Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2019 Apr 1:189:516-532.
doi: 10.1016/j.neuroimage.2019.01.068. Epub 2019 Jan 29.

General functional connectivity: Shared features of resting-state and task fMRI drive reliable and heritable individual differences in functional brain networks

Maxwell L Elliott  1 , Annchen R Knodt  2 , Megan Cooke  2 , M Justin Kim  3 , Tracy R Melzer  4 , Ross Keenan  5 , David Ireland  6 , Sandhya Ramrakha  6 , Richie Poulton  6 , Avshalom Caspi  7 , Terrie E Moffitt  7 , Ahmad R Hariri  2
Affiliations

General functional connectivity: Shared features of resting-state and task fMRI drive reliable and heritable individual differences in functional brain networks

Maxwell L Elliott et al. Neuroimage. .

Abstract

Intrinsic connectivity, measured using resting-state fMRI, has emerged as a fundamental tool in the study of the human brain. However, due to practical limitations, many studies do not collect enough resting-state data to generate reliable measures of intrinsic connectivity necessary for studying individual differences. Here we present general functional connectivity (GFC) as a method for leveraging shared features across resting-state and task fMRI and demonstrate in the Human Connectome Project and the Dunedin Study that GFC offers better test-retest reliability than intrinsic connectivity estimated from the same amount of resting-state data alone. Furthermore, at equivalent scan lengths, GFC displayed higher estimates of heritability than resting-state functional connectivity. We also found that predictions of cognitive ability from GFC generalized across datasets, performing as well or better than resting-state or task data alone. Collectively, our work suggests that GFC can improve the reliability of intrinsic connectivity estimates in existing datasets and, subsequently, the opportunity to identify meaningful correlates of individual differences in behavior. Given that task and resting-state data are often collected together, many researchers can immediately derive more reliable measures of intrinsic connectivity through the adoption of GFC rather than solely using resting-state data. Moreover, by better capturing heritable variation in intrinsic connectivity, GFC represents a novel endophenotype with broad applications in clinical neuroscience and biomarker discovery.

