Striatal gray matter volumes,externalizing traits,and N-back task performance: An exploratory study of sex differences using the human connectome project data

Abstract

The striatum is implicated in externalizing traits and cognitive dysfunction. The ventral and dorsal striatal subregions may play differentiable roles in externalizing behaviors and executive functions. We employed voxel-based morphometry to estimate the striatal gray matter volumes (GMVs) of 968 young adults (510 women) of the Human Connectome Project. We examined sex differences in striatal GMVs, tested how striatal GMVs related to an externalizing trait (Extn), and 2-back memory efficiency (Eff₂), and examined whether these relationships exhibited sex differences. Men showed significantly higher ventral striatum (VS) and lentiform nucleus (LN) GMVs as well as higher Extn and Eff₂ than women. Across all subjects, greater caudate and LN GMVs were correlated significantly with lower Eff₂ though with limited effect sizes (r = −0.140, p < .001 and r = −0.093, p = .004, respectively) and the latter correlation was carried primarily by women (z = −3.070, p = .002, slope test). VS GMV showed positive correlation with Extn (r = 0.085, p = .008). Together, striatal GMVs are significantly but only weakly associated with externalizing behaviors and cognitive dysfunction in young adults. As key hubs of the fronto-striatal circuits, the ventral and dorsal striatum may contribute differently to externalizing psychopathology.

Keywords

basal ganglia personality working memory imaging sex

Introduction

Externalizing behaviors, including impulsivity, often co-occur with other mental and behavioral problems (Chan et al., 2008). Deficits in executive functions have been associated with multiple domains of externalizing problems, including alcohol, marijuana, and other drug use as well as childhood conduct disorder and adult antisocial behavior (Gruber et al., 2012; Mathias et al., 2007; Morgan & Lilienfeld, 2000; Weissenborn & Duka, 2003). Working memory represents a key process of executive functions (Baddeley, 1996; Chen et al., 2019; Diamond, 2013; Li et al., 2021). Individuals with lower working memory capacity in both visual and auditory modality showed higher vulnerability to excessive alcohol use (Gunn & Finn, 2013). A longitudinal study demonstrated that poorer working memory performance indexed by digit span score at baseline predicted more aggressive behaviors at 3-year follow-up in healthy young adults (Karlsgodt et al., 2015). These findings suggest a shared mechanism between externalizing behaviors and executive dysfunction. Examination of the brain structures and functions subserving working memory and other executive functions may advance the understanding of the pathophysiology of externalizing disorders.

As a part of the fronto-striatal circuit, the ventral striatum (VS) is associated with externalizing traits as well as a wide array of cognitive processes, including working memory (Beck et al., 2009; Bjork et al., 2010; Koechlin et al., 2002; van der Meer & Redish, 2011). VS response to reward as compared to non-reward in an incentive task was positively correlated with externalizing score (Bjork et al., 2010), although the association was not observed in a recent study with the card guessing task of the Human Connectome Project (HCP) data (Hyatt et al., 2020). The VS showed higher activation in the 2- as compared to 0- and 1-back trials in the N-back working memory task (Satterthwaite et al., 2012). With a small subsample of the HCP data, a recent study also showed higher BOLD responses to 2- versus 0-back in a superior cluster of VS (Grill et al., 2021). As with the VS, the dorsal striatum (DS), comprising the caudate and lentiform nuclei, also partakes in cognitive processing (Balleine et al., 2007). In the stop signal task, the caudate head and lentiform nucleus showed higher and lower activity, respectively, in relation to the capacity of response inhibition, as reflected in the stop signal reaction time (Li et al., 2008). These findings suggest that both VS and DS dysfunction may contribute to impulsive behaviors and cognitive deficits.

The roles of the VS and DS in impulsive behaviors or externalizing traits may also reflect in volumetrics. Higher impulsivity as quantified by delay discounting was associated with greater DS but not VS gray matter volume (GMV) in 70 healthy adults (Tschernegg et al., 2015). An earlier study showed that reduced DS GMV was linked to higher sensitivity to reward in undergraduates (Barrós-Loscertales et al., 2006). Impulsivity measured with the Barratt Impulsivity Scale (BIS) and delay discounting was correlated positively with the GMVs of medial and dorsolateral prefrontal cortex but negatively with the VS GMV in young healthy adults (Cho et al., 2013). Another study showed that the volumes of subcortical regions including the VS, hypothalamus and anterior thalamus were positively, whereas those of cortical regions including fronto-medial cortex and bilateral insula were negatively (Cohen’s d = 0.19), related to impulsivity as quantified with a temporal discounting task in a large sample of adolescents (Mackey et al., 2017). However, a recent study with the HCP sample associated 2-back accuracy in the N-back task with the GMVs of bilateral lateral orbitofrontal cortex, left cuneus, right middle temporal gyrus, right pars orbitalis, and left inferior temporal gyrus but not the striatum (Owens et al., 2018). These contrasting findings raise questions whether striatal GMVs are indeed related to externalizing traits and cognitive functioning in adults.

Compared to women, men engage more frequently in externalizing behaviors (Cross et al., 2011; Hicks et al., 2007; Weinstein & Dannon, 2015; Zucker, 2008) and show higher prevalence in impulse-control and conduct disorders (Corr & McNaughton, 2016; Frick & Thornton, 2017; Torrubia et al., 2001). The neural bases of the sex differences have been under extensive investigation, much with a focus on the striatal circuits. Boys and girls showed different patterns in VS volumetric development (Dennison et al., 2013; Raznahan et al., 2010), which may account for higher vulnerability of males to externalizing pathology (Dir & Hulvershorn, 2019). On the other hand, although men presented larger VS GMV than women, impulsivity as evaluated with the Karolinska Scales of Personality was not significantly correlated with VS GMV in men and women combined or separately (Caravaggio et al., 2017). In clinical studies, boys with attention-deficit/hyperactivity disorder (ADHD) showed lower VS GMV as compared to typically developing (TD) boys, but no volumetric differences were detected in ADHD relative to TD girls (Wang et al., 2018). Young men relative to women showed larger globus pallidus and putamen but not caudate or VS GMV (Rijpkema et al., 2012). These findings suggest the need to examine sex differences in striatal GMVs and in the relationships between striatal GMVs and externalizing behaviors and cognitive functions across multiple instruments and in a large sample.

