Trait positive affect is associated with hippocampal volume and change in caudate volume across adolescence.

Cogn Affect Behav Neurosci (2015) 15:80–94 DOI 10.3758/s13415-014-0319-2

Trait positive affect is associated with hippocampal volume and change in caudate volume across adolescence Meg Dennison & Sarah Whittle & Murat Yücel & Michelle L. Byrne & Orli Schwartz & Julian G. Simmons & Nicholas B. Allen

Published online: 18 September 2014 # Psychonomic Society, Inc. 2014

Abstract Trait positive affect (PA) in childhood confers both risk and resilience to psychological and behavioral difficulties in adolescence, although explanations for this association are lacking. Neurodevelopment in key areas associated with positive affect is ongoing throughout adolescence, and is likely to be related to the increased incidence of disorders of positive affect during this period of development. The aim of this study was to prospectively explore the relationship between trait indices of PA and brain development in subcortical reward regions during early to mid-adolescence in a community sample of adolescents. A total of 89 (46 male, 43 female) adolescents participated in magnetic resonance imaging assessments during both early and mid-adolescence (mean age at baseline = 12.6 years, SD = 0.45; mean follow-up period = 3.78 years, SD = 0.21) and also completed self-report measures of trait positive and negative affect (at baseline). To M. Dennison : M. L. Byrne : O. Schwartz : J. G. Simmons : N. B. Allen (*) Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Victoria 3010, Australia e-mail: [email protected] S. Whittle : M. Yücel Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia S. Whittle : N. B. Allen Orygen Youth Health, Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia M. Yücel Monash Clinical and Imaging Neuroscience (MCIN) Laboratory, Monash Biomedical Imaging and School of Psychological Sciences, Monash University, Clayton, Victoria, Australia N. B. Allen Department of Psychology, University of Oregon, Eugene, Oregon, USA

examine the specificity of these effects, the relation between negative affect and brain development was also examined. The degree of volume reduction in the right caudate over time was predicted by PA. Independent of time, larger hippocampal volumes were associated with higher PA, and negative affect was associated with smaller left amygdala volume. The moderating effect of negative affect on the development of the left caudate varied as a function of lifetime psychiatric history. These findings suggest that early to mid-adolescence is an important period whereby neurodevelopmental processes may underlie key phenotypes conferring both risk and resilience for emotional and behavioral difficulties later in life. Keywords Basal ganglia . Amygdala . Development . Personality . Reward . Hippocampus Trait positive affect (PA) in childhood is associated with both risk and resilience to psychological and behavioral difficulties that emerge in adolescence, a period in the life span characterized by a marked increase in the incidence of disorders of positive affectivity (Kessler et al., 2005; Merikangas et al., 2010). PA is a stable dimension of affect and refers to the extent to which a person feels enthusiastic, active, and alert. High PA is a state of high energy, full concentration, and pleasurable engagement, whereas low PA is typified by sadness and lethargy (Watson, Clark, & Carey, 1988). Although higher levels of PA are associated with a number of cognitive, interpersonal, and physiological benefits that promote resilience during adolescence (Diamond & Aspinwall, 2003; Lee & Robok, 2002; Lyubomirsky, King, & Diener, 2005; Tugade, Fredrickson, & Feldman Barrett, 2004; Wills, Sandy, Shinar, & Yaeger, 1999), lower levels of trait PA are associated with increased risk for a range of negative outcomes, including problematic substance use (Colder & Chassin, 1997; Hatzigiakoumis, Martinotti, Giannantonio, &

Cogn Affect Behav Neurosci (2015) 15:80–94

Janiri, 2011; Wills et al., 1999), depression (Forbes & Dahl, 2005; Lonigan, Phillips, & Hooe, 2003), and externalizing problems (Kim, Walden, Harris, Karrass, & Catron, 2007). It has been proposed that neurodevelopment during adolescence may contribute to the changes in behavior associated with PA, including increased risk taking (Forbes et al., 2010; Steinberg, 2008; Urošević, Collins, Muetzel, Lim, & Luciana, 2012), as well as the increased vulnerability for psychopathology during this period (Forbes & Dahl, 2005). Given these coincident effects, understanding how trait PA is associated with neurodevelopment throughout late childhood into adolescence is important for understanding the mechanisms that promote emotional maturity and resilience throughout adolescence into adulthood (Klimstra, Hale, Raaijmakers, Branje, & Meeus, 2009), as well as those that may contribute to the onset of adolescent psychopathology (Forbes & Dahl, 2005). In this study, we suggest that individual differences in temperamental PA may predict the nature and level of engagement with positive emotional experiences during adolescence. This may give rise to differential activation in regions associated with positive em otion, which may in turn alter neurodevelopmental processes during this sensitive period (Greenough & Black, 2011). Although the neurological processes underlying trait PA are likely complex and involve numerous brain regions (Russo & Nestler, 2013), several regions are reliably associated with core components of PA, including both high approach (“wanting”) and low approach (“liking”) emotions (Aldridge & Berridge, 2010; Berridge, Robinson, & Aldridge, 2009; Harmon-Jones & Gable, 2008; Kringelbach & Berridge, 2010). Studies have clarified more specific roles of both the ventral and dorsal striatum in the experience of PA, particularly in the anticipation of rewards and the initiation of appetitive behavior, respectively (Balleine, Delgado, & Hikosaka, 2007; Chambers, Taylor, & Potenza, 2003; Delgado, 2007; Kringelbach & Berridge, 2009; Nestler & Carlezon, 2006; O’Doherty et al., 2004; Olds & Milner, 1954). Research findings have shown that increases in nucleus accumbens neuron activity and dopamine release in both ventral and dorsal striatum are observed during both expectation and experience of rewards (Adinoff, 2004; de la FuenteFernández et al., 2002; Doyon, Anders, Ramachandra, Czachowski, & Gonzales, 2005; Koepp et al., 1998; Schultz, 2002; Volkow et al., 2002). A number of functional imaging (fMRI) studies in adults have shown increases in ventral striatal activity associated with a variety of pleasant stimuli. These include euphoric responses to dextroamphetamine (Drevets et al., 2001), cocaine-induced euphoria (Breiter et al., 1997), increased monetary reward (Cohen, Young, Baek, Kessler, & Ranganath, 2005; Knutson, Adams, Fong, & Hommer, 2001; O’Doherty, Kringelbach, Rolls, Hornak, & Andrews, 2001), pleasurable responses to music (Blood & Zatorre, 2001), and viewing beautiful faces (Aharon et al.,

