The further development of the LCS is illustrated in **Figures 2C,D** and **3A–C**. At CS 18/19 (6.5 GW), the cells forming the LCS had increased in number, concurrent with a generalized growth of the SVZ of GE and cortex anlage. The calretinin+ stream now extended ventrally and formed cell aggregates in the developing POC/endopiriform complex (**Figure 3C**). The more lateral Tbr1+ stream reached the same ventral level, but occupied the entire SVZ and ventral cortical territories (in early human corticogenesis, Tbr1 marks both SVZ progenitor cells and postmitotic migratory neurons). Concurrently, the first representatives of the calretinin+ pioneer plate (Meyer et al., 2000) had appeared in the lateral cortex (**Figure 2C**), and became more evident at CS 20 (7 GW) (**Figure 3C**). In most embryonic brains from this period, a conspicuous hole may mark the site of the future internal capsule (IC) (**Figures 3A– C**) (see also plates 190 A and B in Bayer and Altman, 2007). Mitotic figures were numerous in the striatal anlage, but absent from the LCS, which was thus a non-proliferating structure (**Figure 3A**). In the embryonic stages, the RGF [see below section Migration from the PSB to the Insula along the Radial Glia Fascicle (RGF)] had not yet formed; after its appearance around 11 GW neurons migrating into the insula used the RGF as a migration substrate, and the LCS might thus be considered as its forerunner.

At 8–10 GW, a highly complex neuronal configuration characterized the insula and adjacent POC/endopiriform area,

(black) and calretinin (brown). Tbr1+ pallial cells descend from the PSB (asterisk) to the future POC and insula (I), representing the pallial part of the LCS. The cortex is still in the early preplate stage. Calretinin+ cells in the subventricular zone (SVZ) of the lateral ganglionic eminence (LGE) form the subpallial component of the LCS. (C,D) PSB and LCS at Carnegie stage (CS) 19, double-stained for PCNA (brown) and calretinin (black) in C, and for Tbr1 (black) and calretinin (brown) in D. The calretinin+ cells at the PSB and in the POC are mainly post-mitotic; both the pallial, Tbr1+, and the subpallial, calretinin+ components of the LCS have largely increased in size, along with the growth of the SVZ at the PSB. The arrows in B,D indicate the proposed direction of the LCS. BG, basal ganglia; GE, ganglionic eminences. Bars: in A,C: 100 µm; in B,D: 50 µm.

determined following Bayer and Altman (2007). In the POC, aggregates of Tbr1+ cells were intermixed with CR+/Tbr1− neurons forming complex nuclear structures (**Figures 3D– F**). We considered the loosening of the compact Tbr1+ lateral cortical plate (CP), together with a narrowing of the marginal zone compared to the POC, as landmarks defining the territory of the insular cortex. Calretinin marked neurons with a pyramidal shape, which corresponded to the deep pioneer cells representing the presubplate (Meyer et al., 2000), in what we propose as the ventralmost extension of the prospective insula (**Figure 3F**). They were separated from the

superficial pioneer neurons by calretinin-negative CP neurons. Regarding the distribution of CR+ deep pioneer neurons, the transition between insula and POC appeared as a gradual one. Importantly, at this early age of 8 GW, the anteroventral insula had already a laminated organization, indicating that its deep layers were already formed as derivatives of the PSB via the LCS.

#### The Developing Internal Capsule Crosses the PSB and Delimits the Rostro-Caudal Extent of the Insula

The IC is an important landmark in the early fetal brain; at 8 GW it was recognizable at the PSB at the level of the prospective frontal cortex (FC) as a CR-negative fiber bundle (**Figure 3E**), but not at intermediate and caudal levels, where the IC had not yet approached the PSB. At 8 GW, the caudal PSB, prior to the crossing of the IC, consisted of a proliferating SVZ positive for PCNA, Tbr1, and Tbr2 (**Figures 4A–C**). Around 9/10 GW, the IC also crossed a more caudal, midinsular level of the PSB (**Figure 4D**), while its posterior limb had just entered the GE but still not reached the PSB near the parietotemporal (PT) junction (**Figure 4E**). Concurrently, the lateral ventricle adopted a C-shaped curvature, growing in both a rostral (frontal lobe) and ventral (temporal lobe) direction. As a consequence of the ventricular curvature, in coronal sections

the ventral half of the telencephalon appeared almost like a mirror image of the dorsal half (**Figures 4D–F**). The PSB followed the curvature of the lateral ventricle; its posterior limit with the caudal part of the GE was recognizable by its SVZ, which was positive for Tbr2 and vimentin (**Figures 4E,F**). At 10 GW, the SVZ of the caudal PSB prior to the crossing of the IC was particularly broad, (**Figure 4E**) and was even wider than the SVZ of its neighboring parietal and temporal areas.

The sylvian fossa appeared at 11–12 GW, before the formation of the circular sulcus and the opercula. The insula was medially delimited by putamen and external capsule (EC), and separated from the proliferating ventricular zone (VZ) and SVZ by the growing basal ganglia (BG) and the IC, so that migrating excitatory neurons could reach the insula only indirectly via radial glia fibers crossing the IC and circumventing the putamen. To identify the insular cortex before the formation of a distinctive Sylvian fissure, we had to rely on its topographical relationships with deep structures and fiber tracts, which remain constant during development and persist into adulthood. Rostrally, the insula began at the intersection of EC and IC at the level of the PF cortex (**Figure 5A**), while caudally it was delimited by the posterior limb of the IC and the PT junction (**Figure 5C**). At this time point, the insular cortex

(asterisks) is indicated by arrows. Only those structures mentioned in the text are represented. D, diencephalon; SP, cortical subplate (in white).

had no distinctive features that would allow a cytoarchitectonic definition.

# Migration from the PSB to the Insula along the Radial Glia Fascicle (RGF)

We tried to identify the possible proliferative sources and their migratory routes into the insula through analysis of radial glia architecture. At 11–12 GW, basal or outer radial glia (oRG) appeared in the SVZ at the PSB, which now occupied a PCNA+ and Tbr2+ wedge-shaped area at the intersection of IC and EC. This SVZ was wider than the SVZ in the adjacent cortex (**Figures 6A,B**). Radial glia processes originating in the SVZ at the PSB were positive for vimentin and nestin, and assembled in a distinct RGF coursing along the prospective EC. Following the curvature of the PSB, the RGF originating from its SVZ formed a continuous band of radial glia fibers. According to their origin, four subdivisions of the RGF could be established, which remained constant during the first half of gestation: PF, fronto-parietal (FP), PT, and T, temporal radial glia fibers, which took different directions to reach their destination in the insula. Descending RGF component FP merged with ascending RGF component T (**Figures 5B**, **6D,E**), whereas component PF descended without an ascending counterpart (**Figure 5A**). Component PT at the caudal end of the BG (putamen islands interspersed between the posterior limb of the IC) (**Figure 5C**) coursed straight to the caudal insula, where also an intermediate zone was recognizable. Along its course, the RGF fanned out and entered the insula at different dorso-ventral levels, maintaining an initially parallel orientation up to the point where descending and ascending components met (**Figure 5B**).

At 13/14 GW, migrating neurons, still positive for Tbr1, followed the direction of the RGF into the insula, populating its subplate and CP, clearly delimiting cortical and subcortical territories (**Figures 6C,G**). After this age, migrating neurons were Tbr1-negative. The wedge-shaped SVZ, marked with vimentin (**Figures 6D–F**), nestin, PCNA (**Figures 6A,D**), and Tbr2 (**Figures 6B,F**), progressively increased in width, partially entering the IC, and expanded in parallel with the proliferation of the outer SVZ (oSVZ) in the adjacent opercula. We reconstructed the course and orientation of the RGF components into the insula in a horizontal section at 21 GW (**Figure 7**). As expected, the RGF arose from both the dorsal and ventral oSVZ at the PSB, although the dorsal RGF predominated. The orientation of the fibers leaving the fascicle and entering the insular subplate is shown for different levels (**Figure 7**, from 1–4). At levels 1 and 2, fibers emerging from the RGF component FP took a descending course, while at level 3 (component T) they ascended. Level 4 shows radial glia fibers in the intermediate zone of the temporal operculum, which seemed to bend and course toward subplate and CP of the superior TC rather than into the insula. The RGF was compressed in the EC and contained also vimentin+ cells. However, PCNA+ mitoses were rare, and we did not observe Tbr2+ progenitor cells in this location; this indicates that the territory of the RGF was not an extension of the proliferative oSVZ.

Importantly, the PF PSB (RGF component PF) extended farther rostrally than the temporal one (RGF component T) (**Figure 5A**); in consequence, the most anterior region of the insula received radial glia fibers only from the PF PSB. The middle and posterior insular regions, in turn, received radial fiber-mediated migrations from the dorsal (FP) and ventral (temporal) PSB (**Figure 5B**). Even more caudally, the posterior end of the insula was close to the PT junction, and was also populated by large numbers of oRG cells and processes. At this level, an intermediate zone was recognizable as a fiber-rich layer continuous with the intermediate zone of the adjacent cortices (**Figure 5C**).

The distinct subregions of the insular lobe were thus connected more or less abundantly via the RGF with the proliferative SVZ along the PSB. The differential availability of progenitor cells may be the basis for the differential layering of the subareas of the insular lobe. It is remarkable that the insular lobe, despite its rather distant relationship with the proliferating oSVZ, and the limited progenitor pool at the PSB, is able to fold once migration is finished (**Figure 1B**).

# Fronto-Parietal and Temporal PSB at Midgestation

Vimentin, a marker of radial glia, does not define the PSB. To determine the origin of the RGF at the PSB at midgestation (21GW), we compared adjacent sections stained for vimentin and Tbr2 (**Figure 8**). When reconstructing the photomosaics for both markers from high magnification microphotographs, we noticed that the periventricular layers, inner and outer SVZ, were quite different in the various lobes. Similarly, the fiber tracts also differed, and widely varied in thickness. Particularly in the frontal and parietal lobes, the intermediate zone (future white matter) was at this time point much wider than in the temporal lobe. In parallel, Tbr2+ and PCNA+ cells extended much farther into the IZ in FP areas than in the TC. In the TC, the anterior commissure seemed to represent an obstacle

for the RGF, since it was traversed neither by the RGF nor did it contain Tbr2+ cells. We limited our study to the PSB and the adjacent regions, where the Tbr2+ progenitor cells became less and less numerous toward the IC (**Figures 8B,D**), and were basically absent at the level of the insula. The RGF was more difficult to discern than in earlier stages, but still very thick, prominent radial glia fibers emerged at the PSB and crossed the inner fibrous layer and adjacent IC (**Figures 8A,C**).

While at early fetal stages the PSB extended medially beyond the cortico-striatal sulcus, at midgestation it had shifted laterally in the temporal lobe, but not in the frontal lobe. Future studies will show how the PSB in different lobes behaves toward the end of cortical neurogenesis.

FIGURE 8 | Photographic reconstructions of the PSB in coronal sections at 21 GW. Vimentin (A,C) shows the distribution and orientation of radial glia cells and fibers in the frontal (A) and temporal (C) periventricular zones and the RGF route into the Insula (white arrows). Tbr2 (B,D) marks the PSB (arrow in B), which at this age is less well defined than at earlier fetal stages, and in the temporal lobe does not extend medially beyond the striato-cortical sulcus (asterisks in all panels; in D, the PSB is indicated by a dotted line). Tbr2+ and vimentin+ progenitor cells cross the periventricular fiber layers (Inner fibrous layer, IFL) and extend far into the oSVZ and even the intermediate zone (IZ). Note the complexity of fiber tracts in the temporal lobe, due to the presence of the anterior commissure (AC). The large black dots in the inner (i) SVZ in D are stained blood vessels. Bars: in A: 240 µm, in B: 140 µm, in C: 250 µm, in D: 190 µm.

# Prenatal Development of Lamination in the Insula

During the first half of gestation, the insular cortex had a uniform structure, with a CP formed by densely aggregated immature neurons. On the whole, the insular CP was narrower than that of the adjacent opercular areas. The first clear appearance of layering was at midgestation (20/21 GW) (**Figures 9A–F**), when MAP2 (**Figure 9D**) and Tbr1 (**Figures 9C,F**) immunostaining (in maturing human cortical neurons, Tbr1 is cytoplasmic) indicated the presence of a distinct layer V, or inner pyramidal layer, which was particular prominent in the anterior agranular insula (**Figure 9C**). However, compared with the Betz cells in the adjacent primary motor cortex (**Figure 9E**), the dimensions and proportions of layer V pyramids in the anterior insula were rather reduced, as well as their positivity for MAP2. The deep layer VI and subplate were also Tbr1+, although in this case staining was nuclear (**Figure 9F**). The subplate was directly continuous with the subplate of the adjacent opercular cortices, but considerably reduced in width. Remarkably, the rostral and intermediate insula lacked an intermediate zone, characterized by horizontal fibers traversed by clusters of migrating neurons (Bystron et al., 2008; **Figures 6E,G**, **8C**). In the posterior insula, the intermediate zone was present, traversed by calretinin+ fibers from the posterior limb of the IC (not shown).

Also at midgestation, we observed a difference in the distribution of the Reelin+ axonal plexus of the Cajal–Retzius cells in the lower marginal zone (Meyer and González-Gómez, 2017): The plexus, characteristic of isocortex and important for laminar arrangement of neurons, was absent in the periallocortical transition area (**Figure 9B**), but appeared in the more dorsal, isocortical insula. Cajal–Retzius cells were, however, abundant in the upper marginal zone all over the insula.

