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Rong Chen, PhD, MS, MTR

Academic Title:

Associate Professor

Primary Appointment:

Diagnostic Radiology and Nuclear Medicine

Administrative Title:

Associate Vice Chair, Artificial Intelligence


100 N Greene St, 411

Phone (Primary):




Education and Training

Southeast University, China, B.S. 1996, Biomedical Engineering
The Graduate School of Chinese Academy of Sciences, China, M.S. 1999, Electrical Engineering
Washington State University, Pullman, WA, Ph.D. 2003, Electrical and Computer Engineering
University of Pennsylvania, Philadelphia, PA, Postdoctoral Researcher, 2005, Radiology
University of Pennsylvania, Philadelphia, PA, M.T.R 2012, Translational Research


Dr. Chen has a strong background in computational neuroscience, machine learning, neuroimaging, and clinical and translational research. Dr. Chen’s research focuses on leveraging machine learning and neuroimaging to understand the relationship between brain and behavior across scales, leading to next-generation AI, a deeper understanding of mechanisms of cognition, emotion, and decision making, and novel therapeutic concepts for brain disease such as Alzheimer’s disease, Parkinson’s disease, substance use disorder, autism, sickle cell disease, and HIV. He has 20 years of experience in advanced modeling, algorithm, and software development. He has released two open-source biomedical data mining software packages on NITRC: the GAMMA suite and Advanced Connectivity Analysis.

Dr. Chen is Associate Vice Chair of AI, Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine. He is a senior member of IEEE. He is an editorial member of Journal of Signal Processing Systems, Frontier in Computational Neuroscience, Frontiers in Neurorobotics, the Open Neuroimaging Journal, and the Neuroradiology Journal. As the PI, Dr. Chen conducts research funded by NIH NINDS, NIDA, NIA, the BRAIN initiative, and Oracle. 

Research/Clinical Keywords

Computational Neuroscience, Machine Learning, Neuroimaging, Biomedical Data Analysis, Translational Medicine, Clinical Research

Highlighted Publications


Chen R. Bayesian Coherence Analysis for Microcircuit Structure Learning, Neuroinformatics, 2022. (Learn a Markov network with Bayesian network learning)

Chen R., Causal network inference for neural ensemble activity, Neuroinformatics, 19(3), 515-527, 2021. (A machine learning framework of causal inference based on neural activity data)

Barbera G, Liang B, Zhang LF, Gerfen CR, Culurciello E, Chen R#, Li Y#, Lin DT#. Spatially Compact Neural Clusters In The Dorsal Striatum Encode Locomotion Relevant Information. 92(1):202-213, Neuron. 2016. # co-corresponding authors. (The first study for calcium imaging based neural decoding). 

Chen R, Herskovits EH, Graphical-Model-based Morphometric Analysis. IEEE Transaction on Medical Imaging. Vol 24, 1237-1248, October 2005. (A Bayesian computational framework for high-dimensional neuroimaging data)

Chen R, Sivakumar K, Kargupta H, Collective Mining of Bayesian Networks from Distributed Heterogeneous Data, Knowledge and Information Systems. 6(2):164-187, 2004. (The first study about distributed Bayesian network learning)


