718 research outputs found
Atlas Toolkit: Fast registration of 3D morphological datasets in the absence of landmarks
Image registration is a gateway technology for Developmental Systems Biology, enabling computational analysis of related datasets within a shared coordinate system. Many registration tools rely on landmarks to ensure that datasets are correctly aligned; yet suitable landmarks are not present in many datasets. Atlas Toolkit is a Fiji/ImageJ plugin collection offering elastic group-wise registration of 3D morphological datasets, guided by segmentation of the interesting morphology. We demonstrate the method by combinatorial mapping of cell signalling events in the developing eyes of chick embryos, and use the integrated datasets to predictively enumerate Gene Regulatory Network states
Neurite imaging reveals microstructural variations in human cerebral cortical gray matter
We present distinct patterns of neurite distribution in the human cerebral cortex using diffusion magnetic resonance imaging (MRI). We analyzed both high-resolution structural (T1w and T2w images) and diffusion MRI data in 505 subjects from the Human Connectome Project. Neurite distributions were evaluated using the neurite orientation dispersion and density imaging (NODDI) model, optimized for gray matter, and mapped onto the cortical surface using a method weighted towards the cortical mid-thickness to reduce partial volume effects. The estimated neurite density was high in both somatosensory and motor areas, early visual and auditory areas, and middle temporal area (MT), showing a strikingly similar distribution to myelin maps estimated from the T1w/T2w ratio. The estimated neurite orientation dispersion was particularly high in early sensory areas, which are known for dense tangential fibers and are classified as granular cortex by classical anatomists. Spatial gradients of these cortical neurite properties revealed transitions that colocalize with some areal boundaries in a recent multi-modal parcellation of the human cerebral cortex, providing mutually supportive evidence. Our findings indicate that analyzing the cortical gray matter neurite morphology using diffusion MRI and NODDI provides valuable information regarding cortical microstructure that is related to but complementary to myeloarchitecture
EEG-Based Quantification of Cortical Current Density and Dynamic Causal Connectivity Generalized across Subjects Performing BCI-Monitored Cognitive Tasks.
Quantification of dynamic causal interactions among brain regions constitutes an important component of conducting research and developing applications in experimental and translational neuroscience. Furthermore, cortical networks with dynamic causal connectivity in brain-computer interface (BCI) applications offer a more comprehensive view of brain states implicated in behavior than do individual brain regions. However, models of cortical network dynamics are difficult to generalize across subjects because current electroencephalography (EEG) signal analysis techniques are limited in their ability to reliably localize sources across subjects. We propose an algorithmic and computational framework for identifying cortical networks across subjects in which dynamic causal connectivity is modeled among user-selected cortical regions of interest (ROIs). We demonstrate the strength of the proposed framework using a "reach/saccade to spatial target" cognitive task performed by 10 right-handed individuals. Modeling of causal cortical interactions was accomplished through measurement of cortical activity using (EEG), application of independent component clustering to identify cortical ROIs as network nodes, estimation of cortical current density using cortically constrained low resolution electromagnetic brain tomography (cLORETA), multivariate autoregressive (MVAR) modeling of representative cortical activity signals from each ROI, and quantification of the dynamic causal interaction among the identified ROIs using the Short-time direct Directed Transfer function (SdDTF). The resulting cortical network and the computed causal dynamics among its nodes exhibited physiologically plausible behavior, consistent with past results reported in the literature. This physiological plausibility of the results strengthens the framework's applicability in reliably capturing complex brain functionality, which is required by applications, such as diagnostics and BCI
Template-free Articulated Neural Point Clouds for Reposable View Synthesis
Dynamic Neural Radiance Fields (NeRFs) achieve remarkable visual quality when
synthesizing novel views of time-evolving 3D scenes. However, the common
reliance on backward deformation fields makes reanimation of the captured
object poses challenging. Moreover, the state of the art dynamic models are
often limited by low visual fidelity, long reconstruction time or specificity
to narrow application domains. In this paper, we present a novel method
utilizing a point-based representation and Linear Blend Skinning (LBS) to
jointly learn a Dynamic NeRF and an associated skeletal model from even sparse
multi-view video. Our forward-warping approach achieves state-of-the-art visual
fidelity when synthesizing novel views and poses while significantly reducing
the necessary learning time when compared to existing work. We demonstrate the
versatility of our representation on a variety of articulated objects from
common datasets and obtain reposable 3D reconstructions without the need of
object-specific skeletal templates. Code will be made available at
https://github.com/lukasuz/Articulated-Point-NeRF
Defining thalamic nuclei and topographic connectivity gradients in vivo.
