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1.  Connectivity network measures predict volumetric atrophy in mild cognitive impairment 
Neurobiology of aging  2014;36(0 1):S113-S120.
Alzheimer’s disease (AD) is characterized by cortical atrophy and disrupted anatomical connectivity, and leads to abnormal interactions between neural systems. Diffusion weighted imaging (DWI) and graph theory can be used to evaluate major brain networks, and detect signs of a breakdown in network connectivity. In a longitudinal study using both DWI and standard MRI, we assessed baseline white matter connectivity patterns in 30 subjects with mild cognitive impairment (MCI; mean age: 71.8+/−7.5 yrs; 18M/12F) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Using both standard MRI-based cortical parcellations and whole-brain tractography, we computed baseline connectivity maps from which we calculated global “small-world” architecture measures, including mean clustering coefficient (MCC) and characteristic path length (CPL). We evaluated whether these baseline network measures predicted future volumetric brain atrophy in MCI subjects, who are at risk for developing AD, as determined by 3D Jacobian “expansion factor maps” between baseline and 6-month follow-up anatomical scans. This study suggests that DWI-based network measures may be a novel predictor of AD progression.
doi:10.1016/j.neurobiolaging.2014.04.038
PMCID: PMC4276308  PMID: 25444606
Graph theory; brain networks; white matter; DTI; tractography; ADNI; TBM; small worldness; connectivity
2.  Seemingly Unrelated Regression empowers detection of network failure in dementia 
Neurobiology of aging  2014;36(0 1):S103-S112.
Brain connectivity is progressively disrupted in Alzheimer’s disease (AD). Here we used a seemingly unrelated regression (SUR) model to enhance the power to identify structural connections related to cognitive scores. We simultaneously solved regression equations with different predictors and used correlated errors among the equations to boost power for associations with brain networks. Connectivity maps were computed to represent the brain’s fiber networks from diffusion-weighted MRI scans of 200 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We first identified a pattern of brain connections related to clinical decline using standard regressions powered by this large sample size. As AD studies with a large number of DTI scans are rare, it is important to detect effects in smaller samples using simultaneous regression modeling like SUR. Diagnosis of MCI or AD is well known to be associated with ApoE genotype and educational level. In a subsample with no apparent associations using the general linear model, power was boosted with our SUR model--combining genotype, educational level, and clinical diagnosis.
doi:10.1016/j.neurobiolaging.2014.02.032
PMCID: PMC4276318  PMID: 25257986
Brain connectivity; neuroimaging genetics; HARDI tractography; seemingly unrelated regression (SUR); APOE4; multivariate analysis
3.  DTI-based maximum density path analysis and classification of Alzheimer’s disease 
Neurobiology of aging  2014;36(0 1):S132-S140.
Characterizing brain changes in Alzheimer’s disease (AD) is important for patient prognosis, and for assessing brain deterioration in clinical trials. In this diffusion tensor imaging study, we used a new fiber-tract modeling method to investigate white matter integrity in 50 elderly controls (CTL), 113 people with mild cognitive impairment (MCI), and 37 AD patients. After clustering tractography using an ROI atlas, we used a shortest path graph search through each bundle’s fiber density map to derive maximum density paths (MDPs), which we registered across subjects. We calculated the fractional anisotropy (FA) and mean diffusivity (MD) along all MDPs and found significant MD and FA differences between AD patients and CTL subjects as well as MD differences between CTL and late MCI subjects. MD and FA were also associated with widely used clinical scores (MMSE). As an MDP is a compact, low-dimensional representation of white matter organization, we tested the utility of DTI measures along these MDPs as features for support vector machine (SVM) based classification of AD.
doi:10.1016/j.neurobiolaging.2014.05.037
PMCID: PMC4283487  PMID: 25444597
ADNI; tractography; DTI; fiber tract modeling; white matter; connectivity; SVM; classification
4.  Genome-wide interaction analysis reveals replicated epistatic effects on brain structure 
Neurobiology of aging  2014;36(0 1):S151-S158.
The discovery of several genes that affect risk for Alzheimer's disease ignited a worldwide search for Single Nucleotide Polymorphisms (SNPs), common genetic variants that affect the brain. Genome-wide search of all possible SNP-SNP interactions is challenging and rarely attempted, due to the complexity of conducting ∼1011 pairwise statistical tests. However, recent advances in machine learning, e.g., iterative sure independence screening (SIS), make it possible to analyze datasets with vastly more predictors than observations. Using an implementation of the SIS algorithm (called EPISIS), we performed a genome-wide interaction analysis testing all possible SNP-SNP interactions affecting regional brain volumes measured on MRI and mapped using tensor-based morphometry. We identified a significant SNP-SNP interaction between rs1345203 and rs1213205 that explains 1.9% of the variance in temporal lobe volume. We mapped the whole-brain, voxelwise effects of the interaction in the ADNI dataset and separately in an independent replication dataset of healthy twins (QTIM). Each additional loading in the interaction effect was associated with ∼5% greater brain regional brain volume (a protective effect) in both ADNI and QTIM samples.
doi:10.1016/j.neurobiolaging.2014.02.033
PMCID: PMC4332874  PMID: 25264344
epistasis; interaction; genome-wide; GWAS; GWIA; sure independence screening; tensor-based morphometry
5.  Rich club network analysis shows distinct patterns of disruption in frontotemporal dementia and Alzheimer’s disease 
Mathematics and visualization  2014;2014:13-22.
