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. Author manuscript; available in PMC: 2019 Sep 1.
Published in final edited form as: Neurosci Biobehav Rev. 2018 Jul 1;92:417–436. doi: 10.1016/j.neubiorev.2018.06.004

Puberty and the human brain: insights into adolescent development

Nandita Vijayakumar a, Zdena Op de Macks a, Elizabeth A Shirtcliff b, Jennifer H Pfeifer a
PMCID: PMC6234123  NIHMSID: NIHMS980141  PMID: 29972766

Abstract

Alongside the exponential flourish of research on age-related trajectories of human brain development during childhood and adolescence in the past two decades, there has been an increase in the body of work examining the association between pubertal development and brain maturation. This review systematically examines empirical research on puberty-related structural and functional brain development in humans, with the aim of identifying convergent patterns of associations. We emphasize longitudinal studies, and discuss pervasive but oft-overlooked methodological issues that may be contributing to inconsistent findings and hindering progress (e.g., conflating distinct pubertal indices and different measurement instruments). We also briefly evaluate support for prominent models of adolescent neurodevelopment that hypothesize puberty-related changes in brain regions involved in affective and motivational processes. For the field to progress, replication studies are needed to help resolve current inconsistencies and gain a clearer understanding of pubertal associations with brain development in humans, knowledge that is crucial to make sense of the changes in psychosocial functioning, risk behavior, and mental health during adolescence.

Keywords: puberty, adolescence, hormones, brain development, structural MRI, functional MRI


Adolescence is a time of risk and resilience, when both positive and negative lifetime trajectories unfold (Dahl, 2004). This developmental period is also shaped by the release of pubertal hormones that trigger the process of sexual maturation, resulting in a myriad of physical and biological changes encompassing increased growth and metabolic rate, alterations in fat and muscle, breast and genital development and the appearance of secondary sex characteristics. At the same time, adolescents experience marked changes in social, emotional, and cognitive processes that ultimately enable them to attain adult roles and responsibilities (Choudhury, 2010). Along with educational and vocational achievement, this period sees a child dependent on their parents progress to a relatively independent young adult who is more responsible for their own behaviour and actions (Davey et al., 2008). In addition, significant changes in brain structure and function have been identified during this period (Crone and Dahl, 2012; Mills and Tamnes, 2014). One way to integrate across these multilevel changes is to conceptualize puberty as referring to the biological changes and adolescence as referring to the social changes (Sisk and Foster, 2004), with neurodevelopment as a potential mediator of the association between biochemical and psychosocial changes, and thus between puberty and adolescence (Blakemore et al., 2010).

Over the last two decades, there has been much research using MRI to investigate anatomical and functional changes in the brain during adolescence. While most of these studies have focused on the effect of age, there has been a more recent rise in the number of articles examining the effect of pubertal development on the brain. Greater understanding of these associations is crucial to make sense of the psychosocial changes occurring during adolescence, such as heightened social sensitivity and self-awareness (Blakemore and Mills, 2014; Pfeifer and Peake, 2012; Weil et al., 2013), increased parental conflict (Marceau et al., 2012), and social influences on decision-making (Chein et al., 2011; Weigard et al., 2014). Therefore, we aim to systematically review research examining puberty-related brain development, with specific emphasis on studies using longitudinal designs.

1. Puberty

Pubertal development occurs in two phases, adrenarche and gonadarche,which are triggered by activation of the hypothalamic-pituitary-adrenal and hypothalamic-pituitary-gonadal axes, respectively. Adrenarche is the earliest sign of puberty, typically occurring between the ages of 6 and 9 years, and earlier in girls than boys (Biro et al., 2014; Patton and Viner, 2007; Tung et al., 2004). It begins when the adrenal glands release androgens, such as dehydroepiandrosterone (DHEA) and its sulphate (DHEA-S; Palmert et al., 2001). These hormones continue to increase until the early 20s and are responsible for the development of some secondary sex characteristics, including pubic hair growth, body odor and acne (Havelock et al., 2004). Gonadarche is triggered by the hypothalamus releasing substantial amounts of gonadotropin-releasing hormone (GnRH) in a pulsatile manner during sleep (Plant and Barker-Gibb, 2004; Veldhuis, 1996). This reactivates the “dormant” hypothalamic-pituitary-gonadal axis, which was first active during prenatal and early postnatal life, and subsequently shut down by inhibitory gamma-aminobutyric acid inputs to the hypothalamus (Ojeda et al., 2006; Schulz et al., 2009; Sisk and Foster, 2004). The pulsatile release of GnRH triggers the pituitary to produce follicle stimulating and luteinizing hormones (FSH and LH), which in turn stimulate the ovaries and testes to produce sex steroid hormones, such as estrogen and testosterone. These hormones are ultimately responsible for reproductive maturity and other secondary sex characteristics, with estrogen stimulating breast growth, menstruation, and ovulation in females, and testosterone stimulating testicular development and voice changes in males. While it remains uncertain what process triggers the initial increase in GnRH release, it is hypothesised that a combination of metabolic regulation, energy storage and sleep regulation are responsible (Sisk and Foster, 2004). Gonadarche occurs earlier in females, between 9 and 14 years of age,compared to onset in males between 10 and 15 years of age (Dorn et al., 2006).

1.2. Measurement of puberty

1.2.1. Physical changes

The measurement of pubertal development is complicated by the multitude of biological and physical changes, as it remains uncertain which of these features is the best representation of maturation. The most prominent system for measuring pubertal development was proposed by Tanner (1962) who conceptualized pubertal maturation as the progression through five stages of physical development based on changes in the breast (for females), genitalia, and pubic hair. Tanner Stage (TS) 1 is considered to be pre-pubescent, TS 2–4 reflect intermediate stages and TS 5 signifies reproductive maturity. This characterization of pubertal stage is ideally measured using physical examination by health professionals, but is also commonly assessed using picture based self-report (Dorn and Susman, 2002). Perhaps an even more frequently used measurement of pubertal stage is the self- (or parent-) reported Pubertal Development Scale (PDS), which assesses height growth, body hair and skin changes, as well as breast development and menstruation in females and facial hair and voice changes in males (Petersen et al., 1988). This measure, however, does not directly map onto Tanner staging as it struggles to capture adrenarche and early gonadal development (see section 4.1.1 for further discussion; Dorn et al., 2006). Furthermore, given that self-report scales often suffer from inaccuracies associated with age, relative pubertal stage, and ethnicity (Shirtcliff et al., 2009), physical examination is often considered to be the gold standard (Dorn et al., 2006), but is also subject to its own limitations (e.g., arguably the most expensive and intrusive method).

Aside from pubertal stage, inter-individual differences in pubertal maturation can also be described using the concepts of timing and tempo. Timing describes the pubertal status of a child relative to their same-sex and -age peers, commonly examined by measuring pubertal maturation in a sample of similarly aged individuals or statistically regressing age from puberty (Dorn and Biro, 2011; Ellis and Essex, 2007). In contrast, pubertal tempo refers to the rate of progression through the pubertal stages, and thus requires ideally (a minimum of) three repeated measurements to permit calculation (Marceau et al., 2011). Although research has identified associations between tempo and timing, the direction of association remains inconsistent with some finding faster tempo in earlier maturers (Apter and Vihko, 1985; Marceau et al., 2011), but others finding the inverse association (Pantsiotou et al., 2008).

1.2.2. Hormones

Hormones are one of the most direct indices of biological changes occurring during this developmental period. However, there is skepticism for using hormones as the sole marker of pubertal maturation, as there is wide individual variability in hormone levels both within and across pubertal stages (Dorn et al., 2006). There are also multiple issues associated with their measurement that need to be considered. For example, estradiol levels can vary across the day and menstrual cycle in females(Buvat and Buvat-Herbaut, 1981), and although testosterone is relatively stable across the cycle, researchers still have to contend with a circadian rhythm (Liening et al., 2010). Studies attempt to overcome some of these issues by collecting more than one sample, usually immediately following awakening when hormones are at their highest concentrations (Matchock et al., 2007), and frequently within the early follicular phase in menstruating females when stage of the cycle is known for certain.

Aside from pubertal maturation, hormone levels also reflect genes, environmental factors, such as diet (Soliman et al., 2014) and being in an MRI scanner (Eatough et al., 2009), behaviors (e.g., exercise; Di Luigi et al., 2006) and other hormone levels (e.g., DHEA/S conversion to testosterone and aromatization to estrogen; Ubuka and Tsutsui, 2014). Differing results have also been identified with various methods of collecting and assaying samples (Handelsman and Wartofsky, 2013; Vesper et al., 2014, 2008; Vesper and Botelho, 2010). Of particular importance, saliva and blood samples index different aspects of hormone levels. Sex steroid hormones are bound to proteins during transportation, such as the sex hormone– binding globulin (SHBG) protein, and only a small percentage are unbound and free to act on receptors, including those within the brain. While saliva samples measure these unbound or “active” hormone levels, serum indexes total levels encompassing both bound and unbound hormones (Hofman, 2001), although measurement of SHBG allows the estimation of active hormone levels (Södergård et al., 1982). Urine samples also measure unbound hormone levels, but they reflect cumulative levels and are affected by urinary density or creatine concentration (Demir et al., 1994). Finally, collection can be intrusive (e.g., blood samples) and/or time consuming (e.g., repeated urine or saliva samples) with certain methodologies.

1.2. Neurobiological changes during puberty

Along with the physical changes occurring during puberty, there are a number of concurrent neural changes during this developmental period. Indeed, it has long been hypothesized that the hormonal shifts driving physical development may also be influencing brain maturation. Phoenix et al. (1959) proposed that hormone-related behavior changes during adolescence were instantiated within the brain; exposure to sex steroid hormones early in life masculinized and defeminized neural circuits, and subsequent release of these sex hormones during gonadarche gave rise to sex-typical behaviors through their actions on sexually differentiated circuits (Schulz et al., 2009). These processes can be differentiated into ‘organizational effects’ that refer to permanent changes in neural structure, and ‘activational effects’ that refer to temporary changes in the activity of neural systems (Sisk and Foster, 2004). Organizational actions of steroid hormones are limited to sensitive periods – prior to or after these windows of time, hormones have limited effects on the brain’s structure.

The organization-activation hypothesis has continued to evolve since its introduction. It has long been known that hormones have organizational effects during early neural development, with research in the 1960s and 70s identifying maximally sensitive periods during pre- and peri-natal life when increases in testosterone resulted in masculinization of the male neural circuitry and comparative absence resulted in feminization of the female neural system (Wallen and Baum, 2002). Thus it was initially hypothesized that only activational effects were present during puberty, with gonadal steroid hormones re-activating dormant neural circuits that facilitate reproductive and non-reproductive behaviors (e.g., changes in stress responsivity). However, more recent animal evidence suggests that puberty is a second period when hormones exert organizational effects on the structure of neural circuits (e.g., reviewed in Schulz et al., 2009), including those directly related to the facilitation of reproductive behaviors (i.e., hypothalamus), as well as indirectly via influences on attentional and motivational tendencies (Hebbard et al., 2003; Romeo and Sisk, 2001; Sato et al., 2008).

Conceptualization of this model remains largely driven by animal research (see reviews: Juraska and Willing, 2017; Schulz et al., 2009; Sisk and Foster, 2004). Only recently, with the comparatively new flourish of neuroimaging techniques, have researchers been able to examine data in support of this model in humans. This is increasingly recognized as a crucial area of research that is necessary to gain a better understanding of the complex socio-emotional changes experienced by human adolescents, including trajectories towards adaptive and maladaptive outcomes. Initial investigations into the role of puberty arose from structural MRI studies on grey matter that identified earlier age-related peaks in cortical development in females compared to males (Gogtay et al., 2004; Lenroot et al., 2007), which seemed to correspond with the time of pubertal onset in each sex. Although this finding has not been consistently replicated (Ducharme et al., 2016; Pfefferbaum et al., 2015; Vijayakumar et al., 2016; Wierenga et al., 2014), it did result in a number of structural and functional MRI studies investigating the role of puberty in neurobiological development. These studies have employed various methods of assessing puberty, as well as different ways of controlling for, or testing interactions with, age. Furthermore, fMRI studies have utilized different tasks to examine various aspects of social, emotional, and cognitive function. We aim to systematically review the structural and functional MRI research in order to identify the pattern of findings emerging from the literature to date, extending a prior review that specifically focused on brain structure (Herting and Sowell, 2017).

The inclusionary criteria comprised the use of pubertal measures (either physical and/or hormonal indices) and neuroimaging methodologies in a sample of children and/or adolescents (see Figure S1). We specifically focus on typical development, and direct interested readers to a recent review that has addressed atypical populations (i.e., disorders of sex development; Bramble et al., 2017). The literature will be broadly grouped into two main categories: brain structure (grey and white matter) and function, and specific emphasis will be placed on longitudinal studies. We highlight when studies have chosen to control for age in pubertal analyses, thus indexing pubertal timing as opposed to stage. We also note when studies have employed pubertal stage assessments other than the gold standard of physical examination, particularly when inconsistent findings are present. We summarize findings at the end of each section, attempting to identify consistent effects in relation to i) brain regions, ii) the index of pubertal maturation, and iii) potential sex differences. Finally, we apply the findings to dominant models of adolescent neurodevelopment that have historically posited pubertal effects on certain aspects of brain maturation.

2. Structural MRI studies

Studies examining the relationship between puberty and structural brain development fall into two major categories: i) investigations into changes in grey matter that contains neuron bodies and supporting glial cells, and ii) investigations into changes in white matter that contains the myelinated axon fibers of neurons.

2.1. Grey matter changes

Investigations into grey matter primarily employ two main analytic methods: i) voxel-based morphometry (VBM) that tests for differences in grey matter density, and ii) surface-based morphometry (SBM) that provides estimates of cortical thickness, surface area and volume. Research on normative developmental patterns of grey matter during adolescence has identified increases in surface area, along with reductions in thickness and volume with age (Ducharme et al., 2016, 2015; Mills et al., 2016; Vijayakumar et al., 2016; Wierenga et al., 2014). Similarly, research on puberty has identified negative associations between global grey matter volume and pubertal stage and gonadal hormone levels, although more inconsistencies are evident when controlling for age (see Table 1; Bramen et al., 2011; Paus et al., 2010; Peper et al., 2009a; Pfefferbaum et al., 2015). Of more interest, though, is research that examines regional differences in these associations given i) variation in the density of hormone receptors (see Holder and Blaustein, 2014) and ii) different rates of maturation across the brain (Tamnes et al., 2010; Vijayakumar et al., 2016; Wierenga et al., 2014).

Table 1:

Pubertal associations with global cortical grey matter.

  Without Age With Age Age Sample size
  Female Male Female Male    
Pubertal stage        
Peper et al., 2009b         9 214
Koolschijn et al., 2014         8–25 215
Bramen et al., 2011         10–14 80
Pfefferbaum et al., 2015         12–22 674
Testosterone        
Peper et al., 2009c       10–15 78
Koolschijn et al., 2014         8–25 215
Bramen et al., 2011         10–14 80
Paus et al., 2010     12–18 419
Estradiol        
Peper et al., 2009c         10–15 78
Koolschijn et al., 2014         8–25 215
positive
negative
null

Note: only cross-sectional studies are presented.

2.1.1. Cortical development

2.1.1.1. Cross-sectional research

Similar to studies on age-related grey matter changes, studies examining associations between pubertal stage and cortical grey matter have predominantly identified negative associations across adolescence (refer to Table 2 for an overview of all structural studies that were reviewed). This includes extensive and widespread negative associations between PDS scores and regional density/thickness and volume (Hu et al., 2013; Koolschijn et al., 2014; Peper et al., 2009b; Pfefferbaum et al., 2015). Findings from studies that accounted for age are depicted in Figure 1.

Table 2.

Overview of studies on pubertal associations with grey and white matter research in humans.

