基于静息状态功能连接的口吃规划和运动亚型证据

IF 2.1 2区 心理学 Q1 AUDIOLOGY & SPEECH-LANGUAGE PATHOLOGY Brain and Language Pub Date : 2024-05-03 DOI:10.1016/j.bandl.2024.105417
Hannah P. Rowe , Jason A. Tourville , Alfonso Nieto-Castanon , Emily O. Garnett , Ho Ming Chow , Soo-Eun Chang , Frank H. Guenther
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引用次数: 0

摘要

我们测试了根据发音器速度梯度方向(GODIVA)模型提出的假设,即口吃成人(AWS)可能根据皮质-基底神经节规划或运动环路内不同的连接性而组成亚型。对 91 名口吃成人和 79 名对照者的静息状态功能连接进行了测量,以了解 GODIVA 模型的所有连接情况。根据主成分分析,两个连接占了 AWS 连接变异的大部分:左丘脑 - 左额叶下沟后部(规划环路成分)和左辅助运动区 - 左腹侧前运动皮层(运动环路成分)。使用这两种连接的 k-means 聚类算法发现了三个 AWS 聚类。簇 1 在两个连接点上都与对照组有显著差异;簇 2 仅在计划环路上有显著差异;簇 3 仅在运动环路上有显著差异。这些发现表明口吃存在计划和运动亚型。
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Evidence for planning and motor subtypes of stuttering based on resting state functional connectivity

We tested the hypothesis, generated from the Gradient Order Directions Into Velocities of Articulators (GODIVA) model, that adults who stutter (AWS) may comprise subtypes based on differing connectivity within the cortico-basal ganglia planning or motor loop. Resting state functional connectivity from 91 AWS and 79 controls was measured for all GODIVA model connections. Based on a principal components analysis, two connections accounted for most of the connectivity variability in AWS: left thalamus – left posterior inferior frontal sulcus (planning loop component) and left supplementary motor area – left ventral premotor cortex (motor loop component). A k-means clustering algorithm using the two connections revealed three clusters of AWS. Cluster 1 was significantly different from controls in both connections; Cluster 2 was significantly different in only the planning loop; and Cluster 3 was significantly different in only the motor loop. These findings suggest the presence of planning and motor subtypes of stuttering.

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来源期刊
Brain and Language
Brain and Language 医学-神经科学
CiteScore
4.50
自引率
8.00%
发文量
82
审稿时长
20.5 weeks
期刊介绍: An interdisciplinary journal, Brain and Language publishes articles that elucidate the complex relationships among language, brain, and behavior. The journal covers the large variety of modern techniques in cognitive neuroscience, including functional and structural brain imaging, electrophysiology, cellular and molecular neurobiology, genetics, lesion-based approaches, and computational modeling. All articles must relate to human language and be relevant to the understanding of its neurobiological and neurocognitive bases. Published articles in the journal are expected to have significant theoretical novelty and/or practical implications, and use perspectives and methods from psychology, linguistics, and neuroscience along with brain data and brain measures.
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