Derrek P Hibar

Publications (24)89.97 Total impact

• Article: Genome-wide scan of healthy human connectome discovers SPON1 gene variant influencing dementia severity.

  Neda Jahanshad, Priya Rajagopalan, Xue Hua, Derrek P Hibar, Talia M Nir, Arthur W Toga, Clifford R Jack, Andrew J Saykin, Robert C Green, Michael W Weiner, Sarah E Medland, Grant W Montgomery, Narelle K Hansell, Katie L McMahon, Greig I de Zubicaray, Nicholas G Martin, Margaret J Wright, Paul M Thompson
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  ABSTRACT: Aberrant connectivity is implicated in many neurological and psychiatric disorders, including Alzheimer's disease and schizophrenia. However, other than a few disease-associated candidate genes, we know little about the degree to which genetics play a role in the brain networks; we know even less about specific genes that influence brain connections. Twin and family-based studies can generate estimates of overall genetic influences on a trait, but genome-wide association scans (GWASs) can screen the genome for specific variants influencing the brain or risk for disease. To identify the heritability of various brain connections, we scanned healthy young adult twins with high-field, high-angular resolution diffusion MRI. We adapted GWASs to screen the brain's connectivity pattern, allowing us to discover genetic variants that affect the human brain's wiring. The association of connectivity with the SPON1 variant at rs2618516 on chromosome 11 (11p15.2) reached connectome-wide, genome-wide significance after stringent statistical corrections were enforced, and it was replicated in an independent subsample. rs2618516 was shown to affect brain structure in an elderly population with varying degrees of dementia. Older people who carried the connectivity variant had significantly milder clinical dementia scores and lower risk of Alzheimer's disease. As a posthoc analysis, we conducted GWASs on several organizational and topological network measures derived from the matrices to discover variants in and around genes associated with autism (MACROD2), development (NEDD4), and mental retardation (UBE2A) significantly associated with connectivity. Connectome-wide, genome-wide screening offers substantial promise to discover genes affecting brain connectivity and risk for brain diseases.
  Proceedings of the National Academy of Sciences 03/2013; · 9.68 Impact Factor
• Article: 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.

  Florence F Roussotte, Neda Jahanshad, Derrek P Hibar, Elizabeth R Sowell, Omid Kohannim, Marina Barysheva, Narelle K Hansell, Katie L McMahon, Greig I de Zubicaray, Grant W Montgomery, Nicholas G Martin, Margaret J Wright, Arthur W Toga, Clifford R Jack, Michael W Weiner, Paul M Thompson
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  ABSTRACT: 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. Hum Brain Mapp, 2013. © 2013 Wiley Periodicals, Inc.
  Human Brain Mapping 02/2013; · 5.88 Impact Factor
• Source

  Available from: Florence Roussotte

  Article: 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

  Florence F. Roussotte, Neda Jahanshad, Derrek P. Hibar, Elizabeth R. Sowell, Omid Kohannim, Marina Barysheva, Narelle K. Hansell, Katie L. McMahon, Greig I. de Zubicaray, Grant W. Montgomery, Nicholas G. Martin, Margaret J. Wright, Arthur W. Toga, Clifford R. Jack, Michael W. Weiner, Paul M. Thompson, and the ADNI
  Human Brain Mapping 01/2013; · 5.88 Impact Factor
• Article: Unbiased Tensor-Based Morphometry: Improved Robustness and Sample Size Estimates for Alzheimer's Disease Clinical Trials.

