Skip to main content
NCBI home page
Human Brain Mapping logoLink to Human Brain Mapping
. 2018 Sep 12;39(12):4903–4912. doi: 10.1002/hbm.24332

Decreased BOLD fluctuations in lateral temporal cortices of premature born adults

Jing Shang 1,2, Josef G Bäuml 1,3, Nikolaos Koutsouleris 2, Marcel Daamen 4, Nicole Baumann 5, Claus Zimmer 3, Peter Bartmann 6, Henning Boecker 4, Dieter Wolke 5,7, Christian Sorg 1,3,8,✉
PMCID: PMC6866310  PMID: 30208256

Abstract

Lasting volume reductions in subcortical and temporal‐insular cortices after premature birth suggest altered ongoing activity in these areas. We hypothesized altered fluctuations in ongoing neural excitability and activity, as measured by slowly fluctuating blood oxygenation of resting‐state functional MRI (rs‐fMRI), in premature born adults, with altered fluctuations being linked with underlying brain volume reductions. To investigate this hypothesis, 94 very preterm/very low birth weight (VP/VLBW) and 92 full‐term born young adults underwent structural and rs‐fMRI data acquisition with voxel‐based morphometry and amplitude of low‐frequency fluctuations (ALFF) as main outcome measure. In VP/VLBW adults, ALFF was reduced in lateral temporal cortices, and this reduction was positively associated with lower birth weight. Regions of reduced ALFF overlapped with reduced brain volume. On the one hand, ALFF reduction remained after controlling for volume loss, supporting the functional nature of ALFF reductions. On the other hand, ALFF decreases were positively associated with underlying brain volume loss, indicating a relation between structural and functional changes. Furthermore, within the VP/VLBW group, reduced ALFF was associated with reduced IQ, indicating the behavioral relevance of ALFF decreases in temporal cortices. These results demonstrate long‐term impact of premature birth on ongoing BOLD fluctuations in lateral temporal cortices, which are linked with brain volume reductions. Data suggest permanently reduced fluctuations in ongoing neural excitability and activity in structurally altered lateral temporal cortices after premature birth.

Keywords: BOLD fluctuations, lateral temporal cortices, premature birth, resting‐state fMRI

1. INTRODUCTION

Premature birth, that is, preterm birth before 37 weeks of gestation and/or birth at low birth weight below 2,500 g, has a worldwide prevalence of more than 10% (Blencowe et al., 2012; Volpe, 2009). It is associated with an increased risk for birth complications and adverse long‐term outcomes including brain functionality (Volpe, 2009). Particularly, premature born individuals have a higher risk for long‐term neurocognitive impairments, psychiatric disorders, and lower socioeconomic status (SES) (D'Onofrio et al., 2013; Nosarti et al., 2012). Risk for adverse outcomes increases substantially for very premature born individuals, that is, born very preterm (VP; gestational age < 32 weeks) and/or with very low birth weight (VLBW; <1,500 g) (Nosarti et al., 2012; Saigal & Doyle, 2008). The increased risk for adverse neurocognitive outcomes results from brain maturation abnormalities induced by adverse perinatal events such as brain injury due to hypoxia‐ischemia, brain hemorrhage, infections, and other inflammatory processes as well as neonatal pain and stress (Volpe, 2009).

At the microscopic level, these processes primarily impair the development of premyelinating oligodendrocytes, GABA‐ergic interneurons, and subplate neurons, which play a fundamental role in the development of cortical microstructure, morphology, and connectivity (Back et al., 2002; Buser et al., 2012; Dean et al., 2013; Deng, 2010; Kinney et al., 2012; Salmaso, Jablonska, Scafidi, Vaccarino, & Gallo, 2014). For example, during gestational weeks 15–35, different populations of subplate neurons control ingrowing of thalamocortical, basal forebrain cholinergic, and cortico‐cortical afferents into cortical microcircuits (for review, see Hoerder‐Suabedissen & Molnar, 2015): these processes are often impaired in prematurity due to the vulnerability of subplate neurons to perinatal hypoxia (Volpe, 2009), resulting in reduced subplate neuron arborization and local microcircuit development (McClendon et al., 2017). At the macroscopic level, while impairments in white matter integrity are widespread (Ball et al., 2014; Ball et al., 2012; Eikenes, Lohaugen, Brubakk, Skranes, & Haberg, 2011; Meng et al., 2015; Skranes et al., 2007), gray matter volume reductions focus consistently on selected subcortical and cortical regions such as the thalamus, striatum, and medial and lateral temporal cortices (Ball et al., 2013; Grothe et al., 2017; Karolis et al., 2017; Meng et al., 2015; Nosarti et al., 2008; Pierson et al., 2007). Consistent gray matter changes, together with microstructural changes, suggest that premature birth might impact very basic local physiological brain processes such as ongoing brain activity. Indeed, previous functional imaging studies—mainly task‐ and resting‐state functional MRI (rs‐fMRI) (Bauml et al., 2014; Daamen et al., 2015; Daamen et al., 2014; Damaraju et al., 2010; Doria et al., 2010; Froudist‐Walsh et al., 2015; Lubsen et al., 2011; Smyser et al., 2010; White et al., 2014)—and also some PET studies such as striatal F‐Dopa PET (Froudist‐Walsh et al., 2017) demonstrated overlapping structural and functional brain changes in premature born individuals. For example, premature born adults with perinatal brain injury have reduced dopamine synthesis capacity in the striatum, measured by F‐Dopa‐PET, in which the volume is also typically reduced after premature birth (Froudist‐Walsh et al., 2017). The coherence of slowly fluctuating ongoing activity, measured by correlated blood oxygenation fluctuations of rs‐fMRI, is altered in the striatum, thalamus, and lateral temporal cortices, with these alterations being linked with correspondent volume loss (Bauml et al., 2014). Particularly, the last finding is of interest when looking for changed brain structure after premature birth accompanied with basic functional changes. Recent studies suggest that slowly fluctuating blood oxygenation of rs‐fMRI reflect slowly fluctuating ongoing neural excitability and activity (Biswal et al., 2010; Ma et al., 2016; Mateo, Knutsen, Tsai, Shih, & Kleinfeld, 2017; Matsui, Murakami, & Ohki, 2016; Raichle, 2011; Sanchez‐Vives, Massimini, & Mattia, 2017; Schwalm et al., 2017; Zang et al., 2007), which—at least for the cortex—represents a kind of fundamental cortical “default activity” generated by basic cortical microcircuits (Sanchez‐Vives et al., 2017). Therefore, the link between correlated blood oxygenation and underlying volume loss after premature birth suggests potential changes in basic, slowly fluctuating, ongoing neural excitability and activity particularly in regions of consistent brain volume loss, likely due to prematurity‐induced brain microstructure changes. To address this issue, this study tested the following hypothesis: fluctuations in ongoing neural excitability and activity, as measured by rs‐fMRI, are lastingly altered in premature born individuals, with alterations occurring mainly in brain areas of significant brain volume loss, a loss in volume that could potentially be linked with these alterations.

