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.

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 dramatically during infancy with global CBF at 18 months almost five times of the CBF around birth. Taken together, the literature suggests significant but nonuniformly distributed CBF increases across the brain regions during infancy, consistent to the measured heterogeneous rCBF increase pattern (Figures 2 and 3) in this study.

Furthermore, the differentiated cerebral metabolic pattern reflected by measured rCBF distribution (Figure 2) at a specific age and differential increase rates of the rCBF (Figure 3) from 0 to 24 months are strikingly consistent with spatiotemporally differentiated functional (Cao et al., 2017a; Doria et al., 2010; Fransson et al., 2007; Gao et al., 2009; Smyser et al., 2010) and structural (Ouyang et al., 2019a) maturational processes. In developmental brains, cellular processes supporting differential functional emergence require extra oxygen and glucose delivery through cerebral blood flow to meet the metabolic demand. In Hebb’s principle, 'neurons that fire together wire together.' Through the synaptogenesis in neuronal maturation, the neurons within a certain functional network system tend to have more synchronized activity in a more mature stage than in an immature stage. As shown in the diagram in Figure 5—figure supplement 3, cellular activities in developmental brains, such as synaptogenesis critical for brain circuit formation, need extra energy more than that in the stable and matured stage. Neurons do not have internal reserves of energy in the form of sugar or oxygen. The demand of extra energy requires an increase in rCBF to deliver more oxygen and glucose for the formation of brain networks. In the context of infant brain development, there is a cascade of events of CBF increase, CMRO2 and CMRGlu increase, synaptogenesis and synaptic efficacy increase, blood oxygenation level-dependent (BOLD) signal synchronization increase, and functional connectivity increase, shown in the bottom of Figure 5—figure supplement 3. Such spatial correlation of rCBF to the FC in the functional network ROIs was found in adults (Liang et al., 2013). Higher rCBF has also been found in the DMN in children 6–20 years of age (Liu et al., 2018). Consistent with the diagram shown in Figure 5—figure supplement 3, Figure 4 revealed significant correlation between FC and rCBF in the DMN network, and Figure 5 identified this significant correlation between FC and rCBF specifically in the DMN network, but not in the primary Vis or SM networks. Collectively, these results (Figures 4 and 5) were well aligned with the hypothesis that faster rCBF increase in the DMN underlies the emergence of the DMN reflected by significant FC increases. The revealed physiology–function relationship may shed light on physiological underpinnings of brain functional network emergence.

It is noteworthy to highlight a few technical details below. First, this study benefits from multimodal MRI allowing measuring functional network emergence and rCBF of the same cohort of infants. Simultaneous rCBF and FC measurements enabled us to probe relationship of brain physiology and function during infant development. Second, a nonparametric permutation analysis without a prior hypothesis at the voxel level across the whole brain was conducted to confirm the coupling of rCBF and FC is specific in the DMN regions (Figure 5), not in the primary sensorimotor (Vis or SM) regions (Figure 5—figure supplement 2), demonstrating the robustness of the results on physiological underpinning of functional network emergence in the DMN. Third, the pCASL perfusion MRI was calibrated by PC MRI so that the errors caused by varying labeling efficiency and varying T1 value of arterial blood among infant subjects can be ameliorated. This subject-specific calibration process therefore enhanced the accuracy of rCBF measurements for their potential use as a standard reference. For studies without additional PC MRI acquired for subject-specific calibration, utilizing developmentally appropriate or even subject-specific T1 values of arterial blood is encouraged to improve the accuracy of rCBF measurements. Given the lack of consensus on pCASL acquisition parameters for infants, future research focusing on systematically optimizing the pCASL acquisition protocol in the infant population is needed. Fourth, the ROIs for rCBF measurements were obtained by data-driven ICA of the same sub-cohort of infant subjects aged 12–24 months instead of transferred ROIs from certain brain parcellation atlases. Since most of the parcellation atlases were built based on adult brain data and all these atlases were established based on other subject groups, ROIs delineated from the same cohort improve accuracy of coupling analysis. There are several limitations in this study that can be improved in future investigations. All data were acquired from a cross-sectional cohort. To minimize the inter-subject variability, future study with a longitudinal cohort of infants is warranted. With relatively small size of infant brains, spatial resolution of the pCASL and rs-fMRI can be further improved too to improve imaging measurement accuracy. With relatively larger voxel size of the pCASL scan, partial volume effects could lead to bias of the rCBF measurement. Due to smaller brain and thinner brain cortex of younger infants, using consistent pCASL acquisition voxel size could lead to heterogeneous partial volume effects across infants with different brain sizes and cortical thicknesses. The rCBF biases related to partial volume effects were minimized with calibration of individual PC MRI from all infants. Relatively larger pCASL acquisition voxel size might also contribute to blurring the smaller red areas in the occipital lobe of the brain into a larger continuous red region, although higher rCBF values at the base of the brain and occipital regions than in the frontal regions were observed in infant and neonate brains (e.g., Kim et al., 2018; Lemaître et al., 2021; Wang et al., 2008; Satterthwaite et al., 2014). We believe that the rCBF increase pattern during maturation is more complicated than relatively simplified posterior-to-anterior and inferior-to-superior gradients. It also presents a primary-to-association gradient reproducibly found in the literature (see Sydnor et al., 2021 for review). Such mixed patterns are consistent with the maturation pattern demonstrated in Figure 3a. More clear maturation gradient could benefit from future rCBF maps of higher signal-to-noise ratio and higher resolution. To further improve the statistical power, larger infant sample size will be beneficial in the future studies. Finally, although the rCBF from pCASL perfusion MRI is highly correlated with the CMRO2 and CMRGlu measured from PET, rCBF is not a direct measurement of the rate of energy consumption. Physiology–function relationship studies in infants could benefit from the development of novel noninvasive MR imaging methods as an alternative to PET to measure CMRO2 or CMRGlu without the involvement of radioactive tracer.

