This page has only limited features, please log in for full access.

Unclaimed
Shuo Chen
Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine University of Maryland Baltimore Maryland USA

Basic Info

Basic Info is private.

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Research article
Published: 26 July 2021 in Statistics in Medicine
Reads 0
Downloads 0

Clusterwise statistical inference is the most widely used technique for functional magnetic resonance imaging (fMRI) data analyses. Clusterwise statistical inference consists of two steps: (i) primary thresholding that excludes less significant voxels by a prespecified cut-off (eg, p < . 001 ); and (ii) clusterwise thresholding that controls the familywise error rate caused by clusters consisting of false positive suprathreshold voxels. The selection of the primary threshold is critical because it determines both statistical power and false discovery rate (FDR). However, in most existing statistical packages, the primary threshold is selected based on prior knowledge (eg, p < . 001 ) without taking into account the information in the data. In this article, we propose a data-driven approach to algorithmically select the optimal primary threshold based on an empirical Bayes framework. We evaluate the proposed model using extensive simulation studies and real fMRI data. In the simulation, we show that our method can effectively increase statistical power by 20% to over 100% while effectively controlling the FDR. We then investigate the brain response to the dose-effect of chlorpromazine in patients with schizophrenia by analyzing fMRI scans and generate consistent results.

ACS Style

Yunjiang Ge; Stephanie Hare; Gang Chen; James A. Waltz; Peter Kochunov; L. Elliot Hong; Shuo Chen. Bayes estimate of primary threshold in clusterwise functional magnetic resonance imaging inferences. Statistics in Medicine 2021, 1 .

AMA Style

Yunjiang Ge, Stephanie Hare, Gang Chen, James A. Waltz, Peter Kochunov, L. Elliot Hong, Shuo Chen. Bayes estimate of primary threshold in clusterwise functional magnetic resonance imaging inferences. Statistics in Medicine. 2021; ():1.

Chicago/Turabian Style

Yunjiang Ge; Stephanie Hare; Gang Chen; James A. Waltz; Peter Kochunov; L. Elliot Hong; Shuo Chen. 2021. "Bayes estimate of primary threshold in clusterwise functional magnetic resonance imaging inferences." Statistics in Medicine , no. : 1.

Journal article
Published: 02 March 2021 in Schizophrenia Research
Reads 0
Downloads 0

We hypothesized that cerebral white matter deficits in schizophrenia (SZ) are driven in part by accelerated white matter aging and are associated with cognitive deficits. We used a machine learning model to predict individual age from diffusion tensor imaging features and calculated the delta age (Δage) as the difference between predicted and chronological age. Through this approach, we translated multivariate white matter imaging features into an age-scaled metric and used it to test the temporal trends of accelerated aging-related white matter deficit in SZ and its association with the cognition. A feature selection procedure was first employed to choose fractional anisotropy values in 34 of 43 white fiber tracts. Using these features, a machine learning model was trained based on a training set consisted of 107 healthy controls (HC). The brain age of 166 SZs and 107 HCs in the testing set were calculated using this model. Then, we examined the SZ-HC group effect on Δage and whether this effect was moderated by chronological age using the regression spline model. The results showed that Δage was significantly elevated in the age > 30 group in patients (p < 0.001) but not in age ≤ 30 group (p = 0.364). Δage in patients was significantly and negatively associated with both working memory (β = −0.176, p = 0.007) and processing speed (β = −0.519, p = 0.035) while adjusting sex and chronological age. Overall, these findings indicate that the Δage is elevated in SZs and become significantly from the third decade of life; the increase of Δage in SZs is associated with the declined neurocognitive performance.

ACS Style

Jingtao Wang; Peter Kochunov; Hemalatha Sampath; Kathryn S. Hatch; Meghann C. Ryan; Fuzhong Xue; Jahanshad Neda; Thompson Paul; Britta Hahn; James Gold; James Waltz; L. Elliot Hong; Shuo Chen. White matter brain aging in relationship to schizophrenia and its cognitive deficit. Schizophrenia Research 2021, 230, 9 -16.

AMA Style

Jingtao Wang, Peter Kochunov, Hemalatha Sampath, Kathryn S. Hatch, Meghann C. Ryan, Fuzhong Xue, Jahanshad Neda, Thompson Paul, Britta Hahn, James Gold, James Waltz, L. Elliot Hong, Shuo Chen. White matter brain aging in relationship to schizophrenia and its cognitive deficit. Schizophrenia Research. 2021; 230 ():9-16.

