Abstract
The salience network (SN), ventral attention network (VAN), dorsal attention network (DAN) and default mode network (DMN) have shown significant interactions and overlapping functions in bottom-up and top-down mechanisms of attention. In the present study, we tested if the SN, VAN, DAN and DMN connectivity can infer the gestational age (GA) at birth in a study group of 88 healthy neonates, scanned at 40 weeks of post-menstrual age, and with GA at birth ranging from 28 to 40 weeks. We also ascertained whether the connectivity within each of the SN, VAN, DAN and DMN was able to infer the average functional connectivity of the others. The ability to infer GA at birth or another network's connectivity was evaluated using a multivariate data-driven framework. The VAN, DAN and the DMN inferred the GA at birth (p < 0.05). The SN, DMN and VAN were able to infer the average connectivity of the other networks (p < 0.05). Mediation analysis between VAN’s and DAN’s inference on GA at birth found reciprocal transmittance of change with GA at birth of VAN’s and DAN’s connectivity (p < 0.05). Our findings suggest that the VAN has a prominent role in bottom-up salience detection in early infancy and that the role of the VAN and the SN may overlap in the bottom-up control of attention.
Similar content being viewed by others
Availability of data and materials
The data that support the findings of this study are available on request from the corresponding author (VO). The data are not publicly available due to privacy and consent reasons.
Code availability
The custom code is available on request from the corresponding author (VO).
References
Abdi H, Williams LJ, Valentin D (2013) Multiple factor analysis: principal component analysis for multitable and multiblock data sets. Wiley Interdiscip Rev Comput Stat 5(2):149–179. https://doi.org/10.1002/WICS.1246
Afif A, Bouvier R, Buenerd A, Trouillas J, Mertens P (2007) Development of the human fetal insular cortex: study of the gyration from 13 to 28 gestational weeks. Brain Struct Funct 212(3–4):335–346. https://doi.org/10.1007/S00429-007-0161-1
Anderson JS, Ferguson MA, Lopez-Larson M, Yurgelun-Todd D (2011) Connectivity gradients between the default mode and attention control networks. Brain Connectivity 1(2):147–157. https://doi.org/10.1089/BRAIN.2011.0007
Andrews-Hanna JR, Reidler JS, Huang C, Buckner RL (2010) Evidence for the default network’s role in spontaneous cognition. J Neurophysiol 104(1):322–335. https://doi.org/10.1152/JN.00830.2009
Anticevic A, Repovs G, Shulman GL, Barch DM (2010) When less is more: TPJ and default network deactivation during encoding predicts working memory performance. Neuroimage 49(3):2638–2648. https://doi.org/10.1016/J.NEUROIMAGE.2009.11.008
Avants BB, Yushkevich P, Pluta J, Minkoff D, Korczykowski M, Detre J, Gee JC (2010) The optimal template effect in hippocampus studies of diseased populations. Neuroimage 49(3):2457–2466. https://doi.org/10.1016/J.NEUROIMAGE.2009.09.062
Avants BB, Tustison NJ, Song G, Cook PA, Klein A, Gee JC (2011) A reproducible evaluation of ants similarity metric performance in brain image registration. Neuroimage 54(3):2033–2044. https://doi.org/10.1016/J.NEUROIMAGE.2010.09.025
Ball G, Aljabar P, Arichi T, Tusor N, Cox D, Merchant N, Nongena P, Hajnal JV, Edwards AD, Counsell SJ (2016) Machine-learning to characterise neonatal functional connectivity in the preterm brain. Neuroimage 124(Pt A):267–275. https://doi.org/10.1016/J.NEUROIMAGE.2015.08.055
Bishop CM (2006) Pattern recognition and machine learning
Bright MG, Tench CR, Murphy K (2017) Potential pitfalls when denoising resting state FMRI data using nuisance regression. Neuroimage 154(July):159–168. https://doi.org/10.1016/J.NEUROIMAGE.2016.12.027
Buckner RL, Andrews-Hanna JR, Schacter DL (2008) The brain’s default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci 1124(March):1–38. https://doi.org/10.1196/ANNALS.1440.011
Buckner RL, DiNicola LM (2019) The brain's default network: updated anatomy, physiology and evolving insights. Nat Rev Neurosci 20(10):593–608. https://doi.org/10.1038/s41583-019-0212-7
Chiarelli AM, Sestieri C, Navarra R, Wise RG, Caulo M (2021) Distinct effects of prematurity on MRI metrics of brain functional connectivity, activity, and structure: univariate and multivariate analyses. Hum Brain Mapp 42(11):3593–3607. https://doi.org/10.1002/HBM.25456