Maps including regional cerebral blood flow (rCBF) maps and functional connectivity maps from multimodal infant MRI datasets including pseudo-continuous arterial-spin-labelled perfusion MRI and resting-state MRI of forty-eight infants are publicly available from Huang lab GitHub repository (https://github.com/haohuanglab/infant_perfusion_function, copy archived at swh:1:rev:34071e49232d7960ff6b78464b55077da136d868). However, we cannot openly share the raw, unprocessed MRI data, because The Institutional Review Board of Beijing Children's Hospital Research Ethics Committee (Approval number 2016-36) specifies the participants did not give consent for these data to be released publicly. The raw MRI data can be made available to individual researchers on informal request to the corresponding author through email hao.huang@pennmedicine.upenn.edu. Source code used in analysis and related documentation are also available at the Huang lab GitHub repository (https://github.com/haohuanglab/infant_perfusion_function).

1. 1. Alsop DC
   2. Detre JA
   3. Golay X
   4. Günther M
   5. Hendrikse J
   6. Hernandez-Garcia L
   7. Lu H
   8. MacIntosh BJ
   9. Parkes LM
   10. Smits M
   11. van Osch MJP
   12. Wang DJJ
   13. Wong EC
   14. Zaharchuk G

   (2015) Recommended implementation of arterial spin-labeled perfusion MRI for clinical applications: a consensus of the ISMRM perfusion Study Group and the European Consortium for ASL in dementia

   Magnetic Resonance in Medicine 73:102–116.

   https://doi.org/10.1002/mrm.25197

   • PubMed
   • Google Scholar
2. 1. Altman DI
   2. Powers WJ
   3. Perlman JM
   4. Herscovitch P
   5. Volpe SL
   6. Volpe JJ

   (1988) Cerebral blood flow requirement for brain viability in newborn infants is lower than in adults

   Annals of Neurology 24:218–226.

   https://doi.org/10.1002/ana.410240208

   • PubMed
   • Google Scholar
3. 1. Altman DI
   2. Perlman JM
   3. Volpe JJ
   4. Powers WJ

   (1993) Cerebral oxygen metabolism in newborns

   Pediatrics 92:99–104.

   https://doi.org/10.1542/peds.92.1.99

   • PubMed
   • Google Scholar
4. 1. Aslan S
   2. Xu F
   3. Wang PL
   4. Uh J
   5. Yezhuvath US
   6. van Osch M
   7. Lu H

   (2010) Estimation of labeling efficiency in pseudocontinuous arterial spin labeling

   Magnetic Resonance in Medicine 63:765–771.

   https://doi.org/10.1002/mrm.22245

   • PubMed
   • Google Scholar
5. 1. Beckmann CF
   2. DeLuca M
   3. Devlin JT
   4. Smith SM

   (2005) Investigations into resting-state connectivity using independent component analysis

   Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 360:1001–1013.

   https://doi.org/10.1098/rstb.2005.1634

   • PubMed
   • Google Scholar
6. 1. Bouma GJ
   2. Muizelaar JP

   (1990) Relationship between cardiac output and cerebral blood flow in patients with intact and with impaired autoregulation

   Journal of Neurosurgery 73:368–374.

   https://doi.org/10.3171/jns.1990.73.3.0368

   • PubMed
   • Google Scholar
7. 1. Buckner RL
   2. Andrews-Hanna JR
   3. Schacter DL

   (2008) The brain’s default network: anatomy, function, and relevance to disease

   Annals of the New York Academy of Sciences 1124:1–38.