Authors N (F) Age Puberty Study design Imaging
modalit
Image
processing
Outcome of interest
Asato et al., 2010 112 (63) 8–28 TS (pic) Cross-sectional DTI voxelwise TBSS RD
Barendse et al., 2018 87 (47) 9–10 DHEA, TEST (saliva) Cross-sectional DTI voxelwise TBSS FA, MD, RA, AD
Bava et al., 2011 58 (29) 12–14 PDS Cross-sectional DTI voxelwise TBSS FA, MD, RD, AD
Blanton et al., 2012 54 (54) 9–16 TS, menarcheal status Cross-sectional sMRI VBM Amygdala &
hippocampus volumes
Bramen et al., 2011 80, 48 10–14 TS, TEST (blood) Cross-sectional sMRI SBM Subcortical & global
GM volume
Bramen et al., 2012 85 (49) 10–14 TEST (blood) Cross-sectional sMRI SBM Vertex-level CT
Brouwer et al., 2015 113 (53) 9–12 LH (urine), FSH
(urine), EST (urine),
TEST (saliva)
Single cohort
longitudinal
sMRI VBM Voxel-level GM
Chavarria et al., 2014 124 (62) 5–18 PDS Cross-sectional sMRI VBM Corpus callosum volume
Genc et al.,
2017
74 (31) 9–12 PDS Cross-sectional DTI Fixel-based
analysis
Fibre density, fibre
cross-section
Goddings et al., 2014 275 (117);
711 scans
7–20 TS (pic) Accelerated
longitudinal
sMRI SBM Subcortical volumes
Herting et al., 2012 77 (39) 10–16 PDS, TEST, EST
(blood)
Cross-sectional DTI voxelwise TBSS FA, MD, RD, AD
Herting et al., 2014 116 (59);
189 scans
10–14 TS, TEST, EST
(blood)
Single cohort
longitudinal
sMRI SBM Global & subcortical
volumes
Herting et al., 2015 81 (48) 10–14 TS, TEST, EST
(blood)
Single cohort
longitudinal
sMRI SBM Vertex-level CT & SA
Herting et al., 2017 33 (15);
66 scans
10–20 PDS Accelerated
longitudinal
DTI voxelwise TBSS FA, MD, RD, AD
Herve et al., 2009 404 (200) 12–18 TEST (blood, active
levels estimated)
Cross-sectional sMRI VBM Corticospinal tract
volume
Hu et al., 2013 306 (167) 4–18 PDS Cross-sectional sMRI VBM Mesial temporal lobe
volume
Klauser et al., 2015 85 (48) 9 DHEA (saliva) Cross-sectional sMRI VBM Voxel-level WM
Koolschijn et al., 2014 215 (113) 8–25 PDS, TEST (saliva),
EST (saliva), LH
(urine)
Cross-sectional sMRI SBM Global GM &
subcortical
volume, vertex-level CT
Menzies et al., 2015 61 (0) 13–16 TS (pic), TEST, EST,
DHEA (saliva)
Cross-sectional DTI voxelwise TBSS FA, MD, RD, AD
Murray et al., 2016 95 (50) 9 DHEA, TEST (saliva) Cross-sectional sMRI SBM Pituitary volume
Neufang et al., 2009 46 (23) 8–15 TEST, EST (blood) Cross-sectional sMRI VBM Voxel-level GM
Nguyen et al., 2013 255 (143);
407 scans
4–22 DHEA, TEST (saliva) Accelerated
longitudinal
sMRI SBM Vertex-level CT
Nguyen et al., 2012 281 (154);
469 scans
4–22 PDS, TEST (saliva) Accelerated
longitudinal
sMRI SBM Vertex-level CT
Paus et al., 2010 204 (0) 12–18 TEST (blood, active
levels estimated), AR
gene
Cross-sectional sMRI VBM Global GM & WM
volume, voxel-level WM
Peper et al., 2008 104 (47) 9 LH (urine) Cross-sectional sMRI VBM Voxel-level WM
Peper et al., 2009a 78 (41) 10–15 TEST (saliva), EST
(urine)
Cross-sectional sMRI VBM Global & voxel-level
GM, WM
Peper et al., 2009b 214 (107) 9 TS Cross-sectional sMRI VBM Global & voxel-level
GM, WM
Peper et al., 2010 85 (46) 10–15 LH (urine), FSH
(urine), EST (urine),
TEST (saliva)
Cross-sectional sMRI VBM Pituitary &
hypothalamus volumes
Peper et al., 2015 258 (132) 8–25 TEST, EST (saliva) Cross-sectional DTI deterministic
tractography
FA, MD, RD, LD
Perrin et al., 2008 204 (0) 12–18 TEST (blood, active
levels estimated)
Cross-sectional sMRI VBM Global WM volume
Perrin et al., 2009 408 (204) 12–18 PDS Cross-sectional sMRI VBM Voxel-level WM
Pfefferbaum et al., 2015 674, (340) 12–22 PDS Cross-sectional sMRI SBM Global WM volume
Pangelinan et al., 2016 941 (480) 12–19 PDS, TEST (blood,
active levels
estimated)
Cross-sectional sMRI VBM Corticospinal tract
volume
Satterthwaite et al., 2014 524 (335) 10–22 TS (pic) Cross-sectional sMRI VBM Amygdala &
hippocampus volumes
Schutter et al.,
2017
149 (76) 12–27 TEST (saliva) Cross-sectional sMRI SBM Cerebellar volume
Urosevic et al., 2014 126 (63) 9–18 PDS, TS (pic) Cross-sectional sMRI SBM Subcortical volumes
Whittle et al., 2012 154 (72) 11–16 PDS Cross-sectional sMRI SBM Pituitary volume
Wong et al., 2014 962 (495) 11–19 PDS, TEST, EST
(blood)
Cross-sectional sMRI VBM Pituitary volume

NB: The “Puberty” column only notes a single method of collection when it was utilized for all hormones. AD = axial diffusivity, AR = angroden receptor, CT = cortical thickness, DHEA = dehydroepiandrosterone, DTI = diffusion tensor imaging, EST = Estradiol, FA = fractional anisoptropy, FSH = follicle- stimulating hormone, GM = grey matter, LD = longitudinal diffusivity, LH = lutenizing hormone, MD = mean diffusivity, PDS = Pubertal Development Scale, RA = radial diffusivity, SA = surface area, SBM = surface-based morphometry, sMRI = structural MRI, TBSS = tract-based spatial statistics, TEST = testosterone TS = Tanner stage, VBM = voxel-based morphometry, WM = white matter

Figure 1.

Figure 1.

Pubertal associations with regional cortical grey matter (from crosssectional studies), after accounting for age. Darker arrows represent lateral findings, while lighter arrows represent medial findings. Dashed lines connect findings from same study that fall within same anatomical subdivisions. Upward and downward arrows represent positive and negative correlations, respectively. Readers who would like further orientation to brain structure may consider exploring the interactive viewer available at http://www.brainfacts.org/3D-Brain. Pubertal stage: Hu et al., 2013; Koolschijn et al., 2014; Peper et al., 2009b; Pfefferbaum et al., 2015. Testosterone: Bramen et al., 2012; Koolschijn et al., 2014; Neufang et al., 2009 (null effects: Peper et al., 2009a). Estradiol: Brouwer et al., 2015; Koolschijn et al., 2014; Neufang et al., 2009; Peper et al., 2009a.

Similar negative associations have been identified in relation to testosterone levels. Almost all of these studies controlled for age and much of the results converge in the frontal lobe, particularly in the orbitofrontal cortex (OFC) and anterior cingulatecortex (ACC) (see Figure 1; Bramen et al., 2012; Koolschijn et al., 2014). Some sex differences have been noted, with one study finding stronger negative associations in males within the left parietal lobe in 8–15 year olds (Neufang et al., 2009), and another finding stronger negative associations in females of a similar age in the frontal, inferior parietal and middle temporal gyri (Bramen et al., 2012). Sex differences in the occipital lobe have also been characterized by negative associations with testosterone levels in females and positive associations in males (Bramen et al., 2012). Nevertheless, thus far, it is difficult to confirm any strong sex differences for specific brain regions.

Negative associations between estradiol levels and brain structure also predominate, with all studies controlling for age (see Figure 1; Brouwer et al., 2015; Koolschijn et al., 2014). Two of these studies found sex differences characterized by negative relationships between estradiol levels and prefrontal (PFC) and parietal cortices in females alone (Brouwer et al., 2015; Peper et al., 2009a), although some positive associations in females have been identified in the inferior temporal, middle occipital and middle frontal regions (Peper et al., 2009a).

2.1.1.2. Longitudinal research

Longitudinal studies are especially helpful for research on puberty as they allow for age and pubertal stage to be more easily differentiated than cross-sectional research. Unfortunately, only four longitudinal studies were identified that examined pubertal associations with cortical development. Using an accelerated longitudinal sample of 4 to 22 year olds, Nguyen and colleagues (2012) found significant negative associations between testosterone and thickness in more developed adolescents (PDS “stages” 3–5, following conversion from raw scores). This included the left posterior cingulate, precuneus, dorsolateral (dl)PFC and ACC in males, and right somatosensory cortex in females. These effects accounted for age and remained significant when controlling for PDS. While similar effects were present when examining PDS alone, they did not survive correction for testosterone levels. These results are largely consistent with cross-sectional findings, but also suggest that cortical thinning may be driven by testosterone and emerges over the course of pubertal development with normative increases in testosterone.

Subsequent analyses of this sample identified significant positive associations between DHEA and the left dlPFC, right entorhinal and perirhinal cortices, as well as the temporoparietal junction (TPJ), in the pre-pubertal group (PDS stages 1–2; Nguyen et al., 2013). DHEA levels also moderated the association between testosterone and regional thickness, such that it was stronger in those with lower DHEA levels relative to those with higher DHEA levels. The authors speculate that different competing time-dependent processes may mediate these interactive effects, and emphasize the importance of examining hormone levels together rather than in isolation, along with the importance of adrenarche as a critical period for brain development.

While accelerated longitudinal designs permit the examination of a large age range within a shorter time frame, non-accelerated designs have increased power to examine intraindividual change. Employing such a design, Herting and colleagues (2015) constrained the age span of participants at each wave given their primary interest in pubertal processes, recruiting 10–12 year old females and 12–14 year old males. Over a two-year follow-up (and after controlling for age and pubertal stage at baseline) greater increases in estradiol were related to greater thinning in the left middle temporal cortex in females, while greater TS change was related to less thinning of the superior frontal and right superior temporal cortices (the latter finding being stronger in females). The opposing findings of pubertal stage and estradiol are hypothesized to arise from TS capturing a broader range of the various hormonal processes occurring during puberty. They also identified sex-specific associations between pubertal changes and maturation of cortical surface area, suggesting that puberty may have unique effects on differing properties of the cortical mantle. This is not surprising given that thickness and surface area capture distinct cellular processes (Chenn and Walsh, 2002). Further research on surface area may thus provide novel insight into pubertal effects that are neglected when focusing on thickness, or potentially obscured when investigating volumetric estimates that are the product of surface area and thickness.

Interestingly, another non-accelerated study of 9 year old twins failed to identify any associations between grey matter development and changes in testosterone, estradiol or LH levels over 3 years (Brouwer et al., 2015). Aside from the age range of the sample, another potentially important difference from Herting and colleagues’ (2015) study is the use of VBM methodology, which suggests that analytic technique may be a source of noise in the literature (see section 4.1.2 for further discussion). They did find that changes in FSH levels were positively associated with changes in the left PFC, left hippocampus, and right cerebellar density in females. More than half of significant voxels were explained by environmental factors unique to the individual, as opposed to common environment or shared genetic factors, indicating an important role of environmental factors in adolescent brain development.

2.1.2. Subcortical development

2.1.2.1. Cross-sectional research

There has been particular interest in the effect of puberty on the hippocampus and amygdala given the prevalence of sex steroid hormone receptors in these regions (Abdelgadir et al., 1999).

Focusing on the amygdala, there is support for sex differences in the effect of pubertal stage (see Figure 2), with one study identifying a negative association with TS in females and a positive association in males (Bramen et al., 2011). Others have found converging evidence when considering specific aspects of pubertal development, including negative associations between amygdala volume and breast development in females (Blanton et al., 2012; Hu et al., 2013), and positive associations with hair and skin changes in males (Hu et al., 2013). In relation to the hippocampus, there is some support for reductions in volume with increasing pubertal stage (Blanton et al., 2012; Neufang et al., 2009), although two studies have noted differing patterns of association in males and females (see Figure 2; Bramen et al., 2011; Hu et al., 2013). Across both the amygdala and hippocampus, much more inconsistencies exist when considering pubertal timing, across different measurement indices (TS: Blanton et al., 2012; Bramen et al., 2011; Peper et al., 2009b; PDS: Koolschijn et al., 2014; factor score of PDS and pictorial ratings: Urosevic et al., 2014) and also when using large age-spans (Koolschijn et al., 2014; Satterthwaite et al., 2014; Urosevic et al., 2014).

Figure 2.

Figure 2.

Pubertal associations with subcortical grey matter (from cross-sectional studies) in males (M) and females (F). Circles represent findings from studies that ran analyses both controlling and not controlling for age. Pubertal stage: Blanton et al., 2012*; Bramen et al., 2011*; Hu et al., 2013*; Koolschijn et al., 2014; Neufang et al., 2009; Peper et al., 2009b; Satterthwaite et al., 2014*; Urosevic et al., 2014. Testosterone: Bramen et al., 2011*; Koolschijn et al., 2014; Neufang et al., 2009; Peper et al., 2009a; Brouwer et al., 2015. Estradiol: Koolschijn et al., 2014; Neufang et al., 2009; Peper et al., 2009a; Brouwer et al., 2015. *Studies only examined amygdala and hippocampus.

Results are predominantly non-significant when considering the relationship between gonadal hormones and either amygdala or hippocampus volume. Most studies controlled for age, and only one out of these five identified significant associations between testosterone levels and volume (positive with amygdala and negative with hippocampus; Neufang et al., 2009). None of the four studies on estradiol levels identified significant relationships with either structure. The only study that did not control for age found a significant negative association between testosterone levels and amygdala volume in females alone, but no such association with the hippocampus (Bramen et al., 2011).

The nucleus accumbens (NAcc) is of particular interest in this literature given behavioral changes in reward processing during adolescence (Silverman et al., 2015), but there is minimal support for the morphological properties of this region being associated with puberty (see Figure 2; Brouwer et al., 2015; Koolschijn et al., 2014; Neufang et al., 2009; Peper et al., 2009a; Peper et al., 2009b). The only study to find effects noted significantly smaller volumes in more mature females and a trend towards larger volumes in more mature males, after accounting for age in a sample of 9–18 year olds (based on a factor score of PDS and pictorial ratings; Urosevic et al., 2014).

Another subcortical region that has received some interest given its role in the secretion of FSH and LH is the pituitary gland. Two out of four studies identified positive associations with PDS score, both with (Whittle et al., 2012; Wong et al., 2014) and without (Wong et al., 2014) controlling for age. Others have noted positive associations with estradiol, testosterone, FSH and DHEA levels, most of which remains when accounting for age (Murray et al., 2016; Peper et al., 2010; Wong et al., 2014). There is also some support for pubertal effects in the diencephalon (i.e., hypothalamus, mammillary bodies and thalamus; Bramen et al., 2011; Neufang et al., 2009; Urosevic et al., 2014), although others have failed to replicate these results (Koolschijn et al., 2014; Peper et al., 2010, 2009a).

2.1.2.2. Longitudinal research

Comparatively fewer longitudinal studies have examined associations between puberty and subcortical development. An accelerated longitudinal study of 7–20 year olds identified positive associations between TS (based on pictorial ratings) and amygdala and hippocampus volume, but negative associations with striatal structures (i.e., NAcc, caudate, pallidum and putamen; Goddings et al., 2014). Significant interactions between age and puberty, as well as qualitative sex differences in development trajectories, were identified. Quantitative sex differences in the relationship between TS and subcortical volume were also reported in a non-accelerated longitudinal study (described in section 2.1.1.2), along with significant interactions between age, testosterone levels and sex (Herting et al., 2014). As illustrated in Figure 3, amygdala findings from both studies are broadly consistent with sex differences in cross-sectional research. However, they do suggest nonlinear changes in subcortical volume as a function of pubertal development, and emphasize the need for further longitudinal research to explore such trajectories.

Figure 3.

Figure 3.

Pubertal associations with amygdala development from longitudinal studies. a) Goddings et al., 2014; b,c) Herting et al., 2014.

Moving beyond traditional investigations of structural brain development, a series of studies by Nguyen and colleagues (2017, 2016a, 2016b) examined the relationship between puberty and structural covariance (i.e., how structural properties, such as volume or thickness, of different regions correlate with each other Alexander-Bloch et al., 2013; Zielinski et al., 2010). In an accelerated-longitudinal sample of 6–22 year olds, testosterone levels moderated covariance between the amygdala and right OFC, such that individuals with lower testosterone levels exhibited positive covariance (i.e., larger amygdala and greater cortical thickness), whereas those with higher levels exhibited negative covariance (Nguyen et al., 2016b). Results remained significant when controlling for pubertal (PDS) stage and estradiol. Similar associations were identified between DHEA levels and cortico-amygdala covariance (Nguyen et al., 2016a). Testosterone levels also moderated coritco-hippocampus covariance in males, although in the opposite direction to the amygdala (Nguyen et al., 2017). Analyses across all studies accounted for age-related maturation. These studies extend the structural neuroimaging literature by using a network-based approach to examine the relationship between hormones and coordinated development of brain regions. Such an approach is valuable given that animal literature predominantly supports direct mechanisms within the hypothalamus, amygdala, hippocampus, medial PFC and visual cortex (Juraska and Willing, 2017), but pubertal associations with grey matter structure are pervasive across the cortex.

2.1.3. Summary of grey matter research

There appears to be a general pattern of reductions in cortical grey matter associated with greater pubertal (stage) development and testosterone levels, and predominantly similar associations when accounting for age (i.e., pubertal timing). Less consistency in these patterns exists for estradiol levels, although negative associations dominate in females. There is also some preliminary evidence that different hormones may play a role in different periods of development, with DHEA and testosterone being implicated in early and late pubertal stages, respectively, and exhibiting different patterns of association with cortical structure. Although not specifically tested, DHEA results are consistent with adrenarche-related brain changes occurring earlier in development. Across the different pubertal indices, the most consistent effects appear to be present in the frontal lobe, although the temporal lobes have also been identified to a lesser extent. Finally, it is thus far difficult to identify clear sex differences in the associations between pubertal developmental and cortical grey matter structures.

