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1.  ELECTRON MICROSCOPIC STRUCTURE OF MYXOMA VIRUS AND SOME REACTIVABLE DERIVATIVES 
Journal of Bacteriology  1964;87(2):454-460.
Padgett, Billie L. (University of Wisconsin, Madison), Margaret J. Wright, Anne Jayne, and Duard L. Walker. Electron microscopic structure of myxoma virus and some reactivable derivatives. J. Bacteriol. 87:454–460. 1964.—The phosphotungstate negative staining technique was used to study the fine structure of myxoma virus particles and of reactivable derivatives in an electron microscope. Two general types of particles were observed in preparations of myxoma virus grown in rabbit kidney cells in tissue culture. The most common form is rounded or rectangular, with an average length and width of 296 by 254 mμ. The surface of this type of particle is convoluted, and is composed of tubular elements arranged in a complex fashion; the particle is frequently surrounded by a membrane. The second form, which was seen less frequently, is more rectangular and slightly larger. It has a closely fitting membrane, a finely granular surface, and the only internal structures discernible are present in a zone about 150 A wide, just beneath the membrane. Reactivable myxoma virus particles were prepared by treating myxoma virus with heat, urea, sodium dodecyl sulfate, and ethyl ether. The appearance of the particles after inactivation by heat, urea, and sodium dodecyl sulfate is described.
Images
PMCID: PMC277030  PMID: 14151071
2.  A Commonly Carried Genetic Variant in the Delta Opioid Receptor Gene, OPRD1, is Associated with Smaller Regional Brain Volumes: Replication in Elderly and Young Populations 
Human brain mapping  2013;35(4):1226-1236.
Delta opioid receptors are implicated in a variety of psychiatric and neurological disorders. These receptors play a key role in the reinforcing properties of drugs of abuse, and polymorphisms in OPRD1 (the gene encoding delta opioid receptors) are associated with drug addiction. Delta opioid receptors are also involved in protecting neurons against hypoxic and ischemic stress. Here, we first examined a large sample of 738 elderly participants with neuroimaging and genetic data from the Alzheimer’s Disease Neuroimaging Initiative. We hypothesized that common variants in OPRD1 would be associated with differences in brain structure, particularly in regions relevant to addictive and neurodegenerative disorders. One very common variant (rs678849) predicted differences in regional brain volumes. We replicated the association of this single-nucleotide polymorphism with regional tissue volumes in a large sample of young participants in the Queensland Twin Imaging study. Although the same allele was associated with reduced volumes in both cohorts, the brain regions affected differed between the two samples. In healthy elderly, exploratory analyses suggested that the genotype associated with reduced brain volumes in both cohorts may also predict cerebrospinal fluid levels of neurodegenerative biomarkers, but this requires confirmation. If opiate receptor genetic variants are related to individual differences in brain structure, genotyping of these variants may be helpful when designing clinical trials targeting delta opioid receptors to treat neurological disorders.
doi:10.1002/hbm.22247
PMCID: PMC4046708  PMID: 23427138
neuroimaging; genetics; neurodegeneration; drug addiction; opiates
3.  Genome-wide association identifies genetic variants associated with lentiform nucleus volume in N=1345 young and elderly subjects 
Brain imaging and behavior  2013;7(2):102-115.
Deficits in lentiform nucleus volume and morphometry are implicated in a number of genetically influenced disorders, including Parkinson’s disease, schizophrenia, and ADHD. Here we performed genome-wide searches to discover common genetic variants associated with differences in lentiform nucleus volume in human populations. We assessed structural MRI scans of the brain in two large genotyped samples: the Alzheimer’s Disease Neuroimaging Initiative (ADNI; N=706) and the Queensland Twin Imaging Study (QTIM; N=639). Statistics of association from each cohort were combined meta-analytically using a fixed-effects model to boost power and to reduce the prevalence of false positive findings. We identified a number of associations in and around the flavin-containing monooxygenase (FMO) gene cluster. The most highly associated SNP, rs1795240, was located in the FMO3 gene; after meta-analysis, it showed genome-wide significant evidence of association with lentiform nucleus volume (PMA=4.79×10−8). This commonly-carried genetic variant accounted for 2.68 % and 0.84 % of the trait variability in the ADNI and QTIM samples, respectively, even though the QTIM sample was on average 50 years younger. Pathway enrichment analysis revealed significant contributions of this gene to the cytochrome P450 pathway, which is involved in metabolizing numerous therapeutic drugs for pain, seizures, mania, depression, anxiety, and psychosis. The genetic variants we identified provide replicated, genome-wide significant evidence for the FMO gene cluster’s involvement in lentiform nucleus volume differences in human populations.
doi:10.1007/s11682-012-9199-7
PMCID: PMC3779070  PMID: 22903471
Basal ganglia; Genome-wide association study (GWAS); MRI; Replication; Morphometry; Drug metabolism
4.  Test-Retest Reliability of Graph Theory Measures of Structural Brain Connectivity 
The human connectome has recently become a popular research topic in neuroscience, and many new algorithms have been applied to analyze brain networks. In particular, network topology measures from graph theory have been adapted to analyze network efficiency and ‘small-world’ properties. While there has been a surge in the number of papers examining connectivity through graph theory, questions remain about its test-retest reliability (TRT). In particular, the reproducibility of structural connectivity measures has not been assessed. We examined the TRT of global connectivity measures generated from graph theory analyses of 17 young adults who underwent two high-angular resolution diffusion (HARDI) scans approximately 3 months apart. Of the measures assessed, modularity had the highest TRT, and it was stable across a range of sparsities (a thresholding parameter used to define which network edges are retained). These reliability measures underline the need to develop network descriptors that are robust to acquisition parameters.
PMCID: PMC4039303  PMID: 23286144
5.  Genetic clustering on the hippocampal surface for genome-wide association studies 
Imaging genetics aims to discover how variants in the human genome influence brain measures derived from images. Genome-wide association scans (GWAS) can screen the genome for common differences in our DNA that relate to brain measures. In small samples, GWAS has low power as individual gene effects are weak and one must also correct for multiple comparisons across the genome and the image. Here we extend recent work on genetic clustering of images, to analyze surface-based models of anatomy using GWAS. We performed spherical harmonic analysis of hippocampal surfaces, automatically extracted from brain MRI scans of 1254 subjects. We clustered hippocampal surface regions with common genetic influences by examining genetic correlations (rg) between the normalized deformation values at all pairs of surface points. Using genetic correlations to cluster surface measures, we were able to boost effect sizes for genetic associations, compared to clustering with traditional phenotypic correlations using Pearson's r.
