A Constrained ICA-EMD Model for Group Level fMRI Analysis.

Independent component analysis (ICA), being a data-driven method, has been shown to be a powerful tool for functional magnetic resonance imaging (fMRI) data analysis. One drawback of this multivariate approach is that it is not, in general, compatible with the analysis of group data. Various techniques have been proposed to overcome this limitation of ICA. In this paper, a novel ICA-based workflow for extracting resting-state networks from fMRI group studies is proposed. An empirical mode decomposition (EMD) is used, in a data-driven manner, to generate reference signals that can be incorporated into a constrained version of ICA (cICA), thereby eliminating the inherent ambiguities of ICA. The results of the proposed workflow are then compared to those obtained by a widely used group ICA approach for fMRI analysis. In this study, we demonstrate that intrinsic modes, extracted by EMD, are suitable to serve as references for cICA. This approach yields typical resting-state patterns that are consistent over subjects. By introducing these reference signals into the ICA, our processing pipeline yields comparable activity patterns across subjects in a mathematically transparent manner. Our approach provides a user-friendly tool to adjust the trade-off between a high similarity across subjects and preserving individual subject features of the independent components.

Frequency-Resolved Dynamic Functional Connectivity Reveals Scale-Stable Features of Connectivity-States.

Investigating temporal variability of functional connectivity is an emerging field in connectomics. Entering dynamic functional connectivity by applying sliding window techniques on resting-state fMRI (rs-fMRI) time courses emerged from this topic. We introduce frequency-resolved dynamic functional connectivity (frdFC) by means of multivariate empirical mode decomposition (MEMD) followed up by filter-bank investigations. In general, we find that MEMD is capable of generating time courses to perform frdFC and we discover that the structure of connectivity-states is robust over frequency scales and even becomes more evident with decreasing frequency. This scale-stability varies with the number of extracted clusters when applying k-means. We find a scale-stability drop-off from k = 4 to k = 5 extracted connectivity-states, which is corroborated by null-models, simulations, theoretical considerations, filter-banks, and scale-adjusted windows. Our filter-bank studies show that filter design is more delicate in the rs-fMRI than in the simulated case. Besides offering a baseline for further frdFC research, we suggest and demonstrate the use of scale-stability as a possible quality criterion for connectivity-state and model selection. We present first evidence showing that connectivity-states are both a multivariate, and a multiscale phenomenon. A data repository of our frequency-resolved time-series is provided.

Empirical mode decomposition of multiple ECG leads for catheter ablation long-term outcome prediction in persistent atrial fibrillation.

Predictive models arouse increasing interest in clinical practice, not only to improve successful intervention rates but also to extract information of diverse physiological disorders. This is the case of persistent atrial fibrillation (AF), the most common cardiac arrhythmia in adults. Currently, catheter ablation (CA) is one of the preferred therapies to face this disease. However, selecting the best responders to CA by standard noninvasive techniques such as the electrocardiogram (ECG) remains a challenge. This work presents different predictive models for determining long-term CA outcome based on the dominant frequency (DF) of atrial activity measured in the ECG. The ensemble empirical mode decomposition (EEMD) is employed to obtain the intrinsic mode functions (IMFs) composing the ECG signal in each lead. The IMF DFs computed in multiple leads are then combined into a logistic regression (LR) model. The IMF DF features are discriminant enough to reach 79% accuracy for long-term CA outcome prediction, outperforming other methods based on DF computation. Our study shows EEMD as a valuable alternative to extract clinically relevant spectral information from AF ECGs and confirms the advantage of LR to build multivariate predictive models as compared with univariate analysis.

Quinolones: review of psychiatric and neurological adverse reactions.

