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10 results on '"Schwartz, Y."'

1. The role of diversity in data-driven analysis of multi-subject fMRI data: Comparison of approaches based on independence and sparsity using global performance metrics.

Author

Long Q, Bhinge S, Levin-Schwartz Y, Boukouvalas Z, Calhoun VD, and Adalı T

Subjects
Adult, Brain diagnostic imaging, Brain physiopathology, Female, Functional Neuroimaging standards, Humans, Image Processing, Computer-Assisted standards, Magnetic Resonance Imaging, Male, Middle Aged, Nerve Net diagnostic imaging, Nerve Net physiopathology, Principal Component Analysis, Schizophrenia diagnostic imaging, Algorithms, Brain physiology, Data Interpretation, Statistical, Functional Neuroimaging methods, Image Processing, Computer-Assisted methods, Nerve Net physiology, Schizophrenia physiopathology
Abstract

Data-driven methods have been widely used in functional magnetic resonance imaging (fMRI) data analysis. They extract latent factors, generally, through the use of a simple generative model. Independent component analysis (ICA) and dictionary learning (DL) are two popular data-driven methods that are based on two different forms of diversity-statistical properties of the data-statistical independence for ICA and sparsity for DL. Despite their popularity, the comparative advantage of emphasizing one property over another in the decomposition of fMRI data is not well understood. Such a comparison is made harder due to the differences in the modeling assumptions between ICA and DL, as well as within different ICA algorithms where each algorithm exploits a different form of diversity. In this paper, we propose the use of objective global measures, such as time course frequency power ratio, network connection summary, and graph theoretical metrics, to gain insight into the role that different types of diversity have on the analysis of fMRI data. Four ICA algorithms that account for different types of diversity and one DL algorithm are studied. We apply these algorithms to real fMRI data collected from patients with schizophrenia and healthy controls. Our results suggest that no one particular method has the best performance using all metrics, implying that the optimal method will change depending on the goal of the analysis. However, we note that in none of the scenarios we test the highly popular Infomax provides the best performance, demonstrating the cost of exploiting limited form of diversity., (© 2018 Wiley Periodicals, Inc.)

Published
2019
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2. A method to compare the discriminatory power of data-driven methods: Application to ICA and IVA.

Author

Levin-Schwartz Y, Calhoun VD, and Adalı T

Subjects
Brain physiopathology, Data Interpretation, Statistical, Humans, Neuropsychological Tests, Schizophrenia physiopathology, Schizophrenic Psychology, Brain diagnostic imaging, Brain Mapping methods, Data Science methods, Image Processing, Computer-Assisted methods, Magnetic Resonance Imaging, Schizophrenia diagnostic imaging
Abstract

Background: The widespread application of data-driven factorization-based methods, such as independent component analysis (ICA), to functional magnetic resonance imaging data facilitates the study of neural function and how it is disrupted by psychiatric disorders such as schizophrenia. While the increasing number of these methods motivates a comparison of their relative performance, such a comparison is difficult to perform on real fMRI data, since the ground truth is, relatively, unknown and the alignment of factors across different methods is impractical and imprecise., New Method: We present a novel method, global difference maps (GDMs), to compare the results of different fMRI analysis techniques on real fMRI data, quantify their relative performances, and highlight the differences between the decompositions visually., Comparison With Existing Methods: We apply this method to compare the performances of two different factorization-based methods, ICA and its multiset extension independent vector analysis (IVA), for the analysis of fMRI data from 109 patients with schizophrenia and 138 healthy controls during the performance of three tasks., Results: Through this application of GDMs, we find that IVA can determine regions that are more discriminatory between patients and controls than ICA, though IVA is less effective at emphasizing regions found in only a subset of the tasks., Conclusions: These results demonstrate that GDMs are an effective way to compare the performances of different factorization-based methods as well as regression-based analyses., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Published
2019
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3. Atlases of cognition with large-scale human brain mapping.

Author

Varoquaux G, Schwartz Y, Poldrack RA, Gauthier B, Bzdok D, Poline JB, and Thirion B

Subjects
Area Under Curve, Bayes Theorem, Databases, Factual, Hearing, Humans, Magnetic Resonance Imaging, Motor Skills, ROC Curve, Reproducibility of Results, Brain physiology, Brain Mapping methods, Cognition physiology, Functional Neuroimaging methods, Neuroimaging methods
Abstract

To map the neural substrate of mental function, cognitive neuroimaging relies on controlled psychological manipulations that engage brain systems associated with specific cognitive processes. In order to build comprehensive atlases of cognitive function in the brain, it must assemble maps for many different cognitive processes, which often evoke overlapping patterns of activation. Such data aggregation faces contrasting goals: on the one hand finding correspondences across vastly different cognitive experiments, while on the other hand precisely describing the function of any given brain region. Here we introduce a new analysis framework that tackles these difficulties and thereby enables the generation of brain atlases for cognitive function. The approach leverages ontologies of cognitive concepts and multi-label brain decoding to map the neural substrate of these concepts. We demonstrate the approach by building an atlas of functional brain organization based on 30 diverse functional neuroimaging studies, totaling 196 different experimental conditions. Unlike conventional brain mapping, this functional atlas supports robust reverse inference: predicting the mental processes from brain activity in the regions delineated by the atlas. To establish that this reverse inference is indeed governed by the corresponding concepts, and not idiosyncrasies of experimental designs, we show that it can accurately decode the cognitive concepts recruited in new tasks. These results demonstrate that aggregating independent task-fMRI studies can provide a more precise global atlas of selective associations between brain and cognition., Competing Interests: The authors have declared that no competing interests exist regarding the content of the manuscript.

