More info after the jump.

Press release:

Dear Colleague,

I am writing to let you know that we have just launched our first data set mapping gene expression across an adult human brain.   As with all Allen Institute Atlases, the data is freely available at www.brain-map.org For this first release, we have included:

· Spatially mapped microarray data for over 700 distinct anatomic locations throughout the brain and containing information for over 62,000 gene probes with 93% of known genes represented by at least 2 probes

· A hierarchical anatomic naming system (ontology) integrating leading schemes for different brain regions

· Searches by anatomic region, probe, or gene, as well as queries for comparing expression among pre-selected structure sets

· Expert neuroanatomic annotation of brain structures and delineation of areas sampled for microarray analysis

· An in situ hybridization study from a separate adult human brain that characterizes 55 genes in subcortical regions extending from the front of the caudate through posterior substantia nigra, and a smaller set of 10 genes through the hypothalamus. The 55-gene set focuses on the glutamatergic and GABAergic systems.

We hope that you might find these to be valuable resources, and will be continuing to add data from additional brains (8-10 in total) and improving our tools for analysis over the next few years.  As always, we appreciate your feedback, both good and bad.  If the tools and data have helped in some meaningful way, please let us know…though we can track basic usage of the data through web hits, your personal stories really help justify the continued support of the existing resources and our ability to create more.

Allan R. Jones, PhD
Chief Executive Officer
Allen Institute for Brain Science
551 N. 34th St.
Seattle WA 98103

Consider a a cortical neuron in V1, layer 2/3, whose output shows sharp orientation tuning. What are the orientation tunings of the most important inputs to that neuron? What is the spatial distribution of these inputs in the neuron’s dendritic tree?

Here’s three possibilities. (1) You might expect the neuron to collect inputs which are broadly tuned for that same orientation (the “weak-bias model”). (2) Or, you might expect that the neuron as a whole collects inputs with various tunings, but that each dendritic branches would tend to collect inputs with a certain orientation. (3) Or, neither of these could be the case; maybe the inputs just take all sorts of orientations, randomly distributed among the dendritic tree. Here a picture of these possibilities from the News and Views:

Jia, Rochefort, Chen, and Konnerth analyzed the orientation tuning of such neurons as well as the orientation tuning of the calcium dynamics within the neuron’s dendritic tree. Their results support the third option (inputs with heterogenous tuning, spatially mixed).

While hyperpolarizing the cell, they found “calcium hotspots” in the dendritic tree, that is, places where there was a noticeable, localized calcium signal in response to stimulation. They then analyzed the orientation tuning of these hotspots. Figure 3b shows three hotspots and their calcium response to various drifting gratings (oriented visual stimuli):

Figure 3c shows what the orientation tuning was for all of the hotspots in one neuron:

The main results are that the orientation tuning of the hotspots is heterogeneous (all sorts of different tunings are found), and that there is no discernible spatial pattern to where the differently tuned hotspots are located within the dendritic tree.

Furthermore, they compared the histogram of the orientation tuning of hotspots between sharply tuned neurons and broadly tuned neurons, and found that they were similar, supporting the hypothesis that whatever it is that makes some neurons have sharper orientation than others tuning in their output, the cause is something other than having sharper orientation tuning in their inputs. Fig. 4d (OSI stands for “orientation selectivity index”):

Here’s an excerpt from the Nature editor’s summary: “Whether…. tuning is already encoded in a neuron’s dendritic inputs or whether the neuron itself computes its selective response has been unclear….They discover that, while all neurons receive distributed input signals coding for multiple stimulus orientations, each neuron makes its own ‘decision’ as to the orientation preference of its firing output.”

Some cautionary notes: (A} the News and Views makes it sound as if this study established linear dendritic summation. As far as I can tell, the study didn’t test that directly. (B) above, I said that possiblity 3 is that the inputs are “randomly distributed”; in the study, however, although the distribution SEEMED random, it’s possible that it is just organized in some complicated way that made it look random. (C) I could be wrong about this, but as far as I can tell, there’s no guarantee that the calcium hotspots are the “most important” synaptic inputs; they might be ones which just happen to have a high density of calcium channels (D) they are only looking in about four planes of focus and getting about 13 hotspots per neuron, so this is only a small proportion of all of the synapses (E) even if the set of strong synapses showed heterogeneous tuning, there could be many weak synapses that all have tuning that matches the output tuning. (F) I defined the hotspots as “noticeable, localized calcium signal in response to stimulation”, but this is pretty subjective. The article does not exactly specify an algorithm which was used to pick out the hotspots from within their imaging data. All the methods has to say about it is, “Transient changes in Ca2+ fluorescence (?f/f) were systematically examined by an adaptive algorithm, which involved small regions of interest (ROIs) of 3?×?4?µm, noise filtering and pattern matching.”

When we learn new information we use only a tiny fraction of the neurons in our brain for that particular memory trace. In order to allow the molecular study of those specific neurons we combined elements of the tet system with a promoter that is activated by high level neural activity (the cfos promoter) to generate mice in which a genetic tag can be introduced into neurons that are active at a given point in time. The tag can be maintained for a prolonged period, creating a precise record of the neural activity pattern at a specific point in time. Using fear conditioning we found that the same neurons activated during learning were reactivated when the animal recalled the fearful event. We also found that these neurons were no longer activated following memory extinction, consistent with the idea that extinction modifies a component of the original memory trace.

