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Cognitive Development During Adulthood01:30

Cognitive Development During Adulthood

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Cognitive development continues throughout adulthood, undergoing significant shifts across early, middle, and late stages. Individual transition occurs from adolescent idealism to pragmatic and adaptable thinking in early adulthood. During this period, individuals learn to integrate personal beliefs with the recognition that other perspectives are equally valid. Exposure to the complexities of modern society, diverse experiences, and higher education contribute to this adaptive thought process,...
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Multi-input and Multi-variable systems01:22

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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混合多模式多任务学习用于预测主观认知衰退的进展轨迹.

Minhui Yu1, Yuqi Fang2, Yunbi Liu3

  • 1Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC 27599, USA.

Neural networks : the official journal of the International Neural Network Society
|February 22, 2025
PubMed
概括

本研究引入了一种混合多模式学习框架 (HM2L),通过融合MRI和PET数据来改善主观认知衰退 (SCD) 进展的预测. HM2L有效地归因缺失的数据和转移知识,优于现有方法.

关键词:
这就是为什么MRI是MRI.多种方式的核聚变.在这里,PET是PET.主观认知衰退 主观认知衰退

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科学领域:

  • 神经成像是一种神经成像.
  • 机器学习 机器学习
  • 医疗数据融合 医学数据融合

背景情况:

  • 整合MRI和PET数据用于疾病进展预测是具有挑战性的,因为模式差异.
  • 小样本大小和缺少的数据 (PET) 是神经退行性疾病研究中的常见问题.

研究的目的:

  • 开发一种混合多模式多任务学习 (HM2L) 框架,用于预测主观认知衰退 (SCD) 的进展.
  • 通过跨领域的知识转移,解决缺少PET数据和小样本大小的挑战.

主要方法:

  • 拟议的HM2L框架包括缺少的PET归算,多模式特征提取与软max-三重制约,以及基于注意力的融合.
  • 从一个大型数据集 (795名受试者) 运用转移学习策略到两个小型SCD队列 (136名受试者).
  • 分类标签和临床分数 (MMSE,GDS) 的多任务预测.

主要成果:

  • 在共同预测SCD类别标签和临床分数方面,HM2L显著超过了最先进的方法.
  • 在SCD受试者中观察到较低的迷你精神状态检查 (MMSE) 评分,这些SCD受试者进展到轻度认知障碍.
  • 在SCD进展和老年抑郁量表 (GDS) 分数之间确定了复杂的关系.

结论:

  • 在神经退行性疾病研究中,HM2L框架为多模式数据融合和知识转移提供了有效的方法.
  • 可实现SCD进展轨迹的准确预测,有助于早期诊断和干预计划.
  • 这些发现突显了MMSE和GDS在随着时间的推移跟踪SCD患者的认知变化和情绪方面的实用性.