Quantitative aspects of the microvascular system in macaque visual cortex

The basic principle of the most frequently used functional neuroimaging methods is the brain’s local dynamic regulation of blood flow. For a correct interpretation of neuroimaging results the structural and functional neurovascular coupling underlying this regulation must be well understood. Here we report quantitative anatomical data of the microvasculature in the macaque visual cortex. Formalin-fixed frozen sections of 4 animals (M. mulatta) were processed for double fluorescence immunohistochemistry. Sections were incubated with anti-collagen type IV and DAPI to stain for vessels and cell nuclei. In one additional animal, the anti-collagen procedure was combined with cytochrome oxidase staining in V1. The length density (LD), surface density (SD), volume fraction (VF) and diameter (D) of the vessels were stereologically determined. Furthermore, synchrotron-based computed tomographies (SRCT) of formalin-fixed and barium sulfate-perfused brain samples from another 2 animals were used to corroborate the histological results. In V1, the vascular density was highest in layer IVc- (LD 674.7 mm/mm3, SD 15.2 mm2/mm3, VF 2.6 , D 7.2 microns) and lowest in layer I (LD 461.5 mm/mm3, SD 10.9 mm2/mm3, VF 1.9 , D 7.5 microns). In all extrastriate visual areas analyzed (V2, V3, V4, V5), the vascular density was generally lower, and the difference between layer IV and the remaining layers was less prominent when compared to V1. These density values were similar compared to the ones tomographically obtained from SRCT. The vascular density in cytochrome oxidase rich blobs in V1 was 14 higher as compared to the interblob region. In summary, V1 is different from all extrastriate areas analyzed with respect to the laminar vessel distribution and overall vascular density. Differences between extrastriate areas were negligible. The overall vascular volume fraction in visual cortex derived from immunostaining was approximately 2 , a value that was well reproduced by the SRCT

Ready ... Go: Amplitude of the fMRI Signal Encodes Expectation of Cue Arrival Time

What happens when the brain awaits a signal of uncertain arrival time, as when a sprinter waits for the starting pistol? And what happens just after the starting pistol fires? Using functional magnetic resonance imaging (fMRI), we have discovered a novel correlate of temporal expectations in several brain regions, most prominently in the supplementary motor area (SMA). Contrary to expectations, we found little fMRI activity during the waiting period; however, a large signal appears after the “go” signal, the amplitude of which reflects learned expectations about the distribution of possible waiting times. Specifically, the amplitude of the fMRI signal appears to encode a cumulative conditional probability, also known as the cumulative hazard function. The fMRI signal loses its dependence on waiting time in a “countdown” condition in which the arrival time of the go cue is known in advance, suggesting that the signal encodes temporal probabilities rather than simply elapsed time. The dependence of the signal on temporal expectation is present in “no-go” conditions, demonstrating that the effect is not a consequence of motor output. Finally, the encoding is not dependent on modality, operating in the same manner with auditory or visual signals. This finding extends our understanding of the relationship between temporal expectancy and measurable neural signals

Local field potential phase and spike timing convey information about different visual features in primary visual cortex

The natural visual environment is characterized by both “what/where” aspects (image features such as contrast or orientation which are defined by the relationship between visual signals simultaneously presented at different points in space) and “when” aspects, describing the temporal variations of the image features. Both “when” and “what/where” information is necessary to describe and understand the natural visual environment, and to take appropriate behavioral decisions. While “where” can be considered embedded as retinotopy, it is likely that localized neural populations in the visual cortex keep a simultaneous representation of both “what” and “when” aspects of the visual stimuli. However, little is yet known about how the spike trains of neurons in primary visual cortex encode both sources of information. The traditional hypothesis in systems neuroscience is that sensory variables are represented by a rate code, i.e. all sensory information is encoded by the number of spikes emitted over relatively long time windows. Although the relevance of rate in encoding static features is well established, this code can be inherently ambiguous in changing environments [1] and it is unlikely that this code is rich enough to represent simultaneously different types of information. Therefore here we explore the hypothesis that the timing of spikes is a crucial variable in representing both “what” and “when” aspects of the natural visual environment. To address these issues, we recorded single unit activity and LFPs in primary visual cortex of opiate anaesthetized macaques during the binocular presentation of naturalistic color movies. By means of computational analysis, we extracted several image features (color, orientation, luminance, space and time contrast, motion) from the receptive fields of each single neuron. We then considered two different spike timing codes previously studied in both the auditory [2] and the visual cortex [3]. In the first code, which we call spike patterns code, sequences of spike times from single neurons are measured (with a resolution of the order of 10 ms) with respect to the time course of the external stimulus. In the second code, which we call phase of firing code, spikes are measured with respect to the phase of the concurrent low frequency LFPs recorded from the same electrode as the spikes. We then used these data to investigate systematically which types of neural codes carry information about the static features of the image and which neural codes carry information about the time course of these features. We found that both “when” and “what” aspects are encoded simultaneously by spike times of visual cortical neurons. However, “what” and “when” are encoded by two different neural information streams; “what” aspects are encoded (on a fine scale of few ms) by spike patterns, and “when” stimulus aspects are encoded by the phase of firing (on a coarse scale of hundreds of ms)

