Brain Circuitry Supporting Multi-Organ Autonomic Outflow in Response to Nausea.

While autonomic outflow is an important co-factor of nausea physiology, central control of this outflow is poorly understood. We evaluated sympathetic (skin conductance level) and cardiovagal (high-frequency heart rate variability) modulation, collected synchronously with functional MRI (fMRI) data during nauseogenic visual stimulation aimed to induce vection in susceptible individuals. Autonomic data guided analysis of neuroimaging data, using a stimulus-based (analysis windows set by visual stimulation protocol) and percept-based (windows set by subjects' ratings) approach. Increased sympathetic and decreased parasympathetic modulation was associated with robust and anti-correlated brain activity in response to nausea. Specifically, greater autonomic response was associated with reduced fMRI signal in brain regions such as the insula, suggesting an inhibitory relationship with premotor brainstem nuclei. Interestingly, some sympathetic/parasympathetic specificity was noted. Activity in default mode network and visual motion areas was anti-correlated with parasympathetic outflow at peak nausea. In contrast, lateral prefrontal cortical activity was anti-correlated with sympathetic outflow during recovery, soon after cessation of nauseogenic stimulation. These results suggest divergent central autonomic control for sympathetic and parasympathetic response to nausea. Autonomic outflow and the central autonomic network underlying ANS response to nausea may be an important determinant of overall nausea intensity and, ultimately, a potential therapeutic target.

Investigation of the electrophysiological correlates of negative BOLD response during intermittent photic stimulation: An EEG-fMRI study.

Although the occurrence of concomitant positive BOLD responses (PBRs) and negative BOLD responses (NBRs) to visual stimuli is increasingly investigated in neuroscience, it still lacks a definite explanation. Multimodal imaging represents a powerful tool to study the determinants of negative BOLD responses: the integration of functional Magnetic Resonance Imaging (fMRI) and electroencephalographic (EEG) recordings is especially useful, since it can give information on the neurovascular coupling underlying this complex phenomenon. In the present study, the brain response to intermittent photic stimulation (IPS) was investigated in a group of healthy subjects using simultaneous EEG-fMRI, with the main objective to study the electrophysiological mechanisms associated with the intense NBRs elicited by IPS in extra-striate visual cortex. The EEG analysis showed that IPS induced a desynchronization of the basal rhythm, followed by the instauration of a novel rhythm driven by the visual stimulation. The most interesting results emerged from the EEG-informed fMRI analysis, which suggested a relationship between the neuronal rhythms at 10 and 12 Hz and the BOLD dynamics in extra-striate visual cortex. These findings support the hypothesis that NBRs to visual stimuli may be neuronal in origin rather than reflecting pure vascular phenomena. Hum Brain Mapp 37:2247-2262, 2016. © 2016 Wiley Periodicals, Inc.

Investigation of negative BOLD responses in human brain through NIRS technique. A visual stimulation study.

Despite negative blood oxygenation level dependent (BOLD) responses to visual stimuli have recently gained considerable interest, the explanation for their underlying neuronal and vascular mechanisms is still controversial. In the present study, a multimodal experimental approach is presented to shed light on the negative BOLD phenomenon in the human brain. In particular, information from functional magnetic resonance imaging (fMRI) and near infrared spectroscopy (NIRS) was integrated to confirm and gain insight into the phenomenon of negative BOLD responses (NBRs) to unpatterned intermittent photic stimulation (IPS) in healthy subjects. Eight healthy subjects participated in the study. Consistent findings emerged from the activation analysis of fMRI and NIRS data and the comparison of BOLD and hemoglobin responses at the single channel level showed that NBRs are related to a decrease in oxyhemoglobin (HbO) combined with a lower increase in deoxyhemoglobin (HHb), corresponding to a decrease in total hemoglobin (THb) and estimated cerebral blood volume (CBV). The HbO and HHb variations were significant in at least one channel in six subjects out of eight (p<0.05). The NIRS technique allowed obtaining valuable information on the vascular determinants of the NBRs, since the discrimination between HbO, HHb and THb information provided a more comprehensive view of the negative BOLD phenomenon. The within and between subject heterogeneous BOLD-Hb temporal relations pave the way to further investigations into the neurovascular properties of NBRs.

