BACKGROUND:Pharmacological tolerance is defined as a decrease in the effect of a drug over time, or the need to increase the dose to achieve the same effect. It has not been established whether repeated exposure to sevoflurane induces tolerance in children.METHODS:We conducted an observational study in children younger than 6 years of age scheduled for multiple radiotherapy sessions with sevoflurane anesthesia. To evaluate the development of sevoflurane tolerance, we analyzed changes in electroencephalographic spectral power at induction, across sessions. We fitted individual and group-level linear regression models to evaluate the correlation between the outcomes and sessions. In addition, a linear mixed-effect model was used to evaluate the association between radiotherapy sessions and outcomes.RESULTS:Eighteen children were included and the median number of radiotherapy sessions per child was 28 (interquartile range: 10 to 33). There was no correlation between induction time and radiotherapy sessions. At the group level, the linear mixed-effect model showed, in a subgroup of patients, that alpha relative power and spectral edge frequency 95 were inversely correlated with the number of anesthesia sessions. Nonetheless, this subgroup did not differ from the other subjects in terms of age, sex, or the total number of radiotherapy sessions.CONCLUSIONS:Our results suggest that children undergoing repeated anesthesia exposure for radiotherapy do not develop tolerance to sevoflurane. However, we found that a group of patients exhibited a reduction in the alpha relative power as a function of anesthetic exposure. These results may have implications that justify further studies.

We present a study of an artificial neural architecture that predict human ocular scanpaths while they are free-viewing different images types. This analysis is made by comparing different metrics that encompass scanpath patterns, these metrics aim to measure spatial and temporal errors; such as the MSE, ScanMatch, cross-correlogram peaks, and MultiMatch. Our methodology begin by choosing one architecture and training different parametric models per subject and image type, this allows to adjust the models to each person and a given set of images. We find out that there is a clear difference in prediction when people free-view images with high visual content (high-frequency contents) and low visual content (no-frequency contents). The input features selected for predicting the scanpath are saliency maps calculated from foveated images together with the past of the ocular scanpath of subjects, modeled by our architecture called FovSOS-FSD (Foveated Saliency and Ocular Scanpath with Feature Selection and Direct Prediction).The results of this study could be used to improve the design of gaze-controlled interfaces, virtual reality, as well as to better understand how humans visually explore their surroundings and pave a way to make future research.

This article presents a transdisciplinary analysis of the challenges in fusing neuroscience concepts with artificial intelligence (AI) to create AI systems inspired by biological cognition. We explore the structural and functional disparities between the neocortex’s canonical microcircuits and existing AI models, focusing on architectural differences, learning mechanisms, and energy efficiency. The discussion extends to adapting non-goal-oriented learning and dynamic neuronal connections from biological brains to enhance AI’s flexibility and efficiency. This work underscores the potential of neuroscientific insights to revolutionize AI development, advocating for a paradigm shift towards more adaptable and brain-like AI systems. We conclude that there is a major room for bioinspiration by focusing on developing architecture, objective functions, and learning rules using a local instead of a global approach.

In this work we present SaFiDe, a deterministic method to detect eye movements (saccades and fixations) from eye-trace data. We developed this method for human and nonhuman primate data from video- and coil-recorded eye traces and further applied the algorithm to eye traces computed from electrooculograms. All the data analyzed were from free-exploration paradigms, where the main challenge was to detect periods of saccades and fixations that were uncued by the task. The method uses velocity and acceleration thresholds, calculated from the eye trace, to detect saccade and fixation periods. We show that our fully deterministic method detects saccades and fixations from eye traces during free visual exploration. The algorithm was implemented in MATLAB, and the code is publicly available on a GitHub repository.•The algorithm presented is entirely deterministic, simplifying the comparison between subjects and tasks.•Thus far, the algorithm presented can operate over video-based eye tracker data, human electrooculogram records, or monkey scleral eye coil data.

