Identifying Boys With Autism Spectrum Disorder Based on Whole-Brain Resting-State Interregional Functional Connections Using a Boruta-Based Support Vector Machine Approach.

An increasing number of resting-state functional magnetic resonance neuroimaging (R-fMRI) studies have used functional connections as discriminative features for machine learning to identify patients with brain diseases. However, it remains unclear which functional connections could serve as highly discriminative features to realize the classification of autism spectrum disorder (ASD). The aim of this study was to find ASD-related functional connectivity patterns and examine whether these patterns had the potential to provide neuroimaging-based information to clinically assist with the diagnosis of ASD by means of machine learning. We investigated the whole-brain interregional functional connections derived from R-fMRI. Data were acquired from 48 boys with ASD and 50 typically developing age-matched controls at NYU Langone Medical Center from the publicly available Autism Brain Imaging Data Exchange I (ABIDE I) dataset; the ASD-related functional connections identified by the Boruta algorithm were used as the features of support vector machine (SVM) to distinguish patients with ASD from typically developing controls (TDC); a permutation test was performed to assess the classification performance. Approximately, 92.9% of participants were correctly classified by a combined SVM and leave-one-out cross-validation (LOOCV) approach, wherein 95.8% of patients with ASD were correctly identified. The default mode network (DMN) exhibited a relatively high network degree and discriminative power. Eight important brain regions showed a high discriminative power, including the posterior cingulate cortex (PCC) and the ventrolateral prefrontal cortex (vlPFC). Significant correlations were found between the classification scores of several functional connections and ASD symptoms (p < 0.05). This study highlights the important role of DMN in ASD identification. Interregional functional connections might provide useful information for the clinical diagnosis of ASD.

Intranasal Oxytocin Increases Perceptual Salience of Faces in the Absence of Awareness.

OBJECTIVE: The neuropeptide oxytocin has been found to improve human social cognition and promote prosocial behavior. However, it is still unclear about the mechanisms underlying these effects of oxytocin on neural processes, such as visual perception and awareness. Especially, it is still unclear whether oxytocin influences perceptual salience of social stimuli in the absence of awareness. METHODS: In a randomized double-blind, placebo-controlled trial we applied an interocular suppression paradigm and eye tracking methods to investigate the influence of intranasally administered oxytocin on perceptual salience of social stimuli. Suppression times and pupillometric data were measured during subjects being presented with gradually introduced pictures of social stimuli (neutral expression faces) or nonsocial stimuli (grayscale watch pictures) that were suppressed and invisible in 10 men who were administered 24 IU oxytocin and 10 men who were administered a placebo. RESULTS: The results demonstrated that the oxytocin group perceived social stimuli more quickly accompanied by subsequent larger increasing pupil diameter than nonsocial stimuli, indicating an increased unconscious salience of social stimuli. CONCLUSION: These findings provided new insights into oxytocin's modulatory role to social information processing, suggesting that oxytocin might enhance attentional bias to social stimuli even after removal of awareness.

Are ADC values of readout-segmented echo-planar diffusion-weighted imaging (RESOLVE) correlated with pathological prognostic factors in rectal adenocarcinoma?

BACKGROUND: Diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) values as imaging biomarkers of rectal cancer are currently a hot research spot. The use of ADC values for preoperative judgment of pathological features in rectal cancer has been generally accepted. The image quality evaluation of conventional diffusion is severe deformation, and the measurement of ADC values can easily lead to bias. Readout-segmented echo-planar diffusion-weighted imaging (RESOLVE) provides high signal-to-noise ratio images and significantly reduces distortions caused by magnetosensitive effects. The purpose of this study was to explore the correlations between ADC values of RESOLVE and pathological prognostic factors in rectal adenocarcinoma. METHODS: We collected pathological data of 89 patients with pathologically confirmed rectal adenocarcinoma who directly underwent surgical resection without receiving adjuvant therapy. The patients were grouped according to the pathologic type, gross classification, degree of differentiation, TN stage, and immunohistochemical expression of epidermal growth factor receptor (EGFR). RESULTS: RESOLVE ADC values of rectal cancer were measured at b = 800, and correlations between the RESOLVE ADC values obtained in different groups were analysed. We found that RESOLVE ADC values in the ulcer-type group were significantly higher than those in the eminence-type group. CONCLUSION: RESOLVE ADC values in different pathologic types of rectal cancer were significantly different. RESOLVE ADC values in the EGFR-positive group were significantly lower than those in the EGFR-negative group. There was no significant difference in RESOLVE ADC values between different degrees of pathologic differentiation, TN stages, and positive or negative lymph nodes. The quantitative description of RESOLVE ADC values could be used to assess the biological behaviour of rectal adenocarcinoma.

Adult community-acquired pneumonia with unusually enlarged mediastinal lymph nodes: A case report.

Adult community-acquired pneumonia (ACAP) is the most prevalent pulmonary infectious disease that may be asymptomatic or have varying clinical presentations. Patients with ACAP often present with enlarged mediastinal lymph nodes on their chest computed tomography images. However, large irregular swollen lymph nodes are rarely reported in ACAP, and may therefore be confused with enlarged lymph node masses. In the present case report, the patient presented with lymph node masses, which were ameliorated to their normal size following antimicrobial treatment. The patient was 24 years old and otherwise healthy, which led to a pronounced and excessive immune response to pneumonia in the lymph nodes. Atypical pneumonia is difficult to diagnose based on imaging features. The present case report demonstrates that patients with pneumonia may present with unusually enlarged mediastinal lymph nodes, which are most likely, a result of a strong immune response to pneumonia.

A simplified computational memory model from information processing.

This paper is intended to propose a computational model for memory from the view of information processing. The model, called simplified memory information retrieval network (SMIRN), is a bi-modular hierarchical functional memory network by abstracting memory function and simulating memory information processing. At first meta-memory is defined to express the neuron or brain cortices based on the biology and graph theories, and we develop an intra-modular network with the modeling algorithm by mapping the node and edge, and then the bi-modular network is delineated with intra-modular and inter-modular. At last a polynomial retrieval algorithm is introduced. In this paper we simulate the memory phenomena and functions of memorization and strengthening by information processing algorithms. The theoretical analysis and the simulation results show that the model is in accordance with the memory phenomena from information processing view.

Topology Universality and Dissimilarity in a Class of Scale-Free Networks.

We study the effect of subtle changes on the evolution in the scale-free (SF) networks. Three extended models are evolved based on competition and inner anti-preferential deletion in growth and preferential attachment processes. By nonlinear and dynamic controlling on randomness and determinacy, three models can self-organize into scale-free networks, and diverse scaling exponents appear. Moreover, the model with more determinacy has more stringent parameter control than randomness, especially in the edge deletion. Our results suggest that the nature of the topology universality and dissimilarity in SF networks may be the subtle changes of randomness and determinacy.