Here, with the HCP data, we investigated the relationships between striatal GMV, externalizing behavior, and executive function and to explore potential sex differences in these relationships in young adults. We examined how striatal (VS, caudate, and lentiform nucleus) GMVs may relate to externalizing traits as assessed with the Achenbach Adult Self Report (ASR), as well as cognitive function quantified by N-back performance, specifically 2-back efficiency. Although the contrasting findings of the literature did not allow us to formulate specific hypotheses (other than perhaps male dominance in externalizing traits), we had two issues to explore. First, we tested sex differences in striatal GMVs, externalizing trait/behavior, and cognitive function. Second, we examined the correlations between striatal GMVs and behavioral measures and sex differences in these correlations. We believe that the study would inform the roles and sex differences in the roles of striatal volumetrics in externalizing behavior and cognitive function. The potential findings may also facilitate future meta-analyses to revisit these issues in systems neuroscience.

Materials and methods

Dataset: subjects

Behavioral and 3T magnetic resonance (MR) imaging data from 1206 healthy young adult participants (1113 with structural MR scans) were collected from 2012 to 2015 in the 1200 Subjects Release (S1200) of the HCP (https://www.humanconnectome.org/study/hcp-young-adult/document/1200-subjects-data-release). Individuals with severe neurodevelopmental, neuropsychiatric, or neurologic disorders were not included in the dataset. A group of 124 participants did not participate in the N-back task. An additional group of 114 participants were excluded due to excessive head movement (translation >2 mm or rotation >2° in any dimension) or problematic image quality after normalization. The final sample size of the current study was 968 (510 women; age: 29.53 ± 3.56 years). We have obtained permission from the HCP to use the Open and Restricted Access data for the current study. All recruitment procedures and informed consent forms, including consent to share de-identified data, were approved by the Washington University Institutional Review Board (code 00011725).

Dataset: assessments of externalizing trait and N-back performance

Participants were assessed with the Achenbach Adult Self Report (ASR), a part of the Achenbach System of Empirically Based Assessment (ASEBA) taxonomy (Achenbach & Rescorla, 2003), with the ASR_Extn_Raw score (Extn) quantifying the extent of externalizing behaviors and problems.

Participants completed the N-back task in two runs with each consisting of eight blocks. Four sets of pictures (body part, face, place, and tool) were used as stimuli. At the start of each block, a cue was displayed for 2.5 s to indicate the block type (0- or 2-back). In the 0-back blocks, the cue also indicated a specific target for participants to identify. In the 2-back blocks, participants were required to identify whether the target stimulus in the current trial matches the stimulus two trials before. In each trial, the target stimulus was presented for 2 s, followed by an inter-trial interval of 0.5 s. Each block consisted of 10 trials, and a “null” block of 15 s was presented every two blocks. The blocks were presented in fixed orders in the two runs (first: 2,0,2,0,2,2,0,0; second: 2,0,2,0,0,2,0,2). The median accuracy rates (AR) and response time (RT) of 0- and 2-back represent the primary performance measures. However, in many (including the HCP) N-back studies, participants tend to perform at ceiling AR at the expense of RT (Hockey & Geffen, 2004; Hur et al., 2017; Meule, 2017). Thus, to eliminate the effect of speed-accuracy tradeoff, we computed the efficiency of the 2-back trials (Eff₂ = AR₂/RT₂) as a more sensitive measure of N-back performance, with a higher Eff₂ indicating better performance and cognitive function (Mackie et al., 2013).

MRI acquisition and voxel-based morphometry

A customized 3T Siemens Connectome Skyra with a standard 32-channel Siemens receiver head coil and a body transmission coil was used in the MRI scanning. T1-weighted high-resolution structural images were acquired using a 3D MPRAGE sequence with 0.7 mm isotropic resolution (FOV = 224 × 224 mm, matrix = 320 × 320, 256 sagittal slices, TR = 2400 ms, TE = 2.14 ms, TI = 1000 ms, FA = 8°).

We used voxel-based morphometry (VBM) to estimate the GMVs of brain regions with the CAT12 toolbox. The details of VBM analysis have been described earlier (Chen, Chaudhary, et al., 2022; Ide et al., 2020). We followed the suggested defaults of the CAT12 manual (Gaser & Dahnke, 2016; http://dbm.neuro.uni-jena.de/vbm): 1) individuals’ structural images were spatially normalized to the same stereotactic space; 2) the normalized images were segmented into distinct brain tissues (gray matter, white matter, and cerebrospinal fluid); and 3) the gray matter (GM) images were smoothed. Specifically, a spatial adaptive non-local means (SANLM) denoising filter was first applied to the initial voxel-based processing (Manjón et al., 2010), followed by internal resampling to accommodate low-resolution images and anisotropic spatial resolutions. Subsequently, data were processed by bias-correction and affine-registration, followed by the unified segmentation (Ashburner & Friston, 2005). After segmentation, the brain was parcellated into left and right hemisphere, subcortical areas, and cerebellum by skull-stripping. A local intensity transformation of all tissue classes was performed to reduce the effects of higher gray matter intensities in the motor cortex, basal ganglia, or occipital lobe, followed by adaptive maximum a posteriori (AMAP) segmentation with partial volume estimation (Tohka et al., 2004). The tissue segments were then spatially normalized to a common reference space using DARTEL registrations (Ashburner, 2007). The GM maps were smoothed by convolution with an isotropic Gaussian kernel (FWHM = 8 mm). Data quality was checked by using the modules of display slices and VBM data homogeneity in the CAT12.

Group data analyses

We used the VS mask, generated by using both cytoarchitectonic and topographical criteria, as in earlier studies (Zaborszky et al., 2008; Zhang et al., 2017). The masks of caudate and lentiform nucleus were obtained from the Automated Anatomic Labeling atlas (Tzourio-Mazoyer et al., 2002). In addition, total intracranial volume (TIV) of each participant was estimated and used in the second-level analyses as a covariate to correct for brain sizes. In group statistics, we performed two-sample t tests to examine the sex differences in the GMVs of the striatal masks and of the whole brain, with age, years of education, and TIV as covariates.

