What Is the Optimal Value of the g-Ratio for Myelinated Fibers in the Rat CNS? A Theoretical Approach

BACKGROUND:The biological process underlying axonal myelination is complex and often prone to injury and disease. The ratio of the inner axonal diameter to the total outer diameter or g-ratio is widely utilized as a functional and structural index of optimal axonal myelination. Based on the speed of fiber conduction, Rushton was the first to derive a theoretical estimate of the optimal g-ratio of 0.6 [1]. This theoretical limit nicely explains the experimental data for myelinated axons obtained for some peripheral fibers but appears significantly lower than that found for CNS fibers. This is, however, hardly surprising given that in the CNS, axonal myelination must achieve multiple goals including reducing conduction delays, promoting conduction fidelity, lowering energy costs, and saving space. METHODOLOGY/PRINCIPAL FINDINGS:In this study we explore the notion that a balanced set-point can be achieved at a functional level as the micro-structure of individual axons becomes optimized, particularly for the central system where axons tend to be smaller and their myelin sheath thinner. We used an intuitive yet novel theoretical approach based on the fundamental biophysical properties describing axonal structure and function to show that an optimal g-ratio can be defined for the central nervous system (approximately 0.77). Furthermore, by reducing the influence of volume constraints on structural design by about 40%, this approach can also predict the g-ratio observed in some peripheral fibers (approximately 0.6). CONCLUSIONS/SIGNIFICANCE:These results support the notion of optimization theory in nervous system design and construction and may also help explain why the central and peripheral systems have evolved different g-ratios as a result of volume constraints

Altering the trajectory of early postnatal cortical development can lead to structural and behavioural features of autism

<p>Abstract</p> <p>Background</p> <p>Autism is a behaviourally defined neurodevelopmental disorder with unknown etiology. Recent studies in autistic children consistently point to neuropathological and functional abnormalities in the temporal association cortex (TeA) and its associated structures. It has been proposed that the trajectory of postnatal development in these regions may undergo accelerated maturational alterations that predominantly affect sensory recognition and social interaction. Indeed, the temporal association regions that are important for sensory recognition and social interaction are one of the last regions to mature suggesting a potential vulnerability to early maturation. However, direct evaluation of the emerging hypothesis that an altered time course of early postnatal development can lead to an ASD phenotype remains lacking.</p> <p>Results</p> <p>We used electrophysiological, histological, and behavioural techniques to investigate if the known neuronal maturational promoter valproate, similar to that in culture systems, can influence the normal developmental trajectory of TeA <it>in vivo</it>. Brain sections obtained from postnatal rat pups treated with VPA <it>in vivo </it>revealed that almost 40% of cortical cells in TeA prematurely exhibited adult-like intrinsic electrophysiological properties and that this was often associated with gross cortical hypertrophy and a reduced predisposition for social play behaviour.</p> <p>Conclusions</p> <p>The co-manifestation of these functional, structural and behavioural features suggests that alteration of the developmental time course in certain high-order cortical networks may play an important role in the neurophysiological basis of autism.</p

Posterior temporal association cortex:a comprehensive in vitro electrophysiological study in rats

Bibliography: p. 189-21

Mechanisms of Hierarchical Cortical Maturation

Cortical information processing is structurally and functionally organized into hierarchical pathways, with primary sensory cortical regions providing modality specific information and associative cortical regions playing a more integrative role. Historically, there has been debate as to whether primary cortical regions mature earlier than associative cortical regions, or whether both primary and associative cortical regions mature simultaneously. Identifying whether primary and associative cortical regions mature hierarchically or simultaneously will not only deepen our understanding of the mechanisms that regulate brain maturation, but it will also provide fundamental insight into aspects of adolescent behavior, learning, neurodevelopmental disorders and computational models of neural processing. This mini-review article summarizes the current evidence supporting the sequential and hierarchical nature of cortical maturation, and then proposes a new cellular model underlying this process. Finally, unresolved issues associated with hierarchical cortical maturation are also addressed

What We Have Learned about Autism Spectrum Disorder from Valproic Acid

Two recent epidemiological investigations in children exposed to valproic acid (VPA) treatment in utero have reported a significant risk associated with neurodevelopmental disorders and autism spectrum disorder (ASD) in particular. Parallel to this work, there is a growing body of animal research literature using VPA as an animal model of ASD. In this focused review we first summarize the epidemiological evidence linking VPA to ASD and then comment on two important neurobiological findings linking VPA to ASD clinicopathology, namely, accelerated or early brain overgrowth and hyperexcitable networks. Improving our understanding of how the drug VPA can alter early development of neurological systems will ultimately improve our understanding of ASD

