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Quantitative assessment of neurodevelopmental maturation: a comprehensive systematic literature review of artificial intelligence-based brain age prediction in pediatric populations. 神经发育成熟度的定量评估:基于人工智能的儿科人群脑年龄预测的全面系统文献综述。
IF 2.5 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-11-12 eCollection Date: 2024-01-01 DOI: 10.3389/fninf.2024.1496143
Eric Dragendorf, Eva Bültmann, Dominik Wolff

Introduction: Over the past few decades, numerous researchers have explored the application of machine learning for assessing children's neurological development. Developmental changes in the brain could be utilized to gauge the alignment of its maturation status with the child's chronological age. AI is trained to analyze changes in different modalities and estimate the brain age of subjects. Disparities between the predicted and chronological age can be viewed as a biomarker for a pathological condition. This literature review aims to illuminate research studies that have employed AI to predict children's brain age.

Methods: The inclusion criteria for this study were predicting brain age via AI in healthy children up to 12 years. The search term was centered around the keywords "pediatric," "artificial intelligence," and "brain age" and was utilized in PubMed and IEEEXplore. The selected literature was then examined for information on data acquisition methods, the age range of the study population, pre-processing, methods and AI techniques utilized, the quality of the respective techniques, model explanation, and clinical applications.

Results: Fifty one publications from 2012 to 2024 were included in the analysis. The primary modality of data acquisition was MRI, followed by EEG. Structural and functional MRI-based studies commonly used publicly available datasets, while EEG-based studies typically relied on self-recruitment. Many studies utilized pre-processing pipelines provided by toolkit suites, particularly in MRI-based research. The most frequently used model type was kernel-based learning algorithms, followed by convolutional neural networks. Overall, prediction accuracy may improve when multiple acquisition modalities are used, but comparing studies is challenging. In EEG, the prediction error decreases as the number of electrodes increases. Approximately one-third of the studies used explainable artificial intelligence methods to explain the model and chosen parameters. However, there is a significant clinical translation gap as no study has tested their model in a clinical routine setting.

Discussion: Further research should test on external datasets and include low-quality routine images for MRI. T2-weighted MRI was underrepresented. Furthermore, different kernel types should be compared on the same dataset. Implementing modern model architectures, such as convolutional neural networks, should be the next step in EEG-based research studies.

简介在过去的几十年里,许多研究人员都在探索如何应用机器学习来评估儿童的神经系统发育。大脑的发育变化可以用来衡量其成熟状态与儿童的实际年龄是否一致。人工智能经过训练,可以分析不同模式的变化,并估算受试者的大脑年龄。预测年龄与实际年龄之间的差异可被视为病理状况的生物标记。本文献综述旨在阐明采用人工智能预测儿童脑年龄的研究:本研究的纳入标准是通过人工智能预测 12 岁以下健康儿童的脑年龄。搜索关键词围绕 "儿科"、"人工智能 "和 "脑年龄",并使用 PubMed 和 IEEEXplore。然后对所选文献进行检查,以了解数据采集方法、研究人群的年龄范围、预处理、所使用的方法和人工智能技术、相关技术的质量、模型解释和临床应用等信息:本次分析共收录了 2012 年至 2024 年间发表的 51 篇论文。数据采集的主要方式是磁共振成像,其次是脑电图。基于结构和功能磁共振成像的研究通常使用公开的数据集,而基于脑电图的研究通常依赖于自我招募。许多研究利用了工具包套件提供的预处理管道,尤其是在基于核磁共振成像的研究中。最常用的模型类型是基于核的学习算法,其次是卷积神经网络。总体而言,当使用多种采集模式时,预测准确率可能会有所提高,但对研究进行比较具有挑战性。在脑电图中,预测误差随着电极数量的增加而减小。大约三分之一的研究使用了可解释的人工智能方法来解释模型和所选参数。然而,由于没有一项研究在临床常规环境中测试过他们的模型,因此在临床转化方面还存在很大差距:讨论:进一步的研究应在外部数据集上进行测试,并纳入低质量的核磁共振常规图像。T2加权核磁共振成像的代表性不足。此外,还应在同一数据集上比较不同的内核类型。在基于脑电图的研究中,下一步应采用卷积神经网络等现代模型架构。
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引用次数: 0
Spectral graph convolutional neural network for Alzheimer's disease diagnosis and multi-disease categorization from functional brain changes in magnetic resonance images. 光谱图卷积神经网络用于从磁共振图像中的大脑功能变化诊断阿尔茨海默病和多种疾病分类。
IF 2.5 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-10-30 eCollection Date: 2024-01-01 DOI: 10.3389/fninf.2024.1495571
Hadeel Alharbi, Roben A Juanatas, Abdullah Al Hejaili, Se-Jung Lim

