A Multidimensional Neural Maturation Index Reveals Reproducible Developmental Patterns in Children and Adolescents

Exploring associations between psychotic experiences and structural brain age: a population-based study in late adolescence

Neuroimaging studies show advanced structural "brain age" in schizophrenia and related psychotic disorders, potentially reflecting aberrant brain ageing or maturation. The extent to which altered brain age is associated with subthreshold psychotic experiences (PE) in youth remains unclear. We investigated the association between PE and brain-predicted age difference (brain-PAD) in late adolescence using a population-based sample of 117 participants with PE and 115 without PE (aged 19-21 years) from the Avon Longitudinal Study of Parents and Children. Brain-PAD was estimated using a publicly available machine learning model previously trained on a combination of region-wise T1-weighted grey-matter measures. We found little evidence for an association between PEs and brain-PAD after adjusting for age and sex (Cohen's d = -0.21 [95% CI -0.47, 0.05], p = 0.11). While there was some evidence for lower brain-PAD in those with PEs relative to those without PEs after additionally adjusting for parental social class (Cohen's d = -0.31 [95% CI -0.58, -0.03], p = 0.031) or birth weight (Cohen's d = -0.29 [95% CI -0.55, -0.03], p = 0.038), adjusting for maternal education or childhood IQ did not alter the primary results. These findings do not support the notion of advanced brain age in older adolescents with PEs. However, they weakly suggest there might be a younger-looking brain in those individuals, indicative of subtle delays in structural brain maturation. Future studies with larger samples covering a wider age range and multimodal measures could further investigate brain age as a marker of psychotic experiences in youth.

Diverging Effects of Violence Exposure and Psychiatric Symptoms on Amygdala-Prefrontal Maturation During Childhood and Adolescence

BACKGROUND

Violence exposure during childhood and adolescence is associated with increased prevalence and severity of psychopathology. Neurobiological correlates suggest that abnormal maturation of emotion-related brain circuitry, such as the amygdala-prefrontal cortex (PFC) circuit, may underlie the development of psychiatric symptoms after exposure. However, it remains unclear how amygdala-PFC circuit maturation is related to psychiatric risk in the context of violence.

METHODS

In this study, we analyzed individual differences in amygdala-PFC circuit maturity using data collected from the PNC (Philadelphia Neurodevelopmental Cohort) (n = 1133 youths). Neurodevelopment models of amygdala-PFC resting-state functional connectivity were built using deep learning and trained to predict chronological age in typically developing youths (not violence exposed and without a psychiatric diagnosis). Using the brain age gap estimate, an index of relative circuit maturation, patterns of atypical neurodevelopment were investigated.

RESULTS

Violence exposure was associated with delayed maturation of basolateral amygdala (BLA)-PFC circuits, driven by increased BLA-medial orbitofrontal cortex functional connectivity. In contrast, increased psychiatric symptoms were associated with advanced maturation of BLA-PFC functional connectivity, driven by decreased BLA-dorsolateral PFC functional connectivity.

CONCLUSIONS

Delayed frontoamygdala maturation after exposure to violence suggests atypical, but adaptive, development of threat appraisal processes, potentially reflecting a greater threat generalization characteristic of younger children. Advanced circuit maturation with increasing symptoms suggests divergent neurodevelopmental mechanisms underlying illness after emotion circuits have adapted to adversity, exacerbated by preexisting vulnerabilities to early maturation. Disentangling the effects of adversity and psychopathology on neurodevelopment is crucial for helping youths recover from violence and preventing illness from continuing into adulthood.

Assessing the association between global structural brain age and polygenic risk for schizophrenia in early adulthood: A recall-by-genotype study

Neuroimaging studies consistently show advanced brain age in schizophrenia, suggesting that brain structure is often 'older' than expected at a given chronological age. Whether advanced brain age is linked to genetic liability for schizophrenia remains unclear. In this pre-registered secondary data analysis, we utilised a recall-by-genotype approach applied to a population-based subsample from the Avon Longitudinal Study of Parents and Children to assess brain age differences between young adults aged 21-24 years with relatively high (n = 96) and low (n = 93) polygenic risk for schizophrenia (SCZ-PRS). A global index of brain age (or brain-predicted age) was estimated using a publicly available machine learning model previously trained on a combination of region-wise gray-matter measures, including cortical thickness, surface area and subcortical volumes derived from T1-weighted magnetic resonance imaging (MRI) scans. We found no difference in mean brain-PAD (the difference between brain-predicted age and chronological age) between the high- and low-SCZ-PRS groups, controlling for the effects of sex and age at time of scanning (b = -.21; 95% CI -2.00, 1.58; p = .82; Cohen's d = -.034; partial R2 = .00029). These findings do not support an association between SCZ-PRS and brain-PAD based on global age-related structural brain patterns, suggesting that brain age may not be a vulnerability marker of common genetic risk for SCZ. Future studies with larger samples and multimodal brain age measures could further investigate global or localised effects of SCZ-PRS.

Childhood socioeconomic status and the pace of structural neurodevelopment: accelerated, delayed, or simply different?

