首页 > 最新文献

Frontiers in Computational Neuroscience最新文献

英文 中文
Editorial: Computational modeling and machine learning methods in neurodevelopment and neurodegeneration: from basic research to clinical applications. 社论:神经发育和神经变性中的计算建模和机器学习方法:从基础研究到临床应用。
IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-11-08 eCollection Date: 2024-01-01 DOI: 10.3389/fncom.2024.1514220
Noemi Montobbio, Roberto Maffulli, Anees Abrol, Pablo Martínez-Cañada
{"title":"Editorial: Computational modeling and machine learning methods in neurodevelopment and neurodegeneration: from basic research to clinical applications.","authors":"Noemi Montobbio, Roberto Maffulli, Anees Abrol, Pablo Martínez-Cañada","doi":"10.3389/fncom.2024.1514220","DOIUrl":"https://doi.org/10.3389/fncom.2024.1514220","url":null,"abstract":"","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"18 ","pages":"1514220"},"PeriodicalIF":2.1,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11581899/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142709341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Simulated synapse loss induces depression-like behaviors in deep reinforcement learning. 在深度强化学习中,模拟突触丢失会诱发类似抑郁的行为。
IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-11-06 eCollection Date: 2024-01-01 DOI: 10.3389/fncom.2024.1466364
Eric Chalmers, Santina Duarte, Xena Al-Hejji, Daniel Devoe, Aaron Gruber, Robert J McDonald

Deep Reinforcement Learning is a branch of artificial intelligence that uses artificial neural networks to model reward-based learning as it occurs in biological agents. Here we modify a Deep Reinforcement Learning approach by imposing a suppressive effect on the connections between neurons in the artificial network-simulating the effect of dendritic spine loss as observed in major depressive disorder (MDD). Surprisingly, this simulated spine loss is sufficient to induce a variety of MDD-like behaviors in the artificially intelligent agent, including anhedonia, increased temporal discounting, avoidance, and an altered exploration/exploitation balance. Furthermore, simulating alternative and longstanding reward-processing-centric conceptions of MDD (dysfunction of the dopamine system, altered reward discounting, context-dependent learning rates, increased exploration) does not produce the same range of MDD-like behaviors. These results support a conceptual model of MDD as a reduction of brain connectivity (and thus information-processing capacity) rather than an imbalance in monoamines-though the computational model suggests a possible explanation for the dysfunction of dopamine systems in MDD. Reversing the spine-loss effect in our computational MDD model can lead to rescue of rewarding behavior under some conditions. This supports the search for treatments that increase plasticity and synaptogenesis, and the model suggests some implications for their effective administration.

深度强化学习(Deep Reinforcement Learning)是人工智能的一个分支,它使用人工神经网络来模拟生物体内发生的基于奖励的学习。在这里,我们对深度强化学习方法进行了修改,对人工网络中神经元之间的连接施加了抑制效应--模拟在重度抑郁症(MDD)中观察到的树突棘缺失效应。令人惊讶的是,这种模拟的树突棘缺失足以在人工智能代理中诱发各种类似 MDD 的行为,包括失神、时间折扣增加、回避和探索/开发平衡的改变。此外,模拟以奖赏处理为中心的 MDD 的其他长期概念(多巴胺系统功能障碍、奖赏折现改变、情境依赖学习率、探索增加)也不会产生相同的 MDD 类行为。这些结果支持将 MDD 视为大脑连通性降低(从而导致信息处理能力下降)而非单胺失衡的概念模型--尽管计算模型为 MDD 中多巴胺系统功能障碍提供了一种可能的解释。在我们的 MDD 计算模型中,逆转脊髓丧失效应可以在某些条件下挽救奖赏行为。这支持了对增加可塑性和突触生成的治疗方法的探索,而该模型也为这些治疗方法的有效应用提供了一些启示。
{"title":"Simulated synapse loss induces depression-like behaviors in deep reinforcement learning.","authors":"Eric Chalmers, Santina Duarte, Xena Al-Hejji, Daniel Devoe, Aaron Gruber, Robert J McDonald","doi":"10.3389/fncom.2024.1466364","DOIUrl":"10.3389/fncom.2024.1466364","url":null,"abstract":"<p><p>Deep Reinforcement Learning is a branch of artificial intelligence that uses artificial neural networks to model reward-based learning as it occurs in biological agents. Here we modify a Deep Reinforcement Learning approach by imposing a suppressive effect on the connections between neurons in the artificial network-simulating the effect of dendritic spine loss as observed in major depressive disorder (MDD). Surprisingly, this simulated spine loss is sufficient to induce a variety of MDD-like behaviors in the artificially intelligent agent, including anhedonia, increased temporal discounting, avoidance, and an altered exploration/exploitation balance. Furthermore, simulating alternative and longstanding reward-processing-centric conceptions of MDD (dysfunction of the dopamine system, altered reward discounting, context-dependent learning rates, increased exploration) does not produce the same range of MDD-like behaviors. These results support a conceptual model of MDD as a reduction of brain connectivity (and thus information-processing capacity) rather than an imbalance in monoamines-though the computational model suggests a possible explanation for the dysfunction of dopamine systems in MDD. Reversing the spine-loss effect in our computational MDD model can lead to rescue of rewarding behavior under some conditions. This supports the search for treatments that increase plasticity and synaptogenesis, and the model suggests some implications for their effective administration.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"18 ","pages":"1466364"},"PeriodicalIF":2.1,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11576168/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142681336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Systematic review of cognitive impairment in drivers through mental workload using physiological measures of heart rate variability. 利用心率变异性的生理测量方法,通过精神工作量对驾驶员认知障碍进行系统审查。
IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-10-31 eCollection Date: 2024-01-01 DOI: 10.3389/fncom.2024.1475530
Mansoor S Raza, Mohsin Murtaza, Chi-Tsun Cheng, Muhana M A Muslam, Bader M Albahlal