What We Have Learned about Autism Spectrum Disorder from Valproic Acid

Two recent epidemiological investigations in children exposed to valproic acid (VPA) treatment in utero have reported a significant risk associated with neurodevelopmental disorders and autism spectrum disorder (ASD) in particular. Parallel to this work, there is a growing body of animal research literature using VPA as an animal model of ASD. In this focused review we first summarize the epidemiological evidence linking VPA to ASD and then comment on two important neurobiological findings linking VPA to ASD clinicopathology, namely, accelerated or early brain overgrowth and hyperexcitable networks. Improving our understanding of how the drug VPA can alter early development of neurological systems will ultimately improve our understanding of ASD.Peer Reviewe

Optimized g-ratio for different neural systems.

<p>A: Efficiency index curves for a 1 µm diameter axon where δ = β = 1 (CNS-black curve) and where volume is less of a constraint <i>δ</i> = 0.6β (PNS-grey curve). Note that the peak is shifted to the right indicating that when volume is less of a constraint then the optimal sheath thickness is larger. B: Plots of <i>d_i</i> versus <i>d_o</i> representing different neural systems. In grey (circles) is when volume is less of a constraint (<i>δ</i> = 0.6<i>β</i>). In black (triangles) is when volume is as equally important as the other parameters (<i>δ</i> = <i>β</i> = 1) in defining an optimal structure (re-plotted from previous figure for comparison). The slopes of the lines, which represent the g-ratio, correspond to approximately 0.58–0.59 for the grey (PNS; correlation coefficient r = 1.0, R² = 0.99; p<0.0001) and 0.76–0.77 for the black (CNS). Curves were generated using the parameters defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007754#pone-0007754-g001" target="_blank">Figure 1</a> and were the same for both the “CNS” and “PNS” plots with the exception of <i>δ</i>.</p

Some summarized experimental g-ratio data.

<p>Some previously published g-ratio values for myelinated axons. The data are reported as the mean value (or range of means - except for the anterior commissure that only reported a range). Note that mean values are in good agreement with our predictions (g-ratio<i>_observed</i>≈0.76–0.81) for CNS and some PNS axons. Sources: a, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007754#pone.0007754-Arnett1" target="_blank">[10]</a>; b, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007754#pone.0007754-Benninger1" target="_blank">[12]</a>; c, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007754#pone.0007754-Mason1" target="_blank">[16]</a>; d, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007754#pone.0007754-Waxman3" target="_blank">[32]</a>; e, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007754#pone.0007754-Guy1" target="_blank">[15]</a>; f, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007754#pone.0007754-Chau1" target="_blank">[36]</a>; g, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007754#pone.0007754-Blakemore2" target="_blank">[13]</a>; h, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007754#pone.0007754-Ehrlich1" target="_blank">[63]</a>; i, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007754#pone.0007754-Grandis1" target="_blank">[64]</a>; j, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007754#pone.0007754-Michailov1" target="_blank">[65]</a>; k, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007754#pone.0007754-Wallace1" target="_blank">[66]</a>; l, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007754#pone.0007754-Jeronimo1" target="_blank">[67]</a>; m, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007754#pone.0007754-Malik1" target="_blank">[68]</a>; n, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007754#pone.0007754-Fahrenkamp1" target="_blank">[69]</a>; o, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007754#pone.0007754-Thomas1" target="_blank">[70]</a>; p, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007754#pone.0007754-Kerns1" target="_blank">[58]</a>; q, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007754#pone.0007754-Fraher1" target="_blank">[71]</a>. ^†signifies data from the present study (rat internal capsule raw data; 0.78±0.01 SEM, n = 85; and rat brainstem raw data; 0.81±0.01 SEM, n = 70).</p

The model can predict an optimized level of myelination for a given axon.

<p>A: Relative efficiency index with increasing lamellae (i.e. increasing sheath thickness) without volume as a constraint (i.e., <i>δ</i> = 0). A global optimum does not exist. B: Top, a schematic illustrating a 2 µm inner diameter (<i>d_i</i>) axon with an increasing (1→3) myelin sheath thickness; where the total myelin sheath thickness equals the difference between the outer (<i>d_o</i>) and inner diameters (i.e., <i>d_o</i>-<i>d_i</i>). Bottom, relative efficiency index for different myelin sheath thicknesses. “2” represents the level of “optimized” (i.e, maximal efficiency) myelination for this particular axon. “1” and “3” illustrate that lower or higher levels of myelination provide a less efficient myelo-architectural design. Here volume is a constraint (<i>δ</i> = <i>β</i> = 1).</p