In the ages examined after midgestation, 24 and 25 GW, the overall immaturity of the insular CP persisted, and there was no evidence for a future differentiation into an agranular, dysgranular and granular cortex. Since at 32 GW the insula presented an adult-like morphology, cytoarchitectonic maturation would take place during the interval between 25 and 32 GW. Similarly, in our coronal sections at 24 and 25 GW, we were unable to distinguish incipient insular sulcation and gyration.

## DISCUSSION

The insula derives from the PSB of all cortical areas adjacent to the Sylvian fissure: PF, frontal, parietal, and temporal, and thus constitutes a central node of the human cortex. Radial migrations from the PSB to the insula have to cross the growing IC, and circumvent the BG, and reach the insula via a RGF that course in the EC. According to its origins, the RGF has four main components, parts of which merge, and may determine the granular, dysgranular and granular character of the insular sectors.

## The Anatomy of the Developing Insula

The human insular lobe is shaped by the curvature of the lateral ventricle, and lies embedded between PF, frontal, and parietal areas dorsally, and temporal areas ventrally, from which it is separated by the Sylvian fissure and the circular sulcus. Its position external to putamen and IC prevents direct contacts with the periventricular proliferative zones, and radial migrations to the insula have to take indirect, unusual routes to reach

P, putamen; SVZ, SVZ of the temporal horn which opens at more caudal levels. Bars: in A: 400 µm; in C, for B,C,F: 160 µm; in D, for D and E: 75 µm.

and populate the distant lobe. Using radial glia architectonics and the pallial markers Tbr1 and Tbr2 (Hevner et al., 2003; Englund et al., 2005), we identified the PSB as the origin of the neurons destined to form the insular cortex. During early fetal development, the PSB follows the curvature of the ventricle, and thus extends from PF to temporal levels. We suggest that local differences of the PSB along its extent through the various lobes contribute to the multifaceted lamination pattern of the insular cortex. The different timing of the entrance of the IC into the cortex traversing the PSB (Krsnik et al., 2017), with rostral parts preceding more caudal parts, is an additional influencing factor, which will require further studies.

The finding that the architecture of the insula is contingent on size and orientation of the lateral ventricle and the curved shape of the PSB, explains the differences in insular structure reported in a variety of mammals. Even though comparative studies tend to emphasize common principles of brain structure in order to establish homologies (Cat: Clascá et al., 1997; monkey: Gallay et al., 2012; Evrard et al., 2014), anatomical studies in a wide variety of mammalian species usually not studied in the laboratory, including the dolphin (Jacobs et al., 1984; Casanova et al., 2010), demonstrated an extremely variable shape, general organization, lamination and cellular specialization of the insula, to the point that there is no recognizable common model of organization of the mammalian insular cortex (Butti and Hof, 2010). The relationship between insula and claustrum is similarly controversial. We neglected the claustrum, because we did not detect migratory mechanisms similar to those of the insula (Mufson and Mesulam, 1982; Nieuwenhuys, 2012). Developmental gene expression studies postulated that insula and insular claustrum are formed from the lateral pallium (Watson and Puelles, 2017). It may be argued that the mouse is not the

best model for the human cortex, taking into account recent studies suggesting that the ancestor of mammals was probably a gyrencephalic animal (O'Leary et al., 2013; Lewitus et al., 2014). The magnitude of species differences suggests that the development of the insula is best understood when examined together with the anatomical landmarks that define this lobule in a given species.

In any case, the human insula should be considered in the context of its complex cognitive, social and emotional functions, including empathy, altruistic behavior, self-awareness, interoception, mindfulness, and consciousness (Craig, 2009; Fan et al., 2011; Fischer et al., 2016; Tusche et al., 2016; Laneri et al., 2017), or, as expressed by Craig (2010), the "sentient-self." In keeping with these human-specific functions, a volumetric comparison of the insula of human and non-human primates revealed that in terms of absolute volumes the left and right agranular insula are among the most enlarged cortical areas relative to the chimpanzee (Bauernfeind et al., 2013). The degree of "granularity" of a cytoarchitectonic area is thus unrelated to its involvement in networks engaged in human cognitive functions such as those attributed to the anterior insula.

#### The PSB Is the Origin of the Insula

The PSB has been extensively studied in terms of comparative anatomy, establishing homologies with the anterior dorsal ventricular ridge (ADVR) of sauropsids, and the fate of the cells in the LCS in sauropsids and mammals (Molnár and Butler, 2002), as well as in terms of developmental gene expression. Gene expression studies in the PSB of mice showed that the PSB is the main source of POC, claustrum, olfactory bulb, olfactory tubercle, and amygdala, and that the cells destined to these centers migrate ventrally via the LCS (Medina et al., 2004; Carney et al., 2006; Cocas et al., 2011). The insula as a PSB derivative has received less attention, probably because it is so unconspicuous in the rodent. The PSB is certainly an important landmark in the rodent brain, where it represents the boundary between pallium and subpallium, and is implicated in dorsoventral patterning of the telencephalon (Stoykova et al., 2000; Yun et al., 2001), but to what extent is it relevant for the human brain? Molnár and Butler (2002) recognized the evolutionary potential of the PSB, and also its function as an initial barrier zone for crossing cortico-thalamic and thalamocortical axons in the IC. However, the rodent studies did not take into account the enormous regression of olfactory structures in the microsmatic human, which rests importance from this aspect of the LCS. Instead, we show that the early descending migrations from the human PSB are mostly destined to the insula, although they also contribute Tbr1+ and calretinin+ cells to the POC. Furthermore, the rostro-caudal extension of the PSB is hugely increased due to the size increase of both, cortical progenitor zones and GE. Another important factor is the IC, which crosses the PSB (Molnár et al., 2012), and thus represents an additional obstacle for radial migration from the PSB to the insula. We suggest that the prominence of the PSB in human is an important factor for the development of the insular lobe. Furthermore, in the human brain, the insula does not represent the most lateral part of the cortex as in the rodent, but rather emerges as the core cortical region that makes possible the mirror arrangement of dorsal (FP) and ventral (temporal) cortical lobes. The central anatomical position of the insula in the human telencephalon is also paralleled by a similar central functional relevance, since especially the dorsal anterior insula can be considered a critical hub in connectivity networks of the human brain (Uddin et al., 2014).

# Radial Glia Architectonics Reveal a Migration Route from the PSB to the Insula

Radial glia has important roles in cortex development: It serves as a guidance substrate for radially migrating neurons (Rakic, 1971), and is also the principal progenitor cell type of the developing telencephalon (Malatesta et al., 2000; Miyata et al., 2001; Noctor et al., 2001; Tamamaki et al., 2001). In early embryonic stages, radial glia somata are confined to the VZ as apical radial glia, while in later stages, basal or oRG forms the proliferating cell population in the SVZ (Fietz et al., 2010; Hansen et al., 2010; Reillo et al., 2011). An oSVZ (Smart et al., 2002) is more prominent in gyrencephalic brains than in the lissencephalic rodent, and has been suggested to play key roles in the folding of the neocortex in gyrencephalic species because of its abundance in oRG that promote cortical expansion (Fietz et al., 2010; Hansen et al., 2010; Lewitus et al., 2013; Martínez-Martínez et al., 2016). On the other hand, comparisons of lissencephalic primates, gyrencephalic rodents, and carnivores suggested that the cytoarchitectonic subdivisions of the SVZ are an evolutionary trend and not a primate-specific feature, and that a substantial population of oRG exists unrelated to the degree of cortical folding (García-Moreno et al., 2012; Kelava et al., 2012; Martínez-Cerdeño et al., 2012, 2016). The expression of transcription factors Pax6 and Tbr2 varies between the various types of progenitor cells and species (Hansen et al., 2010; Kelava et al., 2012; Betizeau et al., 2013; Cunningham et al., 2013), although the expression of Tbr2 in neural precursor cells can be used for defining the boundaries of the SVZ both developmentally and evolutionarily (Martínez-Cerdeño et al., 2016). It is not clear whether also the intermediate progenitor cells, which lack a polarized process, express the pan-radial glia marker vimentin. In any case, the vimentin-expressing radial glia cells in the SVZ of our human material displayed the various morphotypes described in the monkey (Betizeau et al., 2013), where they indicate differential mitotic potentials and cell-cycle parameters. Radial glia-guided locomotion is the migration mode of excitatory cortical neurons on their route through intermediate zone, subplate and CP, until they reach the marginal zone, detach from the radial glia fiber, and change to a somatic translocation mode (Tissir and Goffinet, 2003; Sekine et al., 2011). The critical question is the degree of horizontal dispersion along the radial glia route. Radially migrating neurons may change from one radial glia fiber to an adjacent one, so that the strictly radial orientation of a radial glia fiber does not necessarily imply a similar radial course of the migrating neurons (Reillo et al., 2011; Gertz and Kriegstein, 2015). It is thus possible that the origin of neurons in the insula might be more extensive than

described here, and that the SVZ of the adjacent opercula contribute neuroblasts dispersing tangentially into the insula. We suggest, however, that it is precisely the absence of tangential dispersion between adjacent lobes that leads to the enormous growth of the opercula, versus the restricted expansion of the insula. The distinct RGF into the insula is another argument for an origin from a specific sector of the oSVZ at the PSB. The RGF represents the main radial migration substrate from the PSB to the insula, and is set apart from the non-fasciculated oRG fibers originating from the oSVZ of the opercula. It is possible though that tangential dispersion within the spatially compressed RGF contributes to the inhomogeneities of layer IV. An important feature of the human RGF is that it is composed of descending and ascending fibers and thus connects the derivatives of the FP and temporal SVZ, which act in concert in the formation of the insula. It appeared, however, in our material that the dominant source of insular neurons is the dorsal PSB, whereas the temporal PSB contribution is less substantial. Future studies using region-specific markers might solve this question.

### The Variability of the Inner Granular Layer of the Human Insula

The human neocortex displays a large diversity of size and density of neurons, which are arranged in six horizontal layers of variable width. This diversity is the foundation of the cytoarchitectonic areas described by Brodmann (1909) and Von Economo and Koskinas (1925).

A major criterion in these classifications is the differentiation of the granular layers II and IV, which define the degree of granularity of a given area. Primary sensory areas have a particularly prominent layer IV, which is the main target of thalamo-cortical fibers, and populated by its principal neurons: spiny stellate cells, star pyramids, and small to very small "dwarf pyramids" (Von Economo and Koskinas, 1925). Spiny stellate cells are glutamatergic excitatory neurons (Conti et al., 1989; Feldmeyer et al., 1999), which establish asymmetric synapses mainly with dendritic spines (Saint Marie and Peters, 1985), give rise to interlaminar projections (Qi and Feldmeyer, 2016), and even connect adjacent areas (Meyer and Albus, 1981). In human auditory cortex, layer IV is populated by transitional forms between spiny stellate and small pyramidal cells (Meyer et al., 1989). However, layer IV is also prominent in many other cortical areas, such as the posterior insula, where anatomical and functional studies stressed its afferent sensory (gustatory, auditory, and vestibular) input (Augustine, 1996; Kurth et al., 2010).

There is a general agreement in the basic classification of the insula into agranular, dysgranular and granular parts (Mesulam and Mufson, 1985), even though the number of subareas varies between authors (reviewed by Nieuwenhuys, 2012). In

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Afif, A., Bouvier, R., Buenerd, A., Trouillas, J., and Mertens, P. (2007). Development of the human fetal insular cortex: study of the gyration from 13 to 28 our perinatal material we tried, following Von Economo and Koskinas (1925), to section the insula in a plane perpendicular to the main axis of most insular gyri. However, the insular gyri undergo subtle changes in orientation, have multiple dimples and small subsulci, which altogether distort the naturally vertical columnar arrangement of all layers including layer IV, and may give the impression of a distinct cytoarchitectonic subdivision.

As described here, the insula derives from the PSB, which in turn is continuous with the VZ and SVZ of the adjacent lobes. In this sense, the agranular character of the anterior insula reflects the trend of the agranular PF and frontal areas, whereas the granular caudal insula resembles the hypergranular character of the adjacent parietal and temporal sensory areas. The dysgranular character of the intermediate areas of the insula may be attributed to the contribution and possible intermixture of RGF compartments FP and T, as well as to the possibility of tangential dispersion within the RGF.

In the literature on the oSVZ, it is often emphasized that the subgranular layers V and VI derive from the VZ and inner SVZ, whereas the supragranular layers III and II originate from the oSVZ (e.g., Smart et al., 2002; Lukaszewicz et al., 2005; Nowakowski et al., 2016), leaving open the question of the origin of layer IV. Species differences may account for this apparent neglect. As shown by Martínez-Cerdeño et al. (2012), the peak in number of Tbr2+ progenitors and mitotic divisions in the oSVZ of the macaque somatosensory cortex occurs during the generation of layer IV, whereas in rat and ferret this peak is at the end of cortical neurogenesis, when the supragranular layers are born. It is thus tempting to propose that layer IV of the human insula derives predominantly from the oSVZ. The degree of granularity of a given cytoarchitectonic area may thus depend on the availability of oSVZ progenitors characterized by high-output cycling parameters during a precise time window (Betizeau et al., 2013), along with the presence of an adequate migration substrate. The generation of a cell-rich layer IV is an important issue particularly in the primate and human brain, where the classification into anatomical and cytoarchitectonic areas is largely based on the granularity of the inner granular layer. Interestingly, the "highest" cognitive functions within the insular lobe are attributed to the agranular anterior insula, which demonstrates that the agranular character of a cortical area is not an indication of less complex functions. It would be desirable that future ontogenetic and phylogenetic studies of the cerebral cortex become more focused on the peculiarities of the human brain (Clowry et al., 2010).

