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Purpose To introduce a study design and statistical analysis framework to assess the repeatability, reproducibility, and minimal detectable changes (MDCs) of metabolite concentrations determined by in vivo MRS. Methods An unbalanced nested study design was chosen to acquire in vivo MRS data within different repeatability and reproducibility scenarios. A spin-echo, full-intensity acquired localized (SPECIAL) sequence was employed at 7 T utlizing three different inversion pulses: a hyperbolic secant (HS), a gradient offset independent adiabaticity (GOIA), and a wideband, uniform rate, smooth truncation (WURST) pulse. Metabolite concentrations, Cramer-Rao lower bounds (CRLBs) and coefficients of variation (CVs) were calculated. Both Bland-Altman analysis and a restricted maximum-likelihood estimation (REML) analysis were performed to estimate the different variance contributions of the repeatability and reproducibility of the measured concentration. A Bland-Altmann analysis of the spectral shape was performed to assess the variance of the spectral shape, independent of quantification model influences. Results For the used setup, minimal detectable changes of brain metabolite concentrations were found to be between 0.40 µmol/g and 2.23 µmol/g. CRLBs account for only 16 % to 74 % of the total variance of the metabolite concentrations. The application of gradient-modulated inversion pulses in SPECIAL led to slightly improved repeatability, but overall reproducibility appeared to be limited by differences in positioning, calibration, and other day-to-day variations throughout different sessions. Conclusion A framework is introduced to estimate the precision of metabolite concentrations obtained by MRS in vivo, and the minimal detectable changes for 13 metabolite concentrations measured at 7 T using SPECIAL are obtained.

Urbanicity is a growing environmental challenge for mental health. Here, we investigate correlations of urbanicity with brain structure and function, neuropsychology and mental illness symptoms in young people from China and Europe (total n = 3,867). We developed a remote-sensing satellite measure (UrbanSat) to quantify population density at any point on Earth. UrbanSat estimates of urbanicity were correlated with brain volume, cortical surface area and brain network connectivity in the medial prefrontal cortex and cerebellum. UrbanSat was also associated with perspective-taking and depression symptoms, and this was mediated by neural variables. Urbanicity effects were greatest when urban exposure occurred in childhood for the cerebellum, and from childhood to adolescence for the prefrontal cortex. As UrbanSat can be generalized to different geographies, it may enable assessments of correlations of urbanicity with mental illness and resilience globally. Xu et al. show that satellite-measured urbanicity (living in a densely populated area) is correlated with brain volume, cortical surface area and brain network connectivity in a sample of 3,867 people from China and Europe.

With the growth of decentralized/federated analysis approaches in neuroimaging, the opportunities to study brain disorders using data from multiple sites has grown multi-fold. One such initiative is the Neuromark, a fully automated spatially constrained independent component analysis (ICA) that is used to link brain network abnormalities among different datasets, studies, and disorders while leveraging subject-specific networks. In this study, we implement the neuromark pipeline in COINSTAC, an open-source neuroimaging framework for collaborative/decentralized analysis. Decentralized exploratory analysis of nearly 2000 resting-state functional magnetic resonance imaging datasets collected at different sites across two cohorts and co-located in different countries was performed to study the resting brain functional network connectivity changes in adolescents who smoke and consume alcohol. Results showed hypoconnectivity across the majority of networks including sensory, default mode, and subcortical domains, more for alcohol than smoking, and decreased low frequency power. These findings suggest that global reduced synchronization is associated with both tobacco and alcohol use. This proof-of-concept work demonstrates the utility and incentives associated with large-scale decentralized collaborations spanning multiple sites.

Alcohol use disorder (AUD) is a chronic and fatal disease. The main impediment of the AUD therapy is a high probability of relapse to alcohol abuse even after prolonged abstinence. The molecular mechanisms of cue-induced relapse are not well established, despite the fact that they may offer new targets for the treatment of AUD. Using a comprehensive animal model of AUD, virally-mediated and amygdala-targeted genetic manipulations by CRISPR/Cas9 technology and ex vivo electrophysiology, we identify a mechanism that selectively controls cue-induced alcohol relapse and AUD symptom severity. This mechanism is based on activity-regulated cytoskeleton-associated protein (Arc)/ARG3.1-dependent plasticity of the amygdala synapses. In humans, we identified single nucleotide polymorphisms in the ARC gene and their methylation predicting not only amygdala size, but also frequency of alcohol use, even at the onset of regular consumption. Targeting Arc during alcohol cue exposure may thus be a selective new mechanism for relapse prevention.

