A novel spatiotemporal tool for the automatic classification of fMRI noise based on Independent Component Analysis.

In this study, a semi-automatic, easy-to-use classification method for the identification and removal of fMRI noise is proposed and tested. The method relies on subject-level spatial independent component analysis (ICA) of fMRI data. Starting from a reference set of labeled independent components (ICs), novel ICs are classified as physiological/artefactual by combining a spatial correlation (SC) analysis with the reference ICs and relative power spectral (PS) analysis. Here, ICs from a task-based fMRI dataset were used as reference. SC and SP thresholds were set using a test dataset (5 subjects, same fMRI protocol) based on Receiving Operating Characteristic curves. The tool performance and versatility were measured on a resting-state fMRI dataset (5 subjects). Our results show that the method can automatically identify noise-related ICs with accuracy, specificity and sensitivity higher than 80% across different fMRI protocols. These findings also suggest that the reference set provided in the present study might be used to mark ICs coming from independent taskrelated or resting-state fMRI datasets.Clinical relevance- The new method will be included in a userfriendly, open-source tool for removal of noisy contributions from fMRI datasets to be used in clinical and research practices.

Exploration of Web-Sites Affects Autonomic Responses Related to Unconscious Emotions.

In this work we are interested in analyzing any correlations between physiological parameters, extracted from signals such as Electrocardiogram, respiratory signal and Skin Conductance, and self-reported indices related to emotional or cognitive stimulations. For this purpose, an experiment involving twenty participants with a mean age of 25±5 years of both sexes (13 males and 7 females) was carried out. The protocol included the navigation in simulated web-sites and the vision of two different commercial products (utilitarian and hedonistic). At the end of the navigation, a questionnaire was submitted to the subject in order to measure his/her feelings and emotions in a qualitative and subjective way. Quantitative features were extracted from the physiological signals recorded during the execution of the protocol. We performed a correlation analysis between self-reported and physiological responses related to Arousal, Pleasure, Expectancy and Situational Involvement. Findings showed that when a consumer is exposed to a utilitarian product, the physiological emotional responses are disassociated from the self-reported ones. For the hedonistic product, instead, self-reported measures significantly correlate with physiological arousal features like the combined effect of cardiac and respiratory activity and the Heart Rate.

Functional Clustering approach for the analysis of Stereo-EEG activity patterns in correspondence of epileptic seizures.

In this study, a functional clustering approach is proposed and tested for the identification of brain functional networks emerging during sleep-related seizures. Stereo-EEG signals recorded in patients with Type II Focal Cortical Dysplasia (FCD type II), were analyzed. This novel approach is able to identify the network configuration changes in pre-ictal and early ictal periods, by grouping Stereo-EEG signals on the basis of the Cluster Index, after wavelet multiscale decomposition. Results showed that the proposed method is able to detect clusters of interacting leads, mainly overlapped on the Epileptogenic Zone (EZ) identified by a clinical expert, with distinctive configurations related to analyzed frequency ranges. This suggested the presence of coupling activities between the elements of the epileptic system at different frequency scales.

Effects of standardized Ginkgo biloba extract on the acquisition, retrieval and extinction of conditioned suppression: Evidence that short-term memory and long-term memory are differentially modulated.

Studies in our laboratory have characterized the putative neuromodulatory effects of a standardized extract of the green leaves of Ginkgo biloba (EGb), which comprises a formulation of 24% ginkgo-flavoglycosides and 6% ginkgo-terpenoid lactones, on conditioned suppression. This model comprises a suitable animal model for investigating the behavioral changes and pharmacological mechanisms that underlie fear memory and anxiety. The characterization of the effects on distinct stages of fear memory or fear extinction will help illustrate both the beneficial and harmful effects. Three hundred adult male Wistar rats were randomly assigned to 30 groups according to the treatment as follows: i-ii) control groups (CS-US and CSno-US); iii) vehicle group (12% Tween®80); and iv-vi) EGb groups (250, 500 and 1000mgkg(-1)); or experimental procedures designed to assess the effects of EGb treatment prior to the acquisition (n=20 per group) and retrieval of conditioned fear (n=10 per group) or prior to the extinction training (n=10 per group) and extinction retention test (n=10 per group). Furthermore, to better understand the effects of acute EGb treatment on fear memory, we conducted two additional analyses: the acquisition of within- and between-session extinction of fear memory (short- and long-term memory, respectively). No difference was identified between the control and treatment groups during the retention test (P>0.05), with the exception of the CSno-US group in relation to all groups (P<0.05). A between-session analysis indicated that EGb at 250mgkg(-1) facilitated the acquisition of extinction fear memory, which was verified by the suppression ration in the first trial of extinction training (SR=0.39) and the extinction retention test session (SR=0.53, P<0.05), without impairments in fear memory acquisition, which were evaluated during the retention test (SR=0.79). Moreover, EGb administered at 1000mgkg(-1) prior to conditioning did not enhance the long-term extinction memory, i.e., it did not prevent the return of extinguished fear memory in the extinction retention test, in which the spontaneous recovery of fear was demonstrated (SR=0.63, P<0.05); however, it significantly facilitated short-term memory as verified by data from the within-session extinction (1 to 8-10 trials) during the retention test (SR=0.73 to SR=0.59; P<0.05) and the extinction retention test (SR=0.63 to SR=0.41; P<0.05). Moreover, spontaneous recovery was identified in response to a higher dose of EGb when administered prior to extinction training (SR=0.75, P<0.05) and the extinction retention test (SR=0.70; P<0.05). At dose of 500mgkg(-1) EGb reduced the suppression ratio when administered prior to the retention test (SR=0.57) and extinction training (SR=0.55; P<0.05) without preventing the acquisition of fear memory, which suggests that EGb has anti-anxiety effects. Taken together, the current findings suggest that EGb differentially modulates short- and long-term memory, as well as anxiety-like behavior. The actions of EGb may provide information regarding the beneficial effects in the prevention and treatment of neurocognitive impairments and anxiety disorders. Additional analyses are necessary to facilitate an understanding of these effects; however, previous data from our group suggest that GABAergic, serotoninergic and glutamatergic receptors are potential targets of the effects of EGb on conditioned suppression.

