Skip to main content
SpringerLink
Log in

Cognitive function: holarchy or holacracy?

  • Review Article
  • Published:
Neurological Sciences Aims and scope Submit manuscript

Abstract

Cognition is the most complex function of the brain. When exploring the inner workings of cognitive processes, it is crucial to understand the complexity of the brain’s dynamics. This paper aims to describe the integrated framework of the cognitive function, seen as the result of organization and interactions between several systems and subsystems. We briefly describe several organizational concepts, spanning from the reductionist hierarchical approach, up to the more dynamic theory of open complex systems. The homeostatic regulation of the mechanisms responsible for cognitive processes is showcased as a dynamic interplay between several anticorrelated mechanisms, which can be found at every level of the brain’s organization, from molecular and cellular level to large-scale networks (e.g., excitation-inhibition, long-term plasticity-long-term depression, synchronization-desynchronization, segregation-integration, order-chaos). We support the hypothesis that cognitive function is the consequence of multiple network interactions, integrating intricate relationships between several systems, in addition to neural circuits.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Carrera E, Tononi G (2014) Diaschisis: past, present, future. Brain J Neurol 137(Pt 9):2408–2422

    Google Scholar 

  2. Agnati LF, Marcoli M, Maura G, Woods A, Guidolin D (2018) The brain as a “hyper-network”: the key role of neural networks as main producers of the integrated brain actions especially via the “broadcasted” neuroconnectomics. J Neural Transm Vienna Austria 1996 125(6):883–897

    Google Scholar 

  3. Neisser U (1967) Cognitive psychology, 1st edn. Prentice Hall, Englewood Cliffs, NJ 351 p

    Google Scholar 

  4. Trewavas A (2006) A brief history of systems biology. Plant Cell 18(10):2420–2430

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Cognitive science as complexity science - Favela (2020) WIREs Cognitive Science - Wiley Online Library [Internet] [cited 2020 Sep 28] Available from: https://onlinelibrarywileycom/doi/abs/101002/wcs1525

  6. Koestler A (1968) The ghost in the machine, First American edn. Macmillan, London 384 p

  7. Amazoncom (2020) Holacracy: The new management system for a rapidly changing world eBook: Robertson, Brian J: Kindle Store Available from: https://wwwamazoncom/Holacracy-Management-System-Rapidly-Changing-ebook/dp/B00PF6QM6K

  8. Holacracy (2020) In: Wikipedia [Internet] [cited 2020 Sep 28] Available from: https://enwikipediaorg/w/indexphp?title=Holacracy&oldid=966668492

  9. Salthe SN, Matsuno K (1995) Self-organization in hierarchical systems. J Soc Evol Syst 18(4):327–338

    Google Scholar 

  10. Busseniers E (2014) Hierarchical organization versus self-organization ArXiv14021670 Cs [Internet] [cited 2020 Sep 28]; Available from: http://arxivorg/abs/14021670

  11. Choi I, Lee J-Y, Lee S-H (2018) Bottom-up and top-down modulation of multisensory integration. Curr Opin Neurobiol 52:115–122

    CAS  PubMed  Google Scholar 

  12. Feltz B, Crommelinck M, Goujon P (eds) (2006) Self-organization and emergence in life sciences [Internet] Springer Netherlands [cited 2020 Sep 28] (Synthese Library) Available from: https://wwwspringercom/gp/book/9781402039164

  13. Muresanu DF, Buzoianu A, Florian SI, von Wild T (2012) Towards a roadmap in brain protection and recovery. J Cell Mol Med 16(12):2861–2871

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Guidolin D, Anderlini D, Marcoli M, Cortelli P, Calandra-Buonaura G, Woods AS et al (2019) A new integrative theory of brain-body-ecosystem medicine: from the hippocratic holistic view of medicine to our modern society. Int J Environ Res Public Health 16(17):28

    Google Scholar 

  15. Kamimura D, Tanaka Y, Hasebe R, Murakami M (2019) Bidirectional communication between neural and immune systems Int Immunol

  16. Williams CL, Garcia-Reyero N, Martyniuk CJ, Tubbs CW, Bisesi JH (2020) Regulation of endocrine systems by the microbiome: perspectives from comparative animal models. Gen Comp Endocrinol 292:113437