   https://doi.org/10.1196/annals.1440.011

   • PubMed
   • Google Scholar
8. 1. Bystron I
   2. Blakemore C
   3. Rakic P

   (2008) Development of the human cerebral cortex: Boulder Committee revisited

   Nature Reviews. Neuroscience 9:110–122.

   https://doi.org/10.1038/nrn2252

   • PubMed
   • Google Scholar
9. 1. Cao M
   2. He Y
   3. Dai Z
   4. Liao X
   5. Jeon T
   6. Ouyang M
   7. Chalak L
   8. Bi Y
   9. Rollins N
   10. Dong Q
   11. Huang H

   (2017a) Early development of functional network segregation revealed by connectomic analysis of the preterm human brain

   Cerebral Cortex 27:1949–1963.

   https://doi.org/10.1093/cercor/bhw038

   • PubMed
   • Google Scholar
10. 1. Cao M
    2. Huang H
    3. He Y

    (2017b) Developmental connectomics from infancy through early childhood

    Trends in Neurosciences 40:494–506.

    https://doi.org/10.1016/j.tins.2017.06.003

    • PubMed
    • Google Scholar
11. 1. Chiron C
    2. Raynaud C
    3. Mazière B
    4. Zilbovicius M
    5. Laflamme L
    6. Masure MC
    7. Dulac O
    8. Bourguignon M
    9. Syrota A

    (1992)

    Changes in regional cerebral blood flow during brain maturation in children and adolescents

    The Journal of Nuclear Medicine: Official Publication, Society of Nuclear Medicine 33:696–703.

    • Google Scholar
12. 1. Chiron C
    2. Jambaque I
    3. Nabbout R
    4. Lounes R
    5. Syrota A
    6. Dulac O

    (1997) The right brain hemisphere is dominant in human infants

    Brain 120 (Pt 6):1057–1065.

    https://doi.org/10.1093/brain/120.6.1057

    • PubMed
    • Google Scholar
13. 1. Chugani HT
    2. Phelps ME

    (1986) Maturational changes in cerebral function in infants determined by 18FDG positron emission tomography

    Science 231:840–843.

    https://doi.org/10.1126/science.3945811

    • PubMed
    • Google Scholar
14. 1. Chugani HT
    2. Phelps ME
    3. Mazziotta JC

    (1987) Positron emission tomography study of human brain functional development

    Annals of Neurology 22:487–497.

    https://doi.org/10.1002/ana.410220408

    • PubMed
    • Google Scholar
15. 1. Cusack R
    2. McCuaig O
    3. Linke AC

    (2018) Methodological challenges in the comparison of infant fMRI across age groups

    Developmental Cognitive Neuroscience 33:194–205.

    https://doi.org/10.1016/j.dcn.2017.11.003

    • PubMed
    • Google Scholar
16. Book

    1. Deming WE

    (1943)

    Statistical Adjutement of Data

    Wiley.

    • Google Scholar
17. 1. Detre JA
    2. Alsop DC

    (1999) Perfusion magnetic resonance imaging with continuous arterial spin labeling: methods and clinical applications in the central nervous system

    European Journal of Radiology 30:115–124.

    https://doi.org/10.1016/s0720-048x(99)00050-9

    • PubMed
    • Google Scholar
18. 1. Dittmer JC
    2. Dawson RM

    (1961) The isolation of a new lipid, triphosphoinositide, and monophosphoinositide from ox brain

    The Biochemical Journal 81:535–540.

    https://doi.org/10.1042/bj0810535

    • PubMed
    • Google Scholar
19. 1. Doria V
    2. Beckmann CF
    3. Arichi T
    4. Merchant N
    5. Groppo M
    6. Turkheimer FE
    7. Counsell SJ
    8. Murgasova M
    9. Aljabar P
    10. Nunes RG
    11. Larkman DJ
    12. Rees G
    13. Edwards AD

    (2010) Emergence of resting state networks in the preterm human brain

    PNAS 107:20015–20020.

    https://doi.org/10.1073/pnas.1007921107

    • PubMed
    • Google Scholar
20. 1. Doyle-Thomas KAR
    2. Lee W
    3. Foster NEV
    4. Tryfon A
    5. Ouimet T
    6. Hyde KL
    7. Evans AC
    8. Lewis J
    9. Zwaigenbaum L
    10. Anagnostou E
    11. NeuroDevNet ASD Imaging Group

    (2015) Atypical functional brain connectivity during rest in autism spectrum disorders

    Annals of Neurology 77:866–876.

    https://doi.org/10.1002/ana.24391

    • PubMed
    • Google Scholar
21. 1. Fox PT
    2. Raichle ME

    (1986) Focal physiological uncoupling of cerebral blood flow and oxidative metabolism during somatosensory stimulation in human subjects

    PNAS 83:1140–1144.

