Skip to main content
SpringerLink
Log in

Integrated and segregated frequency architecture of the human brain network

  • Original Article
  • Published:
Brain Structure and Function Aims and scope Submit manuscript

Abstract

The frequency of brain activity modulates the relationship between the brain and human behavior. Insufficient understanding of frequency-specific features may thus lead to inconsistent explanations of human behavior. However, to date, the frequency-specific features of the human brain functional network at the whole-brain level remain poorly understood. Here, we used resting-state fMRI data and graph-theory analyses to investigate the frequency-specific characteristics of fMRI signals in 12 frequency bands (frequency range 0.01–0.7 Hz) in 75 healthy participants. We found that brain regions with higher level and more complex functions had a more variable functional connectivity pattern but engaged less in higher frequency ranges. Moreover, brain regions that engaged in fewer frequency bands played more integrated roles (i.e., higher network participation coefficient and lower within-module degree) in the functional network, whereas regions that engaged in broader frequency ranges exhibited more segregated functions (i.e., lower network participation coefficient and higher within-module degree). Finally, behavioral analyses revealed that regional frequency variability was associated with a spectrum of behavioral functions from sensorimotor functions to complex cognitive and social functions. Taken together, our results showed that segregated functions are executed in wide frequency ranges, whereas integrated functions are executed mainly in lower frequency ranges. These frequency-specific features of brain networks provided crucial insights into the frequency mechanism of fMRI signals, suggesting that signals in higher frequency ranges should be considered for their relation to cognitive functions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

All the data used in this study are collected from the “100 unrelated subjects” sample of a published database Human Connectome Project (HCP). The raw data are available at https://db.humanconnectome.org.

References

  • Baria AT, Baliki MN, Parrish T, Apkarian AV (2011) Anatomical and functional assemblies of brain BOLD oscillations. J Neurosci 31:7910–7919

    CAS  PubMed  PubMed Central  Google Scholar 

  • Birn RM, Diamond JB, Smith MA, Bandettini PA (2006) Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI. NeuroImage 31:1536–1548

    PubMed  Google Scholar 

  • Birn RM, Smith MA, Jones TB, Bandettini PA (2008) The respiration response function: the temporal dynamics of fMRI signal fluctuations related to changes in respiration. NeuroImage 40:644–654

    PubMed  Google Scholar 

  • Biswal B, Yetkin FZ, Haughton VM, Hyde JS (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34:537–541

    CAS  PubMed  Google Scholar 

  • Boubela RN, Kalcher K, Huf W, Kronnerwetter C, Filzmoser P, Moser E (2013) Beyond noise: using temporal ICA to extract meaningful information from high-frequency fMRI signal fluctuations during rest. Front Hum Neurosci 7:168

    PubMed  PubMed Central  Google Scholar 

  • Braboszcz C, Delorme A (2011) Lost in thoughts: neural markers of low alertness during mind wandering. NeuroImage 54:3040–3047

    PubMed  Google Scholar 

  • Bright MG, Tench CR, Murphy K (2017) Potential pitfalls when denoising resting state fMRI data using nuisance regression. NeuroImage 154:159–168

    PubMed  Google Scholar 

  • Brooks JC, Beckmann CF, Miller KL, Wise RG, Porro CA, Tracey I, Jenkinson M (2008) Physiological noise modelling for spinal functional magnetic resonance imaging studies. NeuroImage 39:680–692

    PubMed  Google Scholar 

  • Buzsáki G, Draguhn A (2004) Neuronal oscillations in cortical networks. Science 304:1926–1929

    PubMed  Google Scholar 

  • Chang C, Cunningham JP, Glover GH (2009) Influence of heart rate on the BOLD signal: the cardiac response function. NeuroImage 44:857–869

    PubMed  Google Scholar 

  • Chen JE, Glover GH (2015) BOLD fractional contribution to resting-state functional connectivity above 0.1 Hz. NeuroImage 107:207–218

    PubMed  Google Scholar 

  • Chen JE, Jahanian H, Glover GH (2017) Nuisance regression of high-frequency functional magnetic resonance imaging data: denoising can be noisy. Brain Connectivity 7:13–24

