Wednesday, 6 August 2014

Connectome


   
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White matter tracts within a human brain, as visualized by MRI tractography.
A connectome is a comprehensive map of neural connections in the brain, and may be thought of as its "wiring diagram". More broadly, a connectome would include the mapping of all neural connections within an organism's nervous system.
The production and study of connectomes, known as connectomics, may range in scale from a detailed map of the full set of neurons and synapses within part or all of the nervous system of an organism to a macro scale description of the functional and structural connectivity between all cortical areas and subcortical structures. The term "connectome" is used primarily in scientific efforts to capture, map, and understand the organization of neural interactions within the brain.
Research has successfully constructed the full connectome of one animal: the roundworm C. elegans (White et al., 1986,[1] Varshney et al., 2011[2]). Partial connectomes of a mouse retina [3] and mouse primary visual cortex [4] have also been successfully constructed. Bock et al.'s complete 12TB data set is publicly available at Open Connectome Project.
The ultimate goal of connectomics is to map the human brain. This effort is pursued by the Human Connectome Project, sponsored by the National Institutes of Health, whose focus is to build a network map of the human brain in healthy, living adults.




Origin and usage of the term "connectome"[edit]

In 2005, Dr. Olaf Sporns at Indiana University and Dr. Patric Hagmann at Lausanne University Hospital independently and simultaneously suggested the term "connectome" to refer to a map of the neural connections within the brain. This term was directly inspired by the ongoing effort to sequence the human genetic code—to build a genome.
"Connectomics" (Hagmann, 2005) has been defined as the science concerned with assembling and analyzing connectome data sets.[5]
In their 2005 paper, The Human Connectome, a structural description of the human brain, Sporns et al. wrote:
To understand the functioning of a network, one must know its elements and their interconnections. The purpose of this article is to discuss research strategies aimed at a comprehensive structural description of the network of elements and connections forming the human brain. We propose to call this dataset the human "connectome," and we argue that it is fundamentally important in cognitive neuroscience and neuropsychology. The connectome will significantly increase our understanding of how functional brain states emerge from their underlying structural substrate, and will provide new mechanistic insights into how brain function is affected if this structural substrate is disrupted.[6]
In his 2005 Ph.D. thesis, From diffusion MRI to brain connectomics, Hagmann wrote:
It is clear that, like the genome, which is much more than just a juxtaposition of genes, the set of all neuronal connections in the brain is much more than the sum of their individual components. The genome is an entity it-self, as it is from the subtle gene interaction that [life] emerges. In a similar manner, one could consider the brain connectome, set of all neuronal connections, as one single entity, thus emphasizing the fact that the huge brain neuronal communication capacity and computational power critically relies on this subtle and incredibly complex connectivity architecture.[5]
Pathways through cerebral white matter can be charted by histological dissection and staining, by degeneration methods, and by axonal tracing. Axonal tracing methods form the primary basis for the systematic charting of long-distance pathways into extensive, species-specific anatomical connection matrices between gray matter regions. Landmark studies have included the areas and connections of the visual cortex of the macaque (Felleman and Van Essen, 1991)[7] and the thalamo-cortical system in the feline brain (Scannell et al., 1999).[8] The development of neuroinformatics databases for anatomical connectivity allow for continual updating and refinement of such anatomical connection maps. The online macaque cortex connectivity tool CoCoMac (Kötter, 2004)[9] is a prominent example of such a database.
In the human brain, the significance of the connectome stems from the realization that the structure and function of the human brain are intricately linked, through multiple levels and modes of brain connectivity. There are strong natural constraints on which neurons or neural populations can interact, or how strong or direct their interactions are. Indeed, the foundation of human cognition lies in the pattern of dynamic interactions shaped by the connectome.
However, structure-function relationships in the brain are unlikely to reduce to simple one-to-one mappings. In fact, the connectome can evidently support a great number of variable dynamic states, depending on current sensory inputs, global brain state, learning and development. Some changes in functional state may involve rapid changes of structural connectivity at the synaptic level, as has been elucidated by two-photon imaging experiments showing the rapid appearance and disappearance of dendritic spines (Bonhoeffer and Yuste, 2002).[10]
Despite such complex and variable structure-function mappings, the connectome is an indispensable basis for the mechanistic interpretation of dynamic brain data, from single-cell recordings to functional neuroimaging.
The term "connectome" was more recently popularized by Sebastian Seung's "I am my Connectome" speech given at the 2010 TED conference, which discusses the high-level goals of mapping the human connectome, as well as ongoing efforts to build a three-dimensional neural map of brain tissue at the microscale.[11] In 2012, Seung published the book Connectome: How the Brain's Wiring Makes Us Who We Are.