## AUTHOR CONTRIBUTIONS

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 González-Arnay, González-Gómez and Meyer. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Cell Type-Specific Structural Organization of the Six Layers in Rat Barrel Cortex

#### Rajeevan T. Narayanan, Daniel Udvary and Marcel Oberlaender\*

*Max Planck Group: In Silico Brain Sciences, Center of Advanced European Studies and Research, Bonn, Germany*

The cytoarchitectonic subdivision of the neocortex into six layers is often used to describe the organization of the cortical circuitry, sensory-evoked signal flow or cortical functions. However, each layer comprises neuronal cell types that have different genetic, functional and/or structural properties. Here, we reanalyze structural data from some of our recent work in the posterior-medial barrel-subfield of the vibrissal part of rat primary somatosensory cortex (vS1). We quantify the degree to which somata, dendrites and axons of the 10 major excitatory cell types of the cortex are distributed with respect to the cytoarchitectonic organization of vS1. We show that within each layer, somata of multiple cell types intermingle, but that each cell type displays dendrite and axon distributions that are aligned to specific cytoarchitectonic landmarks. The resultant quantification of the structural composition of each layer in terms of the cell type-specific number of somata, dendritic and axonal path lengths will aid future studies to bridge between layer- and cell type-specific analyses.

#### Edited by:

*Kathleen S. Rockland, Boston University School of Medicine, United States*

#### Reviewed by:

*Dirk Feldmeyer, RWTH Aachen University, Germany Guy Elston, Centre for Cognitive Neuroscience, Australia*

#### \*Correspondence:

*Marcel Oberlaender<EMAIL_ADDRESS>
Received: *29 June 2017* Accepted: *28 September 2017* Published: *13 October 2017*

#### Citation:

*Narayanan RT, Udvary D and Oberlaender M (2017) Cell Type-Specific Structural Organization of the Six Layers in Rat Barrel Cortex. Front. Neuroanat. 11:91. doi: 10.3389/fnana.2017.00091* Keywords: barrel cortex, whisker touch, soma, dendrite, axon

More than a century ago, Brodmann (Brodmann, 1909) described that the shapes and diameters of neuron somata vary as a function of cortical depth (see Garey, 1994 for an English translation of Brodmann's original work). These differences correlate with systematic changes in neuron densities along the vertical cortex axis (i.e., from the pial surface toward the white matter), which gave rise to the concept of cytoarchitectonic layers (Brodmann, 1909). The neocortex is typically subdivided into six layers, i.e., layers 1-6 (L1-6). Even though cortical layers are purely defined by vertical gradients in soma size, shape or density, and there are many exceptions to the division into six layers across cortical areas and species (see Elston, 2002, 2003; Spruston, 2008; DeFelipe, 2011; Elston et al., 2011; Kaas, 2013; Rockland, 2017, for comparative studies and reviews on some aspects of structural cortical heterogeneity), this concept has been widely accepted to provide a first order criterion to discriminate between neuronal cell types. Countless studies have thus grouped neurons by their laminar soma locations and provided layer-specific analyses.

Moreover, layers are often used as a synonym for elementary computational units when describing the organization of cortical circuits. For example, in primary sensory cortices, a general motif, referred to as a "canonical circuit" has been proposed (reviewed in Douglas and Martin, 2004). According to this theory, L4 is regarded as the major thalamorecipient layer and thus as the starting point of cortical information processing. Information from L4 is then thought to propagate through the cortical column, first to L2/3 from where it is then relayed to L5/6, the primary output layers of the cortex. However, each layer is populated by genetically (e.g., Zeisel et al., 2015; Tasic et al., 2016), biophysically (e.g., Ferrante et al., 2017), physiologically (e.g., de Kock et al., 2007) and morphologically (e.g., Narayanan et al., 2015) diverse neuron populations. This heterogeneity of each layer—as well as the general structural heterogeneity across cortical areas and species—raises the question to what extent grouping of neurons by their laminar soma locations is an appropriate simplification to study and describe the structural and functional organization of cortical circuits.

In this review article, we reanalyze some of our recent work about the structural and functional organization of the posteriormedial barrel-subfield in the vibrissal part of rat primary somatosensory cortex (vS1, i.e., barrel cortex). On the example of this well-studied, yet—when compared to other cortical areas and species—uniquely organized primary sensory area (e.g., the rodent barrel cortex is characterized by neuron-dense aggregates in L4 (i.e., barrels) that form a somatotopic map of the facial whiskers), we seek to address the following general questions: How layer-specific are physiological and morphological properties of individual excitatory neurons, and how homogeneous are cortical layers with respect to the resultant structure-function neuronal cell types?

First, we define the borders between the six layers of rat vS1 by precise measurements of the 3D distributions of all excitatory and inhibitory neuron somata (Meyer et al., 2013). Second, we group in vivo recorded excitatory neurons, whose dendrite and axon morphologies have been reconstructed, into 10 structurefunction cell types (Oberlaender et al., 2012; Narayanan et al., 2015). Third, these datasets are combined by registration into a precise anatomical reference frame of rat vS1 (Egger et al., 2012), which allows quantifying cell type-specific soma, dendrite and axon distributions with respect to the six layers of vS1. Finally, we determine the number of somata, as well as the amounts of dendrites and axons that each of the ten cell types contributes to each of the six layers within the volume of an average cortical barrel column. The present review article thus provides a quantitative account of the structural heterogeneity of cortical layers, which will help relating layer-specific to cell type-specific studies of cortex organization.

#### DEFINING CORTICAL LAYERS IN RAT vS1

To define layer borders, we had previously reported the number and 3D distribution of all excitatory and inhibitory somata in entire rat vS1 (Meyer et al., 2013). We had sliced the brains of four animals (28–29 day old, male Wistar rats) into consecutive 50µm thick vibratome sections, at an angle that is approximately tangential to vS1. The sections were stained with NeuN (Mullen et al., 1992) and GAD67 (Kaufman et al., 1986), to label the somata of all neurons and to discriminate between excitatory and inhibitory somata, respectively. The tangential cutting plane allowed identification of the L4 barrels, which were extrapolated to barrel columns (**Figures 1A–D**). Thus, we were able to determine the number and vertical distributions of all excitatory and inhibitory somata for each individual barrel column (**Figures 1E–G**). The resultant soma density profiles along the respective vertical column axes were then used to define and calculate layer borders (see also Meyer et al., 2010), which are represented by the following distances from the pial surface: L1- L2/3: 157 ± 16µm, L2/3-L4: 575 ± 57µm, L4-L5: 900 ± 50µm, L5-L6: 1,411 ± 28µm; L6-white matter (WM): 1,973 ± 44µm (Meyer et al., 2013).

Two caveats about these layer border values should, however, be noted. First, it was not possible to define the border between L2 and L3 based on the overall soma density distribution. This is in line with several previous attempts that failed to identify specific anatomical features that distinguish L2 from L3 (e.g., Lund, 1973; Somogyi et al., 1981; Fitzpatrick et al., 1983; Hendry et al., 1987; Lund and Wu, 1997). L2 and L3 are thus often grouped as one layer (i.e., L2/3), even though evidence for functional differences between neurons in L2 and L3 has accumulated from different cortical areas (e.g., Shepherd and Svoboda, 2005; Bureau et al., 2006; Gur and Snodderly, 2008). In our data, the vertical distribution of inhibitory somata was, in general, different compared to the layer-defining distribution of excitatory neuron somata (**Figure 1F**). Inhibitory somata were densest in upper L2/3 and at the border between L4 and L5 (Meyer et al., 2011). The distribution of inhibitory somata thus provided a quantitative basis for an anatomical separation between L2 and L3. The average border between L2 and L3 was hence calculated as 296 ± 30µm. Potentially reflecting the different developmental origins of excitatory (Gorski et al., 2002) and inhibitory neurons (Anderson et al., 1997), the different vertical soma distributions question whether grouping inhibitory neurons by their soma locations within cortical layers is an appropriate strategy to describe the organizational principles of inhibitory circuits. For example, the densest distributions of inhibitory neurons in upper L2/3 and at the L4/5 border could underlie the observation that sensory-evoked firing rates of excitatory neurons that are located at these depths are typically much lower than those in deeper parts of L2/3 and L5 (e.g., de Kock et al., 2007). For the remainder of this review article, we will thus restrict our analyses of layer-specific structure and function to excitatory cell types.

The second caveat is that layer borders deviate between barrel columns. The differences do not reflect a linear scaling with cortical thickness. Instead, we found that the diameter of cortical barrel columns compensates for differences in cortical thickness across vS1, resulting in largely the same volume for barrel columns that represent whiskers of the same row along the animals' snout (Egger et al., 2012). More specifically, barrel columns representing whiskers at the bottom of the snout, the so-called E-row, have a volume that is more than three times larger compared to those barrel columns that represent whiskers at the top of the snout (i.e., A-row). The cortical depth changes substantially within and across whisker rows (e.g., by 303µm from A2 to E2), whereas the location and thickness of L4 is largely preserved across barrel columns (e.g. the depth of L4 changes only by 116µm from A2 to E2) (**Figure 1G**). Consequently, the depths and extents of the cortical layers change in a whisker-specific manner. Assigning functional data to a particular cortical layer by measuring the recording or imaging depth, may hence result in mixing neurons from different layers. For the remainder of this review article, we will thus restrict our analyses to the column representing the D2 whisker.

FIGURE 1 | Defining the cortical layers of rat vS1. (A) Schematic illustration of the somatotopic organization of vS1 into whisker related barrel columns. (B) Top view onto vS1 shows the center locations of all NeuN positive somata with respect to barrel columns. Somata in columns representing whiskers in the A-to-E row (i.e., top-to-bottom of the snout) are colored in red, pink, yellow, green, and blue, respectively. Somata in columns representing the greek whiskers (i.e., α-δ) and those between columns (i.e., septa) are colored in gray and white, respectively. (C) Semi-coronal view of cross-section through vS1, showing barrel columns representing arc-2 whiskers (dashed region in B). (D) Maximum-z projections of image stacks with center locations of all somata (top: NeuN) and the subset of inhibitory somata (bottom: GAD67) for example volume in vS1. (E) Left: Average distribution of excitatory somata in 2D projection view. Dashed lines represent layer borders. Right: 1D vertical density profile of excitatory somata averaged across entire vS1. Shaded regions reflect SD. (F) Corresponding to panel E: distribution of inhibitory somata (left), 1D vertical density profile of inhibitory somata (right panel). (G) 1D vertical density profiles of excitatory and inhibitory somata averaged across columns representing A2, C2, and E2 whiskers, respectively. Note the shift in depth of layer borders across barrel columns. (B–G) are adapted and modified from Meyer et al. (2013) with permission.

#### DEFINING STRUCTURE-FUNCTION CELL TYPES IN RAT vS1

To define cell types of excitatory neurons, we had previously reported a dataset that comprised the in vivo activity and 3D morphology of individual neurons (Oberlaender et al., 2012), whose somata were located across the entire cortical depth (**Figure 2A**). We had performed cell-attached recordings in anesthetized animals (28–35 day old, male Wistar rats) and measured spiking patterns during periods of ongoing activity (i.e., without stimulation) and during passive deflections of the somatotopically aligned whisker (de Kock et al., 2007). Following the functional measurements, the recorded neurons were labeled with biocytin (Pinault, 1996; Narayanan et al., 2014), which allowed for post hoc reconstructions of their soma, dendrite and intracortical (IC) axon morphologies (Narayanan et al., 2015). The neuron tracings were augmented with reconstructions of the pial surface, WM tract and barrel field in L4, which allowed for precise registration into an average geometrical reference frame of rat vS1 (Egger et al., 2012). Registration compensated for variability in cutting angle and tissue shrinkage across animals, allowing to determine the 3D position of the recorded neuron with ∼50µm accuracy. Combining this dataset with the layer borders described above, we were able to investigate how neuronal morphology and in vivo spiking correlate with the neurons' locations in L2-6 (Oberlaender et al., 2012).

In line with several studies that investigated activity patterns across layers—in different sensory systems, species and during different behavioral states (e.g., Vinje and Gallant, 2000; Deweese et al., 2003; Brecht et al., 2004; Olshausen and Field, 2004)—our dataset showed that spiking activity during periods of ongoing activity is sparse (1.09 ± 1.32 Hz, n = 57), with neurons in L5 representing, on average, the most active population (1.72 ± 1.56 Hz, n = 29). During whisker stimulation, spiking activity increased primarily within L4 and L5 (**Figure 2A**). However, whisker-evoked (and ongoing) spike rates deviated substantially within each layer, ranging from neurons that did not respond to the stimulus (or decreased spiking compared to ongoing periods) to neurons that increased spike rates by more than 20 Hz within the first 50 ms after stimulation. Both observations, first that sensory stimulation evokes spiking activity most prominently within L4 and L5, and second that responses are highly variable within layers, are generalizable to other stimuli, sensory systems and species (Harris and Shepherd, 2015).