Diffusion imaging and brain connectivity analyses can reveal the underlying organizational patterns of the human brain, described as complex networks of densely interlinked regions. Here, we analyzed 1.5-Tesla whole-brain diffusion-weighted images from 64 participants – 15 patients with behavioral variant frontotemporal (bvFTD) dementia, 19 with early-onset Alzheimer’s disease (EOAD), and 30 healthy elderly controls. Based on whole-brain tractography, we reconstructed structural brain connectivity networks to map connections between cortical regions. We examined how bvFTD and EOAD disrupt the weighted ‘rich club’ – a network property where high-degree network nodes are more interconnected than expected by chance. bvFTD disrupts both the nodal and global organization of the network in both low- and high-degree regions of the brain. EOAD targets the global connectivity of the brain, mainly affecting the fiber density of high-degree (highly connected) regions that form the rich club network. These rich club analyses suggest distinct patterns of disruptions among different forms of dementia.
doi:10.1007/978-3-319-11182-7_2
PMCID: PMC4492471  PMID: 26161050
6.  Algebraic connectivity of brain networks shows patterns of segregation leading to reduced network robustness in Alzheimer's disease 
Measures of network topology and connectivity aid the understanding of network breakdown as the brain degenerates in Alzheimer's disease (AD). We analyzed 3-Tesla diffusion-weighted images from 202 patients scanned by the Alzheimer's Disease Neuroimaging Initiative – 50 healthy controls, 72 with early- and 38 with late-stage mild cognitive impairment (eMCI/lMCI) and 42 with AD. Using whole-brain tractography, we reconstructed structural connectivity networks representing connections between pairs of cortical regions. We examined, for the first time in this context, the network's Laplacian matrix and its Fiedler value, describing the network's algebraic connectivity, and the Fiedler vector, used to partition a graph. We assessed algebraic connectivity and four additional supporting metrics, revealing a decrease in network robustness and increasing disarray among nodes as dementia progressed. Network components became more disconnected and segregated, and their modularity increased. These measures are sensitive to diagnostic group differences, and may help understand the complex changes in AD.
doi:10.1007/978-3-319-11182-7_6
PMCID: PMC4669194  PMID: 26640830
brain network; algebraic connectivity; Fiedler value; modularity; Alzheimer's disease
7.  Obesity Gene NEGR1 Associated with White Matter Integrity in Healthy Young Adults 
NeuroImage  2014;102(0 2):548-557.
Obesity is a crucial public health issue in developed countries, with implications for cardiovascular and brain health as we age. A number of commonly-carried genetic variants are associated with obesity. Here we aim to see whether variants in obesity-associated genes - NEGR1, FTO, MTCH2, MC4R, LRRN6C, MAP2K5, FAIM2, SEC16B, ETV5, BDNF-AS, ATXN2L, ATP2A1, KCTD15, and TNN13K - are associated with white matter microstructural properties, assessed by high angular resolution diffusion imaging (HARDI) in young healthy adults between 20–30 years of age from the Queensland Twin Imaging study (QTIM). We began with a multi-locus approach testing how a number of common genetic risk factors for obesity at the single nucleotide polymorphism (SNP) level may jointly influence white matter integrity throughout the brain and found a wide spread genetic effect. Risk allele rs2815752 in NEGR1 was most associated with lower white matter integrity across a substantial portion of the brain. Across the area of significance in the bilateral posterior corona radiata, each additional copy of the risk allele was associated with a 2.2% lower average FA. This is the first study to find an association between an obesity risk gene and differences in white matter integrity. As our subjects were young and healthy, our results suggest that NEGR1 has effects on brain structure independent of its effect on obesity.
doi:10.1016/j.neuroimage.2014.07.041
PMCID: PMC4269485  PMID: 25072390
8.  Serum Cholesterol and Variant in Cholesterol-Related Gene CETP Predict White Matter Microstructure 
Neurobiology of aging  2014;35(11):2504-2513.
Several common genetic variants influence cholesterol levels, which play a key role in overall health. Myelin synthesis and maintenance are highly sensitive to cholesterol concentrations, and abnormal cholesterol levels increase the risk for various brain diseases, including Alzheimer's disease (AD). We report significant associations between higher serum cholesterol (CHOL) levels and high-density lipoproteins (HDL) and higher fractional anisotropy in 403 young adults (23.8±2.4 years) scanned with diffusion imaging and anatomical MRI at 4 Tesla. By fitting a multi-locus genetic model within white matter areas associated with CHOL, we found that a set of 18 cholesterol-related SNPs implicated in AD risk predicted FA. We focused on the SNP with the largest individual effects - CETP (rs5882) – and found that increased G-allele dosage was associated with higher FA and lower radial and mean diffusivities in voxel-wise analyses of the whole brain. A follow-up analysis detected WM associations with rs5882 in the opposite direction in 78 older individuals (74.3±7.3 years). Cholesterol levels may influence WM integrity, and cholesterol-related genes may exert age-dependent effects.
doi:10.1016/j.neurobiolaging.2014.05.024
PMCID: PMC4198330  PMID: 24997672
brain structure; DTI; imaging genetics; cholesterol; development; aging
9.  Automatic Clustering and Population Analysis of White Matter Tracts using Maximum Density Paths 
NeuroImage  2014;0:284-295.