Published
2018
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4. The Brainomics/Localizer database.

Author

Papadopoulos Orfanos D, Michel V, Schwartz Y, Pinel P, Moreno A, Le Bihan D, and Frouin V

Subjects
Adolescent, Adult, Databases, Genetic, Female, Humans, Male, Middle Aged, Young Adult, Brain anatomy & histology, Brain diagnostic imaging, Brain physiology, Databases, Factual, Functional Neuroimaging, Magnetic Resonance Imaging
Abstract

The Brainomics/Localizer database exposes part of the data collected by the in-house Localizer project, which planned to acquire four types of data from volunteer research subjects: anatomical MRI scans, functional MRI data, behavioral and demographic data, and DNA sampling. Over the years, this local project has been collecting such data from hundreds of subjects. We had selected 94 of these subjects for their complete datasets, including all four types of data, as the basis for a prior publication; the Brainomics/Localizer database publishes the data associated with these 94 subjects. Since regulatory rules prevent us from making genetic data available for download, the database serves only anatomical MRI scans, functional MRI data, behavioral and demographic data. To publish this set of heterogeneous data, we use dedicated software based on the open-source CubicWeb semantic web framework. Through genericity in the data model and flexibility in the display of data (web pages, CSV, JSON, XML), CubicWeb helps us expose these complex datasets in original and efficient ways., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Published
2017
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5. Sample-poor estimation of order and common signal subspace with application to fusion of medical imaging data.

Author

Levin-Schwartz Y, Song Y, Schreier PJ, Calhoun VD, and Adalı T

Subjects
Humans, Multimodal Imaging, Multivariate Analysis, Principal Component Analysis, Schizophrenia diagnostic imaging, Brain diagnostic imaging, Brain Mapping methods, Electroencephalography methods, Magnetic Resonance Imaging methods
Abstract

Due to their data-driven nature, multivariate methods such as canonical correlation analysis (CCA) have proven very useful for fusion of multimodal neurological data. However, being able to determine the degree of similarity between datasets and appropriate order selection are crucial to the success of such techniques. The standard methods for calculating the order of multimodal data focus only on sources with the greatest individual energy and ignore relations across datasets. Additionally, these techniques as well as the most widely-used methods for determining the degree of similarity between datasets assume sufficient sample support and are not effective in the sample-poor regime. In this paper, we propose to jointly estimate the degree of similarity between datasets and their order when few samples are present using principal component analysis and canonical correlation analysis (PCA-CCA). By considering these two problems simultaneously, we are able to minimize the assumptions placed on the data and achieve superior performance in the sample-poor regime compared to traditional techniques. We apply PCA-CCA to the pairwise combinations of functional magnetic resonance imaging (fMRI), structural magnetic resonance imaging (sMRI), and electroencephalogram (EEG) data drawn from patients with schizophrenia and healthy controls while performing an auditory oddball task. The PCA-CCA results indicate that the fMRI and sMRI datasets are the most similar, whereas the sMRI and EEG datasets share the least similarity. We also demonstrate that the degree of similarity obtained by PCA-CCA is highly predictive of the degree of significance found for components generated using CCA., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Published
2016
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6. FTO, obesity and the adolescent brain.

Author

Melka MG, Gillis J, Bernard M, Abrahamowicz M, Chakravarty MM, Leonard GT, Perron M, Richer L, Veillette S, Banaschewski T, Barker GJ, Büchel C, Conrod P, Flor H, Heinz A, Garavan H, Brühl R, Mann K, Artiges E, Lourdusamy A, Lathrop M, Loth E, Schwartz Y, Frouin V, Rietschel M, Smolka MN, Ströhle A, Gallinat J, Struve M, Lattka E, Waldenberger M, Schumann G, Pavlidis P, Gaudet D, Paus T, and Pausova Z

Subjects
Adipose Tissue metabolism, Adiposity genetics, Adolescent, Alpha-Ketoglutarate-Dependent Dioxygenase FTO, Anthropometry, Body Mass Index, Brain metabolism, Female, Genetic Predisposition to Disease, Humans, Male, Obesity metabolism, Proteins metabolism, Brain anatomy & histology, Obesity genetics, Proteins genetics
Abstract

