Neuroscience informatics最新文献_第4页

Morphometric characterization of early- and late-onset Parkinson's disease: An ROI-based study of classification and correlation 早发和晚发帕金森病的形态计量学特征：基于roi的分类和相关性研究

Neuroscience informatics

Pub Date : 2025-08-22 DOI: 10.1016/j.neuri.2025.100228

Sadhana Kumari , Bharti Rana , Shefali Chaudhary , Roopa Rajan , S. Senthil Kumaran , Achal Kumar Srivastava , Leve Joseph Devarajan

Introduction

Parkinson's disease (PD) is associated with progressive neurodegeneration, particularly involving cortico-basal ganglia-thalamo-cortical circuits that underlie motor and cognitive functions. We investigated the morphological brain features derived from structural MRI to differentiate early (EOPD) and late-onset PD (LOPD) from age-related healthy controls.

Methods

3D T1-weighted MRI was acquired in 114 subjects (27 EOPD, 32 YHC, 28 LOPD, and 27 OHC). Gray matter volume (GMV), white matter volumes (WMV), fractal dimension (FD), gyrification index (GI), and cortical thickness (CT) were extracted using CAT12 software. Three tasks, (i) identification of statistically significant regions, (ii) automatic diagnosis using machine learning using individual and combined features, and (iii) correlation study were performed to quantify the relationship between morphological features and clinical variables.

Results

EOPD exhibited a reduction in GMV and cortical complexity in frontal, parietal and temporal lobes compared to YHC. We achieved the highest classification accuracy of 89.06% using FD and CT for EOPD vs YHC, 90.91% using GMV, WMV and FD for LOPD vs OHC and 89.29% using WMV and FD for EOPD vs LOPD after data augmentation for class balancing. EOPD revealed a negative correlation of GMV with UPDRS II (in medial frontal cortex, precuneus and supplementary motor cortex), FD with UPDRS III in pericalcarine; GI and UPDRS II in transverse temporal and pars opercularis; CT with UPDRS III in superior frontal regions.

Conclusion

Distinct morphometric changes were observed in patients with EOPD and LOPD in comparison with HC, suggesting the utility of morphological measures in early diagnosis of PD.

帕金森病（PD）与进行性神经退行性变有关，特别是涉及运动和认知功能基础的皮质-基底神经节-丘脑-皮质回路。我们研究了来自结构MRI的脑形态学特征，以区分早期（EOPD）和晚发性PD （LOPD）与年龄相关的健康对照。方法对114例患者（EOPD 27例，YHC 32例，LOPD 28例，OHC 27例）进行3d t1加权MRI检查。采用CAT12软件提取脑灰质体积（GMV）、白质体积（WMV）、分形维数（FD）、旋转指数（GI）、皮质厚度（CT）。三个任务，(i)识别统计显著区域，（ii）使用机器学习使用单个和组合特征进行自动诊断，以及（iii）进行相关性研究，以量化形态学特征与临床变量之间的关系。结果与YHC相比，opd表现出额叶、顶叶和颞叶的GMV和皮质复杂性降低。使用FD和CT对EOPD与YHC的分类准确率为89.06%，使用GMV、WMV和FD对LOPD与OHC的分类准确率为90.91%，使用WMV和FD对EOPD与LOPD的分类准确率为89.29%。EOPD显示GMV与UPDRS II（内侧额叶皮质、楔前叶和辅助运动皮质）呈负相关，FD与UPDRS III呈负相关；颞部和包部的GI和UPDRS II；额上区UPDRS III CT检查。结论与HC相比，EOPD和LOPD患者的形态学变化明显，提示形态学检测在PD早期诊断中的应用。

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引用次数: 0

Enhancing neuromolecular imaging classification in low-data regimes with generative machine learning: A case study in HDAC PET/MR imaging of alcohol use disorder 利用生成式机器学习增强低数据体制下的神经分子成像分类：酒精使用障碍的HDAC PET/MR成像案例研究