Alzheimer's disease (AD) is a progressive neurological disorder characterized by the gradual deterioration of cognitive functions, leading to dementia and significantly impacting the quality of life for millions of people worldwide. Early and accurate diagnosis is crucial for the effective management and treatment of this debilitating condition. This study introduces a novel framework based on Spectral Graph Convolutional Neural Networks (SGCNN) for diagnosing AD and categorizing multiple diseases through the analysis of functional changes in brain structures captured via magnetic resonance imaging (MRI). To assess the effectiveness of our approach, we systematically analyze structural modifications to the SGCNN model through comprehensive ablation studies. The performance of various Convolutional Neural Networks (CNNs) is also evaluated, including SGCNN variants, Base CNN, Lean CNN, and Deep CNN. We begin with the original SGCNN model, which serves as our baseline and achieves a commendable classification accuracy of 93%. In our investigation, we perform two distinct ablation studies on the SGCNN model to examine how specific structural changes impact its performance. The results reveal that Ablation Model 1 significantly enhances accuracy, achieving an impressive 95%, while Ablation Model 2 maintains the baseline accuracy of 93%. Additionally, the Base CNN model demonstrates strong performance with a classification accuracy of 93%, whereas both the Lean CNN and Deep CNN models achieve 94% accuracy, indicating their competitive capabilities. To validate the models' effectiveness, we utilize multiple evaluation metrics, including accuracy, precision, recall, and F1-score, ensuring a thorough assessment of their performance. Our findings underscore that Ablation Model 1 (SGCNN Model 1) delivers the highest predictive accuracy among the tested models, highlighting its potential as a robust approach for Alzheimer's image classification. Ultimately, this research aims to facilitate early diagnosis and treatment of AD, contributing to improved patient outcomes and advancing the field of neurodegenerative disease diagnosis.

阿尔茨海默病(AD)是一种渐进性神经系统疾病,其特点是认知功能逐渐退化,导致痴呆,严重影响全球数百万人的生活质量。早期准确的诊断对于有效管理和治疗这种使人衰弱的疾病至关重要。本研究介绍了一种基于谱图卷积神经网络(SGCNN)的新型框架,通过分析磁共振成像(MRI)捕捉到的大脑结构的功能变化,诊断痴呆症并对多种疾病进行分类。为了评估我们方法的有效性,我们通过全面的消融研究系统地分析了对 SGCNN 模型的结构修改。我们还评估了各种卷积神经网络(CNN)的性能,包括 SGCNN 变体、Base CNN、Lean CNN 和 Deep CNN。我们从原始 SGCNN 模型开始,该模型是我们的基准模型,分类准确率高达 93%,值得称赞。在研究中,我们对 SGCNN 模型进行了两次不同的消融研究,以考察特定的结构变化对其性能的影响。结果显示,消融模型 1 显著提高了准确率,达到了令人印象深刻的 95%,而消融模型 2 则保持了 93% 的基线准确率。此外,Base CNN 模型表现强劲,分类准确率达到 93%,而 Lean CNN 和 Deep CNN 模型的准确率均达到 94%,这表明它们具有很强的竞争力。为了验证模型的有效性,我们采用了多种评估指标,包括准确率、精确度、召回率和 F1 分数,以确保对其性能进行全面评估。我们的研究结果表明,在所测试的模型中,消融模型 1(SGCNN 模型 1)的预测准确率最高,凸显了其作为阿尔茨海默氏症图像分类的稳健方法的潜力。这项研究的最终目的是促进阿尔茨海默病的早期诊断和治疗,改善患者的预后,推动神经退行性疾病诊断领域的发展。
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引用次数: 0
Commentary: Accelerating spiking neural network simulations with PymoNNto and PymoNNtorch. 评论:利用 PymoNNto 和 PymoNNtorch 加速尖峰神经网络仿真
IF 2.5 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-10-23 eCollection Date: 2024-01-01 DOI: 10.3389/fninf.2024.1446620
Hans Ekkehard Plesser
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引用次数: 0
Fuzzy C-means clustering algorithm applied in computed tomography images of patients with intracranial hemorrhage. 模糊 C-means 聚类算法在颅内出血患者计算机断层扫描图像中的应用。
IF 2.5 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-10-23 eCollection Date: 2024-01-01 DOI: 10.3389/fninf.2024.1440304
Lintao Zhang, Dewen Song, Huiying Qiu, Lin Ye, Zengliang Xu

