Semantic wiki:
http://www.connectome.ch/wiki/Main_Page

Desktop viewer:
http://connectomeviewer.org/viewer “Multi-Modal Multi-Level Network and Neuroimaging Visualization and Analysis” (screencasts)

The authors, Cutting, DeLong, and Nothelfer, speculate that the pink noise bit is being driven by some process that is related to attention, since there are some other results (which they cite) showing the relevance of pink noise to attention.

However, IMDB ratings were not positively correlated with the pink-noise-ness of the movie (partial correlation with release date factored out).

Incidentally, this guy did his PhD thesis on cognitive science explanations for film editing techniques.

James E. Cutting, Jordan E. DeLong, and Christine E. Nothelfer. Attention and the Evolution of Hollywood Film. Psychological Science February 2010 , first published on February 5, 2010 doi:10.1177/0956797610361679.

Octave/MATLAB toolkit for analysis of spike train data. Open source. Information theory-y.

“The task administered to the salmon involved completing an open-ended mentalizing task. The salmon was shown a series of photographs depicting human individuals in social situations with a specified emotional valence. The salmon was asked to determine what emotion the individual in the photo must have been experiencing.”

Voxels in the data were searched to find voxels which show a statistically significant correlation with the experimental condition. 16 such voxels were found (without doing any correction). As the poster says, “Across the 130,000 voxels in a typical fMRI volume the probability of a false positive is almost certain”. The poster recommends correcting with either “overall false discovery rate (FDR) … based on a method defined by Benjamini and Hochberg (1995)” or “overall familywise error rate (FWER) …. using algorithms originally devised by Friston et al. (1994)” (with either correction, no voxels were found).

http://prefrontal.org/files/posters/Bennett-Salmon-2009.pdf

I’m a fan of it because it is an open-access journal featuring a “tiered system” and more.  From their website:

The Frontiers Journal Series is not just another journal. It is a new approach to scientific publishing. As service to scientists, it is driven by researchers for researchers but it also serves the interests of the general public. Frontiers disseminates research in a tiered system that begins with original articles submitted to Specialty Journals. It evaluates research truly democratically and objectively based on the reading activity of the scientific communities and the public. And it drives the most outstanding and relevant research up to the next tier journals, the Field Journals.

I’m a big fan of the variety of specialty journals they have:

• Aging Neuroscience
• Behavioral Neuroscience
• Cellular Neuroscience
• Computational Neuroscience
• Enteric Neuroscience
• Evolutionary Neuroscience
• Human Neuroscience
• Integrative Neuroscience
• Molecular Neuroscience
• Neural Circuits
• Neuroanatomy
• Neuroenergetics
• Neuroengineering
• Neurogenesis
• Neurogenomics
• Neuroinformatics
• Neuromethods
• Neuropharamacology
• Neuroprosthetics
• Neurorobotics
• Synaptic Neuroscience
• Systems Neuroscience

From the News and Views, I was intrigued to learn that previous transcriptome analyses of adult human brains found very little difference in gene expression between brain areas:

[...] this suggests that it is the gene expression during development that largely determines higher brain functions by specifying the complexity of neural connections. Numerically, the most important genes relating to cognitive differences between species may be genes that specify how the machinery is put together. In support of this hypothesis, many of the identified differentially expressed genes in this study are related to processes involved in connection formation, such as axonal guidance and cell adhesion.

An impressive 76% of all human genes are expressed in the developing fetal brain. Of those, 33% are differentially expressed over brain regions (13 regions were examined) and 28% are alternatively spliced. The differentially expressed genes are also ones that seem to have evolved the most recently. Even in these early (midgestation) stages, left-right asymmetry was seen, such as the localization of the language-associated FOXP2 genes to Broca’s area.

Of interest to computational folks, they find that gene expression follows power-law scaling (as many other naturally occurring “small-worlds” networks do) with certain hub genes connected to many others and certain spoke genes with relatively few connections. Unsupervised hierarchical clustering is used in this analysis.