The thalamus consists of multiple nuclei that have been previously defined by their chemoarchitectual and cytoarchitectual properties ex vivo. These form discrete, functionally specialized, territories with topographically arranged graduated patterns of connectivity. However, previous in vivo thalamic parcellation with MRI has been hindered by substantial inter-individual variability or discrepancies between MRI derived segmentations and histological sections. Here, we use the Euclidean distance to characterize probabilistic tractography distributions derived from diffusion MRI. We generate 12 feature maps by performing voxel-wise parameterization of the distance histograms (6 feature maps) and the distribution of three-dimensional distance transition gradients generated by applying a Sobel kernel to the distance metrics. We use these 12 feature maps to delineate individual thalamic nuclei, then extract the tractography profiles for each and calculate the voxel-wise tractography gradients. Within each thalamic nucleus, the tractography gradients were topographically arranged as distinct non-overlapping cortical networks with transitory overlapping mid-zones. This work significantly advances quantitative segmentation of the thalamus in vivo using 3T MRI. At an individual subject level, the thalamic segmentations consistently achieve a close relationship with a priori histological atlas information, and resolve in vivo topographic gradients within each thalamic nucleus for the first time. Additionally, these techniques allow individual thalamic nuclei to be closely aligned across large populations and generate measures of inter-individual variability that can be used to study both basic function and pathological processes in vivo
Temporal dynamics of MEG phase information during speech perception: Segmentation and neural communication using mutual information and phase locking
The incoming speech stream contains a rich amount of temporal
information. In particular, information on slow time scales, the delta
and theta band (125 - 1000 ms, 1 - 8 Hz), corresponds to prosodic
and syllabic information while information on faster time scales (20-40 ms, 25 - 50 Hz) corresponds to feature/phonemic information. In order for speech perception to occur, this signal must be segregated into meaningful units of analysis and then processed in a distributed network of brain regions. Recent evidence suggests that low frequency phase information in the delta and theta bands of the Magnetoencephalography (MEG) signal plays an important role for tracking and segmenting the incoming signal into units of analysis. This thesis utilized a novel method of analysis, Mutual Information (MI) to characterize the relative information contributions of these low frequency phases. Reliable information pertaining to the stimulus was present in both delta and theta bands (3 - 5 Hz, 5 - 7 Hz) and information within each of these three sub-bands was independent of each other. A second experiment demonstrated that the information present in these bands differed significantly for speech and a non-speech control condition, suggesting that contrary to previous results, a purely acoustic hypothesis of this segmentation is not supported. A third experiment found that both low (delta and theta) and high (gamma) frequency information is utilized to facilitate communication between brain areas thought to underlie speech perception. Distinct auditory/speech networks that operated exclusively using these frequencies were revealed, suggesting a privileged role for these timescales for neural communication between brain regions. Taken together these results suggest that timescales that correspond linguistically to important aspects of the speech stream also facilitate segmentation of the incoming signal and communication between brain areas that perform neural computation
Can we identify non-stationary dynamics of trial-to-trial variability?"
Identifying sources of the apparent variability in non-stationary scenarios is a fundamental problem in many biological data analysis settings. For instance, neurophysiological responses to the same task often vary from each repetition of the same experiment (trial) to the next. The origin and functional role of this observed variability is one of the fundamental questions in neuroscience. The nature of such trial-to-trial dynamics however remains largely elusive to current data analysis approaches. A range of strategies have been proposed in modalities such as electro-encephalography but gaining a fundamental insight into latent sources of trial-to-trial variability in neural recordings is still a major challenge. In this paper, we present a proof-of-concept study to the analysis of trial-to-trial variability dynamics founded on non-autonomous dynamical systems. At this initial stage, we evaluate the capacity of a simple statistic based on the behaviour of trajectories in classification settings, the trajectory coherence, in order to identify trial-to-trial dynamics. First, we derive the conditions leading to observable changes in datasets generated by a compact dynamical system (the Duffing equation). This canonical system plays the role of a ubiquitous model of non-stationary supervised classification problems. Second, we estimate the coherence of class-trajectories in empirically reconstructed space of system states. We show how this analysis can discern variations attributable to non-autonomous deterministic processes from stochastic fluctuations. The analyses are benchmarked using simulated and two different real datasets which have been shown to exhibit attractor dynamics. As an illustrative example, we focused on the analysis of the rat's frontal cortex ensemble dynamics during a decision-making task. Results suggest that, in line with recent hypotheses, rather than internal noise, it is the deterministic trend which most likely underlies the observed trial-to-trial variability. Thus, the empirical tool developed within this study potentially allows us to infer the source of variability in in-vivo neural recordings
Laterally constrained low-rank seismic data completion via cyclic-shear transform
A crucial step in seismic data processing consists in reconstructing the
wavefields at spatial locations where faulty or absent sources and/or receivers
result in missing data. Several developments in seismic acquisition and
interpolation strive to restore signals fragmented by sampling limitations;
still, seismic data frequently remain poorly sampled in the source, receiver,
or both coordinates. An intrinsic limitation of real-life dense acquisition
systems, which are often exceedingly expensive, is that they remain unable to
circumvent various physical and environmental obstacles, ultimately hindering a
proper recording scheme. In many situations, when the preferred reconstruction
method fails to render the actual continuous signals, subsequent imaging
studies are negatively affected by sampling artefacts. A recent alternative
builds on low-rank completion techniques to deliver superior restoration
results on seismic data, paving the way for data kernel compression that can
potentially unlock multiple modern processing methods so far prohibited in 3D
field scenarios. In this work, we propose a novel transform domain revealing
the low-rank character of seismic data that prevents the inherent matrix
enlargement introduced when the data are sorted in the midpoint-offset domain
and develop a robust extension of the current matrix completion framework to
account for lateral physical constraints that ensure a degree of proximity
similarity among neighbouring points. Our strategy successfully interpolates
missing sources and receivers simultaneously in synthetic and field data
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