The intricate interplay between driver cognitive dysfunction, mental workload (MWL), and heart rate variability (HRV) provides a captivating avenue for investigation within the domain of transportation safety studies. This article provides a systematic review and examines cognitive hindrance stemming from mental workload and heart rate variability. It scrutinizes the mental workload experienced by drivers by leveraging data gleaned from prior studies that employed heart rate monitoring systems and eye tracking technology, thereby illuminating the correlation between cognitive impairment, mental workload, and physiological indicators such as heart rate and ocular movements. The investigation is grounded in the premise that the mental workload of drivers can be assessed through physiological cues, such as heart rate and eye movements. The study discovered that HRV and infrared (IR) measurements played a crucial role in evaluating fatigue and workload for skilled drivers. However, the study overlooked potential factors contributing to cognitive impairment in drivers and could benefit from incorporating alternative indicators of cognitive workload for deeper insights. Furthermore, investigated driving simulators demonstrated that an eco-safe driving Human-Machine Interface (HMI) significantly promoted safe driving behaviors without imposing excessive mental and visual workload on drivers. Recommendations were made for future studies to consider additional indicators of cognitive workload, such as subjective assessments or task performance metrics, for a more comprehensive understanding.

驾驶员认知功能障碍、脑力劳动负荷(MWL)和心率变异性(HRV)之间错综复杂的相互作用,为交通安全研究领域提供了一个引人入胜的调查途径。本文对精神工作量和心率变异性引起的认知障碍进行了系统回顾和研究。文章利用之前采用心率监测系统和眼动跟踪技术进行的研究中收集的数据,仔细研究了驾驶员所经历的脑力劳动负荷,从而揭示了认知障碍、脑力劳动负荷以及心率和眼动等生理指标之间的相关性。调查的前提是,可以通过心率和眼球运动等生理线索来评估驾驶员的脑力劳动负荷。研究发现,心率变异和红外线(IR)测量在评估熟练驾驶员的疲劳和工作量方面起着至关重要的作用。然而,该研究忽略了导致驾驶员认知障碍的潜在因素,如果能纳入认知工作量的替代指标,将有助于获得更深入的见解。此外,对驾驶模拟器的调查表明,生态安全驾驶人机界面(HMI)可显著促进安全驾驶行为,而不会给驾驶员带来过多的精神和视觉工作量。建议今后的研究考虑认知工作量的其他指标,如主观评估或任务绩效指标,以便更全面地了解情况。
{"title":"Systematic review of cognitive impairment in drivers through mental workload using physiological measures of heart rate variability.","authors":"Mansoor S Raza, Mohsin Murtaza, Chi-Tsun Cheng, Muhana M A Muslam, Bader M Albahlal","doi":"10.3389/fncom.2024.1475530","DOIUrl":"10.3389/fncom.2024.1475530","url":null,"abstract":"<p><p>The intricate interplay between driver cognitive dysfunction, mental workload (MWL), and heart rate variability (HRV) provides a captivating avenue for investigation within the domain of transportation safety studies. This article provides a systematic review and examines cognitive hindrance stemming from mental workload and heart rate variability. It scrutinizes the mental workload experienced by drivers by leveraging data gleaned from prior studies that employed heart rate monitoring systems and eye tracking technology, thereby illuminating the correlation between cognitive impairment, mental workload, and physiological indicators such as heart rate and ocular movements. The investigation is grounded in the premise that the mental workload of drivers can be assessed through physiological cues, such as heart rate and eye movements. The study discovered that HRV and infrared (IR) measurements played a crucial role in evaluating fatigue and workload for skilled drivers. However, the study overlooked potential factors contributing to cognitive impairment in drivers and could benefit from incorporating alternative indicators of cognitive workload for deeper insights. Furthermore, investigated driving simulators demonstrated that an eco-safe driving Human-Machine Interface (HMI) significantly promoted safe driving behaviors without imposing excessive mental and visual workload on drivers. Recommendations were made for future studies to consider additional indicators of cognitive workload, such as subjective assessments or task performance metrics, for a more comprehensive understanding.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"18 ","pages":"1475530"},"PeriodicalIF":2.1,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11560442/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142617217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Facial emotion recognition using deep quantum and advanced transfer learning mechanism. 利用深度量子和高级迁移学习机制进行面部情绪识别。
IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-10-30 eCollection Date: 2024-01-01 DOI: 10.3389/fncom.2024.1435956
Shtwai Alsubai, Abdullah Alqahtani, Abed Alanazi, Mohemmed Sha, Abdu Gumaei

Introduction: Facial expressions have become a common way for interaction among humans. People cannot comprehend and predict the emotions or expressions of individuals through simple vision. Thus, in psychology, detecting facial expressions or emotion analysis demands an assessment and evaluation of decisions for identifying the emotions of a person or any group during communication. With the recent evolution of technology, AI (Artificial Intelligence) has gained significant usage, wherein DL (Deep Learning) based algorithms are employed for detecting facial expressions.

Methods: The study proposes a system design that detects facial expressions by extracting relevant features using a Modified ResNet model. The proposed system stacks building-blocks with residual connections and employs an advanced extraction method with quantum computing, which significantly reduces computation time compared to conventional methods. The backbone stem utilizes a quantum convolutional layer comprised of several parameterized quantum-filters. Additionally, the research integrates residual connections in the ResNet-18 model with the Modified up Sampled Bottle Neck Process (MuS-BNP), retaining computational efficacy while benefiting from residual connections.

