焦点功能: - 麻省理工学院人工智能研究专业

焦点功能:
Bridging Scales and Levels

Editorial: Bridging Scales and Levels

Emma K. Towlson

1,2

and Fabrizio De Vico Fallani

3,4

1Center for Complex Network Research, Northeastern University, 波士顿, 嘛 02115, 美国
2Media Laboratory, 麻省理工学院, 剑桥, 嘛 02139, 美国
3Inria, Aramis project-team, F-75013, 巴黎, 法国
Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS UMR 7225,
Sorbonne Université, F-75013, 巴黎, 法国

抽象的

Network neuroscience strives to understand the networks of the brain on all spatiotemporal
scales and levels of observation. Current experimental and theoretical capabilities are
beginning to facilitate a more holistic perspective, uniting these networks. This focus feature,
“Bridging Scales and Levels,” aims to document current research and looks to future progress
towards this vision.

One of the greatest assets of network neuroscience is its ability to transcend and unite disparate
disciplinary approaches to understanding the brain. This is particularly valuable when it comes
to spatiotemporal scales. From genes and proteins interacting in the cell, to signaling within
神经元群, to the integration of cortical regions, and even the brain itself within
身体, the brain is inescapably, on all scales, a network. Different timescales govern the
operation of each part, from milliseconds-long molecular interactions, to the seconds and
minutes for circuit and whole brain dynamics, to days and upwards for some brain-behavior
and social interactions, and neurological diseases. At a given spatiotemporal scale, 有
a further choice of the level on which to observe and question. These levels range from that
of the single component (a gene of interest, the dynamics of a single neuron, or the role of a
brain region), to circuitry and sub-systems, to the whole population. 最终, each of these
pieces must be united to complete the puzzles presented by the brain. And that means ﬁnding
ways to bridge the scales and levels.

The given scale and level often guides the type of approach taken, and the disciplinary speciﬁc
knowledge and skills required. 例如, collecting fMRI data relies on imaging technol-
奥吉; data analysis may need expertise in signal processing, 机器学习, 统计数据, ETC.
The scientiﬁc question and the outcome interpretation need crucial inputs from neuroscience-
related disciplines. To piece all of these components together, there is thus a need within
network neuroscience for forums within which these distinct communities can connect, 分享
ideas, and learn to speak each other’s languages. This is what we set out to provide, 什么时候
we held a one-day satellite symposium on Network Neuroscience at the international meeting
NetSci 2017, in Indianapolis – the annual ﬂagship conference for the Network Science Society.
The strong attendance of the satellite, including around 120 参与者, is testament to the
enthusiasm within the network science community for neuroscientiﬁc applications and inspi-
rations. Our ambitions necessarily included bridging communities further aﬁeld – welcoming
in neuroscientiﬁc researchers who may not traditionally have attended a network science con-
参考. This satellite was thus pivotal in that it marked the transition from the highly popular
Brain Networks satellite that had run for the previous two years to a full-ﬂedged Network
Neuroscience satellite, enveloping all aspects of the ﬁeld.

The result was a fast-paced, energetic, and vibrant day with representation from all corners
of network neuroscience, via 17 oral presentations and nearly 40 posters. One talk was

开放访问

杂志

引文: Towlson, 乙. K., & De Vico
Fallani, F. (2018). Editorial: Bridging
Scales and Levels. 网络
神经科学, 2(3), 303–305.
https://doi.org/10.1162/netn_e_00059

DOI:
https://doi.org/10.1162/netn_e_00059

版权: © 2018
麻省理工学院
在知识共享下发布
归因 4.0 国际的
(抄送 4.0) 执照

麻省理工学院出版社

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Editorial: Bridging Scales and Levels

delivered remotely from Europe, and all talks were streamed via Facebook live (其中许多
obtained >200 views on the same day). Presenters were invited to submit their work to
网络神经科学, to provide a snapshot of the topics of the day, and to ignite wider
debate around the theme of this focus feature: “Bridging Scales and Levels.”