Unsupervised EEG preictal interval identification in patients with drug-resistant epilepsy.

Typical seizure prediction models aim at discriminating interictal brain activity from pre-seizure electrographic patterns. Given the lack of a preictal clinical definition, a fixed interval is widely used to develop these models. Recent studies reporting preictal interval selection among a range of fixed intervals show inter- and intra-patient preictal interval variability, possibly reflecting the heterogeneity of the seizure generation process. Obtaining accurate labels of the preictal interval can be used to train supervised prediction models and, hence, avoid setting a fixed preictal interval for all seizures within the same patient. Unsupervised learning methods hold great promise for exploring preictal alterations on a seizure-specific scale. Multivariate and univariate linear and nonlinear features were extracted from scalp electroencephalography (EEG) signals collected from 41 patients with drug-resistant epilepsy undergoing presurgical monitoring. Nonlinear dimensionality reduction was performed for each group of features and each of the 226 seizures. We applied different clustering methods in searching for preictal clusters located until 2 h before the seizure onset. We identified preictal patterns in 90% of patients and 51% of the visually inspected seizures. The preictal clusters manifested a seizure-specific profile with varying duration (22.9 ± 21.0 min) and starting time before seizure onset (47.6 ± 27.3 min). Searching for preictal patterns on the EEG trace using unsupervised methods showed that it is possible to identify seizure-specific preictal signatures for some patients and some seizures within the same patient.

Assessment of Singularities in the EEG During A-Phases of Sleep Based on Wavelet Decomposition.

Electroencephalography (EEG) signals convey information related to different processes that take place in the brain. From the EEG fluctuations during sleep, it is possible to establish the sleep stages and identify short events, commonly related to a specific physiological process or pathology. Some of these short events (called A-phases) present an organization and build up the concept of the Cyclic Alternating Pattern (CAP) phenomenon. In general, the A-phases abruptly modify the EEG fluctuations, and a singular behavior could occur. With the aim to quantify the abrupt changes during A-phases, in this work the wavelet analysis is considered to compute Hölder exponents, which measure the singularity strength. We considered time windows of 2s outside and 5s inside A-phases onset (or offset). A total number of 5121 A-phases from 9 healthy participants and 10 patients with periodic leg movements were analyzed. Within an A-phase the Hölder numerical value tends to be 0.6, which implies a less abrupt singularity. Whereas outside of A-phases, it is observed that the Hölder value is approximately equal to 0.3, which implies stronger singularities, i.e., a more evident discontinuity in the signal behavior. In addition, it seems that the number of singularities increases inside of A-phases. The numerical results suggest that the EEG naturally conveys singularities modified by the A-phase occurrence, and this information could help to conceptualize the CAP phenomenon from a new perspective based on the sharpness of the EEG instead of the oscillatory way.

Heart rate variability analysis for the identification of the preictal interval in patients with drug-resistant epilepsy.

Electrocardiogram (ECG) recordings, lasting hours before epileptic seizures, have been studied in the search for evidence of the existence of a preictal interval that follows a normal ECG trace and precedes the seizure's clinical manifestation. The preictal interval has not yet been clinically parametrized. Furthermore, the duration of this interval varies for seizures both among patients and from the same patient. In this study, we performed a heart rate variability (HRV) analysis to investigate the discriminative power of the features of HRV in the identification of the preictal interval. HRV information extracted from the linear time and frequency domains as well as from nonlinear dynamics were analysed. We inspected data from 238 temporal lobe seizures recorded from 41 patients with drug-resistant epilepsy from the EPILEPSIAE database. Unsupervised methods were applied to the HRV feature dataset, thus leading to a new perspective in preictal interval characterization. Distinguishable preictal behaviour was exhibited by 41% of the seizures and 90% of the patients. Half of the preictal intervals were identified in the 40 min before seizure onset. The results demonstrate the potential of applying clustering methods to HRV features to deepen the current understanding of the preictal state.

Consumers Emotional Responses to Functional and Hedonic Products: A Neuroscience Research.