Results: The proposed model demonstrates superior performance by overcoming the issue of maximum similarity within varied facial expressions. The system's ability to accurately detect and differentiate between expressions is measured using performance metrics such as accuracy, F1-score, recall, and precision.

Discussion: This performance analysis confirms the efficacy of the proposed system, highlighting the advantages of quantum computing in feature extraction and the integration of residual connections. The model achieves quantum superiority, providing faster and more accurate computations compared to existing methodologies. The results suggest that the proposed approach offers a promising solution for facial expression recognition tasks, significantly improving both speed and accuracy.

简介面部表情已成为人际交往的一种常见方式。人们无法通过简单的视觉来理解和预测个人的情绪或表情。因此,在心理学中,面部表情检测或情绪分析需要对决策进行评估和评价,以便在交流过程中识别一个人或任何群体的情绪。随着近年来技术的发展,人工智能(AI)得到了广泛应用,其中基于深度学习(DL)的算法被用于检测面部表情:本研究提出了一种系统设计,通过使用修改后的 ResNet 模型提取相关特征来检测面部表情。与传统方法相比,该系统采用先进的量子计算提取方法,大大减少了计算时间。主干利用由多个参数化量子滤波器组成的量子卷积层。此外,该研究还将 ResNet-18 模型中的残余连接与改进的上采样瓶颈过程(MuS-BNP)整合在一起,在保留计算效率的同时从残余连接中获益:结果:所提出的模型克服了不同面部表情中最大相似度的问题,表现出卓越的性能。使用准确率、F1 分数、召回率和精确度等性能指标衡量了系统准确检测和区分不同表情的能力:该性能分析证实了所提系统的功效,凸显了量子计算在特征提取和整合残差连接方面的优势。与现有方法相比,该模型实现了量子优势,提供了更快更准确的计算。结果表明,所提出的方法为面部表情识别任务提供了一种有前途的解决方案,大大提高了速度和准确性。
{"title":"Facial emotion recognition using deep quantum and advanced transfer learning mechanism.","authors":"Shtwai Alsubai, Abdullah Alqahtani, Abed Alanazi, Mohemmed Sha, Abdu Gumaei","doi":"10.3389/fncom.2024.1435956","DOIUrl":"10.3389/fncom.2024.1435956","url":null,"abstract":"<p><strong>Introduction: </strong>Facial expressions have become a common way for interaction among humans. People cannot comprehend and predict the emotions or expressions of individuals through simple vision. Thus, in psychology, detecting facial expressions or emotion analysis demands an assessment and evaluation of decisions for identifying the emotions of a person or any group during communication. With the recent evolution of technology, AI (Artificial Intelligence) has gained significant usage, wherein DL (Deep Learning) based algorithms are employed for detecting facial expressions.</p><p><strong>Methods: </strong>The study proposes a system design that detects facial expressions by extracting relevant features using a Modified ResNet model. The proposed system stacks building-blocks with residual connections and employs an advanced extraction method with quantum computing, which significantly reduces computation time compared to conventional methods. The backbone stem utilizes a quantum convolutional layer comprised of several parameterized quantum-filters. Additionally, the research integrates residual connections in the ResNet-18 model with the Modified up Sampled Bottle Neck Process (MuS-BNP), retaining computational efficacy while benefiting from residual connections.</p><p><strong>Results: </strong>The proposed model demonstrates superior performance by overcoming the issue of maximum similarity within varied facial expressions. The system's ability to accurately detect and differentiate between expressions is measured using performance metrics such as accuracy, F1-score, recall, and precision.</p><p><strong>Discussion: </strong>This performance analysis confirms the efficacy of the proposed system, highlighting the advantages of quantum computing in feature extraction and the integration of residual connections. The model achieves quantum superiority, providing faster and more accurate computations compared to existing methodologies. The results suggest that the proposed approach offers a promising solution for facial expression recognition tasks, significantly improving both speed and accuracy.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"18 ","pages":"1435956"},"PeriodicalIF":2.1,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11557492/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142617216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
BrainNet: an automated approach for brain stress prediction utilizing electrodermal activity signal with XLNet model. BrainNet:利用 XLNet 模型的皮电活动信号进行脑应激预测的自动化方法。
IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-10-24 eCollection Date: 2024-01-01 DOI: 10.3389/fncom.2024.1482994
Liao Xuanzhi, Abeer Hakeem, Linda Mohaisen, Muhammad Umer, Muhammad Attique Khan, Shrooq Alsenan, Shtwai Alsubai, Nisreen Innab

Brain stress monitoring has emerged as a critical research area for understanding and managing stress and neurological health issues. This burgeoning field aims to provide accurate information and prediction about individuals' stress levels by analyzing behavioral data and physiological signals. To address this emerging problem, this research study proposes an innovative approach that uses an attention mechanism-based XLNet model (called BrainNet) for continuous stress monitoring and stress level prediction. The proposed model analyzes streams of brain data, including behavioral and physiological signal patterns using Swell and WESAD datasets. Testing on the Swell multi-class dataset, the model achieves an impressive accuracy of 95.76%. Furthermore, when evaluated on the WESAD dataset, it demonstrates even higher accuracy, reaching 98.32%. When applied to the binary classification of stress and no stress using the Swell dataset, the model achieves an outstanding accuracy of 97.19%. Comparative analysis with other previously published research studies underscores the superior performance of the proposed approach. In addition, cross-validation confirms the significance, efficacy, and robustness of the model in brain stress level prediction and aligns with the goals of smart diagnostics for understanding neurological behaviors.

