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The COVID-19 pandemic and its social and economic consequences have had adverse impacts on physical and mental health worldwide and exposed all segments of the population to protracted uncertainty and daily disruptions. The CoRonavIruS health and Impact Survey (CRISIS) was developed for use as an easy to implement and robust questionnaire covering key domains relevant to mental distress and resilience during the pandemic. Ongoing studies using CRISIS include international studies of COVID-related ill health conducted during different phases of the pandemic and follow-up studies of cohorts characterized before the COVID pandemic. In the current work, we demonstrate the feasibility, psychometric structure, and construct validity of this survey. We then show that pre-existing mood states, perceived COVID risk, and lifestyle changes are strongly associated with negative mood states during the pandemic in population samples of adults and in parents reporting on their children in the US and UK. These findings are highly reproducible and we find a high degree of consistency in the power of these factors to predict mental health during the pandemic.

•There is a growing need to identify benchmark parameters in advancing a low dimensional representation of functional connectivity (i.e., gradients) into a reliable biomarker.•Here, we explored multidimensional parameter space in calculating functional gradients to improve their reproducibility, reliability and predictive validity.•We demonstrated that more reproducible and reliable gradient markers tend to have higher predictive power for unseen phenotypic scores across various cognitive domains.•We showed that the low-dimensional connectivity gradient approach could outperform conventional edge-based analyses in terms of predicting phenotypic scores.•We highlight the necessity of optimizing parameters for new imaging methods before their widespread deployment. Despite myriad demonstrations of feasibility, the high dimensionality of fMRI data remains a critical barrier to its utility for reproducible biomarker discovery. Recent efforts to address this challenge have capitalized on dimensionality reduction techniques applied to resting-state fMRI, identifying principal components of intrinsic connectivity which describe smooth transitions across different cortical systems, so called “connectivity gradients”. These gradients recapitulate neurocognitively meaningful organizational principles that are present in both human and primate brains, and also appear to differ among individuals and clinical populations. Here, we provide a critical assessment of the suitability of connectivity gradients for biomarker discovery. Using the Human Connectome Project (discovery subsample=209; two replication subsamples= 209 × 2) and the Midnight scan club (n = 9), we tested the following key biomarker traits – reliability, reproducibility and predictive validity – of functional gradients. In doing so, we systematically assessed the effects of three analytical settings, including i) dimensionality reduction algorithms (i.e., linear vs. non-linear methods), ii) input data types (i.e., raw time series, [un-]thresholded functional connectivity), and iii) amount of the data (resting-state fMRI time-series lengths). We found that the reproducibility of functional gradients across algorithms and subsamples is generally higher for those explaining more variances of whole-brain connectivity data, as well as those having higher reliability. Notably, among different analytical settings, a linear dimensionality reduction (principal component analysis in our study), more conservatively thresholded functional connectivity (e.g., 95–97%) and longer time-series data (at least ≥20mins) was found to be preferential conditions to obtain higher reliability. Those gradients with higher reliability were able to predict unseen phenotypic scores with a higher accuracy, highlighting reliability as a critical prerequisite for validity. Importantly, prediction accuracy with connectivity gradients exceeded that observed with more traditional edge-based connectivity measures, suggesting the added value of a low-dimensional and multivariate gradient approach. Finally, the present work highlights the importance and benefits of systematically exploring the parameter space for new imaging methods before widespread deployment.

Increasing the reproducibility of neuroimaging measurement addresses a central impediment to the advancement of human neuroscience and its clinical applications. Recent efforts demonstrating variance in functional brain organization within and between individuals shows a need for improving reproducibility of functional parcellations without long scan times. We apply bootstrap aggregation, or bagging, to the problem of improving reproducibility in functional parcellation. We use two large datasets to demonstrate that compared to a standard clustering framework, bagging improves the reproducibility and test-retest reliability of both cortical and subcortical functional parcellations across a range of sites, scanners, samples, scan lengths, clustering algorithms, and clustering parameters (e.g., number of clusters, spatial constraints). With as little as 6 ​min of scan time, bagging creates more reproducible group and individual level parcellations than standard approaches with twice as much data. This suggests that regardless of the specific parcellation strategy employed, bagging may be a key method for improving functional parcellation and bringing functional neuroimaging-based measurement closer to clinical impact.

