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Information-Theoretic Characterization of Blood Panel Predictors for Brain Atrophy and Cognitive Decline in the Elderly.

Proceedings. IEEE International Symposium on Biomedical Imaging

Madsen SK, Ver Steeg G, Mezher A, Jahanshad N, Nir TM, Hua X, Gutman BA, Galstyan A, Thompson PM.
PMID: 26413208
Proc IEEE Int Symp Biomed Imaging. 2015 Apr;2015:980-984. doi: 10.1109/ISBI.2015.7164035.

Cognitive decline in old age is tightly linked with brain atrophy, causing significant burden. It is critical to identify which biomarkers are most predictive of cognitive decline and brain atrophy in the elderly. In 566 older adults from the...

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Madsen SK, Ver Steeg G, Mezher A, et al. Information-Theoretic Characterization of Blood Panel Predictors for Brain Atrophy and Cognitive Decline in the Elderly. Proc IEEE Int Symp Biomed Imaging. 2015;2015:980-984doi: 10.1109/ISBI.2015.7164035.
Madsen, S. K., Ver Steeg, G., Mezher, A., Jahanshad, N., Nir, T. M., Hua, X., Gutman, B. A., Galstyan, A., Thompson, P. M. (2015). Information-Theoretic Characterization of Blood Panel Predictors for Brain Atrophy and Cognitive Decline in the Elderly. Proceedings. IEEE International Symposium on Biomedical Imaging, 2015980-984. https://doi.org/10.1109/ISBI.2015.7164035
Madsen, Sarah K, et al. "Information-Theoretic Characterization of Blood Panel Predictors for Brain Atrophy and Cognitive Decline in the Elderly." Proceedings. IEEE International Symposium on Biomedical Imaging vol. 2015 (2015): 980-984. doi: https://doi.org/10.1109/ISBI.2015.7164035
Madsen SK, Ver Steeg G, Mezher A, Jahanshad N, Nir TM, Hua X, Gutman BA, Galstyan A, Thompson PM. Information-Theoretic Characterization of Blood Panel Predictors for Brain Atrophy and Cognitive Decline in the Elderly. Proc IEEE Int Symp Biomed Imaging. 2015 Apr;2015:980-984. doi: 10.1109/ISBI.2015.7164035. PMID: 26413208; PMCID: PMC4578218.
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A Transformation Similarity Constraint for Groupwise Nonlinear Registration in Longitudinal Neuro Imaging Studies.

Proceedings of SPIE--the International Society for Optical Engineering

Fleishman GM, Gutman BA, Fletcher PT, Thompson P.
PMID: 25914796
Proc SPIE Int Soc Opt Eng. 2015 Mar 20;9413. doi: 10.1117/12.2080841.

Patients with Alzheimer's disease and other brain disorders often show a similar spatial distribution of volume change throughout the brain over time,

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Fleishman GM, Gutman BA, Fletcher PT, et al. A Transformation Similarity Constraint for Groupwise Nonlinear Registration in Longitudinal Neuro Imaging Studies. Proc SPIE Int Soc Opt Eng. 2015;9413doi: 10.1117/12.2080841.
Fleishman, G. M., Gutman, B. A., Fletcher, P. T., & Thompson, P. (2015). A Transformation Similarity Constraint for Groupwise Nonlinear Registration in Longitudinal Neuro Imaging Studies. Proceedings of SPIE--the International Society for Optical Engineering, 9413. https://doi.org/10.1117/12.2080841
Fleishman, Greg M, et al. "A Transformation Similarity Constraint for Groupwise Nonlinear Registration in Longitudinal Neuro Imaging Studies." Proceedings of SPIE--the International Society for Optical Engineering vol. 9413 (2015). doi: https://doi.org/10.1117/12.2080841
Fleishman GM, Gutman BA, Fletcher PT, Thompson P. A Transformation Similarity Constraint for Groupwise Nonlinear Registration in Longitudinal Neuro Imaging Studies. Proc SPIE Int Soc Opt Eng. 2015 Mar 20;9413. doi: 10.1117/12.2080841. PMID: 25914796; PMCID: PMC4406356.
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Deep Learning based Classification of FDG-PET Data for Alzheimers Disease Categories.

