Maternal treatment with selective serotonin reuptake inhibitors during pregnancy and delayed neonatal adaptation: a population-based cohort study.

OBJECTIVE: Selective serotonin reuptake inhibitor (SSRI) use is common in pregnancy. It is associated with delayed neonatal adaptation. Most previous studies have not adjusted for the severity of maternal mental health disorders or examined the impact of SSRI type and dosage. We examined whether treatment with SSRIs in late pregnancy (after 20 weeks) is associated with delayed neonatal adaptation independent of maternal depression and anxiety. DESIGN, SETTING AND PATIENTS: Retrospective population-based birth cohort of 280 090 term infants born at 15 Kaiser Permanente Northern California hospitals, 2011-2019. Individual-level pharmacy, maternal, pregnancy and neonatal data were obtained from electronic medical records. EXPOSURE: Dispensed maternal SSRI prescription after 20 weeks of pregnancy. MAIN OUTCOME MEASURES: Delayed neonatal adaptation defined as a 5 min Apgar score ≤5, resuscitation at birth or admission to a neonatal intensive care unit for respiratory support. Secondary outcomes included each individual component of the primary outcome and more severe neonatal outcomes (pulmonary hypertension, hypoxic-ischaemic encephalopathy and seizures). RESULTS: 7573 (2.7%) infants were exposed to SSRIs in late pregnancy. Delayed neonatal adaptation occurred in 11.2% of exposed vs 4.4% of unexposed infants (relative risk 2.52 (95% CI 2.36 to 2.70)). After multivariable adjustment, there was an association between SSRI exposure and delayed neonatal adaptation (adjusted OR 2.14 (95% CI 1.96 to 2.32)). This association was dose dependent. Escitalopram and fluoxetine were associated with the highest risk of delayed neonatal adaptation. CONCLUSIONS: Infants exposed to SSRIs have increased risks of delayed adaptation in a type and dose-dependent relationship, pointing toward a causal relationship.

Germline biomarkers predict toxicity to anti-PD1/PDL1 checkpoint therapy.

BACKGROUND: There is great interest in finding ways to identify patients who will develop toxicity to cancer therapies. This has become especially pressing in the era of immune therapy, where toxicity can be long-lasting and life-altering, and primarily comes in the form of immune-related adverse effects (irAEs). Treatment with the first drugs in this class, anti-programmed death 1 (anti-PD1)/programmed death-ligand 1 (PDL1) checkpoint therapies, results in grade 2 or higher irAEs in up to 25%-30% of patients, which occur most commonly within the first 6 months of treatment and can include arthralgias, rash, pruritus, pneumonitis, diarrhea and/or colitis, hepatitis, and endocrinopathies. We tested the hypothesis that germline microRNA pathway functional variants, known to predict altered systemic stress responses to cancer therapies, would predict irAEs in patients across cancer types. METHODS: MicroRNA pathway variants were evaluated for an association with grade 2 or higher toxicity using four classifiers on 62 patients with melanoma, and then the panel's performance was validated on 99 patients with other cancer types. Trained classifiers included classification trees, LASSO-regularized logistic regression, boosted trees, and random forests. Final performance measures were reported on the training set using leave-one-out cross validation and validated on held-out samples. The predicted probability of toxicity was evaluated for its association, if any, with response categories to anti-PD1/PDL1 therapy in the melanoma cohort. RESULTS: A biomarker panel was identified that predicts toxicity with 80% accuracy (F1=0.76, area under the curve (AUC)=0.82) in the melanoma training cohort and 77.6% accuracy (F1=0.621, AUC=0.778) in the pan-cancer validation cohort. In the melanoma cohort, the predictive probability of toxicity was not associated with response categories to anti-PD1/PDL1 therapy (p=0.70). In the same cohort, the most significant biomarker of toxicity in RAC1, predicting a greater than ninefold increased risk of toxicity (p<0.001), was also not associated with response to anti-PD1/PDL1 therapy (p=0.151). CONCLUSIONS: A germline microRNA-based biomarker signature predicts grade 2 and higher irAEs to anti-PD1/PDL1 therapy, regardless of tumor type, in a pan-cancer manner. These findings represent an important step toward personalizing checkpoint therapy, the use of which is growing rapidly.

Biomechanical changes in the lumbar spine following spaceflight and factors associated with postspaceflight disc herniation.

