Relating Macroscopic Measures of Brain Activity to Fast, Dynamic Neuronal Interactions

In this article we used biologically plausible simulations of coupled neuronal populations to address the relationship between phasic and fast coherent neuronal interactions and macroscopic measures of activity that are integrated over time, such as the BOLD response in functional magnetic resonance imaging. Event-related, dynamic correlations were assessed using joint peristimulus time histograms and, in particular, the mutual information between stimulus-induced transients in two populations. This mutual information can be considered as an index of functional connectivity. Our simulations showed that functional connectivity or dynamic integration between two populations increases with mean background activity and stimulus-related rate modulation. Furthermore, as the background activity increases, the populations become increasingly sensitive to the intensity of the stimulus in terms of a predisposition to transient phase locking. This reflects an interaction between background activity and stimulus intensity in producing dynamic correlations, in that background activity augments stimulus-induced coherence modulation. This is interesting from a computational perspective because background activity establishes a context that may have a profound effect on event-related interactions or functional connectivity between neuronal populations. Finally, total firing rates, which subsume both background activity and stimulus-related rate modulation, were almost linearly related to the expression of dynamic correlations over large ranges of activities. These observations show that under the assumptions implicit in our model, rate-specific metrics based on rate or coherence modulation may be different perspectives on the same underlying dynamics. This suggests that activity (averaged over all peristimulus times), as measured in neuroimaging, may be tightly coupled to the expression of dynamic correlations.

[1]  O. Prospero-Garcia,et al.  Reliability of Spike Timing in Neocortical Neurons , 1995 .

[2]  Ad Aertsen,et al.  Dynamics of functional coupling in the cerebral cortex , 1994 .

[3]  Ad Aertsen,et al.  Dynamics of Activity in Neuronal Networks Give Rise to Fast Modulations of Functional Connectivity , 1990 .

[4]  Kurt Wiesenfeld,et al.  Stochastic resonance and the benefits of noise: from ice ages to crayfish and SQUIDs , 1995, Nature.

[5]  G. Edelman,et al.  Neural dynamics in a model of the thalamocortical system. II. The role of neural synchrony tested through perturbations of spike timing. , 1997, Cerebral cortex.

[6]  G L Gerstein,et al.  Mutual temporal relationships among neuronal spike trains. Statistical techniques for display and analysis. , 1972, Biophysical journal.

[7]  Karl J. Friston,et al.  The Role of the Thalamus in “Top Down” Modulation of Attention to Sound , 1996, NeuroImage.

[8]  A. J. Collins,et al.  Introduction to Multivariate Analysis , 1982 .

[9]  H Preißl,et al.  Dynamics of activity and connectivity in physiological neuronal networks , 1991 .

[10]  Ravi S. Menon,et al.  Imaging function in the working brain with fMRI , 2001, Current Opinion in Neurobiology.

[11]  G. Edelman,et al.  Neural dynamics in a model of the thalamocortical system. I. Layers, loops and the emergence of fast synchronous rhythms. , 1997, Cerebral cortex.

[12]  Idan Segev,et al.  Methods in Neuronal Modeling , 1988 .

[13]  A. J. Collins,et al.  Introduction To Multivariate Analysis , 1981 .

[14]  A. Aertsen,et al.  Dynamics of neuronal interactions in monkey cortex in relation to behavioural events , 1995, Nature.

[15]  A. Aertsen,et al.  Neuronal assemblies , 1989, IEEE Transactions on Biomedical Engineering.

[16]  M. Cynader,et al.  Quantitative distribution of GABA-immunopositive and -immunonegative neurons and synapses in the monkey striate cortex (area 17). , 1992, Cerebral cortex.

[17]  Karl J. Friston,et al.  The Relationship Between Synchronization Among Neuronal Populations and Their Mean Activity Levels , 1999, Neural Computation.

[18]  C. Koch,et al.  Multiple channels and calcium dynamics , 1989 .

[19]  D. Perkel,et al.  Simultaneously Recorded Trains of Action Potentials: Analysis and Functional Interpretation , 1969, Science.

[20]  R. Traub,et al.  A model of a CA3 hippocampal pyramidal neuron incorporating voltage-clamp data on intrinsic conductances. , 1991, Journal of neurophysiology.