Image processing and analysis methods for the Adolescent Brain Cognitive Development Study

Hagler, Donald J.; Hatton, Sean N.; Cornejo, M. Daniela; Makowski, Carolina; Fair, Damien A.; Dick, Anthony Steven; Sutherland, Matthew T.; Casey, B. J.; Deanna, M; Harms, Michael P.; Watts, Richard; Bjork, James M.; Garavan, Hugh; Hilmer, Laura; Pung, Christopher J.; Sicat, Chelsea Sue; Kuperman, Joshua; Bartsch, Hauke; Xue, Feng; Heitzeg, Mary M.; Laird, Angela R.; Trinh, Thanh T.; González, Raúl; Tapert, Susan F.; Riedel, Michael C.; Squeglia, Lindsay M.; Hyde, Luke W.; Rosenberg, Monica D.; Earl, Eric; Howlett, Katia Delrahim; Baker, Fiona C.; Soules, Mary E.; Diaz, Jazmin; Leon, Octavio Ruiz de; Thompson, Wesley K.; Neale, Michael C.; Herting, Megan M.; Sowell, Elizabeth R.; Alvarez, Ruben P.; Hawes, Samuel W.; Sánchez, Mariana; Bodurka, Jerzy; Breslin, Florence J.; Morris, Amanda Sheffield; Paulus, Martin P.; Simmons, W. Kyle; Polimeni, Jon̈athan R.; Kouwe, André van der; Nencka, Andrew S.; Gray, Kevin M.; Pierpaoli, Carlo; Matochik, John A.; Noronha, Antonio; Aklin, Will M.; Conway, Kevin P.; Glantz, Meyer D.; Hoffman, Elizabeth A.; Little, Roger; Lopez, Marsha F.; Pariyadath, Vani; Weiss, Susan R.B.; Wolff-Hughes, Dana L.; DelCarmen-Wiggins, Rebecca; Ewing, Sarah W. Feldstein; Miranda-Domínguez, Óscar; Nagel, Bonnie J.; Perrone, Anders; Sturgeon, Darrick; Goldstone, Aimée; Pfefferbaum, Adolf; Pohl, Kilian M.; Prouty, Devin; Uban, Kristina A.; Bookheimer, Susan Y.; Dapretto, Mirella; Galván, Adriana; Bagot, Kara; Giedd, Jay N.; Infante, M. Alejandra; Jacobus, Joanna; Patrick, Kevin; Shilling, Paul D.; Desikan, Rahul S.; Li, Yang; Sugrue, Leo P.; Banich, Marie T.; Friedman, Naomi P.; Hewitt, John K.; Hopfer, Christian J.; Sakai, Joseph T.; Tanabe, Jody; Cottler, Linda B.; Nixon, Sara Jo; Chang, Linda; Cloak, Christine; Ernst, Thomas; Reeves, Gloria; Kennedy, David N.; Heeringa, Steve; Peltier, Scott; Schulenberg, John E.; Sripada, Chandra; Zucker, Robert A.; Iacono, William G.; Luciana, Mónica; Calabro, Finnegan J.; Clark, Duncan B.; Lewis, David A.; Luna, Beatríz; Schirda, Claudiu; Brima, Tufikameni; Foxe, John J.; Freedman, Edward G.; Mruzek, Daniel W.; Mason, Michael; Huber, Rebekah S.; McGlade, Erin; Prescot, Andrew P.; Renshaw, Perry F.; Yurgelun‐Todd, Deborah; Allgaier, Nicholas; Dumas, Julie A.; Ivanova, Masha Y.; Potter, Alexandra; Florsheim, Paul; Larson, Christine L.; Medina, Krista Lisdahl; Charness, Michael E.; Fuemmeler, Bernard F.; Hettema, John M.; Maes, Hermine H.; Steinberg, Joel L.; Anokhin, Andrey P.; Glaser, Paul E.A.; Heath, Andrew C.; Madden, Pamela A. F.; Baskin‐Sommers, Arielle; Constable, R. Todd; Grant, Steven; Dowling, Gayathri J.; Brown, Sandra A.; Jernigan, Terry L.; Dale, Anders M.

doi:10.1016/j.neuroimage.2019.116091

Cited by 733 publications

(824 citation statements)

References 116 publications

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“…Pre-processing of all MRI data for ABCD was conducted using in-house software at the Center for Multimodal Imaging and Genetics (CMIG) at University of California San Diego (UCSD) as outlined in Hagler et al (Hagler et al, 2019). Manual quality control was performed prior to the full image preprocessing and structural scans with poor image quality as well as those that did not pass FreeSurfer QC were excluded from all analyses.…”

Section: Imaging Pre-processingmentioning

confidence: 99%

Determining the association between regionalisation of cortical morphology and cognition in 10,145 children

Zhao

Loughnan

et al. 2019

Preprint

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During development individuals undergo protracted changes in cortical morphology, which coincide with changes in cognition. A number of studies have measured the association between brain structure and cognition in children, however these frequently rely on mass univariate statistics or ROI-based comparisons and do not always measure cortical morphology relative to global brain measures. After controlling for global brain measures, we can see a residual regionalisation pattern indicating the size or thickness of different regions relative to the total cortical surface area or mean thickness. Individual variability in this regionalisation may be important for predicting between subject variability in cognitive performance. Here we sought to determine whether the relative configuration of cortical architecture across the whole cortex was associated with cognition using a novel multivariate omnibus statistical test (MOSTest). This method is optimally powered to detect associations that are widely distributed across the cortex. We then used the Polyvertex score (PVS) to quantify the magnitude of the association between vertex-wise cortical morphology and cognitive performance. We have determined that the relative pattern of cortical architecture, after removing the variance associated with global brain measures, predicted unique variance associated with cognition across different modalities.