We also compared men and women directly in whole-brain analyses in a two-sample t test with the same covariates and evaluated the results at a voxel p < .05 corrected for family-wise error (FWE) of multiple comparisons, on the basis of Gaussian random field theory as implemented in SPM. Clusters were overlaid on the ch2better template and reported in MNI coordinates (Collins et al., 1994). Brain regions were identified by reference to the AAL atlas and Duvernoy (1999).

We computed the partial correlation coefficients in regressions between the VS, DS [caudate and lentiform nucleus (LN) combined], caudate, and LN GMVs and Extn, and Eff₂ first for men and women separately, with age, years of education, and TIV as covariates. If the correlations did not show sex differences in slope tests, we combined men and women in the regressions, with sex, age, years of education, and TIV as covariates.

In addition to ROI analyses focusing on the striatum, we performed voxel-wise regression each against Eff₂ and externalizing score with the same set of covariates for all subjects and for men and women separately, in the hope that these data may help future meta-analyses.

We computed the statistical power of the HCP sample in detecting the effect sizes reported of the correlation between VS volume and impulsivity (Cohen’s d ∼ 0.18) in 1830 adolescents (Mackey et al., 2017). Using G*Power (Faul et al., 2007, 2009), we estimated that a sample size of 61, 74, and 99 would have same power to detect an effect size of 0.18, at an α = 0.05, considering a total of 3 covariates. Thus, with a sample size of 968, the current cohort would have adequate statistical power to detect these effect sizes.

Results

Clinical and behavioral measures and striatal GMVs

The descriptive statistics and results of two-sample t tests on sex differences are presented in Table 1. Men were significantly younger and showed higher total intra-cranial TIV, Extn, and Eff₂ than women. Men and women did not differ in years of education. Men and women did not differ in race and ethnicity composition or in total household income (Supplementary Table S1).

Table 1.

Descriptive statistics in men and women with t and p values of sex differences.

	Men (n = 458)	Women (n = 510)	t	p
Age (years)	28.0 (3.7)	29.5 (3.6)	−6.81	<0.001*
Education (years)	14.9 (1.9)	15.0 (1.9)	−1.21	0.225
TIV (ccm)	1623 (154)	1391 (123)	24.50	<0.001*
Extn	10.2 (7.3)	7.4 (6.1)	5.52	<0.001*
Eff₂	0.092 (0.019)	0.087 (0.019)	3.53	<0.001*

Note: All values are mean (SD). TIV: total intracranial volume; Extn: externalizing raw score (ASR_Extn_Raw); Eff₂: efficiency of 2-back. The t and p values are based on two-sample t tests between men and women. The two-sample t tests of TIV, Extn, and Eff₂ were conducted controlling for age and education. *Significant at a corrected p < 0.05/5 = 0.01.

As shown in Figure 1, with age, years of education, and TIV as covariates, men showed higher VS (16.7 ± 1.7 vs. 14.8 ± 1.5 ccm) and lentiform nucleus (31.6 ± 2.8 vs. 28.0 ± 2.7 ccm) GMV relative to women (p’s < .001). Men and women did not differ significantly in the caudate GMV (21.6 ± 2.6 vs. 19.9 ± 2.3 ccm; p = .799). To examine whether the sex differences in VS and lentiform nucleus GMV manifest in voxel-wise analysis and how men and women may differ in the GMVs of other brain regions, we compared men and women for the whole-brain with the same covariates. As shown in Supplementary Figure S1 and Table S2, the whole-brain analysis showed higher GMVs in the VS and in a large cluster that included the putamen and pallidum.

Figure 1.

Sex differences in striatal GMVs, with age, years of education, and TIV as covariates. (A) VS mask in green; (B) caudate mask in yellow; (C) lentiform nucleus mask in magenta. The histograms on the right show the mean ± SD of striatal GMVs. ***p < 0.001.

Correlations of striatal GMVs with externalizing score and N-back performance

We first examined the correlations of striatal GMVs with Extn and Eff₂ for men and women separately controlling for age, years of education, and TIV, and whether men and women differed in the slopes of regressions. Sex difference was only found in the correlation between lentiform nucleus GMV and Eff₂ (see below). Thus, Figure 2 shows the latter regression separately for men and women and all others for the full sample. The statistics are summarized in Supplementary Tables S3 and S4. For the full sample, we evaluated the results with correction for 6 regressions (3 ROIs and two measures) or at a p value of 0.05/(3x2) = 0.0083. With sex, age, years of education, and TIV as covariates, the results showed that 1) VS GMV was positively correlated with Extn (r = 0.085, p = .008); 2) caudate GMV was negatively correlated with Eff₂ (r = −0.140, p < .001); and 3) lentiform nucleus GMV was negatively correlated with Eff₂ (r = −0.093, p = .004). The latter correlation was significant in women (r = −0.140, p = .002) but not in men (r = −0.057, p = .227), and the sex difference was confirmed by a slope test (z = −3.070, p = .002).

Figure 2.

The partial correlations between striatal GMVs (VS in green, caudate in yellow, and lentiform nucleus in magenta) and (A) Extn and (B) Eff₂ for the full sample, with sex, age, years of education, and TIV as covariates. Solid and dashed lines represent the regression lines and 95% confidence intervals, respectively. Circles/triangles represent individual residual data. Note: The last panel shows the partial correlations between lentiform nucleus GMV and Eff₂ for men (in blue, r_M) and women (in gray, r_F) separately.

We also examined the correlations of DS (caudate and lentiform nucleus combined) GMV with Extn and Eff₂ in the full sample as well as in men and women separately controlling for the same set of covariates. The statistics are summarized in Supplementary Tables S5. Lower DS GMV was correlated with greater Eff₂ in the full sample (r = −0.134, p < .001) and in women alone (r = −0.156, p < .001), but not in men (r = −0.114, p = .015), considering a corrected p value <0.05/6 = 0.0083. However, the slope test did not show significant sex differences in the correlations (p’s ≥ 0.254).

