Author response: Infant brain regional cerebral blood flow increases supporting emergence of the default-mode network

Abstract

Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Human infancy is characterized by most rapid regional cerebral blood flow (rCBF) increases across lifespan and emergence of a fundamental brain system default-mode network (DMN). However, how infant rCBF changes spatiotemporally across the brain and how the rCBF increase supports emergence of functional networks such as DMN remains unknown. Here, by acquiring cutting-edge multi-modal MRI including pseudo-continuous arterial-spin-labeled perfusion MRI and resting-state functional MRI of 48 infants cross-sectionally, we elucidated unprecedented 4D spatiotemporal infant rCBF framework and region-specific physiology–function coupling across infancy. We found that faster rCBF increases in the DMN than visual and sensorimotor networks. We also found strongly coupled increases of rCBF and network strength specifically in the DMN, suggesting faster local blood flow increase to meet extraneuronal metabolic demands in the DMN maturation. These results offer insights into the physiological mechanism of brain functional network emergence and have important implications in altered network maturation in brain disorders. Editor's evaluation In this paper, the authors find a link between the emergence of functional connectivity (FC) and changes in regional Cerebral Blood Flow (rCBF) in human infancy from birth to 24 months of age, which will be of interest to the increasing field investigating how the establishment of the brain's functional organization is linked to neurodevelopmental and psychiatric conditions. The data quality and complementarity are impressive for infants over this developmental period (0-2 years). Most of the key claims of the manuscript are well supported by the data. However, the relatively sparse sample and cross-sectional nature do limit interpretation. https://doi.org/10.7554/eLife.78397.sa0 Decision letter Reviews on Sciety eLife's review process Introduction The adult human brain receives 15–20% of cardiac output despite only representing 2% of body mass (Bouma and Muizelaar, 1990; Satterthwaite et al., 2014). Vast energy demand from the human brain starts from infancy, which is characterized by fastest energy expenditure increase across lifespan (Pontzer et al., 2021). Infancy is also the most dynamic phase of brain development across entire lifespan with fastest functional and structural brain development. For example, during infancy the brain size increases dramatically in parallel with rapid elaboration of new synapses, reaching 80–90% of lifetime maximum by age of year 2 (Knickmeyer et al., 2008; Ouyang et al., 2019a; Pfefferbaum et al., 1994). Structural and functional changes of infant brain are underlaid by rapid and precisely regulated (Huang et al., 2013; Silbereis et al., 2016) spatiotemporal cellular and molecular processes, including neurogenesis and neuronal migration (Rakic, 1995; Sidman and Rakic, 1973), synaptic formation (Huttenlocher and Dabholkar, 1997), dendritic arborization (Bystron et al., 2008; Ouyang et al., 2019b), axonal growth (Haynes et al., 2005; Innocenti and Price, 2005), and myelination (Miller et al., 2012; Yakovlev, 1967). These developmental processes demand rapidly increasing energy consumption of the brain. However, there have been few whole-brain mappings of heterogeneous infant brain regional cerebral blood flow (rCBF) changes across landmark infant ages from 0 to 24 months thus far, impeding understanding of energy expenditure across functional systems of early developing brain. As a result of differential neuronal growth across cortex, functional networks in the human brain develop differentially following the order from primary sensorimotor to higher-order cognitive systems (Cao et al., 2017a; Huang and Vasung, 2014; Sidman and Rakic, 1982; Tau and Peterson, 2010; Yu et al., 2016). The default-mode network (DMN) (Raichle et al., 2001) is widely recognized as a fundamental neurobiological system associated with cognitive processes that are directed toward the self and has important implication in typical and atypical brain development (Buckner et al., 2008). Unlike primary sensorimotor (SM) and visual (Vis) networks emerging relatively earlier around and before birth (Cao et al., 2017b; Doria et al., 2010; Smyser et al., 2010), emergence of the vital resting-state DMN is not well established until late infancy (Gao et al., 2009). Till date, it has been unclear how emergence of vital functional networks such as DMN is coupled with rCBF increase during infancy. Regional brain metabolism, including glucose utilization and oxygen consumption, is closely coupled to regional CBF (rCBF) that delivers the glucose and oxygen needed to sustain metabolic needs (Raichle et al., 2001; Vaishnavi et al., 2010). Infant rCBF has been conventionally measured with positron emission tomography (PET) (Altman et al., 1988; Altman et al., 1993; Chugani and Phelps, 1986; Chugani et al., 1987) and single-photon emission computerized tomography (SPECT) (Chiron et al., 1992), which are not applicable to infants due to the associated exposure to radioactive tracers. By labeling the blood in internal carotid and vertebral arteries in neck and measuring downstream labeled arterial blood in brain, arterial-spin-labeled (ASL) (Alsop et al., 2015; Detre and Alsop, 1999) perfusion MRI provides a method for noninvasive quantifying rCBF without requiring radioactive tracers or exogenous contrast agents. Accordingly, ASL is especially suitable for rCBF measurements of infants (Ouyang et al., 2017; Lemaître et al., 2021; Wang et al., 2008) and children (Jain et al., 2012; Satterthwaite et al., 2014). Phase-contrast (PC) MRI, utilizing the phase shift proportional to velocity of the blood spins, has also been used to measure global CBF of the entire brain (Liu et al., 2019). Through integration of pseudo-continuous ASL (pCASL) and PC MRI, rCBF measured from pCASL can be calibrated by global CBF from PC MRI for more accurate infant brain rCBF measurement (Aslan et al., 2010; Ouyang et al., 2017). With rCBF closely related to regional cerebral metabolic rate of oxygen (CMRO2) and glucose (CMRGlu) at the resting state in human brain (Fox and Raichle, 1986; Gur et al., 2009; Paulson et al., 2010; Vaishnavi et al., 2010), rCBF could be used as a surrogate measure of local cerebral metabolic level for resting infant brains. Early developing human brain functional networks can be reproducibly measured with resting-state fMRI (rs-fMRI). For example, a large scale of functional architecture at birth (Cao et al., 2017b; Doria et al., 2010; Fransson et al., 2007) has been revealed with rs-fMRI. Functional networks consist of densely linked hub regions to support efficient neuronal signaling and communication. These hub regions can be delineated with data-driven independent component analysis (ICA) of rs-fMRI data and serve as functional regions of interest (ROIs) for testing physiology–function relationship. Meeting metabolic demands in these hub ROIs is critical for functional network maturation. In fact, spatial correlation of rCBF to the functional connectivity (FC) in these functional ROIs was found in adult brains (Liang et al., 2013). Altered DMN plays a vital role in neurodevelopmental disorders such as autism (Doyle-Thomas et al., 2015; Lynch et al., 2013; Padmanabhan et al., 2017; Washington et al., 2014). Thus, understanding physiological underpinning of the DMN maturation offers invaluable insights into the mechanism of typical and atypical brain development. We hypothesized heterogeneous rCBF maps at landmark infant ages and faster rCBF increase in brain regions of higher cognitive functions (namely DMN regions) during infancy than those of primary sensorimotor functions where functional networks emerge before or around birth (Cao et al., 2017a; Cao et al., 2017b; Doria et al., 2010; Fransson et al., 2007; Smyser et al., 2010; Peng et al., 2020). Furthermore, with rCBF as an indicator of local metabolic level of glucose and oxygen consumption, we hypothesized that strongly coupled rCBF and FC increase specifically in the DMN regions during infancy to meet extra metabolic demand of DMN maturation. In this study, we acquired multi-modal MRI, including both pCASL perfusion MRI, and rs-fMRI, of 48 infants aged 0–24 months to quantify rCBF and