| Perception of social experiences and cortical thickness change together throughout early adolescence: findings from the ABCD cohort | 10.15154/1524763 | Early adolescence is a dynamic period of social and cortical development amidst rapid hormonal and puberty changes. We examined how differences and changes in positive social experiences and cortical thickness co-develop from age 9-11 and 11-13 years in the ABCD cohort (N~12,000). We used Bivariate Latent Change Score Models to capture cortical development (modelling mean whole-brain cortical thickness) and positive social experiences (modelling caregiver monitoring, family cohesion, prosocial behaviour, number of friends, school engagement, school involvement, and neighborhood safety). We found that positive social experiences decreased between age 9-11 years (baseline) and 11-13 years (2-year-follow-up), indicating that social experiences were perceived as less positive over time. We found evidence for correlated change, such that a greater reduction in positive social experiences was associated with a greater reduction in cortical thickness (est=5.23, SE=1.31, p<.001, standardized coefficient=.08), which did not differ between males and females in early and late puberty stages. We found mixed evidence for sex-specific relationships between puberty stage and social experiences. The evidence supports a transactional model of development, in that positive social experiences and cortical thickness change together throughout early adolescence. The findings also highlight the importance of supporting youth in early adolescence through school transitions. | 652/13803 | Secondary Analysis | Shared |
| Youth Screen Media Activity Patterns and Associations With Behavioral Developmental Measures and Resting-state Brain Functional Connectivity | 10.15154/1526465 | Objective Screen media activity (SMA) consumes considerable time in youth’s lives, raising concerns about the effects it may have on youth development. Disentangling mixed associations between youth’s SMA and developmental measures should move beyond overall screen time and consider types and patterns of SMA. We aimed to identify reliable and generalizable SMA patterns among youth and examine their associations with behavioral developmental measures and developing brain functional connectivity. Method Three waves of the Adolescent Brain and Cognitive Development (ABCD) data were examined. The Lifespan Human Connectome Project in Development (HCP-D) was interrogated as an independent sample. ABCD participants included 11,878 children at baseline. HCP-D participants included 652 children and adolescents. Youth-reported SMA and behavioral developmental measures (neurocognitive performance, behavioral problems, psychotic-like experiences, impulsivity, and sensitivities to punishment/reward) were assessed with validated instruments. We identified SMA patterns in the ABCD baseline data using K-means clustering and sensitivity analyses. The generalizability and stability of the identified SMA patterns were examined in HCP-D data and ABCD follow-up waves, respectively. Relationships were examined between SMA patterns and behavioral and brain (resting-state brain functional connectivity [RSFC]) measures using linear-mixed-effect modelling with false-discovery-rate (FDR) correction. Results SMA data from 11,815 children (Meanage = 119.0 months, SDage = 7.5; 6,159 (52.1%) boys) were examined, and 3,151 (26.7%) demonstrated a video-centric higher-frequency SMA pattern and 8,666 (73.3%) demonstrated a lower-frequency pattern. The SMA patterns were validated in similarly-aged HCP-D youth. Compared to the lower-frequency-SMA-pattern group, the video-centric-higher-frequency-SMA-pattern group showed poorer neurocognitive performance (Beta=-0.12, 95%CI, [-0.08, -0.16], FDR-corrected p<.001), more total behavioral problems (Beta=0.13, 95%CI, [0.09, 0.18], FDR-corrected p<.001), and more psychotic-like experiences (Beta=0.31, 95%CI, [0.27, 0.36], FDR-corrected p<.001). The video-centric-higher-frequency-SMA-pattern group demonstrated higher impulsivity, more sensitivity to punishment/reward and altered RSFC among brain areas implicated previously in cognitive processes. Most of the associations persisted with age in the ABCD data, with more individuals (n=3,378, 30.4%) in the video-centric higher-frequency SMA group at one-year follow-up. A social-communication-centric SMA pattern was observed in HCP-D adolescents. Conclusion Video-centric SMA patterns are reliable and generalizable during late childhood. A higher-frequency-video-entertainment-SMA-pattern group showed altered RSFC and poorer developmental measures that persisted longitudinally. The findings suggest public health strategies aiming to decrease excessive time spent by children on video-entertainment-related SMA are needed. Further studies are needed to examine potential video-centric/social-centric SMA bifurcation to understand dynamic changes and trajectories of SMA patterns and related outcomes developmentally. | 652/12528 | Secondary Analysis | Shared |
| Longitudinal development of sex differences in the limbic system is associated with age, puberty and mental health | 10.15154/zk5y-pc91 | Sex differences in mental health become more evident across adolescence, with a two-fold increase of prevalence of mood disorders in females compared to males. The brain underpinnings remain understudied. Here, we investigated the role of age, puberty and mental health in determining the longitudinal development of sex differences in brain structure. We captured sex differences in limbic and non-limbic structures using machine learning models trained in cross-sectional brain imaging data of 1132 youths, yielding limbic and non-limbic estimates of brain sex. Applied to two independent longitudinal samples (total: 8184 youths), our models revealed pronounced sex differences in brain structure with increasing age. For females, brain sex was sensitive to pubertal development (menarche) over time and, for limbic structures, to mood-related mental health. Our findings highlight the limbic system as a key contributor to the development of sex differences in the brain and the potential of machine learning models for brain sex classification to investigate sex-specific processes relevant to mental health. | 652/12479 | Secondary Analysis | Shared |
| Environmental Risk Factors and Psychotic-like Symptoms in Children Aged 9-11 | 10.15154/1519221 | Objective: Research implicates environmental risk factors, including correlates of urbanicity, deprivation, and environmental toxins, in psychotic-like experiences (PLEs). The current study examined associations between several types of environmental risk factors and PLEs in school-age children, whether these associations were specific to PLEs or generalized to other psychopathology, and examined possible neural mechanisms for significant associations.
