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Brown B, Nguyen LT, Morales I, Cardinale EM, Tseng WL, McKay CC, Kircanski K, Brotman MA, Pine DS, Leibenluft E, Linke JO. Associations Between Neighborhood Resources and Youths' Response to Reward Omission in a Task Modeling Negatively Biased Environments. J Am Acad Child Adolesc Psychiatry 2024:S0890-8567(24)00253-3. [PMID: 38763411 DOI: 10.1016/j.jaac.2024.05.011] [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: 10/03/2023] [Revised: 03/05/2024] [Accepted: 05/10/2024] [Indexed: 05/21/2024]
Abstract
OBJECTIVE Neighborhoods provide essential resources (eg, education, safe housing, green space) that influence neurodevelopment and mental health. However, we need a clearer understanding of the mechanisms mediating these relationships. Limited access to neighborhood resources may hinder youths from achieving their goals and, over time, shape their behavioral and neurobiological response to negatively biased environments blocking goals and rewards. METHOD To test this hypothesis, 211 youths (aged ∼13.0 years, 48% boys, 62% identifying as White, 75% with a psychiatric disorder diagnosis) performed a task during functional magnetic resonance imaging. Initially, rewards depended on performance (unbiased condition); but later, rewards were randomly withheld under the pretense that youths did not perform adequately (negatively biased condition), a manipulation that elicits frustration, sadness, and a broad response in neural networks. We investigated associations between the Childhood Opportunity Index (COI), which quantifies access to youth-relevant neighborhood features in 1 metric, and the multimodal response to the negatively biased condition, controlling for age, sex, medication, and psychopathology. RESULTS Youths from less-resourced neighborhoods responded with less anger (p < .001, marginal R2 = 0.42) and more sadness (p < .001, marginal R2 = 0.46) to the negatively biased condition than youths from well-resourced neighborhoods. On the neurobiological level, lower COI scores were associated with a more localized processing mode (p = .039, marginal R2 = 0.076), reduced connectivity between the somatic-motor-salience and the control network (p = .041, marginal R2 = 0.040), and fewer provincial hubs in the somatic-motor-salience, control, and default mode networks (all pFWE < .05). CONCLUSION The present study adds to a growing literature documenting how inequity may affect the brain and emotions in youths. Future work should test whether findings generalize to more diverse samples and should explore effects on neurodevelopmental trajectories and emerging mood disorders during adolescence. DIVERSITY & INCLUSION STATEMENT One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. One or more of the authors of this paper received support from a program designed to increase minority representation in science. We actively worked to promote sex and gender balance in our author group. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group.
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Affiliation(s)
- Berron Brown
- National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland
| | - Lynn T Nguyen
- National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland
| | - Isaac Morales
- National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland
| | | | | | - Cameron C McKay
- National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland
| | - Katharina Kircanski
- National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland
| | - Melissa A Brotman
- National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland
| | - Daniel S Pine
- National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland
| | - Ellen Leibenluft
- National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland
| | - Julia O Linke
- UTHealth, Houston, Texas, and the University of Freiburg, Germany.
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Chan DC, Kim C, Kang RY, Kuhn MK, Beidler LM, Zhang N, Proctor EA. Cytokine expression patterns predict suppression of vulnerable neural circuits in a mouse model of Alzheimer's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.17.585383. [PMID: 38559177 PMCID: PMC10979954 DOI: 10.1101/2024.03.17.585383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Alzheimer's disease is a neurodegenerative disorder characterized by progressive amyloid plaque accumulation, tau tangle formation, neuroimmune dysregulation, synapse an neuron loss, and changes in neural circuit activation that lead to cognitive decline and dementia. Early molecular and cellular disease-instigating events occur 20 or more years prior to presentation of symptoms, making them difficult to study, and for many years amyloid-β, the aggregating peptide seeding amyloid plaques, was thought to be the toxic factor responsible for cognitive deficit. However, strategies targeting amyloid-β aggregation and deposition have largely failed to produce safe and effective therapies, and amyloid plaque levels poorly correlate with cognitive outcomes. However, a role still exists for amyloid-β in the variation in an individual's immune response to early, soluble forms of aggregates, and the downstream consequences of this immune response for aberrant cellular behaviors and creation of a detrimental tissue environment that harms neuron health and causes changes in neural circuit activation. Here, we perform functional magnetic resonance imaging of awake, unanesthetized Alzheimer's disease mice to map changes in functional connectivity over the course of disease progression, in comparison to wild-type littermates. In these same individual animals, we spatiotemporally profile the immune milieu by measuring cytokines, chemokines, and growth factors across various brain regions and over the course of disease progression from pre-pathology through established cognitive deficit. We identify specific signatures of immune activation predicting hyperactivity followed by suppression of intra- and then inter-regional functional connectivity in multiple disease-relevant brain regions, following the pattern of spread of amyloid pathology.