PMCID: PMC4024454  PMID: 24579201
heritability; GWAS; clustering; hippocampus; 3D surfaces; imaging genetics
6.  Harmonization of Neuroticism and Extraversion phenotypes across inventories and cohorts in the Genetics of Personality Consortium: an application of Item Response Theory 
van den Berg, Stéphanie M. | de Moor, Marleen H. M. | McGue, Matt | Pettersson, Erik | Terracciano, Antonio | Verweij, Karin J. H. | Amin, Najaf | Derringer, Jaime | Esko, Tõnu | van Grootheest, Gerard | Hansell, Narelle K. | Huffman, Jennifer | Konte, Bettina | Lahti, Jari | Luciano, Michelle | Matteson, Lindsay K. | Viktorin, Alexander | Wouda, Jasper | Agrawal, Arpana | Allik, Jüri | Bierut, Laura | Broms, Ulla | Campbell, Harry | Smith, George Davey | Eriksson, Johan G. | Ferrucci, Luigi | Franke, Barbera | Fox, Jean-Paul | de Geus, Eco J. C. | Giegling, Ina | Gow, Alan J. | Grucza, Richard | Hartmann, Annette M. | Heath, Andrew C. | Heikkilä, Kauko | Iacono, William G. | Janzing, Joost | Jokela, Markus | Kiemeney, Lambertus | Lehtimäki, Terho | Madden, Pamela A. F. | Magnusson, Patrik K. E. | Northstone, Kate | Nutile, Teresa | Ouwens, Klaasjan G. | Palotie, Aarno | Pattie, Alison | Pesonen, Anu-Katriina | Polasek, Ozren | Pulkkinen, Lea | Pulkki-Råback, Laura | Raitakari, Olli T. | Realo, Anu | Rose, Richard J. | Ruggiero, Daniela | Seppälä, Ilkka | Slutske, Wendy S. | Smyth, David C. | Sorice, Rossella | Starr, John M. | Sutin, Angelina R. | Tanaka, Toshiko | Verhagen, Josine | Vermeulen, Sita | Vuoksimaa, Eero | Widen, Elisabeth | Willemsen, Gonneke | Wright, Margaret J. | Zgaga, Lina | Rujescu, Dan | Metspalu, Andres | Wilson, James F. | Ciullo, Marina | Hayward, Caroline | Rudan, Igor | Deary, Ian J. | Räikkönen, Katri | Arias Vasquez, Alejandro | Costa, Paul T. | Keltikangas-Järvinen, Liisa | van Duijn, Cornelia M. | Penninx, Brenda W. J. H. | Krueger, Robert F. | Evans, David M. | Kaprio, Jaakko | Pedersen, Nancy L. | Martin, Nicholas G. | Boomsma, Dorret I.
Behavior Genetics  2014;44:295-313.
Mega- or meta-analytic studies (e.g. genome-wide association studies) are increasingly used in behavior genetics. An issue in such studies is that phenotypes are often measured by different instruments across study cohorts, requiring harmonization of measures so that more powerful fixed effect meta-analyses can be employed. Within the Genetics of Personality Consortium, we demonstrate for two clinically relevant personality traits, Neuroticism and Extraversion, how Item-Response Theory (IRT) can be applied to map item data from different inventories to the same underlying constructs. Personality item data were analyzed in >160,000 individuals from 23 cohorts across Europe, USA and Australia in which Neuroticism and Extraversion were assessed by nine different personality inventories. Results showed that harmonization was very successful for most personality inventories and moderately successful for some. Neuroticism and Extraversion inventories were largely measurement invariant across cohorts, in particular when comparing cohorts from countries where the same language is spoken. The IRT-based scores for Neuroticism and Extraversion were heritable (48 and 49 %, respectively, based on a meta-analysis of six twin cohorts, total N = 29,496 and 29,501 twin pairs, respectively) with a significant part of the heritability due to non-additive genetic factors. For Extraversion, these genetic factors qualitatively differ across sexes. We showed that our IRT method can lead to a large increase in sample size and therefore statistical power. The IRT approach may be applied to any mega- or meta-analytic study in which item-based behavioral measures need to be harmonized.
Electronic supplementary material
The online version of this article (doi:10.1007/s10519-014-9654-x) contains supplementary material, which is available to authorized users.
doi:10.1007/s10519-014-9654-x
PMCID: PMC4057636  PMID: 24828478
Personality; Item-Response Theory; Measurement; Genome-wide association studies; Consortium; Meta-analysis
7.  Development of Insula Connectivity Between Ages 12 and 30 Revealed by High Angular Resolution Diffusion Imaging 
Human brain mapping  2013;35(4):1790-1800.
The insula, hidden deep within the Sylvian fissures, has proven difficult to study from a connectivity perspective. Most of our current information on the anatomical connectivity of the insula comes from studies of nonhuman primates and post mortem human dissections. To date, only two neuroimaging studies have successfully examined the connectivity of the insula. Here we examine how the connectivity of the insula develops between ages 12 and 30, in 307 young adolescent and adult subjects scanned with 4-Tesla high angular resolution diffusion imaging (HARDI). The density of fiber connections between the insula and the frontal and parietal cortex decreased with age, but the connection density between the insula and the temporal cortex generally increased with age. This trajectory is in line with well-known patterns of cortical development in these regions. In addition, males and females showed different developmental trajectories for the connection between the left insula and the left precentral gyrus. The insula plays many different roles, some of them affected in neuropsychiatric disorders; this information on the insula's connectivity may help efforts to elucidate mechanisms of brain disorders in which it is implicated.
