State-Dependent tACS Effects Reveal the Potential Causal Role of Prestimulus Alpha Traveling Waves in Visual Contrast Detection.

The intricate relationship between prestimulus alpha oscillations and visual contrast detection variability has been the focus of numerous studies. However, the causal impact of prestimulus alpha traveling waves on visual contrast detection remains largely unexplored. In our research, we sought to discern the causal link between prestimulus alpha traveling waves and visual contrast detection across different levels of mental fatigue. Using electroencephalography alongside a visual detection task with 30 healthy adults (13 females; 17 males), we identified a robust negative correlation between prestimulus alpha forward traveling waves (FTWs) and visual contrast threshold (VCT). Inspired by this correlation, we utilized 45/-45° phase-shifted transcranial alternating current stimulation (tACS) in a sham-controlled, double-blind, within-subject experiment with 33 healthy adults (23 females; 10 males) to directly modulate these alpha traveling waves. After the application of 45° phase-shifted tACS, we observed a substantial decrease in FTW and an increase in backward traveling waves, along with a concurrent increase in VCT, compared with the sham condition. These changes were particularly pronounced under a low fatigue state. The findings of state-dependent tACS effects reveal the potential causal role of prestimulus alpha traveling waves in visual contrast detection. Moreover, our study highlights the potential of 45/-45° phase-shifted tACS in cognitive modulation and therapeutic applications.

An EEG-based systematic explainable detection framework for probing and localizing abnormal patterns in Alzheimer's disease.

Objective.Electroencephalography (EEG) is a potential source of downstream biomarkers for the early diagnosis of Alzheimer's disease (AD) due to its low-cost, noninvasive, and portable advantages. Accurately detecting AD-induced patterns from EEG signals is essential for understanding AD-related neurodegeneration at the EEG level and further evaluating the risk of AD at an early stage. This paper proposes a deep learning-based, functional explanatory framework that probes AD abnormalities from short-sequence EEG data.Approach.The framework is a learning-based automatic detection system consisting of three encoding pathways that analyze EEG signals in frequency, complexity, and synchronous domains. We integrated the proposed EEG descriptors with the neural network components into one learning system to detect AD patterns. A transfer learning-based model was used to learn the deep representations, and a modified generative adversarial module was attached to the model to overcome feature sparsity. Furthermore, we utilized activation mapping to obtain the AD-related neurodegeneration at brain rhythm, dynamic complexity, and functional connectivity levels.Main results.The proposed framework can accurately (100%) detect AD patterns based on our raw EEG recordings without delicate preprocessing. Meanwhile, the system indicates that (a) the power of different brain rhythms exhibits abnormal in the frontal lobes of AD patients, and such abnormality spreads to central lobes in the alpha and beta rhythms, (b) the difference in nonlinear complexity varies with the temporal scales, and (c) all the connections of pair-wise brain regions except bilateral temporal connectivity are weak in AD patterns. The proposed method outperforms other related methods in detection performance.Significance.We provide a new method for revealing abnormalities and corresponding localizations in different feature domains of EEG from AD patients. This study is a significant foundation for our future work on identifying individuals at high risk of AD at an early stage.

Epileptic Seizure Detection Using Brain-Rhythmic Recurrence Biomarkers and ONASNet-Based Transfer Learning.

OBJECTIVE: The electroencephalogram (EEG) tool has great potential for real-time monitoring of abnormal brain activities, such as preictal and ictal seizures. Developing an EEG-based detection system for patients with epilepsy is vital for clinical management and targeted therapy. METHODS: This paper proposes a single-channel seizure detection system using brain-rhythmic recurrence biomarkers (BRRM) and an optimized model (ONASNet). BRRM is a direct mapping of the recurrence morphology of brain rhythms in phase space; it reflects the nonlinear dynamics of original EEG signals. The architecture of ONASNet is determined through a modified neural network searching strategy. Then, we exploited transfer learning to apply ONASNet to our EEG data. The combination of BRRM and ONASNet leverages the multiple channels of a neural network to extract features from different brain rhythms simultaneously. RESULTS: We evaluated the efficiency of BRRM-ONASNet on the real EEG recordings derived from Bonn University. In the experiments, different transfer-learning models (TLMs) are respectively constructed using ONASNet and seven well-known neural network structures (VGG16/VGG19/ResNet50/InceptionV3/DenseNet121/Xception/NASNet). Moreover, we compared those TLMs by model size, computing complexity, learning capability, and prediction latency. ONASNet outperforms other structures by strong learning capability, high stability, small model size, short latency, and less requirement of computing resources. Comparing BRRM-ONASNet with other existing methods, our work performs better than others with 100% accuracy under the identical dataset and same detection task. Contributions: The proposed method in this study, analyzing nonlinear features from phase-space representations using a deep neural network, provides new insights for EEG decoding. The successful application of this method in epileptic-seizure detection contributes to computationally medical assistance for epilepsy.

An enhanced EEG microstate recognition framework based on deep neural networks: an application to Parkinson's disease.

