Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 1,378

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

The Chronnectome: Time-Varying Connectivity Networks as the Next Frontier in fMRI Data Discovery

Open access
Calhoun, Vince D., Miller, Robyn, Pearlson, Godfrey, Adalı, Tulay·2014·Neuron
DOI 10.1016/j.neuron.2014.10.015
Open this paper in CoChat — free account

Abstract

Recent years have witnessed a rapid growth of interest in moving functional magnetic resonance imaging (fMRI) beyond simple scan-length averages and into approaches that capture time-varying properties of connectivity. In this Perspective we use the term “chronnectome” to describe metrics that allow a dynamic view of coupling. In the chronnectome, coupling refers to possibly time-varying levels of correlated or mutually informed activity between brain regions whose spatial properties may also be temporally evolving. We primarily focus on multivariate approaches developed in our group and review a number of approaches with an emphasis on matrix decompositions such as principle component analysis and independent component analysis. We also discuss the potential these approaches offer to improve characterization and understanding of brain function. There are a number of methodological directions that need to be developed further, but chronnectome approaches already show great promise for the study of both the healthy and the diseased brain.

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.2014.10.015

Cites

6 of 6 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 6 references

Research works printed but not linked: 65

  1. Allen, 2011, A baseline for the multivariate comparison of resting-state networks, Front Syst Neurosci, 5, 2, 10.3389/fnsys.2011.00002 · DOI
  2. Allen, 2012, Capturing inter-subject variability with group independent component analysis of fMRI data: a simulation study, Neuroimage, 59, 4141, 10.1016/j.neuroimage.2011.10.010 · DOI
  3. Anderson, 2014, Independent vector analysis: identification conditions and performance bounds, IEEE Trans. Signal Process., 62, 4399, 10.1109/TSP.2014.2333554 · DOI
  4. Arbabshirani, 2013, Classification of schizophrenia patients based on resting-state functional network connectivity, Front. Neurosci., 7, 133, 10.3389/fnins.2013.00133 · DOI
  5. Bargmann, 2013, From the connectome to brain function, Nat. Methods, 10, 483, 10.1038/nmeth.2451 · DOI · Paper page
  6. Bullmore, 2009, Complex brain networks: graph theoretical analysis of structural and functional systems, Nat. Rev. Neurosci., 10, 186, 10.1038/nrn2575 · DOI · Paper page
  7. Calhoun, 2012, Multisubject independent component analysis of fMRI: a decade of intrinsic networks, default mode, and neurodiagnostic discovery, IEEE. Rev. Biomed. Eng., 5, 60, 10.1109/RBME.2012.2211076 · DOI
  8. Calhoun, 2008, Modulation of temporally coherent brain networks estimated using ICA at rest and during cognitive tasks, Hum. Brain Mapp., 29, 828, 10.1002/hbm.20581 · DOI · Paper page
  9. Calhoun, 2012, Exploring the psychosis functional connectome: aberrant intrinsic networks in schizophrenia and bipolar disorder, Front. Psychiatry, 2, 75, 10.3389/fpsyt.2011.00075 · DOI
  10. Calhoun, 2013, Independent component analysis for brain FMRI does indeed select for maximal independence, PLoS ONE, 8, e73309, 10.1371/journal.pone.0073309 · DOI
  11. Chang, 2010, Time-frequency dynamics of resting-state brain connectivity measured with fMRI, Neuroimage, 50, 81, 10.1016/j.neuroimage.2009.12.011 · DOI · Paper page
  12. Cribben, 2012, Dynamic connectivity regression: determining state-related changes in brain connectivity, Neuroimage, 61, 907, 10.1016/j.neuroimage.2012.03.070 · DOI
  13. Cuthbert, 2010, Toward new approaches to psychotic disorders: the NIMH Research Domain Criteria project, Schizophr. Bull., 36, 1061, 10.1093/schbul/sbq108 · DOI · Paper page
  14. Damaraju, 2014, Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia, Neuroimage Clin, 5, 298, 10.1016/j.nicl.2014.07.003 · DOI
  15. Daubechies, 2009, Independent component analysis for brain fMRI does not select for independence, Proc. Natl. Acad. Sci. USA, 106, 10415, 10.1073/pnas.0903525106 · DOI
  16. Di, 2013, Dynamic brain functional connectivity modulated by resting-state networks, Brain Struct. Funct.
