A longitudinal resource for studying connectome development and its psychiatric associations during childhood

Inverse relationship between nodal strength and nodal power: Insights from separate resting fMRI and EEG datasets

BACKGROUND

Regional neural response and network properties have traditionally been studied separately. However, growing evidence suggests a close interplay between regional activity and inter-regional connectivity. This study aimed to examine the relationship between global functional connectivity and regional spontaneous activity, termed the global-to-local relationship.

NEW METHOD

Resting-state fMRI data were parcellated using MOSI (modular analysis and similarity measurements), enabling multi-resolution functional partitioning. For each parcellated cluster, the mean amplitude of low-frequency fluctuations (node power) and its average functional connectivity with the remaining cortex (node strength) were computed. Correlation analyses assessed their relationship. A supplementary analysis was conducted on EEG data (1-30 Hz).

RESULTS

A significant negative correlation between node strength and regional power was observed in MRI datasets. One-sample t-tests confirmed robustness across different MOSI resolutions, with individual P values at the level 10-4 to 10-5. The negative relationship was also found in EEG data but was restricted to delta (1-4 Hz) and theta (4-8 Hz) bands.

COMPARISON WITH EXISTING METHODS

This study introduces two key novel aspects. First, it applies MOSI to the entire cortex, enhancing the comprehensiveness of network analysis. Second, it examines the global influence on regional neural activity, rather than limiting the focus to a specific network.

CONCLUSIONS

A robust negative relationship between node strength and node power was consistently observed across both MRI and EEG datasets, particularly in lower frequency bands (up to 8 Hz). These findings suggest a framework for investigating how global connectivity shapes regional neural activity, with inhibitory coupling as a potential underlying mechanism.

Common and unique network basis for externally and internally driven flexibility in cognition: From a developmental perspective

Flexibility is a hallmark of cognitive control and can be driven externally and internally, corresponding to reactive and spontaneous flexibility. However, the convergence and divergence between these two types of flexibility and their underlying neural basis during development remain largely unknown. In this study, we aimed to determine the common and unique networks for reactive and spontaneous flexibility as a function of age and sex, leveraging both cross-sectional and longitudinal resting-state functional magnetic resonance imaging datasets with different temporal resolutions (N = 249, 6-35 years old). Functional connectivity strength and nodal flexibility, derived from static and dynamic frameworks respectively, were utilized. We found similar quadratic effects of age on reactive and spontaneous flexibility, which were mediated by the functional connectivity strength and nodal flexibility of the frontoparietal network. Divergence was observed, with the nodal flexibility of the ventral attention network at the baseline visit uniquely predicting the increase in reactive flexibility 24-30 months later, while the nodal flexibility or functional connectivity strength of the dorsal attention network could specifically predict the increase in spontaneous flexibility. Sex differences were found in tasks measuring reactive and spontaneous flexibility simultaneously, which were moderated by the nodal flexibility of the dorsal attention network. This study advances our understanding of distinct types of flexibility in cognition and their underlying mechanisms throughout developmental stages. Our findings also suggest the importance of studying specific types of cognitive flexibility abnormalities in developmental neuropsychiatric disorders.

Reproducible Brain Charts: An open data resource for mapping brain development and its associations with mental health

Major mental disorders are increasingly understood as disorders of brain development. Large and heterogeneous samples are required to define generalizable links between brain development and psychopathology. To this end, we introduce the Reproducible Brain Charts (RBC), an open data resource that integrates data from 5 large studies of brain development in youth from three continents (N=6,346; 45% Female). Confirmatory bifactor models were used to create harmonized psychiatric phenotypes that capture major dimensions of psychopathology. Following rigorous quality assurance, neuroimaging data were carefully curated and processed using consistent pipelines in a reproducible manner with DataLad, the Configurable Pipeline for the Analysis of Connectomes (C-PAC), and FreeSurfer. Initial analyses of RBC data emphasize the benefit of careful quality assurance and data harmonization in delineating developmental effects and associations with psychopathology. Critically, all RBC data - including harmonized psychiatric phenotypes, unprocessed images, and fully processed imaging derivatives - are openly shared without a data use agreement via the International Neuroimaging Data-sharing Initiative. Together, RBC facilitates large-scale, reproducible, and generalizable research in developmental and psychiatric neuroscience.

