High-resolution three-dimensional reconstruction of a whole yeast cell using focused-ion beam scanning electron microscopy

We developed an approach for focused gallium-ion beam scanning electron microscopy with energy filtered detection of backscattered electrons to create near isometric voxels for high-resolution whole cell visualization. Specifically, this method allowed us to create three-dimensional volumes of high-pressure frozen, freeze-substituted Saccharomyces cerevisiae yeast cells with pixel resolutions down to 3 nm/pixel in x, y, and z, supported by both empirical data and Monte Carlo simulations. As a result, we were able to segment and quantify data sets of numerous targeted subcellular structures/organelles at high-resolution, including the volume, volume percentage, and surface area of the endoplasmic reticulum, cell wall, vacuoles, and mitochondria from an entire cell. Sites of mitochondrial and endoplasmic reticulum interconnectivity were readily identified in rendered data sets. The ability to visualize, segment, and quantify entire eukaryotic cells at high-resolution (potentially sub-5 nanometers isotropic voxels) will provide new perspectives and insights of the inner workings of cells.     

Automated,High Accuracy Classification of Parkinsonian Disorders: A Pattern Recognition Approach

Progressive supranuclear palsy (PSP), multiple system atrophy (MSA) and idiopathic Parkinson’s disease (IPD) can be clinically indistinguishable, especially in the early stages, despite distinct patterns of molecular pathology. Structural neuroimaging holds promise for providing objective biomarkers for discriminating these diseases at the single subject level but all studies to date have reported incomplete separation of disease groups. In this study, we employed multi-class pattern recognition to assess the value of anatomical patterns derived from a widely available structural neuroimaging sequence for automated classification of these disorders. To achieve this, 17 patients with PSP, 14 with IPD and 19 with MSA were scanned using structural MRI along with 19 healthy controls (HCs). An advanced probabilistic pattern recognition approach was employed to evaluate the diagnostic value of several pre-defined anatomical patterns for discriminating the disorders, including: (i) a subcortical motor network; (ii) each of its component regions and (iii) the whole brain. All disease groups could be discriminated simultaneously with high accuracy using the subcortical motor network. The region providing the most accurate predictions overall was the midbrain/brainstem, which discriminated all disease groups from one another and from HCs. The subcortical network also produced more accurate predictions than the whole brain and all of its constituent regions. PSP was accurately predicted from the midbrain/brainstem, cerebellum and all basal ganglia compartments; MSA from the midbrain/brainstem and cerebellum and IPD from the midbrain/brainstem only. This study demonstrates that automated analysis of structural MRI can accurately predict diagnosis in individual patients with Parkinsonian disorders, and identifies distinct patterns of regional atrophy particularly useful for this process.     

Measuring Granger Causality between cortical regions from voxelwise fMRI BOLD signals with LASSO

Functional brain network studies using the Blood Oxygen-Level Dependent (BOLD) signal from functional Magnetic Resonance Imaging (fMRI) are becoming increasingly prevalent in research on the neural basis of human cognition. An important problem in functional brain network analysis is to understand directed functional interactions between brain regions during cognitive performance. This problem has important implications for understanding top-down influences from frontal and parietal control regions to visual occipital cortex in visuospatial attention, the goal motivating the present study. A common approach to measuring directed functional interactions between two brain regions is to first create nodal signals by averaging the BOLD signals of all the voxels in each region, and to then measure directed functional interactions between the nodal signals. Another approach, that avoids averaging, is to measure directed functional interactions between all pairwise combinations of voxels in the two regions. Here we employ an alternative approach that avoids the drawbacks of both averaging and pairwise voxel measures. In this approach, we first use the Least Absolute Shrinkage Selection Operator (LASSO) to pre-select voxels for analysis, then compute a Multivariate Vector AutoRegressive (MVAR) model from the time series of the selected voxels, and finally compute summary Granger Causality (GC) statistics from the model to represent directed interregional interactions. We demonstrate the effectiveness of this approach on both simulated and empirical fMRI data. We also show that averaging regional BOLD activity to create a nodal signal may lead to biased GC estimation of directed interregional interactions. The approach presented here makes it feasible to compute GC between brain regions without the need for averaging. Our results suggest that in the analysis of functional brain networks, careful consideration must be given to the way that network nodes and edges are defined because those definitions may have important implications for the validity of the analysis.     