  Xue Hua, Derrek P Hibar, Christopher R K Ching, Christina P Boyle, Priya Rajagopalan, Boris A Gutman, Alex D Leow, Arthur W Toga, Clifford R Jack, Danielle Harvey, Michael W Weiner, Paul M Thompson
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  ABSTRACT: Various neuroimaging measures are being evaluated for tracking Alzheimer's disease progression in therapeutic trials, including measures of structural brain change based on repeated scanning of patients with magnetic resonance imaging (MRI). Methods to compute brain change must be robust to scan quality. Biases may arise if any scans are thrown out, as this can lead to the true changes being overestimated or underestimated. Here we analyzed the full ADNI-1 MRI dataset and assessed several sources of bias that can arise when tracking brain changes with structural brain imaging methods, as part of a pipeline for tensor-based morphometry (TBM). In all healthy subjects who completed MRI scanning at screening, 6, 12, and 24months, brain atrophy was essentially linear with no detectable bias in longitudinal measures. In power analyses for clinical trials based on these change measures, only 39AD and 95 MCI subjects were needed for a 24-month trial to detect a 25% reduction in the average rate of change using a two-sided test (α=0.05, power=80%). Further sample size reductions were achieved by stratifying the data into Apolipoprotein E (ApoE) ε4 carriers vs. non-carriers. We show how selective data exclusion affects sample size estimates, motivating an objective comparison of different analysis techniques based on statistical power and robustness. TBM is an unbiased, robust, high-throughput imaging surrogate marker for large, multi-site neuroimaging studies and clinical trials of AD and MCI.
  NeuroImage 11/2012; · 5.89 Impact Factor
• Article: Common folate gene variant, MTHFR C677T, is associated with brain structure in two independent cohorts of people with mild cognitive impairment

  Priya Rajagopalan, Neda Jahanshad, Jason L Stein, Xue Hua, Sarah K Madsen, Omid Kohannim, Derrek P Hibar, Arthur W Toga, Clifford R Jack Jr, Andrew J Saykin, Robert C Green, Michael W Weiner, Joshua C Bis, Lewis H Kuller, Mario Riverol, James T Becker, Oscar L Lopez, Paul M Thompson
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  ABSTRACT: A commonly carried C677T polymorphism in a folate-related gene, MTHFR, is associated with higher plasma homocysteine, a well-known mediator of neuronal damage and brain atrophy. As homocysteine promotes brain atrophy, we set out to discover whether people carrying the C677T MTHFR polymorphism which increases homocysteine, might also show systematic differences in brain structure. Using tensor-based morphometry, we tested this association in 359 elderly Caucasian subjects with mild cognitive impairment (MCI) (mean age: 75 ± 7.1 years) scanned with brain MRI and genotyped as part of Alzheimer's disease Neuroimaging Initiative. We carried out a replication study in an independent, non-overlapping sample of 51 elderly Caucasian subjects with MCI (mean age: 76 ± 5.5 years), scanned with brain MRI and genotyped for MTHFR, as part of the Cardiovascular Health Study. At each voxel in the brain, we tested to see where regional volume differences were associated with carrying one or more MTHFR ‘T’ alleles. In ADNI subjects, carriers of the MTHFR risk allele had detectable brain volume deficits, in the white matter, of up to 2-8% per risk T allele locally at baseline and showed accelerated brain atrophy of 0.5-1.5% per T allele at 1 year follow-up, after adjusting for age and sex. We replicated these brain volume deficits of up to 5-12% per MTHFR T allele in the independent cohort of CHS subjects. As expected, the associations weakened after controlling for homocysteine levels, which the risk gene affects. The MTHFR risk variant may thus promote brain atrophy by elevating homocysteine levels. This study aims to investigate the spatially detailed effects of this MTHFR polymorphism on brain structure in 3D, pointing to a causal pathway that may promote homocysteine-mediated brain atrophy in elderly people with MCI.
  NeuroImage: Clinical. 10/2012;
• Article: Genome-wide association identifies genetic variants associated with lentiform nucleus volume in N = 1345 young and elderly subjects.

  Derrek P Hibar, Jason L Stein, April B Ryles, Omid Kohannim, Neda Jahanshad, Sarah E Medland, Narelle K Hansell, Katie L McMahon, Greig I de Zubicaray, Grant W Montgomery, Nicholas G Martin, Margaret J Wright, Andrew J Saykin, Clifford R Jack, Michael W Weiner, Arthur W Toga, Paul M Thompson
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  ABSTRACT: 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 (P ( MA ) = 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.
  Brain Imaging and Behavior 08/2012; · 1.66 Impact Factor
• Article: EnigmaVis: online interactive visualization of genome-wide association studies of the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium.