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2001). Furthermore, fMRI studies have shown that both ventral and dorsal striatum are sensitive to the valence of an outcome (i.e., reward or punishment) and that dorsal activity was associated with the magnitude of reward (Delgado, 2007; Delgado, Nystrom, Fissell, Noll, & Fiez, 2000). Another structure within the basal ganglia, the pallidum, has been linked to pleasure associated with the experience of rewards (Aldridge & Berridge, 2010; Berridge, 2003; Berridge & Kringelbach, 2008). The ventral pallidum contains a “hedonic hotspot” in its posterior half, in which neuronal events can lead to amplifications of a sensory pleasure (Peciña & Berridge, 2005; Peciña, Smith & Berridge, 2006; Smith & Berridge, 2005). In hedonic hotspots, microinjections of opioids and other neurochemicals have been shown to increase the experience of pleasure associated with sweet sensations, which, in animal research, is operationalized by enhanced, species-specific liking responses (Smith & Berridge, 2005). In addition, structural and functional abnormalities in the basal ganglia have been associated with disorders of PA, including mood disorders (Bora, Harrison, Davey, Yücel, & Pantelis, 2012; Marchand & Yurgelun-Todd, 2010; Pizzagalli et al., 2009) and substance use (Churchwell, Carey, Ferrett, Stein, & Yurgelun-Todd, 2012; Ersche et al., 2011; Ersche et al., 2012), with some of these abnormalities being specifically linked to the experience of anhedonia and/or low PA (Forbes et al., 2009; Hatzigiakoumis et al., 2011; Heinz, Schmidt, & Reischies, 1994; Russo & Nestler, 2013; Wacker, Dillon, & Pizzagalli, 2009). These findings further support the links between the psychological construct of PA and its neural substrates. Although some research using neuroimaging techniques has investigated these relationships in adolescents (Churchwell et al., 2012; Forbes et al., 2009), most of the research has been conducted with adult samples. To date, no longitudinal structural neuroimaging studies have been conducted during adolescence. Recent research has suggested dynamic neurodevelopment of subcortical regions during childhood and adolescence that is regionally specific (Dennison et al., 2013; Lenroot et al., 2007; Ostby et al., 2009; Tamnes et al., 2013; Wierenga et al., 2014). For example, for certain striatal areas, such as the putamen and caudate, the transition from early to midadolescence adolescence is associated with reductions in volume, whereas the pallidum and limbic regions such as the hippocampus, but not the amygdala, experience continued growth during this period (Dennison et al., 2013). Thus, in order to comprehensively assess the relationship between brain structure and PA during the childhood and adolescence periods, longitudinal research is imperative (as with crosssectional measurements, larger or smaller volumes of any given region may be “good” or “bad,” depending on the region and the particular point along the developmental trajectory at which volume is assessed). Although it has been theorized that structural neurodevelopment of subcortical regions during adolescence might be associated with aspects of

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PA such as reward processing (Steinberg, 2004), to date, no longitudinal research with adolescent samples has been conducted to directly address this question. The present study is the first longitudinal examination of the relationship between trait PA and structural neurodevelopment in a large community sample of 12 year olds over a 4-year follow-up. We specifically examined the longitudinal changes in volume for structures within the basal ganglia dopamine network, including the nucleus accumbens, caudate, putamen, and pallidum. In order to explore the specificity of PA to any observed effects, we also examined the relationship between negative affect (NA) and brain structural change, and regions associated more generally with emotion processing, but not specific to PA (i.e., amygdala and hippocampus), were also explored. Both the amygdala and the hippocampus are known to play roles in the attentional and learning aspects of emotion (Baxter & Murray, 2002; Calder, Lawrence, & Young, 2001; Kringelbach & Berridge, 2010), and it is likely that they have a superordinate function that often operates independent of valence, and as part of a broader and overlapping affective circuitry (Ernst & Fudge, 2009). Specifically, both the hippocampus and the amygdala are connected with dopaminergic circuits related to the functioning of the basal ganglia (Swerdlow & Koob, 1987), and the role of both structures in reward related learning is increasingly understood (Adcock, Thangavel, Whitfield-Gabrieli, Knutson, & Gabrieli, 2006; Baxter & Murray, 2002; Liu et al., 2004; Schott et al., 2006; Wittmann, Bunzeck, Dolan, & Düzel, 2007; Wittmann et al., 2005). Wittmann et al. (2007) suggested that the hippocampus may be primed by the dopaminergic system to encode for novelty, and the amygdala may play an important role in the arousal of both positive and negative affect, which in turn may contribute to the appropriate selection of both avoidance and approach behaviors in certain contexts (Bechara, Damasio, Damasio, & Lee, 1999). We hypothesized that trait PA would specifically predict the development of regions in the basal ganglia from early to midadolescence, whereas the development of the amygdala and hippocampus, regions more broadly associated with emotion processing (i.e., independent of valence), may be related to both trait PA and trait NA. Specifically, we expected that lower PA would be associated with deviant brain development relative to the normal trends described in both our previous work (Dennison et al., 2013), and similar studies (Tamnes et al., 2013; Wierenga et al., 2014). The present study extends on this previous work, in which we described normative developmental change in a healthy subset of the present sample, and considers how individual differences in temperament are related to deviation from healthy patterns in a larger community sample. However, given the absence of data exploring these developmental relationships, predictions about the direction of the effects were not made, leading to more exploratory analyses. Because normative trends for


neurodevelopmental change have been described in detail for a healthy subset of this sample (Dennison et al., 2013), further description of developmental trends was not provided in the present investigation. In order to study the effects of PA across its full spectrum (i.e., from those with extreme low to extreme high scores), the exclusion of participants with a history of psychiatric illness was not desirable (because such individuals likely exhibit some of the lowest levels of PA). However, in order to assess the unique effects of temperament on brain development, independent from the possible effects of psychiatric illness, we ran secondary analyses comparing the effects of PA and NA on brain development for these regions in a nonpsychiatric group.

