77,277 research outputs found
Improved physiological noise regression in fNIRS: a multimodal extension of the General Linear Model using temporally embedded Canonical Correlation Analysis
For the robust estimation of evoked brain activity from functional Near-Infrared Spectroscopy (fNIRS) signals, it is crucial to reduce nuisance signals from systemic physiology and motion. The current best practice incorporates short-separation (SS) fNIRS measurements as regressors in a General Linear Model (GLM). However, several challenging signal characteristics such as non-instantaneous and non-constant coupling are not yet addressed by this approach and additional auxiliary signals are not optimally exploited. We have recently introduced a new methodological framework for the unsupervised multivariate analysis of fNIRS signals using Blind Source Separation (BSS) methods. Building onto the framework, in this manuscript we show how to incorporate the advantages of regularized temporally embedded Canonical Correlation Analysis (tCCA) into the supervised GLM. This approach allows flexible integration of any number of auxiliary modalities and signals. We provide guidance for the selection of optimal parameters and auxiliary signals for the proposed GLM extension. Its performance in the recovery of evoked HRFs is then evaluated using both simulated ground truth data and real experimental data and compared with the GLM with short-separation regression. Our results show that the GLM with tCCA significantly improves upon the current best practice, yielding significantly better results across all applied metrics: Correlation (HbO max. +45%), Root Mean Squared Error (HbO max. -55%), F-Score (HbO up to 3.25-fold) and p-value as well as power spectral density of the noise floor. The proposed method can be incorporated into the GLM in an easily applicable way that flexibly combines any available auxiliary signals into optimal nuisance regressors. This work has potential significance both for conventional neuroscientific fNIRS experiments as well as for emerging applications of fNIRS in everyday environments, medicine and BCI, where high Contrast to Noise Ratio is of importance for single trial analysis.Published versio
Evaluation of Automatic Video Captioning Using Direct Assessment
We present Direct Assessment, a method for manually assessing the quality of
automatically-generated captions for video. Evaluating the accuracy of video
captions is particularly difficult because for any given video clip there is no
definitive ground truth or correct answer against which to measure. Automatic
metrics for comparing automatic video captions against a manual caption such as
BLEU and METEOR, drawn from techniques used in evaluating machine translation,
were used in the TRECVid video captioning task in 2016 but these are shown to
have weaknesses. The work presented here brings human assessment into the
evaluation by crowdsourcing how well a caption describes a video. We
automatically degrade the quality of some sample captions which are assessed
manually and from this we are able to rate the quality of the human assessors,
a factor we take into account in the evaluation. Using data from the TRECVid
video-to-text task in 2016, we show how our direct assessment method is
replicable and robust and should scale to where there many caption-generation
techniques to be evaluated.Comment: 26 pages, 8 figure
Discovering Neuronal Cell Types and Their Gene Expression Profiles Using a Spatial Point Process Mixture Model
Cataloging the neuronal cell types that comprise circuitry of individual
brain regions is a major goal of modern neuroscience and the BRAIN initiative.
Single-cell RNA sequencing can now be used to measure the gene expression
profiles of individual neurons and to categorize neurons based on their gene
expression profiles. While the single-cell techniques are extremely powerful
and hold great promise, they are currently still labor intensive, have a high
cost per cell, and, most importantly, do not provide information on spatial
distribution of cell types in specific regions of the brain. We propose a
complementary approach that uses computational methods to infer the cell types
and their gene expression profiles through analysis of brain-wide single-cell
resolution in situ hybridization (ISH) imagery contained in the Allen Brain
Atlas (ABA). We measure the spatial distribution of neurons labeled in the ISH
image for each gene and model it as a spatial point process mixture, whose
mixture weights are given by the cell types which express that gene. By fitting
a point process mixture model jointly to the ISH images, we infer both the
spatial point process distribution for each cell type and their gene expression
profile. We validate our predictions of cell type-specific gene expression
profiles using single cell RNA sequencing data, recently published for the
mouse somatosensory cortex. Jointly with the gene expression profiles, cell
features such as cell size, orientation, intensity and local density level are
inferred per cell type
The Emergence of Gravitational Wave Science: 100 Years of Development of Mathematical Theory, Detectors, Numerical Algorithms, and Data Analysis Tools
On September 14, 2015, the newly upgraded Laser Interferometer
Gravitational-wave Observatory (LIGO) recorded a loud gravitational-wave (GW)
signal, emitted a billion light-years away by a coalescing binary of two
stellar-mass black holes. The detection was announced in February 2016, in time
for the hundredth anniversary of Einstein's prediction of GWs within the theory
of general relativity (GR). The signal represents the first direct detection of
GWs, the first observation of a black-hole binary, and the first test of GR in
its strong-field, high-velocity, nonlinear regime. In the remainder of its
first observing run, LIGO observed two more signals from black-hole binaries,
one moderately loud, another at the boundary of statistical significance. The
detections mark the end of a decades-long quest, and the beginning of GW
astronomy: finally, we are able to probe the unseen, electromagnetically dark
Universe by listening to it. In this article, we present a short historical
overview of GW science: this young discipline combines GR, arguably the
crowning achievement of classical physics, with record-setting, ultra-low-noise
laser interferometry, and with some of the most powerful developments in the
theory of differential geometry, partial differential equations,
high-performance computation, numerical analysis, signal processing,
statistical inference, and data science. Our emphasis is on the synergy between
these disciplines, and how mathematics, broadly understood, has historically
played, and continues to play, a crucial role in the development of GW science.
We focus on black holes, which are very pure mathematical solutions of
Einstein's gravitational-field equations that are nevertheless realized in
Nature, and that provided the first observed signals.Comment: 41 pages, 5 figures. To appear in Bulletin of the American
Mathematical Societ
More than skin deep: body representation beyond primary somatosensory cortex
The neural circuits underlying initial sensory processing of somatic information are relatively well understood. In contrast, the processes that go beyond primary somatosensation to create more abstract representations related to the body are less clear. In this review, we focus on two classes of higher-order processing beyond somatosensation. Somatoperception refers to the process of perceiving the body itself, and particularly of ensuring somatic perceptual constancy. We review three key elements of somatoperception: (a) remapping information from the body surface into an egocentric reference frame (b) exteroceptive perception of objects in the external world through their contact with the body and (c) interoceptive percepts about the nature and state of the body itself. Somatorepresentation, in contrast, refers to the essentially cognitive process of constructing semantic knowledge and attitudes about the body, including: (d) lexical-semantic knowledge about bodies generally and one’s own body specifically, (e) configural knowledge about the structure of bodies, (f) emotions and attitudes directed towards one’s own body, and (g) the link between physical body and psychological self. We review a wide range of neuropsychological, neuroimaging and neurophysiological data to explore the dissociation between these different aspects of higher somatosensory function
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