Cortical Sheet Research Articles

Reproducibility and Reliability of Quantitative and Weighted T1 and T2∗ Mapping for Myelin-Based Cortical Parcellation at 7 Tesla.

Different magnetic resonance (MR) parameters, such as R1 (=1/T1) or T2∗, have been used to visualize non-invasively the myelin distribution across the cortical sheet. Myelin contrast is consistently enhanced in the primary sensory and some higher order cortical areas (such as MT or the cingulate cortex), which renders it suitable for subject-specific anatomical cortical parcellation. However, no systematic comparison has been performed between the previously proposed MR parameters, i.e., the longitudinal and transversal relaxation values (or their ratios), for myelin mapping at 7 Tesla. In addition, usually these MR parameters are acquired in a non-quantitative manner (“weighted” parameters). Here, we evaluated the differences in ‘parcellability,’ contrast-to-noise ratio (CNR) and inter- and intra-subject variability and reproducibility, respectively, between high-resolution cortical surface maps based on these weighted MR parameters and their quantitative counterparts in ten healthy subjects. All parameters were obtained in a similar acquisition time and possible transmit- or receive-biases were removed during post-processing. It was found that CNR per unit time and parcellability were lower for the transversal compared to the longitudinal relaxation parameters. Further, quantitative R1 was characterized by the lowest inter- and intra-subject coefficient of variation (5.53 and 1.63%, respectively), making R1 a better parameter to map the myelin distribution compared to the other parameters. Moreover, quantitative MRI approaches offer the advantage of absolute rather than relative characterization of the underlying biochemical composition of the tissue, allowing more reliable comparison within subjects and between healthy subjects and patients. Finally, we explored two parcellation methods (thresholding the MR parameter values vs. surface gradients of these values) to determine areal borders based on the cortical surface pattern. It is shown that both methods are partially observer-dependent, needing manual interaction (i.e., choice of threshold or connecting high gradient values) to provide unambiguous borders.

Individual-specific features of brain systems identified with resting state functional correlations

Recent work has made important advances in describing the large-scale systems-level organization of human cortex by analyzing functional magnetic resonance imaging (fMRI) data averaged across groups of subjects. However, new findings have emerged suggesting that individuals’ cortical systems are topologically complex, containing small but reliable features that cannot be observed in group-averaged datasets, due in part to variability in the position of such features along the cortical sheet. This previous work has reported only specific examples of these individual-specific system features; to date, such features have not been comprehensively described. Here we used fMRI to identify cortical system features in individual subjects within three large cross-subject datasets and one highly sampled within-subject dataset. We observed system features that have not been previously characterized, but 1) were reliably detected across many scanning sessions within a single individual, and 2) could be matched across many individuals. In total, we identified forty-three system features that did not match group-average systems, but that replicated across three independent datasets. We described the size and spatial distribution of each non-group feature. We further observed that some individuals were missing specific system features, suggesting individual differences in the system membership of cortical regions. Finally, we found that individual-specific system features could be used to increase subject-to-subject similarity. Together, this work identifies individual-specific features of human brain systems, thus providing a catalog of previously unobserved brain system features and laying the foundation for detailed examinations of brain connectivity in individuals.

Distributions of Cells and Neurons across the Cortical Sheet in Old World Macaques

According to previous research, cell and neuron densities vary across neocortex in a similar manner across primate taxa. Here, we provide a more extensive examination of this effect in macaque monkeys. We separated neocortex from the underlying white matter in 4 macaque monkey hemispheres (1 Macaca nemestrina, 2 Macaca radiata, and 1 Macaca mulatta), manually flattened the neocortex, and divided it into smaller tissue pieces for analysis. The number of cells and neurons were determined for each piece across the cortical sheet using flow cytometry. Primary visual cortex had the most densely packed neurons and primary motor cortex had the least densely packed neurons. With respect to differences in brain size between cases, there was little variability in the total cell and neuron numbers within specific areas, and overall trends were similar to what has been previously described in Old World baboons and other primates. The average hemispheric total cell number per hemisphere ranged from 2.9 to 3.7 billion, while the average total neuron number ranged from 1.3 to 1.7 billion neurons. The visual cortex neuron densities were predictably higher, ranging from 18.2 to 34.7 million neurons/cm² in macaques, in comparison to a range of 9.3-17.7 million neurons/cm² across cortex as a whole. The results support other evidence that neuron surface densities vary across the cortical sheet in a predictable pattern within and across primate taxa.