    https://doi.org/10.1073/pnas.83.4.1140

    • PubMed
    • Google Scholar
22. 1. Fox MD
    2. Snyder AZ
    3. Vincent JL
    4. Raichle ME

    (2007) Intrinsic fluctuations within cortical systems account for intertrial variability in human behavior

    Neuron 56:171–184.

    https://doi.org/10.1016/j.neuron.2007.08.023

    • PubMed
    • Google Scholar
23. 1. Fransson P
    2. Skiöld B
    3. Horsch S
    4. Nordell A
    5. Blennow M
    6. Lagercrantz H
    7. Aden U

    (2007) Resting-state networks in the infant brain

    PNAS 104:15531–15536.

    https://doi.org/10.1073/pnas.0704380104

    • PubMed
    • Google Scholar
24. 1. Gao W
    2. Zhu H
    3. Giovanello KS
    4. Smith JK
    5. Shen D
    6. Gilmore JH
    7. Lin W

    (2009) Evidence on the emergence of the brain’s default network from 2-week-old to 2-year-old healthy pediatric subjects

    PNAS 106:6790–6795.

    https://doi.org/10.1073/pnas.0811221106

    • PubMed
    • Google Scholar
25. 1. Greicius MD
    2. Krasnow B
    3. Reiss AL
    4. Menon V

    (2003) Functional connectivity in the resting brain: a network analysis of the default mode hypothesis

    PNAS 100:253–258.

    https://doi.org/10.1073/pnas.0135058100

    • PubMed
    • Google Scholar
26. 1. Greicius MD
    2. Srivastava G
    3. Reiss AL
    4. Menon V

    (2004) Default-mode network activity distinguishes alzheimer’s disease from healthy aging: evidence from functional MRI

    PNAS 101:4637–4642.

    https://doi.org/10.1073/pnas.0308627101

    • PubMed
    • Google Scholar
27. 1. Greicius MD
    2. Supekar K
    3. Menon V
    4. Dougherty RF

    (2009) Resting-state functional connectivity reflects structural connectivity in the default mode network

    Cerebral Cortex 19:72–78.

    https://doi.org/10.1093/cercor/bhn059

    • PubMed
    • Google Scholar
28. 1. Gur RC
    2. Ragland JD
    3. Reivich M
    4. Greenberg JH
    5. Alavi A
    6. Gur RE

    (2009) Regional differences in the coupling between resting cerebral blood flow and metabolism may indicate action preparedness as a default state

    Cerebral Cortex 19:375–382.

    https://doi.org/10.1093/cercor/bhn087

    • PubMed
    • Google Scholar
29. 1. Haynes RL
    2. Borenstein NS
    3. Desilva TM
    4. Folkerth RD
    5. Liu LG
    6. Volpe JJ
    7. Kinney HC

    (2005) Axonal development in the cerebral white matter of the human fetus and infant

    The Journal of Comparative Neurology 484:156–167.

    https://doi.org/10.1002/cne.20453

    • PubMed
    • Google Scholar
30. 1. Herscovitch P
    2. Raichle ME

    (1985) What is the correct value for the brain -- blood partition coefficient for water?

    Journal of Cerebral Blood Flow and Metabolism 5:65–69.

    https://doi.org/10.1038/jcbfm.1985.9

    • PubMed
    • Google Scholar
31. 1. Huang H
    2. Jeon T
    3. Sedmak G
    4. Pletikos M
    5. Vasung L
    6. Xu X
    7. Yarowsky P
    8. Richards LJ
    9. Kostovic I
    10. Sestan N
    11. Mori S

    (2013) Coupling diffusion imaging with histological and gene expression analysis to examine the dynamics of cortical areas across the fetal period of human brain development

    Cerebral Cortex 23:2620–2631.

    https://doi.org/10.1093/cercor/bhs241

    • PubMed
    • Google Scholar
32. 1. Huang H.
    2. Vasung L

    (2014) Gaining insight of fetal brain development with diffusion MRI and histology

    International Journal of Developmental Neuroscience 32:11–22.

    https://doi.org/10.1016/j.ijdevneu.2013.06.005

    • PubMed
    • Google Scholar
33. 1. Huttenlocher PR
    2. Dabholkar AS

    (1997) Regional differences in synaptogenesis in human cerebral cortex

    The Journal of Comparative Neurology 387:167–178.

    https://doi.org/10.1002/(sici)1096-9861(19971020)387:2<167::aid-cne1>3.0.co;2-z

    • PubMed
    • Google Scholar
34. 1. Innocenti GM
    2. Price DJ

    (2005) Exuberance in the development of cortical networks

    Nature Reviews. Neuroscience 6:955–965.

