Connect with us

Health

Correspondence of functional connectivity gradients across human isocortex, cerebellum, and hippocampus

Correspondence of functional connectivity gradients across human isocortex, cerebellum, and hippocampus

 


  • Barbas, H. General cortical and special prefrontal connections: principles from structure to function. Annu. Rev. Neurosci. 38, 269–289 (2015).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • García-Cabezas, M. Á., Zikopoulos, B. & Barbas, H. The structural model: a theory linking connections, plasticity, pathology, development and evolution of the cerebral cortex. Brain Struct. Funct. 224, 985–1008 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Haueis, P. Multiscale modeling of cortical gradients: the role of mesoscale circuits for linking macro- and microscale gradients of cortical organization and hierarchical information processing. NeuroImage 232, 117846 (2021).

  • Hilgetag, C. C., Goulas, A. & Changeux, J.-P. A natural cortical axis connecting the outside and inside of the human brain. Netw. Neurosci. 6, 950–959 (2022).

  • Hilgetag, C. C. & Goulas, A. ‘Hierarchy’ in the organization of brain networks. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190319 (2020).

    Article 

    Google Scholar
     

  • Mesulam, M. From sensation to cognition. Brain 121, 1013–1052 (1998).

    Article 
    PubMed 

    Google Scholar
     

  • Paquola, C. et al. Convergence of cortical types and functional motifs in the human mesiotemporal lobe. eLife 9, e60673 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Paquola, C. et al. Microstructural and functional gradients are increasingly dissociated in transmodal cortices. PLoS Biol. 17, e3000284 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Paquola, C. et al. The unique cytoarchitecture and wiring of the human default mode network. Preprint at bioRxiv https://doi.org/10.1101/2021.11.22.469533 (2021)

  • Pijnenburg, R. et al. Biological characteristics of connection-wise resting-state functional connectivity strength. Cereb. Cortex 29, 4646–4653 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Valk, S. L. et al. Shaping brain structure: genetic and phylogenetic axes of macroscale organization of cortical thickness. Sci. Adv. 6, eabb3417 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Valk, S. L. et al. Genetic and phylogenetic uncoupling of structure and function in human transmodal cortex. Nat. Commun. 13, 2341 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Xu, T. et al. Cross-species functional alignment reveals evolutionary hierarchy within the connectome. NeuroImage 223, 117346 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Scholtens, L. H., Feldman Barrett, L. & van den Heuvel, M. P. Cross-species evidence of interplay between neural connectivity at the micro- and macroscale of connectome organization in human, mouse, and rat brain. Brain Connect. 8, 595–603 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • van den Heuvel, M. P. et al. Multimodal analysis of cortical chemoarchitecture and macroscale fMRI resting-state functional connectivity. Hum. Brain Mapp. 37, 3103–3113 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Markov, N. T. et al. Cortical high-density counterstream architectures. Science 342, 1238406 (2013).

  • Hong, S.-J. et al. Toward a connectivity gradient-based framework for reproducible biomarker discovery. NeuroImage 223, 117322 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Huntenburg, J. M., Bazin, P.-L. & Margulies, D. S. Large-scale gradients in human cortical organization. Trends Cogn. Sci. 22, 21–31 (2018).

    Article 
    PubMed 

    Google Scholar
     

  • Vos de Wael, R. et al. BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets. Commun. Biol. 3, 1–10 (2020).

    Article 

    Google Scholar
     

  • Buckner, R. L., Krienen, F. M. & Yeo, B. T. T. Opportunities and limitations of intrinsic functional connectivity MRI. Nat. Neurosci. 16, 832–837 (2013).

    Article 
    PubMed 

    Google Scholar
     

  • Biswal, B., Zerrin Yetkin, F., Haughton, V. M. & Hyde, J. S. Functional connectivity in the motor cortex of resting human brain using echo-planar mri. Magn. Reson. Med. 34, 537–541 (1995).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Bethlehem, R. A. I. et al. Dispersion of functional gradients across the adult lifespan. NeuroImage 222, 117299 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Margulies, D. S. et al. Situating the default-mode network along a principal gradient of macroscale cortical organization. Proc. Natl Acad. Sci. USA 113, 12574–12579 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Vázquez-Rodríguez, B. et al. Gradients of structure–function tethering across neocortex. Proc. Natl Acad. Sci. USA 116, 21219–21227 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang, J. et al. Intrinsic functional connectivity is organized as three interdependent gradients. Sci. Rep. 9, 1–14 (2019).


