Brain structural complexity and consciousness
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Keywords

Consciousness
Sleep
Brain structural complexity
Individual differences
Multimodal brain imaging

How to Cite

Song, C. (2021). Brain structural complexity and consciousness. Philosophy and the Mind Sciences, 2. https://doi.org/10.33735/phimisci.2021.9185

Abstract

Structure shapes function. Understanding what is structurally special about the brain that allows it to generate consciousness remains a fundamental scientific challenge. Recently, advances in brain imaging techniques have made it possible to measure the structure of human brain, from the morphology of neurons and neuronal connections to the gross anatomy of brain regions, in-vivo and non-invasively. Using advanced brain imaging techniques, it was discovered that the structural diversity between neurons and the topology of neuronal connections, as opposed to the sheer number of neurons or neuronal connections, are key to consciousness. When the structural diversity is high and the connections follow a modular topology, neurons will become functionally differentiable and functionally integrable with one another. The high levels of differentiation and integration, in turn, enable the brain to produce the richest conscious experiences from the smallest number of neurons and neuronal connections. Consequently, across individuals, those with a smaller brain volume but a higher structural diversity tend to have richer conscious experiences than those with a larger brain volume but a lower structural diversity. Moreover, within individuals, a reduction in neuronal connections, if accompanied by an increase in structural diversity, will result in richer conscious experiences, and vice versa. These findings suggest that having a larger number of neurons and neuronal connections is not necessarily beneficial for consciousness; in contrast, an optimal brain architecture for consciousness is one where the richest conscious experiences are generated from the smallest number of neurons and neuronal connections, at the minimal cost of biological material, physical space, and metabolic energy.

https://doi.org/10.33735/phimisci.2021.9185
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References

Amunts, K., Lenzen, M., Friederici, A. D., Schleicher, A., Morosan, P., Palomero-Gallagher, N., & Zilles, K. (2010). Broca’s region: Novel organizational principles and multiple receptor mapping. PLoS Biology, 8(9), e1000489. https://doi.org/ 10.1371/journal.pbio.1000489

Amunts, K., & Zilles, K. (2015). Architectonic mapping of the human brain beyond brodmann. Neuron, 88(6), 1086–1107. https://doi.org/10.1016/j.neuron.2015.12.001

Andrews, T. J., Halpern, S. D., & Purves, D. (1997). Correlated size variations in human visual cortex, lateral geniculate nucleus, and optic tract. The Journal of Neuroscience, 17 (8), 2859–2868. https://doi.org/10.1523/JNEUROSCI.17- 08- 02859.1997

Arancibia, I. L., Ford, M. C., Cossell, L., Ishida, K., Tohyama, K., & Attwell, D. (2017). Node of ranvier length as a potential regulator of myelinated axon conduction speed. eLife, 6. https://doi.org/10.7554/eLife.23329

Assaf, Y., & Basser, P. J. (2005). Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain. NeuroImage, 27(1), 48–58. https://doi.org/10.1016/j.neuroimage.2005.03.042

Assaf, Y., Blumenfeld, T., Yovel, Y., & Basser, P. J. (2008). Axcaliber a method for measuring axon diameter distribution from diffusion MRI. Magnetic Resonance in Medicine, 59(6), 1347–1354. https://doi.org/10.1002/mrm.21577

Bail, R. L., Bonafina, A., Espuny-Camacho, I., & Nguyen, L. (2021). Learning about cell lineage, cellular diversity and evolu- tion of the human brain through stem cell models. Current Opinion in Neurobiology, 66, 166–177. https://doi.org/https: //doi.org/10.1016/j.conb.2020.10.018

Bakken, T. E., Jorstad, N. L., Hu, Q., Lake, B. B., Tian, W., Kalmbach, B. E., Crow, M., Hodge, R. D., Krienen, F. M., Sorensen, S. A., Eggermont, J., Yao, Z., Aevermann, B. D., Aldridge, A. I., Bartlett, A., Bertagnolli, D., Casper, T., Castanon, R. G., Crichton, K., ... Lein, E. S. (2020). Evolution of cellular diversity in primary motor cortex of human, marmoset monkey, and mouse. bioRxiv, 2020.03.31.016972. https://doi.org/10.1101/2020.03.31.016972

