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Development of functional and structural connectivity within the default mode network in young children

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Supekar, Kaustubh, Uddin, Lucina Q., Prater, Katherine, Amin, Hitha, Greicius, Michael D., Menon, Vinod·2010·NeuroImage
DOI 10.1016/j.neuroimage.2010.04.009
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Abstract

Functional and structural maturation of networks comprised of discrete regions is an important aspect of brain development. The default-mode network (DMN) is a prominent network which includes the posterior cingulate cortex (PCC), medial prefrontal cortex (mPFC), medial temporal lobes (MTL), and angular gyrus (AG). Despite increasing interest in DMN function, little is known about its maturation from childhood to adulthood. Here we examine developmental changes in DMN connectivity using a multimodal imaging approach by combining resting-state fMRI, voxel-based morphometry and diffusion tensor imaging-based tractography. We found that the DMN undergoes significant developmental changes in functional and structural connectivity, but these changes are not uniform across all DMN nodes. Convergent structural and functional connectivity analyses suggest that PCC-mPFC connectivity along the cingulum bundle is the most immature link in the DMN of children. Both PCC and mPFC also showed gray matter volume differences, as well as prominent macrostructural and microstructural differences in the dorsal cingulum bundle linking these regions. Notably, structural connectivity between PCC and left MTL was either weak or non-existent in children, even though functional connectivity did not differ from that of adults. These results imply that functional connectivity in children can reach adult-like levels despite weak structural connectivity. We propose that maturation of PCC-mPFC structural connectivity plays an important role in the development of self-related and social-cognitive functions that emerge during adolescence. More generally, our study demonstrates how quantitative multimodal analysis of anatomy and connectivity allows us to better characterize the heterogeneous development and maturation of brain networks.

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Research works printed but not linked: 35