In recent years, intracerebral hemorrhage (ICH) has garnered significant attention as a severe cerebrovascular disorder. To enhance the accuracy of ICH detection and segmentation, this study proposed an improved fuzzy C-means (FCM) algorithm and performed a comparative analysis with both traditional FCM and advanced convolutional neural network (CNN) algorithms. Experiments conducted on the publicly available CT-ICH dataset evaluated the performance of these three algorithms in predicting ICH volume. The results demonstrated that the improved FCM algorithm offered notable improvements in computational time and resource consumption compared to the traditional FCM algorithm, while also showing enhanced accuracy. However, it still lagged behind the CNN algorithm in areas such as feature extraction, model generalization, and the ability to handle complex image structures. The study concluded with a discussion of potential directions for further optimizing the FCM algorithm, aiming to bridge the performance gap with CNN algorithms and provide a reference for future research in medical image processing.

近年来,脑出血(ICH)作为一种严重的脑血管疾病备受关注。为了提高 ICH 检测和分割的准确性,本研究提出了一种改进的模糊 C-means (FCM) 算法,并与传统的 FCM 算法和先进的卷积神经网络 (CNN) 算法进行了比较分析。在公开的 CT-ICH 数据集上进行的实验评估了这三种算法在预测 ICH 体积方面的性能。结果表明,与传统的 FCM 算法相比,改进的 FCM 算法在计算时间和资源消耗方面都有显著改善,同时还显示出更高的准确性。不过,它在特征提取、模型泛化和处理复杂图像结构的能力等方面仍落后于 CNN 算法。研究最后讨论了进一步优化 FCM 算法的潜在方向,旨在缩小与 CNN 算法的性能差距,为未来医学图像处理研究提供参考。
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引用次数: 0
Early detection of mild cognitive impairment through neuropsychological tests in population screenings: a decision support system integrating ontologies and machine learning. 在人群筛查中通过神经心理学测试及早发现轻度认知障碍:整合本体论和机器学习的决策支持系统。
IF 2.5 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-10-16 eCollection Date: 2024-01-01 DOI: 10.3389/fninf.2024.1378281
Alba Gómez-Valadés, Rafael Martínez-Tomás, Sara García-Herranz, Atle Bjørnerud, Mariano Rincón

Machine learning (ML) methodologies for detecting Mild Cognitive Impairment (MCI) are progressively gaining prevalence to manage the vast volume of processed information. Nevertheless, the black-box nature of ML algorithms and the heterogeneity within the data may result in varied interpretations across distinct studies. To avoid this, in this proposal, we present the design of a decision support system that integrates a machine learning model represented using the Semantic Web Rule Language (SWRL) in an ontology with specialized knowledge in neuropsychological tests, the NIO ontology. The system's ability to detect MCI subjects was evaluated on a database of 520 neuropsychological assessments conducted in Spanish and compared with other well-established ML methods. Using the F2 coefficient to minimize false negatives, results indicate that the system performs similarly to other well-established ML methods (F2TE2  = 0.830, only below bagging, F2BAG  = 0.832) while exhibiting other significant attributes such as explanation capability and data standardization to a common framework thanks to the ontological part. On the other hand, the system's versatility and ease of use were demonstrated with three additional use cases: evaluation of new cases even if the acquisition stage is incomplete (the case records have missing values), incorporation of a new database into the integrated system, and use of the ontology capabilities to relate different domains. This makes it a useful tool to support physicians and neuropsychologists in population-based screenings for early detection of MCI.