Linear ensemble-coding in midbrain superior colliculus specifies the saccade kinematics

Recently, we proposed an ensemble-coding scheme of the midbrain superior colliculus (SC) in which, during a saccade, each spike emitted by each recruited SC neuron contributes a fixed minivector to the gaze-control motor output. The size and direction of this 'spike vector' depend exclusively on a cell's location within the SC motor map (Goossens and Van Opstal, in J Neurophysiol 95: 2326-2341, 2006). According to this simple scheme, the planned saccade trajectory results from instantaneous linear summation of all spike vectors across the motor map. In our simulations with this model, the brainstem saccade generator was simplified by a linear feedback system, rendering the total model (which has only three free parameters) essentially linear. Interestingly, when this scheme was applied to actually recorded spike trains from 139 saccade-related SC neurons, measured during thousands of eye movements to single visual targets, straight saccades resulted with the correct velocity profiles and nonlinear kinematic relations ('main sequence properties' and 'component stretching'). Hence, we concluded that the kinematic nonlinearity of saccades resides in the spatial-temporal distribution of SC activity, rather than in the brainstem burst generator. The latter is generally assumed in models of the saccadic system. Here we analyze how this behaviour might emerge from this simple scheme. In addition, we will show new experimental evidence in support of the proposed mechanism.     

The role of a midbrain network in competitive stimulus selection

A midbrain network interacts with the well-known frontoparietal forebrain network to select stimuli for gaze and spatial attention. The midbrain network, containing the superior colliculus (SC; optic tectum, OT, in non-mammalian vertebrates) and the isthmic nuclei, helps evaluate the relative priorities of competing stimuli and encodes them in a topographic map of space. Behavioral experiments in monkeys demonstrate an essential contribution of the SC to stimulus selection when the relative priorities of competing stimuli are similar. Neurophysiological results from the owl OT demonstrate a neural correlate of this essential contribution of the SC/OT. The multi-layered, spatiotopic organization of the midbrain network lends itself to the analysis and modeling of the mechanisms underlying stimulus selection for gaze and spatial attention.     

BOLD temporal dynamics of rat superior colliculus and lateral geniculate nucleus following short duration visual stimulation

Background

The superior colliculus (SC) and lateral geniculate nucleus (LGN) are important subcortical structures for vision. Much of our understanding of vision was obtained using invasive and small field of view (FOV) techniques. In this study, we use non-invasive, large FOV blood oxygenation level-dependent (BOLD) fMRI to measure the SC and LGN''s response temporal dynamics following short duration (1 s) visual stimulation.

Methodology/Principal Findings

Experiments are performed at 7 tesla on Sprague Dawley rats stimulated in one eye with flashing light. Gradient-echo and spin-echo sequences are used to provide complementary information. An anatomical image is acquired from one rat after injection of monocrystalline iron oxide nanoparticles (MION), a blood vessel contrast agent. BOLD responses are concentrated in the contralateral SC and LGN. The SC BOLD signal measured with gradient-echo rises to 50% of maximum amplitude (PEAK) 0.2±0.2 s before the LGN signal (p<0.05). The LGN signal returns to 50% of PEAK 1.4±1.2 s before the SC signal (p<0.05). These results indicate the SC signal rises faster than the LGN signal but settles slower. Spin-echo results support these findings. The post-MION image shows the SC and LGN lie beneath large blood vessels. This subcortical vasculature is similar to that in the cortex, which also lies beneath large vessels. The LGN lies closer to the large vessels than much of the SC.

Conclusions/Significance

The differences in response timing between SC and LGN are very similar to those between deep and shallow cortical layers following electrical stimulation, which are related to depth-dependent blood vessel dilation rates. This combined with the similarities in vasculature between subcortex and cortex suggest the SC and LGN timing differences are also related to depth-dependent dilation rates. This study shows for the first time that BOLD responses in the rat SC and LGN following short duration visual stimulation are temporally different.     