Churchill NW, Yourganov G, Oder A, Tam F, Graham SJ, Strother SC (2012) Optimizing preprocessing and analysis pipelines for single-subject FMRI: 2. Interactions with ICA, PCA, task contrast and inter-subject heterogeneity. PLoS ONE 1:1. https://doi.org/10.1371/JOURNAL.PONE.0031147
Ciric R, Wolf DH, Power JD, Roalf DR, Baum GL, Ruparel K, Shinohara RT et al (2017) Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity. Neuroimage 154(July):174–187. https://doi.org/10.1016/J.NEUROIMAGE.2017.03.020
Corbetta M, Shulman GL (2002) Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci 3(3):201–215. https://doi.org/10.1038/nrn755
Corbetta M, Patel G, Shulman GL (2008) The reorienting system of the human brain: from environment to theory of mind. Neuron 58(3):306. https://doi.org/10.1016/J.NEURON.2008.04.017
Cox RW (1996) AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 29(3):162–173. https://doi.org/10.1006/CBMR.1996.0014
Crottaz-Herbette S, Menon V (2006) Where and when the anterior cingulate cortex modulates attentional response: combined FMRI and ERP evidence. J Cogn Neurosci 18(5):766–780. https://doi.org/10.1162/jocn.2006.18.5.766
DiQuattro NE, Geng JJ (2011) Contextual knowledge configures attentional control networks. J Neurosci 31(49):18026–18035. https://doi.org/10.1523/JNEUROSCI.4040-11.2011
Dolgin E (2010) This Is your brain online: the functional connectomes project. Nat Med 16(4):351. https://doi.org/10.1038/NM0410-351B
Doria V, Beckmann CF, Arichi T, Merchant N, Groppo M, Turkheimer FE, Counsell SJ et al (2010) Emergence of resting state networks in the preterm human brain. Proc Natl Acad Sci USA 107(46):20015–20020. https://doi.org/10.1073/PNAS.1007921107
Dosenbach NUF, Fair DA, Cohen AL, Schlaggar BL, Petersen SE (2008) A dual-networks architecture of top-down control. Trends Cogn Sci 12(3):99. https://doi.org/10.1016/J.TICS.2008.01.001
Eckert MA, Menon V, Walczak A, Ahlstrom J, Denslow S, Horwitz A, Dubno JR (2009) At the heart of the ventral attention system: the right anterior insula. Hum Brain Mapp 30(8):2530. https://doi.org/10.1002/HBM.20688
Egeth HE, Yantis S (1997) Visual attention: control, representation, and time course. Annu Rev Psychol 48:269–297. https://doi.org/10.1146/ANNUREV.PSYCH.48.1.269
Esposito R, Cieri F, Chiacchiaretta P, Cera N, Lauriola M, Di Giannantonio M, Tartaro A, Ferretti A (2018) Modifications in resting state functional anticorrelation between default mode network and dorsal attention network: comparison among young adults, healthy elders and mild cognitive impairment patients. Brain Imaging Behav 12(1):127–141. https://doi.org/10.1007/S11682-017-9686-Y
Fair DA, Dosenbach NUF, Church JA, Cohen AL, Brahmbhatt S, Miezin FM, Barch DM, Raichle ME, Petersen SE, Schlaggar BL (2007) Development of distinct control networks through segregation and integration. Proc Natl Acad Sci USA 104(33):13507–13512. https://doi.org/10.1073/PNAS.0705843104
Farrant K, Uddin LQ (2015) Asymmetric development of dorsal and ventral attention networks in the human brain. Dev Cogn Neurosci 12:165–174. https://doi.org/10.1016/J.DCN.2015.02.001
Filzmoser P, Liebmann B, Varmuza K (2009) Repeated double cross validation. J Chemom 23(4):160–171. https://doi.org/10.1002/CEM.1225
Gao W, Sarael Alcauter J, Smith K, Gilmore JH, Lin W (2015) Development of human brain cortical network architecture during infancy. Brain Struct Funct. https://doi.org/10.1007/s00429-014-0710-3
Gilmore JH, Knickmeyer RC, Gao W (2018) Imaging structural and functional brain development in early childhood. Nat Rev Neurosci 19(3):123–137. https://doi.org/10.1038/nrn.2018.1
Huopaniemi I, Suvitaival T, Nikkilä J, Orešič M, Kaski S (2009) Two-way analysis of high-dimensional collinear data. In: Proceedings of the 2009th European conference on machine learning and knowledge discovery in databases—volume part I. In two-way analysis of high-dimensional collinear data. https://doi.org/10.5555/3121576.3121597
Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM (2012) FSL. Neuroimage 62(2):782–790. https://doi.org/10.1016/J.NEUROIMAGE.2011.09.015
Kim H (2010) Dissociating the roles of the default-mode, dorsal, and ventral networks in episodic memory retrieval. Neuroimage 50(4):1648–1657. https://doi.org/10.1016/J.NEUROIMAGE.2010.01.051
