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Kohannim O, Huang JC, Hathout GM. Detection of subthreshold atrophy in crossed cerebellar degeneration via two-compartment mathematical modeling of cell density in DWI: A proof of concept study. Med Hypotheses 2018; 120:96-100. [PMID: 30220350 DOI: 10.1016/j.mehy.2018.08.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Accepted: 08/28/2018] [Indexed: 11/26/2022]
Abstract
Crossed cerebellar diaschisis (CCD) refers to transneuronal degeneration of the corticopontocerebellar pathway, resulting in atrophy of cerebellum contralateral to supratentorial pathology. CCD is traditionally diagnosed on nuclear medicine studies. Our aim is to apply a biexponential diffusion model, composed of intracellular and extracellular compartments, to the detection of subthreshold CCD on DWI, with the calculated fraction of the intracellular compartment as a proposed measure of cell density. At a voxel-by-voxel basis, we solve for intracellular and extracellular coefficients in each side of the cerebellum and compare the distribution of coefficients between each hemisphere. We demonstrate, in all six CCD cases, a significantly lower contribution of the intracellular compartment to the cerebellar hemisphere contralateral to supratentorial pathology (p < 0.01). In a separate, proof-of-concept case of pontine stroke, we also demonstrate reduced intracellular coefficients in bilateral cerebellar hemispheres, excluding middle cerebellar peduncles (p < 0.01). Our findings are consistent with a decreased intracellular fraction, presumably a surrogate for reduced cellular density in corticopontocerebellar degeneration, despite normal-appearing scans. Our approach allows detection of subthreshold structural changes and offers the additional advantage of applicability to most clinical cases, where only three DWI beta values are available.
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Affiliation(s)
- Omid Kohannim
- Department of Radiology, UCLA David Geffen School of Medicine, Los Angeles, CA 90095, United States.
| | - Jimmy C Huang
- Department of Radiology, UCLA David Geffen School of Medicine, Los Angeles, CA 90095, United States; Department of Radiology, Department of Veteran Affairs, Los Angeles, CA 90073, United States; Department of Radiology, Olive View-UCLA Medical Center, Sylmar, CA 91342, United States
| | - Gasser M Hathout
- Department of Radiology, UCLA David Geffen School of Medicine, Los Angeles, CA 90095, United States; Department of Radiology, Department of Veteran Affairs, Los Angeles, CA 90073, United States; Department of Radiology, Olive View-UCLA Medical Center, Sylmar, CA 91342, United States
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Apostolova LG, Hwang KS, Avila D, Elashoff D, Kohannim O, Teng E, Sokolow S, Jack CR, Jagust WJ, Shaw L, Trojanowski JQ, Weiner MW, Thompson PM. Brain amyloidosis ascertainment from cognitive, imaging, and peripheral blood protein measures. Neurology 2015; 84:729-37. [PMID: 25609767 DOI: 10.1212/wnl.0000000000001231] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND The goal of this study was to identify a clinical biomarker signature of brain amyloidosis in the Alzheimer's Disease Neuroimaging Initiative 1 (ADNI1) mild cognitive impairment (MCI) cohort. METHODS We developed a multimodal biomarker classifier for predicting brain amyloidosis using cognitive, imaging, and peripheral blood protein ADNI1 MCI data. We used CSF β-amyloid 1-42 (Aβ42) ≤ 192 pg/mL as proxy measure for Pittsburgh compound B (PiB)-PET standard uptake value ratio ≥ 1.5. We trained our classifier in the subcohort with CSF Aβ42 but no PiB-PET data and tested its performance in the subcohort with PiB-PET but no CSF Aβ42 data. We also examined the utility of our biomarker signature for predicting disease progression from MCI to Alzheimer dementia. RESULTS The CSF training classifier selected Mini-Mental State Examination, Trails B, Auditory Verbal Learning Test delayed recall, education, APOE genotype, interleukin 6 receptor, clusterin, and ApoE protein, and achieved leave-one-out accuracy of 85% (area under the curve [AUC] = 0.8). The PiB testing classifier achieved an AUC of 0.72, and when classifier self-tuning was allowed, AUC = 0.74. The 36-month disease-progression classifier achieved AUC = 0.75 and accuracy = 71%. CONCLUSIONS Automated classifiers based on cognitive and peripheral blood protein variables can identify the presence of brain amyloidosis with a modest level of accuracy. Such methods could have implications for clinical trial design and enrollment in the near future. CLASSIFICATION OF EVIDENCE This study provides Class II evidence that a classification algorithm based on cognitive, imaging, and peripheral blood protein measures identifies patients with brain amyloid on PiB-PET with moderate accuracy (sensitivity 68%, specificity 78%).
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Affiliation(s)
- Liana G Apostolova
- From the Departments of Neurology (L.G.A., K.S.H., D.A., O.K., E.T., P.M.T.), Medicine Statistics Core (D.E.), and School of Nursing (S.S.), David Geffen School of Medicine at University of California, Los Angeles; Institute for Neuroinformatics (P.M.T.), Keck School of Medicine, University of Southern California, Los Angeles; Veterans Affairs Greater Los Angeles Healthcare System (E.T.); Department of Diagnostic Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Department of Public Health and Neuroscience (W.J.J.), University of California, Berkeley; Department of Pathology and Laboratory Medicine (L.S., J.Q.T.), University of Pennsylvania School of Medicine, Philadelphia; Department of Radiology (M.W.W.), University of California, San Francisco; and Department of Veterans Affairs Medical Center (M.W.W.), San Francisco, CA.
