Quantitative Psychology and Statistics Lab

Selected Publications (View Google Scholar Page) 1.   Taylor, A., Zhang, F., Niu, X., Heywood, A., Stocks, J., Feng, G., Popuri, K., Beg, M.F., Wang, L.; Alzheimer's Disease Neuroimaging Initiative (2022), Investigating the temporal pattern of neuroimaging-based brain age estimation as a biomarker for Alzheimer's Disease related neurodegeneration. Neuroimage 263, 119621. 2.   Zhang, F., and Gou, J. (2022), A Unified Framework for Estimation in Lognormal Models. Journal of Business & Economic Statistics, 40(4), 1583-1595. 3.    Niu, X., Gou, J., Chang, H., Lowe, M., and Zhang, F. (2022), Classification model with weighted regularization to improve the reproducibility of neuroimaging signature selection. Statistics in Medicine. 4.    Niu, X., Taylor, A., Shinohara, R., Kounios, J., and Zhang, F. (2022), Multidimensional brain-age prediction reveals altered brain developmental trajectory in psychiatric disorders. Cerebral Cortex. 5.    Zhang, F., and Gou, J. (2022), Machine learning assessment of risk factors for depression in later adulthood. The Lancet Regional Health–Europe, 18. 6.    Goldstein, S.P., Zhang, F., Klasnja, P., Hoover, A., Wing, R.R., and Thomas, J.G. (2022), Optimizing Just-in-Time Adaptive Intervention to Improve Dietary Adherence in Behavioral Obesity Treatment: Study Protocol for a Micro-randomized Trial. JMIR Research Protocols 10(2), e33568.    7.    Landrigan, J., Zhang, F., and Mirman, D. (2021), A Data-Driven Approach to Post-Stroke Aphasia Classification and Lesion-Based Prediction. Brain 144(5), 1372-1383. 8.    Zhang, F., and Gou, J. (2021), Refined Critical Boundary with Enhanced Statistical Power for Non-Directional Two-Sided Tests in Group Sequential Designs with Multiple Endpoints. Statistical Papers 62(3), 1265-1290. 9.    Niu, X., Zhang, F., Kounios, J., and Liang, H. (2020), Improved Prediction of Brain Age Using Multimodal Neuroimaging Data. Human Brain Mapping 41(6), 1626-1643. 10.  Oh, Y., Chesebrough, C., Erickson, B., Zhang, F., and Kounios, J. (2020), An Insight-Related Neural Reward Signal. NeuroImage 214, 116757. 11.  Juarascio, A.S., Crochiere, R.J., Tapera, T.M., Palermo, M., and Zhang, F. (2020), Momentary Changes in Heart Rate Variability Can Detect Risk for Emotional Eating Episodes. Appetite. 12.  Rosen, D.S., Oh, Y., Erickson, B., Zhang, F., Kim, Y., and Kounios, J. (2020), Dual-Process Contributions to Creativity in Jazz Improvisations: An SPM-EEG Study. NeuroImage 213, 116632. 13.   Benson, L., Zhang, F., Espel-Huynh, H., Wilkinson, L., and Lowe, M.R. (2020), Weight Variability During Self-Monitored Weight Loss Predicts Future Weight Loss Outcome. International Journal of Obesity 44, 1360-1367. 14.   Apollonsky, N., Lerner, N., Zhang, F., Raybagkar, D., Eng, J., and Tarazi, R. (2020), Laboratory Biomarkers, Cerebral Blood Flow Velocity and Intellectual Function in Children with Sickle Cell Disease. Advances in Hematology 2020, 9. 15.   Manasse, S.M., Lampe, E.W., Gillikin, L., Payne-Reichert, A., Zhang, F., Juarascio, A.S., and Forman, E.M. (2020), The Project REBOOT Protocol: Evaluating a Personalized Inhibitory Control Training as an Adjunct to Cognitive Behavioral Therapy for Bulimia Nervosa and Binge Eating Disorders. International Journal of Eating Disorders 53(6), 1007-1013. 16.   Espel-Huynh, H., Thompson-Brenner, H., Boswell, J.F., Zhang, F., Juarascio, A.S., and Lowe, M.R. (2020). Development and Validation of a Progress Monitoring Tool Tailored for Use in Intensive Eating Disorder Treatment. European Eating Disorders Review 28(2), 223-236. 17.    Zhang, F., and Gou, J. (2019), Refined Critical Boundary with Enhanced Statistical Power for Non-Directional Two-Sided Tests in Group Sequential Designs with Multiple Endpoints. Statistical Papers. 18.   