At risk of being risky: The relationship between "brain age" under emotional states and risk preference

Brain network connectivity during peer evaluation in adolescent females: Associations with age, pubertal hormones, timing, and status

Despite copious data linking brain function with changes to social behavior and mental health, little is known about how puberty relates to brain functioning. We investigated the specificity of brain network connectivity associations with pubertal indices and age to inform neurodevelopmental models of adolescence. We examined how brain network connectivity during a peer evaluation fMRI task related to pubertal hormones (dehydroepiandrosterone and testosterone), pubertal timing and status, and age. Participants were 99 adolescents assigned female at birth aged 9-15 (M = 12.38, SD = 1.81) enriched for the presence of internalizing symptoms. Multivariate analysis revealed that within Salience, between Frontoparietal - Reward and Cinguloopercular - Reward network connectivity were associated with all measures of pubertal development and age. Specifically, Salience connectivity linked with age, pubertal hormones, and status, but not timing. In contrast, Frontoparietal - Reward connectivity was only associated with hormones. Finally, Cinguloopercular - Reward connectivity related to age and pubertal status, but not hormones or timing. These results provide evidence that the salience processing underlying peer evaluation is jointly influenced by various indices of puberty and age, while coordination between cognitive control and reward circuitry is related to pubertal hormones, pubertal status, and age in unique ways.

Cumulative Effects of Resting-State Connectivity Across All Brain Networks Significantly Correlate with Attention-Deficit Hyperactivity Disorder Symptoms

Identification of replicable neuroimaging correlates of attention-deficit hyperactivity disorder (ADHD) has been hindered by small sample sizes, small effects, and heterogeneity of methods. Given evidence that ADHD is associated with alterations in widely distributed brain networks and the small effects of individual brain features, a whole-brain perspective focusing on cumulative effects is warranted. The use of large, multisite samples is crucial for improving reproducibility and clinical utility of brain-wide MRI association studies. To address this, a polyneuro risk score (PNRS) representing cumulative, brain-wide, ADHD-associated resting-state functional connectivity was constructed and validated using data from the Adolescent Brain Cognitive Development (ABCD, N = 5,543, 51.5% female) study, and was further tested in the independent Oregon-ADHD-1000 case-control cohort (N = 553, 37.4% female). The ADHD PNRS was significantly associated with ADHD symptoms in both cohorts after accounting for relevant covariates (p < 0.001). The most predictive PNRS involved all brain networks, though the strongest effects were concentrated among the default mode and cingulo-opercular networks. In the longitudinal Oregon-ADHD-1000, non-ADHD youth had significantly lower PNRS (Cohen's d = -0.318, robust p = 5.5 × 10-4) than those with persistent ADHD (age 7-19). The PNRS, however, did not mediate polygenic risk for ADHD. Brain-wide connectivity was robustly associated with ADHD symptoms in two independent cohorts, providing further evidence of widespread dysconnectivity in ADHD. Evaluation in enriched samples demonstrates the promise of the PNRS approach for improving reproducibility in neuroimaging studies and unraveling the complex relationships between brain connectivity and behavioral disorders.

Perinatal Western-style diet exposure associated with decreased microglial counts throughout the arcuate nucleus of the hypothalamus in Japanese macaques

Perinatal exposure to a high-fat, high-sugar Western-style diet (WSD) is associated with altered neural circuitry in the melanocortin system. This association may have an underlying inflammatory component, as consumption of a WSD during pregnancy can lead to an elevated inflammatory environment. Our group previously demonstrated that prenatal WSD exposure was associated with increased markers of inflammation in the placenta and fetal hypothalamus in Japanese macaques. In this follow-up study, we sought to determine whether this heightened inflammatory state persisted into the postnatal period, as prenatal exposure to inflammation has been shown to reprogram offspring immune function and long-term neuroinflammation would present a potential means for prolonged disruptions to microglia-mediated neuronal circuit formation. Neuroinflammation was approximated in 1-yr-old offspring by counting resident microglia and peripherally derived macrophages in the region of the hypothalamus examined in the fetal study, the arcuate nucleus (ARC). Microglia and macrophages were immunofluorescently stained with their shared marker, ionized calcium-binding adapter molecule 1 (Iba1), and quantified in 11 regions along the rostral-caudal axis of the ARC. A mixed-effects model revealed main effects of perinatal diet (P = 0.011) and spatial location (P = 0.003) on Iba1-stained cell count. Perinatal WSD exposure was associated with a slight decrease in the number of Iba1-stained cells, and cells were more densely located in the center of the ARC. These findings suggest that the heightened inflammatory state experienced in utero does not persist postnatally. This inflammatory response trajectory could have important implications for understanding how neurodevelopmental disorders progress.NEW & NOTEWORTHY Prenatal Western-style diet exposure is associated with increased microglial activity in utero. However, we found a potentially neuroprotective reduction in microglia count during early postnatal development. This trajectory could inform the timing of disruptions to microglia-mediated neuronal circuit formation. Additionally, this is the first study in juvenile macaques to characterize the distribution of microglia along the rostral-caudal axis of the arcuate nucleus of the hypothalamus. Nearby neuronal populations may be greater targets during inflammatory insults.

Brain age prediction across the human lifespan using multimodal MRI data

Measuring differences between an individual's age and biological age with biological information from the brain have the potential to provide biomarkers of clinically relevant neurological syndromes that arise later in human life. To explore the effect of multimodal brain magnetic resonance imaging (MRI) features on the prediction of brain age, we investigated how multimodal brain imaging data improved age prediction from more imaging features of structural or functional MRI data by using partial least squares regression (PLSR) and longevity data sets (age 6-85 years). First, we found that the age-predicted values for each of these ten features ranged from high to low: cortical thickness (R = 0.866, MAE = 7.904), all seven MRI features (R = 0.8594, MAE = 8.24), four features in structural MRI (R = 0.8591, MAE = 8.24), fALFF (R = 0.853, MAE = 8.1918), gray matter volume (R = 0.8324, MAE = 8.931), three rs-fMRI feature (R = 0.7959, MAE = 9.744), mean curvature (R = 0.7784, MAE = 10.232), ReHo (R = 0.7833, MAE = 10.122), ALFF (R = 0.7517, MAE = 10.844), and surface area (R = 0.719, MAE = 11.33). In addition, the significance of the volume and size of brain MRI data in predicting age was also studied. Second, our results suggest that all multimodal imaging features, except cortical thickness, improve brain-based age prediction. Third, we found that the left hemisphere contributed more to the age prediction, that is, the left hemisphere showed a greater weight in the age prediction than the right hemisphere. Finally, we found a nonlinear relationship between the predicted age and the amount of MRI data. Combined with multimodal and lifespan brain data, our approach provides a new perspective for chronological age prediction and contributes to a better understanding of the relationship between brain disorders and aging.

Youth Perceived Social Support and Symptom Distress: A Random-Intercept Cross-Lagged Panel Model

Although social support and mental health associations have been extensively investigated, their reciprocal relations in vulnerable youth remain understudied. This study investigated the relations between perceived social support and symptom distress over time whilst differentiating between support from caregivers and significant others. The sample included 257 youth (79% self-identified women, Mage = 19.2, SD = 2.5) who were receiving mental health treatment. Using a Random-Intercept Cross-Lagged Panel Model, results revealed no significant concurrent associations, between-person effects, or cross-lagged effects. The autoregressive effects suggested that perceived social support from caregivers was relatively stable over time, while symptom distress and support from a significant other were not. In all, this study challenged the validity of the social causation and social erosion models in the context of perceived social support and symptom distress among vulnerable youth, revealing an absence of significant reciprocal associations. The stable nature of perceived social support from caregivers compared to support from significant others was highlighted. The study design, hypotheses, and target analyses were preregistered under https://osf.io/f4qpg .

Roper and Race: the Nature and Effects of Death Penalty Exclusions for Juveniles and the “Late Adolescent Class”

In Roper v. Simmons (2005), the US Supreme Court raised the minimum age at which someone could be subjected to capital punishment, ruling that no one under the age of 18 at the time of their crime could be sentenced to death. The present article discusses the legal context and rationale by which the Court established the current age-based limit on death penalty eligibility as well as the scientific basis for a recent American Psychological Association Resolution that recommended extending that limit to include members of the “late adolescent class” (i.e., persons from 18 to 20 years old). In addition, we present new data that address the little-discussed but important racial/ethnic implications of these age-based limits to capital punishment, both for the already established Roper exclusion and the APA-proposed exclusion for the late adolescent class. In fact, a much higher percentage of persons in the late adolescent class who were sentenced to death in the post-Roper era were non-White, suggesting that their age-based exclusion would help to remedy this problematic pattern.

Carotenoids improve the development of cerebral cortical networks in formula-fed infant macaques

Nutrition during the first years of life has a significant impact on brain development. This study characterized differences in brain maturation from birth to 6 months of life in infant macaques fed formulas differing in content of lutein, β-carotene, and other carotenoids using Magnetic Resonance Imaging to measure functional connectivity. We observed differences in functional connectivity based on the interaction of diet, age and brain networks. Post hoc analysis revealed significant diet-specific differences between insular-opercular and somatomotor networks at 2 months of age, dorsal attention and somatomotor at 4 months of age, and within somatomotor and between somatomotor-visual and auditory-dorsal attention networks at 6 months of age. Overall, we found a larger divergence in connectivity from the breastfeeding group in infant macaques fed formula containing no supplemental carotenoids in comparison to those fed formula supplemented with carotenoids. These findings suggest that carotenoid formula supplementation influences functional brain development.

Resting-state functional connectivity identifies individuals and predicts age in 8-to-26-month-olds

Resting-state functional connectivity (rsFC) measured with fMRI has been used to characterize functional brain maturation in typically and atypically developing children and adults. However, its reliability and utility for predicting development in infants and toddlers is less well understood. Here, we use fMRI data from the Baby Connectome Project study to measure the reliability and uniqueness of rsFC in infants and toddlers and predict age in this sample (8-to-26 months old; n = 170). We observed medium reliability for within-session infant rsFC in our sample, and found that individual infant and toddler's connectomes were sufficiently distinct for successful functional connectome fingerprinting. Next, we trained and tested support vector regression models to predict age-at-scan with rsFC. Models successfully predicted novel infants' age within ± 3.6 months error and a prediction R² = .51. To characterize the anatomy of predictive networks, we grouped connections into 11 infant-specific resting-state functional networks defined in a data-driven manner. We found that connections between regions of the same network-i.e. within-network connections-predicted age significantly better than between-network connections. Looking ahead, these findings can help characterize changes in functional brain organization in infancy and toddlerhood and inform work predicting developmental outcome measures in this age range.

Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms

OBJECTIVE

Magnetic resonance imaging (MRI) has shown that estimated brain age is deviant from chronological age in various common brain disorders. Brain age estimation could be useful for investigating patterns of brain maturation and integrity, aiding to elucidate brain mechanisms underlying these heterogeneous conditions. Here, we examined functional brain age in two large samples of children and adolescents and its relation to mental health.

METHODS

We used resting-state fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC; n = 1126, age range 8-22 years) to estimate functional connectivity between brain networks, and utilized these as features for brain age prediction. We applied the prediction model to 1387 individuals (age range 8-22 years) in the Healthy Brain Network sample (HBN). In addition, we estimated brain age in PNC using a cross-validation framework. Next, we tested for associations between brain age gap and various aspects of psychopathology and cognitive performance.

RESULTS

Our model was able to predict age in the independent test samples, with a model performance of r = 0.54 for the HBN test set, supporting consistency in functional connectivity patterns between samples and scanners. Linear models revealed a significant association between brain age gap and psychopathology in PNC, where individuals with a lower estimated brain age, had a higher overall symptom burden. These associations were not replicated in HBN.

DISCUSSION

Our findings support the use of brain age prediction from fMRI-based connectivity. While requiring further extensions and validations, the approach may be instrumental for detecting brain phenotypes related to intrinsic connectivity and could assist in characterizing risk in non-typically developing populations.

Adolescent decision making: A decade in review

Research in the past decade has highlighted the nuances of adolescent decision making. In this review article, we summarize several themes evident in the field of developmental science including the redefinition of adolescence and the ways in which adolescent decision-making capabilities converge with or diverge from those of adults. While the decision-making process is similar for adolescents and adults in contexts that encourage deliberation and reflection, adolescents and adults differ in contexts which preclude deliberation vis-à-vis high emotional arousal. We also discuss the reconceptualization of adolescent behavior, including risk taking, as adaptive. That is, characteristics of adolescence, including impulsivity, the importance of peers, and novelty seeking, are normative, evolutionarily advantageous, and essential for positive development. While these features manifest in negative, health-compromising ways (e.g., risky driving and criminal behavior), they also foster growth and exploration. We conclude with a discussion of potential avenues for future research.

Adolescent Brain Development and Contextual Influences: A Decade in Review

Adolescence is a developmental period characterized by substantial psychological, biological, and neurobiological changes. This review discusses the past decade of research on the adolescent brain, as based on the overarching framework that development is a dynamic process both within the individual and between the individual and external inputs. As such, this review focuses on research showing that the development of the brain is influenced by multiple ongoing and dynamic elements. It highlights the implications this body of work on behavioral development and offers areas of opportunity for future research in the coming decade.

Acceleration of Biological Aging and Underestimation of Subjective Age Are Risk Factors for Severe COVID-19

In an epidemic, it is important to have methods for reliable and rapid assessment of risk groups for severe forms of the disease for their priority vaccination and for the application of preventive lockdown measures. The aim of this study was to investigate risk factors for severe forms of COVID-19 in adults using indicators of biological and subjective aging. Longitudinal studies evaluated the severity of the disease and the number of cases. Respondents (447) were divided into "working group" and "risk group" (retirees with chronic diseases). During the lockdown period (in mid-2020), accelerated aging was observed in the group of workers (by 3.9-8 years for men and an increase at the tendency level for women). However, the respondents began to feel subjectively younger (by 3.3-7.2 years). In the risk group, there were no deviations from the expected biopsychological aging. The number of cases at the end of 2020 was 31% in workers and 0% in the risk group. Reasonably, the risk group followed the quarantine rules more strictly by 1.5 times. In working men, indicators of relative biological and relative subjective aging (measured in both 2019 and mid-2020) significantly influenced the incidence at the end of 2020. In women, only the indicators obtained in mid-2020 had a significant impact. The relative biological aging of an individual tested in the middle of 2020 had a direct impact on the risk of infection (p < 0.05) and on the probability of death (p < 0.0001). On the contrary, an increase in the relative subjective (psychological) aging index reduced the risk of infection (at the tendency level, p = 0.06) and the risk of death (p < 0.0001). Both the risk of infection and the risk of death increased with calendar age at the tendency level. Conclusions: Indicators of individual relative biological and subjective aging affect the probability of getting COVID-19 and its severity. The combination of high indicators of biological aging and underestimated indicators of subjective aging is associated with increased chances of developing severe forms of the disease.

Characterizing the Network Architecture of Emotion Regulation Neurodevelopment

The ability to regulate emotions is key to goal attainment and well-being. Although much has been discovered about neurodevelopment and the acquisition of emotion regulation, very little of this work has leveraged information encoded in whole-brain networks. Here we employed a network neuroscience framework to parse the neural underpinnings of emotion regulation skill acquisition, while accounting for age, in a sample of children and adolescents (N = 70, 34 female, aged 8-17 years). Focusing on three key network metrics-network differentiation, modularity, and community number differences between active regulation and a passive emotional baseline-we found that the control network, the default mode network, and limbic network were each related to emotion regulation ability while controlling for age. Greater network differentiation in the control and limbic networks was related to better emotion regulation ability. With regards to network community structure (modularity and community number), more communities and more crosstalk between modules (i.e., less modularity) in the control network were associated with better regulatory ability. By contrast, less crosstalk (i.e., greater modularity) between modules in the default mode network was associated with better regulatory ability. Together, these findings highlight whole-brain connectome features that support the acquisition of emotion regulation in youth.

