Diffusion Tensor Imaging to Predict Neurodevelopmental Impairment in Infants after Hypoxic-Ischemic Injury

OBJECTIVE

 Magnetic resonance imaging (MRI) is the standard of care for evaluation of brain injury after hypoxic-ischemic encephalopathy (HIE) in term newborns. This study utilizes diffusion tensor imaging (DTI) to (1) identify infants at highest risk of development of cerebral palsy (CP) following HIE and to (2) identify regions of the brain critical to normal fidgety general movements (GMs) at 3 to 4 months of postterm. Absence of these normal, physiological movements is highly predictive of CP.

STUDY DESIGN

 Term infants treated with hypothermia for HIE from January 2017 to December 2021 were consented for participation and had brain MRI with DTI after rewarming. The Prechtl's General Movements Assessment was performed at 12 to 16 weeks of age. Structural MRIs were reviewed for abnormalities, and DTI data were processed with the FMRIB Software Library. Infants underwent the Bayley Scales of Infant and Toddler Development III test at 24 months.

RESULTS

 Forty-five infant families were consented; three infants died prior to MRI and were excluded, and a fourth infant was excluded due to diagnosis of a neuromuscular disorder. Twenty-one infants were excluded due to major movement artifact on diffusion images. Ultimately, 17 infants with normal fidgety GMs were compared with 3 infants with absent fidgety GMs with similar maternal and infant characteristics. Infants with absent fidgety GMs had decreased fractional anisotropy of several important white matter tracts, including the posterior limb of the internal capsule, optic radiations, and corpus callosum (p < 0.05). All three infants with absent fidgety GMs and two with normal GMs went on to be diagnosed with CP.

CONCLUSION

 This study identifies white matter tracts of the brain critical to development of normal fidgety GMs in infants at 3 to 4 months of postterm using advanced MRI techniques. These findings identify those at highest risk for CP among infants with moderate/severe HIE prior to hospital discharge.

KEY POINTS

· HIE has devastating impacts on families and infants.. · Diffusion MRI identifies infants at highest risk for developing neurodevelopmental impairment.. · Normal general movements of infancy are generated by key white matter tracts..

A novel Ontology-guided Attribute Partitioning ensemble learning model for early prediction of cognitive deficits using quantitative Structural MRI in very preterm infants

Structural magnetic resonance imaging studies have shown that brain anatomical abnormalities are associated with cognitive deficits in preterm infants. Brain maturation and geometric features can be used with machine learning models for predicting later neurodevelopmental deficits. However, traditional machine learning models would suffer from a large feature-to-instance ratio (i.e., a large number of features but a small number of instances/samples). Ensemble learning is a paradigm that strategically generates and integrates a library of machine learning classifiers and has been successfully used on a wide variety of predictive modeling problems to boost model performance. Attribute (i.e., feature) bagging method is the most commonly used feature partitioning scheme, which randomly and repeatedly draws feature subsets from the entire feature set. Although attribute bagging method can effectively reduce feature dimensionality to handle the large feature-to-instance ratio, it lacks consideration of domain knowledge and latent relationship among features. In this study, we proposed a novel Ontology-guided Attribute Partitioning (OAP) method to better draw feature subsets by considering the domain-specific relationship among features. With the better-partitioned feature subsets, we developed an ensemble learning framework, which is referred to as OAP-Ensemble Learning (OAP-EL). We applied the OAP-EL to predict cognitive deficits at 2 years of age using quantitative brain maturation and geometric features obtained at term equivalent age in very preterm infants. We demonstrated that the proposed OAP-EL approach significantly outperformed the peer ensemble learning and traditional machine learning approaches.

Prenatal opioid exposure is associated with smaller brain volumes in multiple regions

BACKGROUND

The impact of prenatal opioid exposure on brain development remains poorly understood.

METHODS

We conducted a prospective study of term-born infants with and without prenatal opioid exposure. Structural brain MRI was performed between 40 and 48 weeks postmenstrual age. T2-weighted images were processed using the Developing Human Connectome Project structural pipeline. We compared 63 relative regional brain volumes between groups.

