Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is faidy difficult to distinguish the cause o...Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is faidy difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors. Then a diagnostic modeling and reasoning system using the dynamic uncertain causality graph was proposed. A modularized modeling scheme was presented to reduce the complexity of model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. A ＂chaining＂ inference algorithm and weighted logic operation mechanism were employed to guarantee the exactness and efficiency of diagnostic rea- soning under situations of incomplete and uncertain information. Moreover, the causal interactions among diseases and symptoms intuitively demonstrated the reasoning process in a graphical manner. Verification was performed using 203 randomly pooled clinical cases, and the accuracy was 99.01% and 84.73%, respectively, with or without laboratory tests in the model. The solutions were more explicable and convincing than common methods such as Bayesian Networks, further increasing the objectivity of clinical decision-making. The promising results indicated that our model could be potentially used in intelligent diagnosis and help decrease public health expenditure.展开更多
Venanico-Filho et al. developed an elegant matrix formulation for dynamic analysis by frequency domain （FD）, but the convergence, causality and extended period need further refining. In the present paper, it was arg...Venanico-Filho et al. developed an elegant matrix formulation for dynamic analysis by frequency domain （FD）, but the convergence, causality and extended period need further refining. In the present paper, it was argued that： （1） under reasonable assumptions （approximating the frequency response function by the discrete Fourier transform of the discretized unitary impulse response function）, the matrix formulation by FD is equivalent to a circular convolution; （2） to avoid the wraparound interference, the excitation vector and impulse response must be padded with enough zeros; （3） provided that the zero padding requirement satisfied, the convergence and accuracy of direct time domain analysis, which is equivalent to that by FD, are guaranteed by the numerical integration scheme; （4） the imaginary part of the computational response approaching zero is due to the continuity of the impulse response functions.展开更多
Dynamic casual modeling of functional magnetic resonance imaging（fMRI） signals is employed to explore critical emotional neurocircuitry under sad stimuli. The intrinsic model of emotional loops is built on the basis...Dynamic casual modeling of functional magnetic resonance imaging（fMRI） signals is employed to explore critical emotional neurocircuitry under sad stimuli. The intrinsic model of emotional loops is built on the basis of Papez＇s circuit and related prior knowledge, and then three modulatory connection models are established. In these models, stimuli are placed at different points, which represents they affect the neural activities between brain regions, and these activities are modulated in different ways. Then, the optimal model is selected by Bayesian model comparison. From group analysis, patients＇ intrinsic and modulatory connections from the anterior cingulate cortex （ACC） to the right inferior frontal gyrus （rlFG） are significantly higher than those of the control group. Then the functional connection parameters of the model are selected as classifier features. The classification accuracy rate from the support vector machine（SVM） classifier is 80.73%, which, to some extent, validates the effectiveness of the regional connectivity parameters for depression recognition and provides a new approach for the clinical diagnosis of depression.展开更多
Discovering causality from observed time series data is of great importance in various disciplines but also a challenging task.In recent years,cross-mapping methods have been developed to solve the non-separability or...Discovering causality from observed time series data is of great importance in various disciplines but also a challenging task.In recent years,cross-mapping methods have been developed to solve the non-separability or false-negative problem that traditional methods,e.g.,Granger causality or transfer entropy,cannot handle.However,these cross-mapping methods suffer still from nonlinearity and robustness problems on the noisy data.Here,we propose cross-mapping cardinality(CMC),which detects direct causality in a robust and nonlinear manner by quantifying the intersectional cardinality(IC)from the neighbors of the cause variable to the cross-mapping neighbors of the effect variable in the delay embedding space.We theoretically and computationally show the new causal concept“IC concavity”,i.e.concave IC curve against the neighbor size implies causality in the sense of dynamical causality,in contrast to the non-causality of linear IC curve.Thus,the causal strength is measured reliably by the IC curve,which exploits both IC continuity and information transfer of the cross-mapping function from effect to cause variables.Through verification on various simulated and real-world datasets,the accuracy and robustness of CMC are demonstrated significantly better than existing methods.In particular,we validated CMC with the pulse data from motor cortex neurons by training a rhesus monkey to conduct a flexible manual interception experiment.CMC effectively identified the causal relations between neurons while the traditional methods failed.In summary,our approach with the new concept of IC concavity provides a powerful data-driven tool for detecting dynamical causality in complex systems.展开更多