Method: The current study used data from 10,328 9-11-year-olds from the Adolescent Brain Cognitive Development (ABCD) study. Hierarchical linear models examined associations between PLEs and geocoded environmental risk factors, and whether associations generalized to internalizing/externalizing symptoms. Mediation models examined whether structural MRI abnormalities (e.g., intracranial volume) mediated associations between PLEs and environmental risk factors.
Results: The results found specific types of environmental risk factors, namely measures of urbanicity (i.e., drug offense exposure, less perception of neighborhood safety), deprivation (including overall deprivation, rate of poverty, fewer years at residence), and lead exposure risk, were associated with PLEs. These associations showed evidence of stronger associations with PLEs than internalizing/externalizing symptoms (especially overall deprivation, poverty, drug offense exposure, and lead exposure risk). There was evidence that brain volume mediated between 11-25% of the associations between poverty, perception of neighborhood safety, and lead exposure risk with PLEs.
Conclusions: These results are the first to find support for neural measures partially mediating the association between PLEs and environmental exposures. Furthermore, the current study replicated and extended recent findings of the association between PLEs and environmental exposures, finding evidence for specific associations with correlates of urbanicity, deprivation, and lead exposure risk.
| 11/11898 | Secondary Analysis | Shared |
| Associations Among Environmental Unpredictability, Changes in Resting-State Functional Connectivity, and Adolescent Psychopathology in the ABCD study | 10.15154/jeex-3768 | Background: Unpredictability is a core but understudied dimension of adversity and has been receiving increasing attention recently. The effects of unpredictability on psychopathology and the underlying neural mechanisms, however, remain unclear. It is also unknown how unpredictability interacts with other dimensions of adversity in predicting brain development and psychopathology of youth.
Methods: We applied cluster robust standard errors accounting for clustering structure to examine how unpredictability alters the developmental changes in resting-state functional connectivity (rsFC) of large-scale brain networks implicated in psychopathology, as well as the moderating role of deprivation, using data from the Adolescent Brain Cognitive Development (ABCD) study, which included four measurements from baseline (mean ± SD age, 119.13 ± 7.51 months; 2815 females) to 3-year follow-up (N = 5885).
Results: After controlling threat, unpredictability was associated with decreased rsFC within default mode network (DMN) and increased rsFC between cingulo-opercular network (CON) and DMN. Neighborhood educational deprivation moderated the associations between unpredictability and changes in rsFC within DMN and fronto-parietal network (FPN), as well as between CON and DMN. Changes in rsFC between CON and DMN mediated the association between unpredictability and externalizing problems. Neighborhood educational deprivation moderated the indirect pathway from unpredictability to externalizing problems via changes in rsFC between CON and DMN.
Conclusions: Our findings shed light on the neural mechanisms underlying the association between unpredictability and adolescents’ psychopathology and the moderating role of deprivation, highlighting the significance of providing stable environment and abundant educational opportunities to facilitate optimal development. | 1/11868 | Secondary Analysis | Shared |
| A multi-cohort study of resting-state connectivity alterations in attention-deficit/hyperactivity disorder | 10.15154/1527788 | Most studies examining connectomic abnormalities associated with ADHD have used small, underpowered samples and thus produced inconsistent findings. Here we combined data from the Adolescent Brain Cognitive Development (ABCD) and Lifespan Human Connectome Project Development (HCP-D) cohorts (NDAR collections #2573, #2846 and #3165), as well as datasets from non-NDAR sources including the ADHD-200, Healthy Brain Network (HBN), National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) and Neurobehavioral Clinical Research (NCR) cohorts. We aimed to identify network-level resting-state features associated ADHD diagnosis and traits in this large multi-cohort sample. We applied the same 36-parameter+despiking pipeline to subjects from all datasets, and combined data using mega-analytic mixed models, which included nested random intercepts for study, site and family ID. In the group comparison, we compared 1301 subjects with diagnosed ADHD against 1301 unaffected controls (total N=2,602; 1710 males (65.72%); mean age=10.86 years, sd=2.05). Patients and controls were 1:1 nearest neighbor matched on in-scanner motion and key demographic variables. Associations between ADHD-traits and resting-state connectivity were assessed in a large multi-cohort sample (N=10,113). ADHD diagnosis was associated with less anticorrelation between the default mode and salience/ventral attention (B=0.009, t=3.45, p-FDR=0.004, d=0.14, 95% CI=0.004, 0.014), somatomotor (B=0.008, t=3.49, p-FDR=0.004, d=0.14, 95% CI=0.004, 0.013), and dorsal attention networks (B=0.01, t=4.28, p-FDR<0.001, d=0.17, 95% CI=0.006, 0.015). These results were robust to sensitivity analyses considering comorbid internalizing problems, externalizing problems and psychostimulant medication. Similar findings were observed when examining ADHD traits. Finding associations between ADHD and connectivity of the default mode to task-positive networks is consistent with default mode network models of disorder, although all effect sizes were small. | 494/8817 | Secondary Analysis | Shared |
| Lifespan development of brain asymmetry | 10.15154/3er3-dc69 | Lateralization is a fundamental principle of structural brain organization. In vivo imaging of brain asymmetry is essential for deciphering lateralized brain functions and their disruption in neurodevelopmental and neurodegenerative disorders. Here, we present a normative framework for benchmarking brain asymmetry across the lifespan, developed from an aggregated sample of 128 primary neuroimaging studies, including 177,701 scans from 138,231 individuals, jointly spanning the age range from 20 post menstrual weeks to 102 years. This resource includes comprehensive, hemisphere-specific brain growth charts for multiple neuroimaging phenotypes: regional cortical grey matter volume, thickness, surface area, and subcortical volumes. Our findings reveal distinct spatial patterns of asymmetry, with early leftward asymmetry observed in association cortices and late rightward asymmetry in sensory regions. These trajectories support theories of the neuroplasticity of asymmetry and the role of both genetic and environmental factors in shaping brain lateralization. Additionally, we provide tools to generate asymmetry centile scores, which allow the quantification of individual deviations from typical asymmetry throughout the lifespan and can be applied to unseen data or clinical populations. We demonstrate the utility of these models by highlighting group-level differences in asymmetry in autism spectrum disorder, schizophrenia, and Alzheimer’s disease, and exploring genetic correlations with hemispheric specialization. To facilitate further research, we have made this normative framework freely available as an interactive open-access resource (upon publication), offering an essential tool to advance both basic and clinical neuroscience. | 652/8552 | Secondary Analysis | Shared |