RESULTS

Twenty-nine infants with prenatal opioid exposure and 42 unexposed controls were included. The groups had similar demographics, except exposed infants had lower birth weights, more maternal smoking and maternal Hepatitis C, fewer mothers with a college degree, and were more likely non-Hispanic White. After controlling for sex, postmenstrual age at scan, birth weight, and maternal education, exposed infants had significantly smaller relative volumes of the deep gray matter, bilateral thalamic ventrolateral nuclei, bilateral insular white matter, bilateral subthalamic nuclei, brainstem, and cerebrospinal fluid. Exposed infants had larger relative volumes of the right cingulate gyrus white matter and left occipital lobe white matter.

CONCLUSIONS

Infants with prenatal opioid exposure had smaller brain volumes in multiple regions compared to controls, with two regions larger in the opioid-exposed group. Further research should focus on the relative contributions of maternal opioids and other exposures.

IMPACT

Prenatal opioid exposure is associated with developmental and behavioral consequences, but the direct effects of opioids on the developing human brain are poorly understood. Prior small studies using MRI have shown smaller regional brain volumes in opioid-exposed infants and children. After controlling for covariates, infants with prenatal opioid exposure scanned at 40-48 weeks postmenstrual age had smaller brain volumes in multiple regions compared to controls, with two regions larger in the opioid-exposed group. This adds to the literature showing potential impact of prenatal opioid exposure on the developing brain.

Effects of prenatal opioid exposure on functional networks in infancy

Prenatal opioid exposure has been linked to altered neurodevelopment and visual problems such as strabismus and nystagmus. The neural substrate underlying these alterations is unclear. Resting-state functional connectivity MRI (rsfMRI) is an advanced and well-established technique to evaluate brain networks. Few studies have examined the effects of prenatal opioid exposure on resting-state network connectivity in infancy. In this pilot study, we characterized network connectivity in opioid-exposed infants (n = 19) and controls (n = 20) between 4–8 weeks of age using both a whole-brain connectomic approach and a seed-based approach. Prenatal opioid exposure was associated with differences in distribution of betweenness centrality and connection length, with positive connections unique to each group significantly longer than common connections. The unique connections in the opioid-exposed group were more often inter-network connections while unique connections in controls and connections common to both groups were more often intra-network. The opioid-exposed group had smaller network volumes particularly in the primary visual network, but similar network strength as controls. Network topologies as determined by dice similarity index were different between groups, particularly in visual and executive control networks. These results may provide insight into the neural basis for the developmental and visual problems associated with prenatal opioid exposure.

Diffusion MRI Microstructural Abnormalities at Term-Equivalent Age Are Associated with Neurodevelopmental Outcomes at 3 Years of Age in Very Preterm Infants

BACKGROUND AND PURPOSE

Microstructural white matter abnormalities on DTI using Tract-Based Spatial Statistics at term-equivalent age are associated with cognitive and motor outcomes at 2 years of age or younger. However, neurodevelopmental tests administered at such early time points are insufficiently predictive of mild-moderate motor and cognitive impairment at school age. Our objective was to evaluate the microstructural antecedents of cognitive and motor outcomes at 3 years' corrected age in a cohort of very preterm infants.

MATERIALS AND METHODS

We prospectively recruited 101 very preterm infants (<32 weeks' gestational age) and performed DTI at term-equivalent age. The Differential Ability Scales, 2nd ed, Verbal and Nonverbal subtests, and the Bayley Scales of Infant and Toddler Development, 3rd ed, Motor subtest, were administered at 3 years of age. We correlated DTI metrics from Tract-Based Spatial Statistics with the Bayley Scales of Infant and Toddler Development, 3rd ed, and the Differential Ability Scales, 2nd ed, scores with correction for multiple comparisons.

RESULTS

Of the 101 subjects, 84 had high-quality DTI data, and of these, 69 returned for developmental testing (82%). Their mean (SD) gestational age was 28.4 (2.5) weeks, and birth weight was 1121.4 (394.1) g. DTI metrics were significantly associated with Nonverbal Ability in the corpus callosum, posterior thalamic radiations, fornix, and inferior longitudinal fasciculus and with Motor scores in the corpus callosum, internal and external capsules, posterior thalamic radiations, superior and inferior longitudinal fasciculi, cerebral peduncles, and corticospinal tracts.

CONCLUSIONS

We identified widespread microstructural white matter abnormalities in very preterm infants at term that were significantly associated with cognitive and motor development at 3 years' corrected age.

