Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 1,141

The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.

Individual Variability in Functional Connectivity Architecture of the Human Brain

Open access
Mueller, Sophia, Wang, Danhong, Fox, Michael D., Yeo, B.T. Thomas, Sepulcre, Jorge, Sabuncu, Mert R., Shafee, Rebecca, Lu, Jie, Liu, Hesheng·2013·Neuron
DOI 10.1016/j.neuron.2012.12.028
Open this paper in CoChat — free account

Abstract

Summary The fact that people think or behave differently from one another is rooted in individual differences in brain anatomy and connectivity. Here we used repeated-measurement resting-state functional MRI to explore inter-subject variability in connectivity. Individual differences in functional connectivity were heterogeneous across the cortex, with significantly higher variability in heteromodal association cortex and lower variability in unimodal cortices. Inter-subject variability in connectivity was significantly correlated with the degree of evolutionary cortical expansion, suggesting a potential evolutionary root of functional variability. The connectivity variability was also related to variability in sulcal depth but not cortical thickness, positively correlated with the degree of long-range connectivity but negatively correlated with local connectivity. A meta-analysis further revealed that regions predicting individual differences in cognitive domains are predominantly located in regions of high connectivity variability. Our findings have potential implications for understanding brain evolution and development, guiding intervention, and interpreting statistical maps in neuroimaging.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.neuron.2012.12.028

