زهرا نقره علي زاده

Brain connectivity refers to the interactions between different brain regions that play a crucial role in information processing an‎d the execution of voluntary behaviors. In the human brain, these connections include, on the one han‎d, intra-regional connections (which relate to the connections an‎d activities within a specific brain region) an‎d, on the other han‎d, inter-regional connections that provide communication between different brain regions, especially at the cognitive an‎d motor levels. These communication networks are made possible through the synchrony of neural oscillations an‎d the exchange of information between neurons an‎d different brain regions, which is essential for complex processes such as movement preparation an‎d execution. This study aimed to investigate the neural mechanisms underlying the control of voluntary han‎d movements in rhesus monkeys. For this purpose, two studies were designed to analyze the functional connectivity patterns of the brain in three behavioral states: resting, motor preparation, an‎d movement execution. In these studies, two functional connectivity indices were used, including mutual information (MI) an‎d partial phase-locked value (PPLV). The MI method allows the identification of statistical dependencies by measuring the sharing of information between brain regions, while the PPLV method reveals the instantaneous phase synchrony between regions by measuring the synchrony of neural oscillations. In this study, intra- an‎d inter-regional connections between motor cortex regions including the primary motor cortex (M1), ventral premotor cortex (PMV), an‎d dorsal premotor cortex (PMD), supplementary motor area (SMA), an‎d prefrontal regions including the ventrolateral prefrontal cortex (VLPFC) an‎d dorsolateral prefrontal cortex (DLPFC) were analyzed. Based on the connectivity matrices obtained from both methods, network indices including node strength, clustering coefficient, local efficiency, an‎d global efficiency were calculated an‎d compared in three motor states. The results of both methods showed that from the resting state to the preparation state an‎d then to the movement state, the amount of functional connectivity at the network level increases significantly. In the preparation state, the clustering coefficient an‎d local efficiency indices showed a greater increase, indicating an increase in functional segregation; that is, the co-functional areas in the motor an‎d prefrontal cortex were organized into coherent local clusters to process information related to motor planning an‎d decision-making. In contrast, in the movement state, a significant increase was observed in the node strength an‎d global efficiency indices, indicating functional integration; that is, the brain network activates long-range communication pathways between more distant areas for effective movement execution an‎d facilitates the flow of information between motor an‎d cognitive networks.

ارتباطات مغزي , عملكردهاي موتوري , روش هاي غيرخطي

brain connectivity , motor functions , nonlinear methods

Zahra Noghrealizade

Dr Abbas Erfanian Omidvar