The cortical column: a structure without a function

Primary somatosensory cortex organization for engineering artificial somatosensation

Somatosensory deficits from stroke, spinal cord injury, or other neurologic damage can lead to a significant degree of functional impairment. The primary (SI) and secondary (SII) somatosensory cortices encode information in a medial to lateral organization. SI is generally organized topographically, with more discrete cortical representations of specific body regions. SII regions corresponding to anatomical areas are less discrete and may represent a more functional rather than topographic organization. Human somatosensory research continues to map cortical areas of sensory processing with efforts primarily focused on hand and upper extremity information in SI. However, research into SII and other body regions is lacking. In this review, we synthesize the current state of knowledge regarding the cortical organization of human somatosensation and discuss potential applications for brain computer interface. In addition to accurate individualized mapping of cortical somatosensation, further research is required to uncover the neurophysiological mechanisms of how somatosensory information is encoded in the cortex.

Variation and convergence in the morpho-functional properties of the mammalian neocortex

Man's natural inclination to classify and hierarchize the living world has prompted neurophysiologists to explore possible differences in brain organisation between mammals, with the aim of understanding the diversity of their behavioural repertoires. But what really distinguishes the human brain from that of a platypus, an opossum or a rodent? In this review, we compare the structural and electrical properties of neocortical neurons in the main mammalian radiations and examine their impact on the functioning of the networks they form. We discuss variations in overall brain size, number of neurons, length of their dendritic trees and density of spines, acknowledging their increase in humans as in most large-brained species. Our comparative analysis also highlights a remarkable consistency, particularly pronounced in marsupial and placental mammals, in the cell typology, intrinsic and synaptic electrical properties of pyramidal neuron subtypes, and in their organisation into functional circuits. These shared cellular and network characteristics contribute to the emergence of strikingly similar large-scale physiological and pathological brain dynamics across a wide range of species. These findings support the existence of a core set of neural principles and processes conserved throughout mammalian evolution, from which a number of species-specific adaptations appear, likely allowing distinct functional needs to be met in a variety of environmental contexts.

Markov Blankets and Mirror Symmetries-Free Energy Minimization and Mesocortical Anatomy

A theoretical account of development in mesocortical anatomy is derived from the free energy principle, operating in a neural field with both Hebbian and anti-Hebbian neural plasticity. An elementary structural unit is proposed, in which synaptic connections at mesoscale are arranged in paired patterns with mirror symmetry. Exchanges of synaptic flux in each pattern form coupled spatial eigenmodes, and the line of mirror reflection between the paired patterns operates as a Markov blanket, so that prediction errors in exchanges between the pairs are minimized. The theoretical analysis is then compared to the outcomes from a biological model of neocortical development, in which neuron precursors are selected by apoptosis for cell body and synaptic connections maximizing synchrony and also minimizing axonal length. It is shown that this model results in patterns of connection with the anticipated mirror symmetries, at micro-, meso- and inter-arial scales, among lateral connections, and in cortical depth. This explains the spatial organization and functional significance of neuron response preferences, and is compatible with the structural form of both columnar and noncolumnar cortex. Multi-way interactions of mirrored representations can provide a preliminary anatomically realistic model of cortical information processing.

An operating principle of the cerebral cortex, and a cellular mechanism for attentional trial-and-error pattern learning and useful classification extraction

A feature of the brains of intelligent animals is the ability to learn to respond to an ensemble of active neuronal inputs with a behaviorally appropriate ensemble of active neuronal outputs. Previously, a hypothesis was proposed on how this mechanism is implemented at the cellular level within the neocortical pyramidal neuron: the apical tuft or perisomatic inputs initiate "guess" neuron firings, while the basal dendrites identify input patterns based on excited synaptic clusters, with the cluster excitation strength adjusted based on reward feedback. This simple mechanism allows neurons to learn to classify their inputs in a surprisingly intelligent manner. Here, we revise and extend this hypothesis. We modify synaptic plasticity rules to align with behavioral time scale synaptic plasticity (BTSP) observed in hippocampal area CA1, making the framework more biophysically and behaviorally plausible. The neurons for the guess firings are selected in a voluntary manner via feedback connections to apical tufts in the neocortical layer 1, leading to dendritic Ca2+ spikes with burst firing, which are postulated to be neural correlates of attentional, aware processing. Once learned, the neuronal input classification is executed without voluntary or conscious control, enabling hierarchical incremental learning of classifications that is effective in our inherently classifiable world. In addition to voluntary, we propose that pyramidal neuron burst firing can be involuntary, also initiated via apical tuft inputs, drawing attention toward important cues such as novelty and noxious stimuli. We classify the excitations of neocortical pyramidal neurons into four categories based on their excitation pathway: attentional versus automatic and voluntary/acquired versus involuntary. Additionally, we hypothesize that dendrites within pyramidal neuron minicolumn bundles are coupled via depolarization cross-induction, enabling minicolumn functions such as the creation of powerful hierarchical "hyperneurons" and the internal representation of the external world. We suggest building blocks to extend the microcircuit theory to network-level processing, which, interestingly, yields variants resembling the artificial neural networks currently in use. On a more speculative note, we conjecture that principles of intelligence in universes governed by certain types of physical laws might resemble ours.

Modular Phoneme Processing in Human Superior Temporal Gyrus

Modular organization is fundamental to cortical processing, but its presence is human association cortex is unknown. We characterized phoneme processing with 128-1024 channel micro-arrays at 50-200µm pitch on superior temporal gyrus of 7 patients. High gamma responses were highly correlated within ~1.7mm diameter modules, sharply delineated from adjacent modules with distinct time-courses and phoneme-selectivity. We suggest that receptive language cortex may be organized in discrete processing modules.

Cytoarchitectonic and connection stripes in the dysgranular insular cortex in the macaque monkey

The insula has been classically divided into broad granular, dysgranular, and agranular architectonic sectors. We previously proposed a novel partition, dividing each sector into four to seven sharply delimited architectonic areas, with the dysgranular areas being possibly further subdivided into subtle horizontal partitions or "stripes." In architectonics, discrete subparcellations are prone to subjective variability and need being supported with additional neuroanatomical methods. Here, using a secondary analysis of indirect connectional data in the rhesus macaque monkey, we examined the spatial relationship between the dysgranular architectonic stripes and tract-tracing labeling patterns produced in the insula with injections of neuronal tracers in other cortical regions. The injections consistently produced sharply delimited patches of anterograde and/or retrograde labeling, which formed stripes across consecutive coronal sections of the insula. While the overall pattern of labeling on individual coronal sections varied with the injection site, the boundaries of the patches consistently coincided with architectonic boundaries on an adjacent cyto- (Nissl) and/or myelo- (Gallyas) architectonic section. This overlap supports the existence of a fine dysgranular stripe-like partition of the primate insula, with possibly major implications for interoceptive processing in primates including humans. The modular organization of the insula could underlie a serial stream of integration from a dorsal primary interoceptive cortex toward progressively more ventral egocentric "self-agency" and allocentric "social" dysgranular processing units.

