Investigating neuromagnetic brain responses against chromatic flickering stimuli by wavelet entropies

BACKGROUND: Photosensitive epilepsy is a type of reflexive epilepsy triggered by various visual stimuli including colourful ones. Despite the ubiquitous presence of colorful displays, brain responses against different colour combinations are not properly studied. METHODOLOGY/PRINCIPAL FINDINGS: Here, we studied the photosensitivity of the human brain against three types of chromatic flickering stimuli by recording neuromagnetic brain responses (magnetoencephalogram, MEG) from nine adult controls, an unmedicated patient, a medicated patient, and two controls age-matched with patients. Dynamical complexities of MEG signals were investigated by a family of wavelet entropies. Wavelet entropy is a newly proposed measure to characterize large scale brain responses, which quantifies the degree of order/disorder associated with a multi-frequency signal response. In particular, we found that as compared to the unmedicated patient, controls showed significantly larger wavelet entropy values. We also found that Renyi entropy is the most powerful feature for the participant classification. Finally, we also demonstrated the effect of combinational chromatic sensitivity on the underlying order/disorder in MEG signals. CONCLUSIONS/SIGNIFICANCE: Our results suggest that when perturbed by potentially epileptic-triggering stimulus, healthy human brain manages to maintain a non-deterministic, possibly nonlinear state, with high degree of disorder, but an epileptic brain represents a highly ordered state which making it prone to hyper-excitation. Further, certain colour combination was found to be more threatening than other combinations

Methods of electroencephalographic signal analysis for detection of small hidden changes

The aim of this study was to select and evaluate methods sensitive to reveal small hidden changes in the electroencephalographic (EEG) signal. Two original methods were considered

‘Functional Connectivity’ Is a Sensitive Predictor of Epilepsy Diagnosis after the First Seizure

Background: Although epilepsy affects almost 1 % of the world population, diagnosis of this debilitating disease is still difficult. The EEG is an important tool for epilepsy diagnosis and classification, but the sensitivity of interictal epileptiform discharges (IEDs) on the first EEG is only 30–50%. Here we investigate whether using ‘functional connectivity ’ can improve the diagnostic sensitivity of the first interictal EEG in the diagnosis of epilepsy. Methodology/Principal Findings: Patients were selected from a database with 390 standard EEGs of patients after a first suspected seizure. Patients who were later diagnosed with epilepsy (i.e. $two seizures) were compared to matched nonepilepsy patients (with a minimum follow-up of one year). The synchronization likelihood (SL) was used as an index of functional connectivity of the EEG, and average SL per patient was calculated in seven frequency bands. In total, 114 patients were selected. Fifty-seven patients were diagnosed with epilepsy (20 had IEDs on their EEG) and 57 matched patients had other diagnoses. Epilepsy patients had significantly higher SL in the theta band than non-epilepsy patients. Furthermore, theta band SL proved to be a significant predictor of a diagnosis of epilepsy. When only those epilepsy patients without IEDs were considered (n = 74), theta band SL could predict diagnosis with specificity of 76 % and sensitivity of 62%. Conclusion/Significance: Theta band functional connectivity may be a useful diagnostic tool in diagnosing epilepsy

Effect of intracellular lipid accumulation in a new model of non-alcoholic fatty liver disease

<p>Abstract</p> <p>Background</p> <p><it>In vitro </it>exposure of liver cells to high concentrations of free fatty acids (FFA) results in fat overload which promotes inflammatory and fibrogenic response similar to those observed in patients with Non-Alcoholic Fatty Liver Disease (NAFLD) and Non-Alcoholic Steatohepatitis (NASH). Since the mechanisms of this event have not been fully characterized, we aimed to analyze the fibrogenic stimuli in a new <it>in vitro </it>model of NASH.</p> <p>Methods</p> <p>HuH7 cells were cultured for 24 h in an enriched medium containing bovine serum albumin and increasing concentrations of palmitic and oleic acid at a molar ratio of 1:2 (palmitic and oleic acid, respectively). Cytotoxic effect, apoptosis, oxidative stress, and production of inflammatory and fibrogenic cytokines were measured.</p> <p>Results</p> <p>FFA induces a significant increment in the intracellular content of lipid droplets. The gene expression of interleukin-6, interleukin-8 and tumor necrosis factor alpha was significantly increased. The protein level of interleukin-8 was also increased. Intracellular lipid accumulation was associated to a significant up-regulation in the gene expression of transforming growth factor beta 1, alpha 2 macroglobulin, vascular endothelial growth factor A, connective tissue growth factor, insulin-like growth factor 2, thrombospondin 1. Flow cytometry analysis demonstrated a significant increment of early apoptosis and production of reactive oxygen species.</p> <p>Conclusions</p> <p>The exposure of hepatocytes to fatty acids elicits inflammation, increase of oxidative stress, apoptosis and production of fibrogenic cytokines. These data support a primary role of FFA in the pathogenesis of NAFLD and NASH.</p

Brain activation of the defensive and appetitive survival systems in obsessive compulsive disorder

Several studies have shown that basic emotions are responsible for a significant enhancement of early visual processes and increased activation in visual processing brain regions. It may be possible that the cognitive uncertainty and repeated behavioral checking evident in Obsessive Compulsive Disorder (OCD) is due to the existence of abnormalities in basic survival circuits, particularly those associated with the visual processing of the physical characteristics of emotional-laden stimuli. The objective of the present study was to test if patients with OCD show evidence of altered basic survival circuits, particularly those associated with the visual processing of the physical characteristics of emotional stimuli. Fifteen patients with OCD and 12 healthy controls underwent functional magnetic resonance imaging acquisition while being exposed to emotional pictures, with different levels of arousal, intended to trigger the defensive and appetitive basic survival circuits. Overall, the present results seem to indicate dissociation in the activity of the defense and appetitive survival systems in OCD. Results suggest that the clinical group reacts to basic threat with a strong activation of the defensive system mobilizing widespread brain networks (i.e., frontal, temporal, occipital-parietal, and subcortical nucleus) and blocking the activation of the appetitive system when facing positive emotional triggers from the initial stages of visual processing (i.e., superior occipital gyrus)

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

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

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases