False positives in neuroimaging genetics using voxel-based morphometry data

Voxel-wise statistical inference is commonly used to identify significant experimental effects or group differences in both functional and structural studies of the living brain. Tests based on the size of spatially extended clusters of contiguous suprathreshold voxels are also widely used due to th...

Full description

Saved in:

Bibliographic Details
Published in	NeuroImage (Orlando, Fla.) Vol. 54; no. 2; pp. 992 - 1000
Main Authors	Silver, Matt, Montana, Giovanni, Nichols, Thomas E.
Format	Journal Article
Language	English
Published	United States Elsevier Inc 15.01.2011 Elsevier Limited Academic Press
Subjects	Alzheimer's disease Brain Brain - pathology Brain - physiopathology Clusters Cognition Disorders - genetics Cognition Disorders - pathology Cognitive ability False Positive Reactions Functional anatomy Gaussian Genetic diversity Genetics Genotype Genotypes Humans Image processing Imaging Inference Magnetic Resonance Imaging Medical imaging Morphometry Neurodegenerative diseases Neuroimaging Neurosciences Polymorphism, Single Nucleotide Schizophrenia Single-nucleotide polymorphism Smoothness Statistical methods Statistics Structure-function relationships Studies Substantia grisea Thresholds
Online Access	Get full text
ISSN	1053-8119 1095-9572 1095-9572
DOI	10.1016/j.neuroimage.2010.08.049

Cover

Abstract	Voxel-wise statistical inference is commonly used to identify significant experimental effects or group differences in both functional and structural studies of the living brain. Tests based on the size of spatially extended clusters of contiguous suprathreshold voxels are also widely used due to their typically increased statistical power. In “imaging genetics”, such tests are used to identify regions of the brain that are associated with genetic variation. However, concerns have been raised about the adequate control of rejection rates in studies of this type. A previous study tested the effect of a set of ‘null’ SNPs on brain structure and function, and found that false positive rates were well-controlled. However, no similar analysis of false positive rates in an imaging genetic study using cluster size inference has yet been undertaken. We measured false positive rates in an investigation of the effect of 700 pre-selected null SNPs on grey matter volume using voxel-based morphometry (VBM). As VBM data exhibit spatially-varying smoothness, we used both non-stationary and stationary cluster size tests in our analysis. Image and genotype data on 181 subjects with mild cognitive impairment were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). At a nominal significance level of 5%, false positive rates were found to be well-controlled (3.9–5.6%), using a relatively high cluster-forming threshold, αc=0.001, on images smoothed with a 12mm Gaussian kernel. Tests were however anticonservative at lower cluster-forming thresholds (αc=0.01, 0.05), and for images smoothed using a 6mm Gaussian kernel. Here false positive rates ranged from 9.8 to 67.6%. In a further analysis, false positive rates using simulated data were observed to be well-controlled across a wide range of conditions. While motivated by imaging genetics, our findings apply to any VBM study, and suggest that parametric cluster size inference should only be used with high cluster-forming thresholds and smoothness. We would advocate the use of nonparametric methods in other cases. ►RFT nonstationary cluster size test on VBM is found to be invalid based on empirical null studies. ►Invalid inferences (inflated false positive risk) were found with 3 voxel smoothing. ►With 6 voxel smoothing, invalid inferences were found with 6 voxel smoothing and alpha=0.01 cluster-forming threshold. ►RFT nonstationary test only found to be valid with 6 voxel smoothing with alpha=0.001 cluster-forming threshold. ►Equivalent Gaussian data simulations produced valid inferences, suggesting VBM data violates RFT assumptions and motivates the use of nonparametric permutation methods.
AbstractList	Voxel-wise statistical inference is commonly used to identify significant experimental effects or group differences in both functional and structural studies of the living brain. Tests based on the size of spatially extended clusters of contiguous suprathreshold voxels are also widely used due to their typically increased statistical power. In “imaging genetics”, such tests are used to identify regions of the brain that are associated with genetic variation. However, concerns have been raised about the adequate control of rejection rates in studies of this type. A previous study tested the effect of a set of ‘null’ SNPs on brain structure and function, and found that false positive rates were well-controlled. However, no similar analysis of false positive rates in an imaging genetic study using cluster size inference has yet been undertaken. We measured false positive rates in an investigation of the effect of 700 pre-selected null SNPs on grey matter volume using voxel-based morphometry (VBM). As VBM data exhibit spatially-varying smoothness, we used both non-stationary and stationary cluster size tests in our analysis. Image and genotype data on 181 subjects with mild cognitive impairment were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). At a nominal significance level of 5%, false positive rates were found to be well-controlled (3.9–5.6%), using a relatively high cluster-forming threshold, αc=0.001, on images smoothed with a 12mm Gaussian kernel. Tests were however anticonservative at lower cluster-forming thresholds (αc=0.01, 0.05), and for images smoothed using a 6mm Gaussian kernel. Here false positive rates ranged from 9.8 to 67.6%. In a further analysis, false positive rates using simulated data were observed to be well-controlled across a wide range of conditions. While motivated by imaging genetics, our findings apply to any VBM study, and suggest that parametric cluster size inference should only be used with high cluster-forming thresholds and smoothness. We would advocate the use of nonparametric methods in other cases. ►RFT nonstationary cluster size test on VBM is found to be invalid based on empirical null studies. ►Invalid inferences (inflated false positive risk) were found with 3 voxel smoothing. ►With 6 voxel smoothing, invalid inferences were found with 6 voxel smoothing and alpha=0.01 cluster-forming threshold. ►RFT nonstationary test only found to be valid with 6 voxel smoothing with alpha=0.001 cluster-forming threshold. ►Equivalent Gaussian data simulations produced valid inferences, suggesting VBM data violates RFT assumptions and motivates the use of nonparametric permutation methods. Voxel-wise statistical inference is commonly used to identify significant experimental effects or group differences in both functional and structural studies of the living brain. Tests based on the size of spatially extended clusters of contiguous suprathreshold voxels are also widely used due to their typically increased statistical power. In "imaging genetics", such tests are used to identify regions of the brain that are associated with genetic variation. However, concerns have been raised about the adequate control of rejection rates in studies of this type. A previous study tested the effect of a set of 'null' SNPs on brain structure and function, and found that false positive rates were well-controlled. However, no similar analysis of false positive rates in an imaging genetic study using cluster size inference has yet been undertaken. We measured false positive rates in an investigation of the effect of 700 pre-selected null SNPs on grey matter volume using voxel-based morphometry (VBM). As VBM data exhibit spatially-varying smoothness, we used both non-stationary and stationary cluster size tests in our analysis. Image and genotype data on 181 subjects with mild cognitive impairment were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). At a nominal significance level of 5%, false positive rates were found to be well-controlled (3.9-5.6%), using a relatively high cluster-forming threshold, alpha sub(c) = 0.001, on images smoothed with a 12 mm Gaussian kernel. Tests were however anticonservative at lower cluster-forming thresholds ( alpha sub(c) = 0.01, 0.05), and for images smoothed using a 6 mm Gaussian kernel. Here false positive rates ranged from 9.8 to 67.6%. In a further analysis, false positive rates using simulated data were observed to be well-controlled across a wide range of conditions. While motivated by imaging genetics, our findings apply to any VBM study, and suggest that parametric cluster size inference should only be used with high cluster-forming thresholds and smoothness. We would advocate the use of nonparametric methods in other cases. Voxel-wise statistical inference is commonly used to identify significant experimental effects or group differences in both functional and structural studies of the living brain. Tests based on the size of spatially extended clusters of contiguous suprathreshold voxels are also widely used due to their typically increased statistical power. In "imaging genetics", such tests are used to identify regions of the brain that are associated with genetic variation. However, concerns have been raised about the adequate control of rejection rates in studies of this type. A previous study tested the effect of a set of 'null' SNPs on brain structure and function, and found that false positive rates were well-controlled. However, no similar analysis of false positive rates in an imaging genetic study using cluster size inference has yet been undertaken. We measured false positive rates in an investigation of the effect of 700 pre-selected null SNPs on grey matter volume using voxel-based morphometry (VBM). As VBM data exhibit spatially-varying smoothness, we used both non-stationary and