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New Trends in Connectomics

Editorial: New Trends in Connectomics

1
奥拉夫·斯波恩斯

and Danielle S. Bassett

2,3,4,5

1Department of Psychological and Brain Sciences, 印第安纳大学, 布卢明顿, 在, 美国
2Department of Bioengineering, 宾夕法尼亚大学, 费城, PA, 美国
3Department of Physics and Astronomy, 宾夕法尼亚大学, 费城, PA, 美国
4神经内科, Hospital of the University of Pennsylvania, 费城, PA, 美国
5Department of Electrical and Systems Engineering, 宾夕法尼亚大学, 费城, PA, 美国

抽象的

Connectomics is an integral part of network neuroscience. The ﬁeld has undergone rapid
expansion over recent years and increasingly involves a blend of experimental and
computational approaches to brain connectivity. This Focus Feature on “New Trends in
Connectomics” aims to track the progress of the ﬁeld and its many applications across
different neurobiological systems and species.

The idea that connections among neural elements are crucial for brain function has been
central to modern neuroscience almost since its inception. Building on this idea, the emerg-
ing ﬁeld of connectomics adds several new and important components. 第一的, connectomics
provides comprehensive maps of neural connections, with the ultimate goal of achieving com-
plete coverage of any given nervous system. 第二, connectomics delivers insights into the
principles that underlie network architecture and uncovers how these principles support net-
work function. These dual aims can be accomplished through the conﬂuence of new experi-
mental techniques for mapping connections and new network science methods for modeling
and analyzing the resulting large connectivity datasets. 因此, connectomics naturally blends
empirical and computational approaches to gain fundamentally new insights into structure and
function of brain networks.

Connectomics continues to expand rapidly. Since the term “connectome” was ﬁrst intro-
引入 2005 (斯波恩斯, 托诺尼, & Kötter, 2005), the number of scientiﬁc articles devoted to
connectomics has risen continuously (数字 1). Since networks can be built on many spa-
tial scales and with a variety of experimental techniques, there is a growing need to integrate
across these different ways of constructing brain networks and to provide opportunities to ex-
change insights, 数据, and models. This need motivated the organization of a recent Keystone
Symposium on connectomics with the explicit goal to foster scientiﬁc exchange among largely
disconnected communities of researchers studying connectomes in different organisms at dif-
ferent scales with different measurement techniques. One central objective was to promote
empirical and computational approaches that apply across scales, for example by leveraging
the tools of network science (Bassett & 斯波恩斯, 2017). In March 2017, 大约 100 骗局-
nectomics researchers gathered in Santa Fe (New Mexico) to exchange ideas and to discuss the
future of the ﬁeld. All presenters at the workshop were invited to submit their work to Network
神经科学, to be gathered into a Focus Feature entitled “New Trends in Connectomics.”

The result, presented in this new issue of the journal, offers a panoramic overview of an
emerging ﬁeld, with papers that cover a range of techniques applied in different species,
and that combine empirical data, 造型, and computation. Mattar, Thompson-Schill, 和
Bassett (2018) address how functional brain networks change over the course of learning, spe-
ciﬁcally those connections that link different network communities. Heitmann and Breakspear
(2018) address the important role of nonlinear system dynamics in generating ﬂuctuations in

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杂志

引文: 斯波恩斯, 奥。, & Bassett, D. S.
(2018). Editorial: New Trends in
Connectomics. 网络神经科学,
2(2), 125–127. https://doi.org/
10.1162/netn_e_00052

DOI:
https://doi.org/10.1162/netn_e_00052

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: New Trends in Connectomics

数字 1. The growth of connectomics as indexed by the number of published articles and ci-
tations. Publication and citation counts were retrieved from Web of Science on March 12, 2018,
using the search term “connectom*” in either topic or title. Through the end of 2017, a total of
2,684 articles were published, accruing a total of 47,725 citations. These counts likely under-
estimate the impact of connectomics, as many relevant articles do not reference the term
“connectom*” in either topic or title.

