Author response: Oligodendrocyte-mediated myelin plasticity and its role in neural synchronization
Year: 2023
Type: peer-review
Abstract: Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Materials and methods Appendix 1 Appendix 2 Data availability References Decision letter Author response Article and author information Metrics Abstract Temporal synchrony of signals arriving from different neurons or brain regions is essential for proper neural processing. Nevertheless, it is not well understood how such synchrony is achieved and maintained in a complex network of time-delayed neural interactions. Myelin plasticity, accomplished by oligodendrocytes (OLs), has been suggested as an efficient mechanism for controlling timing in brain communications through adaptive changes of axonal conduction velocity and consequently conduction time delays, or latencies; however, local rules and feedback mechanisms that OLs use to achieve synchronization are not known. We propose a mathematical model of oligodendrocyte-mediated myelin plasticity (OMP) in which OLs play an active role in providing such feedback. This is achieved without using arrival times at the synapse or modulatory signaling from astrocytes; instead, it relies on the presence of global and transient OL responses to local action potentials in the axons they myelinate. While inspired by OL morphology, we provide the theoretical underpinnings that motivated the model and explore its performance for a wide range of its parameters. Our results indicate that when the characteristic time of OL's transient intracellular responses to neural spikes is between 10 and 40 ms and the firing rates in individual axons are relatively low (10 Hz), the OMP model efficiently synchronizes correlated and time-locked signals while latencies in axons carrying independent signals are unaffected. This suggests a novel form of selective synchronization in the CNS in which oligodendrocytes play an active role by modulating the conduction delays of correlated spike trains as they traverse to their targets. Editor's evaluation This paper presents a new mathematical model describing biologically plausible feedback that glial cells might use to properly modify the conduction velocity in axons and promote optimal timing of neural impulses through changes in myelination. This work provides an important step forward by providing the theory for myelin-mediated neuronal plasticity. https://doi.org/10.7554/eLife.81982.sa0 Decision letter Reviews on Sciety eLife's review process Introduction Temporal precision required in neural processing can range from sub-millisecond in sound localization and echolocation tasks to milliseconds and hundreds of milliseconds in perceptual and motor system signal processing. Often, this is a consequence of individual neural cells or brain regions requiring a narrow time window to integrate signals arriving from multiple sources. Signals traveling from distant regions commonly traverse complex conduction paths along which conduction velocity (CV) is not constant and undergoes dynamical changes and perturbations, particularly during the development. This will alter the arrival times of action potentials which may undermine the required temporal precision for information processing. It has been argued for more than a decade that a solution to this problem is the adaptive adjustment of the CV through a mechanism of myelin plasticity (MP) Fields, 2005; Fields, 2008, which postulates that myelination is an adaptive and neural activity-dependent process. Modifying myelin sheath thickness and node of Ranvier structure provides the most efficient means to alter conduction delays through changes in CV. There is growing evidence that myelin plasticity is important for fear conditioning Pan et al., 2020; Steadman et al., 2020, spatial learning Wang et al., 2020; Steadman et al., 2020, and is shown to be essential for motor skill learning Bacmeister et al., 2020; Kato et al., 2020; McKenzie et al., 2014; Xiao et al., 2016. Yet, very little progress has been made in understanding the local learning rules in this new form of plasticity and what feedback oligodendrocytes (OLs) use to properly adjust myelination in the CNS. OLs are mostly located far from the target neurons and lack direct feedback on what the desired CV is because the information about the arrival times of the action potentials, that is spikes, is not available at these intermediate locations. Moreover, in most studies of activity-dependent myelination (ADM) Pajevic et al., 2014; Fields, 2015; Dutta et al., 2019; Stevens et al., 2002; Talidou et al., 2021 the precise timing of individual spikes is ignored. It is well known that the introduction of time delays can change both stability and synchronizability at a system level, which then provides an indirect mechanism for MP to affect both the stability Pajevic et al., 2014 and synchronization, for example in a network of spiking neurons Talidou et al., 2021. However, these schemes are based on the activity rate in the connections and do not explicitly include spike timing information in their local rules. In principle, the arrival times at the target neuron can be explicitly used as the feedback signal, via learning curves similar to that of spike-timing-dependent plasticity (STDP) Bi and Poo, 1998 but with some important differences. In STDP, the crucial parameter is the pre- and post-synaptic spike time difference, Δt, the sign of which determines whether long-term potentiation or depression occurs, with Δt=0 marking the sharp transition between the two. For