1
|
Yan Y, Yang Z, Chen L. High-quality models for assessing the effects of environmental pollutants on the nervous system: 3D brain organoids. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 284:116876. [PMID: 39146594 DOI: 10.1016/j.ecoenv.2024.116876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Revised: 08/07/2024] [Accepted: 08/09/2024] [Indexed: 08/17/2024]
Abstract
The prevalence of environmental problems and the increasing risk of human exposure to environmental pollutants have become a global concern. The increasing environmental pollution is one of the main reasons for the rising incidence of most neurological-related diseases in recent years. However, the ethical constraints of direct human research and the racial limitations of animal models have slowed the progress of research in this area. The purpose of this study is to review the neurotoxicity of different environmental pollutants on the brain using brain organoids as a new model and to conclude that brain organoids may play a key role in assessing the mechanisms by which environmental pollutants affect neurogenesis and cause neurological pathogenesis. To accurately determine the negative effects of environmental pollutants on the nervous system, self-organizing brain organoids that are highly similar to the developing brain have become a new model system for studying the effects of environmental pollutants on human brain development and disease. This study uses brain organoids as a model to summarize the neurotoxicity of different environmental pollutants on the nervous system, including structural changes in brain organoids, inhibition of neuronal differentiation and migration, impairment of mitochondrial function, damage to cellular cilia, and influence on signaling pathways. In conclusion, exposure to environmental pollutants may cause different neurotoxicity to the nervous system. Therefore, it is crucial to understand how to use brain organoids to ameliorate neurological disorders caused by environmental pollution.
Collapse
Affiliation(s)
- Yu Yan
- Academy of Medical Engineering and Translational Medicine, Medical College, Tianjin University, Tianjin 300072, China
| | - Ziye Yang
- Academy of Medical Engineering and Translational Medicine, Medical College, Tianjin University, Tianjin 300072, China
| | - Liqun Chen
- Academy of Medical Engineering and Translational Medicine, Medical College, Tianjin University, Tianjin 300072, China.
| |
Collapse
|
2
|
Demeter DV, Greene DJ. The promise of precision functional mapping for neuroimaging in psychiatry. Neuropsychopharmacology 2024:10.1038/s41386-024-01941-z. [PMID: 39085426 DOI: 10.1038/s41386-024-01941-z] [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: 03/18/2024] [Revised: 06/14/2024] [Accepted: 07/17/2024] [Indexed: 08/02/2024]
Abstract
Precision functional mapping (PFM) is a neuroimaging approach to reliably estimate metrics of brain function from individual people via the collection of large amounts of fMRI data (hours per person). This method has revealed much about the inter-individual variation of functional brain networks. While standard group-level studies, in which we average brain measures across groups of people, are important in understanding the generalizable neural underpinnings of neuropsychiatric disorders, many disorders are heterogeneous in nature. This heterogeneity often complicates clinical care, leading to patient uncertainty when considering prognosis or treatment options. We posit that PFM methods may help streamline clinical care in the future, fast-tracking the choice of personalized treatment that is most compatible with the individual. In this review, we provide a history of PFM studies, foundational results highlighting the benefits of PFM methods in the pursuit of an advanced understanding of individual differences in functional network organization, and possible avenues where PFM can contribute to clinical translation of neuroimaging research results in the way of personalized treatment in psychiatry.
Collapse
Affiliation(s)
- Damion V Demeter
- Department of Cognitive Science, University of California San Diego, La Jolla, CA, USA.
| | - Deanna J Greene
- Department of Cognitive Science, University of California San Diego, La Jolla, CA, USA.
| |
Collapse
|
3
|
Laatsch J, Stein F, Maier S, Matthies S, Sobanski E, Alm B, Tebartz van Elst L, Krug A, Philipsen A. Neural correlates of inattention in adults with ADHD. Eur Arch Psychiatry Clin Neurosci 2024:10.1007/s00406-024-01872-2. [PMID: 39073447 DOI: 10.1007/s00406-024-01872-2] [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: 02/05/2024] [Accepted: 07/15/2024] [Indexed: 07/30/2024]
Abstract
In the last two decades, numerous magnetic resonance imaging (MRI) studies have examined differences in cortical structure between individuals with Attention-Deficit/Hyperactivity Disorder (ADHD) and healthy controls. These studies primarily emphasized alterations in gray matter volume (GMV) and cortical thickness (CT). Still, the scientific literature is notably scarce in regard to investigating associations of cortical structure with ADHD psychopathology, specifically inattention within adults with ADHD. The present study aimed to elucidate neurobiological underpinnings of inattention beyond GMV and CT by including cortical gyrification, sulcal depth, and fractal dimension. Building upon the Comparison of Methylphenidate and Psychotherapy in Adult ADHD Study (COMPAS), cortical structure parameters were investigated using 141 T1-weighted anatomical scans of adult patients with ADHD. All brain structural analyses were performed using the threshold-free cluster enhancement (TFCE) approach and the Computational Anatomy Toolbox (CAT12) integrated into the Statistical Parametric Mapping Software (Matlab Version R2021a). Results revealed significant correlations of inattention in multiple brain regions. Cortical gyrification was negatively correlated, whereas cortical thickness and fractal dimension were positively associated with inattention. The clusters showed widespread distribution across the cerebral cortex, with both hemispheres affected. The cortical regions most prominently affected included the precuneus, para-, pre-, and postcentral gyri, superior parietal lobe, and posterior cingulate cortex. This study highlights the importance of cortical alterations in attentional processes in adults with ADHD. Further research in this area is warranted to elucidate intricacies of inattention in adults with ADHD to potentially enhance diagnostic accuracy and inform personalized treatment strategies.
Collapse
Affiliation(s)
- Jonathan Laatsch
- Department of Psychiatry und Psychotherapy, University Hospital Bonn, Bonn, Germany.
| | - Frederike Stein
- Department of Psychiatry und Psychotherapy, University of Marburg, Marburg, Germany
| | - Simon Maier
- Department of Psychiatry and Psychotherapy, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Swantje Matthies
- Department of Psychiatry and Psychotherapy, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Esther Sobanski
- Department of Child and Adolescent Psychiatry Lucerne, Lucerne, Switzerland
- Department of Psychiatry and Psychotherapy, Medical Faculty of Mannheim, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany
| | - Barbara Alm
- Department of Psychiatry and Psychotherapy, Medical Faculty of Mannheim, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany
| | - Ludger Tebartz van Elst
- Department of Psychiatry and Psychotherapy, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Axel Krug
- Department of Psychiatry und Psychotherapy, University Hospital Bonn, Bonn, Germany
| | - Alexandra Philipsen
- Department of Psychiatry und Psychotherapy, University Hospital Bonn, Bonn, Germany
| |
Collapse
|
4
|
Bernardo D, Xie X, Verma P, Kim J, Liu V, Numis AL, Wu Y, Glass HC, Yap PT, Nagarajan SS, Raj A. Simulation-based Inference of Developmental EEG Maturation with the Spectral Graph Model. ARXIV 2024:arXiv:2405.02524v3. [PMID: 39040639 PMCID: PMC11261974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
The spectral content of macroscopic neural activity evolves throughout development, yet how this maturation relates to underlying brain network formation and dynamics remains unknown. Here, we assess the developmental maturation of electroencephalogram spectra via Bayesian model inversion of the spectral graph model, a parsimonious whole-brain model of spatiospectral neural activity derived from linearized neural field models coupled by the structural connectome. Simulation-based inference was used to estimate age-varying spectral graph model parameter posterior distributions from electroencephalogram spectra spanning the developmental period. This model-fitting approach accurately captures observed developmental electroencephalogram spectral maturation via a neurobiologically consistent progression of key neural parameters: long-range coupling, axonal conduction speed, and excitatory:inhibitory balance. These results suggest that the spectral maturation of macroscopic neural activity observed during typical development is supported by age-dependent functional adaptations in localized neural dynamics and their long-range coupling across the macroscopic structural network.
Collapse
Affiliation(s)
- Danilo Bernardo
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Xihe Xie
- Department of Neuroscience, Weill Cornell Medicine, New York, NY, USA
| | - Parul Verma
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Jonathan Kim
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Virginia Liu
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Adam L. Numis
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Ye Wu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Hannah C. Glass
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Srikantan S. Nagarajan
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| |
Collapse
|
5
|
Wen X, Zhao Y, Chen G, Zhang H, Zhang D. Constructing fine-grained spatiotemporal neonatal functional atlases with spectral functional network learning. Hum Brain Mapp 2024; 45:e26718. [PMID: 38825985 PMCID: PMC11144955 DOI: 10.1002/hbm.26718] [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: 11/23/2023] [Revised: 04/22/2024] [Accepted: 05/06/2024] [Indexed: 06/04/2024] Open
Abstract
The early stages of human development are increasingly acknowledged as pivotal in laying the groundwork for subsequent behavioral and cognitive development. Spatiotemporal (4D) brain functional atlases are important in elucidating the development of human brain functions. However, the scarcity of such atlases for early life stages stems from two primary challenges: (1) the significant noise in functional magnetic resonance imaging (fMRI) that complicates the generation of high-quality atlases for each age group, and (2) the rapid and complex changes in the early human brain that hinder the maintenance of temporal consistency in 4D atlases. This study tackles these challenges by integrating low-rank tensor learning with spectral embedding, thereby proposing a novel, data-driven 4D functional atlas generation framework based on spectral functional network learning (SFNL). This method utilizes low-rank tensor learning to capture common functional connectivity (FC) patterns across different ages, thus optimizing FCs for each age group to improve the temporal consistency of functional networks. Incorporating spectral embedding aids in mitigating potential noise in FC networks derived from fMRI data by reconstructing networks in the spectral space. Utilizing SFNL-generated functional networks enables the creation of consistent and highly qualified spatiotemporal functional atlases. The framework was applied to the developing Human Connectome Project (dHCP) dataset, generating the first neonatal 4D functional atlases with fine-grained temporal and spatial resolutions. Experimental evaluations focusing on functional homogeneity, reliability, and temporal consistency demonstrated the superiority of our framework compared to existing methods for constructing 4D atlases. Additionally, network analysis experiments, including individual identification, functional systems development, and local efficiency assessments, further corroborate the efficacy and robustness of the generated atlases. The 4D atlases and related codes will be made publicly accessible (https://github.com/zhaoyunxi/neonate-atlases).