Over the years, researchers have enriched the postulation that hedonic products generate deeper emotional reactions and feelings in the consumer than functional products. However, recent research empirically proves that hedonic products are more affect-rich only for some consumer segments or for specific consumption contexts. We argue that such inconsistency may derive from the nature of the emotions assessed that is strictly dependent on their empirical measurement and not from the mere existence of emotions themselves. Self-reported methods of evaluating consumer experience, on which prior studies are grounded, only assess conscious emotions the consumer can recognize and report, but not unconscious feelings, happening without individual awareness. The present work takes this challenge by conducting a laboratory experiment in which subjects are exposed to both a utilitarian product and a hedonic product. Physiological measures have been adopted to investigate unconscious emotional responses and self-reported measures to assess conscious emotions toward the products. Specifically, physiological data regarding the subjects' cardiac activity, respiratory activity, electrodermal activity, and cerebral activity have been collected and complemented with a survey. Results confirm that both functional and hedonic products generate emotional responses in consumers. Further, findings show that when a consumer is exposed to a functional product, the physiological emotional responses are disassociated from the self-reported ones. A diverse pattern is depicted for hedonic products. We suggest an alternative explanation for the apparent lack of affect-rich experiences elicited by functional products and the need to reconsider emotional responses for these products.

Exploration of the physiological response to an online gambling task by frequency domain analysis of the electrodermal activity.

Online gambling has dramatically increased over the last decades, thus the study of the underlying physiological mechanisms could be helpful to better understand related disorders. Specifically, physiological arousal is well-known to play a key role in gambling behavior. In the present study, unconventional frequency feature of the electrodermal activity (EDA) was extracted (EDASympn) and compared to the most common heart rate variability (HRV) spectral parameters (LF, HF, HFn, LF/HF) to measure arousal during an online gambling session. 46 subjects played online slot machines for 30 minutes, while EDA and ECG were recorded. In the analysis the gaming session was divided into three 10-minutes-long phases. A one-way repeated measures analysis of variance was carried out for each spectral parameter, with the game phases as within-subjects factor. All the calculated parameters showed significant differences between the initial phase of the game and the last two (p < 0.001). In particular, EDAsympn displayed a reciprocal trend with respect to HFn: an initial increase (decrease for HFn) was followed by a plateau phase. LF exhibited a significant difference also between the second and the third phases. EDA frequency-domain analysis appears to be a promising method for physiological arousal assessment, by showing the same discriminative power of HRV spectral components. Further research is needed to emphasize these findings.Clinical Relevance-This promotes the use of a new and easy-to-implement method to assess sympathetic activity.

Removal of the ballistocardiographic artifact from EEG-fMRI data: a canonical correlation approach.

The simultaneous recording of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) can give new insights into how the brain functions. However, the strong electromagnetic field of the MR scanner generates artifacts that obscure the EEG and diminish its readability. Among them, the ballistocardiographic artifact (BCGa) that appears on the EEG is believed to be related to blood flow in scalp arteries leading to electrode movements. Average artifact subtraction (AAS) techniques, used to remove the BCGa, assume a deterministic nature of the artifact. This assumption may be too strong, considering the blood flow related nature of the phenomenon. In this work we propose a new method, based on canonical correlation analysis (CCA) and blind source separation (BSS) techniques, to reduce the BCGa from simultaneously recorded EEG-fMRI. We optimized the method to reduce the user's interaction to a minimum. When tested on six subjects, recorded in 1.5 T or 3 T, the average artifact extracted with BSS-CCA and AAS did not show significant differences, proving the absence of systematic errors. On the other hand, when compared on the basis of intra-subject variability, we found significant differences and better performance of the proposed method with respect to AAS. We demonstrated that our method deals with the intrinsic subject variability specific to the artifact that may cause averaging techniques to fail.

Investigating the Optimal Baseline Positioning to Maximize Cognitive Experimental Outcome.