doi:10.1002/hbm.22292
PMCID: PMC4017914  PMID: 23836455
insula; development; tractography; HARDI; structural connectivity
8.  DEVELOPMENT OF THE “RICH CLUB” IN BRAIN CONNECTIVITY NETWORKS FROM 438 ADOLESCENTS & ADULTS AGED 12 TO 30 
The ‘rich club’ coefficient describes a phenomenon where a network's hubs (high-degree nodes) are on average more intensely interconnected than lower-degree nodes. Networks with rich clubs often have an efficient, higher-order organization, but we do not yet know how the rich club emerges in the living brain, or how it changes as our brain networks develop. Here we chart the developmental trajectory of the rich club in anatomical brain networks from 438 subjects aged 12-30. Cortical networks were constructed from 68×68 connectivity matrices of fiber density, using whole-brain tractography in 4-Tesla 105-gradient high angular resolution diffusion images (HARDI). The adult and younger cohorts had rich clubs that included different nodes; the rich club effect intensified with age. Rich-club organization is a sign of a network's efficiency and robustness. These concepts and findings may be advantageous for studying brain maturation and abnormal brain development.
doi:10.1109/ISBI.2013.6556552
PMCID: PMC4017916  PMID: 24827471
rich club coefficient; high angular resolution diffusion imaging (HARDI); tractography; network analyses; development; structural connectivity
9.  Identification of seven loci affecting mean telomere length and their association with disease 
Codd, Veryan | Nelson, Christopher P. | Albrecht, Eva | Mangino, Massimo | Deelen, Joris | Buxton, Jessica L. | Jan Hottenga, Jouke | Fischer, Krista | Esko, Tõnu | Surakka, Ida | Broer, Linda | Nyholt, Dale R. | Mateo Leach, Irene | Salo, Perttu | Hägg, Sara | Matthews, Mary K. | Palmen, Jutta | Norata, Giuseppe D. | O’Reilly, Paul F. | Saleheen, Danish | Amin, Najaf | Balmforth, Anthony J. | Beekman, Marian | de Boer, Rudolf A. | Böhringer, Stefan | Braund, Peter S. | Burton, Paul R. | de Craen, Anton J. M. | Denniff, Matthew | Dong, Yanbin | Douroudis, Konstantinos | Dubinina, Elena | Eriksson, Johan G. | Garlaschelli, Katia | Guo, Dehuang | Hartikainen, Anna-Liisa | Henders, Anjali K. | Houwing-Duistermaat, Jeanine J. | Kananen, Laura | Karssen, Lennart C. | Kettunen, Johannes | Klopp, Norman | Lagou, Vasiliki | van Leeuwen, Elisabeth M. | Madden, Pamela A. | Mägi, Reedik | Magnusson, Patrik K.E. | Männistö, Satu | McCarthy, Mark I. | Medland, Sarah E. | Mihailov, Evelin | Montgomery, Grant W. | Oostra, Ben A. | Palotie, Aarno | Peters, Annette | Pollard, Helen | Pouta, Anneli | Prokopenko, Inga | Ripatti, Samuli | Salomaa, Veikko | Suchiman, H. Eka D. | Valdes, Ana M. | Verweij, Niek | Viñuela, Ana | Wang, Xiaoling | Wichmann, H.-Erich | Widen, Elisabeth | Willemsen, Gonneke | Wright, Margaret J. | Xia, Kai | Xiao, Xiangjun | van Veldhuisen, Dirk J. | Catapano, Alberico L. | Tobin, Martin D. | Hall, Alistair S. | Blakemore, Alexandra I.F. | van Gilst, Wiek H. | Zhu, Haidong | Erdmann, Jeanette | Reilly, Muredach P. | Kathiresan, Sekar | Schunkert, Heribert | Talmud, Philippa J. | Pedersen, Nancy L. | Perola, Markus | Ouwehand, Willem | Kaprio, Jaakko | Martin, Nicholas G. | van Duijn, Cornelia M. | Hovatta, Iiris | Gieger, Christian | Metspalu, Andres | Boomsma, Dorret I. | Jarvelin, Marjo-Riitta | Slagboom, P. Eline | Thompson, John R. | Spector, Tim D. | van der Harst, Pim | Samani, Nilesh J.
Nature genetics  2013;45(4):422-427e2.
Inter-individual variation in mean leukocyte telomere length (LTL) is associated with cancer and several age-associated diseases. Here, in a genome-wide meta-analysis of 37,684 individuals with replication of selected variants in a further 10,739 individuals, we identified seven loci, including five novel loci, associated with mean LTL (P<5x10−8). Five of the loci contain genes (TERC, TERT, NAF1, OBFC1, RTEL1) that are known to be involved in telomere biology. Lead SNPs at two loci (TERC and TERT) associate with several cancers and other diseases, including idiopathic pulmonary fibrosis. Moreover, a genetic risk score analysis combining lead variants at all seven loci in 22,233 coronary artery disease cases and 64,762 controls showed an association of the alleles associated with shorter LTL with increased risk of CAD (21% (95% CI: 5–35%) per standard deviation in LTL, p=0.014). Our findings support a causal role of telomere length variation in some age-related diseases.
doi:10.1038/ng.2528
PMCID: PMC4006270  PMID: 23535734
10.  Genetic effects on the cerebellar role in working memory: Same brain, different genes? 
NeuroImage  2013;86:392-403.
Over the past several years, evidence has accumulated showing that the cerebellum plays a significant role in cognitive function. Here we show, in a large genetically informative twin sample (n = 430; aged 16–30 years), that the cerebellum is strongly, and reliably (n = 30 rescans), activated during an n-back working memory task, particularly lobules I–IV, VIIa Crus I and II, IX and the vermis. Monozygotic twin correlations for cerebellar activation were generally much larger than dizygotic twin correlations, consistent with genetic influences. Structural equation models showed that up to 65% of the variance in cerebellar activation during working memory is genetic (averaging 34% across significant voxels), most prominently in the lobules VI, and VIIa Crus I, with the remaining variance explained by unique/unshared environmental factors. Heritability estimates for brain activation in the cerebellum agree with those found for working memory activation in the cerebral cortex, even though cerebellar cyto-architecture differs substantially. Phenotypic correlations between BOLD percent signal change in cerebrum and cerebellum were low, and bivariate modeling indicated that genetic influences on the cerebellum are at least partly specific to the cerebellum. Activation on the voxel-level correlated very weakly with cerebellar gray matter volume, suggesting specific genetic influences on the BOLD signal. Heritable signals identified here should facilitate discovery of genetic polymorphisms influencing cerebellar function through genome-wide association studies, to elucidate the genetic liability to brain disorders affecting the cerebellum.
