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Neuroscientists have devoted efforts to explore potential brain recovery after prolonged abstinence in heroin users (HU). However, not much is known about whether frontostriatal circuits can recover after prolonged abstinence in HU. An eight-month longitudinal study was carried out for HU. Two MRI scans were obtained at baseline (HU1) and 8-month follow-up (HU2). The functional and structural connectivities of dorsal and ventral frontostriatal pathways were measured by resting-state functional connectivity (RSFC) and diffusion tensor imaging (DTI). Correlation analyses were employed to reveal the associations between neuroimaging and behavioral changes. Results suggested that relative to healthy controls (HCs), HU1 showed lower fractional anisotropy (FA) in the right dorsolateral prefrontal cortex (DLPFC)-to-caudate tracts and medial orbitofrontal cortex (mOFC)-to-nucleus accumbens (NAc) tracts as well as decreased RSFC in the left mOFC-NAc circuits. Longitudinal results revealed reduced craving and enhanced cognitive control in HU2 compared with HU1. After prolonged abstinence, HU2 showed increased FA values in the right DLPFC-caudate and mOFC-NAc tracts as well as increased RSFC strength in the bilateral mOFC-NAc circuits compared with HU1. In addition, changes in RSFC and FA values in the right mOFC-NAc circuit were negatively correlated with craving score changes. Similarly, negative correlations were also found between changes of RSFC in the bilateral DLPFC-caudate circuits and TMT-A scores. We provided scientific evidence for brain recovery of the dorsal and ventral frontostriatal circuits in HU after prolonged abstinence, and these circuits may be potential neuroimaging biomarkers for cognition and craving changes.

Accurately positioning and segmenting thymoma from computed tomography (CT) images is of great importance for an image-driven thymoma analysis. In clinical practice, the diagnosis and segmentation of thymomas for radiologists are time-consuming and inefficient tasks. Thus, it is necessary to develop a method to accurately and efficiently realize automatic segmentation of thymoma. Here, a dense skip connection encoding–decoding model (DSC-Net), which is a deep convolutional neural network, was proposed to perform automatic segmentation of thymoma with the ability to fuse feature maps under receptive fields of different scales. An image preprocessing method was also proposed to provide much more texture information and enhance the contrast between thymoma and its surrounding tissues. A total of 310 subjects who underwent contrast-enhanced CT scanning were included in this ethically-approved retrospective study. All of the CT slices were manually labeled by four experienced radiologists, and 80% of images were included in the training set and the rest were included in the testing set. The performance of segmentation was evaluated by calculating the accuracy, intersection over union (IoU), and Boundary F1 contour matching score (BFScore) between the predicted segmentation and the manual labels. For segmentation of thymoma in the testing set, the accuracy, IoU and BFScore were 92.96%, 87.86% and 0.9087 respectively. Compared to the U-Net method, the DSC-Net model improved IoU by 3.94%. In addition, the efficacy and robustness of DSC-Net in segmentation of different patients and different types of thymoma classified by the WHO histological classification criteria were verified. The proposed preprocessing method and DSC-Net demonstrated improved performance in segmentation of thymomas, suggesting the ability to provide consistent delineation and assist radiologists in their clinical applications.

The “hunger” hormone ghrelin regulates food-intake and preference for high-calorie (HC) food through modulation of the mesocortico-limbic dopaminergic pathway. Laparoscopic sleeve gastrectomy (LSG) is an effective bariatric surgery to treat morbid obesity. We tested the hypothesis that LSG-induced reductions in appetite and total ghrelin levels in blood are associated with reduced prefrontal brain reactivity to food cues. A functional magnetic resonance imaging (fMRI) cue-reactivity task with HC and low-calorie (LC) food pictures was used to investigate brain reactivity in 22 obese participants tested before and one month after bariatric surgery (BS). Nineteen obese controls (Ctr) without surgery were also tested at baseline and one-month later. LSG significantly decreased (1) fasting plasma concentrations of total ghrelin, leptin and insulin, (2) craving for HC food, and (3) brain activation in the right dorsolateral prefrontal cortex (DLPFC) in response to HC vs. LC food cues (P(FWE) < 0.05). LSG-induced reduction in DLPFC activation to food cues were positively correlated with reduction in ghrelin levels and reduction in craving ratings for food. Psychophysiological interaction (PPI) connectivity analyses showed that the right DLPFC had stronger connectivity with the ventral anterior cingulate cortex (vACC) after LSG; and changes in BMI were negatively correlated with changes in connectivity between the right DLPFC and vACC in the LSG group only. These findings suggest that LSG-induced weight-loss may be related to reductions in ghrelin, possibly leading to decreased food craving and hypothetically reducing DLPFC response to the HC food cues.