We introduce a framework for population analysis of white matter tracts based on diffusion-weighted images of the brain. The framework enables extraction of fibers from high angular resolution diffusion images (HARDI); clustering of the fibers based partly on prior knowledge from an atlas; representation of the fiber bundles compactly using a path following points of highest density (maximum density path; MDP); and registration of these paths together using geodesic curve matching to find local correspondences across a population. We demonstrate our method on 4-Tesla HARDI scans from 565 young adults to compute localized statistics across 50 white matter tracts based on fractional anisotropy (FA). Experimental results show increased sensitivity in the determination of genetic influences on principal fiber tracts compared to the tract-based spatial statistics (TBSS) method. Our results show that the MDP representation reveals important parts of the white matter structure and considerably reduces the dimensionality over comparable fiber matching approaches.
doi:10.1016/j.neuroimage.2014.04.033
PMCID: PMC4065851  PMID: 24747738
HARDI; tractography; MRI; brain; clustering; atlas; Dijkstra; shortest path; geodesic distance; Hough; connectivity; maximum density path; curve registration; longest path
10.  Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: comparing meta and mega analytical approaches for data pooling 
NeuroImage  2014;95:136-150.
Combining datasets across independent studies can boost statistical power by increasing the numbers of observations and can achieve more accurate estimates of effect sizes. This is especially important for genetic studies where a large number of observations are required to obtain sufficient power to detect and replicate genetic effects. There is a need to develop and evaluate methods for joint-analytical analyses of rich datasets collected in imaging genetics studies. The ENIGMA-DTI consortium is developing and evaluating approaches for obtaining pooled estimates of heritability through meta-and mega-genetic analytical approaches, to estimate the general additive genetic contributions to the intersubject variance in fractional anisotropy (FA) measured from diffusion tensor imaging (DTI). We used the ENIGMA-DTI data harmonization protocol for uniform processing of DTI data from multiple sites. We evaluated this protocol in five family-based cohorts providing data from a total of 2248 children and adults (ages: 9–85) collected with various imaging protocols. We used the imaging genetics analysis tool, SOLAR-Eclipse, to combine twin and family data from Dutch, Australian and Mexican-American cohorts into one large “mega-family”. We showed that heritability estimates may vary from one cohort to another. We used two meta-analytical (the sample-size and standard-error weighted) approaches and a mega-genetic analysis to calculate heritability estimates across-population. We performed leave-one-out analysis of the joint estimates of heritability, removing a different cohort each time to understand the estimate variability. Overall, meta- and mega-genetic analyses of heritability produced robust estimates of heritability.
doi:10.1016/j.neuroimage.2014.03.033
PMCID: PMC4043878  PMID: 24657781
Diffusion Tensor Imaging (DTI); Imaging Genetics; Heritability; Meta-analysis; Multi-site; Reliability
11.  Impact of family structure and common environment on heritability estimation for neuroimaging genetics studies using Sequential Oligogenic Linkage Analysis Routines 
Journal of Medical Imaging  2014;1(1):014005.
Abstract.
Imaging genetics is an emerging methodological field that combines genetic information with medical imaging-derived metrics to understand how genetic factors impact observable phenotypes. In order for a trait to be a reasonable phenotype in an imaging genetics study, it must be heritable: at least some proportion of its variance must be due to genetic influences. The Sequential Oligogenic Linkage Analysis Routines (SOLAR) imaging genetics software can estimate the heritability of a trait in complex pedigrees. We investigate the ability of SOLAR to accurately estimate heritability and common environmental effects on simulated imaging phenotypes in various family structures. We found that heritability is reliably estimated with small family-based studies of 40 to 80 individuals, though subtle differences remain between the family structures. In an imaging application analysis, we found that with 80 subjects in any of the family structures, estimated heritability of white matter fractional anisotropy was biased by <10% for every region of interest. Results from these studies can be used when investigators are evaluating power in planning genetic analyzes.
doi:10.1117/1.JMI.1.1.014005
PMCID: PMC4281883  PMID: 25558465
heritability; imaging genetics; power calculation; statistical analysis
12.  Neuroimaging and Genetic Risk for Alzheimer’s Disease and Addiction-Related Degenerative Brain Disorders 
Brain imaging and behavior  2014;8(2):217-233.
