Advanced Search
Display options
Filter resources
Text Availability
Article type
Publication date
Species
Language
Sex
Age
Showing 1 to 12 of 171 entries
Sorted by: Best Match Show Resources per page
Heritability of Hippocampal Formation Sub-region Volumes.

Journal of neurology and neuroscience

Greenspan KS, Arakelian CR, van Erp TGM.
PMID: 28503392
J Neurol Neurosci. 2016;7(6). doi: 10.21767/2171-6625.1000159. Epub 2016 Nov 14.

BACKGROUND: Hippocampal formation (HF) volume and episodic memory performance are substantially heritable, but HF subregion heritability estimates and their possible shared genetic variance with episodic memory performance remain to be determined.METHODS AND FINDINGS: This study provides heritability estimates for...

Cite
Greenspan KS, Arakelian CR, van Erp TGM. Heritability of Hippocampal Formation Sub-region Volumes. J Neurol Neurosci. 2016;7(6)doi: 10.21767/2171-6625.1000159.
Greenspan, K. S., Arakelian, C. R., & van Erp, T. G. M. (2016). Heritability of Hippocampal Formation Sub-region Volumes. Journal of neurology and neuroscience, 7(6), . https://doi.org/10.21767/2171-6625.1000159
Greenspan, Kiefer S, et al. "Heritability of Hippocampal Formation Sub-region Volumes." Journal of neurology and neuroscience vol. 7,6 (2016). doi: https://doi.org/10.21767/2171-6625.1000159
Greenspan KS, Arakelian CR, van Erp TGM. Heritability of Hippocampal Formation Sub-region Volumes. J Neurol Neurosci. 2016;7(6). doi: 10.21767/2171-6625.1000159. Epub 2016 Nov 14. PMID: 28503392; PMCID: PMC5424602.
Copy Download .nbib Format: NLM
APPLYING SPARSE CODING TO SURFACE MULTIVARIATE TENSOR-BASED MORPHOMETRY TO PREDICT FUTURE COGNITIVE DECLINE.

Proceedings. IEEE International Symposium on Biomedical Imaging

Zhang J, Stonnington C, Li Q, Shi J, Bauer RJ, Gutman BA, Chen K, Reiman EM, Thompson PM, Ye J, Wang Y.
PMID: 27499829
Proc IEEE Int Symp Biomed Imaging. 2016 Apr;2016:646-650. doi: 10.1109/ISBI.2016.7493350.

Alzheimer's disease (AD) is a progressive brain disease. Accurate diagnosis of AD and its prodromal stage, mild cognitive impairment, is crucial for clinical trial design. There is also growing interests in identifying brain imaging biomarkers that help evaluate AD...

Cite
Zhang J, Stonnington C, Li Q, et al. APPLYING SPARSE CODING TO SURFACE MULTIVARIATE TENSOR-BASED MORPHOMETRY TO PREDICT FUTURE COGNITIVE DECLINE. Proc IEEE Int Symp Biomed Imaging. 2016;2016:646-650doi: 10.1109/ISBI.2016.7493350.
Zhang, J., Stonnington, C., Li, Q., Shi, J., Bauer, R. J., Gutman, B. A., Chen, K., Reiman, E. M., Thompson, P. M., Ye, J., & Wang, Y. (2016). APPLYING SPARSE CODING TO SURFACE MULTIVARIATE TENSOR-BASED MORPHOMETRY TO PREDICT FUTURE COGNITIVE DECLINE. Proceedings. IEEE International Symposium on Biomedical Imaging, 2016646-650. https://doi.org/10.1109/ISBI.2016.7493350
Zhang, Jie, et al. "APPLYING SPARSE CODING TO SURFACE MULTIVARIATE TENSOR-BASED MORPHOMETRY TO PREDICT FUTURE COGNITIVE DECLINE." Proceedings. IEEE International Symposium on Biomedical Imaging vol. 2016 (2016): 646-650. doi: https://doi.org/10.1109/ISBI.2016.7493350
Zhang J, Stonnington C, Li Q, Shi J, Bauer RJ, Gutman BA, Chen K, Reiman EM, Thompson PM, Ye J, Wang Y. APPLYING SPARSE CODING TO SURFACE MULTIVARIATE TENSOR-BASED MORPHOMETRY TO PREDICT FUTURE COGNITIVE DECLINE. Proc IEEE Int Symp Biomed Imaging. 2016 Apr;2016:646-650. doi: 10.1109/ISBI.2016.7493350. PMID: 27499829; PMCID: PMC4974012.
Copy Download .nbib Format: NLM
Cracking the brain's genetic code.