Neuroscience informatics

Pub Date : 2025-08-14 DOI: 10.1016/j.neuri.2025.100225

Tyler N. Meyer , Olga Andreeva , Roger D. Weiss , Wei Ding , Iris Shen , Changning Wang , Ping Chen , Tewodros Mulugeta Dagnew

Introduction

Positron Emission Tomography (PET) is a vital modality for investigating brain related disorders. However, data scarcity especially for novel molecular targets like neuroepigenetic enzymes combined with difficult-to-recruit patient populations limits the development of machine learning (ML) models. Our primary objective is to enhance single-subject classification of neuromolecular imaging data and facilitate biomarker discovery. We demonstrate our approach using histone deacetylase (HDAC) PET/MR imaging in Alcohol Use Disorder (AUD).

Methods

We propose Catalysis Training pipeline, a framework that augments real imaging data with high-quality synthetic data generated by a Wasserstein Conditional Generative Adversarial Network (WCGAN). Using [¹¹C]Martinostat PET/MR imaging, we extracted 1-D standardized uptake value ratio (SUVR) tabular features representing HDAC enzyme expression density across eight cingulate subregions. These were used to train and test ML classifiers, including Support Vector Machine (SVM), XGBoost, and Random Forest, under leave-one-out cross-validation.

Results

Integrating synthetic data in the training process improved classification accuracy significantly: +26% for XGBoost and Random Forest (from 59% to 85%), and +18% for SVM (from 70% to 88%). Synthetic samples improved model generalizability. Key hemispheric and subregional cingulate HDAC patterns were also identified as potential biomarkers.

Conclusion

Our results demonstrate that generative AI can help overcome data scarcity in low-data regime neuroimaging applications. Catalysis Training provides a scalable strategy to enhance ML-driven biomarker discovery and disease classification, especially for rare or difficult-to-study disorders like AUD. Clinically, cingulate HDAC expression measured by [¹¹C]Martinostat PET/MR shows promise as an objective biomarker for AUD, complementing DSM-based diagnosis and informing novel treatment strategies.

正电子发射断层扫描（PET）是研究脑相关疾病的重要方式。然而，数据稀缺，尤其是神经表观遗传酶等新分子靶点的数据稀缺，加上难以招募的患者群体，限制了机器学习（ML）模型的发展。我们的主要目标是加强神经分子成像数据的单学科分类，促进生物标志物的发现。我们在酒精使用障碍（AUD）中使用组蛋白去乙酰化酶（HDAC） PET/MR成像证明了我们的方法。方法我们提出了催化训练管道，这是一个利用Wasserstein条件生成对抗网络（WCGAN）生成的高质量合成数据增强真实成像数据的框架。使用[11C]Martinostat PET/MR成像，我们提取了代表8个扣带亚区HDAC酶表达密度的1-D标准化摄取值比（SUVR）表格特征。这些被用来训练和测试ML分类器，包括支持向量机（SVM）、XGBoost和随机森林，在留一交叉验证下。结果在训练过程中集成合成数据显著提高了分类准确率：XGBoost和Random Forest的分类准确率为+26%（从59%提高到85%），SVM的分类准确率为+18%（从70%提高到88%）。合成样本提高了模型的泛化能力。关键的半球和分区域扣带HDAC模式也被确定为潜在的生物标志物。我们的研究结果表明，生成式人工智能可以帮助克服低数据机制神经成像应用中的数据稀缺性。Catalysis Training提供了一种可扩展的策略，以增强机器学习驱动的生物标志物发现和疾病分类，特别是对于罕见或难以研究的疾病，如AUD。在临床上，[11C]Martinostat PET/MR测量的扣带HDAC表达有望作为AUD的客观生物标志物，补充基于dsm的诊断并为新的治疗策略提供信息。

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引用次数: 0

Exploring KAN as a next-generation replacement for MLPs in EEG-based seizure detection 探索KAN作为下一代mlp在基于脑电图的癫痫检测中的替代品

