A phenome-wide examination of neural and cognitive function

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 (138 subjects) and individuals with neuropsychiatric disorders including schizophrenia (58 subjects), bipolar disorder (49 subjects), and attention deficit/hyperactivity disorder (45 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.

[1]  Brian B. Avants,et al.  N4ITK: Improved N3 Bias Correction , 2010, IEEE Transactions on Medical Imaging.

[2]  David Atkinson,et al.  Automatic correction of motion artifacts in magnetic resonance images using an entropy focus criterion , 1997, IEEE Transactions on Medical Imaging.

[3]  M. Giannelli,et al.  Characterization of Nyquist ghost in EPI‐fMRI acquisition sequences implemented on two clinical 1.5 T MR scanner systems: effect of readout bandwidth and echo spacing , 2010, Journal of applied clinical medical physics.

[4]  Oluwasanmi Koyejo,et al.  Toward open sharing of task-based fMRI data: the OpenfMRI project , 2013, Front. Neuroinform..

[5]  Stephen M. Smith,et al.  Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.

[6]  Thomas E. Nichols.  Notes on Creating a Standardized Version of DVARS , 2017, 1704.01469.

[7]  Caroline A. Montojo,et al.  Neural substrates of inhibitory control deficits in 22q11.2 deletion syndrome. , 2015, Cerebral cortex.

[8]  Tyrone D. Cannon,et al.  Predicting risky choices from brain activity patterns , 2014, Proceedings of the National Academy of Sciences.

[9]  Eliza Congdon,et al.  Differences in neural activation as a function of risk-taking task parameters , 2013, Front. Neurosci..

[10]  Jean-Philippe Thiran,et al.  Automatic quality assessment in structural brain magnetic resonance imaging , 2009, Magnetic resonance in medicine.

[11]  Caroline A. Montojo,et al.  Neural mechanisms of response inhibition and impulsivity in 22q11.2 deletion carriers and idiopathic attention deficit hyperactivity disorder☆ , 2015, NeuroImage: Clinical.

[12]  Arno Klein,et al.  A reproducible evaluation of ANTs similarity metric performance in brain image registration , 2011, NeuroImage.

[13]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[14]  P. Basser,et al.  MR diffusion tensor spectroscopy and imaging. , 1994, Biophysical journal.

[15]  R W Cox,et al.  AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. , 1996, Computers and biomedical research, an international journal.

[16]  Gregory G. Brown,et al.  A technique for the deidentification of structural brain MR images , 2007, Human brain mapping.

[17]  Satrajit S. Ghosh,et al.  The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments , 2016, Scientific Data.

[18]  Eliza Congdon,et al.  Neural activation during response inhibition in adult attention-deficit/hyperactivity disorder: Preliminary findings on the effects of medication and symptom severity , 2014, Psychiatry Research: Neuroimaging.

[19]  Eliza Congdon,et al.  Default mode network connectivity and reciprocal social behavior in 22q11.2 deletion syndrome. , 2014, Social cognitive and affective neuroscience.

[20]  Lee Friedman,et al.  Measurement of Signal-to-Noise and Contrast-to-Noise in the fBIRN Multicenter Imaging Study , 2006, Journal of Digital Imaging.

[21]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.