Chicago/Turabian Style

Jingtao Wang; Peter Kochunov; Hemalatha Sampath; Kathryn S. Hatch; Meghann C. Ryan; Fuzhong Xue; Jahanshad Neda; Thompson Paul; Britta Hahn; James Gold; James Waltz; L. Elliot Hong; Shuo Chen. 2021. "White matter brain aging in relationship to schizophrenia and its cognitive deficit." Schizophrenia Research 230, no. : 9-16.

Accepted manuscript
Published: 28 January 2021 in Bioinformatics
Reads 0
Downloads 0

Motivation The analysis of gene co-expression network (GCN) is critical in examining the gene-gene interactions and learning the underlying complex yet highly organized gene regulatory mechanisms. Numerous clustering methods have been developed to detect communities of co-expressed genes in the large network. The assumed independent community structure, however, can be oversimplified and may not adequately characterize the complex biological processes. Results We develop a new computational package to extract interconnected communities from gene co-expression network. We consider a pair of communities be interconnected if a subset of genes from one community is correlated with a subset of genes from another community. The interconnected community structure is more flexible and provides a better fit to the empirical co-expression matrix. To overcome the computational challenges, we develop efficient algorithms by leveraging advanced graph norm shrinkage approach. We validate and show the advantage of our method by extensive simulation studies. We then apply our interconnected community detection method to an RNA-seq data from The Cancer Genome Atlas (TCGA) Acute Myeloid Leukemia (AML) study and identify essential interacting biological pathways related to the immune evasion mechanism of tumor cells. Availabilityand implementation The software is available at Github: https://github.com/qwu1221/ICN and Figshare: https://figshare.com/articles/software/ICN-package/13229093. Supplementary information Supplementary data are available at Bioinformatics online.

ACS Style

Qiong Wu; Tianzhou Ma; Qingzhi Liu; Donald K Milton; Yuan Zhang; Shuo Chen. ICN: extracting interconnected communities in gene co-expression networks. Bioinformatics 2021, 1 .

AMA Style

Qiong Wu, Tianzhou Ma, Qingzhi Liu, Donald K Milton, Yuan Zhang, Shuo Chen. ICN: extracting interconnected communities in gene co-expression networks. Bioinformatics. 2021; ():1.

Chicago/Turabian Style

Qiong Wu; Tianzhou Ma; Qingzhi Liu; Donald K Milton; Yuan Zhang; Shuo Chen. 2021. "ICN: extracting interconnected communities in gene co-expression networks." Bioinformatics , no. : 1.

Journal article
Published: 26 January 2021 in NeuroImage: Clinical
Reads 0
Downloads 0

Neurological and psychiatric illnesses are associated with regional brain deficit patterns that bear unique signatures and capture illness-specific characteristics. The Regional Vulnerability Index (RVI) was developed to quantify brain similarity by comparing individual white matter microstructure, cortical gray matter thickness and subcortical gray matter structural volume measures with neuroanatomical deficit patterns derived from large-scale meta-analytic studies. We tested the specificity of the RVI approach for major depressive disorder (MDD) and Alzheimer’s disease (AD) in a large epidemiological sample of UK Biobank (UKBB) participants (N = 19,393; 9138 M/10,255F; age = 64.8 ± 7.4 years). Compared to controls free of neuropsychiatric disorders, participants with MDD (N = 2,248; 805 M/1443F; age = 63.4 ± 7.4) had significantly higher RVI-MDD values (t = 5.6, p = 1·10−8), but showed no detectable difference in RVI-AD (t = 2.0, p = 0.10). Subjects with dementia (N = 7; 4 M/3F; age = 68.6 ± 8.6 years) showed significant elevation in RVI-AD (t = 4.2, p = 3·10−5) but not RVI-MDD (t = 2.1, p = 0.10) compared to controls. Even within affective illnesses, participants with bipolar disorder (N = 54) and anxiety disorder (N = 773) showed no significant elevation in whole-brain RVI-MDD. Participants with Parkinson’s disease (N = 37) showed elevation in RVI-AD (t = 2.4, p = 0.01) while subjects with stroke (N = 247) showed no such elevation (t = 1.1, p = 0.3). In summary, we demonstrated elevation in RVI-MDD and RVI-AD measures in the respective illnesses with strong replicability that is relatively specific to the respective diagnoses. These neuroanatomic deviation patterns offer a useful biomarker for population-wide assessments of similarity to neuropsychiatric illnesses.