Neuroscience informatics

Pub Date : 2025-08-14 DOI: 10.1016/j.neuri.2025.100226

Eman Allogmani

Epilepsy is a chronic neurological disorder characterized by recurrent seizures due to abnormal brain activity. Accurate detection of seizures from electroencephalogram (EEG) signals is critical, but it is often challenged by signal noise and class imbalance in real-world data. In this study, we systematically evaluate Kolmogorov–Arnold Networks (KANs)—a recent neural architecture based on the Kolmogorov–Arnold representation theorem—as an alternative to Multi-Layer Perceptrons (MLPs) for EEG-based seizure classification, with a focus on model robustness under noisy conditions. This is the first comprehensive evaluation of KAN's robustness under multiplicative noise in the context of EEG seizure detection. Experiments were conducted using two widely used EEG datasets: the Bonn dataset and the CHB-MIT Scalp EEG dataset. Across multiple network configurations and varying levels of multiplicative noise, we assess performance using F1 Score, AUROC, AUPRC, Sensitivity, and Specificity. Our findings show that KAN achieves more stable performance than MLPs under noisy conditions, particularly in smaller architectures. These results suggest that KAN may offer a robust and generalizable approach for seizure detection in noise-prone clinical settings.

癫痫是一种慢性神经系统疾病，其特征是由于大脑活动异常引起的反复发作。从脑电图（EEG）信号中准确检测癫痫发作是至关重要的，但它经常受到现实数据中信号噪声和类别不平衡的挑战。在本研究中，我们系统地评估了Kolmogorov-Arnold网络（KANs）——一种基于Kolmogorov-Arnold表示定理的最新神经结构——作为多层感知器（mlp）的替代方案，用于基于脑电图的癫痫分类，重点关注了模型在噪声条件下的鲁棒性。这是在脑电图癫痫发作检测的背景下，首次对KAN在乘性噪声下的鲁棒性进行综合评价。实验使用两个广泛使用的脑电数据集：波恩数据集和CHB-MIT头皮脑电数据集。在多个网络配置和不同级别的乘法噪声中，我们使用F1评分、AUROC、AUPRC、灵敏度和特异性来评估性能。我们的研究结果表明，在噪声条件下，特别是在较小的架构中，KAN比mlp实现了更稳定的性能。这些结果表明，KAN可能为易受噪声影响的临床环境中的癫痫发作检测提供了一种强大且可推广的方法。

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引用次数: 0

Decoding memory with explainable AI: A large-scale EEG-based machine learning study of encoding vs. retrieval 用可解释的人工智能解码记忆：基于脑电图的大规模机器学习研究编码与检索

Neuroscience informatics

Pub Date : 2025-08-14 DOI: 10.1016/j.neuri.2025.100227

Mohammed Tawshif Hossain , Adnan Sami Sarker , Arnab Chowdhury , Rajesh Mitra , Raiyan Rahman , M.R.C. Mahdy

Understanding the distinct neural signatures that differentiate memory encoding from retrieval remains a key challenge in cognitive neuroscience. This study applies machine learning to EEG data from the Penn Electrophysiology of Encoding and Retrieval Study (PEERS), involving 100 participants across over 400 sessions, to classify these cognitive states. We used Discrete Wavelet Transform (DWT) on EEG signals from six critical brain regions and evaluated seven machine learning models. Gradient Boosting emerged as the most effective classifier, achieving 81.97% accuracy and a 91.62% AUC. To interpret this performance, we applied Explainable AI (XAI) methods, specifically SHapley Additive exPlanations (SHAP). This analysis revealed that theta-band relative energy, especially in the Left and Right Anterior Superior (LAS/RAS) regions, was the most influential predictor. Low theta-band energy and RMS values were particularly indicative of encoding states. Topographic maps provided further validation, showing significant neural differences in anterior regions, notably within the theta range. However, the study is limited by the use of a fixed 2.5 s analysis window and demographic skew in the dataset, which may affect generalizability. Future work should address these issues through varied windowing strategies and more diverse populations. This study advances understanding of cognitive memory processes and supports the development of adaptive, memory-aware AI systems, contributing to both neuroscience and neurotechnology.