    PubMed  PubMed Central  Google Scholar 

  • Chen JE, Polimeni JR, Bollmann S, Glover GH (2019) On the analysis of rapidly sampled fMRI data. NeuroImage 188:807–820

    PubMed  Google Scholar 

  • Christoff K, Gordon AM, Smallwood J, Smith R, Schooler JW (2009) Experience sampling during fMRI reveals default network and executive system contributions to mind wandering. Proc Natl Acad Sci 106:8719–8724

    CAS  PubMed  PubMed Central  Google Scholar 

  • Cioli C, Abdi H, Beaton D, Burnod Y, Mesmoudi S (2014) Differences in human cortical gene expression match the temporal properties of large-scale functional networks. PLoS ONE 9:e115913

    PubMed  PubMed Central  Google Scholar 

  • Coifman RR, Lafon S, Lee AB, Maggioni M, Nadler B, Warner F, Zucker SW (2005) Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. Proc Natl Acad Sci 102:7426–7431

    CAS  PubMed  PubMed Central  Google Scholar 

  • Collins CE, Airey DC, Young NA, Leitch DB, Kaas JH (2010) Neuron densities vary across and within cortical areas in primates. Proc Natl Acad Sci USA 107:15927–15932

    CAS  PubMed  PubMed Central  Google Scholar 

  • Cordes D, Haughton VM, Arfanakis K, Carew JD, Turski PA, Moritz CH, Meyerand ME (2001) Frequencies contributing to functional connectivity in the cerebral cortex in “resting-state” data. Am J Neuroradiol 22:1326–1333

    CAS  PubMed  PubMed Central  Google Scholar 

  • Crossley NA, Mechelli A, Vértes PE, Wintonbrown TT, Patel AX, Ginestet CE, Bullmore ET (2013) Cognitive relevance of the community structure of the human brain functional coactivation network. Proc Natl Acad Sci 110:11583–11588

    CAS  PubMed  PubMed Central  Google Scholar 

  • Curtis CE, D’Esposito M (2003) Persistent activity in the prefrontal cortex during working memory. Trends Cogn Sci 7:415–423

    PubMed  Google Scholar 

  • Dai Z, Lin Q, Li T, Wang X, Yuan H, Yu X, Wang H (2019) Disrupted structural and functional brain networks in Alzheimer’s disease. Neurobiol Aging 75:71–82

    CAS  PubMed  Google Scholar 

  • Dale AM (1999) Optimal experimental design for event-related fMRI. Hum Brain Mapp 8:109–114

    CAS  PubMed  PubMed Central  Google Scholar 

  • Davey CE, Grayden DB, Egan GF, Johnston LA (2013) Filtering induces correlation in fMRI resting state data. NeuroImage 64:728–740

    PubMed  Google Scholar 

  • De Luca M, Beckmann C, De Stefano N, Matthews P, Smith SM (2006) fMRI resting state networks define distinct modes of long-distance interactions in the human brain. NeuroImage 29:1359–1367

    PubMed  Google Scholar 

  • Desjardins AE, Kiehl KA, Liddle PF (2001) Removal of confounding effects of global signal in functional MRI analyses. NeuroImage 13:751–758

    CAS  PubMed  Google Scholar 

  • Donner TH, Siegel M, Fries P, Engel AK (2009) Buildup of choice-predictive activity in human motor cortex during perceptual decision making. Curr Biol 19:1581–1585

    CAS  PubMed  Google Scholar 

  • Feinberg DA, Moeller S, Smith SM, Auerbach E, Ramanna S, Glasser MF, Yacoub E (2010) Multiplexed echo planar imaging for sub-second whole brain FMRI and fast diffusion imaging. PLoS ONE 5:e15710

    CAS  PubMed  PubMed Central  Google Scholar 

  • Finn ES, Shen X, Scheinost D, Rosenberg MD, Huang J, Chun MM, Constable RT (2015) Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat Neurosci 18:1664–1671

    CAS  PubMed  PubMed Central  Google Scholar 

  • Fox MD, Zhang D, Snyder AZ, Raichle ME (2009) The global signal and observed anticorrelated resting state brain networks. J Neurophysiol 101:3270–3283