Correlation of topographic surface and volume data from three-dimensional electron microscopy

Three-dimensional(3D) reconstructions from tilt series in an electron microscope show in general an anisotropic resolution due to an instrumentally limited tilt angle. As a consequence, the information in the z direction is blurred, thus making it difficult to detect the boundary of the reconstructed structures. In contrast, high-resolution topography data from microscopic surface techniques provide exactly complementary information. The combination of topographic surface and volume data leads to a better understanding of the 3D structure. The new correlation procedure presented determines both the height scaling of the topographic surface and the relative position of surface and volume data, thus allowing information to be combined. Experimental data for crystalline T4 bacteriophage polyheads were used to test the new method. Three-dimensional volume data were reconstructed from a negatively stained tilt series. Topographic data for both surfaces were obtained by surface relief reconstruction of electron micrographs of freeze-dried and unidirectionally metal-shadowed polyheads. The combined visualization of volume data with the scaled and aligned surface data shows that the correlation technique yields meaningful results. The reported correlation method may be applied to surface data obtained by any microscopic technique yielding topographic data.     

Eph and ephrin signaling in the formation of topographic maps

The axonal connections between the retina and its midbrain target, the superior colliculus (SC), is mapped topographically, such that the spatial relationships of cell bodies in the retina are maintained when terminating in the SC. Topographic map development uses a Cartesian mapping system such that each axis of the retina is mapped independently. Along the nasal-temporal mapping axis, EphAs and ephrin-As, are graded molecular cues required for topographic mapping while the dorsal-ventral axis is mapped in part via EphB and ephrin-Bs. Because both Ephs and ephrins are cell surface molecules they can signal in the forward and reverse directions. Eph/ephrin signaling leads to changes in cytoskeletal dynamics that lead to actin depolymerization and endocytosis guiding axons via attraction and repulsion.     

Layer-specific fMRI reflects different neuronal computations at different depths in human V1

Recent work has established that cerebral blood flow is regulated at a spatial scale that can be resolved by high field fMRI to show cortical columns in humans. While cortical columns represent a cluster of neurons with similar response properties (spanning from the pial surface to the white matter), important information regarding neuronal interactions and computational processes is also contained within a single column, distributed across the six cortical lamina. A basic understanding of underlying neuronal circuitry or computations may be revealed through investigations of the distribution of neural responses at different cortical depths. In this study, we used T(2)-weighted imaging with 0.7 mm (isotropic) resolution to measure fMRI responses at different depths in the gray matter while human subjects observed images with either recognizable or scrambled (physically impossible) objects. Intact and scrambled images were partially occluded, resulting in clusters of activity distributed across primary visual cortex. A subset of the identified clusters of voxels showed a preference for scrambled objects over intact; in these clusters, the fMRI response in middle layers was stronger during the presentation of scrambled objects than during the presentation of intact objects. A second experiment, using stimuli targeted at either the magnocellular or the parvocellular visual pathway, shows that laminar profiles in response to parvocellular-targeted stimuli peak in more superficial layers. These findings provide new evidence for the differential sensitivity of high-field fMRI to modulations of the neural responses at different cortical depths.     

Surface morphology of kidney, ureters and urinary bladder models based on data from the visible human male

The aim of the present work was to create a simplified high-resolution three-dimensional model of kidneys, ureters and urinary bladder in a data form suitable for finite element/volume based numerical simulations. The exterior morphology of the organs was based on images from the Visible Human Male data set. In both the right and left kidney, there were defined their topographic relations to the neighbouring anatomical structures. This model of kidneys, ureters and urinary bladder will be incorporated into the model of The Visible Human Male abdomen and pelvis and it is ready to be used for numerical simulations in urinary system biomechanics.     