In emotional and cognitive research, the baseline is commonly used for standardization purposes in order to have a reference for the identification of the activation. Since no previous studies have investigated which moment of the experiment could be considered optimal for baseline evaluation, we designed an experimental protocol to analyze which time interval could be considered more effective in highlighting differences between the baseline state and the cognitive effort exhibited during tasks (specifically, reaction and working memory tasks). Several indexes were extracted from EEG signals during the visualization of the considered baseline stimuli and the execution of tasks. From our results, as regards to the considered Global Field Power (GFP) indexes (Attention and Memorization indexes), the last baseline stimulus seems to be the best one to highlight the difference in cognitive workload between the individual baseline condition and the two cognitive tasks. Instead, in terms of Engagement index (EI), the difference between Reaction Task (RT) and the individual baseline condition seems to be best highlighted by the relaxing video right after performed tasks. In conclusion, the best baseline position to maximize the differences in cognitive workload may vary among the considered indexes because of confounding effects and individual differences, but further analyses are required to validate this result.

Multiscale Functional Clustering Reveals Frequency Dependent Brain Organization in Type II Focal Cortical Dysplasia With Sleep Hypermotor Epilepsy.

OBJECTIVE: A multiscale functional clustering approach is proposed to investigate the organization of the epileptic networks during different sleep stages and in relation with the occurrence of seizures. METHOD: Stereo-electroencephalographic signals from seven pharmaco-resistant epileptic patients (focal cortical dysplasia type II) were analyzed. The discrete wavelet transform provided a multiscale framework on which a data-driven functional clustering procedure was applied, based on multivariate measures of integration and mutual information. The most interacting functional clusters (FCs) within the sampled brain areas were extracted. RESULTS: FCs characterized by strongly integrated activity were observed mostly in the beta and alpha frequency bands, immediately before seizure onset and in deep sleep stages. These FCs generally included the electrodes from the epileptogenic zone. Furthermore, repeatable patterns were found across ictal events in the same patient. CONCLUSION: In line with previous studies, our findings provide evidence of the important role of beta and alpha activity in seizures generation and support the relation between epileptic activity and sleep stages. SIGNIFICANCE: Despite the small number of subjects included in the study, the present results suggest that the proposed multiscale functional clustering approach is a useful tool for the identification of the frequency-dependent mechanisms underlying seizure generation.

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.

Towards the development of physiological models for emotions evaluation.

In the last decades numerous researches have revealed a strong link between emotions and several physiological responses. However, the automatic recognition of emotions still remains a challenge. In this work we describe a novel approach to estimate valence, arousal and dominance values from various biological parameters (derived from electrodermal activity, heart rate variability signal and electroencephalography), by means of multiple linear regression models. The models training was performed by using a set of pictures pre-evaluated in terms of valence, arousal and dominance, selected from the International Affective Picture System (IAPS) database. By using the step-wise regression method, all the possible combinations of considered biological parameters were tested as input variables for the models. The three multiple linear regression models that could provide the best fit for IAPS pictures valence, arousal and dominance values were selected. The features included in the optimal models were the average of the inter-beat duration (mean RR), the EEG spectral power computed in alpha, beta and theta frequency bands (Alpha, Beta and Theta power) and the average value of EDA signal (mean EDA). The obtained models show an overall good performance in predicting valence, arousal and dominance values.

Integrated IoT intelligent system for the automatic detection of cardiac variability.

Detection of abnormal cardiac events during clinical examination is a matter of chances, as such events may not happen at that precise moment. We therefore propose the implementation and evaluation of a mobile based system that allows a real-time detection of cardiovascular problems related to heart-rate variability. Our approach is to integrate an Internet of Things eHealth kit based on Arduino and validated algorithms for heart rate variability to build a low-cost, reliable and scalable solution. 12 healthy users have evaluated the system in different scenarios to assess the best performing algorithm and the best windowing interval. Finally, a mobile system based on an Android application which integrated the Pan and Tompkins algorithm with a 20 seconds windowing and a module to retrieve real-time electrocardiography through a Bluetooth interface was implemented and assessed.

Validating an Algorithm for Automatic Scoring of Inspiratory Flow Limitation Within a Range of Recording Settings.