Genetic variations in fat mass- and obesity (FTO)-associated gene, a well-replicated gene locus of obesity, appear to be associated also with reduced regional brain volumes in elderly. Here, we examined whether FTO is associated with total brain volume in adolescence, thus exploring possible developmental effects of FTO. We studied a population-based sample of 598 adolescents recruited from the French Canadian founder population in whom we measured brain volume by magnetic resonance imaging. Total fat mass was assessed with bioimpedance and body mass index was determined with anthropometry. Genotype-phenotype associations were tested with Merlin under an additive model. We found that the G allele of FTO (rs9930333) was associated with higher total body fat [TBF (P = 0.002) and lower brain volume (P = 0.005)]. The same allele was also associated with higher lean body mass (P = 0.03) and no difference in height (P = 0.99). Principal component analysis identified a shared inverse variance between the brain volume and TBF, which was associated with FTO at P = 5.5 × 10(-6). These results were replicated in two independent samples of 413 and 718 adolescents, and in a meta-analysis of all three samples (n = 1729 adolescents), FTO was associated with this shared inverse variance at P = 1.3 × 10(-9). Co-expression networks analysis supported the possibility that the underlying FTO effects may occur during embryogenesis. In conclusion, FTO is associated with shared inverse variance between body adiposity and brain volume, suggesting that this gene may exert inverse effects on adipose and brain tissues. Given the completion of the overall brain growth in early childhood, these effects may have their origins during early development.

Published
2013
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7. Cohort-level brain mapping: learning cognitive atoms to single out specialized regions.

Author

Varoquaux G, Schwartz Y, Pinel P, and Thirion B

Subjects
Humans, Image Enhancement methods, Imaging, Three-Dimensional methods, Reproducibility of Results, Sensitivity and Specificity, Algorithms, Artificial Intelligence, Brain physiology, Brain Mapping methods, Image Interpretation, Computer-Assisted methods, Magnetic Resonance Imaging methods, Nerve Net physiology, Pattern Recognition, Automated methods
Abstract

Functional Magnetic Resonance Imaging (fMRI) studies map the human brain by testing the response of groups of individuals to carefully-crafted and contrasted tasks in order to delineate specialized brain regions and networks. The number of functional networks extracted is limited by the number of subject-level contrasts and does not grow with the cohort. Here, we introduce a new group-level brain mapping strategy to differentiate many regions reflecting the variety of brain network configurations observed in the population. Based on the principle of functional segregation, our approach singles out functionally-specialized brain regions by learning group-level functional profiles on which the response of brain regions can be represented sparsely. We use a dictionary-learning formulation that can be solved efficiently with on-line algorithms, scaling to arbitrary large datasets. Importantly, we model inter-subject correspondence as structure imposed in the estimated functional profiles, integrating a structure-inducing regularization with no additional computational cost. On a large multi-subject study, our approach extracts a large number of brain networks with meaningful functional profiles.

Published
2013
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8. Very large fMRI study using the IMAGEN database: sensitivity-specificity and population effect modeling in relation to the underlying anatomy.

Author

Thyreau B, Schwartz Y, Thirion B, Frouin V, Loth E, Vollstädt-Klein S, Paus T, Artiges E, Conrod PJ, Schumann G, Whelan R, and Poline JB

Subjects
Algorithms, Databases, Factual, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Models, Neurological, Models, Statistical, Normal Distribution, Oxygen blood, Population, Reproducibility of Results, Brain anatomy & histology, Brain Mapping methods
Abstract

In this paper we investigate the use of classical fMRI Random Effect (RFX) group statistics when analyzing a very large cohort and the possible improvement brought from anatomical information. Using 1326 subjects from the IMAGEN study, we first give a global picture of the evolution of the group effect t-value from a simple face-watching contrast with increasing cohort size. We obtain a wide activated pattern, far from being limited to the reasonably expected brain areas, illustrating the difference between statistical significance and practical significance. This motivates us to inject tissue-probability information into the group estimation, we model the BOLD contrast using a matter-weighted mixture of Gaussians and compare it to the common, single-Gaussian model. In both cases, the model parameters are estimated per-voxel for one subgroup, and the likelihood of both models is computed on a second, separate subgroup to reflect model generalization capacity. Various group sizes are tested, and significance is asserted using a 10-fold cross-validation scheme. We conclude that adding matter information consistently improves the quantitative analysis of BOLD responses in some areas of the brain, particularly those where accurate inter-subject registration remains challenging., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Published
2012
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9. Improving accuracy and power with transfer learning using a meta-analytic database.