用于检测轻度认知障碍(MCI)的机器学习(ML)方法正在逐渐普及,以管理大量处理过的信息。然而,ML 算法的黑箱性质和数据的异质性可能会导致不同研究的解释各不相同。为了避免这种情况,在本提案中,我们提出了一个决策支持系统的设计方案,该系统将使用语义网络规则语言(SWRL)表示的机器学习模型集成到一个具有神经心理测试专业知识的本体(NIO 本体)中。该系统检测 MCI 受试者的能力在一个包含 520 项西班牙语神经心理学评估的数据库中进行了评估,并与其他成熟的 ML 方法进行了比较。使用 F2 系数来最小化假阴性,结果表明该系统的性能与其他成熟的 ML 方法类似(F2TE2 = 0.830,仅低于袋式方法,F2BAG = 0.832),同时还表现出其他重要属性,如解释能力和数据标准化,由于本体论部分的存在,该系统可实现通用框架。另一方面,该系统的多功能性和易用性也通过三个附加用例得到了体现:即使采集阶段不完整(病例记录有缺失值),也能对新病例进行评估;将新数据库纳入集成系统;利用本体功能将不同领域联系起来。这使得该系统成为支持医生和神经心理学家进行基于人群的筛查以早期发现 MCI 的有用工具。
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引用次数: 0
Research on ECG signal reconstruction based on improved weighted nuclear norm minimization and approximate message passing algorithm. 基于改进的加权核规范最小化和近似信息传递算法的心电信号重建研究。
IF 2.5 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-10-08 eCollection Date: 2024-01-01 DOI: 10.3389/fninf.2024.1454244
Bing Zhang, Xishun Zhu, Fadia Ali Khan, Sajjad Shaukat Jamal, Alanoud Al Mazroa, Rab Nawaz

In order to improve the energy efficiency of wearable devices, it is necessary to compress and reconstruct the collected electrocardiogram data. The compressed data may be mixed with noise during the transmission process. The denoising-based approximate message passing (AMP) algorithm performs well in reconstructing noisy signals, so the denoising-based AMP algorithm is introduced into electrocardiogram signal reconstruction. The weighted nuclear norm minimization algorithm (WNNM) uses the low-rank characteristics of similar signal blocks for denoising, and averages the signal blocks after low-rank decomposition to obtain the final denoised signal. However, under the influence of noise, there may be errors in searching for similar blocks, resulting in dissimilar signal blocks being grouped together, affecting the denoising effect. Based on this, this paper improves the WNNM algorithm and proposes to use weighted averaging instead of direct averaging for the signal blocks after low-rank decomposition in the denoising process, and validating its effectiveness on electrocardiogram signals. Experimental results demonstrate that the IWNNM-AMP algorithm achieves the best reconstruction performance under different compression ratios and noise conditions, obtaining the lowest PRD and RMSE values. Compared with the WNNM-AMP algorithm, the PRD value is reduced by 0.17∼4.56, the P-SNR value is improved by 0.12∼2.70.

