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1. Cerebral blood flow and cardiovascular risk effects on resting brain regional homogeneity

Author

Adhikari, Bhim M, Hong, L Elliot, Zhao, Zhiwei, Wang, Danny JJ, Thompson, Paul M, Jahanshad, Neda, Zhu, Alyssa H, Holiga, Stefan, Turner, Jessica A, van Erp, Theo GM, Calhoun, Vince D, Hatch, Kathryn S, Bruce, Heather, Hare, Stephanie M, Chiappelli, Joshua, Goldwaser, Eric L, Kvarta, Mark D, Ma, Yizhou, Du, Xiaoming, Nichols, Thomas E, Shuldiner, Alan R, Mitchell, Braxton D, Dukart, Juergen, Chen, Shuo, and Kochunov, Peter

Subjects
Arterial-spin labeling, Correlation, Local functional connectivity, Multivariate mediation analysis, Resting state functional MRI, Regional Homegeneity, Cerebral Blood Flow, Mediation Analysis, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery, Biomedical and clinical sciences, Health sciences
Abstract

Regional homogeneity (ReHo) is a measure of local functional brain connectivity that has been reported to be altered in a wide range of neuropsychiatric disorders. Computed from brain resting-state functional MRI time series, ReHo is also sensitive to fluctuations in cerebral blood flow (CBF) that in turn may be influenced by cerebrovascular health. We accessed cerebrovascular health with Framingham cardiovascular risk score (FCVRS). We hypothesize that ReHo signal may be influenced by regional CBF; and that these associations can be summarized as FCVRS→CBF→ReHo. We used three independent samples to test this hypothesis. A test-retest sample of N = 30 healthy volunteers was used for test-retest evaluation of CBF effects on ReHo. Amish Connectome Project (ACP) sample (N = 204, healthy individuals) was used to evaluate association between FCVRS and ReHo and testing if the association diminishes given CBF. The UKBB sample (N = 6,285, healthy participants) was used to replicate the effects of FCVRS on ReHo. We observed strong CBF→ReHo links (p

Published
2022

3. Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium

Author

Zhu, Xi, Kim, Yoojean, Ravid, Orren, He, Xiaofu, Suarez-Jimenez, Benjamin, Zilcha-Mano, Sigal, Lazarov, Amit, Lee, Seonjoo, Abdallah, Chadi G., Angstadt, Michael, Averill, Christopher L., Baird, C. Lexi, Baugh, Lee A., Blackford, Jennifer U., Bomyea, Jessica, Bruce, Steven E., Bryant, Richard A., Cao, Zhihong, Choi, Kyle, Cisler, Josh, Cotton, Andrew S., Daniels, Judith K., Davenport, Nicholas D., Davidson, Richard J., DeBellis, Michael D., Dennis, Emily L., Densmore, Maria, deRoon-Cassini, Terri, Disner, Seth G., Hage, Wissam El, Etkin, Amit, Fani, Negar, Fercho, Kelene A., Fitzgerald, Jacklynn, Forster, Gina L., Frijling, Jessie L., Geuze, Elbert, Gonenc, Atilla, Gordon, Evan M., Gruber, Staci, Grupe, Daniel W, Guenette, Jeffrey P., Haswell, Courtney C., Herringa, Ryan J., Herzog, Julia, Hofmann, David Bernd, Hosseini, Bobak, Hudson, Anna R., Huggins, Ashley A., Ipser, Jonathan C., Jahanshad, Neda, Jia-Richards, Meilin, Jovanovic, Tanja, Kaufman, Milissa L., Kennis, Mitzy, King, Anthony, Kinzel, Philipp, Koch, Saskia B.J., Koerte, Inga K., Koopowitz, Sheri M., Korgaonkar, Mayuresh S., Krystal, John H., Lanius, Ruth, Larson, Christine L., Lebois, Lauren A.M., Li, Gen, Liberzon, Israel, Lu, Guang Ming, Luo, Yifeng, Magnotta, Vincent A., Manthey, Antje, Maron-Katz, Adi, May, Geoffery, McLaughlin, Katie, Mueller, Sven C., Nawijn, Laura, Nelson, Steven M., Neufeld, Richard W.J., Nitschke, Jack B, O'Leary, Erin M., Olatunji, Bunmi O., Olff, Miranda, Peverill, Matthew, Phan, K. Luan, Qi, Rongfeng, Quidé, Yann, Rektor, Ivan, Ressler, Kerry, Riha, Pavel, Ross, Marisa, Rosso, Isabelle M., Salminen, Lauren E., Sambrook, Kelly, Schmahl, Christian, Shenton, Martha E., Sheridan, Margaret, Shih, Chiahao, Sicorello, Maurizio, Sierk, Anika, Simmons, Alan N., Simons, Raluca M., Simons, Jeffrey S., Sponheim, Scott R., Stein, Murray B., Stein, Dan J., Stevens, Jennifer S., Straube, Thomas, Sun, Delin, Théberge, Jean, Thompson, Paul M., Thomopoulos, Sophia I., van der Wee, Nic J.A., van der Werff, Steven J.A., van Erp, Theo G.M., van Rooij, Sanne J.H., van Zuiden, Mirjam, Varkevisser, Tim, Veltman, Dick J., Vermeiren, Robert R.J.M., Walter, Henrik, Wang, Li, Wang, Xin, Weis, Carissa, Winternitz, Sherry, Xie, Hong, Zhu, Ye, Wall, Melanie, Neria, Yuval, and Morey, Rajendra A.