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Affiliation(s)
- Dennis C Chan
- Department of Neurosurgery, Penn State College of Medicine, Hershey, PA, USA
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
- Center for Neural Engineering, Pennsylvania State University, University Park, PA, USA
- Center for Neurotechnology in Mental Health Research, Pennsylvania State University, University Park, PA, USA
| | - ChaeMin Kim
- Department of Neurosurgery, Penn State College of Medicine, Hershey, PA, USA
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA
| | - Rachel Y Kang
- Department of Neurosurgery, Penn State College of Medicine, Hershey, PA, USA
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA
| | - Madison K Kuhn
- Department of Neurosurgery, Penn State College of Medicine, Hershey, PA, USA
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
- Center for Neural Engineering, Pennsylvania State University, University Park, PA, USA
| | - Lynne M Beidler
- Department of Neurosurgery, Penn State College of Medicine, Hershey, PA, USA
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA
| | - Nanyin Zhang
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
- Center for Neural Engineering, Pennsylvania State University, University Park, PA, USA
- Center for Neurotechnology in Mental Health Research, Pennsylvania State University, University Park, PA, USA
| | - Elizabeth A Proctor
- Department of Neurosurgery, Penn State College of Medicine, Hershey, PA, USA
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
- Center for Neural Engineering, Pennsylvania State University, University Park, PA, USA
- Department of Engineering Science & Mechanics, Pennsylvania State University, University Park, PA, USA
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Wang R, Fan Y, Wu Y, Zang YF, Zhou C. Lifespan associations of resting-state brain functional networks with ADHD symptoms. iScience 2022; 25:104673. [PMID: 35832890 PMCID: PMC9272385 DOI: 10.1016/j.isci.2022.104673] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 04/26/2022] [Accepted: 06/21/2022] [Indexed: 12/04/2022] Open
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is increasingly being diagnosed in both children and adults, but the neural mechanisms that underlie its distinct symptoms and whether children and adults share the same mechanism remain poorly understood. Here, we used a nested-spectral partition approach to study resting-state brain networks of ADHD patients (n = 97) and healthy controls (HCs, n = 97) across the lifespan (7–50 years). Compared to the linear lifespan associations of brain segregation and integration with age in HCs, ADHD patients have a quadratic association in the whole-brain and in most functional systems, whereas the limbic system dominantly affected by ADHD has a linear association. Furthermore, the limbic system better predicts hyperactivity, and the salient attention system better predicts inattention. These predictions are shared in children and adults with ADHD. Our findings reveal a lifespan association of brain networks with ADHD and provide potential shared neural bases of distinct ADHD symptoms in children and adults. ADHD patients have a quadratic association with age in brain functional networks Limbic system better predicts hyperactivity across the lifespan Salient attention system better predicts the inattention across the lifespan The predictions on symptom scores are shared in ADHD children and adults
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Wang R, Su X, Chang Z, Lin P, Wu Y. Flexible brain transitions between hierarchical network segregation and integration associated with cognitive performance during a multisource interference task. IEEE J Biomed Health Inform 2021; 26:1835-1846. [PMID: 34648461 DOI: 10.1109/jbhi.2021.3119940] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Cognition involves locally segregated and globally integrated processing. This process is hierarchically organized and linked to evidence from hierarchical modules in brain networks. However, researchers have not clearly determined how flexible transitions between these hierarchical processes are associated with cognitive behavior. Here, we designed a multisource interference task (MSIT) and introduced the nested-spectral partition (NSP) method to detect hierarchical modules in brain functional networks. By defining hierarchical segregation and integration across multiple levels, we showed that the MSIT requires higher network segregation in the whole brain and most functional systems but generates higher integration in the control system. Meanwhile, brain networks have more flexible transitions between segregated and integrated configurations in the task state. Crucially, higher functional flexibility in the resting state, less flexibility in the task state and more efficient switching of the brain from resting to task states were associated with better task performance. Our hierarchical modular analysis was more effective at detecting alterations in functional organization and the phenotype of cognitive performance than graph-based network measures at a single level.