Loss of high-level perceptual knowledge of object structure in DAT

Peer reviewe

Age-related changes in matching novel objects across viewpoints

Peer reviewedPreprin

Functional Brain Imaging in the Clinical Assessment of Consciousness

Recent findings suggest that functional brain imaging might be used to identify consciousness in patients diagnosed with persistent vegetative state and minimally conscious state. Michael Rafii and James Brewer discuss the potential for fMRI's wider implementation in clinical practice, and associated caveats

Causal relationships between frequency bands of extracellular signals in visual cortex revealed by an information theoretic analysis

Characterizing how different cortical rhythms interact and how their interaction changes with sensory stimulation is important to gather insights into how these rhythms are generated and what sensory function they may play. Concepts from information theory, such as Transfer Entropy (TE), offer principled ways to quantify the amount of causation between different frequency bands of the signal recorded from extracellular electrodes; yet these techniques are hard to apply to real data. To address the above issues, in this study we develop a method to compute fast and reliably the amount of TE from experimental time series of extracellular potentials. The method consisted in adapting efficiently the calculation of TE to analog signals and in providing appropriate sampling bias corrections. We then used this method to quantify the strength and significance of causal interaction between frequency bands of field potentials and spikes recorded from primary visual cortex of anaesthetized macaques, both during spontaneous activity and during binocular presentation of naturalistic color movies. Causal interactions between different frequency bands were prominent when considering the signals at a fine (ms) temporal resolution, and happened with a very short (ms-scale) delay. The interactions were much less prominent and significant at coarser temporal resolutions. At high temporal resolution, we found strong bidirectional causal interactions between gamma-band (40–100 Hz) and slower field potentials when considering signals recorded within a distance of 2 mm. The interactions involving gamma bands signals were stronger during movie presentation than in absence of stimuli, suggesting a strong role of the gamma cycle in processing naturalistic stimuli. Moreover, the phase of gamma oscillations was playing a stronger role than their amplitude in increasing causations with slower field potentials and spikes during stimulation. The dominant direction of causality was mainly found in the direction from MUA or gamma frequency band signals to lower frequency signals, suggesting that hierarchical correlations between lower and higher frequency cortical rhythms are originated by the faster rhythms

Parametric study of EEG sensitivity to phase noise during face processing

<b>Background: </b> The present paper examines the visual processing speed of complex objects, here faces, by mapping the relationship between object physical properties and single-trial brain responses. Measuring visual processing speed is challenging because uncontrolled physical differences that co-vary with object categories might affect brain measurements, thus biasing our speed estimates. Recently, we demonstrated that early event-related potential (ERP) differences between faces and objects are preserved even when images differ only in phase information, and amplitude spectra are equated across image categories. Here, we use a parametric design to study how early ERP to faces are shaped by phase information. Subjects performed a two-alternative force choice discrimination between two faces (Experiment 1) or textures (two control experiments). All stimuli had the same amplitude spectrum and were presented at 11 phase noise levels, varying from 0% to 100% in 10% increments, using a linear phase interpolation technique. Single-trial ERP data from each subject were analysed using a multiple linear regression model. <b>Results: </b> Our results show that sensitivity to phase noise in faces emerges progressively in a short time window between the P1 and the N170 ERP visual components. The sensitivity to phase noise starts at about 120–130 ms after stimulus onset and continues for another 25–40 ms. This result was robust both within and across subjects. A control experiment using pink noise textures, which had the same second-order statistics as the faces used in Experiment 1, demonstrated that the sensitivity to phase noise observed for faces cannot be explained by the presence of global image structure alone. A second control experiment used wavelet textures that were matched to the face stimuli in terms of second- and higher-order image statistics. Results from this experiment suggest that higher-order statistics of faces are necessary but not sufficient to obtain the sensitivity to phase noise function observed in response to faces. <b>Conclusion: </b> Our results constitute the first quantitative assessment of the time course of phase information processing by the human visual brain. We interpret our results in a framework that focuses on image statistics and single-trial analyses

Neuronal Shot Noise and Brownian 1/f21/f^2 Behavior in the Local Field Potential

We demonstrate that human electrophysiological recordings of the local field potential (LFP) from intracranial electrodes, acquired from a variety of cerebral regions, show a ubiquitous $1/f^2$ scaling within the power spectrum. We develop a quantitative model that treats the generation of these fields in an analogous way to that of electronic shot noise, and use this model to specifically address the cause of this $1/f^2$ Brownian noise. The model gives way to two analytically tractable solutions, both displaying Brownian noise: 1) uncorrelated cells that display sharp initial activity, whose extracellular fields slowly decay and 2) rapidly firing, temporally correlated cells that generate UP-DOWN states

use of bone health agents bhas in patients pts with metastatic castration resistant prostate cancer mcrpc treated with radium 223 ra 223 after abiraterone abi an interim review of reassure

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