Proceedings of the National Academy of Sciences of the United States of America

Thompson PM.
PMID: 26582794
Proc Natl Acad Sci U S A. 2015 Dec 15;112(50):15269-70. doi: 10.1073/pnas.1520702112. Epub 2015 Nov 18.

No abstract available.

Cite
Thompson PM. Cracking the brain's genetic code. Proc Natl Acad Sci U S A. 2015;112(50):15269-70doi: 10.1073/pnas.1520702112.
Thompson, P. M. (2015). Cracking the brain's genetic code. Proceedings of the National Academy of Sciences of the United States of America, 112(50), 15269-70. https://doi.org/10.1073/pnas.1520702112
Thompson, Paul M. "Cracking the brain's genetic code." Proceedings of the National Academy of Sciences of the United States of America vol. 112,50 (2015): 15269-70. doi: https://doi.org/10.1073/pnas.1520702112
Thompson PM. Cracking the brain's genetic code. Proc Natl Acad Sci U S A. 2015 Dec 15;112(50):15269-70. doi: 10.1073/pnas.1520702112. Epub 2015 Nov 18. PMID: 26582794; PMCID: PMC4687571.
Copy Download .nbib Format: NLM
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...

Cite
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.
Copy Download .nbib Format: NLM
AUTOMATED MULTI-ATLAS LABELING OF THE FORNIX AND ITS INTEGRITY IN ALZHEIMER'S DISEASE.

Proceedings. IEEE International Symposium on Biomedical Imaging

Jin Y, Shi Y, Zhan L, Thompson PM.
PMID: 26413203
Proc IEEE Int Symp Biomed Imaging. 2015 Apr;2015:140-143. doi: 10.1109/ISBI.2015.7163835.

Alzheimer's disease is the most common form of dementia. Diffusion imaging provides information on white matter integrity not available with standard MRI, revealing additional information on how Alzheimer's disease affects the brain. Here we implemented and tested a multi-atlas...

Cite
Jin Y, Shi Y, Zhan L, et al. AUTOMATED MULTI-ATLAS LABELING OF THE FORNIX AND ITS INTEGRITY IN ALZHEIMER'S DISEASE. Proc IEEE Int Symp Biomed Imaging. 2015;2015:140-143doi: 10.1109/ISBI.2015.7163835.
Jin, Y., Shi, Y., Zhan, L., Thompson, P. M. (2015). AUTOMATED MULTI-ATLAS LABELING OF THE FORNIX AND ITS INTEGRITY IN ALZHEIMER'S DISEASE. Proceedings. IEEE International Symposium on Biomedical Imaging, 2015140-143. https://doi.org/10.1109/ISBI.2015.7163835
Jin, Yan, et al. "AUTOMATED MULTI-ATLAS LABELING OF THE FORNIX AND ITS INTEGRITY IN ALZHEIMER'S DISEASE." Proceedings. IEEE International Symposium on Biomedical Imaging vol. 2015 (2015): 140-143. doi: https://doi.org/10.1109/ISBI.2015.7163835
Jin Y, Shi Y, Zhan L, Thompson PM. AUTOMATED MULTI-ATLAS LABELING OF THE FORNIX AND ITS INTEGRITY IN ALZHEIMER'S DISEASE. Proc IEEE Int Symp Biomed Imaging. 2015 Apr;2015:140-143. doi: 10.1109/ISBI.2015.7163835. PMID: 26413203; PMCID: PMC4578317.
Copy Download .nbib Format: NLM
Response to Dr Fried & Dr Kievit, and Dr Malhi et al.

Molecular psychiatry

Schmaal L, Veltman DJ, van Erp TG, Sämann PG, Frodl T, Jahanshad N, Loehrer E, Vernooij MW, Niessen WJ, Ikram MA, Wittfeld K, Grabe HJ, Block A, Hegenscheid K, Hoehn D, Czisch M, Lagopoulos J, Hatton SN, Hickie IB, Goya-Maldonado R, Krämer B, Gruber O, Couvy-Duchesne B, Rentería ME, Strike LT, Wright MJ, de Zubicaray GI, McMahon KL, Medland SE, Gillespie NA, Hall GB, van Velzen LS, van Tol MJ, van der Wee NJ, Veer IM, Walter H, Schramm E, Normann C, Schoepf D, Konrad C, Zurowski B, McIntosh AM, Whalley HC, Sussmann JE, Godlewska BR, Fischer FH, Penninx BW, Thompson PM, Hibar DP.
PMID: 26903270
Mol Psychiatry. 2016 Jun;21(6):726-8. doi: 10.1038/mp.2016.9. Epub 2016 Feb 23.