Socioeconomic status (SES) is associated with children's brain and behavioral development. Several theories propose that early experiences of adversity or low SES can alter the pace of neurodevelopment during childhood and adolescence. These theories make contrasting predictions about whether adverse experiences and low SES are associated with accelerated or delayed neurodevelopment. We contextualize these predictions within the context of normative development of cortical and subcortical structure and review existing evidence on SES and structural brain development to adjudicate between competing hypotheses. Although none of these theories are fully consistent with observed SES-related differences in brain development, existing evidence suggests that low SES is associated with brain structure trajectories more consistent with a delayed or simply different developmental pattern than an acceleration in neurodevelopment.

Changes in patterns of age-related network connectivity are associated with risk for schizophrenia

Alterations in fMRI-based brain functional network connectivity (FNC) are associated with schizophrenia (SCZ) and the genetic risk or subthreshold clinical symptoms preceding the onset of SCZ, which often occurs in early adulthood. Thus, age-sensitive FNC changes may be relevant to SCZ risk-related FNC. We used independent component analysis to estimate FNC from childhood to adulthood in 9,236 individuals. To capture individual brain features more accurately than single-session fMRI, we studied an average of three fMRI scans per individual. To identify potential familial risk-related FNC changes, we compared age-related FNC in first-degree relatives of SCZ patients mostly including unaffected siblings (SIB) with neurotypical controls (NC) at the same age stage. Then, we examined how polygenic risk scores for SCZ influenced risk-related FNC patterns. Finally, we investigated the same risk-related FNC patterns in adult SCZ patients (oSCZ) and young individuals with subclinical psychotic symptoms (PSY). Age-sensitive risk-related FNC patterns emerge during adolescence and early adulthood, but not before. Young SIB always followed older NC patterns, with decreased FNC in a cerebellar-occipitoparietal circuit and increased FNC in two prefrontal-sensorimotor circuits when compared to young NC. Two of these FNC alterations were also found in oSCZ, with one exhibiting reversed pattern. All were linked to polygenic risk for SCZ in unrelated individuals (R2 varied from 0.02 to 0.05). Young PSY showed FNC alterations in the same direction as SIB when compared to NC. These results suggest that age-related neurotypical FNC correlates with genetic risk for SCZ and is detectable with MRI in young participants.

Intrinsic brain activity is increasingly complex and develops asymmetrically during childhood and early adolescence

A large body of evidence suggests that brain signal complexity (BSC) may be an important indicator of healthy brain functioning or alternately, a harbinger of disease and dysfunction. However, despite recent progress our current understanding of how BSC emerges and evolves in large-scale networks, and the factors that shape these dynamics, remains limited. Here, we utilized resting-state functional near-infrared spectroscopy (rs-fNIRS) to capture and characterize the nature and time course of BSC dynamics within large-scale functional networks in 107 healthy participants ranging from 6-13 years of age. Age-dependent increases in spontaneous BSC were observed predominantly in higher-order association areas including the default mode (DMN) and attentional (ATN) networks. Our results also revealed asymmetrical developmental patterns in BSC that were specific to the dorsal and ventral ATN networks, with the former showing a left-lateralized and the latter demonstrating a right-lateralized increase in BSC. These age-dependent laterality shifts appeared to be more pronounced in females compared to males. Lastly, using a machine-learning model, we showed that BSC is a reliable predictor of chronological age. Higher-order association networks such as the DMN and dorsal ATN demonstrated the most robust prognostic power for predicting ages of previously unseen individuals. Taken together, our findings offer new insights into the spatiotemporal patterns of BSC dynamics in large-scale intrinsic networks that evolve over the course of childhood and adolescence, suggesting that a network-based measure of BSC represents a promising approach for tracking normative brain development and may potentially aid in the early detection of atypical developmental trajectories.

A systematic review of multimodal brain age studies: Uncovering a divergence between model accuracy and utility

Brain aging is a complex, multifaceted process that can be challenging to model in ways that are accurate and clinically useful. One of the most common approaches has been to apply machine learning to neuroimaging data with the goal of predicting age in a data-driven manner. Building on initial brain age studies that were derived solely from T1-weighted scans (i.e., unimodal), recent studies have incorporated features across multiple imaging modalities (i.e., "multimodal"). In this systematic review, we show that unimodal and multimodal models have distinct advantages. Multimodal models are the most accurate and sensitive to differences in chronic brain disorders. In contrast, unimodal models from functional magnetic resonance imaging were most sensitive to differences across a broad array of phenotypes. Altogether, multimodal imaging has provided us valuable insight for improving the accuracy of brain age models, but there is still much untapped potential with regard to achieving widespread clinical utility.

Multiscale functional connectivity patterns of the aging brain learned from harmonized rsfMRI data of the multi-cohort iSTAGING study

To learn multiscale functional connectivity patterns of the aging brain, we built a brain age prediction model of functional connectivity measures at seven scales on a large fMRI dataset, consisting of resting-state fMRI scans of 4186 individuals with a wide age range (22 to 97 years, with an average of 63) from five cohorts. We computed multiscale functional connectivity measures of individual subjects using a personalized functional network computational method, harmonized the functional connectivity measures of subjects from multiple datasets in order to build a functional brain age model, and finally evaluated how functional brain age gap correlated with cognitive measures of individual subjects. Our study has revealed that functional connectivity measures at multiple scales were more informative than those at any single scale for the brain age prediction, the data harmonization significantly improved the brain age prediction performance, and the data harmonization in the functional connectivity measures' tangent space worked better than in their original space. Moreover, brain age gap scores of individual subjects derived from the brain age prediction model were significantly correlated with clinical and cognitive measures. Overall, these results demonstrated that multiscale functional connectivity patterns learned from a large-scale multi-site rsfMRI dataset were informative for characterizing the aging brain and the derived brain age gap was associated with cognitive and clinical measures.