| | - Kristy S Hwang
- From the Departments of Neurology (L.G.A., K.S.H., D.A., O.K., E.T., P.M.T.), Medicine Statistics Core (D.E.), and School of Nursing (S.S.), David Geffen School of Medicine at University of California, Los Angeles; Institute for Neuroinformatics (P.M.T.), Keck School of Medicine, University of Southern California, Los Angeles; Veterans Affairs Greater Los Angeles Healthcare System (E.T.); Department of Diagnostic Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Department of Public Health and Neuroscience (W.J.J.), University of California, Berkeley; Department of Pathology and Laboratory Medicine (L.S., J.Q.T.), University of Pennsylvania School of Medicine, Philadelphia; Department of Radiology (M.W.W.), University of California, San Francisco; and Department of Veterans Affairs Medical Center (M.W.W.), San Francisco, CA
| | - David Avila
- From the Departments of Neurology (L.G.A., K.S.H., D.A., O.K., E.T., P.M.T.), Medicine Statistics Core (D.E.), and School of Nursing (S.S.), David Geffen School of Medicine at University of California, Los Angeles; Institute for Neuroinformatics (P.M.T.), Keck School of Medicine, University of Southern California, Los Angeles; Veterans Affairs Greater Los Angeles Healthcare System (E.T.); Department of Diagnostic Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Department of Public Health and Neuroscience (W.J.J.), University of California, Berkeley; Department of Pathology and Laboratory Medicine (L.S., J.Q.T.), University of Pennsylvania School of Medicine, Philadelphia; Department of Radiology (M.W.W.), University of California, San Francisco; and Department of Veterans Affairs Medical Center (M.W.W.), San Francisco, CA
| | - David Elashoff
- From the Departments of Neurology (L.G.A., K.S.H., D.A., O.K., E.T., P.M.T.), Medicine Statistics Core (D.E.), and School of Nursing (S.S.), David Geffen School of Medicine at University of California, Los Angeles; Institute for Neuroinformatics (P.M.T.), Keck School of Medicine, University of Southern California, Los Angeles; Veterans Affairs Greater Los Angeles Healthcare System (E.T.); Department of Diagnostic Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Department of Public Health and Neuroscience (W.J.J.), University of California, Berkeley; Department of Pathology and Laboratory Medicine (L.S., J.Q.T.), University of Pennsylvania School of Medicine, Philadelphia; Department of Radiology (M.W.W.), University of California, San Francisco; and Department of Veterans Affairs Medical Center (M.W.W.), San Francisco, CA
| | - Omid Kohannim
- From the Departments of Neurology (L.G.A., K.S.H., D.A., O.K., E.T., P.M.T.), Medicine Statistics Core (D.E.), and School of Nursing (S.S.), David Geffen School of Medicine at University of California, Los Angeles; Institute for Neuroinformatics (P.M.T.), Keck School of Medicine, University of Southern California, Los Angeles; Veterans Affairs Greater Los Angeles Healthcare System (E.T.); Department of Diagnostic Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Department of Public Health and Neuroscience (W.J.J.), University of California, Berkeley; Department of Pathology and Laboratory Medicine (L.S., J.Q.T.), University of Pennsylvania School of Medicine, Philadelphia; Department of Radiology (M.W.W.), University of California, San Francisco; and Department of Veterans Affairs Medical Center (M.W.W.), San Francisco, CA
| | - Edmond Teng
- From the Departments of Neurology (L.G.A., K.S.H., D.A., O.K., E.T., P.M.T.), Medicine Statistics Core (D.E.), and School of Nursing (S.S.), David Geffen School of Medicine at University of California, Los Angeles; Institute for Neuroinformatics (P.M.T.), Keck School of Medicine, University of Southern California, Los Angeles; Veterans Affairs Greater Los Angeles Healthcare System (E.T.); Department of Diagnostic Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Department of Public Health and Neuroscience (W.J.J.), University of California, Berkeley; Department of Pathology and Laboratory Medicine (L.S., J.Q.T.), University of Pennsylvania School of Medicine, Philadelphia; Department of Radiology (M.W.W.), University of California, San Francisco; and Department of Veterans Affairs Medical Center (M.W.W.), San Francisco, CA
| | - Sophie Sokolow
- From the Departments of Neurology (L.G.A., K.S.H., D.A., O.K., E.T., P.M.T.), Medicine Statistics Core (D.E.), and School of Nursing (S.S.), David Geffen School of Medicine at University of California, Los Angeles; Institute for Neuroinformatics (P.M.T.), Keck School of Medicine, University of Southern California, Los Angeles; Veterans Affairs Greater Los Angeles Healthcare System (E.T.); Department of Diagnostic Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Department of Public Health and Neuroscience (W.J.J.), University of California, Berkeley; Department of Pathology and Laboratory Medicine (L.S., J.Q.T.), University of Pennsylvania School of Medicine, Philadelphia; Department of Radiology (M.W.W.), University of California, San Francisco; and Department of Veterans Affairs Medical Center (M.W.W.), San Francisco, CA
| | - Clifford R Jack
- From the Departments of Neurology (L.G.A., K.S.H., D.A., O.K., E.T., P.M.T.), Medicine Statistics Core (D.E.), and School of Nursing (S.S.), David Geffen School of Medicine at University of California, Los Angeles; Institute for Neuroinformatics (P.M.T.), Keck School of Medicine, University of Southern California, Los Angeles; Veterans Affairs Greater Los Angeles Healthcare System (E.T.); Department of Diagnostic Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Department of Public Health and Neuroscience (W.J.J.), University of California, Berkeley; Department of Pathology and Laboratory Medicine (L.S., J.Q.T.), University of Pennsylvania School of Medicine, Philadelphia; Department of Radiology (M.W.W.), University of California, San Francisco; and Department of Veterans Affairs Medical Center (M.W.W.), San Francisco, CA
| | - William J Jagust
- From the Departments of Neurology (L.G.A., K.S.H., D.A., O.K., E.T., P.M.T.), Medicine Statistics Core (D.E.), and School of Nursing (S.S.), David Geffen School of Medicine at University of California, Los Angeles; Institute for Neuroinformatics (P.M.T.), Keck School of Medicine, University of Southern California, Los Angeles; Veterans Affairs Greater Los Angeles Healthcare System (E.T.); Department of Diagnostic Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Department of Public Health and Neuroscience (W.J.J.), University of California, Berkeley; Department of Pathology and Laboratory Medicine (L.S., J.Q.T.), University of Pennsylvania School of Medicine, Philadelphia; Department of Radiology (M.W.W.), University of California, San Francisco; and Department of Veterans Affairs Medical Center (M.W.W.), San Francisco, CA
