Causal visual interactions as revealed by an information theoretic measure and fMRI

In the present study, we evaluated the direction of the effective connectivity between fMRI activations in neural structures mediating preserved visual function in a patient with homonymous hemianopsia due to a posterior cerebral artery stroke. Although the lesion affected the primary visual cortex, the visual abilities of this patient included above-chance verbal reports of movement and color change as well as the discrimination of movement direction in his hemianopic field. These abilities were coupled with awareness (Riddoch syndrome). The strength and the direction of the interactions between visual regions were assessed by applying directed transinformation (T), a nonparametric information theoretic causal measure sensitive to linear as well as to nonlinear interactions. In the healthy hemisphere, T identified a strong flow of information from visual area V1 to V5 during stimulation by visual movement and from V1 to V4/V8 during stimulation by color change. In addition, during color change stimulation, a bi-directional flow was observed between V4/V8 and V5, suggesting crosstalk between these regions. In the lesioned hemisphere, the color change stimulation evoked a stronger flow from V5 to V4/V8 and a flow from V4/V8 to V2. These observations provide support for the hypothesis that visual information is mediated via subcortical pathways that bypass V1 and project first to higher-tier visual areas V5 and V4/V8 then subsequently to lower-tier area V2.

[1]  C. Schroeder,et al.  A spatiotemporal profile of visual system activation revealed by current source density analysis in the awake macaque. , 1998, Cerebral cortex.

[2]  H. Harashima,et al.  A time‐series analysis method based on the directed transinformation , 1984 .

[3]  Stanley Fahn Recent Advances in EEG and EMG Data Processing , 1983, Neurology.

[4]  James Theiler,et al.  Testing for nonlinearity in time series: the method of surrogate data , 1992 .

[5]  Schreiber,et al.  Measuring information transfer , 2000, Physical review letters.

[6]  J-M Hopf,et al.  Dynamics of feature binding during object-selective attention , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Hualou Liang,et al.  Temporal dynamics of information flow in the cerebral cortex , 2001, Neurocomputing.

[8]  S. Zeki,et al.  The Riddoch syndrome: insights into the neurobiology of conscious vision. , 1998, Brain : a journal of neurology.

[9]  Nikos K Logothetis,et al.  On the nature of the BOLD fMRI contrast mechanism. , 2004, Magnetic resonance imaging.

[10]  P. Cavanagh,et al.  Retinotopy and color sensitivity in human visual cortical area V8 , 1998, Nature Neuroscience.

[11]  Moon,et al.  Estimation of mutual information using kernel density estimators. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[12]  Lorella Battelli,et al.  The effect of expectation on facilitation of colour/form conjunction tasks by TMS over area V5 , 2003, Neuropsychologia.

[13]  R. Gallager Information Theory and Reliable Communication , 1968 .

[14]  R. Willison Recent Advances in EEG and EMG Data Processing , 1982 .

[15]  S. Hillyard,et al.  Delayed Striate Cortical Activation during Spatial Attention , 2002, Neuron.

[16]  J. Simonoff Multivariate Density Estimation , 1996 .

[17]  A. Cowey,et al.  Retinal ganglion cells labelled from the pulvinar nucleus in macaque monkeys , 1994, Neuroscience.

[18]  G. Riddoch DISSOCIATION OF VISUAL PERCEPTIONS DUE TO OCCIPITAL INJURIES, WITH ESPECIAL REFERENCE TO APPRECIATION OF MOVEMENT , 1917 .

[19]  Karl J. Friston,et al.  Dynamic causal modeling for EEG and MEG , 2009, Human brain mapping.

[20]  Tohru Ozaki,et al.  Evaluating frequency-wise directed connectivity of BOLD signals applying relative power contribution with the linear multivariate time-series models , 2005, NeuroImage.

[21]  Hans-Jochen Heinze,et al.  Analysis of pathways mediating preserved vision after striate cortex lesions , 2002, Annals of neurology.

[22]  A. McIntosh,et al.  Network interactions among limbic cortices, basal forebrain, and cerebellum differentiate a tone conditioned as a Pavlovian excitor or inhibitor: fluorodeoxyglucose mapping and covariance structural modeling. , 1994, Journal of neurophysiology.

[23]  C. Granger Investigating causal relations by econometric models and cross-spectral methods , 1969 .

[24]  Mingzhou Ding,et al.  Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance , 2001, Biological Cybernetics.

[25]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[26]  Karl J. Friston,et al.  Multivariate Autoregressive Modelling of fMRI time series , 2003 .

[27]  Leslie G. Ungerleider Two cortical visual systems , 1982 .

[28]  Karl J. Friston Functional and effective connectivity in neuroimaging: A synthesis , 1994 .

[29]  R. Mansfield,et al.  Analysis of visual behavior , 1982 .

[30]  I. Miller Probability, Random Variables, and Stochastic Processes , 1966 .

[31]  J. Martinerie,et al.  Statistical assessment of nonlinear causality: application to epileptic EEG signals , 2003, Journal of Neuroscience Methods.

[32]  Karl J. Friston,et al.  A Dynamic Causal Modeling Study on Category Effects: BottomUp or TopDown Mediation? , 2003, Journal of Cognitive Neuroscience.

[33]  C. D. Kemp,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[34]  L. Benevento,et al.  The organization of connections between the pulvinar and visual area MT in the macaque monkey , 1983, Brain Research.

[35]  A. F. Seila,et al.  A Batching Approach to Quantile Estimation in Regenerative Simulations , 1982 .

[36]  Alexander Thiele,et al.  Neural Correlates of Chromatic Motion Perception , 2001, Neuron.

[37]  S J Luck,et al.  Direct and indirect integration of event‐related potentials, functional magnetic resonance images, and single‐unit recordings , 1999, Human brain mapping.

[38]  Athanasios Papoulis,et al.  Probability, Random Variables and Stochastic Processes , 1965 .

[39]  Lawrence C. Sincich,et al.  Bypassing V1: a direct geniculate input to area MT , 2004, Nature Neuroscience.

[40]  T D Albright,et al.  The Contribution of Color to Motion Processing in Macaque Middle Temporal Area , 1999, The Journal of Neuroscience.

[41]  C. Büchel,et al.  Modulation of connectivity in visual pathways by attention: cortical interactions evaluated with structural equation modelling and fMRI. , 1997, Cerebral cortex.

[42]  Rainer Goebel,et al.  Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping. , 2003, Magnetic resonance imaging.

[43]  Pietro Pietrini,et al.  Cerebral metabolic pattern in obsessive-compulsive disorder: Altered intercorrelations between regional rates of glucose utilization , 1991, Psychiatry Research: Neuroimaging.

[44]  S Zeki,et al.  Improbable areas in the visual brain , 2003, Trends in Neurosciences.

[45]  J W Belliveau,et al.  Borders of multiple visual areas in humans revealed by functional magnetic resonance imaging. , 1995, Science.

[46]  Mark E. Pflieger,et al.  Time-lagged causal information: A new metric for effective connectivity analysis , 2003 .

[47]  M. Gazzaniga,et al.  Combined spatial and temporal imaging of brain activity during visual selective attention in humans , 1994, Nature.