Maternal smoking during pregnancy and cortical structure in children with attention-deficit/hyperactivity disorder.

Maternal smoking during pregnancy (MSDP) is considered a risk factor for ADHD. While the mechanisms underlying this association are not well understood, MSDP may impact the developing brain in ways that lead to ADHD. Here, we investigated the effect of prenatal smoking exposure on cortical brain structures in children with ADHD using two methods of assessing prenatal exposure: maternal recall and epigenetic typing. Exposure groups were defined according to: (1) maternal recall (+MSDP: n = 24; -MSDP: n = 85) and (2) epigenetic markers (EM) (+EM: n = 14 -EM: n = 21). CIVET-1.1.12 and RMINC were used to acquire cortical brain measurements and perform statistical analyses, respectively. The vertex with highest significance was tested for association with Continuous Performance Test (CPT) dimensions. While no differences of brain structures were identified between +MSDP and -MSDP, +EM children (n = 10) had significantly smaller surface area in the right orbitofrontal cortex (ROFc), middle temporal cortex (RTc) and parahippocampal gyrus (RPHg) (15% FDR) compared to -EM children (n = 20). Cortical surface area in the RPHg significantly correlated with CPT commission errors T-scores. This study suggests that molecular markers may better define exposure to environmental risks, as compared to human recall.

Cumulative exposure to ADHD medication is inversely related to hippocampus subregional volume in children.

BACKGROUND: Although there is some evidence for a normalization of brain structure following exposure to ADHD medication, literature on the effects of duration and dose of continued use on the brain is scarce. Here, we investigated the association between cumulative exposure to medication (range 1 week to 4.69 years) and cortical structures and subcortical volumes in a clinical sample of children with ADHD taking medication (n = 109). To the best of our knowledge, this is the first structural MRI study investigating the effects of cumulative exposure to medication on subregional volumes in children treated for ADHD. METHODS: Cumulative exposure to ADHD medication (CEM) was defined as the product of duration on medication (days) and dose (mg/day), yielding the area under the curve (total mg). Cortical thickness and surface area measurements (CIVET-1.1.12), and subcortical volumes in 51 regions (MAGeT-Brain) were analyzed using general linear modelling. RESULTS: Significant effects of CEM were found in two subregions of the left hippocampus, the CA1 (df = 95; q = 0.003) and the strata radiatum/lacunosum/moleculare (df = 95; q = 0.003). Specifically, higher CEM was associated with smaller volumes within these subregions. No effects of medication exposure were detected on cortical thickness or surface area. CONCLUSIONS: Although this study is cross-sectional, the results found within this sample of children show that prolonged ADHD medication use at higher doses is significantly associated with smaller hippocampus volumes in specific subregions. More research is required to determine whether these results are reproduced in other samples of children of ADHD, and further, whether these are beneficial or off-target effects of the medication.

Maturational trajectories of pericortical contrast in typical brain development.

In the last few years, a significant amount of work has aimed to characterize maturational trajectories of cortical development. The role of pericortical microstructure putatively characterized as the gray-white matter contrast (GWC) at the pericortical gray-white matter boundary and its relationship to more traditional morphological measures of cortical morphometry has emerged as a means to examine finer grained neuroanatomical underpinnings of cortical changes. In this work, we characterize the GWC developmental trajectories in a representative sample (n = 394) of children and adolescents (~4 to ~22 years of age), with repeated scans (1-3 scans per subject, total scans n = 819). We tested whether linear, quadratic, or cubic trajectories of contrast development best described changes in GWC. A best-fit model was identified vertex-wise across the whole cortex via the Akaike Information Criterion (AIC). GWC across nearly the whole brain was found to significantly change with age. Cubic trajectories were likeliest for 63% of vertices, quadratic trajectories were likeliest for 20% of vertices, and linear trajectories were likeliest for 16% of vertices. A main effect of sex was observed in some regions, where males had a higher GWC than females. However, no sex by age interactions were found on GWC. In summary, our results suggest a progressive decrease in GWC at the pericortical boundary throughout childhood and adolescence. This work contributes to efforts seeking to characterize typical, healthy brain development and, by extension, can help elucidate aberrant developmental trajectories.