ACS Style

Peter Kochunov; Meghann C. Ryan; Qifan Yang; Kathryn S. Hatch; Alyssa Zhu; Sophia I. Thomopoulos; Neda Jahanshad; Lianne Schmaal; Paul M. Thompson; Shuo Chen; XiaoMing Du; Bhim M. Adhikari; Heather Bruce; Stephanie Hare; Eric L. Goldwaser; Mark D. Kvarta; Thomas E. Nichols; L. Elliot Hong. Comparison of regional brain deficit patterns in common psychiatric and neurological disorders as revealed by big data. NeuroImage: Clinical 2021, 29, 102574 .

AMA Style

Peter Kochunov, Meghann C. Ryan, Qifan Yang, Kathryn S. Hatch, Alyssa Zhu, Sophia I. Thomopoulos, Neda Jahanshad, Lianne Schmaal, Paul M. Thompson, Shuo Chen, XiaoMing Du, Bhim M. Adhikari, Heather Bruce, Stephanie Hare, Eric L. Goldwaser, Mark D. Kvarta, Thomas E. Nichols, L. Elliot Hong. Comparison of regional brain deficit patterns in common psychiatric and neurological disorders as revealed by big data. NeuroImage: Clinical. 2021; 29 ():102574.

Chicago/Turabian Style

Peter Kochunov; Meghann C. Ryan; Qifan Yang; Kathryn S. Hatch; Alyssa Zhu; Sophia I. Thomopoulos; Neda Jahanshad; Lianne Schmaal; Paul M. Thompson; Shuo Chen; XiaoMing Du; Bhim M. Adhikari; Heather Bruce; Stephanie Hare; Eric L. Goldwaser; Mark D. Kvarta; Thomas E. Nichols; L. Elliot Hong. 2021. "Comparison of regional brain deficit patterns in common psychiatric and neurological disorders as revealed by big data." NeuroImage: Clinical 29, no. : 102574.

Research article
Published: 22 January 2021 in Statistics in Medicine
Reads 0
Downloads 0

Link prediction is a fundamental problem in network analysis. In a complex network, links can be unreported and/or under detection limits due to heterogeneous sources of noise and technical challenges during data collection. The incomplete network data can lead to an inaccurate inference of network based data analysis. We propose a parametric link prediction model and consider latent links as misclassified binary outcomes. We develop new algorithms to optimize model parameters and yield robust predictions of unobserved links. Theoretical properties of the predictive model are also discussed. We apply the new method to a partially observed social network data and incomplete brain network data. The results demonstrate that our method outperforms the existing latent‐link prediction methods.

ACS Style

Qiong Wu; Zhen Zhang; Tianzhou Ma; James Waltz; Donald Milton; Shuo Chen. Link predictions for incomplete network data with outcome misclassification. Statistics in Medicine 2021, 40, 1519 -1534.

AMA Style

Qiong Wu, Zhen Zhang, Tianzhou Ma, James Waltz, Donald Milton, Shuo Chen. Link predictions for incomplete network data with outcome misclassification. Statistics in Medicine. 2021; 40 (6):1519-1534.

Chicago/Turabian Style

Qiong Wu; Zhen Zhang; Tianzhou Ma; James Waltz; Donald Milton; Shuo Chen. 2021. "Link predictions for incomplete network data with outcome misclassification." Statistics in Medicine 40, no. 6: 1519-1534.

Preprint content
Published: 11 December 2020
Reads 0
Downloads 0

Fine-mapping is an analytical step to perform causal prioritization of the polymorphic variants on a trait-associated genomic region observed from genome-wide association studies (GWAS). The prioritization of causal variants can be challenging due to the linkage disequilibrium (LD) patterns among hundreds to thousands of polymorphisms associated with a trait. We propose a novel ℓ0 graph norm shrinkage algorithm to select causal variants from dense LD blocks consisting of highly correlated SNPs that may not be proximal or contiguous. We extract dense LD blocks and perform regression shrinkage to calculate a prioritization score to select a parsimonious set of causal variants. Our approach is computationally efficient and allows performing fine-mapping on thousands of polymorphisms. We demonstrate its application using a large UK Biobank (UKBB) sample related to nicotine addiction. Our results suggest that polymorphic variances in both neighboring and distant variants can be consolidated into dense blocks of highly correlated loci. Simulations were used to evaluate and compare the performance of our method and existing fine-mapping algorithms. The results demonstrated that our method outperformed comparable fine-mapping methods with increased sensitivity and reduced false-positive error rate regarding causal variant selection. The application of this method to smoking severity trait in UKBB sample replicated previously reported loci and suggested the causal prioritization of genetic effects on nicotine dependency.

