Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @ UniTn, 38068 Rovereto, Italy; IMT Institute for Advanced Studies, Lucca, Italy.
Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA.
Neuroimage. 2014 Feb 15;87:403-15. doi: 10.1016/j.neuroimage.2013.09.050. Epub 2013 Sep 29.
Laboratory mouse models represent a powerful tool to elucidate the biological foundations of disease, but translation to and from human studies rely upon valid cross-species measures. Resting-state functional connectivity (rsFC) represents a promising translational probe of brain function; however, no convincing demonstration of the presence of distributed, bilateral rsFC networks in the mouse brain has yet been reported. Here we used blood oxygen level dependent (BOLD) and cerebral blood volume (CBV) weighted fMRI to demonstrate the presence of robust and reproducible resting-state networks in the mouse brain. Independent-component analysis (ICA) revealed inter-hemispheric homotopic rsFC networks encompassing several established neuro-anatomical systems of the mouse brain, including limbic, motor and parietal cortex, striatum, thalamus and hippocampus. BOLD and CBV contrast produced consistent networks, with the latter exhibiting a superior anatomical preservation of brain regions close to air-tissue interfaces (e.g. ventral hippocampus). Seed-based analysis confirmed the inter-hemispheric specificity of the correlations observed with ICA and highlighted the presence of distributed antero-posterior networks anatomically homologous to the human salience network (SN) and default-mode network (DMN). Consistent with rsFC investigations in humans, BOLD and CBV-weighted fMRI signals in the DMN-like network exhibited spontaneous anti-correlation with neighbouring fronto-parietal areas. These findings demonstrate the presence of robust distributed intrinsic functional connectivity networks in the mouse brain, and pave the way for the application of rsFC readouts in transgenic models to investigate the biological underpinnings of spontaneous BOLD fMRI fluctuations and their derangement in pathological states.
实验室小鼠模型是阐明疾病生物学基础的有力工具，但从人类研究到动物模型的转化依赖于有效的跨物种测量方法。静息态功能连接（rsFC）是一种很有前途的脑功能转化探针；然而，目前还没有令人信服的证据表明小鼠大脑中存在分布式、双侧 rsFC 网络。在这里，我们使用血氧水平依赖（BOLD）和脑血容量（CBV）加权 fMRI 来证明小鼠大脑中存在强大且可重复的静息态网络。独立成分分析（ICA）揭示了半球间同型 rsFC 网络，包括小鼠大脑的几个已建立的神经解剖系统，包括边缘系统、运动和顶叶皮层、纹状体、丘脑和海马体。BOLD 和 CBV 对比产生了一致的网络，后者对靠近气-组织界面的脑区（如腹侧海马体）具有更好的解剖学保留。种子点分析证实了 ICA 观察到的半球间相关性的特异性，并强调了存在分布式前-后网络，与人类显着网络（SN）和默认模式网络（DMN）在解剖学上同源。与人类 rsFC 研究一致，DMN 样网络中的 BOLD 和 CBV 加权 fMRI 信号自发地与相邻的额-顶区域呈负相关。这些发现证明了小鼠大脑中存在强大的分布式内在功能连接网络，为 rsFC 读数在转基因模型中的应用铺平了道路，以研究自发 BOLD fMRI 波动的生物学基础及其在病理状态下的紊乱。
