bioRxiv Science⌕ Search

Biology subjects

Meschke, E. X.

Publications and source records attributed to Meschke, E. X..

2 recordsLinked to original sources

A map of the cortical functional network mediating naturalistic navigation

Navigation through the real world requires close coordination of perception, planning, and motor actions. Prior neuroimaging studies that used controlled stimuli and tasks have suggested that navigation-related information is represented broadly across the human cerebral cortex. While three regions in the anterior visual cortex have been well-studied1, the extent and functional properties of regions in the parietal and prefrontal cortices are not as well-characterized 2. To map and characterize the full cortical network that underpins active navigation in the real world, we used functional magnetic resonance imaging to record brain activity from participants performing a naturalistic navigation task. Banded ridge regression was used to fit high-dimensional encoding models for 28,134 features to this data. Results show that naturalistic navigation is supported by a network of 11 functionally distinct cortical regions: five prefrontal and three parietal regions, along with three regions in the visual cortex that had been identified and characterized in previous studies. Analysis of encoding model weights shows that these 11 regions transform perceptual inputs through decision-making processes to produce action outputs, and are organized along distributed cortical functional gradients. These results provide a unified description of the functional properties and organization of the cortical network that mediates naturalistic navigation. We anticipate that these maps will provide rich targets to inform more targeted future studies of human navigation.

neuroscience↗

Model connectivity: leveraging the power of encoding models to overcome the limitations of functional connectivity

Functional connectivity (FC) is the most popular method for recovering functional networks of brain areas with fMRI. However, because FC is defined as temporal correlations in brain activity, FC networks are confounded by noise and lack a precise functional role. To overcome these limitations, we developed model connectivity (MC). MC is defined as similarities in encoding model weights, which quantify reliable functional activity in terms of interpretable stimulus- or task-related features. To compare FC and MC, both methods were applied to a naturalistic story listening dataset. FC recovered spatially broad networks that are confounded by noise, and that lack a clear role during natural language comprehension. By contrast, MC recovered spatially localized networks that are robust to noise, and that represent distinct categories of semantic concepts. Thus, MC is a powerful data-driven approach for recovering and interpreting the functional networks that support complex cognitive processes.

neuroscience↗