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. Psychosom Med, 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., & Zhan, L. (2024). Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model. IEEE Trans Neural Netw Learn Syst, 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. Front Neurosci, 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 Trans Med 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. J Alzheimers Dis, 95(4), 1449-1467.SAGE Publications. doi: 10.3233/JAD-230035.

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. Comput Biol Med, 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. Front Comput Neurosci, 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. Comput Biol Med, 166, 107488.Elsevier. doi: 10.1016/j.compbiomed.2023.107488.

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. Med Image Anal, 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 Behav, 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. Netw Neurosci, 6(2), 420-444.MIT Press. doi: 10.1162/netn_a_00220.

Fu, X., Sun, Z., Tang, H., Zou, E.M., Huang, H., Wang, Y., & Zhan, L. (2022). 3D bi-directional transformer U-Net for medical image segmentation. Front Big Data, 5, 1080715.Frontiers. doi: 10.3389/fdata.2022.1080715.

Morrissey, Z.D., Gao, J., Zhan, L., Li, W., Fortel, I., Saido, T., Saito, T., Bakker, A., Mackin, S., Ajilore, O., Lazarov, O., & Leow, A.D. (2022). Hippocampal functional connectivity across age in an App knock-in mouse model of Alzheimer's disease. Front Aging Neurosci, 14, 1085989.Frontiers. doi: 10.3389/fnagi.2022.1085989.

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. Front Neurosci, 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. Proc IEEE Int Symp Biomed Imaging, 2022, 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. Comput Sci Inf Technol, 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 Netw, 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. Front Aging Neurosci, 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. Cereb 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., Krafty, R.T., Aizenstein, H.J., & Zhan, L. (2020). White Matter Integrity Underlying Depressive Symptoms in Dementia Caregivers. Am J Geriatr 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., Ma, X.B., Ma, X.Y., Ma, Y.Q., Marshall, C., Martinez Caicedo, D.A., McDonald, K.T., McKeown, R.D., Mitchell, I., Mora Lepin, L., 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., & Daya Bay Collaboration. (2019). Extraction of the ^{235}U and ^{239}Pu Antineutrino Spectra at Daya Bay. Phys Rev Lett, 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. Am J Clin Nutr, 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), 649-650.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. Front 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. Netw Neurosci, 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., & Alzheimer’s Disease Neuroimaging Initiative and National Alzheimer’s Coordinating Center. (2018). The Added Value of Diffusion-Weighted MRI-Derived Structural Connectome in Evaluating Mild Cognitive Impairment: A Multi-Cohort Validation1. In Zhang, Y. (Ed.). J Alzheimers Dis, 64(1), 149-169.SAGE Publications. 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. Hum Brain Mapp, 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., & Alzheimer's Disease Neuroimaging Initiative. (2017). 3D tract-specific local and global analysis of white matter integrity in Alzheimer's disease. Hum Brain Mapp, 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., & Alzheimer's Diseaase Neuroimaginng Initiative (ADNI). (2017). Fractional anisotropy derived from the diffusion tensor distribution function boosts power to detect Alzheimer's disease deficits. Magn Reson Med, 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. J Comp Neurol, 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. Psychol Med, 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. J Psychiatr Res, 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 Clin, 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 Res, 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 Inform, 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. Hum Brain Mapp, 36(9), 3653-3665.Wiley. doi: 10.1002/hbm.22869.

Zhan, L., Liu, Y., Wang, Y., Zhou, J., Jahanshad, N., Ye, J., Thompson, P.M., & Alzheimer's Disease Neuroimaging Initiative (ADNI). (2015). Boosting brain connectome classification accuracy in Alzheimer's disease using higher-order singular value decomposition. Front Neurosci, 9(JUL), 257.Frontiers. doi: 10.3389/fnins.2015.00257.

Zhan, L., Zhou, J., Wang, Y., Jin, Y., Jahanshad, N., Prasad, G., Nir, T.M., Leonardo, C.D., Ye, J., Thompson, P.M., & For The Alzheimer's Disease Neuroimaging Initiative. (2015). Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer's disease. Front Aging Neurosci, 7(APR), 48.Frontiers. doi: 10.3389/fnagi.2015.00048.

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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. Hum Brain Mapp, 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. Front Neuroinform, 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 clinical correlates. Psychiatry Res, 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. Biol 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. Hum Brain Mapp, 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 Connect, 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.

GadElkarim, J.J., Schonfeld, D., Ajilore, O., Zhan, L., Zhang, A.F., Feusner, J.D., Thompson, P.M., Simon, T.J., Kumar, A., & Leow, A.D. (2012). A framework for quantifying node-level community structure group differences in brain connectivity networks. Med Image Comput Comput Assist Interv, 15(Pt 2), 196-203.Springer Berlin Heidelberg. doi: 10.1007/978-3-642-33418-4_25.

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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.

