The ABCD Study of Neurodevelopment: Identifying Neurocircuit Targets for Prevention and Treatment of Adolescent Substance Abuse

Bjork, James M.; Straub, Lisa K.; Provost, Rosellen G.; Neale, Michael C.

doi:10.1007/s40501-017-0108-y

Cited by 90 publications

(64 citation statements)

References 77 publications

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“…Several initiatives, e.g. HCP Lifespan, the Connectomes Related to Human Disease, ABCD (Bjork et al, 2017) and the UK Biobank (Miller et al, 2016) ) are now taking advantage of higher spatial and temporal resolution of MR data made possible using multi-band (multi-slice) acquisition (Ugurbil et al, 2013). Going forward, we hope that studies of such methodological quality will become the norm, rather than the exception.…”

Section: Discussionmentioning

confidence: 99%

ciftify: A framework for surface-based analysis of legacy MR acquisitions

Dickie

Antičević

Smith

et al. 2018

Preprint

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HIGHLIGHTS• the ciftify package allows for grayordinate-based (CIFTI format) analysis of non-Human Connectome Project (i.e. legacy) MR acquisitions • The workflow and dependencies are distributed as a Docker container, following the BIDS-app interface • Additional ciftify utilities aid in downstream analysis of CIFTI images • We intend for this work to offer bridging solution for legacy data that will allow many researchers to adopt CIFTI format analyses ABSTRACTThe preprocessing pipelines of the Human Connectome Project (HCP) were made publicly available for the neuroimaging community to apply the HCP analytic approach to data from non-HCP sources. The HCP analytic approach is surface-based for the cerebral cortex, uses the CIFTI "grayordinate" file format, provides greater statistical sensitivity than traditional volume-based analysis approaches, and allows for a more neuroanatomically-faithful representation of data. However, the HCP pipelines require the acquisition of specific images (namely T2w and field map) that historically have often not been acquired. Massive amounts of this 'legacy' data could benefit from the adoption of HCP-style methods. However, there is currently no published framework, to our knowledge, for adapting HCP preprocessing to "legacy" data. Here we present the ciftify project, a parsimonious analytic framework for adapting key modules from the HCP pipeline into existing structural workflows using FreeSurfer's recon_all structural and existing functional preprocessing workflows. Within this framework, any functional dataset with an accompanying (i.e. T1w) anatomical data can be analyzed in CIFTI format. To simplify usage for new data, the workflow has been bundled with

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Section: Discussionmentioning

confidence: 99%

ciftify: A framework for surface-based analysis of legacy MR acquisitions

Dickie

Antičević

Smith

et al. 2018

Preprint

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“…Very few efforts have tried to combine information across assays and disorders. Efforts such as ABCD 30 to collect longitudinal imaging, behavioral, and clinical data at scale have only just started, but is focused primarily substance abuse.…”

Section: Integrating Neuroimaging With Other Phenotyping Approaches Amentioning

confidence: 99%

Will Neuroimaging Produce a Clinical Tool for Psychiatry?

Ghosh¹,

Baker²

2019

Psychiatric Annals

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The increasing incidence, awareness, and social and economic impact of mental health disorders, the current status quo of treatment options and their limited success, and the extensive investment into brain imaging research raises an important question for the future behavioral medicine: will neuroimaging produce a clinical tool for psychiatry? Significant advances in neuroimaging over the past two decades allow psychiatric clinicians to peer into the living, functioning brain. Neuroimaging has been used to diagnose mental illnesses, to predict treatment outcomes, to find new stratifications of psychiatric disorders, and to provide therapy. The development of computational techniques, alongside several population neuroimaging efforts worldwide, increase the prospect for a first neuroimaging-based clinical tool. In this article, we describe the formidable challenges to creating such a tool and forecast how current institutions can solve them through social, technical, educational, and policy changes, improving data sharing practices, advances in technology, and integration between neuroimaging and other emerging information streams.

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“…For the current report, we analyzed data acquired in the subset of ABCD participants (see (Bjork et al, 2017;Casey et al, 2018;Lisdahl et al, 2018;Volkow et al, 2017)…”

Section: Abcd -Multiband Datasetmentioning

confidence: 99%

Correction of respiratory artifacts in MRI head motion estimates

Fair

Miranda-Domínguez

Snyder³

et al. 2018

Preprint

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Head motion represents one of the greatest technical obstacles for brain MRI. Accurate detection of artifacts induced by head motion requires precise estimation of movement.However, this estimation may be corrupted by factitious effects owing to main field fluctuations generated by body motion. In the current report, we examine head motion estimation in multiband resting state functional connectivity MRI (rs-fcMRI) data from the Adolescent Brain and Cognitive Development (ABCD) Study and a comparison 'single-shot' dataset from OregonHealth & Science University. We show unequivocally that respirations contaminate movement estimates in functional MRI and that respiration generates apparent head motion not associated with degraded quality of functional MRI. We have developed a novel approach using a bandstop filter that accurately removes these respiratory effects. Subsequently, we demonstrate that utilizing this filter improves post-processing data quality. Lastly, we demonstrate the real-time implementation of motion estimate filtering in our FIRMM (Framewise Integrated Real-Time MRI Monitoring) software package.(FIRMM) . FIRMM provides near instantaneous analysis of head motion (framewise displacement = FD) during scanning. This feature enables the duration of resting state fMRI scanning to be dynamically determined, that is, for as long as is necessary to acquire a prescribed quantity of data meeting a fixed quality assurance criterion. Thus, FIRMM helps to ensure that an adequate quantity of resting state data is acquired in most participants.It also reduces the need for 'overscanning' all participants in order to ensure adequate acquisitions across subjects. Moreover, subjects who cannot suppress excessive motion can be identified promptly and efficiently excluded from the study. Additionally, FIRMM alerts the scanner operator to changes in participant behavior (e.g., sleep, or increased movement because of discomfort), and also can be utilized to give motion feedback to participants themselves. These advances have been thoroughly detailed in prior reports Greene et al., 2018).Here, we address new issues that have arisen when calculating head motion estimates (FD) fMRI data. Multiband imaging (simultaneous multi-slice sequences [SMS]) is a recently developed technique that substantially speeds up fMRI by exciting multiple slices simultaneously (Feinberg and Yacoub, 2012;Moeller et al., 2010;Uğurbil et al., 2013;Xu et al., 2013). Thus, the time needed to acquire one whole brain volume can be reduced to well below 1 sec. While multiband sequences offer several clear advantages and are now becoming widely used, they come at the cost of reducing the temporal signal to noise ratio (tSNR; (Chen et al., 2015)) and, potentially, new types of 'slice-leakage' artifacts (Barth et al., 2016;Todd et al., 2016).Another unanticipated consequence of the improved temporal resolution of SMS sequences is corruption of head motion estimation (FD) arising from the interaction between echo-planar imaging (EPI) and small perturbatio...

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The ABCD Study of Neurodevelopment: Identifying Neurocircuit Targets for Prevention and Treatment of Adolescent Substance Abuse

Cited by 90 publications

References 77 publications

ciftify: A framework for surface-based analysis of legacy MR acquisitions

ciftify: A framework for surface-based analysis of legacy MR acquisitions

Will Neuroimaging Produce a Clinical Tool for Psychiatry?

Correction of respiratory artifacts in MRI head motion estimates

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