Hemodynamic and EEG Time-Courses During Unilateral Hand Movement in Patients with Cortical Myoclonus. An EEG-fMRI and EEG-TD-fNIRS Study.

Multimodal human brain mapping has been proposed as an integrated approach capable of improving the recognition of the cortical correlates of specific neurological functions. We used simultaneous EEG-fMRI (functional magnetic resonance imaging) and EEG-TD-fNIRS (time domain functional near-infrared spectroscopy) recordings to compare different hemodynamic methods with changes in EEG in ten patients with progressive myoclonic epilepsy and 12 healthy controls. We evaluated O2Hb, HHb and Blood oxygen level-dependent (BOLD) changes and event-related desynchronization/synchronization (ERD/ERS) in the α and ß bands of all of the subjects while they performed a simple motor task. The general linear model was used to obtain comparable fMRI and TD-fNIRS activation maps. We also analyzed cortical thickness in order to evaluate any structural changes. In the patients, the TD-NIRS and fMRI data significantly correlated and showed a significant lessening of the increase in O2Hb and the decrease in BOLD. The post-movement ß rebound was minimal or absent in patients. Cortical thickness was moderately reduced in the motor area of the patients and correlated with the reduction in the hemodynamic signals. The fMRI and TD-NIRS results were consistent, significantly correlated and showed smaller hemodynamic changes in the patients. This finding may be partially attributable to mild cortical thickening. However, cortical hyperexcitability, which is known to generate myoclonic jerks and probably accounts for the lack of EEG ß-ERS, did not reflect any increased energy requirement. We hypothesize that this is due to a loss of inhibitory neuronal components that typically fire at high frequencies.

Application of dynamical analyses of heart rate to rhythm classification and prognosis.

We have developed numerical approaches to dynamical analysis of heart rates, measured as interbeat or RR, intervals, based on entropy and fluctuation analyses in a large data base of consecutive Holter monitor recordings. In Part I, we present a RR interval-based classifier that distinguishes normal sinus rhythm (NSR), atrial fibrillation (AF) and sinus rhythm with ectopy with an accuracy of 99%, 81% and 77%respectively, using 10-minute segments. In Part II, we present 2-year mortality estimation based on the entropy calculations. The major finding is that normal dynamics identify a very low risk group. Taken together, these results point to automated analysis of heart rate time series with important clinical applications.

Insomnia types and sleep microstructure dynamics.

This work aims to investigate sleep microstructure as expressed by Cyclic Alternating Pattern (CAP), and its possible alterations in pathological sleep. Three groups, of 10 subjects each, are considered: a) normal sleep, b) psychophysiological insomnia, and c) sleep misperception. One night sleep PSG and sleep macro- micro structure annotations were available per subject. The statistical properties and the dynamics of CAP events are in focus. Multiscale and non-linear methods are presented for the analysis of the microstructure event time series, applied for each type of CAP events, and their combination. The results suggest that a) both types of insomnia present CAP differences from normal sleep related to hyperarousal, b) sleep misperception presents more extensive differences from normal, potentially reflecting multiple sleep mechanisms, c) there are differences between the two types of insomnia as regard to the intertwining of events of different subtypes. The analysis constitutes a contribution towards new markers for the quantitative characterization of insomnia, and its subtypes.

