Huifang Wang - Academia.edu (original) (raw)

Papers by Huifang Wang

doi: 10.3389/fnins.2014.00405 A systematic framework for functional connectivity measures

Frontiers in Neuroscience, Dec 9, 2014

Various methods have been proposed to characterize the functional connectivity between nodes in a... more Various methods have been proposed to characterize the functional connectivity between nodes in a network measured with different modalities (electrophysiology, functional magnetic resonance imaging etc.). Since different measures of functional connectivity yield different results for the same dataset, it is important to assess when and how they can be used. In this work, we provide a systematic framework for evaluating the performance of a large range of functional connectivity measures-based upon a comprehensive portfolio of models generating measurable responses. Specifically, we benchmarked 42 methods using 10,000 simulated datasets from 5 different types of generative models with different connectivity structures. Since all functional connectivity methods require the setting of some parameters (window size and number, model order etc.), we first optimized these parameters using performance criteria based upon (threshold free) ROC analysis. We then evaluated the performance of the methods on data simulated with different types of models. Finally, we assessed the performance of the methods against different levels of signal-to-noise ratios and network configurations. A MATLAB toolbox is provided to perform such analyses using other methods and simulated datasets.

NeuroImage, Feb 1, 2018

Many analysis methods exist to extract graphs of functional connectivity from neuronal networks. ... more Many analysis methods exist to extract graphs of functional connectivity from neuronal networks. Confidence in the results is limited because, (i) different methods give different results, (ii) parameter setting directly influences the final result, and (iii) systematic evaluation of the results is not always performed. Here, we introduce MULAN (MULtiple method ANalysis), which assumes an ensemble based approach combining multiple analysis methods and fuzzy logic to extract graphs with the most probable structure. In order to reduce the dependency on parameter settings, we determine the best set of parameters using a genetic algorithm on simulated datasets, whose temporal structure is similar to the experimental one. After a validation step, the selected set of parameters is used to analyze experimental data. The final step cross-validates experimental subsets of data and provides a direct estimate of the most likely graph and our confidence in the proposed connectivity. A systematic evaluation validates our strategy against empirical stereotactic electroencephalography (SEEG) and functional magnetic resonance imaging (fMRI) data.

One-third of 50 million epilepsy patients worldwide suffer from drug resistant epilepsy and are c... more One-third of 50 million epilepsy patients worldwide suffer from drug resistant epilepsy and are candidates for surgery. Precise estimates of the epileptogenic zone networks (EZNs) are crucial for planning intervention strategies. Here, we present the Virtual Epileptic Patient (VEP), a multimodal probabilistic modeling framework for personalized end-to-end analysis of brain imaging data of drug resistant epilepsy patients. The VEP uses data-driven, personalized virtual brain models derived from patient-specific anatomical (such as T1-MRI, DW-MRI, and CT scan) and functional data (such as stereo-EEG). It employs Markov Chain Monte Carlo (MCMC) and optimization methods from Bayesian inference to estimate a patient’s EZN while considering robustness, convergence, sensor sensitivity, and identifiability diagnostics. We describe both high-resolution neural field simulations and a low-resolution neural mass model inversion. The VEP workflow was evaluated retrospectively with 53 epilepsy pa...

doi: 10.3389/fnins.2014.00405 A systematic framework for functional connectivity measures

NeuroImage, Jan 28, 2016

Individual variability has clear effects upon the outcome of therapies and treatment approaches. ... more Individual variability has clear effects upon the outcome of therapies and treatment approaches. The customization of healthcare options to the individual patient should accordingly improve treatment results. We propose a novel approach to brain interventions based on personalized brain network models derived from non-invasive structural data of individual patients. Along the example of a patient with bitemporal epilepsy, we show step by step how to develop a Virtual Epileptic Patient (VEP) brain model and integrate patient-specific information such as brain connectivity, epileptogenic zone and MRI lesions. Using high-performance computing, we systematically carry out parameter space explorations, fit and validate the brain model against the patient's empirical stereotactic EEG (SEEG) data and demonstrate how to develop novel personalized strategies towards therapy and intervention.