Genome-wide analysis of gene dosage in 24,092 individuals estimates that 10,000 genes modulate cognitive ability.

Genomic copy number variants (CNVs) are routinely identified and reported back to patients with neuropsychiatric disorders, but their quantitative effects on essential traits such as cognitive ability are poorly documented. We have recently shown that the effect size of deletions on cognitive ability can be statistically predicted using measures of intolerance to haploinsufficiency. However, the effect sizes of duplications remain unknown. It is also unknown if the effect of multigenic CNVs are driven by a few genes intolerant to haploinsufficiency or distributed across tolerant genes as well. Here, we identified all CNVs > 50 kilobases in 24,092 individuals from unselected and autism cohorts with assessments of general intelligence. Statistical models used measures of intolerance to haploinsufficiency of genes included in CNVs to predict their effect size on intelligence. Intolerant genes decrease general intelligence by 0.8 and 2.6 points of intelligence quotient when duplicated or deleted, respectively. Effect sizes showed no heterogeneity across cohorts. Validation analyses demonstrated that models could predict CNV effect sizes with 78% accuracy. Data on the inheritance of 27,766 CNVs showed that deletions and duplications with the same effect size on intelligence occur de novo at the same frequency. We estimated that around 10,000 intolerant and tolerant genes negatively affect intelligence when deleted, and less than 2% have large effect sizes. Genes encompassed in CNVs were not enriched in any GOterms but gene regulation and brain expression were GOterms overrepresented in the intolerant subgroup. Such pervasive effects on cognition may be related to emergent properties of the genome not restricted to a limited number of biological pathways.

Using a simulation centre to evaluate preliminary acceptability and impact of an artificial intelligence-powered clinical decision support system for depression treatment on the physician-patient interaction.

BACKGROUND: Recently, artificial intelligence-powered devices have been put forward as potentially powerful tools for the improvement of mental healthcare. An important question is how these devices impact the physician-patient interaction. AIMS: Aifred is an artificial intelligence-powered clinical decision support system (CDSS) for the treatment of major depression. Here, we explore the use of a simulation centre environment in evaluating the usability of Aifred, particularly its impact on the physician-patient interaction. METHOD: Twenty psychiatry and family medicine attending staff and residents were recruited to complete a 2.5-h study at a clinical interaction simulation centre with standardised patients. Each physician had the option of using the CDSS to inform their treatment choice in three 10-min clinical scenarios with standardised patients portraying mild, moderate and severe episodes of major depression. Feasibility and acceptability data were collected through self-report questionnaires, scenario observations, interviews and standardised patient feedback. RESULTS: All 20 participants completed the study. Initial results indicate that the tool was acceptable to clinicians and feasible for use during clinical encounters. Clinicians indicated a willingness to use the tool in real clinical practice, a significant degree of trust in the system's predictions to assist with treatment selection, and reported that the tool helped increase patient understanding of and trust in treatment. The simulation environment allowed for the evaluation of the tool's impact on the physician-patient interaction. CONCLUSIONS: The simulation centre allowed for direct observations of clinician use and impact of the tool on the clinician-patient interaction before clinical studies. It may therefore offer a useful and important environment in the early testing of new technological tools. The present results will inform further tool development and clinician training materials.

Genetic correlations and genome-wide associations of cortical structure in general population samples of 22,824 adults.

Cortical thickness, surface area and volumes vary with age and cognitive function, and in neurological and psychiatric diseases. Here we report heritability, genetic correlations and genome-wide associations of these cortical measures across the whole cortex, and in 34 anatomically predefined regions. Our discovery sample comprises 22,824 individuals from 20 cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank. We identify genetic heterogeneity between cortical measures and brain regions, and 160 genome-wide significant associations pointing to wnt/ß-catenin, TGF-ß and sonic hedgehog pathways. There is enrichment for genes involved in anthropometric traits, hindbrain development, vascular and neurodegenerative disease and psychiatric conditions. These data are a rich resource for studies of the biological mechanisms behind cortical development and aging.

Global and Regional Development of the Human Cerebral Cortex: Molecular Architecture and Occupational Aptitudes.