The connectome at multiple scales[edit]

Brain networks can be defined at different levels of scale, corresponding to levels of spatial resolution in brain imaging (Kötter, 2007, Sporns, 2010).[12][13] These scales can be roughly categorized as microscale, mesoscale and macroscale. Ultimately, it may be possible to join connectomic maps obtained at different scales into a single hierarchical map of the neural organization of a given species that ranges from single neurons to populations of neurons to larger systems like cortical areas. Given the methodological uncertainties involved in inferring connectivity from the primary experimental data, and given that there are likely to be large differences in the connectomes of different individuals, any unified map will likely rely on probabilistic representations of connectivity data (Sporns et al., 2005).[6]
Mapping the connectome at the "microscale" (micrometer resolution) means building a complete map of the neural systems, neuron-by-neuron. The challenge of doing this becomes obvious: the number of neurons comprising the brain easily ranges into the billions in more highly evolved organisms. The human cerebral cortex alone contains on the order of 1010 neurons linked by 1014 synaptic connections.[14] By comparison, the number of base-pairs in a human genome is 3×109. A few of the main challenges of building a human connectome at the microscale today include: (1) data collection would take years given current technology; (2) machine vision tools to annotate the data remain in their infancy, and are inadequate; and (3) neither theory nor algorithms are readily available for the analysis of the resulting brain-graphs. To address the data collection issues, several groups are building high-throughput serial electron microscopes (Kasthuri et al., 2009; Bock et al. 2011). To address the machine-vision and image-processing issues, the Open Connectome Project is alg-sourcing (algorithm outsourcing) this hurdle. Finally, statistical graph theory is an emerging discipline which is developing sophisticated pattern recognition and inference tools to parse these brain-graphs (Goldenberg et al., 2009).
A "mesoscale" connectome corresponds to a spatial resolution of hundreds of micrometers. Rather than attempt to map each individual neuron, a connectome at the mesoscale would attempt to capture anatomically and/or functionally distinct neuronal populations, formed by local circuits (e.g. cortical columns) that link hundreds or thousands of individual neurons. This scale still presents a very ambitious technical challenge at this time and can only be probed on a small scale with invasive techniques or very high field MRI on a local scale.
A connectome at the macroscale (millimeter resolution) attempts to capture large brain systems that can be parcellated into anatomically distinct modules (areas, parcels or nodes), each having a distinct pattern of connectivity. Connectomic databases at the mesoscale and macroscale may be significantly more compact than those at cellular resolution, but they require effective strategies for accurate anatomical or functional parcellation of the neural volume into network nodes (for complexities see, e.g., Wallace et al., 2004).[15]

Mapping the connectome at the cellular level[edit]

