A data-driven, multi-domain brain gray matter signature as a powerful biomarker associated with several clinical outcomes.

IF 4 Q1 CLINICAL NEUROLOGY
Evan Fletcher, Brandon Gavett, Sarah Tomaszewski Farias, Keith Widaman, Rachel Whitmer, Audrey P Fan, Maria Corrada, Charles DeCarli, Dan Mungas
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引用次数: 0

Abstract

Introduction: Characterizing pathological changes in the brain that underlie cognitive impairment, including Alzheimer's disease and related disorders, is central to clinical concerns of prevention, diagnosis, and treatment.

Methods: We describe the properties of a brain gray matter region ("Union Signature") that is derived from four behavior-specific, data-driven signatures in a discovery cohort.

Results: In a separate validation set, the Union Signature demonstrates clinically relevant properties. Its associations with episodic memory, executive function, and Clinical Dementia Rating Sum of Boxes are stronger than those of several standardly accepted brain measures (e.g., hippocampal volume, cortical gray matter) and other previously developed brain signatures. The ability of the Union Signature to classify clinical syndromes among normal, mild cognitive impairment, and dementia exceeds that of the other measures.

Discussion: The Union Signature is a powerful, multipurpose correlate of clinically relevant outcomes and a strong classifier of clinical syndromes.

Highlights: Data-driven brain signatures are potentially valuable in models of cognitive aging.In previous work, we outlined rigorous validation of signatures for memory.This work demonstrates a signature predicting multiple clinical measures.This could be useful in models of interventions for brain support of cognition.

数据驱动的多领域大脑灰质特征是与多种临床结果相关的强大生物标志物。
引言描述认知障碍(包括阿尔茨海默病和相关疾病)所导致的大脑病理变化是预防、诊断和治疗等临床问题的核心:方法:我们描述了一个大脑灰质区域("Union Signature")的特性,该区域是由发现队列中的四个行为特异性数据驱动特征得出的:结果:在一个单独的验证集中,Union Signature 显示了与临床相关的特性。结果:在单独的验证集中,Union Signature 显示出了与临床相关的特性,它与外显记忆、执行功能和临床痴呆评级方框总和之间的关联性强于几种标准的脑测量指标(如海马体积、皮层灰质)和其他以前开发的脑特征。联合特征对正常、轻度认知障碍和痴呆临床综合征的分类能力超过了其他测量方法:讨论:联合特征是临床相关结果的一个功能强大的多用途相关指标,也是临床综合征的一个强有力的分类指标:在以前的工作中,我们概述了对记忆特征的严格验证。这项工作展示了一种可预测多种临床指标的特征,这可能对大脑支持认知的干预模型有用。
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来源期刊
CiteScore
7.80
自引率
7.50%
发文量
101
审稿时长
8 weeks
期刊介绍: Alzheimer''s & Dementia: Diagnosis, Assessment & Disease Monitoring (DADM) is an open access, peer-reviewed, journal from the Alzheimer''s Association® that will publish new research that reports the discovery, development and validation of instruments, technologies, algorithms, and innovative processes. Papers will cover a range of topics interested in the early and accurate detection of individuals with memory complaints and/or among asymptomatic individuals at elevated risk for various forms of memory disorders. The expectation for published papers will be to translate fundamental knowledge about the neurobiology of the disease into practical reports that describe both the conceptual and methodological aspects of the submitted scientific inquiry. Published topics will explore the development of biomarkers, surrogate markers, and conceptual/methodological challenges. Publication priority will be given to papers that 1) describe putative surrogate markers that accurately track disease progression, 2) biomarkers that fulfill international regulatory requirements, 3) reports from large, well-characterized population-based cohorts that comprise the heterogeneity and diversity of asymptomatic individuals and 4) algorithmic development that considers multi-marker arrays (e.g., integrated-omics, genetics, biofluids, imaging, etc.) and advanced computational analytics and technologies.
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