**Figure 2B** shows the dendrite morphologies of three example neurons that were located approximately at the same

cortical depths within L4 and L5, respectively. The neurons differed in their whisker-evoked spike rates and had different dendrite morphologies. We therefore investigated whether the morphological differences of neurons within and across layers can account for the functional variability. We extracted morphological and topological dendrite parameters (see Oberlaender et al., 2012; Narayanan et al., 2015, for a list and definition of the parameters) for 153 in vivo recorded/labeled neurons and applied an objective clustering algorithm (Ankerst et al., 1999) to subdivide our sample into dendritic cell types. The classification determined 10 dendritic cell types, which resembled those previously reported in several studies that performed in vitro recording/labeling experiments in acute brain slices (see Feldmeyer et al., 2013 for a review). We adopted the cell type naming conventions from the in vitro studies and refer to excitatory neurons in vS1 as: L2 pyramidal neurons (L2py), L3py (Petersen and Crochet, 2013), L4py, spiny stellates and star pyramids in L4 (L4ss and L4sp) (Staiger et al., 2004), slenderand thick-tufted pyramids in L5 (L5st and L5tt) (Wise and Jones, 1977), and corticocortical and corticothalamic pyramids in L6 (L6cc and L6ct) (Zhang and Deschenes, 1997; Kumar and Ohana, 2008). The group of L6cc is subdivided into "typical" and "atypical" neurons (DeFelipe and Farinas, 1992), and we refer to the subtypes as L6cc and L6inv, respectively. Typical L6cc are characterized by the lack of apical tuft dendrites (Zhang and Deschenes, 1997; Kumar and Ohana, 2008). The group of L6inv comprises a variety of rare dendritic morphologies, e.g., neurons whose apical dendrite projects toward the white matter (i.e., inverted pyramids).

Grouping the neurons by their respective dendritic cell type could account for some of the functional variability within layers (e.g., whisker-evoked spiking of L4ss/sp vs. L4py: 6.53 vs. 1.90 Hz, or L5st vs. L5tt: 0.97 vs. 6.69 Hz, **Figure 2C**). The relationship between dendritic cell type and in vivo function is likely to extend to other experimental conditions (**Figure 2D**). For example, in contrast to L5tt, L5st increase spiking activity during the rhythmic back-and-forth movements of whiskers (i.e., whisking), with spike times being correlated to specific whisker positions (i.e., phase) of the whisking cycle (de Kock and Sakmann, 2009).

# LAYER-SPECIFIC ORGANIZATION OF EXCITATORY CELL TYPES IN RAT vS1

Previous in vitro studies had revealed several relationships between the dendritic cell type and genetic/molecular profiles

(e.g., in L5 Groh et al., 2010), intrinsic physiological properties (e.g., in L5 Hattox and Nelson, 2007), local connectivity patterns (e.g., in L5 Brown and Hestrin, 2009) or brainwide input populations (e.g., in L5 Kim et al., 2015). Not entirely surprising, we found that the cell type-specific structurefunction-relationships extend to in vivo activity patterns. The ten classes described above may thus represent the major excitatory cell types of rat vS1, and potentially of all sensory

cortices (reviewed in Harris and Shepherd, 2015). Therefore, we quantified the degree to which the structural properties of each (dendritic) cell type are organized with respect to the six (somatic) layers of the cortex.

Somata of the respective cell types are not restricted to the layer that is suggested by their naming convention (**Figure 3A**). Specifically, somata of neurons with the dendritic morphology of L2py are found throughout L2 and L3, but are more frequent within L2 (see **Table 1** for the fraction of neurons per cell type per layer). Similarly, somata of L3py are distributed throughout L3 and L4, but are not found in L2. In contrast, somata of L4ss and L4py are largely restricted to L4, whereas those of L4sp are also found in lower L3. L5st and L5tt are restricted to L5, with L5st being more abundant in upper L5 (i.e., L5A: ∼80/20% L5st/L5tt) compared to L5tt that are more abundant in deep L5 (i.e., L5B: ∼40/60% L5st/L5tt) (Oberlaender et al., 2011). Somata of L6ct and L6inv are largely restricted to L6. In contrast, L6cc are distributed around the L5/6 border (i.e. in deep L5, see also Kasper et al., 1994 and upper L6). Thus, within each layer, somata from multiple excitatory cell types intermingle. The only layer border that separated between somata of different excitatory cell types was the L4/5 border (Narayanan et al., 2015).

Next, we calculated a convex hull around the dendrites from all cells that had been assigned to a respective cell type (**Figure 3A**). Remarkably, these dendritic "innervation volumes" showed several relationships with layers (see also Elston et al., 1997, 1999). Specifically, dendrites of L2py are restricted to L1- 3 and those of L3py are restricted to L1-4. In contrast, dendrites of L4py and L4sp do not innervate L1 and are restricted to L2- 4. Dendrites of L4ss are largely confined to L4. Dendrites of L5st and L5tt extend across L1 to L5 and terminate at the L5/6 border. Dendrites of L6cc and L6ct range from L4-6, whereas those of L6inv remain within L5 and L6. Thus, even though soma distributions of the 10 cell types are only loosely related to layers and the borders between them, and dendrites of each cell type extend across multiple layers, the dendrite distributions display cell type-specific relationships with cytoarchitectonic landmarks (**Figure 3B**).

Finally, we investigated whether IC axon distributions of each cell type are also organized with respect to layers. In a previous study, we had shown that the IC axon projections of individual excitatory neurons are correlated with their respective dendritic cell type (Narayanan et al., 2015). The cell type-specific axon differences were only partly reflected by parameters such as overall path lengths or topology, but originated primarily from different vertical (i.e., laminar, **Figure 3B**) and horizontal (i.e., trans-columnar) projection patterns (Narayanan et al., 2015). Specifically, axons of L2py densely innervate L1-3 and less densely L5, with the two innervation peaks coinciding with the centers of L3 and L5, respectively. Axons of L3py have similar axon projection patterns compared to L2py, but innervation of L5 is as dense as innervation of L3, where the peak coincides with the L2/3 border. Axons of the three L4 cell types deviate from most other cell types, as they do not innervate L1. Apart from this difference, the vertical axon profile of L4py resembles the one of the L3py. Axons of L4sp and L4ss are restricted to L2-4. However, axons of L4sp are most dense within L2/3 (the innervation peak coincides with the L2/3 border), whereas axons of L4ss are equally dense in L2/3 and L4. Axons of both, L5st and L5tt, innervate L1-5, but in contrast to L5tt, L5st project densely to L2 and L3 (the innervation peak coincides with the L2/3 border). Axons of L6cc and L6inv innervate the entire cortical depth from the pial surface to the WM tract, whereas L6ct axons are sparse and restricted to L4-6. Axons of L6inv are sparse in L1-4, but dense in L5 and L6 (the innervation peak coincides with the L5/6 border). In contrast, axons of L6cc are most elaborate within L5-6, but also abundant in L3-4, and sparsely innervate L1-2.

We conclude that IC axon morphologies are cell typespecific and layer-related, because the peaks, minima and/or vertical extents of their respective axon density profiles coincide with layer borders and/or centers in a cell type-specific manner. Moreover, we had shown previously that the layerrelated distributions of cell type-specific IC axon projection patterns are linked to multiple horizontal (i.e., trans-columnar) organizational principles (Narayanan et al., 2015). With the exception of L4ss and L6ct, the majority of axon from neurons of each cell type is located outside the barrel column containing the soma. These trans-columnar, horizontal patterns are subdivided into three principles: (1) axons that extend widest along barrel columns that represent whiskers of the same row (i.e., "rowish" axons), (2) axons that extend widest orthogonal to the row [i.e., along barrel columns that represent whiskers of the same arc ("arcish")], or (3) axons that extend unspecifically to all neighboring columns and even beyond (Narayanan et al., 2015). Interestingly, and independent of the cell type, arcish axons are primarily confined to L1-4, rowish axon projections to L5-6.

In summary, dendrite and axon distributions of the 10 excitatory cell types in rat vS1 are organized with respect to

FIGURE 4 | Cell type-specific organization of cortical layers in rat vS1. (A) Semi-coronal view of the cell type-specific soma distribution in a dense model of an average cortical barrel column. (B) Corresponding to panel A: model of the cell type-specific distribution of dendrites. (C) Corresponding to A: model of the cell type-specific distribution of axons. (D) Quantification of cell type-specific structural composition of each layer with respect to somata (top rows), dendrites (center rows), and axons (bottom rows). Dendritic and axonal length represent all neurons in vS1 whose respective dendrites and/or axons extend into the volume of the modeled barrel column. (A–C) were adapted and modified from Oberlaender et al., 2012; Meyer et al., 2013; Egger et al., 2014; Narayanan et al., 2015 with permission.

cytoarchitectonic layers. However, the laminar landmarks that coincide with the respective dendrite or axon distributions are different and specific for each cell type. For example, even though somata of L2py and L3py intermingle within L3, the vertical extents of their dendrite distributions coincide with the vertical extents of L1-3 and L1-4, respectively. The vertical axon distributions of these two cell types delineate the center and extent of L5, whereas their peak axon densities in the superficial layers coincide with the center of L2 and the L2/3 border, respectively. To provide a comprehensive overview of the degrees to which dendrite and axon distributions of the different cell types are organized with respect to layers, we calculated the relative amount of dendrite and axon path length within each layer for each cell type (**Figure 3C**).

# CELL TYPE-SPECIFIC ORGANIZATION OF CORTICAL LAYERS IN RAT vS1

Finally, we quantified the cell type-specific structural composition of each layer. We had previously reported an approach to generate a dense model of the 3D distributions of cell type-specific somata, dendrites and axons within an average cortical barrel column (Egger et al., 2014). Specifically, we used the 153 reconstructed and registered neuron morphologies to estimate the frequency of occurrence of somata from each cell type along the vertical cortex axis (i.e., at 50 µm resolution), as well as the relative overlaps between cell types (Egger et al., 2014). This allowed assigning each neuron from the measured and registered soma distribution to one of the ten cell types (**Figure 4A**). The resultant model of an average barrel column representing the D2 whisker comprises 17,816 excitatory somata (see **Table 2** for the number of neurons per cell type). Each soma in this model is represented by a dendrite morphology of the respective cell type (**Figure 4B**), whose registered soma depth was within 50µm from the location in the model (Egger et al., 2014). This upscaling of the reconstructions resulted in a total dendritic path length that is found within an average barrel column of 246 m (i.e., originating from all excitatory neurons within entire vS1; see also **Table 2** for dendritic path lengths contributed by each cell type). Similarly, we upscaled the registered axon morphologies to the respective number of neurons per cell type (**Figure 4C**). The resultant total (i.e., from all excitatory neurons in vS1) axonal path length within the average barrel column model is 1,599 m (see **Table 2** for axon path lengths by each cell type).

By combining the dense structural model of an average barrel column with our quantification of the layer borders, we estimated the number of somata, as well as the dendritic and axonal path lengths that each of the 10 cell types contributes to each of the six layers (**Figure 4D**). Within the average column model, the majority of dendrites in L1 originates from L2py, followed by L5tt, L3py, and L5st. The majority of axons originates from L2py, followed by L3py and L5st (see also **Table 1**). Similar to L1, the majority of the dendrites in L2 originates from L2py, followed by L3py and L5tt. The majority of axons in L2 originates to almost equal amounts from L2py, L3py, and L5st. In L3, L3py, followed by L2py and L4sp contribute the most dendrites, whereas axons in L3 originate from L3py, followed by L5st and the three cell types in L4. The cell type contributing the most dendrites to L4 are L3py, followed by the three L4 cell types. The majority of axon in L4 originates from L3py, L4ss, L4sp, and L6cc. Layer 5 comprises dendrites primarily from L5tt, closely followed by L5st and L6ct. In turn, the majority of axon in L5 originates from L3py, followed by L6cc, followed by similar contributions from all remaining cell types. Layer 6 is more homogeneous than the other layers, with the majority of dendrites and axons originating from the three L6 cell types.

In this review article, we combined several of our previous studies to quantify the degree to which soma, dendrite and axon distributions of the ten major excitatory cell types of rat vS1 are organized with respect to cytoarchitectonic layers. Even

TABLE 2 | Number of somata, and length of dendrites and axon that each of the 10 major excitatory cell types contributes to the model of an average cortical barrel column.

though the cell types are purely defined by dendrite morphology, intrinsic properties (Feldmeyer et al., 2013), in vivo function (de Kock et al., 2007; Oberlaender et al., 2012), as well as IC axon projection patterns (Narayanan et al., 2015) are cell typespecific. Defining the six layers based on precise measurements of neuron soma distributions revealed that each layer comprises somata of multiple cell types. Nonetheless, dendrite and IC axon innervation patterns of each cell type showed distributions that were related to several laminar landmarks (i.e., center/extent of layers or layer borders). The respective laminar landmarks depended on the cell type, and typically deviated between dendrites and axons within a cell type. Thus, cytoarchitectonic layers can be regarded as a structural reference frame, which gives rise to several cell type-specific dendrite and IC axon projection patterns.