| | - Leslie Shaw
- From the Departments of Neurology (L.G.A., K.S.H., D.A., O.K., E.T., P.M.T.), Medicine Statistics Core (D.E.), and School of Nursing (S.S.), David Geffen School of Medicine at University of California, Los Angeles; Institute for Neuroinformatics (P.M.T.), Keck School of Medicine, University of Southern California, Los Angeles; Veterans Affairs Greater Los Angeles Healthcare System (E.T.); Department of Diagnostic Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Department of Public Health and Neuroscience (W.J.J.), University of California, Berkeley; Department of Pathology and Laboratory Medicine (L.S., J.Q.T.), University of Pennsylvania School of Medicine, Philadelphia; Department of Radiology (M.W.W.), University of California, San Francisco; and Department of Veterans Affairs Medical Center (M.W.W.), San Francisco, CA
| | - John Q Trojanowski
- From the Departments of Neurology (L.G.A., K.S.H., D.A., O.K., E.T., P.M.T.), Medicine Statistics Core (D.E.), and School of Nursing (S.S.), David Geffen School of Medicine at University of California, Los Angeles; Institute for Neuroinformatics (P.M.T.), Keck School of Medicine, University of Southern California, Los Angeles; Veterans Affairs Greater Los Angeles Healthcare System (E.T.); Department of Diagnostic Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Department of Public Health and Neuroscience (W.J.J.), University of California, Berkeley; Department of Pathology and Laboratory Medicine (L.S., J.Q.T.), University of Pennsylvania School of Medicine, Philadelphia; Department of Radiology (M.W.W.), University of California, San Francisco; and Department of Veterans Affairs Medical Center (M.W.W.), San Francisco, CA
| | - Michael W Weiner
- From the Departments of Neurology (L.G.A., K.S.H., D.A., O.K., E.T., P.M.T.), Medicine Statistics Core (D.E.), and School of Nursing (S.S.), David Geffen School of Medicine at University of California, Los Angeles; Institute for Neuroinformatics (P.M.T.), Keck School of Medicine, University of Southern California, Los Angeles; Veterans Affairs Greater Los Angeles Healthcare System (E.T.); Department of Diagnostic Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Department of Public Health and Neuroscience (W.J.J.), University of California, Berkeley; Department of Pathology and Laboratory Medicine (L.S., J.Q.T.), University of Pennsylvania School of Medicine, Philadelphia; Department of Radiology (M.W.W.), University of California, San Francisco; and Department of Veterans Affairs Medical Center (M.W.W.), San Francisco, CA
| | - Paul M Thompson
- From the Departments of Neurology (L.G.A., K.S.H., D.A., O.K., E.T., P.M.T.), Medicine Statistics Core (D.E.), and School of Nursing (S.S.), David Geffen School of Medicine at University of California, Los Angeles; Institute for Neuroinformatics (P.M.T.), Keck School of Medicine, University of Southern California, Los Angeles; Veterans Affairs Greater Los Angeles Healthcare System (E.T.); Department of Diagnostic Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Department of Public Health and Neuroscience (W.J.J.), University of California, Berkeley; Department of Pathology and Laboratory Medicine (L.S., J.Q.T.), University of Pennsylvania School of Medicine, Philadelphia; Department of Radiology (M.W.W.), University of California, San Francisco; and Department of Veterans Affairs Medical Center (M.W.W.), San Francisco, CA
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Hibar DP, Stein JL, Jahanshad N, Kohannim O, Hua X, Toga AW, McMahon KL, de Zubicaray GI, Martin NG, Wright MJ, Weiner MW, Thompson PM. Genome-wide interaction analysis reveals replicated epistatic effects on brain structure. Neurobiol Aging 2015; 36 Suppl 1:S151-8. [PMID: 25264344 PMCID: PMC4332874 DOI: 10.1016/j.neurobiolaging.2014.02.033] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2013] [Revised: 02/10/2014] [Accepted: 02/16/2014] [Indexed: 11/24/2022]
Abstract
The discovery of several genes that affect the 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 because of the complexity of conducting approximately 10(11) pairwise statistical tests. However, recent advances in machine learning, for example, iterative sure independence screening, make it possible to analyze data sets with vastly more predictors than observations. Using an implementation of the sure independence screening algorithm (called EPISIS), we performed a genome-wide interaction analysis testing all possible SNP-SNP interactions affecting regional brain volumes measured on magnetic resonance imaging 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 Alzheimer's Disease Neuroimaging Initiative data set and separately in an independent replication data set of healthy twins (Queensland Twin Imaging). Each additional loading in the interaction effect was associated with approximately 5% greater brain regional brain volume (a protective effect) in both Alzheimer's Disease Neuroimaging Initiative and Queensland Twin Imaging samples.
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Affiliation(s)
- Derrek P Hibar
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Jason L Stein
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Omid Kohannim
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Xue Hua
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Arthur W Toga
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Katie L McMahon
- Centre for Magnetic Resonance, School of Psychology, University of Queensland, Brisbane, Queensland, Australia
| | - Greig I de Zubicaray
- Functional Magnetic Resonance Imaging Laboratory, School of Psychology, University of Queensland, Brisbane, Queensland, Australia
| | - Nicholas G Martin
- Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, Brisbane, Australia
| | - Margaret J Wright
- Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, Brisbane, Australia
| | - Michael W Weiner
- Department of Radiology, UC San Francisco, San Francisco, CA, USA; Department of Medicine, UC San Francisco, San Francisco, CA, USA; Department of Psychiatry, UC San Francisco, San Francisco, CA, USA; Department of Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA.