Bassett, D. S., & Gazzaniga, M. S. (2011). Understanding complexity in the human brain. Trends in Cognitive Sciences, 15(5), 200–209. https://doi.org/10.1016/j.tics.2011.03.006

Bekkers, J. M. (2011). Pyramidal neurons. Current Biology, 21(24), R975. https://doi.org/10.1016/j.cub.2011.10.037

Bernardi, G., Cecchetti, L., Siclari, F., Buchmann, A., Yu, X., Handjaras, G., Bellesi, M., Ricciardi, E., Kecskemeti, S. R., Riedner, B. A., Alexander, A. L., Benca, R. M., Ghilardi, M. F., Pietrini, P., Cirelli, C., & Tononi, G. (2016). Sleep reverts changes in human gray and white matter caused by wake-dependent training. NeuroImage, 129, 367–377. https://doi. org/10.1016/j.neuroimage.2016.01.020

Boly, M., Massimini, M., Tsuchiya, N., Postle, B. R., Koch, C., & Tononi, G. (2017). Are the neural correlates of consciousness in the front or in the back of the cerebral cortex? Clinical and neuroimaging evidence. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, 37(40), 9603–9613. https://doi.org/10.1523/JNEUROSCI.3218-16.2017

Brown, K. M., Gillette, T. A., & Ascoli, G. A. (2008). Quantifying neuronal size: Summing up trees and splitting the branch difference. Seminars in Cell & Developmental Biology, 19(6), 485–493. https://doi.org/10.1016/j.semcdb.2008.08.005

Bryant, D. M., & Mostov, K. E. (2008). From cells to organs: Building polarized tissue. Nature Reviews Molecular Cell Biology, 9(11), 887–901. https://doi.org/10.1038/nrm2523

Bullmore, E., & Sporns, O. (2009). Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186–198. https://doi.org/10.1038/nrn2575

Chen, C.-H., Panizzon, M. S., Eyler, L. T., Jernigan, T. L., Thompson, W., Fennema-Notestine, C., Jak, A. J., Neale, M. C., Franz, C. E., Hamza, S., Lyons, M. J., Grant, M. D., Fischl, B., Seidman, L. J., Tsuang, M. T., Kremen, W. S., & Dale, A. M. (2011). Genetic influences on cortical regionalization in the human brain. Neuron, 72(4), 537–544. https://doi.org/10. 1016/j.neuron.2011.08.021

Chereau, R., Saraceno, G. E., Angibaud, J., Cattaert, D., & Nägerl, U. V. (2017). Superresolution imaging reveals activity- dependent plasticity of axon morphology linked to changes in action potential conduction velocity. Proceedings of the National Academy of Sciences, 114(6), 1401–1406. https://doi.org/10.1073/pnas.1607541114

Chklovskii, D. (2004). Synaptic connectivity and neuronal morphology: Two sides of the same coin. Neuron, 43(5), 609–617. https://doi.org/10.1016/S0896- 6273(04)00498- 2

Cirelli, C. (2013). Sleep and synaptic changes. Current Opinion in Neurobiology, 23(5), 841–846. https://doi.org/10.1016/j. conb.2013.04.001

Clarke, S., & Miklossy, J. (1990). Occipital cortex in man: Organization of callosal connections, related myelo- and cytoar- chitecture, and putative boundaries of functional visual areas. The Journal of Comparative Neurology, 298(2), 188–214. https://doi.org/10.1002/cne.902980205

Clune, J., Mouret, J.-B., & Lipson, H. (2013). The evolutionary origins of modularity. Proceedings of the Royal Society B: Biological Sciences, 280(1755), 20122863. https://doi.org/10.1098/rspb.2012.2863

Crick, F., & Koch, C. (2003). A framework for consciousness. Nature Neuroscience, 6(2), 119–126. https://doi.org/10.1038/ nn0203- 119