ACS Style

Chen Mo; Zhenyao Ye; Kathryn Hatch; Yuan Zhang; Qiong Wu; Song Liu; Peter Kochunov; L. Elliot Hong; Tianzhou Ma; Shuo Chen. Genetic Fine-mapping with Dense Linkage Disequilibrium Blocks: genetics of nicotine dependence. 2020, 1 .

AMA Style

Chen Mo, Zhenyao Ye, Kathryn Hatch, Yuan Zhang, Qiong Wu, Song Liu, Peter Kochunov, L. Elliot Hong, Tianzhou Ma, Shuo Chen. Genetic Fine-mapping with Dense Linkage Disequilibrium Blocks: genetics of nicotine dependence. . 2020; ():1.

Chicago/Turabian Style

Chen Mo; Zhenyao Ye; Kathryn Hatch; Yuan Zhang; Qiong Wu; Song Liu; Peter Kochunov; L. Elliot Hong; Tianzhou Ma; Shuo Chen. 2020. "Genetic Fine-mapping with Dense Linkage Disequilibrium Blocks: genetics of nicotine dependence." , no. : 1.

Preprint content
Published: 09 October 2020
Reads 0
Downloads 0

Group-level brain connectome analysis has attracted increasing interest in neuropsychiatric research with the goal of identifying connectomic subnetworks (subgraphs) that are systematically associated with brain disorders. However, extracting disease-related subnetworks from the whole brain connectome has been challenging, because no prior knowledge is available regarding the sizes and locations of the subnetworks. In addition, neuroimaging data is often mixed with substantial noise that can further obscure informative subnetwork detection. We propose a likelihood-based adaptive dense subgraph discovery (ADSD) model to extract disease-related subgraphs from the group-level whole brain connectome data. Our method is robust to both false positive and false negative errors of edge-wise inference and thus can lead to a more accurate discovery of latent disease-related connectomic subnetworks. We develop computationally efficient algorithms to implement the novel ADSD objective function and derive theoretical results to guarantee the convergence properties. We apply the proposed approach to a brain fMRI study for schizophrenia research and identify well-organized and biologically meaningful subnetworks that exhibit schizophrenia-related salience network centered connectivity abnormality. Analysis of synthetic data also demonstrates the superior performance of the ADSD method for latent subnetwork detection in comparison with existing methods in various settings.

ACS Style

Qiong Wu; Xiaoqi Huang; Adam Culbreth; James Waltz; L. Elliot Hong; Shuo Chen. Extracting Brain Disease-Related Connectome Subgraphs by Adaptive Dense Subgraph Discovery. 2020, 1 .

AMA Style

Qiong Wu, Xiaoqi Huang, Adam Culbreth, James Waltz, L. Elliot Hong, Shuo Chen. Extracting Brain Disease-Related Connectome Subgraphs by Adaptive Dense Subgraph Discovery. . 2020; ():1.

Chicago/Turabian Style

Qiong Wu; Xiaoqi Huang; Adam Culbreth; James Waltz; L. Elliot Hong; Shuo Chen. 2020. "Extracting Brain Disease-Related Connectome Subgraphs by Adaptive Dense Subgraph Discovery." , no. : 1.

Preprint content
Published: 04 October 2020
Reads 0
Downloads 0

Cluster-wise statistical inference is the most widely used technique for functional magnetic resonance imaging (fMRI) data analyses. Cluster-wise statistical inference consists of two steps: i) primary thresholding that excludes less significant voxels by a pre-specified cut-off (e.g., p

ACS Style

Yunjiang Ge; Stephanie Hare; Gang Chen; James Waltz; Peter Kochunov; Elliot Hong; Shuo Chen. Bayes Estimate of Primary Threshold in Cluster-wise fMRI Inferences. 2020, 1 .

