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Residual and bidirectional LSTM for epileptic seizure detection. 用于癫痫发作检测的残差和双向 LSTM。
IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-06-17 eCollection Date: 2024-01-01 DOI: 10.3389/fncom.2024.1415967
Wei Zhao, Wen-Feng Wang, Lalit Mohan Patnaik, Bao-Can Zhang, Su-Jun Weng, Shi-Xiao Xiao, De-Zhi Wei, Hai-Feng Zhou

Electroencephalogram (EEG) plays a pivotal role in the detection and analysis of epileptic seizures, which affects over 70 million people in the world. Nonetheless, the visual interpretation of EEG signals for epilepsy detection is laborious and time-consuming. To tackle this open challenge, we introduce a straightforward yet efficient hybrid deep learning approach, named ResBiLSTM, for detecting epileptic seizures using EEG signals. Firstly, a one-dimensional residual neural network (ResNet) is tailored to adeptly extract the local spatial features of EEG signals. Subsequently, the acquired features are input into a bidirectional long short-term memory (BiLSTM) layer to model temporal dependencies. These output features are further processed through two fully connected layers to achieve the final epileptic seizure detection. The performance of ResBiLSTM is assessed on the epileptic seizure datasets provided by the University of Bonn and Temple University Hospital (TUH). The ResBiLSTM model achieves epileptic seizure detection accuracy rates of 98.88-100% in binary and ternary classifications on the Bonn dataset. Experimental outcomes for seizure recognition across seven epilepsy seizure types on the TUH seizure corpus (TUSZ) dataset indicate that the ResBiLSTM model attains a classification accuracy of 95.03% and a weighted F1 score of 95.03% with 10-fold cross-validation. These findings illustrate that ResBiLSTM outperforms several recent deep learning state-of-the-art approaches.

脑电图(EEG)在癫痫发作的检测和分析中起着举足轻重的作用,全世界有 7000 多万人受到癫痫发作的影响。然而,用于癫痫检测的脑电信号的可视化解读既费力又费时。为了应对这一挑战,我们引入了一种简单而高效的混合深度学习方法,名为 ResBiLSTM,用于利用脑电信号检测癫痫发作。首先,我们定制了一个一维残差神经网络(ResNet),以巧妙地提取脑电信号的局部空间特征。然后,将获得的特征输入双向长短期记忆(BiLSTM)层,以模拟时间相关性。这些输出特性通过两个全连接层进一步处理,以实现最终的癫痫发作检测。ResBiLSTM 的性能在波恩大学和坦普尔大学医院(TUH)提供的癫痫发作数据集上进行了评估。在波恩数据集的二元和三元分类中,ResBiLSTM 模型的癫痫发作检测准确率达到 98.88%-100%。在 TUH 癫痫发作语料库 (TUSZ) 数据集上进行的七种癫痫发作类型的发作识别实验结果表明,ResBiLSTM 模型的分类准确率为 95.03%,在 10 倍交叉验证下的加权 F1 分数为 95.03%。这些结果表明,ResBiLSTM 的表现优于最近几种最先进的深度学习方法。
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引用次数: 0
Design and evaluation of a global workspace agent embodied in a realistic multimodal environment 设计和评估在现实多模态环境中体现的全局工作空间代理
IF 3.2 4区 医学 Q2 Neuroscience Pub Date : 2024-06-14 DOI: 10.3389/fncom.2024.1352685
Rousslan Fernand Julien Dossa, Kai Arulkumaran, Arthur Juliani, Shuntaro Sasai, Ryota Kanai
As the apparent intelligence of artificial neural networks (ANNs) advances, they are increasingly likened to the functional networks and information processing capabilities of the human brain. Such comparisons have typically focused on particular modalities, such as vision or language. The next frontier is to use the latest advances in ANNs to design and investigate scalable models of higher-level cognitive processes, such as conscious information access, which have historically lacked concrete and specific hypotheses for scientific evaluation. In this work, we propose and then empirically assess an embodied agent with a structure based on global workspace theory (GWT) as specified in the recently proposed “indicator properties” of consciousness. In contrast to prior works on GWT which utilized single modalities, our agent is trained to navigate 3D environments based on realistic audiovisual inputs. We find that the global workspace architecture performs better and more robustly at smaller working memory sizes, as compared to a standard recurrent architecture. Beyond performance, we perform a series of analyses on the learned representations of our architecture and share findings that point to task complexity and regularization being essential for feature learning and the development of meaningful attentional patterns within the workspace.