Evaluating the causal effect of tobacco smoking on white matter brain aging: a two-sample Mendelian randomization analysis in UK Biobank

BACKGROUND AND AIMS

Tobacco smoking is a risk factor for impaired brain function, but its causal effect on white matter brain aging remains unclear. This study aimed to measure the causal effect of tobacco smoking on white matter brain aging.

DESIGN

Mendelian randomization (MR) analysis using two non-overlapping data sets (with and without neuroimaging data) from UK Biobank (UKB). The group exposed to smoking and control group consisted of current smokers and never smokers, respectively. Our main method was generalized weighted linear regression with other methods also included as sensitivity analysis.

SETTING

United Kingdom.

PARTICIPANTS

The study cohort included 23 624 subjects [10 665 males and 12 959 females with a mean age of 54.18 years, 95% confidence interval (CI) = 54.08, 54.28].

MEASUREMENTS

Genetic variants were selected as instrumental variables under the MR analysis assumptions: (1) associated with the exposure; (2) influenced outcome only via exposure; and (3) not associated with confounders. The exposure smoking status (current versus never smokers) was measured by questionnaires at the initial visit (2006-10). The other exposure, cigarettes per day (CPD), measured the average number of cigarettes smoked per day for current tobacco users over the life-time. The outcome was the 'brain age gap' (BAG), the difference between predicted brain age and chronological age, computed by training machine learning model on a non-overlapping set of never smokers.

FINDINGS

The estimated BAG had a mean of 0.10 (95% CI = 0.06, 0.14) years. The MR analysis showed evidence of positive causal effect of smoking behaviors on BAG: the effect of smoking is 0.21 (in years, 95% CI = 6.5 × 10-3 , 0.41; P-value = 0.04), and the effect of CPD is 0.16 year/cigarette (UKB: 95% CI = 0.06, 0.26; P-value = 1.3 × 10-3 ; GSCAN: 95% CI = 0.02, 0.31; P-value = 0.03). The sensitivity analyses showed consistent results.

CONCLUSIONS

There appears to be a significant causal effect of smoking on the brain age gap, which suggests that smoking prevention can be an effective intervention for accelerated brain aging and the age-related decline in cognitive function.

Total Sleep Deprivation Increases Brain Age Prediction Reversibly in Multisite Samples of Young Healthy Adults

Sleep loss pervasively affects the human brain at multiple levels. Age-related changes in several sleep characteristics indicate that reduced sleep quality is a frequent characteristic of aging. Conversely, sleep disruption may accelerate the aging process, yet it is not known what will happen to the age status of the brain if we can manipulate sleep conditions. To tackle this question, we used an approach of brain age to investigate whether sleep loss would cause age-related changes in the brain. We included MRI data of 134 healthy volunteers (mean chronological age of 25.3 between the age of 19 and 39 years, 42 females/92 males) from five datasets with different sleep conditions. Across three datasets with the condition of total sleep deprivation (>24 h of prolonged wakefulness), we consistently observed that total sleep deprivation increased brain age by 1-2 years regarding the group mean difference with the baseline. Interestingly, after one night of recovery sleep, brain age was not different from baseline. We also demonstrated the associations between the change in brain age after total sleep deprivation and the sleep variables measured during the recovery night. By contrast, brain age was not significantly changed by either acute (3 h time-in-bed for one night) or chronic partial sleep restriction (5 h time-in-bed for five continuous nights). Together, the convergent findings indicate that acute total sleep loss changes brain morphology in an aging-like direction in young participants and that these changes are reversible by recovery sleep.SIGNIFICANCE STATEMENT Sleep is fundamental for humans to maintain normal physical and psychological functions. Experimental sleep deprivation is a variable-controlling approach to engaging the brain among different sleep conditions for investigating the responses of the brain to sleep loss. Here, we quantified the response of the brain to sleep deprivation by using the change of brain age predictable with brain morphologic features. In three independent datasets, we consistently found increased brain age after total sleep deprivation, which was associated with the change in sleep variables. Moreover, no significant change in brain age was found after partial sleep deprivation in another two datasets. Our study provides new evidence to explain the brainwide effect of sleep loss in an aging-like direction.

Longitudinal changes in within-salience network functional connectivity mediate the relationship between childhood abuse and neglect, and mental health during adolescence

BACKGROUND

Understanding the neurobiological underpinnings of childhood maltreatment is vital given consistent links with poor mental health. Dimensional models of adversity purport that different types of adversity likely have distinct neurobiological consequences. Adolescence is a key developmental period, during which deviations from normative neurodevelopment may have particular relevance for mental health. However, longitudinal work examining links between different forms of maltreatment, neurodevelopment, and mental health is limited.

METHODS

In the present study, we explored associations between abuse, neglect, and longitudinal development of within-network functional connectivity of the salience (SN), default mode (DMN), and executive control network in 142 community residing adolescents. Resting-state fMRI data were acquired at age 16 (T1; M = 16.46 years, s.d. = 0.52, 66F) and 19 (T2; mean follow-up period: 2.35 years). Mental health data were also collected at T1 and T2. Childhood maltreatment history was assessed prior to T1.