    Google Scholar
     

  • Braga, R. M., Sharp, D. J., Leeson, C., Wise, R. J. S. & Leech, R. Echoes of the brain within default mode, association, and heteromodal cortices. J. Neurosci. 33, 14031–14039 (2013).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sepulcre, J., Sabuncu, M. R., Yeo, T. B., Liu, H. & Johnson, K. A. Stepwise connectivity of the modal cortex reveals the multimodal organization of the human brain. J. Neurosci. 32, 10649–10661 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Szinte, M. & Knapen, T. Visual organization of the default network. Cereb. Cortex 30, 3518–3527 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Sydnor, V. J. et al. Neurodevelopment of the association cortices: patterns, mechanisms, and implications for psychopathology. Neuron 109, 2820–2846 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Finlay, B. L. & Uchiyama, R. Developmental mechanisms channeling cortical evolution. Trends Neurosci. 38, 69–76 (2015).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Valk, S. L. et al. Changing the social brain: plasticity along macro-scale axes of functional connectivity following social mental training. Preprint at bioRxiv https://doi.org/10.1101/2020.11.11.377895 (2021).

  • Fernandino, L. et al. Concept representation reflects multimodal abstraction: a framework for embodied semantics. Cereb. Cortex 26, 2018–2034 (2016).

    Article 
    PubMed 

    Google Scholar
     

  • Corbetta, M. & Shulman, G. L. Control of goal-directed and stimulus-driven attention in the brain. Nat. Rev. Neurosci. 3, 201–215 (2002).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Dosenbach, N. U. F. et al. Distinct brain networks for adaptive and stable task control in humans. Proc. Natl Acad. Sci. USA 104, 11073–11078 (2007).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Miller, E. K. & Cohen, J. D. An integrative theory of prefrontal cortex function. Annu. Rev. Neurosci. 24, 167–202 (2001).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Uddin, L. Q. Salience processing and insular cortical function and dysfunction. Nat. Rev. Neurosci. 16, 55–61 (2015).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Mckeown, B. et al. The relationship between individual variation in macroscale functional gradients and distinct aspects of ongoing thought. NeuroImage 220, 117072 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Guell, X., Schmahmann, J. D., Gabrieli, J. D. & Ghosh, S. S. Functional gradients of the cerebellum. eLife 7, e36652 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Guell, X. et al. Functional territories of human dentate nucleus. Cereb. Cortex 30, 2401–2417 (2020).

  • Kharabian Masouleh, S., Plachti, A., Hoffstaedter, F., Eickhoff, S. & Genon, S. Characterizing the gradients of structural covariance in the human hippocampus. NeuroImage 218, 116972 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Przeździk, I., Faber, M., Fernández, G., Beckmann, C. F. & Haak, K. V. The functional organisation of the hippocampus along its long axis is gradual and predicts recollection. Cortex 119, 324–335 (2019).

    Article 
    PubMed 

    Google Scholar
     

  • Vos de Wael, R. et al. Anatomical and microstructural determinants of hippocampal subfield functional connectome embedding. Proc. Natl Acad. Sci. USA 115, 10154–10159 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Shadmehr, R., Smith, M. A. & Krakauer, J. W. Error correction, sensory prediction, and adaptation in motor control. Annu. Rev. Neurosci. 33, 89–108 (2010).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Sokolov, A. A., Miall, R. C. & Ivry, R. B. The cerebellum: adaptive prediction for movement and cognition. Trends Cogn. Sci. 21, 313–332 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Buzsáki, G. & Tingley, D. Space and time: the hippocampus as a sequence generator. Trends Cogn. Sci. 22, 853–869 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kumaran, D., Hassabis, D. & McClelland, J. L. What learning systems do intelligent agents need? Complementary learning systems theory updated. Trends Cogn. Sci. 20, 512–534 (2016).

    Article 
    PubMed 

    Google Scholar
     

  • Babayan, B. M. et al. A hippocampo-cerebellar centred network for the learning and execution of sequence-based navigation. Sci. Rep. 7, 17812 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Iglói, K. et al. Interaction between hippocampus and cerebellum Crus I in sequence-based but not place-based navigation. Cereb. Cortex 25, 4146–4154 (2015).

    Article 
    PubMed 

    Google Scholar
     

  • Onuki, Y., Van Someren, E. J. W., De Zeeuw, C. I. & Van der Werf, Y. D. Hippocampal–cerebellar interaction during spatio-temporal prediction. Cereb. Cortex 25, 313–321 (2015).

    Article 
    PubMed 

    Google Scholar
     

  • Watson, T. C. et al. Anatomical and physiological foundations of cerebello-hippocampal interaction. eLife 8, e41896 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yu, W. & Krook-Magnuson, E. Cognitive collaborations: bidirectional functional connectivity between the cerebellum and the hippocampus. Front. Syst. Neurosci. 9, 177 (2015).

  • Van Essen, D. C. et al. The WU-Minn human connectome project: an overview. NeuroImage 80, 62–79 (2013).