Published
2023
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5. A comparison of methods to harmonize cortical thickness measurements across scanners and sites

Author

Sun, Delin, Rakesh, Gopalkumar, Haswell, Courtney C., Logue, Mark, Baird, C. Lexi, O'Leary, Erin N., Cotton, Andrew S., Xie, Hong, Tamburrino, Marijo, Chen, Tian, Dennis, Emily L., Jahanshad, Neda, Salminen, Lauren E., Thomopoulos, Sophia I., Rashid, Faisal, Ching, Christopher R.K., Koch, Saskia B.J., Frijling, Jessie L., Nawijn, Laura, van Zuiden, Mirjam, Zhu, Xi, Suarez-Jimenez, Benjamin, Sierk, Anika, Walter, Henrik, Manthey, Antje, Stevens, Jennifer S., Fani, Negar, van Rooij, Sanne J.H., Stein, Murray, Bomyea, Jessica, Koerte, Inga K., Choi, Kyle, van der Werff, Steven J.A., Vermeiren, Robert R.J.M., Herzog, Julia, Lebois, Lauren A.M., Baker, Justin T., Olson, Elizabeth A., Straube, Thomas, Korgaonkar, Mayuresh S., Andrew, Elpiniki, Zhu, Ye, Li, Gen, Ipser, Jonathan, Hudson, Anna R., Peverill, Matthew, Sambrook, Kelly, Gordon, Evan, Baugh, Lee, Forster, Gina, Simons, Raluca M., Simons, Jeffrey S., Magnotta, Vincent, Maron-Katz, Adi, du Plessis, Stefan, Disner, Seth G., Davenport, Nicholas, Grupe, Daniel W., Nitschke, Jack B., deRoon-Cassini, Terri A., Fitzgerald, Jacklynn M., Krystal, John H., Levy, Ifat, Olff, Miranda, Veltman, Dick J., Wang, Li, Neria, Yuval, De Bellis, Michael D., Jovanovic, Tanja, Daniels, Judith K., Shenton, Martha, van de Wee, Nic J.A., Schmahl, Christian, Kaufman, Milissa L., Rosso, Isabelle M., Sponheim, Scott R., Hofmann, David Bernd, Bryant, Richard A., Fercho, Kelene A., Stein, Dan J., Mueller, Sven C., Hosseini, Bobak, Phan, K. Luan, McLaughlin, Katie A., Davidson, Richard J., Larson, Christine L., May, Geoffrey, Nelson, Steven M., Abdallah, Chadi G., Gomaa, Hassaan, Etkin, Amit, Seedat, Soraya, Harpaz-Rotem, Ilan, Liberzon, Israel, van Erp, Theo G.M., Quidé, Yann, Wang, Xin, Thompson, Paul M., and Morey, Rajendra A.