To test this hypothesis, we assessed 94 VP/VLBW and 92 full‐term (FT) born young adults at a median age of 26 years with rs‐fMRI and structural MRI (sMRI). The amplitude of low‐frequency fluctuations (ALFF) of ongoing rs‐fMRI signals is a widely used proxy of ongoing BOLD fluctuations (Zang et al., 2007). Voxel‐based morphometry (VBM) of sMRI data were used to estimate brain volume changes. To ensure the functional nature of prematurity effects on ALFF, we controlled for effects of prematurity‐related structural changes on ALFF by including VBM values as covariates in a voxel‐wise fashion. To test whether group effects on ALFF were indeed linked with brain volume, prematurity, and adult neurocognitive performance, we performed additional correlation analyses with variables of VBM values, prematurity, and IQ scores in the VP/VLBW group.

2. MATERIALS AND METHODS

2.1. Participants

Participants were recruited as part of the prospective Bavarian Longitudinal Study (BLS) (Riegel, Orth, Wolke, & Osterlund, 1995; Wolke & Meyer, 1999), a geographically defined whole‐population study of neonatal at‐risk infants born in South Bavaria. Of the initial 682 infants born VP/VLBW, 411 were eligible for the 26‐year follow‐up assessment, and 260 (63.3%) participated in psychological assessments (Breeman, Jaekel, Baumann, Bartmann, & Wolke, 2015). Of the initial 916 term born control infants from the same obstetric hospitals and alive at 6 years, 350 were randomly selected as term controls within the stratification variables of sex and family SES to be comparable with the VP/VLBW sample. Of these, 308 were eligible for the 26‐year follow‐up assessment and 229 (74.4%) participated in psychological assessments. Of the sample assessed in adulthood, 101 VP/VLBW and 102 FT individuals underwent MRI at 26 years of age. MRI assessments were carried out at two different sites: the Department of Neuroradiology, Klinikum Rechts der Isar, Technische Universität München, Germany (N = 138) and the Department of Radiology, University Hospital Bonn, Germany (N = 67). The study was approved by the local ethics committees of the Klinikum Rechts der Isar and University Hospital Bonn. All study participants gave written informed consent and received travel expenses and a payment for attendance. A detailed description of participants, particularly including MRI‐based brain abnormalities, can be found in Supporting Information.

2.2. Birth‐related variables

Gestational age (GA) was estimated from maternal reports on the first day of the last menstrual period and serial ultrasounds during pregnancy. In cases where the 2 measures differed by more than 2 weeks, clinical assessment at birth with the Dubowitz method was applied (Dubowitz, Dubowitz, & Goldberg, 1970). Maternal age, birth weight (BW), and Intensity of Neonatal Treatment Index (INTI)—which reflects the duration and intensity of medical treatment after birth and family SES at birth—were obtained from obstetric records (Gutbrod, Wolke, Soehne, Ohrt, & Riegel, 2000; Riegel et al., 1995).

2.3. Cognitive assessments

General cognitive performance was assessed by independent trained psychologists using the German version of the Wechsler Adult Intelligence Scale (WAIS III) (von Aster, Neubauer, & Horn, 2006) and converted to age‐normalized and Full‐Scale IQ (FSIQ) scores at the median age of 26 years.

2.4. Image acquisition

At both sites, MRI data acquisition was initially performed on Philips Achieva 3 T TX systems (Achieva, Philips, the Netherlands), using an 8‐channel SENSE head coil. Owing to a scanner upgrade, data acquisition in Bonn had to switch to Philips Ingenia 3 T system with an 8‐channel SENSE head coil after N = 17 participants. After N = 133 participants, data acquisition in Munich switched to the same Philips Ingenia 3 T model as in Bonn. To account for possible confounds introduced by scanner differences, data analyses included scanner identities as covariates of no interest. Across all scanners, sequence parameters were kept identical. Scanners were checked regularly to provide optimal scanning conditions. MRI physicists at the University Hospital Bonn and Klinikum rechts der Isar regularly scanned imaging phantoms to ensure within‐scanner signal stability over time. Signal‐to‐noise ratio (SNR) was not significantly different between scanners (one‐way ANOVA with factor “scanner‐ID” [Bonn 1, Bonn 2, Munich 1, Munich 2]; F(3,182) = 1.84, p = .11). Resting‐state fMRI data were collected for 10 min 52 s from a gradient‐echo echo‐planar sequence (TE = 35 ms, TR = 2,608 ms, flip angle = 90°, FOV = 230 mm2, matrix size = 64 × 63, 41 slices, thickness = 3.58 and 0 mm interslice gap, reconstructed voxel size = 3.59 × 3.59 × 3.59 mm3) resulting in 250 volumes of BOLD fMRI data per subject. Subsequently, a high‐resolution T1‐weighted 3D‐MPRAGE sequence (TI = 1,300 ms, TR = 7.7 ms, TE = 3.9 ms, flip angle = 15°; 180 sagittal slices, FOV = 256 × 256 × 180 mm, reconstruction matrix = 256 × 256; reconstructed voxel size = 1 × 1 × 1 mm3) was acquired. Immediately before undergoing the resting‐state sequence, subjects were instructed to keep their eyes closed and to restrain from falling asleep. We verified that subjects stayed awake by interrogating via intercom immediately after the rs‐fMRI scan.

2.5. Data preprocessing

Preprocessing and measure definition were carried out using SPM12 (http://www.fil.ion.ucl.ac.uk/spm) and DPARSF (Chao‐Gan & Yu‐Feng, 2010). For each participant, functional volumes were realigned to correct for head motion and coregistered to each subject's high‐resolution structural T1 image. Subsequently, the T1‐weighted image was segmented using Unified Segmentation (Ashburner & Friston, 2005). To transform individual images into common MNI (Montreal Neurological Institute) space, segmentation‐based normalization parameters were applied to the coregistered structural and functional data. Data from 17 subjects (7 VP/VLBW subjects and 10 FT subjects) were excluded from further analysis due to excessive head motion defined as a cumulative translation or rotation >3 mm or 3° (cumulative translation VP/VLBW 1.14 ± 0.9 mm, FT 1.22 ± 0.8 mm; cumulative rotation VP/VLBW 0.6 ± 0.5°, FT 0.64 ± 0.52°). To estimate motion‐induced artifacts, temporal SNR (tSNR), point‐to‐point head motion, and frame‐wise displacement were assessed for each subject (Murphy, Bodurka, & Bandettini, 2007; Power, Barnes, Snyder, Schlaggar, & Petersen, 2012; Van Dijk, Sabuncu, & Buckner, 2012). Two‐sample t tests yielded no significant differences between groups regarding mean point‐to‐point translation or rotation of any direction (p > .1), tSNR (p > .25), and frame‐wise displacement (p > .3). One should note that we did not apply additional “scrubbing” procedures to remove outliers in fMRI volumes (Power et al., 2012), as suggested by (Babu & Stoica, 2010; Yan et al., 2013). Removal of noncontiguous time points alters the underlying temporal structure of the data, precluding conventional frequency‐based analyses of rs‐fMRI data; that is, the fast Fourier transformation‐based ALFF, the main outcome of our study.

