Resting state networks in patients with acute disorders of consciousness after severe traumatic brain injury - Ordóñez-Rubiano, Edgar G.; Castañeda-Duarte, Marcelo A.; Baeza-Antón, Laura; Romo-Quebradas, Jorge A.; Perilla-Estrada, Juan P.; Perilla-Cepeda, Tito A.; Enciso-Olivera, Cesar O.; Rudas, Jorge; Marín-Muñoz, Jorge H.; Pulido, Cristian; Gómez, Francisco; Martínez, Darwin; et al. (Clinical neurology and neurosurgery, 2024)
This study aims to describe resting state networks (RSN) in patients with disorders of consciousness (DOC)s after acute severe traumatic brain injury (TBI). Adult patients with TBI with a GCS score <8 who remained in a coma, minimally conscious state (MCS), or unresponsive wakefulness syndrome (UWS), between 2017 and 2020 were included. Blood-oxygen-level dependent imaging was performed to compare their RSN with 10 healthy volunteers. Of a total of 293 patients evaluated, only 13 patients were included according to inclusion criteria: 7 in coma (54%), 2 in MCS (15%), and 4 (31%) had an UWS. RSN analysis showed that the default mode network (DMN) was present and symmetric in 6 patients (46%), absent in 1 (8%), and asymmetric in 6 (46%). The executive control network (ECN) was present in all patients but was asymmetric in 3 (23%). The right ECN was absent in 2 patients (15%) and the left ECN in 1 (7%). The medial visual network was present in 11 (85%) patients. Finally, the cerebellar network was symmetric in 8 patients (62%), asymmetric in 1 (8%), and absent in 4 (30%). A substantial impairment in activation of RSN is demonstrated in patients with DOC after severe TBI in comparison with healthy subjects. Three patterns of activation were found: normal/complete activation, 2) asymmetric activation or partially absent, and 3) absent activation. •Impaired consciousness following severe traumatic brain injury is a major cause of disability.•No tests or scales can accurately predict disorders of consciousness (DOC) prognosis.•rsfMRI reveals resting state networks linked to DOC severity.•Key brain networks might predict DOC outcomes.