BrainMass: Advancing Brain Network Analysis for Diagnosis With Large-Scale Self-Supervised Learning

Foundation models pretrained on large-scale datasets via self-supervised learning demonstrate exceptional versatility across various tasks. Due to the heterogeneity and hard-to-collect medical data, this approach is especially beneficial for medical image analysis and neuroscience research, as it streamlines broad downstream tasks without the need for numerous costly annotations. However, there has been limited investigation into brain network foundation models, limiting their adaptability and generalizability for broad neuroscience studies. In this study, we aim to bridge this gap. In particular, 1) we curated a comprehensive dataset by collating images from 30 datasets, which comprises 70,781 samples of 46,686 participants. Moreover, we introduce pseudo-functional connectivity (pFC) to further generates millions of augmented brain networks by randomly dropping certain timepoints of the BOLD signal; 2) we propose the BrainMass framework for brain network self-supervised learning via mask modeling and feature alignment. BrainMass employs Mask-ROI Modeling (MRM) to bolster intra-network dependencies and regional specificity. Furthermore, Latent Representation Alignment (LRA) module is utilized to regularize augmented brain networks of the same participant with similar topological properties to yield similar latent representations by aligning their latent embeddings. Extensive experiments on eight internal tasks and seven external brain disorder diagnosis tasks show BrainMass's superior performance, highlighting its significant generalizability and adaptability. Nonetheless, BrainMass demonstrates powerful few/zero-shot learning abilities and exhibits meaningful interpretation to various diseases, showcasing its potential use for clinical applications.

Polyconnectomic Scoring of Functional Connectivity Patterns Across Eight Neuropsychiatric and Three Neurodegenerative Disorders

BACKGROUND

Neuropsychiatric and neurodegenerative disorders involve diverse changes in brain functional connectivity. As an alternative to approaches that search for specific mosaic patterns of affected connections and networks, we used polyconnectomic scoring to quantify disorder-related whole-brain connectivity signatures into interpretable, personalized scores.

METHODS

The polyconnectomic score (PCS) measures the extent to which an individual's functional connectivity mirrors the whole-brain circuitry characteristics of a trait. We computed PCSs for 8 neuropsychiatric conditions (attention-deficit/hyperactivity disorder, anxiety-related disorders, autism spectrum disorder, obsessive-compulsive disorder, bipolar disorder, major depressive disorder, schizoaffective disorder, and schizophrenia) and 3 neurodegenerative conditions (Alzheimer's disease, frontotemporal dementia, and Parkinson's disease) across 22 datasets with resting-state functional magnetic resonance imaging data from 10,667 individuals (5325 patients, 5342 control participants). We also examined PCSs in 26,673 individuals from the population-based UK Biobank cohort.

RESULTS

PCSs were consistently higher in out-of-sample patients across 6 of the 8 neuropsychiatric and across all 3 investigated neurodegenerative disorders ([minimum, maximum]: area under the receiver operating characteristic curve = [0.55, 0.73], false discovery rate-corrected p [pFDR] = [1.8 × 10-16, 4.5 × 10-2]). Individuals with elevated PCS levels for neuropsychiatric conditions exhibited higher neuroticism (pFDR < 9.7 × 10-5), lower cognitive performance (pFDR < 5.3 × 10-5), and lower general well-being (pFDR < 9.7 × 10-4).

CONCLUSIONS

Our findings reveal generalizable whole-brain connectivity alterations in brain disorders. Polyconnectomic scoring effectively aggregates disorder-related signatures across the entire brain into an interpretable, participant-specific metric. A toolbox is provided for PCS computation.