Revisiting the Neural Architecture of Adolescent Decision-Making: Univariate and Multivariate Evidence for System-Based Models

Understanding adolescent decision-making is significant for informing basic models of neurodevelopment as well as for the domains of public health and criminal justice. System-based theories posit that adolescent decision-making is guided by activity related to reward and control processes. While successful at explaining behavior, system-based theories have received inconsistent support at the neural level, perhaps because of methodological limitations. Here, we used two complementary approaches to overcome said limitations and rigorously evaluate system-based models. Using decision-level modeling of fMRI data from a risk-taking task in a sample of 2000+ decisions across 51 human adolescents (25 females, mean age = 15.00 years), we find support for system-based theories of decision-making. Neural activity in lateral PFC and a multivariate pattern of cognitive control both predicted a reduced likelihood of risk-taking, whereas increased activity in the NAcc predicted a greater likelihood of risk-taking. Interactions between decision-level brain activity and age were not observed. These results garner support for system-based accounts of adolescent decision-making behavior.SIGNIFICANCE STATEMENT Adolescent decision-making behavior is of great import for basic science, and carries equally consequential implications for public health and criminal justice. While dominant psychological theories seeking to explain adolescent decision-making have found empirical support, their neuroscientific implementations have received inconsistent support. This may be partly because of statistical approaches used by prior neuroimaging studies of system-based theories. We used brain modeling, an approach that predicts behavior from brain activity, of univariate and multivariate neural activity metrics to better understand how neural components of psychological systems guide decision behavior in adolescents. We found broad support for system-based theories such that neural systems involved in cognitive control predicted a reduced likelihood to make risky decisions, whereas value-based systems predicted greater risk-taking propensity.

Maturity of gray matter structures and white matter connectomes, and their relationship with psychiatric symptoms in youth

Brain predicted age difference, or BrainPAD, compares chronological age to an age estimate derived by applying machine learning (ML) to MRI brain data. BrainPAD studies in youth have been relatively limited, often using only a single MRI modality or a single ML algorithm. Here, we use multimodal MRI with a stacked ensemble ML approach that iteratively applies several ML algorithms (AutoML). Eligible participants in the Healthy Brain Network (N = 489) were split into training and test sets. Morphometry estimates, white matter connectomes, or both were entered into AutoML to develop BrainPAD models. The best model was then applied to a held‐out evaluation dataset, and associations with psychometrics were estimated. Models using morphometry and connectomes together had a mean absolute error of 1.18 years, outperforming models using a single MRI modality. Lower BrainPAD values were associated with more symptoms on the CBCL (p_corr = .012) and lower functioning on the Children's Global Assessment Scale (p_corr = .012). Higher BrainPAD values were associated with better performance on the Flanker task (p_corr = .008). Brain age prediction was more accurate using ComBat‐harmonized brain data (MAE = 0.26). Associations with psychometric measures remained consistent after ComBat harmonization, though only the association with CGAS reached statistical significance in the reduced sample. Our findings suggest that BrainPAD scores derived from unharmonized multimodal MRI data using an ensemble ML approach may offer a clinically relevant indicator of psychiatric and cognitive functioning in youth.

Pitfalls in brain age analyses

Over the past decade, there has been an abundance of research on the difference between age and age predicted using brain features, which is commonly referred to as the “brain age gap.” Researchers have identified that the brain age gap, as a linear transformation of an out‐of‐sample residual, is dependent on age. As such, any group differences on the brain age gap could simply be due to group differences on age. To mitigate the brain age gap's dependence on age, it has been proposed that age be regressed out of the brain age gap. If this modified brain age gap is treated as a corrected deviation from age, model accuracy statistics such as R² will be artificially inflated to the extent that it is highly improbable that an R² value below .85 will be obtained no matter the true model accuracy. Given the limitations of proposed brain age analyses, further theoretical work is warranted to determine the best way to quantify deviation from normality.

Brain age prediction in schizophrenia: Does the choice of machine learning algorithm matter?

Brain-predicted age difference (brainPAD) has been used in schizophrenia to assess individual-level deviation in the biological age of the patients' brain (i.e., brain-age) from normative reference brain structural datasets. There is marked inter-study variation in brainPAD in schizophrenia which is commonly attributed to sample heterogeneity. However, the potential contribution of the different machine learning algorithms used for brain-age estimation has not been systematically evaluated. Here, we aimed to assess variation in brain-age estimated by six commonly used algorithms [ordinary least squares regression, ridge regression, least absolute shrinkage and selection operator regression, elastic-net regression, linear support vector regression, and relevance vector regression] when applied to the same brain structural features from the same sample. To assess reproducibility we used data from two publically available samples of healthy individuals (n = 1092 and n = 492) and two further samples, from the Icahn School of Medicine at Mount Sinai (ISMMS) and the Center of Biomedical Research Excellence (COBRE), comprising both patients with schizophrenia (n = 90 and n = 76) and healthy individuals (n = 200 and n = 87). Performance similarity across algorithms was compared within each sample using correlation analyses and hierarchical clustering. Across all samples ordinary least squares regression, the only algorithm without a penalty term, performed markedly worse. All other algorithms showed comparable performance but they still yielded variable brain-age estimates despite being applied to the same data. Although brainPAD was consistently higher in patients with schizophrenia, it varied by algorithm from 3.8 to 5.2 years in the ISMMS sample and from to 4.5 to 11.7 years in the COBRE sample. Algorithm choice introduces variations in brain-age and may confound inter-study comparisons when assessing brainPAD in schizophrenia.