The Journal of neuroscience : the official journal of the Society for Neuroscience, Jan 16, 2015

Adaptive behaviors are built on the arbitrary linkage of sensory inputs to actions and goals. Alt... more Adaptive behaviors are built on the arbitrary linkage of sensory inputs to actions and goals. Although the sensorimotor and associative frontostriatal circuits are known to mediate arbitrary visuomotor mappings, the underlying corticocortico dynamics remain elusive. Here, we take a novel approach exploiting gamma-band neural activity to study the human cortical networks and corticocortical functional connectivity mediating arbitrary visuomotor mapping. Single-trial gamma-power time courses were estimated for all Brodmann areas by combing magnetoencephalographic and MRI data with spectral analysis and beam-forming techniques. Linear correlation and Granger causality analyses were performed to investigate functional connectivity between cortical regions. The performance of visuomotor associations was characterized by an increase in gamma-power and functional connectivity over the sensorimotor and frontoparietal network, in addition to medial prefrontal areas. The superior parietal are...

NeuroImage, 2018

Many analysis methods exist to extract graphs of functional connectivity from neuronal networks. ... more Many analysis methods exist to extract graphs of functional connectivity from neuronal networks. Confidence in the results is limited because, (i) different methods give different results, (ii) parameter setting directly influences the final result, and (iii) systematic evaluation of the results is not always performed. Here, we introduce MULAN (MULtiple method ANalysis), which assumes an ensemble based approach combining multiple analysis methods and fuzzy logic to extract graphs with the most probable structure. In order to reduce the dependency on parameter settings, we determine the best set of parameters using a genetic algorithm on simulated datasets, whose temporal structure is similar to the experimental one. After a validation step, the selected set of parameters is used to analyze experimental data. The final step cross-validates experimental subsets of data and provides a direct estimate of the most likely graph and our confidence in the proposed connectivity. A systemati...

Frontiers in Neuroscience, 2014

Various methods have been proposed to characterize the functional connectivity between nodes in a... more Various methods have been proposed to characterize the functional connectivity between nodes in a network measured with different modalities (electrophysiology, functional magnetic resonance imaging etc.). Since different measures of functional connectivity yield different results for the same dataset, it is important to assess when and how they can be used. In this work, we provide a systematic framework for evaluating the performance of a large range of functional connectivity measures-based upon a comprehensive portfolio of models generating measurable responses. Specifically, we benchmarked 42 methods using 10,000 simulated datasets from 5 different types of generative models with different connectivity structures. Since all functional connectivity methods require the setting of some parameters (window size and number, model order etc.), we first optimized these parameters using performance criteria based upon (threshold free) ROC analysis. We then evaluated the performance of the methods on data simulated with different types of models. Finally, we assessed the performance of the methods against different levels of signal-to-noise ratios and network configurations. A MATLAB toolbox is provided to perform such analyses using other methods and simulated datasets.

Through the analysis of different fault characteristics on single-phase fault for transmission li... more Through the analysis of different fault characteristics on single-phase fault for transmission lines with shunt reactors，one fault nature identification method based on least squares waveform estimation is presented。The method uses the equivalent model as the reference prototype，utilizes time domain waveform estimation to obtain the similarity of calculated and measured value on fault-phase terminal voltage，and then identifies the fault nature by constructing objective function with comparison to the setting value。Simulation results show that the proposed method not only can exactly distinguish permanent fault from transient fault on different conditions，but also can obtain the fault disappear time for transient fault，So it achieve the goal of high-speed reclosure in transient fault and reliable misalignment in permanent fault。

2007 IEEE International Conference on Grey Systems and Intelligent Services, 2007

... Respectively speaking, DRNN is a dynamic network system, which can distinguish the dynamic sy... more ... Respectively speaking, DRNN is a dynamic network system, which can distinguish the dynamic system on line. ... NN2 2 r Generalized Object Compensator Decoupling Fig.2. Block diagram ofdecoupling control 861 ... 1 1 u y ∂ ∂ is information of object, and this information can be ...

