Liang Zhan

Morrissey, Z.D., Gao, J., Shetti, A., Li, W., Zhan, L., Li, W., Fortel, I., Saido, T., Saito, T., Ajilore, O., Cologna, S.M., Lazarov, O., & Leow, A.D. (2024). Temporal Alterations in White Matter in An App Knock-In Mouse Model of Alzheimer's Disease. ENEURO, 11(2), eneuro.0496-eneu23.2024.Society for Neuroscience. doi: 10.1523/ENEURO.0496-23.2024.

Ortiz-Whittingham, L.R., Zhan, L., Ortiz-Chaparro, E.N., Baumer, Y., Zenk, S., Lamar, M., & Powell-Wiley, T.M. (2024). Neighborhood Perceptions Are Associated With Intrinsic Amygdala Activity and Resting-State Connectivity With Salience Network Nodes Among Older Adults. PSYCHOSOMATIC MEDICINE, 86(2), 116-123.Wolters Kluwer. doi: 10.1097/PSY.0000000000001272.

Tang, H., Dai, S., Zou, E.M., Liu, G., Ahearn, R., Krafty, R., Modo, M., & Zhan, L. (2024). Ex-Vivo Hippocampus Segmentation Using Diffusion-Weighted MRI. MATHEMATICS, 12(7), 940.MDPI. doi: 10.3390/math12070940.

Tang, H., Ma, G., Guo, L., Fu, X., Huang, H., & Zhang, L. (2024). Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 35(6), 7363-7375.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TNNLS.2022.3220220.

Banihashemi, L., Lv, J., Wu, M., & Zhan, L. (2023). Editorial: Current advances in multimodal human brain imaging and analysis across the lifespan: From mapping to state prediction. FRONTIERS IN NEUROSCIENCE, 17, 1153035.Frontiers. doi: 10.3389/fnins.2023.1153035.

Cherloo, M.N., Mijani, A.M., Zhan, L., & Daliri, M.R. (2023). A novel multiclass-based framework for P300 detection in BCI matrix speller: Temporal EEG patterns of non-target trials vary based on their position to previous target stimuli. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 123, 106381.Elsevier. doi: 10.1016/j.engappai.2023.106381.

Cui, H., Dai, W., Zhu, Y., Kan, X., Gu, A.A.C., Lukemire, J., Zhan, L., He, L., Guo, Y., & Yang, C. (2023). BrainGB: A Benchmark for Brain Network Analysis With Graph Neural Networks. IEEE TRANSACTIONS ON MEDICAL IMAGING, 42(2), 493-506.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TMI.2022.3218745.

Fortel, I., Zhan, L., Ajilore, O., Wu, Y., Mackin, S., & Leow, A. (2023). Disrupted excitation-inhibition balance in cognitively normal individuals at risk of Alzheimer’s disease. bioRxiv, 4(09-01), 2023.08.21.554061.Cold Spring Harbor Laboratory. doi: 10.1101/2023.08.21.554061.

Fortel, I., Zhan, L., Ajilore, O., Wu, Y., Mackin, S., & Leow, A. (2023). Disrupted Excitation-Inhibition Balance in Cognitively Normal Individuals at Risk of Alzheimer's Disease. JOURNAL OF ALZHEIMERS DISEASE, 95(4), 1449-1467.IOS Press. doi: 10.3233/JAD-230035.

Fu, X., Sun, Z., Tang, H., Zou, E.M., Huang, H., Wang, Y., & Zhan, L. (2023). 3D bi-directional transformer U-Net for medical image segmentation. FRONTIERS IN BIG DATA, 5, 1080715.Frontiers. doi: 10.3389/fdata.2022.1080715.

Jia, H., Tang, H., Ma, G., Cai, W., Huang, H., Zhan, L., & Xia, Y. (2023). A convolutional neural network with pixel-wise sparse graph reasoning for COVID-19 lesion segmentation in CT images. COMPUTERS IN BIOLOGY AND MEDICINE, 155, 106698.Elsevier. doi: 10.1016/j.compbiomed.2023.106698.

Manos, T., Diaz-Pier, S., Fortel, I., Driscoll, I., Zhan, L., & Leow, A. (2023). Enhanced simulations of whole-brain dynamics using hybrid resting-state structural connectomes. FRONTIERS IN COMPUTATIONAL NEUROSCIENCE, 17, 1295395.Frontiers. doi: 10.3389/fncom.2023.1295395.

Mijani, A., Cherloo, M.N., Tang, H., & Zhan, L. (2023). Spectrum-Enhanced TRCA (SE-TRCA): A novel approach for direction detection in SSVEP-based BCI. COMPUTERS IN BIOLOGY AND MEDICINE, 166, 107488.Elsevier. doi: 10.1016/j.compbiomed.2023.107488.

Morrissey, Z.D.D., Gao, J., Zhan, L., Li, W., Fortel, I., Saido, T., Saito, T., Bakker, A., Mackin, S., Ajilore, O., Lazarov, O., & Leow, A.D.D. (2023). Hippocampal functional connectivity across age in an App knock-in mouse model of Alzheimer's disease. FRONTIERS IN AGING NEUROSCIENCE, 14, 1085989.Frontiers. doi: 10.3389/fnagi.2022.1085989.