BACKGROUND CONTEXT: For chronic low back pain, the causal mechanisms between pathological features from imaging and patient symptoms are unclear. For instance, disc herniations can often be present without symptoms. There remains a need for improved knowledge of the pathophysiological mechanisms that explore spinal tissue damage and clinical manifestations of pain and disability. Spaceflight and astronaut health provides a rare opportunity to study potential low back pain mechanisms longitudinally. Spaceflight disrupts diurnal loading on the spine and several lines of evidence indicate that astronauts are at a heightened risk for low back pain and disc herniation following spaceflight. PURPOSE: To examine the relationship between prolonged exposure to microgravity and the elevated incidence of postflight disc herniation, we conducted a longitudinal study to track the spinal health of twelve NASA astronauts before and after approximately 6 months in space. We hypothesize that the incidence of postflight disc herniation and low back complaints associates with spaceflight-included muscle atrophy and pre-existing spinal pathology. STUDY DESIGN: This is a prospective longitudinal study. PATIENT SAMPLE: Our sample included a cohort of twelve astronaut crewmembers. OUTCOME MEASURES: From 3T MRI, we quantified disc water content (ms), disc degeneration (Pfirrmann grade), vertebral endplate irregularities, facet arthropathy and/ fluid, high intensity zones, disc herniation, multifidus total cross-sectional area (cm2), multifidus lean muscle cross-sectional area (cm2), and muscle quality/composition (%). From quantitative fluoroscopy we quantified, maximum flexion-extension ROM (°), maximum lateral bending ROM (°), and maximum translation (%). Lastly, patient outcomes and clinical notes were used for identifying postflight symptoms associated with disc herniations from 3T MRI. METHODS: Advanced imaging data from 3T MRI were collected at three separate time points in relation to spending six months in space: (1) within a year before launch ("pre-flight"), (2) within a week after return to Earth ("post-flight"), and (3) between 1 and 2 months after return to Earth ("recovery"). Fluoroscopy of segmental kinematics was collected at preflight and postflight timepoints. We assessed the effect of spaceflight and postflight recovery on longitudinal changes in spinal structure and function, as well as differences between crew members who did and did not present a symptomatic disc herniation following spaceflight. RESULTS: Half of our astronauts (n=6) experienced new symptoms associated with a new or previously asymptomatic lumbar disc protrusion or extrusion following spaceflight. We observed decreased multifidus muscle quality following spaceflight in the lower lumbar spine, with a reduced percentage of lean muscle at L4L5 (-6.2%, p=.009) and L5S1 (-7.0%, p=.006) associated with the incidence of new disc herniation. Additionally, we observed reduced lumbar segment flexion-extension ROM for L2L3 (-17.2%, p=.006) and L3L4 (-20.5%, p=.02) following spaceflight, and furthermore that reduced ROM among the upper three lumbar segments (-24.1%, p=.01) associated with the incidence of disc herniation. Existing endplate pathology was most prevalent in the upper lumbar spine and associated with reduced segmental ROM (-20.5%, p=.02). CONCLUSIONS: In conclusion from a 10-year study investigating the effects of spaceflight on the lumbar spine and risk for disc herniation, we found the incidence of lumbar disc herniation following spaceflight associates with compromised multifidus muscle quality and spinal segment kinematics, as well as pre-existing spinal endplate irregularities. These findings suggest differential effects of spinal stiffness and muscle loss in the upper versus lower lumbar spine regions that may specifically provoke risk for symptomatic disc herniation in the lower lumbar spine following spaceflight. Results from this study provide a unique longitudinal assessment of mechanisms and possible risk factors for developing disc herniations and related low back pain. Furthermore, these findings will help inform physiologic countermeasures to maintain spinal health in astronauts during long-duration missions in space.

Cortically constrained shape recognition: Automated white matter tract segmentation validated in the pediatric brain.

BACKGROUND AND PURPOSE: Manual segmentation of white matter (WM) bundles requires extensive training and is prohibitively labor-intensive for large-scale studies. Automated segmentation methods are necessary in order to eliminate operator dependency and to enable reproducible studies. Significant changes in the WM landscape throughout childhood require flexible methods to capture the variance across the span of brain development. METHODS: Here, we describe a novel automated segmentation tool called Cortically Constrained Shape Recognition (CCSR), which combines two complementary approaches: (1) anatomical connectivity priors based on FreeSurfer-derived regions of interest and (2) shape priors based on 3-dimensional streamline bundle atlases applied using RecoBundles. We tested the performance and repeatability of this approach by comparing volume and diffusion metrics of the main language WM tracts that were both manually and automatically segmented in a pediatric cohort acquired at the UCSF Dyslexia Center (n = 59; 25 females; average age: 11 ± 2; range: 7-14). RESULTS: The CCSR approach showed high agreement with the expert manual segmentations: across all tracts, the spatial overlap between tract volumes showed an average Dice Similarity Coefficient (DSC) of 0.76, and the fractional anisotropy (FA) on average had a Lin's Concordance Correlation Coefficient (CCC) of 0.81. The CCSR's repeatability in a subset of this cohort achieved a DSC of 0.92 on average across all tracts. CONCLUSION: This novel automated segmentation approach is a promising tool for reproducible large-scale tractography analyses in pediatric populations and particularly for the quantitative assessment of structural connections underlying various clinical presentations in neurodevelopmental disorders.

