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DeRosa J, Friedman NP, Calhoun V, Banich MT. Neurodevelopmental subtypes of functional brain organization in the ABCD study using a rigorous analytic framework. Neuroimage 2024; 299:120827. [PMID: 39245397 DOI: 10.1016/j.neuroimage.2024.120827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 08/02/2024] [Accepted: 08/31/2024] [Indexed: 09/10/2024] Open
Abstract
The current study demonstrates that an individual's resting-state functional connectivity (RSFC) is a dependable biomarker for identifying differential patterns of cognitive and emotional functioning during late childhood. Using baseline RSFC data from the Adolescent Brain Cognitive Development (ABCD) study, which includes children aged 9-11, we identified four distinct RSFC subtypes. We introduce an integrated methodological pipeline for testing the reliability and importance of these subtypes. In the Identification phase, Leiden Community Detection defined RSFC subtypes, with their reproducibility confirmed through a split-sample technique in the Validation stage. The Evaluation phase showed that distinct cognitive and mental health profiles are associated with each subtype, with the Predictive phase indicating that subtypes better predict various cognitive and mental health characteristics than individual RSFC connections. The Replication stage employed bootstrapping and down-sampling methods to substantiate the reproducibility of these subtypes further. This work allows future explorations of developmental trajectories of these RSFC subtypes.
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Affiliation(s)
- Jacob DeRosa
- Department of Psychology and Neuroscience, University of Colorado Boulder, United States; Institute of Cognitive Science, University of Colorado Boulder, United States.
| | - Naomi P Friedman
- Department of Psychology and Neuroscience, University of Colorado Boulder, United States; Institute for Behavioral Genetics, University of Colorado Boulder, United States
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, United States
| | - Marie T Banich
- Department of Psychology and Neuroscience, University of Colorado Boulder, United States; Institute of Cognitive Science, University of Colorado Boulder, United States
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2
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Pozeg P, Jöhr J, Prior JO, Diserens K, Dunet V. Explaining recovery from coma with multimodal neuroimaging. J Neurol 2024; 271:6274-6288. [PMID: 39090230 PMCID: PMC11377522 DOI: 10.1007/s00415-024-12591-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 07/06/2024] [Accepted: 07/18/2024] [Indexed: 08/04/2024]
Abstract
The aim of this prospective, observational cohort study was to investigate and assess diverse neuroimaging biomarkers to predict patients' neurological recovery after coma. 32 patients (18-76 years, M = 44.8, SD = 17.7) with disorders of consciousness participated in the study. Multimodal neuroimaging data acquired during the patient's hospitalization were used to derive cortical glucose metabolism (18F-fluorodeoxyglucose positron emission tomography/computed tomography), and structural (diffusion-weighted imaging) and functional connectivity (resting-state functional MRI) indices. The recovery outcome was defined as a continuous composite score constructed from a multivariate neurobehavioral recovery assessment administered upon the discharge from the hospital. Fractional anisotropy-based white matter integrity in the anterior forebrain mesocircuit (r = 0.72, p < .001, 95% CI: 0.87, 0.45), and the functional connectivity between the antagonistic default mode and dorsal attention resting-state networks (r = - 0.74, p < 0.001, 95% CI: - 0.46, - 0.88) strongly correlated with the recovery outcome. The association between the posterior glucose metabolism and the recovery outcome was moderate (r = 0.38, p = 0.040, 95% CI: 0.66, 0.02). Structural (adjusted R2 = 0.84, p = 0.003) or functional connectivity biomarker (adjusted R2 = 0.85, p = 0.001), but not their combination, significantly improved the model fit to predict the recovery compared solely to bedside neurobehavioral evaluation (adjusted R2 = 0.75). The present study elucidates an important role of specific MRI-derived structural and functional connectivity biomarkers in diagnosis and prognosis of recovery after coma and has implications for clinical care of patients with severe brain injury.
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Affiliation(s)
- Polona Pozeg
- Departement of Medical Radiology, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 46, 1011, Lausanne, Switzerland
| | - Jane Jöhr
- Acute Neurorehabilitation Unit, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, 1011, Lausanne, Switzerland
| | - John O Prior
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital and University of Lausanne, 1011, Lausanne, Switzerland
| | - Karin Diserens
- Acute Neurorehabilitation Unit, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, 1011, Lausanne, Switzerland
| | - Vincent Dunet
- Departement of Medical Radiology, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 46, 1011, Lausanne, Switzerland.
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Jirsaraie RJ, Gatavins MM, Pines AR, Kandala S, Bijsterbosch JD, Marek S, Bogdan R, Barch DM, Sotiras A. Mapping the neurodevelopmental predictors of psychopathology. Mol Psychiatry 2024:10.1038/s41380-024-02682-7. [PMID: 39107582 DOI: 10.1038/s41380-024-02682-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 07/13/2024] [Accepted: 07/22/2024] [Indexed: 08/10/2024]
Abstract
Neuroimaging research has uncovered a multitude of neural abnormalities associated with psychopathology, but few prediction-based studies have been conducted during adolescence, and even fewer used neurobiological features that were extracted across multiple neuroimaging modalities. This gap in the literature is critical, as deriving accurate brain-based models of psychopathology is an essential step towards understanding key neural mechanisms and identifying high-risk individuals. As such, we trained adaptive tree-boosting algorithms on multimodal neuroimaging features from the Lifespan Human Connectome Developmental (HCP-D) sample that contained 956 participants between the ages of 8 to 22 years old. Our feature space consisted of 1037 anatomical, 1090 functional, and 192 diffusion MRI features, which were used to derive models that separately predicted internalizing symptoms, externalizing symptoms, and the general psychopathology factor. We found that multimodal models were the most accurate, but all brain-based models of psychopathology yielded out-of-sample predictions that were weakly correlated with actual symptoms (r2 < 0.15). White matter microstructural properties, including orientation dispersion indices and intracellular volume fractions, were the most predictive of general psychopathology, followed by cortical thickness and functional connectivity. Spatially, the most predictive features of general psychopathology were primarily localized within the default mode and dorsal attention networks. These results were mostly consistent across all dimensions of psychopathology, except orientation dispersion indices and the default mode network were not as heavily weighted in the prediction of internalizing and externalizing symptoms. Taken with prior literature, it appears that neurobiological features are an important part of the equation for predicting psychopathology but relying exclusively on neural markers is clearly not sufficient, especially among adolescent samples with subclinical symptoms. Consequently, risk factor models of psychopathology may benefit from incorporating additional sources of information that have also been shown to explain individual differences, such as psychosocial factors, environmental stressors, and genetic vulnerabilities.
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Affiliation(s)
- Robert J Jirsaraie
- Division of Computational and Data Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Martins M Gatavins
- Lifespan Brain Institute, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA
| | - Adam R Pines
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Sridhar Kandala
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Janine D Bijsterbosch
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Scott Marek
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- AI for Health Institute, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Ryan Bogdan
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Deanna M Barch
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Aristeidis Sotiras
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.
- Institute for Informatics, Data Science & Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.
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Ren J, Loughnan R, Xu B, Thompson WK, Fan CC. Estimating the total variance explained by whole-brain imaging for zero-inflated outcomes. Commun Biol 2024; 7:836. [PMID: 38982203 PMCID: PMC11233705 DOI: 10.1038/s42003-024-06504-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 06/25/2024] [Indexed: 07/11/2024] Open
Abstract
There is a dearth of statistical models that adequately capture the total signal attributed to whole-brain imaging features. The total signal is often widely distributed across the brain, with individual imaging features exhibiting small effect sizes for predicting neurobehavioral phenotypes. The challenge of capturing the total signal is compounded by the distribution of neurobehavioral data, particularly responses to psychological questionnaires, which often feature zero-inflated, highly skewed outcomes. To close this gap, we have developed a novel Variational Bayes algorithm that characterizes the total signal captured by whole-brain imaging features for zero-inflated outcomes. Our zero-inflated variance (ZIV) estimator estimates the fraction of variance explained (FVE) and the proportion of non-null effects (PNN) from large-scale imaging data. In simulations, ZIV demonstrates superior performance over other linear models. When applied to data from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study, we found that whole-brain imaging features contribute to a larger FVE for externalizing behaviors compared to internalizing behaviors. Moreover, focusing on features contributing to the PNN, ZIV estimator localized key neurocircuitry associated with neurobehavioral traits. To the best of our knowledge, the ZIV estimator is the first specialized method for analyzing zero-inflated neuroimaging data, enhancing future studies on brain-behavior relationships and improving the understanding of neurobehavioral disorders.
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Affiliation(s)
- Junting Ren
- Division of Biostatistics, Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, 9500 Gilman Street, La Jolla, 92093, CA, USA.
| | - Robert Loughnan
- Center for Human Development, University of California San Diego, 9500 Gilman Drive, La Jolla, 92093, CA, USA
- Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, 74136, OK, USA
| | - Bohan Xu
- Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, 74136, OK, USA
| | - Wesley K Thompson
- Division of Biostatistics, Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, 9500 Gilman Street, La Jolla, 92093, CA, USA
- Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, 74136, OK, USA
| | - Chun Chieh Fan
- Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, 74136, OK, USA.