PubMed Disclaimer

Figures

Fig. 1
Fig. 1
Test-retest reliability of intrinsic connectivity increases as the amount of data used to estimate either RSFC (A) or GFC (B) increases. Stacked bar plots from the HCP dataset displaying the proportion of functional connections (i.e., edges) across neural networks as defined by (Power et al., 2011) that meet criteria for excellent, good, moderate, and poor reliability as indexed by ICCs.
Fig. 2
Fig. 2
Test-retest reliability of the intrinsic connectivity of canonical neural networks derived from either RSFC or GFC from the HCP scales with the amount of data available for analysis. Intra-Class Correlation (ICC) matrices for RSFC (left panel) and GFC (right panel) demonstrate comparable gains in reliability with increasing amounts of data across common intrinsic networks (Power et al., 2011). 5 and 10 min are written in red because these are common scan lengths for resting-state scans. 30 and 40 min are written in blue because many researchers have collected this amount of fMRI data when resting-state and task scans are combined. To the bottom left of ICC matrices is the color key for the ICCs, with a histogram indicating the density of ICCs for the corresponding graph.
Fig. 3
Fig. 3
The improvement in test-retest reliability of intrinsic connectivity as a function of adding data varies across canonical functional networks. In the left panel mean ICC for RSFC are displayed for within-network connections across 7 previously defined networks (Yeo et al., 2011) for a variety of scan lengths. In the right panel the same data are displayed for GFC. There are clear and consistent differences between networks. The limbic network consistently has the lowest mean ICC, while the default mode network consistently has the highest mean ICC.
Fig. 4
Fig. 4
The estimated heritability of RSFC and GFC varies as a function of scan length. Panels A, C and D display the mean A (additive genetics), E (non-shared environment + error) and C (shared environment) component estimates and 95% confidence intervals around those estimates, derived from ACE modeling of the twin data in the HCP. Scan length increases the A component and decreases the E component while having little effect on the C component. GFC also consistently has more variance attributable to the A component and lower variance attributable to the E component relative to RSFC. In panel B the % of heritable edges for RSFC and GFC are displayed across a variety of scan lengths. A heritable edge is defined as an edge with a lower bound of the 95% confidence interval that is larger than 0 in the ACE model.
Fig. 5
Fig. 5
GFC is better than RSFC at predicting cognitive ability both within and between samples. Results from CPM models predicting cognitive ability from RSFC and GFC. The x-axis displays predictions from leave-one-out cross validation within sample and out-of-sample models trained using the Dunedin Study dataset and tested using the HCP dataset. Predictive utility is displayed as % variance explained (r2).
Fig. 6
Fig. 6
Out-of-sample prediction of cognitive ability for GFC is better than RSFC and as good as task-derived intrinsic connectivity. All models were trained on intrinsic connectivity data from the Dunedin Study and tested using data from the HCP. Models were trained and tested with the same type of data. With task data this meant that models were trained and tested with tasks that have a comparable (parallel) task in both the HCP and Dunedin study. Predictive utility is displayed as % variance explained (r2).
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Aarts A.A., Anderson J.E., Anderson C.J., Attridge P.R., Attwood A., Axt J., Babel M., Bahník Š., Baranski E., Barnett-Cowan M., Bartmess E., Beer J., Bell R., Bentley H., Beyan L., Binion G., Borsboom D., Bosch A., Bosco F.A., Bowman S.D., Brandt M.J., Braswell E., Brohmer H., Brown B.T., Brown K., Brüning J., Calhoun-Sauls A., Callahan S.P., Chagnon E., Chandler J., Chartier C.R., Cheung F., Christopherson C.D., Cillessen L., Clay R., Cleary H., Cloud M.D., Conn M., Cohoon J., Columbus S., Cordes A., Costantini G., Alvarez L.D.C., Cremata E., Crusius J., DeCoster J., DeGaetano M.A., Penna N.D., Den Bezemer B., Deserno M.K., Devitt