Proceedings of SPIE--the International Society for Optical Engineering

Singh S, Srivastava A, Mi L, Caselli RJ, Chen K, Goradia D, Reiman EM, Wang Y.
PMID: 29263566
Proc SPIE Int Soc Opt Eng. 2017 Oct;10572. doi: 10.1117/12.2294537. Epub 2017 Nov 17.

Fluorodeoxyglucose (FDG) positron emission tomography (PET) measures the decline in the regional cerebral metabolic rate for glucose, offering a reliable metabolic biomarker even on presymptomatic Alzheimer's disease (AD) patients. PET scans provide functional information that is unique and unavailable...

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Singh S, Srivastava A, Mi L, et al. Deep Learning based Classification of FDG-PET Data for Alzheimers Disease Categories. Proc SPIE Int Soc Opt Eng. 2017;10572doi: 10.1117/12.2294537.
Singh, S., Srivastava, A., Mi, L., Caselli, R. J., Chen, K., Goradia, D., Reiman, E. M., & Wang, Y. (2017). Deep Learning based Classification of FDG-PET Data for Alzheimers Disease Categories. Proceedings of SPIE--the International Society for Optical Engineering, 10572. https://doi.org/10.1117/12.2294537
Singh, Shibani, et al. "Deep Learning based Classification of FDG-PET Data for Alzheimers Disease Categories." Proceedings of SPIE--the International Society for Optical Engineering vol. 10572 (2017). doi: https://doi.org/10.1117/12.2294537
Singh S, Srivastava A, Mi L, Caselli RJ, Chen K, Goradia D, Reiman EM, Wang Y. Deep Learning based Classification of FDG-PET Data for Alzheimers Disease Categories. Proc SPIE Int Soc Opt Eng. 2017 Oct;10572. doi: 10.1117/12.2294537. Epub 2017 Nov 17. PMID: 29263566; PMCID: PMC5733797.
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Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group.

Molecular psychiatry

Han LKM, Dinga R, Hahn T, Ching CRK, Eyler LT, Aftanas L, Aghajani M, Aleman A, Baune BT, Berger K, Brak I, Filho GB, Carballedo A, Connolly CG, Couvy-Duchesne B, Cullen KR, Dannlowski U, Davey CG, Dima D, Duran FLS, Enneking V, Filimonova E, Frenzel S, Frodl T, Fu CHY, Godlewska BR, Gotlib IH, Grabe HJ, Groenewold NA, Grotegerd D, Gruber O, Hall GB, Harrison BJ, Hatton SN, Hermesdorf M, Hickie IB, Ho TC, Hosten N, Jansen A, Kähler C, Kircher T, Klimes-Dougan B, Krämer B, Krug A, Lagopoulos J, Leenings R, MacMaster FP, MacQueen G, McIntosh A, McLellan Q, McMahon KL, Medland SE, Mueller BA, Mwangi B, Osipov E, Portella MJ, Pozzi E, Reneman L, Repple J, Rosa PGP, Sacchet MD, Sämann PG, Schnell K, Schrantee A, Simulionyte E, Soares JC, Sommer J, Stein DJ, Steinsträter O, Strike LT, Thomopoulos SI, van Tol MJ, Veer IM, Vermeiren RRJM, Walter H, van der Wee NJA, van der Werff SJA, Whalley H, Winter NR, Wittfeld K, Wright MJ, Wu MJ, Völzke H, Yang TT, Zannias V, de Zubicaray GI, Zunta-Soares GB, Abé C, Alda M, Andreassen OA, Bøen E, Bonnin CM, Canales-Rodriguez EJ, Cannon D, Caseras X, Chaim-Avancini TM, Elvsåshagen T, Favre P, Foley SF, Fullerton JM, Goikolea JM, Haarman BCM, Hajek T, Henry C, Houenou J, Howells FM, Ingvar M, Kuplicki R, Lafer B, Landén M, Machado-Vieira R, Malt UF, McDonald C, Mitchell PB, Nabulsi L, Otaduy MCG, Overs BJ, Polosan M, Pomarol-Clotet E, Radua J, Rive MM, Roberts G, Ruhe HG, Salvador R, Sarró S, Satterthwaite TD, Savitz J, Schene AH, Schofield PR, Serpa MH, Sim K, Soeiro-de-Souza MG, Sutherland AN, Temmingh HS, Timmons GM, Uhlmann A, Vieta E, Wolf DH, Zanetti MV, Jahanshad N, Thompson PM, Veltman DJ, Penninx BWJH, Marquand AF, Cole JH, Schmaal L.
PMID: 32424236
Mol Psychiatry. 2021 Sep;26(9):5124-5139. doi: 10.1038/s41380-020-0754-0. Epub 2020 May 18.

Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large...

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Han LKM, Dinga R, Hahn T, et al. Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group. Mol Psychiatry. 2020;26(9):5124-5139doi: 10.1038/s41380-020-0754-0.
Han, L. K. M., Dinga, R., Hahn, T., Ching, C. R. K., Eyler, L. T., Aftanas, L., Aghajani, M., Aleman, A., Baune, B. T., Berger, K., Brak, I., Filho, G. B., Carballedo, A., Connolly, C. G., Couvy-Duchesne, B., Cullen, K. R., Dannlowski, U., Davey, C. G., Dima, D., Duran, F. L. S., Enneking, V., Filimonova, E., Frenzel, S., Frodl, T., Fu, C. H. Y., Godlewska, B. R., Gotlib, I. H., Grabe, H. J., Groenewold, N. A., Grotegerd, D., Gruber, O., Hall, G. B., Harrison, B. J., Hatton, S. N., Hermesdorf, M., Hickie, I. B., Ho, T. C., Hosten, N., Jansen, A., Kähler, C., Kircher, T., Klimes-Dougan, B., Krämer, B., Krug, A., Lagopoulos, J., Leenings, R., MacMaster, F. P., MacQueen, G., McIntosh, A., McLellan, Q., McMahon, K. L., Medland, S. E., Mueller, B. A., Mwangi, B., Osipov, E., Portella, M. J., Pozzi, E., Reneman, L., Repple, J., Rosa, P. G. P., Sacchet, M. D., Sämann, P. G., Schnell, K., Schrantee, A., Simulionyte, E., Soares, J. C., Sommer, J., Stein, D. J., Steinsträter, O., Strike, L. T., Thomopoulos, S. I., van Tol, M. J., Veer, I. M., Vermeiren, R. R. J. M., Walter, H., van der Wee, N. J. A., van der Werff, S. J. A., Whalley, H., Winter, N. R., Wittfeld, K., Wright, M. J., Wu, M. J., Völzke, H., Yang, T. T., Zannias, V., de Zubicaray, G. I., Zunta-Soares, G. B., Abé, C., Alda, M., Andreassen, O. A., Bøen, E., Bonnin, C. M., Canales-Rodriguez, E. J., Cannon, D., Caseras, X., Chaim-Avancini, T. M., Elvsåshagen, T., Favre, P., Foley, S. F., Fullerton, J. M., Goikolea, J. M., Haarman, B. C. M., Hajek, T., Henry, C., Houenou, J., Howells, F. M., Ingvar, M., Kuplicki, R., Lafer, B., Landén, M., Machado-Vieira, R., Malt, U. F., McDonald, C., Mitchell, P. B., Nabulsi, L., Otaduy, M. C. G., Overs, B. J., Polosan, M., Pomarol-Clotet, E., Radua, J., Rive, M. M., Roberts, G., Ruhe, H. G., Salvador, R., Sarró, S., Satterthwaite, T. D., Savitz, J., Schene, A. H., Schofield, P. R., Serpa, M. H., Sim, K., Soeiro-de-Souza, M. G., Sutherland, A. N., Temmingh, H. S., Timmons, G. M., Uhlmann, A., Vieta, E., Wolf, D. H., Zanetti, M. V., Jahanshad, N., Thompson, P. M., Veltman, D. J., Penninx, B. W. J. H., Marquand, A. F., Cole, J. H., & Schmaal, L. (2021). Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group. Molecular psychiatry, 26(9), 5124-5139. https://doi.org/10.1038/s41380-020-0754-0