Mindfulness training preserves sustained attention and resting state anticorrelation between default-mode network and dorsolateral prefrontal cortex: A randomized controlled trial

Mindfulness training can enhance cognitive control, but the neural mechanisms underlying such enhancement in children are unknown. Here, we conducted a randomized controlled trial (RCT) with sixth graders (mean age 11.76 years) to examine the impact of 8 weeks of school-based mindfulness training, relative to coding training as an active control, on sustained attention and associated resting-state functional brain connectivity. At baseline, better performance on a sustained-attention task correlated with greater anticorrelation between the default mode network (DMN) and right dorsolateral prefrontal cortex (DLPFC), a key node of the central executive network. Following the interventions, children in the mindfulness group preserved their sustained-attention performance (i.e., fewer lapses of attention) and preserved DMN-DLPFC anticorrelation compared to children in the active control group, who exhibited declines in both sustained attention and DMN-DLPFC anticorrelation. Further, change in sustained-attention performance correlated with change in DMN-DLPFC anticorrelation only within the mindfulness group. These findings provide the first causal link between mindfulness training and both sustained attention and associated neural plasticity. Administered as a part of sixth graders' school schedule, this RCT supports the beneficial effects of school-based mindfulness training on cognitive control.

Comparative Dynamics of Individual Ageing among the Investigative Type of Professionals Living in Russia and Russian Migrants to the EU Countries

The goal of this study was to uncover the influence of professional activity, migration, and gender on dynamics of subjective age and ageing biomarkers. We examined the representatives of investigative types of professions (ITP), 30–75 years old in Russia, (101/62 women), and Russian migrants to the European Union, (101/56 women). ITPs appeared to be ageing slower than statistical standards; men age faster than women; the pre-retirement group (51–65 years old) showed acceleration of relative biological ageing in the Russian sample (women +4.5 years, men +10.7 years) against the EU sample, suggesting a boost of pre-retirement stress in Russia; subjectively, Russian people (51–65 years old) feel close to their chronological age, while EU people perceive themselves far below their calendar age (men—lower by 20.4, women—lower by 10.9 years). The subjective ageing depends on the country of residence, while biological ageing depends on occupation, gender, and negative expectations of retirement.

Lateralized Connectivity between Globus Pallidus and Motor Cortex is Associated with Freezing of Gait in Parkinson's Disease

Freezing of gait (FoG) is a brief, episodic absence or marked reduction of forward progression of the feet, despite the intention to walk, that is common in people with Parkinson's disease (PD). We hypothesized that not only motor, but higher level cognitive and attention areas may be impaired in freezers. In this study, we aimed to characterize differences in cortical and subcortical functional connectivity specific to FoG. We examined resting state neuroimaging and objective measures of FoG severity and gait from 103 individuals (28 PD + FoG, 36 PD - FoG and 39 healthy controls). Inertial sensors were used to quantify freezing severity and gait. Groups with and without FoG were matched on age, disease severity, cognitive status, and levodopa medication. MRI data was processed using surface-based registration. High-quality imaging data were used to characterize differences in connectivity specific to FoG using a pre-defined set of Regions of Interest (ROIs) and validated using whole-brain connectivity analysis. Associations between functional connectivity and objective measures of FoG were determined via predictive modeling using hold-out cross validation. We found that connectivity between the left globus pallidus (GP) and left somatosensory cortex and between two brain areas in the default and insular/vestibular networks exhibited significant differences specific to FoG and were also strong and significant predictors of FoG severity. Our findings suggest that the interplay among motor, default and vestibular areas of the left cortex are critical in the pathology of FoG.

A set of functionally-defined brain regions with improved representation of the subcortex and cerebellum

An important aspect of network-based analysis is robust node definition. This issue is critical for functional brain network analyses, as poor node choice can lead to spurious findings and misleading inferences about functional brain organization. Two sets of functional brain nodes from our group are well represented in the literature: (1) 264 volumetric regions of interest (ROIs) reported in Power et al., 2011, and (2) 333 cortical surface parcels reported in Gordon et al., 2016. However, subcortical and cerebellar structures are either incompletely captured or missing from these ROI sets. Therefore, properties of functional network organization involving the subcortex and cerebellum may be underappreciated thus far. Here, we apply a winner-take-all partitioning method to resting-state fMRI data to generate novel functionally-constrained ROIs in the thalamus, basal ganglia, amygdala, hippocampus, and cerebellum. We validate these ROIs in three datasets using several criteria, including agreement with existing literature and anatomical atlases. Further, we demonstrate that combining these ROIs with established cortical ROIs recapitulates and extends previously described functional network organization. This new set of ROIs is made publicly available for general use, including a full list of MNI coordinates and functional network labels.

