Are cortical columns restricted to somatosensory cortical sections?

From this previous question, it seems like evidence for the minicolumn organisation of the neocortex seems to be primarily based off observations around the sensory parts of the cortex, such as the primary visual cortex and the barrel cortex. Have column-like organisation been observed in non-sensory regions of the neocortex?

Cortical columns (also called minicolumns) are not limited to the somatosensory cortex. As shown in this paper (which discusses how the miniucolumns change in response to aging), it is also present in the associative cortex.

However, it should be noted that "minicolumn" in this paper is defined as neurons that are close together and form a minimum spanning tree, as shown below.

Connectivity doesn't seem to be analysed, probably because connectivity information is really painful to collect, but I'm not sure given I'm an amateur when it comes to neuroscience.

S1 long-term plasticity

S1 long-term plasticity refers to persistent modifications in the structure or functioning of the primary somatosensory cortex (S1). These modifications are proposed to underlie learning and memory of tactile information, as well as recovery of function after injury. As in other primary cortical areas, long-term plasticity can arise as a result of peripheral injury (lesion-induced plasticity) or after changes in the spatial or temporal pattern of the sensory input (use-dependent and experience-dependent plasticity). This article focuses on studies of long-term changes in the barrel cortex, the area of S1 containing a topographic map of the whiskers found on the snout of rodents. It only describes plasticity in the adult brain, as opposed to developmental plasticity in the young during a critical period. Indeed, in the 1960s, it was thought that functional properties in primary sensory cortices were fixed in the adult. In the 1980s, the first experimental evidence for adult plasticity was provided by exploring the interaction between sensory representations and behavioral learning. Plastic modifications fall in two broad categories. Structural plasticity identifies changes in anatomical properties of neurons and circuits and is relatively restricted in the adult brain. Functional plasticity refers to changes in the response properties of neurons and neural networks, and can be mediated by intrinsic plasticity or synaptic plasticity.


The brains of three adult male bottlenose dolphins (Tursiops truncatus) were analyzed in detail in this study. These specimens were perfused by gravity with 40 l of Windle's fluid in situ using a cannula inserted into the descending aorta in animals that had been euthanized for medical reasons. The brains were then extracted and postfixed in 8% formalin for 3 months (Jacobs et al., 1971 , 1979 ). The brains were then dehydrated in graded alcohol solutions, embedded in celloidin, and cut serially at 35 μm on a modified large specimen microtome (Mico Instruments, Cambridge, MA). Each brain was cut in one of three planes (coronal, sagittal, horizontal) relative to the beak-fluke axis of the animal. Two 1:5 series of adjacent sections throughout these brains were stained for myelin with the Loyez-Weigert method or for Nissl substance with the Bielchowsky-Plien cresyl violet method (Bertrand, 1930 ). The sections were mounted on large glass slides and coverslipped in clarite for examination.

Additional adult specimens from Tursiops, a beluga whale (Delphinapterus leucas), a long-finned pilot whale (Globicephala melas), and a Cuvier's beaked whale (Ziphius cavirostris) were used for comparison across a few species. These specimens were obtained from stranded animals within a few hours of death and were fixed by immersion in neutral formalin for several months. Local samples of the regions corresponding to the primary visual and primary auditory cortex [i.e., from the mid-posterior portion of the lateral gyrus and the mid-posterior region of the suprasylvian gyrus, respectively (Sokolov et al., 1972 Supin et al., 1978 Morgane et al., 1988 )] were obtained from these cases, cryoprotected in graded sucrose solution, and cut on a cryostat (Reichert Jung, Vienna, Austria) at 60 μm and series of sections were then stained with cresyl violet.

All histological preparations were examined on a Zeiss Axiophot 2 photomicroscope with 5×, 10×, and 20× Fluar and Apochromat objectives (Zeiss, Oberkochen, Germany). Photomicrographs were acquired using a 10× PlanApochromat lens and an Optronics Microfire digital camera (Optronics, Goleta, CA). Photomontages were digitally assembled with Virtual Slice software (MicroBrightField, Williston, VT) and processed with Adobe Photoshop CS 8.0. The nomenclature of gyri and sulci follows that proposed by Morgane et al. ( 1980 ).


