Many cancers and neuro‐related diseases display significant phenotypic and genetic heterogeneity across subjects and subpopulations. Characterizing such heterogeneity could transform our understanding of the etiology of these conditions and inspire new approaches to urgently needed prevention, diagnosis, treatment, and prognosis. However, most existing statistical methods face major challenges in delineating such heterogeneity at both the group and individual levels. The aim of this article is to propose a novel statistical disease‐mapping (SDM) framework to address some of these challenges. We develop an efficient estimation method to estimate unknown parameters in SDM and delineate individual and group disease maps. Statistical inference procedures such as hypothesis‐testing problems are also investigated for parameters of interest. Both simulation studies and real data analysis on the ADNI hippocampal surface dataset show that our SDM not only effectively detects diseased regions in each patient but also provides a group disease‐mapping analysis of Alzheimer subgroups.

中文翻译：

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异种神经影像学研究的统计疾病作图

许多癌症和神经相关疾病在受试者和亚人群之间表现出明显的表型和遗传异质性。表征这种异质性可以改变我们对这些疾病病因的理解，并激发出新的方法来紧急地进行预防，诊断，治疗和预后。然而，大多数现有的统计方法在描述群体和个人层面的这种异质性方面都面临着重大挑战。本文的目的是提出一种新颖的统计疾病映射（SDM）框架来应对其中的一些挑战。我们开发了一种有效的估算方法来估算SDM中的未知参数，并描绘出个体和群体疾病图。还对诸如假设检验问题之类的统计推断程序进行了研究，以获取感兴趣的参数。