The present study aimed to assess the efficacy of Near-infrared spectroscopy (NIRS) real-time neurofeedback (NF) vs. atomoxetine (AT) in children with attention deficit hyperactivity disorder (ADHD). A parallel-group study was conducted to enroll children with ADHD between 8 and 12 years of age. Participants were assigned into the NIRS group and AT group as their wish. Subjects in the NIRS group received 12 sessions of NF training within 6 weeks, and subjects in the AT group were given oral medication. Changes in Swanson, Nolan, and Pelham-V rating scales (SNAP-IV), and performance of Go/No-Go and N-back working memory tasks at week 3, 6 and 8 were evaluated. Forty-nine patients completed the study, including 18 ADHD in the NIRS group and 31 in the AT group. Total scores of SNAP-IV significantly decreased from baseline to week 3, week 6, and week 8 in both groups. Patients in the NIRS group showed significant lower scores on the inattention subscale of SNAP-IV at week 3 and week 6, compared to the AT group. NIRS group had a shorter reaction time during the Go/No-Go task at week 6 and fewer errors during 2-back than the AT group at week 3. The findings revealed that NIRS real-time NF is more efficacious relative to AT in improving behavioral performance, highlighting its potential role and advantages in treating patients with ADHD.

Functional constipation (FC) is a common functional gastrointestinal disorder (FGID) with a higher prevalence in clinical practice. The primary brain regions involved in emotional arousal regulation, somatic, sensory and motor control processing have been identified with neuroimaging in FGID. It remains unclear how these factors interact to influence the baseline brain activity of patients with FC. In the current study, we combined resting-state fMRI (RS-fMRI) with Granger causality analysis (GCA) to investigate the causal interactions of the brain areas in 14 patients with FC and in 26 healthy controls (HC). Our data showed significant differences in baseline brain activities in a number of major brain regions implicated in emotional process modulation (i.e. dorsal anterior cingulate cortex-dACC, anterior insula-aINS, orbitofrontal cortex-OFC, hippocampus-HIPP), somatic and sensory processing, and motor control (i.e., supplementary motor area-SMA, precentral gyrus-PreCen) (P

We proposed an effective face recognition method based on the discriminative locality preserving vectors method (DLPV). Using the analysis of eigenspectrum modeling of locality preserving projections, we selected the reliable face variation subspace of LPP to construct the locality preserving vectors to characterize the data set. The discriminative locality preserving vectors (DLPV) method is based on the discriminant analysis on the locality preserving vectors. Furthermore, the theoretical analysis showed that the DLPV is viewed as a generalized discriminative common vector, null space linear discriminant analysis and null space discriminant locality preserving projections, which gave the intuitive motivation of our method. Extensive experimental results obtained on four well-known face databases (ORL, Yale, Extended Yale B and CMU PIE) demonstrated the effectiveness of the proposed DLPV method.

