interdatabase variability in cortical thickness measurements|Interdatabase Variability in Cortical Thickness Measurements.

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interdatabase variability in cortical thickness measurements|Interdatabase Variability in Cortical Thickness Measurements. : trade The longitudinal pipeline of FreeSurfer (v. 6.0, Fischl, 2012) as implemented in the FreeSurfer BIDS-App (Gorgolewski et al., 2017) was used to obtain thickness and area . 31 de mai. de 2023 · Quem está fora: Fernando Henrique (transição); Ramiro, Rafael Bilu e Wesley Gasolina (lesionados); Richard (afastado); e Kaiki (com a seleção brasileira sub .

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Cortical thinning rates were significantly different between databases in all 68 parcellated regions (ANCOVA, P < 0.001). Subtle differences were observed in correlation matrices and bootstrapping convergence.

We investigate several aspects of these databases, including: 1) differences between databases of cortical thinning rates versus age, 2) correlation of cortical thinning . The results showed regional age-related cortical thinning, WM volume increases, and changes in diffusion parameters. Cortical thickness was the most strongly age-related . Interdatabase Variability in Cortical Thickness Measurements. 3.2. Interindividual variation in cortical thickness. Across age‐groups (early, middle, and late life), interindividual variability in regional cortical thickness, as measured by pooled .

The longitudinal pipeline of FreeSurfer (v. 6.0, Fischl, 2012) as implemented in the FreeSurfer BIDS-App (Gorgolewski et al., 2017) was used to obtain thickness and area .Longitudinal-specific strategies for acquisition and processing have potentially significant benefits including more consistent estimates of intra-subject measurements while retaining predictive . In addition, cortical thickness estimates show consistent within-subject variability and reliability. Importantly, cortical thickness estimates in visual areas are well .

Normative cerebral cortical thickness for human visual areas

In this study, we compared four harmonization methods. First, we tested linear mixed-effects modeling (LME), also known as the mixed-effects mega-analysis (ME-Mega) ( . We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models . Such unwanted sources of variation, which we refer to as "scanner effects", can hinder the detection of imaging features associated with clinical covariates of interest and cause spurious findings. In this paper, we investigate scanner effects in two large multi-site studies on cortical thickness measurements across a total of 11 scanners.While normalized intensities are not as predictive of AD as other brain features, such as hippocampal volume and cortical thickness [Wolz et al., 2011], this nevertheless shows that RAVEL-corrected intensities are more representative of true biological variation than intensity-normalized intensities alone.

Introduction. Although elegant work has established an indisputable general relationship between cortical morphology, cytoarchitecture and function (Broca, 1861; Fischl et al., 2008), the precise nature of this relationship is unclear.Moreover, there is a considerable degree of inter-individual variability in the large-scale structural features of the brain, which .

The thickness of the motor cortex is often underestimated in MRI thickness measurement , probably because of the high degree of intracortical myelination that affects the gray–white contrast, causing the white matter surface to be too close to the gray surface, such that cortical thickness is underestimated [10,14]. The calcarine sulcus is .Request PDF | On May 8, 2015, Ilkka Laakso and others published Inter-subject Variability in Electric Fields of Motor Cortical tDCS | Find, read and cite all the research you need on ResearchGateLongitudinal studies, on the other hand, can significantly reduce inter-subject measurement variability. The ANTs longitudinal cortical thickness pipeline extends the ANTs cortical thickness pipeline for longitudinal studies which takes into account various bias issues previously discussed in the literature [12, 14, 19]. 2.2 Extraction of cortical thickness measurements For the extraction of the cortical thickness measurements, we ran the ANTs cortical thickness pipeline, which has been shown to provide accurate and robust cortical thickness measurements [Tustison et al.,2014]. We used an average labeled template previously constructed from a subset of

thickness variation itself, cortical thickness estimates (CT) may be seen as a more straightforward measure of brain structural features (Winkler et al., 2018, 2010). Accordingly, variability in cortical thickness could be expected to show relatively straightforward andSuch unwanted sources of variation, which we refer to as “scanner effects”, can hinder the detection of imaging features associated with clinical covariates of interest and cause spurious findings. In this paper, we investigate scanner effects in two large multi-site studies on cortical thickness measurements across a total of 11 scanners.In this work, isotropic Gaussian smoothing has been applied to the cortical volume. Although this is a simple, practical way to improve the signal-to-noise ratio of the cortical thickness measurements and to account for anatomical variability, it is also disadvantageous due to the highly curved nature of the cortex.