Published
2022
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8. ENIGMA and the individual: Predicting factors that affect the brain in 35 countries worldwide

Author

Thompson, Paul M, Andreassen, Ole A, Arias-Vasquez, Alejandro, Bearden, Carrie E, Boedhoe, Premika S, Brouwer, Rachel M, Buckner, Randy L, Buitelaar, Jan K, Bulayeva, Kazima B, Cannon, Dara M, Cohen, Ronald A, Conrod, Patricia J, Dale, Anders M, Deary, Ian J, Dennis, Emily L, de Reus, Marcel A, Desrivieres, Sylvane, Dima, Danai, Donohoe, Gary, Fisher, Simon E, Fouche, Jean-Paul, Francks, Clyde, Frangou, Sophia, Franke, Barbara, Ganjgahi, Habib, Garavan, Hugh, Glahn, David C, Grabe, Hans J, Guadalupe, Tulio, Gutman, Boris A, Hashimoto, Ryota, Hibar, Derrek P, Holland, Dominic, Hoogman, Martine, Pol, Hilleke E Hulshoff, Hosten, Norbert, Jahanshad, Neda, Kelly, Sinead, Kochunov, Peter, Kremen, William S, Lee, Phil H, Mackey, Scott, Martin, Nicholas G, Mazoyer, Bernard, McDonald, Colm, Medland, Sarah E, Morey, Rajendra A, Nichols, Thomas E, Paus, Tomas, Pausova, Zdenka, Schmaal, Lianne, Schumann, Gunter, Shen, Li, Sisodiya, Sanjay M, Smit, Dirk JA, Smoller, Jordan W, Stein, Dan J, Stein, Jason L, Toro, Roberto, Turner, Jessica A, van den Heuvel, Martijn P, van den Heuvel, Odile L, van Erp, Theo GM, van Rooij, Daan, Veltman, Dick J, Walter, Henrik, Wang, Yalin, Wardlaw, Joanna M, Whelan, Christopher D, Wright, Margaret J, Ye, Jieping, and Consortium, for the ENIGMA

Subjects
Biomedical and Clinical Sciences, Health Sciences, Genetics, Mental Health, Prevention, Brain Disorders, Neurosciences, 2.1 Biological and endogenous factors, Aetiology, Mental health, Neurological, Good Health and Well Being, Brain Diseases, Genome-Wide Association Study, Humans, Mental Disorders, Multicenter Studies as Topic, ENIGMA Consortium, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery, Biomedical and clinical sciences, Health sciences
Abstract

In this review, we discuss recent work by the ENIGMA Consortium (http://enigma.ini.usc.edu) - a global alliance of over 500 scientists spread across 200 institutions in 35 countries collectively analyzing brain imaging, clinical, and genetic data. Initially formed to detect genetic influences on brain measures, ENIGMA has grown to over 30 working groups studying 12 major brain diseases by pooling and comparing brain data. In some of the largest neuroimaging studies to date - of schizophrenia and major depression - ENIGMA has found replicable disease effects on the brain that are consistent worldwide, as well as factors that modulate disease effects. In partnership with other consortia including ADNI, CHARGE, IMAGEN and others1, ENIGMA's genomic screens - now numbering over 30,000 MRI scans - have revealed at least 8 genetic loci that affect brain volumes. Downstream of gene findings, ENIGMA has revealed how these individual variants - and genetic variants in general - may affect both the brain and risk for a range of diseases. The ENIGMA consortium is discovering factors that consistently affect brain structure and function that will serve as future predictors linking individual brain scans and genomic data. It is generating vast pools of normative data on brain measures - from tens of thousands of people - that may help detect deviations from normal development or aging in specific groups of subjects. We discuss challenges and opportunities in applying these predictors to individual subjects and new cohorts, as well as lessons we have learned in ENIGMA's efforts so far.