Feature-Selective Attentional Modulations in Human Frontoparietal Cortex

Control over visual selection has long been framed in terms of a dichotomy between "source" and "site," where top-down feedback signals originating in frontoparietal cortical areas modulate or bias sensory processing in posterior visual areas. This distinction is motivated in part by observations that frontoparietal cortical areas encode task-level variables (e.g., what stimulus is currently relevant or what motor outputs are appropriate), while posterior sensory areas encode continuous or analog feature representations. Here, we present evidence that challenges this distinction. We used fMRI, a roving searchlight analysis, and an inverted encoding model to examine representations of an elementary feature property (orientation) across the entire human cortical sheet while participants attended either the orientation or luminance of a peripheral grating. Orientation-selective representations were present in a multitude of visual, parietal, and prefrontal cortical areas, including portions of the medial occipital cortex, the lateral parietal cortex, and the superior precentral sulcus (thought to contain the human homolog of the macaque frontal eye fields). Additionally, representations in many-but not all-of these regions were stronger when participants were instructed to attend orientation relative to luminance. Collectively, these findings challenge models that posit a strict segregation between sources and sites of attentional control on the basis of representational properties by demonstrating that simple feature values are encoded by cortical regions throughout the visual processing hierarchy, and that representations in many of these areas are modulated by attention. Influential models of visual attention posit a distinction between top-down control and bottom-up sensory processing networks. These models are motivated in part by demonstrations showing that frontoparietal cortical areas associated with top-down control represent abstract or categorical stimulus information, while visual areas encode parametric feature information. Here, we show that multivariate activity in human visual, parietal, and frontal cortical areas encode representations of a simple feature property (orientation). Moreover, representations in several (though not all) of these areas were modulated by feature-based attention in a similar fashion. These results provide an important challenge to models that posit dissociable top-down control and sensory processing networks on the basis of representational properties.

MRI-guided stereotaxic brain surgery in the infant and adult common marmoset.

In the past decade, the New World common marmoset (Callithrix jacchus) has taken a seminal position in neurobiological research, fueled in part by its smooth cortical sheet, which allows cortical areas to be easily accessed by current technologies on the dorsal surface of the brain. In this protocol, we describe a method for the precision placement of agents (e.g., tracers or neurotoxins) into small brain regions of the infant and adult marmoset, using an MRI-guided approach. This strategy uses a protocol for prolonged anesthesia without the need for intubation that we have recently developed, alongside appropriate analgesia and monitoring. The protocol can be readily adapted to be used together with advanced research techniques, such as two-photon microscopy and optical imaging. Including a 5-d postoperative care plan, this protocol takes 7 d to complete. The protocol requires a team of personnel experienced in marmoset care and handling, and small-animal neurosurgery; an assistant for monitoring the animal and assisting with anesthesia; and an MRI technician.

Relationship Between Cortical Gyrification, White Matter Connectivity, and Autism Spectrum Disorder.

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition, which is accompanied by differences in gray matter neuroanatomy and white matter connectivity. However, it is unknown whether these differences are linked or reflect independent aetiologies. Using a multimodal neuroimaging approach, we therefore examined 51 male adults with ASD and 48 neurotypical controls to investigate the relationship between gray matter local gyrification (lGI) and white matter diffusivity in associated fiber tracts. First, ASD individuals had a significant increase in gyrification around the left pre- and post-central gyrus. Second, white matter fiber tracts originating and/or terminating in the cluster of increased lGI had a significant increase in axial diffusivity. This increase in diffusivity was predominantly observed in tracts in close proximity to the cortical sheet. Last, we demonstrate that the increase in lGI was significantly correlated with increased diffusivity of short tracts. This relationship was not significantly modulated by a main effect of group (i.e., ASD), which was more closely associated with gray matter gyrification than white matter diffusivity. Our findings suggest that differences in gray matter neuroanatomy and white matter connectivity are closely linked, and may reflect common rather than distinct aetiological pathways.