Dale, A. M., Fischl, B., & Sereno, M. I. (1999). Cortical surface-based analysis I: Segmentation and surface reconstruction. NeuroImage, 9, 179–194. https://doi.org/10.1006/nimg.1998.0395

Darmanis, S., Sloan, S. A., Zhang, Y., Enge, M., Caneda, C., Shuer, L. M., Hayden Gephart, M. G., Barres, B. A., & Quake, S. R. (2015). A survey of human brain transcriptome diversity at the single cell level. Proceedings of the National Academy of Sciences, 112(23), 7285–7290. https://doi.org/10.1073/pnas.1507125112

Eberwine, J., Sul, J.-Y., Bartfai, T., & Kim, J. (2014). The promise of single-cell sequencing. Nature Methods, 11(1), 25–27. https://doi.org/10.1038/nmeth.2769

Edwards, L. J., Kirilina, E., Mohammadi, S., & Weiskopf, N. (2018). Microstructural imaging of human neocortex in vivo. NeuroImage, 182, 184–206. https://doi.org/10.1016/j.neuroimage.2018.02.055

Fink, S. B. (2016). A deeper look at the “neural correlate of consciousness.” Frontiers in Psychology, 7, 1044. https://doi.org/ 10.3389/fpsyg.2016.01044

Firmin, L., Field, P., Maier, M. A., Kraskov, A., Kirkwood, P. A., Nakajima, K., Lemon, R. N., & Glickstein, M. (2014). Axon diameters and conduction velocities in the macaque pyramidal tract. Journal of Neurophysiology, 112(6), 1229–1240. https://doi.org/10.1152/jn.00720.2013

Fischl, B., Sereno, M. I., & Dale, A. M. (1999). Cortical surface-based analysis II: Inflation, flattening, and a surface-based coordinate system. NeuroImage, 9, 195–207. https://doi.org/10.1006/nimg.1998.0396

Forsman, A. (2014). Effects of genotypic and phenotypic variation on establishment are important for conservation, inva- sion, and infection biology. Proceedings of the National Academy of Sciences, 111(1), 302–307. https://doi.org/10.1073/ pnas.1317745111

Gallos, L. K., Makse, H. A., & Sigman, M. (2012). A small world of weak ties provides optimal global integration of self- similar modules in functional brain networks. Proceedings of the National Academy of Sciences, 109(8), 2825–2830. https://doi.org/10.1073/pnas.1106612109

Gilbert, C., & Wiesel, T. (1989). Columnar specificity of intrinsic horizontal and corticocortical connections in cat visual cortex. The Journal of Neuroscience, 9(7), 2432–2442. https://doi.org/10.1523/JNEUROSCI.09-07-02432.1989

Girvan, M., & Newman, M. E. J. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 99(12), 7821–7826. https://doi.org/10.1073/pnas.122653799

Glasser, M. F., Coalson, T. S., Robinson, E. C., Hacker, C. D., Harwell, J., Yacoub, E., Ugurbil, K., Andersson, J., Beckmann, C. F., Jenkinson, M., Smith, S. M., & Van Essen, D. C. (2016). A multi-modal parcellation of human cerebral cortex. Nature, 536(7615), 171–178. https://doi.org/10.1038/nature18933

Glasser, M. F., Smith, S. M., Marcus, D. S., Andersson, J. L. R., Auerbach, E. J., Behrens, T. E. J., Coalson, T. S., Harms, M. P., Jenkinson, M., Moeller, S., Robinson, E. C., Sotiropoulos, S. N., Xu, J., Yacoub, E., Ugurbil, K., & Van Essen, D. C. (2016). The human connectome project’s neuroimaging approach. Nature Neuroscience, 19(9), 1175–1187. https: //doi.org/10.1038/nn.4361

Harris, J. A., Mihalas, S., Hirokawa, K. E., Whitesell, J. D., Choi, H., Bernard, A., Bohn, P., Caldejon, S., Casal, L., Cho, A., Feiner, A., Feng, D., Gaudreault, N., Gerfen, C. R., Graddis, N., Groblewski, P. A., Henry, A. M., Ho, A., Howard, R., ... Zeng, H. (2019). Hierarchical organization of cortical and thalamic connectivity. Nature, 575(7781), 195–202. https://doi.org/10.1038/s41586- 019- 1716- z

