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. METHODS: We performed a structured literature search of the OVID/MEDLINE, EMBASE, and PUBMED databases following the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Articles published from 2016 to 2023 that met predefined inclusion and exclusion criteria were included. Data extracted included surgical subspecialty using 3DP, application of 3DP, and any reported satisfaction measures of trainees. A thorough analysis of pooled data was performed to evaluate satisfaction rates among studies. RESULTS: A total of 85 studies were included. The median number of participants was 18 (interquartile range 10-27). Fourteen surgical disciplines were represented, with ear, nose, and throat/otolaryngology having the highest recorded utilization of 3DP models among residents and medical students (22.0%), followed by neurosurgery (14.0%) and urology (12.0%). 3DP models were created most frequently to model soft tissue (35.3%), bone (24.7%), vessel (14.1%), mixed (16.4%), or whole organs (6.66%) (Fig.1). Feedback from trainees was overwhelmingly positive regarding the fidelity of the models and their support for integration into their training programs. Among trainees, the combined satisfaction rate with their use in the curriculum was 95% (95% confidence interval, 0.92-0.97), and the satisfaction rate with the model fidelity was 90% (95% confidence interval, 0.86-0.94). CONCLUSIONS: There is wide variation in the surgical specialties utilizing 3DP models in training. These models are effective in increasing trainee comfort with both common and rare scenarios and are associated with a high degree of resident support and satisfaction. Plastic surgery programs may benefit from the integration of this technology, potentially strengthening future surgical curricula. Objective evaluations of their pedagogic effects on residents are areas of future research.

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. Using a mean-field model of thalamocortical activity, we studied the effect of several stimulation protocols, varying the pulse shape, duration, amplitude, and frequency, as well as a target-phase using a closed-loop approach. We evaluated the effect of these parameters on slow-oscillations (SO) and sleep-spindles (SP), considering: (i) the power at the frequency bands of interest, (ii) the number of SO and SP, (iii) co-occurrences between SO and SP, and (iv) synchronization of SP with the up-peak of the SO. The first three targets are maximized using a decreasing ramp pulse with a pulse duration of 50 ms. Also, we observed a reduction in the number of SO when increasing the stimulus energy by rising its amplitude. To assess the target-phase parameter, we applied closed-loop stimulation at 0°, 45°, and 90° of the phase of the narrow-band filtered ongoing activity, at 0.85 Hz as central frequency. The 0° stimulation produces better results in the power and number of SO and SP than the rhythmic or random stimulation. On the other hand, stimulating at 45° or 90° change the timing distribution of spindles centers but with fewer co-occurrences than rhythmic and 0° phase. Finally, we propose the application of closed-loop stimulation at the rising zero-cross point using pulses with a decreasing ramp shape and 50 ms of duration for future experimental work.

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. In this paper, we propose to extend EEMD-CCA in order to include an EMG array as information to aid the removal of artifacts, assessing the performance gain achieved when the number of EMG channels grow. By filtering adaptively (recursive least squares, EMG array as reference) each component resulting from CCA, we aim to ameliorate the distortion of brain signals induced by artifacts and denoising methods. We simulated several noise scenarios based on a linear contamination model, between real and synthetic EEG and EMG signals, and varied the number of EMG channels available to the filter. Our results exhibit a substantial improvement in the performance as the number of EMG electrodes increase from 2 to 16. Further increasing the number of EMG channels up to 128 did not have a significant impact on the performance. We conclude by recommending the use of EMG electrodes to filter components, as it is a computationally inexpensive enhancement that impacts significantly on performance using only a few electrodes.

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.Objective.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.Methods.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). We evaluated spectro-temporal features of entrainment as a function of the stimulation frequency, the resonant frequency of the neural populations comprising the NMM, and the coupling strength between cortical columns. Furthermore, we tested if the entrainment persistence depended on the phase of the EEG-like oscillation at the time the stimulus ended.Main Results.The entrainment of the column that received the stimulation was maximum when the frequency of the entrainer was within a narrow range around the resonant frequency of the column. When this occurred, entrainment persisted for several cycles after the stimulus terminated, and the propagation of the entrainment to other columns was facilitated. Propagation also depended on the resonant frequency of the second column, and the coupling strength between columns. The duration of the persistence of the entrainment depended on the phase of the neural oscillation at the time the entrainer terminated, such that falling phases (fromπ/2 to 3π/2 in a sine function) led to longer persistence than rising phases (from 0 toπ/2 and 3π/2 to 2π).Significance.The study bridges between models of neural oscillations and empirical electrophysiology, providing insights to the mechanisms underlying neural entrainment and the use of rhythmic sensory stimulation for neuroenhancement.

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. To validate the performance and generality of our method, we generated synthetic data, where we found that our method accurately detects neuronal ensembles for a wide range of simulation parameters. We found that our method outperforms current alternative methodologies. We used spike trains of retinal ganglion cells obtained from multi-electrode array recordings under a simple ON-OFF light stimulus to test our method. We found a consistent stimuli-evoked ensemble activity intermingled with spontaneously active ensembles and irregular activity. Our results suggest that the early visual system activity could be organized in distinguishable functional ensembles. We provide a Graphic User Interface, which facilitates the use of our method by the scientific community.