Multiple markers of cortical morphology reveal evidence of supragranular thinning in schizophrenia.

In vivo structural neuroimaging can reliably identify changes to cortical morphology and its regional variation but cannot yet relate these changes to specific cortical layers. We propose, however, that by synthesizing principles of cortical organization, including relative contributions of different layers to sulcal and gyral thickness, regional patterns of variation in thickness of different layers across the cortical sheet and profiles of layer variation across functional hierarchies, it is possible to develop indirect morphological measures as markers of more specific cytoarchitectural changes. We developed four indirect measures sensitive to changes specifically occurring in supragranular cortical layers, and applied these to test the hypothesis that supragranular layers are disproportionately affected in schizophrenia. Our findings from the four different measures converge to indicate a predominance of supragranular thinning in schizophrenia, independent of medication and illness duration. We propose that these indirect measures offer novel ways of identifying layer-specific cortical changes, offering complementary in vivo observations to existing post-mortem studies.

Cortical Resonance Frequencies Emerge from Network Size and Connectivity.

Neural oscillations occur within a wide frequency range with different brain regions exhibiting resonance-like characteristics at specific points in the spectrum. At the microscopic scale, single neurons possess intrinsic oscillatory properties, such that is not yet known whether cortical resonance is consequential to neural oscillations or an emergent property of the networks that interconnect them. Using a network model of loosely-coupled Wilson-Cowan oscillators to simulate a patch of cortical sheet, we demonstrate that the size of the activated network is inversely related to its resonance frequency. Further analysis of the parameter space indicated that the number of excitatory and inhibitory connections, as well as the average transmission delay between units, determined the resonance frequency. The model predicted that if an activated network within the visual cortex increased in size, the resonance frequency of the network would decrease. We tested this prediction experimentally using the steady-state visual evoked potential where we stimulated the visual cortex with different size stimuli at a range of driving frequencies. We demonstrate that the frequency corresponding to peak steady-state response inversely correlated with the size of the network. We conclude that although individual neurons possess resonance properties, oscillatory activity at the macroscopic level is strongly influenced by network interactions, and that the steady-state response can be used to investigate functional networks.

Relating the transcriptional and structural architecture of mouse cortical areas

Relating the transcriptional and structural architecture of mouse cortical areas

Riemannian Statistical Analysis of Cortical Geometry with Robustness to Partial Homology and Misalignment.

Typical studies of the geometry of the cerebral cortical structure focus on either cortical folding or thickness. They rely on spatial normalization, but use cortical descriptors that are sensitive to misregistration arising from the well-known problems of partial homologies between subject brains and local optima in nonlinear registration. In contrast to these approaches, we propose a novel framework for studying the geometry of the entire cortical sheet, subsuming its folding and thickness characteristics. We propose a novel descriptor of local cortical geometry to increase robustness to partial homology and misregistration. The proposed descriptor lies on a Riemannian manifold, and we describe a method for hypothesis testing on manifolds for cross-sectional studies. Results on simulated and clinical data show the benefits of the proposed approach for detecting between-group differences with greater accuracy and consistency.

Relating extrinsic connections to the intrinsic architecture of the cerebral cortex