Shirkavand, R., Zhan, L., Huang, H., Shen, L., & Thompson, P.M. (2023). Incomplete Multimodal Learning for Complex Brain Disorders Prediction.

Tang, H., Guo, L., Fu, X., Wang, Y., Mackin, S., Ajilore, O., Leow, A.D., Thompson, P.M., Huang, H., & Zhan, L. (2023). Signed graph representation learning for functional-to-structural brain network mapping. MEDICAL IMAGE ANALYSIS, 83, 102674.Elsevier. doi: 10.1016/j.media.2022.102674.

Boots, E.A., Zhan, L., Castellanos, K.J., Barnes, L.L., Tussing-Humphreys, L., & Lamar, M. (2022). Inflammatory markers and tract-based structural connectomics in older adults with a preliminary exploration of associations by race. BRAIN IMAGING AND BEHAVIOR, 16(1), 130-140.Springer Nature. doi: 10.1007/s11682-021-00483-y.

Fortel, I., Butler, M., Korthauer, L.E., Zhan, L., Ajilore, O., Sidiropoulos, A., Wu, Y., Driscoll, I., Schonfeld, D., & Leow, A. (2022). Inferring excitation-inhibition dynamics using a maximum entropy model unifying brain structure and function. NETWORK NEUROSCIENCE, 6(2), 420-444.MIT Press. doi: 10.1162/netn_a_00220.

Tang, H., Fu, X., Guo, L., Wang, Y., Mackin, S., Ajilore, O., Leow, A., Thompson, P., Huang, H., & Zhan, L. (2022). Functional2Structural: Cross-Modality Brain Networks Representation Learning.

Tang, H., Guo, L., Fu, X., Qu, B., Ajilore, O., Wang, Y., Thompson, P.M., Huang, H., Leow, A.D., & Zhan, L. (2022). A Hierarchical Graph Learning Model for Brain Network Regression Analysis. FRONTIERS IN NEUROSCIENCE, 16, 963082.Frontiers. doi: 10.3389/fnins.2022.963082.

Tang, H., Guo, L., Fu, X., Qu, B., Thompson, P.M., Huang, H., & Zhan, L. (2022). Hierarchical Brain Embedding Using Explainable Graph Learning. 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 00, 1-5.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/isbi52829.2022.9761543.

Yu, J., Kong, Z., Zhan, L., Shen, L., & He, L. (2022). Tensor-Based Multi-Modality Feature Selection and Regression for Alzheimer's Disease Diagnosis. Computer Science and Information Technology, 12(18), 123-134.Academy and Industry Research Collaboration Center (AIRCC). doi: 10.5121/csit.2022.121812.

Morrissey, Z.D., Zhan, L., Ajilore, O., & Leow, A.D. (2021). rest2vec: Vectorizing the resting-state functional connectome using graph embedding. NEUROIMAGE, 226, 117538.Elsevier. doi: 10.1016/j.neuroimage.2020.117538.

Tang, H., Jia, H., Cai, W., Huang, H., Xia, Y., & Zhan, L. (2021). Boundary-aware Graph Reasoning for Semantic Segmentation.

Tang, H., Ma, G., He, L., Huang, H., & Zhan, L. (2021). CommPOOL: An interpretable graph pooling framework for hierarchical graph representation learning. NEURAL NETWORKS, 143, 669-677.Elsevier. doi: 10.1016/j.neunet.2021.07.028.

Boots, E.A., Castellanos, K.J., Zhan, L., Barnes, L.L., Tussing-Humphreys, L., Deoni, S.C.L., & Lamar, M. (2020). Inflammation, Cognition, and White Matter in Older Adults: An Examination by Race. FRONTIERS IN AGING NEUROSCIENCE, 12, 553998.Frontiers. doi: 10.3389/fnagi.2020.553998.

Fortel, I., Korthauer, L.E., Morrissey, Z., Zhan, L., Ajilore, O., Wolfson, O., Driscoll, I., Schonfeld, D., & Leow, A. (2020). Connectome Signatures of Hyperexcitation in Cognitively Intact Middle-Aged Female APOE-ε4 Carriers. CEREBRAL CORTEX, 30(12), 6350-6362.Oxford University Press (OUP). doi: 10.1093/cercor/bhaa190.

Morrissey, Z.D., Zhan, L., Ajilore, O., & Leow, A.D. (2020). rest2vec: Vectorizing the resting-state functional connectome using graph embedding. 2020.05.10.085332.Cold Spring Harbor Laboratory. doi: 10.1101/2020.05.10.085332.

Smagula, S.F., Stahl, S.T., Santini, T., Banihashemi, L., Hall, M.H., Ibrahim, T.S., Reynolds, C.F.I.I.I., Krafty, R.T., Aizenstein, H.J., & Zhan, L. (2020). White Matter Integrity Underlying Depressive Symptoms in Dementia Caregivers. AMERICAN JOURNAL OF GERIATRIC PSYCHIATRY, 28(5), 578-582.Elsevier. doi: 10.1016/j.jagp.2019.11.010.

Tang, H., Ma, G., Chen, Y., Guo, L., Wang, W., Zeng, B., & Zhan, L. (2020). Adversarial Attack on Hierarchical Graph Pooling Neural Networks.

Wei, L., Zhan, L., Cao, J., & Wang, W. (2020). Improving the energy resolution of the reactor antineutrino energy reconstruction with positron direction. Radiation Detection Technology and Methods, 4(3), 356-361.Springer Nature. doi: 10.1007/s41605-020-00191-z.