My interest in cortical circuits began during my doctoral work in my adviser Otto Creutzfeldt's department at the Max Plank Institute for Psychiatry in Munich. In his laboratory, both intracellular recording from cortical neurons and research in neuroanatomy was pursued (Creutzfeldt, 1993 ). In fact, one of my first publications dealt with an attempt to understand the functional structure of the receptive field (RF) of retinal ganglion cells based on their dendrite anatomy and simulated functional input mapping (Creutzfeldt et al. 1968 ). As it became clear to me that synapses are the structures that are essential for understanding connections in the CNS, I applied for a postdoctoral position in Bernard Katz's laboratory at University College London, supported by a fellowship from the British Council and the earnings of my wife Christiane working as an eye doctor at the Moorfields Eye Hospital. At University College London, Bill Betz and myself developed a method to prepare muscle cells in vitro where the neuromuscular synapse was ‘disjuncted’, meaning that a nerve terminal separated from the muscle fibre, leaving a bare endplate with a high density of functional acetylcholine receptors (Betz & Sakmann, 1973 ). I also watched the wonders of membrane noise analysis, introduced by Bernard Katz and Ricardo Miledi. They derived the first estimates of ‘elementary’ acetylcholine-gated ion channel signals at the neuromuscular endplate (Katz & Miledi, 1972 ) as well as the density of these receptors derived from toxin binding. In short, physiology had become molecular. Moving to Göttingen, I combined efforts with Erwin Neher to try to measure elementary events as single-channel currents directly, a task in which we ultimately succeeded and proved the ion channel concept of membrane excitability. We shared a Nobel prize in 1991 ‘for discoveries concerning the function of single ion channels in cells’. In collaboration with Shosaku Numa, we identified the subunit composition of endplate channels, their channel-forming subunits and single amino acids that determine the size of ion flow through open channels, as summarized in my Nobel Lecture (Sakmann, 1992 ).

Unexpectedly, patch pipettes became more useful than initially thought, because one could not only record small membrane currents with extracellular pipettes but could also gain low-resistance access to the interior of a cell and thereby record the intracellular membrane potential (in the whole-cell recording configuration) from small cells, such as mammalian brain cells. We used this recording configuration to examine synaptic transmission at a giant CNS synapse with precise control of pre- and postsynaptic membrane voltage and ion composition to sharpen the picture of local non-uniform calcium ion signalling at the presynaptic membrane that drives transmitter release. This work is summarized in my Hodgkin–Huxley–Katz Lecture (Meinrenken et al. 2003 ). The whole-cell recording configuration also brought me back to my initial field of interest, which was to understand signalling in neuronal pathways in subcellular detail.

In this Paton Prize Lecture, I will give a personal account of what we found using whole-cell and loose-patch unit recording from nerve cells in brain slices and intact brains by summarizing results on electrical signalling within neurons and then review the significance of intraneuronal signalling for network dynamics on a short time scale (coincidence detection of spatially separate synaptic inputs) and on a longer time scale (the strengthening and weakening of synaptic connectivity).

Cortical and Subcortical Contributions to Activity-Dependent Plasticity in Primate Somatosensory Cortex

After manipulations of the periphery that reduce or enhance input to the somatosensory cortex, affected parts of the body representation will contract or expand, often over many millimeters. Various mechanisms, including divergence of preexisting connections, expression of latent synapses, and sprouting of new synapses, have been proposed to explain such phenomena, which probably underlie altered sensory experiences associated with limb amputation and peripheral nerve injury in humans. Putative cortical mechanisms have received the greatest emphasis but there is increasing evidence for substantial reorganization in subcortical structures, including the brainstem and thalamus, that may be of sufficient extent to account for or play a large part in representational plasticity in somatosensory cortex. Recent studies show that divergence of ascending connections is considerable and sufficient to ensure that small alterations in map topography at brainstem and thalamic levels will be amplified in the projection to the cortex. In the long term, slow, deafferentation-dependent transneuronal atrophy at brainstem, thalamic, and even cortical levels are operational in promoting reorganizational changes, and the extent to which surviving connections can maintain a map is a key to understanding differences between central and peripheral deafferentation.


Of the 109 participants 10 participants were excluded due to artifacts in their MEG and/or MRI data. The remaining 99 participants, 55 controls and 44 PWH, did not statistically differ in age, sex, education, ethnicity, or other demographic variables (see Table 1). Fifteen of the 44 PWH met the criteria for HAND according to the Frascati guidelines. For the PWH, time since diagnosis, current CD4, and CD4 nadir were recorded at the time of enrollment.

Control (n = 55) PWH (n = 44) Significance
Age (years) 42.5 ± 10.6 45.9 ± 10.0 p = .358
Sex 32 M, 23 F 26 M, 18 F p = .991
Handedness 51 R, 4 L 41 R, 3 L p = .931
Weight (kg) 91.9 ± 23.8 85.1 ± 23.5 p = .212
Education (years) 16.8 ± 2.34 14.5 ± 2.22 p = .500
Time on cART (years) 9.12 ± 6.01
Time since HIV diagnosis (years) 10.8 ± 6.48
CD4 nadir (cells/μl) 209 ± 159
Current CD4 (cells/μl) 792 ± 460
  • Note: Values displayed are mean ± SD.
  • Abbreviations: cART, combination antiretroviral therapy F, female L, left M, male R, right.