    CAS  PubMed  Google Scholar 

  17. Osman ME, Hannafin MJ (1992) Metacognition research and theory: analysis and implications for instructional design. Educ Technol Res Dev 40(2):83–99

    Google Scholar 

  18. Flavell JH (1979) Metacognition and cognitive monitoring: a new area of cognitive–developmental inquiry. Am Psychol 34(10):906–911

    Google Scholar 

  19. Bhome R, McWilliams A, Huntley JD, Fleming SM, Howard RJ (2019) Metacognition in functional cognitive disorder- a potential mechanism and treatment target. Cognit Neuropsychiatry 24(5):311–321

    Google Scholar 

  20. Smallwood J, Schooler JW (2015) The science of mind wandering: empirically navigating the stream of consciousness. Annu Rev Psychol 66:487–518

    PubMed  Google Scholar 

  21. Mooneyham BW, Schooler JW (2013) The costs and benefits of mind-wandering: a review. Can J Exp Psychol Rev Can Psychol Exp 67(1):11–18

    Google Scholar 

  22. Beggs JM, Timme N (2012) Being critical of criticality in the brain. Front Physiol 3:163

    PubMed  PubMed Central  Google Scholar 

  23. Cocchi L, Gollo LL, Zalesky A, Breakspear M (2017) Criticality in the brain: a synthesis of neurobiology, models and cognition. Prog Neurobiol 158:132–152

    PubMed  Google Scholar 

  24. Meunier CNJ, Chameau P, Fossier PM (2017) Modulation of synaptic plasticity in the cortex needs to understand all the players Front Synaptic Neurosci [Internet] [cited 2020 Sep 28];9 Available from: https://wwwncbinlmnihgov/pmc/articles/PMC5285384/

  25. Alcamí P, Pereda AE (2019) Beyond plasticity: the dynamic impact of electrical synapses on neural circuits. Nat Rev Neurosci 20(5):253–271

    PubMed  Google Scholar 

  26. Scholkmann F (2015) Two emerging topics regarding long-range physical signaling in neurosystems: membrane nanotubes and electromagnetic fields. J Integr Neurosci 14(2):135–153

    PubMed  Google Scholar 

  27. Bera BK, Rakshit S, Ghosh D, Kurths J (2019) Spike chimera states and firing regularities in neuronal hypernetworks. Chaos Woodbury N 29(5):053115

    Google Scholar 

  28. Majhi S, Bera BK, Ghosh D, Perc M (2019) Chimera states in neuronal networks: A review. Phys Life Rev 28:100–121

    PubMed  Google Scholar 

  29. Moosavi SA, Montakhab A, Valizadeh A (2018) Coexistence of scale-invariant and rhythmic behavior in self-organized criticality. Phys Rev E 98(2–1):022304

    CAS  PubMed  Google Scholar 

  30. Miller SR, Yu S, Plenz D (2019) The scale-invariant, temporal profile of neuronal avalanches in relation to cortical γ-oscillations. Sci Rep 9(1):16403

    PubMed  PubMed Central  Google Scholar 

  31. Perlovsky LI, Kozma R (2007) Neurodynamics of cognition and consciousness In: Perlovsky LI, Kozma R (eds) Neurodynamics of cognition and consciousness [Internet] Berlin, Heidelberg: Springer; 2007 [cited 2020 Sep 29] p 1–8 (Understanding Complex Systems) Available from: https://doi.org/10.1007/978-3-540-73267-9_1

  32. Kelso JAS (2012) Multistability and metastability: understanding dynamic coordination in the brain. Philos Trans R Soc Lond B Biol Sci 367(1591):906–918

    PubMed  PubMed Central  Google Scholar 

  33. Hagmann P, Cammoun L, Gigandet X, Gerhard S, Grant PE, Wedeen V et al (2010) MR connectomics: Principles and challenges. J Neurosci Methods 194(1):34–45

    PubMed  Google Scholar 

  34. Cabral J, Hugues E, Sporns O, Deco G (2011) Role of local network oscillations in resting-state functional connectivity. NeuroImage 57(1):130–139

    PubMed  Google Scholar 

  35. de Pasquale F, Della Penna S, Snyder AZ, Marzetti L, Pizzella V, Romani GL et al (2012) A cortical core for dynamic integration of functional networks in the resting human brain. Neuron 74(4):753–764

