Juan F. Ramirez-Villegas

Heart Rate Variability Dynamics for the Prognosis of Cardiovascular Risk

Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis.

Wavelet packet energy, Tsallis entropy and statistical parameterization for support vector-based and neural-based classification of mammographic regions

This work develops a support vector and neural-based classification of mammographic regions by applying statistical, wavelet packet energy and Tsallis entropy parameterization. From the first four wavelet packet decomposition levels, four different feature sets were evaluated using two-sample Kolmogorov-Smirnov test (KS-test) and, in one case, principal component analysis (PCA). Feature selection was performed applying a hybrid scheme integrating non-parametric KS-test, correlation analysis, a logistic regression (LR) model and sequential forward selection (SFS). The top selected features (depending on the selected wavelet decomposition level) produced the best classification performances in comparison to other well-known feature selection methods. The classification of the data was carried out using several support vector machine (SVM) schemes and multi-layer perceptron (MLP) neural networks. The new set of features improved significantly the classification performance of mammographic regions using conventional SVMs and MLPs.

Diversity of sharp-wave–ripple LFP signatures reveals differentiated brain-wide dynamical events

Significance Sharp-wave–ripple (SPW-R) episodes observed in the electrical activity of mammalian hippocampus are traditionally associated to memory consolidation during sleep but have been recently observed during active behavior. Their involvement in various cognitive functions suggests the existence of SPW-R subtypes engaged in distinct neuronal activity patterns at multiple scales. We use concurrent electrophysiological and functional MRI (fMRI) recordings in macaque monkeys to investigate this hypothesis. We discover several subtypes of SPW-R with distinct electrophysiological properties. Importantly, fMRI recordings reveal differences between the large-scale signatures of SPW-R subtypes, indicating differentiated interactions with neocortex, and contributions of neuromodulatory pathways to the SPW-R phenomenon. Understanding the detailed properties of hippocampal SPW-Rs at multiple scales will provide new insights on the function of memory systems. Sharp-wave–ripple (SPW-R) complexes are believed to mediate memory reactivation, transfer, and consolidation. However, their underlying neuronal dynamics at multiple scales remains poorly understood. Using concurrent hippocampal local field potential (LFP) recordings and functional MRI (fMRI), we study local changes in neuronal activity during SPW-R episodes and their brain-wide correlates. Analysis of the temporal alignment between SPW and ripple components reveals well-differentiated SPW-R subtypes in the CA1 LFP. SPW-R–triggered fMRI maps show that ripples aligned to the positive peak of their SPWs have enhanced neocortical metabolic up-regulation. In contrast, ripples occurring at the trough of their SPWs relate to weaker neocortical up-regulation and absent subcortical down-regulation, indicating differentiated involvement of neuromodulatory pathways in the ripple phenomenon mediated by long-range interactions. To our knowledge, this study provides the first evidence for the existence of SPW-R subtypes with differentiated CA1 activity and metabolic correlates in related brain areas, possibly serving different memory functions.

A saliency-based bottom-up visual attention model for dynamic scenes analysis

This work proposes a model of visual bottom-up attention for dynamic scene analysis. Our work adds motion saliency calculations to a neural network model with realistic temporal dynamics [(e.g., building motion salience on top of De Brecht and Saiki Neural Networks 19:1467–1474, (2006)]. The resulting network elicits strong transient responses to moving objects and reaches stability within a biologically plausible time interval. The responses are statistically different comparing between earlier and later motion neural activity; and between moving and non-moving objects. We demonstrate the network on a number of synthetic and real dynamical movie examples. We show that the model captures the motion saliency asymmetry phenomenon. In addition, the motion salience computation enables sudden-onset moving objects that are less salient in the static scene to rise above others. Finally, we include strong consideration for the neural latencies, the Lyapunov stability, and the neural properties being reproduced by the model.

Color coding in the cortex: a modified approach to bottom-up visual attention

Itti and Koch’s (Vision Research 40:1489–1506, 2000) saliency-based visual attention model is a broadly accepted model that describes how attention processes are deployed in the visual cortex in a pure bottom-up strategy. This work complements their model by modifying the color feature calculation. Evidence suggests that S-cone responses are elicited in the same spatial distribution and have the same sign as responses to M-cone stimuli; these cells are tentatively referred to as red-cyan. For other cells, the S-cone input seems to be aligned with the L-cone input; these cells might be green-magenta cells. To model red-cyan and green-magenta double-opponent cells, we implement a center-surround difference approach of the aforementioned model. The resulting color maps elicited enhanced responses to color salient stimuli when compared to the classic ones at high statistical significance levels. We also show that the modified model improves the prediction of locations attended by human viewers.

Microcalcification Detection in Digitized Mammograms: A Neurobiologically-Inspired Approach

A Computer-Aided Diagnosis (CAD) system is a set of automatic or semi-automatic tools developed to assist radiologists in the detection and/or classification of abnormalities presented in diagnostic images of different modalities. Although on the early phase of research and development CAD systems were criticized by some computer scientists; regardless of this criticism, nowadays’ experimental evidence indicates that success rates of radiologists increase significantly when they are helped by these systems: In mammography, researchers have reported results from prospective studies on a large number of screenees, regarding the effect of CAD on the detection rate of breast cancer. Although there is a large variation in the results, it is important to note that all of these studies indicated an increase in the detection rates of breast cancer with the use of CAD; as a consequence of this, using CAD contributes to decrease cancer-related deceases due to the early detection of cancer signs.

Microcalcification Detection in Mammograms Using Difference of Gaussians Filters and a Hybrid Feedforward-Kohonen Neural Network

This work develops a microcalcifications’ detection system in mammograms by using difference of Gaussians filters (DoG) and artificial neural networks (ANN). The digital image processing proposed show the basic wavelet-based behavior of DoG as a mother function frequently used in several vision tasks, and in this case, used in order to enhance the microcalcifications’ traces in standard mammograms and further to achieve its detection via ANN. In order to achieve this, a segmentation algorithm is implemented for reaching a threshold in already processed images, and finally, the resultant information is given to the ANN. The neural network used to perform the detection is a hybrid feedforward-Kohonen one, implemented with a hard-limit transfer function in the first layer and a self-organizing map (SOM) responsible for microcalcifications’ topologic adjustment in the second layer. Basically, this clustering method gave us a robust solution of the problem and the detection was performed efficiently. There are no considerations relative to morphologic analysis of microcalcifications for diagnosis in this work.