Oligodendrocyte-mediated myelin plasticity and its role in neural synchronization

  1. Sinisa Pajevic
  2. Dietmar Plenz
  3. Peter J Basser
  4. R Douglas Fields

  • Curated by eLife

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    eLife Assessment:

    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 problem is of great importance in the field of neuronal plasticity. The mathematical model is solid and predicts that individual oligodendrocytes are able to modify their myelination pattern in response to correlated action potentials. This work provides an important step forward by providing the theory for myelin-mediated neuronal plasticity. The study will benefit from adapting physiological parameters for oligodendrocytes that are guided by experimental data.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 agreed to share their name with the authors.)

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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.

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  1. Version published to 10.7554/elife.81982 on eLife
  2. eLife

    eLife Assessment:

    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 problem is of great importance in the field of neuronal plasticity. The mathematical model is solid and predicts that individual oligodendrocytes are able to modify their myelination pattern in response to correlated action potentials. This work provides an important step forward by providing the theory for myelin-mediated neuronal plasticity. The study will benefit from adapting physiological parameters for oligodendrocytes that are guided by experimental data.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 agreed to share their name with the authors.)

  3. eLife

    Reviewer #1 (Public Review):

    In this study, Pajevic and colleagues devised a mathematical model to study the biophysical properties of oligodendrocytes (OLs) in mediating myelin plasticity in the brain. The authors suggest that the OLs can sense the neuronal firing and in a calculated manner release a factor (M), which can locally regulate myelin sheath addition or removal at a given axonal segment. As far as my expertise goes, the modeling work seems quite sound and robust. But, the critical issue is that many parameters the authors chose for generating the oligodendrocyte-mediated myelin plasticity (OMP) model are hypothetical from a physiological point of view. For several modeling parameters, it was challenging to relate to the 'real' electrophysiology quantities recorded for OLs. For example, it is unclear what the ultra-fast signaling factor (G) could be at OLs/myelin segments, which can act at a speed of 40ms. Another assumption is that OLs release a potent factor called M, which can instantaneously promote the formation of new myelin internodes or stabilize the existing node. In addition, the release of such factor locally by OLs to self-maintain the myelin sheath has not been experimentally demonstrated yet. Throughout the manuscript, I felt that OLs were morphed into neuron-like cells exhibiting fast responsive electrophysiological properties. But, the actual experimental electrophysiological recordings or Ca2+ imaging data suggest that OLs operate in seconds rather than milliseconds. Hence, the study requires selecting physiological parameters that are more "realistic" for OLs and are guided by the rich sets of published experimental data.

  4. eLife

    Reviewer #2 (Public Review):

    Pajevic et. al build and analyze a theoretical model for exploring potential mechanisms by which oligodendrocyte (OL) cells may achieve synchronization of spike-arrival timing across axons. The problem of temporal synchronization is of great importance to neuroscience, and the notion of myelin plasticity as a modulator of axonal conduction delays is exciting and under-studied. Their model assumes a closed-loop interaction between OL cells and neural spike timing; in contrast to previous models, here the sensing and acting of OLs along axonal bundles is entirely local and does not depend on arrival times at the target synapse. The proposed model builds on a global variable carried within each OL cell, which responds to spikes from all axons to which it has processed. This variable then modulates the level of a local myelin promoter, specific to each axon. They show, mainly through numerical simulation, that their model can robustly synchronize between groups of axons with correlated firing while leaving axon groups firing at independent timing unchanged. Further, the performance of their model is not significantly affected by mixed groups of correlated and independently spiking axons.

    This work has the potential to impact the field in two ways. First, the ideas explored here may inspire further theoretical research into the mechanisms of spike-arrival timing via myelin plasticity. Second, through this model, the authors generate a number of concrete predictions regarding the mechanics of OL-mediated myelin plasticity. It would be intriguing to test these predictions experimentally, a process which in itself may lead to additional discoveries and novel insights.

    The manuscript is in general well written, here I point out some aspects of the model that can benefit from further discussion:
    (1) An essential ingredient of the model is the OL-specific global signal, G, which can in principle be a result of a cascade of responses to neuronal spiking activity. This paper focuses on the simplest case, where the cascade consists of a single response function, and the authors propose that the physiological signal that carries G might be the intracellular calcium level. This is an interesting idea, and in my opinion, could be elaborated on. For example, how does model behavior depend on the number of cascade processes, n? What are the physiological signals that might correspond to additional levels in the cascade? Are there specific measurements that could be done to validate/invalidate the proposed role of calcium?
    (2) One of the concrete predictions from the model is that optimal synchronization occurs in the regime of low firing rates. How does this result depend on the model choice? For example, does this change in the more general model OMP-n? Does it depend on the homeostatically set myelin removal rate? Do the authors have an idea as to the mechanism underlying this optimal point?

  5. Version published to 10.1101/2022.07.03.498570v2 on bioRxiv
  6. Version published to 10.1101/2022.07.03.498570v1 on bioRxiv