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Liang Zhan

Assistant Professor
Google Scholar Electrical and Computer Engineering Bioengineering Department

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Ph.D., University of California, Los Angeles, 2011

Boots, E.A., Zhan, L., Castellanos, K.J., Barnes, L.L., Tussing-Humphreys, L., & Lamar, M. (2021). Inflammatory markers and tract-based structural connectomics in older adults with a preliminary exploration of associations by race. BRAIN IMAGING AND BEHAVIOR.Springer Science and Business Media LLC. doi: 10.1007/s11682-021-00483-y.

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 BV. doi: 10.1016/j.neuroimage.2020.117538.

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 BV. 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 Media SA. 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-epsilon 4 Carriers. CEREBRAL CORTEX, 30(12), 6350-6362.Oxford University Press (OUP). doi: 10.1093/cercor/bhaa190.

Smagula, S.F., Stahl, S.T., Santini, T., Banihashemi, L., Hall, M.H., Ibrahim, T.S., III, R.C.F., 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 BV. doi: 10.1016/j.jagp.2019.11.010.

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 Science and Business Media LLC. 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 BV. 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 BV. 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.Informa UK Limited. 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.Oxford University Press (OUP). 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 BV. 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. 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., 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 Media SA. 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 - Journals. 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 epsilon 4 carriers. NEUROIMAGE, 178, 295-305.Elsevier BV. 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. J Alzheimers Dis, 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., & 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. JOURNAL OF COMPARATIVE NEUROLOGY, 525(15), 3251-3265.Wiley. doi: 10.1002/cne.24274.

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 BV. 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 BV. 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 BV. 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 Science and Business Media LLC. 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. (2015). Boosting brain connectome classification accuracy in Alzheimer's disease using higher-order singular value decomposition. Frontiers in Neuroscience, 9(JUL).Frontiers Media SA. 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. Frontiers in Aging Neuroscience, 7(APR).Frontiers Media SA. doi: 10.3389/fnagi.2015.00048.

Gadelkarim, J.J., Ajilore, O., Schonfeld, D., Zhan, L., Thompson, P.M., Feusner, J.D., Kumar, A., Altshuler, L.L., & Leow, A.D. (2014). Investigating brain community structure abnormalities in bipolar disorder using path length associated community estimation. Hum Brain Mapp, 35(5), 2253-2264.Wiley. doi: 10.1002/hbm.22324.

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 BV. 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).Frontiers Media SA. 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 Science and Business Media LLC. 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 BV. 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 BV. 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 Connectivity, 3(1), 72-86.Mary Ann Liebert Inc. 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 Science and Business Media LLC. 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.

Leow, A.D., Zhan, L., Arienzo, D., GadElkarim, J.J., Zhang, A.F., Ajilore, O., Kumar, A., Thompson, P.M., & Feusner, J.D. (2012). Hierarchical structural mapping for globally optimized estimation of functional networks. Med Image Comput Comput Assist Interv, 15(Pt 2), 228-236.Springer Berlin Heidelberg. doi: 10.1007/978-3-642-33418-4_29.

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 Science and Business Media LLC. 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 BV. 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 Berlin Heidelberg. doi: 10.1007/978-3-642-04268-3_104.

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

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

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. The Importance of Being Negative: A serious treatment of non-trivial edges in brain functional connectome.

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).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. 986-990).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 (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12261 LNCS, (pp. 635-645).Springer International Publishing. doi: 10.1007/978-3-030-59710-8_62.

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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 (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11768 LNCS, (pp. 503-511).Springer International Publishing. 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), 2019-April, (pp. 169-172).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 (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11846 LNCS, (pp. 30-38).Springer International Publishing. doi: 10.1007/978-3-030-33226-6_4.

Zhan, L. (2019). Latest results on the measurement of reactor antineutrino oscillation at Daya Bay. In Proceedings of Science, 340.

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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 (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11846 LNCS, (pp. 84-92).Springer International Publishing. doi: 10.1007/978-3-030-33226-6_10.

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Zhan, L., & Bay, D. (2018). Latest results of reactor antineutrino flux and spectrum at Daya Bay. In Neutrino 2018 - 28th International Conference on Neutrino Physics and Astrophysics, Conference Proceedings.

