The Relationship Between General Intelligence and Cortical Structure in Healthy Individuals

Brain volumes, cognitive, and adaptive skills in school-age children with Down syndrome

BACKGROUND

Down syndrome (DS) is the most common congenital neurodevelopmental disorder, present in about 1 in every 700 live births. Despite its prevalence, literature exploring the neurobiology underlying DS and how this neurobiology is related to behavior is limited. This study fills this gap by examining cortical volumes and behavioral correlates in school-age children with DS.

METHODS

School-age children (mean = 9.7 years ± 1.1) underwent comprehensive assessments, including cognitive and adaptive assessments, as well as an MRI scan without the use of sedation. Children with DS (n = 35) were compared to available samples of typically developing (TD; n = 80) and ASD children (n = 29). ANOVAs were conducted to compare groups on cognitive and adaptive assessments. ANCOVAs (covarying for age, sex, and total cerebral volume; TCV) compared cortical brain volumes between groups. Correlations between behavioral metrics and cortical and cerebellar volumes (separately for gray (GM) and white matter (WM)) were conducted separately by group.

RESULTS

As expected, children with DS had significantly lower cognitive skills compared to ASD and TD children. Daily Living adaptive skills were comparable between ASD children and children with DS, and both groups scored lower than TD children. Children with DS exhibited a smaller TCV compared to ASD and TD children. Additionally, when controlling for TCV, age, and sex, children with DS had significantly smaller total GM and tissue volumes. Cerebellum volumes were significantly correlated with Daily Living adaptive behaviors in the DS group only.

CONCLUSIONS

Despite children with DS exhibiting lower cognitive skills and smaller brain volume overall than children with ASD, their deficits in Socialization and Daily Living adaptive skills are comparable. Differences in lobar volumes (e.g., Right Frontal GM/WM, Left Frontal WM, and Left and Right Temporal WM) were observed above and beyond overall differences in total volume. The correlation between cerebellum volumes and Daily Living adaptive behaviors in the DS group provides a novel area to explore in future research.

Inferring neurocognition using artificial intelligence on brain MRIs

Brain magnetic resonance imaging (MRI) offers a unique lens to study neuroanatomic support of human neurocognition. A core mystery is the MRI explanation of individual differences in neurocognition and its manifestation in intelligence. The past four decades have seen great advancement in studying this century-long mystery, but the sample size and population-level studies limit the explanation at the individual level. The recent rise of big data and artificial intelligence offers novel opportunities. Yet, data sources, harmonization, study design, and interpretation must be carefully considered. This review aims to summarize past work, discuss rising opportunities and challenges, and facilitate further investigations on artificial intelligence inferring human neurocognition.

From gifted to high potential and twice exceptional: A state-of-the-art meta-review

Despite the abundant literature on intelligence and high potential individuals, there is still a lack of international consensus on the terminology and clinical characteristics associated to this population. It has been argued that unstandardized use of diagnosis tools and research methods make comparisons and interpretations of scientific and epidemiological evidence difficult in this field. If multiple cognitive and psychological models have attempted to explain the mechanisms underlying high potentiality, there is a need to confront new scientific evidence with the old, to uproot a global understanding of what constitutes the neurocognitive profile of high-potential in gifted individuals. Another particularly relevant aspect of applied research on high potentiality concerns the challenges faced by individuals referred to as "twice exceptional" in the field of education and in their socio-affective life. Some individuals have demonstrated high forms of intelligence together with learning, affective or neurodevelopmental disorders posing the question as to whether compensating or exacerbating psycho-cognitive mechanisms might underlie their observed behavior. Elucidating same will prove relevant to questions concerning the possible need for differential diagnosis tools, specialized educational and clinical support. A meta-review of the latest findings from neuroscience to developmental psychology, might help in the conception and reviewing of intervention strategies.

