Inattention and reaction time variability are linked to ventromedial prefrontal volume in adolescents

Background Neuroimaging studies of attention-deficit/hyperactivity disorder (ADHD) have most commonly reported volumetric abnormalities in the basal ganglia, cerebellum, and prefrontal cortices. Few studies have examined the relationship between ADHD symptomatology and brain structure in population-based samples. We investigated the relationship between dimensional measures of ADHD symptomatology, brain structure, and reaction time variability—an index of lapses in attention. We also tested for associations between brain structural correlates of ADHD symptomatology and maps of dopaminergic gene expression. Methods Psychopathology and imaging data were available for 1538 youths. Parent ratings of ADHD symptoms were obtained using the Development and Well-Being Assessment and the Strengths and Difficulties Questionnaire (SDQ). Self-reports of ADHD symptoms were assessed using the youth version of the SDQ. Reaction time variability was available in a subset of participants. For each measure, whole-brain voxelwise regressions with gray matter volume were calculated. Results Parent ratings of ADHD symptoms (Development and Well-Being Assessment and SDQ), adolescent self-reports of ADHD symptoms on the SDQ, and reaction time variability were each negatively associated with gray matter volume in an overlapping region of the ventromedial prefrontal cortex. Maps of DRD1 and DRD2 gene expression were associated with brain structural correlates of ADHD symptomatology. Conclusions This is the first study to reveal relationships between ventromedial prefrontal cortex structure and multi-informant measures of ADHD symptoms in a large population-based sample of adolescents. Our results indicate that ventromedial prefrontal cortex structure is a biomarker for ADHD symptomatology. These findings extend previous research implicating the default mode network and dopaminergic dysfunction in ADHD

Bayesian causal network modeling suggests adolescent cannabis use accelerates prefrontal cortical thinning

International audienceAbstract While there is substantial evidence that cannabis use is associated with differences in human brain development, most of this evidence is correlational in nature. Bayesian causal network (BCN) modeling attempts to identify probable causal relationships in correlational data using conditional probabilities to estimate directional associations between a set of interrelated variables. In this study, we employed BCN modeling in 637 adolescents from the IMAGEN study who were cannabis naïve at age 14 to provide evidence that the accelerated prefrontal cortical thinning found previously in adolescent cannabis users by Albaugh et al. [1] is a result of cannabis use causally affecting neurodevelopment. BCNs incorporated data on cannabis use, prefrontal cortical thickness, and other factors related to both brain development and cannabis use, including demographics, psychopathology, childhood adversity, and other substance use. All BCN algorithms strongly suggested a directional relationship from adolescent cannabis use to accelerated cortical thinning. While BCN modeling alone does not prove a causal relationship, these results are consistent with a body of animal and human research suggesting that adolescent cannabis use adversely affects brain development

Bandelier archaeological excavation project : summer 1990 excavations at Burnt Mesa Pueblo and Casa del Rito

Includes maps, figures, and photos. Best copy available. Edited by Timothy A. Kohler and Matthew J. Root with contributions by David Albaugh, Michele Gray, Douglas R. Harro, Timothy A. Kohler, Angela R. Linse, Meredith H. Matthews, Michael V. Reilly, Matthew J. Root and W. Nicholas Trierweiler.Museum of Anthropolog

Bayesian causal network modeling suggests adolescent cannabis use accelerates prefrontal cortical thinning.

While there is substantial evidence that cannabis use is associated with differences in human brain development, most of this evidence is correlational in nature. Bayesian causal network (BCN) modeling attempts to identify probable causal relationships in correlational data using conditional probabilities to estimate directional associations between a set of interrelated variables. In this study, we employed BCN modeling in 637 adolescents from the IMAGEN study who were cannabis naïve at age 14 to provide evidence that the accelerated prefrontal cortical thinning found previously in adolescent cannabis users by Albaugh et al. [1] is a result of cannabis use causally affecting neurodevelopment. BCNs incorporated data on cannabis use, prefrontal cortical thickness, and other factors related to both brain development and cannabis use, including demographics, psychopathology, childhood adversity, and other substance use. All BCN algorithms strongly suggested a directional relationship from adolescent cannabis use to accelerated cortical thinning. While BCN modeling alone does not prove a causal relationship, these results are consistent with a body of animal and human research suggesting that adolescent cannabis use adversely affects brain development

Examination of the association between exposure to childhood maltreatment and brain structure in young adults: a machine learning analysis

