Neuroscience informatics最新文献

A proof-of-concept study on the use of large language models for assessing research methodology in neuroimaging 一项关于使用大型语言模型评估神经影像学研究方法的概念验证研究

Neuroscience informatics

Pub Date : 2026-01-23 DOI: 10.1016/j.neuri.2026.100262

Brock Pluimer , Apeksha Sridhar , Ishtiaq Mawla , Helen Mengxuan Wu , Roshni Lulla , Sarah Hennessy , Patrick Sadil , Rishab Iyer , Eric Ichesco , Anson Kairys , Max Egan , Jonas Kaplan , Richard E. Harris

Careful evaluation of research methodology is fundamental to scientific progress but represents a significant burden on human experts. The complexity of functional MRI (fMRI) methods makes transparent reporting, as suggested by OHBM COBIDAS guidelines, particularly critical. Large Language Models (LLMs) present a potential solution for rapid, scalable methodological assessment. We evaluated three state-of-the-art LLMs (Gemini 2.5 Pro, Claude 4 Sonnet, ChatGPT-o3-pro) against human expert ratings. Fifty fMRI articles (taken from 2016 to 2025) were independently evaluated by ten human experts and three LLMs using an 82-item COBIDAS-based rubric. Human raters demonstrated excellent inter-rater reliability (ICC = 0.801), while LLMs showed poor internal agreement (ICC = 0.254). When comparing total scores across papers, Gemini showed strong positive correlation with human consensus (r = 0.693, p < 0.0001), Claude showed moderate positive correlation (r = 0.394, p = 0.004), while ChatGPT showed negative correlation (r = −0.172, p = 0.233). Gemini maintained high reliability when added to human raters (combined ICC = 0.811), achieving 85.3 % exact agreement and 98.8 % within-1-point agreement. Domain-specific analysis revealed Gemini's consistently high agreement across all six COBIDAS sections (experimental design: 0.915, statistical modeling: 0.880), while ChatGPT and Claude showed weaker, more variable performance. Obvious differences emerged in determining non-applicable items: humans marked 40.5 % as not applicable versus 32.3 % for Gemini, 9.2 % for ChatGPT and 21.1 % for Claude. ChatGPT exhibited extreme score volatility, with papers ranging from 0 to 121 points compared to humans' 44.2–77.7 range. LLM scoring required 1–7 min versus 30–35 min for humans. This proof-of-concept study demonstrates that LLM-assisted methodological evaluation is feasible for complex neuroimaging research and could likely be applied to other research fields.

对研究方法的仔细评估是科学进步的基础，但对人类专家来说却是一项重大负担。功能MRI （fMRI）方法的复杂性使得OHBM COBIDAS指南所建议的透明报告尤为重要。大型语言模型（llm）为快速、可扩展的方法学评估提供了一个潜在的解决方案。我们评估了三个最先进的法学硕士（Gemini 2.5 Pro, Claude 4 Sonnet, chatgpt - 03 - Pro）与人类专家的评级。50篇fMRI文章（取自2016年至2025年）由10位人类专家和3位法学硕士使用基于cobidas的82项标准独立评估。人类评分者表现出优秀的评分者之间的可靠性（ICC = 0.801），而llm表现出较差的内部一致性（ICC = 0.254）。在比较论文总分时，Gemini与人类共识呈强正相关（r = 0.693, p < 0.0001）， Claude与人类共识呈中度正相关（r = 0.394, p = 0.004）， ChatGPT与人类共识呈负相关（r = - 0.172, p = 0.233）。当与人类评分者相结合时，Gemini保持了高可靠性（综合ICC = 0.811），达到85.3%的精确一致性和98.8%的1点以内一致性。特定领域的分析显示，Gemini在所有六个COBIDAS部分（实验设计：0.915，统计建模：0.880）的一致性始终很高，而ChatGPT和Claude表现出更弱、更多变的表现。在确定不适用的项目上出现了明显的差异：人类将40.5%标记为不适用，而双子座为32.3%，ChatGPT为9.2%，克劳德为21.1%。ChatGPT表现出极端的得分波动，论文的得分范围在0到121分之间，而人类的得分范围在44.2到77.7分之间。LLM评分需要1-7分钟，而人类评分需要30-35分钟。这项概念验证研究表明，法学硕士辅助的方法评估对于复杂的神经影像学研究是可行的，并且可能应用于其他研究领域。