Structural brain correlates of non-verbal cognitive ability in 5-year-old children: Findings from the FinnBrain birth cohort study

Non-verbal cognitive ability predicts multiple important life outcomes, for example, school and job performance. It has been associated with parieto-frontal cortical anatomy in prior studies in adult and adolescent populations, while young children have received relatively little attention. We explored the associations between cortical anatomy and non-verbal cognitive ability in 165 5-year-old participants (mean scan age 5.40 years, SD 0.13; 90 males) from the FinnBrain Birth Cohort study. T1-weighted brain magnetic resonance images were processed using FreeSurfer. Non-verbal cognitive ability was measured using the Performance Intelligence Quotient (PIQ) estimated from the Block Design and Matrix Reasoning subtests from the Wechsler Preschool and Primary Scale of Intelligence (WPPSI-III). In vertex-wise general linear models, PIQ scores associated positively with volumes in the left caudal middle frontal and right pericalcarine regions, as well as surface area in left the caudal middle frontal, left inferior temporal, and right lingual regions. There were no associations between PIQ and cortical thickness. To the best of our knowledge, this is the first study to examine structural correlates of non-verbal cognitive ability in a large sample of typically developing 5-year-olds. The findings are generally in line with prior findings from older age groups, with the important addition of the positive association between volume / surface area in the right medial occipital region and non-verbal cognitive ability. This finding adds to the literature by discovering a new brain region that should be considered in future studies exploring the role of cortical structure for cognitive development in young children.

A Bivariate Twin Study of Cortical Surface Area and Verbal and Nonverbal Intellectual Skills in Adolescence

Understanding the biological basis of cognitive differences between individuals is the goal in human intelligence research. The surface area of the cortex is considered to be a key determinant of human intelligence. Adolescence is a period of development characterized by physiological, emotional, behavioral, and psychosocial changes, which is related to the recombination and optimization of the cerebral cortex, and cognitive ability changes significantly in children and adolescents. This study examined the effects of common genetic and environmental factors between the surface area of the cerebral cortex and intelligence in typical developing adolescents (twins, n = 114, age 12-18 years old). Cortical surface area data were parsed into subregions (i.e., frontal, parietal, occipital, and temporal areas) and intelligence into verbal and nonverbal skills. We found a phenotypic correlation between regional surface areas and verbal intelligence. No correlation was observed between regional surface areas and nonverbal intelligence, except for the occipital lobe and the right hemisphere. In the bivariate twin analyses, the differences in phenotypic correlation between regional surface areas and verbal intelligence were not due to unshared environmental effects or measurement error, but to genetic effects. In summary, the current study has broadened the previous genetic investigations of cognitive ability and cortical surface area.

Age differences in cortical thickness and their association with cognition in chimpanzee (Pan troglodytes)

Humans and chimpanzees are genetically similar and share a number of life history, behavioral, cognitive and neuroanatomical similarities. Notwithstanding, our understanding of age-related changes in cognitive and motor functions in chimpanzees remains largely unstudied despite recent evident demonstrating that chimpanzees exhibit many of the same neuropathological features of Alzheimer's disease observed in human postmortem brains. Here, we examined age-related differences in cognition and cortical thickness measured from magnetic resonance images in a sample of 215 chimpanzees ranging in age between 9 and 54 years. We found that chimpanzees showed global and region-specific thinning of cortex with increasing age. Further, within the elderly cohort, chimpanzees that performed better than average had thicker cortex in frontal, temporal and parietal regions compared to chimpanzees that performed worse than average. Independent of age, we also found sex differences in cortical thickness in 4 brain regions. Males had higher adjusted cortical thickness scores for the caudal anterior cingulate, rostral anterior cingulate, and medial orbital frontal while females had higher values for the inferior parietal cortex. We found no evidence that increasing age nor sex was associated with asymmetries in cortical thickness. Moreover, age-related differences in cognitive function were only weakly associated with asymmetries in cortical thickness. In summary, as has been reported in humans and other primates, elderly chimpanzees show thinner cortex and variation in cortical thickness is associated with general cognitive functions.