随着人工神经网络(ANN)智能化的发展,人们越来越多地将其与人脑的功能网络和信息处理能力相提并论。这种比较通常侧重于特定模式,如视觉或语言。下一个前沿领域是利用人工神经网络的最新进展,设计和研究更高层次认知过程的可扩展模型,如有意识的信息获取,而这些认知过程历来缺乏用于科学评估的具体而明确的假设。在这项研究中,我们提出了一个基于全局工作空间理论(GWT)的具身代理,并对其进行了实证评估。与之前利用单一模态的全局工作空间理论(GWT)工作不同,我们的代理接受了基于真实视听输入的三维环境导航训练。我们发现,与标准的递归架构相比,全局工作空间架构在工作记忆容量较小的情况下表现得更好、更稳健。除了性能之外,我们还对我们架构的学习表征进行了一系列分析,并分享了一些发现,这些发现表明任务复杂性和正则化对于工作空间内的特征学习和有意义的注意模式的发展至关重要。
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引用次数: 0
SS-DRPL: self-supervised deep representation pattern learning for voice-based Parkinson's disease detection SS-DRPL:基于语音的帕金森病检测的自我监督深度表征模式学习
IF 3.2 4区 医学 Q2 Neuroscience Pub Date : 2024-06-12 DOI: 10.3389/fncom.2024.1414462
Tae Hoon Kim, Moez Krichen, Stephen Ojo, Gabriel Avelino R. Sampedro, Meznah A. Alamro
Parkinson's disease (PD) is a globally significant health challenge, necessitating accurate and timely diagnostic methods to facilitate effective treatment and intervention. In recent years, self-supervised deep representation pattern learning (SS-DRPL) has emerged as a promising approach for extracting valuable representations from data, offering the potential to enhance the efficiency of voice-based PD detection. This research study focuses on investigating the utilization of SS-DRPL in conjunction with deep learning algorithms for voice-based PD classification. This study encompasses a comprehensive evaluation aimed at assessing the accuracy of various predictive models, particularly deep learning methods when combined with SS-DRPL. Two deep learning architectures, namely hybrid Long Short-Term Memory and Recurrent Neural Networks (LSTM-RNN) and Deep Neural Networks (DNN), are employed and compared in terms of their ability to detect voice-based PD cases accurately. Additionally, several traditional machine learning models are also included to establish a baseline for comparison. The findings of the study reveal that the incorporation of SS-DRPL leads to improved model performance across all experimental setups. Notably, the LSTM-RNN architecture augmented with SS-DRPL achieves the highest F1-score of 0.94, indicating its superior ability to detect PD cases using voice-based data effectively. This outcome underscores the efficacy of SS-DRPL in enabling deep learning models to learn intricate patterns and correlations within the data, thereby facilitating more accurate PD classification.