- Department of Radiology, School of Medicine, University of California San Diego, 9500 Gilman Drive, La Jolla, 92093, CA, USA.
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Clancy KJ, Devignes Q, Ren B, Pollmann Y, Nielsen SR, Howell K, Kumar P, Belleau EL, Rosso IM. Spatiotemporal dynamics of hippocampal-cortical networks underlying the unique phenomenological properties of trauma-related intrusive memories. Mol Psychiatry 2024; 29:2161-2169. [PMID: 38454081 PMCID: PMC11408261 DOI: 10.1038/s41380-024-02486-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 02/07/2024] [Accepted: 02/12/2024] [Indexed: 03/09/2024]
Abstract
Trauma-related intrusive memories (TR-IMs) possess unique phenomenological properties that contribute to adverse post-traumatic outcomes, positioning them as critical intervention targets. However, transdiagnostic treatments for TR-IMs are scarce, as their underlying mechanisms have been investigated separate from their unique phenomenological properties. Extant models of more general episodic memory highlight dynamic hippocampal-cortical interactions that vary along the anterior-posterior axis of the hippocampus (HPC) to support different cognitive-affective and sensory-perceptual features of memory. Extending this work into the unique properties of TR-IMs, we conducted a study of eighty-four trauma-exposed adults who completed daily ecological momentary assessments of TR-IM properties followed by resting-state functional magnetic resonance imaging (rs-fMRI). Spatiotemporal dynamics of anterior and posterior hippocampal (a/pHPC)-cortical networks were assessed using co-activation pattern analysis to investigate their associations with different properties of TR-IMs. Emotional intensity of TR-IMs was inversely associated with the frequency and persistence of an aHPC-default mode network co-activation pattern. Conversely, sensory features of TR-IMs were associated with more frequent co-activation of the HPC with sensory cortices and the ventral attention network, and the reliving of TR-IMs in the "here-and-now" was associated with more persistent co-activation of the pHPC and the visual cortex. Notably, no associations were found between HPC-cortical network dynamics and conventional symptom measures, including TR-IM frequency or retrospective recall, underscoring the utility of ecological assessments of memory properties in identifying their neural substrates. These findings provide novel insights into the neural correlates of the unique features of TR-IMs that are critical for the development of individualized, transdiagnostic treatments for this pervasive, difficult-to-treat symptom.
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Affiliation(s)
- Kevin J Clancy
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA, USA.
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
| | - Quentin Devignes
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Boyu Ren
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Laboratory for Psychiatric Biostatistics, McLean Hospital, Belmont, MA, USA
| | - Yara Pollmann
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA, USA
| | - Sienna R Nielsen
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA, USA
| | - Kristin Howell
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA, USA
| | - Poornima Kumar
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Emily L Belleau
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Isabelle M Rosso
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
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Tsai CJ, Lin HY, Gau SSF. Correlation of altered intrinsic functional connectivity with impaired self-regulation in children and adolescents with ADHD. Eur Arch Psychiatry Clin Neurosci 2024:10.1007/s00406-024-01787-y. [PMID: 38906983 DOI: 10.1007/s00406-024-01787-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 02/16/2024] [Indexed: 06/23/2024]
Abstract
BACKGROUND Attention-deficit hyperactivity disorder (ADHD) has a high prevalence of co-occurring impaired self-regulation (dysregulation), exacerbating adverse outcomes. Neural correlates underlying impaired self-regulation in ADHD remain inconclusive. We aimed to investigate the impact of dysregulation on intrinsic functional connectivity (iFC) in children with ADHD and the correlation of iFC with dysregulation among children with ADHD relative to typically developing controls (TDC). METHODS Resting-state functional MRI data of 71 children with ADHD (11.38 ± 2.44 years) and 117 age-matched TDC were used in the final analysis. We restricted our analyses to resting-state networks (RSNs) of interest derived from independent component analysis. Impaired self-regulation was estimated based on the Child Behavioral Checklist-Dysregulation Profile. RESULTS Children with ADHD showed stronger iFC than TDC in the left frontoparietal network, somatomotor network (SMN), visual network (VIS), default-mode network (DMN), and dorsal attention network (DAN) (FWE-corrected alpha < 0.05). After adding dysregulation levels as an extra regressor, the ADHD group only showed stronger iFC in the VIS and SMN. ADHD children with high dysregulation had higher precuneus iFC within DMN than ADHD children with low dysregulation. Angular gyrus iFC within DMN was positively correlated with dysregulation in the ADHD group but negatively correlated with dysregulation in the TDC group. Functional network connectivity showed ADHD had a greater DMN-DAN connection than TDC, regardless of the dysregulation level. CONCLUSIONS Our findings suggest that DMN connectivity may contribute to impaired self-regulation in ADHD. Impaired self-regulation should be considered categorical and dimensional moderators for the neural correlates of altered iFC in ADHD.
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Affiliation(s)
- Chia-Jui Tsai
- Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Hsiang-Yuan Lin
- Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Susan Shur-Fen Gau
- Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan.
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, No. 7, Chung-Shan South Road, Taipei, 10002, Taiwan.
- Graduate Institute of Brain and Mind Sciences, College of Medicine, National Taiwan University Hospital, Taipei, Taiwan.
- Department of Psychology, College of Science, National Taiwan University, Taipei, Taiwan.
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Xiao X, Hammond C, Salmeron BJ, Wang D, Gu H, Zhai T, Nguyen H, Lu H, Ross TJ, Yang Y. Brain Functional Connectome Defines a Transdiagnostic Dimension Shared by Cognitive Function and Psychopathology in Preadolescents. Biol Psychiatry 2024; 95:1081-1090. [PMID: 37769982 PMCID: PMC10963340 DOI: 10.1016/j.biopsych.2023.08.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Revised: 07/27/2023] [Accepted: 08/30/2023] [Indexed: 10/03/2023]
Abstract
BACKGROUND Cognitive function and general psychopathology are two important classes of human behavior dimensions that are individually related to mental disorders across diagnostic categories. However, whether these two transdiagnostic dimensions are linked to common or distinct brain networks that convey resilience or risk for the development of psychiatric disorders remains unclear. METHODS The current study is a longitudinal investigation with 11,875 youths from the Adolescent Brain Cognitive Development (ABCD) Study at ages 9 to 10 years at the onset of the study. A machine learning approach based on canonical correlation analysis was used to identify latent dimensional associations of the resting-state functional connectome with multidomain behavioral assessments including cognitive functions and psychopathological measures. For the latent resting-state functional connectivity factor showing a robust behavioral association, its ability to predict psychiatric disorders was assessed using 2-year follow-up data, and its genetic association was evaluated using twin data from the same cohort. RESULTS A latent functional connectome pattern was identified that showed a strong and generalizable association with the multidomain behavioral assessments (5-fold cross-validation: ρ = 0.68-0.73 for the training set [n = 5096]; ρ = 0.56-0.58 for the test set [n = 1476]). This functional connectome pattern was highly heritable (h2 = 74.42%, 95% CI: 56.76%-85.42%), exhibited a dose-response relationship with the cumulative number of psychiatric disorders assessed concurrently and at 2 years post-magnetic resonance imaging scan, and predicted the transition of diagnosis across disorders over the 2-year follow-up period. CONCLUSIONS These findings provide preliminary evidence for a transdiagnostic connectome-based measure that underlies individual differences in the development of psychiatric disorders during early adolescence.
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Affiliation(s)
- Xiang Xiao
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
| | - Christopher Hammond
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Betty Jo Salmeron
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
| | - Danni Wang
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
| | - Hong Gu
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
| | - Tianye Zhai
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
| | - Hieu Nguyen
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
| | - Hanbing Lu
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
| | - Thomas J Ross
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
| | - Yihong Yang
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland.
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Watters H, Davis A, Fazili A, Daley L, LaGrow TJ, Schumacher EH, Keilholz S. Infraslow dynamic patterns in human cortical networks track a spectrum of external to internal attention. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.22.590625. [PMID: 38712098 PMCID: PMC11071428 DOI: 10.1101/2024.04.22.590625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Early efforts to understand the human cerebral cortex focused on localization of function, assigning functional roles to specific brain regions. More recent evidence depicts the cortex as a dynamic system, organized into flexible networks with patterns of spatiotemporal activity corresponding to attentional demands. In functional MRI (fMRI), dynamic analysis of such spatiotemporal patterns is highly promising for providing non-invasive biomarkers of neurodegenerative diseases and neural disorders. However, there is no established neurotypical spectrum to interpret the burgeoning literature of dynamic functional connectivity from fMRI across attentional states. In the present study, we apply dynamic analysis of network-scale spatiotemporal patterns in a range of fMRI datasets across numerous tasks including a left-right moving dot task, visual working memory tasks, congruence tasks, multiple resting state datasets, mindfulness meditators, and subjects watching TV. We find that cortical networks show shifts in dynamic functional connectivity across a spectrum that tracks the level of external to internal attention demanded by these tasks. Dynamics of networks often grouped into a single task positive network show divergent responses along this axis of attention, consistent with evidence that definitions of a single task positive network are misleading. Additionally, somatosensory and visual networks exhibit strong phase shifting along this spectrum of attention. Results were robust on a group and individual level, further establishing network dynamics as a potential individual biomarker. To our knowledge, this represents the first study of its kind to generate a spectrum of dynamic network relationships across such an axis of attention.