Variations in brain activity patterns reveal impairments of motor and cognitive functions in the human brain. Electroencephalogram (EEG) microstates embody brain activity patterns at a microscopic time scale. However, current microstate analysis method can only recognize less than 90% of EEG signals per subject, which severely limits the characterization of dynamic brain activity. As an application to early Parkinson's disease (PD), we propose an enhanced EEG microstate recognition framework based on deep neural networks, which yields recognition rates from 90% to 99%, as accompanied by a strong anti-artifact property. Additionally, gradient-weighted class activation mapping, as a visualization technique, is employed to locate the activated functional brain regions of each microstate class. We find that each microstate class corresponds to a particular activated brain region. Finally, based on the improved identification of microstate sequences, we explore the EEG microstate characteristics and their clinical associations. We show that the decreased occurrences of a particular microstate class reflect the degree of cognitive decline in early PD, and reduced transitions between certain microstates suggest injury in motor-related brain regions. The novel EEG microstate recognition framework paves the way to revealing more effective biomarkers for early PD.

Microscope-Integrated OCT-Guided Volumetric Measurements of Subretinal Blebs Created by a Suprachoroidal Approach.

Purpose: To investigate the use of imaging modalities in the volumetric measurement of the subretinal space and examine the volume of subretinal blebs created by a subretinal drug delivery device utilizing microscope-integrated optical coherence tomography (MIOCT). Methods: An MIOCT image-based volume measurement method was developed and assessed for accuracy and reproducibility by imaging ceramic spheres of known size that were surgically implanted into ex vivo porcine eyes. This method was then used to measure subretinal blebs created in 10 porcine eyes by injection of balanced salt solution utilizing a subretinal delivery device via a suprachoroidal cannula. Bleb volumes obtained from MIOCT were compared to the intended injection volume. Results: Validation of image-based volume measurements of ceramic spheres showed accuracy to ±0.029 µL (5.6%) for objects imaged over the posterior pole and ±0.025 µL (4.8%) over peripheral retina. The mean expected injection volume from extraocular tests of the suprachoroidal cannula was 66.44 µL (σ = 2.4 µL). The mean injection volume as measured by the MIOCT imaging method was 54.8 µL (σ = 12.3 µL), or 82.48% of expected injection volume. Conclusions: MIOCT can measure the volume of subretinal blebs with accuracy and precision. The novel suprachoroidal approach using a subretinal delivery device was able to deliver greater than 80% of expected injection volume into the subretinal space, as assessed by MIOCT. Translational Relevance: MIOCT provides a method for visualization, and analysis of images enables surgeons to quantify and evaluate the success of subretinal drug delivery via a suprachoroidal approach.

Lightweight Learning-Based Automatic Segmentation of Subretinal Blebs on Microscope-Integrated Optical Coherence Tomography Images.

PURPOSE: Subretinal injections of therapeutics are commonly used to treat ocular diseases. Accurate dosing of therapeutics at target locations is crucial but difficult to achieve using subretinal injections due to leakage, and there is no method available to measure the volume of therapeutics successfully administered to the subretinal location during surgery. Here, we introduce the first automatic method for quantifying the volume of subretinal blebs, using porcine eyes injected with Ringer's lactate solution as samples. DESIGN: Ex vivo animal study. METHODS: Microscope-integrated optical coherence tomography was used to obtain 3D visualization of subretinal blebs in porcine eyes at Duke Eye Center. Two different injection phases were imaged and analyzed in 15 eyes (30 volumes), selected from a total of 37 eyes. The inclusion/exclusion criteria were set independently from the algorithm-development and testing team. A novel lightweight, deep learning-based algorithm was designed to segment subretinal bleb boundaries. A cross-validation method was used to avoid selection bias. An ensemble-classifier strategy was applied to generate final results for the test dataset. RESULTS: The algorithm performs notably better than 4 other state-of-the-art deep learning-based segmentation methods, achieving an F1 score of 93.86 ± 1.17% and 96.90 ± 0.59% on the independent test data for entry and full blebs, respectively. CONCLUSION: The proposed algorithm accurately segmented the volumetric boundaries of Ringer's lactate solution delivered into the subretinal space of porcine eyes with robust performance and real-time speed. This is the first step for future applications in computer-guided delivery of therapeutics into the subretinal space in human subjects.

Biomarkers for Alzheimer's Disease Defined by a Novel Brain Functional Network Measure.

OBJECTIVE: This paper aims to explore affordable biomarkers of Alzheimer's disease (AD) based on noninvasive, low cost, and portability electroencephalography (EEG) signals. METHODS: By combining multiscale analysis and embedding space theory, a novel strategy was developed for constructing brain functional network inferred from generalized composite multiscale entropy vector (GCMSEV). Functional network analysis and seed analysis were used for comparing AD pattern versus control pattern. Machine learning methods were employed for proving the effectiveness of our method. RESULTS: Patients with AD exhibited hypoconnectivity over the whole scalp, especially for long-range connections. Significant decreased connections between frontal and other regions reveals that the transmission of signals related to frontal hub is indeed damaged due to AD. The predictors consist of interfrontal and left frontal-right occipital connections that led to a good performance for distinguishing AD patients and normal subjects with over 96% classification accuracy and 0.98 parametric area under curve. CONCLUSION: Above findings demonstrated the superior power of the EEG markers quantified by our GCMSEV network, as the indicator of abnormal functional connectivity in the brain of AD patients. SIGNIFICANCE: This paper develops a novel EEG-based strategy for functional connectivity quantification and enriches the topographical biomarkers used for neurophysiological assessment.