Bi-stable perception is a strong instance of cognitive self-organization, providing a research model for how 'the brain makes up its mind.' The complexity of perceptual bistability prevents a simple attribution of functions to areas, because many cognitive processes, recruiting multiple brain regions, are simultaneously involved. The functional magnetic resonance imaging (fMRI) evidence suggests the activation of a large network of distant brain areas. Concurrently, electroencephalographic and magnetoencephalographic (MEEG) literature shows sub second oscillatory activity and phase synchrony on several frequency bands. Strongly represented are beta and gamma bands, often associated with neural/cognitive integration processes. The spatial extension and short duration of brain activities suggests the need for a fast, large-scale neural coordination mechanism. To address the range of temporo-spatial scales involved, we systematize the current knowledge from mathematical models, cognitive sciences and neuroscience at large, from single-cell- to system-level research, including evidence from human and non-human primates. Surprisingly, despite evidence spanning through different organization levels, models, and experimental approaches, the scarcity of integrative studies is evident. In a final section of the review we dwell on the reasons behind such scarcity and on the need of integration in order to achieve a real understanding of the complexities underlying bi-stable perception processes.

Abstract Variations in human behavior correspond to the adaptation of the nervous system to different internal and environmental demands. Attention, a cognitive process for weighing environmental demands, changes over time. Pupillary activity, which is affected by fluctuating levels of cognitive processing, appears to identify neural dynamics that relate to different states of attention. In mice, for example, pupil dynamics directly correlate with brain state fluctuations. Although, in humans, alpha-band activity is associated with inhibitory processes in cortical networks during visual processing, and its amplitude is modulated by attention, conclusive evidence linking this narrowband activity to pupil changes in time remains sparse. We hypothesize that, as alpha activity and pupil diameter indicate attentional variations over time, these two measures should be comodulated. In this work, we recorded the electroencephalographic (EEG) and pupillary activity of 16 human subjects who had their eyes fixed on a gray screen for 1 min. Our study revealed that the alpha-band amplitude and the high-frequency component of the pupil diameter covariate spontaneously. Specifically, the maximum alpha-band amplitude was observed to occur ∼300 ms before the peak of the pupil diameter. In contrast, the minimum alpha-band amplitude was noted to occur ∼350 ms before the trough of the pupil diameter. The consistent temporal coincidence of these two measurements strongly suggests that the subject’s state of attention, as indicated by the EEG alpha amplitude, is changing moment to moment and can be monitored by measuring EEG together with the diameter pupil.

Abstract Background Currently, the diagnosis of schizophrenia is made solely based on interviews and behavioral observations by a trained psychiatrist. Technologies such as electroencephalography (EEG) are used for differential diagnosis and not to support the psychiatrist’s positive diagnosis. Here, we show the potential of EEG recordings as biomarkers of the schizophrenia syndrome. EEG (electroencephalography) differences between patients with schizophrenia (SCZ) and controls have been reported. Tasks used are complex and specialized, not necessarily resemble natural stimuli/ environment to which the brain is adapted. We tested if SCZ global cognitive deficits could be described by EEG features using an ecological and simple approach. Methods We recorded EEG while schizophrenia patients freely viewed natural scenes, and we analyzed the average EEG activity locked to the image onset. We compared occipital ERPs obtained from 11 subjects with SCZ and 9 aged-- matched healthy controls (HC) during free-- exploration of images. Image categories included Plain Gray, Pink Noise and Landscapes (n=10 each). ERPs locked to image onset were obtained from occipital electrodes ader ocular artifacts rejection (by ICA decomposition). Results We found significant differences between patients and healthy controls in occipital areas approximately 500 ms after image onset. These differences were used to train a classifier to discriminate the schizophrenia patients from the controls. The best classifier had 81% sensitivity for the detection of patients and specificity of 59% for the detection of controls, with an overall accuracy of 71%. We observed a positive wave after NS (natural scenes) landscape image onset, with late differences between the SZ patients and HCs. After visual inspection of the ERPs from each area (frontal, central, parietal, and occipital), we found significant differences only in the occipital ERP. It had two positive peaks in the HCs but a reduced second peak in the SZ patients. The median ERP at 0.4–0.6 s after image onset for the HCs was 4.14 μ V and 1.55 μ V for the SZ patients. The patients had a significant decrease in their ERP amplitude compared to the HCs (p = 0.01, Z = −2.5, T = 82, WRS test). Only the occipital electrodes showed differences in this period with the NS images. No other differences between the HC and SZ groups were found at other locations or time periods. We found significant differences between HC and SZ groups at the occipital electrodes only for the NS. Neither gray (p = 0.29, Z = −1.06, T = 101, WRS test) nor pink noise images (p = 0.93, Z = −0.07, T = 114, WRS test) showed significant differences between the HCs and SZ patients at any group of electrodes at this or any other time period. With an accuracy of 71% we are able to classified subjects. We performed 1350 cross--validation leaving 4 subjects out (two SCZ and two controls). 70.5% of the subjects with schizophrenia were correctly detected. Discussion This study shows that EEG features can differentiate between SCZ and HC in a simple, instruction--free visual task. Differences in late potentials (>300 ms) and in more complex images suggests deficits in top--down (cognitive) rather than bottom--up (perception) mechanisms. These results indicate that EEG signals from a free-viewing paradigm discriminate patients from healthy controls and have the potential to become a tool for the psychiatrist to support the positive diagnosis of schizophrenia.

