Impaired brain functional connectivity and complexity in mild cognitive decline

Mild cognitive impairment (MCI) is often considered a precursor to Alzheimer's disease (AD). Then, a better understanding of MCI neural correlates may inform more effective therapeutic interventions before irreversible changes occur in the brain, potentially delaying the onset of AD. Resting-state functional magnetic resonance imaging (rs-fMRI) has proven to be a powerful tool for investigating brain functional connectivity (FC) in MCI patients; however, integrating such analysis with graph theory and brain complexity (entropy) remains an underexplored yet promising avenue for understanding MCI-related changes. Therefore, we aimed to identify patterns of neural dysfunction and changes in brain complexity that may help differentiate mild cognitive decline from normal aging. We included 44 patients with an MCI diagnosis (75 ± 8 years; 26 men and 18 women) and 40 controls (77 ± 7 years; 26 men and 14 women). Conventional rs-FC served as a well-established foundation for further analyses. Graph theory was applied since it has gained prominence to investigate the structure of brain networks and identify patients with dementia. Sample entropy was measured to assess the complex and dynamic functioning of the brain. Reduced functional connectivity, cost, degree, entropy, and increased average path length were observed in MCI patients compared to controls. Alterations converged to temporal and frontal areas, insula, thalamus, and hippocampus and were involved in language processing, spatial attention and perception, and memory. Functional connectivity alterations seemed to precede topological changes expected for AD patients. Moreover, altered entropy suggested an initial brain disability to maintain efficient network integration in a memory-related region. Therefore, our findings emphasize the importance of integrating functional connectivity analysis, graph theory, and entropy to understand brain changes in MCI better. These complementary approaches offer a more comprehensive view of the neural dysfunctions associated with cognitive decline, providing a promising foundation for identifying biomarkers that could predict progression to neurodegenerative diseases, such as Alzheimer's disease.