Instruction cues are widely employed for research on neural mechanisms during movement preparation.However,their influence on brain connectivity during movement has not received much attention.Herein,15 healthy subjec...Instruction cues are widely employed for research on neural mechanisms during movement preparation.However,their influence on brain connectivity during movement has not received much attention.Herein,15 healthy subjects completed two experimental tasks including either instructed or voluntary movements;meanwhile electroencephalogram(EEG)data were synchronously recorded.Based on source analysis and related literature,six movement-related brain regions were selected,including the left/right supplementary motor area(SMA),left/right inferior frontal gyrus(iFg),and left/right postcentral gyrus(pCg).After assuming 10 a priori models of regional brain connectivity,we evaluated the optimal connectivity model between brain regions for the two scenarios using the dynamic causality model(DCM).During voluntary movement,the movement originated in the SMA,passed through the iFg of the prefrontal lobe,and then returned to the main sensory cortex of the pCg.In the instructed movement,the movement originated in the iFg,and then was transmitted to the pCg and the SMA,as well as from the pCg to the SMA.In contrast to the preparation process of voluntary movement,there were long-range information interactions between the iFg and pCg.Further,almost the same brain regions were active during movement preparation under both voluntary and instructed movement tasks,which evidences certain similarities in dynamic brain connectivity,that is,the brain has direct connections between the bilateral SMA,bilateral pCg,and bilateral SMA,indicating that the both brain hemispheres work together during the movement preparation phase.The results suggest that the network during the preparation process of instructed movements is more complex than voluntary movements.展开更多
An important and unresolved question is how human brain regions process information and interact with each other in intertemporal choice related to gains and losses. Using psychophysiological interaction and dynamic c...An important and unresolved question is how human brain regions process information and interact with each other in intertemporal choice related to gains and losses. Using psychophysiological interaction and dynamic causal modeling analyses, we investigated the functional interactions between regions involved in the decision- making process while participants performed temporal discounting tasks in both the gains and losses domains. We found two distinct intrinsic valuation systems underlying temporal discounting in the gains and losses domains： gains were specifically evaluated in the medial regions, including the medial prefrontal and orbitofrontal cortices, and losses were evaluated in the lateral dorsolateral prefrontal cortex. In addition, immediate reward or pun- ishment was found to modulate the functional interactions between the dorsolateral prefrontal cortex and distinct regions in both the gains and losses domains： in the gains domain, the mesolimbic regions; in the losses domain, the medial prefrontal cortex, anterior cingulate cortex, and insula. These findings suggest that intertemporal choice of gains and losses might involve distinct valuation systems, and more importantly, separate neural interactions may implement the intertemporal choices of gains and losses. These findings may provide a new biological perspective for understanding the neural mechanisms underlying intertemporal choice of gains and losses.展开更多
A number of studies have indicated that disor- ders of consciousness result from multifocal injuries as well as from the impaired functional and anatomical connectivity between various anterior forebrain regions. Howe...A number of studies have indicated that disor- ders of consciousness result from multifocal injuries as well as from the impaired functional and anatomical connectivity between various anterior forebrain regions. However, the specific causal mechanism linking these regions remains unclear. In this study, we used spectral dynamic causal modeling to assess how the effective connections （ECs） between various regions differ between individuals. Next, we used connectome-based predictive modeling to evaluate the performance of the ECs in predicting the clinical scores of DOC patients. We found increased ECs from the striatum to the globus pallidus as well as from the globus pallidus to the posterior cingulate cortex, and decreased ECs from the globus pallidus to the thalamus and from the medial prefrontal cortex to the striatum in DOC patients as compared to healthy controls. Prediction of the patients＇ outcome was effective using the negative ECs as features. In summary, the present study highlights a key role of the thalamo-basal ganglia-cortical loop in DOCs and supports the anterior forebrain mesocircuit hypothesis. Furthermore, EC could be potentially used to assess the consciousness level.展开更多
