Neuroscience informatics最新文献

Evaluating the effect of point-sampling on univariate point and interval forecasting of cerebral physiologic signals using ARIMA modeling in acute traumatic neural injury 用ARIMA模型评价点采样对急性创伤性神经损伤脑生理信号单变量点区间预测的效果

Neuroscience informatics

Pub Date : 2025-12-17 DOI: 10.1016/j.neuri.2025.100248

Nuray Vakitbilir , Kevin Y. Stein , Tobias Bergmann , Noah Silvaggio , Amanjyot Singh Sainbhi , Abrar Islam , Logan Froese , Rakibul Hasan , Mansoor Hayat , Marcel Aries , Frederick A. Zeiler

High-resolution physiological signals, such as intracranial pressure (ICP) and regional cerebral oxygen saturation (rSO₂), are critical for managing traumatic brain injury (TBI) by enabling continuous monitoring of cerebral autoregulation and vascular reactivity. These signals provide essential insights into brain perfusion dynamics, supporting timely clinical interventions. However, the high temporal resolution of these data introduces challenges in real-time use, integration into predictive models, and computational efficiency. Consequently, resolution reduction techniques are essential for simplifying the data while retaining critical features necessary for accurate prediction and modeling. Using the Multi-omic Analytics and Integrative Neuroinformatics in the HUman Brain (MAIN-HUB) Lab database, high-frequency cerebral physiologic dataset, we aimed to evaluate the effects of point-sampling resolution reduction on point and interval predictions using the autoregressive integrated moving average (ARIMA) model for both raw signals and derived indices. Temporal resolution was reduced by selecting the first value within non-overlapping intervals, ranging from 1-min (min) to 12-h windows. A total of 125 patient data was analyzed across various temporal resolutions. The results indicated that ARIMA models performed well at higher resolutions (e.g., 1-min), capturing short-term physiological dynamics with lower errors. However, as resolution decreased, errors and variability increased, particularly for signals like mean arterial pressure (MAP) and cerebral perfusion pressure (CPP), which exhibit rapid or complex physiological changes. The findings underscore the need to carefully balance temporal resolution, model performance, and computational efficiency, especially when dealing with high-frequency physiological data in clinical settings.

高分辨率的生理信号，如颅内压（ICP）和区域脑氧饱和度（rSO2），通过连续监测大脑自动调节和血管反应性，对治疗创伤性脑损伤（TBI）至关重要。这些信号提供了脑灌注动力学的基本见解，支持及时的临床干预。然而，这些数据的高时间分辨率在实时使用、集成到预测模型和计算效率方面带来了挑战。因此，分辨率降低技术对于简化数据，同时保留准确预测和建模所需的关键特征是必不可少的。利用人脑多组学分析和综合神经信息学（MAIN-HUB）实验室数据库、高频脑生理学数据集，我们旨在利用原始信号和衍生指数的自回归综合移动平均（ARIMA）模型，评估点采样分辨率降低对点和区间预测的影响。通过选择非重叠间隔内的第一个值来降低时间分辨率，范围从1分钟（min）到12小时窗口。在不同的时间分辨率下，共分析了125例患者的数据。结果表明，ARIMA模型在高分辨率（例如1分钟）下表现良好，以较低的误差捕获短期生理动力学。然而，随着分辨率的降低，误差和变异性增加，特别是对于平均动脉压（MAP）和脑灌注压（CPP）等信号，它们表现出快速或复杂的生理变化。研究结果强调了仔细平衡时间分辨率、模型性能和计算效率的必要性，特别是在临床环境中处理高频生理数据时。

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引用次数: 0

EEG-based classification in psychiatry using motif discovery 基于脑电图的精神病学基序发现分类

Neuroscience informatics

Pub Date : 2025-11-12 DOI: 10.1016/j.neuri.2025.100242

Melanija Kraljevska , Kateřina Hlaváčková-Schindler , Lukas Miklautz , Claudia Plant

In current medical practice, patients undergoing treatment for depression typically must wait four to six weeks before clinicians can assess their response to medication, due to the delayed onset of noticeable effects from antidepressants. Identifying treatment response at an earlier stage is of great importance, as it can reduce both the emotional and economic burden associated with prolonged treatment. We present a novel Motif Discovery Framework (MDF) that extracts dynamic features from EEG time series data to distinguish between treatment responders and non-responders in depression. Our findings show that MDF can predict treatment response with high precision as early as the 7th day of treatment, significantly reducing the waiting time for patients. Furthermore, we demonstrate that MDF generalizes well to classification tasks in other psychiatric conditions, including schizophrenia, Alzheimer’s disease, and dementia. Overall, our experiments show that MDF outperforms relevant benchmarks. The high precision of our classification framework underscores the potential of EEG dynamic properties-represented as motifs-to support clinical decision-making and ultimately enhance patient quality of life.