2.6. Data analysis: Outcome variables and statistical analysis

2.6.1. Amplitude of low‐frequency fluctuations

As a first step of analysis, nuisance covariates—including six head motion parameters, white matter, global brain signal, and cerebrospinal fluid signal intensities—were regressed out from preprocessed resting‐state fMRI data. Subsequently, the data were smoothed using a Gaussian kernel with a full‐width at half‐maximum of 6 mm. Then, after linear‐trend removal, the time series were transformed to the frequency domain using fast Fourier transformation to obtain the power spectrum. To calculate the ALFF, the power spectrum was square‐rooted and averaged across 0.01–0.1 Hz at each voxel. Finally, the ALFF of each voxel was then divided by the global mean of ALFF values for standardization (Zang et al., 2007). To test for group differences, voxel‐wise ALFF maps per subject were entered into a general linear model as implemented in SPM12, with the factor “group,” and the covariates “sex,” “scanner identity,” and “frame‐wise displacement.” Significance was tested using two‐sample t tests (p < .05, corrected for family‐wise error [FWE] at cluster‐level).

2.6.2. VBM and ALFF

We analyzed gray matter volumes to investigate the relationship between ALFF and underlying gray matter volume changes. Voxel‐wise gray matter volumes were analyzed using VBM as implemented in VBM8 (http://dbm.neuro.uni-jena.de/vbm.html). T1‐weighted images were corrected for bias‐field inhomogeneity, registered using linear (12‐parameter affine) and nonlinear transformations, and segmented into gray matter (GM), white matter, and cerebrospinal fluid within the same generative model. The resulting GM images were modulated to account for structural changes resulting from the normalization process. Here, we only considered nonlinear changes, so that further analyses did not have to account for differences in head size. Finally, images were smoothed with a Gaussian kernel of 6 mm (FWHM). For group comparisons, voxel‐wise two‐sample t tests were performed (p < .05 FWE‐corrected), controlling for sex and scanner identity.

Recent findings suggest that between‐group differences in measures derived from fMRI signals may potentially be influenced by underlying structural differences in gray matter volumes (He et al., 2007; Oakes et al., 2007). To ensure the functional nature of potential ALFF changes in premature born adults, we performed voxel‐wise linear regression analysis—namely, residualizing ALFF values for gray matter volume—as an approximation to correct for likely nonlinear impact of brain structure changes on ALFF. Resulting residuals entered voxel‐wise general linear models (see above) and were tested for significance using two‐sample t tests (p < .05, FWE cluster‐level corrected), controlling for sex, scanner identity, and FD.

2.6.3. Correlation between ALFF, underlying gray matter, prematurity, and cognitive performance variables

To analyze the association between aberrant ALFF and underlying gray matter, prematurity and cognitive performance averaged ALFF values among voxels of brain areas with ALFF abnormalities were extracted for all 94 VP/VLBW subjects and associated with averaged VBM values (the same voxels as for averaged ALFF), birth‐related variables (GA, BW, and INTI), and the cognitive performance variable (full‐scale IQ). These associations were investigated via three partial correlation analyses, as implemented in SPSS (Statistical Package for the Social Sciences). Each correlation approach was controlled for sex, scanner identity, and frame‐wise displacement, and the significance threshold was set at 0.05.

3. RESULTS

3.1. Sample characteristics

Group demographic characteristics and clinical background variables are shown in Table 1. VP/VLBW and FT group did not differ with respect to age (p = .277), gender (p = .786), SES at birth (p = .253), or maternal age (p = .956). By design, VP/VLBW adults had significantly lower GA (p < .001) and BW (p < .001), and were hospitalized for longer time (p < .001). VP/VLBW individuals had significantly lower WAIS‐III Full‐Scale IQ scores (p = .001).

Table 1.

Sample characteristics

Full‐term born group (n = 92) VP/VLBW born group (n = 94) Statistical comparison
M SD Range M SD Range
Sex (male/female) 53/39 56/38 p = .786
Age (years) 26.8 ±0.7 26–29 26.7 ±0.6 26–28 p = .277
GA (weeks) 39.7 ±1.1 37–42 30.5 ±2.0 25–36 p < .001
BW (g) 3,413 ±433 2,450–4,670 1,319 ±309 630–2,070 p < .001
Hospital (days) 6.8 ±2.4 2–15 72.8 ±26.0 24–170 p < .001
INTI ‐ ‐ ‐ 11.70 3.84 3–19.8 ‐
SESa 29/41/22 1−3 27/42/25 1–3 p = .253
Maternal age 29.4 ±5.2 18–42 29.4 ±4.7 17–41 p = .956
Full‐scale IQb 102.9 ±11.9 77–130 94.5 ±12.9 64–131 p < .001
Open in a new tab

GA = gestational age; BW = birth weight; Hospital = duration of hospitalization; INTI = Intensity of Neonatal Treatment (Morbidity) Index; SES = socioeconomic status at birth; maternal age = maternal age at birth; IQ = intelligence quotient.

Statistical comparisons: sex, SES with χ 2 statistics; age, GA, BW, Hospital, maternal age, and IQ with two sample t tests.

a

1 = upper class, 2 = middle class, 3 = lower class.

b

Data are based on 90 VLBW preterm and 89 full‐term subjects, respectively.

3.2. ALFF decrease in temporal cortices and its relation to underlying brain structure in VP/VLBW born adults

Voxel‐wise two‐sample t tests of ALFF maps demonstrated significant ALFF reductions in an extended cluster of the left lateral temporal and insular cortex and ALFF increases in the thalamus of VP/VLBW born adults compared with mature born adults (p < .05, FWE cluster‐level corrected) (Figure 1 and Table 2). To ensure that observed ALFF reductions were independent from our methodological approach including global brain signal removal, we controlled for global brain signal removal by performing the same analysis pipeline but without global signal removal. We found again ALFF reductions in lateral temporal cortices (Supporting Information, Figure S1), demonstrating that temporal cortices ALFF reductions in premature born adults are not confounded by global brain signal removal.

Figure 1.

Figure 1

Open in a new tab

Aberrant ALFF in premature born adults. Statistical parametric map of group comparison for ALFF between VP/VLBW and FT born adults, two‐sample t test, p < .05 FWE‐corrected (Table 2). Color bars indicate t values for increased/decreased ALFF in the VP/VLBW group. Abbreviations: ALFF, amplitude of low‐frequency fluctuations; FT, full‐term; VP/VLBW, very preterm/very low birth weight

Table 2.

Group‐different brain clusters for ALLF

Brain region Cluster size T values x

MNI

y

z p value
Thalamus 66 4.29 ‐3 −12 −12 .006
Temporal‐insular cortex 224 −6.65 −36 9 −24 <.001
−5.36 −54 −3 −15
−4.45 −54 6 0
Open in a new tab

Statistical analysis: two‐sample t test (p < .05, FWE cluser‐level correction), correct for gender, scanners, and frame‐wise displacement as covariates of no interest.

