## Investigating the functional role of callosal connections with dynamic causal models (2005)

Venue: | Ann N Y Acad Sci |

Citations: | 6 - 1 self |

### BibTeX

@ARTICLE{Stephan05investigatingthe,

author = {Klaas E. Stephan and A Will D. Penny and A John C. Marshall and Gereon R. Fink and Karl J. Friston A},

title = {Investigating the functional role of callosal connections with dynamic causal models},

journal = {Ann N Y Acad Sci},

year = {2005},

volume = {1064},

pages = {16--36}

}

### OpenURL

### Abstract

ABSTRACT: The anatomy of the corpus callosum has been described in considerable detail. Tracing studies in animals and human postmortem experiments are currently complemented by diffusion-weighted imaging, which enables noninvasive investigations of callosal connectivity to be conducted. In contrast to the wealth of anatomical data, little is known about the principles by which interhemispheric integration is mediated by callosal connections. Most importantly, we lack insights into the mechanisms that determine the functional role of callosal connections in a context-dependent fashion. These mechanisms can now be disclosed by models of effective connectivity that explain neuroimaging data from paradigms that manipulate interhemispheric interactions. In this article, we demonstrate that dynamic causal modeling (DCM), in conjunction with Bayesian model selection (BMS), is a powerful approach to disentangling the various factors that determine the functional role of callosal connections. We first review the theoretical foundations of DCM and BMS before demonstrating the application of these techniques to empirical data from a single subject.

### Citations

749 | Structural Equations with Latent Variables - Bollen - 1989 |

181 |
Dynamic causal modelling
- Friston, Harrison, et al.
- 2003
(Show Context)
Citation Context ...of visual stimuli in the periphery of one visual hemifield ensures that the contralateral visual cortex receives the stimulus information first. Therefore, 23 (10) (d θ /2)lnN > d θ ⇒ N > e 2 ≈ 7.39. =-=(11)-=- BF ij = p(y|m i )/p(y|m j ). (12)s24 ANNALS NEW YORK ACADEMY OF SCIENCES one knows that any area that is in the hemisphere ipsilateral to stimulus presentation can only receive this information if it... |

133 | Dynamics of blood flow and oxygenation changes during brain activation: the Balloon model - Buxton, Wong, et al. - 1998 |

97 | The endophenotype concept in psychiatry: Etymology and strategic intentions - Gottesman, Gould - 2003 |

84 |
Exploring complex networks”, Nature 410
- Strogatz
- 2001
(Show Context)
Citation Context ...etina to the visual cortex, presentation of visual stimuli in the periphery of one visual hemifield ensures that the contralateral visual cortex receives the stimulus information first. Therefore, 23 =-=(10)-=- (d θ /2)lnN > d θ ⇒ N > e 2 ≈ 7.39. (11) BF ij = p(y|m i )/p(y|m j ). (12)s24 ANNALS NEW YORK ACADEMY OF SCIENCES one knows that any area that is in the hemisphere ipsilateral to stimulus presentatio... |

72 |
Detection and modelling of non-Gaussian apparent diffusion coefficient profiles in human brain data
- Alexander, Barker, et al.
- 2002
(Show Context)
Citation Context ...on the prior density, for example, the prior covariance of the intrinsic connections (see eq. 9). This is problematic in the context of DCM for fMRI because this prior covariance is defined in a model=-=(9)-=-sSTEPHAN et al.: ROLE OF CALLOSAL CONNECTIONS WITH DCM specific fashion to ensure that the probability of obtaining an unstable system is very small. (Specifically, this is achieved by choosing the pr... |

66 | Broca's region revisited: Cytoarchitecture and intersubject variability - Amunts, Schleicher, et al. - 1999 |

55 | Posterior parietal cortex in rhesus monkey: I. Parcellation of areas based on distinctive limbic and sensory corticocortical connections - Cavada, Goldman-Rakic - 1989 |

54 | A neural mass model for MEG/EEG: coupling and neuronal dynamics, NeuroImage 20 - David, Friston - 2003 |

50 | Bayesian estimation of dynamical systems: an application to fMRI - Friston - 2002 |