Using Developmental Science to Distinguish Adolescents and Adults Under the Law

A developmental scientific perspective on drawing legal age boundaries begins with the premise that the age at which the rights and responsibilities of adulthood are conferred to minors must align with the psychological capacities and skills necessary to exercise good judgment in specific contexts. This article examines three aspects of development relevant to this analysis: cognitive capabilities, especially those that support reasoned and deliberative decision making; psychosocial capacities, especially those that facilitate self-regulation under conditions of social or emotional arousal; and neurobiological maturation in brain regions and systems that undergird these cognitive and psychosocial skills. We conclude that the maturation of the capacity to reason and deliberate systematically precedes, by as much as five years, the maturation of the ability to exercise self-regulation, especially in socially and emotionally arousing contexts. Legal age boundaries should distinguish between two very different decision-making contexts: those that allow for unhurried, logical reflection and those that do not.

Machine Learning With Neuroimaging: Evaluating Its Applications in Psychiatry

Psychiatric disorders are complex, involving heterogeneous symptomatology and neurobiology that rarely involves the disruption of single, isolated brain structures. In an attempt to better describe and understand the complexities of psychiatric disorders, investigators have increasingly applied multivariate pattern classification approaches to neuroimaging data and in particular supervised machine learning methods. However, supervised machine learning approaches also come with unique challenges and trade-offs, requiring additional study design and interpretation considerations. The goal of this review is to provide a set of best practices for evaluating machine learning applications to psychiatric disorders. We discuss how to evaluate two common efforts: 1) making predictions that have the potential to aid in diagnosis, prognosis, and treatment and 2) interrogating the complex neurophysiological mechanisms underlying psychopathology. We focus here on machine learning as applied to functional connectivity with magnetic resonance imaging, as an example to ground discussion. We argue that for machine learning classification to have translational utility for individual-level predictions, investigators must ensure that the classification is clinically informative, independent of confounding variables, and appropriately assessed for both performance and generalizability. We contend that shedding light on the complex mechanisms underlying psychiatric disorders will require consideration of the unique utility, interpretability, and reliability of the neuroimaging features (e.g., regions, networks, connections) identified from machine learning approaches. Finally, we discuss how the rise of large, multisite, publicly available datasets may contribute to the utility of machine learning approaches in psychiatry.

Evaluating the Prediction of Brain Maturity From Functional Connectivity After Motion Artifact Denoising

The ability to make individual-level predictions from neuroanatomy has the potential to be particularly useful in child development. Previously, resting-state functional connectivity (RSFC) MRI has been used to successfully predict maturity and diagnosis of typically and atypically developing individuals. Unfortunately, submillimeter head motion in the scanner produces systematic, distance-dependent differences in RSFC and may contaminate, and potentially facilitate, these predictions. Here, we evaluated individual age prediction with RSFC after stringent motion denoising. Using multivariate machine learning, we found that 57% of the variance in individual RSFC after motion artifact denoising was explained by age, while 4% was explained by residual effects of head motion. When RSFC data were not adequately denoised, 50% of the variance was explained by motion. Reducing motion-related artifact also revealed that prediction did not depend upon characteristics of functional connections previously hypothesized to mediate development (e.g., connection distance). Instead, successful age prediction relied upon sampling functional connections across multiple functional systems with strong, reliable RSFC within an individual. Our results demonstrate that RSFC across the brain is sufficiently robust to make individual-level predictions of maturity in typical development, and hence, may have clinical utility for the diagnosis and prognosis of individuals with atypical developmental trajectories.

It is time for an ethical, evidence-based approach to youth presenting to the ED with an opioid overdose

Currently, there is a dangerous inconsistency between our current understanding of adolescent development and the effects of drugs on cognition when compared to our collective approach to youth who present in the emergency department with an opioid overdose. We call upon practitioners to embrace a new paradigm and we ask the Canadian Pediatric Society (CPS) to spearhead the development of guidelines to advise on best practices to manage youth who present to the emergency department with an illicit drug overdose.

Age-related differences in task-induced brain activation is not task specific: Multivariate pattern generalization between metacognition, cognition and perception

Adolescence is associated with widespread maturation of brain structures and functional connectivity profiles that shift from local to more distributed and better integrated networks, which are active during a variety of cognitive tasks. Nevertheless, the approach to examine task-induced developmental brain changes is function-specific, leaving the question open whether functional maturation is specific to the particular cognitive demands of the task used, or generalizes across different tasks. In the present study we examine the hypothesis that functional brain maturation is driven by global changes in how the brain handles cognitive demands. Multivariate pattern classification analysis (MVPA) was used to examine whether age discriminative task-induced activation patterns generalize across a wide range of information processing levels. 25 young (13-years old) and 22 old (17-years old) adolescents performed three conceptually different tasks of metacognition, cognition and visual processing. MVPA applied within each task indicated that task-induced brain activation is consistent and reliably different between ages 13 and 17. These age-discriminative activation patterns proved to be common across the different tasks used, despite the differences in cognitive demands and brain structures engaged by each of the three tasks. MVP classifiers trained to detect age-discriminative patterns in brain activation during one task were significantly able to decode age from brain activation maps during execution of other tasks with accuracies between 63 and 75%. The results emphasize that age-specific characteristics of task-induced brain activation have to be understood at the level of brain-wide networks that show maturational changes in their organization and processing efficacy during adolescence.

