Scientists use brain imaging to reveal the movies in our mind.

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

Two approaches

Broadly, there are two approaches to synaptic tagging: 1) mRNA is distributed widely and translated locally at tagged locations; 2) protein products are distributed widely in the bodies of dendrites but only contact/off-load at tagged synaptic specializations. This News & Views gives a nice overview of the two papers, which find approach 1) in Aplysia cultures with sensorin mRNA and approach 2) in rat hippocampal neurons with Vesl-1S/Homer-1a protein. It amazes me that both were found pretty much simultaneously, but that might have more to do with the use of the photoconvertible Dendra2 protein than anything else.

With both approaches, we still don’t know why mRNA/protein is directed to a certain location. That is, we can visualize synaptic tagging but we don’t know what is the tag, its ontogeny, or the mechanism of tagging. But that might not be so important to understanding more about neural function. These new tools might allow us to image plasticity at many synapses at once, perhaps even in vivo. But before that, more work is needed… does the optical signal (from the Dendra fusion protein) correlate with degree of potentiation? Can we detect plasticity in the opposite direction, ie. synaptic depression, through another tag?  (As a sidenote to approach 1), the use of 5′ and 3′ UTRs as a sort of molecular zipcode is also intriguing.)

1. 3D PALM (open access): Using 2-photon and photoactivatable proteins, the authors image beyond the usual sub-wavelength TIRF limits. They image over multiple microns with 50nm resolution.
2. Neuroeconomics:  Low digit ratio (2d:4d) predicts financial success in traders. Okay, measure the length of your index and ring fingers. (Not sure if this analysis applies for the ladies; the authors only used men in the study.) Calculate the ratio (2d/4d); longer ring fingers signify greater fetal androgen exposure. The mean value is about 0.96. As the authors say,
   

   Digit ratios have been found to predict performance in competitive sports, such as soccer, rugby, basketball, and skiing, so 2D:4D may also predict the risk preferences and physical speed required for high-frequency trading.

   A strong correlation (r~0.5) was found between low digit ratios and profits in short-term trading. So, they take on more risk and make more money. What I want to know is how well the low 2d:4d ratio traders did over the last 6 months!

An amazing paper from Neuron demonstrating adaptive (circadian clock-governed) binning in the retina, based on dopamine modulation of gap junction (electrical) synapses between retinal photodetectors. During the day, abundant dopamine release weakens gap junctions coupling rods and cones together so that visual acuity is high. When light is scarce (at night), there is less dopamine and the electrical coupling between rods and cones is increased. This is analogous to on-chip binning in CCD (digital) cameras. Binning increases signal (in light-limited systems, eg. seeing at night) by increasing optical input area and by reducing single element noise (ie. noise at different photoreceptors should be independent) at the cost of resolution. So, the retina activates photoreceptor binning at night to boost low-light signals and deactivates it during the day to increase resolution. The dopamine comes from cells in the interplexiform layer, whose dopamine release is itself governed by melatonin projections.

Also, I never knew that gap junction strengths were directly modifiable. It looks like the D2 receptors are G-protein coupled to PKA, which acts on the gap junctions.

Two amazing things here:

1. Plants missing photosynthetic enzymes of their own that migrate directionally toward “victim” plants. This behavior has an uncanny resemblance to axon guidance. Make sure to view the time-lapse video in the NYT article. Here’s an image from the PSU website:
   
   
2. Plants capable of identifying kin and “being nice” to kin while going into a competitive mode of root growth with non-kin. Amazing.

It refreshing to see this kind of interesting behavior without any neurons involved. It makes me think (realize) that the idea of a neuron or a neural system has many components and there might not be any good reason to assume that a single cell must have all of those properties or none of them. Something like a neuron-like cell that’s not a neuron in the classical sense. Anyone know of other examples?

Here’s a really thorough 2007 article from J. Microscopy that does a nice job of comparing wide-field/deconvolution, spinning disk confocal, and laser scanning confocal microscopy. Punchline is after the jump.

A recommendation based on specimen contrast:

So that our work may achieve its goal of establishing guidelines for choosing between different modes of 3-D microscopy, we summarize here the main result of our comparisons. First, the magnitude of a simple specimen-dependent parameter that we have called the haziness index (H) provides a numerical criterion for choosing the most appropriate mode of microscopy. H is computed as a ratio of background in a WF image over signal: the intensity of background from out-of-focus fluorescence divided by the intensity of the fluorescence from a small in-focus object. ‘Small’ here means comparable in size to the Airy disk. In practice, the intensity of such small objects will be unmeasurable in a WF microscope for specimens with very large background, so it will typically be necessary to estimate H by some indirect means. For instance, it will often be possible to find a thin edge of the specimen where the background is not overwhelming and measurement of the intensity of some small object of interest is possible in a WF microscope. This number could then be combined with a WF measurement of the background in the thickest region of the specimen to give a good estimate of H. Alternatively, if both signal and background could be measured in a thin specimen, then knowing its thickness relative to a thicker specimen would be sufficient to calculate the value of H in the regions too thick for WF microscopy. In this regard, the graph in Fig. 2(B) will be useful, showing the decreasing contribution to the background from planes that are very far from focus.

Naturally, this sort of extrapolation will be somewhat imprecise, but fortunately all that is required is an order-of-magnitude estimate of H. For specimens with H less than ~20, WF microscopy with deconvolution can provide the best images. Over the range 20 < H < 200, spinning disk confocals are the best choice. From 200 < H < 1000, it will probably be necessary to use a spot scanning confocal, and beyond H = 1000, none of these methods is likely to be satisfactory.

I highly recommend taking a look at Figure 7 and the Results section on Contrast if you’re interested in the specifics. This paper seems very thorough.