  Nic M Novak, Jason L Stein, Sarah E Medland, Derrek P Hibar, Paul M Thompson, Arthur W Toga
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  ABSTRACT: In an attempt to increase power to detect genetic associations with brain phenotypes derived from human neuroimaging data, we recently conducted a large-scale, genome-wide association meta-analysis of hippocampal, brain, and intracranial volume through the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium. Here, we present a freely available online interactive tool, EnigmaVis, which makes it easy to visualize the association results generated by the consortium alongside allele frequency, genes, and functional annotations. EnigmaVis runs natively within the web browser, and generates plots that show the level of association between brain phenotypes at user-specified genomic positions. Uniquely, EnigmaVis is dynamic; users can interact with elements on the plot in real time. This software will be useful when exploring the effect on brain structure of particular genetic variants influencing neuropsychiatric illness and cognitive function. Future projects of the consortium and updates to EnigmaVis will also be displayed on the site. EnigmaVis is freely available online at http://enigma.loni.ucla.edu/enigma-vis/
  Twin Research and Human Genetics 06/2012; 15(3):414-8. · 1.70 Impact Factor
• Article: Alzheimer's disease risk gene, GAB2, is associated with regional brain volume differences in 755 young healthy twins.

  Derrek P Hibar, Neda Jahanshad, Jason L Stein, Omid Kohannim, Arthur W Toga, Sarah E Medland, Narelle K Hansell, Katie L McMahon, Greig I de Zubicaray, Grant W Montgomery, Nicholas G Martin, Margaret J Wright, Paul M Thompson
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  ABSTRACT: 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.
  Twin Research and Human Genetics 06/2012; 15(3):286-95. · 1.70 Impact Factor
• Source

  Available from: qimr.edu.au

  Article: Predicting white matter integrity from multiple common genetic variants.

  Omid Kohannim, Neda Jahanshad, Meredith N Braskie, Jason L Stein, Ming-Chang Chiang, April H Reese, Derrek P Hibar, Arthur W Toga, Katie L McMahon, Greig I de Zubicaray, Sarah E Medland, Grant W Montgomery, Nicholas G Martin, Margaret J Wright, Paul M Thompson
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  ABSTRACT: 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.
  Neuropsychopharmacology: official publication of the American College of Neuropsychopharmacology 04/2012; 37(9):2012-9. · 6.99 Impact Factor
• Article: Brain structure in healthy adults is related to serum transferrin and the H63D polymorphism in the HFE gene.

  Neda Jahanshad, Omid Kohannim, Derrek P Hibar, Jason L Stein, Katie L McMahon, Greig I de Zubicaray, Sarah E Medland, Grant W Montgomery, John B Whitfield, Nicholas G Martin, Margaret J Wright, Arthur W Toga, Paul M Thompson
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  ABSTRACT: Control of iron homeostasis is essential for healthy central nervous system function: iron deficiency is associated with cognitive impairment, yet iron overload is thought to promote neurodegenerative diseases. Specific genetic markers have been previously identified that influence levels of transferrin, the protein that transports iron throughout the body, in the blood and brain. Here, we discovered that transferrin levels are related to detectable differences in the macro- and microstructure of the living brain. We collected brain MRI scans from 615 healthy young adult twins and siblings, of whom 574 were also scanned with diffusion tensor imaging at 4 Tesla. Fiber integrity was assessed by using the diffusion tensor imaging-based measure of fractional anisotropy. In bivariate genetic models based on monozygotic and dizygotic twins, we discovered that partially overlapping additive genetic factors influenced transferrin levels and brain microstructure. We also examined common variants in genes associated with transferrin levels, TF and HFE, and found that a commonly carried polymorphism (H63D at rs1799945) in the hemochromatotic HFE gene was associated with white matter fiber integrity. This gene has a well documented association with iron overload. Our statistical maps reveal previously unknown influences of the same gene on brain microstructure and transferrin levels. This discovery may shed light on the neural mechanisms by which iron affects cognition, neurodevelopment, and neurodegeneration.
  Proceedings of the National Academy of Sciences 01/2012; 109(14):E851-9. · 9.68 Impact Factor
• Article: Discovery and Replication of Gene Influences on Brain Structure Using LASSO Regression.

