Schizophrenia phenotypes are suggestive of impaired cortical plasticity in the disease, but the mechanisms of these deficits are unknown. Genomic association studies have implicated a large number of genes that regulate neuromodulation and plasticity, indicating that the plasticity deficits have a genetic origin. Here, we used biochemically detailed computational modeling of postsynaptic plasticity to investigate how schizophrenia-associated genes regulate long-term potentiation (LTP) and depression (LTD). We combined our model with data from postmortem RNA expression studies (CommonMind gene-expression datasets) to assess the consequences of altered expression of plasticity-regulating genes for the amplitude of LTP and LTD. Our results show that the expression alterations observed post mortem, especially those in the anterior cingulate cortex, lead to impaired protein kinase A (PKA)-pathway-mediated LTP in synapses containing GluR1 receptors. We validated these findings using a genotyped electroencephalogram (EEG) dataset where polygenic risk scores for synaptic and ion channel-encoding genes as well as modulation of visual evoked potentials were determined for 286 healthy controls. Our results provide a possible genetic mechanism for plasticity impairments in schizophrenia, which can lead to improved understanding and, ultimately, treatment of the disorder.

Vigilance represents an ability to sustain prolonged attention and plays a crucial role in ensuring the reliability and optimal performance of various tasks. In this report, we describe a MultiModal Vigilance (MMV) dataset comprising seven physiological signals acquired during two Brain-Computer Interface (BCI) tasks. The BCI tasks encompass a rapid serial visual presentation (RSVP)-based target image retrieval task and a steady-state visual evoked potential (SSVEP)-based cursor-control task. The MMV dataset includes four sessions of seven physiological signals for 18 subjects, which encompasses electroencephalogram(EEG), electrooculogram (EOG), electrocardiogram (ECG), photoplethysmogram (PPG), electrodermal activity (EDA), electromyogram (EMG), and eye movement. The MMV dataset provides data from four stages: 1) raw data, 2) pre-processed data, 3) trial data, and 4) feature data that can be directly used for vigilance estimation. We believe this dataset will achieve flexible reuse and meet the various needs of researchers. And this dataset will greatly contribute to advancing research on physiological signal-based vigilance research and estimation.

BACKGROUND: The domain of brain-computer interface (BCI) technology has experienced significant expansion in recent years. However, the field continues to face a pivotal challenge due to the dearth of high-quality datasets. This lack of robust datasets serves as a bottleneck, constraining the progression of algorithmic innovations and, by extension, the maturation of the BCI field.
RESULTS: This study details the acquisition and compilation of electroencephalogram data across 3 distinct dual-frequency steady-state visual evoked potential (SSVEP) paradigms, encompassing over 100 participants. Each experimental condition featured 40 individual targets with 5 repetitions per target, culminating in a comprehensive dataset consisting of 21,000 trials of dual-frequency SSVEP recordings. We performed an exhaustive validation of the dataset through signal-to-noise ratio analyses and task-related component analysis, thereby substantiating its reliability and effectiveness for classification tasks.
CONCLUSIONS: The extensive dataset presented is set to be a catalyst for the accelerated development of BCI technologies. Its significance extends beyond the BCI sphere and holds considerable promise for propelling research in psychology and neuroscience. The dataset is particularly invaluable for discerning the complex dynamics of binocular visual resource distribution.