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引用次数: 0

From text to code – Leveraging machine learning for neurology outpatient clinical coding 从文本到代码——利用机器学习进行神经病学门诊临床编码

Neuroscience informatics

Pub Date : 2026-01-19 DOI: 10.1016/j.neuri.2026.100257

Elena Purcaru , Michael George , Matthew Stammers , Christopher Kipps

Background

Most neurological care is delivered in outpatient settings without mandated clinical coding. The clinical records remain stored as unstructured text with inconsistent formatting. There is a significant opportunity to increase the value of these data through automated clinical coding utilising natural language processing (NLP). While existing models for full ICD-10 clinical coding lack sufficient accuracy for clinical use, 60 % of neurology outpatient cases fall into just five diagnostic categories. This suggests that a simplified coding system could enhance feasibility and serve as a foundation for more complex coding schemes.

Objective

We propose a simplified coding system of 29 codes for neurology outpatient episodes. We evaluate several machine learning methods in a supervised single-label classification task on real-world outpatient care notes.

Methods

We collected outpatient care notes created between 15 November 2018 and 2 December 2022. The training dataset included 14,917 care notes, most of which were annotated with ICD-10 codes during routine care and subsequently mapped to 29 simplified diagnostic categories. An external validation set of 1,042 randomly selected encounters was retrospectively coded.

Models included logistic regression, support vector machine, bidirectional LSTM, BERT-based models (DistilBERT, RoBERTa), and a generative large language model (LLM), Mistral 7B. All but the LLM were trained via 10-fold stratified cross-validation; final models were trained on the complete dataset.

Results

DistilBERT and RoBERTa outperformed traditional models, with F1-scores of 81.73 (95 % CI: 79.02–84.13) and 81.16 (95 % CI: 78.84–83.76), respectively. The LLM–DistilBERT hybrid performed worse than all but BiLSTM and produced “medical hallucinations,” making it unsuitable for clinical use. The training data were highly imbalanced. BERT-based models showed strong performance on high-frequency categories, with F1-scores over 85 % for the top five classes. At a 0.85 confidence threshold, DistilBERT achieved 96 % accuracy on 64 % of the external validation set.

Conclusions

BERT-based NLP models perform well in classifying neurology outpatient clinic notes when a reduced set of diagnostic categories is used. In a human-in-the-loop workflow, such models can meaningfully reduce the manual coding workload while preserving accuracy. To our knowledge, this is the first applied study of automated clinical coding in neurology outpatient care.

背景：大多数神经系统护理是在门诊进行的，没有强制性的临床编码。临床记录仍然存储为格式不一致的非结构化文本。通过利用自然语言处理（NLP）的自动临床编码，有一个重要的机会来增加这些数据的价值。虽然现有的ICD-10完整临床编码模型缺乏临床使用的足够准确性，但60%的神经病学门诊病例仅属于五种诊断类别。这表明简化的编码系统可以提高可行性，并为更复杂的编码方案奠定基础。目的提出一种简化的神经内科门诊发作码系统。我们评估了几种机器学习方法在一个有监督的单标签分类任务对现实世界的门诊护理记录。方法收集2018年11月15日至2022年12月2日期间创建的门诊记录。训练数据集包括14,917份护理记录，其中大部分在常规护理期间使用ICD-10代码进行注释，随后映射为29个简化诊断类别。对随机选择的1042次遭遇的外部验证集进行回顾性编码。模型包括逻辑回归、支持向量机、双向LSTM、基于bert的模型（DistilBERT, RoBERTa）和生成式大型语言模型（LLM） Mistral 7B。除LLM外，其余均通过10倍分层交叉验证进行训练；最后的模型在完整的数据集上进行训练。结果distilbert和RoBERTa的f1评分分别为81.73 （95% CI: 79.02 ~ 84.13）和81.16 (95% CI: 78.84 ~ 83.76)，优于传统模型。LLM-DistilBERT混合物的表现比除BiLSTM之外的其他混合物都差，而且会产生“医学幻觉”，因此不适合临床使用。训练数据高度不平衡。基于bert的模型在高频类别上表现出色，前五个类别的f1得分超过85%。在0.85的置信阈值下，在64%的外部验证集上，蒸馏器达到了96%的准确度。结论当使用简化的诊断类别集时，基于bert的NLP模型在神经内科门诊病历分类方面表现良好。在人在循环的工作流中，这样的模型可以有效地减少手工编码的工作量，同时保持准确性。据我们所知，这是神经病学门诊护理中首次应用自动临床编码的研究。