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Apostolova LG, Hwang KS, Kohannim O, Avila D, Elashoff D, Jack CR, Shaw L, Trojanowski JQ, Weiner MW, Thompson PM. ApoE4 effects on automated diagnostic classifiers for mild cognitive impairment and Alzheimer's disease. Neuroimage Clin 2014; 4:461-72. [PMID: 24634832 PMCID: PMC3952354 DOI: 10.1016/j.nicl.2013.12.012] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/26/2013] [Revised: 12/24/2013] [Accepted: 12/24/2013] [Indexed: 01/30/2023]
Abstract
Biomarkers are the only feasible way to detect and monitor presymptomatic Alzheimer's disease (AD). No single biomarker can predict future cognitive decline with an acceptable level of accuracy. In addition to designing powerful multimodal diagnostic platforms, a careful investigation of the major sources of disease heterogeneity and their influence on biomarker changes is needed. Here we investigated the accuracy of a novel multimodal biomarker classifier for differentiating cognitively normal (NC), mild cognitive impairment (MCI) and AD subjects with and without stratification by ApoE4 genotype. 111 NC, 182 MCI and 95 AD ADNI participants provided both structural MRI and CSF data at baseline. We used an automated machine-learning classifier to test the ability of hippocampal volume and CSF Aβ, t-tau and p-tau levels, both separately and in combination, to differentiate NC, MCI and AD subjects, and predict conversion. We hypothesized that the combined hippocampal/CSF biomarker classifier model would achieve the highest accuracy in differentiating between the three diagnostic groups and that ApoE4 genotype will affect both diagnostic accuracy and biomarker selection. The combined hippocampal/CSF classifier performed better than hippocampus-only classifier in differentiating NC from MCI and NC from AD. It also outperformed the CSF-only classifier in differentiating NC vs. AD. Our amyloid marker played a role in discriminating NC from MCI or AD but not for MCI vs. AD. Neurodegenerative markers contributed to accurate discrimination of AD from NC and MCI but not NC from MCI. Classifiers predicting MCI conversion performed well only after ApoE4 stratification. Hippocampal volume and sex achieved AUC = 0.68 for predicting conversion in the ApoE4-positive MCI, while CSF p-tau, education and sex achieved AUC = 0.89 for predicting conversion in ApoE4-negative MCI. These observations support the proposed biomarker trajectory in AD, which postulates that amyloid markers become abnormal early in the disease course while markers of neurodegeneration become abnormal later in the disease course and suggests that ApoE4 could be at least partially responsible for some of the observed disease heterogeneity. Multimodal classifiers have better predictive power than unimodal classifier. ApoE4 significantly affects diagnostic discriminability in the MCI and dementia stages. Our data supports the hypothesized biomarker trajectory in AD.
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Key Words
- AD, Alzheimer's disease
- ADNI
- ADNI, Alzheimer's Disease Neuroimaging Initiative
- AUC, area under the curve
- Abeta
- Alzheimer's disease
- ApoE, apolipoprotein E
- Aβ, Amyloid beta
- Aβ42, Amyloid beta with 42 amino acid residues
- CSF, cerebrospinal fluid
- Diagnosis
- Hippocampus atrophy
- ICBM, International Consortium for Brain Mapping
- MCI, mild cognitive impairment
- MCIc, MCI converters
- MCInc, MCI nonconverters
- MMSE, Mini-Mental State Examination
- NC, normal control
- ROC, receiver operating curve
- SVM, support vector machine
- Tau
- p-tau, phosphorylated tau protein
- t-tau, total tau protein
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Affiliation(s)
- Liana G Apostolova
- Department of Neurology, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Kristy S Hwang
- Department of Neurology, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Omid Kohannim
- Imaging genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - David Avila
- Department of Neurology, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - David Elashoff
- Department of Medicine Statistics Core, UCLA, Los Angeles, CA, USA
| | - Clifford R Jack
- Department of Diagnostic Radiology, Mayo Clinic, Rochester, MN, USA
| | - Leslie Shaw
- Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Michael W Weiner
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA ; Department of Veteran's Affairs Medical Center, San Francisco, CA, USA
| | - Paul M Thompson
- Imaging genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
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Kohannim O, Hua X, Rajagopalan P, Hibar DP, Jahanshad N, Grill JD, Apostolova LG, Toga AW, Jack CR, Weiner MW, Thompson PM. Multilocus genetic profiling to empower drug trials and predict brain atrophy. Neuroimage Clin 2013; 2:827-35. [PMID: 24179834 PMCID: PMC3777716 DOI: 10.1016/j.nicl.2013.05.007] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2013] [Revised: 04/14/2013] [Accepted: 05/11/2013] [Indexed: 12/16/2022]
Abstract
Designers of clinical trials for Alzheimer's disease (AD) and mild cognitive impairment (MCI) are actively considering structural and functional neuroimaging, cerebrospinal fluid and genetic biomarkers to reduce the sample sizes needed to detect therapeutic effects. Genetic pre-selection, however, has been limited to Apolipoprotein E (ApoE). Recently discovered polymorphisms in the CLU, CR1 and PICALM genes are also moderate risk factors for AD; each affects lifetime AD risk by ~ 10–20%. Here, we tested the hypothesis that pre-selecting subjects based on these variants along with ApoE genotype would further boost clinical trial power, relative to considering ApoE alone, using an MRI-derived 2-year atrophy rate as our outcome measure. We ranked subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) based on their cumulative risk from these four genes. We obtained sample size estimates in cohorts enriched in subjects with greater aggregate genetic risk. Enriching for additional genetic biomarkers reduced the required sample sizes by up to 50%, for MCI trials. Thus, AD drug trial enrichment with multiple genotypes may have potential implications for the timeliness, cost, and power of trials. ApoE genotype status helps enrich MCI trials, using a structural MRI outcome measure. CLU, PICALM and CR1 risk genes boost potential MCI trial power beyond ApoE alone. CLU, PICALM and CR1 show significant, aggregate effects on TBM maps of brain atrophy.