OBJECTIVE: To quantitatively evaluate visual evoked potentials (VEPs) in prosthetic vision and simulated visual reduction.
METHODS: Four blind patients implanted with the Argus II retinal prosthesis and seven sighted controls participated. VEPs were recorded with pattern-reversal stimuli (2 cycles of a horizontal square wave grating, 0.1 cycle/degree) at 1.07 reversals per second (rps) for Argus II subjects and 3.37 rps for controls. Argus II patients had both eyes patched, viewing the pattern solely through their implant. Controls viewed the pattern monocularly, either with their best-corrected vision or with simulated visual reduction (field restriction, added blur or reduced display contrast).
RESULTS: VEPs recorded in Argus II patients displayed a similar shape to normal VEPs when controls viewed the pattern without simulated visual reduction. In sighted controls, adding blur significantly delayed the P100 peak time by 8.7 ms, 95% CI (0.9, 16.6). Reducing stimulus contrast to 32% and 6% of full display contrast significantly decreased P100 amplitude to 55% (37%, 82%) and 20% (13%, 31%), respectively. Restriction on the field of view had no impact on either the amplitude or the peak latency of P100.
CONCLUSIONS: The early visual cortex in retinal prosthesis users remains responsive to retinal input, showing a similar response profile to that of sighted controls. Pattern-reversal VEP offers valuable insights for objectively evaluating artificial vision therapy systems (AVTSs) when selecting, fitting and training implant users, but the uncertainties in the exact timing and location of electrode stimulation must be considered when interpreting the results.

Steady-state visual evoked potentials (SSVEPs) are widely used for brain-computer interfaces (BCIs) as they provide a stable and efficient means to connect the computer to the brain with a simple flickering light. Previous studies focused on low-density frequency division multiplexing techniques, i.e. typically employing one or two light-modulation frequencies during a single flickering light stimulation. Here we show that it is possible to encode information in SSVEPs excited by high-density frequency division multiplexing, involving hundreds of frequencies. We then demonstrate the ability to transmit entire images from the computer to the brain/EEG read-out in relatively short times. High-density frequency multiplexing also allows to implement a photonic neural network utilizing SSVEPs, that is applied to simple classification tasks and exhibits promising scalability properties by connecting multiple brains in series. Our findings open up new possibilities for the field of neural interfaces, holding potential for various applications, including assistive technologies and cognitive enhancements, to further improve human-machine interactions.

Steady-state visually evoked potential is one of the active explorations in the brain-computer interface research. Electroencephalogram based brain computer interface studies have been widely applied to perceive solutions for real-world problems in the healthcare domain. The classification of externally bestowed visual stimuli of different frequencies on a human was experimented to identify the need of paralytic people. Although many classifiers are at the fingertip of machine learning technology, recent research has proven that ensemble learning is more efficacious than individual classifiers. Despite its efficiency, ensemble learning technology exhibits certain drawbacks like taking more time on selecting the optimal classifier subset. This research article utilizes the Harris Hawk Optimization algorithm to select the best classifier subset from the given set of classifiers. The objective of the research is to develop an efficient multi-classifier model for electroencephalogram signal classification. The proposed model utilizes the Boruta Feature Selection algorithm to select the prominent features for classification. Thus selected prominent features are fed into the multi-classifier subset which has been generated by the Harris Hawk Optimization algorithm. The results of the multi-classifier ensemble model are aggregated using Stacking, Bagging, Boosting, and Voting. The proposed model is evaluated against the acquired dataset and produces a promising accuracy of 96.1%, 98.7%, 91.91%, and 99.01% with the ensemble techniques respectively. The proposed model is also validated with other performance metrics such as sensitivity, specificity, and F1-Score. The experimental results show that the proposed model proves its supremacy in segregating the multi-class classification problem with high accuracy.

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In order to determine the effect of nystagmus on objective visual acuity (VA) estimates, we compared subjective (VApsych) and objective (VEP, VAVEP) VA estimates in participants with nystagmus. For this purpose, 20 participants with nystagmus (NY) caused by idiopathic infantile nystagmus, albinism, achiasma or acquired nystagmus were recruited in this study. Estimates of BCVA (best corrected visual acuity) were determined psychophysically (VApsych; FrACT, Freiburg visual acuity test) and electrophysiologically (VAVEP; EP2000) according to ISCEV (International Society of Clinical Electrophysiology of Vision) guidelines. For each participant the eye with the stronger fixation instability [Nidek microperimeter (MP-1), Nidek Instruments] was included for further analysis. VApsych vs VAVEP were compared via paired t-tests and the correlation of the difference between VApsych and VAVEP (∆VA) vs the degree of fixation instability was tested with Pearson correlation (r). We found VAVEP to be better than VApsych [by 0.12 Logarithm of the Minimum Angle of Resolution (logMAR); mean ± standard error (SE) of VAVEP vs VApsych: 0.176 ± 0.06 vs. 0.299 ± 0.06, P = 0.017] and ∆VA to be correlated linearly with the degree of fixation instability (r2 = 0.21,p = 0.048). In conclusion, on average we report a small VA overestimation, around 1 line, for VAVEP compared to VApsych in NY. This overestimation depended on the magnitude of the fixation instability. As a rule of thumb, a reduction of the fixation probability in the central 4° from 100 to 50% leads on average to a VAVEP overestimation of around 0.25 logMAR, i.e. 2.5 lines.