Hartwell, L. H., Hopfield, J. J., Leibler, S., & Murray, A. W. (1999). From molecular to modular cell biology. Nature, 402(S6761), C47–C52. https://doi.org/10.1038/35011540

Horowitz, A., Barazany, D., Tavor, I., Bernstein, M., Yovel, G., & Assaf, Y. (2015). In vivo correlation between axon diameter and conduction velocity in the human brain. Brain Structure and Function, 220(3), 1777–1788. https://doi.org/10.1007/ s00429- 014- 0871- 0

Hwang, K., Bertolero, M. A., Liu, W. B., & D’Esposito, M. (2017). The human thalamus is an integrative hub for functional brain networks. The Journal of Neuroscience, 37(23), 5594–5607. https://doi.org/10.1523/JNEUROSCI.0067-17.2017

Jones, D. K., Alexander, D. C., Bowtell, R., Cercignani, M., Dell’Acqua, F., McHugh, D. J., Miller, K. L., Palombo, M., Parker, G. J. M., Rudrapatna, U. S., & Tax, C. M. W. (2018). Microstructural imaging of the human brain with a super- scanner: 10 key advantages of ultra-strong gradients for diffusion MRI. NeuroImage, 182, 8–38. https://doi.org/10.1016/ j.neuroimage.2018.05.047

Joyner, A. H., J., C. R., Bloss, C. S., Bakken, T. E., Rimol, L. M., Melle, I., Agartz, I., Djurovic, S., Topol, E. J., Schork, N. J., Andreassen, O. A., & Dale, A. M. (2009). A common MECP2 haplotype associates with reduced cortical surface area in humans in two independent populations. Proceedings of the National Academy of Sciences, 106(36), 15483–15488. https://doi.org/10.1073/pnas.0901866106

Kaas, J. H. (2012). Evolution of columns, modules, and domains in the neocortex of primates. Proceedings of the National Academy of Sciences, 109(Supplement_1), 10655–10660. https://doi.org/10.1073/pnas.1201892109

Kaas, J. H. (2000). Why is brain size so important: Design problems and solutions as neocortex gets bigger or smaller. Brain and Mind, 7–23. https://doi.org/10.1023/A:1010028405318

Kanai, R., & Rees, G. (2011). The structural basis of inter-individual differences in human behaviour and cognition. Nature Reviews. Neuroscience, 12(4), 231–242. https://doi.org/10.1038/nrn3000

Kashtan, N., & Alon, U. (2005). Spontaneous evolution of modularity and network motifs. Proceedings of the National Academy of Sciences, 102(39), 13773–13778. https://doi.org/10.1073/pnas.0503610102

Ko, H., Hofer, S. B., Pichler, B., Buchanan, K. A., Sjöström, P. J., & Mrsic-Flogel, T. D. (2011). Functional specificity of local synaptic connections in neocortical networks. Nature, 473(7345), 87–91. https://doi.org/10.1038/nature09880

Koch, C. (2019). The feeling of life itself: Why consciousness is widespread but can’t be computed. The MIT Press.

Koch, C., Massimini, M., Boly, M., & Tononi, G. (2016). Neural correlates of consciousness: Progress and problems. Nature Reviews Neuroscience, 17(5), 307–321. https://doi.org/10.1038/nrn.2016.22

Krause, A. J., Simon, E. B., Mander, B. A., Greer, S. M., Saletin, J. M., Goldstein-Piekarski, A. N., & Walker, M. P. (2017). The sleep-deprived human brain. Nature Reviews Neuroscience, 18(7), 404–418. https://doi.org/10.1038/nrn.2017.55

Lake, B. B., Ai, R., Kaeser, G. E., Salathia, N. S., Yung, Y. C., Liu, R., Wildberg, A., Gao, D., Fung, H.-L., Chen, S., Vija- yaraghavan, R., Wong, J., Chen, A., Sheng, X., Kaper, F., Shen, R., Ronaghi, M., Fan, J.-B., Wang, W., ... Zhang, K. (2016). Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science, 352(6293), 1586–1590. https://doi.org/10.1126/science.aaf1204

