Longitudinal monitoring of the mouse brain reveals heterogenous network trajectories during aging.
Journal
Communications biology
ISSN: 2399-3642
Titre abrégé: Commun Biol
Pays: England
ID NLM: 101719179
Informations de publication
Date de publication:
20 Feb 2024
20 Feb 2024
Historique:
received:
30
05
2023
accepted:
30
01
2024
medline:
21
2
2024
pubmed:
21
2
2024
entrez:
21
2
2024
Statut:
epublish
Résumé
The human aging brain is characterized by changes in network efficiency that are currently best captured through longitudinal resting-state functional MRI (rs-fMRI). These studies however are challenging due to the long human lifespan. Here we show that the mouse animal model with a much shorter lifespan allows us to follow the functional network organization over most of the animal's adult lifetime. We used a longitudinal study of the functional connectivity of different brain regions with rs-fMRI under anesthesia. Our analysis uncovers network modules similar to those reported in younger mice and in humans (i.e., prefrontal/default mode network (DMN), somatomotor and somatosensory networks). Statistical analysis reveals different patterns of network reorganization during aging. Female mice showed a pattern akin to human aging, with de-differentiation of the connectome, mainly due to increases in connectivity of the prefrontal/DMN cortical networks to other modules. Our male cohorts revealed heterogenous aging patterns with only one group confirming the de- differentiation, while the majority showed an increase in connectivity of the somatomotor cortex to the Nucleus accumbens. In summary, in line with human work, our analysis in mice supports the concept of de-differentiation in the aging mammalian brain and reveals additional trajectories in aging mice networks.
Identifiants
pubmed: 38378942
doi: 10.1038/s42003-024-05873-8
pii: 10.1038/s42003-024-05873-8
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
210Subventions
Organisme : Kempestiftelserna (Kempe Foundations)
ID : JCK-1922.2
Informations de copyright
© 2024. The Author(s).
Références
Grady, C. The cognitive neuroscience of ageing. Nat. Rev. Neurosci. 13, 491–505 (2012).
pubmed: 22714020
pmcid: 3800175
doi: 10.1038/nrn3256
Nyberg, L. et al. Biological and environmental predictors of heterogeneity in neurocognitive ageing: Evidence from Betula and other longitudinal studies. Ageing Res Rev. 64, 101184 (2020).
pubmed: 32992046
doi: 10.1016/j.arr.2020.101184
Querfurth, H. W. & LaFerla, F. M. Alzheimer’s disease. N. Engl. J. Med. 362, 329–344 (2010).
pubmed: 20107219
doi: 10.1056/NEJMra0909142
Nyberg, L. et al. Longitudinal evidence for diminished frontal cortex function in aging. Proc. Natl Acad. Sci. USA 107, 22682–22686 (2010).
pubmed: 21156826
pmcid: 3012514
doi: 10.1073/pnas.1012651108
Craik, F. I. M. & Salthouse, T. A. The Handbook Of Aging And Cognition. 3 edn. (Lawrence Erlbaum Associates, 2011).
Biswal, B., Yetkin, F. Z., Haughton, V. M. & Hyde, J. S. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34, 537–541 (1995).
pubmed: 8524021
doi: 10.1002/mrm.1910340409
Power, J. D. et al. Functional network organization of the human brain. Neuron 72, 665–678 (2011).
pubmed: 22099467
pmcid: 3222858
doi: 10.1016/j.neuron.2011.09.006
Fox, M. D. et al. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl Acad. Sci. USA 102, 9673–9678 (2005).
pubmed: 15976020
pmcid: 1157105
doi: 10.1073/pnas.0504136102
Fornito, A., Zalesky, A. & Bullmore, E. Fundamentals Of Brain Network Analysis. (Elsevier Academic Press, 2016).