3.1 MEG sensor-level analysis

Robust broadband synchronizations spanning 10–90 Hz were observed in several MEG sensors near the sensorimotor and parietal regions during the 100 ms directly following the onset of electrical stimulation (p < .001, corrected). These responses, especially those in gamma frequencies, were considerably stronger in the 50 ms immediately following each electrical stimulation (Figure 1a). Thus, we focused our beamformer analyses on these 50 ms time windows (i.e., 0–50 ms and 500–550 ms) following each stimulation and the 20–75 Hz range. Our main analyses started at 20 Hz as this was the lowest frequency that we could precisely resolve using a 50 ms time interval. We stopped at 75 Hz on the high end as the relative power of the neuronal responses decreased sharply thereafter, particularly in response to the second stimulus.

3.2 HIV-related alterations in somatosensory cortical structure and function

Beamformer output images indicated robust responses in the contralateral somatosensory hand region of the postcentral gyrus following each stimulation (Figure 1b). The peak locations of the responses to the first and second stimulations were virtually identical in the contralateral postcentral gyrus, and these locations were highly similar across the two groups (Figure 1c). As described in the Methods section, the beamformer images were grand averaged across all participants and stimulations, and then virtual sensor data were extracted in each participant from the peak voxel in the grand-averaged image. The envelope of the resulting time series was then computed for the 20–75 Hz frequency range.

To examine group differences in sensory processing, we computed two mixed-model 2 × 2 analysis of variances (ANOVAs) (stimulation-by-group). Participants with responses outside of ±2.5 SD were excluded from these analyses (n = 2). The first examined response amplitude and the second tested peak frequency. The analysis of response amplitude indicated a main effect of stimulation, such that the response to stimulation 2 was weaker than that to stimulation 1 across all participants (F[1,96] = 45.65, p < .001). In other words, there was a significant somatosensory gating effect across all participants (Figure 2b). In contrast, neither the main effect of the group (p = .159) nor the interaction were significant (p = .776). As per peak frequency, the main effect of stimulation was significant (F[1,96] = 4.19, p < .05), indicating that the peak frequency of the response to the second stimulation (mean = 49.7, SD = 14.9) was higher than that to the first stimulation (mean = 45.0, SD = 14.9). Similar to the amplitude findings, neither the main effect of the group (p = .385) nor the interaction were significant (p = .386).

Next, we examined the strength of spontaneous neural activity during the baseline period by computing the mean amplitude from −700 to −300 ms using the absolute power time series (i.e., not baseline corrected). Independent sample t-tests indicated significantly stronger spontaneous power in the PWH relative to controls (p = .001 Figure 2c,d). We further explored the extent to which these group-level differences in neural activity may be explained by group differences in cortical structure, namely cortical thickness within the left postcentral gyrus.

3.3 Local cortical thickness mediates the effect of HIV on spontaneous neural activity

To evaluate differences in cortical thickness, we computed the mean cortical thickness in each participant within an 8 mm sphere centered on the peak somatosensory response in the left postcentral gyrus, which was derived from the grand-averaged MEG beamformer images. These mean values were examined using independent sample t tests which revealed significant group differences (Figure 3a) such that PWH had significantly reduced cortical thickness relative to controls. Then, to evaluate the extent to which gray matter thickness within the left postcentral gyrus mediated the effects between HIV and spontaneous neural activity, we tested a mediation model in which diagnostic group (i.e., PWH or control) predicted gray matter thickness within the left postcentral gyrus which subsequently predicted spontaneous neural activity within the same brain tissue. As identified in our primary analysis, there was a significant total effect of HIV on spontaneous activity (β = .31, b = 4.44, 95% CI [1.51, 7.30]),

such that PWH tended to have higher resting power relative to controls. Importantly, there was also a significant mediation whereby cortical thickness in the left postcentral gyrus partially explained the relationship between HIV and spontaneous neural activity during the baseline (indirect: β = .080, b = 1.15, 95% CI [.087, 2.65] Figure 4). Specifically, PWH had significantly reduced postcentral gyrus thickness relative to healthy controls (β = −.39, b = −.062, p < .001), and this reduced thickness subsequently predicted stronger spontaneous power during the baseline period (β = −.21, b = −18.59, p = .040). Note that the direct effect of HIV on spontaneous activity remained significant (β = .23, b = 3.29, 95% CI [.33, 5.94]). There were no other statistically significant relationships between group membership and brain structure or function.

To follow up these analyses, we tested an exploratory model exclusively in PWH to determine whether indices of disease severity could explain the noted effects of HIV-infection on cortical thickness and spontaneous power in the left postcentral gyrus. The model was structured such that current CD4 counts, CD4 nadir, and time since HIV diagnosis all served as predictors of spontaneous activity before somatosensory stimulation with gray matter thickness in the left postcentral gyrus modeled as a mediator. Indices of HIV disease were allowed to freely correlate. Postcentral gyrus thickness was significantly predicted by CD4 nadir (β = .31, b = .004, p = .04) indicating that individuals with lower CD4 nadir tended to have thinner gray matter in the left postcentral gyrus (Figure 3b). However, there were no other significant associations between markers of HIV disease and brain structure or function, nor were there any significant indirect effects.