大脑压力监测已成为了解和管理压力及神经健康问题的一个重要研究领域。这一新兴领域旨在通过分析行为数据和生理信号,提供有关个人压力水平的准确信息和预测。为了解决这个新出现的问题,本研究提出了一种创新方法,即使用基于注意力机制的 XLNet 模型(称为 BrainNet)进行连续压力监测和压力水平预测。该模型利用 Swell 和 WESAD 数据集分析大脑数据流,包括行为和生理信号模式。在 Swell 多类数据集上进行测试时,该模型达到了令人印象深刻的 95.76% 的准确率。此外,在 WESAD 数据集上进行评估时,准确率更高,达到了 98.32%。当使用 Swell 数据集对压力和无压力进行二元分类时,该模型的准确率达到了 97.19%。与之前发表的其他研究成果的对比分析凸显了所提方法的卓越性能。此外,交叉验证证实了该模型在大脑压力水平预测中的重要性、有效性和稳健性,并与理解神经行为的智能诊断目标相一致。
{"title":"BrainNet: an automated approach for brain stress prediction utilizing electrodermal activity signal with XLNet model.","authors":"Liao Xuanzhi, Abeer Hakeem, Linda Mohaisen, Muhammad Umer, Muhammad Attique Khan, Shrooq Alsenan, Shtwai Alsubai, Nisreen Innab","doi":"10.3389/fncom.2024.1482994","DOIUrl":"https://doi.org/10.3389/fncom.2024.1482994","url":null,"abstract":"<p><p>Brain stress monitoring has emerged as a critical research area for understanding and managing stress and neurological health issues. This burgeoning field aims to provide accurate information and prediction about individuals' stress levels by analyzing behavioral data and physiological signals. To address this emerging problem, this research study proposes an innovative approach that uses an attention mechanism-based XLNet model (called BrainNet) for continuous stress monitoring and stress level prediction. The proposed model analyzes streams of brain data, including behavioral and physiological signal patterns using Swell and WESAD datasets. Testing on the Swell multi-class dataset, the model achieves an impressive accuracy of 95.76%. Furthermore, when evaluated on the WESAD dataset, it demonstrates even higher accuracy, reaching 98.32%. When applied to the binary classification of stress and no stress using the Swell dataset, the model achieves an outstanding accuracy of 97.19%. Comparative analysis with other previously published research studies underscores the superior performance of the proposed approach. In addition, cross-validation confirms the significance, efficacy, and robustness of the model in brain stress level prediction and aligns with the goals of smart diagnostics for understanding neurological behaviors.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"18 ","pages":"1482994"},"PeriodicalIF":2.1,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11540660/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142603906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Latent dynamics of primary sensory cortical population activity structured by fluctuations in the local field potential. 由局部场电位波动构成的初级感觉皮层群体活动的潜伏动态。
IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-10-23 eCollection Date: 2024-01-01 DOI: 10.3389/fncom.2024.1445621
Audrey Sederberg, Aurélie Pala, Garrett B Stanley

Introduction: As emerging technologies enable measurement of precise details of the activity within microcircuits at ever-increasing scales, there is a growing need to identify the salient features and patterns within the neural populations that represent physiologically and behaviorally relevant aspects of the network. Accumulating evidence from recordings of large neural populations suggests that neural population activity frequently exhibits relatively low-dimensional structure, with a small number of variables explaining a substantial fraction of the structure of the activity. While such structure has been observed across the brain, it is not known how reduced-dimension representations of neural population activity relate to classical metrics of "brain state," typically described in terms of fluctuations in the local field potential (LFP), single-cell activity, and behavioral metrics.

Methods: Hidden state models were fit to spontaneous spiking activity of populations of neurons, recorded in the whisker area of primary somatosensory cortex of awake mice. Classic measures of cortical state in S1, including the LFP and whisking activity, were compared to the dynamics of states inferred from spiking activity.

Results: A hidden Markov model fit the population spiking data well with a relatively small number of states, and putative inhibitory neurons played an outsize role in determining the latent state dynamics. Spiking states inferred from the model were more informative of the cortical state than a direct readout of the spiking activity of single neurons or of the population. Further, the spiking states predicted both the trial-by-trial variability in sensory responses and one aspect of behavior, whisking activity.

Discussion: Our results show how classical measurements of brain state relate to neural population spiking dynamics at the scale of the microcircuit and provide an approach for quantitative mapping of brain state dynamics across brain areas.