High-density recording reveals sparse clusters (but not columns) for shape and texture encoding in macaque V4

Macaque area V4 includes neurons that exhibit exquisite selectivity for visual form and surface texture, but their functional organization across laminae is unknown. We used high-density Neuropixels probes in two awake monkeys to characterize shape and texture tuning of dozens of neurons simultaneously across layers. We found sporadic clusters of neurons that exhibit similar tuning for shape and texture: ~20% exhibited similar tuning with their neighbors. Importantly, these clusters were confined to a few layers, seldom 'columnar' in structure. This was the case even when neurons were strongly driven, and exhibited robust contrast invariance for shape and texture tuning. We conclude that functional organization in area V4 is not columnar for shape and texture stimulus features and in general organization maybe at a coarse scale (e.g. encoding of 2D vs 3D shape) rather than at a fine scale in terms of similarity in tuning for specific features (as in the orientation columns in V1). We speculate that this may be a direct consequence of the great diversity of inputs integrated by V4 neurons to build variegated tuning manifolds in a high-dimensional space.

A role for ocular dominance in binocular integration

Neurons in the primate primary visual cortex (V1) combine left- and right-eye information to form a binocular output. Controversy surrounds whether ocular dominance, the preference of these neurons for one eye over the other, is functionally relevant. Here, we demonstrate that ocular dominance impacts gain control during binocular combination. We recorded V1 spiking activity while monkeys passively viewed grating stimuli. Gratings were either presented to one eye (monocular), both eyes with the same contrasts (binocular balanced), or both eyes with different contrasts (binocular imbalanced). We found that contrast placed in a neuron's dominant eye was weighted more strongly than contrast placed in a neuron's non-dominant eye. This asymmetry covaried with neurons' ocular dominance. We then tested whether accounting for ocular dominance within divisive normalization improves the fit to neural data. We found that ocular dominance significantly improved model performance, with interocular normalization providing the best fits. These findings suggest that V1 ocular dominance is relevant for response normalization during binocular stimulation.

Stimulus edges induce orientation tuning in superior colliculus

Orientation columns exist in the primary visual cortex (V1) of cat and primates but not mouse. Intriguingly, some recent studies reported the presence of orientation and direction columns in the mouse superficial superior colliculus (sSC), while others reported a lack of columnar organization therein. Using in vivo calcium imaging of sSC in the awake mouse brain, we found that the presence of columns is highly stimulus dependent. Specifically, we observed orientation and direction columns formed by sSC neurons retinotopically mapped to the edge of grating stimuli. For both excitatory and inhibitory neurons in sSC, orientation selectivity can be induced by the edge with their preferred orientation perpendicular to the edge orientation. Furthermore, we found that this edge-induced orientation selectivity is associated with saliency encoding. These findings indicate that the tuning properties of sSC neurons are not fixed by circuit architecture but rather dependent on the spatiotemporal properties of the stimulus.

Visual experience-dependent development of ocular dominance columns in pigmented rats

Despite previous agreement of the absence of cortical column structure in the rodent visual cortex, we have recently revealed a presence of ocular dominance columns (ODCs) in the primary visual cortex (V1) of adult Long-Evans rats. In this study, we deepened understanding of characteristics of rat ODCs. We found that this structure was conserved in Brown Norway rats, but not in albino rats; therefore, it could be a structure generally present in pigmented wild rats. Activity-dependent gene expression indicated that maturation of eye-dominant patches takes more than 2 weeks after eye-opening, and this process is visual experience dependent. Monocular deprivation during classical critical period strongly influenced size of ODCs, shifting ocular dominance from the deprived eye to the opened eye. On the other hand, transneuronal anterograde tracer showed a presence of eye-dominant patchy innervation from the ipsilateral V1 even before eye-opening, suggesting the presence of visual activity-independent genetic components of developing ODCs. Pigmented C57BL/6J mice also showed minor clusters of ocular dominance neurons. These results provide insights into how visual experience-dependent and experience-independent components both contribute to develop cortical columns during early postnatal stages, and indicate that rats and mice can be excellent models to study them.

Local and long-distance organization of prefrontal cortex circuits in the marmoset brain

The prefrontal cortex (PFC) has dramatically expanded in primates, but its organization and interactions with other brain regions are only partially understood. We performed high-resolution connectomic mapping of the marmoset PFC and found two contrasting corticocortical and corticostriatal projection patterns: "patchy" projections that formed many columns of submillimeter scale in nearby and distant regions and "diffuse" projections that spread widely across the cortex and striatum. Parcellation-free analyses revealed representations of PFC gradients in these projections' local and global distribution patterns. We also demonstrated column-scale precision of reciprocal corticocortical connectivity, suggesting that PFC contains a mosaic of discrete columns. Diffuse projections showed considerable diversity in the laminar patterns of axonal spread. Altogether, these fine-grained analyses reveal important principles of local and long-distance PFC circuits in marmosets and provide insights into the functional organization of the primate brain.

Imaging the columnar functional organization of human area MT+ to axis-of-motion stimuli using VASO at 7 Tesla

Cortical columns of direction-selective neurons in the motion sensitive area (MT) have been successfully established as a microscopic feature of the neocortex in animals. The same property has been investigated at mesoscale (<1 mm) in the homologous brain area (hMT+, V5) in living humans by using ultra-high field functional magnetic resonance imaging (fMRI). Despite the reproducibility of the selective response to axis-of-motion stimuli, clear quantitative evidence for the columnar organization of hMT+ is still lacking. Using cerebral blood volume (CBV)-sensitive fMRI at 7 Tesla with submillimeter resolution and high spatial specificity to microvasculature, we investigate the columnar functional organization of hMT+ in 5 participants perceiving axis-of-motion stimuli for both blood oxygenation level dependent (BOLD) and vascular space occupancy (VASO) contrast mechanisms provided by the used slice-selective slab-inversion (SS-SI)-VASO sequence. With the development of a new searchlight algorithm for column detection, we provide the first quantitative columnarity map that characterizes the entire 3D hMT+ volume. Using voxel-wise measures of sensitivity and specificity, we demonstrate the advantage of using CBV-sensitive fMRI to detect mesoscopic cortical features by revealing higher specificity of axis-of-motion cortical columns for VASO as compared to BOLD contrast. These voxel-wise metrics also provide further insights on how to mitigate the highly debated draining veins effect. We conclude that using CBV-VASO fMRI together with voxel-wise measurements of sensitivity, specificity and columnarity offers a promising avenue to quantify the mesoscopic organization of hMT+ with respect to axis-of-motion stimuli. Furthermore, our approach and methodological developments are generalizable and applicable to other human brain areas where similar mesoscopic research questions are addressed.

Mutual consistency of multiple visual feature maps constrains combined map topology

The primary visual cortex (V1) is the first cortical area that processes visual information relayed from the thalamus. The topologies permitted in joint ocular dominance (OD), orientation preference (OP), and direction preference (DP) maps in V1 are considered, with the aim of finding a maximally symmetric periodic case that can serve as the basis for perturbations toward natural realizations. It is shown that mutual consistency of the maps selects just two possible such lattice structures, and that one of these is much closer to experiment than the other. This comprises a hexagonal lattice of alternating positive and negative OP singularities, with each unit cell or hypercolumn containing four such singularities, each of which radiates three DP discontinuities that follow OP contours and end at OP singularities of opposite sign. Other DP discontinuities emanate at 90 degrees to the midpoints of the ones that link OP singularities, and cross OP contours perpendicularly. These features explain experimentally observed relationships between DP discontinuities and OP contours, including sudden approximately 90-degree changes of direction in the former.