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Affiliation(s)
| | - Aleah Davis
- Agnes Scott College
- Georgia Institute of Technology School of Psychology
| | | | - Lauren Daley
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
| | - TJ LaGrow
- Georgia Institute of Technology School of Electrical and Computer Engineering
| | | | - Shella Keilholz
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
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DeRosa J, Friedman NP, Calhoun V, Banich MT. Neurodevelopmental Subtypes of Functional Brain Organization in the ABCD Study Using a Rigorous Analytic Framework. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.16.585343. [PMID: 38559171 PMCID: PMC10979961 DOI: 10.1101/2024.03.16.585343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
The current study demonstrates that an individual's resting-state functional connectivity (RSFC) is a dependable biomarker for identifying differential patterns of cognitive and emotional functioning during late childhood. Using baseline RSFC data from the Adolescent Brain Cognitive Development (ABCD) study, which includes children aged 9-11, we identified four distinct RSFC subtypes We introduce an integrated methodological pipeline for testing the reliability and importance of these subtypes. In the Identification phase, Leiden Community Detection defined RSFC subtypes, with their reproducibility confirmed through a split-sample technique in the Validation stage. The Evaluation phase showed that distinct cognitive and mental health profiles are associated with each subtype, with the Predictive phase indicating that subtypes better predict various cognitive and mental health characteristics than individual RSFC connections. The Replication stage employed bootstrapping and down-sampling methods to substantiate the reproducibility of these subtypes further. This work allows future explorations of developmental trajectories of these RSFC subtypes.
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Affiliation(s)
- Jacob DeRosa
- Department of Psychology and Neuroscience, University of Colorado Boulder
- Institute of Cognitive Science, University of Colorado Boulder
| | - Naomi P. Friedman
- Department of Psychology and Neuroscience, University of Colorado Boulder
- Institute for Behavioral Genetics, University of Colorado Boulder
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University
| | - Marie T. Banich
- Department of Psychology and Neuroscience, University of Colorado Boulder
- Institute of Cognitive Science, University of Colorado Boulder
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10
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Albertina EA, Barch DM, Karcher NR. Internalizing Symptoms and Adverse Childhood Experiences Associated With Functional Connectivity in a Middle Childhood Sample. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:50-59. [PMID: 35483606 PMCID: PMC9596616 DOI: 10.1016/j.bpsc.2022.04.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 03/13/2022] [Accepted: 04/09/2022] [Indexed: 05/11/2023]
Abstract
BACKGROUND Research has found overlapping associations in adults of resting-state functional connectivity (RSFC) to both internalizing disorders (e.g., depression, anxiety) and a history of traumatic events. The present study aimed to extend this previous research to a younger sample by examining RSFC associations with both internalizing symptoms and adverse childhood experiences (ACEs) in middle childhood. METHODS We used generalized linear mixed models to examine associations between a priori within- and between-network RSFC with child-reported internalizing symptoms and ACEs using the Adolescent Brain Cognitive Development dataset (N = 10,168, mean age = 9.95 years, SD = 0.627). RESULTS We found that internalizing symptoms and ACEs were associated with both multiple overlapping and unique RSFC network patterns. Both ACEs and internalizing symptoms were associated with a reduced anticorrelation between the default mode network and the dorsal attention network. However, internalizing symptoms were uniquely associated with lower within-network default mode network connectivity, while ACEs were uniquely associated with both lower between-network connectivity of the auditory network and cingulo-opercular network, and higher within-network frontoparietal network connectivity. CONCLUSIONS The present study points to overlap in the RSFC associations with internalizing symptoms and ACEs, as well as important areas of specificity in RSFC associations. Many of the RSFC associations found have been previously implicated in attentional control functions, including modulation of attention to sensory stimuli. This may have critical importance in understanding internalizing symptoms and outcomes of ACEs.
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Affiliation(s)
- Emily A Albertina
- Department of Psychiatry, Washington University in St. Louis School of Medicine, St. Louis, Missouri.
| | - Deanna M Barch
- Department of Psychiatry, Washington University in St. Louis School of Medicine, St. Louis, Missouri
| | - Nicole R Karcher
- Department of Psychiatry, Washington University in St. Louis School of Medicine, St. Louis, Missouri
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11
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Thomas SA, Ryan SK, Gilman J. Resting state network connectivity is associated with cognitive flexibility performance in youth in the Adolescent Brain Cognitive Development Study. Neuropsychologia 2023; 191:108708. [PMID: 37898357 PMCID: PMC10842068 DOI: 10.1016/j.neuropsychologia.2023.108708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Revised: 10/13/2023] [Accepted: 10/25/2023] [Indexed: 10/30/2023]
Abstract
Cognitive flexibility is an executive functioning skill that develops in childhood, and when impaired, has transdiagnostic implications for psychiatric disorders. To identify how intrinsic neural architecture at rest is linked to cognitive flexibility performance, we used the data-driven method of independent component analysis (ICA) to investigate resting state networks (RSNs) and their whole-brain connectivity associated with levels of cognitive flexibility performance in children. We hypothesized differences by cognitive flexibility performance in RSN connectivity strength in cortico-striatal circuitry, which would manifest via the executive control network, right and left frontoparietal networks (FPN), salience network, default mode network (DMN), and basal ganglia network. We selected participants from the Adolescent Brain Cognitive Development (ABCD) Study who scored at the 25th, ("CF-Low"), 50th ("CF-Average"), or 75th percentiles ("CF-High") on a cognitive flexibility task, were early to middle puberty, and did not exhibit significant psychopathology (n = 967, 47.9% female; ages 9-10). We conducted whole-brain ICA, identifying 14 well-characterized RSNs. Groups differed in connectivity strength in the right FPN, anterior DMN, and posterior DMN. Planned comparisons indicated CF-High had stronger connectivity between right FPN and supplementary motor/anterior cingulate than CF-Low. CF-High had more anti-correlated connectivity between anterior DMN and precuneus than CF-Average. CF-Low had stronger connectivity between posterior DMN and supplementary motor/anterior cingulate than CF-Average. Post-hoc correlations with reaction time by trial type demonstrated significant associations with connectivity. In sum, our results suggest childhood cognitive flexibility performance is associated with DMN and FPN connectivity strength at rest, and that there may be optimal levels of connectivity associated with task performance that vary by network.
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Affiliation(s)
- Sarah A Thomas
- Bradley Hasbro Children's Research Center, 25 Hoppin St., Box #36, Providence, RI, 02903, USA; Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA; Carney Institute for Brain Science, Brown University, Box 1901, 164 Angell St., 4th Floor, Providence, RI, 02912, USA.
| | - Sarah K Ryan
- Bradley Hasbro Children's Research Center, 25 Hoppin St., Box #36, Providence, RI, 02903, USA.
| | - Jodi Gilman
- Massachusetts General Hospital (MGH) Department of Psychiatry, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
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Hoy N, Lynch SJ, Waszczuk MA, Reppermund S, Mewton L. Transdiagnostic biomarkers of mental illness across the lifespan: A systematic review examining the genetic and neural correlates of latent transdiagnostic dimensions of psychopathology in the general population. Neurosci Biobehav Rev 2023; 155:105431. [PMID: 37898444 DOI: 10.1016/j.neubiorev.2023.105431] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/26/2023] [Accepted: 10/21/2023] [Indexed: 10/30/2023]
Abstract
This systematic review synthesizes evidence from research investigating the biological correlates of latent transdiagnostic dimensions of psychopathology (e.g., the p-factor, internalizing, externalizing) across the lifespan. Eligibility criteria captured genomic and neuroimaging studies investigating general and/or specific dimensions in general population samples across all age groups. MEDLINE, Embase, and PsycINFO were searched for relevant studies published up to March 2023 and 46 studies were selected for inclusion. The results revealed several biological correlates consistently associated with transdiagnostic dimensions of psychopathology, including polygenic scores for ADHD and neuroticism, global surface area and global gray matter volume. Shared and unique associations between symptom dimensions are highlighted, as are potential age-specific differences in biological associations. Findings are interpreted with reference to key methodological differences across studies. The included studies provide compelling evidence that the general dimension of psychopathology reflects common underlying genetic and neurobiological vulnerabilities that are shared across diverse manifestations of mental illness. Substantive interpretations of general psychopathology in the context of genetic and neurobiological evidence are discussed.