    PubMed  PubMed Central  Google Scholar 

  • Garcés P, Pereda E, Hernández-Tamames JA, Del-Pozo F, Maestú F, Ángel Pineda-Pardo J (2016) Multimodal description of whole brain connectivity: a comparison of resting state MEG, fMRI, and DWI. Hum Brain Mapp 37:20–34

    PubMed  Google Scholar 

  • Glasser MF, Sotiropoulos SN, Wilson JA, Coalson TS, Fischl B, Andersson JL, Xu J, Jbabdi S, Webster M, Polimeni JR, Van Essen DC (2013) The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 80:105–124

    PubMed  Google Scholar 

  • Glover GH, Li TQ, Ress D (2000) Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magn Reson Med 44:162–167

    CAS  PubMed  Google Scholar 

  • Gohel SR, Biswal BB (2015) Functional integration between brain regions at rest occurs in multiple-frequency bands. Brain Connectivity 5:23–34

    PubMed  PubMed Central  Google Scholar 

  • Guimera R, Amaral LAN (2005) Cartography of complex networks: modules and universal roles. J Stat Mech Theory Exp 2005:P02001

    Google Scholar 

  • Hallquist MN, Hwang K, Luna B (2013) The nuisance of nuisance regression: spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity. NeuroImage 82:208–225

    PubMed  Google Scholar 

  • Hawrylycz MJ, Lein ES, Guillozet-Bongaarts AL, Shen EH, Ng L, Miller JA, van de Lagemaat LN, Smith KA, Ebbert A, Riley ZL, Abajian C, Beckmann CF, Bernard A, Bertagnolli D, Boe AF, Cartagena PM, Chakravarty MM, Chapin M, Chong J, Dalley RA, Jones AR (2012) An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489:391–399

    CAS  PubMed  PubMed Central  Google Scholar 

  • Hennig J, Zhong K, Speck O (2007) MR-encephalography: fast multi-channel monitoring of brain physiology with magnetic resonance. NeuroImage 34:212–219

    PubMed  Google Scholar 

  • Hipp JF, Siegel M (2015) BOLD fMRI correlation reflects frequency-specific neuronal correlation. Curr Biol 25:1368–1374

    CAS  PubMed  Google Scholar 

  • Huntenburg JM, Bazin PL, Margulies DS (2018) Large-scale gradients in human cortical organization. Trends Cogn Sci 22:21–31

    PubMed  Google Scholar 

  • Larkman DJ, Hajnal JV, Herlihy AH, Coutts GA, Young IR, Ehnholm G (2001) Use of multicoil arrays for separation of signal from multiple slices simultaneously excited. J Magn Reson Imaging 13:313–317

    CAS  PubMed  Google Scholar 

  • Laufs H, Kleinschmidt A, Beyerle A, Eger E, Salek-Haddadi A, Preibisch C, Krakow K (2003) EEG-correlated fMRI of human alpha activity. NeuroImage 19:1463–1476

    CAS  PubMed  Google Scholar 

  • Lee HL, Zahneisen B, Hugger T, LeVan P, Hennig J (2013) Tracking dynamic resting-state networks at higher frequencies using MR-encephalography. NeuroImage 65:216–222

    PubMed  Google Scholar 

  • Leopold DA, Murayama Y, Logothetis NK (2003) Very slow activity fluctuations in monkey visual cortex: implications for functional brain imaging. Cereb Cortex 13:422–433

    PubMed  Google Scholar 

  • Lewis LD, Setsompop K, Rosen BR, Polimeni JR (2016) Fast fMRI can detect oscillatory neural activity in humans. Proc Natl Acad Sci 113:E6679–E6685

    CAS  PubMed  PubMed Central  Google Scholar 

  • Lin FH, Wald LL, Ahlfors SP, Hämäläinen MS, Kwong KK, Belliveau JW (2006) Dynamic magnetic resonance inverse imaging of human brain function. Magn Reson Med 56:787–802