Inter-Software Consistency on the Estimation of Subcortical Structure Volume in Different Age Groups

There is still little research on the consistency among the subcortical volume estimates of different software packages. It is also unclear whether there are age-related differences in the inter-software consistency. The current study aimed to examine the consistency of three commonly used automated software packages and the effect of age on inter-software consistency. We analyzed T1-weighted structural images from two public datasets, in which the subjects were divided into four age groups ranging from childhood and adolescence to late adulthood. We chose three mainstream automated software packages including FreeSurfer, CAT, and FSL, to estimate the volumes of seven subcortical structures, including thalamus, caudate, putamen, pallidum, hippocampus, amygdala, and accumbens. We used the intraclass correlation coefficient (ICC) and Pearson correlation coefficient (PCC) to quantify inter-software consistency and compared the consistency measures among the age groups. As a measure of validity, we additionally evaluated the predictive power of each software package's estimates for predicting age. The results showed good inter-software consistency in the thalamus, caudate, putamen, and hippocampus, moderate consistency in the pallidum, and poor consistency in the amygdala and accumbens. Significant differences in the inter-software consistency were not observed among the age groups in most cases. FreeSurfer exhibited higher age prediction accuracy than CAT and FSL. The current study showed that the inter-software consistency on the subcortical volume estimation varies with structures but generally not with age groups, which has important implications for the interpretation and reproducibility of neuroimaging findings.

Brain age prediction via cross-stratified ensemble learning

As an important biomarker of neural aging, the brain age reflects the integrity and health of the human brain. Accurate prediction of brain age could help to understand the underlying mechanism of neural aging. In this study, a cross-stratified ensemble learning algorithm with staking strategy was proposed to obtain brain age and the derived predicted age difference (PAD) using T1-weighted magnetic resonance imaging (MRI) data. The approach was characterized as by implementing two modules: one was three base learners of 3D-DenseNet, 3D-ResNeXt, 3D-Inception-v4; another was 14 secondary learners of liner regressions. To evaluate performance, our method was compared with single base learners, regular ensemble learning algorithms, and state-of-the-art (SOTA) methods. The results demonstrated that our proposed model outperformed others models, with three metrics of mean absolute error (MAE), root mean-squared error (RMSE), and coefficient of determination (R2) of 2.9405 years, 3.9458 years, and 0.9597, respectively. Furthermore, there existed significant differences in PAD among the three groups of normal control (NC), mild cognitive impairment (MCI) and Alzheimer's disease (AD), with an increased trend across NC, MCI, and AD. It was concluded that the proposed algorithm could be effectively used in computing brain aging and PAD, and offering potential for early diagnosis and assessment of normal brain aging and AD.

Personality traits as predictors of depression across the lifespan

BACKGROUND

Depression is a major public health concern. A barrier for research has been the heterogeneous nature of depression, complicated by the categorical diagnosis of depression which is based on a cluster of symptoms, each with its own etiology. To address the multifactorial etiology of depression and its high comorbidity with anxiety, we aimed to examine the relations between personality traits, diverse behavioral, cognitive and physical measures, and depression and anxiety over the lifespan.

METHOD

Our sample was drawn from the NKI-RS, a community-based lifespan sample (N = 1494 participants aged 6 to 85). Analyses included multivariate approach and general linear models for group comparisons and dimensional analyses, respectively. A machine learning model was trained to predict depression using many factors including personality traits.

RESULTS

Depression and anxiety were both characterized by increased neuroticism and introversion, but did not differ between themselves. Comorbidity had an additive effect on personality vulnerability. Dimensionally, depression was only associated with personality in adolescence, where it was positively correlated with neuroticism, and negatively correlated with extraversion, agreeableness, and conscientiousness. The relationship between anxiety and personality changed over time, with neuroticism and conscientiousness being the most salient traits. Our machine learning model predicted depression with 70 % accuracy with neuroticism and extraversion contributing most.

LIMITATIONS

Due to the cross-sectional design, conclusions cannot be drawn about causal relationships between personality and depression.

CONCLUSION

These results underscore the impact of personality on depressive disorders and provide novel insights on how personality contributes to depression across the lifespan.

