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Showing 1 to 11 of 11 entries
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Bifactor models and rotations: exploring the extent to which multidimensional data yield univocal scale scores.

Journal of personality assessment

Reise SP, Moore TM, Haviland MG.
PMID: 20954056
J Pers Assess. 2010 Nov;92(6):544-59. doi: 10.1080/00223891.2010.496477.

The application of psychological measures often results in item response data that arguably are consistent with both unidimensional (a single common factor) and multidimensional latent structures (typically caused by parcels of items that tap similar content domains). As such,...

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Reise SP, Moore TM, Haviland MG. Bifactor models and rotations: exploring the extent to which multidimensional data yield univocal scale scores. J Pers Assess. 2010;92(6):544-59doi: 10.1080/00223891.2010.496477.
Reise, S. P., Moore, T. M., & Haviland, M. G. (2010). Bifactor models and rotations: exploring the extent to which multidimensional data yield univocal scale scores. Journal of personality assessment, 92(6), 544-59. https://doi.org/10.1080/00223891.2010.496477
Reise, Steven P, et al. "Bifactor models and rotations: exploring the extent to which multidimensional data yield univocal scale scores." Journal of personality assessment vol. 92,6 (2010): 544-59. doi: https://doi.org/10.1080/00223891.2010.496477
Reise SP, Moore TM, Haviland MG. Bifactor models and rotations: exploring the extent to which multidimensional data yield univocal scale scores. J Pers Assess. 2010 Nov;92(6):544-59. doi: 10.1080/00223891.2010.496477. PMID: 20954056; PMCID: PMC2981404.
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Literature Mapping with PubAtlas - extending PubMed with a 'BLASTing interface'.

Summit on translational bioinformatics

Parker DS, Chu WW, Sabb FW, Toga AW, Bilder RM.
PMID: 21347177
Summit Transl Bioinform. 2009 Mar 01;2009:90-4.

PubAtlas (www.pubatlas.org) is a web service and standalone program providing literature maps for the biomedical research literature. It accepts user-defined sets of terms (PubMed queries) as input, and permits 'BLASTing' of one set against another: for all terms x...

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Parker DS, Chu WW, Sabb FW, et al. Literature Mapping with PubAtlas - extending PubMed with a 'BLASTing interface'. Summit Transl Bioinform. 2009;2009:90-4
Parker, D. S., Chu, W. W., Sabb, F. W., Toga, A. W., & Bilder, R. M. (2009). Literature Mapping with PubAtlas - extending PubMed with a 'BLASTing interface'. Summit on translational bioinformatics, 200990-4.
Parker, D S, et al. "Literature Mapping with PubAtlas - extending PubMed with a 'BLASTing interface'." Summit on translational bioinformatics vol. 2009 (2009): 90-4.
Parker DS, Chu WW, Sabb FW, Toga AW, Bilder RM. Literature Mapping with PubAtlas - extending PubMed with a 'BLASTing interface'. Summit Transl Bioinform. 2009 Mar 01;2009:90-4. PMID: 21347177; PMCID: PMC3041555.
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Shared and distinct white matter abnormalities in schizophrenia and bipolar disorder.

Progress in neuro-psychopharmacology & biological psychiatry

Joo SW, Kim H, Jo YT, Yoon W, Kim Y, Lee J.
PMID: 33188830
Prog Neuropsychopharmacol Biol Psychiatry. 2021 Jun 08;108:110175. doi: 10.1016/j.pnpbp.2020.110175. Epub 2020 Nov 12.

While white matter impairments play an integral part in the pathophysiology of schizophrenia and bipolar disorder, the literature on white matter abnormality differences between the two disorders is insufficient. The University of California Los Angeles Consortium for Neuropsychiatric Phenomic...

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Joo SW, Kim H, Jo YT, et al. Shared and distinct white matter abnormalities in schizophrenia and bipolar disorder. Prog Neuropsychopharmacol Biol Psychiatry. 2020;108:110175doi: 10.1016/j.pnpbp.2020.110175.
Joo, S. W., Kim, H., Jo, Y. T., Yoon, W., Kim, Y., & Lee, J. (2021). Shared and distinct white matter abnormalities in schizophrenia and bipolar disorder. Progress in neuro-psychopharmacology & biological psychiatry, 108110175. https://doi.org/10.1016/j.pnpbp.2020.110175
Joo, Sung Woo, et al. "Shared and distinct white matter abnormalities in schizophrenia and bipolar disorder." Progress in neuro-psychopharmacology & biological psychiatry vol. 108 (2021): 110175. doi: https://doi.org/10.1016/j.pnpbp.2020.110175
Joo SW, Kim H, Jo YT, Yoon W, Kim Y, Lee J. Shared and distinct white matter abnormalities in schizophrenia and bipolar disorder. Prog Neuropsychopharmacol Biol Psychiatry. 2021 Jun 08;108:110175. doi: 10.1016/j.pnpbp.2020.110175. Epub 2020 Nov 12. PMID: 33188830.
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Risk Taking and Sensitivity to Punishment in Children with ADHD, ODD, ADHD+ODD, and Controls.

