Winner of the Brazier awardThe dysmature EEG pattern in infants with bronchopulmonary dysplasia and its prognostic implications☆
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Cited by (47)
Dysmature patterns of newborn EEG recordings: Biological markers of transitory brain dysfunction or brain injury
2022, European Journal of Paediatric NeurologyCitation Excerpt :In a patient with a dysmature pattern in the first and the second EEG, we later recorded a normal EEG and normal development at 6 years of age. As this patient was the only newborn with such findings, we cannot compare this observation to a study by Hahn et al. [3] who found that the presence of dysmature patterns in near-term EEGs were associated with significantly less favourable outcomes. In his study on preterms with bronchopulmonary dysplasia and the need for ventilatory support, out of 23 children with dysmature patterns, 83% either died or had neurological sequelae; meanwhile, among infants who did not have dysmature patterns, 62% had a normal neurological outcome.
Electroencephalography in the Preterm and Term Infant
2017, Fetal and Neonatal Physiology, 2-Volume SetCharacteristics and clinical significance of delta brushes in the EEG of premature infants
2016, Clinical Neurophysiology PracticeCitation Excerpt :All five neonates with later cognitive impairment had delta brushes after 39 weeks along with other features of dysmaturity (Hayakawa et al., 1997). Using delta brush incidence, alongside three other EEG measures, to assess dysmaturity, 83% of neonates with bronchopulmonary dysplasia associated with a dysmature EEG at 37–42 weeks had a poor outcome, compared to 23% with normal EEGs (Hahn and Tharp, 1990). On the other hand, EEG dysmaturity has been followed by normal outcome, but in only one study of three subjects (Tharp et al., 1981).
Cognitive outcome at 5years in very premature children without severe early cerebral abnormalities: Relationships with EEG at 6 weeks after birth
2013, Neurophysiologie CliniqueCitation Excerpt :These chronic-stage EEG patterns allow detecting the risks of motor impairment in infants with usually small lesions that had not been diagnosed by early EEG and neuroimaging techniques (cerebral ultrasound scan, MRI). As regards dysmature EEGs recorded in infants born near term, Hahn and Tharp [17] showed that this EEG pattern was associated with “significantly less favourable outcome”. In other studies, chronic-stage EEG abnormalities appeared to be useful to detect a risk for cognitive impairments [8,18,19,25,37].
Development of a novel robust measure for interhemispheric synchrony in the neonatal EEG: Activation Synchrony Index (ASI)
2013, NeuroImageCitation Excerpt :Synchrony of this kind is traditionally estimated from the temporal coincidence of the intermittent bouts of activity (often called bursts; a.k.a. spontaneous activity transients, SATs; see Table 1 in Vanhatalo and Kaila, 2010) during quiet sleep that is characterized by a background pattern called trace alternant/discontinu. Prior studies have shown, for instance, that interhemispheric synchrony of this kind increases towards term age, and that many clinical compromises in brain function will lead to a decrease in synchrony, identified as an increased asynchrony (Hahn and Tharp, 1990; Koeda et al., 1995; Lombroso, 1979; Monod et al., 1972; Walsh et al., 2011). Strikingly in this context, there are no unanimous and/or unequivocal definitions of interhemispheric (a) synchrony, and likewise, there are no established, plausible paradigms for its measurement.
Phase synchrony in the early preterm EEG: Development of methods for estimating synchrony in both oscillations and events
2012, NeuroImageCitation Excerpt :Since the first successful recordings of neonatal EEG, its interpretation has been based on visual classification of individual EEG waveforms, and to some extent, on the pattern of spontaneous ‘background’ activity (Andre et al., 2010; Holmes and Lombroso, 1993). Visual assessment of ‘asynchrony’ (an undefined concept; cf. Walsh et al., 2011) is considered as a sign of maturation and/or abnormality in the EEG record (Hahn and Tharp, 1990; Walsh et al., 2011), although visual review is very ambiguous in discerning synchrony of this kind. Interplay between brain areas (or EEG signals) may be assessed in different ways (see Fig. 1 in Bressler and Menon, 2010; Dauwels et al., 2010; Uhlhaas et al., 2009; Vanhatalo and Palva, 2011), and it is traditionally performed on pairwise comparison of two EEG signals at a time.
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This research was supported by NIH Grant NS12151 (B.R. Tharp) and Premature Research Center Grant RR-00081. Postdoctoral support was provided by NIH Epilepsy Training Program, NS07280 (J.S. Hahn).
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We would like to thank Ron L. Ariagno, M.D. and Roger B. Baldwin, Ph.D. of the neonatalogy division for their helpful suggestions, and Ms. Nancy Thompson, Ms. Phyllis T. Hall, and Ms. Mary Kay Burton for technical assistance. Statistical assistance was kindly provided by Lincoln Moses, Ph.D.