Somatosensory Cortex/Primary Somatosensory Cortex


Have you ever wondered if you feel things the same way other people do? How do you know ‘red’ is really the same red to everyone? Maybe the person next to you sees green as red. These thought-provoking questions can’t be answered precisely with science, but we can learn more about how external stimuli, like colors, are processed in the brain. This is where the somatosensory cortex comes in. This part of the brain processes sensations, or external stimuli, from our environment. Before we learn more about the somatosensory cortex, we need to learn a little bit about brain anatomy and where the somatosensory cortex is located.

somatosensory cortex

The primary somatosensory cortex is located in the postcentral gyrus and is part of the somatosensory system. It was initially defined from surface stimulation studies of Wilder Penfield, and parallel surface potential studies of Bard, Woolsey, and Marshall. Although initially defined to be roughly the same as Brodmann areas 3, 1 and 2, more recent work by Kaas has suggested that for homogeny with other sensory fields only area 3 should be referred to as “primary somatosensory cortex”, as it receives the bulk of the thalamocortical projections from the sensory input fields.

At the primary somatosensory cortex, tactile representation is orderly arranged (in an inverted fashion) from the toe (at the top of the cerebral hemisphere) to mouth (at the bottom). However, somebody parts may be controlled by partially overlapping regions of cortex. Each cerebral hemisphere of the primary somatosensory cortex only contains a tactile representation of the opposite (contralateral) side of the body. The amount of primary somatosensory cortex devoted to a body part is not proportional to the absolute size of the body surface, but, instead, to the relative density of cutaneous tactile receptors on that body part. The density of cutaneous tactile receptors on a body part is generally indicative of the degree of sensitivity of tactile stimulation experienced at a said body part. For this reason, the human lips and hands have a larger representation than other body parts.


Brodmann areas 3, 1, and 2 make up the primary somatosensory cortex of the human brain (or S1). Because Brodmann sliced the brain somewhat obliquely, he encountered area 1 first however, from anterior to posterior, the Brodmann designations are 3, 1, and 2, respectively.

Brodmann area (BA) 3 is subdivided into areas 3a and 3b. Where BA 1 occupies the apex of the postcentral gyrus, the rostral border of BA 3a is in the nadir of the Central sulcus and is caudally followed by BA 3b, then BA 1, with BA 2 following and ending in the nadir of the postcentral sulcus. BA 3b is now conceived as the primary somatosensory cortex because 1) it receives dense inputs from the NP nucleus of the thalamus 2) its neurons are highly responsive to somatosensory stimuli, but not other stimuli 3) lesions here impair somatic sensation and 4) electrical stimulation evokes the somatic sensory experience. BA 3a also receives dense input from the thalamus however, this area is concerned with proprioception.

Areas 1 and 2 receive dense inputs from BA 3b. The projection from 3b to 1 primarily relays texture information the projection to area 2 emphasizes size and shape. Lesions confined to these areas produce predictable dysfunction in texture, size, and shape discrimination.

The somatosensory cortex, like another neocortex, is layered. Like other sensory cortex (i.e., visual and auditory) the thalamic inputs project into layer IV, which in turn projects into other layers. As in other sensory cortices, S1 neurons are grouped together with similar inputs and responses into vertical columns that extend across cortical layers (e.g., As shown by Vernon Mountcastle, into alternating layers of slowly adapting and rapidly adapting neurons or spatial segmentation of the vibrissae on mouse/rat cerebral cortex).

This area of cortex, as shown by Wilder Penfield and others, is organized somatotopically, having the pattern of a homunculus. That is, the legs and trunk fold over the midline the arms and hands are along the middle of the area shown here, and the face is near the bottom of the figure. While it is not well-shown here, the lips and hands are enlarged on a proper homunculus, since a larger number of neurons in the cerebral cortex are devoted to processing information from these areas.

The positions of Brodmann areas 3, 1, and 2 are – from the nadir of the central sulcus toward the apex of the postcentral gyrus – 3a, 3b, 1, and 2, respectively.