Journal of psychopathology and behavioral assessment

Humphreys KL, Lee SS.
PMID: 33132494
J Psychopathol Behav Assess. 2011;33(3):299-307. doi: 10.1007/s10862-011-9237-6. Epub 2011 Jun 11.

We used the Balloon Analog Risk Task (BART) to examine risk taking and sensitivity to punishment, two relevant aspects of behavioral inhibition, in 203 school-age children with attention-deficit/hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), ADHD+ODD, and controls. Participants earned...

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Humphreys KL, Lee SS. Risk Taking and Sensitivity to Punishment in Children with ADHD, ODD, ADHD+ODD, and Controls. J Psychopathol Behav Assess. 2011;33(3):299-307doi: 10.1007/s10862-011-9237-6.
Humphreys, K. L., & Lee, S. S. (2011). Risk Taking and Sensitivity to Punishment in Children with ADHD, ODD, ADHD+ODD, and Controls. Journal of psychopathology and behavioral assessment, 33(3), 299-307. https://doi.org/10.1007/s10862-011-9237-6
Humphreys, Kathryn L, and Lee, Steve S. "Risk Taking and Sensitivity to Punishment in Children with ADHD, ODD, ADHD+ODD, and Controls." Journal of psychopathology and behavioral assessment vol. 33,3 (2011): 299-307. doi: https://doi.org/10.1007/s10862-011-9237-6
Humphreys KL, Lee SS. Risk Taking and Sensitivity to Punishment in Children with ADHD, ODD, ADHD+ODD, and Controls. J Psychopathol Behav Assess. 2011;33(3):299-307. doi: 10.1007/s10862-011-9237-6. Epub 2011 Jun 11. PMID: 33132494; PMCID: PMC7597674.
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Nonlinear manifold learning in functional magnetic resonance imaging uncovers a low-dimensional space of brain dynamics.

Human brain mapping

Gao S, Mishne G, Scheinost D.
PMID: 34184812
Hum Brain Mapp. 2021 Oct 01;42(14):4510-4524. doi: 10.1002/hbm.25561. Epub 2021 Jun 29.

Large-scale brain dynamics are believed to lie in a latent, low-dimensional space. Typically, the embeddings of brain scans are derived independently from different cognitive tasks or resting-state data, ignoring a potentially large-and shared-portion of this space. Here, we establish...

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Gao S, Mishne G, Scheinost D. Nonlinear manifold learning in functional magnetic resonance imaging uncovers a low-dimensional space of brain dynamics. Hum Brain Mapp. 2021;42(14):4510-4524doi: 10.1002/hbm.25561.
Gao, S., Mishne, G., & Scheinost, D. (2021). Nonlinear manifold learning in functional magnetic resonance imaging uncovers a low-dimensional space of brain dynamics. Human brain mapping, 42(14), 4510-4524. https://doi.org/10.1002/hbm.25561
Gao, Siyuan, et al. "Nonlinear manifold learning in functional magnetic resonance imaging uncovers a low-dimensional space of brain dynamics." Human brain mapping vol. 42,14 (2021): 4510-4524. doi: https://doi.org/10.1002/hbm.25561
Gao S, Mishne G, Scheinost D. Nonlinear manifold learning in functional magnetic resonance imaging uncovers a low-dimensional space of brain dynamics. Hum Brain Mapp. 2021 Oct 01;42(14):4510-4524. doi: 10.1002/hbm.25561. Epub 2021 Jun 29. PMID: 34184812; PMCID: PMC8410525.
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Decoding continuous variables from neuroimaging data: basic and clinical applications.

Frontiers in neuroscience

Cohen JR, Asarnow RF, Sabb FW, Bilder RM, Bookheimer SY, Knowlton BJ, Poldrack RA.
PMID: 21720520
Front Neurosci. 2011 Jun 15;5:75. doi: 10.3389/fnins.2011.00075. eCollection 2011.

The application of statistical machine learning techniques to neuroimaging data has allowed researchers to decode the cognitive and disease states of participants. The majority of studies using these techniques have focused on pattern classification to decode the type of...