To assure the functional nature of ALFF reductions, we controlled for confounding influences of volumetric changes in VP/VLBW born adults, using VBM analyses of sMRI data (Figure 2). First, we found volume reductions in the VP/VLBW group for temporal cortices and subcortical structures such as the thalamus and basal ganglia (Figure 2a and Supporting Information, Table S1). Volume reductions overlap with ALFF reductions in the left lateral temporal cortex and with ALFF increases in the thalamus (Figure 2b). Second, after controlling for voxel‐wise VBM scores, a two‐sample t test still revealed residualized ALFF reductions in the left temporal cortex in VP/VLBW, while ALFF increases in the thalamus did not remain (Figure 2c). This result supports the idea that ALFF reductions in the left temporal cortex are of physiological nature and not totally explained by underlying volume loss.

Figure 2.

Figure 2

Open in a new tab

ALFF and volumetric changes in premature born adults. (a) Statistical parametric map of group comparison for VBM between VP/VLBW and FT born adults, two‐sample t test, p < .05 FWE‐corrected (Supporting Information, Table S1). Decreased VBM on VP/VLBW group is shown in yellow and increased VBM in turquoise. (b) Overlap (red) of changes in ALFF (green and blue; see Figure 1) and VBM (yellow, only VBM reductions) in premature born adults. (c) VBM‐residualized ALFF reductions in premature born adults, two‐sample t test, p < .05 FWE‐corrected. (d) Temporal cortices ALFF reductions are correlated with temporal cortices VBM reductions, partial correlation, p < .05. Abbreviations: ALFF, amplitude of low‐frequency fluctuations; FT, full‐term; VBM, voxel‐based morphometry; VP/VLBW, very preterm/very low birth weight

To further test whether “true” temporal ALFF reductions were indeed related to prematurity, we performed partial correlation analyses between ALFF (i.e., averaged ALFF scores of group difference clusters) and birth‐related variables (i.e., GA, BW, and INTI) in the VP/VLBW group only (Figure 3). We found a positive correlation between left temporal ALFF and BW (r = .251, p = .019), demonstrating that temporal ALFF reductions were linked with prematurity.

Figure 3.

Figure 3

Open in a new tab

Temporal cortices ALFF and birth weight in premature born adults. Reduced temporal cortices ALFF (Figure 1) is correlated with reduced BW, partial correlation, p < .05. Abbreviation: BW, birth weight

To further analyze the relationship between temporal ALFF reductions and underlying brain structure, we correlated—in the VP/VLBW group only—averaged ALFF values with VBM values (Figure 2d). We found a positive correlation between ALFF and VBM values in the left lateral temporal cortex (r = .231, p = .029), demonstrating that temporal ALFF reductions are associated with underlying brain volume loss. To test whether this relation between temporal cortex activity fluctuations and brain structure is specific for premature born adults, we performed additional correlation analysis for the link between ALFF and VBM across full‐term born persons. We did not find a significant correlation, indicating the specificity of the link between temporal cortex activity fluctuations and underlying structure for prematurity.

3.3. ALFF reductions and cognitive performance

To test whether temporal ALFF reductions are associated with changes in cognitive performance, we performed correlation analyses between averaged ALFF values and general cognitive performance in the VP/VLBW group only (Figure 4). We found a positive correlation between ALFF and full‐scale IQ (r = .267, p = .013), indicating the cognitive relevance of temporal ALFF reductions after premature birth.

Figure 4.

Figure 4

Open in a new tab

Temporal cortices ALFF and IQ in premature born adults. Reduced temporal cortices ALFF (Figure 1) is correlated with reduced full‐scale IQ, partial correlation, p < .05. Abbreviation: IQ, intelligence quotient

4. DISCUSSION

To investigate whether BOLD fluctuations are altered after premature birth, we explored the amplitude of low BOLD frequency fluctuations (ALFF) based on resting‐state fMRI data from 94 VP/VLBW and 92 full‐term born adults. ALFF was reduced in left lateral temporal cortices of VP/VLBW adults. To the best of our knowledge, this is the first report of aberrant BOLD fluctuations in premature born individuals. Furthermore, we found that temporal ALFF reductions remained after controlling for overlapping gray matter volume reductions, pointing toward the functional nature of temporal ALFF decreases. On the other hand, temporal ALFF reductions were linked with volume reductions, suggesting the dependence of ALFF decreases on underlying structural changes. Finally, temporal ALFF reductions were linked with IQ reductions, demonstrating their behavioral significance. In the following section, we discuss these single findings in more detail, focusing particularly on the relation between temporal ALFF reductions and underlying structural changes.

In very premature born adults, ALFF was reduced in both the insula and the lateral and anterior temporal cortices, and increased in the thalamus (Figure 1 and Table 2). ALFF changes overlapped with gray matter volume reductions in VP/VLBW adults, particularly in the temporal cortices and thalamus (Figure 2a,b). The pattern of volume reductions in subcortical areas, such as the thalamus and striatum as well as in temporal‐insular cortices, is in line with previous studies (Ball et al., 2013; Karolis et al., 2017; Nosarti et al., 2008; Pierson et al., 2007). In the left temporal cortex, reduced ALFF remained after controlling for gray matter volume (Figure 2c), supporting the functional nature of temporal ALFF reductions in VP/VLBW born adults. Furthermore, temporal ALFF reductions correlated with birth weight (Figure 3), independently from GA or medical complications at birth, suggesting that ALLF reductions are indeed linked with premature birth. Finally, temporal cortex ALFF reductions correlated with IQ reductions in premature born persons (Figure 4), indicating the functional relevance of temporal ALFF changes. Based on these findings, we conclude that premature birth has lasting, relevant long‐term effects on slowly fluctuating ongoing BOLD activity in the lateral temporal cortex.

Previous studies demonstrated that ALFF, particularly in the lateral temporal cortices, is sensitive not only to the effects of typical brain development and aging but also to the changes in neurodevelopmental disorders (Biswal et al., 2010; Itahashi et al., 2015; Yu et al., 2014). For example, Biswal et al. (2010) showed aging effects on ALFF mainly in cortical midline structures such as anterior and posterior cingulate and also in lateral temporal cortices; decreased ALFF has been observed in temporal cortices and insula of patients with schizophrenia (Yu et al., 2014) and in lateral and inferior temporal cortices of patients with autism (Itahashi et al., 2015). This overlap of findings suggests that ALFF variation, particularly in the temporal cortices, may strongly covary with developmental brain changes and thus may represent a potential surrogate marker for neurodevelopmental brain disorders. This overlap, however, does not point to identical mechanisms underlying ALFF changes in temporal cortices across distinct developmental conditions; that is, ALFF reductions in prematurity and schizophrenia may have distinct underlying causes, but they may converge on macroscopically similar ALFF alteration patterns.

