Funktionelle und effektive Konnektivität

Zusammenfassung

Neurophysiologische und bildgebende Verfahren zur Messung von Hirnaktivität, wie fMRI oder EEG, werden in den Neurowissenschaften eingesetzt, um Prozesse funktioneller Spezialisierung und funktioneller Integration im menschlichen Gehirn zu untersuchen. Funktionelle Integration kann auf zwei verschiedene Arten beschrieben werden: funktionelle Konnektivität und effektive Konnektivität. Während die funktionelle Konnektivität lediglich statistische Abhängigkeiten zwischen Zeitreihen beschreibt, erfordert das Konzept der effektiven Konnektivität ein mechanistisches Modell der kausalen Effekte, die den beobachteten Daten zu Grunde liegen. Dieser Artikel fasst die konzeptionellen und methodischen Grundlagen moderner Techniken für die Analyse funktioneller und effektiver Konnektivität auf der Basis von fMRI und elektrophysiologischen Daten zusammen. Ein besonderer Schwerpunkt liegt dabei auf dem Dynamic Causal Modelling (DCM), einem neuen Verfahren zur Analyse nichtlinearer neuronaler Systeme. Diese Methode besitzt ein vielversprechendes Potenzial für klinische Anwendungen, z. B. zur Entschlüsselung pathophysiologischer Mechanismen bei Hirnerkrankungen und zur Etablierung neurophysiologisch fundierter diagnostischer Klassifikationen.

Abstract

Neurophysiological and imaging procedures to measure brain activity, such as fMRI or EEG, are employed in neuroscience to investigate processes of functional specialisation and functional integration in the human brain. Functioal integration can be described in two distinct ways: functional connectivity and effective connectivity. Whereas functional connectivity merely describes the statistical dependence between two time series, the concept of effective connectivity requires a mechanistic model of the causative effects upon which the data to be observed are based. This article summarises the conceptual and methodological principles of modern techniques for the analysis of functional and effective connectivity on the basis of fMRI and electrophysiological data. Particular emphasis is placed on dynamic causal modelling (DCM), a new procedure for the analysis of non-linear neuronal systems. This method has a highly promising potential for clinical applications, e. g., for decoding pathological mechanisms in brain diseases and for the establishment of neurologically valid diagnostic classifications.

Schlüsselwörter

Dynamic Causal Modelling (DCM) - Modellvergleich - Bayes - synaptische Plastizität

Key words

dynamic causal modelling (DCM) - model comparisons - Bayesian procedure - synaptic plasticity

