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.

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.

Assessment of agreement between manual and automated processing of liver MR elastography for shear stiffness estimation in children and young adults with autoimmune liver disease.

PURPOSE: To compare automated versus standard of care manual processing of 2D gradient recalled echo (GRE) liver MR Elastography (MRE) in children and young adults. MATERIALS AND METHODS: 2D GRE liver MRE data from research liver MRI examinations performed as part of an autoimmune liver disease registry between March 2017 and March 2020 were analyzed retrospectively. All liver MRE data were acquired at 1.5 T with 60 Hz mechanical vibration frequency. For manual processing, two independent readers (R1, R2) traced regions of interest on scanner generated shear stiffness maps. Automated processing was performed using MREplus+ (Resoundant Inc.) using 90% (A90) and 95% (A95) confidence masks. Agreement was evaluated using intra-class correlation coefficients (ICC) and Bland-Altman analyses. Classification performance was evaluated using receiver operating characteristic curve (ROC) analyses. RESULTS: In 65 patients with mean age of 15.5 ± 3.8 years (range 8-23 years; 35 males) median liver shear stiffness was 2.99 kPa (mean 3.55 ± 1.69 kPa). Inter-reader agreement for manual processing was very strong (ICC = 0.99, mean bias = 0.01 kPa [95% limits of agreement (LoA): - 0.41 to 0.44 kPa]). Correlation between manual and A95 automated processing was very strong (R1: ICC = 0.988, mean bias = 0.13 kPa [95% LoA: - 0.40 to 0.68 kPa]; R2: ICC = 0.987, mean bias = 0.13 kPa [95% LoA: - 0.44 to 0.69 kPa]). Automated measurements were perfectly replicable (ICC = 1.0; mean bias = 0 kPa). CONCLUSION: Liver shear stiffness values obtained using automated processing showed excellent agreement with manual processing. Automated processing of liver MRE was perfectly replicable.

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.

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.