Additional Publication Citations

Peer-reviewed journal articles

  1. Chen, R., Zhang, X.M., The Development of a New Type of Intelligent Surface Pressure Measuring Instrument, Instrument Technique and Sensor, Dec. 1997
  2. Chen, R., Liu, X.J., Zou, M.Y., Binary Image Restoration Based on MRF, Journal of Image and Graphics, Oct. 1999
  3. Chen, R., Sivakumar, K, Kargupta, H, Collective Mining of Bayesian Networks from Distributed Heterogeneous data, Knowledge and Information Systems, 6(2):164-187, 2004
  4. Chen, R., Herskovits, E. H., Graphical-Model-based Morphometric Analysis, IEEE Transaction on medical imaging, Vol 24, 1237-1248, Oct. 2005
  5. Chen, R., Herskovits, E. H., Network analysis of Mild Cognitive Impairment, NeuroImage, Vol 29, 1252-1259, 2006
  6. Chen, R., Herskovits E. H., Clinical Diagnosis based on Bayesian Classification of Functional Magnetic-resonance Data. Neuroinformatics, 5(3):178-88, Fall, 2007
  7. Chen, R., Herskovits E. H., Graphical-model-based Multivariate Analysis of Functional Magnetic Resonance Data, NeuroImage, Vol 35, 635-647, Apr. 2007
  8. Chen, R., Hillis A. E., Pawlak M., and Herskovits E. H., Voxelwise Bayesian Lesion Deficit Analysis, NeuoImage, Vol 40, 1633-1642, May, 2008
  9. Herskovits E. H., Chen, R., Integrating Data-Mining Support into a Brain-Image Database Using Open-Source Components, Advance in Medical Sciences, 18:1-10, Apr. 2008
  10. Chen, R., Pawlak, M., Flynn, T., Krejza, J., Herskovits, E. H., Melhem, E., Brain morphometry and IQ measurements in children with sickle cell disease, Journal of Developmental & Behavioral Pediatrics, 30(6):509-17, 2009
  11. Jiao, Y., Chen, R., Ke, X, Chu, KK, Lu ZH, Herskovits, E. H, Predictive models of autism spectrum disorder based on brain regional cortical thickness, NeuroImage, 50(2):589-99, 2010 (Experimental design, data analysis)
  12. Chen, R., Herskovits E. H, Voxel-based Bayesian lesion-symptom mapping, NeuroImage, 49:1, 597-602, 2010
  13. Chen, R., Herskovits E. H, Machine-learning techniques for building a diagnostic model for very mild dementia, NeuroImage, 52(1):234-44, 2010
  14. Krejza J, Chen R, Romanowicz G, Kwiatkowski JL, Ichord R, Arkuszewski M, Zimmerman R, Ohene-Frempong K, Desiderio L, Melhem ER., Sickle cell disease and imaging: inter-hemispheric differences in blood flow Doppler parameters, Stroke.42:1, 81-6, 2011 (Data analysis)
  15. Chen, R., Jiao Y., Herskovits E. H, Structural MRI in Autism Spectrum Disorder, invited paper, Pediatric Research, 69:63r-8r, 2011.
  16. Arkuszewski, M., Krejza, J., Chen, R., Kwiatkowski, J., Ichord, R., Zimmerman, R., Ohene-Frempong, K., Desiderio, L., E.R. Melhem, Sickle Cell Disease: Reference Values and Interhemispheric Differences of Nonimaging Transcranial Doppler Blood Flow Parameters, American Journal of Neuroradiology, 32(8):1444-50, 2011
  17. Jiao, Y., Chen, R., Cheng, L., Ke, X., Chu K., Lu ZH., Herskovits. E. H., Predictive models for subtypes of autism spectrum disorder based on single-nucleotide polymorphisms and magnetic resonance imaging, Advance in Medical Sciences, 56(2):334-42, 2011
  18. Jiao, Y., Chen, R., Ke, X., Cheng Li, Chu, K., Lu ZH, Herskovits, E. H., Single Nucleotide Polymorphisms Predict Symptom Severity of Autism Spectrum Disorder, Journal of Autism and Developmental Disorders, 42(6):971-83, 2011