Published
2017

10. Increased power by harmonizing structural MRI site differences with the ComBat batch adjustment method in ENIGMA

Author

Glahn, David, Pearlson, Godfrey, Hong, Elliot, Krug, Axel, Carr, Vaughan, Tooney, Paul, Cooper, Gavin, Rasser, Paul, Michie, Patricia, Catts, Stanley, Gur, Raquel, Gur, Ruben, Yang, Fude, Fan, Fengmei, Chen, Jingxu, Guo, Hua, Tan, Shuping, Wang, Zhiren, Xiang, Hong, Piras, Federica, Assogna, Francesca, Salvador, Raymond, McKenna, Peter, Bonvino, Aurora, King, Margaret, Kaiser, Stefan, Nguyen, Dana, Pineda-Zapata, Julian, Radua, Joaquim, Vieta, Eduard, Shinohara, Russell, Kochunov, Peter, Quidé, Yann, Green, Melissa J., Weickert, Cynthia S., Weickert, Thomas, Bruggemann, Jason, Kircher, Tilo, Nenadić, Igor, Cairns, Murray J., Seal, Marc, Schall, Ulrich, Henskens, Frans, Fullerton, Janice M., Mowry, Bryan, Pantelis, Christos, Lenroot, Rhoshel, Cropley, Vanessa, Loughland, Carmel, Scott, Rodney, Wolf, Daniel, Satterthwaite, Theodore D., Tan, Yunlong, Sim, Kang, Piras, Fabrizio, Spalletta, Gianfranco, Banaj, Nerisa, Pomarol-Clotet, Edith, Solanes, Aleix, Albajes-Eizagirre, Anton, Canales-Rodríguez, Erick J., Sarro, Salvador, Di Giorgio, Annabella, Bertolino, Alessandro, Stäblein, Michael, Oertel, Viola, Knöchel, Christian, Borgwardt, Stefan, du Plessis, Stefan, Yun, Je-Yeon, Kwon, Jun Soo, Dannlowski, Udo, Hahn, Tim, Grotegerd, Dominik, Alloza, Clara, Arango, Celso, Janssen, Joost, Díaz-Caneja, Covadonga, Jiang, Wenhao, Calhoun, Vince, Ehrlich, Stefan, Yang, Kun, Cascella, Nicola G., Takayanagi, Yoichiro, Sawa, Akira, Tomyshev, Alexander, Lebedeva, Irina, Kaleda, Vasily, Kirschner, Matthias, Hoschl, Cyril, Tomecek, David, Skoch, Antonin, van Amelsvoort, Therese, Bakker, Geor, James, Anthony, Preda, Adrian, Weideman, Andrea, Stein, Dan J., Howells, Fleur, Uhlmann, Anne, Temmingh, Henk, López-Jaramillo, Carlos, Díaz-Zuluaga, Ana, Fortea, Lydia, Martinez-Heras, Eloy, Solana, Elisabeth, Llufriu, Sara, Jahanshad, Neda, Thompson, Paul, Turner, Jessica, and van Erp, Theo

Published
2020
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14. Heritability and reliability of automatically segmented human hippocampal formation subregions.

Author

Whelan, Christopher D, Hibar, Derrek P, van Velzen, Laura S, Zannas, Anthony S, Carrillo-Roa, Tania, McMahon, Katie, Prasad, Gautam, Kelly, Sinéad, Faskowitz, Joshua, deZubiracay, Greig, Iglesias, Juan E, van Erp, Theo GM, Frodl, Thomas, Martin, Nicholas G, Wright, Margaret J, Jahanshad, Neda, Schmaal, Lianne, Sämann, Philipp G, Thompson, Paul M, and Alzheimer's Disease Neuroimaging Initiative

Subjects
Alzheimer's Disease Neuroimaging Initiative, Hippocampus, Humans, Alzheimer Disease, Magnetic Resonance Imaging, Anxiety Disorders, Depressive Disorder, Phenotype, Algorithms, Image Processing, Computer-Assisted, Software, Adult, Aged, Aged, 80 and over, Middle Aged, Female, Male, Neuroimaging, Neurology & Neurosurgery, Medical and Health Sciences, Psychology and Cognitive Sciences
Abstract