FC, respectively. RCBF at the voxel level and in functional network ROIs were measured to test the hypothesis of spatiotemporally differential rCBF increases during infancy. Maturation of FC in the DMN was delineated. Correlation of FC increase and rCBF increase in the DMN ROIs was tested and further confirmed with data-driven permutation analysis, the latter of which was to examine whether the coupling of rCBF and FC takes place only in the DMN during infancy. Results Emergence of the DMN during early brain development Figure 1a shows the emergence of the DMN in typically developing brain from 0 to 24 months as measured using rs-fMRI with a posterior cingulate cortex (PCC, a vital hub of DMN network) seed region indicated by the black dash line. At around birth (0 months), the DMN is still immature with weak FC between PCC and other DMN regions, including medial prefrontal cortex (MPFC), inferior posterior lobule (IPL), and lateral temporal cortex (LTC) (Figure 1a). During infant brain development from 0 to 24 months, Figure 1a shows that the functional connectivity between MPFC, IPL, or ITC and PCC gradually strengthens. Figure 1b shows the FC–age correlation r value map. It can be appreciated from Figure 1b that across the cortical surface relatively higher r values are only located at the DMN regions (except the seed PCC). Figure 1 with 2 supplements see all Download asset Open asset Emergence of functional connectivity (FC) within the default-mode network (DMN) during infancy. The maps of the DMN FC (PCC as a seed) at representative ages from 0 to 24 months are demonstrated in (a), and the map of correlation coefficient of FC (PCC as a seed) and age is demonstrated in (b). In (a), gradually emerging FC of other DMN regions (including MPFC, IPL, and LTC) to the PCC from 0 to 24 months can be appreciated. The PCC is delineated by the black dashed contour. In (b), stronger correlation between FC (PCC as a seed) and age is localized in DMN subregions IPL, ITL, and MPFC. Abbreviations of DMN subregions: IPL: inferior posterior lobule; LTC: lateral temporal cortex; MPFC: medial prefrontal cortex; PCC: posterior cingulate cortex. For robust and consistent identification of functional network ROI of infants, three functional network ROIs, including DMN, visual (Vis) network, and sensorimotor (SM) network, were generated from rs-fMRI data of infant aged 12–24 months, as shown in Figure 1—figure supplement 1. After applying these network ROIs to measure functional connectivity changes of infants of all ages from 0 to 24 months, we found significant increase of the FC only within the DMN (r = 0.31, p<0.05), but not in the Vis (r = 0.048, p=0.745) or SM (r = 0.087, p=0.559), network regions (Figure 1—figure supplement 2), indicating significant functional development in the DMN, but not in the Vis or SM network. Faster rCBF increases in the DMN hub regions during infant brain development The labeling plane and imaging slices of pCASL perfusion MRI of a representative infant brain, reconstructed internal carotid and vertebral arteries, and four PC MR images of the target arteries are shown in Figure 2a. The rCBF maps of infant brains were calculated based on pCASL perfusion MRI and calibrated by PC MRI. As an overview, axial rCBF maps of typically developing brains at milestone ages of 1, 6, 12, 18, and 24 months are demonstrated in Figure 2b. The rCBF maps with high gray/white matter contrasts can be appreciated by a clear contrast between white matter and gray matter. A general increase of blood flow across the brain gray matter from birth to 2 years of age is readily observed. Heterogeneous rCBF distribution at a given infant age can be appreciated from these maps. For example, higher rCBF values in primary visual cortex compared to other brain regions are clear in younger infant at around 1 month. Figure 2b also demonstrates differential rCBF increases across brain regions. RCBF increases are prominent in the PCC, indicated by green arrows. On the other hand, rCBF in the visual cortex is already higher (indicated by blue arrows) than other brain regions in early infancy and increases slowly across infant development. The adopted pCASL protocol is highly reproducible with