Skip to main content
SpringerLink
Log in

Pathologies in functional connectivity, feedback control and robustness: a global workspace perspective on autism spectrum disorders

  • Review
  • Published:
Cognitive Processing Aims and scope Submit manuscript

Abstract

We study the background to problems of functional connectivity in autism spectrum disorders within the neurocognitive framework of the global workspace model. This we proceed to do by observing network irregularities detracting from that of a well-formed small world network architecture. This is discussed in terms of pathologies in functional connectivity and lack of central coherence disrupting inter-network communication thus impairing effective cognitive action. A typical coherence-connectivity measure as a by-product of various neuroimaging results is considered. This is related to a model of feedback control in which a coherence function in the frequency domain is modified by an environmentally determined interaction parameter. With respect to the latter, we discuss the stability question that in theory may counterbalance inessential metabolic costs and incoherence of processing. We suggest that factors such as local overconnectivity and global underconnectivity, along with acute over-expenditure of metabolic costs give rise to instability within the connective core of the workspace.

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
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. Recent findings Edelman et al. (2011) reveal that a mechanism for GW can be provided by the ‘Dynamic Core’ hypothesis in terms of re-entrantly projecting neural signals throughout the brain cortex, such that a mental image, for instance, instantaneously activates a large number of cortical regions. Likewise, the spatio-temporal features of intra-cortical phase transitions is homologous to that of the broadcasting process in the GW (Freeman et al. 2012; Shanahan 2010).

  2. Mathematical details pertaining to how giant components form in a semi-random graph can be found in Erdős and Rényi (1960), Watts and Strogatz (1998). Here they can be reasonably thought of as the network representations of the dominant coalitions that enter into the connective core while overcoming their competitors.

  3. The criteria for defining functions such as \(\mathrm{Coh}(S,f)\) in relationship to discrete time series methods providing statistical measures of causality, is surveyed in Sakkalis (2011).

  4. ‘Adiabatic’ means that the changes are slow enough to allow the necessary limit theorems to function. ‘Stationary’ means that, between pieces the probabilities hardly change, and ‘piecewise’ means that these properties hold between phase transitions which are described using renormalization methods (Wallace 2005a). ‘Ergodic’ means that in the long term, correlated sequences of symbols are generated at an average rate equal to their joint probabilities. Consequently, the Shannon entropy then becomes the long-term average of the ‘surprise’ element.

  5. We recall the striking duality, as proposed by Shannon (1959) between the properties of an information source with distortion measure, and those of a communication channel. This duality is further enhanced if channels can be assigned ‘message’ costs, so that the problem becomes one of finding a source that is suited to the channel at a tolerable level. This is the essence of the ‘tuning’ version of the Shannon Coding Theorem (see Wallace 2005a) which professes the channel as formally ‘transmitted’ by the signal. A dual channel capacity is thus definable in terms of a channel probability distribution that maximizes information transmission, once given a fixed message probability distribution.

  6. Heuristically, the estimates for stability can be taken as those derived from analysis of the Bode integral formula that further determines orders of ‘fragility’ as they are relevant for robustness (Åström and Murray 2008; Csete and Doyle 2002).

  7. The neurobiological explanation for such disproportionality mainly points toward increased glutamatergic signaling (excitation), on the one hand, and reduction in GABAergic signaling (inhibition) on the other (Rubenstein and Merzenich 2003).

References

  • Achard S, Salvador R, Whitcher B, Suckling J, Bullmore E (2006) A resilient, low-frequency, small-world, human brain functional network with highly connected association cortical hubs. J Neurosci 26(1):63–72

    Article  CAS  PubMed  Google Scholar 

  • Åström KJ, Murray RM (2008) Feedback control. Princeton University Press, Princeton, NJ

    Google Scholar 

  • Atlan H, Cohen I (1998) Immune information, self-organization and meaning. Int Immunol 10:711–717

    Article  CAS  PubMed  Google Scholar 

  • Axmacher N et al (2010) Cross-frequency coupling supports multi-item working memory in the human hippocampus. Proc Natl Acad Sci USA 107:3228–3233

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Baars B (1998) A cognitive theory of consciousness. Cambridge University Press, New York

    Google Scholar 

  • Baars B, Franklin S (2003) How conscious experience and working memory interact. Trends Cogn Sci 73:166–172