Author

Schwartz Y, Varoquaux G, Pallier C, Pinel P, Poline JB, and Thirion B

Subjects
Algorithms, Brain Mapping, Humans, Image Interpretation, Computer-Assisted methods, Information Storage and Retrieval, Pattern Recognition, Automated methods, Reproducibility of Results, Sensitivity and Specificity, Brain physiology, Databases, Factual, Image Enhancement methods, Magnetic Resonance Imaging methods, Meta-Analysis as Topic
Abstract

Typical cohorts in brain imaging studies are not large enough for systematic testing of all the information contained in the images. To build testable working hypotheses, investigators thus rely on analysis of previous work, sometimes formalized in a so-called meta-analysis. In brain imaging, this approach underlies the specification of regions of interest (ROIs) that are usually selected on the basis of the coordinates of previously detected effects. In this paper, we propose to use a database of images, rather than coordinates, and frame the problem as transfer learning: learning a discriminant model on a reference task to apply it to a different but related new task. To facilitate statistical analysis of small cohorts, we use a sparse discriminant model that selects predictive voxels on the reference task and thus provides a principled procedure to define ROIs. The benefits of our approach are twofold. First it uses the reference database for prediction, i.e., to provide potential biomarkers in a clinical setting. Second it increases statistical power on the new task. We demonstrate on a set of 18 pairs of functional MRI experimental conditions that our approach gives good prediction. In addition, on a specific transfer situation involving different scanners at different locations, we show that voxel selection based on transfer learning leads to higher detection power on small cohorts.

Published
2012
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10. Identification of common variants associated with human hippocampal and intracranial volumes