We have carried out meta-analyses of genome-wide association studies (GWAS) (n = 23 784) of the first two principal components (PCs) that group together cortical regions with shared variance in their surface area. PC1 (global) captured variations of most regions, whereas PC2 (visual) was specific to the primary and secondary visual cortices. We identified a total of 18 (PC1) and 17 (PC2) independent loci, which were replicated in another 25 746 individuals. The loci of the global PC1 included those associated previously with intracranial volume and/or general cognitive function, such as MAPT and IGF2BP1. The loci of the visual PC2 included DAAM1, a key player in the planar-cell-polarity pathway. We then tested associations with occupational aptitudes and, as predicted, found that the global PC1 was associated with General Learning Ability, and the visual PC2 was associated with the Form Perception aptitude. These results suggest that interindividual variations in global and regional development of the human cerebral cortex (and its molecular architecture) cascade-albeit in a very limited manner-to behaviors as complex as the choice of one's occupation.

Maternal antenatal depression and child mental health: Moderation by genomic risk for attention-deficit/hyperactivity disorder.

Maternal antenatal depression strongly influences child mental health but with considerable inter-individual variation that is, in part, linked to genotype. The challenge is to effectively capture the genotypic influence. We outline a novel approach to describe genomic susceptibility to maternal antenatal depression focusing on child emotional/behavioral difficulties. Two cohorts provided measures of maternal depression, child genetic variation, and child mental health symptoms. We constructed a conventional polygenic risk score (PRS) for attention-deficit/hyperactivity disorder (ADHD) (PRSADHD) that significantly moderated the association between maternal antenatal depression and internalizing problems at 60 months (p = 2.94 × 10-4, R2 = .18). We then constructed an interaction PRS (xPRS) based on a subset of those single nucleotide polymorphisms from the PRSADHD that most accounted for the moderation of the association between maternal antenatal depression and child outcome. The interaction between maternal antenatal depression and this xPRS accounted for a larger proportion of the variance in child emotional/behavioral problems than models based on any PRSADHD (p = 5.50 × 10-9, R2 = .27), with similar findings in the replication cohort. The xPRS was significantly enriched for genes involved in neuronal development and synaptic function. Our study illustrates a novel approach to the study of genotypic moderation on the impact of maternal antenatal depression on child mental health and highlights the utility of the xPRS approach. These findings advance our understanding of individual differences in the developmental origins of mental health.

Cortical surface area variations within the dorsolateral prefrontal cortex are better predictors of future cognitive performance than fluid ability and working memory / Las variaciones de superficie cortical en la corteza dorsolateral prefrontal predicen mejor el futuro desempeño cognitivo que la inteligencia fluida y la memoria operativa

Background: Are cognitive and biological variables useful for predicting future behavioral outcomes?. Method: In two independent groups, we measured a set of cognitive (fluid and crystallized intelligence, working memory, and attention control) and biological (cortical thickness and cortical surface area) variables on two occasions separated by six months, to predict behavioral outcomes of interest (performance on an adaptive version of the n-back task) measured twelve and eighteen months later. We followed three stages: discovery, validation, and generalization. In the discovery stage, cognitive/biological variables and the behavioral outcome of interest were assessed in a group of individuals (in-sample). In the validation stage, the cognitive and biological variables were related with a parallel version of the behavioral outcome assessed several months later. In the generalization stage, the validation findings were tested in an independent group of individuals (out-of-sample). Results: The key finding revealed that cortical surface area variations within the right dorsolateral prefrontal cortex predict the behavioral outcome of interest in both groups, whereas the cognitive variables failed to show reliable predictive validity. Conclusions: Individual differences in biological variables might predict future behavioral outcomes better than cognitive variables concurrently correlated with these behavioral outcomes

