DanieleMarinazzo博士是比利时根特大学数据分析系的正教授。十多年来，他一直在向我们展示我们可以从脑成像数据中提取更多信息和见解——从EEG和MEG到fMRI。从技术上讲，他是一名统计物理学家，但实际上，他是一名网络神经科学家和建模分析师，不断推动着领域前进。
在这个播客中，他讨论了一些最近的论文，这些论文探讨了我们如何通过仔细的基准测试和周密的实验设计，可能提高新发现和模型的影响力和相关性。
他谈到了他希望在功能连接研究中从相关性转向因果关系的愿望，他讨论了格兰杰因果关系，以及从成对相关性转向多变量相关性。
此外，他还深入探讨了血流动力学的极限——这些极限可能在一定程度上被克服，正如他引人注目的工作所显示的，血流动力学响应功能，其在空间上的变化，可以仅使用静息状态数据在体素级别估计。
他还讨论了使用伽马频率一致性作为特征来估算和绘制大脑中兴奋/抑制比率的工作。这具有潜在的深远的临床和研究应用。
最后，他与欧洲脑计划合作创建了一个有用的网站 ebrains (https://www.ebrains.eu)，Marinazzo博士和我们讨论这个网站，该网站作为共享数据和代码探索的仓库和工具，并封装了该项目参与人员协作的成果。
这是一次全方位的、令人大开眼界的讨论，来自一位不仅深入网络建模战线，而且是开放科学和跨学科持续参与的坚定支持者的杰出科学家。
制片人：
OmerFaruk Gulban
AlfieWearn
StephaniaAssimopoulos
Neurosalience#S4E13 with Daniele Marinazzo - Networks, causality, new ideas to advance thefield
Dr.Daniele Marinazzo is a full professor in the department of data analysis at theUniversity of Ghent, in Belgium. For over a decade he has been showing us whatfurther information and insight we may extract from brain imaging data - fromEEG and MEG to fMRI. He is technically a statistical physicist, but in reality,he is a network neuroscientist and data modeler who is constantly pushing theenvelope.
In thispodcast he discusses some recent papers that go into how we might be able toimprove the impact and relevance of new findings and models through carefulbenchmarking and well considered experimental design.
He talksabout his desire to move from correlation to causation in functionalconnectivity studies, he discusses granger causality, as well as moving frompairwise correlation to multivariate correlation.
Furthermore,he delves into the limits of hemodynamics - limits that may be pushed back to adegree, as suggested by his compelling work showing that hemodynamic responsefunction, which varies over space, may be estimated on a voxel-wise basis usingresting state data alone.
His workin estimating and mapping the Excitation/Inhibition ratio in the brain by usinggamma frequency coherence as a signature was also discussed. This haspotentially profound clinical and research applications.
Lastly,his collaborative work with the European Human Brain Project towards thecreation of the useful website, called ebrains (https://www.ebrains.eu), wasdiscussed, which serves as a repository and tool for exploring shared data andcode, as well as providing a user-friendly encapsulation of the project'scollective effort.
It is anall-around fun, eye-opening discussion featuring an outstanding scientist whois not only deep in the trenches of network modelling, but also a strongproponent of open science and constant engagement across disciplines.
Episodeproducers:
OmerFaruk Gulban
AlfieWearn
StephaniaAssimopoulos
ReferencedPapers:
MikaRubinov. Circular and unified analysis in network neuroscience. eLife. 2023;12:e79559. Doi: 10.7554/eLife.79559
Reid AT,et al. Advancing functional connectivity research from association tocausation. Nat Neurosci. 2019 Nov;22(11):1751-1760. Doi:10.1038/s41593-019-0510-4.
Valdes-SosaPA et al. Effective connectivity: Influence, causality and biophysicalmodelling. Neuroimage. 2009; 58(2): 339-361. Doi:10.1016/j.neuroimage.2011.03.058.
Wu GR,et al. A blind deconvolution approach to recover effective connectivity brainnetworks from resting state fMRI data. Medical Image Analysis. 2013;17(3):365-374. Doi: 10.1016/j.media.2013.01.003.
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