| ComBatLS: A location- and scale-preserving method for multi-site image harmonization | 10.15154/sr0j-g796 | Recent work has leveraged massive datasets and advanced harmonization methods to construct normative models of neuroanatomical features and benchmark individuals’ morphology. However, current harmonization tools do not preserve the effects of biological covariates including sex and age on features’ variances; this failure may induce error in normative scores, particularly when such factors are distributed unequally across sites. Here, we introduce a new extension of the popular ComBat harmonization method, ComBatLS, that preserves biological variance in features’ locations and scales. We use UK Biobank data to show that ComBatLS robustly replicates individuals’ normative scores better than other ComBat methods when subjects are assigned to sex-imbalanced synthetic “sites”. Additionally, we demonstrate that ComBatLS significantly reduces sex biases in normative scores compared to traditional methods. Finally, we show that ComBatLS successfully harmonizes consortium data collected across over 50 studies. R implementation of ComBatLS is available at https://github.com/andy1764/ComBatFamily. | 652/6514 | Secondary Analysis | Shared |
| Evidence for embracing normative modeling | 10.15154/9c5r-0h50 | In this work, we expand the normative model repository introduced in Rutherford et al., 2022a to include normative models charting lifespan trajectories of structural surface area and brain functional connectivity, measured using two unique resting-state network atlases (Yeo-17 and Smith-10), and an updated online platform for transferring these models to new data sources. We showcase the value of these models with a head-to-head comparison between the features output by normative modeling and raw data features in several benchmarking tasks: mass univariate group difference testing (schizophrenia versus control), classification (schizophrenia versus control), and regression (predicting general cognitive ability). Across all benchmarks, we show the advantage of using normative modeling features, with the strongest statistically significant results demonstrated in the group difference testing and classification tasks. We intend for these accessible resources to facilitate the wider adoption of normative modeling across the neuroimaging community. | 652/2599 | Secondary Analysis | Shared |
| Optimizing Biophysical Large-Scale Brain Circuit Models With Deep Neural Networks | 10.15154/my03-az79 | Biophysical modeling provides mechanistic insights into brain function, spanning single-neuron dynamics to large-scale circuit models. These models are governed by biologically meaningful parameters, many of which can be experimentally measured. Some parameters are unknown, and optimizing them improves fit to experimental data, enhancing biological plausibility. However, existing methods require repeated, computationally expensive numerical integration of differential equations, limiting scalability to population-level datasets. Here, we introduce DELSSOME (DEep Learning for Surrogate Statistics Optimization in MEan field modeling), a framework that bypasses numerical integration by directly predicting whether parameter sets produce realistic brain dynamics. Across three large-scale circuit models, DELSSOME achieves a 1500-8000× speedup over numerical integration in predicting model realism. When embedded within an evolutionary optimization strategy, DELSSOME enables 50-100× faster parameter estimation without sacrificing agreement with numerical integration. Because of computational constraints, most studies simulate large-scale circuit models only at the group level. DELSSOME enables efficient individual-level optimization of the feedback inhibition control model. By collating 12,005 individuals across 14 datasets, we derive – for the first time – normative trajectories of cortical E/I ratio across the lifespan, revealing new insights into sex differences and network-specific patterns. This acceleration enables population-scale mechanistic modeling and unlocks new opportunities for understanding brain function. | 652/1377 | Secondary Analysis | Shared |
| Spectral normative modeling of brain structure | 10.15154/2kd4-ek70 | Normative modeling in neuroscience aims to characterize interindividual variation in brain phenotypes and establish reference ranges, or brain charts, against which individuals can be compared. Normative models are typically limited to coarse spatial scales due to computational constraints, limiting their spatial specificity. Furthermore, dependence on fixed parcellation atlases limits their adaptability to alternative parcellation schemes. To overcome these key limitations, we propose spectral normative modeling (SNM), which leverages brain eigenmodes to efficiently generate normative ranges for arbitrarily defined regions of interest. Training SNM on over 78,000 healthy brain scans, we generate accurate lifespan thickness growth charts across different spatial scales, from millimeters to the whole brain. These charts reveal three principal thickness growth gradients, aligning neurotypical cortical change with established anatomical, genetic, and functional hierarchies. We further demonstrate SNM’s utility by elucidating high-resolution individual cortical atrophy patterns that characterize the heterogeneous expression of neurodegeneration in Alzheimer’s disease. SNM lays the groundwork for a new generation of spatially precise brain charts, offering substantial potential to drive advances in individualized precision medicine. | 652/1377 | Secondary Analysis | Shared |
