Infraslow dynamic patterns in human cortical networks track a spectrum of external to internal attention.

Cortical networks exhibit remarkable adaptability, shifting their dynamic connectivity based on attentional demands. In a comprehensive analysis of fMRI data from over 200 participants across various tasks—like visual working memory and mindfulness—strong phase shifts were observed in somatosensory and visual networks. Notably, responses diverged within the so-called task positive network, challenging previous definitions. These findings suggest that understanding these dynamics could lead to individualized biomarkers for neurodegenerative diseases, enhancing insights into brain function in biological sciences and neuroscience.

Study at a glance

Abstract

Early efforts to understand the human cerebral cortex focused on localization of function, assigning functional roles to specific brain regions. More recent evidence depicts the cortex as a dynamic system, organized into flexible networks with patterns of spatiotemporal activity corresponding to attentional demands. In functional MRI (fMRI), dynamic analysis of such spatiotemporal patterns is highly promising for providing non-invasive biomarkers of neurodegenerative diseases and neural disorders. However, there is no established neurotypical spectrum to interpret the burgeoning literature of dynamic functional connectivity from fMRI across attentional states. In the present study, we apply dynamic analysis of network-scale spatiotemporal patterns in a range of fMRI datasets across numerous tasks including a left-right moving dot task, visual working memory tasks, congruence tasks, multiple resting state datasets, mindfulness meditators, and subjects watching TV. We find that cortical networks show shifts in dynamic functional connectivity across a spectrum that tracks the level of external to internal attention demanded by these tasks. Dynamics of networks often grouped into a single task positive network show divergent responses along this axis of attention, consistent with evidence that definitions of a single task positive network are misleading. Additionally, somatosensory and visual networks exhibit strong phase shifting along this spectrum of attention. Results were robust on a group and individual level, further establishing network dynamics as a potential individual biomarker. To our knowledge, this represents the first study of its kind to generate a spectrum of dynamic network relationships across such an axis of attention.