Literature DB >> 28334882

Decreased integration and information capacity in stroke measured by whole brain models of resting state activity.

Mohit H Adhikari1, Carl D Hacker2, Josh S Siegel3, Alessandra Griffa4,5, Patric Hagmann4,5, Gustavo Deco1,6, Maurizio Corbetta2,3,7,8.   

Abstract

While several studies have shown that focal lesions affect the communication between structurally normal regions of the brain, and that these changes may correlate with behavioural deficits, their impact on brain's information processing capacity is currently unknown. Here we test the hypothesis that focal lesions decrease the brain's information processing capacity, of which changes in functional connectivity may be a measurable correlate. To measure processing capacity, we turned to whole brain computational modelling to estimate the integration and segregation of information in brain networks. First, we measured functional connectivity between different brain areas with resting state functional magnetic resonance imaging in healthy subjects (n = 26), and subjects who had suffered a cortical stroke (n = 36). We then used a whole-brain network model that coupled average excitatory activities of local regions via anatomical connectivity. Model parameters were optimized in each healthy or stroke participant to maximize correlation between model and empirical functional connectivity, so that the model's effective connectivity was a veridical representation of healthy or lesioned brain networks. Subsequently, we calculated two model-based measures: 'integration', a graph theoretical measure obtained from functional connectivity, which measures the connectedness of brain networks, and 'information capacity', an information theoretical measure that cannot be obtained empirically, representative of the segregative ability of brain networks to encode distinct stimuli. We found that both measures were decreased in stroke patients, as compared to healthy controls, particularly at the level of resting-state networks. Furthermore, we found that these measures, especially information capacity, correlate with measures of behavioural impairment and the segregation of resting-state networks empirically measured. This study shows that focal lesions affect the brain's ability to represent stimuli and task states, and that information capacity measured through whole brain models is a theory-driven measure of processing capacity that could be used as a biomarker of injury for outcome prediction or target for rehabilitation intervention.
© The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  functional connectivity; information capacity; integration; whole-brain modelling

Mesh:

Year:  2017        PMID: 28334882      PMCID: PMC6075429          DOI: 10.1093/brain/awx021

Source DB:  PubMed          Journal:  Brain        ISSN: 0006-8950            Impact factor:   13.501


  54 in total

1.  Electrophysiological signatures of resting state networks in the human brain.

Authors:  D Mantini; M G Perrucci; C Del Gratta; G L Romani; M Corbetta
Journal:  Proc Natl Acad Sci U S A       Date:  2007-08-01       Impact factor: 11.205

2.  Decreased corticospinal tract fractional anisotropy predicts long-term motor outcome after stroke.

Authors:  Josep Puig; Gerard Blasco; Josep Daunis-I-Estadella; Götz Thomalla; Mar Castellanos; Jaume Figueras; Sebastián Remollo; Cecile van Eendenburg; Javier Sánchez-González; Joaquín Serena; Salvador Pedraza
Journal:  Stroke       Date:  2013-05-07       Impact factor: 7.914

3.  Stability of muscle synergies for voluntary actions after cortical stroke in humans.

Authors:  Vincent C K Cheung; Lamberto Piron; Michela Agostini; Stefano Silvoni; Andrea Turolla; Emilio Bizzi
Journal:  Proc Natl Acad Sci U S A       Date:  2009-10-30       Impact factor: 11.205

4.  How local excitation-inhibition ratio impacts the whole brain dynamics.

Authors:  Gustavo Deco; Adrián Ponce-Alvarez; Patric Hagmann; Gian Luca Romani; Dante Mantini; Maurizio Corbetta
Journal:  J Neurosci       Date:  2014-06-04       Impact factor: 6.167

5.  Identification of optimal structural connectivity using functional connectivity and neural modeling.

Authors:  Gustavo Deco; Anthony R McIntosh; Kelly Shen; R Matthew Hutchison; Ravi S Menon; Stefan Everling; Patric Hagmann; Viktor K Jirsa
Journal:  J Neurosci       Date:  2014-06-04       Impact factor: 6.167

6.  Clinical correlates of white matter findings on cranial magnetic resonance imaging of 3301 elderly people. The Cardiovascular Health Study.