Linking cortex and contraction-Integrating models along the corticomuscular pathway

Computational models of the neuromusculoskeletal system provide a deterministic approach to investigate input-output relationships in the human motor system. Neuromusculoskeletal models are typically used to estimate muscle activations and forces that are consistent with observed motion under healthy and pathological conditions. However, many movement pathologies originate in the brain, including stroke, cerebral palsy, and Parkinson's disease, while most neuromusculoskeletal models deal exclusively with the peripheral nervous system and do not incorporate models of the motor cortex, cerebellum, or spinal cord. An integrated understanding of motor control is necessary to reveal underlying neural-input and motor-output relationships. To facilitate the development of integrated corticomuscular motor pathway models, we provide an overview of the neuromusculoskeletal modelling landscape with a focus on integrating computational models of the motor cortex, spinal cord circuitry, α-motoneurons  and skeletal muscle in regard to their role in generating voluntary muscle contraction. Further, we highlight the challenges and opportunities associated with an integrated corticomuscular pathway model, such as challenges in defining neuron connectivities, modelling standardisation, and opportunities in applying models to study emergent behaviour. Integrated corticomuscular pathway models have applications in brain-machine-interaction, education, and our understanding of neurological disease.

Spatial organization of neuron-astrocyte interactions in the somatosensory cortex

Microcircuits in the neocortex are functionally organized along layers and columns, which are the fundamental modules of cortical information processing. While the function of cortical microcircuits has focused on neuronal elements, much less is known about the functional organization of astrocytes and their bidirectional interaction with neurons. Here, we show that Cannabinoid type 1 receptor (CB1R)-mediated astrocyte activation by neuron-released endocannabinoids elevate astrocyte Ca2+ levels, stimulate ATP/adenosine release as gliotransmitters, and transiently depress synaptic transmission in layer 5 pyramidal neurons at relatively distant synapses (˃20 μm) from the stimulated neuron. This astrocyte-mediated heteroneuronal synaptic depression occurred between pyramidal neurons within a cortical column and was absent in neurons belonging to adjacent cortical columns. Moreover, this form of heteroneuronal synaptic depression occurs between neurons located in particular layers, following a specific connectivity pattern that depends on a layer-specific neuron-to-astrocyte signaling. These results unravel the existence of astrocyte-mediated nonsynaptic communication between cortical neurons and that this communication is column- and layer-specific, which adds further complexity to the intercellular signaling processes in the neocortex.

Biocompatible and Long-Term Monitoring Strategies of Wearable, Ingestible and Implantable Biosensors: Reform the Next Generation Healthcare

Sensors enable the detection of physiological indicators and pathological markers to assist in the diagnosis, treatment, and long-term monitoring of diseases, in addition to playing an essential role in the observation and evaluation of physiological activities. The development of modern medical activities cannot be separated from the precise detection, reliable acquisition, and intelligent analysis of human body information. Therefore, sensors have become the core of new-generation health technologies along with the Internet of Things (IoTs) and artificial intelligence (AI). Previous research on the sensing of human information has conferred many superior properties on sensors, of which biocompatibility is one of the most important. Recently, biocompatible biosensors have developed rapidly to provide the possibility for the long-term and in-situ monitoring of physiological information. In this review, we summarize the ideal features and engineering realization strategies of three different types of biocompatible biosensors, including wearable, ingestible, and implantable sensors from the level of sensor designing and application. Additionally, the detection targets of the biosensors are further divided into vital life parameters (e.g., body temperature, heart rate, blood pressure, and respiratory rate), biochemical indicators, as well as physical and physiological parameters based on the clinical needs. In this review, starting from the emerging concept of next-generation diagnostics and healthcare technologies, we discuss how biocompatible sensors revolutionize the state-of-art healthcare system unprecedentedly, as well as the challenges and opportunities faced in the future development of biocompatible health sensors.

Excitatory-inhibitory recurrent dynamics produce robust visual grids and stable attractors

Spatially modulated grid cells have been recently found in the rat secondary visual cortex (V2) during active navigation. However, the computational mechanism and functional significance of V2 grid cells remain unknown. To address the knowledge gap, we train a biologically inspired excitatory-inhibitory recurrent neural network to perform a two-dimensional spatial navigation task with multisensory input. We find grid-like responses in both excitatory and inhibitory RNN units, which are robust with respect to spatial cues, dimensionality of visual input, and activation function. Population responses reveal a low-dimensional, torus-like manifold and attractor. We find a link between functional grid clusters with similar receptive fields and structured excitatory-to-excitatory connections. Additionally, multistable torus-like attractors emerged with increasing sparsity in inter- and intra-subnetwork connectivity. Finally, irregular grid patterns are found in recurrent neural network (RNN) units during a visual sequence recognition task. Together, our results suggest common computational mechanisms of V2 grid cells for spatial and non-spatial tasks.

Interdigitated Columnar Representation of Personal Space and Visual Space in Human Parietal Cortex

Personal space (PS) is the space around the body that people prefer to maintain between themselves and unfamiliar others. Intrusion into personal space evokes discomfort and an urge to move away. Physiologic studies in nonhuman primates suggest that defensive responses to intruding stimuli involve the parietal cortex. We hypothesized that the spatial encoding of interpersonal distance is initially transformed from purely sensory to more egocentric mapping within human parietal cortex. This hypothesis was tested using 7 Tesla (7T) fMRI at high spatial resolution (1.1 mm isotropic), in seven subjects (four females, three males). In response to visual stimuli presented at a range of virtual distances, we found two categories of distance encoding in two corresponding radially-extending columns of activity within parietal cortex. One set of columns (P columns) responded selectively to moving and stationary face images presented at virtual distances that were nearer (but not farther) than each subject's behaviorally-defined personal space boundary. In most P columns, BOLD response amplitudes increased monotonically and nonlinearly with increasing virtual face proximity. In the remaining P columns, BOLD responses decreased with increasing proximity. A second set of parietal columns (D columns) responded selectively to disparity-based distance cues (near or far) in random dot stimuli, similar to disparity-selective columns described previously in occipital cortex. Critically, in parietal cortex, P columns were topographically interdigitated (nonoverlapping) with D columns. These results suggest that visual spatial information is transformed from visual to body-centered (or person-centered) dimensions in multiple local sites within human parietal cortex.SIGNIFICANCE STATEMENT Recent COVID-related social distancing practices highlight the need to better understand brain mechanisms which regulate "personal space" (PS), which is defined by the closest interpersonal distance that is comfortable for an individual. Using high spatial resolution brain imaging, we tested whether a map of external space is transformed from purely visual (3D-based) information to a more egocentric map (related to personal space) in human parietal cortex. We confirmed this transformation and further showed that it was mediated by two mutually segregated sets of columns: one which encoded interpersonal distance and another that encoded visual distance. These results suggest that the cortical transformation of sensory-centered to person-centered encoding of space near the body involves short-range communication across interdigitated columns within parietal cortex.

