text:
Resveratrol enhances HBV replication through activating Sirt1-PGC-1α-PPARα pathway

The population of hepatitis B combined with a number of metabolic disorders is increasing significantly. Resveratrol (RSV) has been used as a preclinical drug for the treatment of the metabolic disorders. However, the impact of RSV on HBV replication remains unknown. In this study, the HBV-expressing hepatocelluar carcinoma cell line and mouse model created by hydrodynamic injection of viral DNA were used. We found that RSV activates Sirt1, which in turn deacetylates PGC-1α and subsequently increases the transcriptional activity of PPARα, leading to the enhanced HBV transcription and replication in vitro and in vivo. In addition, we found that this pathway is also required for fasting-induced HBV transcription. Taken together, this study identifies that RSV enhances HBV transcription and replication especially acting on the core promoter, which depends on Sirt1-PGC-1α-PPARα pathway. We conclude that RSV may exacerbate the progression of hepatitis B and that patients with hepatitis B infection should be cautious taking RSV as a dietary supplement.

RSV increases HBV replication in vitro and in vivo.
We first tested the toxicity of RSV in HepG2. 2.15 cells and determined the optimal dose for the later treatment. The 50% inhibitory concentration (IC50) of cell viability for RSV treatment was 106.3 μM and then the 50 μM was an effective concentration chosen for the following experiments of RSV (Fig. 1a). To explore the impact of RSV on HBV replication in HepG2.2.15 cells, equivalent numbers of cells were exposed to 50 μM RSV or control vehicle for 72 hours and HBV DNA in the culture medium was measured by real-time qPCR. We found that RSV treatment dose-dependently upregulated HBV DNA levels by nearly 8 times higher than the control (Fig. 1b). Same results were obtained in HepG2 (data not show) and Huh7 cells transiently transfected with 1.3mer HBV genomic DNA (Fig. 1c). We then established an HBV replication mouse model with hydrodynamic injection of 1.3mer HBV genomic DNA (10 μg/per mouse) according to a previous study 24 . After injection, the serum HBV DNA number in mice remained at a low level of [10 3 ] IU/ml for more than one month (Fig. 1d), which is consistent with the previous data 25 . After treatment of RSV (100 mg/kg/d) via daily oral gavage of RSV for 2 weeks, the serum HBV DNA levels were significantly increased and maintained at a higher level towards the end point of the drug administration (Fig. 1e), suggesting that RSV is a strong stimulator for HBV DNA replication.
RSV activates the HBV core promoter. To further investigate the mechanism by which RSV upregulates HBV replication, we measured the pregenomic RNA (pgRNA) and preC RNA in HepG2.2.15 cells by real-time qPCR, HBcAg expression by Western blot, and the HBsAg and HBeAg contents released into the culture medium by ELISA. We found that RSV treatment increased not only pgRNA and preC RNA levels by almost 6-fold and 4-fold respectively, but also HBeAg and HBcAg levels by nearly 5-fold and 3-fold respectively, with a slight increase in HBsAg expression (Fig. 2a), suggesting that the transcription of HBV DNA was activated by the RSV treatment, especially at the region of viral core promoter. Our in vivo experiments further supported these observations. RSV treatment (100 mg/kg/d) for 2 weeks increased HBsAg and HBeAg levels in circulation  (Fig. 2b,c), indicating that the viral core promoter plays a key role in the process. We then measured the activities of the preS1, preS2 and core promoters respectively in HepG2 cells. We found that while RSV treatment increased the core promoter activity by nearly 16-fold, it activated preS1 and preS2 promoters with much less potency (Fig. 2d). To further confirm the results in vivo, 6 μg of 1.3× HBV-Cp-Luc plasmid DNA was hydromatically injected into the mice. 72 hours later, the mice were treated with RSV for 20 minutes. The activity of the core promoter in mice liver was measured by the animal imaging system in real time. We found that RSV increased the luciferase signals in the liver by nearly 1.3-fold, compared with the vehicle control (Fig. 2e). These results suggest that RSV promotes HBV replication by activating the HBV core promoter. In vivo luciferase analysis of mice was observed after administration of water (control group, NC) or RSV (experimental group, Exp). Representative images for visualization of luciferase activity (left) and their quantitative analysis data (right) were shown. The gels have been run under the same experimental conditions. *p < 0.05 with n = 6 /group.  26 . To identify the transcription factor(s) involved in the activation of the core promoter by RSV, we knocked down various HBV-related transcriptional factors including Jun Proto-Oncogene (c-Jun), peroxisome proliferator-activated receptor, alpha/retinoid x receptor, alpha (PPARα/RXRα), CCAAT/Enhancer binding protein (C/EBP), alpha (C/EBPα), farnesoid x -activated receptor (FXRα), and hepatocyte nuclear factor 4, alpha (HNF4α) in HepG2.215 cells by small interfering RNAs (siRNAs). Real-time qPCR data demonstrated that knockdown of PPARα or RXRα abolished RSV-induced HBV replication ( Fig. 3a), while knockdown of the others didn't affect supernatant HBV DNA levels (Fig. 3b). To examine whether PPARα /RXRα could regulate the HBV core promoter, we knocked down PPARα or RXRα by siRNA in HepG2 cells following pGL3-Cp transfection and RSV treatment. Evidently, knockdown of either PPAR or RXR reduced the luciferase signal of the core promoter (Fig. 3c). To further evaluate the activity of PPARα / RXRα affected by RSV treatment, we measured the expression of PPARα /RXRα target genes by RT-qPCR in HepG2.2.15 cells and the mouse liver. RSV stimulated the expression of PPARα target genes promoting fatty acid oxidation (FAO), such as key mitochondrial medium chain acyl-CoA dehydrogenase (MCAD), microsome cytochrome P450 in FAO and mitochondrial 3-hydroxy-3-methylglutaryl-CoA synthase (mHMG-CoAs) in ketogenesis (Fig. 3d). Taken together, the activation of PPARα /RXRα on core promoter plays a key role in RSV-induced HBV transcription.
Sirt1/PGC-1α is required for RSV-induced HBV replication. RSV can act as a positive regulator of sirtuin 1 (Sirt1) to improve the metabolic process of aging, hepatic steatosis and diabetes [27][28][29][30][31] . To examine whether Sirt1 is also involved in RSV-induced HBV replication, we measured the expression of endogenous Sirt1 as well as its activity in HepG2.2.15 cells and mouse liver. We found that RSV not only increased Sirt1 mRNA and protein levels ( Fig. 4a,b), but also enhanced the ratio of NAD/NADH (Fig. 4c), consistent with the previous reports 32, 33 .
To examine whether Sirt1 contributes to RSV-induced HBV replication, we measured HBV DNA in the culture media from Sirt1 knockdown cells (Fig. 4d). Knockdown of Sirt1 significantly decreased the HBV DNA levels by almost 80% compared to the control (Fig. 4e). The activation of HBV core promoter by RSV was also substantially blocked by Sirt1 knockdown (Fig. 4f). In addition, nicotinamide (NAM), a Sirt1 activity inhibitor, also dramatically reduced RSV-stimulated HBV core promoter activity (Fig. 4g). These data demonstrated an essential role of Sirt1 in RSV-stimulated HBV replication.
Previous studies have demonstrated that in hepatic lipid metabolism, Sirt1 can activate PPARα via deacetylating peroxisome proliferator-activated receptor gamma coactivator 1, alpha (PGC-1α ) 34 . To analyze whether PGC-1α is a signal that links Sirt1 to PPARα /RXRα in RSV-induced HBV replication, we examined the PGC-1α acetylation in HepG2.2.15 cells. RSV treatment significantly decreased the acetylation level of PGC-1α protein in HepG2.2.15 cells (Fig. 5a) and increased the interaction between endogenous Sirt1 and PGC-1α (Fig. 5b). Pretreatment of nicotinamide (NAM), a Sirt1 activity inhibitor, restored the acetylated PGC-1α level to that of the untreated group (Fig. 5a). To determine whether PGC-1α is required for RSV-induced HBV replication, we knocked down PGC-1α by siRNA in HepG2.2.15 cells. PGC-1α deficiency caused a significant decrease of HBV DNA in the culture medium compared to the control (Fig. 5c). Similarly, knockdown of PGC-1α significantly reduced RSV-stimulated core promoter activity in HepG2 cells (Fig. 5d). In sum, these findings indicate that Sirt1 activated by RSV directly regulates PGC-1α acetylation, resulting in RSV-induced HBV replication.
Activation of PPARα is required for fasting-induced HBV replication. Calorie restriction has a similar effect on energy metabolism to that of RSV treatment 35,36 . It was reported that fasting can induce HBV gene expression through PGC-1α 37 . Given that the fact that PPARα 's transcriptional activity can be activated by fasting 38,39 and the essential role of PGC-1α /PPARα axis in mediating RSV-induced HBV replication, we reckoned that PPARα may be required for fasting-triggered HBV replication. To test this idea, the mice were starved for different time periods after injection of 1.3 × HBV-Cp-Luc plasmid DNA. The core promoter activity, assessed by the luciferase intensity, was time-dependently increased in all fasted mice (Fig. 6a,e). To verify the role of (g) Sirt1 antagonist NAM markedly blocked RSV-stimulated activity of the viral core promoter. HepG2 cells were cotransfected with pGL3-Cp and pRL-TK plasmids for 24 hours and cells were then treated with RSV (50 μM) or RSV (50 μM) plus NAM (10 mM) for 72 h. The gels have been run under the same experimental conditions. All experiments were repeated at least three times with consistent results. Bar graphs represent the means ± SEM, n = 3 (*p < 0.05).
PPARα in the process, the hepatic expression of PPARα targeted genes including MCAD, cytochrome P450 and mHMG-CoAs was measured by RT-qPCR. Starvation up-regulated the mRNA levels of all the genes measured ( Fig. 6b), similar to those caused by RSV treatment (Fig. 3d). In addition, GW6471, a specific PPARα inhibitor, significantly blocked the enhanced activity of the HBV core promoter by fasting in the time course of starvation (Fig. 6d,e), suggesting an essential role of PPARα activation in fasting-induced HBV transcription.