Current non-invasive imaging techniques cannot capture the brain's activity on a neuron-by-neuron level. Mapping the connectome at the cellular level in vertebrates currently requires post-mortem microscopic analysis of limited portions of brain tissue. Non-optical techniques that rely on high-throughput DNA sequencing have been proposed recently by Tony Zador (CSHL).[citation needed]
Traditional histological circuit-mapping approaches rely on imaging and include light-microscopic techniques for cell staining, injection of labeling agents for tract tracing, or reconstruction of serially sectioned tissue blocks via electron microscopy (EM). Each of these classical approaches has specific drawbacks when it comes to deployment for connectomics. The staining of single cells, e.g. with the Golgi stain, to trace cellular processes and connectivity suffers from the limited resolution of light-microscopy as well as difficulties in capturing long-range projections. Tract tracing, often described as the "gold standard" of neuroanatomy for detecting long-range pathways across the brain, generally only allows the tracing of fairly large cell populations and single axonal pathways. EM reconstruction was successfully used for the compilation of the C. elegans connectome (White et al., 1986).[1] However, applications to larger tissue blocks of entire nervous systems have traditionally had difficulty with projections that span longer distances.
Recent advances in mapping neural connectivity at the cellular level offer significant new hope for overcoming the limitations of classical techniques and for compiling cellular connectome data sets (Livet et al., 2007; Lichtman et al., 2008).[16][17][18] Using Brainbow, a combinatorial color labeling method based on the stochastic expression of several fluorescent proteins, Lichtman and colleagues were able to mark individual neurons with one of over 100 distinct colors. The labeling of individual neurons with a distinguishable hue then allows the tracing and reconstruction of their cellular structure including long processes within a block of tissue.
In March 2011, the journal Nature published a pair of articles on micro-connectomes: Bock et al.[4] and Briggman et al.[3] In both articles, the authors first characterized the functional properties of a small subset of cells, and then manually traced a subset of the processes emanating from those cells to obtain a partial subgraph. In alignment with the principles of open-science, the authors of Bock et al. (2011) have released their data for public access. The full resolution 12TB dataset from Bock et al. is available at the Open Connectome Project.
In 2012, a Citizen science project called EyeWire began attempting to crowdsource the mapping of the connectome through an interactive game.[19]
Scaling up ultrastructural circuit mapping to the whole mouse brain is currently underway (Mikula, 2012).[20]
An alternative approach to mapping connectivity was recently proposed by Zador and colleagues (Zador et al., 2012).[21] Zador's technique, called BOINC (barcoding of individual neuronal connections) uses high-throughput sequencing to map neural circuits. Briefly, the approach consists of (1) labelling each neuron with a unique DNA barcode; (2) transferring barcodes between synaptically coupled neurons (for example using PRV); and (3) fusion of barcodes to represent a synaptic pair. This approach has the potential to be cheap, fast, and extremely high-throughput.

Mapping the connectome at the macro scale[edit]

Established methods of brain research, such as axonal tracing, provided early avenues for building connectome data sets. However, more recent advances in living subjects has been made by the use of non-invasive imaging technologies such as diffusion magnetic resonance imaging and functional magnetic resonance imaging (fMRI). The first, when combined with tractography allows reconstruction of the major fiber bundles in the brain. The second allows the researcher to capture the brain's network activity (either at rest or while performing directed tasks), enabling the identification of structurally and anatomically distinct areas of the brain that are functionally connected.
Notably, the goal of the Human Connectome Project, led by the WU-Minn consortium, is to build a structural and functional map of the healthy human brain at the macro scale, using a combination of multiple imaging technologies and resolutions.

Recent advances in connectivity mapping[edit]

Tractographic reconstruction of neural connections via DTI
Over the past few years, several investigators have attempted to map the large-scale structural architecture of the human cerebral cortex. One attempt exploited cross-correlations in cortical thickness or volume across individuals (He et al., 2007).[22] Such gray-matter thickness correlations have been postulated as indicators for the presence of structural connections. A drawback of the approach is that it provides highly indirect information about cortical connection patterns and requires data from large numbers of individuals to derive a single connection data set across a subject group.
Other investigators have attempted to build whole-brain connection matrices from diffusion imaging data. One group of researchers (Iturria-Medina et al., 2008)[23] has constructed connectome data sets using diffusion tensor imaging (DTI)[24][25] followed by the derivation of average connection probabilities between 70-90 cortical and basal brain gray matter areas. All networks were found to have small-world attributes and "broad-scale" degree distributions. An analysis of betweenness centrality in these networks demonstrated high centrality for the precuneus, the insula, the superior parietal and the superior frontal cortex. Another group (Gong et al. 2008)[26] has applied DTI to map a network of anatomical connections between 78 cortical regions. This study also identified several hub regions in the human brain, including the precuneus and the superior frontal gyrus.
Hagmann et al. (2007)[27] constructed a connection matrix from fiber densities measured between homogeneously distributed and equal-sized regions of interest (ROIs) numbering between 500 and 4000. A quantitative analysis of connection matrices obtained for approximately 1000 ROIs and approximately 50,000 fiber pathways from two subjects demonstrated an exponential (one-scale) degree distribution as well as robust small-world attributes for the network. The data sets were derived from diffusion spectrum imaging (DSI) (Wedeen, 2005),[28] a variant of diffusion-weighted imaging[29][30] that is sensitive to intra-voxel heterogeneities in diffusion directions caused by crossing fiber tracts and thus allows more accurate mapping of axonal trajectories than other diffusion imaging approaches (Wedeen, 2008).[31] The combination of whole-head DSI datasets acquired and processed according to the approach developed by Hagmann et al. (2007)[27] with the graph analysis tools conceived initially for animal tracing studies (Sporns, 2006; Sporns, 2007)[32][33] allow a detailed study of the network structure of human cortical connectivity (Hagmann et al., 2008).[34] The human brain network was characterized using a broad array of network analysis methods including core decomposition, modularity analysis, hub classification and centrality. Hagmann et al. presented evidence for the existence of a structural core of highly and mutually interconnected brain regions, located primarily in posterior medial and parietal cortex. The core comprises portions of the posterior cingulate cortex, the precuneus, the cuneus, the paracentral lobule, the isthmus of the cingulate, the banks of the superior temporal sulcus, and the inferior and superior parietal cortex, all located in both cerebral hemispheres.
More recently, Connectograms have been used to visualize full-brain data by placing cortical areas around a circle, organized by lobe.[35][36] Inner circles then depict cortical metrics on a color scale. White matter fiber connections in DTI data are then drawn between these cortical regions and weighted by FA and strength of the connection. Such graphs have even been used to analyze the damage done to the famous traumatic brain injury patient Phineas Gage.[37]