Neuroimaging offers a powerful means to assess the trajectory of brain degeneration in a variety of disorders, including Alzheimer’s disease (AD). Here we describe how multimodal imaging can be used to study the changing brain during the different stages of AD. We integrate findings from a range of studies using magnetic resonance imaging (MRI), positron emission tomography (PET), functional MRI (fMRI) and diffusion weighted imaging (DWI). Neuroimaging reveals how risk genes for degenerative disorders affect the brain, including several recently discovered genetic variants that may disrupt brain connectivity. We review some recent neuroimaging studies of genetic polymorphisms associated with increased risk for late-onset Alzheimer’s disease (LOAD). Some genetic variants that increase risk for drug addiction may overlap with those associated with degenerative brain disorders. These common associations offer new insight into mechanisms underlying neurodegeneration and addictive behaviors, and may offer new leads for treating them before severe and irreversible neurological symptoms appear.
doi:10.1007/s11682-013-9263-y
PMCID: PMC3992278  PMID: 24142306
Alzheimer’s disease; imaging genetics; multi-modal imaging; neurodegeneration; addiction
13.  Whole-genome analyses of whole-brain data: working within an expanded search space 
Nature neuroscience  2014;17(6):791-800.
Large-scale comparisons of patients and healthy controls have unearthed genetic risk factors associated with a range of neurological and psychiatric illnesses. Meanwhile, brain imaging studies are increasing in size and scope, revealing disease and genetic effects on brain structure and function, and implicating neural pathways and causal mechanisms. With the advent of global neuroimaging consortia, imaging studies are now well powered to discover genetic variants that reliably affect the brain. Genetic analyses of brain measures from tens of thousands of people are being extended to test genetic associations with signals at millions of locations in the brain. Connectome-wide, genome-wide scans can jointly screen brain circuits and genomes, presenting new statistical challenges. There is a growing need for the community to establish and enforce standards in this developing field to ensure robust findings. Here we discuss how neuroimagers and geneticists have formed alliances to discover how genetic factors affect the brain. The field is rapidly advancing with ultra-high-resolution imaging and whole-genome sequencing. We recommend a rigorous approach to neuroimaging genomics that capitalizes on its recent successes and ensures the reliability of future discoveries.
doi:10.1038/nn.3718
PMCID: PMC4300949  PMID: 24866045
14.  Common genetic variants influence human subcortical brain structures 
Hibar, Derrek P. | Stein, Jason L. | Renteria, Miguel E. | Arias-Vasquez, Alejandro | Desrivières, Sylvane | Jahanshad, Neda | Toro, Roberto | Wittfeld, Katharina | Abramovic, Lucija | Andersson, Micael | Aribisala, Benjamin S. | Armstrong, Nicola J. | Bernard, Manon | Bohlken, Marc M. | Boks, Marco P. | Bralten, Janita | Brown, Andrew A. | Chakravarty, M. Mallar | Chen, Qiang | Ching, Christopher R. K. | Cuellar-Partida, Gabriel | den Braber, Anouk | Giddaluru, Sudheer | Goldman, Aaron L. | Grimm, Oliver | Guadalupe, Tulio | Hass, Johanna | Woldehawariat, Girma | Holmes, Avram J. | Hoogman, Martine | Janowitz, Deborah | Jia, Tianye | Kim, Sungeun | Klein, Marieke | Kraemer, Bernd | Lee, Phil H. | Olde Loohuis, Loes M. | Luciano, Michelle | Macare, Christine | Mather, Karen A. | Mattheisen, Manuel | Milaneschi, Yuri | Nho, Kwangsik | Papmeyer, Martina | Ramasamy, Adaikalavan | Risacher, Shannon L. | Roiz-Santiañez, Roberto | Rose, Emma J. | Salami, Alireza | Sämann, Philipp G. | Schmaal, Lianne | Schork, Andrew J. | Shin, Jean | Strike, Lachlan T. | Teumer, Alexander | van Donkelaar, Marjolein M. J. | van Eijk, Kristel R. | Walters, Raymond K. | Westlye, Lars T. | Whelan, Christopher D. | Winkler, Anderson M. | Zwiers, Marcel P. | Alhusaini, Saud | Athanasiu, Lavinia | Ehrlich, Stefan | Hakobjan, Marina M. H. | Hartberg, Cecilie B. | Haukvik, Unn K. | Heister, Angelien J. G. A. M. | Hoehn, David | Kasperaviciute, Dalia | Liewald, David C. M. | Lopez, Lorna M. | Makkinje, Remco R. R. | Matarin, Mar | Naber, Marlies A. M. | McKay, D. Reese | Needham, Margaret | Nugent, Allison C. | Pütz, Benno | Royle, Natalie A. | Shen, Li | Sprooten, Emma | Trabzuni, Daniah | van der Marel, Saskia S. L. | van Hulzen, Kimm J. E. | Walton, Esther | Wolf, Christiane | Almasy, Laura | Ames, David | Arepalli, Sampath | Assareh, Amelia A. | Bastin, Mark E. | Brodaty, Henry | Bulayeva, Kazima B. | Carless, Melanie A. | Cichon, Sven | Corvin, Aiden | Curran, Joanne E. | Czisch, Michael | de Zubicaray, Greig I. | Dillman, Allissa | Duggirala, Ravi | Dyer, Thomas D. | Erk, Susanne | Fedko, Iryna O. | Ferrucci, Luigi | Foroud, Tatiana M. | Fox, Peter T. | Fukunaga, Masaki | Gibbs, J. Raphael | Göring, Harald H. H. | Green, Robert C. | Guelfi, Sebastian | Hansell, Narelle K. | Hartman, Catharina A. | Hegenscheid, Katrin | Heinz, Andreas | Hernandez, Dena G. | Heslenfeld, Dirk J. | Hoekstra, Pieter J. | Holsboer, Florian | Homuth, Georg | Hottenga, Jouke-Jan | Ikeda, Masashi | Jack, Clifford R. | Jenkinson, Mark | Johnson, Robert | Kanai, Ryota | Keil, Maria | Kent, Jack W. | Kochunov, Peter | Kwok, John B. | Lawrie, Stephen M. | Liu, Xinmin | Longo, Dan L. | McMahon, Katie L. | Meisenzahl, Eva | Melle, Ingrid | Mohnke, Sebastian | Montgomery, Grant W. | Mostert, Jeanette C. | Mühleisen, Thomas W. | Nalls, Michael A. | Nichols, Thomas E. | Nilsson, Lars G. | Nöthen, Markus M. | Ohi, Kazutaka | Olvera, Rene L. | Perez-Iglesias, Rocio | Pike, G. Bruce | Potkin, Steven G. | Reinvang, Ivar | Reppermund, Simone | Rietschel, Marcella | Romanczuk-Seiferth, Nina | Rosen, Glenn D. | Rujescu, Dan | Schnell, Knut | Schofield, Peter R. | Smith, Colin | Steen, Vidar M. | Sussmann, Jessika E. | Thalamuthu, Anbupalam | Toga, Arthur W. | Traynor, Bryan J. | Troncoso, Juan | Turner, Jessica A. | Valdés Hernández, Maria C. | van ’t Ent, Dennis | van der Brug, Marcel | van der Wee, Nic J. A. | van Tol, Marie-Jose | Veltman, Dick J. | Wassink, Thomas H. | Westman, Eric | Zielke, Ronald H. | Zonderman, Alan B. | Ashbrook, David G. | Hager, Reinmar | Lu, Lu | McMahon, Francis J. | Morris, Derek W. | Williams, Robert W. | Brunner, Han G. | Buckner, Randy L. | Buitelaar, Jan K. | Cahn, Wiepke | Calhoun, Vince D. | Cavalleri, Gianpiero L. | Crespo-Facorro, Benedicto | Dale, Anders M. | Davies, Gareth E. | Delanty, Norman | Depondt, Chantal | Djurovic, Srdjan | Drevets, Wayne C. | Espeseth, Thomas | Gollub, Randy L. | Ho, Beng-Choon | Hoffmann, Wolfgang | Hosten, Norbert | Kahn, René S. | Le Hellard, Stephanie | Meyer-Lindenberg, Andreas | Müller-Myhsok, Bertram | Nauck, Matthias | Nyberg, Lars | Pandolfo, Massimo | Penninx, Brenda W. J. H. | Roffman, Joshua L. | Sisodiya, Sanjay M. | Smoller, Jordan W. | van Bokhoven, Hans | van Haren, Neeltje E. M. | Völzke, Henry | Walter, Henrik | Weiner, Michael W. | Wen, Wei | White, Tonya | Agartz, Ingrid | Andreassen, Ole A. | Blangero, John | Boomsma, Dorret I. | Brouwer, Rachel M. | Cannon, Dara M. | Cookson, Mark R. | de Geus, Eco J. C. | Deary, Ian J. | Donohoe, Gary | Fernández, Guillén | Fisher, Simon E. | Francks, Clyde | Glahn, David C. | Grabe, Hans J. | Gruber, Oliver | Hardy, John | Hashimoto, Ryota | Hulshoff Pol, Hilleke E. | Jönsson, Erik G. | Kloszewska, Iwona | Lovestone, Simon | Mattay, Venkata S. | Mecocci, Patrizia | McDonald, Colm | McIntosh, Andrew M. | Ophoff, Roel A. | Paus, Tomas | Pausova, Zdenka | Ryten, Mina | Sachdev, Perminder S. | Saykin, Andrew J. | Simmons, Andy | Singleton, Andrew | Soininen, Hilkka | Wardlaw, Joanna M. | Weale, Michael E. | Weinberger, Daniel R. | Adams, Hieab H. H. | Launer, Lenore J. | Seiler, Stephan | Schmidt, Reinhold | Chauhan, Ganesh | Satizabal, Claudia L. | Becker, James T. | Yanek, Lisa | van der Lee, Sven J. | Ebling, Maritza | Fischl, Bruce | Longstreth, W. T. | Greve, Douglas | Schmidt, Helena | Nyquist, Paul | Vinke, Louis N. | van Duijn, Cornelia M. | Xue, Luting | Mazoyer, Bernard | Bis, Joshua C. | Gudnason, Vilmundur | Seshadri, Sudha | Ikram, M. Arfan | Martin, Nicholas G. | Wright, Margaret J. | Schumann, Gunter | Franke, Barbara | Thompson, Paul M. | Medland, Sarah E.