RESULTS

Abuse and neglect were both found to be associated with increases in within-SN functional connectivity from age 16 to 19. Further, there were sex differences in the association between neglect and changes in within-DMN connectivity. Finally, increases in within-SN connectivity were found to mediate the association between abuse/neglect and lower problematic substance use and higher depressive symptoms at age 19.

CONCLUSIONS

Our findings suggest that childhood maltreatment is associated with altered neurodevelopmental trajectories, and that changes in salience processing may be linked with risk and resilience for the development of depression and substance use problems during adolescence, respectively. Further work is needed to understand the distinct neurodevelopmental and mental health outcomes of abuse and neglect.

Benchmarking the generalizability of brain age models: Challenges posed by scanner variance and prediction bias

Machine learning has been increasingly applied to neuroimaging data to predict age, deriving a personalized biomarker with potential clinical applications. The scientific and clinical value of these models depends on their applicability to independently acquired scans from diverse sources. Accordingly, we evaluated the generalizability of two brain age models that were trained across the lifespan by applying them to three distinct early-life samples with participants aged 8-22 years. These models were chosen based on the size and diversity of their training data, but they also differed greatly in their processing methods and predictive algorithms. Specifically, one brain age model was built by applying gradient tree boosting (GTB) to extracted features of cortical thickness, surface area, and brain volume. The other model applied a 2D convolutional neural network (DBN) to minimally preprocessed slices of T1-weighted scans. Additional model variants were created to understand how generalizability changed when each model was trained with data that became more similar to the test samples in terms of age and acquisition protocols. Our results illustrated numerous trade-offs. The GTB predictions were relatively more accurate overall and yielded more reliable predictions when applied to lower quality scans. In contrast, the DBN displayed the most utility in detecting associations between brain age gaps and cognitive functioning. Broadly speaking, the largest limitations affecting generalizability were acquisition protocol differences and biased brain age estimates. If such confounds could eventually be removed without post-hoc corrections, brain age predictions may have greater utility as personalized biomarkers of healthy aging.

Predicting aging trajectories of decline in brain volume, cortical thickness and fractional anisotropy in schizophrenia

Brain-age prediction is a novel approach to assessing deviated brain aging trajectories in different diseases. However, most studies have used an average brain age gap (BAG) of individuals with schizophrenia of different illness durations for comparison with healthy participants. Therefore, this study investigated whether declined brain structures as reflected by BAGs may be present in schizophrenia in terms of brain volume, cortical thickness, and fractional anisotropy across different illness durations. We used brain volume, cortical thickness, and fractional anisotropy as features to train three models from the training dataset. Three models were applied to predict brain ages in the hold-out test and schizophrenia datasets and calculate BAGs. We divided the schizophrenia dataset into multiple groups based on the illness duration using a sliding time window approach for ANCOVA analysis. The brain volume and cortical thickness models revealed that, in comparison with healthy controls, individuals with schizophrenia had larger BAGs across different illness durations, whereas the BAG in terms of fractional anisotropy did not differ from that of healthy controls after disease onset. Moreover, the BAG at the initial stage of schizophrenia was the largest in the cortical thickness model. In contrast, the BAG from approximately two decades after disease onset was the largest in the brain volume model. Our findings suggest that schizophrenia differentially affects the decline of different brain structures during the disease course. Moreover, different trends of decline in thickness and volume-based measures suggest a differential decline in dimensions of brain structure throughout the course of schizophrenia.

The Role of School Environment in Brain Structure, Connectivity, and Mental Health in Children: A Multimodal Investigation

BACKGROUND

Much work has been dedicated to understanding the effects of adverse home environments on brain development. While the school social and learning environment plays a role in child development, little work has been done to investigate the impact of the school environment on the developing brain. The goal of the present study was to examine associations between the school environment, brain structure and connectivity, and mental health.

METHODS

In this preregistered study we investigated these questions in a large sample of adolescents (9-10 years of age) from the Adolescent Brain Cognitive Development (ABCD) Study. We examined the association between school environment and gray matter (n = 10,435) and white matter (n = 10,770) structure and functional connectivity (n = 9528). We then investigated multivariate relationships between school-associated brain measures and mental health.

RESULTS

School environment was associated with connectivity of the auditory and retrosplenial temporal network as well as of higher-order cognitive networks like the cingulo-opercular, default mode, ventral attention, and frontoparietal networks. Multivariate analyses revealed that connectivity of the cingulo-opercular and default mode networks was also associated with mental health.

CONCLUSIONS

Findings shed light on the neural mechanisms through which favorable school environments may contribute to positive mental health outcomes in children. Our findings have implications for interventions targeted at promoting positive youth functioning through improving school environments.

Multidimensional brain-age prediction reveals altered brain developmental trajectory in psychiatric disorders

Brain-age prediction has emerged as a novel approach for studying brain development. However, brain regions change in different ways and at different rates. Unitary brain-age indices represent developmental status averaged across the whole brain and therefore do not capture the divergent developmental trajectories of various brain structures. This staggered developmental unfolding, determined by genetics and postnatal experience, is implicated in the progression of psychiatric and neurological disorders. We propose a multidimensional brain-age index (MBAI) that provides regional age predictions. Using a database of 556 individuals, we identified clusters of imaging features with distinct developmental trajectories and built machine learning models to obtain brain-age predictions from each of the clusters. Our results show that the MBAI provides a flexible analysis of region-specific brain-age changes that are invisible to unidimensional brain-age. Importantly, brain-ages computed from region-specific feature clusters contain complementary information and demonstrate differential ability to distinguish disorder groups (e.g., depression and oppositional defiant disorder) from healthy controls. In summary, we show that MBAI is sensitive to alterations in brain structures and captures distinct regional change patterns that may serve as biomarkers that contribute to our understanding of healthy and pathological brain development and the characterization and diagnosis of psychiatric disorders.