Zhan, L., Le, Q., Feng, Z., Lou, Y., & Li, H. (2020). Microstructures and Mechanical Properties of Mg-4.5Gd- 2.6Nd-0.5Zn-0.5Zr New Casting Alloy. Xiyou Jinshu Cailiao Yu Gongcheng/Rare Metal Materials and Engineering, 49(8), 2644-2648.

Zhan, L., Le, Q.C., Feng, Z.J., Yue, Y., & Ma, Y.B. (2020). Microstructure and Mechanical Properties of Mg-2.6Nd-1.5Gd-0.5Zn- 0.5Zr Casting Magnesium Alloy. Zhuzao/Foundry, 69(12), 1298-1303.

Adey, D., An, F.P., Balantekin, A.B., Band, H.R., Bishai, M., Blyth, S., Cao, D., Cao, G.F., Cao, J., Chang, J.F., Chang, Y., Chen, H.S., Chen, S.M., Chen, Y., Chen, Y.X., Cheng, J., Cheng, Z.K., Cherwinka, J.J., Chu, M.C., Chukanov, A., Cummings, J.P., Dash, N., Deng, F.S., Ding, Y.Y., Diwan, M.V., Dohnal, T., Dove, J., Dvořák, M., Dwyer, D.A., Gonchar, M., Gong, G.H., Gong, H., Gu, W.Q., Guo, J.Y., Guo, L., Guo, X.H., Guo, Y.H., Guo, Z., Hackenburg, R.W., Hans, S., He, M., Heeger, K.M., Heng, Y.K., Higuera, A., Hor, Y.K., Hsiung, Y.B., Hu, B.Z., Hu, J.R., Hu, T., Hu, Z.J., Huang, H.X., Huang, X.T., Huang, Y.B., Huber, P., Jaffe, D.E., Jen, K.L., Jetter, S., Ji, X.L., Ji, X.P., Johnson, R.A., Jones, D., Kang, L., Kettell, S.H., Koerner, L.W., Kohn, S., Kramer, M., Langford, T.J., Lebanowski, L., Lee, J., Lee, J.H.C., Lei, R.T., Leitner, R., Leung, J.K.C., Li, C., Li, F., Li, H.L., Li, Q.J., Li, S., Li, S.C., Li, S.J., Li, W.D., Li, X.N., Li, X.Q., Li, Y.F., Li, Z.B., Liang, H., Lin, C.J., Lin, G.L., Lin, S., Lin, S.K., Ling, J.J., Link, J.M., Littenberg, L., Littlejohn, B.R., Liu, J.C., Liu, J.L., Liu, Y., Liu, Y.H., Lu, C., Lu, H.Q., Lu, J.S., Luk, K.B., Ma, X.B., Ma, X.Y., Ma, Y.Q., Marshall, C., Caicedo, D.A.M., McDonald, K.T., McKeown, R.D., Mitchell, I., Lepin, L.M., Napolitano, J., Naumov, D., Naumova, E., Ochoa-Ricoux, J.P., Olshevskiy, A., Pan, H.R., Park, J., Patton, S., Pec, V., Peng, J.C., Pinsky, L., Pun, C.S.J., Qi, F.Z., Qi, M., Qian, X., Raper, N., Ren, J., Rosero, R., Roskovec, B., Ruan, X.C., Steiner, H., Sun, J.L., Treskov, K., Tse, W.H., Tull, C.E., Viren, B., Vorobel, V., Wang, C.H., Wang, J., Wang, M., Wang, N.Y., Wang, R.G., Wang, W., Wang, W., Wang, X., Wang, Y., Wang, Y.F., Wang, Z., Wang, Z., Wang, Z.M., Wei, H.Y., Wei, L.H., Wen, L.J., Whisnant, K., White, C.G., Wong, H.L.H., Wong, S.C.F., Worcester, E., Wu, Q., Wu, W.J., Xia, D.M., Xing, Z.Z., Xu, J.L., Xue, T., Yang, C.G., Yang, L., Yang, M.S., Yang, Y.Z., Ye, M., Yeh, M., Young, B.L., Yu, H.Z., Yu, Z.Y., Yue, B.B., Zeng, S., Zeng, Y., Zhan, L., Zhang, C., Zhang, C.C., Zhang, F.Y., Zhang, H.H., Zhang, J.W., Zhang, Q.M., Zhang, R., Zhang, X.F., Zhang, X.T., Zhang, Y.M., Zhang, Y.M., Zhang, Y.X., Zhang, Y.Y., Zhang, Z.J., Zhang, Z.P., Zhang, Z.Y., Zhao, J., Zhou, L., Zhuang, H.L., & Zou, J.H. (2019). A high precision calibration of the nonlinear energy response at Daya Bay. Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment, 940, 230-242.Elsevier. doi: 10.1016/j.nima.2019.06.031.