O., Dewitte L., Dobolyi D.G., Dodson G.T., Donnellan M.B., Donohue R., Dore R.A., Dorrough A., Dreber A., Dugas M., Dunn E.W., Easey K., Eboigbe S., Eggleston C., Embley J., Epskamp S., Errington T.M., Estel V., Farach F.J., Feather J., Fedor A., Fernández-Castilla B., Fiedler S., Field J.G., Fitneva S.A., Flagan T., Forest A.L., Forsell E., Foster J.D., Frank M.C., Frazier R.S., Fuchs H., Gable P., Galak J., Galliani E.M., Gampa A., Garcia S., Gazarian D., Gilbert E., Giner-Sorolla R., Glöckner A., Goellner L., Goh J.X., Goldberg R., Goodbourn P.T., Gordon-McKeon S., Gorges B., Gorges J., Goss J., Graham J., Grange J.A., Gray J., Hartgerink C., Hartshorne J., Hasselman F., Hayes T., Heikensten E., Henninger F., Hodsoll J., Holubar T., Hoogendoorn G., Humphries D.J., Hung C.O.Y., Immelman N., Irsik V.C., Jahn G., Jäkel F., Jekel M., Johannesson M., Johnson L.G., Johnson D.J., Johnson K.M., Johnston W.J., Jonas K., Joy-Gaba J.A., Kappes H.B., Kelso K., Kidwell M.C., Kim S.K., Kirkhart M., Kleinberg B., Knežević G., Kolorz F.M., Kossakowski J.J., Krause R.W., Krijnen J., Kuhlmann T., Kunkels Y.K., Kyc M.M., Lai C.K., Laique A., Lakens D., Lane K.A., Lassetter B., Lazarević L.B., Le Bel E.P., Lee K.J., Lee M., Lemm K., Levitan C.A., Lewis M., Lin L., Lin S., Lippold M., Loureiro D., Luteijn I., MacKinnon S., Mainard H.N., Marigold D.C., Martin D.P., Martinez T., Masicampo E.J., Matacotta J., Mathur M., May M., Mechin N., Mehta P., Meixner J., Melinger A., Miller J.K., Miller M., Moore K., Möschl M., Motyl M., Müller S.M., Munafo M., Neijenhuijs K.I., Nervi T., Nicolas G., Nilsonne G., Nosek B.A., Nuijten M.B., Olsson C., Osborne C., Ostkamp L., Pavel M., Penton-Voak I.S., Perna O., Pernet C., Perugini M., Pipitone R.N., Pitts M., Plessow F., Prenoveau J.M., Rahal R.M., Ratliff K.A., Reinhard D., Renkewitz F., Ricker A.A., Rigney A., Rivers A.M., Roebke M., Rutchick A.M., Ryan R.S., Sahin O., Saide A., Sandstrom G.M., Santos D., Saxe R., Schlegelmilch R., Schmidt K., Scholz S., Seibel L., Selterman D.F., Shaki S., Simpson W.B., Sinclair H.C., Skorinko J.L.M., Slowik A., Snyder J.S., Soderberg C., Sonnleitner C., Spencer N., Spies J.R., Steegen S., Stieger S., Strohminger N., Sullivan G.B., Talhelm T., Tapia M., Te Dorsthorst A., Thomae M., Thomas S.L., Tio P., Traets F., Tsang S., Tuerlinckx F., Turchan P., Valášek M., Van’t Veer A.E., Van Aert R., Van Assen M., Van Bork R., Van De Ven M., Van Den Bergh D., Van Der Hulst M., Van Dooren R., Van Doorn J., Van Renswoude D.R., Van Rijn H., Vanpaemel W., Echeverría A.V., Vazquez M., Velez N., Vermue M., Verschoor M., Vianello M., Voracek M., Vuu G., Wagenmakers E.J., Weerdmeester J., Welsh A., Westgate E.C., Wissink J., Wood M., Woods A., Wright E., Wu S., Zeelenberg M., Zuni K. Estimating the reproducibility of psychological science. Science. 2015;349(80) doi: 10.1126/science.aac4716. - DOI - PubMed
    1. Adams H.H.H., Hibar D.P., Chouraki V., Stein J.L., Nyquist P.A., Rentería M.E., Trompet S., Arias-Vasquez A., Seshadri S., Desrivières S., Beecham A.H., Jahanshad N., Wittfeld K., Van Der Lee S.J., Abramovic L., Alhusaini S., Amin N., Andersson M., Arfanakis K., Aribisala B.S., Armstrong N.J., Athanasiu L., Axelsson T., Beiser A., Bernard M., Bis J.C., Blanken L.M.E., Blanton S.H., Bohlken M.M., Boks M.P., Bralten J., Brickman A.M., Carmichael O., Chakravarty M.M., Chauhan G., Chen Q., Ching C.R.K., Cuellar-Partida G., Braber A. Den, Doan N.T., Ehrlich S., Filippi I., Ge T., Giddaluru S., Goldman A.L., Gottesman R.F., Greven C.U., Grimm O., Griswold M.E., Guadalupe T., Hass J., Haukvik U.K., Hilal S., Hofer E., Hoehn D., Holmes A.J., Hoogman M., Janowitz D., Jia T., Kasperaviciute D., Kim S., Klein M., Kraemer B., Lee P.H., Liao J., Liewald D.C.M., Lopez L.M., Luciano M., Macare C., Marquand A., Matarin M., Mather K.A., Mattheisen M., Mazoyer B., McKay D.R., McWhirter R., Milaneschi Y., Mirza-Schreiber N., Muetzel R.L., Maniega S.M., Nho K., Nugent A.C., Loohuis L.M.O., Oosterlaan J., Papmeyer M., Pappa I., Pirpamer L., Pudas S., Pütz B., Rajan K.B., Ramasamy A., Richards J.S., Risacher S.L., Roiz-Santiañez R., Rommelse N., Rose E.J., Royle N.A., Rundek T., Sämann P.G., Satizabal C.L., Schmaal L., Schork A.J., Shen L., Shin J., Shumskaya