帕金森病(Parkinson's disease,PD)是一项全球性的重大健康挑战,需要准确及时的诊断方法来促进有效的治疗和干预。近年来,自监督深度表征模式学习(SS-DRPL)已成为从数据中提取有价值表征的一种有前途的方法,有望提高基于语音的帕金森病检测效率。本研究主要探讨如何将 SS-DRPL 与深度学习算法相结合,用于基于语音的 PD 分类。本研究包括一项综合评估,旨在评估各种预测模型,特别是深度学习方法与 SS-DRPL 结合使用时的准确性。研究采用了两种深度学习架构,即混合长短期记忆和递归神经网络(LSTM-RNN)和深度神经网络(DNN),并比较了它们准确检测基于语音的 PD 病例的能力。此外,还采用了几种传统的机器学习模型,以建立比较基线。研究结果表明,在所有实验设置中,SS-DRPL 的加入都提高了模型的性能。值得注意的是,添加了 SS-DRPL 的 LSTM-RNN 架构的 F1 分数最高,达到了 0.94,这表明该架构具有利用语音数据有效检测 PD 病例的卓越能力。这一结果凸显了 SS-DRPL 在使深度学习模型学习数据中错综复杂的模式和相关性方面的功效,从而有助于更准确地进行 PD 分类。
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引用次数: 0
Enhancing brain tumor classification in MRI scans with a multi-layer customized convolutional neural network approach 利用多层定制卷积神经网络方法增强磁共振成像扫描中的脑肿瘤分类功能
IF 3.2 4区 医学 Q2 Neuroscience Pub Date : 2024-06-12 DOI: 10.3389/fncom.2024.1418546
Eid Albalawi, Arastu Thakur, D. Dorai, Surbhi Bhatia Khan, T. Mahesh, Ahlam Almusharraf, Khursheed Aurangzeb, Muhammad Shahid Anwar
The necessity of prompt and accurate brain tumor diagnosis is unquestionable for optimizing treatment strategies and patient prognoses. Traditional reliance on Magnetic Resonance Imaging (MRI) analysis, contingent upon expert interpretation, grapples with challenges such as time-intensive processes and susceptibility to human error.This research presents a novel Convolutional Neural Network (CNN) architecture designed to enhance the accuracy and efficiency of brain tumor detection in MRI scans.The dataset used in the study comprises 7,023 brain MRI images from figshare, SARTAJ, and Br35H, categorized into glioma, meningioma, no tumor, and pituitary classes, with a CNN-based multi-task classification model employed for tumor detection, classification, and location identification. Our methodology focused on multi-task classification using a single CNN model for various brain MRI classification tasks, including tumor detection, classification based on grade and type, and tumor location identification.The proposed CNN model incorporates advanced feature extraction capabilities and deep learning optimization techniques, culminating in a groundbreaking paradigm shift in automated brain MRI analysis. With an exceptional tumor classification accuracy of 99%, our method surpasses current methodologies, demonstrating the remarkable potential of deep learning in medical applications.This study represents a significant advancement in the early detection and treatment planning of brain tumors, offering a more efficient and accurate alternative to traditional MRI analysis methods.
为了优化治疗策略和患者预后,及时准确诊断脑肿瘤的必要性毋庸置疑。传统的磁共振成像(MRI)分析依赖于专家解读,面临着时间密集型流程和易受人为错误影响等挑战。本研究提出了一种新型卷积神经网络(CNN)架构,旨在提高磁共振成像扫描中脑肿瘤检测的准确性和效率。研究中使用的数据集包括来自 figshare、SARTAJ 和 Br35H 的 7,023 张脑核磁共振图像,分为胶质瘤、脑膜瘤、无肿瘤和垂体瘤四类,并采用基于 CNN 的多任务分类模型进行肿瘤检测、分类和位置识别。我们的方法侧重于使用单个 CNN 模型进行多任务分类,以完成各种脑磁共振成像分类任务,包括肿瘤检测、基于等级和类型的分类以及肿瘤位置识别。我们的方法的肿瘤分类准确率高达 99%,超越了当前的方法,展示了深度学习在医疗应用中的巨大潜力。这项研究代表了脑肿瘤早期检测和治疗规划领域的重大进展,为传统的 MRI 分析方法提供了更高效、更准确的替代方案。
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引用次数: 0
Pathological cell assembly dynamics in a striatal MSN network model 纹状体 MSN 网络模型中的病态细胞组装动力学
IF 3.2 4区 医学 Q2 Neuroscience Pub Date : 2024-06-06 DOI: 10.3389/fncom.2024.1410335
Astrid Correa, Adam Ponzi, Vladimir M. Calderón, Rosanna Migliore