However, there is converging evidence that puberty may contribute to sexually dimorphic changes seen in the amygdala. Multiple studies found that females exhibited negative associations with pubertal stage, while males exhibited positive associations. Moreover, longitudinal research suggests complex nonlinear interactions between puberty, age and sex. While both sexes show predominantly negative associations between pubertal stage and hippocampus volume, some studies did identify sex differences. While animal research indicates that such sex differences are related to the effects of sex steroids on receptors, support for hormonal associations with subcortical structure is limited in crosssectional studies on humans. However, the only longitudinal study does suggest that subcortical maturation is related to hormone levels, highlighting the need for further targeted longitudinal research to fully understand these relationships.

2.2. White matter changes

Initial research on white matter development was conducted using VBM analyses, but more recent work has employed Diffusion Tensor Imaging (DTI) to measure the diffusion of water molecules through tissues, providing a measure of net directionality and magnitude (diffusivity). The net directionality of diffusion is indexed by fractional anisotropy (FA), which is often assumed to reflect myelination, but has also been associated with axonal loss (Barkovich, 2000), changes in cell packing density (Beaulieu, 2002) , myelin pathology (Shimony et al., 1999), as well as less coherent or crossing fibers within a voxel (Mädler et al., 2008; Virta et al., 1999). Mean diffusivity (MD) is another frequently used index that quantifies overall diffusion within a particular voxel in any direction, with higher values suggestive of disrupted axonal integrity. While other diffusivity indices exist (i.e., axial and radial diffusivity), we focus on MD as it is the most commonly studied metric thus far, but list studies using other indices in Table 2. Research on normative developmental patterns of white matter integrity has identified significant increases in FA and decreases in MD during childhood and adolescence across all the major fiber tracts (Asato et al., 2010; Pfefferbaum et al., 2015). Next, we review the relationship between puberty and white matter development, with an overview of the findings presented in Table 3.

Table 3.

Pubertal associations with (a) white matter volume/density and (b) DTI indices of FA and MD.

  A) Volume/density
  Without age With age
Pubertal stage
Chavarria et al., 2014 CC
Pfefferbaum et al., 2015 global
Perrin et al., 2009 all lobes
Peper et al., 2009b occipital
Pangelinan et al., 2016 CST
Testosterone
Paus et al., 2010 global
Herve et al., 2009 CST CST
Perrin et al., 2008 global
Peper et al., 2009c global/regional
Pangelinan et al., 2016 CST
Estradiol
Paus et al., 2010 global/regional
B) FA MD
  Female Male Female Male
Pubertal stage
Bava et al., 2012 CST, SCR ILF, forceps major
Herting et al., 2012 insula
  superior front superior front
Menzies et al., 2015 SLF, ILF, CLT, CST SLF, ILF, CLT, CST
Testosterone
Barendse et al., 2018
Herting et al., 2012 precentral superior temp, front, angular gyrus, thalamus, CC, IC superior front
Peper et al., 2015 subcortico-temp
Menzies et al., 2015 * SLF, ILF, CLT, CST
Estradiol
Herting et al., 2012 angular gyrus, IC, SLF Cingulum, superior front, precuneus, thalamus
Peper et al., 2015
Menzies et al., 2015 * SLF, ILF, CLT, CST
positive
negative
null

Note: Only findings from cross-sectional studies are presented. Within part b), analyses that did not include age in model are marked with “*”. CC = corpus callosum, CLT = cortico-limbic tract, CST = cortico-spinal tract, front = frontal lobe, IC = internal capsule, ILF = inferior longitudinal fasiculus, SCR = Superior corona radiata, SLF = superior longitudinal fasiculus, temp = temporal lobe.

2.2.1. Cross-sectional research

2.2.1.1. VBM studies

As highlighted in Table 3a, a number of studies have identified positive associations between global/regional white matter volume and pubertal (PDS) stage (Chavarria et al., 2014; Perrin et al., 2009; Pfefferbaum et al., 2015) as well as testosterone levels (Hervé et al., 2009; Paus et al., 2010; Perrin et al., 2008). However, when accounting for age, inconsistent effects are present for both pubertal stage (Pangelinan et al., 2016; Peper et al., 2009b) and testosterone (Hervé et al., 2009; Pangelinan et al., 2016; Peper et al., 2009a). The only study focusing on estradiol also failed to identify any associations with global or regional white matter when controlling for age (Peper et al., 2009a). Studies on separate 9 year old samples have found negative associations between DHEA levels and white matter density around the left anterior corona radiata (Klauser et al., 2015), and positive associations between LH levels and white matter density in the right splenium of the corpus callosum and superior frontal gyrus, bilateral middle temporal cortex and left cingulum (Peper et al., 2008).

2.2.1.2. DTI studies

Findings from DTI studies are summarized in Table 3b. There appears to be a trend for increased FA at higher pubertal stages. Such positive associations with PDS score have been identified in the internal capsule, superior temporal, superior and inferior frontal, and angular gyri, after controlling for age in a sample of 10–16 years (Herting et al., 2012). A similar trending positive association was identified in a study of 13–16 year old boys using TS (based on pictorial ratings; Menzies et al., 2015). However, negative PDS associations with FA have also been noted in the superior frontal and precentral gyri in females (Herting et al., 2012), and others have failed to identify any significant associations (Bava et al., 2011). Only one out of three studies identified significant associations between pubertal stage and MD. Specifically, reduced MD was found at later pubertal stages in the superior and inferior longitudinal fasciculus, cortico-subcortical and projection tracts (Menzies et al., 2015).

Four studies have examined associations of gonadal hormonal levels with white matter development using DTI. One of the three studies to examine FA found positive associations with testosterone levels in the internal capsule, corpus callosum, and superior temporal, frontal, and angular gyri in males, and precentral gyrus in females, in a sample of 10–16 year olds after controlling for age (Herting et al., 2012). Interestingly, two out of four studies identified positive associations between testosterone levels and MD after controlling for age - within the superior frontal gyrus in 10–16 year old males (Herting et al., 2012), and subcortico-temporal tract in 8–25 year old females (Peper et al., 2015). This pattern of findings contradicts expected negative associations based on age- and pubertal stage-related changes in MD. However, the only study that did not control for age did find such negative associations between testosterone levels and MD in a cluster comprising the superior and inferior longitudinal fasciculi, as well as cortico-limbic and -spinal tracts, in 13–16 year old males (Menzies et al., 2015).

When considering estradiol, one of the two studies examining FA identified positive associations in the bilateral inferior cingulum and precuneus in males, and negative ssociations in the right angular gyrus and superior longitudinal fasciculus in females when controlling for age (Herting et al., 2012). None of the three studies examining MD found significant associations when either controlling (Herting et al., 2012; Peper et al., 2015) or not controlling for age (Menzies et al., 2015).

Two studies have examined the role of DHEA in white matter development; one study of 13–16 year old boys failed to identify significant associations with MD (Menzies et al., 2015), but another study of 9 year olds found widespread positive associations with MD (strongest peaks were in the splenium, superior and posterior corona radiata and superior longitudinal fasiculus; Barendse et al., 2018). We hypothesize that inconsistencies likely relate to age differences between the samples, and may reflect the role of DHEA in white matter development during adrenarche. Barendse and colleagues (2018) also found that DHEA and testosterone levels interacted, such that children with lower DHEA levels had negative associations between testosterone and FA, and positive associations with MD, relative to children with higher DHEA levels. These findings echo the interactive effects of testosterone and DHEA on grey matter development (Nguyen et al., 2013), further highlighting the value of exploring the interplay between different hormones in predicting brain development.

2.2.2. Longitudinal research

One longitudinal study of white matter development using SBM estimates found greater increases in global white matter volume at earlier TS in a sample of 10–16 year olds, and similar associations with testosterone (trend-level) and estradiol (examined in females alone; Herting et al., 2014). A subsequent DTI study of 10–18 year olds followed up after a 2year interval identified unique effects of adrenal and gonadal changes on FA (Herting et al., 2017). Specifically, adrenal changes were related to increased FA in the thalamus and precentral gyrus, while gonadal changes were related to reductions in FA in the corpus callosum (genu), superior and anterior corona radiata, and superior frontal gyrus. In addition, sex differences were characterized by gonadal changes being related to increased FA in males, but decreased FA in females, in the superior frontal and precentral gyrus.

2.2.3. Summary of white matter research

A somewhat consistent picture appears when examining the relationship between pubertal maturation and changes in white matter volume or density, with a general trend of increased white matter density/volume over time. Similar effects are present for testosterone levels, as well as estradiol levels in females. Findings are mixed when considering associations with DTI indices; while there is some support for positive associations between pubertal stage and FA, findings are inconsistent when considering the relationship between gonadal hormones and FA, and between any pubertal index and MD, with overall minimal research to date. Of the significant findings that were identified, as with the grey matter literature, many of these effects (across both pubertal stage and testosterone) lay within the frontal and temporal lobes, as well as cortico-cortical and cortico-subcortical association tracts that connect these regions. Thus far, no consistent sex differences have been identified in the literature. Finally, there is preliminary evidence from cross-sectional and longitudinal studies that adrenal changes also play an important role in white matter development.

3. Functional MRI studies

Studies examining the relationship between puberty and functional brain development have largely focused on two different aspects of psychosocial functioning: i) affective processes underlying motivational and emotional tendencies, and ii) cognitive processes that enable individuals to understand and interpret social situations. However, it should be noted that affective and cognitive processes work in conjunction with each other to support social functioning. In addition, we discuss a few preliminary studies that have examined non-social cognitive processes that are also known to develop during adolescence, as well as resting-state functional connectivity. Refer to Table 4 for an overview of all functional studies that were reviewed.

Table 4.

Overview of studies on pubertal associations with brain function in humans.

Authors N (F) Age Puberty Study design Imaging
modality
Imaging details
Alarcon et al., 2014 49 (23) 10–16 TEST (blood) Cross-sectional fMRI spatial working memory
Braams et al., 2015 249 (147) 8–27 PDS, TEST (saliva) Accelerated longitudinal fMRI reward sensitivity – outcome
Cservenka et al., 2015 44 (22) 10–15 PDS, TEST, EST (blood) Cross-sectional fMRI affective faces – emotional
incongruence
Fareri et al., 2015 50 (23) 4–23 TEST (saliva) Cross-sectional rs-fc MRI VS seed
Ferri et al., 2014 60 (60) 8–15 TS (pic), PDS Cross-sectional fMRI affective faces – emotional
reactivity
Forbes et al., 2010 77 (40) 11–13 TEST (blood) Cross-sectional fMRI reward sensitivity –
anticipation & outcome
Forbes et al., 2011 76 (40) 11–13 TS Cross-sectional fMRI affective faces – emotional
reactivity
Goddings et al., 2012 42 (42) 11–13.7 TS, menarcheal status,
TEST, EST, DHEA (saliva)
Cross-sectional fMRI social cognition – mentalising
Jankowski et al., 2014 18 (9) 11–14 PDS Cross-sectional fMRI social cognition – self vs other
evaluations
Klapwijk et al., 2013 35 (35) 11–13.7 TS, menarcheal status,
TEST, EST, DHEA (saliva)
Cross-sectional fMRI (PPI) social cognition – mentalising
Le Moult et al., 2015 36 (36) 9–14 menarcheal status Cross-sectional fMRI reward sensitivity –
anticipation
Moore et al., 2012 45 (26) 10–13 PDS Single cohort
longitudinal
fMRI affective faces – emotional
reactivity
Morgan et al., 2013 72 (40) 11–13 TS Cross-sectional fMRI reward sensitivity –
anticipation & outcome
Op de Macks et al., 2011 50 (33) 10–16 TEST, EST, DHEA (saliva) Cross-sectional fMRI reward sensitivity – outcome
Op de Macks et al., 2016a 58 (58) 11–13 PDS, TEST, EST (saliva) Cross-sectional fMRI risk taking
Op de Macks et al., 2016b 58 (58) 11–13 PDS, TEST, EST (saliva) Cross-sectional fMRI risk taking – social vs monetary
feedback
Peters et al., 2014 268 (138) 8–25 PDS, TEST, EST (saliva) Cross-sectional fMRI rule-learning with performance
feedback
Peters et al., 2015 173 (86) 12–25 TEST (saliva) Cross-sectional rs-fc MRI amygdala seed
Pfeifer et al., 2013 27 (18) 10–13 PDS Single cohort longitudinal fMRI social cognition – self vs other
evaluations
Schweinsburg et al., 2005 49 (25) 12–17 PDS Cross-sectional fMRI spatial working memory
Spielberg et al., 2014 38 (21) 11–15 TEST (blood) Single cohort longitudinal fMRI affective faces – emotional
reactivity
Spielberg et al., 2015 41 (21) 11–15 TEST (blood) Single cohort longitudinal fMRI (PPI) affective faces – emotional
reactivity
Telzer et al., 2015 30 9–16 PDS – parent Cross-sectional fMRI affective faces – opposite sex
Tyborowska et al., 2016 47 (26) 14 TEST (saliva) Cross-sectional fMRI affective faces – emotional
incongruence
van Duijvenvoorde et al., 2014 47 (32); 31
(18)
10–16; 2-year
follow-up
PDS Cross-sectional and
accelerated longitudinal
fMRI reward sensitivity – outcome
Whittle et a., 2015 83 (43) 9 DHEA (saliva) Cross-sectional fMRI affective faces – emotional
reactivity

NB: The “Puberty” column only notes a single method of collection when it was utilized for all hormones.

DHEA = dehydroepiandrosterone; EST = Estradiol, fMRI = functional MRI; PDS = Pubertal Development Scale; PPI = Psychophysiological interactions; rs-fc MRI = resting-state functional connectivity MRI; TEST = testosterone; TS = Tanner stage; VBM = voxel-based morphometry; VS = ventral striatum

3.1. Reward processes

Reward-related processing has largely been investigated as neural responses to monetary gains in gambling and card-guessing paradigms. A recent quantitative metaanalysis showed that although adolescents and adults activate similar regions during reward processing – including the ventral and dorsal striatum, insula, and posterior cingulate cortex – reward-related neural activation tends to be greater in adolescents compared to adults (Silverman et al., 2015). Furthermore, despite conflicting findings from cross-sectional studies (reviewed in Galvan, 2010), a longitudinal study provides evidence for a mid-adolescent peak in reward-related activation in the ventral striatum (VS; Braams et al., 2015).

3.1.1. Cross-sectional studies

Studies focusing on the effect of pubertal development on reward-related neural processing are highlighted in Figure 4. Reward-related processing is often separated into the anticipation and receipt of reward. When considering the anticipation phase, a study of 9–14 year old females found that menarcheal status and TS (based on pictorial ratings) were related to greater VS activation during anticipation of both gain (trending) and loss in a monetary incentive delay task (LeMoult et al., 2015). Another study of 11–13 year olds found that more pubertally advanced adolescents (“mid/late: TS 3–5” compared to “pre/early: TS 1–2” groups) exhibited less BA10 activation during reward anticipation (Morgan et al., 2013), although prior analyses of the same data failed to identify any effects of pubertal timing (i.e., controlling for age) within the VS or medial PFC (Forbes et al., 2010).

Figure 4.

Figure 4.

Pubertal associations with reward-related brain function. a) Darker arrows represent lateral findings, while lighter arrows represent medial findings. Dashed circles highlight findings that did not control for age. NB: Results using lowered (uncorrected) statistical thresholds from Op de Macks et al. (2011) are not presented.

b) Striatal findings (all studies used ROIs). NB: Longitudinal findings from Braams et al., (2015) are not presented. T statistics and correlation coefficients were converted to Z statistics for consistency across studies.

Conflicting results have also been observed when considering the effect of puberty on neural activation to reward outcomes. One study found that adolescents at later TS exhibited less caudate and more medial PFC (extending into the dorsal ACC) activation in 11–13 year olds, after controlling for age (Forbes et al., 2010), but separate analyses of the same sample found less rostral ACC activation without controlling for age (Morgan et al., 2013). Others have failed to identify significant effects when using self-report measures of pubertal stage in both early adolescent and extended age ranges, with and without controlling for age (Op de Macks et al., 2016b; van Duijvenvoorde et al., 2014).

Focusing on testosterone, the only study investigating reward anticipation identified positive associations with caudate activity in males, but not females, when controlling for age (Forbes et al., 2010). Positive associations with medial OFC activity have also been found in 11–13 year old females when choosing to play on risky trials, after controlling for age (Op de Macks et al., 2016b). Of the three studies that examined reward outcomes, two identified positive associations between testosterone levels and VS activity during early to mid-adolescence, with (Alarcón et al., 2017) and without controlling for age (Op de Macks et al., 2011). The third study, however, found negative associations with caudate activity in 11–13 year olds, after controlling for age (Forbes et al., 2010).

Finally, estradiol levels have been found to positively correlate with activation to monetary reward outcomes in a sample of 10–16 year olds, within the dorsal striatum, dlPFC, and medial PFC (using an uncorrected threshold; Op de Macks et al., 2011). Positive associations have also been identified with NAcc activation during decision making (choosing to play vs. pass) in 11–13 year old females after accounting for age (Op de Macks et al., 2016b), and similar associations with anterior insula activity were present when choosing to play in social, but not monetary, feedback conditions (Op de Macks et al., 2016a). However, null effects have also been noted for reward outcomes in a sample of 12–17 years, after accounting for age (Alarcón et al., 2017).