在目前的医疗实践中，由于抗抑郁药的明显效果延迟发作，接受抑郁症治疗的患者通常必须等待4到6周，临床医生才能评估他们对药物的反应。在早期阶段确定治疗反应是非常重要的，因为它可以减少与长期治疗相关的情绪和经济负担。我们提出了一种新的Motif发现框架（MDF），从脑电图时间序列数据中提取动态特征，以区分抑郁症治疗反应者和无反应者。我们的研究结果表明，MDF早在治疗第7天就可以高精度地预测治疗反应，显著减少患者的等待时间。此外，我们证明MDF可以很好地推广到其他精神疾病的分类任务，包括精神分裂症、阿尔茨海默病和痴呆症。总的来说，我们的实验表明MDF优于相关基准测试。我们的分类框架的高精度强调了EEG动态特性的潜力——以基序表示——以支持临床决策并最终提高患者的生活质量。

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引用次数: 0

Attention-Gated CNN and discrete wavelet transform based ensemble framework for brain hemorrhage classification 基于注意力门控CNN和离散小波变换的脑出血分类集成框架

Neuroscience informatics

Pub Date : 2025-11-12 DOI: 10.1016/j.neuri.2025.100243

Srutanik Bhaduri , Rasel Mondal , Prateek Sarangi , Vinod Kumar Kurmi , Swati Goyal , Lovely Kaushal , Mahek Sodani , Tanmay Basu

Brain hemorrhage, or Intracranial Hemorrhage (ICH), is a critical medical condition requiring rapid diagnosis. Existing Convolutional Neural Network (CNN) models often struggle to differentiate similar hemorrhage subtypes like Epidural (EDH) and Subdural (SDH) due to a lack of specific spatial feature identification. This study aims to develop a robust classification framework to address this challenge. We propose an ensemble framework combining two complementary models. The first is an attention-gated 2D CNN designed to highlight subtle hemorrhagic regions. The second is a multi-level Discrete Wavelet Transform (DWT) model that analyzes images in the frequency domain to capture deeper contextual and textural information from the 3D brain volume. The proposed ensemble model was evaluated on the RSNA, CQ500, and a new GMC clinical dataset. The empirical study demonstrates that our model consistently outperforms state-of-the-art methods across standard evaluation metrics, including accuracy, macro-averaged AUC-ROC, specificity, sensitivity, and F1-score. The novel ensembling of an attention-gated CNN and a DWT-based model provides a more comprehensive feature representation, leading to significantly improved accuracy and robustness in ICH classification, particularly in distinguishing challenging subtypes like EDH and SDH.

脑出血或颅内出血（ICH）是一种需要快速诊断的危重医学病症。由于缺乏特定的空间特征识别，现有的卷积神经网络（CNN）模型往往难以区分类似的出血亚型，如硬膜外（EDH）和硬膜下（SDH）。本研究旨在开发一个强大的分类框架来应对这一挑战。我们提出了一个结合两个互补模型的集成框架。第一个是注意门控的二维CNN，用来突出细微的出血区域。第二种是多层离散小波变换（DWT）模型，该模型在频域分析图像，从3D脑体积中捕获更深层次的上下文和纹理信息。在RSNA、CQ500和一个新的GMC临床数据集上对所提出的集成模型进行了评估。实证研究表明，我们的模型在标准评估指标上始终优于最先进的方法，包括准确性、宏观平均AUC-ROC、特异性、敏感性和f1评分。注意力门控CNN和基于dwt的模型的新颖组合提供了更全面的特征表示，从而显著提高了ICH分类的准确性和鲁棒性，特别是在区分EDH和SDH等具有挑战性的亚型方面。

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引用次数: 0

Deep learning for fetal brain imaging: A systematic review and framework towards privacy-preserving neurodevelopmental informatics 胎儿脑成像的深度学习：对隐私保护神经发育信息学的系统回顾和框架