Primary challenge for macroscale connectomics: determining parcellations of the brain[edit]

The initial explorations in macroscale human connectomics were done using either equally sized regions or anatomical regions with unclear relationship to the underlying functional organization of the brain (e.g. gyral and sulcal-based regions). While much can be learned from these approaches, it is highly desirable to parcellate the brain into functionally distinct parcels: brain regions with distinct architectonics, connectivity, function, and/or topography (Felleman and Van Essen, 1991).[38] Accurate parcellation allows each node in the macroscale connectome to be more informative by associating it with a distinct connectivity pattern and functional profile. Parcellation of localized areas of cortex have been accomplished using diffusion tractography (Beckmann et al. 2009)[39] and functional connectivity (Nelson et al. 2010)[40] to non-invasively measure connectivity patterns and define cortical areas based on distinct connectivity patterns. Such analyses may best be done on a whole brain scale and by integrating non-invasive modalities. Accurate whole brain parcellation may lead to more accurate macroscale connectomes for the normal brain, which can then be compared to disease states.

Mapping functional connectivity to complement anatomical connectivity[edit]

Using functional MRI (fMRI) in the resting state and during tasks, functions of the connectome circuits are being studied.[41] Just as detailed road maps of the earth's surface do not tell us much about the kind of vehicles that travel those roads or what cargo they are hauling, to understand how neural structures result in specific functional behavior such as consciousness, it is necessary to build theories that relate functions to anatomical connectivity.[42]

See also[edit]

References[edit]

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The science behind Obama’s BRAIN project.


The science behind Obama’s BRAIN project.

Posted   Blogger Ref http://www.p2pfoundation.net/Multi-Dimensional_Science     


On April 2nd, 2013 President Obama announced the BRAIN initiative, a project aimed at understanding brain activities at a level of detail that is unprecedented. A lot has been said and written about the initiative but some people feel in the dark as to what are its precise aims. Although announcing the project before the aims were set in concrete might have raised some worries1,2,3,12, I personally see it as a fresh and open way to proceed. It gives a chance for everyone to express their opinion. Sebastian Seung’s Twitter comment illustrates this very well:
@emckiernan13 @eperlste Not earmarked…you should join the debate by making constructive arguments about how to spend the money. SebastianSeung, 2 Apr 2013.
For this post I will leave aside the political aspect – I think that as a French Canadian my view on American politics is of limited interest. For the curious, I will simply say that I believe it is a very exciting project. It might end up having just as much impact as the big science projects that the USA have been leading during the last century including the exploration of space and the sequencing of the human genome.
What I would like to do is regroup the information that is currently available on the scientific aspect and technical challenges that await the BRAIN project and present them to you in the simplest terms possible. I think it is important for me to specify that I have no particular access to the scientists on that project and the rest of this text is not “insider” information – it is based on published references that are listed at the end of the post.
The BRAIN project proponents argue that it is time for a “large-scale effort in neuroscience to create and apply a new generation of tools to enable the functional mapping and control of neural activity in brains with cellular and millisecond resolution”4.
Cell Patch Clamp
A row of neurons in the CA1 region of the hippocampus, a part of the brain involved in memory. If you look closely, there is a V-shaped glass piece on top of one of the middle neurons. This is a patch electrode, one of the techniques currently used to record specific neurons in the brain. The challenge of the BRAIN project is to develop a technique that has similar resolution and precision but that could be applied to millions of neurons instead of just one.Photo from Rosentod released in the public domain.