MP, such discontinuous learning curves would be unstable and hence must be smoothly ramped across the Δt=0 line Eurich et al., 1999; Pajevic et al., 2015. More importantly, any such feedback information at the target will have to be passed in a retrograde fashion, which is problematic since OLs are mostly located very far from the post-synaptic targets of the axons they myelinate. The same problem applies to schemes in which a network of Kuramoto oscillators is studied and the feedback is based on the phase differences Noori et al., 2020. To develop spike-timing-dependent myelination (STDM) rules, it becomes important to consider schemes in which the mediators of feedback have to act locally and adjust the delays only based on local signal timing information, where the final arrival time error is not available. In this work, we propose models in which OLs are not only the myelinating agents but also serve as the mediators, providing feedback through the interaction with spikes in different axons. We call this form of STDM oligodendrocyte-mediated myelin plasticity (OMP). Specifically, we focus on a particular type of OMP, which uses the transient temporal profile of the OL responses to neural spikes as a reference for adjusting CV and relative time delays. We use theoretical arguments, mathematical modeling, and simulations to show that even the simplest form of OMP models can lead to effective synchronization of correlated and time-locked spikes while leaving temporally uncorrelated spikes unaffected. Oligodendrocyte-mediated myelin plasticity (OMP) Our OMP model was inspired by the fact that OL morphology differs drastically from that of Schwann cell (SC), which myelinate axons in the PNS. The most important morphological difference between these two myelinating cell types is that the OL extends many of its processes to myelinate multiple axons, while the SC myelinates only a single axon (Figure 1A and B). We hypothesize that this difference comes from the distinct functional roles myelination plays in the PNS and CNS. In the PNS, the goal is to maximize the CV, while in the CNS the presumed goal is to optimize the synchrony of signals arriving from multiple sources. The OL-axon connectivity is schematically depicted in Figure 1C and quantified via the myelination matrix, ℳ. A single OL can have up to 50 such processes extended to axons within a 100–200 μm distance from the soma, but with a tendency to maximize the number of axons it can myelinate, making it unlikely that a given OL would myelinate the same axon twice Dumas et al., 2015; Walsh et al., 2016. We postulate that this morphology enables a single OL to integrate and compare signals from different axons and act as a mediator in providing the needed feedback for adjusting the relative timing of different signals via dynamic regulation of the CV along these axons. In Figure 1D, we outline a general form for such spike-response OMP models, consisting of three essential steps, described below. In these continuous-time models, the transient nature of the temporal profile of the OL response to an action potential plays a crucial role in creating the needed reference for adjusting the relative delays on different axons. Figure 1 with 4 supplements see all Download asset Open asset OMP motivation and model description. (A) Cross-section of myelinated axon bundle in CNS illustrates that myelin thickness on adjacent axons differs. (B) Single OL (green) myelinates many different axons (purple), which is in stark contrast to Schwann cells in the PNS, which myelinate only one axon. (C) OL-axon connectivity: OLs tend to avoid placing multiple processes on a single axon, thus maximizing the number of axons it myelinates. (D) Schematic depiction of the OMP-1 model which contains only the three basic steps required for this type of OMP to work. (E) Spike-response curves R(t) for increasing values of τG. (F) The equation governing the release of G(t) is linear and the response to multiple spikes (vertical lines), is the linear sum of individual responses (dashed lines). The release of M after each spike at any given OL process/axon will be proportional to the amplitude of G(t) at the time of spike (red dot for red spike). (G) The sum over responses can also be viewed as sampling of R(t) in reverse time. Panels A and B have been adapted from Chapter 45 Figure 1 in Fields, 2013, used with permission. The first essential element of OMP is the release of a global intracellular signaling factor, G, after each neural spike on any of the axons myelinated by a given OL. This response has a characteristic transient temporal profile, R(t) (Step 1 in Figure 1D). When the OL encounters a sequence of spike trains, the resulting global intracellular signal, G(t), will simply be the sum of the individual responses and will fluctuate in time, thus providing a common and time-dependent reference to all of its processes. To allow for differential myelination between different axons, a local myelin-promoting factor, M, is also required. It is released after each neuronal spike (Step 2) on a given axon a, but its release is catalyzed by G and hence is proportional to the global signal, G(t). The OL process that myelinates axon a has a dynamically changing concentration of such local factor, Ma(t), that depends on the temporal profile of the spike trains and will generally differ between the axons myelinated by a given OL. This difference in local concentration of M allows for selective modification of the CV and the conduction delays, τa. One can envision more elaborate OMP models (e.g. OMP-n in Figure 1—figure supplement 1), in which the release of M can depend on several different global factors, GM, which potentially are released via a cascade of events triggered by the original factor G. Here, we use the simplest model of this kind, OMP-1, which has only one global signal, G, that does both, responding to neuronal spikes and modulating the local release of Ma at axon a, and for the remainder of this work, we are going to refer to the OMP-1 model simply as the OMP model. The last step (Step 3) represents two continuous processes, one being the conversion of M into myelin with some addition rate, λA, and the second being the steady removal of myelin with rate λR Dutta et al., 2019, resulting in time-varying conduction delays, τa(t). The dynamics of the OMP model and its main variables G(t), Ma(t), and τa(t), is governed by a set of equations that implement steps 1–3 (Equation 8, Equation 11, and Equation 12), but also include presumed homeostatic regulation of the myelin conversion/removal rates (Equation 17), which is needed for the long-time stability of the model. An example of their time progression is shown in Figure 1—figure supplement 2. These equations govern the behavior of a population of OL at a particular segment located at distance xo along the axonal bundle (see Figure 1G). The full OMP model simulates the net delays across a discrete number of such segments, NO, each containing its own set of equations. We call this sequence of OL segments the oligo-chain (OC), which is graphically depicted in Figure 2A. Based on theoretical arguments elaborated in the next subsection, we expect OMP to synchronize correlated/time-locked signals on different axons while leaving the axons carrying independent signals unaltered (Figure 2B), even in situations where individual OL myelinates axons carrying both types of signals (Figure 2C). We study its ability to synchronize signals arriving from multiple sources that are temporally dispersed by fixed delays, representing persistent relative temporal delays among NA axons that arise either from developmental and other structural disturbances in the conduction pathways, or are due to fixed temporal sequences of activations between different sources. We expect those fixed delays, td(a),a=1,…,NA, to be compensated via corresponding adaptive delays of the myelinated axonal segments, τa,a=1,…,NA, to facilitate synchronous arrival, as depicted graphically in Figure 2D. We test this ability of the OMP model in our simulations that are, together with the details of the model, described in Materials and Methods. Figure 2 with 2 supplements see all Download asset Open asset OMP simulations with action potentials/spikes conducted along 'oligodendrocyte' chain (OC). (A) OC with NO 'effective oligodendrocytes' (segments), myelinating NA axons (ℳ is an NO×NA matrix of ones). (B) examples of 'pure' spikes used in our simulations: correlated versus independent spikes, and Poisson versus regular spiking. Gray lines are moving averages of all spikes using a sliding Gaussian window with 10 ms RMS width. (C) example of 'mixed' signals in which axons in Group 1 are conducting independent spikes and those in Group 2 carry correlated spikes. The two groups can potentially interfere with the expected synchronization behavior of each 'pure' group. Different groups also spikes time-locked within each but independent between the (D) depiction of time delays between the signal and the delays are not and the in spike times as they the axonal bundle of myelinated axons, delays are that the the of the fixed and adaptive delays, not the axons. OMP theory While OL morphology inspired our OMP model, the motivation was also by basic theoretical which provide about the synchronization We that the spike trains in individual axons have the that are independent and with that are by a process. We the rate as and the time with The for which is a to G(t) from spikes on axon a, can then be by that the sum over spikes in Equation 10 can be as sampling the response in reverse time (see Figure 1G). Here, the important parameter is the time to the last as the spikes are the from making a of where the spike times on axon a for a given OL. we the sum of the as the for can be as For uncorrelated due to any of the axons is to a resulting in of M only by the the synchronization for time-locked signals when the OL myelinates many axons is a more particularly when Gaussian with is to the process and also due to in the learning equation (see Materials and When from all axons, of spikes will generally to be In most this will depend on the form of R(t) and For for the of two axons and a given fixed we to consider the where and where (see Figure 1—figure supplement in to the for as a of and For the axon becomes the in which the myelination is making its CV of (Figure 1—figure supplement We such in the Appendix B for a with regular and without The more with increasing even when is and this is for most of the processes governing the spiking dynamics on a given axon. However, in the of a Poisson process we can its to for and The for at any of the along the axons, is simply The for M in the of Poisson spiking is also For when the concentration of M at the axon is while for the axon where the of their is than which will lead to a in CV of the axon, hence This also the of OMP synchronization, expected to be most efficient for which at low firing rates and a spike response time, (Figure 1—figure supplement In the of multiple axons, the its and we can the expected concentration of M on axon a as The Equation 