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Cited by 2Neural dynamics and brain functionEEG and Brain-Computer InterfacesFunctional Brain Connectivity Studies

Neurophysiological correlates to the human brain complexity through q-statistical analysis of electroencephalogram

Open access
Abramov, Dimitri Marques, Quintanilha, Daniel de Freitas, Lima, Henrique Santos, Costa, Roozemeria Pereira, Kamil-Leite, Carla, Lazarev, Vladimir V., Tsallis, Constantino·2025·Scientific Reports
DOI 10.1038/s41598-025-21156-6PMC PMC12537874
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Abstract

The prospects of assessing neural complexity (NC) by q-statistics of the systemic organization of different types and levels of brain activity were studied. In 70 adult subjects, NC was assessed via the parameter q of q-statistics, applied to the ongoing EEG and of 20 scalp points (channels). The NC were estimated both globally for all channels (AllCh) and locally (for each single channel) in different Functional States (FSs). The values of q was compared among FSs and single channels, as well they were correlated with the power of θ (4-8Hz), [Formula: see text] (15-25Hz) and others EEG bands, in each FS. The value of q across all FSs was higher for AllCh than for the single channels FSs. Consistently with previous studies, we found a negative correlation between NC and age. The FSs did not influence the q of the EEG in AllCh, although locally the FS modulated q in a consistent manner (e.g., reducing q in posterior sites with eyes closed). The q was correlated positively with the power of the θ and negatively with that of the [Formula: see text] band in general. These findings support the idea that, as a first approach, q-statistics seems to be related to the human NC. The relationship between q and θ power aligns with greater NC during FSs such as listening music and resting with eyes open, which is consistent with high-order representations rather than low-informative attentional tasks (OddBall).

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  • Abramov et al. [25] showed that global neural complexity assessed via q-statistics exceeded the sum of local complexities, consistent with non-additive, irreducible brain organization.

    “Abramov et al. [25] showed that global neural complexity assessed via q-statistics exceeded the sum of local complexities, consistent with non-additive, irreducible brain organization.”

    From Quantum-Inspired and Non-Classical Approaches to Consciousness: Models, Evidence and Constraints (2026) · cites this paper as [25]

    The Results (q-statistic) report that the all-channel q value was significantly higher than the average q value of individual channels; the Discussion interprets this as non-linear behavior between parts and whole. The paper does not report that global complexity exceeded the sum of local complexities, so the claim overstates the comparison. (LLM, conf=0.90)

    Experimental — accuracy not yet measured

  • Abramov et al. demonstrated that q-statistics derived from EEG event-interval distributions can differentiate global from local neural complexity.

    “Abramov et al. [25] demonstrated that q-statistics derived from EEG event-interval distributions can differentiate global from local neural complexity, with functional states selectively modulating local complexity at specific scalp sites.”

    From Quantum-Inspired and Non-Classical Approaches to Consciousness: Models, Evidence and Constraints (2026) · cites this paper as [25]

  • Functional states selectively modulate local complexity at specific scalp sites.

    “Abramov et al. [25] demonstrated that q-statistics derived from EEG event-interval distributions can differentiate global from local neural complexity, with functional states selectively modulating local complexity at specific scalp sites.”

    From Quantum-Inspired and Non-Classical Approaches to Consciousness: Models, Evidence and Constraints (2026) · cites this paper as [25]

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Research works printed but not linked: 4

  1. [12]Gell-Mann, M. & Tsallis, C. (eds) Nonextensive Entropy—Interdisciplinary Applications (Oxford University Press, Oxford, 2004).
  2. [25]Niedermeyer, E., da Silva, F. L., editors. Niedermeyer’s electroencephalography: basic principles, clinical applications, and related fields. 6th ed. Philadelphia: Lippincott Williams Wilkins (2011).
  3. [42]Wechsler, D. WAIS-III - Escala Wechsler de Inteligência para Adultos: manual técnico. Pearson Clinical Brasil (2004).
  4. [71]de Oliveira, R. M., Brito, S., da Silva, L. R. & Tsallis, C. Connecting complex networks to nonadditive entropies. Sci. Rep. 11 (1), 1130. 10.1038/s41598-020-80939 (2021). 33441951 PMC7806741 · DOI

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Authors
Abramov, D. M., Quintanilha, D. D. F., Lima, H. S., Costa, R. P., Kamil-Leite, C., Lazarev, V. V., & Tsallis, C.
Affiliations
  • Instituto Nacional de Saúde da Mulher, da Criança e do Adolescente Fernandes Figueira (BR)
  • Centro Brasileiro de Pesquisas Físicas (BR)
  • Santa Fe Institute (US)
Corresponding author
Abramov, Dimitri Marques
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2025-10-20
Volume
15
Issue
1
Pages
36502-36502
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-025-21156-6
PMC
PMC12537874
OpenAlex
W4415370806
Cited by
2
References
80
Citations by year
2026: 2
FWCI
1.13
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University)
Funders
  • Foundation for Scientific and Technological Development in Health (NDN)
  • Conselho Nacional de Desenvolvimento Científico e Tecnológico (NDN)
  • Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (NDN)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Keywords
neural complexity, q-statistics, EEG, functional state, EEG frequency bands, theta power, alpha power, age effects, oddball paradigm, music listening, resting state
MeSH terms
Adult, Brain, Electroencephalography, Female, Humans, Male, Middle Aged, Young Adult

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