Functional interactions among neurons within single columns of macaque V1

Recent developments in high-density neurophysiological tools now make it possible to record from hundreds of single neurons within local, highly interconnected neural networks. Among the many advantages of such recordings is that they dramatically increase the quantity of identifiable, functional interactions between neurons thereby providing an unprecedented view of local circuits. Using high-density, Neuropixels recordings from single neocortical columns of primary visual cortex in nonhuman primates, we identified 1000s of functionally interacting neuronal pairs using established crosscorrelation approaches. Our results reveal clear and systematic variations in the synchrony and strength of functional interactions within single cortical columns. Despite neurons residing within the same column, both measures of interactions depended heavily on the vertical distance separating neuronal pairs, as well as on the similarity of stimulus tuning. In addition, we leveraged the statistical power afforded by the large numbers of functionally interacting pairs to categorize interactions between neurons based on their crosscorrelation functions. These analyses identified distinct, putative classes of functional interactions within the full population. These classes of functional interactions were corroborated by their unique distributions across defined laminar compartments and were consistent with known properties of V1 cortical circuitry, such as the lead-lag relationship between simple and complex cells. Our results provide a clear proof-of-principle for the use of high-density neurophysiological recordings to assess circuit-level interactions within local neuronal networks.

Unification of free energy minimization, spatiotemporal energy, and dimension reduction models of V1 organization: Postnatal learning on an antenatal scaffold

Developmental selection of neurons and synapses so as to maximize pulse synchrony has recently been used to explain antenatal cortical development. Consequences of the same selection process—an application of the Free Energy Principle—are here followed into the postnatal phase in V1, and the implications for cognitive function are considered. Structured inputs transformed via lag relay in superficial patch connections lead to the generation of circumferential synaptic connectivity superimposed upon the antenatal, radial, “like-to-like” connectivity surrounding each singularity. The spatiotemporal energy and dimension reduction models of cortical feature preferences are accounted for and unified within the expanded model, and relationships of orientation preference (OP), space frequency preference (SFP), and temporal frequency preference (TFP) are resolved. The emergent anatomy provides a basis for “active inference” that includes interpolative modification of synapses so as to anticipate future inputs, as well as learn directly from present stimuli. Neurodynamic properties are those of heteroclinic networks with coupled spatial eigenmodes.

Characteristic columnar connectivity caters to cortical computation: Replication, simulation, and evaluation of a microcircuit model

The neocortex, and with it the mammalian brain, achieves a level of computational efficiency like no other existing computational engine. A deeper understanding of its building blocks (cortical microcircuits), and their underlying computational principles is thus of paramount interest. To this end, we need reproducible computational models that can be analyzed, modified, extended and quantitatively compared. In this study, we further that aim by providing a replication of a seminal cortical column model. This model consists of noisy Hodgkin-Huxley neurons connected by dynamic synapses, whose connectivity scheme is based on empirical findings from intracellular recordings. Our analysis confirms the key original finding that the specific, data-based connectivity structure enhances the computational performance compared to a variety of alternatively structured control circuits. For this comparison, we use tasks based on spike patterns and rates that require the systems not only to have simple classification capabilities, but also to retain information over time and to be able to compute nonlinear functions. Going beyond the scope of the original study, we demonstrate that this finding is independent of the complexity of the neuron model, which further strengthens the argument that it is the connectivity which is crucial. Finally, a detailed analysis of the memory capabilities of the circuits reveals a stereotypical memory profile common across all circuit variants. Notably, the circuit with laminar structure does not retain stimulus any longer than any other circuit type. We therefore conclude that the model's computational advantage lies in a sharper representation of the stimuli.

The inevitable inequality of cortical columns

The idea of columns as an organizing cortical unit emerged from physiologic studies in the sensory systems. Connectional studies and molecular markers pointed to widespread presence of modular label that necessitated revision of the classical concept of columns. The general principle of cortical systematic variation in laminar structure is at the core of cortical organization. Systematic variation can be traced to the phylogenetically ancient limbic cortices, which have the simplest laminar structure, and continues through eulaminate cortices that show sequential elaboration of their six layers. Connections are governed by relational rules, whereby columns or modules with a vertical organization represent the feedforward mode of communication from earlier- to later processing cortices. Conversely, feedback connections are laminar-based and connect later- with earlier processing areas; both patterns are established in development. Based on studies in primates, the columnar/modular pattern of communication appears to be newer in evolution, while the broadly based laminar pattern represents an older system. The graded variation of cortices entails a rich variety of patterns of connections into modules, layers, and mixed arrangements as the laminar and modular patterns of communication intersect in the cortex. This framework suggests an ordered architecture poised to facilitate seamless recruitment of areas in behavior, in patterns that are affected in diseases of developmental origin.

The cortical spectrum: A robust structural continuum in primate cerebral cortex revealed by histological staining and magnetic resonance imaging

High-level characterizations of the primate cerebral cortex sit between two extremes: on one end the cortical mantle is seen as a mosaic of structurally and functionally unique areas, and on the other it is seen as a uniform six-layered structure in which functional differences are defined solely by extrinsic connections. Neither of these extremes captures the crucial neuroanatomical finding: that the cortex exhibits systematic gradations in architectonic structure. These gradations have been shown to predict cortico-cortical connectivity, which in turn suggests powerful ways to ground connectomics in anatomical structure, and by extension cortical function. A challenge to widespread use of this concept is the labor-intensive and invasive nature of histological staining, which is the primary means of recognizing anatomical gradations. Here we show that a novel computational analysis technique can provide a coarse-grained picture of cortical variation. For each of 78 cortical areas spanning the entire cortical mantle of the rhesus macaque, we created a high dimensional set of anatomical features derived from captured images of cortical tissue stained for myelin and SMI-32. The method involved semi-automated de-noising of images, and enabled comparison of brain areas without hand-labeling of features such as layer boundaries. We applied multidimensional scaling (MDS) to the dataset to visualize similarity among cortical areas. This analysis shows a systematic variation between weakly laminated (limbic) cortices and sharply laminated (eulaminate) cortices. We call this smooth continuum the “cortical spectrum”. We also show that this spectrum is visible within subsystems of the cortex: the occipital, parietal, temporal, motor, prefrontal, and insular cortices. We compared the MDS-derived spectrum with a spectrum produced using T1- and T2-weighted magnetic resonance imaging (MRI) data derived from macaque, and found close agreement of the two coarse-graining methods. This suggests that T1w/T2w data, routinely obtained in human MRI studies, can serve as an effective proxy for data derived from high-resolution histological methods. More generally, this approach shows that the cortical spectrum is robust to the specific method used to compare cortical areas, and is therefore a powerful tool to understand the principles of organization of the primate cortex.

Remodeling of the Cortical Structural Connectome in Posttraumatic Stress Disorder: Results From the ENIGMA-PGC Posttraumatic Stress Disorder Consortium

BACKGROUND

Posttraumatic stress disorder (PTSD) is accompanied by disrupted cortical neuroanatomy. We investigated alteration in covariance of structural networks associated with PTSD in regions that demonstrate the case-control differences in cortical thickness (CT) and surface area (SA).

METHODS

Neuroimaging and clinical data were aggregated from 29 research sites in >1300 PTSD cases and >2000 trauma-exposed control subjects (ages 6.2-85.2 years) by the ENIGMA-PGC (Enhancing Neuro Imaging Genetics through Meta Analysis-Psychiatric Genomics Consortium) PTSD working group. Cortical regions in the network were rank ordered by the effect size of PTSD-related cortical differences in CT and SA. The top-n (n = 2-148) regions with the largest effect size for PTSD > non-PTSD formed hypertrophic networks, the largest effect size for PTSD < non-PTSD formed atrophic networks, and the smallest effect size of between-group differences formed stable networks. The mean structural covariance (SC) of a given n-region network was the average of all positive pairwise correlations and was compared with the mean SC of 5000 randomly generated n-region networks.

