Graph theory demonstrates lower nodal efficiency and transitivity in the Default Mode Network 6-12 months after admission for COVID-19 pneumonia

Motivation: Neural functional networks provide insights into the intrinsic function and integrity of brain areas. Goal(s): The aim of the current study was to investigate the levels of functional integration and segregation of brain areas within the default mode network (DMN) in a cohort of adults 6-12 months after admission for SARS-CoV-2 pneumonia. Approach: We used a functional atlas and graph-theoretical framework to model the topology of brain areas within the DMN. Results: We found reduced functional integration within the DMN of adults previously admitted with SARS-CoV-2. Affected brain areas are involved in higher-cognitive memory processing functions. Impact: Investigating the brain’s functional organization in the context of post-acute infection with SARS-CoV-2 can offer a window into the neuropathology of long COVID-19 symptoms.