Christopher D. Smyser

Longitudinal Analysis of Neural Network Development in Preterm Infants

Application of resting state functional connectivity magnetic resonance imaging (fcMRI) to the study of prematurely born infants enables assessment of the earliest forms of cerebral connectivity and characterization of its early development in the human brain. We obtained 90 longitudinal fcMRI data sets from a cohort of preterm infants aged from 26 weeks postmenstrual age (PMA) through term equivalent age at PMA-specific time points. Utilizing seed-based correlation analysis, we identified resting state networks involving varied cortical regions, the thalamus, and cerebellum. Identified networks demonstrated a regionally variable age-specific pattern of development, with more mature forms consisting of localized interhemispheric connections between homotopic counterparts. Anatomical distance was found to play a critical role in the rate of connection development. Prominent differences were noted between networks identified in term control versus premature infants at term equivalent, including in the thalamocortical connections critical for neurodevelopment. Putative precursors of the default mode network were detected in term control infants but were not identified in preterm infants, including those at term equivalent. Identified patterns of network maturation reflect the intricate relationship of structural and functional processes present throughout this important developmental period and are consistent with prior investigations of neurodevelopment in this population.

Resting-State fMRI: A Review of Methods and Clinical Applications

SUMMARY: Resting-state fMRI measures spontaneous low-frequency fluctuations in the BOLD signal to investigate the functional architecture of the brain. Application of this technique has allowed the identification of various RSNs, or spatially distinct areas of the brain that demonstrate synchronous BOLD fluctuations at rest. Various methods exist for analyzing resting-state data, including seed-based approaches, independent component analysis, graph methods, clustering algorithms, neural networks, and pattern classifiers. Clinical applications of resting-state fMRI are at an early stage of development. However, its use in presurgical planning for patients with brain tumor and epilepsy demonstrates early promise, and the technique may have a future role in providing diagnostic and prognostic information for neurologic and psychiatric diseases.

Neonatal intensive care unit stress is associated with brain development in preterm infants

Although many perinatal factors have been linked to adverse neurodevelopmental outcomes in very premature infants, much of the variation in outcome remains unexplained. The impact on brain development of 1 potential factor, exposure to stressors in the neonatal intensive care unit, has not yet been studied in a systematic, prospective manner.

Alterations in Brain Structure and Neurodevelopmental Outcome in Preterm Infants Hospitalized in Different Neonatal Intensive Care Unit Environments

OBJECTIVE To evaluate associations between neonatal intensive care unit (NICU) room type (open ward and private room) and medical outcomes; neurobehavior, electrophysiology, and brain structure at hospital discharge; and developmental outcomes at 2 years of age. STUDY DESIGN In this prospective longitudinal cohort study, we enrolled 136 preterm infants born <30 weeks gestation from an urban, 75-bed level III NICU from 2007-2010. Upon admission, each participant was assigned to a bedspace in an open ward or private room within the same hospital, based on space and staffing availability, where they remained for the duration of hospitalization. The primary outcome was developmental performance at 2 years of age (n = 86 infants returned for testing, which was 83% of survivors) measured using the Bayley Scales of Infant and Toddler Development, 3rd Edition. Secondary outcomes were: (1) medical factors throughout the hospitalization; (2) neurobehavior; and (3) cerebral injury and maturation (determined by magnetic resonance imaging and electroencephalography). RESULTS At term equivalent age, infants in private rooms were characterized by a diminution of normal hemispheric asymmetry and a trend toward having lower amplitude integrated electroencephalography cerebral maturation scores (P = .02; β = -0.52 [CI -0.95, -0.10]). At age 2 years, infants from private rooms had lower language scores (P = .006; β = -8.3 [CI -14.2, -2.4]) and a trend toward lower motor scores (P = .02; β = -6.3 [CI -11.7, -0.99]), which persisted after adjustment for potential confounders. CONCLUSION These findings raise concerns that highlight the need for further research into the potential adverse effects of different amounts of sensory exposure in the NICU environment.

