How brain reacts to attack at a local region
Tu, W.; Ma, Z.; Ma, Y.; Zhang, N.
Show abstract
The architecture of brain networks has been extensively studied in multiple species. However, exactly how the brain network reconfigures when a local region stops functioning remains elusive. By combining chemogenetics and resting-state functional magnetic resonance imaging (rsfMRI) in awake rodents, we investigated the causal impact of acutely inactivating a hub region (i.e. dorsal anterior cingulate cortex) on brain network properties. We found that disrupting hub activity profoundly changed the function the default-mode network (DMN), and this change was associated with altered DMN-related behavior. Suppressing hub activity also impacted the topological architecture of the whole-brain network in network resilience, segregation and small worldness, but not network integration. This study has established a system that allows for mechanistically dissecting the relationship between local regions and brain network properties. Our data provide direct evidence supporting the hypothesis that acute dysfunction of a brain hub can cause large-scale network changes. This study opens an avenue of manipulating brain networks by controlling hub-node activity.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Chemogenetic Stimulation of Tonic Locus Coeruleus Activity Strengthens the Default Mode Network 98%
- Neuronal dynamics of the default mode network and anterior insular cortex: Intrinsic properties and modulation by salient stimuli 96%
- Chemogenetic dissection of the primate prefronto-subcortical pathways for working memory and decision-making 95%
Similar papers in this journal
- Brain-wide connectivity and novelty response of the dorsal endopiriform nucleus in mice 95%
- Optogenetic Activation of Striatal D1/D2 Medium Spiny Neurons Differentially Engages Downstream Connected Areas Beyond the Basal Ganglia 94%
- The mouse claustrum synaptically connects cortical network motifs 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.