Somatosensory Cortex Function/Primary Somatosensory Cortex Function


The somatosensory cortex receives all sensory input from the body. Cells that are part of the brain or nerves that extend into the body are called neurons. Neurons that sense feelings in our skin, pain, visual, or auditory stimuli, all send their information to the somatosensory cortex for processing. The following diagram shows how sensations in the skin are sent through neurons to the brain for processing.

somatosensory cortex function

The skin transmits signals through other neurons to the brain sensory to brain pathway. Each neuron takes its information to a specific place in the somatsensory cortex. Next, that part of the somatosensory cortex gets to work on figuring out what the information means. Think of it like scientists sending data to a data analyst. Each scientist, like the neuron, gathers information and sends it to a master analyzer or the somatosensory cortex.
Some neurons are very important and a big chunk of the somatosensory cortex is devoted to understanding their information. The senior scientist sends the most important information to our analyst, and he spends a lot of time understanding it. However, our junior scientists or volunteers gather less important information, so our analyst, or somatosensory cortex, spend less time on that data.


The brain is the control center of the whole body. It is made up of a right and left side, or lobes, which are connected in the middle by the corpus colossum. Each lobe is devoted to a different function. The outer layer of the brain is called the cerebral cortex. Think of it like the skin on fruit, the skin is the cerebral cortex, and the fruit is the white insides of the apple. The cerebral cortex helps with processing and higher-order thinking skills, like reasoning, language, and interpreting the environment. This image shows a cross-section of the brain, with the cerebral cortex shown as the dark outline.

The somatosensory cortex is a part of the cerebral cortex and is located in the middle of the brain. This image shows the somatosensory cortex, highlighted in blue in the brain.

somatosensory cortex location

Confusing cortical columns

The late developmental neurobiologist, and a member of the National Academy of Sciences, Victor Hamburger told me during one of our discussions about the distinction between boring data and exciting concepts, “one can spend an entire lifetime correcting a flawed paper published in reputable journal and still loose the battle if people like the basic idea” (V. Hamburger, personal communication). An example of the longevity of basically incorrect information is the phenomenon of “The basic uniformity in structure of the neocortex,” published in 1980 by Rockel, Hiorns, and Powell (1). This highly influential paper had obvious problems at almost every level: The authors selected an arbitrary 30-μm-wide, 25-μm-deep vertical cortical “column” between the pia and the bottom of the cortex, because the ruler in the graticule of the oil-immersion eyepiece on their microscope had a 30-μm marker and their histological sections were 25 μm thick then, they estimated that the number of neurons within this “minicolumn” is 110 in all cytoarchitectonic areas examined, without any correction for the cell size and finally, based on this dubious finding, they made a broad generalization that the magic number of 110 is constant in all mammalian species (rodents, carnivore, and primates, including human) in all cytoarchitectonic areas (except the primary visual cortex in primates). This finding led them to conclude that, “the intrinsic structure of the neocortex is basically more uniform than has been thought and that differences in cytoarchitecture and function reflects differences in connections.”

Most neuroscientists recognized the problems with both the method used and the data obtained, but many found the simple concept of the uniformity of the cortex across various modalities as well as during evolution of neocortical expansion highly attractive. Although at least six research articles have directly refuted the accuracy of the data of Rockel et al. as well as the validity of their generalization (e.g., ref. 2), according to the Institute of Scientific Information (ISI), their article has been cited ≈500 times. It is discussed often in the major reference books of neuroscience and brain evolution, and is used widely in computational models of cortical operations. The concept of uniformity across all species appealed to philosophers who liked the idea that the difference between animals and human are just quantitative. It also provided the scientific basis of the so-called “tabula rasa hypothesis” of cortical development, which assumes that all cytoarchitectonic areas are specified from the initially homogeneous and equipotential cortex by input from the periphery.

This is why the article by Herculano-Housel et al. (3) in this issue of PNAS serves a useful purpose, even though it does not report any unexpected results. Using a state-of-the-art unbiased stereology method, the authors show convincingly that the density of neurons in the neocortex varies as much as three times even among the highly related primate species. The results from the two studies are difficult to compare because Rockel et al. (1) counted the number of neurons in very small vertical cylinders (minicolumns), whereas Herculano-Housel et al. estimated the density of neurons in a larger volume of cortical tissue that can be more affected by the amount of neuropile. However, the main goal of the work of Herculano-Housel et al. seems to be to dispel the lingering perception that the data reported by Rockel et al. are basically valid and to emphasize, once again, that the simple concept of basic uniformity of the cortex, which may appear attractive, is basically incorrect. I, however, feel that Herculano-Housel et al. did not go far enough in addressing the related problem that is caused by the frequent misuse of the term “cortical column.”

The term cortical “column” is used in so many ways that it can be very confusing to the nonspecialist.

Classical anatomists have emphasized the laminar deployment of the neocortex but were also aware of the prominent columnar organization as visualized in Golgi impregnated material and as is particularly compelling in the Nissl-stained sections of the human cerebral cortex (4). However, the concept of functional columns received deserved attention only after Vernon Mountcastle (5) discovered that the neurons arranged vertically (or radially in the convoluted cerebrum) in the form of columns spanning the width of the primate somatosensory cortex respond to a single receptive field at the periphery. This, and subsequent research by many others, has shown that the cortical columns consist of an array of iterative neuronal groups (also called modules) that extend radially across cellular layers VI to II with layer I at the top (6–10). The neurons within a given column are stereotypically interconnected in the vertical dimension, share extrinsic connectivity, and hence act as basic functional units subserving a set of common static and dynamic cortical operations that include not only sensory and motor areas but also association areas subserving the highest cognitive functions (8, 9, 11).