为了提高可穿戴设备的能效,有必要对收集到的心电图数据进行压缩和重构。压缩数据在传输过程中可能会混入噪声。基于去噪的近似消息传递(AMP)算法在重构噪声信号方面表现出色,因此基于去噪的 AMP 算法被引入到心电图信号重构中。加权核规范最小化算法(WNNM)利用相似信号块的低秩特征进行去噪,对低秩分解后的信号块进行平均,得到最终的去噪信号。然而,在噪声的影响下,搜索相似块时可能会出现误差,导致不相似的信号块被归为一组,影响去噪效果。基于此,本文对 WNNM 算法进行了改进,提出在去噪过程中使用加权平均代替低阶分解后信号块的直接平均,并在心电信号上验证了其有效性。实验结果表明,在不同的压缩比和噪声条件下,IWNNM-AMP 算法的重建性能最佳,获得了最低的 PRD 值和 RMSE 值。与 WNNM-AMP 算法相比,PRD 值降低了 0.17∼4.56,P-SNR 值提高了 0.12∼2.70。
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引用次数: 0
Can micro-expressions be used as a biomarker for autism spectrum disorder? 微表达可用作自闭症谱系障碍的生物标记吗?
IF 2.5 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-10-03 eCollection Date: 2024-01-01 DOI: 10.3389/fninf.2024.1435091
Mindi Ruan, Na Zhang, Xiangxu Yu, Wenqi Li, Chuanbo Hu, Paula J Webster, Lynn K Paul, Shuo Wang, Xin Li

Introduction: Early and accurate diagnosis of autism spectrum disorder (ASD) is crucial for effective intervention, yet it remains a significant challenge due to its complexity and variability. Micro-expressions are rapid, involuntary facial movements indicative of underlying emotional states. It is unknown whether micro-expression can serve as a valid bio-marker for ASD diagnosis.

Methods: This study introduces a novel machine-learning (ML) framework that advances ASD diagnostics by focusing on facial micro-expressions. We applied cutting-edge algorithms to detect and analyze these micro-expressions from video data, aiming to identify distinctive patterns that could differentiate individuals with ASD from typically developing peers. Our computational approach included three key components: (1) micro-expression spotting using Shallow Optical Flow Three-stream CNN (SOFTNet), (2) feature extraction via Micron-BERT, and (3) classification with majority voting of three competing models (MLP, SVM, and ResNet).

Results: Despite the sophisticated methodology, the ML framework's ability to reliably identify ASD-specific patterns was limited by the quality of video data. This limitation raised concerns about the efficacy of using micro-expressions for ASD diagnostics and pointed to the necessity for enhanced video data quality.

Discussion: Our research has provided a cautious evaluation of micro-expression diagnostic value, underscoring the need for advancements in behavioral imaging and multimodal AI technology to leverage the full capabilities of ML in an ASD-specific clinical context.

导言:自闭症谱系障碍(ASD)的早期准确诊断对有效干预至关重要,但由于其复杂性和多变性,诊断仍是一项重大挑战。微表情是一种快速、不自主的面部动作,表明潜在的情绪状态。微表情能否作为诊断 ASD 的有效生物标记尚不得而知:本研究介绍了一种新颖的机器学习(ML)框架,通过关注面部微表情来推进 ASD 诊断。我们采用最先进的算法来检测和分析视频数据中的微表情,旨在找出可将 ASD 患者与发育正常的同龄人区分开来的独特模式。我们的计算方法包括三个关键部分:(1) 使用浅层光流三流 CNN(SOFTNet)发现微表情;(2) 通过 Micron-BERT 提取特征;(3) 使用三个竞争模型(MLP、SVM 和 ResNet)的多数票进行分类:尽管采用了复杂的方法,但由于视频数据的质量问题,ML 框架可靠识别 ASD 特定模式的能力受到了限制。这一局限性引发了人们对使用微表情进行 ASD 诊断的有效性的担忧,并指出了提高视频数据质量的必要性:我们的研究对微表情的诊断价值进行了谨慎的评估,强调了在行为成像和多模态人工智能技术方面取得进步的必要性,以便在针对 ASD 的临床环境中充分利用人工智能的全部功能。
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引用次数: 0
Reproducible brain PET data analysis: easier said than done. 可重复的脑 PET 数据分析:说起来容易做起来难。
IF 2.5 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-09-30 eCollection Date: 2024-01-01 DOI: 10.3389/fninf.2024.1420315
Maryam Naseri, Sreekrishna Ramakrishnapillai, Owen T Carmichael

