Kehui Chen

University of Pittsburgh

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Hans‐Georg Müller

University of California, Davis

David A. Lewis

University of Pittsburgh

Joel Wood

University of Pittsburgh

Raquel E. Gur

California University of Pennsylvania

Vishwajit L. Nimgaonkar

University of Pittsburgh

Ruben C. Gur

California University of Pennsylvania

Jing Lei

Carnegie Mellon University

Wei Xue

China University of Geosciences

Robert H. Yolken

Johns Hopkins University

Faith Dickerson

Sheppard Pratt Health System

Cooperative Institutions

University of Pittsburgh

University of Pittsburgh Medical Center

University of California, Davis

Mansoura University

Kanazawa University

Center for the Neural Basis of Cognition

University of North Carolina at Chapel Hill

Atal Bihari Vajpayee Institute of Medical Sciences and Dr. Ram Manohar Lohia Hospital

Carnegie Mellon University

San Diego State University

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Frequent-voting Independence Screening for Data of Different Types or Different Dimensions

Statistica Sinica (2024)

Haeun Moon Kehui Chen

Modern datasets often include different types of variables with complex features, making variable selection particularly challenging.For example, a measure of dependence with the response variable may not be directly comparable among predictor variables of different types and different dimensions.To address this challenge, this work proposes a frequent-voting based independent screening method for variable selection, which avoids a direct comparison of the dependence measure among different variables.Asymptotic analyses show that the proposed method selects all of the active variables with probability converging to one.We also demonstrate its great finite sample performance through numerical experiments and the application to an ADHD study.

Independence

10.5705/ss.202023.0070

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Peripheral Markers of Stress Response Across the Spectrum of Suicidal Thoughts and Behavior

Biological Psychiatry (2022)

Nadine Melhem David A. Lewis Anna L. Marsland David Brent Dara Sakolsky

Suicidal Behavior

10.1016/j.biopsych.2022.02.155

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Distinct Physiological Maturation of Parvalbumin-Positive Neuron Subtypes in Mouse Prefrontal Cortex

Journal of Neuroscience (2017)

Takeaki Miyamae Kehui Chen David A. Lewis Guillermo González‐Burgos

Parvalbumin-positive (PV+) neurons control the timing of pyramidal cell output in cortical neuron networks. In the prefrontal cortex (PFC), PV+ neuron activity is involved in cognitive function, suggesting that PV+ neuron maturation is critical for cognitive development. The two major PV+ neuron subtypes found in the PFC, chandelier cells (ChCs) and basket cells (BCs), are thought to play different roles in cortical circuits, but the trajectories of their physiological maturation have not been compared. Using two separate mouse lines, we found that in the mature PFC, both ChCs and BCs are abundant in superficial layer 2, but only BCs are present in deeper laminar locations. This distinctive laminar distribution was observed by postnatal day 12 (P12), when we first identified ChCs by the presence of axon cartridges. Electrophysiology analysis of excitatory synapse development, starting at P12, showed that excitatory drive remains low throughout development in ChCs, but increases rapidly before puberty in BCs, with an earlier time course in deeper-layer BCs. Consistent with a role of excitatory synaptic drive in the maturation of PV+ neuron firing properties, the fast-spiking phenotype showed different maturation trajectories between ChCs and BCs, and between superficial versus deep-layer BCs. ChC and BC maturation was nearly completed, via different trajectories, before the onset of puberty. These findings suggest that ChC and BC maturation may contribute differentially to the emergence of cognitive function, primarily during prepubertal development.SIGNIFICANCE STATEMENT Parvalbumin-positive (PV+) neurons tightly control pyramidal cell output. Thus PV+ neuron maturation in the prefrontal cortex (PFC) is crucial for cognitive development. However, the relative physiological maturation of the two major subtypes of PV+ neurons, chandelier cells (ChCs) and basket cells (BCs), has not been determined. We assessed the maturation of ChCs and BCs in different layers of the mouse PFC, and found that, from early postnatal age, ChCs and BCs differ in laminar location. Excitatory synapses and fast-spiking properties matured before the onset of puberty in both cell types, but following cell type-specific developmental trajectories. Hence, the physiological maturation of ChCs and BCs may contribute to the emergence of cognitive function differentially, and predominantly during prepubertal development.

Neocortex

10.1523/jneurosci.3325-16.2017

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Citations (126)

Remote Sensing Image Classification Based on Lightweight Network and Pruning

2021 China Automation Congress (CAC) (2022)

Zexi Wang Wei Xue Kehui Chen Shaopeng Ma

In recent years, deep neural network has continuously improved the performance of remote sensing image classification. Though The deep neural network model is powerful, it is difficult to deploy on resource-constrained hardware platforms such as mobile terminal devices and embedded devices due to the large number of network parameters. In this paper, a novel lightweight network (LW-Net) model and a network pruning method are used for remote sensing image classification. This LW-Net model adopts a net block unit to obtain more characteristic graphs with less computational complexity. The proposed network pruning method uses the sparsity regularization on the influence factor in BN layer to automatically identified and pruned unimportant channels to make the model structure simpler. Experimental results demonstrate compared with traditional deep neural networks, the proposed model with the network pruning method can greatly reduce the computational complexity and model parameters with a little accuracy loss.