    PubMed  PubMed Central  Google Scholar 

  36. Schroeder CE, Lakatos P (2009) The gamma oscillation: master or slave? Brain Topogr 22(1):24–26

    PubMed  Google Scholar 

  37. Bullmore E, Sporns O (2012) The economy of brain network organization. Nat Rev Neurosci 13(5):336–349

    CAS  PubMed  Google Scholar 

  38. Hellyer PJ, Scott G, Shanahan M, Sharp DJ, Leech R (2015) Cognitive Flexibility through Metastable Neural Dynamics Is Disrupted by Damage to the Structural Connectome. J Neurosci Off J Soc Neurosci 35(24):9050–9063

    CAS  Google Scholar 

  39. van den Heuvel MP, Sporns O (2011) Rich-club organization of the human connectome. J Neurosci Off J Soc Neurosci 31(44):15775–15786

    Google Scholar 

  40. Sporns O (2018) Graph theory methods: applications in brain networks. Dialogues Clin Neurosci 20(2):111–121

    PubMed  PubMed Central  Google Scholar 

  41. de Pasquale F, Corbetta M, Betti V, Della Penna S (2017) Cortical cores in network dynamics NeuroImage

  42. Sporns O (2013) Structure and function of complex brain networks. Dialogues Clin Neurosci 15(3):247–262

    PubMed  PubMed Central  Google Scholar 

  43. Solé RV, Valverde S (2004) Information theory of complex networks: on evolution and architectural constraints In: Ben-Naim E, Frauenfelder H, Toroczkai Z, editors Complex networks [Internet] Berlin, Heidelberg: Springer [cited 2020 Sep 29] p 189–207 (Lecture Notes in Physics) Available from: https://doi.org/10.1007/978-3-540-44485-5_9

  44. Chen Y, Wang S, Hilgetag CC, Zhou C (2017) Features of spatial and functional segregation and integration of the primate connectome revealed by trade-off between wiring cost and efficiency. PLoS Comput Biol 13(9):e1005776

    PubMed  PubMed Central  Google Scholar 

  45. null SR, null JL, Taya F, deSouza J, Thakor NV, Bezerianos A (2017) Dynamic functional segregation and integration in human brain network during complex tasks. IEEE Trans Neural Syst Rehabil Eng Publ IEEE Eng Med Biol Soc 25(6):547–556

    Google Scholar 

  46. Douw L, Schoonheim MM, Landi D, van der Meer ML, Geurts JJG, Reijneveld JC et al (2011) Cognition is related to resting-state small-world network topology: an magnetoencephalographic study. Neuroscience 175:169–177

    CAS  PubMed  Google Scholar 

  47. Lin S-J, Baumeister TR, Garg S, McKeown MJ (2018) Cognitive profiles and hub vulnerability in parkinson’s disease. Front Neurol 9:482

    PubMed  PubMed Central  Google Scholar 

  48. Lee K, Khoo HM, Lina J-M, Dubeau F, Gotman J, Grova C (2018) Disruption, emergence and lateralization of brain network hubs in mesial temporal lobe epilepsy. NeuroImage Clin 20:71–84

    PubMed  PubMed Central  Google Scholar 

  49. Larivière S, Ward NS, Boudrias M-H (2018) Disrupted functional network integrity and flexibility after stroke: Relation to motor impairments. NeuroImage Clin 19:883–891

    PubMed  PubMed Central  Google Scholar 

  50. Han K, Chapman SB, Krawczyk DC (2016) Disrupted Intrinsic connectivity among default, dorsal attention, and frontoparietal control networks in individuals with chronic traumatic brain injury. J Int Neuropsychol Soc JINS 22(2):263–279

    PubMed  Google Scholar 

  51. Váša F, Shanahan M, Hellyer PJ, Scott G, Cabral J, Leech R (2015) Effects of lesions on synchrony and metastability in cortical networks. NeuroImage 118:456–467

    PubMed  Google Scholar 

  52. Popov T, Westner BU, Silton RL, Sass SM, Spielberg JM, Rockstroh B et al (2018) Time course of brain network reconfiguration supporting inhibitory control. J Neurosci Off J Soc Neurosci 38(18):4348–4356