While a great deal of recent effort has focused on addressing a perceived reproducibility crisis within brain structural magnetic resonance imaging (MRI) and functional MRI research communities, this article argues that brain positron emission tomography (PET) research stands on even more fragile ground, lagging behind efforts to address MRI reproducibility. We begin by examining the current landscape of factors that contribute to reproducible neuroimaging data analysis, including scientific standards, analytic plan pre-registration, data and code sharing, containerized workflows, and standardized processing pipelines. We then focus on disparities in the current status of these factors between brain MRI and brain PET. To demonstrate the positive impact that further developing such reproducibility factors would have on brain PET research, we present a case study that illustrates the many challenges faced by one laboratory that attempted to reproduce a community-standard brain PET processing pipeline. We identified key areas in which the brain PET community could enhance reproducibility, including stricter reporting policies among PET dedicated journals, data repositories, containerized analysis tools, and standardized processing pipelines. Other solutions such as mandatory pre-registration, data sharing, code availability as a condition of grant funding, and online forums and standardized reporting templates, are also discussed. Bolstering these reproducibility factors within the brain PET research community has the potential to unlock the full potential of brain PET research, propelling it toward a higher-impact future.

最近,脑结构磁共振成像(MRI)和功能磁共振成像(MRI)研究界将大量精力集中在解决可重复性危机上,而本文认为脑正电子发射断层扫描(PET)研究的基础更加脆弱,落后于解决 MRI 可重复性问题的努力。我们首先考察了当前有助于神经成像数据分析可重复性的各种因素,包括科学标准、分析计划预注册、数据和代码共享、容器化工作流程和标准化处理管道。然后,我们将重点关注这些因素在脑 MRI 和脑 PET 之间的现状差异。为了证明进一步开发这些可重复性因素将对脑 PET 研究产生的积极影响,我们介绍了一个案例研究,该案例说明了一家实验室在试图复制社区标准脑 PET 处理管道时所面临的诸多挑战。我们确定了脑 PET 社区可以提高可重复性的关键领域,包括 PET 专用期刊之间更严格的报告政策、数据存储库、容器化分析工具和标准化处理管道。此外,还讨论了其他解决方案,如强制预注册、数据共享、将代码可用性作为资助条件、在线论坛和标准化报告模板等。在脑 PET 研究界加强这些可重复性因素有可能释放脑 PET 研究的全部潜力,推动其走向更有影响力的未来。
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引用次数: 0
Artificial intelligence role in advancement of human brain connectome studies. 人工智能在推动人类大脑连接组研究中的作用。
IF 2.5 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-09-20 eCollection Date: 2024-01-01 DOI: 10.3389/fninf.2024.1399931
Dorsa Shekouh, Helia Sadat Kaboli, Mohammadreza Ghaffarzadeh-Esfahani, Mohammadmahdi Khayamdar, Zeinab Hamedani, Saeed Oraee-Yazdani, Alireza Zali, Elnaz Amanzadeh

Neurons are interactive cells that connect via ions to develop electromagnetic fields in the brain. This structure functions directly in the brain. Connectome is the data obtained from neuronal connections. Since neural circuits change in the brain in various diseases, studying connectome sheds light on the clinical changes in special diseases. The ability to explore this data and its relation to the disorders leads us to find new therapeutic methods. Artificial intelligence (AI) is a collection of powerful algorithms used for finding the relationship between input data and the outcome. AI is used for extraction of valuable features from connectome data and in turn uses them for development of prognostic and diagnostic models in neurological diseases. Studying the changes of brain circuits in neurodegenerative diseases and behavioral disorders makes it possible to provide early diagnosis and development of efficient treatment strategies. Considering the difficulties in studying brain diseases, the use of connectome data is one of the beneficial methods for improvement of knowledge of this organ. In the present study, we provide a systematic review on the studies published using connectome data and AI for studying various diseases and we focus on the strength and weaknesses of studies aiming to provide a viewpoint for the future studies. Throughout, AI is very useful for development of diagnostic and prognostic tools using neuroimaging data, while bias in data collection and decay in addition to using small datasets restricts applications of AI-based tools using connectome data which should be covered in the future studies.