Nature  2015;520(7546):224-229.
The highly complex structure of the human brain is strongly shaped by genetic influences1. Subcortical brain regions form circuits with cortical areas to coordinate movement2, learning, memory3 and motivation4, and altered circuits can lead to abnormal behaviour and disease2. To investigate how common genetic variants affect the structure of these brain regions, here we conduct genome-wide association studies of the volumes of seven subcortical regions and the intracranial volume derived from magnetic resonance images of 30,717 individuals from 50 cohorts. We identify five novel genetic variants influencing the volumes of the putamen and caudate nucleus. We also find stronger evidence for three loci with previously established influences on hippocampal volume5 and intracranial volume6. These variants show specific volumetric effects on brain structures rather than global effects across structures. The strongest effects were found for the putamen, where a novel intergenic locus with replicable influence on volume (rs945270; P = 1.08 × 10−33; 0.52% variance explained) showed evidence of altering the expression of the KTN1 gene in both brain and blood tissue. Variants influencing putamen volume clustered near developmental genes that regulate apoptosis, axon guidance and vesicle transport. Identification of these genetic variants provides insight into the causes of variability inhuman brain development, and may help to determine mechanisms of neuropsychiatric dysfunction.
doi:10.1038/nature14101
PMCID: PMC4393366  PMID: 25607358
15.  Mapping White Matter Integrity in Elderly People with HIV 
Human brain mapping  2013;35(3):975-992.
People with HIV are living longer as combination antiretroviral therapy (cART) becomes more widely available. However, even when plasma viral load is reduced to untraceable levels, chronic HIV infection is associated with neurological deficits and brain atrophy beyond that of normal aging. HIV is often marked by cortical and subcortical atrophy, but the integrity of the brain’s white matter (WM) pathways also progressively declines. Few studies focus on older cohorts where normal aging may be compounded with HIV infection to influence deficit patterns. In this relatively large diffusion tensor imaging (DTI) study, we investigated abnormalities in WM fiber integrity in 56 HIV+ adults with access to cART (mean age: 63.9 ± 3.7 years), compared to 31 matched healthy controls (65.4 ± 2.2 years). Statistical 3D maps revealed the independent effects of HIV diagnosis and age on fractional anisotropy (FA) and diffusivity, but we did not find any evidence for an age by diagnosis interaction in our current sample. Compared to healthy controls, HIV patients showed pervasive FA decreases and diffusivity increases throughout WM. We also assessed neuropsychological (NP) summary z-score associations. In both patients and controls, fiber integrity measures were associated with NP summary scores. The greatest differences were detected in the corpus callosum and in the projection fibers of the corona radiata. These deficits are consistent with published NP deficits and cortical atrophy patterns in elderly people with HIV.
doi:10.1002/hbm.22228
PMCID: PMC3775847  PMID: 23362139
brain integrity; white matter; diffusion tensor imaging; cognition; HIV; cART
16.  Genetics of Path Lengths in Brain Connectivity Networks: HARDI-Based Maps in 457 Adults 
Brain connectivity analyses are increasingly popular for investigating organization. Many connectivity measures including path lengths are generally defined as the number of nodes traversed to connect a node in a graph to the others. Despite its name, path length is purely topological, and does not take into account the physical length of the connections. The distance of the trajectory may also be highly relevant, but is typically overlooked in connectivity analyses. Here we combined genotyping, anatomical MRI and HARDI to understand how our genes influence the cortical connections, using whole-brain tractography. We defined a new measure, based on Dijkstra’s algorithm, to compute path lengths for tracts connecting pairs of cortical regions. We compiled these measures into matrices where elements represent the physical distance traveled along tracts. We then analyzed a large cohort of healthy twins and show that our path length measure is reliable, heritable, and influenced even in young adults by the Alzheimer’s risk gene, CLU.
doi:10.1007/978-3-642-33530-3_3
PMCID: PMC4288784  PMID: 25584366
Structural connectivity; neuroimaging genetics; Dijkstra’s algorithm; HARDI tractography; path length
17.  Registering Cortical Surfaces Based on Whole-Brain Structural Connectivity and Continuous Connectivity Analysis 
We present a framework for registering cortical surfaces based on tractography-informed structural connectivity. We define connectivity as a continuous kernel on the product space of the cortex, and develop a method for estimating this kernel from tractography fiber models. Next, we formulate the kernel registration problem, and present a means to non-linearly register two brains’ continuous connectivity profiles. We apply theoretical results from operator theory to develop an algorithm for decomposing the connectome into its shared and individual components. Lastly, we extend two discrete connectivity measures to the continuous case, and apply our framework to 98 Alzheimer’s patients and controls. Our measures show significant differences between the two groups.