Quinolones are a class of antibacterial agents for the treatment of several infectious diseases (e.g. urinary and respiratory tract infections). They are used worldwide due to their broad spectrum of activity, high bioavailability and good safety profile. The safety profile varies from quinolone to quinolone. The aim of this article was to review the neurological and psychiatric adverse drug reaction (ADR) profile of quinolones, using a literature search strategy designed to identify case reports and case series. A literature search using PubMed/MEDLINE (from inception to 31 October 2010) was performed to identify case reports and case series related to quinolone-associated neurological and psychiatric ADRs. The search was conducted in two phases: the first phase was the literature search and in the second phase relevant articles were identified through review of the references of the selected articles. Relevant articles were defined as articles referring to adverse events/reactions associated with the use of any quinolone. Abstracts referring to animal studies, clinical trials and observational studies were excluded. Identified case reports were analysed by age group, sex, active substances, dosage, concomitant medication, ambulatory or hospital-based event and seriousness, after Medical Dictionary for Regulatory Activities (MedDRA®) coding. From a total of 828 articles, 83 were identified as referring to nervous system and/or psychiatric disorders induced by quinolones. 145 individual case reports were extracted from the 83 articles. 40.7% of the individual case reports belonged to psychiatric disorders only, whereas 46.9% related to neurological disorders only. Eight (5.5%) individual case reports presented both neurological and psychiatric ADRs. Ciprofloxacin, ofloxacin and pefloxacin were the quinolones with more neurological and psychiatric ADRs reported in the literature. Ciprofloxacin has been extensively used worldwide, which may explain the higher number of reports, while for ofloxacin and pefloxacin, the number of reports may be over-representative. A total of 232 ADRs were identified from the selected articles, with 206 of these related to psychiatric and/or neurological ADRs. The other 26 were related to other body systems but were reported together with the reactions of interest. Mania, insomnia, acute psychosis and delirium were the most frequently reported psychiatric adverse events; grand mal convulsion, confusional state, convulsions and myoclonus were the most frequently reported neurological adverse events. Several aspects should be taken into account in the development of CNS adverse effects, such as the pharmacokinetics of quinolones, chemical structure and quinolone uptake in the brain. These events may affect not only susceptible patients but also 'healthy' patients.

Singular spectrum analysis for an automated solvent artifact removal and baseline correction of 1D NMR spectra.

NMR spectroscopy in biology and medicine is generally performed in aqueous solutions, thus in (1)H NMR spectroscopy, the dominant signal often stems from the partly suppressed solvent and can be many orders of magnitude larger than the resonances of interest. Strong solvent signals lead to a disappearance of weak resonances of interest close to the solvent artifact and to base plane variations all over the spectrum. The AUREMOL-SSA/ALS approach for automated solvent artifact removal and baseline correction has been originally developed for multi-dimensional NMR spectroscopy. Here, we describe the necessary adaptations for an automated application to one-dimensional NMR spectra. Its core algorithm is still based on singular spectrum analysis (SSA) applied on time domain signals (FIDs) and it is still combined with an automated baseline correction (ALS) in the frequency domain. However, both steps (SSA and ALS) have been modified in order to achieve optimal results when dealing with one-dimensional spectra. The performance of the method has been tested on one-dimensional synthetic and experimental spectra including the back-calculated spectrum of HPr protein and an experimental spectrum of a human urine sample. The latter has been recorded with the typically used NOESY-type 1D pulse sequence including water pre-saturation. Furthermore, the fully automated AUREMOL-SSA/ALS procedure includes the managing of oversampled, digitally filtered and zero-filled data and the correction of the frequency domain phase shift caused by the group delay time shift from the digital finite response filtering.

Spatiotemporal ICA applied to retinotopic fMRI data.

We use two spatiotemporal Independent Component Analysis algorithms, stJADE and stSOBI, to analyse data from a retinotopic functional magnetic resonance imaging experiment and compare their performance to the analysis of the same data with the spatial ICA done with JADE. This kind of experimental setting has the advantage that the activation in the brain can be estimated fairly easily and therefore can be used as well defined benchmark. We show that stSOBI can outperform sJADE and exhibits quite stable behaviour while stJADE critically depends on the quality of the chosen parameter settings for each subject.

Automated solvent artifact removal and base plane correction of multidimensional NMR protein spectra by AUREMOL-SSA.

Strong solvent signals lead to a disappearance of weak protein signals close to the solvent resonance frequency and to base plane variations all over the spectrum. AUREMOL-SSA provides an automated approach for solvent artifact removal from multidimensional NMR protein spectra. Its core algorithm is based on singular spectrum analysis (SSA) in the time domain and is combined with an automated base plane correction in the frequency domain. The performance of the method has been tested on synthetic and experimental spectra including two-dimensional NOESY and TOCSY spectra and a three-dimensional (1)H,(13)C-HCCH-TOCSY spectrum. It can also be applied to frequency domain spectra since an optional inverse Fourier transformation is included in the algorithm.