    Article 
    PubMed 

    Google Scholar
     

  • Holmes, A. J. et al. Brain genomics superstruct project initial data release with structural, functional, and behavioral measures. Sci. Data 2, 1–16 (2015).

    Article 

    Google Scholar
     

  • Yeo, B. T. T. et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 1125–1165 (2011).

    Article 
    PubMed 

    Google Scholar
     

  • Coifman, R. R. & Lafon, S. Diffusion maps. Appl. Comput. Harmon. Anal. 21, 5–30 (2006).

    Article 

    Google Scholar
     

  • Li, Q. et al. Atypical neural topographies underpin dysfunctional pattern separation in temporal lobe epilepsy. Brain 144, 2486–2498 (2021).

  • Yang, S. et al. The thalamic functional gradient and its relationship to structural basis and cognitive relevance. NeuroImage 218, 116960 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Kulaga-Yoskovitz, J. et al. Multi-contrast submillimetric 3 Tesla hippocampal subfield segmentation protocol and dataset. Sci. Data 2, 1–9 (2015).

    Article 

    Google Scholar
     

  • Katsumi, Y., Theriault, J. E., Quigley, K. S. & Barrett, L. F. Allostasis as a core feature of hierarchical gradients in the human brain. Netw. Neurosci. 6, 1010–1031 (2022).

    Article 

    Google Scholar
     

  • Alexander-Bloch, A. F. et al. On testing for spatial correspondence between maps of human brain structure and function. Neuroimage 178, 540–551 (2018).

    Article 
    PubMed 

    Google Scholar
     

  • Blazquez Freches, G. et al. Principles of temporal association cortex organisation as revealed by connectivity gradients. Brain Struct. Funct. 225, 1245–1260 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Faber, M., Przezdzik, I., Fernandez, G., Haak, K. V. & Beckmann, C. F. Overlapping connectivity gradients in the anterior temporal lobe underlie semantic cognition. Preprint at bioRxiv https://doi.org/10.1101/2020.05.28.121137 (2020).

  • Haak, K. V., Marquand, A. F. & Beckmann, C. F. Connectopic mapping with resting-state fMRI. NeuroImage 170, 83–94 (2018).

    Article 
    PubMed 

    Google Scholar
     

  • Raut, R. V., Snyder, A. Z. & Raichle, M. E. Hierarchical dynamics as a macroscopic organizing principle of the human brain. Proc. Natl Acad. Sci. USA 117, 20890–20897 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Shafiei, G. et al. Topographic gradients of intrinsic dynamics across neocortex. Elife 9, e62116 (2020).

  • Vogel, J. W. et al. A molecular gradient along the longitudinal axis of the human hippocampus informs large-scale behavioral systems. Nat. Commun. 11, 1–17 (2020).

    Article 

    Google Scholar
     

  • Marquand, A. F., Haak, K. V. & Beckmann, C. F. Functional corticostriatal connection topographies predict goal-directed behaviour in humans. Nat. Hum. Behav. 1, 1–9 (2017).

    Article 

    Google Scholar
     

  • Buckner, R. L., Krienen, F. M., Castellanos, A., Diaz, J. C. & Yeo, B. T. T. The organization of the human cerebellum estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 2322–2345 (2011).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ábrahám, H. et al. Myelination in the human hippocampal formation from midgestation to adulthood. Int. J. Dev. Neurosci. 28, 401–410 (2010).

    Article 
    PubMed 

    Google Scholar
     

  • Aggleton, J. P. Multiple anatomical systems embedded within the primate medial temporal lobe: Implications for hippocampal function. Neurosci. Biobehav. Rev. 36, 1579–1596 (2012).

    Article 
    PubMed 

    Google Scholar
     

  • Rolls, E. T. Pattern separation, completion, and categorisation in the hippocampus and neocortex. Neurobiol. Learn. Mem. 129, 4–28 (2016).

    Article 
    PubMed 

    Google Scholar
     

  • van Strien, N. M., Cappaert, N. L. M. & Witter, M. P. The anatomy of memory: an interactive overview of the parahippocampal–hippocampal network. Nat. Rev. Neurosci. 10, 272–282 (2009).

    Article 
    PubMed 

    Google Scholar
     

  • Fox, M. D. et al. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl Acad. Sci. USA 102, 9673–9678 (2005).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Fanselow, M. S. & Dong, H.-W. Are the dorsal and ventral hippocampus functionally distinct structures? Neuron 65, 7–19 (2010).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Strange, B. A., Witter, M. P., Lein, E. S. & Moser, E. I. Functional organization of the hippocampal longitudinal axis. Nat. Rev. Neurosci. 15, 655–669 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Barrett, L. F. The theory of constructed emotion: an active inference account of interoception and categorization. Soc. Cogn. Affect. Neurosci. 12, 1–23 (2017).