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Cohen JR, Asarnow RF, Sabb FW, et al. Decoding continuous variables from neuroimaging data: basic and clinical applications. Front Neurosci. 2011;5:75doi: 10.3389/fnins.2011.00075.
Cohen, J. R., Asarnow, R. F., Sabb, F. W., Bilder, R. M., Bookheimer, S. Y., Knowlton, B. J., & Poldrack, R. A. (2011). Decoding continuous variables from neuroimaging data: basic and clinical applications. Frontiers in neuroscience, 575. https://doi.org/10.3389/fnins.2011.00075
Cohen, Jessica R, et al. "Decoding continuous variables from neuroimaging data: basic and clinical applications." Frontiers in neuroscience vol. 5 (2011): 75. doi: https://doi.org/10.3389/fnins.2011.00075
Cohen JR, Asarnow RF, Sabb FW, Bilder RM, Bookheimer SY, Knowlton BJ, Poldrack RA. Decoding continuous variables from neuroimaging data: basic and clinical applications. Front Neurosci. 2011 Jun 15;5:75. doi: 10.3389/fnins.2011.00075. eCollection 2011. PMID: 21720520; PMCID: PMC3118657.
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The cognitive atlas: toward a knowledge foundation for cognitive neuroscience.

Frontiers in neuroinformatics

Poldrack RA, Kittur A, Kalar D, Miller E, Seppa C, Gil Y, Parker DS, Sabb FW, Bilder RM.
PMID: 21922006
Front Neuroinform. 2011 Sep 06;5:17. doi: 10.3389/fninf.2011.00017. eCollection 2011.

Cognitive neuroscience aims to map mental processes onto brain function, which begs the question of what "mental processes" exist and how they relate to the tasks that are used to manipulate and measure them. This topic has been addressed...

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Poldrack RA, Kittur A, Kalar D, et al. The cognitive atlas: toward a knowledge foundation for cognitive neuroscience. Front Neuroinform. 2011;5:17doi: 10.3389/fninf.2011.00017.
Poldrack, R. A., Kittur, A., Kalar, D., Miller, E., Seppa, C., Gil, Y., Parker, D. S., Sabb, F. W., & Bilder, R. M. (2011). The cognitive atlas: toward a knowledge foundation for cognitive neuroscience. Frontiers in neuroinformatics, 517. https://doi.org/10.3389/fninf.2011.00017
Poldrack, Russell A, et al. "The cognitive atlas: toward a knowledge foundation for cognitive neuroscience." Frontiers in neuroinformatics vol. 5 (2011): 17. doi: https://doi.org/10.3389/fninf.2011.00017
Poldrack RA, Kittur A, Kalar D, Miller E, Seppa C, Gil Y, Parker DS, Sabb FW, Bilder RM. The cognitive atlas: toward a knowledge foundation for cognitive neuroscience. Front Neuroinform. 2011 Sep 06;5:17. doi: 10.3389/fninf.2011.00017. eCollection 2011. PMID: 21922006; PMCID: PMC3167196.
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Invited Paper: The Rediscovery of Bifactor Measurement Models.

Multivariate behavioral research

Reise SP.
PMID: 24049214
Multivariate Behav Res. 2012 Sep 01;47(5):667-696. doi: 10.1080/00273171.2012.715555.

Bifactor latent structures were introduced over 70 years ago, but only recently has bifactor modeling been rediscovered as an effective approach to modeling

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Reise SP. Invited Paper: The Rediscovery of Bifactor Measurement Models. Multivariate Behav Res. 2012;47(5):667-696doi: 10.1080/00273171.2012.715555.
Reise, S. P. (2012). Invited Paper: The Rediscovery of Bifactor Measurement Models. Multivariate behavioral research, 47(5), 667-696. https://doi.org/10.1080/00273171.2012.715555
Reise, Steven P. "Invited Paper: The Rediscovery of Bifactor Measurement Models." Multivariate behavioral research vol. 47,5 (2012): 667-696. doi: https://doi.org/10.1080/00273171.2012.715555
Reise SP. Invited Paper: The Rediscovery of Bifactor Measurement Models. Multivariate Behav Res. 2012 Sep 01;47(5):667-696. doi: 10.1080/00273171.2012.715555. PMID: 24049214; PMCID: PMC3773879.
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Differences in neural activation as a function of risk-taking task parameters.

Frontiers in neuroscience

Congdon E, Bato AA, Schonberg T, Mumford JA, Karlsgodt KH, Sabb FW, London ED, Cannon TD, Bilder RM, Poldrack RA.
PMID: 24137106
Front Neurosci. 2013 Sep 30;7:173. doi: 10.3389/fnins.2013.00173. eCollection 2013.