Han, Laura K M, et al. "Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group." Molecular psychiatry vol. 26,9 (2021): 5124-5139. doi: https://doi.org/10.1038/s41380-020-0754-0
Han LKM, Dinga R, Hahn T, Ching CRK, Eyler LT, Aftanas L, Aghajani M, Aleman A, Baune BT, Berger K, Brak I, Filho GB, Carballedo A, Connolly CG, Couvy-Duchesne B, Cullen KR, Dannlowski U, Davey CG, Dima D, Duran FLS, Enneking V, Filimonova E, Frenzel S, Frodl T, Fu CHY, Godlewska BR, Gotlib IH, Grabe HJ, Groenewold NA, Grotegerd D, Gruber O, Hall GB, Harrison BJ, Hatton SN, Hermesdorf M, Hickie IB, Ho TC, Hosten N, Jansen A, Kähler C, Kircher T, Klimes-Dougan B, Krämer B, Krug A, Lagopoulos J, Leenings R, MacMaster FP, MacQueen G, McIntosh A, McLellan Q, McMahon KL, Medland SE, Mueller BA, Mwangi B, Osipov E, Portella MJ, Pozzi E, Reneman L, Repple J, Rosa PGP, Sacchet MD, Sämann PG, Schnell K, Schrantee A, Simulionyte E, Soares JC, Sommer J, Stein DJ, Steinsträter O, Strike LT, Thomopoulos SI, van Tol MJ, Veer IM, Vermeiren RRJM, Walter H, van der Wee NJA, van der Werff SJA, Whalley H, Winter NR, Wittfeld K, Wright MJ, Wu MJ, Völzke H, Yang TT, Zannias V, de Zubicaray GI, Zunta-Soares GB, Abé C, Alda M, Andreassen OA, Bøen E, Bonnin CM, Canales-Rodriguez EJ, Cannon D, Caseras X, Chaim-Avancini TM, Elvsåshagen T, Favre P, Foley SF, Fullerton JM, Goikolea JM, Haarman BCM, Hajek T, Henry C, Houenou J, Howells FM, Ingvar M, Kuplicki R, Lafer B, Landén M, Machado-Vieira R, Malt UF, McDonald C, Mitchell PB, Nabulsi L, Otaduy MCG, Overs BJ, Polosan M, Pomarol-Clotet E, Radua J, Rive MM, Roberts G, Ruhe HG, Salvador R, Sarró S, Satterthwaite TD, Savitz J, Schene AH, Schofield PR, Serpa MH, Sim K, Soeiro-de-Souza MG, Sutherland AN, Temmingh HS, Timmons GM, Uhlmann A, Vieta E, Wolf DH, Zanetti MV, Jahanshad N, Thompson PM, Veltman DJ, Penninx BWJH, Marquand AF, Cole JH, Schmaal L. Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group. Mol Psychiatry. 2021 Sep;26(9):5124-5139. doi: 10.1038/s41380-020-0754-0. Epub 2020 May 18. PMID: 32424236; PMCID: PMC8589647.
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Regional relationships between CSF VEGF levels and Alzheimer's disease brain biomarkers and cognition.

Neurobiology of aging

Tubi MA, Kothapalli D, Hapenney M, Feingold FW, Mack WJ, King KS, Thompson PM, Braskie MN.
PMID: 34126466
Neurobiol Aging. 2021 Sep;105:241-251. doi: 10.1016/j.neurobiolaging.2021.04.025. Epub 2021 May 21.

Vascular endothelial growth factor (VEGF) is a complex signaling protein that supports vascular and neuronal function. Alzheimer's disease (AD) -neuropathological hallmarks interfere with VEGF signaling and modify previously detected positive associations between cerebral spinal fluid (CSF) VEGF and cognition...

