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.

翻译：神经影像学通过磁共振成像（MRI）、功能磁共振成像（fMRI）、脑电图（EEG）和正电子发射断层扫描（PET）等技术，描绘人脑的结构、功能与连接，极大地深化了我们对人脑的理解。这些技术推动了从早期大脑发育到神经退行性与神经精神疾病等贯穿生命周期的重大突破。然而，大脑是一个复杂的多尺度系统，神经影像测量数据相应地具有高维特性。这带来了重大的统计挑战，包括测量噪声、运动相关伪影、显著的被试间及站点/扫描仪间变异，以及现代研究庞大的数据规模。本文围绕四个关键领域探讨神经影像学中的统计机遇与挑战：（i）从出生至20岁的大脑发育，（ii）成年与衰老大脑，（iii）神经退行性与神经精神疾病，以及（iv）大脑编码与解码。在简要介绍主要成像技术后，我们回顾前沿研究，强调数据与建模方面的挑战，并指出统计学家可深入探索的研究机遇。最后我们强调，统计学家、神经科学家与临床医生之间的紧密协作，对于将神经影像学的进展转化为更精准的诊断、更深入的机制理解以及更个性化的治疗方案至关重要。