Although the anatomical and functional columnarity of the neocortex has never been in doubt, the size, cell composition, synaptic organization, expression of signaling molecules, and function of various types of “columns” are dramatically different. Columns could be defined by cell constellation, pattern of connectivity, myelin content, staining property, magnitude of gene expression, or functional properties. For example, there are ocular dominance columns, orientation columns, hypercolumns, and color columns, to mention only those described in the primary visual cortex (12), that differ from each other as well as from the columns of the alternating callosal and ipsilateral projection in the frontal lobe (8) or various minicolumns advocated by Szentgahotai (7), Eccles (9), Buxhoeveden and Casanova (10), and a more recent detailed reconstruction of barrel field columns by Sakmann and colleagues (13) and their visibility in vivo by neuroimaging (14). The only connections between these diverse structures and concepts is that they refer to the vertical or radial columnar organization of its elements as opposed to the horizontal or laminar organization that is more explicit in histological preparations of the mature neocortex. Thus, the term cortical “column” is used in so many ways that it can be very confusing to the nonspecialist if not more precisely defined.

The term and concept of radial cortical columns is also used in developmental neurobiology of the cerebral cortex. Thus, I have used the term “ontogenetic column” in designating the cohorts of cortical neurons that originate, over time, from a single neuronal progenitor (15). The clonally related postmitotic neurons are initially deployed in a geometrically perfect columnar pattern in the embryonic primate cerebrum that the temptation to use the term column was irresistible (see figure 1 in ref. 15). The ontogenetic columns are also evident in alive slice preparations (16) and in vivo labeling of neuronal lineages using gene transfer tracing (17). Columnar organization is particularly evident in the developing primate cerebral cortex where neurons are positioned into a radial array with crystalline regularity that depends on a sequential production and the directed migration of neurons that requires the orchestration of multiple molecular events and complex cell–cell interactions (11).

The relationship between ontogenetic and various functional columns has not been adequately investigated. However, it has been clear from the beginning that any functional column in the adult cerebral cortex must consist of several ontogenetic columns (polyclones), depending on their function and that neurons from different clones intermix with the adjacent columns as they migrate across the intermediate zone (18). It was initially evident and clearly stated that the ontogenetic columns in various cytoarchitectonic areas are different and thus contradict the notion of the homogeneity of the neocortex (15). This variability can explain both the differential expansion of individual functional areas and the introduction of new areas during evolution as formulated in the protomap hypothesis (15, 19). The concept of polyclones was supported by the finding of wider alternating columns of gene expression in transgenic and chimeric mice (19–21). The morphological visibility of ontogenetic columns is diminished after arrival of the interneurons, glial cells, and afferents, which all participate in the formation of the neuropile with a myriad of synaptic connections that constitute diverse types of functional columns described by the anatomists and physiologists (11).

The longevity of the concept of the basic uniformity in structure of the neocortex is in part due to the high regard for Tom Powell, a professor at Oxford and a leading neuroanatomist at the time. The other part is that the conclusions of Rockel, Hiorns, and Powell (1), that the cortex consists of columns and that it expands during evolution more in surface than in thickness, are obvious and still stand. Also still standing, as clearly pointed out by Herculano-Housel et al. (3), is the existence of ontogenetic columns and the validity of the radial unit hypothesis as the basis for understanding its evolutionary expansion at the cellular and molecular level. A new challenge is to reconstruct the complete cellular and synaptic circuits and develop model cortical columns that are dedicated to each function to determine how deviation of this pattern affects behavior.


Distributed information processing in the cortex calls for large-scale mesoscopic monitoring of dynamic neuronal activity at the timescale of behaviour. Here we used a novel transgenic mouse line expressing the genetically encoded voltage indicator (GEVI) chimeric VSFP Butterfly (chiVSFP) under the tightly regulated tetO (TRE) promoter, and achieved chiVSFP expression in pyramidal neurons for cell class-specific monitoring of cortical electrical activity in a chronic preparation. We first show that multi-whisker somatosensory stimulation triggers different sensory processing dynamics in the primary somatosensory cortices between the wakening and fully awake/alert brain states. We then analysed how primary sensory information is broadcasted from the primary somatosensory cortex over both cortical hemispheres in both brain state conditions. Dual-hemisphere monitoring showed a novel cortex-wide hyperpolarising activity following the initial sensory-evoked depolarising response in the wakening state. In contrast, in the awake state, sensory-evoked activity is more spatially discrete, with a prominent bilateral hyperpolarising response that emerges first over the motor cortices and subsequently in the sensory cortices in an anatomically sequential manner. Additionally, we observed secondary response components that may be associated with higher order sensory processing.