Under normal conditions the principal cells of the striatum, medium spiny neurons (MSNs), show structured cell assembly activity patterns which alternate sequentially over exceedingly long timescales of many minutes. It is important to understand this activity since it is characteristically disrupted in multiple pathologies, such as Parkinson's disease and dyskinesia, and thought to be caused by alterations in the MSN to MSN lateral inhibitory connections and in the strength and distribution of cortical excitation to MSNs. To understand how these long timescales arise we extended a previous network model of MSN cells to include synapses with short-term plasticity, with parameters taken from a recent detailed striatal connectome study. We first confirmed the presence of sequentially switching cell clusters using the non-linear dimensionality reduction technique, Uniform Manifold Approximation and Projection (UMAP). We found that the network could generate non-stationary activity patterns varying extremely slowly on the order of minutes under biologically realistic conditions. Next we used Simulation Based Inference (SBI) to train a deep net to map features of the MSN network generated cell assembly activity to MSN network parameters. We used the trained SBI model to estimate MSN network parameters from ex-vivo brain slice calcium imaging data. We found that best fit network parameters were very close to their physiologically observed values. On the other hand network parameters estimated from Parkinsonian, decorticated and dyskinetic ex-vivo slice preparations were different. Our work may provide a pipeline for diagnosis of basal ganglia pathology from spiking data as well as for the design pharmacological treatments.
在正常情况下,纹状体的主要细胞--中刺神经元(MSN)--显示出结构化的细胞集结活动模式,这些活动模式在几分钟的超长时间尺度内依次交替进行。了解这种活动非常重要,因为它在帕金森病和运动障碍等多种病症中都会受到破坏,而且被认为是由 MSN 与 MSN 之间的横向抑制连接以及皮质对 MSN 的兴奋强度和分布的改变引起的。为了了解这些长时间尺度是如何产生的,我们扩展了以前的 MSN 细胞网络模型,以包括具有短期可塑性的突触,其参数取自最近的纹状体连接组详细研究。我们首先利用非线性降维技术--统一表层逼近和投影(UMAP)--确认了有序切换的细胞簇的存在。我们发现,在生物现实条件下,该网络能产生变化极其缓慢的非稳态活动模式,其变化量级可达几分钟。接下来,我们使用模拟推理(SBI)训练一个深度网,将 MSN 网络生成的细胞组装活动特征映射到 MSN 网络参数上。我们利用训练好的 SBI 模型,从体外脑片钙成像数据中估算 MSN 网络参数。我们发现,最佳拟合网络参数与其生理观测值非常接近。另一方面,从帕金森、去皮质和运动障碍的体外切片制备中估算出的网络参数则有所不同。我们的工作可为从尖峰数据诊断基底神经节病理学以及设计药物治疗提供一个管道。
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引用次数: 0
Synergy quality assessment of muscle modules for determining learning performance using a realistic musculoskeletal model 利用逼真的肌肉骨骼模型对肌肉模块进行协同质量评估,以确定学习成绩
IF 3.2 4区 医学 Q2 Neuroscience Pub Date : 2024-05-30 DOI: 10.3389/fncom.2024.1355855
Akito Fukunishi, Kyo Kutsuzawa, Dai Owaki, Mitsuhiro Hayashibe
How our central nervous system efficiently controls our complex musculoskeletal system is still debated. The muscle synergy hypothesis is proposed to simplify this complex system by assuming the existence of functional neural modules that coordinate several muscles. Modularity based on muscle synergies can facilitate motor learning without compromising task performance. However, the effectiveness of modularity in motor control remains debated. This ambiguity can, in part, stem from overlooking that the performance of modularity depends on the mechanical aspects of modules of interest, such as the torque the modules exert. To address this issue, this study introduces two criteria to evaluate the quality of module sets based on commonly used performance metrics in motor learning studies: the accuracy of torque production and learning speed. One evaluates the regularity in the direction of mechanical torque the modules exert, while the other evaluates the evenness of its magnitude. For verification of our criteria, we simulated motor learning of torque production tasks in a realistic musculoskeletal system of the upper arm using feed-forward neural networks while changing the control conditions. We found that the proposed criteria successfully explain the tendency of learning performance in various control conditions. These result suggest that regularity in the direction of and evenness in magnitude of mechanical torque of utilized modules are significant factor for determining learning performance. Although the criteria were originally conceived for an error-based learning scheme, the approach to pursue which set of modules is better for motor control can have significant implications in other studies of modularity in general.