Alcohol use disorder (AUD) is a chronic and fatal disease. The main impediment of the AUD therapy is a high probability of relapse to alcohol abuse even after prolonged abstinence. The molecular mechanisms of cue-induced relapse are not well established, despite the fact that they may offer new targets for the treatment of AUD. Using a comprehensive animal model of AUD, virally-mediated and amygdala-targeted genetic manipulations by CRISPR/Cas9 technology and ex vivo electrophysiology, we identify a mechanism that selectively controls cue-induced alcohol relapse and AUD symptom severity. This mechanism is based on activity-regulated cytoskeleton-associated protein (Arc)/ARG3.1-dependent plasticity of the amygdala synapses. In humans, we identified single nucleotide polymorphisms in the ARC gene and their methylation predicting not only amygdala size, but also frequency of alcohol use, even at the onset of regular consumption. Targeting Arc during alcohol cue exposure may thus be a selective new mechanism for relapse prevention.

Purpose To investigate how the simultaneous exposure to gradient and RF fields affects the temperature rise in patients with a metallic hip prosthesis during an MRI session. Methods In silico analysis was performed with an anatomically realistic human model with CoCrMo hip implant in 12 imaging positions. The analysis was performed at 1.5 T and 3 T, considering four clinical sequences: turbo spin-echo, EPI, gradient-echo, and true fast imaging sequence with steady precession. The exposure to gradient and RF fields was evaluated separately and superposed, by adopting an ad hoc computational algorithm. Temperature increase within the body, rather than specific absorption rate, was used as a safety metric. Results With the exception of gradient-echo, all investigated sequences produced temperature increases higher than 1 K after 360 seconds, at least for one body position. In general, RF-induced heating dominates the turbo spin-echo sequence, whereas gradient-induced heating prevails with EPI; the situation with fast imaging sequence with steady precession is more diversified. The RF effects are enhanced when the implant is within the RF coil, whereas the effects of gradient fields are maximized if the prosthesis is outside the imaging region. Cases for which temperature-increase thresholds were exceeded were identified, together with the corresponding amount of tissue mass involved and the exposure time needed to reach these limits. Conclusion The analysis confirms that risky situations may occur when a patient carrying a hip implant undergoes an MRI exam and that, in some cases, the gradient field heating may be significant. In general, exclusion criteria only based on whole-body specific absorption rate may not be sufficient to ensure patients' safety.

BackgroundAdolescent onset of depression is associated with long-lasting negative consequences. Identifying adolescents at risk for developing depression would enable the monitoring of risk factors and the development of early intervention strategies. Using machine learning to combine several risk factors from multiple modalities might allow prediction of depression onset at the individual level. MethodsA subsample of a multisite longitudinal study in adolescents, the IMAGEN study, was used to predict future (subthreshold) major depressive disorder onset in healthy adolescents. Based on 2-year and 5-year follow-up data, participants were grouped into the following: 1) those developing a diagnosis of major depressive disorder or subthreshold major depressive disorder and 2) healthy control subjects. Baseline measurements of 145 variables from different modalities (clinical, cognitive, environmental, and structural magnetic resonance imaging) at age 14 years were used as input to penalized logistic regression (with different levels of penalization) to predict depression onset in a training dataset (n = 407). The features contributing the highest to the prediction were validated in an independent hold-out sample (three independent IMAGEN sites; n = 137). ResultsThe area under the receiver operating characteristic curve for predicting depression onset ranged between 0.70 and 0.72 in the training dataset. Baseline severity of depressive symptoms, female sex, neuroticism, stressful life events, and surface area of the supramarginal gyrus contributed most to the predictive model and predicted onset of depression, with an area under the receiver operating characteristic curve between 0.68 and 0.72 in the independent validation sample. ConclusionsThis study showed that depression onset in adolescents can be predicted based on a combination multimodal data of clinical characteristics, life events, personality traits, and brain structure variables.