To guide optimal behaviours, the integration of sensory information with other cognitive components such as memory, attention, and motivation is crucial. Accruing evidence currently emphasises the recruitment of spatially distant cortical regions to achieve goal-directed behaviour 3,4,5,10 , but even the processing and integration of sensory information alone recruits dynamic activity of different regions across a large cortical space 37 . Furthermore, functional and dynamic neuronal computations also involve hyperpolarising activity, as well as subthreshold voltage fluctuations, both of which cannot be captured using the mainstream GECI-based imaging approaches. To fully understand the circuit mechanisms underlying behaviour it is necessary to directly monitor neuronal activity with high temporal resolution and high spatial coverage. Voltage sensitive dye imaging (VSDI) has advanced considerably towards achieving this goal 31,38 , but is methodologically blind to cellular identity and unsuitable for studies extending over several weeks. Recent progress in GEVI development and GEVI-based mesoscopic voltage imaging approach provides an exclusive opportunity for observing cell type-specific population electrical activity with sufficient spatiotemporal resolution and coverage 12,13,14,15,17,18,27,39,40 . We note that, single-photon imaging is likely dominated by activity in the superficial layers due to penetrance of visible light in tissue.

The new transgenic mouse line (tetO-chiVSFP) expresses the ratiometric FRET-based GEVI chiVSFP under the control of a transactivator (tTA). CaMK2A-tTAtetO-chiVSFP mice expressed chiVSFP in pyramidal neurons (under the CaMK2A promoter) across cortical layers (Fig. 1, Supplementary Fig. S1A). The mouse model showed structural and functional stability for up to 13 months following thin-skull cranial window implant (Supplementary Fig. S1B), demonstrating that GEVIs allow longitudinal monitoring of neuronal activity in vivo. Cortex-wide monitoring and the registration of cortical regions onto connectivity maps, such as the Allen Institute Mouse Brain Atlas used here, allows standardised fluorescence signal attribution across animals. Optical monitoring using differential dual emission GEVIs (Fig. 1E) offers the advantage of improved separation of haemodynamic and voltage signals, which can be problematic for monochromatic in vivo widefield optical imaging experiments in the mammalian brain 12,20,22,23,24,25 .

In the wakening condition, GEVI imaging of sensory-evoked population voltage responses from pyramidal neurons revealed triphasic responses in contra SSp-bfd, similar to previous indications from VSDI 37,41,42,43 , but with the additional circuit delineation of pyramidal cell types (Fig. 2). In the awake state, on the other hand, the sensory-evoked depolarising response in contra SSp-bfd monophasically returns to baseline (Fig. 2A), and this extended response duration in the awake cortex also is in line with observations from VSDI single whisker stimulation experiments 44 . Using isoflurane, we confirmed this altered population voltage response is not anaesthetic-specific (Fig. 2B).

Extending previous work, we found whisker stimulation-evoked responses with early onset similar to that of contra SSp-bfd in two adjacent cortical areas, SSs and VISrl. SSs displays a sensory-evoked response similar to that of SSp-bfd under both wakening and awake conditions (Figs 3 and 5). In line with existing cellular electrophysiology observations 45 , the similar response onset delay indicates parallel thalamic inputs into SSs. Previous experiments inactivating the primary somatosensory area have shown to modulate SSs response properties. Since our experimental design does not involve decision making or behavioural output, the brain state dependent signalling in SSp-bfd and SSs represents sensory processing.

The early onset response in VISrl is distinctly different from the activity in VISp (Fig. 5), indicating that VISrl response reflects, at least in part, primary somatosensory processing rather than multimodal sensory integration or secondary processing of visual information. Existing evidence from cellular-resolution electrophysiology and calcium dye imaging has observed multisensory circuitries in VISrl 46 , and recent GECI functional imaging experiments have also identified additional cortical areas in the retinotopic map that intrude into other regions including the somatosensory cortex 32 . To be cautious, we like to note that we cannot exclude scattered responses from barrel cortex or imperfect mapping of cortical space to the Allen Mouse Brain Atlas. Together with our current observations, this highlights the distributed nature of sensory information processing involving multiple cortical areas.

The high spatial coverage of mesoscopic imaging allows us to monitor dynamic activity changes simultaneously across both hemispheres (Figs 3–5 Movies S1–S4). As somatosensory inputs in rodents are entirely crossed until reaching the cortex, we anticipated an ipsilateral homotopic echo response, as cellular-level electrophysiology observations indicated 28 . In contrast, in our study, the initial depolarising response in the ipsilateral hemisphere is not restricted to SSp-bfd in both wakening and awake conditions (Figs 3 and 4). Under wakening condition, the initial cortex-wide depolarising response reaches similar amplitudes across sensory and motor cortices in the ipsilateral hemisphere (Figs 3–5 Movies S1 and S2). This cortex-wide broadcasting could be facilitated by slow wave activity present during the sedated brain state 47 , and this could be restricted by the enhanced synaptic inhibition at wakefulness 48 .