The Key Role of Magnetic Resonance Imaging in the Detection of Neurodegenerative Diseases-Associated Biomarkers: A Review

Neurodegenerative diseases (NDs), including chronic disease such as Alzheimer's disease, Parkinson's disease, Huntington's disease, and multiple sclerosis, and acute diseases like traumatic brain injury and ischemic stroke are characterized by progressive degeneration, brain tissue damage and loss of neurons, accompanied by behavioral and cognitive dysfunctions. So far, there are no complete cures for NDs; thus, early and timely diagnoses are essential and beneficial to patients' treatment. Magnetic resonance imaging (MRI) has become one of the advanced medical imaging techniques widely used in the clinical examination of NDs due to its non-invasive diagnostic value. In this review, research published in English in current decade from PubMed electronic database on the use of MRI to detect specific biomarkers of NDs was collected, summarized, and discussed, which provides valuable suggestions for the early diagnosis, prevention, and treatment of NDs in the clinic.

Hyper and hypo attention networks activations affect social development in children with autism spectrum disorder

Children with autism spectrum disorder (ASD) experience a range of social and non-social attention deficits. To date, most studies assessed the neurological framework or discrete behavioral traits related to one attention network, leaving a gap in the understanding of the developmental cascade affecting the inter-relations among attention networks in ASD in a pervasive manner. We propose a theoretical framework that integrates the behavioral deficits and neurological manifestations through a cohesive developmental prism of attention networks’ activations while assessing their impact on social deficits in children with ASD. Insights arising from the model suggest hyper-and-hypoactivation of posterior attention networks leads to an altered prefrontal anterior attention network weight in ways that conjointly impact social performance in ASD. This perspective on how attention networks develop and interact in ASD may inform future research directions regarding ASD and attention development.

Associations of Irritability With Functional Connectivity of Amygdala and Nucleus Accumbens in Adolescents and Young Adults With ADHD

OBJECTIVE

Irritability is a common characteristic in ADHD. We examined whether dysfunction in neural connections supporting threat and reward processing was related to irritability in adolescents and young adults with ADHD.

METHOD

We used resting-state fMRI to assess connectivity of amygdala and nucleus accumbens seeds in those with ADHD (n = 34) and an age- and gender-matched typically-developing comparison group (n = 34).

RESULTS

In those with ADHD, irritability was associated with atypical functional connectivity of both seed regions. Amygdala seeds showed greater connectivity with right inferior frontal gyrus and caudate/putamen, and less connectivity with precuneus. Nucleus accumbens seeds showed altered connectivity with middle temporal gyrus and precuneus.

CONCLUSION

The irritability-ADHD presentation is associated with atypical functional connectivity of reward and threat processing regions with cognitive control and emotion processing regions. These patterns provide novel evidence for irritability-associated neural underpinnings in adolescents and young adults with ADHD. The findings suggest cognitive and behavioral treatments that address response to reward, including omission of an expected reward and irritability, may be beneficial for ADHD.

Advanced Brain-Age in Psychotic Psychopathology: Evidence for Transdiagnostic Neurodevelopmental Origins

Schizophrenia is characterized by abnormal brain structure such as global reductions in gray matter volume. Machine learning models trained to estimate the age of brains from structural neuroimaging data consistently show advanced brain-age to be associated with schizophrenia. Yet, it is unclear whether advanced brain-age is specific to schizophrenia compared to other psychotic disorders, and whether evidence that brain structure is "older" than chronological age actually reflects neurodevelopmental rather than atrophic processes. It is also unknown whether advanced brain-age is associated with genetic liability for psychosis carried by biological relatives of people with schizophrenia. We used the Brain-Age Regression Analysis and Computation Utility Software (BARACUS) prediction model and calculated the residualized brain-age gap of 332 adults (163 individuals with psychotic disorders: 105 schizophrenia, 17 schizoaffective disorder, 41 bipolar I disorder with psychotic features; 103 first-degree biological relatives; 66 controls). The model estimated advanced brain-ages for people with psychosis in comparison to controls and relatives, with no differences among psychotic disorders or between relatives and controls. Specifically, the model revealed an enlarged brain-age gap for schizophrenia and bipolar disorder with psychotic features. Advanced brain-age was associated with lower cognitive and general functioning in the full sample. Among relatives, cognitive performance and schizotypal symptoms were related to brain-age gap, suggesting that advanced brain-age is associated with the subtle expressions associated with psychosis. Exploratory longitudinal analyses suggested that brain aging was not accelerated in individuals with a psychotic disorder. In sum, we found that people with psychotic disorders, irrespective of specific diagnosis or illness severity, show indications of non-progressive, advanced brain-age. These findings support a transdiagnostic, neurodevelopmental formulation of structural brain abnormalities in psychotic psychopathology.

Brain age in mood and psychotic disorders: a systematic review and meta-analysis

OBJECTIVE

To evaluate whether accelerated brain aging occurs in individuals with mood or psychotic disorders.

