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The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments.

Scientific data

Gorgolewski KJ, Auer T, Calhoun VD, Craddock RC, Das S, Duff EP, Flandin G, Ghosh SS, Glatard T, Halchenko YO, Handwerker DA, Hanke M, Keator D, Li X, Michael Z, Maumet C, Nichols BN, Nichols TE, Pellman J, Poline JB, Rokem A, Schaefer G, Sochat V, Triplett W, Turner JA, Varoquaux G, Poldrack RA.
PMID: 27326542
Sci Data. 2016 Jun 21;3:160044. doi: 10.1038/sdata.2016.44.

The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain....

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Gorgolewski KJ, Auer T, Calhoun VD, et al. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data. 2016;3:160044doi: 10.1038/sdata.2016.44.
Gorgolewski, K. J., Auer, T., Calhoun, V. D., Craddock, R. C., Das, S., Duff, E. P., Flandin, G., Ghosh, S. S., Glatard, T., Halchenko, Y. O., Handwerker, D. A., Hanke, M., Keator, D., Li, X., Michael, Z., Maumet, C., Nichols, B. N., Nichols, T. E., Pellman, J., Poline, J. B., Rokem, A., Schaefer, G., Sochat, V., Triplett, W., Turner, J. A., Varoquaux, G., & Poldrack, R. A. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific data, 3160044. https://doi.org/10.1038/sdata.2016.44
Gorgolewski, Krzysztof J, et al. "The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments." Scientific data vol. 3 (2016): 160044. doi: https://doi.org/10.1038/sdata.2016.44
Gorgolewski KJ, Auer T, Calhoun VD, Craddock RC, Das S, Duff EP, Flandin G, Ghosh SS, Glatard T, Halchenko YO, Handwerker DA, Hanke M, Keator D, Li X, Michael Z, Maumet C, Nichols BN, Nichols TE, Pellman J, Poline JB, Rokem A, Schaefer G, Sochat V, Triplett W, Turner JA, Varoquaux G, Poldrack RA. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data. 2016 Jun 21;3:160044. doi: 10.1038/sdata.2016.44. PMID: 27326542; PMCID: PMC4978148.
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The default modes of reading: modulation of posterior cingulate and medial prefrontal cortex connectivity associated with comprehension and task focus while reading.

Frontiers in human neuroscience

Smallwood J, Gorgolewski KJ, Golchert J, Ruby FJ, Engen H, Baird B, Vinski MT, Schooler JW, Margulies DS.
PMID: 24282397
Front Hum Neurosci. 2013 Nov 12;7:734. doi: 10.3389/fnhum.2013.00734. eCollection 2013.

Reading is a fundamental human capacity and yet it can easily be derailed by the simple act of mind-wandering. A large-scale brain network, referred to as the default mode network (DMN), has been shown to be involved in both...

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Smallwood J, Gorgolewski KJ, Golchert J, et al. The default modes of reading: modulation of posterior cingulate and medial prefrontal cortex connectivity associated with comprehension and task focus while reading. Front Hum Neurosci. 2013;7:734doi: 10.3389/fnhum.2013.00734.
Smallwood, J., Gorgolewski, K. J., Golchert, J., Ruby, F. J., Engen, H., Baird, B., Vinski, M. T., Schooler, J. W., & Margulies, D. S. (2013). The default modes of reading: modulation of posterior cingulate and medial prefrontal cortex connectivity associated with comprehension and task focus while reading. Frontiers in human neuroscience, 7734. https://doi.org/10.3389/fnhum.2013.00734
Smallwood, Jonathan, et al. "The default modes of reading: modulation of posterior cingulate and medial prefrontal cortex connectivity associated with comprehension and task focus while reading." Frontiers in human neuroscience vol. 7 (2013): 734. doi: https://doi.org/10.3389/fnhum.2013.00734
Smallwood J, Gorgolewski KJ, Golchert J, Ruby FJ, Engen H, Baird B, Vinski MT, Schooler JW, Margulies DS. The default modes of reading: modulation of posterior cingulate and medial prefrontal cortex connectivity associated with comprehension and task focus while reading. Front Hum Neurosci. 2013 Nov 12;7:734. doi: 10.3389/fnhum.2013.00734. eCollection 2013. PMID: 24282397; PMCID: PMC3825257.
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A Practical Guide for Improving Transparency and Reproducibility in Neuroimaging Research.

PLoS biology

Gorgolewski KJ, Poldrack RA.
PMID: 27389358
PLoS Biol. 2016 Jul 07;14(7):e1002506. doi: 10.1371/journal.pbio.1002506. eCollection 2016 Jul.