Neuroscience informatics

Pub Date : 2025-10-28 DOI: 10.1016/j.neuri.2025.100241

Sayma Alam Suha, Rifat Shahriyar

Fetal neurodevelopment is a complex process of neural growth during pregnancy, where early detection of abnormalities is vital, and deep learning offers promising techniques for this purpose. The objective of this systematic review is to investigate deep learning applications in fetal neurodevelopment, aiming to synthesize cutting-edge research, examine methodologies, identify research gaps, and propose a federated learning framework. Following PRISMA 2020 guidelines, 55 peer-reviewed articles were selected from an initial 900 records across major databases and additional sources where each article was examined through six specific data extraction criteria. Peer-reviewed articles from 2005 to 2025, specifically those exploring automated deep learning for fetal neurodevelopment using clinical images were included, while non-deep learning analyses were excluded. Risk of bias was qualitatively assessed based on design, data diversity, validation, and reporting. Key scopes of the studies included brain segmentation and regionalization (50.91%), structural measurement (12.73%), image reconstruction, enhancement and synthesis (21.82%) and predictive modeling and clinical classification (14.55%) which also distinguishes between tasks involving pixel-level analysis and image-level predictions. The 55 included studies used diverse datasets (753 to 433,000 images) as well as synthetic image data in some recent works covering wide-ranging gestational ages, mainly using MRI and ultrasound images. The systematic analysis explicitly categorizes each study by task type, applied methodology (U-Net variants, transformer-based models, CNNs, implicit neural representations), and corresponding evaluation metrics—segmentation (DSC, IoU, HD95), classification (Accuracy, Precision, AUC), regression (MAE, RMSE, R²), and reconstruction (PSNR, SSIM), facilitating standardized performance comparisons and establishing clear benchmarks for future research in automated fetal brain imaging. Significant gaps that were identified include inadequate data diversity, privacy measures, limited clinical interpretability and validity of AI models, and insufficient integration of multimodal data. To address these challenges, a unified framework is proposed that integrates multimodal data fusion, explainable artificial intelligence (XAI) paradigms, and federated learning architectures complemented by synthetic data generation techniques to ensure robust privacy preservation in real-world application. This work was not specifically funded, and the review was not registered.

胎儿神经发育是怀孕期间神经生长的一个复杂过程，早期发现异常是至关重要的，深度学习为这一目的提供了有前途的技术。本系统综述的目的是研究深度学习在胎儿神经发育中的应用，旨在综合前沿研究，检查方法，确定研究空白，并提出一个联合学习框架。根据PRISMA 2020指南，从主要数据库和其他来源的初始900条记录中选择了55篇同行评议的文章，其中每篇文章都通过六个特定的数据提取标准进行了检查。2005年至2025年的同行评审文章，特别是那些利用临床图像探索胎儿神经发育自动化深度学习的文章被纳入其中，而非深度学习分析被排除。偏倚风险根据设计、数据多样性、验证和报告进行定性评估。主要研究范围包括脑分割和区域化（50.91%）、结构测量（12.73%）、图像重建、增强和合成（21.82%）以及预测建模和临床分类（14.55%），并区分涉及像素级分析和图像级预测的任务。这55项纳入的研究使用了不同的数据集（753至43.3万张图像），以及一些近期作品中的合成图像数据，涵盖了广泛的胎龄，主要使用MRI和超声图像。系统分析根据任务类型、应用方法（U-Net变量、基于变压器的模型、cnn、隐式神经表征）和相应的评估指标(分割（DSC、IoU、HD95）、分类（准确度、精度、AUC）、回归（MAE、RMSE、R2）和重建（PSNR、SSIM）对每项研究进行了明确的分类，促进了标准化的性能比较，并为未来的自动化胎儿脑成像研究建立了明确的基准。发现的重大差距包括数据多样性不足、隐私措施、人工智能模型的临床可解释性和有效性有限，以及多模式数据整合不足。为了应对这些挑战，提出了一个统一的框架，该框架集成了多模态数据融合、可解释的人工智能（XAI）范式和联合学习架构，辅以合成数据生成技术，以确保在实际应用中健壮的隐私保护。这项工作没有特别资助，评论也没有注册。