    https://doi.org/10.1038/nrn1790

    • PubMed
    • Google Scholar
35. 1. Jain V
    2. Duda J
    3. Avants B
    4. Giannetta M
    5. Xie SX
    6. Roberts T
    7. Detre JA
    8. Hurt H
    9. Wehrli FW
    10. Wang DJJ

    (2012) Longitudinal reproducibility and accuracy of pseudo-continuous arterial spin-labeled perfusion MR imaging in typically developing children

    Radiology 263:527–536.

    https://doi.org/10.1148/radiol.12111509

    • PubMed
    • Google Scholar
36. 1. Kim HG
    2. Lee JH
    3. Choi JW
    4. Han M
    5. Gho SM
    6. Moon Y

    (2018) Multidelay arterial spin-labeling MRI in neonates and infants: cerebral perfusion changes during brain maturation

    AJNR. American Journal of Neuroradiology 39:1912–1918.

    https://doi.org/10.3174/ajnr.A5774

    • PubMed
    • Google Scholar
37. 1. Knickmeyer RC
    2. Gouttard S
    3. Kang C
    4. Evans D
    5. Wilber K
    6. Smith JK
    7. Hamer RM
    8. Lin W
    9. Gerig G
    10. Gilmore JH

    (2008) A structural MRI study of human brain development from birth to 2 years

    The Journal of Neuroscience 28:12176–12182.

    https://doi.org/10.1523/JNEUROSCI.3479-08.2008

    • PubMed
    • Google Scholar
38. 1. Lemaître H
    2. Augé P
    3. Saitovitch A
    4. Vinçon-Leite A
    5. Tacchella JM
    6. Fillon L
    7. Calmon R
    8. Dangouloff-Ros V
    9. Lévy R
    10. Grévent D
    11. Brunelle F
    12. Boddaert N
    13. Zilbovicius M

    (2021) Rest functional brain maturation during the first year of life

    Cerebral Cortex 31:1776–1785.

    https://doi.org/10.1093/cercor/bhaa325

    • PubMed
    • Google Scholar
39. 1. Li A
    2. Gong L
    3. Xu F

    (2011) Brain-state-independent neural representation of peripheral stimulation in rat olfactory bulb

    PNAS 108:5087–5092.

    https://doi.org/10.1073/pnas.1013814108

    • PubMed
    • Google Scholar
40. 1. Liang X
    2. Zou Q
    3. He Y
    4. Yang Y

    (2013) Coupling of functional connectivity and regional cerebral blood flow reveals a physiological basis for network hubs of the human brain

    PNAS 110:1929–1934.

    https://doi.org/10.1073/pnas.1214900110

    • PubMed
    • Google Scholar
41. 1. Liu P
    2. Chalak LF
    3. Krishnamurthy LC
    4. Mir I
    5. Peng S
    6. Huang H
    7. Lu H

    (2016) T1 and T2 values of human neonatal blood at 3 tesla: dependence on hematocrit, oxygenation, and temperature

    Magnetic Resonance in Medicine 75:1730–1735.

    https://doi.org/10.1002/mrm.25775

    • PubMed
    • Google Scholar
42. 1. Liu F
    2. Duan Y
    3. Peterson BS
    4. Asllani I
    5. Zelaya F
    6. Lythgoe D
    7. Kangarlu A

    (2018) Resting state cerebral blood flow with arterial spin labeling MRI in developing human brains

    European Journal of Paediatric Neurology 22:642–651.

    https://doi.org/10.1016/j.ejpn.2018.03.003

    • PubMed
    • Google Scholar
43. 1. Liu P.
    2. Qi Y
    3. Lin Z
    4. Guo Q
    5. Wang X
    6. Lu H

    (2019) Assessment of cerebral blood flow in neonates and infants: a phase-contrast MRI study

    NeuroImage 185:926–933.

    https://doi.org/10.1016/j.neuroimage.2018.03.020

    • PubMed
    • Google Scholar
44. 1. Lynch CJ
    2. Uddin LQ
    3. Supekar K
    4. Khouzam A
    5. Phillips J
    6. Menon V

    (2013) Default mode network in childhood autism: posteromedial cortex heterogeneity and relationship with social deficits

    Biological Psychiatry 74:212–219.

    https://doi.org/10.1016/j.biopsych.2012.12.013

    • PubMed
    • Google Scholar
45. 1. Miller DJ
    2. Duka T
    3. Stimpson CD
    4. Schapiro SJ
    5. Baze WB
    6. McArthur MJ
    7. Fobbs AJ
    8. Sousa AMM
    9. Sestan N
    10. Wildman DE
    11. Lipovich L
    12. Kuzawa CW
    13. Hof PR
    14. Sherwood CC

    (2012) Prolonged myelination in human neocortical evolution

    PNAS 109:16480–16485.

