On time and space in the brain: A relativistic pseudo-diffusion framework
Open accessAbstract
Considering that the propagation speed of action potentials in the brain connectome has a finite limit and that present is ill-defined in the brain we apply concepts borrowed from the theories of special and general relativity to introduce the view that time and space are tightly blended in the brain. It is shown that the brain functional and structural features can be unified through a combined brain “spacetime”. This 4-dimensional brain spacetime presents a functional curvature generated by brain activity, in a similar way gravitational masses give our 4-dimensional Universe spacetime its curvature. After laying its foundations and developing this framework using a relativistic pseudo-diffusion model of neural propagation, we explore how this whole-brain framework may shed light on brain functional features and dysfunction phenotypes (clinical expression of diseases) observed in some neuropsychiatric and consciousness disorders. Because action potentials in the brain connectome propagate with a finite velocity limit, simultaneity across brain nodes is only relative. A new concept is emerging where time and space in the brain, as in the Universe, are tightly mingled through a combined “spacetime”. This 4-dimensional spacetime merging brain structure and function presents a curvature generated by brain activity, in a similar way gravitational masses give our Universe spacetime its curvature, driving activity flow within the brain.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Because neural propagation has a finite maximum speed within the connectome, a relativistic formalism using a brain-specific limiting velocity is required.
“However, because neural propagation has a finite maximum speed within the connectome, a relativistic formalism using a brain-specific limiting velocity is required, which is not trivial matter (Le Bihan, 2020).”
From 40 Years of diffusion MRI in the brain: From history to emerging frontiers (2026) · cites this paper as Le Bihan, 2020
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Zur Elektrodynamik bewegter Körper
Einstein, A. (1905) · Annalen der Physik · doi:10.1002/andp.19053221004 · Cited by 4,385 · see citing works
Network hubs in the human brain
van den Heuvel, M. P., & Sporns, O. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.09.012 · Cited by 2,123 · see citing works
The dynamic brain: from spiking neurons to neural masses and cortical fields
Deco, G., Jirsa, V. K., Robinson, P. A., Breakspear, M., & Friston, K. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000092 · Cited by 1,084 · see citing works
Controllability of structural brain networks
Gu, S., Pasqualetti, F., Cieslak, M., Telesford, Q. K., Yu, A. B., Kahn, A. E., Medaglia, J. D., Vettel, J. M., Miller, M. B., Grafton, S. T., et al. (2015) · Nature Communications · doi:10.1038/ncomms9414 · Cited by 912 · see citing works
Signature of consciousness in the dynamics of resting-state brain activity
Barttfeld, P., Uhrig, L., Sitt, J. D., Sigman, M., Jarraya, B., & Dehaene, S. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1418031112 · Cited by 670 · see citing works
Optimally controlling the human connectome: the role of network topology
Betzel, R. F., Gu, S., Medaglia, J. D., Pasqualetti, F., & Bassett, D. S. (2016) · Scientific Reports · doi:10.1038/srep30770 · Cited by 247 · see citing works
Mapping Structural Connectivity Using Diffusion <scp>MRI</scp> : Challenges and Opportunities
Yeh, C., Jones, D. K., Liang, X., Descoteaux, M., & Connelly, A. (2021) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27188 · Cited by 187 · see citing works
Towards a neuronal gauge theory
Sengupta, B., Tozzi, A., Cooray, G. K., Douglas, P. K., & Friston, K. J. (2016) · PLOS Biology · doi:10.1371/journal.pbio.1002400 · Cited by 132 · see citing works
Flagged first, then by citations
Research works printed but not linked: 13
- Tozzi A. The Physical features of visual imagery. Preprint 201911.0256.
- C.W. Misner , K.S. Thorne , J.A. Wheeler , Gravity, Princeton University Press, 2017 .
- Einstein A. (a). Über die von der molekularkinetischen Theorie der Wärme geforderte Bewegung von in ruhenden Flüssigkeiten suspendierten Teilchen Annalen der Physik (ser. 4), 1905; 17: 549-560.
- J. Dunkel , P. Talkner , P. Hanggi , Relativistic diffusion processes and random walk models, Physical Review D 75 (2007) 043001 .
- G. Hesselmann , C.A. Kell , E. Eger , A. Kleinschmidt , Spontaneous local variations in ongoing neural activity bias perceptual decisions, Proc. Natl Acad. Sci. 105 (31) (2008) 10894-10989 .
- K. Friston , A theory of cortical responses, Philos. Trans. R. Soc. Lond. B Biol. Sci. 360 (2015) 815-936 .
- Sepideh Sadaghiani , Jean-Baptiste Poline , Andreas Kleinschmidt , Mark D'Esposito , Ongoing dynamics in large-scale functional connectivity predict perception, Proc. Natl Acad. Sci. 112 (July7(27)) (2015) 8463-8468 .
- https://doi.org/ 10.1371/journal.pbio.2001494.s016 (movie from [) · DOI
- S.W. Hawking, T. Hertog, A smooth exit from eternal inflation? J. High Energy Phys. (2018) 147 https://doi.org/, doi: 10.1007/JHEP04(2018)147 . · DOI
- C. Giusti , E. Pastalkova , C. Curto , V. Itskov , Clique topology reveals intrinsic ge- ometric structure in neural correlations, Proc. Natl Acad. Sci. 112 (44) (2015) 13455-13460 .
- P. Broca , Sur le principe des localisations cérébrales, Bulletin de la Société d'Anthropologie tome II (1861) 190-204 .
- Bertolero M.A., Blevins A.S., Baum G.L., Gur R.C., Gur, R.E., Roalf D.R., Satterth- waite T.D., Basset D.S. The network architecture of the human brain is modularly encoded in the genome (arXiv preprint 1905.07606). · arXiv
- B.D. Fulcher , A. Fornito , A transcriptional signature of hub connectivity in the mouse connectome, Proc. Natl Acad. Sci. 113 (5) (2016) 1435-1440 .
Cited by
Sorted by citations
40 Years of diffusion MRI in the brain: From history to emerging frontiers
Le Bihan, D. (2026) · Imaging Neuroscience · doi:10.1162/imag.a.1365
“…as the thalamic nuclei. pret faulty connections revealed by DTI in relation to psychiatric phenotypes, including hallucinations in schizophrenia ( Le Bihan, 2020), delayed conscious access in schizophrenia and bipolar disorder ( Berkovitch et al., 2021), and depressive symptoms associated with…”
- Authors
- Le Bihan, D.
- ORCID
- Le Bihan, Denis
- Venue
- Brain Multiphysics
- Publisher
- Elsevier BV
- Published
- 2020-11
- Volume
- 1
- Pages
- 100016
- ISSN
- 2666-5220 (ISSN-L)
- OpenAlex
- W3106263114
- Cited by
- 30
- References
- 8
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 4 · 2023: 7 · 2022: 5 · 2021: 4
- FWCI
- 2.15
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Brain Multiphysics (publishedVersion, cc-by-nc-nd)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- finite speed of propagation, brain connectome, relativity of simultaneity, neuropsychiatric disorders, consciousness disorders
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