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Affiliation(s)
- Nicholas Hoy
- The Matilda Centre for Research in Mental Health and Substance Use, University of Sydney, Sydney, Australia; Centre for Healthy Brain Ageing, University of New South Wales, Sydney, Australia.
| | - Samantha J Lynch
- The Matilda Centre for Research in Mental Health and Substance Use, University of Sydney, Sydney, Australia; Department of Psychiatry, Université de Montréal, Montreal, Canada; Research Centre, CHU Sainte-Justine, Montreal, Canada
| | - Monika A Waszczuk
- Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, United States
| | - Simone Reppermund
- Centre for Healthy Brain Ageing, University of New South Wales, Sydney, Australia; Department of Developmental Disability Neuropsychiatry, University of New South Wales, Sydney, Australia
| | - Louise Mewton
- The Matilda Centre for Research in Mental Health and Substance Use, University of Sydney, Sydney, Australia
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Feng Y, Zhu Y, Guo X, Luo X, Dang C, Liu Q, Xu C, Kang S, Yin G, Liang T, Wang Y, Liu L, Sun L. Exploring the Potential "Brain-Cognition-Behavior" Relationship in Children With ADHD Based on Resting-State Brain Local Activation and Functional Connectivity. J Atten Disord 2023; 27:1638-1649. [PMID: 37688472 DOI: 10.1177/10870547231197206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/11/2023]
Abstract
OBJECTIVE Exploring how abnormal brain function in children with ADHD affects executive function and ultimately leads to behavioral impairment provides a theoretical basis for clinically targeted neurotherapy and cognitive training. METHOD Amplitude of low frequency fluctuations (ALFF), regional homogeneity (ReHo), and seed-based FC were analyzed in 53 ADHD and 52 healthy controls. The "brain-cognition-behavior" relationship was further explored using mediation analysis. RESULTS ADHD showed abnormal local activation in the middle temporal gyrus (MTG), inferior occipital gyrus and inferior frontal gyrus (IFG) and reduced FC between the IFG and the cerebellum. ADHD diagnosis may affect ALFF of MTG and further modulate shift and finally affect inattentive symptoms. It may also affect the total symptoms through the FC of the IFG with the cerebellum. CONCLUSION ADHD showed extensive spontaneous activity abnormalities and frontal-cerebellar FC impairments. Localized functional abnormalities in the MTG may affect the shift in EF, resulting in attention deficit behavior.
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Affiliation(s)
- Yuan Feng
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Yu Zhu
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Xiaojie Guo
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Xiangsheng Luo
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Chen Dang
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Qianrong Liu
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Chenyang Xu
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Simin Kang
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Gaohan Yin
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Taizhu Liang
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Yufeng Wang
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Lu Liu
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Li Sun
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
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14
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Chang SE, Lenartowicz A, Hellemann GS, Uddin LQ, Bearden CE. Variability in Cognitive Task Performance in Early Adolescence Is Associated With Stronger Between-Network Anticorrelation and Future Attention Problems. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2023; 3:948-957. [PMID: 37881561 PMCID: PMC10593900 DOI: 10.1016/j.bpsgos.2022.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 09/22/2022] [Accepted: 11/09/2022] [Indexed: 11/28/2022] Open
Abstract
Background Intraindividual variability (IIV) during cognitive task performance is a key behavioral index of attention and a consistent marker of attention-deficit/hyperactivity disorder. In adults, lower IIV has been associated with anticorrelation between the default mode network (DMN) and dorsal attention network (DAN)-thought to underlie effective allocation of attention. However, whether these behavioral and neural markers of attention are 1) associated with each other and 2) can predict future attention-related deficits has not been examined in a developmental, population-based cohort. Methods We examined relationships at the baseline visit between IIV on 3 cognitive tasks, DMN-DAN anticorrelation, and parent-reported attention problems using data from the Adolescent Brain Cognitive Development (ABCD) Study (N = 11,878 participants, ages 9 to 10 years, female = 47.8%). We also investigated whether behavioral and neural markers of attention at baseline predicted attention problems 1, 2, and 3 years later. Results At baseline, greater DMN-DAN anticorrelation was associated with lower IIV across all 3 cognitive tasks (B = 0.22 to 0.25). Older age at baseline was associated with stronger DMN-DAN anticorrelation and lower IIV (B = -0.005 to -0.0004). Weaker DMN-DAN anticorrelation and IIV were cross-sectionally associated with attention problems (B = 1.41 to 7.63). Longitudinally, lower IIV at baseline was associated with less severe attention problems 1 to 3 years later, after accounting for baseline attention problems (B = 0.288 to 0.77). Conclusions The results suggest that IIV in early adolescence is associated with worsening attention problems in a representative cohort of U.S. youth. Attention deficits in early adolescence may be important for understanding and predicting future cognitive and clinical outcomes.
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Affiliation(s)
- Sarah E. Chang
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, California
| | - Agatha Lenartowicz
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, California
| | - Gerhard S. Hellemann
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, California
- Department of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Lucina Q. Uddin
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, California
| | - Carrie E. Bearden
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, California
- Department of Psychology, University of California, Los Angeles, Los Angeles, California
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15
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Picci G, Petro NM, Son JJ, Agcaoglu O, Eastman JA, Wang YP, Stephen JM, Calhoun VD, Taylor BK, Wilson TW. Transdiagnostic indicators predict developmental changes in cognitive control resting-state networks. Dev Psychopathol 2023:1-11. [PMID: 37615120 PMCID: PMC11140239 DOI: 10.1017/s0954579423001013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/25/2023]
Abstract
Over the past decade, transdiagnostic indicators in relation to neurobiological processes have provided extensive insight into youth's risk for psychopathology. During development, exposure to childhood trauma and dysregulation (i.e., so-called AAA symptomology: anxiety, aggression, and attention problems) puts individuals at a disproportionate risk for developing psychopathology and altered network-level neural functioning. Evidence for the latter has emerged from resting-state fMRI studies linking mental health symptoms and aberrations in functional networks (e.g., cognitive control (CCN), default mode networks (DMN)) in youth, although few of these investigations have used longitudinal designs. Herein, we leveraged a three-year longitudinal study to identify whether traumatic exposures and concomitant dysregulation trigger changes in the developmental trajectories of resting-state functional networks involved in cognitive control (N = 190; 91 females; time 1 Mage = 11.81). Findings from latent growth curve analyses revealed that greater trauma exposure predicted increasing connectivity between the CCN and DMN across time. Greater levels of dysregulation predicted reductions in within-network connectivity in the CCN. These findings presented in typically developing youth corroborate connectivity patterns reported in clinical populations, suggesting there is predictive utility in using transdiagnostic indicators to forecast alterations in resting-state networks implicated in psychopathology.
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Affiliation(s)
- Giorgia Picci
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA
- Center for Pediatric Brain Health, Boys Town National Research Hospital, Boys Town, NE, USA
- Department of Pharmacology & Neuroscience, Creighton University, Omaha, NE, USA
| | - Nathan M Petro
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA
- Center for Pediatric Brain Health, Boys Town National Research Hospital, Boys Town, NE, USA
| | - Jake J Son
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA
- Center for Pediatric Brain Health, Boys Town National Research Hospital, Boys Town, NE, USA
- College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA
| | - Oktay Agcaoglu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of technology, and Emory University, Atlanta, GA, USA
| | - Jacob A Eastman
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA
- Center for Pediatric Brain Health, Boys Town National Research Hospital, Boys Town, NE, USA
| | - Yu-Ping Wang
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA
| | | | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of technology, and Emory University, Atlanta, GA, USA
| | - Brittany K Taylor
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA
- Center for Pediatric Brain Health, Boys Town National Research Hospital, Boys Town, NE, USA
- Department of Pharmacology & Neuroscience, Creighton University, Omaha, NE, USA
| | - Tony W Wilson
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA
- Center for Pediatric Brain Health, Boys Town National Research Hospital, Boys Town, NE, USA
- Department of Pharmacology & Neuroscience, Creighton University, Omaha, NE, USA
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16
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Lombardo D, Kaufmann T. Different patterns of intrinsic functional connectivity at the default mode and attentional networks predict crystalized and fluid abilities in childhood. Cereb Cortex Commun 2023; 4:tgad015. [PMID: 37675438 PMCID: PMC10477707 DOI: 10.1093/texcom/tgad015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 08/01/2023] [Accepted: 08/14/2023] [Indexed: 09/08/2023] Open
Abstract
Crystallized abilities are skills used to solve problems based on experience, while fluid abilities are linked to reasoning without evoke prior knowledge. To what extent crystallized and fluid abilities involve dissociated or overlapping neural systems is debatable. Due to often deployed small sample sizes or different study settings in prior work, the neural basis of crystallized and fluid abilities in childhood remains largely unknown. Here we analyzed within and between network connectivity patterns from resting-state functional MRI of 2707 children between 9 and 10 years from the ABCD study. We hypothesized that differences in functional connectivity at the default mode network (DMN), ventral, and dorsal attentional networks (VAN, DAN) explain differences in fluid and crystallized abilities. We found that stronger between-network connectivity of the DMN and VAN, DMN and DAN, and VAN and DAN predicted crystallized abilities. Within-network connectivity of the DAN predicted both crystallized and fluid abilities. Our findings reveal that crystallized abilities rely on the functional coupling between attentional networks and the DMN, whereas fluid abilities are associated with a focal connectivity configuration at the DAN. Our study provides new evidence into the neural basis of child intelligence and calls for future comparative research in adulthood during neuropsychiatric diseases.