The observation that dendrites and axons of excitatory cortical cell types are organized with respect to laminar landmarks—and may hence be referred to as layer-specific—does not imply that layers are organized in a cell type-specific manner. This is because somata of multiple cell types intermingle within and across layers (i.e., only the L4/5 border represents a cell type border), and the layer-specific organization of dendrites and axons is different for each of those intermingling cell types. Hence, using layers as a synonym for cell types, for example when describing cortical circuits, may yield ambiguous results. For example, the canonical pathway theory suggests that thalamocortical input to L4 is first relayed to L2/3 and then to L5. However, apart from L4ss and L4sp that primarily project their axons to L2-4, L4 comprises also L3py and L4py, which have additional dense axon projections directly to L5. The present quantifications of the layer-related morphological organization of excitatory cell types (**Figure 3**) and the cell type-specific structural composition of cortical layers (**Figure 4**) may hence aid future studies to better interpret layerspecific measurements or manipulations with respect to the underlying cell types and circuits.

The layer-related structural organization of excitatory cell types, as reviewed here, is not limited to the local cortical circuitry, but extends to long-range pathways. For example, thalamocortical axons originating in the ventral posterior medial nucleus of the thalamus (VPM) define the extent of L4 and coincide with the L5/6 border in rat vS1. In contrast, axonal projections into vS1 from neurons in the posterior medial nucleus of the thalamus (POm) delineate L1 and coincide with the L4/5 border (Wimmer et al., 2010). In addition to layer-related thalamocortical input patterns, cortical output neurons have specific vertical soma distributions that reflect their respective long-range target areas. For example, VPMprojecting L6ct are located in upper L6 (i.e., L6A), whereas those that project additionally to POm are found in L6B (Zhang and Deschenes, 1997). Similarly, somata of intratelencephalic L5 neurons (ITs)—which are defined by long-range axons that project to the striatum and other cortical areas (Harris and Shepherd, 2015)—are more frequent in L5A, whereas those that project to subcortical targets (i.e., pyramidal tract neurons, PTs Wise and Jones, 1977) are primarily located in L5B. Moreover, we have recently shown that somata of PTs form two sublayers that reflect specific subcortical targets (e.g., POm-projectors are more superficial within L5B than those PTs that innervate the Pons, Rojas-Piloni et al., 2017). The 10 major excitatory cell types of the cortex may thus be divided into further subtypes that differ in their long-range targets, and/or additional structural and functional properties (for a review, e.g., DeFelipe and Farinas, 1992; Spruston, 2008; Harris and Shepherd, 2015) that go beyond those described here (e.g., density and distribution of spines Elston, 2002), and whose respective vertical soma

## REFERENCES

distributions define sublayers within the six cytoarchitectonic layers.

#### AUTHOR CONTRIBUTIONS

MO designed the review. RN and DU performed data analysis. All authors wrote the paper.

#### ACKNOWLEDGMENTS

Funding was provided by the Bernstein Center for Computational Neuroscience, funded by German Federal Ministry of Education and Research Grant BMBF/FKZ 01GQ1002, the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No 633428), and the Center of Advanced European Studies and Research (caesar).

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Narayanan, Udvary and Oberlaender. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# A Perspective on Cortical Layering and Layer-Spanning Neuronal Elements

#### Matthew E. Larkum<sup>1</sup> \*, Lucy S. Petro<sup>2</sup> , Robert N. S. Sachdev <sup>1</sup> \* and Lars Muckli <sup>2</sup> \*

<sup>1</sup>Neurocure Center for Excellence, Charité Universitätsmedizin Berlin & Humboldt Universität, Berlin, Germany, <sup>2</sup>Centre for Cognitive Neuroimaging, Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom

This review article addresses the function of the layers of the cerebral cortex. We develop the perspective that cortical layering needs to be understood in terms of its functional anatomy, i.e., the terminations of synaptic inputs on distinct cellular compartments and their effect on cortical activity. The cortex is a hierarchical structure in which feed forward and feedback pathways have a layer-specific termination pattern. We take the view that the influence of synaptic inputs arriving at different cortical layers can only be understood in terms of their complex interaction with cellular biophysics and the subsequent computation that occurs at the cellular level. We use high-resolution fMRI, which can resolve activity across layers, as a case study for implementing this approach by describing how cognitive events arising from the laminar distribution of inputs can be interpreted by taking into account the properties of neurons that span different layers. This perspective is based on recent advances in measuring subcellular activity in distinct feed-forward and feedback axons and in dendrites as they span across layers.

Keywords: feedback, feedforward networks, top-down processing, calcium spikes, apical dendrite, ultra-highfield fMRI, layer fMRI

## CONCEPTUAL SHIFT

Neuroscience has seen a dramatic evolution since the early anatomical investigations of the 19th and 20th centuries.  This approach was enormously successful in describing the structure of the cerebral cortex as a laminar structure based on the cytoarchitecture. Reaching its zenith in the first half of the 20th century (Defelipe et al., 1988), ever more detailed descriptions of the precise configuration of cells and axons promised to explain the function of the cerebral cortex. However, with the development of techniques for recording activity directly from the neurons of the brain, the original focus on a faithful anatomical description gave way to more simplified descriptions of the functional anatomy, i.e., organization and connectivity of structures (Hubel and Wiesel, 1962; Mountcastle, 1978). Here, various models were offered for describing the organization of the cortex (**Figure 1B**, ''Connectivity''). The increased emphasis on connectivity often came at the expense of the complexity of the components, mostly treated as simple point neurons. In this article, we take the view that a full description of a laminar structure like the cortex will require the successful marriage of both the components and the connectivity that captures an adequate description of both aspects and their interplay.

#### Edited by:

Kathleen S. Rockland, Boston University, United States

#### Reviewed by:

Stewart Shipp, University College London, United Kingdom Andreas H. Burkhalter, Washington University in St. Louis, United States

#### \*Correspondence:

Matthew E. Larkum<EMAIL_ADDRESS>Robert N. S. Sachdev<EMAIL_ADDRESS>Lars Muckli<EMAIL_ADDRESS>
Received: 02 August 2017 Accepted: 19 June 2018 Published: 17 July 2018

#### Citation:

Larkum ME, Petro LS, Sachdev RNS and Muckli L (2018) A Perspective on Cortical Layering and Layer-Spanning Neuronal Elements. Front. Neuroanat. 12:56. doi: 10.3389/fnana.2018.00056

# WHAT DEFINES A LAYER AS A SUBUNIT OF FUNCTION?

Treating neurons as single points comes at a great price in a layered structure like the cortex. In fact, neurons have complicated morphologies and properties that typically span multiple layers. It is conventional to refer to ''cells of a specific layer.'' The description ''layer 5 neuron,'' for instance, conventionally refers to a neuron whose cell body lies in layer 5. However, the properties of this cell are distributed across multiple cortical layers throughout their dendrites and axon. The merging of components and connectivity in a description of the layered cortex needs to take into account the location and influence of synaptic inputs and the resultant electrical events that occur within layer-spanning neurons (**Figure 1**). For example, layer 5a and layer 5b pyramidal cells differ predominantly in their properties defined by the apical dendrites (Major et al., 2013).

A description of the cortex emphasizing connectivity between simple components—named after the cell body location—is seductive, particularly to computational neuroscientists, but also to physiologists who measure the ''activity'' of a neuron by recording from the cell body. It turns out that the neurons generate action potentials very close to the cell body, which is also the location that is most accessible for recording the neuron output. However, there is no functional consequence to this fact from the perspective of an input-output description of the cortical circuit and in particular for ascribing functionality to layers (see **Box 1**). Neither the input nor the output is actually best described as located at the cell body. Both the inputs (postsynaptic potentials) and the outputs (transmitter release) of a single neuron could literally occur in any and all of the layers of the cortex. From this perspective, there is no such thing as a ''L5 pyramidal neuron.'' Nevertheless, there is a general correspondence between the cytoarchitecture, and between the layering apparent due to cell bodies and the layering of axonal terminations (**Figure 1A**, middle and right).

The last few decades have witnessed a huge increase in our knowledge about the properties of dendrites and how they integrate synaptic input (Spruston, 2008; Major et al., 2013; Grienberger et al., 2015). For instance, we now know that pyramidal neurons in the cortex have various types of local dendritic spikes (Na+, Ca2<sup>+</sup> and NMDA) and diverse distributions of ion channels that influence the propagation and local integration of synaptic potentials. It is now clear that these neurons are more complex than simple point neurons but a canonical description has yet to emerge. Ideally, we should be able to describe neurons in functional terms with reference to the number of compartments, the organization of input and output and their relationship to the layering of the cortex. Nevertheless, with the advent of optical methods it has become possible to directly image activity in particular axons, in dendrites and in cell bodies situated in particular layers (Svoboda et al., 1997; Petreanu et al., 2007; Jia et al., 2010; Andermann et al., 2013; Kim and Kastner, 2013) which promises to increase our understanding. Functional MRI, measuring BOLD contrast which is a combination of blood flow, blood volume and blood oxygenation, is better linked to neuronal activity by summed energy consumption than by spiking neuron output. With the increased spatial resolution of fMRI in recent years, BOLD is now measured at different cortical depths and can therefore be used to characterize the summed energy consumption in different layers of cortex. Great advances have also been made in anatomical approaches for examining brain connectivity at all scales (Bassett and Sporns, 2017). Notably missing so far

BOX 1 | Re-evaluating the somato-centric perspective in layered structures. Since Cajal (1894) proposed the Neuron Doctrine, it has been clear that information (by and large) flows uni-directionally across neurons in the nervous system. This has lead to a pervasive and reductive description of how neurons operate that is apparent both in the way most neuroscientists talk about "activity" in the brain and the way the operation of neurons is formalized (A). Here, axon terminals are frequently described as the "input" to a neuron, which is usually represented only as a "conceptual cell body" that emits "output." This perspective has survived more than a century since Cajal because, we argue, it fits the intuition that cell bodies are both physically prominent and provide a convenient locus for recording action potentials. In fact, the physical situation, which is well understood but frequently overlooked, is that the output of a neuron almost always manifests as the release of transmitter at the axon terminals whereas the input is best described as synaptic currents located directly abutting the terminals (B). From a methodological point of view, this means that methods such as fMRI are typically describing energy related to release of transmitter at axon terminals (see main text) and local field potentials describe synaptic currents located nearby. With the advent of methodologies that can more precisely resolve the layering of the cortex (e.g., high-res fMRI), it becomes necessary to shift perspectives in order to interpret the signals. Because of the close apposition of input and output, the choice of label for synapses reduces quickly to semantics and it is not the argument here that the nomenclature needs to change. However, the current "somato-centric" perspective that we owe originally to Cajal cannot be used to explain the data emerging with modern techniques. It is increasingly well understood that the "function" of a neuron (i.e., the transformation from input to output) occurs via the process of dendritic integration that in the cortex frequently occurs in active and layer-spanning dendritic trees (C). Thus, the computation of a cortical neuron is actually a complex spatio-temporal phenomenon that transforms inputs arriving over various layers to output delivered to various other layers (D). In this transformation process, the cell body specifies neither the location of the processing nor the output of the neuron and could in principle be collapsed to a dimensionless node in any specific layer without substantially changing the input/output function of the neuron. It is therefore not correct from a functional perspective to attribute the "activity" of a cell to the layer in which the cell body is located. The interpretation of BOLD signals from high-res fMRI recordings, for instance, cannot be attributed simply to the spiking neuronal activity occurring in the same layers as the BOLD signal, but rather to the summed mostly post synaptic membrane potential fluctuations of dendrites whose cell body is elsewhere.

is a coherent integration of these revolutionary thrusts in the neurosciences.

In summary, our perspective on cortical layering is the following:

# COMBINING THE COMPONENTS AND CONNECTIVITY IN A DESCRIPTION OF THE CORTEX

To date, models of cortex that include the laminar structure are only a small proportion of the total and these models tend to ignore the dendrites or treat them only cursorily (Spratling and Johnson, 2001; Spratling, 2002; Raizada and Grossberg, 2003; Thomson and Bannister, 2003; Grossberg, 2007; George and Hawkins, 2009). By using simplified components, such models may fail to successfully account for the interactions across layers that are sometimes carried out within the neurons themselves. To achieve this, a description of the full repertoire of dendritic properties with respect to the different cortical lamina will be necessary. The final goal of such a model is a description of how the inputs between and within cortical areas are transformed into laminar-specific output throughout the system.

A simple example of such a model was proposed in a recent hypothesis of how the dendritic calcium spike in pyramidal neurons might associate feed-forward and feedback information streams arriving at different cortical layers (**Figure 2A**; Larkum, 2013). Here, the pyramidal neuron acts like a coincidence detector for simultaneous input to the upper and lower cortical layers with Ca2<sup>+</sup> spikes facilitated by back-propagating action potentials (BAC firing; Larkum et al., 1999). Whether or not the cortex operates exactly in this fashion is still an open question. Evidence in favor of this particular hypothesis was recently demonstrated by showing that the threshold for perception correlates with dendritic calcium spikes in layer 5 pyramidal neurons and that down-regulating the calcium spikes suppressed perception at threshold stimulus levels (Takahashi et al., 2016).

On the other hand, alternative explanations have been offered to explain exactly which inputs lead to calcium spike firing. For instance, feedback inputs also arrive in the lower layers and possibly triggering the BAC firing mechanism on their own (Manita et al., 2015, 2017). However, the fact remains that the calcium spike apparently has an effect on the perceptual threshold that cannot be explained by models with point neurons. The main upshot is that it is necessary to have an account of how cellular processes such as local spikes and subcellular propagation (Major et al., 2013; Stuart and Spruston, 2015) interact with inputs and under what behavioral circumstances in order to interpret any given recordings and explain complex behavior.