Adey, D., An, F.P., Balantekin, A.B., Band, H.R., Bishai, M., Blyth, S., Cao, D., Cao, G.F., Cao, J., Chang, J.F., Chang, Y., Chen, H.S., Chen, S.M., Chen, Y., Chen, Y.X., Cheng, J., Cheng, Z.K., Cherwinka, J.J., Chu, M.C., Chukanov, A., Cummings, J.P., Dash, N., Deng, F.S., Ding, Y.Y., Diwan, M.V., Dohnal, T., Dove, J., Dvořák, M., Dwyer, D.A., Gonchar, M., Gong, G.H., Gong, H., Gu, W.Q., Guo, J.Y., Guo, L., Guo, X.H., Guo, Y.H., Guo, Z., Hackenburg, R.W., Hans, S., He, M., Heeger, K.M., Heng, Y.K., Higuera, A., Hor, Y.K., Hsiung, Y.B., Hu, B.Z., Hu, J.R., Hu, T., Hu, Z.J., Huang, H.X., Huang, X.T., Huang, Y.B., Huber, P., Jaffe, D.E., Jen, K.L., Ji, X.L., Ji, X.P., Johnson, R.A., Jones, D., Kang, L., Kettell, S.H., Koerner, L.W., Kohn, S., Kramer, M., Langford, T.J., Lee, J., Lee, J.H.C., Lei, R.T., Leitner, R., Leung, J.K.C., Li, C., Li, F., Li, H.L., Li, Q.J., Li, S., Li, S.C., Li, S.J., Li, W.D., Li, X.N., Li, X.Q., Li, Y.F., Li, Z.B., Liang, H., Lin, C.J., Lin, G.L., Lin, S., Ling, J.J., Link, J.M., Littenberg, L., Littlejohn, B.R., Liu, J.C., Liu, J.L., Liu, Y., Liu, Y.H., Lu, C., Lu, H.Q., Lu, J.S., Luk, K.B., B., X., Y., X., Q., Y., Marshall, C., Caicedo, D.A.M., McDonald, K.T., McKeown, R.D., Mitchell, I., Lepin, L.M., Napolitano, J., Naumov, D., Naumova, E., Ochoa-Ricoux, J.P., Olshevskiy, A., Pan, H.R., Park, J., Patton, S., Pec, V., Peng, J.C., Pinsky, L., Pun, C.S.J., Qi, F.Z., Qi, M., Qian, X., Raper, N., Ren, J., Rosero, R., Roskovec, B., Ruan, X.C., Steiner, H., Sun, J.L., Treskov, K., Tse, W.H., Tull, C.E., Viren, B., Vorobel, V., Wang, C.H., Wang, J., Wang, M., Wang, N.Y., Wang, R.G., Wang, W., Wang, W., Wang, X., Wang, Y., Wang, Y.F., Wang, Z., Wang, Z., Wang, Z.M., Wei, H.Y., Wei, L.H., Wen, L.J., Whisnant, K., White, C.G., Wong, H.L.H., Wong, S.C.F., Worcester, E., Wu, Q., Wu, W.J., Xia, D.M., Xing, Z.Z., Xu, J.L., Xue, T., Yang, C.G., Yang, L., Yang, M.S., Yang, Y.Z., Ye, M., Yeh, M., Young, B.L., Yu, H.Z., Yu, Z.Y., Yue, B.B., Zeng, S., Zeng, Y., Zhan, L., Zhang, C., Zhang, C.C., Zhang, F.Y., Zhang, H.H., Zhang, J.W., Zhang, Q.M., Zhang, R., Zhang, X.F., Zhang, X.T., Zhang, Y.M., Zhang, Y.M., Zhang, Y.X., Zhang, Y.Y., Zhang, Z.J., Zhang, Z.P., Zhang, Z.Y., Zhao, J., Zhou, L., Zhuang, H.L., & Zou, J.H. (2019). Extraction of the U235 and Pu239 Antineutrino Spectra at Daya Bay. Physical Review Letters, 123(11), 111801.American Physical Society (APS). doi: 10.1103/physrevlett.123.111801.

Boots, E.A., Zhan, L., Dion, C., Karstens, A.J., Peven, J.C., Ajilore, O., & Lamar, M. (2019). Cardiovascular disease risk factors, tract-based structural connectomics, and cognition in older adults. NEUROIMAGE, 196, 152-160.Elsevier. doi: 10.1016/j.neuroimage.2019.04.024.

Carr, R., Coleman, J., Danilov, M., Gratta, G., Heeger, K., Huber, P., Hor, Y., Kawasaki, T., Kim, S.B., Kim, Y., Learned, J., Lindner, M., Nakajima, K., Nikkel, J., Seo, S.H., Suekane, F., Vacheret, A., Wang, W., Wilhelmi, J., & Zhan, L. (2019). Neutrino-Based Tools for Nuclear Verification and Diplomacy in North Korea. Science & Global Security, 27(1), 15-28.Taylor & Francis. doi: 10.1080/08929882.2019.1603007.

Karstens, A.J., Tussing-Humphreys, L., Zhan, L., Rajendran, N., Cohen, J., Dion, C., Zhou, X.J., & Lamar, M. (2019). Associations of the Mediterranean diet with cognitive and neuroimaging phenotypes of dementia in healthy older adults. AMERICAN JOURNAL OF CLINICAL NUTRITION, 109(2), 361-368.Elsevier. doi: 10.1093/ajcn/nqy275.