PMCID: PMC4283762  PMID: 25320795
Diffusion MRI; Cortical Surface Registration; Connectivity Analysis; Data Fusion
18.  Impact of family structure and common environment on heritability estimation for neuroimaging genetics studies using Sequential Oligogenic Linkage Analysis Routines 
Imaging genetics is an emerging methodological field that combines genetic information with medical imaging-derived metrics to understand how genetic factors impact observable phenotypes. In order for a trait to be a reasonable phenotype in an imaging genetics study, it must be heritable: at least some proportion of its variance must be due to genetic influences. The Sequential Oligogenic Linkage Analysis Routines (SOLAR) imaging genetics software can estimate the heritability of a trait in complex pedigrees. We investigate the ability of SOLAR to accurately estimate heritability and common environmental effects on simulated imaging phenotypes in various family structures. We found that heritability is reliably estimated with small family-based studies of 40 to 80 individuals, though subtle differences remain between the family structures. In an imaging application analysis, we found that with 80 subjects in any of the family structures, estimated heritability of white matter fractional anisotropy was biased by <10% for every region of interest. Results from these studies can be used when investigators are evaluating power in planning genetic analyzes.
doi:10.1117/1.JMI.1.1.014005
PMCID: PMC4281883  PMID: 25558465
heritability; imaging genetics; power calculation; statistical analysis
19.  Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer’s disease 
Alzheimer’s disease (AD) involves a gradual breakdown of brain connectivity, and network analyses offer a promising new approach to track and understand disease progression. Even so, our ability to detect degenerative changes in brain networks depends on the methods used. Here we compared several tractography and feature extraction methods to see which ones gave best diagnostic classification for 202 people with AD, mild cognitive impairment or normal cognition, scanned with 41-gradient diffusion-weighted magnetic resonance imaging as part of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) project. We computed brain networks based on whole brain tractography with nine different methods – four of them tensor-based deterministic (FACT, RK2, SL, and TL), two orientation distribution function (ODF)-based deterministic (FACT, RK2), two ODF-based probabilistic approaches (Hough and PICo), and one “ball-and-stick” approach (Probtrackx). Brain networks derived from different tractography algorithms did not differ in terms of classification performance on ADNI, but performing principal components analysis on networks helped classification in some cases. Small differences may still be detectable in a truly vast cohort, but these experiments help assess the relative advantages of different tractography algorithms, and different post-processing choices, when used for classification.
doi:10.3389/fnagi.2015.00048
PMCID: PMC4396191  PMID: 25926791
Alzheimer’s disease; brain network; tractography; classification; PCA; GLRAM; diffusion MRI
20.  Boosting brain connectome classification accuracy in Alzheimer's disease using higher-order singular value decomposition 
Alzheimer's disease (AD) is a progressive brain disease. Accurate detection of AD and its prodromal stage, mild cognitive impairment (MCI), are crucial. There is also a growing interest in identifying brain imaging biomarkers that help to automatically differentiate stages of Alzheimer's disease. Here, we focused on brain structural networks computed from diffusion MRI and proposed a new feature extraction and classification framework based on higher order singular value decomposition and sparse logistic regression. In tests on publicly available data from the Alzheimer's Disease Neuroimaging Initiative, our proposed framework showed promise in detecting brain network differences that help in classifying different stages of Alzheimer's disease.
doi:10.3389/fnins.2015.00257
PMCID: PMC4513242  PMID: 26257601
Alzheimer's disease; mild cognitive impairment; diffusion MRI; connectome; high-order SVD; classification
21.  Relation between variants in the neurotrophin receptor gene, NTRK3, and white matter integrity in healthy young adults 
NeuroImage  2013;82:146-153.
The NTRK3 gene (also known as TRKC) encodes a high affinity receptor for the neurotrophin 3′-nucleotidase (NT3), which is implicated in oligodendrocyte and myelin development. We previously found that white matter integrity in young adults related to genetic variants in genes encoding neurotrophins and their receptors. This underscores the importance of neurotrophins for white matter development. NTRK3 variants are putative risk factors for schizophrenia, bipolar disorder, and obsessive-compulsive disorder hoarding, suggesting that some NTRK3 variants may affect the brain.
To test this, we scanned 392 healthy adult twins and their siblings (mean age, 23.6 ± 2.2 years; range: 20-29 years) with 105-gradient 4-Tesla diffusion tensor imaging (DTI). We identified 18 single nucleotide polymorphisms (SNPs) in the NTRK3 gene that have been associated with neuropsychiatric disorders. We used a multi-SNP model, adjusting for family relatedness, age, and sex, to relate these variants to voxelwise fractional anisotropy (FA) – a DTI measure of white matter integrity.
