Evolution, brain anatomy, and autism

An article that I wrote about one year ago became public access and can be downloaded from either PubMed or Researchgate. The title of the article is, “The Modular Organization of the Cerebral Cortex: Evolutionary Significance and Possible Links to Neurodevelopmental Conditions”. For those interested in evolution and autism this might offer an interesting read. I have copied the first few paragraphs of the article below.  Those interested in reading the whole manuscript can click on the  link provided in this sentence. The article starts with an introduction as to how the brain is organized from an evolutionary perspective.

INTRODUCTION

To some extent our understanding of the brain has been dependent on our ability to establish representational maps that communicate spatial information accurately and reproducibly. Early topographic localization schemes for the cerebral cortex were bottom-up approaches that clustered together microscopic units into larger regions based on characteristics of their somatic morphology, pigment distribution, pathological susceptibility, or myelin architecture. The successful use of defined anatomical characteristics and consequent uniformity of the different parcellation schemes across brains of a given species suggests the presence of discrete scalable modules (Diez et al., 2015; Fischi and Sereno, 2018). These units or modules lessen the evolutionary cost entailed in the creation of customized regions responsive to ever-changing environmental exigencies. In the end, the presence of homologous modules across different species attests to the evolutionary preference for standardization rather than customization, all at the expense of performance (Kuratani, 2009).
The Nobel laureate Francois Jacob once argued that evolution had “tinkered” with developmental biology. Evolution’s desultory approach explained why, despite the influence of natural selection, there were many homologies, as well as imperfections, across organisms (Jacob, 1977). Indeed, growth and differentiation across different organisms appears constrained by similar genes within the evo-devo gene toolkit. From this perspective, the brain is an agglomeration of structural improvisations piled one on top of another through millions of years of evolution with each layer providing for added complexity and a potential bottleneck for information processing (textbox 1) (Linden, 2008; Blazek et al., 2011). Simply stated, “It must be borne in mind that evolution is a tendentious, almost bureaucratic process. Once something novel has been invented, it’s generally retained and not thrown away. The new is built upon the old, but does not replace it” (Freitas, 2008). Some complex functions and behaviors are therefore novel outcomes of a system not originally intended for them. (Casanova and Tillquist, 2008). The imperfect disposition1 of brain parts is only counterbalanced by the presence and versatility of modules whose weak linkages provide for adaptive phenotypic variations (Kirschner an Gerhart, 2006).
Weak linkages explain how different parts of a system are coupled so that changes in one of its modules or compartments do not seriously affect other parts of the system. In addition, weak linkages promote the combination of modules depending on prevailing environmental exigencies. The benefits entailed by having modules with weak linkages, in the midst of an otherwise inefficient system, would make them a conserved property.
The brain according to Kitano (2007), is a modular, weakly linked system that exhibits a clear tradeoff between robustness and fragility. It is therefore unsurprising that Brodal in his classic textbook “ Neurological Anatomy in Relation to Clinical Medicine ” made the observation that the nervous system is composed of a multitude of minor units, each with its particular structural organization, specific with respect to its finer intrinsic organization as well as with its connections with other units (Brodal, 1981). According to Szentàgothai, the basic reason for looking at the organization of the brain in terms of modules is that it offers a framework for the functional interpretation of structural data (Szentàgothai, 1975).

THE NEURON

The concept that the nervous system is made up of discrete cellular elements each recapitulating the holistic properties of the brain was a generalization of Matthias Schleiden and Theodor Schwann’s cell theory as applied to the nervous system. Within this framework, the presence of neurons as discrete cellular elements and units of information processing arose from the pioneering work of Santiago Ramón y Cajal. According to Cajal, neurons were polarized in such a way that impulses were conducted cellulipetally along dendrites and cellulifugally along axons (Bertucchi, 1999). In order to comply with the polarization of the neuron, each cell was compartmentalized both functionally and anatomically by a membrane possessing: 1) a receptive component for input, 2) an impulse-initiating component, and 3) a transmitter-releasing component (Brown, 2001).The initial assessments of dynamic polarization were based solely on histological features and disregarded contemporaneous findings by Sherrington showing that neuronal conduction was reversible (Bertucchi, 1999). Indeed, exceptions to the “generalized neuron” have posed a frequent inconvenience to neuroscientists. Axons may bear a receptive surface and dendrites have voltage-gated ion channels that allow them to generate action potentials. Some interneurons and dopaminergic cells grow their axons from a dendrite while others may lack either dendrites or axons. Sometimes synapses connect a dendrite to another dendrite or an axon to another axon. Dendrites can transmit signals from the cell body and release via exocytosis a variety of neurotransmitters (Ludwig, et al., 2016). Furthermore, the role of the neuron as the sole unit of information processing has been questioned given the fact that glial networks provide a functional syncytium for electrical and chemical signaling (Froes et al., 1999). Neuronal differences have thus engendered a plethora of classifications for neurons based on their location, shape, size, released neurotransmitter(s), and function. In this regard, the plurality of neurons illustrates differences in kind rather than degree.Neurons are dependent on the actions of other neurons. According to Brown (2001): “A reflex arc of just two neurons is an abstraction, useful for discussion, which does not exist in nature. Even in systems such as the monosynaptic myotatic reflex in mammals, in which there is one set of afferent and one set of efferent neurons, many neurons are involved.” Indeed, “No matter how complicated a single neuron may be, it cannot play a role in the processing of information without interacting with other neurons” (Shepherd and Koch, 1998). This interdependence of neurons is contrary to the definition of a module (vide infra) as a self-contained unit. Moreover, evidence from cell cultures suggests that this interdependence is crucial for neuronal differentiation.Primary neuronal cultures can be manipulated to induce morphological changes characteristic of cell differentiation. A cell culture is initiated when a number of cells are seeded into a flask or plate. Dispersed postmitotic cells extend processes and synapse with one another. Once neurons differentiate they rarely divide. The survival of these cells and their maturation process (e.g., synapse formation, dendrite morphology) is heavily dependent on their seeding density (Biffi et al. 2013). In the case of neuronal cultures, the required seeding density varies between 80 and 300 cells/mm2 (Sellstrom et al., 1999).Neuronal cultures recapitulate in vitro the phenomenon of neuroplasticity, especially during the time period encumbered by brain development. The long-term survival of neurons depends on their making contact with other neurons. Indeed, a mouse that lacks an essential protein for neurotransmitter release, has a wiring plan that looks normal. However, in the days following the stage for synapse formation there is massive cell loss (Verhage et al., 2000). Depriving neurons of their connections dooms any attempts at survival. This vital interdependence makes it difficult to provide an operational definition of a neuron as a module of information processing. In defining the term neuron as a phenomenon (i.e., information processing unit) it would be necessary to repeat the term (i.e., neuron) in the framework of the explanation. Such an argument lends itself to an infinite regress.