No abstract available.

Cite
Schmaal L, Veltman DJ, van Erp TG, et al. Response to Dr Fried & Dr Kievit, and Dr Malhi et al. Mol Psychiatry. 2016;21(6):726-8doi: 10.1038/mp.2016.9.
Schmaal, L., Veltman, D. J., van Erp, T. G., Sämann, P. G., Frodl, T., Jahanshad, N., Loehrer, E., Vernooij, M. W., Niessen, W. J., Ikram, M. A., Wittfeld, K., Grabe, H. J., Block, A., Hegenscheid, K., Hoehn, D., Czisch, M., Lagopoulos, J., Hatton, S. N., Hickie, I. B., Goya-Maldonado, R., Krämer, B., Gruber, O., Couvy-Duchesne, B., Rentería, M. E., Strike, L. T., Wright, M. J., de Zubicaray, G. I., McMahon, K. L., Medland, S. E., Gillespie, N. A., Hall, G. B., van Velzen, L. S., van Tol, M. J., van der Wee, N. J., Veer, I. M., Walter, H., Schramm, E., Normann, C., Schoepf, D., Konrad, C., Zurowski, B., McIntosh, A. M., Whalley, H. C., Sussmann, J. E., Godlewska, B. R., Fischer, F. H., Penninx, B. W., Thompson, P. M., & Hibar, D. P. (2016). Response to Dr Fried & Dr Kievit, and Dr Malhi et al. Molecular psychiatry, 21(6), 726-8. https://doi.org/10.1038/mp.2016.9
Schmaal, L, et al. "Response to Dr Fried & Dr Kievit, and Dr Malhi et al." Molecular psychiatry vol. 21,6 (2016): 726-8. doi: https://doi.org/10.1038/mp.2016.9
Schmaal L, Veltman DJ, van Erp TG, Sämann PG, Frodl T, Jahanshad N, Loehrer E, Vernooij MW, Niessen WJ, Ikram MA, Wittfeld K, Grabe HJ, Block A, Hegenscheid K, Hoehn D, Czisch M, Lagopoulos J, Hatton SN, Hickie IB, Goya-Maldonado R, Krämer B, Gruber O, Couvy-Duchesne B, Rentería ME, Strike LT, Wright MJ, de Zubicaray GI, McMahon KL, Medland SE, Gillespie NA, Hall GB, van Velzen LS, van Tol MJ, van der Wee NJ, Veer IM, Walter H, Schramm E, Normann C, Schoepf D, Konrad C, Zurowski B, McIntosh AM, Whalley HC, Sussmann JE, Godlewska BR, Fischer FH, Penninx BW, Thompson PM, Hibar DP. Response to Dr Fried & Dr Kievit, and Dr Malhi et al. Mol Psychiatry. 2016 Jun;21(6):726-8. doi: 10.1038/mp.2016.9. Epub 2016 Feb 23. PMID: 26903270; PMCID: PMC4876636.
Copy Download .nbib Format: NLM
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,

Cite
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.
Copy Download .nbib Format: NLM
Impact of family structure and common environment on heritability estimation for neuroimaging genetics studies using Sequential Oligogenic Linkage Analysis Routines.

Journal of medical imaging (Bellingham, Wash.)

Koran ME, Thornton-Wells TA, Jahanshad N, Glahn DC, Thompson PM, Blangero J, Nichols TE, Kochunov P, Landman BA.
PMID: 25558465
J Med Imaging (Bellingham). 2014 Jun 27;1(1):014005. doi: 10.1117/1.JMI.1.1.014005.

Imaging genetics is an emerging methodological field that combines genetic information with medical imaging-derived metrics to understand how genetic factors impact observable phenotypes. In order for a trait to be a reasonable phenotype in an imaging genetics study, it...