    Article 
    PubMed 

    Google Scholar
     

  • Kleckner, I. R. et al. Evidence for a large-scale brain system supporting allostasis and interoception in humans. Nat. Hum. Behav. 1, 1–14 (2017).

    Article 

    Google Scholar
     

  • Levinthal, D. J. & Strick, P. L. The motor cortex communicates with the kidney. J. Neurosci. 32, 6726–6731 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Levinthal, D. J. & Strick, P. L. Multiple areas of the cerebral cortex influence the stomach. Proc. Natl Acad. Sci. USA 117, 13078–13083 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Touroutoglou, A., Andreano, J., Dickerson, B. C. & Barrett, L. F. The tenacious brain: how the anterior mid-cingulate contributes to achieving goals. Cortex 123, 12–29 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Kong, R. et al. Spatial topography of individual-specific cortical networks predicts human cognition, personality, and emotion. Cereb. Cortex 29, 2533–2551 (2019).

    Article 
    PubMed 

    Google Scholar
     

  • Hong, S.-J. et al. Atypical functional connectome hierarchy in autism. Nat. Commun. 10, 1–13 (2019).

    Article 

    Google Scholar
     

  • Karapanagiotidis, T. et al. The psychological correlates of distinct neural states occurring during wakeful rest. Sci. Rep. 10, 21121 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • van den Heuvel, M. P., Mandl, R. C. W., Kahn, R. S. & Hulshoff Pol, H. E. Functionally linked resting-state networks reflect the underlying structural connectivity architecture of the human brain. Hum. Brain Mapp. 30, 3127–3141 (2009).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Barrett, L. F. & Simmons, W. K. Interoceptive predictions in the brain. Nat. Rev. Neurosci. 16, 419–429 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chanes, L. & Barrett, L. F. Redefining the role of limbic areas in cortical processing. Trends Cogn. Sci. 20, 96–106 (2016).

    Article 
    PubMed 

    Google Scholar
     

  • Hutchinson, J. B. & Barrett, L. F. The power of predictions: an emerging paradigm for psychological research. Curr. Dir. Psychol. Sci. 28, 280–291 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P. & Pezzulo, G. Active inference: a process theory. Neural Comput. 29, 1–49 (2017).

    Article 
    PubMed 

    Google Scholar
     

  • Pezzulo, G., Zorzi, M. & Corbetta, M. The secret life of predictive brains: what’s spontaneous activity for? Trends Cogn. Sci. 25, 730–743 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Smith, R., Thayer, J. F., Khalsa, S. S. & Lane, R. D. The hierarchical basis of neurovisceral integration. Neurosci. Biobehav. Rev. 75, 274–296 (2017).

    Article 
    PubMed 

    Google Scholar
     

  • Rao, R. P. N. & Ballard, D. H. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat. Neurosci. 2, 79–87 (1999).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Keller, G. B. & Mrsic-Flogel, T. D. Predictive processing: a canonical cortical computation. Neuron 100, 424–435 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Picard, F. & Friston, K. Predictions, perception, and a sense of self. Neurology 83, 1112–1118 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Barron, H. C., Auksztulewicz, R. & Friston, K. Prediction and memory: a predictive coding account. Prog. Neurobiol. 192, 101821 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gravina, M. T. & Sederberg, P. B. The neural architecture of prediction over a continuum of spatiotemporal scales. Curr. Opin. Behav. Sci. 17, 194–202 (2017).

    Article 

    Google Scholar
     

  • Liu, K., Sibille, J. & Dragoi, G. Generative predictive codes by multiplexed hippocampal neuronal tuplets. Neuron 99, 1329–1341.e6 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Sherman, B. E. & Turk-Browne, N. B. Statistical prediction of the future impairs episodic encoding of the present. Proc. Natl Acad. Sci. USA 117, 22760–22770 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ito, M. Control of mental activities by internal models in the cerebellum. Nat. Rev. Neurosci. 9, 304–313 (2008).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Kawato, M. Internal models for motor control and trajectory planning. Curr. Opin. Neurobiol. 9, 718–727 (1999).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Wolpert, D. M., Miall, R. C. & Kawato, M. Internal models in the cerebellum. Trends Cogn. Sci. 2, 338–347 (1998).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Berkes, P., Orbán, G., Lengyel, M. & Fiser, J. Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment. Science 331, 83–87 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Feldman, H. & Friston, K. J. Attention, uncertainty, and free-energy. Front. Hum. Neurosci. 4, 215 (2010).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kanai, R., Komura, Y., Shipp, S. & Friston, K. Cerebral hierarchies: predictive processing, precision and the pulvinar. Philos. Trans. R. Soc. B Biol. Sci. 370, 20140169 (2015).

