Depth- and curvature-based quantitative susceptibility mapping analyses of cortical iron in Alzheimer's disease.

In addition to amyloid beta plaques and neurofibrillary tangles, Alzheimer's disease (AD) has been associated with elevated iron in deep gray matter nuclei using quantitative susceptibility mapping (QSM). However, only a few studies have examined cortical iron, using more macroscopic approaches that cannot assess layer-specific differences. Here, we conducted column-based QSM analyses to assess whether AD-related increases in cortical iron vary in relation to layer-specific differences in the type and density of neurons. We obtained global and regional measures of positive (iron) and negative (myelin, protein aggregation) susceptibility from 22 adults with AD and 22 demographically matched healthy controls. Depth-wise analyses indicated that global susceptibility increased from the pial surface to the gray/white matter boundary, with a larger slope for positive susceptibility in the left hemisphere for adults with AD than controls. Curvature-based analyses indicated larger global susceptibility for adults with AD versus controls; the right hemisphere versus left; and gyri versus sulci. Region-of-interest analyses identified similar depth- and curvature-specific group differences, especially for temporo-parietal regions. Finding that iron accumulates in a topographically heterogenous manner across the cortical mantle may help explain the profound cognitive deterioration that differentiates AD from the slowing of general motor processes in healthy aging.

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.

Asymmetries in Global Perception Are Represented in Near- versus Far-Preferring Clusters in Human Visual Cortex.

Human perception is more "global" when stimuli are viewed within the lower (rather than the upper) visual field. This phenomenon is typically considered as a 2-D phenomenon, likely due to differential neural processing within dorsal versus ventral cortical areas that represent lower versus upper visual fields, respectively. Here we test a novel hypothesis that this vertical asymmetry in global processing is a 3-D phenomenon associated with (1) higher ecological relevance of low-spatial frequency (SF) components in encoding near (compared with far) visual objects and (2) the fact that near objects are more frequently found in lower rather than upper visual fields. Using high-resolution fMRI, collected within an ultra-high-field (7 T) scanner, we found that the extent of vertical asymmetry in global visual processing in human subjects (n = 10) was correlated with the fMRI response evoked by disparity-varying stimuli in human cortical area V3A. We also found that near-preferring clusters in V3A, located within stereoselective cortical columns, responded more selectively than far-preferring clusters, to low-SF features. These findings support the hypothesis that vertical asymmetry in global processing is a 3-D (not a 2-D) phenomenon, associated with the function of the stereoselective columns within visual cortex, especially those located within visual area V3A.SIGNIFICANCE STATEMENT Here we test and confirm a new hypothesis: fine-scale neural mechanisms underlying the vertical asymmetry in global visual processing. According to this hypothesis, the asymmetry in global visual processing is a 3-D (rather than a 2-D) phenomenon, reflected in the function of fine-scale cortical structures (clusters and columns) underlying depth perception. Our findings highlight the importance of considering these structures, as regions of interest, in clarifying the neural mechanisms underlying visual perception. The results also highlight the importance of statistics of natural scenes in shaping human visual perception.

Functionally specific optogenetic modulation in primate visual cortex.

In primates, visual perception is mediated by brain circuits composed of submillimeter nodes linked together in specific networks that process different types of information, such as eye specificity and contour orientation. We hypothesized that optogenetic stimulation targeted to cortical nodes could selectively activate such cortical networks. We used viral transfection methods to confer light sensitivity to neurons in monkey primary visual cortex. Using intrinsic signal optical imaging and single-unit electrophysiology to assess effects of targeted optogenetic stimulation, we found that (i) optogenetic stimulation of single ocular dominance columns (eye-specific nodes) revealed preferential activation of nearby same-eye columns but not opposite-eye columns, and (ii) optogenetic stimulation of single orientation domains increased visual response of matching orientation domains and relatively suppressed nonmatching orientation selectivity. These findings demonstrate that optical stimulation of single nodes leads to modulation of functionally specific cortical networks related to underlying neural architecture.

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.

Sub-millimeter fMRI reveals multiple topographical digit representations that form action maps in human motor cortex.