Gray matter associations with extinction-induced neural activation in patients with anxiety disorders

The relationship between structural characteristics and extinction-induced brain activations in anxiety disorders (ANX) remains a space for greater exploration. In this study, we assessed gray matter volume (GMV) and its associated functional activations during fear extinction memory recall in an ANX cohort. We performed voxel-based morphometry analysis to examine GMVs from ANX (n = 92) and controls (n = 73). We further examined the correlation between GMVs and extinction-induced neural activations during recall across groups. In the patients' group, we observed decreased GMV in the anterior hippocampus and increased GMV in the dorsolateral prefrontal cortex (dlPFC). Hippocampal volume was positively correlated with ventromedial prefrontal cortex activation in healthy controls, while it was negatively correlated with dorsal anterior cingulate cortex (dACC) activation in ANX. The dlPFC volume was positively correlated with activations of dACC, pre- and post-central gyrus, and supramarginal gyrus only in healthy controls. Therefore, the link between structural and functional imbalance within the hippocampus and dlPFC might contribute to the pathophysiology of ANX. In the controls, the relationship between structural variance in the hippocampus and dlPFC and extinction-induced neural activations is consistent with a greater ability to regulate fear responding; associations that were absent in the ANX cohort. Furthermore, our findings of structure-function abnormalities within key nodes of emotional homeostasis in ANX point to dlPFC as a potential neural node to target using neuromodulation tools.

Examine sex differences in autism spectrum disorder in school-aged children and adolescents with fluent language

There are noteworthy sex disparities in the prevalence of autism spectrum disorders (ASD), while findings regarding the sex differences in core symptoms are inconsistent. There are few relevant studies on sex differences in mainland China. This study was dedicated to a deeper understanding of the impact of sex differences on the clinical presentation of ASD with fluent language. We retrospectively studied 301 children with ASD (58 females) and utilized raw scores from the ADI-R and ADOS and the intelligence quotient (IQ) to measure symptomatology. Based on the Full-Scale IQ (FS-IQ), a binary split of average, above-average IQ (high-IQ), and below-average IQ (low IQ) occurs at 85. Across the entire sample, males and females are comparable in the FS-IQ, while males scored higher in the Perceptual Reasoning Index (PRI) (F = 7.812, p = 0.006). ADI-R did not find any statistically significant sex differences in the diagnostic cutoff score satisfaction or the raw domain scores. While a significant effect of sex on ADOS social affect domain scores was found in the total sample [λ = 0.970, partial η²  = 0.030, F (3,295) = 3.019, p = 0.030]. Tests of between-subjects effects revealed that males scored higher than females mainly in the ADOS reciprocal social interaction subcategory (partial η²  = 0.022, F = 6.563, p = 0.011). Stratified analysis revealed that the effect of sex on ADOS reciprocal social interaction subcategory scores only significant in the low-IQ children with ASD (partial η²  = 0.092, F = 10.088, p = 0.002). In general, overall cognitive functioning is similar across males and females with ASD, while males have a higher perceptual reasoning ability. Females with ASD are more likely to have comorbid intellectual impairment than males, and they could require additional intervention support. Autistic children with low IQs are more likely to exhibit sex differences in their core symptoms than children with high IQs. Intelligence plays a key role in sex-based differences in the core symptoms of ASD.

Predictive models demonstrate age-dependent association of subcortical volumes and cognitive measures

Whether brain matter volume is correlated with cognitive functioning and higher intelligence is controversial. We explored this relationship by analysis of data collected on 193 healthy young and older adults through the "Leipzig Study for Mind-Body-Emotion Interactions" (LEMON) study. Our analysis involved four cognitive measures: fluid intelligence, crystallized intelligence, cognitive flexibility, and working memory. Brain subregion volumes were determined by magnetic resonance imaging. We normalized each subregion volume to the estimated total intracranial volume and conducted training simulations to compare the predictive power of normalized volumes of large regions of the brain (i.e., gray matter, cortical white matter, and cerebrospinal fluid), normalized subcortical volumes, and combined normalized volumes of large brain regions and normalized subcortical volumes. Statistical tests showed significant differences in the performance accuracy and feature importance of the subregion volumes in predicting cognitive skills for young and older adults. Random forest feature selection analysis showed that cortical white matter was the key feature in predicting fluid intelligence in both young and older adults. In young adults, crystallized intelligence was best predicted by caudate nucleus, thalamus, pallidum, and nucleus accumbens volumes, whereas putamen, amygdala, nucleus accumbens, and hippocampus volumes were selected for older adults. Cognitive flexibility was best predicted by the caudate, nucleus accumbens, and hippocampus in young adults and caudate and amygdala in older adults. Finally, working memory was best predicted by the putamen, pallidum, and nucleus accumbens in the younger group, whereas amygdala and hippocampus volumes were predictive in the older group. Thus, machine learning predictive models demonstrated an age-dependent association between subcortical volumes and cognitive measures. These approaches may be useful in predicting the likelihood of age-related cognitive decline and in testing of approaches for targeted improvement of cognitive functioning in older adults.