Lau, H., & Rosenthal, D. (2011). Empirical support for higher-order theories of conscious awareness. Trends in Cognitive Sciences, 15(8), 365–373. https://doi.org/10.1016/j.tics.2011.05.009

Le Bihan, D., & Iima, M. (2015). Diffusion MRI: What water tells us about biological tissues. PLOS Biology, 13(7), e1002203. https://doi.org/10.1371/journal.pbio.1002203

Lisman, J. E. (2017). Locke’s view of the hard problem of consciousness and its implications for neuroscience and computer science. Frontiers in Psychology, 8, 1069. https://doi.org/10.3389/fpsyg.2017.01069

Macaulay, I. C., Ponting, C. P., & Voet, T. (2017). Single-cell multiomics: Multiple measurements from single cells. Trends in Genetics, 33(2), 155–168. https://doi.org/10.1016/j.tig.2016.12.003

Mountcastle, V. (1997). The columnar organization of the neocortex. Brain, 120(4), 701–722. https://doi.org/10.1093/brain/ 120.4.701

Newman, M. E. J. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103(23), 8577–8582. https://doi.org/10.1073/pnas.0601602103

Norberg, J., Swaney, D. P., Dushoff, J., Lin, J., Casagrandi, R., & Levin, S. A. (2001). Phenotypic diversity and ecosystem functioning in changing environments: A theoretical framework. Proceedings of the National Academy of Sciences, 98(20), 11376–11381. https://doi.org/10.1073/pnas.171315998

Odegaard, B., Knight, R. T., & Lau, H. (2017). Should a few null findings falsify prefrontal theories of conscious perception? The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, 37(40), 9593–9602. https://doi.org/10. 1523/JNEUROSCI.3217- 16.2017

Oizumi, M., Albantakis, L., & Tononi, G. (2014). From the phenomenology to the mechanisms of consciousness: Integrated information theory 3.0. PLoS Computational Biology, 10(5), e1003588. https://doi.org/10.1371/journal.pcbi.1003588

Palombo, M., Ligneul, C., Najac, C., Le Douce, J., Flament, J., Escartin, C., Hantraye, P., Brouillet, E., Bonvento, G., & Valette, J. (2016). New paradigm to assess brain cell morphology by diffusion-weighted MR spectroscopy in vivo. Proceedings of the National Academy of Sciences, 113(24), 6671–6676. https://doi.org/10.1073/pnas.1504327113

Palombo, M., Shemesh, N., Ronen, I., & Valette, J. (2018). Insights into brain microstructure from in vivo DW-MRS. Neu- roImage, 182, 97–116. https://doi.org/10.1016/j.neuroimage.2017.11.028

Palomero, N., & Zilles, K. (2019). Cortical layers: Cyto-, myelo-, receptor- and synaptic architecture in human cortical areas. NeuroImage, 197, 716–741. https://doi.org/10.1016/j.neuroimage.2017.08.035

Pan, R. K., & Sinha, S. (2009). Modularity produces small-world networks with dynamical time-scale separation. Europhysics Letters, 85(6), 68006. https://doi.org/10.1209/0295-5075/85/68006

Panda, A., Mehta, B. B., Coppo, S., Jiang, Y., Ma, D., Seiberlich, N., Griswold, M. A., & Gulani, V. (2017). Magnetic resonance fingerprinting: An overview. Current Opinion in Biomedical Engineering, 3, 56–66. https://doi.org/10.1016/j.cobme. 2017.11.001

Panizzon, M. S., Fennema-Notestine, C., Eyler, L. T., Jernigan, T. L., Prom-Wormley, E., Neale, M., Jacobson, K., Lyons, M. J., Grant, M. D., Franz, C. E., Xian, H., Tsuang, M., Fischl, B., Seidman, L., Dale, A., & Kremen, W. S. (2009). Distinct genetic influences on cortical surface area and cortical thickness. Cerebral Cortex, 19(11), 2728–2735. https://doi.org/ 10.1093/cercor/bhp026

