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T1- and T2-weighted (T1w and T2w) images are essential for tissue classification and anatomical localization in Magnetic Resonance Imaging (MRI) analyses. However, these anatomical data can be challenging to acquire in non-sedated neonatal cohorts, which are prone to high amplitude movement and display lower tissue contrast than adults. As a result, one of these modalities may be missing or of such poor quality that they cannot be used for accurate image processing, resulting in subject loss. While recent literature attempts to overcome these issues in adult populations using synthetic imaging approaches, evaluation of the efficacy of these methods in pediatric populations and the impact of these techniques in conventional MR analyses has not been performed. In this work, we present two novel methods to generate pseudo-T2w images: the first is based in deep learning and expands upon previous models to 3D imaging without the requirement of paired data, the second is based in nonlinear multi-atlas registration providing a computationally lightweight alternative. We demonstrate the anatomical accuracy of pseudo-T2w images and their efficacy in existing MR processing pipelines in two independent neonatal cohorts. Critically, we show that implementing these pseudo-T2w methods in resting-state functional MRI analyses produces virtually identical functional connectivity results when compared to those resulting from T2w images, confirming their utility in infant MRI studies for salvaging otherwise lost subject data., Competing Interests: Declaration of Competing Interest The authors do not report any competing interests., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

The importance of motion correction when processing resting state functional magnetic resonance imaging (rs-fMRI) data is well-established in adult cohorts. This includes adjustments based on self-limited, large amplitude subject head motion, as well as factitious rhythmic motion induced by respiration. In adults, such respiration artifact can be effectively removed by applying a notch filter to the motion trace, resulting in higher amounts of data retained after frame censoring (e.g., "scrubbing") and more reliable correlation values. Due to the unique physiological and behavioral characteristics of infants and toddlers, rs-fMRI processing pipelines, including methods to identify and remove colored noise due to subject motion, must be appropriately modified to accurately reflect true neuronal signal. These younger cohorts are characterized by higher respiration rates and lower-amplitude head movements than adults; thus, the presence and significance of comparable respiratory artifact and the subsequent necessity of applying similar techniques remain unknown. Herein, we identify and characterize the consistent presence of respiratory artifact in rs-fMRI data collected during natural sleep in infants and toddlers across two independent cohorts (aged 8-24 months) analyzed using different pipelines. We further demonstrate how removing this artifact using an age-specific notch filter allows for both improved data quality and data retention in measured results. Importantly, this work reveals the critical need to identify and address respiratory-driven head motion in fMRI data acquired in young populations through the use of age-specific motion filters as a mechanism to optimize the accuracy of measured results in this population., Competing Interests: Declaration of Competing Interest Oregon Health and Sciences University and Eric Earl have a financial interest in Nous Imaging, Inc., a company that may have a commercial interest in the results of this research and technology. Additionally, Dr. Damien Fair is co-founder and equity holder of Nous Imaging Inc., which has licensed FIRMM Software. These interests have been reviewed and managed by the University of Minnesota and Oregon Health and Sciences University in accordance with their Conflict of Interest policies., (Copyright © 2021. Published by Elsevier Inc.)

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 brain's 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., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Mo1, LFA-1, and p150,95 are structurally related glycoproteins of the CD11/CD18 complex that are expressed on the membrane of human leukocytes. In the neutrophil, the surface expression of the CD11/CD18 complex is up-modulated (Mo1 greater than p150,95 much greater than LFA-1) by stimulatory factors that include calcium ionophore A23187, phorbol myristate acetate (PMA), and N-L-formyl-L-leucyl-L-phenylalanine (fMLP). Here, in an immunofluorescence analysis, we have examined CD11/CD18 glycoprotein expression by human monocytes, pulmonary alveolar macrophages (PAM, obtained by bronchoalveolar lavage), and breast milk macrophages (BMM) as compared to neutrophils before and after exposure to A23187 (1 microM), fMLP (0.1 microM), or PMA (0.1 microgram/ml) for 15 min at 37 degrees C. Unstimulated monocytes within unfractionated blood mononuclear cells kept at 4 degrees C (n = 13) expressed all three CD11/CD18 glycoproteins, and exposure to A23187 resulted in significant increases in the surface expression of Mo1 (median of 5.7-fold), LFA-1 (median of 2.1-fold), and p150,95 (median of 7.2-fold). Exposure to fMLP- or PMA-stimulated increases of lesser magnitude. CD11/CD18 expression by PAM (n = 9) was barely detectable and was unaffected by exposure to A23187. In contrast, BMM (n = 11) expressed all three CD11/CD18 glycoproteins (with considerable variability among specimens), but no increase was stimulated by A23187. These results demonstrate that monocytes, like neutrophils, have the capacity to respond to activating factors with an increase in CD11/CD18 glycoprotein expression; macrophage differentiation is accompanied by a loss (PAM) or retention (BMM) of CD11/CD18 expression that is unmodulated in response to activation.