Spontaneous Functional Recovery after Focal Damage in Neuronal Cultures

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Abstract

Damage in biological neuronal networks triggers a complex functional reorganization whose mechanisms are still poorly understood. To delineate this reorganization process, here we investigate the functional alterations of in vitro rat cortical circuits following localized laser ablation. The analysis of the functional network configuration before and after ablation allowed us to quantify the extent of functional alterations and the characteristic spatial and temporal scales along recovery. We observed that damage precipitated a fast rerouting of information flow that restored network’s communicability in about 15 min. Functional restoration was led by the immediate neighbors around trauma but was orchestrated by the entire network. Our in vitro setup exposes the ability of neuronal circuits to articulate fast responses to acute damage, and may serve as a proxy to devise recovery strategies in actual brain circuits. Moreover, this biological setup can become a benchmark to empirically test network theories about the spontaneous recovery in dynamical networks.

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Most cited references 25

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Disruptions of network connectivity predict impairment in multiple behavioral domains after stroke.

Joshua Siegel, Lenny Ramsey, Abraham Snyder … (2016)

Deficits following stroke are classically attributed to focal damage, but recent evidence suggests a key role of distributed brain network disruption. We measured resting functional connectivity (FC), lesion topography, and behavior in multiple domains (attention, visual memory, verbal memory, language, motor, and visual) in a cohort of 132 stroke patients, and used machine-learning models to predict neurological impairment in individual subjects. We found that visual memory and verbal memory were better predicted by FC, whereas visual and motor impairments were better predicted by lesion topography. Attention and language deficits were well predicted by both. Next, we identified a general pattern of physiological network dysfunction consisting of decrease of interhemispheric integration and intrahemispheric segregation, which strongly related to behavioral impairment in multiple domains. Network-specific patterns of dysfunction predicted specific behavioral deficits, and loss of interhemispheric communication across a set of regions was associated with impairment across multiple behavioral domains. These results link key organizational features of brain networks to brain-behavior relationships in stroke.

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Connectivity-based approaches in stroke and recovery of function.

Christian Grefkes, G Fink (2014)

After focal damage, cerebral networks reorganise their structural and functional anatomy to compensate for both the lesion itself and remote effects. Novel developments in the analysis of functional neuroimaging data enable us to assess in vivo the specific contributions of individual brain areas to recovery of function and the effect of treatment on cortical reorganisation. Connectivity analyses can be used to investigate the effect of stroke on cerebral networks, and help us to understand why some patients make a better recovery than others. This systems-level view also provides insights into how neuromodulatory interventions might target pathological network configurations associated with incomplete recovery. In the future, such analyses of connectivity could help to optimise treatment regimens based on the individual network pathology underlying a particular neurological deficit, thereby opening the way for stratification of patients based on the possible response to an intervention. Copyright © 2014 Elsevier Ltd. All rights reserved.

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Is Open Access

Non-Stationarity in the “Resting Brain’s” Modular Architecture

David T. W. Jones, Prashanthi Vemuri, Matthew Murphy … (2012)

Task-free functional magnetic resonance imaging (TF-fMRI) has great potential for advancing the understanding and treatment of neurologic illness. However, as with all measures of neural activity, variability is a hallmark of intrinsic connectivity networks (ICNs) identified by TF-fMRI. This variability has hampered efforts to define a robust metric of connectivity suitable as a biomarker for neurologic illness. We hypothesized that some of this variability rather than representing noise in the measurement process, is related to a fundamental feature of connectivity within ICNs, which is their non-stationary nature. To test this hypothesis, we used a large (n = 892) population-based sample of older subjects to construct a well characterized atlas of 68 functional regions, which were categorized based on independent component analysis network of origin, anatomical locations, and a functional meta-analysis. These regions were then used to construct dynamic graphical representations of brain connectivity within a sliding time window for each subject. This allowed us to demonstrate the non-stationary nature of the brain’s modular organization and assign each region to a “meta-modular” group. Using this grouping, we then compared dwell time in strong sub-network configurations of the default mode network (DMN) between 28 subjects with Alzheimer’s dementia and 56 cognitively normal elderly subjects matched 1∶2 on age, gender, and education. We found that differences in connectivity we and others have previously observed in Alzheimer’s disease can be explained by differences in dwell time in DMN sub-network configurations, rather than steady state connectivity magnitude. DMN dwell time in specific modular configurations may also underlie the TF-fMRI findings that have been described in mild cognitive impairment and cognitively normal subjects who are at risk for Alzheimer’s dementia.