What do neuroanatomical networks reveal about the ontology of human cognitive abilities?

Over the last decades, cognitive psychology has come to a fair consensus about the human intelligence ontological structure. However, it remains an open question whether anatomical properties of the brain support the same ontology. The present study explored the ontological structure derived from neuroanatomical networks associated with performance on 15 cognitive tasks indicating various abilities. Results suggest that the brain-derived (neurometric) ontology partly agrees with the cognitive performance-derived (psychometric) ontology complemented with interpretable differences. Moreover, the cortical areas associated with different inferred abilities are segregated, with little or no overlap. Nevertheless, these spatially segregated cortical areas are integrated via denser white matter structural connections as compared with the general brain connectome. The integration of ability-related cortical networks constitutes a neural counterpart to the psychometric construct of general intelligence, while the consistency and difference between psychometric and neurometric ontologies represent crucial pieces of knowledge for theory building, clinical diagnostics, and treatment.

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Psychometric and neurometric cognitive ontologies are partly equivalent

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Ability-related brain areas are ontologically segregated with little to no overlap

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However, ability-related brain areas are densely interconnected by fiber tracts

Brain Regions Identified as Being Associated with Verbal Reasoning through the Use of Imaging Regression via Internal Variation

Brain-imaging data have been increasingly used to understand intellectual disabilities. Despite significant progress in biomedical research, the mechanisms for most of the intellectual disabilities remain unknown. Finding the underlying neurological mechanisms has been proved difficult, especially in children due to the rapid development of their brains. We investigate verbal reasoning, which is a reliable measure of individuals' general intellectual abilities, and develop a class of high-order imaging regression models to identify brain subregions which might be associated with this specific intellectual ability. A key novelty of our method is to take advantage of spatial brain structures, and specifically the piecewise smooth nature of most imaging coefficients in the form of high-order tensors. Our approach provides an effective and urgently needed method for identifying brain subregions potentially underlying certain intellectual disabilities. The idea behind our approach is a carefully constructed concept called Internal Variation (IV). The IV employs tensor decomposition and provides a computationally feasible substitution for Total Variation (TV), which has been considered in the literature to deal with similar problems but is problematic in high order tensor regression. Before applying our method to analyze the real data, we conduct comprehensive simulation studies to demonstrate the validity of our method in imaging signal identification. Then, we present our results from the analysis of a dataset based on the Philadelphia Neurodevelopmental Cohort for which we preprocessed the data including re-orienting, bias-field correcting, extracting, normalizing and registering the magnetic resonance images from 978 individuals. Our analysis identified a subregion across the cingulate cortex and the corpus callosum as being associated with individuals' verbal reasoning ability, which, to the best of our knowledge, is a novel region that has not been reported in the literature. This finding is useful in further investigation of functional mechansims for verbal reasoning.

Network-wise surface-based morphometric insight into the cortical neural circuitry underlying irritability in adolescents

Previous studies examining structural brain correlates of irritability have taken a region-specific approach and have been relatively inconsistent. In a sample of adolescents with and without clinically impairing irritability, the current study examines: (i) cortical volume (CV) in canonical functional networks; (ii) the association between the CV of functional networks and severity of irritability; and (iii) the extent to which IQ mediates the association between structural abnormalities and severity of irritability. Structural MRI and IQ data were collected from 130 adolescents with high irritability (mean age = 15.54±1.83 years, 58 females, self-reported Affective Reactivity Index [ARI] ≥ 4) and 119 adolescents with low irritability (mean age = 15.10±1.93 years, 39 females, self-reported ARI < 4). Subject-specific network-wise CV was estimated after parcellating the whole brain into 17 previously reported functional networks. Our Multivariate Analysis of Covariance (MANCOVA) revealed that adolescents with high irritability had significantly reduced CV of the bilateral control and default-mode networks (p < 0.05) relative to adolescents with low irritability. Multiple regression analyses showed a significant negative association between the control network CV and the severity of irritability. Mediation analysis showed that IQ partially mediated the association between the control network CV and the severity of irritability. Follow-up analysis on subcortical volume (SCV) showed that adolescents with high irritability had reduced bilateral SCV within the amygdala relative to adolescents with low irritability. Reduced CV within bilateral control and default networks and reduced SCV within bilateral amygdala may represent core features of the pathophysiology of irritability. The current data also indicate the potential importance of a patient's IQ in determining how pathophysiology related to the control network is expressed.