doi:10.1016/j.neuroimage.2013.10.006
PMCID: PMC3925745  PMID: 24128737
Cerebellum; Heritability; Genetics; Functional MRI; Working memory; Twin study
11.  The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data 
Thompson, Paul M. | Stein, Jason L. | Medland, Sarah E. | Hibar, Derrek P. | Vasquez, Alejandro Arias | Renteria, Miguel E. | Toro, Roberto | Jahanshad, Neda | Schumann, Gunter | Franke, Barbara | Wright, Margaret J. | Martin, Nicholas G. | Agartz, Ingrid | Alda, Martin | Alhusaini, Saud | Almasy, Laura | Almeida, Jorge | Alpert, Kathryn | Andreasen, Nancy C. | Andreassen, Ole A. | Apostolova, Liana G. | Appel, Katja | Armstrong, Nicola J. | Aribisala, Benjamin | Bastin, Mark E. | Bauer, Michael | Bearden, Carrie E. | Bergmann, Ørjan | Binder, Elisabeth B. | Blangero, John | Bockholt, Henry J. | Bøen, Erlend | Bois, Catherine | Boomsma, Dorret I. | Booth, Tom | Bowman, Ian J. | Bralten, Janita | Brouwer, Rachel M. | Brunner, Han G. | Brohawn, David G. | Buckner, Randy L. | Buitelaar, Jan | Bulayeva, Kazima | Bustillo, Juan R. | Calhoun, Vince D. | Cannon, Dara M. | Cantor, Rita M. | Carless, Melanie A. | Caseras, Xavier | Cavalleri, Gianpiero L. | Chakravarty, M. Mallar | Chang, Kiki D. | Ching, Christopher R. K. | Christoforou, Andrea | Cichon, Sven | Clark, Vincent P. | Conrod, Patricia | Coppola, Giovanni | Crespo-Facorro, Benedicto | Curran, Joanne E. | Czisch, Michael | Deary, Ian J. | de Geus, Eco J. C. | den Braber, Anouk | Delvecchio, Giuseppe | Depondt, Chantal | de Haan, Lieuwe | de Zubicaray, Greig I. | Dima, Danai | Dimitrova, Rali | Djurovic, Srdjan | Dong, Hongwei | Donohoe, Gary | Duggirala, Ravindranath | Dyer, Thomas D. | Ehrlich, Stefan | Ekman, Carl Johan | Elvsåshagen, Torbjørn | Emsell, Louise | Erk, Susanne | Espeseth, Thomas | Fagerness, Jesen | Fears, Scott | Fedko, Iryna | Fernández, Guillén | Fisher, Simon E. | Foroud, Tatiana | Fox, Peter T. | Francks, Clyde | Frangou, Sophia | Frey, Eva Maria | Frodl, Thomas | Frouin, Vincent | Garavan, Hugh | Giddaluru, Sudheer | Glahn, David C. | Godlewska, Beata | Goldstein, Rita Z. | Gollub, Randy L. | Grabe, Hans J. | Grimm, Oliver | Gruber, Oliver | Guadalupe, Tulio | Gur, Raquel E. | Gur, Ruben C. | Göring, Harald H. H. | Hagenaars, Saskia | Hajek, Tomas | Hall, Geoffrey B. | Hall, Jeremy | Hardy, John | Hartman, Catharina A. | Hass, Johanna | Hatton, Sean N. | Haukvik, Unn K. | Hegenscheid, Katrin | Heinz, Andreas | Hickie, Ian B. | Ho, Beng-Choon | Hoehn, David | Hoekstra, Pieter J. | Hollinshead, Marisa | Holmes, Avram J. | Homuth, Georg | Hoogman, Martine | Hong, L. Elliot | Hosten, Norbert | Hottenga, Jouke-Jan | Hulshoff Pol, Hilleke E. | Hwang, Kristy S. | Jack, Clifford R. | Jenkinson, Mark | Johnston, Caroline | Jönsson, Erik G. | Kahn, René S. | Kasperaviciute, Dalia | Kelly, Sinead | Kim, Sungeun | Kochunov, Peter | Koenders, Laura | Krämer, Bernd | Kwok, John B. J. | Lagopoulos, Jim | Laje, Gonzalo | Landen, Mikael | Landman, Bennett A. | Lauriello, John | Lawrie, Stephen M. | Lee, Phil H. | Le Hellard, Stephanie | Lemaître, Herve | Leonardo, Cassandra D. | Li, Chiang-shan | Liberg, Benny | Liewald, David C. | Liu, Xinmin | Lopez, Lorna M. | Loth, Eva | Lourdusamy, Anbarasu | Luciano, Michelle | Macciardi, Fabio | Machielsen, Marise W. J. | MacQueen, Glenda M. | Malt, Ulrik F. | Mandl, René | Manoach, Dara S. | Martinot, Jean-Luc | Matarin, Mar | Mather, Karen A. | Mattheisen, Manuel | Mattingsdal, Morten | Meyer-Lindenberg, Andreas | McDonald, Colm | McIntosh, Andrew M. | McMahon, Francis J. | McMahon, Katie L. | Meisenzahl, Eva | Melle, Ingrid | Milaneschi, Yuri | Mohnke, Sebastian | Montgomery, Grant W. | Morris, Derek W. | Moses, Eric K. | Mueller, Bryon A. | Muñoz Maniega, Susana | Mühleisen, Thomas W. | Müller-Myhsok, Bertram | Mwangi, Benson | Nauck, Matthias | Nho, Kwangsik | Nichols, Thomas E. | Nilsson, Lars-Göran | Nugent, Allison C. | Nyberg, Lars | Olvera, Rene L. | Oosterlaan, Jaap | Ophoff, Roel A. | Pandolfo, Massimo | Papalampropoulou-Tsiridou, Melina | Papmeyer, Martina | Paus, Tomas | Pausova, Zdenka | Pearlson, Godfrey D. | Penninx, Brenda W. | Peterson, Charles P. | Pfennig, Andrea | Phillips, Mary | Pike, G. Bruce | Poline, Jean-Baptiste | Potkin, Steven G. | Pütz, Benno | Ramasamy, Adaikalavan | Rasmussen, Jerod | Rietschel, Marcella | Rijpkema, Mark | Risacher, Shannon L. | Roffman, Joshua L. | Roiz-Santiañez, Roberto | Romanczuk-Seiferth, Nina | Rose, Emma J. | Royle, Natalie A. | Rujescu, Dan | Ryten, Mina | Sachdev, Perminder S. | Salami, Alireza | Satterthwaite, Theodore D. | Savitz, Jonathan | Saykin, Andrew J. | Scanlon, Cathy | Schmaal, Lianne | Schnack, Hugo G. | Schork, Andrew J. | Schulz, S. Charles | Schür, Remmelt | Seidman, Larry | Shen, Li | Shoemaker, Jody M. | Simmons, Andrew | Sisodiya, Sanjay M. | Smith, Colin | Smoller, Jordan W. | Soares, Jair C. | Sponheim, Scott R. | Sprooten, Emma | Starr, John M. | Steen, Vidar M. | Strakowski, Stephen | Strike, Lachlan | Sussmann, Jessika | Sämann, Philipp G. | Teumer, Alexander | Toga, Arthur W. | Tordesillas-Gutierrez, Diana | Trabzuni, Daniah | Trost, Sarah | Turner, Jessica | Van den Heuvel, Martijn | van der Wee, Nic J. | van Eijk, Kristel | van Erp, Theo G. M. | van Haren, Neeltje E. M. | van ‘t Ent, Dennis | van Tol, Marie-Jose | Valdés Hernández, Maria C. | Veltman, Dick J. | Versace, Amelia | Völzke, Henry | Walker, Robert | Walter, Henrik | Wang, Lei | Wardlaw, Joanna M. | Weale, Michael E. | Weiner, Michael W. | Wen, Wei | Westlye, Lars T. | Whalley, Heather C. | Whelan, Christopher D. | White, Tonya | Winkler, Anderson M. | Wittfeld, Katharina | Woldehawariat, Girma | Wolf, Christiane | Zilles, David | Zwiers, Marcel P. | Thalamuthu, Anbupalam | Schofield, Peter R. | Freimer, Nelson B. | Lawrence, Natalia S. | Drevets, Wayne
Brain Imaging and Behavior  2014;8:153-182.
The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA’s first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way.
doi:10.1007/s11682-013-9269-5
PMCID: PMC4008818  PMID: 24399358
Genetics; MRI; GWAS; Consortium; Meta-analysis; Multi-site
12.  Development of Brain Structural Connectivity between Ages 12 and 30: A 4-Tesla Diffusion Imaging Study in 439 Adolescents and Adults 
NeuroImage  2012;64:671-684.
Understanding how the brain matures in healthy individuals is critical for evaluating deviations from normal development in psychiatric and neurodevelopmental disorders. The brain’s anatomical networks are profoundly re-modeled between childhood and adulthood, and diffusion tractography offers unprecedented power to reconstruct these networks and neural pathways in vivo. Here we tracked changes in structural connectivity and network efficiency in 439 right-handed individuals aged 12 to 30 (211 female/126 male adults, mean age=23.6, SD=2.19; 31 female/24 male 12 year olds, mean age=12.3, SD=0.18; and 25 female/22 male 16 year olds, mean age=16.2, SD=0.37). All participants were scanned with high angular resolution diffusion imaging (HARDI) at 4 Tesla. After we performed whole brain tractography, 70 cortical gyral-based regions of interest were extracted from each participant’s co-registered anatomical scans. The degree of fiber connections between all pairs of cortical regions, or nodes, were found to create symmetric fiber density matrices, reflecting the structural brain network. From those 70×70 matrices we computed graph theory metrics characterizing structural connectivity. Several key global and nodal metrics changed across development, showing increased network integration, with some connections pruned and others strengthened. The increases and decreases in fiber density, however, were not distributed proportionally across the brain. The frontal cortex had a disproportionate number of decreases in fiber density while the temporal cortex had a disproportionate number of increases in fiber density. This large-scale analysis of the developing structural connectome offers a foundation to develop statistical criteria for aberrant brain connectivity as the human brain matures.
doi:10.1016/j.neuroimage.2012.09.004
PMCID: PMC3603574  PMID: 22982357
HARDI; structural connectivity; graph theory; development
13.  Meta-analysis of genome-wide association studies for personality 
Molecular psychiatry  2010;17(3):337-349.
Personality can be thought of as a set of characteristics that influence people’s thoughts, feelings, and behaviour across a variety of settings. Variation in personality is predictive of many outcomes in life, including mental health. Here we report on a meta-analysis of genome-wide association (GWA) data for personality in ten discovery samples (17 375 adults) and five in-silico replication samples (3 294 adults). All participants were of European ancestry. Personality scores for Neuroticism, Extraversion, Openness to Experience, Agreeableness, and Conscientiousness were based on the NEO Five-Factor Inventory. Genotype data were available of ~2.4M Single Nucleotide Polymorphisms (SNPs; directly typed and imputed using HAPMAP data). In the discovery samples, classical association analyses were performed under an additive model followed by meta-analysis using the weighted inverse variance method. Results showed genome-wide significance for Openness to Experience near the RASA1 gene on 5q14.3 (rs1477268 and rs2032794, P = 2.8 × 10−8 and 3.1 × 10−8) and for Conscientiousness in the brain-expressed KATNAL2 gene on 18q21.1 (rs2576037, P = 4.9 × 10−8). We further conducted a gene-based test that confirmed the association of KATNAL2 to Conscientiousness. In-silico replication did not, however, show significant associations of the top SNPs with Openness and Conscientiousness, although the direction of effect of the KATNAL2 SNP on Conscientiousness was consistent in all replication samples. Larger scale GWA studies and alternative approaches are required for confirmation of KATNAL2 as a novel gene affecting Conscientiousness.