  19. Chen, R., Herskovits E. H, Graphical model based multivariate analysis (GAMMA): an open-source, cross-platform neuroimaging data analysis software package, Neuroinformatics, 10(2):119-27, 2011
  20. Chen, R., Resnick, S., Davatzikos, C., Herskovits E. H., Dynamic Bayesian network modeling for longitudinal brain morphometry, NeuroImage, 59(3):2330-8, 2011
  21. Chen, R., Young, K., Chao L. L., Miller, B., Yaffe K., Weiner, M., Herskovits E. H., Prediction of Conversion from Mild Cognitive Impairment to Alzheimer Disease Based on Bayesian Data Mining with Ensemble Learning, The Neuroradiology Journal, 25:1, 5-16, 2012
  22. Kumar, M., Kim, S., Pickup, S., Chen, R., Fairless A., Ittyerah, R., Abel, T., Brodkin, E., Poptani, H., Longitudinal in-vivo diffusion tensor imaging for assessing brain developmental changes in BALB/cJ mice, a model of reduced sociability relevant to autism, Brain Research, 1455:56-67, 2012
  23. Chen, R., Wang, S., Poptani, H., Melhem, E. R., Herskovits, E. H. A Bayesian diagnostic system to differentiate glioblastomas from solitary brain metastases. Neuroradiol J. 10;26(2):175-83. 2013
  24. Arkuszewski M., Krejza J., Chen R., Melhem E. R., Sickle cell anemia: reference values of cerebral blood flow determined by continuous arterial spin labeling MRI. Neuroradiol J. 10;26(2):191-200. 2013
  25. Arkuszewski, M., Krejza, J., Chen, R., Ichord, R., Kwiatkowski, J., Bilello M., Zimmerman R., Ohene-Frempong K., Melhem, E. R., Prevalence of intracranial stenosis and silent cerebral infarcts in children with sickle cell anemia and low risk of stroke, International Journal of Stroke. 8(7):E50-1. DOI:10.1111/ijs.12115. 2013
  26. Arkuszewski, M., Krejza, J., Chen, R., Ichord, R., Kwiatkowski, JL., Bilello, M., Zimmerman, R., Ohene-Frempong, K., Melhem, E., Sickle cell anemia: intracranial stenosis and silent cerebral infarcts in children with low risk of stroke. Adv Med Sci. Mar;59(1):108-13, 2014
  27. Liu, Y., Wang, T., Chen, X., Zhang, J., Zhou, G., Wang, Z., Chen, R., Tract-based Bayesian multivariate analysis of mild traumatic brain injury. Comput Math Methods Med. 2014:120182. doi: 10.1155/2014/120182, 2014
  28. Chen, R., Herskovits, E. H., Examining the multifactorial nature of a cognitive process using Bayesian brain-behavior modeling. Comput Med Imaging Graph, 41:117-25, 2014
  29. Hickok, G., Rogalsky, C., Chen, R., Herskovits, E. H., Townsley, S., Hillis, A., Partially Overlapping Sensorimotor Networks Underlie Speech Praxis and Verbal Short-Term Memory: Evidence from Apraxia of Speech Following Acute Stroke, 8:649, Hum. Neurosci. 2014
  30. Chen, R., Herskovits, E. H., Bayesian predictive modeling based on multidimensional connectivity profiling, Neuroradiol J. 28(1), 5-11, 2015
  31. Chen, HJ., Chen, R., Yang, M., Teng, GJ., Herskovits, E. H., Identification of minimal hepatic encephalopathy in patients with cirrhosis based on white matter imaging and Bayesian data mining, AJNR, 36(3):481-7, 2015 (Experimental design, data analysis, data interpretation)
  32. Chen, R., Arkuszewski M, Krejza J., Zimmerman R., Herskovits, E. H., Melhem E. R. A prospective longitudinal brain-morphometry study of children with sickle-cell disease, AJNR, 36(2), 403-410, 2015
