Search

Background: Real-time functional magnetic resonance imaging neurofeedback (rtfMRI-nf) has proven to be a powerful technique to help subjects to gauge and enhance emotional control. Traditionally, rtfMRI-nf has focused on emotional regulation through self-regulation of amygdala. Recently, rtfMRI studies have observed that regulation of a target brain region is accompanied by connectivity changes beyond the target region. Therefore, the aim of present study is to investigate the use of connectivity between amygdala and prefrontal regions as the target of neurofeedback training in healthy individuals and subjects with a life-time history of major depressive disorder (MDD) performing an emotion regulation task. Method: Ten remitted MDD subjects and twelve healthy controls (HC) performed an emotion regulation task in 4 runs of rtfMRI-nf training followed by one transfer run without neurofeedback conducted in a single session. The functional connectivity between amygdala and prefrontal cortex was presented as a feedback bar concurrent with the emotion regulation task. Participants' emotional state was measured by the Positive and Negative Affect Schedule (PANAS) prior to and following the rtfMRI-nf. Psychological assessments were used to determine subjects' history of depression. Results: Participants with a history of MDD showed a trend of decreasing functional connectivity across the four rtfMRI-nf runs, and there was a marginally significant interaction between the MDD history and number of training runs. The HC group showed a significant increase of frontal cortex activation between the second and third neurofeedback runs. Comparing PANAS scores before and after connectivity-based rtfMRI-nf, we observed a significant decrease in negative PANAS score in the whole group overall, and a significant decrease in positive PANAS score in the MDD group alone.

It is a difficult task for both professional investors and individual traders continuously making profit in stock market. With the development of computer science and deep reinforcement learning, Buy\&Hold (B\&H) has been oversteped by many artificial intelligence trading algorithms. However, the information and process are not enough, which limit the performance of reinforcement learning algorithms. Thus, we propose a parallel-network continuous quantitative trading model with GARCH and PPO to enrich the basical deep reinforcement learning model, where the deep learning parallel network layers deal with 3 different frequencies data (including GARCH information) and proximal policy optimization (PPO) algorithm interacts actions and rewards with stock trading environment. Experiments in 5 stocks from Chinese stock market show our method achieves more extra profit comparing with basical reinforcement learning methods and bench models.

Wen Zhang,1 Zhao Qing,1 Yongwei Hu,2 Mingran Shao,1 Jiaming Lu,1 Junxia Wang,1 Ming Li,1 Xin Zhang,1 Zuzana Nedelska,3 Jakub Hort,3 Zhishun Wang,4 Tong Qiao,5 Bing Zhang1 1Department of Radiology, Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, People’s Republic of China; 2Department of Vascular and Interventional Radiology, Wujin People’s Hospital Affiliated to Jiangsu University, Changzhou, People’s Republic of China; 3Department of Neurology, Memory Clinic, 2nd Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic; 4Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA; 5Department of Vascular Surgery, Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, People’s Republic of ChinaCorrespondence: Bing ZhangDepartment of Radiology, Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No. 321 Zhongshan Road, Nanjing 210008, People’s Republic of ChinaTel +86 15851803070Email zhangbing_nanjing@nju.edu.cnTong QiaoDepartment of Vascular Surgery, Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No. 321 Zhongshan Road, Nanjing 210008, People’s Republic of ChinaTel +86 15951985338Email qiaotongmail@aliyun.comPurpose: Our objectives were to assess the abnormalities of subcortical nuclei by combining volume and shape analyses and potential association with cognitive impairment.Patients and Methods: Twenty-nine patients with severe ACS of the unilateral internal carotid artery and 31 controls were enrolled between January 2017 to August 2018. All participants underwent a comprehensive neuropsychological evaluation, blood lipid biochemical measurements, and structural magnetic resonance imaging (MRI) to measure subcortical volumes and sub-regional shape deformations. Basic statistics, correction for multiple comparisons. Seventeen ACS patients underwent carotid endarterectomy (CEA) within one week after baseline measurements, cognitive assessments and MRI scans were repeated 6 months after CEA.Results: The ACS patients had higher apolipoprotein B/apolipoprotein A1 (ApoB/ApoA1) ratio and worse performance in all cognitive domains than controls. Moreover, the ACS patients showed more profound thalamic atrophy assessed by shape and volume analysis, especially in the medial dorsal thalamus. No significant differences were found in other subcortical nuclei after multiple comparisons correction. At baseline, thalamic atrophy correlated with cognitive impairment and ApoB/ApoA1 ratio. Furthermore, mediation analysis at baseline showed that the association of carotid intima-media thickness with executive functioning was mediated by thalamic volume. After CEA, cognitive improvement and increase in the bilateral medial dorsal thalamic volume were observed.Conclusion: Our study identified the distinct atrophy of subcortical nuclei and their association with cognition in patients with ACS. Assessments of the thalamus by volumetric and shape analysis may provide an early marker for cerebral ischemia and reperfusion after CEA.Keywords: asymptomatic carotid stenosis, cognitive impairment, magnetic resonance imaging, endarterectomy, thalamus