导言:随着新兴技术能够以越来越高的尺度精确测量微电路内活动的细节,人们越来越需要识别神经群中代表网络生理和行为相关方面的显著特征和模式。对大型神经群的记录所积累的证据表明,神经群活动经常表现出相对低维的结构,少量变量就能解释活动结构的大部分。方法:在清醒小鼠初级躯体感觉皮层胡须区记录的神经元群的自发尖峰活动拟合了隐态模型。将 S1 中皮层状态的传统测量方法(包括 LFP 和拂动活动)与根据尖峰活动推断出的状态动态进行了比较:结果:隐马尔可夫模型很好地拟合了状态数量相对较少的群体尖峰数据,推定的抑制性神经元在决定潜伏状态动态方面发挥了巨大作用。从模型中推断出的尖峰状态比直接读出单个神经元或群体的尖峰活动更能反映大脑皮层的状态。此外,尖峰状态还能预测感觉反应的逐次试验变异性和行为的一个方面--拂动活动:我们的研究结果表明了大脑状态的经典测量方法与微电路尺度上神经群尖峰动态的关系,并为跨脑区大脑状态动态的定量映射提供了一种方法。
{"title":"Latent dynamics of primary sensory cortical population activity structured by fluctuations in the local field potential.","authors":"Audrey Sederberg, Aurélie Pala, Garrett B Stanley","doi":"10.3389/fncom.2024.1445621","DOIUrl":"10.3389/fncom.2024.1445621","url":null,"abstract":"<p><strong>Introduction: </strong>As emerging technologies enable measurement of precise details of the activity within microcircuits at ever-increasing scales, there is a growing need to identify the salient features and patterns within the neural populations that represent physiologically and behaviorally relevant aspects of the network. Accumulating evidence from recordings of large neural populations suggests that neural population activity frequently exhibits relatively low-dimensional structure, with a small number of variables explaining a substantial fraction of the structure of the activity. While such structure has been observed across the brain, it is not known how reduced-dimension representations of neural population activity relate to classical metrics of \"brain state,\" typically described in terms of fluctuations in the local field potential (LFP), single-cell activity, and behavioral metrics.</p><p><strong>Methods: </strong>Hidden state models were fit to spontaneous spiking activity of populations of neurons, recorded in the whisker area of primary somatosensory cortex of awake mice. Classic measures of cortical state in S1, including the LFP and whisking activity, were compared to the dynamics of states inferred from spiking activity.</p><p><strong>Results: </strong>A hidden Markov model fit the population spiking data well with a relatively small number of states, and putative inhibitory neurons played an outsize role in determining the latent state dynamics. Spiking states inferred from the model were more informative of the cortical state than a direct readout of the spiking activity of single neurons or of the population. Further, the spiking states predicted both the trial-by-trial variability in sensory responses and one aspect of behavior, whisking activity.</p><p><strong>Discussion: </strong>Our results show how classical measurements of brain state relate to neural population spiking dynamics at the scale of the microcircuit and provide an approach for quantitative mapping of brain state dynamics across brain areas.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"18 ","pages":"1445621"},"PeriodicalIF":2.1,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11537859/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142590127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Multi-stage semi-supervised learning enhances white matter hyperintensity segmentation. 多阶段半监督学习增强了白质超强度分割。
IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-10-22 eCollection Date: 2024-01-01 DOI: 10.3389/fncom.2024.1487877
Kauê T N Duarte, Abhijot S Sidhu, Murilo C Barros, David G Gobbi, Cheryl R McCreary, Feryal Saad, Richard Camicioli, Eric E Smith, Mariana P Bento, Richard Frayne

Introduction: White matter hyperintensities (WMHs) are frequently observed on magnetic resonance (MR) images in older adults, commonly appearing as areas of high signal intensity on fluid-attenuated inversion recovery (FLAIR) MR scans. Elevated WMH volumes are associated with a greater risk of dementia and stroke, even after accounting for vascular risk factors. Manual segmentation, while considered the ground truth, is both labor-intensive and time-consuming, limiting the generation of annotated WMH datasets. Un-annotated data are relatively available; however, the requirement of annotated data poses a challenge for developing supervised machine learning models.

Methods: To address this challenge, we implemented a multi-stage semi-supervised learning (M3SL) approach that first uses un-annotated data segmented by traditional processing methods ("bronze" and "silver" quality data) and then uses a smaller number of "gold"-standard annotations for model refinement. The M3SL approach enabled fine-tuning of the model weights with the gold-standard annotations. This approach was integrated into the training of a U-Net model for WMH segmentation. We used data from three scanner vendors (over more than five scanners) and from both cognitively normal (CN) adult and patients cohorts [with mild cognitive impairment and Alzheimer's disease (AD)].

Results: An analysis of WMH segmentation performance across both scanner and clinical stage (CN, MCI, AD) factors was conducted. We compared our results to both conventional and transfer-learning deep learning methods and observed better generalization with M3SL across different datasets. We evaluated several metrics (F-measure, IoU, and Hausdorff distance) and found significant improvements with our method compared to conventional (p < 0.001) and transfer-learning (p < 0.001).

Discussion: These findings suggest that automated, non-machine learning, tools have a role in a multi-stage learning framework and can reduce the impact of limited annotated data and, thus, enhance model performance.