Zhang, F., and Gou, J. (2019), Control of False Positive Rates in Clusterwise fMRI Inferences. Journal of Applied Statistics 46(11), 1956-1972. 19.    Zhang, F., Wang, J.-P., Jiang, W. (2019). An Integrative Classification Model for Multiple Sclerosis Lesion Detection in Multimodal MRI. Statistics and Its Interface 12(2), 193-202. 20.    Liang, H., Zhang, F., and Niu, X. (2019) Investigating Systematic Bias in Brain Age Estimation with Application to PTSD. Human Brain Mapping 40(11), 3143-3152. 21.    Wang, L., Heywood, A., Stocks, J., Bae, J., Ma, D., Popuri, K., Toga A., Kantarci, K., Younes, L., Mackenzie, I.R., Beg, M.F., Zhang, F., and Rosen, H. (2019), Grant Report on PREDICT-ADFTD: Multimodal Imaging Prediction of AD/FTD and Differential Diagnosis. Journal of Psychiatry and Brain Science 4, e190017. 22.    Butryn, M.L., Godfrey, K., Martinelli, M., Roberts, S.R., Forman, E.M., and Zhang, F. (2019), Digital Self-Monitoring: Does Adherence or Association with Outcomes Differ by Self-Monitoring Target? Obesity Science & Practice 6(2), 126-133. 23.    Low M.Y., Lacson, C., Zhang, F., Kesslick, A., and Bradt, J. (2019), Vocal Music Therapy for Chronic Pain: A Mixed Methods Feasibility Study. Journal of Alternative and Complementary Medicine 26(2), 113-122. 24.    Kerrigan, S.G., Forman, E.M., Patel, M., Williams, D., Zhang, F., Crosby, R., and Butryn, M.L. (2019), Evaluating the Feasibility, Acceptability, and Effects of Deposit Contracts with and without Daily Feedback to Promote Physical Activity. Journal of Physical Activity & Health 17(1), 29-36. 25.    Forman, E.M., Goldstein, S.P., Crochiere, R.J., Butryn, M.L., Juarascio, A.S., Zhang, F., and Foster, G.D. (2019), Randomized Controlled Trial of OnTrack, a Just-in-Time Adaptive Intervention Designed to Enhance Weight Loss. Translational Behavioral Medicine 9(6), 989-1001. 26.    Hamner, T., Hepburn, S., Zhang, F., Fidler, D., Robinson Rosenberg, C., Robins, D.L., and Lee, N.R. (2019), Cognitive and Autism Symptom Profiles in Comorbid Down Syndrome and Autism Spectrum Disorder. Journal of Developmental & Behavioral Pediatrics 41(3), 172-179. 27.    Godfrey, M., Hepburn, S., Fidler, D., Tapera, T., Zhang, F., Robinson, C., Lee, N.R. (2019). Autism spectrum disorder (ASD) symptom profiles of children with comorbid Down syndrome (DS) and ASD: A comparison with children with DS-only and ASD-only. Research in Developmental Disabilities 89, 83-93. 28.    Schumacher, L.M., Kerrigan, S.G., Remmert, J.E., Call, C.C., Zhang, F., & Butryn, M.L. (2019). I think therefore I am? Examining the relationship between exercise identity and exercise behavior during behavioral weight loss treatment. Psychology of Sport & Exercise 43, 123-127. 29.    Butryn, M.L., Martinelli, M.K., Remmert, J.E., Roberts, S.R., Zhang, F., Forman, E.M., & Manasse, S.M. (2019). Executive functioning as a predictor of weight loss and physical activity outcomes. Annals of Behavioral Medicine 53(10), 909-917. 30.    Duan, H., Wang, X., Wang, Z., Xue, W., Kan, Y., Hu, W., and Zhang, F. (2019), Acute Stress Shapes Creative Cognition in Trait Anxiety. Frontiers in Psychology 10, 1517. 31.    Lowe, M.R., Marmorstein, N., Iacono, W., Rosenbaum, D., Espel-Huynh, H., Muratore, A. F., Lantz, E., and Zhang, F. (2019). Body concerns and BMI as predictors of disordered eating and body mass in girls: An 18-year longitudinal investigation. Journal of Abnormal Psychology 128(1), 32-43. 32.    Zhang, F., Tapera, T.M., and Gou, J. (2018), Application of a New Dietary Pattern Analysis Method in Nutritional Epidemiology. BMC Medical Research Methodology 18, 119. 33.    Zhang, F., Yang, E., Niu, X., and Zhu Y. (2018). Joint Modeling of the Association between NIH Funding and Its Three Primary Outcomes: Patents, Publications, and Citation Impact. Scientometrics 117(1), 591-602. 34.    