我们的中枢神经系统如何有效控制复杂的肌肉骨骼系统仍存在争议。肌肉协同作用假说假定存在协调多块肌肉的功能神经模块,从而简化了这一复杂系统。基于肌肉协同作用的模块化可以促进运动学习,同时又不影响任务的完成。然而,模块化在运动控制中的有效性仍存在争议。这种模糊性可能部分源于忽略了模块化的性能取决于相关模块的机械方面,如模块施加的扭矩。为了解决这个问题,本研究根据运动学习研究中常用的性能指标:扭矩产生的准确性和学习速度,引入了两个标准来评估模块集的质量。其中一个标准评估模块产生机械扭矩方向的规律性,另一个标准评估其大小的均匀性。为了验证我们的标准,我们使用前馈神经网络模拟了上臂真实肌肉骨骼系统的扭矩产生任务的运动学习,同时改变了控制条件。我们发现,所提出的标准成功地解释了各种控制条件下学习成绩的变化趋势。这些结果表明,所使用模块的机械扭矩方向的规律性和大小的均匀性是决定学习成绩的重要因素。虽然这些标准最初是为基于错误的学习方案而设计的,但这种追求哪组模块更适合运动控制的方法对其他一般模块化研究具有重要意义。
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引用次数: 0
DT-SCNN: dual-threshold spiking convolutional neural network with fewer operations and memory access for edge applications DT-SCNN:双阈值尖峰卷积神经网络,运算和内存访问更少,适用于边缘应用
IF 3.2 4区 医学 Q2 Neuroscience Pub Date : 2024-05-30 DOI: 10.3389/fncom.2024.1418115
Fuming Lei, Xu Yang, Jian Liu, Runjiang Dou, Nanjian Wu
The spiking convolutional neural network (SCNN) is a kind of spiking neural network (SNN) with high accuracy for visual tasks and power efficiency on neuromorphic hardware, which is attractive for edge applications. However, it is challenging to implement SCNNs on resource-constrained edge devices because of the large number of convolutional operations and membrane potential (Vm) storage needed. Previous works have focused on timestep reduction, network pruning, and network quantization to realize SCNN implementation on edge devices. However, they overlooked similarities between spiking feature maps (SFmaps), which contain significant redundancy and cause unnecessary computation and storage. This work proposes a dual-threshold spiking convolutional neural network (DT-SCNN) to decrease the number of operations and memory access by utilizing similarities between SFmaps. The DT-SCNN employs dual firing thresholds to derive two similar SFmaps from one Vm map, reducing the number of convolutional operations and decreasing the volume of Vms and convolutional weights by half. We propose a variant spatio-temporal back propagation (STBP) training method with a two-stage strategy to train DT-SCNNs to decrease the inference timestep to 1. The experimental results show that the dual-thresholds mechanism achieves a 50% reduction in operations and data storage for the convolutional layers compared to conventional SCNNs while achieving not more than a 0.4% accuracy loss on the CIFAR10, MNIST, and Fashion MNIST datasets. Due to the lightweight network and single timestep inference, the DT-SCNN has the least number of operations compared to previous works, paving the way for low-latency and power-efficient edge applications.