The neurobiological bases of the association between development and psychopathology remain poorly understood. Here, we identify a shared spatial pattern of cortical thickness (CT) in normative development and several psychiatric and neurological disorders. Principal component analysis (PCA) was applied to CT of 68 regions in the Desikan-Killiany atlas derived from three large-scale datasets comprising a total of 41,075 neurotypical participants. PCA produced a spatially broad first principal component (PC1) that was reproducible across datasets. Then PC1 derived from healthy adult participants was compared to the pattern of CT differences associated with psychiatric and neurological disorders comprising a total of 14,886 cases and 20,962 controls from seven ENIGMA disease-related working groups, normative maturation and aging comprising a total of 17,697 scans from the ABCD Study® and the IMAGEN developmental study, and 17,075 participants from the ENIGMA Lifespan working group, as well as gene expression maps from the Allen Human Brain Atlas. Results revealed substantial spatial correspondences between PC1 and widespread lower CT observed in numerous psychiatric disorders. Moreover, the PC1 pattern was also correlated with the spatial pattern of normative maturation and aging. The transcriptional analysis identified a set of genes including KCNA2, KCNS1 and KCNS2 with expression patterns closely related to the spatial pattern of PC1. The gene category enrichment analysis indicated that the transcriptional correlations of PC1 were enriched to multiple gene ontology categories and were specifically over-represented starting at late childhood, coinciding with the onset of significant cortical maturation and emergence of psychopathology during the prepubertal-to-pubertal transition. Collectively, the present study reports a reproducible latent pattern of CT that captures interregional profiles of cortical changes in both normative brain maturation and a spectrum of psychiatric disorders. The pubertal timing of the expression of PC1-related genes implicates disrupted neurodevelopment in the pathogenesis of the spectrum of psychiatric diseases emerging during adolescence.

Recent longitudinal studies in youth have reported MRI correlates of prospective anxiety symptoms during adolescence, a vulnerable period for the onset of anxiety disorders. However, their predictive value has not been established. Individual prediction through machine-learning algorithms might help bridge the gap to clinical relevance. A voting classifier with Random Forest, Support Vector Machine and Logistic Regression algorithms was used to evaluate the predictive pertinence of gray matter volumes of interest and psychometric scores in the detection of prospective clinical anxiety. Participants with clinical anxiety at age 18-23 (N = 156) were investigated at age 14 along with healthy controls (N = 424). Shapley values were extracted for in-depth interpretation of feature importance. Prospective prediction of pooled anxiety disorders relied mostly on psychometric features and achieved moderate performance (area under the receiver operating curve = 0.68), while generalized anxiety disorder (GAD) prediction achieved similar performance. MRI regional volumes did not improve the prediction performance of prospective pooled anxiety disorders with respect to psychometric features alone, but they improved the prediction performance of GAD, with the caudate and pallidum volumes being among the most contributing features. To conclude, in non-anxious 14 year old adolescents, future clinical anxiety onset 4-8 years later could be individually predicted. Psychometric features such as neuroticism, hopelessness and emotional symptoms were the main contributors to pooled anxiety disorders prediction. Neuroanatomical data, such as caudate and pallidum volume, proved valuable for GAD and should be included in prospective clinical anxiety prediction in adolescents.

BACKGROUND: While drinking, one must eventually decide between the immediate rewarding effect of alcohol and the delayed reward of a healthier lifestyle. Individuals differ in their devaluation of a delayed reward based on the time required to receive it, the so called delay discounting. Previous studies showed that adolescents discount steeper than adults and that steeper delay discounting was associated with heavier alcohol use in both groups. METHODS: In a large-scale longitudinal study, we investigated whether higher rates of delay discounting are a developmental antecedent or a consequence of alcohol use during adolescent development. As part of the IMAGEN project, 2220 adolescents completed the Monetary Choice questionnaire as delay discounting measure and the Alcohol Use Disorders Identification Test as well as the Timeline-Follow-Back interview at the ages 14, 16, 18 and 22. Bivariate latent growth curve models were applied to investigate the relationship between delay discounting and drinking. To further explore the consequences of drinking, we computed the cumulative alcohol consumption to correlate it with the development of discounting. A subsample of 221 participants completed an intertemporal choice task during functional magnetic resonance imaging at ages 14, 16 and 18. Repeated-measures ANOVA was used to differentiate the development of neural processing during intertemporal choices between high-risk and low-risk drinkers. RESULTS: Overall, high rates of delay discounting at age 14 predicted a stronger increase of drinking over 8 years. In turn, an average, moderate alcohol use did not affect delay discounting from age 14 to 22. Of note, we found indicators for less brain activity in top-down control areas during intertemporal choices in those participants who drank more. CONCLUSIONS: Concluding, steep delay discounting is a predictor rather than a consequence of alcohol use in low-level drinking adolescents. Consideration is given to sampling strategies and reliability concerns for future longitudinal studies.