4 is an important that that with Poisson spiking and fixed time delays along different axons, the differential of the myelination factor will myelinate the axons more than the The in Equation 4 but well with the values for ms (Figure 1—figure supplement Results Our for the synchronization of the OMP model is the in the spike arrival times across all axons, where for fixed delays to some and are the adaptive delays (see Figure The with some mostly due to the among fixed delays, and in the of time, due to the OMP is to to We the time of during and we call it a synchronization profile, We study the performance of the OMP model by the synchronization for a wide range of the OMP model parameter a The of parameter values in these simulations are in Appendix while the of the OMP model is given in In most we a number of synchronization one for each parameter which then as averages over all but by a given parameter of or using a model and described in Materials and methods (see also Figure supplement In we focus on the long-time which represents the long-time ability of the OMP model to synchronize signals for a given parameter 1 of used in the OMP model OMP spiking signal and data and and other For each we provide a and the range of the parameter or its in the of the of used in this model transient response time for R(t) rate for factor removal of OL in of on on adaptive delays of adaptive model OL signal (Equation of M on axon a (Equation for axon a (Equation or NO×NA for OC segment (Equation removal rate (Equation parameter when signal time firing in Poisson of given to for fixed in firing of 10 of during of each of after each profile during OMP values for by and via model time for via model rate for fixed fixed on axon for the of We show in Figure the of as well as the model when a number of synchronization using a wide range of OMP Figure supplement and Figure supplement 1 for The results indicate that the OMP model as When the myelinated axons conducted correlated spikes, we a in the conduction that a in change was for independent spikes resulting in synchronization to the constant model, (see for synchronization Figure supplement For correlated spikes, a single to synchrony was most commonly In several was not and which can be individual particularly for NO and (Figure the OMP model with only a single OL has an due to the presence of fixed delays, which be by increasing and This however, for any of when are used (Figure see also Figure 1—figure supplement 1 and Figure supplement These can be in Figure where the averages of all by NO are The for NO to in of the number of spikes When in time, it becomes that more OLs in an OC OC stability as well as the rate, the averages over a number of the error is and the or other of To all we to different models, as described in Methods. In Figure we show the and the model for different values of all indicate with increasing NO, synchrony is and For most of the OMP simulations a to a of all the arrival time from the ms to for ms this to with 1 Figure with 2 supplements see all Download asset Open asset OMP model behavior as a of OMP and signal parameters. (A) When spikes are show change in synchronization in the model for correlated signals synchronization occurs, on and τG. (B) individual OMP synchronization simulations for with ms ms lines are for and lines for indicate the ms ms ms (C) of on the number of NO, in the We show both the based on the time, as well as when in of the number of neural spikes by the OLs (D) of of on the number of OL in (E) in and for of the M for two axon which well the of (F) of and the synchronization time on the number of axons, NA values are only for as of for different NA all spike For correlated spikes, the of synchronization on two temporal the characteristic response time of the and the which was to be the same for all axons. In Figure we the synchronization as a of and the results shown are for (see Appendix For very performance was the fact that for the spike the window between spikes in different axons is the other spike responses last the resulting G(t) to release M, particularly for (Figure 1—figure supplement we that for intermediate values of synchronization was achieved and was efficient for firing rates In Figure we study the of and the synchronization time on the number of axons that OLs myelinate, The results are by the firing rate For a low firing rate of optimal performance was achieved for synchronization with in firing rate and number of axons to In increasing NA up synchronization for all firing rates The synchronization achieved at for an intermediate number of axons also with the of synchronization at We that myelinating more than axons not synchronization for any of the spiking to OLs more than 50 processes. In Figure we the ability of the OMP model to sources of signals by groups of axons carry spikes with different or different from different groups are responses in the same and hence interfere and can potentially the expected behavior for the 'pure' that ability to synchronize the correlated of or synchronize the independent In Figure and we show the results when one of axons correlated and independent spikes. In Figure we explore the of different groups carrying signals that are correlated within each but not between We within each and compare it with the signal the number of axons being in all We an in synchronization only for correlated with only of the synchronization performance due to the presence of other groups
Source: eLife
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,500
OpenAlex ID: W4367057623