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引用次数: 0

A study on the potential relationship between the diagnosis and functional connectivity in the brain in major depressive disorder 重度抑郁症的诊断与脑功能连接的潜在关系研究

Neuroscience informatics

Pub Date : 2026-01-15 DOI: 10.1016/j.neuri.2026.100258

Tsubasa Sasaki , Yoshiyuki Hirano

Background

Many studies on resting-state functional connectivity (FC) in major depressive disorder (MDD) have investigated FC as a biomarker of disease pathogenesis. However, few studies have examined conditional dependencies among FC, clinical status, and demographic variables. Considering such dependencies allows the identification of direct relationships obscured by spurious correlations.

Aim

This study aimed to examine the neural mechanisms of MDD and propose a structural relationship between FC and MDD, focusing on sulcal regions.

Methods

Using a large dataset of 431 healthy controls and 235 MDD patients with MDD, we combined partial least squares (PLS)-based feature extraction with logistic regression and light gradient boosting machine (LightGBM) models for diagnostic classification, followed by Bayesian network (BN) analysis employing a directed acyclic graph.

Results

The classification models demonstrated moderate accuracy (logistic regression: area under the curve [AUC] = 0.735; LightGBM: AUC = 0.710). Structure learning with the Max–Min Hill-Climbing algorithm revealed direct edges from the MDD diagnosis to variables derived from the BDI and PLS components, but no direct parent nodes of MDD were identified. Intervention simulation showed that the MDD diagnosis significantly reduced FC in the default mode network (DMN), dorsal attention network, and between subcortical structures and cortex.

Conclusion

MDD diagnosis is associated with disease-specific disruptions not only in the DMN but also across multiple networks, underscoring the need to consider widespread network dysfunction in the pathophysiology of MDD. Future longitudinal and interventional research is required to clarify the causal relationships between the diagnosis and brain function.

背景许多关于重度抑郁症（MDD）静息状态功能连接（FC）的研究都将FC作为疾病发病机制的生物标志物。然而，很少有研究考察FC、临床状态和人口统计学变量之间的条件依赖性。考虑到这样的依赖关系，可以识别被虚假相关性掩盖的直接关系。目的本研究旨在探讨MDD的神经机制，并提出FC与MDD之间的结构关系，重点关注脑沟区。方法利用431名健康对照和235名MDD合并MDD患者的大型数据集，将基于偏最小二乘（PLS）的特征提取与逻辑回归和光梯度增强机（LightGBM）模型相结合进行诊断分类，然后采用有向无环图进行贝叶斯网络（BN）分析。结果分类模型具有中等准确度（logistic回归：曲线下面积[AUC] = 0.735; LightGBM: AUC = 0.710）。使用Max-Min爬坡算法的结构学习揭示了从MDD诊断到BDI和PLS分量衍生变量的直接边缘，但没有识别出MDD的直接父节点。干预模拟显示，MDD诊断显著降低了默认模式网络（DMN）、背侧注意网络以及皮层下结构与皮层之间的FC。结论MDD的诊断不仅与DMN的疾病特异性破坏有关，而且与多个网络的疾病特异性破坏有关，这强调了在MDD的病理生理中考虑广泛的网络功能障碍的必要性。未来的纵向和介入研究需要澄清诊断和脑功能之间的因果关系。

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引用次数: 0

Integrating cross-sectional imaging data into functional outcome prediction models for acute ischemic stroke of the anterior circulation 将横断成像数据整合到前循环急性缺血性卒中的功能预后预测模型中

Neuroscience informatics

Pub Date : 2026-01-14 DOI: 10.1016/j.neuri.2026.100260

Frank te Nijenhuis , Matthijs van der Sluijs , Pieter Jan van Doormaal , Wim van Zwam , Jeannette Hofmeijer , Xucong Zhang , Sandra Cornelissen , Danny Ruijters , Ruisheng Su , Theo van Walsum