Currently, the diagnosis of schizophrenia is made solely based on interviews and behavioral observations by a trained psychiatrist. Technologies such as electroencephalography(EEG) are used for differential diagnosis and not to support the psychiatrist's positive diagnosis. Here, we show the potential of EEG recordings as biomarkers of the schizophrenia syndrome. We recorded EEG while schizophrenia patients freely viewed natural scenes, and we analyzed the average EEG activity locked to the image onset. We found significant differences between patients and healthy controls in occipital areas approximately 500 ms after image onset. These differences were used to train a classifier to discriminate the schizophrenia patients from the controls. The best classifier had 81% sensitivity for the detection of patients and specificity of 59% for the detection of controls, with an overall accuracy of 71%. These results indicate that EEG signals from a free-viewing paradigm discriminate patients from healthy controls and have the potential to become a tool for the psychiatrist to support the positive diagnosis of schizophrenia.

Perception is the result of ongoing brain activity combined with sensory stimuli. In natural vision, changes in the visual input typically occur as the result of self-initiated eye movements. Nonetheless, in most studies, stimuli are flashed, and natural eye movements are avoided or restricted. As a consequence, the neural sensory processing associated with active vision is poorly understood. Here, we show that occipital event-related potentials (ERP) to eye movements during free exploration of natural images exhibited different amplitudes, time course and motor dependency than that from the same flashed stimuli. We found that the ERP to visual fixations doubles in P1 magnitude and does not show a late component, which is classically seen with flashed stimuli 1,2 . In addition, we discovered that the ERP to the saccade onset was as large as the ERP to fixations onset, with an early component that preceded the visual input, suggesting that a motor modulation was associated with the saccades 3 . Furthermore, the use of different visual scenes revealed that both the ERP amplitude and time course were dependent on the type of image explored. Our results demonstrated that during active vision, the nervous system engages a mechanism of sensory modulation that is precisely timed to the self-initiated stimulus changes. This mechanism could help coordinate neural activity across different cortical areas and, by extension, serve as a general mechanism for the global coordination of neural networks.

This work sheds light on the possibility of using control strategies to set the parameters of electric stimulation, a commonly used technique in severe human central nervous system diseases. Currently, parameters of electric stimulation are set through a trial and error process, with a lot of undesirable side effects. Accordingly, and based on the problem of having a population of sick neurons embedded in a population of normal neurons, this work explores the possibility of using a control system based on the behavior of healthy neurons to set current parameters able to modify the electric behavior of sick neurons. Specifically, we posit a knowledge-based expert control system that modifies the firing mode of a thalamic neuron by applying a control stimulation current, with the aim of making it fire in the same mode as a reference thalamic neuron. The controller parameters are tuned based on some characteristics of neurons that have to be determined through experiments before their application, but this controller does not require a detailed mathematical model of each neuron. Simulation results indicate that the proposed system satisfies the control objectives.