    Article 

    Google Scholar
     

  • Parr, T. & Friston, K. J. Attention or salience? Curr. Opin. Psychol. 29, 1–5 (2019).

    Article 
    PubMed 

    Google Scholar
     

  • Beul, S. F. & Hilgetag, C. C. Towards a “canonical” agranular cortical microcircuit. Front. Neuroanat. 8, 165 (2015).

  • Bastos, A. M., Lundqvist, M., Waite, A. S., Kopell, N. & Miller, E. K. Layer and rhythm specificity for predictive routing. Proc. Natl Acad. Sci. USA 117, 31459–31469 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Muckli, L. et al. Contextual feedback to superficial layers of V1. Curr. Biol. 25, 2690–2695 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Buckner, R. L. The serendipitous discovery of the brain’s default network. NeuroImage 62, 1137–1145 (2012).

    Article 
    PubMed 

    Google Scholar
     

  • Stawarczyk, D., Bezdek, M. A. & Zacks, J. M. Event representations and predictive processing: the role of the midline default network core. Top. Cogn. Sci. 13, 164–186 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • Smallwood, J. et al. The default mode network in cognition: a topographical perspective. Nat. Rev. Neurosci. 22, 503–513 (2021).

  • Dixon, M. L. et al. Heterogeneity within the frontoparietal control network and its relationship to the default and dorsal attention networks. Proc. Natl Acad. Sci. USA 115, E1598–E1607 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hasson, U., Chen, J. & Honey, C. J. Hierarchical process memory: memory as an integral component of information processing. Trends Cogn. Sci. 19, 304–313 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Deneve, S. Bayesian spiking neurons I: inference. Neural Comput. 20, 91–117 (2008).

    Article 
    PubMed 

    Google Scholar
     

  • Power, J. D. et al. Functional network organization of the human brain. Neuron 72, 665–678 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Touroutoglou, A., Hollenbeck, M., Dickerson, B. C. & Barrett, L. F. Dissociable large-scale networks anchored in the right anterior insula subserve affective experience and attention. NeuroImage 60, 1947–1958 (2012).

    Article 
    PubMed 

    Google Scholar
     

  • Ullsperger, M., Danielmeier, C. & Jocham, G. Neurophysiology of performance monitoring and adaptive behavior. Physiol. Rev. 94, 35–79 (2014).

    Article 
    PubMed 

    Google Scholar
     

  • Craig, A. D. How do you feel — now? The anterior insula and human awareness. Nat. Rev. Neurosci. 10, 59–70 (2009).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Workman, A. D., Charvet, C. J., Clancy, B., Darlington, R. B. & Finlay, B. L. Modeling transformations of neurodevelopmental sequences across mammalian species. J. Neurosci. 33, 7368–7383 (2013).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sherwood, C. C., Bauernfeind, A. L., Bianchi, S., Raghanti, M. A. & Hof, P. R. Human brain evolution writ large and small. Prog. Brain Res. 195, 237–254 (2012).

  • Sherwood, C. C., Bauernfeind, A. L., Verendeev, A., Raghanti, M. A. & Hof, P. R. 4.08 – Evolutionary Specializations of Human Brain Microstructure. in Evolution of Nervous Systems (ed. Kaas, J. H.) 121–139 (Academic Press, 2017).

  • Barrett, L. F. & Finlay, B. L. Concepts, goals and the control of survival-related behaviors. Curr. Opin. Behav. Sci. 24, 172–179 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sultan, F. et al. Unravelling cerebellar pathways with high temporal precision targeting motor and extensive sensory and parietal networks. Nat. Commun. 3, 924 (2012).

    Article 
    PubMed 

    Google Scholar
     

  • Tanaka, H., Ishikawa, T., Lee, J. & Kakei, S. The cerebro-cerebellum as a locus of forward model: a review. Front. Syst. Neurosci. 14, 19 (2020).

  • Roth, M. J., Synofzik, M. & Lindner, A. The cerebellum optimizes perceptual predictions about external sensory events. Curr. Biol. 23, 930–935 (2013).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Synofzik, M., Lindner, A. & Thier, P. The cerebellum updates predictions about the visual consequences of one’s behavior. Curr. Biol. 18, 814–818 (2008).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Molinari, M. & Masciullo, M. The implementation of predictions during sequencing. Front. Cell. Neurosci. 13, 439 (2019).

  • Herzfeld, D. J., Vaswani, P. A., Marko, M. K. & Shadmehr, R. A memory of errors in sensorimotor learning. Science 345, 1349–1353 (2014).

  • Wei, K. & Körding, K. Relevance of error: what drives motor adaptation? J. Neurophysiol. 101, 655–664 (2009).