Pearce, J. M. S. (2013). The neuroanatomy of herophilus. European Neurology, 69(5), 292–295. https://doi.org/10.1159/ 000346232

Rakic, P. (1988). Specification of cerebral cortical areas. Science (New York, N.Y.), 241(4862), 170–176. https://doi.org/10.1126/ science.3291116

Ravasz, E. (2002). Hierarchical organization of modularity in metabolic networks. Science, 297(5586), 1551–1555. https: //doi.org/10.1126/science.1073374

Schwarzkopf, D. S., Song, C., & Rees, G. (2011). The surface area of human V1 predicts the subjective experience of object size. Nature Neuroscience, 14(1), 28–30. https://doi.org/10.1038/nn.2706

Shipp, S. (2005). The importance of being agranular: A comparative account of visual and motor cortex. Philosophical Transactions of the Royal Society B: Biological Sciences, 360(1456), 797–814. https://doi.org/10.1098/rstb.2005.1630

Siclari, F., LaRocque, J., Postle, B., & Tononi, G. (2013). Assessing sleep consciousness within subjects using a serial awak- ening paradigm. Frontiers in Psychology, 4, 542. https://doi.org/10.3389/fpsyg.2013.00542

Silver, M. A., & Kastner, S. (2009). Topographic maps in human frontal and parietal cortex. Trends in Cognitive Sciences, 13(11), 488–495. https://doi.org/10.1016/j.tics.2009.08.005

Sole, R., & Valverde, S. (2006). Are network motifs the spandrels of cellular complexity? Trends in Ecology & Evolution, 21(8), 419–422. https://doi.org/10.1016/j.tree.2006.05.013

Song, C., Haun, A. M., & Tononi, G. (2017). Plasticity in the structure of visual space. eNeuro, 4(3). https://doi.org/10.1523/ ENEURO.0080- 17.2017

Song, C., Havlin, S., & Makse, H. A. (2005). Self-similarity of complex networks. Nature, 433(7024), 392–395. https://doi. org/10.1038/nature03248

Song, C., & Rees, G. (2018). Intra-hemispheric integration underlies perception of tilt illusion. NeuroImage, 175, 80–90. https://doi.org/10.1016/j.neuroimage.2018.03.073

Song, C., Schwarzkopf, D. S., Kanai, R., & Rees, G. (2015). Neural population tuning links visual cortical anatomy to human visual perception. Neuron, 85(3), 641–656. https://doi.org/10.1016/j.neuron.2014.12.041

Song, C., Schwarzkopf, D. S., Kanai, R., & Rees, G. (2011). Reciprocal anatomical relationship between primary sensory and prefrontal cortices in the human brain. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, 31(26), 9472–9480. https://doi.org/10.1523/JNEUROSCI.0308-11.2011

Song, C., Schwarzkopf, D. S., Lutti, A., Li, B., Kanai, R., & Rees, G. (2013). Effective connectivity within human primary visual cortex predicts interindividual diversity in illusory perception. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, 33(48), 18781–18791. https://doi.org/10.1523/JNEUROSCI.4201-12.2013

Song, C., Schwarzkopf, D. S., & Rees, G. (2011). Interocular induction of illusory size perception. BMC Neuroscience, 12, 27. https://doi.org/10.1186/1471- 2202- 12- 27

Song, C., Schwarzkopf, D. S., & Rees, G. (2013). Variability in visual cortex size reflects tradeoff between local orientation sensitivity and global orientation modulation. Nature Communications, 4, 2201. https://doi.org/10.1038/ncomms3201

Song, C., & Tagliazucchi, E. (2020). Linking the nature and functions of sleep: Insights from multimodal imaging of the sleeping brain. Current Opinion in Physiology, 15, 29–36. https://doi.org/10.1016/j.cophys.2019.11.012

Sporns, O. (2013). Network attributes for segregation and integration in the human brain. Current Opinion in Neurobiology, 23(2), 162–171. https://doi.org/10.1016/j.conb.2012.11.015

Sporns, O., & Betzel, R. F. (2016). Modular brain networks. Annual Review of Psychology, 67(1), 613–640. https://doi.org/10. 1146/annurev- psych- 122414- 033634

Sporns, O., Tononi, G., & Kötter, R. (2005). The human connectome: A structural description of the human brain. PLoS Computational Biology, 1(4), e42. https://doi.org/10.1371/journal.pcbi.0010042

Sutherland, S. (1995). The Macmillan dictionary of psychology. Macmillan International Higher Education.

