Pharmacopsychiatry 2012; 45(S 01): S57-S64
DOI: 10.1055/s-0032-1309001
Original Paper
© Georg Thieme Verlag KG Stuttgart · New York

Functional Graph Alterations in Schizophrenia: A Result from a Global Anatomic Decoupling?

J. Cabral
1   Theoretical and Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
2   Department of Psychiatry, University of Oxford, Oxford, U.K.
,
M. L. Kringelbach
2   Department of Psychiatry, University of Oxford, Oxford, U.K.
3   Center of Functionally Integrative Neuroscience (CFIN), Aarhus University, Aarhus, Denmark
,
G. Deco
1   Theoretical and Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
4   Institut Català de Recerca i Estudis Avançats, Barcelona, Spain
› Author Affiliations
Further Information

Publication History

Publication Date:
07 May 2012 (online)

Abstract

Introduction:

During rest, the brain exhibits slow hemodynamic fluctuations (<0.1 Hz) that are correlated across spatially segregated brain regions, defining functional networks. Resting-state functional networks of people with schizophrenia were found to have graph properties that differ from those of control subjects. Namely, functional graphs from patients exhibit reduced small-worldness, increased hierarchy, lower clustering, improved efficiency and greater robustness. Notably, most of these parameters correlate with patients’ cognitive performance.

Methods:

To test if a brain-wide coupling deficit could be at the origin of such network reorganization, we use a model of resting-state activity where the coupling strength can be manipulated. For a range of coupling values, the simulated functional graphs obtained were characterized using graph theory.

Results:

For a coupling range, simulated graphs shared properties of healthy resting-state functional graphs. On decreasing the coupling strength, the resultant functional graphs exhibited a topological reorganization, in the same way as described in schizophrenia.

Discussion:

This work shows how complex functional graph alterations reported in schizophrenia can be accounted for by a decrease in the structural coupling strength. These results are corroborated by reports of lower white matter density in schizophrenia.