尖峰卷积神经网络(SCNN)是一种尖峰神经网络(SNN),在视觉任务中具有高精确度,在神经形态硬件上具有高能效,对边缘应用很有吸引力。然而,由于需要大量卷积运算和膜电位(Vm)存储,在资源受限的边缘设备上实现 SCNN 是一项挑战。以前的工作主要集中在减少时间步长、网络剪枝和网络量化上,以实现在边缘设备上实施 SCNN。然而,他们忽略了尖峰特征图(SFmaps)之间的相似性,这些特征图包含大量冗余,会造成不必要的计算和存储。本研究提出了一种双阈值尖峰卷积神经网络(DT-SCNN),通过利用 SFmaps 之间的相似性来减少运算次数和内存访问。DT-SCNN 采用双发射阈值,从一个 Vm 映射中推导出两个相似的 SF 映射,从而减少了卷积操作的数量,并将 Vm 和卷积权重的体积减少了一半。实验结果表明,与传统 SCNN 相比,双阈值机制减少了卷积层 50% 的操作和数据存储,同时在 CIFAR10、MNIST 和时尚 MNIST 数据集上的准确率损失不超过 0.4%。由于采用了轻量级网络和单时间步推理,DT-SCNN 的操作次数与以前的作品相比最少,为低延迟、高能效的边缘应用铺平了道路。
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引用次数: 0
Simulated dynamical transitions in a heterogeneous marmoset pFC cluster 模拟异构狨猴 pFC 集群的动态转变
IF 3.2 4区 医学 Q2 Neuroscience Pub Date : 2024-05-28 DOI: 10.3389/fncom.2024.1398898
Bernard A. Pailthorpe
Network analysis of the marmoset cortical connectivity data indicates a significant 3D cluster in and around the pre-frontal cortex. A multi-node, heterogeneous neural mass model of this six-node cluster was constructed. Its parameters were informed by available experimental and simulation data so that each neural mass oscillated in a characteristic frequency band. Nodes were connected with directed, weighted links derived from the marmoset structural connectivity data. Heterogeneity arose from the different link weights and model parameters for each node. Stimulation of the cluster with an incident pulse train modulated in the standard frequency bands induced a variety of dynamical state transitions that lasted in the range of 5–10 s, suggestive of timescales relevant to short-term memory. A short gamma burst rapidly reset the beta-induced transition. The theta-induced transition state showed a spontaneous, delayed reset to the resting state. An additional, continuous gamma wave stimulus induced a new beating oscillatory state. Longer or repeated gamma bursts were phase-aligned with the beta oscillation, delivering increasing energy input and causing shorter transition times. The relevance of these results to working memory is yet to be established, but they suggest interesting opportunities.
对狨猴皮层连接数据的网络分析表明,在前额叶皮层及其周围有一个重要的三维集群。我们为这个六节点群构建了一个多节点、异质神经块模型。该模型的参数参考了现有的实验和模拟数据,因此每个神经块都在一个特征频率带内振荡。根据狨猴结构连通性数据得出的有向、加权链接将节点连接起来。每个节点的链接权重和模型参数不同,因此会产生异质性。用标准频带调制的入射脉冲串刺激集群,会诱发各种动态状态转换,持续时间在 5-10 秒之间,这表明了与短时记忆相关的时间尺度。一个短伽玛脉冲串迅速重置了贝塔诱导的转换。θ诱导的过渡状态显示出一种自发的、延迟的重置静息状态。额外的、持续的伽玛波刺激会诱发新的跳动振荡状态。较长或重复的伽玛脉冲与β振荡相位对齐,提供了更多的能量输入,并缩短了过渡时间。这些结果与工作记忆的相关性尚待确定,但它们暗示了有趣的机会。
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引用次数: 0
Multi-sequence generative adversarial network: better generation for enhanced magnetic resonance imaging images 多序列生成对抗网络:更好地生成增强型磁共振成像图像
IF 3.2 4区 医学 Q2 Neuroscience Pub Date : 2024-05-22 DOI: 10.3389/fncom.2024.1365238
Leizi Li, Jingchun Yu, Yijin Li, Jinbo Wei, Ruifang Fan, Dieen Wu, Yufeng Ye