47 | Thermal Analysis - Richardson - 1989 |

44 |
Laminar origins and terminations of cortical connections of the occipital lobe in the rhesus monkey
- Rockland, Pandya
- 1979
(Show Context)
Citation Context ... of one visual hemifield ensures that the contralateral visual cortex receives the stimulus information first. Therefore, 23 (10) (d θ /2)lnN > d θ ⇒ N > e 2 ≈ 7.39. (11) BF ij = p(y|m i )/p(y|m j ). =-=(12)-=-s24 ANNALS NEW YORK ACADEMY OF SCIENCES one knows that any area that is in the hemisphere ipsilateral to stimulus presentation can only receive this information if it is transferred through the corpus... |

39 | On the actions that one nerve cell can have on another: Distinguishing “drivers” from “modulators - Sherman, Guillery |

38 |
Beyond phrenology: What can neuroimaging tell us about distributed circuitry
- Friston
- 2002
(Show Context)
Citation Context ...e parameters θn z that define the functional architecture and interactions among brain regions at a neuronal level (n is not an exponent, but simply denotes “neural”): · [ 1 … k ] = = F(z, u, θn z ). =-=(1)-=- · z · z · In this neural state equation, the state z and the inputs u are time-dependent, whereas the parameters are time-invariant. In DCM, F has the bilinear form z = Az + + Cu. (2) · m ujBj z ∑ j ... |

38 | Network analysis of cortical visual pathways mapped with PET - McIntosh, Grady, et al. - 1994 |

35 |
A framework for a streamline-based probabilistic index of connectivity (PICo) using a structural interpretation of MRI diffusion measurements
- Parker, Haroon, et al.
- 2003
(Show Context)
Citation Context ...(7) the model evidence can be considered as a normalization constant for the product of the likelihood of the data and the prior probability of the parameters; therefore, p(y|m) = �p(y|θ,m)p(θ|m) dθ. =-=(8)-=- Here, the number of free parameters (as well as the functional form) are considered by the integration. Unfortunately, this integral cannot usually be solved analytically; therefore, an approximation... |

32 | When a good fit can be bad - Pitt, Myung - 2002 |

27 | Where in the brain does visual attention select the forest and the trees? Nature 382: 626–628 - Fink, Halligan, et al. - 1996 |

17 | Network participation indices: characterizing component roles for information processing in neural networks - Kötter, Stephan - 2003 |

16 | Time is of the essence: A conjecture that hemispheric specialization arises from interhemispheric conduction delay - Ringo, Doty, et al. - 1994 |

14 | On the role of general system theory for functional neuroimaging - Stephan - 2004 |

12 | The missing link: The role of interhemispheric interaction in attentional processing - Banich - 1998 |

12 | Stochastic models of neuronal dynamics - Harrison, David, et al. - 2005 |

9 |
Mapping of histologically identified long fiber tracts in human cerebral hemispheres to the MRI volume of a reference brain: position and spatial variability of the optic radiation
- Bqrgel, Schormann, et al.
- 1999
(Show Context)
Citation Context ... if one considers that the solution to a linear ordinary differential equation of the form z = Az is an exponential function (compare the state equation in equation 2). · 21 (4) y = h(u, θ) + Xβ + ε. =-=(5)-=- p(cTηθ|y > γ) = φN[(cTηθ|y − γ)/( c )]. (6) T Cθ ycs22 ANNALS NEW YORK ACADEMY OF SCIENCES Bayesian Model Selection (BMS) A generic problem encountered by any kind of modeling approach is the questio... |

8 | Role of the input in visual hemispheric asymmetries - Sergent - 1983 |

7 | Metacontrol of hemispheric function in human split-brain patients - Levy, Trevarthen - 1976 |

5 | Organization of callosal linkages in visual area V2 of macaque monkey - Abel, O’Brien, et al. - 2000 |

5 | Interhemispheric transmission of information and functional asymmetry of the human brain - Nowicka, Grabowska, et al. - 1996 |

4 | The afferent and efferent callosal connections of retinotopically defined areas in cat cortex - Segraves, C - 1982 |

3 | Neuronal asymmetries in primary visual cortex of dyslexic and nondyslexic - Jenner, Rosen, et al. - 1999 |