BACKGROUND: Mowat-Wilson syndrome (MWS) is a rare genetic condition resulting in multiple congenital anomalies, including facial dysmorphism, structural anomalies of the internal organs, functional disorders, and, although less commonly, ocular abnormalities. To present a child with MWS and eye abnormalities.
METHODS: A 3-year-old boy was born at 37 weeks of pregnancy with dysmorphic features, neurodevelopmental disorders, genetically confirmed MWS, nystagmus, strabismus, and suspicion of congenital glaucoma. Ophthalmic examination was carried out under general anesthesia; eyeball ultrasound and electrophysiological examination (flash visual evoked potentials) were also performed.
RESULTS: The examinations revealed nystagmus, a normal response of pupils to light in both eyes, and normal intraocular pressure, that is, 17 and 18 mm Hg in the right and left eye, respectively. Corneal thickness was 606 µm in the right eye and 588 µm in the left eye. Gonioscopy revealed displacement of Schwalbe line anterior to the limbus of the cornea (posterior embryotoxon). Fundus examination revealed a pink optic disk with a cup-to-disc ratio of 0.5, macular pigment regrouping, and normal blood vessels. Flash visual evoked potentials: P2 latency was normal. P2 amplitude from the left hemisphere was reduced to 50%, and P2 amplitude over the right hemisphere was normal.
CONCLUSIONS: Children with genetically determined congenital anomalies need regular ophthalmic checkups to accurately assess the eye and determine the prospects of vision function development.

UNASSIGNED: Targeted drug delivery to the optic nerve head may be useful in the preclinical study and later clinical management of optic neuropathies, however, there are no FDA-approved drug delivery systems to achieve this. The purpose of this work was to develop an optic nerve head drug delivery technique.
UNASSIGNED: Different strategies to approach the optic nerve head were investigated, including standard intravitreal and retroorbital injections. A novel SupraChoroidal-to-Optic-NervE (SCONE) delivery was optimized by creating a sclerotomy and introducing a catheter into the suprachoroidal space. Under direct visualization, the catheter was guided to the optic nerve head. India ink was injected. The suprachoroidal approach was performed in New Zealand White rabbit eyes in vivo (25 animals total). Parameters, including microneedle size and design, catheter design, and catheter tip angle, were optimized ex vivo and in vivo.
UNASSIGNED: Out of the candidate optic nerve head approaches, intravitreal, retroorbital, and suprachoroidal approaches were able to localize India ink to within 2 mm of the optic nerve. The suprachoroidal approach was further investigated, and after optimization, was able to deposit India ink directly within the optic nerve head in up to 80% of attempts. In eyes with successful SCONE delivery, latency and amplitude of visual evoked potentials was not different than the naïve untreated eye.
UNASSIGNED: SCONE delivery can be used for targeted drug delivery to the optic nerve head of rabbits without measurable toxicity measured anatomically or functionally. Successful development of this system may yield novel opportunities to study optic nerve head-specific drug delivery in animal models, and paradigm-shifting management strategies for treating optic neuropathies.
UNASSIGNED: Here we demonstrate data on a new method for targeted delivery to the optic nerve head, addressing a significant unmet need in therapeutics for optic neuropathies.