理解区分记忆编码和检索的不同神经特征仍然是认知神经科学的一个关键挑战。这项研究将机器学习应用于宾夕法尼亚大学编码和检索电生理学研究（PEERS）的脑电图数据，涉及100名参与者，跨越400多个会议，对这些认知状态进行分类。我们使用离散小波变换（DWT）对来自6个关键脑区的脑电图信号进行处理，并评估了7种机器学习模型。Gradient Boosting是最有效的分类器，准确率达到81.97%，AUC为91.62%。为了解释这种表现，我们应用了可解释人工智能（Explainable AI， XAI）方法，特别是SHapley加性解释（SHAP）。该分析显示，theta波段相对能量，特别是在左右前上（LAS/RAS）区域，是最具影响力的预测因子。低波段能量和RMS值特别表明编码状态。地形图提供了进一步的验证，显示了显著的神经差异在前部区域，特别是在θ波范围内。然而，该研究受到使用固定的2.5 s分析窗口和数据集中的人口统计偏差的限制，这可能会影响通用性。未来的工作应该通过不同的窗口策略和更多样化的人群来解决这些问题。这项研究促进了对认知记忆过程的理解，并支持了自适应、记忆感知的人工智能系统的发展，为神经科学和神经技术做出了贡献。

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引用次数: 0

Impact of physical activity and cardiorespiratory fitness on brain morphology among overweight and obese populations: A systematic review and meta-analysis of neuroimaging studies 身体活动和心肺健康对超重和肥胖人群脑形态的影响：神经影像学研究的系统回顾和荟萃分析

Neuroscience informatics

Pub Date : 2025-08-05 DOI: 10.1016/j.neuri.2025.100224

Dilip Shettigar , Suresh Sukumar , Rajagopal Kadavigere , K. Vaishali , Nitika C. Panakkal , Winniecia Dkhar , Abhimanyu Pradhan , Baskaran Chandrasekaran , Hari Prakash Palaniswamy , Poovitha Shruthi Paramashiva , Sneha Ravichandran , Sathya Sabina Muthu , Koustubh Kamath

引用次数: 0

AI-enhanced diagnosis of very late-onset schizophrenia-like psychosis: A step toward preventing dementia in older adults 人工智能对极晚发性精神分裂症样精神病的增强诊断：预防老年人痴呆的一步

Neuroscience informatics

Pub Date : 2025-07-28 DOI: 10.1016/j.neuri.2025.100223

Ali Allahgholi , Ava Mazhari

The rapid aging of the global population, projected to reach 2.1 billion individuals aged 60 and older by 2050, is associated with an increased prevalence of mental health conditions, particularly dementia and psychosis. Among these, very late-onset schizophrenia-like psychosis (VLOSLP), defined as occurring after age 60, poses significant diagnostic challenges due to overlapping neurobiological changes and medical conditions common in older adults. Studies have indicated a higher risk of dementia in patients with VLOSLP, emphasizing the necessity for ongoing symptom monitoring. In recent years, artificial intelligence (AI), particularly deep learning (DL) and machine learning (ML), has shown promise in enhancing disease diagnosis through advanced medical imaging techniques. This study aims to classify VLOSLP using MRI images from patients aged 60 and older, obtained from the COBRE and MCICshare databases via the SchizoConnect platform. To address the challenge of limited data, synthetic images were generated using Generative Adversarial Networks (GAN) following preprocessing techniques. These images were then classified using a Support Vector Machine (SVM) classifier, with feature extraction performed through Zernike moments. The findings achieved an area under the curve (AUC) of 0.98, contributing to more accurate diagnoses of VLOSLP and facilitating better management and monitoring of this complex condition in the aging population.