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Wang R, Liu M, Cheng X, Wu Y, Hildebrandt A, Zhou C. Segregation, integration, and balance of large-scale resting brain networks configure different cognitive abilities. Proc Natl Acad Sci U S A 2021; 118:e2022288118. [PMID: 34074762 PMCID: PMC8201916 DOI: 10.1073/pnas.2022288118] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Diverse cognitive processes set different demands on locally segregated and globally integrated brain activity. However, it remains an open question how resting brains configure their functional organization to balance the demands on network segregation and integration to best serve cognition. Here we use an eigenmode-based approach to identify hierarchical modules in functional brain networks and quantify the functional balance between network segregation and integration. In a large sample of healthy young adults (n = 991), we combine the whole-brain resting state functional magnetic resonance imaging (fMRI) data with a mean-filed model on the structural network derived from diffusion tensor imaging and demonstrate that resting brain networks are on average close to a balanced state. This state allows for a balanced time dwelling at segregated and integrated configurations and highly flexible switching between them. Furthermore, we employ structural equation modeling to estimate general and domain-specific cognitive phenotypes from nine tasks and demonstrate that network segregation, integration, and their balance in resting brains predict individual differences in diverse cognitive phenotypes. More specifically, stronger integration is associated with better general cognitive ability, stronger segregation fosters crystallized intelligence and processing speed, and an individual's tendency toward balance supports better memory. Our findings provide a comprehensive and deep understanding of the brain's functioning principles in supporting diverse functional demands and cognitive abilities and advance modern network neuroscience theories of human cognition.
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Affiliation(s)
- Rong Wang
- College of Science, Xi'an University of Science and Technology, Xi'an 710054, China
- Department of Physics, Centre for Nonlinear Studies, Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Hong Kong
- School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an 710049, China
- State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi'an Jiaotong University, Xi'an 710049, China
| | - Mianxin Liu
- Department of Physics, Centre for Nonlinear Studies, Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Hong Kong
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
| | - Xinhong Cheng
- College of Science, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Ying Wu
- School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an 710049, China
- State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi'an Jiaotong University, Xi'an 710049, China
- National Demonstration Center for Experimental Mechanics Education, Xi'an Jiaotong University, Xi'an 710049, China
| | - Andrea Hildebrandt
- Department of Psychology, Carl von Ossietzky Universität Oldenburg, 26129 Oldenburg, Germany;
- Research Center Neurosensory Science, Carl von Ossietzky Universität Oldenburg, 26129 Oldenburg, Germany
| | - Changsong Zhou
- Department of Physics, Centre for Nonlinear Studies, Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Hong Kong;
- Department of Physics, Zhejiang University, Hangzhou 310027, China
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Zangrossi A, Zanzotto G, Lorenzoni F, Indelicato G, Cannas Aghedu F, Cermelli P, Bisiacchi PS. Resting-state functional brain connectivity predicts cognitive performance: An exploratory study on a time-based prospective memory task. Behav Brain Res 2021; 402:113130. [PMID: 33444694 DOI: 10.1016/j.bbr.2021.113130] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 12/22/2020] [Accepted: 01/05/2021] [Indexed: 11/16/2022]
Abstract
Resting-state functional brain connectivity (rsFC) is in wide use for the investigation of a variety of cognitive neuroscience phenomena. In the first phase of this study, we explored the changes in EEG-reconstructed rsFC in young vs. older adults, in the both the open-eyes (OE) and the closed-eyes (CE) conditions. The results showed significant differences in several rsFC network metrics in the two age groups, confirming and detailing established knowledge that aging modulates brain functional organisation. In the study's second phase we investigated the role of rsFC architecture on cognitive performance through a time-based Prospective Memory task involving participants who monitored the passage of time to perform a specific action at an appropriate time in the future. Regression models revealed that the monitoring strategy (i.e. the number of clock checks) can be predicted by rsFC graph metric, specifically, eccentricity and betweenness in the OE condition, and assortativity in the CE condition. These results show for the first time how metrics qualifying functional brain connectivity at rest can account for the differences in the way individuals strategically handle cognitive loads in the Prospective Memory domain.