2008 International Conference on Information and Automation, 2008

Mobile robot navigation is one of the important domains in mobile robot technologiespsila researc... more Mobile robot navigation is one of the important domains in mobile robot technologiespsila research. This problem is divided into two categories of basic sub-problems: path planning and motion planning. In this paper we integrate the path planning and motion planning of a robot into a uniform framework, which is described by a hybrid system. Hybrid systems combine discrete and continuous behavior so that we can deal with the complexity of the environment at the discrete level and dynamics of a robot at the continuous level. At the discrete level, on the triangulation of the state space of a robot, a dual graph is constructed following a proposed target attractive principle. Then based on this dual graph, we design an incremental heuristic search algorithm to deal with dynamic environment and find an optimal sequence of adjacent triangles. At the continuous level, a motion planning algorithms that can autonomously generate the translational and rotational velocities for the robot to travel along the given sequence of triangles. Simulation results demonstrate the correctness and effectiveness of the approach.

2010 IEEE International Conference on Robotics and Automation, 2010

This paper studies the controllability of pairs of identical nonholonomic vehicles maintaining a ... more This paper studies the controllability of pairs of identical nonholonomic vehicles maintaining a constant distance. The study provides controllability results for the five most common types of robot vehicles: Dubins, Reeds-Shepp, differential drive, car-like and convexified Reeds-Shepp. The challenge of achieving controllability of such systems is that their admissible control domains depend on configuration variables. A theorem of controllability specifical

49th IEEE Conference on Decision and Control (CDC), 2010

This paper studies the controllability of formations of n identical aircraft maintaining constant... more This paper studies the controllability of formations of n identical aircraft maintaining constant distances. Aircraft are modeled as a planar kinematic system with constant velocity and curvature bounds. The challenges of achieving controllability of such system are that it is an affine system with drift and its admissible controls are determined by its configuration variables. We begin with the study of a pair of aircraft maintaining a constant distance. As a result, we show that if the specified distance is sufficiently large, a pair of aircraft is completely controllable, i.e. can be steered between any two arbitrary configurations. In case of small distances, a description of the reachable sets is provided. Finally, we provide the controllability results for three basic formations of n aircraft.

Catalysis Letters, 2008

MgO-Al 2 O 3 mixed oxides were prepared and used as the support of Co-Mo-based water-gas shift re... more MgO-Al 2 O 3 mixed oxides were prepared and used as the support of Co-Mo-based water-gas shift reaction (WGSR) catalysts. X-ray diffraction (XRD) characterization showed that the MgO-Al 2 O 3 mixed oxides support is composed of MgO, c-Al 2 O 3 , and magnesiaalumina spinel. The MgO-Al 2 O 3 mixed oxides-supported Co-Mo-based catalysts exhibited high shift activity at high temperature (360-450°C) and high stability. The addition of potassium enhanced the activities but affected adversely the stabilities of Co-Mo-based catalysts. ESR characterization shows that the Mo 5+ species are not connected with the WGSR activity. Magnesium in the support may be closely related with the formation of formate species intermediate for the WGSR.

This paper addresses the problem of computing a time-optimal trajectory between two configuration... more This paper addresses the problem of computing a time-optimal trajectory between two configurations of a car- like robot. Since 1990, two remarkable results of this problem have been achieved: the sufficient family containing all types of optimal trajectories between any two configurations, and then the optimal synthesis determining a type of optimal trajectories for two specified configurations. Our contribution is

Frontiers in Neuroscience, 2014

Various methods have been proposed to characterize the functional connectivity between nodes in a... more Various methods have been proposed to characterize the functional connectivity between nodes in a network measured with different modalities (electrophysiology, functional magnetic resonance imaging etc.). Since different measures of functional connectivity yield different results for the same dataset, it is important to assess when and how they can be used. In this work, we provide a systematic framework for evaluating the performance of a large range of functional connectivity measures-based upon a comprehensive portfolio of models generating measurable responses. Specifically, we benchmarked 42 methods using 10,000 simulated datasets from 5 different types of generative models with different connectivity structures. Since all functional connectivity methods require the setting of some parameters (window size and number, model order etc.), we first optimized these parameters using performance criteria based upon (threshold free) ROC analysis. We then evaluated the performance of the methods on data simulated with different types of models. Finally, we assessed the performance of the methods against different levels of signal-to-noise ratios and network configurations. A MATLAB toolbox is provided to perform such analyses using other methods and simulated datasets.