Discussion
RSV has attracted tremendous attentions in the past years due to its beneficial effects in a number of diseases (e.g. metabolic or cardiovascular diseases, aging, cancer ) exerted by its protective functions such as antioxidant properties 15,40 , anti-inflammatory 41 and anti-proliferative functions 42 respectively. Although RSV was found to have effects on several viral infections, this study is the first time to demonstrate that RSV actually induces HBV replication in vitro and in vivo. Given the fact that there are increasing successful cases of preclinical trials of RSV treatment on a variety of human diseases 43,44 , our data raises a serious concern about the risk of possible side effects of RSV, such as treatment of RSV in patients with metabolic disorders simultaneously infected with HBV. In addition, RSV exists in a number of fruits such as red grapes (50-100 mg/g) 45 and drinks such as red wine (0.2 mg/l to 5.8 mg/l) 46 at a notable concentrations. Patients with HBV infection, especially HBV carriers and occult infection patients, would have a risk of HBV recurrence when drinking over-dosed red wine. Given the drinking culture in large populations worldwide, additional studies would be required to determine the guideline amount for red wine drinking in HBV patients.
Our data are consistent with previous finding that RSV is considered as an activator of Sirt1 47 , a member of the mammalian sirtuins or HDAC class III. Ren 49 . The discrepancy between the two studies discussed above may be partially due to different cell models and experimental conditions used. In our study, unlike Act3, RSV alone displays a strong ability to promote HBV replication in a Sirt1-PGC-1α and PPARα /RXRα -dependent manner. Sirt1, PGC-1α and PPARα / RXRα proteins appear to constitute a sub-network that modulates the HBV core promoter activity and viral transcription. RSV-induced Sirt1 activation mediates HBV transcription, and inhibition of Sirt1 by NAM attenuates this effect, suggesting that activation of Sirt1 is required for RSV-induced HBV replication. Given the limited effects of Sirt1 overexpression or activation by Act3 on the HBV transcription, the most likely explanation is that RSV has both Sirt1-dependent and independent functions 50 , which can simultaneously activate other signals to augment the function of this sub-network, such as activation of PPARα /RXRα through other pathways.
Fasting induces metabolic responses that allow mammals to survive for a long period of energy deprivation. Dramatic changes in gluconeogenesis and fatty acid oxidation are prominent features of the energy-metabolic responses. Shlomai A et al. demonstrated that PGC-1α controls HBV replication through nutritional signals and interestingly, PGC-1α co-activates HNF4α , a key enzyme of gluconeogenesis, to promote HBV replication 37 . However, we think that the role of HNF4α is not conclusive due to lack of loss of function study to demonstrate the necessity of HNF4α in this process. In our study, although the expression of gluconeugenic genes was upregulated after 7-hour fasting, there was no similar change in the gene expression in the time points of prolonged fasting (Fig. 6c), suggesting that the activation of gluconeogenesis is not associated with fasting-induced HBV viral replication. Instead, we found that genes involved in FAO were closely associated with fasting-induced HBV transcription and replication. This was further confirmed by our experiments where inhibition of PPARα , a key enzyme of FAO, alleviated fasting-induced HBV transcription in vivo, providing strong evidence that FAO plays an important role in HBV replication in the hosts exposed to nutrient/energy deficiency.
In summary, this study demonstrates that RSV has a strong ability to enhance HBV replication through its core promoter. As shown in Fig. 7, RSV activates Sirt1 and enhances HBV replication in a PGC1α -PPARα /RXRα dependent mechanism, which resembles the pathway of fasting-induced FAO and HBV replication. Supplement of HBV patients with RSV presents a potential risk of hepatitis B recurrence.

Animal experiments.
Our animal studies were conducted in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals according to the regulation in the People's Republic of China. The protocol was approved by the Committee on the Ethics of Animal Experiments of Zhejiang University (Approval No. X1201231). All procedures were made to minimize suffering. C57BL/6 mice (male, 6-8 weeks old), obtained from Shanghai Laboratory Animal Center (Shanghai, China), were used for animal experiments. For the whole HBV analysis, ten micrograms of HBV plasmid DNA (pHBV) in a volume of PBS equivalent to 10% of the mouse body weight were injected via tail vein on day 0 according to the method 24 . Twenty eight days after the hydrodynamic injection, mice were divided into two groups as follows: RSV group and H2O group. Mice received either RSV (100 mg/kg mouse body weight) or vehicle (water) by oral gavage every day for 2 weeks. The chosen dose was based on a previous study 51 . RSV was re-suspended in water. Mice were sacrificed at 35 and 42 days after injection. Liver tissues and sera were collected for pathologic and biochemical analysis.
For animal imaging experiments, mice were injected with six micrograms of the 1.3× HBV-Cp-Luc construct and analysis was performed 72 h after the hydrodynamic injection 37 . After mice received an intragastric administration of RSV (100 mg/kg/d) or vehicle (water) for 20 minutes, mice were anesthetized with isoflurane, and D-luciferin potassium salt (122796, Perkin Elmer, USA) at 150 mg/kg was then intraperitoneal injection (i.p.) injected. Visualization of luciferase activity was performed on the Lumazone FM1024 equipment (Nippon Roper, Japan). Data analysis including quantification was performed by using the SlideBook ™ 4.0 software (Intelligent Imaging Innovations, USA). Luciferase activity of every animal was quantified and expressed as the ratio relative to the baseline activity.
For starvation experiments, male littermates were separated into individual cages at the beginning of each fasting experiment. Fasting was initiated at 5:00 p.m. Mice injected with the 1.3x HBV-Cp-Luc plasmid at 72 h prior to fasting were divided into two groups. The experimental group (n = 6) was fasted (free access to water was allowed) at indicated times (0, 7, 24, 48 h) and allowed to a subsequent 24-hour refeeding. The control group (n = 6) was let for continuous free feeding. The luciferase activity of core promoter was visualized at indicated times. Meanwhile, another 30 mice (divided into 5 groups: 0 h, fasting 7 h, fasting 24 h, fasting 48 h, refed, n = 6) treated as indicated above were killed and liver tissues were collected and immediately frozen in liquid nitrogen for further analysis. To investigate the effect of inhibiting activity of PPARα on the viral core promoter under nutritional deprivation, mice with HBV-Cp-Luc plasmid were administered acute intraperitoneal injection of GW6471 (1 mg/kg, a PPARα antagonist) 30 min prior to defined fasting time points. The dose was chosen based on the previous pilot study 52 . Control animals received the same volume of the vehicle (1% aqueous solution of DMSO). Luciferase activity of every animal was quantified at all time points.
For the detection of pgRNA and preC RNA, the primers used were based on the previous pilot study 54 . In brief, the cDNA product was used in each of three separate amplification reactions with BC1 (5′ -GGAAAGAAGT CAGAAGGCAA) as the common 3′ antisense primer and 5′ sense primers: (1) PCP (5′ -GGTCTGCGCACCAGCACC) for the specific detection of preC RNA transcripts, (2) PGP (5′ -CACCTCTGCCTAATCATC) for monitoring total CP-directed RNA transcription (pgRNA plus preC RNA), and (3) M3 (5′ -CTGGGAGGAGTTGGGGG AGGAGATT) for detecting residual HBV DNA contamination. The levels of pgRNA transcripts were calculated by subtracting preC RNA levels from total CP-directed transcription.

Protein-protein interaction analysis and Western blot. Endogenous protein-protein interaction in
cells was examined by co-immunoprecipitation experiments using anti-Sirt1 and anti-PGC-1α antibodies. Cells were lysed with Cell Lysis Buffer (1× ) (#9803, Cell Signaling Technology, USA) containing 1 mM PMSF, 10 mM nicotinamide and 10 μM TSA. Lysates were centrifuged (13,000g, 4 °C, 10 min) and the supernatants were used for immunoprecipitation. 50 μl of fresh protein G magnetic beads (#LSKMAGG02, Millipore, USA) were added and incubated with 1 μg (2 μL) of anti-Sirt1 or 2.4 μg (12 μl) of anti-PGC-1α for 10 minutes with continuous mixing at room temperature. Cell lysate samples (400 μg) and the immobilized capture antibody were then incubated at 4 °C with continuous mixing overnight. Immunocomplexes were washed several times, denatured with 80 μl 2× Laemmli sample buffer (10 min, 95 °C) and then analyzed by Western blot. The immunoprecipitates were separated by SDS-PAGE and immunoblotted using antibodies against Sirt1 and PGC-1α .