    Article  Google Scholar 

  • Baars B, Franklin S, Ramsoy TZ (2013) Global workspace dynamics: cortical ‘binding and propagation’ enables conscious contents. Front Psychol 4 (Article 200)

  • Baron-Cohen S, Belmonte MK (2005) Autism: a window onto the development of the social and the analytic brain. Annu Rev Neurosci 28:109–126

    Article  CAS  PubMed  Google Scholar 

  • Barrat A, Barthélemy M, Vespignani A (2008) Dynamical processes on complex networks. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Bassett DS, Bullmore E (2006) Small world brain networks. Neurosci 12(6):512–523

    Google Scholar 

  • Beggs JM (2008) The criticality hypothesis: how local cortical networks might optimize information processing. Phil Trans R Soc A 366:329–343

    Article  PubMed  Google Scholar 

  • Belmonte MK, Baron-Cohen S (2004) Small-world network properties and the emergence of social cognition: evidence from functional studies of autism. In 3rd international conference on development and learning: developing social brains, San Diego, CA

  • Belmonte MK, Allen G, Beckel-Mitchener A, Boulanger LM, Carper RA, Web SJ (2004) Autism and abnormal development of brain connectivity. J Neurosci 24(42):9228–9231

    Article  CAS  PubMed  Google Scholar 

  • Belmonte MK, Bourgeron T (2006) Fragile X syndrome and autism at the intersection of genetic and neural networks. Nat Neurosci 9(10):1221–1225

    Article  CAS  PubMed  Google Scholar 

  • Bennett C (1988) Logical depth and physical complexity. In: Herkin R (ed) The universal turing machine: a half-century survey. Oxford University Press, Oxford, pp 227–257

    Google Scholar 

  • Bracewell RN (2000) The Fourier transform and its applications. McGraw Hill, Boston, MA

    Google Scholar 

  • Casanova MF (2007) The neurobiology of autism. Brain Pathol 17:422–423

    Article  PubMed  Google Scholar 

  • Chauhan A, Chauhan V (2006) Oxidative stress in autism. Pathopysiology 13:171–181

    Article  CAS  Google Scholar 

  • Clark A (1997) Being there: putting brain, body and world together again. MIT Press, Cambridge, MA

    Google Scholar 

  • Cohen I (2000) Tending Adam’s garden: evolving the cognitive immune self. Academic Press, New York

    Google Scholar 

  • Courchesne E, Pierce K (2005) Why the frontal cortex in autism might be talking only to itself: local over-connectivity but long distance disconnection. Curr Opin Neurobiol 15:225–230

    Article  CAS  PubMed  Google Scholar 

  • Courchesne E et al (2007) Mapping early brain development in autism. Neuron 56:1–15

    Article  Google Scholar 

  • Cover T, Thomas J (1991) Elements of information theory. Wiley, New York

    Book  Google Scholar 

  • Csete M, Doyle JC (2002) Reverse engineering of biological complexity. Science 295:1664–1669

    Article  CAS  PubMed  Google Scholar 

  • David O, Cosmelli D, Friston KJ (2004) Evaluation of different measures of functional connectivity using a neural mass model. NeuroImage 21:659–673

    Article  PubMed  Google Scholar 

  • Dehaene S, Naccache L (2001) Towards a cognitive neuroscience of consciousness: basic evidence and a workspace framework. Cognition 79:1–37

    Article  CAS  PubMed  Google Scholar 

  • Dehaene S, Changeux JP (2005) Ongoing spontaneous activity controls access to consciousness: a neuronal model for inattentional blindness. PLoS Biol 3(5):e141

    Article  PubMed Central  PubMed  Google Scholar 

  • Dehaene S (2009) Conscious and nonconscious processes: distinct forms of evidence accumulation. Sémin Poincaré XII:89–114

    Google Scholar 

  • Delbeuck X, Van der Linden M, Collette F (2003) Alzheimer’s disease as a disconnection syndrome? Neuropsychol Rev 13(2):79–92

    Article  CAS  PubMed  Google Scholar 

  • Dinstein I, Heeger DJ, Loenzi L, Minshew NJ, Malach R, Behrmann M (2012) Unreliable evoked responses in autism. Neuron 71:981–991

    Article  Google Scholar 

  • Dominguez LG, Veázquez J-LP, Gálan RF (2013) A model of functional brain connectivity and background noise as a biomarker for cognitive phenotypes:application to autism. PLoS ONE 8(4):e61493