3.1.2. Longitudinal studies

Only one study was identified in the fMRI literature that employed multilevel modelling to analyze accelerated longitudinal data. Braams and colleagues (2015) identified a quadratic effect of age on NAcc response to wins over losses in a gambling task, characterized by a mid-adolescent peak in activation, in a sample of 827 year olds. In comparison, NAcc response increased linearly with PDS score. Similar linear associations were found in relation to testosterone levels, and moreover, testosterone effects were close to three times larger than the PDS.

3.2. Social-affective processes

Research on social-affective processing has commonly examined neural responses to emotionally-laden faces. Many of these studies have revealed greater subcortical activation during adolescence, including some evidence for an adolescent peak in amygdala activation in response to fearful faces (Guyer et al., 2008; Hare et al., 2008; Monk et al., 2003). Others have identified greater VS activation in response to happy faces relative to rest (i.e., null) events (Pfeifer et al., 2011; Somerville et al., 2011), suggesting there might be enhanced processing of both negatively- and positively-valenced social stimuli during adolescence.

3.2.1. Cross-sectional studies

An overview of studies examining social-affective processes is presented in Figure 5. Only two cross-sectional studies were identified that investigated pubertal associations with neural processing of emotionally-laden facial stimuli. Both studies found reductions in amygdala activation to emotionally neutral faces with pubertal development, with ( Forbes et al., 2011) and without controlling (Ferri et al., 2014) for age. The former study additionally found that mid/late-pubertal adolescents (TS 34), compared to those at earlier stages, exhibited less ventrolateral (vl)PFC reactivity to fearful faces, but more reactivity to angry faces (Forbes et al., 2011). These findings were interpreted as a reduction in threat-related brain function for ambiguous stimuli (i.e., fear and neutral faces), paired with an increase for unambiguous stimuli (i.e., angry faces), with pubertal maturation.

Figure 5.

Figure 5.

Pubertal associations with affect-related brain function. a) Darker arrows represent lateral findings, while lighter arrows represent medial findings. Dashed circles highlight findings that did not control for age. Dashed lines connect findings from same study that fall within the same anatomical subdivision. b) Amygdala findings (studies using whole brain analyses highlighted in bold). NB: Longitudinal findings from Spielberg et al. (2014; 2015) on task-based connectivity are not presented. T statistics and correlation coefficients were converted to Z statistics for consistency across studies.

No cross-sectional studies were identified that examined the effect of gonadal hormones on reactivity to emotionally-laden facial stimuli. However, DHEA levels have been shown to be (predominantly) negatively associated with functional activation in 9 year olds, including cingulate activity while viewing positively- and negatively-valenced emotional faces, as well as caudate and dlPFC activity to negatively-valenced faces (Whittle et al., 2015).

Aside from the aforementioned investigations of emotions, studies have also used facial stimuli to examine other factors that may influence social-affective processing. For example, amygdala and VS activation has been found to increase with pubertal stage (PDS) when viewing opposite-sex relative to same-sex affective faces in a sample of 9–16 year olds, after accounting for age (Telzer et al., 2015). Interestingly, increasing age was related to lower amygdala activation with and without controlling for PDS, suggesting that pubertal maturation relative to sameaged peers has a unique effect of making opposite sex faces more salient.

Finally, there is some evidence that neural activation during emotional conflict is association with gonadal hormones. Neural responses to emotional conflict, defined as emotion-incongruent vs. -congruent face-word stimuli, were positively related to estradiol levels in the right dorsal ACC and left cerebellum, but negatively associated with testosterone levels in the left putamen and middle frontal gyrus, in a sample of 10–15 year old males (Cservenka et al., 2015). Similar direction of associations with each hormone was present in females, although regions of significance differed (e.g., testosterone effects were in the precuneus). Another study defining emotional conflict as engaging in an approach response to angry faces and avoidance response to happy faces also found negative associations between testosterone levels and amygdala and thalamus activity in 14 year olds (Tyborowska et al., 2016). However, positive associations were also identified in the anterior PFC.

3.2.2. Longitudinal studies

A longitudinal study of early to mid-adolescents (i.e., 11 – 15 years) identified increased amygdala and NAcc activity (Spielberg et al., 2014), as well as reduced amygdala-OFC functional coupling (Spielberg et al., 2015), to threat-related stimuli (i.e., angry/fearful faces compared to neutral faces/shapes) with increases in testosterone. Others have noted a generalized increase in reactivity to positively- and negatively-valenced emotions with pubertal development. Specifically, Moore and colleagues (2012) found that earlier pubertal timing (PDS regressed for age) was positively related to activity in the amygdala, thalamus and visual cortical areas at age 10, and this increased in magnitude and extent at age 13. At 13 years of age, pubertal timing was also related to greater reactivity in the temporal pole, vlPFC and dorsomedial (dm)PFC.

3.3. Social-cognitive processes

Among the most crucial cognitive abilities to support adolescents’ engagement in social behaviors is mentalizing, the ability to recognize and interpret other people’s feelings, intentions, and desires (Frith and Frith, 2003). Advanced perspective-taking continues to be refined throughout adolescence (Dumontheil et al., 2010; Sodian, 2011), and is supported by the development of the medial PFC, precuneus, anterior temporal and temporo-parietal cortices. Adolescence is also characterized by the emergence of a differentiated sense of self and intensification of self-evaluative processes, especially in the social domain. Neural activation, particularly in the vmPFC and VS, increases over adolescence when engaging in self-evaluative processes (Jankowski et al., 2014; Pfeifer et al., 2013).

3.3.1. Cross-sectional studies

Testosterone levels have been found to positively correlate with activity in social brain regions during mentalizing, specifically within the anterior temporal cortex (ATC) in 11–14 year old girls, with and without controlling for age (Goddings et al., 2012). Similar trend-level associations were present for other hormones (estradiol and DHEA), but not for pubertal stage. Meanwhile, dmPFC activation was negatively correlated with age even after controlling for hormones, suggesting potentially independent effects of puberty and age on different brain regions. Followup analyses of the same sample identified puberty-related changes in functional connectivity of the social brain network; dmPFC-ATC coupling was stronger in later TS stages, and dmPFC-TPJ connectivity was stronger at higher estradiol levels, after accounting for age (Klapwijk et al., 2013).

When considering self-evaluative processes, bilateral VS activation increased with pubertal stage (PDS) during social, but not academic, evaluations in a sample of 11–14 year olds (Jankowski et al., 2014). These findings remained when controlling for age.

3.3.2. Longitudinal studies

Only one longitudinal study has related neural activity elicited by socialcognitive processes and pubertal development. Change in pubertal stage (PDS) in 1013 year olds was related to greater increases in vmPFC activation when evaluating the self, relative to another target in the social, but not academic domain. While age (operationalized by study wave) also produced increases in vmPFC activity during self-evaluation in both domains, the effect of pubertal stage on social self-evaluation remained even after controlling for age (Pfeifer et al., 2013).

3.4. Cognitive control processes

To date, comparatively less research has examined pubertal influences on cognitive control processes (e.g., working memory, inhibitory control, cognitive flexibility, performance monitoring). While these abilities are present early in development, higher-order executive processes continue to be refined throughout adolescence, along with continued maturation of implicated neural substrates, including the dlPFC, ACC and parietal cortices (Asato et al., 2006; Huizinga et al., 2006; Luna, 2009).

An early study investigating pubertal effects on brain function found that pubertal stage was negatively associated with activation in the right superior parietal cortex, after controlling for age, in a sample of 12–17 year olds performing a spatial working memory task (Schweinsburg et al., 2005). However, it was not associated with activation in numerous other frontal and parietal clusters that were related to age. Similarly, more recent imaging studies have failed to identify a role of puberty in cognitive function. For example, sex differences in neural activation related to spatial working memory, after accounting for age effects, were not mediated by testosterone levels in 10–16 year olds (Alarcon et al., 2014). Furthermore, neither testosterone, estradiol, or pubertal stage were associated with neural activation during feedback learning in a sorting task after accounting for age, which itself was significantly associated with neural response in a sample of 8– 25 year olds (Peters et al., 2014).

3.5. Resting-state functional connectivity

The examination of intrinsic or resting-state functional connectivity in the brain can be approached using either seed-based analyses or topological properties of brain networks using graph theory. Only two studies were identified that examined pubertal associations with resting-state functional connectivity, which used seedbased approaches focusing on the amygdala and VS.

There has been particular interest in examining developmental changes in resting-state functional connectivity between the amygdala and medial PFC, with preliminary data suggesting age-related increases in connectivity with the vmPFC (Gabard-Durnam et al., 2014). In comparison, a similar cluster within the vmPFC was found to exhibit decreased connectivity with the amygdala at higher testosterone levels in a sample of 12–25 year olds, after controlling for age (Peters et al., 2015), consistent with findings of reduced amygdala-OFC task-based connectivity with increasing testosterone levels (Spielberg et al., 2015; discussed in section 3.2.2). While age-related findings have been interpreted as reflecting greater integration of the fronto-limbic network with development (Alarcon et al., 2015; Gabard-Durnam et al., 2014), the hormonal findings suggest potentially unique developmental processes being captured by puberty and age.

In comparison to the amygdala, resting-state functional connectivity of the VS broadly decreases with age across a number of regions, including the temporal lobe, precuneus, amygdala, medial PFC and ACC (Fareri et al., 2015; Padmanabhan et al., 2013). Similar reductions in VS-subgenual ACC connectivity were identified with increasing testosterone levels in a sample of 4–23 year olds, and moreover, testosterone mediated the negative association between age and connectivity (Fareri et al., 2015). In comparison to the integration hypothesis discussed above, the authors suggest these changes may reflect functional specialization of the VS and subgenual ACC to support differential processes over time.

3.6. Summary of functional research

Overall, certain aspects of brain function seem to be more closely related to pubertal maturation than others. Gonadal hormone levels seem largely associated with increases in functional activation elicited by various aspects of reward-related processing within the VS and medial PFC, while more inconsistencies exist in relation to pubertal stage. In comparison, both pubertal stage and testosterone levels appear to be associated with decreases in amygdala activation elicited by neutral faces, and decreases in lateral PFC responses to fearful faces.

There is also some indication that functional connectivity of regions may decrease in relation to specific emotions (i.e., fear) with pubertal maturation. Findings also indicate a general pattern of increased activation of social brain regions when engaging in social-cognitive processes such as mentalizing and self-evaluation, as well as potentially increased connectivity among these regions, with pubertal maturation. In comparison, the limited studies focusing on cognitive control suggest that pubertal maturation may not be related to associated neural responses. Finally, there is preliminary evidence that DHEA relates to socioemotional processing during adrenarche and that increases in testosterone reduces resting-state functional connectivity between limbic and medial prefrontal regions. However, it is important to note inconsistencies in the literature that hinder strong conclusions, and to draw attention to methodological considerations discussed in section 4.1 that limit the replication of findings.

4. Overview

It is evident from this systematic review of structural and functional neuroimaging research that certain aspects of brain development are more strongly related to pubertal maturation than others. Structural development of subcortical regions is the most extensively investigated area thus far, with findings highlighting sex differences in the relationship between pubertal stage and amygdala volume, and to a lesser extent, hippocampal volume. The limited longitudinal research suggests that gonadal hormones may be responsible, despite inconsistent support from crosssectional studies. In the cortex, reductions in grey matter are evident with increasing pubertal stage, earlier pubertal timing, and rising testosterone levels. These effects are most pronounced in the frontal lobe, followed by the temporal lobe. Consistent with these cortical findings, but still preliminary in nature, are associations between pubertal development and white matter in similar regions, as well as cortico-cortical and cortico-subcortical association tracts that connect these regions. Importantly, longitudinal studies implicate the role of pubertal tempo and intra-individual changes in hormone levels on cortical and subcortical development during adolescence; specifically that nonlinear trajectories may best describe certain associations between pubertal maturation and brain development. Taken together, findings suggest that longitudinal pubertal and hormonal processes, rather than absolute stages or levels, are more likely to shed valuable insight on brain development.

Drawing conclusions from the functional neuroimaging literature is more difficult given variations in task design, but there appears to be an overall trend of increased activity associated with motivational processing in the VS and medial PFC with rising gonadal hormone levels. Similar puberty-related increases in the processing of emotionally-laden faces have been identified within the amygdala and prefrontal cortices, although functional connectivity of these regions may decrease with maturation. There is also a general pattern of increased activity associated with social-cognitive processes in the social brain network with pubertal maturation. However, it is important to reiterate acknowledgment of inconsistencies in the literature, particularly when comparing different pubertal and hormonal indices, that hinder strong conclusions.

Conflicting findings in the literature likely arise, at least partly, from vast variations in study design, such as the age range of participants, pubertal measurements, neuroimaging processing techniques, and sampling and analytic strategies. This has resulted in very few empirical investigations of overlapping aspects of pubertal and brain development. Consequently, it is difficult to draw robust conclusions regarding the consistency and replicability of findings. In order to aide progression of the field and evolution of our theories, we discuss methodological considerations when interpreting current findings, as well as planning future studies.

Box 1. Models of adolescent neurodevelopment.

The interest in examining pubertal associations with brain development in humans largely originated from prominent models of adolescent neurodevelopment, which historically postulated a mismatch between the development of subcortical regions implicated in affective reactivity and cortical regions implicated in cognitive control (Casey et al., 2008; Nelson et al., 2005; Spielberg et al., 2015). In particular, the dual-systems (Steinberg, 2008) and social information processing network (SIPN; Nelson et al., 2005) models, and to a lesser degree the imbalance model (Somerville and Casey, 2010), proposed that pubertal maturation was driving alterations in neural processing of affective stimuli around early adolescence, while age- or experiencerelated maturation was responsible for more protracted changes in the cognitive regulatory system. Although these models differ in a number of ways, this mismatch was broadly held responsible for numerous adverse outcomes during adolescence (e.g., health-risking behaviors, substance abuse, depression). As such, the examination of pubertal associations with brain development represent crucial empirical assessments of the prevailing heuristic models of adolescent neurodevelopment. However, we do note that all models have evolved significantly since their introductions, and while puberty remains a core component of the dualsystems model (Shulman et al., 2016; Smith et al., 2013), emphasis on puberty within the SIPN and imbalance models appears to have decreased (Casey et al., 2016; Nelson et al., 2016).

Our review of the functional neuroimaging literature suggests that certain indices of pubertal maturation do influence motivational and affective processing. However, the very limited studies examining cognitive control have thus far failed to identify any effects of puberty. While these findings may be considered to provide preliminary support for aforementioned models, it should be noted that there are a number of null findings for motivational and social-affective processes. It is also possible that current models have deterred more extensive research on cognitive control. When considering specific models in more detail, further inconsistencies arise. For example, the dual-systems model hypothesizes an inverted-U shaped trajectory of striatal activation to reward processing (Shulman et al., 2016), but the only study to examine such nonlinear associations between puberty (both stage and testosterone levels) and reward-related activation identified solely linear effects across adolescence and young adulthood (Braams et al., 2015). Such conflicting findings, in combination with certain aspects of brain development being more extensively investigated than others, hinders strong conclusions from being drawn regarding support for “mismatch” models of adolescent neurodevelopment. Findings of pubertal associations with social-cognitive processes also highlight the need for our theories to account for social factors that play a prominent role in influencing adolescent behavior, as proposed by a number of research groups (Crone and Dahl, 2012; Kilford et al., 2016; Nelson et al., 2016; Pfeifer and Allen, 2016; van den Bos and Eppinger, 2016). Overlooking these changes may result in an inaccurate assumption that behavioral changes related to pubertal maturation are solely driven by brain regions subserving reward and other affective processes.

“Mismatch” models are also rooted in early structural neuroimaging findings that showed protracted age-related maturation of prefrontal regions implicated in cognitive control and emotion regulation (Gogtay et al., 2004; Shaw et al., 2008), relative to hypothesized earlier maturation of subcortical regions that were presumed to be related to puberty. In support of prevalent theories, development of the amygdala and hippocampus does appear to be linked to puberty. However, less attention has been directed to the structure of the VS, with primarily null findings, despite the importance of this region to models of adolescent neurodevelopment. Moreover, our review indicates that frontal regions are among the most consistently correlated cortical structures with pubertal maturation, including the superior and inferior frontal, and anterior cingulate, cortices, that are thought to play a role in emotional, attentional and cognitive control. These cortical regions also appear to exhibit more consistent associations with gonadal hormones in comparison to subcortical structures. Such findings contradict theories of solely age- or experiencedriven cortical maturation of regions subserving emotional and cognitive control, thus challenging the premise of “mismatch” models. Cortical associations with pubertal maturation may also be driven by neural interactions with subcortical systems, either via structural or functional connectivity, and there is increasing acknowledgement of this network perspective in more recent adaptations of these models (e.g., Casey et al., 2016). Further empirical studies using such network analytic approaches are now required to advance this line of research.

Box 2. Pubertal mechanisms.