AMA Style

Yunjiang Ge, Stephanie Hare, Gang Chen, James Waltz, Peter Kochunov, Elliot Hong, Shuo Chen. Bayes Estimate of Primary Threshold in Cluster-wise fMRI Inferences. . 2020; ():1.

Chicago/Turabian Style

Yunjiang Ge; Stephanie Hare; Gang Chen; James Waltz; Peter Kochunov; Elliot Hong; Shuo Chen. 2020. "Bayes Estimate of Primary Threshold in Cluster-wise fMRI Inferences." , no. : 1.

Journal article
Published: 23 August 2020 in Entropy
Reads 0
Downloads 0

We propose a new metric to characterize the complexity of weighted complex networks. Weighted complex networks represent a highly organized interactive process, for example, co-varying returns between stocks (financial networks) and coordination between brain regions (brain connectivity networks). Although network entropy methods have been developed for binary networks, the measurement of non-randomness and complexity for large weighted networks remains challenging. We develop a new analytical framework to measure the complexity of a weighted network via graph embedding and point pattern analysis techniques in order to address this unmet need. We first perform graph embedding to project all nodes of the weighted adjacency matrix to a low dimensional vector space. Next, we analyze the point distribution pattern in the projected space, and measure its deviation from the complete spatial randomness. We evaluate our method via extensive simulation studies and find that our method can sensitively detect the difference of complexity and is robust to noise. Last, we apply the approach to a functional magnetic resonance imaging study and compare the complexity metrics of functional brain connectivity networks from 124 patients with schizophrenia and 103 healthy controls. The results show that the brain circuitry is more organized in healthy controls than schizophrenic patients for male subjects while the difference is minimal in female subjects. These findings are well aligned with the established sex difference in schizophrenia.

ACS Style

Shuo Chen; Zhen Zhang; Chen Mo; Qiong Wu; Peter Kochunov; L. Elliot Hong. Characterizing the Complexity of Weighted Networks via Graph Embedding and Point Pattern Analysis. Entropy 2020, 22, 925 .

AMA Style

Shuo Chen, Zhen Zhang, Chen Mo, Qiong Wu, Peter Kochunov, L. Elliot Hong. Characterizing the Complexity of Weighted Networks via Graph Embedding and Point Pattern Analysis. Entropy. 2020; 22 (9):925.

Chicago/Turabian Style

Shuo Chen; Zhen Zhang; Chen Mo; Qiong Wu; Peter Kochunov; L. Elliot Hong. 2020. "Characterizing the Complexity of Weighted Networks via Graph Embedding and Point Pattern Analysis." Entropy 22, no. 9: 925.

Original article
Published: 17 June 2020 in Therapeutic Drug Monitoring
Reads 0
Downloads 0

Background: Clozapine is the most effective antipsychotic for treatment-resistant schizophrenia. Although serum clozapine levels can help guide treatment, they are underutilized owing to requirements for frequent venous blood draws and lack of immediate results. Methods: Clozapine levels measured with a novel immunoassay technology (which enables point-of-care development) were compared with those measured by standard liquid chromatography/tandem mass spectrometry (LC-MS/MS). Frozen serum aliquots of 117 samples (N=48 patients with schizophrenia on clozapine; N=24 patients with schizophrenia not on clozapine; N=45 healthy controls) were sent to a national reference laboratory (NRL) for clozapine level determination by LC-MS/MS, and matching samples were subjected to novel immunoassay (three runs). At a later date, another frozen aliquot from the same date was sent to the NRL for repeat testing. Results: The NRL obtained 18 false positive clozapine results (mean 42.39 ± 32.06, range 21–159 ng/mL) in participants not on clozapine (N=3) and healthy controls (N=15). The immunoassay showed no false positive clozapine results. The clozapine levels were correlated between both assays (r=0.84, p <0.0001), despite 16% higher clozapine levels with immunoassay (482.08 ± 270.88 ng/mL immunoassay, 414.98 ± 186.29 ng/mL LC-MS/MS (p=0.03)). Agreement analysis using concordance correlation coefficient (CCC) for LC-MS/MS of the two aliquots yielded CCC=0.869; 95% CI=0.690–0.970, whereas higher agreement results were observed for the three runs of immunoassay (CCC=0.99; 95% CI=0.979–0.997). Conclusions: The lack of false positives observed with immunoassay, higher repeat performance agreement, and good correlation with LC-MS/MS may indicate the more robust performance of immunoassay than that of LC-MS/MS clozapine level determination.