    Article 
    PubMed 

    Google Scholar
     

  • Smith, M. A., Ghazizadeh, A. & Shadmehr, R. Interacting adaptive processes with different timescales underlie short-term motor learning. PLoS Biol. 4, e179 (2006).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Thach, W. T. Discharge of cerebellar neurons related to two maintained postures and two prompt movements. I. Nuclear cell output. J. Neurophysiol. 33, 527–536 (1970).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Schmahmann, J. D. & Pandya, D. N. The Cerebrocerebellar System. Int. Rev. Neurobiol. 41, 31–60 (1997).

  • Apps, R. & Watson, T. C. 48 – Cerebro-Cerebellar Connections. in Handbook of the Cerebellum and Cerebellar Disorders (eds. Manto, M., Schmahmann, J. D., Rossi, F., Gruol, D. L. & Koibuchi, N.) 1131–1153 (Springer, 2013).

  • Glickstein, M., May, J. G. & Mercier, B. E. Corticopontine projection in the macaque: the distribution of labelled cortical cells after large injections of horseradish peroxidase in the pontine nuclei. J. Comp. Neurol. 235, 343–359 (1985).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Kelly, R. M. & Strick, P. L. Cerebellar loops with motor cortex and prefrontal cortex of a nonhuman primate. J. Neurosci. 23, 8432–8444 (2003).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Schmahmann, J. D. From movement to thought: anatomic substrates of the cerebellar contribution to cognitive processing. Hum. Brain Mapp. 4, 174–198 (1996).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Zhu, J.-N. & Wang, J.-J. The cerebellum in feeding control: possible function and mechanism. Cell. Mol. Neurobiol. 28, 469–478 (2008).

    Article 
    PubMed 

    Google Scholar
     

  • Zhu, J.-N., Yung, W.-H., Kwok-Chong Chow, B., Chan, Y.-S. & Wang, J.-J. The cerebellar-hypothalamic circuits: potential pathways underlying cerebellar involvement in somatic-visceral integration. Brain Res. Rev. 52, 93–106 (2006).

    Article 
    PubMed 

    Google Scholar
     

  • Lisman, J. & Redish, A. D. Prediction, sequences and the hippocampus. Philos. Trans. R. Soc. B Biol. Sci. 364, 1193–1201 (2009).

    Article 

    Google Scholar
     

  • Pezzulo, G., Kemere, C. & der Meer, M. A. A. Internally generated hippocampal sequences as a vantage point to probe future-oriented cognition. Ann. N. Y. Acad. Sci. 1396, 144–165 (2017).

    Article 
    PubMed 

    Google Scholar
     

  • Witter, M. P., Doan, T. P., Jacobsen, B., Nilssen, E. S. & Ohara, S. Architecture of the entorhinal cortex A review of entorhinal anatomy in rodents with some comparative notes. Front. Syst. Neurosci. 11, 46 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Insausti, R. & Amaral, D. G. 24 – Hippocampal Formation. in The Human Nervous System(eds. Mai, J. K., Paxinos, G.) 896–942 (Elsevier, 2012).

  • Amaral, D. G. & Cowan, W. M. Subcortical afferents to the hippocampal formation in the monkey. J. Comp. Neurol. 189, 573–591 (1980).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Barbas, H. & Blatt, G. J. Topographically specific hippocampal projections target functionally distinct prefrontal areas in the rhesus monkey. Hippocampus 5, 511–533 (1995).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Insausti, R. & Muñoz, M. Cortical projections of the non-entorhinal hippocampal formation in the cynomolgus monkey (Macaca fascicularis). Eur. J. Neurosci. 14, 435–451 (2001).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Paleja, M., Girard, T. A., Herdman, K. A. & Christensen, B. K. Two distinct neural networks functionally connected to the human hippocampus during pattern separation tasks. Brain Cogn. 92, 101–111 (2014).

    Article 

    Google Scholar
     

  • Rochefort, C., Lefort, J. & Rondi-Reig, L. The cerebellum: a new key structure in the navigation system. Front. Neural Circuits 7, 35 (2013).

  • Bohne, P., Schwarz, M. K., Herlitze, S. & Mark, M. D. A new projection from the deep cerebellar nuclei to the hippocampus via the ventrolateral and laterodorsal thalamus in mice. Front. Neural Circuits 13, 51 (2019).

  • Arrigo, A. et al. Constrained spherical deconvolution analysis of the limbic network in human, with emphasis on a direct cerebello-limbic pathway. Front. Hum. Neurosci. 8, 987 (2014).

  • Baldassano, C. et al. Discovering event structure in continuous narrative perception and memory. Neuron 95, 709–721.e5 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Molinari, M. et al. Cerebellum and detection of sequences, from perception to cognition. Cerebellum 7, 611–615 (2008).

