Association of Network Connectivity via Resting State Functional MRI with Consciousness, Mortality, and Outcomes in Neonatal Acute Brain Injury

Abstract

Background An accurate and comprehensive test of integrated brain network function is needed for neonates during the acute brain injury period to inform on morbidity. This retrospective cohort study aimed to assess whether integrated brain network function by resting state functional MRI, acquired during the acute period in neonates with brain injury, is associated with acute exam, neonatal mortality, and 5-month outcomes. Methods This study included 40 consecutive neonates with resting state functional MRI acquired 1-22 days after suspected brain insult from March 2018 to July 2019 at Phoenix Childrens Hospital. Acute period exam and test results were assigned ordinal scores based on severity as documented by respective treating specialists. Analyses (Fisher exact, Wilcoxon-rank sum test, ordinal/multinomial logistic regression) examined association of resting state networks with demographics, presentation, neurological exam, electroencephalogram, anatomical MRI, magnetic resonance spectroscopy, passive task functional MRI, and outcomes of discharge condition, outpatient development, motor tone, seizure, and mortality. Results Subjects had a mean (standard deviation) gestational age of 37.8 (2.6) weeks, a majority were male (63%), with diagnosis of hypoxic ischemic encephalopathy (68%). Other findings at birth included mild distress (48%), moderately abnormal neurological exam (33%), and consciousness characterized as awake but irritable (40%). Significant associations after multiple testing corrections were detected for resting state networks: basal ganglia with outpatient developmental delay (odds ratio [OR], 14.5; 99.4% confidence interval [CI], 2.00-105; P<.001) and motor tone/weakness (OR, 9.98; 99.4% CI, 1.72-57.9; P<.001); language/frontal-parietal network with discharge condition (OR, 5.13; 99.4% CI, 1.22-21.5; P=.002) and outpatient developmental delay (OR, 4.77; 99.4% CI, 1.21-18.7; P=.002); default mode network with discharge condition (OR, 3.72; 99.4% CI, 1.01-13.78; P=.006) and neurological exam (P=.002 (FE); OR, 11.8; 99.4% CI, 0.73-191; P=.01 (OLR)); seizure onset zone with motor tone/weakness (OR, 3.31; 99.4% CI, 1.08-10.1; P=.003). Resting state networks were not detected in only three neonates, who died prior to discharge. Conclusions This study provides level 3 evidence (OCEBM Levels of Evidence Working Group) that the degree of abnormality of resting state networks in neonatal acute brain injury is associated with acute exam and outcomes. Total lack of brain network detection was only found in patients who did not survive.

Treatable brain network biomarkers in children in coma using task and resting-state functional MRI: a case series

“…It is worth noting that the rs-fMRI network biomarker's performance in a case with a poor outcome, like patient 3 who exhibited no detectable networks, serves as a worst-case-scenario control and provides a contrasting demonstration compared to patients 1 and 2. Due to the immaturity of the developing brain in expressing consciousness through examination, the likelihood of misdiagnosis in children and neonates may be even higher than the reported 49% misdiagnosis rate in adults with DoC ( 14 , 37 ). In cases similar to patients 2 and 3, where individuals exhibit unresponsiveness alongside spontaneous and/or stimulus-induced movements, interpreting these movements as purposeful, seizure activity, or non-epileptic reflexes/automatisms becomes challenging.…”

“…Rs-fMRI has the potential to identify deep regions involved in epileptogenesis that may not manifest observable clinical signs ( 14 , 27 , 36 , 50 – 52 ) and may not be detectable through surface recording EEG electrodes ( 53 , 54 ). This was evident in the cases of patients 1 and 2, where rs-fMRI signals were less affected by spatial resolution limitations.…”

“…It has been previously employed in a population of pediatric patients with intractable epilepsy, primarily resulting from prior brain injuries and congenital causes. Moreover, subsequent repeat studies in the same individuals have shown correlations with changes in their clinical condition, thereby enhancing the validation of its individualized application (13).…”

“…These subsignals, referred to as independent components (ICs), can originate from brain networks or sources of noise, such as the MRI scanner, respiration, cardiovascular activity, and cerebrospinal fluid pulsation. Differentiating between noise and brain networks presents a challenging task, as evidenced in the relevant literature (11,13,(23)(24)(25)(26).…”

“…Consistent with prior studies (10,28), the standard local false discovery rate was employed to establish an IC threshold of P < 0.05. To differentiate between noise components and neuronal signals from established resting-state networks, expert personnel conducted manual assessments based on spatial patterns (11) and a BOLD frequency <10 Hz/100 (11,13).…”