Lubsen J, Vohr B, Myers E, Hampson M, Lacadie C, Schneider KC, Katz KH, Constable RT, Ment LR (2011) Microstructural and functional connectivity in the developing preterm brain. Semin Perinatol 35(1):34–43. https://doi.org/10.1053/J.SEMPERI.2010.10.006
Mackinnon DP (2012) Introduction to statistical mediation analysis, pp 1–477. https://doi.org/10.4324/9780203809556
Majerus S, Attout L, Argembeau A, Degueldre C, Fias W, Maquet P, Martinez Perez T et al (2012) Attention supports verbal short-term memory via competition between dorsal and ventral attention networks. Cerebral Cortex (new York, N.y.: 1991) 22(5):1086–1097. https://doi.org/10.1093/CERCOR/BHR174
Matsuyoshi D, Ikeda T, Sawamoto N, Kakigi R, Fukuyama H, Osaka N (2010) Task-irrelevant memory load induces inattentional blindness without temporo-parietal suppression. Neuropsychologia 48(10):3094–3101. https://doi.org/10.1016/J.NEUROPSYCHOLOGIA.2010.06.021
Menon V (2011) Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cogn Sci 15(10):483–506. https://doi.org/10.1016/J.TICS.2011.08.003
Menon V, Uddin LQ (2010) Saliency, switching, attention and control: a network model of insula function. Brain Struct Funct 214(5–6):655. https://doi.org/10.1007/S00429-010-0262-0
Murphy K, Fox MD (2017) Towards a consensus regarding global signal regression for resting state functional connectivity MRI. Neuroimage 154(July):169–173. https://doi.org/10.1016/J.NEUROIMAGE.2016.11.052
Nie J, Li G, Shen D (2013) Development of cortical anatomical properties from early childhood to early adulthood. Neuroimage 76(August):216–224. https://doi.org/10.1016/J.NEUROIMAGE.2013.03.021
Parr T, Friston KJ (2017) Working memory, attention, and salience in active inference. Sci Rep 7(1):1–21. https://doi.org/10.1038/s41598-017-15249-0
Parr T, Friston KJ (2019) Attention or salience? Curr Opin Psychol 29(October):1–5. https://doi.org/10.1016/J.COPSYC.2018.10.006
Posner MI, Rothbart MK (2012) Development of attention networks. In: Cognition and brain development: converging evidence from various methodologies, pp 61–83. https://doi.org/10.1037/14043-004
Preacher KJ, Leonardelli GJ (2010) Calculation for the sobel test: an interactive calculation tool for mediation testson tests. http://quantpsy.org/sobel/sobel.htm
Pruett JR Jr, Kandala S, Hoertel S, Snyder AZ, Elison JT, Nishino T et al (2015) Accurate age classification of 6 and 12 month-old infants based on resting-state functional connectivity magnetic resonance imaging data. Dev Cogn Neurosci 12:123. https://doi.org/10.1016/J.DCN.2015.01.003
Raichle ME (2015) The brain’s default mode network. Annu Rev Neurosci 38(July):433–447. https://doi.org/10.1146/ANNUREV-NEURO-071013-014030
Rosenthal G, Váša F, Griffa A, Hagmann P, Amico E, Goñi J, Avidan G, Sporns O (2018) Mapping higher-order relations between brain structure and function with embedded vector representations of connectomes. https://doi.org/10.1038/s41467-018-04614-w. Accessed 9 Jan 2022
Schafer RJ, Lacadie C, Vohr B, Kesler SR, Katz KH, Schneider KC, Pugh KR et al (2009) Alterations in functional connectivity for language in prematurely born adolescents. Brain 132(3):661–670. https://doi.org/10.1093/BRAIN/AWN353
Shang J, Fisher P, Bäuml JG, Daamen M, Baumann N, Zimmer C, Bartmann P et al (2019) A machine learning investigation of volumetric and functional MRI abnormalities in adults born preterm. Hum Brain Mapp 40(14):4239–4252. https://doi.org/10.1002/HBM.24698
Shi F, Yap P-T, Guorong Wu, Jia H, Gilmore JH, Lin W, Shen D (2011) Infant brain atlases from neonates to 1- and 2-year-olds. PLoS ONE 6(4):e18746. https://doi.org/10.1371/JOURNAL.PONE.0018746
Shulman GL, McAvoy MP, Cowan MC, Astafiev SV, Tansy AP, d’Avossa G, Corbetta M (2003) Quantitative analysis of attention and detection signals during visual search. J Neurophysiol 90(5):3384–3397. https://doi.org/10.1152/JN.00343.2003
Shulman GL, Astafiev SV, McAvoy MP, d’ Avossa G, Corbetta M (2007) Right TPJ deactivation during visual search: functional significance and support for a filter hypothesis. Cerebral Cortex (new York, N.y.: 1991) 17(11):2625–2633. https://doi.org/10.1093/CERCOR/BHL170
Smyser CD, Inder TE, Shimony JS, Hill JE, Degnan AJ, Snyder AZ, Neil JJ (2010) Longitudinal analysis of neural network development in preterm infants. Cerebral Cortex (new York, N.y.: 1991) 20(12):2852–2862. https://doi.org/10.1093/CERCOR/BHQ035