The human brain coordinates a wide variety of motor activities. On a large scale, the cortical motor system is topographically organized such that neighboring body parts are represented by neighboring brain areas. This homunculus-like somatotopic organization along the central sulcus has been observed using neuroimaging for large body parts such as the face, hands and feet. However, on a finer scale, invasive electrical stimulation studies show deviations from this somatotopic organization that suggest an organizing principle based on motor actions rather than body part moved. It has not been clear how the action-map organization principle of the motor cortex in the mesoscopic (sub-millimeter) regime integrates into a body map organization principle on a macroscopic scale (cm). Here we developed and applied advanced mesoscopic (sub-millimeter) fMRI and analysis methodology to non-invasively investigate the functional organization topography across columnar and laminar structures in humans. Compared to previous methods, in this study, we could capture locally specific blood volume changes across entire brain regions along the cortical curvature. We find that individual fingers have multiple mirrored representations in the primary motor cortex depending on the movements they are involved in. We find that individual digits have cortical representations up to 3 â€‹mm apart from each other arranged in a column-like fashion. These representations are differentially engaged depending on whether the digits' muscles are used for different motor actions such as flexion movements, like grasping a ball or retraction movements like releasing a ball. This research provides a starting point for non-invasive investigation of mesoscale topography across layers and columns of the human cortex and bridges the gap between invasive electrophysiological investigations and large coverage non-invasive neuroimaging.

Estimates of cortical column orientation improve MEG source inversion.

Determining the anatomical source of brain activity non-invasively measured from EEG or MEG sensors is challenging. In order to simplify the source localization problem, many techniques introduce the assumption that current sources lie on the cortical surface. Another common assumption is that this current flow is orthogonal to the cortical surface, thereby approximating the orientation of cortical columns. However, it is not clear which cortical surface to use to define the current source locations, and normal vectors computed from a single cortical surface may not be the best approximation to the orientation of cortical columns. We compared three different surface location priors and five different approaches for estimating dipole vector orientation, both in simulations and visual and motor evoked MEG responses. We show that models with source locations on the white matter surface and using methods based on establishing correspondences between white matter and pial cortical surfaces dramatically outperform models with source locations on the pial or combined pial/white surfaces and which use methods based on the geometry of a single cortical surface in fitting evoked visual and motor responses. These methods can be easily implemented and adopted in most M/EEG analysis pipelines, with the potential to significantly improve source localization of evoked responses.

The impact of ultra-high field MRI on cognitive and computational neuroimaging.

The ability to measure functional brain responses non-invasively with ultra high field MRI (7 T and above) represents a unique opportunity in advancing our understanding of the human brain. Compared to lower fields (3 T and below), ultra high field MRI has an increased sensitivity, which can be used to acquire functional images with greater spatial resolution, and greater specificity of the blood oxygen level dependent (BOLD) signal to the underlying neuronal responses. Together, increased resolution and specificity enable investigating brain functions at a submillimeter scale, which so far could only be done with invasive techniques. At this mesoscopic spatial scale, perception, cognition and behavior can be probed at the level of fundamental units of neural computations, such as cortical columns, cortical layers, and subcortical nuclei. This represents a unique and distinctive advantage that differentiates ultra high from lower field imaging and that can foster a tighter link between fMRI and computational modeling of neural networks. So far, functional brain mapping at submillimeter scale has focused on the processing of sensory information and on well-known systems for which extensive information is available from invasive recordings in animals. It remains an open challenge to extend this methodology to uniquely human functions and, more generally, to systems for which animal models may be problematic. To succeed, the possibility to acquire high-resolution functional data with large spatial coverage, the availability of computational models of neural processing as well as accurate biophysical modeling of neurovascular coupling at mesoscopic scale all appear necessary.

Spatial specificity of the functional MRI blood oxygenation response relative to neuronal activity.

Previous attempts at characterizing the spatial specificity of the blood oxygenation level dependent functional MRI (BOLD fMRI) response by estimating its point-spread function (PSF) have conventionally relied on retinotopic spatial representations of visual stimuli in area V1. Consequently, their estimates were confounded by the width and scatter of receptive fields of V1 neurons. Here, we circumvent these limits by instead using the inherent cortical spatial organization of ocular dominance columns (ODCs) to determine the PSF for both Gradient Echo (GE) and Spin Echo (SE) BOLD imaging at 7 Tesla. By applying Markov chain Monte Carlo sampling on a probabilistic generative model of imaging ODCs, we quantified the PSFs that best predict the spatial structure and magnitude of differential ODCs' responses. Prior distributions for the ODC model parameters were determined by analyzing published data of cytochrome oxidase patterns from post-mortem histology of human V1 and of neurophysiological ocular dominance indices. The average PSF full-widths at half-maximum obtained from differential ODCs' responses following the removal of voxels influenced by contributions from macroscopic blood vessels were 0.86 mm (SE) and 0.99 mm (GE). Our results provide a quantitative basis for the spatial specificity of BOLD fMRI at ultra-high fields, which can be used for planning and interpretation of high-resolution differential fMRI of fine-scale cortical organizations.

