Dynamic regional homogeneity alterations and cognitive impairment in patients with moderate and severe obstructive sleep apnea

Alterations in dynamic regional homogeneity within default mode network in patients with thyroid-associated ophthalmopathy

Thyroid-associated ophthalmopathy (TAO) is a significant autoimmune eye disease known for causing exophthalmos and substantial optic nerve damage. Prior investigations have solely focused on static functional MRI (fMRI) scans of the brain in TAO patients, neglecting the assessment of temporal variations in local brain activity. This study aimed to characterize alterations in dynamic regional homogeneity (dReHo) in TAO patients and differentiate between TAO patients and healthy controls using support vector machine (SVM) classification. Thirty-two patients with TAO and 32 healthy controls underwent resting-state fMRI scans. We calculated dReHo using sliding-window methods to evaluate changes in regional brain activity and compared these findings between the two groups. Subsequently, we employed SVM, a machine learning algorithm, to investigate the potential use of dReHo maps as diagnostic markers for TAO. Compared to healthy controls, individuals with active TAO demonstrated significantly higher dReHo values in the right angular gyrus, left precuneus, right inferior parietal as well as the left superior parietal gyrus. The SVM model demonstrated an accuracy ranging from 65.62 to 68.75% in distinguishing between TAO patients and healthy controls based on dReHo variability in these identified brain regions, with an area under the curve of 0.70 to 0.76. TAO patients showed increased dReHo in default mode network-related brain regions. The accuracy of classifying TAO patients and healthy controls based on dReHo was notably high. These results offer new insights for investigating the pathogenesis and clinical diagnostic classification of individuals with TAO.

Altered functional connectivity of cerebellar subregions in male patients with obstructive sleep apnea: A resting-state fMRI study

PURPOSE

Previous studies have demonstrated impaired cerebellar function in patients with obstructive sleep apnea (OSA), which is associated with impaired cognition. However, the effects of OSA on resting-state functional connectivity (FC) in the cerebellum has not been determined. The purpose of this study was to investigate resting-state FC of the cerebellar subregions and its relevance to clinical symptoms in patients with OSA.

METHODS

Sixty-eight patients with OSA and seventy-two healthy controls (HCs) were included in the study. Eight subregions of the cerebellum were selected as regions of interest, and the FC values were calculated for each subregion with other voxels. A correlation analysis was performed to examine the relationship between clinical and cognitive data.

RESULTS

Patients with OSA showed higher FC in specific regions, including the right lobule VI with the right posterior middle temporal gyrus and right angular gyrus, the right Crus I with the bilateral precuneus/left superior parietal lobule, and the right Crus II with the precuneus/right posterior cingulate cortex. Furthermore, the oxygen depletion index was negatively correlated with aberrant FC between the right Crus II and the bilateral precuneus / right posterior cingulate cortex in OSA patients (p = 0.004).

CONCLUSION

The cerebellum is functionally lateralized and closely linked to the posterior default mode network. Higher FC is related to cognition, emotion, language, and sleep in OSA. Abnormal FC may offer new neuroimaging evidence and insights for a deeper comprehension of OSA-related alterations.

Altered dynamic and static brain activity and functional connectivity in COVID-19 patients: a preliminary study

This study aimed to investigate the effects of COVID-19 on brain functional activity through resting-state functional MRI (rs-fMRI). fMRI scans were conducted on a cohort of 42 confirmed COVID-19-positive patients and 46 healthy controls (HCs) to assess brain functional activity. A combination of dynamic and static amplitude of low-frequency fluctuations (dALFF/sALFF) and dynamic and static functional connectivity (dFC/sFC) was used for evaluation. Abnormal brain regions identified were then used as feature inputs in the model to evaluate support vector machine (SVM) capability in recognizing COVID-19 patients. Moreover, the random forest (RF) model was employed to verify the stability of SVM diagnoses for COVID-19 patients. Compared to HCs, COVID-19 patients exhibited a decrease in sALFF in the right lingual gyrus and the left medial occipital gyrus and an increase in dALFF in the right straight gyrus. Moreover, there was a decline in sFC between both lingual gyri and the right superior occipital gyrus and a reduction in dFC with the precentral gyrus. The dynamic and static combined ALFF and FC could distinguish between COVID-19 patients and the HCs with an accuracy of 0.885, a specificity of 0.818, a sensitivity of 0.933 and an area under the curve of 0.909. The combination of dynamic and static ALFF and FC can provide information for detecting brain functional abnormalities in COVID-19 patients.