    CAS  Google Scholar 

  53. Corbetta M, Siegel JS, Shulman GL (2018) On the low dimensionality of behavioral deficits and alterations of brain network connectivity after focal injury. Cortex J Devoted Study Nerv Syst Behav 107:229–237

    Google Scholar 

  54. Siegel JS, Ramsey LE, Snyder AZ, Metcalf NV, Chacko RV, Weinberger K et al (2016) Disruptions of network connectivity predict impairment in multiple behavioral domains after stroke. Proc Natl Acad Sci U S A 113(30):E4367–E4376

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Kabbara A, El Falou W, Khalil M, Wendling F, Hassan M (2017) The dynamic functional core network of the human brain at rest. Sci Rep 7(1):2936

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Chand GB, Wu J, Hajjar I, Qiu D (2017) Interactions of the salience network and its subsystems with the default-mode and the central-executive networks in normal aging and mild cognitive impairment. Brain Connect 7(7):401–412

    PubMed  PubMed Central  Google Scholar 

  57. Farrant K, Uddin LQ (2015) Asymmetric development of dorsal and ventral attention networks in the human brain. Dev Cogn Neurosci 12:165–174

    PubMed  PubMed Central  Google Scholar 

  58. Wadden KP, Woodward TS, Metzak PD, Lavigne KM, Lakhani B, Auriat AM et al (2015) Compensatory motor network connectivity is associated with motor sequence learning after subcortical stroke. Behav Brain Res 286:136–145

    PubMed  PubMed Central  Google Scholar 

  59. Jilka SR, Scott G, Ham T, Pickering A, Bonnelle V, Braga RM et al (2014) Damage to the salience network and interactions with the default mode network. J Neurosci Off J Soc Neurosci 34(33):10798–10807

    CAS  Google Scholar 

  60. Leech R, Kamourieh S, Beckmann CF, Sharp DJ (2011) Fractionating the default mode network: distinct contributions of the ventral and dorsal posterior cingulate cortex to cognitive control. J Neurosci Off J Soc Neurosci 31(9):3217–3224

    CAS  Google Scholar 

  61. Wisniewski D, Reverberi C, Momennejad I, Kahnt T, Haynes J-D (2015) The Role of the Parietal Cortex in the Representation of Task-Reward Associations. J Neurosci Off J Soc Neurosci 35(36):12355–12365

    CAS  Google Scholar 

  62. Silton RL, Heller W, Towers DN, Engels AS, Spielberg JM, Edgar JC et al (2010) The time course of activity in dorsolateral prefrontal cortex and anterior cingulate cortex during top-down attentional control. NeuroImage 50(3):1292–1302

    PubMed  Google Scholar 

  63. Pietrzak RH, Cohen H, Snyder PJ (2007) Spatial learning efficiency and error monitoring in normal aging: an investigation using a novel hidden maze learning test. Arch Clin Neuropsychol Off J Natl Acad Neuropsychol 22(2):235–245

    Google Scholar 

  64. Piccoli T, Valente G, Linden DEJ, Re M, Esposito F, Sack AT et al (2015) The default mode network and the working memory network are not anti-correlated during all phases of a working memory task. PLOS ONE 10(4):e0123354

    PubMed  PubMed Central  Google Scholar 

  65. de Pasquale F, Sabatini U, Della Penna S, Sestieri C, Caravasso CF, Formisano R et al (2013) The connectivity of functional cores reveals different degrees of segregation and integration in the brain at rest. NeuroImage 69:51–61

    PubMed  Google Scholar 

  66. Scalf PE, Ahn J, Beck DM, Lleras A (2014) Trial history effects in the ventral attentional network. J Cogn Neurosci 26(12):2789–2797

    PubMed  Google Scholar 

  67. de Pasquale F, Della Penna S, Sporns O, Romani GL, Corbetta M (2016) A dynamic core network and global efficiency in the resting human brain. Cereb Cortex N Y N 1991 26(10):4015–4033

    Google Scholar 

  68. Adhikari MH, Hacker CD, Siegel JS, Griffa A, Hagmann P, Deco G et al (2017) Decreased integration and information capacity in stroke measured by whole brain models of resting state activity. Brain J Neurol 140(4):1068–1085