Optimization of functional MRI for detection, decoding and high-resolution imaging of the response patterns of cortical columns.

The capacity of functional MRI (fMRI) to resolve cortical columns depends on several factors. These include the spatial scale of the columnar pattern, the point-spread of the fMRI response, the voxel size, and the signal-to-noise ratio (SNR) considering thermal and physiological noise. However, it remains unknown how these factors combine, and what is the voxel size that optimizes fMRI of cortical columns. Here we combine current knowledge into a quantitative model of fMRI of realistic patterns of cortical columns with different spatial scales and degrees of irregularity. We compare different approaches for identifying patterns of cortical columns, including univariate and multivariate based detection, multi-voxel pattern analysis (MVPA) based decoding, and high-resolution imaging and reconstruction of the pattern of cortical columns. We present the dependence of the performance of each approach on the parameters of the imaged pattern as well as those of the data acquisition. In addition, we predict voxel sizes that optimize fMRI of cortical columns under various scenarios. We found that all measures associated with multivariate detection and decoding could be approximately calculated from a measure we termed "multivariate contrast-to-noise ratio" (mv-CNR), which is a function of the contrast-to-noise ratio (CNR) and number of voxels. Furthermore, mv-CNR implied that the optimal voxel width for detection and decoding is independent of changes in response amplitude, SNR and imaged volume that are not caused by changes in voxel size. For regular patterns, optimal voxel widths for detection, decoding and imaging/reconstructing the pattern of cortical columns were approximately half the main cycle length of the organization. Optimal voxel widths for irregular patterns were less dependent on the main cycle length, and differed between univariate detection, multivariate detection and decoding, and reconstruction. We compared the effects of different factors of Gradient Echo fMRI at 3 Tesla (T), Gradient Echo fMRI at 7T, and Spin-Echo fMRI at 7T on the detection, decoding, and reconstruction measures considered and found that in all cases the width of the fMRI point-spread had the most significant effect. In contrast, different response amplitudes and noise characteristics played a relatively minor role. We recommend specific voxel widths for optimal univariate detection, for multivariate detection and decoding, and for high-resolution imaging of cortical columns under these three data-acquisition scenarios. Our study supports the planning, optimization, and interpretation of high-resolution fMRI of cortical columns and the decoding of information conveyed by these columns.

Collective Activity of Many Bistable Assemblies Reproduces Characteristic Dynamics of Multistable Perception.

UNLABELLED: The timing of perceptual decisions depends on both deterministic and stochastic factors, as the gradual accumulation of sensory evidence (deterministic) is contaminated by sensory and/or internal noise (stochastic). When human observers view multistable visual displays, successive episodes of stochastic accumulation culminate in repeated reversals of visual appearance. Treating reversal timing as a "first-passage time" problem, we ask how the observed timing densities constrain the underlying stochastic accumulation. Importantly, mean reversal times (i.e., deterministic factors) differ enormously between displays/observers/stimulation levels, whereas the variance and skewness of reversal times (i.e., stochastic factors) keep characteristic proportions of the mean. What sort of stochastic process could reproduce this highly consistent "scaling property?" Here we show that the collective activity of a finite population of bistable units (i.e., a generalized Ehrenfest process) quantitatively reproduces all aspects of the scaling property of multistable phenomena, in contrast to other processes under consideration (Poisson, Wiener, or Ornstein-Uhlenbeck process). The postulated units express the spontaneous dynamics of attractor assemblies transitioning between distinct activity states. Plausible candidates are cortical columns, or clusters of columns, as they are preferentially connected and spontaneously explore a restricted repertoire of activity states. Our findings suggests that perceptual representations are granular, probabilistic, and operate far from equilibrium, thereby offering a suitable substrate for statistical inference. SIGNIFICANCE STATEMENT: Spontaneous reversals of high-level perception, so-called multistable perception, conform to highly consistent and characteristic statistics, constraining plausible neural representations. We show that the observed perceptual dynamics would be reproduced quantitatively by a finite population of distinct neural assemblies, each with locally bistable activity, operating far from the collective equilibrium (generalized Ehrenfest process). Such a representation would be consistent with the intrinsic stochastic dynamics of neocortical activity, which is dominated by preferentially connected assemblies, such as cortical columns or clusters of columns. We predict that local neuron assemblies will express bistable dynamics, with spontaneous active-inactive transitions, whenever they contribute to high-level perception.

