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In an effort to consolidate disparate threads, I am now blogging exclusively here. I anticipate that I will be discussing much neuroscience, and in particular Alzheimer’s, at that blog. Even if you don’t want to subscribe to the new blog, thanks for reading. 

In everyday life, your muscles, metabolism, and nervous system work together to ensure that your cerebral blood flow meets the metabolic needs of your various brain regions. So if you are trying to scrutinize an impressionist painting, your body will likely relocate more blood flow to your visual cortex.

Following a stroke, this cerebral blood flow regulation is impaired. But, the degree and spread of the impairment is unknown. To investigate this, Hu et al. measured systemic blood pressure (BP) and used a transcranial doppler to measure cerebral blood flow velocity (BFV) at the same time.

In their model, better regulation of cerebral blood flow corresponds to a sharper phase shift between blood pressure (BP) and cerebral blood flow velocity (BFV). Individuals with the highest score of a 9 on their autoregulation index (ARI) have more regulation than those with the lowest score of 0, which corresponds to no phase shift.

When they compared patients who had experienced MCA infarcts (a common type of stroke) and healthy controls, they found that stroke patients had significantly less phase coupling between blood pressure and cerebral blood flow. This effect was pronounced over a wide range of blood pressure oscillation frequencies.

Given enough time and the right conditions, can the body repair its ability to regulate cerebral blood flow following a stroke? When the researchers examined this, they found no statistically significant difference between the BFV-BP phase difference and time since stroke.

But, that doesn’t mean that there’s a statistically significant lack of difference. So, further longitudinal studies will be needed to help clarify whether, in certain people in certain environments, the brain improves its cerebral regulation following stroke.

Reference

Hu K, Lo M-T, Peng C-K, Liu Y, Novak V (2012) A Nonlinear Dynamic Approach Reveals a Long-Term Stroke Effect on Cerebral Blood Flow Regulation at Multiple Time Scales. PLoS Comput Biol 8(7): e1002601. doi:10.1371/journal.pcbi.1002601

In investigating a crime, to pinpoint the culprit, the saying goes, “follow the money.” In science, the saying is (or at least, should be), “follow the ATP.”

A six month old paper acts as a nice review on this topic. The authors stratify tissue types based on the degree of myelination (none, developing, and adult). This is shown here,

where:

  • action potential use is on voltage-gated Na+/K+-ATPases
  • synapse use is on postsynaptic membrane currents, presynaptic calcium entry, and neurotransmitter/vesicle cycling
  • oligodendrocyte resting potential use is continuous Na+/K+ pumps
  • housekeeping use is on protein/lipid synthesis and intracellular trafficking of molecules/organelles

That’s way more than I would have expected on housekeeping. But by far their most surprising finding is that the cost of maintaining the resting potentials in oligodendrocytes is so large that myelination doesn’t usually save energy on net–it depends on the firing rate of the neuron. That’s a heterodox bomb.

I suppose that myelination not leading to energy saving is weak evidence in favor of it doing something else, aside from speeding up spikes. Like, allowing for plasticity.

Reference

Harris; Atwood (2012). “The Energetics of CNS White Matter”Journal of NeuroscienceDOI:10.1523/JNEUROSCI.3430-11.2012

A nice, basic study looks at how altering the location of inhibition onto a pyramidal cell neurite affects its spiking properties. Their inhibition is meant to mimic the effects of cortical interneurons (e.g., basket cells, Martinotti cells), which project onto pyramidal cells each with their own stereotyped spatial distributions.

Elucidating these basic structure-function relationships will make synapse-level connectomics data more useful to determine the function of interneuron types.

Here’s just one of many examples in their extensive report. When they applied inhibition (GABA) to pyramidal cell dendrites further from the soma than their excitatory signal (laser-based glutamate uncaging), which they called “distal inhibition”, it led to an increased threshold required for a spike to occur. But, it didn’t change the intensity of that spike when it did occur.

In constrast, when they applied inhibition to pyramidal cell dendrites between the excitatory signal and the soma, which they called “on-the-path inhibition”, it both slightly increased the depolarization threshold and reduced the spike heights when they did occur. You can see this all below.

As an example of how this could be used, let’s say that, on the basis of connectomics data, you discover that a certain set of cells send projections to pyramidal cells which are systematically distal to the projections from a different set of cells.

What you can then say is that the former class of cells is acting to increase the depolarization threshold which the latter set of cells needs to exceed in order to induce those pyramidal cells to spike. Pretty cool.