RESULTS

Patients with PTSD, relative to non-PTSD control subjects, exhibited lower mean SC in CT-based and SA-based atrophic networks. Comorbid depression, sex, and age modulated covariance differences of PTSD-related structural networks.

CONCLUSIONS

Covariance of structural networks based on CT and cortical SA are affected by PTSD and further modulated by comorbid depression, sex, and age. The SC networks that are perturbed in PTSD comport with converging evidence from resting-state functional connectivity networks and networks affected by inflammatory processes and stress hormones in PTSD.

A model for the development of binocular congruence in primary visual cortex

Neurons in primary visual cortex are selective for stimulus orientation, and a neuron's preferred orientation changes little when the stimulus is switched from one eye to the other. It has recently been shown that monocular orientation preferences are uncorrelated before eye opening; how, then, do they become aligned during visual experience? We aimed to provide a model for this acquired congruence. Our model, which simulates the cat's visual system, comprises multiple on-centre and off-centre channels from both eyes converging onto neurons in primary visual cortex; development proceeds in two phases via Hebbian plasticity in the geniculocortical synapse. First, cortical drive comes from waves of activity drifting across each retina. The result is orientation tuning that differs between the two eyes. The second phase begins with eye opening: at each visual field location, on-centre cortical inputs from one eye can cancel off-centre inputs from the other eye. Synaptic plasticity reduces the destructive interference by up-regulating inputs from one eye at the expense of its fellow, resulting in binocular congruence of orientation tuning. We also show that orthogonal orientation preferences at the end of the first phase result in ocular dominance, suggesting that ocular dominance is a by-product of binocular congruence.

Are Grid-Like Representations a Component of All Perception and Cognition?

Grid cells or grid-like responses have been reported in the rodent, bat and human brains during various spatial and non-spatial tasks. However, the functions of grid-like representations beyond the classical hippocampal formation remain elusive. Based on accumulating evidence from recent rodent recordings and human fMRI data, we make speculative accounts regarding the mechanisms and functional significance of the sensory cortical grid cells and further make theory-driven predictions. We argue and reason the rationale why grid responses may be universal in the brain for a wide range of perceptual and cognitive tasks that involve locomotion and mental navigation. Computational modeling may provide an alternative and complementary means to investigate the grid code or grid-like map. We hope that the new discussion will lead to experimentally testable hypotheses and drive future experimental data collection.

Analytic Model for Feature Maps in the Primary Visual Cortex

A compact analytic model is proposed to describe the combined orientation preference (OP) and ocular dominance (OD) features of simple cells and their mutual constraints on the spatial layout of the combined OP-OD map in the primary visual cortex (V1). This model consists of three parts: (i) an anisotropic Laplacian (AL) operator that represents the local neural sensitivity to the orientation of visual inputs; and (ii) obtain a receptive field (RF) operator that models the anisotropic spatial projection from nearby neurons to a given V1 cell over scales of a few tenths of a millimeter and combines with the AL operator to give an overall OP operator; and (iii) a map that describes how the parameters of these operators vary approximately periodically across V1. The parameters of the proposed model maximize the neural response at a given OP with an OP tuning curve fitted to experimental results. It is found that the anisotropy of the AL operator does not significantly affect OP selectivity, which is dominated by the RF anisotropy, consistent with Hubel and Wiesel's original conclusions that orientation tuning width of V1 simple cell is inversely related to the elongation of its RF. A simplified and idealized OP-OD map is then constructed to describe the approximately periodic local OP-OD structure of V1 in a compact form. It is shown explicitly that the OP map can be approximated by retaining its dominant spatial Fourier coefficients, which are shown to suffice to reconstruct its basic spatial structure. Moreover, this representation is a suitable form to analyze observed OP maps compactly and to be used in neural field theory (NFT) for analyzing activity modulated by the OP-OD structure of V1. Application to independently simulated V1 OP structure shows that observed irregularities in the map correspond to a spread of dominant coefficients in a circle in Fourier space. In addition, there is a strong bias toward two perpendicular directions when only a small patch of local map is included. The bias is decreased as the amount of V1 included in the Fourier transform is increased.

Columnar and Laminar Segregation of Retinal Input to the Primate Superior Colliculus Revealed by Anterograde Tracer Injection Into Each Eye

Purpose

After the lateral geniculate nucleus, the superior colliculus is the richest target of retinal projections in primates. Hubel et al. used tritium autoradiography to show that axon terminals emanating from one eye form irregular columns in the stratum griseum superficiale. Unlabeled gaps were thought to be filled by the other eye, but this assumption was never tested directly.

Methods

Experiments were performed in two normal macaques. In monkey 1, [³H]proline was injected into the left eye and the pattern of radiolabeling was examined in serial cross-sections through the entire superior colliculus. In monkey 2, cholera toxin subunit B conjugated to Alexa 488 was injected into the right eye and cholera toxin subunit B - Alexa 594 was injected into the left eye. The two fluorescent labels were compared in a reconstruction of the superior colliculus prepared from serial sections.

Results

In monkey 1, irregular columns of axon terminals were present in the superficial grey. The projection from the peripheral retina was stronger than the projection from the macula. In monkey 2, the two fluorescent Alexa tracers mainly interdigitated: a conspicuous gap in one label was usually filled by a clump of the other label. There was also partial laminar segregation of ocular inputs. In the far peripheral field representation, the contralateral eye's input generally terminated closer to the tectal surface. In the midperiphery the eyes switched, bringing the ipsilateral input nearer the surface.

Conclusions

Direct retinal input to the macaque superior colliculus is segregated into alternating columns and strata, despite the fact that tectal cells respond robustly to stimulation of either eye.

A Machine Learning Approach Involving Functional Connectivity Features to Classify Rest-EEG Psychogenic Non-Epileptic Seizures from Healthy Controls

Until now, clinicians are not able to evaluate the Psychogenic Non-Epileptic Seizures (PNES) from the rest-electroencephalography (EEG) readout. No EEG marker can help differentiate PNES cases from healthy subjects. In this paper, we have investigated the power spectrum density (PSD), in resting-state EEGs, to evaluate the abnormalities in PNES affected brains. Additionally, we have used functional connectivity tools, such as phase lag index (PLI), and graph-derived metrics to better observe the integration of distributed information of regular and synchronized multi-scale communication within and across inter-regional brain areas. We proved the utility of our method after enrolling a cohort study of 20 age- and gender-matched PNES and 19 healthy control (HC) subjects. In this work, three classification models, namely support vector machine (SVM), linear discriminant analysis (LDA), and Multilayer perceptron (MLP), have been employed to model the relationship between the functional connectivity features (rest-HC versus rest-PNES). The best performance for the discrimination of participants was obtained using the MLP classifier, reporting a precision of 85.73%, a recall of 86.57%, an F1-score of 78.98%, and, finally, an accuracy of 91.02%. In conclusion, our results hypothesized two main aspects. The first is an intrinsic organization of functional brain networks that reflects a dysfunctional level of integration across brain regions, which can provide new insights into the pathophysiological mechanisms of PNES. The second is that functional connectivity features and MLP could be a promising method to classify rest-EEG data of PNES form healthy controls subjects.