In acute ischemic stroke, large vessel occlusions of the anterior circulation are increasingly treated with endovascular therapy (EVT). The efficacy of this therapy depends on adequate treatment selection. Treatment decisions can be based on predictions of functional outcome. Most existing studies predict functional outcomes using clinical parameters. We set out to study functional outcome prediction performance by integrating imaging in a multimodal setting. Using a multi-center dataset containing 2927 patients, we compare the functional outcome prediction performances of clinical baseline models, including the clinically validated MR PREDICTS decision tool, image-based models with deep learning networks, and a multimodal approach combining clinical and imaging information. The predicted outcome measure is dichotomized modified Rankin Scale score 90 days after EVT. We perform sanity checks, hyperparameter optimization, and comparisons of effectiveness of using CTA, NCCT, or both images as input. Our experiments show that information extracted from CTA or NCCT images does not significantly improve the performance, as quantified using AUC, of functional outcome prediction methods compared to a baseline model. The multimodal approach may replace radiologically derived biomarkers, as its performance is non-inferior.

在急性缺血性卒中中，前循环大血管闭塞越来越多地采用血管内治疗（EVT）。这种疗法的疗效取决于适当的治疗选择。治疗决定可以基于对功能结果的预测。大多数现有研究使用临床参数预测功能结果。我们开始通过在多模态环境中整合成像来研究功能预后预测性能。使用包含2927例患者的多中心数据集，我们比较了临床基线模型的功能结局预测性能，包括临床验证的MR预测决策工具，基于图像的深度学习网络模型，以及结合临床和影像学信息的多模式方法。预测预后指标为EVT后90天的二分类修正兰金量表评分。我们执行完整性检查、超参数优化，并比较使用CTA、NCCT或两种图像作为输入的有效性。我们的实验表明，与基线模型相比，从CTA或NCCT图像中提取的信息并没有显著提高功能结果预测方法的性能（使用AUC进行量化）。多模式方法可以取代放射学衍生的生物标志物，因为它的性能不差。

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引用次数: 0

Evaluating SegResNet for single-modality meningioma segmentation on T1 contrast-enhanced MRI on a New Zealand clinical cohort 评估SegResNet在新西兰临床队列T1增强MRI上单模态脑膜瘤分割的效果

Neuroscience informatics

Pub Date : 2026-01-13 DOI: 10.1016/j.neuri.2026.100261

Jiantao Shen , Sung-Min Jun , Samantha J. Holdsworth , Gonzalo Maso Talou , Jason A. Correia , Hamid Abbasi

Accurate and automated meningioma segmentation remains a biomedical engineering challenge, particularly when relying on single-modality MRI data. We evaluate SegResNet, a U-Net-based deep learning architecture, for meningioma segmentation using 817 T1 contrast-enhanced (T1CE) magnetic resonance imaging (MRI) images from 282 patients across Auckland, New Zealand. We investigate the effect of incorporating additional images from the 2023 Brain Tumor Segmentation (BraTS) meningioma challenge during training on model performance. The baseline model trained solely on the Auckland dataset achieved 75.67 % mean Dice. Incorporating an additional 200 and 400 BraTS images improved segmentation performance to 77.89 % and 76.73 %, respectively. A separate experiment involving pre-training on BraTS data followed by fine-tuning on Auckland data achieved 75.90 % Dice. Our results suggest that while leveraging external datasets can enhance model robustness, the extent of improvement depends on dataset heterogeneity and alignment with the target domain.

Analysis of a subset of images unaffected by skull-stripping artifacts indicated notably higher segmentation accuracy (up to 84.02 % Dice), highlighting the influence of preprocessing on performance. Evaluations using the 2023 and 2024 BraTS lesion-wise metrics demonstrated the importance of context-appropriate metric selection. Our findings highlight the adaptability of SegResNet to a single-modality T1CE – a widely available sequence in standard clinical protocols – clinical dataset and emphasize how public data integration, careful preprocessing, and task-aligned evaluation can support robust segmentation models for diverse and resource-constrained environments.