Cites

21 of 21 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 21 references

Research works printed but not linked: 39

  1. Amunts, 1999, Broca’s region revisited: cytoarchitecture and intersubject variability, J. Comp. Neurol., 412, 319, 10.1002/(SICI)1096-9861(19990920)412:2<319::AID-CNE10>3.0.CO;2-7 · DOI · Paper page
  2. Brett, 2001, Using the Talairach atlas with the MNI template, Neuroimage, 13, S85, 10.1016/S1053-8119(01)91428-4 · DOI · Paper page
  3. Brun, 2009, Mapping the regional influence of genetics on brain structure variability—a tensor-based morphometry study, Neuroimage, 48, 37, 10.1016/j.neuroimage.2009.05.022 · DOI
  4. Bürgel, 2006, White matter fiber tracts of the human brain: three-dimensional mapping at microscopic resolution, topography and intersubject variability, Neuroimage, 29, 1092, 10.1016/j.neuroimage.2005.08.040 · DOI · Paper page
  5. Chen, 2011, Genetic influences on cortical regionalization in the human brain, Neuron, 72, 537, 10.1016/j.neuron.2011.08.021 · DOI
  6. Clancy, 2000, The course of human events: predicting the timing of primate neural development, Dev. Sci., 3, 57, 10.1111/1467-7687.00100 · DOI
  7. Connolly, 1992, The measurement of handedness: a cross-cultural comparison of samples from England and Papua New Guinea, Neuropsychologia, 30, 13, 10.1016/0028-3932(92)90010-J · doi:10.1016/0028-3932(92)90010-j
  8. Elston, 2009, Spinogenesis and pruning scales across functional hierarchies, J. Neurosci., 29, 3271, 10.1523/JNEUROSCI.5216-08.2009 · DOI · Paper page
  9. Fischl, 1999, Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system, Neuroimage, 9, 195, 10.1006/nimg.1998.0396 · DOI · Paper page
  10. Fox, 2010, Clinical applications of resting state functional connectivity, Front. Syst. Neurosci., 4, 19
  11. Fox, 2012, Identification of reproducible individualized targets for treatment of depression with TMS based on intrinsic connectivity, Neuroimage, 66C, 151
  12. Fransson, 2011, The functional architecture of the infant brain as revealed by resting-state fMRI, Cereb. Cortex, 21, 145, 10.1093/cercor/bhq071 · DOI · Paper page
  13. Frost, 2012, Measuring structural-functional correspondence: spatial variability of specialised brain regions after macro-anatomical alignment, Neuroimage, 59, 1369, 10.1016/j.neuroimage.2011.08.035 · DOI · Paper page
  14. Glendenning, 1998, Comparative morphometry of mammalian central auditory systems: variation in nuclei and form of the ascending system, Brain Behav. Evol., 51, 59, 10.1159/000006530 · DOI
  15. Goldman-Rakic, 1988, Topography of cognition: parallel distributed networks in primate association cortex, Annu. Rev. Neurosci., 11, 137, 10.1146/annurev.ne.11.030188.001033 · DOI · Paper page
  16. Greve, 2009, Accurate and robust brain image alignment using boundary-based registration, Neuroimage, 48, 63, 10.1016/j.neuroimage.2009.06.060 · DOI · Paper page
  17. Hill, 2010, A surface-based analysis of hemispheric asymmetries and folding of cerebral cortex in term-born human infants, J. Neurosci., 30, 2268, 10.1523/JNEUROSCI.4682-09.2010 · DOI · Paper page
  18. Jacobs, 1997, Life-span dendritic and spine changes in areas 10 and 18 of human cortex: a quantitative Golgi study, J. Comp. Neurol., 386, 661, 10.1002/(SICI)1096-9861(19971006)386:4<661::AID-CNE11>3.0.CO;2-N · doi:10.1002/(sici)1096-9861(19971006)386:4<661::aid-cne11>3.0.co;2-n
  19. Kaas, 2005, From mice to men: the evolution of the large, complex human brain, J. Biosci., 30, 155, 10.1007/BF02703695 · DOI
  20. Kaas, 2006, Evolution of the neocortex, Curr. Biol., 16, R910, 10.1016/j.cub.2006.09.057 · DOI · Paper page
  21. Kessler, 2007, Lifetime prevalence and age-of-onset distributions of mental disorders in the World Health Organization’s World Mental Health Survey Initiative, World Psychiatry, 6, 168
  22. Laird, 2010, Comparison of the disparity between Talairach and MNI coordinates in functional neuroimaging data: validation of the Lancaster transform, Neuroimage, 51, 677, 10.1016/j.neuroimage.2010.02.048 · DOI · Paper page
  23. Liu, 2009, Task-free presurgical mapping using functional magnetic resonance imaging intrinsic activity, J. Neurosurg., 111, 746, 10.3171/2008.10.JNS08846 · DOI
  24. Lu, 2012, Rat brains also have a default mode network, Proc. Natl. Acad. Sci. USA, 109, 3979, 10.1073/pnas.1200506109 · DOI · Paper page
  25. Petanjek, 2011, Extraordinary neoteny of synaptic spines in the human prefrontal cortex, Proc. Natl. Acad. Sci. USA, 108, 13281, 10.1073/pnas.1105108108 · DOI · Paper page
  26. Rademacher, 2001, Variability and asymmetry in the human precentral motor system. A cytoarchitectonic and myeloarchitectonic brain mapping study, Brain, 124, 2232, 10.1093/brain/124.11.2232 · DOI · Paper page
  27. Rimol, 2010, Cortical thickness is influenced by regionally specific genetic factors, Biol. Psychiatry, 67, 493, 10.1016/j.biopsych.2009.09.032 · DOI
  28. Semendeferi, 2001, Prefrontal cortex in humans and apes: a comparative study of area 10, Am. J. Phys. Anthropol., 114, 224, 10.1002/1096-8644(200103)114:3<224::AID-AJPA1022>3.0.CO;2-I · doi:10.1002/1096-8644(200103)114:3<224::aid-ajpa1022>3.0.co;2-i
  29. Sepulcre, 2010, The organization of local and distant functional connectivity in the human brain, PLoS Comput. Biol., 6, e1000808, 10.1371/journal.pcbi.1000808 · DOI · Paper page
  30. Shaw, 2008, Neurodevelopmental trajectories of the human cerebral cortex, J. Neurosci., 28, 3586, 10.1523/JNEUROSCI.5309-07.2008 · DOI · Paper page
  31. Sugiura, 2007, Analysis of intersubject variability in activation: an application to the incidental episodic retrieval during recognition test, Hum. Brain Mapp., 28, 49, 10.1002/hbm.20256 · DOI
  32. Thompson, 2001, Genetic influences on brain structure, Nat. Neurosci., 4, 1253, 10.1038/nn758 · DOI · Paper page
  33. Van Dijk, 2012, The influence of head motion on intrinsic functional connectivity MRI, Neuroimage, 59, 431, 10.1016/j.neuroimage.2011.07.044 · DOI · Paper page
  34. van Essen, 2005, A Population-Average, Landmark- and Surface-based (PALS) atlas of human cerebral cortex, Neuroimage, 28, 635, 10.1016/j.neuroimage.2005.06.058 · DOI · Paper page
  35. van Essen, 2007, Surface-based and probabilistic atlases of primate cerebral cortex, Neuron, 56, 209, 10.1016/j.neuron.2007.10.015 · DOI · Paper page
  36. van Essen, 2012, Cortical parcellations of the macaque monkey analyzed on surface-based atlases, Cereb. Cortex, 22, 2227, 10.1093/cercor/bhr290 · DOI · Paper page
  37. Yeo, 2010, Learning task-optimal registration cost functions for localizing cytoarchitecture and function in the cerebral cortex, IEEE Trans. Med. Imaging, 29, 1424, 10.1109/TMI.2010.2049497 · DOI
  38. Zhang, 2010, Disease and the brain’s dark energy, Nat. Rev. Neurol., 6, 15, 10.1038/nrneurol.2009.198 · DOI · Paper page
  39. Zilles, 1997, Quantitative analysis of sulci in the human cerebral cortex: development, regional heterogeneity, gender difference, asymmetry, intersubject variability and cortical architecture, Hum. Brain Mapp., 5, 218, 10.1002/(SICI)1097-0193(1997)5:4<218::AID-HBM2>3.0.CO;2-6 · DOI · Paper page

Cited by

See all 1,141

Metadata

Authors
Mueller, S., Wang, D., Fox, M. D., Yeo, B. T. T., Sepulcre, J., Sabuncu, M. R., Shafee, R., Lu, J., & Liu, H.
Venue
Neuron
Publisher
Elsevier BV
Published
2013-02
Volume
77
Issue
3
Pages
586-595
ISSN
0896-6273 (ISSN-L)
DOI
10.1016/j.neuron.2012.12.028
OpenAlex
W2140203142
Cited by
1,141
References
21
FWCI
25.58
Type
journal article
Language
en
Open access
bronze
Licence

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Individual Variability in Functional Connectivity Architecture of the Human Brain. https://app.cochat.ai/papers/10.1016/j.neuron.2012.12.028
The paper
Mueller, S., Wang, D., Fox, M. D., Yeo, B. T. T., Sepulcre, J., Sabuncu, M. R., Shafee, R., Lu, J., & Liu, H. (2013). Individual Variability in Functional Connectivity Architecture of the Human Brain. Neuron. https://doi.org/10.1016/j.neuron.2012.12.028

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.