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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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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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Mapping Mental Function to Brain Structure: How Can Cognitive Neuroimaging Succeed?.

Perspectives on psychological science : a journal of the Association for Psychological Science

Poldrack RA.
PMID: 25076977
Perspect Psychol Sci. 2010 Nov;5(6):753-61. doi: 10.1177/1745691610388777.

The goal of cognitive neuroscience is to identify the mapping between brain function and mental processing. In this article, I examine the strategies that have been used to identify such mappings and argue that they may be fundamentally unable...

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Poldrack RA. Mapping Mental Function to Brain Structure: How Can Cognitive Neuroimaging Succeed?. Perspect Psychol Sci. 2010;5(6):753-61doi: 10.1177/1745691610388777.
Poldrack, R. A. (2010). Mapping Mental Function to Brain Structure: How Can Cognitive Neuroimaging Succeed?. Perspectives on psychological science : a journal of the Association for Psychological Science, 5(6), 753-61. https://doi.org/10.1177/1745691610388777
Poldrack, Russell A. "Mapping Mental Function to Brain Structure: How Can Cognitive Neuroimaging Succeed?." Perspectives on psychological science : a journal of the Association for Psychological Science vol. 5,6 (2010): 753-61. doi: https://doi.org/10.1177/1745691610388777
Poldrack RA. Mapping Mental Function to Brain Structure: How Can Cognitive Neuroimaging Succeed?. Perspect Psychol Sci. 2010 Nov;5(6):753-61. doi: 10.1177/1745691610388777. PMID: 25076977; PMCID: PMC4112478.
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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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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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Showing 1 to 7 of 7 entries