  17. DiLuca, 2014, The cost of brain diseases: a burden or a challenge?, Neuron, 82, 1205, 10.1016/j.neuron.2014.05.044 · DOI
  18. Doron, 2012, Dynamic network structure of interhemispheric coordination, Proc. Natl. Acad. Sci. USA, 109, 18661, 10.1073/pnas.1216402109 · DOI · Paper page
  19. Du, 2014, Exploring difference and overlap between schizophrenia, schizoaffective and bipolar disorders using resting-state brain functional networks, IEEE EMBS
  20. Eavani, 2013, Identifying patterns in temporal variation of functional connectivity using resting state fmri, Proc. IEEE Int. Symp. Biomed. Imaging, 2013, 1086
  21. Erhardt, 2011, On network derivation, classification, and visualization: a response to Habeck and Moeller, Brain Connect., 1, 1, 10.1089/brain.2011.0022 · DOI
  22. Erhardt, 2011, Comparison of multi-subject ICA methods for analysis of fMRI data, Hum. Brain Mapp., 32, 2075, 10.1002/hbm.21170 · DOI
  23. Erhardt, 2012, SimTB, a simulation toolbox for fMRI data under a model of spatiotemporal separability, Neuroimage, 59, 4160, 10.1016/j.neuroimage.2011.11.088 · DOI
  24. Friston, 2003, Dynamic causal modelling, Neuroimage, 19, 1273, 10.1016/S1053-8119(03)00202-7 · DOI · Paper page
  25. Garrity, 2007, Aberrant “default mode” functional connectivity in schizophrenia, Am. J. Psychiatry, 164, 450, 10.1176/ajp.2007.164.3.450 · DOI · Paper page
  26. Gonzalez-Castillo, 2012, Whole-brain, time-locked activation with simple tasks revealed using massive averaging and model-free analysis, Proc. Natl. Acad. Sci. USA, 109, 5487, 10.1073/pnas.1121049109 · DOI
  27. Handwerker, 2012, Periodic changes in fMRI connectivity, Neuroimage, 63, 1712, 10.1016/j.neuroimage.2012.06.078 · DOI
  28. Havlicek, 2011, Dynamic modeling of neuronal responses in fMRI using cubature Kalman filtering, Neuroimage, 56, 2109, 10.1016/j.neuroimage.2011.03.005 · DOI
  29. Hosokawa, 2009, Brain glucose metabolism difference between bipolar and unipolar mood disorders in depressed and euthymic states, Prog. Neuropsychopharmacol. Biol. Psychiatry, 33, 243, 10.1016/j.pnpbp.2008.11.014 · DOI
  30. Hutchison, 2013, Dynamic functional connectivity: promise, issues, and interpretations, Neuroimage, 80, 360, 10.1016/j.neuroimage.2013.05.079 · DOI · Paper page
  31. Hutchison, 2013, Resting-state networks show dynamic functional connectivity in awake humans and anesthetized macaques, Hum. Brain Mapp., 34, 2154, 10.1002/hbm.22058 · DOI · Paper page
  32. Insel, 2010, Research domain criteria (RDoC): toward a new classification framework for research on mental disorders, Am. J. Psychiatry, 167, 748, 10.1176/appi.ajp.2010.09091379 · DOI · Paper page
  33. Jafri, 2008, A method for functional network connectivity among spatially independent resting-state components in schizophrenia, Neuroimage, 39, 1666, 10.1016/j.neuroimage.2007.11.001 · DOI
  34. Keilholz, 2014, The neural basis of time-varying resting state functional connectivity, Brain Connect., 10.1089/brain.2014.0250 · DOI
  35. Keilholz, 2013, Dynamic properties of functional connectivity in the rodent, Brain Connect, 3, 31, 10.1089/brain.2012.0115 · DOI
  36. Keshavan, 2011, A dimensional approach to the psychosis spectrum between bipolar disorder and schizophrenia: the Schizo-Bipolar Scale, Schizophr. Res., 133, 250, 10.1016/j.schres.2011.09.005 · DOI
  37. Kim, 2006, Independent vector analysis: definition and algorithms, Proc. 40th Asilomar Conf. Signals Systems Comput., 1393
  38. Kiviniemi, 2011, A sliding time-window ICA reveals spatial variability of the default mode network in time, Brain Connect., 1, 339, 10.1089/brain.2011.0036 · DOI
  39. Koenig, 2002, Millisecond by millisecond, year by year: normative EEG microstates and developmental stages, Neuroimage, 16, 41, 10.1006/nimg.2002.1070 · DOI
  40. Kopell, 2014, Beyond the Connectome: The Dynome, Neuron, 83, 1319, 10.1016/j.neuron.2014.08.016 · DOI
  41. Kucyi, 2014, Dynamic functional connectivity of the default mode network tracks daydreaming, Neuroimage, 100, 471, 10.1016/j.neuroimage.2014.06.044 · DOI
  42. Larson-Prior, 2011, Modulation of the brain’s functional network architecture in the transition from wake to sleep, Prog. Brain Res., 193, 277, 10.1016/B978-0-444-53839-0.00018-1 · DOI
  43. Leonardi, 2013, Principal components of functional connectivity: a new approach to study dynamic brain connectivity during rest, Neuroimage, 83, 937, 10.1016/j.neuroimage.2013.07.019 · DOI