Adverse event monitoring and reporting in pediatric neuromodulatory studies: A systematic review

Neuromodulatory interventions are relatively novel and approaches to studying harms and tolerability have varied. Using a checklist based on guidelines from Good Clinical Practice and the Harms Extension of the CONSORT (Consolidated Standards of Reporting Trials) Statement, we identified how adverse events are measured, assessed, and reported in studies evaluating neuromodulation for the treatment of mental and neurodevelopmental disorders among children and adolescents. A systematic literature review identified 56 experimental and quasi-experimental studies evaluating transcranial magnetic stimulation (TMS), transcranial alternating (tACS) or direct (tDCS) current stimulation, transcranial pulse stimulation (TPS), and vagus or trigeminal nerve stimulation (VNS or TNS). For 22 studies (39%), the types of adverse events to be monitored were identified, and for 31 studies (55%), methods for collecting adverse event data were described. Methods for assessing adverse events were less commonly described with 23 studies (41%) having details on assessing event severity, and 11 studies (20%) having details on assessing event causality. Among 31 studies with reported results, headache, skin irritation, and general pain or discomfort were the most reported across studies. Seizure, untoward medical occurrences, and intracranial bleeding, edema, or other intracranial pathology were considered serious events, but these events were not reported as occurring in any results-based papers. Taken together, the findings from this review indicate that most studies of pediatric neuromodulatory interventions did not include descriptions of adverse event monitoring and evaluation. Comprehensive event monitoring and reporting across studies can significantly augment the current knowledge base.

Functional connectivity development along the sensorimotor-association axis enhances the cortical hierarchy

Human cortical maturation has been posited to be organized along the sensorimotor-association axis, a hierarchical axis of brain organization that spans from unimodal sensorimotor cortices to transmodal association cortices. Here, we investigate the hypothesis that the development of functional connectivity during childhood through adolescence conforms to the cortical hierarchy defined by the sensorimotor-association axis. We tested this pre-registered hypothesis in four large-scale, independent datasets (total n = 3355; ages 5-23 years): the Philadelphia Neurodevelopmental Cohort (n = 1207), Nathan Kline Institute-Rockland Sample (n = 397), Human Connectome Project: Development (n = 625), and Healthy Brain Network (n = 1126). Across datasets, the development of functional connectivity systematically varied along the sensorimotor-association axis. Connectivity in sensorimotor regions increased, whereas connectivity in association cortices declined, refining and reinforcing the cortical hierarchy. These consistent and generalizable results establish that the sensorimotor-association axis of cortical organization encodes the dominant pattern of functional connectivity development.

Age, Motion, Medical, and Psychiatric Associations With Incidental Findings in Brain MRI

Importance

Few investigations have evaluated rates of brain-based magnetic resonance imaging (MRI) incidental findings (IFs) in large lifespan samples, their stability over time, or their associations with health outcomes.

Objectives

To examine rates of brain-based IFs across the lifespan, their persistence, and their associations with phenotypic indicators of behavior, cognition, and health; to compare quantified motion with radiologist-reported motion and evaluate its associations with IF rates; and to explore IF consistency across multiple visits.

Design, Setting, and Participants

This cross-sectional study included participants from the Nathan Kline Institute-Rockland Sample (NKI-RS), a lifespan community-ascertained sample, and the Healthy Brain Network (HBN), a cross-sectional community self-referred pediatric sample focused on mental health and learning disorders. The NKI-RS enrolled participants (ages 6-85 years) between March 2012 and March 2020 and had longitudinal participants followed up for as long as 4 years. The HBN enrolled participants (ages 5-21 years) between August 2015 and October 2021. Clinical neuroradiology MRI reports were coded for radiologist-reported motion as well as presence, type, and clinical urgency (category 1, no abnormal findings; 2, no referral recommended; 3, consider referral; and 4, immediate referral) of IFs. MRI reports were coded from June to October 2021. Data were analyzed from November 2021 to February 2023.

Main Outcomes and Measures

Rates and type of IFs by demographic characteristics, health phenotyping, and motion artifacts; longitudinal stability of IFs; and Euler number in projecting radiologist-reported motion.