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Affiliation(s)
- Andrea Zangrossi
- Department of Neuroscience, University of Padova, Padova, Italy; Padova Neuroscience Center (PNC), University of Padova, Padova, Italy.
| | - Giovanni Zanzotto
- Padova Neuroscience Center (PNC), University of Padova, Padova, Italy; Department of General Psychology, University of Padova, Padova, Italy
| | | | - Giuliana Indelicato
- York Cross-disciplinary Centre for Systems Analysis, Department of Mathematics, University of York, UK
| | | | | | - Patrizia Silvia Bisiacchi
- Padova Neuroscience Center (PNC), University of Padova, Padova, Italy; Department of General Psychology, University of Padova, Padova, Italy
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Radmanesh M, Jalili M, Kozlowska K. Activation of Functional Brain Networks in Children With Psychogenic Non-epileptic Seizures. Front Hum Neurosci 2020; 14:339. [PMID: 33192376 PMCID: PMC7477327 DOI: 10.3389/fnhum.2020.00339] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 08/03/2020] [Indexed: 02/03/2023] Open
Abstract
Objectives Psychogenic non-epileptic seizures (PNES) have been hypothesized to emerge in the context of neural networks instability. To explore this hypothesis in children, we applied a graph theory approach to examine connectivity in neural networks in the resting-state EEG in 35 children with PNES, 31 children with other functional neurological symptoms (but no PNES), and 75 healthy controls. Methods The networks were extracted from Laplacian-transformed time series by a coherence connectivity estimation method. Results Children with PNES (vs. controls) showed widespread changes in network metrics: increased global efficiency (gamma and beta bands), increased local efficiency (gamma band), and increased modularity (gamma and alpha bands). Compared to controls, they also had higher levels of autonomic arousal (e.g., lower heart variability); more anxiety, depression, and stress on the Depression Anxiety and Stress Scales; and more adverse childhood experiences on the Early Life Stress Questionnaire. Increases in network metrics correlated with arousal. Children with other functional neurological symptoms (but no PNES) showed scattered and less pronounced changes in network metrics. Conclusion The results indicate that children with PNES present with increased activation of neural networks coupled with increased physiological arousal. While this shift in functional organization may confer a short-term adaptive advantage-one that facilitates neural communication and the child's capacity to respond self-protectively in the face of stressful life events-it may also have a significant biological cost. It may predispose the child's neural networks to periods of instability-presenting clinically as PNES-when the neural networks are faced with perturbations in energy flow or with additional demands.
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Affiliation(s)
| | - Mahdi Jalili
- School of Engineering, RMIT University, Melbourne, VIC, Australia
| | - Kasia Kozlowska
- Department of Psychological Medicine, The Children's Hospital at Westmead, Sydney, NSW, Australia.,The University of Sydney School of Medicine, Sydney, NSW, Australia.,Westmead Institute for Medical Research, Sydney, NSW, Australia
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