METHODS

A systematic review following PRISMA guidelines was conducted. A meta-analysis was then performed to assess neuroimaging-derived brain age gap in three independent groups: (1) schizophrenia and first-episode psychosis, (2) major depressive disorder, and (3) bipolar disorder.

RESULTS

A total of 18 papers were included. The random-effects model meta-analysis showed a significantly increased neuroimaging-derived brain age gap relative to age-matched controls for the three major psychiatric disorders, with schizophrenia (3.08; 95%CI [2.32; 3.85]; p < 0.01) presenting the largest effect, followed by bipolar disorder (1.93; [0.53; 3.34]; p < 0.01) and major depressive disorder (1.12; [0.41; 1.83]; p < 0.01). The brain age gap was larger in older compared to younger individuals.

CONCLUSION

Individuals with mood and psychotic disorders may undergo a process of accelerated brain aging reflected in patterns captured by neuroimaging data. The brain age gap tends to be more pronounced in older individuals, indicating a possible cumulative biological effect of illness burden.

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.

Differential Patterns of Delayed Emotion Circuit Maturation in Abused Girls With and Without Internalizing Psychopathology

OBJECTIVE

Childhood abuse represents one of the most potent risk factors for developing psychopathology, especially in females. Evidence suggests that exposure to early-life adversity may be related to advanced maturation of emotion processing neural circuits. However, it remains unknown whether abuse is related to early circuit maturation and whether maturation patterns depend on the presence of psychopathology.

METHODS

A multisite sample of 234 girls (ages 8-18 years) completed clinical assessment, maltreatment histories, and high-resolution T1-weighted structural MRI. Girls were stratified by abuse history and internalizing disorder diagnosis into typically developing (no abuse/no diagnosis), resilient (abuse/no diagnosis), and susceptible (abuse/current diagnosis) groups. Machine learning models of normative brain development were aggregated in a stacked generalization framework trained to predict chronological age using gray matter volume in whole-brain, emotion, and language circuit parcellations. Brain age gap estimations (BrainAGEs; predicted age minus true chronological age) were calculated as indices of relative circuit maturation.

RESULTS

Childhood abuse was related to reduced BrainAGE (delayed maturation) specific to emotion circuits. Delayed emotion circuit BrainAGE was further related to increased hyperarousal symptoms. Childhood physical neglect was associated with increased whole-brain BrainAGE (advanced maturation). Neural contributors to emotion circuit BrainAGE differed in girls with and without an internalizing diagnosis, especially in the lateral prefrontal, parietal, and insular cortices and the hippocampus.

CONCLUSIONS

Abuse exposure in girls is associated with a delayed structural maturation pattern specific to emotion circuitry, a potentially adaptive mechanism enhancing threat generalization. Physical neglect, on the other hand, is associated with a broader brain-wide pattern of advanced structural maturation. The differential influence of fronto-parietal cortices and the hippocampus on emotion circuit maturity in resilient girls may represent neurodevelopmental markers of reduced psychiatric risk following abuse.

Characterizing effects of age, sex and psychosis symptoms on thalamocortical functional connectivity in youth

The thalamus is composed of multiple nuclei densely connected with the cortex in an organized manner, forming parallel thalamocortical networks critical to sensory, motor, and cognitive functioning. Thalamocortical circuit dysfunction has been implicated in multiple neurodevelopmental disorders, including schizophrenia, which also often exhibit sex differences in prevalence, clinical characteristics, and neuropathology. However, very little is known about developmental and sex effects on thalamocortical networks in youth. The present study characterized the effects of age, sex and psychosis symptomatology in anatomically constrained thalamocortical networks in a large community sample of youth (n = 1100, aged 8-21) from the Philadelphia Neurodevelopmental Cohort (PNC). Cortical functional connectivity of seven anatomically defined thalamic nuclear groups were examined: anterior, mediodorsal, ventral lateral, ventral posterolateral, pulvinar, medial and lateral geniculate nuclear groups. Age and sex effects were characterized using complementary thalamic region-of-interest (ROI) to cortical ROI and voxel-wise analyses. Effects of clinical symptomatology were analyzed by separating youth into three groups based on their clinical symptoms; typically developing youth (n = 298), psychosis spectrum youth (n = 320), and youth with other psychopathologies (n = 482). As an exploratory analysis, association with PRIME scores were used as a dimensional measure of psychopathology. Age effects were broadly characterized by decreasing connectivity with sensory/motor cortical areas, and increasing connectivity with heteromodal prefrontal and parietal cortical areas. This pattern was most pronounced for thalamic motor and sensory nuclei. Females showed greater connectivity between multiple thalamic nuclear groups and the visual cortex compared to males, while males showed greater connectivity with the inferior frontal and orbitofrontal cortices. Youth with psychosis spectrum symptoms showed a subtle decrease in thalamic connectivity with the premotor and prefrontal cortices. Across all youth, greater PRIME scores were associated with lower connectivity between the prefrontal cortex and mediodorsal thalamus. By characterizing typical development in anatomically constrained thalamocortical networks, this study provides an anchor for conceptualizing disruptions to the integrity of these networks observed in neurodevelopmental disorders.

Development of brain behavior integration systems related to criminal culpability from childhood to young adulthood: Does it stop at 18 years?

