Relationship between brain function (aEEG) and brain structure (MRI) and their predictive value for neurodevelopmental outcome of preterm infants

Abstract

To improve the prediction of neurodevelopmental outcome in very preterm infants, this study used the combination of amplitude-integrated electroencephalography (aEEG) within the first 72 h of life and cranial magnetic resonance imaging (MRI) at term equivalent age. A single-center cohort of 38 infants born before 32 weeks of gestation was subjected to both investigations. Structural measurements were performed on MRI. Multiple regression analysis was used to identify independent factors including functional and structural brain measurements associated with outcome at a corrected age of 24 months. aEEG parameters significantly correlated with MRI measurements. Reduced deep gray matter volume was associated with low Burdjalov Score on day 3 (p < 0.0001) and day 1–3 (p = 0.0012). The biparietal width and the transcerebellar diameter were related to Burdjalov Score on day 1 (p = 0.0111; p = 0.0002). The final multiple regression analysis revealed independent predictors of neurodevelopmental outcome: intraventricular hemorrhage (p = 0.0060) and interhemispheric distance (p = 0.0052) for mental developmental index; Burdjalov Score day 1 (p = 0.0201) and interhemispheric distance (p = 0.0142) for psychomotor developmental index.Conclusion: Functional aEEG parameters were associated with altered brain maturation on MRI. The combination of aEEG and MRI contributes to the prediction of outcome at 24 months. What is Known: • Prematurity remains a risk factor for impaired neurodevelopment.• aEEG is used to measure brain activity in preterm infants and cranial MRI is performed to identify structural gray and white matter abnormalities with impact on neurodevelopmental outcome. What is New: • aEEG parameters observed within the first 72 h of life were associated with altered deep gray matter volumes, biparietal width, and transcerebellar diameter at term equivalent age.• The combination of aEEG and MRI contributes to the prediction of neurodevelopmental outcome at 2 years of corrected age in very preterm infants.Electronic supplementary materialThe online version of this article (10.1007/s00431-018-3166-2) contains supplementary material, which is available to authorized users.

Relationship Between Early Functional and Structural Brain Developments and Brain Injury in Preterm Infants

“…Moreover, maturational features can be useful for the prognosis of long-term neurodevelopmental outcome [5][6][7][8]. In addition, magnetic resonance imaging (MRI) is a valuable neuroimaging modality to detect preterm brain abnormalities, such as white matter injury and cerebellar hemorrhages, difficult to detect using cranial ultrasound [9,10]. Sequential MRI allows longitudinal assessment of structural brain development, such as the integrity and growth of brain structures, and it is predictive of long-term outcome [11,12].…”

Association between development of early cortical activity on amplitude-integrated EEG and 5-years neurodevelopmental outcome in the preterm infant

“…This might be due to substantial differences in the assessment and analyses methods of aEEG recordings, analysed aEEG time points as well as outcome measurements and compositions of combined outcomes. In line with our aEEG assessment methods Bruns et al (17) and Huning et al (28) used the total maturity score by Burdjalov et al(5) and the background pattern assessment by Hellstrom-Westas et al (2), whereas others (12) applied different aEEG background scoring systems (29) as well as quantitative analysis methods such as interburst intervals (13) and dichotomized aEEG tracings into normal and abnormal (12, 14) patterns. This substantial variability in aEEG analysis methods hampers the comparability among studies and with our results.…”

Cognitive outcome of early school‐aged children born very preterm is not predicted by early short‐term amplitude‐integrated electroencephalography

“…This might be due to substantial differences in the assessment and analysis methods of aEEG recordings, analysed aEEG time points, outcome measurements and compositions of combined outcomes. In line with our aEEG assessment methods, Huning et al used the total maturity score by Burdjalov et al and the background pattern assessment by Hellstrom‐Westas et al, whereas others applied different aEEG background scoring systems and quantitative analysis methods such as interburst intervals, and dichotomised aEEG tracings into normal and abnormal patterns. This substantial variability in aEEG analysis methods hampers the comparability among studies, and with our results.…”