静息状态fMRI连接对人脑层流连接结构非常敏感。

Q1 Computer Science
Gopikrishna Deshpande, Yun Wang, Jennifer Robinson
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引用次数: 3

摘要

先前的侵入性研究表明,人类新皮层灰质包含细胞结构不同的层,其与大脑其他部分的结构连接存在显著差异。鉴于解剖和功能磁共振成像(fMRI)的空间分辨率最近有所提高,我们假设来自fMRI的静息状态功能连接(FC)对层特异性丘脑-皮层和皮质-皮层微回路敏感。利用7 T时获得的亚毫米静息状态fMRI数据,我们发现:(1)整个丘脑与皮层I、VI层之间的FC明显强于丘脑与其他层之间的FC。体感觉丘脑(腹侧后外侧核,VPL)与初级体感觉皮层第四、六层之间的FC较其他层强;(2)浅层(I-III层)同源区域之间的半球间皮质-皮质FC较深层(V-VI层)强。这些发现与之前的侵入性研究推断的结构连接一致,这些研究表明:(i)整个丘脑中的m型神经元向第一层投射;(ii)第vi层的锥体神经元以所有丘脑核为目标,(iii) VPL中的c型神经元投射到第iv层并接受初级体感皮层第vi层的输入,(iv)同源皮质区域之间80%的胶质投射神经元连接浅层。我们的研究结果首次证明静息状态fMRI对皮层层之间的结构连接很敏感(以前是通过侵入性研究推断的),特别是在丘脑-皮层和皮质-皮层网络中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Resting state fMRI connectivity is sensitive to laminar connectional architecture in the human brain.

Resting state fMRI connectivity is sensitive to laminar connectional architecture in the human brain.

Resting state fMRI connectivity is sensitive to laminar connectional architecture in the human brain.

Resting state fMRI connectivity is sensitive to laminar connectional architecture in the human brain.

Previous invasive studies indicate that human neocortical graymatter contains cytoarchitectonically distinct layers, with notable differences in their structural connectivity with the rest of the brain. Given recent improvements in the spatial resolution of anatomical and functional magnetic resonance imaging (fMRI), we hypothesize that resting state functional connectivity (FC) derived from fMRI is sensitive to layer-specific thalamo-cortical and cortico-cortical microcircuits. Using sub-millimeter resting state fMRI data obtained at 7 T, we found that: (1) FC between the entire thalamus and cortical layers I and VI was significantly stronger than between the thalamus and other layers. Furthermore, FC between somatosensory thalamus (ventral posterolateral nucleus, VPL) and layers IV, VI of the primary somatosensory cortex were stronger than with other layers; (2) Inter-hemispheric cortico-cortical FC between homologous regions in superficial layers (layers I-III) was stronger compared to deep layers (layers V-VI). These findings are in agreement with structural connections inferred from previous invasive studies that showed that: (i) M-type neurons in the entire thalamus project to layer-I; (ii) Pyramidal neurons in layer-VI target all thalamic nuclei, (iii) C-type neurons in the VPL project to layer-IV and receive inputs from layer-VI of the primary somatosensory cortex, and (iv) 80% of collosal projecting neurons between homologous cortical regions connect superficial layers. Our results demonstrate for the first time that resting state fMRI is sensitive to structural connections between cortical layers (previously inferred through invasive studies), specifically in thalamo-cortical and cortico-cortical networks.

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来源期刊
Brain Informatics
Brain Informatics Computer Science-Computer Science Applications
CiteScore
9.50
自引率
0.00%
发文量
27
审稿时长
13 weeks
期刊介绍: Brain Informatics is an international, peer-reviewed, interdisciplinary open-access journal published under the brand SpringerOpen, which provides a unique platform for researchers and practitioners to disseminate original research on computational and informatics technologies related to brain. This journal addresses the computational, cognitive, physiological, biological, physical, ecological and social perspectives of brain informatics. It also welcomes emerging information technologies and advanced neuro-imaging technologies, such as big data analytics and interactive knowledge discovery related to various large-scale brain studies and their applications. This journal will publish high-quality original research papers, brief reports and critical reviews in all theoretical, technological, clinical and interdisciplinary studies that make up the field of brain informatics and its applications in brain-machine intelligence, brain-inspired intelligent systems, mental health and brain disorders, etc. The scope of papers includes the following five tracks: Track 1: Cognitive and Computational Foundations of Brain Science Track 2: Human Information Processing Systems Track 3: Brain Big Data Analytics, Curation and Management Track 4: Informatics Paradigms for Brain and Mental Health Research Track 5: Brain-Machine Intelligence and Brain-Inspired Computing
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