Inspiratory Flow Limitation (IFL) is a phenomenon associated with narrowing of the upper airway, preventing an increase in inspiratory airflow despite an elevation in intrathoracic pressure. It has been shown that quantification of IFL might complement information provided by standard indices such as the apnea-hypopnea index (AHI) in characterizing sleep disordered breathing and identifying subclinical disease. Defining guidelines for visual scoring of IFL has been of increasing interest, and automated methods are desirable to avoid inter-scorer variability and allow analysis of large datasets. In addition, as recording instrumentation and practices may vary across hospitals and laboratories, it is useful to assess the influence of the recording parameters on the accuracy of the automated classification. We employed nasal pressure signals recorded as part of polysomnography (PSG) studies in 7 patients. Two experts independently classified approximately 2000 breaths per subject as IFL or non-IFL, and we used the consensus scoring as the gold standard. For each breath, we derived features indicative of the shape and frequency content of the signals and used them to train and validate a Support Vector Machine (SVM) to distinguish IFL from non-IFL breaths. We also assessed the effect of signal filtering (down-sampling and baseline-removal) on classification performance. The performance of the classifier was excellent (accuracy ~93%) for the raw signals (collected at 125 Hz with no filtering), and decreased for increasing high-pass cut-off frequencies (fc = [0.05, 0.1, 0.15, 0.2] Hz) down to 84% for fc= 0.2 Hz and for decreasing sampling rate (fs = [20, 50, 75, 100] Hz) down to ~85% for fs=20 Hz. Loss of performance was minimized when the classifier was re-trained using data with matched filtering characteristics (accuracy > 89%). We can conclude that the SVM feature-based algorithm provides a reliable and efficient tool for breath-by-breath classification.

Evaluation and application of a RBF neural network for online single-sweep extraction of SEPs during scoliosis surgery.

A method for on-line single sweep detection of somatosensory evoked potentials (SEPs) during intraoperative neuromonitoring is proposed. It is based on a radial-basis function neural network with Gaussian activations. In order to improve its tracking capabilities, the radial-basis functions location is partially learnt sweep-by-sweep; the training algorithm is effective, though consistent with real-time applications. This new detection method has been tested on simulated data so as to set the network parameters. Moreover, it has been applied to real recordings obtained from a new neuromonitoring technique which is based on the simultaneous observation of the SEP and of the evoked H-reflex elicited by the same electric stimulus. The SEPs have been extracted using the neural network and the results have then been compared to those obtained by ARX filtering and correlated with the spinal cord integrity information obtained by the H-reflex. The proposed algorithm has been proved to be particularly effective and suitable for single-sweep detection. It is able to track both sudden and smooth signal changes of both amplitude and latency and the needed computational time is moderate.

EEG Analysis During Active and Assisted Repetitive Movements: Evidence for Differences in Neural Engagement.

Two key ingredients of a successful neuro-rehabilitative intervention have been identified as intensive and repetitive training and subject's active participation, which can be coupled in an active robot-assisted training. To exploit these two elements, we recorded electroencephalography, electromyography and kinematics signals from nine healthy subjects performing a 2×2 factorial design protocol, with subject's volitional intention and robotic glove assistance as factors. We quantitatively evaluated primary sensorimotor, premotor and supplementary motor areas activation during movement execution by computing event-related desynchronization (ERD) patterns associated to mu and beta rhythms. ERD patterns showed a similar behavior for all investigated regions: statistically significant ERDs began earlier in conditions requiring subject's volitional contribution; ERDs were prolonged towards the end of movement in conditions in which the robotic assistance was present. Our study suggests that the combination between subject volitional contribution and movement assistance provided by the robotic device (i.e., active robot-assisted modality) is able to provide early brain activation (i.e., earlier ERD) associated with stronger proprioceptive feedback (i.e., longer ERD). This finding might be particularly important for neurological patients, where movement cannot be completed autonomously and passive/active robot-assisted modalities are the only possibilities of execution.

Multivariate and multiorgan analysis of cardiorespiratory variability signals: the CAP sleep case.

