Zhu Wei, Zhang Guangle, Zhu Xiao-Hong, Chen Wei
Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, United States.
Imaging Neurosci (Camb). 2025 Apr 22;3. doi: 10.1162/imag_a_00543. eCollection 2025.
Probing neuronal activity and functional connectivity at cortical layer and sub-cortical nucleus level provides opportunities for mapping local and remote neural circuits and resting-state networks (RSN) critical for understanding cognition and behaviors. However, conventional resting-state fMRI (rs-fMRI) has been applied predominantly at relatively low spatial resolution and macroscopic level, unable to obtain laminar-specific information and neural circuits across the cortex at mesoscopic level. In addition, it is lack of sophisticated processing pipeline to deal with small laminar structures in rodent brains. To fill this gap, we conducted a high-resolution rs-fMRI study of mouse brain at ultra-high field and developed an fMRI preprocessing pipeline that features in random matrix theory-based principal component analysis to remove thermal noise, non-rigid image registration strategy to improve head motion estimation, one-time image voxel shift correction to minimize multi-interpolation-induced spatial blur, and improve subject-level alignment to facilitate group analysis. By applying this pipeline to the high-resolution mouse rs-fMRI with atlas-based connectivity analysis, we achieved high-quality hierarchical connectomes covering from large brain regions to cortical layers, and between white matter bundle fibers and cortices in mice. We demonstrate the hierarchical connectomes connecting to three representative brain regions: somatosensory areas, hippocampal regions, and lateral forebrain white matter bundles, showing previously undetected networks. The distinct laminar-specific networks evidence that the spontaneous neuronal activity is not uniform across the cortical layers in the resting brain, consistent with the layer-specific neuronal projection patterns that were observed in AAV viral tracer projections. Additionally, we also observed extended functional connections in areas with sparse viral tracer projections. The feasibility of achieving laminar-specific connectomes with distinct RSNs provides opportunities to study neural circuits and brain functions at multiple scales, though achieving high fidelity and specificity in mapping laminar-specific connectomes may require even higher spatial resolution.
在皮层和皮层下核水平探测神经元活动和功能连接，为绘制对理解认知和行为至关重要的局部和远程神经回路及静息态网络（RSN）提供了机会。然而，传统的静息态功能磁共振成像（rs-fMRI）主要应用于相对较低的空间分辨率和宏观水平，无法在介观水平获得跨皮层的层特异性信息和神经回路。此外，它缺乏处理啮齿动物大脑中层状小结构的复杂处理流程。为了填补这一空白，我们在超高场对小鼠大脑进行了高分辨率rs-fMRI研究，并开发了一种功能磁共振成像预处理流程，其特点包括基于随机矩阵理论的主成分分析以去除热噪声、非刚性图像配准策略以改善头部运动估计、一次性图像体素移位校正以最小化多重插值引起的空间模糊，以及改善个体水平的对齐以促进组分析。通过将该流程应用于基于图谱的连接性分析的高分辨率小鼠rs-fMRI，我们获得了覆盖从小脑区域到皮层层，以及小鼠白质束纤维和皮层之间的高质量分层连接组。我们展示了连接到三个代表性脑区的分层连接组：体感区、海马区和外侧前脑白质束，显示出以前未检测到的网络。不同的层特异性网络证明，静息大脑中自发神经元活动在皮层各层并不均匀，这与在腺相关病毒（AAV）示踪剂投射中观察到的层特异性神经元投射模式一致。此外，我们还在病毒示踪剂投射稀疏的区域观察到了扩展的功能连接。实现具有不同RSN的层特异性连接组的可行性为在多个尺度上研究神经回路和脑功能提供了机会，尽管在绘制层特异性连接组时实现高保真度和特异性可能需要更高的空间分辨率。