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).IEEE. doi: 10.1109/eit.2017.8053382.

Isaev, D.Y., Nir, T.M., Jahanshad, N., Villalon-Reina, J.E., Zhan, L., Leow, A.D., & Thompson, P.M. (2017). Improved clinical diffusion MRI reliability using a tensor distribution function compared to a single tensor. In 12th International Symposium on Medical Information Processing and Analysis, 10160.SPIE. doi: 10.1117/12.2257281.

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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 (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9556, (pp. 33-44).Springer International Publishing. 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.ASEE Conferences. 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 International Publishing. doi: 10.1007/978-3-319-28588-7_19.

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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).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 (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9250, (pp. 105-114).Springer International Publishing. 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 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 2015-July, (pp. 985-989).IEEE. 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 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 2015-July, (pp. 131-135).IEEE. doi: 10.1109/isbi.2015.7163833.

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Dennis, E.L., Zhan, L., Jahanshad, N., Mueller, B.A., Jin, Y., Lenglet, C., Yacoub, E., Sapiro, G., Ugurbil, K., Harel, N., Toga, A.W., Lim, K.O., & Thompson, P.M. (2014). Rich Club Analysis of Structural Brain Connectivity at 7 Tesla Versus 3 Tesla. In Mathematics and Visualization, 0, (pp. 209-218).Springer International Publishing. doi: 10.1007/978-3-319-02475-2_19.

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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).

Zhan, L., Nie, Z., Ye, J., Wang, Y., Jin, Y., Jahanshad, N., Prasad, G., de Zubicaray, G.I., McMahon, K.L., Martin, N.G., Wright, M.J., & Thompson, P.M. (2014). Multiple Stages Classification of Alzheimer’s Disease Based on Structural Brain Networks Using Generalized Low Rank Approximations (GLRAM). In Mathematics and Visualization, 39, (pp. 35-44).Springer International Publishing. doi: 10.1007/978-3-319-11182-7_4.

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 2013 IEEE 10th International Symposium on Biomedical Imaging, (pp. 512-515).IEEE. doi: 10.1109/isbi.2013.6556524.

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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 (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7511 LNCS, (pp. 196-203). doi: 10.1007/978-3-642-33418-4_25.

Jin, Y., Shi, Y., Zhan, L., Li, J., de Zubicaray, G.I., McMahon, K.L., Martin, N.G., Wright, M.J., & Thompson, P.M. (2012). Automatic Population HARDI White Matter Tract Clustering by Label Fusion of Multiple Tract Atlases. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7509 LNCS, (pp. 147-156).Springer Berlin Heidelberg. doi: 10.1007/978-3-642-33530-3_12.

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Leow, A.D., Zhan, L., Arienzo, D., Gadelkarim, J.J., Zhang, A.F., Ajilore, O., Kumar, A., Thompson, P.M., & Feusner, J.D. (2012). Hierarchical structural mapping for globally optimized estimation of functional networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7511 LNCS, (pp. 228-236).

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 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), (pp. 530-533).IEEE. doi: 10.1109/isbi.2012.6235602.

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Zhan, L., Leow, A.D., Aganj, I., Lenglet, C., Sapiro, G., Yacoub, E., Harel, N., Toga, A.W., & Thompson, P.M. (2011). Differential information content in staggered multiple shell hardi measured by the tensor distribution function. In 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, (pp. 305-309).IEEE. doi: 10.1109/isbi.2011.5872411.

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Hageman, N., Leow, A., Shattuck, D., Zhan, L., Thompson, P., Zhu, S., & Toga, A. (2009). Segmenting crossing fiber geometries using fluid mechanics tensor distribution function tractography. In 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, (pp. 1390-1393).IEEE. doi: 10.1109/isbi.2009.5193325.

Kim, Y., Thompson, P.M., Toga, A.W., Vese, L., & Zhan, L. (2009). HARDI Denoising: Variational Regularization of the Spherical Apparent Diffusion Coefficient sADC. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5636 LNCS, (pp. 515-527).Springer Berlin Heidelberg. doi: 10.1007/978-3-642-02498-6_43.

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Zhan, L., Leow, A.D., Zhu, S., Barysheva, 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. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5761 LNCS(PART 1), (pp. 845-852). doi: 10.1007/978-3-642-04268-3_104.

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