stationary cluster size tests in our analysis. Image and genotype data on 181 subjects with mild cognitive impairment were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). At a nominal significance level of 5%, false positive rates were found to be well-controlled (3.9-5.6%), using a relatively high cluster-forming threshold, α(c)=0.001, on images smoothed with a 12 mm Gaussian kernel. Tests were however anticonservative at lower cluster-forming thresholds (α(c)=0.01, 0.05), and for images smoothed using a 6mm Gaussian kernel. Here false positive rates ranged from 9.8 to 67.6%. In a further analysis, false positive rates using simulated data were observed to be well-controlled across a wide range of conditions. While motivated by imaging genetics, our findings apply to any VBM study, and suggest that parametric cluster size inference should only be used with high cluster-forming thresholds and smoothness. We would advocate the use of nonparametric methods in other cases.Voxel-wise statistical inference is commonly used to identify significant experimental effects or group differences in both functional and structural studies of the living brain. Tests based on the size of spatially extended clusters of contiguous suprathreshold voxels are also widely used due to their typically increased statistical power. In "imaging genetics", such tests are used to identify regions of the brain that are associated with genetic variation. However, concerns have been raised about the adequate control of rejection rates in studies of this type. A previous study tested the effect of a set of 'null' SNPs on brain structure and function, and found that false positive rates were well-controlled. However, no similar analysis of false positive rates in an imaging genetic study using cluster size inference has yet been undertaken. We measured false positive rates in an investigation of the effect of 700 pre-selected null SNPs on grey matter volume using voxel-based morphometry (VBM). As VBM data exhibit spatially-varying smoothness, we used both non-stationary and stationary cluster size tests in our analysis. Image and genotype data on 181 subjects with mild cognitive impairment were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). At a nominal significance level of 5%, false positive rates were found to be well-controlled (3.9-5.6%), using a relatively high cluster-forming threshold, α(c)=0.001, on images smoothed with a 12 mm Gaussian kernel. Tests were however anticonservative at lower cluster-forming thresholds (α(c)=0.01, 0.05), and for images smoothed using a 6mm Gaussian kernel. Here false positive rates ranged from 9.8 to 67.6%. In a further analysis, false positive rates using simulated data were observed to be well-controlled across a wide range of conditions. While motivated by imaging genetics, our findings apply to any VBM study, and suggest that parametric cluster size inference should only be used with high cluster-forming thresholds and smoothness. We would advocate the use of nonparametric methods in other cases. Voxel-wise statistical inference is commonly used to identify significant experimental effects or group differences in both functional and structural studies of the living brain. Tests based on the size of spatially extended clusters of contiguous suprathreshold voxels are also widely used due to their typically increased statistical power. In "imaging genetics", such tests are used to identify regions of the brain that are associated with genetic variation. However, concerns have been raised about the adequate control of rejection rates in studies of this type. A previous study tested the effect of a set of 'null' SNPs on brain structure and function, and found that false positive rates were well-controlled. However, no similar analysis of false positive rates in an imaging genetic study using cluster size inference has yet been undertaken. We measured false positive rates in an investigation of the effect of 700 pre-selected null SNPs on grey matter volume using voxel-based morphometry (VBM). As VBM data exhibit spatially-varying smoothness, we used both non-stationary and stationary cluster size tests in our analysis. Image and genotype data on 181 subjects with mild cognitive impairment were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). At a nominal significance level of 5%, false positive rates were found to be well-controlled (3.9-5.6%), using a relatively high cluster-forming threshold, α(c)=0.001, on images smoothed with a 12 mm Gaussian kernel. Tests were however anticonservative at lower cluster-forming thresholds (α(c)=0.01, 0.05), and for images smoothed using a 6mm Gaussian kernel. Here false positive rates ranged from 9.8 to 67.6%. In a further analysis, false positive rates using simulated data were observed to be well-controlled across a wide range of conditions. While motivated by imaging genetics, our findings apply to any VBM study, and suggest that parametric cluster size inference should only be used with high cluster-forming thresholds and smoothness. We would advocate the use of nonparametric methods in other cases. Voxel-wise statistical inference is commonly used to identify significant experimental effects or group differences in both functional and structural studies of the living brain. Tests based on the size of spatially extended clusters of contiguous suprathreshold voxels are also widely used due to their typically increased statistical power. In “imaging genetics”, such tests are used to identify regions of the brain that are associated with genetic variation. However, concerns have been raised about the adequate control of rejection rates in studies of this type. A previous study tested the effect of a set of ‘null’ SNPs on brain structure and function, and found that false positive rates were well-controlled. However, no similar analysis of false positive rates in an imaging genetic study using cluster size inference has yet been undertaken.We measured false positive rates in an investigation of the effect of 700 pre-selected null SNPs on grey matter volume using voxel-based morphometry (VBM). As VBM data exhibit spatially-varying smoothness, we used both non-stationary and stationary cluster size tests in our analysis. Image and genotype data on 181 subjects with mild cognitive impairment were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). At a nominal significance level of 5%, false positive rates were found to be well-controlled (3.9–5.6%), using a relatively high cluster-forming threshold, αc=0.001, on images smoothed with a 12mm Gaussian kernel. Tests were however anticonservative at lower cluster-forming thresholds (αc=0.01, 0.05), and for images smoothed using a 6mm Gaussian kernel. Here false positive rates ranged from 9.8 to 67.6%. In a further analysis, false positive rates using simulated data were observed to be well-controlled across a wide range of conditions.While motivated by imaging genetics, our findings apply to any VBM study, and suggest that parametric cluster size inference should only be used with high cluster-forming thresholds and smoothness. We would advocate the use of nonparametric methods in other cases. Voxel-wise statistical inference is commonly used to identify significant experimental effects or group differences in both functional and structural studies of the living brain. Tests based on the size of spatially extended clusters of contiguous suprathreshold voxels are also widely used due to their typically increased statistical power. In “imaging genetics”, such tests are used to identify regions of the brain that are associated with genetic variation. However, concerns have been raised about the adequate control of rejection rates in studies of this type. A previous study tested the effect of a set of ‘null’ SNPs on brain structure and function, and found that false positive rates were well-controlled. However, no similar analysis of false positive rates in an imaging genetic study using cluster size inference has yet been undertaken. We measured false positive rates in an investigation of the effect of 700 pre-selected null SNPs on grey matter volume using voxel-based morphometry (VBM). As VBM data exhibit spatially-varying smoothness, we used both non-stationary and stationary cluster size tests in our analysis. Image and genotype data on 181 subjects with mild cognitive impairment were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). At a nominal significance level of 5%, false positive rates were found to be well-controlled (3.9–5.6%), using a relatively high cluster-forming threshold, α c = 0.001, on images smoothed with a 12 mm Gaussian kernel. Tests were however anticonservative at lower cluster-forming thresholds ( α c = 0.01, 0.05), and for images smoothed using a 6 mm Gaussian kernel. Here false positive rates ranged from 9.8 to 67.6%. In a further analysis, false positive rates using simulated data were observed to be well-controlled across a wide range of conditions. While motivated by imaging genetics, our findings apply to any VBM study, and suggest that parametric cluster size inference should only be used with high cluster-forming thresholds and smoothness. We would advocate the use of nonparametric methods in other cases. ►RFT nonstationary cluster size test on VBM is found to be invalid based on empirical null studies. ►Invalid inferences (inflated false positive risk) were found with 3 voxel smoothing. ►With 6 voxel smoothing, invalid inferences were found with 6 voxel smoothing and alpha=0.01 cluster-forming threshold. ►RFT nonstationary test only found to be valid with 6 voxel smoothing with alpha=0.001 cluster-forming threshold. ►Equivalent Gaussian data simulations produced valid inferences, suggesting VBM data violates RFT assumptions and motivates the use of nonparametric permutation methods.
Author	Montana, Giovanni Silver, Matt Nichols, Thomas E.