Cite
Koran ME, Thornton-Wells TA, Jahanshad N, et al. Impact of family structure and common environment on heritability estimation for neuroimaging genetics studies using Sequential Oligogenic Linkage Analysis Routines. J Med Imaging (Bellingham). 2014;1(1):014005doi: 10.1117/1.JMI.1.1.014005.
Koran, M. E., Thornton-Wells, T. A., Jahanshad, N., Glahn, D. C., Thompson, P. M., Blangero, J., Nichols, T. E., Kochunov, P., & Landman, B. A. (2014). Impact of family structure and common environment on heritability estimation for neuroimaging genetics studies using Sequential Oligogenic Linkage Analysis Routines. Journal of medical imaging (Bellingham, Wash.), 1(1), 014005. https://doi.org/10.1117/1.JMI.1.1.014005
Koran, Mary Ellen, et al. "Impact of family structure and common environment on heritability estimation for neuroimaging genetics studies using Sequential Oligogenic Linkage Analysis Routines." Journal of medical imaging (Bellingham, Wash.) vol. 1,1 (2014): 014005. doi: https://doi.org/10.1117/1.JMI.1.1.014005
Koran ME, Thornton-Wells TA, Jahanshad N, Glahn DC, Thompson PM, Blangero J, Nichols TE, Kochunov P, Landman BA. Impact of family structure and common environment on heritability estimation for neuroimaging genetics studies using Sequential Oligogenic Linkage Analysis Routines. J Med Imaging (Bellingham). 2014 Jun 27;1(1):014005. doi: 10.1117/1.JMI.1.1.014005. PMID: 25558465; PMCID: PMC4281883.
Copy Download .nbib Format: NLM
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...

Cite
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.
Copy Download .nbib Format: NLM
An Optimal Transportation based Univariate Neuroimaging Index.

Proceedings. IEEE International Conference on Computer Vision

Mi L, Zhang W, Zhang J, Fan Y, Goradia D, Chen K, Reiman EM, Gu X, Wang Y.
PMID: 29225556
Proc IEEE Int Conf Comput Vis. 2017;2017:182-191.

The alterations of brain structures and functions have been considered closely correlated to the change of cognitive performance due to neurodegenerative diseases such as Alzheimer's disease. In this paper, we introduce a variational framework to compute the optimal transformation...

Cite
Mi L, Zhang W, Zhang J, et al. An Optimal Transportation based Univariate Neuroimaging Index. Proc IEEE Int Conf Comput Vis. 2017;2017:182-191
Mi, L., Zhang, W., Zhang, J., Fan, Y., Goradia, D., Chen, K., Reiman, E. M., Gu, X., & Wang, Y. (2017). An Optimal Transportation based Univariate Neuroimaging Index. Proceedings. IEEE International Conference on Computer Vision, 2017182-191.
Mi, Liang, et al. "An Optimal Transportation based Univariate Neuroimaging Index." Proceedings. IEEE International Conference on Computer Vision vol. 2017 (2017): 182-191.
Mi L, Zhang W, Zhang J, Fan Y, Goradia D, Chen K, Reiman EM, Gu X, Wang Y. An Optimal Transportation based Univariate Neuroimaging Index. Proc IEEE Int Conf Comput Vis. 2017;2017:182-191. PMID: 29225556; PMCID: PMC5719504.
Copy Download .nbib Format: NLM
The ENIGMA-Epilepsy working group: Mapping disease from large data sets.

Human brain mapping

Sisodiya SM, Whelan CD, Hatton SN, Huynh K, Altmann A, Ryten M, Vezzani A, Caligiuri ME, Labate A, Gambardella A, Ives-Deliperi V, Meletti S, Munsell BC, Bonilha L, Tondelli M, Rebsamen M, Rummel C, Vaudano AE, Wiest R, Balachandra AR, Bargalló N, Bartolini E, Bernasconi A, Bernasconi N, Bernhardt B, Caldairou B, Carr SJA, Cavalleri GL, Cendes F, Concha L, Desmond PM, Domin M, Duncan JS, Focke NK, Guerrini R, Hamandi K, Jackson GD, Jahanshad N, Kälviäinen R, Keller SS, Kochunov P, Kowalczyk MA, Kreilkamp BAK, Kwan P, Lariviere S, Lenge M, Lopez SM, Martin P, Mascalchi M, Moreira JCV, Morita-Sherman ME, Pardoe HR, Pariente JC, Raviteja K, Rocha CS, Rodríguez-Cruces R, Seeck M, Semmelroch MKHG, Sinclair B, Soltanian-Zadeh H, Stein DJ, Striano P, Taylor PN, Thomas RH, Thomopoulos SI, Velakoulis D, Vivash L, Weber B, Yasuda CL, Zhang J, Thompson PM, McDonald CR.
PMID: 32468614
Hum Brain Mapp. 2020 May 29; doi: 10.1002/hbm.25037. Epub 2020 May 29.