Author

Stein, Jason L, Medland, Sarah E, Bernard, Manon, Nauck, Matthias, Nöthen, Markus M., Olvera, Rene L, Pandolfo, Massimo, Pike, G Bruce, Puls, Ralf, Reinvang, Ivar, Rentería, Miguel E, Rietschel, Marcella, Roffman, Joshua L, Brown, Andrew A, Royle, Natalie A, Rujescu, Dan, Savitz, Jonathan, Schnack, Hugo G, Schnell, Knut, Seiferth, Nina, Smith, Colin, Steen, Vidar M, Valdés Hernández, Maria C, Van den Heuvel, Martijn, Cannon, Dara M, van der Wee, Nic J, Van Haren, Neeltje E M, Veltman, Joris A, Völzke, Henry, Walker, Robert, Westlye, Lars T, Whelan, Christopher D, Agartz, Ingrid, Boomsma, Dorret I, Cavalleri, Gianpiero L, Chakravarty, M Mallar, Dale, Anders M, Djurovic, Srdjan, Drevets, Wayne C, Hagoort, Peter, Hall, Jeremy, Heinz, Andreas, Jack, Clifford R, Foroud, Tatiana M, Le Hellard, Stephanie, Macciardi, Fabio, Christoforou, Andrea, Montgomery, Grant W, Poline, Jean Baptiste, Porteous, David J, Sisodiya, Sanjay M, Starr, John M, Sussmann, Jessika, Toga, Arthur W, Veltman, Dick J, Walter, Henrik, Weiner, Michael W, Domin, Martin, Initiative, Alzheimer's Disease Neuroimaging, Consortium, EPIGEN, Consortium, IMAGEN, Group, Saguenay Youth Study, Bis, Joshua C, Ikram, M Arfan, Smith, Albert V, Gudnason, Vilmundur, Tzourio, Christophe, Vernooij, Meike W, Grimm, Oliver, Launer, Lenore J, DeCarli, Charles, Seshadri, Sudha, Consortium, Cohorts for Heart and Aging Research in Genomic Epidemiology, Andreassen, Ole A, Apostolova, Liana G, Bastin, Mark E, Blangero, John, Brunner, Han G, Buckner, Randy L, Hollinshead, Marisa, Cichon, Sven, Coppola, Giovanni, de Zubicaray, Greig I, Deary, Ian J, Donohoe, Gary, de Geus, Eco J C, Espeseth, Thomas, Fernández, Guillén, Glahn, David C, Grabe, Hans J, Holmes, Avram J, Hardy, John, Hulshoff Pol, Hilleke E, Jenkinson, Mark, Kahn, René S, McDonald, Colm, McIntosh, Andrew M, McMahon, Francis J, McMahon, Katie L, Meyer-Lindenberg, Andreas, Morris, Derek W, Homuth, Georg, Müller-Myhsok, Bertram, Nichols, Thomas E, Ophoff, Roel A, Paus, Tomas, Pausova, Zdenka, Penninx, Brenda W, Potkin, Steven G, Sämann, Philipp G, Saykin, Andrew J, Schumann, Gunter, Vasquez, Alejandro Arias, Hottenga, Jouke-Jan, Smoller, Jordan W, Wardlaw, Joanna M, Weale, Michael E, Martin, Nicholas G, Franke, Barbara, Wright, Margaret J, Thompson, Paul M, Consortium, Enhancing Neuro Imaging Genetics through Meta-Analysis, Weiner, Michael, Aisen, Paul, Langan, Camilla, Petersen, Ronald, Jagust, William, Trojanowki, John Q, Beckett, Laurel, Green, Robert C, Morris, John, Liu, Enchi, Lopez, Lorna M, Montine, Tom, Gamst, Anthony, Thomas, Ronald G, Donohue, Michael, Walter, Sarah, Gessert, Devon, Sather, Tamie, Hansell, Narelle K, Harvey, Danielle, Kornak, John, Dale, Anders, Bernstein, Matthew, Felmlee, Joel, Fox, Nick, Hwang, Kristy S, Thompson, Paul, Schuff, Norbert, Alexander, Gene, Bandy, Dan, Koeppe, Robert A, Foster, Norm, Reiman, Eric M, Chen, Kewei, Mathis, Chet, Kim, Sungeun, Cairns, Nigel J, Taylor-Reinwald, Lisa, Trojanowki, J. Q., Shaw, Les, Lee, Virginia M Y, Korecka, Magdalena, Crawford, Karen, Neu, Scott, Laje, Gonzalo, Potkin, Steven, Shen, Li, Kachaturian, Zaven, Frank, Richard, Snyder, Peter J, Molchan, Susan, Kaye, Jeffrey, Quinn, Joseph, Lee, Phil H, Lind, Betty, Dolen, Sara, Schneider, Lon S, Pawluczyk, Sonia, Spann, Bryan M, Brewer, James, Vanderswag, Helen, Heidebrink, Judith L, Lord, Joanne L, Liu, Xinmin, Johnson, Kris, Doody, Rachelle S, Villanueva-Meyer, Javier, Chowdhury, Munir, Stern, Yaakov, Honig, Lawrence S, Bell, Karen L, Morris, John C, Ances, Beau, Carroll, Maria, Loth, Eva, Leon, Sue, Mintun, Mark A, Schneider, Stacy, Marson, Daniel, Griffith, Randall, Clark, David, Grossman, Hillel, Mitsis, Effie, Romirowsky, Aliza, deToledo-Morrell, Leyla, Hibar, Derrek P, Lourdusamy, Anbarasu, Shah, Raj C, Duara, Ranjan, Varon, Daniel, Roberts, Peggy, Albert, Marilyn, Onyike, Chiadi, Kielb, Stephanie, Rusinek, Henry, de Leon, Mony J, Glodzik, Lidia, Mattingsdal, Morten, De Santi, Susan, Doraiswamy, P Murali, Petrella, Jeffrey R, Coleman, R Edward, Arnold, Steven E, Karlawish, Jason H, Wolk, David, Smith, Charles D, Jicha, Greg, Hardy, Peter, Mohnke, Sebastian, Lopez, Oscar L, Oakley, MaryAnn, Simpson, Donna M, Porsteinsson, Anton P, Goldstein, Bonnie S, Martin, Kim, Makino, Kelly M, Ismail, M Saleem, Mulnard, Ruth A, Thai, Gaby, Maniega, Susana Muñoz, Mc-Adams-Ortiz, Catherine, Womack, Kyle, Mathews, Dana, Quiceno, Mary, Diaz-Arrastia, Ramon, King, Richard, Weiner, Myron, Martin-Cook, Kristen, DeVous, Michael, Levey, Allan I, Nho, Kwangsik, Lah, James J, Cellar, Janet S, Burns, Jeffrey M, Anderson, Heather S, Swerdlow, Russell H, Apostolova, Liana, Lu, Po H, Bartzokis, George, Silverman, Daniel H S, Graff-Radford, Neill R, Nugent, Allison C, Parfitt, Francine, Johnson, Heather, Farlow, Martin R, Hake, Ann Marie, Matthews, Brandy R, Herring, Scott, van Dyck, Christopher H, Carson, Richard E, MacAvoy, Martha G, Chertkow, Howard, O'Brien, Carol, Bergman, Howard, Hosei, Chris, Black, Sandra, Stefanovic, Bojana, Caldwell, Curtis, Hsiung, Ging-Yuek Robin, Feldman, Howard, Mudge, Benita, Assaly, Michele, Kertesz, Andrew, Papmeyer, Martina, Rogers, John, Trost, Dick, Bernick, Charles, Munic, Donna, Kerwin, Diana, Mesulam, Marek-Marsel, Lipowski, Kristina, Wu, Chuang-Kuo, Johnson, Nancy, Sadowsky, Carl, Pütz, Benno, Martinez, Walter, Villena, Teresa, Turner, Raymond