    https://doi.org/10.1073/pnas.1117943109

    • PubMed
    • Google Scholar
46. 1. Ouyang M
    2. Liu P
    3. Jeon T
    4. Chalak L
    5. Heyne R
    6. Rollins NK
    7. Licht DJ
    8. Detre JA
    9. Roberts TPL
    10. Lu H
    11. Huang H

    (2017) Heterogeneous increases of regional cerebral blood flow during preterm brain development: preliminary assessment with pseudo-continuous arterial spin labeled perfusion MRI

    NeuroImage 147:233–242.

    https://doi.org/10.1016/j.neuroimage.2016.12.034

    • PubMed
    • Google Scholar
47. 1. Ouyang M
    2. Dubois J
    3. Yu Q
    4. Mukherjee P
    5. Huang H

    (2019a) Delineation of early brain development from fetuses to infants with diffusion MRI and beyond

    NeuroImage 185:836–850.

    https://doi.org/10.1016/j.neuroimage.2018.04.017

    • PubMed
    • Google Scholar
48. 1. Ouyang M
    2. Jeon T
    3. Sotiras A
    4. Peng Q
    5. Mishra V
    6. Halovanic C
    7. Chen M
    8. Chalak L
    9. Rollins N
    10. Roberts TPL
    11. Davatzikos C
    12. Huang H

    (2019b) Differential cortical microstructural maturation in the preterm human brain with diffusion kurtosis and tensor imaging

    PNAS 116:4681–4688.

    https://doi.org/10.1073/pnas.1812156116

    • PubMed
    • Google Scholar
49. 1. Padmanabhan A
    2. Lynch CJ
    3. Schaer M
    4. Menon V

    (2017) The default mode network in autism

    Biological Psychiatry. Cognitive Neuroscience and Neuroimaging 2:476–486.

    https://doi.org/10.1016/j.bpsc.2017.04.004

    • PubMed
    • Google Scholar
50. 1. Paulson OB
    2. Hasselbalch SG
    3. Rostrup E
    4. Knudsen GM
    5. Pelligrino D

    (2010) Cerebral blood flow response to functional activation

    Journal of Cerebral Blood Flow and Metabolism 30:2–14.

    https://doi.org/10.1038/jcbfm.2009.188

    • PubMed
    • Google Scholar
51. 1. Peng Q
    2. Ouyang M
    3. Wang J
    4. Yu Q
    5. Zhao C
    6. Slinger M
    7. Li H
    8. Fan Y
    9. Hong B
    10. Huang H

    (2020) Regularized-ncut: robust and homogeneous functional parcellation of neonate and adult brain networks

    Artificial Intelligence in Medicine 106:101872.

    https://doi.org/10.1016/j.artmed.2020.101872

    • PubMed
    • Google Scholar
52. 1. Pfefferbaum A
    2. Mathalon DH
    3. Sullivan EV
    4. Rawles JM
    5. Zipursky RB
    6. Lim KO

    (1994) A quantitative magnetic resonance imaging study of changes in brain morphology from infancy to late adulthood

    Archives of Neurology 51:874–887.

    https://doi.org/10.1001/archneur.1994.00540210046012

    • PubMed
    • Google Scholar
53. 1. Pontzer H
    2. Yamada Y
    3. Sagayama H
    4. Ainslie PN
    5. Andersen LF
    6. Anderson LJ
    7. Arab L
    8. Baddou I
    9. Bedu-Addo K
    10. Blaak EE
    11. Blanc S
    12. Bonomi AG
    13. Bouten CVC
    14. Bovet P
    15. Buchowski MS
    16. Butte NF
    17. Camps SG
    18. Close GL
    19. Cooper JA
    20. Cooper R
    21. Das SK
    22. Dugas LR
    23. Ekelund U
    24. Entringer S
    25. Forrester T
    26. Fudge BW
    27. Goris AH
    28. Gurven M
    29. Hambly C
    30. El Hamdouchi A
    31. Hoos MB
    32. Hu S
    33. Joonas N
    34. Joosen AM
    35. Katzmarzyk P
    36. Kempen KP
    37. Kimura M
    38. Kraus WE
    39. Kushner RF
    40. Lambert EV
    41. Leonard WR
    42. Lessan N
    43. Martin C
    44. Medin AC
    45. Meijer EP
    46. Morehen JC
    47. Morton JP
    48. Neuhouser ML
    49. Nicklas TA
    50. Ojiambo RM
    51. Pietiläinen KH
    52. Pitsiladis YP
    53. Plange-Rhule J
    54. Plasqui G
    55. Prentice RL
    56. Rabinovich RA
    57. Racette SB
    58. Raichlen DA
    59. Ravussin E
    60. Reynolds RM
    61. Roberts SB
    62. Schuit AJ
    63. Sjödin AM
    64. Stice E
    65. Urlacher SS
    66. Valenti G
    67. Van Etten LM
    68. Van Mil EA
    69. Wells JCK
    70. Wilson G
    71. Wood BM
    72. Yanovski J
    73. Yoshida T
    74. Zhang X
    75. Murphy-Alford AJ
    76. Loechl C
    77. Luke AH
    78. Rood J
    79. Schoeller DA
    80. Westerterp KR
    81. Wong WW
    82. Speakman JR
    83. IAEA DLW Database Consortium