Method Participants and procedure The participants were a community sample of 89 adolescents (46 males, 43 females) recruited from schools across metropolitan Melbourne, Australia, as part of a larger longitudinal cohort study (the Orygen Adolescent Development Study [ADS]), the aim of which was to prospectively investigate risk factors for depression. In order to maximize variability in such factors, in the ADS researchers screened a large number of early adolescents (n = 2,453) on key affective temperaments known to promote risk and resilience for psychopathology, from primary schools in and around metropolitan Melbourne, Australia, and selected a smaller sample of adolescents (i.e., 415) to participate in further longitudinal assessments. These 415 adolescents represented an oversampling of those with high and low temperamental risk for psychopathology, and an undersampling of those with an intermediate level of risk, resulting in a distribution that retained the variance associated with the larger screening sample but that was still normally distributed (see Yap, Allen, & Ladouceur, 2008, and Supplement 1 of the present study). The 89 individuals for whom data are included in the present study represent those right-handed participants who completed brain imaging from two assessment waves of the study, who did not have a history of depressive illness or substance use disorder prior to baseline, and who did not have a chronic illness, language or learning disability, or use of medicines known to affect nervous system functioning, other than psychotropic medications (see the Psychiatric Diagnoses section below). Adolescents were invited to participate in two waves of assessments, at approximately ages 12 and 17. For both waves, adolescents completed diagnostic interviews (Orvaschel, 1994; assessing DSM-IV Axis I psychopathology) and structural magnetic resonance imaging (MRI) scans. Trait positive affect was assessed at Wave 1 using the Positive Affect Negative Affect Scale (PANAS; Watson, Clark, &


Tellegen, 1988). For this scale, participants were required to report on what was “generally true” for them. PA and NA have typically been measured using self-report questionnaires, such as the PANAS (Watson et al. 1988a, b) and the Behavioural Activation/Behavioural Inhibition Scales (BIS/BAS; see Carver & White, 1994). The PANAS was designed to directly measure affect and can thus be used as a measure of trait affect, whereas the BIS/BAS scales assess behavioral correlates of these affective dimensions. In a comparison of multiple methods of assessment of PA and NA using self-report, informant reports, and aggregated momentary/diary data, Schimmack (2007) concluded that although multimethod studies are desirable, studies with a single method and appropriate sample sizes are likely to produce valid results most of the time. In Wave 1, intelligence was assessed by a short form of the Wechsler Intelligence Scale for Children, Fourth Edition (WISC-IV; Sattler & Dumont, 2004; Wechsler, 2003), and handedness was measured with the Edinburgh Handedness Inventory (Oldfield, 1971). Some response items on the PANAS were missing for seven participants, and these data were imputed using the expectation maximization (EM) algorithm in SPSS (Dempster, Laird, & Rubin, 1977; Little & Rubin, 1987). The EM algorithm is a technique that finds maximum likelihood estimates in parametric models for incomplete data. This procedure preserves the distributional characteristics of the nonmissing values on the item. Socioeconomic status (SES) was estimated using the Australian National University Four (ANU4) Scale (Jones & McMillan, 2001), which is based on parent report of occupation and has a range of 0 to 100. In this sample, ANU4 scores appeared strongly bimodal, with groups clustered around a score of 80 and between 30 and 40. Thus, ANU4 scores were dichotomized into two categories, high and low, to reflect the observed distribution. Note that for the attrition analyses presented below, socioeconomic disadvantage was determined by using the postcodes of the school addresses to calculate an index of relative socioeconomic disadvantage (Australian Bureau of Statistics, 2011). The final sample (N = 89) did not differ from the school screening sample (i.e., N = 2,452) on sex [Pearson’s χ2(1) = 0.77, p = .68], socioeconomic disadvantage [F(1, 2440) = 0.49, p = .49], geographic location within the cohort catchment area [Pearson’s χ2(4) = 2.47, p = .65], or type of educational institution attended [Pearson’s χ2(2) = 0.50, p = .78]. In each wave, after complete description of the procedures to the participants, written informed consent was obtained from all adolescents and from their parents/guardians, in accordance with the guidelines of the Human Research Ethics Committee of the University of Melbourne, Australia. Psychiatric diagnoses At baseline, the Schedule for Affective Disorders and Schizophrenia for School-Age Children—Present and

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Lifetime (KSADS-PL) interview (Kaufman et al., 1997) was used to screen and exclude any adolescent with a history of depressive or substance use disorder (SUD). These exclusion criteria were used because the overarching study from which these data were derived was designed to prospectively examine the onset of mood and SUDs during adolescence. This interview was administered again at the follow-up wave in order to assess the onset of DSM-IV Axis I psychopathology. Participants with a lifetime history of anxiety and/or behavioral disorders prior to baseline were not excluded, given that these may indeed be risk factors for the onset of depression and SUDs during adolescence. This led to the exclusion of only one participant from the 415 participants invited to be involved in the study. This is highly consistent with epidemiological reports, which have shown that, relative to childhood, the risk for depression and SUDs markedly increases during adolescence (Kessler et al., 2005; Merikangas et al., 2010). Therefore, we argue that our sample was not biased by this exclusion criterion and is representative of a community sample. Nine participants met the KSADS-PL criteria for at least one psychiatric diagnosis before the baseline scan. These diagnoses included specific phobia (three), enuresis (two), generalized anxiety disorder (one), oppositional defiant disorder (one), social phobia (two; one case was comorbid with specific phobia), and obsessive compulsive disorder (one). By the time of the follow-up scan, a further 18 participants met the KSADS criteria for at least one psychiatric diagnosis. Depressive illness (including adjustment disorder, major depressive disorder, and depressive disorder not otherwise specified) was observed in 12 participants. Eight of the participants with a depressive illness also had a lifetime history of at least one other psychiatric disorder, including anxiety disorders, conduct and oppositional defiant disorders, enuresis, and substance dependence. A further nine participants met the criteria for at least one psychiatric diagnosis between baseline and follow-up who did not develop a depressive illness. These diagnoses included alcohol abuse, specific phobia, obsessive compulsive disorder, and conduct and oppositional defiant disorders. There were no cases of psychotic disorders or bipolar disorders. Eighteen of the participants with psychiatric diagnoses had received either psychological therapy or counseling delivered by either a private psychologist or a school counselor in relation to psychiatric illness or poor coping/life stress. One participant was treated for depression with sertraline for one week, whereas another had seen a pediatrician in relation to externalizing diagnoses and had been treated with dexamphetamine medication for a period of 18 months starting at age 11. More specific details about this group, including specific comorbidities and durations of illness, can be reviewed in Supplement 2.