Alternative analyses: voxel-wise regression

We employed ROI analyses because of the focus on the striatum. However, we also performed voxel-wise regression each against Eff₂ and externalizing score with the same set of covariates for all subjects and for men and women separately. The results are shown in Supplementary Figure S2 and Table S6. Across all subjects, higher Eff₂ was correlated with higher GMVs in the cerebellum and lower GMVs in bilateral caudate and putamen and left pallidum. In women alone, right supramarginal, postcentral gyri, caudate, and putamen showed GMVs in negative correlations with Eff₂. Men did not show any significant clusters in correlation, positive or negative, with Eff₂. No clusters showed any significant correlations with externalizing score in any group.

Discussion

With a well-powered sample size, the current study examined how striatal GMVs related to externalizing trait and working memory efficiency, and whether these relationships exhibited sex differences. Men showed significantly higher VS and lentiform nucleus GMVs as well as higher externalizing scores and working memory efficiency than women. VS GMV showed a positive correlation with the externalizing trait across all subjects.

Across all subjects, greater caudate and lentiform nucleus GMVs were correlated significantly with lower working memory efficiency, and the latter correlation was carried primarily by women. We highlight the main findings in discussion.

Striatal GMVs, externalizing traits, and cognition

Men showed higher VS and lentiform nucleus GMV, externalizing score, and efficiency of 2-back memory in comparison to women. However, the sex differences in striatal GMVs were not mirrored by the differences in behavioral measures, with the possible exception that greater VS GMV was associated with more externalizing problems. In accord with the latter finding, pathological gambling, an externalizing disorder, was associated with higher VS GMV (Koehler et al., 2015). In an earlier study, both men and women showed positive correlations between VS GMV and obesity, a medical condition that implicates impaired control of eating (Horstmann et al., 2011). The latter and current results are consistent to the extent that over-eating and obesity can be associated with externalizing forms of psychopathology (Mitchell et al., 2014; Slane et al., 2010) and with the caveat that pathological eating involves other distinct psychological and biological processes.

The current finding can also be considered with a recent work of over 10,000 children from the Adolescent Brain Cognition Development project showing a positive correlation between VS GMV and behavioral activation system (BAS) traits in both boys and girls (Ide et al., 2020). Thus, it is possible that the VS GMV may represent a sex-shared volumetric marker of externalizing traits, with males showing both higher VS GMV and externalizing dispositions than females. Note that the effect size (r = 0.085) of the correlation in the current sample of young adults was much lower than the effect size (Cohen’s d ∼ 0.123) reported of children in Ide et al. The weaker correlation perhaps reflects the differences between the ASR, which assesses more general externalizing behaviors, and the BAS scale, which specifically assesses traits of reward-seeking propensity (Hamilton et al., 2012; Kambouropoulos & Staiger, 2001; Studer et al., 2016), as supported by the VS (Daniel & Pollmann, 2014; Pagnoni et al., 2002). Moreover, gray matter loss as an index of maturation occurs during the development of brain regions that support cognitive functions, including the frontoparietal cortex and subcortical structures such as basal ganglia (Giedd et al., 2006). Thus, the correlations between VS GMV and externalizing traits may manifest differently in children and young adults.

Higher caudate and lentiform nucleus GMVs were negatively correlated with working memory efficiency in the full sample. Interconnected with the prefrontal cortex (Zhang et al., 2012), the DS plays pivotal roles in multiple domains of cognitive function. The caudate and lentiform nucleus showed higher activations in individuals who performed better in a wide array of cognitive tasks (Brovelli et al., 2011; Grahn et al., 2008; Melrose et al., 2007; Monchi et al., 2006; Rubia et al., 2006). In particular, the DS as part of the parieto-fronto-striatal circuit showed greater responses to high versus low load in the N-back task across ages (Mencarelli et al., 2019; Yaple et al., 2019). These seemingly contrasting findings suggest that both volumetric and functional measures are needed in describing individual differences in cognitive performance. More studies and meta-analyses in particular are needed to examine the potential relationships between regional structural and functional measures in link with behavioral traits and cognitive functions.

Sex differences

In accord with earlier reports (Lotze et al., 2019; Ruigrok et al., 2014; Sanchis-Segura et al., 2019), we observed higher GMVs of bilateral putamen and pallidum in men relative to women. Connected with the VS, the lentiform nucleus is part of the cognitive motor circuit (Turner & Desmurget, 2010) that supports motivated behavior (Root et al., 2015; Smith et al., 2009); thus, higher VS and lentiform nucleus GMV may be associated with impulsive and other externalizing behaviors and potentially cognitive dysfunction. However, we observed here that, although men showed higher externalizing score than women, they also demonstrated better working memory efficiency than women. Thus, sex differences in striatal GMVs did not appear to implicate the same pattern of sex differences in behaviors and cognition. In linear regressions, higher lentiform nucleus GMVs were correlated with lower working memory efficiency, although this relationship appeared to be carried primarily by women, who showed a significantly stronger correlation than men. This finding, although hard to explain, is broadly consistent with the afore-mentioned study of obesity showing that women but not men demonstrated a positive correlation between putamen GMV and body mass index (Horstmann et al., 2011). Importantly, the findings of higher striatal volumes and better cognitive performance in men versus women do not imply a positive correlation between striatal volume and cognitive performance in inter-subject variation, either in men or in women. That is, group differences and inter-subject differences within the group are not necessarily commensurate.

More research is needed to understand the cerebral morphometrical bases of sex-dependent cognitive functions and behavioral traits. For instance, these findings suggest the importance in investigating sex differences in the neural phenotypes of mental conditions implicating impulsivity and cognitive deficits, such as ADHD (Chen et al., 2021b; Ruigrok et al., 2014). Human lesion studies may provide causal evidence for the distinct contribution of a specific brain structure to functional processes and behavioral traits and how these causal roles may differ between the sexes.

Limitations and conclusion

A few limitations need to be considered for the current study. Firstly, although comprising a large number of well-characterized participants, the HCP sample is largely non-clinical. The lack of cases with extreme externalizing behaviors or cognitive dysfunctions may account for the relatively weak correlations between striatal GMVs and behavioral metrics. Secondly, ASR captured the overall levels but not specific domains of externalizing traits. Studies are needed to employ multiple instruments to quantify externalizing behaviors. Thirdly, it is challenging to make direct inferences from regional GMVs to complex behavioral traits. Morphometrical findings provide potential answers to how brain supports behavior. However, as the mapping between brain structure and function is not straightforward (Zaborszky, 2021), investigations of group and individual differences in both structure and function of the brain are needed to fully understand the neural bases of behavior in health or illness (MacDonald et al., 2006).