准确和自动化的脑膜瘤分割仍然是生物医学工程的挑战，特别是当依赖单模态MRI数据时。我们使用来自新西兰奥克兰282名患者的817 T1对比增强（T1CE）磁共振成像（MRI）图像，对基于u - net的深度学习架构SegResNet进行脑膜瘤分割评估。我们研究了在训练中加入来自2023脑肿瘤分割（BraTS）脑膜瘤挑战的额外图像对模型性能的影响。仅在奥克兰数据集上训练的基线模型达到了平均骰子的75.67%。结合额外的200和400 BraTS图像，分割性能分别提高到77.89%和76.73%。另一项单独的实验涉及对BraTS数据进行预训练，然后对奥克兰数据进行微调，获得了75.90%的Dice。我们的研究结果表明，虽然利用外部数据集可以增强模型的鲁棒性，但改进的程度取决于数据集的异质性和与目标域的一致性。对未受头骨剥离伪影影响的图像子集的分析表明，分割精度显著提高（高达84.02% Dice），突出了预处理对性能的影响。使用2023年和2024年BraTS的横向指标进行评估，证明了选择适合环境的指标的重要性。我们的研究结果强调了SegResNet对单模态T1CE（标准临床协议中广泛使用的序列）的适应性，并强调了公共数据集成、仔细预处理和任务对齐评估如何支持多样化和资源受限环境下的稳健分割模型。

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引用次数: 0

TRELLIS -enhanced surface features for comprehensive intracranial aneurysm analysis TRELLIS增强的表面特征用于颅内动脉瘤的综合分析

Neuroscience informatics

Pub Date : 2026-01-13 DOI: 10.1016/j.neuri.2026.100259

Clément Hervé, Paul Garnier, Jonathan Viquerat, Elie Hachem

Intracranial aneurysms pose a significant clinical risk yet are difficult to detect, delineate, and model due to limited annotated 3D data. We propose a cross-domain feature-transfer approach that leverages the latent geometric embeddings learned by TRELLIS, a generative model trained on large-scale non-medical 3D datasets, to augment neural networks for aneurysm analysis. By replacing conventional point normals or mesh descriptors with TRELLIS surface features, we systematically enhance three downstream tasks: (i) classifying aneurysms versus healthy vessels in the Intra3D dataset, (ii) segmenting aneurysm and vessel regions on 3D meshes, and (iii) predicting time-evolving blood-flow fields using a graph neural network on the AnXplore dataset. Our experiments show that the inclusion of these features yields strong gains in accuracy, F1-score, and segmentation quality over state-of-the-art baselines, and reduces simulation error by 15%. These results illustrate the broader potential of transferring 3D representations from general-purpose generative models to specialized medical tasks.

颅内动脉瘤具有显著的临床风险，但由于有限的注释三维数据，难以检测，描绘和建模。我们提出了一种跨域特征转移方法，该方法利用TRELLIS学习的潜在几何嵌入来增强动脉瘤分析的神经网络。TRELLIS是一种基于大规模非医疗3D数据集训练的生成模型。通过用TRELLIS表面特征取代传统的点法线或网格描述符，我们系统地增强了三个下游任务：(i)在Intra3D数据集中对动脉瘤和健康血管进行分类，（ii）在3D网格上分割动脉瘤和血管区域，以及（iii）在AnXplore数据集中使用图神经网络预测随时间变化的血流场。我们的实验表明，与最先进的基线相比，这些特征的包含在准确性、f1分数和分割质量方面产生了巨大的收益，并将模拟误差减少了15%。这些结果说明了将3D表示从通用生成模型转移到专业医疗任务的更广泛潜力。

引用次数: 0

Advances in acquisition and post-processing optimization of IVIM MRI for brain imaging: A systematic review IVIM MRI脑成像采集及后处理优化研究进展综述

Neuroscience informatics

Pub Date : 2026-01-06 DOI: 10.1016/j.neuri.2025.100256

Abhijith S. , Saikiran Pendem , Rajagopal Kadavigere , Priyanka , Dharmesh Singh , Priya P.S.

Purpose

Diffusion-weighted MRI is widely used to probe brain microstructure, but its signal reflects both diffusion and perfusion effects. Intravoxel Incoherent Motion (IVIM) MRI enables non-contrast separation of these components, offering potential clinical value in neuroimaging. However, clinical translation remains limited due to variability in acquisition and post-processing methods. This systematic review evaluates optimization strategies aimed at improving the accuracy, reproducibility, and clinical utility of IVIM parameters in brain.