| Temporal and Spatial Scales of Human Resting-state Cortical Activity Across the Lifespan | 10.15154/5f0d-z111 | Sensorimotor and cognitive abilities undergo substantial changes throughout the human lifespan, but the corresponding changes in the functional properties of cortical network remain poorly understood. This can be studied using temporal and spatial scales of functional magnetic resonance imaging (fMRI) signals, which provide a robust description of the topological structure and temporal dynamics of neural activity. For example, timescales of resting-state fMRI signals can parsimoniously predict a significant amount of the individual variability in functional connectivity networks identified in adult human brains. In the present study, we quantified and compared temporal and spatial scales in resting-state fMRI data collected from 2,352 subjects between the ages of 5 and 100 in Developmental, Young Adult, and Aging datasets from Human Connectome Project. For most cortical regions, we found that both temporal and spatial scales largely decreased with age across most cortical areas throughout the lifespan, with the visual cortex and the limbic network consistently showing the largest and smallest scales, respectively. For some prefrontal regions, however, these two scales displayed non-monotonic trajectories during adolescence and peaked around the same time during adolescence and decreasing throughout the rest of the lifespan. We also found that cortical myelination increased monotonically throughout the lifespan, and its rate of change was significantly correlated with the changes in both temporal and spatial scales across different cortical regions in adulthood. These findings suggest that temporal and spatial scales in fMRI signals, as well as cortical myelination, are closely coordinated during both development and aging. | 652/1377 | Secondary Analysis | Shared |
| The structure of neuroanatomical variation within bilinguals | 10.15154/1528104 | We have developed this CVAE method that was useful for Autism (Aglinskas et al 2022). We think it might be useful for bilingualism research because bilingualism is similarly variable. We need access to this database to test our ideas. | 652/1375 | Secondary Analysis | Shared |
| Release the Krakencoder: A unified brain connectome translation and fusion tool | 10.15154/per3-s328 | Brain connectivity can be estimated in many ways, depending on modality and processing strategy. Here we present the Krakencoder, a joint connectome mapping tool that simultaneously, bidirectionally translates between structural (SC) and functional connectivity (FC), and across different atlases and processing choices via a common latent representation. These mappings demonstrate unprecedented accuracy and individual-level identifiability; the mapping between SC and FC has identifiability 42-54% higher than existing models. The Krakencoder combines all connectome flavors via a shared low-dimensional latent space. This “fusion” representation i) better reflects familial relatedness, ii) preserves age- and sex-relevant information and iii) enhances cognition-relevant information. The Krakencoder can be applied without retraining to new, out-of-age-distribution data while still preserving inter-individual differences in the connectome predictions and familial relationships in the latent representations. The Krakencoder is a significant leap forward in capturing the relationship between multi-modal brain connectomes in an individualized, behaviorally- and demographically-relevant way. | 608/1324 | Secondary Analysis | Shared |
| Human Connectome Project-Development (HCP-D) Release 1.0 | 10.15154/1503530 | Initial release of data from the Human Connectome Project in Development (ages 5-21). Release includes basic demographic data (sex, age, race/ethnicity, handedness) and unprocessed imaging data for all modalities (structural, resting state fMRI, task fMRI, diffusion, and ASL) for 655 subjects and preprocessed structural imaging data for 84 subjects. Full release documentation available at: https://www.humanconnectome.org/study/hcp-lifespan-development/documentation.
| 655/655 | Primary Analysis | Shared |
| Creating a population-averaged structural connectomic brain atlas dataset from HCP-aging subjects | 10.15154/wjhf-4v02 | Population-averaged brain atlases, that are represented in a standard space with anatomical labels, are instrumental tools in neurosurgical planning and the study of neurodegenerative conditions. Traditional brain atlases are primarily derived from anatomical scans and contain limited information regarding the axonal organization of the white matter. With the advance of diffusion MRI that allows the modeling of fiber orientation distribution (FOD) in the brain tissue, there is an increasing interest for a population-averaged FOD template, especially based on a large healthy aging cohort, to offer structural connectivity information for connectomic surgery and analysis of neurodegeneration. We will create a set of multi-contrast structural connectomic MRI atlases, including T1w, T2w, and FOD templates, along with the associated whole brain tractograms. The templates will be made using multi-contrast group-wise registration based on 3T MRIs of a large cohort of subjects from the Human Connectome Project in Aging (HCP-A). To enhance the usability, probabilistic tissue maps and segmentation of 22 subcortical structures will be provided. Finally, the subthalamic nucleus shown in the atlas will be parcellated into sensorimotor, limbic, and associative sub-regions based on their structural connectivity to facilitate the analysis and planning of deep brain stimulation procedures. | 652/652 | Primary Analysis | Shared |
| Decoding Age-specific Changes in Brain Functional Connectivity Using a Sliding-window Based Clustering Method | 10.15154/1528403 | Functional magnetic resonance imaging (fMRI) permits detailed study of human brain function. Understanding the age-specific development of neural circuits in the typically developing brain may help us generate new hypotheses for developmental psychopathologies. Functional connectivity (FC), defined as the statistical associations between two brain regions, has been widely used in estimating functional networks from fMRI data. Previous research has shown that the evolution of FC does not follow a linear trend, particularly from childhood to young adulthood. Thus, this work aims to detect the nuanced FC changes with age from the non-linear curves and identify age-period-specific FC development patterns. We proposed a sliding-window based clustering approach to identify refined age interval of FC development. We used resting-state fMRI (rs-fMRI) data from the human connectome project-development (HCP-D), which recruited children, adolescents, and young adults aged from 5 to 21 years. Our analyses revealed different developmental patterns of resting-state FC by sex. In general, females matured earlier than males, but males had a faster development rate during age 100 -120 months. We identified four developmental phases: network construction in late childhood, segregation and integration construction in adolescence, network pruning in young adulthood, and a unique phase in males -- U-shape development. In addition, we investigated the sex effect on the slopes of FC-age correlation. Males had higher slopes during late childhood and young adulthood. These results inform trajectories of normal FC development, information that can in the future be used to pinpoint when development might go awry in neurodevelopmental disorders. | 652/652 | Secondary Analysis | Shared |