Collapse
Affiliation(s)
- Xuyun Wen
- College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
| | - Yunxi Zhao
- College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
| | - Geng Chen
- School of Computer ScienceNorthwestern Polytechnical UniversityShanxiChina
| | - Han Zhang
- School of Biomedical EngineeringShanghaiTech UniversityShanghaiChina
| | - Daoqiang Zhang
- College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
| |
Collapse
|
6
|
Sibilia F, Jost-Mousseau C, Banaschewski T, Barker GJ, Büchel C, Desrivières S, Flor H, Grigis A, Garavan H, Gowland P, Heinz A, Ittermann B, Martinot JL, Martinot MLP, Artiges E, Nees F, Orfanos DP, Poustka L, Millenet S, Fröhner JH, Smolka MN, Walter H, Whelan R, Schumann G, Bokde AL. The relationship between negative life events and cortical structural connectivity in adolescents. IBRO Neurosci Rep 2024; 16:201-210. [PMID: 38348392 PMCID: PMC10859284 DOI: 10.1016/j.ibneur.2024.01.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 01/27/2024] [Indexed: 02/15/2024] Open
Abstract
Adolescence is a crucial period for physical and psychological development. The impact of negative life events represents a risk factor for the onset of neuropsychiatric disorders. This study aims to investigate the relationship between negative life events and structural brain connectivity, considering both graph theory and connectivity strength. A group (n = 487) of adolescents from the IMAGEN Consortium was divided into Low and High Stress groups. Brain networks were extracted at an individual level, based on morphological similarity between grey matter regions with regions defined using an atlas-based region of interest (ROI) approach. Between-group comparisons were performed with global and local graph theory measures in a range of sparsity levels. The analysis was also performed in a larger sample of adolescents (n = 976) to examine linear correlations between stress level and network measures. Connectivity strength differences were investigated with network-based statistics. Negative life events were not found to be a factor influencing global network measures at any sparsity level. At local network level, between-group differences were found in centrality measures of the left somato-motor network (a decrease of betweenness centrality was seen at sparsity 5%), of the bilateral central visual and the left dorsal attention network (increase of degree at sparsity 10% at sparsity 30% respectively). Network-based statistics analysis showed an increase in connectivity strength in the High stress group in edges connecting the dorsal attention, limbic and salience networks. This study suggests negative life events alone do not alter structural connectivity globally, but they are associated to connectivity properties in areas involved in emotion and attention.
Collapse
Affiliation(s)
- Francesca Sibilia
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Coline Jost-Mousseau
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
- Paris Institute of Technology for Life, Food and Environmental Sciences, Paris, France
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Gareth J. Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom
| | - Christian Büchel
- University Medical Centre Hamburg-Eppendorf, House W34, 3.OG, Martinistr. 52, 20246, Hamburg, Germany
| | - Sylvane Desrivières
- Medical Research Council - Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom
| | - Herta Flor
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, 68131 Mannheim, Germany
| | - Antoine Grigis
- NeuroSpin, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, 05405 Burlington, VT, USA
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Andreas Heinz
- Charité – Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany
| | - Bernd Ittermann
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Saclay, University Paris Descartes – Sorbonne Paris Cité; and Maison de Solenn, Paris, France
| | - Marie-Laure Paillère Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Saclay, University Paris Descartes; and AP-HP.Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Sud, University Paris Descartes - Sorbonne Paris Cité; and Psychiatry Department 91G16, Orsay Hospital, France
| | - Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
| | | | - Luise Poustka
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, von-Siebold-Str. 5, 37075, Göttingen, Germany
| | - Sabina Millenet
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Juliane H. Fröhner
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Michael N. Smolka
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Henrik Walter
- Charité – Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
| | - Gunter Schumann
- Medical Research Council - Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom
| | - Arun L.W. Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - IMAGEN Consortium
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
- Paris Institute of Technology for Life, Food and Environmental Sciences, Paris, France
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom
- University Medical Centre Hamburg-Eppendorf, House W34, 3.OG, Martinistr. 52, 20246, Hamburg, Germany
- Medical Research Council - Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, 68131 Mannheim, Germany
- NeuroSpin, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France
- Departments of Psychiatry and Psychology, University of Vermont, 05405 Burlington, VT, USA
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
- Charité – Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Saclay, University Paris Descartes – Sorbonne Paris Cité; and Maison de Solenn, Paris, France
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Saclay, University Paris Descartes; and AP-HP.Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Sud, University Paris Descartes - Sorbonne Paris Cité; and Psychiatry Department 91G16, Orsay Hospital, France
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, von-Siebold-Str. 5, 37075, Göttingen, Germany
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
| |
Collapse
|
7
|
Gonuguntla V, Adebisi AT, Veluvolu KC. Identification of Optimal and Most Significant Event Related Brain Functional Network. IEEE Trans Neural Syst Rehabil Eng 2024; 32:1906-1915. [PMID: 38722721 DOI: 10.1109/tnsre.2024.3399308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2024]
Abstract
Advancements in network science have facilitated the study of brain communication networks. Existing techniques for identifying event-related brain functional networks (BFNs) often result in fully connected networks. However, determining the optimal and most significant network representation for event-related BFNs is crucial for understanding complex brain networks. The presence of both false and genuine connections in the fully connected network requires network thresholding to eliminate false connections. However, a generalized framework for thresholding in network neuroscience is currently lacking. To address this, we propose four novel methods that leverage network properties, energy, and efficiency to select a generalized threshold level. This threshold serves as the basis for identifying the optimal and most significant event-related BFN. We validate our methods on an openly available emotion dataset and demonstrate their effectiveness in identifying multiple events. Our proposed approach can serve as a versatile thresholding technique to represent the fully connected network as an event-related BFN.
Collapse
|
8
|
Luo Z, Yin E, Yan Y, Zhao S, Xie L, Shen H, Zeng LL, Wang L, Hu D. Sleep deprivation changes frequency-specific functional organization of the resting human brain. Brain Res Bull 2024; 210:110925. [PMID: 38493835 DOI: 10.1016/j.brainresbull.2024.110925] [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: 11/29/2023] [Revised: 02/13/2024] [Accepted: 03/10/2024] [Indexed: 03/19/2024]
Abstract
Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies have widely explored the temporal connection changes in the human brain following long-term sleep deprivation (SD). However, the frequency-specific topological properties of sleep-deprived functional networks remain virtually unclear. In this study, thirty-seven healthy male subjects underwent resting-state fMRI during rested wakefulness (RW) and after 36 hours of SD, and we examined frequency-specific spectral connection changes (0.01-0.08 Hz, interval = 0.01 Hz) caused by SD. First, we conducted a multivariate pattern analysis combining linear SVM classifiers with a robust feature selection algorithm, and the results revealed that accuracies of 74.29%-84.29% could be achieved in the classification between RW and SD states in leave-one-out cross-validation at different frequency bands, moreover, the spectral connection at the lowest and highest frequency bands exhibited higher discriminative power. Connection involving the cingulo-opercular network increased most, while connection involving the default-mode network decreased most following SD. Then we performed a graph-theoretic analysis and observed reduced low-frequency modularity and high-frequency global efficiency in the SD state. Moreover, hub regions, which were primarily situated in the cerebellum and the cingulo-opercular network after SD, exhibited high discriminative power in the aforementioned classification consistently. The findings may indicate the frequency-dependent effects of SD on the functional network topology and its efficiency of information exchange, providing new insights into the impact of SD on the human brain.
Collapse
Affiliation(s)
- Zhiguo Luo
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, China; Intelligent Game and Decision Laboratory, Beijing 100071, China; Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China; College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
| | - Erwei Yin
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, China; Intelligent Game and Decision Laboratory, Beijing 100071, China; Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China.
| | - Ye Yan
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, China; Intelligent Game and Decision Laboratory, Beijing 100071, China; Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China
| | - Shaokai Zhao
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, China; Intelligent Game and Decision Laboratory, Beijing 100071, China; Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China
| | - Liang Xie
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, China; Intelligent Game and Decision Laboratory, Beijing 100071, China; Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China
| | - Hui Shen
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
| | - Ling-Li Zeng
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
| | - Lubin Wang
- The Brain Science Center, Beijing Institute of Basic Medical Sciences, Beijing 102206, China
| | - Dewen Hu
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China.
| |
Collapse
|
9
|
Zhi D, Jiang R, Pearlson G, Fu Z, Qi S, Yan W, Feng A, Xu M, Calhoun V, Sui J. Triple Interactions Between the Environment, Brain, and Behavior in Children: An ABCD Study. Biol Psychiatry 2024; 95:828-838. [PMID: 38151182 PMCID: PMC11006588 DOI: 10.1016/j.biopsych.2023.12.019] [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: 07/27/2023] [Revised: 12/14/2023] [Accepted: 12/18/2023] [Indexed: 12/29/2023]
Abstract
BACKGROUND Environmental exposures play a crucial role in shaping children's behavioral development. However, the mechanisms by which these exposures interact with brain functional connectivity and influence behavior remain unexplored. METHODS We investigated the comprehensive environment-brain-behavior triple interactions through rigorous association, prediction, and mediation analyses, while adjusting for multiple confounders. Particularly, we examined the predictive power of brain functional network connectivity (FNC) and 41 environmental exposures for 23 behaviors related to cognitive ability and mental health in 7655 children selected from the Adolescent Brain Cognitive Development (ABCD) Study at both baseline and follow-up. RESULTS FNC demonstrated more predictability for cognitive abilities than for mental health, with cross-validation from the UK Biobank study (N = 20,852), highlighting the importance of thalamus and hippocampus in longitudinal prediction, while FNC+environment demonstrated more predictive power than FNC in both cross-sectional and longitudinal prediction of all behaviors, especially for mental health (r = 0.32-0.63). We found that family and neighborhood exposures were common critical environmental influencers on cognitive ability and mental health, which can be mediated by FNC significantly. Healthy perinatal development was a unique protective factor for higher cognitive ability, whereas sleep problems, family conflicts, and adverse school environments specifically increased risk of poor mental health. CONCLUSIONS This work revealed comprehensive environment-brain-behavior triple interactions based on the ABCD Study, identified cognitive control and default mode networks as the most predictive functional networks for a wide repertoire of behaviors, and underscored the long-lasting impact of critical environmental exposures on childhood development, in which sleep problems were the most prominent factors affecting mental health.
