interdiscip med j Interdisciplinary Medical Journal 2980-1915 Hatay Mustafa Kemal University 10.17944/interdiscip.1676000 Neurosciences (Other) Sinirbilim (Diğer) Resting state is not actually a state of rest, as confirmed by the loss of physiological complexity in brain dynamics https://orcid.org/0000-0003-1676-0648 Özel Hasan Fehmi MANISA CELAL BAYAR UNIVERSITY, MANİSA HEALTH SERVICES VOCATIONAL SCHOOL https://orcid.org/0000-0001-6617-604X Kazdağlı Hasan IZMIR UNIVERSITY OF ECONOMICS, HEALTH SERVICES VOCATIONAL SCHOOL 04 16 2026 17 57 10 18 04 14 2025 12 18 2025 Copyright © 2023, Interdisciplinary Medical Journal 2023 Interdisciplinary Medical Journal

Objective: The human brain operates with complex, non-linear dynamics that enable adaptability to cognitive demands. Physiological complexity, measured through electroencephalography (EEG), provides insights into neural organization and function. This study examines how cognitive load induced by verbal mental tasks affects brain complexity using fractal analysis.Method: EEG data from 36 healthy young adults (9 males and 27 females, aged 18–26 years) were obtained from the publicly available PhysioNet database. These recordings were collected with prior ethical approval and informed consent, and no new data were acquired for the present study. Participants completed a resting-state session followed by a mental arithmetic task with verbal commands. Detrended fluctuation analysis (DFA) was employed to assess EEG complexity, and a novel domain-based complexity loss parameter (dS) was introduced to quantify deviations from an idealized reference. Statistical comparisons were conducted using two-way ANOVA with Šídák correction, and all analyses were performed using GraphPad Prism version 10 (GraphPad Software, San Diego, CA, USA).Results: Cognitive load led to a significant reduction in DFA values, particularly in the temporal and frontal regions, indicating decreased physiological complexity. dS values increased significantly in the temporal regions, supporting the hypothesis that cognitive demand alters neural dynamics. These findings align with the default mode network concept, highlighting a shift from a high-complexity resting state to a more structured and synchronized state under cognitive load.Conclusion: The results suggest that physiological complexity decreases during verbal cognitive tasks, with the strongest effects in temporal regions. This supports the use of EEG fractal analysis in assessing cognitive workload and neural efficiency. Future studies should explore its applications in clinical and human-computer interaction contexts.

 EEG physiological complexity cognitive load fractal analysis DFA mental workload verbal tasks He F and Yuan Y. Nonlinear system identification of neural systems from neurophysiological signals. Neuroscience 2021;458:213-228. https://doi.org/10.1016/j.neuroscience.2020.12.001 Gao J, Hu J, Tung WW. Complexity measures of brain wave dynamics. Cogn Neurodyn 2011;5(2):171. https://doi.org/10.1007/s11571-011-9151-3 Juen Z, Shen-Hsing A. Brain entropy, fractal dimensions and predictability: a review of complexity measures for EEG in healthy and neuropsychiatric populations. European Journal of Neuroscience 2022;56(7):5047-5069. https://doi.org/10.1111/ejn.15800 Yang AC, Wang SJ, Lai KL, Tsai CF, Yang CH, Hwang JP, et al. Cognitive and neuropsychiatric correlates of EEG dynamic complexity in patients with Alzheimer’s disease. Prog Neuropsychopharmacol Biol Psychiatry 2013;47(2):52-61. https://doi.org/10.1016/j.pnpbp.2013.07.022 Margreeth Smits F, Porcaro C, Cottone C, Cancelli A, Maria Rossini P, Tecchio