1,655 research outputs found
Motion clouds: model-based stimulus synthesis of natural-like random textures for the study of motion perception
Choosing an appropriate set of stimuli is essential to characterize the
response of a sensory system to a particular functional dimension, such as the
eye movement following the motion of a visual scene. Here, we describe a
framework to generate random texture movies with controlled information
content, i.e., Motion Clouds. These stimuli are defined using a generative
model that is based on controlled experimental parametrization. We show that
Motion Clouds correspond to dense mixing of localized moving gratings with
random positions. Their global envelope is similar to natural-like stimulation
with an approximate full-field translation corresponding to a retinal slip. We
describe the construction of these stimuli mathematically and propose an
open-source Python-based implementation. Examples of the use of this framework
are shown. We also propose extensions to other modalities such as color vision,
touch, and audition
modCHIMERA: A novel murine closed-head model of moderate traumatic brain injury
AbstractTraumatic brain injury is a major source of global disability and mortality. Preclinical TBI models are a crucial component of therapeutic investigation. We report a tunable, monitored model of murine non-surgical, diffuse closed-head injuryâmodCHIMERAâcharacterized by impact as well as linear and rotational acceleration. modCHIMERA is based on the Closed-Head Impact Model of Engineered Rotational Acceleration (CHIMERA) platform. We tested this model at 2 energy levels: 1.7 and 2.1 Joulesâsubstantially higher than previously reported for this system. Kinematic analysis demonstrated linear acceleration exceeding injury thresholds in humans, although outcome metrics tracked impact energy more closely than kinematic parameters. Acute severity metrics were consistent with a complicated-mild or moderate TBI, a clinical population characterized by high morbidity but potentially reversible pathology. Axonal injury was multifocal and bilateral, neuronal death was detected in the hippocampus, and microglial neuroinflammation was prominent. Acute functional analysis revealed prolonged post-injury unconsciousness, and decreased spontaneous behavior and stimulated neurological scores. Neurobehavioral deficits were demonstrated in spatial learning/memory and socialization at 1-month. The overall injury profile of modCHIMERA corresponds with the range responsible for a substantial portion of TBI-related disability in humans. modCHIMERA should provide a reliable platform for efficient analysis of TBI pathophysiology and testing of treatment modalities.</jats:p
Building connectomes using diffusion MRI: why, how and but
Why has diffusion MRI become a principal modality for mapping connectomes in vivo? How do different image acquisition parameters, fiber tracking algorithms and other methodological choices affect connectome estimation? What are the main factors that dictate the success and failure of connectome reconstruction? These are some of the key questions that we aim to address in this review. We provide an overview of the key methods that can be used to estimate the nodes and edges of macroscale connectomes, and we discuss open problems and inherent limitations. We argue that diffusion MRI-based connectome mapping methods are still in their infancy and caution against blind application of deep white matter tractography due to the challenges inherent to connectome reconstruction. We review a number of studies that provide evidence of useful microstructural and network properties that can be extracted in various independent and biologically-relevant contexts. Finally, we highlight some of the key deficiencies of current macroscale connectome mapping methodologies and motivate future developments
Impact of corpus callosum integrity on functional interhemispheric connectivity and cognition in healthy subjects
To examine the corpus callosum's (CC) integrity in terms of fractional anisotropy (FA) and how it affects resting-state hemispheric connectivity (rs-IHC) and cognitive function in healthy individuals. Sixty-eight healthy individuals were recruited for the study. The global FA (gFA) and FA values of each CC tract (forceps minor, body, tapetum, and forceps major) were evaluated using diffusion-weighted imaging (DWI) sequences. The homotopic functional connectivity technique was used to quantify the effects of FA in the CC tracts on bilateral functional connectivity, including the confounding effect of gFA. Brain regions with higher or lower rs-IHC were identified using the threshold-free cluster enhancement family-wise error-corrected p-value of 0.05. The null hypothesis was rejected if the p-value was <= 0.05 for the nonparametric partial correlation technique. Several clusters of increased rs-IHC were identified in relation to the FA of individual CC tracts, each with a unique topographic distribution and extension. Only forceps minor FA values correlated with cognitive scores. The integrity of CC influences rs-IHC differently in healthy subjects. Specifically, forceps minor anisotropy impacts rs-IHC and cognition more than other CC tracts do
Event-Driven Imaging in Turbid Media: A Confluence of Optoelectronics and Neuromorphic Computation
In this paper a new optical-computational method is introduced to unveil
images of targets whose visibility is severely obscured by light scattering in
dense, turbid media. The targets of interest are taken to be dynamic in that
their optical properties are time-varying whether stationary in space or
moving. The scheme, to our knowledge the first of its kind, is human vision
inspired whereby diffuse photons collected from the turbid medium are first
transformed to spike trains by a dynamic vision sensor as in the retina, and
image reconstruction is then performed by a neuromorphic computing approach
mimicking the brain. We combine benchtop experimental data in both reflection
(backscattering) and transmission geometries with support from physics-based
simulations to develop a neuromorphic computational model and then apply this