    PubMed  Google Scholar 

  • Lin FH, Chu YH, Hsu YC, Lin JFL, Tsai KWK, Tsai SY, Kuo WJ (2015) Significant feed-forward connectivity revealed by high frequency components of BOLD fMRI signals. NeuroImage 121:69–77

    PubMed  Google Scholar 

  • Logothetis NK, Pauls J, Augath M, Trinath T, Oeltermann A (2001) Neurophysiological investigation of the basis of the fMRI signal. Nature 412:150–157

    CAS  PubMed  Google Scholar 

  • Macey PM, Macey KE, Kumar R, Harper RM (2004) A method for removal of global effects from fMRI time series. NeuroImage 22(1):360–366

    PubMed  Google Scholar 

  • Margulies DS, Ghosh SS, Goulas A, Falkiewicz M, Huntenburg JM, Langs G, Petrides M (2016) Situating the default-mode network along a principal gradient of macroscale cortical organization. Proc Natl Acad Sci 113:12574–12579

    CAS  PubMed  PubMed Central  Google Scholar 

  • Maris E, Fries P, van Ede F (2016) Diverse phase relations among neuronal rhythms and their potential function. Trends Neurosci 39:86–99

    CAS  PubMed  Google Scholar 

  • Mason MF, Norton MI, Van Horn JD, Wegner DM, Grafton ST, Macrae CN (2007) Wandering minds: the default network and stimulus-independent thought. Science 315:393–395

    CAS  PubMed  PubMed Central  Google Scholar 

  • Moeller S, Yacoub E, Olman CA, Auerbach E, Strupp J, Harel N, Uğurbil K (2010) Multiband multislice GE-EPI at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI. Magn Reson Med 63:1144–1153

    PubMed  PubMed Central  Google Scholar 

  • Murphy K, Birn RM, Handwerker DA, Jones TB, Bandettini PA (2009) The impact of global signal regression on resting state correlations: are anti-correlated networks introduced? NeuroImage 44:893–905

    Article  PubMed  Google Scholar 

  • Murray JD, Bernacchia A, Freedman DJ, Romo R, Wallis JD, Cai X, Lee D (2014) A hierarchy of intrinsic timescales across primate cortex. Nat Neurosci 17:1661

    CAS  PubMed  PubMed Central  Google Scholar 

  • Niazy RK, Xie J, Miller K, Beckmann CF, Smith SM (2011) Spectral characteristics of resting state networks. Prog Brain Res 193:259–276

    PubMed  Google Scholar 

  • Penttonen M, Buzsáki G (2003) Natural logarithmic relationship between brain oscillators. Thalamus Relat Syst 2:145–152

    Google Scholar 

  • Power JD, Barnes K, Snyder AZ, Schlaggar BL, Petersen SE (2012) Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage 59:2142–2154

    PubMed  Google Scholar 

  • Preti MG, Van De Ville D (2019) Decoupling of brain function from structure reveals regional behavioral specialization in humans. Nat Commun 10:4747

    PubMed  PubMed Central  Google Scholar 

  • Saad ZS, Gotts SJ, Murphy K, Chen G, Jo HJ, Martin A, Cox RW (2012) Trouble at rest: how correlation patterns and group differences become distorted after global signal regression. Brain Connectivity 2:25–32

    PubMed  PubMed Central  Google Scholar 

  • Salvador R, Martinez A, Pomarol-Clotet E, Gomar J, Vila F, Sarró S, Bullmore E (2008) A simple view of the brain through a frequency-specific functional connectivity measure. NeuroImage 39:279–289

    CAS  PubMed  Google Scholar 

  • Siegel M, Donner TH, Engel AK (2012) Spectral fingerprints of large-scale neuronal interactions. Nat Rev Neurosci 13:121

    CAS  PubMed  Google Scholar 

  • Smith-Collins AP, Luyt K, Heep A, Kauppinen RA (2015) High frequency functional brain networks in neonates revealed by rapid acquisition resting state fMRI. Hum Brain Mapp 36:2483–2494

    PubMed  PubMed Central  Google Scholar 

  • Sporns O (2013) Network attributes for segregation and integration in the human brain. Curr Opin Neurobiol 23:162–171

