Connectionists: New Papers for Computational Neuroimaging and Neuroscience

[Stephen Jose hanson]

>From the RUMBA Lab   Rutgers University Mind Brain Analysis

Three new papers for Computational Neuroimaging and Neuroscience 

Hanson SJ, Gagliardi AD, and Hanson C. (2009) Solving the brain
synchrony eigenvalue problem: conservation of temporal dynamics (fMRI)
over subjects doing the same task. Journal of computational
neuroscience. Aug;27(1):103-14. 

This paper describes  a new method for extracting synchronous temporal
dynamics across subjects doing passive 
viewing task such as a movie while they are being brain scanned. "....
This general question can be framed in a dynamical systems context and
further be posed as an eigenvalue problem about the conservation of
synchrony across all brains simultaneously. We show that solving the
problem results in a non-arbitrary measure of temporal dynamics across
brains that scales over any number of subjects, stabilizes with
increasing sample size, and varies systematically across tasks and
stimulus conditions." 

PDF here:
http://sites.google.com/site/rumbalab/publications-1/papers/2009

Poldrack, R., Halchenko Y., and Hanson, S.J. (in press).  Decoding the
large-scale structure of brain function by classifying mental states
across individuals, Psychological Science. 

"...Using a variety of classifier techniques, we achieved
cross-validated classification accuracy of 80% across individuals
(chance = 13%). Using a neural network classifier, we recovered a
low-dimensional representation common to all the cognitive-perceptual
tasks in our data set, and we used an ontology of cognitive processes to
determine the cognitive concepts most related to each dimension. These
results revealed a small set of large-scale networks that map cognitive
processes across a highly diverse set of mental tasks, suggesting a
novel way to characterize the neural basis of cognition."

PDF here:
http://sites.google.com/site/rumbalab/publications-1/papers/in-press

Ramsey, J. D., Hanson, S. J., Hanson, C., Halchenko, Y. O., Poldrack, R.
A., and Glymour, C. (2009). Six problems for causal inference from fmri.
NeuroImage.

"...To find actual effective connectivity relations, search methods must
accommodate indirect measurements of nonlinear time series dependencies,
feedback, multiple subjects possibly varying in identified regions of
interest, and unknown possible location-dependent variations in BOLD
response delays. We describe combinations of procedures that under these
conditions find feed-forward sub-structure characteristic of a group of
subjects. The method is illustrated with an empirical data set and
confirmed with simulations of time series of non-linear, randomly
generated, effective connectivities, with feedback, subject to random
differences of BOLD delays, with regions of interest missing at random
for some subjects, measured with noise approximating the signal to noise
ratio of the empirical data."

PDF here:
http://sites.google.com/site/rumbalab/publications-1/papers/in-press



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