Despite evidence supporting a relationship between impulsivity and naturalistic risk-taking, the relationship of impulsivity with laboratory-based measures of risky decision-making remains unclear. One factor contributing to this gap in our understanding is the degree to which different risky decision-making...

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Congdon E, Bato AA, Schonberg T, et al. Differences in neural activation as a function of risk-taking task parameters. Front Neurosci. 2013;7:173doi: 10.3389/fnins.2013.00173.
Congdon, E., Bato, A. A., Schonberg, T., Mumford, J. A., Karlsgodt, K. H., Sabb, F. W., London, E. D., Cannon, T. D., Bilder, R. M., & Poldrack, R. A. (2013). Differences in neural activation as a function of risk-taking task parameters. Frontiers in neuroscience, 7173. https://doi.org/10.3389/fnins.2013.00173
Congdon, Eliza, et al. "Differences in neural activation as a function of risk-taking task parameters." Frontiers in neuroscience vol. 7 (2013): 173. doi: https://doi.org/10.3389/fnins.2013.00173
Congdon E, Bato AA, Schonberg T, Mumford JA, Karlsgodt KH, Sabb FW, London ED, Cannon TD, Bilder RM, Poldrack RA. Differences in neural activation as a function of risk-taking task parameters. Front Neurosci. 2013 Sep 30;7:173. doi: 10.3389/fnins.2013.00173. eCollection 2013. PMID: 24137106; PMCID: PMC3786224.
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From a deep learning model back to the brain-Identifying regional predictors and their relation to aging.

Human brain mapping

Levakov G, Rosenthal G, Shelef I, Raviv TR, Avidan G.
PMID: 32320123
Hum Brain Mapp. 2020 Aug 15;41(12):3235-3252. doi: 10.1002/hbm.25011. Epub 2020 Apr 22.

We present a Deep Learning framework for the prediction of chronological age from structural magnetic resonance imaging scans. Previous findings associate increased brain age with neurodegenerative diseases and higher mortality rates. However, the importance of brain age prediction goes...

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Levakov G, Rosenthal G, Shelef I, et al. From a deep learning model back to the brain-Identifying regional predictors and their relation to aging. Hum Brain Mapp. 2020;41(12):3235-3252doi: 10.1002/hbm.25011.
Levakov, G., Rosenthal, G., Shelef, I., Raviv, T. R., & Avidan, G. (2020). From a deep learning model back to the brain-Identifying regional predictors and their relation to aging. Human brain mapping, 41(12), 3235-3252. https://doi.org/10.1002/hbm.25011
Levakov, Gidon, et al. "From a deep learning model back to the brain-Identifying regional predictors and their relation to aging." Human brain mapping vol. 41,12 (2020): 3235-3252. doi: https://doi.org/10.1002/hbm.25011
Levakov G, Rosenthal G, Shelef I, Raviv TR, Avidan G. From a deep learning model back to the brain-Identifying regional predictors and their relation to aging. Hum Brain Mapp. 2020 Aug 15;41(12):3235-3252. doi: 10.1002/hbm.25011. Epub 2020 Apr 22. PMID: 32320123; PMCID: PMC7426775.
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Preprocessed Consortium for Neuropsychiatric Phenomics dataset.

F1000Research

Gorgolewski KJ, Durnez J, Poldrack RA.
PMID: 29152222
F1000Res. 2017 Jul 28;6:1262. doi: 10.12688/f1000research.11964.2. eCollection 2017.

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,...

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Gorgolewski KJ, Durnez J, Poldrack RA. Preprocessed Consortium for Neuropsychiatric Phenomics dataset. F1000Res. 2017;6:1262doi: 10.12688/f1000research.11964.2.
Gorgolewski, K. J., Durnez, J., & Poldrack, R. A. (2017). Preprocessed Consortium for Neuropsychiatric Phenomics dataset. F1000Research, 61262. https://doi.org/10.12688/f1000research.11964.2
Gorgolewski, Krzysztof J, et al. "Preprocessed Consortium for Neuropsychiatric Phenomics dataset." F1000Research vol. 6 (2017): 1262. doi: https://doi.org/10.12688/f1000research.11964.2
Gorgolewski KJ, Durnez J, Poldrack RA. Preprocessed Consortium for Neuropsychiatric Phenomics dataset. F1000Res. 2017 Jul 28;6:1262. doi: 10.12688/f1000research.11964.2. eCollection 2017. PMID: 29152222; PMCID: PMC5664981.
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