A phenome-wide examination of neural and cognitive function

Poldrack, Russell A.; Congdon, Eliza; Triplett, William; Gorgolewski, Krzysztof J.; Karlsgodt, Katherine H.; Mumford, Jeanette A.; Sabb, Fred W.; Freimer, Nelson B.; London, Edythe D.; Cannon, Tyrone D.; Bilder, Robert M.

doi:10.1038/sdata.2016.110

Cited by 300 publications

(320 citation statements)

References 32 publications

(23 reference statements)

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“…We downloaded the UCLA Consortium for Neuropsychiatric Phenomics (CNP) dataset from the public database OpenfMRI (30). The CNP dataset (29) comprised multimodal imaging and behavioral data from 272 participants, including 130 healthy individuals (HC), 49 patients with bipolar disorder type I (BD), 43 patients with attentiondeficit/hyperactivity disorder (ADHD), 39 patients with schizophrenia (SZ) and 11 patients with schizoaffective disorder (SZAD).…”

Section: Participantsmentioning

confidence: 99%

“…Details about participant recruitment can be found elsewhere (29). Briefly, healthy adults were recruited via community advertisements from the Los Angeles area, while patients were recruited via local clinics and online portals.…”

Section: Participantsmentioning

confidence: 99%

“…In this study, we utilized data from the UCLA Consortium for Neuropsychiatric Phenomics (29), a unique dataset in which the imaging and behavioral assessment were focused on working memory and inhibitory control, two domains that are exceedingly relevant in multiple psychiatric disorders. This allowed us to examine behavioral phenotypes beyond the clinical symptoms typically examined in many transdiagnostic studies (10,11,14,15,17).…”

Section: Introductionmentioning

confidence: 99%

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Somatosensory-Motor Dysconnectivity Spans Multiple Transdiagnostic Dimensions of Psychopathology

Kebets

Holmes

Orban

et al. 2019

Preprint

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word count: 247 Main text word count: 3980 Number of figures: 4 Number of tables: 1 Number of supplemental files: 1 AbstractBackground: There is considerable interest in a dimensional transdiagnostic approach to psychiatry. Most transdiagnostic studies have derived factors based only on clinical symptoms, which might miss possible links between psychopathology, cognitive processes and personality traits. Furthermore, many psychiatric studies focus on higher-order association brain networks, thus neglecting the potential influence of huge swaths of the brain. Methods:A multivariate data-driven approach (partial least squares; PLS) was utilized to identify latent components linking a large set of clinical, cognitive and personality measures to whole-brain resting-state functional connectivity (RSFC) patterns across 224 participants.The participants were either healthy (N=110) or diagnosed with bipolar disorder (N=40), attention-deficit/hyperactivity disorder (N=37), schizophrenia (N=29) or schizoaffective disorder (N=8). In contrast to traditional case-control analyses, the diagnostic categories were not utilized in the PLS analysis, but were helpful for interpreting the components.Results: Our analyses revealed three latent components corresponding to general psychopathology, cognitive dysfunction and impulsivity. Each component was associated with a unique whole-brain RSFC signature and shared across all participants. The components were robust across multiple control analyses and replicated using independent task functional magnetic resonance imaging data from the same participants. Strikingly, all three components featured connectivity alterations within the somatosensory-motor network, and its connectivity with subcortical structures and cortical executive networks. Conclusions:We identified three distinct dimensions with dissociable (but overlapping) whole-brain RSFC signatures across healthy individuals and individuals with psychiatric illness, providing potential intermediate phenotypes that span across diagnostic categories.Our results suggest expanding the focus of psychiatric neuroscience beyond higher-order brain networks.

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Section: Participantsmentioning

confidence: 99%

Section: Participantsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Somatosensory-Motor Dysconnectivity Spans Multiple Transdiagnostic Dimensions of Psychopathology

Kebets

Holmes

Orban

et al. 2019

Preprint

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“…To examine the ability of ETAC to find true positives, a collection of task datasets was downloaded from OpenFMRI.org. The UCLA Consortium for Neuropsychiatric Phenomics LA5c Study (Poldrack et al, 2016; OpenFMRI collection ds000030) was chosen, in particular the pattern matching and encoding "pamenc" task, using 78 control subjects (four control subjects' data had to be discarded due to excessive head motion). This collection of data was selected since it is a relatively large study, and the pamenc task robustly engages large segments of the brain involving vision, spatial perception, and language.…”

Section: Tests 3: Reliabilitymentioning

confidence: 99%

Equitable Thresholding and Clustering (ETAC): A novel method for FMRI clustering in AFNI

Cox¹

2018

Preprint

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This paper describes a hybrid method to threshold FMRI group statistical maps derived from voxelwise second-level statistical analyses. The proposed "Equitable Thresholding and Clustering" (ETAC) approach seeks to reduce the dependence of clustering results on arbitrary parameter values by using multiple sub-tests, each equivalent to a standard FMRI clustering analysis, to make decisions about which groups of voxels are potentially significant. The union of these sub-test results decides which voxels are accepted. The approach adjusts the clusterthresholding parameter of each sub-test in an equitable way, so that the individual false positive rates (FPRs) are balanced across sub-tests to achieve a desired final FPR (e.g., 5%). ETAC utilizes resampling methods to estimate the FPR, and thus does not rely on parametric assumptions about the spatial correlation of FMRI noise. The approach was validated with pseudo-task timings in resting state brain data. Additionally, a task FMRI data collection was used to compare ETAC's true positive detection power vs. a standard cluster detection method, demonstrating that ETAC is able to detect true results and control false positives while reducing reliance on arbitrary analysis parameters.

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“…For a full description of the paradigms for each task, please refer to 1 . We analysed the task data using FSL 17 and AFNI…”

Section: Volume-based Task Analysismentioning

confidence: 99%

Preprocessed Consortium for Neuropsychiatric Phenomics dataset

2017

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Here we present preprocessed MRI data of 265 participants from the Consortium for Neuropsychiatric Phenomics (CNP) dataset. The preprocessed dataset includes minimally preprocessed data in the native, MNI and surface spaces accompanied with potential confound regressors, tissue probability masks, brain masks and transformations. In addition the preprocessed dataset includes unthresholded group level and single subject statistical maps from all tasks included in the original dataset. We hope that availability of this dataset will greatly accelerate research.

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A phenome-wide examination of neural and cognitive function

Cited by 300 publications

References 32 publications

Somatosensory-Motor Dysconnectivity Spans Multiple Transdiagnostic Dimensions of Psychopathology

Somatosensory-Motor Dysconnectivity Spans Multiple Transdiagnostic Dimensions of Psychopathology

Equitable Thresholding and Clustering (ETAC): A novel method for FMRI clustering in AFNI

Preprocessed Consortium for Neuropsychiatric Phenomics dataset

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