Child Maltreatment, Delayed Reward Discounting, and Alcohol and Other Drug Use Problems: The Moderating Role of Heart Rate Variability

BACKGROUND

Child maltreatment (CM) is robustly associated with youth risk for addictive behaviors, and recent findings suggest that this may be mediated through impulsive discounting of future rewards. However, research indicates that youth self-regulation (emotional and cognitive), particularly in peer contexts, is critical to consider in the study of decision making. This study aimed to examine the indirect link between CM and alcohol and other drug use problems, through delayed reward discounting (DRD), among a community sample of emerging adults. Further, this investigation aimed to examine whether this indirect link was moderated by heart rate variability (HRV), a physiological proxy for regulation of stress reactivity.

METHODS

A sample of emerging adults (N = 225; Mage  = 21.56; SDage  = 2.24; 52.9% female) was assessed at 2 time points, with 1 year between assessments. The sample was comprised of rural emerging adults from lower socioeconomic backgrounds. DRD was examined using a monetary choice task, and HRV reactivity was derived during a social stress task.

RESULTS

Increased CM experiences were significantly linked to riskier DRD. HRV reactivity amplified the indirect effect between CM and alcohol use problems via riskier DRD.

CONCLUSIONS

The results demonstrate that the connection between CM and alcohol use problems via impulsive decision making is modulated by acute stress response reactivity, as indexed by HRV.

Feeling How Old I Am: Subjective Age Is Associated With Estimated Brain Age

While the aging process is a universal phenomenon, people perceive and experience one's aging considerably differently. Subjective age (SA), referring to how individuals experience themselves as younger or older than their actual age, has been highlighted as an important predictor of late-life health outcomes. However, it is unclear whether and how SA is associated with the neurobiological process of aging. In this study, 68 healthy older adults underwent a SA survey and magnetic resonance imaging (MRI) scans. T1-weighted brain images of open-access datasets were utilized to construct a model for age prediction. We utilized both voxel-based morphometry (VBM) and age-prediction modeling techniques to explore whether the three groups of SA (i.e., feels younger, same, or older than actual age) differed in their regional gray matter (GM) volumes, and predicted brain age. The results showed that elderly individuals who perceived themselves as younger than their real age showed not only larger GM volume in the inferior frontal gyrus and the superior temporal gyrus, but also younger predicted brain age. Our findings suggest that subjective experience of aging is closely related to the process of brain aging and underscores the neurobiological mechanisms of SA as an important marker of late-life neurocognitive health.

Correlated Gene Expression and Anatomical Communication Support Synchronized Brain Activity in the Mouse Functional Connectome

Cognition and behavior depend on synchronized intrinsic brain activity that is organized into functional networks across the brain. Research has investigated how anatomical connectivity both shapes and is shaped by these networks, but not how anatomical connectivity interacts with intra-areal molecular properties to drive functional connectivity. Here, we present a novel linear model to explain functional connectivity by integrating systematically obtained measurements of axonal connectivity, gene expression, and resting-state functional connectivity MRI in the mouse brain. The model suggests that functional connectivity arises from both anatomical links and inter-areal similarities in gene expression. By estimating these effects, we identify anatomical modules in which correlated gene expression and anatomical connectivity support functional connectivity. Along with providing evidence that not all genes equally contribute to functional connectivity, this research establishes new insights regarding the biological underpinnings of coordinated brain activity measured by BOLD fMRI.SIGNIFICANCE STATEMENT Efforts at characterizing the functional connectome with fMRI have risen exponentially over the last decade. Yet despite this rise, the biological underpinnings of these functional measurements are still primarily unknown. The current report begins to fill this void by investigating the molecular underpinnings of the functional connectome through an integration of systematically obtained structural information and gene expression data throughout the rodent brain. We find that both white matter connectivity and similarity in regional gene expression relate to resting-state functional connectivity. The current report furthers our understanding of the biological underpinnings of the functional connectome and provides a linear model that can be used to streamline preclinical animal studies of disease.

Maternal IL-6 during pregnancy can be estimated from newborn brain connectivity and predicts future working memory in offspring

Several lines of evidence support the link between maternal inflammation during pregnancy and increased likelihood of neurodevelopmental and psychiatric disorders in offspring. This longitudinal study seeks to advance understanding regarding implications of systemic maternal inflammation during pregnancy, indexed by plasma interleukin-6 (IL-6) concentrations, for large-scale brain system development and emerging executive function skills in offspring. We assessed maternal IL-6 during pregnancy, functional magnetic resonance imaging acquired in neonates, and working memory (an important component of executive function) at 2 years of age. Functional connectivity within and between multiple neonatal brain networks can be modeled to estimate maternal IL-6 concentrations during pregnancy. Brain regions heavily weighted in these models overlap substantially with those supporting working memory in a large meta-analysis. Maternal IL-6 also directly accounts for a portion of the variance of working memory at 2 years of age. Findings highlight the association of maternal inflammation during pregnancy with the developing functional architecture of the brain and emerging executive function.

