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Basics of Multivariate Analysis in Neuroimaging Data
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复杂的特征遗传学的教训可能有助于我们克服神经成像复制危机
1Social, Genetic & Developmental Psychiatry Centre, King's College, London, UK.
概括
复杂的特征遗传学和神经成像可以通过采用遗传学研究的五个教训来提高可靠性,包括更大的样本大小和数据共享. 这些策略增强了发现特征和疾病的生物相关的发现.
科学领域:
- 神经成像是一种神经成像.
- 复杂的特征遗传学复杂的特征遗传学
背景情况:
- 这两个领域都面临着在确定共同特征和疾病的可靠生物相关性方面面临的挑战.
- 复制性问题在人口级遗传学和个体间神经成像研究中普遍存在.
研究的目的:
- 突出从基因学研究中得到的5个关键教训,适用于神经成像.
- 提出方法来提高神经成像研究的稳定性和可复制性.
主要方法:
- 审查复杂特征遗传学研究中的成功实践.
- 将这些已知遗传学方法应用于神经成像数据分析.
- 检查从候选基因研究和全基因组关联研究中得出的经验教训.
主要成果:
- 放弃单个大脑区域过于简单地绘制特征.
- 强调增加样本大小的重要性,以获得可靠的调查结果.
- 激励大规模无变体测试和总结级数据共享,以促进协作和元分析.
结论:
- 采用遗传学实践,如大规模数据分析和共享,可以增强神经成像发现.
- 将遗传学方法应用于神经成像数据,可以获得更强大的结果.
- 这些策略可以通过可靠的发现加速神经成像研究的演变.
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