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Tubi MA, Kothapalli D, Hapenney M, et al. Regional relationships between CSF VEGF levels and Alzheimer's disease brain biomarkers and cognition. Neurobiol Aging. 2021;105:241-251doi: 10.1016/j.neurobiolaging.2021.04.025.
Tubi, M. A., Kothapalli, D., Hapenney, M., Feingold, F. W., Mack, W. J., King, K. S., Thompson, P. M., Braskie, M. N. (2021). Regional relationships between CSF VEGF levels and Alzheimer's disease brain biomarkers and cognition. Neurobiology of aging, 105241-251. https://doi.org/10.1016/j.neurobiolaging.2021.04.025
Tubi, Meral A, et al. "Regional relationships between CSF VEGF levels and Alzheimer's disease brain biomarkers and cognition." Neurobiology of aging vol. 105 (2021): 241-251. doi: https://doi.org/10.1016/j.neurobiolaging.2021.04.025
Tubi MA, Kothapalli D, Hapenney M, Feingold FW, Mack WJ, King KS, Thompson PM, Braskie MN. Regional relationships between CSF VEGF levels and Alzheimer's disease brain biomarkers and cognition. Neurobiol Aging. 2021 Sep;105:241-251. doi: 10.1016/j.neurobiolaging.2021.04.025. Epub 2021 May 21. PMID: 34126466; PMCID: PMC8544907.
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HIPPOCAMPUS MORPHOMETRY STUDY ON PATHOLOGY-CONFIRMED ALZHEIMER'S DISEASE PATIENTS WITH SURFACE MULTIVARIATE MORPHOMETRY STATISTICS.

Proceedings. IEEE International Symposium on Biomedical Imaging

Wu J, Zhang J, Shi J, Chen K, Caselli RJ, Reiman EM, Wang Y.
PMID: 30123414
Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:1555-1559. doi: 10.1109/ISBI.2018.8363870. Epub 2018 May 24.

Alzheimer's disease (AD) is one of the most prevalent neurodegenerative diseases in elderly and the incidence of this disease is increasing with older ages. One of the hallmarks of AD is the accumulation of beta-amyloid plaques (A

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Wu J, Zhang J, Shi J, et al. HIPPOCAMPUS MORPHOMETRY STUDY ON PATHOLOGY-CONFIRMED ALZHEIMER'S DISEASE PATIENTS WITH SURFACE MULTIVARIATE MORPHOMETRY STATISTICS. Proc IEEE Int Symp Biomed Imaging. 2018;2018:1555-1559doi: 10.1109/ISBI.2018.8363870.
Wu, J., Zhang, J., Shi, J., Chen, K., Caselli, R. J., Reiman, E. M., & Wang, Y. (2018). HIPPOCAMPUS MORPHOMETRY STUDY ON PATHOLOGY-CONFIRMED ALZHEIMER'S DISEASE PATIENTS WITH SURFACE MULTIVARIATE MORPHOMETRY STATISTICS. Proceedings. IEEE International Symposium on Biomedical Imaging, 20181555-1559. https://doi.org/10.1109/ISBI.2018.8363870
Wu, Jianfeng, et al. "HIPPOCAMPUS MORPHOMETRY STUDY ON PATHOLOGY-CONFIRMED ALZHEIMER'S DISEASE PATIENTS WITH SURFACE MULTIVARIATE MORPHOMETRY STATISTICS." Proceedings. IEEE International Symposium on Biomedical Imaging vol. 2018 (2018): 1555-1559. doi: https://doi.org/10.1109/ISBI.2018.8363870
Wu J, Zhang J, Shi J, Chen K, Caselli RJ, Reiman EM, Wang Y. HIPPOCAMPUS MORPHOMETRY STUDY ON PATHOLOGY-CONFIRMED ALZHEIMER'S DISEASE PATIENTS WITH SURFACE MULTIVARIATE MORPHOMETRY STATISTICS. Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:1555-1559. doi: 10.1109/ISBI.2018.8363870. Epub 2018 May 24. PMID: 30123414; PMCID: PMC6097541.
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SPECTRAL GRAPH THEORY AND GRAPH ENERGY METRICS SHOW EVIDENCE FOR THE ALZHEIMER'S DISEASE DISCONNECTION SYNDROME IN .