  Omid Kohannim, Derrek P Hibar, Jason L Stein, Neda Jahanshad, Xue Hua, Priya Rajagopalan, Arthur W Toga, Clifford R Jack, Michael W Weiner, Greig I de Zubicaray, Katie L McMahon, Narelle K Hansell, Nicholas G Martin, Margaret J Wright, Paul M Thompson
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  ABSTRACT: We implemented least absolute shrinkage and selection operator (LASSO) regression to evaluate gene effects in genome-wide association studies (GWAS) of brain images, using an MRI-derived temporal lobe volume measure from 729 subjects scanned as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI). Sparse groups of SNPs in individual genes were selected by LASSO, which identifies efficient sets of variants influencing the data. These SNPs were considered jointly when assessing their association with neuroimaging measures. We discovered 22 genes that passed genome-wide significance for influencing temporal lobe volume. This was a substantially greater number of significant genes compared to those found with standard, univariate GWAS. These top genes are all expressed in the brain and include genes previously related to brain function or neuropsychiatric disorders such as MACROD2, SORCS2, GRIN2B, MAGI2, NPAS3, CLSTN2, GABRG3, NRXN3, PRKAG2, GAS7, RBFOX1, ADARB2, CHD4, and CDH13. The top genes we identified with this method also displayed significant and widespread post hoc effects on voxelwise, tensor-based morphometry (TBM) maps of the temporal lobes. The most significantly associated gene was an autism susceptibility gene known as MACROD2. We were able to successfully replicate the effect of the MACROD2 gene in an independent cohort of 564 young, Australian healthy adult twins and siblings scanned with MRI (mean age: 23.8 ± 2.2 SD years). Our approach powerfully complements univariate techniques in detecting influences of genes on the living brain.
  Frontiers in Neuroscience 01/2012; 6:115.
• Article: PREDICTING TEMPORAL LOBE VOLUME ON MRI FROM GENOTYPES USING L(1)-L(2) REGULARIZED REGRESSION.

  Omid Kohannim, Derrek P Hibar, Neda Jahanshad, Jason L Stein, Xue Hua, Arthur W Toga, Clifford R Jack, Michael W Weiner, Paul M Thompson
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  ABSTRACT: Penalized or sparse regression methods are gaining increasing attention in imaging genomics, as they can select optimal regressors from a large set of predictors whose individual effects are small or mostly zero. We applied a multivariate approach, based on L(1)-L(2)-regularized regression (elastic net) to predict a magnetic resonance imaging (MRI) tensor-based morphometry-derived measure of temporal lobe volume from a genome-wide scan in 740 Alzheimer's Disease Neuroimaging Initiative (ADNI) subjects. We tuned the elastic net model's parameters using internal crossvalidation and evaluated the model on independent test sets. Compared to 100,000 permutations performed with randomized imaging measures, the predictions were found to be statistically significant (p ~ 0.001). The rs9933137 variant in the RBFOX1 gene was a highly contributory genotype, along with rs10845840 in GRIN2B and rs2456930, discovered previously in a univariate genomewide search.
  Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging 01/2012;
• Article: DISCOVERY OF GENES THAT AFFECT HUMAN BRAIN CONNECTIVITY: A GENOME-WIDE ANALYSIS OF THE CONNECTOME.

  Neda Jahanshad, Derrek P Hibar, April Ryles, Arthur W Toga, Katie L McMahon, Greig I de Zubicaray, Narelle K Hansell, Grant W Montgomery, Nicholas G Martin, Margaret J Wright, Paul M Thompson
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  ABSTRACT: 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.
  Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging 01/2012;
• Article: Voxelwise gene-wide association study (vGeneWAS): multivariate gene-based association testing in 731 elderly subjects.