    Google Scholar 

  69. Griffis JC, Metcalf NV, Corbetta M, Shulman GL (2020) Damage to the shortest structural paths between brain regions is associated with disruptions of resting-state functional connectivity after stroke. NeuroImage 210:116589

    PubMed  Google Scholar 

  70. Roberts JA, Gollo LL, Abeysuriya RG, Roberts G, Mitchell PB, Woolrich MW et al (2019) Metastable brain waves. Nat Commun 10(1):1056

    PubMed  PubMed Central  Google Scholar 

  71. Duch W (2019) Autism spectrum disorder and deep attractors in neurodynamics In: Cutsuridis V (ed) Multiscale models of brain disorders [Internet] Cham: Springer International Publishing; [cited 2020 Sep 29] p 135–46 (Springer Series in Cognitive and Neural Systems) Available from: https://doi.org/10.1007/978-3-030-18830-6_13

  72. He BJ, Zempel JM, Snyder AZ, Raichle ME (2010) The temporal structures and functional significance of scale-free brain activity. Neuron 66(3):353–369

    CAS  PubMed  PubMed Central  Google Scholar 

  73. He BJ (2014) Scale-free brain activity: past, present, and future. Trends Cogn Sci 18(9):480–487

    PubMed  PubMed Central  Google Scholar 

  74. Fox MD, Raichle ME (2007) Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci 8(9):700–711

    CAS  PubMed  Google Scholar 

  75. Herman P, Sanganahalli BG, Hyder F, Eke A (2011) Fractal analysis of spontaneous fluctuations of the BOLD signal in rat brain. NeuroImage 58(4):1060–1069

    PubMed  PubMed Central  Google Scholar 

  76. Liu Z, Fukunaga M, de Zwart JA, Duyn JH (2010) Large-scale spontaneous fluctuations and correlations in brain electrical activity observed with magnetoencephalography. NeuroImage 51(1):102–111

    PubMed  PubMed Central  Google Scholar 

  77. Murta T, Leite M, Carmichael DW, Figueiredo P, Lemieux L (2015) Electrophysiological correlates of the BOLD signal for EEG-informed fMRI. Hum Brain Mapp 36(1):391–414

    PubMed  Google Scholar 

  78. Keller CJ, Bickel S, Honey CJ, Groppe DM, Entz L, Craddock RC et al (2013) Neurophysiological investigation of spontaneous correlated and anticorrelated fluctuations of the BOLD signal. J Neurosci Off J Soc Neurosci 33(15):6333–6342

    CAS  Google Scholar 

  79. Pan W-J, Thompson G, Magnuson M, Majeed W, Jaeger D, Keilholz S (2011) Broadband local field potentials correlate with spontaneous fluctuations in functional magnetic resonance imaging signals in the rat somatosensory cortex under isoflurane anesthesia. Brain Connect 1(2):119–131

    PubMed  PubMed Central  Google Scholar 

  80. McDonough IM, Nashiro K (2014) Network complexity as a measure of information processing across resting-state networks: evidence from the Human Connectome Project. Front Hum Neurosci 8:409

    PubMed  PubMed Central  Google Scholar 

  81. Zappasodi F, Pasqualetti P, Rossini PM, Tecchio F (2019) Acute phase neuronal activity for the prognosis of stroke recovery. Neural Plast 2019:1971875

    PubMed  PubMed Central  Google Scholar 

  82. Al-Nuaimi AH, Jammeh E, Sun L, Ifeachor E (2017) Higuchi fractal dimension of the electroencephalogram as a biomarker for early detection of Alzheimer’s disease. Annu Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Annu Int Conf 2017:2320–2324

    Google Scholar 

  83. Al-Qazzaz NK, Ali SHBM, Ahmad SA, Islam MS, Escudero J (2018) Discrimination of stroke-related mild cognitive impairment and vascular dementia using EEG signal analysis. Med Biol Eng Comput 56(1):137–157

    PubMed  Google Scholar 

  84. Namazi H, Aghasian E, Ala TS (2019) Fractal-based classification of electroencephalography (EEG) signals in healthy adolescents and adolescents with symptoms of schizophrenia. Technol Health Care Off J Eur Soc Eng Med 27(3):233–241