    (2021) Daily energy expenditure through the human life course

    Science 373:808–812.

    https://doi.org/10.1126/science.abe5017

    • PubMed
    • Google Scholar
54. 1. Raichle ME
    2. MacLeod AM
    3. Snyder AZ
    4. Powers WJ
    5. Gusnard DA
    6. Shulman GL

    (2001) A default mode of brain function

    PNAS 98:676–682.

    https://doi.org/10.1073/pnas.98.2.676

    • PubMed
    • Google Scholar
55. 1. Raichle ME

    (2010) Two views of brain function

    Trends in Cognitive Sciences 14:180–190.

    https://doi.org/10.1016/j.tics.2010.01.008

    • PubMed
    • Google Scholar
56. 1. Raichle ME

    (2015) The brain’s default mode network

    Annual Review of Neuroscience 38:433–447.

    https://doi.org/10.1146/annurev-neuro-071013-014030

    • PubMed
    • Google Scholar
57. 1. Rakic P

    (1995) Radial versus tangential migration of neuronal clones in the developing cerebral cortex

    PNAS 92:11323–11327.

    https://doi.org/10.1073/pnas.92.25.11323

    • PubMed
    • Google Scholar
58. 1. Satterthwaite TD
    2. Shinohara RT
    3. Wolf DH
    4. Hopson RD
    5. Elliott MA
    6. Vandekar SN
    7. Ruparel K
    8. Calkins ME
    9. Roalf DR
    10. Gennatas ED
    11. Jackson C
    12. Erus G
    13. Prabhakaran K
    14. Davatzikos C
    15. Detre JA
    16. Hakonarson H
    17. Gur RC
    18. Gur RE

    (2014) Impact of puberty on the evolution of cerebral perfusion during adolescence

    PNAS 111:8643–8648.

    https://doi.org/10.1073/pnas.1400178111

    • PubMed
    • Google Scholar
59. 1. Settergren G
    2. Lindblad BS
    3. Persson B

    (1976) Cerebral blood flow and exchange of oxygen, glucose, ketone bodies, lactate, pyruvate and amino acids in infants

    Acta Paediatrica Scandinavica 65:343–353.

    https://doi.org/10.1111/j.1651-2227.1976.tb04896.x

    • PubMed
    • Google Scholar
60. 1. Sidman RL
    2. Rakic P

    (1973) Neuronal migration, with special reference to developing human brain: a review

    Brain Research 62:1–35.

    https://doi.org/10.1016/0006-8993(73)90617-3

    • PubMed
    • Google Scholar
61. Book

    1. Sidman RL
    2. Rakic P

    (1982)

    Development of the Human Central Nervous System, in Histology and Histopathology of the Nervous System

    Thomas, Springfield.

    • Google Scholar
62. 1. Silbereis JC
    2. Pochareddy S
    3. Zhu Y
    4. Li M
    5. Sestan N

    (2016) The cellular and molecular landscapes of the developing human central nervous system

    Neuron 89:248–268.

    https://doi.org/10.1016/j.neuron.2015.12.008

    • PubMed
    • Google Scholar
63. 1. Smith SM
    2. Fox PT
    3. Miller KL
    4. Glahn DC
    5. Fox PM
    6. Mackay CE
    7. Filippini N
    8. Watkins KE
    9. Toro R
    10. Laird AR
    11. Beckmann CF

    (2009) Correspondence of the brain’s functional architecture during activation and rest

    PNAS 106:13040–13045.

    https://doi.org/10.1073/pnas.0905267106

    • PubMed
    • Google Scholar
64. 1. Smyser CD
    2. Inder TE
    3. Shimony JS
    4. Hill JE
    5. Degnan AJ
    6. Snyder AZ
    7. Neil JJ

    (2010) Longitudinal analysis of neural network development in preterm infants

    Cerebral Cortex 20:2852–2862.

    https://doi.org/10.1093/cercor/bhq035

    • PubMed
    • Google Scholar
65. 1. Suzuki C
    2. Kosugi M
    3. Magata Y

    (2021) Conscious rat PET imaging with soft immobilization for quantitation of brain functions: comprehensive assessment of anesthesia effects on cerebral blood flow and metabolism

    EJNMMI Research 11:1–11.

