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首页> 外文期刊>Philosophical Transactions of the Royal Society of London, Series B. Biological Sciences >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

机译：分析人口神经科学的脑网络：贝叶斯哲学的案例

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摘要

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.
机译：网络连接指纹是当今最好的选择，以获得个人的大脑和认知的忠实采样。广泛可用的MRI扫描仪可以提供丰富的信息，进入网络招聘和重新配置，现在尺寸为数百和数千人。在这里，我们考虑使用贝叶斯策略分析这种连接型材的优点。这些分析技术提供了研究的网络耦合现象的全概率估计，提供分析机械以以连贯的方式分开认识论不确定性和生物变异性，使我们迈向途径超越二进制陈述与生效的不存在，并且提供播放时所有模型参数的可信度估计，从而使单亲预测具有严格的不确定性间隔。我们通过自闭症谱系疾病作为一种经常性主题，以攻击的基于网络在神经科学的方法中，我们争论的脆性界限将需要仔细的概率答案。

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