Archive for the ‘Structure to Function’ Category

What is the mechanism by which dendritic spines can change structure over a rapid time course? Though this may seem esoteric, it is probably how memories form and is thus utterly essential to neuroscience. Two new papers present some relevant data.


Two-photon imaging data of dendritic spines, from Wikipedia User:Tmhoogland

First, as has been shown several times, Harward et al show that glutamate uncaging at single dendritic spines leads to a rapid increase in spine volume after only ~ 1 minute that degrades over a period of several more minutes:


Harward et al; doi:10.1038/nature19766

Along the same time course as the dendritic spine volume increase, these authors also detected TrkB activation (using their amazing new FRET sensor), which was largely in the activated spine but also traveled to nearby spines and the dendrite itself:


Harward et al; doi:10.1038/nature19766


In what is to me probably their most compelling experiment, they show that hippocampal slices without BDNF have highly impaired volume changes in response to glutamate, and that this can be rescued by the addition of BDNF:


Harward et al; doi:10.1038/nature19766

They also present several lines of evidence that this is an autocrine mechanism, with BDNF released from spines by exosomes and binding to TrkB receptors on the same spine.

In a separate article in which most of the same authors contributed, they show that another protein, Rac1, is activated (ie, GTP-bound, leading to fluorescence) very quickly following glutamate uncaging at single spines:



Hedrick et al; doi:10.1038/nature19784

They also show that a similar rapid course of activation following glutamate uncaging occurs for the other Rho GTPases Cdc42 and RhoA.

Interestingly, they also show that these proteins mediate synaptic crosstalk, whereby the activation of one dendritic spines causes nearby dendritic spines to increase in strength. After several more experiments, here is their diagram explaining this mechanism:


Hedrick et al; doi:10.1038/nature19784

Overall I find their data trustworthy and important. The most interesting subsequent question for me is whether endogenous amounts of CaMKII, BDNF, TrkB, and Rho GTPase signaling components (e.g., Cdc42, RhoA, Rac1) vary across dendritic spines, and whether this helps mediate variability in spine-specific and spine neighbor-specific degrees of plasticity. My guess is that they do, but AFAICT it remains to be shown.

If it is true that spines, dendrites, and neurons vary in the expression and distribution of these proteins, then any attempt to build models of the brain, as well as models of individual brains that have any sort of dynamic component, probably need to measure and model the local densities of these protein mediators of plasticity.

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It has been well-established for over a decade that synaptic vesicle release further away from a particular receptor cluster is associated with a decreased probability of receptor open state and therefore a decreased postsynaptic current (at least at glutamatergic synapses).


Franks et al 2003; PMC2944019

A few months ago Tang et al published an article in which they reported live imaging of cultured rat hippocampal neurons to investigate this.

They showed that critical vesicle priming and fusion proteins are preferentially found near to one another within presynaptic active zones. Moreover, these regions were associated with higher levels of postsynaptic receptors and scaffolding proteins.

On this basis, the authors suggest that there are transynaptic columns, which they call “nanocolumns” (I employ scare quotes here quite intentionally because I don’t prefix any word with nano- until I am absolutely forced to).

They have a nice YouTube video visualizing this arrangement at a synapse:

They propose that this arrangement allows the densest presynaptic active zones to match the densest postsynaptic receptor densities, maximizing the efficiency, and therefore strength, of the synapse.

In their most elegant validation experiment of this model, they inhibited synapses by activating postsynaptic NMDA receptors and found that this led to a decreased correspondence between synaptic active zones and postsynaptic densities (PSDs).


Tang et al 2016; doi:10.1038/nature19058

As you can see, the time-scale of the effect of NMDA receptor activation was pretty fast, at only 5 mins. My guess is that this effect is so fast because active positive regulation maintains the column organization, and without it, proteins rapidly diffuse away.

It is almost certain that synaptic cleft adhesion systems or retrograde signaling mechanisms regulate synaptic column organization, and the race is on to identify them and precisely how they work.

In the meantime, Tang et al’s work is a great example of synaptic strength variability that is dependent on protein localization, and should inform our models of how the brain works.

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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.


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

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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.


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

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A cool study shows that knocking out the regulatory protein Epac2 in mice has large effects on the structural stability of basal dendrites in pyramidal neurons.

This is not the first study to have demonstrated this type of selective regulation, but it’s still surprising. The apical dendrites are so similar and close to the basal ones; why wouldn’t a regulatory molecule affect both classes?

The fact that they are regulated differentially shows that each structural component of neurons is finely tuned. This is weak evidence in favor of the theory that the neuronal morphology carries lots of information.


Srivastava DP, Woolfrey KM, Jones KA, Anderson CT, Smith KR, et al. (2012) An Autism-Associated Variant of Epac2 Reveals a Role for Ras/Epac2 Signaling in Controlling Basal Dendrite Maintenance in Mice. PLoS Biol 10(6): e1001350. doi:10.1371/journal.pbio.1001350

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