Sporns, O. Graph theory methods: applications in brain networks. Dialogues Clin. Neurosci. 20, 111–121 (2018).
pubmed: 30250388
pmcid: 6136126
doi: 10.31887/DCNS.2018.20.2/osporns
Crossley, N. A. et al. The hubs of the human connectome are generally implicated in the anatomy of brain disorders. Brain 137, 2382–2395 (2014).
pubmed: 25057133
pmcid: 4107735
doi: 10.1093/brain/awu132
Wig, G. S. Segregated systems of human brain networks. Trends Cogn. Sci. 21, 981–996 (2017).
pubmed: 29100737
doi: 10.1016/j.tics.2017.09.006
Chan, M. Y. et al. Long-term prognosis and educational determinants of brain network decline in older adult individuals. Nat. Aging 1, 1053–1067 (2021).
pubmed: 35382259
pmcid: 8979545
doi: 10.1038/s43587-021-00125-4
Varangis, E., Habeck, C. G., Razlighi, Q. R. & Stern, Y. The effect of aging on resting state connectivity of predefined networks in the brain. Front. Aging Neurosci. 11, 234 (2019).
pubmed: 31555124
pmcid: 6737010
doi: 10.3389/fnagi.2019.00234
Geerligs, L., Renken, R. J., Saliasi, E., Maurits, N. M. & Lorist, M. M. A brain-wide study of age-related changes in functional connectivity. Cereb. Cortex 25, 1987–1999 (2015).
pubmed: 24532319
doi: 10.1093/cercor/bhu012
Wang, R. et al. Segregation, integration, and balance of large-scale resting brain networks configure different cognitive abilities. Proc. Natl Acad. Sci. USA 118, e2022288118 (2021).
pubmed: 34074762
pmcid: 8201916
doi: 10.1073/pnas.2022288118
Pedersen, R. et al. When functional blurring becomes deleterious: reduced system segregation is associated with less white matter integrity and cognitive decline in aging. Neuroimage 242, 118449 (2021).
pubmed: 34358662
doi: 10.1016/j.neuroimage.2021.118449
Grandjean, J. et al. Common functional networks in the mouse brain revealed by multi-centre resting-state fMRI analysis. Neuroimage 205, 116278 (2020).
pubmed: 31614221
doi: 10.1016/j.neuroimage.2019.116278
Egimendia, A. et al. Aging reduces the functional brain networks strength—a resting state fmri study of healthy mouse brain. Front. Aging Neurosc. https://doi.org/10.3389/fnagi.2019.00277 (2019).
Wang, S., Lai, X., Deng, Y. & Song, Y. Correlation between mouse age and human age in anti-tumor research: Significance and method establishment. Life Sci. 242, 117242 (2020).
pubmed: 31891723
doi: 10.1016/j.lfs.2019.117242
Nyberg, L., Pudas, S. & Lundquist, A. Cognitive Neuroscience of Aging: Linking cognitive and cerebral aging (eds R. Cabeza, L. Nyberg, & D. Park) (Oxford University Press, 2004).
Belloy, M. E. et al. Quasi-periodic patterns of neural activity improve classification of Alzheimer’s disease in mice. Sci. Rep. 8, 10024 (2018).
pubmed: 29968786
pmcid: 6030071
doi: 10.1038/s41598-018-28237-9
Gozzi, A. & Schwarz, A. J. Large-scale functional connectivity networks in the rodent brain. Neuroimage 127, 496–509 (2016).
pubmed: 26706448
doi: 10.1016/j.neuroimage.2015.12.017
Gutierrez-Barragan, D. et al. Unique spatiotemporal fMRI dynamics in the awake mouse brain. Curr. Biol. 32, 631–644 e636 (2022).
pubmed: 34998465
pmcid: 8837277
doi: 10.1016/j.cub.2021.12.015
Akeju, O. et al. Disruption of thalamic functional connectivity is a neural correlate of dexmedetomidine-induced unconsciousness. eLife https://doi.org/10.7554/elife.04499 (2014).
Avelar-Pereira, B., Bäckman, L., Wåhlin, A., Nyberg, L. & Salami, A. Age-related differences in dynamic interactions among default mode, frontoparietal control, and dorsal attention networks during resting-state and interference resolution. Front. Aging Neurosci. 9, 152–152 (2017).
pubmed: 28588476
pmcid: 5438979
doi: 10.3389/fnagi.2017.00152
Paasonen, J., Stenroos, P., Salo, R. A., Kiviniemi, V. & Grohn, O. Functional connectivity under six anesthesia protocols and the awake condition in rat brain. Neuroimage 172, 9–20 (2018).
pubmed: 29414498
doi: 10.1016/j.neuroimage.2018.01.014
Lidster, K., Owen, K., Browne, W. J. & Prescott, M. J. Cage aggression in group-housed laboratory male mice: an international data crowdsourcing project. Sci. Rep. https://doi.org/10.1038/s41598-019-51674-z (2019).