Peven, J.C., Chen, Y., Guo, L., Zhan, L., Boots, E.A., Dion, C., Libon, D.J., Heilman, K.M., & Lamar, M. (2019). The oblique effect: The relationship between profiles of visuospatial preference, cognition, and brain connectomics in older adults. NEUROPSYCHOLOGIA, 135, 107236.Elsevier. doi: 10.1016/j.neuropsychologia.2019.107236.

Carr, R., Coleman, J., Gratta, G., Heeger, K., Huber, P., Hor, Y., Kawasaki, T., Kim, S.B., Kim, Y., Learned, J., Lindner, M., Nakajima, K., Seo, S.H., Suekane, F., Vacheret, A., Wang, W., & Zhan, L. (2018). Neutrino physics for Korean diplomacy. In Sills, J. (Ed.). Science, 362(6415), 649.2-64650.American Association for the Advancement of Science (AAAS). doi: 10.1126/science.aav8136.

Conrin, S.D., Zhan, L., Morrissey, Z.D., Xing, M., Forbes, A., Maki, P., Milad, M.R., Ajilore, O., & Leow, A.D. (2018). Sex-by-age differences in the resting-state brain connectivity.

Conrin, S.D., Zhan, L., Morrissey, Z.D., Xing, M., Forbes, A., Maki, P., Milad, M.R., Ajilore, O., Langenecker, S.A., & Leow, A.D. (2018). From Default Mode Network to the Basal Configuration: Sex Differences in the Resting-State Brain Connectivity as a Function of Age and Their Clinical Correlates. FRONTIERS IN PSYCHIATRY, 9(AUG), 365.Frontiers. doi: 10.3389/fpsyt.2018.00365.

Keiriz, J.J.G., Zhan, L., Ajilore, O., Leow, A.D., & Forbes, A.G. (2018). NeuroCave: A web-based immersive visualization platform for exploring connectome datasets. Network Neuroscience, 2(3), 344-361.MIT Press. doi: 10.1162/netn_a_00044.

Korthauer, L.E., Zhan, L., Ajilore, O., Leow, A., & Driscoll, I. (2018). Disrupted topology of the resting state structural connectome in middle-aged APOE ε4 carriers. NEUROIMAGE, 178, 295-305.Elsevier. doi: 10.1016/j.neuroimage.2018.05.052.

Wang, Q., Guo, L., Thompson, P.M., Jack, C.R., Dodge, H., Zhan, L., Zhou, J., & Center, F.T.A.D.N.I.A.N.A.C. (2018). The Added Value of Diffusion-Weighted MRI-Derived Structural Connectome in Evaluating Mild Cognitive Impairment: A Multi-Cohort Validation1. In Zhang, Y. (Ed.). Journal of Alzheimer's Disease, 64(1), 149-169.IOS Press. doi: 10.3233/jad-171048.

Zhan, L., Jenkins, L.M., Zhang, A., Conte, G., Forbes, A., Harvey, D., Angkustsiri, K., Goodrich-Hunsaker, N.J., Durdle, C., Lee, A., Schumann, C., Carmichael, O., Kalish, K., Leow, A.D., & Simon, T.J. (2018). Baseline connectome modular abnormalities in the childhood phase of a longitudinal study on individuals with chromosome 22q11.2 deletion syndrome. HUMAN BRAIN MAPPING, 39(1), 232-248.Wiley. doi: 10.1002/hbm.23838.

Jin, Y., Huang, C., Daianu, M., Zhan, L., Dennis, E.L., Reid, R.I., Jack, C.R., Zhu, H., Thompson, P.M., & Initiative, A.D.N. (2017). 3D tract‐specific local and global analysis of white matter integrity in Alzheimer's disease. Human Brain Mapping, 38(3), 1191-1207.Wiley. doi: 10.1002/hbm.23448.

Nir, T.M., Jahanshad, N., Villalon‐Reina, J.E., Isaev, D., Zavaliangos‐Petropulu, A., Zhan, L., Leow, A.D., Jack, C.R., Weiner, M.W., Thompson, P.M., & Initiative, F.T.A.D.N. (2017). Fractional anisotropy derived from the diffusion tensor distribution function boosts power to detect Alzheimer's disease deficits. Magnetic Resonance in Medicine, 78(6), 2322-2333.Wiley. doi: 10.1002/mrm.26623.

Zhan, L., Jenkins, L.M., Wolfson, O.E., GadElkarim, J.J., Nocito, K., Thompson, P.M., Ajilore, O.A., Chung, M.K., & Leow, A.D. (2017). The significance of negative correlations in brain connectivity. JOURNAL OF COMPARATIVE NEUROLOGY, 525(15), 3251-3265.Wiley. doi: 10.1002/cne.24274.

Zhan, L., Jenkins, L.M., Wolfson, O.E., GadElkarim, J.J., Nocito, K., Thompson, P.M., Ajilore, O.A., Chung, M.K., & Leow, A.D. (2016). The Importance of Being Negative: A serious treatment of non-trivial edges in brain functional connectome.

Zhang, A., Leow, A., Zhan, L., GadElkarim, J., Moody, T., Khalsa, S., Strober, M., & Feusner, J.D. (2016). Brain connectome modularity in weight-restored anorexia nervosa and body dysmorphic disorder. Psychological Medicine, 46(13), 2785-2797.Cambridge University Press (CUP). doi: 10.1017/s0033291716001458.