  Derrek P Hibar, Jason L Stein, Omid Kohannim, Neda Jahanshad, Andrew J Saykin, Li Shen, Sungeun Kim, Nathan Pankratz, Tatiana Foroud, Matthew J Huentelman, Steven G Potkin, Clifford R Jack, Michael W Weiner, Arthur W Toga, Paul M Thompson
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  ABSTRACT: Imaging traits provide a powerful and biologically relevant substrate to examine the influence of genetics on the brain. Interest in genome-wide, brain-wide search for influential genetic variants is growing, but has mainly focused on univariate, SNP-based association tests. Moving to gene-based multivariate statistics, we can test the combined effect of multiple genetic variants in a single test statistic. Multivariate models can reduce the number of statistical tests in gene-wide or genome-wide scans and may discover gene effects undetectable with SNP-based methods. Here we present a gene-based method for associating the joint effect of single nucleotide polymorphisms (SNPs) in 18,044 genes across 31,662 voxels of the whole brain in 731 elderly subjects (mean age: 75.56±6.82SD years; 430 males) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Structural MRI scans were analyzed using tensor-based morphometry (TBM) to compute 3D maps of regional brain volume differences compared to an average template image based on healthy elderly subjects. Using the voxel-level volume difference values as the phenotype, we selected the most significantly associated gene (out of 18,044) at each voxel across the brain. No genes identified were significant after correction for multiple comparisons, but several known candidates were re-identified, as were other genes highly relevant to brain function. GAB2, which has been previously associated with late-onset AD, was identified as the top gene in this study, suggesting the validity of the approach. This multivariate, gene-based voxelwise association study offers a novel framework to detect genetic influences on the brain.
  NeuroImage 06/2011; 56(4):1875-91. · 5.89 Impact Factor
• Article: Hippocampal volume is related to body mass index in Alzheimer's disease.

  April J Ho, Cyrus A Raji, Priya Saharan, Andrew DeGiorgio, Sarah K Madsen, Derrek P Hibar, Jason L Stein, James T Becker, Oscar L Lopez, Arthur W Toga, Paul M Thompson
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  ABSTRACT: Obesity is associated with lower brain volumes in early Alzheimer's disease, but its effects on hippocampal volumes are unclear, as weight loss is also associated with Alzheimer's disease. To address this question, we applied an automated hippocampal mapping method to brain MRI scans for 162 patients with Alzheimer's disease. We hypothesized that obesity, measured by body mass index, would be associated with lower hippocampal volumes in mildly affected patients. Statistical maps showed a selective pattern of hippocampal volume differences that were significantly associated with body mass index. Associations were detected in the anterior hippocampus, and confirmed by permutation testing. Cardiovascular risk factors, such as high body mass index, may promote additional neurodegenerative changes, and should therefore be considered in epidemiological studies and clinical trials.
  Neuroreport 01/2011; 22(1):10-4. · 1.66 Impact Factor
• Source

  Available from: Clifford R Jack

  Conference Proceeding: Principal components regression: Multivariate, gene-based tests in imaging genomics.

  Derrek P. Hibar, Jason L. Stein, Omid Kohannim, Neda Jahanshad, Clifford R. Jack, Michael Weiner, Arthur W. Toga, Paul M. Thompson
  Proceedings of the 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011, March 30 - April 2, 2011, Chicago, Illinois, USA; 01/2011
• Source

  Available from: Clifford R Jack

  Conference Proceeding: Boosting power to detect genetic associations in imaging using multi-locus, genome-wide scans and ridge regression.

  Omid Kohannim, Derrek P. Hibar, Jason L. Stein, Neda Jahanshad, Clifford R. Jack, Michael Weiner, Arthur W. Toga, Paul M. Thompson
  Proceedings of the 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011, March 30 - April 2, 2011, Chicago, Illinois, USA; 01/2011
• Article: Multilocus genetic analysis of brain images.