Freund, J. et al. Emergence of individuality in genetically identical mice. Science 340, 756–759 (2013).
pubmed: 23661762
doi: 10.1126/science.1235294
Biswal, B. B. et al. Toward discovery science of human brain function. Proc. Natl Acad. Sci. USA 107, 4734–4739 (2010).
pubmed: 20176931
pmcid: 2842060
doi: 10.1073/pnas.0911855107
Ficek-Tani, B. et al. Sex differences in default mode network connectivity in healthy aging adults. Cereb. Cortex 33, 6139–6151 (2022).
pmcid: 10183749
doi: 10.1093/cercor/bhac491
Scheinost, D. et al. Sex differences in normal age trajectories of functional brain networks. Hum. Brain Mapp. 36, 1524–1535 (2015).
pubmed: 25523617
doi: 10.1002/hbm.22720
Sole-Padulles, C. et al. No association between loneliness, episodic memory and hippocampal volume change in young and healthy older adults: A Longitudinal European Multicenter Study. Front Aging Neurosci. 14, 795764 (2022).
pubmed: 35283753
pmcid: 8905540
doi: 10.3389/fnagi.2022.795764
Karalija, N. et al. Longitudinal dopamine D2 receptor changes and cerebrovascular health in aging. Neurology 99, e1278–e1289 (2022).
pubmed: 35790424
pmcid: 9576296
doi: 10.1212/WNL.0000000000200891
Febo, M. et al. Longitudinal characterization and biomarkers of age and sex differences in the decline of spatial memory. Front. Aging Neurosci. https://doi.org/10.3389/fnagi.2020.00034 (2020).
Stowie, A. C. & Glass, J. D. Longitudinal study of changes in daily activity rhythms over the lifespan in individual male and female C57BL/6J mice. J. Biol. Rhythms 30, 563–568 (2015).
pubmed: 26271539
doi: 10.1177/0748730415598023
Rosenfeld, C. S. Sex-dependent differences in voluntary physical activity. J. Neurosci. Res. 95, 279–290 (2017).
pubmed: 27870424
pmcid: 5120617
doi: 10.1002/jnr.23896
Brann, D. W., Dhandapani, K., Wakade, C., Mahesh, V. B. & Khan, M. M. Neurotrophic and neuroprotective actions of estrogen: basic mechanisms and clinical implications. Steroids 72, 381–405 (2007).
pubmed: 17379265
pmcid: 2048656
doi: 10.1016/j.steroids.2007.02.003
An, X. L. et al. Strain and sex differences in anxiety-like and social behaviors in C57BL/6J and BALB/cJ mice. Exp. Anim. 60, 111–123 (2011).
pubmed: 21512266
doi: 10.1538/expanim.60.111
Premoli, M., Petroni, V., Bulthuis, R., Bonini, S. A. & Pietropaolo, S. Ultrasonic vocalizations in adult C57BL/6J mice: the role of sex differences and repeated testing. Front. Behav. Neurosci. 16, 883353 (2022).
pubmed: 35910678
pmcid: 9330122
doi: 10.3389/fnbeh.2022.883353
Donoghue, J. P. & Herkenham, M. Neostriatal projections from individual cortical fields conform to histochemically distinct striatal compartments in the rat. Brain Res. 365, 397–403 (1986).
pubmed: 3004664
doi: 10.1016/0006-8993(86)91658-6
Gerfen, C. R. The neostriatal mosaic: compartmentalization of corticostriatal input and striatonigral output systems. Nature 311, 461–464 (1984).
pubmed: 6207434
doi: 10.1038/311461a0
Rathod, Y. D. & Di Fulvio, M. The feeding microstructure of male and female mice. PLoS ONE 16, e0246569 (2021).
pubmed: 33539467
pmcid: 7861458
doi: 10.1371/journal.pone.0246569
Ragozzino, M. E., Adams, S. & Kesner, R. P. Differential involvement of the dorsal anterior cingulate and prelimbic-infralimbic areas of the rodent prefrontal cortex in spatial working memory. Behav. Neurosci. 112, 293–303 (1998).