This means recording many neurons in the brain and having high precision in terms of time and space. Millisecond resolution means knowing the state of the neuron a thousand times per second. It is also expected that the techniques will provide high spatial resolution. Cellular resolution means that neurons are being recorded one by one (see illustration on the left), not as an ensemble as is the case in brain imaging. These two levels of precision are rather common in neurophysiology. We know how to insert tiny electrodes in the brain to record or stimulate a couple of neurons – the technique is already being applied to cure some diseases. What would be novel here is that instead of recording 1, 2 or 3 neurons, the BRAIN project would develop tools allowing us to record from thousands to millions of neurons simultaneously. A million neurons is approximately what would be needed to fully record the brain of the zebrafish4.
How will neuroscientists be able to record a million neurons if the current widespread techniques only allow recording a small number of them ? The answer is that we do not know exactly yet – the goal of the project is to develop the technologies – but some possibilities come to mind and are being raised by the proponents of the BRAIN project. I will thus review those possibilities and point out to a specific challenge, pro and con for each of these techniques. This should provide you with an overview of why the BRAIN project constitutes an advancement and what kind of technological progress we can expect from it. This is in no way an extensive list but a (hopefully) reader-friendly description of the potential avenues that have been discussed.
1. The molecular ticker tape.
The BRAIN project’s main goal is to develop technologies to record brain activities. One of the challenges that has been discussed and that neuroscientists might try to tackle is to create a molecular ticker tape. Developing this technique can be considered a “moon shot”. We do not know if it will work, we have no idea how practical it will be, but if developed correctly the payoff will be huge. In principle it is a very realistic way to record neural activities, but no one has applied it yet. It consists in using a molecule that is expressed in biological cells: the DNA polymerase (DNAP), or similar molecules that produce long strings of other molecules.
DNA Polymerase
Illustration of the DNA polymerase, one of the molecules that might be used to create molecules of DNA that would be used as a molecular “ticker tape” (a recording) of neural activity.Photo from Yikrazuul released under the Creative Commons license.


Those small molecular machines are part of our normal biological arsenal. They are in charge of copying our DNA. The DNA is the molecule which contains the code to construct all proteins of our body. The polymerase makes operations on the DNA at a very small scale (we cannot see those using a traditional microscope). The polymerase is essential for creating the copies of DNA that are transferred to daughter cells when cells divide during development. Thanks to the polymerase and many other enzymes, the DNA that is present at first in the egg is copied to every cell of the embryo and later on to every cell of the body of the animal as it develops. DNA strings pop out from one side of the polymerase a little bit like a long sheet of paper would come out of a printer.
The idea of using polymerases such as this one to record neural activities has been discussed by many, including George Church and Konrad P. Kording5,6. Since the enzyme is already working well to write information on DNA sequences, scientists think they can “harness” it to write the neural activity of a neuron onto a DNA string. This is due to a fortunate “defect” in this molecule; they tend to make errors – not copying DNA perfectly. In particular, the more calcium there is in the cell, the more likely the polymerase makes errors in copying the DNA. There is no particular reason for that, it just doesn’t make perfect copies. Luckily, when neurons are active, it is usually followed with an increase of calcium in their cell body and dendrites. Thus one can imagine a DNA polymerase that would generate a dummy string of DNA in every neuron of the brain and would make more errors when there is calcium in the neuron. The DNA would be like a micro-document, kept in a region of the cell where it probably would not interfere with normal cell functions. Recovering the DNA dummies of each neurons would allow us to have a “recording” of the moments when the polymerase was making no error (silent neurons) and when it was making errors (active neurons). This would literally provide us with a history of the neural activity of specific cells.
It seems that strings of DNA would not get too long and that we might be able to get data from extended periods. Alivisatos and colleagues made a rough estimation of about 7 days of neural activity that could be encoded in a tiny 5-μm-diameter pack of synthetic DNA7.
The capability of DNA for dense information storage is quite remarkable. In principle, a 5-μm-diameter synthetic cell could hold at least 6 billion base pairs of DNA, which could encode 7 days of spiking data at 100 Hz with 100-fold redundancy.
There is a great post at the Nucleus Ambiguous blog about the molecular ticker tape method.
Challenges
1. Create the custom version of the DNA polymerase and show that it works.
2. Study alternatives to the calcium-induced errors.
There are many reasons why the calcium-induced errors technique that is for now the most developed approach (although not completely developed yet) might not be the best way to record neural activity. First, the calcium enters relatively slowly in the neuron when they get excited. Second the concentrations of calcium, when neurons are excited, vary depending of where you are in the cell.
One possibility to overcome these problems would be to stick the DNA polymerase to another molecule that would confer additional properties. For instance, the sodium channel is a molecule present in almost all neurons and it detects small changes in voltage in neurons already. Perhaps we could rely on this sodium channel hitting on the polymerase a little bit like a hammer on a nail. There is actually a number of channels like the sodium channel that can be thought of as alternatives to improve the technique but none of them, including the sodium channel, have been tested or are known to work in any way. I have no doubt that these possibilities are currently being considered in the laboratories interested in developing such techniques.
3. Solve the problem of which cell is being recorded.
When extracting the “recordings” from the brain, we will end up with millions of DNA strings but it will be impossible to know from which neuron they come. To be valid the molecular ticker tape approach will require imprinting every DNA string with some sort of bar code, which could allow us to know from which neuron the recording comes. It is still unknown what will be made to address this issue but some of the possibilities include generating a specific random code for each neuron8. This idea requires more development to become practical. There is also a series of molecules that are more or less concentrated in certain spots of the brain such as the molecule called Sonic hedgehog. Detecting those molecules and printing the information on the DNA string might be one way to know where the recording comes from.
Pro
- This would be the best technique that we can think of to record all neurons in a brain.
Con
- It is not developed yet.
2. Optical imaging.
The technique of optical imaging is already widely used to record neural activities. I myself have been using it. It consists in inserting a molecule that reacts to light into the neurons. Calcium imaging is one example in which the molecule inserted in the neuron changes the color it emits based on the concentration of calcium in the cell. The more the neuron is excited, the more it appears as “bright” under the microscope. This allows filming the part of the brain of interest and tracking the activity of dozens, up to hundreds or thousands of neurons at the same time.
Calcium imaging of neurons
Neurons of a mouse viewed using calcium imaging. Note that the mouse was anesthetised for the moment the photo was taken to avoid movement.Photo from Mittmann et al. 2011 reproduced with special permission from Nature Neuroscience. The license that applies to this blog does not apply to this photo.
Challenge