While there is substantial evidence that cannabis use is associated with differences in brain structure and function, most of this evidence is correlational in nature. This is particularly true regarding the association of adolescent cannabis use on human brain development, which cannot be tested in an experimental approach. Bayesian causal network (BCN) modeling attempts to identify probable causal associations in correlational data by using the conditional probabilities among a set of interrelated variables to estimate directional associations between those variables. The current report builds on a recent analysis conducted by Albaugh et al. (2021) that found an association between neurodevelopment and cannabis use in the IMAGEN study of adolescent brain development. Here, we employ BCN modeling on the same sample to provide evidence that the associations found previously are driven by cannabis use affecting neurodevelopment and not, for example, by a pre-existing neurodevelopmental trajectory that also promotes cannabis use. Structural MRI was acquired at ages 14 and 19, from which average cortical thickness was derived for a region of interest in the dorsal prefrontal cortex identified by Albaugh et al. as differing in adolescents who initiated cannabis use between ages 14 and 19. Adolescents were all cannabis naïve at age 14 and 46% had used cannabis at least once by age 19. We tested multiple learning algorithms with a variety of different parameters to build BCNs that would describe the relationship between cortical thickness and cannabis use. All BCN models strongly suggested a directional relationship from cannabis use between the ages of 14 and 19 to accelerated cortical thinning during that same period. Acknowledging that BCN modeling cannot prove a causal relationship between adolescent cannabis use and accelerated cortical thinning, these results are consistent with a body of preclinical and human research suggesting that adolescent cannabis use adversely affects brain development.

The electrophysiological activities in the human body generate electric and magnetic fields that can be measured noninvasively by electrodes on the skin, or even, not requiring any contact, by magnetometers. This includes the measurement of electrical activity of brain, heart, muscles and nerves that can be measured in vivo and allows to analyze functional processes with high temporal resolution. To measure these extremely small magnetic biosignals, traditionally highly sensitive superconducting quantum-interference devices have been used, together with advanced magnetic shields. Recently, they have been complemented in usability by a new class of sensors, optically pumped magnetometers (OPMs). These quantum sensors offer a high sensitivity without requiring cryogenic temperatures, allowing the design of small and flexible sensors for clinical applications. In this letter, we describe the advantages of these upcoming OPMs in two exemplary applications that were recently carried out at Physikalisch-Technische Bundesanstalt (PTB): (1) magnetocardiography (MCG) recorded during exercise and (2) auditory-evoked fields registered by magnetoencephalography.

As airborne ultrasound can be found in many technical applications and everyday situations, the question as to whether sounds at these frequencies can be heard by human beings or whether they present a risk to their hearing system is of great practical relevance. To objectively study these issues, the monaural hearing threshold in the frequency range from 14 to 24 kHz was determined for 26 test subjects between 19 and 33 years of age using pure tone audiometry. The hearing threshold values increased strongly with increasing frequency up to around 21 kHz, followed by a range with a smaller slope toward 24 kHz. The number of subjects who could respond positively to the threshold measurements decreased dramatically above 21 kHz. Brain activation was then measured by means of magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) and with acoustic stimuli at the same frequencies, with sound pressure levels (SPLs) above and below the individual threshold. No auditory cortex activation was found for levels below the threshold. Although test subjects reported audible sounds above the threshold, no brain activity was identified in the above-threshold case under current experimental conditions except at the highest sensation level, which was presented at the lowest test frequency.