SYSTEMS AND MODULES

A system is a grouping of items arranged into a unified whole so as to perform a common goal. A modular design reduces the complexity of a system by subdividing the same into smaller parts. This organizational scheme provides for the integration of different tasks while simultaneously allowing for the functional independence of its parts. Modules self-organize into complex systems under a variety of factors rather than a predefined blueprint (Stam et al., 2010). In biological systems, self-organization allows for environmental and physical factors to play a role in molding the final product. After examining the cytoarchitectural organization of the cerebral cortex, Arbib and Erdi (2000) concluded that, “modular architectonics may be seen as a pattern resulting from the dynamics of self-organization rather than being completely laid down in the genome.”In the cerebral cortex, connectivity helps define the scale and spatial boundaries of self-organizing modules (Batuev and Babmindra, 1993; Clune et al., 2013). Elements of a module are held in tightly interconnected groups or clusters (Mounier et al., 2010). The underlying organizational scheme follows the principle of an economy of wiring where neurons performing a particular function, and in need of communication with each other, do so more efficiently if they are held close together (Shipp, 2007). By way of contrast, connectivity between modules is reduced or looser (figure 1a,b) (Girvan and Newman, 2002). This property of modules is known as a community structure or clustering (Girvan and Newman, 2002). The identification of connectivity patterns indicative of modular assemblies provides a frame of reference that facilitates our understanding of the power, strength, redundancy, and scalability of a system.Circuits in which components maintain links with their immediate neighbors require shorter and fewer projections. In biological systems, selective pressures have clustered these connections in a small world network; a topology that optimizes connectedness while minimizing wiring costs (Sporns and Honey, 2006; Mounier et al., 2010). In this framework, those nodes that exhibit more frequent connections with each other serve as hubs of activity for the whole system (Sporns et al., 2007; Bullmore and Sporns, 2009). This modular arrangement and its defining pattern of connectivity, generalized throughout the cerebral cortex, results in an arrangement consistent with modeled network activity patterns (Muller-Linow et al., 2008; a critical review is provided by Hilgetag and Goulas, 2016).The clustering of neuronal connections into specific patterns at multiple levels of organization is a defining feature of the mammalian brain (Callaway, 2002; Bassett and Bullmore, 2006; for a review see Buxhoeveden and Casanova, 2005a). Axonal studies have shown that connectivity in these clusters is primarily local (intracolumnar) in nature and may be established early during brain development (Douglas and Martin, 1998). Ventricular injections of enhanced green fluorescent protein (EGFP)-expressing retroviruses (labeling ontogenetic clones of neurons) combined with multiple electrode whole-cell recording (to study synapse formation) illustrate an increased propensity towards establishing connections among neighboring sister cells during corticogenesis (Yu et al., 2009). The transient electrical coupling between radially aligned sister excitatory neurons regulates the subsequent formation of specific chemical synapses in the neocortex and establishes the basic microcircuit of the cerebral cortex (Yu et al., 2009, 2012).

2 responses to “Evolution, brain anatomy, and autism

  1. My daughter, Isabella, is 16, and has autism. She is verbal and very smart. I would like to have her undergo TMS treatment, under your supervision. My second son, (now 25) who was diagnosed with Early Onset Bipolar Disorder underwent TMS treatment with the guidance of Dr. Mark George at MUSC ten years ago. My son attributes his full recovery to TMS treatments. I can expound further if you’d like. Two to three years ago I requested that Dr. George treat my autistic daughter using TMS, but he said that research was unclear and in early stages regarding treatment of autism with TMS. I also met and chatted with John Robison at a conference in Columbia, SC, but he would not bend in either direction as to whether he thought it a good idea for Isabella to receive TMS treatment. Please let me know how/when/where/who so that we can begin this journey.
    Sincerely,
    Mary Baker Stevens

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    • Wish I could be of more help but presently retired. The federal government biased funding research initiatives towards larger projects emphasizing genetics. I never saw much in terms of treatment interventions. In the end I was squeezed out. Fortunate however that I can spend more time with my family and be there for them helping them. My oldest child is severely handicapped and now I can be there for him. TMS still remains in the research realm as Dr. George said. I would probably check at clinicaltrials.gov and use TMS and autism as key words. Hopefully there is a clinical trial near you. Let me know if I can be of any further help. Best regards

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