| Deviation in development of dorsal association tracts during preadolescence links to concurrent and future cognitive performance and transdiagnostic psychopathology | 10.15154/spz4-rf81 | Many psychiatric disorders begin during adolescence, coinciding with the rapid development of brain white matter (WM). However, it remains unclear whether deviations from normal WM development during this period contribute to psychopathology. In this study, we developed normative models of brain age based on specific WM tracts using three large-scale developmental datasets ( ~ 10,000 subjects). We found that tract-specific deviations in WM development of association and limbic/subcortical systems were linked to concurrent and future cognition and psychopathology. The spatial pattern of the association system aligned closely with high-order brain networks and mitochondrial maps. Importantly, delayed brain-age especially in dorsal association tracts predicted psychiatric disorders across diagnoses and disorder onset over a 2-year follow-up. By identifying tract-specific WM development during preadolescence as a predictor of cognitive capacity and psychiatric risks, this study provides a framework for tracking individualized brain development and understanding the neurobiological underpinnings of cognition and transdiagnostic psychopathology. | 652/652 | Secondary Analysis | Shared |
| Human Connectome Project-Development (HCP-D) Release 2.0 | 10.15154/1520708 | The 2.0 release of data from the Human Connectome Project in Development (healthy participants, ages 5-21) includes visit 1 (V1) preprocessed structural and functional imaging data, unprocessed V1 imaging data for all modalities (structural, resting state fMRI, task fMRI, diffusion, and ASL), and non-imaging demographic and behavioral assessment data for 652 participants. For details of all the measures included in this release and access instructions see the Lifespan HCP-Development Release 2.0 documentation link below. | 652/652 | Primary Analysis | Shared |
| Integrated brain connectivity analysis with fMRI, DTI, and sMRI powered by interpretable graph neural networks | 10.15154/qcw2-dq85 | Multimodal neuroimaging data modeling has become a widely used approach but confronts considerable challenges due to their heterogeneity, which encompasses variability in data types, scales, and formats across modalities. This variability necessitates the deployment of advanced computational methods to integrate and interpret diverse datasets within a cohesive analytical framework. In our research, we combine functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and structural MRI (sMRI) for joint analysis. This integration capitalizes on the unique strengths of each modality and their inherent interconnections, aiming for a comprehensive understanding of the brain’s connectivity and anatomical characteristics. Utilizing the Glasser atlas for parcellation, we integrate imaging-derived features from multiple modalities—functional connectivity from fMRI, structural connectivity from DTI, and anatomical features from sMRI—within consistent regions. Our approach incorporates a masking strategy to differentially weight neural connections, thereby facilitating an amalgamation of multimodal imaging data. This technique enhances interpretability at the connectivity level, transcending traditional analyses centered on singular regional attributes. The model is applied to the Human Connectome Project’s Development study to elucidate the associations between multimodal imaging and cognitive functions throughout youth. The analysis demonstrates improved prediction accuracy and uncovers crucial anatomical features and neural connections, deepening our understanding of brain structure and function. This study not only advances multimodal neuroimaging analytics by offering a novel method for integrative analysis of diverse imaging modalities but also improves the understanding of intricate relationships between brain’s structural and functional networks and cognitive development. | 652/652 | Secondary Analysis | Shared |
| Local-global functional gradients of the thalamus capturing different aspects of thalamic structure and function | 10.15154/4vvp-qn12 | The thalamus, a core hub positioned deep in the center of the brain, has a highly complex inter-regional communication pattern. The thalamic functional connectome has received increasing attention, showcasing its pivotal role in various high-order cognitive processes and development. However, thalamic connectome profiles across different spatial scales have not been systematically investigated. More specifically, it is unknown how each thalamic voxel is functionally connected to one another on a local level, and how thalamic voxels are functionally embedded within large-scale cortical systems on a global level. Leveraging the recent connectopic gradient mapping techniques and incorporating our methodological updates, we shed light on the thalamic local-global connectome profiles by characterizing their respective functional gradients. We show that the local gradients of the thalamus primarily reflect its internal anatomical configuration, while its global counterparts predominantly reflect the thalamus’s inherent role in supporting various cognitive functions. | 652/652 | Secondary Analysis | Shared |
| Spatiotemporal patterns of cortical microstructural maturation in children and adolescents with diffusion MRI | 10.15154/nzry-he54 | Neocortical maturation is a dynamic process that proceeds in a hierarchical manner; however, the spatiotemporal organization of cortical microstructure with diffusion MRI has yet to be fully defined. This study characterized cortical microstructural maturation using diffusion MRI (fwe-DTI and NODDI multi-compartment modeling) in a cohort of 637 children and adolescents between 8 and 21 years of age. We found spatially heterogeneous developmental patterns broadly demarcated into functional domains where NODDI metrics increased, and fwe-DTI metrics decreased with age. By applying nonlinear growth models in a vertex-wise analysis, we observed a general posterior-to-anterior pattern of maturation, where the fwe-DTI measures mean diffusivity (MD) and radial diffusivity (RD) reached peak maturation earlier than the NODDI metrics neurite density index. Using non-negative matrix factorization, we found occipito-parietal cortical regions that correspond to lower-order sensory domains mature earlier than fronto-temporal higher-order association domains. Our findings corroborate previous histological and neuroimaging studies that show spatially-varying patterns of cortical maturation that may reflect unique developmental processes of cytoarchitectonically-determined regional patterns of change. | 652/652 | Primary Analysis | Shared |
| Revealing the spatial pattern of brain hemodynamic sensitivity to healthy aging through sparse DCM | 10.15154/hk8k-pm84 | Age-related changes in the BOLD response could reflect neuro-vascular coupling modifications rather than simply impairments in neural functioning. In this study, we propose the use of a generative dynamic causal model (DCM) to decouple neuronal and vascular factors in the BOLD signal, with the aim of characterizing the whole-brain spatial pattern of hemodynamic sensitivity to healthy aging, as well as to test the role of hemodynamic features as independent predictors in an age-classification model.