Collapse
Affiliation(s)
- Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Godfrey Pearlson
- Department of Psychiatry and Neurobiology, Yale School of Medicine, New Haven, Connecticut
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Emory University, and Georgia State University, Atlanta, Georgia
| | - Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Weizheng Yan
- National Institute on Alcohol Abuse and Alcoholism, Lab of Neuroimaging, National Institutes of Health, Bethesda, Maryland
| | - Aichen Feng
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Ming Xu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Emory University, and Georgia State University, Atlanta, Georgia.
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Emory University, and Georgia State University, Atlanta, Georgia.
| |
Collapse
|
10
|
Luo AC, Sydnor VJ, Pines A, Larsen B, Alexander-Bloch AF, Cieslak M, Covitz S, Chen AA, Esper NB, Feczko E, Franco AR, Gur RE, Gur RC, Houghton A, Hu F, Keller AS, Kiar G, Mehta K, Salum GA, Tapera T, Xu T, Zhao C, Salo T, Fair DA, Shinohara RT, Milham MP, Satterthwaite TD. Functional connectivity development along the sensorimotor-association axis enhances the cortical hierarchy. Nat Commun 2024; 15:3511. [PMID: 38664387 PMCID: PMC11045762 DOI: 10.1038/s41467-024-47748-w] [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: 08/28/2023] [Accepted: 04/10/2024] [Indexed: 04/28/2024] Open
Abstract
Human cortical maturation has been posited to be organized along the sensorimotor-association axis, a hierarchical axis of brain organization that spans from unimodal sensorimotor cortices to transmodal association cortices. Here, we investigate the hypothesis that the development of functional connectivity during childhood through adolescence conforms to the cortical hierarchy defined by the sensorimotor-association axis. We tested this pre-registered hypothesis in four large-scale, independent datasets (total n = 3355; ages 5-23 years): the Philadelphia Neurodevelopmental Cohort (n = 1207), Nathan Kline Institute-Rockland Sample (n = 397), Human Connectome Project: Development (n = 625), and Healthy Brain Network (n = 1126). Across datasets, the development of functional connectivity systematically varied along the sensorimotor-association axis. Connectivity in sensorimotor regions increased, whereas connectivity in association cortices declined, refining and reinforcing the cortical hierarchy. These consistent and generalizable results establish that the sensorimotor-association axis of cortical organization encodes the dominant pattern of functional connectivity development.
Collapse
Affiliation(s)
- Audrey C Luo
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Valerie J Sydnor
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Adam Pines
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94305, USA
| | - Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, 55455, USA
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN, 55455, USA
| | - Aaron F Alexander-Bloch
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Matthew Cieslak
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sydney Covitz
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Andrew A Chen
- Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, 29425, USA
| | | | - Eric Feczko
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
| | - Alexandre R Franco
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, 10962, USA
- Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, 10016, USA
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Audrey Houghton
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Fengling Hu
- Penn Statistics in Imaging and Visualization Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arielle S Keller
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Gregory Kiar
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
| | - Kahini Mehta
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Giovanni A Salum
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
- Section on Negative Affect and Social Processes, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Tinashe Tapera
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, 02115, USA
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
| | - Chenying Zhao
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Taylor Salo
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, 55455, USA
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN, 55455, USA
- Institute of Child Development, College of Education and Human Development, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, 10962, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| |
Collapse
|
11
|
Yan ZX, He Z, Jiang LH, Zou X. Age-related trajectories of the development of social cognition. Front Psychol 2024; 15:1348781. [PMID: 38711752 PMCID: PMC11071648 DOI: 10.3389/fpsyg.2024.1348781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 03/20/2024] [Indexed: 05/08/2024] Open
Abstract
Age-related trajectories of intrinsic functional connectivity (iFC), which represent the interconnections between discrete regions of the human brain, for processes related to social cognition (SC) provide evidence for social development through neural imaging and can guide clinical interventions when such development is atypical. However, due to the lack of studies investigating brain development over a wide range of ages, the neural mechanisms of SC remain poorly understood, although considerable behavior-related evidence is available. The present study mapped vortex-wise iFC features between SC networks and the entire cerebral cortex by using common functional networks, creating the corresponding age-related trajectories. Three networks [moral cognition, theory of mind (ToM), and empathy] were selected as representative SC networks. The Enhanced Nathan Kline Institute-Rockland Sample (NKI-RS, N = 316, ages 8-83 years old) was employed delineate iFC characteristics and construct trajectories. The results showed that the SC networks display unique and overlapping iFC profiles. The iFC of the empathy network, an age-sensitive network, with dorsal attention network was found to exhibit a linear increasing pattern, that of the ventral attention network was observed to exhibit a linear decreasing pattern, and that of the somatomotor and dorsal attention networks was noted to exhibit a quadric-concave iFC pattern. Additionally, a sex-specific effect was observed for the empathy network as it exhibits linear and quadric sex-based differences in iFC with the frontoparietal and vision networks, respectively. The iFC of the ToM network with the ventral attention network exhibits a pronounced quadric-convex (inverted U-shape) trajectory. No linear or quadratic trajectories were noted in the iFC of the moral cognition network. These findings indicate that SC networks exhibit iFC with both low-level (somatomotor, vision) and high-level (attention and control) networks along specific developmental trajectories. The age-related trajectories determined in this study advance our understanding of the neural mechanisms of SC, providing valuable references for identification and intervention in cases of development of atypical SC.
Collapse
Affiliation(s)
- Zhi-Xiong Yan
- Guangxi Center of Developmental Population Neuroscience, Nanning Normal University, Nanning, China
| | - Zhe He
- Guangxi Center of Developmental Population Neuroscience, Nanning Normal University, Nanning, China
| | - Ling-Hui Jiang
- Guangxi Center of Developmental Population Neuroscience, Nanning Normal University, Nanning, China
| | - Xia Zou
- Continuing Education School, Guangxi College for Preschool Education, Nanning, China
| |
Collapse
|
12
|
Silemek AH, Chen H, Sati P, Gao W. The Brain's First "Traffic Map" through Unified Structural and Functional Connectivity (USFC) Modeling. RESEARCH SQUARE 2024:rs.3.rs-4184305. [PMID: 38699356 PMCID: PMC11065057 DOI: 10.21203/rs.3.rs-4184305/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
The brain's white matter connections are thought to provide the structural basis for its functional connections between distant brain regions but how our brain selects the best structural routes for effective functional communications remains poorly understood. In this study, we propose a Unified Structural and Functional Connectivity (USFC) model and use an "economical assumption" to create the brain's first "traffic map" reflecting how frequently each structural connection segment of the brain is used to achieve the global functional communication system. The resulting USFC map highlights regions in the subcortical, default-mode, and salience networks as the most heavily traversed nodes and a midline frontal-caudate-thalamus-posterior cingulate-visual cortex corridor as the backbone of the whole brain connectivity system. Our results further revealed a striking negative association between structural and functional connectivity strengths in routes supporting negative functional connections as well as much higher efficiency metrics in the USFC connectome when compared to structural and functional ones alone. Overall, the proposed USFC model opens up a new window for effective brain connectome modeling and provides a considerable leap forward in brain mapping efforts for a better understanding of the brain's fundamental communication mechanisms.
Collapse
|
13
|
Alvino FG, Gini S, Minetti A, Pagani M, Sastre-Yagüe D, Barsotti N, De Guzman E, Schleifer C, Stuefer A, Kushan L, Montani C, Galbusera A, Papaleo F, Lombardo MV, Pasqualetti M, Bearden CE, Gozzi A. Synaptic-dependent developmental dysconnectivity in 22q11.2 deletion syndrome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.29.587339. [PMID: 38585897 PMCID: PMC10996624 DOI: 10.1101/2024.03.29.587339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Chromosome 22q11.2 deletion is among the strongest known genetic risk factors for neuropsychiatric disorders, including autism and schizophrenia. Brain imaging studies have reported disrupted large-scale functional connectivity in people with 22q11 deletion syndrome (22q11DS). However, the significance and biological determinants of these functional alterations remain unclear. Here, we use a cross-species design to investigate the developmental trajectory and neural underpinnings of brain dysconnectivity in 22q11DS. We find that LgDel mice, an established mouse model of 22q11DS, exhibit age-specific patterns of functional MRI (fMRI) dysconnectivity, with widespread fMRI hyper-connectivity in juvenile mice reverting to focal hippocampal hypoconnectivity over puberty. These fMRI connectivity alterations are mirrored by co-occurring developmental alterations in dendritic spine density, and are both transiently normalized by developmental GSK3β inhibition, suggesting a synaptic origin for this phenomenon. Notably, analogous hyper- to hypoconnectivity reconfiguration occurs also in human 22q11DS, where it affects hippocampal and cortical regions spatially enriched for synaptic genes that interact with GSK3β, and autism-relevant transcripts. Functional dysconnectivity in somatomotor components of this network is predictive of age-dependent social alterations in 22q11.2 deletion carriers. Taken together, these findings suggest that synaptic-related mechanisms underlie developmentally mediated functional dysconnectivity in 22q11DS.