ACS Style

Tiffany Buckley; Christopher Kitchen; Gopal Vyas; Nathan A. Siegfried; Eshetu Tefera; Shuo Chen; Bethany A. DiPaula; Deanna L. Kelly. Comparison of Novel Immunoassay With Liquid Chromatography/Tandem Mass Spectrometry (LC-MS/MS) for Therapeutic Drug Monitoring of Clozapine. Therapeutic Drug Monitoring 2020, 42, 771 -777.

AMA Style

Tiffany Buckley, Christopher Kitchen, Gopal Vyas, Nathan A. Siegfried, Eshetu Tefera, Shuo Chen, Bethany A. DiPaula, Deanna L. Kelly. Comparison of Novel Immunoassay With Liquid Chromatography/Tandem Mass Spectrometry (LC-MS/MS) for Therapeutic Drug Monitoring of Clozapine. Therapeutic Drug Monitoring. 2020; 42 (5):771-777.

Chicago/Turabian Style

Tiffany Buckley; Christopher Kitchen; Gopal Vyas; Nathan A. Siegfried; Eshetu Tefera; Shuo Chen; Bethany A. DiPaula; Deanna L. Kelly. 2020. "Comparison of Novel Immunoassay With Liquid Chromatography/Tandem Mass Spectrometry (LC-MS/MS) for Therapeutic Drug Monitoring of Clozapine." Therapeutic Drug Monitoring 42, no. 5: 771-777.

Other
Published: 20 February 2020
Reads 0
Downloads 0

IntroductionTemporomandibular disorder (TMD) is a common musculoskeletal pain condition with development of chronic symptoms in 49% of patients. Although a number of biological factors have shown an association with chronic TMD in cross-sectional and case control studies, there are currently no biomarkers that can predict the development of chronic symptoms. The PREDICT study aims to undertake analytical validation of a novel peak alpha frequency (PAF) and corticomotor excitability (CME) biomarker signature using a human model of the transition to sustained myofascial temporomandibular pain (masseter intramuscular injection of nerve growth factor [NGF]). This paper describes, a-priori, the methods and analysis plan.Methods and analysisThis study uses a multi-site longitudinal, experimental study to follow individuals for a period of 30 days as they progressively develop and experience complete resolution of NGF-induced muscle pain. 150 healthy participants will be recruited. Participants will complete twice daily electronic pain dairies from Day 0 to Day 30 and undergo assessment of pressure pain thresholds, and recording of PAF and CME on Days 0, 2 and 5. Intramuscular injection of NGF will be given into the right masseter muscle on Days 0 and 2. The primary outcome is pain sensitivity.Ethics and disseminationEthical approval has been obtained from The University of New South Wales (HC190206) and the University of Maryland Baltimore (HP-00085371). Dissemination will occur through presentations at National and International conferences and publications in international peer-reviewed journals.Registration detailsClinicalTrials.gov: NCT04241562 (prospective)STRENGTHS AND LIMITATIONS OF THIS STUDYPREDICT is the first study to undertake analytical validation of a peak alpha frequency and corticomotor excitability biomarker signature. The study will determine the sensitivity, specificity and accuracy of this biomarker signature at predicting pain sensitivity.PREDICT will establish the reportable range of test results and determine automation and simplification of methods for biomarker detection in the clinic.The methods and statistical analysis plan are pre-specified to ensure reporting transparency.Future patient studies will be required for clinical validation.

ACS Style

David A Seminowicz; Katarzyna Bilska; Nahian S Chowdhury; Patrick Skippen; Samantha K Millard; Alan Ki Chiang; Shuo Chen; Andrew J Furman; Siobhan M Schabrun. A novel cortical biomarker signature for predicting pain sensitivity: protocol for the PREDICT longitudinal analytical validation study. 2020, 1 .

AMA Style

David A Seminowicz, Katarzyna Bilska, Nahian S Chowdhury, Patrick Skippen, Samantha K Millard, Alan Ki Chiang, Shuo Chen, Andrew J Furman, Siobhan M Schabrun. A novel cortical biomarker signature for predicting pain sensitivity: protocol for the PREDICT longitudinal analytical validation study. . 2020; ():1.