Exposure to maltreatment during childhood is associated with structural changes throughout the brain. However, the structural differences that are most strongly associated with maltreatment remain unclear given the limited number of whole-brain studies. The present study used machine learning to identify if and how brain structure distinguished young adults with and without a history of maltreatment. Young adults (ages 18–21, n = 384) completed an assessment of childhood trauma exposure and a structural MRI as part of the IMAGEN study. Elastic net regularized regression was used to identify the structural features that identified those with a history of maltreatment. A generalizable model that included 7 cortical thicknesses, 15 surface areas, and 5 subcortical volumes was identified (area under the receiver operating characteristic curve = 0.71, p < 0.001). Those with a maltreatment history had reduced surface areas and cortical thicknesses primarily in fronto-temporal regions. This group also had larger cortical thicknesses in occipital regions and surface areas in frontal regions. The results suggest childhood maltreatment is associated with multiple measures of structure throughout the brain. The use of a large sample without exposure to adulthood trauma provides further evidence for the unique contribution of childhood trauma to brain structure. The identified regions overlapped with regions associated with psychopathology in adults with maltreatment histories, which offers insights as to how these disorders manifest

Association of cannabis use during adolescence with neurodevelopment.

Importance: Animal studies have shown that the adolescent brain is sensitive to disruptions in endocannabinoid signaling, resulting in altered neurodevelopment and lasting behavioral effects. However, few studies have investigated ties between cannabis use and adolescent brain development in humans. Question: To what extent is cannabis use associated with magnetic resonance imaging–measured cerebral cortical thickness development during adolescence? Findings: In this cohort study, linear mixed-effects model analysis using 1598 magnetic resonance images from 799 participants revealed that cannabis use was associated with accelerated age-related cortical thinning from 14 to 19 years of age in predominantly prefrontal regions. The spatial pattern of cannabis-related cortical thinning was significantly associated with a positron emission tomography–assessed map of cannabinoid 1 receptor availability. Meaning: Results suggest that cannabis use during middle to late adolescence may be associated with altered cerebral cortical development, particularly in regions rich in cannabinoid 1 receptors

Brain structural covariance network differences in adults with alcohol dependence and heavy drinking adolescents

Background and aimsGraph theoretic analysis of structural covariance networks (SCN) provides an assessment of brain organization that has not yet been applied to alcohol dependence (AD). We estimated whether SCN differences are present in adults with AD and heavy drinking adolescents at age 19 and age 14, prior to substantial exposure to alcohol.DesignCross-sectional sample of adults and a cohort of adolescents. Correlation matrices for cortical thicknesses across 68 regions were summarized with graph theoretic metrics.Setting and participants745 adults with AD and 979 non-dependent controls from 24 sites curated by the ENIGMA-Addiction working group, and 297 hazardous drinking adolescents and 594 controls at age 14 and 19 from the IMAGEN study, all from Europe.MeasurementsMetrics of network segregation (modularity, clustering coefficient, and local efficiency) and integration (average shortest path length and global efficiency).FindingsThe younger AD adults had lower network segregation and higher integration relative to non-dependent controls. Compared with controls, the hazardous drinkers at age 19 showed lower modularity (Area-under-the-curve [AUC] difference = -0.0142, confidence interval [CI] 95% [-0.1333, 0.0092]; p-value = 0.017), clustering coefficient (AUC difference = -0.0164 CI 95% [-0.1456, 0.0043], p-value = 0.008), and local efficiency (AUC difference = -0.0141 CI 95% [-0.0097, 0.0034], p-value = 0.010), as well as lower average shortest path length (AUC difference = -0.0405 CI 95% [-0.0392, 0.0096]; p-value = 0.021) and higher global efficiency (AUC difference = 0.0044 CI 95% [-0.0011, 0.0043]; p-value = 0.023). The same pattern was present at age 14 with lower clustering coefficient (AUC difference = -0.0131 CI 95% [-0.1304, 0.0033]; p-value = 0.024), lower average shortest path length (AUC difference = -0.0362 CI 95% [-0.0334, 0.0118]; p-value = 0.019), and higher global efficiency (AUC difference = 0.0035 CI 95% [-0.0011, 0.0038]; p-value = 0.048).ConclusionsCross-sectional analyses indicate a specific structural covariance network profile is an early marker of alcohol dependence in adults. Similar effects in a cohort of heavy drinking adolescents, observed both at age 19 and prior to substantial alcohol exposure at age 14, suggest that this pattern may be a pre-existing risk factor for problematic drinking