Epilepsy is a common and serious neurological disorder, with many different constituent conditions characterized by their electro clinical, imaging, and genetic features. MRI has been fundamental in advancing our understanding of brain processes in the epilepsies. Smaller-scale studies have...

Cite
Sisodiya SM, Whelan CD, Hatton SN, et al. The ENIGMA-Epilepsy working group: Mapping disease from large data sets. Hum Brain Mapp. 2020;doi: 10.1002/hbm.25037.
Sisodiya, S. M., Whelan, C. D., Hatton, S. N., Huynh, K., Altmann, A., Ryten, M., Vezzani, A., Caligiuri, M. E., Labate, A., Gambardella, A., Ives-Deliperi, V., Meletti, S., Munsell, B. C., Bonilha, L., Tondelli, M., Rebsamen, M., Rummel, C., Vaudano, A. E., Wiest, R., Balachandra, A. R., Bargalló, N., Bartolini, E., Bernasconi, A., Bernasconi, N., Bernhardt, B., Caldairou, B., Carr, S. J. A., Cavalleri, G. L., Cendes, F., Concha, L., Desmond, P. M., Domin, M., Duncan, J. S., Focke, N. K., Guerrini, R., Hamandi, K., Jackson, G. D., Jahanshad, N., Kälviäinen, R., Keller, S. S., Kochunov, P., Kowalczyk, M. A., Kreilkamp, B. A. K., Kwan, P., Lariviere, S., Lenge, M., Lopez, S. M., Martin, P., Mascalchi, M., Moreira, J. C. V., Morita-Sherman, M. E., Pardoe, H. R., Pariente, J. C., Raviteja, K., Rocha, C. S., Rodríguez-Cruces, R., Seeck, M., Semmelroch, M. K. H. G., Sinclair, B., Soltanian-Zadeh, H., Stein, D. J., Striano, P., Taylor, P. N., Thomas, R. H., Thomopoulos, S. I., Velakoulis, D., Vivash, L., Weber, B., Yasuda, C. L., Zhang, J., Thompson, P. M., McDonald, C. R. (2020). The ENIGMA-Epilepsy working group: Mapping disease from large data sets. Human brain mapping, . https://doi.org/10.1002/hbm.25037
Sisodiya, Sanjay M, et al. "The ENIGMA-Epilepsy working group: Mapping disease from large data sets." Human brain mapping vol. (2020). doi: https://doi.org/10.1002/hbm.25037
Sisodiya SM, Whelan CD, Hatton SN, Huynh K, Altmann A, Ryten M, Vezzani A, Caligiuri ME, Labate A, Gambardella A, Ives-Deliperi V, Meletti S, Munsell BC, Bonilha L, Tondelli M, Rebsamen M, Rummel C, Vaudano AE, Wiest R, Balachandra AR, Bargalló N, Bartolini E, Bernasconi A, Bernasconi N, Bernhardt B, Caldairou B, Carr SJA, Cavalleri GL, Cendes F, Concha L, Desmond PM, Domin M, Duncan JS, Focke NK, Guerrini R, Hamandi K, Jackson GD, Jahanshad N, Kälviäinen R, Keller SS, Kochunov P, Kowalczyk MA, Kreilkamp BAK, Kwan P, Lariviere S, Lenge M, Lopez SM, Martin P, Mascalchi M, Moreira JCV, Morita-Sherman ME, Pardoe HR, Pariente JC, Raviteja K, Rocha CS, Rodríguez-Cruces R, Seeck M, Semmelroch MKHG, Sinclair B, Soltanian-Zadeh H, Stein DJ, Striano P, Taylor PN, Thomas RH, Thomopoulos SI, Velakoulis D, Vivash L, Weber B, Yasuda CL, Zhang J, Thompson PM, McDonald CR. The ENIGMA-Epilepsy working group: Mapping disease from large data sets. Hum Brain Mapp. 2020 May 29; doi: 10.1002/hbm.25037. Epub 2020 May 29. PMID: 32468614.
Copy Download .nbib Format: NLM
Genetic contributors to risk of schizophrenia in the presence of a 22q11.2 deletion.