Background: Evidence from neuroimaging has implicated abnormal cerebral cortical patterns in schizophrenia. Application of machine learning techniques is required for identifying a structural signature at the individual level reflecting neurobiological substrates of schizophrenia. Methods: 52 Patients and 66 healthy controls were recruited within the same period. Multivariate computation was used to examine the abnormalities of cortical features in schizophrenia. Features were selected by least absolute shrinkage and the selection operator (LASSO) method. The diagnostic capacity of multi-dimensional cortical neuroanatomical pattern-based classification was evaluated based on diagnostic tests. Findings: The features of mean curvature (left insula and left inferior frontal gyrus), cortical thickness (left fusiform gyrus), and metric distortion (left cuneus and right superior temporal gyrus) revealed both group differences and diagnostic capacity. Area under the receiver operating characteristic curve was 0.88, and the sensitivity, specificity, and accuracy were 94%, 82%, and 88% respectively. There was a positive association between the index score derived from multi-dimensional patterns and symptom severity (r = 0.40, P< 0.01) for patients. Our findings demonstrated a view of cortical differences with the capacity to discriminate patients with schizophrenia. Structural neuroimaging-based signatures hold potential promise of paving the road for clinical utility in schizophrenia. Interpretation: Our findings demonstrate a view of cortical differences with capacity to discriminate patients with schizophrenia. Structural neuroimaging-based signatures hold potential promise of paving the road for clinical utility in schizophrenia. Funding Statement:This study was supported by the National Basic Research Program of China under Grant Nos. 2014CB543203 and 2015CB856403, the National Natural Science Foundation of China under Grant Nos. 81471811, 81471738 and 61401346, the Fundamental Research Funds for the Central Universities (Dr Qin) and grants 81571651 from the National Natural Science Foundation of China and 2017ZDXM-SF-048 from the Key Research and Development Program of Shaanxi Province (Dr Yin). Declaration of Interest: No disclosure was reported. Ethics Approval Statement: This study was approved by the local Research Ethics Committee (Xijing Hospital, Fourth Military Medical University). All participants gave written informed consent after a complete description of this study

The aim of this work is to investigate the differences of effective connectivity of the default mode network (DMN) in Alzheimer's disease (AD) patients and normal controls (NC). The technique of independent component analysis (ICA) was applied to identify DMN components and multivariate Granger causality analysis (mGCA) was used to explore an effective connectivity pattern. We found that: (i) connections in AD were decreased than those in NC, in terms of intensity and quantity. Posterior cingulated cortex (PCC) exhibited significant activity in NC as it connected with most of the other regions within the DMN. Besides, the PCC was the convergence center which only received interactions from other regions; (ii) right inferior temporal cortex (rITC) in the NC exhibited stronger interactions with other regions within the DMN compared with AD patients; and (iii) interactions between medial prefrontal cortex (MPFC) and bilateral inferior parietal cortex (IPC) in the NC were weaker than those in AD patients. These findings may implicate a brain dysfunction in AD patients and reveal more pathophysiological characteristics of AD.

To assess the longitudinal gray matter (GM) and white matter (WM) changes between repeated observations 1 year apart in a group of the early clinical stage of migraine patients without aura, and to explore the relationship of such structural changes with headache activity, we studied patients newly diagnosed with episodic migraine lasting 8 to 14 weeks. Optimized voxel-based morphometry and tract-based spatial statistical analyses were used to evaluate changes in GM and WM by using 3-dimensional T1-weighted and diffusion-tensor imaging, respectively. At the 1-year follow-up examination, GM reduction was observed in the dorsolateral and medial part of the superior frontal gyrus, orbitofrontal cortex, hippocampus, precuneus, and primary and secondary somatosensory cortices. No significant differences were found in the fractional anisotropy and longitudinal, radial, and mean diffusivity of WM in migraine patients without aura within a year. Negative results were found for the association between changes in headache activity parameters and GM. Our results indicated that the GM and WM changed in different pathophysiological conditions of migraine patients without aura. The WM probably evolves slowly in the course of migraine chronicity. Perspective Our study found early involvement of GM reduction of sensory-discriminative brain regions in the pathologic process of migraine, but the WM did not exhibit significant changes in the same time interval. GM reduction in sensory-discriminative brain regions may characterize the pathophysiological features of migraine patients without aura in its early stage.