Chicago/Turabian Style

David A Seminowicz; Katarzyna Bilska; Nahian S Chowdhury; Patrick Skippen; Samantha K Millard; Alan Ki Chiang; Shuo Chen; Andrew J Furman; Siobhan M Schabrun. 2020. "A novel cortical biomarker signature for predicting pain sensitivity: protocol for the PREDICT longitudinal analytical validation study." , no. : 1.

Preprint
Published: 25 November 2019
Reads 0
Downloads 0

SummaryLink prediction is a fundamental problem in network analysis. In a complex network, links can be unreported and/or under detection limits due to heterogeneous noises and technical challenges during data collection. The incomplete network data can lead to an inaccurate inference of network based data analysis. We propose a new link prediction model that builds on the exponential random graph model (ERGM) by considering latent links as misclassified binary outcomes. We develop new algorithms to optimize model parameters and yield robust predictions of unobserved links. The new method is applied to a partially observed social network data and incomplete brain network data. The results demonstrate that our method outperforms the existing latent-contact prediction methods.

ACS Style

Qiong Wu; Zhen Zhang; James Waltz; Tianzhou Ma; Donald Milton; Shuo Chen. Predicting Latent Links from Incomplete Network Data Using Exponential Random Graph Model with Outcome Misclassification. 2019, 852798 .

AMA Style

Qiong Wu, Zhen Zhang, James Waltz, Tianzhou Ma, Donald Milton, Shuo Chen. Predicting Latent Links from Incomplete Network Data Using Exponential Random Graph Model with Outcome Misclassification. . 2019; ():852798.

Chicago/Turabian Style

Qiong Wu; Zhen Zhang; James Waltz; Tianzhou Ma; Donald Milton; Shuo Chen. 2019. "Predicting Latent Links from Incomplete Network Data Using Exponential Random Graph Model with Outcome Misclassification." , no. : 852798.

Preprint
Published: 08 September 2019
Reads 0
Downloads 0

We consider group-level statistical inference for networks, where outcomes are multivariate edge variables constrained in an adjacency matrix. The graph notation is used to represent a network, where nodes are identical biological units (e.g. brain regions) shared across subjects and edge-variables indicate the strengths of interactive relationships between nodes. Edge-variables vary across subjects and may be associated with covariates of interest. The statistical inference for multivariate edge-variables is challenging because both localized inference on individual edges and the joint inference of a combinatorial of edges (network-level) are desired. Different from conventional multivariate variables (e.g. omics data), the inference of a combinatorial of edges is closely linked with network topology and graph combinatorics. We propose a novel objective function with

ACS Style

Shuo Chen; Qiong Wu; L. Elliot Hong. Graph combinatorics based group-level network inference. 2019, 758490 .

AMA Style

Shuo Chen, Qiong Wu, L. Elliot Hong. Graph combinatorics based group-level network inference. . 2019; ():758490.

Chicago/Turabian Style

Shuo Chen; Qiong Wu; L. Elliot Hong. 2019. "Graph combinatorics based group-level network inference." , no. : 758490.

Journal article
Published: 09 July 2019 in Computational Statistics & Data Analysis
Reads 0
Downloads 0

Emerging brain connectivity network studies suggest that interactions betweenvarious distributed neuronal populations may be characterized by an organized complex topological structure. Many neuropsychiatric disorders are associated with altered topological patterns of brain connectivity. Therefore, a key inquiry of connectivity analysis is to detect group-level differentially expressed connectome patterns from the massive neuroimaging data. Recently, statistical methods have been developed to detect differentially expressed connectivity features at a subnetwork level, extending more commonly applied edge level analysis. However, the graph topological structures in these methods are limited to community/cliques which may not effectively uncover the underlying complex and disease-related brain circuits/subnetworks. Building on these previous subnetwork detection methods, a new statistical approach is developed to automatically identify the latent differentially expressed brain connectivity subnetworks with k-partite graph topological structures from large brain connectivity matrices. In addition, statistical inferential techniques are provided to test the detected topological structure. The new methods are evaluated via extensive simulation studies and then applied to resting state fMRI data (24 cases and 18 controls) for Parkinson’s disease research. A differentially expressed connectivity network with the k-partite graph topological structure is detected which reveals underlying neural features distinguishing Parkinson’s disease patients from healthy control subjects.

