Abstract
PurposeTo investigate the effect of sleep disorder (SD) on the changes of brain network dysfunction in mild cognitive impairment (MCI), we compared network connectivity patterns among MCI, SD, and comorbid MCI and sleep disorders (MCI-SD) patients using resting state functional magnetic resonance imaging (RS-fMRI).Patients and MethodsA total of 60 participants were included in this study, 20 each with MCI, SD, or MCI-SD. And all participants underwent structural and functional MRI scanning. The default-mode network (DMN) was extracted by independent component analysis (ICA), and regional functional connectivity strengths were calculated and compared among groups.ResultsCompared to MCI patients, The DMN of MCI-SD patients demonstrated weaker functional connectivity with left middle frontal gyrus, right superior marginal gyrus, but stronger connectivity with the left parahippocampus, left precuneus and left middle temporal gyrus. Compared to the SD group, MCI-SD patients demonstrated weaker functional connectivity with right transverse temporal gyrus (Heschl’s gyrus), right precentral gyrus, and left insula, but stronger connectivity with posterior cerebellum, right middle occipital gyrus, and left precuneus.ConclusionPatients with MCI-SD show unique changes in brain network connectivity patterns compared to MCI or SD alone, likely reflecting a broader functional disconnection and the need to recruit more brain regions for functional compensation.
Highlights
The incidence and prevalence of dementia are rising in many regions due to population aging
As expected according to group stratification criteria, Pittsburgh Sleep Quality Index (PSQI) scores were markedly higher in the SD group (16.00 ± 1.17) and mild cognitive impairment (MCI)-SD group (11.90 ± 2.80) compared to the MCI group
The regions with significant differences in functional connectivity were concentrated within the DMN in the MCI-SD group compared with those of the MCI group; while compared with the SD group, the functional connectivity between DMN and external brain regions showed significant differences (In the analysis, we mainly focused on DMN, but when using independent component analysis (ICA) to extract DMN, the extracted brain network covered a small part of the regions outside DMN, so we observed such a result)
Summary
The incidence and prevalence of dementia are rising in many regions due to population aging. According to estimates by Alzheimer’s Disease International, more than 50 million people worldwide had dementia in 2019, and this number will increase to 152 million by 2050 (Lopez and Kuller, 2019). Dementia results in impairment of learning, memory, understanding, orientation, Functional Brain Connectivity in MCI-SD computation and other cognitive functions. Alzheimer’s disease (AD) is the most common type of dementia, accounting for about 50–70% of all cases (Julie and Mary, 2014). MCI is a borderline between normal aging and dementia and is widely considered a precursor to AD and other neurodegenerative disorders. Since MCI has the potential to remain stable and not deteriorate or even reverse into a normal cognitive state, it has become a goal to study the transition mechanism of MCI to AD and how to effectively prevent its further development
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