Recent years have seen an increase in alarming signals regarding the lack of replicability in neuroscience, psychology, and other related fields. To avoid a widespread crisis in neuroimaging research and consequent loss of credibility in the public eye, we...

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Gorgolewski KJ, Poldrack RA. A Practical Guide for Improving Transparency and Reproducibility in Neuroimaging Research. PLoS Biol. 2016;14(7):e1002506doi: 10.1371/journal.pbio.1002506.
Gorgolewski, K. J., & Poldrack, R. A. (2016). A Practical Guide for Improving Transparency and Reproducibility in Neuroimaging Research. PLoS biology, 14(7), e1002506. https://doi.org/10.1371/journal.pbio.1002506
Gorgolewski, Krzysztof J, and Poldrack, Russell A. "A Practical Guide for Improving Transparency and Reproducibility in Neuroimaging Research." PLoS biology vol. 14,7 (2016): e1002506. doi: https://doi.org/10.1371/journal.pbio.1002506
Gorgolewski KJ, Poldrack RA. A Practical Guide for Improving Transparency and Reproducibility in Neuroimaging Research. PLoS Biol. 2016 Jul 07;14(7):e1002506. doi: 10.1371/journal.pbio.1002506. eCollection 2016 Jul. PMID: 27389358; PMCID: PMC4936733.
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NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain.

Frontiers in neuroinformatics

Gorgolewski KJ, Varoquaux G, Rivera G, Schwarz Y, Ghosh SS, Maumet C, Sochat VV, Nichols TE, Poldrack RA, Poline JB, Yarkoni T, Margulies DS.
PMID: 25914639
Front Neuroinform. 2015 Apr 10;9:8. doi: 10.3389/fninf.2015.00008. eCollection 2015.

Here we present NeuroVault-a web based repository that allows researchers to store, share, visualize, and decode statistical maps of the human brain. NeuroVault is easy to use and employs modern web technologies to provide informative visualization of data without...

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Gorgolewski KJ, Varoquaux G, Rivera G, et al. NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain. Front Neuroinform. 2015;9:8doi: 10.3389/fninf.2015.00008.
Gorgolewski, K. J., Varoquaux, G., Rivera, G., Schwarz, Y., Ghosh, S. S., Maumet, C., Sochat, V. V., Nichols, T. E., Poldrack, R. A., Poline, J. B., Yarkoni, T., & Margulies, D. S. (2015). NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain. Frontiers in neuroinformatics, 98. https://doi.org/10.3389/fninf.2015.00008
Gorgolewski, Krzysztof J, et al. "NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain." Frontiers in neuroinformatics vol. 9 (2015): 8. doi: https://doi.org/10.3389/fninf.2015.00008
Gorgolewski KJ, Varoquaux G, Rivera G, Schwarz Y, Ghosh SS, Maumet C, Sochat VV, Nichols TE, Poldrack RA, Poline JB, Yarkoni T, Margulies DS. NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain. Front Neuroinform. 2015 Apr 10;9:8. doi: 10.3389/fninf.2015.00008. eCollection 2015. PMID: 25914639; PMCID: PMC4392315.
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Adaptive thresholding for reliable topological inference in single subject fMRI analysis.

Frontiers in human neuroscience

Gorgolewski KJ, Storkey AJ, Bastin ME, Pernet CR.
PMID: 22936908
Front Hum Neurosci. 2012 Aug 25;6:245. doi: 10.3389/fnhum.2012.00245. eCollection 2012.

Single subject fMRI has proved to be a useful tool for mapping functional areas in clinical procedures such as tumor resection. Using fMRI data, clinicians assess the risk, plan and execute such procedures based on thresholded statistical maps. However,...

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Gorgolewski KJ, Storkey AJ, Bastin ME, et al. Adaptive thresholding for reliable topological inference in single subject fMRI analysis. Front Hum Neurosci. 2012;6:245doi: 10.3389/fnhum.2012.00245.
Gorgolewski, K. J., Storkey, A. J., Bastin, M. E., & Pernet, C. R. (2012). Adaptive thresholding for reliable topological inference in single subject fMRI analysis. Frontiers in human neuroscience, 6245. https://doi.org/10.3389/fnhum.2012.00245
Gorgolewski, Krzysztof J, et al. "Adaptive thresholding for reliable topological inference in single subject fMRI analysis." Frontiers in human neuroscience vol. 6 (2012): 245. doi: https://doi.org/10.3389/fnhum.2012.00245
Gorgolewski KJ, Storkey AJ, Bastin ME, Pernet CR. Adaptive thresholding for reliable topological inference in single subject fMRI analysis. Front Hum Neurosci. 2012 Aug 25;6:245. doi: 10.3389/fnhum.2012.00245. eCollection 2012. PMID: 22936908; PMCID: PMC3427544.
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PyBIDS: Python tools for BIDS datasets.