Behavioral, Anatomical and Heritable Convergence of Affect and Cognition in Superior Frontal Cortex

Cognitive abilities and affective experience are key human traits that are interrelated in behavior and brain. Individual variation of cognitive and affective traits, as well as brain structure, has been shown to partly underlie genetic effects. However, to what extent affect and cognition have a shared genetic relationship with local brain structure is incompletely understood. Here we studied phenotypic and genetic correlations of cognitive and affective traits in behavior and brain structure (cortical thickness, surface area and subcortical volumes) in the pedigree-based Human Connectome Project sample (N = 1091). Both cognitive and affective trait scores were highly heritable and showed significant phenotypic correlation on the behavioral level. Cortical thickness in the left superior frontal cortex showed a phenotypic association with both affect and cognition. Decomposing the phenotypic correlations into genetic and environmental components showed that the associations were accounted for by shared genetic effects between the traits. Quantitative functional decoding of the left superior frontal cortex further indicated that this region is associated with cognitive and emotional functioning. This study provides a multi-level approach to study the association between affect and cognition and suggests a convergence of both in superior frontal cortical thickness.

Blue-Light Therapy Strengthens Resting-State Effective Connectivity within Default-Mode Network after Mild TBI

BACKGROUND

Emerging evidence suggests that post concussive symptoms, including mood changes, may be improved through morning blue-wavelength light therapy (BLT). However, the neurobiological mechanisms underlying these effects remain unknown. We hypothesize that BLT may influence the effective brain connectivity (EC) patterns within the default-mode network (DMN), particularly involving the medial prefrontal cortex (MPFC), which may contribute to improvements in mood.

METHODS

Resting-state functional MRI data were collected from 41 healthy-controls (HCs) and 28 individuals with mild traumatic brain injury (mTBI). Individuals with mTBI also underwent a diffusion-weighted imaging scan and were randomly assigned to complete either 6 weeks of daily morning BLT (N = 14) or amber light therapy (ALT; N = 14). Advanced spectral dynamic causal modeling (sDCM) and diffusion MRI connectometry were used to estimate EC patterns and structural connectivity strength within the DMN, respectively.

RESULTS

The sDCM analysis showed dominant connectivity pattern following mTBI (pre-treatment) within the hemisphere contralateral to the one observed for HCs. BLT, but not ALT, resulted in improved directional information flow (ie, EC) from the left lateral parietal cortex (LLPC) to MPFC within the DMN. The improvement in EC from LLPC to MPFC was accompanied by stronger structural connectivity between the 2 areas. For the BLT group, the observed improvements in function and structure were correlated (at a trend level) with changes in self-reported happiness.

CONCLUSIONS

The current preliminary findings provide empirical evidence that morning short-wavelength light therapy could be used as a novel alternative rehabilitation technique for mTBI.

TRIAL REGISTRY

The research protocols were registered in the ClinicalTrials.gov database (CT Identifiers NCT01747811 and NCT01721356).

Cortical and subcortical responsiveness to intensive adaptive working memory training: An MRI surface-based analysis

Working memory training (WMT) has been shown to have effects on cognitive performance, the precise effects and the underlying neurobiological mechanisms are, however, still a matter of debate. In particular, the impact of WMT on gray matter morphology is still rather unclear. In the present study, 59 healthy middle‐aged participants (age range 50–65 years) were pseudo‐randomly single‐blinded allocated to an 8‐week adaptive WMT or an 8‐week nonadaptive intervention. Before and after the intervention, high resolution magnetic resonance imaging (MRI) was performed and cognitive test performance was assessed in all participants. Vertex‐wise cortical volume, thickness, surface area, and cortical folding was calculated. Seven subcortical volumes of interest and global mean cortical thickness were also measured. Comparisons of symmetrized percent change (SPC) between groups were conducted to identify group by time interactions. Greater increases in cortical gyrification in bilateral parietal regions, including superior parietal cortex and inferior parietal lobule as well as precuneus, greater increases in cortical volume and thickness in bilateral primary motor cortex, and changes in surface area in bilateral occipital cortex (medial and lateral occipital cortex) were detected in WMT group after training compared to active controls. Structural training‐induced changes in WM‐related regions, especially parietal regions, might provide a better brain processing environment for higher WM load.