In this view, DCM was applied to the resting-state fMRI data of a cohort of 126 healthy individuals in a wide age range, providing reliable estimates of the hemodynamic response function (HRF) for each subject and each region of interest. Then, some features characterizing each HRF curve were extracted and used to fit a multivariate logistic regression model to predict the age class of each individual. Ultimately, we tested the final predictive model on an independent dataset of 338 healthy subjects selected from the Human Connectome Project Aging (HCP-A) and Development (HCP-D) cohorts. Our results entail the spatial heterogeneity of the age effects on the hemodynamic component, since its impact resulted to be strongly region- and population-specific, discouraging any space-invariant corrective procedures that attempt to correct for vascular factors when carrying out functional studies involving groups with different ages. Moreover, we demonstrated that a strong interaction exists between some specific hemodynamic features and age, further supporting the essential role of the hemodynamic factor as independent predictor of biological aging, rather than a simple confounding variable.
| 152/641 | Secondary Analysis | Shared |
| How does machine learning bias in prediction of behavioral phenotypes relate to individual characteristics? | 10.15154/8e8k-jf34 | Brain-based prediction of behavioral phenotypes is promising for precision medicine. However, predictive models in the field have commonly shown limited accuracy and individual bias. For instance, participants of ethnic/racial minority (e.g., African Americans) are more likely to be misclassified or show larger prediction errors, compared to white American participants. Addressing these challenges requires a better understanding of the brain-based and sociodemographic factors related to prediction biases. Here, we investigated the association between the prediction errors of brain-based models predicting over 100 behavioral measures and eight variables of sociodemographic or scan-related individual characteristics. These associations are examined in two developmental and one young adult cohorts (sample size = 4278, 432, and 784 respectively). We found that mental health measures in developmental cohorts tend to be more easily biased by the confounding factors. In particular, head motion, head size, and sex/gender are consistently related to the machine learning biases in multiple behavioral measures. Our work highlights the multifactorial nature of the prediction errors generated by the brain-based predictive models. Overall, our results call for greater attention to model bias in developmental population, in particular when developing AI-based approaches for the early diagnosis of psychiatric disorders. | 610/610 | Secondary Analysis | Shared |
| Brain Topology Underlying Executive Functions Across the Lifespan: Focus on the Default Mode Network | 10.15154/pvjz-h270 | While traditional neuroimaging approaches to the study of executive functions (EFs) have typically employed task-evoked paradigms, resting state studies are gaining popularity as a tool for investigating inter-individual variability in the functional connectome and its relationship to cognitive performance outside of the scanner. Using resting state functional magnetic resonance imaging data from the Human Connectome Project Lifespan database, the present study capitalised on graph theory to chart cross-sectional variations in the intrinsic functional organisation of the frontoparietal (FPN) and the default mode (DMN) networks in 500 healthy individuals (from 10 to 100 years of age), to investigate the neural underpinnings of EFs across the lifespan. Topological properties of both the FPN and DMN were predictive of EF performance, but not of a control task of picture naming, providing specificity in support for a tight link between neuro-functional and cognitive-behavioural efficiency within the EF domain. The topological organisation of the DMN, however, appeared more sensitive to age-related changes relative to that of the FPN. Because the DMN matures earlier in life than the FPN, it is more susceptible to neurodegenerative changes. Moreover, because its activity is stronger in conditions of resting state, the DMN might be easier to measure in noncompliant populations and in those at the extremes of the life-span curve, namely very young or elder participants. Here, we argue that the study of its functional architecture in relation to higher order cognition across the lifespan might, thus, be of greater interest compared with what has been traditionally thought. | 93/500 | Secondary Analysis | Shared |
| Characterising grey-white matter relationships in recent-onset psychosis and its association with cognitive function. | 10.15154/dbr7-8e09 | Individuals with recent-onset psychosis (ROP) present widespread grey matter (GM) reductions and white matter (WM) abnormalities. While prior studies used univariate approaches, understanding how multiple GM regions relate to WM tracts is important, as psychosis involves network-level brain dysfunction. Understanding characteristic GM-WM patterns may also clarify the basis of cognitive impairments, which are potentially linked to network dysfunction in psychosis. Using multivariate analysis, we examined whole-brain GM-WM relationships and their association with cognitive abilities in ROP. We used T1 and diffusion-weighted images from 71 non-affective ROP individuals (age 22.09 ± 3.08) and 71 matched controls (age 22.05 ± 3.21). We performed multiblock partial least squares correlation (MB-PLS-C) to identify GM-WM patterns based on GM thickness or surface area and WM fractional anisotropy (FA), and examined their associations with cognitive abilities. MB-PLS-C identified a 'GM thickness'-'WM FA' pattern representing group differences, explaining 12.38 % of the variance and associated with frontal and temporal GM regions and seven WM tracts around subcortical structures. MB-PLS-C also identified a 'GM surface area'-'WM FA' pattern showing group differences, explaining 18.92 % and related with cingulate, frontal, temporal, and parietal GM regions and 15 WM tracts, including the inferior cerebellar peduncle and corona radiata. The 'GM thickness'-'WM FA' pattern describing group differences was significantly correlated with processing speed in ROP. MB-PLS-C identified differential whole-brain GM-WM relationships, indicating a potential signature of brain alterations in ROP. Our findings of a relationship between processing speed and GM-WM patterns for GM thickness have implications for our understanding of brain-behaviour relationships in psychosis. | 230/466 | Primary Analysis | Shared |