Event Abstract Back to Event Relating extrinsic connections to the intrinsic architecture of the cerebral cortex Claus C. Hilgetag1, 2* 1 University Medical Center Hamburg-Eppendorf, Department of Computational Neuroscience, Germany 2 Boston University, Department of Health Sciences, United States The nerve fibre projections that connect different areas of the cortical sheet constitute a complex structural network for neural communication in the brain. How is the organization of this cortical connectome related to other features of brain architecture? We addressed the question by systematically investigating the relation of structural parameters of cortical areas, such as their distance, similarity in cortical cytoarchitecture as described by cortical lamination or neuronal density, as well as similarity of cortical thickness, to essential features of long-range cortico-cortical connections, such as their presence or absence as well as patterns of laminar projection origins and terminations. Across different species (primate, cat, mouse) and different cortical lobes, the essential features of cortico-cortical connections varied consistently and strongly with the cytoarchitectonic similarity of cortical areas. By contrast, such relations were not found consistently for distance or similarity of cortical thickness. As gradients in cortical cytoarchitecture also determine the specific organisation of the local micro-circuits of different areas, our findings illuminate general principles of neural wiring, linking the organization of long-range connections to intrinsic circuits of the cerebral cortex. Keywords: connectomes, Cerebral Cortex, Cytoarchitecture, connectivity, human brain, monkey, Cats, Mouse Conference: Neuroinformatics 2016, Reading, United Kingdom, 3 Sep - 4 Sep, 2016. Presentation Type: Investigator presentations Topic: General neuroinformatics Citation: Hilgetag CC (2016). Relating extrinsic connections to the intrinsic architecture of the cerebral cortex. Front. Neuroinform. Conference Abstract: Neuroinformatics 2016. doi: 10.3389/conf.fninf.2016.20.00017 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 27 Apr 2016; Published Online: 18 Jul 2016. * Correspondence: Dr. Claus C Hilgetag, University Medical Center Hamburg-Eppendorf, Department of Computational Neuroscience, Hamburg, D-20246, Germany, c.hilgetag@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Claus C Hilgetag Google Claus C Hilgetag Google Scholar Claus C Hilgetag PubMed Claus C Hilgetag Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Pycortex: an interactive surface visualizer for fMRI.

Surface visualizations of fMRI provide a comprehensive view of cortical activity. However, surface visualizations are difficult to generate and most common visualization techniques rely on unnecessary interpolation which limits the fidelity of the resulting maps. Furthermore, it is difficult to understand the relationship between flattened cortical surfaces and the underlying 3D anatomy using tools available currently. To address these problems we have developed pycortex, a Python toolbox for interactive surface mapping and visualization. Pycortex exploits the power of modern graphics cards to sample volumetric data on a per-pixel basis, allowing dense and accurate mapping of the voxel grid across the surface. Anatomical and functional information can be projected onto the cortical surface. The surface can be inflated and flattened interactively, aiding interpretation of the correspondence between the anatomical surface and the flattened cortical sheet. The output of pycortex can be viewed using WebGL, a technology compatible with modern web browsers. This allows complex fMRI surface maps to be distributed broadly online without requiring installation of complex software.

Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream.

Converging evidence suggests that the primate ventral visual pathway encodes increasingly complex stimulus features in downstream areas. We quantitatively show that there indeed exists an explicit gradient for feature complexity in the ventral pathway of the human brain. This was achieved by mapping thousands of stimulus features of increasing complexity across the cortical sheet using a deep neural network. Our approach also revealed a fine-grained functional specialization of downstream areas of the ventral stream. Furthermore, it allowed decoding of representations from human brain activity at an unsurpassed degree of accuracy, confirming the quality of the developed approach. Stimulus features that successfully explained neural responses indicate that population receptive fields were explicitly tuned for object categorization. This provides strong support for the hypothesis that object categorization is a guiding principle in the functional organization of the primate ventral stream.

Superficial white matter fiber systems impede detection of long-range cortical connections in diffusion MR tractography

In vivo tractography based on diffusion magnetic resonance imaging (dMRI) has opened new doors to study structure-function relationships in the human brain. Initially developed to map the trajectory of major white matter tracts, dMRI is used increasingly to infer long-range anatomical connections of the cortex. Because axonal projections originate and terminate in the gray matter but travel mainly through the deep white matter, the success of tractography hinges on the capacity to follow fibers across this transition. Here we demonstrate that the complex arrangement of white matter fibers residing just under the cortical sheet poses severe challenges for long-range tractography over roughly half of the brain. We investigate this issue by comparing dMRI from very-high-resolution ex vivo macaque brain specimens with histological analysis of the same tissue. Using probabilistic tracking from pure gray and white matter seeds, we found that ∼50% of the cortical surface was effectively inaccessible for long-range diffusion tracking because of dense white matter zones just beneath the infragranular layers of the cortex. Analysis of the corresponding myelin-stained sections revealed that these zones colocalized with dense and uniform sheets of axons running mostly parallel to the cortical surface, most often in sulcal regions but also in many gyral crowns. Tracer injection into the sulcal cortex demonstrated that at least some axonal fibers pass directly through these fiber systems. Current and future high-resolution dMRI studies of the human brain will need to develop methods to overcome the challenges posed by superficial white matter systems to determine long-range anatomical connections accurately.