In conclusion, we argue that it is fundamentally important first to examine all cell types of the cortex and describe and encapsulate their properties and the way they integrate synaptic inputs (**Figures 2B,C**). This task can be started in vitro but eventually must be validated in vivo under awake behaving conditions. Second, having obtained the biophysical facts about the components of the cortex, it will be necessary to develop abstractions of these components so that their functionality can be captured in a model. Third, this information has to be combined with the connectivity of the cortex so that the influence of particular inputs can be included in the model. With this information in hand, it becomes possible to interpret laminarspecific data collected from the cortex whether it comes in the form of electrical recordings from particular cells in particular layers or imaging of brain activity at various levels of resolution and various cortical depths.

# A CASE STUDY—ULTRA HIGH-RESOLUTION fMRI

To elucidate this argument, we take a case study using the relatively new technique of ultra high-resolution fMRI that allows researchers to investigate brain activity non-invasively in human subjects with the ability to separate different depth layers in cortex (**Figure 3**). With this technique, blood oxygenation leveldependent signals (BOLD) can be measured for the top, middle and bottom thirds of the cortex. This, in turn, approximates to the layers known to roughly segregate according to synaptic inputs from feedback vs. feed forward connectivity (Petro and Muckli, 2016, 2017). We take this example because it highlights both the gains that can be made by demanding informed interpretations and the pitfalls of proceeding without them. It is ideal as a case study because, unlike traditional fMRI, this is a technique that can be applied to, and in our opinion calls for, an understanding of the operation of the cortex from a laminar perspective (Muckli et al., 2015; Huber et al., 2017; Lawrence et al., 2017).

But what conclusions about high-res fMRI recordings are valid? For example, suppose higher BOLD signals are detected at one depth vs. another? Signals in fMRI studies are often conflated with neuronal ''activity.'' This is problematic on a number of levels. BOLD signals largely reflect the energy consumption of neural processing that require oxygen and glucose rather than a direct reflection of neuronal activity. Therefore, the BOLD signal most likely reflects locations of high synaptic activity (Logothetis et al., 2001; Viswanathan and Freeman, 2007; Logothetis, 2008) because the process of synaptic transmission has the largest energy requirement. Importantly, the activity of excitatory and inhibitory neurons would simply combine on this view despite their opposite affects. Nevertheless, even with this understanding, it is often implied that high synaptic activity translates to high post-synaptic activity. However, the post-synaptic effect will depend on the complexities of the post-synaptic targets. For instance, large synaptic input to the upper layers that might occur due to an increase in feedback information would have a complex relationship to the firing of neurons that have distal dendrites projecting to the upper layers and their cell bodies and proximal dendrites in deeper layers. The major effect of such activity might be to depolarize the apical dendrites of pyramidal neurons whose cell bodies are in lower layers and whose axons terminate mostly in different cortical areas. Or, the same input may activate dendrites targeting inhibitory neurons that have the opposite effect on the dendrites of pyramidal neurons. Furthermore, the relative proportion and timing of inputs to different cortical layers will lead to further complexities that are mostly determined by the biophysics of the cells themselves. Some of these predictions

FIGURE 3 | Cortical surface reconstruction. Left hemisphere in the human acquired with high resolution 7T fMRI, overlaid with grid lines depicting cortical depths from superficial (red) to deep (purple) layers; (left upper) at the boundary of V2 and V3 right and lower indicate cortical depth levels in V1 (Muckli et al., 2015, adapted with permission). With this method, voxels in fMRI are labeled depending on their cortical depth and incorporated into depth-specific analyses. In the above example, the fMRI data is at 0.8 mm<sup>3</sup> . Even though the bands of cortical depth level measured with fMRI are still insufficient to separate all six anatomical layers of cortex, there are important gradients that are functionally different in their processing of internal mental states, which we can capture by separating the layers into upper, middle and lower (i.e., by separating feedforward and feedback processing).

can be guessed already from our knowledge of the operation of components such as layer 5 pyramidal neurons but even for these well-studied neurons their description is probably still not complete. There is not yet a consensus description of these neurons that encapsulates their function under many simple input patterns. For other neurons of the cortex the situation is even less clear such that predictions of what happens under various different conditions remain wild guesses at best. Furthermore, most of the biophysical information we have so far about the operation of subcellular membrane potential dynamics is derived from recordings from rodent neurons under controlled conditions in vitro. What will happen in human neurons that are much larger (Mohan et al., 2015; Deitcher et al., 2017) and may have different properties (Eyal et al., 2016)? Good predictions about what signals should be expected using ultra high-resolution fMRI in humans during cognitive tasks will require a coherent theory of what occurs with different patterns of laminar input in different areas under different conditions.

 At present, these high-resolution brain imaging studies have not tested the theories and tasks of their standard brain imaging counterparts at 3 Tesla. We anticipate studies of learning, memory, multisensory processing and consciousness which are surely forthcoming at laminar resolution. These studies would build on a number of successful proof-of-concept findings, measuring, for example, BOLD activation in layers of primary visual cortex in response to its preferred stimulus of contrast-reversing checkerboards (De Martino et al., 2013); cerebral blood volume and BOLD signal changes in layers of human primary motor cortex during finger tapping (Guidi et al., 2016); and layer-dependent population receptive field sizes in human primary visual cortex (Fracasso et al., 2016). While these studies provide conceptual advances in human sensory and motor systems, they do not address the laminar influence of feedback vs. feedforward sources, or in functional terms how cognitive processing interacts with sensory processing. For example, when viewing natural scene images, cortical feedback transmits the brain's internal model of the scene back to primary visual cortex, predominantly the superficial layers (Muckli et al., 2015). In superficial layers, this feedback is detected with fMRI as changes in the activation distribution measured in multi-voxel pattern activity. Another finding relevant for the question of top-down feedback modulation of vision to cortical layers, is the demonstration that the deep layers of V1 are more active for perceptual filling-in of contours, a form of modal completion, (Kok et al., 2016). This was shown as a univariate increase of activity in deep layers. Taken together, both studies suggest how cognitive function interacts with sensory processing on a sub-neuronal level; top-down expectations are present in the dendritic tree of superficial layers of V1, and when combined with specific contextual information they can trigger activity in deeper layers and the illusory percept of visual contour filling-in. Although this interpretation of both studies has not yet been tested directly, it highlights the potential of ultra-high-field fMRI in detecting the functional properties of sub-neuronal compartments. Combined multimethod experiments are ongoing to establish this interpretation across species and scales. Another example of top-down modulation in layers of sensory cortex is provided by De Martino et al. (2015) who show that attention sharpens the representation of acoustic information mainly in the superficial layers of human primary auditory cortex. All of these studies give rise to the question of the circuit and systems level mechanisms of topdown, feedback processing. In a first demonstration of its kind, human laminar fMRI was used to derive information flow between cortical areas (Huber et al., 2017). Using BOLD and CBV (cerebral blood volume) measures, the authors revealed somatosensory and premotor input in upper layers of M1 and cortico-spinal motor output in the deeper layers. Moreover they found layer-specific functional connectivity of M1 and somatosensory and premotor areas using laminar restingstate fMRI. Technical advances in high-resolution fMRI will ensure that we can improve spatial coverage, meaning in future we can image even larger areas of cortex, that are capturing information flow between additional communicating regions.

 All of these topics will be bolstered by (and are essential components of) what we propose here: the necessity of interpreting layer-specific fMRI data with knowledge of the laminar distribution of inputs and layer-spanning cellular compartments. We need a theory that incorporates the complex properties of neuronal components whose dendrites and axons span multiple layers because, while these current studies already highlight the tremendous potential of this technique for the study of cognitive function in higher brain areas, it will remain difficult to fully disentangle the effects of feed forward and feedback inputs in human cortex without understanding the underlying biophysics. The interpretation of the layer specific fMRI signal therefore requires an a priori theory of cortical function, describing the functional consequences of the laminar distribution of synaptic inputs for a neuron. Even for studies in rodents where there is a constant evolution in the methods, and where the temporal and spatial resolution is much higher than with fMRI, the ability to collect large data sets is ultimately meaningless without a theory (Jonas and Kording, 2017).

The promise of high-resolution fMRI is in allowing a window into the neuro-computational unit of cortical layers during the most elaborate cognitive states, for example, emotion, inner speech, empathy and mental time travel. Non-invasive approaches are crucial because we are not close to being able to image activity at a cellular or subcellular resolution in human beings, or to applying experiments where one manipulates specific pathways using approaches like optogenetics. On the other hand, this situation does not mean that the underlying cellular and subcellular dynamics can therefore be ignored without influencing the interpretation. If brain imaging can reveal a functionally-relevant meso-scale abstraction of the synaptic inputs in micro-scale neuroscience, this will amount to an important breakthrough. At some point in the very near future it may then become possible to talk about consequences of activity in cortical areas during cognitive tasks in the context of feed forward and feedback inputs, and activity in the different layers.

# LINKING COMPLEX COMPONENTS WITH LAMINAR CONNECTIVITY—WHAT STILL NEEDS TO BE DONE?

In our opinion it would be unwise to use oversimplified assumptions (such as point neurons) as a substitute for our ignorance. Fundamental questions remain regarding many aspects of cortical layering and how this interacts with the complexities of layer-spanning neurons with active dendrites. For instance, we lack a satisfactory account of what role ''basal'' dendrites play, that are largely limited to the same or adjacent layer as the cell body. It has been suggested that local dendritic NMDA spikes in these dendrites offer multiple, independent integrations of incoming signals (Mel, 1992; Polsky et al., 2009). In fact, this observation has been extended to the tuft dendrites of pyramidal neurons (Larkum et al., 2009; Palmer et al., 2014) and may be a generalization for thin dendrites of other neurons (Lavzin et al., 2012). In general, however, we still lack the specific and necessary information to accurately describe the input/output function of most neurons and their relationship to the layering of the cortex. In fact, most modern methods do not measure either input or output directly (see **Box 1**) but rather action potential activity at the cell body. In cases where interesting processes occur in the dendrite but the cell fires no action potential, the underlying events might still be detectable with methods such as high-resolution fMRI.

Perhaps most importantly, the learning rules for synaptic connectivity are yet to be linked conclusively to the full range of intrinsic activity and laminar circuitry. There is good evidence that dendritic Na<sup>+</sup> and Ca2<sup>+</sup> spikes influence synaptic plasticity (Kampa et al., 2006; Sjöström and Häusser, 2006; Losonczy et al., 2008) as well as NMDA spikes (Gordon et al., 2006; Brandalise et al., 2016). Nevertheless, an integrated theory of how all these isolated phenomena combine to result in network rewiring is still lacking. Recent intriguing results from the Magee group suggest that some combination of these intrinsic dendritic properties may be transformative in explaining learning in the hippocampus (Bittner et al., 2015; Grienberger et al., 2017). In the neocortex there is accumulating evidence that plasticity occurs at feedback and 2nd order thalamic synapses on to the apical tuft dendrites of L5 neurons in the upper layers of the cortex (Gambino et al., 2014; Cichon and Gan, 2015; Miyamoto et al., 2016). All these forms of plasticity depend on this activation of intrinsic dendritic activity. The exact conditions or rules that control this kind of learning are still being determined but it is clear that they cannot be understood or described without reference to both the laminar pattern of connectivity and the intrinsic properties of the neurons that process these inputs in distinct compartments lying in distinct cortical lamina.

At the present time, most laboratories still focus on recordings from cell bodies and some even still report data without reference to the cortical layer or cell type from which they are taken. Two-photon imaging approaches have opened up the possibility of collecting data from compartments other than the cell body but recording data from anything other than cell bodies is still the exception rather than the rule. In the meantime, improvements to 2-photon imaging (Ji et al., 2016; Papadopoulos et al., 2016), other methodologies such as prisms inserted into the cortex (Andermann et al., 2013) and high-resolution fMRI now make it possible to include

#### REFERENCES

laminar-specific information. Standard methodologies such as vertically oriented linear arrays like Michigan Probes have long been useful for probing laminar issues (BeMent et al., 1986; Cauller and Kulics, 1991) and can be used to probe the relative influence of feed forward and feedback influences in the cortex (van Kerkoerle et al., 2017). It was recently shown that intrinsic excitability of pyramidal neurons such as calcium spikes are also easily detected by these devices (Suzuki and Larkum, 2017).

In summary, it is now possible to move beyond a simple description of cortex in terms of point neurons and pointto-point connections in favor of a richer understanding that includes vertical features such as axonal termination layers and subcellular compartments spanning several layers. We have learned enough about the properties of particular neurons such as the layer 5 neuron to be able to say for certain that their active dendritic properties interact with the location of synaptic inputs in a very complex but important way. In our view, it is essential to investigate these features in all neurons and synaptic input pathways in order to understand the layering of the cortex.

# AUTHOR CONTRIBUTIONS

ML, LP, RS and LM wrote the article.

# FUNDING

This work was supported by the European Union's Horizon 2020 research and innovation program and Euratom research and training program 20142018 (under grant agreement No. 670118 to ML). This project has received funding from the European Union's Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 720270 and 785907 (Human Brain Project SGA1 and SGA2). Deutsche Forschungsgemeinschaft (Grant No. LA 3442/3-1 and Grant No. LA 3442/5-1 to ML); and European Research Council (ERC StG 2012\_311751-''Brain reading of contextual feedback and predictions'' to LM).

humans at 7T: improved specificity with 3D GRASE. PLoS One 8:e60514. doi: 10.1371/journal.pone.0060514

MRI at 7 and 9.4T. Neuroimage 164, 48–58. doi: 10.1016/j.neuroimage. 2017.03.058

**Conflict of Interest Statement**: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Larkum, Petro, Sachdev and Muckli. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# What Is the Evidence for Inter-laminar Integration in a Prefrontal Cortical Minicolumn?