Significant findings beg the question of what underlying mechanisms might be mediating the relationship between puberty and brain development. Much of our knowledge comes from animal research, where there is evidence for changes in neurons and supporting processes during puberty, such as pruning of dendrites and synapses in the amygdala, hippocampus and medial PFC (Cooke and Woolley, 2005; Drzewiecki et al., 2015; Zehr et al., 2008, 2006), and increased myelination in the corpus callosum (Kim and Juraska, 1997). Rising testosterone levels increased the number of neurons in the amygdala in male Syrian hamsters (De Lorme et al., 2012), and androgen administration during puberty increased spine density in the amygdala and hippocampus (Cunningham et al., 2007). Comparatively, ovariectomy in female rats resulted in greater neurons and glia, as well as dendritic spines, in the medial PFC and visual cortex (Antonio Mun˜oz-Cueto et al., 1990; Koss et al., 2015). These findings are suggestive of sex differences in the role of gonadal hormones in neural organization; consistently, the removal of gonads prior to puberty resulted in less sex differences within the hypothalamus and amygdala (Ahmed et al., 2008). A number of studies have also noted changes in receptor density during puberty, including increased androgen receptors in the hypothalamus and amygdala following the onset of puberty (Kashon and Sisk, 1994) and more specifically with rising testosterone levels (Meek et al., 1997). Estrogen receptor (ER) α has been found to decrease prior to pubertal onset within the medial PFC, while ER β comparatively increased, in mice (Westberry and Wilson, 2012). For a recent review of the animal literature, refer to Juraska & Willing (2017).

Interpretation of findings also needs to consider indirect mechanisms that may be partly responsible for the relationship between puberty and brain development. Alternate hypotheses largely focus on socio-environmental influences associated with the myriad of physical and behavioral changes. Specifically, adolescents may be treated differently by others in their environment (e.g., peers, parents, teachers) as a result of their physical changes, or alterations in their own behaviors could trigger different socio-environmental influences (Blakemore, 2008; Mendle et al., 2012). Based on a growing literature implicating a role of the environment in structural and function brain development (Richmond et al., 2016; Schriber and Guyer, 2016), this represents an important area for future research to more thoroughly investigate and incorporate environmental influences into our theoretical and analytic models.

4.1. Methodological considerations

4.1.1. Pubertal assessments

One of the largest sources of noise within this literature likely relates to the conflation of pubertal stage and timing. When puberty is examined alone, higher scores indicate greater pubertal maturation. When age is incorporated via study design or statistical modelling, the resultant pubertal metric is more akin to “stage-for-age” (i.e., timing), with higher scores indicating greater maturation than same-aged peers. While both pubertal stage and timing are important, they are fundamentally different constructs that the current neuroimaging literature has largely failed to differentiate. Many studies that account for age do not specifically interpret findings as reflecting pubertal timing. However, our review suggests that normative transition through pubertal stages influences brain development differently than earlier or later maturation relative to same-aged peers. Greater awareness of the distinction between these constructs will eliminate some of the inconsistencies in the literature. It may also allow us to develop a clearer understanding of what is more important for normative versus aberrant trajectories of brain development, as well as potential underlying mechanisms.

Inconsistencies in findings are also likely to be impacted by variations in the measurement of pubertal stage. As discussed in the introduction (section 1.2.1) PDS and TS tap into different aspects of pubertal maturation, with PDS under-representing gonadal development. As such, individuals with low PDS scores can actually be quite physically developed according to TS, particularly in males as the PDS does not track genital development (i.e., penile and testicular changes) despite it primarily defining the first 3 Tanner stages (Deardorff et al., in press). Given these differences, certain inconsistencies in the literature between TS and PDS may, to some extent, reflectnon linear relationships between puberty and brain development. For example, some studies identified amygdala volume increases with TS in males (Bramen et al., 2011; Neufang et al., 2009), but other studies that might be capturing later pubertal development with the PDS have failed to replicate these effects (Koolschijn et al., 2014). One way to minimize this issue is to use scoring methods that try to transform the PDS into TS, such as that proposed by Shirtcliff et al. (2009) that differentially codes gonadal and adrenal hormonal signals of physical development. Studies using self-report measures may also benefit from using both the PDS and pictorial ratings of TS to ensure all phases of development are better captured.

More refined coding schemes of puberty will also help us overcome the use of global operationalizations that integrate a number of physical changes, each developing at differing rates, including those driven by adrenal vs. gonadal hormones. This seems particularly important given a growing literature on brain development related to adrenarche during late childhood (see Byrne et al., 2017), with potentially differential patterns of brain development to gonadarche (i.e., Herting et al., 2017; Nguyen et al., 2013, vs 2012). Others have shown that different aspects of physical development (i.e., hair, breast, voice) exhibit varied associations with subcortical structure (Blanton et al., 2012; Hu et al., 2013). As such, we speculate that reliance on global metrics may be obscuring important information.

Another important methodological consideration is the analytic strategy employed by studies to account for the correlated nature of certain hormones with each other and/or pubertal stage (Shirtcliff et al., 2009). Many studies examine each hormonal/pubertal index separately, thus limiting the specificity of their findings (as results may be driven by shared variance among different pubertal indices). On the other hand, modelling multiple indices within a given analysis is likely to significantly change the interpretation of findings, particularly when the same approach is employed across sex. For example, controlling for testosterone in pubertal stage analyses would account for different variance in males and females. It is not possible to suggest a single strategy to deal with this issue; rather, analysis should be determined based on the research question of interest and findings need to be carefully interpreted given the analytic strategy.

As described in section 1.2.2, it is also widely known that extraneous factors such as the circadian rhythm and menstruation, as well as anovulation in early female adolescents, are additional sources of variance for basal hormone levels (Berenbaum et al., 2015). This may account for the oftentimes inconsistent or null findings in relation to estradiol. More frequent and systematic measurements of hormones, particularly of estradiol across the menstrual cycle, may help us better capture pubertal processes and identify potential neurodevelopmental effects (Shirtcliff et al., 2009). Finally, inconsistent findings may also be attributable to the use of saliva vs. blood samples that measure free and total hormone levels, respectively (see Herting et al., 2017 for further discussion).

4.1.2. Neuroimaging assessments

Inconsistencies in the current literature may be, at least partly, also attributable to differences in neuroimaging methodologies across studies. This can be difficult to overcome as studies frequently employ newer and more advanced techniques than prior literature. This is particularly evident in structural neuroimaging, with a shift from VBM to SBM and DTI for grey and white matter properties, respectively. In the functional neuroimaging literature, studies frequently employ different experimental paradigms to examine the same topic of interest. For example, some studies on reward-related processing model the choice to engage in reward-related behaviors, others focus on the subsequent anticipatory processes, and some examine neural response to reward outcomes. These design variations, in combination with differences in pubertal indices and methods of controlling for age, result in only one or two studies investigating a particular topic of interest and using comparable measures and analysis techniques (as highlighted in Figures 4 and 5). While it remains essential that we continue to advance our methods, it is equally important to replicate studies where possible to consolidate prior findings, particularly when it is feasible to achieve both goals. For example, studies using an affective faces task could report pubertal associations with reactivity to different emotionally-laden stimuli in addition to other experimental manipulations, such as the gender of facial stimuli.

Another important consideration in neuroimaging analyses is the choice between whole-brain and region-of-interest (ROI) analyses. While some studies have employed the latter approach with specific hypotheses regarding the location of effects, this can hinder conclusions about the specificity of pubertal relationships to particular brain regions. The inclusion of whole brain (i.e., vertex/voxel-wise or parcellation-based) analyses in NeuroVault (or as supplementary material) can help overcome this issue, and also facilitates comparison across studies with differing ROIs. NeuroVault can also help overcome the issue of differing methods of correction for multiple comparisons across studies, which can potentially bias findings when lower thresholds are employed with ROI analyses. In addition, whole brain analyses can highlight potentially unexpected areas of significance that converge across different studies. This combination of hypothesis-driven and exploratory approaches is most likely to help us build on current findings as well as identify promising paths for future research.

4.1.3. Study design and analysis

While the literature is dominated by cross-sectional studies, inferences about developmental processes from these studies can be misleading (Kraemer et al., 2000), given individual differences in puberty and brain structure/function. Some crosssectional studies attempt to maximize variance in pubertal maturation while minimizing potential age-effects by sampling participants within a narrow age-band around the onset of puberty, although these designs often under-represent the upper range of pubertal development. Alternatively, studies with broader age ranges have to deal with high correlations between puberty and age, along with issues related to controlling for age when specifically interested in pubertal stage (see section 4.1.1). Only longitudinal designs with repeated within-subjects measurements are able to distinguish differences between age- and puberty-related maturation.

While there is an increasing number of longitudinal studies, those examining pubertal tempo (i.e., intra-individual change) have predominantly modelled linear trajectories given limitations with the number of time points available. However, a child transitioning from TS 1 to 2 is likely to be experiencing very different brain changes from an adolescent going from TS 4 to 5. An analytic model that imposes a linear effect of pubertal tempo assumes similarity across both these transitions. This speaks to the importance of examining nonlinear trajectories that account for potentially differential effects of puberty at different stages of development. Ideally, studies would have a minimum of four time points to study nonlinear trajectories at the individual level (i.e., without over-fitting models or relying on group-level changes).

Another important consideration for longitudinal studies is the overall duration, as researchers need to sample over a decade to capture the entire age span during which puberty may impact brain development. Moreover, hormone levels continue to change following TS 5 (Braams et al., 2015), highlighting potentially longer periods of interest. Accelerated longitudinal designs can cover greater age spans with a shorter study duration, but are less powered to capture intra-individual processes during any given time frame. This issue is exemplified when studies are not specifically designed to study pubertal processes, and thus recruit cohorts based on age. Future studies using this design may benefit from selecting cohorts based on pubertal stage to increase their power to examine pubertal questions. As discussed by Herting et al. (2017), time interval between assessments is another important consideration, with participant burden also playing a role.

Finally, we note that most studies in the literature undertake multiple sets of analyses, whether this relates to the examination of numerous ROIs and/or comparison across different measures of pubertal maturation. Oftentimes, statistical thresholds do not account for these multiple comparisons, especially if examination in a given region is strongly hypothesis-driven. While lowered statistical thresholds can be beneficial in early exploratory research where it is important to minimize false negatives, moving forward, we need to ensure that appropriate multiple comparison procedures are undertaken to account for the plurality of analyses in future hypothesis-driven studies that build on current findings.

4.2. Functional relevance

Aside from the biological changes occurring during adolescence, this period is also characterized by significant affective, behavioral and social changes, as well as a sharp increase in the prevalence of psychopathology (World Health Organization, 2014). It is often postulated that puberty-related brain development may be partly responsible for these functional outcomes. However, very few studies have examined associations between puberty, brain development and health behaviors or mental health outcomes. Most of these studies focused on pubertal timing, as earlier maturation relative to same-age peers has consistently been implicated in mental health problems during adolescence (Harden et al., 2012; Harden and Mendle, 2012). Findings highlight a potential role of pituitary structure, with larger volumes mediating the relationship between earlier pubertal timing and increases in depressive symptomatology (Whittle et al., 2012), as well as the relationship between greater DHEA levels and social anxiety symptoms (Murray et al., 2016). When considering brain function, blunted activation of the posterior insula to happy faces mediated the relationship between DHEA levels and externalizing symptoms (Whittle et al., 2015). Even fewer studies have examined the role of puberty and brain development in adolescent health risking behaviors. Pubertal stage has been found to moderate the association between limbic brain structure and risky sexual behaviors (Feldstein Ewing et al., 2018), and greater testosterone levels has been associated with increased alcohol use in males via amygdala-OFC resting state connectivity (Peters et al., 2015). Interestingly, there has been no research to date on how puberty and brain development may together predict positive outcomes such as prosociality, normative sexual development and health promoting behaviors (see Suleiman et al., 2017). Such a shift away from the “risk framework” will help us identify how biological processes ultimately promote the goals of adolescence, such as attaining adult social roles, responsibilities and status.

4.3. Conclusions

The extant literature highlights that pubertal development is associated with concurrent neurobiological maturation during adolescence. Regional variation exists, with both structure and function of subcortical and frontal regions being the most consistently implicated. However, results may be biased from neuroimaging practices that focus on these regions. Many inconsistent findings were also identified, and we presume that variations in study design, including measurement of pubertal maturation and consideration of age effects (i.e., timing vs. stage), are important contributing factors. In addition, potential nonlinear effects of puberty might be obscuring important findings. Moving forward, replication studies are needed to help us resolve inconsistencies and gain a clearer understanding of pubertal associations with brain development. Longitudinal investigations that are better able to distinguish pubertal and age-related processes are critical. Finally, integration of different neuroimaging modalities, and the assessment of multiple hormones in addition to physical changes, may help us develop a broader understanding of pubertal influences on brain development and associated functioning.

Supplementary Material

Figure S1.

PRISMA flowchart of systematic review. An electronic search was conducted in PubMed using the key words adolescence, brain, MRI OR DTI OR fMRI, and puberty OR hormones OR pubertal hormones OR gonadarche OR adrenarche OR gonadal hormones OR adrenarcheal hormones, to identify studies published in this field to date (February 2018). Inclusion criteria included i) the use of pubertal measures (either physical and/or hormonal indices), ii) neuroimaging methodologies (sMRI, DTI, fMRI, rs-fcMRI), iii) in a sample of typically developing children and/or adolescents (lower limit of age range less than 18 years).

Highlights.

  1. Pubertal associations with sub cortical and frontal brain regions.

  2. Distinct role of pubertal stage and timing on brain development.

  3. Longitudinal studies highlight importance of examining non-linear associations.

  4. Discuss potential sources of inconsistencies and recommend future directions.

Acknowledgments

Funding

This work was supported by the National Institute of Health grants P50 DA035763

(PIs: Chamberlain and Fisher) and R01 MH107418 (PI: Pfeifer).

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Declarations of interest: none