Importance An increasing prevalence of adult attention-deficit/hyperactivity disorder (ADHD) diagnosis and treatment has been reported in clinical settings and administrative data in the United States. However, there are limited data on recent trends of adult ADHD diagnosis among racial/ethnic subgroups. Objective To examine trends, including associated demographic characteristics, psychiatric diagnoses, and negative outcomes, in the prevalence and incidence of adult ADHD diagnosis among 7 racial/ethnic groups during a 10-year period. Design, Setting, and Participants This cohort study investigated trends in the diagnosis of ADHD in adults who identified as African American or black, Native American, Pacific Islander, Latino or Hispanic, non-Hispanic white, Asian American, or other using the Kaiser Permanente Northern California health plan medical records. A total of 5 282 877 adult patients and 867 453 children aged 5 to 11 years who received care at Kaiser Permanente Northern California from January 1, 2007, to December 31, 2016, were included. Data analysis was performed from January 2017 through September 2019. Exposures Period of ADHD diagnosis. Main Outcomes and Measures Prevalence and incidence of licensed mental health clinician–diagnosed ADHD in adults and prevalence of licensed mental health clinician–diagnosed ADHD in children aged 5 to 11 years. Results Of 5 282 877 adult patients (1 155 790 [21.9%] aged 25-34 years; 2 667 562 [50.5%] women; 2 204 493 [41.7%] white individuals), 59 371 (1.12%) received diagnoses of ADHD. Prevalence increased from 0.43% in 2007 to 0.96% in 2016. Among 867 453 children aged 5 to 11 years (424 449 [48.9%] girls; 260 236 [30.0%] white individuals), prevalence increased from 2.96% in 2007 to 3.74% in 2016. During the study period, annual adult ADHD prevalence increased for every race/ethnicity, but white individuals consistently had the highest prevalence rates (white individuals: 0.67%-1.42%; black individuals: 0.22%-0.69%; Native American individuals: 0.56%-1.14%; Pacific Islander individuals: 0.11%-0.39%; Hispanic or Latino individuals: 0.25%-0.65%; Asian American individuals: 0.11%-0.35%; individuals from other races/ethnicities: 0.29%-0.71%). Incidence of ADHD diagnosis per 10 000 person-years increased from 9.43 in 2007 to 13.49 in 2016. Younger age (eg, >65 years vs 18-24 years: odds ratio [OR], 0.094; 95% CI, 0.088-0.101;P < .001), male sex (women: OR, 0.943; 95% CI, 0.928-0.959;P < .001), white race (eg, Asian patients vs white patients: OR, 0.248; 95% CI, 0.240-0.257;P < .001), being divorced (OR, 1.131; 95% CI, 1.093-1.171;P < .001), being employed (eg, retired vs employed persons: OR, 0.278; 95% CI, 0.267-0.290;P < .001), and having a higher median education level (OR, 2.156; 95% CI, 2.062-2.256;P < .001) were positively associated with odds of ADHD diagnosis. Having an eating disorder (OR, 5.192; 95% CI, 4.926-5.473;P < .001), depressive disorder (OR, 4.118; 95% CI, 4.030-4.207;P < .001), bipolar disorder (OR, 4.722; 95% CI, 4.556-4.894;P < .001), or anxiety disorder (OR, 2.438; 95% CI, 2.385-2.491;P < .001) was associated with higher odds of receiving an ADHD diagnosis. Adults with ADHD had significantly higher odds of frequent health care utilization (OR, 1.303; 95% CI, 1.272-1.334;P < .001) and sexually transmitted infections (OR, 1.289; 95% CI 1.251-1.329;P < .001) compared with adults with no ADHD diagnosis. Conclusions and Relevance This study confirmed the reported increases in rates of ADHD diagnosis among adults, showing substantially lower rates of detection among minority racial/ethnic subgroups in the United States. Higher odds of negative outcomes reflect the economic and personal consequences that substantiate the need to improve assessment and treatment of ADHD in adults.

Using brain atlases to localize regions of interest is a requirement for making neuroscientifically valid statistical inferences. These atlases, represented in volumetric or surface coordinate spaces, can describe brain topology from a variety of perspectives. Although many human brain atlases have circulated the field over the past fifty years, limited effort has been devoted to their standardization. Standardization can facilitate consistency and transparency with respect to orientation, resolution, labeling scheme, file storage format, and coordinate space designation. Our group has worked to consolidate an extensive selection of popular human brain atlases into a single, curated, open-source library, where they are stored following a standardized protocol with accompanying metadata, which can serve as the basis for future atlases. The repository containing the atlases, the specification, as well as relevant transformation functions is available in the neuroparc OSF registered repository or https://github.com/neurodata/neuroparc .