    https://doi.org/10.1186/s13550-021-00787-6

    • Google Scholar
66. 1. Sydnor VJ
    2. Larsen B
    3. Bassett DS
    4. Alexander-Bloch A
    5. Fair DA
    6. Liston C
    7. Mackey AP
    8. Milham MP
    9. Pines A
    10. Roalf DR
    11. Seidlitz J
    12. Xu T
    13. Raznahan A
    14. Satterthwaite TD

    (2021) Neurodevelopment of the association cortices: patterns, mechanisms, and implications for psychopathology

    Neuron 109:2820–2846.

    https://doi.org/10.1016/j.neuron.2021.06.016

    • PubMed
    • Google Scholar
67. 1. Tau GZ
    2. Peterson BS

    (2010) Normal development of brain circuits

    Neuropsychopharmacology 35:147–168.

    https://doi.org/10.1038/npp.2009.115

    • PubMed
    • Google Scholar
68. 1. Vaishnavi SN
    2. Vlassenko AG
    3. Rundle MM
    4. Snyder AZ
    5. Mintun MA
    6. Raichle ME

    (2010) Regional aerobic glycolysis in the human brain

    PNAS 107:17757–17762.

    https://doi.org/10.1073/pnas.1010459107

    • PubMed
    • Google Scholar
69. 1. Varela M
    2. Hajnal JV
    3. Petersen ET
    4. Golay X
    5. Merchant N
    6. Larkman DJ

    (2011) A method for rapid in vivo measurement of blood T1

    NMR in Biomedicine 24:80–88.

    https://doi.org/10.1002/nbm.1559

    • PubMed
    • Google Scholar
70. 1. Varela M
    2. Petersen ET
    3. Golay X
    4. Hajnal JV

    (2015) Cerebral blood flow measurements in infants using look-locker arterial spin labeling

    Journal of Magnetic Resonance Imaging 41:1591–1600.

    https://doi.org/10.1002/jmri.24716

    • PubMed
    • Google Scholar
71. 1. Wang Z
    2. Fernández-Seara M
    3. Alsop DC
    4. Liu WC
    5. Flax JF
    6. Benasich AA
    7. Detre JA

    (2008) Assessment of functional development in normal infant brain using arterial spin labeled perfusion MRI

    NeuroImage 39:973–978.

    https://doi.org/10.1016/j.neuroimage.2007.09.045

    • PubMed
    • Google Scholar
72. 1. Washington SD
    2. Gordon EM
    3. Brar J
    4. Warburton S
    5. Sawyer AT
    6. Wolfe A
    7. Mease-Ference ER
    8. Girton L
    9. Hailu A
    10. Mbwana J
    11. Gaillard WD
    12. Kalbfleisch ML
    13. VanMeter JW

    (2014) Dysmaturation of the default mode network in autism

    Human Brain Mapping 35:1284–1296.

    https://doi.org/10.1002/hbm.22252

    • PubMed
    • Google Scholar
73. 1. Yakovlev P

    (1967)

    Regional Development of the Brain in Early Life

    3–70, The myelogenetic cycles of regional maturation of the brain, Regional Development of the Brain in Early Life, Blackwell Scientific Publications.

    • Google Scholar
74. 1. Yu Q
    2. Ouyang A
    3. Chalak L
    4. Jeon T
    5. Chia J
    6. Mishra V
    7. Sivarajan M
    8. Jackson G
    9. Rollins N
    10. Liu S
    11. Huang H

    (2016) Structural development of human fetal and preterm brain cortical plate based on population-averaged templates

    Cerebral Cortex 26:4381–4391.

    https://doi.org/10.1093/cercor/bhv201

    • PubMed
    • Google Scholar

Author details

1. Qinlin Yu

   1. Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, United States
   2. Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States

   Contribution

   Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

2. Minhui Ouyang

   1. Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, United States
   2. Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States

   Contribution

   Software, Formal analysis, Validation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8013-2553

3. John Detre

   1. Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
   2. Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States

   Contribution

   Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

4. Huiying Kang

   1. Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, United States
   2. Department of Radiology, Beijing Children’s Hospital, Capital Medical University, Beijing, China

   Contribution

   Resources, Data curation, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

5. Di Hu

   1. Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, United States
   2. Department of Radiology, Beijing Children’s Hospital, Capital Medical University, Beijing, China

   Contribution

   Resources, Data curation, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

6. Bo Hong

   Department of Biomedical Engineering, Tsinghua University, Beijing, China

   Contribution

   Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2900-6791

7. Fang Fang

   School of Psychological and Cognitive Sciences, Peking University, Beijing, China

   Contribution

   Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

8. Yun Peng

   Department of Radiology, Beijing Children’s Hospital, Capital Medical University, Beijing, China

   Contribution

   Resources, Data curation, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

9. Hao Huang

   1. Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, United States
   2. Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   huangh6@email.chop.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9103-4382

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