Ajilore, O., Vizueta, N., Walshaw, P., Zhan, L., Leow, A., & Altshuler, L.L. (2015). Connectome signatures of neurocognitive abnormalities in euthymic bipolar I disorder. Journal of Psychiatric Research, 68, 37-44.Elsevier. doi: 10.1016/j.jpsychires.2015.05.017.

Dennis, E.L., Jin, Y., Villalon-Reina, J.E., Zhan, L., Kernan, C.L., Babikian, T., Mink, R.B., Babbitt, C.J., Johnson, J.L., Giza, C.C., Thompson, P.M., & Asarnow, R.F. (2015). White matter disruption in moderate/severe pediatric traumatic brain injury: Advanced tract-based analyses. NeuroImage Clinical, 7, 493-505.Elsevier. doi: 10.1016/j.nicl.2015.02.002.

Madsen, S.K., Zai, A., Pirnia, T., Arienzo, D., Zhan, L., Moody, T.D., Thompson, P.M., & Feusner, J.D. (2015). Cortical thickness and brain volumetric analysis in body dysmorphic disorder. Psychiatry Research, 232(1), 115-122.Elsevier. doi: 10.1016/j.pscychresns.2015.02.003.

Ye, A.Q., Ajilore, O.A., Conte, G., GadElkarim, J., Thomas-Ramos, G., Zhan, L., Yang, S., Kumar, A., Magin, R.L., G. Forbes, A., & Leow, A.D. (2015). The intrinsic geometry of the human brain connectome. Brain Informatics, 2(4), 197-210.Springer Nature. doi: 10.1007/s40708-015-0022-2.

Ye, A.Q., Zhan, L., Conrin, S., GadElKarim, J., Zhang, A., Yang, S., Feusner, J.D., Kumar, A., Ajilore, O., & Leow, A. (2015). Measuring embeddedness: Hierarchical scale‐dependent information exchange efficiency of the human brain connectome. Human Brain Mapping, 36(9), 3653-3665.Wiley. doi: 10.1002/hbm.22869.

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Jin, Y., Shi, Y., Zhan, L., Gutman, B.A., de Zubicaray, G.I., McMahon, K.L., Wright, M.J., Toga, A.W., & Thompson, P.M. (2014). Automatic clustering of white matter fibers in brain diffusion MRI with an application to genetics. NeuroImage, 100, 75-90.Elsevier. doi: 10.1016/j.neuroimage.2014.04.048.

Leow, A., Harvey, D., Goodrich-Hunsaker, N.J., Gadelkarim, J., Kumar, A., Zhan, L., Rivera, S.M., & Simon, T.J. (2014). Altered Structural Brain Connectome in Young Adult Fragile X Premutation Carriers. HUMAN BRAIN MAPPING, 35(9), 4518-4530.Wiley. doi: 10.1002/hbm.22491.

Ajilore, O., Zhan, L., GadElkarim, J., Zhang, A., Feusner, J.D., Yang, S., Thompson, P.M., Kumar, A., & Leow, A. (2013). Constructing the resting state structural connectome. Frontiers in Neuroinformatics, 7(DEC), 30.Frontiers. doi: 10.3389/fninf.2013.00030.

Arienzo, D., Leow, A., Brown, J.A., Zhan, L., GadElkarim, J., Hovav, S., & Feusner, J.D. (2013). Abnormal Brain Network Organization in Body Dysmorphic Disorder. Neuropsychopharmacology, 38(6), 1130-1139.Springer Nature. doi: 10.1038/npp.2013.18.

Feusner, J.D., Arienzo, D., Li, W., Zhan, L., GadElkarim, J., Thompson, P.M., & Leow, A.D. (2013). White matter microstructure in body dysmorphic disorder and its clinicalcorrelates. Psychiatry Research, 211(2), 132-140.Elsevier. doi: 10.1016/j.pscychresns.2012.11.001.

Leow, A., Ajilore, O., Zhan, L., Arienzo, D., GadElkarim, J., Zhang, A., Moody, T., Van Horn, J., Feusner, J., Kumar, A., Thompson, P., & Altshuler, L. (2013). Impaired Inter-Hemispheric Integration in Bipolar Disorder Revealed with Brain Network Analyses. Biological Psychiatry, 73(2), 183-193.Elsevier. doi: 10.1016/j.biopsych.2012.09.014.

Zhan, L., Jahanshad, N., Ennis, D.B., Jin, Y., Bernstein, M.A., Borowski, B.J., Jack, C.R., Toga, A.W., Leow, A.D., & Thompson, P.M. (2013). Angular versus spatial resolution trade‐offs for diffusion imaging under time constraints. Human Brain Mapping, 34(10), 2688-2706.Wiley. doi: 10.1002/hbm.22094.

Zhan, L., Mueller, B.A., Jahanshad, N., Jin, Y., Lenglet, C., Yacoub, E., Sapiro, G., Ugurbil, K., Harel, N., Toga, A.W., Lim, K.O., & Thompson, P.M. (2013). Magnetic Resonance Field Strength Effects on Diffusion Measures and Brain Connectivity Networks. Brain Connectivity, 3(1), 72-86.Mary Ann Liebert. doi: 10.1089/brain.2012.0114.

Zhang, A., Ajilore, O., Zhan, L., GadElkarim, J., Korthauer, L., Yang, S., Leow, A., & Kumar, A. (2013). White Matter Tract Integrity of Anterior Limb of Internal Capsule in Major Depression and Type 2 Diabetes. Neuropsychopharmacology, 38(8), 1451-1459.Springer Nature. doi: 10.1038/npp.2013.41.