Decoding eye-of-origin outside of awareness.

In the primary visual cortex of many mammals, ocular dominance columns segregate information from the two eyes. Yet under controlled conditions, most human observers are unable to correctly report the eye to which a stimulus has been shown, indicating that this information is lost during subsequent processing. This study investigates whether eye-of-origin information is available in the pattern of electrophysiological activity evoked by visual stimuli, recorded using EEG and decoded using multivariate pattern analysis. Observers (N=24) viewed sine-wave grating and plaid stimuli of different orientations, shown to either the left or right eye (or both). Using a support vector machine, eye-of-origin could be decoded above chance at around 140 and 220ms post stimulus onset, yet observers were at chance for reporting this information. Other stimulus features, such as binocularity, orientation, spatial pattern, and the presence of interocular conflict (i.e. rivalry), could also be decoded using the same techniques, though all of these were perceptually discriminable above chance. A control analysis found no evidence to support the possibility that eye dominance was responsible for the eye-of-origin effects. These results support a structural explanation for multivariate decoding of electrophysiological signals - information organised in cortical columns can be decoded, even when observers are unaware of this information.

Estimating cortical column sensory networks in rodents from micro-electrocorticograph (µECoG) recordings.

Micro-electrocorticograph (µECoG) arrays offer the flexibility to record local field potentials (LFPs) from the surface of the cortex, using high density electrodes that are sub-mm in diameter. Research to date has not provided conclusive evidence for the underlying signal generation of µECoG recorded LFPs, or if µECoG arrays can capture network activity from the cortex. We studied the pervading view of the LFP signal by exploring the spatial scale at which the LFP can be considered elemental. We investigated the underlying signal generation and ability to capture functional networks by implanting, µECoG arrays to record sensory-evoked potentials in four rats. The organization of the sensory cortex was studied by analyzing the sensory-evoked potentials with two distinct modeling techniques: (1) The volume conduction model, that models the electrode LFPs with an electrostatic representation, generated by a single cortical generator, and (2) the dynamic causal model (DCM), that models the electrode LFPs with a network model, whose activity is generated by multiple interacting cortical sources. The volume conduction approach modeled activity from electrodes separated < 1000 µm, with reasonable accuracy but a network model like DCM was required to accurately capture activity > 1500 µm. The extrinsic network component in DCM was determined to be essential for accurate modeling of observed potentials. These results all point to the presence of a sensory network, and that µECoG arrays are able to capture network activity in the neocortex. The estimated DCM network models the functional organization of the cortex, as signal generators for the µECoG recorded LFPs, and provides hypothesis-testing tools to explore the brain.

Can visual information encoded in cortical columns be decoded from magnetoencephalography data in humans?

It is a principal open question whether noninvasive imaging methods in humans can decode information encoded at a spatial scale as fine as the basic functional unit of cortex: cortical columns. We addressed this question in five magnetoencephalography (MEG) experiments by investigating a columnar-level encoded visual feature: contrast edge orientation. We found that MEG signals contained orientation-specific information as early as approximately 50 ms after stimulus onset even when controlling for confounds, such as overrepresentation of particular orientations, stimulus edge interactions, and global form-related signals. Theoretical modeling confirmed the plausibility of this empirical result. An essential consequence of our results is that information encoded in the human brain at the level of cortical columns should in general be accessible by multivariate analysis of electrophysiological signals.

Cerebral blood volume MRI with intravascular superparamagnetic iron oxide nanoparticles.

The cerebral blood volume (CBV) is a crucial physiological indicator of tissue viability and vascular reactivity. Thus, noninvasive CBV mapping has been of great interest. For this, ultrasmall superparamagnetic iron oxide (USPIO) nanoparticles, including monocrystalline iron oxide nanoparticles, can be used as long-half-life, intravascular susceptibility agents of CBV MRI measurements. Moreover, CBV-weighted functional MRI (fMRI) with USPIO nanoparticles provides enhanced sensitivity, reduced large vessel contribution and improved spatial specificity relative to conventional blood oxygenation level-dependent fMRI, and measures a single physiological parameter that is easily interpretable. We review the physiochemical and magnetic properties, and pharmacokinetics, of USPIO nanoparticles in brief. We then extensively discuss quantifications of baseline CBV, vessel size index and functional CBV change. We also provide reviews of dose-dependent sensitivity, vascular filter function, specificity, characteristics and impulse response function of CBV fMRI. Examples of CBV fMRI specificity at the laminar and columnar resolution are provided. Finally, we briefly review the application of CBV measurements to functional and pharmacological studies in animals. Overall, the use of USPIO nanoparticles can determine baseline CBV and its changes induced by functional activity and pharmacological interventions.