Fluctuations in blood oxygenation, as reflected by ALFF, are assumed to indicate slow fluctuations of macroscopic brain activity, which in turn reflect fluctuations in ongoing neuronal activity and excitability (Biswal et al., 2010; Ma et al., 2016; Mateo et al., 2017; Matsui et al., 2016; Raichle, 2011; Sanchez‐Vives et al., 2017; Schwalm et al., 2017; Zang et al., 2007). In more detail, restricted to the cortex, local cortical microcircuits generate spontaneously slow activity fluctuations of alternating active (i.e., up‐state) and inactive (i.e., down‐state) phases at frequencies of below 1 Hz (Sanchez‐Vives et al., 2017). Recent studies using simultaneous neuronal imaging and optical imaging/fMRI in animals, providing both simultaneous neuronal and hemodynamic blood oxygenation‐related information, have demonstrated that blood oxygenation fluctuations reflect slow fluctuations in excitatory activity (Ma et al., 2016; Schwalm et al., 2017) and their coherence (Mateo et al., 2017; Matsui et al., 2016). Applying these findings to reduced ALFF in temporal cortices of premature born adults, they suggest that correspondent changes in slow neuronal activity fluctuations may exist in prematurely born subjects (Arichi et al., 2012). To get definitive evidence for this suggestion, simultaneous EEG‐fMRI experiments in premature born individuals are necessary (Arichi et al., 2017).

Furthermore, slow fluctuations in ongoing neural activity and excitability are thought to represent basic cortical “default” activity, which is generated locally by basic microcircuits (Sanchez‐Vives et al., 2017). For example, cortical in vitro slices produce slow fluctuating ongoing activity spontaneously (Sanchez‐Vives & McCormick, 2000), and simple artificial cell assembly architectures simulate slow ongoing activity fluctuations (Markram et al., 2015). These findings indicate that slow ongoing fluctuations depend on basic underlying structural microcircuitry, which, in turn, are aberrant after premature birth (Ball et al., 2013; Dean et al., 2013; McClendon et al., 2017). For example, while Ball et al. (2013) showed impaired cortical microstructure in preterm born infants via diffusion imaging, Dean et al. (2013) demonstrated that aberrant cortical diffusion imaging signals were associated with reduced dendritic arborization in premature born sheep. McClendon et al. (2017), in turn, showed that transient hypoxic episodes in premature born sheep reduce their subplate neuron dendritic arborization and subsequent microcircuit development. These points together suggest that aberrant cortical slow fluctuations in premature born individuals might be linked to aberrant underlying gray matter structure. Indeed, we found that reduced temporal ALFF was associated with reduced underlying brain volume (Figure 2d). This finding suggests that impaired cortical development after premature birth may impact on basic ongoing cortical activity, particularly in the lateral temporal cortices via aberrant structural microcircuits. One possibility to test this further might be to link cortical microstructural indices (such as those derived from diffusion imaging, e.g., in Ball et al., 2013) with ALFF‐based measures. Conclusively, premature birth might alter “default” slow fluctuations in ongoing neural activity and excitability in the temporal cortices, potentially via changes in underlying microstructure.

While these points provide a general argument for the link between ALFF and slow fluctuations in local cortical activity, on one hand, and underlying brain structure and cortical microcircuits, on the other hand, it is not clear why ALFF changes arise specifically in the temporal cortices. Specific lateral temporal cortex changes after premature birth have been reported also in other modalities, such as task‐fMRI studies (Gozzo et al., 2009; Schafer et al., 2009; Wilke, Hauser, Krageloh‐Mann, & Lidzba, 2014), resting‐state fMRI (Bauml et al., 2014; White et al., 2014), or diffusion tensor imaging (Aeby et al., 2013; Northam et al., 2012). In particular, we recently found in largely the same individuals that coherence of ongoing BOLD fluctuations—that is, intrinsic functional connectivity (iFC)—is aberrant in temporal cortices, and that these functional connectivity changes were linked with underlying temporal gray matter loss (Bauml et al., 2014). One should note the difference between basic ongoing BOLD fluctuations—that is, ALFF—and correlated ongoing BOLD fluctuations—that is, iFC. Such remarkable convergence of changes across distinct modalities and ages after premature delivery supports the idea that a basic process of temporal cortex‐dependent brain development might be affected by prematurity. In the following section, we will speculate as to whether some specific microscopic developments might underpin such focus on the temporal cortex. We are aware that this speculation is clearly beyond our experimental approach, but it might be a useful way to better understand the regional specificity of our findings in terms of testable hypotheses.

Cortical development depends critically on subplate neurons and their development (Hoerder‐Suabedissen & Molnar, 2015; McClendon et al., 2017; Salmaso et al., 2014; Volpe, 2009). The subplate zone below cortical layer 6, which includes different populations of subplate neurons, represents a dynamic “waiting compartment” for ingrowing thalamocortical afferents (Rakic, 1976), basal forebrain cholinergic afferents (Kostovic, 1986), and corticocortical afferents (deAzevedo, Hedin‐Pereira, & Lent, 1997), showing the largest activity in gestational weeks 15–35 and being critical for local microcircuit development. In particular, in lateral temporo‐parietal regions, subplate neuron growth has its highest rates (Corbett‐Detig et al., 2011). As premature birth is known to affect subplate neuron development (Deng, 2010; Kinney et al., 2012; Salmaso et al., 2014; Volpe, 2009), we speculate—due the overlap of our findings of lateral temporal cortex‐focused ALFF reductions with high rate subplate growth in the temporal cortices—that ALFF in the temporal cortices might reflect late consequences of subplate neuron development aberrances in prematurity. It is clear that, to test this idea, future neuropathological and/or translational studies with animal models are necessary.

4.1. Strength and limitations

Some points should be carefully considered when interpreting our results. First, the current sample is biased to VP/VLBW adults with less severe neonatal complications, less functional impairments, and higher IQ. Individuals with stronger birth complications and/or severe lasting impairments in the initial BLS sample were more likely to be excluded in initial screening for MRI or to reject MRI scanning or even continuation in the study. Thus, differences in ALFF between VP/VLBW and term control adults reported here are conservative estimates of true differences. Second, the study sample was limited by MRI‐ and study‐related contraindications including a history of severe neurological disorders (e.g., epilepsy, multiple sclerosis, cerebral hemorrhage, traumatic brain injury, tinnitus), severe back problems, (potential) pregnancy, severely impaired vision, and nonremovable ferromagnetic implants (e.g., pacemakers). Third, the current sample size is large (94 VP/VLBW and 92 FT adults), enhancing the generalizability of our findings. Fourth, head motion in VP/VLBW adults during scanning and scanning at multiple scanners used in this study may confound imaging‐derived brain connectivity measures. This study controlled for these effects as strictly as possible; however, subtle influences of these confounds cannot be ruled out completely.

5. CONCLUSION

Slowly fluctuating BOLD activity is reduced in lateral temporal cortices after very premature birth, with these functional changes being linked with underlying structural changes.

CONFLICT OF INTEREST

The authors declare no conflict of interest.

