Semiparametric curve alignment and shift density estimation for biological data

Assume that we observe a large number of curves, all of them with identical, although unknown, shape, but with a different random shift. The objective is to estimate the individual time shifts and their distribution. Such an objective appears in several biological applications like neuroscience or ECG signal processing, in which the estimation of the distribution of the elapsed time between repetitive pulses with a possibly low signal-noise ratio, and without a knowledge of the pulse shape is of interest. We suggest an M-estimator leading to a three-stage algorithm: we split our data set in blocks, on which the estimation of the shifts is done by minimizing a cost criterion based on a functional of the periodogram; the estimated shifts are then plugged into a standard density estimator. We show that under mild regularity assumptions the density estimate converges weakly to the true shift distribution. The theory is applied both to simulations and to alignment of real ECG signals. The estimator of the shift distribution performs well, even in the case of low signal-to-noise ratio, and is shown to outperform the standard methods for curve alignment.Comment: 30 pages ; v5 : minor changes and correction in the proof of Proposition 3.

How much baseline correction do we need in ERP research? Extended GLM model can replace baseline correction while lifting its limits

Baseline correction plays an important role in past and current methodological debates in ERP research (e.g. the Tanner v. Maess debate in Journal of Neuroscience Methods), serving as a potential alternative to strong highpass filtering. However, the very assumptions that underlie traditional baseline also undermine it, making it statistically unnecessary and even undesirable and reducing signal-to-noise ratio. Including the baseline interval as a predictor in a GLM-based statistical approach allows the data to determine how much baseline correction is needed, including both full traditional and no baseline correction as subcases, while reducing the amount of variance in the residual error term and thus potentially increasing statistical power

1/ f Noise and Machine Intelligence in a Nonlinear Dopant Atom Network

Noise exists in nearly all physical systems ranging from simple electronic devices such as transistors to complex systems such as neural networks. To understand a system's behavior, it is vital to know the origin of the noise and its characteristics. Recently, it was shown that the nonlinear electronic properties of a disordered dopant atom network in silicon can be exploited for efficiently executing classification tasks through “material learning.” Here, we study the dopant network's intrinsic 1/f noise arising from Coulomb interactions, and its impact on the features that determine its computational abilities, viz., the nonlinearity and the signal‐to‐noise ratio (SNR), is investigated. The findings on optimal SNR and nonlinear transformation of data by this nonlinear network provide a guideline for the scaling of physical learning machines and shed light on neuroscience from a new perspective

DTIPrep: quality control of diffusion-weighted images

pre-printIn the last decade, diffusion MRI (dMRI) studies of the human and animal brain have been used to investigate a multitude of pathologies and drug-related effects in neuroscience research. Study after study identifies white matter (WM) degeneration as a crucial biomaker for all these diseases. The tool of choice for studying WM is dMRI however, dMRI has inherently low signal-to-noise ratio and its acquisition requires a relatively long scan time; in fact, the high loads required occasionally stress scanner hardware past the point of physical failure

Structural adaptive smoothing for single-subject analysis in SPM: the aws4SPM-toolbox

There exists a variety of software tools for analyzing functional Magnetic Resonance Imaging data. A very popular one is the freely available SPM package by the Functional Imaging Laboratory at the Wellcome Department of Imaging Neuroscience. In order to enhance the signal-to-noise ratio it provides the possibility to smooth the data in a pre-processing step by a Gaussian filter. However, this comes at the cost of reducing the effective resolution. In a series of recent papers it has been shown, that using a structural adaptive smoothing algorithm based on the Propagation-Separation method allows for enhanced signal detection while preserving the shape and spatial extent of the activation areas. Here, we describe our implementation of this algorithm as a toolbox for SPM

Causality estimates among brain cortical areas by Partial Directed Coherence: simulations and application to real data

The problem of the definition and evaluation of brain connectivity has become a central one in neuroscience during the latest years, as a way to understand the organization and interaction of cortical areas during the execution of cognitive or motor tasks. Among various methods established during the years, the Partial Directed Coherence (PDC) is a frequency-domain approach to this problem, based on a multivariate autoregressive modeling of time series and on the concept of Granger causality. In this paper we propose the use of the PDC method on cortical signals estimated from high resolution EEG recordings, a non invasive method which exhibits a higher spatial resolution than conventional cerebral electromagnetic measures. The principle contributions of this work are the results of a simulation study, testing the performances of PDC, and a statistical analysis (via the ANOVA, analysis of variance) of the influence of different levels of Signal to Noise Ratio and temporal length, as they have been systematically imposed on simulated signals. An application to high resolution EEG recordings during a foot movement is also presented

Cerebral correlates and statistical criteria of cross-modal face and voice integration

Perception of faces and voices plays a prominent role in human social interaction, making multisensory integration of cross-modal speech a topic of great interest in cognitive neuroscience. How to define po- tential sites of multisensory integration using functional magnetic resonance imaging (fMRI) is currently under debate, with three statistical criteria frequently used (e.g., super-additive, max and mean criteria). In the present fMRI study, 20 participants were scanned in a block design under three stimulus conditions: dynamic unimodal face, unimodal voice and bimodal face–voice. Using this single dataset, we examine all these statistical criteria in an attempt to define loci of face–voice integration. While the super-additive and mean criteria essentially revealed regions in which one of the unimodal responses was a deactivation, the max criterion appeared stringent and only highlighted the left hippocampus as a potential site of face– voice integration. Psychophysiological interaction analysis showed that connectivity between occipital and temporal cortices increased during bimodal compared to unimodal conditions. We concluded that, when investigating multisensory integration with fMRI, all these criteria should be used in conjunction with ma- nipulation of stimulus signal-to-noise ratio and/or cross-modal congruency

Design and Initial Characterization of a Small Near-Infrared Fluorescent Calcium Indicator

Near-infrared (NIR) genetically encoded calcium indicators (GECIs) are becoming powerful tools for neuroscience. Because of their spectral characteristics, the use of NIR GECIs helps to avoid signal loss from the absorption by body pigments, light-scattering, and autofluorescence in mammalian tissues. In addition, NIR GECIs do not suffer from cross-excitation artifacts when used with common fluorescent indicators and optogenetics actuators. Although several NIR GECIs have been developed, there is no NIR GECI currently available that would combine the high brightness in cells and photostability with small size and fast response kinetics. Here, we report a small FRET-based NIR fluorescent calcium indicator iGECInano. We characterize iGECInano in vitro, in non-neuronal mammalian cells, and primary mouse neurons. iGECInano demonstrates the improvement in the signal-to-noise ratio and response kinetics compared to other NIR GECIs.Peer reviewe

Diffuse Correlation Spectroscopy: A Review of Recent Advances in Parallelisation and Depth Discrimination Techniques

Diffuse correlation spectroscopy is a non-invasive optical modality used to measure cerebral blood flow in real time, and it has important potential applications in clinical monitoring and neuroscience. As such, many research groups have recently been investigating methods to improve the signal-to-noise ratio, imaging depth, and spatial resolution of diffuse correlation spectroscopy. Such methods have included multispeckle, long wavelength, interferometric, depth discrimination, time-of-flight resolution, and acousto-optic detection strategies. In this review, we exhaustively appraise this plethora of recent advances, which can be used to assess limitations and guide innovation for future implementations of diffuse correlation spectroscopy that will harness technological improvements in the years to come