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Neuroimaging Acquisition and preprocessing At baseline and follow-up, structural imaging of the entire brain was conducted using MRI. The baseline scans were conducted at the Brain Research Institute in Melbourne, Australia, on a 3-T GE scanner. A T1-weighted sequence was performed with the following parameters: repetition time = 36 ms, echo time = 9 ms, flip angle = 35°, field of view = 20 cm2, pixel matrix = 410 × 410, and 124 T1-weighted contiguous 1.5-mm-thick slices (voxel dimensions = 0.4883 × 0.4883 × 1.5 mm). Due to unforeseen circumstances, follow-up scans were conducted at the Royal Children’s Hospital (RCH), Melbourne, Australia, on a 3-T Siemens scanner with the following parameters: repetition time = 1,900 ms, echo time = 2.24 ms, flip angle = 9°, field of view = 23 cm, and 176 T1-weighted contiguous 0.9-mm thick slices (voxel dimensions = 0.9 mm3). All images were visually screened before preprocessing to eliminate poor quality images from the analysis. For longitudinal and/or multisite studies, the stability of image acquisition is critical but may be compromised in several ways (see Leung et al., 2010). For example, instrument-related differences between sites (e.g., scanner manufacturer, field strength) or instrument upgrades within sites may compromise image comparability (Han et al., 2006). In the present study, steps were employed to address two main sources of error (i.e., geometric distortion and voxel dimension drifts; Clarkson et al., 2009; Jovicich et al., 2006; Wang & Doddrell, 2005) that are known to result from multisite and/or longitudinal scanning, in order to improve the comparability of the images acquired at the two different time points and scanner sites. Firstly, all images were corrected for tissue signal inhomogeneity, which has been shown to result from geometric distortion (Wang & Doddrell, 2005). This was achieved via N3 correction (optimized for 3-T images; Zheng, Chee, & Zagorodnov, 2009), a nonparametric nonuniformity intensity normalization method (Sled, Zijdenbos, & Evans, 1998). Secondly, voxel dimension drift had been successfully corrected in previous studies using linear registration procedures (e.g., Clarkson et al., 2009). We addressed this issue by processing all images using a longitudinal stream in Freesurfer (Version 4.5; http://surfer.nmr.mgh.harvard.edu/fswiki/ LongitudinalProcessing), which involves the creation of an unbiased within-subjects template space and average image (Reuter & Fischl, 2011) using robust, inverse consistent registration (Reuter, Rosas, & Fischl, 2010). Information from this subject template is used to initialize the longitudinal image processing at several locations in order to increase repeatability and statistical power. This method guarantees inverse consistency (i.e., symmetry), can deal with different intensity scales, and automatically estimates a sensitivity parameter to detect outlier regions in the images. The resulting registrations are highly accurate, due to their ability to ignore


outlier regions, and show superior robustness with respect to noise, to intensity scaling, and outliers when compared to alternate registration tools (Reuter et al., 2010). Furthermore, to investigate the possibility of interscanner bias, an interscanner reliability study was performed (see Dennison et al., 2013, and Supplement 3 for full details). The evidence from this study did not provide evidence of interscanner related bias. All FreeSurfer image processing was completed using a high-performance computing facility: an AMD Opteron system using CentOS 5 Linux, hosted by the Victorian Partnership for Advanced Computing, Melbourne, Australia. FreeSurfer output was reviewed on the same workstation. Subcortical volumes Volumes of the caudate, putamen, nucleus accumbens, pallidum, and hippocampus were estimated using FreeSurfer by means of an automated segmentation procedure, which has been described previously (Dale, Fischl, & Sereno, 1999; Fischl et al., 2002; Fischl, Sereno, & Dale, 1999). Samples of the segmentation outcomes from the FreeSurfer analysis are depicted in Fig. 1. This procedure automatically assigns a neuroanatomical label to each voxel in an MRI volume based on probabilistic information estimated automatically from a manually labeled training set. The atlas renormalization procedure used in FreeSurfer reduces the sensitivity of the whole-brain segmentation method to changes in scanner platforms and improves its accuracy and robustness, which also facilitates multisite neuroanatomical imaging studies (Han & Fischl, 2007). Furthermore, the N3 correction utilized has been shown to improve automated segmentation processing (Boyes et al., 2008; Zheng et al., 2009). There is

Putamen

Caudate Pallidum

Nucleus Accumbens

Hippocampus Amygdala

Fig. 1 Example FreeSurfer segmentation volumes (midline sagittal view) for the nucleus accumbens, caudate, pallidum, putamen, hippocampus, and amygdala. Note: that amygdala volumes were traced manually, although FreeSurfer volumes are displayed here


evidence for good reliability of the delineation of the hippocampus, caudate, putamen, and pallidum using this software (Babalola et al., 2009; Fischl et al., 2002; Morey et al., 2009; Morey et al., 2010). Although reliability estimates have been slightly lower for the nucleus accumbens, automated delineation of this structure is similarly reliably to manual tracing (Babalola et al., 2009; Frazier et al., 2008). Whole-brain volumes were calculated at both time points as the total of all gray and white matter, as per the FreeSurfer segmentation. Amygdala volumes were estimated using a manual procedure, due to evidence that automated segmentation is less reliable than manual tracing for this structure (Boccardi et al., 2011; Morey et al., 2009). The method used to trace the amygdala has previously been described in detail (Whittle et al., 2008; Whittle et al., 2009). Briefly, the posterior boundary of the amygdala was marked by the first appearance of amygdala gray matter above the temporal horn. The lateral border was marked superiorly by the thin strip of white matter separating the amygdala from the claustrum and tail of the caudate, and inferiorly by the temporal stem and extension of the temporal horn. The medial border was marked superiorly by the semilunar gyrus, and inferiorly by subamygdaloid white matter, which separates the amygdala from the entorhinal cortex. The anterior boundary was marked by the joining of the optic chiasm, or the point at which the lateral sulcus closes to form the entorhinal sulcus (whichever was more posterior). Amygdala volumes were traced by two trained researchers blind to participants’ characteristics, and reliability was assessed by both researchers by tracing ten randomly selected images. The intraclass correlation coefficients (ICCs) for interrater reliability were .84 and .83 for the right and left amygdala volumes, respectively. The ICCs for intrarater reliability ranged between .83 and .93 for both researchers. All subcortical volumes were separated by hemisphere and corrected for whole-brain volume using a covariance procedure (Jack et al., 1989; Sanfilipo, Benedict, Zivadinov, & Bakshi, 2004), and these corrected volumes were used in all subsequent analyses. Data analysis Hierarchical linear regressions using the clustered robust standard errors method were conducted for each of the subcortical brain volume measures and the whole-brain volume (Diggle, Heagerty, Liang, & Zeger, 2002; Rogers, 1993; White, 1980; Williams, 2000; Wooldridge, 2000), which were separated by hemisphere due to previous developmental findings of laterality effects (Dennison et al., 2013). The within-subjects or “clustered” variable was time, with PA, NA, and lifetime psychiatric diagnosis (LPD) as the between-subjects variables. In the first step, the covariates sex, intelligence, and SES time, as well as the two-way interactions between time and the covariates, were entered. In the second step, the main effects