pubmed: 9588479
doi: 10.1037/0735-7044.112.2.293
Gisquet-Verrier, P. & Delatour, B. The role of the rat prelimbic/infralimbic cortex in working memory: not involved in the short-term maintenance but in monitoring and processing functions. Neuroscience 141, 585–596 (2006).
pubmed: 16713111
doi: 10.1016/j.neuroscience.2006.04.009
Delatour, B. & Gisquet-Verrier, P. Prelimbic cortex specific lesions disrupt delayed-variable response tasks in the rat. Behav. Neurosci. 110, 1282–1298 (1996).
pubmed: 8986332
doi: 10.1037/0735-7044.110.6.1282
Laubach, M., Amarante, L. M., Swanson, K. & White, S. R. What, if anything, is rodent prefrontal cortex? eneuro 5, ENEURO.0315–0318 (2018).
pubmed: 30406193
doi: 10.1523/ENEURO.0315-18.2018
Giacobbo, B. L. et al. The aged striatum: evidence of molecular and structural changes using a longitudinal multimodal approach in mice. Front. Aging Neurosci. 14, 795132 (2022).
pubmed: 35140600
pmcid: 8818755
doi: 10.3389/fnagi.2022.795132
Tustison, N. J. et al. N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29, 1310–1320 (2010).
pubmed: 20378467
pmcid: 3071855
doi: 10.1109/TMI.2010.2046908
Delora, A. et al. A simple rapid process for semi-automated brain extraction from magnetic resonance images of the whole mouse head. J. Neurosci. Methods 257, 185–193 (2016).
pubmed: 26455644
doi: 10.1016/j.jneumeth.2015.09.031
Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W. & Smith, S. M. FSL. Neuroimage 62, 782–790 (2012).
pubmed: 21979382
doi: 10.1016/j.neuroimage.2011.09.015
Cox, R. W. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 29, 162–173 (1996).
pubmed: 8812068
doi: 10.1006/cbmr.1996.0014
Jenkinson, M., Bannister, P., Brady, M. & Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17, 825–841 (2002).
pubmed: 12377157
doi: 10.1006/nimg.2002.1132
Wang, Q. et al. The allen mouse brain common coordinate framework: a 3D reference atlas. Cell 181, 936–953.e920 (2020).
pubmed: 32386544
pmcid: 8152789
doi: 10.1016/j.cell.2020.04.007
Boubela, R. N. et al. Beyond noise: using temporal ICA to extract meaningful information from high-frequency fMRI signal fluctuations during rest. Front. Hum. Neurosci. 7, 168 (2013).
pubmed: 23641208
pmcid: 3640215
doi: 10.3389/fnhum.2013.00168
Pan, W.-J., Thompson, G. J., Magnuson, M. E., Jaeger, D. & Keilholz, S. Infraslow LFP correlates to resting-state fMRI BOLD signals. Neuroimage 74, 288–297 (2013).
pubmed: 23481462
doi: 10.1016/j.neuroimage.2013.02.035
Lein, E. S. et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168–176 (2007).
pubmed: 17151600
doi: 10.1038/nature05453
Swanson, L. W. Brain Maps: Structure of the Rat Brain. Vol. 3rd rev. ed (Academic, 2004).
Hof, P. R. Comparative Cytoarchitectonic Atlas Of The C57BL/6 and 129/Sv Mouse Brains (Elsevier, 2000).
Blondel, V. D., Guillaume, J. L., Hendrickx, J. M., de Kerchove, C. & Lambiotte, R. Local leaders in random networks. Phys. Rev. E 77, 036114 (2008).
doi: 10.1103/PhysRevE.77.036114
Lancichinetti, A. & Fortunato, S. Consensus clustering in complex networks. Sci. Rep. 2, 336 (2012).
pubmed: 22468223
pmcid: 3313482
doi: 10.1038/srep00336
Sun, Y., Danila, B., Josić, K. & Bassler, K. E. Improved community structure detection using a modified fine-tuning strategy. Europhys. Lett. 86, 28004 (2009).