F. Electroencephalographic Fractal Dimension in Healthy Ageing and Alzheimer’s Disease. PloS one, 2016;11(2):e0149587. https://doi.org/10.1371/journal.pone.0149587 Buckner RL, Andrews-Hanna JR, Schacter DL. The brain’s default network: anatomy, function, and relevance to disease. Annals of the New York Academy of Sciences, 2008;1124(1):1-38. https://doi.org/10.1196/annals.1440.011 Vatansever D, Menon DK, Manktelow AE, Sahakian BJ, Stamatakis EA. Default mode network connectivity during task execution. Neuroimage 2015;122:96-104. https://doi.org/10.1016/j.neuroimage.2015.07.053 Fernández A, Zuluaga P, Abásolo D, Gómez C, Serra A, Méndez MA, et al. Brain oscillatory complexity across the life span. Clinical Neurophysiology 2012;123(11):2154-62. https://doi.org/10.1016/j.clinph.2012.04.025 Gonzalez-Burgos G, Cho RY, Lewis DA. Alterations in cortical network oscillations and parvalbumin neurons in schizophrenia. Biol Psychiatry 2015;77(12):1031-40. https://doi.org/10.1016/j.biopsych.2015.03.010 Takahashi T, Cho RY, Mizuno T, Kikuchi M, Murata T, Takahashi K, et al. Antipsychotics reverse abnormal EEG complexity in drug-naive schizophrenia: A multiscale entropy analysis. Neuroimage 2010;51(1):173-82. https://doi.org/10.1016/j.neuroimage.2010.02.009 Xiang J, Li C, Li H, Cao R, Wang B, Han X, et al. The detection of epileptic seizure signals based on fuzzy entropy. J Neurosci Methods 2015;243:18-25. https://doi.org/10.1016/j.jneumeth.2015.01.015 Bosl WJ, Tager-Flusberg H, Nelson CA. EEG Analytics for Early Detection of Autism Spectrum Disorder: A data-driven approach. Sci Rep. 2018;8(1):6828. https://doi.org/10.1038/s41598-018-24318-x Kramer AF. Physiological metrics of mental workload: A review of recent progress. Multiple-task performance. 1st ed. London: CRC Press; 2020:279-328. Huang J, Traylor Z, Choo S, Nam CS. Neural Correlates of Mental Workload During Multitasking: a Dynamic Causal Modeling Study. Proceedings of the human factors and ergonomics society annual meeting. 1st ed. Los Angeles: SAGE Publications; 2021. https://doi.org/10.1177/1071181321651003 Antonenko P, Paas F, Grabner R, van Gog T. Using Electroencephalography to Measure Cognitive Load. Educ Psychol Rev. 2010;22(4):425-38. https://doi.org/10.1007/s10648-010-9130-y Raufi B, Longo L. An Evaluation of the EEG Alpha-to-Theta and Theta-to-Alpha Band Ratios as Indexes of Mental Workload. Front Neuroinform 2022;16:861967. https://doi.org/10.3389/fninf.2022.861967 Anjum MF, Espinoza A, Cole R, Singh A, May P, Uc E, et al. Resting-state EEG measures cognitive impairment in Parkinson’s disease. npj Parkinson’s Disease 2023;10(1):6. https://doi.org/10.1038/s41531-023-00602-0 Abásolo D, Simons S, Morgado da Silva R, Tononi G, Vyazovskiy V V. Lempel-Ziv complexity of cortical activity during sleep and waking in rats. J Neurophysiol 2015;113(7):2742-52. https://doi.org/10.1152/jn.00575.2014 Meisel C, Bailey K, Achermann P, Plenz D. Decline of long-range temporal correlations in the human brain during sustained wakefulness. Sci Rep 2017;7(1):11825. https://doi.org/10.1038/s41598-017-12140-w Hou D, Wang C, Chen Y, Wang W, Du J. Long-range temporal correlations of broadband EEG oscillations for depressed subjects following different hemispheric cerebral infarction. Cogn Neurodyn 2017;11(6):529-38. https://doi.org/10.1007/s11571-017-9451-3 Zyma I, Tukaev S, Seleznov I, Kiyono K, Popov A, Chernykh M, et al. Electroencephalograms during mental arithmetic task performance. Data. 2019;4(1). https://doi.org/10.3390/data4010014 Goldberger AL, Peng CK, Lipsitz LA. What is physiologic complexity and how does it change with aging and disease? Neurobiology of aging 2002;23(1):23-26. https://doi.org/10.1016/S0197-4580(01)00266-4 Peng CK, Havlin S, Stanley HE, Goldberger AL. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos: An Interdisciplinary Journal of Nonlinear Science 1995;5(1):82-7. https://doi.org/10.1063/1.166141 Ozel HF, Kazdagli H. A simple approach to determine loss of physiological complexity in heart rate series. Biomed Phys Eng Express 2023;9(4). https://doi.org/10.1088/2057-1976/acd254 Jiang Z, Ning Y, An B, Li A, Feng H. Detecting Mental EEG Properties Using Detrended Fluctuation Analysis. 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2006. p. 2017-2020. https://doi.org/10.1109/IEMBS.2005.1616852 Knecht S, Deppe M, Dräger B, Bobe L, Lohmann H, Ringelstein EB, et al. Language lateralization in healthy right-handers. Brain 2000;123(1):74-81. https://doi.org/10.1093/brain/123.1.74 Pujol J, Deus J, Losilla JM, Capdevila A. Cerebral lateralization of language in normal left-handed people studied by functional MRI. Neurology. 1999;52(5):1038-43. doi: 10.1212/wnl.52.5.1038. Springer JA, Binder JR, Hammeke TA, Swanson SJ, Frost JA, Bellgowan PSF, et al. Language dominance in neurologically normal and epilepsy subjects: a functional MRI study. Brain 1999;122(11):2033-45. https://doi.org/10.1093/brain/122.11.2033 Li Z, Li J, Xia Y, Feng P, Feng F. Variation Trends of Fractal Dimension in Epileptic EEG Signals. Algorithms. 2021;14(11):316. https://doi.org/10.3390/a14110316 Liu J, Yang Q, Yao B, Brown RW, Yue GH. Linear Correlation Between Fractal Dimension of EEG Signal and Handgrip Force. Biol Cybern. 2005;93(2):131-40. https://doi.org/10.1007/s00422-005-0561-3 Rahatabad FN, Rangraz P, Dalir M, Nasrabadi AM. The Relation Between Chaotic Feature of Surface EEG and Muscle Force. J Med Signals Sens. 2021;11(4):229-36. https://doi.org/10.4103/jmss.JMSS_47_20 Shenhav A, Botvinick MM, Cohen JD. The expected value of control: An integrative theory of anterior cingulate cortex function. Neuron 2013;79(2):217. https://doi.org/10.1016/j.neuron.2013.07.007 Raghavendra BS, Dutt DN, Halahalli HN, John JP. Complexity analysis of EEG in patients with schizophrenia using fractal dimension. Physiol Meas. 2009;30(8):795-808. https://doi.org/10.1088/0967-3334/30/8/005 Zappasodi F, Olejarczyk E, Marzetti L, Assenza G, Pizzella V, Tecchio F. Fractal dimension of EEG activity senses neuronal impairment in acute stroke. PLoS One. 2014 Jun 26;9(6). https://doi.org/10.1371/journal.pone.0100199 Chen QF, Zhang XH, Zou TX, Huang NX, Chen HJ. Reduced Cortical Complexity in Cirrhotic Patients with Minimal Hepatic Encephalopathy. Neural Plast 2020;2020(1):7364649. https://doi.org/10.1155/2020/7364649 Al-Nuaimi AHH, Jammeh E, Sun L, Ifeachor E. Complexity Measures for Quantifying Changes in Electroencephalogram in Alzheimer's Disease. Complexity 2018;2018(1):8915079. https://doi.org/10.1155/2018/8915079 Uthayakumar R, Easwaramoorthy D. Epileptic seizure detection in EEG signals using multifractal analysis and wavelet transform. Fractals 2013;21(2):1350011. https://doi.org/10.1142/S0218348X13500114 Ruiz de Miras J, Derchi CC, Atzori T, Mazza A, Arcuri P, Salvatore A, et al. Spatio-Temporal Fractal Dimension Analysis from Resting State EEG Signals in Parkinson's Disease. Entropy. 2023;25(7). https://doi.org/10.3390/e25071017 Jenkins AC. Rethinking Cognitive Load: A Default-Mode Network Perspective. Trends Cogn Sci 2019;23(7):531-3. https://doi.org/10.1016/j.tics.2019.04.008 Crittenden BM, Mitchell DJ, Duncan J. Recruitment of the default mode network during a demanding act of executive control. Elife. 2015;4:e06481. https://doi.org/10.7554/elife.06481.