Signals from different systems are analyzed during sleep on a beat-to-beat basis to provide a quantitative measure of synchronization with the heart rate variability (HRV) signal, oscillations of which reflect the action of the autonomic nervous system. Beat-to-beat variability signals synchronized to QRS occurrence on ECG signals were extracted from respiration, electroencephalogram (EEG) and electromyogram (EMG) traces. The analysis was restricted to sleep stage 2. Cyclic alternating pattern (CAP) periods were detected from EEG signals and the following conditions were identified: stage 2 non-CAP (2 NCAP), stage 2 CAP (2 CAP) and stage 2 CAP with myoclonus (2 CAP MC). The coupling relationships between pairs of variability signals were studied in both the time and frequency domains. Passing from 2 NCAP to 2 CAP, sympathetic activation is indicated by tachycardia and reduced respiratory arrhythmia in the heart rate signal. At the same time, we observed a marked link between EEG and HRV at the CAP frequency. During 2 CAP MC, the increased synchronization involved myoclonus and respiration. The underlying mechanism seems to be related to a global control system at the central level that involves the different systems.

User-Independent Recognition of Sports Activities From a Single Wrist-Worn Accelerometer: A Template-Matching-Based Approach.

GOAL: To investigate the accuracy of template matching for classifying sports activities using the acceleration signal recorded with a wearable sensor. METHODS: A population of 29 normal weight and 19 overweight subjects was recruited to perform eight common sports activities, while body movement was measured using a triaxial accelerometer placed at the wrist. User- and axis-independent acceleration signal templates were automatically extracted to represent each activity category and recognize activity types. Five different similarity measures between example signals and templates were compared: Euclidean distance, dynamic time warping (DTW), derivative DTW, correlation and an innovative index, and combining distance and correlation metrics ( Rce). Template-based activity recognition was compared to statistical-learning classifiers, such as Naïve Bayes, decision tree, logistic regression (LR), and artificial neural network (ANN) trained using time- and frequency-domain signal features. Each algorithm was tested on data from a holdout group of 15 normal weight and 19 overweight subjects. RESULTS: The Rce index outperformed other template-matching metrics by achieving recognition rate above 80% for the majority of the activities. Template matching showed robust classification accuracy when tested on unseen data and in case of limited training examples. LR and ANN achieved the highest overall recognition accuracy  âˆ¼  85% but showed to be more vulnerable to misclassification error than template matching on overweight subjects' data. CONCLUSION: Template matching can be used to classify sports activities using the wrist acceleration signal. SIGNIFICANCE: Automatically extracted template prototypes from the acceleration signal may be used to enhance accuracy and generalization properties of statistical-learning classifiers.

Neuroimaging brainstem circuitry supporting cardiovagal response to pain: a combined heart rate variability/ultrahigh-field (7 T) functional magnetic resonance imaging study.

Central autonomic control nuclei in the brainstem have been difficult to evaluate non-invasively in humans. We applied ultrahigh-field (7 T) functional magnetic resonance imaging (fMRI), and the improved spatial resolution it affords (1.2 mm isotropic), to evaluate putative brainstem nuclei that control and/or sense pain-evoked cardiovagal modulation (high-frequency heart rate variability (HF-HRV) instantaneously estimated through a point-process approach). The time-variant HF-HRV signal was used to guide the general linear model analysis of neuroimaging data. Sustained (6 min) pain stimulation reduced cardiovagal modulation, with the most prominent reduction evident in the first 2 min. Brainstem nuclei associated with pain-evoked HF-HRV reduction were previously implicated in both autonomic regulation and pain processing. Specifically, clusters consistent with the rostral ventromedial medulla, ventral nucleus reticularis (Rt)/nucleus ambiguus (NAmb) and pontine nuclei (Pn) were found when contrasting sustained pain versus rest. Analysis of the initial 2-min period identified Rt/NAmb and Pn, in addition to clusters consistent with the dorsal motor nucleus of the vagus/nucleus of the solitary tract and locus coeruleus. Combining high spatial resolution fMRI and high temporal resolution HF-HRV allowed for a non-invasive characterization of brainstem nuclei, suggesting that nociceptive afference induces pain-processing brainstem nuclei to function in concert with known premotor autonomic nuclei in order to affect the cardiovagal response to pain.