| Preserved white matter structure at the grey matter–white matter interface despite widespread cortical thinning in early psychosis | 10.15154/fweb-pv54 | Background
Early psychosis is characterized by widespread cortical grey matter (GM) reductions. White matter (WM) alterations have also been reported. The GM–WM interface may represent a link between cortical GM and WM alterations that remains understudied. As the interface matures later in development than deep WM, it may be particularly vulnerable to neurodevelopmental disruptions in schizophrenia-spectrum disorders. However, its complex fiber architecture and proximity to the cortex pose challenges for conventional diffusion tensor imaging.
Methods
We applied an advanced diffusion MRI approach to assess fiber density (FD) in the GM–WM interface of 78 individuals with early psychosis (age 22.0 ± 3.0, 29.5% female) and 78 controls (age 21.8 ± 3.2, 29.5% female). We examined group differences in FD at the GM–WM interface and cortical measures, and assessed spatial correlations between regional effect sizes of FD and cortical measures.
Results
Widespread cortical thinning was observed, whereas no significant group differences were identified in FD at the GM–WM interface. This finding remained robust after accounting for partial-volume effects and was consistent with the template-space analysis. The spatial distribution of FD effect sizes showed significant positive correlations with those of cortical thickness and surface area.
Conclusions
Despite widespread cortical GM alterations, WM microstructure at the GM–WM interface was relatively preserved in early psychosis, while spatial correspondence between GM and WM measures at the interface was observed. These findings suggest that cortical alterations are pronounced in early psychosis, whereas WM changes may manifest later in the course of illness. | 230/404 | Primary Analysis | Shared |
| Psychological Resilience and Neurodegenerative Risk: A Connectomics-Transcriptomics Investigation in Healthy Adolescent and Middle-Aged Females | 10.15154/1526352 | Adverse life events can inflict substantial long-term damage, which, paradoxically, has been posited to stem from initially adaptative responses to the challenges encountered in one’s environment. Thus, identification of the mechanisms linking resilience against recent stressors to longer-term psychological vulnerability is key to understanding optimal functioning across multiple timescales. To address this issue, our study tested the relevance of neuro-reproductive maturation and senescence, respectively, to both resilience and longer-term risk for pathologies characterised by accelerated brain aging, specifically, Alzheimer’s Disease (AD). Graph theoretical and partial least squares analyses were conducted on multimodal imaging, reported biological aging and recent adverse experience data from the Lifespan Human Connectome Project (HCP). Availability of reproductive maturation/senescence measures restricted our investigation to adolescent (N =178) and middle-aged (N=146) females. Psychological resilience was linked to age-specific brain senescence patterns suggestive of precocious functional development of somatomotor and control-relevant networks (adolescence) and earlier aging of default mode and salience/ventral attention systems (middle adulthood). Biological aging showed complementary associations with the neural patterns relevant to resilience in adolescence (positive relationship) versus middle-age (negative relationship). Transcriptomic and expression quantitative trait locus data analyses linked the neural aging patterns correlated with psychological resilience in middle adulthood to gene expression patterns suggestive of increased AD risk. Our results imply a partially antagonistic relationship between resilience against proximal stressors and longer-term psychological adjustment in later life. They thus underscore the importance of fine-tuning extant views on successful coping by considering the multiple timescales across which age-specific processes may unfold. | 178/324 | Secondary Analysis | Shared |
| Genetic Risk Predicts Adolescent Mood Pathology via Sexual Differentiation of Brain Function and Physiological Aging | 10.15154/te4p-qr97 | Studies focused on dichotomous sex differences report that internalizing disorders are more prevalent among females. Recent evidence challenges this traditional approach to sex differences, indicating instead that the human brain is best described along a continuum of sexual differentiation. Thus, assessments of the degree to which a brain is sexually differentiated could provide more nuanced insights into the mechanisms underpinning mental ill-health. To examine the under-explored possibility that psychiatric risk varies with sexual differentiation in brain function, we use longitudinal (N = 199) and cross-sectional (N =277) data from male and female youths. 336 healthy young adults constitute the reference group for estimating neural functional coupling patterns that vary between males and females. In the longitudinal sample, sexual differentiation along a canonical sensory-association functional hierarchy at ages 9-10 correlates, in a sex-dependent manner, with pubertal development and immune/metabolic dysregulation at ages 11-12. The sexual differentiation and physiological profiles sequentially mediate the relationship between genetic risk and rising internalizing/externalizing symptoms. The links between sexual differentiation, physiology and psychopathology are replicated in the cross-sectional sample and shown to hold across sexes. Thus, our study emphasizes the importance of integrating continuous assessments of sexual differentiation and physiology in personalizing psychiatric intervention in adolescence. | 277/277 | Secondary Analysis | Shared |
