统计学助力神经影像，破解大脑发育与疾病研究难题
Statistical Opportunities in Neuroimaging
- 从出生到20岁大脑发育的统计建模新方法
- 解决高维数据噪声与个体间差异带来的分析挑战
- 适合统计学家与神经科学交叉研究者参考
神经影像技术（如MRI、fMRI、EEG、PET）极大推动了人类大脑结构、功能和连接性的理解，涵盖从早期脑发育到神经退行性及精神疾病的研究。然而，大脑是多尺度复杂系统，神经影像数据维度极高，面临测量噪声、运动伪影、个体与扫描仪间显著差异，以及现代研究规模庞大的统计挑战。本文探讨神经影像中四大关键领域的统计机遇与挑战：(i) 出生至20岁脑发育；(ii) 成人及老化脑；(iii) 神经退行性疾病与精神障碍；(iv) 大脑编码与解码。在简要介绍主要成像技术后，综述前沿研究，强调数据与建模难点，并指出统计学家可参与的关键研究方向。最后强调，统计学家、神经科学家与临床医生的紧密合作，对实现更精准诊断、深入机制理解及个性化治疗至关重要。
原文摘要 · Abstract (English)
Neuroimaging has profoundly enhanced our understanding of the human brain by characterizing its structure, function, and connectivity through modalities like MRI, fMRI, EEG, and PET. These technologies have enabled major breakthroughs across the lifespan, from early brain development to neurodegenerative and neuropsychiatric disorders. Despite these advances, the brain is a complex, multiscale system, and neuroimaging measurements are correspondingly high-dimensional. This creates major statistical challenges, including measurement noise, motion-related artifacts, substantial inter-subject and site/scanner variability, and the sheer scale of modern studies. This paper explores statistical opportunities and challenges in neuroimaging across four key areas: (i) brain development from birth to age 20, (ii) the adult and aging brain, (iii) neurodegeneration and neuropsychiatric disorders, and (iv) brain encoding and decoding. After a quick tutorial on major imaging technologies, we review cutting-edge studies, underscore data and modeling challenges, and highlight research opportunities for statisticians. We conclude by emphasizing that close collaboration among statisticians, neuroscientists, and clinicians is essential for translating neuroimaging advances into improved diagnostics, deeper mechanistic insight, and more personalized treatments.
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