Patient-specific cortical electrodes for sulcal and gyral implantation.

Noninvasive localization of certain brain functions may be mapped on a millimetre level. However, the interelectrode spacing of common clinical brain surface electrodes still remains around 10 mm. Here, we present details on development of electrodes for attaining higher quality electrocorticographic signals for use in functional brain mapping and brain-machine interface (BMI) technologies. We used platinum-plate-electrodes of 1-mm diameter to produce sheet electrodes after the creation of individualized molds using a 3-D printer and a press system that sandwiched the electrodes between personalized silicone sheets. We created arrays to fit the surface curvature of the brain and inside the central sulcus, with interelectrode distances of 2.5 mm (a density of 16 times previous standard types). Rat experiments undertaken indicated no long term toxicity. We were also able to custom design, rapidly manufacture, safely implant, and confirm the efficacy of personalized electrodes, including the capability to attain meaningful high-gamma-band information in an amyotrophic lateral sclerosis patient. We developed cortical sheet electrodes with a high-spatial resolution, tailor-made to match an individual's brain. This sheet electrode may contribute to the higher performance of BMI's.

Locally-Optimized Inter-Subject Alignment of Functional Cortical Regions

Inter-subject registration of cortical areas is necessary in functional imaging (fMRI) studies for making inferences about equivalent brain function across a population. However, many high-level visual brain areas are defined as peaks of functional contrasts and it is usually difficult to identify clear anatomical landmarks and boundaries for these areas, due to large variability in their cortical position. As a consequence, previous methods usually fail to accurately map such functional regions of interest (ROIs) across participants. To address this problem, we propose a locally optimized registration method that directly predicts the location of a seed ROI on a separate target cortical sheet by maximizing the functional correlation between regions and simultaneously constraining the global structure of the mapping, while allowing for non-local deformations in its topology. Our registration method outperformed two canonical baselines (anatomical landmark-based AFNI alignment and cortical curvature-based FreeSurfer alignment) in the percentage of overlap between predicted region and ground truth LOC. Furthermore, the maps obtained using our method are more consistent across subjects than both baseline measures. Consequently, our method has the ability to directly and immediately improve the quality of group maps for high-level visual areas in countless fMRI studies. This would dramatically increase the statistical power of such studies, as a more accurate mapping to a common space implies less smoothing and larger effect sizes.

The cellular composition of the marsupial neocortex

In the current investigation we examined the number and proportion of neuronal and non-neuronal cells in the primary sensory areas of the neocortex of a South American marsupial, the short-tailed opossum (Monodelphis domestica). The primary somatosensory (S1), auditory (A1), and visual (V1) areas were dissected from the cortical sheet and compared with each other and the remaining neocortex using the isotropic fractionator technique. We found that although the overall sizes of V1, S1, A1, and the remaining cortical regions differed from each other, these divisions of the neocortex contained the same number of neurons, but the remaining cortex contained significantly more non-neurons than the primary sensory regions. In addition, the percent of neurons was higher in A1 than in the remaining cortex and the cortex as a whole. These results are similar to those seen in non-human primates. Furthermore, these results indicate that in some respects, such as number of neurons, the neocortex is homogenous across its extent, whereas in other aspects of organization, such as non-neuronal number and percentage of neurons, there is non-uniformity. Whereas the overall pattern of neuronal distribution is similar between short-tailed opossums and eutherian mammals, short-tailed opossum have a much lower cellular and neuronal density than other eutherian mammals. This suggests that the high neuronal density cortices of mammals such as rodents and primates may be a more recently evolved characteristic that is restricted to eutherians, and likely contributes to the complex behaviors we see in modern mammals.