 
  • References

  • 1 Friston KJ. Theoretical neurobiology and schizophrenia. Br Med Bull 1996; 52: 644-655
  • 2 Friston KJ, Frith CD. Schizophrenia: a disconnection syndrome?. Clin Neurosci 1995; 3: 89-97
  • 3 Bluhm RL, Miller J, Lanius RA et al. Spontaneous low-frequency fluctuations in the BOLD signal in schizophrenic patients: anomalies in the default network. Schizophr Bull 2007; 33: 1004-1012
  • 4 Liang M, Zhou Y, Jiang T et al. Widespread functional disconnectivity in schizophrenia with resting-state functional magnetic resonance imaging. Neuroreport 2006; 17: 209-213
  • 5 Winterer G, Ziller M, Dorn H et al. Schizophrenia: reduced signal-to-noise ratio and impaired phase-locking during information processing. Clin Neurophysiol 2000; 111: 837-849
  • 6 Friston KJ. Schizophrenia and the disconnection hypothesis. Acta Psychiatr Scand Suppl 1999; 395: 68-79
  • 7 Winterer G, Weinberger DR. Genes, dopamine and cortical signal-to-noise ratio in schizophrenia. Trends Neurosci 2004; 27: 683-690
  • 8 Winterer G. Cortical microcircuits in schizophrenia  −  The dopamine hypothesis revisited. Pharmacopsychiatry 2006; 39: S68-S71
  • 9 Winterer G. Why do patients with schizophrenia smoke?. Curr Opin Psychiatr 2010; 23: 112-119
  • 10 Mobascher A, Warbrick T, Brinkmeyer J et al. Nicotine effects on attention in schizophrenia: a simultaneous EEG-fMRI study. Eur Neuropsychopharm 2011; 21: S515-S516
  • 11 Skudlarski P, Jagannathan K, Anderson K et al. Brain connectivity is not only lower but different in schizophrenia: a combined anatomical and functional approach. Biol Psychiatry 2010; 68: 61-69
  • 12 Mitelman SA, Newmark RE, Torosjan Y et al. White matter fractional anisotropy and outcome in schizophrenia. Schizophr Res 2006; 87: 138-159
  • 13 Lim KO, Hedehus M, Moseley M et al. Compromised white matter tract integrity in schizophrenia inferred from diffusion tensor imaging. Arch Gen Psychiatry 1999; 56: 367-374
  • 14 Zalesky A, Fornito A, Seal ML et al. Disrupted axonal fiber connectivity in schizophrenia. Biol Psychiatry 2011; 69: 80-89
  • 15 Biswal B, Yetkin FZ, Haughton VM et al. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 1995; 34: 537-541
  • 16 Greicius MD, Supekar K, Menon V et al. Resting-state functional connectivity reflects structural connectivity in the default mode network. Cereb Cortex 2009; 19: 72-78
  • 17 Honey CJ, Sporns O, Cammoun L et al. Predicting human resting-state functional connectivity from structural connectivity. Proc Natl Acad Sci USA 2009; 106: 2035-2040
  • 18 Lynall ME, Bassett DS, Kerwin R et al. Functional connectivity and brain networks in schizophrenia. J Neurosci 2010; 30: 9477-9487
  • 19 Sporns O, Chialvo DR, Kaiser M et al. Organization, development and function of complex brain networks. Trends Cogn Sci 2004; 8: 418-425
  • 20 Shenton ME, Dickey CC, Frumin M et al. A review of MRI findings in schizophrenia. Schizophr Res 2001; 49: 1-52
  • 21 Knöchel C, Oertel-Knöchel V, Schönmeyer R et al. Interhemispheric hypoconnectivity in schizophrenia: Fiber integrity and volume differences of the corpus callosum in patients and unaffected relatives. Neuroimage 2012; 59: 926-934
  • 22 Konrad A, Winterer G. Disturbed structural connectivity in schizophrenia  −  Primary factor in pathology or epiphenomenon?. Schizophrenia Bull 2008; 34: 72-92
  • 23 Hoptman MJ, Ardekani BA, Butler PD et al. DTI and impulsivity in schizophrenia: a first voxelwise correlational analysis. Neuroreport 2004; 15: 2467-2470
  • 24 Skelly LR, Calhoun V, Meda SA et al. Diffusion tensor imaging in schizophrenia: relationship to symptoms. Schizophr Res 2008; 98: 157-162
  • 25 Cabral J, Hugues E, Sporns O et al. Role of local network oscillations in resting-state functional connectivity. Neuroimage 2011; 57: 130-139
  • 26 Gong G, Rosa-Neto P, Carbonell F et al. Age- and gender-related differences in the cortical anatomical network. J Neurosci 2009; 29: 15684-15693
  • 27 Tzourio-Mazoyer N, Landeau B, Papathanassiou D et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 2002; 15: 273-289
  • 28 Jenkinson M, Bannister P, Brady M et al. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 2002; 17: 825-841
  • 29 Collins D, Neelin P, Peters T et al. Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J Comput Assist Tomography 1994; 18: 192-205
  • 30 Behrens TE, Woolrich MW, Jenkinson M et al. Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn Reson Med 2003; 50: 1077-1088
  • 31 Behrens TE, Berg HJ, Jbabdi S et al. Probabilistic diffusion tractography with multiple fibre orientations: What can we gain?. Neuroimage 2007; 34: 144-155
  • 32 Achard S, Bullmore E. Efficiency and cost of economical brain functional networks. PLoS Comput Biol 2007; 3: e17
  • 33 Bartos M, Vida I, Jonas P. Synaptic mechanisms of synchronized gamma oscillations in inhibitory interneuron networks. Nat Rev Neurosci 2007; 8: 45-56
  • 34 Brunel N. Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. J Comput Neurosci 2000; 8: 183-208
  • 35 Brunel N, Wang XJ. What determines the frequency of fast network oscillations with irregular neural discharges? I. Synaptic dynamics and excitation-inhibition balance. J Neurophysiol 2003; 90: 415-430
  • 36 Borgers C, Kopell N. Synchronization in networks of excitatory and inhibitory neurons with sparse, random connectivity. Neural Comput 2003; 15: 509-538
  • 37 Acebron JA, Bonilla LL, Vicente CJP et al. The Kuramoto model: A simple paradigm for synchronization phenomena. Rev Mod Phys 2005; 77: 137-185
  • 38 Friston KJ, Harrison L, Penny W. Dynamic causal modelling. Neuroimage 2003; 19: 1273-1302
  • 39 Shanahan M. Metastable chimera states in community-structured oscillator networks. Chaos 2010; 20
  • 40 Tononi G, Sporns O, Edelman GM. A measure for brain complexity: relating functional segregation and integration in the nervous system. Proc Natl Acad Sci U S A 1994; 91: 5033-5037
  • 41 Latora V, Marchiori M. Efficient behavior of small-world networks. Phys Rev Lett 2001; 87: 198701
  • 42 Ravasz E, Barabasi AL. Hierarchical organization in complex networks. Phys Rev E Stat Nonlin Soft Matter Phys 2003; 67: 026112
  • 43 Bassett DS, Bullmore E, Verchinski BA et al. Hierarchical organization of human cortical networks in health and schizophrenia. J Neurosci 2008; 28: 9239-9248
  • 44 Watts DJ, Strogatz SH. Collective dynamics of 'small-world' networks. Nature 1998; 393: 440-442
  • 45 Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 2010; 52: 1059-1069
  • 46 Friston KJ. The disconnection hypothesis. Schizophr Res 1998; 30: 115-125
  • 47 Stephan KE, Baldeweg T, Friston KJ. Synaptic plasticity and dysconnection in schizophrenia. Biol Psychiatry 2006; 59: 929-939
  • 48 Coyle JT, Tsai G, Goff D. Converging evidence of NMDA receptor hypofunction in the pathophysiology of schizophrenia. Ann N Y Acad Sci 2003; 1003: 318-327
  • 49 Supekar K, Menon V, Rubin D et al. Network analysis of intrinsic functional brain connectivity in Alzheimer's disease. Plos Comput Biol 2008; 4: e1000100
  • 50 Boccaletti S, Latora V, Moreno Y et al. Complex networks: Structure and dynamics. Physics Reports 2006; 424: 175-308
  • 51 Humphries MD, Gurney K, Prescott TJ. The brainstem reticular formation is a small-world, not scale-free, network. Proc Biol Sci 2006; 273: 503-511
  • 52 Achard S, Salvador R, Whitcher B et al. A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. J Neurosci 2006; 26: 63-72