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Affiliation(s)
- Diego Lombardo
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Calwerstraße 14, 72076 Tübingen, Germany
| | - Tobias Kaufmann
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Calwerstraße 14, 72076 Tübingen, Germany
- German Center for Mental Health (DZPG), Partner Site Tübingen, Calwerstraße 14, 72076 Tübingen, Germany
- Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Kirkeveien 166, 0450 Oslo, Norway
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17
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Ren J, Loughnan R, Xu B, Thompson WK, Fan CC. Estimating the Total Variance Explained by Whole-Brain Imaging for Zero-inflated Outcomes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.14.553270. [PMID: 37645753 PMCID: PMC10462013 DOI: 10.1101/2023.08.14.553270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Zero-inflated outcomes are very common in behavioral data, particularly for responses to psychological questionnaires. Modeling these challenging distributions is further exacerbated by the absence of established statistical models capable of characterizing total signals attributed to whole-brain imaging features, making the accurate assessment of brain-behavior relationships particularly formidable. Given this critical need, we have developed a novel variational Bayes algorithm that characterizes the total signal captured by whole-brain imaging features for zero-inflated outcomes . Our zero-inflated variance (ZIV) estimator robustly estimates the fraction of variance explained (FVE) and the proportion of non-null effects from large-scale imaging data. In simulations, ZIV outperformed other linear prediction algorithms. Applying ZIV to data from one of the largest neuroimaging studies, the Adolescent Brain Cognitive Development SM (ABCD) Study, we found that whole-brain imaging features have a larger FVE for externalizing compared to internalizing behavior. We also demonstrate that the ZIV estimator, especially applied to focal sub-scales, can localize key neurocircuitry associated with human behavior.
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18
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Hehr A, Huntley ED, Marusak HA. Getting a Good Night's Sleep: Associations Between Sleep Duration and Parent-Reported Sleep Quality on Default Mode Network Connectivity in Youth. J Adolesc Health 2023; 72:933-942. [PMID: 36872118 PMCID: PMC10198813 DOI: 10.1016/j.jadohealth.2023.01.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 12/07/2022] [Accepted: 01/04/2023] [Indexed: 03/07/2023]
Abstract
PURPOSE Sleep plays an important role in healthy neurocognitive development, and poor sleep is linked to cognitive and emotional dysfunction. Studies in adults suggest that shorter sleep duration and poor sleep quality may disrupt core neurocognitive networks, particularly the default mode network (DMN)-a network implicated in internal cognitive processing and rumination. Here, we examine the relationships between sleep and within- and between-network resting-state functional connectivity (rs-FC) of the DMN in youth. METHODS This study included 3,798 youth (11.9 ± 0.6 years, 47.5% female) from the Adolescent Brain Cognitive Development cohort. Sleep duration and wake after sleep onset (WASO) were quantified using Fitbit watch recordings, and parent-reported sleep disturbances were measured using the Sleep Disturbance Scale for Children. We focused on rs-FC between the DMN and anticorrelated networks (i.e., dorsal attention network [DAN], frontoparietal network, salience network). RESULTS Both shorter sleep duration and greater sleep disturbances were associated with weaker within-network DMN rs-FC. Shorter sleep duration was also associated with weaker anticorrelation (i.e., higher rs-FC) between the DMN and two anticorrelated networks: the DAN and frontoparietal network. Greater WASO was also associated with DMN-DAN rs-FC, and the effects of WASO on rs-FC were most pronounced among children who slept fewer hours/night. DISCUSSION Together, these data suggest that different aspects of sleep are associated with distinct and interactive alterations in resting-state brain networks. Alterations in core neurocognitive networks may confer increased risk for emotional psychopathology and attention-related vulnerabilities. Our findings contribute to the growing number of studies demonstrating the importance of healthy sleep practices in youth.
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Affiliation(s)
- Aneesh Hehr
- Department of Psychiatry & Behavioral Neurosciences, School of Medicine, Wayne State University, Detroit, Michigan
| | - Edward D Huntley
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, Michigan
| | - Hilary A Marusak
- Department of Psychiatry & Behavioral Neurosciences, School of Medicine, Wayne State University, Detroit, Michigan; Karmanos Cancer Institute, Detroit, Michigan; Merrill Palmer Skillman Institute for Child and Family Development, Wayne State University, Detroit, Michigan.
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19
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Privodnova EY, Slobodskaya HR, Savostyanov AN, Bocharov AV, Saprigyn AE, Knyazev GG. Fast changes in default and control network activity underlying intraindividual response time variability in childhood: Does age and sex matter? Dev Psychobiol 2023; 65:e22382. [PMID: 37073590 DOI: 10.1002/dev.22382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 10/10/2022] [Accepted: 02/08/2023] [Indexed: 04/20/2023]
Abstract
Intraindividual response time variability (RTV) is considered as a general marker of neurological health. In adults, the central executive and salience networks (task-positive networks, TPN) and the default mode network (DMN) are critical for RTV. Given that RTV decreases with growing up, and that boys are likely somewhat behind girls with respect to the network development, we aimed to clarify age and sex effects. Electroencephalogram was recorded during Stroop-like test performance in 124 typically developing children aged 5-12 years. Network fluctuations were calculated as changes of current source density (CSD) in regions of interest (ROIs) from pretest to 1-s test interval. In boys, TPN activation (CSD increase in ROIs included in the TPN) was associated with lower RTV, suggesting a greater engagement of attentional control. In children younger than 9.5 years, higher response stability was associated with the predominance of TPN activation over DMN activation (CSD increase in ROIs included in the TPN > that in the DMN); this predominance increased with age, suggesting that variability among younger children may be due to network immaturity. These findings suggest that the TPN and DMN may play different roles within the network mechanisms of RTV in boys and girls and at different developmental stages.
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Affiliation(s)
- Evgeniya Yu Privodnova
- Laboratory of Cognitive Physiology, Scientific Research Institute of Neurosciences and Medicine, Novosibirsk, Russia
- Department of Psychology, Institute of Medicine and Psychology, Novosibirsk State University, Novosibirsk, Russia
| | - Helena R Slobodskaya
- Department of Child Development and Individual Differences, Scientific Research Institute of Neurosciences and Medicine, Novosibirsk, Russia
| | - Alexander N Savostyanov
- Laboratory of Differential Psychophysiology, Scientific Research Institute of Neurosciences and Medicine, Novosibirsk, Russia
- Institute for the Humanities, Novosibirsk State University, Novosibirsk, Russia
| | - Andrey V Bocharov
- Laboratory of Differential Psychophysiology, Scientific Research Institute of Neurosciences and Medicine, Novosibirsk, Russia
- Institute for the Humanities, Novosibirsk State University, Novosibirsk, Russia
| | - Alexander E Saprigyn
- Laboratory of Differential Psychophysiology, Scientific Research Institute of Neurosciences and Medicine, Novosibirsk, Russia
- Laboratory of Psychological Genetics, Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia
| | - Gennady G Knyazev
- Laboratory of Differential Psychophysiology, Scientific Research Institute of Neurosciences and Medicine, Novosibirsk, Russia
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20
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Pines A, Keller AS, Larsen B, Bertolero M, Ashourvan A, Bassett DS, Cieslak M, Covitz S, Fan Y, Feczko E, Houghton A, Rueter AR, Saggar M, Shafiei G, Tapera TM, Vogel J, Weinstein SM, Shinohara RT, Williams LM, Fair DA, Satterthwaite TD. Development of top-down cortical propagations in youth. Neuron 2023; 111:1316-1330.e5. [PMID: 36803653 PMCID: PMC10121821 DOI: 10.1016/j.neuron.2023.01.014] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 12/08/2022] [Accepted: 01/18/2023] [Indexed: 02/19/2023]
Abstract
Hierarchical processing requires activity propagating between higher- and lower-order cortical areas. However, functional neuroimaging studies have chiefly quantified fluctuations within regions over time rather than propagations occurring over space. Here, we leverage advances in neuroimaging and computer vision to track cortical activity propagations in a large sample of youth (n = 388). We delineate cortical propagations that systematically ascend and descend a cortical hierarchy in all individuals in our developmental cohort, as well as in an independent dataset of densely sampled adults. Further, we demonstrate that top-down, descending hierarchical propagations become more prevalent with greater demands for cognitive control as well as with development in youth. These findings emphasize that hierarchical processing is reflected in the directionality of propagating cortical activity and suggest top-down propagations as a potential mechanism of neurocognitive maturation in youth.