Reference

Jadi M, Polsky A, Schiller J, Mel BW (2012) Location-Dependent Effects of Inhibition on Local Spiking in Pyramidal Neuron Dendrites. PLoS Comput Biol 8(6): e1002550. doi:10.1371/journal.pcbi.1002550

Due to randomness in neurodevelopment, it is an unavoidable constraint on neural morphology that some synapses will be further away from the soma than others.

And due to the vagaries of membrane electrophysiology, membrane potential changes will degrade continuously on their journey to the soma.

So, in the absence of an adjustment mechanism, the potentials of more distant synapses would be more degraded when they reach the soma and have a proportionally smaller impact on whether the neuron should spike.

For certain cell types, evolution would probably like to eliminate this bias (“no ion channel taxation without representation”). But the mechanism by which they might do so is unclear.

A modeling study provides intriguing evidence that hippocampal pyramidal neurons adjust for this via homeostatic scaling of the maximum calcium concentration at synapses following action potential backpropagation.

As you can see below, the authors found a strongly negative correlation between the distance from a spine to the soma and its maximum calcium concentration.

Because distance from the soma and EPSP degradation are correlated, they also found a strongly negative association between the degree to which excitatory potentials attenuate on their journey to the soma and their max calcium concentration.

left = model pyramidal neuron with spines; colored circles = locations of spines demarcated in the scatter plots; path distance = 3d separation from the soma to that spine

The main problem with the model is that if synapses really are in a true “synaptic democracy”, then homeostatic scaling to this feature wouldn’t be strong enough to be the only reason why. Figure 8F simply does not show a distribution of jointly independent variables.

Plus, figure 1H shows that almost all of the variance in peak calcium concentration is for synapses <400 microns from the soma. How would you achieve equality of representation between synapses 400 um and 800 um away?

Still, this is a nice example of a structure-function relationship at the synapse level and is a hypothesis wholly worth testing.

Reference

Sterratt DC, Groen MR, Meredith RM, van Ooyen A (2012) Spine Calcium Transients Induced by Synaptically-Evoked Action Potentials Can Predict Synapse Location and Establish Synaptic Democracy. PLoS Comput Biol 8(6): e1002545. doi:10.1371/journal.pcbi.1002545

1) Scale mismatch between the synapse-synapse level and the kind of description you want to acquire about the nervous system for a particular goal. He argues that the point at which the interesting neural computation works might be at the mesoscale. It might be enough to know the statistics of how nerve cells work at the synapse level if you want to predict behavior.

2) Structure-function relationships are elusive in the nervous system. It’s harder to understand the information that is being propagated to the nervous system because its purpose is so much more nebulous than a typical organ, like a kidney.

3) Computation-substrate relationships are elusive in general. The structure of an information processing machine doesn’t tell you about the processing it performs. For example, you can analyze in finest detail the microprocessor structure in a computer, and it will constrain the possible ways it can act, but it won’t tell you what actual operating system it is using.

Here is a link to the video of Movshon’s opening remarks. He also mentions the good-if-true point that the set of connections of C. elegans is known, but our understanding of its physiology hasn’t “been materially enhanced” by having that connectome.

The rest of the debate was entertaining but not all that vitriolic. Movshon and Seung do not appear to disagree on all that much.

I personally lean towards Seung’s side. This is not so much due to the specifics (many of which can be successfully quibbled with), but rather due to the reference class of genomics, a set of technologies and methods which have proven to be very fruitful and well worth the investment.

During development, one axon typically comes to dominate each set of synaptic sites at a neuromuscular junction. This means that just one neuron controls each muscle fiber, allowing for specificity of motor function.

A nice application of laser irradiation allows researchers to intervene in the formation of axonal branches in developing mice to study this.

What they found was that irradiating the axon currently occupying the site spurred a sequence of events (presumably involving molecular signaling) that led nearby axons (often smaller ones) to take it over.

A 67 second, soundless video of one 1,000-step simulation of this process demonstrates the concepts behind this finding.

In the simulation, each circle represents a synaptic site, and each color an innervating axon. There are originally six colors.

At each of the 1,000 time steps, one axon is withdrawn from a randomly chosen site, and an adjacent one (possibly of the same color) takes it over.

The territory controlled by one axon increases (with negative acceleration) until it comes to dominate all the sites.

Although it is possible that a qualitatively different process occurs for axonal inputs to nerve cells, odds are that a similar sort of evolution via competition helps drive CNS phenomena such as memory. (Because evolution tends to re-use useful processes.)

Reference

Turney SG, Lichtman JW (2012) Reversing the Outcome of Synapse Elimination at Developing Neuromuscular Junctions In Vivo: Evidence for Synaptic Competition and Its Mechanism. PLoS Biol 10(6): e1001352. doi:10.1371/journal.pbio.1001352

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