Imagine your brain as a complex web of connections, constantly shifting and adapting. Well, scientists have discovered that this intricate network can reveal early signs of Alzheimer’s disease. Subjective cognitive decline (SCD), the first stage of Alzheimer’s, has been linked to abnormal dynamics in the brain’s functional networks. In a study, researchers used advanced imaging techniques to analyze the resting-state functional magnetic resonance imaging (fMRI) of participants with SCD, Alzheimer’s patients, and normal controls. They found three distinct states of information transformation in the brain: low, middle, and high connectivity levels. These states differed significantly between the groups, and the middle state showed a positive correlation with cognitive scores. Moreover, when examining specific brain networks, such as the default mode network and sensorimotor network, researchers identified significant differences between controls and individuals with SCD or Alzheimer’s. The findings suggest that analyzing dynamic functional connectivity could potentially serve as an early detection tool for Alzheimer’s. By understanding these changes in brain network dynamics, we can develop targeted interventions to slow down or prevent the progression of this devastating disease! Explore the groundbreaking research on dynamic functional networks in Alzheimer’s by clicking below.

Subjective cognitive decline (SCD) is considered to be the preclinical stage of Alzheimer’s disease (AD) and has the potential for the early diagnosis and intervention of AD. It was implicated that CSF-tau, which increases very early in the disease process in AD, has a high sensitivity and specificity to differentiate AD from normal aging, and the highly connected brain regions behaved more tau burden in patients with AD. Thus, a highly connected state measured by dynamic functional connectivity may serve as the early changes of AD. In this study, forty-five normal controls (NC), thirty-six individuals with SCD, and thirty-five patients with AD were enrolled to obtain the resting-state functional magnetic resonance imaging scanning. Sliding windows, Pearson correlation, and clustering analysis were combined to investigate the different levels of information transformation states. Three states, namely, the low state, the middle state, and the high state, were characterized based on the strength of functional connectivity between each pair of brain regions. For the global dynamic functional connectivity analysis, statistically significant differences were found among groups in the three states, and the functional connectivity in the middle state was positively correlated with cognitive scales. Furthermore, the whole brain was parcellated into four networks, namely, default mode network (DMN), cognitive control network (CCN), sensorimotor network (SMN), and occipital-cerebellum network (OCN). For the local network analysis, statistically significant differences in CCN for low state and SMN for middle state and high state were found in normal controls and patients with AD. Meanwhile, the differences were also found in normal controls and individuals with SCD. In addition, the functional connectivity in SMN for high state was positively correlated with cognitive scales. Converging results showed the changes in dynamic functional states in individuals with SCD and patients with AD. In addition, the changes were mainly in the high strength of the functional connectivity state.

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