The human hippocampal formation can be divided into a set of cytoarchitecturally and functionally distinct subregions, involved in different aspects of memory formation. Neuroanatomical disruptions within these subregions are associated with several debilitating brain disorders including Alzheimer's disease, major depression, schizophrenia, and bipolar disorder. Multi-center brain imaging consortia, such as the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) consortium, are interested in studying disease effects on these subregions, and in the genetic factors that affect them. For large-scale studies, automated extraction and subsequent genomic association studies of these hippocampal subregion measures may provide additional insight. Here, we evaluated the test-retest reliability and transplatform reliability (1.5T versus 3T) of the subregion segmentation module in the FreeSurfer software package using three independent cohorts of healthy adults, one young (Queensland Twins Imaging Study, N=39), another elderly (Alzheimer's Disease Neuroimaging Initiative, ADNI-2, N=163) and another mixed cohort of healthy and depressed participants (Max Planck Institute, MPIP, N=598). We also investigated agreement between the most recent version of this algorithm (v6.0) and an older version (v5.3), again using the ADNI-2 and MPIP cohorts in addition to a sample from the Netherlands Study for Depression and Anxiety (NESDA) (N=221). Finally, we estimated the heritability (h(2)) of the segmented subregion volumes using the full sample of young, healthy QTIM twins (N=728). Test-retest reliability was high for all twelve subregions in the 3T ADNI-2 sample (intraclass correlation coefficient (ICC)=0.70-0.97) and moderate-to-high in the 4T QTIM sample (ICC=0.5-0.89). Transplatform reliability was strong for eleven of the twelve subregions (ICC=0.66-0.96); however, the hippocampal fissure was not consistently reconstructed across 1.5T and 3T field strengths (ICC=0.47-0.57). Between-version agreement was moderate for the hippocampal tail, subiculum and presubiculum (ICC=0.78-0.84; Dice Similarity Coefficient (DSC)=0.55-0.70), and poor for all other subregions (ICC=0.34-0.81; DSC=0.28-0.51). All hippocampal subregion volumes were highly heritable (h(2)=0.67-0.91). Our findings indicate that eleven of the twelve human hippocampal subregions segmented using FreeSurfer version 6.0 may serve as reliable and informative quantitative phenotypes for future multi-site imaging genetics initiatives such as those of the ENIGMA consortium.

Published
2016

15. Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images

Author

Carass, Aaron, Cuzzocreo, Jennifer L., Han, Shuo, Hernandez-Castillo, Carlos R., Rasser, Paul E., Ganz, Melanie, Beliveau, Vincent, Dolz, Jose, Ben Ayed, Ismail, Desrosiers, Christian, Thyreau, Benjamin, Romero, José E., Coupé, Pierrick, Manjón, José V., Fonov, Vladimir S., Collins, D. Louis, Ying, Sarah H., Onyike, Chiadi U., Crocetti, Deana, Landman, Bennett A., Mostofsky, Stewart H., Thompson, Paul M., and Prince, Jerry L.

Published
2018
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16. White matter microstructural abnormalities in girls with chromosome 22q11.2 deletion syndrome, Fragile X or Turner syndrome as evidenced by diffusion tensor imaging.

Author

Villalon-Reina, Julio, Jahanshad, Neda, Beaton, Elliott, Toga, Arthur W, Thompson, Paul M, and Simon, Tony J

Subjects
Brain, Neural Pathways, Nerve Fibers, Myelinated, Humans, Fragile X Syndrome, Turner Syndrome, Image Interpretation, Computer-Assisted, Anisotropy, Adolescent, Child, Female, Diffusion Tensor Imaging, 22q11 Deletion Syndrome, Connectivity, Diffusion tensor imaging, Genetic diseases, Neurodevelopmental diseases, Neurology & Neurosurgery, Medical and Health Sciences, Psychology and Cognitive Sciences
Abstract

Children with chromosome 22q11.2 deletion syndrome (22q11.2DS), Fragile X syndrome (FXS), or Turner syndrome (TS) are considered to belong to distinct genetic groups, as each disorder is caused by separate genetic alterations. Even so, they have similar cognitive and behavioral dysfunctions, particularly in visuospatial and numerical abilities. To assess evidence for common underlying neural microstructural alterations, we set out to determine whether these groups have partially overlapping white matter abnormalities, relative to typically developing controls. We scanned 101 female children between 7 and 14years old: 25 with 22q11.2DS, 18 with FXS, 17 with TS, and 41 aged-matched controls using diffusion tensor imaging (DTI). Anisotropy and diffusivity measures were calculated and all brain scans were nonlinearly aligned to population and site-specific templates. We performed voxel-based statistical comparisons of the DTI-derived metrics between each disease group and the controls, while adjusting for age. Girls with 22q11.2DS showed lower fractional anisotropy (FA) than controls in the association fibers of the superior and inferior longitudinal fasciculi, the splenium of the corpus callosum, and the corticospinal tract. FA was abnormally lower in girls with FXS in the posterior limbs of the internal capsule, posterior thalami, and precentral gyrus. Girls with TS had lower FA in the inferior longitudinal fasciculus, right internal capsule and left cerebellar peduncle. Partially overlapping neurodevelopmental anomalies were detected in all three neurogenetic disorders. Altered white matter integrity in the superior and inferior longitudinal fasciculi and thalamic to frontal tracts may contribute to the behavioral characteristics of all of these disorders.