PGC-1α acetylation assays. PGC-1α acetylation level was measured by immunoprecipitation of PGC-1α
followed by Western blot using anti-acetyl-lysine antibody. PGC-1α and acetylation levels were assayed using specific antibodies for PGC-1α and acetyl-lysine.
Luciferase ashsay. HepG2 cells in 96-well plates containing 2.0 × 10 4 cells were transiently transfected with the reporter vector (pGL3-Cp, pGL3-S1p and pGL3-S2p) by using Lipofectamine 3000 according to the manufacturer's instructions. Transfection mixtures for each well comprised 100 ng of promoter reporter plasmid and 10 ng of plasmid pRL-TK, serving as an internal control to normalize the transfection efficiency. Six hours after transfection, RSV was added to the medium as indicated and cells were incubated for 3 days. Then Firefly and Renilla luciferase activities were measured by using a Dual-Glo ® Luciferase Assay System kit (E2940, Promega, USA) according to the manufacturer's instructions. The luciferase activity was determined on a GloMax microplate luminometer (Promega, USA). NAD+/NADH ratio assay. HepG2.2.1.5 cells were grown to 55% confluency in a 6 cm 2 tissue culture plate.
Cells were subsequently treated with media containing DMSO or RSV (50 μM). After 24 hours of treatment, cells were lifted with trypsin, washed twice with cold PBS and pelleted through centrifugation. NAD + /NADH ratio was performed using the EnzyChrom NAD/NADH Assay Kit (E2ND-48, Bioassay Systems, USA). NAD and NADH contents were normalized by protein concentrations in cell lysates. Detection of serum HBV antigen and DNA. Serum HBsAg and HBeAg levels were detected automatically by Abbott i2000SR using the Architect HBsAg and HBeAg Reagent kits (Abbott Diagnostics, Abbott Park, IL, USA). Serum HBV DNA copies were measured using the Fluorescence Quantitative PCR Detection Kit for Hepatitis B Virus DNA (ACON Biotech Co. Ltd, Hangzhou, China). The HBsAg, HBeAg and HBV DNA in the culture medium were measured similarly. All of these assays were conducted following the manufacturers' instructions.

Immunohistochemistry.
Statistics. Data were analyzed using GraphPad Prism v5.0a (GraphPad Software, Inc., SanDiego, USA). Data were presented as mean ± SEM. Statistical significance of the differences was determined using Student t test. Differences were considered significant when P < 0.05.


s2fieldsofstudy: Biology Psychology

text:
Chemogenetic inhibition of the medial prefrontal cortex reverses the effects of REM sleep loss on sucrose consumption

Rapid eye movement (REM) sleep loss is associated with increased consumption of weight-promoting foods. The prefrontal cortex (PFC) is thought to mediate reward anticipation. However, the precise role of the PFC in mediating reward responses to highly palatable foods (HPF) after REM sleep deprivation is unclear. We selectively reduced REM sleep in mice over a 25–48 hr period and chemogenetically inhibited the medial PFC (mPFC) by using an altered glutamate-gated and ivermectin-gated chloride channel that facilitated neuronal inhibition through hyperpolarizing infected neurons. HPF consumption was measured while the mPFC was inactivated and REM sleep loss was induced. We found that REM sleep loss increased HPF consumption compared to control animals. However, mPFC inactivation reversed the effect of REM sleep loss on sucrose consumption without affecting fat consumption. Our findings provide, for the first time, a causal link between REM sleep, mPFC function and HPF consumption. DOI: http://dx.doi.org/10.7554/eLife.20269.001

Introduction
A strong link exists between insufficient sleep and weight gain. A study of more than 30,000 participants found that those who slept less than 6 hr per night were more likely to gain weight over a 1year period compared to people who slept 7 to 8 hr per night (Watanabe et al., 2010). Moreover, when healthy adults obtain insufficient sleep (i.e., 5 hr per night) they are more likely to consume weight-promoting foods compared to persons who obtain sufficient sleep (i.e., 9 hr per night; Markwald et al., 2013). Rapid eye movement (REM) sleep loss itself may be sufficient to increase consumption of weight promoting, highly palatable foods (HPF). Shechter et al. (2012) found that REM sleep loss in humans, over a period of 5 d, was inversely associated with hunger ratings and fat consumption. Indeed, increased intake of weight promoting, highly palatable foods may, over time, lead to adverse health outcomes such as obesity, diabetes and cardiovascular diseases.
Several studies have attempted to uncover the underlying neural mechanisms responsible for mediating an increased desire to consume HPF in response to sleep deprivation. Healthy adults who were subjected to one night of total sleep loss reported an increased desire to consume high-calorie foods compared to non-sleep deprived controls (Greer et al., 2013). In addition, sleep deprivation decreased activation, assessed by fMRI scans, in areas of the brain thought to mediate reward anticipation and inhibitory control, namely the anterior cingulate and orbitofrontal cortex, respectively (Greer et al., 2013;Liu et al., 2011;Stuss, 2011). However, the relationship between brain function and sleep loss are not consistent. For example, presentation of HPF to sleep deprived healthy adults was not associated with anterior cingulate activation (St. Onge et al., 2014) while in other instances a positive association was found between appetite for HPF and anterior cingulate function (Benedict et al., 2012). Therefore, the current investigation sought to determine the role of the medial prefrontal cortex (medial PFC, mPFC) in mediating appetite for highly palatable foods in mice experiencing REM sleep loss. To achieve this aim, we used a well-validated chemogenetic method (Lerchner et al., 2007) to produce a modified invertebrate chloride channel (GluClab) that when activated by the drug ivermectin (IVM) facilitates neuronal inhibition in the mPFC over several days. We then examined the effect of mPFC inactivation and REM sleep loss on appetite for HPF in mice.

Results
Wire-mesh-grid device exposure produced REM sleep loss During the dark period, mice exposed to a wire-mesh-grid device (WMGD) in their housing cage ( Figure 1A) over a 72-hr period showed significantly less REM sleep after device introduction compared to baseline polygraphic recordings obtained without the device (F 3,6 = 23.835, p = 0.001; Figure 1B and C, McEown et al., 2016). There was a significant REM sleep reduction of 58% observed between baseline and the 25-48 hr dark time period (p = 0.026) and a 56% REM sleep reduction observed between baseline and the 49-72 hr dark time period (p = 0.020). In addition, a 46% REM sleep reduction was found when comparing the 0-24 hr and 25-48 hr dark time periods (p = 0.009). Moreover, we did not observe any rebound in REM sleep during the first 3 hr of the 25-48 hr light period (all comparisons p > 0.05; Figure 1-figure supplement 1). A significant increase in wakefulness was also produced by the device during the dark period (F 3,6 = 6.886, p = 0.023). Specifically, a 23% increase in wakefulness during the dark period was observed between baseline and the 49-72 hr time period (p = 0.042). No differences were observed between time periods, during the dark phase, for slow-wave sleep (SWS)-also known as non-REM sleep-(F 3,6 = 4.605, p = 0.053). In addition, differences between time periods, during the light phase, for wake (F 3,6 = 1.608, p = 0.284), REM (F 3,6 = 1.103, 0.418) and SWS (F 3,6 = 1.434, p = 0.323) were also nonsignificant.
Exposing mice to the WMGD significantly affected sleep episode numbers during the 25-48 hr light and dark periods ( Figure 2A). Specifically, WMGD exposure increased the number of SWS episodes during the light period, lasting 120 to 239 s, by 44% compared to baseline measures taken without the WMGD (t 2 = À18.898, p = 0.003). Furthermore, WMGD exposure decreased the number of REM episodes during the dark period, lasting 60 to 119 s, by 69% compared to baseline (t 2 = 9.430, p=0.011). On the other hand, WMGD exposure increased the number of wake episodes during the light period, lasting 30 to 59 s, by 42% compared to baseline (t 2 = À4.355, p = 0.049). The mean duration of SWS, REM sleep and wake episodes was not significantly different after WMGD exposure ( Figure 2B).
These results suggest that REM sleep is specifically reduced during the dark period in mice after WMGD exposure for the 25-48 hr time period, or longer, and thus the WMGD may be useful to elucidate the role of REM sleep loss on HPF consumption.