Results

A total of 1300 NKI-RS participants (781 [60.1%] female; mean [SD] age, 38.9 [21.8] years) and 2772 HBN participants (976 [35.2%] female; mean [SD] age, 10.0 [3.5] years) had health phenotyping and neuroradiology-reviewed MRI scans. IFs were common, with 284 of 2956 children (9.6%) and 608 of 1107 adults (54.9%) having IFs, but rarely of clinical concern (category 1: NKI-RS, 619 [47.6%]; HBN, 2561 [92.4%]; category 2: NKI-RS, 647 [49.8%]; HBN, 178 [6.4%]; category 3: NKI-RS, 79 [6.1%]; HBN, 30 [1.1%]; category 4: NKI-RS: 12 [0.9%]; HBN, 6 [0.2%]). Overall, 46 children (1.6%) and 79 adults (7.1%) required referral for their IFs. IF frequency increased with age. Elevated blood pressure and BMI were associated with increased T2 hyperintensities and age-related cortical atrophy. Radiologist-reported motion aligned with Euler-quantified motion, but neither were associated with IF rates.

Conclusions and Relevance

In this cross-sectional study, IFs were common, particularly with increasing age, although rarely clinically significant. While T2 hyperintensity and age-related cortical atrophy were associated with BMI and blood pressure, IFs were not associated with other behavioral, cognitive, and health phenotyping. Motion may not limit clinical IF detection.

The significance and limited influence of cerebrovascular reactivity on age and sex effects in task- and resting-state brain activity

Functional MRI measures the blood-oxygen-level dependent signals, which provide an indirect measure of neural activity mediated by neurovascular responses. Cerebrovascular reactivity affects both task-induced and resting-state blood-oxygen-level dependent activity and may confound inter-individual effects, such as those related to aging and biological sex. We examined a large dataset containing breath-holding, checkerboard, and resting-state tasks. We used the breath-holding task to measure cerebrovascular reactivity, used the checkerboard task to obtain task-based activations, and quantified resting-state activity with amplitude of low-frequency fluctuations and regional homogeneity. We hypothesized that cerebrovascular reactivity would be correlated with blood-oxygen-level dependent measures and that accounting for these correlations would result in better estimates of age and sex effects. We found that cerebrovascular reactivity was correlated with checkerboard task activations in the visual cortex and with amplitude of low-frequency fluctuations and regional homogeneity in widespread fronto-parietal regions, as well as regions with large vessels. We also found significant age and sex effects in cerebrovascular reactivity, some of which overlapped with those observed in amplitude of low-frequency fluctuations and regional homogeneity. However, correcting for the effects of cerebrovascular reactivity had very limited influence on the estimates of age and sex. Our results highlight the limitations of accounting for cerebrovascular reactivity with the current breath-holding task.

Cerebrovascular reactivity increases across development in multiple networks as revealed by a breath-holding task: A longitudinal fMRI study

Functional magnetic resonance imaging (fMRI) has been widely used to understand the neurodevelopmental changes that occur in cognition and behavior across childhood. The blood-oxygen-level-dependent (BOLD) signal obtained from fMRI is understood to be comprised of both neuronal and vascular information. However, it is unclear whether the vascular response is altered across age in studies investigating development in children. Since the breath-hold (BH) task is commonly used to understand cerebrovascular reactivity (CVR) in fMRI studies, it can be used to account for developmental differences in vascular response. This study examines how the cerebrovascular response changes over age in a longitudinal children's BH data set from the Nathan Kline Institute (NKI) Rockland Sample (aged 6-18 years old at enrollment). A general linear model approach was applied to derive CVR from BH data. To model both the longitudinal and cross-sectional effects of age on BH response, we used mixed-effects modeling with the following terms: linear, quadratic, logarithmic, and quadratic-logarithmic, to find the best-fitting model. We observed increased BH BOLD signals in multiple networks across age, in which linear and logarithmic mixed-effects models provided the best fit with the lowest Akaike information criterion scores. This shows that the cerebrovascular response increases across development in a brain network-specific manner. Therefore, fMRI studies investigating the developmental period should account for cerebrovascular changes that occur with age.