    CAS  PubMed  Google Scholar 

  • Sporns O, Betzel RF (2016) Modular brain networks. Annu Rev Psychol 67:613–640

    PubMed  Google Scholar 

  • Störmer VS, Feng W, Martinez A, McDonald JJ, Hillyard SA (2016) Salient, irrelevant sounds reflexively induce alpha rhythm desynchronization in parallel with slow potential shifts in visual cortex. J Cogn Neurosci 28:433–445

    PubMed  Google Scholar 

  • Thompson WH, Fransson P (2015) The frequency dimension of fMRI dynamic connectivity: network connectivity, functional hubs and integration in the resting brain. NeuroImage 121:227–242

    PubMed  Google Scholar 

  • Thompson GJ, Merritt MD, Pan WJ, Magnuson ME, Grooms JK, Jaeger D, Keilholz SD (2013) Neural correlates of time-varying functional connectivity in the rat. NeuroImage 83:826–836

    PubMed  Google Scholar 

  • Thompson WH, Kastrati G, Finc K, Wright J, Shine JM, Poldrack RA (2019) Time-varying nodal measures with temporal community structure: a cautionary note to avoid misquantification. BioRxiv 659508

  • Tomasi D, Volkow ND (2011) Functional connectivity hubs in the human brain. NeuroImage 57(3):908–917

    PubMed  Google Scholar 

  • Tsushima Y, Sasaki Y, Watanabe T (2006) Greater disruption due to failure of inhibitory control on an ambiguous distractor. Science 314:1786–1788

    CAS  PubMed  Google Scholar 

  • Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Joliot M (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 15:273–289

    CAS  PubMed  Google Scholar 

  • van den Heuvel MP, Sporns O (2013) Network hubs in the human brain. Trends Cogn Sci 17:683–696

    PubMed  Google Scholar 

  • van den Heuvel MP, de Lange SC, Zalesky A, Seguin C, Yeo BT, Schmidt R (2017) Proportional thresholding in resting-state fMRI functional connectivity networks and consequences for patient-control connectome studies: Issues and recommendations. NeuroImage 152:437–449

    PubMed  Google Scholar 

  • van Den Heuvel MP, Pol HEH (2010) Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur Neuropsychopharmacol 20:519–534

    PubMed  Google Scholar 

  • Van Dijk KRA, Sabuncu MR, Buckner RL (2012) The influence of head motion on in-trinsic functional connectivity MRI. NeuroImage 59:431–438

    PubMed  Google Scholar 

  • Van Wijk BC, Stam CJ, Daffertshofer A (2010) Comparing brain networks of different size and connectivity density using graph theory. PLoS ONE 5:e13701

    PubMed  PubMed Central  Google Scholar 

  • van Essen DC, Smith SM, Barch DM, Behrens TE, Yacoub E, Ugurbil K, Consortium WMH (2013) The WU-Minn human connectome project: an overview. NeuroImage 80:62–79

    PubMed  Google Scholar 

  • Wagstyl K, Ronan L, Goodyer IM, Fletcher PC (2015) Cortical thickness gradients in structural hierarchies. NeuroImage 111:241–250

    PubMed  Google Scholar 

  • Wang JH, Zuo XN, Gohel S, Milham MP, Biswal BB, He Y (2011) Graph theoretical analysis of functional brain networks: test-retest evaluation on short-and long-term resting-state functional MRI data. PLoS ONE 6:e21976

    CAS  PubMed  PubMed Central  Google Scholar 

  • Wang J, Wang X, Xia M, Liao X, Evans A, He Y (2015) GRETNA: a graph theoretical network analysis toolbox for imaging connectomics. Front Hum Neurosci 9:386

    CAS  PubMed  PubMed Central  Google Scholar 

  • Wang Y, Zhu L, Zou Q, Cui Q, Liao W, Duan X, Chen H (2018) Frequency dependent hub role of the dorsal and ventral right anterior insula. NeuroImage 165:112–117

