Functional cortical gradients capture the brains large-scale organization along sensory-to-association (G1) and visual-to-somatomotor (G2) axes, yet the laminar circuitry that supports these axes remains largely unknown. Leveraging whole-brain submillimeter 7T resting-state fMRI, we estimated functional connectivity within deep, middle, and superficial cortical depths, integrated these into a multilayer network, and derived depth-resolved functional gradients across the cortex. We then computed an inter-regional dissimilarity index quantifying how distinct each region is from the rest of the cortex, and discovered a systematic dissociation across depth: the superficial-layer index closely followed G1, whereas the deep-layer index aligned with G2. The deep-layer index peaked in receive-dominant regions, consistent with deep layers as principal targets of feedback projections, while the superficial-layer index was maximal in cytoarchitecturally less differentiated transmodal cortex, consistent with its dense recurrent circuitry. Together, these findings demonstrate that functional gradients relate to laminar connectivity, and establish a mesoscale framework for decomposing whole-brain cortical connectivity.
功能性皮层梯度捕捉了大脑沿感觉-联合轴（G1）和视觉-躯体运动轴（G2）的大尺度组织，但支持这些轴的层状回路在很大程度上仍未知。利用全脑亚毫米级7T静息态功能磁共振成像，我们估算了深层、中层和浅层皮层深度内的功能连接，将其整合为多层网络，并推导出跨皮层的深度分辨功能梯度。随后，我们计算了一个区域间差异指数，量化每个区域与皮层其余部分的差异程度，并发现了跨深度的系统性分离：浅层指数紧密跟随G1，而深层指数与G2对齐。深层指数在接收主导区域达到峰值，这与深层作为反馈投射主要目标一致；而浅层指数在细胞结构分化程度较低的跨模态皮层中最大，这与其密集的循环回路一致。总之，这些发现表明功能梯度与层状连接相关，并建立了一个分解全脑皮层连接的中尺度框架。
Degutis, J. K. et al. · CC-BY 4.0