  33. Yang M, Yang YR, Li HJ, Lu XS, Shi YM, Liu B, Chen HJ, Teng GJ, Chen R, Herskovits EH. Combining diffusion tensor imaging and gray matter volumetry to investigate motor functioning in chronic stroke. PLoS One. May 12;10(5), 2015
  34. Herskovits EH, Hong LE, Kochunov P, Sampath H, Chen R. Edge-Centered DTI Connectivity Analysis: Application to Schizophrenia. Neuroinformatics. 13(4):501-509, 2015
  35. Chen, R., Herskovits, E. H. and ADNI, Predictive structural dynamic network analysis, Journal of neuroscience methods, 245:58-63, 2015
  36. Wang Q, Chen R, JaJa J, Jin Y, Hong LE. Herskovits EH, Connectivity-Based Brain Parcellation - A Connectivity-Based Atlas for Schizophrenia Research, Neuroinformatics, 14(1):83-97, 2016
  37. Wang Z, Wu W, Liu Y, Wang T, Chen X, Zhang J, Zhou G, Chen R. Altered cerebellar white matter integrity in patients with mild traumatic brain injury in the acute stage. PLoS One, 11(3): e0151489, 2016
  38. Chen, R., Nixon, E., Herskovits, E. H., Advanced Connectivity Analysis (ACA): a large scale functional connectivity data mining environment, Neuroinformatics, 14(2): 191-9, 2016
  39. Chen, R., Krejza, J., Arkuszewski, M., Zimmerman, R., Herskovits, E., Melhem, E., Brain morphometric analysis predicts decline of intelligence quotient in children with sickle cell disease: a preliminary study, Advances in Medical Sciences, 62(1): 151-157, 2017
  40. Barbera G, Liang B, Zhang LF, Gerfen CR, Culurciello E, Chen R#, Li Y#, Lin DT#. Spatially compact neural clusters in the dorsal striatum encode locomotion relevant information. 92(1):202-213, Neuron. 2016. # Co-corresponding authors.
  41. Jiao Y, Wang XH, Chen R, Tang TY, Zhu XQ, Teng GJ., Predictive models of minimal hepatic encephalopathy for cirrhotic patients based on large-scale brain intrinsic connectivity networks, Sep 14;7(1):11512, Sci Rep. 2017
  42. Chen HJ, Shi HB, Jiang LF, Chen L, Chen R., Disrupted topological organization of brain structural network associated with prior overt hepatic encephalopathy in cirrhotic patients, European Radiology, 28(1): 85-95, 2017
  43. Chen R., Zheng YJ, Nixon E, Herskovits E, Dynamic network model with continuous valued nodes for longitudinal brain morphometry, NeuroImage, 155:605-611, 2017
  44. Dreizin D., Bodanapally U., Boscak A., Tirada N., Issa G., Nascone J., Bivona L., Mascarenhas D., O’Toole R., Nixon E., Chen R., Siegel E., CT Prediction Model for Major Arterial Injury after Blunt Pelvic Ring Disruption, Radiology, 287(3): 1061-1069, 2017
  45. Naragum V., Jindal G., Miller T., Kole M., Shivashankar R., Merino J., Cole J., Chen R., Kohler N., Gandhi D., Functional Independence After Stroke Thrombectomy Using Thrombolysis in Cerebral Infarction Grade 2c, A New Aim of Successful Revascularization, World Neurosurgery, 119:e928-e933. doi: 10.1016/j.wneu.2018.08.006, 2018
  46. Miller TR, Giacon L, Kole MJ, Chen R., Jindal G., Gandhi D., Onyx embolization with the Apollo detachable tip microcatheter: A single-center experience. Interv Neuroradiol, 24(3):339-344. doi: 10.1177/1591019918758494, 2018
  47. Liang HJ., Chang L., Chen R., Oishi K., Ernst T., Independent and Combined Effects of Chronic HIV-Infection and Tobacco Smoking on Brain Microstructure, Journal of Neuroimmune Pharmacology, 13(4):509-522. doi: 10.1007/s11481-018-9810-9, 2018