Information flow among auditory and language processing-related regions implicated in the pathophysiology of auditory verbal hallucinations（AVHs） in schizophrenia（SZ） remains unclear. In this study, we used stocha...Information flow among auditory and language processing-related regions implicated in the pathophysiology of auditory verbal hallucinations（AVHs） in schizophrenia（SZ） remains unclear. In this study, we used stochastic dynamic causal modeling（s DCM） to quantify connections among the left dorsolateral prefrontal cortex（inner speech monitoring）, auditory cortex（auditory processing）, hippocampus（memory retrieval）, thalamus（information filtering）, and Broca＇s area（language production） in 17 first-episode drug-na？¨ve SZ patients with AVHs, 15 without AVHs, and 19 healthy controls using resting-state functional magnetic resonance imaging.Finally, we performed receiver operating characteristic（ROC） analysis and correlation analysis between image measures and symptoms. s DCM revealed an increasedsensitivity of auditory cortex to its thalamic afferents and a decrease in hippocampal sensitivity to auditory inputs in SZ patients with AVHs. The area under the ROC curve showed the diagnostic value of these two connections to distinguish SZ patients with AVHs from those without AVHs. Furthermore, we found a positive correlation between the strength of the connectivity from Broca＇s area to the auditory cortex and the severity of AVHs. These findings demonstrate, for the first time, augmented AVHspecific excitatory afferents from the thalamus to the auditory cortex in SZ patients, resulting in auditory perception without external auditory stimuli. Our results provide insights into the neural mechanisms underlying AVHs in SZ. This thalamic-auditory cortical-hippocampal dysconnectivity may also serve as a diagnostic biomarker of AVHs in SZ and a therapeutic target based on direct in vivo evidence.展开更多
Disorders of sex development(DSD)are a group of rare complex clinical syndromes with multiple etiologies.Distinguishing the various causes of DSD is quite difficult in clinical practice,even for senior general physici...Disorders of sex development(DSD)are a group of rare complex clinical syndromes with multiple etiologies.Distinguishing the various causes of DSD is quite difficult in clinical practice,even for senior general physicians because of the similar and atypical clinical manifestations of these conditions.In addition,DSD are difficult to diagnose because most primary doctors receive insufficient training for DSD.Delayed diagnoses and misdiagnoses are common for patients with DSD and lead to poor treatment and prognoses.On the basis of the principles and algorithms of dynamic uncertain causality graph(DUCG),a diagnosis model for DSD was jointly constructed by experts on DSD and engineers of artificial intelligence.“Chaining”inference algorithm and weighted logic operation mechanism were applied to guarantee the accuracy and efficiency of diagnostic reasoning under incomplete situations and uncertain information.Verification was performed using 153 selected clinical cases involving nine common DSD-related diseases and three causes other than DSD as the differential diagnosis.The model had an accuracy of 94.1%,which was significantly higher than that of interns and third-year residents.In conclusion,the DUCG model has broad application prospects as a computer-aided diagnostic tool for DSDrelated diseases.展开更多
In this paper, we are interested in exploring the dynamic causal relationships among two sets of three variables in different quarters. One set is futures sugar closing price in Zhengzhou futures exchange market （ZC...In this paper, we are interested in exploring the dynamic causal relationships among two sets of three variables in different quarters. One set is futures sugar closing price in Zhengzhou futures exchange market （ZC）, spot sugar price in Zhengzhou （ZS） and futures sugar closing price in New York futures exchange market（NC） and the other includes futures sugar opening price in Zhengzhou （ZO）, ZS and NC. For each quarter, we first use Bayesian model selection to obtain the optimal causal graph with the highest BD scores and then use Bayesian model averaging approach to explore the causal relationship between every two variables. From the real data analysis, the two conclusions almost coincide, which shows that the two methods are practical.展开更多
Chunk decomposition is defined as a cognitive process which breaks up familiar items into several parts to reorganize them in an alternative approach.The present study investigated the effective connectivity of visual...Chunk decomposition is defined as a cognitive process which breaks up familiar items into several parts to reorganize them in an alternative approach.The present study investigated the effective connectivity of visual streams in chunk decomposition through dynamic causal modeling(DCM).The results revealed that chunk familiarity and perceptual tightness made a combined contribution to highlight not only the "what" and the "where" streams,but also the effective connectivity from the left inferior temporal gyrus to the left superior parietal lobule.展开更多
基金supported by the Medical and Health Research Program of Zhejiang Province(No.2015KYB128)the Zhejiang Provincial Natural Science Foundation(No.LQ15H030004),China
文摘Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is faidy difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors. Then a diagnostic modeling and reasoning system using the dynamic uncertain causality graph was proposed. A modularized modeling scheme was presented to reduce the complexity of model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. A ＂chaining＂ inference algorithm and weighted logic operation mechanism were employed to guarantee the exactness and efficiency of diagnostic rea- soning under situations of incomplete and uncertain information. Moreover, the causal interactions among diseases and symptoms intuitively demonstrated the reasoning process in a graphical manner. Verification was performed using 203 randomly pooled clinical cases, and the accuracy was 99.01% and 84.73%, respectively, with or without laboratory tests in the model. The solutions were more explicable and convincing than common methods such as Bayesian Networks, further increasing the objectivity of clinical decision-making. The promising results indicated that our model could be potentially used in intelligent diagnosis and help decrease public health expenditure.