doi: 10.3389/fnins.2014.00405 A systematic framework for functional connectivity measures

Frontiers in Neuroscience, Dec 9, 2014

Various methods have been proposed to characterize the functional connectivity between nodes in a... more Various methods have been proposed to characterize the functional connectivity between nodes in a network measured with different modalities (electrophysiology, functional magnetic resonance imaging etc.). Since different measures of functional connectivity yield different results for the same dataset, it is important to assess when and how they can be used. In this work, we provide a systematic framework for evaluating the performance of a large range of functional connectivity measures-based upon a comprehensive portfolio of models generating measurable responses. Specifically, we benchmarked 42 methods using 10,000 simulated datasets from 5 different types of generative models with different connectivity structures. Since all functional connectivity methods require the setting of some parameters (window size and number, model order etc.), we first optimized these parameters using performance criteria based upon (threshold free) ROC analysis. We then evaluated the performance of the methods on data simulated with different types of models. Finally, we assessed the performance of the methods against different levels of signal-to-noise ratios and network configurations. A MATLAB toolbox is provided to perform such analyses using other methods and simulated datasets.

NeuroImage, Feb 1, 2018

Many analysis methods exist to extract graphs of functional connectivity from neuronal networks. ... more Many analysis methods exist to extract graphs of functional connectivity from neuronal networks. Confidence in the results is limited because, (i) different methods give different results, (ii) parameter setting directly influences the final result, and (iii) systematic evaluation of the results is not always performed. Here, we introduce MULAN (MULtiple method ANalysis), which assumes an ensemble based approach combining multiple analysis methods and fuzzy logic to extract graphs with the most probable structure. In order to reduce the dependency on parameter settings, we determine the best set of parameters using a genetic algorithm on simulated datasets, whose temporal structure is similar to the experimental one. After a validation step, the selected set of parameters is used to analyze experimental data. The final step cross-validates experimental subsets of data and provides a direct estimate of the most likely graph and our confidence in the proposed connectivity. A systematic evaluation validates our strategy against empirical stereotactic electroencephalography (SEEG) and functional magnetic resonance imaging (fMRI) data.

One-third of 50 million epilepsy patients worldwide suffer from drug resistant epilepsy and are c... more One-third of 50 million epilepsy patients worldwide suffer from drug resistant epilepsy and are candidates for surgery. Precise estimates of the epileptogenic zone networks (EZNs) are crucial for planning intervention strategies. Here, we present the Virtual Epileptic Patient (VEP), a multimodal probabilistic modeling framework for personalized end-to-end analysis of brain imaging data of drug resistant epilepsy patients. The VEP uses data-driven, personalized virtual brain models derived from patient-specific anatomical (such as T1-MRI, DW-MRI, and CT scan) and functional data (such as stereo-EEG). It employs Markov Chain Monte Carlo (MCMC) and optimization methods from Bayesian inference to estimate a patient’s EZN while considering robustness, convergence, sensor sensitivity, and identifiability diagnostics. We describe both high-resolution neural field simulations and a low-resolution neural mass model inversion. The VEP workflow was evaluated retrospectively with 53 epilepsy pa...

doi: 10.3389/fnins.2014.00405 A systematic framework for functional connectivity measures

NeuroImage, Jan 28, 2016

Individual variability has clear effects upon the outcome of therapies and treatment approaches. ... more Individual variability has clear effects upon the outcome of therapies and treatment approaches. The customization of healthcare options to the individual patient should accordingly improve treatment results. We propose a novel approach to brain interventions based on personalized brain network models derived from non-invasive structural data of individual patients. Along the example of a patient with bitemporal epilepsy, we show step by step how to develop a Virtual Epileptic Patient (VEP) brain model and integrate patient-specific information such as brain connectivity, epileptogenic zone and MRI lesions. Using high-performance computing, we systematically carry out parameter space explorations, fit and validate the brain model against the patient's empirical stereotactic EEG (SEEG) data and demonstrate how to develop novel personalized strategies towards therapy and intervention.

