社论
Editorial: Focus feature on consciousness
and cognition
Randy McIntosh1, Sean Hill2, and Olaf Sporns3
1Institute for Neuroscience and Neurotechnology, Department of Biomedical Physiology and Kinesiology, Simon Fraser
大学, Burnaby, BC, 加拿大
2Krembil Centre for Neuroinformatics, Departments of Psychiatry and Psychology, 多伦多大学, 多伦多, 在, 加拿大
3Department of Psychological and Brain Sciences, 印第安纳大学, 布卢明顿, 在, 美国
关键词: Connectivity, Brain networks, 大脑动力学, 意识, 认识
开放访问
杂志
抽象的
Consciousness and cognition are an increasing focus of theoretical and experimental research
in neuroscience, leveraging the methods and tools of brain dynamics and connectivity. 这
Focus Feature brings together a collection of articles that examine the various roles of brain
networks in computational and dynamic models, and in studies of physiological and
neuroimaging processes that underpin and enable behavioral and cognitive function.
Network neuroscience is a relatively new field but has arguably transformed how we approach
the study of the brain. Over the past 20 年, the focus on connections and circuits evolved in
parallel with technological developments that gave us the ability to measure much of the neural
activity unfolding in the functioning brain. These new data brought challenges to our thinking
and gave rise to new ideas and concepts—on dynamics and networks, and on the roles of con-
nectivity and information in shaping cognition and behavior. Many of these ideas arose and
challenges were addressed openly in a series of meetings called the Brain Connectivity Work-
店铺 (BCW). The inaugural event took place in Dusseldorf, 德国, 在 2002, organized by Rolf
Kotter and Karl Friston. The aim was to establish a dialogue between researchers in theoretical
and computational neuroscience, and in neuroscience methods and experimentation centering
on the emerging field of brain connectivity and networks. The meeting format was and still is
独特的, with an emphasis on a brief presentation (不多于 15 min) and extended periods
of discussion to focus on the core issues of brain function. The 19th BCW meeting, 起初
planned to take place in Toronto but cancelled due to Covid-19, happened virtually in 2021. 它
was organized to bring some perspective on how the field of network neuroscience had evolved,
with a particular focus on whether the network perspective had supported any new ideas about
higher brain function. 可以说, the answer to this was a cautious “yes.”
After the meeting, we invited the attendees to submit papers that captured some of their key
消息. What is common for all these papers is the use of network terms and concepts to
make their case. The clearest examples of this are the papers from Liu, 贝策尔, and Misic
(2022) and Hilgetag, Goulas, and Changeux (2022). 刘等人. examine the link between func-
tional and structural connectivity, where functional connections are reweighted based on their
underlying structural and geometric embedding. They provide a quantitative framework for
evaluating polysynaptic functional connections. Hilgetag et al. provide a complementary
paper that links the connectivity features of the human brain to our unique cognitive abilities.
引文: McIntosh, R。, 爬坡道, S。, &
斯波恩斯, 氧. (2022). Editorial: Focus
feature on consciousness and
认识. 网络神经科学, 6(4),
934–936. https://doi.org/10.1162/netn_e
_00273
DOI:
https://doi.org/10.1162/netn_e_00273
已收到: 16 八月 2022
利益争夺: 作者有
声明不存在竞争利益
存在.
通讯作者:
奥拉夫·斯波恩斯
osporns@indiana.edu
处理编辑器:
奥拉夫·斯波恩斯
版权: © 2022
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: Focus feature on consciousness and cognition
These features span both local and global aspects of brain network organization. The impor-
tance of the temporal domains is central to the papers from John et al. (2022) and Wang and
Halassa (2022). John et al. emphasize time and propose dynamical systems theory as a frame-
work to evolve a better understanding of the temporal richness of network dynamics. 王
and Halassa present a detailed exploration of thalamocortical interactions in learning, 展示-
ing that cortex-thalamus cortico-striatal loops interact over different timescales to enable
元学习. Network dynamics are more directly related to cognitive functions and
extended to clinical applications in the remaining papers. Demertzi et al. (2022) explore
the patterns of interactions between networks across time in relation to segregation and inte-
gration. They link anticorrelations to variations in local and global network inhibition, 哪个
is purported to be essential for conscious mental activity. Katsumi, Theriault, Quigley, 和
Barrett (2022) connect allostatic prediction (predictions needed to coordinate internal sys-
特姆斯) and network gradients of functional connectivity. This work moves associations of
psychological functions from specific regions or networks to whole-brain phenomena with
allostatic features. Kang, Galdo, and Turner (2022) further expand the structure-function rela-
tion by estimating clusters of regions based on structural connectivity, and then use the clusters
as constraints in a factor analysis of functional connectivity and map their relation to a range of
cognitive functions. Karvelis et al. (2022) review studies that use computational models based
on empirical data to predict treatment response in major depressive disorder. They suggest that
a generative modeling approach may be more sensitive for prediction but also make the case
that treatment outcomes should be evaluated across a continuum and with multiple measures
to better capture a patient’s unique circumstance. Our Focus Feature concludes with work
from Cruzat et al. (2022), which examines changes in signatures of brain network dynamics
(turbulence) in relation to the administration of psychedelic drugs. One common finding
across drug types was that the psychedelic state seems to increase information transfer through
long-range spatial scales. The thread of network-level explanations and predictions is com-
mon across all papers and illustrates the advances in neuroscience that have originated from
the network perspective.