Antecedentes: ¿Predicen las variables cognitivas y biológicas el futuro desempeño cognitivo? Método: en dos grupos independientes de participantes se miden variables cognitivas (inteligencia fluida y cristalizada, memoria operativa y control atencional) y biológicas (grosor y superficie cortical) en dos ocasiones separadas por seis meses, para predecir el desempeño en la tarea n-back valorado doce y dieciocho meses después. Se completan tres etapas: descubrimiento, validación y generalización. En la de descubrimiento se valoran en un grupo de individuos las variables cognitivas/biológicas y el desempeño a predecir. En la de validación, se relacionan las mismas variables con una versión paralela de la n-back completada meses después. En la de generalización, los resultados de la validación se replican en un grupo independiente de individuos. Resultados: las variaciones de superficie cortical en la corteza dorsolateral prefrontal derecha predicen el desempeño cognitivo en los dos grupos independientes de individuos, mientras que las variables cognitivas no contribuyen a la predicción del desempeño futuro. Conclusiones: las diferencias individuales en determinadas variables biológicas predicen el desempeño cognitivo mejor que las variables cognitivas que correlacionan concurrentemente con ese desempeño

Cortical surface area variations within the dorsolateral prefrontal cortex are better predictors of future cognitive performance than fluid ability and working memory.

BACKGROUND: Are cognitive and biological variables useful for predicting future behavioral outcomes? METHOD: In two independent groups, we measured a set of cognitive (fluid and crystallized intelligence, working memory, and attention control) and biological (cortical thickness and cortical surface area) variables on two occasions separated by six months, to predict behavioral outcomes of interest (performance on an adaptive version of the n-back task) measured twelve and eighteen months later. We followed three stages: discovery, validation, and generalization. In the discovery stage, cognitive/biological variables and the behavioral outcome of interest were assessed in a group of individuals (in-sample). In the validation stage, the cognitive and biological variables were related with a parallel version of the behavioral outcome assessed several months later. In the generalization stage, the validation findings were tested in an independent group of individuals (out-of-sample). RESULTS: The key finding revealed that cortical surface area variations within the right dorsolateral prefrontal cortex predict the behavioral outcome of interest in both groups, whereas the cognitive variables failed to show reliable predictive validity. CONCLUSIONS: Individual differences in biological variables might predict future behavioral outcomes better than cognitive variables concurrently correlated with these behavioral outcomes.

Time-lagged associations between cognitive and cortical development from childhood to early adulthood.

Throughout childhood and adolescence, humans experience marked changes in cortical structure and cognitive ability. Cortical thickness and surface area, in particular, have been associated with cognitive ability. Here we ask the question: What are the time-related associations between cognitive changes and cortical structure maturation. Identifying a developmental sequence requires multiple measurements of these variables from the same individuals across time. This allows capturing relations among the variables and, thus, finding whether (a) developmental cognitive changes follow cortical structure maturation, (b) cortical structure maturation follows cognitive changes, or (c) both processes influence each other over time. Four hundred and thiry children and adolescents (age range = 6.01-22.28 years) completed the Wechsler Abbreviated Scale of Intelligence battery and were MRI scanned at 3 time points separated by ≈2 years (Mage T1 = 10.60, SD = 3.58; Mage T2 = 12.63, SD = 3.62; Mage T3 = 14.49, SD = 3.55). Latent change score models were applied to quantify age-related relationships among the variables of interest. Our results indicate that cortical and cognitive changes related to each other reciprocally. Specifically, the magnitude or rate of the change in each variable at any occasion-and not the previous level-was predictive of later changes. These results were replicated for brain regions selected according to the coordinates identified in the Basten et al.'s (2015) meta-analysis, to the parieto-frontal integration theory (Jung & Haier, 2007) and to the whole cortex. Potential implications regarding brain plasticity and cognitive enhancement are discussed. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Dissecting genetic cross-talk between ADHD and other neurodevelopmental disorders: Evidence from behavioural, pharmacological and brain imaging investigations.

Several epidemiological and genetic studies have provided evidence of an overlap between neurodevelopmental disorders. However, the details of the etiological pathways remain to be elucidated. In this study, we garnered the findings of previous GWAS, conducted with schizophrenia and bipolar disorder. We conducted an exploratory study to examine the association between these SNPs and quantitative clinical/ behavioural/ cognitive/ structural brain parameters, as well as response to treatment with a fixed dose of methylphenidate, in a relatively large sample of children with ADHD. Family-based association tests were conducted with nine tag SNPs with 602 nuclear families. In addition, structural magnetic resonance imaging (sMRI) was conducted in a subset of children with ADHD (n = 76). Of the 9 tag SNPs examined, rs1602565 showed a significant association with ADHD, several dimensional measures and response to treatment. An association was also observed between rs1006737 (CACNA1C) and performance IQ. In addition, significant reductions in cortical thickness measurements were observed with the risk allele in rs1006737. These results provide preliminary evidence for putative shared genetic vulnerability between childhood ADHD and other neurodevelopmental disorders.