Introduction Our flexible and adaptive interactions with the environment are guided by our individual representation of the physical world, estimated through sensation and evaluation of available information against prior knowledge. When linking sensory evidence with higher-level expectations for action, the central nervous system (CNS) in typically developing (TD) individuals relies in part on distributed and interacting cortical regions to communicate neuronal signals flexibly across the brain. Increasing evidence suggests that the balance between levels of signal and noise during information processing may be disrupted in individuals with Autism Spectrum Disorders (ASD). Methods Participants with and without ASD performed a visuospatial interference task while undergoing functional Magnetic Resonance Imaging (fMRI). We empirically estimated parameters characterizing participants’ latencies and their subtle fluctuations (noise accumulation) over the 16-min scan. We modeled hemodynamic activation and used seed-based analyses of neural coupling to study dysfunction in interference-specific connectivity in a subset of ASD participants who were nonparametrically matched to TD participants on age, male-to-female ratio, and magnitude of movement during the scan. Results Stochastic patterns of response fluctuations reveal significantly higher noise-to-signal levels and a more random and noisy structure in ASD versus TD participants, and in particular ASD adults who have the greatest clinical autistic deficits. While individuals with ASD show an overall weaker modulation of interference-specific functional connectivity relative to TD individuals, in particular between the seeds of Anterior Cingulate Cortex (ACC) and Inferior Parietal Sulcus (IPS) and the rest of the brain, we found that in ASD, higher uncertainty during the task is linked to increased interference-specific coupling between bilateral anterior insula and prefrontal cortex. Conclusions Subtle and informative differences in the structure of experiencing information exist between ASD and TD individuals. Our findings reveal in ASD an atypical capacity to apply previously perceived information in a manner optimal for adaptive functioning, plausibly revealing suboptimal message-passing across the CNS.

Objective: To study resting cerebral blood flow in children and adults with developmental stuttering. Methods: We acquired pulsed arterial spin labeling magnetic resonance imaging data in 26 participants with stuttering and 36 healthy, fluent controls. While covarying for age, sex, and IQ, we compared perfusion values voxel-wise across diagnostic groups and assessed correlations of perfusion with stuttering severity within the stuttering group and with measures of motor speed in both groups. Results: We detected lower regional Cerebral Blood Flow (rCBF) at rest in the stuttering group compared with healthy controls in Broca's area bilaterally and the superior frontal gyrus. rCBF values in Broca's area bilaterally correlated inversely with the severity of stuttering and extended posteriorly into other portions of the language loop. We also found increased rCBF in cerebellar nuclei and parietal cortex in the stuttering group compared with healthy controls. Findings were unchanged in child-only analyses and when excluding participants with comorbid illnesses or those taking medication. Conclusions: rCBF is reduced in Broca's region in persons who stutter. More severe stuttering is associated with even greater reductions in rCBF to Broca's region, additive to the underlying putative trait reduction in rCBF relative to control values. Moreover, a greater abnormality in rCBF in the posterior language loop is associated with more severe symptoms, suggesting that a common pathophysiology throughout the language loop likely contributes to stuttering severity.

Individuals with autism spectrum disorders (ASD) often have difficulty recognizing and interpreting facial expressions of emotion, which may impair their ability to navigate and communicate successfully in their social, interpersonal environments. Characterizing specific differences between individuals with ASD and their typically developing (TD) counterparts in the neural activity subserving their experience of emotional faces may provide distinct targets for ASD interventions. Thus we used functional magnetic resonance imaging (fMRI) and a parametric experimental design to identify brain regions in which neural activity correlated with ratings of arousal and valence for a broad range of emotional faces. Participants (51 ASD, 84 TD) were group-matched by age, sex, IQ, race, and socioeconomic status. Using task-related change in blood-oxygen-level-dependent (BOLD) fMRI signal as a measure, and covarying for age, sex, FSIQ, and ADOS scores, we detected significant differences across diagnostic groups in the neural activity subserving the dimension of arousal but not valence. BOLD-signal in TD participants correlated inversely with ratings of arousal in regions associated primarily with attentional functions, whereas BOLD-signal in ASD participants correlated positively with arousal ratings in regions commonly associated with impulse control and default-mode activity. Only minor differences were detected between groups in the BOLD signal correlates of valence ratings. Our findings provide unique insight into the emotional experiences of individuals with ASD. Although behavioral responses to face-stimuli were comparable across diagnostic groups, the corresponding neural activity for our ASD and TD groups differed dramatically. The near absence of group differences for valence correlates and the presence of strong group differences for arousal correlates suggest that individuals with ASD are not atypical in all aspects of emotion-processing. Studying these similarities and differences may help us to understand the origins of divergent interpersonal emotional experience in persons with ASD. Hum Brain Mapp 37:443-461, 2016. © 2015 Wiley Periodicals, Inc.

International audience

International audience

SOA-based systems o er high degree of exibility and interoperabil- ity. However, the securing of SOA-based applications is still a challenge. Although some related techniques have been proposed and presented in academia and industry, it is still dicult to check SOA quality in security aspect from an architecture view. In this thesis project, a method for security analysis in SOA is intro- duced and investigated. The method intends to be used for analyzing security of SOA-based systems on architecture level. To demonstrate the method, a prototype supporting the method is introduced and imple- mented. And the method and prototype are also evaluated respectively based on Technology Acceptance Model. The evaluation result shows that the prototype supporting the method is a promising inspection tool to detect software vulnerability.

International audience

International audience

International audience

International audience

International audience

International audience

International audience

Many computational models assume that reinforcement learning relies on changes in synaptic efficacy between cortical regions representing stimuli and striatal regions involved in response selection, but this assumption has thus far lacked empirical support in humans. We recorded hemodynamic signals with fMRI while participants navigated a virtual maze to find hidden rewards. We fitted a reinforcement-learning algorithm to participants' choice behavior and evaluated the neural activity and the changes in functional connectivity related to trial-by-trial learning variables. Activity in the posterior putamen during choice periods increased progressively during learning. Furthermore, the functional connections between the sensorimotor cortex and the posterior putamen strengthened progressively as participants learned the task. These changes in corticostriatal connectivity differentiated participants who learned the task from those who did not. These findings provide a direct link between changes in corticostriatal connectivity and learning, thereby supporting a central assumption common to several computational models of reinforcement learning.

To identify the neural substrate of self-regulatory control in children and adults with Tourette syndrome (TS).