Methods

Registered in PROSPERO and conducted according to PRISMA guidelines, a systematic search across five databases was performed. Original peer-reviewed studies focusing on optimization of IVIM acquisition or post-processing in human brain imaging were included, while reviews and studies lacking methodological detail were excluded. Study quality was assessed using a customized QUADAS-2 tool. Due to methodological heterogeneity, an effect direction plot was applied instead of meta-analysis.

Results

Out of 1,668 identified records, 14 studies were included. Acquisition strategies such as optimised b-value sampling, cardiac gating, and advanced sequences reduced parameter variability by up to 40 %. Post-processing methods, including Bayesian fitting, deep learning–based models, and advanced denoising, improved parameter accuracy by up to 99 % and precision by up to 95 %. Effect direction analysis demonstrated significant positive effects on accuracy and clinical utility (p < 0.001) and repeatability (p < 0.05), while scan-time reduction showed no significant benefit (p > 0.05). No study reported gold-standard validation.

Conclusion

Optimization of IVIM acquisition and post-processing enhances parameter robustness and reproducibility in brain MRI, though protocol heterogeneity remains a major obstacle to standardization and clinical adoption.

目的磁共振弥散加权成像被广泛应用于脑显微结构探测，但其信号同时反映了弥散和灌注效应。体素内非相干运动（IVIM） MRI能够实现这些成分的非对比分离，在神经成像中提供潜在的临床价值。然而，由于获取和后处理方法的差异，临床翻译仍然有限。本系统综述评估了优化策略，旨在提高大脑IVIM参数的准确性、可重复性和临床实用性。方法在普洛斯佩罗注册，并根据PRISMA指南，在五个数据库中进行系统检索。原始的同行评议研究集中于优化IVIM采集或人脑成像后处理，而缺乏方法学细节的综述和研究被排除在外。使用定制的QUADAS-2工具评估研究质量。由于方法的异质性，我们采用效应方向图代替meta分析。结果在1,668份确定的记录中，纳入了14项研究。采集策略，如优化的b值采样，心脏门控，和先进的序列减少参数可变性高达40%。后处理方法，包括贝叶斯拟合、基于深度学习的模型和高级去噪，将参数准确度提高了99%，精度提高了95%。效果方向分析显示，该方法对准确性、临床实用性（p < 0.001）和重复性（p < 0.05）均有显著的积极作用，而缩短扫描时间则无显著的益处（p < 0.05）。没有研究报告金标准验证。结论IVIM采集和后处理的优化提高了脑MRI参数的稳健性和可重复性，但方案的异质性仍然是标准化和临床应用的主要障碍。

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引用次数: 0

Neuroscience informatics

Pub Date : 2026-01-03 DOI: 10.1016/j.neuri.2025.100255

My N. Nguyen, Yoshiki Kubota, Akimasa Hirata

This study investigated age-related changes of brain fiber pathways from diffusion tensor imaging (DTI) tractograms with directional decomposition. Two hundred subjects were stratified into three age groups. Tractograms were generated at two levels: from individual DTI images (subject-level), and from group-averaged images (group-level). Fiber tracking was performed within the cerebral white matter, brainstem, thalamus, and cerebellum at both the levels. Each tractogram was decomposed into directional tracts. At the subject-level, original and decomposed tracts were used to quantify tract density and correlations with age. Tract density was highest in the thalamus and brainstem, while the cerebellum showed the greatest inter-subject variability. Tract count exhibited some significant correlations with age: in cerebral white matter, it decreased overall, especially along S-I and A-P directions; in thalamus, S-I and A-P tracts decreased, while L-R and mixed-direction tracts increased. The brainstem tracts demonstrated its overall stability during aging. At the group level, ∼60 % of brainstem tracts were oriented along the S–I direction, and ∼64 % of cerebellar tracts along the A–P direction. Notably, the posterolateral tracts of the cerebellum showed asymmetry, with the left side associated with visuospatial processing, containing fewer tracts than the right side associated with language pathways. These findings highlight region- and direction-specific changes with age, revealing structural patterns that are not captured by conventional scalar measures. They suggested candidate biomarkers for brain aging and provided useful references for longitudinal neuroimaging and brain stimulation studies, with potential applications in the early detection of neurodegeneration and optimization of stimulation strategies.

