BackgroundEnhancing the effectiveness of current pain relief strategies is a persistent clinical challenge. Although transcutaneous electrical nerve stimulation (TENS) is used in various painful conditions, its effectiveness may decline over time, requiring additional pain management strategies. Immersive virtual reality (VR) with personalized visuo-tactile stimulation has demonstrated analgesic properties. Nevertheless, whether visuo-tactile stimulation can enhance the pain-relieving outcomes of TENS and its underlying neurophysiological mechanisms remains largely unknown. ObjectiveThe study aims to investigate whether the integration of visuo-tactile stimulation with TENS can enhance the pain-relieving outcomes of TENS alone, and we also aim to explore the brain mechanisms underlying the analgesic effect of this integrated intervention. MethodsIn this study, 75 healthy participants were enrolled and randomly assigned to 1 of 3 groups: congruent TENS-VR (TENS-ConVR) and 2 control groups (incongruent TENS-VR [TENS-InVR] and TENS alone). In the context of TENS-ConVR, we combined TENS and VR by connecting TENS-induced paresthesia with personalized visual bodily feedback. The visual feedback was designed to align with the spatiotemporal patterns of the paresthesia induced by TENS. A pain rating task and a 32-channel electroencephalography were applied. ResultsTwo-way ANOVAs showed that TENS-ConVR exhibited a statistically greater reduction in pain rating (F1,48=6.84; P=.01) and N2 amplitude (F1,48=5.69; P=.02) to high-intensity pain stimuli before and after stimulation than TENS alone. The reduction of brain activity was stronger in participants who reported stronger pain-relieving outcomes. TENS-ConVR reduced the brain oscillation in the gamma band, whereas this result was not found in TENS alone. ConclusionsThis study observed that combining TENS and visual stimulation in a single solution could enhance the pain-relieving effect of TENS, which has the potential to improve the effectiveness of current pain management treatments. Trial RegistrationChinese Clinical Trial Registry ChiCTR2500098834; https://www.chictr.org.cn/showprojEN.html?proj=254171

Background: Brain recovery phenomenon after long-term abstinence had been reported in substance use disorders. Yet, few longitudinal studies have been conducted to observe the abnormal dynamic functional connectivity (dFNC) of large-scale brain networks and recovery after prolonged abstinence in heroin users. Objective: The current study will explore the brain network dynamic connection reconfigurations after prolonged abstinence in heroin users (HUs). Methods: The 10-month longitudinal design was carried out for 40 HUs. The 40 healthy controls (HCs) were also enrolled. Group independent component analysis (GICA) and dFNC analysis were employed to detect the different dFNC patterns of addiction-related ICNs between HUs and HCs. The temporal properties and the graph-theoretical properties were calculated. Whether the abnormalities would be reconfigured in HUs after prolonged abstinence was then investigated. Results: Based on eight functional networks extracted from GICA, four states were identified by the dFNC analysis. Lower mean dwell time and fraction rate in state4 were found for HUs, which were increased toward HCs after prolonged abstinence. In this state, HUs at baseline showed higher dFNC of RECN-aSN, aSN- aSN and dDMN-pSN, which decreased after protracted abstinence. A similar recovery phenomenon was found for the global efficiency and path length in abstinence HUs. Mean while, the abnormal dFNC strength was correlated with craving both at baseline and after abstinence. Conclusion: Our longitudinal study observed the large-scale brain network reconfiguration from the dynamic perspective in HUs after prolonged abstinence and improved the understanding of the neurobiology of prolonged abstinence in HUs.