最后, if you want to more directly experience the talks and discussions that took place at
the meeting, you can access all of them on the BCW YouTube channel. We feel that the unique
format of the Brain Connectivity Workshop, which prioritizes intensive audience participation
and interaction, is something we desperately need in present-day neuroscience. We tend to get
overwhelmed by technological wizardry and the accompanying abundance of data while los-
ing sight of what we are trying to learn. BCW gives us a chance to talk about this. We hope you
find inspiration in these pages.
参考
Cruzat, J。, Perl, 是. S。, Escrichs, A。, Vohryzek, J。, Timmermann, C。,
Roseman, L。, … Kringelbach, 中号. L. (2022). Effects of classic psy-
chedelic drugs on turbulent signatures in brain dynamics. 网-
work Neuroscience, 6(4), 1104–1124. https://doi.org/10.1162
/netn_a_00250
Demertzi, A。, Kucyi, A。, Ponce-Alvarez, A。, Keliris, G. A。, Whitfield-
Gabrieli, S。, & 德科, G. (2022). Functional network antagonism
and consciousness. 网络神经科学, 6(4), 998–1009.
https://doi.org/10.1162/netn_a_00244
希尔格塔格, C. C。, Goulas, A。, & 尚古克斯, J. 磷. (2022). A natural cortical
axis connecting the outside and inside of the human brain. 网络
神经科学, 6(4), 950–959. https://doi.org/10.1162/netn_a
_00256
约翰, 是. J。, Sawyer, K. S。, 斯里尼瓦桑, K., 穆勒, 乙. J。, Munn, 乙. R。, &
Shine, J. 中号. (2022). It’s about time: Linking dynamical systems
with human neuroimaging to understand the brain. 网络
神经科学, 6(4), 960–979. https://doi.org/10.1162/netn_a
_00230
Kang, 我。, Galdo, M。, & 车工, 乙. 中号. (2022). Constraining functional
coactivation with a cluster-based structural connectivity net-
工作. 网络神经科学, 6(4), 1032–1065. https://doi.org
/10.1162/netn_a_00242
网络神经科学
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Editorial: Focus feature on consciousness and cognition
Karvelis, P。, Charlton, C. E., Allohverdi, S. G。, Bedford, P。, Hauke,
D. J。, & Diaconescu, A. 氧. (2022). Computational approaches to
treatment response prediction in major depression using brain
activity and behavioral data: A systematic review. Network Neu-
roscience, 6(4), 1066–1103. https://doi.org/10.1162/netn_a
_00233
Katsumi, Y。, Theriault, J. E., Quigley, K. S。, & Barrett, L. F. (2022).
Allostasis as a core feature of hierarchical gradients in the human
脑. 网络神经科学, 6(4), 1010–1031. https://doi.org
/10.1162/netn_a_00240
刘, Z. Q., 贝策尔, 右. F。, & Misic, 乙. (2022). Benchmarking functional con-
nectivity by the structure and geometry of the human brain. 网络
神经科学, 6(4), 937–949. https://doi.org/10.1162/netn_a_00236
王, 中号. B., & Halassa, 中号. 中号. (2022). Thalamocortical contribu-
tion to flexible learning in neural systems. Network Neurosci-
恩斯, 6(4), 980–997. https://doi.org/10.1162/netn_a_00235
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