Proceedings. IEEE International Symposium on Biomedical Imaging

Daianu M, Mezher A, Jahanshad N, Hibar DP, Nir TM, Jack CR, Weiner MW, Bernstein MA, Thompson PM.
PMID: 26413205
Proc IEEE Int Symp Biomed Imaging. 2015 Apr;2015:458-461. doi: 10.1109/ISBI.2015.7163910.

Our understanding of network breakdown in Alzheimer's disease (AD) is likely to be enhanced through advanced mathematical descriptors. Here, we applied spectral graph theory to provide novel metrics of structural connectivity based on 3-Tesla diffusion weighted images in 42...

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Daianu M, Mezher A, Jahanshad N, et al. SPECTRAL GRAPH THEORY AND GRAPH ENERGY METRICS SHOW EVIDENCE FOR THE ALZHEIMER'S DISEASE DISCONNECTION SYNDROME IN . Proc IEEE Int Symp Biomed Imaging. 2015;2015:458-461doi: 10.1109/ISBI.2015.7163910.
Daianu, M., Mezher, A., Jahanshad, N., Hibar, D. P., Nir, T. M., Jack, C. R., Weiner, M. W., Bernstein, M. A., & Thompson, P. M. (2015). SPECTRAL GRAPH THEORY AND GRAPH ENERGY METRICS SHOW EVIDENCE FOR THE ALZHEIMER'S DISEASE DISCONNECTION SYNDROME IN . Proceedings. IEEE International Symposium on Biomedical Imaging, 2015458-461. https://doi.org/10.1109/ISBI.2015.7163910
Daianu, Madelaine, et al. "SPECTRAL GRAPH THEORY AND GRAPH ENERGY METRICS SHOW EVIDENCE FOR THE ALZHEIMER'S DISEASE DISCONNECTION SYNDROME IN ." Proceedings. IEEE International Symposium on Biomedical Imaging vol. 2015 (2015): 458-461. doi: https://doi.org/10.1109/ISBI.2015.7163910
Daianu M, Mezher A, Jahanshad N, Hibar DP, Nir TM, Jack CR, Weiner MW, Bernstein MA, Thompson PM. SPECTRAL GRAPH THEORY AND GRAPH ENERGY METRICS SHOW EVIDENCE FOR THE ALZHEIMER'S DISEASE DISCONNECTION SYNDROME IN . Proc IEEE Int Symp Biomed Imaging. 2015 Apr;2015:458-461. doi: 10.1109/ISBI.2015.7163910. PMID: 26413205; PMCID: PMC4578320.
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Detecting Genetic Risk Factors for Alzheimer's Disease in Whole Genome Sequence Data via Lasso Screening.

Proceedings. IEEE International Symposium on Biomedical Imaging

Yang T, Wang J, Sun Q, Hibar DP, Jahanshad N, Liu L, Wang Y, Zhan L, Thompson PM, Ye J.
PMID: 26413209
Proc IEEE Int Symp Biomed Imaging. 2015 Apr;2015:985-989. doi: 10.1109/ISBI.2015.7164036.

Genetic factors play a key role in Alzheimer's disease (AD). The Alzheimer's Disease Neuroimaging Initiative (ADNI) whole genome sequence (WGS) data offers new power to investigate mechanisms of AD by combining entire genome sequences with neuroimaging and clinical data....

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Yang T, Wang J, Sun Q, et al. Detecting Genetic Risk Factors for Alzheimer's Disease in Whole Genome Sequence Data via Lasso Screening. Proc IEEE Int Symp Biomed Imaging. 2015;2015:985-989doi: 10.1109/ISBI.2015.7164036.
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. Proceedings. IEEE International Symposium on Biomedical Imaging, 2015985-989. https://doi.org/10.1109/ISBI.2015.7164036
Yang, Tao, et al. "Detecting Genetic Risk Factors for Alzheimer's Disease in Whole Genome Sequence Data via Lasso Screening." Proceedings. IEEE International Symposium on Biomedical Imaging vol. 2015 (2015): 985-989. doi: https://doi.org/10.1109/ISBI.2015.7164036
Yang T, Wang J, Sun Q, Hibar DP, Jahanshad N, Liu L, Wang Y, Zhan L, Thompson PM, Ye J. Detecting Genetic Risk Factors for Alzheimer's Disease in Whole Genome Sequence Data via Lasso Screening. Proc IEEE Int Symp Biomed Imaging. 2015 Apr;2015:985-989. doi: 10.1109/ISBI.2015.7164036. PMID: 26413209; PMCID: PMC4578226.
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Gene interactions and structural brain change in early-onset Alzheimer's disease subjects using the pipeline environment.