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引用次数: 0

Power-to-power cross-frequency coupling as a novel approach for temporal lobe seizure detection and analysis 功率-功率交叉频率耦合作为一种新的颞叶癫痫检测和分析方法

Neuroscience informatics

Pub Date : 2025-10-28 DOI: 10.1016/j.neuri.2025.100240

Bar Lehmann, Andrei V. Medvedev

Objective

Power-to-power cross-frequency coupling (CFC) is a novel method to index the dynamic spatio-temporal interactions between brain rhythms, including high frequency oscillations (HFOs). This research evaluates this promising method's capacity for seizure detection with intracranial EEG. Seizures can be conceptualized as composites of different electrographic patterns including (1) spike, (2) ripple-on-spike, and (3) ripple-on-oscillation. This study also performs a basic CFC analysis of each of these components which has potential to further the understanding of epileptogenic processes.

Methods

In this study, deep learning networks including Stacked Sparse Autoencoder (SSAE) and Long Short Term Memory (LSTM) are trained to detect seizures and help characterize CFC patterns for these three common seizure components. The analysis uses intracranial EEG (iEEG) records from the ieeg.org (Mayo Clinic files) database. Temporal Lobe Epilepsy (TLE) seizures (

n = 120

) from 26 patients were analyzed along with segments of background activity. Power-to-power coupling was calculated between all frequencies 1–250 Hz pairwise using the EEGLAB toolbox. CFC matrices of seizure and background activity were used as training or testing inputs to the autoencoder.

Results

The trained network was able to recognize background and seizure segments (not used in training) with a sensitivity of 90.2%, specificity of 96.8% and overall accuracy of 93.4%. The three seizure components (spike, ripple-on-spike, ripple-on-oscillation) were also observed to have unique CFC signatures.

Conclusions

The results provide evidence both for (1) the relevance of power-to-power coupling (PPC) for TLE seizure detection in iEEG, as well as (2) there existing unique PPC signatures of three common seizure components.

目的功率-功率交叉频率耦合（CFC）是一种用于表征脑节律（包括高频振荡）之间动态时空相互作用的新方法。本研究评估了该方法在颅内脑电图检测癫痫发作中的应用前景。癫痫发作可以被定义为不同电图模式的复合，包括(1)尖峰，(2)尖峰上的波纹，(3)振荡上的波纹。本研究还对这些成分进行了基本的氯氟化碳分析，这有可能进一步了解癫痫发生过程。方法在本研究中，深度学习网络包括堆叠稀疏自编码器（SSAE）和长短期记忆（LSTM）进行训练，以检测癫痫发作，并帮助表征这三种常见癫痫发作成分的CFC模式。分析使用来自ieeg.org（梅奥诊所档案）数据库的颅内脑电图（iEEG）记录。对26例颞叶癫痫（TLE）发作（n=120）及其背景活动片段进行分析。使用EEGLAB工具箱两两计算所有频率1-250 Hz之间的功率-功率耦合。癫痫发作和背景活动的CFC矩阵被用作自动编码器的训练或测试输入。结果训练后的神经网络能够识别背景和未用于训练的癫痫片段，灵敏度为90.2%，特异性为96.8%，总体准确率为93.4%。三种发作成分（尖峰，尖峰上的波纹，振荡上的波纹）也被观察到具有独特的CFC特征。结论电-电耦合（power-to-power coupling， PPC）与eeg中TLE发作检测的相关性，以及三种常见发作成分存在独特的PPC特征。