  48. Yan T., Wang W., Yang L., Chen K., Chen R., Han Y., Rich club disturbances of the human connectome from subjective cognitive decline to Alzheimer’s disease, Theranostics, 8(12):3237-3255, 2018
  49. Qiu WL, Chen R., Chen X, Zhang HF, Song L, Cui WJ, Zhang JJ, Ye DD, Zhang YF, Wang ZQ, Oridonin-loaded and GPC1 Targeted Gold Nanoparticle for Multimodal Imaging and Therapy in Pancreatic Cancer, International Journal of Nanomedicine, 13:6809-6827, doi: 10.2147/IJN.S177993, 2018 (Data analysis)
  50. Liang B., Zhang LF., Barbera G., Fang WT., Zhang J., Chen XC, Chen R., Li Y., LinDT., Distinct and Dynamic ON and OFF Neural Ensembles in the Prefrontal Cortex Code Social Exploration, Neuron, 100(3):700-714, doi: 10.1016/j.neuron.2018.08.043, 2018 (Data analysis)
  51. White C., Dharaiya E., Dalal S., Chen R., Haramati L., Vancouver Risk Calculator compared to ACR LungRads in Predicting Malignancy: Analysis of the National Lung Screening Trial, Radiology, 291(1):205-211, 2019
  52. Roberts B.M., White M.G., Patton M.H, Chen R., Mathur B., Ensemble encoding of action speed by striatal fast-spiking interneurons, Brain Structure and Function, 224(7):2567-2576, 2019 (Data analysis and modeling)
  53. Qiu W, Zhang H, Chen X, Song L, Cui W, Ren S, Wang Y, Guo K, Li D, Chen R., Wang ZQ. A GPC1-targeted and gemcitabine-loaded biocompatible nanoplatform for pancreatic cancer multimodal imaging and therapy. Nanomedicine (Lond), 14(17):2339-2353, 2019
  54. Ren S, Chen X, Cui W, Chen R., Guo K, Zhang H, Chen S, Wang ZQ. Differentiation of chronic mass-forming pancreatitis from pancreatic ductal adenocarcinoma using contrast-enhanced computed tomography. Cancer Management and Research, 11:7857–7866, 2019
  55. Ren S, Zhang J, Chen J, Cui W, Zhao R, Qiu W, Duan S, Chen R, Chen X, Wang ZQ. Evaluation of Texture Analysis for the Differential Diagnosis of Mass-Forming Pancreatitis from Pancreatic Ductal Adenocarcinoma on Contrast-Enhanced CT Images. Oncol. 2019. doi: 10.3389/fonc.2019.01171.
  56. Duan N., Rao M., Chen X., Yin YY, Wang ZQ, Chen R., Predicting necrosis in adnexal torsion in women of reproductive age using magnetic resonance imaging, European Radiology, 30(2):1054-1061, 2020
  57. Duan N, Chen X, Yin Y, Wang Z, Chen R., Comparison between magnetic resonance hysterosalpingography and conventional hysterosalpingography: direct visualization of the fallopian tubes using a novel MRI contrast agent mixture. Acta Radiol. 2020
  58. Qiu WL, Duan N, Chen X, Ren S, Zhang YF, Wang ZQ, Chen R., Pancreatic Ductal Adenocarcinoma: Machine Learning–Based Quantitative Computed Tomography Texture Analysis for Prediction of Histopathological Grade, Cancer Management and Research, 11:9253-9264, 2019
  59. Liang C, Shao Q, Zhang W, Yang M, Chang Q, Chen R., Chen JF. Smcr8 deficiency disrupts axonal transport-dependent lysosomal function and promotes axonal swellings and gain of toxicity in C9ALS/FTD mouse models, Human molecular genetics, 28(23):3940-3953, 2019
  60. Zhang W, Ma Li, Yang M, Shao Q, Xu J, Lu ZP, Zhao Z, Chen R., Chai Y, and Chen JF, Cerebral organoid and mouse models reveal a RAB39b-PI3K-mTOR pathway dependent dysregulation of cortical development leading to macrocephaly/autism phenotypes, Genes & Development, 34(7-8):580-597, 2020
