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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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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 4 of 4 entries