AuthorAffiliation	b Department of Statistics & Warwick Manufacturing Group, University of Warwick, UK a Statistics Section, Department of Mathematics, Imperial College London, UK
AuthorAffiliation_xml	– name: a Statistics Section, Department of Mathematics, Imperial College London, UK – name: b Department of Statistics & Warwick Manufacturing Group, University of Warwick, UK
Author_xml	– sequence: 1 givenname: Matt surname: Silver fullname: Silver, Matt organization: Statistics Section, Department of Mathematics, Imperial College London, UK – sequence: 2 givenname: Giovanni surname: Montana fullname: Montana, Giovanni organization: Statistics Section, Department of Mathematics, Imperial College London, UK – sequence: 3 givenname: Thomas E. surname: Nichols fullname: Nichols, Thomas E. email: t.e.nichols@warwick.ac.uk organization: Department of Statistics & Warwick Manufacturing Group, University of Warwick, UK
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/20849959$$D View this record in MEDLINE/PubMed
BookMark	eNqNkktv1DAURiNURB_wF1AkFrDJcB3Hjr1BlIoCohIbWFuOczP1kLEH2xkx_x5HU6bQBczKr-Mj-97vvDhx3mFRlAQWBAh_vVo4nIK3a73ERQ15G8QCGvmoOCMgWSVZW5_Mc0YrQYg8Lc5jXAGAJI14UpzWIBopmTwrPl_rMWK58dEmu8VYWlce3NYtyyU6TNbEcorzcut_4lh1OmJfrn3Y3Po1prAre5300-LxMNue3Y0Xxbfr91-vPlY3Xz58urq8qQyvRao4GwwT2nRENKKT0AgKAgeghuiO8pZrImkrARrS9pRAX9O2oSIzAINgSC-KN3vvZurW2Bt0KehRbUJ-c9gpr636-8TZW7X0W0WBU0p5Fry8EwT_Y8KY1NpGg-OoHfopKsFoI1re0P-TRPCGiRoy-eqfJOEtqQVnZEZfPEBXfgoul0wRlvtScxBtpp7_-cvD9373LgNiD5jgYww4HBACao6JWqn7mKg5JgqEyjG5r-DhqrFJJ-vnetnxGMG7vQBzn7cWg4rGojPY24Amqd7bYyRvH0jMaJ01evyOu-MUvwCIPfVd
CitedBy_id	crossref_primary_10_1016_j_neuropsychologia_2013_12_006 crossref_primary_10_3390_nu12010127 crossref_primary_10_1002_hbm_25153 crossref_primary_10_1016_j_mri_2018_01_004 crossref_primary_10_1016_j_neuroimage_2013_01_058 crossref_primary_10_3389_fnhum_2015_00681 crossref_primary_10_1371_journal_pone_0023175 crossref_primary_10_1111_biom_13114 crossref_primary_10_3389_fnhum_2020_492990 crossref_primary_10_1016_j_neuroimage_2012_03_069 crossref_primary_10_1016_j_parkreldis_2023_105457 crossref_primary_10_3389_fninf_2014_00072 crossref_primary_10_1002_hbm_23369 crossref_primary_10_1093_scan_nsu033 crossref_primary_10_1016_j_jalz_2014_10_012 crossref_primary_10_1016_j_bbr_2019_112145 crossref_primary_10_1016_j_neuroimage_2012_04_014 crossref_primary_10_1038_s41598_022_11724_5 crossref_primary_10_1371_journal_pone_0122914 crossref_primary_10_1016_j_tics_2022_12_011 crossref_primary_10_1371_journal_pone_0194422 crossref_primary_10_1007_s11682_013_9262_z crossref_primary_10_1007_s00429_013_0677_5 crossref_primary_10_3389_fpsyt_2024_1491042 crossref_primary_10_1016_j_neuroimage_2015_02_002 crossref_primary_10_1016_j_biopsych_2012_05_022 crossref_primary_10_1007_s00787_023_02231_7 crossref_primary_10_1016_j_neuroimage_2012_08_037 crossref_primary_10_1093_scan_nsu144 crossref_primary_10_1002_hbm_23638 crossref_primary_10_1093_cercor_bhw020 crossref_primary_10_1093_cercor_bhw141 crossref_primary_10_1016_j_neuroimage_2013_04_004 crossref_primary_10_1007_s11682_017_9690_2 crossref_primary_10_1016_j_neuroimage_2019_03_008 crossref_primary_10_3389_fnhum_2015_00103 crossref_primary_10_1038_s42003_021_02435_0 crossref_primary_10_1016_j_neuroimage_2011_09_064 crossref_primary_10_1016_j_neuroimage_2015_05_094 crossref_primary_10_1371_journal_pone_0122666 crossref_primary_10_1093_cercor_bhs061 crossref_primary_10_1016_j_jalz_2014_11_001 crossref_primary_10_1016_j_neuroimage_2012_07_012 crossref_primary_10_1016_j_nicl_2019_101989 crossref_primary_10_1146_annurev_linguist_030514_124819 crossref_primary_10_3389_fpsyg_2017_00443 crossref_primary_10_1111_biom_13460 crossref_primary_10_1093_schbul_sbv180 crossref_primary_10_1177_0271678X20974961 crossref_primary_10_1016_j_cortex_2022_09_014 crossref_primary_10_1002_hbm_24078 crossref_primary_10_1093_biostatistics_kxx051 crossref_primary_10_1111_ejn_13801 crossref_primary_10_1038_srep31231 crossref_primary_10_1002_jnr_23901 crossref_primary_10_1007_s00429_019_01969_8 crossref_primary_10_1016_j_eplepsyres_2012_11_008 crossref_primary_10_1002_hbm_24905 crossref_primary_10_1016_j_neuroimage_2017_12_072 crossref_primary_10_1016_j_neuroscience_2017_08_004 crossref_primary_10_1093_cercor_bhw008 crossref_primary_10_1007_s11682_017_9713_z crossref_primary_10_1016_j_dcn_2015_06_001 crossref_primary_10_1016_j_cobeha_2018_12_009 crossref_primary_10_1016_j_neuroimage_2014_11_060 crossref_primary_10_1016_j_neubiorev_2015_02_008 crossref_primary_10_1093_cercor_bhy189 crossref_primary_10_2463_mrms_rev_2021_0096 crossref_primary_10_1016_j_neuroimage_2019_116158 crossref_primary_10_3389_fnins_2015_00018 crossref_primary_10_1002_hbm_23453 crossref_primary_10_3389_fninf_2014_00029 crossref_primary_10_1007_s11682_020_00338_y crossref_primary_10_1002_hbm_22638 crossref_primary_10_1016_j_bbr_2017_11_004 crossref_primary_10_1093_cercor_bhaa119 crossref_primary_10_1007_s00221_019_05517_y crossref_primary_10_1017_S1366728916001206 crossref_primary_10_1002_erv_2346 crossref_primary_10_1073_pnas_1602809113 crossref_primary_10_1016_j_neuroimage_2013_12_058 crossref_primary_10_1038_s41598_017_10104_8 crossref_primary_10_1016_j_neuropsychologia_2015_11_012 crossref_primary_10_1016_j_neuroimage_2011_10_063 crossref_primary_10_1007_s00429_014_0857_y crossref_primary_10_1007_s11682_015_9396_2 crossref_primary_10_1016_j_smrv_2015_03_001 crossref_primary_10_1016_j_neuroimage_2014_11_004 crossref_primary_10_1093_cercor_bhaa127 crossref_primary_10_1002_wics_1457 crossref_primary_10_1016_j_neulet_2017_05_066 crossref_primary_10_3389_fnins_2021_708387 crossref_primary_10_1002_hbm_25374 crossref_primary_10_1016_j_jneumeth_2018_06_017 crossref_primary_10_1016_j_pscychresns_2015_01_008 crossref_primary_10_1016_j_neuroimage_2013_01_034 crossref_primary_10_1016_j_pscychresns_2016_01_006 crossref_primary_10_3389_fpsyg_2019_00155 crossref_primary_10_3389_fneur_2016_00147 crossref_primary_10_1080_01621459_2018_1448826 crossref_primary_10_1523_JNEUROSCI_0598_14_2015 crossref_primary_10_1007_s00429_012_0444_z crossref_primary_10_3758_s13415_013_0165_7 crossref_primary_10_1016_j_cortex_2019_12_022 crossref_primary_10_1073_pnas_1602413113 crossref_primary_10_1016_j_neuroimage_2017_02_079 crossref_primary_10_1371_journal_pone_0101372 crossref_primary_10_1038_s41597_022_01338_x crossref_primary_10_1002_gepi_21854 crossref_primary_10_1002_hbm_22890 crossref_primary_10_1016_j_jalz_2013_05_1769 crossref_primary_10_1111_jon_12461 crossref_primary_10_1016_j_jalz_2011_09_172 crossref_primary_10_1093_cercor_bhw331 crossref_primary_10_1016_j_neuroimage_2011_05_055 crossref_primary_10_1038_s42003_021_01974_w crossref_primary_10_1016_j_neuropsychologia_2017_04_004 crossref_primary_10_1556_2006_2020_00066 crossref_primary_10_1093_scan_nsu041 crossref_primary_10_1016_j_pscychresns_2016_05_004 crossref_primary_10_1155_2016_5940634 crossref_primary_10_3389_fneur_2024_1267349
Cites_doi	10.1002/(SICI)1097-0193(1996)4:1<58::AID-HBM4>3.0.CO;2-O 10.1002/(SICI)1097-0193(1996)4:1<74::AID-HBM5>3.0.CO;2-M 10.1002/hbm.20401 10.1016/j.neuroimage.2003.08.003 10.1038/mp.2008.127 10.1016/j.neuroimage.2010.01.056 10.1016/j.neuroimage.2009.09.006 10.1016/j.neuroimage.2010.01.042 10.1016/j.neuroimage.2006.11.037 10.1016/j.biopsych.2005.11.005 10.1006/nimg.2002.1153 10.1016/j.neuroimage.2008.10.003 10.1016/j.neuroimage.2009.11.041 10.1214/009053607000000406 10.1006/nimg.1996.0074 10.1016/j.neulet.2008.11.035 10.1006/nimg.1996.0248 10.1002/(SICI)1097-0193(1999)8:2/3<98::AID-HBM5>3.0.CO;2-F 10.1073/pnas.0901866106 10.1016/j.neuroimage.2007.07.007 10.1038/nrn1993 10.1191/0962280203sm341ra 10.1016/j.neuroimage.2005.04.045 10.1006/nimg.2000.0582 10.1016/j.neuroimage.2008.11.006 10.1016/j.neuroimage.2008.05.021 10.1016/j.neuroimage.2009.11.034 10.1002/hbm.460020402 10.1016/j.neuroimage.2004.01.041 10.1080/10673220600642945 10.1016/j.neuroimage.2007.11.058 10.1016/j.neuroimage.2008.10.057 10.1093/schbul/sbn155 10.1016/j.neuroimage.2009.01.009 10.1002/jmri.21049
ContentType	Journal Article
Copyright	2010 Elsevier Inc. Copyright © 2010 Elsevier Inc. All rights reserved. 2010. Elsevier Inc. 2011 Elsevier Inc. 2010 Elsevier Inc.