References

  1. Abdelgadir SE, Roselli CE, Choate JV, Resko JA, 1999. Androgen receptor messenger ribonucleic acid in brains and pituitaries of male rhesus monkeys: studies on distribution, hormonal control, and relationship to luteinizing hormone secretion. Biol. Reprod 60, 1251–1256. [DOI] [PubMed] [Google Scholar]
  2. Ahmed EI, Zehr JL, Schulz KM, Lorenz BH, DonCarlos LL, Sisk CL, 2008. Pubertal hormones modulate the addition of new cells to sexually dimorphic brain regions. Nat. Neurosci 11, 995–997. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Alarcon G, Cservenka A, Fair DA, Nagel BJ, 2014. Sex differences in the neural substrates of spatial working memory during adolescence are not mediated by endogenous testosterone. Brain Res 1593, 40–54. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Alarcón G, Cservenka A, Nagel BJ, 2017. Adolescent neural response to reward is related to participant sex and task motivation. Brain Cogn 111, 51–62. 10.1016/j.bandc.2016.10.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Alarcon G, Cservenka A, Rudolph MD, Fair DA, Nagel BJ, 2015. Developmental sex differences in resting state functional connectivity of amygdala sub-regions. NeuroImage 115, 235–244. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Alexander-Bloch A, Giedd JN, Bullmore E, 2013. Imaging structural co-variance between human brain regions. Nat. Rev. Neurosci 14, 322–336. 10.1038/nrn3465 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Antonio Mun˜oz-Cueto J, Miguel Garci´a-Segura L, Ruiz-Marcos A, 1990. Developmental sex differences and effect of ovariectomy on the number of cortical pyramidal cell dendritic spines. Brain Res 515, 64–68. 10.1016/00068993(90)90577-X [DOI] [PubMed] [Google Scholar]
  8. Apter D, Vihko R, 1985. Premenarcheal endocrine changes in relation to age at menarche. Clin. Endocrinol. (Oxf.) 22, 753–760. [DOI] [PubMed] [Google Scholar]
  9. Asato MR, Sweeney JA, Luna B, 2006. Cognitive processes in the development of TOL performance. Neuropsychologia 44, 2259–2269. 10.1016/j.neuropsychologia.2006.05.010 [DOI] [PubMed] [Google Scholar]
  10. Asato MR, Terwilliger R, Woo J, Luna B, 2010. White matter development in adolescence: a DTI study. Cereb. Cortex, 20, 2122–2131. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Barendse MEA, Simmons JG, Byrne ML, Seal ML, Patton G, Mundy L, Wood SJ, Olsson CA, Allen NB, Whittle S, 2018. Brain structural connectivity during adrenarche: Associations between hormone levels and white matter microstructure. Psychoneuroendocrinology 88, 70–77. 10.1016/j.psyneuen.2017.11.009 [DOI] [PubMed] [Google Scholar]
  12. Barkovich AJ, 2000. Concepts of myelin and myelination in neuroradiology. Am. J. Neuroradiol 21, 1099–1109. [PMC free article] [PubMed] [Google Scholar]
  13. Bava S, Boucquey V, Goldenberg D, Thayer RE, Ward M, Jacobus J, Tapert SF, 2011. Sex differences in adolescent white matter architecture. Brain Res 1375, 41–48. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Beaulieu C, 2002. The basis of anisotropic water diffusion in the nervous system - a technical review. NMR Biomed 15, 435–455. 10.1002/nbm.782 [DOI] [PubMed] [Google Scholar]
  15. Berenbaum SA, Beltz AM, Corley R, 2015. The importance of puberty for adolescent development: conceptualization and measurement. Adv. Child Dev. Behav 48, 53–92. 10.1016/bs.acdb.2014.11.002 [DOI] [PubMed] [Google Scholar]
  16. Biro FM, Pinney SM, Huang B, Baker ER, Walt Chandler D, Dorn LD, 2014. Hormone changes in peripubertal girls. J. Clin. Endocrinol. Metab 99, 3829–3835. 10.1210/jc.2013-4528 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Blakemore S-J, 2008. Development of the social brain during adolescence. Q. J. Exp. Psychol 61, 40–49. [DOI] [PubMed] [Google Scholar]
  18. Blakemore S-J, Burnett S, Dahl RE, 2010. The role of puberty in the developing adolescent brain. Hum. Brain Mapp 31, 926–933. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Blakemore S-J, Mills KL, 2014. Is adolescence a sensitive period for sociocultural processing? Annu. Rev. Psychol 65, 187–207. 10.1146/annurevpsych-010213-115202 [DOI] [PubMed] [Google Scholar]
  20. Blanton RE, Cooney RE, Joormann J, Eugene F, Glover GH, Gotlib IH, 2012. Pubertal stage and brain anatomy in girls. Neuroscience 217, 105–112. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Braams BR, van Duijvenvoorde ACK, Peper JS, Crone EA, 2015Longitudinal changes in adolescent risk-taking: a comprehensive study of neural responses to rewards, pubertal development, and risk-taking behavior. J. Neurosci 35, 7226–7238. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Bramble MS, Lipson A, Vashist N, Vilain E, 2017. Effects of chromosomal sex and hormonal influences on shaping sex differences in brain and behavior: Lessons from cases of disorders of sex development. J. Neurosci. Res 95, 65–74. 10.1002/jnr.23832 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Bramen JE, Hranilovich JA, Dahl RE, Chen J, Rosso C, Forbes EE, Dinov ID, Worthman CM, Sowell ER, 2012. Sex matters during adolescence: testosterone-related cortical thickness maturation differs between boys and girls. PLoS ONE 7, e33850. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Bramen JE, Hranilovich JA, Dahl RE, Forbes EE, Chen J, Toga AW, Dinov ID, Worthman CM, Sowell ER, 2011. Puberty influences medial temporal lobe and cortical gray matter maturation differently in boys than girls matched for sexual maturity. Cereb. Cortex 21, 636–646. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Brouwer RM, Koenis MMG, Schnack HG, van Baal GC, van Soelen ILC, Boomsma DI, Hulshoff Pol HE, 2015. Longitudinal development of hormone levels and grey matter density in 9 and 12-year-old twins. Behav. Genet 45, 313–323. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Buvat J, Buvat-Herbaut M, 1981. Changes in the gonadotrophins, in the prolactin and in the sexual steroid levels throughout the normal menstrual cycle. J. Gynecol. Obstet. Biol. Reprod 10, 99–108. [PubMed] [Google Scholar]
  27. Byrne ML, Whittle S, Vijayakumar N, Dennison M, Simmons JG, Allen NB, 2017. A systematic review of adrenarche as a sensitive period in neurobiological development and mental health. Dev. Cogn. Neurosci, Sensitive periods across development 25, 12–28. 10.1016/j.dcn.2016.12.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Casey BJ, Galván A, Somerville LH, 2016. Beyond simple models of adolescence to an integrated circuit-based account: A commentary. Dev. Cogn. Neurosci 17, 128–130. 10.1016/j.dcn.2015.12.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Casey BJ, Jonrd RM, Hare TA, 2008. The adolescent brain. Ann. N. Y. Acad. Sci 1124, 111–126. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Chavarria MC, Sanchez FJ, Chou Y-Y, Thompson PM, Luders E, 2014. Puberty in the corpus callosum. Neuroscience 265, 1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Chein J, Albert D, O’Brien L, Uckert K, Steinberg L, 2011. Peers increase adolescent risk taking by enhancing activity in the brain’s reward circuitry. Dev. Sci 14, F1–10. 10.1111/j.1467-7687.2010.01035.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Chenn A, Walsh CA, 2002. Regulation of cerebral cortical size by control of cell cycle exit in neural precursors. Science 297, 365–369. 10.1126/science.1074192 [DOI] [PubMed] [Google Scholar]
  33. Choudhury S, 2010. Culturing the adolescent brain: what can neuroscience learn from anthropology? Soc. Cogn. Affect. Neurosci 5, 159–167. 10.1093/scan/nsp030 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Cooke BM, Woolley CS, 2005. Gonadal hormone modulation of dendrites in the mammalian CNS. J. Neurobiol 64, 34–46. 10.1002/neu.20143 [DOI] [PubMed] [Google Scholar]
  35. Crone EA, Dahl RE, 2012. Understanding adolescence as a period of socialaffective engagement and goal flexibility. Nat. Rev. Neurosci 13, 636–650. 10.1038/nrn3313 [DOI] [PubMed] [Google Scholar]
  36. Cservenka A, Stroup ML, Etkin A, Nagel BJ, 2015. The effects of age, sex, and hormones on emotional conflict-related brain response during adolescence. Brain Cogn 99, 135–150. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Cunningham RL, Claiborne BJ, McGinnis MY, 2007. Pubertal exposure to anabolic androgenic steroids increases spine densities on neurons in the limbic system of male rats. Neuroscience 150, 609–615. 10.1016/j.neuroscience.2007.09.038 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Dahl RE, 2004. Adolescent brain development: a period of vulnerabilities and opportunities. Keynote address. Ann. N. Y. Acad. Sci 1021, 1–22. 10.1196/annals.1308.001 [DOI] [PubMed] [Google Scholar]
  39. Davey CG, Yücel M, Allen NB, 2008. The emergence of depression in adolescence: development of the prefrontal cortex and the representation of reward. Neurosci. Biobehav. Rev 32, 1–19. 10.1016/j.neubiorev.2007.04.016 [DOI] [PubMed] [Google Scholar]
  40. De Lorme KC, Schulz KM, Salas-Ramirez KY, Sisk CL, 2012. Pubertal testosterone organizes regional volume and neuronal number within the medial amygdala of adult male Syrian hamsters. Brain Res 1460, 33–40. 10.1016/j.brainres.2012.04.035 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Deardorff J, Hoyt LT, Carter R, Shirtcliff EA (in press). Exploring the complexity of puberty: Understudied populations and processes. Journal of Research on Adolescence [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Demir A, Alfthan H, Stenman UH, Voutilainen R, 1994. A clinically useful method for detecting gonadotropins in children: assessment of luteinizing hormone and follicle-stimulating hormone from urine as an alternative to serum by ultrasensitive time-resolved immunofluorometric assays. Pediatr. Res 36, 221–226. 10.1203/00006450-199408000-00014 [DOI] [PubMed] [Google Scholar]
  43. Di Luigi L, Baldari C, Gallotta MC, Perroni F, Romanelli F, Lenzi A, Guidetti L, 2006. Salivary steroids at rest and after a training load in young male athletes: relationship with chronological age and pubertal development. Int. J. Sports Med 27, 709–717. 10.1055/s-2005-872931 [DOI] [PubMed] [Google Scholar]
  44. Dorn LD, Biro FM, 2011. Puberty and its measurement: A decade in review. J.Res. Adolesc 21, 180–195. 10.1111/j.1532-7795.2010.00722.x [DOI] [Google Scholar]
  45. Dorn LD, Dahl RE, Woodward HR, Biro F, 2006. Defining the boundaries of early adolescence: A user’s guide to assessing pubertal status and pubertal timing in research with adolescents. Appl. Dev. Sci 10, 30–56. 10.1207/s1532480xads1001_3 [DOI] [Google Scholar]
  46. Dorn LD, Susman EJ, 2002. Puberty script: Assessment of physical development in boys and girls. Cincinnati OH Cincinnati Child; Hosp. Med. Cent [Google Scholar]
  47. Drzewiecki CM, Willing J, Juraska JM, 2015. Changes in the number of synapses in the medial prefrontal cortex across adolescence. Presented at the Neuroscience Meeting Planner Chicago, IL: Society for Neuroscience. [Google Scholar]
  48. Ducharme S, Albaugh MD, Nguyen T-V, Hudziak JJ, Mateos-Pérez JM, Labbe A, Evans AC, Karama S, 2016. Trajectories of cortical thickness maturation in normal brain development — The importance of quality control procedures. NeuroImage 125, 267–279. 10.1016/j.neuroimage.2015.10.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Ducharme S, Albaugh MD, Nguyen T-V, Hudziak JJ, Mateos-Pérez JM, Labbe A, Evans AC, Karama S, 2015. Trajectories of cortical surface area and cortical volume maturation in normal brain development. Data Brief 5, 929–938. 10.1016/j.dib.2015.10.044 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Dumontheil I, Houlton R, Christoff K, Blakemore S-J, 2010. Development of relational reasoning during adolescence. Dev. Sci 13, F15–F24. 10.1111/j.1467-7687.2010.01014.x [DOI] [PubMed] [Google Scholar]
  51. Eatough EM, Shirtcliff EA, Hanson JL, Pollak SD, 2009. Hormonal reactivity to MRI scanning in adolescents. Psychoneuroendocrinology 34, 1242–1246. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Ellis BJ, Essex MJ, 2007. Family environments, adrenarche, and sexual maturation: a longitudinal test of a life history model. Child Dev 78, 1799–1817. 10.1111/j.1467-8624.2007.01092.x [DOI] [PubMed] [Google Scholar]
  53. Fareri DS, Gabard-Durnam L, Goff B, Flannery J, Gee DG, Lumian DS, Caldera C, Tottenham N, 2015. Normative development of ventral striatal resting state connectivity in humans. NeuroImage 118, 422–437. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Feldstein Ewing SW, Hudson KA, Caouette J, Mayer AR, Thayer RE, Ryman SG, Bryan AD (2018). Sexual risk-taking and subcortical brain volume in adolescence. Ann. Behav. Med 52, 393–405. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Ferri J, Bress JN, Eaton NR, Proudfit GH, 2014. The impact of puberty and social anxiety on amygdala activation to faces in adolescence. Dev. Neurosci 36, 239–249. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Forbes EE, Phillips ML, Silk JS, Ryan ND, Dahl RE, 2011. Neural systems of threat processing in adolescents: role of pubertal maturation and relation to measures of negative affect. Dev. Neuropsychol 36, 429–452. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Forbes EE, Ryan ND, Phillips ML, Manuck SB, Worthman CM, Moyles DL, Tarr JA, Sciarrillo SR, Dahl RE, 2010. Healthy adolescents’ neural response to reward: associations with puberty, positive affect, and depressive symptoms. JAAC 49, 162–72.e1–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Frith U, Frith CD, 2003. Development and neurophysiology of mentalizing. Philos. Trans. R. Soc. Lond. B. Biol. Sci 358, 459–473. 10.1098/rstb.2002.1218 [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Gabard-Durnam LJ, Flannery J, Goff B, Gee DG, Humphreys KL, Telzer E, Hare T, Tottenham N, 2014. The development of human amygdala functional connectivity at rest from 4 to 23 years: a cross-sectional study. NeuroImage 95, 193–207. 10.1016/j.neuroimage.2014.03.038 [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Galvan A, 2010. Adolescent development of the reward system. Front. Hum. Neurosci 4 10.3389/neuro.09.006.2010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Goddings A-L, Burnett Heyes S, Bird G, Viner RM, Blakemore S-J, 2012. The relationship between puberty and social emotion processing. Dev. Sci 15, 801–811. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Goddings A-L, Mills KL, Clasen LS, Giedd JN, Viner RM, Blakemore SJ, 2014. The influence of puberty on subcortical brain development. NeuroImage 88, 242–251. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Gogtay N, Giedd JN, Lusk L, Hayashi KM, Greenstein D, Vaituzis AC, Nugent TF, Herman DH, Clasen LS, Toga AW, Rapoport JL, Thompson PM, 2004. Dynamic mapping of human cortical development during childhood through early adulthood. Proc. Natl. Acad. Sci. U. S. A 101, 8174–8179. 10.1073/pnas.0402680101 [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Guyer AE, Monk CS, McClure-Tone EB, Nelson EE, Roberson-Nay R, Adler AD, Fromm SJ, Leibenluft E, Pine DS, Ernst M, 2008. A developmental examination of amygdala response to facial expressions. J. Cogn. Neurosci 20, 1565–1582. 10.1162/jocn.2008.20114 [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Handelsman DJ, Wartofsky L, 2013. Requirement for mass spectrometry sex steroid assays. J. Clin. Endocrinol. Metab 98, 3971–3973. 10.1210/jc.2013-3375 [DOI] [PubMed] [Google Scholar]
  66. Harden KP, Mendle J, 2012. Gene-environment interplay in the association between pubertal timing and delinquency in adolescent girls. J. Abnorm. Psychol 121, 73–87. 10.1037/a0024160 [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Harden KP, Mendle J, Kretsch N, 2012. Environmental and genetic pathways between early pubertal timing and dieting in adolescence: distinguishing between objective and subjective timing. Psychol. Med 42, 183–193. 10.1017/S0033291711000961 [DOI] [PubMed] [Google Scholar]
  68. Hare TA, Tottenham N, Galvan A, Voss HU, Glover GH, Casey BJ, 2008. Biological substrates of emotional reactivity and regulation in adolescence during an emotional go-nogo task. Biol. Psychiatry 63, 927–934. 10.1016/j.biopsych.2008.03.015015 [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Havelock JC, Auchus RJ, Rainey WE, 2004. The rise in adrenal androgen biosynthesis: adrenarche. Semin. Reprod. Med 22, 337–347. 10.1055/s-2004-861550 [DOI] [PubMed] [Google Scholar]
