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Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy
Published on: May 27, 2020
Aberrant brain functional connectivity in patients with dysthyroid optic neuropathy: a resting-state fMRI study
Jin-Ling Lu1, Yu-Hui Zhou1, Huan-Huan Chen2
1Department of Radiology, The First Affiliated Hospital With Nanjing Medical University, Nanjing, China.
Objectives:
We aimed to investigate the alterations of static and dynamic brain functional connectivity in dysthyroid optic neuropathy (DON) using resting-state functional MRI (rs-fMRI) with the voxel-mirrored homotopic connectivity (VMHC) and degree centrality (DC) methods.
Materials And Methods:
Seventy-five thyroid-associated ophthalmopathy (TAO) patients (29 DON and 46 non-DON) and 32 healthy controls (HCs) were prospectively recruited. Static and dynamic VMHC (sVMHC and dVMHC) and DC (sDC and dDC) values were measured and compared among groups. Support-vector machine (SVM) classification method was employed to examine the diagnostic performance of identified models in distinguishing DON patients from non-DON patients.
Results:
Compared to non-DON patients, DON patients showed decreased sVMHC in cerebellar lobule VI, inferior occipital gyrus (IOG) and superior occipital gyrus (SOG), alongside decreased dVMHC in IOG. DON patients also exhibited decreased sDC in bilateral middle occipital gyrus (MOG) in comparison to non-DON patients. Meanwhile, DON patients had lower sVMHC in lingual gyrus and MOG, together with decreased sDC in left MOG compared to HCs. Additionally, dVMHC in IOG of DON patients was positively correlated with the quality-of-life scores for appearance (r = 0.419, p = 0.033). When detecting DON, combined model (sVMHC+dVMHC+sDC) showed best diagnostic performance (AUC = 0.9528), followed by the dVMHC model (AUC = 0.9378), sDC model (AUC = 0.8598), and sVMHC model (AUC = 0.8193).
Conclusion:
Brain functional connectivity differed between TAO patients with and without DON, which may reflect the underlying neural mechanism of disease. The dVMHC index showed promising discriminative potential for identifying DON. Combining static and dynamic metrics could further optimize diagnostic efficiency.