Ioan Opris\*, Stephano Chang and Brian R. Noga

The Miami Project to Cure Paralysis, Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, FL, United States

The objective of this perspective article is to examine columnar inter-laminar integration during the executive control of behavior. The integration hypothesis posits that perceptual and behavioral signals are integrated within the prefrontal cortical interlaminar microcircuits. Inter-laminar minicolumnar activity previously recorded from the dorsolateral prefrontal cortex (dlPFC) of nonhuman primates, trained in a visual delay match-to-sample (DMS) task, was re-assessed from an integrative perspective. Biomorphic multielectrode arrays (MEAs) played a unique role in the in vivo recording of columnar cell firing in the dlPFC layers 2/3 and 5/6. Several integrative aspects stem from these experiments: 1. Functional integration of perceptual and behavioral signals across cortical layers during executive control. The integrative effect of dlPFC minicolumns was shown by: (i) increased correlated firing on correct vs. error trials; (ii) decreased correlated firing when the number of non-matching images increased; and (iii) similar spatial firing preference across cortical-striatal cells during spatial-trials, and less on object-trials. 2. Causal relations to integration of cognitive signals by the minicolumnar turbo-engines. The inter-laminar integration between the perceptual and executive circuits was facilitated by stimulating the infra-granular layers with firing patterns obtained from supra-granular layers that enhanced spatial preference of percent correct performance on spatial trials. 3. Integration across hierarchical levels of the brain. The integration of intention signals (visual spatial, direction) with movement preparation (timing, velocity) in striatum and with the motor command and posture in midbrain is also discussed. These findings provide evidence for inter-laminar integration of executive control signals within brain's prefrontal cortical microcircuits.

#### Edited by:

Javier DeFelipe, Cajal Institute (CSIC), Spain

#### Reviewed by:

Srikanth Ramaswamy, École Polytechnique Fédérale de Lausanne, Switzerland Conrado Arturo Bosman, University of Amsterdam, Netherlands

#### \*Correspondence:

Ioan Opris<EMAIL_ADDRESS>
Received: 09 August 2017 Accepted: 27 November 2017 Published: 14 December 2017

#### Citation:

Opris I, Chang S and Noga BR (2017) What Is the Evidence for Inter-laminar Integration in a Prefrontal Cortical Minicolumn? Front. Neuroanat. 11:116. doi: 10.3389/fnana.2017.00116 Keywords: integration, coordination, cortical layers, cortical minicolumns, microcircuits, prefrontal cortex, executive function

## INTRODUCTION

Neural integration can be defined as the summation of excitatory and inhibitory synaptic inputs, which governs the generation of an action potential (Arnold et al., 2004). Integration of various neuronal signals within and between prefrontal cortical microcircuits plays a crucial role in cognition, perception and action (Penfield, 1958; Miller and Cohen, 2001; Opris et al., 2013; Bastos et al., 2015). Voluntary action is based on the intention to achieve a goal, and this goal determines how planning and subsequent actions lead to its achievement. However, recent studies point to evidence for neural integration of voluntary action in a prefrontal cortical minicolumn (Opris et al., 2011, 2012b, 2013; Bastos et al., 2015).

The prefrontal cortex may be regarded as an assembly of interconnected neuronal cells that sends and receives projections to/from virtually all cortical areas (sensory, motor and association), as well as to/from multiple subcortical structures (Miller and Cohen, 2001). As pointed out by Vernon Mountcastle, it is critical to identify the arrays of inputs and outputs (Mountcastle, 1997), in order to understand the integrative role of the prefrontal cortex (Miller and Cohen, 2001) and its functional organization (Kritzer and Goldman-Rakic, 1995).

Functional specialization of the brain employs cortical layers that differ from three to six layers (Shepherd, 2011). The sixth layer of cortex is, in fact, an adaptation observed exclusively in mammals, although it shares some commonalities with other species (Aboitiz, 2011; DeFelipe, 2011; Bosman and Aboitiz, 2015). A complementary role in the expression of brain function is played by the cortical minicolumns that interconnects the layers and forms functional microcircuits (Mountcastle, 1957, 1997; Shepherd and Grillner, 2010; DeFelipe et al., 2012). Just imagine if the supra-granular and infra-granular laminae would be disconnected, across the entire brain, then, one interface of the brain will be sensing without perceiving (i.e., being able to interpret their meaning) and the other interface will act/move randomly, without having a goal.

In the prefrontal cortex, the vertical ''chains'' of neurons called minicolumns (Mountcastle, 1957, 1997; Buxhoeveden and Casanova, 2002) are surrounded by inhibitory cells, forming a curtain of inhibition (Szentágothai and Arbib, 1975; see the **Schematic diagram**). Cortical neurons within prefrontal minicolumns are inter-connected across all layers: three upper layers (L1–L3), one granular layer (L4) and two lower layers (L5/L6). The granular layer receives the ''sensory inputs'' via the thalamus (Constantinople and Bruno, 2013). According to the concept of ''three-stratum'' functional module, lower layers execute the associative computations elaborated in upper layers (Buxhoeveden and Casanova, 2002; Casanova et al., 2011; Opris, 2013). The upper layers consist of small pyramidal cells that form vertical connections with the larger pyramidal neurons of the lower layers that generate most of the output from the cerebral cortex to other cortical/subcortical parts of the brain (Buxhoeveden and Casanova, 2002; Gabbott et al., 2003, 2005). Cortical microcircuit modules are forming topographic maps in visual and motor cortices (Mountcastle, 1997; Thomson and Lamy, 2007; Kaas, 2012), as well as in auditory (Allen et al., 2017), somatic (Mountcastle, 1997), taste (Peng et al., 2015) and smell (Qu et al., 2016) modalities of these sensory cortices. A modular network can be partitioned into nodes that are ''densely interconnected internally'' but only sparsely to other subsets (Chung et al., 2016).

The aim of this perspective is to examine the integration of perceptual and behavioral signals in the inter-laminar prefrontal cortical microcircuits (Opris et al., 2011, 2012a,b, 2013; Opris and Casanova, 2014). Given that the prefrontal cortex is the ''seat'' of the highest brain functions (Fuster, 2001), understanding the site of integration and the functional role of this computational mechanism is essential.

# Cortical Layers and Functional Integration

Let's begin with few questions that address the core of modular structure and its functional role.

#### Why Have Cortical Layers?

Cortical layers serve multiple purposes: some serve more local microcircuits while others act as interconnecting loops (thalamo-cortical) or large-scale networks (bottom-up) that have a multifunctional role.

information from Ventral Tegmental Area (VTA) and Thalamus (TH). BG stands for Basal Ganglia, DA for Dopamine, Glu for Glutamate and GABA for gamma butyric acid.

#### What Is the Function of Layering?

Being part of the brain's connectome (Hilgetag et al., 2016) including the entirety of brain's connections, cortical layers serve local microcircuits processing sensory or motor information, as well as the interconnected loops (thalamo-cortical) or largescale networks: (i) bottom-up, sensory-to-motor; (ii) top-down, cognitive-to-motor; and (iii) inter-hemispheric, for coordination of various behaviors, serving a multifunctional purpose (Miller and Phelps, 2010; Makarova et al., 2011; Georgopoulos, 2015).

#### Do Neurons in Cortex Integrate Information Across Different Layers or Across Columns?

Indeed, neurons from different cortical layers/columns communicate to each other via synchronized firing (Romo et al., 2003; Opris et al., 2013) across cortical layers, quantified by cross-correlation histograms (CCHs). Also, neuronal integration has been studied using ''noise and signal correlations'' or LFP-spike interactions (Bosman and Aboitiz, 2015).

and average peri-event histograms (PEHs) obtained from two cell pairs recorded simultaneously from L2/3 (blue) and L5 (red) in minicolumn format over ± 2.0 s relative to match phase (A) onset (0.0 s) in a single DMS session. Cross-correlation histograms (CCHs) for the same cell pairs in each minicolumn are shown (between raster-PEH displays) in left and right mini-columns for Pre (black, −2.0 s to 0.0 s) and Post (green, 0.0 s to +2.0 s) time intervals relative to match phase onset (M, 0.0 s). CCHs show increased inter-laminar synchronization (larger correlation peaks) for both cell pairs during target selection in the match phase (green, post) relative to similar correlations between the same cell pairs constructed before phase onset (pre, −2.0 s to 0.0 s). With permission from Opris et al. (2012b).

#### What Is the Evidence for Integration of Information Across Several Layers?

As seen in **Figure 1**, the neurons in prefrontal cortical layers L2/3 and L5 change firing patterns during the presentation of the matching target (together with the non-matching distractors), resulting in inter-laminar CCHs changes (increases the number of coincident spikes) that may represent translaminar integration of information (Foxworthy et al., 2013; Opris et al., 2013). Information flows through cortical layers in a feed-forward manner (Bastos et al., 2015), going from layer 4, to layers 2/3 and onwards (Bastos et al., 2012). Another view maintains that cortical layers can have distinct inputs that activate them, triggering spikes when the integration input bypasses a threshold (Opris and Casanova, 2014).

#### What Is the Hypothesis of Prefrontal Cortical Inter-laminar Integration?

The integrative hypothesis posits that integration of perceptual signals from supra-granular layers with the behavioral signals in the infra-granular layers takes place in the prefrontal cortical inter-laminar microcircuits (Opris et al., 2011, 2012a,b, 2013; Takeuchi et al., 2011).

#### What Is the Function of Each Prefrontal Cortical Layer?

Layer 1: Cross-modality modulation is achieved through layer L1 cell-mediated inhibitory and disinhibitory circuits (Ibrahim et al., 2016). Neurons in layer L1 form ''canonical neuronal circuits'' to control information processes in both supra- and infra-granular cortical layers (Lee et al., 2015). Projections of neurons to layer L1 from all cortical inputs has been shown to make a significant contribution to the integration process throughout the neocortex (Mitchell and Cauller, 2001).

Layer 2/3: This layer provides the integration of perceptual stimuli by inter-areal bottom up connections and the integration of perception with action by trans-laminar connections and top-down cortico-subcortical connections (Opris et al., 2011, 2012a,b, 2013).

Layer 3: This layer provides inter-hemispheric callosal connections (Georgopoulos, 2015).

Layer 4: The primary visual cortex receives orientation- and direction-tuned inputs from thalamus in layer 4 (Sun et al., 2016).

Layer 5: Pyramidal neurons in this layer integrate inputs from many sources and distribute outputs to cortical and subcortical structures (Kim et al., 2015). Cortical-subcortical neurons project to subcortical structures in the thalamo-cortical loop, integrate information and generate responses more relevant to movement control, while cortico-cortical neurons are more important in visual perception.

Layer 6: This layer projects to the thalamus (Opris and Casanova, 2014).

## What Is the Evidence for Cortical Inter-laminar Integration?

#### Evidence for Functional Integration in Cortical Minicolumns

It is obvious that cortical neurons that interconnect to each other across layers play an important role in the emergence of brain function. To demonstrate this we show in **Figure 1** an ideal example that depicts simultaneously recorded cells with differential firing in adjacent minicolums and inter-laminar layers during the match epoch of the DMS task (**Figure 1A**). Functional integration may be pursued based on: (i) interlaminar; and/or (ii) inter-columnar neuronal interactions. The cross-correlograms (**Figure 1C**) that quantify these interactions between cells provide crucial evidence for the integration process (Opris et al., 2012b).

#### Columnar Inter-laminar Processing in Prefrontal Cortex during "Target Selection"

**Figure 1C** shows an example of inter-laminar interaction between two cell pairs with individual firing rates displayed in raster/peri-event histograms (PEHs) during the epoch between matching image presentation (match phase onset) and offset of the matching ''target'' (0.0 s ± 2.0 s). Cell pairs in this study (Opris et al., 2012b) were recorded on ''appropriate sets of adjacent pads'' (corresponding to Minicolumns 1 and 2) on the biomorphic multielectrode arrays (MEAs), shown in the illustration of both trans-laminar cell pairs in L2/3 and L5/6 (**Figure 1C**). Neurons in both, upper and lower layers showed ''significant increases'' in mean firing rate (p < 0.001) in the ''post''-match phase (0.0 s to 2.0 s) associated with target selection (**Figure 1A**) and the decision making phase of the task (Opris et al., 2012b), relative to the ''pre'' match phase (−2.0 s to 0.0 s). An important observation

thalamo-cortical loop. The radius of polar plots is represented in Hz and tuning amplitude is measured in Hz, as well. Asterisks: ∗∗p < 0.001, ANOVA.

highlighted with this type of recording configuration was that layer L2/3 neurons in the same minicolumn, exhibited higher firing rates in the ''post''-match epoch (0.0 s to +2.0 s; **Figure 1C**, upper raster/PEHs) than neurons in L5 of the same, over the same time period (**Figure 1C**, lower raster/PEHs).