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'.

Optical Imaging-Based Guidance of Viral Microinjections and Insertion of a Laminar Electrophysiology Probe Into a Predetermined Barrel in Mouse Area S1BF.

Wide-field Optical Imaging of Intrinsic Signals (OI-IS; Grinvald et al., 1986) is a method for imaging functional brain hemodynamic responses, mainly used to image activity from the surface of the cerebral cortex. It localizes small functional modules - such as cortical columns - with great spatial resolution and spatial specificity relative to the site of increases in neuronal activity. OI-IS is capable of imaging responses either through an intact or thinned skull or following a craniotomy. Therefore, it is minimally invasive, which makes it ideal for survival experiments. Here we describe OI-IS-based methods for guiding microinjections of optogenetics viral vectors in proximity to small functional modules (S1 barrels) of the cerebral cortex and for guiding the insertion of electrodes for electrophysiological recording into such modules. We validate our proposed methods by tissue processing of the cerebral barrel field area, revealing the track of the electrode in a predetermined barrel. In addition, we demonstrate the use of optical imaging to visualize the spatial extent of the optogenetics photostimulation, making it possible to estimate one of the two variables that conjointly determine which region of the brain is stimulated. Lastly, we demonstrate the use of OI-IS at high-magnification for imaging the upper recording contacts of a laminar probe, making it possible to estimate the insertion depth of all contacts relative to the surface of the cortex. These methods support the precise positioning of microinjections and recording electrodes, thus overcoming the variability in the spatial position of fine-scale functional modules.

What we can learn from the complex architecture of single axons.

Anterogradely labeled connections at the single-axon level provide unparalleled spatial and quantitative data as well as a novel perspective on laminar, columnar, hierarchical and other aspects of cortical organization. Here, I briefly summarize single-axon results from representative examples of thalamocortical, corticocortical, callosal, and lateral intrinsic connections, with attention to implications for cortical organization. Particularly worth emphasizing is the intricate spatial configuration and striking morphometric heterogeneity of individual axons even within the same system of connections. A short section touches on patterns of axonal trajectories in the distal, preterminal few millimeters. Emphasis is on studies in nonhuman primates from about 1983 to present, with non-viral tracers and 2-D reconstruction (i.e., compressed z-axis) in the early visual cortical pathway. The last section recapitulates what this approach can tell us about inter-areal communication and cortical organization, and possible implications for dynamics and effective connectivity, and concludes with comments on open questions and future directions.

Synergistic Coding of Visual Information in Columnar Networks.

Incoming stimuli are encoded collectively by populations of cortical neurons, which transmit information by using a neural code thought to be predominantly redundant. Redundant coding is widely believed to reflect a design choice whereby neurons with overlapping receptive fields sample environmental stimuli to convey similar information. Here, we performed multi-electrode laminar recordings in awake monkey V1 to report significant synergistic interactions between nearby neurons within a cortical column. These interactions are clustered non-randomly across cortical layers to form synergy and redundancy hubs. Homogeneous sub-populations comprising synergy hubs decode stimulus information significantly better compared to redundancy hubs or heterogeneous sub-populations. Mechanistically, synergistic interactions emerge from the stimulus dependence of correlated activity between neurons. Our findings suggest a refinement of the prevailing ideas regarding coding schemes in sensory cortex: columnar populations can efficiently encode information due to synergistic interactions even when receptive fields overlap and shared noise between cells is high.

Somatosensory maps.

Somatosensory areas containing topographic maps of the body surface are a major feature of parietal cortex. In primates, parietal cortex contains four somatosensory areas, each with its own map, with the primary cutaneous map in area 3b. Rodents have at least three parietal somatosensory areas. Maps are not isomorphic to the body surface, but magnify behaviorally important skin regions, which include the hands and face in primates, and the whiskers in rodents. Within each map, intracortical circuits process tactile information, mediate spatial integration, and support active sensation. Maps may also contain fine-scale representations of touch submodalities, or direction of tactile motion. Functional representations are more overlapping than suggested by textbook depictions of map topography. The whisker map in rodent somatosensory cortex is a canonic system for studying cortical microcircuits, sensory coding, and map plasticity. Somatosensory maps are plastic throughout life in response to altered use or injury. This chapter reviews basic principles and recent findings in primate, human, and rodent somatosensory maps.