On the Origins of Signal Variance in FMRI of the Human Midbrain at High Field

Functional Magnetic Resonance Imaging (fMRI) in the midbrain at 7 Tesla suffers from unexpectedly low temporal signal to noise ratio (TSNR) compared to other brain regions. Various methodologies were used in this study to quantitatively identify causes of the noise and signal differences in midbrain fMRI data. The influence of physiological noise sources was examined using RETROICOR, phase regression analysis, and power spectral analyses of contributions in the respiratory and cardiac frequency ranges. The impact of between-shot phase shifts in 3-D multi-shot sequences was tested using a one-dimensional (1-D) phase navigator approach. Additionally, the effects of shared noise influences between regions that were temporally, but not functionally, correlated with the midbrain (adjacent white matter and anterior cerebellum) were investigated via analyses with regressors of ‘no interest’. These attempts to reduce noise did not improve the overall TSNR in the midbrain. In addition, the steady state signal and noise were measured in the midbrain and the visual cortex for resting state data. We observed comparable steady state signals from both the midbrain and the cortex. However, the noise was 2–3 times higher in the midbrain relative to the cortex, confirming that the low TSNR in the midbrain was not due to low signal but rather a result of large signal variance. These temporal variations did not behave as known physiological or other noise sources, and were not mitigated by conventional strategies. Upon further investigation, resting state functional connectivity analysis in the midbrain showed strong intrinsic fluctuations between homologous midbrain regions. These data suggest that the low TSNR in the midbrain may originate from larger signal fluctuations arising from functional connectivity compared to cortex, rather than simply reflecting physiological noise.     

Hemodynamic traveling waves in human visual cortex

Functional MRI (fMRI) experiments rely on precise characterization of the blood oxygen level dependent (BOLD) signal. As the spatial resolution of fMRI reaches the sub-millimeter range, the need for quantitative modelling of spatiotemporal properties of this hemodynamic signal has become pressing. Here, we find that a detailed physiologically-based model of spatiotemporal BOLD responses predicts traveling waves with velocities and spatial ranges in empirically observable ranges. Two measurable parameters, related to physiology, characterize these waves: wave velocity and damping rate. To test these predictions, high-resolution fMRI data are acquired from subjects viewing discrete visual stimuli. Predictions and experiment show strong agreement, in particular confirming BOLD waves propagating for at least 5-10 mm across the cortical surface at speeds of 2-12 mm s-1. These observations enable fundamentally new approaches to fMRI analysis, crucial for fMRI data acquired at high spatial resolution.     

Assessment of single-vessel cerebral blood velocity by phase contrast fMRI

Current approaches to high-field functional MRI (fMRI) provide 2 means to map hemodynamics at the level of single vessels in the brain. One is through changes in deoxyhemoglobin in venules, i.e., blood oxygenation level–dependent (BOLD) fMRI, while the second is through changes in arteriole diameter, i.e., cerebral blood volume (CBV) fMRI. Here, we introduce cerebral blood flow–related velocity-based fMRI, denoted CBFv-fMRI, which uses high-resolution phase contrast (PC) MRI to form velocity measurements of flow. We use CBFv-fMRI in measure changes in blood velocity in single penetrating microvessels across rat parietal cortex. In contrast to the venule-dominated BOLD and arteriole-dominated CBV fMRI signals, CBFv-fMRI is comparable from both arterioles and venules. A single fMRI platform is used to map changes in blood pO₂ (BOLD), volume (CBV), and velocity (CBFv). This combined high-resolution single-vessel fMRI mapping scheme enables vessel-specific hemodynamic mapping in animal models of normal and diseased states and further has translational potential to map vascular dementia in diseased or injured human brains with ultra–high-field fMRI.

This study presents a phase contrast-based, high field MRI-based approach for the functional mapping of cerebral blood velocity in individual cortical arterioles and venules in the rat cortex; this approach can be combined with previously established approaches to map BOLD, CBV, and blood velocity from penetrating microvessels.     

Use of Anisotropy, 3D Segmented Atlas,and Computational Analysis to Identify Gray Matter Subcortical Lesions Common to Concussive Injury from Different Sites on the Cortex