Advances in functional magnetic resonance imaging (fMRI) have significantly enhanced our understanding of the striatal system of both humans and non-human primates (NHP) over the last few decades. However, its circuit-level functional anatomy remains poorly understood, partly because in-vivo fMRI cannot directly perturb a brain system and map its casual input-output relationship. Also, routine 3T fMRI has an insufficient spatial resolution. We performed electrical microstimulation (EM) of the striatum in lightly-anesthetized NHPs while simultaneously mapping whole-brain activation, using contrast-enhanced fMRI at ultra-high-field 7T. By stimulating multiple positions along the striatum's main (dorsal-to-ventral) axis, we revealed its complex functional circuit concerning mutually connected subsystems in both cortical and subcortical areas. Indeed, within the striatum, there were distinct brain activation patterns across different stimulation sites. Specifically, dorsal stimulation revealed a medial-to-lateral elongated shape of activation in upper caudate and putamen areas, whereas ventral stimulation evoked areas confined to the medial and lower caudate. Such dorsoventral gradients also appeared in neocortical and thalamic activations, indicating consistent embedding profiles of the striatal system across the whole brain. These findings reflect different forms of within-circuit and inter-regional neuronal connectivity between the dorsal and ventromedial striatum. These patterns both shared and contrasted with previous anatomical tract-tracing and in-vivo resting-state fMRI studies. Our approach of combining microstimulation and whole-brain fMRI mapping in NHPs provides a unique opportunity to integrate our understanding of a targeted brain area's meso- and macro-scale functional systems.

Moving from group level to individual level functional parcellation maps is a critical step for developing a rich understanding of the links between individual variation in functional network architecture and cognitive and clinical phenotypes. Still, the identification of functional units in the brain based on intrinsic functional connectivity and its dynamic variations between and within subjects remains challenging. Recently, the bootstrap analysis of stable clusters (BASC) framework was developed to quantify the stability of functional brain networks both across and within subjects. This multi-level approach utilizes bootstrap resampling for both individual and group-level clustering to delineate functional units based on their consistency across and within subjects, while providing a measure of their stability. Here, we optimized the BASC framework for functional parcellation of the basal ganglia by investigating a variety of clustering algorithms and similarity measures. Reproducibility and test-retest reliability were computed to validate this analytic framework as a tool to describe inter-individual differences in the stability of functional networks. The functional parcellation revealed by stable clusters replicated previous divisions found in the basal ganglia based on intrinsic functional connectivity. While we found moderate to high reproducibility, test-retest reliability was high at the boundaries of the functional units as well as within their cores. This is interesting because the boundaries between functional networks have been shown to explain most individual phenotypic variability. The current study provides evidence for the consistency of the parcellation of the basal ganglia, and provides the first group level parcellation built from individual-level cluster solutions. These novel results demonstrate the utility of BASC for quantifying inter-individual differences in the functional organization of brain regions, and encourage usage in future studies. •Reproducible and reliable individual level parcellations of the basal ganglia.•Parcellation consistent across sites, session, and clustering techniques.•Provides a basis to study individual differences in brain parcellation.

Background Although studies of adults show that pre-existing mental disorders increase risk for COVID-19 infection and severity, there is limited information about this association among youth. Mental disorders in general as well as specific types of disorders may influence the ability to comply with risk-mitigation strategies to reduce COVID-19 infection and transmission. Methods Youth compliance (rated as “Never,” “Sometimes,” “Often,” or “Very often/Always”) with risk mitigation was reported by parents on the CoRonavIruS Health Impact Survey (CRISIS) in January 2021. The sample comprised 314 female and 514 male participants from the large-scale Child Mind Institute Healthy Brain Network, a transdiagnostic self-referred, community sample of children and adolescents (ages 5–21). Responses were summarized using factor analysis of risk mitigation, and their associations with lifetime mental disorders (assessed via structured diagnostic interviews) were identified with linear regression analyses (adjusted for covariates). All analyses used R Project for Statistical Computing for Mac (v.4.0.5). Results A two-factor model was the best-fitting solution. Factor 1 (avoidance behaviors) included avoiding groups, indoor settings, and other peoples’ homes; avoidance scores were higher among youth with any anxiety disorder (p = .01). Factor 2 (hygiene behaviors) included using hand sanitizer, washing hands, and maintaining social distance; hygiene scores were lower among youth with ADHD (combined type) (p = .02). Mask wearing was common (90%), did not load on either factor, and was not associated with any mental health disorder. Conclusion and relevance Although most mental disorders examined were not associated with risk mitigation, youth with ADHD characterized by hyperactivity plus inattention may need additional support to consistently engage in risk-mitigation behaviors. Enhancing risk-mitigation strategies among at-risk groups of youth may help reduce COVID-19 infection and transmission.