doi:10.1038/mp.2010.128
PMCID: PMC3785122  PMID: 21173776
Personality; Five-Factor Model; Genome-wide association; Meta-analysis; Genetic variants
14.  Alzheimer’s Disease Risk Gene, GAB2, is Associated with Regional Brain Volume Differences in 755 Young Healthy Twins 
The development of late-onset Alzheimer’s disease (LOAD) is under strong genetic control and there is great interest in the genetic variants that confer increased risk. The Alzheimer’s disease risk gene, growth factor receptor bound protein 2-associated protein (GAB2), has been shown to provide a 1.27–1.51 increased odds of developing LOAD for rs7101429 major allele carriers, in case-control analysis. GAB2 is expressed across the brain throughout life, and its role in LOAD pathology is well understood. Recent studies have begun to examine the effect of genetic variation in the GAB2 gene on differences in the brain. However, the effect of GAB2 on the young-adult brain has yet to be considered. Here we found a significant association between the GAB2 gene and morphological brain differences in 755 young-adult twins (469 females) (M = 23.1, SD = 3.1 years), using a gene-based test with principal components regression (PCReg). Detectable differences in brain morphology are therefore associated with variation in the GAB2 gene, even in young adults, long before the typical age of onset of Alzheimer’s disease.
doi:10.1017/thg.2012.15
PMCID: PMC3785377  PMID: 22856364
GAB2; imaging genetics; tensor-based morphometry; Alzheimer’s disease
15.  Altered Structural Brain Connectivity in Healthy Carriers of the Autism Risk Gene, CNTNAP2 
Brain connectivity  2011;1(6):447-459.
Recently, carriers of a common variant in the autism risk gene, CNTNAP2, were found to have altered functional brain connectivity using functional MRI. Here we scanned 328 young adults with high-field (4-Tesla) diffusion imaging, to test the hypothesis that carriers of this gene variant would have altered structural brain connectivity. All participants (209 females, 119 males, age: 23.4 +/−2.17 SD years) were scanned with 105-gradient high angular diffusion imaging (HARDI) at 4 Tesla. After performing a whole-brain fiber tractography using the full angular resolution of the diffusion scans, 70 cortical surface-based regions of interest were created from each individual’s co-registered anatomical data to compute graph metrics for all pairs of cortical regions. In graph theory analyses, subjects homozygous for the risk allele (CC) had lower characteristic path length, greater small-worldness and global efficiency in whole brain analyses, as well as greater eccentricity (maximum path length) in 60 of 70 nodes in regional analyses. These results were not reducible to differences in more commonly studied traits such as fiber density or fractional anisotropy. This is the first study to link graph theory metrics of brain structural connectivity to a common genetic variant linked with autism and will help us understand the neurobiology of circuits implicated in risk for autism.
doi:10.1089/brain.2011.0064
PMCID: PMC3420970  PMID: 22500773
structural connectivity; HARDI; autism; CNTNAP2; graph theory; twins
16.  Predicting White Matter Integrity from Multiple Common Genetic Variants 
Neuropsychopharmacology  2012;37(9):2012-2019.
Several common genetic variants have recently been discovered that appear to influence white matter microstructure, as measured by diffusion tensor imaging (DTI). Each genetic variant explains only a small proportion of the variance in brain microstructure, so we set out to explore their combined effect on the white matter integrity of the corpus callosum. We measured six common candidate single-nucleotide polymorphisms (SNPs) in the COMT, NTRK1, BDNF, ErbB4, CLU, and HFE genes, and investigated their individual and aggregate effects on white matter structure in 395 healthy adult twins and siblings (age: 20–30 years). All subjects were scanned with 4-tesla 94-direction high angular resolution diffusion imaging. When combined using mixed-effects linear regression, a joint model based on five of the candidate SNPs (COMT, NTRK1, ErbB4, CLU, and HFE) explained ∼6% of the variance in the average fractional anisotropy (FA) of the corpus callosum. This predictive model had detectable effects on FA at 82% of the corpus callosum voxels, including the genu, body, and splenium. Predicting the brain's fiber microstructure from genotypes may ultimately help in early risk assessment, and eventually, in personalized treatment for neuropsychiatric disorders in which brain integrity and connectivity are affected.
doi:10.1038/npp.2012.49
PMCID: PMC3398730  PMID: 22510721
neuroimaging; brain structure; DTI; genetics; genetic profiles; prediction; imaging; clinical or preclinical; neuroanatomy; neurogenetics; pharmacogenetics / pharmacogenomics; neuroimaging; brain structure; DTI; genetics; genetic profiles
17.  A Genome-Wide Study on the Perception of the Odorants Androstenone and Galaxolide 
Chemical Senses  2012;37(6):541-552.
Twin pairs and their siblings rated the intensity of the odorants amyl acetate, androstenone, eugenol, Galaxolide, mercaptans, and rose (N = 1573). Heritability was established for ratings of androstenone (h 2 = 0.30) and Galaxolide (h2 = 0.34) but not for the other odorants. Genome-wide association analysis using 2.3 million single nucleotide polymorphisms indicated that the most significant association was between androstenone and a region without known olfactory receptor genes (rs10966900, P = 1.2 × 10−7). A previously reported association between the olfactory receptor OR7D4 and the androstenone was not detected until we specifically typed this gene (P = 1.1 × 10−4). We also tested these 2 associations in a second independent sample of subjects and replicated the results either fully (OR7D4, P = 0.00002) or partially (rs10966900, P = 0.010; N = 266). These findings suggest that 1) the perceived intensity of some but not all odorants is a heritable trait, 2) use of a current genome-wide marker panel did not detect a known olfactory genotype–phenotype association, and 3) person-to-person differences in androstenone perception are influenced by OR7D4 genotype and perhaps by variants of other genes.