{"title":"Power-to-power cross-frequency coupling as a novel approach for temporal lobe seizure detection and analysis","authors":"Bar Lehmann, Andrei V. Medvedev","doi":"10.1016/j.neuri.2025.100240","DOIUrl":"10.1016/j.neuri.2025.100240","url":null,"abstract":"<div><h3>Objective</h3><div>Power-to-power cross-frequency coupling (CFC) is a novel method to index the dynamic spatio-temporal interactions between brain rhythms, including high frequency oscillations (HFOs). This research evaluates this promising method's capacity for seizure detection with intracranial EEG. Seizures can be conceptualized as composites of different electrographic patterns including (1) spike, (2) ripple-on-spike, and (3) ripple-on-oscillation. This study also performs a basic CFC analysis of each of these components which has potential to further the understanding of epileptogenic processes.</div></div><div><h3>Methods</h3><div>In this study, deep learning networks including Stacked Sparse Autoencoder (SSAE) and Long Short Term Memory (LSTM) are trained to detect seizures and help characterize CFC patterns for these three common seizure components. The analysis uses intracranial EEG (iEEG) records from the ieeg.org (Mayo Clinic files) database. Temporal Lobe Epilepsy (TLE) seizures (<span><math><mi>n</mi><mo>=</mo><mn>120</mn></math></span>) from 26 patients were analyzed along with segments of background activity. Power-to-power coupling was calculated between all frequencies 1–250 Hz pairwise using the EEGLAB toolbox. CFC matrices of seizure and background activity were used as training or testing inputs to the autoencoder.</div></div><div><h3>Results</h3><div>The trained network was able to recognize background and seizure segments (not used in training) with a sensitivity of 90.2%, specificity of 96.8% and overall accuracy of 93.4%. The three seizure components (spike, ripple-on-spike, ripple-on-oscillation) were also observed to have unique CFC signatures.</div></div><div><h3>Conclusions</h3><div>The results provide evidence both for (1) the relevance of power-to-power coupling (PPC) for TLE seizure detection in iEEG, as well as (2) there existing unique PPC signatures of three common seizure components.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 4","pages":"Article 100240"},"PeriodicalIF":0.0,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Entity-augmented neuroscience knowledge retrieval using ontology and semantic understanding capability of LLM 基于LLM本体和语义理解能力的实体增强神经科学知识检索

Neuroscience informatics

Pub Date : 2025-10-28 DOI: 10.1016/j.neuri.2025.100237

Pralaypati Ta , Sriram Venkatesaperumal , Keerthi Ram , Mohanasankar Sivaprakasam

Neuroscience research publications encompass a vast wealth of knowledge. Accurately retrieving existing information and discovering new insights from this extensive literature is essential for advancing the field. However, when knowledge is dispersed across multiple sources, current state-of-the-art retrieval methods often struggle to extract the necessary information. A knowledge graph (KG) can integrate and link knowledge from multiple sources. However, existing methods for constructing KGs in neuroscience often rely on labeled data and require domain expertise. Acquiring large-scale, labeled data for a specialized area like neuroscience presents significant challenges. This work proposes novel methods for constructing KG from unlabeled large-scale neuroscience research corpus utilizing large language models (LLM), neuroscience ontology, and text embeddings. We analyze the semantic relevance of neuroscience text segments identified by LLM for building the knowledge graph. We also introduce an entity-augmented information retrieval algorithm to extract knowledge from the KG. Several experiments were conducted to evaluate the proposed approaches. The results demonstrate that our methods significantly enhance knowledge discovery from the unlabeled neuroscience research corpus. The performance of the proposed entity and relation extraction method is comparable to the existing supervised method. It achieves an F1 score of 0.84 for entity extraction from the unlabeled data. The knowledge obtained from the KG improves answers to over 52% of neuroscience questions from the PubMedQA dataset and questions generated using selected neuroscience entities.

神经科学研究出版物包含了大量的知识。准确地检索现有信息，并从这些广泛的文献中发现新的见解，对于推进该领域至关重要。然而，当知识分散在多个来源时，当前最先进的检索方法往往难以提取必要的信息。知识图（KG）可以整合和链接来自多个来源的知识。然而，现有的神经科学中构建kg的方法往往依赖于标记数据，需要领域的专业知识。为像神经科学这样的专业领域获取大规模的、有标签的数据是一项重大挑战。这项工作提出了利用大型语言模型（LLM）、神经科学本体和文本嵌入从未标记的大规模神经科学研究语料库中构建KG的新方法。我们分析由LLM识别的神经科学文本片段的语义相关性，构建知识图谱。我们还引入了一种实体增强信息检索算法来从知识库中提取知识。进行了几个实验来评估所提出的方法。结果表明，我们的方法显著提高了未标记神经科学研究语料库的知识发现。所提出的实体和关系提取方法的性能与现有的监督方法相当。对于从未标记数据中提取实体，它达到了0.84的F1分数。从KG获得的知识提高了来自PubMedQA数据集和使用选定的神经科学实体生成的问题的52%以上的神经科学问题的答案。

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引用次数: 0

Feature analysis of depression patients' house-tree-person drawings using convolutional neural networks 基于卷积神经网络的抑郁症患者屋树人图特征分析

Neuroscience informatics

Pub Date : 2025-10-28 DOI: 10.1016/j.neuri.2025.100239

Liu Zhenyi , Ye Cun Chun

Objective

This study explores the use of Convolutional Neural Networks (CNNs) to analyze House-Tree-Person (HTP) drawings for the classification of depression severity, addressing the subjectivity and limitations of traditional psychological assessment methods.