GluClab-mPFC neuronal inhibition reverses the effect REM sleep loss on HPF consumption
Next, we investigated the effect of REM sleep loss on HPF consumption and whether changes in the latter one depend on the mPFC. Therefore, we used a chemogenetic system that involves a Caenorhabditis elegans glutamate-gated and IVM-gated chloride channel that was mutagenically modified to abolish sensitivity to glutamate while retaining sensitivity to IVM (Lerchner et al., 2007). The . Black and white bars in (B) indicate dark and light period, respectively. *p < 0.05 and **p < 0.01 indicate significant differences between baseline and WMGD exposure (n = 3). Figure 1 continued on next page altered heteromeric channel, which is made up of an a-subunit and b-subunit, prevents the firing of action potentials by hyperpolarizing the cell membrane for several days after administration of IVM.
Two adeno-associated viruses (AAV) carrying the a-subunit or b-subunit of the IVM-gated GluClab channel (AAV-GluCla-IVM and AAV-GluClb-IVM) were stereotaxically co-injected bilaterally into the mPFC of wild-type mice ( Figure 3A and B). To detect expression of the GluClab channel, we labeled neurons with anti-green fluorescent protein (GFP) antibodies to stain the fusion proteins of the GluCla-subunit and GluClb-subunit with yellow and cyan fluorescent proteins, respectively. Heat maps depicting AAV location and spread within the mPFC are displayed in Figures 3B and  4A. The virus successfully targeted the mPFC, for mice used in all experiments, including the cingulate, prelimbic and infralimbic cortices ( Figure 3B). IVM-injected mice had markedly attenuated Fos expression in the mPFC compared to mice injected with propylene glycol, indicating that the GluClab successfully inhibited neuronal activity in the mPFC ( Figure 3C). SWS, REM and wakefulness amounts were non-significant when comparing the light and dark phases during the 25-48 hr time period for mPFC inactivated mice to the light and dark phases of the baseline day before the IVM injection ( Figure 3D; all comparisons p>0.05).
We then examined the effect of REM sleep loss on sucrose consumption and whether changes in sucrose consumption depend on the mPFC (F 3,16 = 3.206, p = 0.051; see Figure 4, Panel B). We compared non-sleep deprived mPFC inactivated mice to non-sleep deprived control mice and found no difference between these groups in sucrose (propylene glycol injected mice: p = 0.391; saline injected mice: p = 0.350) or fat consumption (propylene glycol injected mice: p = 0.575; saline injected mice: p = 0.434); indicating that mPFC inactivation, alone, did not affect sucrose or fat consumption in mice (Figure 4-figure supplement 1, Panel A). Moreover, we found that REM sleep loss increased sucrose consumption by 27% compared to non-sleep deprived saline injected controls (p = 0.039) and 35% compared to non-sleep deprived propylene glycol injected controls (p = 0.033). On the other hand, mPFC neuronal inhibition reversed the effect of REM sleep loss on sucrose consumption. Specifically, mPFC inactivated, REM sleep deprived mice consumed 49% less sucrose compared to REM sleep deprived controls (p = 0.004). Whereas sucrose consumption was non-significant between mPFC inactivated, REM sleep deprived mice and non-sleep deprived propylene glycol injected (p = 0.205) and saline injected controls (p=0.060).
Furthermore, the effect of REM sleep loss on fat consumption was also examined and whether changes in fat consumption depend on the mPFC (F 3,16 = 7.778, p = 0.002; see Figure 4, Panel B). We found that REM sleep loss increased fat consumption by 26% compared to non-sleep deprived saline injected controls (p = 0.001) and 35% compared to non-sleep deprived propylene glycol injected controls (p = 0.000). However, mPFC inhibition paired with REM sleep loss did not affect fat consumption compared to sleep deprived controls (p = 0.199).

Discussion
We were successfully able to mitigate the increased consumption of sucrose that resulted from REM sleep loss. We achieved this aim by chemogenetically inhibiting mPFC function. These results are the first to demonstrate a clear, causative link between mPFC function, REM sleep and HPF consumption. Specifically, REM sleep loss increased consumption of two highly palatable foods, namely sucrose and fat. Whereas, mPFC inhibition completely reversed increased sucrose intake by attenuating the effect of REM sleep loss on sucrose consumption. On the other hand, mPFC inhibition did not attenuate the effect of REM sleep loss on fat consumption.
The PFC appears to play a role in mediating goal directed behavior toward highly appetitive food stimuli. Brain circuits responsible for judging the palatability of foods such as taste, texture and smell are mediated by areas of the PFC including the orbitofrontal and anterior cingulate cortex (Rolls, 2015). More specifically, the anterior cingulate appears to mediate taste for glucose in primates as feeding fruit juice (i.e., a high-glucose food) to satiety decreased neuronal firing in the anterior cingulate cortex using single cell recordings, whereas initial juice feeding increased neuronal firing in these animals (Rolls, 2008). Chocolate flavor also increases activation, as measured by fMRI, in the anterior cingulate cortex in humans (Rolls and McCabe, 2007). In addition, obese patients display increased PFC activity in response to high-calorie visual food stimuli; specifically, increased activity in the orbitofrontal and anterior cingulate cortex (Stoeckel et al., 2008;Rothemund et al., 2007). Alterations in PFC brain activity may, over time, result in the inability of these persons to inhibit salient food cues leading to excess high calorie food consumption and weight gain. Our results support these data by directly manipulating mPFC function in mice, which resulted in a substantial decrease in sucrose consumption, but had no effect on fat consumption. On the other hand, it is unknown whether consumption of other highly palatable foods may be effected by mPFC inactivation and/or REM disruption (e.g., foods high in salt content). Future research is needed to explore this possibility. The mPFC contains heterogeneous neurons that include some inhibitory GABAergic interneurons (approximately 20%) and excitatory pyramidal neurons (approximately 80%; (Riga et al., 2014). Moreover, mPFC neurons expressing dopamine D1 receptors mediate food consumption (Land et al., 2014); however, it is currently unknown what role this neuronal subtype may play in controlling highly palatable food consumption in response to REM sleep disruption.
Furthermore, in humans REM sleep loss adversely affects dietary behavior which may lead to weight gain. For example, REM sleep loss in humans enhances the desire to consume food and increases food consumption (Gonnissen et al., 2013;Shechter et al., 2012). In addition, REM sleep loss in children and adolescents, of 1 hr per night measured over three nights, was associated with a marked increase in the likelihood of these children being overweight (Liu et al., 2008). In agreement with these findings, we observed that reducing REM sleep, over a 25-48 hr period, during the dark phase resulted in increased HPF consumption. On the other hand, it should be noted that, in the current investigation, REM sleep was not effected during the light phase. It is unknown why our observed reduction in REM sleep was restricted to the dark phase and not during the light phase. Indeed, sleep pressure is greater during the light phase compared to the dark phase in mice; therefore, it is possible that our device was only able to produce REM reduction during the dark phase when sleep pressure was weaker. In addition, the REM sleep disruption observed in the current investigation did not occur during the first 24 hr after WMGD introduction. A plausible explanation is that the sleep reducing effect of our device requires time to build-up and thus produce disturbances in sleep.
In conclusion, our results demonstrated that mPFC inhibition reduced sucrose consumption in REM sleep deprived mice. Our results clearly indicate that increased HPF consumption, resulting from REM sleep loss, is mediated by mPFC function thereby providing clear findings that elucidate the role of the mPFC and REM sleep loss in mediating unhealthy dietary behavior, which may lead to adverse health outcomes. A male mouse line on a C57BL/6 background, which was maintained at the International Institute of Integrative Sleep Medicine and weighed between 23 and 34 g (9-10 weeks old) at the beginning of the experiments, was used for all experiments. The animals were individually housed in insulated sound-proof chambers maintained at an ambient room temperature of 22˚C ± 1˚C with a relative humidity of 60 ± 2% in an automatically controlled 12 hr light/dark cycle (lights on at 8:00, lights off at 20:00). Food and water were available ad libitum. No method of blinding or randomization was used for any of the experiments. This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All animals were handled according to approved institutional animal care and use committee (IACUC) protocols (#16-359) of the University of Tsukuba. All protocols aimed to reduce the number of animals used for each experiment and to minimize pain or discomfort. IVM was obtained from Merial Company.

Stereotaxic AAV injection and placement of EEG/EMG electrodes
Surgeries for AAV injections were conducted under pentobarbital anaesthesia (50 mg/kg, intraperitoneal [i.p.]). Using aseptic techniques, mice were injected stereotaxically into the mPFC (n = 14) with recombinant GluCla-AAV and GluClb-AAV (250 nl/injection, 1.1 Â 10 11 particles/ml), with a glass micropipette and an air pressure injector system (Chamberlin et al., 1998). The following coordinates were used for injections into the mPFC of C57BL/6 mice according to the atlas of Paxinos and Franklin (2001). Bilateral injections were made at 1.7 mm anterior and 0.4 mm lateral to bregma, and 2.0 mm below the dural surface.
Mice were also chronically implanted with EEG and electromyogram (EMG) electrodes for polysomnography (n = 6) as previously outlined (Oishi et al., 2016). Briefly, the implant comprised two stainless steel screws (1 mm diameter) serving as EEG electrodes, one of which was placed epidurally over the right frontal cortex (1.0 mm anterior and 1.5 mm lateral to bregma) and the other over the right parietal cortex (1.0 mm anterior and 1.5 mm lateral to lambda). Two insulated Teflon-coated, silver wires (0.2 mm in diameter), which were placed bilaterally into the trapezius muscles, served as EMG electrodes. Both EEG and EMG electrodes were connected to a microconnector, and the whole assembly was then fixed to the skull with self-curing dental acrylic resin. Animals were allowed one week for postoperative recovery before being placed in experimental housing cages (Figure 1, Panel A) for a 24 hr habituation period and connected with recording leads.

EEG/EMG recording
After the 24-hr habituation period, mice were injected with vehicle (propylene glycol, i.p.) at 16:30. Baseline polygraphic recording commenced at 17:00 and terminated at 16:00 the following day. After baseline recordings were obtained, mice (n = 3) were injected with IVM (10 mg/kg; i.p.) at 16:30 on the following day to initiate virus activation. Polygraphic recordings subsequently commenced at 17:00 for a period of 72 hr.
A separate group of EEG/EMG implanted mice (n = 3) were tested using the preceding methods with the exception that mice did not receive AAV infusions and did not receive i.p. injections. Instead, a wire-mesh-grid cage bottom (purchased from Oriental Yeast Company, Tokyo, Japan) was introduced, at 16:30 the day after baseline polygraphic recording, for a period of 72 hr. The wiremesh-grid cage bottom was constructed from stainless steel and measured: 22 cm (length) Â 19.5 cm (width) Â 2.5 cm (height) with the grid consisting of 1 cm 2 openings (see Figure 1, Panel A).

Vigilance state assessment based on EEG/EMG/locomotor activity recordings
The EEG/EMG signals were amplified and filtered , then digitised at a sampling rate of 128 Hz, and recorded using SLEEPSIGN software (Kohtoh et al., 2008) (Kissei Comtec). In addition, locomotor activity was recorded with an infrared photocell sensor (Biotex). The vigilance states were scored offline by 10 s epochs into three stages, including waking, SWS, and REM sleep, according to standard criteria (Oishi et al., 2016). As a final step, defined vigilance stages were examined visually, and corrected when necessary.

