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本页整理公开书目信息,不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成,未完成全文核验,可能有误;具体假设、证明与结论请核对原文。摘要保留来源文本,未经模型改写。

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引用量采用 OpenAlex 单条关联记录,不合计多个记录;关联记录可能包含正式发表版本,不等于此版本单独引用。缺失不视为零,引用指标不代表论文质量。

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分类依据与纠错

AI 根据所提供的论文材料归类:摘要讨论脑影像张量回归与空间正则化,内部变差并非工具变量,未直接对应所列细分。

反馈分类问题

正在核对关联版本…

代码、数据与补充材料

正在核对对应版本的资源…

关注这篇论文的作者
Long Feng
Xuan Bi
Heping Zhang

利用内部变差成像回归识别与言语推理相关的脑区

研究问题
如何利用脑影像的空间结构与系数分段平滑性,建立可计算的高阶回归模型,识别与言语推理能力相关的脑区?
方法与思路
作者提出基于张量分解的内部变差概念,作为高阶张量回归中总变差的可计算替代,并结合模拟与预处理后的影像分析。
材料陈述的贡献
摘要称模拟支持成像信号识别,在 978 人数据中发现跨扣带皮层和胼胝体的关联子区域,并称其为此前未报道区域。
阅读关注点
应核对内部变差与总变差的关系、张量分解和调参方式,以及影像预处理、关联筛选与新发现声明的证据。
阅读边界
仅据版本未注明的 OpenAlex 索引摘要解读;新区域是作者声明,摘要未提供外部验证或因果机制证据,IV 此处指内部变差。

原文摘要

Brain-imaging data have been increasingly used to understand intellectual disabilities. Despite significant progress in biomedical research, the mechanisms for most of the intellectual disabilities remain unknown. Finding the underlying neurological mechanisms has been proved difficult, especially in children due to the rapid development of their brains. We investigate verbal reasoning, which is a reliable measure of individuals' general intellectual abilities, and develop a class of high-order imaging regression models to identify brain subregions which might be associated with this specific intellectual ability. A key novelty of our method is to take advantage of spatial brain structures, and specifically the piecewise smooth nature of most imaging coefficients in the form of high-order tensors. Our approach provides an effective and urgently needed method for identifying brain subregions potentially underlying certain intellectual disabilities. The idea behind our approach is a carefully constructed concept called Internal Variation (IV). The IV employs tensor decomposition and provides a computationally feasible substitution for Total Variation (TV), which has been considered in the literature to deal with similar problems but is problematic in high order tensor regression. Before applying our method to analyze the real data, we conduct comprehensive simulation studies to demonstrate the validity of our method in imaging signal identification. Then, we present our results from the analysis of a dataset based on the Philadelphia Neurodevelopmental Cohort for which we preprocessed the data including re-orienting, bias-field correcting, extracting, normalizing and registering the magnetic resonance images from 978 individuals. Our analysis identified a subregion across the cingulate cortex and the corpus callosum as being associated with individuals' verbal reasoning ability, which, to the best of our knowledge, is a novel region that has not been reported in the literature. This finding is useful in further investigation of functional mechansims for verbal reasoning.

笔记与分组
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