Objective: White matter microstructure alterations have recently been associated with depressive episodes during adolescence, but it is unknown whether they predate depression. The authors investigated whether subthreshold depression in adolescence is associated with white matter microstructure variations and whether they relate to depression outcome. Method: Adolescents with subthreshold depression (N=96) and healthy control subjects (N=336) drawn from a community-based cohort were compared using diffusion tensor imaging and whole brain tract-based spatial statistics (TBSS) at age 14 to assess white matter microstructure. They were followed up at age 16 to assess depression. Probabilistic tractography was used to reconstruct white matter streamlines spreading from the regions identified in the TBSS analysis and along bundles implicated in emotion regulation, the uncinate fasciculus and the cingulum. The authors searched for mediating effects of white matter microstructure on the relationship between baseline subthreshold depression and depression at follow-up, and then explored the specificity of the findings. Results: Lower fractional anisotropy (FA) and higher radial diffusivity were found in the anterior corpus callosum in the adolescents with subthreshold depression. Tractography analysis showed that they also had lower FA in the right cingulum streamlines, along with lower FA and higher mean diffusivity in tracts connecting the corpus callosum to the anterior cingulate cortex. The relation between subthreshold depression at baseline and depression at follow-up was mediated by FA values in the latter tracts, and lower FA values in those tracts distinctively predicted higher individual risk for depression. Conclusions: Early FA variations in tracts projecting from the corpus callosum to the anterior cingulate cortex may denote a higher risk of transition to depression in adolescents.

Neuropathological changes associated with Alzheimers disease (AD) can occur decades before clinical symptoms. We investigated whether neurodevelopment and/or neurodegeneration affects the risk of AD, through reducing structural brain reserve and/or accelerating brain atrophy, respectively. We used bidirectional two-sample Mendelian randomization to estimate the effects of genetic liability to AD on global and regional cortical thickness, total intracranial volume, volume of subcortical structures and cerebral white matter in 36,842 participants aged eight to 81 years across five independent cohorts, and the effects of global and regional cortical thickness and subcortical volumes on AD risk in 94,337 participants. Our findings show that AD risk alleles have an age-dependent effect on a range of cortical and subcortical brain measures that starts in mid-life, in non-clinical populations. Evidence for such effects across childhood and young adulthood is weak. We also found little evidence to suggest brain morphology alters AD risk. Thus, genetic liability to AD is likely to alter mechanisms and/or rates of neurodegeneration, rather than reduce structural brain reserve.

Urbanicity is a growing environmental challenge for mental-health. While the impact of urban life on brain and behavior might be distinct in different sociocultural conditions and geographies, there might exist features shared between regions. To investigate correlations of urbanicity with brain structure and function, neuropsychology and mental illness symptoms in young people from China and Europe, we developed a remote-sensing satellite-measure termed ‘UrbanSat’ quantifying population-density, a general measure of urbanicity. UrbanSat is correlated with brain volume, surface area and brain-network-connectivity in the medial prefrontal cortex and cerebellum, which mediate its effect on perspective-taking and depression- symptoms. Susceptibility to high population-density is greatest during childhood for the cerebellum and from childhood to adolescence for the prefrontal cortex. As UrbanSat can be generalized to different geographies, it will enable assessing the impact of urbanicity on mental illness and resilience globally, especially in young people where prevention and early interventions are most effective.

Magnetoencephalography (MEG) is a neuroimaging technique for measuring magnetic signals in vicinity of the head. With various source localization algorithms we can estimate the excited areas inside cortex. Standard MEG devices use SQUID-based channels which, despite their very good signal-to-noise ratio (SNR) have numerous drawbacks. As an alternative to these, commercial optically pumped magnetometers (OPMs) have recently developed to such a degree, that they are suitable for measuring magnetic fields in MEG. In this work we present measurements of the brain auditory evoked fields (AEF) with a system of 15 OPM sensors, that can detect both radial and tangential components of the magnetic field. These results are compared to the results obtained with the SQUID system. However, the quantitative comparison of two MEG systems is not trivial. We present a method for comparing two MEG system, which operates on the principle of minimum norm estimate (MNE) source localization algorithm. We show, that performance of the MEG system consisting of a small number of OPM sensors is slightly worse, but still comparable to results of the complete standard squid system with 125 gradiometers.

Extensive research has demonstrated that rs1360780, a common single nucleotide polymorphism within the FKBP5 gene, interacts with early-life stress in predicting psychopathology. Previous results suggest that carriers of the TT genotype of rs1360780 who were exposed to child abuse show differences in structure and functional activation of emotion-processing brain areas belonging to the salience network. Extending these findings on intermediate phenotypes of psychopathology, we examined if the interaction between rs1360780 and child abuse predicts resting-state functional connectivity (rsFC) between the amygdala and other areas of the salience network. We analyzed data of young European adults from the general population (N = 774; mean age = 18.76 years) who took part in the IMAGEN study. In the absence of main effects of genotype and abuse, a significant interaction effect was observed for rsFC between the right centromedial amygdala and right posterior insula (p < .025, FWE-corrected), which was driven by stronger rsFC in TT allele carriers with a history of abuse. Our results suggest that the TT genotype of rs1360780 may render individuals with a history of abuse more vulnerable to functional changes in communication between brain areas processing emotions and bodily sensations, which could underlie or increase risk for psychopathology.