  70. Hebbard PC, King RR, Malsbury CW, Harley CW, 2003. Two organizational effects of pubertal testosterone in male rats: transient social memory and a shift away from long-term potentiation following a tetanus in hippocampal CA1. Exp. Neurol 182, 470–475. [DOI] [PubMed] [Google Scholar]
  71. Herting MM, Gautam P, Spielberg JM, Dahl RE, Sowell ER, 2015. A longitudinal study: changes in cortical thickness and surface area during pubertal maturation. PLoS ONE 10, e0119774. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Herting MM, Gautam P, Spielberg JM, Kan E, Dahl RE, Sowell ER, 2014. The role of testosterone and estradiol in brain volume changes across adolescence: a longitudinal structural MRI study. Hum. Brain Mapp 35, 5633–5645. 10.1002/hbm.22575 [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Herting MM, Kim R, Uban KA, Kan E, Binley A, Sowell ER, 2017. Longitudinal changes in pubertal maturation and white matter microstructure. Psychoneuroendocrinology 81, 70–79. 10.1016/j.psyneuen.2017.03.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Herting MM, Maxwell EC, Irvine C, Nagel BJ, 2012. The impact of sex, puberty, and hormones on white matter microstructure in adolescents. Cereb. Cortex 22, 1979–1992. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Herting MM, Sowell ER, 2017. Puberty and structural brain development in humans. Front. Neuroendocrinol 44, 122–137. 10.1016/j.yfrne.2016.12.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Hervé P-Y, Leonard G, Perron M, Pike B, Pitiot A, Richer L, Veillette S, Pausova Z, Paus T, 2009. Handedness, motor skills and maturation of the corticospinal tract in the adolescent brain. Hum. Brain Mapp 30, 3151–3162. [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Hofman LF, 2001. Human saliva as a diagnostic specimen. J. Nutr 131, 1621S–5S. [DOI] [PubMed] [Google Scholar]
  78. Holder MK, Blaustein JD, 2014. Puberty and adolescence as a time of vulnerability to stressors that alter neurobehavioral processes. Front. Neuroendocrinol 35, 89–110. 10.1016/j.yfrne.2013.10.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Hu S, Pruessner JC, Coupé P, Collins DL, 2013. Volumetric analysis of medial temporal lobe structures in brain development from childhood to adolescence. NeuroImage 74, 276–287. [DOI] [PubMed] [Google Scholar]
  80. Huizinga M, Dolan CV, van der Molen MW, 2006. Age-related change in executive function: developmental trends and a latent variable analysis. Neuropsychologia 44, 2017–2036. 10.1016/j.neuropsychologia.2006.01.010 [DOI] [PubMed] [Google Scholar]
  81. Jankowski KF, Moore WE, Merchant JS, Kahn LE, Pfeifer JH, 2014. But do you think I’m cool? Developmental differences in striatal recruitment during direct and reflected social self-evaluations. Accid. Anal. Prev 8, 40–54. [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Juraska JM, Willing J, 2017. Pubertal onset as a critical transition for neural development and cognition. Brain Res 1654, 87–94. 10.1016/j.brainres.2016.04.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  83. Kashon ML, Sisk CL, 1994. Pubertal maturation is associated with an increase in the number of androgen receptor-immunoreactive cells in the brains of male ferrets. Brain Res. Dev. Brain Res 78, 237–242. [DOI] [PubMed] [Google Scholar]
  84. Kilford EJ, Garrett E, Blakemore S-J, 2016. The development of social cognition in adolescence: An integrated perspective. Neurosci. Biobehav. Rev 70, 106–120. 10.1016/j.neubiorev.2016.08.016 [DOI] [PubMed] [Google Scholar]
  85. Kim JH, Juraska JM, 1997. Sex differences in the development of axon number in the splenium of the rat corpus callosum from postnatal day 15 through 60. Brain Res. Dev. Brain Res 102, 77–85. [DOI] [PubMed] [Google Scholar]
  86. Klapwijk ET, Goddings A-L, Burnett Heyes S, Bird G, Viner RM, Blakemore S-J, 2013. Increased functional connectivity with puberty in the mentalising network involved in social emotion processing. Horm. Behav 64, 314–322. [DOI] [PMC free article] [PubMed] [Google Scholar]
  87. Klauser P, Whittle S, Simmons JG, Byrne ML, Mundy LK, Patton GC, Fornito A, Allen NB, 2015. Reduced frontal white matter volume in children with early onset of adrenarche. Psychoneuroendocrinology 52, 111–118. [DOI] [PubMed] [Google Scholar]
  88. Koolschijn PCMP, Peper JS, Crone EA, 2014. The influence of sex steroids on structural brain maturation in adolescence. PLoS ONE 9, e83929. [DOI] [PMC free article] [PubMed] [Google Scholar]
  89. Koss WA, Lloyd MM, Sadowski RN, Wise LM, Juraska JM, 2015. Gonadectomy before puberty increases the number of neurons and glia in the medial prefrontal cortex of female, but not male, rats. Dev. Psychobiol 57, 305–312. 10.1002/dev.21290 [DOI] [PMC free article] [PubMed] [Google Scholar]
  90. Kraemer HC, Yesavage JA, Taylor JL, Kupfer D, 2000. How can we learn about developmental processes from cross-sectional studies, or can we? Am. J. Psychiatry 157, 163–171. 10.1176/appi.ajp.157.2.163 [DOI] [PubMed] [Google Scholar]
  91. LeMoult J, Colich NL, Sherdell L, Hamilton JP, Gotlib IH, 2015. Influence of menarche on the relation between diurnal cortisol production and ventral striatum activity during reward anticipation. Soc. Cogn. Affect. Neurosci 10, 1244–1250. [DOI] [PMC free article] [PubMed] [Google Scholar]
  92. Lenroot RK, Gogtay N, Greenstein DK, Wells EM, Wallace GL, Clasen LS, Blumenthal JD, Lerch J, Zijdenbos AP, Evans AC, Thompson PM, Giedd JN, 2007. Sexual dimorphism of brain developmental trajectories during childhood and adolescence. NeuroImage 36, 1065–1073. 10.1016/j.neuroimage.2007.03.053 [DOI] [PMC free article] [PubMed] [Google Scholar]
  93. Liening SH, Stanton SJ, Saini EK, Schultheiss OC, 2010. Salivary testosterone, cortisol, and progesterone: two-week stability, interhormone correlations, and effects of time of day, menstrual cycle, and oral contraceptive use on steroid hormone levels. Physiol. Behav 99, 8–16. 10.1016/j.physbeh.2009.10.001 [DOI] [PubMed] [Google Scholar]
  94. Luna B, 2009. Developmental changes in cognitive control through adolescence. Adv. Child Dev. Behav 37, 233–278. [DOI] [PMC free article] [PubMed] [Google Scholar]
  95. Mädler B, Drabycz SA, Kolind SH, Whittall KP, MacKay AL, 2008. Is diffusion anisotropy an accurate monitor of myelination? Correlation of multicomponent T2 relaxation and diffusion tensor anisotropy in human brain. Magn. Reson. Imaging 26, 874–888. 10.1016/j.mri.2008.01.047 [DOI] [PubMed] [Google Scholar]
  96. Marceau K, Dorn LD, Susman EJ, 2012. Stress and puberty-related hormone reactivity, negative emotionality, and parent–adolescent relationships. Psychoneuroendocrinology 37, 1286–1298. 10.1016/j.psyneuen.2012.01.001 [DOI] [PubMed] [Google Scholar]
  97. Marceau K, Ram N, Houts RM, Grimm KJ, Susman EJ, 2011. Individual differences in boys’ and girls’ timing and tempo of puberty: modeling development with nonlinear growth models. Dev. Psychol 47, 1389–1409. 10.1037/a0023838 [DOI] [PMC free article] [PubMed] [Google Scholar]
  98. Matchock RL, Dorn LD, Susman EJ, 2007. Diurnal and seasonal cortisol, testosterone, and DHEA rhythms in boys and girls during puberty. Chronobiol. Int 24, 969–990. 10.1080/07420520701649471 [DOI] [PubMed] [Google Scholar]
  99. Meek LR, Romeo RD, Novak CM, Sisk CL, 1997. Actions of testosterone in prepubertal and postpubertal male hamsters: dissociation of effects on reproductive behavior and brain androgen receptor immunoreactivity. Horm. Behav 31, 75–88. 10.1006/hbeh.1997.1371 [DOI] [PubMed] [Google Scholar]
  100. Mendle J, Harden KP, Brooks-Gunn J, Graber JA, 2012. Peer relationships and depressive symptomatology in boys at puberty. Dev. Psychol 48, 429–435. 10.1037/a0026425 [DOI] [PMC free article] [PubMed] [Google Scholar]
  101. Menzies L, Goddings A-L, Whitaker KJ, Blakemore S-J, Viner RM, 2015. The effects of puberty on white matter development in boys. Dev. Cogn. Neurosci 11, 116–128. [DOI] [PMC free article] [PubMed] [Google Scholar]
  102. Mills KL, Goddings A-L, Herting MM, Meuwese R, Blakemore S-J, Crone EA, Dahl RE, Güroğlu B, Raznahan A, Sowell ER, Tamnes CK, 2016. Structural brain development between childhood and adulthood: Convergence across four longitudinal samples. Neuroimage 141, 273–281. 10.1016/j.neuroimage.2016.07.044 [DOI] [PMC free article] [PubMed] [Google Scholar]
  103. Mills KL, Tamnes CK, 2014. Methods and considerations for longitudinal structural brain imaging analysis across development. Dev. Cogn. Neurosci 9, 172–190. 10.1016/j.dcn.2014.04.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  104. Monk CS, McClure EB, Nelson EE, Zarahn E, Bilder RM, Leibenluft E, Charney DS, Ernst M, Pine DS, 2003. Adolescent immaturity in attention-related brain engagement to emotional facial expressions. NeuroImage 20, 420–428. [DOI] [PubMed] [Google Scholar]
  105. Moore WE 3rd, Pfeifer JH, Masten CL, Mazziotta JC, Iacoboni M, Dapretto M, 2012. Facing puberty: associations between pubertal development and neural responses to affective facial displays. Soc. Cogn. Affect. Neurosci 7, 35–43. [DOI] [PMC free article] [PubMed] [Google Scholar]
  106. Morgan JK, Olino TM, McMakin DL, Ryan ND, Forbes EE, 2013. Neural response to reward as a predictor of increases in depressive symptoms in adolescence. Neurobiol. Dis 52, 66–74. [DOI] [PMC free article] [PubMed] [Google Scholar]
  107. Murray CR, Simmons JG, Allen NB, Byrne ML, Mundy LK, Seal ML, Patton GC, Olsson CA, Whittle S, 2016. Associations between dehydroepiandrosterone (DHEA) levels, pituitary volume, and social anxiety in children. Psychoneuroendocrinology 64, 31–39. 10.1016/j.psyneuen.2015.11.004 [DOI] [PubMed] [Google Scholar]
  108. Nelson E, Jarcho JM, Guyer AE, 2016. Social re-orientation and brain development: An expanded and updated view. Dev. Cogn. Neurosci., Special Section: The Developmental Neuroscience of Adolescence: Revisiting, Refining, and Extending Seminal Models 17, 118–127. 10.1016/j.dcn.2015.12.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  109. Nelson E, Leibenluft E, McClure E, Pine D, 2005. The social re-orientation of adolescence: a neuroscience perspective on the process and its relation to psychopathology. Psychol. Med 35, 163–174. [DOI] [PubMed] [Google Scholar]
  110. Neufang S, Specht K, Hausmann M, Güntürkün O, Herpertz-Dahlmann B, Fink GR, Konrad K, 2009. Sex differences and the impact of steroid hormones on the developing human brain. Cereb. Cortex 19, 464–473. [DOI] [PubMed] [Google Scholar]
  111. Nguyen T-V, Gower P, Albaugh MD, Botteron KN, Hudziak JJ, Fonov VS, Collins L, Ducharme S, McCracken JT, 2016a. The developmental relationship between DHEA and visual attention is mediated by structural plasticity of cortico-amygdalar networks. Psychoneuroendocrinology 70, 122–133. 10.1016/j.psyneuen.2016.05.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  112. Nguyen T-V, Lew J, Albaugh MD, Botteron KN, Hudziak JJ, Fonov VS, Collins DL, Ducharme S, McCracken JT, 2017. Sex-specific associations of testosterone with prefrontal-hippocampal development and executive function. Psychoneuroendocrinology 76, 206–217. 10.1016/j.psyneuen.2016.12.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  113. Nguyen T-V, McCracken J, Ducharme S, Botteron KN, Mahabir M, Johnson W, Israel M, Evans AC, Karama S, for the Brain Development Cooperative Group, 2012. Testosterone-related cortical maturation across childhood and adolescence. Cereb. Cortex 23, 1424–1432. [DOI] [PMC free article] [PubMed] [Google Scholar]
  114. Nguyen T-V, McCracken JT, Albaugh MD, Botteron KN, Hudziak JJ, Ducharme S, 2016b. A testosterone-related structural brain phenotype predicts aggressive behavior from childhood to adulthood. Psychoneuroendocrinology 63, 109–118. 10.1016/j.psyneuen.2015.09.021 [DOI] [PMC free article] [PubMed] [Google Scholar]
  115. Nguyen T-V, McCracken JT, Ducharme S, Cropp BF, Botteron KN, Evans AC, Karama S, 2013. Interactive effects of dehydroepiandrosterone and testosterone on cortical thickness during early brain development. J. Neurosci 33, 10840–10848. [DOI] [PMC free article] [PubMed] [Google Scholar]
  116. Ojeda SR, Lomniczi A, Mastronardi C, Heger S, Roth C, Parent A-S, Matagne V, Mungenast AE, 2006. Minireview: the neuroendocrine regulation of puberty: is the time ripe for a systems biology approach? Endocrinology 147, 1166–1174. 10.1210/en.2005-1136 [DOI] [PubMed] [Google Scholar]
  117. Op de Macks ZA, Bunge SA, Bell ON, Kriegsfeld LJ, Kayser AS, Dahl RE, 2016a. The effect of social rank feedback on risk taking and associated reward processes in adolescent girls. Soc. Cogn. Affect. Neurosci 12, 240–250. [DOI] [PMC free article] [PubMed] [Google Scholar]
  118. Op de Macks ZA, Bunge SA, Bell ON, Wilbrecht L, Kriegsfeld LJ, Kayser AS, Dahl RE, 2016b. Risky decision-making in adolescent girls: The role of pubertal hormones and reward circuitry. Psychoneuroendocrinology 74, 77–91. [DOI] [PubMed] [Google Scholar]
  119. Op de Macks ZA, Gunther Moor B, Overgaauw S, Guroglu B, Dahl RE, Crone EA, 2011. Testosterone levels correspond with increased ventral striatum activation in response to monetary rewards in adolescents. Accid. Anal. Prev 1, 506–516. [DOI] [PMC free article] [PubMed] [Google Scholar]
  120. Padmanabhan A, Lynn A, Foran W, Luna B, O’Hearn K, 2013. Age related changes in striatal resting state functional connectivity in autism. Front. Hum. Neurosci 7 10.3389/fnhum.2013.00814 [DOI] [PMC free article] [PubMed] [Google Scholar]
  121. Palmert MR, Hayden DL, Mansfield MJ, Crigler JF, Crowley WF, Chandler DW, Boepple PA, 2001. The longitudinal study of adrenal maturation during gonadal suppression: evidence that adrenarche is a gradual process. J. Clin. Endocrinol. Metab 86, 4536–4542. 10.1210/jcem.86.9.7863 [DOI] [PubMed] [Google Scholar]
  122. Pangelinan MM, Leonard G, Perron M, Pike GB, Richer L, Veillette S, Pausova Z, Paus T, 2016. Puberty and testosterone shape the corticospinal tract during male adolescence. Brain Struct. Funct 221, 1083–1094. [DOI] [PubMed] [Google Scholar]
  123. Pantsiotou S, Papadimitriou A, Douros K, Priftis K, Nicolaidou P, Fretzayas A, 2008. Maturational tempo differences in relation to the timing of the onset of puberty in girls. Acta Paediatr. Oslo Nor 1992 97, 217–220. 10.1111/j.1651-2227.2007.00598.x [DOI] [PubMed] [Google Scholar]
  124. Patton GC, Viner R, 2007. Pubertal transitions in health. Lancet Lond. Engl 369, 1130–1139. 10.1016/S0140-6736(07)60366-3 [DOI] [PubMed] [Google Scholar]
  125. Paus T, Nawaz-Khan I, Leonard G, Perron M, Pike GB, Pitiot A, Richer L, Susman E, Veillette S, Pausova Z, 2010. Sexual dimorphism in the adolescent brain: Role of testosterone and androgen receptor in global and local volumes of grey and white matter. Horm. Behav 57, 63–75. 10.1016/j.yhbeh.2009.08.004 [DOI] [PubMed] [Google Scholar]
  126. Peper JS, Brouwer RM, Schnack HG, van Baal GC, van Leeuwen M, van den Berg SM, Delemarre-Van de Waal HA, Boomsma DI, Kahn RS, Hulshoff Pol HE, 2009a. Sex steroids and brain structure in pubertal boys and girls. Psychoneuroendocrinology 34, 332–342. [DOI] [PubMed] [Google Scholar]
  127. Peper JS, Brouwer RM, Schnack HG, Van Baal GCM, van Leeuwen M, van den Berg SM, Delemarre-Van de Waal HA, Janke AL, Collins DL, Evans AC, Boomsma DI, Kahn RS, Hulshoff Pol HE, 2008. Cerebral white matter in early puberty is associated with luteinizing hormone concentrations. Psychoneuroendocrinology 33, 909–915. [DOI] [PubMed] [Google Scholar]
  128. Peper JS, Brouwer RM, van Leeuwen M, Schnack HG, Boomsma DI, Kahn RS, Hulshoff Pol HE, 2010. HPG-axis hormones during puberty: a study on the association with hypothalamic and pituitary volumes. Psychoneuroendocrinology 35, 133–140. [DOI] [PubMed] [Google Scholar]
  129. Peper JS, de Reus MA, van den Heuvel MP, Schutter DJLG, 2015. Short fused? associations between white matter connections, sex steroids, and aggression across adolescence. Hum. Brain Mapp 36, 1043–1052. 10.1002/hbm.22684 [DOI] [PMC free article] [PubMed] [Google Scholar]
  130. Peper JS, Schnack HG, Brouwer RM, Van Baal GCM, Pjetri E, Székely E, van Leeuwen M, van den Berg SM, Collins DL, Evans AC, Boomsma DI, Kahn RS, Hulshoff Pol HE, 2009b. Heritability of regional and global brain structure at the onset of puberty: a magnetic resonance imaging study in 9-year-old twin pairs. Hum. Brain Mapp 30, 2184–2196. [DOI] [PMC free article] [PubMed] [Google Scholar]
