通过大脑网络的深度学习揭示了跨生命周期的潜在大脑疾病的共同光谱
Mianxin Liu1,2, Jingyang Zhang1, Yao Wang3
1School of Biomedical Engineering, State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai 201210, China.
iScience
|November 29, 2023
概括
这项研究使用深度学习和功能性MRI数据来识别早期和晚年脑疾病中常见的大脑网络变化. 研究结果揭示了一系列疾病,有助于了解并发症.
科学领域:
- 神经科学是一个神经科学.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 在人类一生中,大脑疾病可能具有共同的潜在病理变化.
- 有限的神经成像证据可以澄清这些共同点和关系.
研究的目的:
- 调查早期和晚年大脑疾病中共享的神经成像变化的研究.
- 用功能磁共振成像 (fMRI) 来分类多种大脑疾病.
主要方法:
- 开发了一个受限制的单个分支深度学习模型.
- 利用了4410名参与者 (包括健康对照组) 的多站点fMRI数据.
- 分类为5种不同的早期和晚年脑部疾病.
主要成果:
- 实现了62.6 ± 1.9%的整体分类准确度.
- 识别出常受影响的功能子网络:默认模式,执行控制,视觉和边缘网络.
- 观察到年轻患者和老年患者的连续分布,支持了年轻患者和老年患者的持续分布.
- 疾病的频谱的疾病的频谱.
- 一个概念,一个概念.
结论:
- 深度学习模型可以检测跨不同脑部疾病的共享功能性脑部变化.
- 研究结果支持整个生命周期中持续存在的一系列大脑疾病.
- 这种方法增强了对疾病并发症和时间关联的理解.
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