Precise ''functional connections'' between single units (cells) within each minicolumn were provided by cross-correlation histograms (CCHs), represented for individual L2/3 and L5 cell pairs recorded on vertically positioned electrode pads (**Figure 1B**) of the MEA (Opris et al., 2011, 2012b, 2013; Takeuchi et al., 2011). Normalized synchronized firing (shown by CCHs) for both minicolumns were shown in **Figure 1C** for cell firing in the displayed PEHs: (i) ''pre''-match epoch (−2.0 s to 0.0 s, Pre, black curve); or (ii) ''post''-match epoch (0.0 s to +2.0 s, Post, green) for the same cell pairs. Although both CCHs depict a ''significantly correlated firing'' (p < 0.001; t-test), the differences in max correlation for both neuron pairs suggest that inter-laminar firing was more synchronized in the ''post''-match epoch (0.0–2.0 s) than in the ''pre''-match epoch (p < 0.001; t-test).

#### Laminar Inter-columnar Processing in Prefrontal Cortex during "Target Selection"

The demonstration of functional connections between individual cells within same cortical layer and different minicolumns was provided by CCHs (Opris et al., 2012a) plotted for individual L2/3-vs.-L2/3 and L5-vs.-L5 cell pairs, recorded on horizontally positioned electrode pads on the MEA. Normalized correlograms for cell pairs are presented in **Figure 1C**. Although both correlograms (L2/3-vs.-L2/3 and L5-vs.-L5) show significantly correlated firing (Post vs. Pre: p < 0.001), the differences in peak correlation for both cell pairs indicate that intra-laminar intercolumnar firing was more synchronized in layer 2/3 than in layer 5 (p < 0.001). This supports the idea of laminar integration in supra-granular layers (Petro and Muckli, 2017).

#### Cortical-Subcortical Interaction

Cortical-subcortical integration of sensory and motor signals occurs during cortico-striatal interactions funneling signals in the cortico-thalamic loops (McFarland and Haber, 2002). Two recent results reported by Opris et al. (2013) and Santos et al. (2014) have demonstrated differential (pre)frontal cortico-

inter-laminar cell pairs. Scatter plot showing differential distributions of individual CCH peak correlation coefficients on trials with low vs. high numbers of images for the same cell pairs (n = 27) comprising the population CCH. ∗∗p < 0.001, <sup>∗</sup>p < 0.01, ANOVA.

striatal interaction during the planning and preparation for movement in monkeys.

#### Laminar Inter-hemispheric Processing in Prefrontal Cortex in Layer 3

Neurons in Layer 3 of fronto-parietal cortices provide important interhemispheric callosal connections necessary for the coordination of movement (Georgopoulos, 2015). Obviously, inter-hemispheric integration is playing a key role such as coordination.

#### Comparison of Inter-laminar Integration in the Correct vs. Error Trials

One way to test whether trans-laminar integration takes place in cortical minicolumns is to compare the inter-laminar firing in correct vs. error trials. A trial was considered correct when the animal responded adequately to each instruction of the DMS task's event sequence (shown in **Figure 1A**), receiving a drop of juice as reward, or an error trial, when the animal failed to obey one or more instructions, and was not rewarded (Opris et al., 2012b). Experiments by Opris et al. (2012b) compared correlated firing of pairs of cells from layers L2/3 and L5 during the match epoch (dealing with target selection) under correct vs. error trials. **Figure 2A** shows that cell pairs (in layers L2/3 and L5) that exhibited increased firing during the match epoch in correct trials (left), reduced their firing on error trials, with the inappropriate target being selected (right). The trend of increased (respectively decreased) correlated firing in correct (respectively error) trials was present across entire subpopulation of prefrontal cell pairs, as shown in **Figure 2A**. Moreover, the significant increase (decrease) in the mean CCH peaks (p < 0.001), provides evidence for the enhanced (lack of) inter-laminar correlated firing between L2/3 and L5 in correct (error) trials, as shown in **Figure 2B**. Taken together, these unique simultaneous recordings in prefrontal cortical layers (and minicolumns) of rhesus macaques, during in vivo performance of a cognitive task, provide evidence for prefrontal cortical inter-laminar integration during correct trials and lack of integration during error trials.

line and fed to MIMO model shown in (A). Shaded areas indicate time of match response execution during DMS trial, and the illustrated firing in L5 which is the same pattern as the delivered stimulation on trials with inappropriate L2/3 firing. (C) Changes in cumulative response latencies (processing time) from match phase onset ("0") during trials with stimulation delivered in the manner shown in (A,B,D). Increase in performance across trials with increasing difficulty as a function of the number of match phase distracter images on trials that received MIMO stimulation in the manner shown in (A). (D) Differential effects of MIMO stimulation on spatial vs. object trials showing more enhancement on spatial trials ranging in delays of 1–20 s. ∗∗p < 0.001, ANOVA.

#### Integration of Spatial Perception with Action

During the perception-to-action cycle, when the brain plans a movement, the executive mechanism coordinates the interactions between the environment and the perceptual/sensory-motor/executive systems (Fuster, 2000; Opris et al., 2011, 2013). On top of the executive hierarchy, the prefrontal microcircuits are assumed to ''bind perceptual and executive control'' functional signals to coordinate goal-driven behavior (**Figure 3A**). Here, we highlight the recorded neuronal ''firing simultaneously in prefrontal cortical layers and the caudate-putamen'' of rhesus monkeys (see **Figure 3B**), trained in a spatial-vs.-object version of the visual match-to-sample task (Opris et al., 2013). During the perception and executive selection epochs, cell firing in the prefrontal layers and caudateputamen exhibited ''preferences for the same location'' on spatial-trials, but not on object-trials.

#### Transformation of Perceptual Signals into Action

When the perceptual-executive circuit is activated by the stimulation of prefrontal infra-granular-layers with electrical stimuli ''patterns'' obtained from supra-granular-layers, Opris et al. (2013) could replicate spatial preferences (similar to neural tuning) in percent correct performance, on spatial trials (**Figure 3C**). These results show that inter-laminar prefrontal microcircuits play causal roles in the perception-to-action cycle (Mahan and Georgopoulos, 2013; Opris et al., 2013).

#### Does the Number of Distractor Images Affect the Inter-laminar Integration?

In the DMS task in **Figure 1A** the distractor images are those images that represent a different object than the sample image. As shown previously (Opris et al., 2011, 2012a,b, 2013), a major factor influencing the selection of the matching target in the match phase of the DMS task was the number of distractor images presented with the ''sample'' image in a given trial (see **Figure 4**). The increase in task difficulty via increasing the number of distractors, allowed the animal to make a sizeable amount of error trials for comparison to the correct trials (Opris et al., 2012a). In **Figure 4A** is shown a gradual ''decrease in cell pair firing in layers L2/3 and L5'' as a function of the number of images presented during the matching phase. Consistent with our prior results (Opris et al., 2012a,b), neuronal population mean firing rates in layers L2/3 and L5 (**Figure 4B**) confirmed this decrease as a function of the number of distractors in the match phase (p < 0.001). Moreover, this decrease was also expressed in terms of ''correlated firing'' between L2/3- L5 cell pairs, as shown in **Figures 4C,D**, in which CCHs on trials with few (2 or 3) distracter images exhibited significantly higher correlations than on trials with more (6 or 7) distracter images (p < 0.01). The decrease in inter-laminar correlated firing is consistent with a decrease in integration caused by the increase in the number of distractor targets. Likely the decrease in task performance is due to an increase in the ''cognitive

FIGURE 6 | The hierarchical executive control system for movement. The executive control system is anatomically organized via a hierarchical architecture of cortical modules (layers, minicolumns with microcircuits), subcortical nuclei (basal ganglia and thalamus with cortical-subcortical-thalamic loops; hippocampus and hypothalamus), brainstem (midbrain, pons, medulla with cortical-brainstem networks), and spinal cord (locomotor central pattern generators, CPG). At the higher level in the hierarchy are the columnar laminar modules of frontal (cognitive), parietal (motor), extrastriate (visual), temporal, orbital frontal cortices (emotion) and entorhinal (limbic), Beneath, are the subcortical structures: striatum (caudate, Cn, putamen, Pu and nucleus accumbens, NAcc), globus pallidus [GPe/GPi (external/internal segments), the dopaminergic ventral tegmental area (VTA), the ventral pallidum (VP), substantia nigra (SNc/SNr: pars compacta/pars reticulata), subthalamic nucleus (STN) and the thalamus. Motivation and emotion are processed by the hypothalamus and amygdala, respectively. The coordination of the navigation systems involves the frontal cortex and the hippocampus (Hipp). The subthalamic locomotor region (SLR) is a subcortical center for coordinating locomotion. At the level of the brain-stem and spinal cord, locomotion is initiated by the direct activation or disinhibition of the mesencephalic locomotor region (MLR) and/or the reticulospinal (RS) pathway. Stimulation of the MLR activates reticulospinal neurons which project through the ventrolateral funiculus to activate spinal locomotor central pattern generator neurons, in part, by the release of excitatory amino acids. The reticulospinal pathway is considered to comprise the primary "command pathway" for the initiation of locomotion. MLR stimulation also activates in parallel, multiple monoaminergic descending pathways during centrally-generated locomotion. The flexor (F) and extensor (E) components of the locomotor CPG are activated/modulated by descending bilateral reticulospinal and monoaminergic projections as well as by crossed excitatory (I) and inhibitory (•) segmental projections from the CPG opposite to it. Sensory afferents from skin and muscles innervate spinal neurons in the dorsal horn, intermediate zone and ventral horn to fine-tune the locomotor step cycle. Details of the flexor and extensor components of the CPG are omitted in order to emphasize general interconnections between them and their target neurons. LC, locus ceruleus; SubC, subceruleus; KF, Kölliker-Fuse; DRG, dorsal root ganglia; L, left; R, right; TA, tibialis anterior; Sart, Sartorius; FDL, flexor digitorum longus; LG, lateral gastrocnemius; ST, semitendinosus.

work load'' of the task (Kelley and Lavie, 2011; Opris et al., 2011).

#### Causal Relations to Columnar Integration

To test causal relationship to columnar inter-laminar integration, the recorded firing of neurons was examined via a nonlinear multi-input multi-output (MIMO) model, which extracted and characterized multi-laminar firing patterns during performance in the DMS task (Hampson et al., 2012). **Figure 5** provides causal evidence for the integration of natural signal flowing through the PFC microcircuits with the injected signal via stimulation. Minicolumnar neuronal firing of the PFC neurons was recorded from rhesus macaques trained in a DMS task, via biomorphic MEAs that provided signals from neurons in prefrontal cortical layers 2/3 and 5 (Opris et al., 2015a). The MIMO device sent patterns of electrical stimuli via stimulation to prefrontal cortical layer 5, during columnar ''inter-laminar integration'' at the time of target selection (Opris, 2013). Such stimulation improved (augmented) normal task performance significantly (**Figure 5D**). The executive control circuit was facilitated by applying stimulation patterns in the prefrontal cortical infragranular-layers similar to the patterns coming from the supragranular-layers that produced enhanced spatial preference in performance percentage, similar to neural tuning. Thus, interlaminar prefrontal microcircuits may play causative roles in the cognitive perception-to-action cycle (Hampson et al., 2012; Opris, 2013). Such causal relations to integration of cognitive signals provide direct evidence that cortical minicolumns may represent the turbo-engines of the brain (Jones, 2000; Jones and Rakic, 2010; Opris et al., 2013; Chapman and Mudar, 2014).

#### Integration across Hierarchical Levels of Processing

**Figure 6** illustrates modular integration across hierarchical levels. Executive control has been defined as the brain's ability ''to control thought and action'' by coordinating ''multiple systems and mechanisms across multiple brain areas'' to pursue a goal (Miller and Phelps, 2010). Examples of executive control functions include: attention, working memory, decision making, intention or motor plan and behavioral inhibition (Fuster and Bressler, 2012). Thus, the ability of the brain to exercise executive control over behavior relies upon the integration of the multiple microcircuitries (sensory, motor, reward), loops (thalamo-cortical) and large scale networks (bottom-up or topdown) with distributive encoding organized in a hierarchical manner.

The hierarchy of brain functions was introduced by Joaquin Fuster based on Hughlings Jackson's assumption that the neocortex is the climax of the nervous system and it controls (activates and/or inhibits) the functions of lower levels. However, cortical disease led to two sets of findings: ''negative'' signs and symptoms from ''loss of the controlling cortex'' and ''positive'' ones from the ''emergence of the lower center'' (Fuster, 1990; York and Steinberg, 2011). This implied an anatomical and physiological hierarchy of centers within the brain, with higher ones activating or suppressing the function of lower ones (Fuster, 1990; York and Steinberg, 2011).

Our focus is on the hierarchy of neural circuitry underlying the executive control of behavior (**Figure 6**) spanning from the frontal and parietal cortices, subcortical structures like the basal ganglia and thalamus, the brainstem and the spinal cord. The hierarchical integration of various stimuli (Hirabayashi et al., 2013a,b) in the executive function, may follow a bottom-up integration of visual information (Felleman and Van Essen, 1991) while the coordination of movement kinematics is performed in a top-down manner according to a prior intention/plan (Noga and Opris, 2017). At the top of the executive hierarchy are the frontal (premotor) and parietal (motor) cortical microcircuits, interconnected within thalamocortical loops through cortico-striatal projections and further in the brainstem to the mesencephalic locomotor region (MLR) and the central pattern generators in the spinal cord (Noga and Opris, 2017).

# FUTURE DIRECTIONS