    Article 
    PubMed 

    Google Scholar
     

  • Van Overwalle, F., Manto, M., Leggio, M. & Delgado-García, J. M. The sequencing process generated by the cerebellum crucially contributes to social interactions. Med. Hypotheses 128, 33–42 (2019).

    Article 
    PubMed 

    Google Scholar
     

  • Zacks, J. M. et al. Event perception: a mind-brain perspective. Psychol. Bull. 133, 273–293 (2007).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hasselmo, M. E. What is the function of hippocampal theta rhythm?—Linking behavioral data to phasic properties of field potential and unit recording data. Hippocampus 15, 936–949 (2005).

    Article 
    PubMed 

    Google Scholar
     

  • Barrett, L. F., Quigley, K. S. & Hamilton, P. An active inference theory of allostasis and interoception in depression. Philos. Trans. R. Soc. B Biol. Sci. 371, 20160011 (2016).

    Article 

    Google Scholar
     

  • Smith, R., Badcock, P. & Friston, K. J. Recent advances in the application of predictive coding and active inference models within clinical neuroscience. Psychiatry Clin. Neurosci. 75, 3–13 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • Park, B. et al. An expanding manifold in transmodal regions characterizes adolescent reconfiguration of structural connectome organization. eLife 10, e64694 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tian, Y., Margulies, D. S., Breakspear, M. & Zalesky, A. Topographic organization of the human subcortex unveiled with functional connectivity gradients. Nat. Neurosci. 23, 1421–1432 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Glasser, M. F. et al. The minimal preprocessing pipelines for the Human Connectome Project. NeuroImage 80, 105–124 (2013).

    Article 
    PubMed 

    Google Scholar
     

  • Smith, S. M. et al. Resting-state fMRI in the Human Connectome Project. NeuroImage 80, 144–168 (2013).

    Article 
    PubMed 

    Google Scholar
     

  • Griffanti, L. et al. ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging. NeuroImage 95, 232–247 (2014).

    Article 
    PubMed 

    Google Scholar
     

  • Salimi-Khorshidi, G. et al. Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers. NeuroImage 90, 449–468 (2014).

    Article 
    PubMed 

    Google Scholar
     

  • Glasser, M. F. et al. A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Robinson, E. C. et al. MSM: a new flexible framework for multimodal surface matching. NeuroImage 100, 414–426 (2014).

    Article 
    PubMed 

    Google Scholar
     

  • Beckmann, C. F. & Smith, S. M. Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans. Med. Imaging 23, 137–152 (2004).

    Article 
    PubMed 

    Google Scholar
     

  • Fischl, B., Liu, A. & Dale, A. M. Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex. IEEE Trans. Med. Imaging 20, 70–80 (2001).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Fischl, B., Sereno, M. I., Tootell, R. B. H. & Dale, A. M. High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum. Brain Mapp. 8, 272–284 (1999).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Fischl, B., Sereno, M. I. & Dale, A. M. Cortical surface-based analysis: II: inflation, flattening, and a surface-based coordinate system. NeuroImage 9, 195–207 (1999).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Li, J. et al. Global signal regression strengthens association between resting-state functional connectivity and behavior. NeuroImage 196, 126–141 (2019).

    Article 
    PubMed 

    Google Scholar
     

  • Fischl, B. FreeSurfer. NeuroImage 62, 774–781 (2012).

    Article 
    PubMed 

    Google Scholar
     

  • Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W. & Smith, S. M. FSL. NeuroImage 62, 782–790 (2012).

    Article 
    PubMed 

    Google Scholar
     

  • Avants, B. B. et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage 54, 2033–2044 (2011).

    Article 
    PubMed 

    Google Scholar
     

  • Greve, D. N. & Fischl, B. Accurate and robust brain image alignment using boundary-based registration. NeuroImage 48, 63–72 (2009).

    Article 
    PubMed 

    Google Scholar
     

  • Power, J. D. et al. Methods to detect, characterize, and remove motion artifact in resting state fMRI. NeuroImage 84, 320–341 (2014).

    Article 
    PubMed 

    Google Scholar
     

  • Jenkinson, M., Bannister, P., Brady, M. & Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage 17, 825–841 (2002).

    Article 
    PubMed 

    Google Scholar
     

  • Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L. & Petersen, S. E. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage 59, 2142–2154 (2012).

    Article 
    PubMed 

    Google Scholar
     

  • Smith, S. M. et al. Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage 23, S208–S219 (2004).

    Article 
    PubMed 

    Google Scholar
     

  • Gordon, E. M. et al. Generation and evaluation of a cortical area parcellation from resting-state correlations. Cereb. Cortex 26, 288–303 (2016).