    Google Scholar 

  85. Čukić M, Stokić M, Radenković S, Ljubisavljević M, Simić S, Savić D (2020) Nonlinear analysis of EEG complexity in episode and remission phase of recurrent depression. Int J Methods Psychiatr Res 29(2):e1816

    PubMed  Google Scholar 

  86. Namazi H, Aghasian E, Ala TS (2020) Complexity-based classification of EEG signal in normal subjects and patients with epilepsy. Technol Health Care Off J Eur Soc Eng Med 28(1):57–66

    Google Scholar 

  87. Saenger VM, Ponce-Alvarez A, Adhikari M, Hagmann P, Deco G, Corbetta M (2018) Linking entropy at rest with the underlying structural connectivity in the healthy and lesioned brain. Cereb Cortex N Y N 1991 28(8):2948–2958

    Google Scholar 

  88. Grieder M, Wang DJJ, Dierks T, Wahlund L-O, Jann K (2018) Default mode network complexity and cognitive decline in mild Alzheimer’s disease. Front Neurosci 12:770

    PubMed  PubMed Central  Google Scholar 

  89. Allen EA, Damaraju E, Plis SM, Erhardt EB, Eichele T, Calhoun VD (2014) Tracking whole-brain connectivity dynamics in the resting state. Cereb Cortex N Y N 1991 24(3):663–676

    Google Scholar 

  90. Wang DJJ, Jann K, Fan C, Qiao Y, Zang Y-F, Lu H et al (2018) Neurophysiological basis of multi-scale entropy of brain complexity and its relationship with functional connectivity. Front Neurosci 12:352

    PubMed  PubMed Central  Google Scholar 

  91. Liu M, Song C, Liang Y, Knöpfel T, Zhou C (2019) Assessing spatiotemporal variability of brain spontaneous activity by multiscale entropy and functional connectivity. NeuroImage 198:198–220

    PubMed  Google Scholar 

  92. Richiardi J, Altmann A, Milazzo A-C, Chang C, Chakravarty MM, Banaschewski T et al (2015) BRAIN NETWORKS Correlated gene expression supports synchronous activity in brain networks. Science 348(6240):1241–1244

    CAS  PubMed  PubMed Central  Google Scholar 

  93. Waldron D (2015) Human genetics: gene expression and functional brain networks [Internet] Nat Rev Genet [cited 2018 Aug 6] Available from: https://wwwnaturecom/articles/nrg3986

  94. Chatterjee P, Roy D, Bhattacharyya M, Bandyopadhyay S (2017) Biological networks in Parkinson’s disease: an insight into the epigenetic mechanisms associated with this disease. BMC Genomics 18(1):721

    PubMed  PubMed Central  Google Scholar 

  95. Pérez-Palma E, Bustos BI, Villamán CF, Alarcón MA, Avila ME, Ugarte GD et al (2014) Overrepresentation of glutamate signaling in Alzheimer’s disease: network-based pathway enrichment using meta-analysis of genome-wide association studies. PloS One 9(4):e95413

    PubMed  PubMed Central  Google Scholar 

  96. Pita-Juárez Y, Altschuler G, Kariotis S, Wei W, Koler K, Green C et al (2018) The Pathway Coexpression Network: Revealing pathway relationships. PLoS Comput Biol 14(3):e1006042

    PubMed  PubMed Central  Google Scholar 

  97. Wildenhain J, Crampin EJ (2006) Reconstructing gene regulatory networks: from random to scale-free connectivity. Syst Biol 153(4):247–256

    CAS  Google Scholar 

  98. Fu D, Tan P, Kuznetsov A, Molkov YI (2014) Chaos and robustness in a single family of genetic oscillatory networks. PloS One 9(3):e90666

    PubMed  PubMed Central  Google Scholar 

  99. Nido GS, Ryan MM, Benuskova L, Williams JM (2015) Dynamical properties of gene regulatory networks involved in long-term potentiation. Front Mol Neurosci 8:42

    PubMed  PubMed Central  Google Scholar 

  100. Hu Y, Chen X, Gu H, Yang Y (2013) Resting-state glutamate and GABA concentrations predict task-induced deactivation in the default mode network. J Neurosci Off J Soc Neurosci 33(47):18566–18573