  61. Trent G., Ye .N, Chopr J., Chen R., Wong-You-Cheong J., Naslund M., Siddiqui M., Wnorowski A., Performance of PI-RADS v2 assessment categories assigned prior to MR-US fusion biopsy in a new fusion biopsy program, Clinical Imaging, 64:29-34, 2020
  62. Ren S., Zhao R., Zhang JJ, Guo K., Gu XY, Duan SF, Wang ZQ., Chen R., Diagnostic accuracy of unenhanced CT texture analysis to differentiate mass-forming pancreatitis from pancreatic ductal adenocarcinoma. Abdom Radiol, 2020
  63. Lee Y., Xie J., Lee EJ, Sudarsanan S., Lin DT, Chen R.*, Bhattacharyya S.S.*, Real-time Neuron Detection and Neural Signal Extraction Platform for Miniature Calcium Imaging, Frontiers in Computational Neuroscience, 2020
  64. Liang HJ, Tang WK, Chu W., Ernst T., Chen R., Chang L., Striatal and White Matter Volumes in Chronic Ketamine Users with or without Recent Regular Stimulants Use, Drug and Alcohol Dependence, 213:108063, 2020
  65. Chen R.*, Lee KH, Herskovits EH., Computational framework for detection of subtypes of neuropsychiatric disorders based on DTI-derived anatomical connectivity, The Neuroradiology Journal, 2020
  66. Wang J., Ren S., Liu YK, Guo K., Chen X., Wang ZQ, Chen R., Carcinoid Tumorlets Co-Existing with Chronic Pulmonary Inflammatory Processes: Imaging Findings and Histological Appearances, Medical Science Monitor, 2020
  67. Ren S., Zhao R., Cui WJ, Qiu WL, Guo K., Duan SF, Wang ZQ, Chen R., Computed Tomography-Based Radiomics Signature for the Preoperative Differentiation of Pancreatic Adenosquamous Carcinoma from Pancreatic Ductal Adenocarcinoma, Frontiers in Oncology, 2020
  68. Liu YK, Wu W., Chen X., Wu M., Hu G., Zhou G., Wang ZQ*, Chen R.*, Aberrant correlation between the default mode and salience networks in mild traumatic brain injury, Frontiers in Computational Neuroscience, 2020. (Co-corresponding author)
  69. Dreizin, D., Rosales, R., Li, G., Syed, H. and Chen, R., Volumetric Markers of Body Composition May Improve Personalized Prediction of Major Arterial Bleeding After Pelvic Fracture: A Secondary Analysis of the Baltimore CT Prediction Model Cohort. Canadian Association of Radiologists Journal, p.0846537120952508. 2020
  70. Hossain R., Chelala L., Sleilaty G., Amin S., Vairavamurthy J., Chen R., Gupta A., Jeudy J., White C., Preprocedure CT Findings of Right Heart Failure as a Predictor of Mortality After Transcatheter Aortic Valve Replacement, AJR. American journal of roentgenology, 2020
  71. Wu XM., Bhattacharyya SS., Chen R.*, WGEVIA: A Graph Level Embedding Method for Microcircuit Data, Frontiers in Computational Neuroscience, 2020
  72. Lee KH., Wu XM., Lee YS., Lin DT., Bhattacharyya SS., Chen R.*, Neural Decoding on Imbalanced Calcium Imaging Data with a Network of Support Vector Machines, Advanced Robotics, 2020
  73. Chen R., Causal network inference for neural ensemble activity, in press, Neuroinformatics, 2020. (A machine learning framework of causal inference based on neural activity data)
  74. Qiu W, Wang Z, Chen R., Shi H, Ma Y, Zhou H, Li M, Li W, Chen H, Zhou H., Xiaoai Jiedu Recipe suppresses hepatocellular carcinogenesis through the miR-200b-3p /Notch1 axis. Cancer Management Res. 2020