Molecular psychiatry

Cleynen I, Engchuan W, Hestand MS, Heung T, Holleman AM, Johnston HR, Monfeuga T, McDonald-McGinn DM, Gur RE, Morrow BE, Swillen A, Vorstman JAS, Bearden CE, Chow EWC, van den Bree M, Emanuel BS, Vermeesch JR, Warren ST, Owen MJ, Chopra P, Cutler DJ, Duncan R, Kotlar AV, Mulle JG, Voss AJ, Zwick ME, Diacou A, Golden A, Guo T, Lin JR, Wang T, Zhang Z, Zhao Y, Marshall C, Merico D, Jin A, Lilley B, Salmons HI, Tran O, Holmans P, Pardinas A, Walters JTR, Demaerel W, Boot E, Butcher NJ, Costain GA, Lowther C, Evers R, van Amelsvoort TAMJ, van Duin E, Vingerhoets C, Breckpot J, Devriendt K, Vergaelen E, Vogels A, Crowley TB, McGinn DE, Moss EM, Sharkus RJ, Unolt M, Zackai EH, Calkins ME, Gallagher RS, Gur RC, Tang SX, Fritsch R, Ornstein C, Repetto GM, Breetvelt E, Duijff SN, Fiksinski A, Moss H, Niarchou M, Murphy KC, Prasad SE, Daly EM, Gudbrandsen M, Murphy CM, Murphy DG, Buzzanca A, Fabio FD, Digilio MC, Pontillo M, Marino B, Vicari S, Coleman K, Cubells JF, Ousley OY, Carmel M, Gothelf D, Mekori-Domachevsky E, Michaelovsky E, Weinberger R, Weizman A, Kushan L, Jalbrzikowski M, Armando M, Eliez S, Sandini C, Schneider M, Béna FS, Antshel KM, Fremont W, Kates WR, Belzeaux R, Busa T, Philip N, Campbell LE, McCabe KL, Hooper SR, Schoch K, Shashi V, Simon TJ, Tassone F, Arango C, Fraguas D, García-Miñaúr S, Morey-Canyelles J, Rosell J, Suñer DH, Raventos-Simic J, Epstein MP, Williams NM, Bassett AS.
PMID: 32015465
Mol Psychiatry. 2021 Aug;26(8):4496-4510. doi: 10.1038/s41380-020-0654-3. Epub 2020 Feb 03.

Schizophrenia occurs in about one in four individuals with 22q11.2 deletion syndrome (22q11.2DS). The aim of this International Brain and Behavior 22q11.2DS Consortium (IBBC) study was to identify genetic factors that contribute to schizophrenia, in addition to the ~20-fold...