HPF data collection
HPF consumption measurements were obtained from mice that did not receive EEG/EMG electrode implantation (n = 24). After AAV infusion, mice were singly placed into testing cages without the wire-mesh-grid-cage bottoms for 7 days. During these seven days mice had free access to laboratory chow. On day 8, mice were injected with IVM (10 mg/kg, i.p.; n = 4) or vehicle (propylene glycol; n = 4) at 16:30 and singly placed back into their housing cages with the addition of the wire-meshgrid cage bottoms at 17:00 (Figure 1, Panel A). The highly palatable foods were introduced on day 8 and food consumption was subsequently measured every 24 hr at 17:00 over a total period of 72 hr. In addition, three controls groups were used in which one group of mice did not receive AAV infusions and were injected with saline (n = 9), one group of mice received AAV infusions and were injected with propylene glycol (n = 3), and one group of mice received AAV infusions and were injected with IVM (n = 4). All three groups were not exposed to the wire-mesh-grid cage bottom (i. e., the non-sleep deprived groups). During the 72 hr test period, all mice had free access to white chocolate (DARS brand, Morinaga Company, Tokyo, Japan), a high-fat diet (HFD-60, Oriental Yeast Company, Tokyo, Japan) and standard laboratory chow (see Supplementary file 1 for the composition of each highly palatable diet). Total amounts of sucrose and fat consumption were calculated from each food type (i.e., sucrose and fat amounts from the white chocolate and from the high-fat diet) 25-48 hr after initial food exposure.

Immunohistochemistry for GFP and Fos immunoreactive cell expression
When data collection, outlined above, was complete mice were deeply anesthetized using chloral hydrate (500 mg/kg, i.p.) and perfused through the left ventricle of the heart with saline and subsequently perfused with neutral buffered 10% formalin solution. Brains were then removed and placed into vials containing 10% formalin solution for 1 week. Brains were subsequently transferred to vials containing 20% sucrose in phosphate-buffered saline (PBS) for 24 hr at 4˚C to reduce freezing artifacts. Brains were then frozen using dry ice, placed on a freezing microtome and sectioned at 40 mm. Immunohistochemistry was undertaken on free floating brain sections as previously described (Lazarus et al., 2011). In brief, sections were rinsed in PBS, incubated in 0.3% hydrogen peroxide in 0.25% Triton X-100 in PBS (PBT) for 30 min at room temperature, rinsed in PBS and incubated, overnight at room temperature, in rabbit GFP primary antibody (Molecular Probes, RRID:AB_221569, lot number 1293114) at a dilution of 1:20,000 or rabbit Fos primary antibody (Millipore, RRID:AB_ 2106755, lot number D00058535) at a dilution of 1:20,000 in PBT with 0.02% sodium azide. After overnight incubation, brain sections were rinsed in PBS for 1 hr and then incubated for 90 min in biotinylated anti-rabbit antibody (Jackson ImmunoResearch) at a dilution of 1:1000. These sections were then treated with avidin-biotin complex (1:1000; Vectastain ABC Elite kit, Vector Labs) for 1 hr and immunoreactive cells were visualised by reaction with 3,3'-diaminobenzidine and 0.01% hydrogen peroxide. Tissue sections mounted on glass slides were scanned with a Hamamatsu Nano-Zoomer-XR Digital slide scanner (Hamamatsu Photonics), and digital photomicrographs were analyzed with Hamamatsu NDPView software v2.4.26.
AAV location was assessed by visualizing GFP expression in brain sections using Hamamatsu NDPView software v2.4.26. Virus location figures (see Figure 3, Panel B and Figure 4, Panel A) were produced by using the following procedure: (a) observing virus location and spread within brain sections; (b) transforming digital photomicrographs images to color-scale images; and (c) superimposing these color-scale images to obtain a summation of AAV location for all mice.
Furthermore, Fos immunoreactive cell expression was measured using two AAV infused mice that were injected with either propylene glycol or IVM, exposed to the WMGD for 48 hr and fed a diet of white chocolate, HFD and chow. After WMGD exposure, mice were sacrificed during the dark period (i.e. at 21:00). Immunohistochemistry procedures were then undertaken as outlined above. Fos immunoreactive cell imaging, at 20 times magnification, was produced using Hamamatsu NDPView software v2.4.26 (see Panel C, Figure 3) by focusing on an area of interest located within the mPFC at 1.7 mm anterior and 0.4 mm lateral to bregma, and 2.0 mm below the dural surface.

Statistical analyses
Means and ± S.E.M. are expressed for all statistical comparisons. For comparisons between two groups, two-tailed paired samples Student's t-tests were used followed by Bonferroni corrections. In addition, Pearson correlations were used to compare associations between two groups. Repeated measures ANOVA or one-way ANOVA were used when comparing groups of three or more followed by Fisher's Probable Least-Squares Difference (PLSD) post hoc tests. For comparisons between independent samples, the Levene's test was used to assess homogeneity of variance and for paired sample comparisons the Mauchly's test was used to assess sphericity. All comparisons were considered statistically significant at p < 0.05. Statistics for all data are reported in Supplementary file 2.


extfieldsofstudy: Medicine Psychology

text:
Altered resting-state whole-brain functional networks of neonates with intrauterine growth restriction

The feasibility to use functional MRI (fMRI) during natural sleep to assess low-frequency basal brain activity fluctuations in human neonates has been demonstrated, although its potential to characterise pathologies of prenatal origin has not yet been exploited. In the present study, we used intrauterine growth restriction (IUGR) as a model of altered neurodevelopment due to prenatal condition to show the suitability of brain networks to characterise functional brain organisation at neonatal age. Particularly, we analysed resting-state fMRI signal of 20 neonates with IUGR and 13 controls, obtaining whole-brain functional networks based on correlations of BOLD signal in 90 grey matter regions of an anatomical atlas (AAL). Characterisation of the networks obtained with graph theoretical features showed increased network infrastructure and raw efficiencies but reduced efficiency after normalisation, demonstrating hyper-connected but sub-optimally organised IUGR functional brain networks. Significant association of network features with neurobehavioral scores was also found. Further assessment of spatiotemporal dynamics displayed alterations into features associated to frontal, cingulate and lingual cortices. These findings show the capacity of functional brain networks to characterise brain reorganisation from an early age, and their potential to develop biomarkers of altered neurodevelopment. in individual functional brain IUGR theory We further characterised functional spatiotemporal dynamics and assessed network nodes with altered temporal IUGR but also on occipital, parietal and sub-cortical regions. Nodal strength of RSN long-term effects of this The specific connectivity fingerprint could underlie the increased prevalence of IUGR subjects developing disorders of neural development some the links of IUGR with neurodevelopmental individualised biomarkers of the long-term prognosis of infants with IUGR in order to be able to clinically intervene. Although multiple comparisons problems could preclude interpretations of direct partial correlations of network features with NBAS, the ordinal regression of functional network features and NBAS severity score is especially relevant as it is robust to this problem. These results are in line with previous findings reporting an association of network features of structural brain networks with neurodevelopment in IUGR (Batalle Batalle In addition, MRI in fetal functional brain early biomarker We combination of multi-modal features of brain networks improve the assessment of the risk of neurodevelopmental problems of prenatal origin since an early age, being its translation to the clinical practice in the mid-term horizon.

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A C C E P T E D ACCEPTED MANUSCRIPT Batalle et al. 2 Title: Altered resting-state whole-brain functional networks of neonates with intrauterine growth restriction

INTRODUCTION
Intrauterine growth restriction (IUGR) affects 5-10% of all pregnancies in developed countries and it is a major public health issue, being associated with short-and long-term neurodevelopmental and cognitive dysfunctions (Arcangeli, Thilaganathan, Hooper, Khan, & Bhide, 2012;Baschat, 2013;Løhaugen et al., 2013). The characterisation of underlying brain alterations supporting these dysfunctions and the prediction of the subset of the population with a higher risk of altered neurodevelopmental outcomes are among the challenges of modern fetal medicine and paediatrics. Magnetic resonance imaging (MRI) has been used to characterise structural brain alterations underlying neurodevelopmental dysfunctions of subjects with IUGR at different stages of development, starting in-utero (Egaña-Ugrinovic, Sanz-Cortes, Sanz-Cortes et al., 2013), persisting at neonatal and early infancy (De Bie et al., 2011;Dubois et al., 2008;Esteban et al., 2010;Lodygensky et al., 2008;Padilla et al., 2011;Tolsa et al., 2004) and at adolescence (Martinussen et al., 2009;Skranes et al., 2005). In the recent years, the knowledge of structural brain organisation has significantly advanced with the assessment of the macroscopic circuitry of connections of the brain with structural brain networks obtained from MRI (Hagmann, 2005;Sporns, Tononi, & Kotter, 2005). Importantly, graph theoretical features have been used to characterise brain networks (Bullmore & Sporns, 2009), allowing to comprehensibly describe with a few network features the underlying brain connectivity organisation. This approach has been demonstrated to be useful to characterise a wide-range of pathologies and conditions that affect brain connectivity (Bassett & Bullmore, 2009). Based on anatomical and diffusion MRI, this technique has been promising in the study of IUGR, allowing to demonstrate alterations in the structural brain network organisation and its association with altered neurodevelopment in one- year-old infants (Batalle et al., 2012;Batalle et al., 2013), school-age infants (Fischi-Gomez et al., 2014), and in an animal model of long-term IUGR (Batalle et al., 2014). However, it remains unknown if there is brain reorganisation at a functional level in this population, and if it can be detected at neonatal age.

