Two neat tools concerned with the “connectome” (i.e. the pattern of connections in the nervous system):
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 statistics of shot durations in 150 films from 1935 to 2005 were analyzed. From about 1970 to the present, the power spectrum of shot durations in individual films has tended to become more like pink noise (power ~= 1/f). Also, autocorrelation shows that the lengths of nearby shots has become more and more correlated.
Octave/MATLAB toolkit for analysis of spike train data. Open source. Information theory-y.
This poster, by Bennett, Baird, Miller, and Wolford, provides a memorable reminder that you have to do a statistical correction for multiple comparisons when you datamine a large number of things for correlation.
“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.”
The journal, Frontiers in Neuroscience, edited by Idan Segev, has made it Volume 3, issue 1. Launching last year at the Society for Neuroscience conference, its probably the newest Neuroscience-related journal.
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.
A cutting-edge application of the Affy total human exome GeneChip (4X coverage per exon, 40X coverage per gene): Functional and Evolutionary Insights into Human Brain Development through Global Transcriptome Analysis.
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.
Nature is reporting on potential flaw in multiple imaging (fMRI) studies of social neuroscience. Ed Vul (a graduate student in my dept) and colleagues have a paper in press that says that many of the high correlations between brain regions and social behavior are implausible, given the inherent variability/noise in fMRI. Furthermore, based on a survey of methods from individual investigators, they created a list of papers that commit, in their view, a statistical mistake (non-independence). Naturally, the authors named in the paper aren’t happy and, according to the Nature article, several rebuttals are in the works. At the very least, to my non-expert eyes, this seems like an important discussion to have about data analysis and methodology.