Supporting information

Appendix S1 Supporting Information

Click here for additional data file. (13.2MB, docx)

ACKNOWLEDGMENTS

The authors thank all current and former members of the Bavarian Longitudinal Study Group who contributed to general study organization, recruitment, and data collection, management, and subsequent analyses, including (in alphabetical order) Barbara Busch, Stephan Czeschka, Claudia Grünzinger, Christian Koch, Diana Kurze, Sonja Perk, Andrea Schreier, Antje Strasser, Julia Trummer, and Eva van Rossum. The authors are grateful to the staff of the Department of Neuroradiology in Munich and the Department of Radiology in Bonn for their help in data collection. Most importantly, they thank all their study participants and their families for their efforts to take part in this study. This study was supported by Chinese Scholar Council (CSC, File No: 201708080036 to JS), Deutsche Forschungsgemeinschaft (SO 1336/1‐1 to CS), German Federal Ministry of Education and Science (BMBF 01ER0801 to PB and DW, BMBF 01ER0803 to CS) and the Kommission für Klinische Forschung, Technische Universität München (KKF 8765162 to CS).

Shang J, Bäuml JG, Koutsouleris N, et al. Decreased BOLD fluctuations in lateral temporal cortices of premature born adults. Hum Brain Mapp. 2018;39:4903–4912. 10.1002/hbm.24332

Funding information Chinese Scholar Council, Grant/Award Number: 201708080036 ; Deutsche Forschungsgemeinschaft, Grant/Award Number: SO 1336/1‐1; German Federal Ministry of Education and Science, Grant/ Award Number: BMBF 01ER0801 BMBF 01ER0803 ; Technische Universität München, Grant/Award Number: KKF 8765162

