Vecchio Daniela, Piras Federica, Natalizi Federica, Banaj Nerisa, Pellicano Clelia, Piras Fabrizio
Neuropsychiatry Laboratory, Department of Clinical Neuroscience and Neurorehabilitation, IRCCS Santa Lucia Foundation, Rome 00179, Italy.
Department of Psychology, 'Sapienza' University of Rome, Rome 00185, Italy.
Brain Commun. 2025 Jan 21;7(1):fcaf027. doi: 10.1093/braincomms/fcaf027. eCollection 2025.
Alzheimer's disease is a disabling neurodegenerative disorder for which no effective treatment currently exists. To predict the diagnosis of Alzheimer's disease could be crucial for patients' outcome, but current Alzheimer's disease biomarkers are invasive, time consuming or expensive. Thus, developing MRI-based computational methods for Alzheimer's disease early diagnosis would be essential to narrow down the phenotypic measures predictive of cognitive decline. Amnestic mild cognitive impairment (aMCI) is associated with higher risk for Alzheimer's disease, and here, we aimed to identify MRI-based quantitative rules to predict aMCI to possible Alzheimer's disease conversion, applying different machine learning algorithms sequentially. At baseline, T1-weighted brain images were collected for 104 aMCI patients and processed to obtain 146 volumetric measures of cerebral grey matter regions [regions of interest (ROIs)]. One year later, patients were classified as converters (aMCI-c = 32) or non-converters, i.e. clinically and neuropsychologically stable (aMCI-s = 72) based on cognitive performance. Feature selection was performed by random forest (RF), and the identified seven ROIs volumetric data were used to implement support vector machine (SVM) and decision tree (DT) classification algorithms. Both SVM and DT reached an average accuracy of 86% in identifying aMCI-c and aMCI-s. DT found a critical threshold volume of the right entorhinal cortex (EC-r) as the first feature for differentiating aMCI-c/aMCI-s. Almost all aMCI-c had an EC-r volume <1286 mm, while more than half of the aMCI-s patients had a volume above the identified threshold for this structure. Other key regions for the classification between aMCI-c/aMCI-s were the left lateral occipital (LOC-l), the middle temporal gyrus and the temporal pole cortices. Our study reinforces previous evidence suggesting that the morphometry of the EC-r and LOC-l best predicts aMCI to Alzheimer's disease conversion. Further investigations are needed prior to deeming our findings as a broadly applicable predictive framework. However, here, a first indication was derived for volumetric thresholds that, being easy to obtain, may assist in early identification of Alzheimer's disease in clinical practice, thus contributing to establishing MRI as a useful non-invasive prognostic instrument for dementia onset.
阿尔茨海默病是一种致残性神经退行性疾病，目前尚无有效的治疗方法。预测阿尔茨海默病的诊断对患者的预后可能至关重要，但目前的阿尔茨海默病生物标志物具有侵入性、耗时或昂贵等缺点。因此，开发基于磁共振成像（MRI）的计算方法用于阿尔茨海默病的早期诊断对于缩小预测认知衰退的表型指标范围至关重要。遗忘型轻度认知障碍（aMCI）与患阿尔茨海默病的较高风险相关，在此，我们旨在确定基于MRI的定量规则，以预测aMCI向可能的阿尔茨海默病转化，依次应用不同的机器学习算法。在基线时，收集了104例aMCI患者的T1加权脑图像，并进行处理以获得146个脑灰质区域[感兴趣区域（ROIs）]的体积测量值。一年后，根据认知表现将患者分为转化者（aMCI-c = 32）或非转化者，即临床和神经心理学稳定者（aMCI-s = 72）。通过随机森林（RF）进行特征选择，并使用确定的7个ROIs体积数据来实施支持向量机（SVM）和决策树（DT）分类算法。SVM和DT在识别aMCI-c和aMCI-s方面的平均准确率均达到86%。DT发现右侧内嗅皮质（EC-r）的临界阈值体积是区分aMCI-c/aMCI-s的首要特征。几乎所有aMCI-c患者的EC-r体积<1286 mm，而超过一半的aMCI-s患者该结构的体积高于确定的阈值。aMCI-c/aMCI-s分类的其他关键区域是左侧枕叶外侧（LOC-l）、颞中回和颞极皮质。我们的研究强化了先前的证据，表明EC-r和LOC-l的形态测量最能预测aMCI向阿尔茨海默病的转化。在将我们的发现视为广泛适用的预测框架之前，还需要进一步研究。然而，在此得出了体积阈值的首个指标，该指标易于获得，可能有助于在临床实践中早期识别阿尔茨海默病，从而有助于将MRI确立为一种用于痴呆症发病的有用的非侵入性预后工具。