  1. Bammer, 2002, Diffusion tensor imaging using single-shot SENSE-EPI, Magn. Reson. Med., 48, 128, 10.1002/mrm.10184 · DOI · Paper page
  2. Barnea-Goraly, 2005, White matter development during childhood and adolescence: a cross-sectional diffusion tensor imaging study, Cereb. Cortex, 15, 1848, 10.1093/cercor/bhi062 · DOI · Paper page
  3. Basser, 1995, Inferring microstructural features and the physiological state of tissues from diffusion-weighted images, NMR Biomed., 8, 333, 10.1002/nbm.1940080707 · DOI · Paper page
  4. Basser, 2000, In vivo fiber tractography using DT-MRI data, Magn. Reson. Med., 44, 625, 10.1002/1522-2594(200010)44:4<625::AID-MRM17>3.0.CO;2-O · DOI
  5. Basser, 1996, Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI, J. Magn. Reson. B, 111, 209, 10.1006/jmrb.1996.0086 · DOI · Paper page
  6. Behrens, 2007, Probabilistic diffusion tractography with multiple fibre orientations: What can we gain?, NeuroImage, 34, 144, 10.1016/j.neuroimage.2006.09.018 · DOI · Paper page
  7. Binder, 2009, Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies, Cereb. Cortex, 10.1093/cercor/bhp055 · DOI · Paper page
  8. Chambers, 2004, Fast and slow parietal pathways mediate spatial attention, Nat. Neurosci., 7, 217, 10.1038/nn1203 · DOI
  9. Conturo, 1999, Tracking neuronal fiber pathways in the living human brain, Proc. Natl. Acad. Sci. U. S. A., 96, 10422, 10.1073/pnas.96.18.10422 · DOI · Paper page
  10. Cordes, 2001, Frequencies contributing to functional connectivity in the cerebral cortex in “resting-state” data, AJNR Am. J. Neuroradiol., 22, 1326
  11. De Luca, 2005, Blood oxygenation level dependent contrast resting state networks are relevant to functional activity in the neocortical sensorimotor system, Exp. Brain Res., 167, 587, 10.1007/s00221-005-0059-1 · DOI
  12. Fair, 2007, Development of distinct control networks through segregation and integration, Proc. Natl. Acad. Sci. U. S. A., 104, 13507, 10.1073/pnas.0705843104 · DOI · Paper page
  13. Fox, 2005, The human brain is intrinsically organized into dynamic, anticorrelated functional networks, Proc. Natl. Acad. Sci. U. S. A., 102, 9673, 10.1073/pnas.0504136102 · DOI · Paper page
  14. Glover, 1998, Self-navigated spiral fMRI: interleaved versus single-shot, Magn. Reson. Med., 39, 361, 10.1002/mrm.1910390305 · DOI · Paper page
  15. Glover, 2001, Spiral-in/out BOLD fMRI for increased SNR and reduced susceptibility artifacts, Magn. Reson. Med., 46, 515, 10.1002/mrm.1222 · DOI · Paper page
  16. Hayasaka, 2004, Nonstationary cluster-size inference with random field and permutation methods, NeuroImage, 22, 676, 10.1016/j.neuroimage.2004.01.041 · DOI · Paper page
  17. Honey, 2009, Predicting human resting-state functional connectivity from structural connectivity, Proc. Natl. Acad. Sci. U. S. A., 106, 2035, 10.1073/pnas.0811168106 · DOI · Paper page
  18. Jones, 2004, The effect of gradient sampling schemes on measures derived from diffusion tensor MRI: a Monte Carlo study, Magn. Reson. Med., 51, 807, 10.1002/mrm.20033 · DOI · Paper page
  19. Liu, 2008, Disrupted small-world networks in schizophrenia, Brain, 131, 945, 10.1093/brain/awn018 · DOI · Paper page
  20. Milner, 2005, The medial temporal-lobe amnesic syndrome, Psychiatr. Clin. North Am., 28, 609
  21. Morcom, 2007, Does the brain have a baseline? Why we should be resisting a rest, NeuroImage, 37, 1073, 10.1016/j.neuroimage.2006.09.013 · DOI · Paper page
  22. Mori, 1999, Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging, Ann. Neurol., 45, 265, 10.1002/1531-8249(199902)45:2<265::AID-ANA21>3.0.CO;2-3 · DOI
  23. Nir, 2007, Coupling between neuronal firing rate, gamma LFP, and BOLD fMRI is related to interneuronal correlations, Curr. Biol., 17, 1275, 10.1016/j.cub.2007.06.066 · DOI · Paper page
  24. Nir, 2008, Interhemispheric correlations of slow spontaneous neuronal fluctuations revealed in human sensory cortex, Nat. Neurosci., 11, 1100, 10.1038/nn.2177 · DOI · Paper page
  25. Pajevic, 2002, A continuous tensor field approximation of discrete DT-MRI data for extracting microstructural and architectural features of tissue, J. Magn. Reson., 154, 85, 10.1006/jmre.2001.2452 · DOI
  26. Polli, 2005, Rostral and dorsal anterior cingulate cortex make dissociable contributions during antisaccade error commission, Proc. Natl. Acad. Sci. U. S. A., 102, 15700, 10.1073/pnas.0503657102 · DOI
  27. Press, 2002
  28. Salvador, 2005, Neurophysiological architecture of functional magnetic resonance images of human brain, Cereb. Cortex, 15, 1332, 10.1093/cercor/bhi016 · DOI · Paper page
  29. Shaw, 2008, Neurodevelopmental trajectories of the human cerebral cortex, J Neurosci, 28, 3586, 10.1523/JNEUROSCI.5309-07.2008 · DOI · Paper page
  30. Shmueli, 2007, Low-frequency fluctuations in the cardiac rate as a source of variance in the resting-state fMRI BOLD signal, NeuroImage, 38, 306, 10.1016/j.neuroimage.2007.07.037 · DOI · Paper page
  31. Skudlarski, 2008, Measuring brain connectivity: diffusion tensor imaging validates resting state temporal correlations, NeuroImage, 43, 554, 10.1016/j.neuroimage.2008.07.063 · DOI · Paper page
  32. Spreng, 2009, The common neural basis of autobiographical memory, prospection, navigation, theory of mind, and the default mode: a quantitative meta-analysis, J Cogn Neurosci, 21, 489, 10.1162/jocn.2008.21029 · DOI · Paper page
  33. Stark, 2008, Regional variation in interhemispheric coordination of intrinsic hemodynamic fluctuations, J Neurosci, 28, 13754, 10.1523/JNEUROSCI.4544-08.2008 · DOI · Paper page
  34. Sun, 2004, Measuring interregional functional connectivity using coherence and partial coherence analyses of fMRI data, NeuroImage, 21, 647, 10.1016/j.neuroimage.2003.09.056 · DOI
  35. Thompson, 2005, Structural MRI and brain development, Int. Rev. Neurobiol., 67, 285, 10.1016/S0074-7742(05)67009-2 · DOI

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Metadata

Authors
Supekar, K., Uddin, L. Q., Prater, K., Amin, H., Greicius, M. D., & Menon, V.
Venue
NeuroImage
Publisher
Elsevier BV
Published
2010-08
Volume
52
Issue
1
Pages
290-301
ISSN
1053-8119 (ISSN-L)
DOI
10.1016/j.neuroimage.2010.04.009
OpenAlex
W2096424283
Cited by
530
References
32
FWCI
21.72
Type
journal article
Language
en
Open access
green
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Funders
  • National Institutes of Health (HD047520, HD059205, NS058899)
  • National Science Foundation (BCS-0449927)

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Supekar, K., Uddin, L. Q., Prater, K., Amin, H., Greicius, M. D., & Menon, V. (2010). Development of functional and structural connectivity within the default mode network in young children. NeuroImage. https://doi.org/10.1016/j.neuroimage.2010.04.009

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