BACKGROUND:It is unknown whether repetitive Transcranial Magnetic Stimulation (rTMS) could improve sleep quality by modulating electroencephalography (EEG) connectivity of insomnia disorder (ID) patients. Great heterogeneity had been found in the clinical outcomes of rTMS for ID. The study aimed to investigate the potential mechanisms of rTMS therapy for ID and develop models to predict clinical outcomes. METHODS:In Study 1, 50 ID patients were randomly divided into active and sham groups, and subjected to 20 sessions of treatment with 1 Hz rTMS over the left dorsolateral prefrontal cortex. EEG during awake, Polysomnography, and clinical assessment were collected and analyzed before and after rTMS. In Study 2, 120 ID patients were subjected to active rTMS stimulation and were then separated into optimal and sub-optimal groups due to the median of Pittsburgh Sleep Quality Index reduction rate. Machine learning models were developed based on baseline EEG coherence to predict rTMS treatment effects. RESULTS:In Study 1, decreased EEG coherence in theta and alpha bands were observed after rTMS treatment, and changes in theta band (F7-O1) coherence were correlated with changes in sleep efficiency. In Study 2, baseline EEG coherence in theta, alpha, and beta bands showed the potential to predict the treatment effects of rTMS for ID. CONCLUSION:rTMS improved sleep quality of ID patients by modulating the abnormal EEG coherence. Baseline EEG coherence between certain channels in theta, alpha, and beta bands could act as potential biomarkers to predict the therapeutic effects.

Despite burgeoning evidence for cortical hyperarousal in insomnia disorder, the existing results on electroencephalography spectral features are highly heterogeneous. Phase‐amplitude coupling, which refers to the modulation of the low‐frequency phase to a high‐frequency amplitude, is probably a more sensitive quantitative measure for characterizing abnormal neural oscillations and explaining the therapeutic effect of repetitive transcranial magnetic stimulation in the treatment of patients with insomnia disorder. Sixty insomnia disorder patients were randomly divided into the active and sham treatment groups to receive 4 weeks of repetitive transcranial magnetic stimulation treatment. Behavioral assessments, resting‐state electroencephalography recordings, and sleep polysomnography recordings were performed before and after repetitive transcranial magnetic stimulation treatment. Forty good sleeper controls underwent the same assessment. We demonstrated that phase‐amplitude coupling values in the frontal and temporal lobes were weaker in Insomnia disorder patients than in those with good sleeper controls at baseline and that phase‐amplitude coupling values near the intervention area were significantly enhanced after active repetitive transcranial magnetic stimulation treatment. Furthermore, the enhancement of phase‐amplitude coupling values was significantly correlated with the improvement of sleep quality. This study revealed the potential of phase‐amplitude coupling in assessing the severity of insomnia disorder and the efficacy of repetitive transcranial magnetic stimulation treatment, providing new insights on the abnormal physiological mechanisms and future treatments for insomnia disorder.