Collapse
Affiliation(s)
- F G Alvino
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Italy
| | - S Gini
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Italy
- Center for Mind and Brain Sciences, University of Trento, Rovereto, Italy
| | - A Minetti
- Department of Biology, Unit of Cell and Developmental Biology, University of Pisa, Pisa, Italy
| | - M Pagani
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Italy
- IMT School for Advanced Studies, Lucca, Italy
| | - D Sastre-Yagüe
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Italy
- Center for Mind and Brain Sciences, University of Trento, Rovereto, Italy
| | - N Barsotti
- Centro per l'Integrazione della Strumentazione Scientifica dell'Universita di Pisa (CISUP), Pisa, Italy
| | - E De Guzman
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Italy
| | - C Schleifer
- Departments of Psychiatry and Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California at Los Angeles, Los Angeles, California
| | - A Stuefer
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Italy
- Center for Mind and Brain Sciences, University of Trento, Rovereto, Italy
| | - L Kushan
- Departments of Psychiatry and Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California at Los Angeles, Los Angeles, California
| | - C Montani
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Italy
| | - A Galbusera
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Italy
| | - F Papaleo
- Genetics of Cognition Laboratory, Neuroscience area, Istituto Italiano di Tecnologia, Genova, Italy
| | - M V Lombardo
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - M Pasqualetti
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Italy
- Centro per l'Integrazione della Strumentazione Scientifica dell'Universita di Pisa (CISUP), Pisa, Italy
| | - C E Bearden
- Departments of Psychiatry and Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California at Los Angeles, Los Angeles, California
| | - A Gozzi
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Italy
| |
Collapse
|
14
|
Meghji S, Hilderley AJ, Murias K, Brooks BL, Andersen J, Fehlings D, Dlamini N, Kirton A, Carlson HL. Executive functioning, ADHD symptoms and resting state functional connectivity in children with perinatal stroke. Brain Imaging Behav 2024; 18:263-278. [PMID: 38038867 PMCID: PMC11156742 DOI: 10.1007/s11682-023-00827-w] [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] [Accepted: 11/19/2023] [Indexed: 12/02/2023]
Abstract
Perinatal stroke describes a group of focal, vascular brain injuries that occur early in development, often resulting in lifelong disability. Two types of perinatal stroke predominate, arterial ischemic stroke (AIS) and periventricular venous infarction (PVI). Though perinatal stroke is typically considered a motor disorder, other comorbidities commonly exist including attention-deficit hyperactivity disorder (ADHD) and deficits in executive function. Rates of ADHD symptoms are higher in children with perinatal stroke and deficits in executive function may also occur but underlying mechanisms are not known. We measured resting state functional connectivity in children with perinatal stroke using previously established dorsal attention, frontoparietal, and default mode network seeds. Associations with parental ratings of executive function and ADHD symptoms were examined. A total of 120 participants aged 6-19 years [AIS N = 31; PVI N = 30; Controls N = 59] were recruited. In comparison to typically developing peers, both the AIS and PVI groups showed lower intra- and inter-hemispheric functional connectivity values in the networks investigated. Group differences in between-network connectivity were also demonstrated, showing weaker anticorrelations between task-positive (frontoparietal and dorsal attention) and task-negative (default mode) networks in stroke groups compared to controls. Both within-network and between-network functional connectivity values were highly associated with parental reports of executive function and ADHD symptoms. These results suggest that differences in functional connectivity exist both within and between networks after perinatal stroke, the degree of which is associated with ADHD symptoms and executive function.
Collapse
Affiliation(s)
- Suraya Meghji
- Calgary Pediatric Stroke Program, Alberta Children's Hospital, 28 Oki Drive NW, Calgary, AB, Canada
- Alberta Children's Hospital Research Institute, 28 Oki Drive NW, Calgary, AB, Canada
| | - Alicia J Hilderley
- Calgary Pediatric Stroke Program, Alberta Children's Hospital, 28 Oki Drive NW, Calgary, AB, Canada
- Alberta Children's Hospital Research Institute, 28 Oki Drive NW, Calgary, AB, Canada
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Kara Murias
- Alberta Children's Hospital Research Institute, 28 Oki Drive NW, Calgary, AB, Canada
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - Brian L Brooks
- Alberta Children's Hospital Research Institute, 28 Oki Drive NW, Calgary, AB, Canada
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
- Neurosciences Program, Alberta Children's Hospital, Calgary, AB, Canada
- Department of Psychology, University of Calgary, Calgary, AB, Canada
| | - John Andersen
- Department of Pediatrics, University of Alberta, Edmonton, AB, Canada
| | - Darcy Fehlings
- Department of Pediatrics, University of Toronto, Toronto, ON, Canada
| | - Nomazulu Dlamini
- Department of Pediatrics, University of Toronto, Toronto, ON, Canada
- Children's Stroke Program, Division of Neurology, Hospital for Sick Children, Toronto, ON, Canada
| | - Adam Kirton
- Calgary Pediatric Stroke Program, Alberta Children's Hospital, 28 Oki Drive NW, Calgary, AB, Canada
- Alberta Children's Hospital Research Institute, 28 Oki Drive NW, Calgary, AB, Canada
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
- Department of Radiology, University of Calgary, Calgary, AB, Canada
| | - Helen L Carlson
- Calgary Pediatric Stroke Program, Alberta Children's Hospital, 28 Oki Drive NW, Calgary, AB, Canada.
- Alberta Children's Hospital Research Institute, 28 Oki Drive NW, Calgary, AB, Canada.
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.
| |
Collapse
|
15
|
Feng X, Piper RJ, Prentice F, Clayden JD, Baldeweg T. Functional brain connectivity in children with focal epilepsy: A systematic review of functional MRI studies. Seizure 2024; 117:164-173. [PMID: 38432080 DOI: 10.1016/j.seizure.2024.02.021] [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: 12/18/2023] [Revised: 02/26/2024] [Accepted: 02/27/2024] [Indexed: 03/05/2024] Open
Abstract
Epilepsy is increasingly recognised as a brain network disorder and many studies have investigated functional connectivity (FC) in children with epilepsy using functional MRI (fMRI). This systematic review of fMRI studies, published up to November 2023, investigated profiles of FC changes and their clinical relevance in children with focal epilepsy compared to healthy controls. A literature search in PubMed and Web of Science yielded 62 articles. We categorised the results into three groups: 1) differences in correlation-based FC between patients and controls; 2) differences in other FC measures between patients and controls; and 3) associations between FC and disease variables (for example, age of onset), cognitive and seizure outcomes. Studies revealed either increased or decreased FC across multiple brain regions in children with focal epilepsy. However, findings lacked consistency: conflicting FC alterations (decreased and increased FC) co-existed within or between brain regions across all focal epilepsy groups. The studies demonstrated overall that 1) interhemispheric connections often displayed abnormal connectivity and 2) connectivity within and between canonical functional networks was decreased, particularly for the default mode network. Focal epilepsy disrupted FC in children both locally (e.g., seizure-onset zones, or within-brain subnetworks) and globally (e.g., whole-brain network architecture). The wide variety of FC study methodologies limits clinical application of the results. Future research should employ longitudinal designs to understand the evolution of brain networks during the disease course and explore the potential of FC biomarkers for predicting cognitive and postsurgical seizure outcomes.
Collapse
Affiliation(s)
- Xiyu Feng
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, University College London, 30 Guilford, London WC1N 1EH, United Kingdom
| | - Rory J Piper
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, University College London, 30 Guilford, London WC1N 1EH, United Kingdom; Department of Neurosurgery, Great Ormond Street Hospital, London, United Kingdom
| | - Freya Prentice
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, University College London, 30 Guilford, London WC1N 1EH, United Kingdom
| | - Jonathan D Clayden
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, University College London, 30 Guilford, London WC1N 1EH, United Kingdom
| | - Torsten Baldeweg
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, University College London, 30 Guilford, London WC1N 1EH, United Kingdom.
| |
Collapse
|
16
|
Chinichian N, Lindner M, Yanchuk S, Schwalger T, Schöll E, Berner R. Modeling brain network flexibility in networks of coupled oscillators: a feasibility study. Sci Rep 2024; 14:5713. [PMID: 38459077 PMCID: PMC10923875 DOI: 10.1038/s41598-024-55753-8] [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: 09/19/2023] [Accepted: 02/27/2024] [Indexed: 03/10/2024] Open
Abstract
Modeling the functionality of the human brain is a major goal in neuroscience for which many powerful methodologies have been developed over the last decade. The impact of working memory and the associated brain regions on the brain dynamics is of particular interest due to their connection with many functions and malfunctions in the brain. In this context, the concept of brain flexibility has been developed for the characterization of brain functionality. We discuss emergence of brain flexibility that is commonly measured by the identification of changes in the cluster structure of co-active brain regions. We provide evidence that brain flexibility can be modeled by a system of coupled FitzHugh-Nagumo oscillators where the network structure is obtained from human brain Diffusion Tensor Imaging (DTI). Additionally, we propose a straightforward and computationally efficient alternative macroscopic measure, which is derived from the Pearson distance of functional brain matrices. This metric exhibits similarities to the established patterns of brain template flexibility that have been observed in prior investigations. Furthermore, we explore the significance of the brain's network structure and the strength of connections between network nodes or brain regions associated with working memory in the observation of patterns in networks flexibility. This work enriches our understanding of the interplay between the structure and function of dynamic brain networks and proposes a modeling strategy to study brain flexibility.
Collapse
Affiliation(s)
- Narges Chinichian
- Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany.
- Psychiatry Department, Charité-Universitätsmedizin, Berlin, Germany.
- Bernstein Center for Computational Neuroscience, Berlin, Germany.
| | - Michael Lindner
- Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany
- Potsdam Institute for Climate Impact Research, Potsdam, Germany
| | - Serhiy Yanchuk
- Potsdam Institute for Climate Impact Research, Potsdam, Germany
- Institute of Mathematics, Humboldt Universität zu Berlin, Berlin, Germany
- School of Mathematical Sciences, University College Cork, Cork, Ireland
| | - Tilo Schwalger
- Bernstein Center for Computational Neuroscience, Berlin, Germany
- Institute of Mathematics, Technische Universität Berlin, Berlin, Germany
| | - Eckehard Schöll
- Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
- Potsdam Institute for Climate Impact Research, Potsdam, Germany
| | - Rico Berner
- Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany
- Department of Physics, Humboldt Universität zu Berlin, Berlin, Germany
| |
Collapse
|
17
|
Lineham A, Avila-Quintero VJ, Bloch MH, Dwyer J. Exploring Predictors of Ketamine Response in Adolescent Treatment-Resistant Depression. J Child Adolesc Psychopharmacol 2024; 34:73-79. [PMID: 38170185 PMCID: PMC11262580 DOI: 10.1089/cap.2023.0047] [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] [Indexed: 01/05/2024]
Abstract
Objective: Ketamine has proved effective as a rapid-acting antidepressant agent, but treatment is not effective for everyone (approximately a quarter to a half of patients). Some adult studies have begun to investigate predictors of ketamine's antidepressant response, but no studies have examined this in adolescents with depression. Methods: We conducted a secondary data analysis of adolescents who participated in a randomized, single-dose, midazolam-controlled crossover trial of ketamine for adolescents with treatment-resistant depression. We examined the relationship between 19 exploratory demographic and clinical variables and depression symptom improvement (using the Montgomery-Åsberg Depression Rating Scale [MADRS]) at 1 and 7 days postinfusion. Results: Subjects who had fewer medication trials of both antidepressant medications and augmentation treatments were more likely to experience depression symptom improvement with ketamine. Subjects with shorter duration of their current depressive episode were more likely to experience depression symptom improvement with ketamine. Subjects currently being treated with selective serotonin reuptake inhibitor medications, and not being treated with serotonin-norepinephrine reuptake inhibitor medications, also experienced greater symptom improvement with ketamine. When receiving the midazolam control, less severe depressive symptoms, as measured by the Children's Depression Rating Scale (CDRS) (but not MADRS), and a comorbid attention-deficit/hyperactivity disorder diagnosis were associated with increased response. Conclusions: Findings should be viewed as preliminary and exploratory given the small sample size and multiple secondary analyses. Identifying meaningful predictors of ketamine response is important to inform future therapeutic use of this compound, however, considerably more research is warranted before such clinical guidance is established. The trial was registered in clinicaltrials.gov with the identifier NCT02579928.