Background Autism spectrum disorder (ASD) is a heritable condition related to brain development that affects a person’s perception and socialization with others. Here, we examined variability in the brain morphology in ASD children and adolescent individuals at the level of brain cortical structural profiles and the level of each brain regional measure. . Cortical thickness measurements are then estimated using the DiReCT algorithm [Das et al., 2009]. Briefly, the DiReCT method estimates the GM/WM interface and the GM/CSF interface and computes a diffeomorphic mapping between the two interfaces, from which thickness is derived. . Without harmonization, the percentage of variation in the . Age-related decline in the brain: a longitudinal study on inter-individual variability of cortical thickness, area, volume, and cognition July 2021 NeuroImage 240:118370

To evaluate how well SAN preserves biological variation in the cortical thickness data, we first select a subset of vertices that exceed the 80th percentile of the CASH scores, which result in 1871 and 1873 vertices from the left and right hemispheres, respectively. . Reliability of mri‐derived measurements of human cerebral cortical . Changes in cortical thickness, surface area, gyrification, WMH, and DTI-ALPS were subtype-specific in FTD. . Considering that gyrification has been thought to be a surrogate measure of underlying brain connectivity, 43 the observed results of LGI reductions thus may signify varying degrees of brain dysconnectivity in the three subtypes, . In a recent large-scale analysis of cortical thickness over the life span using cross-sectional data, most brain regions showed a steep decrease during the second and third decades of life and an attenuated or plateaued slope afterwards (Frangou et al., 2020), which is in line with the decelerating decline of cortical thickness over 8 years .

Hutton, C. et al. Voxel-based cortical thickness measurements in MRI. Neuroimage 40 , 1701–1710 (2008). Article PubMed Google ScholarMagnetic Resonance Imaging (MRI) studies have shown that cortical volume declines with age. Although volume is a multiplicative measure consisting of thickness and area, few studies have focused on both its components. Information on decline variability and associations between person-specific changes of different brain metrics, brain regions, and cognition is sparse. In . Previous studies on age-related changes in cortical and hippocampal morphology were not designed or able to reveal the complex spatial patterns of changes across the lifespan. To this end, the current study examined these changes in a decade-by-decade manner by comparing consecutive age decades at the vertex-wise level. Additionally, the lifespan .

The multimodal components tend to be located in anatomically-related brain areas, suggesting a morphological and possibly functional relationship. The local components show relationships between surface-based cortical thickness and arealization, voxel-based morphometry (VBM), and between three different DTI measures.

The phenomenon of cortical thinning with age has been well established; however, the measured rate of change varies between studies. . Interdatabase Variability in Cortical Thickness Measurements. M Ethan MacDonald, Rebecca J Williams, Nils D Forkert, Avery J L Berman, Cheryl R McCreary, Richard Frayne, G Bruce Pike . The source of this . We took two different approaches to measure inter-individual variability in change, which are described below. . from the population mean for the measure (i.e. average cortical thickness). Specifically, Level1 : Y ij = b 1 + b 2 X 2 + b 3 X 3 + b 4 X 4 + b 5 X 2 X 4 + b 6 X 3 X 4 + b 7 i age ij + f (age ij) + b 0 i + e ij. For cortical thickness, there was again variability in relation between mean thickness for parcels and image quality (t-statistic range = -2.376–6.571), with modest associations in the aggregate (mean t-statistic = 1.510 ± 2.04). A total of 430 genes belonging to various pathways were identified as associated with brain cortical morphometric variation. The pathways, including short-term memory, regulation of system process .

Low MPD values indicate low variability and therefore, values closest to 0 are considered to be the most reproducible (McGuire et al., 2017). A random sample of ICCs and MPDs were validated in IBM SPSS Statistics 26 (IBM) to verify accuracy. . For cortical thickness measurements (Image C), 16 regions had an ICC<0.7 (minimum value = 0.402 . Comparison of cortical thickness from the BigBrain with von Economo and Koskinas histological measurement and MRI cortical thickness data from the . boundaries were extended beyond layer VI and beyond the pial surface between 0.25 mm and 0.75 mm so as to match the variability of cortical extent in the test profile data set. Training profiles .

the process of accurately measuring the cortical thickness on the sub -millimeter scale. For example, a complete labeling of a human brain from a high -resolution T1 -weighted MRI scan can take a trained anatomist days to complete, and even this labo r-intensive procedure only allows the measurement of cortical volume , not cortical thickness .

Longitudinal Mapping of Cortical Thickness Measurements: An

Normative cerebral cortical thickness for human visual areas