As the most common sleep disorder among adults, insomnia disorder is associated with substantial deleterious effects on mental and physical health and quality of life [[1]Stein M.B. et al.Genome-wide analysis of insomnia disorder.Mol Psychiatr. 2018; 23: 2238-2250Google Scholar], such as impaired memory consolidation and executive function [[2]Wardle-Pinkston S. Slavish D.C. Taylor D.J. Insomnia and cognitive performance: a systematic review and meta-analysis.Sleep Med Rev. 2019; 48: 101205Google Scholar]. The hyperarousal hypothesis is a robust framework for the conceptualization of insomnia etiology, and provides a potential target for intervention [[3]Kalmbach D.A. et al.Hyperarousal and sleep reactivity in insomnia: current insights.Nat Sci Sleep. 2018; 10: 193Google Scholar]. Specifically, disturbed sleep in insomnia patients is associated with greater cortical metabolism [[4]Nofzinger E.A. et al.Functional neuroimaging evidence for hyperarousal in insomnia.Am J Psychiatr. 2004; 161: 2126-2128Google Scholar]. Therefore, repetitive transcranial magnetic stimulation (rTMS) over the left dorsal lateral prefrontal cortex (DLPFC) has been widely used in the treatment of insomnia disorders, and subjective sleep improvements are commonly reported. However, findings based on objective sleep measures (polysomnography, PSG) were not consistent, and the underlying mechanisms of rTMS therapy for insomnia remain unknown [[5]Herrero Babiloni A. et al.The effects of non-invasive brain stimulation on sleep disturbances among different neurological and neuropsychiatric conditions: a systematic review.Sleep Med Rev. 2021; 55: 101381Google Scholar]. In the current study, we employed a double-blind, sham-controlled experimental design. Twenty active sessions of 1Hz rTMS on the left DLPFC in real group and 20 sham sessions in sham groups were carried out over four consecutive weeks. Both subjective sleep data indicated by Pittsburgh Sleep Quality Index (PSQI) and Insomnia Severity Index (ISI) and objective sleep data measured by polysomnography (PSG) were collected and compared between pre- and post-treatment. To shed some light on the potential durability of rTMS treatment for insomnia, the PSQI and ISI were acquired one-month later after their last rTMS session. Although the stimulation target is commonly a single brain region, converging evidence suggests that the beneficial effects of rTMS may be mediated via distributed networks. Thus, the undirected and directed interaction changes of the left DLPFC-centered brain circuits were examined by resting-state functional connectivity (RSFC) and Granger causality analysis (GCA) between pre- and post-treatment. The study was approved by the local Institutional Review Board and was registered in the Chinese Clinical Trial Registry (No. ChiCTR2100042449). According to DSM-5 criteria, we enrolled 44 insomnia patients (28 female; mean age: 43.8 ± 10.3 years; mean ± SD) at the Second Hospital of Hebei Medical University, Shijiazhuang, China. Twenty-two age-, gender- and education-matched healthy controls were enrolled as well. Written and informed consent was obtained from all participants. Insomnia patients were separated into real (n = 22) or sham groups (n = 22) randomly. Then, 20 active or sham sessions of left DLPFC rTMS were delivered over 4 consecutive weeks (5 times/week; Fig. 1. A). Insomnia disorder patients were assessed before and after real or sham rTMS treatment, including behavioral scales (PSQI, ISI, MoCA, MMSE, BAI, BDI), PSG recording (Grael 4K system, Australia) and MRI scanning (Philips Achieva 3.0T, Netherland). Due to personal reasons, the number of people who completed pre- and post-treatment PSG data collection was 17 (real group) and 18 (sham group). Active low-frequency rTMS was administered using a MagPro R30 TMS stimulator with a Fig. 1-shaped coil (MagVenture, Denmark) with the “5-cm rule” to locate the left DLPFC [[6]Yuan K. et al.Potential neural mechanism of single session transcranial magnetic stimulation on smoking craving.Sci China Inf Sci. 2020; 63: 1-3Google Scholar]. The rTMS stimulation was delivered at 1Hz, and the stimulus intensity was set at 80% of the resting motor threshold (RMT). Sham rTMS is also carried out as the coil is turned away from the skull at 90°. No side effects were reported during or after brain stimulation. The sleep stage classification was conducted by employing YASA algorithm on the PSG data [[7]Vallat R. Walker M.P. An open-source, high-performance tool for automated sleep staging.Elife. 