Pruning

Contextual image classification

Regularization

10.1109/cac57257.2022.10056093

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Efficient Underground Target Detection of Urban Roads in Ground-Penetrating Radar Images Based on Neural Networks

Remote Sensing (2023)

Wei Xue Kehui Chen Ting Li Li Liu Jian Zhang

Ground-penetrating radar (GPR) is an important nondestructive testing (NDT) tool for the underground exploration of urban roads. However, due to the large amount of GPR data, traditional manual interpretation is time-consuming and laborious. To address this problem, an efficient underground target detection method for urban roads based on neural networks is proposed in this paper. First, robust principal component analysis (RPCA) is used to suppress the clutter in the B-scan image. Then, three time-domain statistics of each A-scan signal are calculated as its features, and one backpropagation (BP) neural network is adopted to recognize A-scan signals to obtain the horizontal regions of targets. Next, the fusion and deletion (FAD) algorithm is used to further optimize the horizontal regions of targets. Finally, three time-domain statistics of each segmented A-scan signal in the horizontal regions of targets are extracted as the features, and another BP neural network is employed to recognize the segmented A-scan signals to obtain the vertical regions of targets. The proposed method is verified with both simulation and real GPR data. The experimental results show that the proposed method can effectively locate the horizontal ranges and vertical depths of underground targets for urban roads and has higher recognition accuracy and less processing time than the traditional segmentation recognition methods.

Ground-Penetrating Radar

SIGNAL (programming language)

Backpropagation

10.3390/rs15051346

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400. Dysfunctional Emotion Discrimination in Schizophrenia is Associated with HSV-1 Infection and Improves with Antiviral Treatment

Biological Psychiatry (2017)

Vishwajit L. Nimgaonkar Triptish Bhatia Joel Wood Satish Iyengar Sreelatha Narayana

Dysfunctional family

HSL and HSV

Lytic cycle

10.1016/j.biopsych.2017.02.417

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Nonparametric covariance estimation for mixed longitudinal studies, with applications in midlife women's health

arXiv (Cornell University) (2017)

Anru R. Zhang Kehui Chen

In mixed longitudinal studies, a group of subjects enter the study at different ages (cross-sectional) and are followed for successive years (longitudinal). In the context of such studies, we consider nonparametric covariance estimation with samples of noisy and partially observed functional trajectories. The proposed algorithm is based on a noniterative sequential-aggregation scheme with only basic matrix operations and closed-form solutions in each step. The good performance of the proposed method is supported by both theory and numerical experiments. We also apply the proposed procedure to a study on the working memory of midlife women, based on data from the Study of Women's Health Across the Nation (SWAN).

Longitudinal Study

Longitudinal data

Analysis of covariance

10.48550/arxiv.1711.00101

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Altered Gradients of Glutamate and Gamma-Aminobutyric Acid Transcripts in the Cortical Visuospatial Working Memory Network in Schizophrenia

Biological Psychiatry (2017)

Gil D. Hoftman Samuel J. Dienel H. Holly Bazmi Yun Zhang Kehui Chen

Dorsolateral prefrontal cortex

Gamma-Aminobutyric Acid

10.1016/j.biopsych.2017.11.029

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Citations (60)

Modeling Conditional Distributions for Functional Responses, With Application to Traffic Monitoring via GPS-Enabled Mobile Phones

Technometrics (2013)

Kehui Chen Hans‐Georg Müller

Motivated by problems involving a traffic monitoring system in which trajectory data are obtained from Global Positioning System-enabled mobile phones, we propose a novel approach to functional regression modeling, where instead of the usual mean regression the entire distribution of functional responses is modeled conditionally on predictors. An approach that sensibly balances flexibility and stability is obtained by assuming that the response functions are drawn from a Gaussian process, the mean and covariance function of which depend on predictors. The dependence of the mean function and covariance function of the response on the predictors is modeled additively. We demonstrate the proposed methods by constructing predicted curves and corresponding prediction regions for traffic velocity trajectories for a future time period, using current traffic velocity fields as predictor functions. The proposed functional regression and conditional distribution approach is of general interest for functional response settings, where in addition to predicting the conditional mean response function one is also interested in predicting the covariance surface of the random response functions, conditional on predictor curves.

Conditional probability distribution

Conditional expectation

10.1080/00401706.2013.842933

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Citations (13)

Modeling Repeated Functional Observations

Journal of the American Statistical Association (2012)

Kehui Chen Hans‐Georg Müller

Abstract We introduce a new methodological framework for repeatedly observed and thus dependent functional data, aiming at situations where curves are recorded repeatedly for each subject in a sample. Our methodology covers the case where the recordings of the curves are scheduled on a regular and dense grid and also situations more typical for longitudinal studies, where the timing of recordings is often sparse and random. The proposed models lead to an interpretable and straightforward decomposition of the inherent variation in repeatedly observed functional data and are implemented through a straightforward two-step functional principal component analysis. We provide consistency results and asymptotic convergence rates for the estimated model components. We compare the proposed model with an alternative approach via a two-dimensional Karhunen-Loève expansion and illustrate it through the analysis of longitudinal mortality data from period lifetables that are repeatedly observed for a sample of countries over many years, and also through simulation studies. This article has online supplementary materials. Keywords: AsymptoticsFunctional data analysisFunctional principal componentsHierarchical modelLifetableLongitudinal dataMortalityRate of convergenceRepeated measuresUniform convergence SUPPLEMENTARY MATERIALS A: Auxiliary Results and Proofs B: Comparisons with the Karhunen-Loève Expansion C: List of Countries Included in Mortality Data Analysis D: Additional Simulations E: Eigenanalysis of the Random Functions ξ1(s) and ξ2(s) for the Mortality Data This research was supported by NSF grants DMS08-0619 and DMS11-04426. The authors gratefully acknowledge the very constructive comments of two referees, an associate editor, and the editor.

Constructive

10.1080/01621459.2012.734196

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Citations (95)