Collapse
Affiliation(s)
- Alice Lineham
- Child Study Center, Yale School of Medicine, New Haven, Connecticut, USA
| | | | - Michael H. Bloch
- Child Study Center, Yale School of Medicine, New Haven, Connecticut, USA
- Department of Psychiatry and Yale School of Medicine, New Haven, Connecticut, USA
| | - Jennifer Dwyer
- Child Study Center, Yale School of Medicine, New Haven, Connecticut, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| |
Collapse
|
18
|
Voldsbekk I, Kjelkenes R, Frogner ER, Westlye LT, Alnæs D. Testing the sensitivity of diagnosis-derived patterns in functional brain networks to symptom burden in a Norwegian youth sample. Hum Brain Mapp 2024; 45:e26631. [PMID: 38379514 PMCID: PMC10879903 DOI: 10.1002/hbm.26631] [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: 10/24/2023] [Revised: 01/30/2024] [Accepted: 02/06/2024] [Indexed: 02/22/2024] Open
Abstract
Aberrant brain network development represents a putative aetiological component in mental disorders, which typically emerge during childhood and adolescence. Previous studies have identified resting-state functional connectivity (RSFC) patterns reflecting psychopathology, but the generalisability to other samples and politico-cultural contexts has not been established. We investigated whether a previously identified cross-diagnostic case-control and autism spectrum disorder (ASD)-specific pattern of RSFC (discovery sample; aged 5-21 from New York City, USA; n = 1666) could be validated in a Norwegian convenience-based youth sample (validation sample; aged 9-25 from Oslo, Norway; n = 531). As a test of generalisability, we investigated if these diagnosis-derived RSFC patterns were sensitive to levels of symptom burden in both samples, based on an independent measure of symptom burden. Both the cross-diagnostic and ASD-specific RSFC pattern were validated across samples. Connectivity patterns were significantly associated with thematically appropriate symptom dimensions in the discovery sample. In the validation sample, the ASD-specific RSFC pattern showed a weak, inverse relationship with symptoms of conduct problems, hyperactivity and prosociality, while the cross-diagnostic pattern was not significantly linked to symptoms. Diagnosis-derived connectivity patterns in a developmental clinical US sample were validated in a convenience sample of Norwegian youth, however, they were not associated with mental health symptoms.
Collapse
Affiliation(s)
- Irene Voldsbekk
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University HospitalOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Rikka Kjelkenes
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University HospitalOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Erik R. Frogner
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University HospitalOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Lars T. Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University HospitalOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of Oslo, Department of Neurology, Oslo University HospitalOsloNorway
| | - Dag Alnæs
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University HospitalOsloNorway
| |
Collapse
|
19
|
Hill AT, Bailey NW, Zomorrodi R, Hadas I, Kirkovski M, Das S, Lum JAG, Enticott PG. EEG microstates in early-to-middle childhood show associations with age, biological sex, and alpha power. Hum Brain Mapp 2023; 44:6484-6498. [PMID: 37873867 PMCID: PMC10681660 DOI: 10.1002/hbm.26525] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 10/04/2023] [Accepted: 10/06/2023] [Indexed: 10/25/2023] Open
Abstract
Electroencephalographic (EEG) microstates can provide a unique window into the temporal dynamics of large-scale brain networks across brief (millisecond) timescales. Here, we analysed fundamental temporal features of microstates extracted from the broadband EEG signal in a large (N = 139) cohort of children spanning early-to-middle childhood (4-12 years of age). Linear regression models were used to examine if participants' age and biological sex could predict the temporal parameters GEV, duration, coverage, and occurrence, for five microstate classes (A-E) across both eyes-closed and eyes-open resting-state recordings. We further explored associations between these microstate parameters and posterior alpha power after removal of the 1/f-like aperiodic signal. The microstates obtained from our neurodevelopmental EEG recordings broadly replicated the four canonical microstate classes (A to D) frequently reported in adults, with the addition of the more recently established microstate class E. Biological sex served as a significant predictor in the regression models for four of the five microstate classes (A, C, D, and E). In addition, duration and occurrence for microstate E were both found to be positively associated with age for the eyes-open recordings, while the temporal parameters of microstates C and E both exhibited associations with alpha band spectral power. Together, these findings highlight the influence of age and sex on large-scale functional brain networks during early-to-middle childhood, extending understanding of neural dynamics across this important period for brain development.
Collapse
Affiliation(s)
- Aron T. Hill
- Cognitive Neuroscience Unit, School of PsychologyDeakin UniversityGeelongAustralia
- Department of Psychiatry, Central Clinical SchoolMonash UniversityMelbourneAustralia
| | - Neil W. Bailey
- Monarch Research InstituteMonarch Mental Health GroupSydneyAustralia
- School of Medicine and PsychologyThe Australian National UniversityCanberraAustralia
| | - Reza Zomorrodi
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental HealthUniversity of TorontoTorontoCanada
| | - Itay Hadas
- Department of Psychiatry, School of MedicineUniversity of California San DiegoLa JollaCaliforniaUSA
| | - Melissa Kirkovski
- Cognitive Neuroscience Unit, School of PsychologyDeakin UniversityGeelongAustralia
- Institute for Health and SportVictoria UniversityMelbourneAustralia
| | - Sushmit Das
- Azrieli Adult Neurodevelopmental CentreCentre for Addiction and Mental HealthTorontoCanada
| | - Jarrad A. G. Lum
- Cognitive Neuroscience Unit, School of PsychologyDeakin UniversityGeelongAustralia
| | - Peter G. Enticott
- Cognitive Neuroscience Unit, School of PsychologyDeakin UniversityGeelongAustralia
- Department of Psychiatry, Central Clinical SchoolMonash UniversityMelbourneAustralia
| |
Collapse
|
20
|
Crestol A, Rajagopal S, Lissaman R, LaPlume AA, Pasvanis S, Olsen RK, Einstein G, Jacobs EG, Rajah MN. Menopause Status and Within-Group Differences in Chronological Age Affect the Functional Neural Correlates of Spatial Context Memory in Middle-Aged Females. J Neurosci 2023; 43:8756-8768. [PMID: 37903593 PMCID: PMC10727179 DOI: 10.1523/jneurosci.0663-23.2023] [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: 04/13/2023] [Revised: 10/10/2023] [Accepted: 10/11/2023] [Indexed: 11/01/2023] Open
Abstract
Reductions in the ability to encode and retrieve past experiences in rich spatial contextual detail (episodic memory) are apparent by midlife-a time when most females experience spontaneous menopause. Yet, little is known about how menopause status affects episodic memory-related brain activity at encoding and retrieval in middle-aged premenopausal and postmenopausal females, and whether any observed group differences in brain activity and memory performance correlate with chronological age within group. We conducted an event-related task fMRI study of episodic memory for spatial context to address this knowledge gap. Multivariate behavioral partial least squares was used to investigate how chronological age and retrieval accuracy correlated with brain activity in 31 premenopausal females (age range, 39.55-53.30 years; mean age, 44.28 years; SD age, 3.12 years) and 41 postmenopausal females (age range, 46.70-65.14 years; mean age, 57.56 years; SD age, 3.93 years). We found that postmenopausal status, and advanced age within postmenopause, was associated with lower spatial context memory. The fMRI analysis showed that only in postmenopausal females, advanced age was correlated with decreased activity in occipitotemporal, parahippocampal, and inferior parietal cortices during encoding and retrieval, and poorer spatial context memory performance. In contrast, only premenopausal females exhibited an overlap in encoding and retrieval activity in angular gyrus, midline cortical regions, and prefrontal cortex, which correlated with better spatial context retrieval accuracy. These results highlight how menopause status and chronological age, nested within menopause group, affect episodic memory and its neural correlates at midlife.SIGNIFICANCE STATEMENT This is the first fMRI study to examine how premenopause and postmenopause status affect the neural correlates of episodic memory encoding and retrieval, and how chronological age contributes to any observed group similarities and differences. We found that both menopause status (endocrine age) and chronological age affect spatial context memory and its neural correlates. Menopause status directly affected the direction of age-related and performance-related correlations with brain activity in inferior parietal, parahippocampal, and occipitotemporal cortices across encoding and retrieval. Moreover, we found that only premenopausal females exhibited cortical reinstatement of encoding-related activity in midline cortical, prefrontal, and angular gyrus, at retrieval. This suggests that spatial context memory abilities may rely on distinct brain systems at premenopause compared with postmenopause.