Literatur

• 1 Acs F, Greenlee MW. Connectivity modulation of early visual processing areas during covert and overt tracking tasks.  Neuroimage. 2008;  41 ((2)) 380-388
• 2 Aertsen A, Preißl H. Dynamics of activity and connectivity in physiological neuronal networks. In: HG S, editor Nonlinear Dynamics and Neuronal Networks. New York: VCH Publishers 1999: 281-302
• 3 Akaike H. A new look at the statistical model identification IEEE Trans.  Automatic Control. 1974;  19 716-723
• 4 Allen P, Mechelli A, Stephan KE. et al . Fronto-temporal interactions during overt verbal initiation and suppression.  J Cogn Neurosci. 2008;  20 ((9)) 1656-1669
• 5 Allen P, Stephan KE, Mechelli A. et al . Cingulate activity and fronto-temporal connectivity in people with prodromal signs of psychosis.  Neuroimage. 2010;  49 ((1)) 947-955
• 6 Breakspear M. “Dynamic” connectivity in neural systems: theoretical and empirical considerations.  Neuroinformatics. 2004;  2 ((2)) 205-226
• 7 Buchel C, Friston KJ. Modulation of connectivity in visual pathways by attention: cortical interactions evaluated with structural equation modelling and fMRI.  Cereb Cortex. 1997;  7 ((8)) 768-778
• 8 Bullmore E, Horwitz B, Honey G. et al . How good is good enough in path analysis of fMRI data?.  Neuroimage. 2000;  11 ((4)) 289-301
• 9 Buxton RB, Wong EC, Frank LR. Dynamics of blood flow and oxygenation changes during brain activation: the balloon model.  Magn Reson Med. 1998;  39 ((6)) 855-864
• 10 Chen CC, Henson RN, Stephan KE. et al . Forward and backward connections in the brain: a DCM study of functional asymmetries.  Neuroimage. 2009;  45 ((2)) 453-462
• 11 Chen CC, Kiebel SJ, Friston KJ. Dynamic causal modelling of induced responses.  Neuroimage. 2008;  41 ((4)) 1293-1312
• 12 David O, Friston KJ. A neural mass model for MEG/EEG: coupling and neuronal dynamics.  Neuroimage. 2003;  20 ((3)) 1743-1755
• 13 David O, Guillemain I, Saillet S. et al . Identifying neural drivers with functional MRI: an electrophysiological validation.  PLoS Biol. 2008;  6 ((12)) 2683-2697
• 14 David O, Kiebel SJ, Harrison LM. et al . Dynamic causal modeling of evoked responses in EEG and MEG.  Neuroimage. 2006;  30 ((4)) 1255-1272
• 15 den Ouden HE, Friston KJ, Daw ND. et al . A dual role for prediction error in associative learning.  Cereb Cortex. 2009;  19 ((5)) 1175-1185
• 16 Eickhoff SB, Dafotakis M, Grefkes C. et al . Central adaptation following heterotopic hand replantation probed by fMRI and effective connectivity analysis.  Exp Neurol. 2008;  212 ((1)) 132-144
• 17 Friston K. A theory of cortical responses.  Philos Trans R Soc Lond B Biol Sci. 2005;  360 ((1456)) 815-836
• 18 Friston K, Mattout J, Trujillo-Barreto N. et al . Variational free energy and the Laplace approximation.  Neuroimage. 2007;  34 ((1)) 220-234
• 19 Friston KJ. Functional and effective connectivity in neuroimaging: a synthesis.  Hum Brain Mapp. 1994;  2 56-78
• 20 Friston KJ. Bayesian estimation of dynamical systems: an application to fMRI.  Neuroimage. 2002a;  16 ((2)) 513-530
• 21 Friston KJ. Beyond phrenology: What can neuroimaging tell us abut distributed circuitry?.  Ann Rev Neurosci. 2002b;  25 221-250
• 22 Friston KJ, Buechel C, Fink GR. et al . Psychophysiological and modulatory interactions in neuroimaging.  Neuroimage. 1997;  6 ((3)) 218-229
• 23 Friston KJ, Frith CD, Liddle PF. et al . Functional connectivity: the principal-component analysis of large (PET) data sets.  J Cereb Blood Flow Metab. 1993;  13 ((1)) 5-14
• 24 Friston KJ, Harrison L, Penny W. Dynamic causal modelling.  Neuroimage. 2003;  19 ((4)) 1273-1302
• 25 Friston KJ, Mechelli A, Turner R. et al . Nonlinear responses in fMRI: the Balloon model, Volterra kernels, and other hemodynamics.  Neuroimage. 2000;  12 ((4)) 466-477
• 26 Friston KJ, Trujillo-Barreto N, Daunizeau J. DEM: a variational treatment of dynamic systems.  Neuroimage. 2008;  41 ((3)) 849-885
• 27 Garrido MI, Kilner JM, Kiebel SJ. et al . Dynamic causal modelling of evoked potentials: a reproducibility study.  Neuroimage. 2007;  36 ((3)) 571-580
• 28 Goebel R, Roebroeck A, Kim DS. et al . Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping.  Magn Reson Imaging. 2003;  21 ((10)) 1251-1261
• 29 Grefkes C, Nowak DA, Eickhoff SB. et al . Cortical connectivity after subcortical stroke assessed with functional magnetic resonance imaging.  Ann Neurol. 2008;  63 ((2)) 236-246
• 30 Grol MJ, Majdandzic J, Stephan KE. et al . Parieto-frontal connectivity during visually guided grasping.  J Neurosci. 2007;  27 ((44)) 11877-11887
• 31 Harrison L, Penny WD, Friston K. Multivariate autoregressive modeling of fMRI time series.  Neuroimage. 2003;  19 ((4)) 1477-1491
• 32 Horwitz B, Tagamets MA, McIntosh AR. Neural modeling, functional brain imaging, and cognition.  Trends Cogn Sci. 1999;  3 ((3)) 91-98