    PubMed  Google Scholar 

  • Weissenbacher A, Kasess C, Gerstl F, Lanzenberger R, Moser E, Windischberger C (2009) Correlations and anticorrelations in resting-state functional connectivity MRI: a quantitative comparison of preprocessing strategies. NeuroImage 47:1408–1416

    PubMed  Google Scholar 

  • Welvaert M, Rosseel Y (2013) On the definition of signal-to-noise ratio and contrast-to-noise ratio for fMRI data. PLoS ONE 8:e77089

    CAS  PubMed  PubMed Central  Google Scholar 

  • Wu CW, Gu H, Lu H, Stein EA, Chen J-H, Yang Y (2008) Frequency specificity of functional connectivity in brain networks. NeuroImage 42:1047–1055

    PubMed  Google Scholar 

  • Xia M, Wang J, He Y (2013) BrainNet Viewer: a network visualization tool for human brain connectomics. PLoS ONE 8:e68910

    CAS  PubMed  PubMed Central  Google Scholar 

  • Yan CG, Zang YF (2010) DPARSF: a MATLAB TOOLBOX for “pipeline” data analysis of resting-state fMRI. Front Syst Neurosci 4:13

    Google Scholar 

  • Yang GJ, Murray JD, Repovs G, Cole MW, Savic A, Glasser MF et al (2014) Altered global brain signal in schizophrenia. Proc Nat Acad Sci 111:7438–7443

    CAS  PubMed  PubMed Central  Google Scholar 

  • Yeo BT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, Buckner RL (2011) The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol 106:1125–1165

    PubMed  Google Scholar 

  • Zahneisen B, Grotz T, Lee KJ, Ohlendorf S, Reisert M, Zaitsev M, Hennig J (2011) Three-dimensional MR-encephalography: fast volumetric brain imaging using rosette trajectories. Magn Reson Med 65:1260–1268

    PubMed  Google Scholar 

  • Zalesky A, Fornito A, Harding IH, Cocchi L, Yücel M, Pantelis C, Bullmore ET (2010) Whole-brain anatomical networks: does the choice of nodes matter? NeuroImage 50:970–983

    PubMed  Google Scholar 

  • Zhang J, Cheng W, Liu Z, Zhang K, Lei X, Yao Y, Lu G (2016) Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders. Brain 139:2307–2321

    PubMed  Google Scholar 

  • Zuo XN, Di Martino A, Kelly C, Shehzad ZE, Gee DG, Klein DF, Castellanos FX, Biswal BB, Milham MP (2010) The oscillating brain: complex and reliable. NeuroImage 49:1432–1445

    PubMed  Google Scholar 

  • Zuo XN, Ehmke R, Mennes M, Imperati D, Castellanos FX, Sporns O, Milham MP (2012) Network centrality in the human functional connectome. Cereb Cortex 22:1862–1875

    PubMed  Google Scholar 

Download references

Funding

This work was supported by the National Natural Science Foundation of China (NSFC) (nos. 81601559, 61772569), the Guangdong Basic and Applied Basic Research Foundation (no. 2019A1515012148) and the Fundamental Research Funds for the Central Universities (nos. 19wkzd20, 20wkzd11).

Author information

Authors and Affiliations

  1. Department of Psychology, Sun Yat-Sen University, Guangzhou, 510006, China

    Junji Ma, Ying Lin, Chuanlin Hu, Jinbo Zhang, Yangyang Yi & Zhengjia Dai

Authors
  1. Junji Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ying Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chuanlin Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jinbo Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yangyang Yi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zhengjia Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhengjia Dai.

Ethics declarations

Conflict of interest

The authors have no conflict of interest to declare.

Ethics approval

Data of the current study were acquired from a public database Human Connectome Project (HCP). The HCP project was approved by the local Institutional Review Board at Washington University in St. Louis.

Consent to participate and consent for publication

Written informed consents have been obtained from all participants in the Human Connectome Project.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 19598 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ma, J., Lin, Y., Hu, C. et al. Integrated and segregated frequency architecture of the human brain network. Brain Struct Funct 226, 335–350 (2021). https://doi.org/10.1007/s00429-020-02174-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00429-020-02174-8

Keywords

Search

Navigation