1. To achieve the degree of spatial resolution and the number of neurons that the BRAIN project is targeting, the method will have to be improved if it is intended to record neurons in behaving mammals. For now the technique is mostly being used on neurons maintained in artificial conditions and we do not know if the thickness of the brain will be an insurmountable obstacle to record many neurons in animals. In animals with very transparent and small brains like zebrafish, it might still be possible. The big challenge is that we need to send light to the neurons and then receive the light emitted from the neurons.
Pro
- Realistic method that has been proven to work already.
Con
- Difficult to apply in the deep parts of the brain and is generally more useful on neurons maintained in artificial conditions, rarely in behaving animals or humans.
3. Silicon-based nanoprobes.
With progresses in microelectronics, one could expect that it might become possible to simply create such small electrical probes that it would be possible to insert thousands of them seemlessly in the brain, thus providing many thousands of recording sites7. These techniques are already being used – with about a hundred working recording sites on a probe being available. For now they have been used in the development of brain-machine interfaces, which are prosthetics that patients can use to control artifical limbs with their brain9,10.
Challenge
1. Make them smaller with more recording sites.
Pro
- Realistic method that has been proven to work already.
Con
- The technique is invasive and requires adding a piece of electronic in the brain.
Other aspects.
In the most recent text published by the proponents of the BRAIN project4, they also mention that the activity of neurons in the brain will not only need to be recorded but that technologies should also be further developed to alter neural activity. There are already optical techniques that allow researchers to send ray of lights to brain regions in which the neurons have been equipped with light receptors – this technique can be used to shut down or excite some neurons11. This is essential to researchers to assess whether the neurons are actively involved in some specific behavior.
There are also many techniques that are appearing to map connections between neurons, some of which rely on contributions from the general public such as EyeWire. Although these techniques show us the connections without revealing the functions, it is likely that they could be used in combinations with the previously discussed techniques to map the connections between the recorded neurons.
One aspect that has been much less discussed is what kind of behavior will be studied. It’s one thing to say we get neural recordings of millions of neurons, it’s another thing to decide what we want subjects to do. Will the project include early attempts at applying those methods to understand perception, motor control, decision making? It seems that for now the precise experiments that will be run is left open.
Finally one interesting question left is how the data will be made accessible to the wide neuroscientific community and even the general public4. The authors mention that the huge amount of data coming from such high numbers of neurons might require analysis by a wide community. It remains to be determined what means will be taken to make this data accessible but I think it would be very nice if members of the general public could participate in digging through those numerous recordings!
I hope I have clarified what the aims of the project were, as far as what has been discussed in public scientific forums up to now. Keep in mind that we will not have a first official document from the committee being consulted for this project before the end of the year so everything could change.
References
1. Christopher Chabris (2013) How Much BAM for the Buck, and Other Thoughts on the Brain Activity Map Project. http://http://blog.chabris.com.
2. John Markoff (2013) Obama Seeking to Boost Study of Human Brain. New York Times.
3. Jeanne Garbarino (2013) A 3 Billion Dollar Mistake: Why the American government should think twice about a Brain Activity Map (BAM). http://http://incubator.rockefeller.edu.
4. A. Paul Alivisatos, Miyoung Chun, George M. Church, Karl Deisseroth, John P. Donoghue, Ralph J. Greenspan, Paul L. McEuen, Michael L. Roukes, Terrence J. Sejnowski, Paul S. Weiss, Rafael Yuste (2013) The Brain Activity Map. Science 339:1284-1285.
5. Bradley Michael Zamft, Adam H. Marblestone, Konrad Kording, Daniel Schmidt, Daniel Martin-Alarcon, Keith Tyo, Edward S. Boyden, George Church (2012) Measuring Cation Dependent DNA Polymerase Fidelity Landscapes by Deep Sequencing . PLoS ONE 7:e43876.
6. Konrad P. Kording (2011) Of Toasters and Molecular Ticker Tapes. PLoS Computational Biology 7:e1002291.
7. A. Paul Alivisatos, Miyoung Chun, George M. Church, Ralph J. Greenspan, Michael L. Roukes, Rafael Yuste (2012) The Brain Activity Map Project and the Challenge of Functional Connectomics. 74:970–974.
8. Anthony M. Zador, Joshua Dubnau, Hassana K. Oyibo, Huiqing Zhan, Gang Cao, Ian D. Peikon (2013) Sequencing the Connectome. PLoS Biology 10:e1001411.
9. Hochberg LR, Bacher D, Jarosiewicz B, Masse NY, Simeral JD, Vogel J, Haddadin S, Liu J, Cash SS, van der Smagt P, Donoghue JP (2012) Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 485:372-5.
10. Pais-Vieira M, Lebedev MA, Wiest MC, Nicolelis MA (2013) Simultaneous top-down modulation of the primary somatosensory cortex and thalamic nuclei during active tactile discrimination. Journal of Neuroscience 33:4076-93.
11. Kim TI, McCall JG, Jung YH, Huang X, Siuda ER, Li Y, Song J, Song YM, Pao HA, Kim RH, Lu C, Lee SD, Song IS, Shin G, Al-Hasani R, Kim S, Tan MP, Huang Y, Omenetto FG, Rogers JA, Bruchas MR. (2013) Injectable, cellular-scale optoelectronics with applications for wireless optogenetics. Science 340:211-6.
12. Scicurious (2013) The BRAIN Initiative: BAM or BUST?. Scientific American Blogs.


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