确定与ADHD儿童和青少年认知功能相关的功能性大脑连接的发育变化
Brian Pho1, Ryan Andrew Stevenson2, Sara Saljoughi3
1Program in Neuroscience, University of Western Ontario, London, ON, Canada.
Developmental cognitive neuroscience
|August 25, 2024
概括
机器学习模型使用大脑功能连接成功预测了患有注意力缺陷/多动障碍 (ADHD) 的儿童的认知能力. 这些预测模式在ADHD青少年的童年和青春期之间有所不同.
科学领域:
- 神经科学是一个神经科学.
- 发展心理学 发展心理学
- 认知科学 认知科学
背景情况:
- 患有注意力缺陷/多动障碍 (ADHD) 的年轻人经常表现出执行功能缺陷.
- 大脑功能连接的差异与ADHD的认知障碍有关.
- 与ADHD青少年认知相关的大脑功能性质的发育变化尚未得到充分理解.
研究的目的:
- 描述与多动症青少年认知能力相关的大脑功能连接的发育变化.
- 研究如何机器学习可以根据ADHD的功能性大脑网络预测认知表现.
- 确定特定的大脑网络,预测ADHD不同发育阶段的认知能力.
主要方法:
- 分析了373名患有ADHD的青少年和106名神经类型 (NT) 个体 (6-16岁) 的fMRI数据.
- 机器学习模型被用来识别电影观看期间功能网络连接的预测模式.
- 使用样本外交叉验证来评估认知能力的预测准确性.
主要成果:
- 机器学习模型准确地预测了ADHD儿童 (6-11岁) 的智商,视觉空间,语言理解和流动推理.
- 在患有多动症的青少年 (12-16岁) 中,预测准确度没有达到.
- 关键的预测连接从幼儿时代的默认模式,记忆检索和背部注意网络转移到中年时代的体运动,环眼和前侧双眼网络.
结论:
- 机器学习可以在整个发育过程中识别与ADHD认知能力相关的不同功能连接配置文件.
- 大脑网络对ADHD认知功能的贡献在童年和青春期之间有很大差异.
- 这项研究强调了神经成像和机器学习的潜力,以了解ADHD的认知发展.
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