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for PA, NA, and LTD were entered. In the third step, all twoway interaction terms were entered, and in the final step, the three 3-way interaction terms between the variables were entered; for an overview of the regression analysis plan, see Table 1. The method described by Benjamini and Yekutieli (B–Y; 2001) was used to correct for multiple comparisons. According to the method and the number of comparisons (13: 6 regions of interest [ROIs] ×2 hemispheres + the whole-brain volume), the critical value was set at .0157 (for a table of critical values derived from the B–Y method, see Narum, 2006). Unlike Bonferroni corrections, which are considered to be too conservative in dependent data sets (Armitage, Berry, & Matthews, 2002), or earlier false discovery rate (FDR) approaches (i.e., Benjamini & Hochberg, 1995), which can lead to large Type I error rates (Narum, 2006), the B–Y method was designed to accommodate dependence among the hypothesis tests while providing a more conservative Type I error rate than earlier FDR approaches (Narum, 2006). Note that uncorrected p values are reported in the results below. Due to positive skewness, an inverse transformation was applied to the NA variable. Note that the effects of age at baseline and of time (in months) between baseline and follow-up scans on ROI volumes and ROI volume change were examined via correlational analysis. We found a weak but statistically significant negative correlation between length of the delay between scans and change in the left putamen (r = –.26, p = .016). However, when length of the delay between scans was included in the regression analysis for the left putamen, the pattern of significant findings was not changed. The data were analyzed using the statistical packages STATA 12 (StataCorp, 2011) and SPSS Version 19 (SPSS for Mac, 2011).

Results The demographic characteristics of the sample are described in Table 2. We found no sex differences in age Table 1 Analysis plan Step 1 (Covariates)

Step 2

Step 3

Step 4

FSIQ Sex SES Time Time × Sex Time × FSIQ Time × SES

PA NA LPD

Time × PA Time × NA Time × LPD NA × PA NA × LPD PA × LPD

PA × NA × Time PA × LPD × Time NA × LPD × Time

FSIQ = intelligence; SES = socioeconomic status; PA = positive affect; NA = negative affect; LPD = lifetime psychiatric diagnosis

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Table 2 Sample characteristics and psychological measures Sex

Sample size Age at baseline (years)a Age at follow up (years)a Delay (months)a Estimate full scale IQa Percentage low SES Positive affect (PA)a Negative affect (NA)a

Psychiatric Status

Male

Female

LPD Negative

LPD Positive

Total

46 12.7; 0.48; 11.4–13.6 16.4; 0.53; 15.0–17.6 44.8b; 1.8; 42–49 107.7; 11.7; 79–132 27 % 33.0; 7.3; 12–50 16.2; 5.2; 10–27

43 12.6; 0.42; 11.9–13.7 16.4; 0.52; 15.5–17.5 45.8b; 2.8; 40–55 104.3; 10.7; 87–123 38 % 35.6; 6.5; 21–50 16.3; 6.8; 10–44

62 12.7; 0.47; 11.4–13.7 16.4; 0.53; 15.0–17.6 45.1; 2.3; 40–52 106.1; 11.7; 83–132 31 % 35.2c; 6.4; 21–50 15.8; 5.8; 10–44

27 12.6; 0.43; 11.9–13.5 16.5; 0.49; 15.5–17.5 45.9; 2.9; 42–55 106.4; 10.5; 79–125 37 % 32.2c; 7.9; 12–46 17.3; 6.5; 10–33

89 12.6; 0.45; 11.4–13.7 16.4; 0.51; 15.0–17.6 45.3; 2.5; 40–55 106.2; 11.3; 79–132 32 % 34.3; 7; 12–50 16.3; 6.0; 10–44

LPD Negative = no lifetime history of psychiatric diagnosis. LPD Positive = lifetime history of psychiatric illness. a Presented in the order mean; standard deviation; minimum–maximum values. b Significant difference between males and females, p < .05, two-tailed. c Significant difference between adolescents with a psychiatric history and those without, p < .05, one-tailed

at either baseline or follow-up [respectively, t(87) = 0.977, p = .331; t(87) = 0.003, p = .998] or in proportions of low/high SES [χ2(1) = 1.30, p = .254]. Nor were there any difference between adolescents who had lifetime history of psychiatric illness in age at either baseline or follow-up [respectively, t(87) = 0.34, p = .733; t(87) = –0.37, p = .712] or in proportions of low/high SES [χ2(1) = 0.35, p = .554]. On average, the period of delay between scans was 45.3 months, ranging from 40 to 55 months. On average, the delay for girls was one month more than that for boys (45.8 vs. 44.8, respectively), and although this difference was statistically significant [t(87) = –2.29, p = .025], the importance of this difference from a developmental perspective was considered to be minimal, particularly given the large sample size and the length of the delay period. The difference in delays as a function of psychiatric illness was not significant [t(87) = –1.41, p = .164]. We found no significant differences in IQ between males and females [t(87) = 1.385, p = .169] or as a function of psychiatric illness [t(87) = –0.131, p = .896]. The PANAS scores by sex and psychiatric status are also reported in Table 2. PA and NA were uncorrelated (r = .001, p = .992). No significant differences were apparent between males and females on PA [t(87) = 1.82, p = .075] or NA [t(87) = 0.27, p = .786]. We expected that children with a psychiatric history would have higher NA and lower PA, and therefore one-tailed tests were conducted. We found no significant differences between adolescents with a lifetime history of psychiatric illness on NA [t(87) = –1.00, p = .160]; however, children with a psychiatric history did score significantly lower on PA than did children without a psychiatric history [t(87) = 1.89, p = .031]. Affect and brain volume Average brain volumes and significant change over time for each ROI are described in Table 3. Independent of time,