Association between carotid atheroma and cerebral cortex structure at age 73 years.

OBJECTIVE: To examine the relationship between carotid atherosclerosis and cerebral cortical thickness and investigate whether cortical thickness mediates the association between carotid atheroma and relative cognitive decline. METHODS: We assessed 554 community-dwelling subjects (male/female: 296/258) from the Lothian Birth Cohort 1936 who underwent brain magnetic resonance imaging and carotid Doppler ultrasound studies at age 73 years. The relationship between carotid atherosclerosis markers (internal carotid artery stenosis, intima-media thickness, velocity, pulsatility, and resistivity indexes) and vertex-wide cerebral cortical thickness was examined cross-sectionally, controlling for gender, extensive vascular risk factors (VRFs), and intelligence quotient at age 11 (IQ-11). We also determined the association between carotid stenosis and a composite measure of fluid intelligence at age 73 years. A mediation model was applied to examine whether cortical thickness mediated the relationship between carotid stenosis and cognitive function. RESULTS: A widespread negative association was identified between carotid stenosis (median = 15%) and cerebral cortical thickness at age 73 years, independent of the side of carotid stenosis, other carotid measures, VRFs, and IQ-11. This association increased in an almost dose-response relationship from mild to severe degrees of carotid stenosis, across the anterior and posterior circulation territories. A negative association was also noted between carotid stenosis and fluid intelligence (standardized beta coefficient = -0.151, p = 0.001), which appeared partly (approximately 22%) mediated by carotid stenosis-related thinning of the cerebral cortex. INTERPRETATION: The findings suggest that carotid stenosis represents a marker of processes that accelerate aging of the cerebral cortex and cognition that is in part independent of measurable VRFs. Cortical thinning within the anterior and posterior circulation territories partially mediated the relationship between carotid atheroma and fluid intelligence. Ann Neurol 2018;84:576-587.

Role of DHEA and cortisol in prefrontal-amygdalar development and working memory.

There is accumulating evidence that both dehydroepiandrosterone (DHEA) and cortisol play an important role in regulating physical maturation and brain development. High DHEA levels tend to be associated with neuroprotective and indirect anabolic effects, while high cortisol levels tend to be associated with catabolic and neurotoxic properties. Previous literature has linked the ratio between DHEA and cortisol levels (DC ratio) to disorders of attention, emotional regulation and conduct, but little is known as to the relationship between this ratio and brain development. Due to the extensive links between the amygdala and the cortex as well as the known amygdalar involvement in emotional regulation, we examined associations between DC ratio, structural covariance of the amygdala with whole-brain cortical thickness, and validated report-based measures of attention, working memory, internalizing and externalizing symptoms, in a longitudinal sample of typically developing children and adolescents 6-22 years of age. We found that DC ratio predicted covariance between amygdalar volume and the medial anterior cingulate cortex, particularly in the right hemisphere. DC ratio had a significant indirect effect on working memory through its impact on prefrontal-amygdalar covariance, with higher DC ratios associated with a prefrontal-amygdalar covariance pattern predictive of higher scores on a measure of working memory. Taken together, these findings support the notion, as suggested by animal and in vitro studies, that there are opposing effects of DHEA and cortisol on brain development in humans, and that these effects may especially target prefrontal-amygdalar development and working memory, in a lateralized fashion.

Widespread associations between trait conscientiousness and thickness of brain cortical regions.

