Neural Analysis Image
The ability to encode noxious stimulus intensity is essential for studying the neural processing of pain perception. It is well accepted that the intensity information is transmitted within both sensory and affective pathways.
Fig 1 Neuronal responses in the medial (ACC, MD) and lateral (SI, VPL) pain pathways induced by laser stimulation. (Image by IOP)
Fig 2 The averaged single-unit response to laser stimulation for each brain region of interest at differing stimulation intensity levels. (Image by IOP)
Fig 3 The capacity of neural ensemble discrimination of different stimulus intensities measured by discriminant analysis. (Image by IOP)
However, it remains unclear what the encoding patterns are in the thalamocortical brain regions, and whether the dual pain systems share similar responsibility in intensity coding.
Dr. LUO Fei and Dr. WANG Jinyan’s team at the Key Laboratory of Mental Health of the Institute of Psychology, has recently employed the technique of multichannel single-unit recording of neural activity in freely-moving awake animals to investigate the activity of individual neurons and neuronal ensembles in the rat brain following the application of noxious laser stimuli of increasing intensity to the hindpaw. Four brain regions were monitored, including two within the lateral sensory pain pathway, namely, the ventral posterior lateral thalamic nuclei and the primary somatosensory cortex, and two in the medial pathway, namely, the medial dorsal thalamic nuclei and the anterior cingulate cortex. Neuron number, firing rate, and ensemble spike count codings were examined in this study.
The results showed that the noxious laser stimulation evoked double-peak responses in all recorded brain regions. Significant correlations were found between the laser intensity and the number of responsive neurons, the firing rates, as well as the mass spike counts (MSCs). MSC coding was generally more efficient than the other two methods. Moreover, the coding capacities of neurons in the two pathways were comparable.
This study demonstrated the collective contribution of medial and lateral pathway neurons to the noxious intensity coding. Additionally, they provide evidence that ensemble spike count may be the most reliable method for coding pain intensity in the brain.
Copyright © 2002 – 2011 Chinese Academy of Sciences
http://www.molecularpain.com/content/7/1/64/abstract
Daniel Margala and David Kirkby (as team DeepZot) placed first in the Mapping Dark Matter challenge. Daniel agreed to answer a few questions for No Free Hunch as part of our series of posts on the best Mapping Dark Matter entries.
What was your background prior to entering Mapping Dark Matter?
I graduated from the University of California, Los Angeles in 2009 with a B.S. in Physics. In the course of my studies at UCLA, I learned Linux system administration and various scripting languages managing a cluster of servers and data archive for an astro-particle research group. I became interested in numerical analysis while investigating the polarity and momentum of muons produced in the atmosphere by incident cosmic rays. Currently, I am a PhD student in the Physics and Astronomy Department at the University of California, Irvine. My advisor (and DeepZot team member), Prof. David Kirkby, and I are using the Baryon Oscillation Spectroscopic Survey (BOSS) to study the distribution of matter in our universe at the largest volumes. My work with BOSS has primarily focused on the operations software at the telescope, specifically, with the interfaces to the BOSS spectrograph, located at Apache Point Observatory in New Mexico.
How did you come to form a team together?
I became interesting in working with David during a conversation that included an avid discussion of programming languages at a department event (where I was lured by the prospect of free food and drink, the perfect bait for graduate students). I began working on a variety of projects with David for about half a year, ranging from cosmology to electrical engineering, before we started working on the related GREAT10 challenge.
What made you decide to enter?
The GREAT10 challenge was a perfect opportunity for me to bring my freshly developed proficiency in numerical analysis to bear. As a student looking to gain experience, this was also a chance to contribute to the forefront of analysis techniques employed by the weak lensing community. The comparatively compact size and similarity between data sets made participating in the MDM challenge very attractive. The ellipticity measurement (galaxy shape) in the MDM competition was a critical step in our GREAT10 analysis, where the goal is to disentangle the shear (due to dark matter) from the intrinsic galaxy shape.
What was your most important insight into the dataset?
The most important insight was that the pixel-level residuals are a powerful tool for finding the best-fit models for galaxy and star images. We were able to assess the quality of various image models and parameters studying distributions of residuals across sets of images. This was essential to our method, which consisted of a pixel-level maximum-likelihood fit to each star and galaxy image.
The images below demonstrate our fit for a single galaxy image. From left to right, we see the original image (zoomed in on the center), our fitted model with the same resolution, and a higher resolution version of the fitted model:
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