higher levels of PA predicted larger hippocampal volumes in both the left [b = 16.4, t(78) = 4.06, p = .0001] and right [b = 12.8, t(78) = 2.88, p = .005] hemispheres. Additionally, independent of time, higher levels of NA predicted smaller left amygdala volumes [b = –2460.70, t(78) = 2.63, p = .010]. We observed a significant interaction between time and PA for the right caudate [b = –9.86, t(72) = 2.51, p = .014], which is depicted in Fig. 2. Although those with higher levels of PA experienced a greater reduction in caudate volume over time,

Table 3 Means and standard deviations (SDs) of whole-brain volume corrected brain volumes Baseline

Follow-Up

Brain Region

M

SD

M

SD

Right accumbens Left accumbens Right pallidum* ^

713 496 1,929

97 93 215

683 544 2,031

87 78 242

Left pallidum* ^ Right caudate Left caudate* ^^ Right putamen Left putamen* ^^ Right hippocampus* ^ Left hippocampus* ^ Right amygdala* ^^ Left amygdala Whole-brain volume* ^

2,105 4,395 4,447 7,240 7,350 4,503 4,446 1,841 1,888 1,347,195

217 449 453 689 607 353 359 236 204 117,792

2,361 4,298 4,303 7,172 7,101 4,729 4,598 1,765 1,865 1,379,413

289 474 469 690 610 386 376 211 243 136,746

* Change over time was significant after adjusting for multiple comparisons (i.e., p < .016) after controlling for sex, IQ, and SES and their interactions with time (i.e., the covariates in Step 1; see Table 1). Note that these findings may vary from those reported in Dennison et al. (2013), since the sample reported here included both healthy adolescents and those with a lifetime psychiatric history. ^ Increase in volume. ^^ Decrease in volume


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Fig. 2 Regression lines depicting relationship between time and right caudate volume, as moderated by positive affect (PA). The middle lines in each set of three lines (bold) represent regression lines for 1 SD above (solid line = high PA) and below (dashed line = low PA) the mean, and the upper and lower lines represent upper and lower 95 % confidence intervals of these lines

PA did not predict the baseline volume [b = 4.83, t(72) = 0.70, p = .482] or follow-up volume [b = –4.03, t(72) = 0.27, p = .785] of this region. Finally, a significant three-way interaction was apparent between NA, time, and LPD status for the left caudate [b = 5,683.5, t(679) = 2.87, p = .005], which is depicted in Fig. 3. Further investigations in a reduced model revealed that change in left caudate volume was moderated by NA for the LPDpositive group at a trend level [b = 3,213.6, t(81) = 2.32, p = .023], but not for the LPD-negative group [b = –1,566.8, t(81) = 1.58, p = .118]. For the LPD-positive group, lower levels of NA predicted greater decreases in left caudate volume over time; however, NA did not predict left caudate volumes at baseline [b = –6,493.71, t(81) = 1.80, p = .075] or follow-up [b = –3,280.07, t(81) = 0.83, p = .406]. No significant findings involved the interaction term between PA and NA for any of the ROIs.

Discussion This study describes for the first time associations between trait positive affect and the development of the basal ganglia, hippocampus, and amygdala in a sample of adolescents who were selected on the basis of temperamental risk and resilience for emotional and behavioral problems in adolescence. Moderating effects of PA were observed for the development of the right caudate, with higher levels of PA predicting more pronounced decreases in volume—that is, higher PA predicted an exaggeration of the pattern associated with healthy developmental trends described previously (Dennison et al., 2013). Independent of time, lower levels of PA were associated with smaller hippocampal volumes bilaterally. These effects of PA

Fig. 3 Regression lines depicting relationship between time and left caudate volume, as moderated by negative affect (NA) for adolescents without a lifetime psychiatric history (upper graph) and those with a psychiatric history (lower graph). The middle lines in each set of three lines (bold) represent regression lines for 1 SD above (solid lines = high NA) and below (dashed lines = low NA) the mean, and the upper and lower lines represent upper and lower 95 % confidence intervals of these lines

on brain development did not vary in relation to the experience of a lifetime psychiatric diagnosis. With respect to NA, higher levels predicted smaller left amygdala volumes, and the moderating effect of NA on the development of the left caudate varied as a function of psychiatric history. In further exploring this interaction, a trend was observed whereby NA moderated development of the left caudate only for adolescents with a psychiatric history, such that greater decreases in left caudate volume were observed for children with lower NA in this group.

Associations with positive affect Change in right caudate volume, but not in baseline or followup volume, was associated with PA, which may suggest that although the moderating effects of PA are present during early to mid-adolescence, differences between individuals high and low in trait PA may not manifest structurally until later in adolescence or adulthood. This finding shows that the moderating effects of PA on striatal structure only begin to