2021; 10: e70092Google Scholar]. Then quantitative variables were calculated, such as sleep efficiency (SE), non-rapid eye movement sleep stages (NREM3) sleep duration, and sleep onset latency (SOL). RSFC and GCA were used to identify the rTMS-induced changes of the left DLPFC functional circuits in insomnia patients. Further technical details are provided in the Supplementary material. Two-way ANOVA revealed significant “treatment × time” interaction effect in both subjective sleeping measures (PSQI: F = 15.40, p = 0.0003; ISI: F = 44.92, p = 0.0001) and objective sleeping measurements (SE: F = 32.45, p = 0.0001; NREM3: F = 7.987, p = 0.0079; SOL: F = 9.356, p = 0.0044). In the real rTMS group, subjective sleep improvement was found by showing reduced PSQI (t = 5.97, p = 0.0001) and ISI (t = 6.54, p = 0.0001) (Fig. 1. B). Similarly, objective sleep improvement was found, as evidenced by increased sleep efficiency (t = 3.55, p = 0.0027), NREM3 sleep duration (t = 2.20, p = 0.043), and shorter sleep onset latency (t = 2.58, p = 0.02) (Fig. 1. C). One-month follow-up revealed a significant improvement in subjective sleeping measurements (PSQI, ISI) compared with the pre-treatment period (PSQI: t = 3.88, p = 0.0011; ISI: t = 6.59, p = 0.0001) (Fig. 1. D). However, those findings mentioned above were not detected in the sham group (p > 0.05). Neither real nor sham rTMS produced significant differences in MMSE, MoCA, BDI and BAI scores between pre- and post-rTMS treatment. Meanwhile, reduced RSFC between the left DLPFC and right superior frontal gyrus (SFG) was observed in insomnia patients after real rTMS treatment relative to baseline (FWE corrected, p < 0.05), which approached the average level of healthy controls (Fig. 1. F). Furthermore, GCA revealed disrupted functional coupling from the left hippocampus to the left DLPFC after real treatment (FWE corrected, p < 0.05) (Fig. 1. G). The RSFC changes of the left DLPFC-middle temporal lobe circuit were significantly positively associated with sleep improvement (i.e., PSQI changes) in the real group (r = 0.763, p = 0.0001, Fig. 1. E). Our pilot study validated the effectiveness and investigated the durability of rTMS therapy on insomnia disorders by demonstrating both subjective and objective sleep quality improvement. Moreover, we explored the possible mechanisms of the rTMS therapeutic effect for insomnia by using fMRI. The 1Hz rTMS over left DLPFC showed the potential to normalize the hyperexcited functional connections between the left DLPFC-right SFG cortex in insomnia patients (Fig. 1. F). Further, we observed disrupted cortico-hippocampal interactions in insomnia patients, and rTMS may have rewired these impaired connections (Fig. 1. G). This may supports its use as a novel intervention for memory consolidation deficits in insomnia patients [[8]Girardeau G. Lopes-dos-Santos V. Brain neural patterns and the memory function of sleep.Science. 2021; 374: 560-564Google Scholar]. Finally, our findings revealed the remote effects occurred in rTMS treatment for insomnia disorder [[9]Castrillon G. et al.The physiological effects of noninvasive brain stimulation fundamentally differ across the human cortex.Sci Adv. 2020; 6: eaay2739Google Scholar], which should be taken into consideration when using rTMS for brain disorders. In conclusion, we reported therapeutic improvement in sleep and potential underlying mechanisms of 1HZ rTMS therapy over the left DLPFC in insomnia disorders, which were associated with the modulation of the left DLPFC centered pathway. The lack of significant improvement in memory tasks in the current study leaves the possible link between the cortico-hippocampal interaction and memory performance unresolved. Thus, our findings should be considered preliminary and need to be replicated. Meanwhile, more accurate location of targets, advanced stimulation patterns and predictions of treatment outcomes [[10]Liu S. et al.Brain responses to drug cues predict craving changes in abstinent heroin users: a preliminary study.Neuroimage. 2021; 237: 118169Google Scholar] are also encouraged to improve rTMS therapy. This work was supported by National Natural Science Foundation of China (Grant Nos. 81871426, 81871430). The authors report no other conflicts of interest. We thank all the participants who volunteered their time to take part in this research. We are appreciated for the help in English writing contributed by Peter Manza from the Laboratory of Neuroimaging, NIAAA, NIH. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The following is the Supplementary data to this article: Download .docx (.03 MB) Help with docx files Multimedia component 1