MRI is one of the commonly used diagnostic methods in clinical practice, especially in brain diseases. There are many sequences in MRI, but T1CE images can only be obtained by using contrast agents. Many patients (such as cancer patients) must undergo alignment of multiple MRI sequences for diagnosis, especially the contrast-enhanced magnetic resonance sequence. However, some patients such as pregnant women, children, etc. find it difficult to use contrast agents to obtain enhanced sequences, and contrast agents have many adverse reactions, which can pose a significant risk. With the continuous development of deep learning, the emergence of generative adversarial networks makes it possible to extract features from one type of image to generate another type of image.We propose a generative adversarial network model with multimodal inputs and end-to-end decoding based on the pix2pix model. For the pix2pix model, we used four evaluation metrics: NMSE, RMSE, SSIM, and PNSR to assess the effectiveness of our generated model.Through statistical analysis, we compared our proposed new model with pix2pix and found significant differences between the two. Our model outperformed pix2pix, with higher SSIM and PNSR, lower NMSE and RMSE. We also found that the input of T1W images and T2W images had better effects than other combinations, providing new ideas for subsequent work on generating magnetic resonance enhancement sequence images. By using our model, it is possible to generate magnetic resonance enhanced sequence images based on magnetic resonance non-enhanced sequence images.This has significant implications as it can greatly reduce the use of contrast agents to protect populations such as pregnant women and children who are contraindicated for contrast agents. Additionally, contrast agents are relatively expensive, and this generation method may bring about substantial economic benefits.
磁共振成像是临床上常用的诊断方法之一,尤其适用于脑部疾病。磁共振成像有多种序列,但只有使用造影剂才能获得 T1CE 图像。许多患者(如癌症患者)必须接受多种磁共振序列的排列诊断,尤其是造影剂增强磁共振序列。然而,一些患者如孕妇、儿童等很难使用造影剂获得增强序列,而且造影剂有很多不良反应,会带来很大风险。随着深度学习的不断发展,生成式对抗网络的出现使得从一种类型的图像中提取特征生成另一种类型的图像成为可能。对于 pix2pix 模型,我们使用了四个评估指标:通过统计分析,我们将提出的新模型与 pix2pix 进行了比较,发现两者之间存在显著差异。我们的模型优于 pix2pix,SSIM 和 PNSR 更高,NMSE 和 RMSE 更低。我们还发现,输入 T1W 图像和 T2W 图像的效果优于其他组合,这为后续生成磁共振增强序列图像的工作提供了新思路。通过使用我们的模型,可以在磁共振非增强序列图像的基础上生成磁共振增强序列图像。这具有重大意义,因为它可以大大减少造影剂的使用,保护孕妇和儿童等造影剂禁忌人群。此外,造影剂相对昂贵,这种生成方法可能会带来巨大的经济效益。
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引用次数: 0
A mutual information measure of phase-amplitude coupling using gamma generalized linear models 利用伽马广义线性模型的相位-振幅耦合互信息测量法
IF 3.2 4区 医学 Q2 Neuroscience Pub Date : 2024-05-22 DOI: 10.3389/fncom.2024.1392655
Andrew S. Perley, Todd P. Coleman
Cross frequency coupling (CFC) between electrophysiological signals in the brain is a long-studied phenomenon and its abnormalities have been observed in conditions such as Parkinson's disease and epilepsy. More recently, CFC has been observed in stomach-brain electrophysiologic studies and thus becomes an enticing possible target for diseases involving aberrations of the gut-brain axis. However, current methods of detecting coupling, specifically phase-amplitude coupling (PAC), do not attempt to capture the phase and amplitude statistical relationships.In this paper, we first demonstrate a method of modeling these joint statistics with a flexible parametric approach, where we model the conditional distribution of amplitude given phase using a gamma distributed generalized linear model (GLM) with a Fourier basis of regressors. We perform