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Affiliation(s)
- Omid Kohannim
- Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
| | - Xue Hua
- Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
| | - Priya Rajagopalan
- Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
| | - Derrek P. Hibar
- Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
| | - Joshua D. Grill
- Mary Easton Center for Alzheimer's Disease Research, UCLA School of Medicine, Los Angeles, CA, USA
| | - Liana G. Apostolova
- Mary Easton Center for Alzheimer's Disease Research, UCLA School of Medicine, Los Angeles, CA, USA
| | - Arthur W. Toga
- Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
| | | | - Michael W. Weiner
- Depts. of Radiology, Medicine and Psychiatry, UCSF, San Francisco, CA, USA
- Dept. of Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Paul M. Thompson
- Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
- Corresponding author at: Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA. Tel.: + 1 310 206 2101; fax: + 1 310 206 5518.
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Hibar DP, Stein JL, Ryles AB, Kohannim O, Jahanshad N, Medland SE, Hansell NK, McMahon KL, de Zubicaray GI, Montgomery GW, Martin NG, Wright MJ, Saykin AJ, Jack CR, Weiner MW, Toga AW, Thompson PM. Genome-wide association identifies genetic variants associated with lentiform nucleus volume in N = 1345 young and elderly subjects. Brain Imaging Behav 2013; 7:102-15. [PMID: 22903471 PMCID: PMC3779070 DOI: 10.1007/s11682-012-9199-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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.
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Affiliation(s)
- Derrek P. Hibar
- Imaging Genetics Center at the Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
| | - Jason L. Stein
- Imaging Genetics Center at the Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
| | - April B. Ryles
- Imaging Genetics Center at the Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
| | - Omid Kohannim
- Imaging Genetics Center at the Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
| | - Neda Jahanshad
- Imaging Genetics Center at the Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
| | - Sarah E. Medland
- Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, Brisbane, Australia
- Neurogenetics Laboratory, Queensland Institute of Medical Research, Brisbane, Australia
- Broad Institute of Harvard and MIT, Boston, MA, USA
| | - Narelle K. Hansell
- Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, Brisbane, Australia
| | - Katie L. McMahon
- Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia
| | - Greig I. de Zubicaray
- Functional Magnetic Resonance Imaging Laboratory, School of Psychology, University of Queensland, Brisbane, Queensland, Australia
| | - Grant W. Montgomery
- Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, Brisbane, Australia
| | - Nicholas G. Martin
- Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, Brisbane, Australia
| | - Margaret J. Wright
- Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, Brisbane, Australia
| | - Andrew J. Saykin
- Center for Neuroimaging, Department of Radiology and Imaging Science, Indiana University School of Medicine, Indianapolis, IN, USA
| | | | - Michael W. Weiner
- Departments of Radiology, Medicine, Psychiatry, UC San Francisco, San Francisco, CA, USA
- Department of Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Arthur W. Toga
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
| | - Paul M. Thompson
- Imaging Genetics Center at the Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
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7
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Braskie MN, Kohannim O, Jahanshad N, Chiang MC, Barysheva M, Toga AW, Ringman JM, Montgomery GW, McMahon KL, de Zubicaray GI, Martin NG, Wright MJ, Thompson PM. Relation between variants in the neurotrophin receptor gene, NTRK3, and white matter integrity in healthy young adults. Neuroimage 2013; 82:146-53. [PMID: 23727532 DOI: 10.1016/j.neuroimage.2013.05.095] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2012] [Revised: 05/20/2013] [Accepted: 05/22/2013] [Indexed: 01/10/2023] Open
Abstract
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 is related to common 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.
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Affiliation(s)
- Meredith N Braskie
- Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA 90095, USA
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8
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Abstract
Antiretroviral therapies have become widely available, and as a result, individuals infected with the human immunodeficiency virus (HIV) are living longer, and becoming integrated into the geriatric population. Around half of the HIV+ population shows some degree of cognitive impairment, but it is unknown how their neural networks and brain connectivity compare to those of noninfected people. Here we combined magnetic resonance imaging-based cortical parcellations with high angular resolution diffusion tensor imaging tractography in 55 HIV-seropositive patients and 30 age-matched controls, to map white matter connections between cortical regions. We set out to determine selective virus-associated disruptions in the brain's structural network. All individuals in this study were aged 60-80, with full access to antiretroviral therapy. Frontal and motor connections were compromised in HIV+ individuals. HIV+ people who carried the apolipoprotein E4 allele (ApoE4) genotype-which puts them at even greater risk for neurodegeneration-showed additional network structure deficits in temporal and parietal connections. The ApoE4 genotype interacted with duration of illness. Carriers showed greater brain network inefficiencies the longer they were infected. Neural network deficiencies in HIV+ populations exceed those typical of normal aging, and are worse in those genetically predisposed to brain degeneration. This work isolates neuropathological alterations in HIV+ elders, even when treated with antiretroviral therapy. Network impairments may contribute to the neuropsychological abnormalities in elderly HIV patients, who will soon account for around half of all HIV+ adults.