• 33 Jirsa VK. Connectivity and dynamics of neural information processing.  Neuroinformatics. 2004;  2 ((2)) 183-204
• 34 Kass RE, Raftery AE. Bayes factors.  J Am Stat Assoc. 1995;  90 773-795
• 35 Kiebel SJ, David O, Friston KJ. Dynamic causal modelling of evoked responses in EEG/MEG with lead field parameterization.  Neuroimage. 2006;  30 ((4)) 1273-1284
• 36 Kiebel SJ, Kloppel S, Weiskopf N. et al . Dynamic causal modeling: a generative model of slice timing in fMRI.  Neuroimage. 2007;  34 ((4)) 1487-1496
• 37 Marreiros AC, Kiebel SJ, Friston KJ. Dynamic causal modelling for fMRI: a two-state model.  Neuroimage. 2008;  39 ((1)) 269-278
• 38 Marshall JC, Fink GR. Cerebral localization, then and now.  Neuroimage. 2003;  20 ((Suppl 1)) S2-7
• 39 McIntosh AR, Gonzales-Lima F. Structural equation modelling and its application to network analysis in functional brain imaging.  Brain Mapp. 1994;  2 2-22
• 40 Mechelli A, Price CJ, Noppeney U. et al . A dynamic causal modeling study on category effects: bottom-up or top-down mediation?.  J Cogn Neurosci. 2003;  15 ((7)) 925-934
• 41 Montague PR, Dayan P, Sejnowski TJ. A framework for mesencephalic dopamine systems based on predictive Hebbian learning.  J Neurosci. 1996;  16 ((5)) 1936-1947
• 42 Moran RJ, Kiebel SJ, Stephan KE. et al . A neural mass model of spectral responses in electrophysiology.  Neuroimage. 2007;  37 ((3)) 706-720
• 43 Moran RJ, Stephan KE, Kiebel SJ. et al . Bayesian estimation of synaptic physiology from the spectral responses of neural masses.  Neuroimage. 2008;  42 ((1)) 272-284
• 44 Moran RJ, Stephan KE, Seidenbecher T. et al . Dynamic causal models of steady-state responses.  Neuroimage. 2009;  44 ((3)) 796-811
• 45 Noppeney U, Josephs O, Hocking J. et al . The effect of prior visual information on recognition of speech and sounds.  Cereb Cortex. 2008;  18 ((3)) 598-609
• 46 Penny WD, Litvak V, Fuentemilla L. et al . Dynamic causal models for phase coupling.  J Neurosci Methods. 2009; 
• 47 Penny WD, Stephan KE, Mechelli A. et al . Comparing dynamic causal models.  Neuroimage. 2004a;  22 ((3)) 1157-1172
• 48 Penny WD, Stephan KE, Mechelli A. et al . Modelling functional integration: a comparison of structural equation and dynamic causal models.  Neuroimage. 2004b;  23 ((Suppl 1)) S264-74
• 49 Pitt MA, Myung IJ. When a good fit can be bad.  Trends Cogn Sci. 2002;  6 ((10)) 421-425
• 50 Salinas E, Sejnowski TJ. Gain modulation in the central nervous system: where behavior, neurophysiology, and computation meet.  Neuroscientist. 2001;  7 ((5)) 430-440
• 51 Sarter M, Hasselmo ME, Bruno JP. et al . Unraveling the attentional functions of cortical cholinergic inputs: interactions between signal-driven and cognitive modulation of signal detection.  Brain Res Brain Res Rev. 2005;  48 ((1)) 98-111
• 52 Schultz W, Dickinson A. Neuronal coding of prediction errors.  Annu Rev Neurosci. 2000;  23 473-500
• 53 Schwarz G. Estimating the dimension of a model.  Ann Stat. 1978;  6 461-464
• 54 Sonty SP, Mesulam MM, Weintraub S. et al . Altered effective connectivity within the language network in primary progressive aphasia.  J Neurosci. 2007;  27 ((6)) 1334-1345
• 55 Stephan KE. On the role of general system theory for functional neuroimaging.  J Anat. 2004;  205 ((6)) 443-470
• 56 Stephan KE, Fink GR, Marshall JC. Mechanisms of hemispheric specialization: insights from analyses of connectivity.  Neuropsychologia. 2007a;  45 ((2)) 209-228
• 57 Stephan KE, Friston KJ, Frith CD. Dysconnection in schizophrenia: from abnormal synaptic plasticity to failures of self-monitoring.  Schizophr Bull. 2009a;  35 ((3)) 509-527
• 58 Stephan KE, Harrison LM, Kiebel SJ. et al . Dynamic causal models of neural system dynamics:current state and future extensions.  J Biosci. 2007b;  32 ((1)) 129-144
• 59 Stephan KE, Harrison LM, Penny WD. et al . Biophysical models of fMRI responses.  Curr Opin Neurobiol. 2004;  14 ((5)) 629-635
• 60 Stephan KE, Kasper L, Harrison LM. et al . Nonlinear dynamic causal models for fMRI.  Neuroimage. 2008;  42 ((2)) 649-662
• 61 Stephan KE, Marshall JC, Penny WD. et al . Interhemispheric integration of visual processing during task-driven lateralization.  J Neurosci. 2007c;  27 ((13)) 3512-3522
• 62 Stephan KE, Penny WD, Daunizeau J. et al . Bayesian model selection for group studies.  Neuroimage. 2009b;  46 ((4)) 1004-1017
• 63 Stephan KE, Penny WD, Marshall JC. et al . Investigating the functional role of callosal connections with dynamic causal models.  Ann N Y Acad Sci. 2005;  1064 16-36
• 64 Stephan KE, Tittgemeyer M, Knosche TR. et al . Tractography-based priors for dynamic causal models.  Neuroimage. 2009c;  47 ((4)) 1628-1638
• 65 Stephan KE, Weiskopf N, Drysdale PM. et al . Comparing hemodynamic models with DCM.  Neuroimage. 2007d;  38 ((3)) 387-401
• 66 Zucker RS, Regehr WG. Short-term synaptic plasticity.  Annu Rev Physiol. 2002;  64 355-405

Korrespondenzadresse