全球人口迅速老龄化，预计到2050年60岁及以上人口将达到21亿，这与精神健康状况，特别是痴呆症和精神病的患病率上升有关。其中，非常晚发性精神分裂症样精神病（VLOSLP），定义为发生在60岁以后，由于重叠的神经生物学变化和老年人常见的医疗条件，给诊断带来了重大挑战。研究表明，VLOSLP患者痴呆的风险更高，强调了持续监测症状的必要性。近年来，人工智能（AI），特别是深度学习（DL）和机器学习（ML），在通过先进的医学成像技术增强疾病诊断方面显示出了希望。本研究旨在通过SchizoConnect平台从COBRE和MCICshare数据库获得60岁及以上患者的MRI图像，对VLOSLP进行分类。为了解决有限数据的挑战，在预处理技术之后使用生成对抗网络（GAN）生成合成图像。然后使用支持向量机（SVM）分类器对这些图像进行分类，并通过泽尼克矩进行特征提取。研究结果达到了0.98的曲线下面积（AUC），有助于更准确地诊断VLOSLP，并有助于更好地管理和监测老年人群中这一复杂疾病。

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引用次数: 0

Deep learning model for patient emotion recognition using EEG-tNIRS data 基于EEG-tNIRS数据的患者情绪识别深度学习模型

Neuroscience informatics

Pub Date : 2025-07-22 DOI: 10.1016/j.neuri.2025.100219

Mohan Raparthi , Nischay Reddy Mitta , Vinay Kumar Dunka , Sowmya Gudekota , Sandeep Pushyamitra Pattyam , Venkata Siva Prakash Nimmagadda

This study presents a novel approach that integrates electroencephalogram (EEG) and functional near-infrared spectroscopy (tNIRS) data to enhance emotion classification accuracy. A Modality-Attentive Multi-Channel Graph Convolution Model (MAMP-GF) is introduced, leveraging GraphSAGE-based representation learning to capture inter-channel relationships. Multi-level feature extraction techniques, including Channel Features (CF), Statistical Features (SF), and Graph Features (GF), are employed to maximize the discriminative power of EEG-tNIRS signals. To enhance modality fusion, we propose and evaluate three fusion strategies: MA-GF, MP-GF, and MA-MP-GF, which integrate graph convolutional networks with a modality attention mechanism. The model is trained and validated using EEG and tNIRS data collected from 30 subjects exposed to emotionally stimulating video clips. Experimental results demonstrate that the proposed MA-MP-GF fusion model achieves 98.77% accuracy in subject-dependent experiments, significantly outperforming traditional single-modal and other multimodal fusion methods. In cross-subject validation, the model attains a 55.53% accuracy, highlighting its robustness despite inter-subject variability. The findings illustrate that the proposed graph convolution fusion approach, combined with modality attention, effectively enhances emotion recognition accuracy and stability. This research underscores the potential of EEG-tNIRS fusion in real-time, non-invasive emotion monitoring, paving the way for advanced applications in personalized healthcare and affective computing.

本研究提出了一种结合脑电图（EEG）和功能近红外光谱（tNIRS）数据的新方法，以提高情绪分类的准确性。引入了一种模态关注的多通道图卷积模型（MAMP-GF），利用基于graphsage的表示学习来捕获通道间关系。采用通道特征（CF）、统计特征（SF）和图特征（GF）等多层次特征提取技术，最大限度地提高了EEG-tNIRS信号的判别能力。为了增强模态融合，我们提出并评估了三种融合策略：MA-GF、MP-GF和MA-MP-GF，它们将图卷积网络与模态注意机制相结合。该模型是通过从30名观看情绪刺激视频片段的受试者中收集的EEG和tnir数据进行训练和验证的。实验结果表明，MA-MP-GF融合模型在主体相关实验中准确率达到98.77%，显著优于传统的单模态和其他多模态融合方法。在跨主题验证中，该模型达到55.53%的准确率，突出了其鲁棒性，尽管存在不同主题的差异。研究结果表明，本文提出的图卷积融合方法与模态关注相结合，有效地提高了情感识别的准确性和稳定性。这项研究强调了EEG-tNIRS融合在实时、非侵入性情绪监测中的潜力，为个性化医疗保健和情感计算的高级应用铺平了道路。

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引用次数: 0

A systematic review and meta-analysis on the diagnostic accuracy of artificial intelligence and computer-aided diagnosis of lumbar prolapsed intervertebral disc 人工智能和计算机辅助诊断腰椎间盘突出症诊断准确性的系统回顾和荟萃分析