Authors:  W T Longstreth; T A Manolio; A Arnold; G L Burke; N Bryan; C A Jungreis; P L Enright; D O'Leary; L Fried
Journal:  Stroke       Date:  1996-08       Impact factor: 7.914

7.  Resting interhemispheric functional magnetic resonance imaging connectivity predicts performance after stroke.

Authors:  Alex R Carter; Serguei V Astafiev; Catherine E Lang; Lisa T Connor; Jennifer Rengachary; Michael J Strube; Daniel L W Pope; Gordon L Shulman; Maurizio Corbetta
Journal:  Ann Neurol       Date:  2010-03       Impact factor: 10.422

8.  Impact of in-scanner head motion on multiple measures of functional connectivity: relevance for studies of neurodevelopment in youth.

Authors:  Theodore D Satterthwaite; Daniel H Wolf; James Loughead; Kosha Ruparel; Mark A Elliott; Hakon Hakonarson; Ruben C Gur; Raquel E Gur
Journal:  Neuroimage       Date:  2012-01-02       Impact factor: 6.556

9.  Registration of [18F]FDG microPET and small-animal MRI.

Authors:  Douglas J Rowland; Joel R Garbow; Richard Laforest; Abraham Z Snyder
Journal:  Nucl Med Biol       Date:  2005-08       Impact factor: 2.408

10.  Resting state network estimation in individual subjects.

Authors:  Eric C Leuthardt; Maurizio Corbetta; Carl D Hacker; Timothy O Laumann; Nicholas P Szrama; Antonello Baldassarre; Abraham Z Snyder
Journal:  Neuroimage       Date:  2013-06-02       Impact factor: 6.556

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  27 in total

1.  Damage to the shortest structural paths between brain regions is associated with disruptions of resting-state functional connectivity after stroke.

Authors:  Joseph C Griffis; Nicholas V Metcalf; Maurizio Corbetta; Gordon L Shulman
Journal:  Neuroimage       Date:  2020-01-30       Impact factor: 6.556

2.  Reply: Defining a functional network homeostasis after stroke: EEG-based approach is complementary to functional MRI.

Authors:  Mohit H Adhikari; Gustavo Deco; Maurizio Corbetta
Journal:  Brain       Date:  2017-12-01       Impact factor: 13.501

Review 3.  [The importance of neuronal networks for motor rehabilitation after a stroke].

Authors:  F C Hummel
Journal:  Nervenarzt       Date:  2017-08       Impact factor: 1.214

Review 4.  Brain networks and their relevance for stroke rehabilitation.

Authors:  Adrian G Guggisberg; Philipp J Koch; Friedhelm C Hummel; Cathrin M Buetefisch
Journal:  Clin Neurophysiol       Date:  2019-04-15       Impact factor: 3.708

5.  Altered dynamics of brain segregation and integration in poststroke aphasia.

Authors:  Jing Guo; Bharat B Biswal; Shaoqiang Han; Jiao Li; Siqi Yang; Mi Yang; Huafu Chen
Journal:  Hum Brain Mapp       Date:  2019-04-23       Impact factor: 5.038

6.  Toward neuroimaging-based network biomarkers for transient ischemic attack.

Authors:  Yating Lv; Xiujie Han; Yulin Song; Yu Han; Chengshu Zhou; Dan Zhou; Fuding Zhang; Qiming Xue; Jinling Liu; Lijuan Zhao; Cairong Zhang; Lingyu Li; Jinhui Wang
Journal:  Hum Brain Mapp       Date:  2019-04-19       Impact factor: 5.038

7.  Temporal exponential random graph models of longitudinal brain networks after stroke.

Authors:  Catalina Obando; Charlotte Rosso; Joshua Siegel; Maurizio Corbetta; Fabrizio De Vico Fallani
Journal:  J R Soc Interface       Date:  2022-03-02       Impact factor: 4.118

Review 8.  Mapping correlated neurological deficits after stroke to distributed brain networks.

Authors:  Joshua S Siegel; Gordon L Shulman; Maurizio Corbetta
Journal:  Brain Struct Funct       Date:  2022-07-26       Impact factor: 3.748

9.  Linking Entropy at Rest with the Underlying Structural Connectivity in the Healthy and Lesioned Brain.

Authors:  Victor M Saenger; Adrián Ponce-Alvarez; Mohit Adhikari; Patric Hagmann; Gustavo Deco; Maurizio Corbetta
Journal:  Cereb Cortex       Date:  2018-08-01       Impact factor: 5.357

10.  Connectivity Measures Differentiate Cortical and Subcortical Sub-Acute Ischemic Stroke Patients.

Authors:  Chiara Fanciullacci; Alessandro Panarese; Vincenzo Spina; Michael Lassi; Alberto Mazzoni; Fiorenzo Artoni; Silvestro Micera; Carmelo Chisari
Journal:  Front Hum Neurosci       Date:  2021-07-01       Impact factor: 3.169

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