The job of a neuropathologist is to establish the existence of abnormalities in the conformation of both cells and tissue. This endeavor is not all that different from that of a chess player, who rather than memorizing endless move sequences guiding him/her though the middle game he/she goes after those spatial configurations of pieces that have proven of benefit in the past. In this regard the neuropathologist first examines his slides at low magnification to establish the presence of gross tissue reactions like edema, ischemia, or even a tumor.  Once the status of the tissue has been ascertained the neuropathologist zeroes down at higher magnification on individuals cells to study the configuration of their bodies and nuclei.

Screening of cells is a detailed process that itself can be divided into characterizing the status of both the cell body and the nucleus. The body or soma of a cell indicates how the same has differentiated and showcases its “type”. The cell body readily identifies a cell as coming from the membranes covering the brain, neurons, lining of blood vessels, etc. Indeed, based on its cell body you can recognize whether a given cell is derived from the skin or inner parts of the body. The nucleus, on the other hand, gives an idea as to the activity of the cell rather than cell type. Nuclear features, for example, are very important when making a diagnosis of a tumor, as their cells are actively dividing and the nucleus shows distinctive features.

Neurons themselves give the impression of metabolically active cells.  The majority of neurons (about 75% of them) in the cortex are very large and acquire a pyramidal configuration.  Their nucleus is correspondingly large and its genetic material dissolved, meaning that it is actively being transcribed. The nucleloli (the ribosome factory of the cell) of pyramidal cells are enormous. Other types of cells that are similarly active in the body belong to glands. Both glands and neuronal tissue secrete chemicals in large quantities albeit through different mechanisms and for different purposes.

Figure: A neuron stained with the Nissl technique. Nissl stains make apparent features of the cytoplasm and nucleus that indicate the high metabolic activity of the cell. Stained organelles are called the rough endoplasmic reticulum (also called Nissl bodies) and nucleolus.

Regressive changes, such as with cell shrinkage or small bodies, imply a withering away of the cell. Early neurologists called it “abiotrophy” to signify a loss of a vital nutritive factor. In regressive states the optimal biochemical environment of the cell deviates from normal without itself becoming life-threatening. The insidious process provides for long-standing unfavorable living conditions that manifest themselves as shrinkage of the cell body (soma) and its projections as well as distortions in shape.  Occasional mechanisms involved in regressive changes include the aging process, oxidative stress, and reduced protein synthesis.

Figure: Striking regressive changes seen in neurons caused by lack of oxygen.  The pyramidal cells are shrunken and cytoplasmic borders retracted. The shrunk cytoplasm can be inferred by the cavities surrounding each pyramidal cell.  Elements within the cytoplasm and nucleus appear homogenized and the nucleolus has disappeared.  Changes suggest the lack of vitality or metabolic activity of the cell, a prelude to cell death.

Small cells have been described for both cortical and subcortical neurons in primary as well as syndromic autism. The findings of increased-cell packing density and smaller neuronal (soma) size were first reported in autism by Bauman and Kemper.  Brain areas involved included the amygdala (primarily the medial, central, and cortical nuclei), hippocampal complex, subiculum, entorhinal cortex, medial septal nuclei, and mammillary bodies (for a summary of findings see Bauman and Kemper, 2005).  The authors considered the small cells as examples of developmental arrest, but did not provide evidence for their assertion nor did they ever attempt to pursue this point. Note: The small cells in autism do not display characteristics of regressive changes nor for that matter of developmental arrest.  According to this investigator they look normal except for size and can only be distinguished by population analysis with aid of a computer.

Why are small neurons of importance and why do such findings need to be pursued?

Autism is traditionally regarded as a neurodevelopmental condition that manifests itself behaviorally within the first 3 years of a person’s life. Postmortem studies have shown that minicolumnar width in these patients is significantly diminished specially at their periphery. Other studies have shown that the cell soma of cortical and subcortical neurons in both typical and syndromic autism is reduced. Small neurons invariably suggest the presence of neuropathology. According to the literature, small neurons provide a variety of phenotypes spanning developmental arrest, a stage leading to cell death, aposklesis, or abiotrophy (i.e., cell withering associated with neurodegeneration), and, in some cases, a type of non-apoptotic dark cell degeneration.