Psychiatry investigation

Moon SW, Dinov ID, Zamanyan A, Shi R, Genco A, Hobel S, Thompson PM, Toga AW.
PMID: 25670955
Psychiatry Investig. 2015 Jan;12(1):125-35. doi: 10.4306/pi.2015.12.1.125. Epub 2015 Jan 12.

OBJECTIVE: This article investigates subjects aged 55 to 65 from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to broaden our understanding of early-onset (EO) cognitive impairment using neuroimaging and genetics biomarkers.METHODS: Nine of the subjects had EO-AD (Alzheimer's disease)...

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Moon SW, Dinov ID, Zamanyan A, et al. Gene interactions and structural brain change in early-onset Alzheimer's disease subjects using the pipeline environment. Psychiatry Investig. 2015;12(1):125-35doi: 10.4306/pi.2015.12.1.125.
Moon, S. W., Dinov, I. D., Zamanyan, A., Shi, R., Genco, A., Hobel, S., Thompson, P. M., Toga, A. W. (2015). Gene interactions and structural brain change in early-onset Alzheimer's disease subjects using the pipeline environment. Psychiatry investigation, 12(1), 125-35. https://doi.org/10.4306/pi.2015.12.1.125
Moon, Seok Woo, et al. "Gene interactions and structural brain change in early-onset Alzheimer's disease subjects using the pipeline environment." Psychiatry investigation vol. 12,1 (2015): 125-35. doi: https://doi.org/10.4306/pi.2015.12.1.125
Moon SW, Dinov ID, Zamanyan A, Shi R, Genco A, Hobel S, Thompson PM, Toga AW. Gene interactions and structural brain change in early-onset Alzheimer's disease subjects using the pipeline environment. Psychiatry Investig. 2015 Jan;12(1):125-35. doi: 10.4306/pi.2015.12.1.125. Epub 2015 Jan 12. PMID: 25670955; PMCID: PMC4310910.
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Communication of brain network core connections altered in behavioral variant frontotemporal dementia but possibly preserved in early-onset Alzheimer's disease.

Proceedings of SPIE--the International Society for Optical Engineering

Daianu M, Jahanshad N, Mendez MF, Bartzokis G, Jimenez EE, Thompson PM.
PMID: 25848494
Proc SPIE Int Soc Opt Eng. 2015 Mar 20;9413. doi: 10.1117/12.2082352.

Diffusion imaging and brain connectivity analyses can assess white matter deterioration in the brain, revealing the underlying patterns of how brain structure declines. Fiber tractography methods can infer neural pathways and connectivity patterns, yielding sensitive mathematical metrics of network...

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Daianu M, Jahanshad N, Mendez MF, et al. Communication of brain network core connections altered in behavioral variant frontotemporal dementia but possibly preserved in early-onset Alzheimer's disease. Proc SPIE Int Soc Opt Eng. 2015;9413doi: 10.1117/12.2082352.
Daianu, M., Jahanshad, N., Mendez, M. F., Bartzokis, G., Jimenez, E. E., & Thompson, P. M. (2015). Communication of brain network core connections altered in behavioral variant frontotemporal dementia but possibly preserved in early-onset Alzheimer's disease. Proceedings of SPIE--the International Society for Optical Engineering, 9413. https://doi.org/10.1117/12.2082352
Daianu, Madelaine, et al. "Communication of brain network core connections altered in behavioral variant frontotemporal dementia but possibly preserved in early-onset Alzheimer's disease." Proceedings of SPIE--the International Society for Optical Engineering vol. 9413 (2015). doi: https://doi.org/10.1117/12.2082352
Daianu M, Jahanshad N, Mendez MF, Bartzokis G, Jimenez EE, Thompson PM. Communication of brain network core connections altered in behavioral variant frontotemporal dementia but possibly preserved in early-onset Alzheimer's disease. Proc SPIE Int Soc Opt Eng. 2015 Mar 20;9413. doi: 10.1117/12.2082352. PMID: 25848494; PMCID: PMC4384394.
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Brain Imaging Genomics: Integrated Analysis and Machine Learning.