Published
2013

19. MULTI-SOURCE FEATURE LEARNING FOR JOINT ANALYSIS OF INCOMPLETE MULTIPLE HETEROGENEOUS NEUROIMAGING DATA

Author

Yuan, Lei, Wang, Yalin, Thompson, Paul M., Narayan, Vaibhav A., and Ye, Jieping

Abstract

Analysis of incomplete data is a big challenge when integrating large-scale brain imaging datasets from different imaging modalities. In the Alzheimer’s Disease Neuroimaging Initiative (ADNI), for example, over half of the subjects lack cerebrospinal fluid (CSF) measurements; an independent half of the subjects do not have fluorodeoxyglucose positron emission tomography (FDG-PET) scans; many lack proteomics measurements. Traditionally, subjects with missing measures are discarded, resulting in a severe loss of available information. In this paper, we address this problem by proposing an incomplete Multi-Source Feature (iMSF) learning method where all the samples (with at least one available data source) can be used. To illustrate the proposed approach, we classify patients from the ADNI study into groups with Alzheimer’s disease (AD), mild cognitive impairment (MCI) and normal controls, based on the multi-modality data. At baseline, ADNI’s 780 participants (172 AD, 397 MCI, 211 NC), have at least one of four data types: magnetic resonance imaging (MRI), FDG-PET, CSF and proteomics. These data are used to test our algorithm. Depending on the problem being solved, we divide our samples according to the availability of data sources, and we learn shared sets of features with state-of-the-art sparse learning methods. To build a practical and robust system, we construct a classifier ensemble by combining our method with four other methods for missing value estimation. Comprehensive experiments with various parameters show that our proposed iMSF method and the ensemble model yield stable and promising results.

Published
2012

23. Atlas-based hippocampus segmentation in Alzheimer's disease and mild cognitive impairment.

Author

Carmichael, Owen T, Aizenstein, Howard A, Davis, Simon W, Becker, James T, Thompson, Paul M, Meltzer, Carolyn Cidis, and Liu, Yanxi

Subjects
Aged, Algorithms, Alzheimer Disease, Atrophy, Cognition Disorders, Female, Functional Laterality, Hippocampus, Humans, Image Processing, Computer-Assisted, Male, Models, Statistical, Software
Abstract

This study assesses the performance of public-domain automated methodologies for MRI-based segmentation of the hippocampus in elderly subjects with Alzheimer's disease (AD) and mild cognitive impairment (MCI). Structural MR images of 54 age- and gender-matched healthy elderly individuals, subjects with probable AD, and subjects with MCI were collected at the University of Pittsburgh Alzheimer's Disease Research Center. Hippocampi in subject images were automatically segmented by using AIR, SPM, FLIRT, and the fully deformable method of Chen to align the images to the Harvard atlas, MNI atlas, and randomly selected, manually labeled subject images ("cohort atlases"). Mixed-effects statistical models analyzed the effects of side of the brain, disease state, registration method, choice of atlas, and manual tracing protocol on the spatial overlap between automated segmentations and expert manual segmentations. Registration methods that produced higher degrees of geometric deformation produced automated segmentations with higher agreement with manual segmentations. Side of the brain, presence of AD, choice of reference image, and manual tracing protocol were also significant factors contributing to automated segmentation performance. Fully automated techniques can be competitive with human raters on this difficult segmentation task, but a rigorous statistical analysis shows that a variety of methodological factors must be carefully considered to insure that automated methods perform well in practice. The use of fully deformable registration methods, cohort atlases, and user-defined manual tracings are recommended for highest performance in fully automated hippocampus segmentation.

