Mapping Resting State Networks: Insights into motor impairment of Acute Post Stroke Patients

Stroke is the most common neurological condition that causes disability or in more severe cases death. The course of recovery after a stroke and the most appropriate rehabilitation and treatment methods in this context remain uncertain. Although the structural damage caused by a stroke may be localized in a specific brain area, dysfunction may occur remotely in areas of the brain that are far from the area of damage. Areas that are directly damaged or indirectly affected are organized into a larger functional network that is in dynamic balance with other networks in the brain. Therefore, measurement of resting state functional brain networks can predict the clinical picture and prognosis of stroke. The aim of this research is to investigate the relationship between resting state functional brain networks and motor Decadency in acute stroke patients. Twenty right-handed patients, 17 of whom were men and 3 of whom were women, with an average age of 58.8+7.3 and their first stroke, were included in the study. In the acute period, fMRI was also applied to patients to evaluate the resting state brain networks. Patients were also evaluated with the MoCa and NIHSS scales. Resting status of the patient-The correlation between the obtained resting status network components and the NIHSS scores was evaluated to analyze the fMRI records. The patients' NIHSS scores were 2.65 (+1.47) and their MoCa scores were 20.89 (+4.01). In the resting state brain networks analysis, the Default Mode Network (DMN) and Sensorimotor Network (SMN) emerged as the most significant networks. The functional connectivity of the Default Mode Network with the left Cerebellum and Precuneus cortex decreased as NIHSS scores increased, while the functional connectivity of the SMN with the Frontal Operculum, Inferior Frontal Gyrus and Insular Cortex showed high activity as NIHSS scores increased. Our findings reveal that there is a correlation between the neurological symptoms that occur in the acute phase of stroke and the functional brain networks in the resting state. This finding reveals that there is a basis for predicting post-stroke prognosis and even planning rehabilitation in a specific way through resting state functional brain networks.