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manifest sometime after early adolescence, highlighting this period of development as an important stage of differentiation. Although speculative, this finding is consistent with the notion that striatal development is sensitive to the experience of positive emotions specifically during the adolescent period. This may be because adolescence is associated with dynamic development of the striatum; previous research has shown that the caudate reaches peak volume during early adolescence, and then undergoes a period of pruning throughout adolescence (Lenroot & Giedd, 2006). Although the mechanisms behind the associations between caudate development and PA are unknown, trait differences in PA, which in themselves are likely to be biologically based (Whittle, Allen, Lubman, & Yücel, 2006), might lead to differing degrees of engagement with reward experiences during adolescence. This might in turn alter how this neural system matures, possibly by modulating dopamine activity in this region. It has been shown that the dopamine activity in the caudate appears to play a critical role in instrumental reward learning (Koepp et al., 1998; O’Doherty et al., 2004; Volkow et al., 2002), which is essential to the adaptive execution of motivated behavior. Instrumental reward learning involves actively approaching and engaging with rewards; thus, it is plausible that the association between differences in caudate development and PA may be related to differences in the experience of rewarding stimuli during this period of life. This hypothesis is consistent with the findings of Forbes et al. (2009), who showed that for adolescents, diminished activation of the caudate during both reward anticipation and outcome was associated with lower subjective report of PA. Independent of time, NA, and psychiatric status, lower levels of PA were associated with smaller bilateral hippocampal volumes. Although the mechanisms for this association are yet unknown, research with rats has shown that the experience of positive emotion is associated with hippocampal cell proliferation (HCP) and survival in both adult and juvenile animals (Wohr et al., 2009; Yamamuro et al., 2010). This is broadly consistent with our finding of greater volume in this region in the context of higher PA. More specifically, Wohr et al. found that the effects of increased HCP were only observed in animals that had a trait-like predisposition to experiencing positive emotion. Interestingly, Yamamuro et al. provided evidence that brain-derived neurotrophic factor (BDNF), the neurotrophin that has been well studied in the relationship between neurogenesis and negative stress (for a review, see Balu & Lucki, 2009), was not altered by the experience of positive emotion, suggesting that these effects occur through a BDNF-independent pathway. Given the consistency between these animal studies and our findings, further studies into the presence of two pathways to HCP, one related to PA and one related to stress, may be an important neurobiological target for further investigations into the association between PA and resilience to the effects of negative/


stressful life events (i.e., better coping; for a review, see Lyubomirsky et al., 2005). The absence of relationships of the nucleus accumbens and pallidum with PA was surprising, given previous reports (Berridge & Kringelbach, 2008; Keedwell, Andrew, Williams, Brammer, & Phillips, 2005; Urošević et al., 2012; Wacker et al., 2009). This was particularly surprising for the nucleus accumbens, given the findings reported by Urošević et al., prospectively linking larger accumbens volumes in adolescence to greater levels of reward responsiveness and reporting positive associations between change in nucleus accumbens volume and concurrent increases in motivational drive. Comparisons between this study and the present findings are difficult, due to differences in design (i.e., mixed cohort) and data analysis. For example, Urošević et al. combined participants into three adolescent groups using larger age ranges (i.e., 3-year spans) than were used in the present study, and their reports of developmental trends vary from those we have previously described (Dennison et al., 2013). It may be the case that the association between PA and nucleus accumbens volumes does not manifest until later in adolescence, which would be consistent with the previous findings (Urošević et al., 2012; Wacker et al., 2009). The failure to observe an association with the pallidum may relate to the volumetric measurement of this structure. The positive hedonic properties associated with the pallidum have been specifically linked to a smaller subregion within the ventral region (Aldridge & Berridge, 2010; Berridge & Kringelbach, 2008; Berridge et al., 2009; Smith & Berridge, 2005). The global ROI volumetric measure used in this study may not have been sufficiently sensitive to changes in the subregion associated with positive emotion. Another area for further consideration in explaining these null findings will be the distinction between positive emotions for different levels of approach motivation (Gable & HarmonJones, 2008; Gilbert, 2012; Harmon-Jones & Gable, 2008). “High-approach” positive emotions, such as enthusiasm, desire, and excitement, can be characterized as pregoal, rewardseeking emotions that are associated with an urge to act or approach, and they may also map on Berridge and colleagues’ (2009) notion of “wanting.” “Low-approach” positive emotions, such as interest, contentment, love, amusement, and gratitude, are theorized to be experienced either after a reward or goal is obtained or when a goal or reward is not immediately relevant, such that the intensity of the urge to perform an action is relatively low (Bryant, 2003; Gable & HarmonJones, 2010; Harmon-Jones & Gable, 2008). In fact, high levels of reward-seeking activity may interfere with the experience of low-approach positive affect. This notion of lowapproach positive emotion may be consistent with Berridge and colleagues’ (2009) notion of “liking.” The hedonic properties associated with the pallidum focus on liking and sensory pleasure, which, in this region, have to some extent been


dissociated from the high-approach positive emotions of “wanting” (Aldridge & Berridge, 2010; Berridge, 2003). The measure of PA used in this study may not have been sufficiently sensitive in detecting or differentiating between these different types of PA. Further consideration of models that make a distinction between types of positive emotions in terms of levels of approach motivation (i.e., Harmon-Jones & Gable, 2008) are needed. Second, a broader question exists as to whether the neurodevelopment of different striatal regions in adolescence is associated more strongly with high-approach emotions relative to low-approach positive emotions (Gilbert, 2012), which is consistent with the behavioral findings of increased reward responsiveness and motivational drive (Urošević et al., 2012). The findings of developmental sensitivity to PA in the caudate—the function of which, conceptually, is more strongly associated with high-approach positive affect (i.e., instrumental reward learning)—are consistent with the hypothesis that neurodevelopment during adolescence may drive high- rather than low-approach positive emotions. To our knowledge, this has been the first investigation of the relationship between trait PA and amygdala volume during adolescence. Although decreases in amygdala volume have been reported in (unmedicated) depression (Hamilton, Siemer, & Gotlib, 2008), only amygdala function has been previously linked to PA (e.g., anhedonia)—although the findings are indeed mixed, and the results may be explained by differences in the types of stimuli (i.e., faces vs. money) and the tasks used in functional paradigms (see Forbes et al., 2009; Keedwell et al., 2005). The failure to observe an association between the amygdala and PA was surprising, given its established role in processing and learning about affective stimuli (Baxter & Murray, 2002). However, lesion studies in animals have shown that the amygdala is not always necessary for successful reward learning (for a review, see Baxter & Murray, 2002). As in the pallidum results described above, it may be the case that the measure of PA used in this study did not tap into more specific components of PA that are amygdala dependent, such as using representations of reward for updating current stimulus–value associations (Baxter & Murray, 2002). Associations with negative affect Although the association between higher NA and smaller amygdala volume found in the present study is consistent with extensive evidence linking the experience of negative emotions, such as fear and anxiety, to the structure and function of the amygdala (Calder et al., 2001), the direction differs from that in a recent study with a large sample of healthy adults reporting that larger amygdala volumes were linked to heightened trait NA (Holmes et al., 2012). These findings suggest that developmental stage may be a key variable to consider in order to accurately characterize the relationships between affect and structure volume. The failure to find an association