Considerations and recent advances in nanoscale interfaces with neuronal and cardiac networks

Nanoscale interfaces with biological tissue, principally made with nanowires (NWs), are envisioned as minimally destructive to the tissue and as scalable tools to directly transduce the electrochemical activity of a neuron at its finest resolution. This review lays the foundations for understanding the material and device considerations required to interrogate neuronal activity at the nanoscale. We first discuss the electrochemical nanoelectrode-neuron interfaces and then present new results concerning the electrochemical impedance and charge injection capacities of millimeter, micrometer, and nanometer scale wires with Pt, PEDOT:PSS, Si, Ti, ITO, IrO x , Ag, and AgCl materials. Using established circuit models for NW-neuron interfaces, we discuss the impact of having multiple NWs interfacing with a single neuron on the amplitude and temporal characteristics of the recorded potentials. We review state of the art advances in nanoelectrode-neuron interfaces, the standard control experiments to investigate their electrophysiological behavior, and present recent high fidelity recordings of intracellular potentials obtained with ultrasharp NWs developed in our laboratory that naturally permeate neuronal cell bodies. Recordings from arrays and individually addressable electrically shorted NWs are presented, and the long-term stability of intracellular recording is discussed and put in the context of established techniques. Finally, a perspective on future research directions and applications is presented.

Traveling Waves in Quasi-One-Dimensional Neuronal Minicolumns

Traveling waves of neuronal activity in the cortex have been observed in vivo. These traveling waves have been correlated to various features of observed cortical dynamics, including spike timing variability and correlated fluctuations in neuron membrane potential. Although traveling waves are typically studied as either strictly one-dimensional or two-dimensional excitations, here we investigate the conditions for the existence of quasi-one-dimensional traveling waves that could be sustainable in parts of the brain containing cortical minicolumns. For that, we explore a quasi-one-dimensional network of heterogeneous neurons with a biologically influenced computational model of neuron dynamics and connectivity. We find that background stimulus reliably evokes traveling waves in networks with local connectivity between neurons. We also observe traveling waves in fully connected networks when a model for action potential propagation speed is incorporated. The biological properties of the neurons influence the generation and propagation of the traveling waves. Our quasi-one-dimensional model is not only useful for studying the basic properties of traveling waves in neuronal networks; it also provides a simplified representation of possible wave propagation in columnar or minicolumnar networks found in the cortex.

The Expression Patterns of Cytochrome Oxidase and Immediate-Early Genes Show Absence of Ocular Dominance Columns in the Striate Cortex of Squirrel Monkeys Following Monocular Inactivation

Because at least some squirrel monkeys lack ocular dominance columns (ODCs) in the striate cortex (V1) that are detectable by cytochrome oxidase (CO) histochemistry, the functional importance of ODCs on stereoscopic 3-D vision has been questioned. However, conventional CO histochemistry or trans-synaptic tracer study has limited capacity to reveal cortical functional architecture, whereas the expression of immediate-early genes (IEGs), c-FOS and ZIF268, is more directly responsive to neuronal activity of cortical neurons to demonstrate ocular dominance (OD)-related domains in V1 following monocular inactivation. Thus, we wondered whether IEG expression would reveal ODCs in the squirrel monkey V1. In this study, we first examined CO histochemistry in V1 of five squirrel monkeys that were subjected to monocular enucleation or tetrodotoxin (TTX) treatment to address whether there is substantial cross-individual variation as reported previously. Then, we examined the IEG expression of the same V1 tissue to address whether OD-related domains are revealed. As a result, staining patterns of CO histochemistry were relatively homogeneous throughout layer 4 of V1. IEG expression was also moderate and homogeneous throughout layer 4 of V1 in all cases. On the other hand, the IEG expression was patchy in accordance with CO blobs outside layer 4, particularly in infragranular layers, although they may not directly represent OD clusters. Squirrel monkeys remain an exceptional species among anthropoid primates with regard to OD organization, and thus are potentially good subjects to study the development and function of ODCs.

Contribution of animal models toward understanding resting state functional connectivity

Functional connectivity, which reflects the spatial and temporal organization of intrinsic activity throughout the brain, is one of the most studied measures in human neuroimaging research. The noninvasive acquisition of resting state functional magnetic resonance imaging (rs-fMRI) allows the characterization of features designated as functional networks, functional connectivity gradients, and time-varying activity patterns that provide insight into the intrinsic functional organization of the brain and potential alterations related to brain dysfunction. Functional connectivity, hence, captures dimensions of the brain's activity that have enormous potential for both clinical and preclinical research. However, the mechanisms underlying functional connectivity have yet to be fully characterized, hindering interpretation of rs-fMRI studies. As in other branches of neuroscience, the identification of the neurophysiological processes that contribute to functional connectivity largely depends on research conducted on laboratory animals, which provide a platform where specific, multi-dimensional investigations that involve invasive measurements can be carried out. These highly controlled experiments facilitate the interpretation of the temporal correlations observed across the brain. Indeed, information obtained from animal experimentation to date is the basis for our current understanding of the underlying basis for functional brain connectivity. This review presents a compendium of some of the most critical advances in the field based on the efforts made by the animal neuroimaging community.

Establishing a New Link between Fuzzy Logic, Neuroscience, and Quantum Mechanics through Bayesian Probability: Perspectives in Artificial Intelligence and Unconventional Computing

Human interaction with the world is dominated by uncertainty. Probability theory is a valuable tool to face such uncertainty. According to the Bayesian definition, probabilities are personal beliefs. Experimental evidence supports the notion that human behavior is highly consistent with Bayesian probabilistic inference in both the sensory and motor and cognitive domain. All the higher-level psychophysical functions of our brain are believed to take the activities of interconnected and distributed networks of neurons in the neocortex as their physiological substrate. Neurons in the neocortex are organized in cortical columns that behave as fuzzy sets. Fuzzy sets theory has embraced uncertainty modeling when membership functions have been reinterpreted as possibility distributions. The terms of Bayes' formula are conceivable as fuzzy sets and Bayes' inference becomes a fuzzy inference. According to the QBism, quantum probabilities are also Bayesian. They are logical constructs rather than physical realities. It derives that the Born rule is nothing but a kind of Quantum Law of Total Probability. Wavefunctions and measurement operators are viewed epistemically. Both of them are similar to fuzzy sets. The new link that is established between fuzzy logic, neuroscience, and quantum mechanics through Bayesian probability could spark new ideas for the development of artificial intelligence and unconventional computing.

Toward understanding the neural code of the brain

More and more, the neurosciences and the sciences concerned with mind and cognition are burying fundamental questions under layers of professional methodology. I therefore welcome Biological Cybernetics' invitation to comment on two of my papers, (von der Malsburg 1973) and (von der Malsburg and Schneider 1986) (henceforth referred to as (I) and (II)) as an opportunity to address two fundamental questions about brain and mind: How is the brain's structure generated? and How is mental content expressed by the brain's physical states? Those two questions are deeply entangled with each other and play a kind of gateway role on the way to making progress with the issues of perception, intelligence, creativity and consciousness.