Personality characteristics, not clinical symptoms, are associated with anhedonia in a community sample: A preliminary investigation

Anhedonia is a salient transdiagnostic psychiatric symptom associated with increased illness severity and chronicity. Anhedonia is also present to varying degrees in non-clinical cohorts. Here, we sought to examine factors influencing expression of anhedonia. Participants (N = 335) were recruited through the Nathan Kline Institute-Rockland Sample, an initiative to deeply phenotype a large community sample across the lifespan. Utilizing a data-driven approach, we evaluated associations between anhedonia severity, indexed by Snaith-Hamilton Pleasure Scale (SHAPS), and 20 physical, developmental, and clinical measures, including Structured Clinical Interview for DSM-IV, Beck Depression Inventory, State-Trait Anxiety Inventory, NEO Five-Factor Inventory-3 (NEO-FFI-3), BMI, Hemoglobin A1C, and demography. Using a bootstrapped AIC-based backward selection algorithm, seven variables were retained in the final model: NEO-FFI-3 agreeableness, extraversion, and openness to experience; BMI; sex; ethnicity; and race. Though median SHAPS scores were greater in participants with psychiatric diagnoses (18.5) than those without (17.0) (U = 12238.5, z = 2.473, p = 0.013), diagnosis and symptom measures were not retained as significant predictors in the final robust linear model. Participants scoring higher on agreeableness, extraversion, and openness to experience reported significantly lower anhedonia. These results demonstrate personality as a mild-to-moderate but significant driver of differences in experiencing pleasure in a community sample.

Association of Intrinsic Functional Connectivity between the Locus Coeruleus and Salience Network with Attentional Ability

The locus coeruleus (LC) is a brainstem region associated with broad neural arousal because of norepinephrine production, but it has increasingly been associated with specific cognitive processes. These include sustained attention, with deficits associated with various neuropsychological disorders. Neural models of attention deficits have focused on interrupted dynamics between the salience network (SAL) with the frontoparietal network, which has been associated with task-switching and processing of external stimuli, respectively. Conflicting findings for these regions suggest the possibility of upstream signaling leading to attention dysfunction, and recent research suggests LC involvement. In this study, resting-state functional connectivity and behavioral performance on an attention task was examined within 584 individuals. Analysis revealed significant clusters connected to LC activity in the SAL. Given previous findings that attention deficits may be caused by SAL network switching dysfunctions, findings here further suggest that dysfunction in LC-SAL connectivity may impair attention.

Quantifying brain connectivity signatures by means of polyconnectomic scoring

A broad range of neuropsychiatric disorders are associated with alterations in macroscale brain circuitry and connectivity. Identifying consistent brain patterns underlying these disorders by means of structural and functional MRI has proven challenging, partly due to the vast number of tests required to examine the entire brain, which can lead to an increase in missed findings. In this study, we propose polyconnectomic score (PCS) as a metric designed to quantify the presence of disease-related brain connectivity signatures in connectomes. PCS summarizes evidence of brain patterns related to a phenotype across the entire landscape of brain connectivity into a subject-level score. We evaluated PCS across four brain disorders (autism spectrum disorder, schizophrenia, attention deficit hyperactivity disorder, and Alzheimer's disease) and 14 studies encompassing ~35,000 individuals. Our findings consistently show that patients exhibit significantly higher PCS compared to controls, with effect sizes that go beyond other single MRI metrics ([min, max]: Cohen's d = [0.30, 0.87], AUC = [0.58, 0.73]). We further demonstrate that PCS serves as a valuable tool for stratifying individuals, for example within the psychosis continuum, distinguishing patients with schizophrenia from their first-degree relatives (d = 0.42, p = 4 × 10-3, FDR-corrected), and first-degree relatives from healthy controls (d = 0.34, p = 0.034, FDR-corrected). We also show that PCS is useful to uncover associations between brain connectivity patterns related to neuropsychiatric disorders and mental health, psychosocial factors, and body measurements.

A longitudinal resource for population neuroscience of school-age children and adolescents in China