Chronic peer victimization has long-term impacts on mental health; however, the biological mediators of this adverse relationship are unknown. We sought to determine whether adolescent brain development is involved in mediating the effect of peer victimization on psychopathology. We included participants (n = 682) from the longitudinal IMAGEN study with both peer victimization and neuroimaging data. Latent profile analysis identified groups of adolescents with different experiential patterns of victimization. We then associated the victimization trajectories and brain volume changes with depression, generalized anxiety, and hyperactivity symptoms at age 19. Repeated measures ANOVA revealed time-by-victimization interactions on left putamen volume (F = 4.38, p = 0.037). Changes in left putamen volume were negatively associated with generalized anxiety (t = -2.32, p = 0.020). Notably, peer victimization was indirectly associated with generalized anxiety via decreases in putamen volume (95% CI = 0.004-0.109). This was also true for the left caudate (95% CI = 0.002-0.099). These data suggest that the experience of chronic peer victimization during adolescence might induce psychopathology-relevant deviations from normative brain development. Early peer victimization interventions could prevent such pathological changes.

High adolescent alcohol consumption is predictive for alcohol problems later in life. To tailor interventions, early identification of risk groups for adolescent alcohol consumption is important. The IMAGEN dataset was utilized to investigate predictors for problematic alcohol consumption at age 18-20 years as a function self and parental personality and drug-related measures as well as life-events and cognitive variables all assessed at age 14 years (N = 1404). For this purpose the binary partitioning algorithm ctree was used in an explorative analysis. The algorithm recursively selects significant input variables and splits the outcome variable based on these, yielding a conditional inference tree. Four significant split variables, namely Place of residence, the Disorganization subscale of the Temperament and Character Inventory, Sex, and the Sexuality subscale of the life-events questionnaire were found to distinguish between adolescents scoring high or low on the Alcohol Use Disorders Identification Test about five years later (all p 0.001). The analyis adds to the literature on predictors of adolescent drinking problems using a large European sample. The identified split variables could easily be collected in community samples. If their validity is proven in independent samples, they could facilitate intervention studies in the field of adolescent alcohol prevention.

Youths with attention-deficit/hyperactivity disorder symptomatology often exhibit residual inattention and/or hyperactivity in adulthood; however, this is not true for all individuals. We recently reported that dimensional, multi-informant ratings of hyperactive/inattentive symptoms are associated with ventromedial prefrontal cortex (vmPFC) structure. Herein, we investigate the degree to which vmPFC structure during adolescence predicts hyperactive/inattentive symptomatology at 5-year follow-up. Structural equation modeling was used to test the extent to which adolescent vmPFC volume predicts hyperactive/inattentive symptomatology 5 years later in early adulthood. 1104 participants (M = 14.52 years, standard deviation = 0.42; 583 females) possessed hyperactive/inattentive symptom data at 5-year follow-up, as well as quality controlled neuroimaging data and complete psychometric data at baseline. Self-reports of hyperactive/inattentive symptomatology were obtained during adolescence and at 5-year follow-up using the Strengths and Difficulties Questionnaire (SDQ). At baseline and 5-year follow-up, a hyperactive/inattentive latent variable was derived from items on the SDQ. Baseline vmPFC volume predicted adult hyperactive/inattentive symptomatology (standardized coefficient = −0.274, P < 0.001) while controlling for baseline hyperactive/inattentive symptomatology. These results are the first to reveal relations between adolescent brain structure and adult hyperactive/inattentive symptomatology, and suggest that early structural development of the vmPFC may be consequential for the subsequent expression of hyperactive/inattentive symptoms.

Non-acute effects of cannabis on neurocognition in adolescents remain unclear with most studies being cross-sectional. Therefore, the aim of this longitudinal, multi-center study was to examine the effects of cannabis use on cognitive abilities in participants emerging into adulthood. Data on substance use as well as neurocognitive measures were assessed in 804 adolescents (441 females, 363 males) at age 14 and 19. First, cross-sectional analyses of baseline and follow-up data were conducted using analyses of covariance (ANCOVA). Second, repeated measures ANCOVAs were conducted to quantify longitudinal change. Cross-sectionally, we detected no significant neurocognitive differences before initiation of drug use. However, after controlling for confounders, light cannabis use as well as late-onset thereof was associated with increased decision-making skills both cross-sectionally at follow-up as well as longitudinally compared to non-using controls. In summary, our data suggests that decision-making is not impaired when cannabis is used in moderation and onset of use occurs after the age of 15. In addition, we find no evidence to support the presumption that cannabis consumption leads to a decline in neurocognitive ability.