Collapse
Affiliation(s)
- Arielle Crestol
- Integrated Program in Neuroscience, McGill University, Montréal, Quebec H3A 1A1, Canada
| | | | - Rikki Lissaman
- Douglas Mental Health University Institute, Verdun, Quebec H4H 1R3, Canada
- Department of Psychiatry, McGill University, Montréal, Quebec H3A 1A1, Canada
| | - Annalise A LaPlume
- Douglas Mental Health University Institute, Verdun, Quebec H4H 1R3, Canada
- Department of Psychology, Toronto Metropolitan University, Toronto, Ontario M5B 2K3, Canada
| | | | - Rosanna K Olsen
- Rotman Research Institute, Baycrest Centre and University of Toronto, Toronto, Ontario M6A 2E1, Canada
- Department of Psychology, University of Toronto, Toronto, Ontario M5S 3G3, Canada
| | - Gillian Einstein
- Rotman Research Institute, Baycrest Centre and University of Toronto, Toronto, Ontario M6A 2E1, Canada
- Department of Psychology, University of Toronto, Toronto, Ontario M5S 3G3, Canada
| | - Emily G Jacobs
- Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, California 93106-9660
| | - M Natasha Rajah
- Douglas Mental Health University Institute, Verdun, Quebec H4H 1R3, Canada
- Department of Psychology, McGill University, Montréal, Quebec H3A 1G1, Canada
- Department of Psychiatry, McGill University, Montréal, Quebec H3A 1A1, Canada
- Department of Psychology, Toronto Metropolitan University, Toronto, Ontario M5B 2K3, Canada
| |
Collapse
|
21
|
Kirse HA, Bahrami M, Lyday RG, Simpson SL, Peterson-Sockwell H, Burdette JH, Laurienti PJ. Differences in Brain Network Topology Based on Alcohol Use History in Adolescents. Brain Sci 2023; 13:1676. [PMID: 38137124 PMCID: PMC10741456 DOI: 10.3390/brainsci13121676] [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: 09/21/2023] [Revised: 11/10/2023] [Accepted: 11/28/2023] [Indexed: 12/24/2023] Open
Abstract
Approximately 6 million youth aged 12 to 20 consume alcohol monthly in the United States. The effect of alcohol consumption in adolescence on behavior and cognition is heavily researched; however, little is known about how alcohol consumption in adolescence may alter brain function, leading to long-term developmental detriments. In order to investigate differences in brain connectivity associated with alcohol use in adolescents, brain networks were constructed using resting-state functional magnetic resonance imaging data collected by the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) from 698 youth (12-21 years; 117 hazardous drinkers and 581 no/low drinkers). Analyses assessed differences in brain network topology based on alcohol consumption in eight predefined brain networks, as well as in whole-brain connectivity. Within the central executive network (CEN), basal ganglia network (BGN), and sensorimotor network (SMN), no/low drinkers demonstrated stronger and more frequent connections between highly globally efficient nodes, with fewer and weaker connections between highly clustered nodes. Inverse results were observed within the dorsal attention network (DAN), visual network (VN), and frontotemporal network (FTN), with no/low drinkers demonstrating weaker connections between nodes with high efficiency and increased frequency of clustered nodes compared to hazardous drinkers. Cross-sectional results from this study show clear organizational differences between adolescents with no/low or hazardous alcohol use, suggesting that aberrant connectivity in these brain networks is associated with risky drinking behaviors.
Collapse
Affiliation(s)
- Haley A. Kirse
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA; (H.A.K.); (M.B.); (R.G.L.); (S.L.S.); (H.P.-S.); (J.H.B.)
- Graduate Program, Wake Forest Graduate School of Arts and Sciences, Integrative Physiology and Pharmacology, Winston-Salem, NC 27101, USA
| | - Mohsen Bahrami
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA; (H.A.K.); (M.B.); (R.G.L.); (S.L.S.); (H.P.-S.); (J.H.B.)
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA
| | - Robert G. Lyday
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA; (H.A.K.); (M.B.); (R.G.L.); (S.L.S.); (H.P.-S.); (J.H.B.)
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA
| | - Sean L. Simpson
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA; (H.A.K.); (M.B.); (R.G.L.); (S.L.S.); (H.P.-S.); (J.H.B.)
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA
| | - Hope Peterson-Sockwell
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA; (H.A.K.); (M.B.); (R.G.L.); (S.L.S.); (H.P.-S.); (J.H.B.)
| | - Jonathan H. Burdette
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA; (H.A.K.); (M.B.); (R.G.L.); (S.L.S.); (H.P.-S.); (J.H.B.)
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA
| | - Paul J. Laurienti
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA; (H.A.K.); (M.B.); (R.G.L.); (S.L.S.); (H.P.-S.); (J.H.B.)
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA
| |
Collapse
|
22
|
Fan Z, Xi X, Wang T, Li H, Maofeng W, Li L, Lü Z. Effect of tDCS on corticomuscular coupling and the brain functional network of stroke patients. Med Biol Eng Comput 2023; 61:3303-3317. [PMID: 37667074 DOI: 10.1007/s11517-023-02905-z] [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] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 08/09/2023] [Indexed: 09/06/2023]
Abstract
Transcranial direct current stimulation (tDCS) is an emerging brain intervention technique that has gained growing attention in recent years in the rehabilitation area. In this paper, we investigated the efficacy of tDCS in the rehabilitation process of stroke patients, utilizing corticomuscular coupling (CMC) and brain functional network analysis. Specifically, we examined changes in CMC relationships between the treatment and control groups before and after rehabilitation by transfer entropy (TE), and constructed brain functional networks by TE. We further calculated features of the functional networks, including node degree, global efficiency, clustering coefficient, characteristic path length, and small world index. Our results demonstrate that CMC in patients increased significantly after treatment, with greater improvements in the tDCS group, particularly within the beta and gamma bands. In addition, the functional brain network analysis revealed enhanced connectivity between brain regions, improved information processing capacity, and increased transmission efficiency in patients as their condition improved. Notably, treatment with tDCS resulted in more significant improvements than the sham group, with a statistically significant difference observed after rehabilitation treatment (p < 0.05). These findings provide compelling evidence regarding the role of tDCS in the treatment of stroke and highlight the potential of this approach in stroke rehabilitation. The use of tDCS for therapeutic interventions in stroke rehabilitation can significantly improve the coupling of patients' functional brain networks. Also, using Transfer Entropy (TE) as a characteristic of CMC, tDCS was found to significantly enhance patients' TE, i.e. enhanced CMC.
Collapse
Affiliation(s)
- Zhuyao Fan
- HDU-ITMO Joint Institute, Hangzhou Dianzi University, Hangzhou, 310018, China
- School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Xugang Xi
- HDU-ITMO Joint Institute, Hangzhou Dianzi University, Hangzhou, 310018, China.
- School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China.
| | - Ting Wang
- HDU-ITMO Joint Institute, Hangzhou Dianzi University, Hangzhou, 310018, China
- School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Hangcheng Li
- Hangzhou Mingzhou Naokang Rehabilitation Hospital, Hangzhou, 311215, China
| | - Wang Maofeng
- Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, 322100, China
| | - Lihua Li
- School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Zhong Lü
- Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, 322100, China
| |
Collapse
|
23
|
Carosella KA, Wiglesworth A, Bendezú JJ, Brower R, Mirza S, Mueller BA, Cullen KR, Klimes-Dougan B. Patterns of experience, expression, and physiology of stress relate to depressive symptoms and self-injurious thoughts and behaviors in adolescents: a person-centered approach. Psychol Med 2023; 53:7902-7912. [PMID: 37609891 PMCID: PMC10755230 DOI: 10.1017/s0033291723002003] [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] [Received: 10/07/2022] [Revised: 05/24/2023] [Accepted: 06/30/2023] [Indexed: 08/24/2023]
Abstract
BACKGROUND Preliminary evidence shows that discordance in stress experience, expression, and physiology (EEP) in adolescents is linked to depression, suicidal ideation (SI), non-suicidal self-injury (NSSI), and brain functioning. This study employs person-centered analysis to probe the relationship between stress responses, psychopathology, and neural patterns in female adolescents who are oversampled for engagement in NSSI. METHODS Adolescent females (N = 109, ages 12-17) underwent a social stress test from which self-report measures of stress experience, observer ratings of stress expression, and physiological metrics of stress (via salivary cortisol) were obtained. Multi-trajectory modeling was employed to identify concordant and discordant stress EEP groups. Depressive symptoms, SI and attempt, NSSI engagement, frontal and limbic activation to emotional stimuli, and resting state fronto-limbic connectivity were examined in the EEP groups derived from the multi-trajectory models. RESULTS Four groups were identified, three of which demonstrated relatively concordant EEP and one which demonstrated discordant EEP (High Experience-High Expression-Low Physiology). Further, replicating past research, the High Experience-High Expression-Low Physiology discordant group exhibited higher depressive symptoms, SI, suicide attempt, and NSSI episodes (only for sensitivity analyses based on past year) relative to other EEP groups. No significant group differences in brain functioning emerged. CONCLUSION Results indicate that within-person, multi-level patterns in stress responding capture risk for dysfunction including depression and self-injurious thoughts and behaviors. Further interrogating of system-level stress functioning may better inform assessment and intervention efforts.
Collapse
Affiliation(s)
| | - Andrea Wiglesworth
- Department of Psychology, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Jason José Bendezú
- Department of Psychology, The Pennsylvania State University, University Park Campus, University Park, PA, USA
| | - Rylee Brower
- Department of Psychology, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Salahudeen Mirza
- Department of Psychology, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Bryon A. Mueller
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Kathryn R. Cullen
- Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Bonnie Klimes-Dougan
- Department of Psychology, University of Minnesota Twin Cities, Minneapolis, MN, USA
| |
Collapse
|
24
|
Picci G, Ott LR, Penhale SH, Taylor BK, Johnson HJ, Willett MP, Okelberry HJ, Wang Y, Calhoun VD, Stephen JM, Wilson TW. Developmental changes in endogenous testosterone have sexually-dimorphic effects on spontaneous cortical dynamics. Hum Brain Mapp 2023; 44:6043-6054. [PMID: 37811842 PMCID: PMC10619376 DOI: 10.1002/hbm.26496] [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: 11/19/2022] [Revised: 04/18/2023] [Accepted: 09/13/2023] [Indexed: 10/10/2023] Open
Abstract
The transition from childhood to adolescence is associated with an influx of sex hormones, which not only facilitates physical and behavioral changes, but also dramatic changes in neural circuitry. While previous work has shown that pubertal hormones modulate structural and functional brain development, few of these studies have focused on the impact that such hormones have on spontaneous cortical activity, and whether these effects are modulated by sex during this critical developmental window. Herein, we examined the effect of endogenous testosterone on spontaneous cortical activity in 71 typically-developing youth (ages 10-17 years; 32 male). Participants completed a resting-state magnetoencephalographic (MEG) recording, structural MRI, and provided a saliva sample for hormone analysis. MEG data were source-reconstructed and the power within five canonical frequency bands (delta, theta, alpha, beta, and gamma) was computed. The resulting power spectral density maps were analyzed via vertex-wise ANCOVAs to identify spatially specific effects of testosterone and sex by testosterone interactions, while covarying out age. We found robust sex differences in the modulatory effects of testosterone on spontaneous delta, beta, and gamma activity. These interactions were largely confined to frontal cortices and exhibited a stark switch in the directionality of the correlation from the low (delta) to high frequencies (beta/gamma). For example, in the delta band, greater testosterone related to lower relative power in prefrontal cortices in boys, while the reverse pattern was found for girls. These data suggest testosterone levels are uniquely related to the development of spontaneous cortical dynamics during adolescence, and such levels are associated with different developmental patterns in males and females within regions implicated in executive functioning.