That quote is from an abstract for a talk by Mark Mayford that will be given next week at UCSD. However, the following paper seems to report those results:

Leon G. Reijmers, Brian L. Perkins, Naoki Matsuo, Mark Mayford. Localization of a Stable Neural Correlate of Associative Memory. Science 31 August 2007: Vol. 317. no. 5842, pp. 1230 – 1233.

In addition, here’s the rest of the talk abstract, which seems to report new results:

One fundamental question in memory research has been how this nuclear to synaptic communication occurs. Using the cfos transgenic approach to specifically focus on activated circuits, we found in a recent study that glutamate receptors get specifically targeted to synapses that are altered with learning. That is, learning produces a sort of molecular tag at certain synapses that allows them to capture the newly synthesized receptors arriving from the nucleus hours after the learning event. Thus, the synapses that are altered in strength to produce a short-term memory must be primed, or tagged, to receive new receptor in order for that memory to be maintained long-term.

A few days later, Henry Markram, leader of the Blue Brain Project at EPFL, sent off an e-mail to IBM CTO Bernard Meyerson harshly criticizing the IBM press release, and cc’ed several reporters. This brought a spate of shock media into the usually placid arena of computational neuroscience reporting, with headlines such as “IBM’s cat-brain sim a ’scam,’ says Swiss boffin: Neuroscientist hairs on end”, and “Meow! IBM cat brain simulation dissed as ‘hoax’ by rival scientist”.  One reporter chose to highlight the rivalry as cat versus rat, using the different animal model choice of the two researchers as a theme.  Since then, additional criticisms from Markram have appeared online.

Find out more after the jump.

In the aftermath, IBM has stood behind the announcement, citing for Network World their team’s involvement with “Stanford University, University of Wisconsin-Madison, Cornell University, Columbia University Medical Center, University of California-Merced and Lawrence Berkeley National Laboratory” as defense.  Who are the researchers they are standing behind?  According to Modha’s blog, they are:

• Stanford University: Brian A. Wandell (Prof of Psychology, Electrical Engineering), H.-S. Philip Wong (Prof of Electrical Engineering)
• Cornell University: Rajit Manohar (Prof of Electrical Engineering)
• Columbia University Medical Center: Stefano Fusi (Prof of Theoretical Neuroscience)
• University of Wisconsin-Madison: Giulio Tononi (Prof of Psychiatry)
• University of California-Merced: Christopher Kello (Prof of Cognitive Science)

For this neurodude, it is interesting how this disagreement may be symbolic of the gap that still remains between neuroscience and AI.  Markram is a neuroscientist turned technologist, while Modha is a computer engineer who wants to derive technological insight from biological  systems.  They are approaching the ideal of reverse engineering the brain from very different perspectives, and its only natural that they value different milestones.  The IBM team, even with the additional professors on their team, still lacks mainstream neuroscientists to help validate their claims.  That being said, the public realization of this could be a positive thing for both fields.  Although some frustration has resulted from this, this could be a great opportunity for the breakdown of walls between these fields.

In the end though, it does seem like Markram has a point.  The IBM press release clearly went too far.  Whether the angry public e-mail was the best strategic way to make the point remains to be seen.  It will be interesting to see what the next move from the IBM team will look like.

A very cool article on a new open source, online system to crowd source the assemblage of data in neuroscience from the Voice of San Diego.  From the article:

Traditionally, the study of the brain was organized somewhat like an archipelago. Neuroscientists would inhabit their own island or peninsula of the brain, and see little reason to venture elsewhere.

Molecular neuroscientists, who study how DNA and RNA function in the brain, didn’t share their work with cognitive specialists who study how psychological and cognitive functions are produced by the brain, for example.

But there has been an awakening to the idea that brains of humans and mammals should be studied like the complex, and interrelated systems that they are. Neuroscientists realized that they had to start collaborating across disciplines and sharing their data if they wanted to make advances in their own field.

[...]

Ellisman and his UCSD colleagues have devised a solution: crowdsource a brain. And this week they unveiled their years-long project — the Whole Brain Catalog — at the annual convention of the Society for Neuroscience, the largest gathering of brain experts in the world.

You can also see an impressive  artists rendition of the Whole Brain Catalog on YouTube.

UPDATE 10/27: Looks like Voice of San Diego scooped the New York Times, who just posted on this topic in today’s bits blog.

Full disclosure: I am intimately involved with this project.

Watch it online at the TED website.

My colleague Robert Wang and I created an online collaborative DNA Vector analysis program called everyVECTOR. We were initially motivated because all of the existing commercial software are really expensive and the free ones are not as nicely designed/intuitive to use. Also, I was always frustrated with collaborators sending me text files of DNA sequences that weren’t annotated and confusing to read.

[...] You can also the public interface (without registration) by visiting here.

We released everyVECTOR last week and so far we have received good responses from people. We have around 200 users now from the past week, mostly from the Stanford and bay area universities.

I hope all of you molecular biologists can give everyVECTOR a try and give Feng some feedback. It certainly seems much more affordable (ie. free) than its well-known competitors. I’m a big fan of web-based tools myself and find them invaluable in doing simple sequence calculations for my own projects (one of my favs is the Sequence Manipulation Suite).

Also, apologies for the decreased posting frequency… I’m trying to graduate these days and there just doesn’t seem to be enough hours for everything. I hope to return to full force soon.

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