doi:10.1093/chemse/bjs008
PMCID: PMC3452230  PMID: 22362865
androstenone; Galaxolide; genetic twin modeling; genome-wide association study; heritability; twins
18.  Identification of common variants associated with human hippocampal and intracranial volumes 
Stein, Jason L | Medland, Sarah E | Vasquez, Alejandro Arias | Hibar, Derrek P | Senstad, Rudy E | Winkler, Anderson M | Toro, Roberto | Appel, Katja | Bartecek, Richard | Bergmann, Ørjan | Bernard, Manon | Brown, Andrew A | Cannon, Dara M | Chakravarty, M Mallar | Christoforou, Andrea | Domin, Martin | Grimm, Oliver | Hollinshead, Marisa | Holmes, Avram J | Homuth, Georg | Hottenga, Jouke-Jan | Langan, Camilla | Lopez, Lorna M | Hansell, Narelle K | Hwang, Kristy S | Kim, Sungeun | Laje, Gonzalo | Lee, Phil H | Liu, Xinmin | Loth, Eva | Lourdusamy, Anbarasu | Mattingsdal, Morten | Mohnke, Sebastian | Maniega, Susana Muñoz | Nho, Kwangsik | Nugent, Allison C | O’Brien, Carol | Papmeyer, Martina | Pütz, Benno | Ramasamy, Adaikalavan | Rasmussen, Jerod | Rijpkema, Mark | Risacher, Shannon L | Roddey, J Cooper | Rose, Emma J | Ryten, Mina | Shen, Li | Sprooten, Emma | Strengman, Eric | Teumer, Alexander | Trabzuni, Daniah | Turner, Jessica | van Eijk, Kristel | van Erp, Theo G M | van Tol, Marie-Jose | Wittfeld, Katharina | Wolf, Christiane | Woudstra, Saskia | Aleman, Andre | Alhusaini, Saud | Almasy, Laura | Binder, Elisabeth B | Brohawn, David G | Cantor, Rita M | Carless, Melanie A | Corvin, Aiden | Czisch, Michael | Curran, Joanne E | Davies, Gail | de Almeida, Marcio A A | Delanty, Norman | Depondt, Chantal | Duggirala, Ravi | Dyer, Thomas D | Erk, Susanne | Fagerness, Jesen | Fox, Peter T | Freimer, Nelson B | Gill, Michael | Göring, Harald H H | Hagler, Donald J | Hoehn, David | Holsboer, Florian | Hoogman, Martine | Hosten, Norbert | Jahanshad, Neda | Johnson, Matthew P | Kasperaviciute, Dalia | Kent, Jack W | Kochunov, Peter | Lancaster, Jack L | Lawrie, Stephen M | Liewald, David C | Mandl, René | Matarin, Mar | Mattheisen, Manuel | Meisenzahl, Eva | Melle, Ingrid | Moses, Eric K | Mühleisen, Thomas W | 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 | 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 | 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 | 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 | 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 | Bis, Joshua C | Ikram, M Arfan | Smith, Albert V | Gudnason, Vilmundur | Tzourio, Christophe | Vernooij, Meike W | Launer, Lenore J | DeCarli, Charles | Seshadri, Sudha | Andreassen, Ole A | Apostolova, Liana G | Bastin, Mark E | Blangero, John | Brunner, Han G | Buckner, Randy L | 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 | 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 | 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 | Smoller, Jordan W | Wardlaw, Joanna M | Weale, Michael E | Martin, Nicholas G | Franke, Barbara | Wright, Margaret J | Thompson, Paul M
Nature genetics  2012;44(5):552-561.
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 disease1,2 and is reduced in schizophrenia3, major depression4 and mesial temporal lobe epilepsy5. Whereas many brain imaging phenotypes are highly heritable6,7, 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 × 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 × 10−12). We also identified a suggestive association with total brain volume at rs10494373 within DDR2 (1q23.3; N = 6,500; P = 5.81 × 10−7).
doi:10.1038/ng.2250
PMCID: PMC3635491  PMID: 22504417
19.  The Genetic Correlation between Height and IQ: Shared Genes or Assortative Mating? 
PLoS Genetics  2013;9(4):e1003451.
Traits that are attractive to the opposite sex are often positively correlated when scaled such that scores increase with attractiveness, and this correlation typically has a genetic component. Such traits can be genetically correlated due to genes that affect both traits (“pleiotropy”) and/or because assortative mating causes statistical correlations to develop between selected alleles across the traits (“gametic phase disequilibrium”). In this study, we modeled the covariation between monozygotic and dizygotic twins, their siblings, and their parents (total N = 7,905) to elucidate the nature of the correlation between two potentially sexually selected traits in humans: height and IQ. Unlike previous designs used to investigate the nature of the height–IQ correlation, the present design accounts for the effects of assortative mating and provides much less biased estimates of additive genetic, non-additive genetic, and shared environmental influences. Both traits were highly heritable, although there was greater evidence for non-additive genetic effects in males. After accounting for assortative mating, the correlation between height and IQ was found to be almost entirely genetic in nature. Model fits indicate that both pleiotropy and assortative mating contribute significantly and about equally to this genetic correlation.
Author Summary
Traits that are attractive to the opposite sex are often positively correlated when scaled such that scores increase with attractiveness, and this correlation typically has a genetic component. Such traits can be genetically correlated due to genes that affect both traits and/or because assortative mating (people choosing mates who are similar to themselves) causes statistical correlations to develop between selected alleles across the traits. In this study, we used a large (total N = 7,905), genetically informative dataset to understand why two potentially sexually selected traits in humans—height and IQ—are correlated. We found that both shared genes and assortative mating were about equally important in causing the relationship between these two traits. To our knowledge, this is the first study that has been able to disambiguate the two principal reasons—shared genes versus assortative mating—for why traits can be genetically correlated.
doi:10.1371/journal.pgen.1003451
PMCID: PMC3617178  PMID: 23593038
20.  Hierarchical topological network analysis of anatomical human brain connectivity and differences related to sex and kinship 
NeuroImage  2011;59(4):3784-3804.
Modern non-invasive brain imaging technologies, such as diffusion weighted magnetic resonance imaging (DWI), enable the mapping of neural fiber tracts in the white matter, providing a basis to reconstruct a detailed map of brain structural connectivity networks. Brain connectivity networks differ from random networks in their topology, which can be measured using small worldness, modularity, and high-degree nodes (hubs). Still, little is known about how individual differences in structural brain network properties relate to age, sex, or genetic differences. Recently, some groups have reported brain network biomarkers that enable differentiation among individuals, pairs of individuals, and groups of individuals. In addition to studying new topological features, here we provide a unifying general method to investigate topological brain networks and connectivity differences between individuals, pairs of individuals, and groups of individuals at several levels of the data hierarchy, while appropriately controlling false discovery rate (FDR) errors. We apply our new method to a large dataset of high quality brain connectivity networks obtained from High Angular Resolution Diffusion Imaging (HARDI) tractography in 303 young adult twins, siblings, and unrelated people. Our proposed approach can accurately classify brain connectivity networks based on sex (93% accuracy) and kinship (88.5% accuracy). We find statistically significant differences associated with sex and kinship both in the brain connectivity networks and in derived topological metrics, such as the clustering coefficient and the communicability matrix.
doi:10.1016/j.neuroimage.2011.10.096
PMCID: PMC3551467  PMID: 22108644
Anatomical brain connectivity; Complex networks; Diffusion weighted MRI; Topological analysis; Hierarchical analysis; False discovery rate; Sex and kinship brain network differences
21.  Loci affecting gamma-glutamyl transferase in adults and adolescents show age × SNP interaction and cardiometabolic disease associations 
Human Molecular Genetics  2011;21(2):446-455.