Tononi, G. (1998). Complexity and coherency: Integrating information in the brain. Trends in Cognitive Sciences, 2(12),

–484. https://doi.org/10.1016/S1364-6613(98)01259-5

Tononi, G., Boly, M., Massimini, M., & Koch, C. (2016). Integrated information theory: From consciousness to its physical

substrate. Nature Reviews Neuroscience, 17(7), 450–461. https://doi.org/10.1038/nrn.2016.44

Tononi, G., & Cirelli, C. (2014). Sleep and the price of plasticity: From synaptic and cellular homeostasis to memory

consolidation and integration. Neuron, 81(1), 12–34. https://doi.org/10.1016/j.neuron.2013.12.025

Tononi, G., & Edelman, G. M. (1998). Consciousness and complexity. Science, 282, 1846–1851. https://doi.org/10.1126/

science.282.5395.1846

Tononi, G., Sporns, O., & Edelman, G. M. (1999). Measures of degeneracy and redundancy in biological networks. Proceed-

ings of the National Academy of Sciences, 96(6), 3257–3262. https://doi.org/10.1073/pnas.96.6.3257

Tononi, G., Sporns, O., & Edelman, G. M. (1994). A measure for brain complexity: Relating functional segregation and integration in the nervous system. Proceedings of the National Academy of Sciences, 91(11), 5033–5037. https://doi.org/ 10.1073/pnas.91.11.5033

Tubbs, R. S. (2015). Anatomy is to physiology as geography is to history. Clinical Anatomy, 28(2), 151. https://doi.org/10. 1002/ca.22526

Vivo, L. de, Bellesi, M., Marshall, W., Bushong, E. A., Ellisman, M. H., Tononi, G., & Cirelli, C. (2017). Ultrastructural evidence for synaptic scaling across the wake/sleep cycle. Science, 355(6324), 507–510. https://doi.org/10.1126/science.aah5982

Wandell, B. A., Dumoulin, S. O., & Brewer, A. A. (2007). Visual field maps in human cortex. Neuron, 56(2), 366–383. https://doi.org/10.1016/j.neuron.2007.10.012

Wandell, B. A., & Winawer, J. (2015). Computational neuroimaging and population receptive fields. Trends in Cognitive Sciences, 19(6), 349–357. https://doi.org/10.1016/j.tics.2015.03.009

Weiskopf, N., Suckling, J., Williams, G., Correia, M. M., Inkster, B., Tait, R., Ooi, C., Bullmore, E. T., & Lutti, A. (2013). Quan- titative multi-parameter mapping of R1, PD(*), MT, and R2(*) at 3T: A multi-center validation. Frontiers in Neuroscience, 7, 95. https://doi.org/10.3389/fnins.2013.00095

Weliky, M., Kandler, K., Fitzpatrick, D., & Katz, L. C. (1995). Patterns of excitation and inhibition evoked by horizontal connections in visual cortex share a common relationship to orientation columns. Neuron, 15(3), 541–552. https: //doi.org/10.1016/0896- 6273(95)90143- 4

Windt, J. M. (2015). Dreaming a conceptual framework for philosophy of mind and empirical research. The MIT Press. http: //www.jstor.org/stable/j.ctt17kk7qt

Yu, Y.-C., Bultje, R. S., Wang, X., & Shi, S.-H. (2009). Specific synapses develop preferentially among sister excitatory neurons in the neocortex. Nature, 458(7237), 501–504. https://doi.org/10.1038/nature07722

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