Literature DB >> 34588302

Metabolism modulates network synchrony in the aging brain.

Corey Weistuch1,2, Lilianne R Mujica-Parodi1,3,4,5,6, Rostam M Razban1, Botond Antal3,6, Helena van Nieuwenhuizen5, Anar Amgalan1,3,5, Ken A Dill7,2,5,8.   

Abstract

Brain aging is associated with hypometabolism and global changes in functional connectivity. Using functional MRI (fMRI), we show that network synchrony, a collective property of brain activity, decreases with age. Applying quantitative methods from statistical physics, we provide a generative (Ising) model for these changes as a function of the average communication strength between brain regions. We find that older brains are closer to a critical point of this communication strength, in which even small changes in metabolism lead to abrupt changes in network synchrony. Finally, by experimentally modulating metabolic activity in younger adults, we show how metabolism alone-independent of other changes associated with aging-can provide a plausible candidate mechanism for marked reorganization of brain network topology.

Entities:  

Keywords:  aging; criticality; fMRI; neurometabolism; synchrony

Mesh:

Year:  2021        PMID: 34588302      PMCID: PMC8501850          DOI: 10.1073/pnas.2025727118

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  52 in total

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Journal:  Neuroimage       Date:  2015-07-29       Impact factor: 6.556

2.  Energetic cost of brain functional connectivity.

Authors:  Dardo Tomasi; Gene-Jack Wang; Nora D Volkow
Journal:  Proc Natl Acad Sci U S A       Date:  2013-07-29       Impact factor: 11.205

Review 3.  Synaptic energy use and supply.

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Journal:  Neuron       Date:  2012-09-06       Impact factor: 17.173

4.  Glucose metabolism in normal aging and Alzheimer's disease: Methodological and physiological considerations for PET studies.

Authors:  Lisa Mosconi
Journal:  Clin Transl Imaging       Date:  2013-08

5.  The global signal in fMRI: Nuisance or Information?

Authors:  Thomas T Liu; Alican Nalci; Maryam Falahpour
Journal:  Neuroimage       Date:  2017-02-16       Impact factor: 6.556

6.  Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python.

Authors:  Krzysztof Gorgolewski; Christopher D Burns; Cindee Madison; Dav Clark; Yaroslav O Halchenko; Michael L Waskom; Satrajit S Ghosh
Journal:  Front Neuroinform       Date:  2011-08-22       Impact factor: 4.081

7.  Meta-analysis of Alzheimer's disease risk with obesity, diabetes, and related disorders.

Authors:  Louis A Profenno; Anton P Porsteinsson; Stephen V Faraone
Journal:  Biol Psychiatry       Date:  2009-04-09       Impact factor: 13.382

8.  Searching for collective behavior in a large network of sensory neurons.

Authors:  Gašper Tkačik; Olivier Marre; Dario Amodei; Elad Schneidman; William Bialek; Michael J Berry
Journal:  PLoS Comput Biol       Date:  2014-01-02       Impact factor: 4.475

Review 9.  The Role of Cerebrovascular-Reactivity Mapping in Functional MRI: Calibrated fMRI and Resting-State fMRI.

Authors:  J Jean Chen; Claudine J Gauthier
Journal:  Front Physiol       Date:  2021-03-25       Impact factor: 4.566

10.  Editorial: Origins of the Resting-State fMRI Signal.

Authors:  J Jean Chen; Peter Herman; Shella Keilholz; Garth J Thompson
Journal:  Front Neurosci       Date:  2020-10-27       Impact factor: 4.677

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  1 in total

1.  Type 2 diabetes mellitus accelerates brain aging and cognitive decline: Complementary findings from UK Biobank and meta-analyses.

Authors:  Botond Antal; Liam P McMahon; Syed Fahad Sultan; Andrew Lithen; Deborah J Wexler; Bradford Dickerson; Eva-Maria Ratai; Lilianne R Mujica-Parodi
Journal:  Elife       Date:  2022-05-24       Impact factor: 8.713

  1 in total

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