Scott, Johnson, Kathleen, Reynolds, Brigid, Sperling, Reisa A, Johnson, Keith A, Marshall, Gad, Frey, Meghan, Yesavage, Jerome, Ramasamy, Adaikalavan, Taylor, Joy L, Lane, Barton, Rosen, Allyson, Tinklenberg, Jared, Sabbagh, Marwan, Belden, Christine, Jacobson, Sandra, Kowall, Neil, Killiany, Ronald, Budson, Andrew E, Senstad, Rudy E, Rasmussen, Jerod, Norbash, Alexander, Johnson, Patricia Lynn, Obisesan, Thomas O, Wolday, Saba, Bwayo, Salome K, Lerner, Alan, Hudson, Leon, Ogrocki, Paula, Fletcher, Evan, Carmichael, Owen, Rijpkema, Mark, Olichney, John, Kittur, Smita, Borrie, Michael, Lee, T-Y, Bartha, Rob, Johnson, Sterling, Asthana, Sanjay, Carlsson, Cynthia M, Risacher, Shannon L, Preda, Adrian, Nguyen, Dana, Tariot, Pierre, Fleisher, Adam, Reeder, Stephanie, Bates, Vernice, Capote, Horacio, Rainka, Michelle, Scharre, Douglas W, Kataki, Maria, Roddey, J Cooper, Zimmerman, Earl A, Celmins, Dzintra, Brown, Alice D, Pearlson, Godfrey D, Blank, Karen, Anderson, Karen, Santulli, Robert B, Schwartz, Eben S, Sink, Kaycee M, Rose, Emma J, Williamson, Jeff D, Garg, Pradeep, Watkins, Franklin, Ott, Brian R, Querfurth, Henry, Tremont, Geoffrey, Salloway, Stephen, Malloy, Paul, Correia, Stephen, Rosen, Howard J, Ryten, Mina, Miller, Bruce L, Mintzer, Jacobo, Longmire, Crystal Flynn, Spicer, Kenneth, Finger, Elizabeth, Rachinsky, Irina, Drost, Dick, Cavalleri, Gianpiero, Alhusaini, Saud, Delanty, Norman, Whelan, Christopher, Sisodiya, Sanjay, Kasperaviciute, Dalia, Matarin, Mar, Depondt, Chantal, Goldstein, David B, Heinzen, Erin L, Shianna, Kevin, Sprooten, Emma, Radtke, Rodney, Ottmann, Ruth, Sergievsky, G. H., Schumann, G., Conrod, P., Reed, L., Barker, G., Williams, S., Loth, E., Struve, M., Strengman, Eric, Lourdusamy, A., Cattrell, A., Nymberg, C., Topper, L., Smith, L., Havatzias, S., Stueber, K., Mallik, C., Stacey, D., Wong, C Peng, Teumer, Alexander, Werts, H., Andrew, C., Desrivieres, S., Heinz, A., Gallinat, J., Häke, I., Ivanov, N., Klär, A., Reuter, J., Winkler, Anderson M, Trabzuni, Daniah, Palafox, C., Hohmann, C., Schilling, C., Lüdemann, K., Romanowski, A., Ströhle, A., Wolff, E., Rapp, M., Ittermann, B., Brühl, R., Turner, Jessica, Ihlenfeld, A., Walaszek, B., Schubert, F., Garavan, H., Connolly, C., Jones, J., Lalor, E., McCabe, E., Ní Shiothcháin, A., Whelan, R., van Eijk, Kristel, Spanagel, R., Leonardi-Essmann, F., Sommer, W., Flor, H., Vollstaedt-Klein, S., Nees, F., Banaschewski, T., Poustka, L., Steiner, S., Mann, K., van Erp, Theo G M, Buehler, M., Rietschel, M., Stolzenburg, E., Schmal, C., Schirmbeck, F., Paus, T., Gowland, P., Heym, N., Lawrence, C., Newman, C., van Tol, Marie-Jose, Pausova, Z., Smolka, M., Huebner, T., Ripke, S., Mennigen, E., Muller, K., Ziesch, V., Büchel, C., Bromberg, U., Fadai, T., Wittfeld, Katharina, Lueken, L., Yacubian, J., Finsterbusch, J., Martinot, J. L., Artiges, E., Bordas, N., de Bournonville, S., Bricaud, Z., Gollier Briand, F., Lemaitre, H., Wolf, Christiane, Massicotte, J., Miranda, R., Paillère Martinot, M. L., Penttilä, J., Poline, J. B., Barbot, A., Schwartz, Y., Lalanne, C., Frouin, V., Thyreau, B., Woudstra, Saskia, Dalley, J., Mar, A., Robbins, T., Subramaniam, N., Theobald, D., Richmond, N., de Rover, M., Molander, A., Jordan, E., Robinson, E., Aleman, Andre, Hipolata, L., Moreno, M., Arroyo, M., Stephens, D., Ripley, T., Crombag, H., Pena, Y., Lathrop, M., Zelenika, D., Heath, S., Lanzerath, D., Heinrichs, B., Spranger, T., Fuchs, B., Speiser, C., Resch, F., Haffner, J., Parzer, P., Brunner, R., Klaassen, A., Toro, Roberto, Almasy, Laura, Klaassen, I., Constant, P., Mignon, X., Thomsen, T., Zysset, S., Vestboe, A., Ireland, J., Rogers, J., Binder, Elisabeth B, Chakravarty, Mallar, Smith, Albert Vernon, van der Lijn, Fedde, Crivello, Fabrice, Fornage, Myriam, Shulman, Joshua M, Brohawn, David G, Schmidt, Helena, Srikanth, Velandai, Schuur, Maaike, Yu, Lei, Choi, Seung-Hoan, Sigurdsson, Sigurdur, Verhaaren, Benjamin F J, DeStefano, Anita L, Lambert, Jean-Charles, Cantor, Rita M, Struchalin, Maksim, Stankovich, Jim, Ibrahim-Verbaas, Carla A, Fleischman, Debra, Zijdenbos, Alex, den Heijer, Tom, Mazoyer, Bernard, Coker, Laura H, Enzinger, Christian, Danoy, Patrick, Carless, Melanie A, Amin, Najaf, Arfanakis, Konstantinos, van Buchem, Mark A, de Bruijn, Renée F A G, Beiser, Alexa, Dufouil, Carole, Huang, Juebin, Cavalieri, Margherita, Thomson, Russell, Niessen, Wiro J, Corvin, Aiden, Chibnik, Lori B, Gislason, Gauti K, Hofman, Albert, Pikula, Aleksandra, Amouyel, Philippe, Freeman, Kevin B, Phan, Thanh G, Oostra, Ben A, Nalls, Michael A, Uitterlinden, Andre G, Czisch, Michael, Au, Rhoda, Elbaz, Alexis, Beare, Richard J, van Swieten, John C, Lopez, Oscar, Harris, Tamara B, Chouraki, Vincent, Breteler, Monique M B, De Jager, Philip L, Becker, James T, Curran, Joanne E, Knopman, David, Fazekas, Franz, Wolf, Philip A, van der Lugt, Aad, Longstreth, W. T., Brown, Mathew A, Bennett, David A, van Duijn, Cornelia M, Davies, Gail, Mosley, Thomas H, Schmidt, Reinhold, de Almeida, Marcio A A, Appel, Katja, Duggirala, Ravi, Dyer, Thomas D, Erk, Susanne, Fagerness, Jesen, Fox, Peter T, Freimer, Nelson B, Gill, Michael, Göring, Harald