No abstract

Previous studies have explored resting‐state functional connectivity (rs‐FC) of the amygdala in patients with autism spectrum disorder (ASD). However, it remains unclear whether there are frequency‐specific FC alterations of the amygdala in ASD and whether FC in specific frequency bands can be used to distinguish patients with ASD from typical controls (TCs). Data from 306 patients with ASD and 314 age‐matched and sex‐matched TCs were collected from 28 sites in the Autism Brain Imaging Data Exchange database. The bilateral amygdala, defined as the seed regions, was used to perform seed‐based FC analyses in the conventional, slow‐5, and slow‐4 frequency bands at each site. Image‐based meta‐analyses were used to obtain consistent brain regions across 28 sites in the three frequency bands. By combining generative adversarial networks and deep neural networks, a deep learning approach was applied to distinguish patients with ASD from TCs. The meta‐analysis results showed frequency band specificity of FC in ASD, which was reflected in the slow‐5 frequency band instead of the conventional and slow‐4 frequency bands. The deep learning results showed that, compared with the conventional and slow‐4 frequency bands, the slow‐5 frequency band exhibited a higher accuracy of 74.73%, precision of 74.58%, recall of 75.05%, and area under the curve of 0.811 to distinguish patients with ASD from TCs. These findings may help us to understand the pathological mechanisms of ASD and provide preliminary guidance for the clinical diagnosis of ASD.

Objective: CSF1R-related leukoencephalopathy is an adult-onset white matter disease with high disability and mortality, while current diagnostic approaches are prone to misdiagnosis and not sensitive enough for preclinical alternations. This study introduced amplitude of low-frequency uctuations (ALFF) and regional homogeneity (ReHo) based on resting-state functional MRI (rsfMRI) to compare the spontaneous brain activities of patients and healthy controls, aiming to provide early clues for disease onset and enhance our understanding of the disease.Methods: RsfMRI was performed on 11 patients and 23 healthy controls and preprocessed for calculation of ALFF and ReHo. Permutation tests with threshold free cluster enhancement (number of permutations =5,000) was applied for comparison. Voxels with P value<0.05 (family-wise error corrected) and cluster size>10 voxels was considered with signi cant difference.Results: Compared to controls, the patient group showed decreased ALFF in right paracentral lobule and precentral gyrus, and increased ALFF in left dorsolateral superior frontal gyrus, left postcentral gyrus, left precentral gyrus, right precuneus, as well as bilateral insula, parahippocampal gyrus, hippocampus, midbrain and cingulate gyrus. Decreased ReHo was found in bilateral supplementary motor area and paracentral lobule of patients, while ReHo increased in right superior occipital gyrus, right precentral gyrus, left angular gyrus, as well as bilateral parahippocampal gyrus, hippocampus, middle occipital gyrus, supramarginal gyrus and extra-nuclear.Conclusion: These results revealed altered spontaneous brain activities in CSF1R-related leukoencephalopathy, especially in limbic system and supplementary motor area, which may serve as an early biomarker for the onset, and shed light on disease mechanisms.