| Modelling the Developing Connectome - Graph Representation Learning with Variational Autoencoders | 10.15154/1528540 | The functional development of the human brain exhibits high inter-subject variability, which is influenced by factors such as genetic predisposition, environmental influences and individual learning experiences. A broadly accessible imaging modality for analysis of the brain’s functional development is found in resting state functional Magnetic Resonance Imaging (rs-fMRI), a non-invasive imaging technique covering the whole brain. However, analysis of rs-fMRI data in the context of developmental studies is challenging. Compared to adults, paediatric cohorts show higher in-scanner movement, which leads to erroneous intensity changes in the signal. Such artefacts, if not accounted for, lead to systematic errors in subsequent rs-fMRI analysis. Furthermore, approaches for developmental rs-
fMRI analysis must be able to capture general trends rather than individual differences due to inter-subject variability. In this work, a framework for analysis of the topological properties of the developing brain is presented. First, a paediatric preprocessing pipeline is developed and evaluated for its performance in removing in-scanner-movement-related artefacts. Second, an adaption of the graph-based Variational Autoencoder (VAE) is developed for representation learning on brain graphs derived from developmental rs- fMRI data in the age range of 7.5 to 18.5 years. Additionally, a graph-based conditional VAE is proposed, with the goal to enhance the VAE representation learning abilities. Furthermore, the VAEs are compared to a Bayesian regression baseline. The evaluation of the VAE-based approaches is threefold: First, the models’ brain graph reconstruction capabilities are evaluated in terms of the Mean Squared Error (MSE) and a correlation-based error measure. Second, the quality of datasets generated by the models is measured in terms of Maximum Mean Discrepancy (MMD) based on graph statistics distributions. Finally, the ability to capture developmental trajectories of a developmental dataset is investigated by statistical analysis using Network Based Statistic (NBS). Results show that the developmental processes available in the used dataset cannot be modelled exactly by the presented modelling approaches. Additionally, extending the VAE with a condition does not improve its representation learning capabilities. However, trends, such as the continuous development of the Default Mode Network (DMN), are captured successfully by the graph-based VAE. | 249/249 | Primary Analysis | Shared |
| Effects of Youth Sport Participation on Neural Processing During Response Inhibition in Children | 10.15154/38w1-b557 | Purpose: Youth sports offer many benefits to developing children, but collision sports introduce additional risks from exposure to repetitive head impacts (RHIs). Past research has linked this exposure to reduced cognitive performance, including response inhibition. The current study aimed to probe how participation in collision sports affects inhibitory control in current youth collision sport athletes, compared to peers who participate in non-contact sports or non-sport activities.
Methods: We used functional magnetic resonance imaging (fMRI) data acquired in the context of a response inhibition (CARIT) task from the Lifespan Human Connectome Project Development (HCP-D) Study to investigate how inhibitory control processing may differ among three groups: youth athletes who currently participate in non-contact sports (n=70; 13.2±2.7 yrs), youth athletes who currently participate in collision sports (n=48; 12.9±2.6 yrs), and current youth participants of non-sport activities (n=57; 14.3±2.5 yrs). Group differences on task-based behavioral measures (accuracy, reaction time) were assessed using generalized linear models (GLMs). A whole-brain univariate fMRI analysis using a GLM approach was conducted to identify task-related regional differences in Blood Oxygenation Level Dependent signal.
Results: No group differences were observed in behavioral task performance (p-values above 0.242). However, there were differences in neural recruitment of the left Superior Temporal Gyrus region (MNI: -58, -40, 10; k = 102 voxels; peak voxel z-value = 4.24; p <. 001, cluster-corrected) when comparing the two sport groups to the non-sport activity group - athletes activated this region more than non-sport peers during inhibited response trials.
Conclusions: Sport participation may influence differential processing during active response inhibition, perhaps signaling differences in task strategy, however collision sport participation shows no distinct deleterious effect. | 175/175 | Primary Analysis | Shared |
| Local and global reward learning in the lateral frontal cortex show differential development during human adolescence | 10.15154/1528961 | Reward-guided choice is fundamental for adaptive behaviour and depends on several component processes supported by prefrontal cortex. Here, across three studies, we show that two such component processes, linking reward to specific choices and estimating the global reward state, develop during human adolescence and are linked to the lateral portions of the prefrontal cortex. These processes reflect the assignment of rewards contingently to local choices, or noncontingently, to choices that make up the global reward history. Using matched experimental tasks and analysis platforms, we show the influence of both mechanisms increase during adolescence (study 1) and that lesions to lateral frontal cortex (that included and/or disconnected both orbitofrontal and insula cortex) in human adult patients (study 2) and macaque monkeys (study 3) impair both local and global reward learning. Developmental effects were distinguishable from the influence of a decision bias on choice behaviour, known to depend on medial prefrontal cortex. Differences in local and global assignments of reward to choices across adolescence, in the context of delayed grey matter maturation of the lateral orbitofrontal and anterior insula cortex, may underlie changes in adaptive behaviour. | 60/60 | Primary Analysis | Shared |