In conclusion, we demonstrated higher externalizing traits and striatal GMVs in men than in women. Lentiform nucleus GMV showed a significant negative correlation with working memory efficiency in women but not in men, and the difference was confirmed by a slope test. However, overall, the volumetric and behavioral measures are correlated with limited effect sizes. These findings contrasted with earlier reports of smaller sample sizes and may have implications of studies of the cerebral volumetric bases of externalizing psychopathologies.

Supplemental Material

sj-pdf-1-epp-10.1177_20438087221080057 – Supplemental Material for Striatal gray matter volumes, externalizing traits, and N-back task performance: An exploratory study of sex differences using the human connectome project data

Supplemental Material for, sj-pdf-1-epp-10.1177_20438087221080057 Striatal gray matter volumes, externalizing traits, and N-back task performance: An exploratory study of sex differences using the human connectome project data by Yu Chen and Chiang-Shan R Li in Journal of Experimental Psychopathology

Footnotes

Acknowledgment

The funding agencies otherwise have no roles in the conceptualization of the study, data collection and analysis, or the decision to publish these results. We thank Guangfei Li for assistance in curating the HCP data.

Author Contributions

Both authors contributed to conceptualization of the study, data analysis and interpretation, literature review and writing of the manuscript.

Declaration of conflicting interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: The authors declare no competing interests in the current study.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The current study was supported by NIH grants DA045189, DA044749, and DA051922.

Supplemental material

Supplemental material for this article is available online.

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References

Achenbach

T. M.

Rescorla

(2003) Manual for the ASEBA adult forms & profiles. University of Vermont.

Ashburner

(2007) A fast diffeomorphic image registration algorithm.. Neuroimage. 38, 95–113.

Ashburner

Friston

(2005) Unified segmentation. Neuroimage. 26(3): 839–851, https://doi.org/10.1016/j.neuroimage.2005.02.018.

Baddeley

(1996) Exploring the central executive. The Quarterly Journal of Experimental Psychology Section A, 49(1): 5–28. https://doi.org/10.1080/713755608.

Balleine

B. W.

Delgado

M. R.

Hikosaka

(2007) The role of the dorsal striatum in reward and decision-making. The Journal of Neuroscience: the Official Journal of the Society for Neuroscience, 27(31): 8161–8165, https://doi.org/10.1523/JNEUROSCI.1554-07.2007.

Beck

Schlagenhauf

Wüstenberg

Hein

Kienast

Kahnt

Schmack

Hägele

Knutson

Heinz

(2009) Ventral striatal activation during reward anticipation correlates with impulsivity in alcoholics. Biological Psychiatry, 66(8): 734–742, https://doi.org/10.1016/j.biopsych.2009.04.035.

Bjork

J.M.

Chen

Smith

A. R.

Hommer

D. W.

(2010) Incentive-elicited mesolimbic activation and externalizing symptomatology in adolescents. Clinical Psychology & Psychotherapy, 51(7): 827–837, https://doi.org/10.1111/j.1469-7610.2009.02201.x.

Brovelli

Nazarian

Meunier

Boussaoud

(2011) Differential roles of caudate nucleus and putamen during instrumental learning. Neuroimage, 57(4): 1580–1590, https://doi.org/10.1016/j.neuroimage.2011.05.059.

Caravaggio

Plitman

Chung

J. K.

Gerretsen

Kim

Iwata

Chakravarty

Remington

Graff-Guerrero

(2017) Trait impulsiveness is related to smaller post-commissural putamen volumes in males but not females. The European Journal of Neuroscience, 46(7): 2253–2264, https://doi.org/10.1111/ejn.13661.

10.

Chan

Y.-F.

Dennis

M. L.

Funk

R. R.

(2008) Prevalence and comorbidity of major internalizing and externalizing problems among adolescents and adults presenting to substance abuse treatment. Journal of Substance Abuse Treatment, 34(1): 14–24, https://doi.org/10.1016/j.jsat.2006.12.031.

11.

Chen

Chaudhary

Wang

C.-S.R.

(2022) Gray matter volumes of the insula and anterior cingulate cortex and their dysfunctional roles in cigarette smoking. Addiction Neuroscience, 1: 100003, https://doi.org/10.1016/j.addicn.2021.100003.

12.

Chen

Ide

J. S.

Luo

C.-S.R.

(2021) Sex differences in attention deficit hyperactivity symptom severity and functional connectivity of the dorsal striatum in young adults. Neuroimage: Reports, 1(2): 100025, https://doi.org/10.1016/j.ynirp.2021.100025.

13.

Chen

Spagna

Kim

T. H.

Chen

Fan

(2019) Testing a cognitive control model of human intelligence. Scientific Reports, 9(1): 2898, https://doi.org/10.1038/s41598-019-39685-2.

14.

Cho

S.S.

Pellecchia

Aminian

Ray

Segura

Obeso

Strafella

A. P.

(2013) Morphometric correlation of impulsivity in medial prefrontal cortex. Brain Topography, 26(3): 479–487, https://doi.org/10.1007/s10548-012-0270-x.

15.

Collins

D. L.

Neelin

Peters

T. M.

Evans

A.C.

(1994) Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. Journal of Computer Assisted Tomography, 18(2): 192–205, https://doi.org/10.1097/00004728-199403000-00005.

16.

Corr

P. F.

McNaughton

(2016) Neural mechanisms of low trait anxiety and risk for externalizing behavior. In: Beauchaine, T., & Hinshaw, S. (eds). The Oxford handbook of externalizing spectrum disorders. Oxford University Press, pp. 220–238.

17.

Cross

C. P.

Copping

L. T.

Campbell

(2011) Sex differences in impulsivity: a meta-analysis. Psychological Bulletin, 137(1): 97–130, https://doi.org/10.1037/a0021591.

18.

Daniel

Pollmann

(2014) A universal role of the ventral striatum in reward-based learning: evidence from human studies. Neurobiology of Learning and Memory, 114: 90–100, https://doi.org/10.1016/j.nlm.2014.05.002.

19.