文摘Venanico-Filho et al. developed an elegant matrix formulation for dynamic analysis by frequency domain （FD）, but the convergence, causality and extended period need further refining. In the present paper, it was argued that： （1） under reasonable assumptions （approximating the frequency response function by the discrete Fourier transform of the discretized unitary impulse response function）, the matrix formulation by FD is equivalent to a circular convolution; （2） to avoid the wraparound interference, the excitation vector and impulse response must be padded with enough zeros; （3） provided that the zero padding requirement satisfied, the convergence and accuracy of direct time domain analysis, which is equivalent to that by FD, are guaranteed by the numerical integration scheme; （4） the imaginary part of the computational response approaching zero is due to the continuity of the impulse response functions.
基金The National Natural Science Foundation of China(No.30900356,81071135)
文摘Dynamic casual modeling of functional magnetic resonance imaging（fMRI） signals is employed to explore critical emotional neurocircuitry under sad stimuli. The intrinsic model of emotional loops is built on the basis of Papez＇s circuit and related prior knowledge, and then three modulatory connection models are established. In these models, stimuli are placed at different points, which represents they affect the neural activities between brain regions, and these activities are modulated in different ways. Then, the optimal model is selected by Bayesian model comparison. From group analysis, patients＇ intrinsic and modulatory connections from the anterior cingulate cortex （ACC） to the right inferior frontal gyrus （rlFG） are significantly higher than those of the control group. Then the functional connection parameters of the model are selected as classifier features. The classification accuracy rate from the support vector machine（SVM） classifier is 80.73%, which, to some extent, validates the effectiveness of the regional connectivity parameters for depression recognition and provides a new approach for the clinical diagnosis of depression.
基金supported by National Key R&D Program(2022YFA1004800,2022YFC2704600,2022YFC2704604)National Science Foundation of China(12131020,31930022,12026608)+5 种基金CAS Strategic Priority Research Program(XDB38040400,XDB32040103)Lingang Lab(LG202105-01-02)Shanghai Municipal Science and Technology Major Project(2021SHZDZX)Special Fund for Science and Technology Innovation Strategy of Guangdong Province(2021B0909050004,2021B0909060002)Major Key Project of Peng Cheng Laboratory(PCL2021A12)JST Moonshot R&D(JPMJMS2021).
文摘Discovering causality from observed time series data is of great importance in various disciplines but also a challenging task.In recent years,cross-mapping methods have been developed to solve the non-separability or false-negative problem that traditional methods,e.g.,Granger causality or transfer entropy,cannot handle.However,these cross-mapping methods suffer still from nonlinearity and robustness problems on the noisy data.Here,we propose cross-mapping cardinality(CMC),which detects direct causality in a robust and nonlinear manner by quantifying the intersectional cardinality(IC)from the neighbors of the cause variable to the cross-mapping neighbors of the effect variable in the delay embedding space.We theoretically and computationally show the new causal concept“IC concavity”,i.e.concave IC curve against the neighbor size implies causality in the sense of dynamical causality,in contrast to the non-causality of linear IC curve.Thus,the causal strength is measured reliably by the IC curve,which exploits both IC continuity and information transfer of the cross-mapping function from effect to cause variables.Through verification on various simulated and real-world datasets,the accuracy and robustness of CMC are demonstrated significantly better than existing methods.In particular,we validated CMC with the pulse data from motor cortex neurons by training a rhesus monkey to conduct a flexible manual interception experiment.CMC effectively identified the causal relations between neurons while the traditional methods failed.In summary,our approach with the new concept of IC concavity provides a powerful data-driven tool for detecting dynamical causality in complex systems.