H H, Bartecek, Richard, Hagler, Donald J, Hoehn, David, Holsboer, Florian, Hoogman, Martine, Hosten, Norbert, Jahanshad, Neda, Johnson, Matthew P, Kent, Jack W, Kochunov, Peter, Bergmann, Ørjan, Lancaster, Jack L, Lawrie, Stephen M, Liewald, David C, Mandl, René, Mattheisen, Manuel, Meisenzahl, Eva, Melle, Ingrid, Moses, Eric K, Mühleisen, Thomas W, David Geffen School of Medicine [Los Angeles], University of California [Los Angeles] (UCLA), University of California-University of California, Queensland Institute of Medical Research, Radboud University Medical Center [Nijmegen], Yale University School of Medicine, Génétique Humaine et Fonctions Cognitives, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Universität Greifswald - University of Greifswald, Universität Heidelberg [Heidelberg], University Medical Center [Utrecht], University of Oslo (UiO), University of Toronto, National University of Ireland [Galway] (NUI Galway), Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada], University of Bergen (UiB), Harvard University [Cambridge], VU University Amsterdam, University of Edinburgh, Structure et Réactivité des Systèmes Moléculaires Complexes (SRSMC), Institut de Chimie du CNRS (INC)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Broad Institute of MIT and Harvard (BROAD INSTITUTE), Harvard Medical School [Boston] (HMS)-Massachusetts Institute of Technology (MIT)-Massachusetts General Hospital [Boston], National Institutes of Health [Bethesda] (NIH), Department of Forensic and Neurodevelopmental Sciences, King‘s College London, Institute of Psychiatry, Psychology & Neuroscience, King's College London, Georgia State University, University System of Georgia (USG), Department of Psychiatry and Human Behavior [Irvine], University of California [Irvine] (UCI), Leiden University Medical Center (LUMC), Dundee Technopole, CXR Biosciences Ltd, University of Groningen [Groningen], Molecular and Cellular Therapeutics, Royal College of Surgeons in Ireland (RCSI), Department of Genetics, Southwest Foundation for Biomedical Research, Bijvoet Center of Biomolecular Research [Utrecht], Utrecht University [Utrecht], Neurology Division, Beaumont Hospital, Dublin 9, Ireland, Beaumont Hospital, The University of Texas Health Science Center at Houston (UTHealth), Center for Neurobehavioral Genetics, Max Planck Institute of Psychiatry, Max-Planck-Gesellschaft, Department of Computer Science, Durham University, Laboratoire des symbioses tropicales et méditerranéennes (UMR LSTM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université Montpellier 1 (UM1)-Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Charité - UniversitätsMedizin = Charité - University Hospital [Berlin], University of California, Institute of Neurology [London], University College of London [London] (UCL), University of California [San Francisco] (UCSF), Department of Medicine, University of Washington [Seattle], Erasmus University Medical Center [Rotterdam] (Erasmus MC), Centre Émile Durkheim (CED), Sciences Po Bordeaux - Institut d'études politiques de Bordeaux (IEP Bordeaux)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Università degli Studi di Salermo, Università degli Studi di Salerno (UNISA), School of Psychology, University of Queensland, University of Queensland [Brisbane], Hartford Hospital, Lancaster University, Centre for Advanced Imaging, McConnell Brain Imaging Centre (MNI), McGill University = Université McGill [Montréal, Canada]-McGill University = Université McGill [Montréal, Canada], Stanley Center for Psychiatric Research, Harvard Medical School [Boston] (HMS)-Massachusetts Institute of Technology (MIT)-Massachusetts General Hospital [Boston]-Harvard Medical School [Boston] (HMS)-Massachusetts Institute of Technology (MIT)-Massachusetts General Hospital [Boston], Faculteit Medische Wetenschappen/UMCG, Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Perceptual and Cognitive Neuroscience (PCN), Biological Psychology, Neuroscience Campus Amsterdam - Brain Imaging, EMGO+ - Mental Health, EPIGEN Consortium, IMAGENConsortium, Saguenay Youth Study Group, the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium, Psychiatry, NCA - Brain Imaging, EMGO - Mental health, Vrije universiteit = Free university of Amsterdam [Amsterdam] (VU), Virology, Epidemiology, Clinical Chemistry, Erasmus MC other, Radiology & Nuclear Medicine, University of California (UC)-University of California (UC), Yale School of Medicine [New Haven, Connecticut] (YSM), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Universität Heidelberg [Heidelberg] = Heidelberg University, Harvard University, Vrije Universiteit Amsterdam [Amsterdam] (VU), University of California [Irvine] (UC Irvine), Universiteit Leiden, University of California (UC), University of California [San Francisco] (UC San Francisco), Università degli Studi di Salerno = University of Salerno (UNISA), University of Iceland [Reykjavik], McGill University, University of Bergen (UIB), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Bijvoet Center of Biomolecular Research, Charité - Universitätsmedizin Berlin / Charite - University Medicine Berlin, UMR5116, Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), and McGill University-McGill University