Dennison

Whittle

Yücel

Vijayakumar

Kline

Simmons

Allen

N.B.

(2013) Mapping subcortical brain maturation during adolescence: Evidence of hemisphere‐and sex‐specific longitudinal changes. Developmental Science, 16(5): 772–791, https://doi.org/10.1111/desc.12057.

20.

Diamond

(2013) Executive functions. Annual Review of Psychology, 64: 135–168, https://doi.org/10.1146/annurev-psych-113011-143750.

21.

Dir

A. L.

Hulvershorn

L. A.

(2019) Sex differences in adolescent neurobiological risk for substance use and substance use disorders. Current Addiction Reports ; 6(4): 514–521, https://doi.org/10.1007/s40429-019-00276-w.

22.

Duvernoy

(1999) The human brain: surface, three-dimensional sectional anatomy with MRI, and blood supply, 2: Springer.

23.

Faul

Erdfelder

Buchner

Lang

A.-G.

(2009) Statistical power analyses using G* Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4): 1149–1160, https://doi.org/10.3758/BRM.41.4.1149.

24.

Faul

Erdfelder

Lang

A.-G.

Buchner

(2007) G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2): 175–191, https://doi.org/10.3758/bf03193146.

25.

Frick

P. J.

Thornton

L.C.

(2017) A brief history of the diagnostic classification of childhood externalizing disorders. The Wiley Blackwell handbook of developmental psychopathology, pp. 475–495, https://doi.org/10.1002/9781118554470.ch23.

26.

Gaser

Dahnke

(2016) CAT-a computational anatomy toolbox for the analysis of structural MRI data. http://www.neuro.uni-jena.de/hbm2016/GaserHBM2016.pdf.

27.

Giedd

J. N.

Clasen

L. S.

Lenroot

Greenstein

Wallace

G. L.

Ordaz

Molloy

E. A.

Blumenthal

J.D.

Tossell

J.W.

Stayer

Samango-Sprouse

C. A.

Shen

Davatzikos

Merke

Chrousos

G. P.

(2006) Puberty-related influences on brain development. Molecular and Cellular Endocrinology, 254-255: 154–162, https://doi.org/10.1016/j.mce.2006.04.016.

28.

Grahn

J. A.

Parkinson

J. A.

Owen

A. M.

(2008) The cognitive functions of the caudate nucleus. Progress in Neurobiology, 86(3): 141–155, https://doi.org/10.1016/j.pneurobio.2008.09.004.

29.

Grill

Nyberg

Rieckmann

(2021) Neural correlates of reward processing: Functional dissociation of two components within the ventral striatum. Brain Behav, 11(2): Article e01987, https://doi.org/10.1002/brb3.1987.

30.

Gruber

S. A.

Sagar

K. A.

Dahlgren

M. K.

Racine

Lukas

S.E.

(2012) Age of onset of marijuana use and executive function. Psychology of Addictive Behaviors, 26(3): 496–506, https://doi.org/10.1037/a0026269.

31.

Gunn

R. L.

Finn

P. R.

(2013) Impulsivity partially mediates the association between reduced working memory capacity and alcohol problems. Alcohol, 47(1): 3–8, https://doi.org/10.1016/j.alcohol.2012.10.003.

32.

Hamilton

K. R.

Sinha

Potenza

M. N.

(2012) Hazardous drinking and dimensions of impulsivity, behavioral approach, and inhibition in adult men and women. Alcoholism: Clinical and Experimental Research, 36(6): 958–966, https://doi.org/10.1111/j.1530-0277.2011.01708.x.

33.

Hicks

B. M.

Blonigen

D. M.

Kramer

M. D.

Krueger

R. F.

Patrick

C. J.

Iacono

W. G.

McGue

(2007) Gender differences and developmental change in externalizing disorders from late adolescence to early adulthood: A longitudinal twin study. Journal of Abnormal Psychology, 116(3): 433, https://doi.org/10.1037/0021-843X.116.3.433.

34.

Hockey

Geffen

(2004) The concurrent validity and test?retest reliability of a visuospatial working memory task. Intelligence, 32(6): 591–605, https://doi.org/10.1016/j.intell.2004.07.009.

35.

Horstmann

Busse

F. P.

Mathar

Muller

Lepsien

Schlogl

Kabisch

Kratzsch

Neumann

Stumvoll

Villringer

Pleger

(2011) Obesity-related differences between women and men in brain structure and goal-directed behavior. Front Hum Neurosci, 5: 58, https://doi.org/10.3389/fnhum.2011.00058.

36.

Hur

Iordan

A. D.

Dolcos

Berenbaum

(2017) Emotional influences on perception and working memory. Cognition & Emotion, 31(6): 1294–1302, https://doi.org/10.1080/02699931.2016.1213703.

37.

Hyatt

C. S.

Hallowell

E. S.

Owens

M. M.

Weiss

B. M.

Sweet

L. H.

Miller

J. D.

(2020) An fMRI investigation of the relations between Extraversion, internalizing psychopathology, and neural activation following reward receipt in the Human Connectome Project sample. Personal Neurosci, 3: Article e13, https://doi.org/10.1017/pen.2020.11.

38.

Ide

J. S.

H. T.

Chen

T. M.

C.S.P.

Zhornitsky

C.R.

(2020) Gray matter volumetric correlates of behavioral activation and inhibition system traits in children: An exploratory voxel-based morphometry study of the ABCD project data. Neuroimage, 220: 117085, https://doi.org/10.1016/j.neuroimage.2020.117085.

39.

Kambouropoulos

Staiger

P. K.

(2001) The influence of sensitivity to reward on reactivity to alcohol‐related cues. Addiction, 96(8): 1175–1185, https://doi.org/10.1046/j.1360-0443.2001.968117510.x.

40.

Karlsgodt

K. H.

Bato

A. A.

Blair

M. A.

DeRosse

Szeszko

P. R.

Malhotra

A. K.

(2015) White matter microstructure in the executive network associated with aggression in healthy adolescents and young adults. [Social Cognitive and Affective Neuroscience Electronic Resource], 10(9): 1251–1256, https://doi.org/10.1093/scan/nsv015.

41.