intraclass correlation coefficient (ICC) 0.8854 calculated from pCASL scans of a randomly selected infant subject aged 17.6 month, shown in Figure 2—figure supplement 1. With rCBF measured at these functional network ROIs, Figure 2—figure supplement 2 quantitatively exhibits spatial inhomogeneity of rCBF distribution regardless of age. These quantitative measurements are consistent to the observation of heterogeneous rCBF distribution in Figure 2b. Specifically, as shown in Figure 2—figure supplement 2, significant heterogeneity of rCBF was found across regions (F(6, 282) = 122.6, p<10–10) with an analysis of variance (ANOVA) test. With further paired t-test between regions, the highest and lowest rCBF was found in the Vis (82.1 ± 2.19 ml/100 g/min) and SM (49.1 ± 1.49 ml/100 g/min) regions, respectively (all ts(47) > 4.17, p<0.05, false discovery rate [FDR] corrected), while rCBF in the DMN (67.8 ± 2.08 ml/100 g/min) regions was in the middle (all ts (47) > 2.87, p<0.05, FDR corrected). Within the DMN, rCBF in the PCC (75.4 ± 2.19 ml/100 g/min) and LTC (72.0 ± 2.82 ml/100 g/min) regions were significantly higher than rCBF in the MPFC (60.7 ± 2.24 ml/100 g/min) (both ts(47) > 8.22, p<0.05, FDR corrected) and IPL regions (59.4 ± 1.96 ml/100 g/min) (both ts(47) > 7.87, p<0.05, FDR corrected). After comparing corresponding rCBF measures of different network ROIs between left and right hemisphere for evaluating rCBF asymmetry, we found significantly higher (ts(47) = 3.82, p<0.05) rCBF in the SM network ROI in the right hemisphere (50.8 ± 1.67 ml/100 g/min) compared to that in the left hemisphere (47.8 ± 1.43 ml/100 g/min) while no significant rCBF difference was found in the DMN or Vis network ROIs between two hemispheres. This finding of rCBF asymmetry in the SM network ROI is consistent to the previous studies (Chiron et al., 1997; Lemaître et al., 2021). Figure 2 with 2 supplements see all Download asset Open asset Acquisition of high-quality infant pseudo-continuous arterial-spin-labeled (pCASL) perfusion and phase contrast (PC) MRI and resultant axial regional cerebral blood flow (rCBF) maps at different infant ages. (a) Labeling plane (red line) and imaging volume (blue box) of pCASL perfusion MRI are shown on the mid-sagittal slice of T1-weighted image of a representative infant on the left panels. Axial and sagittal view of MR angiography with reconstructed internal carotid and vertebral arteries are shown in the middle of panel (a). On the right of panel (a), the coronal view of the reconstructed arteries is placed in the middle with four slices (shown as blue bars) of the PC MR scans positioned perpendicular to the respective feeding arteries. The PC MR images are shown on the four panels surrounding the coronal view of the angiography. These PC MR images measure the global cerebral blood flow of internal carotid and vertebral arteries and are used to calibrate rCBF. (b) rCBF maps of representative typically developing (TD) infant brains at 1, 6, 12, 18, and 24 months from left to right. Axial slices of rCBF maps from inferior to superior are shown from bottom to top of the panel b for each TD infant brain. Green arrows point to the posterior cingulate cortex (a hub of the DMN network) characterized by relatively lower rCBF at early infancy and prominent rCBF increases from 1 to 24 months. Blue arrows point to the visual cortex characterized by relatively higher rCBF at early infancy and relatively mild rCBF increase from 1 to 24 months. Figure 3a shows cortical maps of linearly fitted rCBF values of infant brains from 0 to 24 months. Consistent with nonuniform profile of the rCBF maps observed in Figure 2b, the three-dimensionally reconstructed rCBF distribution maps in Figure 3a are also not uniform at each milestone infant age. RCBF increases from 0 to 24 months across cortical regions are apparent, as demonstrated by the relatively high rCBF–age correlation r values across the cortical surface in Figure 3b. Heterogeneity of rCBF increases across all brain voxels can be more clearly appreciated in Figure 3a and b compared to Figure 2b. Significant interaction between regions and age was found (F(6, 322) = 2.45, p<0.05) with an analysis of covariance (ANCOVA) test