本研究利用定向分解扩散张量成像（DTI）图研究脑纤维通路的年龄相关性变化。200名受试者被分为三个年龄组。在两个层次上生成束状图：来自个体DTI图像（受试者水平）和来自组平均图像（组水平）。在脑白质、脑干、丘脑和小脑两个水平上进行纤维跟踪。每个束图被分解成方向束。在受试者水平上，使用原始和分解的束来量化束密度及其与年龄的相关性。丘脑和脑干的束密度最高，而小脑则表现出最大的主体间变异性。脑道数与年龄有显著的相关性：脑白质总体减少，尤其是沿S-I和A-P方向；丘脑S-I束和A-P束减少，L-R束和混合方向束增加。脑干束在衰老过程中表现出整体稳定性。在组水平上，约60%的脑干束沿S-I方向定向，约64%的小脑束沿A-P方向定向。值得注意的是，小脑的后外侧束表现出不对称，左侧与视觉空间处理有关，比右侧与语言通路有关的束少。这些发现突出了区域和方向随年龄的变化，揭示了传统标量测量无法捕获的结构模式。他们提出了脑老化的候选生物标志物，为纵向神经成像和脑刺激研究提供了有用的参考，在神经退行性疾病的早期检测和刺激策略的优化方面具有潜在的应用价值。

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引用次数: 0

Spatiotemporal dynamics of TMS-Evoked responses: A dual damped sine model analysis of cortical site and stimulation condition effects tms诱发反应的时空动态：皮质部位和刺激条件效应的双重阻尼正弦模型分析

Neuroscience informatics

Pub Date : 2025-12-25 DOI: 10.1016/j.neuri.2025.100254

Damián Jan

Background

Transcranial magnetic stimulation combined with EEG (TMS-EEG) provides a non-invasive window into cortical excitability and connectivity. However, interpreting TMS-evoked potentials (TEPs) remains challenging due to pervasive artifacts and the limited physiological interpretability of descriptive analytical approaches.

New method

We introduce the Dual Damped Sine (DDS) model, a parametric framework that decomposes TEPs into physiologically meaningful parameters: amplitudes (A₁, A₂), frequencies (f₁, f₂), and damping constants (γ₁, γ₂). We applied DDS to the publicly available OpenNeuro dataset ds001849 to assess its ability to capture site- and condition-specific cortical responses.

Results

DDS achieved excellent model fits (median R² ≈ 0.95; RMSE ≤10⁻⁶) and revealed significant site- and condition-specific differences in the early TEP window (15–80 ms). Active TMS produced larger amplitudes and stronger damping, particularly at DLPFC, with frequencies constrained to physiological bands. These findings are consistent with previous evidence that early TEP components reflect site-specific cortical activation (Siebner et al., 2019; Freedberg et al., 2020).

Comparison with existing methods:While traditional similarity metrics quantify global waveform differences, DDS provides mechanistic interpretation of TEP dynamics through its parametric decomposition. The model captures how cortical responses evolve in time, offering insights into excitatory-inhibitory dynamics.

Conclusions

DDS represents a novel analytical approach that not only confirms established findings about early TEP specificity but also provides physiologically interpretable parameters describing cortical response dynamics. This parametric framework advances TMS-EEG methodology by bridging the gap between waveform analysis and neurophysiological interpretation.

经颅磁刺激联合脑电图（TMS-EEG）提供了一个研究皮层兴奋性和连通性的非侵入性窗口。然而，由于普遍存在的人工产物和描述性分析方法的有限生理可解释性，解释tms诱发电位（TEPs）仍然具有挑战性。我们引入了双阻尼正弦（DDS）模型，这是一个参数框架，将tep分解为有生理意义的参数：振幅（A1, A2），频率（f1, f2）和阻尼常数（γ1, γ2）。我们将DDS应用于公开可用的OpenNeuro数据集ds001849，以评估其捕获部位和条件特异性皮层反应的能力。结果dds获得了极好的模型拟合（中位数R2≈0.95;RMSE≤10−6），并在早期TEP窗口（15-80 ms）显示出显著的部位和条件特异性差异。主动经颅磁刺激产生更大的振幅和更强的阻尼，特别是在DLPFC，频率限制在生理波段。这些发现与之前的证据一致，即早期TEP成分反映了部位特异性皮层激活（Siebner et al., 2019; Freedberg et al., 2020）。与现有方法的比较：传统的相似性度量量化了全局波形差异，而DDS通过参数分解提供了TEP动态的机制解释。该模型捕捉了皮层反应如何随时间演变，为兴奋-抑制动力学提供了见解。结论sdds代表了一种新的分析方法，不仅证实了早期TEP特异性的既定发现，而且提供了描述皮质反应动力学的生理可解释参数。该参数框架通过弥合波形分析和神经生理解释之间的差距，推进了TMS-EEG方法。