Properties of beta oscillations in Dup15q syndrome.

BACKGROUND: Duplications of 15q11.2-q13.1 (Dup15q syndrome) are highly penetrant for autism, intellectual disability, hypotonia, and epilepsy. The 15q region harbors genes critical for brain development, particularly UBE3A and a cluster of gamma-aminobutyric acid type A receptor (GABAAR) genes. We recently described an electrophysiological biomarker of the syndrome, characterized by excessive beta oscillations (12-30 Hz), resembling electroencephalogram (EEG) changes induced by allosteric modulation of GABAARs. In this follow-up study, we tested a larger cohort of children with Dup15q syndrome to comprehensively examine properties of this EEG biomarker that would inform its use in future clinical trials, specifically, its (1) relation to basic clinical features, such as age, duplication type, and epilepsy; (2) relation to behavioral characteristics, such as cognition and adaptive function; (3) stability over time; and (4) reproducibility of the signal in clinical EEG recordings. METHODS: We computed EEG power and beta peak frequency (BPF) in a cohort of children with Dup15q syndrome (N = 41, age range 9-189 months). To relate EEG parameters to clinical (study 1) and behavioral features (study 2), we examined age, duplication type, epilepsy, cognition, and daily living skills (DLS) as predictors of beta power and BPF. To evaluate stability over time (study 3), we derived the intraclass correlation coefficients (ICC) from beta power and BPF computed from children with multiple EEG recordings (N = 10, age range 18-161 months). To evaluate reproducibility in a clinical setting (study 4), we derived ICCs from beta power computed from children (N = 8, age range 19-96 months), who had undergone both research EEG and clinical EEG. RESULTS: The most promising relationships between EEG and clinical traits were found using BPF. BPF was predicted both by epilepsy status (R2 = 0.11, p = 0.038) and the DLS component of the Vineland Adaptive Behavior Scale (R2 = 0.17, p = 0.01). Beta power and peak frequency showed high stability across repeated visits (beta power ICC = 0.93, BPF ICC = 0.92). A reproducibility analysis revealed that beta power estimates are comparable between research and clinical EEG (ICC = 0.94). CONCLUSIONS: In this era of precision health, with pharmacological and neuromodulatory therapies being developed and tested for specific genetic etiologies of neurodevelopmental disorders, quantification and examination of mechanistic biomarkers can greatly improve clinical trials. To this end, the robust beta oscillations evident in Dup15q syndrome are clinically reproducible and stable over time. With future preclinical and computational studies that will help disentangle the underlying mechanism, it is possible that this biomarker could serve as a robust measure of drug target engagement or a proximal outcome measure in future disease modifying intervention trials.

Covariate-adjusted region-referenced generalized functional linear model for EEG data.

Electroencephalography (EEG) studies produce region-referenced functional data in the form of EEG signals recorded across electrodes on the scalp. It is of clinical interest to relate the highly structured EEG data to scalar outcomes such as diagnostic status. In our motivating study, resting-state EEG is collected on both typically developing (TD) children and children with autism spectrum disorder (ASD) aged 2 to 12 years old. The peak alpha frequency (PAF), defined as the location of a prominent peak in the alpha frequency band of the spectral density, is an important biomarker linked to neurodevelopment and is known to shift from lower to higher frequencies as children age. To retain the most amount of information from the data, we consider the oscillations in the spectral density within the alpha band, rather than just the peak location, as a functional predictor of diagnostic status (TD vs ASD), adjusted for chronological age. A covariate-adjusted region-referenced generalized functional linear model is proposed for modeling scalar outcomes from region-referenced functional predictors, which utilizes a tensor basis formed from one-dimensional discrete and continuous bases to estimate functional effects across a discrete regional domain while simultaneously adjusting for additional nonfunctional covariates, such as age. The proposed methodology provides novel insights into differences in neural development of TD and ASD children. The efficacy of the proposed methodology is investigated through extensive simulation studies.