    Article 
    PubMed 

    Google Scholar
     

  • Diedrichsen, J., Balsters, J. H., Flavell, J., Cussans, E. & Ramnani, N. A probabilistic MR atlas of the human cerebellum. NeuroImage 46, 39–46 (2009).

    Article 
    PubMed 

    Google Scholar
     

  • Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31, 968–980 (2006).

    Article 
    PubMed 

    Google Scholar
     

  • Frazier, J. A. et al. Structural brain magnetic resonance imaging of limbic and thalamic volumes in pediatric bipolar disorder. Am. J. Psychiatry 162, 1256–1265 (2005).

    Article 
    PubMed 

    Google Scholar
     

  • Li, Q. et al. Atypical neural topographies underpin dysfunctional pattern separation in temporal lobe epilepsy. Brain 144, 2486–2498 (2021).

  • Manjón, J. V. & Coupé, P. volBrain: An Online MRI Brain Volumetry System. Front. Neuroinform. 10, 30 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Romero, J. E., Coupé, P. & Manjón, J. V. HIPS: a new hippocampus subfield segmentation method. NeuroImage 163, 286–295 (2017).

    Article 
    PubMed 

    Google Scholar
     

  • Lowe, A. J. et al. Targeting age-related differences in brain and cognition with multimodal imaging and connectome topography profiling. Hum. Brain Mapp. 40, 5213–5230 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Royer, J. et al. Myeloarchitecture gradients in the human insula: Histological underpinnings and association to intrinsic functional connectivity. NeuroImage 216, 116859 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Katsumi, Y. et al. yutakatsumi/gradient_correlations: v1.0.0. Zenodo https://doi.org/10.5281/zenodo.7764905 (2023).

  • Sources

    1/ https://Google.com/

    2/ https://www.nature.com/articles/s42003-023-04796-0

    The mention sources can contact us to remove/changing this article

    What Are The Main Benefits Of Comparing Car Insurance Quotes Online

    LOS ANGELES, CA / ACCESSWIRE / June 24, 2020, / Compare-autoinsurance.Org has launched a new blog post that presents the main benefits of comparing multiple car insurance quotes. For more info and free online quotes, please visit https://compare-autoinsurance.Org/the-advantages-of-comparing-prices-with-car-insurance-quotes-online/ The modern society has numerous technological advantages. One important advantage is the speed at which information is sent and received. With the help of the internet, the shopping habits of many persons have drastically changed. The car insurance industry hasn't remained untouched by these changes. On the internet, drivers can compare insurance prices and find out which sellers have the best offers. View photos The advantages of comparing online car insurance quotes are the following: Online quotes can be obtained from anywhere and at any time. Unlike physical insurance agencies, websites don't have a specific schedule and they are available at any time. Drivers that have busy working schedules, can compare quotes from anywhere and at any time, even at midnight. Multiple choices. Almost all insurance providers, no matter if they are well-known brands or just local insurers, have an online presence. Online quotes will allow policyholders the chance to discover multiple insurance companies and check their prices. Drivers are no longer required to get quotes from just a few known insurance companies. Also, local and regional insurers can provide lower insurance rates for the same services. Accurate insurance estimates. Online quotes can only be accurate if the customers provide accurate and real info about their car models and driving history. Lying about past driving incidents can make the price estimates to be lower, but when dealing with an insurance company lying to them is useless. Usually, insurance companies will do research about a potential customer before granting him coverage. Online quotes can be sorted easily. Although drivers are recommended to not choose a policy just based on its price, drivers can easily sort quotes by insurance price. Using brokerage websites will allow drivers to get quotes from multiple insurers, thus making the comparison faster and easier. For additional info, money-saving tips, and free car insurance quotes, visit https://compare-autoinsurance.Org/ Compare-autoinsurance.Org is an online provider of life, home, health, and auto insurance quotes. This website is unique because it does not simply stick to one kind of insurance provider, but brings the clients the best deals from many different online insurance carriers. In this way, clients have access to offers from multiple carriers all in one place: this website. On this site, customers have access to quotes for insurance plans from various agencies, such as local or nationwide agencies, brand names insurance companies, etc. "Online quotes can easily help drivers obtain better car insurance deals. All they have to do is to complete an online form with accurate and real info, then compare prices", said Russell Rabichev, Marketing Director of Internet Marketing Company. CONTACT: Company Name: Internet Marketing CompanyPerson for contact Name: Gurgu CPhone Number: (818) 359-3898Email: [email protected]: https://compare-autoinsurance.Org/ SOURCE: Compare-autoinsurance.Org View source version on accesswire.Com:https://www.Accesswire.Com/595055/What-Are-The-Main-Benefits-Of-Comparing-Car-Insurance-Quotes-Online View photos

    ExBUlletin

    to request, modification Contact us at Here or [email protected]