Long-term treatment with standardized extract of Ginkgo biloba L. enhances the conditioned suppression of licking in rats by the modulation of neuronal and glial cell function in the dorsal hippocampus and central amygdala.

Our group previously demonstrated that short-term treatment with a standardized extract of Ginkgo biloba (EGb) changed fear-conditioned memory by modulating gene expression in the hippocampus, amygdaloid complex and prefrontal cortex. Although there are few controlled studies that support the long-term use of EGb for the prevention and/or treatment of memory impairment, the chronic use of Ginkgo is common. This study evaluated the effects of chronic treatment with EGb on the conditioned emotional response, assessed by the suppression of ongoing behavior and in the modulation of gene and protein expression. Male adult Wistar rats were treated over 28days and assigned to five groups (n=10) as follows: positive control (4mgkg(-1) Diazepam), negative control (12% Tween 80), EGb groups (0.5 and 1.0gkg(-1)) and the naïve group. The suppression of the licking response was calculated for each rat in six trials. Our results provide further evidence for the efficacy of EGb on memory. For the first time, we show that long-term treatment with the highest dose of EGb improves the fear memory and suggests that increased cAMP-responsive element-binding protein (CREB)-1 and glial fibrillary acidic protein (GFAP) mRNA and protein (P<0.001) in the dorsal hippocampus and amygdaloid complex and reduced growth and plasticity-associated protein 43 (GAP-43) (P<0.01) in the hippocampus are involved in this process. The fear memory/treatment-dependent changes observed in our study suggest that EGb might be effective for memory enhancement through its effect on the dorsal hippocampus and amygdaloid complex.

From neurovascular coupling to neurovascular cascade: a study on neural, autonomic and vascular transients in attention.

Mental processes bring about neural, vascular and autonomic changes in the brain cortex. Due to the different nature of these modifications, their onsets show no synchrony and time dynamics is often strongly dissimilar. After acquiring data from a group of 16 subjects, we estimated temporal correlation between task and signals in order to assess possible influences induced by an attentive task on electroencephalographic (EEG), heart rate variability (HRV), oxy- and deoxy-haemoglobin concentration signals. We also investigated correlations and time delays between couples of different biological signals. This allowed for the isolation of a subgroup of subjects showing similar tracks. Cardiac frequency and deoxy-haemoglobin signals displayed a strong positive correlation with the task design, while EEG alpha rhythm and oxygenation showed a negative correlation. Neural electrical response was nearly instantaneous with respect to the task progression, and autonomic response showed a mean delay of about 15 s and a slower hemodynamic response (mean delay above 20 s) was finally induced. Globally, the task elicited a cascade of responses, in which delays can be quantified.

Predictive modeling of cardiovascular complications in incident hemodialysis patients.

The administration of hemodialysis (HD) treatment leads to the continuous collection of a vast quantity of medical data. Many variables related to the patient health status, to the treatment, and to dialyzer settings can be recorded and stored at each treatment session. In this study a dataset of 42 variables and 1526 patients extracted from the Fresenius Medical Care database EuCliD was used to develop and apply a random forest predictive model for the prediction of cardiovascular events in the first year of HD treatment. A ridge-lasso logistic regression algorithm was then applied to the subset of variables mostly involved in the prediction model to get insights in the mechanisms underlying the incidence of cardiovascular complications in this high risk population of patients.

Predicting EEG complexity from sleep macro and microstructure.

This work investigates the relation between the complexity of electroencephalography (EEG) signal, as measured by fractal dimension (FD), and normal sleep structure in terms of its macrostructure and microstructure. Sleep features are defined, encoding sleep stage and cyclic alternating pattern (CAP) related information, both in short and long term. The relevance of each sleep feature to the EEG FD is investigated, and the most informative ones are depicted. In order to quantitatively assess the relation between sleep characteristics and EEG dynamics, a modeling approach is proposed which employs subsets of the sleep macrostructure and microstructure features as input variables and predicts EEG FD based on these features of sleep micro/macrostructure. Different sleep feature sets are investigated along with linear and nonlinear models. Findings suggest that the EEG FD time series is best predicted by a nonlinear support vector machine (SVM) model, employing both sleep stage/transitions and CAP features at different time scales depending on the EEG activation subtype. This combination of features suggests that short-term and long-term history of macro and micro sleep events interact in a complex manner toward generating the dynamics of sleep.

Study of neurovascular and autonomic response in a divided attention test by means of EEG, ECG and NIRS signals.