In schizophrenia, patients display dysfunctions during the execution of simple visual tasks such as antisaccade or smooth pursuit. In more ecological scenarios, such as free viewing of natural images, patients appear to make fewer and longer visual fixations and display shorter scanpaths. It is not clear whether these measurements reflect alterations in their proficiency to perform basic eye movements, such as saccades and fixations, or are related to high-level mechanisms, such as exploration or attention. We utilized free exploration of natural images of different complexities as a model of an ecological context where normally operative mechanisms of visual control can be accurately measured. We quantified visual exploration as Euclidean distance, scanpaths, saccades, and visual fixation, using the standard SR-Research eye tracker algorithm (SR). We then compared this result with a computation that includes microsaccades (EM). We evaluated eight schizophrenia patients and corresponding healthy controls (HC). Next, we tested whether the decrement in the number of saccades and fixations, as well as their increment in duration reported previously in schizophrenia patients, resulted from the increasing occurrence of undetected microsaccades. We found that when utilizing the standard SR algorithm, patients displayed shorter scanpaths as well as fewer and shorter saccades and fixations. When we employed the EM algorithm, the differences in these parameters between patients and HC were no longer significant. On the other hand, we found that image complexity plays an important role in exploratory behaviors, demonstrating that this factor explains most of differences between eye-movement behaviors in schizophrenia patients. These results help elucidate the mechanisms of visual motor control that are affected in schizophrenia and contribute to the finding of adequate markers for diagnosis and treatment for this condition.

Neurophysiological data are widely affected by different forms of signal artifacts. In electroencephalographic recordings from the scalp, eye blinks are a main contribution as a source of signal alteration. Different approaches have been used to improve on this problem, from the rejection of part of the signal, to corrections through linear decomposition methods. A widely used technique is independent component analysis (ICA). Different studies have shown the suitability of ICA to correct a variety of artifact sources, but to our knowledge, there is no evidence of the effect of ICA in the phase of a signal, over time. This is of importance because the phase is a critical component of the physiological signals that has been implicated in several neural mechanisms. The aim of this work is to assess the level of phase distortion that ICA can potentially introduce to real and simulated data.

Neural synchrony, measured as phase synchronization of electroencephalogram (EEG) signals, have been studied in relation with different cognitive task. However, not as much is known about how phase-shift relates to this process. Here we propose a method to detect phase-shift on single-trial EEG signals and assess its correlation with subjects' behavior. As preliminary result, this methodology was successfully applied in EEG recorded from three subjects during a bi-stable visual perceptual task.

In this article, a new approach to offline signature verification, based on a general-purpose wide baseline matching methodology, is proposed. Instead of detecting and matching geometric, signature-dependent features, as it is usually done, in the proposed approach local interest points are detected in the signature images, then local descriptors are computed in the neighborhood of these points, and afterwards these descriptors are compared using local and global matching procedures. The final verification is carried out using a Bayes classifier. It is important to remark that the local interest points do not correspond to any signature-dependent fiducial point, but to local maxima in a scale-space representation of the signature images. The proposed system is validated using the GPDS signature database, where it achieves a FRR of 16.4

A new approach to automatic fingerprint verification based on a general-purpose wide baseline matching methodology is here proposed. The approach is not based on the standard ridge-minutiae-based framework. Instead of detecting and matching the standard structural features, local interest points are detected in the fingerprints, then local descriptors are computed in the neighborhood of these points, and afterwards these descriptors are compared using local and global matching procedures. Then, a final verification is carried out by a Bayes classifier. The methodology is validated using the FVC2004 dataset, where competitive results are obtained.

In this article is proposed a new approach to automatic fingerprint verification that is not based on the standard ridge-minutiae-based framework, but in a general-purpose wide baseline matching methodology. Instead of detecting and matching the standard structural features, in the proposed approach local interest points are detected in the fingerprint, then local descriptors are computed in the neighborhood of these points, and afterwards these descriptors are compared using local and global matching procedures. The final verification is carried out by a Bayes classifier. It is important to remark that the local interest points do not correspond to minutiae or singular points, but to local maxima in a scale-space representation of the fingerprint images. The proposed system has 4 variants that are validated using the FVC2004 test protocol. The best variant, which uses an enhanced fingerprint image, SDoG interest points and SIFT descriptors, achieves a FRR of 20.9% and a FAR of 5.7% in the FVC2004-DB1 test database, without using any minutia or singular points' information.