Koechlin

Danek

Burnod

Grafman

(2002) Medial prefrontal and subcortical mechanisms underlying the acquisition of motor and cognitive action sequences in humans. Neuron, 35(2): 371–381, https://doi.org/10.1016/s0896-6273(02)00742-0.

42.

Koehler

Hasselmann

Wustenberg

Heinz

Romanczuk-Seiferth

(2015) Higher volume of ventral striatum and right prefrontal cortex in pathological gambling. Brain Structure & Function, 220(1): 469–477, https://doi.org/10.1007/s00429-013-0668-6.

43.

Chen

T. M.

Wang

Tang

C.R.

(2021) Neural correlates of individual variation in two-back working memory and the relationship with fluid intelligence. Scientific Reports, 11(1): 9980, https://doi.org/10.1038/s41598-021-89433-8.

44.

C.S.

Yan

Sinha

Lee

T. W.

(2008) Subcortical processes of motor response inhibition during a stop signal task. Neuroimage, 41(4): 1352–1363, https://doi.org/10.1016/j.neuroimage.2008.04.023.

45.

Lotze

Domin

Gerlach

F. H.

Gaser

Lueders

Schmidt

C. O.

Neumann

(2019) Novel findings from 2,838 Adult Brains on Sex Differences in Gray Matter Brain Volume. Scientific Reports, 9(1): 1671, https://doi.org/10.1038/s41598-018-38239-2.

46.

MacDonald

S. W.

Nyberg

Backman

(2006) Intra-individual variability in behavior: links to brain structure, neurotransmission and neuronal activity. Trends in Neurosciences, 29(8): 474–480, https://doi.org/10.1016/j.tins.2006.06.011.

47.

Mackey

Chaarani

Kan

K. J.

Spechler

P. A.

Orr

Banaschewski

Barker

Bokde

A.L.W.

Bromberg

Buchel

Cattrell

Conrod

P.J.

Desrivieres

Flor

Frouin

Gallinat

Gowland

Heinz

Ittermann

Consortium

(2017) Brain Regions Related to Impulsivity Mediate the Effects of Early Adversity on Antisocial Behavior. Biological Psychiatry, 82(4): 275–282. https;//doi.org/10.1016/j.biopsych.2015.12.027.

48.

Mackie

M.-A.

Van Dam

N. T.

Fan

(2013) Cognitive control and attentional functions. Brain and Cognition, 82(3): 301–312, https://doi.org/10.1016/j.bandc.2013.05.004.

49.

Manjón

J. V.

Coupé

Martí‐Bonmatí

Collins

D. L.

Robles

(2010) Adaptive non‐local means denoising of MR images with spatially varying noise levels. Journal of Magnetic Resonance Imaging: JMRI, 31(1): 192–203.

50.

Mathias

C. W.

Stanford

M. S.

Marsh

D. M.

Frick

P. J.

Moeller

F.G.

Swann

A.C.

Dougherty

D.M.

(2007) Characterizing aggressive behavior with the Impulsive/Premeditated Aggression Scale among adolescents with conduct disorder. Psychiatry Research, 151(3): 231–242, https://doi.org/10.1016/j.psychres.2006.11.001.

51.

Melrose

R. J.

Poulin

R. M.

Stern

C. E.

(2007) An fMRI investigation of the role of the basal ganglia in reasoning. Brain Research, 1142: 146–158, https://doi.org/10.1016/j.brainres.2007.01.060.

52.

Mencarelli

Neri

Mom

Menardi

Rossi

Santarnecchi

(2019) Stimuli, presentation modality, and load-specific brain activity patterns during n-back task. Hum Brain Mapp, 40(13): 3810–3831, https://doi.org/10.1002/hbm.24633.

53.

Meule

(2017) Reporting and interpreting working memory performance in n-back Tasks. Front Psychol, 8: 352, https://doi.org/10.3389/fpsyg.2017.00352.

54.

Mitchell

K. S.

Wolf

E.J.

Reardon

A. F.

Miller

M. W.

(2014) Association of eating disorder symptoms with internalizing and externalizing dimensions of psychopathology among men and women. Int J Eat Disord, 47(8): 860–869, https://doi.org/10.1002/eat.22300.

55.

Monchi

Petrides

Strafella

A. P.

Worsley

K. J.

Doyon

(2006) Functional role of the basal ganglia in the planning and execution of actions. Annals of Neurology, 59(2): 257–264, https://doi.org/10.1002/ana.20742.

56.

Morgan

A. B.

Lilienfeld

S. O.

(2000) A meta-analytic review of the relation between antisocial behavior and neuropsychological measures of executive function. Clinical Psychology Review, 20(1): 113–136, https://doi.org/10.1016/s0272-7358(98)00096-8.

57.

Owens

M. M.

Duda

Sweet

L. H.

MacKillop

(2018) Distinct functional and structural neural underpinnings of working memory. Neuroimage, 174: 463–471, https://doi.org/10.1016/j.neuroimage.2018.03.022.

58.

Pagnoni

Zink

C. F.

Montague

P. R.

Berns

G. S.

(2002) Activity in human ventral striatum locked to errors of reward prediction. Nature Neuroscience, 5(2): 97–98, https://doi.org/10.1038/nn802.

59.

Raznahan

Lee

Stidd

Long

Greenstein

Clasen

Addington

Gogtay

Rapoport

J. L.

Giedd

J. N.

(2010) Longitudinally mapping the influence of sex and androgen signaling on the dynamics of human cortical maturation in adolescence. Proceedings of the National Academy of Sciences, 107(39): 16988–16993, https://doi.org/10.1073/pnas.1006025107.

60.

Rijpkema

Everaerd

van der Pol

Franke

Tendolkar

Fernandez

(2012) Normal sexual dimorphism in the human basal ganglia. Hum Brain Mapp, 33(5): 1246–1252, https://doi.org/10.1002/hbm.21283.

61.

Root

D.H.

Melendez

R. I.

Zaborszky

Napier

T. C.

(2015) The ventral pallidum: Subregion-specific functional anatomy and roles in motivated behaviors. Progress in Neurobiology, 130: 29–70, https://doi.org/10.1016/j.pneurobio.2015.03.005.

62.