Goldstein, S.P., Zhang, F., Thomas, J.G., Butryn, M.L., Herbert, J.D., and Forman, E.M. (2018). Application of Machine Learning to Predict Dietary Lapses During Weight Loss. Journal of Diabetes Sciences and Technology 12(5), 1045-1052. 35.    Lowe, M.R., Butryn, M.L., and Zhang, F. (2018). Evaluation of Meal Replacements and a Home Food Environment Intervention for Long-term Weight Loss: A Randomized Controlled Trial. The American Journal of Clinical Nutrition 107(1), 12-19. 36.    Forman, E.M., Goldstein, S.P., Zhang, F., Evans, B. C., Manasse S.M., Butryn, M.L., Juarascio, A.S., Abichandani, P., Martin, G.J., and Foster, G.D. (2018). OnTrack: Development and Feasibility of a Smartphone App Designed to Predict and Prevent Dietary Lapses. Translational Behavioral Medicine 9, 236-245. 37.    Erickson, B., Truelove-Hill, M., Oh, Y., Anderson, J., Zhang, F., Kounios, J. (2018). Resting-State Brain Oscillations Predict Trait-like Cognitive Styles. Neuropsychologia 120, 1-8. 38.    Call, C.C., Schumacher, L.M., Rosenbaum, D.L., Convertino, A.D., Zhang, F., Butryn, M.L. (2018). Participant and interventionist perceptions of challenges during behavioral weight loss treatment. Journal of Behavioral Medicine, 1-12. 39.    Zhang, F. (2017). Resting-state functional connectivity abnormalities in adolescent depression. EBioMedicine 17, 20-21. 40.    Gou, J., and Zhang, F. (2017), Experience Simpson's Paradox in the Classroom. The American Statistician 71(1), 61-66. 41.    Manasse, S.M., Flack, D., Dochat, C., Zhang, F., Butryn, M.L., Forman, E.M. (2017), Not so fast: The Impact of Impulsivity on Weight Loss Varies by Treatment Type. Appetite 113, 193-199. 42.    Rosenbaum, D.L., Espel, H.M., Butryn, M., Zhang, F., and Lowe, M.R. (2017). Daily self-weighing and weight gain prevention: A longitudinal study of college-aged women. Journal of Behavioral Medicine 40(5), 846-853. 43.    Butryn, M.L., Forman, E.M., Lowe, M.R., Gorin, A., Zhang, F., and Schaumberg, K. (2017). Efficacy of environmental and acceptance-based enhancements to behavioral weight loss treatment: the ENACT trial. Obesity 25(5), 866-872. 44.    Goldstein, S.P., Evans, B.C., Flack, D., Juarascio, A.S., Manasse, S.M., Zhang, F., and Forman, E.M. (2017). Return of the JITAI: Applying a just-in-time adaptive intervention framework to the development of m-Health solutions for addictive behaviors. International Journal of Behavioral Medicine 24(5), 673-682. 45.    Zhang, F., Jiang, W., Wong, P.C.M., and Wang, J.-P. (2016), Bayesian Probit Model with Spatially Varying Coefficients and Its Application to Functional Magnetic Resonance Imaging. Statistics in Medicine 35(24), 4380-4397. 46.    Zhang, F., and Gou, J. (2016), A P-value Model for Theoretical Power Analysis and its Applications in Multiple Testing Procedures. BMC Medical Research Methodology 16, 135. 47.    Schumacher, L.M., Gaspar, M.E., Remmert, J., Zhang, F., Forman, E.M., and Butryn, M.L. (2016), Small Weight Gains During Obesity Treatment: Normative or Cause for Concern? Obesity Science & Practice 2(4), 366-375. 48.    Manasse, S.M., Espel, H.M., Kerrigan, S.G., Schumacher, L.M., Zhang, F., Forman, E.M.,  and Juarascio, A.S. (2016), Does Impulsivity Predict Treatment Outcome for Binge Eating Disorder? A multimodal investigation. Appetite 105, 172-179. 49.    Viswanathan, V., Shultz, D., Block M., Blood A.J., Breiter, H.C., Calder B., Chamberlain L., Lee N., Livengood S., Mulhern, F., Raman, K., Stern, D.B., and Zhang, F. (2016), Using fMRI Analysis to Unpack a Portion of Prospect Theory for Advertising/Marketing Understanding. Rediscovering the Essentiality of Marketing, 453-470. 50.    Zhang, F., Wang, J.-P., Kim, J., Todd, P., and Wong, P.C.M. (2015), Decoding Multiple Sound Categories in the Human Temporal Cortex Using High Resolution fMRI. PLOS ONE 10(2), e0117303. 51.    