The number of diffusion tensor imaging (DTI) studies regarding the human spine has considerably increased and it is challenging because of the spine’s small size and artifacts associated with the most commonly used clinical imaging method. A novel segmentation method based on the reduced field-of-view (rFOV) DTI dataset is presented in cervical spinal canal cerebrospinal fluid, spinal cord grey matter and white matter classification in both healthy volunteers and patients with neuromyelitis optica (NMO) and multiple sclerosis (MS). Due to each channel based on high resolution rFOV DTI images providing complementary information on spinal tissue segmentation, we want to choose a different contribution map from multiple channel images. Via principal component analysis (PCA) and a hybrid diffusion filter with a continuous switch applied on fourteen channel features, eigen maps can be obtained and used for tissue segmentation based on the Bayesian discrimination method. Relative to segmentation by a pair of expert readers, all of the automated segmentation results in the experiment fall in the good segmentation area and performed well, giving an average segmentation accuracy of about 0.852 for cervical spinal cord grey matter in terms of volume overlap. Furthermore, this has important applications in defining more accurate human spinal cord tissue maps when fusing structural data with diffusion data. rFOV DTI and the proposed automatic segmentation outperform traditional manual segmentation methods in classifying MR cervical spinal images and might be potentially helpful for detecting cervical spine diseases in NMO and MS.

The aim of this study was to investigate the relevance of the basal ganglia (BG) in pathogenesis of migraine by assessing the abnormal volume and resting-state networks of the BG in migraine patients without aura (MWoA). The volume of the subsets in the BG was compared between 40 MWoA and 40 age- and gender-matched healthy controls. The resting-state functional connectivity of BG subsets with abnormal volume was also investigated. Reduced volume in the left caudate and the right nucleus accumbens (NAc) was detected in the migraine group compared with healthy controls; meanwhile, increased functional connectivity between the BG and several brain regions within nociceptive and somatosensory processing pathways was observed. Correlation analysis revealed significant correlations between the volume of the bilateral caudate and right NAc and disease duration. In addition, an increased monthly frequency of migraine attack was associated with increased functional connectivity between the bilateral caudate and left insula, and longer disease duration was correlated with increased functional connectivity between the right NAc and bilateral anterior cingulate cortex. Our results revealed abnormal volume of BG and dysfunctional dynamics during interictal resting state within pain pathways of the BG in MWoA, which validated the association between the BG and migraine.Our findings revealed the presence of reduced volume in NAc and caudate of the BG and interictal dysfunctional dynamics within BG networks in MWoA. The abnormal structure and function within the pain-related pathways of the BG were possibly associated with impaired pain processing and modulatory processes in MWoA.

An attention network test (ANT) provides a behavioral measure of the efficiency of the three attention networks (alerting, orienting and executive networks) within a single task. In the present study, we investigated the effect of passive hyperthermia on the attention network with event-related functional magnetic resonance imaging (fMRI). The behavioral results showed that passive hyperthermia of 50 °C and 40% relative humidity impaired the executive function, but showed no effect on the alerting and orienting networks. The fMRI results showed that: (i) passive hyperthermia enhanced the activity in the right superior frontal gyrus and depressed the activity in the right middle occipital gyrus, left inferior parietal lobule and left culmen in the alerting network, (ii) passive hyperthermia enhanced the activity in the temporal lobe and depressed the activity in the frontal lobe, parietal lobe and occipital lobe in the orienting network, and (iii) passive hyperthermia enhanced the activity in the dorsolateral prefrontal cortex but did not affect the activity in the anterior cingulate. We concluded that passive hyperthermia impaired executive function, especially the efficiency of resolving conflict and the negative effects of passive hyperthermia on alerting and orienting were overcome through variant regional brain activation.

Previous functional imaging studies on heroin addicts have focused on abnormal brain functions based on specific tasks, while few fMRI studies concentrated on the resting-state abnormalities of heroin-dependent individuals. In the current study, we applied the pattern classification technique, which employs the feature extraction method of non-negative matrix factorization (NMF) and a support vector machine (SVM) classifier. Its main purpose was to characterize the discrepancy in activation patterns between heroin-dependent individuals and healthy subjects during the resting state. The results displayed a high accuracy in the activation pattern differences of the two groups, which included the orbitofrontal cortex pm, cingulate gyrus, frontal and para-limbic regions such as the anterior cingulate cortex (ACC), hippocampal/parahippocampal region, amygdala, caudate, putamen, as well as the posterior insula and thalamus. These findings indicate that significant biomarkers exist among the network of circuits that are involved in drug abuse. The implications from our study may help explain the behavioral and neuropsychological deficits in heroin-dependent individuals and shed light on the mechanisms underlying heroin addiction. (C) 2011 Elsevier B.V. All rights reserved.