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.

Heavy drinker adolescents: altered brainstem microstructure.The cortical-cerebellar circuit is vulnerable to heavy drinking (HD) in adults. We hypothesized early microstructural modifications of the pons/midbrain region, containing core structures of the reward system, in HD adolescents. Thirty-two otherwise symptom-free HDs at age 14 (HD14) and 24 abstainers becoming HDs at age 16 (HD16) were identified in the community with the Alcohol Use Disorders Identification Test (AUDIT) and compared with abstainers. The monetary incentive delay (MID) task assessed reward-sensitive performance. Voxelwise statistics of diffusion tensor imaging (DTI) values in the thalamo-ponto-mesencephalic region were obtained using tract-based spatial statistics. Projections between the ventral tegmental area (VTA) and the nucleus accumbens (NAcc) were identified by probabilistic tractography. Lower fraction of anisotropy and higher radial diffusivity (RD) values were detected in the upper dorsal pons of HD14 adolescents, and a trend for higher RD in HD16, compared with abstainers. When expecting reward, HD14 had higher MID task success scores than abstainers, and success scores were higher with a lower number of tracts in all adolescents. In symptom-free community adolescents, a region of lower white matter (WM) integrity in the pons at age 14 was associated with current HD and predicted HD at age 16. HD was related to reward sensitivity.

Magnetoencephalography (MEG) is a noninvasive neuroimaging technique for measuring the activity in the brain. In the vicinity of the head it measures the magnetic field produced by the electric currents in neurons. From the measured magnetic field, using various computational techniques, we determine the location of the source. In this paper, we present the main methods for localizing activated areas in the brain with the data obtained from a 128-channel superconducting quantum interference (SQUID) system. Great emphasis is on solving the inverse problem, i.e. finding the source from the measured magnetic field around the head, with the minimization of the forward model for calculating the magnetic field inside a conducting sphere. We also demonstrate each step in the image processing of the magnetic resonance imaging (MRI), which we use for a more precise source localization. We address all the major drawbacks when using the SQUID gradiometers. As an alternative, we present the new generation of magnetometers, optically pumped magnetometers (OPM), which operate at room temperature. Compared to SQUIDs they can be placed closer to the head with the use of a custom 3D printed sensor holder. We also present the first test results with a custom system of 15 OPMs.

Mental disorders represent an increasing personal and financial burden and yet treatment development has stagnated in recent decades. Current disease classifications do not reflect psychobiological mechanisms of psychopathology, nor the complex interplay of genetic and environmental factors, likely contributing to this stagnation. Ten years ago, the longitudinal IMAGEN study was designed to comprehensively incorporate neuroimaging, genetics, and environmental factors to investigate the neural basis of reinforcement-related behaviour in normal adolescent development and psychopathology. In this article, we describe how insights into the psychobiological mechanisms of clinically relevant symptoms obtained by innovative integrative methodologies applied in IMAGEN have informed our current and future research aims. These aims include the identification of symptom groups that are based on shared psychobiological mechanisms and the development of markers that predict disease course and treatment response in clinical groups. These improvements in precision medicine will be achieved, in part, by employing novel methodological tools that refine the biological systems we target. We will also implement our approach in low- and medium-income countries to understand how distinct environmental, socio-economic and cultural conditions influence the development of psychopathology. Together, IMAGEN and related initiatives strive to reduce the burden of mental disorders by developing precision medicine approaches globally.