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Affiliation(s)
- Neda Jahanshad
- Imaging Genetics Center, Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
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9
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Chi EC, Allen GI, Zhou H, Kohannim O, Lange K, Thompson PM. IMAGING GENETICS VIA SPARSE CANONICAL CORRELATION ANALYSIS. Proc IEEE Int Symp Biomed Imaging 2013; 2013:740-743. [PMID: 24443689 DOI: 10.1109/isbi.2013.6556581] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The collection of brain images from populations of subjects who have been genotyped with genome-wide scans makes it feasible to search for genetic effects on the brain. Even so, multivariate methods are sorely needed that can search both images and the genome for relationships, making use of the correlation structure of both datasets. Here we investigate the use of sparse canonical correlation analysis (CCA) to home in on sets of genetic variants that explain variance in a set of images. We extend recent work on penalized matrix decomposition to account for the correlations in both datasets. Such methods show promise in imaging genetics as they exploit the natural covariance in the datasets. They also avoid an astronomically heavy statistical correction for searching the whole genome and the entire image for promising associations.
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Affiliation(s)
- Eric C Chi
- Department of Human Genetics, UCLA School of Medicine, Los Angeles, CA, USA
| | | | - Hua Zhou
- Department of Statistics, North Carolina State University, Raleigh, NC, USA
| | - Omid Kohannim
- Imaging Genetics Center, Lab. of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA, USA
| | - Kenneth Lange
- Department of Human Genetics, UCLA School of Medicine, Los Angeles, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Lab. of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA, USA
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10
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Roussotte FF, Jahanshad N, Hibar DP, Sowell ER, Kohannim O, Barysheva M, Hansell NK, McMahon KL, de Zubicaray GI, Montgomery GW, Martin NG, Wright MJ, Toga AW, Jack CR, Weiner MW, Thompson PM. 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. Hum Brain Mapp 2013; 35:1226-36. [PMID: 23427138 DOI: 10.1002/hbm.22247] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2012] [Revised: 11/01/2012] [Accepted: 11/30/2012] [Indexed: 02/02/2023] Open
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.
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Affiliation(s)
- Florence F Roussotte
- Imaging Genetics Center, Laboratory of Neuro Imaging, Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California; Department of Pediatrics, Developmental Cognitive Neuroimaging Laboratory (DCNL), University of Southern California, Los Angeles, California
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11
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Hibar DP, Stein JL, Jahanshad N, Kohannim O, Toga AW, McMahon KL, de Zubicaray GI, Montgomery GW, Martin NG, Wright MJ, Weiner MW, Thompson PM. Exhaustive search of the SNP-sNP interactome identifies epistatic effects on brain volume in two cohorts. Med Image Comput Comput Assist Interv 2013; 16:600-7. [PMID: 24505811 PMCID: PMC4109883 DOI: 10.1007/978-3-642-40760-4_75] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
The SNP-SNP interactome has rarely been explored in the context of neuroimaging genetics mainly due to the complexity of conducting approximately 10(11) pairwise statistical tests. However, recent advances in machine learning, specifically the iterative sure independence screening (SIS) method, have enabled the analysis of datasets where the number of predictors is much larger than the number of observations. Using an implementation of the SIS algorithm (called EPISIS), we used exhaustive search of the genome-wide, SNP-SNP interactome to identify and prioritize SNPs for interaction analysis. We identified a significant SNP pair, rs1345203 and rs1213205, associated with temporal lobe volume. We further examined the full-brain, voxelwise effects of the interaction in the ADNI dataset and separately in an independent dataset of healthy twins (QTIM). We found that each additional loading in the epistatic effect was associated with approximately 5% greater brain regional brain volume (a protective effect) in both the ADNI and QTIM samples.
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Affiliation(s)
- Derrek P Hibar
- Imaging Genetics Center, Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, USA
| | - Jason L Stein
- Imaging Genetics Center, Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, USA
| | - Omid Kohannim
- Imaging Genetics Center, Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, USA
| | - Arthur W Toga
- Imaging Genetics Center, Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, USA
| | - Katie L McMahon
- Center for Magnetic Resonance, School of Psychology, University of Queensland, Brisbane, Australia
| | - Greig I de Zubicaray
- Functional Magnetic Resonance Imaging Laboratory, School of Psychology, University of Queensland, Brisbane, Australia
| | - Grant W Montgomery
- Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, Brisbane, Australia
| | - Nicholas G Martin
- Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, Brisbane, Australia
| | - Margaret J Wright
- Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, Brisbane, Australia
| | | | - Paul M Thompson
- Imaging Genetics Center, Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, USA
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12
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Grill JD, Di L, Lu PH, Lee C, Ringman J, Apostolova LG, Chow N, Kohannim O, Cummings JL, Thompson PM, Elashoff D. Estimating sample sizes for predementia Alzheimer's trials based on the Alzheimer's Disease Neuroimaging Initiative. Neurobiol Aging 2013; 34:62-72. [PMID: 22503160 PMCID: PMC3412892 DOI: 10.1016/j.neurobiolaging.2012.03.006] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2011] [Revised: 02/24/2012] [Accepted: 03/08/2012] [Indexed: 11/21/2022]
Abstract
This study modeled predementia Alzheimer's disease clinical trials. Longitudinal data from cognitively normal (CN) and mild cognitive impairment (MCI) participants in the Alzheimer's Disease Neuroimaging Initiative were used to calculate sample size requirements for trials using outcome measures, including the Clinical Dementia Rating scale sum of boxes, Mini-Mental State Examination, Alzheimer's Disease Assessment Scale-cognitive subscale with and without delayed recall, and the Rey Auditory Verbal Learning Task. We examined the impact on sample sizes of enrichment for genetic and biomarker criteria, including cerebrospinal fluid protein and neuroimaging analyses. We observed little cognitive decline in the CN population at 36 months, regardless of the enrichment strategy. Nonetheless, in CN subjects, using Rey Auditory Verbal Learning Task total as an outcome at 36 months required the fewest subjects across enrichment strategies, with apolipoprotein E genotype ε4 carrier status requiring the fewest (n = 499 per arm to demonstrate a 25% reduction in disease progression). In MCI, enrichment reduced the required sample sizes for trials, relative to estimates based on all subjects. For MCI, the Clinical Dementia Rating scale sum of boxes consistently required the smallest sample sizes. We conclude that predementia clinical trial conduct in Alzheimer's disease is enhanced by the use of biomarker inclusion criteria.