Collapse
Affiliation(s)
- Giorgia Picci
- Institute for Human Neuroscience, Boys Town National Research HospitalBoys TownNebraskaUSA
- Center for Pediatric Brain Health, Boys Town National Research HospitalBoys TownNebraskaUSA
| | - Lauren R. Ott
- Institute for Human Neuroscience, Boys Town National Research HospitalBoys TownNebraskaUSA
- Center for Pediatric Brain Health, Boys Town National Research HospitalBoys TownNebraskaUSA
| | - Samantha H. Penhale
- Institute for Human Neuroscience, Boys Town National Research HospitalBoys TownNebraskaUSA
- Center for Pediatric Brain Health, Boys Town National Research HospitalBoys TownNebraskaUSA
| | - Brittany K. Taylor
- Institute for Human Neuroscience, Boys Town National Research HospitalBoys TownNebraskaUSA
- Center for Pediatric Brain Health, Boys Town National Research HospitalBoys TownNebraskaUSA
| | - Hallie J. Johnson
- Institute for Human Neuroscience, Boys Town National Research HospitalBoys TownNebraskaUSA
- Center for Pediatric Brain Health, Boys Town National Research HospitalBoys TownNebraskaUSA
| | - Madelyn P. Willett
- Institute for Human Neuroscience, Boys Town National Research HospitalBoys TownNebraskaUSA
- Center for Pediatric Brain Health, Boys Town National Research HospitalBoys TownNebraskaUSA
| | - Hannah J. Okelberry
- Institute for Human Neuroscience, Boys Town National Research HospitalBoys TownNebraskaUSA
- Center for Pediatric Brain Health, Boys Town National Research HospitalBoys TownNebraskaUSA
| | - Yu‐Ping Wang
- Department of Biomedical EngineeringTulane UniversityNew OrleansLouisianaUSA
| | - Vince D. Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of TechnologyEmory UniversityAtlantaGeorgiaUSA
| | | | - Tony W. Wilson
- Institute for Human Neuroscience, Boys Town National Research HospitalBoys TownNebraskaUSA
- Center for Pediatric Brain Health, Boys Town National Research HospitalBoys TownNebraskaUSA
- Department of Pharmacology & NeuroscienceCreighton UniversityOmahaNebraskaUSA
| |
Collapse
|
25
|
Mariani Wigley ILC, Björnsdotter M, Scheinin NM, Merisaari H, Saunavaara J, Parkkola R, Bonichini S, Montirosso R, Karlsson L, Karlsson H, Tuulari JJ. Infants' sex affects neural responses to affective touch in early infancy. Dev Psychobiol 2023; 65:e22419. [PMID: 37860896 DOI: 10.1002/dev.22419] [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: 04/04/2023] [Revised: 06/26/2023] [Accepted: 08/12/2023] [Indexed: 10/21/2023]
Abstract
Social touch is closely related to the establishment and maintenance of social bonds in humans, and the sensory brain circuit for gentle brushing is already active soon after birth. Brain development is known to be sexually dimorphic, but the potential effect of sex on brain activation to gentle touch remains unknown. Here, we examined brain activation to gentle skin stroking, a tactile stimulation that resembles affective or social touch, in term-born neonates. Eighteen infants aged 11-36 days, recruited from the FinnBrain Birth Cohort Study, were included in the study. During natural sleep, soft brush strokes were applied to the skin of the right leg during functional magnetic resonance imaging (fMRI) at 3 cm/s velocity. We examined potential differences in brain activation between males (n = 10) and females (n = 8) and found that females had larger blood oxygenation level dependent (BOLD) responses (brushing vs. rest) in bilateral orbitofrontal cortex (OFC), right ventral striatum and bilateral inferior striatum, pons, and cerebellum compared to males. Moreover, the psychophysiological interactions (PPI) analysis, setting the left and right OFC as seed regions, revealed significant differences between males and females. Females exhibited stronger PPI connectivity between the left OFC and posterior cingulate or cuneus. Our work suggests that social touch neural responses are different in male and female neonates, which may have major ramifications for later brain, cognitive, and social development. Finally, many of the sexually dimorphic brain responses were subcortical, not captured by surface-based neuroimaging, indicating that fMRI will be a relevant technique for future studies.
Collapse
Affiliation(s)
| | - Malin Björnsdotter
- Department of Affective Psychiatry, Sahlgrenska University Hospital, Gothenburg, Sweden
- Center for Cognitive and Computational Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Noora M Scheinin
- Department of Clinical Medicine, Turku Brain and Mind Center, FinnBrain Birth Cohort Study, University of Turku, Turku, Finland
- Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland
| | - Harri Merisaari
- Department of Clinical Medicine, Turku Brain and Mind Center, FinnBrain Birth Cohort Study, University of Turku, Turku, Finland
- Department of Radiology, University of Turku, Turku, Finland
| | - Jani Saunavaara
- Department of Radiology, University of Turku, Turku, Finland
| | - Riitta Parkkola
- Department of Radiology, University of Turku, Turku, Finland
- Department of Radiology, Turku University Hospital, Turku, Finland
| | - Sabrina Bonichini
- Department of Developmental and Social Psychology, University of Padua, Padua, Italy
| | - Rosario Montirosso
- 0-3 Center for the at-Risk Infant, Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Linnea Karlsson
- Department of Clinical Medicine, Turku Brain and Mind Center, FinnBrain Birth Cohort Study, University of Turku, Turku, Finland
- Centre for Population Health Research, Turku University Hospital, University of Turku, Turku, Finland
- Turku Collegium for Science, Medicine and Technology, University of Turku, Turku, Finland
| | - Hasse Karlsson
- Department of Clinical Medicine, Turku Brain and Mind Center, FinnBrain Birth Cohort Study, University of Turku, Turku, Finland
- Centre for Population Health Research, Turku University Hospital, University of Turku, Turku, Finland
- Turku Collegium for Science, Medicine and Technology, University of Turku, Turku, Finland
| | - Jetro J Tuulari
- Department of Clinical Medicine, Turku Brain and Mind Center, FinnBrain Birth Cohort Study, University of Turku, Turku, Finland
- Centre for Population Health Research, Turku University Hospital, University of Turku, Turku, Finland
- Turku Collegium for Science, Medicine and Technology, University of Turku, Turku, Finland
- Department of Psychiatry, University of Oxford, Oxford, UK
| |
Collapse
|
26
|
Song X, Chai L. Graph Signal Smoothness Based Feature Learning of Brain Functional Networks in Schizophrenia. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3854-3863. [PMID: 37768796 DOI: 10.1109/tnsre.2023.3320135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/30/2023]
Abstract
In this paper we study the brain functional network of schizophrenic patients based on resting-state fMRI data. Different from the region of interest (ROI)-level brain networks that describe the connectivity between brain regions, this paper constructs a subject-level brain functional network that describes the similarity between subjects from a graph signal processing (GSP) perspective. Based on the subject graph, we introduce the concept of graph signal smoothness to analyze the abnormal brain regions (feature brain regions) in which schizophrenic patients produce abnormal functional connections and to quantitatively rank the degree of abnormality of brain regions. We find that in the patients' brain networks, many new connections appear and some common connections are strengthened. The feature brain regions can be easily found according to the value of connection differences. Finally, we validate the learned feature brain regions by the results of two types of statistical analyses (ROI-to-ROI analysis and seed-to-voxel analysis), and the feature brain regions derived from graph signal smoothness are indeed the brain regions with significant differences in the statistical analysis, which illustrates the potential of graph signal smoothness for use in quantitative analysis of brain networks.
Collapse
|
27
|
Park S, Park D, Kim MJ. Similarity in functional connectome architecture predicts teenage grit. Soc Cogn Affect Neurosci 2023; 18:nsad047. [PMID: 37700673 PMCID: PMC10549957 DOI: 10.1093/scan/nsad047] [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: 04/13/2023] [Revised: 07/14/2023] [Accepted: 09/05/2023] [Indexed: 09/14/2023] Open
Abstract
Grit is a personality trait that encapsulates the tendency to persevere and maintain consistent interest for long-term goals. While prior studies found that grit predicts positive behavioral outcomes, there is a paucity of work providing explanatory evidence from a neurodevelopmental perspective. Based on previous research suggesting the utility of the functional connectome (FC) as a developmental measure, we tested the idea that individual differences in grit might be, in part, rooted in brain development in adolescence and emerging adulthood (N = 64, 11-19 years of age). Our analysis showed that grit was associated with connectome stability across conditions and connectome similarity across individuals. Notably, inter-subject representational similarity analysis revealed that teenagers who were grittier shared similar FC architecture with each other, more so than those with lower grit. Our findings suggest that individuals with high levels of grit are more likely to exhibit a converging pattern of whole-brain functional connectivity, which may underpin subsequent beneficial behavioral outcomes.
Collapse
Affiliation(s)
- Sujin Park
- Department of Psychology, Sungkyunkwan University, Seoul 03063, South Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon 16419, South Korea
| | - Daeun Park
- Department of Psychology, Sungkyunkwan University, Seoul 03063, South Korea
| | - M Justin Kim
- Department of Psychology, Sungkyunkwan University, Seoul 03063, South Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon 16419, South Korea
| |
Collapse
|
28
|
Uddin LQ, Betzel RF, Cohen JR, Damoiseaux JS, De Brigard F, Eickhoff SB, Fornito A, Gratton C, Gordon EM, Laird AR, Larson-Prior L, McIntosh AR, Nickerson LD, Pessoa L, Pinho AL, Poldrack RA, Razi A, Sadaghiani S, Shine JM, Yendiki A, Yeo BTT, Spreng RN. Controversies and progress on standardization of large-scale brain network nomenclature. Netw Neurosci 2023; 7:864-905. [PMID: 37781138 PMCID: PMC10473266 DOI: 10.1162/netn_a_00323] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 05/10/2023] [Indexed: 10/03/2023] Open
Abstract
Progress in scientific disciplines is accompanied by standardization of terminology. Network neuroscience, at the level of macroscale organization of the brain, is beginning to confront the challenges associated with developing a taxonomy of its fundamental explanatory constructs. The Workgroup for HArmonized Taxonomy of NETworks (WHATNET) was formed in 2020 as an Organization for Human Brain Mapping (OHBM)-endorsed best practices committee to provide recommendations on points of consensus, identify open questions, and highlight areas of ongoing debate in the service of moving the field toward standardized reporting of network neuroscience results. The committee conducted a survey to catalog current practices in large-scale brain network nomenclature. A few well-known network names (e.g., default mode network) dominated responses to the survey, and a number of illuminating points of disagreement emerged. We summarize survey results and provide initial considerations and recommendations from the workgroup. This perspective piece includes a selective review of challenges to this enterprise, including (1) network scale, resolution, and hierarchies; (2) interindividual variability of networks; (3) dynamics and nonstationarity of networks; (4) consideration of network affiliations of subcortical structures; and (5) consideration of multimodal information. We close with minimal reporting guidelines for the cognitive and network neuroscience communities to adopt.