神经元是互动细胞,通过离子连接,在大脑中形成电磁场。这种结构直接在大脑中发挥作用。连接组是从神经元连接中获得的数据。由于在各种疾病中大脑神经回路会发生变化,因此研究连接组可以揭示特殊疾病的临床变化。探索这些数据及其与疾病之间关系的能力将帮助我们找到新的治疗方法。人工智能(AI)是一系列功能强大的算法,用于寻找输入数据与结果之间的关系。人工智能用于从连接组数据中提取有价值的特征,进而用于开发神经系统疾病的预后和诊断模型。通过研究神经退行性疾病和行为障碍中大脑回路的变化,可以提供早期诊断并制定有效的治疗策略。考虑到研究脑部疾病的困难,利用连接组数据是增进对这一器官了解的有益方法之一。在本研究中,我们对已发表的利用连接组数据和人工智能研究各种疾病的研究进行了系统回顾,并重点分析了这些研究的优势和不足,旨在为今后的研究提供一个视角。总的来说,人工智能对于利用神经影像数据开发诊断和预后工具非常有用,但数据收集和衰减中的偏差以及使用小数据集限制了基于人工智能的工具在连接组数据中的应用,这一点应在未来的研究中加以关注。
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引用次数: 0
Cooperation objective evaluation in aviation: validation and comparison of two novel approaches in simulated environment 航空合作目标评估:在模拟环境中验证和比较两种新方法
IF 3.5 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-09-18 DOI: 10.3389/fninf.2024.1409322
Rossella Capotorto, Vincenzo Ronca, Nicolina Sciaraffa, Gianluca Borghini, Gianluca Di Flumeri, Lorenzo Mezzadri, Alessia Vozzi, Andrea Giorgi, Daniele Germano, Fabio Babiloni, Pietro Aricò
IntroductionIn operational environments, human interaction and cooperation between individuals are critical to efficiency and safety. These states are influenced by individuals' cognitive and emotional states. Human factor research aims to objectively quantify these states to prevent human error and maintain constant performances, particularly in high-risk settings such as aviation, where human error and performance account for a significant portion of accidents.MethodsThus, this study aimed to evaluate and validate two novel methods for assessing the degree of cooperation among professional pilots engaged in real-flight simulation tasks. In addition, the study aimed to assess the ability of the proposed metrics to differentiate between the expertise levels of operating crews based on their levels of cooperation. Eight crews were involved in the experiments, consisting of four crews of Unexperienced pilots and four crews of Experienced pilots. An expert trainer, simulating air traffic management communication on one side and acting as a subject matter expert on the other, provided external evaluations of the pilots' mental states during the simulation. The two novel approaches introduced in this study were formulated based on circular correlation and mutual information techniques.Results and discussionThe findings demonstrated the possibility of quantifying cooperation levels among pilots during realistic flight simulations. In addition, cooperation time is found to be significantly higher (p &lt; 0.05) among Experienced pilots compared to Unexperienced ones. Furthermore, these preliminary results exhibited significant correlations (p &lt; 0.05) with subjective and behavioral measures collected every 30 s during the task, confirming their reliability.
导言在操作环境中,人与人之间的互动与合作对效率和安全至关重要。这些状态受到个人认知和情绪状态的影响。人因研究旨在客观量化这些状态,以防止人为失误并保持稳定的性能,特别是在航空等高风险环境中,人为失误和性能占事故的很大一部分。方法因此,本研究旨在评估和验证两种新方法,用于评估参与真实飞行模拟任务的专业飞行员之间的合作程度。此外,该研究还旨在评估所提出的指标是否能够根据合作程度来区分机组人员的专业水平。八名机组人员参与了实验,其中四名为无经验飞行员,四名为有经验飞行员。一名专家培训师一边模拟空中交通管理通信,一边作为主题专家,对飞行员在模拟过程中的心理状态进行外部评估。本研究中引入的两种新方法是基于循环相关和互信息技术制定的。此外,还发现有经验的飞行员与无经验的飞行员相比,合作时间明显较长(p &p;lt;0.05)。此外,这些初步结果与任务期间每 30 秒收集一次的主观和行为测量结果有明显的相关性(p & lt; 0.05),证实了其可靠性。
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引用次数: 0
期刊
Frontiers in Neuroinformatics
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