Copyright_xml	– notice: 2010 Elsevier Inc. – notice: Copyright © 2010 Elsevier Inc. All rights reserved. – notice: 2010. Elsevier Inc. – notice: 2011 Elsevier Inc. 2010 Elsevier Inc.
CorporateAuthor	the Alzheimer's Disease Neuroimaging Initiative Alzheimer's Disease Neuroimaging Initiative
CorporateAuthor_xml	– name: the Alzheimer's Disease Neuroimaging Initiative – name: Alzheimer's Disease Neuroimaging Initiative
DBID	6I. AAFTH AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7TK 7X7 7XB 88E 88G 8AO 8FD 8FE 8FH 8FI 8FJ 8FK ABUWG AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ K9. LK8 M0S M1P M2M M7P P64 PHGZM PHGZT PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS PSYQQ Q9U RC3 7U5 L7M 7X8 7QO 5PM
DOI	10.1016/j.neuroimage.2010.08.049
DatabaseName	ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Neurosciences Abstracts Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Psychology Database (Alumni) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Natural Science Collection ProQuest Hospital Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection ProQuest Central Natural Science Collection ProQuest One Community College ProQuest Central Korea Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) Biological Sciences ProQuest Health & Medical Collection Medical Database Psychology Database Biological Science Database Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic (New) ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China ProQuest One Psychology ProQuest Central Basic Genetics Abstracts Solid State and Superconductivity Abstracts Advanced Technologies Database with Aerospace MEDLINE - Academic Biotechnology Research Abstracts PubMed Central (Full Participant titles)
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) ProQuest One Psychology ProQuest Central Student Technology Research Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Health & Medical Research Collection Genetics Abstracts Health Research Premium Collection Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest Biological Science Collection ProQuest Central Basic ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Psychology Journals (Alumni) Biological Science Database ProQuest SciTech Collection Neurosciences Abstracts ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts ProQuest Health & Medical Complete ProQuest Medical Library ProQuest Psychology Journals ProQuest One Academic UKI Edition Engineering Research Database ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) Advanced Technologies Database with Aerospace Solid State and Superconductivity Abstracts MEDLINE - Academic Biotechnology Research Abstracts
DatabaseTitleList	Genetics Abstracts MEDLINE - Academic MEDLINE ProQuest One Psychology Technology Research Database
Database_xml	– sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 3 dbid: BENPR name: ProQuest Central url: http://www.proquest.com/pqcentral?accountid=15518 sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Medicine Statistics
EISSN	1095-9572
EndPage	1000
ExternalDocumentID	PMC3063336 3388728261 20849959 10_1016_j_neuroimage_2010_08_049 S1053811910011316
Genre	Research Support, Non-U.S. Gov't Journal Article Research Support, N.I.H., Extramural
GrantInformation_xml	– fundername: Wellcome Trust – fundername: Medical Research Council grantid: G0900908 – fundername: NIA NIH HHS grantid: U24 AG021886 – fundername: NIA NIH HHS grantid: U19 AG010483 – fundername: NIA NIH HHS grantid: P30 AG010129 – fundername: NIA NIH HHS grantid: U01 AG024904 – fundername: NIA NIH HHS grantid: K01 AG030514
GroupedDBID	--- --K --M .~1 0R~ 123 1B1 1RT 1~. 1~5 4.4 457 4G. 5RE 5VS 7-5 71M 7X7 88E 8AO 8FE 8FH 8FI 8FJ 8P~ 9JM AABNK AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AATTM AAXKI AAXLA AAXUO AAYWO ABBQC ABCQJ ABFNM ABFRF ABIVO ABJNI ABMAC ABUWG ABXDB ACDAQ ACGFO ACGFS ACIEU ACPRK ACRLP ACRPL ACVFH ADBBV ADCNI ADEZE ADFRT ADMUD ADNMO AEBSH AEFWE AEIPS AEKER AENEX AEUPX AFJKZ AFKRA AFPUW AFTJW AFXIZ AGCQF AGUBO AGWIK AGYEJ AHHHB AHMBA AIEXJ AIIUN AIKHN AITUG AJRQY AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU ANZVX AXJTR AZQEC BBNVY BENPR BHPHI BKOJK BLXMC BNPGV BPHCQ BVXVI CCPQU CS3 DM4 DU5 DWQXO EBS EFBJH EFKBS EJD EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN FYUFA G-Q GBLVA GNUQQ GROUPED_DOAJ HCIFZ HMCUK HZ~ IHE J1W KOM LG5 LK8 LX8 M1P M29 M2M M2V M41 M7P MO0 MOBAO N9A O-L O9- OAUVE OVD OZT P-8 P-9 P2P PC. PHGZM PHGZT PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PSYQQ PUEGO Q38 ROL RPZ SAE SCC SDF SDG SDP SES SSH SSN SSZ T5K TEORI UKHRP UV1 YK3 ZU3 ~G- 3V. 6I. AACTN AADPK AAFTH AAIAV ABLVK ABYKQ AFKWA AJBFU AJOXV AMFUW C45 EFLBG HMQ LCYCR RIG SNS ZA5 .1- .FO 29N 53G AAFWJ AAQXK AAYXX ABMZM ADFGL ADVLN ADXHL AFPKN AFRHN AGHFR AGQPQ AGRNS AIGII AJUYK AKRLJ ALIPV APXCP ASPBG AVWKF AZFZN CAG CITATION COF FEDTE FGOYB G-2 HDW HEI HMK HMO HVGLF OK1 R2- SEW WUQ XPP Z5R ZMT CGR CUY CVF ECM EIF NPM 7TK 7XB 8FD 8FK FR3 K9. P64 PKEHL PQEST PQUKI PRINS Q9U RC3 7U5 L7M 7X8 ACLOT ~HD 7QO 5PM
ID	FETCH-LOGICAL-c628t-65fc58acb1848b9048308ef03c1ab3676a1937900417d310d23743808e00f85e3
IEDL.DBID	7X7
ISSN	1053-8119 1095-9572
IngestDate	Thu Aug 21 14:32:22 EDT 2025 Sat Sep 27 18:00:09 EDT 2025 Sun Sep 28 07:29:15 EDT 2025 Fri Sep 05 06:16:44 EDT 2025 Wed Aug 13 06:14:21 EDT 2025 Mon Jul 21 06:01:19 EDT 2025 Thu Apr 24 23:09:59 EDT 2025 Tue Jul 01 02:14:39 EDT 2025 Fri Feb 23 02:39:29 EST 2024 Tue Aug 26 16:36:50 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	2
Language	English
License	http://creativecommons.org/licenses/by/3.0 https://www.elsevier.com/tdm/userlicense/1.0 Copyright © 2010 Elsevier Inc. All rights reserved. Open Access under CC BY 3.0 license
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c628t-65fc58acb1848b9048308ef03c1ab3676a1937900417d310d23743808e00f85e3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (www.loni.ucla.edu/ADNI). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete list of ADNI investigators is available at http://www.loni.ucla.edu/ADNI/Collaboration/ADNI_Manuscript_Citations.pdf.
OpenAccessLink	https://www.sciencedirect.com/science/article/pii/S1053811910011316
PMID	20849959
PQID	1549926087
PQPubID	2031077
PageCount	9
ParticipantIDs	pubmedcentral_primary_oai_pubmedcentral_nih_gov_3063336 proquest_miscellaneous_853487643 proquest_miscellaneous_818645820 proquest_miscellaneous_1671286510 proquest_journals_1549926087 pubmed_primary_20849959 crossref_primary_10_1016_j_neuroimage_2010_08_049 crossref_citationtrail_10_1016_j_neuroimage_2010_08_049 elsevier_sciencedirect_doi_10_1016_j_neuroimage_2010_08_049 elsevier_clinicalkey_doi_10_1016_j_neuroimage_2010_08_049
ProviderPackageCode	CITATION AAYXX
PublicationCentury	2000
PublicationDate	2011-01-15
PublicationDateYYYYMMDD	2011-01-15
PublicationDate_xml	– month: 01 year: 2011 text: 2011-01-15 day: 15
PublicationDecade	2010
PublicationPlace	United States
PublicationPlace_xml	– name: United States – name: Amsterdam
PublicationTitle	NeuroImage (Orlando, Fla.)
