Marmoset brain segmentation from deconvolved magnetic resonance images and estimated label maps.

PURPOSE: The proposed method aims to create label maps that can be used for the segmentation of animal brain MR images without the need of a brain template. This is achieved by performing a joint deconvolution and segmentation of the brain MR images. METHODS: It is based on modeling locally the image statistics using a generalized Gaussian distribution (GGD) and couples the deconvolved image and its corresponding labels map using the GGD-Potts model. Because of the complexity of the resulting Bayesian estimators of the unknown model parameters, a Gibbs sampler is used to generate samples following the desired posterior probability. RESULTS: The performance of the proposed algorithm is assessed on simulated and real MR images by the segmentation of enhanced marmoset brain images into its main compartments using the corresponding label maps created. Quantitative assessment showed that this method presents results that are comparable to those obtained with the classical method-registering the volumes to a brain template. CONCLUSION: The proposed method of using labels as prior information for brain segmentation provides a similar or a slightly better performance compared with the classical reference method based on a dedicated template.

Exploring the Correlation Between M/EEG Source-Space and fMRI Networks at Rest.

Magneto/electro-encephalography (M/EEG) source connectivity is an emerging approach to estimate brain networks with high temporal and spatial resolutions. Here, we aim to evaluate the effect of functional connectivity (FC) methods on the correlation between M/EEG source-space and fMRI networks at rest. Two main FC families are tested: (i) FC methods that do not remove zero-lag connectivity including Phase Locking Value (PLV) and Amplitude Envelope Correlation (AEC) and (ii) FC methods that remove zero-lag connections such as Phase Lag Index (PLI) and two orthogonalisation approaches combined with PLV (PLVCol, PLVPas) and AEC (AECCol, AECPas). Methods are evaluated on resting state M/EEG signals recorded from healthy participants at rest (N = 74). Networks obtained by each FC method are compared with fMRI networks (obtained from the Human Connectome Project). Results show low correlations for all FC methods, however PLV and AEC networks are significantly correlated with fMRI networks (ρ = 0.12, p = 1.93 × 10-8 and ρ = 0.06, p = 0.007, respectively), while other methods are not. These observations are consistent for all M/EEG frequency bands and for different FC matrices threshold. Our main message is to be careful in selecting FC methods when comparing or combining M/EEG with fMRI. We consider that more comparative studies based on simulation and real data and at different levels (node, module or sub networks) are still needed in order to improve our understanding on the relationships between M/EEG source-space networks and fMRI networks at rest.

Decreased integration of EEG source-space networks in disorders of consciousness.

Increasing evidence links disorders of consciousness (DOC) with disruptions in functional connectivity between distant brain areas. However, to which extent the balance of brain network segregation and integration is modified in DOC patients remains unclear. Using high-density electroencephalography (EEG), the objective of our study was to characterize the local and global topological changes of DOC patients' functional brain networks. Resting state high-density-EEG data were collected and analyzed from 82 participants: 61 DOC patients recovering from coma with various levels of consciousness (EMCS (n = 6), MCS+ (n = 29), MCS- (n = 17) and UWS (n = 9)), and 21 healthy subjects (i.e., controls). Functional brain networks in five different EEG frequency bands and the broadband signal were estimated using an EEG connectivity approach at the source level. Graph theory-based analyses were used to evaluate their relationship with decreasing levels of consciousness as well as group differences between healthy volunteers and DOC patient groups. Results showed that networks in DOC patients are characterized by impaired global information processing (network integration) and increased local information processing (network segregation) as compared to controls. The large-scale functional brain networks had integration decreasing with lower level of consciousness.

Early-warning of ARDS using novelty detection and data fusion.

Acute respiratory distress syndrome (ARDS) is a critical condition that disturbs the respiratory system and may lead to death. Early identification of this syndrome is crucial for the implementation of preventive measures. The present paper focuses on the prediction of the onset of this syndrome using physiological records of patients. Heart rate, respiratory rate, peripheral arterial oxygen saturation and mean airway blood pressure were considered. The method proposed in this paper uses first distance-based novelty detection that allows detecting deviations from normal states for each signal. Then, linear and nonlinear kernel-based data fusion algorithms are introduced to combine the individual signal decisions. The proposed method is evaluated using the MIMIC II physiological database. As a result, ARDS is detected in the early phases of occurrence with sensitivity and specificity of 65% and 100% respectively for the combination of all the signals in study. Moreover, the proposed method outperforms current state-of-the-art methods in real-time surveillance of ARDS using only physiological data with an average prediction before 39 h of onset.