Neuroscience informatics

Pub Date : 2025-07-22 DOI: 10.1016/j.neuri.2025.100221

Sandeep Pattnaik , Manu Goyal , Rajneesh Kumar Gujral , Amit Mittal

Introduction

Lumbar prolapsed intervertebral disc (PIVD) is a debilitating lower back condition, whose accurate and timely diagnosis is crucial for its effective management. Artificial intelligence (AI) and computer-aided diagnosis (CAD) techniques have the potential to revolutionise diagnosis by improving accuracy, efficiency, and objectivity. This systematic review with meta-analysis thus aims to thoroughly assess the available knowledge on the usability of different AI and CAD-based tools in lumbar PIVD diagnosis.

Methods

A systematic search of electronic databases, between June and August 2024 for relevant full-text studies. The primary outcomes for review included the diagnostic accuracy (of each AI and CAD system. Subsequently, a meta-analysis was conducted to synthesise the results of the included studies.

Result

A total of eight studies were identified, evaluating thirteen CAD or AI systems. The meta-analysis involved three of the studies, and it demonstrated a high pooled sensitivity (0.901, 95% CI: 0.871–0.924) and specificity (0.919, 95% CI: 0.898–0.936) for lumbar PIVD diagnosis.

Conclusion

To conclude, these findings strongly support the potential of AI/CAD systems to improve the accuracy and efficiency of lumbar PIVD diagnosis.

Prospero ID

CRD42023444785

腰椎间盘突出症（PIVD）是一种使腰背部衰弱的疾病，准确及时的诊断对其有效治疗至关重要。人工智能（AI）和计算机辅助诊断（CAD）技术有可能通过提高准确性、效率和客观性来彻底改变诊断。因此，本系统综述和荟萃分析旨在全面评估不同人工智能和基于cad的工具在腰椎PIVD诊断中的可用性。方法系统检索电子数据库，于2024年6 - 8月间进行相关全文研究。评估的主要结果包括每个AI和CAD系统的诊断准确性。随后，进行荟萃分析以综合纳入研究的结果。结果共确定了8项研究，评估了13个CAD或AI系统。荟萃分析涉及三项研究，结果显示腰椎PIVD诊断具有较高的综合敏感性（0.901,95% CI: 0.871-0.924）和特异性（0.919,95% CI: 0.898-0.936）。综上所述，这些发现有力地支持了AI/CAD系统在提高腰椎PIVD诊断的准确性和效率方面的潜力。普洛斯彼罗IDCRD42023444785

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引用次数: 0

Reinforcement learning in artificial intelligence and neurobiology 人工智能和神经生物学中的强化学习

Neuroscience informatics

Pub Date : 2025-07-22 DOI: 10.1016/j.neuri.2025.100220

Tursun Alkam, Andrew H Van Benschoten, Ebrahim Tarshizi

Reinforcement learning (RL), a computational framework rooted in behavioral psychology, enables agents to learn optimal actions through trial and error. It now powers intelligent systems across domains such as autonomous driving, robotics, and logistics, solving tasks once thought to require human cognition. As RL reshapes artificial intelligence (AI), it raises a critical question in neuroscience: does the brain learn through similar mechanisms? Growing evidence suggests it does.

To bridge this interdisciplinary gap, this review introduces core RL concepts to neuroscientists and clinicians with limited AI exposure. We outline the agent–environment interaction loop and describe key architectures including model-free, model-based, and meta-RL. We then examine how advances in deep RL have generated testable hypotheses about neural computation and behavior. In parallel, we discuss how neurobiological findings, especially the role of dopamine in encoding reward prediction errors, have inspired biologically grounded RL models. Empirical studies reveal neural correlates of RL algorithms in the basal ganglia, prefrontal cortex, and hippocampus, supporting their roles in planning, memory, and decision-making. We also highlight clinical applications, including how RL frameworks are used to model cognitive decline and psychiatric disorders, while acknowledging limitations in scaling RL to biological complexity.

Looking ahead, RL offers powerful tools for understanding brain function, guiding brain–machine interfaces, and personalizing psychiatric treatment. The convergence of RL and neuroscience offers a promising interdisciplinary lens for advancing our understanding of learning and decision-making in both artificial agents and the human brain.