We evaluated neurovascular and autonomic response to a Divided Attention task within a group of 16 healthy subjects, by means of Electroencephalography, Electrocardiography, functional Near Infrared Spectroscopy techniques, acquired simultaneously. We exctracted Alpha (8-13,5 Hz) and Beta (13,5-30 Hz) power rhythms with a spectral autoregressive residual model, and inter-beat-interval (RR series) and separated superficial (extracortical) and depth NIRS contribution. Cross Correlation Function at different time lags was then calculated between each signal and the task, modeled as a square wave and among couples of signals, in order to evaluate the sequence of activation of the different physiological districts involved and the common information shared. Results showed the presence of a cascade of responses and a strong influence by the block task on each signal, representative of the neurovascular coupling elicited by the cognitive cerebral activation.

CAP sleep in insomnia: new methodological aspects for sleep microstructure analysis.

This work aims to propose new methodologies for the quantitative characterization of insomnia. Sleep microstructure, as expressed by Cyclic Alternatic pattern (CAP) sleep, is studied and differences between normal sleepers and insomniacs are investigated. The dynamic in the structure of CAP activation events is studied by use of wavelet analysis and the content of events, i.e. EEG dynamics, is studied in terms of complexity analysis. Both in structure and content, features exhibiting statistically significant differences are proposed, opening new perspectives for the understanding and the quantitative characterization of sleep and its disorders.

Advances in monitoring cardiovascular signals. Contribution of nonlinear signal processing.

Monitoring procedures are the basis to evaluate the clinical state of patients and to assess changes in their status, thus providing necessary interventions in time. To obtain this important objective it is necessary to integrate technological development with systems performing biomedical knowledge extraction and classification. Methods extracting non linear characteristics from HRV signal are presented and discussed to stress that integrated and multiparametric signal processing approaches can contribute to new diagnostic and classification indices. Examples report heart rate variability analysis in long periods in patients with cardiovascular disease. Fetal ECG monitoring is another example. In this case, coupling nonlinear parameters and linear time and frequency techniques increases diagnostic power and reliability of the monitoring. The paper shows that integrated signal analysis is very helpful to describe pathophysiological mechanisms involved in the cardiovascular and neural system control. It is a reliable basis to set up knowledge-based monitoring systems.

Assessment of the EEG complexity during activations from sleep.

The present study quantitatively analyzes the EEG characteristics during activations (Act) that occur during NREM sleep, and constitute elements of sleep microstructure (i.e. the Cyclic Alternating Pattern). The fractal dimension (FD) and the sample entropy (SampEn) measures were used to study the different sleep stages and the Act that build up the sleep structure. Polysomnographic recordings from 10 good sleepers were analyzed. The complexity indexes of the Act were compared with the non-activation (NAct) periods during non-REM sleep. In addition, complexity measures among the different Act subtypes (A1, A2 and A3) were analyzed. A3 presented a quite similar complexity independently of the sleep stage, while A1 and A2 showed higher complexity in light sleep than during deep sleep. The current results suggest that Act present a hierarchic complexity between subtypes A3 (higher), A2 (intermediate) and A1 (lower) in all sleep stages.

EEG complexity during sleep: on the effect of micro and macro sleep structure.

This work investigates the relation between EEG complexity measures, in particular Fractal Dimension and Sample Entropy, and sleep structure, in terms of both macrostructure, i.e. sleep stages, and microstructure, i.e. phase A activation of CAP sleep. Activation phases are compared with the non-activation periods of non-REM sleep. The study suggests that complexity features can serve as consistent descriptors of sleep dynamics and can potentially assist in the classification of sleep stages.

Long-term correlations and complexity analysis of the heart rate variability signal during sleep. Comparing normal and pathologic subjects.

BACKGROUND: Physiological sleep is characterized by different cyclic phenomena, such as REM, nonREM phases and the Cyclic Alternating Pattern (CAP), that are associated to characteristic patterns in the heart rate variability (HRV) signal. Disruption of such rhythms due to sleep disorders, for example insomnia or apnea syndrome, alters the normal sleep patterns and the dynamics of the HRV recorded during the night. OBJECTIVES: In this paper we analyze long-term and complexity dynamics of the HRV signal recorded during sleep in different groups of subjects. The aim is to investigate whether the calculated indices are able to capture the different characteristics and to discriminate among the groups of subjects, classified according sleep disorders or cardiovascular pathologies. METHODS: Parameters, able to detect the fractal-like behavior of a signal and to measure the regularity and complexity of a time series, are calculated on the HRV signal acquired during the night. Different groups of subjects were analyzed: healthy subjects with high sleep efficiency, healthy subjects with low sleep efficiency, subjects affected by insomnia, heart failure patients, subjects affected by obstructive sleep apnea. RESULTS: The evaluated parameters show significant differences in the groups of subjects considered in this work. In particular heart failure patients have significant lower entropy and complexity values, whereas apnea patients show an increased irregularity when compared with normal subjects with high sleep efficiency. CONCLUSIONS: This work proposes indices that can be used as global descriptors of the dynamics of the whole night and can discriminate among different groups of subjects.