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Author and article information

Journal

Journal ID (nlm-ta): eNeuro

Journal ID (iso-abbrev): eNeuro

Journal ID (hwp): eneuro

Journal ID (pmc): eneuro

Journal ID (publisher-id): eNeuro

Title: eNeuro

Publisher: Society for Neuroscience

ISSN (Electronic): 2373-2822

Publication date (Electronic preprint): 9 December 2019

Publication date (Electronic): 2 January 2020

Publication date Collection: Jan-Feb 2020

Volume: 7

Issue: 1

Electronic Location Identifier: ENEURO.0254-19.2019

Affiliations

[1 ]Departament de Física de la Matèria Condensada, Universitat de Barcelona , Barcelona 08028, Spain

[2 ]Universitat de Barcelona Institute of Complex Systems (UBICS) , Barcelona 08028, Spain

[3 ]GOTHAM Lab–Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza , Zaragoza 50018, Spain

[4 ]Department of Condensed Matter Physics, University of Zaragoza , Zaragoza 50009, Spain

[5 ]Departament de Biomedicina, Facultat de Medicina, Institut de Neurociències, Universitat de Barcelona , Barcelona 08036, Spain

[6 ]Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED)

[7 ]ICFO–Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology , Castelldefels 08860, Spain

[8 ]Universidad ECCI, Bogotá 111311, Colombia

[9 ]Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona 43007, Spain

Author notes

The authors declare no competing financial interests.

Author contributions: S.T., J.A., P.L.-A., and J.S. designed research; S.T., J.A., and O.E.O. performed research; S.T., E.E.-P., C.G., D.T., and A.A. contributed unpublished reagents/analytic tools; S.T., E.E.-P., D.T., and J.S. analyzed data; S.T. and J.S. wrote the paper.

This research is part of MESO-BRAIN. The MESO-BRAIN Project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme Grant 713140 (to S.T., E.E.-P., J.A., O.E.O., P.L.-A., J.S.). J.S. and S.T. were supported by the Spanish Ministerio de Economia y Competitividad Projects FIS2013-41144-P, FIS2016-78507-C2-2-P, and FIS2017-90782-REDT (IBERSINC) and by the Generalitat de Catalunya Grant 2017-SGR-1061. C.G. was supported by Juan de la Cierva-Formación (Ministerio de Ciencia, Innovación y Universidades) and by the James S. McDonnell Foundation Postdoctoral Fellowship Grant 220020457. A.A. was supported by the Generalitat de Catalunya Project 2017-SGR-896, Spanish MINECO Projects FIS2015-71582-C2-1 and FIS2017-90782-REDT, ICREA Academia, and the James S. McDonnell Foundation Grant 220020325. J.A., O.E.O., and P.L.-A. were supported by the Spanish Ministerio de Economia y Competitividad (AEI/FEDER) Project FIS2016-80455-R, the “Severo Ochoa” Programme for Centers of Excellence in R&D (SEV-2015-0522), Fundació Privada Cellex, Fundación Mig-Puig, Generalitat de Catalunya through the CERCA program, and Laserlab-Europe (EU-H2020 654148).

Correspondence should be addressed to Jordi Soriano at jordi.soriano@ 123456ub.edu .

Author information

Daniel Tornero http://orcid.org/0000-0002-4812-4091

Omar E. Olarte http://orcid.org/0000-0003-1988-6967

Pablo Loza-Alvarez http://orcid.org/0000-0002-3129-1213

Jordi Soriano http://orcid.org/0000-0003-2676-815X

Article

Publisher ID: eN-MNT-0254-19

DOI: 10.1523/ENEURO.0254-19.2019

PMC ID: 6984807

PubMed ID: 31818830

SO-VID: 6d461c8b-2c36-410e-93c8-a62aaeb6c10e

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.

History

Date received : 27 June 2019

Date revision received : 18 November 2019

Date accepted : 29 November 2019

Page count

Figures: 5, Tables: 0, Equations: 55, References: 39, Pages: 13, Words: 10666

Funding

Funded by: http://doi.org/10.13039/501100007601EC | Horizon 2020 (EU Framework Programme for Research and Innovation)

Award ID: 713140

Award ID: Laserlab Europe 654148

Funded by: Ministerio de Econom ía y Competitividad

Award ID: FIS2013-41144-P

Award ID: FIS2016-78507-C2-2-P

Award ID: FIS2017-90782-REDT

Award ID: FIS2015-71582-C2-1

Award ID: FIS2017-90782-REDT

Award ID: FIS2016-80455-R

Funded by: http://doi.org/10.13039/501100002809Generalitat de Catalunya (Government of Catalonia)

Award ID: 2017-SGR-1061

Award ID: 2017-SGR-896

Award ID: CERCA Programme

Funded by: http://doi.org/10.13039/501100003741Institució Catalana de Recerca i Estudis Avançats (ICREA)

Funded by: http://doi.org/10.13039/100000913James S. McDonnell Foundation (JSMF)

Award ID: 220020325

Funded by: Severo Ochoa Programme

Award ID: SEV-2015-0522

Funded by: http://doi.org/10.13039/100008050Fundación Cellex (Cellex Foundation)

Funded by: Fundación Mig-puig

Custom metadata

cover-date January/February 2020

Keywords: calcium imaging,focal damage,functional recovery,laser microsurgery,network neuroscience,neuronal cultures

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Keywords: calcium imaging, focal damage, functional recovery, laser microsurgery, network neuroscience, neuronal cultures

Spontaneous Functional Recovery after Focal Damage in Neuronal Cultures

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Abstract

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Neuronal Signaling

Most cited references 25

Disruptions of network connectivity predict impairment in multiple behavioral domains after stroke.

Connectivity-based approaches in stroke and recovery of function.

Non-Stationarity in the “Resting Brain’s” Modular Architecture

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