    Article  PubMed Central  PubMed  Google Scholar 

  • Edelman GM, Gally JA, Baars BJ (2011) Biology of consciousness. Front Psychol 73(1):43–52. doi:10.3389/fpsyg.2011.00004

    Google Scholar 

  • Elia N (2004) When Bode meets Shannon: control-oriented feedback communication schemes. IEEE Trans Autom Control 49(9):1477–1488

    Article  Google Scholar 

  • English T (1996) Evaluation of evolutionary and genetic optimizers: no free lunch. In: Fogel L, Angeline P, Back T (eds) Evolutionary programming V: proceedings of the fifth annual conference on evolutionary programming. MIT Press, Cambridge, MA, pp 163–169

    Google Scholar 

  • Erdős P, Rényi A (1960) On the evolution of random graphs. Publ Math Inst Hung Acad Sci 5:17–61

    Google Scholar 

  • Feynman R (1996) Feynman lectures on computation. Addison-Wesley, Reading, MA

    Google Scholar 

  • Freeman WJ, Ahlfors SP, Menon V (2009) Combining fMRI with EEG and MEG in order to characterise mesoscopic patterns of brain activity related to cognition. Int J Psychophysiol 73(1):43–52 (Lorig TS ed)

    Article  PubMed Central  PubMed  Google Scholar 

  • Freeman WJ, Kozma R, Vitiello G, (2012) Adaptation of the generalized Carnot cycle to describe thermodynamics of cerebral cortex. In: 2012 IEEE world congress on computational intelligence, Australia, pp 3229–3236. IEEE Press, Brisbane

  • Fries P (2005) A mechanism for cognitive dynamics:neuronal communication through neuronal coherence. Trends Cogn Sci 9:474–480

    Article  PubMed  Google Scholar 

  • Fries P (2009) Neuronal gamma-band synchronization as a fundamental process in cortical computation. Annu Rev Neurosci 32:209–224

    Article  CAS  PubMed  Google Scholar 

  • Friston K (2010) The free-energy principle: a unified brain theory? Nat Rev Neurosci 11(2):127–138

    Article  CAS  PubMed  Google Scholar 

  • Friston K (2011) Functional and effective connectivity: a review. Brain Connect 1(1):13–36

    Article  PubMed  Google Scholar 

  • Friston K (2012) Self-organisation, inference and cognition: comment on “Consciousness, crosstalk, and the mereological fallacy: an evolutionary perspective” by Rodrick Wallace. Phys Life Rev 9(4):456–457

    Article  PubMed  Google Scholar 

  • Frith U (1989) Autism: explaining the enigma. Blackwell, Oxford, UK

    Google Scholar 

  • Frith U, Happe F (1994) Autism: beyond ‘theory of mind’. Cognition 50:115–132

    Article  CAS  PubMed  Google Scholar 

  • Frye RE, DeLaTorre R, Taylor H, Slattery J, Melnyk S, Chowdhury N, James SJ (2013) Redox metabolism abnormalities in autistic children associated with mitochondrial disease. Transl Psychiatry 3:e273. doi:10.1038/tp.2013.51

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Gaillard R, Dehaene S, Adam C et al (2009) Converging intracranial markers of conscious access. PLS Biol 7(3):0472–0474

    CAS  Google Scholar 

  • Glazebrook JF, Wallace R (2009a) Small worlds and red queens in the global workspace: an information-theoretic approach. Cogn Syst Res 10:333–365

    Article  Google Scholar 

  • Glazebrook JF, Wallace R (2009b) Rate distortion manifolds as model spaces for cognitive information. Informatica 33:309–345

    Google Scholar 

  • Grandin T (1992) An inside view of autism. In: Schopler E, Mesibov MB (eds) High functioning individuals with autism. Plenum, New York

    Google Scholar 

  • Hagmann P, Kurant M, Gigandet X, Thiran P, Wedeen VJ, Meuli R, Thiran J-Ph (2007) Mapping human whole-brain structural networks with diffusion MRI. PLos ONE Issue 7:e597

    Article  Google Scholar 

  • Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ, Sporns O (2008) Mapping the structural core of the human cerebral cortex. PLoS Biol 6(7):e159

    Article  PubMed Central  PubMed  Google Scholar 

  • Huber K (2007) Fragile X syndrome: molecular mechanisms of cognitive dysfunction. Am J Psychiatry 164(4):556