Association between emotional intelligence and effective brain connectome: A large-scale spectral DCM study

INTRODUCTION

Emotional Intelligence (EI) is a well-documented aspect of social and interpersonal functioning, but the underlying neural mechanisms for this capacity remain poorly understood. Here we used advanced brain connectivity techniques to explore the associations between EI and effective connectivity (EC) within four functional brain networks.

METHODS

The Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) was used to collect EI data from 55 healthy individuals (mean age = 30.56±8.3 years, 26 males). The MSCEIT comprises two area cores - experiential EI (T1) and strategic EI (T2). The T1 core included two sub-scales - perception of emotions (S1) and using emotions to facilitate thinking (S2), and the T2 core included two sub-scales - understanding of emotions (S3) and management of emotions (S4). All participants underwent structural and resting-state functional magnetic resonance imaging (rsfMRI) scans. The spectral dynamic causal modeling approach was implemented to estimate EC within four networks of interest - the default-mode network (DMN), dorsal attention network (DAN), control-execution network (CEN) and salience network (SN). The strength of EC within each network was correlated with the measures of EI, with correlations at p_FDR < 0.05 considered as significant.

RESULTS

There was no significant association between any of the measures of EI and EC strength within the DMN and DAN. For CEN, however, we found that there were significant negative associations between EC strength from the right anterior prefrontal cortex (RAPFC) to the left anterior prefrontal cortex (LAPFC) and both S2 and T1, and significant positive associations between EC strength from LAPFC to RAPFC and S2. EC strength from the right superior parietal cortex (SPC) to RAPFC also showed significant negative association with S4 and T2. For the SN, S3 showed significant negative association with EC strength from the right insula to RAPFC and significant positive association with EC strength from the left insula to dorsal anterior cingulate cortex (DACC).

CONCLUSIONS

We provide evidence that the negative ECs within the right hemisphere, and from the right to left hemisphere, and positive ECs within the left hemisphere and from the left to right hemisphere of CEN (involving bilateral frontal and right parietal region) and SN (involving right frontal, anterior cingulate and bilateral insula) play a significant role in regulating and processing emotions. These findings also suggest that measures of EC can be utilized as important biomarkers to better understand the underlying neural mechanisms of EI.

Linking Brain Biology to Intellectual Endowment: A Review on the Associations of Human Intelligence With Neuroimaging Data

Human intelligence has always been a fascinating subject for scientists. Since the inception of Spearman's general intelligence in the early 1900s, there has been significant progress towards characterizing different aspects of intelligence and its relationship with structural and functional features of the brain. In recent years, the invention of sophisticated brain imaging devices using Diffusion-Weighted Imaging (DWI) and functional Magnetic Resonance Imaging (fMRI) has allowed researchers to test hypotheses about neural correlates of intelligence in humans.This review summarizes recent findings on the associations of human intelligence with neuroimaging data. To this end, first, we review the literature that has related brain morphometry to intelligence. Next, we elaborate on the applications of DWI and restingstate fMRI on the investigation of intelligence. Then, we provide a survey of literature that has used multimodal DWI-fMRI to shed light on intelligence. Finally, we discuss the state-of-the-art of individualized prediction of intelligence from neuroimaging data and point out future strategies. Future studies hold promising outcomes for machine learning-based predictive frameworks using neuroimaging features to estimate human intelligence.