  Derrek P Hibar, Omid Kohannim, Jason L Stein, Ming-Chang Chiang, Paul M Thompson
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  ABSTRACT: The quest to identify genes that influence disease is now being extended to find genes that affect biological markers of disease, or endophenotypes. Brain images, in particular, provide exquisitely detailed measures of anatomy, function, and connectivity in the living brain, and have identified characteristic features for many neurological and psychiatric disorders. The emerging field of imaging genomics is discovering important genetic variants associated with brain structure and function, which in turn influence disease risk and fundamental cognitive processes. Statistical approaches for testing genetic associations are not straightforward to apply to brain images because the data in brain images is spatially complex and generally high dimensional. Neuroimaging phenotypes typically include 3D maps across many points in the brain, fiber tracts, shape-based analyses, and connectivity matrices, or networks. These complex data types require new methods for data reduction and joint consideration of the image and the genome. Image-wide, genome-wide searches are now feasible, but they can be greatly empowered by sparse regression or hierarchical clustering methods that isolate promising features, boosting statistical power. Here we review the evolution of statistical approaches to assess genetic influences on the brain. We outline the current state of multivariate statistics in imaging genomics, and future directions, including meta-analysis. We emphasize the power of novel multivariate approaches to discover reliable genetic influences with small effect sizes.
  Frontiers in genetics. 01/2011; 2:73.
• Source

  Available from: Clifford R Jack

  Article: Sex and age differences in atrophic rates: an ADNI study with n=1368 MRI scans.

  Xue Hua, Derrek P Hibar, Suh Lee, Arthur W Toga, Clifford R Jack, Michael W Weiner, Paul M Thompson
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  ABSTRACT: We set out to determine factors that influence the rate of brain atrophy in 1-year longitudinal magnetic resonance imaging (MRI) data. With tensor-based morphometry (TBM), we mapped the 3-dimensional profile of progressive atrophy in 144 subjects with probable Alzheimer's disease (AD) (age: 76.5 +/- 7.4 years), 338 with amnestic mild cognitive impairment (MCI; 76.0 +/- 7.2), and 202 healthy controls (77.0 +/- 5.1), scanned twice, 1 year apart. Statistical maps revealed significant age and sex differences in atrophic rates. Brain atrophic rates were about 1%-1.5% faster in women than men. Atrophy was faster in younger than older subjects, most prominently in mild cognitive impairment, with a 1% increase in the rates of atrophy and 2% in ventricular expansion, for every 10-year decrease in age. TBM-derived atrophic rates correlated with reduced beta-amyloid and elevated tau levels (n = 363) at baseline, baseline and progressive deterioration in clinical measures, and increasing numbers of risk alleles for the ApoE4 gene. TBM is a sensitive, high-throughput biomarker for tracking disease progression in large imaging studies; sub-analyses focusing on women or younger subjects gave improved sample size requirements for clinical trials.
  Neurobiology of aging 08/2010; 31(8):1463-80. · 5.94 Impact Factor
• Source

  Available from: Clifford R Jack

  Article: Boosting power for clinical trials using classifiers based on multiple biomarkers.

  Omid Kohannim, Xue Hua, Derrek P Hibar, Suh Lee, Yi-Yu Chou, Arthur W Toga, Clifford R Jack, Michael W Weiner, Paul M Thompson
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  ABSTRACT: Machine learning methods pool diverse information to perform computer-assisted diagnosis and predict future clinical decline. We introduce a machine learning method to boost power in clinical trials. We created a Support Vector Machine algorithm that combines brain imaging and other biomarkers to classify 737 Alzheimer's disease Neuroimaging initiative (ADNI) subjects as having Alzheimer's disease (AD), mild cognitive impairment (MCI), or normal controls. We trained our classifiers based on example data including: MRI measures of hippocampal, ventricular, and temporal lobe volumes, a PET-FDG numerical summary, CSF biomarkers (t-tau, p-tau, and Abeta(42)), ApoE genotype, age, sex, and body mass index. MRI measures contributed most to Alzheimer's disease (AD) classification; PET-FDG and CSF biomarkers, particularly Abeta(42), contributed more to MCI classification. Using all biomarkers jointly, we used our classifier to select the one-third of the subjects most likely to decline. In this subsample, fewer than 40 AD and MCI subjects would be needed to detect a 25% slowing in temporal lobe atrophy rates with 80% power--a substantial boosting of power relative to standard imaging measures.
  Neurobiology of aging 08/2010; 31(8):1429-42. · 5.94 Impact Factor