Journal of open source software

Yarkoni T, Markiewicz CJ, de la Vega A, Gorgolewski KJ, Salo T, Halchenko YO, McNamara Q, DeStasio K, Poline JB, Petrov D, Hayot-Sasson V, Nielson DM, Carlin J, Kiar G, Whitaker K, DuPre E, Wagner A, Tirrell LS, Jas M, Hanke M, Poldrack RA, Esteban O, Appelhoff S, Holdgraf C, Staden I, Thirion B, Kleinschmidt DF, Lee JA, Visconti di Oleggio Castello M, Notter MP, Blair R.
PMID: 32775955
J Open Source Softw. 2019;4(40). doi: 10.21105/joss.01294. Epub 2019 Aug 12.

No abstract available.

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Yarkoni T, Markiewicz CJ, de la Vega A, et al. PyBIDS: Python tools for BIDS datasets. J Open Source Softw. 2019;4(40)doi: 10.21105/joss.01294.
Yarkoni, T., Markiewicz, C. J., de la Vega, A., Gorgolewski, K. J., Salo, T., Halchenko, Y. O., McNamara, Q., DeStasio, K., Poline, J. B., Petrov, D., Hayot-Sasson, V., Nielson, D. M., Carlin, J., Kiar, G., Whitaker, K., DuPre, E., Wagner, A., Tirrell, L. S., Jas, M., Hanke, M., Poldrack, R. A., Esteban, O., Appelhoff, S., Holdgraf, C., Staden, I., Thirion, B., Kleinschmidt, D. F., Lee, J. A., Visconti di Oleggio Castello, M., Notter, M. P., & Blair, R. (2019). PyBIDS: Python tools for BIDS datasets. Journal of open source software, 4(40), . https://doi.org/10.21105/joss.01294
Yarkoni, Tal, et al. "PyBIDS: Python tools for BIDS datasets." Journal of open source software vol. 4,40 (2019). doi: https://doi.org/10.21105/joss.01294
Yarkoni T, Markiewicz CJ, de la Vega A, Gorgolewski KJ, Salo T, Halchenko YO, McNamara Q, DeStasio K, Poline JB, Petrov D, Hayot-Sasson V, Nielson DM, Carlin J, Kiar G, Whitaker K, DuPre E, Wagner A, Tirrell LS, Jas M, Hanke M, Poldrack RA, Esteban O, Appelhoff S, Holdgraf C, Staden I, Thirion B, Kleinschmidt DF, Lee JA, Visconti di Oleggio Castello M, Notter MP, Blair R. PyBIDS: Python tools for BIDS datasets. J Open Source Softw. 2019;4(40). doi: 10.21105/joss.01294. Epub 2019 Aug 12. PMID: 32775955; PMCID: PMC7409983.
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The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments.

Scientific data

Gorgolewski KJ, Auer T, Calhoun VD, Craddock RC, Das S, Duff EP, Flandin G, Ghosh SS, Glatard T, Halchenko YO, Handwerker DA, Hanke M, Keator D, Li X, Michael Z, Maumet C, Nichols BN, Nichols TE, Pellman J, Poline JB, Rokem A, Schaefer G, Sochat V, Triplett W, Turner JA, Varoquaux G, Poldrack RA.
PMID: 27326542
Sci Data. 2016 Jun 21;3:160044. doi: 10.1038/sdata.2016.44.

The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain....

Cite
Gorgolewski KJ, Auer T, Calhoun VD, et al. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data. 2016;3:160044doi: 10.1038/sdata.2016.44.
Gorgolewski, K. J., Auer, T., Calhoun, V. D., Craddock, R. C., Das, S., Duff, E. P., Flandin, G., Ghosh, S. S., Glatard, T., Halchenko, Y. O., Handwerker, D. A., Hanke, M., Keator, D., Li, X., Michael, Z., Maumet, C., Nichols, B. N., Nichols, T. E., Pellman, J., Poline, J. B., Rokem, A., Schaefer, G., Sochat, V., Triplett, W., Turner, J. A., Varoquaux, G., & Poldrack, R. A. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific data, 3160044. https://doi.org/10.1038/sdata.2016.44
Gorgolewski, Krzysztof J, et al. "The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments." Scientific data vol. 3 (2016): 160044. doi: https://doi.org/10.1038/sdata.2016.44
Gorgolewski KJ, Auer T, Calhoun VD, Craddock RC, Das S, Duff EP, Flandin G, Ghosh SS, Glatard T, Halchenko YO, Handwerker DA, Hanke M, Keator D, Li X, Michael Z, Maumet C, Nichols BN, Nichols TE, Pellman J, Poline JB, Rokem A, Schaefer G, Sochat V, Triplett W, Turner JA, Varoquaux G, Poldrack RA. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data. 2016 Jun 21;3:160044. doi: 10.1038/sdata.2016.44. PMID: 27326542; PMCID: PMC4978148.
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Making data sharing count: a publication-based solution.