Methods

A dataset of 1,020 HTP drawings from adults aged 25–30 was collected, consisting of 432 healthy controls, 336 patients with moderate depression, and 252 patients with severe depression. The drawings were labeled based on the Hamilton Depression Scale (HAMD). A CNN model was trained and optimized using cross-validation to extract and classify depression-related visual features. The model's performance was evaluated using accuracy, recall, F1-score, and area under the ROC curve (AUC).

Results

The CNN model demonstrated a classification accuracy of 89% for distinguishing normal and depressed individuals, with an AUC of 0.96. In differentiating moderate from severe depression, the model achieved an AUC of 1.00, indicating near-perfect classification. The extracted features, such as line clarity and detail richness, correlated with depression severity, confirming their diagnostic relevance.

Conclusion

The study validates CNN-based image analysis as an effective and objective method for depression assessment using HTP drawings. The model not only improves accuracy but also offers potential applications in automated mental health screening. Future research should integrate multimodal data, such as speech and physiological signals, to enhance diagnostic precision.

目的探讨利用卷积神经网络（cnn）对屋树人（House-Tree-Person， HTP）图进行抑郁症严重程度分类的方法，解决传统心理评估方法的主观性和局限性。方法收集25 ~ 30岁成人HTP图1020张，其中健康对照432例，中度抑郁患者336例，重度抑郁患者252例。这些图画是根据汉密尔顿抑郁量表（HAMD）进行标记的。使用交叉验证对CNN模型进行训练和优化，提取和分类抑郁症相关的视觉特征。使用准确率、召回率、f1评分和ROC曲线下面积（AUC）来评估模型的性能。结果CNN模型对正常和抑郁个体的分类准确率为89%，AUC为0.96。在区分中度抑郁症和重度抑郁症时，该模型的AUC为1.00，表明分类接近完美。提取的特征，如线条清晰度和细节丰富度，与抑郁症严重程度相关，证实了它们的诊断相关性。结论基于cnn的图像分析是一种有效、客观的HTP图抑郁评价方法。该模型不仅提高了准确性，而且在自动心理健康筛查中提供了潜在的应用。未来的研究应整合多模态数据，如语音和生理信号，以提高诊断精度。

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引用次数: 0

Letter to editor regarding “A systematic review and meta-analysis on the diagnostic accuracy of artificial intelligence and computer-aided diagnosis of lumbar prolapsed intervertebral disc” 关于“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评价和荟萃分析”的致编辑信

Neuroscience informatics

Pub Date : 2025-09-30 DOI: 10.1016/j.neuri.2025.100235

Estanislao Arana

引用次数: 0

Smart web interface for student mental health prediction using machine learning with blockchain technology 使用区块链技术的机器学习进行学生心理健康预测的智能网络界面

Neuroscience informatics

Pub Date : 2025-09-30 DOI: 10.1016/j.neuri.2025.100236

Mishu Deb Nath , Md. Khabir Uddin Ahamed , Omayer Ahmed , Tanvir Ahmed , Sujit Roy , Mohammed Nasir Uddin

Student mental health is becoming a growing global concern, with more students facing psychological stress, anxiety, and related disorders. These mental health challenges often develop gradually and, if ignored, can negatively affect a student's academic performance and personal life. Early detection is essential, but high costs, limited resources, and time constraints often hinder it. The study proposes a machine learning-based approach to predict and assess student mental health, addressing this problem. Using rich psychological and behavioral data, the system can identify early signs of mental distress. An extensive evaluation of 12 machine learning models identified the top six performers. Logistic regression, Decision Tree, Extra Tree, Adaboost, Gradient Boosting, and XGBoost. Among these, the fine-tuned Random Forest algorithm achieved the highest performance, with an impressive accuracy of 95.6%. To ensure practical implementation, a Streamlit-based application was developed. This application enables educators and mental health professionals to perform real-time analysis and receive predictions in a clear and user-friendly format. The study incorporates blockchain technology to ensure the secure handling of sensitive data. Data collected through the Web interface, such as responses to mental health questionnaires, is securely stored using blockchain technology. This integrated system offers a reliable and scalable solution for monitoring and supporting student mental health.

