Normal brain MRI findings were observed in half of the patients. Lesions in the hippocampus were the most common MR imaging abnormal finding. The presence of hippocampal lesions is the main MR imaging predictor for poor prognosis in patients with anti--methyl-D-aspartate receptor encephalitis.

AIMThe aim of the present study was to characterize the rhythm of 24-h intraocular pressure (IOP) and ocular perfusion pressure (OPP) in patients with ocular hypertension (OHTN), in order to provide a reference for the clinical diagnosis and treatment of OHTN. METHODSAccording to the diagnostic criteria, 107 patients with OHTN were included, and an age- and sex-matched healthy control group (71 patients) was selected. The IOP and blood pressure (BP) of the OHTN and the healthy control groups were recorded every 2 h over a 24-h period. BP was measured using a digital automatic BP monitor, and IOP was measured using a non-contact tonometer. RESULTSThe maximum, minimum and mean IOP were significantly higher in subjects with OHTN than in the healthy control group (P<0.05), and the maximum, minimum and mean MOPP were significantly lower in the OHTN group than in the healthy control group (P<0.05). The minimum and mean SOPP and DOPP values of the OHTN group were lower than those of the healthy control group (P<0.05), and the IOP, MOPP, SOPP and DOPP diurnal and nocturnal fluctuation values were significantly greater in the OHTN group than in the healthy control group (P<0.05). The peak and trough IOP times of the two groups coincided, which tended to be low during the day and higher at night. The peak and trough MOPP and SOPP times of the two groups also coincided, and were primarily higher during the day and lower at night. The 24-h DOPP in the healthy control group was generally higher during the daytime and lower at night, with peak values between 19:00-23:00 h, and trough values between 3:00-7:00 h. No obvious day-to-night fluctuations were observed in the OHTN group. CONCLUSIONThe OPP of patients with OHTN is lower, and the 24-h OPP fluctuates more than that of healthy control subjects. This may be an important blood flow factor for the progression to primary open angle glaucoma in patients with OHTN.

White matter degradation has been proposed as one possible explanation for age-related cognitive decline. The human brain is, however, a network and it may be more appropriate to relate cognitive functions to properties of the network rather than specific brain regions. Cognitive domains were measured annually (mean follow-up = 1.25 ± 0.61 years), including processing speed, memory, language, visuospatial, and executive functions. Diffusion tensor imaging was performed at baseline in 90 clinically normal older adults (aged 54-86). We report on graph theory-based analyses of diffusion tensor imaging tract-derived connectivity. The machine learning approach was used to predict the rate of cognitive decline from white matter connectivity data. The reduced efficacy of white matter networks could predict the performance of these cognitive domains except memory. The predicted scores were significantly correlated with the real scores. For the local regions for predicting the cognitive changes, the right precuneus, left inferior parietal lobe and cuneus are the most important regions for predicting monthly change of executive function; some left partial and occipital regions are the most important for the changed of attention; the right frontal and temporal regions are the most important for the changed of language. Our findings suggested that the global white matter connectivity characteristics are the valuable predictive index for the longitudinal cognitive decline. For the first time, topological efficiency of white matter connectivity maps which related to special domains of cognitive decline in the elderly are identified. Dijk et al., 2008). Meanwhile, other studies found the WM tracts predict the episodic memory over time, but not executive function (Fjell et al., 2016; Rabin et al., 2018). The potential difficulties in interpreting these findings include the selection of white matter tracts examined and the specific tests used to assess the different cognitive domains. However, data are lacking on the association between the brain's connectivity patterns and cognitive abilities in generally healthy non-demented older participants The widespread degeneration of WM connectivity between brain regions makes network analysis an appropriate way to explore the possible neuropathological mechanisms of cognitive aging at the system level. The whole WM networks exhibit several topological properties, such as global and local network efficiency, 2005; van