Frontiers in neuroscience

Gorgolewski KJ, Margulies DS, Milham MP.
PMID: 23390412
Front Neurosci. 2013 Feb 06;7:9. doi: 10.3389/fnins.2013.00009. eCollection 2013.

The neuroimaging community has been increasingly called up to openly share data. Although data sharing has been a cornerstone of large-scale data consortia, the incentive for the individual researcher remains unclear. Other fields have benefited from embracing a data...

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Gorgolewski KJ, Margulies DS, Milham MP. Making data sharing count: a publication-based solution. Front Neurosci. 2013;7:9doi: 10.3389/fnins.2013.00009.
Gorgolewski, K. J., Margulies, D. S., & Milham, M. P. (2013). Making data sharing count: a publication-based solution. Frontiers in neuroscience, 79. https://doi.org/10.3389/fnins.2013.00009
Gorgolewski, Krzysztof J, et al. "Making data sharing count: a publication-based solution." Frontiers in neuroscience vol. 7 (2013): 9. doi: https://doi.org/10.3389/fnins.2013.00009
Gorgolewski KJ, Margulies DS, Milham MP. Making data sharing count: a publication-based solution. Front Neurosci. 2013 Feb 06;7:9. doi: 10.3389/fnins.2013.00009. eCollection 2013. PMID: 23390412; PMCID: PMC3565154.
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Data sharing in neuroimaging research.

Frontiers in neuroinformatics

Poline JB, Breeze JL, Ghosh S, Gorgolewski K, Halchenko YO, Hanke M, Haselgrove C, Helmer KG, Keator DB, Marcus DS, Poldrack RA, Schwartz Y, Ashburner J, Kennedy DN.
PMID: 22493576
Front Neuroinform. 2012 Apr 05;6:9. doi: 10.3389/fninf.2012.00009. eCollection 2012.

Significant resources around the world have been invested in neuroimaging studies of brain function and disease. Easier access to this large body of work should have profound impact on research in cognitive neuroscience and psychiatry, leading to advances in...

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Poline JB, Breeze JL, Ghosh S, et al. Data sharing in neuroimaging research. Front Neuroinform. 2012;6:9doi: 10.3389/fninf.2012.00009.
Poline, J. B., Breeze, J. L., Ghosh, S., Gorgolewski, K., Halchenko, Y. O., Hanke, M., Haselgrove, C., Helmer, K. G., Keator, D. B., Marcus, D. S., Poldrack, R. A., Schwartz, Y., Ashburner, J., & Kennedy, D. N. (2012). Data sharing in neuroimaging research. Frontiers in neuroinformatics, 69. https://doi.org/10.3389/fninf.2012.00009
Poline, Jean-Baptiste, et al. "Data sharing in neuroimaging research." Frontiers in neuroinformatics vol. 6 (2012): 9. doi: https://doi.org/10.3389/fninf.2012.00009
Poline JB, Breeze JL, Ghosh S, Gorgolewski K, Halchenko YO, Hanke M, Haselgrove C, Helmer KG, Keator DB, Marcus DS, Poldrack RA, Schwartz Y, Ashburner J, Kennedy DN. Data sharing in neuroimaging research. Front Neuroinform. 2012 Apr 05;6:9. doi: 10.3389/fninf.2012.00009. eCollection 2012. PMID: 22493576; PMCID: PMC3319918.
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Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python.

Frontiers in neuroinformatics

Gorgolewski K, Burns CD, Madison C, Clark D, Halchenko YO, Waskom ML, Ghosh SS.
PMID: 21897815
Front Neuroinform. 2011 Aug 22;5:13. doi: 10.3389/fninf.2011.00013. eCollection 2011.

Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. Several sophisticated software packages (e.g., AFNI, BrainVoyager, FSL, FreeSurfer, Nipy, R, SPM) are used to process and analyze large and...