  44. Leonardi, 2014, Disentangling dynamic networks: Separated and joint expressions of functional connectivity patterns in time, Hum. Brain Mapp., 10.1002/hbm.22599 · DOI
  45. Lindquist, 2014, Evaluating dynamic bivariate correlations in resting-state fMRI: A comparison study and a new approach, Neuroimage, 101, 531, 10.1016/j.neuroimage.2014.06.052 · DOI
  46. Ma, 2011, Automatic identification of functional clusters in FMRI data using spatial dependence, IEEE Trans. Biomed. Eng., 58, 3406, 10.1109/TBME.2011.2167149 · DOI
  47. Ma, 2014, Dynamic changes of spatial functional network connectivity in healthy individuals and schizophrenia patients using independent vector analysis, Neuroimage, 90, 196, 10.1016/j.neuroimage.2013.12.063 · DOI
  48. Meyer, 2009, The misdiagnosis of bipolar disorder as a psychotic disorder: some of its causes and their influence on therapy, J. Affect. Disord., 112, 174, 10.1016/j.jad.2008.04.022 · DOI
  49. Miller, 2014, Higher-Dimensional Meta-State Analysis of Resting fMRI Reveals Reduced 4D Spatiotemporal Spectral Dynamism in Schizophrenia, IEEE EMBS
  50. Miller, 2014, Higher dimensional analysis shows reduced dynamism of time-varying network connectivity in schizophrenia patients, IEEE EMBS
  51. Narayanan, 2014, Resting state electroencephalogram oscillatory abnormalities in schizophrenia and psychotic bipolar patients and their relatives from the bipolar and schizophrenia network on intermediate phenotypes study, Biol. Psychiatry, 76, 456, 10.1016/j.biopsych.2013.12.008 · DOI
  52. Pascual-Marqui, 1995, Segmentation of brain electrical activity into microstates: model estimation and validation, IEEE Trans. Biomed. Eng., 42, 658, 10.1109/10.391164 · DOI
  53. Prichard, 1994, Generating surrogate data for time series with several simultaneously measured variables, Phys. Rev. Lett., 73, 951, 10.1103/PhysRevLett.73.951 · DOI
  54. Rashid, 2014, Dynamic connectivity states estimated from resting fMRI identify differences among schizophrenia, bipolar disorder, and healthy control subjects, Frontiers in Human Neuroscience, 10.3389/fnhum.2014.00897 · DOI
  55. Sakoğlu, 2010, A method for evaluating dynamic functional network connectivity and task-modulation: application to schizophrenia, MAGMA, 23, 351, 10.1007/s10334-010-0197-8 · DOI
  56. Shirer, 2011, Decoding subject-driven cognitive states with whole-brain connectivity patterns, Cereb. Cortex, 22, 158, 10.1093/cercor/bhr099 · DOI · Paper page
  57. Smith, 2011, Network modelling methods for FMRI, Neuroimage, 54, 875, 10.1016/j.neuroimage.2010.08.063 · DOI · Paper page
  58. Sporns, 2011, The human connectome: a complex network, Ann. N Y Acad. Sci., 1224, 109, 10.1111/j.1749-6632.2010.05888.x · DOI
  59. Tagliazucchi, 2014, Decoding wakefulness levels from typical fMRI resting-state data reveals reliable drifts between wakefulness and sleep, Neuron, 82, 695, 10.1016/j.neuron.2014.03.020 · DOI
  60. Tagliazucchi, 2013, Breakdown of long-range temporal dependence in default mode and attention networks during deep sleep, Proc. Natl. Acad. Sci. USA, 110, 15419, 10.1073/pnas.1312848110 · DOI
  61. Tagliazucchi, 2014, Enhanced repertoire of brain dynamical states during the psychedelic experience, Hum. Brain Mapp., 10.1002/hbm.22562 · DOI
  62. van den Heuvel, 2012, High-cost, high-capacity backbone for global brain communication, Proc. Natl. Acad. Sci. USA, 109, 11372, 10.1073/pnas.1203593109 · DOI · Paper page
  63. Yu, 2011, Altered small-world brain networks in temporal lobe in patients with schizophrenia performing an auditory oddball task, Front. Syst. Neurosci., 5, 7
  64. Yu, 2013, State-related functional integration and functional segregation brain networks in schizophrenia, Schizophr. Res., 150, 450, 10.1016/j.schres.2013.09.016 · DOI
  65. Zalesky, 2012, The relationship between regional and inter-regional functional connectivity deficits in schizophrenia, Hum. Brain Mapp., 33, 2535, 10.1002/hbm.21379 · DOI

Cited by

Metadata

Authors
Calhoun, V. D., Miller, R., Pearlson, G., & Adalı, T.
Venue
Neuron
Publisher
Elsevier BV
Published
2014-10
Volume
84
Issue
2
Pages
262-274
ISSN
0896-6273 (ISSN-L)
DOI
10.1016/j.neuron.2014.10.015
OpenAlex
W1968248619
Cited by
1,378
References
6
FWCI
40.01
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

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.