where age was used as a covariate. With DMN functional network regions including PCC, MPFC, IPL, and LTC as well as Vis and SM network regions delineated in Figure 1—figure supplement 1b as ROIs, rCBF trajectories in Figure 3c demonstrate that rCBF in these ROIs all increase significantly with age (Vis: r = 0.53, p<10–4; SM: r = 0.52, p<10–4; DMN: r = 0.7, p<10–7; DMN_PCC: r = 0.66, p<10–6; DMN_MPFC: r = 0.67, p<10–6; DMN_IPL: r = 0.66, p<10–6; DMN_LTC: r = 0.72, p<10–8). Using the trajectory of primary sensorimotor (SM) (black line and circles) in Figure 3c as a reference, rCBF increase rates across functional network ROIs are also heterogeneous (Figure 3c). Specifically, significantly higher (all p<0.05, FDR corrected) rCBF increase rate was found in total DMN ROIs (1.59 ml/100 g/min/month) and individual DMN ROIs, including DMN_PCC (1.57 ml/100 g/min/month), DMN_MPFC (1.63 ml/100 g/min/month), DMN_IPL (1.42 ml/100 g/min/month), and DMN_LTC (2.22 ml/100 g/min/month) compared to in the SM ROI (0.85 ml/100 g/min/month). Although the rCBF growth rate in the Vis (1.27 ml/100 g/min/month) ROIs is higher than that in the SM ROIs, this difference is not significant (p=0.13). Collectively, Figures 2 and 3 show that the CBF increases significantly and differentially across brain regions during infancy, with rCBF in the DMN hub regions increasing faster than rCBF in the SM and Vis regions (Figure 3). The 4D spatiotemporal whole-brain rCBF changes during infant development are presented in Video 1. Figure 3 Download asset Open asset 4D spatiotemporal regional cerebral blood flow (rCBF) dynamics and faster rCBF increases in the default-mode network (DMN) hub regions during infancy. (a) Medial (top row) and lateral (bottom row) views of fitted rCBF profiles of the infant brain at 0, 6, 12, 18, and 24 months in the custom-made infant template space demonstrate heterogeneous rCBF increase across the brain regions. (b) Medial (top) and lateral (bottom) views of rCBF–age correlation coefficient (r) map are demonstrated. (c) The scatterplots of rCBF measurements in the primary sensorimotor (SM) network (black circle and black line), visual (Vis) network (blue circle and blue line), and total and individual DMN hub regions (DMN_MPFC, DMN_PCC, DMN_IPL, and DMN_LTC) (blue circle and blue line) of all studied infants demonstrate differential rCBF increase rates. * next to network name in each plot indicates significant (false discovery rate [FDR]-corrected p<0.05) differences of rCBF trajectory slopes from that of SM used as a reference and shown in a black dashed line. See legend of Figure 1 for abbreviations of the DMN subregions. Video 1 Download asset This video cannot be played in place because your browser does support HTML5 video. You may still download the video for offline viewing. Download as MPEG-4 Download as WebM Download as Ogg Video of the 4D spatiotemporal whole-brain dynamics of regional cerebral blood flow from 0 to 24 months. Coupling between rCBF and FC within DMN during infant brain development To test the hypothesis that rCBF increases in the DMN regions underlie emergence of this vital functional network, correlation between rCBF and FC was conducted across randomly selected voxels within the DMN of all infants aged 0–12 months (Figure 4a) and all infants aged 12–24 months (Figure 4b). Significant correlations (p<0.001) were found in both age groups. Partial correlation analysis between rCBF and FC after regressing out age effects also confirmed significant correlation between rCBF and FC in the DMN regions in both 0–12-month (p<0.001) and 12–24-month (p<0.001) groups excluding the age effect. We further tested whether functional emergence of the DMN represented by increases of FC within the DMN (namely DMN FC) was correlated to rCBF increases specifically in the DMN regions, but not in primary sensorimotor (Vis or SM) regions. Figure 5a shows correlations between the DMN FC and rCBF at the DMN (red lines), Vis (green lines), or SM (blue lines) voxels. The correlations between the DMN FC and averaged rCBF in the DMN, Vis, or SM region are represented by thickened lines in Figure 5a. A correlation map (Figure 5b) between the DMN FC and rCBF across the entire brain