基金the Technology Project of Henan Province(No.202102310210)the Key Project of Discipline Construction of Zhengzhou University(No.XKZDQY201905)。
文摘Instruction cues are widely employed for research on neural mechanisms during movement preparation.However,their influence on brain connectivity during movement has not received much attention.Herein,15 healthy subjects completed two experimental tasks including either instructed or voluntary movements;meanwhile electroencephalogram(EEG)data were synchronously recorded.Based on source analysis and related literature,six movement-related brain regions were selected,including the left/right supplementary motor area(SMA),left/right inferior frontal gyrus(iFg),and left/right postcentral gyrus(pCg).After assuming 10 a priori models of regional brain connectivity,we evaluated the optimal connectivity model between brain regions for the two scenarios using the dynamic causality model(DCM).During voluntary movement,the movement originated in the SMA,passed through the iFg of the prefrontal lobe,and then returned to the main sensory cortex of the pCg.In the instructed movement,the movement originated in the iFg,and then was transmitted to the pCg and the SMA,as well as from the pCg to the SMA.In contrast to the preparation process of voluntary movement,there were long-range information interactions between the iFg and pCg.Further,almost the same brain regions were active during movement preparation under both voluntary and instructed movement tasks,which evidences certain similarities in dynamic brain connectivity,that is,the brain has direct connections between the bilateral SMA,bilateral pCg,and bilateral SMA,indicating that the both brain hemispheres work together during the movement preparation phase.The results suggest that the network during the preparation process of instructed movements is more complex than voluntary movements.
基金supported by the National Natural Science Foundation of China(71471171,71071150,91432302,31620103905,31471005,and 71761167001)the Science Frontier Program of the Chinese Academy of Sciences(QYZDJSSW-SMC019)+2 种基金the Shenzhen Peacock Plan(KQTD2015033016104926)the Guangdong Pearl River Talents Plan Innovative and Entrepreneurial Team(2016ZT06S220)the CAS Key Laboratory of Behavioral Science,Institute of Psychology(Y5CX052003)
文摘An important and unresolved question is how human brain regions process information and interact with each other in intertemporal choice related to gains and losses. Using psychophysiological interaction and dynamic causal modeling analyses, we investigated the functional interactions between regions involved in the decision- making process while participants performed temporal discounting tasks in both the gains and losses domains. We found two distinct intrinsic valuation systems underlying temporal discounting in the gains and losses domains： gains were specifically evaluated in the medial regions, including the medial prefrontal and orbitofrontal cortices, and losses were evaluated in the lateral dorsolateral prefrontal cortex. In addition, immediate reward or pun- ishment was found to modulate the functional interactions between the dorsolateral prefrontal cortex and distinct regions in both the gains and losses domains： in the gains domain, the mesolimbic regions; in the losses domain, the medial prefrontal cortex, anterior cingulate cortex, and insula. These findings suggest that intertemporal choice of gains and losses might involve distinct valuation systems, and more importantly, separate neural interactions may implement the intertemporal choices of gains and losses. These findings may provide a new biological perspective for understanding the neural mechanisms underlying intertemporal choice of gains and losses.
基金supported by National Natural Science Foundation of China (81471654, 81428013, 81371535, and 81271548)the Natural Science Foundation of Guangdong Province, China (2015A030313609)+1 种基金Planned Science and Technology Project of Guangzhou Municipality, China (20160402007 and 201604020184)the Innovation Project of The Graduate School of South China Normal University
文摘A number of studies have indicated that disor- ders of consciousness result from multifocal injuries as well as from the impaired functional and anatomical connectivity between various anterior forebrain regions. However, the specific causal mechanism linking these regions remains unclear. In this study, we used spectral dynamic causal modeling to assess how the effective connections （ECs） between various regions differ between individuals. Next, we used connectome-based predictive modeling to evaluate the performance of the ECs in predicting the clinical scores of DOC patients. We found increased ECs from the striatum to the globus pallidus as well as from the globus pallidus to the posterior cingulate cortex, and decreased ECs from the globus pallidus to the thalamus and from the medial prefrontal cortex to the striatum in DOC patients as compared to healthy controls. Prediction of the patients＇ outcome was effective using the negative ECs as features. In summary, the present study highlights a key role of the thalamo-basal ganglia-cortical loop in DOCs and supports the anterior forebrain mesocircuit hypothesis. Furthermore, EC could be potentially used to assess the consciousness level.
