Assessing feature relevance in NPLS models by VIP

Multilinear PLS (NPLS) and its discriminant version (NPLS-DA) are very diffuse tools to model multi-way data arrays. Analysis of NPLS weights and NPLS regression coefficients allows data patterns, feature correlation and covariance structure to be depicted. In this study we propose an extension of the Variable Importance in Projection (VIP) parameter to multi-way arrays in order to highlight the most relevant features to predict the studied dependent properties either for interpretative purposes or to operate feature selection. The VIPs are implemented for each mode of the data array and in the case of multivariate dependent responses considering both the cases of expressing VIP with respect to each single y-variable and of taking into account all y-variables altogether. Three different applications to real data are presented: i) NPLS has been used to model the properties of bread loaves from near infrared spectra of dough, acquired at different leavening times, and corresponding to different flour formulations. VIP values were used to assess the spectral regions mainly involved in determining flour performance; ii) assessing the authenticity of extra virgin olive oils by NPLS-DA elaboration of gas chromatography/mass spectrometry data (GC–MS). VIP values were used to assess both GC and MS discriminant features; iii) NPLS analysis of a fMRI-BOLD experiment based on a pain paradigm of acute prolonged pain in healthy volunteers, in order to reproduce efficiently the corresponding psychophysical pain profiles. VIP values were used to identify the brain regions mainly involved in determining the pain intensity profile

Atrial natriuretic factor in essential hypertension : echocardiographic and humoral correlates

Aim of this study was to assess the relationship between plasma concentration of atrial natriuretic factor (ANF) and its two-dimensional echocardiographic (left ventricular mass, left atrium diameter) and humoral (plasma renin and aldosterone) variables in essential hypertension (EH). We evaluated 32 patients with uncomplicated mild to moderate EH and 10 controls. They were studied in the supine position after 7 days of constant dietary sodium intake and were off therapy since at least 3 weeks. ANF values overlapped between EH patients and controls (27.8 +/- 11.5 vs. 19.5 +/- 7.4 pg/ml, p = NS). In EH, no significant correlation was found between ANF values and left ventricular mass (r = 0.29), left atrial diameter (r = 0.04), mean arterial blood pressure (r = 0.26), plasma renin activity (r = 0.00), and aldosterone (r = 0.26). In EH, ANF values overlapped between the 15 patients with hypertrophy and the 17 patients with normal ventricular mass: 30.3 +/- 17 vs. 25.6 +/- 10.6 pg/ms (p = NS). We conclude that there is a substantial overlap in plasma ANF values between mild to moderate uncomplicated EH and controls, and left ventricular hypertrophy is not a major independent stimulus to ANF release in EH

Visual outcome and poor prognostic factors in acute retinal necrosis syndrome

Objective: To evaluate the impact of selected clinical parameters on the mid-/long-term visual outcome of patients with acute retinal necrosis (ARN) Design: A retrospective cohort study Methods: Setting: Two University Hospitals (Parma, Italy; Lausanne, Switzerland). Participants: Thirty-nine non-HIV patients (39 eyes) with ARN, as confirmed by polymerase chain reaction on intraocular samples. The following potential predictors were tested using linear regression models: age, sex, etiology, best-corrected visual acuity (BCVA) on admission, delay between ARN symptom onset and treatment initiation, and surgery (performed or not). Main outcome: BCVA at the final follow up Results: Thirty-nine of 39 non-HIV patients (22 men and 17 women; mean age, 50 years) diagnosed with ARN were enrolled in the study. Etiologies were: varicella-zoster virus in 25 eyes (64%), herpes simplex viruses in the remaining 14 eyes. The average follow-up duration was 19 ± 13 months. All patients had undergone systemic antivirals; surgery was performed in 16 eyes. The mean delay between onset of visual symptoms and antiviral treatment initiation was 15 ± 31 days (range, 1–180 days). The mean BCVA at baseline was 0.83 ± 0.75 logMAR, while the mean final BCVA was 0.75 ± 0.81 logMAR. Both initial BCVA and treatment delay (TD) were significantly correlated with the final BCVA (p < 0.05). Conclusions: Initial BCVA and TD seem to be significant predictors of mid-/long-term visual outcome in non-HIV patients affected by ARN

From BOLD-FMRI signals to the prediction of subjective pain perception through a regularization algorithm

Functional magnetic resonance imaging, in particular theBOLD-fMRI technique, plays a dominant role in humanbrain mapping studies, mostly because of its noninvasivenessand relatively high spatio-temporal resolution.The main goal of fMRI data analysis has been to revealthe distributed patterns of brain areas involved in specificfunctions, by applying a variety of statistical methods withmodel-based or data-driven approaches. In the last years,several studies have taken a different approach, where thedirection of analysis is reversed in order to probe whetherfMRI signals can be used to predict perceptual or cognitivestates. In this study we test the feasibility of predicting theperceived pain intensity in healthy volunteers, based on fMRIsignals collected during an experimental pain paradigm lastingseveral minutes. In particular, we introduce a methodologicalapproach based on new regularization learning algorithmsfor regression problems.Functional magnetic resonance imaging, in particular the BOLD-fMRI technique, plays a dominant role in human brain mapping studies, mostly because of its non-invasiveness and relatively high spatio-temporal resolution. The main goal of fMRI data analysis has been to reveal the distributed patterns of brain areas involved in specific functions, by applying a variety of statistical methods with model-based or data-driven approaches. In the last years, several studies have taken a different approach, where the direction of analysis is reversed in order to probe whether fMRI signals can be used to predict perceptual or cognitive states. In this study we test the feasibility of predicting the perceived pain intensityin healthy volunteers, based on fMRI signals collected during an experimental pain paradigm lasting several minutes. In particular, we introduce a methodological approach based on new regularization learning algorithms for regression problems. \ua9 EURASIP, 2009

Predicting subjective pain perception based on BOLD-fMRI signals: a new machine learning approach

Functional magnetic resonance imaging, in particular the BOLD-fMRI technique, plays a dominant role in human brain mapping studies, mostly because of its non-invasiveness, good spatial and acceptable temporal resolution in comparison with other techniques. The main goal of fMRI data analysis has been to reveal the distributed patterns of brain areas involved in specific functions and their interactions, by applying a variety of univariate or multivariate statistical methods with model-basedor data-driven approaches. In the last few years, a growing number of studies have taken a different approach, where the direction of analysis is reversed in order to probe whether fMRI signals can be used to predict perceptual or cognitive states. In this study we wished to test the feasibility of predicting the perceived pain intensity in healthy volunteers, based on fMRI signals collected during an experimental pain paradigm lasting several minutes. To this end, we tested and optimized one methodological approach based on new regularization learning algorithms on this regression problem

A regularization algorithm for decoding perceptual profiles

In this study we wished to test the feasibility of predicting the perceived pain intensity in healthy volunteers, based on fMRI signals collected during an experimental pain paradigm lasting several minutes. This model of acute prolonged (tonic) pain bears some similarities with clinically relevant conditions, such as prolonged ongoing activity in nociceptors and spontaneous fluctuations of perceived pain intensity over time.To predict individual pain profile, we tested and optimized one methodological approach based on new regularization learning algorithms on this regression problem