During the past decade, cognitive neuroscience has been calling for population diversity to address the challenge of validity and generalizability, ushering in a new era of population neuroscience. The developing Chinese Color Nest Project (devCCNP, 2013-2022), the first ten-year stage of the lifespan CCNP (2013-2032), is a two-stages project focusing on brain-mind development. The project aims to create and share a large-scale, longitudinal and multimodal dataset of typically developing children and adolescents (ages 6.0-17.9 at enrolment) in the Chinese population. The devCCNP houses not only phenotypes measured by demographic, biophysical, psychological and behavioural, cognitive, affective, and ocular-tracking assessments but also neurotypes measured with magnetic resonance imaging (MRI) of brain morphometry, resting-state function, naturalistic viewing function and diffusion structure. This Data Descriptor introduces the first data release of devCCNP including a total of 864 visits from 479 participants. Herein, we provided details of the experimental design, sampling strategies, and technical validation of the devCCNP resource. We demonstrate and discuss the potential of a multicohort longitudinal design to depict normative brain growth curves from the perspective of developmental population neuroscience. The devCCNP resource is shared as part of the "Chinese Data-sharing Warehouse for In-vivo Imaging Brain" in the Chinese Color Nest Project (CCNP) - Lifespan Brain-Mind Development Data Community ( https://ccnp.scidb.cn ) at the Science Data Bank.

The Significance and Limited Influence of Cerebrovascular Reactivity on Age and Sex Effects in Task- and Resting-State Brain Activity

Functional MRI (fMRI) measures the blood-oxygen-level dependent (BOLD) signals, which provide an indirect measure of neural activity mediated by neurovascular responses. Cerebrovascular reactivity affects both task-induced and resting-state BOLD activity and may confound inter-individual effects observed in BOLD-based measures, such as those related to aging and biological sex. To investigate this, we examined a large open-access fMRI dataset containing a breath-holding task, checkerboard task, and resting-state scans. We used the breath-holding task to measure cerebrovascular reactivity, used the checkerboard task to obtain task-based activations, and from the resting-state data, we quantified the resting-state amplitude of low-frequency fluctuations (ALFF), and resting-state regional homogeneity (ReHo). We hypothesized that cerebrovascular reactivity would be correlated with BOLD measures and that accounting for these correlations would result in better estimates of age and sex effects. Our analysis showed that cerebrovascular reactivity was correlated with checkerboard task activations in the visual cortex and with ALFF and ReHo in widespread fronto-parietal regions, as well as regions with large vessels. We also found significant age and sex effects in cerebrovascular reactivity, some of which overlapped with those observed in ALFF and ReHo scores. Finally, we demonstrated that correcting for the effects of cerebrovascular reactivity had very limited influence on the estimates of age and sex. Our results highlight the limitations of accounting for cerebrovascular reactivity with the current breath-holding task.

brainlife.io: A decentralized and open source cloud platform to support neuroscience research

Neuroscience research has expanded dramatically over the past 30 years by advancing standardization and tool development to support rigor and transparency. Consequently, the complexity of the data pipeline has also increased, hindering access to FAIR data analysis to portions of the worldwide research community. brainlife.io was developed to reduce these burdens and democratize modern neuroscience research across institutions and career levels. Using community software and hardware infrastructure, the platform provides open-source data standardization, management, visualization, and processing and simplifies the data pipeline. brainlife.io automatically tracks the provenance history of thousands of data objects, supporting simplicity, efficiency, and transparency in neuroscience research. Here brainlife.io's technology and data services are described and evaluated for validity, reliability, reproducibility, replicability, and scientific utility. Using data from 4 modalities and 3,200 participants, we demonstrate that brainlife.io's services produce outputs that adhere to best practices in modern neuroscience research.

Cerebrovascular reactivity increases across development in multiple networks as revealed by a breath-holding task: a longitudinal fMRI study

Functional magnetic resonance imaging (fMRI) has been widely used to understand the neurodevelopmental changes that occur in cognition and behavior across childhood. The blood-oxygen-level-dependent (BOLD) signal obtained from fMRI is understood to be comprised of both neuronal and vascular information. However, it is unclear whether the vascular response is altered across age in studies investigating development in children. Since the breath-hold task is commonly used to understand cerebrovascular reactivity in fMRI studies, it can be used to account for developmental differences in vascular response. This study examines how the cerebrovascular response changes over age in a longitudinal children's breath-hold dataset from the Nathan Kline Institute (NKI) Rockland Sample (ages 6 to 18 years old at enrollment). A general linear model (GLM) approach was applied to derive cerebrovascular reactivity from breath-hold data. To model both the longitudinal and cross-sectional effects of age on breath-hold response, we used mixed effects modeling with the following terms: linear, quadratic, logarithmic, and quadratic-logarithmic, to find the best-fitting model. We observed increased breath-hold BOLD signal in multiple networks across age, in which linear and logarithmic mixed effects models provided the best fit with the lowest Akaike Information Criterion (AIC) scores. This shows that the cerebrovascular response increases across development in a brain network-specific manner. Therefore, fMRI studies investigating the developmental period should account for cerebrovascular changes which occur with age.