    Article  PubMed  Google Scholar 

  • Itturia-Medina Y, Sotero RC, Canales-Rodríguez EJ, Alemán-Gómez Y, Melie-García L (2008) Studying the human brain anatomical network via diffusion-weighted MRI and graph theory. Neuroimage 40:1064–1076

    Article  Google Scholar 

  • Just M, Keller T, Malave V, Kana R, Varma S (2012) Autism as a neural disorder: a theory of frontal-posterior underconnectivity. Neurosci Biobehav Rev 36:1292–1313

    Article  PubMed Central  PubMed  Google Scholar 

  • Kitano H (2004) Biological robustness. Nat Rev Genet 5(11):826–837

    Article  CAS  PubMed  Google Scholar 

  • Kozma R, Puljic M, Balister P, Bollobas B, Freeman W (2004) Neuropercolation: a random cellular automata approach to spatio-temporal neurodynamics. Lecture notes in computer science, vol 3305, pp 435–443. Springer, New York

  • Latora V, Marchiori M (2001) Effcient behavior of small-world networks. Phys Rev Lett 87:198701

    Article  CAS  PubMed  Google Scholar 

  • Lennie P (2003) The cost of cortical computation. Curr Biol 13:493–497

    Article  CAS  PubMed  Google Scholar 

  • Maia TV, Cleeremans A (2005) Consciousness: converging insights from connectionist modeling and neuroscience. Trends Cogn Sci 9(8):397–404

    Article  PubMed  Google Scholar 

  • Marco EJ, Hinkley LBN, Hill SS, Nagarajan SS (2011) Sensory processing in autism: a review of neurophysiologic findings. Pediatr Res 69(5):48R

    Article  PubMed Central  PubMed  Google Scholar 

  • Martins NC, Dahleh MA (2008) Feedback control in the presence of noisy channels: “Bode-like” fundamental limitations of performance. IEEE Trans Autom Control 53(7):1604–1615

    Article  Google Scholar 

  • Minshew NJ, Williams DL (2007) The new biology of autism. Arch Neurol 64(10):945–950

    Article  PubMed Central  PubMed  Google Scholar 

  • Müller R-A, Shih P, Keehn B, Deyoe JR, Leyden KM, Shukla DK (2011) Underconnected, but how? A survey of functional connectivity MRI studies in autism spectrum disorders. Cereb Cortex 21:2233–2243

    Article  PubMed Central  PubMed  Google Scholar 

  • Nair G, Fagnani F, Zampieri S, Evans R (2007) Feedback control under data rate constraints: an overview. Proc IEEE 95:108–137

    Article  Google Scholar 

  • Peters JM, Taquet M, Vega C et al (2013) Brain functional networks in syndromic and non-syndromic autism: a graph theoretical study of EEG connectivity. BMC Med 11:54. doi:10.1186/1741-17015-11-54

    Article  PubMed Central  PubMed  Google Scholar 

  • Rippon G, Brock J, Brown C, Boucher J (2007) Disordered connectivity in the autistic brain: challenges for the “new psychophysiology”. Int J Psychophysiol 63:164–172

    Article  PubMed  Google Scholar 

  • Rubenstein JLR, Merzenich MM (2003) Model of autism: increased ratio of excitation/inhibition in key neural systems. Genes Brain Behav 2:255–267

    Article  CAS  PubMed  Google Scholar 

  • Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52:1059–1069

    Article  PubMed  Google Scholar 

  • Sakkalis V (2011) Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG. Comput Biol Med 41:1110–1117

    Article  CAS  PubMed  Google Scholar 

  • Segel LA, Bar-Or RL (1999) On the role of feedback in promoting conflicting goals of the adaptive immune system. J Immunol 163:1342–1349

    CAS  PubMed  Google Scholar 

  • Sengupta B, Stemmler MB, Friston KJ (2013) Information and efficiency in the nervous system: a synthesis. PLoS Comput Biol 9(7):e1003157. doi:10.1371/journal.pcbi.1003157

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Sengupta B, Stemmler MB (2014) Power consumption during neuronal computation. Proc IEEE 102(5):738–750. doi:10.1109/JPROC.2014.2307755

    Article  Google Scholar 

  • Shanahan M (2010) Embodiment and the inner life. Oxford University Press, Oxford

    Google Scholar 

  • Shanahan M (2012) The brain’s connective core and its role in animal cognition. Phil Trans R Soc B 367:2704–2714