FA was optimally predicted (based on the highest false discovery rate critical p), by five SNPs (rs1017412, rs2114252, rs16941261, rs3784406, and rs7176429; overall FDR critical p = 0.028). Gene effects were widespread and included the corpus callosum genu and inferior longitudinal fasciculus - regions implicated in several neuropsychiatric disorders and previously associated with other neurotrophin-related genetic variants in an overlapping sample of subjects. NTRK3 genetic variants, and neurotrophins more generally, may influence white matter integrity in brain regions implicated in neuropsychiatric disorders.
doi:10.1016/j.neuroimage.2013.05.095
PMCID: PMC3948328  PMID: 23727532
Fractional anisotropy; diffusion tensor imaging; single nucleotide polymorphism; schizophrenia; obsessive compulsive disorder; bipolar disorder
22.  LEFT VERSUS RIGHT HEMISPHERE DIFFERENCES IN BRAIN CONNECTIVITY: 4-TESLA HARDI TRACTOGRAPHY IN 569 TWINS 
Diffusion imaging can map anatomical connectivity in the living brain, offering new insights into fundamental questions such as how the left and right brain hemispheres differ. Anatomical brain asymmetries are related to speech and language abilities, but less is known about left/right hemisphere differences in brain wiring. To assess this, we scanned 457 young adults (age 23.4±2.0 SD years) and 112 adolescents (age 12-16) with 4-Tesla 105-gradient high-angular resolution diffusion imaging. We extracted fiber tracts throughout the brain with a Hough transform method. A 70×70 connectivity matrix was created, for each subject, based on the proportion of fibers intersecting 70 cortical regions. We identified significant differences in the proportions of fibers intersecting left and right hemisphere cortical regions. The degree of asymmetry in the connectivity matrices varied with age, as did the asymmetry in network topology measures such as the small-world effect.
doi:10.1109/ISBI.2012.6235601
PMCID: PMC4232939  PMID: 25404993
tractography; high angular resolution diffusion imaging (HARDI); small-world effect; connectome; laterality
23.  ATLAS-BASED FIBER CLUSTERING FOR MULTI-SUBJECT ANALYSIS OF HIGH ANGULAR RESOLUTION DIFFUSION IMAGING TRACTOGRAPHY 
High angular resolution diffusion imaging (HARDI) allows in vivo analysis of the white matter structure and connectivity. Based on orientation distribution functions (ODFs) that represent the directionality of water diffusion at each point in the brain, tractography methods can recover major axonal pathways. This enables tract-based analysis of fiber integrity and connectivity. For multi-subject comparisons, fibers may be clustered into bundles that are consistently found across subjects. To do this, we scanned 20 young adults with HARDI at 4 T. From the reconstructed ODFs, we performed whole-brain tractography with a novel Hough transform method. We then used measures of agreement between the extracted 3D curves and a co-registered probabilistic DTI atlas to select key pathways. Using median filtering and a shortest path graph search, we derived the maximum density path to compactly represent each tract in the population. With this tract-based method, we performed tract-based analysis of fractional anisotropy, and assessed how the chosen tractography algorithm influenced the results. The resulting method may expedite population-based statistical analysis of HARDI and DTI.
doi:10.1109/ISBI.2011.5872405
PMCID: PMC4232949  PMID: 25404992
tractography; clustering; Dijkstra’s shortest path; multi-subject analysis; fiber bundles
24.  Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: A pilot project of the ENIGMA–DTI working group 
NeuroImage  2013;81:455-469.
The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium was set up to analyze brain measures and genotypes from multiple sites across the world to improve the power to detect genetic variants that influence the brain. Diffusion tensor imaging (DTI) yields quantitative measures sensitive to brain development and degeneration, and some common genetic variants may be associated with white matter integrity or connectivity. DTI measures, such as the fractional anisotropy (FA) of water diffusion, may be useful for identifying genetic variants that influence brain microstructure. However, genome-wide association studies (GWAS) require large populations to obtain sufficient power to detect and replicate significant effects, motivating a multi-site consortium effort. As part of an ENIGMA–DTI working group, we analyzed high-resolution FA images from multiple imaging sites across North America, Australia, and Europe, to address the challenge of harmonizing imaging data collected at multiple sites. Four hundred images of healthy adults aged 18–85 from four sites were used to create a template and corresponding skeletonized FA image as a common reference space. Using twin and pedigree samples of different ethnicities, we used our common template to evaluate the heritability of tract-derived FA measures. We show that our template is reliable for integrating multiple datasets by combining results through meta-analysis and unifying the data through exploratory mega-analyses. Our results may help prioritize regions of the FA map that are consistently influenced by additive genetic factors for future genetic discovery studies. Protocols and templates are publicly available at (http://enigma.loni.ucla.edu/ongoing/dti-working-group/).
doi:10.1016/j.neuroimage.2013.04.061
PMCID: PMC3729717  PMID: 23629049
Diffusion Tensor Imaging (DTI); Imaging genetics; Heritability; Meta-analysis; Multi-site; Reliability
25.  White matter microstructural abnormalities in girls with chromosome 22q11.2 deletion syndrome, Fragile X or Turner syndrome as evidenced by diffusion tensor imaging 
NeuroImage  2013;81:441-454.
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 14 years 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.
doi:10.1016/j.neuroimage.2013.04.028
PMCID: PMC3947617  PMID: 23602925
Diffussion Tensor Imaging; Genetic diseases; Neurodevelopmental diseases; Connectivity

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