  131. Perrin JS, Hervé P-Y, Leonard G, Perron M, Pike GB, Pitiot A, Richer L, Veillette S, Pausova Z, Paus T, 2008. Growth of white matter in the adolescent brain: Role of testosterone and androgen receptor. J. Neurosci 28, 9519–9524. [DOI] [PMC free article] [PubMed] [Google Scholar]
  132. Perrin JS, Leonard G, Perron M, Pike GB, Pitiot A, Richer L, Veillette S, Pausova Z, Paus T, 2009. Sex differences in the growth of white matter during adolescence. NeuroImage 45, 1055–1066. 10.1016/j.neuroimage.2009.01.023 [DOI] [PubMed] [Google Scholar]
  133. Peters S, Braams BR, Raijmakers MEJ, Koolschijn PCMP, Crone EA, 2014. The neural coding of feedback learning across child and adolescent development. Cogn. Neurosci. J. Of 26, 1705–1720. [DOI] [PubMed] [Google Scholar]
  134. Peters S, Jolles DJ, van Duijvenvoorde ACK, Crone EA, Peper JS, 2015. The link between testosterone and amygdala-orbitofrontal cortex connectivity in adolescent alcohol use. Psychoneuroendocrinology 53, 117–126. [DOI] [PubMed] [Google Scholar]
  135. Petersen AC, Crockett L, Richards M, Boxer A, 1988. A self-report measure of pubertal status: Reliability, validity, and initial norms. J. Youth Adolesc 17, 117–133. 10.1007/BF01537962 [DOI] [PubMed] [Google Scholar]
  136. Pfefferbaum A, Rohlfing T, Pohl KM, Lane B, Chu W, Kwon D, Nolan Nichols B, Brown SA, Tapert SF, Cummins K, Thompson WK, Brumback T, Meloy MJ, Jernigan TL, Dale A, Colrain IM, Baker FC, Prouty D, De Bellis MD, Voyvodic JT, Clark DB, Luna B, Chung T, Nagel BJ, Sullivan EV, 2015. Adolescent development of cortical and white matter structure in the NCANDA sample: Role of sex, ethnicity, puberty, and alcohol drinking. Cereb. Cortex 26, 4101–4121. [DOI] [PMC free article] [PubMed] [Google Scholar]
  137. Pfeifer JH, Allen NB, 2016. The audacity of specificity: Moving adolescent developmental neuroscience towards more powerful scientific paradigms and translatable models. Dev. Cogn. Neurosci 17, 131–137. 10.1016/j.dcn.2015.12.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  138. Pfeifer JH, Kahn LE, Merchant JS, Peake SJ, Veroude K, Masten CL, Lieberman MD, Mazziotta JC, Dapretto M, 2013. Longitudinal change in the neural bases of adolescent social self-evaluations: effects of age and pubertal development. J. Neurosci 33, 7415–7419. [DOI] [PMC free article] [PubMed] [Google Scholar]
  139. Pfeifer JH, Masten CL, Moore WE 3rd, Oswald TM, Mazziotta JC, Iacoboni M, Dapretto M, 2011. Entering adolescence: resistance to peer influence, risky behavior, and neural changes in emotion reactivity. Neuron 69, 1029–1036. 10.1016/j.neuron.2011.02.019 [DOI] [PMC free article] [PubMed] [Google Scholar]
  140. Pfeifer JH, Peake SJ, 2012. Self-development: integrating cognitive, socioemotional, and neuroimaging perspectives. Dev. Cogn. Neurosci 2, 55–69. 10.1016/j.dcn.2011.07.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  141. Phoenix CH, Goy RW, Gerall AA, Young WC, 1959. Organizing action of prenatally administered testosterone propionate on the tissues mediating mating behavior in the female guinea pig. Endocrinology 65, 369–382. 10.1210/endo-65-3-369 [DOI] [PubMed] [Google Scholar]
  142. Plant TM, Barker-Gibb ML, 2004. Neurobiological mechanisms of puberty in higher primates. Hum. Reprod. Update 10, 67–77. [DOI] [PubMed] [Google Scholar]
  143. Richmond S, Johnson KA, Seal ML, Allen NB, Whittle S, 2016. Development of brain networks and relevance of environmental and genetic factors: A systematic review. Neurosci. Biobehav. Rev 71, 215–239. 10.1016/j.neubiorev.2016.08.024 [DOI] [PubMed] [Google Scholar]
  144. Romeo RD, Sisk CL, 2001. Pubertal and seasonal plasticity in the amygdala. Brain Res 889, 71–77. [DOI] [PubMed] [Google Scholar]
  145. Sato SM, Schulz KM, Sisk CL, Wood RI, 2008. Adolescents and androgens, receptors and rewards. Horm. Behav 53, 647–658. 10.1016/j.yhbeh.2008.01.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  146. Satterthwaite TD, Vandekar S, Wolf DH, Ruparel K, Roalf DR, Jackson C, Elliott MA, Bilker WB, Calkins ME, Prabhakaran K, Davatzikos C, Hakonarson H, Gur RE, Gur RC, 2014. Sex differences in the effect of puberty on hippocampal morphology. JAAC 53, 341–50.e1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  147. Schriber RA, Guyer AE, 2016. Adolescent neurobiological susceptibility to social context. Dev. Cogn. Neurosci 19, 1–18. 10.1016/j.dcn.2015.12.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  148. Schulz KM, Molenda-Figueira HA, Sisk CL, 2009. Back to the future: The organizational-activational hypothesis adapted to puberty and adolescence. Horm. Behav 55, 597–604. 10.1016/j.yhbeh.2009.03.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  149. Schweinsburg AD, Nagel BJ, Tapert SF, 2005. fMRI reveals alteration of spatial working memory networks across adolescence. J. Int. Neuropsychol. Soc 11, 631–644. 10.1017/S1355617705050757 [DOI] [PMC free article] [PubMed] [Google Scholar]
  150. Shaw P, Kabani NJ, Lerch JP, Eckstrand K, Lenroot R, Gogtay N, Greenstein D, Clasen L, Evans A, Rapoport JL, Giedd JN, Wise SP, 2008. Neurodevelopmental trajectories of the human cerebral cortex. J. Neurosci 28, 3586–3594. [DOI] [PMC free article] [PubMed] [Google Scholar]
  151. Shimony JS, McKinstry RC, Akbudak E, Aronovitz JA, Snyder AZ, Lori NF, Cull TS, Conturo TE, 1999. Quantitative diffusion-tensor anisotropy brain MR imaging: normative human data and anatomic analysis. Radiology 212, 770–784. 10.1148/radiology.212.3.r99au51770 [DOI] [PubMed] [Google Scholar]
  152. Shirtcliff EA, Dahl RE, Pollak SD, 2009. Pubertal development: correspondence between hormonal and physical development. Child Dev 80, 327–337. 10.1111/j.1467-8624.2009.01263.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  153. Shulman EP, Smith AR, Silva K, Icenogle G, Duell N, Chein J, Steinberg L, 2016. The dual systems model: Review, reappraisal, and reaffirmation. Dev. Cogn. Neurosci 17, 103–117. 10.1016/j.dcn.2015.12.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  154. Silverman MH, Jedd K, Luciana M, 2015. Neural networks involved in adolescent reward processing: An activation likelihood estimation meta-analysis of functional neuroimaging studies. NeuroImage 122, 427–439. 10.1016/j.neuroimage.2015.07.083 [DOI] [PMC free article] [PubMed] [Google Scholar]
  155. Sisk CL, Foster DL, 2004. The neural basis of puberty and adolescence. Nat. Neurosci 7, 1040–1047. 10.1038/nn1326 [DOI] [PubMed] [Google Scholar]
  156. Smith AR, Chein J, Steinberg L, 2013. Impact of socio-emotional context, brain development, and pubertal maturation on adolescent risk-taking. Horm. Behav 64, 323–332. [DOI] [PMC free article] [PubMed] [Google Scholar]
  157. Södergård R, Bäckström T, Shanbhag V, Carstensen H, 1982. Calculation of free and bound fractions of testosterone and estradiol-17 beta to human plasma proteins at body temperature. J. Steroid Biochem 16, 801–810. [DOI] [PubMed] [Google Scholar]
  158. Sodian B, 2011. Theory of mind in infancy. Child Dev. Perspect 5, 39–43. 10.1111/j.1750-8606.2010.00152.x [DOI] [Google Scholar]
  159. Soliman A, De Sanctis V, Elalaily R, 2014. Nutrition and pubertal development. Indian J. Endocrinol. Metab 18, S39–S47. 10.4103/2230-8210.145073 [DOI] [PMC free article] [PubMed] [Google Scholar]
  160. Somerville LH, Casey BJ, 2010. Developmental neurobiology of cognitive control and motivational systems. Curr. Opin. Neurobiol 20, 236–241. [DOI] [PMC free article] [PubMed] [Google Scholar]
  161. Somerville LH, Hare T, Casey B, 2011. Frontostriatal maturation predicts cognitive control failure to appetitive cues in adolescents. J. Cogn. Neurosci 23, 2123–2134. 10.1162/jocn.2010.21572 [DOI] [PMC free article] [PubMed] [Google Scholar]
  162. Spielberg JM, Forbes EE, Ladouceur CD, Worthman CM, Olino TM, Ryan ND, Dahl RE, 2015. Pubertal testosterone influences threat-related amygdalaorbitofrontal cortex coupling. Soc. Cogn. Affect. Neurosci 10, 408–415. [DOI] [PMC free article] [PubMed] [Google Scholar]
  163. Spielberg JM, Olino TM, Forbes EE, Dahl RE, 2014. Exciting fear in adolescence: Does pubertal development alter threat processing? Dev. Cogn. Neurosci 8, 86–95. 10.1016/j.dcn.2014.01.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  164. Steen RG, Hamer RM, Lieberman JA, 2007. Measuring brain volume by MR imaging: Impact of measurement precision and natural variation on sample size requirements. Am. J. Neuroradiol 28, 1119–1125. 10.3174/ajnr.A0537 [DOI] [PMC free article] [PubMed] [Google Scholar]
  165. Steinberg L, 2008. A social neuroscience perspective on adolescent risk-taking. Dev. Rev. DR 28, 78–106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  166. Suleiman AB, Galván A, Harden KP, Dahl RE, 2017. Becoming a sexual being: The ‘elephant in the room’of adolescent brain development. Dev. Cogn. Neurosci 25, 209–220. 10.1016/j.dcn.2016.09.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  167. Tamnes CK, Ostby Y, Fjell AM, Westlye LT, Due-Tønnessen P, Walhovd KB, 2010. Brain maturation in adolescence and young adulthood: regional agerelated changes in cortical thickness and white matter volume and microstructure. Cereb. Cortex 20, 534–548. 10.1093/cercor/bhp118 [DOI] [PubMed] [Google Scholar]
  168. Tanner JM, 1962. Growth at Adolescence (2nd edition). Blackwell Publishing, Oxford. [Google Scholar]
  169. Telzer EH, Flannery J, Humphreys KL, Goff B, Gabard-Durman L, Gee DG, Tottenham N, 2015. “The Cooties Effect”: Amygdala reactivity to opposite- versus same-sex faces declines from childhood to adolescence. J. Cogn. Neurosci 27, 1685–1696. [DOI] [PMC free article] [PubMed] [Google Scholar]
  170. Tung Y-C, Lee J-S, Tsai W-Y, Hsiao P-H, 2004. Physiological changes of adrenal androgens in childhood. J. Formos. Med. Assoc. Taiwan Yi Zhi 103, 921–924. [PubMed] [Google Scholar]
  171. Tyborowska A, Volman I, Smeekens S, Toni I, Roelofs K, 2016. Testosterone during puberty shifts emotional control from pulvinar to anterior prefrontal cortex. J. Neurosci 36, 6156–6164. [DOI] [PMC free article] [PubMed] [Google Scholar]
  172. Ubuka T, Tsutsui K, 2014. Review: neuroestrogen regulation of socio-sexual behavior of males. Front. Neurosci 8, 323 10.3389/fnins.2014.00323 [DOI] [PMC free article] [PubMed] [Google Scholar]
  173. Urosevic S, Collins P, Muetzel R, Lim KO, Luciana M, 2014. Pubertal status associations with reward and threat sensitivities and subcortical brain volumes during adolescence. Brain Cogn 89, 15–26. [DOI] [PMC free article] [PubMed] [Google Scholar]
  174. van den Bos W, Eppinger B, 2016. Developing developmental cognitive neuroscience: From agenda setting to hypothesis testing. Dev. Cogn. Neurosci 17, 138–144. 10.1016/j.dcn.2015.12.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  175. van Duijvenvoorde ACK, Op de Macks ZA, Overgaauw S, Gunther Moor B, Dahl RE, Crone EA, 2014. A cross-sectional and longitudinal analysis of rewardrelated brain activation: effects of age, pubertal stage, and reward sensitivity. Brain Cogn 89, 3–14. [DOI] [PubMed] [Google Scholar]
  176. Veldhuis JD, 1996. Neuroendocrine mechanisms mediating awakening of the human gonadotropic axis in puberty. Pediatr. Nephrol. Berl. Ger 10, 304–317. [DOI] [PubMed] [Google Scholar]
  177. Vesper HW, Botelho JC, 2010. Standardization of testosterone measurements in humans. J. Steroid Biochem. Mol. Biol 121, 513–519. 10.1016/j.jsbmb.2010.03.032 [DOI] [PubMed] [Google Scholar]
  178. Vesper HW, Botelho JC, Shacklady C, Smith A, Myers GL, 2008. CDC project on standardizing steroid hormone measurements. Steroids 73, 1286–1292. 10.1016/j.steroids.2008.09.008 [DOI] [PubMed] [Google Scholar]
  179. Vesper HW, Botelho JC, Wang Y, 2014. Challenges and improvements in testosterone and estradiol testing. Asian J. Androl 16, 178–184. 10.4103/1008-682X.122338 [DOI] [PMC free article] [PubMed] [Google Scholar]
  180. Vijayakumar N, Allen NB, Youssef G, Dennison M, Yücel M, Simmons JG, Whittle S, 2016. Brain development during adolescence: A mixed-longitudinal investigation of cortical thickness, surface area, and volume. Hum. Brain Mapp 37, 2027–2038. 10.1002/hbm.23154 [DOI] [PMC free article] [PubMed] [Google Scholar]
  181. Virta A, Barnett A, Pierpaoli C, 1999. Visualizing and characterizing white matter fiber structure and architecture in the human pyramidal tract using diffusion tensor MRI. Magn. Reson. Imaging 17, 1121–1133. [DOI] [PubMed] [Google Scholar]
  182. Wallen K, Baum MJ, 2002. Masculinization and defeminization in altricial and precocial mammals: comparative aspects of steroid hormone action, in: Pfaff DW, Arnold AP, Fahrbach SE, Etgen AM, Rubin RT (Eds.), Hormones, Brain and Behavior Academic Press, San Diego, pp. 385–423. 10.1016/B978-012532104-4/50071-8 [DOI] [Google Scholar]
  183. Weigard A, Chein J, Albert D, Smith A, Steinberg L, 2014. Effects of anonymous peer observation on adolescents’ preference for immediate rewards. Dev. Sci 17, 71–78. 10.1111/desc.12099 [DOI] [PMC free article] [PubMed] [Google Scholar]
  184. Weil LG, Fleming SM, Dumontheil I, Kilford EJ, Weil RS, Rees G, Dolan RJ, Blakemore S-J, 2013. The development of metacognitive ability in adolescence. Conscious. Cogn 22, 264–271. 10.1016/j.concog.2013.01.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  185. Westberry JM, Wilson ME, 2012. Regulation of estrogen receptor alpha gene expression in the mouse prefrontal cortex during early postnatal development. Neurogenetics 13, 159–167. 10.1007/s10048-012-0323-z [DOI] [PubMed] [Google Scholar]
  186. Whittle S, Simmons JG, Byrne ML, Strikwerda-Brown C, Kerestes R, Seal ML, Olsson CA, Dudgeon P, Mundy LK, Patton GC, Allen NB, 2015. Associations between early adrenarche, affective brain function and mental health in children. Soc. Cogn. Affect. Neurosci 10, 1282–1290. [DOI] [PMC free article] [PubMed] [Google Scholar]
  187. Whittle S, Yücel M, Lorenzetti V, Byrne ML, Simmons JG, Wood SJ, Pantelis C, Allen NB, 2012. Pituitary volume mediates the relationship between pubertal timing and depressive symptoms during adolescence. Psychoneuroendocrinology 37, 881–891. [DOI] [PubMed] [Google Scholar]
  188. Wierenga LM, Langen M, Oranje B, Durston S, 2014. Unique developmental trajectories of cortical thickness and surface area. NeuroImage 87, 120–126. 10.1016/j.neuroimage.2013.11.010 [DOI] [PubMed] [Google Scholar]
  189. Wong AP-Y, Pipitone J, Park MTM, Dickie EW, Leonard G, Perron M, Pike BG, Richer L, Veillette S, Chakravarty MM, Pausova Z, Paus T, 2014. Estimating volumes of the pituitary gland from T1- weighted magnetic-resonance images: effects of age, puberty, testosterone, and estradiol. NeuroImage 94, 216–221. [DOI] [PubMed] [Google Scholar]
  190. World Health Organization, 2014. Health for the world’s adolescents: A second chance in the second decade http://apps.who.int/adolescent/seconddecade/files/1612_MNCAH_HWA_Executive_Summary.pdf
  191. Zehr JL, Nichols LR, Schulz KM, Sisk CL, 2008. Adolescent development of neuron structure in dentate gyrus granule cells of male Syrian hamsters. Dev. Neurobiol 68, 1517–1526. 10.1002/dneu.20675 [DOI] [PMC free article] [PubMed] [Google Scholar]
  192. Zehr JL, Todd BJ, Schulz KM, McCarthy MM, Sisk CL, 2006. Dendritic pruning of the medial amygdala during pubertal development of the male Syrian hamster. J. Neurobiol 66, 578–590. 10.1002/neu.20251 [DOI] [PubMed] [Google Scholar]
  193. Zielinski BA, Gennatas ED, Zhou J, Seeley WW, 2010. Network-level structural covariance in the developing brain. Proc. Natl. Acad. Sci 107, 18191–18196. 10.1073/pnas.1003109107 [DOI] [PMC free article] [PubMed] [Google Scholar]

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Supplementary Materials

Figure S1.

PRISMA flowchart of systematic review. An electronic search was conducted in PubMed using the key words adolescence, brain, MRI OR DTI OR fMRI, and puberty OR hormones OR pubertal hormones OR gonadarche OR adrenarche OR gonadal hormones OR adrenarcheal hormones, to identify studies published in this field to date (February 2018). Inclusion criteria included i) the use of pubertal measures (either physical and/or hormonal indices), ii) neuroimaging methodologies (sMRI, DTI, fMRI, rs-fcMRI), iii) in a sample of typically developing children and/or adolescents (lower limit of age range less than 18 years).

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