Prediction complements explanation in understanding the developing brain

A central aim of human neuroscience is understanding the neurobiology of cognition and behavior. Although we have made significant progress towards this goal, reliance on group-level studies of the developed adult brain has limited our ability to explain population variability and developmental changes in neural circuitry and behavior. In this review, we suggest that predictive modeling, a method for predicting individual differences in behavior from brain features, can complement descriptive approaches and provide new ways to account for this variability. Highlighting the outsized scientific and clinical benefits of prediction in developmental populations including adolescence, we show that predictive brain-based models are already providing new insights on adolescent-specific risk-related behaviors. Together with large-scale developmental neuroimaging datasets and complementary analytic approaches, predictive modeling affords us the opportunity and obligation to identify novel treatment targets and individually tailor the course of interventions for developmental psychopathologies that impact so many young people today.

Validation of mobile phone text messages for nicotine and tobacco risk communication among college students: A content analysis

INTRODUCTION

In the United States, young adults have the highest prevalence of tobacco use. The dissemination of mobile phone text messages is a growing strategy for tobacco risk communication among young adults. However, little has been done concerning the design and validation of such text messages. The Texas Tobacco Center of Regulatory Science (Texas-TCORS) has developed a library of messages based on framing (gain- or loss-framed), depth (simple or complex) and appeal (emotional or rational). This study validated the library based on depth and appeal, identified text messages that may need improvement, and explored new themes.

METHODS

The library formed the study sample (N=976 messages). The Linguistic Inquiry and Word Count (LIWC) software of 2015 was used to code for word count, word length and frequency of emotional and cognitive words. Analyses of variance, logistic regression and scatter plots were conducted for validation.

RESULTS

In all, 874 messages agreed with LIWC-coding. Several messages did not agree with LIWC. Ten messages designed to be complex indicated simplicity, while 51 messages designed to be rational exhibited no cognitive words. New relevant themes were identified, such as health (e.g. ‘diagnosis’, ‘cancer’), death (e.g. ‘dead’, ‘lethal’) and social connotations (e.g. ‘parents’, ‘friends’).

CONCLUSIONS

Nicotine and tobacco researchers can safely use, for young adults, messages from the Texas-TCORS library to convey information in the intended style. Future work may expand upon the new themes. Findings will be utilized to develop new campaigns, so that risks of nicotine and tobacco products can be widely disseminated.

Levodopa improves response inhibition and enhances striatal activation in early-stage Parkinson's disease

Dopaminergic medications improve the motor symptoms of Parkinson's disease (PD), but their effect on response inhibition, a critical executive function, remains unclear. Previous studies primarily enrolled patients in more advanced stages of PD, when dopaminergic medication loses efficacy, and patients were typically on multiple medications. Here, we recruited 21 patients in early-stage PD on levodopa monotherapy and 37 age-matched controls to perform the stop-signal task during functional magnetic resonance imaging. In contrast to previous studies reporting null effects in more advanced PD, levodopa significantly improved response inhibition performance in our sample. No significant group differences were found in brain activations to pure motor inhibition or error processing (stop success vs. error trials). However, relative to controls, the PD group showed weaker striatal activations to salient events (infrequent vs. frequent events: stop vs. go trials) and fronto-striatal task-residual functional connectivity; both were restored with levodopa. Thus, levodopa appears to improve an important executive function in early-stage PD via enhanced salient signal processing, shedding new light on the role of dopaminergic signaling in response inhibition.

Development of the emotional brain

In this article, we highlight the importance of dynamic reorganization of neural circuitry during adolescence, as it relates to the development of emotion reactivity and regulation. We offer a neurobiological account of hierarchical, circuit-based changes that coincide with emotional development during this time. Recent imaging studies suggest that the development of the emotional brain involves a cascade of changes in limbic and cognitive control circuitry. These changes are particularly pronounced during adolescence, when the demand for self regulation across a variety of emotional and social situations may be greatest. We propose that hierarchical changes in circuitry, from subcortico-subcortical to subcortico-cortical to cortico-subcortical and finally to cortico-cortical, may underlie the gradual changes in emotion reactivity and regulation throughout adolescence into young adulthood, with changes at each level being necessary for the instantiation of changes at the next level.

Connecting brain responsivity and real-world risk taking: Strengths and limitations of current methodological approaches

In line with the goal of limiting health risk behaviors in adolescence, a growing literature investigates whether individual differences in functional brain responses can be related to vulnerability to engage in risky decision-making. We review this body of work, investigate when and in what way findings converge, and provide best practice recommendations. We identified 23 studies that examined individual differences in brain responsivity and adolescent risk taking. Findings varied widely in terms of the neural regions identified as relating to risky behavior. This heterogeneity is likely due to the abundance of approaches used to assess risk taking, and to the disparity of fMRI tasks. Indeed, brain-behavior correlations were typically found in regions showing a main effect of task. However, results from a test of publication bias suggested that region of interest approaches lacked evidential value. The findings suggest that neural factors differentiating riskier teens are not localized to a single region. Therefore, approaches that utilize data from the entire brain, particularly in predictive analyses, may yield more reliable and applicable results. We discuss several decision points that researchers should consider when designing a study, and emphasize the importance of precise research questions that move beyond a general desire to address adolescent risk taking.