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between hippocampal volumes and NA was also surprising, given previous reports of hippocampal volume being associated with neuroticism (DeYoung et al., 2010) and stress (McEwen, 1999); however, it may be the case that these relationships vary developmentally. The moderating effect of NA on the development of the left caudate varied as a function of psychiatric status. Although only observed at a trend level, the finding that NA moderated decreases in left caudate volume for adolescents with a psychiatric history was surprising. Firstly, it appears inconsistent with reports in healthy adults, which have shown no association between the caudate and NA in a cross-sectional design (Holmes et al., 2012). However, the pattern of findings depicted in Fig. 3 suggests that, although it is not significant at either time point, the moderating effect of NA on left caudate volumes may reduce over time, and may disappear by later adulthood. Secondly, given our finding of an association between PA and caudate development, the finding seems somewhat at odds with the idea that negative and positive emotions are governed by distinct neural pathways (Ernst & Fudge, 2009; Lane et al., 1997). In this group, greater reduction in volume over time was moderately associated with low NA, which is consistent with the idea that a more adaptive temperament seems to be associated with reductions in caudate volume over time. Further research will be required in order to understand the meaning of the specific relationship between NA and brain maturation in those with a lifetime psychiatric history. Limitations Some important limitations should be considered. Our MRI scans were acquired multisite; however, a reliability study suggested that the effects of changing scanners were unlikely to have introduced systematic bias to the longitudinal effects described. Moreover, such effects would be more likely to introduce noise that was unrelated to the variables of interest (i.e., PA), which would only make the hypothesized effects more difficult to observe. Secondly, the measurement of trait PA was limited to a single self-report questionnaire. Although the PANAS has been shown to have good reliability and validity (Crawford & Henry, 2004; Watson et al., 1988b) a multimethod composite assessment of trait PA, including further questionnaires (BIS/BAS scales) and either parental report or (ideally) observational measures, would further validate the measure. As we suggested above, multiple methods of assessment may have allowed for a more comprehensive assessment of low- and high-approach positive emotions (Bryant, 2003). Thirdly, in this study we only examined the effects of PA on the development of brain structure, not of function. Corroboration of the structural findings with

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functional reward-related/affective tasks might help consolidate some of the previous literature showing differential functions of the reward system during this period of life. Finally, we considered differences in the effects of PA and NA on brain structure and development for both healthy children and those with a lifetime history of psychiatric illness, but due to the relatively low incidence of individual illnesses, heterogeneity, and comorbidity of disorders within our psychiatric group, we did not examine the unique effects of individual disorders or the effects of the exact timing of onset of these disorders on the interaction between trait affect and brain development. Future research should consider the unique effects of specific disorders on the association between temperament and brain development.

Conclusion This is the first study to show, in an adolescent community sample, that individual differences in PA are associated with differential brain development from early to mid-adolescence. Our findings demonstrate that volumetric development of one node of the neural reward system, the caudate, is associated with PA during this period. Specifically, high PA, a trait associated with resilience to mental health problems, was associated with more pronounced normative volumetric change in the caudate. Additionally, independent of time, hippocampal volume was related to PA during this time of life. NA was negatively associated with left amygdala volume, and the moderating effects of NA on left caudate development varied with respect to psychiatric history. The present findings suggest that the developmental window between early and mid-adolescence may be critical for interventions that promote resilience to disorders of positive emotion that emerge later in life, although further clarification is needed of exactly what aspects of PA (i.e., wanting, liking, or learning) are related to the development of affective neurocircuitry during this period of life.

Author note This work was in part presented at the Society for Research in Adolescence (SRA) 13th Biennial Conference, Vancouver, Canada, March 8–10, 2012. No authors report competing interests. This research was supported by grants from the Colonial Foundation, the National Health and Medical Research Council (NHMRC; Australia; Program Grant No. 350241), and the Australian Research Council (Discovery Grant No. DP0878136). S.W. is supported by an NHMRC Career Development Fellowship (ID: 1007716). M.D. was supported by an Australian Postgraduate Award. M.Y. is supported by an NHMRC Fellowship (ID: 1021973). Our neuroimaging analysis was facilitated by the Neuropsychiatry Imaging Laboratory at the Melbourne Neuropsychiatry Centre. The authors thank the Brain Research Institute and Royal Children’s Hospital for support in acquiring the neuroimaging data, as well as the families who participated in the study.


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Greater hippocampal volume is associated with PTSD treatment response.

Adiposity is inversely associated with hippocampal volume in African Americans and European Americans with diabetes.

Reduced hippocampal volume is associated with overgeneralization of negative context in individuals with PTSD.

The SEMA5A gene is associated with hippocampal volume, and their interaction is associated with performance on Raven's Progressive Matrices.

Hippocampal Brain Volume Is Associated with Faster Facial Emotion Identification in Older Adults: Preliminary Results.

Low-intensity daily walking activity is associated with hippocampal volume in older adults.

Serotonin transporter gene methylation is associated with hippocampal gray matter volume.

Hippocampal volume is inversely related to PTSD duration.

Hippocampal (subfield) volume and shape in relation to cognitive performance across the adult lifespan.

Hearing Impairment Is Associated with Smaller Brain Volume in Aging.

Subcortical intelligence: caudate volume predicts IQ in healthy adults.

Short-term escitalopram treatment and hippocampal volume.

Comparison of hippocampal volume in dementia subtypes.

CACNA1C risk variant is associated with increased amygdala volume.

Increased mean platelet volume is associated with coronary artery ectasia.

Association of depressive symptoms with hippocampal volume in 1936 adults.

Elevated mean platelet volume is associated with silent cerebral infarction.

Higher cardiorespiratory fitness levels are associated with greater hippocampal volume in breast cancer survivors.

Beating the brain about abuse: Empirical and meta-analytic studies of the association between maltreatment and hippocampal volume across childhood and adolescence.

Aerobic exercise intervention in young people with schizophrenia spectrum disorders; improved fitness with no change in hippocampal volume.

The GABRB1 gene is associated with thalamus volume and modulates the association between thalamus volume and intelligence.

Fibromyalgia patients have reduced hippocampal volume compared with healthy controls.

Multi-modal characterization of rapid anterior hippocampal volume increase associated with aerobic exercise.

Increased hippocampal blood volume and normal blood flow in schizophrenia.