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A C C E P T E D ACCEPTED MANUSCRIPT Batalle et al. 5 Since the seminal study of Biswal et al. (Biswal, Yetkin, Haughton, & Hyde, 1995), the potential of low-frequency components of resting-state functional MRI (rs-fMRI) to obtain whole-brain functional brain networks based on partial correlations of blood oxygen level-dependent (BOLD) signal (Salvador et al., 2005) has been demonstrated. Several studies have demonstrated the feasibility to use rs-fMRI to characterise the functional organisation of the healthy neonatal brain, opening the opportunity to characterise also the alterations in brain organisation due to prenatal conditions, such as IUGR. Using independent component analysis (ICA), the emergence of synchronised spontaneous low-frequency rs-fMRI BOLD signals exhibiting resting state networks (RSN) has been demonstrated in term and preterm infants during light sedation and natural sleep (Fransson et al., 2009;Fransson et al., 2007). Both ICA and seed-based correlation approaches have also been used in longitudinal studies showing the emergence of connections partially or completely matching several RSN during neonatal development (Doria et al., 2010;Gao et al., 2009;Lin et al., 2008;Smyser et al., 2010). However, studies considering whole-brain functional brain networks of the neonatal brain are scarce in the literature. Neonatal networks composed of selected regions of interest (ROIs) were studied by Gao et al. (2009), while voxel-wise networks obtained in a normalised space were obtained by Fransson et al. (Fransson, Aden, Blennow, & Lagercrantz, 2011), showing the presence of cortical hubs and sub-networks associated with these hubs. Finally, Gao et al. (2011) studied the normal evolution of ROI-based functional brain networks from neonatal age to two years of age and its resilience to random attacks, and recently van den Heuvel et al. (2015) studied the evolution of both structural and functional connectivity during preterm brain development.
In the present study we used partial correlations of rs-fMRI BOLD signals averaged into 90 regions of an anatomical brain atlas (Tzourio-Mazoyer et al., 2002) in 13 controls and 20 subjects with IUGR scanned around 44 weeks equivalent post menstrual age (PMA). Using the whole-brain functional networks obtained we characterised alterations in the individual functional brain connectivity of neonates with IUGR using graph theory features. We further characterised functional spatiotemporal dynamics and assessed network nodes with altered temporal characteristics.

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A C C E P T E D ACCEPTED MANUSCRIPT Batalle et al. 6 Finally, the association of individual network features with neonatal neurobehavioral outcomes was also assessed.

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A C C E P T E D

Participants, neurobehavioral assessment and MRI acquisition
The infants of the study were part of a larger prospective research program in IUGR involving fetal assessment and short-and long-term postnatal follow-up at Hospital Clínic (Barcelona, Spain).
The local Ethics Committee approved the study protocol, and written informed consent was obtained from the parents or legal guardians of all the participants (CEIC: 2012/7715). The original sample of the study included a sample of 45 pregnancies with 30 late-onset IUGR and 15 control fetuses. Late-onset IUGR was defined as those fetuses with estimated fetal weight below the 10 th centile according to local reference standards (Figueras et al., 2008) confirmed at birth and delivered after 34 weeks of pregnancy. Control subjects were sampled from general pregnant population and defined as fetuses with fetal estimated weight between 10 th and 90 th centile confirmed at birth. Infants with chromosomal, genetic or structural defects and signs of intrauterine infection or neonatal onset sepsis were excluded from the study. Neonatal data was prospectively recorded including gestational age (GA), birth weight, gender, Apgar at 5 min, umbilical artery pH, neonatal complications and maternal smoking status during pregnancy.
MRI was performed around one month corrected age during natural sleep after feeding the baby. An expert neuroradiologist analysed anatomical images and those infants without overt brain lesions were further analysed. In addition, all acquired images were visually inspected for apparent artefacts and subjects excluded accordingly. Thus, ten of 30 cases and two of the 15 controls were excluded from the study due to white matter (WM) lesions, awakening or excessive movement during acquisition, obtaining a final sample of 13 controls (5 males) and 20 subjects with IUGR (13 males). Characteristics of the population are described in Table 1. Particular care was taken in order to ensure neonatal welfare during the MR acquisition. A pulseoximetry probe was placed around the baby's wrist to monitor oxygen saturation levels throughout the scan, and acoustic noise was minimised with the use of neonatal ear muffs (MiniMuffs ® Neonatal Noise Attenuators, Natus Medical Incorporated, USA). The infant was swaddled with one or two infant sheets before being placed within a vacuum immobiliser, air was removed from the bag and the infant was contained M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT Batalle et al. 8 within a rigid cradle that is shaped to its body, effectively swaddling the infant. The lighting in the scanner room was reduced to aid the infant's sleep but was kept at a level that allows safely monitoring.
Neonatal neurobehavioral performance was assessed at neonatal age with the Neonatal Behavioral Assessment Scale (NBAS) (Nugent & Brazelton, 2000), which evaluates cortical and subcortical functions in 35 items grouped into 6 clusters: habituation, social-interactive, organisation of state, regulation of state, autonomous nervous system and attention (Sagiv et al., 2008). Cluster scores were transformed to z-scores according to a standard population (Costas Moragas, Fornieles Deu, Botet Mussons, Boatella Costa, & de Caceres Zurita, 2007;Sagiv et al., 2008) and defined as abnormal if they have a z-score below minus one. NBAS severity score was defined as the number of abnormal NBAS clusters for each subject. Due to the low successful rate of a valid habituation cluster (12 out of 33), it was excluded from the calculation of NBAS severity score.
Therefore, severity score was only calculated on 29 out of 33 subjects that had valid estimation of social-interactive, organisation of the state, regulation of the state, autonomous nervous system and attention clusters, in a range from 0 to 5.

Pre-processing and network extraction
All anatomical T2-weighted volumes were first skull-striped using BET (Smith, 2002). Brain tissue was segmented into WM, grey matter (GM) and cerebrospinal fluid (CSF) with unified segmentation model (Ashburner & Friston, 2005) available with SPM8. The default adult templates were replaced with specific neonatal tissue probability maps (Shi et al., 2011). AAL atlas (Tzourio-Mazoyer et al., 2002) recently adapted to neonatal population in a T2-weighted template (Shi et al., 2011) was used to parcellate each subject into 90 cortical and sub-cortical regions (Supplementary  (Warfield et al., 2002) was used to obtain an elastic transformation matching the template with each subject's T2 volume. AAL labels were propagated to each subject using this elastic transformation with discrete labelling preserved by nearest neighbour interpolation.
Image pre-processing of BOLD images was mainly performed with SPM8 package. First, intravolume time differences between slices were corrected. Inter-volume geometric displacements were corrected using a six-parameter rigid transformation for each acquired volume and spatially smoothed using a Gaussian kernel with 2 mm full width at half maximum. Correction of head motion effects in the signal was performed by regressing out the 6-parameter head motion profiles previously estimated in each voxel across all the acquisition time. Root mean square frame displacement (FD-RMS) between each volume was estimated using average of rotation and translation parameters differences (Power, Barnes, Snyder, Schlaggar, & Petersen, 2012) using matrix RMS formulation , as available in FSL5.0 Motion Outliers tool. Number of outliers in each subject (>75 th percentile + 1.5 × InterQuartile Range) was also recorded. GM/WM/CSF segmentation and AAL ROI parcellation obtained in T2 anatomical volume were registered to an averaged BOLD volume using an affine transformation. The representative averaged time series corresponding to each ROI were estimated for those voxels belonging to the GM mask and band pass filtered (0.01 -0.15 Hz). This "broad band" has been suggested to be the more reliable for graph theory analysis (Braun et al., 2011). Network edges were calculated as the partial correlation Batalle et al. 10 coefficients obtained between each pair of ROI averaged signal excluding the effects of the signal of the other 88 ROIs, obtaining a 90 x 90 partial correlation matrix for each subject. Pearson coefficients were transformed according to Fisher z-transformation and negative correlation coefficients were excluded, i.e., thresholding the networks with ρ>0. The correlation between each pair of ROIs excluding the effect of the signal common to the rest of ROIs was therefore inferred to be proportional to the connectivity of each given pair of ROIs, obtaining weighted matrices that represent the raw connectivity of RSN of each subject, as shown in Figure 1A. Note that only positive correlations were included

Network normalisation
In order to disentangle network infrastructure from network organisation, i.e., evaluate the organisation of networks independently of their average strength and density (cost), three different approaches were followed. First, different average strength among subjects was neutralised by means of normalisation of each subject's brain network by its total energy (Batalle et al., 2014), that is, given a network C with weights ݓ , for each pair of nodes i, j, we defined a normalised version of the network C with normalised weights defined as ݓ , = ݓ , ∑ ݓ , ∀, ⁄ . Secondly, differences of network density (cost) were neutralised by means of a cost-corrected analysis of network features (Achard & Bullmore, 2007). Following this approach, a binary network was created at different network costs, selecting for each cost-value x the connections with a strongest weight that yield to a cost x. The cost range was limited by the minimum value of network density for any subject included into the analysis, which is the maximum network cost where is possible to fairly compare among all the subjects. Finally, the effect of differences in strength and density were neutralised at the same time by means of a combination of both methods: instead of obtaining a binary network at each network cost, as it was performed in the second approach, the weighted network obtained at each network cost was considered in this third approach. Given that weighted features are much related to the strength of a network (weighted cost), to obtain pure organisational descriptors, the weighted networks obtained at each network cost were normalised as described in the strength normalisation approach. Particularly, for a given network ܥሺ݅, ݆ሻ = ݓ , where ݓ , Batalle et al. 11 represents the weight of the connection between nodes i and j, we defined a cost-corrected network ܥ ௪ ሺ݅, ݆, ݔሻ for each value of cost ݔ . Note that ܥ ௪ ሺ݅, ݆, ݔሻ = ݓ , ሺݔሻ where ݓ , ሺݔሻ = ݓ , if the link between node i and j belongs to the subset of strongest connections that ensure a network density of ݔ and ݓ , ሺݔሻ = 0 otherwise. In this context, normalised cost-corrected network at cost ݔ was defined as ܥ ௪ ሺ݅, ݆, ݔሻ = ݓ , ሺݔሻ ∑ ݓ , ሺݔሻ ∀, ⁄ .