Liang, J., Hong, D., Zhang, F., and Zou, J. (2015), IMSmining: A Tool for Imaging Mass Spectrometry Data Biomarker Selection and Classification. Springer Proceedings in Mathematics & Statistics 139, 155-162. 52.    Manasse, S.M., Espel, H.M., Forman, E.M., Juarascio, A.S., Butryn, M.L., Ruocco, A.C., Zhang, F., and Lowe, M.R. (2015), The Independent and Interacting Effects of Hedonic Hunger and Executive Fuction on Binge Eating. Appetite 89, 16-21. 53.    Block, M.P., Schultz, D.E., Breiter, H., Blood, A., Calder, B., Chamberlain, L., and Zhang, F. (2015), Redefining neuromarketing. In: American Academy of Advertising Conference. Proceedings in American Academy of Advertising, 53. 54.    Breiter, H.C., Block M., Blood A.J., Calder B., Chamberlain L., Lee N., Livengood S., Mulhern, F., Raman, K., Shultz, D., Stern, D.B., Viswanathan, V., and Zhang, F*. (2014), Redefining Neuromarketing as an Integrated Science of Influence. Frontiers in Human Neuroscience 8, 1073. *Co-first author. 55.    Zhang, F., and Hong, D. (2011), Elastic Net Based Framework for Imaging Mass Spectrometry Data Biomarker Selection and Classification. Statistics in Medicine 30, 753-768. 56.    Hong, D., and Zhang, F. (2010), Weighted Elastic Net Model for Mass Spectrometry Imaging Processing. Journal of Mathematical Modeling of Natural Phenomena 5(3), 115-133. 57.    Hong, D., Qin, S.Y., and Zhang, F. (2010), Mathematical Tools and Statistical Techniques for Proteomic Data Mining. International Journal of Mathematics and Computer Science 5(2), 123-140. Invited Presentations 1.   Zhang, F., (2022, September). Data Analytic Strategies for Handling Big Data Sets. Invited talk at the Eating Disorders Research Society Conference, Philadelphia, PA. 2.   Zhang, F., (2022, June). Statistical Modeling Issues in Brain Age Prediction. Invited talk at the 5th International Conference on Econometrics and Statistics (EcoSta), Kyoto, Japan. 3.   Zhang, F., (2021, May). Penalized Multi-state Models for Examining Multimodal Imaging Signatures of Alzheimer's Disease. Invited talk presented at the Statistical Methods in Imaging Conference, Atlanta, GA. 4.    Zhang, F., (2021, April). Machine Learning for Wearables and Smart Devices. Invited talk presented at the Rehabilitation Sciences Research Seminar, Drexel University, Philadelphia, PA. 5.    Zhang, F., Heywood, A., Stocks, J.K., Wang, L. (2020, August). Multi-state Markov Transition Models for Examining Multimodal Imaging Signatures of Alzheimer's Disease. Invited talk presented at the 2020 Joint Statistical Meetings (JSM), Philadelphia, PA. 6.    Zhang, F., Niu, X., and Liang, H. (2019, December). Brain Age Prediction in Adolescents with Anxiety Disorders: A Multi-modal Brain Imaging Study. Invited talk presented at the 11th ICSA International Conference, Hangzhou, China. 7.    Zhang, F., Juarascio, A., and Forman, E. (2019, November). Improved Modeling of Multi-Sensor Mobile Health Data. Invited talk presented at the Biostatistics Seminar, Drexel University, Philadelphia, PA. 8.    Zhang, F., Tapera, T.M., and Juarascio, A. (2019, August). Statistical Modeling for Integrating Data from Multiple Wearable Sensors to Detect Affect Lability. Paper presented at the 2019 Joint Statistical Meetings (JSM), Denver, CO. 9.    Zhang, F., Tapera, T.M., and Juarascio, A. (2019, March). Statistical Modeling for Integrating Data from Multiple Wearable Sensors to Detect Affect Lability. Paper presented at the Eastern North American Region (ENAR) of the International Biometrics Society Meeting, Philadelphia, PA. 10.  Zhang, F., and Niu, X. (2018, December). Hierarchical Bayesian Models for Integrating Multimodal Neuroimaging Data. Paper presented at the 11th International Conference of the ERCIM WG on Computational and Methodological Statistics, Pisa, Italy. 11.  Zhang, F. (2018, October). A Big Data Approach to Understanding Complex Behavioral and Neuroimaging Data. Invited talk presented at the Biostatistics Seminar, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA. 12.  