    Article  PubMed Central  PubMed  Google Scholar 

  • Shannon C (1959) Coding theorems for a discrete source with a fidelity criterion. Inst Radio Eng Int Conv Rec 7:142–163

    Google Scholar 

  • Shin DK, Cho K-H (2013) Recurrent connections form a phase-locking neuronal tuner for frequency dependent selective communication. Sci Rep 3:2519. doi:10.1038/srep02519

    PubMed Central  PubMed  Google Scholar 

  • Sporns O, Tononi G, Edelman GM (2002) Theoretical neuroanatomy and the connectivity of the cerebral cortex. Behav Brain Res 135:69–74

    Article  CAS  PubMed  Google Scholar 

  • Sporns O, Honey CJ (2006) Small worlds inside big brains. Proc Natl Acad Sci USA 103(51):19219–19220

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Stam CJ, Jones BF, Nolte G, Breakspear M, Scheltens Ph (2007) Small world networks and functional connectivity in Alzheimer’s disease. Cereb Cortex 17:92–99

    Article  CAS  PubMed  Google Scholar 

  • Tyszka JM, Kennedy DP, Paul LK, Adolphs R (2014) Largely typical patterns of resting-state functional connectivity in high-functioning adults with autism. Cereb Cortex 24(7):1894–1905

    Article  PubMed  Google Scholar 

  • Varela F, Thompson E, Rosch E (1991) The embodied mind: cognitive science and human experience. MIT Press, Cambridge, MA

    Google Scholar 

  • Wallace R (2005a) Consciousness: a mathematical treatment of the global neuronal workspace model. Springer, New York

    Google Scholar 

  • Wallace R (2005b) A global workspace perspective on mental disorders. Theor Biol Med Model 2:49

    Article  PubMed Central  PubMed  Google Scholar 

  • Wallace R (2012) Consciousness, crosstalk, and the mereological fallacy: an evolutionary perspective. Phys Life Rev 9:426–453

    Article  PubMed  Google Scholar 

  • Wallace R (2014) Metabolic free energy and biological codes: a ‘Data Rate Theorem’ aging model. Bull Math Biol. doi:10.1007/s11538-014-0013-0

  • Wallace R, Fullilove M (2008) Collective consciousness and its discontents: institutional distributed cognition, racial policy, and public health in the United States. Springer, New York

    Book  Google Scholar 

  • Wallace R, Wallace D (2013) A mathematical approach to multilevel, multiscale health interventions: pharmaceutical industry decline and policy response. Imperial College Press, London

    Book  Google Scholar 

  • Wallace R, Glazebrook JF (2013) Statistical models for morphogenesis: crosstalk, diffusion, and the regulation of developmental topology (submitted)

  • Watts DJ, Strogatz SH (1998) Collective dynamics of “small world” networks. Nature 393:440–442

    Article  CAS  PubMed  Google Scholar 

  • Welchew D, Ashwin C, Berkouk K, Salvador R, Suckling J, Baron-Cohen S, Bullmore E (2005) Functional disconnectivity of the medial temporal lobe in Asperger’s syndrome. Biol Psychiatry 57:991–998

    Article  PubMed  Google Scholar 

  • Yeung RW (2008) Information theory and network coding. Springer, New York

    Google Scholar 

  • Zecavati N, Spence SJ (2009) Neurometabolic disorders and dysfunction in autism spectrum disorders. Curr Neurol Neurosci Rep 9:129–136

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

We wish to thank the reviewers and the Action Editor for their constructive criticism and suggestions. Our gratitude is extended to Ryan Boske-Cox for some production assistance.

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, Eastern Illinois University, 600 Lincoln Avenue, Charleston, IL, 61920-3099, USA

    James F. Glazebrook

  2. Division of Epidemiology, The New York State Psychiatric Institute, Box 47, 1051 Riverside Drive, New York, NY, 10032, USA

    Rodrick Wallace

Authors
  1. James F. Glazebrook
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rodrick Wallace
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to James F. Glazebrook.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Glazebrook, J.F., Wallace, R. Pathologies in functional connectivity, feedback control and robustness: a global workspace perspective on autism spectrum disorders. Cogn Process 16, 1–16 (2015). https://doi.org/10.1007/s10339-014-0636-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10339-014-0636-y

Keywords

Search

Navigation