Ok, what the fuck good is anything here helpful to stroke recovery? The whole goal of stroke research is to GET SURVIVORS RECOVERED! This did nothing towards that. Well, more persons to fire!
Dynamic Reorganization Patterns of Brain Modules after Stroke Reflecting Motor Function
Advancements in neuroimaging technologies have significantly deepened our understanding of the neural physiopathology associated with stroke. Nevertheless, the majority of studies ignored the characteristics of dynamic changes in brain networks. The relationship between dynamic changes in brain networks and the severity of motor dysfunction after stroke needs further investigation. From the perspective of multilayer network module reconstruction, we aimed to explore the dynamic reorganization of the brain and its relationship with motor function in subcortical stroke patients.
We recruited 35 healthy individuals and 50 stroke patients with unilateral limb motor dysfunction (further divided into mild-moderate group and severe group). Using dynamic multilayer network modularity analysis, we investigated changes in the dynamic modular reconfiguration of brain networks. Additionally, we assessed longitudinal clinical scale changes in stroke patients. Correlation and regression analyses were employed to explore the relationship between characteristic dynamic indicators and impairment and recovery of motor function, respectively.
We observed increased temporal flexibility in the Default Mode Network (DMN) and decreased recruitment of module reconfiguration in the Attention Network (AN), Sensorimotor Network (SMN), and DMN after stroke. We also observed reduced module loyalty following stroke. Additionally, correlation analysis showed that hyper-flexibility of the DMN was associated with better lower limb motor function performance in stroke patients with mild-to-moderate impairment. Regression analysis indicated that increased flexibility within the DMN and decreased recruitment coefficient within the AN may predict good lower limb function prognosis in patients with mild to moderate motor impairment.
Our study revealed more frequent modular reconfiguration and hyperactive interaction of brain networks after stroke. Notably, dynamic modular remodeling was closely related to the impairment and recovery of motor function. Understanding the temporal module reconfiguration patterns in multilayer networks after stroke can provide valuable information for more targeted treatments.
The brain network can be defined as a collection of functional connectivity (FC) among various brain regions, derived from the statistical interdependence of spontaneous fluctuations in their blood oxygen level-dependent (BOLD) signals [1, 2]. Emerging evidence suggests the temporal variability and instability of brain functional network organization, with interactions among specific networks even during the resting state [3, 4, 5]. To accurately capture changes in time-varying FC, existing static models of functional reorganization need to be improved. Several algorithms have been developed to describe brain dynamics, including K-mean clustering [6], co-activation patterns [7], and principal component analysis [8]. Nevertheless, the majority of previous researches on dynamic networks has either ignored the continuum of the time window or failed to investigate the specific patterns of variation among networks explicitly.
Stroke is a common cause of disability in adults [9]. Function impairment after stroke can be considered a disorder within the brain network. Clinically, stroke patients with similar lesion locations may exhibit markedly diverse motor function outcomes [10]. The pattern of functional reorganization may reflect the severity of the disease, the accumulation of auto-adaptive capacity during disease progression, and the effectiveness of interventions [11, 12, 13]. Pioneering researchers have sought to identify image features that may be biomarkers of motor recovery after stroke. They found that increased FCs between the cortex are associated with recovered grasping performance after stroke [14], and restoration of coupling patterns within the ipsilesional hemisphere over time has been related to the extent of recovery [15]. The advent of dynamical analysis methods has broadened research searching for neuroimaging biomarkers.
Graph theory has confirmed the existence of modular organization within the brain [16, 17, 18]. Each distinct module is responsible for specific functions, and this modular arrangement is thought to enable efficient processing of designated functions while maintaining global network integration [19]. Similarly, the modular organization of the brain exhibits dynamically altering properties. The multilayer network modular algorithm [20], a mathematical extension of conventional networks, can be envisioned as a stack of numerous individual two-dimensional matrix networks, forming a three-dimensional multilayer network. In a dynamic multilayer network, each layer represents FC within a specific time window, and clusters with similar BOLD time courses within each time window are identified as modules [21, 22]. The advantage of the dynamic module detection method lies in its capacity to provide insights into continuous brain dynamics by preserving the temporal order between neighboring layers. Alterations in modular structure are frequently correlated with nervous system diseases, as exemplified in schizophrenia [23], depression [24], autism [25], and bipolar disorder [26]. However, applying this method to the post-stroke brain needs further investigation. Notably, since network properties are not static over time, dynamic changes in the brain network within seconds or minutes may represent a more sensitive measure of impairment and recovery of brain functions.
In this study, we used the multilayer modular network algorithm with temporal resolution to reveal dynamic modular reorganization after stroke. Additionally, we sought to identify the relationship between changes in module characteristics and motor performance after stroke. We hypothesized that substantial dynamic restructuring of the modular structure occurs after stroke, and these aberrant temporal variabilities are related to post-stroke hemiparesis. These findings would facilitate a deeper understanding of the neural mechanisms underlying movement deficits and recovery.
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