A statistical parametric method for the extraction of stimulus dependent activity from intrinsic optical signals

The anterior part of inferotemporal cortex (IT) is thought to be critical for the object recognition and discrimination. To understand the mechanism on how objects are presented by the activation of neurons, and then how the information on the objects is processed in this brain area, the optical imaging technique based on intrinsic signals was applied to directly visualize the responsive neurons when viewing various object images. Instead of using just the ratio of the optical intensities, here we proposed a novel method based on a statistical parametric map for the extraction of the stimulus-dependent signals from the response images. Statistical parameter, t was introduced to evaluate the difference of means between the response and control images obtained with and without visual stimulation. t value for each pixel was computed by comparing the means of optical intensities at the particular pixel in response images and control images, and used to evaluate the significance of the response. The performance of this method was evaluated by the number of spiking neurons. In the region extracted with the proposed method, in as high as 81.8% of penetrations, neurons were responsive, this was significantly higher than 45.5% if in the region extracted by using the ratio of the optical intensities. The results demonstrate that the method proposed here is effective for the signal extraction in the optical imaging experiments.

[1]  Amiram Grinvald,et al.  Iso-orientation domains in cat visual cortex are arranged in pinwheel-like patterns , 1991, Nature.

[2]  Keiji Tanaka,et al.  Functional architecture in monkey inferotemporal cortex revealed by in vivo optical imaging , 1998, Neuroscience Research.

[3]  Keiji Tanaka,et al.  Neuronal selectivities to complex object features in the ventral visual pathway of the macaque cerebral cortex. , 1994, Journal of neurophysiology.

[4]  Edgar N. Sánchez,et al.  Dynamic learning rate (ηD) for recurrent high order neural observer (RHONO): Anaerobic process application , 2011, The 2011 International Joint Conference on Neural Networks.

[5]  D. Ts'o,et al.  Functional organization of primate visual cortex revealed by high resolution optical imaging. , 1990, Science.

[6]  C. Gilbert,et al.  Long-range horizontal connections and their role in cortical reorganization revealed by optical recording of cat primary visual cortex , 1995, Nature.

[7]  Keiji Tanaka,et al.  Effects of shape-discrimination training on the selectivity of inferotemporal cells in adult monkeys. , 1998, Journal of neurophysiology.

[8]  G. Ghose,et al.  Form processing modules in primate area V4. , 1997, Journal of neurophysiology.

[9]  A Grinvald,et al.  Optical imaging reveals the functional architecture of neurons processing shape and motion in owl monkey area MT , 1994, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[10]  Keiji Tanaka,et al.  Optical Imaging of Functional Organization in the Monkey Inferotemporal Cortex , 1996, Science.

[11]  G. Blasdel,et al.  Orientation selectivity, preference, and continuity in monkey striate cortex , 1992, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[12]  César Torres-Huitzil,et al.  Two-phase GA parameter tunning method of CPGs for quadruped gaits , 2011, The 2011 International Joint Conference on Neural Networks.

[13]  Sarrazin Jean-Christophe,et al.  The temporality of consciousness: Computational principles of a single information integration-propagation process (I2P2) , 2011, The 2011 International Joint Conference on Neural Networks.

[14]  A. Grinvald,et al.  Interactions Between Electrical Activity and Cortical Microcirculation Revealed by Imaging Spectroscopy: Implications for Functional Brain Mapping , 1996, Science.

[15]  D. Ts'o,et al.  Cortical functional architecture and local coupling between neuronal activity and the microcirculation revealed by in vivo high-resolution optical imaging of intrinsic signals. , 1990, Proceedings of the National Academy of Sciences of the United States of America.

[16]  Esteban J. Palomo,et al.  Visualisation of network forensics traffic data with a self-organising map for qualitative features , 2011, The 2011 International Joint Conference on Neural Networks.

[17]  R. Frostig,et al.  Optical imaging of neuronal activity. , 1988, Physiological reviews.