The Journal of neuroscience : the official journal of the Society for Neuroscience, Jan 16, 2015

Adaptive behaviors are built on the arbitrary linkage of sensory inputs to actions and goals. Alt... more Adaptive behaviors are built on the arbitrary linkage of sensory inputs to actions and goals. Although the sensorimotor and associative frontostriatal circuits are known to mediate arbitrary visuomotor mappings, the underlying corticocortico dynamics remain elusive. Here, we take a novel approach exploiting gamma-band neural activity to study the human cortical networks and corticocortical functional connectivity mediating arbitrary visuomotor mapping. Single-trial gamma-power time courses were estimated for all Brodmann areas by combing magnetoencephalographic and MRI data with spectral analysis and beam-forming techniques. Linear correlation and Granger causality analyses were performed to investigate functional connectivity between cortical regions. The performance of visuomotor associations was characterized by an increase in gamma-power and functional connectivity over the sensorimotor and frontoparietal network, in addition to medial prefrontal areas. The superior parietal are...

NeuroImage, 2018

Many analysis methods exist to extract graphs of functional connectivity from neuronal networks. ... more Many analysis methods exist to extract graphs of functional connectivity from neuronal networks. Confidence in the results is limited because, (i) different methods give different results, (ii) parameter setting directly influences the final result, and (iii) systematic evaluation of the results is not always performed. Here, we introduce MULAN (MULtiple method ANalysis), which assumes an ensemble based approach combining multiple analysis methods and fuzzy logic to extract graphs with the most probable structure. In order to reduce the dependency on parameter settings, we determine the best set of parameters using a genetic algorithm on simulated datasets, whose temporal structure is similar to the experimental one. After a validation step, the selected set of parameters is used to analyze experimental data. The final step cross-validates experimental subsets of data and provides a direct estimate of the most likely graph and our confidence in the proposed connectivity. A systemati...

Frontiers in Neuroscience, 2014

Various methods have been proposed to characterize the functional connectivity between nodes in a... more Various methods have been proposed to characterize the functional connectivity between nodes in a network measured with different modalities (electrophysiology, functional magnetic resonance imaging etc.). Since different measures of functional connectivity yield different results for the same dataset, it is important to assess when and how they can be used. In this work, we provide a systematic framework for evaluating the performance of a large range of functional connectivity measures-based upon a comprehensive portfolio of models generating measurable responses. Specifically, we benchmarked 42 methods using 10,000 simulated datasets from 5 different types of generative models with different connectivity structures. Since all functional connectivity methods require the setting of some parameters (window size and number, model order etc.), we first optimized these parameters using performance criteria based upon (threshold free) ROC analysis. We then evaluated the performance of the methods on data simulated with different types of models. Finally, we assessed the performance of the methods against different levels of signal-to-noise ratios and network configurations. A MATLAB toolbox is provided to perform such analyses using other methods and simulated datasets.

Through the analysis of different fault characteristics on single-phase fault for transmission li... more Through the analysis of different fault characteristics on single-phase fault for transmission lines with shunt reactors，one fault nature identification method based on least squares waveform estimation is presented。The method uses the equivalent model as the reference prototype，utilizes time domain waveform estimation to obtain the similarity of calculated and measured value on fault-phase terminal voltage，and then identifies the fault nature by constructing objective function with comparison to the setting value。Simulation results show that the proposed method not only can exactly distinguish permanent fault from transient fault on different conditions，but also can obtain the fault disappear time for transient fault，So it achieve the goal of high-speed reclosure in transient fault and reliable misalignment in permanent fault。

2007 IEEE International Conference on Grey Systems and Intelligent Services, 2007

... Respectively speaking, DRNN is a dynamic network system, which can distinguish the dynamic sy... more ... Respectively speaking, DRNN is a dynamic network system, which can distinguish the dynamic system on line. ... NN2 2 r Generalized Object Compensator Decoupling Fig.2. Block diagram ofdecoupling control 861 ... 1 1 u y ∂ ∂ is information of object, and this information can be ...

