Isance regressors (consisting of the parameters from the motion correction, BAY1217389MedChemExpress BAY1217389 linear and quadratic drift, and global signal mean) were regressed out of the seed time course. White matter and CSF signals were not included in the nuisance regressors, as their impact on results from group analysis has been shown to be minimal [37]. The seed time course was band-pass filtered with the pass band between 0.009 and 0.08 Hz, using qhw.v5i4.5120 only the retained frames, as the salient information in RS-fMRI is contained within this frequency band [39?3]. The seed time course was regressed on all voxels in the brain, using the nuisance regressors described above as covariates of no interest. The T-scores were retained for second level analysis. For second-level analysis, each participant was only included if there were at least 40 retained frames from each dataset (eyes open and eyes closed) and at least 100 total frames retained (corresponding to 5 minutes or more acquisition time). This resulted in retaining data from 25 (11 full-term, 14 preterm) out of the 38 participants while discarding data from the other 13 of the participants. A GLM was used with preterm status the variable of interest; and sex, age, eyes open status, square root of number of frames from the eyes open run, and square root of number of frames from the eyes closed run as covariates of no interest. A clustered wild bootstrap (1000 repetitions, Rademacher distribution) was used in order to appropriately handle the different sources of variance (e.g., within-participant, within-family, and between-family). For each bootstrap replication, the residual multipliers were fnins.2015.00094 kept the same for each voxel in order to accurately account for the effects of spatial autocorrelation. Tscores were determined as the mean over the bootstrap repetitions divided by the standard deviation. T-scores were then converted into Z-scores. A Monte-Carlo simulation [36], which estimates intrinsic spatial autocorrelation via “noise” maps obtained from the fit residuals, was used to determine family-wise-error (FWE) statistical significance. FWE corrected p < 0.05 was determined to be at Z > 3.25, spatial filtering at = 4 mm, spatial extent threshold at 100 voxels (= 2700 mm3).PLOS ONE | DOI:10.1371/journal.pone.0130686 June 22,6 /Altered Brain Connectivity in Late Preterm ChildrenResults Patient CharacteristicsWe report results from a community-based cohort of low-to-moderate socioeconomic status preadolescent (ages 9?3) twin pairs born late preterm or at full term. This study is part of an international collaborative longitudinal research program to investigate genetic and environmental influences on prematurity, long-term neurocognitive functioning and general health outcomes. The preadolescents in this study were recruited from a city in Northeast Brazil (Montes Claros, pop. ca. 410,000). This region not only has a high incidence of natural multiple gestation pregnancies but also a high rate of late preterm birth coupled with limited healthcare resources [29]. As a result, none of the preadolescents had ever been hospitalized as infants in a neonatal intensive care unit. FCCP site Therefore, this cohort provides a unique opportunity to analyze the longitudinal impact of prematurity-related stress during the late third trimester of brain development on the organization of developing neural systems. Late preterm birth was defined as gestational age between 34 and 36 weeks. The term controls group was defined as gestational age.Isance regressors (consisting of the parameters from the motion correction, linear and quadratic drift, and global signal mean) were regressed out of the seed time course. White matter and CSF signals were not included in the nuisance regressors, as their impact on results from group analysis has been shown to be minimal [37]. The seed time course was band-pass filtered with the pass band between 0.009 and 0.08 Hz, using qhw.v5i4.5120 only the retained frames, as the salient information in RS-fMRI is contained within this frequency band [39?3]. The seed time course was regressed on all voxels in the brain, using the nuisance regressors described above as covariates of no interest. The T-scores were retained for second level analysis. For second-level analysis, each participant was only included if there were at least 40 retained frames from each dataset (eyes open and eyes closed) and at least 100 total frames retained (corresponding to 5 minutes or more acquisition time). This resulted in retaining data from 25 (11 full-term, 14 preterm) out of the 38 participants while discarding data from the other 13 of the participants. A GLM was used with preterm status the variable of interest; and sex, age, eyes open status, square root of number of frames from the eyes open run, and square root of number of frames from the eyes closed run as covariates of no interest. A clustered wild bootstrap (1000 repetitions, Rademacher distribution) was used in order to appropriately handle the different sources of variance (e.g., within-participant, within-family, and between-family). For each bootstrap replication, the residual multipliers were fnins.2015.00094 kept the same for each voxel in order to accurately account for the effects of spatial autocorrelation. Tscores were determined as the mean over the bootstrap repetitions divided by the standard deviation. T-scores were then converted into Z-scores. A Monte-Carlo simulation [36], which estimates intrinsic spatial autocorrelation via “noise” maps obtained from the fit residuals, was used to determine family-wise-error (FWE) statistical significance. FWE corrected p < 0.05 was determined to be at Z > 3.25, spatial filtering at = 4 mm, spatial extent threshold at 100 voxels (= 2700 mm3).PLOS ONE | DOI:10.1371/journal.pone.0130686 June 22,6 /Altered Brain Connectivity in Late Preterm ChildrenResults Patient CharacteristicsWe report results from a community-based cohort of low-to-moderate socioeconomic status preadolescent (ages 9?3) twin pairs born late preterm or at full term. This study is part of an international collaborative longitudinal research program to investigate genetic and environmental influences on prematurity, long-term neurocognitive functioning and general health outcomes. The preadolescents in this study were recruited from a city in Northeast Brazil (Montes Claros, pop. ca. 410,000). This region not only has a high incidence of natural multiple gestation pregnancies but also a high rate of late preterm birth coupled with limited healthcare resources [29]. As a result, none of the preadolescents had ever been hospitalized as infants in a neonatal intensive care unit. Therefore, this cohort provides a unique opportunity to analyze the longitudinal impact of prematurity-related stress during the late third trimester of brain development on the organization of developing neural systems. Late preterm birth was defined as gestational age between 34 and 36 weeks. The term controls group was defined as gestational age.