This study examines the effects of puberty and sex on the intrinsic functional connectivity (iFC) of brain networks, with a focus on the default-mode network (DMN). Consistently implicated in depressive disorders, the DMN’s function may interact with puberty and sex in the development of these disorders, whose onsets peak in adolescence, and which show strong sex disproportionality (females > males). The main question concerns how the DMN evolves with puberty as a function of sex. These effects are expected to involve within- and between-network iFC, particularly, the salience and the central-executive networks, consistent with the Triple-Network Model. Resting-state scans of an adolescent community sample (n = 304, male/female: 157/147; mean/std age: 14.6/0.41 years), from the IMAGEN database, were analyzed using the AFNI software suite and a data reduction strategy for the effects of puberty and sex. Three midline regions (medial prefrontal, pregenual anterior cingulate, and posterior cingulate), within the DMN and consistently implicated in mood disorders, were selected as seeds. Within- and between-network clusters of the DMN iFC changed with pubertal maturation differently in boys and girls (puberty-X-sex). Specifically, pubertal maturation predicted weaker iFC in girls and stronger iFC in boys. Finally, iFC was stronger in boys than girls independently of puberty. Brain–behavior associations indicated that lower connectivity of the anterior cingulate seed predicted higher internalizing symptoms at 2-year follow-up. In conclusion, weaker iFC of the anterior DMN may signal disconnections among circuits supporting mood regulation, conferring risk for internalizing disorders.

Spatially selective excitation in two dimensions (2D-SSE) utilizing parallel transmission was applied as a means to acquire signal from voxels adapted to the anatomy of interest for in vivo (1) H MR spectroscopy. A novel method to select spectroscopy voxels with arbitrary shapes in two dimensions was investigated. An on-off scheme with an adiabatic slice selective inversion pulse preceding a 2D-SSE pulse together with a segmented inward spiral excitation k-space trajectory enabled rapid free induction decay acquisitions. Performance of the sequence was evaluated in simulations, phantom experiments, and in vivo measurements at 3 T. High spatial fidelity of the excitation profile was achieved for different target shapes and with little off-resonance deterioration. Metabolite concentrations in human brain determined with the new sequence were quantified with Cramer-Rao lower bounds less than 20%. They were in the physiological range and did not deviate systematically from results acquired with a conventional SPECIAL sequence. In conclusion, a new approach for shaped voxel MRS in the human brain is presented, which complements existing sequences. Simulations show that 2D-SSE pulses yield reduced chemical shift artifact when compared with conventional localization methods. Copyright © 2016 John Wiley & Sons, Ltd.

This work focuses on the in silico evaluation of the energy deposed by MRI switched gradient fields in bulk metallic implants and the consequent temperature increase in the surrounding tissues. An original computational strategy, based on the subdivision of the gradient coil switching sequences into sub-signals and on the time-harmonic electromagnetic field solution, allows to realistically simulate the evolution of the phenomena produced by the gradient coils fed according to any MRI sequence. Then, Pennes’ bioheat equation is solved through a Douglas–Gunn time split scheme to compute the time-dependent temperature increase. The procedure is validated by comparison with laboratory results, using a component of a realistic hip implant embedded within a phantom, obtaining an agreement on the temperature increase better than 5%, lower than the overall measurement uncertainty. The heating generated inside the body of a patient with a unilateral hip implant when undergoing an Echo-Planar Imaging (EPI) MRI sequence is evaluated and the role of the parameters affecting the thermal results (body position, coil performing the frequency encoding, effects of thermoregulation) is discussed. The results show that the gradient coils can generate local increases of temperature up to some kelvin when acting without radiofrequency excitation. Hence, their contribution in general should not be disregarded when evaluating patients’ safety.

BackgroundAbnormalities in reward circuit function are considered a core feature of addiction. Yet, it is still largely unknown whether these abnormalities stem from chronic drug use, a genetic predisposition, or both.MethodsIn the present study, we investigated this issue using a large sample of adolescent children by applying structural equation modeling to examine the effects of several dopaminergic polymorphisms of the D1 and D2 receptor type on the reward function of the ventral striatum (VS) and orbital frontal cortex (OFC), and whether this relationship predicted the propensity to engage in early alcohol misuse behaviors at 14 years of age and again at 16 years of age.ResultsThe results demonstrated a regional specificity with which the functional polymorphism rs686 of the D1 dopamine receptor (DRD1) gene and Taq1A of the ANKK1 gene influenced medial and lateral OFC activation during reward anticipation, respectively. Importantly, our path model revealed a significant indirect relationship between the rs686 of the DRD1 gene and early onset of alcohol misuse through a medial OFC × VS interaction.ConclusionsThese findings highlight the role of D1 and D2 in adjusting reward-related activations within the mesocorticolimbic circuitry, as well as in the susceptibility to early onset of alcohol misuse.