The relationship between structure and function remains of prominent interest in network
神经科学. Genetics-based approaches are gaining in popularity, and begin to attempt
to bridge the very small spatial scale of genes with the structure and function of whole brain
地区 (Richiardi et al., 2015). A session on “Function and Dysfunction in the Human Brain”
saw contributions exploring the nature of large-scale whole-brain connectivity when things
work as expected (such as the nature of learning, Bassett and Mattar (2017)), and when they
don’t (such as in Alzheimer’s disease, de Haan (2017)). On pages 306–322, Amico and Goñi
(2018) extend an ICA-based framework to pick out concurrent features of structural and func-
tional connectivity in connectomes from the Human Connecome Project.

30 years on from the seminal mapping of its connectome, C. elegans still commands a big
presence in neuroscience (Yan et al., 2017). 的确, given our unparalleled knowledge of
its structure and behavior, and our experimental power to intervene, it is an organism with
enormous potential to bridge the scales and levels. OpenWorm represents an international,
interdisciplinary online community committed to creating a virtual worm. On pages 323–343,
Olivares, Izquierdo, and Beer (2018) demonstrate, through computational simulations, 那
neurons in the ventral nerve cord are capable of producing a central pattern generator that
drives locomotion.

Modern datasets are arriving with unprecedented, exquisite detail, and are often multimodal
in nature, presenting opportunities to study scales and levels concurrently. There is naturally
a demand for tools to study and integrate them. A “Computational Approaches” session in-
cluded a hands-on data showcase demonstrating the Allen Institute for Brain Science’s latest
data and tool releases. On pages 344–361, Keiriz et al. (2018) introduce a web-based visual-
ization platform with which to explore brain networks for research and clinical purposes; 这
authors demonstrated their platform at the satellite with virtual reality headsets.

Looking to the future of network neuroscience, we must develop theoretical and experimental
tools to link the scales and levels inherent in the brain. This is a challenge which necessarily
includes bridging traditionally disparate research communities, and fostering an environment
for effective communication and collaboration. The journal Network Neuroscience is one such
vital avenue for researchers to share results and keep abreast of the newest advances. Academic
meetings, including NetSci, are also starting to recognize this need, and have begun moving
towards connecting the disjoint landscape. Much work remains to be done towards “Bridging
Scales and Levels,” and the right forums to spark conversation and collaboration towards this
goal are essential.

参考

Allen Institute for Brain Science. (2015). Allen Brain Atlas Data

de Haan, 瓦. (2017). The Virtual Trial. Frontiers in Neuroscience,

Portal. http://brain-map.org

11: 110.

Amico, E., & 戈尼, J. (2018). Mapping Hybrid Functional-structural
Connectivity Traits in the Human Connectome. Network Neuro-
科学, 306–322.

Bassett, D. S。, & Mattar, 中号. G. (2017). A Network Neuroscience
of Human Learning: Potential to Inform Quantitative Theories of
Brain and Behavior. 认知科学的趋势, 21(4), 250–264.

Keiriz, J. J. G。, Zhan, L。, Ajilore, 奥。, Leow, A. D ., & Forbes, A. G.
(2018). NeuroCave: A Web-based Immersive Visualization
Platform for Exploring Connectome Datasets. Network Neuro-
科学, 344–361.

Olivares, 乙. 奥。, Izquierdo, 乙. J。, & 啤酒, 右. D.

(2018). Potential
role of a ventral nerve cord central pattern generator in forward

网络神经科学

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Editorial: Bridging Scales and Levels

and backward locomotion in Caenorhabditis elegans. 网络
神经科学, 323–343

gene expression supports synchronous activity in brain networks.
科学, 348(6240), 1241–1244.

OpenWorm Foundation. (2014). OpenWorm. http://openworm.

org

Richiardi, J。, 阿尔特曼, A。, Milazzo, A. C。, 张, C。, Chakravarty,
中号. M。, Banaschewski, T。, . . . Conrod, 磷. (2015). Correlated

严, G。, Vértes, 磷. E., Towlson, 乙. K., Chew, 是. L。, 沃克, D. S。,
Schafer, 瓦. S。, & 巴拉巴斯, A.-L. (2017). Network control prin-
ciples predict neuron function in the Caenorhabditis elegans
connectome. 自然, 550, 519–523.

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