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Affiliation(s)
- Joshua D Grill
- Mary S. Easton Center for Alzheimer's Disease Research, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA.
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13
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Jahanshad N, Valcour VG, Nir TM, Kohannim O, Busovaca E, Nicolas K, Thompson P. Disrupted brain networks in the aging HIV+ population. Brain Connect 2012. [DOI: 10.1089/brain.2012.0105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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14
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Rajagopalan P, Jahanshad N, Stein JL, Hua X, Madsen SK, Kohannim O, Hibar DP, Toga AW, Jack CR, Saykin AJ, Green RC, Weiner MW, Bis JC, Kuller LH, Riverol M, Becker JT, Lopez OL, Thompson PM. Common folate gene variant, MTHFR C677T, is associated with brain structure in two independent cohorts of people with mild cognitive impairment. Neuroimage Clin 2012; 1:179-87. [PMID: 24179750 PMCID: PMC3757723 DOI: 10.1016/j.nicl.2012.09.012] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2012] [Revised: 08/30/2012] [Accepted: 09/26/2012] [Indexed: 02/05/2023]
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.
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Affiliation(s)
- Priya Rajagopalan
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
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15
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Kohannim O, Hibar DP, Stein JL, Jahanshad N, Hua X, Rajagopalan P, Toga AW, Jack CR, Weiner MW, de Zubicaray GI, McMahon KL, Hansell NK, Martin NG, Wright MJ, Thompson PM. Discovery and Replication of Gene Influences on Brain Structure Using LASSO Regression. Front Neurosci 2012; 6:115. [PMID: 22888310 PMCID: PMC3412288 DOI: 10.3389/fnins.2012.00115] [Citation(s) in RCA: 79] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2012] [Accepted: 07/12/2012] [Indexed: 12/12/2022] Open
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.
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Affiliation(s)
- Omid Kohannim
- Imaging Genetics Center at the Laboratory of Neuro Imaging, Department of Neurology, UCLA School of MedicineLos Angeles, CA, USA
| | - Derrek P. Hibar
- Imaging Genetics Center at the Laboratory of Neuro Imaging, Department of Neurology, UCLA School of MedicineLos Angeles, CA, USA
| | - Jason L. Stein
- Imaging Genetics Center at the Laboratory of Neuro Imaging, Department of Neurology, UCLA School of MedicineLos Angeles, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center at the Laboratory of Neuro Imaging, Department of Neurology, UCLA School of MedicineLos Angeles, CA, USA
| | - Xue Hua
- Imaging Genetics Center at the Laboratory of Neuro Imaging, Department of Neurology, UCLA School of MedicineLos Angeles, CA, USA
| | - Priya Rajagopalan
- Imaging Genetics Center at the Laboratory of Neuro Imaging, Department of Neurology, UCLA School of MedicineLos Angeles, CA, USA
| | - Arthur W. Toga
- Imaging Genetics Center at the Laboratory of Neuro Imaging, Department of Neurology, UCLA School of MedicineLos Angeles, CA, USA
| | | | - Michael W. Weiner
- Department of Radiology, UC San FranciscoSan Francisco, CA, USA
- Department of Medicine, UC San FranciscoSan Francisco, CA, USA
- Department of Psychiatry, UC San FranciscoSan Francisco, CA, USA
- Department of Veterans Affairs Medical CenterSan Francisco, CA, USA
| | | | - Katie L. McMahon
- Center for Advanced Imaging, University of QueenslandBrisbane, QLD, Australia
| | | | | | | | - Paul M. Thompson
- Imaging Genetics Center at the Laboratory of Neuro Imaging, Department of Neurology, UCLA School of MedicineLos Angeles, CA, USA
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16
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Kohannim O, Hibar DP, Jahanshad N, Stein JL, Hua X, Toga AW, Jack CR, Weiner MW, Thompson PM. PREDICTING TEMPORAL LOBE VOLUME ON MRI FROM GENOTYPES USING L(1)-L(2) REGULARIZED REGRESSION. Proc IEEE Int Symp Biomed Imaging 2012:1160-1163. [PMID: 22903144 DOI: 10.1109/isbi.2012.6235766] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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.
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Affiliation(s)
- Omid Kohannim
- Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
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17
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Jahanshad N, Kohannim O, Toga AW, McMahon KL, de Zubicaray GI, Hansell NK, Montgomery GW, Martin NG, Wright MJ, Thompson PM. DIFFUSION IMAGING PROTOCOL EFFECTS ON GENETIC ASSOCIATIONS. Proc IEEE Int Symp Biomed Imaging 2012:944-947. [PMID: 22903274 DOI: 10.1109/isbi.2012.6235712] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Large multi-site image-analysis studies have successfully discovered genetic variants that affect brain structure in tens of thousands of subjects scanned worldwide. Candidate genes have also associated with brain integrity, measured using fractional anisotropy in diffusion tensor images (DTI). To evaluate the heritability and robustness of DTI measures as a target for genetic analysis, we compared 417 twins and siblings scanned on the same day on the same high field scanner (4-Tesla) with two protocols: (1) 94-directions; 2mm-thick slices, (2) 27-directions; 5mm-thickness. Using mean FA in white matter ROIs and FA 'skeletons' derived using FSL, we (1) examined differences in voxelwise means, variances, and correlations among the measures; and (2) assessed heritability with structural equation models, using the classical twin design. FA measures from the genu of the corpus callosum were highly heritable, regardless of protocol. Genome-wide analysis of the genu mean FA revealed differences across protocols in the top associations.