voxels was generated. The procedures of generating this correlation map are illustrated in Figure 5—figure supplement 1. The DMN, Vis, and SM ROIs in Figure 5b were delineated with dashed red, green, and blue contours, respectively, and obtained from Figure 1—figure supplement 1b. Most of the significant correlations (r > rcrit) between the DMN FC and voxel-wise rCBF were found in the voxels in the DMN regions, such as PCC, IPL, and LTC, but not in the Vis or SM regions (Figure 5b). Demonstrated in a radar plot in Figure 5c, much higher percent of voxel with significant correlations between rCBF and the DMN FC was found in the DMN (36.7%, p<0.0001) regions than in the SM (14.6%, p>0.05) or Vis (5.5%, p>0.05) regions. Statistical significance of higher percent of voxels with significant correlations in the DMN (p<0.0001) was confirmed using nonparametric permutation tests with 10,000 permutations. We also conducted the correlation between the Vis FC and rCBF across the brain as well as permutation test. As expected, no significant correlation between the Vis FC and rCBF can be found in any voxel in the DMN, Vis, or SM ROIs, demonstrated in Figure 5—figure supplement 2a. Similar analysis was also conducted for correlation between the SM FC and rCBF across the brain and percent of voxels with significant correlation was close to zero, as demonstrated in Figure 5—figure supplement 2b. Combined with the results shown in Figure 5, the results of coupling between Vis (Figure 5—figure supplement 2a) or SM (Figure 5—figure supplement 2b) FC and rCBF further demonstrated that the selected rCBF-FC coupling can be only found in the DMN ROIs, but not in the Vis or SM network ROIs. Figure 4 Download asset Open asset Significant correlation of regional cerebral blood flow (rCBF) and functional connectivity (FC) at randomly selected 4000 voxels within the default-mode network (DMN) for both infants aged 0–12 months (p<0.001, left scatter plot) and infants aged 12–24 months (p<0.001, right scatter plot). FC is the average of FC of a certain DMN voxel to all other DMN voxels. The DMN regions of interests obtained from a data-driven independent component analysis of resting-state fMRI of the 12–24month infant cohort are shown on the top panels as an anatomical reference. See legend of Figure 1 for abbreviations of the DMN subregions. Figure 5 with 3 supplements see all Download asset Open asset Significant correlation between functional emergence of the default-mode network (DMN) and regional cerebral blood flow (rCBF) increases specifically in the DMN regions, but not in primary sensorimotor (visual or sensorimotor) regions. (a) Correlation of intra-default-mode-network functional connectivity (DMN FC) and rCBF at randomly selected voxels in the DMN (light red lines), visual (Vis, light green lines) and sensorimotor (SM, light blue lines) network regions. Correlations of DMN FC and averaged rCBF in the DMN, Vis, and SM network regions are shown as thickened red, green, and blue lines, respectively. (b) Coupling between the DMN FC and rCBF across the brain can be appreciated by distribution of voxel-wise correlation coefficient (r) obtained from correlation between DMN FC and rCBF at each voxel. The short black line in the color bar indicates critical r value rcrit corresponding to p=0.05. Higher r values can be appreciated in the DMN hub regions including posterior cingulate cortex (PCC), medial prefrontal cortex (MPFC), inferior posterior lobule (IPL), and lateral temporal cortex (LTC) with their boundaries delineated by the dashed dark red contours (from Figure 1—figure supplement 1b). Dashed green and blue contours (also from Figure 1—figure supplement 1b) delineate the Vis and SM network regions, respectively. (c) Radar plot shows significant correlation between rCBF and intra-DMN FC in the DMN network (36.7%, p<0.0001), but not in the Vis (14.6%, p>0.05), or SM (5.5%, p>0.05) networks. The radius represents the percent of the voxels with significant correlations between intra-DMN FC and rCBF in DMN, Vis, and SM network regions, respectively. The dashed line circle indicates critical percent of significant voxels with p=0.05 from 10,000 permutation tests. ***p<0.0001. Discussion We