The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites

There are two popular approaches for automated white matter parcellation using diffusion MRI tractography, including fiber clustering strategies that group white matter fibers according to their geometric trajectories and cortical‐parcellation‐based strategies that focus on the structural connectivity among different brain regions of interest. While multiple studies have assessed test–retest reproducibility of automated white matter parcellations using cortical‐parcellation‐based strategies, there are no existing studies of test–retest reproducibility of fiber clustering parcellation. In this work, we perform what we believe is the first study of fiber clustering white matter parcellation test–retest reproducibility. The assessment is performed on three test–retest diffusion MRI datasets including a total of 255 subjects across genders, a broad age range (5–82 years), health conditions (autism, Parkinson's disease and healthy subjects), and imaging acquisition protocols (three different sites). A comprehensive evaluation is conducted for a fiber clustering method that leverages an anatomically curated fiber clustering white matter atlas, with comparison to a popular cortical‐parcellation‐based method. The two methods are compared for the two main white matter parcellation applications of dividing the entire white matter into parcels (i.e., whole brain white matter parcellation) and identifying particular anatomical fiber tracts (i.e., anatomical fiber tract parcellation). Test–retest reproducibility is measured using both geometric and diffusion features, including volumetric overlap (wDice) and relative difference of fractional anisotropy. Our experimental results in general indicate that the fiber clustering method produced more reproducible white matter parcellations than the cortical‐parcellation‐based method.

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Test–retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering

Zhang

Norton

et al. 2019

“…In detail, the atlases by Smith et al () (hereafter referred to as the Smith‐Atlas), by Yeo et al () (hereafter referred to as the Yeo‐Atlas), and by Shirer et al () (hereafter referred to as the Shirer‐Atlas) were chosen because they have each been cited more than 500 times by January 31, 2019 according to the Altmetric information available on each article's website. The atlas by Gordon et al () (hereafter referred to as the Gordon‐Atlas) was chosen because it is used in the Adolescent Brain Cognitive Development Study (https://abcdstudy.org/) which aims to provide national normative standards for brain FC (Casey et al, ). We included the atlases by Doucet et al () and Doucet, Rasgon, McEwen, Micali, and Frangou () (hereafter referred to as Doucet2011‐Atlas and Doucet2018‐Atlas) that were constructed by our group using resting‐state fMRI data from two independent large samples of healthy participants (Doucet et al, ; Doucet, Rasgon, et al, ).…”

Section: Methodsmentioning

confidence: 99%

Evaluation of the spatial variability in the major resting‐state networks across human brain functional atlases

Doucet

Lee

Frangou

2019

The human brain is intrinsically organized into resting‐state networks (RSNs). Currently, several human brain functional atlases are used to define the spatial constituents of these RSNs. However, there are significant concerns about interatlas variability. In response, we undertook a quantitative comparison of the five major RSNs (default mode [DMN], salience, central executive, sensorimotor, and visual networks) across currently available brain functional atlases (n = 6) in which we demonstrated that (a) similarity between atlases was modest and positively linked to the size of the sample used to construct them; (b) across atlases, spatial overlap among major RSNs ranged between 17 and 76% (mean = 39%), which resulted in variability in their functional connectivity; (c) lower order RSNs were generally spatially conserved across atlases; (d) among higher order RSNs, the DMN was the most conserved across atlases; and (e) voxel‐wise flexibility (i.e., the likelihood of a voxel to change network assignment across atlases) was high for subcortical regions and low for the sensory, motor and medial prefrontal cortices, and the precuneus. In order to facilitate RSN reproducibility in future studies, we provide a new freely available Consensual Atlas of REsting‐state Networks, based on the most reliable atlases.

“…Meta-analyses of reward processing exist (Liu, Hairston, Schrier, & Fan, 2011;Silverman et al, 2015). The MID task is currently used in multisite longitudinal neuroimaging studies, such as IMAGEN (Schumann et al, 2010), and the Adolescent Brain and Cognitive Development study (ABCD; Casey et al, 2018). The MID task is currently used in multisite longitudinal neuroimaging studies, such as IMAGEN (Schumann et al, 2010), and the Adolescent Brain and Cognitive Development study (ABCD; Casey et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Mapping adolescent reward anticipation, receipt, and prediction error during the monetary incentive delay task

Cao

Bennett

Orr

et al. 2018

Self Cite

The functional neuroanatomy and connectivity of reward processing in adults are well documented, with relatively less research on adolescents, a notable gap given this developmental period's association with altered reward sensitivity. Here, a large sample (n = 1,510) of adolescents performed the monetary incentive delay (MID) task during functional magnetic resonance imaging. Probabilistic maps identified brain regions that were reliably responsive to reward anticipation and receipt, and to prediction errors derived from a computational model. Psychophysiological interactions analyses were used to examine functional connections throughout reward processing. Bilateral ventral striatum, pallidum, insula, thalamus, hippocampus, cingulate cortex, midbrain, motor area, and occipital areas were reliably activated during reward anticipation. Bilateral ventromedial prefrontal cortex and bilateral thalamus exhibited positive and negative activation, respectively, during reward receipt. Bilateral ventral striatum was reliably active following prediction errors. Previously, individual differences in the personality trait of sensation seeking were shown to be related to individual differences in sensitivity to reward outcome. Here, we found that sensation seeking scores were negatively correlated with right inferior frontal gyrus activity following reward prediction errors estimated using a computational model. Psychophysiological interactions demonstrated widespread cortical and subcortical connectivity during reward processing, including connectivity between reward‐related regions with motor areas and the salience network. Males had more activation in left putamen, right precuneus, and middle temporal gyrus during reward anticipation. In summary, we found that, in adolescents, different reward processing stages during the MID task were robustly associated with distinctive patterns of activation and of connectivity.