简介:在老年人的磁共振(MR)图像中经常可以观察到白质增厚(WMH),通常表现为流体增强反转恢复(FLAIR)MR 扫描中的高信号强度区域。即使考虑了血管风险因素,WMH 体积增大也与痴呆和中风的风险增加有关。手动分割虽然被认为是基本事实,但却既耗费人力又耗费时间,从而限制了注释 WMH 数据集的生成。未注释的数据相对较多;然而,对注释数据的要求为开发监督机器学习模型带来了挑战:为了应对这一挑战,我们采用了一种多阶段半监督学习(M3SL)方法,首先使用按传统处理方法分割的未注释数据("铜 "和 "银 "质量数据),然后使用数量较少的 "金 "标准注释来完善模型。M3SL 方法能够利用黄金标准注释对模型权重进行微调。这种方法被集成到用于 WMH 切分的 U-Net 模型的训练中。我们使用了来自三家扫描仪供应商(超过五台扫描仪)和认知正常(CN)成人及患者队列(轻度认知障碍和阿尔茨海默病(AD))的数据:对扫描仪和临床阶段(CN、MCI、AD)因素的 WMH 分段性能进行了分析。我们将结果与传统深度学习方法和迁移学习深度学习方法进行了比较,观察到 M3SL 在不同数据集上具有更好的泛化效果。我们评估了几个指标(F-measure、IoU 和 Hausdorff 距离),发现与传统方法(p < 0.001)和迁移学习方法(p < 0.001)相比,我们的方法有显著改善:这些研究结果表明,自动化的非机器学习工具在多阶段学习框架中可以发挥作用,并能减少有限注释数据的影响,从而提高模型性能。
{"title":"Multi-stage semi-supervised learning enhances white matter hyperintensity segmentation.","authors":"Kauê T N Duarte, Abhijot S Sidhu, Murilo C Barros, David G Gobbi, Cheryl R McCreary, Feryal Saad, Richard Camicioli, Eric E Smith, Mariana P Bento, Richard Frayne","doi":"10.3389/fncom.2024.1487877","DOIUrl":"10.3389/fncom.2024.1487877","url":null,"abstract":"<p><strong>Introduction: </strong>White matter hyperintensities (WMHs) are frequently observed on magnetic resonance (MR) images in older adults, commonly appearing as areas of high signal intensity on fluid-attenuated inversion recovery (FLAIR) MR scans. Elevated WMH volumes are associated with a greater risk of dementia and stroke, even after accounting for vascular risk factors. Manual segmentation, while considered the ground truth, is both labor-intensive and time-consuming, limiting the generation of annotated WMH datasets. Un-annotated data are relatively available; however, the requirement of annotated data poses a challenge for developing supervised machine learning models.</p><p><strong>Methods: </strong>To address this challenge, we implemented a multi-stage semi-supervised learning (M3SL) approach that first uses un-annotated data segmented by traditional processing methods (\"bronze\" and \"silver\" quality data) and then uses a smaller number of \"gold\"-standard annotations for model refinement. The M3SL approach enabled fine-tuning of the model weights with the gold-standard annotations. This approach was integrated into the training of a U-Net model for WMH segmentation. We used data from three scanner vendors (over more than five scanners) and from both cognitively normal (CN) adult and patients cohorts [with mild cognitive impairment and Alzheimer's disease (AD)].</p><p><strong>Results: </strong>An analysis of WMH segmentation performance across both scanner and clinical stage (CN, MCI, AD) factors was conducted. We compared our results to both conventional and transfer-learning deep learning methods and observed better generalization with M3SL across different datasets. We evaluated several metrics (<i>F</i>-measure, <i>IoU</i>, and Hausdorff distance) and found significant improvements with our method compared to conventional (<i>p</i> < 0.001) and transfer-learning (<i>p</i> < 0.001).</p><p><strong>Discussion: </strong>These findings suggest that automated, non-machine learning, tools have a role in a multi-stage learning framework and can reduce the impact of limited annotated data and, thus, enhance model performance.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"18 ","pages":"1487877"},"PeriodicalIF":2.1,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11534601/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142582212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Data-centric automated approach to predict autism spectrum disorder based on selective features and explainable artificial intelligence. 基于选择性特征和可解释人工智能的以数据为中心的自闭症谱系障碍自动预测方法。
IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-10-21 eCollection Date: 2024-01-01 DOI: 10.3389/fncom.2024.1489463
Asma Aldrees, Stephen Ojo, James Wanliss, Muhammad Umer, Muhammad Attique Khan, Bayan Alabdullah, Shtwai Alsubai, Nisreen Innab

Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by notable challenges in cognitive function, understanding language, recognizing objects, interacting with others, and communicating effectively. Its origins are mainly genetic, and identifying it early and intervening promptly can reduce the necessity for extensive medical treatments and lengthy diagnostic procedures for those impacted by ASD. This research is designed with two types of experimentation for ASD analysis. In the first set of experiments, authors utilized three feature engineering techniques (Chi-square, backward feature elimination, and PCA) with multiple machine learning models for autism presence prediction in toddlers. The proposed XGBoost 2.0 obtained 99% accuracy, F1 score, and recall with 98% precision with chi-square significant features. In the second scenario, main focus shifts to identifying tailored educational methods for children with ASD through the assessment of their behavioral, verbal, and physical responses. Again, the proposed approach performs well with 99% accuracy, F1 score, recall, and precision. In this research, cross-validation technique is also implemented to check the stability of the proposed model along with the comparison of previously published research works to show the significance of the proposed model. This study aims to develop personalized educational strategies for individuals with ASD using machine learning techniques to meet their specific needs better.

自闭症谱系障碍(ASD)是一种神经发育疾病,其特征是在认知功能、理解语言、识别物体、与他人互动和有效沟通方面存在明显的障碍。其病因主要是遗传,及早发现并及时干预可以减少受 ASD 影响的人接受大量医疗和冗长诊断程序的必要性。这项研究设计了两种类型的实验来分析 ASD。在第一组实验中,作者利用三种特征工程技术(Chi-square、后向特征消除和 PCA)和多种机器学习模型来预测幼儿是否患有自闭症。所提出的 XGBoost 2.0 获得了 99% 的准确率、F1 分数和 98% 的召回率,其中 Chi-square 特征显著。在第二种情况下,主要重点转移到通过评估 ASD 儿童的行为、语言和身体反应来确定适合他们的教育方法。同样,所提出的方法表现出色,准确率、F1 分数、召回率和精确度均达到 99%。在这项研究中,还采用了交叉验证技术来检查所提模型的稳定性,并与以前发表的研究成果进行比较,以显示所提模型的重要性。本研究旨在利用机器学习技术为 ASD 患者制定个性化教育策略,以更好地满足他们的特定需求。
{"title":"Data-centric automated approach to predict autism spectrum disorder based on selective features and explainable artificial intelligence.","authors":"Asma Aldrees, Stephen Ojo, James Wanliss, Muhammad Umer, Muhammad Attique Khan, Bayan Alabdullah, Shtwai Alsubai, Nisreen Innab","doi":"10.3389/fncom.2024.1489463","DOIUrl":"10.3389/fncom.2024.1489463","url":null,"abstract":"<p><p>Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by notable challenges in cognitive function, understanding language, recognizing objects, interacting with others, and communicating effectively. Its origins are mainly genetic, and identifying it early and intervening promptly can reduce the necessity for extensive medical treatments and lengthy diagnostic procedures for those impacted by ASD. This research is designed with two types of experimentation for ASD analysis. In the first set of experiments, authors utilized three feature engineering techniques (Chi-square, backward feature elimination, and PCA) with multiple machine learning models for autism presence prediction in toddlers. The proposed XGBoost 2.0 obtained 99% accuracy, F1 score, and recall with 98% precision with chi-square significant features. In the second scenario, main focus shifts to identifying tailored educational methods for children with ASD through the assessment of their behavioral, verbal, and physical responses. Again, the proposed approach performs well with 99% accuracy, F1 score, recall, and precision. In this research, cross-validation technique is also implemented to check the stability of the proposed model along with the comparison of previously published research works to show the significance of the proposed model. This study aims to develop personalized educational strategies for individuals with ASD using machine learning techniques to meet their specific needs better.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"18 ","pages":"1489463"},"PeriodicalIF":2.1,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11532156/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142575728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A combinatorial deep learning method for Alzheimer's disease classification-based merging pretrained networks. 基于合并预训练网络的阿尔茨海默病分类组合式深度学习方法。
IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-10-17 eCollection Date: 2024-01-01 DOI: 10.3389/fncom.2024.1444019
Houmem Slimi, Ala Balti, Sabeur Abid, Mounir Sayadi

