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Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
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Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ...
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Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...
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对于神经退行性疾病的对比自我监督学习分类.

Vadym Gryshchuk, Devesh Singh, Stefan Teipel

    medRxiv : the preprint server for health sciences
    |July 15, 2024
    PubMed
    概括

    自主监督学习 (SSL) 有效地区分阿尔茨海默病 (AD) 和前叶退行 (FTLD) 使用无标签的MRI扫描. 这种可解释的AI方法显示了高准确度,与监督方法相比,用于神经退行性疾病的分类.

    科学领域:

    • 神经成像是一种神经成像.
    • 人工智能的人工智能
    • 神经学 神经学

    背景情况:

    • 神经退行性疾病,如阿尔茨海默病 (AD) 和前叶退行 (FTLD) 导致通过MRI检测到的明显的大脑体积损失.
    • 监督机器学习用于疾病分类需要广泛的,专业标记的数据集,这些数据集往往很难获得.
    • 自主监督学习 (SSL) 为培训模型提供了一个有希望的替代方案,而不需要标记数据.

    研究的目的:

    • 研究SSL模型的应用,以使用T1加权MRI扫描来区分不同的神经退行性疾病.
    • 评估SSL模型在识别疾病特异性脑缩模式方面的可解释性.
    • 与最先进的监督方法相比,评估SSL模型的性能.

    主要方法:

    • 使用对比的自我监督学习作为特征提取器训练了一个深层卷积神经网络.
    • 一个单层感知器作为下游任务的分类头.
    • 该模型在ADNI,AIBL和FTLDNI队列的2694个T1加权MRI扫描上进行了训练和验证,包括认知正常的对照,AD和FTLD亚型.

    主要成果:

    • 经过SSL训练的特征提取器展示了可泛化和强大的表示来进行分类.
    • 该模型在测试中实现了82%的AD与认知正常 (CN) 的平衡精度,在持久数据集上达到80%.

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  • 对于行为变异前性痴呆症 (BV) 与CN,该模型达到88%的平衡精度.
  • 综合梯度分析突出了标志性缩区域:AD的时间灰质和BV的岛屿.

    • 结论:

    • 通过SSL方法,可以有效地利用无注释的神经成像数据集来训练可靠和可解释的机器学习模型.
    • 开发的SSL模型的性能与神经退行性疾病分类的监督深度学习方法相提并论.
    • 在研究神经系统疾病方面,SSL提供了一种可行的策略,可以利用大量的未标记的神经成像数据.