E., Smith A.V., Sprooten E., Strike L.T., Teumer A., Thomson R., Tordesillas-Gutierrez D., Toro R., Trabzuni D., Vaidya D., Van Der Grond J., Van Der Meer D., Van Donkelaar M.M.J., Van Eijk K.R., Van Erp T.G.M., Van Rooij D., Walton E., Westlye L.T., Whelan C.D., Windham B.G., Winkler A.M., Woldehawariat G., Wolf C., Wolfers T., Xu B., Yanek L.R., Yang J., Zijdenbos A., Zwiers M.P., Agartz I., Aggarwal N.T., Almasy L., Ames D., Amouyel P., Andreassen O.A., Arepalli S., Assareh A.A., Barral S., Bastin M.E., Becker D.M., Becker J.T., Bennett D.A., Blangero J., Van Bokhoven H., Boomsma D.I., Brodaty H., Brouwer R.M., Brunner H.G., Buckner R.L., Buitelaar J.K., Bulayeva K.B., Cahn W., Calhoun V.D., Cannon D.M., Cavalleri G.L., Chen C., Cheng C.Y., Cichon S., Cookson M.R., Corvin A., Crespo-Facorro B., Curran J.E., Czisch M., Dale A.M., Davies G.E., De Geus E.J.C., De Jager P.L., De Zubicaray G.I., Delanty N., Depondt C., Destefano A.L., Dillman A., Djurovic S., Donohoe G., Drevets W.C., Duggirala R., Dyer T.D., Erk S., Espeseth T., Evans D.A., Fedko I.O., Fernández G., Ferrucci L., Fisher S.E., Fleischman D.A., Ford I., Foroud T.M., Fox P.T., Francks C., Fukunaga M., Gibbs J.R., Glahn D.C., Gollub R.L., Göring H.H.H., Grabe H.J., Green R.C., Gruber O., Gudnason V., Guelfi S., Hansell N.K., Hardy J., Hartman C.A., Hashimoto R., Hegenscheid K., Heinz A., Le Hellard S., Hernandez D.G., Heslenfeld D.J., Ho B.C., Hoekstra P.J., Hoffmann W., Hofman A., Holsboer F., Homuth G., Hosten N., Hottenga J.J., Pol H.E.H., Ikeda M., Ikram M.K., Jack C.R., Jenkinson M., Johnson R., Jönsson E.G., Jukema J.W., Kahn R.S., Kanai R., Kloszewska I., Knopman D.S., Kochunov P., Kwok J.B., Lawrie S.M., Lemaître H., Liu X., Longo D.L., Longstreth W.T., Lopez O.L., Lovestone S., Martinez O., Martinot J.L., Mattay V.S., McDonald C., McIntosh A.M., McMahon K.L., McMahon F.J., Mecocci P., Melle I., Meyer-Lindenberg A., Mohnke S., Montgomery G.W., Morris D.W., Mosley T.H., Mühleisen T.W., Müller-Myhsok B., Nalls M.A., Nauck M., Nichols T.E., Niessen W.J., Nöthen M.M., Nyberg L., Ohi K., Olvera R.L., Ophoff R.A., Pandolfo M., Paus T., Pausova Z., Penninx B.W.J.H., Pike G.B., Potkin S.G., Psaty B.M., Reppermund S., Rietschel M., Roffman J.L., Romanczuk-Seiferth N., Rotter J.I., Ryten M., Sacco R.L., Sachdev P.S., Saykin A.J., Schmidt R., Schofield P.R., Sigurdsson S., Simmons A., Singleton A., Sisodiya S.M., Smith C., Smoller J.W., Soininen H., Srikanth V., Steen V.M., Stott D.J., Sussmann J.E., Thalamuthu A., Tiemeier H., Toga A.W., Traynor B.J., Troncoso J., Turner J.A., Tzourio C., Uitterlinden A.G., Hernández M.C.V., Van Der Brug M., Van Der Lugt A., Van Der Wee N.J.A., Van Duijn C.M., Van Haren N.E.M., Van’t Ent D., Van Tol M.J., Vardarajan B.N., Veltman D.J., Vernooij M.W., Völzke H., Walter H., Wardlaw J.M., Wassink T.H., Weale M.E., Weinberger D.R., Weiner M.W., Wen W., Westman E., White T., Wong T.Y., Wright C.B., Zielke H.R., Zonderman A.B., Deary I.J., Decarli C., Schmidt H., Martin N.G., De Craen A.J.M., Wright M.J., Launer L.J., Schumann G., Fornage M., Franke B., Debette S., Medland S.E., Ikram M.A., Thompson P.M. Novel genetic loci underlying human intracranial volume identified through genome-wide association. Nat. Neurosci. 2016;19:1569–1582. doi: 10.1038/nn.4398. - DOI - PMC - PubMed
    1. Adhikari B.M., Jahanshad N., Shukla D., Glahn D.C., Blangero J., Fox P.T., Reynolds R.C., Cox R.W., Fieremans E., Veraart J., Novikov D.S., Nichols T.E., Hong L.E., Thompson P.M., Kochunov P. Comparison of heritability estimates on resting state fMRI connectivity phenotypes using the ENIGMA analysis pipeline. Hum. Brain Mapp. 2018 doi: 10.1002/hbm.24331. - DOI - PMC - PubMed
    1. Anderson J.S., Ferguson M.A., Lopez-Larson M., Yurgelun-Todd D. Reproducibility of single-subject functional connectivity measurements. Am. J. Neuroradiol. 2011;32:548–555. doi: 10.3174/ajnr.A2330. - DOI - PMC - PubMed
    1. Arfanakis K., Cordes D., Haughton V.M., Moritz C.H., Quigley M.A., Meyerand M.E. Combining independent component analysis and correlation analysis to probe interregional connectivity in fMRI task activation datasets. Magn. Reson. Imaging. 2000;18:921–930. doi: 10.1016/S0730-725X(00)00190-9. - DOI - PubMed

Publication types