Introduction: Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory loss, and impaired daily functioning. Despite significant research, AD remains incurable, highlighting the critical need for early diagnosis and intervention to improve patient outcomes. Timely detection plays a crucial role in managing the disease more effectively. Pretrained convolutional neural networks (CNNs) trained on large-scale datasets, such as ImageNet, have been employed for AD classification, providing a head start for developing more accurate models.

Methods: This paper proposes a novel hybrid deep learning approach that combines the strengths of two specific pretrained architectures. The proposed model enhances the representation of AD-related patterns by leveraging the feature extraction capabilities of both networks. We validated this model using a large dataset of MRI images from AD patients. Performance was evaluated in terms of classification accuracy and robustness against noise, and the results were compared to several commonly used models in AD detection.

Results: The proposed hybrid model demonstrated significant performance improvements over individual models, achieving an accuracy classification rate of 99.85%. Comparative analysis with other models further revealed the superiority of the new architecture, particularly in terms of classification rate and resistance to noise interference.

Discussion: The high accuracy and robustness of the proposed hybrid model suggest its potential utility in early AD detection. By improving feature representation through the combination of two pretrained networks, this model could provide clinicians with a more reliable tool for early diagnosis and monitoring of AD progression. This approach holds promise for aiding in timely diagnoses and treatment decisions, contributing to better management of Alzheimer's disease.

前言阿尔茨海默病(AD)是一种进行性神经退行性疾病,以认知能力下降、记忆力减退和日常功能受损为特征。尽管开展了大量研究,但阿尔茨海默病仍无法治愈,这突出表明了早期诊断和干预以改善患者预后的迫切需要。及时发现对更有效地控制疾病起着至关重要的作用。在大规模数据集(如 ImageNet)上训练的预训练卷积神经网络(CNN)已被用于 AD 分类,为开发更精确的模型提供了一个良好的开端:本文提出了一种新型混合深度学习方法,它结合了两种特定预训练架构的优势。通过利用这两种网络的特征提取能力,所提出的模型增强了对注意力缺失症相关模式的表示。我们使用来自 AD 患者的大型 MRI 图像数据集对该模型进行了验证。我们从分类准确性和对噪声的鲁棒性两个方面对其性能进行了评估,并将结果与一些常用的注意力缺失症检测模型进行了比较:结果:与单个模型相比,所提出的混合模型的性能有了显著提高,分类准确率达到 99.85%。与其他模型的对比分析进一步显示了新架构的优越性,尤其是在分类率和抗噪声干扰能力方面:讨论:所提出的混合模型的高准确率和鲁棒性表明,它在早期注意力缺失症检测中具有潜在的实用性。通过结合两个预训练网络来改进特征表示,该模型可以为临床医生提供更可靠的工具,用于早期诊断和监测注意力缺失症的进展。这种方法有望帮助及时做出诊断和治疗决定,为更好地管理阿尔茨海默病做出贡献。
{"title":"A combinatorial deep learning method for Alzheimer's disease classification-based merging pretrained networks.","authors":"Houmem Slimi, Ala Balti, Sabeur Abid, Mounir Sayadi","doi":"10.3389/fncom.2024.1444019","DOIUrl":"10.3389/fncom.2024.1444019","url":null,"abstract":"<p><strong>Introduction: </strong>Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory loss, and impaired daily functioning. Despite significant research, AD remains incurable, highlighting the critical need for early diagnosis and intervention to improve patient outcomes. Timely detection plays a crucial role in managing the disease more effectively. Pretrained convolutional neural networks (CNNs) trained on large-scale datasets, such as ImageNet, have been employed for AD classification, providing a head start for developing more accurate models.</p><p><strong>Methods: </strong>This paper proposes a novel hybrid deep learning approach that combines the strengths of two specific pretrained architectures. The proposed model enhances the representation of AD-related patterns by leveraging the feature extraction capabilities of both networks. We validated this model using a large dataset of MRI images from AD patients. Performance was evaluated in terms of classification accuracy and robustness against noise, and the results were compared to several commonly used models in AD detection.</p><p><strong>Results: </strong>The proposed hybrid model demonstrated significant performance improvements over individual models, achieving an accuracy classification rate of 99.85%. Comparative analysis with other models further revealed the superiority of the new architecture, particularly in terms of classification rate and resistance to noise interference.</p><p><strong>Discussion: </strong>The high accuracy and robustness of the proposed hybrid model suggest its potential utility in early AD detection. By improving feature representation through the combination of two pretrained networks, this model could provide clinicians with a more reliable tool for early diagnosis and monitoring of AD progression. This approach holds promise for aiding in timely diagnoses and treatment decisions, contributing to better management of Alzheimer's disease.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"18 ","pages":"1444019"},"PeriodicalIF":2.1,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11525984/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142557513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Multi-scale asynchronous correlation and 2D convolutional autoencoder for adolescent health risk prediction with limited fMRI data. 利用有限的 fMRI 数据进行青少年健康风险预测的多尺度异步相关性和二维卷积自动编码器。
IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-10-15 eCollection Date: 2024-01-01 DOI: 10.3389/fncom.2024.1478193
Di Gao, Guanghao Yang, Jiarun Shen, Fang Wu, Chao Ji