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.

A machine learning model was developed to evaluate the severity of aortic coarctation (CoA) in infants based on anatomical features measured on CTA. In total, 239 infant patients undergoing both thorax CTA and echocardiography were retrospectively reviewed. The patients were assigned to either mild or severe CoA group based on their pressure gradient on echocardiography. They were further divided into patent ductus arteriosus (PDA) and non-PDA groups. The anatomical features were measured on double-oblique multiplanar reconstructed CTA images. Then, the optimal features were identified by using the Boruta algorithm. Subsequently, the coarctation severity was classified using linear discriminant analysis (LDA). We further investigated the relationship between the anatomical features and re-coarctation using Cox regression. Four anatomical features showed significant differences between the mild and severe CoA groups, including the smallest aortic cross-sectional area indexed to body surface area (p < 0.001), the narrowest aortic diameter (CoA diameter) indexed to height (p < 0.001), the diameter of the descending aorta at the diaphragmatic level (p < 0.001) and weight (p = 0.005). With these features, accuracy of 88.6% and 90.2%, sensitivity of 65.0% and 72.1%, and specificity of 92.9% and 100% were obtained for classifying the CoA severity in the non-PDA and PDA groups, respectively. Moreover, CoA diameter indexed to weight was associated with the risk of re-coarctation. CoA severity can be evaluated by using LDA with anatomical features. When quantifying the severity of CoA and risk of re-coarctation, both anatomical alternations at the CoA site and the growth of the patients need to be considered. • CTA is routinely ordered for infants with coarctation of the aorta; however, whether anatomical variations observed with CTA could be used to assess the severity of CoA remains unknown. • Using the diameter and area of the coarctation site adjusted to body growth as features, the LDA model achieved an accuracy of 88.6% and 90.2% in differentiating between the mild and severe CoA patients in the non-PDA group and PDA group, respectively. • The narrowest aortic diameter (CoA diameter) indexed to weight has a hazard ratio of 10.29 for re-coarctation.

Despite significant progress in treatments for smoking cessation, smoking continues to be a significant public health concern, especially in young adulthood. Thus, developing a predictive model that can classify and characterize the brain-based biomarkers predicting smoking status would be imperative to improving treatment development. In this study, we applied a support vector machine-based classification method to discriminate 70 young male smokers and 70 matched nonsmokers using their diffusion tensor imaging (DTI) data. The classification procedure achieved an average accuracy of 88.6% and an average area under the curve of 0.95. The most discriminative features that contributed to the classification were primarily located in the sagittal stratum (SS), external capsule (EC), superior longitudinal fasciculus (SLF), anterior corona radiata (ACR) and inferior front-occipital fasciculus (IFOF). The following regression analysis showed a significant negatively correlation between the average RD values of the left ACR (r = −0.247, p = 0.039) and FTND. The average MD values in the right EC (r = −0.254, p = 0.034) and RD values in the right IFOF (r = −0.240, p = 0.046) were inversely associated with pack-years. Our findings indicate that the discriminative white matter (WM) features as brain biomarkers provide great predictive power for smoking status and suggest that machine learning techniques can reveal underlying smoking-related neurobiology.

Various EEG features have been proposed for differentiating the consciousness and unconsciousness states during general anesthesia. However, their performance for detecting the fluctuation of consciousness level remains unclear. In this work, we recorded 60-channels EEG data during propofol anesthesia, and extracted 110 EEG features that were shown to be sensitive to the change of consciousness level. Then, we used classification model to evaluate the performance of these features in distinguishing the response state fluctuating around the point of loss of behavioral responsiveness (LOBR) to external stimuli. We found that EEG features, including delta power, SynchFastSlow, and the topographical ratio of alpha power, were efficient in distinguishing the stable change in consciousness level with an accuracy of 95.8%, however, these features performed poorly in distinguishing the response state around the point of LOBR with an accuracy of 66.9%. Using EEG features selected specifically for detecting consciousness fluctuation, approximately 10% improvement in accuracy was obtained. Our results suggested that the EEG features that were sensitive to the stable change of consciousness level and fluctuation of consciousness level were largely different. EEG features including theta band power and functional connectivity are more relevant to the fluctuation of consciousness level.