The rate at which the brain develops from conception to adulthood, and how that maturational process relates to brain's product, behavior, has been a major topic for the neurosciences with implications to legal practice, most dramatically in the case of the death penalty. Based partly on neuroscience evidence for protracted maturation of executive brain systems, the US Supreme Court ruled in 2005 that capital punishment is unconstitutional for crimes committed while under the age of 18. Much more data have since been acquired on multiple indices of brain development and behavior, and a question now facing the courts is whether the cutoff age of 18 has a scientific basis or, from the developmental neuroscience evidence, it is arbitrary and in need for re-evaluation. Here I present methods used by neuroscientists to measure parameters of brain development related to complex behavior, and summarize the major findings to date. Since 2005, there have been several large-scale studies revealing multimodal brain parameters related to major domains of behavior. These studies converge to show protracted development of brain tissue and its connectivity, as well as physiologic parameters of resting-state and task-related activity, which underlie the ability of the brain to process and integrate information. The developmental course of these differences does not reach its apex at 18, indeed it continues into the early 20s. I conclude that this 18 and older population, probably up to around age 23, is therefore just as vulnerable to the effects on behavior (especially behavioral control) of the lack of maturation of these brain structures and functions as are 17-year-olds.

Predicting brain age during typical and atypical development based on structural and functional neuroimaging

Exploring typical and atypical brain developmental trajectories is very important for understanding the normal pace of brain development and the mechanisms by which mental disorders deviate from normal development. A precise and sex-specific brain age prediction model is desirable for investigating the systematic deviation and individual heterogeneity of disorders associated with atypical brain development, such as autism spectrum disorders. In this study, we used partial least squares regression and the stacking algorithm to establish a sex-specific brain age prediction model based on T1-weighted structural magnetic resonance imaging and resting-state functional magnetic resonance imaging. The model showed good generalization and high robustness on four independent datasets with different ethnic information and age ranges. A predictor weights analysis showed the differences and similarities in changes in structure and function during brain development. At the group level, the brain age gap estimation for autistic patients was significantly smaller than that for healthy controls in both the ABIDE dataset and the healthy brain network dataset, which suggested that autistic patients as a whole exhibited the characteristics of delayed development. However, within the ABIDE dataset, the premature development group had significantly higher Autism Diagnostic Observation Schedule (ADOS) scores than those of the delayed development group, implying that individuals with premature development had greater severity. Using these findings, we built an accurate typical brain development trajectory and developed a method of atypical trajectory analysis that considers sex differences and individual heterogeneity. This strategy may provide valuable clues for understanding the relationship between brain development and mental disorders.

Similar but distinct - Effects of different socioeconomic indicators on resting state functional connectivity: Findings from the Adolescent Brain Cognitive Development (ABCD) Study®

Early socioeconomic status (SES) has consistently been associated with child health and cognitive outcomes, in addition to alterations in brain function and connectivity. The goal of the present study was to probe the effects of different facets of SES (parent education, income, and neighborhood disadvantage), that likely represent varying aspects of the environment, on resting state functional connectivity (rsFC). We investigated this question in a large sample of 9475 children (aged 9–10 years) from the Adolescent Brain Cognitive Development (ABCD) Study. Specifically, we analyzed the association between household SES (parent education, income-to-needs ratio) and neighborhood disadvantage, and system-level rsFC using within-sample split-half replication. We then tested whether the associations were unique to each SES measure, and whether household SES and neighborhood disadvantage had interactive effects on rsFC. SES measures had both common and distinct effects on rsFC, with sensory-motor systems (e.g., sensorimotor network) and cognitive networks (e.g., front-parietal network) particularly implicated. Further, the association between neighborhood disadvantage and sensorimotor network connectivity was less pronounced in the presence of high income-to-needs. Findings demonstrate that different facets of SES have distinct and interacting effects on rsFC, highlighting the importance of considering different indicators when studying the effects of SES on the brain.

Multi-channel attention-fusion neural network for brain age estimation: Accuracy, generality, and interpretation with 16,705 healthy MRIs across lifespan

Brain age estimated by machine learning from T1-weighted magnetic resonance images (T1w MRIs) can reveal how brain disorders alter brain aging and can help in the early detection of such disorders. A fundamental step is to build an accurate age estimator from healthy brain MRIs. We focus on this step, and propose a framework to improve the accuracy, generality, and interpretation of age estimation in healthy brain MRIs. For accuracy, we used one of the largest sample sizes (N = 16,705). For each subject, our proposed algorithm first explicitly splits the T1w image, which has been commonly treated as a single-channel 3D image in other studies, into two 3D image channels representing contrast and morphometry information. We further proposed a "fusion-with-attention" deep learning convolutional neural network (FiA-Net) to learn how to best fuse the contrast and morphometry image channels. FiA-Net recognizes varying contributions across image channels at different brain anatomy and different feature layers. In contrast, multi-channel fusion does not exist for brain age estimation, and is mostly attention-free in other medical image analysis tasks (e.g., image synthesis, or segmentation), where treating channels equally may not be optimal. For generality, we used lifespan data 0-97 years of age for real-world utility; and we thoroughly tested FiA-Net for multi-site and multi-scanner generality by two phases of cross-validations in discovery and replication data, compared to most other studies with only one phase of cross-validation. For interpretation, we directly measured each artificial neuron's correlation with the chronological age, compared to other studies looking at the saliency of features where salient features may or may not predict age. Overall, FiA-Net achieved a mean absolute error (MAE) of 3.00 years and Pearson correlation r=0.9840 with known chronological ages in healthy brain MRIs 0-97 years of age, comparing favorably with state-of-the-art algorithms and studies for accuracy and generality across sites and datasets. We also provided interpretations on how different artificial neurons and real neuroanatomy contribute to the age estimation.