PublicationTitleAlternate	Neuroimage
PublicationYear	2011
Publisher	Elsevier Inc Elsevier Limited Academic Press
Publisher_xml	– name: Elsevier Inc – name: Elsevier Limited – name: Academic Press
References	Friston, Holmes, Poline, Price, Frith (bb0050) 1996; 4 Nichols, Hayasaka (bb0105) 2003; 12 Worsley, Marrett, Neelin, Vandal, Friston, Evans (bb0175) 1996; 4 Hayasaka, Phan, Liberzon, Worsley, Nichols (bb0075) 2004; 22 Bergouignan, Chupin, Czechowska, Kinkingnéhun, Lemogne, Bastard, Lepage, Garnero, Colliot, Fossati (bb0015) 2009; 45 Frackowiak, Friston, Frith, Dolan, Price, Zeki, Ashburner, Penny (bb0040) 2003 Ueda, Fujiwara, Miyata, Hirao, Saze, Kawada, Fujimoto, Tanaka, Sawamoto, Fukuyama, Murai (bb0160) 2010; 49 Viviani, Beschoner, Ehrhard, Schmitz, Thöne (bb0165) 2007; 35 Folley, Astur, Jagannathan, Calhoun, Pearlson (bb0035) 2010; 49 Glahn, Thompson, Blangero (bb0055) 2007; 28 Roffman, Weiss, Goff, Rauch, Weinberger (bb0130) 2006; 14 Hariri, Drabant, Weinberger (bb0065) 2006; 59 Hayasaka, Nichols (bb0070) 2003; 20 Ashburner, Friston (bb0010) 2000; 11 Joyner, J., C. R., Bloss, Bakken, Rimol, Melle, Agartz, Djurovic, Topol, Schork, Andreassen, Dale (bb0085) 2009; 106 Meyer-Lindenberg, Nicodemus, Egan, Callicott, Mattay, Weinberger (bb0095) 2008; 40 Pievani, Rasser, Galluzzi, Benussi, Ghidoni, Sabattoli, Bonetti, Binetti, Thompson, Frisoni (bb0110) 2009; 45 Poline, Worsley, Evans, Friston (bb0115) 1997; 5 Moorhead, Job, Spencer, Whalley, Johnstone, Lawrie (bb0100) 2005; 28 Filippini, Rao, Wetten, Gibson, Borrie, Guzman, Kertesz, Loy-English, Williams, Nichols, Whitcher, Matthews (bb0030) 2009; 44 Worsley (bb0170) 2002; 15 Ashburner (bb0005) 2007; 38 Meyer-Lindenberg, Weinberger (bb0090) 2006; 7 Salmond, Ashburner, Vargha-Khadem, A (bb0140) 2002; 1030 Calhoun, Liu, AdalI (bb0020) 2009; 45 Chumbley, Friston (bb0025) 2009; 44 Rosen, Alcantar, Rothlind, Sturm, Kramer, Weiner, Miller (bb0135) 2010; 49 Schwartz, Mitchell, Lahna, Luber, Huckans, Mitchell, Hoffman (bb0145) 2010; 50 Worsley, Marrett, Neelin, Evans (bb0180) 1996; 4 Worsley, Andermann, Koulis, MacDonald, Evans (bb0185) 1999; 8 Hardoon, Ettinger, Mourão-Miranda, Antonova, Collier, Kumari, Williams, Brammer (bb0060) 2009; 450 Jack, Bernstein, Fox, Thompson, Alexander, Harvey, Borowski, Britson, Whitwell, Ward, Dale, Felmlee, Gunter, Hill, Killiany, Schuff, Fox-Bosetti, Lin, Studholme, DeCarli, Krueger, Ward, Metzger, Scott, Mallozzi, Blezek, Levy, Debbins, Fleisher, Albert, Green, Bartzokis, Glover, Mugler, Weiner, Study (bb0080) 2008; 27 Potkin, Turner, Guffanti, Lakatos, Fallon, Nguyen, Mathalon, Ford, Lauriello, Macciardi, FBIRN (bb0125) 2009; 35 Potkin, Turner, Fallon, Lakatos, Keator, Guffanti, Macciardi (bb0120) 2009; 14 Friston, Holmes, Worsley, Poline, Frith, Frackowiak (bb0045) 1995; 2 Taylor, Worsley (bb0155) 2008; 36 Shen, Kim, Risacher, Nho, Swaminathan, West, Foroud, Pankratz, Moore, Sloan, Huentelman, Craig, DeChairo, Potkin, Jack, Weiner, Saykin (bb0150) 2010; 53 Viviani (10.1016/j.neuroimage.2010.08.049_bb0165) 2007; 35 Ashburner (10.1016/j.neuroimage.2010.08.049_bb0005) 2007; 38 Poline (10.1016/j.neuroimage.2010.08.049_bb0115) 1997; 5 Moorhead (10.1016/j.neuroimage.2010.08.049_bb0100) 2005; 28 Pievani (10.1016/j.neuroimage.2010.08.049_bb0110) 2009; 45 Potkin (10.1016/j.neuroimage.2010.08.049_bb0125) 2009; 35 Glahn (10.1016/j.neuroimage.2010.08.049_bb0055) 2007; 28 Bergouignan (10.1016/j.neuroimage.2010.08.049_bb0015) 2009; 45 Hardoon (10.1016/j.neuroimage.2010.08.049_bb0060) 2009; 450 Hariri (10.1016/j.neuroimage.2010.08.049_bb0065) 2006; 59 Joyner (10.1016/j.neuroimage.2010.08.049_bb0085) 2009; 106 Taylor (10.1016/j.neuroimage.2010.08.049_bb0155) 2008; 36 Schwartz (10.1016/j.neuroimage.2010.08.049_bb0145) 2010; 50 Chumbley (10.1016/j.neuroimage.2010.08.049_bb0025) 2009; 44 Folley (10.1016/j.neuroimage.2010.08.049_bb0035) 2010; 49 Nichols (10.1016/j.neuroimage.2010.08.049_bb0105) 2003; 12 Ashburner (10.1016/j.neuroimage.2010.08.049_bb0010) 2000; 11 Filippini (10.1016/j.neuroimage.2010.08.049_bb0030) 2009; 44 Worsley (10.1016/j.neuroimage.2010.08.049_bb0170) 2002; 15 Ueda (10.1016/j.neuroimage.2010.08.049_bb0160) 2010; 49 Calhoun (10.1016/j.neuroimage.2010.08.049_bb0020) 2009; 45 Worsley (10.1016/j.neuroimage.2010.08.049_bb0180) 1996; 4 Rosen (10.1016/j.neuroimage.2010.08.049_bb0135) 2010; 49 Potkin (10.1016/j.neuroimage.2010.08.049_bb0120) 2009; 14 Roffman (10.1016/j.neuroimage.2010.08.049_bb0130) 2006; 14 Hayasaka (10.1016/j.neuroimage.2010.08.049_bb0075) 2004; 22 Frackowiak (10.1016/j.neuroimage.2010.08.049_bb0040) 2003 Salmond (10.1016/j.neuroimage.2010.08.049_bb0140) 2002; 1030 Hayasaka (10.1016/j.neuroimage.2010.08.049_bb0070) 2003; 20 Friston (10.1016/j.neuroimage.2010.08.049_bb0050) 1996; 4 Worsley (10.1016/j.neuroimage.2010.08.049_bb0185) 1999; 8 Friston (10.1016/j.neuroimage.2010.08.049_bb0045) 1995; 2 Meyer-Lindenberg (10.1016/j.neuroimage.2010.08.049_bb0090) 2006; 7 Meyer-Lindenberg (10.1016/j.neuroimage.2010.08.049_bb0095) 2008; 40 Shen (10.1016/j.neuroimage.2010.08.049_bb0150) 2010; 53 Jack (10.1016/j.neuroimage.2010.08.049_bb0080) 2008; 27 Worsley (10.1016/j.neuroimage.2010.08.049_bb0175) 1996; 4
References_xml	– volume: 50 start-page: 1392 year: 2010 end-page: 1401 ident: bb0145 article-title: Global and local morphometric differences in recently abstinent methamphetamine-dependent individuals publication-title: Neuroimage – volume: 106 start-page: 15483 year: 2009 end-page: 15488 ident: bb0085 article-title: A common MECP2 haplotype associates with reduced cortical surface area in humans in two independent populations publication-title: Proc. Natl. Acad. Sci. – volume: 7 start-page: 818 year: 2006 end-page: 827 ident: bb0090 article-title: Intermediate phenotypes and genetic mechanisms of psychiatric disorders publication-title: Nat. Rev. Neurosci. – volume: 15 start-page: S346 year: 2002 ident: bb0170 article-title: Non-stationary FWHM and its effect on statistical inference of fMRI data publication-title: Neuroimage – volume: 36 start-page: 1 year: 2008 end-page: 27 ident: bb0155 article-title: Random fields of multivariate test statistics, with applications to shape analysis publication-title: Ann. Statist. – volume: 8 start-page: 98 year: 1999 end-page: 101 ident: bb0185 article-title: Detecting changes in nonisotropic images publication-title: Hum. Brain Mapp. – volume: 38 start-page: 95 year: 2007 end-page: 113 ident: bb0005 article-title: A fast diffeomorphic image registration algorithm publication-title: Neuroimage – volume: 11 start-page: 805 year: 2000 end-page: 821 ident: bb0010 article-title: Voxel-based morphometry — the methods publication-title: Neuroimage – volume: 14 start-page: 416 year: 2009 end-page: 428 ident: bb0120 article-title: Gene discovery through imaging genetics: identification of two novel genes associated with schizophrenia publication-title: Mol. Psychiatry – volume: 35 start-page: 121 year: 2007 end-page: 130 ident: bb0165 article-title: Non-normality and transformations of random fields, with an application to voxel-based morphometry publication-title: Neuroimage – volume: 1030 start-page: 1027 year: 2002 end-page: 1030 ident: bb0140 article-title: Distributional assumptions in voxel-based morphometry publication-title: Neuroimage – volume: 27 start-page: 685 year: 2008 end-page: 691 ident: bb0080 article-title: The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods publication-title: J. Magn. Reson. Imaging – volume: 4 start-page: 223 year: 1996 end-page: 235 ident: bb0050 article-title: Detecting activations in pet and fMRI: levels of inference and power publication-title: Neuroimage – year: 2003 ident: bb0040 article-title: Human Brain Function – volume: 450 start-page: 281 year: 2009 end-page: 286 ident: bb0060 article-title: Correlation-based multivariate analysis of genetic influence on brain volume publication-title: Neurosci. Lett. – volume: 59 start-page: 888 year: 2006 end-page: 897 ident: bb0065 article-title: Imaging genetics: perspectives from studies of genetically driven variation in serotonin function and corticolimbic affective processing publication-title: Biol. Psychiatry – volume: 35 start-page: 96 year: 2009 end-page: 108 ident: bb0125 article-title: A genome-wide association study of schizophrenia using brain activation as a quantitative phenotype publication-title: Schizophr. Bull. – volume: 49 start-page: 3358 year: 2010 end-page: 3364 ident: bb0135 article-title: Neuroanatomical correlates of cognitive self-appraisal in neurodegenerative disease publication-title: Neuroimage – volume: 45 start-page: 29 year: 2009 end-page: 37 ident: bb0015 article-title: Can voxel based morphometry, manual segmentation and automated segmentation equally detect hippocampal volume differences in acute depression? publication-title: Neuroimage – volume: 45 start-page: 1090 year: 2009 end-page: 1098 ident: bb0110 article-title: Mapping the effect of APOE [epsilon]4 on gray matter loss in Alzheimer's disease in vivo publication-title: Neuroimage – volume: 53 start-page: 1051 year: 2010 end-page: 1063 ident: bb0150 article-title: Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: A study of the ADNI cohort publication-title: Neuroimage – volume: 14 start-page: 78 year: 2006 end-page: 91 ident: bb0130 article-title: Neuroimaging-genetic paradigms: a new approach to investigate the pathophysiology and treatment of cognitive deficits in schizophrenia publication-title: Harv. Rev. Psychiatry – volume: 4 start-page: 74 year: 1996 end-page: 90 ident: bb0180 article-title: Searching scale space for activation in PET images publication-title: Hum. Brain Mapp. – volume: 44 start-page: 724 year: 2009 end-page: 728 ident: bb0030 article-title: Anatomically-distinct genetic associations of apoe [var epsilon]4 allele load with regional cortical atrophy in alzheimer's disease publication-title: Neuroimage – volume: 40 start-page: 655 year: 2008 end-page: 661 ident: bb0095 article-title: False positives in imaging genetics publication-title: Neuroimage – volume: 12 start-page: 419 year: 2003 end-page: 446 ident: bb0105 article-title: Controlling the familywise error rate in functional neuroimaging: a comparative review publication-title: Stat. Meth. Med. Res. – volume: 22 start-page: 676 year: 2004 end-page: 687 ident: bb0075 article-title: Nonstationary cluster-size inference with random field and permutation methods publication-title: Neuroimage – volume: 49 start-page: 2503 year: 2010 end-page: 2508 ident: bb0160 article-title: Investigating association of brain volumes with intracranial capacity in schizophrenia publication-title: Neuroimage – volume: 45 start-page: S163 year: 2009 end-page: S172 ident: bb0020 article-title: A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data publication-title: Neuroimage – volume: 2 start-page: 189 year: 1995 end-page: 210 ident: bb0045 article-title: Statistical parametric maps in functional imaging: a general linear approach publication-title: Hum. Brain Mapp. – volume: 28 start-page: 488 year: 2007 end-page: 501 ident: bb0055 article-title: Neuroimaging endophenotypes: strategies for finding genes influencing brain structure and function publication-title: Hum. Brain Mapp. – volume: 20 start-page: 2343 year: 2003 end-page: 2356 ident: bb0070 article-title: Validating cluster size inference: random field and permutation methods publication-title: Neuroimage – volume: 4 start-page: 58 year: 1996 end-page: 73 ident: bb0175 article-title: A unified statistical approach for determining significant voxels in images of cerebral activation publication-title: Hum. Brain Mapp. – volume: 49 start-page: 3373 year: 2010 end-page: 3384 ident: bb0035 article-title: Anomalous neural circuit function in schizophrenia during a virtual morris water task publication-title: Neuroimage – volume: 5 start-page: 83 year: 1997 end-page: 96 ident: bb0115 article-title: Combining spatial extent and peak intensity to test for activations in functional imaging publication-title: Neuroimage – volume: 28 start-page: 544 year: 2005 end-page: 552 ident: bb0100 article-title: Empirical comparison of maximal voxel and non-isotropic adjusted cluster extent results in a voxel-based morphometry study of comorbid learning disability with schizophrenia publication-title: Neuroimage – volume: 44 start-page: 62 year: 2009 end-page: 70 ident: bb0025 article-title: False discovery rate revisited: FDR and topological inference using Gaussian random fields publication-title: Neuroimage – volume: 4 start-page: 58 year: 1996 ident: 10.1016/j.neuroimage.2010.08.049_bb0175 article-title: A unified statistical approach for determining significant voxels in images of cerebral activation publication-title: Hum. Brain Mapp. doi: 10.1002/(SICI)1097-0193(1996)4:1<58::AID-HBM4>3.0.CO;2-O – volume: 4 start-page: 74 issue: 1 year: 1996 ident: 10.1016/j.neuroimage.2010.08.049_bb0180 article-title: Searching scale space for activation in PET images publication-title: Hum. Brain Mapp. doi: 10.1002/(SICI)1097-0193(1996)4:1<74::AID-HBM5>3.0.CO;2-M – volume: 28 start-page: 488 year: 2007 ident: 10.1016/j.neuroimage.2010.08.049_bb0055 article-title: Neuroimaging endophenotypes: strategies for finding genes influencing brain structure and function publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.20401 – volume: 20 start-page: 2343 issue: 4 year: 2003 ident: 10.1016/j.neuroimage.2010.08.049_bb0070 article-title: Validating cluster size inference: random field and permutation methods publication-title: Neuroimage doi: 10.1016/j.neuroimage.2003.08.003 – volume: 14 start-page: 416 year: 2009 ident: 10.1016/j.neuroimage.2010.08.049_bb0120 article-title: Gene discovery through imaging genetics: identification of two novel genes associated with schizophrenia publication-title: Mol. Psychiatry doi: 10.1038/mp.2008.127 – volume: 50 start-page: 1392 issue: 4 year: 2010 ident: 10.1016/j.neuroimage.2010.08.049_bb0145 article-title: Global and local morphometric differences in recently abstinent methamphetamine-dependent individuals publication-title: Neuroimage doi: 10.1016/j.neuroimage.2010.01.056 – volume: 49 start-page: 2503 issue: 3 year: 2010 ident: 10.1016/j.neuroimage.2010.08.049_bb0160 article-title: Investigating association of brain volumes with intracranial capacity in schizophrenia publication-title: Neuroimage doi: 10.1016/j.neuroimage.2009.09.006 – volume: 53 start-page: 1051 issue: 3 year: 2010 ident: 10.1016/j.neuroimage.2010.08.049_bb0150 article-title: Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: A study of the ADNI cohort publication-title: Neuroimage doi: 10.1016/j.neuroimage.2010.01.042 – volume: 35 start-page: 121 year: 2007 ident: 10.1016/j.neuroimage.2010.08.049_bb0165 article-title: Non-normality and transformations of random