White Matter Injury and Structural Anomalies in Infants with Prenatal Opioid Exposure

Previous studies have not found structural injury or brain malformations in infants and children with prenatal opioid exposure. As part of an ongoing study evaluating neuroimaging in infants with prenatal opioid exposure, we reviewed structural brain MR imaging in 20 term infants with prenatal opioid exposure and 20 term controls at 4-8 weeks of age. We found that 8 of the 20 opioid-exposed infants had punctate white matter lesions or white matter signal abnormality on structural MR imaging, and 2 of the opioid-exposed infants had a septopreoptic fusion anomaly. No controls had white matter injury or structural malformations. Our findings underscore the importance of clinical neurodevelopmental follow-up and the need for more comprehensive imaging and long-term outcomes research following prenatal opioid exposure.

Big data sharing and analysis to advance research in post-traumatic epilepsy

We describe the infrastructure and functionality for a centralized preclinical and clinical data repository and analytic platform to support importing heterogeneous multi-modal data, automatically and manually linking data across modalities and sites, and searching content. We have developed and applied innovative image and electrophysiology processing methods to identify candidate biomarkers from MRI, EEG, and multi-modal data. Based on heterogeneous biomarkers, we present novel analytic tools designed to study epileptogenesis in animal model and human with the goal of tracking the probability of developing epilepsy over time.

Analytic Tools for Post-traumatic Epileptogenesis Biomarker Search in Multimodal Dataset of an Animal Model and Human Patients

Epilepsy is among the most common serious disabling disorders of the brain, and the global burden of epilepsy exerts a tremendous cost to society. Most people with epilepsy have acquired forms of the disorder, and the development of antiepileptogenic interventions could potentially prevent or cure epilepsy in many of them. However, the discovery of potential antiepileptogenic treatments and clinical validation would require a means to identify populations of patients at very high risk for epilepsy after a potential epileptogenic insult, to know when to treat and to document prevention or cure. A fundamental challenge in discovering biomarkers of epileptogenesis is that this process is likely multifactorial and crosses multiple modalities. Investigators must have access to a large number of high quality, well-curated data points and study subjects for biomarker signals to be detectable above the noise inherent in complex phenomena, such as epileptogenesis, traumatic brain injury (TBI), and conditions of data collection. Additionally, data generating and collecting sites are spread worldwide among different laboratories, clinical sites, heterogeneous data types, formats, and across multi-center preclinical trials. Before the data can even be analyzed, these data must be standardized. The Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) is a multi-center project with the overarching goal that epileptogenesis after TBI can be prevented with specific treatments. The identification of relevant biomarkers and performance of rigorous preclinical trials will permit the future design and performance of economically feasible full-scale clinical trials of antiepileptogenic therapies. We have been analyzing human data collected from UCLA and rat data collected from the University of Eastern Finland, both centers collecting data for EpiBioS4Rx, to identify biomarkers of epileptogenesis. Big data techniques and rigorous analysis are brought to longitudinal data collected from humans and an animal model of TBI, epilepsy, and their interaction. The prolonged continuous data streams of intracranial, cortical surface, and scalp EEG from humans and an animal model of epilepsy span months. By applying our innovative mathematical tools via supervised and unsupervised learning methods, we are able to subject a robust dataset to recently pioneered data analysis tools and visualize multivariable interactions with novel graphical methods.

Single-Molecule FRET States, Conformational Interchange, and Conformational Selection by Dye Labels in Calmodulin

We investigate the roles of measurement time scale and the nature of the fluorophores in the FRET states measured for calmodulin, a calcium signaling protein known to undergo pronounced conformational changes. The measured FRET distributions depend markedly on the measurement time scale (nanosecond or microsecond). Comparison of FRET distributions measured by donor fluorescence decay with FRET distributions recovered from single-molecule burst measurements binned over time scales of 90 μs to 1 ms reveals conformational averaging over the intervening time regimes. We find further that, particularly in the presence of saturating Ca(2+), the nature of the measured single-molecule FRET distribution depends markedly on the identity of the FRET pair. The results suggest interchange between conformational states on time scales of hundreds of microseconds or less. Interaction with a fluorophore such as the dye Texas Red alters both the nature of the measured FRET distributions and the dynamics of conformational interchange. The results further suggest that the fluorophore may not be merely a benign reporter of protein conformations in FRET studies, but may in fact alter the conformational landscape.