Zhang, F. (2018, July). A Big Data Approach to Understanding Complex Behavioral and Neuroimaging Data. Seminar talk at Science Technology & Teaching Forum, Key Lab of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi’an, China. 13.  Zhang, F., and Gou, J. (2018, July). Control of False-Positive Rates in Clusterwise fMRI Inferences. Poster presented at the ICSA China Conference with the Focus on Data Science, Qingdao, China. 14.    Zhang, F., and Niu, X. (2018, June). Joint Modeling of Multimodal Neuroimaging Signatures of PTSD. Paper presented at the ICSA Applied Statistics Symposium, New Brunswick, NJ. 15.    Zhang, F., and Niu, X. (2018, June). An Integrative Model for Assessing Multimodal Neuroimaging Signatures of Post-traumatic Stress Disorder. Paper presented at the Statistical Methods in Imaging Conference, Philadelphia, PA. 16.    Zhang, F., Tapera, T.M., Goldstein, S.P., and Forman, E. (2018, March). Improved Modeling of Smartphone-based Ecological Momentary Assessment Data for Dietary Lapse Prediction. Paper presented at the Eastern North American Region (ENAR) of the International Biometrics Society Meeting, Atlanta, GA. 17.    Zhang, F., Tapera, T.M., Goldstein, S.P., and Forman, E. (2017, December). Development of a Smpartphone App and Machine Learning Algorithms to Predict and Prevent Dietary Lapses. Talk presented at the Wearable Computing Group, mHealth Group Seminar, University of Pennsylvania, PA. 18.    Zhang, F. (2017, October). Statistical Modeling for High Dimensional Structured Data with Application to Neuroimaging. Paper presented at the Philadelphia Big Data Symposium, Philadelphia, PA. 19.    Zhang, F., and Niu, X. (2017, July). An Integrative Model for Assessing Multimodal Neuroimaging Signatures of Post-traumatic Stress Disorder. Paper presented at the 2017 Joint Statistical Meetings (JSM), Baltimore, MD. 20.    Zhang, F. (2017, May). Multimodal Neuroimaging, Wearable Computing, and Big Data Integration. Talk presented at the SMART group seminar, Johns Hopkins University, Baltimore, MD. 21.    Zhang, F. (May 2016). Statistical Modeling for High Dimensional Biomedical Imaging Data. Paper presented at Drexel University Math Department Seminar, Philadelphia, PA. 22.    Zhang, F., Jiang, W., and Wang, J.-P. (2015, October). Analytical Modeling for High Dimensional Structured Neuroimaging Data. Seminar talk at the Brain Behavior Lab, University of Pennsylvania, Philadelphia, PA. 23.    Zhang, F. (2015, June). New Statistical Methods for High Dimensional Biomedical Imaging Data Analysis. Paper presented at the Biological Discovery from Big Data Workshop, Philadelphia, PA. 24.    Zhang, F. (2015, April). Statistical Modeling for High Dimensional Structured Data with Application to Neuroimaging. Talk presented at the College of Arts and Sciences Dean’s Seminar Series, Drexel University, Philadelphia. 25.    Zhang, F., Jiang, W., and Wang, J.-P. (2014, September). Bayesian Probit Model with Spatially Varying Coefficients and Its Application to Functional Magnetic Resonance Imaging. Paper presented at the Imaging Genetics Seminar, University of Pennsylvania, Philadelphia, PA. 26.    Zhang, F., and Hong, D. (2011, March). Imaging Mass Spectrometry Data Biomarker Selection and Classification. Paper presented at the Statistics Department Seminar, Northwestern University, Evanston, IL. 27.    Zhang, F., and Hong, D. (2009, October). Recent Progress on Biomarker Selection of IMS Data. Paper presented at the Bio-math Seminar, Math Department, Middle Tennessee State University, Murfreesboro, TN. 28.    Zhang, F., and Hong, D. (2009, May). Variable Selection Methods for IMS Data Analysis. Paper presented at the Mass Spectrometry Research Center Seminar, Vanderbilt University, Nashville, TN.