Granger causality model (GCM) derived from multivariate vector autoregressive models of data has been employed for identifying effective connectivity in the human brain with functional MR imaging (fMRI) and to reveal complex temporal and spatial dynamics underlying a variety of cognitive processes. In the most recent fMRI effective connectivity measures, pairwise GCM has commonly been applied based on single voxel values or average values from special brain areas at the group level. Although a few novel conditional GCM methods have been proposed to quantify the connections between brain areas, our study is the first to propose a viable standardized approach for group analysis of an fMRI data with GCM. To compare the effectiveness of our approach with traditional pairwise GCM models, we applied a well-established conditional GCM to pre-selected time series of brain regions resulting from general linear model (GLM) and group spatial kernel independent component analysis (ICA) of an fMRI dataset in the temporal domain. Datasets consisting of one task-related and one resting-state fMRI were used to investigate connections among brain areas with the conditional GCM method. With the GLM detected brain activation regions in the emotion related cortex during the block design paradigm, the conditional GCM method was proposed to study the causality of the habituation between the left amygdala and pregenual cingulate cortex during emotion processing. For the resting-state dataset, it is possible to calculate not only the effective connectivity between networks but also the heterogeneity within a single network. Our results have further shown a particular interacting pattern of default mode network (DMN) that can be characterized as both afferent and efferent influences on the medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC). These results suggest that the conditional GCM approach based on a linear multivariate vector autoregressive (MVAR) model can achieve greater accuracy in detecting network connectivity than the widely used pairwise GCM, and this group analysis methodology can be quite useful to extend the information obtainable in fMRI.

The rising number of obese individuals on a global scale has led the scientific community to determine the causes for this disease. Besides over-consumption of high-caloric foods and/or endocrine dysfunction, food addiction has been found to be a major culprit for weight gain. Food addiction results from craving certain food or food-substances so as to obtain a state of heightened pleasure, energy or excitement. Major intervention is needed in curbing these cravings and suppressing the appetite to promote weight loss. Functional magnetic resonance imaging (fMRI) has been applied to study why obese individuals overeat. Introduction of this technology will serve as a means in paving the way for new weight loss drugs.

Previous neuroimaging studies have demonstrated both structural and functional damages in heroin-dependent individuals. However, few studies investigated gray matter deficits and abnormal resting-state networks together in heroin-dependent individuals. In the present study, voxel-based morphometry (VBM) was used to identify brain regions with gray matter density reduction. Resting-state fMRI connectivity analysis was employed to assess potential functional abnormalities during resting-state. All clinical significances were investigated by examining their association with duration of heroin use. Compared with healthy subjects, heroin-dependent individuals showed significant reduction in gray matter density in the right dorsolateral prefrontal cortex (DLPFC) and a decrease in resting-state functional connectivity between the right DLPFC and left inferior parietal lobe (IPL). The gray matter density of the right DLPFC and its resting-state functional connectivity with the left IPL both showed significantly negative correlation with duration of heroin use, which were likely to be related to the functional impairments in decision-making and cognitive control exhibited by heroin-dependent individuals. Our findings demonstrated that long heroin dependence impairs the right DLPFC in heroin-dependent individuals, including structural deficits and resting-state functional impairments.

Clinical acupuncture typically involves an effective treatment phase several hours post-therapy. We previously identified regions that carry the time-varied signals based on the BLOCK experimental paradigm. Here we characterize the brain network by applying the graph theory analysis during the post-acupuncture resting state. Our results show gradually increasing connections in the brainstem during verum acupuncture (ACU). The anterior insula plays an important role in connecting the components of the brain networks following ACU. We suggest that acupuncture can induce significant complex response patterns with relatively more robust magnitudes. Our findings provide direct evidence that the post-needling resting state contains acupuncture-related effects that are due to the slow-acting nature of acupuncture.