ACS Style

Shuo Chen; F. DuBois Bowman; Yishi Xing. Detecting and testing altered brain connectivity networks with k-partite network topology. Computational Statistics & Data Analysis 2019, 141, 109 -122.

AMA Style

Shuo Chen, F. DuBois Bowman, Yishi Xing. Detecting and testing altered brain connectivity networks with k-partite network topology. Computational Statistics & Data Analysis. 2019; 141 ():109-122.

Chicago/Turabian Style

Shuo Chen; F. DuBois Bowman; Yishi Xing. 2019. "Detecting and testing altered brain connectivity networks with k-partite network topology." Computational Statistics & Data Analysis 141, no. : 109-122.

Journal article
Published: 01 November 2018 in Computational Statistics & Data Analysis
Reads 0
Downloads 0

Interactions between features of high-dimensional biomedical data often exhibit complex and organized, yet latent, network topological structures. Estimating the non-sparse large covariance matrix of these high-dimensional biomedical data while preserving and recognizing the latent network topology are challenging. A two step procedure is proposed that first detects latent network topological structures from the sample correlation matrix by implementing new penalized optimization and then regularizes the covariance matrix by leveraging the detected network topological information. The network topology guided regularization can reduce false positive and false negative rates simultaneously because it allows edges to borrow strengths from each other precisely. Empirical data examples demonstrate that organized latent network topological structures widely exist in high-dimensional biomedical data across platforms and identifying these network structures can effectively improve estimating covariance matrix and understanding interactive relationships between biomedical features.

ACS Style

Shuo Chen; Jian Kang; Yishi Xing; Yunpeng Zhao; Donald K. Milton. Estimating large covariance matrix with network topology for high-dimensional biomedical data. Computational Statistics & Data Analysis 2018, 127, 82 -95.

AMA Style

Shuo Chen, Jian Kang, Yishi Xing, Yunpeng Zhao, Donald K. Milton. Estimating large covariance matrix with network topology for high-dimensional biomedical data. Computational Statistics & Data Analysis. 2018; 127 ():82-95.

Chicago/Turabian Style

Shuo Chen; Jian Kang; Yishi Xing; Yunpeng Zhao; Donald K. Milton. 2018. "Estimating large covariance matrix with network topology for high-dimensional biomedical data." Computational Statistics & Data Analysis 127, no. : 82-95.

Journal article
Published: 10 September 2018 in Biostatistics
Reads 0
Downloads 0

SUMMARY Brain connectivity studies often refer to brain areas as graph nodes and connections between nodes as edges, and aim to identify neuropsychiatric phenotype-related connectivity patterns. When performing group-level brain connectivity alternation analyses, it is critical to model the dependence structure between multivariate connectivity edges to achieve accurate and efficient estimates of model parameters. However, specifying and estimating dependencies between connectivity edges presents formidable challenges because (i) the dimensionality of parameters in the covariance matrix is high (of the order of the fourth power of the number of nodes); (ii) the covariance between a pair of edges involves four nodes with spatial location information; and (iii) the dependence structure between edges can be related to unknown network topological structures. Existing methods for large covariance/precision matrix regularization and spatial closeness-based dependence structure specification/estimation models may not fully address the complexity and challenges. We develop a new Bayesian nonparametric model that unifies information from brain network areas (nodes), connectivity (edges), and covariance between edges by constructing the function of covariance matrix based on the underlying network topological structure. We perform parameter estimation using an efficient Markov chain Monte Carlo algorithm. We apply our method to resting-state functional magnetic resonance imaging data from 60 subjects of a schizophrenia study and simulated data to demonstrate the performance of our method.

ACS Style

Shuo Chen; Yishi Xing; Jian Kang; Peter Kochunov; L Elliot Hong. Bayesian modeling of dependence in brain connectivity data. Biostatistics 2018, 21, 269 -286.

AMA Style

Shuo Chen, Yishi Xing, Jian Kang, Peter Kochunov, L Elliot Hong. Bayesian modeling of dependence in brain connectivity data. Biostatistics. 2018; 21 (2):269-286.

Chicago/Turabian Style

Shuo Chen; Yishi Xing; Jian Kang; Peter Kochunov; L Elliot Hong. 2018. "Bayesian modeling of dependence in brain connectivity data." Biostatistics 21, no. 2: 269-286.