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Affiliation(s)
- Adam Pines
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94304, USA; The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Arielle S Keller
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Bart Larsen
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Maxwell Bertolero
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Arian Ashourvan
- Department of Psychology, The University of Kansas, Lawrence, KS 66045, USA
| | - Dani S Bassett
- Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA; Departments of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Physics & Astronomy, The University of Pennsylvania, Philadelphia, PA 19104, USA; Santa Fe Institute, Santa Fe, NM 87051, USA
| | - Matthew Cieslak
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Sydney Covitz
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Yong Fan
- Department of Radiology, The University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Eric Feczko
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55414, USA
| | - Audrey Houghton
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55414, USA
| | - Amanda R Rueter
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55414, USA
| | - Manish Saggar
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94304, USA
| | - Golia Shafiei
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Tinashe M Tapera
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Jacob Vogel
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Sarah M Weinstein
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Leanne M Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94304, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55414, USA
| | - Theodore D Satterthwaite
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA.
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21
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Fekson VK, Michaeli T, Rosch KS, Schlaggar BL, Horowitz-Kraus T. Characterizing different cognitive and neurobiological profiles in a community sample of children using a non-parametric approach: An fMRI study. Dev Cogn Neurosci 2023; 60:101198. [PMID: 36652896 PMCID: PMC9853310 DOI: 10.1016/j.dcn.2023.101198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 12/06/2022] [Accepted: 01/11/2023] [Indexed: 01/14/2023] Open
Abstract
Executive Functions (EF) is an umbrella term for a set of mental processes geared towards goal-directed behavior supporting academic skills such as reading abilities. One of the brain's functional networks implicated in EF is the Default Mode Network (DMN). The current study uses measures of inhibitory control, a main sub-function of EF, to create cognitive and neurobiological "inhibitory control profiles" and relate them to reading abilities in a large sample (N = 5055) of adolescents aged 9-10 from the Adolescent Brain Cognitive Development (ABCD) study. Using a Latent Profile Analysis (LPA) approach, data related to inhibitory control was divided into four inhibition classes. For each class, functional connectivity within the DMN was calculated from resting-state data, using a non-parametric algorithm for detecting group similarities. These inhibitory control profiles were then related to reading abilities. The four inhibitory control groups showed significantly different reading abilities, with neurobiologically different DMN segregation profiles for each class versus controls. The current study demonstrates that a community sample of children is not entirely homogeneous and is composed of different subgroups that can be differentiated both behaviorally/cognitively and neurobiologically, by focusing on inhibitory control and the DMN. Educational implications relating these results to reading abilities are noted.
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Affiliation(s)
- Victoria Khalfin Fekson
- Educational Neuroimaging Group, Faculty of Education in Science and Technology, Technion, Israel
| | - Tomer Michaeli
- Faculty of Electrical and Computer Engineering, Technion, Israel
| | - Keri S Rosch
- Kennedy Krieger Institute, Baltimore, MD, USA; Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Bradley L Schlaggar
- Kennedy Krieger Institute, Baltimore, MD, USA; Departments of Neurology and Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Tzipi Horowitz-Kraus
- Educational Neuroimaging Group, Faculty of Education in Science and Technology, Technion, Israel; Kennedy Krieger Institute, Baltimore, MD, USA; Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Faculty of Biomedical Engineering, Technion, Israel.
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22
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Xu Z, Zhao W, Wang H, Tian Y, Lei X. Functional connectivity between dorsal attention and default mode networks mediates subjective sleep duration and depression in young females. J Affect Disord 2023; 325:386-391. [PMID: 36634855 DOI: 10.1016/j.jad.2023.01.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 01/01/2023] [Accepted: 01/04/2023] [Indexed: 01/11/2023]
Abstract
BACKGROUND Depression prevails throughout the world. Young females are more likely to suffer from depression because of lack of sleep. METHODS We recruited 405 young female participants to assess their subjective sleep duration and self-rating depression. The resting-state magnetic resonance imaging (rs-fMRI) data were collected to identify the brain regions related to sleep duration and depression, and a mediating model was established among sleep duration, depression and functional connectivity (FC) of rs-fMRI. RESULTS Correlation analysis indicated that subjective sleep duration was negatively associated with self-rating depression in young females (r = -0.22, p < .001). The network connectivity between dorsal attention network (DAN) and default mode network (DMN) positively correlated with self-rating depression (r = 0.13, p < .05), and negatively correlated with subjective sleep duration (r = -0.14, p < .01). Furthermore, the mediation analysis revealed that the FC of DAN-DMN significantly mediated the effect of sleep duration on depression. LIMITATIONS The study was a cross-section design and the sleep duration of the participants was subjectively reported. Future studies should consider to track the participants longitudinally and to measure the objective sleep duration by actigraph or polysomnography. CONCLUSIONS The participants with less sleep duration are more prone to develop depression feelings. The FC of DAN-DMN mediated the effect of sleep duration on depression. Thus, the FC of DAN-DMN could be consider as a neural target to relieve depression by increasing sleep duration in young females.
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Affiliation(s)
- Ziye Xu
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing 400715, China
| | - Wenrui Zhao
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing 400715, China
| | - Haien Wang
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing 400715, China
| | - Yun Tian
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing 400715, China
| | - Xu Lei
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing 400715, China.
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23
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Yuan D, Hahn S, Allgaier N, Owens MM, Chaarani B, Potter A, Garavan H. Machine learning approaches linking brain function to behavior in the ABCD STOP task. Hum Brain Mapp 2023; 44:1751-1766. [PMID: 36534603 PMCID: PMC9921227 DOI: 10.1002/hbm.26172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 10/13/2022] [Accepted: 11/14/2022] [Indexed: 12/24/2022] Open
Abstract
The stop-signal task (SST) is one of the most common fMRI tasks of response inhibition, and its performance measure, the stop-signal reaction-time (SSRT), is broadly used as a measure of cognitive control processes. The neurobiology underlying individual or clinical differences in response inhibition remain unclear, consistent with the general pattern of quite modest brain-behavior associations that have been recently reported in well-powered large-sample studies. Here, we investigated the potential of multivariate, machine learning (ML) methods to improve the estimation of individual differences in SSRT with multimodal structural and functional region of interest-level neuroimaging data from 9- to 11-year-olds children in the ABCD Study. Six ML algorithms were assessed across modalities and fMRI tasks. We verified that SST activation performed best in predicting SSRT among multiple modalities including morphological MRI (cortical surface area/thickness), diffusion tensor imaging, and fMRI task activations, and then showed that SST activation explained 12% of the variance in SSRT using cross-validation and out-of-sample lockbox data sets (n = 7298). Brain regions that were more active during the task and that showed more interindividual variation in activation were better at capturing individual differences in performance on the task, but this was only true for activations when successfully inhibiting. Cortical regions outperformed subcortical areas in explaining individual differences but the two hemispheres performed equally well. These results demonstrate that the detection of reproducible links between brain function and performance can be improved with multivariate approaches and give insight into a number of brain systems contributing to individual differences in this fundamental cognitive control process.
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Affiliation(s)
- Dekang Yuan
- Department of PsychiatryUniversity of VermontBurlingtonVermontUSA
| | - Sage Hahn
- Department of PsychiatryUniversity of VermontBurlingtonVermontUSA
| | | | - Max M. Owens
- Department of PsychiatryUniversity of VermontBurlingtonVermontUSA
| | - Bader Chaarani
- Department of PsychiatryUniversity of VermontBurlingtonVermontUSA
| | - Alexandra Potter
- Department of PsychiatryUniversity of VermontBurlingtonVermontUSA
| | - Hugh Garavan
- Department of PsychiatryUniversity of VermontBurlingtonVermontUSA
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24
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Chen S, Zhang X, Lin S, Zhang Y, Xu Z, Li Y, Xu M, Hou G, Qiu Y. Suicide risk stratification among major depressed patients based on a machine learning approach and whole-brain functional connectivity. J Affect Disord 2023; 322:173-179. [PMID: 36370913 DOI: 10.1016/j.jad.2022.11.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 08/24/2022] [Accepted: 11/07/2022] [Indexed: 11/11/2022]
Abstract
BACKGROUND Suicide risk stratification and individual-level prediction among major depressive disorder (MDD) is important but unrecognized. Here, we construct models to detect suicidality in MDD using machine learning (ML) and whole-brain functional connectivity (FC). METHODS A cross-sectional assessment was conducted on 200 subjects, including 126 MDD with high suicide risk (HSR; 73 patients with suicidal ideation [SI], 53 patients with suicidal attempts [SA]), 36 patients with low suicide risk (LSR) and 38 healthy controls (HCs). Whole-brain FC features were calculated, the least absolute shrinkage and selection operator (LASSO) method was used for feature selection. A support vector machine (SVM) was performed to build models to distinguish MDD from HCs, and for suicide risk stratification among MDD. Leave-one-out cross-validation (LOOCV) was performed for validation. RESULTS The models constructed using SVM on whole-brain FC had powerful classification efficiency in screening MDD from HCs (accuracy = 88.50 %), and in suicide risk stratification among MDD patients (with accuracy = 84.56 % and 74.60 % in classifying patients with HSR or LSR, and SA or SI, respectively). Subsequent analysis demonstrated that intra-network dysconnectivity in the sensorimotor network and inter-network dysconnectivity between the default and dorsal attention network could characterize HSR and SA in MDD, separately. LIMITATIONS This study was a single center cohort study without external validation. CONCLUSION These findings indicate ML approaches are useful in suicide risk stratification among MDD based on whole-brain FC, which may help to identify individuals with different suicide risks in MDD and provide an individual-level prediction.