fields, with an application to voxel-based morphometry publication-title: Neuroimage doi: 10.1016/j.neuroimage.2006.11.037 – volume: 59 start-page: 888 year: 2006 ident: 10.1016/j.neuroimage.2010.08.049_bb0065 article-title: Imaging genetics: perspectives from studies of genetically driven variation in serotonin function and corticolimbic affective processing publication-title: Biol. Psychiatry doi: 10.1016/j.biopsych.2005.11.005 – volume: 1030 start-page: 1027 year: 2002 ident: 10.1016/j.neuroimage.2010.08.049_bb0140 article-title: Distributional assumptions in voxel-based morphometry publication-title: Neuroimage doi: 10.1006/nimg.2002.1153 – volume: 44 start-page: 724 issue: 3 year: 2009 ident: 10.1016/j.neuroimage.2010.08.049_bb0030 article-title: Anatomically-distinct genetic associations of apoe [var epsilon]4 allele load with regional cortical atrophy in alzheimer's disease publication-title: Neuroimage doi: 10.1016/j.neuroimage.2008.10.003 – volume: 49 start-page: 3358 issue: 4 year: 2010 ident: 10.1016/j.neuroimage.2010.08.049_bb0135 article-title: Neuroanatomical correlates of cognitive self-appraisal in neurodegenerative disease publication-title: Neuroimage doi: 10.1016/j.neuroimage.2009.11.041 – volume: 36 start-page: 1 issue: 1 year: 2008 ident: 10.1016/j.neuroimage.2010.08.049_bb0155 article-title: Random fields of multivariate test statistics, with applications to shape analysis publication-title: Ann. Statist. doi: 10.1214/009053607000000406 – volume: 4 start-page: 223 year: 1996 ident: 10.1016/j.neuroimage.2010.08.049_bb0050 article-title: Detecting activations in pet and fMRI: levels of inference and power publication-title: Neuroimage doi: 10.1006/nimg.1996.0074 – volume: 450 start-page: 281 issue: 3 year: 2009 ident: 10.1016/j.neuroimage.2010.08.049_bb0060 article-title: Correlation-based multivariate analysis of genetic influence on brain volume publication-title: Neurosci. Lett. doi: 10.1016/j.neulet.2008.11.035 – volume: 5 start-page: 83 issue: 2 year: 1997 ident: 10.1016/j.neuroimage.2010.08.049_bb0115 article-title: Combining spatial extent and peak intensity to test for activations in functional imaging publication-title: Neuroimage doi: 10.1006/nimg.1996.0248 – volume: 8 start-page: 98 issue: 2–3 year: 1999 ident: 10.1016/j.neuroimage.2010.08.049_bb0185 article-title: Detecting changes in nonisotropic images publication-title: Hum. Brain Mapp. doi: 10.1002/(SICI)1097-0193(1999)8:2/3<98::AID-HBM5>3.0.CO;2-F – volume: 106 start-page: 15483 issue: 36 year: 2009 ident: 10.1016/j.neuroimage.2010.08.049_bb0085 article-title: A common MECP2 haplotype associates with reduced cortical surface area in humans in two independent populations publication-title: Proc. Natl. Acad. Sci. doi: 10.1073/pnas.0901866106 – volume: 38 start-page: 95 issue: 1 year: 2007 ident: 10.1016/j.neuroimage.2010.08.049_bb0005 article-title: A fast diffeomorphic image registration algorithm publication-title: Neuroimage doi: 10.1016/j.neuroimage.2007.07.007 – volume: 7 start-page: 818 year: 2006 ident: 10.1016/j.neuroimage.2010.08.049_bb0090 article-title: Intermediate phenotypes and genetic mechanisms of psychiatric disorders publication-title: Nat. Rev. Neurosci. doi: 10.1038/nrn1993 – volume: 12 start-page: 419 year: 2003 ident: 10.1016/j.neuroimage.2010.08.049_bb0105 article-title: Controlling the familywise error rate in functional neuroimaging: a comparative review publication-title: Stat. Meth. Med. Res. doi: 10.1191/0962280203sm341ra – year: 2003 ident: 10.1016/j.neuroimage.2010.08.049_bb0040 – volume: 28 start-page: 544 issue: 3 year: 2005 ident: 10.1016/j.neuroimage.2010.08.049_bb0100 article-title: Empirical comparison of maximal voxel and non-isotropic adjusted cluster extent results in a voxel-based morphometry study of comorbid learning disability with schizophrenia publication-title: Neuroimage doi: 10.1016/j.neuroimage.2005.04.045 – volume: 11 start-page: 805 year: 2000 ident: 10.1016/j.neuroimage.2010.08.049_bb0010 article-title: Voxel-based morphometry — the methods publication-title: Neuroimage doi: 10.1006/nimg.2000.0582 – volume: 45 start-page: 29 issue: 1 year: 2009 ident: 10.1016/j.neuroimage.2010.08.049_bb0015 article-title: Can voxel based morphometry, manual segmentation and automated segmentation equally detect hippocampal volume differences in acute depression? publication-title: Neuroimage doi: 10.1016/j.neuroimage.2008.11.006 – volume: 15 start-page: S346 year: 2002 ident: 10.1016/j.neuroimage.2010.08.049_bb0170 article-title: Non-stationary FWHM and its effect on statistical inference of fMRI data publication-title: Neuroimage – volume: 44 start-page: 62 issue: 1 year: 2009 ident: 10.1016/j.neuroimage.2010.08.049_bb0025 article-title: False discovery rate revisited: FDR and topological inference using Gaussian random fields publication-title: Neuroimage doi: 10.1016/j.neuroimage.2008.05.021 – volume: 49 start-page: 3373 issue: 4 year: 2010 ident: 10.1016/j.neuroimage.2010.08.049_bb0035 article-title: Anomalous neural circuit function in schizophrenia during a virtual morris water task publication-title: Neuroimage doi: 10.1016/j.neuroimage.2009.11.034 – volume: 2 start-page: 189 year: 1995 ident: 10.1016/j.neuroimage.2010.08.049_bb0045 article-title: Statistical parametric maps in functional imaging: a general linear approach publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.460020402 – volume: 22 start-page: 676 issue: 2 year: 2004 ident: 10.1016/j.neuroimage.2010.08.049_bb0075 article-title: Nonstationary cluster-size inference with random field and permutation methods publication-title: Neuroimage doi: 10.1016/j.neuroimage.2004.01.041 – volume: 14 start-page: 78 year: 2006 ident: 10.1016/j.neuroimage.2010.08.049_bb0130 article-title: Neuroimaging-genetic paradigms: a new approach to investigate the pathophysiology and treatment of cognitive deficits in schizophrenia publication-title: Harv. Rev. Psychiatry doi: 10.1080/10673220600642945 – volume: 40 start-page: 655 issue: 2 year: 2008 ident: 10.1016/j.neuroimage.2010.08.049_bb0095 article-title: False positives in imaging genetics publication-title: Neuroimage doi: 10.1016/j.neuroimage.2007.11.058 – volume: 45 start-page: S163 issue: 1, Supplement 1 year: 2009 ident: 10.1016/j.neuroimage.2010.08.049_bb0020 article-title: A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data publication-title: Neuroimage doi: 10.1016/j.neuroimage.2008.10.057 – volume: 35 start-page: 96 issue: 1 year: 2009 ident: 10.1016/j.neuroimage.2010.08.049_bb0125 article-title: A genome-wide association study of schizophrenia using brain activation as a quantitative phenotype publication-title: Schizophr. Bull. doi: 10.1093/schbul/sbn155 – volume: 45 start-page: 1090 issue: 4 year: 2009 ident: 10.1016/j.neuroimage.2010.08.049_bb0110 article-title: Mapping the effect of APOE [epsilon]4 on gray matter loss in Alzheimer's disease in vivo publication-title: Neuroimage doi: 10.1016/j.neuroimage.2009.01.009 – volume: 27 start-page: 685 issue: 4 year: 2008 ident: 10.1016/j.neuroimage.2010.08.049_bb0080 article-title: The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods publication-title: J. Magn. Reson. Imaging doi: 10.1002/jmri.21049
SSID	ssj0009148
Score	2.4019423
Snippet	Voxel-wise statistical inference is commonly used to identify significant experimental effects or group differences in both functional and structural studies...