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. METHODS: In young (2-3-month-old) and adult (6-7-month-old) 5xFAD and WT mice, we have studied the physiological response, firing rate, and burst of RGCs to various types of visual stimuli using a multielectrode array system. RESULTS: The firing rate and burst response in 5xFAD RGCs showed hyperactivity at the early stage of AD in young mice, whereas hypoactivity was seen at the later stage of AD in adults. The physiological alterations observed in 5xFAD correlate well with an increase in the expression of glutamate in the ganglion cell layer in young and adults. GABA staining increased in the inner nuclear and plexiform layer, which was more pronounced in the adult than the young 5xFAD retina, altering the excitation/inhibition balance, which could explain the observed early hyperactivity and later hypoactivity in RGC physiology. CONCLUSION: These findings indicate functional changes may be caused by neurochemical alterations of the retina starting at an early stage of the AD disease.

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. We hypothesize that the duration of the persistence of entrainment is determined by the specific functional state of the entrained neural network at the time the stimulus ends. Steady-state visually evoked potentials (SSVEP) were elicited in 19 healthy volunteers at the participants' individual alpha peaks. Visual stimulation consisted of a sinusoidally-varying light terminating at one of four phases: 0, π/2, π, and 3π/2. The persistence duration of the oscillatory activity was analyzed as a function of the terminating phase of the stimulus. Phases of the SSVEP at the stimulus termination were distributed within a constant range of values relative to the phase of the stimulus. Longer persistence durations were obtained when visual stimulation terminated towards the troughs of the alpha oscillations, while shorter persistence durations occurred when stimuli terminated near the peaks. Source localization analysis suggests that the persistence of entrainment reflects the functioning of fronto-occipital neuronal circuits, which might prime the sensory representation of incoming visual stimuli based on predictions about stimulus rhythmicity. Consequently, different states of the network at the end of the stimulation, corresponding to different states of intrinsic neuronal coupling, may determine the time windows over which coding of incoming sensory stimulation is modulated by the preceding oscillatory activity.

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. By stimulating the retina of the diurnal rodent, Octodon degus with MC we show that the RGCs respond to increasingly complex stimuli by narrowing their adjustment curves in response to movement. At the level of the population, complex stimuli produce a sparser code while preserving movement information; therefore, the stimuli are encoded more efficiently. Interestingly, these properties were observed throughout different populations of RGCs. Thus, our results reveal that the response at the level of RGCs is modulated by the naturalness of the stimulus - in particular for motion - which suggests that the tuning to the statistics of natural images already emerges at the level of the retina.

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., oscillations in the electroencephalogram phase-locked to the driving stimuli). The classic interpretation of the amplitude of the steady-state evoked responses assumes a stereotypical time-invariant neural response plus random background fluctuations, such that averaging over repeated presentations of the stimulus recovers the stereotypical response. This approach ignores the dynamics of the steady-state, as in the case of the adaptation elicited by prolonged exposures to the stimulus. To analyze the dynamics of steady-state responses, it can be assumed that the time evolution of the response amplitude is the same in different stimulation runs separated by sufficiently long breaks. Based on this assumption, a method to characterize the time evolution of steady-state responses is presented. A sufficiently large number of recordings are acquired in response to the same experimental condition. Experimental runs (recordings) are column-wise averaged (i.e., runs are averaged but epoch within recordings are not averaged with the preceding segments). The column-wise averaging allows analysis of steady-state responses in recordings with remarkably high signal-to-noise ratios. Therefore, the averaged signal provides an accurate representation of the time evolution of the steady-state response, which can be analyzed in both the time and frequency domains. In this study, a detailed description of the method is provided, using steady-state visually evoked potentials as an example of a response. Advantages and caveats are evaluated based on a comparison with single-trial methods designed to analyze neural entrainment.

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. Here, we use electrophysiological recordings, gathered from retinas of the diurnal rodent Octodon degus, to show that RGCs with peripheral receptive fields (RF) are larger, faster, and have shorter transient responses. This translates into higher sensitivity at high temporal frequencies and a full frequency bandwidth when compared to RGCs with more central RF. We also observed that imbalances between ON and OFF cell populations are preserved with eccentricity. Finally, the high diversity of functional types of RGCs highlights the complexity of the computational strategies implemented in the early stages of visual processing, which could inspire the development of bio-inspired artificial systems.

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. In general, motion direction selectivity is based on a non-linear asymmetric computation inside a receptive field differentiating cell responses between preferred and null direction stimuli. To what extent can biological findings match these considerations? In this review, we outline theoretical and mathematical studies of motion direction selectivity, aiming to map the properties of the models onto the neural circuitry and synaptic connectivity found in the retina. Additionally, we review several compartmental models that have tried to fill this gap. Finally, we discuss the remaining challenges that computational models will have to tackle in order to fully understand the retinal motion direction-selective circuitry.