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Affiliation(s)
- Shengli Chen
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan AVE 89, Nanshan district, Shenzhen 518000, PR China
| | - Xiaojing Zhang
- Guangdong Provincial Key Laboratory of Genome Stability and Disease Prevention and Regional Immunity and Diseases, Department of Pathology, Shenzhen University School of Medicine, Shenzhen, Guangdong 518060, PR China
| | - Shiwei Lin
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan AVE 89, Nanshan district, Shenzhen 518000, PR China
| | - Yingli Zhang
- Department of Depressive Disorders, Shenzhen Mental Health Center, Shenzhen Kangning Hospital, Cuizhu AVE 1080, Luohu district, Shenzhen 518020, China
| | - Ziyun Xu
- Department of Radiology, Shenzhen Mental Health Center, Shenzhen Kangning Hospital, Cuizhu AVE 1080, Luohu district, Shenzhen 518020, China
| | - Yanqing Li
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Duobao AVE 56, Liwan District, Guangzhou, PR China
| | - Manxi Xu
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Duobao AVE 56, Liwan District, Guangzhou, PR China
| | - Gangqiang Hou
- Department of Radiology, Shenzhen Mental Health Center, Shenzhen Kangning Hospital, Cuizhu AVE 1080, Luohu district, Shenzhen 518020, China.
| | - Yingwei Qiu
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan AVE 89, Nanshan district, Shenzhen 518000, PR China.
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25
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Rakesh D, Zalesky A, Whittle S. The Role of School Environment in Brain Structure, Connectivity, and Mental Health in Children: A Multimodal Investigation. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:32-41. [PMID: 35123109 DOI: 10.1016/j.bpsc.2022.01.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Revised: 01/05/2022] [Accepted: 01/20/2022] [Indexed: 02/07/2023]
Abstract
BACKGROUND Much work has been dedicated to understanding the effects of adverse home environments on brain development. While the school social and learning environment plays a role in child development, little work has been done to investigate the impact of the school environment on the developing brain. The goal of the present study was to examine associations between the school environment, brain structure and connectivity, and mental health. METHODS In this preregistered study we investigated these questions in a large sample of adolescents (9-10 years of age) from the Adolescent Brain Cognitive Development (ABCD) Study. We examined the association between school environment and gray matter (n = 10,435) and white matter (n = 10,770) structure and functional connectivity (n = 9528). We then investigated multivariate relationships between school-associated brain measures and mental health. RESULTS School environment was associated with connectivity of the auditory and retrosplenial temporal network as well as of higher-order cognitive networks like the cingulo-opercular, default mode, ventral attention, and frontoparietal networks. Multivariate analyses revealed that connectivity of the cingulo-opercular and default mode networks was also associated with mental health. CONCLUSIONS Findings shed light on the neural mechanisms through which favorable school environments may contribute to positive mental health outcomes in children. Our findings have implications for interventions targeted at promoting positive youth functioning through improving school environments.
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Affiliation(s)
- Divyangana Rakesh
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia.
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia; Melbourne School of Engineering, University of Melbourne, Melbourne, Victoria, Australia
| | - Sarah Whittle
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia.
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26
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Pines AR, Larsen B, Cui Z, Sydnor VJ, Bertolero MA, Adebimpe A, Alexander-Bloch AF, Davatzikos C, Fair DA, Gur RC, Gur RE, Li H, Milham MP, Moore TM, Murtha K, Parkes L, Thompson-Schill SL, Shanmugan S, Shinohara RT, Weinstein SM, Bassett DS, Fan Y, Satterthwaite TD. Dissociable multi-scale patterns of development in personalized brain networks. Nat Commun 2022; 13:2647. [PMID: 35551181 PMCID: PMC9098559 DOI: 10.1038/s41467-022-30244-4] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 04/21/2022] [Indexed: 11/24/2022] Open
Abstract
The brain is organized into networks at multiple resolutions, or scales, yet studies of functional network development typically focus on a single scale. Here, we derive personalized functional networks across 29 scales in a large sample of youths (n = 693, ages 8-23 years) to identify multi-scale patterns of network re-organization related to neurocognitive development. We found that developmental shifts in inter-network coupling reflect and strengthen a functional hierarchy of cortical organization. Furthermore, we observed that scale-dependent effects were present in lower-order, unimodal networks, but not higher-order, transmodal networks. Finally, we found that network maturation had clear behavioral relevance: the development of coupling in unimodal and transmodal networks are dissociably related to the emergence of executive function. These results suggest that the development of functional brain networks align with and refine a hierarchy linked to cognition.
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Affiliation(s)
- Adam R Pines
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Bart Larsen
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Zaixu Cui
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Chinese Institute for Brain Research, 102206, Beijing, China
| | - Valerie J Sydnor
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Maxwell A Bertolero
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Azeez Adebimpe
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Aaron F Alexander-Bloch
- Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Christos Davatzikos
- Department of Radiology, the University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Damien A Fair
- Department of Pediatrics, College of Education and Human Development, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Ruben C Gur
- Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Radiology, the University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Raquel E Gur
- Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Hongming Li
- Department of Radiology, the University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Michael P Milham
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, 10962, USA.,Center for the Developing Brain, Child Mind Institute, New York City, NY, USA
| | - Tyler M Moore
- Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Kristin Murtha
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Linden Parkes
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | | | - Sheila Shanmugan
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sarah M Weinstein
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Danielle S Bassett
- Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Santa Fe Institute, Santa Fe, NM, 87051, USA
| | - Yong Fan
- Department of Radiology, the University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Theodore D Satterthwaite
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA. .,Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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27
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Ellwood-Lowe ME, Irving CN, Bunge SA. Exploring neural correlates of behavioral and academic resilience among children in poverty. Dev Cogn Neurosci 2022; 54:101090. [PMID: 35248821 PMCID: PMC8899231 DOI: 10.1016/j.dcn.2022.101090] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 02/15/2022] [Accepted: 02/19/2022] [Indexed: 11/26/2022] Open
Abstract
Children in poverty must contend with systems that do not meet their needs. We explored what, at a neural level, helps explain children's resilience in these contexts. Lower coupling between lateral frontoparietal network (LFPN) and default mode network (DMN)-linked, respectively, to externally- and internally-directed thought-has previously been associated with better cognitive performance. However, we recently found the opposite pattern for children in poverty. Here, we probed ecologically-valid assessments of performance. In a pre-registered study, we investigated trajectories of network coupling over ages 9-13 and their relation to school grades and attention problems. We analyzed longitudinal data from ABCD Study (N = 8366 children at baseline; 1303 below poverty). The link between cognitive performance and grades was weaker for children in poverty, highlighting the importance of ecologically-valid measures. As predicted, higher LFPN-DMN connectivity was linked to worse grades and attentional problems for children living above poverty, while children below poverty showed opposite tendencies. This interaction between LFPN-DMN connectivity and poverty related to children's grades two years later; however, it was attenuated when controlling for baseline grades and was not related to attention longitudinally. Together, these findings suggest network connectivity is differentially related to performance in real-world settings for children above and below poverty.