Functional Connectivity MRI in Infants: Exploration of the Functional Organization of the Developing Brain

Advanced neuroimaging techniques have been increasingly applied to the study of preterm and term infants in an effort to further define the functional cerebral architecture of the developing brain. Despite improved understanding of the complex relationship between structure and function obtained through these investigations, significant questions remain regarding the nature, location, and timing of the maturational changes which occur during early development. Functional connectivity magnetic resonance imaging (fcMRI) utilizes spontaneous, low frequency (< 0.1 Hz), coherent fluctuations in blood oxygen level dependent (BOLD) signal to identify networks of functional cerebral connections. Due to the intrinsic characteristics of its image acquisition and analysis, fcMRI offers a novel neuroimaging approach well suited to investigation of infants. Recently, this methodology has been successfully applied to examine neonatal populations, defining normative patterns of large-scale neural network development in the maturing brain. The resting-state networks (RSNs) identified in these studies reflect the evolving cerebral structural architecture, presumably driven by varied genetic and environmental influences. Principal features of these investigations and their role in characterization of the tenets of neural network development during this critical developmental period are highlighted in this review. Despite these successes, optimal methods for fcMRI data acquisition and analysis for this population have not yet been defined. Further, appropriate schemes for interpretation and translation of fcMRI results remain unknown, a matter of increasing importance as functional neuroimaging findings are progressively applied in the clinical arena. Notwithstanding these concerns, fcMRI provides insight into the earliest forms of cerebral connectivity and therefore holds great promise for future neurodevelopmental investigations.

Effects of White Matter Injury on Resting State fMRI Measures in Prematurely Born Infants

The cerebral white matter is vulnerable to injury in very preterm infants (born prior to 30 weeks gestation), resulting in a spectrum of lesions. These range from severe forms, including cystic periventricular leukomalacia and periventricular hemorrhagic infarction, to minor focal punctate lesions. Moderate to severe white matter injury in preterm infants has been shown to predict later neurodevelopmental disability, although outcomes can vary widely in infants with qualitatively comparable lesions. Resting state functional connectivity magnetic resonance imaging has been increasingly utilized in neurodevelopmental investigations and may provide complementary information regarding the impact of white matter injury on the developing brain. We performed resting state functional connectivity magnetic resonance imaging at term equivalent postmenstrual age in fourteen preterm infants with moderate to severe white matter injury secondary to periventricular hemorrhagic infarction. In these subjects, resting state networks were identifiable throughout the brain. Patterns of aberrant functional connectivity were observed and depended upon injury severity. Comparisons were performed against data obtained from prematurely-born infants with mild white matter injury and healthy, term-born infants and demonstrated group differences. These results reveal structural-functional correlates of preterm white matter injury and carry implications for future investigations of neurodevelopmental disability.

Resting-State Network Complexity and Magnitude Are Reduced in Prematurely Born Infants

Premature birth is associated with high rates of motor and cognitive disability. Investigations have described resting-state functional magnetic resonance imaging (rs-fMRI) correlates of prematurity in older children, but comparable data in the neonatal period remain scarce. We studied 25 term-born control infants within the first week of life and 25 very preterm infants (born at gestational ages ranging from 23 to 29 weeks) without evident structural injury at term equivalent postmenstrual age. Conventional resting-state network (RSN) mapping revealed only modest differences between the term and prematurely born infants, in accordance with previous work. However, clear group differences were observed in quantitative analyses based on correlation and covariance matrices representing the functional MRI time series extracted from 31 regions of interest in 7 RSNs. In addition, the maximum likelihood dimensionality estimates of the group-averaged covariance matrices in the term and preterm infants were 5 and 3, respectively, indicating that prematurity leads to a reduction in the complexity of rs-fMRI covariance structure. These findings highlight the importance of quantitative analyses of rs-fMRI data and suggest a more sensitive method for delineating the effects of preterm birth in infants without evident structural injury.