REFERENCES

  1. Aeby, A. , De Tiege, X. , Creuzil, M. , David, P. , Baleriaux, D. , Van Overmeire, B. , … Van Bogaert, P. (2013). Language development at 2 years is correlated to brain microstructure in the left superior temporal gyrus at term equivalent age: A diffusion tensor imaging study. NeuroImage, 78, 145–151. [DOI] [PubMed] [Google Scholar]
  2. Arichi, T. , Fagiolo, G. , Varela, M. , Melendez‐Calderon, A. , Allievi, A. , Merchant, N. , … Edwards, A. D. (2012). Development of BOLD signal hemodynamic responses in the human brain. NeuroImage, 63, 663–673. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Arichi, T. , Whitehead, K. , Barone, G. , Pressler, R. , Padormo, F. , Edwards, A. D. , & Fabrizi, L. (2017). Localization of spontaneous bursting neuronal activity in the preterm human brain with simultaneous EEG‐fMRI. eLife, 6, e27814. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Ashburner, J. , & Friston, K. J. (2005). Unified segmentation. NeuroImage, 26, 839–851. [DOI] [PubMed] [Google Scholar]
  5. Babu, P. , & Stoica, P. (2010). Spectral analysis of nonuniformly sampled data – A review. Digital Signal Processing, 20, 359–378. [Google Scholar]
  6. Back, S. A. , Han, B. H. , Luo, N. L. , Chricton, C. A. , Xanthoudakis, S. , Tam, J. , … Holtzman, D. M. (2002). Selective vulnerability of late oligodendrocyte progenitors to hypoxia–ischemia. The Journal of Neuroscience : the Official Journal of the Society for Neuroscience, 22, 455–463. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Ball, G. , Aljabar, P. , Zebari, S. , Tusor, N. , Arichi, T. , Merchant, N. , … Edwards, A. D. (2014). Rich‐club organization of the newborn human brain. Proceedings of the National Academy of Sciences, 111, 7456–7461. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Ball, G. , Boardman, J. P. , Rueckert, D. , Aljabar, P. , Arichi, T. , Merchant, N. , … Counsell, S. J. (2012). The effect of preterm birth on thalamic and cortical development. Cerebral Cortex, 22, 1016–1024. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Ball, G. , Srinivasan, L. , Aljabar, P. , Counsell, S. J. , Durighel, G. , Hajnal, J. V. , … Edwards, A. D. (2013). Development of cortical microstructure in the preterm human brain. Proceedings of the National Academy of Sciences of the United States of America, 110, 9541–9546. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Bauml, J. G. , Daamen, M. , Meng, C. , Neitzel, J. , Scheef, L. , Jaekel, J. , … Sorg, C. (2014). Correspondence between aberrant intrinsic network connectivity and gray‐matter volume in the ventral brain of preterm born adults. Cerebral Cortex, 25, 4135–4145. [DOI] [PubMed] [Google Scholar]
  11. Biswal, B. B. , Mennes, M. , Zuo, X. N. , Gohel, S. , Kelly, C. , Smith, S. M. , … Milham, M. P. (2010). Toward discovery science of human brain function. Proceedings of the National Academy of Sciences of the United States of America, 107, 4734–4739. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Blencowe, H. , Cousens, S. , Oestergaard, M. Z. , Chou, D. , Moller, A.‐B. , Narwal, R. , … Lawn, J. E. (2012). National, regional, and worldwide estimates of preterm birth rates in the year 2010 with time trends since 1990 for selected countries: A systematic analysis and implications. The Lancet, 379, 2162–2172. [DOI] [PubMed] [Google Scholar]
  13. Breeman, L. D. , Jaekel, J. , Baumann, N. , Bartmann, P. , & Wolke, D. (2015). Preterm cognitive function into adulthood. Pediatrics, 136, 415–423. [DOI] [PubMed] [Google Scholar]
  14. Buser, J. R. , Maire, J. , Riddle, A. , Gong, X. , Nguyen, T. , Nelson, K. , … Back, S. A. (2012). Arrested preoligodendrocyte maturation contributes to myelination failure in premature infants. Annals of Neurology, 71, 93–109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Chao‐Gan, Y. , & Yu‐Feng, Z. (2010). DPARSF: A MATLAB toolbox for "pipeline" data analysis of resting‐state fMRI. Frontiers in Systems Neuroscience, 4, 13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Corbett‐Detig, J. , Habas, P. A. , Scott, J. A. , Kim, K. , Rajagopalan, V. , McQuillen, P. S. , … Studholme, C. (2011). 3D global and regional patterns of human fetal subplate growth determined in utero. Brain Structure & Function, 215, 255–263. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. D'Onofrio, B. M. , Class, Q. A. , Rickert, M. E. , Larsson, H. , Langstrom, N. , & Lichtenstein, P. (2013). Preterm birth and mortality and morbidity: A population‐based quasi‐experimental study. JAMA Psychiatry, 70, 1231–1240. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Daamen, M. , Bauml, J. G. , Scheef, L. , Meng, C. , Jurcoane, A. , Jaekel, J. , … Boecker, H. (2015). Neural correlates of executive attention in adults born very preterm. NeuroImage. Clinical, 9, 581–591. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Daamen, M. , Bauml, J. G. , Scheef, L. , Sorg, C. , Busch, B. , Baumann, N. , … Boecker, H. (2014). Working memory in preterm‐born adults: Load‐dependent compensatory activity of the posterior default mode network. Human Brain Mapping, 36, 1121–1137. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Damaraju, E. , Phillips, J. R. , Lowe, J. R. , Ohls, R. , Calhoun, V. D. , & Caprihan, A. (2010). Resting‐state functional connectivity differences in premature children. Frontiers in Systems Neuroscience, 4, 23. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Dean, J. M. , McClendon, E. , Hansen, K. , Azimi‐Zonooz, A. , Chen, K. , Riddle, A. , … Back, S. A. (2013). Prenatal cerebral ischemia disrupts MRI‐defined cortical microstructure through disturbances in neuronal arborization. Science Translational Medicine, 5, 168ra7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. deAzevedo, L. C. , Hedin‐Pereira, C. , & Lent, R. (1997). Callosal neurons in the cingulate cortical plate and subplate of human fetuses. The Journal of Comparative Neurology, 386, 60–70. [DOI] [PubMed] [Google Scholar]
  23. Deng, W. (2010). Neurobiology of injury to the developing brain. Nature Reviews. Neurology, 6, 328–336. [DOI] [PubMed] [Google Scholar]
  24. Doria, V. , Beckmann, C. F. , Arichi, T. , Merchant, N. , Groppo, M. , Turkheimer, F. E. , … Edwards, A. D. (2010). Emergence of resting state networks in the preterm human brain. Proceedings of the National Academy of Sciences of the United States of America, 107, 20015–20020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Dubowitz, L. M. , Dubowitz, V. , & Goldberg, C. (1970). Clinical assessment of gestational age in the newborn infant. The Journal of Pediatrics, 77, 1–10. [DOI] [PubMed] [Google Scholar]
  26. Eikenes, L. , Lohaugen, G. C. , Brubakk, A. M. , Skranes, J. , & Haberg, A. K. (2011). Young adults born preterm with very low birth weight demonstrate widespread white matter alterations on brain DTI. NeuroImage, 54, 1774–1785. [DOI] [PubMed] [Google Scholar]
  27. Froudist‐Walsh, S. , Bloomfield, M. A. P. , Veronese, M. , Kroll, J. , Karolis, V. R. , Jauhar, S. , … Howes, O. (2017). The effect of perinatal brain injury on dopaminergic function and hippocampal volume in adult life. eLife, 6, e29088. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Froudist‐Walsh, S. , Karolis, V. , Caldinelli, C. , Brittain, P. J. , Kroll, J. , Rodriguez‐Toscano, E. , … Nosarti, C. (2015). Very early brain damage leads to remodeling of the working memory system in adulthood: A combined fMRI/tractography study. The Journal of Neuroscience : the Official Journal of the Society for Neuroscience, 35, 15787–15799. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Gozzo, Y. , Vohr, B. , Lacadie, C. , Hampson, M. , Katz, K. H. , Maller‐Kesselman, J. , … Ment, L. R. (2009). Alterations in neural connectivity in preterm children at school age. NeuroImage, 48, 458–463. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Grothe, M. J. , Scheef, L. , Bauml, J. , Meng, C. , Daamen, M. , Baumann, N. , … Sorg, C. (2017). Reduced cholinergic basal forebrain integrity links neonatal complications and adult cognitive deficits after premature birth. Biological Psychiatry, 82, 119–126. [DOI] [PubMed] [Google Scholar]
  31. Gutbrod, T. , Wolke, D. , Soehne, B. , Ohrt, B. , & Riegel, K. (2000). Effects of gestation and birth weight on the growth and development of very low birthweight small for gestational age infants: A matched group comparison. Archives of Disease in Childhood. Fetal and Neonatal Edition, 82, F208–F214. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. He, Y. , Wang, L. , Zang, Y. , Tian, L. , Zhang, X. , Li, K. , & Jiang, T. (2007). Regional coherence changes in the early stages of Alzheimer's disease: A combined structural and resting‐state functional MRI study. NeuroImage, 35, 488–500. [DOI] [PubMed] [Google Scholar]
  33. Hoerder‐Suabedissen, A. , & Molnar, Z. (2015). Development, evolution and pathology of neocortical subplate neurons. Nature Reviews. Neuroscience, 16, 133–146. [DOI] [PubMed] [Google Scholar]