Transcranial direct current stimulation (tDCS) is a widely-used tool to induce neuroplasticity and modulate cortical function by applying weak direct current over the scalp. In this review, we first introduce the underlying mechanism of action, the brief history from discovery to clinical scientific research, electrode positioning and montages, and parameter setup of tDCS. Then, we review tDCS application in clinical samples including people with drug addiction, major depression disorder, Alzheimer's disease, as well as in children. This review covers the typical characteristics and the underlying neural mechanisms of tDCS treatment in such studies. This is followed by a discussion of safety, especially when the current intensity is increased or the stimulation duration is prolonged. Given such concerns, we provide detailed suggestions regarding safety procedures for tDCS operation. Lastly, future research directions are discussed. They include foci on the development of multi-tech combination with tDCS such as with TMS and fMRI; long-term behavioral and morphological changes; possible applications in other research domains, and more animal research to deepen the understanding of the biological and physiological mechanisms of tDCS stimulation.

Amnestic mild cognitive impairment MCI (aMCI) has a high progression to Alzheimer’s disease (AD). Recently, resting-state functional MRI (RS-fMRI) has been increasingly utilized in studying the pathogenesis of aMCI, especially in resting-state networks (RSNs). In the current study, we aimed to explore abnormal RSNs related to memory deficits in aMCI patients compared to the aged-matched healthy control group using RS-fMRI techniques. Firstly, we used ALFF (amplitude of low-frequency fluctuation) method to define the regions of interest (ROIs) which exhibited significant changes in aMCI compared with the control group. Then, we divided these ROIs into different networks in line with prior studies. The aim of this study is to explore the functional connectivity between these ROIs within networks and also to investigate the connectivity between networks. Comparing aMCI to the control group, our results showed that 1) the hippocampus (HIPP) had decreased FC with the medial prefrontal cortex (mPFC) and inferior parietal lobe (IPL), and the mPFC showed increased connectivity to IPL in the default mode network; 2) the thalamus showed decreased FC with the putamen and HIPP, and the HIPP showed increased connectivity to the putamen in the limbic system; 3) the supplementary motor area had decreased FC with the middle temporal gyrus and increased FC with the superior parietal lobe in the sensorimotor network; 4) increased connectivity between the lingual gyrus and middle occipital gyrus in the visual network; and 5) the DMN has reduced inter-network connectivities with the SMN and VN. These findings indicated that functional brain networks involved in cognition such as episodic memory, sensorimotor and visual cognition in aMCI were altered, and provided a new sight in understanding the important subtype of aMCI.

Purpose: The aim of this study is to qualify the network properties of the brain networks between two different mental tasks (play task or rest task) in a healthy population. Methods and Materials: EEG signals were recorded from 19 healthy subjects when performing different mental tasks. Partial directed coherence (PDC) analysis, based on Granger causality (GC), was used to assess the effective brain networks during the different mental tasks. Moreover, the network measures, including degree, degree distribution, local and global efficiency in delta, theta, alpha, and beta rhythms were calculated and analyzed. Results: The local efficiency is higher in the beta frequency and lower in the theta frequency during play task whereas the global efficiency is higher in the theta frequency and lower in the beta frequency in the rest task. Significance: This study reveals the network measures during different mental states and efficiency measures may be used as characteristic quantities for improvement in attentional performance.

BACKGROUND: The CT image reconstruction algorithm based compressed sensing (CS) can be formulated as an optimization problem that minimizes the total-variation (TV) term constrained by the data fidelity and image nonnegativity. There are a lot of solutions to this problem, but the computational efficiency and reconstructed image quality of these methods still need to be improved.OBJECTIVE: To investigate a faster and more accurate mathematical algorithm to settle TV term minimization problem of CT image reconstruction.METHOD: A Nesterov's algorithm (NESTA) is a fast and accurate algorithm for solving TV minimization problem, which can be ascribed to the use of most notably Nesterov's smoothing technique and a subtle averaging of sequences of iterates, which has been shown to improve the convergence properties of standard gradient-descent algorithms. In order to demonstrate the superior performance of NESTA on computational efficiency and image quality, a comparison with Simultaneous Algebraic Reconstruction Technique-TV (SART-TV) and Split-Bregman (SpBr) algorithm is made using a digital phantom study and two physical phantom studies from highly undersampled projection measurements.RESULTS: With only 25% of conventional full-scan dose and, NESTA method reduces the average CT number error from 51.76HU to 9.98HU on Shepp-Logan phantom and reduces the average CT number error from 50.13HU to 0.32HU on Catphan 600 phantom. On an anthropomorphic head phantom, the average CT number error is reduced from 84.21HU to 1.01HU in the central uniform area.CONCLUSIONS: To the best of our knowledge this is the first work that apply the NESTA method into CT reconstruction based CS. Research shows that this method is of great potential, further studies and optimization are necessary.

