"What Makes Us Human: From Genes to Machines", June 2018, Jerusalem:
Are there human-specific neuroanatomical features and where would we find them?
Kathleen Rockland, Boston University, USA
Data on human-specific neuroanatomical features are still distressingly sparse and nonsystematic. At the cellular level, so far there is little evidence of human-specific cell types, at least at the light microscopic level. Von Economo cells, once an attractive candidate, have been found in multiple species; and large cells, such as Betz and Meynert cells, are primate specializations, but not human specific (unless in quantitative population ratios or other characteristics). At a finer level, cell-type specializations may yet be found; for example, in ion channels and receptor distributions. Physiological experiments in fact indicate membrane specializations (G. Eyal et al., 2016; V. Szegedi et al., 2016), and biophysically relevant specializations in synaptic machinery are documented at the ultrastructural level.
Morphometric analyses have identified multiple cell specific criteria such as, for cortical pyramidal cells, larger soma, a larger dendritic arbor, and more spines - properties construed as indicative of greater input convergence and “integrative” capacity (G. Elston, 2001, 2011; S. Bianchi et al., 2013). Similar analyses could in principle be applied more widely to other cell types, across cortical layers and areas, with the expectation that human-specific morphometric features (as are well established for astroglia) might be found. Precision morphometric investigations, still in early stages, already demonstrate new facets of synaptic specializations and neuropil organization. Dense reconstructions from 3-D electron microscopy reveal features of circuitry precision (e.g., P. Fernandez-Gonzalez et al., 2017; Schmidt et al., 2017), which both provide objective criteria for comparisons across cortical areas and species, and hint at new functional principles.
Another rich vein for investigation is the axonal architecture and, in specific, the relationships of intrinsic and extrinsic collateral projections. The facts and principles of axonal divergence (as opposed to input convergence at dendrites) are technically difficult to access, requiring intracellular fills or injections of low titre virus. From available data, all from experimental animals, we know that 1) not all pyramidal neurons have extrinsic projections (is there a larger or smaller number of these in humans?), 2) pyramidal neurons have a variable number of intrinsic and extrinsic collaterals (in rat: Kita and Kita, 2012), and 3) extrinsic projections, as visualized at the light microscopic level, have a highly variable configuration even within one system (Rockland, 2002, 2015). The numbers and identify of postsynaptic targets, for intrinsic and extrinsic collaterals, are unknown for any neuron. We don't know if there is an ordered variability, per area, per species, although this seems reasonable to suppose. Intrinsic cortical collateralization is somewhat better investigated, since this is accessible to in vitro studies; and partial data indicate distinct laminar patterns and spatial distributions (for rodent layer 6: A. Thomson, 2010). A recent study in cat visual cortex, however, indicates that a single neuron can have a surprising mix of both myelinated and unmyelinated intrinsic collaterals (G. Koestinger et al., 2017). How these parameters vary in humans and their functional significance are presently unknown.
What can we conclude about human specific neuroanatomical features? First, that the answers are still pending, but that we have a better idea of places to look and better tools for investigating.
Second, that specialization may be a series of modifications where incrementally altered structures and interactions result in richer repertoires and combinatorics (“complexity”). A concluding thought? A dominating concept in the field has been “stereotyped,” but “diversified” may be a more appropriate framework of inquiry, especially for the complex human brain.