Sex chromosome aneuploidy alters the relationship between neuroanatomy and cognition

Sex chromosome aneuploidy (SCA) increases the risk for cognitive deficits, and confers changes in regional cortical thickness (CT) and surface area (SA). Neuroanatomical correlates of inter-individual variation in cognitive ability have been described in health, but are not well-characterized in SCA. Here, we modeled relationships between general cognitive ability (estimated using full-scale IQ [FSIQ] from Wechsler scales) and regional estimates of SA and CT (from structural MRI scans) in both aneuploid (28 XXX, 55 XXY, 22 XYY, 19 XXYY) and typically-developing euploid (79 XX, 85 XY) individuals. Results indicated widespread decoupling of normative anatomical-cognitive relationships in SCA: we found five regions where SCA significantly altered SA-FSIQ relationships, and five regions where SCA significantly altered CT-FSIQ relationships. The majority of areas were characterized by the presence of positive anatomy-IQ relationships in health, but no or slightly negative anatomy-IQ relationships in SCA. Disrupted anatomical-cognitive relationships generalized from the full cohort to karyotypically defined subcohorts (i.e., XX-XXX; XY-XYY; XY-XXY), demonstrating continuity across multiple supernumerary SCA conditions. As the first direct evidence of altered regional neuroanatomical-cognitive relationships in supernumerary SCA, our findings shed light on potential genetic and structural correlates of the cognitive phenotype in SCA, and may have implications for other neurogenetic disorders.

Sex differences in limbic network and risk-taking propensity in healthy individuals

Little is known about the structural neural substrates that may contribute to sex differences in risk-taking propensity (RTP). A close association between risk-seeking behavior and the emotional-regulation network led us to hypothesize that the sex differences in RTP would be associated with sex differences in brain morphometry of the limbic network (LN). We collected RTP scores using the bubble sheet version of the evaluation of risk (EVAR) scale and neuroanatomical data from 57 healthy individuals (29 males). The EVAR scale included sub-scales measuring recklessness/impulsivity, self-confidence, and need for control (NFC). We observed significant sex differences in NFC showing greater desire for control and dominance in males than females (multivariate analysis of covariance, MANCOVAN: p = .01). Morphometry analysis showed that it was only the right LN, which showed significant sex differences in normalized surface area, normalized cortical volume, and adjusted mean curvature index (females > males) at p < .01 (MANCOVAN, corrected for multiple comparisons). Correlation analysis showed that greater curvature of the right LN was significantly associated with lower desire for control in high-risk events (r = -.31, p = .02 at 95% CI of [-0.53, -0.05]). Our findings suggest that the normalized cortical measures could indicate specific sex differences in brain morphometry, particularly within the LN. The curvature index was the only differentiating factor for greater/lower propensity for risk-taking behavior in overall sample. Therefore, the LN and the curvature measures could be key biomarkers, which play an important role in predicting risk-taking behavior.

Initiation of the Hypothalamic-Pituitary-Gonadal Axis in Young Girls Undergoing Central Precocious Puberty Exerts Remodeling Effects on the Prefrontal Cortex

Central precocious puberty (CPP) has been shown to exert significant effects on psychosocial development. These early puberty-related hormones and psychosocial functional changes are considered to be associated with specific brain development. However, the biological mechanisms underlying the sculpting of human brain architecture and modulation of psychosocial transformation by puberty-related hormonal maturation remain elusive, especially during the early phase of CPP. The current investigation aims to specify the brain regions in which early hormone-related maturation effects occur during CPP and their relationships with psychological functions. 65 young girls (aged 4.3-8.0 years) underwent structural imaging on a 3T MR system, completed psychological tests and performed the gonadotropin-releasing hormone (GnRH) stimulation test to identify hormonal manifestations of hypothalamic-pituitary-gonadal axis (HPG axis) activation. Based on the GnRH test, 28 young girls were identified with CPP, whereas the other 37 girls were identified with non-central precocious puberty (NCPP). Cortical parameters were calculated and compared between the two groups after adjusting for age, weight, and height. Brain regions showing group differences were extracted and correlated with serum hormone levels and psychological parameters. The CPP girls showed thinner cortices primarily in the right rostral middle frontal cortex. This morphological difference was positively correlated with stimulated estradiol (E2) levels. Further, higher E2 levels were significantly associated with higher hyperactivity scores. Premature HPG axis activation in CPP girls at an early stage appears to exert remodeling effects on brain anatomy, primarily in the prefrontal cortex, which may affect psychological development following the emergence of robust changes in sex hormones.