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Zhang, A., Leow, A., Ajilore, O., Lamar, M., Yang, S., Joseph, J., Medina, J., Zhan, L., & Kumar, A. (2012). Quantitative Tract-Specific Measures of Uncinate and Cingulum in Major Depression Using Diffusion Tensor Imaging. Neuropsychopharmacology, 37(4), 959-967.Springer Nature. doi: 10.1038/npp.2011.279.

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Kim, Y., Thompson, P.M., Toga, A.W., Vese, L., & Zhan, L. (2009). HARDI denoising: Variational regularization of the spherical apparent diffusion coefficient sADC. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5636 LNCS, 515-527. doi: 10.1007/978-3-642-02498-6_43.

Leow, A.D., Zhu, S., Zhan, L., McMahon, K., de Zubicaray, G.I., Meredith, M., Wright, M.J., Toga, A.W., & Thompson, P.M. (2009). The tensor distribution function. Magnetic Resonance in Medicine, 61(1), 205-214.Wiley. doi: 10.1002/mrm.21852.

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Hoang, B., Pang, Y., Liang, S., Zhan, L., Thompson, P.M., & Zhou, J. (2024). Distributed Harmonization: Federated Clustered Batch Effect Adjustment and Generalization. In KDD : proceedings International Conference on Knowledge Discovery & Data Mining, 2024, (pp. 5105-5115).Association for Computing Machinery (ACM).United States. doi: 10.1145/3637528.3671590.

Ortiz-Whittingham, L.R., Zhan, L., Ortiz-Chaparro, E.N., Lamar, M., & Powell-Wiley, T. (2022). NEIGHBORHOOD PERCEPTIONS ARE ASSOCIATED WITH AMYGDALAR ACTIVITY AS A MARKER OF CHRONIC STRESS-RELATED NEURAL ACTIVITY. In ANNALS OF BEHAVIORAL MEDICINE, 56(SUPP 1), (p. S504).

Zhang, Y., Zhan, L., Wu, S., Thompson, P., & Huang, H. (2021). Disentangled and Proportional Representation Learning for Multi-view Brain Connectomes. In Lecture Notes in Computer Science, 12907, (pp. 508-518).Springer Nature.Germany. doi: 10.1007/978-3-030-87234-2_48.

Chen, Y., Tang, H., Guo, L., Peven, J.C., Huang, H., Leow, A.D., Lamar, M., & Zhan, L. (2020). A Generalized Framework of Pathlength Associated Community Estimation for Brain Structural Network. In 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 00, (pp. 288-291).Institute of Electrical and Electronics Engineers (IEEE).United States. doi: 10.1109/isbi45749.2020.9098552.

Farazi, M., Zhan, L., Lepore, N., Thompson, P.M., & Wang, Y. (2020). A Univariate Persistent Brain Network Feature Based on the Aggregated Cost of Cycles from the Nested Filtration Networks. In 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 00, (pp. 1-5).Institute of Electrical and Electronics Engineers (IEEE).United States. doi: 10.1109/isbi45749.2020.9098716.

Ganjdanesh, A., Ghasedi, K., Zhan, L., Cai, W., & Huang, H. (2020). Predicting Potential Propensity of Adolescents to Drugs via New Semi-supervised Deep Ordinal Regression Model. In Lecture Notes in Computer Science, 12261, (pp. 635-645).Springer Nature. doi: 10.1007/978-3-030-59710-8_62.

Li, C., Tang, H., Deng, C., Zhan, L., & Liu, W. (2020). Vulnerability vs. Reliability: Disentangled Adversarial Examples for Cross-Modal Learning. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, (pp. 421-429).Association for Computing Machinery (ACM). doi: 10.1145/3394486.3403084.

Wang, Q., Zhan, L., Thompson, P., & Zhou, J. (2020). Multimodal Learning with Incomplete Modalities by Knowledge Distillation. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, (pp. 1828-1838).Association for Computing Machinery (ACM). doi: 10.1145/3394486.3403234.

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Fortel, I., Butler, M., Korthauer, L.E., Zhan, L., Ajilore, O., Driscoll, I., Sidiropoulos, A., Zhang, Y., Guo, L., Huang, H., Schonfeld, D., & Leow, A. (2019). Brain Dynamics Through the Lens of Statistical Mechanics by Unifying Structure and Function. In Lecture Notes in Computer Science, 11768, (pp. 503-511).Springer Nature. doi: 10.1007/978-3-030-32254-0_56.

Guo, L., Tang, H., Wang, Q., Dennis, E., Zhu, D., Huang, H., Ajilore, O., Leow, A.D., & Zhan, L. (2019). Identifying Configurational Abnormalities in Alzheimer’S Disease Progression Using Multi-View Structure Connectome. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 00, (pp. 169-172).Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/isbi.2019.8759373.

Tang, H., Guo, L., Dennis, E., Thompson, P.M., Huang, H., Ajilore, O., Leow, A.D., & Zhan, L. (2019). Classifying Stages of Mild Cognitive Impairment via Augmented Graph Embedding. In Lecture Notes in Computer Science, 11846, (pp. 30-38).Springer Nature. doi: 10.1007/978-3-030-33226-6_4.