In the awake state, the sensory-evoked response pattern is spatially and temporally more segregated in line with the EEG-derived concept of desynchronization. In contrast to the cortex-wide hyperpolarising period in the wakening condition, in the awake state we observed a chain of sequential hyperpolarising activities engaging different anatomically defined regions, from MOs, MOp, to primary sensory regions (SSp-n and SSp-m), with the involvement of SSp-bfd only in the ipsilateral hemisphere (Figs 3–5, Movies S3 and S4). Despite the hemispheric differences in the sensory cortices, largely homotopic responses are observed in the motor cortices across brain states (Figs 3–6 Movies S1–S4). Motor cortices also display a response that is dynamically distinct from those of the sensory cortices, especially in the awake state (Figs 4–5). It shall again be noted that in contrast to other studies using goal-directed paradigms, no goal-related motor actions are required in the current study, therefore, although we do not exclude spontaneous movements, our observations are not confounded by activities related to goal-related decisions or generation of decision-related motor commands. We do not exclude motor reactions, and such motor response may require perception.

Classical electrophysiology and imaging experiments have established that different brain states such as active, resting, sedation or sleep are reflected by characteristic features of neuronal circuit dynamics 1,35,49,50,51,52,53,54,55 . Using cell class-specific GEVI imaging we extended these studies showing how somatosensory-evoked response dynamics of pyramidal neurons reflects the level of sedation, wakefulness and arousal. Using heartbeat frequency as a proxy of the level of sedation and arousal 26,27 (Figs 2A-iv, 6A), we found that, through the transition from light anaesthesia to awake, reduced hyperpolarisation across multiple sensory and motor cortices is accompanied with decreased initial peak amplitude particularly in ipsilateral sensory cortices, and bilateral motor cortices (Supplementary Fig. S5). Interestingly, as the arousal/alertness decreases through the course of the awake imaging session, we observed a decrease in the initial peak amplitude in multiple regions amongst sensory and motor cortices (Supplementary Fig. S5), while the initial peak amplitude in SSp-bfd (Fig. 2, Supplementary Fig. S2) and SSs remained largely constant (and similar to that observed under wakening). This indicates increased cortical broadcasting during increased alertness. These changes in cortex-wide broadcasting likely requires fine-tuned state dependent inhibitory activity 48,56,57,58,59,60 . In line with this idea, we observed a gradual increase in sensory-evoked hyperpolarising amplitude in multiple sensory and motor regions across both hemispheres through the awake imaging session (Supplementary Fig. S5). Furthermore, such fine-tuned inhibition would also contribute to the sensory-evoked sequential pyramidal population hyperpolarising activity from motor to somatosensory cortices in the awake cortex (Figs 3–5).

Cellular-level observations combined with goal-directed behavioural paradigms had indicated a secondary depolarising response component associated with the conscious perception of sensory input 33 . However, since this earlier study included a motor task to implement readout for the perception, the secondary response may be related to the decision-making process or motor task itself. Here we observed a prominent secondary depolarising component in both the somatosensory (Fig. 6A) and motor cortices (Fig. 6B) that likely reflects alertness-dependent processing with sensorimotor integration (Fig. 6).

Our study highlights the potential of brain-wide voltage imaging approaches and sets a foundation for a number of future studies, among which most pressing is the role of different classes of GABAergic cells in sensorimotor integration 59,61,62 .

I would like to thank Dr. Ted Abel for reading a previous version of the manuscript. Furthermore, I would also like to thank Dr. Gabriele Radnikow for many helpful suggestions and careful reading of the final version of this manuscript. This work was supported by the Helmholtz Foundation and project grants from the Helmholtz Alliance on Systems Biology and the DFG Research Group �rrel Cortex Function.”

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Keywords: barrel cortex, cortical column, excitatory connections, long-range collaterals, pyramidal cell, somatosensory cortex, spiny stellate cell

Citation: Feldmeyer D (2012) Excitatory neuronal connectivity in the barrel cortex. Front. Neuroanat. 6:24. doi: 10.3389/fnana.2012.00024

Received: 02 February 2012 Accepted: 15 June 2012
Published online: 11 July 2012.

Idan Segev, The Hebrew University of Jerusalem, Israel
Heiko J. Luhmann, Institut für Physiologie und Pathophysiologie, Germany
Randy M. Bruno, Columbia University, USA

Copyright © 2012 Feldmeyer. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.