2008 International Conference on Information and Automation, 2008

Mobile robot navigation is one of the important domains in mobile robot technologiespsila researc... more Mobile robot navigation is one of the important domains in mobile robot technologiespsila research. This problem is divided into two categories of basic sub-problems: path planning and motion planning. In this paper we integrate the path planning and motion planning of a robot into a uniform framework, which is described by a hybrid system. Hybrid systems combine discrete and continuous behavior so that we can deal with the complexity of the environment at the discrete level and dynamics of a robot at the continuous level. At the discrete level, on the triangulation of the state space of a robot, a dual graph is constructed following a proposed target attractive principle. Then based on this dual graph, we design an incremental heuristic search algorithm to deal with dynamic environment and find an optimal sequence of adjacent triangles. At the continuous level, a motion planning algorithms that can autonomously generate the translational and rotational velocities for the robot to travel along the given sequence of triangles. Simulation results demonstrate the correctness and effectiveness of the approach.

2010 IEEE International Conference on Robotics and Automation, 2010

This paper studies the controllability of pairs of identical nonholonomic vehicles maintaining a ... more This paper studies the controllability of pairs of identical nonholonomic vehicles maintaining a constant distance. The study provides controllability results for the five most common types of robot vehicles: Dubins, Reeds-Shepp, differential drive, car-like and convexified Reeds-Shepp. The challenge of achieving controllability of such systems is that their admissible control domains depend on configuration variables. A theorem of controllability specifical

49th IEEE Conference on Decision and Control (CDC), 2010

This paper studies the controllability of formations of n identical aircraft maintaining constant... more This paper studies the controllability of formations of n identical aircraft maintaining constant distances. Aircraft are modeled as a planar kinematic system with constant velocity and curvature bounds. The challenges of achieving controllability of such system are that it is an affine system with drift and its admissible controls are determined by its configuration variables. We begin with the study of a pair of aircraft maintaining a constant distance. As a result, we show that if the specified distance is sufficiently large, a pair of aircraft is completely controllable, i.e. can be steered between any two arbitrary configurations. In case of small distances, a description of the reachable sets is provided. Finally, we provide the controllability results for three basic formations of n aircraft.

Catalysis Letters, 2008

MgO-Al 2 O 3 mixed oxides were prepared and used as the support of Co-Mo-based water-gas shift re... more MgO-Al 2 O 3 mixed oxides were prepared and used as the support of Co-Mo-based water-gas shift reaction (WGSR) catalysts. X-ray diffraction (XRD) characterization showed that the MgO-Al 2 O 3 mixed oxides support is composed of MgO, c-Al 2 O 3 , and magnesiaalumina spinel. The MgO-Al 2 O 3 mixed oxides-supported Co-Mo-based catalysts exhibited high shift activity at high temperature (360-450°C) and high stability. The addition of potassium enhanced the activities but affected adversely the stabilities of Co-Mo-based catalysts. ESR characterization shows that the Mo 5+ species are not connected with the WGSR activity. Magnesium in the support may be closely related with the formation of formate species intermediate for the WGSR.

This paper addresses the problem of computing a time-optimal trajectory between two configuration... more This paper addresses the problem of computing a time-optimal trajectory between two configurations of a car- like robot. Since 1990, two remarkable results of this problem have been achieved: the sufficient family containing all types of optimal trajectories between any two configurations, and then the optimal synthesis determining a type of optimal trajectories for two specified configurations. Our contribution is

Frontiers in Neuroscience, 2014

Various methods have been proposed to characterize the functional connectivity between nodes in a... more Various methods have been proposed to characterize the functional connectivity between nodes in a network measured with different modalities (electrophysiology, functional magnetic resonance imaging etc.). Since different measures of functional connectivity yield different results for the same dataset, it is important to assess when and how they can be used. In this work, we provide a systematic framework for evaluating the performance of a large range of functional connectivity measures-based upon a comprehensive portfolio of models generating measurable responses. Specifically, we benchmarked 42 methods using 10,000 simulated datasets from 5 different types of generative models with different connectivity structures. Since all functional connectivity methods require the setting of some parameters (window size and number, model order etc.), we first optimized these parameters using performance criteria based upon (threshold free) ROC analysis. We then evaluated the performance of the methods on data simulated with different types of models. Finally, we assessed the performance of the methods against different levels of signal-to-noise ratios and network configurations. A MATLAB toolbox is provided to perform such analyses using other methods and simulated datasets.