Impact of Artificial Intelligence (AI) Image Enhancing Filters on Patient Expectations for Plastic Surgery Outcomes.

Background: Artificial intelligence (AI) technologies use a three-part strategy for facial visual enhancement: (1) Facial Detection, (2) Facial Landmark Detection, and (3) Filter Application (Chen in Arch Fac Plast Surg 21:361-367, 2019). In the context of the surgical patient population, open-source AI algorithms are capable of modifying or simulating images to present potential results of plastic surgery procedures. Our primary aim was to understand whether AI filter use may influence individuals' perceptions and expectations of post-surgical outcomes.

Documentary Analysis of (St. John's Wort) and Its Effect on Depressive Disorders.

, also known as St. John's Wort, pericon, or yellow grass, is known for its antidepressant potential. It could represent a natural alternative to current pharmacological antidepressant treatments, which have a high incidence of side effects in patients and therefore lead to early dropouts.

Three-Dimensional Printing in Surgical Education: An Updated Systematic Review of the Literature.

Introduction: Three-dimensional printing (3DP) is being integrated into surgical practice at a significant pace, from preprocedural planning to procedure simulation. 3DP is especially useful in surgical education, where printed models are highly accurate and customizable. The aim of this study was to evaluate how 3DP is being integrated most recently into surgical residency training.

Modelling neural entrainment and its persistence: influence of frequency of stimulation and phase at the stimulus offset.

Neural entrainment, the synchronization of brain oscillations to the frequency of an external stimuli, is a key mechanism that shapes perceptual and cognitive processes.Using simulations, we investigated the dynamics of neural entrainment, particularly the period following the end of the stimulation, since the persistence (reverberation) of neural entrainment may condition future sensory representations based on predictions about stimulus rhythmicity.Neural entrainment was assessed using a modified Jansen-Rit neural mass model (NMM) of coupled cortical columns, in which the spectral features of the output resembled that of the electroencephalogram (EEG).

Scalable and accurate method for neuronal ensemble detection in spiking neural networks.

We propose a novel, scalable, and accurate method for detecting neuronal ensembles from a population of spiking neurons. Our approach offers a simple yet powerful tool to study ensemble activity. It relies on clustering synchronous population activity (population vectors), allows the participation of neurons in different ensembles, has few parameters to tune and is computationally efficient.

Selection of stimulus parameters for enhancing slow wave sleep events with a neural-field theory thalamocortical model.

Slow-wave sleep cortical brain activity, conformed by slow-oscillations and sleep spindles, plays a key role in memory consolidation. The increase of the power of the slow-wave events, obtained by auditory sensory stimulation, positively correlates with memory consolidation performance. However, little is known about the experimental protocol maximizing this effect, which could be induced by the power of slow-oscillation, the number of sleep spindles, or the timing of both events' co-occurrence.

Retinal Ganglion Cells Functional Changes in a Mouse Model of Alzheimer's Disease Are Linked with Neurotransmitter Alterations.

Background: Alzheimer's disease (AD) is the most prevalent form of dementia worldwide. This neurodegenerative syndrome affects cognition, memory, behavior, and the visual system, particularly the retina.

Objective: This work aims to determine whether the 5xFAD mouse, a transgenic model of AD, displays changes in the function of retinal ganglion cells (RGCs) and if those alterations are correlated with changes in the expression of glutamate and gamma-aminobutyric acid (GABA) neurotransmitters.

Persistence of EEG Alpha Entrainment Depends on Stimulus Phase at Offset.

Neural entrainment is the synchronization of neural activity to the frequency of repetitive external stimuli, which can be observed as an increase in the electroencephalogram (EEG) power spectrum at the driving frequency, -also known as the steady-state response. Although it has been systematically reported that the entrained EEG oscillation persists for approximately three cycles after stimulus offset, the neural mechanisms underpinning it remain unknown. Focusing on alpha oscillations, we adopt the dynamical excitation/inhibition framework, which suggests that phases of entrained EEG signals correspond to alternating excitatory/inhibitory states of the neural circuitry.

Improving EEG Muscle Artifact Removal With an EMG Array.

Removal of artifacts induced by muscle activity is crucial for analysis of the electroencephalogram (EEG), and continues to be a challenge in experiments where the subject may speak, change facial expressions, or move. Ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA) has been proven to be an efficient method for denoising of EEG contaminated with muscle artifacts. EEMD-CCA, likewise the majority of algorithms, does not incorporate any statistical information of the artifact, namely, electromyogram (EMG) recorded over the muscles actively contaminating the EEG.

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials.

Neural entrainment refers to the synchronization of neural activity to the periodicity of sensory stimuli. This synchronization defines the generation of steady-state evoked responses (i.e.

Speed-Selectivity in Retinal Ganglion Cells is Sharpened by Broad Spatial Frequency, Naturalistic Stimuli.

Motion detection represents one of the critical tasks of the visual system and has motivated a large body of research. However, it remains unclear precisely why the response of retinal ganglion cells (RGCs) to simple artificial stimuli does not predict their response to complex, naturalistic stimuli. To explore this topic, we use Motion Clouds (MC), which are synthetic textures that preserve properties of natural images and are merely parameterized, in particular by modulating the spatiotemporal spectrum complexity of the stimulus by adjusting the frequency bandwidths.

Characterization of Retinal Functionality at Different Eccentricities in a Diurnal Rodent.

Although the properties of the neurons of the visual system that process central and peripheral regions of the visual field have been widely researched in the visual cortex and the LGN, they have scarcely been documented for the retina. The retina is the first step in integrating optical signals, and despite considerable efforts to functionally characterize the different types of retinal ganglion cells (RGCs), a clear account of the particular functionality of cells with central vs. peripheral fields is still wanting.

Mathematical analysis and modeling of motion direction selectivity in the retina.

Motion detection is one of the most important and primitive computations performed by our visual system. Specifically in the retina, ganglion cells producing motion direction-selective responses have been addressed by different disciplines, such as mathematics, neurophysiology and computational modeling, since the beginnings of vision science. Although a number of studies have analyzed theoretical and mathematical considerations for such responses, a clear picture of the underlying cellular mechanisms is only recently emerging.