Theory of mind performance and prefrontal connectivity in adolescents at clinical high risk for psychosis

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Onset of psychosis was linked to a lack of age-related improvement in theory of mind.

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Reduced prefrontal connectivity preceded onset of psychosis in high risk youth.

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High risk youth with lower prefrontal connectivity were at greatest risk of psychosis.

Theory of mind(ToM) impairment is a key feature of psychotic disorders and has been documented in individuals at clinical high-risk for psychosis (CHR), suggesting that it may predate illness onset. However, no study to date has examined brain functional correlates of ToM in individuals at CHR during adolescence. The “Reading-the-Mind-in-the-Eyes” test was used to measure ToM performance in 50 CHR youth, 15 of whom transitioned to psychosis (CHR-t) at follow-up (12 ± 6 months) and 36 healthy volunteers. Resting-state functional MRI was acquired to evaluate functional connectivity within the default mode network. Group by age interaction revealed an age-positive association in ToM performance in healthy volunteers, which was not present in adolescents at CHR-t. Intrinsic functional connectivity in the medial prefrontal cortex was reduced in adolescents at CHR-t relative to those who did not transition and to healthy volunteers. Survival analyses revealed that participants at CHR with lower medial prefrontal cortex connectivity were at greatest risk of developing psychosis at follow-up. We demonstrate that lack of age-related maturation of ToM and reduced medial prefrontal cortex connectivity both precede the onset of psychosis during adolescence. Medial prefrontal cortex connectivity holds potential as a brain-based marker for the early identification of transition to psychosis.

The Speed of Development of Adolescent Brain Age Depends on Sex and Is Genetically Determined

Children and adolescents show high variability in brain development. Brain age-the estimated biological age of an individual brain-can be used to index developmental stage. In a longitudinal sample of adolescents (age 9-23 years), including monozygotic and dizygotic twins and their siblings, structural magnetic resonance imaging scans (N = 673) at 3 time points were acquired. Using brain morphology data of different types and at different spatial scales, brain age predictors were trained and validated. Differences in brain age between males and females were assessed and the heritability of individual variation in brain age gaps was calculated. On average, females were ahead of males by at most 1 year, but similar aging patterns were found for both sexes. The difference between brain age and chronological age was heritable, as was the change in brain age gap over time. In conclusion, females and males show similar developmental ("aging") patterns but, on average, females pass through this development earlier. Reliable brain age predictors may be used to detect (extreme) deviations in developmental state of the brain early, possibly indicating aberrant development as a sign of risk of neurodevelopmental disorders.

Unraveling the Consequences of Childhood Maltreatment: Deviations From Typical Functional Neurodevelopment Mediate the Relationship Between Maltreatment History and Depressive Symptoms

BACKGROUND

Childhood maltreatment is associated with lifelong psychiatric sequelae. However, our understanding of neurobiological mechanisms responsible for this association is limited. Childhood maltreatment may confer risk for psychopathology by altering neurodevelopmental trajectories during childhood and adolescence. Longitudinal research, which is essential for examining this question, has been limited.

METHODS

We investigated maltreatment-associated alterations in the development of neural circuitry. Associations between cumulative childhood maltreatment (assessed using a dimensional measure of abuse and neglect via the Childhood Trauma Questionnaire) and the longitudinal development of resting-state functional connectivity (rsFC) were examined in 130 community-residing adolescents. Functional magnetic resonance imaging data were acquired at age 16 (T1; mean ± SD age, 16.46 ± 0.52 years; 66 females) and age 19 (T2; mean follow-up period, 2.35 years; n = 90 with functional magnetic resonance imaging data at both time points).

RESULTS

We found maltreatment to be associated with widespread longitudinal increases in rsFC, primarily between default mode, dorsal attention, and frontoparietal systems. We also found sex-dependent increased maltreatment-associated rsFC in male participants in salience and limbic circuits. Cross-sectional analyses revealed a shift in maltreatment-related rsFC alterations, which were localized to subcortical and sensory circuits at T1 and to frontal circuits at T2. Finally, longitudinal increases in rsFC connectivity mediated the relationship between childhood maltreatment and increased depressive symptoms.

CONCLUSIONS

To our knowledge, this is the first study to examine longitudinal maltreatment-related alterations in rsFC in adolescents. Our findings shed light on the neurodevelopmental consequences of childhood maltreatment and provide evidence for their role in risk for depression.

Using neuroimaging to predict brain age: insights into typical and atypical development and risk for psychopathology

Childhood and adolescence are characterized by complex patterns of changes in brain structure and function. Recently, Truelove-Hill et al. (Truelove-Hill M, Erus G, Bashyam V, Varol E, Sako C, Gur RC, Gur RE, Koutsouleris N, Zhuo C, Fan Y, Wolf DH, Satterthwaite TD, Davatzikos C. J Neurosci 40: 1265-1275, 2020) used a novel machine learning algorithm to capture the subtle nuances of brain maturation during adolescence in two indices based on predicted brain age. In this article, we present an overview of the brain age prediction model used, provide further insight into the utility of this multimodal index to explore typical and atypical development, and discuss avenues for future research.