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Gorgolewski K, Burns CD, Madison C, et al. Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python. Front Neuroinform. 2011;5:13doi: 10.3389/fninf.2011.00013.
Gorgolewski, K., Burns, C. D., Madison, C., Clark, D., Halchenko, Y. O., Waskom, M. L., & Ghosh, S. S. (2011). Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python. Frontiers in neuroinformatics, 513. https://doi.org/10.3389/fninf.2011.00013
Gorgolewski, Krzysztof, et al. "Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python." Frontiers in neuroinformatics vol. 5 (2011): 13. doi: https://doi.org/10.3389/fninf.2011.00013
Gorgolewski K, Burns CD, Madison C, Clark D, Halchenko YO, Waskom ML, Ghosh SS. Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python. Front Neuroinform. 2011 Aug 22;5:13. doi: 10.3389/fninf.2011.00013. eCollection 2011. PMID: 21897815; PMCID: PMC3159964.
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Dynamic network participation of functional connectivity hubs assessed by resting-state fMRI.

Frontiers in human neuroscience

Schaefer A, Margulies DS, Lohmann G, Gorgolewski KJ, Smallwood J, Kiebel SJ, Villringer A.
PMID: 24860458
Front Hum Neurosci. 2014 May 06;8:195. doi: 10.3389/fnhum.2014.00195. eCollection 2014.

Network studies of large-scale brain connectivity have demonstrated that highly connected areas, or "hubs," are a key feature of human functional and structural brain organization. We use resting-state functional MRI data and connectivity clustering to identify multi-network hubs and...

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Schaefer A, Margulies DS, Lohmann G, et al. Dynamic network participation of functional connectivity hubs assessed by resting-state fMRI. Front Hum Neurosci. 2014;8:195doi: 10.3389/fnhum.2014.00195.
Schaefer, A., Margulies, D. S., Lohmann, G., Gorgolewski, K. J., Smallwood, J., Kiebel, S. J., & Villringer, A. (2014). Dynamic network participation of functional connectivity hubs assessed by resting-state fMRI. Frontiers in human neuroscience, 8195. https://doi.org/10.3389/fnhum.2014.00195
Schaefer, Alexander, et al. "Dynamic network participation of functional connectivity hubs assessed by resting-state fMRI." Frontiers in human neuroscience vol. 8 (2014): 195. doi: https://doi.org/10.3389/fnhum.2014.00195
Schaefer A, Margulies DS, Lohmann G, Gorgolewski KJ, Smallwood J, Kiebel SJ, Villringer A. Dynamic network participation of functional connectivity hubs assessed by resting-state fMRI. Front Hum Neurosci. 2014 May 06;8:195. doi: 10.3389/fnhum.2014.00195. eCollection 2014. PMID: 24860458; PMCID: PMC4018560.
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A Coordinate-Based Meta-Analysis of Overlaps in Regional Specialization and Functional Connectivity across Subjective Value and Default Mode Networks.

Frontiers in neuroscience

Acikalin MY, Gorgolewski KJ, Poldrack RA.
PMID: 28154520
Front Neurosci. 2017 Jan 19;11:1. doi: 10.3389/fnins.2017.00001. eCollection 2017.

Previous research has provided qualitative evidence for overlap in a number of brain regions across the subjective value network (SVN) and the default mode network (DMN). In order to quantitatively assess this overlap, we conducted a series of coordinate-based...

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Acikalin MY, Gorgolewski KJ, Poldrack RA. A Coordinate-Based Meta-Analysis of Overlaps in Regional Specialization and Functional Connectivity across Subjective Value and Default Mode Networks. Front Neurosci. 2017;11:1doi: 10.3389/fnins.2017.00001.
Acikalin, M. Y., Gorgolewski, K. J., & Poldrack, R. A. (2017). A Coordinate-Based Meta-Analysis of Overlaps in Regional Specialization and Functional Connectivity across Subjective Value and Default Mode Networks. Frontiers in neuroscience, 111. https://doi.org/10.3389/fnins.2017.00001
Acikalin, M Yavuz, et al. "A Coordinate-Based Meta-Analysis of Overlaps in Regional Specialization and Functional Connectivity across Subjective Value and Default Mode Networks." Frontiers in neuroscience vol. 11 (2017): 1. doi: https://doi.org/10.3389/fnins.2017.00001
Acikalin MY, Gorgolewski KJ, Poldrack RA. A Coordinate-Based Meta-Analysis of Overlaps in Regional Specialization and Functional Connectivity across Subjective Value and Default Mode Networks. Front Neurosci. 2017 Jan 19;11:1. doi: 10.3389/fnins.2017.00001. eCollection 2017. PMID: 28154520; PMCID: PMC5243799.
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