Longitudinal trajectories of childhood and adolescent attention deficit hyperactivity disorder diagnoses in three cohorts

Background

Attention deficit/hyperactivity disorder (ADHD) is usually conceptualized as a childhood-onset neurodevelopmental disorder, in which symptoms either decrease steadily into adulthood or remain stable. A recent study challenged this view, reporting that for most with ADHD, diagnostic status fluctuates with age. We ask if such a 'fluctuating' ADHD symptom trajectory subgroup is present in other population-based and clinic-based cohorts, centered on childhood and adolescence.

Methods

Cohorts were the population-based Adolescent Brain Cognitive Development (ABCD: N = 9735), Neurobehavioral Clinical Research (NCR: N = 258), and the Nathan Kline Institute-Rockland (NKI-Rockland: N = 149). All participants had three or more assessments spanning different age windows. Participants were categorized into developmental diagnostic subgroups: fluctuant ADHD (defined by two or more switches between meeting and not meeting ADHD criteria), remitting ADHD, persisting ADHD, emerging ADHD and never affected. Data were collected between 2011 and 2022. Analyses were performed between May 2022 and April 2023.

Findings

A subgroup with fluctuant child and adolescent ADHD diagnoses was found in all cohorts (29.3% of participants with ADHD in ABCD, 26.6% in NCR and 17% in NKI-Rockland). While the proportion of those with fluctuant ADHD increased with the number of assessments, it never constituted the dominant subgroup.

Interpretation

We provide further evidence in three cohorts for the existence of a fluctuant ADHD diagnostic subgroup during childhood and adolescence, albeit in a minority of cases. Such fluctuant child and adolescent ADHD diagnoses may suggest a natural history more akin to relapsing-remitting mood disorders and/or a marked sensitivity to environmental shifts that occur across development.

Funding

Intramural programs of the NHGRI and NIMH.

Precision behavioral phenotyping as a strategy for uncovering the biological correlates of psychopathology

Our capacity to measure diverse aspects of human biology has developed rapidly in the past decades, but the rate at which these techniques have generated insights into the biological correlates of psychopathology has lagged far behind. The slow progress is partly due to the poor sensitivity, specificity and replicability of many findings in the literature, which have in turn been attributed to small effect sizes, small sample sizes and inadequate statistical power. A commonly proposed solution is to focus on large, consortia-sized samples. Yet it is abundantly clear that increasing sample sizes will have a limited impact unless a more fundamental issue is addressed: the precision with which target behavioral phenotypes are measured. Here, we discuss challenges, outline several ways forward and provide worked examples to demonstrate key problems and potential solutions. A precision phenotyping approach can enhance the discovery and replicability of associations between biology and psychopathology.

The Queensland Twin Adolescent Brain Project, a longitudinal study of adolescent brain development

We describe the Queensland Twin Adolescent Brain (QTAB) dataset and provide a detailed methodology and technical validation to facilitate data usage. The QTAB dataset comprises multimodal neuroimaging, as well as cognitive and mental health data collected in adolescent twins over two sessions (session 1: N = 422, age 9-14 years; session 2: N = 304, 10-16 years). The MRI protocol consisted of T1-weighted (MP2RAGE), T2-weighted, FLAIR, high-resolution TSE, SWI, resting-state fMRI, DWI, and ASL scans. Two fMRI tasks were added in session 2: an emotional conflict task and a passive movie-watching task. Outside of the scanner, we assessed cognitive function using standardised tests. We also obtained self-reports of symptoms for anxiety and depression, perceived stress, sleepiness, pubertal development measures, and risk and protective factors. We additionally collected several biological samples for genomic and metagenomic analysis. The QTAB project was established to promote health-related research in adolescence.

An analysis-ready and quality controlled resource for pediatric brain white-matter research

We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets.