Connectome (50 page)

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letters stand for nothing:
Jarvis et al. 2005.

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dorsal ventricular ridge:
Karten 1997.

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Michale Fee and his collaborators:
Hahnloser, Kozhevnikov, and Fee 2002.

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expect from a synaptic chain:
The synaptic chain is actually a bit too simple a model for HVC. To account for the repetitions of the song motif, the last neurons in the chain would have to make synapses onto the first neurons, creating a circular structure rather than a linear one. And some additional mechanism would be needed to terminate the sequence after a few repetitions.

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like a synaptic chain: Fee and his collaborators estimate that one hundred RA-projecting HVC neurons are spiking during any moment of song (Fee, Kozhevnikov, and Hahnloser 2004) and hypothesize that HVC contains a synaptic chain with one hundred neurons in each link.
Fee and his collaborators estimate that one hundred RA-projecting HVC neurons are spiking during any moment of song (Fee, Kozhevnikov, and Hahnloser 2004) and hypothesize that HVC contains a synaptic chain with one hundred neurons in each link.

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To reveal it:
Ideally, the HVC connectome would come to us naturally unscrambled, so no additional work would be necessary. This would be the case if HVC neurons were arranged so that they spiked in some spatially defined order—for example, from front to back. But actually it appears that neurons are arranged without regard to their spike times (Fee, Kozhevnikov, and Hahnloser 2004).

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computer would be necessary:
Actually, we could still do it by hand if the chain were perfect. But if there are some “inappropriate” connections, such as synapses directed backward, finding a chain becomes more difficult and requires a computer (Seung 2009). Unscrambling neurons is an example of a problem called “graph layout” by computer scientists.

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resemble blinking lights:
These stains fluoresce when illuminated, like a sticker that glows in the dark when illuminated by black light. The amount of fluorescence varies with calcium concentration, which in turn is modulated by spiking.

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might not be able to order:
Actually, this outcome could leave ambiguity. Perhaps a sequential ordering exists, but our unscrambling algorithms are too poor to find it. Computer scientists will have to work hard to make sure that their algorithms are good enough to find any ordering if it exists.

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go backward or jump:
Even if there turn out to be some “inappropriate” connections that violate the sequential ordering, we could still say that the connectome is an
approximation
to a synaptic chain. But if there were too many such connections, then we'd have to say that the chain is a bad model and cannot explain why the network generates sequential activity.

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HVC neurons in young males:
Jun and Jin 2007; Fiete et al. 2010.

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reconnection also plays a role:
This was suggested by Jun and Jin 2007.

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Kevin Briggman:
Briggman, Helmstaedter, and Denk 2011.

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Davi Bock:
Bock et al. 2011.

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great-great-grandma's dog:
What about grounding the memory of the bird's song? If we found an entire bird connectome, we could examine the pathways from each HVC neuron to the vocal muscles. These pathways are thought to transform the abstract sequence in HVC into the specific motor commands required to make sounds. (This transformation appears to be learned by practice too.) Analysis of the connections in these pathways might make it possible to decode the movement signaled by each HVC neuron. This method would require that we identify rules of connection for neurons related to motor control, which are analogous to the part–whole rule for perceptual neurons. In general, grounding memories requires that we trace pathways all the way from the center of the brain to the sensory and motor periphery.

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rules of connection:
Rules of connection can be mathematically formalized as probabilistic models of graph generation based on latent variables at the nodes of the graph (Seung 2009).

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quite improbable too:
Mooney and Prather 2005.

 

12. Comparing

 

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Native American and African myths:
Davis 2005.

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bedrock assumption:
Even more disconcertingly, identical twins challenge the more sweeping axiom that everything is unique—human, animal, or inanimate object. This axiom underlies the lovely claim that no two snowflakes are alike, and may have been behind the animistic beliefs of primitive societies that all objects have souls. Because of mass production by factories, we have grown blasé about material objects that look almost indistinguishable. Such instances were much rarer in the preindustrial world, so I suspect that twins appeared even more magical to our primitive ancestors than they do now. But such thoughts are less relevant for connectomics than fodder for nanotechnologists who promise to make material objects that are truly identical, down to the placement of individual atoms (see, for example, Drexler 1986).

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deviations in DNA sequence:
Machin 2009 discusses both genetic and epigenetic differences between identical twins.

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two complete C. elegans connectomes:
As mentioned earlier, the researchers actually pieced together the connectome using images drawn from several worms. The published
C. elegans
connectome is a mosaic, not a unified representation of an individual worm's nervous system. So we don't have even one complete connectome of an individual worm, much less two.

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David Hall and Richard Russell:
Hall and Russell 1991.

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purebred dogs and horses:
Laboratory animals are generally inbred this way to ensure that they are genetically almost identical, which is supposed to make experiments more repeatable. It's well-known that inbreeding can increase the likelihood of having two defective copies of a gene, and “recessive” disorders are governed by a “two strikes and you're out” rule. This is why many dog breeds have genetic disorders and why European royalty suffered from hemophilia. Since inbreeding probably makes laboratory animals “dumber,” research on them might not be applicable to their wild counterparts.