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Morrissey, Z., Zhan, L., Lee, H., Keiriz, J., Forbes, A., Ajilore, O., Leow, A., & Chung, M. (2018). Phase Angle Spatial Embedding (PhASE). In Lecture Notes in Computer Science, 11072, (pp. 367-374).Springer Nature. doi: 10.1007/978-3-030-00931-1_42.

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Borowczyk, B., Wyss, B., Drane, A., Luttinen, G., Turkmen, A., Baltaci, K., Peng, A.S., & Zhan, L. (2017). Automated Part Management System Capstone Project. In 2017 IEEE International Conference on Electro Information Technology (EIT), (pp. 341-344).Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/eit.2017.8053382.

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Dennis, E.L., Prasad, G., Daianu, M., Zhan, L., Babikian, T., Kernan, C., Mink, R., Babbitt, C., Johnson, J., Giza, C.C., Asarnow, R.F., & Thompson, P.M. (2016). Fiber Tracking in Traumatic Brain Injury: Comparison of 9 Tractography Algorithms. In Lecture Notes in Computer Science, 9556, (pp. 33-44).Springer Nature. doi: 10.1007/978-3-319-30858-6_4.

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Peng, A., Eickhoff, B., Baltaci, K., Zhan, L., & Nelson, R. (2016). Experiences in Developing a Computer Engineering Capstone Design Course with a Start-up Company. In 2016 ASEE Annual Conference & Exposition Proceedings, 2016-June.American Society for Engineering Education. doi: 10.18260/p.26810.

Villalon-Reina, J.E., Nir, T.M., Zhan, L., McMahon, K.L., de Zubicaray, G.I., Wright, M.J., Jahanshad, N., & Thompson, P.M. (2016). Reliability of Structural Connectivity Examined with Four Different Diffusion Reconstruction Methods at Two Different Spatial and Angular Resolutions. In Mathematics and Visualization, none, (pp. 219-231).Springer Nature. doi: 10.1007/978-3-319-28588-7_19.

Wang, Q., Zhan, L., Thompson, P.M., Dodge, H.H., & Zhou, J. (2016). Discriminative Fusion of Multiple Brain Networks for Early Mild Cognitive Impairment Detection. In 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), 2016-June, (pp. 568-572).Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/isbi.2016.7493332.

Zhu, D., Jahanshad, N., Riedel, B.C., Zhan, L., Faskowitz, J., Prasad, G., & Thompson, P.M. (2016). Population Learning of Structural Connectivity by White Matter Encoding and Decoding. In 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), 2016-June, (pp. 554-558).Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/isbi.2016.7493329.

Cao, B., Zhan, L., Kong, X., Yu, P.S., Vizueta, N., Altshuler, L.L., & Leow, A.D. (2015). Identification of Discriminative Subgraph Patterns in fMRI Brain Networks in Bipolar Affective Disorder. In Lecture Notes in Computer Science, 9250, (pp. 105-114).Springer Nature. doi: 10.1007/978-3-319-23344-4_11.

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Yang, T., Wang, J., Sun, Q., Hibar, D.P., Jahanshad, N., Liu, L., Wang, Y., Zhan, L., Thompson, P.M., & Ye, J. (2015). Detecting Genetic Risk Factors for Alzheimer's Disease in Whole Genome Sequence Data via Lasso Screening. In 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2015, (pp. 985-989).Institute of Electrical and Electronics Engineers (IEEE).United States. doi: 10.1109/isbi.2015.7164036.

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Zhan, L., Jahanshad, N., Jin, Y., Nir, T.M., Leonardo, C.D., Bernstein, M.A., Borowski, B., Jack, C.R., & Thompson, P.M. (2014). Understanding scanner upgrade effects on brain integrity & connectivity measures. In 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014, (pp. 234-237).

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Jin, Y., Shi, Y., Zhan, L., de Zubicaray, G.I., McMahon, K.L., Martin, N.G., Wright, M.J., & Thompson, P.M. (2013). LABELING WHITE MATTER TRACTS IN HARDI BY FUSING MULTIPLE TRACT ATLASES WITH APPLICATIONS TO GENETICS. In 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2013, (pp. 512-515).Institute of Electrical and Electronics Engineers (IEEE).United States. doi: 10.1109/isbi.2013.6556524.

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Leow, A., Zhan, L., Ajilore, O., GadElkarim, J., Zhang, A., Arienzo, D., Moody, T., Feusner, J., Kumar, A., Thompson, P., & Altshuler, L. (2012). MEASURING INTER-HEMISPHERIC INTEGRATION IN BIPOLAR AFFECTIVE DISORDER USING BRAIN NETWORK ANALYSES AND HARDI. In 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 1, (pp. 5-8).Institute of Electrical and Electronics Engineers (IEEE).United States. doi: 10.1109/isbi.2012.6235470.

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Tong, M., Kim, Y., Zhan, L., Sapiro, G., Lenglet, C., Mueller, B.A., Thompson, P.M., & Vese, L.A. (2012). A VARIATIONAL MODEL FOR DENOISING HIGH ANGULAR RESOLUTION DIFFUSION IMAGING. In 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 1, (pp. 530-533).Institute of Electrical and Electronics Engineers (IEEE).United States. doi: 10.1109/isbi.2012.6235602.

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