Cite
Cleynen I, Engchuan W, Hestand MS, et al. Genetic contributors to risk of schizophrenia in the presence of a 22q11.2 deletion. Mol Psychiatry. 2020;26(8):4496-4510doi: 10.1038/s41380-020-0654-3.
Cleynen, I., Engchuan, W., Hestand, M. S., Heung, T., Holleman, A. M., Johnston, H. R., Monfeuga, T., McDonald-McGinn, D. M., Gur, R. E., Morrow, B. E., Swillen, A., Vorstman, J. A. S., Bearden, C. E., Chow, E. W. C., van den Bree, M., Emanuel, B. S., Vermeesch, J. R., Warren, S. T., Owen, M. J., Chopra, P., Cutler, D. J., Duncan, R., Kotlar, A. V., Mulle, J. G., Voss, A. J., Zwick, M. E., Diacou, A., Golden, A., Guo, T., Lin, J. R., Wang, T., Zhang, Z., Zhao, Y., Marshall, C., Merico, D., Jin, A., Lilley, B., Salmons, H. I., Tran, O., Holmans, P., Pardinas, A., Walters, J. T. R., Demaerel, W., Boot, E., Butcher, N. J., Costain, G. A., Lowther, C., Evers, R., van Amelsvoort, T. A. M. J., van Duin, E., Vingerhoets, C., Breckpot, J., Devriendt, K., Vergaelen, E., Vogels, A., Crowley, T. B., McGinn, D. E., Moss, E. M., Sharkus, R. J., Unolt, M., Zackai, E. H., Calkins, M. E., Gallagher, R. S., Gur, R. C., Tang, S. X., Fritsch, R., Ornstein, C., Repetto, G. M., Breetvelt, E., Duijff, S. N., Fiksinski, A., Moss, H., Niarchou, M., Murphy, K. C., Prasad, S. E., Daly, E. M., Gudbrandsen, M., Murphy, C. M., Murphy, D. G., Buzzanca, A., Fabio, F. D., Digilio, M. C., Pontillo, M., Marino, B., Vicari, S., Coleman, K., Cubells, J. F., Ousley, O. Y., Carmel, M., Gothelf, D., Mekori-Domachevsky, E., Michaelovsky, E., Weinberger, R., Weizman, A., Kushan, L., Jalbrzikowski, M., Armando, M., Eliez, S., Sandini, C., Schneider, M., Béna, F. S., Antshel, K. M., Fremont, W., Kates, W. R., Belzeaux, R., Busa, T., Philip, N., Campbell, L. E., McCabe, K. L., Hooper, S. R., Schoch, K., Shashi, V., Simon, T. J., Tassone, F., Arango, C., Fraguas, D., García-Miñaúr, S., Morey-Canyelles, J., Rosell, J., Suñer, D. H., Raventos-Simic, J., Epstein, M. P., Williams, N. M., & Bassett, A. S. (2021). Genetic contributors to risk of schizophrenia in the presence of a 22q11.2 deletion. Molecular psychiatry, 26(8), 4496-4510. https://doi.org/10.1038/s41380-020-0654-3
Cleynen, Isabelle, et al. "Genetic contributors to risk of schizophrenia in the presence of a 22q11.2 deletion." Molecular psychiatry vol. 26,8 (2021): 4496-4510. doi: https://doi.org/10.1038/s41380-020-0654-3
Cleynen I, Engchuan W, Hestand MS, Heung T, Holleman AM, Johnston HR, Monfeuga T, McDonald-McGinn DM, Gur RE, Morrow BE, Swillen A, Vorstman JAS, Bearden CE, Chow EWC, van den Bree M, Emanuel BS, Vermeesch JR, Warren ST, Owen MJ, Chopra P, Cutler DJ, Duncan R, Kotlar AV, Mulle JG, Voss AJ, Zwick ME, Diacou A, Golden A, Guo T, Lin JR, Wang T, Zhang Z, Zhao Y, Marshall C, Merico D, Jin A, Lilley B, Salmons HI, Tran O, Holmans P, Pardinas A, Walters JTR, Demaerel W, Boot E, Butcher NJ, Costain GA, Lowther C, Evers R, van Amelsvoort TAMJ, van Duin E, Vingerhoets C, Breckpot J, Devriendt K, Vergaelen E, Vogels A, Crowley TB, McGinn DE, Moss EM, Sharkus RJ, Unolt M, Zackai EH, Calkins ME, Gallagher RS, Gur RC, Tang SX, Fritsch R, Ornstein C, Repetto GM, Breetvelt E, Duijff SN, Fiksinski A, Moss H, Niarchou M, Murphy KC, Prasad SE, Daly EM, Gudbrandsen M, Murphy CM, Murphy DG, Buzzanca A, Fabio FD, Digilio MC, Pontillo M, Marino B, Vicari S, Coleman K, Cubells JF, Ousley OY, Carmel M, Gothelf D, Mekori-Domachevsky E, Michaelovsky E, Weinberger R, Weizman A, Kushan L, Jalbrzikowski M, Armando M, Eliez S, Sandini C, Schneider M, Béna FS, Antshel KM, Fremont W, Kates WR, Belzeaux R, Busa T, Philip N, Campbell LE, McCabe KL, Hooper SR, Schoch K, Shashi V, Simon TJ, Tassone F, Arango C, Fraguas D, García-Miñaúr S, Morey-Canyelles J, Rosell J, Suñer DH, Raventos-Simic J, Epstein MP, Williams NM, Bassett AS. Genetic contributors to risk of schizophrenia in the presence of a 22q11.2 deletion. Mol Psychiatry. 2021 Aug;26(8):4496-4510. doi: 10.1038/s41380-020-0654-3. Epub 2020 Feb 03. PMID: 32015465; PMCID: PMC7396297.
Copy Download .nbib Format: NLM
Showing 1 to 12 of 171 entries