功能连接, an area of considerable interest in studies of spontaneous or “resting”
brain activity. Miranda-Dominguez et al. (2018) use machine-learning techniques to demon-
strate the heritability of “connectotyping”—the identiﬁcation of individuals based on complex
patterns of connectome-wide functional connectivity. Mills et al. (2018) apply functional
connectomics to a human disorder (ADHD) and show that behavioral measures are associ-
ated with speciﬁc changes in the manner in which segregated functional systems interact with
另一个. 李等人. (2018) apply network science concepts to characterize the functional
roles of epileptogenic zones in human electrophysiological data, and suggest that network-
based methodologies may have clinical applications. Kesler, Acton, 饶, and Ray (2018) inves-
tigate structural and functional connectome network properties in a transgenic mouse model
system designed to mimic the pathogenesis of Alzheimer’s disease. Kale, 扎莱斯基, and Gollo
(2018) investigate how the directionality of anatomical projections impacts the estimation of
commonly used graph-theoretical attributes. 最后, 摩根, Achard, Termenon, 布莫尔,
and Vértes (2018) decompose functional brain networks into network motifs and use varia-
tions in motif frequency and composition to probe for generative processes underlying network
形成.

Much discussion at the Keystone Symposium revolved around the issues that will occupy
connectomics in the years to come. 清楚地, continued improvements in measurement accu-
racy and coverage will give us improved connectivity maps, in a wider range of organisms
and at various scales of resolution. With these developments will come an increasing need
to design more advanced computational tools and theoretical frameworks to enable deeper
insight into the fundamental principles of brain network architecture and function. And as
maps and tools improve, the study of brain networks will continue to evolve, moving beyond
descriptive accounts to models that incorporate a rich set of neurobiological mechanisms, 广告-
dress changes in network structure and dynamics, reveal the network basis of cognition and
行为, and enable targeted intervention, prediction, and control. 网络神经科学
will continue to serve as a prime forum for dissemination and discussion in this important ﬁeld
for many years to come.

网络神经科学

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Editorial: New Trends in Connectomics

参考

Bassett, D. S。, & 斯波恩斯, 氧. (2017). Network neuroscience. 自然

神经科学, 20(3), 353.

Heitmann, S。, & Breakspear, 中号. (2018). Putting the “dynamic” back
into dynamic functional connectivity. 网络神经科学,
2(2), 150–174.

Kale, P。, 扎莱斯基, A。, & Gollo, L. L.

(2018). Estimating the
impact of structural directionality: How reliable are undirected
connectomes? 网络神经科学, 2(2), 259–284.

Kesler, S. R。, Acton, P。, 饶, 五、, & Ray, 瓦. J.

(2018). Functional
and structural connectome properties in the 5XFAD transgenic
mouse model of Alzheimer’s disease. 网络神经科学,
2(2), 241–258.

李, A。, Chennuri, B., Subramanian, S。, Yaffe, R。, Gliske, S。, Stacey,
(2018). Using network analy-
W., . . . Gonzalez-Martinez, J.
sis to localize the epileptogenic zone from invasive EEG record-
ings in intractable focal epilepsy. 网络神经科学, 2(2),
218–240.

Mattar, 中号. G。, Thompson-Schill, S. L。, & Bassett, D. S. (2018). 这
network architecture of value learning. 网络神经科学,
2(2), 128–149.

米尔斯, 乙. D ., Miranda-Dominguez, 奥。, 米尔斯, K., Earl, E., Cordova,
M。, Painter, J。, . . . Fair, D. A. (2018). ADHD and attentional
Impaired segregation of task positive and task nega-
控制:
tive brain networks. 网络神经科学, 2(2), 200–217.
Miranda-Dominguez, 奥。, Feczko, E., Grayson, D. S。, Walum, H。,
Nigg, J. T。, & Fair, D. A. (2018). Heritability of the human con-
nectome: A connectotyping study. 网络神经科学, 2(2),
175–199.

摩根, S. E., Achard, S。, Termenon, M。, 布莫尔, 乙. T。, & Vértes, 磷. 乙.
(2018). Low-dimensional morphospace of
topological motifs
in human fMRI brain networks. 网络神经科学, 2(2),
285–302.

斯波恩斯, 奥。, 托诺尼, G。, & Kötter, 右. (2005). The human connec-
tome: A structural description of the human brain. PLoS Compu-
tational Biology, 1(4), e42.

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