The neural correlates of human personality have been of longstanding interest; however, most studies in the field have relied on modest sample sizes and few replicable results have been reported to date. We investigated relationships between personality and brain gray matter in a sample of generally healthy, older (mean age 73 years) adults from Scotland drawn from the Lothian Birth Cohort 1936. Participants (N = 578) completed a brain MRI scan and self-reported Big Five personality trait measures. Conscientiousness trait scores were positively related to brain cortical thickness in a range of regions, including bilateral parahippocampal gyrus, bilateral fusiform gyrus, left cingulate gyrus, right medial orbitofrontal cortex, and left dorsomedial prefrontal cortex. These associations - most notably in frontal regions - were modestly-to-moderately attenuated by the inclusion of biomarker variables assessing allostatic load and smoking status. None of the other personality traits showed robust associations with brain cortical thickness, nor did we observe any personality trait associations with cortical surface area and gray matter volume. These findings indicate that brain cortical thickness is associated with conscientiousness, perhaps partly accounted for by allostatic load and smoking status.

Brain structural differences between 73- and 92-year olds matched for childhood intelligence, social background, and intracranial volume.

Fully characterizing age differences in the brain is a key task for combating aging-related cognitive decline. Using propensity score matching on 2 independent, narrow-age cohorts, we used data on childhood cognitive ability, socioeconomic background, and intracranial volume to match participants at mean age of 92 years (n = 42) to very similar participants at mean age of 73 years (n = 126). Examining a variety of global and regional structural neuroimaging variables, there were large differences in gray and white matter volumes, cortical surface area, cortical thickness, and white matter hyperintensity volume and spatial extent. In a mediation analysis, the total volume of white matter hyperintensities and total cortical surface area jointly mediated 24.9% of the relation between age and general cognitive ability (tissue volumes and cortical thickness were not significant mediators in this analysis). These findings provide an unusual and valuable perspective on neurostructural aging, in which brains from the 8th and 10th decades of life differ widely despite the same cognitive, socioeconomic, and brain-volumetric starting points.

Processing speed and the relationship between Trail Making Test-B performance, cortical thinning and white matter microstructure in older adults.

Part B of the Trail Making Test (TMT-B) is widely used as a quick and easy to administer measure of executive dysfunction. The current study investigated the relationships between TMT-B performance, brain volumes, cortical thickness and white matter water diffusion characteristics in a large sample of older participants, before and after controlling for processing speed. Four hundred and eleven healthy, community-dwelling older adults who were all born in 1936 were assessed on TMT-B, 5 tests of processing speed, and provided contemporaneous structural and diffusion MRI data. Significant relationships were found between slower TMT-B completion times and thinner cortex in the frontal, temporal and inferior parietal regions as well as the Sylvian fissure/insula. Slower TMT-B completion time was also significantly associated with poorer white matter microstructure of the left anterior thalamic radiation, and the right uncinate fasciculus. The majority of these associations were markedly attenuated when additionally controlling for processing speed. These data suggest that individual differences in processing speed contribute to the associations between TMT-B completion time and the grey and white matter structure of older adults.

Enhanced structural connectivity within a brain sub-network supporting working memory and engagement processes after cognitive training.

The structural connectome provides relevant information about experience and training-related changes in the brain. Here, we used network-based statistics (NBS) and graph theoretical analyses to study structural changes in the brain as a function of cognitive training. Fifty-six young women were divided in two groups (experimental and control). We assessed their cognitive function before and after completing a working memory intervention using a comprehensive battery that included fluid and crystallized abilities, working memory and attention control, and we also obtained structural MRI images. We acquired and analyzed diffusion-weighted images to reconstruct the anatomical connectome and we computed standardized changes in connectivity as well as group differences across time using NBS. We also compared group differences relying on a variety of graph-theory indices (clustering, characteristic path length, global and local efficiency and strength) for the whole network as well as for the sub-network derived from NBS analyses. Finally, we calculated correlations between these graph indices and training performance as well as the behavioral changes in cognitive function. Our results revealed enhanced connectivity for the training group within one specific network comprised of nodes/regions supporting cognitive processes required by the training (working memory, interference resolution, inhibition, and task engagement). Significant group differences were also observed for strength and global efficiency indices in the sub-network detected by NBS. Therefore, the connectome approach is a valuable method for tracking the effects of cognitive training interventions across specific sub-networks. Moreover, this approach allowsfor the computation of graph theoretical network metricstoquantifythetopological architecture of the brain networkdetected. The observed structural brain changes support the behavioral results reported earlier (see Colom, Román, et al., 2013).