  34. Itahashi, T. , Yamada, T. , Watanabe, H. , Nakamura, M. , Ohta, H. , Kanai, C. , … Hashimoto, R. (2015). Alterations of local spontaneous brain activity and connectivity in adults with high‐functioning autism spectrum disorder. Molecular Autism, 6, 30. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Karolis, V. R. , Froudist‐Walsh, S. , Kroll, J. , Brittain, P. J. , Tseng, C.‐E. J. , Nam, K.‐W. , … Nosarti, C. (2017). Volumetric grey matter alterations in adolescents and adults born very preterm suggest accelerated brain maturation. Neuroimage, 163, 379–389. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Kinney, H. C. , Haynes, R. L. , Xu, G. , Andiman, S. E. , Folkerth, R. D. , Sleeper, L. A. , & Volpe, J. J. (2012). Neuron deficit in the white matter and subplate in periventricular leukomalacia. Annals of Neurology, 71, 397–406. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Kostovic, I. (1986). Prenatal development of nucleus basalis complex and related fiber systems in man: A histochemical study. Neuroscience, 17, 1047–1077. [DOI] [PubMed] [Google Scholar]
  38. Lubsen, J. , Vohr, B. , Myers, E. , Hampson, M. , Lacadie, C. , Schneider, K. C. , … Ment, L. R. (2011). Microstructural and functional connectivity in the developing preterm brain. Seminars in Perinatology, 35, 34–43. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Ma, Y. , Shaik, M. A. , Kozberg, M. G. , Kim, S. H. , Portes, J. P. , Timerman, D. , & Hillman, E. M. (2016). Resting‐state hemodynamics are spatiotemporally coupled to synchronized and symmetric neural activity in excitatory neurons. Proceedings of the National Academy of Sciences of the United States of America, 113, E8463–E8471. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Markram, H. , Muller, E. , Ramaswamy, S. , Reimann, M. W. , Abdellah, M. , Sanchez, C. A. , … Schurmann, F. (2015). Reconstruction and simulation of neocortical microcircuitry. Cell, 163, 456–492. [DOI] [PubMed] [Google Scholar]
  41. Mateo, C. , Knutsen, P. M. , Tsai, P. S. , Shih, A. Y. , & Kleinfeld, D. (2017). Entrainment of arteriole vasomotor fluctuations by neural activity is a basis of blood‐oxygenation‐level‐dependent "resting‐state" connectivity. Neuron, 96, 936–948.e3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Matsui, T. , Murakami, T. , & Ohki, K. (2016). Transient neuronal coactivations embedded in globally propagating waves underlie resting‐state functional connectivity. Proceedings of the National Academy of Sciences, 113, 6556–6561. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. McClendon, E. , Shaver, D. C. , Degener‐O'Brien, K. , Gong, X. , Nguyen, T. , Hoerder‐Suabedissen, A. , … Back, S. A. (2017). Transient hypoxemia chronically disrupts maturation of preterm fetal ovine subplate neuron arborization and activity. The Journal of Neuroscience : the Official Journal of the Society for Neuroscience, 37, 11912–11929. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Meng, C. , Bauml, J. G. , Daamen, M. , Jaekel, J. , Neitzel, J. , Scheef, L. , … Sorg, C. (2015). Extensive and interrelated subcortical white and gray matter alterations in preterm‐born adults. Brain Structure & Function, 221, 2109–2121. [DOI] [PubMed] [Google Scholar]
  45. Murphy, K. , Bodurka, J. , & Bandettini, P. A. (2007). How long to scan? The relationship between fMRI temporal signal to noise ratio and necessary scan duration. NeuroImage, 34, 565–574. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Northam, G. B. , Liegeois, F. , Tournier, J. D. , Croft, L. J. , Johns, P. N. , Chong, W. K. , … Baldeweg, T. (2012). Interhemispheric temporal lobe connectivity predicts language impairment in adolescents born preterm. Brain, 135, 3781–3798. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Nosarti, C. , Giouroukou, E. , Healy, E. , Rifkin, L. , Walshe, M. , Reichenberg, A. , … Murray, R. M. (2008). Grey and white matter distribution in very preterm adolescents mediates neurodevelopmental outcome. Brain, 131, 205–217. [DOI] [PubMed] [Google Scholar]
  48. Nosarti, C. , Reichenberg, A. , Murray, R. M. , Cnattingius, S. , Lambe, M. P. , Yin, L. , … Hultman, C. M. (2012). Preterm birth and psychiatric disorders in young adult life. Archives of General Psychiatry, 69, E1–E8. [DOI] [PubMed] [Google Scholar]
  49. Oakes, T. R. , Fox, A. S. , Johnstone, T. , Chung, M. K. , Kalin, N. , & Davidson, R. J. (2007). Integrating VBM into the general linear model with voxelwise anatomical covariates. NeuroImage, 34, 500–508. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Pierson, C. R. , Folkerth, R. D. , Billiards, S. S. , Trachtenberg, F. L. , Drinkwater, M. E. , Volpe, J. J. , & Kinney, H. C. (2007). Gray matter injury associated with periventricular leukomalacia in the premature infant. Acta Neuropathologica, 114, 619–631. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Power, J. D. , Barnes, K. A. , Snyder, A. Z. , Schlaggar, B. L. , & Petersen, S. E. (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage, 59, 2142–2154. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Raichle, M. E. (2011). The restless brain. Brain Connectivity, 1, 3–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Rakic, P. (1976). Prenatal genesis of connections subserving ocular dominance in the rhesus monkey. Nature, 261, 467–471. [DOI] [PubMed] [Google Scholar]
  54. Riegel, K. , Orth, B. , Wolke, D. , & Osterlund, K. (1995). Die Entwicklung gefährdet geborener Kinder bis zum 5 Lebensjahr. Stuttgart, Germany, Thieme. [Google Scholar]
  55. Saigal, S. , & Doyle, L. W. (2008). An overview of mortality and sequelae of preterm birth from infancy to adulthood. The Lancet, 371, 261–269. [DOI] [PubMed] [Google Scholar]
  56. Salmaso, N. , Jablonska, B. , Scafidi, J. , Vaccarino, F. M. , & Gallo, V. (2014). Neurobiology of premature brain injury. Nature Neuroscience, 17, 341–346. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Sanchez‐Vives, M. V. , Massimini, M. , & Mattia, M. (2017). Shaping the default activity pattern of the cortical network. Neuron, 94, 993–1001. [DOI] [PubMed] [Google Scholar]
  58. Sanchez‐Vives, M. V. , & McCormick, D. A. (2000). Cellular and network mechanisms of rhythmic recurrent activity in neocortex. Nature Neuroscience, 3, 1027–1034. [DOI] [PubMed] [Google Scholar]
  59. Schafer, R. J. , Lacadie, C. , Vohr, B. , Kesler, S. R. , Katz, K. H. , Schneider, K. C. , … Ment, L. R. (2009). Alterations in functional connectivity for language in prematurely born adolescents. Brain, 132, 661–670. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Schwalm, M. , Schmid, F. , Wachsmuth, L. , Backhaus, H. , Kronfeld, A. , Aedo Jury, F. , … Stroh, A. (2017). Cortex‐wide BOLD fMRI activity reflects locally‐recorded slow oscillation‐associated calcium waves. eLife, 6, e27602. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Skranes, J. , Vangberg, T. R. , Kulseng, S. , Indredavik, M. S. , Evensen, K. A. , Martinussen, M. , … Brubakk, A. M. (2007). Clinical findings and white matter abnormalities seen on diffusion tensor imaging in adolescents with very low birth weight. Brain, 130, 654–666. [DOI] [PubMed] [Google Scholar]
  62. Smyser, C. D. , Inder, T. E. , Shimony, J. S. , Hill, J. E. , Degnan, A. J. , Snyder, A. Z. , & Neil, J. J. (2010). Longitudinal analysis of neural network development in preterm infants. Cerebral Cortex, 20, 2852–2862. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Van Dijk, K. R. , Sabuncu, M. R. , & Buckner, R. L. (2012). The influence of head motion on intrinsic functional connectivity MRI. NeuroImage, 59, 431–438. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Volpe, J. J. (2009). Brain injury in premature infants: A complex amalgam of destructive and developmental disturbances. The Lancet Neurology, 8, 110–124. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. von Aster, M. , Neubauer, A. , & Horn, R. (2006). Wechsler Intelligenztest für Erwachsene WIE. Deutschsprachige Bearbeitung und Adaptation des WAIS‐III von David Wechsler (2., korrigierte Auflage). Frankfurt: Pearson Assessment. [Google Scholar]
  66. White, T. P. , Symington, I. , Castellanos, N. P. , Brittain, P. J. , Froudist Walsh, S. , Nam, K. W. , … Nosarti, C. (2014). Dysconnectivity of neurocognitive networks at rest in very‐preterm born adults. NeuroImage. Clinical, 4, 352–365. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Wilke, M. , Hauser, T. K. , Krageloh‐Mann, I. , & Lidzba, K. (2014). Specific impairment of functional connectivity between language regions in former early preterms. Human Brain Mapping, 35, 3372–3384. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Wolke, D. , & Meyer, R. (1999). Cognitive status, language attainment, and prereading skills of 6‐year‐old very preterm children and their peers: The Bavarian Longitudinal Study. Developmental Medicine & Child Neurology, 41, 94–109. [DOI] [PubMed] [Google Scholar]
  69. Yan, C. G. , Cheung, B. , Kelly, C. , Colcombe, S. , Craddock, R. C. , Di Martino, A. , … Milham, M. P. (2013). A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. NeuroImage, 76, 183–201. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Yu, R. , Chien, Y. L. , Wang, H. L. , Liu, C. M. , Liu, C. C. , Hwang, T. J. , … Tseng, W. Y. (2014). Frequency‐specific alternations in the amplitude of low‐frequency fluctuations in schizophrenia. Human Brain Mapping, 35, 627–637. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Zang, Y. F. , He, Y. , Zhu, C. Z. , Cao, Q. J. , Sui, M. Q. , Liang, M. , … Wang, Y. F. (2007). Altered baseline brain activity in children with ADHD revealed by resting‐state functional MRI. Brain & Development, 29, 83–91. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Appendix S1 Supporting Information

Click here for additional data file. (13.2MB, docx)

Articles from Human Brain Mapping are provided here courtesy of Wiley

RESOURCES