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sophisticated computational methods:
The most basic computational problem of genomics is finding a matching or alignment between two DNA sequences. This is solved by fast approximations to dynamic programming, a formalism first developed in the 1940s and 1950s for solving problems with a one-dimensional or tree structure. Solving the analogous matching problem for two connectomes will be an important computational challenge for connectomics, and is much harder than aligning genomes. Determining whether two connectomes are the same is known as the graph isomorphism problem, for which no polynomial time algorithm is known. Determining whether one connectome is part of another is known as the subgraph isomorphism problem, which is NP-complete.

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known in antiquity:
Gray and white are not the natural colors of living brain tissue, which is pinkish, but rather the colors of preserved brain tissue.

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is all “wires”:
As noted by Kostovic and Rakic 1980, Cajal already observed that there are exceptions to this rule, known as “interstitial neurons.”

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straight out of the base:
This mental picture is a bit confusing, because the cell body looks like an arrowhead pointing in the opposite direction of information flow along the axon.

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150,000 kilometers:
This crude estimate assumes that the density of axons throughout the cerebral white matter is the same as in the corpus callosum, or 380,000 axons per square millimeter (Aboitiz et al. 1992). The estimate also makes use of the total volume of white matter, which is 400 cubic centimeters (Rilling and Insel 1999).

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Myelination speeds up:
The fat in myelin serves as an insulator that prevents leakage of electrical currents out of the axon. This has the effect of boosting the speed at which electrical signals propagate. Electrical signals travel at top speed in myelinated axons, ten or more times faster than in unmyelinated axons. Myelin sheaths are outgrowths of non-neuronal, or glial, cells. Schwann cells myelinate PNS axons, and oligodendrocytes myelinate CNS axons.

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axon enters and branches:
If the axon doesn't branch in a region, it's probably passing through without making synapses.

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almost completely unexplored:
Historically, the white matter of animal brains has been studied by the method of tracer injection. When certain substances are injected into the brain, they are taken up by neurons at that location and transported along axons to other brain regions. By visualizing the destination of such tracer substances, it is possible to identify the regions connected to the injection site. Data from such experiments was compiled in Felleman and Van Essen 1991 to chart the regional connectome of the monkey brain shown here (Figure 51). The Brain Architecture Project, led by Partha Mitra, is systematically applying tracer injections with the goal of producing a complete map of long-range connections in the rodent brain. But the tracer must be injected while the brain is still alive, as its transport depends on active processes in living neurons. Therefore tracer injection is an invasive technique, and is employed only with animal brains. It does not work at all with postmortem human brains. (Certain lipophilic dyes don't depend on active transport, but are difficult to use as tracers in postmortem brains because they travel so slowly.) My proposal of serial light microscopy does not require injection of tracers. Instead of staining just a small bundle of axons, all myelinated axons in the white matter are stained and imaged. This method could potentially be applied to a postmortem human brain. Furthermore, its high spatial resolution prevents the ambiguities that plague diffusion MRI and naked-eye dissection. My proposal is an example of dense reconstruction, which extracts a complete map from a single brain, rather than aggregating data from many brains.

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Diffusion MRI is an exciting:
This method works by measuring the direction dependence of the speed of diffusion of water molecules in the brain. Diffusion along the axis of axons is faster than in the perpendicular direction.

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sparking revisions: Friederici 2009.
Friederici 2009.

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complementary methods:
We've focused on comparing connectomes of different individuals using microscopy. This provides snapshots of connectomes at moments in time. Comparing such snapshots can tell us something about how interventions change the brain. (Recall that Rosenzweig's experiments on environmental enrichment and Antonini and Stryker's experiments on monocular deprivation of V1 relied on comparisons between different animals or populations of animals.) But we would also like to compare the connectomes of a single individual at different times. Unfortunately, there is currently no good way of doing this. A noninvasive method like MRI can follow the evolution of a connectome over time but cannot deliver the neuronal resolution of microscopy. There are ways of improving the snapshots of microscopy by highlighting changes to the connectome, however. There now exist staining methods for making recently strengthened synapses visible, as well as methods that do the same for newly created neurons. It's important to invent ways of labeling
synapses
that were recently created, as well as locations where synapses were recently eliminated. With such images, one could not only quantify the total amount of synapse creation and elimination but go much further, because every created and eliminated synapse would be seen in the context of an entire network. We would know exactly how synapse creation and elimination changed the organization of connectivity, as opposed to a coarse measure like total number of synapses. This would enable us to detect even subtle connectome changes, as well as figure out whether they are causally related to learning.

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brains of the deceased:
I mentioned earlier that the two-photon microscope can be used to observe neurons in living brains. This requires opening or thinning the skull, however. Also, it works only for neurons near the surface of the brain, unless the viewing is done through an optical fiber inserted deep inside, an even more invasive procedure. And it can visualize only neurites that are sparsely labeled.

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present special problems:
The brains may not be well preserved after death; they may suffer from other abnormalities that are not relevant to the mental disorder in question, such as injury caused by stroke; and they may have been changed by drugs if the deceased person was treated for the mental disorder.

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into the genomes of animals:
Nestler and Hyman 2010. Some mental disorders are associated with deletions of parts of the genome, and researchers can create these deletions in animal genomes also.

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simian immunodeficiency virus:
According to one theory, HIV originated when SIV mutated and jumped from monkeys to humans.

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numbers of plaques and tangles:
Oddo et al. 2003.

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“unbiased, hypothesis-free manner”:
Lander 2011.