Neuronal Avalanches Across the Rat Somatosensory Barrel Cortex and the Effect of Single Whisker Stimulation

Since its first experimental signatures, the so called "critical brain hypothesis" has been extensively studied. Yet, its actual foundations remain elusive. According to a widely accepted teleological reasoning, the brain would be poised to a critical state to optimize the mapping of the noisy and ever changing real-world inputs, thus suggesting that primary sensory cortical areas should be critical. We investigated whether a single barrel column of the somatosensory cortex of the anesthetized rat displays a critical behavior. Neuronal avalanches were recorded across all cortical layers in terms of both multi-unit activities and population local field potentials, and their behavior during spontaneous activity compared to the one evoked by a controlled single whisker deflection. By applying a maximum likelihood statistical method based on timeseries undersampling to fit the avalanches distributions, we show that neuronal avalanches are power law distributed for both multi-unit activities and local field potentials during spontaneous activity, with exponents that are spread along a scaling line. Instead, after the tactile stimulus, activity switches to a transient across-layers synchronization mode that appears to dominate the cortical representation of the single sensory input.

Cytochrome oxidase "blobs": a call for more anatomy

An ordered relation of structure and function has been a cornerstone in thinking about brain organization. Like the brain itself, however, this is not straightforward and is confounded both by functional intricacy and structural plasticity (many routes to a given outcome). As a striking case of putative structure-function correlation, this mini-review focuses on the relatively well-characterized pattern of cytochrome oxidase (CO) blobs (aka "patches" or "puffs") in the supragranular layers of macaque monkey visual cortex. The pattern is without doubt visually compelling, and the semi-dichotomous array of CO+ blobs and CO- interblobs is consistent with multiple studies reporting compartment-specific preferential connectivity and distinctive physiological response properties. Nevertheless, as briefly reviewed here, the finer anatomical organization of this system is surprisingly under-investigated, and the relation to functional aspects, therefore, unclear. Microcircuitry, cell type, and three-dimensional spatiotemporal level investigations of the CO+ CO- pattern are needed and may open new views to structure-function organization of visual cortex, and to phylogenetic and ontogenetic comparisons across nonhuman primates (NHP), and between NHP and humans.

Population Models, Not Analyses, of Human Neuroscience Measurements

Selectivity for many basic properties of visual stimuli, such as orientation, is thought to be organized at the scale of cortical columns, making it difficult or impossible to measure directly with noninvasive human neuroscience measurement. However, computational analyses of neuroimaging data have shown that selectivity for orientation can be recovered by considering the pattern of response across a region of cortex. This suggests that computational analyses can reveal representation encoded at a finer spatial scale than is implied by the spatial resolution limits of measurement techniques. This potentially opens up the possibility to study a much wider range of neural phenomena that are otherwise inaccessible through noninvasive measurement. However, as we review in this article, a large body of evidence suggests an alternative hypothesis to this superresolution account: that orientation information is available at the spatial scale of cortical maps and thus easily measurable at the spatial resolution of standard techniques. In fact, a population model shows that this orientation information need not even come from single-unit selectivity for orientation tuning, but instead can result from population selectivity for spatial frequency. Thus, a categorical error of interpretation can result whereby orientation selectivity can be confused with spatial frequency selectivity. This is similarly problematic for the interpretation of results from numerous studies of more complex representations and cognitive functions that have built upon the computational techniques used to reveal stimulus orientation. We suggest in this review that these interpretational ambiguities can be avoided by treating computational analyses as models of the neural processes that give rise to measurement. Building upon the modeling tradition in vision science using considerations of whether population models meet a set of core criteria is important for creating the foundation for a cumulative and replicable approach to making valid inferences from human neuroscience measurements. Expected final online publication date for the Annual Review of Vision Science, Volume 7 is September 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Functional organization of spatial frequency tuning in macaque V1 revealed with two-photon calcium imaging

V1 neurons are functionally organized in orientation columns in primates. Whether spatial frequency (SF) columns also exist is less clear because mixed results have been reported. A definitive solution would be SF functional maps at single-neuron resolution. Here we used two-photon calcium imaging to construct first cellular SF maps in V1 superficial layers of five awake fixating macaques, and studied SF functional organization properties and neuronal tuning characteristics. The SF maps (850 × 850 μm²) showed weak horizontal SF clustering (median clustering index = 1.43 vs. unity baseline), about one sixth as strong as orientation clustering in the same sets of neurons, which argues against a meaningful orthogonal relationship between orientation and SF functional maps. These maps also displayed nearly absent vertical SF clustering between two cortical depths (150 & 300 μm), indicating a lack of SF columnar structures within the superficial layers. The underlying causes might be that most neurons were tuned to a narrow two-octave range of medium frequencies, and many neurons with different SF preferences were often spatially mixed, which disallowed finer grouping of SF tuning. In addition, individual SF tuning functions were often asymmetric, having wider lower frequency branches, which may help encode low SF information for later decoding.

An Evolutionary Microcircuit Approach to the Neural Basis of High Dimensional Sensory Processing in Olfaction

Odor stimuli consist of thousands of possible molecules, each molecule with many different properties, each property a dimension of the stimulus. Processing these high dimensional stimuli would appear to require many stages in the brain to reach odor perception, yet, in mammals, after the sensory receptors this is accomplished through only two regions, the olfactory bulb and olfactory cortex. We take a first step toward a fundamental understanding by identifying the sequence of local operations carried out by microcircuits in the pathway. Parallel research provided strong evidence that processed odor information is spatial representations of odor molecules that constitute odor images in the olfactory bulb and odor objects in olfactory cortex. Paleontology provides a unique advantage with evolutionary insights providing evidence that the basic architecture of the olfactory pathway almost from the start ∼330 million years ago (mya) has included an overwhelming input from olfactory sensory neurons combined with a large olfactory bulb and olfactory cortex to process that input, driven by olfactory receptor gene duplications. We identify a sequence of over 20 microcircuits that are involved, and expand on results of research on several microcircuits that give the best insights thus far into the nature of the high dimensional processing.

Unique scales preserve self-similar integrate-and-fire functionality of neuronal clusters

Brains demonstrate varying spatial scales of nested hierarchical clustering. Identifying the brain's neuronal cluster size to be presented as nodes in a network computation is critical to both neuroscience and artificial intelligence, as these define the cognitive blocks capable of building intelligent computation. Experiments support various forms and sizes of neural clustering, from handfuls of dendrites to thousands of neurons, and hint at their behavior. Here, we use computational simulations with a brain-derived fMRI network to show that not only do brain networks remain structurally self-similar across scales but also neuron-like signal integration functionality ("integrate and fire") is preserved at particular clustering scales. As such, we propose a coarse-graining of neuronal networks to ensemble-nodes, with multiple spikes making up its ensemble-spike and time re-scaling factor defining its ensemble-time step. This fractal-like spatiotemporal property, observed in both structure and function, permits strategic choice in bridging across experimental scales for computational modeling while also suggesting regulatory constraints on developmental and evolutionary "growth spurts" in brain size, as per punctuated equilibrium theories in evolutionary biology.