Correction of Distortion in Flattened Representations of the Cortical Surface Allows Prediction of V1-V3 Functional Organization from Anatomy

Several domains of neuroscience offer map-like models that link location on the cortical surface to properties of sensory representation. Within cortical visual areas V1, V2, and V3, algebraic transformations can relate position in the visual field to the retinotopic representation on the flattened cortical sheet. A limit to the practical application of this structure-function model is that the cortex, while topologically a two-dimensional surface, is curved. Flattening of the curved surface to a plane unavoidably introduces local geometric distortions that are not accounted for in idealized models. Here, we show that this limitation is overcome by correcting the geometric distortion induced by cortical flattening. We use a mass-spring-damper simulation to create a registration between functional MRI retinotopic mapping data of visual areas V1, V2, and V3 and an algebraic model of retinotopy. This registration is then applied to the flattened cortical surface anatomy to create an anatomical template that is linked to the algebraic retinotopic model. This registered cortical template can be used to accurately predict the location and retinotopic organization of these early visual areas from cortical anatomy alone. Moreover, we show that prediction accuracy remains when extrapolating beyond the range of data used to inform the model, indicating that the registration reflects the retinotopic organization of visual cortex. We provide code for the mass-spring-damper technique, which has general utility for the registration of cortical structure and function beyond the visual cortex.

An isomorphic mapping hypothesis of the grid representation

We introduce a grid cell microcircuit hypothesis. We propose the ‘grid in the world’ (evident in grid cell discharges) is generated by a ‘grid in the cortex’. This cortical grid is formed by patches of calbindin-positive pyramidal neurons in layer 2 of medial entorhinal cortex (MEC). Our isomorphic mapping hypothesis assumes three types of isomorphism: (i) metric correspondence of neural space (the two-dimensional cortical sheet) and the external two-dimensional space within patches; (ii) isomorphism between cellular connectivity matrix and firing field; (iii) isomorphism between single cell and population activity. Each patch is a grid cell lattice arranged in a two-dimensional map of space with a neural : external scale of approximately 1 : 2000 in the dorsal part of rat MEC. The lattice behaves like an excitable medium with neighbouring grid cells exciting each other. Spatial scale is implemented as an intrinsic scaling factor for neural propagation speed. This factor varies along the dorsoventral cortical axis. A connectivity scheme of the grid system is described. Head direction input specifies the direction of activity propagation. We extend the theory to neurons between grid patches and predict a rare discharge pattern (inverted grid cells) and the relative location and proportion of grid cells and spatial band cells.

Longitudinal four-dimensional mapping of subcortical anatomy in human development

Growing access to large-scale longitudinal structural neuroimaging data has fundamentally altered our understanding of cortical development en route to human adulthood, with consequences for basic science, medicine, and public policy. In striking contrast, basic anatomical development of subcortical structures such as the striatum, pallidum, and thalamus has remained poorly described--despite these evolutionarily ancient structures being both intimate working partners of the cortical sheet and critical to diverse developmentally emergent skills and disorders. Here, to begin addressing this disparity, we apply methods for the measurement of subcortical volume and shape to 1,171 longitudinally acquired structural magnetic resonance imaging brain scans from 618 typically developing males and females aged 5-25 y. We show that the striatum, pallidum, and thalamus each follow curvilinear trajectories of volume change, which, for the striatum and thalamus, peak after cortical volume has already begun to decline and show a relative delay in males. Four-dimensional mapping of subcortical shape reveals that (i) striatal, pallidal, and thalamic domains linked to specific fronto-parietal association cortices contract with age whereas other subcortical territories expand, and (ii) each structure harbors hotspots of sexually dimorphic change over adolescence--with relevance for sex-biased mental disorders emerging in youth. By establishing the developmental dynamism, spatial heterochonicity, and sexual dimorphism of human subcortical maturation, these data bring our spatiotemporal understanding of subcortical development closer to that of the cortex--allowing evolutionary, basic, and clinical neuroscience to be conducted within a more comprehensive developmental framework.