The face recognition ability varies across individuals. However, it remains elusive how brain anatomical structure is related to the face recognition ability in healthy subjects. In this study, we adopted voxel-based morphometry analysis and machine learning approach to investigate the neural basis of individual face recognition ability using anatomical magnetic resonance imaging. We demonstrated that the gray matter volume (GMV) of the right ventral anterior temporal lobe (vATL), an area sensitive to face identity, is significant positively correlated with the subject's face recognition ability which was measured by the Cambridge face memory test (CFMT) score. Furthermore, the predictive model established by the balanced cross-validation combined with linear regression method revealed that the right vATL GMV can predict subjects' face ability. However, the subjects' Cambridge face memory test scores cannot be predicted by the GMV of the face processing network core brain regions including the right occipital face area (OFA) and the right face fusion area (FFA). Our results suggest that the right vATL may play an important role in face recognition and might provide insight into the neural mechanisms underlying face recognition deficits in patients with pathophysiological conditions such as prosopagnosia.

Visual cognition such as face recognition requests a high degree of functional integration between distributed brain areas of a network. It has been reported that the fusiform gyrus (FG) is an important brain area involved in facial cognition; altered connectivity of FG to some other regions may lead to a deficit in visual cognition especially face recognition. However, whether functional connectivity between the FG and other brain areas changes remains unclear in the resting state in amnestic mild cognitive impairment (aMCI) subjects. Here, we employed a resting-state functional MRI (fMRI) to examine alterations in functional connectivity of left/right FG comparing aMCI patients with age-matched control subjects. Forty-eight aMCI and 38 control subjects from the Alzheimer's disease Neuroimaging Initiative were analyzed. We concentrated on the correlation between low frequency fMRI time courses in the FG and those in all other brain regions. Relative to the control group, we found some discrepant regions in the aMCI group which presented increased or decreased connectivity with the left/right FG including the left precuneus, left lingual gyrus, right thalamus, supramarginal gyrus, left supplementary motor area, left inferior temporal gyrus, and left parahippocampus. More importantly, we also obtained that both left and right FG have increased functional connections with the left middle occipital gyrus (MOG) and right anterior cingulate gyrus (ACC) in aMCI patients. That was not a coincidence and might imply that the MOG and ACC also play a critical role in visual cognition, especially face recognition. These findings in a large part supported our hypothesis and provided a new insight in understanding the important subtype of MCI.

It is well established that expertise modulates evoked brain activity in response to specific stimuli. Recently, researchers have begun to investigate how expertise influences the resting brain. Among these studies, most focused on the connectivity features within/across regions, i.e., connectivity patterns/strength. However, little concern has been given to a more fundamental issue whether or not expertise modulates baseline brain activity. We investigated this question using amplitude of low-frequency (<0.08 Hz) fluctuation (ALFF) as the metric of brain activity and a novel expertise model, i.e., acupuncturists, due to their robust proficiency in tactile perception and emotion regulation. After the psychophysical and behavioral expertise screening procedure, 23 acupuncturists and 23 matched non-acupuncturists (NA) were enrolled. Our results explicated higher ALFF for acupuncturists in the left ventral medial prefrontal cortex (VMPFC) and the contralateral hand representation of the primary somatosensory area (SI) (corrected for multiple comparisons). Additionally, ALFF of VMPFC was negatively correlated with the outcomes of the emotion regulation task (corrected for multiple comparisons). We suggest that our study may reveal a novel connection between the neuroplasticity mechanism and resting state activity, which would upgrade our understanding of the central mechanism of learning. Furthermore, by showing that expertise can affect the baseline brain activity as indicated by ALFF, our findings may have profound implication for functional neuroimaging studies especially those involving expert models, in that difference in baseline brain activity may either smear the spatial pattern of activations for task data or introduce biased results into connectivity-based analysis for resting data.