doi: 10.1209/0295-5075/86/28004
Geerligs, L., Rubinov, M., Cam, C. & Henson, R. N. State and trait components of functional connectivity: individual differences vary with mental state. J. Neurosci. 35, 13949–13961 (2015).
pubmed: 26468196
pmcid: 4604231
doi: 10.1523/JNEUROSCI.1324-15.2015
Chan, M. Y., Park, D. C., Savalia, N. K., Petersen, S. E. & Wig, G. S. Decreased segregation of brain systems across the healthy adult lifespan. Proc. Natl Acad. Sci. USA 111, E4997–E5006 (2014).
pubmed: 25368199
pmcid: 4246293
doi: 10.1073/pnas.1415122111
He, Y., Chen, Z. J. & Evans, A. C. Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cereb. Cortex 17, 2407–2419 (2007).
pubmed: 17204824
doi: 10.1093/cercor/bhl149
Wang, J. et al. Parcellation-dependent small-world brain functional networks: a resting-state fMRI study. Hum. Brain Mapp. 30, 1511–1523 (2009).
pubmed: 18649353
doi: 10.1002/hbm.20623
Zhang, J. et al. Disrupted brain connectivity networks in drug-naive, first-episode major depressive disorder. Biol. Psychiatry 70, 334–342 (2011).
pubmed: 21791259
doi: 10.1016/j.biopsych.2011.05.018
Watts, D. J. & Strogatz, S. H. Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998).
pubmed: 9623998
doi: 10.1038/30918
Latora, V. & Marchiori, M. Efficient behavior of small-world networks. Phys. Rev. Lett. 87, 198701 (2001).
pubmed: 11690461
doi: 10.1103/PhysRevLett.87.198701
Humphries, M. D. & Gurney, K. Network ‘small-world-ness’: a quantitative method for determining canonical network equivalence. PLoS ONE 3, e0002051 (2008).
pubmed: 18446219
doi: 10.1371/journal.pone.0002051
Newman, M. E. Assortative mixing in networks. Phys. Rev. Lett. 89, 208701 (2002).
pubmed: 12443515
doi: 10.1103/PhysRevLett.89.208701
Noldus, R. & Van Mieghem, P. Assortativity in complex networks. J. Complex Netw. 3, 507–542 (2015).
doi: 10.1093/comnet/cnv005
Freeman, L. C. Centrality in networks: I. Conceptual clarification. Soc. Netw. 1, 215–239 (1979).
doi: 10.1016/0378-8733(78)90021-7
Wang, J. et al. GRETNA: a graph theoretical network analysis toolbox for imaging connectomics. Front. Hum. Neurosci. 9, 386 (2015).
pubmed: 26175682
pmcid: 4485071
Bates, D., Machler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
doi: 10.18637/jss.v067.i01
Bernal-Rusiel, J. L., Greve, D. N., Reuter, M., Fischl, B. & Sabuncu, M. R. Statistical analysis of longitudinal neuroimage data with Linear Mixed Effects models. Neuroimage 66, 249–260 (2013).
pubmed: 23123680
doi: 10.1016/j.neuroimage.2012.10.065
R Core Team. (R Foundation for Statistical Computing, 2014).
Gałecki, A. T. & Burzykowski, T. Linear Mixed-effects Models Using R : A Step-by-step Approach (Springer, 2013).
Verbeke, G., Fieuws, S., Molenberghs, G. & Davidian, M. The analysis of multivariate longitudinal data: a review. Stat. Methods Med. Res. 23, 42–59 (2014).
pubmed: 22523185
doi: 10.1177/0962280212445834
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc.: Ser. B (Methodol.) 57, 289–300 (1995).
Sultan, F. Figure data from Longitudinal monitoring of the mouse brain reveals heterogenous network trajectories during aging [Dataset]. Dryad https://datadryad.org/stash/share/NIk2WMQGqp3USlKKAUHemvP6A5N8QQivhWjtQyCIfQk (2024).
Sultan, F. Longitudinal Monitoring of Aging Mouse Brain. OSF https://doi.org/10.5061/dryad.jdfn2z3hr (2024).
SARIF-UMU. Longitudinal-monitoring-of-aging-mouse-brain: initial. Zenodo https://doi.org/10.5281/zenodo.10522014 (2024).