Introduction: Adolescence is a fundamental period of transformation, encompassing extensive physical, psychological, and behavioral changes. Effective health risk assessment during this stage is crucial for timely intervention, yet traditional methodologies often fail to accurately predict mental and behavioral health risks due to the intricacy of neural dynamics and the scarcity of quality-annotated fMRI datasets.

Methods: This study introduces an innovative deep learning-based framework for health risk assessment in adolescents by employing a combination of a two-dimensional convolutional autoencoder (2DCNN-AE) with multi-sequence learning and multi-scale asynchronous correlation information extraction techniques. This approach facilitates the intricate analysis of spatial and temporal features within fMRI data, aiming to enhance the accuracy of the risk assessment process.

Results: Upon examination using the Adolescent Risk Behavior (AHRB) dataset, which includes fMRI scans from 174 individuals aged 17-22, the proposed methodology exhibited a significant improvement over conventional models. It attained a precision of 83.116%, a recall of 84.784%, and an F1-score of 83.942%, surpassing standard benchmarks in most pertinent evaluative measures.

Discussion: The results underscore the superior performance of the deep learning-based approach in understanding and predicting health-related risks in adolescents. It underscores the value of this methodology in advancing the precision of health risk assessments, offering an enhanced tool for early detection and potential intervention strategies in this sensitive developmental stage.

引言青春期是一个基本的转变时期,包括广泛的生理、心理和行为变化。在这一阶段进行有效的健康风险评估对于及时干预至关重要,然而,由于神经动力学的复杂性和高质量标注的 fMRI 数据集的稀缺性,传统方法往往无法准确预测心理和行为健康风险:本研究采用二维卷积自动编码器(2DCNN-AE)与多序列学习和多尺度异步相关信息提取技术相结合的方法,为青少年健康风险评估引入了一种基于深度学习的创新框架。这种方法有助于对 fMRI 数据中的空间和时间特征进行复杂分析,从而提高风险评估过程的准确性:在使用青少年风险行为(AHRB)数据集(其中包括 174 名 17-22 岁个体的 fMRI 扫描)进行检验后,所提出的方法比传统模型有了显著改善。其精确度为 83.116%,召回率为 84.784%,F1 分数为 83.942%,在大多数相关评估指标上都超过了标准基准:结果表明,基于深度学习的方法在理解和预测青少年健康相关风险方面表现出色。讨论:研究结果表明,基于深度学习的方法在理解和预测青少年健康相关风险方面表现出色,并强调了该方法在提高健康风险评估的精确度方面的价值,为在这一敏感的发展阶段进行早期检测和制定潜在干预策略提供了更先进的工具。
{"title":"Multi-scale asynchronous correlation and 2D convolutional autoencoder for adolescent health risk prediction with limited fMRI data.","authors":"Di Gao, Guanghao Yang, Jiarun Shen, Fang Wu, Chao Ji","doi":"10.3389/fncom.2024.1478193","DOIUrl":"https://doi.org/10.3389/fncom.2024.1478193","url":null,"abstract":"<p><strong>Introduction: </strong>Adolescence is a fundamental period of transformation, encompassing extensive physical, psychological, and behavioral changes. Effective health risk assessment during this stage is crucial for timely intervention, yet traditional methodologies often fail to accurately predict mental and behavioral health risks due to the intricacy of neural dynamics and the scarcity of quality-annotated fMRI datasets.</p><p><strong>Methods: </strong>This study introduces an innovative deep learning-based framework for health risk assessment in adolescents by employing a combination of a two-dimensional convolutional autoencoder (2DCNN-AE) with multi-sequence learning and multi-scale asynchronous correlation information extraction techniques. This approach facilitates the intricate analysis of spatial and temporal features within fMRI data, aiming to enhance the accuracy of the risk assessment process.</p><p><strong>Results: </strong>Upon examination using the Adolescent Risk Behavior (AHRB) dataset, which includes fMRI scans from 174 individuals aged 17-22, the proposed methodology exhibited a significant improvement over conventional models. It attained a precision of 83.116%, a recall of 84.784%, and an F1-score of 83.942%, surpassing standard benchmarks in most pertinent evaluative measures.</p><p><strong>Discussion: </strong>The results underscore the superior performance of the deep learning-based approach in understanding and predicting health-related risks in adolescents. It underscores the value of this methodology in advancing the precision of health risk assessments, offering an enhanced tool for early detection and potential intervention strategies in this sensitive developmental stage.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"18 ","pages":"1478193"},"PeriodicalIF":2.1,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11518741/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142544667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
期刊
Frontiers in Computational Neuroscience
全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1