强化学习（RL）是一种植根于行为心理学的计算框架，它使代理能够通过试错来学习最佳行为。它现在为自动驾驶、机器人和物流等领域的智能系统提供动力，解决了曾经被认为需要人类认知的任务。随着强化学习重塑人工智能（AI），它提出了神经科学中的一个关键问题：大脑是否通过类似的机制进行学习？越来越多的证据表明确实如此。为了弥合这一跨学科的差距，本综述向人工智能接触有限的神经科学家和临床医生介绍了核心RL概念。我们概述了代理-环境交互循环，并描述了包括无模型、基于模型和元强化学习在内的关键体系结构。然后，我们研究了深度强化学习的进展如何产生关于神经计算和行为的可测试假设。同时，我们讨论了神经生物学的发现，特别是多巴胺在编码奖励预测错误中的作用，如何启发了基于生物学的RL模型。实证研究揭示了RL算法在基底神经节、前额叶皮层和海马体中的神经关联，支持它们在计划、记忆和决策中的作用。我们还强调了临床应用，包括RL框架如何用于模拟认知能力下降和精神疾病，同时承认将RL扩展到生物复杂性的局限性。展望未来，强化学习为理解大脑功能、指导脑机接口和个性化精神治疗提供了强大的工具。强化学习和神经科学的融合提供了一个很有前途的跨学科视角，可以促进我们对人工智能体和人脑中学习和决策的理解。

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引用次数: 0

Short-window EEG-based auditory attention decoding for neuroadaptive hearing support for smart healthcare 基于短窗口脑电图的听觉注意解码用于智能医疗的神经适应性听力支持

Neuroscience informatics

Pub Date : 2025-07-22 DOI: 10.1016/j.neuri.2025.100222

Ihtiram Raza Khan , Sheng-Lung Peng , Rupali Mahajan , Rajesh Dey

Background

Selective auditory attention the brain's ability to focus on a specific speaker in multi-talker environments is often compromised in individuals with auditory or neurological disorders. While Auditory Attention Decoding (AAD) using EEG has shown promise in detecting attentional focus, existing models primarily utilize temporal or spectral features, often neglecting the synergistic relationships across time, space, and frequency. This limitation significantly reduces decoding accuracy, particularly in short decision windows, which are crucial for real-time applications like neuro-steered hearing aids. This study is to enhance short-window AAD performance by fully leveraging multi-dimensional EEG characteristics.

Methods

To address this, we propose TSF-AADNet, a novel neural framework that integrates temporal–spatial and frequency–spatial features using dual-branch architectures and advanced attention-based fusion.

Results

Tested on KULeuven and DTU datasets, TSF-AADNet achieves 91.8% and 81.1% accuracy at 0.1-second windows—outperforming the state-of-the-art by up to 7.99%.

Conclusions

These results demonstrate the model's potential in enabling precise, real-time attention tracking for hearing impairment diagnostics and next-generation neuroadaptive auditory prosthetics.

选择性听觉注意在多说话的环境中，大脑专注于特定说话者的能力在听觉或神经障碍的个体中经常受到损害。虽然利用脑电图进行听觉注意解码（AAD）在检测注意焦点方面显示出前景，但现有模型主要利用时间或频谱特征，往往忽略了时间、空间和频率之间的协同关系。这种限制大大降低了解码的准确性，特别是在短决策窗口中，这对于神经导向助听器等实时应用至关重要。本研究旨在充分利用脑电图的多维特征，提高短窗口AAD的性能。为了解决这个问题，我们提出了一种新的神经框架TSF-AADNet，它使用双分支架构和先进的基于注意力的融合技术集成了时空和频率空间特征。结果在KULeuven和DTU数据集上测试，TSF-AADNet在0.1秒窗口下的准确率分别达到91.8%和81.1%，比目前最先进的准确率高出7.99%。这些结果证明了该模型在精确、实时的注意力跟踪听力障碍诊断和下一代神经适应性听觉假肢方面的潜力。

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引用次数: 0