Classifying mild cognitive impairment from normal cognition: fMRI complexity matches tau PET performance.

IF 4.4 Q1 CLINICAL NEUROLOGY

Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring Pub Date : 2025-08-12 eCollection Date: 2025-07-01 DOI:10.1002/dad2.70159

Kay Jann, Gilsoon Park, John M Ringman, Hosung Kim

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引用次数: 0

Abstract

Introduction: Cognitive decline in Alzheimer's disease (AD) is closely linked to tau pathology, which leads to loss of synaptic connections and ultimately neurons. While tau positron emission tomography (PET) carries radiation risks, is costly, and often unavailable in clinical settings, brain entropy mapping via resting-state functional magnetic resonance imaging (fMRI) has emerged as a marker of impaired brain function related to tauopathy.

Methods: Using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Estudio de la Enfermedad de Alzheimer en Jalisciences (EEAJ), we investigate the classification performance of fMRI entropy with tau PET in distinguishing cognitively normal (CN) from cognitively impaired (mild cognitive impairment/AD) individuals. Convolutional neural networks, initially trained and evaluated via 5-fold cross-validation on ADNI data, were subsequently tested on an independent external cohort (EEAJ) using an ensemble approach.

Results: The fMRI entropy classifier matched the tau PET model in accuracy and outperformed it in F1 score (0.64 vs. 0.61) and area under the curve (AUC; 0.73 vs. 0.67). On the independent external validation dataset (EEAJ), fMRI sample entropy showed a comparable F1 score (0.88) to tau PET (0.88) and achieved a notably higher AUC (0.94 vs. 0.92).

Discussion: Our findings suggest that fMRI entropy could be a non-invasive imaging marker alternative to tau PET for detecting AD-related cognitive impairment.

Highlights: Functional magnetic resonance imaging (fMRI) complexity matches tau positron emission tomography (PET) in classifying cognitive impairment.Sample entropy and multiscale entropy were used for fMRI-based Alzheimer's disease (AD) classification.3D convolutional neural networks models achieve up to 84% accuracy using fMRI complexity measures.The dorsal attention network was identified as critical for distinguishing mild cognitive impairment/AD.fMRI complexity offers a non-invasive alternative to tau positron emission tomography imaging.

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将轻度认知障碍与正常认知区分：fMRI复杂度与tau PET表现相符。

导读：阿尔茨海默病（AD）的认知能力下降与tau病理密切相关，tau病理导致突触连接的丧失，最终导致神经元的丧失。虽然tau正电子发射断层扫描（PET）有辐射风险，价格昂贵，而且通常在临床环境中不可用，但通过静息状态功能磁共振成像（fMRI）绘制脑熵图已经成为与tau病相关的脑功能受损的标志。方法：利用阿尔茨海默病神经影像学倡议（ADNI）和阿尔茨海默病Enfermedad de Alzheimer en Jalisciences （EEAJ）的数据，我们研究了tau PET在区分认知正常（CN）和认知受损（轻度认知障碍/AD）个体方面的fMRI熵分类性能。卷积神经网络最初通过ADNI数据的5倍交叉验证进行训练和评估，随后使用集成方法在独立外部队列（EEAJ）上进行测试。结果：fMRI熵分类器在准确性上与tau PET模型相匹配，在F1评分（0.64 vs 0.61）和曲线下面积(AUC；0.73 vs. 0.67)。在独立的外部验证数据集（EEAJ）上，fMRI样本熵的F1得分（0.88）与tau PET（0.88）相当，AUC（0.94比0.92）明显更高。讨论：我们的研究结果表明，fMRI熵可以替代tau PET作为检测ad相关认知障碍的非侵入性成像标记。重点：功能磁共振成像（fMRI）的复杂性与tau正电子发射断层扫描（PET）在认知障碍分类上的匹配。样本熵和多尺度熵用于基于fmri的阿尔茨海默病（AD）分类。3D卷积神经网络模型实现高达84%的准确性使用fMRI复杂性措施。背侧注意网络被认为是区分轻度认知障碍/AD的关键。fMRI的复杂性为tau正电子发射断层成像提供了一种非侵入性的替代方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring Medicine-Psychiatry and Mental Health

CiteScore

7.80

自引率

7.50%

发文量

101

审稿时长

8 weeks

期刊介绍： Alzheimer''s & Dementia: Diagnosis, Assessment & Disease Monitoring (DADM) is an open access, peer-reviewed, journal from the Alzheimer''s Association® that will publish new research that reports the discovery, development and validation of instruments, technologies, algorithms, and innovative processes. Papers will cover a range of topics interested in the early and accurate detection of individuals with memory complaints and/or among asymptomatic individuals at elevated risk for various forms of memory disorders. The expectation for published papers will be to translate fundamental knowledge about the neurobiology of the disease into practical reports that describe both the conceptual and methodological aspects of the submitted scientific inquiry. Published topics will explore the development of biomarkers, surrogate markers, and conceptual/methodological challenges. Publication priority will be given to papers that 1) describe putative surrogate markers that accurately track disease progression, 2) biomarkers that fulfill international regulatory requirements, 3) reports from large, well-characterized population-based cohorts that comprise the heterogeneity and diversity of asymptomatic individuals and 4) algorithmic development that considers multi-marker arrays (e.g., integrated-omics, genetics, biofluids, imaging, etc.) and advanced computational analytics and technologies.