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Scan-rescan reliability of subcortical brain volumes derived from automated segmentation.

ABSTRACT: Large-scale longitudinal studies of regional brain volume require reliable quantification using automated segmentation and labeling. However, repeated MR scanning of the same subject, even if using the same scanner and acquisition parameters, does not result in identical images due to small changes in image orientation, changes in prescan parameters, and magnetic field instability. These differences may lead to appreciable changes in estimates of volume for different structures. This study examined scan-rescan reliability of automated segmentation algorithms for measuring several subcortical regions, using both within-day and across-day comparison sessions in a group of 23 normal participants. We found that the reliability of volume measures including percent volume difference, percent volume overlap (Dice's coefficient), and intraclass correlation coefficient (ICC), varied substantially across brain regions. Low reliability was observed in some structures such as the amygdala (ICC = 0.6), with higher reliability (ICC = 0.9) for other structures such as the thalamus and caudate. Patterns of reliability across regions were similar for automated segmentation with FSL/FIRST and FreeSurfer (longitudinal stream). Reliability was associated with the volume of the structure, the ratio of volume to surface area for the structure, the magnitude of the interscan interval, and the method of segmentation. Sample size estimates for detecting changes in brain volume for a range of likely effect sizes also differed by region. Thus, longitudinal research requires a careful analysis of sample size and choice of segmentation method combined with a consideration of the brain structure(s) of interest and the magnitude of the anticipated effects.

SUBMITTER: Morey RA

PROVIDER: S-EPMC3782252 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundPrevious quantitative synthetic MRI of the brain has been solely performed in 2D.PurposeTo evaluate the feasibility of the recently developed sequence 3D-QALAS for brain cortical thickness and volumetric analysis.Study typeReproducibility/repeatability study.SubjectsTwenty-one healthy volunteers (35.6 ± 13.8 years).Field strength/sequence3D T1 -weighted fast spoiled gradient recalled echo (FSPGR) sequence was performed once, and 3D-QALAS sequence was performed twice with a 3T scanner.AssessmentFreeSurfer and FIRST were used to measure cortical thickness and volume of subcortical structures, respectively. Agreement with FSPGR and scan-rescan repeatability were evaluated for 3D-QALAS.Statistical testsPercent relative difference and intraclass correlation coefficient (ICC) were used to assess reproducibility and scan-rescan repeatability of the 3D-QALAS sequence-derived measurements.ResultsPercent relative difference compared with FSPGR in cortical thickness of the whole cortex was 3.1%, and 89% of the regional areas showed less than 10% relative difference in cortical thickness. The mean ICC across all regions was 0.65, and 74% of the structures showed substantial to almost perfect agreement. For volumes of subcortical structures, the median percent relative differences were lower than 10% across all subcortical structures, except for the accumbens area, and all structures showed ICCs of substantial to almost perfect agreement. For the scan-rescan test, percent relative difference in cortical thickness of the whole cortex was 2.3%, and 97% of the regional areas showed less than 10% relative difference in cortical thickness. The mean ICC across all regions was 0.73, and 80% showed substantial to almost perfect agreement. For volumes of subcortical structures, relative differences were less than 10% across all subcortical structures except for the accumbens area, and all structures showed ICCs of substantial to almost perfect agreement.Data conclusion3D-QALAS could be reliably used for measuring cortical thickness and subcortical volumes in most brain regions.Level of evidence3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1834-1842.

Project description:Automated MRI-derived measurements of in-vivo human brain volumes provide novel insights into normal and abnormal neuroanatomy, but little is known about measurement reliability. Here we assess the impact of image acquisition variables (scan session, MRI sequence, scanner upgrade, vendor and field strengths), FreeSurfer segmentation pre-processing variables (image averaging, B1 field inhomogeneity correction) and segmentation analysis variables (probabilistic atlas) on resultant image segmentation volumes from older (n=15, mean age 69.5) and younger (both n=5, mean ages 34 and 36.5) healthy subjects. The variability between hippocampal, thalamic, caudate, putamen, lateral ventricular and total intracranial volume measures across sessions on the same scanner on different days is less than 4.3% for the older group and less than 2.3% for the younger group. Within-scanner measurements are remarkably reliable across scan sessions, being minimally affected by averaging of multiple acquisitions, B1 correction, acquisition sequence (MPRAGE vs. multi-echo-FLASH), major scanner upgrades (Sonata-Avanto, Trio-TrioTIM), and segmentation atlas (MPRAGE or multi-echo-FLASH). Volume measurements across platforms (Siemens Sonata vs. GE Signa) and field strengths (1.5 T vs. 3 T) result in a volume difference bias but with a comparable variance as that measured within-scanner, implying that multi-site studies may not necessarily require a much larger sample to detect a specific effect. These results suggest that volumes derived from automated segmentation of T1-weighted structural images are reliable measures within the same scanner platform, even after upgrades; however, combining data across platform and across field-strength introduces a bias that should be considered in the design of multi-site studies, such as clinical drug trials. The results derived from the young groups (scanner upgrade effects and B1 inhomogeneity correction effects) should be considered as preliminary and in need for further validation with a larger dataset.

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Project description:Reactive and proactive subtypes of aggression have been recognized to help parse etiological heterogeneity of this complex phenotype. With a heritability of about 50%, genetic factors play a role in the development of aggressive behavior. Imaging studies implicate brain structures related to social behavior in aggression etiology, most notably the amygdala and striatum. This study aimed to gain more insight into the pathways from genetic risk factors for aggression to aggression phenotypes. To this end, we conducted genome-wide gene-based cross-trait meta-analyses of aggression with the volumes of amygdala, nucleus accumbens and caudate nucleus to identify genes influencing both aggression and aggression-related brain volumes. We used data of large-scale genome-wide association studies (GWAS) of: (a) aggressive behavior in children and adolescents (EAGLE, N = 18,988); and (b) Magnetic Resonance Imaging (MRI)-based volume measures of aggression-relevant subcortical brain regions (ENIGMA2, N = 13,171). Second, the identified genes were further investigated in a sample of healthy adults (mean age (SD) = 25.28 (4.62) years; 43% male) who had genome-wide genotyping data and questionnaire data on aggression subtypes available (Brain Imaging Genetics, BIG, N = 501) to study their effect on reactive and proactive subtypes of aggression. Our meta-analysis identified two genes, MECOM and AVPR1A, significantly associated with both aggression risk and nucleus accumbens (MECOM) and amygdala (AVPR1A) brain volume. Subsequent in-depth analysis of these genes in healthy adults (BIG), including sex as an interaction term in the model, revealed no significant subtype-specific gene-wide associations. Using cross-trait meta-analysis of brain measures and psychiatric phenotypes, this study generated new hypotheses about specific links between genes, the brain and behavior. Results indicate that MECOM and AVPR1A may exert an effect on aggression through mechanisms involving nucleus accumbens and amygdala volumes, respectively.

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Scan-rescan reliability of subcortical brain volumes derived from automated segmentation.

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