基金supported by the National Key Basic Research and Development Program(973)(2011CB707805)the National Natural Science Foundation of China(81571651,81301199,and 81230035)the Fund for the Dissertation Submitted to Fourth Military Medical University for the Academic Degree of Doctor,China(2014D07)
文摘Information flow among auditory and language processing-related regions implicated in the pathophysiology of auditory verbal hallucinations（AVHs） in schizophrenia（SZ） remains unclear. In this study, we used stochastic dynamic causal modeling（s DCM） to quantify connections among the left dorsolateral prefrontal cortex（inner speech monitoring）, auditory cortex（auditory processing）, hippocampus（memory retrieval）, thalamus（information filtering）, and Broca＇s area（language production） in 17 first-episode drug-na？¨ve SZ patients with AVHs, 15 without AVHs, and 19 healthy controls using resting-state functional magnetic resonance imaging.Finally, we performed receiver operating characteristic（ROC） analysis and correlation analysis between image measures and symptoms. s DCM revealed an increasedsensitivity of auditory cortex to its thalamic afferents and a decrease in hippocampal sensitivity to auditory inputs in SZ patients with AVHs. The area under the ROC curve showed the diagnostic value of these two connections to distinguish SZ patients with AVHs from those without AVHs. Furthermore, we found a positive correlation between the strength of the connectivity from Broca＇s area to the auditory cortex and the severity of AVHs. These findings demonstrate, for the first time, augmented AVHspecific excitatory afferents from the thalamus to the auditory cortex in SZ patients, resulting in auditory perception without external auditory stimuli. Our results provide insights into the neural mechanisms underlying AVHs in SZ. This thalamic-auditory cortical-hippocampal dysconnectivity may also serve as a diagnostic biomarker of AVHs in SZ and a therapeutic target based on direct in vivo evidence.
基金This research was supported by the National Key Research and Development Program of China(No.2016YFC0901501)CAMS Innovation Fund for Medical Science(No.CAMS-2017-I2M–1-011)the Research Project of the Institute of Internet Industry,Tsinghua University,titled“DUCG theory and application of medical aided diagnosis-algorithm of introducing classification variables in DUCG.”。
文摘Disorders of sex development(DSD)are a group of rare complex clinical syndromes with multiple etiologies.Distinguishing the various causes of DSD is quite difficult in clinical practice,even for senior general physicians because of the similar and atypical clinical manifestations of these conditions.In addition,DSD are difficult to diagnose because most primary doctors receive insufficient training for DSD.Delayed diagnoses and misdiagnoses are common for patients with DSD and lead to poor treatment and prognoses.On the basis of the principles and algorithms of dynamic uncertain causality graph(DUCG),a diagnosis model for DSD was jointly constructed by experts on DSD and engineers of artificial intelligence.“Chaining”inference algorithm and weighted logic operation mechanism were applied to guarantee the accuracy and efficiency of diagnostic reasoning under incomplete situations and uncertain information.Verification was performed using 153 selected clinical cases involving nine common DSD-related diseases and three causes other than DSD as the differential diagnosis.The model had an accuracy of 94.1%,which was significantly higher than that of interns and third-year residents.In conclusion,the DUCG model has broad application prospects as a computer-aided diagnostic tool for DSDrelated diseases.
基金Supported by National Natural Science Foundation of China under grant(No.11371062,11671338)Beijing Center for Mathematics and Information Interdisciplinary Science and UIBE NetworkingCollaboration Center for China’s Multinational Business(No.201504YY006A)
文摘In this paper, we are interested in exploring the dynamic causal relationships among two sets of three variables in different quarters. One set is futures sugar closing price in Zhengzhou futures exchange market （ZC）, spot sugar price in Zhengzhou （ZS） and futures sugar closing price in New York futures exchange market（NC） and the other includes futures sugar opening price in Zhengzhou （ZO）, ZS and NC. For each quarter, we first use Bayesian model selection to obtain the optimal causal graph with the highest BD scores and then use Bayesian model averaging approach to explore the causal relationship between every two variables. From the real data analysis, the two conclusions almost coincide, which shows that the two methods are practical.
基金supported by the Knowledge Innovation Program of Chinese Academy of Sciences (Grant No. KSCX2-YW-R-28)the National Natural Science Foundation of China (Grant No. 30770708) the National Hi-Tech Research and Development Program of China (Grant No. 2008AA022604)
文摘Chunk decomposition is defined as a cognitive process which breaks up familiar items into several parts to reorganize them in an alternative approach.The present study investigated the effective connectivity of visual streams in chunk decomposition through dynamic causal modeling(DCM).The results revealed that chunk familiarity and perceptual tightness made a combined contribution to highlight not only the "what" and the "where" streams,but also the effective connectivity from the left inferior temporal gyrus to the left superior parietal lobule.