Serum gamma-glutamyl transferase (GGT) activity is a marker of liver disease which is also prospectively associated with the risk of all-cause mortality, cardiovascular disease, type 2 diabetes and cancers. We have discovered novel loci affecting GGT in a genome-wide association study (rs1497406 in an intergenic region of chromosome 1, P = 3.9 × 10−8; rs944002 in C14orf73 on chromosome 14, P = 4.7 × 10−13; rs340005 in RORA on chromosome 15, P = 2.4 × 10−8), and a highly significant heterogeneity between adult and adolescent results at the GGT1 locus on chromosome 22 (maximum PHET = 5.6 × 10−12 at rs6519520). Pathway analysis of significant and suggestive single-nucleotide polymorphism associations showed significant overlap between genes affecting GGT and those affecting common metabolic and inflammatory diseases, and identified the hepatic nuclear factor (HNF) family as controllers of a network of genes affecting GGT. Our results reinforce the disease associations of GGT and demonstrate that control by the GGT1 locus varies with age.
doi:10.1093/hmg/ddr478
PMCID: PMC3276286  PMID: 22010049
22.  GROUP ACTION INDUCED AVERAGING FOR HARDI PROCESSING 
We consider the problem of processing high angular resolution diffusion images described by orientation distribution functions (ODFs). Prior work showed that several processing operations, e.g., averaging, interpolation and filtering, can be reduced to averaging in the space of ODFs. However, this approach leads to anatomically erroneous results when the ODFs to be processed have very different orientations. To address this issue, we propose a group action induced distance for averaging ODFs, which leads to a novel processing framework on the spaces of orientation (the space of 3D rotations) and shape (the space of ODFs with the same orientation). Experiments demonstrate that our framework produces anatomically meaningful results.
doi:10.1109/ISBI.2012.6235827
PMCID: PMC3420967  PMID: 22903055
biomedical image processing; information geometry; Riemannian manifolds; diffusion magnetic resonance imaging
23.  DIFFUSION IMAGING PROTOCOL EFFECTS ON GENETIC ASSOCIATIONS 
Large multi-site image-analysis studies have successfully discovered genetic variants that affect brain structure in tens of thousands of subjects scanned worldwide. Candidate genes have also associated with brain integrity, measured using fractional anisotropy in diffusion tensor images (DTI). To evaluate the heritability and robustness of DTI measures as a target for genetic analysis, we compared 417 twins and siblings scanned on the same day on the same high field scanner (4-Tesla) with two protocols: (1) 94-directions; 2mm-thick slices, (2) 27-directions; 5mm-thickness. Using mean FA in white matter ROIs and FA ‘skeletons’ derived using FSL, we (1) examined differences in voxelwise means, variances, and correlations among the measures; and (2) assessed heritability with structural equation models, using the classical twin design. FA measures from the genu of the corpus callosum were highly heritable, regardless of protocol. Genome-wide analysis of the genu mean FA revealed differences across protocols in the top associations.
doi:10.1109/ISBI.2012.6235712
PMCID: PMC3420973  PMID: 22903274
imaging genetics; DTI protocol stability; corpus callosum; genome-wide association study; multi-site analysis
24.  CHANGES IN ANATOMICAL BRAIN CONNECTIVITY BETWEEN AGES 12 AND 30: A HARDI STUDY OF 467 ADOLESCENTS AND ADULTS 
Graph theory can be applied to matrices that represent the brain’s anatomical connections, to better understand global properties of anatomical networks, such as their clustering, efficiency and “small-world” topology. Network analysis is popular in adult studies of connectivity, but only one study – in just 30 subjects – has examined how network measures change as the brain develops over this period. Here we assessed the developmental trajectory of graph theory metrics of structural brain connectivity in a cross-sectional study of 467 subjects, aged 12 to 30. We computed network measures from 70×70 connectivity matrices of fiber density generated using whole-brain tractography in 4-Tesla 105-gradient high angular resolution diffusion images (HARDI). We assessed global efficiency and modularity, and both age and age2 effects were identified. HARDI-based connectivity maps are sensitive to the remodeling and refinement of structural brain connections as the human brain develops.
doi:10.1109/ISBI.2012.6235695
PMCID: PMC3420974  PMID: 22903354
graph theory; high angular resolution diffusion imaging (HARDI); tractography; network analyses; development; structural connectivity
25.  DISCOVERY OF GENES THAT AFFECT HUMAN BRAIN CONNECTIVITY: A GENOME-WIDE ANALYSIS OF THE CONNECTOME 
Human brain connectivity is disrupted in a wide range of disorders – from Alzheimer’s disease to autism – but little is known about which specific genes affect it. Here we conducted a genome-wide association for connectivity matrices that capture information on the density of fiber connections between 70 brain regions. We scanned a large twin cohort (N=366) with 4-Tesla high angular resolution diffusion imaging (105-gradient HARDI). Using whole brain HARDI tractography, we extracted a relatively sparse 70×70 matrix representing fiber density between all pairs of cortical regions automatically labeled in co-registered anatomical scans. Additive genetic factors accounted for 1–58% of the variance in connectivity between 90 (of 122) tested nodes. We discovered genome-wide significant associations between variants and connectivity. GWAS permutations at various levels of heritability, and split-sample replication, validated our genetic findings. The resulting genes may offer new leads for mechanisms influencing aberrant connectivity and neurodegeneration.
doi:10.1109/ISBI.2012.6235605
PMCID: PMC3420975  PMID: 22903411
genetics; high angular resolution diffusion imaging (HARDI); cortical surfaces; twin modeling; human connectome

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