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Affiliation(s)
- M E Ellwood-Lowe
- Department of Psychology, University of California, Berkeley, USA.
| | - C N Irving
- Department of Psychology, University of California, Berkeley, USA
| | - S A Bunge
- Department of Psychology, University of California, Berkeley, USA; Helen Wills Neuroscience Institute, University of California, Berkeley, USA
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28
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Deshpande G, Zhao X, Robinson J. Functional Parcellation of the Hippocampus based on its Layer-specific Connectivity with Default Mode and Dorsal Attention Networks. Neuroimage 2022; 254:119078. [PMID: 35276366 DOI: 10.1016/j.neuroimage.2022.119078] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Revised: 01/29/2022] [Accepted: 03/07/2022] [Indexed: 12/25/2022] Open
Abstract
Recent neuroimaging evidence suggests that there might be an anterior-posterior functional differentiation of the hippocampus along the long-axis. The HERNET (hippocampal encoding/retrieval and network) model proposed an encoding/retrieval dichotomy with the anterior hippocampus more connected to the dorsal attention network (DAN) during memory encoding, and the posterior portions more connected to the default mode network (DMN) during retrieval. Evidence both for and against the HERNET model has been reported. In this study, we test the validity of the HERNET model non-invasively in humans by computing functional connectivity (FC) in layer-specific cortico-hippocampal microcircuits. This was achieved by acquiring sub-millimeter functional magnetic resonance imaging (fMRI) data during encoding/retrieval tasks at 7T. Specifically, FC between infra-granular output layers of DAN with hippocampus during encoding and FC between supra-granular input layers of DMN with hippocampus during retrieval were computed to test the predictions of the HERNET model. Our results support some predictions of the HERNET model including anterior-posterior gradient along the long axis of the hippocampus. While preferential relationships between the entire hippocampus and DAN/DMN during encoding/retrieval, respectively, were observed as predicted, anterior-posterior specificity in these network relationships could not be confirmed. The strength and clarity of evidence for/against the HERNET model were superior with layer-specific data compared to conventional volume data.
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Affiliation(s)
- Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL 36849, USA; Department of Psychological Sciences, Auburn University, Auburn, AL, USA; Alabama Advanced Imaging Consortium, Birmingham, AL, USA; Center for Neuroscience, Auburn University, Auburn, AL, USA; Key Laboratory for Learning and Cognition, School of Psychology, Capital Normal University, Beijing, China; Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India; Centre for Brain Research, Indian Institute of Science, Bangalore, India.
| | - Xinyu Zhao
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL 36849, USA; Quora Inc., Mountain View, CA, USA
| | - Jennifer Robinson
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL 36849, USA; Department of Psychological Sciences, Auburn University, Auburn, AL, USA; Alabama Advanced Imaging Consortium, Birmingham, AL, USA; Center for Neuroscience, Auburn University, Auburn, AL, USA
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29
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Lei Y, Zhang D, Qi F, Gao J, Tang M, Ai K, Yan X, Lei X, Shao Z, Su Y, Zhang X. Dysfunctional Interaction Between the Dorsal Attention Network and the Default Mode Network in Patients With Type 2 Diabetes Mellitus. Front Hum Neurosci 2022; 15:796386. [PMID: 35002661 PMCID: PMC8741406 DOI: 10.3389/fnhum.2021.796386] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 12/08/2021] [Indexed: 11/13/2022] Open
Abstract
The risk of cognitive impairment in patients with type 2 diabetes mellitus (T2DM) is significantly higher than that in the general population, but the exact neurophysiological mechanism underlying this is still unclear. An abnormal change in the intrinsic anticorrelation of the dorsal attention network (DAN) and the default mode network (DMN) is thought to be the mechanism underlying cognitive deficits that occur in many psychiatric disorders, but this association has rarely been tested in T2DM. This study explored the relationship between the interaction patterns of the DAN-DMN and clinical/cognitive variables in patients with T2DM. Forty-four patients with T2DM and 47 sex-, age-, and education-matched healthy controls (HCs) underwent neuropsychological assessments, independent component analysis (ICA), and functional network connection analysis (FNC). The relationship of DAN-DMN anticorrelation with the results of a battery of neuropsychological tests was also assessed. Relative to the HC group, the DMN showed decreased functional connectivity (FC) in the right precuneus, and the DAN showed decreased FC in the left inferior parietal lobule (IPL) in patients with T2DM. Subsequent FNC analysis revealed that, compared with the HC group, the T2DM patients displayed significantly increased inter-network connectivity between the DAN and DMN. These abnormal changes were correlated with the scores of multiple neuropsychological assessments (P < 0.05). These findings indicate abnormal changes in the interaction patterns of the DAN-DMN may be involved in the neuropathology of attention and general cognitive dysfunction in T2DM patients.
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Affiliation(s)
- Yumeng Lei
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Dongsheng Zhang
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Fei Qi
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Jie Gao
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Min Tang
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Kai Ai
- Department of Clinical Science, Philips Healthcare, Xi'an, China
| | - Xuejiao Yan
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Xiaoyan Lei
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Zhirong Shao
- Department of Graduate, Xi'an Medical University, Xi'an, China
| | - Yu Su
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Xiaoling Zhang
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
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30
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Yoon L, Carranza AF, Swartz JR. Resting-State Functional Connectivity Associated With Extraversion and Agreeableness in Adolescence. Front Behav Neurosci 2022; 15:644790. [PMID: 35046781 PMCID: PMC8762207 DOI: 10.3389/fnbeh.2021.644790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 12/08/2021] [Indexed: 11/23/2022] Open
Abstract
Although adolescence is a period in which developmental changes occur in brain connectivity, personality formation, and peer interaction, few studies have examined the neural correlates of personality dimensions related to social behavior within adolescent samples. The current study aims to investigate whether adolescents’ brain functional connectivity is associated with extraversion and agreeableness, personality dimensions linked to peer acceptance, social network size, and friendship quality. Considering sex-variant neural maturation in adolescence, we also examined sex-specific associations between personality and functional connectivity. Using resting-state functional magnetic resonance imaging (fMRI) data from a community sample of 70 adolescents aged 12–15, we examined associations between self-reported extraversion and agreeableness and seed-to-whole brain connectivity with the amygdala as a seed region of interest. Then, using 415 brain regions that correspond to 8 major brain networks and subcortex, we explored neural connectivity within brain networks and across the whole-brain. We conducted group-level multiple regression analyses with the regressors of extraversion, agreeableness, and their interactions with sex. Results demonstrated that amygdala connectivity with the postcentral gyrus, middle temporal gyrus, and the temporal pole is positively associated with extraversion in girls and negatively associated with extraversion in boys. Agreeableness was positively associated with amygdala connectivity with the middle occipital cortex and superior parietal cortex, in the same direction for boys and girls. Results of the whole-brain connectivity analysis revealed that the connectivity of the postcentral gyrus, located in the dorsal attention network, with regions in default mode network (DMN), salience/ventral attention network, and control network (CON) was associated with extraversion, with most connections showing positive associations in girls and negative associations in boys. For agreeableness, results of the within-network connectivity analysis showed that connections within the limbic network were positively associated with agreeableness in boys while negatively associated with or not associated with agreeableness in girls. Results suggest that intrinsic functional connectivity may contribute to adolescents’ individual differences in extraversion and agreeableness and highlights sex-specific neural connectivity patterns associated with the two personality dimensions. This study deepens our understanding of the neurobiological correlates of adolescent personality that may lead to different developmental trajectories of social experience.
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Aging and goal-directed cognition: Cognitive control, inhibition, and motivated cognition. PSYCHOLOGY OF LEARNING AND MOTIVATION 2022. [DOI: 10.1016/bs.plm.2022.08.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Associations Between Neighborhood Disadvantage, Resting-State Functional Connectivity, and Behavior in the Adolescent Brain Cognitive Development Study: The Moderating Role of Positive Family and School Environments. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 6:877-886. [PMID: 33771727 DOI: 10.1016/j.bpsc.2021.03.008] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 02/22/2021] [Accepted: 03/15/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND Neighborhood disadvantage has consistently been associated with mental health and cognitive function, in addition to alterations in brain function and connectivity. However, positive environmental influences may buffer these effects. The aim of this study was to examine the association between neighborhood disadvantage and resting-state functional connectivity (rsFC), the moderating role of positive parenting and school environment, and relationships between disadvantage-associated rsFC patterns and mental health and cognition. METHODS In this preregistered study, we tested this hypothesis in a large sample of 7618 children (aged 9-10 years) from the Adolescent Brain Cognitive Development (ABCD) study. Specifically, we analyzed the relationship between neighborhood disadvantage and system-level FC. We also tested whether positive family and school environmental factors and sex moderated effects. Finally, we investigated multivariate relationships between disadvantage-associated rsFC patterns and cognition and mental health. RESULTS Disadvantage was associated with widespread alterations in FC across both higher-order (e.g., default mode network and dorsal attention network) and sensorimotor functional systems, some of which were moderated by positive environments. Implicated connections showed multivariate associations with behavior, whereby disadvantage-associated rsFC was generally associated with worse cognition and mental health. Disadvantage-associated connections also predicted variation in cognitive scores using machine learning models. CONCLUSIONS Our findings shed light on potential mechanisms (i.e., alteration of neural circuitry) through which neighborhood disadvantage may affect youth cognition and mental well-being. This work highlights the importance of positive family and school environments in mitigating some of these effects.
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