Zhan, L., Leow, A.D., Jahanshad, N., Chiang, M.C., Barysheva, M., Lee, A.D., Toga, A.W., McMahon, K.L., de Zubicaray, G.I., Wright, M.J., & Thompson, P.M. (2010). How does angular resolution affect diffusion imaging measures?. Neuroimage, 49(2), 1357-1371.Elsevier. doi: 10.1016/j.neuroimage.2009.09.057.

Kim, Y., Thompson, P.M., Toga, A.W., Vese, L., & Zhan, L. (2009). HARDI denoising: variational regularization of the spherical apparent diffusion coefficient sADC. Inf Process Med Imaging, 21, 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. Magn Reson Med, 61(1), 205-214.Wiley. doi: 10.1002/mrm.21852.

Zhan, L., Leow, A.D., Zhu, S., Baryshev, M., Toga, A.W., McMahon, K.L., de Zubicaray, G.I., Wright, M.J., & Thompson, P.M. (2009). A novel measure of fractional anisotropy based on the tensor distribution function. Med Image Comput Comput Assist Interv, 12(Pt 1), 845-852.Springer Nature. doi: 10.1007/978-3-642-04268-3_104.

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, 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 Med Image Comput Comput Assist Interv, 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 Proc IEEE Int Symp Biomed Imaging, 2020, (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 Proc IEEE Int Symp Biomed Imaging, 2020, (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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Zhang, Y., Zhan, L., Thompson, P.M., & Huang, H. (2019). Biological Knowledge Guided Deep Neural Network for Brain Genotype-Phenotype Association Study. In Lecture Notes in Computer Science, 11846, (pp. 84-92).Springer Nature. doi: 10.1007/978-3-030-33226-6_10.

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.

Sun, M., Baytas, I.M., Zhan, L., Wang, Z., & Zhou, J. (2018). Subspace Network. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, (pp. 2259-2268).Association for Computing Machinery (ACM). doi: 10.1145/3219819.3219966.

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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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Li, Q., Yang, T., Zhan, L., Hibar, D.P., Jahanshad, N., Wang, Y., Ye, J., Thompson, P.M., & Wang, J. (2016). Large-Scale Collaborative Imaging Genetics Studies of Risk Genetic Factors for Alzheimer’s Disease Across Multiple Institutions. In Lecture Notes in Computer Science, 9900, (pp. 335-343).Springer Nature. doi: 10.1007/978-3-319-46720-7_39.

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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 Proc IEEE Int Symp Biomed Imaging, 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., Liu, Y., Zhou, J., Ye, J., & Thompson, P.M. (2015). Boosting Classification Accuracy of Diffusion MRI Derived Brain Networks for the Subtypes of Mild Cognitive Impairment Using Higher Order Singular Value Decomposition. In Proc IEEE Int Symp Biomed Imaging, 2015, (pp. 131-135).Institute of Electrical and Electronics Engineers (IEEE).United States. doi: 10.1109/ISBI.2015.7163833.

Zhu, D., Zhan, L., Faskowitz, J., Daianu, M., Jahanshad, N., de Zubicaray, G.I., McMahon, K.L., Martin, N.G., Wright, M.J., & Thompson, P.M. (2015). GENETIC ANALYSIS OF STRUCTURAL BRAIN CONNECTIVITY USING DICCCOL MODELS OF DIFFUSION MRI IN 522 TWINS. In Proc IEEE Int Symp Biomed Imaging, 2015, (pp. 1167-1171).Institute of Electrical and Electronics Engineers (IEEE).United States. doi: 10.1109/ISBI.2015.7164080.

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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 Proc IEEE Int Symp Biomed Imaging, 2013, (pp. 512-515).Institute of Electrical and Electronics Engineers (IEEE).United States. doi: 10.1109/ISBI.2013.6556524.

Zhan, L., Jahanshad, N., Jin, Y., Toga, A.W., McMahon, K.L., de Zubicaray, G.I., Martin, N.G., Wright, M.J., & Thompson, P.M. (2013). BRAIN NETWORK EFFICIENCY AND TOPOLOGY DEPEND ON THE FIBER TRACKING METHOD: 11 TRACTOGRAPHY ALGORITHMS COMPARED IN 536 SUBJECTS. In 2013 IEEE 10th International Symposium on Biomedical Imaging, (pp. 1134-1137).Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/isbi.2013.6556679.

GadElkarim, J.J., Schonfeld, D., Ajilore, O., Zhan, L., Zhang, A.F., Feusner, J.D., Thompson, P.M., Simon, T.J., Kumar, A., & Leow, A.D. (2012). A Framework for Quantifying Node-Level Community Structure Group Differences in Brain Connectivity Networks. In Lecture Notes in Computer Science, 15(Pt 2), (pp. 196-203).Springer Nature. doi: 10.1007/978-3-642-33418-4_25.

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