Mutations in STAMBP, encoding a deubiquitinating enzyme, cause microcephaly-capillary malformation syndrome

Microcephaly–capillary malformation (MIC-CAP) syndrome is characterized by severe microcephaly with progressive cortical atrophy, intractable epilepsy, profound developmental delay and multiple small capillary malformations on the skin. We used whole-exome sequencing of five patients with MIC-CAP syndrome and identified recessive mutations in STAMBP, a gene encoding the deubiquitinating (DUB) isopeptidase STAMBP (STAM-binding protein, also known as AMSH, associated molecule with the SH3 domain of STAM) that has a key role in cell surface receptor–mediated endocytosis and sorting. Patient cell lines showed reduced STAMBP expression associated with accumulation of ubiquitin-conjugated protein aggregates, elevated apoptosis and insensitive activation of the RAS-MAPK and PI3K-AKT-mTOR pathways. The latter cellular phenotype is notable considering the established connection between these pathways and their association with vascular and capillary malformations. Furthermore, our findings of a congenital human disorder caused by a defective DUB protein that functions in endocytosis implicates ubiquitin-conjugate aggregation and elevated apoptosis as factors potentially influencing the progressive neuronal loss underlying MIC-CAP syndrome.

Real-time multiple linear regression for fMRI supported by time-aware acquisition and processing

Real‐time parametric statistical analysis of functional MRI (fMRI) data would potentially enlarge the scope of experimentation and facilitate its application to clinical populations. A system is described that addresses the need for rapid analysis of fMRI data and lays the foundation for dealing with problems that impede the application of fMRI to clinical populations. The system, I/OWA (Input/Output time‐aWare Architecture), combines a general architecture for sampling and time‐stamping relevant information channels in fMRI (image acquisition, stimulation, subject responses, cardiac and respiratory monitors, etc.) and an efficient approach to manipulating these data, featuring incremental subsecond multiple linear regression. The advantages of the system are the simplification of event timing and efficient and unified data formatting. Substantial parametric analysis can be performed and displayed in real‐time. Immediate (replay) and delayed off‐line analysis can also be performed with the same interface. The capabilities of the system are demonstrated in normal subjects using a polar visual angle phase mapping paradigm. The system provides a time‐accounting infrastructure that readily supports standard and innovative approaches to fMRI. Magn Reson Med 45:289–298, 2001.

Prediction of brain maturity in infants using machine-learning algorithms.

Recent resting-state functional MRI investigations have demonstrated that much of the large-scale functional network architecture supporting motor, sensory and cognitive functions in older pediatric and adult populations is present in term- and prematurely-born infants. Application of new analytical approaches can help translate the improved understanding of early functional connectivity provided through these studies into predictive models of neurodevelopmental outcome. One approach to achieving this goal is multivariate pattern analysis, a machine-learning, pattern classification approach well-suited for high-dimensional neuroimaging data. It has previously been adapted to predict brain maturity in children and adolescents using structural and resting state-functional MRI data. In this study, we evaluated resting state-functional MRI data from 50 preterm-born infants (born at 23-29weeks of gestation and without moderate-severe brain injury) scanned at term equivalent postmenstrual age compared with data from 50 term-born control infants studied within the first week of life. Using 214 regions of interest, binary support vector machines distinguished term from preterm infants with 84% accuracy (p<0.0001). Inter- and intra-hemispheric connections throughout the brain were important for group categorization, indicating that widespread changes in the brains functional network architecture associated with preterm birth are detectable by term equivalent age. Support vector regression enabled quantitative estimation of birth gestational age in single subjects using only term equivalent resting state-functional MRI data, indicating that the present approach is sensitive to the degree of disruption of brain development associated with preterm birth (using gestational age as a surrogate for the extent of disruption). This suggests that support vector regression may provide a means for predicting neurodevelopmental outcome in individual infants.