RS-FetMRI: a MATLAB-SPM Based Tool for Pre-processing Fetal Resting-State fMRI Data.

Resting-state functional magnetic resonance imaging (rs-fMRI) most recently has proved to open a measureless window on functional neurodevelopment in utero. Fetal brain activation and connectivity maps can be heavily influenced by 1) fetal-specific motion effects on the time-series and 2) the accuracy of time-series spatial normalization to a standardized gestational-week (GW) specific fetal template space.Due to the absence of a standardized and generalizable image processing protocol, the objective of the present work was to implement a validated fetal rs-fMRI preprocessing pipeline (RS-FetMRI) divided into 6 inter-dependent preprocessing modules (i.e., M1 to M6) and designed to work entirely as an extension for Statistical Parametric Mapping (SPM).RS-FetMRI pipeline output analyses on rs-fMRI time-series sampled from a cohort of fetuses acquired on both 1.5 T and 3 T MRI scanning systems showed increased efficacy of estimation of the degree of movement coupled with an efficient motion censoring procedure, resulting in increased number of motion-uncorrupted volumes and temporal continuity in fetal rs-fMRI time-series data. Moreover, a "structural-free" SPM-based spatial normalization procedure granted a high degree of spatial overlap with high reproducibility and a significant improvement in whole-brain and parcellation-specific Temporal Signal-to-Noise Ratio (TSNR) mirrored by functional connectivity analysis.To our knowledge, the RS-FetMRI pipeline is the first semi-automatic and easy-to-use standardized fetal rs-fMRI preprocessing pipeline completely integrated in MATLAB-SPM able to remove entry barriers for new research groups into the field of fetal rs-fMRI, for both research or clinical purposes, and ultimately to make future fetal brain connectivity investigations more suitable for comparison and cross-validation.

A hierarchical procedure to select intrauterine and extrauterine factors for methodological validation of preterm birth risk estimation.

BACKGROUND: Etiopathogenesis of preterm birth (PTB) is multifactorial, with a universe of risk factors interplaying between the mother and the environment. It is of utmost importance to identify the most informative factors in order to estimate the degree of PTB risk and trace an individualized profile. The aims of the present study were: 1) to identify all acknowledged risk factors for PTB and to select the most informative ones for defining an accurate model of risk prediction; 2) to verify predictive accuracy of the model and 3) to identify group profiles according to the degree of PTB risk based on the most informative factors. METHODS: The Maternal Frailty Inventory (MaFra) was created based on a systematic review of the literature including 174 identified intrauterine (IU) and extrauterine (EU) factors. A sample of 111 pregnant women previously categorized in low or high risk for PTB below 37 weeks, according to ACOG guidelines, underwent the MaFra Inventory. First, univariate logistic regression enabled p-value ordering and the Akaike Information Criterion (AIC) selected the model including the most informative MaFra factors. Second, random forest classifier verified the overall predictive accuracy of the model. Third, fuzzy c-means clustering assigned group membership based on the most informative MaFra factors. RESULTS: The most informative and parsimonious model selected through AIC included Placenta Previa, Pregnancy Induced Hypertension, Antibiotics, Cervix Length, Physical Exercise, Fetal Growth, Maternal Anxiety, Preeclampsia, Antihypertensives. The random forest classifier including only the most informative IU and EU factors achieved an overall accuracy of 81.08% and an AUC of 0.8122. The cluster analysis identified three groups of typical pregnant women, profiled on the basis of the most informative IU and EU risk factors from a lower to a higher degree of PTB risk, which paralleled time of birth delivery. CONCLUSIONS: This study establishes a generalized methodology for building-up an evidence-based holistic risk assessment for PTB to be used in clinical practice. Relevant and essential factors were selected and were able to provide an accurate estimation of degree of PTB risk based on the most informative constellation of IU and EU factors.

Myosin light chain 3f attenuates age-induced decline in contractile velocity in MHC type II single muscle fibers.

Aging is characterized by a progressive loss of muscle mass and impaired contractility (e.g., decline in force, velocity, and power). Although the slowing of contraction speed in aging muscle is well described, the underlying molecular mechanisms responsible for the decrement in speed are unknown. Myosin heavy chain (MHC) isoforms are the primary molecules determining contractile velocity; however, the contraction speed of single fibers within a given MHC isoform type is variable. Recent evidence proposes that the decline in shortening velocity (Vo) with aging is associated with a decrease in the relative content of essential myosin light chain 3f (MLC(3f) ) isoform. In the current study, we first evaluated the relative content of MLC(3f) isoform and Vo in adult and old rats. We then used recombinant adenovirus (rAd) gene transfer technology to increase MLC(3f) protein content in the MHC type II semimembranosus muscle (SM). We hypothesized that (i) aging would decrease the relative MLC(3f) content and Vo in type II fibers, and (ii) increasing the MLC(3f) content would restore the age-induced decline in Vo. We found that there was an age-related decrement in relative MLC(3f) content and Vo in MHC type II fibers. Increasing MLC(3f) content, as indicated by greater % MLC(3f) and MLC(3f) /MLC(2f) ratio, provided significant protection against age-induced decline in Vo without influencing fiber diameter, force generation, MHC isoform distribution, or causing cellular damage. To the best of our knowledge, these are the first data to demonstrate positive effects of MLC(3f) against slowing of contractile function in aged skeletal muscle.