Subjects
Netherlands Twin Register (NTR), Pathology, 110 012 Social cognition of verbal communication, [SDV]Life Sciences [q-bio], Hippocampus, Genome-wide association study, DCN PAC - Perception action and control, Hippocampal formation, physiopathology [Brain], Bioinformatics, 0302 clinical medicine, 130 000 Cognitive Neurology & Memory, TEMPORAL-LOBE EPILEPSY, 110 014 Public activities, Renal disorder [IGMD 9], 0303 health sciences, medicine.diagnostic_test, Translational research Immune Regulation [ONCOL 3], Brain, Human brain, Genomic disorders and inherited multi-system disorders [DCN PAC - Perception action and control IGMD 3], ALZHEIMERS-DISEASE, medicine.anatomical_structure, Brain size, genetics [Chromosomes, Human, Pair 12], genetics [Polymorphism, Single Nucleotide], Biomarker (medicine), NA+/H+ EXCHANGER, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Genetic Markers, medicine.medical_specialty, 110 000 Neurocognition of Language, DCN MP - Plasticity and memory, A neurocomputational model for the Processing of Linguistic Utterances based on the Unification-Space architecture [110 007 PLUS], BRAIN VOLUME, UNIFIED APPROACH, 110 013 Binding and the MUC-model, Neuroimaging, Biology, GENOTYPE IMPUTATION, Polymorphism, Single Nucleotide, Article, Genomic disorders and inherited multi-system disorders DCN MP - Plasticity and memory [IGMD 3], 03 medical and health sciences, AUTOMATED SEGMENTATION, Meta-Analysis as Topic, SDG 3 - Good Health and Well-being, ddc:570, FUNCTIONAL IMPLICATIONS, Genetics, medicine, Humans, GENOME-WIDE ASSOCIATION, 030304 developmental biology, Chromosomes, Human, Pair 12, Magnetic resonance imaging, Genetic Loci, physiopathology [Hippocampus], 110 009 The human brain and Chinese prosody, Genetics and epigenetic pathways of disease Genomic disorders and inherited multi-system disorders [NCMLS 6], HUMAN HEIGHT, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract

Contains fulltext : 108202.pdf (Publisher’s version ) (Closed access) Identifying genetic variants influencing human brain structures may reveal new biological mechanisms underlying cognition and neuropsychiatric illness. The volume of the hippocampus is a biomarker of incipient Alzheimer's disease and is reduced in schizophrenia, major depression and mesial temporal lobe epilepsy. Whereas many brain imaging phenotypes are highly heritable, identifying and replicating genetic influences has been difficult, as small effects and the high costs of magnetic resonance imaging (MRI) have led to underpowered studies. Here we report genome-wide association meta-analyses and replication for mean bilateral hippocampal, total brain and intracranial volumes from a large multinational consortium. The intergenic variant rs7294919 was associated with hippocampal volume (12q24.22; N = 21,151; P = 6.70 x 10(-16)) and the expression levels of the positional candidate gene TESC in brain tissue. Additionally, rs10784502, located within HMGA2, was associated with intracranial volume (12q14.3; N = 15,782; P = 1.12 x 10(-12)). We also identified a suggestive association with total brain volume at rs10494373 within DDR2 (1q23.3; N = 6,500; P = 5.81 x 10(-7)). 01 mei 2012

Published
2012
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