A roadmap to integrate astrocytes into Systems Neuroscience
Glia. 2019-05-06; 68(1): 5-26
DOI: 10.1002/glia.23632
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1. Glia. 2020 Jan;68(1):5-26. doi: 10.1002/glia.23632. Epub 2019 May 6.
A roadmap to integrate astrocytes into Systems Neuroscience.
Kastanenka KV(1), Moreno-Bote R(2)(3), De Pittà M(4), Perea G(5), Eraso-Pichot
A(6), Masgrau R(6), Poskanzer KE(7), Galea E(3)(6).
Author information:
(1)Department of Neurology, MassGeneral Institute for Neurodegenerative Diseases,
Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.
(2)Center for Brain and Cognition and Department of Information and
Communications Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
(3)ICREA, Barcelona, Spain.
(4)BCAM Basque Center for Applied Mathematics, Bilbao, Spain.
(5)Instituto Cajal, CSIC, Madrid, Spain.
(6)Institut de Neurociències i Departament de Bioquímica, Universitat Autònoma de
Barcelona, Barcelona, Spain.
(7)Department of Biochemistry & Biophysics, Neuroscience Graduate Program, and
Kavli Institute for Fundamental Neuroscience, University of California-San
Francisco, San Francisco, California.
Systems neuroscience is still mainly a neuronal field, despite the plethora of
evidence supporting the fact that astrocytes modulate local neural circuits,
networks, and complex behaviors. In this article, we sought to identify which
types of studies are necessary to establish whether astrocytes, beyond their
well-documented homeostatic and metabolic functions, perform computations
implementing mathematical algorithms that sub-serve coding and higher-brain
functions. First, we reviewed Systems-like studies that include astrocytes in
order to identify computational operations that these cells may perform, using
Ca2+ transients as their encoding language. The analysis suggests that astrocytes
may carry out canonical computations in a time scale of subseconds to seconds in
sensory processing, neuromodulation, brain state, memory formation, fear, and
complex homeostatic reflexes. Next, we propose a list of actions to gain insight
into the outstanding question of which variables are encoded by such
computations. The application of statistical analyses based on machine learning,
such as dimensionality reduction and decoding in the context of complex
behaviors, combined with connectomics of astrocyte-neuronal circuits, is, in our
view, fundamental undertakings. We also discuss technical and analytical
approaches to study neuronal and astrocytic populations simultaneously, and the
inclusion of astrocytes in advanced modeling of neural circuits, as well as in
theories currently under exploration such as predictive coding and
energy-efficient coding. Clarifying the relationship between astrocytic Ca2+ and
brain coding may represent a leap forward toward novel approaches in the study of
astrocytes in health and disease.
© 2019 Wiley Periodicals, Inc.
DOI: 10.1002/glia.23632
PMCID: PMC6832773
PMID: 31058383 [Indexed for MEDLINE]