A phenome-wide examination of neural and cognitive function

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Abstract

This data descriptor outlines a shared neuroimaging dataset from the UCLA Consortium for Neuropsychiatric Phenomics, which focused on understanding the dimensional structure of memory and cognitive control (response inhibition) functions in both healthy individuals (130 subjects) and individuals with neuropsychiatric disorders including schizophrenia (50 subjects), bipolar disorder (49 subjects), and attention deficit/hyperactivity disorder (43 subjects). The dataset includes an extensive set of task-based fMRI assessments, resting fMRI, structural MRI, and high angular resolution diffusion MRI. The dataset is shared through the OpenfMRI project, and is formatted according to the Brain Imaging Data Structure (BIDS) standard.

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A technique for the deidentification of structural brain MR images.

Amanda Bischoff-Grethe, I. Ozyurt, Evelina Busa … (2007)

Due to the increasing need for subject privacy, the ability to deidentify structural MR images so that they do not provide full facial detail is desirable. A program was developed that uses models of nonbrain structures for removing potentially identifying facial features. When a novel image is presented, the optimal linear transform is computed for the input volume (Fischl et al. [2002]: Neuron 33:341-355; Fischl et al. [2004]: Neuroimage 23 (Suppl 1):S69-S84). A brain mask is constructed by forming the union of all voxels with nonzero probability of being brain and then morphologically dilated. All voxels outside the mask with a nonzero probability of being a facial feature are set to 0. The algorithm was applied to 342 datasets that included two different T1-weighted pulse sequences and four different diagnoses (depressed, Alzheimer's, and elderly and young control groups). Visual inspection showed none had brain tissue removed. In a detailed analysis of the impact of defacing on skull-stripping, 16 datasets were bias corrected with N3 (Sled et al. [1998]: IEEE Trans Med Imaging 17:87-97), defaced, and then skull-stripped using either a hybrid watershed algorithm (Ségonne et al. [2004]: Neuroimage 22:1060-1075, in FreeSurfer) or Brain Surface Extractor (Sandor and Leahy [1997]: IEEE Trans Med Imaging 16:41-54; Shattuck et al. [2001]: Neuroimage 13:856-876); defacing did not appreciably influence the outcome of skull-stripping. Results suggested that the automatic defacing algorithm is robust, efficiently removes nonbrain tissue, and does not unduly influence the outcome of the processing methods utilized; in some cases, skull-stripping was improved. Analyses support this algorithm as a viable method to allow data sharing with minimal data alteration within large-scale multisite projects. (c) 2007 Wiley-Liss, Inc.

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Automatic correction of motion artifacts in magnetic resonance images using an entropy focus criterion.

D. Atkinson, D.L.G. Hill, P.N.R. Stoyle … (1997)

We present the use of an entropy focus criterion to enable automatic focusing of motion corrupted magnetic resonance images. We demonstrate the principle using illustrative examples from cooperative volunteers. Our technique can determine unknown patient motion or use knowledge of motion from other measures as a starting estimate. The motion estimate is used to compensate the acquired data and is iteratively refined using the image entropy. Entropy focuses the whole image principally by favoring the removal of motion induced ghosts and blurring from otherwise dark regions of the image. Using only the image data, and no special hardware or pulse sequences, we demonstrate correction for arbitrary rigid-body translational motion in the imaging plane and for a single rotation. Extension to three-dimensional (3-D) and more general motion should be possible. The algorithm is able to determine volunteer motion well. The mean absolute deviation between algorithm and navigator-echo-determined motion is comparable to the displacement step size used in the algorithm. Local deviations from the recorded motion or navigator-determined motion are explained and we indicate how enhanced focus criteria may be derived. In all cases we were able to compensate images for patient motion, reducing blurring and ghosting.

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Inner speech as a retrieval aid for task goals: the effects of cue type and articulatory suppression in the random task cuing paradigm.

Akira Miyake, Michael J Emerson, Francisca Padilla … (2015)

Articulatory suppression has been shown to increase switch costs in the list paradigm (e.g., [J. Exp. Psychol.: General 130 (2001) 641, J Memory Language 48 (2003) 148]). The present dual-task study examined whether this effect generalizes to the random task cuing paradigm. Participants performed color or shape judgments according to explicit word cues (COLOR or SHAPE) or less transparent letter cues ( C for the color task and S for the shape task). In the word cue condition, the switch cost was equivalent for the articulatory suppression and the control (no dual-task) conditions, but, in the letter cue condition, the switch cost was significantly greater for the articulatory suppression condition than for the control condition. These results suggest that inner speech may be recruited as a tool for retrieving and activating the relevant task goal when the task cue is not transparent and hence imposes nonnegligible retrieval demand.

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Author and article information

Journal

Journal ID (nlm-ta): Sci Data

Journal ID (iso-abbrev): Sci Data

Title: Scientific Data

Publisher: Nature Publishing Group

ISSN (Electronic): 2052-4463

Publication date (Electronic): 06 December 2016

Publication date Collection: 2016

Volume: 3

Electronic Location Identifier: 160110

Affiliations

[1 ]Department of Psychology, Stanford University, Stanford, California 94305, USA

[2 ]Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, California 90095, USA

[3 ]Department of Psychology, University of California , Los Angeles, California 90095, USA

[4 ]Center for Healthy Minds, University of Wisconsin—Madison, Madison, Wisconsin 53705, USA

[5 ]Lewis Center for Neuroimaging, University of Oregon, Eugene, Oregon 97403, USA

[6 ]Department of Psychology, Yale University, New Haven, Connecticut 06520, USA

[7 ]Department of Psychiatry, Yale University, New Haven, Connecticut 06520, USA

Author notes

[a ] R.A.P. (email: russpold@ 123456stanford.edu ).

[]

R.A.P.: conception and design of study and data sharing plan, drafting of the article. E.C.: conception and design of study, acquisition and analysis of data, critical review and final approval of the version submitted. W.T.: curation, critical review and final approval of the version submitted. K.J.G.: design of data sharing plan, critical review and final approval of the version submitted. K.H.K.: conception and design of study, acquisition and analysis of data, critical review and final approval of the version submitted. J.A.M.: conception and design of study, final approval of the version submitted. FWS: conception and design of study, final approval of the version submitted. N.B.F.: conception and design of study, final approval of the version submitted. E.D.L.: conception and design of the study, final approval of the version submitted. T.D.C.: conception and design of study, final approval of the version submitted. R.M.B.: conception and design of study and data sharing plan, critical review and final approval of the version submitted.

Article

Publisher Item ID: sdata2016110

DOI: 10.1038/sdata.2016.110

PMC ID: 5139672

PubMed ID: 27922632

SO-VID: e7abe9be-0690-4c63-928c-bcd74b901a5a

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This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0 Metadata associated with this Data Descriptor is available at http://www.nature.com/sdata/ and is released under the CC0 waiver to maximize reuse.

A phenome-wide examination of neural and cognitive function

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Communication Through Coherence

Most cited references 13

A technique for the deidentification of structural brain MR images.

Automatic correction of motion artifacts in magnetic resonance images using an entropy focus criterion.

Inner speech as a retrieval aid for task goals: the effects of cue type and articulatory suppression in the random task cuing paradigm.

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