The death of the cortical column? Patchwork structure and conceptual retirement in neuroscientific practice

In 1981, David Hubel and Torsten Wiesel received the Nobel Prize for their research on cortical columns-vertical bands of neurons with similar functional properties. This success led to the view that "cortical column" refers to the basic building block of the mammalian neocortex. Since the 1990s, however, critics questioned this building block picture of "cortical column" and debated whether this concept is useless and should be replaced with successor concepts. This paper inquires which experimental results after 1981 challenged the building block picture and whether these challenges warrant the elimination "cortical column" from neuroscientific discourse. I argue that the proliferation of experimental techniques led to a patchwork of locally adapted uses of the column concept. Each use refers to a different kind of cortical structure, rather than a neocortical building block. Once we acknowledge this diverse-kinds picture of "cortical column", the elimination of column concept becomes unnecessary. Rather, I suggest that "cortical column" has reached conceptual retirement: although it cannot be used to identify a neocortical building block, column research is still useful as a guide and cautionary tale for ongoing research. At the same time, neuroscientists should search for alternative concepts when studying the functional architecture of the neocortex. keywords: Cortical column, conceptual development, history of neuroscience, patchwork, eliminativism, conceptual retirement.

Cross-species neuroscience: closing the explanatory gap

Neuroscience has seen substantial development in non-invasive methods available for investigating the living human brain. However, these tools are limited to coarse macroscopic measures of neural activity that aggregate the diverse responses of thousands of cells. To access neural activity at the cellular and circuit level, researchers instead rely on invasive recordings in animals. Recent advances in invasive methods now permit large-scale recording and circuit-level manipulations with exquisite spatio-temporal precision. Yet, there has been limited progress in relating these microcircuit measures to complex cognition and behaviour observed in humans. Contemporary neuroscience thus faces an explanatory gap between macroscopic descriptions of the human brain and microscopic descriptions in animal models. To close the explanatory gap, we propose adopting a cross-species approach. Despite dramatic differences in the size of mammalian brains, this approach is broadly justified by preserved homology. Here, we outline a three-armed approach for effective cross-species investigation that highlights the need to translate different measures of neural activity into a common space. We discuss how a cross-species approach has the potential to transform basic neuroscience while also benefiting neuropsychiatric drug development where clinical translation has, to date, seen minimal success. This article is part of the theme issue 'Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity'.

Projection of Orthogonal Tiling from the Retina to the Visual Cortex

In higher mammals, the primary visual cortex (V1) is organized into diverse tuning maps of visual features. The topography of these maps intersects orthogonally, but it remains unclear how such a systematic relationship can develop. Here, we show that the orthogonal organization already exists in retinal ganglion cell (RGC) mosaics, providing a blueprint of the organization in V1. From analysis of the RGC mosaics data in monkeys and cats, we find that the ON-OFF RGC distance and ON-OFF angle of neighboring RGCs are organized into a topographic tiling across mosaics, analogous to the orthogonal intersection of cortical tuning maps. Our model simulation shows that the ON-OFF distance and angle in RGC mosaics correspondingly initiate ocular dominance/spatial frequency tuning and orientation tuning, resulting in the orthogonal intersection of cortical tuning maps. These findings suggest that the regularly structured ON-OFF patterns mirrored from the retina initiate the uniform representation of combinations of map features over the visual space.

Orientation Preference Maps in Microcebus murinus Reveal Size-Invariant Design Principles in Primate Visual Cortex

Orientation preference maps (OPMs) are a prominent feature of primary visual cortex (V1) organization in many primates and carnivores. In rodents, neurons are not organized in OPMs but are instead interspersed in a “salt and pepper” fashion, although clusters of orientation-selective neurons have been reported. Does this fundamental difference reflect the existence of a lower size limit for orientation columns (OCs) below which they cannot be scaled down with decreasing V1 size? To address this question, we examined V1 of one of the smallest living primates, the 60-g prosimian mouse lemur (Microcebus murinus). Using chronic intrinsic signal imaging, we found that mouse lemur V1 contains robust OCs, which are arranged in a pinwheel-like fashion. OC size in mouse lemurs was found to be only marginally smaller compared to the macaque, suggesting that these circuit elements are nearly incompressible. The spatial arrangement of pinwheels is well described by a common mathematical design of primate V1 circuit organization. In order to accommodate OPMs, we found that the mouse lemur V1 covers one-fifth of the cortical surface, which is one of the largest V1-to-cortex ratios found in primates. These results indicate that the primate-type visual cortical circuit organization is constrained by a size limitation and raises the possibility that its emergence might have evolved by disruptive innovation rather than gradual change.

Orientation preference maps are a hallmark of V1 organization in all primates studied thus far, yet they are absent in rodents. It is uncertain whether these structures scale with body or brain size. Using intrinsic signal imaging, Ho et al. reveal the presence of such maps in the V1 of the world’s smallest primate, the mouse lemur (Microcebus murinus).

Forging a path to mesoscopic imaging success with ultra-high field functional magnetic resonance imaging

Functional magnetic resonance imaging (fMRI) studies with ultra-high field (UHF, 7+ Tesla) technology enable the acquisition of high-resolution images. In this work, we discuss recent achievements in UHF fMRI at the mesoscopic scale, on the order of cortical columns and layers, and examine approaches to addressing common challenges. As researchers push to smaller and smaller voxel sizes, acquisition and analysis decisions have greater potential to degrade spatial accuracy, and UHF fMRI data must be carefully interpreted. We consider the impact of acquisition decisions on the spatial specificity of the MR signal with a representative dataset with 0.8 mm isotropic resolution. We illustrate the trade-offs in contrast with noise ratio and spatial specificity of different acquisition techniques and show that acquisition blurring can increase the effective voxel size by as much as 50% in some dimensions. We further describe how different sources of degradations to spatial resolution in functional data may be characterized. Finally, we emphasize that progress in UHF fMRI depends not only on scientific discovery and technical advancement, but also on informal discussions and documentation of challenges researchers face and overcome in pursuit of their goals. This article is part of the theme issue 'Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity'.

Selective Activation of Cortical Columns Using Multichannel Magnetic Stimulation With a Bent Flat Microwire Array

OBJECTIVE

Cortical neural prostheses that aim to restore useful vision, hearing, and tactile sensations require the ability to selectively target different cortical regions simultaneously. Electrical stimulation via intracortical electrodes has been used to create spatial patterns of cortical activation. However, their efficacy remains limited due to the inability of conventional electrodes to confine activation to specific cortical regions around each electrode. Magnetic stimulation from single bent wires can selectively activate pyramidal neurons while avoiding passing axons, thereby confining activation to small cortical regions. This paper presents a novel bent flat microwire array and demonstrates its effectiveness for selective activation of cortical columns in mouse brain slices.

METHODS

A computational model was developed to compare the spatial resolution of magnetic stimulation from bent wire arrays with 280 and 530 μm tip spacings. The same array designs were fabricated for use in electrophysiological experiments, i.e., calcium imaging (GCaMP6s) of mouse brain slices.

RESULTS

All fabricated array designs reliably produced spatially discrete cortical activations at low stimulus amplitudes, but the 280-μm-spacing produced strong interference (constructive or destructive) at high stimulus amplitudes, thereby resulting in single strong activations or two asymmetric activations. 4-channel bent wire arrays with spacing of 340 μm avoided the interference and produced clearer spatial patterns of activation than electrodes.

CONCLUSION

Bent wire array designs can influence the strength or the spatial resolution of multichannel magnetic stimulation.

SIGNIFICANCE

These results suggest that bent microwire arrays can enhance the selectivity of multichannel stimulation of brain and therefore may help to develop reliable and effective cortical neural prostheses.