SourceID	pubmedcentral proquest pubmed crossref elsevier
SourceType	Open Access Repository Aggregation Database Index Database Enrichment Source Publisher
StartPage	992
SubjectTerms	Alzheimer's disease Brain Brain - pathology Brain - physiopathology Clusters Cognition Disorders - genetics Cognition Disorders - pathology Cognitive ability False Positive Reactions Functional anatomy Gaussian Genetic diversity Genetics Genotype Genotypes Humans Image processing Imaging Inference Magnetic Resonance Imaging Medical imaging Morphometry Neurodegenerative diseases Neuroimaging Neurosciences Polymorphism, Single Nucleotide Schizophrenia Single-nucleotide polymorphism Smoothness Statistical methods Statistics Structure-function relationships Studies Substantia grisea Thresholds
SummonAdditionalLinks	– databaseName: Elsevier SD Freedom Collection Journals [SCFCJ] dbid: AIKHN link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB6VrYS4IN6kFGQkrmbtOIkdcaoqVgtVe4FKvVmJ40BQN6m624r-e2Y2Tsry0kpcY08UjWc8M_E3nwHeFNo5o6TiTlSGJ7GPeZm4jMcqwwQ1r2svqcH5-CSbnyYfz9KzHTgcemEIVhn2_n5PX-_W4ck0aHN60TTTT5gZYLjBeoPSGiWzO7AbY7Q3E9g9-HA0P7nl3pVJ3xGXKk4CAdDTw7zWtJHNAp034LzMW0HEmn-OUr9nob-CKX-KTrMHcD-kleyg__KHsOPbR3D3OBycP4ajGZqZZz1E69ovWdOy8ZswejG0I2pnXDICwn9h1913f84pxFVs0eFadAu_urxhBCh9Aqez958P5zzco8BdFpsVz9LapaZwJVZzpsyJRF4YXwvlZFESY1uBWZzOiXpLV5juVbHSRERvvBC1Sb16CpO2a_1zYHGpCqG9Lsq6ImJ6g-VKKXyRG4FvqZII9KA36wLJON11cW4HNNk3e6txSxq3dA1mkkcgR8mLnmhjC5l8WBo7NJLi1mcxGmwh-26U3TC4LaX3B0uwwemXltjucqwPjY7g9TiM7kpnMEXruyuck2lJzcBSRMD-ModIBuk4819TUoWVJmaTETzrzW9UWixMQiRyuBQbhjlOIELxzZG2-bomFkfvVEple_-lmhdwr__vLrlM92GyurzyLzFxW5WvgmP-AB5cRBE priority: 102 providerName: Elsevier
Title	False positives in neuroimaging genetics using voxel-based morphometry data
URI	https://www.clinicalkey.com/#!/content/1-s2.0-S1053811910011316 https://dx.doi.org/10.1016/j.neuroimage.2010.08.049 https://www.ncbi.nlm.nih.gov/pubmed/20849959 https://www.proquest.com/docview/1549926087 https://www.proquest.com/docview/1671286510 https://www.proquest.com/docview/818645820 https://www.proquest.com/docview/853487643 https://pubmed.ncbi.nlm.nih.gov/PMC3063336
Volume	54
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
link	http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3db9MwED-xTUJ7QTC-AqMyEq8edpzEjnhAY1pVGKumiUl9ixLHgaI1GWs3wQt_O3eNkzI-pj7lIb6oPd_5frbvfgfwKtfWGiUVt6I0PApdyIvIJjxUCQLUtKqcpALn43EyOos-TOKJP3Cb-7TKbk1cLtRlY-mM_DVRiaUIvo1-e_GNU9coul31LTQ2YEsiEqHWDXqiV6S7MmpL4WLFDQ7wmTxtfteSL3I6Q6_1CV5mTxCj5r_D09_w888syt_C0vA-3PN4ku23BvAA7rh6B-4e-xvzHdgmNNmSMT-EoyEam2Ntota1m7NpzfofiDGMoTVRUeOcUTr8Z3bdfHfnnAJdyWYNzkgzc4vLH4zSSh_B2fDw08GI-24K3CahWfAkrmxsclvgns4UKVHJC-MqoazMC-Jty1GDOiUCLl0i6CtDpYmO3jghKhM79Rg266Z2T4GFhcqFdjovqpLo6Q1uWgrh8tQI_EoZBaA7JWbWU41Tx4vzrMsp-5qt1J-R-jNqhhmlAche8qKl21hDJu3mKevKSXEBzDAmrCH7ppf1kKOFEmtK73ZmkXnXn2crQw3gZf8anZZuYvLaNVc4JtGSSoKlCID9ZwxRDdKl5m1DYoX7TcSUATxpbbFXWihMRFRyOBU3rLQfQLTiN9_U0y9LenH0UaVU8uz2P_ccttvjdcllvAubi8sr9wLx2aIYwMbeTzlYuuIAtvYPTj-e0PP90WiMz3eH45PTX_YFQXM
linkProvider	ProQuest
linkToHtml	http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3db9MwED-NIcFeEIyPFQYYCR4NdpzEjhBCCKg6uu5pk_pmEseBojXZ1m6wf4q_kbvmo4yPqS97ji9qz-e7n3N3vwN4nmrnjJKKO5EbHgY-4FnoYh6oGAFqUhReUoPzaC8eHISfxtF4DX62vTBUVtn6xIWjzitH38hfEZVYguDb6LdHx5ymRlF2tR2hUZvF0J9_xyvb7M3OB9zfF0HQ_7j_fsCbqQLcxYGZ8zgqXGRSl-HdxmQJUaoL4wuhnEwz4i9LEdPohIiodI7gJw-UJlp244UoTOQVvvcaXA8pxYjnR4_1kuRXhnXrXaS4kTJpKofqerIFP-Vkil6iKSgzLwUxeP47HP4Nd_-s2vwtDPZvw60Gv7J3tcHdgTVfbsKNUZOh34QNQq81-fNdGPbRuD2rC8PO_IxNStb9QIyZDK2XmihnjMrvv7Cz6oc_5BRYczat0AKqqZ-fnDMqY70HB1ei5_uwXlal3wIWZCoV2us0K3Kiwzd4ScqETxMj8C152APdKtG6htqcJmwc2raG7Ztdqt-S-i0N3wyTHshO8qim91hBJmn3ybbtq-hwLcagFWRfd7INxKmhy4rS261Z2MbVzOzyYPTgWfcYnQRlftLSV6e4JtaSWpCl6AH7zxqiNqQk6mVLIoX3W8SwPXhQ22KntECYkKjrcCsuWGm3gGjMLz4pJ18XdOboE5RS8cPL_9xTuDnYH-3a3Z294SPYqD_tSy6jbVifn5z6x4gN59mTxYFk8PmqPcAvJJp1vw
linkToPdf	http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Nb9QwEB2VIlW9ICgfDRQwEhxN7TiJHSGEEGXVsrTiQKW9hcRxYFE3Kd1toX-NX8fMOslSPqq99BxPtGuPZ57jN28AnubaWqOk4laUhkehC3kR2YSHKkGAmlaVk1TgvH-Q7B5G70bxaAV-drUwRKvsYuI8UJeNpW_k2yQlliL4Nnq7amkRH3YGr46_ceogRTetXTsN7yJDd_4dj2_Tl3s7uNbPwnDw9uObXd52GOA2Cc2MJ3FlY5PbAs85pkhJXl0YVwllZV6QllmO-EanJEqlSwRCZag0SbQbJ0RlYqfwvdfgulZRRG0j9EgvBH9l5MvwYsWNlGnLIvLcsrlW5XiCEaMll5nngtQ8_50a_4a-fzI4f0uJg5two8Wy7LV3vluw4uoNWNtvb-s3YJ2QrBeCvg3DATq6Y54kduambFyz_gdi_mToyVRQOWVExf_Mzpof7ohTki3ZpEFvaCZudnLOiNJ6Bw6vZJ7vwmrd1G4TWFioXGin86IqSRrf4IGpEC5PjcC3lFEAupvEzLYy59Rt4yjr-Gxfs8X0ZzT9GTXijNIAZG957KU-lrBJu3XKulJWDL4Z5qMlbF_0ti3c8TBmSeutzi2yNuxMs8UmCeBJ_xgDBt0C5bVrTnFMoiWVI0sRAPvPGJI5pAvVy4bECs-6iGcDuOd9sZ-0UJiIZOxwKS54aT-AJM0vPqnHX-bS5hgflFLJ_cv_3GNYw72fvd87GD6Adf-VX3IZb8Hq7OTUPUSYOCsezfcjg09XHQB-AaJhefI
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=False+positives+in+neuroimaging+genetics+using+voxel-based+morphometry+data&rft.jtitle=NeuroImage+%28Orlando%2C+Fla.%29&rft.au=Silver%2C+Matt&rft.au=Montana%2C+Giovanni&rft.au=Nichols%2C+Thomas+E.&rft.date=2011-01-15&rft.pub=Academic+Press&rft.issn=1053-8119&rft.eissn=1095-9572&rft.volume=54&rft.issue=2&rft.spage=992&rft.epage=1000&rft_id=info:doi/10.1016%2Fj.neuroimage.2010.08.049&rft_id=info%3Apmid%2F20849959&rft.externalDocID=PMC3063336
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1053-8119&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1053-8119&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1053-8119&client=summon