About the Authors: Aïda Bafeta Affiliation: Center for Research and Interdisciplinarity (CRI), Université de Paris, INSERM U1284, Paris, France ORCID logo http://orcid.org/0000-0001-8947-7881 Jason Bobe Affiliation: Institute for Next Generation Healthcare, New York, New York, United States of America ORCID logo http://orcid.org/0000-0003-1864-8609 Jon Clucas Affiliation: MATTER Lab, Child Mind Institute, New York, New York, United States of America ORCID logo http://orcid.org/0000-0001-7590-5806 Pattie Pramila Gonsalves Affiliation: Sangath, New Delhi, India ORCID logo http://orcid.org/0000-0003-3780-4523 Célya Gruson-Daniel Affiliation: COSTECH, Université de Technologie de Compiègne, Compiègne, France; LabCMO, Université du Québec à Montréal, Université Laval, Montreal, Canada ORCID logo http://orcid.org/0000-0003-4247-4637 Kathy L. Hudson Affiliation: Hudson Works LLC, Washington, District of Columbia, United States of America Arno Klein * E-mail: arno@childmind.org (AK); dule@ailfe.org (DM) Affiliation: MATTER Lab, Child Mind Institute, New York, New York, United States of America ORCID logo http://orcid.org/0000-0002-0707-2889 Anirudh Krishnakumar Affiliation: Center for Research and Interdisciplinarity (CRI), Université de Paris, INSERM U1284, Paris, France ORCID logo http://orcid.org/0000-0002-2764-133X Anna McCollister-Slipp Affiliation: Four Lights Consulting LLC, Washington, District of Columbia, United States of America ORCID logo http://orcid.org/0000-0002-8615-6430 Ariel B. Lindner Affiliation: Center for Research and Interdisciplinarity (CRI), Université de Paris, INSERM U1284, Paris, France Dusan Misevic * E-mail: arno@childmind.org (AK); dule@ailfe.org (DM) Affiliation: Center for Research and Interdisciplinarity (CRI), Université de Paris, INSERM U1284, Paris, France ORCID logo http://orcid.org/0000-0002-0126-2980 John A. Naslund Affiliation: Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts, United States of America ORCID logo http://orcid.org/0000-0001-6777-0104 Camille Nebeker Affiliation: Department of Family Medicine and Public Health, School of Medicine, University of California San Diego, San Diego, California, United States of America ORCID logo http://orcid.org/0000-0001-6819-1796 Aki Nikolaidis Affiliation: Center for the Developing Brain, Child Mind Institute, New York, New York, United States of America Irene Pasquetto Affiliation: Harvard Kennedy School, Harvard University, Cambridge, Massachusetts, United States of America ORCID logo http://orcid.org/0000-0002-2790-0629 Gabriela Sanchez Affiliation: University of Geneva, Geneva, Switzerland ORCID logo http://orcid.org/0000-0003-4682-1616 Matthieu Schapira Affiliation: Structural Genomics Consortium and Department of Pharmacology & Toxicology, University of Toronto, Toronto, Canada ORCID logo http://orcid.org/0000-0002-1047-3309 Tohar Scheininger Affiliation: Healthy Brain Network, Child Mind Institute, New York, New York, United States of America ORCID logo http://orcid.org/0000-0003-3674-9999 Félix Schoeller Affiliation: Center for Research and Interdisciplinarity (CRI), Université de Paris, INSERM U1284, Paris, France Anibal Sólon Heinsfeld Affiliation: Center for the Developing Brain, Child Mind Institute, New York, New York, United States of America François Taddei Affiliation: Center for Research and Interdisciplinarity (CRI), Université de Paris, INSERM U1284, Paris, France Introduction We are witnessing a dramatic transformation in the way we do science. The well-established, tried and tested rules and regulations for behavioral and biomedical research involving human participants [18] must demonstrate voluntary participation via informed consent [19–22], perform risk assessment to determine if the probability and magnitude of potential harms are balanced against potential benefits, include those who may benefit most from knowledge gained, consider downstream societal implications, conduct an external review of study procedures before initiating any project, and develop additional protections for vulnerable stakeholders. Do not simply delegate consideration of ethical and responsible research practices solely to research ethics boards (also known as institutional review boards [IRBs]). The Citizen Science Association has also developed and shared materials for conducting an IRB review [28], to help build an ethics review process for the citizen science community.

Characterizing multifaceted individual differences in brain function using neuroimaging is central to biomarker discovery in neuroscience. We provide an integrative toolbox, Reliability eXplorer (ReX), to facilitate the examination of individual variation and reliability as well as the effective direction for optimization of measuring individual differences in biomarker discovery. We also illustrate gradient flows, a two-dimensional field map-based approach to identifying and representing the most effective direction for optimization when measuring individual differences, which is implemented in ReX. The Reliability eXplorer is a tool for assessing measurement reliability in neuroimaging studies.