Published
2005

24. Mapping Cortical Gray Matter Asymmetry Patterns in Adolescents with Heavy Prenatal Alcohol Exposure

Author

Sowell, Elizabeth R, Thompson, Paul M, Peterson, Bradley S, Mattson, Sarah N, Welcome, Suzanne E, Henkenius, Amy L, Riley, Edward P, Jernigan, Terry L, and Toga, Arthur W

Subjects
Biomedical and Clinical Sciences, Clinical Research, Conditions Affecting the Embryonic and Fetal Periods, Pediatric, Substance Misuse, Neurosciences, Alcoholism, Alcohol Use and Health, Mental health, Adolescent, Adult, Brain Mapping, Cerebral Cortex, Child, Dominance, Cerebral, Female, Fetal Alcohol Spectrum Disorders, Humans, Image Processing, Computer-Assisted, Imaging, Three-Dimensional, Magnetic Resonance Imaging, Male, Pregnancy, Reference Values, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery, Biomedical and clinical sciences, Health sciences
Abstract

Here we report on detailed three-dimensional quantitative maps of brain surface and gray matter density asymmetry patterns during normal adolescent development and show how these anatomical features of the brain are disrupted as a result of prenatal exposure to large quantities of alcohol. We studied two independent samples of normally developing children, adolescents, and young adults, totaling 83 subjects from two different research groups, and compared them to 21 individuals with heavy prenatal alcohol exposure. Surface-based image analysis techniques allowed us to match cortical anatomy across subjects and between hemispheres based on manually delineated sulcal landmarks. Quantitative maps of brain surface asymmetry reveal prominent peri-Sylvian hemispheric differences in which the superior temporal and inferior parietal cortices are shifted backward in the left relative to the right hemisphere in both normal and alcohol-exposed subjects. Cortical surface gray matter asymmetry, mapped here in adolescent populations, is most prominent in the posterior inferior temporal lobes (right greater than left), and this effect does not differ between groups of normally developing children, adolescents, or young adults. Alcohol-exposed individuals show a significant reduction in this asymmetry, whether studied with surface-based or more traditional volumetric region of interest analyses. This region of cortex, near the junction of Brodmann's areas 21, 22, and 37, primarily subserves language functions that are known to be impaired on average in the alcohol-exposed subjects. Our findings elucidate regional patterns of brain surface and gray matter asymmetry during normal development and may contribute to a more comprehensive understanding of the neural substrates of cognitive dysfunction after heavy prenatal alcohol exposure.

Published
2002

25. Localizing Age-Related Changes in Brain Structure between Childhood and Adolescence Using Statistical Parametric Mapping

Author

Sowell, Elizabeth R, Thompson, Paul M, Holmes, Colin J, Batth, Rajneesh, Jernigan, Terry L, and Toga, Arthur W

Subjects
Biomedical and Clinical Sciences, Neurosciences, Pediatric, Clinical Research, Biomedical Imaging, Adolescent, Aging, Brain, Child, Female, Humans, Image Processing, Computer-Assisted, Male, Models, Statistical, Reproducibility of Results, myelination, normal development, MRI, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery, Biomedical and clinical sciences, Health sciences
Abstract

Volumetric studies have consistently shown reductions in cerebral gray matter volume between childhood and adolescence, with the most dramatic changes occurring in the more dorsal cortices of the frontal and parietal lobes. The purpose of this study was to examine the spatial location of these changes employing methods typical of functional imaging studies. T1-weighted structural MRI data (1.2 mm) were analyzed for nine normally developing children and nine normal adolescents. Validity and reliability of the tissue segmentation protocol were assessed as part of several preprocessing analyses prior to statistical parametric mapping (SPM). Using SPM96, a simple contrast of average gray matter differences between the two age groups revealed 57 significant clusters (SPM[Z] height threshold, P

Published
1999

27. Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: A pilot project of the ENIGMA–DTI working group

Author

Jahanshad, Neda, Kochunov, Peter V., Sprooten, Emma, Mandl, René C., Nichols, Thomas E., Almasy, Laura, Blangero, John, Brouwer, Rachel M., Curran, Joanne E., de Zubicaray, Greig I., Duggirala, Ravi, Fox, Peter T., Hong, L. Elliot, Landman, Bennett A., Martin, Nicholas G., McMahon, Katie L., Medland, Sarah E., Mitchell, Braxton D., Olvera, Rene L., Peterson, Charles P., Starr, John M., Sussmann, Jessika E., Toga, Arthur W., Wardlaw, Joanna M., Wright, Margaret J., Hulshoff Pol, Hilleke E., Bastin, Mark E., McIntosh, Andrew M., Deary, Ian J., Thompson, Paul M., and Glahn, David C.

Published
2013
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