for image reconstruction of different MNIST characters and image sets by a
dedicated deep spiking neural network algorithm. Image reconstruction is
achieved under conditions of turbidity where an original image is
unintelligible to the human eye or a digital video camera, yet clearly and
quantifiable identifiable when using the new neuromorphic computational
approach
Inferring Latent States and Refining Force Estimates via Hierarchical Dirichlet Process Modeling in Single Particle Tracking Experiments
Optical microscopy provides rich spatio-temporal information characterizing
in vivo molecular motion. However, effective forces and other parameters used
to summarize molecular motion change over time in live cells due to latent
state changes, e.g., changes induced by dynamic micro-environments,
photobleaching, and other heterogeneity inherent in biological processes. This
study focuses on techniques for analyzing Single Particle Tracking (SPT) data
experiencing abrupt state changes. We demonstrate the approach on GFP tagged
chromatids experiencing metaphase in yeast cells and probe the effective forces
resulting from dynamic interactions that reflect the sum of a number of
physical phenomena. State changes are induced by factors such as microtubule
dynamics exerting force through the centromere, thermal polymer fluctuations,
etc. Simulations are used to demonstrate the relevance of the approach in more
general SPT data analyses. Refined force estimates are obtained by adopting and
modifying a nonparametric Bayesian modeling technique, the Hierarchical
Dirichlet Process Switching Linear Dynamical System (HDP-SLDS), for SPT
applications. The HDP-SLDS method shows promise in systematically identifying
dynamical regime changes induced by unobserved state changes when the number of
underlying states is unknown in advance (a common problem in SPT applications).
We expand on the relevance of the HDP-SLDS approach, review the relevant
background of Hierarchical Dirichlet Processes, show how to map discrete time
HDP-SLDS models to classic SPT models, and discuss limitations of the approach.
In addition, we demonstrate new computational techniques for tuning
hyperparameters and for checking the statistical consistency of model
assumptions directly against individual experimental trajectories; the
techniques circumvent the need for "ground-truth" and subjective information.Comment: 25 pages, 6 figures. Differs only typographically from PLoS One
publication available freely as an open-access article at
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.013763
Cerebral white matter status and resting state functional MRI.
White Matter (WM) is a pivotal component of the Central Nervous System (CNS), and its main role is the transmission of the neural impulses within the CNS and between CNS and Peripheral Nervous System (PNS). It is note from literature that changes in the WM affects the function of the CNS with effects on the higher neurological function, included cognition. Further, it has been theorized in the last decades that ageing-associated decline in higher neurological functions, in particular in the neurocognitive sphere, could be at least partly explained by the âdisconnectionâ of the cortical areas of the brain due to the WM degeneration.
Although standard âin-vivoâ imaging biomarkers of WM integrity have not been validated yet for clinical purposes, several researches have demonstrated the correlation between different potential imaging biomarkers and WM integrity.
The aim of the PhD project is to explore and better understanding the effects of WM status on the brain structure, networking and cognition. In particular, we designed three distinct explorative and cross-sectional studies; more specifically, we analyzed the effects of two Magnetic Resonance Imaging (MRI) markers of WM degeneration (the global Fractional Anisotropy (gFA) and the white matter hyperintensities burden (WMHb), respectively) on the brain activity measured with the Resting-State Functional Magnetic Resonance Imaging (rs-fMRI) technique. The project was conducted by analyzing a human population of healthy subjects extracted from the public available dataset âLeipzig Study for Mind-Body-Emotion Interactionsâ (LEMON). The results of these studies have been published during the PhD course on three distinct international scientific papers
Microstructural imaging of the human brain with a 'super-scanner': 10 key advantages of ultra-strong gradients for diffusion MRI
The key component of a microstructural diffusion MRI 'super-scanner' is a dedicated high-strength gradient system that enables stronger diffusion weightings per unit time compared to conventional gradient designs. This can, in turn, drastically shorten the time needed for diffusion encoding, increase the signal-to-noise ratio, and facilitate measurements at shorter diffusion times. This review, written from the perspective of the UK National Facility for In Vivo MR Imaging of Human Tissue Microstructure, an initiative to establish a shared 300 mT/m-gradient facility amongst the microstructural imaging community, describes ten advantages of ultra-strong gradients for microstructural imaging. Specifically, we will discuss how the increase of the accessible measurement space compared to a lower-gradient systems (in terms of Î, b-value, and TE) can accelerate developments in the areas of 1) axon diameter distribution mapping; 2) microstructural parameter estimation; 3) mapping micro-vs macroscopic anisotropy features with gradient waveforms beyond a single pair of pulsed-gradients; 4) multi-contrast experiments, e.g. diffusion-relaxometry; 5) tractography and high-resolution imaging in vivo and 6) post mortem; 7) diffusion-weighted spectroscopy of metabolites other than water; 8) tumour characterisation; 9) functional diffusion MRI; and 10) quality enhancement of images acquired on lower-gradient systems. We finally discuss practical barriers in the use of ultra-strong gradients, and provide an outlook on the next generation of 'super-scanners'
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