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一种数据驱动的多领域脑灰质特征作为与多种临床结果相关的强大生物标志物。

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

作者信息

Fletcher Evan, Gavett Brandon, Farias Sarah Tomaszewski, Widaman Keith, Whitmer Rachel, Fan Audrey P, Corrada Maria, DeCarli Charles, Mungas Dan

机构信息

Department of Neurology University of California Davis California USA.

Alzheimer's Disease Research Center UC Davis School of Medicine Davis California USA.

出版信息

Alzheimers Dement (Amst). 2024 Nov 23;16(4):e70026. doi: 10.1002/dad2.70026. eCollection 2024 Oct-Dec.

DOI:10.1002/dad2.70026
PMID:39583650
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11584916/
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.

摘要

引言

明确包括阿尔茨海默病及相关疾病在内的认知障碍所基于的大脑病理变化,对于预防、诊断和治疗等临床问题至关重要。

方法

我们描述了一个大脑灰质区域(“联合特征”)的特性,该区域源自一个发现队列中的四个行为特异性、数据驱动的特征。

结果

在一个单独的验证集中,联合特征显示出与临床相关的特性。它与情景记忆、执行功能以及临床痴呆评定量表总积分的关联比几种标准认可的脑测量指标(如海马体积、皮质灰质)以及其他先前开发的脑特征更强。联合特征对正常、轻度认知障碍和痴呆患者临床综合征的分类能力超过了其他测量指标。

讨论

联合特征是临床相关结果的强大、多用途关联指标,也是临床综合征的有力分类器。

重点

数据驱动的脑特征在认知衰老模型中可能具有重要价值。在之前的工作中,我们概述了对记忆特征的严格验证。这项工作展示了一个可预测多种临床测量指标的特征。这可能对支持大脑认知的干预模型有用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f66/11584916/b043aba5bf02/DAD2-16-e70026-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f66/11584916/5f54bec969f3/DAD2-16-e70026-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f66/11584916/4f4bcf1002a1/DAD2-16-e70026-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f66/11584916/b043aba5bf02/DAD2-16-e70026-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f66/11584916/5f54bec969f3/DAD2-16-e70026-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f66/11584916/4f4bcf1002a1/DAD2-16-e70026-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f66/11584916/b043aba5bf02/DAD2-16-e70026-g003.jpg

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本文引用的文献

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Toward a statistical validation of brain signatures as robust measures of behavioral substrates.
朝着将大脑特征作为行为基质稳健测量的统计验证方向努力。
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Aging Brain. 2022 Jan 10;2:100027. doi: 10.1016/j.nbas.2021.100027. eCollection 2022.
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