model selection with minimum description length (MDL) principle, demonstrate a method for assessing goodness-of-fit (GOF), and showcase the efficacy of this approach in multiple electroencephalography (EEG) datasets. Secondly, we showcase how we can utilize the mutual information, which operates on the joint distribution, as a canonical measure of coupling, as it is non-zero and non-negative if and only if the phase and amplitude are not statistically independent. In addition, we build off of previous work by Martinez-Cancino et al., and Voytek et al., and show that the information density, evaluated using our method along the given sample path, is a promising measure of time-resolved PAC.Using synthetically generated gut-brain coupled signals, we demonstrate that our method outperforms the existing gold-standard methods for detectable low-levels of phase-amplitude coupling through receiver operating characteristic (ROC) curve analysis. To validate our method, we test on invasive EEG recordings by generating comodulograms, and compare our method to the gold standard PAC measure, Modulation Index, demonstrating comparable performance in exploratory analysis. Furthermore, to showcase its use in joint gut-brain electrophysiology data, we generate topoplots of simultaneous high-density EEG and electrgastrography recordings and reproduce seminal work by Richter et al. that demonstrated the existence of gut-brain PAC. Using simulated data, we validate our method for different types of time-varying coupling and then demonstrate its performance to track time-varying PAC in sleep spindle EEG and mismatch negativity (MMN) datasets.Our new measure of PAC using Gamma GLMs and mutual information demonstrates a promising new way to compute PAC values using the full joint distribution on amplitude and phase. Our measure outperforms the most common existing measures of PAC, and show promising results in identifying time varying PAC in electrophysiological datasets. In addition, we provide for using our method with multiple comparisons and show that our measure potentially has more statistical power in electrophysiologic recordin
大脑电生理信号之间的跨频耦合(CFC)是一种研究已久的现象,在帕金森病和癫痫等疾病中都曾观察到其异常。最近,在胃-脑电生理研究中也观察到了 CFC 现象,因此,CFC 成为了治疗肠-脑轴异常疾病的一个诱人靶点。在本文中,我们首先展示了一种用灵活的参数方法对这些联合统计进行建模的方法,在这种方法中,我们用一个伽马分布的广义线性模型(GLM)对给定相位的振幅条件分布进行建模,该模型具有回归因子的傅立叶基础。我们利用最小描述长度(MDL)原则进行模型选择,展示了评估拟合优度(GOF)的方法,并在多个脑电图(EEG)数据集中展示了这种方法的功效。其次,我们展示了如何利用联合分布上的互信息作为耦合度的典型衡量标准,因为只有当且仅当相位和振幅在统计上不独立时,互信息才是非零和非负的。此外,我们还借鉴了 Martinez-Cancino 等人和 Voytek 等人之前的研究成果,并证明使用我们的方法沿给定采样路径评估的信息密度是一种很有前景的时间分辨 PAC 测量方法。我们使用合成生成的肠脑耦合信号,通过接收器操作特征曲线 (ROC) 分析,证明我们的方法在检测低水平的相位-振幅耦合方面优于现有的黄金标准方法。为了验证我们的方法,我们对有创脑电图记录进行了测试,生成了 Comodulogram,并将我们的方法与黄金标准 PAC 测量方法--调制指数进行了比较,结果表明我们的方法在探索性分析中具有可比性。此外,为了展示该方法在肠脑联合电生理学数据中的应用,我们生成了同步高密度脑电图和电图记录的拓扑图,并重现了 Richter 等人的开创性工作,该工作证明了肠脑 PAC 的存在。我们使用伽马 GLM 和互信息对 PAC 进行了新的测量,展示了一种利用振幅和相位的完整联合分布计算 PAC 值的有前途的新方法。我们的测量方法优于现有最常见的 PAC 测量方法,在识别电生理学数据集中的时变 PAC 方面显示出良好的效果。此外,我们还提供了使用我们的方法进行多重比较的方法,并表明我们的方法在同时使用肠道-大脑数据集的电生理记录中可能具有更强的统计能力。
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Frontiers in Computational Neuroscience
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