Rubia

Smith

A. B.

Woolley

Nosarti

Heyman

Taylor

Brammer

(2006) Progressive increase of frontostriatal brain activation from childhood to adulthood during event-related tasks of cognitive control. Hum Brain Mapp, 27(12): 973–993, https://doi.org/10.1002/hbm.20237.

63.

Ruigrok

A.N.

Salimi-Khorshidi

Lai

M. C.

Baron-Cohen

Lombardo

M. V.

Tait

R. J.

Suckling

(2014) A meta-analysis of sex differences in human brain structure. Neuroscience and Biobehavioral Reviews, 39: 34–50, https://doi.org/10.1016/j.neubiorev.2013.12.004.

64.

Sanchis-Segura

Ibanez-Gual

M. V.

Adrian-Ventura

Aguirre

Gomez-Cruz

A. J.

Avila

Forn

(2019) Sex differences in gray matter volume: how many and how large are they really? Biol Sex Differ, 10(1): 32, https://doi.org/10.1186/s13293-019-0245-7.

65.

Satterthwaite

T. D.

Ruparel

Loughead

Elliott

M. A.

Gerraty

R. T.

Calkins

M. E.

Hakonarson

Gur

R. C.

Gur

R. E.

Wolf

D. H.

(2012) Being right is its own reward: load and performance related ventral striatum activation to correct responses during a working memory task in youth. Neuroimage, 61(3): 723–729, https://doi.org/10.1016/j.neuroimage.2012.03.060.

66.

Slane

J. D.

Burt

S. A.

Klump

K. L.

(2010) The road less traveled: Associations between externalizing behaviors and eating pathology. Int J Eat Disord, 43(2): 149–160, https://doi.org/10.1002/eat.20680.

67.

Smith

K. S.

Tindell

A. J.

Aldridge

J. W.

Berridge

K. C.

(2009) Ventral pallidum roles in reward and motivation. Behavioural Brain Research, 196(2): 155–167, https://doi.org/10.1016/j.bbr.2008.09.038.

68.

Studer

Baggio

Dupuis

Mohler-Kuo

Daeppen

J.-B.

Gmel

(2016) Drinking motives as mediators of the associations between reinforcement sensitivity and alcohol misuse and problems. Frontiers in Psychology, 7: 718, https://doi.org/10.3389/fpsyg.2016.00718.

69.

Tohka

Zijdenbos

Evans

(2004) Fast and robust parameter estimation for statistical partial volume models in brain MRI. Neuroimage, 23(1): 84–97, https://doi.org/10.1016/j.neuroimage.2004.05.007.

70.

Torrubia

Avila

Moltó

Caseras

(2001) The sensitivity to punishment and sensitivity to reward questionnaire (SPSRQ) as a measure of Gray's anxiety and impulsivity dimensions. Personality and Individual Differences, 31(6): 837–862, https://doi.org/10.1016/s0191-8869(00)00183-5.

71.

Tschernegg

Pletzer

Schwartenbeck

Ludersdorfer

Hoffmann

Kronbichler

(2015) Impulsivity relates to striatal gray matter volumes in humans: evidence from a delay discounting paradigm. Front Hum Neurosci, 9: 384, https://doi.org/10.3389/fnhum.2015.00384.

72.

Turner

R. S.

Desmurget

(2010) Basal ganglia contributions to motor control: a vigorous tutor. Current Opinion in Neurobiology, 20(6): 704–716, https://doi.org/10.1016/j.conb.2010.08.022.

73.

Tzourio-Mazoyer

Landeau

Papathanassiou

Crivello

Etard

Delcroix

Mazoyer

Jolio

(2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage, 15: 273–289, https://doi.org/10.1006/nimg.2001.0978.

74.

van der Meer

M. A.

Redish

A.D.

(2011) Ventral striatum: a critical look at models of learning and evaluation. Current Opinion in Neurobiology, 21(3): 387–392, https://doi.org/10.1016/j.conb.2011.02.011.

75.

Wang

Zuo

Liao

Long

Joshi

R.M.

(2018) Gender differences in anomalous subcortical morphology for children with ADHD. Neuroscience Letters, 665: 176–181, https://doi.org/10.1016/j.neulet.2017.12.006.

76.

Weinstein

Dannon

(2015) Is Impulsivity a Male Trait Rather than Female Trait? Exploring the Sex Difference in Impulsivity. Current Behavioral Neuroscience Reports, 2(1): 9–14, https://doi.org/10.1007/s40473-015-0031-8.

77.

Weissenborn

Duka

(2003) Acute alcohol effects on cognitive function in social drinkers: their relationship to drinking habits. Psychopharmacology, 165(3): 306–312, https://doi.org/10.1007/s00213-002-1281-1.

78.

Yaple

Z. A.

Stevens

W. D.

Arsalidou

(2019) Meta-analyses of the n-back working memory task: fMRI evidence of age-related changes in prefrontal cortex involvement across the adult lifespan. Neuroimage, 196: 16–31, https://doi.org/10.1016/j.neuroimage.2019.03.074.

79.

Zaborszky

(2021) Brain structure and function: The first 15 years-a retrospective. Brain Structure & Function, 226(8): 2467–2475, https://doi.org/10.1007/s00429-021-02362-0.

80.

Zaborszky

Hoemke

Mohlberg

Schleicher

Amunts

Zilles

(2008) Stereotaxic probabilistic maps of the magnocellular cell groups in human basal forebrain. Neuroimage, 42: 1127–1141, https://doi.org/10.1016/j.neuroimage.2008.05.055.

81.

Zhang

Fucito

L. M.

Luo

Mazure

C. M.

Zaborszky

C.S.R.

(2017) Resting-state functional connectivity of the basal nucleus of meynert in cigarette smokers: dependence level and gender differences. Nicotine & Tobacco Research, 19(4): 452–459, https://doi.org/10.1093/ntr/ntw209.

82.

Zhang

Ide

J. S.

C. S.

(2012) Resting-state functional connectivity of the medial superior frontal cortex. Cerebral Cortex, 22(1): 99–111, https://doi.org/10.1093/cercor/bhr088.

83.

Zucker (2008) Anticipating problem alcohol use developmentally from childhood into middle adulthood: what have we learned? Addiction, 103(Suppl 1): 100–108, https://doi.org/10.1111/j.1360-0443.2008.02179.x.

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