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Affiliation(s)
- Neda Jahanshad
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA
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18
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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.
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Affiliation(s)
- Derrek P. Hibar
- Laboratory of Neuro Imaging, Department of Neurology, University of California Los Angeles School of MedicineLos Angeles, CA, USA
| | - Omid Kohannim
- Laboratory of Neuro Imaging, Department of Neurology, University of California Los Angeles School of MedicineLos Angeles, CA, USA
| | - Jason L. Stein
- Laboratory of Neuro Imaging, Department of Neurology, University of California Los Angeles School of MedicineLos Angeles, CA, USA
| | - Ming-Chang Chiang
- Laboratory of Neuro Imaging, Department of Neurology, University of California Los Angeles School of MedicineLos Angeles, CA, USA
- Department of Biomedical Engineering, National Yang-Ming UniversityTaipei, Taiwan
| | - Paul M. Thompson
- Laboratory of Neuro Imaging, Department of Neurology, University of California Los Angeles School of MedicineLos Angeles, CA, USA
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Apostolova L, Hwang K, Kohannim O, Coppola G, Klein E, Gao F, Cummings J, Thompson P. IC‐P‐095: Automated Diagnostic Classifiers Using Imaging, Genotyping, and Gene Expression Data. Alzheimers Dement 2011. [DOI: 10.1016/j.jalz.2011.05.060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
| | | | | | | | | | | | - Jeffrey Cummings
- Cleveland Clinic Lou Ruvo Center for Brain HealthLas VegasNevadaUnited States
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20
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Kohannim O, Hua X, Hibar DP, Lee S, Chou YY, Toga AW, Jack CR, Weiner MW, Thompson PM. Boosting power for clinical trials using classifiers based on multiple biomarkers. Neurobiol Aging 2010; 31:1429-42. [PMID: 20541286 PMCID: PMC2903199 DOI: 10.1016/j.neurobiolaging.2010.04.022] [Citation(s) in RCA: 104] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2010] [Revised: 04/06/2010] [Accepted: 04/22/2010] [Indexed: 10/19/2022]
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.
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Affiliation(s)
- Omid Kohannim
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA 90095-1769, USA
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Gargalovic PS, Erbilgin A, Kohannim O, Pagnon J, Wang X, Castellani L, LeBoeuf R, Peterson ML, Spear BT, Lusis AJ. Quantitative trait locus mapping and identification of Zhx2 as a novel regulator of plasma lipid metabolism. ACTA ACUST UNITED AC 2009; 3:60-7. [PMID: 20160197 DOI: 10.1161/circgenetics.109.902320] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND We previously mapped a quantitative trait locus on chromosome 15 in mice contributing to high-density lipoprotein cholesterol and triglyceride levels and now report the identification of the underlying gene. METHODS AND RESULTS We first fine-mapped the locus by studying a series of congenic strains derived from the parental strains BALB/cJ and MRL/MpJ. Analysis of gene expression and sequencing followed by transgenic complementation led to the identification of zinc fingers and homeoboxes 2 (Zhx2), a transcription factor previously implicated in the developmental regulation of alpha-fetoprotein. Reduced expression of the protein in BALB/cJ mice resulted in altered hepatic transcript levels for several genes involved in lipoprotein metabolism. Most notably, the Zhx2 mutation resulted in a failure to suppress expression of lipoprotein lipase, a gene normally silenced in the adult liver, and this was normalized in BALB/cJ mice carrying the Zhx2 transgene. CONCLUSIONS We identified the gene underlying the chromosome 15 quantitative trait locus, and our results show that Zhx2 functions as a novel developmental regulator of key genes influencing lipoprotein metabolism.
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Affiliation(s)
- Peter S Gargalovic
- Department of Medicine, Microbiology, University of California, Los Angeles, CA 90095-1679, USA
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Mungrue IN, Pagnon J, Kohannim O, Gargalovic PS, Lusis AJ. CHAC1/MGC4504 is a novel proapoptotic component of the unfolded protein response, downstream of the ATF4-ATF3-CHOP cascade. J Immunol 2009; 182:466-76. [PMID: 19109178 DOI: 10.4049/jimmunol.182.1.466] [Citation(s) in RCA: 218] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
To understand pathways mediating the inflammatory responses of human aortic endothelial cells to oxidized phospholipids, we previously used a combination of genetics and genomics to model a coexpression network encompassing >1000 genes. CHAC1 (cation transport regulator-like protein 1), a novel gene regulated by ox-PAPC (oxidized 1-palmitoyl-2-arachidonyl-sn-3-glycero-phosphorylcholine), was identified in a co-regulated group of genes enriched for components of the ATF4 (activating transcription factor 4) arm of the unfolded protein response pathway. Herein, we characterize the role of CHAC1 and validate the network model. We first define the activation of CHAC1 mRNA by chemical unfolded protein response-inducers, but not other cell stressors. We then define activation of CHAC1 by the ATF4-ATF3-CHOP (C/EBP homologous protein), and not parallel XBP1 (X box-binding protein 1) or ATF6 pathways, using siRNA and/or overexpression plasmids. To examine the subset of genes downstream of CHAC1, we used expression microarray analysis to identify a list of 227 differentially regulated genes. We validated the activation of TNFRSF6B (tumor necrosis factor receptor superfamily, member 6b), a FASL decoy receptor, in cells treated with CHAC1 small interfering RNA. Finally, we showed that CHAC1 overexpression enhanced apoptosis, while CHAC1 small interfering RNA suppressed apoptosis, as determined by TUNEL, PARP (poly(ADP-ribose) polymerase) cleavage, and AIF (apoptosis-inducing factor) nuclear translocation.
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Affiliation(s)
- Imran N Mungrue
- Department of Medicine, University of California at Los Angeles, CA 90095, USA.
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