Smyser CD, Dosenbach NUF, Smyser TA, Snyder AZ, Rogers CE, Inder TE, Schlaggar BL, Neil JJ (2016) Prediction of brain maturity in infants using machine-learning algorithms. Neuroimage 136(August):1. https://doi.org/10.1016/J.NEUROIMAGE.2016.05.029
Sridharan D, Levitin DJ, Chafe CH, Berger J, Menon V (2007) Neural dynamics of event segmentation in music: converging evidence for dissociable ventral and dorsal networks. Neuron 55(3):521–532. https://doi.org/10.1016/J.NEURON.2007.07.003
Sridharan D, Levitin DJ, Menon V (2008) A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proc Natl Acad Sci 105(34):12569–12574. https://doi.org/10.1073/PNAS.0800005105
Sterzer P, Kleinschmidt A (2010) Anterior insula activations in perceptual paradigms: often observed but barely understood. Brain Struct Funct 214(5–6):611–622. https://doi.org/10.1007/S00429-010-0252-2
Stoecklein S, Hilgendorff A, Li M, Förster K, Flemmer AW, Galiè F, Wunderlich S et al (2020) Variable functional connectivity architecture of the preterm human brain: impact of developmental cortical expansion and maturation. Proc Natl Acad Sci 117(2):1201–1206. https://doi.org/10.1073/PNAS.1907892117
Suo X, Ding H, Li Xi, Zhang Y, Liang M, Zhang Y, Chunshui Yu, Qin W (2021) Anatomical and functional coupling between the dorsal and ventral attention networks. Neuroimage 232(May):117868. https://doi.org/10.1016/J.NEUROIMAGE.2021.117868
Teffer K, Semendeferi K (2012) Human prefrontal cortex: evolution, development, and pathology. Prog Brain Res 195(January):191–218. https://doi.org/10.1016/B978-0-444-53860-4.00009-X
Todd JT, Thaler L, Dijkstra TMH (2015) The effects of field of view on the perception of 3D slant from texture. https://doi.org/10.1016/j.visres.2005.01.003. Accessed 9 Aug 2021
Toulmin H, Beckmann CF, O’Muircheartaigh J, Ball G, Nongena P, Makropoulos A, Ederies A et al (2015) Specialization and integration of functional thalamocortical connectivity in the human infant. Proc Natl Acad Sci USA 112(20):6485–6490. https://doi.org/10.1073/PNAS.1422638112
Uddin LQ (2015) Salience processing and insular cortical function and dysfunction. Nat Rev Neurosci 16(1):55–61. https://doi.org/10.1038/NRN3857
Vossel S, Geng JJ, Fink GR (2014) Dorsal and ventral attention systems: distinct neural circuits but collaborative roles. Neuroscientist 20(2):150–159. https://doi.org/10.1177/1073858413494269
Wang Q, Seghers D, D’Agostino E, Maes F, Vandermeulen D, Suetens P, Hammers A (2005) Construction and validation of mean shape atlas templates for atlas-based brain image segmentation. Inf Process Med Imaging 19:689–700. https://doi.org/10.1007/11505730_57
Wen X, Zhang H, Li G, Liu M, Yin W, Lin W, Zhang J, Shen D (2019) First-year development of modules and hubs in infant brain functional Networks. Neuroimage 185(January):222–235. https://doi.org/10.1016/J.NEUROIMAGE.2018.10.019
Wold S, Ruhe A, Wold H, Dunn III WJ (2006) The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses. SIAM J Sci Stat Comput 5(3):735–743. https://doi.org/10.1137/0905052
Wolf K, Pfeiffer T (2014) The development of attentional resolution. Cogn Dev 29(1):62–80. https://doi.org/10.1016/J.COGDEV.2013.09.004
Funding
The authors received no financial support for the research, authorship, and publication of this article.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors have no conflict of interest to declare.
Ethics approval
All procedures performed in the study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its amendments or comparable ethical standards.
Consent to participate
Informed consent for participation to the present study was obtained by the legal guardian of each one of the children enrolled in the study.
Consent for publication
Consent for publication was obtained by the legal guardian of each one of the children enrolled in the study.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Onofrj, V., Chiarelli, A.M., Wise, R. et al. Interaction of the salience network, ventral attention network, dorsal attention network and default mode network in neonates and early development of the bottom-up attention system. Brain Struct Funct 227, 1843–1856 (2022). https://doi.org/10.1007/s00429-022-02477-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00429-022-02477-y