Network analysis
Graph theory features allow summarising infrastructure and organisation of a brain network represented as an adjacency matrix (binary or weighted). Global functioning of each network was assessed by its infrastructure (average strength), integration (global efficiency) and segregation (local efficiency). Regional characteristics were evaluated by means of nodal strength, assessing the total connectivity of a node in a given network, and nodal efficiency, measuring the efficiency of the sub-network associated to a given node. Nodes with a high nodal efficiency indicate a high tolerance of the network to the elimination of the given node, which is associated to a high clustering of the neighbourhood of this node (Achard & Bullmore, 2007). Formulation and calculation of the graph theory features used to assess each network was based on the definitions and code compiled by Rubinov and Sporns (Rubinov & Sporns, 2009).

Dynamic functional connectivity
Dynamic functional connectivity (DFC) has been defined as the functional connectivity over a sliding time window (Sakoglu et al., 2010). Recently, phase synchronisation has been used to solve the resolution/reliability trade-off of windowing the signal in functional MRI time-series by means of conversion of the real signal into a complex analytic version, with techniques such as Hilbert transform. In the present study, we used similar techniques to study the temporal dynamics of the Note that as ∆߮ ሺݐሻ is constrained between 0 and ߨ, ݓ ு ሺݐሻ will be constrained between 0 and 1, being ݓ ு ሺݐሻ = 1 when the phase of the two regions is identical.
Using this approach, a weighted connectivity matrix based on the phase similarity was constructed at each instant of time, allowing the extraction of temporal-dependent global and regional network features that assess dynamic functional connectivity organisation.
However, given that the subjects are not receiving a specific stimulus that would allow direct correspondence with temporal points and specific brain state, features obtained at each time point are not easily comparable among subjects. Hence, we used Kuramoto order parameter to reorder temporal axis according to the relative level of global synchronisation of each subject, from its lowest to its highest level. Therefore, although after reording we still cannot give interpretation to each time point, overall they can now be interpreted as a percentage of time points showing the M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT Batalle et al. 13 same percentage of synchronisation relative to each subject, this way it is possible to reorder the temporal features obtained for each subject and can be compared as they are obtained based on the same relative level of synchronisation.

Statistical analysis
Comparisons among groups were performed by general linear models (GLM) with gender and smoking status of the mother as a co-factor and GA and PMA at MRI as co-variables. Significance was declared at p<0.05 (uncorrected). Regional alterations were shown using BrainNet viewer (Xia, Wang, & He, 2013

Resting-state networks in IUGR neonates
Infrastructure of the raw weighted partial correlation functional brain networks obtained ( Figure   1B-D) was assessed by comparing weighted graph theoretical features among groups by means of GLM. Importantly, this analysis showed significantly increased values of IUGR average strength (p=0.013), suggesting an increased pattern of weighted connectivity in IUGR networks. As expected of more strongly connected networks, weighted measures of global (p=0.015) and local efficiency (p=0.028) were also increased in IUGR. Network density, however, did not significantly differ between cases and controls.
Analysis of the pure organisational components of brain networks is strongly influenced by differences in the network infrastructure among subjects (Ginestet, Nichols, Bullmore, & Simmons, 2011). In order to assess the effectiveness of the organisation between groups independently of average strength, the effect of different values of average strength among subjects was neutralised M A N U S C R I P T

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Batalle et al. 14 by means of normalisation of each subject's brain network, as previously described in section 2.4.
As a result, significantly reduced normalised local efficiency was observed in IUGR group (p=0.003, Figure 1E). Although non significantly different between IUGR and control group, differences among subjects in network density (cost) could also be influencing the analysis of network organisation when using global network features. In this case, this effect was neutralised by means of a costcorrected analysis (Achard & Bullmore, 2007) also significantly reduced for global and local efficiencies (p=0.013 and p=0.016 respectively). All three different approaches of normalisation yielded the same conclusion: although IUGR had an increased RSN infrastructure characterised by increased average strength yielding to increased raw efficiencies, its organisation was sub-optimal when compared with controls.
In addition to the analysis of global network features, in order to find the regional components that have a higher influence in the network reorganisation observed in IUGR group, differences among groups were assessed using a GLM for each nodal feature using a similar approach as previously used for global network features. Particularly, nodal strength and nodal efficiency were further assessed in its raw and normalised versions (Figure 2). We observed that nodal features of raw networks showed a pattern of alterations in IUGR for both nodal efficiency and strength in frontal areas, but also on occipital, parietal and sub-cortical regions. Nodal strength and efficiency M A N U S C R I P T

Batalle et al. 15
of normalised networks showed a reduced number of alterations in IUGR, although there were still present some significant differences in subnetworks belonging to frontal, temporal and occipital cortices and sub-cortical regions such as amygdala and hippocampus. Note, however, that the nodal alterations reported must be considered exploratory, given that the differences observed did not withstand a false discovery rate (FDR) correction (Benjamini, Krieger, & Yekutieli, 2006).

Dynamic functional connectivity
In order to assess if there were any notable differences in the DFC of IUGR, we first used Kuramoto order parameter R to analyse the global level of synchronisation of each subject's brain at each instant of time ( Figure 3A), being zero for complete asynchrony and 1 for full synchronisation. As the subject' acquisition was not under specific stimuli, rather than analysing the results in a temporal basis, the average value over time was compared. A tendency towards significance of having an increased Kuramoto order parameter was observed in IUGR group (p=0.055), showing that, overall, subjects with IUGR tend to present a more synchronised brain than their control counterparts. The Kuramoto order parameter was further used to sort the assessed, finding a set of nodes with statistically significant differences in IUGR across time ( Figure   3E). Observing the percentage of time that each nodal efficiency is significantly different in IUGR allowed highlighting four regional features altered in IUGR more than two standard deviations

DISCUSSION
Characterisation of the brain changes underlying neurodevelopmental problems in IUGR is a current challenge in modern fetal and paediatric medicine (Ment, Hirtz, & Huppi, 2009). A better understanding of the pathophysiology of this condition is essential to start developing early biomarkers to detect the infants at high risk of having altered neurodevelopmental problems.
Importantly, it has been shown that early individualised interventions significantly improves IUGR neurobehavioral performance at short-and mid-term (Als et al., 2012;McAnulty et al., 2013).
However, given the high prevalence of IUGR and the economic cost of individualised care units, selecting those IUGR infants with a higher risk is essential to appropriately advise parents and optimally use clinical resources. With this long-term goal in mind, in the present study we investigated functional brain networks in IUGR.
The results obtained showed a very specific pattern of alterations in IUGR whole-brain RSN, characterised by a hyper-connectivity of their raw networks. However, when assessing the pure organisational features by means of three different normalisation procedures, we observe a suboptimal organisation in IUGR characterised by decreased global and local efficiency. Further analysis of nodal features showed a spread pattern of regional alterations in IUGR, however, not strong enough to withstand a FDR correction. Spatio-temporal analysis of the signal supported previous results, revealing a tendency of increased overall synchronisation in IUGR, and a set of nodal features that might have an important role in the reorganisation of functional brain networks obtained. These results, together with previously reported alterations in IUGR structural brain networks (Batalle et al., 2012) support the hypothesis that IUGR is a condition strongly associated with brain organisation, as has been suggested to happen with mood and psychiatric disorders.
Albeit it is important to note that establishing a causality with the evidence available is still premature, association of network features with neurobehavioral performance is reported in this and other studies (Batalle et al., 2012;Batalle et al., 2013), postulating IUGR as a candidate to be a brain-network disorder (Rubinov & Bullmore, 2013).