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引用次数: 0

NeuroFusion: A forensic enriched ensemble framework for cerebellum disease classification 神经融合：小脑疾病分类的法医综合框架

Neuroscience informatics

Pub Date : 2025-12-23 DOI: 10.1016/j.neuri.2025.100251

Abu Hanzala , Md Sajjad , Tanjila Akter , Harpreet Kaur , Md Sadekur Rahman

Accurate and timely classification of cerebellar diseases is crucial for effective diagnostic, yet it remains challenging due to the inherent heterogeneity of these disorders and the subtlety of their neuroimaging manifestations. This study investigated a novel multi-stage ensemble framework integrating SE blocks and segmentation-assisted augmentation tailored for limited cerebellum disease MRI data. Dataset included 3296 MRI scans from four classes and we divided dataset into three parts: training, testing, and validation, and their ratio was 64:20:16. However, we performed image forensic analysis on it, such as Error Level Analysis (ELA) and Noise Residual Analysis (NRA). This study used deep learning architectures that can automatically classify cerebellum diseases and compared these models, which included six D-CNNs models, six transfer learning models, and three ensemble models. Another important contribution of our study is the significant improvement in the classification efficiency by strategically integrating squeeze and excitation and label smoothing techniques. We show that fine-tuning significantly improves the diagnostic accuracy of both D-CNNs and transfer learning models on cerebellum MRI data. Notably, our combined models consistently achieve higher performance, with FusionNet-6 reaching an exceptional accuracy of 99.83 %. K-fold cross-validation was performed, yielding consistently high performance with per-class sensitivity and specificity above 99 %. The study also greatly enhances the impact of dataset augmentation techniques, including the use of segmented data to reveal complex interactions that can enhance the performance of some models or, in some cases, dramatically reduce the performance of specific models. These results underscore the immense potential of deep learning ensembles to provide highly accurate and robust diagnostic support for cerebellum diseases, paving the way for more objective and efficient clinical workflows.

准确和及时的小脑疾病分类是有效诊断的关键，但由于这些疾病固有的异质性和其神经影像学表现的微妙性，它仍然具有挑战性。本研究研究了一种新的多阶段集成框架，将SE块和分段辅助增强相结合，为有限的小脑疾病MRI数据量身定制。数据集包括来自四个类的3296个MRI扫描，我们将数据集分为三个部分：训练、测试和验证，它们的比例为64:20:16。然而，我们对其进行了图像取证分析，如误差水平分析（ELA）和噪声残留分析（NRA）。本研究使用了可以自动对小脑疾病进行分类的深度学习架构，并对这些模型进行了比较，其中包括6个d - cnn模型、6个迁移学习模型和3个集成模型。我们研究的另一个重要贡献是通过策略性地整合挤压和激励和标签平滑技术，显著提高了分类效率。我们发现微调显著提高了d - cnn和迁移学习模型在小脑MRI数据上的诊断准确性。值得注意的是，我们的组合模型始终如一地实现更高的性能，FusionNet-6达到了99.83%的卓越准确率。进行K-fold交叉验证，获得一致的高性能，每类灵敏度和特异性均在99%以上。该研究还极大地增强了数据集增强技术的影响，包括使用分段数据来揭示复杂的相互作用，这些相互作用可以增强某些模型的性能，或者在某些情况下显着降低特定模型的性能。这些结果强调了深度学习系统在为小脑疾病提供高度准确和强大的诊断支持方面的巨大潜力，为更客观和有效的临床工作流程铺平了道路。

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