  75. Ren, S., Qian, L., Daniels, M.J., Duan, S., Chen, R. and Wang, Z., Evaluation of contrast-enhanced computed tomography for the differential diagnosis of hypovascular pancreatic neuroendocrine tumors from chronic mass-forming pancreatitis. European Journal of Radiology, 133, p.109360, 2020
  76. Chen HJ., Zhang XH., Shi JY., Jiang SF., Sun YF., Zhang L., Chen R., Thalamic structural connectivity abnormalities in minimal hepatic encephalopathy, Frontiers in Neuroanatomy, 15:7, 2021
  77. Wu XM., Lin DT., Chen R.*, Bhattacharyya SS.*, Learning compact DNN models for behavior prediction from neural activity of calcium imaging, Journal of Signal Processing Systems, 2021
  78. Chen R., Herskovits EH, the Alzheimer’s Disease Neuroimaging Initiative, Machine Learning Detects Distinct Subtypes of Minimal Cognitive Impairment, Journal of Signal Processing Systems, 16, 1-7, 2021
  79. Ma L., Wang J., Ge JL., Wang Y., Zhang W., Du YN., Luo J., Li YP., Wang F., Fan GP., Chen R., Yao B., Zhao Z., Guo ML., Kim WK., Chai Y., Chen JF., Reversing neural circuit and behavior deficit in mice exposed to maternal inflammation by Zika virus, EMBO Rep, 22(8):e51978, 2021
  80. Sun XC, Duan SW, Cao A, Villagomez B, Lin RX, Chen HX, Pi LY, Ren B, Chen R, Chen MJ, Ying ZK, Fang SY, Cao Q, RRY Inhibits Amyloid-β1–42 Peptide Aggregation and Neurotoxicity, Jun 8;5(1):479-495, 2021
  81. Zhang Y, Denman AJ, Liang B, Werner CT, Beacher NJ, Chen R., Li Y, Shaham Y, Barbera G, Lin DT, Detailed mapping of behavior reveals the formation of prelimbic neural ensembles across operant learning, Neuron, 110(4), 674-685, 2022
  82. Chen J, Chen R, Xue C, Qi W, Hu G, Xu W, Chen S, Rao J, Zhang F, Zhang X. Hippocampal-Subregion Mechanisms of Repetitive Transcranial Magnetic Stimulation Causally Associated with Amelioration of Episodic Memory in Amnestic Mild Cognitive Impairment. J Alzheimers Dis. 2022
  83. McKeon PN, Bunce GW, Patton MH, Chen R, Mathur BN, Cortical control of striatal fast-spiking interneuron synchrony, J Physiol. 600(9):2189-2202, 2022
  84. Liang B, Zhang LF, Zhang Y, Werner CT, Beacher NJ, Denman AJ, Li Y, Chen R, Gerfen CR, Barbera G, Lin DT. Striatal direct pathway neurons play leading roles in accelerating rotarod motor skill learning, iScience, 25(5): 104245, 2022
  85. Liang B, Thapa R, Zhang G, Moffitt C, Zhang Y, Zhang LF, Johnston A, Ruby HP, Barbera G, Wong PC, Zhang ZJ, Chen R, Lin DT, Li Y. Aberrant neural activity in prefrontal pyramidal neurons lacking TDP-43 precedes neuron loss, Prog Neurobiol, in press, doi: 10.1016/j.pneurobio.2022.102297, 2022
  86. Xu DF, Chen R*. Meta-Learning for Decoding Neural Activity Data with Noisy Labels, Frontiers in Computational Neuroscience, in press, 2022
  87. Chen R, Bayesian Multisource Data Integration for Explainable Brain-behavior Analysis, Frontiers in Neuroscience, 16:1044680. doi: 10.3389/fnins.2022.1044680, 2022
  88. Chen R, Bayesian Coherence Analysis for Microcircuit Structure Learning, Neuroinformatics, in press, 2022
  89. Xie J, Chen R*, Bhattacharyya S*, A Parameter-Optimization Framework for Neural Decoding Systems, Frontiers in Neuroinformatics, in press, 2023
  90. Varanasi S, Tuli R, Han F, Chen R, Choa FS, Age Related Functional Connectivity Signature Extraction Using Energy-Based Machine Learning Techniques, Sensors, in press, 2023