Traumatic brain injury (TBI) can occur anywhere along the cortical mantel. While the cortical contusions may be random and disparate in their locations, the clinical outcomes are often similar and difficult to explain. Thus a question that arises is, do concussions at different sites on the cortex affect similar subcortical brain regions? To address this question we used a fluid percussion model to concuss the right caudal or rostral cortices in rats. Five days later, diffusion tensor MRI data were acquired for indices of anisotropy (IA) for use in a novel method of analysis to detect changes in gray matter microarchitecture. IA values from over 20,000 voxels were registered into a 3D segmented, annotated rat atlas covering 150 brain areas. Comparisons between left and right hemispheres revealed a small population of subcortical sites with altered IA values. Rostral and caudal concussions were of striking similarity in the impacted subcortical locations, particularly the central nucleus of the amygdala, laterodorsal thalamus, and hippocampal complex. Subsequent immunohistochemical analysis of these sites showed significant neuroinflammation. This study presents three significant findings that advance our understanding and evaluation of TBI: 1) the introduction of a new method to identify highly localized disturbances in discrete gray matter, subcortical brain nuclei without postmortem histology, 2) the use of this method to demonstrate that separate injuries to the rostral and caudal cortex produce the same subcortical, disturbances, and 3) the central nucleus of the amygdala, critical in the regulation of emotion, is vulnerable to concussion.     

A model of the saccade-generating system that accounts for trajectory variations produced by competing visual stimuli

Variable saccade trajectories are produced in visual search paradigms in which multiple potential target stimuli are present. These variable trajectories provide a rich source of information that may lead to a deeper understanding of the basic control mechanisms of the saccadic system. We have used published behavioral observations and neural recordings in the superior colliculus (SC), gathered in monkeys performing visual search paradigms, to guide the construction of a new distributed model of the saccadic system. The new model can account for many of the variations in saccade trajectory produced by the appearance of multiple visual stimuli in a search paradigm. The model uses distributed feedback about current eye motion from the brainstem to the SC to reduce activity there at physiologically realistic rates during saccades. The long-range lateral inhibitory connections between SC cells used in previous models have been eliminated to match recent physiological evidence. The model features interactions between visually activated multiple populations of cells in the SC and distributed and topologically organized inhibitory input to the SC from the SNr to produce some of the types of variable saccadic trajectories, including slightly curved and averaging saccades, observed in visual search tasks. The distributed perisaccadic disinhibition of SC from the substantia nigra (SNr) is assumed to have broad spatial tuning. In order to produce the strongly curved saccades occasionally recorded in visual search, the existence of a parallel input to the saccadic burst generators in addition to that provided by the distributed input from the SC is required. The spatiotemporal form of this additional parallel input is computed based on the assumption that the input from the model SC is realistic. In accordance with other recent models, it is assumed that the parallel input comes from the cerebellum, but our model predicts that the parallel input is delayed during highly curved saccadic trajectories.     

Topographic Factor Analysis: A Bayesian Model for Inferring Brain Networks from Neural Data

The neural patterns recorded during a neuroscientific experiment reflect complex interactions between many brain regions, each comprising millions of neurons. However, the measurements themselves are typically abstracted from that underlying structure. For example, functional magnetic resonance imaging (fMRI) datasets comprise a time series of three-dimensional images, where each voxel in an image (roughly) reflects the activity of the brain structure(s)–located at the corresponding point in space–at the time the image was collected. FMRI data often exhibit strong spatial correlations, whereby nearby voxels behave similarly over time as the underlying brain structure modulates its activity. Here we develop topographic factor analysis (TFA), a technique that exploits spatial correlations in fMRI data to recover the underlying structure that the images reflect. Specifically, TFA casts each brain image as a weighted sum of spatial functions. The parameters of those spatial functions, which may be learned by applying TFA to an fMRI dataset, reveal the locations and sizes of the brain structures activated while the data were collected, as well as the interactions between those structures.     

Negative BOLD fMRI response in the visual cortex carries precise stimulus-specific information

Sustained positive BOLD (blood oxygen level-dependent) activity is employed extensively in functional magnetic resonance imaging (fMRI) studies as evidence for task or stimulus-specific neural responses. However, the presence of sustained negative BOLD activity (i.e., sustained responses that are lower than the fixation baseline) has remained more difficult to interpret. Some studies suggest that it results from local "blood stealing" wherein blood is diverted to neurally active regions without a concomitant change of neural activity in the negative BOLD regions. However, other evidence suggests that negative BOLD is a result of local neural suppression. In both cases, regions of negative BOLD response are usually interpreted as carrying relatively little, if any, stimulus-specific information (hence the predominant reliance on positive BOLD activity in fMRI). Here we show that the negative BOLD response resulting from visual stimulation can carry high information content that is stimulus-specific. Using a general linear model (GLM), we contrasted standard flickering stimuli to a fixation baseline and found regions of the visual cortex that displayed a sustained negative BOLD response, consistent with several previous studies. Within these negative BOLD regions, we compared patterns of fMRI activity generated by flickering Gabors that were systematically shifted in position. As the Gabors were shifted further from each other, the correlation in the spatial pattern of activity across a population of voxels (such as the population of V1 voxels that displayed a negative BOLD response) decreased significantly. Despite the fact that the BOLD signal was significantly negative (lower than fixation baseline), these regions were able to discriminate objects separated by less than 0.5 deg (at approximately 10 deg eccentricity). The results suggest that meaningful, stimulus-specific processing occurs even in regions that display a strong negative BOLD response.     