    CAS  Google Scholar 

  101. Dharmadhikari S, Ma R, Yeh C-L, Stock A-K, Snyder S, Zauber SE et al (2015) Striatal and thalamic GABA level concentrations play differential roles for the modulation of response selection processes by proprioceptive information. NeuroImage 120:36–42

    CAS  PubMed  PubMed Central  Google Scholar 

  102. Haag L, Quetscher C, Dharmadhikari S, Dydak U, Schmidt-Wilcke T, Beste C (2015) Interrelation of resting state functional connectivity, striatal GABA levels, and cognitive control processes. Hum Brain Mapp 36(11):4383–4393

    PubMed  PubMed Central  Google Scholar 

  103. Gulyás AI, Szabó GG, Ulbert I, Holderith N, Monyer H, Erdélyi F et al (2010) Parvalbumin-containing fast-spiking basket cells generate the field potential oscillations induced by cholinergic receptor activation in the hippocampus. J Neurosci Off J Soc Neurosci 30(45):15134–15145

    Google Scholar 

  104. Kann O (2016) The interneuron energy hypothesis: Implications for brain disease. Neurobiol Dis 90:75–85

    CAS  PubMed  Google Scholar 

  105. López ME, Garcés P, Cuesta P, Castellanos NP, Aurtenetxe S, Bajo R et al (2014) Synchronization during an internally directed cognitive state in healthy aging and mild cognitive impairment: a MEG study. Age Dordr Neth 36(3):9643

    Google Scholar 

  106. Kapogiannis D, Reiter DA, Willette AA, Mattson MP (2013) Posteromedial cortex glutamate and GABA predict intrinsic functional connectivity of the default mode network. NeuroImage 64:112–119

  107. Deleglise B, Lassus B, Soubeyre V, Doulazmi M, Brugg B, Vanhoutte P et al (2018) Dysregulated Neurotransmission induces Trans-synaptic degeneration in reconstructed Neuronal Networks. Sci Rep 8(1):11596

    PubMed  PubMed Central  Google Scholar 

  108. Martorell AJ, Paulson AL, Suk H-J, Abdurrob F, Drummond GT, Guan W et al (2019) Multi-sensory gamma stimulation ameliorates Alzheimer’s-associated pathology and improves cognition. Cell 177(2):256–271e22

    CAS  PubMed  PubMed Central  Google Scholar 

  109. Ikeda T, Kobayashi S, Morimoto C (2019) Effects of repetitive transcranial magnetic stimulation on ER stress-related genes and glutamate, γ-aminobutyric acid and glycine transporter genes in mouse brain. Biochem Biophys Rep 17:10–16

    PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Neurosciences, “Iuliu Hatieganu” University of Medicine and Pharmacy, No. 37 Mircea Eliade Street, 400486, Cluj-Napoca, Romania

    Codruta Birle, Dana Slavoaca, Maria Balea, Livia Livint Popa, Ioana Muresanu, Vitalie Vacaras, Stefan Strilciuc & Dafin F. Muresanu

  2. “RoNeuro” Institute for Neurological Research and Diagnostic, Cluj-Napoca, Romania

    Codruta Birle, Dana Slavoaca, Maria Balea, Livia Livint Popa, Ioana Muresanu, Emanuel Stefanescu, Vitalie Vacaras, Stefan Strilciuc & Dafin F. Muresanu

  3. Department of Clinical Neurosciences, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania

    Constantin Dina

  4. Department of Radiology, Faculty of Medicine, “Ovidius” University, Constanta, Romania

    Bogdan Ovidiu Popescu

Authors
  1. Codruta Birle
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dana Slavoaca
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maria Balea
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Livia Livint Popa
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ioana Muresanu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Emanuel Stefanescu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Vitalie Vacaras
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Constantin Dina
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Stefan Strilciuc
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Bogdan Ovidiu Popescu
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Dafin F. Muresanu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

CB and DS conceived the idea of the manuscript. CB, DS, and MB were involved in writing the manuscript. DS, LPL, and ES performed the literature search. DFM and SS critically revised the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Dana Slavoaca.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies involving human participants performed by any of the authors.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Birle, C., Slavoaca, D., Balea, M. et al. Cognitive function: holarchy or holacracy?. Neurol Sci 42, 89–99 (2021). https://doi.org/10.1007/s10072-020-04737-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10072-020-04737-3

Keywords

Search

Navigation