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Interestingly, in a previous study of structural brain networks in one-year-old population, reduced fractional anisotropy (FA) weighted global and local efficiency was found in IUGR (Batalle et al., 2012). Intuitively one would expect to obtain reduced efficiencies in functional brain networks due to a weaker structural connectivity, but in contrary, significantly increased raw efficiencies were found in IUGR neonates. A possible explanation for this discord could be the difference of age in the population under study, as it is a critical period in terms of brain connectivity changes . Although the constraining of functional connectivity to the structural substrate has been demonstrated (Deco et al., 2013;Honey et al., 2009;van den Heuvel et al., 2015), functional and structural brain networks might also be capturing different aspects of brain organisation, and they do not necessarily need to behave in the same manner (Cabral, Hugues, Kringelbach, & Deco, 2012;. Note also that in a previous study on healthy infants, Gao et al. (2011) showed that, from neonatal age to one year of age, more connections presented a reduction of connectivity than those presenting an increase. Thus we cannot discard a possible retard in maturation as an explanation of the general increase in IUGR average strength.
IUGR has been suggested to be a risk factor of developing disorders such as attention deficit hyperactive disorder (ADHD) (Heinonen et al., 2010;Linnet et al., 2006), autism spectrum disorders (ASD) (Gardener, Spiegelman, & Buka, 2011;Moore, Kneitel, Walker, Gilbert, & Xing, 2012) and schizophrenia (Nielsen et al., 2013). Although there are some contradictory reports, generally ASD has been characterised by having functional hyper-connectivity of salience and default mode network (Menon, 2013). However, whole-brain RSN have been reported to show reduced raw local efficiency but increased global efficiency after cost-correction (Rudie et al., 2012). In adult schizophrenia patients, reduced global and local efficiency after cost-correction of RSN has been reported (Liu et al., 2008), but also decreased average functional connectivity (Lynall et al., 2010).
RSN of children with ADHD have been reported to have increased local efficiency for several costs, although raw weighted measures have not been described (Wang et al., 2009). Overall, after comparison with the body of literature we note a unique pattern of alterations in neonatal IUGR brain networks. This pattern of alterations must be confirmed at a later age, being the follow-up of IUGR population a crucial aspect for the characterisation of the evolution of the alterations and M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT Batalle et al. 19 long-term effects of this condition. The specific connectivity fingerprint trajectories that could underlie the increased prevalence of IUGR subjects developing disorders of neural development are not clear, and will be the likely focus of future prospective studies. We hypothesise that some of the links of IUGR with neurodevelopmental disorders could be partially associated with dysfunctions in some specific regional sub-networks produced by brain reorganisation. It is worthwhile to note that although partial correlation networks and temporal dynamics yield to same global functioning conclusions (IUGR has a hyper-connected, hyper-synchronised brain but with a sub-optimal organisation), the regional results obtained by each of these techniques showed different altered regions, suggesting that they assess complementary features of brain organisation, although both highlighted the role of frontal areas. Particularly, analysis of temporal dynamics allowed to obtain a reduced set of regions altered, comprised by frontal, cingulate and lingual cortices. In agreement with the results obtained, several frontal regions with altered structural brain network features have been previously reported in IUGR (Batalle et al., 2012), while evidence of alterations in frontalposterior networks in ASD has been reported in several studies (Maximo, Cadena, & Kana, 2014).
Interestingly, altered nodal efficiency of left lingual gyrus has been specifically reported in structural brain networks of IUGR (Batalle et al., 2012;Batalle et al., 2013) and has also been shown to be decreased in RSN of ADHD patients (Wang et al., 2009). Concerning cingulate cortical areas, FA measures of this area have been strongly associated with altered neurobehavioral performance in a long-term rabbit model of IUGR (Illa et al., 2013). In addition, functional alterations of cingulate areas have been reported in different studies of ASD (Maximo et al., 2014), schizophrenia (Lynall et al., 2010) and ADHD (De La Fuente, Xia, Branch, & Li, 2013).
Finding alterations in the brain network features associated with pathology is by itself relevant for the characterisation of its pathophysiology. In the case of IUGR, the results suggest that brain reorganisation previously demonstrated in the structural substrate is also present at a functional level since neonatal age. This significantly improves the knowledge of this condition, serving as a potential physiological basis for the neurobehavioral alterations reported in these infants (Figueras et al., 2009). Note that given the heterogeneity of the aetiology and progression of this condition, big samples are needed to find differences in neonatal neurobehavior, being even more crucial to M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT Batalle et al. 20 find individualised biomarkers of the long-term prognosis of infants with IUGR in order to be able to clinically intervene. Although multiple comparisons problems could preclude interpretations of direct partial correlations of network features with NBAS, the ordinal regression of functional network features and NBAS severity score is especially relevant as it is robust to this problem. These results are in line with previous findings reporting an association of network features of structural brain networks with neurodevelopment in IUGR (Batalle et al., 2012;Batalle et al., 2013). In addition, recent reports showing the feasibility to assess functional MRI in fetal period reinforce the potential to use functional brain networks as an early biomarker (Thomason et al., 2013). We are confident that combination of multi-modal features of brain networks will significantly improve the assessment of the risk of neurodevelopmental problems of prenatal origin since an early age, being its translation to the clinical practice in the mid-term horizon.
Finally, there are several issues of the study that must be noted. First, we would like to note that the MRI acquisition was performed during natural sleep. Previous reports have associated deepness of sleep with functional connectivity (Horovitz et al., 2009;Larson-Prior et al., 2009); however, although the effects of sedation and sleep in functional connectivity are not fully understood, significant differences have not been found between sedated and non-sedated infants using ICA and seed-based correlation approaches (Doria et al., 2010;Fransson et al., 2011;Fransson et al., 2009). Importantly, neonatal sleep has been suggested to be mainly in active sleep (Biagioni et al., 2005), minimising the possibility that different sleep deepness could be partially explaining some of the results obtained. Regarding the comparability of the results obtained, two previous studies use ROI-parcellation to study whole brain functional brain networks in neonates (Gao et al., 2011;van den Heuvel et al., 2015). Franson et al. (2011) also assessed whole-brain networks in a neonatal population, however, it was performed voxel-wise in a normalised space, obtaining very large networks (4966-by-4966 elements). Albeit the use of voxel-wise networks has some advantages, constraining the networks obtained to an anatomical brain atlas allows a more comprehensible and manageable comparison among studies, especially given the broad use of AAL atlas in the literature. However, regional parcellation of the neonatal brain is also a critical issue and could be a source of bias. This was alleviated by the use of T2-weighted anatomical M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT Batalle et al. 21 volumes which improve WM contrast in neonates (Williams et al., 2005), and by the use of a specific neonatal atlas (Shi et al., 2011). Note that although we correct for gender in the statistical model, in our population the percentage of males is much higher in IUGR group. Previous studies have described gender-related differences in brain connectivity (Ingalhalikar et al., 2014;Tian, Wang, Yan, & He, 2011), although the specific effects of gender differences on functional brain connectivity at such an early age are still largely unknown. We acknowledge the relatively small sample size available for this study. In order to assess the reliability of the results obtained, we applied a resampling approach allowing us to reassess the differences between cases and controls for global network features. After applying bootstrap with 10000 samples we observed that most of the differences reported are still statistically significant after resampling (Supplementary Table 2).
Although the effects associated with IUGR appeared to be consistent when using different approaches, statistical power is limited by this reduced sample size. Further effort to study this very challenging population is needed to confirm the results here presented. With respect of motion during scan, the estimation of motion for both groups was comparable with previously reported for infants and adolescents (Pruim, Mennes, Buitelaar, & Beckmann, 2015), and importantly, we didn't find any significant differences in motion between cases and controls (Table 1), minimising the risk that any of the results reported could be related to different levels of motion in the two groups under study. We acknowledge that the robustness to motion of the approach used to analyse the data, i.e., constructing networks by means of partial correlations within the averaged signal among ROIs, needs to be further assessed (Power, Schlaggar, & Petersen, 2015). However, it should inherently remove signals present throughout the brain, therefore be robust to jerk motion typical in neonatal acquisitions. As the signal of the 88 remaining ROIs is used as a confounder in each of the partial correlations, any signal global to all the brain will be neglected as a possible source of pair-wise correlation. From a specific graph theoretical point of view, partial correlations have been shown to be an effective way to remove head motion effects (Yan, Craddock, He, & Milham, 2013). In addition, the effect of low motion, will also be further compensated by voxel-wise regressing out of the signal the 6 rigid-body features previously used to correct inter-volume motion. We further assessed the effect of motion by adding average FD-RMS and number of frames with high motion M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT Batalle et al. 22 (outliers) as a covariate in the GLM assessing group effects in main network features, without finding any relevant difference with reported results (Supplementary Table 3). Furthermore, we also tested censoring of frames with high motion and regressing them out from correlations when computing connectivity matrices (Supplementary Material), also confirming the main findings reported, suggesting that the motion during acquisition did not have a relevant effect in the graph theory characteristics here reported. Finally, since haemodynamic adaptation in IUGR occurs with blood flow redistribution preferentially to the brain, i.e. the brain sparing effect, we could not discard that part of the changes observed could be related with changes in brain vasculature. Since most of IUGR babies included in our study did not have brain vasodilation, as assessed by Middle Cerebral Artery pulsatility index, we could expect that differences would not be related with changes in neurovasculature. However, early changes in brain blood redistribution could also affect pulsatility index (Garcia-Canadilla et al., 2014), not allowing us to discard this effect in our population.

CONCLUSIONS
In conclusion, the results presented show the feasibility of using functional brain networks at neonatal age to characterise alterations of prenatal origin. Using IUGR as a model of prenatal condition allowed finding a unique pattern of alterations in the functional brain network organisation, associated with neurobehavioral scores. Overall, the observed functional reorganisation in IUGR neonates could be a potential substrate of altered neurodevelopment in infants with IUGR, and together with previous findings, postulate IUGR condition as a possible brain-network disorder.
Association of network features with neurobehavior since neonatal age opens the opportunity to develop early image biomarkers of altered neurodevelopment, a clinical chance to improve the management of a condition that affects up to 10% of the population.   Kuramoto order parameter reordered according to relative value for each subject, from its lowest to its highest value (B). Global (C) and local efficiency (D) at each time point reordered according

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