Nonlinear feature extraction using kernel principal component analysis with non-negative pre-image.

The inherent physical characteristics of many real-life phenomena, including biological and physiological aspects, require adapted nonlinear tools. Moreover, the additive nature in some situations involve solutions expressed as positive combinations of data. In this paper, we propose a nonlinear feature extraction method, with a non-negativity constraint. To this end, the kernel principal component analysis is considered to define the most relevant features in the reproducing kernel Hilbert space. These features are the nonlinear principal components with high-order correlations between input variables. A pre-image technique is required to get back to the input space. With a non-negative constraint, we show that one can solve the pre-image problem efficiently, using a simple iterative scheme. Furthermore, the constrained solution contributes to the stability of the algorithm. Experimental results on event-related potentials (ERP) illustrate the efficiency of the proposed method.

Hilbert-Huang transformation: application to postural stability analysis.

The aim objective of this paper is the analysis of the Centre Of Pressure (COP) time series by the means of the Hilbert Huang Transformation (HHT). The HHT consists of extracting the Intrinsic Mode Functions (IMFs) from an Empirical Mode Decomposition (EMD), and then applying the Hilbert Transformation on the IMFs. The trace of the HHT in the complex plane has a circular form, with each IMF having its own rotation frequency. The area of the circle represents a possible indicator of the postural stability status of the subjects. Experimental results show the effectiveness of the area of this circle in order to identify the post-vibratory effects on standing posture in healthy adult subjects.

Fractal time series analysis of postural stability in elderly and control subjects.

BACKGROUND: The study of balance using stabilogram analysis is of particular interest in the study of falls. Although simple statistical parameters derived from the stabilogram have been shown to predict risk of falls, such measures offer little insight into the underlying control mechanisms responsible for degradation in balance. In contrast, fractal and non-linear time-series analysis of stabilograms, such as estimations of the Hurst exponent (H), may provide information related to the underlying motor control strategies governing postural stability. In order to be adapted for a home-based follow-up of balance, such methods need to be robust, regardless of the experimental protocol, while producing time-series that are as short as possible. The present study compares two methods of calculating H: Detrended Fluctuation Analysis (DFA) and Stabilogram Diffusion Analysis (SDA) for elderly and control subjects, as well as evaluating the effect of recording duration. METHODS: Centre of pressure signals were obtained from 90 young adult subjects and 10 elderly subjects. Data were sampled at 100 Hz for 30 s, including stepping onto and off the force plate. Estimations of H were made using sliding windows of 10, 5, and 2.5 s durations, with windows slid forward in 1-s increments. Multivariate analysis of variance was used to test for the effect of time, age and estimation method on the Hurst exponent, while the intra-class correlation coefficient (ICC) was used as a measure of reliability. RESULTS: Both SDA and DFA methods were able to identify differences in postural stability between control and elderly subjects for time series as short as 5 s, with ICC values as high as 0.75 for DFA. CONCLUSION: Both methods would be well-suited to non-invasive longitudinal assessment of balance. In addition, reliable estimations of H were obtained from time series as short as 5 s.

Identification of a degradation in postural equilibrium invoked by different vibration frequencies on the tibialis anterior tendon.

The aim of the study was to compare the effect of different vibration frequencies applied to the tibialis anterior tendon on the control of postural equilibrium. Sixty-three parameters were analyzed, with 44 of them showing an effect of vibration. The greatest differences were observed for those parameters related to movement in the anteroposterior direction. Such a result was due to the direction of postural sway induced by the vibration. There were no differences between the control condition and vibration at 50 Hz. However, both 70 and 90 Hz vibrations produced similar results. The method of postural perturbation presented here might be useful to test the sensitivity of parameters extracted from COP signals in terms of their capability to detect small changes in postural equilibrium.

Reconstructed phase spaces of intrinsic mode functions. Application to postural stability analysis.

In this contribution, we propose an efficient nonlinear analysis method characterizing postural steadiness. The analyzed signal is the displacement of the centre of pressure (COP) collected from a force plate used for measuring postural sway. The proposed method consists of analyzing the nonlinear dynamics of the intrinsic mode functions (IMF) of the COP signal. The nonlinear properties are assessed through the reconstructed phase spaces of the different IMFs. This study shows some specific geometries of the attractors of some intrinsic modes. Moreover, the volume spanned by the geometric attractors in the reconstructed phase space represents an efficient indicator of the postural stability of the subject. Experiments results corroborate the effectiveness of the method to blindly discriminate young subjects, elderly subjects and subjects presenting a risk of falling.