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Section: Imaging Pre-processingmentioning

confidence: 99%

Determining the association between regionalisation of cortical morphology and cognition in 10,145 children

Zhao

Loughnan

et al. 2019

Preprint

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“…In this work, we have thoroughly examined the reproducibility of the proposed method for the HCP data, and the results are satisfactory. However, the multisite reproducibility of the dMRI data and the resultant analysis is still an open challenge 40,41 . The proposed method may also be used for the task of multi-site harmonization since the tractograms are all mapped to the MDS embedding space in which the white matter tracts can be more comparable than in the streamline space.…”

Section: Discussionmentioning

confidence: 99%

A Measure of Whole-Brain White Matter Topography

Wang¹,

Shi²

2019

Preprint

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The unprecedentedly high-quality large-scale brain imaging datasets, from such as the Human Connectome Project (HCP) and UK-Biobank, provide a unique opportunity for measuring the white matter topography of the human brain. By leveraging the multi-shell diffusion MRI data from the original young adult HCP, we systematically develop a reliable measure of the whole-brain white matter topography, and we coin it topographic vector. As the main result, we find that the three most dominant dimensions of the topographic vectors strongly and linearly correlate with the coordinates of the corresponding streamlines of the whole-brain tractograms. Our results support the earlier prescient hypothesis that brain development follows a "base-plan" established by three (main) chemotactic gradients of early embryogenesis, and they implicate that the whole brain white matter tracts can be represented by vectors of a natural coordinate system. The topographic organisation, or topography, of the brain white matter was first measured using Klingler's brain dissection technique in early post-mortem studies 30 years ago 1 . However, our understanding of it is still limited. We view the topography of brain white matter pathways at two scales: local and global. The local topographic organisation, which is also known as the topographic regularity, has been defined as the preservation of the spatial relationship between neurons in point-to-point or region-toregion axonal connections 2,3 and it has been supported by tracer injection results 4-7 and diffusion magnetic resonance imaging (dMRI) studies 8-11 for various white matter

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“…Apart from these criteria, participants affected by the error related to structural MRI data reported in Known Issues with Data Release 2.0 (https://nda.nih.gov/edit_collection.html?id=2573) were excluded. Finally, we excluded participants with a lower quality of MRI and preprocessing results based on the ABCD Study's FreeSurfer quality control conducted by trained technicians to identify data showing evidence of excessive motion, pial overestimation, white matter underestimation, inhomogeneity, or artifacts (Hagler et al, 2019). Based on these criteria and external quality control screening, there were 2379 remaining participants, collected across 13 sites and 2 different scanners.…”

Section: Methodsmentioning

confidence: 99%

Decomposing complex links between the childhood environment and brain structure in school-aged youth

Hong

Sisk

Caballero

et al. 2020

Preprint

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Childhood experiences play a profound role in conferring risk and resilience for brain and behavioral development. However, how different facets of the environment shape neurodevelopment remains largely unknown. Here we sought to decompose heterogeneous relationships between environmental factors and brain structure in 989 school-aged children from the Adolescent Brain Cognitive Development Study. We applied a cross-modal integration and clustering approach called 'Similarity Network Fusion', which combined two brain morphometrics (i.e., cortical thickness and myelin-surrogate markers), and key environmental factors (i.e., trauma exposure, neighborhood safety, school environment, and family environment) to identify homogeneous subtypes. Depending on the subtyping resolution, results identified two or five subgroups, each characterized by distinct brain structure-environment profiles. Notably, more supportive caregiving and school environments were associated with increased myelination, whereas less supportive caregiving, higher family conflict and psychopathology, and higher perceived neighborhood safety were observed with increased cortical thickness. These subtypes were highly reproducible and predicted externalizing symptoms and overall mental health problems. Our findings support the theory that distinct environmental exposures differentially influence neurodevelopment. Delineating more precise associations between risk factors, protective factors, and brain development may inform approaches to enhance risk identification and optimize interventions targeting specific experiences.

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Image processing and analysis methods for the Adolescent Brain Cognitive Development Study

Cited by 733 publications

References 116 publications

Determining the association between regionalisation of cortical morphology and cognition in 10,145 children

Determining the association between regionalisation of cortical morphology and cognition in 10,145 children

A Measure of Whole-Brain White Matter Topography

Decomposing complex links between the childhood environment and brain structure in school-aged youth

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