High-Field fMRI Reveals Brain Activation Patterns Underlying Saccade Execution in the Human Superior Colliculus

Background

The superior colliculus (SC) has been shown to play a crucial role in the initiation and coordination of eye- and head-movements. The knowledge about the function of this structure is mainly based on single-unit recordings in animals with relatively few neuroimaging studies investigating eye-movement related brain activity in humans.

Methodology/Principal Findings

The present study employed high-field (7 Tesla) functional magnetic resonance imaging (fMRI) to investigate SC responses during endogenously cued saccades in humans. In response to centrally presented instructional cues, subjects either performed saccades away from (centrifugal) or towards (centripetal) the center of straight gaze or maintained fixation at the center position. Compared to central fixation, the execution of saccades elicited hemodynamic activity within a network of cortical and subcortical areas that included the SC, lateral geniculate nucleus (LGN), occipital cortex, striatum, and the pulvinar.

Conclusions/Significance

Activity in the SC was enhanced contralateral to the direction of the saccade (i.e., greater activity in the right as compared to left SC during leftward saccades and vice versa) during both centrifugal and centripetal saccades, thereby demonstrating that the contralateral predominance for saccade execution that has been shown to exist in animals is also present in the human SC. In addition, centrifugal saccades elicited greater activity in the SC than did centripetal saccades, while also being accompanied by an enhanced deactivation within the prefrontal default-mode network. This pattern of brain activity might reflect the reduced processing effort required to move the eyes toward as compared to away from the center of straight gaze, a position that might serve as a spatial baseline in which the retinotopic and craniotopic reference frames are aligned.     

Nonlinear visuoauditory integration in the mouse superior colliculus

Sensory information from different modalities is processed in parallel, and then integrated in associative brain areas to improve object identification and the interpretation of sensory experiences. The Superior Colliculus (SC) is a midbrain structure that plays a critical role in integrating visual, auditory, and somatosensory input to assess saliency and promote action. Although the response properties of the individual SC neurons to visuoauditory stimuli have been characterized, little is known about the spatial and temporal dynamics of the integration at the population level. Here we recorded the response properties of SC neurons to spatially restricted visual and auditory stimuli using large-scale electrophysiology. We then created a general, population-level model that explains the spatial, temporal, and intensity requirements of stimuli needed for sensory integration. We found that the mouse SC contains topographically organized visual and auditory neurons that exhibit nonlinear multisensory integration. We show that nonlinear integration depends on properties of auditory but not visual stimuli. We also find that a heuristically derived nonlinear modulation function reveals conditions required for sensory integration that are consistent with previously proposed models of sensory integration such as spatial matching and the principle of inverse effectiveness.     

Topographic representation of the human body in the occipitotemporal cortex

Large-scale topographic representations of the body have long been established in the somatosensory and motor cortices. Using functional imaging, we identified a topographically organized body part map within the occipitotemporal cortex (OTC), with distinct clusters of voxels showing clear preference for different visually presented body parts. This representation was consistent both across hemispheres and participants. Using converging methods, the preference for specific body parts was demonstrated to be robust and did not merely reflect shape differences between the categories. Finally, execution of (unseen) movements with different body parts resulted in a limited topographic representation of the limbs and trunk, which partially overlapped with the visual body part map. This motor-driven activation in the OTC could not be explained solely by visual or motor imagery of the body parts. This suggests that visual and motor-related information converge within the OTC in a body part specific manner.