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Analysing brain networks in population neuroscience: a case for the Bayesian philosophy

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Analysing brain networks in population neuroscience : a case for the Bayesian philosophy. / Bzdok, Danilo; Floris, Dorothea L.; Marquand, Andre F.

In: Philosophical transactions of the Royal Society of London. Series B, Biological sciences, Vol. 375, No. 1796, 20190661, 13.04.2020.

Research output: Contribution to journalReview article

Harvard

Bzdok, D, Floris, DL & Marquand, AF 2020, 'Analysing brain networks in population neuroscience: a case for the Bayesian philosophy', Philosophical transactions of the Royal Society of London. Series B, Biological sciences, vol. 375, no. 1796, 20190661. https://doi.org/10.1098/rstb.2019.0661

APA

Bzdok, D., Floris, D. L., & Marquand, A. F. (2020). Analysing brain networks in population neuroscience: a case for the Bayesian philosophy. Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 375(1796), [20190661]. https://doi.org/10.1098/rstb.2019.0661

Vancouver

Bzdok D, Floris DL, Marquand AF. Analysing brain networks in population neuroscience: a case for the Bayesian philosophy. Philosophical transactions of the Royal Society of London. Series B, Biological sciences. 2020 Apr 13;375(1796). 20190661. https://doi.org/10.1098/rstb.2019.0661

Author

Bzdok, Danilo ; Floris, Dorothea L. ; Marquand, Andre F. / Analysing brain networks in population neuroscience : a case for the Bayesian philosophy. In: Philosophical transactions of the Royal Society of London. Series B, Biological sciences. 2020 ; Vol. 375, No. 1796.

Bibtex Download

@article{1fcc308d6633483bb7d92a6da8298b2a,
title = "Analysing brain networks in population neuroscience: a case for the Bayesian philosophy",
abstract = "Network connectivity fingerprints are among today's best choices to obtain a faithful sampling of an individual's brain and cognition. Widely available MRI scanners can provide rich information tapping into network recruitment and reconfiguration that now scales to hundreds and thousands of humans. Here, we contemplate the advantages of analysing such connectome profiles using Bayesian strategies. These analysis techniques afford full probability estimates of the studied network coupling phenomena, provide analytical machinery to separate epistemological uncertainty and biological variability in a coherent manner, usher us towards avenues to go beyond binary statements on existence versus non-existence of an effect, and afford credibility estimates around all model parameters at play which thus enable single-subject predictions with rigorous uncertainty intervals. We illustrate the brittle boundary between healthy and diseased brain circuits by autism spectrum disorder as a recurring theme where, we argue, network-based approaches in neuroscience will require careful probabilistic answers. This article is part of the theme issue 'Unifying the essential concepts of biological networks: biological insights and philosophical foundations'.",
keywords = "confounding influences, connectome-based prediction, statistical learning, uncertainty",
author = "Danilo Bzdok and Floris, {Dorothea L.} and Marquand, {Andre F.}",
year = "2020",
month = "4",
day = "13",
doi = "10.1098/rstb.2019.0661",
language = "English",
volume = "375",
journal = "Philosophical transactions of the Royal Society of London. Series B, Biological sciences",
issn = "1471-2970",
number = "1796",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - Analysing brain networks in population neuroscience

T2 - a case for the Bayesian philosophy

AU - Bzdok, Danilo

AU - Floris, Dorothea L.

AU - Marquand, Andre F.

PY - 2020/4/13

Y1 - 2020/4/13

N2 - Network connectivity fingerprints are among today's best choices to obtain a faithful sampling of an individual's brain and cognition. Widely available MRI scanners can provide rich information tapping into network recruitment and reconfiguration that now scales to hundreds and thousands of humans. Here, we contemplate the advantages of analysing such connectome profiles using Bayesian strategies. These analysis techniques afford full probability estimates of the studied network coupling phenomena, provide analytical machinery to separate epistemological uncertainty and biological variability in a coherent manner, usher us towards avenues to go beyond binary statements on existence versus non-existence of an effect, and afford credibility estimates around all model parameters at play which thus enable single-subject predictions with rigorous uncertainty intervals. We illustrate the brittle boundary between healthy and diseased brain circuits by autism spectrum disorder as a recurring theme where, we argue, network-based approaches in neuroscience will require careful probabilistic answers. This article is part of the theme issue 'Unifying the essential concepts of biological networks: biological insights and philosophical foundations'.

AB - Network connectivity fingerprints are among today's best choices to obtain a faithful sampling of an individual's brain and cognition. Widely available MRI scanners can provide rich information tapping into network recruitment and reconfiguration that now scales to hundreds and thousands of humans. Here, we contemplate the advantages of analysing such connectome profiles using Bayesian strategies. These analysis techniques afford full probability estimates of the studied network coupling phenomena, provide analytical machinery to separate epistemological uncertainty and biological variability in a coherent manner, usher us towards avenues to go beyond binary statements on existence versus non-existence of an effect, and afford credibility estimates around all model parameters at play which thus enable single-subject predictions with rigorous uncertainty intervals. We illustrate the brittle boundary between healthy and diseased brain circuits by autism spectrum disorder as a recurring theme where, we argue, network-based approaches in neuroscience will require careful probabilistic answers. This article is part of the theme issue 'Unifying the essential concepts of biological networks: biological insights and philosophical foundations'.

KW - confounding influences

KW - connectome-based prediction

KW - statistical learning

KW - uncertainty

UR - http://www.scopus.com/inward/record.url?scp=85079914784&partnerID=8YFLogxK

U2 - 10.1098/rstb.2019.0661

DO - 10.1098/rstb.2019.0661

M3 - Review article

C2 - 32089111

AN - SCOPUS:85079914784

VL - 375

JO - Philosophical transactions of the Royal Society of London. Series B, Biological sciences

JF - Philosophical transactions of the Royal Society of London. Series B, Biological sciences

SN - 1471-2970

IS - 1796

M1 - 20190661

ER -

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