We hypothesize that the smaller cell soma in the brains of autistic patients are not the result of a neuropathological processes but a necessity imposed by laws of conservation in order to make the system work most efficiently. Results from our group indicate that smaller pyramidal cell size in autism is related to the “even expansion” of minicolumns (Casanova et al., 2011). Smaller cells in this sense are not an advantage or a disadvantage, but simply a fact that “. . .evolution couldn’t readily build us [our brains] in any other way” (Marcus, 2008, p. 154). This finding has important implications in regards to defining difference in brain connectivity that may be particular to the condition.

Small cells define an “intrahemispheric modus operandi” (a preference for operations within a given hemisphere)

Individual size of cells may not decrease with increasing brain size because this parameter is defined by the cell’s connections. According to Vinters and Kleinschmidt-DeMasters (2008), “The volume of the neuronal soma parallels the length of the axon for which it is responsible: the longer the axon, the larger the cell body must be” (p. 2). This correlation is an exigency of the increased metabolic requirements and organelle machinery necessary to sustain a longer axon. It is therefore unsurprising that the main diameter of fibers across longer commissural connections (i.e., corpus callosum) is essentially constant across species (Doty, 2007). This shift in cell/minicolumnar size has biased brain connectivity so that larger brains are most efficient at developing an “intrahemispheric modus operandi” (Doty, 2007, p. 282). Hemispheric specialization thus reflects a solution to the struggle between metabolic exigencies and connectivity.

A decrease in size of pyramidal neurons would likely constrain formation of longer-range, metabolically expensive projections biasing the network toward establishment of local connections. Increased numbers of smaller minicolumns, as seen in autistic patients, would complement this trend. As numbers of modules increase, the number of potential connections required to maintain a constant degree of connectivity among them increases geometrically providing for deficiencies based on limitation in space, signal timing, and metabolic constraints (Casanova, 2004). Optimization of network path length would require a relative increase in short-range connections with selective retention of longer-range transcortical projections linking distributed networks.

Implications for autism

Reductions of pyramidal neuron size corresponding to smaller minicolumns have implications for information-processing within a network and for the clinical features of autism. As metabolic limits preferentially constrain the activity of smaller cells, cortical adaptations emerge featuring locally linked modules with limited information-processing operations. These are distributed within large decentralized networks in which metabolic demands on any cell or module are limited. Such operations, subserved by minicolumns or small networks of minicolumns, increase efficiency by preferentially processing transient changes in focal input and integrating information with neighboring modules, thus limiting activity of individual modules. Decreases in field size of small neuron collaterals would bias cortical organization toward smaller, more integrated minicolumnar networks, providing the basis for increased discriminatory capacity. This sparse distributed coding architecture is especially salient in visual cortex in which discontinuities are extracted from arrays of focal inputs processed in parallel. Pathology, as in autism, in which cortical development is biased toward analogous connectivity among smaller minicolumns may be expected to give rise to a hyperspecific pattern of cortical information-processing (McClelland, 2000) related to the increased discriminative perceptual abilities and impaired ability to integrate information among perceptual, executive, and other cognitive domains.

References

Bauman, M. L., & Kemper, T. L. (2005). Structural brain anatomy in autism: What is the evidence? In M. L. Bauman & T. L. Kemper (Eds.), The neurobiology of autism, 2^nd ed. (pp. 121–135). Baltimore: Johns Hopkins University Press.

Casanova, M. F. (2004). Intracortical circuitry: one of psychiatry’s missing assumptions. Eur. Arch. Psychiatry Clin. Neurosci. 254, 148–151.

Casanova MF, El-Baz AS, Switala A (2011). Laws of conservation as related to brain growth, aging, and evolution:symmetry of the minicolumn, Frontiers in Neuroanatomy 5:66.

Doty, R. W. (2007). “Cortical commisural connections in primates,” in Evolution of Nervous Systems: A Comprehensive Reference, Vol. 4, Primates, eds J. H. Kaas and L. A. Krubitzer (Amsterdam: Elsevier), 277–289.

Marcus, G. (2008). Kluge: The Haphazard Construction of the Human Mind. Boston: Houghton Mifflin.

McClelland, J. L. (2000). The basis of hyperspecificity in autism: a preliminary suggestion based on the properties of neural nets. J. Autism Dev. Disord. 30, 497–502.

Vinters, H. V., and Kleinschmidt- DeMasters, B. K. (2008). “General pathology of the central nervous system,” in Greenfield’s Neuropathology, eds S. Love, D. N. Louis, and D. W. Ellison (London: Hodder Arnold), 1–62.