Proceedings of the IEEE. Institute of Electrical and Electronics Engineers

Shen L, Thompson PM.
PMID: 31902950
Proc IEEE Inst Electr Electron Eng. 2020 Jan;108(1):125-162. doi: 10.1109/JPROC.2019.2947272. Epub 2019 Oct 29.

Brain imaging genomics is an emerging data science field, where integrated analysis of brain imaging and genomics data, often combined with other biomarker, clinical and environmental data, is performed to gain new insights into the phenotypic, genetic and molecular...

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Shen L, Thompson PM. Brain Imaging Genomics: Integrated Analysis and Machine Learning. Proc IEEE Inst Electr Electron Eng. 2019;108(1):125-162doi: 10.1109/JPROC.2019.2947272.
Shen, L., & Thompson, P. M. (2020). Brain Imaging Genomics: Integrated Analysis and Machine Learning. Proceedings of the IEEE. Institute of Electrical and Electronics Engineers, 108(1), 125-162. https://doi.org/10.1109/JPROC.2019.2947272
Shen, Li, and Thompson, Paul M. "Brain Imaging Genomics: Integrated Analysis and Machine Learning." Proceedings of the IEEE. Institute of Electrical and Electronics Engineers vol. 108,1 (2020): 125-162. doi: https://doi.org/10.1109/JPROC.2019.2947272
Shen L, Thompson PM. Brain Imaging Genomics: Integrated Analysis and Machine Learning. Proc IEEE Inst Electr Electron Eng. 2020 Jan;108(1):125-162. doi: 10.1109/JPROC.2019.2947272. Epub 2019 Oct 29. PMID: 31902950; PMCID: PMC6941751.
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The C677T variant in .

Biological psychiatry. Cognitive neuroscience and neuroimaging

Roussotte FF, Hua X, Narr KL, Small GW, Thompson PM.
PMID: 28435933
Biol Psychiatry Cogn Neurosci Neuroimaging. 2017 Apr;2(3):280-288. doi: 10.1016/j.bpsc.2016.09.005.

INTRODUCTION: The C677T functional variant in the methylene-tetrahydrofolate reductase (METHODS AND MATERIALS: Here, 3D magnetic resonance imaging data was analyzed from 738 individuals (age: 75.5 ± 6.8 years; 438 men/300 women) including 173 Alzheimer's patients, 359 subjects with mild...

Cite
Roussotte FF, Hua X, Narr KL, et al. The C677T variant in . Biol Psychiatry Cogn Neurosci Neuroimaging. 2017;2(3):280-288doi: 10.1016/j.bpsc.2016.09.005.
Roussotte, F. F., Hua, X., Narr, K. L., Small, G. W., Thompson, P. M. (2017). The C677T variant in . Biological psychiatry. Cognitive neuroscience and neuroimaging, 2(3), 280-288. https://doi.org/10.1016/j.bpsc.2016.09.005
Roussotte, Florence F, et al. "The C677T variant in ." Biological psychiatry. Cognitive neuroscience and neuroimaging vol. 2,3 (2017): 280-288. doi: https://doi.org/10.1016/j.bpsc.2016.09.005
Roussotte FF, Hua X, Narr KL, Small GW, Thompson PM. The C677T variant in . Biol Psychiatry Cogn Neurosci Neuroimaging. 2017 Apr;2(3):280-288. doi: 10.1016/j.bpsc.2016.09.005. PMID: 28435933; PMCID: PMC5395287.
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