In the current study, we investigated whether or not stimulation at vision and nonvision-related acupoints was able to induce similarity in the time domain, although stimulation at different acupoints could produce similar spatial distributions. This phenomenon still remains uncertain and contradictory. We introduced a novel experimental paradigm using a modified non-repeated event-related (NRER) design, and utilized the methods of independent component analysis (ICA) combined with seed correlated functional connectivity analysis to locate visual cortical activations and to study their temporal characteristics during electro-acupuncture (EAS) at vision-related acupoint GB 37 and nonvision-related acupoint KI 8. Results showed that strong activations were present in the visual cortical areas (BA 17/18/19) at both acupoints, but temporal correlation analysis indicated that they were modulated in opposite directions during the resting state after acupuncture. Our results revealed that acupuncture at vision and nonvision-related acupoints can induce similar activations in spatial distribution but different modulation effects temporally.

Background: Acupuncture has been commonly used for preventing migraine attacks and relieving pain during a migraine, although there is limited knowledge on the physiological mechanism behind this method. The objectives of this study were to compare the differences in brain activities evoked by active acupoints and inactive acupoints and to investigate the possible correlation between clinical variables and brain responses. Methods and Results: A randomized controlled trial and resting-state functional magnetic resonance imaging (fMRI) were conducted. A total of eighty migraineurs without aura were enrolled to receive either active acupoint acupuncture or inactive acupoint acupuncture treatment for 8 weeks, and twenty patients in each group were randomly selected for the fMRI scan at the end of baseline and at the end of treatment. The neuroimaging data indicated that long-term active acupoint therapy elicited a more extensive and remarkable cerebral response compared with acupuncture at inactive acupoints. Most of the regions were involved in the pain matrix, lateral pain system, medial pain system, default mode network, and cognitive components of pain processing. Correlation analysis showed that the decrease in the visual analogue scale (VAS) was significantly related to the increased average Regional homogeneity (ReHo) values in the anterior cingulate cortex in the two groups. Moreover, the decrease in the VAS was associated with increased average ReHo values in the insula which could be detected in the active acupoint group. Conclusions: Long-term active acupoint therapy and inactive acupoint therapy have different brain activities. We postulate that acupuncture at the active acupoint might have the potential effect of regulating some disease-affected key regions and the pain circuitry for migraine, and promote establishing psychophysical pain homeostasis. Trial Registration: Chinese Clinical Trial Registry ChiCTR-TRC-13003635 Citation: Zhao L, Liu J, Zhang F, Dong X, Peng Y, et al. (2014) Effects of Long-Term Acupuncture Treatment on Resting-State Brain Activity in Migraine Patients: A Randomized Controlled Trial on Active Acupoints and Inactive Acupoints. PLoS ONE 9(6): e99538. doi:10.1371/journal.pone.0099538 Editor: Mario D. Cordero, University of Sevilla, Spain Received September 28, 2013; Accepted May 14, 2014; Published June 10, 2014 Copyright: 2014 Zhao et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This study was supported by the National Basic Research Program of China (973 Program, No. 2012CB518501), National Natural Science Foundation of China (Nos. 30901900, 30930112, 81101108), the Project of Administration of Traditional Chinese Medicine of Sichuan Province (No. 2012-E-038), and the Project of Innovative Research Team Research Fund of Sichuan Provincial Education Department (No. 12TD002). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: acuresearch@126.com (FL); liujixin@life.xidian.edu.cn (JL)

Objective The objective of this study was to develop a rodent model of Porphyromonas gingivalis infection during pregnancy. Study Design Sprague Dawley rats were infected intravenously with 10 5 , 10 7 , or 10 9 CFU per dam of P gingivalis strain W83, ATCC 33277, or A7436 at gestational day 14 and necropsied at gestational day 18. Maternal organs were cultured to assess the spread of the infection. Six fetal units (placenta, amniotic fluid, membranes, and fetus) per dam were cultured; additional fetal units were examined by histopathology. Polymerase chain reaction was performed on placentas. Results Colonization rates were dependent on the strain of P gingivalis used and the infection dose. At an infection dose of 10 9 CFU/dam, P gingivalis W83, ATCC 33277, or A7436 was detected in 33%, 83%, or 100% of placentas, respectively. Epithelial hyperplasia, cellular necrosis, and inflammatory infiltrate were observed in infected placental tissues. Conclusion This study demonstrated that P gingivalis can invade both maternal and fetal tissues, resulting in chorioamnionitis and placentitis.