2 |
Cerebral localization, then and now
- MARSHALL, FINK
- 2003
(Show Context)
Citation Context ...F(z, u, θn z ). (1) · z · z · In this neural state equation, the state z and the inputs u are time-dependent, whereas the parameters are time-invariant. In DCM, F has the bilinear form z = Az + + Cu. =-=(2)-=- · m ujBj z ∑ j = 1 The parameters of this bilinear neural state equation, θ n = {A, B 1 , …, B m , C}, can be expressed as partial derivatives of F: A = ∂F/∂z = ∂ /∂z Bj = ∂2 z F/∂z∂uj = (∂/∂uj )(∂ /... |

2 | Interhemispheric integration: I. Symmetry and convergence of the corticocortical connections of the left and the right principal sulcus (PS) and the left and the right supplementary motor area (SMA) in the rhesus monkey - MCGUIRE, BATES, et al. - 1991 |

2 | Insights into the functional specificity of the human corpus callosum - Funnell, Corballis, et al. - 2000 |

2 | Organization of the callosal connections of visual areas V1 and V2 in the macaque monkey - Kennedy, Dehay, et al. - 1986 |

1 |
BECHMANN et al. 2000. Current concepts in neuroanatomical tracing
- KÖBBERT, APPS, et al.
(Show Context)
Citation Context ... content q. The predicted BOLD signal y is a nonlinear function of blood volume and deoxyhemoglobin content: y = λ(v, q). Details of the hemodynamic model can be found in other publications. 11,26,27 =-=(3)-=-sSTEPHAN et al.: ROLE OF CALLOSAL CONNECTIONS WITH DCM By combining the neural and hemodynamic states into a joint state vector x and the neural and hemodynamic parameters into a joint parameter vecto... |

1 |
et al. 2001. Advanced database methodology for the collation of connectivity data on the macaque brain
- STEPHAN, KAMPER, et al.
(Show Context)
Citation Context ... This is easily understood if one considers that the solution to a linear ordinary differential equation of the form z = Az is an exponential function (compare the state equation in equation 2). · 21 =-=(4)-=- y = h(u, θ) + Xβ + ε. (5) p(cTηθ|y > γ) = φN[(cTηθ|y − γ)/( c )]. (6) T Cθ ycs22 ANNALS NEW YORK ACADEMY OF SCIENCES Bayesian Model Selection (BMS) A generic problem encountered by any kind of modeli... |

1 |
BRATZKE et al. 2000. Interhemispheric asymmetries of the modular structure in human temporal cortex. Science 289
- GALUSKE, SCHLOTE, et al.
- 1946
(Show Context)
Citation Context ...ear ordinary differential equation of the form z = Az is an exponential function (compare the state equation in equation 2). · 21 (4) y = h(u, θ) + Xβ + ε. (5) p(cTηθ|y > γ) = φN[(cTηθ|y − γ)/( c )]. =-=(6)-=- T Cθ ycs22 ANNALS NEW YORK ACADEMY OF SCIENCES Bayesian Model Selection (BMS) A generic problem encountered by any kind of modeling approach is the question of model selection: given some observed da... |

1 | et al. 2004. Effects of handedness and gender on macro- and microstructure of the corpus callosum and its subregions: a combined high-resolution and diffusion-tensor MRI study - WESTERHAUSEN, KREUDER, et al. |

1 | MANDL et al. 2003. Focal white matter density changes in schizophrenia: reduced inter-hemispheric connectivity. NeuroImage 21 - POL, E, et al. |

1 | MECHELLI et al. 2004. Comparing dynamic causal models - PENNY, STEPHAN, et al. |

1 | et al. 2000. Nonlinear responses in fMRI: the balloon model, Volterra kernels, and other hemodynamics. NeuroImage 12 - FRISTON, MECHELLI, et al. |

1 | MECHELLI et al. 2004. Modelling functional integration: a comparison of structural equation and dynamic causal models. NeuroImage 23: S264–S274 - PENNY, STEPHAN, et al. |

1 | FRISTON et al. 2003. Lateralized cognitive processes and lateralized task control in the human brain. Science 301 - STEPHAN, MARSHALL, et al. |

1 | et al. 2004. Studying effective connectivity with a neural mass model of evoked MEG/EEG responses - DAVID, HARRISON, et al. |

1 | GRUZELIER et al. 2004. Mismatch negativity potentials and cognitive impairment in schizophrenia - BALDEWEG, KLUGMAN, et al. |