The present study employed dynamic causal modeling to investigate the effective functional connectivity between regions of the neural network involved in top-down letter processing. We used an illusory letter detection paradigm in which participants detected letters while viewing pure noise images. When participants detected letters, the response of the right middle occipital gyrus (MOG) in the visual cortex was enhanced by increased feed-backward connectivity from the left inferior frontal gyrus (IFG). In addition, illusory letter detection increased feed-forward connectivity from the right MOG to the left inferior parietal lobules. Originating in the left IFG, this top-down letter processing network may facilitate the detection of letters by activating letter processing areas within the visual cortex. This activation in turns may highlight the visual features of letters and send letter information to activate the associated phonological representations in the identified parietal region.

Combining with nonnegative matrix factorization(NMF)—a new subspace data analysis method,a novel method for weak ship targets detection is proposed for polarimetric synthetic aperture radar(SAR) image detection.By eigendecomposition of the polarimetric covariance matrix,the eigenvalue sets containing the energy of the polarimetric SAR image can be achieved.Using these eigenvalue sets,the non-negative matrix can be composed.Then the dominating feature of the polarimetric SAR image is extracted using NMF with sparseness constraints,hence,the weak ship targets can be detected.The ocean measured data of both the fully and dual polarimetric SAR have been utilized to validate the effectiveness of the proposed method.

Neural mechanisms underlying word processing have been extensively studied. It has been revealed that when individuals are engaged in active word processing, a complex network of cortical regions is activated. However, it is entirely unknown whether the word-processing regions are intrinsically organized without any explicit processing tasks during the resting state. The present study investigated the intrinsic functional connectivity between word-processing regions during the resting state with the use of fMRI methodology. The low-frequency fluctuations were observed between the left middle fusiform gyrus and a number of cortical regions. They included the left angular gyrus, left supramarginal gyrus, bilateral pars opercularis, and left pars triangularis of the inferior frontal gyrus, which have been implicated in phonological and semantic processing. Additionally, the activations were also observed in the bilateral superior parietal lobule and dorsal lateral prefrontal cortex, which have been suggested to provide top-down monitoring on the visual-spatial processing of words. The findings of our study indicate an intrinsically organized network during the resting state that likely prepares the visual system to anticipate the highly probable word input for ready and effective processing. (C) 2010 Elsevier Ireland Ltd. All rights reserved.

Neuroimaging studies have revealed that several brain regions play an important role in the visual processing of Chinese characters,including the left parts of inferior occipital gyrus,middle fusiform gyrus,superior parietal lobule,and middle frontal gyrus.In order to investigate the intercommunicating pattern among these brain regions in the resting state,the method of the multivariate Granger Causality Model(GCM) is employed to analyze the time series of these brain regions,which could provide robustness to false causality in case of variables with a common source.The results demonstrate that there are significantly bidirectional Granger causality connections between the left inferior occipital gyrus and superior parietal lobule,as well as between the left middle fusiform gyrus and superior parietal lobule;the left inferior occipital gyrus Granger-causes the left middle frontal gyrus;the left middle fusiform gyrus is Granger-caused by the left middle frontal gyrus.All the results above reveal the intercommunications among the brain regions which respond to the visual processing of Chinese characters without any explicit tasks,and indicate a preparatory brain networks for the highly probable character input in the resting state.