revealed strongly coupled rCBF and FC increases specifically in the DMN while establishing unprecedented 4D rCBF spatiotemporal changes during infancy. The tight rCBF-FC relationship found with multimodal infant MRI suggests that DMN emergence is supported by faster local blood flow increase in the DMN to meet metabolic demand, offering refreshing insight into the physiological mechanism underlying early brain functional architecture emergence. The delineated 4D brain perfusion spatiotemporal framework was characterized with heterogeneous rCBF distribution across brain regions at a specific age and differential age-dependent rCBF increase rates across brain regions during infant development, and can be used as quantified standard reference for detecting rCBF alterations (e.g., the z scores) of atypically developing brains. Elucidating the ontogeny of infant brain physiology and its functional correlates could greatly advance current understanding of general principles of early brain development. Gradient of functional network maturations in early brain development has been more extensively characterized with recent rs-fMRI studies. Differential emergence of these functional networks is distinguished by different onset time as well as different maturational rate of various brain functions in a given developmental period. For example, primary sensory and motor functional networks, such as the SM and Vis networks, appear earlier before or around birth (Cao et al., 2017a; Doria et al., 2010; Fransson et al., 2007; Smyser et al., 2010; Peng et al., 2020). Other functional networks involved in heteromodal functions appear later. The DMN (Fox et al., 2007; Greicius et al., 2003; Greicius et al., 2009; Raichle et al., 2001; Raichle, 2015; Smith et al., 2009) is a higher-order functional network. Smyser et al., 2010 found that SM and Vis functional networks mature earlier and demonstrate adult-like pattern for preterm neonate brain, with the DMN much immature and incomplete around birth.Cao et al., 2017a also found rapid maturation of primary sensorimotor functional systems in preterm neonates from 31 to 41 postmenstrual weeks while the DMN remained immature during that period. These previous studies suggest that significant functional maturation in primary sensorimotor networks occur earlier in preterm and perinatal developmental period (Cao et al., 2017a; Doria et al., 2010; Smyser et al., 2010) compared to 0–24-month infancy focused in this study. Functional network emergence in the DMN was found in the developmental infant cohort in Figure 1, marking significant maturation of the DMN in infancy and distinguished network pattern from earlier developmental period. The delineated DMN emergence in this study is also consistent to the literature (Gao et al., 2009). Figure 1—figure supplement 2 further demonstrated significant increase of FC only in the DMN, but not in primary sensorimotor system that already emerged in earlier developmental period. Glucose and oxygen are two primary molecules for energy metabolism in the brain (Raichle et al., 2001; Vaishnavi et al., 2010). Glucose consumed by infant brain represents 30% total amount of glucose (Raichle, 2010; Settergren et al., 1976), more than 15–20% typically seen in adult brain (Bouma and Muizelaar, 1990; Satterthwaite et al., 2014). The cerebral metabolic rates for glucose (CMRGlu) and oxygen (CMRO2) are direct measures of the rate of energy consumption, which parallel the proliferation of synapses in brain during infancy (Raichle, 2010). RCBF delivering glucose and oxygen for energy metabolism in the brain is closely related to CMRGlu and CMRO2 and can serve as a surrogate of these two measurements (Fox and Raichle, 1986; Gur et al., 2009; Paulson et al., 2010; Vaishnavi et al., 2010). In the PET study (Chugani and Phelps, 1986) using CMRGlu measurements, it was found that the local CMRGlu in the sensorimotor cortex almost reaches the highest level in early infancy and then plateaus during rest of infancy, consistent with relatively small changes of rCBF in later infancy in primary sensorimotor ROIs found in this study (Figure 3). On the other hand, the global CBF measured with PC MRI (Liu et al., 2019) increases dramaticall