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Background and aims:Abnormal regional neural activity has been identified by the analysis of the static amplitude of low-frequency fluctuation (ALFF) in the setting of minimal hepatic encephalopathy (MHE). Brain activity is highly dynamic. This work sought to evaluate the temporal variability of ALFF to reveal MHE-related alterations in the dynamics of spontaneous neural activity.MethodsA total of 29 healthy controls and 49 patients with cirrhosis [including 20 patients with MHE and 29 patients without MHE (NHE)] who underwent resting-state functional magnetic resonance imaging and Psychometric Hepatic Encephalopathy Score (PHES) examination were enrolled in this investigation. Utilizing a sliding-window approach, we calculated the dynamic ALFF (dALFF) variability to reflect the temporal dynamics of regional neural activity. An analysis of the correlation between dALFF variability and PHES was performed, and receiver operating characteristic (ROC) curve analysis to determine the potential of the dALFF variability index in identifying MHE was completed.ResultsThe dALFF variability in the bilateral precuneus/posterior cingulate gyrus and left middle frontal gyrus progressively decreased from NHE to MHE group. In cirrhotic patients, the value of dALFF variability in the bilateral precuneus/posterior cingulate gyrus was positively correlated with their neurocognitive performance (r = 0.383 and P = 0.007). The index of dALFF variability in the bilateral precuneus/posterior cingulate gyrus could be used to distinguish NHE and MHE patients, with moderate power (area under the ROC curve = 0.712 and P = 0.012).ConclusionOur findings highlight the existence of aberrant dynamic brain function in MHE, which could underlie the neural basis of cognitive impairments and could be associated with the development of the disease. Analyzing dALFF could facilitate new biomarker identification for MHE.

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AimsTo investigate microstructural impairments of corticospinal tracts (CSTs) with different origins in amyotrophic lateral sclerosis (ALS) using neurite orientation dispersion and density imaging (NODDI).MethodsDiffusion‐weighted imaging data acquired from 39 patients with ALS and 50 controls were used to estimate NODDI and diffusion tensor imaging (DTI) models. Fine maps of CST subfibers originating from the primary motor area (M1), premotor cortex, primary sensory area, and supplementary motor area (SMA) were segmented. NODDI metrics (neurite density index [NDI] and orientation dispersion index [ODI]) and DTI metrics (fractional anisotropy [FA] and mean/axial/radial diffusivity [MD/AD/RD]) were computed.ResultsThe patients with ALS showed microstructural impairments (reflected by NDI, ODI, and FA reductions and MD, AD, and RD increases) in CST subfibers, especially in M1 fibers, which correlated with disease severity. Compared with other diffusion metrics, NDI yielded a higher effect size and detected the greatest extent of CST subfibers damage. Logistic regression analyses based on NDI in M1 subfiber yielded the best diagnostic performance compared with other subfibers and the whole CST.ConclusionsMicrostructural impairment of CST subfibers (especially those originating from M1) is the key feature of ALS. The combination of NODDI and CST subfibers analysis may improve diagnosing performance for ALS.