随着越来越多的学生面临心理压力、焦虑和相关障碍，学生心理健康正在成为一个日益受到全球关注的问题。这些心理健康挑战往往是逐渐发展的，如果忽视，可能会对学生的学习成绩和个人生活产生负面影响。早期检测至关重要，但高昂的成本、有限的资源和时间限制往往会阻碍检测。该研究提出了一种基于机器学习的方法来预测和评估学生的心理健康，以解决这一问题。利用丰富的心理和行为数据，该系统可以识别出精神困扰的早期迹象。对12个机器学习模型的广泛评估确定了表现最好的6个模型。逻辑回归，决策树，额外树，Adaboost，梯度增强和XGBoost。其中，经过微调的Random Forest算法取得了最高的性能，准确率达到了惊人的95.6%。为了确保实际实现，开发了一个基于streamlite的应用程序。该应用程序使教育工作者和心理健康专业人员能够以清晰和用户友好的格式进行实时分析和接收预测。该研究采用区块链技术来确保敏感数据的安全处理。通过Web界面收集的数据，如对心理健康问卷的回答，使用区块链技术安全地存储。这个综合系统为监测和支持学生的心理健康提供了可靠和可扩展的解决方案。

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引用次数: 0

Functional MRI in hypertension – A systematic review of brain connectivity, regional activity, and cognitive impairment 高血压的功能性MRI——对脑连通性、区域活动和认知障碍的系统回顾

Neuroscience informatics

Pub Date : 2025-09-09 DOI: 10.1016/j.neuri.2025.100233

Sathya Sabina Muthu , Suresh Sukumar , Rajagopal Kadavigere , Shivashankar K.N. , K. Vaishali , Ramesh Babu M.G. , Hari Prakash Palaniswamy , Abhimanyu Pradhan , Winniecia Dkhar , Nitika C. Panakkal , Sneha Ravichandran , Dilip Shettigar , Poovitha Shruthi Paramashiva

Hypertension is increasingly recognized as a key contributor to cognitive decline and brain structure and function alterations. Functional Magnetic Resonance Imaging (fMRI) provides a non-invasive means to detect early disruptions in neural networks before clinical symptoms of cognitive impairment emerge. This systematic review explored the application of fMRI in assessing brain functional changes and cognitive performance in individuals with hypertension. A comprehensive search of electronic databases identified eight relevant studies, most of which employed resting-state fMRI techniques. Findings majorly demonstrated that hypertension is associated with altered connectivity within key neural networks, including the default mode network, frontoparietal network, and salience network. Additional observations included reduced regional homogeneity and changes in low-frequency fluctuations. These neural alterations were linked to decreased memory, executive function, and attention. While the findings support the potential of fMRI as an early biomarker for hypertension-related cognitive impairment, the evidence remains limited by the small number of studies and geographic concentration. Nonetheless, fMRI holds promise for clinical application in identifying individuals at risk and guiding timely interventions. Additional longitudinal studies with broader geographic representation are necessary to confirm these insights and facilitate the integration of fMRI into the routine evaluation and management of hypertension-related brain alterations.

高血压越来越被认为是认知能力下降和大脑结构和功能改变的关键因素。功能磁共振成像（fMRI）提供了一种非侵入性手段，在认知障碍的临床症状出现之前检测神经网络的早期中断。本系统综述探讨了功能磁共振成像在高血压患者脑功能变化和认知表现评估中的应用。通过对电子数据库的全面搜索，确定了8项相关研究，其中大多数采用了静息状态功能磁共振成像技术。研究结果主要表明，高血压与关键神经网络的连通性改变有关，包括默认模式网络、额顶叶网络和显著性网络。其他观察结果包括区域均匀性降低和低频波动的变化。这些神经变化与记忆力、执行功能和注意力下降有关。虽然这些发现支持fMRI作为高血压相关认知障碍的早期生物标志物的潜力，但由于研究数量少和地理集中，证据仍然有限。尽管如此，功能磁共振成像仍有望在临床应用中识别有风险的个体并指导及时干预。需要更多具有更广泛地理代表性的纵向研究来证实这些见解，并促进将功能磁共振成像纳入高血压相关脑改变的常规评估和管理。

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引用次数: 0