◽  
...  
2021 ◽  
Author(s):  
Hua Zhu ◽  
Lijun Zuo ◽  
Wanlin Zhu ◽  
Jing Jing ◽  
Zhe Zhang ◽  
...  

Abstract ObjectiveTo characterize brain structural and functional networks in post-stroke patients with or without cognitive impairment. MethodsGraph theory analysis was applied to diffusion-weighted imaging (DWI) data and resting-state functional MRI (fMRI) data from 23 post-stroke patients with cognitive impairment (PSCI), 17 post-stroke patients without cognitive impairment (NPSCI), and 29 healthy controls (HC). Structural and functional connectivity between 90 cortical and subcortical brain regions was estimated and thresholded to construct a set of undirected graphs. Network-based statistics (NBS) was used to characterize altered connectivity patterns among the three groups. ResultsCompared to HC, the PSCI group demonstrated substantial reductions in all three types of connections - rich club, feeder, and local - in structural and functional networks. Specifically, in structural network analysis, reduced connections were observed within basal ganglia and basal ganglia-frontal networks, whereas in the functional network analysis, reduced connections were observed in fronto-parietal network (FPN) and cingulo-opercular networks (CON). Meanwhile, compared to HC, the NPSCI group demonstrated reductions in both feeder and local connections only within occipital area and occipital-temporal structural networks. ConclusionsThe findings of reduced structural connectivity in regions stemming from a basal ganglia core and reduced functional connectivity in FPN and CON may indicate a bottom-up cognitive impairment induced by stroke. Graph analysis and connectomics may aid clinical diagnosis and serve as potential imaging biomarkers for post-stroke patients with cognitive impairment.


2016 ◽  
Vol 37 (12) ◽  
pp. 2310-2316 ◽  
Author(s):  
V. Dunet ◽  
J. Deverdun ◽  
C. Charroud ◽  
E. Le Bars ◽  
F. Molino ◽  
...  

2020 ◽  
Vol 17 (4) ◽  
pp. 373-381
Author(s):  
Wuhai Tao ◽  
Jinping Sun ◽  
Xin Li ◽  
Wen Shao ◽  
Jing Pei ◽  
...  

Background: Subjective Memory Impairment (SMI) may tremendously increase the risk of Alzheimer’s Disease (AD). The full understanding of the neuromechanism of SMI will shed light on the early intervention of AD. Methods: In the current study, 23 Healthy Controls (HC), 22 SMI subjects and 24 amnestic Mild Cognitive Impairment (aMCI) subjects underwent the comprehensive neuropsychological assessment and the resting-state functional magnetic resonance imaging scan. The difference in the connectivity of the Default Mode Network (DMN) and Functional Connectivity (FC) from the Region of Interest (ROI) to the whole brain were compared, respectively. Results: The results showed that HC and SMI subjects had significantly higher connectivity in the region of the precuneus area compared to aMCI subjects. However, from this region to the whole brain, SMI and aMCI subjects had significant FC decrease in the right anterior cingulum, left superior frontal and left medial superior frontal gyrus compared to HC. In addition, this FC change was significantly correlated with the cognitive function decline in participants. Conclusion: Our study indicated that SMI subjects had relatively intact DMN connectivity but impaired FC between the anterior and posterior brain. The findings suggest that long-distance FC is more vulnerable than the short ones in the people with SMI.


2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Federico Calesella ◽  
Alberto Testolin ◽  
Michele De Filippo De Grazia ◽  
Marco Zorzi

AbstractMultivariate prediction of human behavior from resting state data is gaining increasing popularity in the neuroimaging community, with far-reaching translational implications in neurology and psychiatry. However, the high dimensionality of neuroimaging data increases the risk of overfitting, calling for the use of dimensionality reduction methods to build robust predictive models. In this work, we assess the ability of four well-known dimensionality reduction techniques to extract relevant features from resting state functional connectivity matrices of stroke patients, which are then used to build a predictive model of the associated deficits based on cross-validated regularized regression. In particular, we investigated the prediction ability over different neuropsychological scores referring to language, verbal memory, and spatial memory domains. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) were the two best methods at extracting representative features, followed by Dictionary Learning (DL) and Non-Negative Matrix Factorization (NNMF). Consistent with these results, features extracted by PCA and ICA were found to be the best predictors of the neuropsychological scores across all the considered cognitive domains. For each feature extraction method, we also examined the impact of the regularization method, model complexity (in terms of number of features that entered in the model) and quality of the maps that display predictive edges in the resting state networks. We conclude that PCA-based models, especially when combined with L1 (LASSO) regularization, provide optimal balance between prediction accuracy, model complexity, and interpretability.


Author(s):  
Cristina Russo ◽  
Laura Veronelli ◽  
Carlotta Casati ◽  
Alessia Monti ◽  
Laura Perucca ◽  
...  

AbstractMotor learning interacts with and shapes experience-dependent cerebral plasticity. In stroke patients with paresis of the upper limb, motor recovery was proposed to reflect a process of re-learning the lost/impaired skill, which interacts with rehabilitation. However, to what extent stroke patients with hemiparesis may retain the ability of learning with their affected limb remains an unsolved issue, that was addressed by this study. Nineteen patients, with a cerebrovascular lesion affecting the right or the left hemisphere, underwent an explicit motor learning task (finger tapping task, FTT), which was performed with the paretic hand. Eighteen age-matched healthy participants served as controls. Motor performance was assessed during the learning phase (i.e., online learning), as well as immediately at the end of practice, and after 90 min and 24 h (i.e., retention). Results show that overall, as compared to the control group, stroke patients, regardless of the side (left/right) of the hemispheric lesion, do not show a reliable practice-dependent improvement; consequently, no retention could be detected in the long-term (after 90 min and 24 h). The motor learning impairment was associated with subcortical damage, predominantly affecting the basal ganglia; conversely, it was not associated with age, time elapsed from stroke, severity of upper-limb motor and sensory deficits, and the general neurological condition. This evidence expands our understanding regarding the potential of post-stroke motor recovery through motor practice, suggesting a potential key role of basal ganglia, not only in implicit motor learning as previously pointed out, but also in explicit finger tapping motor tasks.


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