Collapse
Affiliation(s)
- Lucina Q. Uddin
- Department of Psychiatry and Biobehavioral Sciences and Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Richard F. Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Jessica R. Cohen
- Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC, USA
| | - Jessica S. Damoiseaux
- Institute of Gerontology and Department of Psychology, Wayne State University, Detroit, MI, USA
| | | | - Simon B. Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | - Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Evan M. Gordon
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, USA
| | - Angela R. Laird
- Department of Physics, Florida International University, Miami, FL, USA
| | - Linda Larson-Prior
- Deptartment of Psychiatry and Department of Neurobiology and Developmental Sciences, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - A. Randal McIntosh
- Institute for Neuroscience and Neurotechnology, Simon Fraser University, Vancouver, BC, Canada
| | | | - Luiz Pessoa
- Department of Psychology, University of Maryland, College Park, MD, USA
| | - Ana Luísa Pinho
- Brain and Mind Institute, Western University, London, Ontario, Canada
| | | | - Adeel Razi
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | - Sepideh Sadaghiani
- Department of Psychology, University of Illinois, Urbana Champaign, IL, USA
| | - James M. Shine
- Brain and Mind Center, University of Sydney, Sydney, Australia
| | - Anastasia Yendiki
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - B. T. Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
| | - R. Nathan Spreng
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
| |
Collapse
|
29
|
Dhamala E, Rong Ooi LQ, Chen J, Ricard JA, Berkeley E, Chopra S, Qu Y, Zhang XH, Lawhead C, Yeo BTT, Holmes AJ. Brain-Based Predictions of Psychiatric Illness-Linked Behaviors Across the Sexes. Biol Psychiatry 2023; 94:479-491. [PMID: 37031778 PMCID: PMC10524434 DOI: 10.1016/j.biopsych.2023.03.025] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 03/27/2023] [Accepted: 03/30/2023] [Indexed: 04/11/2023]
Abstract
BACKGROUND Individual differences in functional brain connectivity can be used to predict both the presence of psychiatric illness and variability in associated behaviors. However, despite evidence for sex differences in functional network connectivity and in the prevalence, presentation, and trajectory of psychiatric illnesses, the extent to which disorder-relevant aspects of network connectivity are shared or unique across the sexes remains to be determined. METHODS In this work, we used predictive modeling approaches to evaluate whether shared or unique functional connectivity correlates underlie the expression of psychiatric illness-linked behaviors in males and females in data from the Adolescent Brain Cognitive Development Study (N = 5260; 2571 females). RESULTS We demonstrate that functional connectivity profiles predict individual differences in externalizing behaviors in males and females but predict internalizing behaviors only in females. Furthermore, models trained to predict externalizing behaviors in males generalize to predict internalizing behaviors in females, and models trained to predict internalizing behaviors in females generalize to predict externalizing behaviors in males. Finally, the neurobiological correlates of many behaviors are largely shared within and across sexes: functional connections within and between heteromodal association networks, including default, limbic, control, and dorsal attention networks, are associated with internalizing and externalizing behaviors. CONCLUSIONS Taken together, these findings suggest that shared neurobiological patterns may manifest as distinct behaviors across the sexes. Based on these results, we recommend that both clinicians and researchers carefully consider how sex may influence the presentation of psychiatric illnesses, especially those along the internalizing-externalizing spectrum.
Collapse
Affiliation(s)
- Elvisha Dhamala
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, New York; Department of Psychology, Yale University, New Haven, Connecticut; Kavli Institute for Neuroscience, Yale University, New Haven, Connecticut.
| | - Leon Qi Rong Ooi
- Centre for Sleep and Cognition and Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme, National University of Singapore, Singapore
| | - Jianzhong Chen
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme, National University of Singapore, Singapore
| | - Jocelyn A Ricard
- Department of Psychology, Yale University, New Haven, Connecticut
| | | | - Sidhant Chopra
- Department of Psychology, Yale University, New Haven, Connecticut
| | - Yueyue Qu
- Department of Psychology, Yale University, New Haven, Connecticut
| | - Xi-Han Zhang
- Department of Psychology, Yale University, New Haven, Connecticut
| | - Connor Lawhead
- Department of Psychology, Yale University, New Haven, Connecticut
| | - B T Thomas Yeo
- Centre for Sleep and Cognition and Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme, National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, Connecticut; Kavli Institute for Neuroscience, Yale University, New Haven, Connecticut; Department of Psychiatry, Yale University, New Haven, Connecticut; Wu Tsai Institute, Yale University, New Haven, Connecticut; Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, New Jersey.
| |
Collapse
|
30
|
Mehrotra D, Dubé L. Accounting for multiscale processing in adaptive real-world decision-making via the hippocampus. Front Neurosci 2023; 17:1200842. [PMID: 37732307 PMCID: PMC10508350 DOI: 10.3389/fnins.2023.1200842] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 08/25/2023] [Indexed: 09/22/2023] Open
Abstract
For adaptive real-time behavior in real-world contexts, the brain needs to allow past information over multiple timescales to influence current processing for making choices that create the best outcome as a person goes about making choices in their everyday life. The neuroeconomics literature on value-based decision-making has formalized such choice through reinforcement learning models for two extreme strategies. These strategies are model-free (MF), which is an automatic, stimulus-response type of action, and model-based (MB), which bases choice on cognitive representations of the world and causal inference on environment-behavior structure. The emphasis of examining the neural substrates of value-based decision making has been on the striatum and prefrontal regions, especially with regards to the "here and now" decision-making. Yet, such a dichotomy does not embrace all the dynamic complexity involved. In addition, despite robust research on the role of the hippocampus in memory and spatial learning, its contribution to value-based decision making is just starting to be explored. This paper aims to better appreciate the role of the hippocampus in decision-making and advance the successor representation (SR) as a candidate mechanism for encoding state representations in the hippocampus, separate from reward representations. To this end, we review research that relates hippocampal sequences to SR models showing that the implementation of such sequences in reinforcement learning agents improves their performance. This also enables the agents to perform multiscale temporal processing in a biologically plausible manner. Altogether, we articulate a framework to advance current striatal and prefrontal-focused decision making to better account for multiscale mechanisms underlying various real-world time-related concepts such as the self that cumulates over a person's life course.
Collapse
Affiliation(s)
- Dhruv Mehrotra
- Integrated Program in Neuroscience, McGill University, Montréal, QC, Canada
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Laurette Dubé
- Desautels Faculty of Management, McGill University, Montréal, QC, Canada
- McGill Center for the Convergence of Health and Economics, McGill University, Montréal, QC, Canada
| |
Collapse
|
31
|
Brynildsen JK, Rajan K, Henderson MX, Bassett DS. Network models to enhance the translational impact of cross-species studies. Nat Rev Neurosci 2023; 24:575-588. [PMID: 37524935 PMCID: PMC10634203 DOI: 10.1038/s41583-023-00720-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/17/2023] [Indexed: 08/02/2023]
Abstract
Neuroscience studies are often carried out in animal models for the purpose of understanding specific aspects of the human condition. However, the translation of findings across species remains a substantial challenge. Network science approaches can enhance the translational impact of cross-species studies by providing a means of mapping small-scale cellular processes identified in animal model studies to larger-scale inter-regional circuits observed in humans. In this Review, we highlight the contributions of network science approaches to the development of cross-species translational research in neuroscience. We lay the foundation for our discussion by exploring the objectives of cross-species translational models. We then discuss how the development of new tools that enable the acquisition of whole-brain data in animal models with cellular resolution provides unprecedented opportunity for cross-species applications of network science approaches for understanding large-scale brain networks. We describe how these tools may support the translation of findings across species and imaging modalities and highlight future opportunities. Our overarching goal is to illustrate how the application of network science tools across human and animal model studies could deepen insight into the neurobiology that underlies phenomena observed with non-invasive neuroimaging methods and could simultaneously further our ability to translate findings across species.
Collapse
Affiliation(s)
- Julia K Brynildsen
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Kanaka Rajan
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael X Henderson
- Parkinson's Disease Center, Department of Neurodegenerative Science, Van Andel Institute, Grand Rapids, MI, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA.
- Santa Fe Institute, Santa Fe, NM, USA.
| |
Collapse
|
32
|
Cao L, Li L, Huang Z, Xia F, Huang R, Ma Y, Qin Y, Wu J, Tong L, Zhang C, Zhang Y, Ren Z. Functional network segregation is associated with higher functional connectivity in endurance runners. Neurosci Lett 2023; 812:137401. [PMID: 37460055 DOI: 10.1016/j.neulet.2023.137401] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 06/21/2023] [Accepted: 07/14/2023] [Indexed: 07/23/2023]
Abstract
Neuroimaging studies have identified significant differences in brain structure, function, and connectivity between endurance runners and healthy controls. However, the topological organization of large-scale functional brain networks remains unexplored in endurance runners. Using resting-state functional magnetic resonance imaging data, this study examined the differences in the topological organization of functional networks between endurance runners (n = 22) and healthy controls (n = 20). Endurance runners had significantly higher clustering coefficients in the whole-brain functional network than healthy controls, but the two did not differ regarding the shortest path length or small-world index. Using network-based statistics, we identified one subnetwork in endurance runners with higher functional connectivity than healthy controls, and the mean functional connectivity of the subnetwork significantly correlated with the three aforementioned small-world parameters. In this subnetwork, the mean clustering coefficient of nodes associated with short-range connections was higher in endurance runners than in healthy controls, but the mean clustering coefficient of nodes associated with long-range connections did not differ between the two groups. In conclusion, using graph theoretical approaches, we revealed significant differences in the topological organization of the whole-brain functional network and functional connectivity between endurance runners and healthy controls. The relationship between these two features suggests that a more segregated network may arise from the optimization of the identified subnetwork in endurance runners. These findings are possibly the neural basis underlying the good performance of endurance runners in endurance running.
Collapse
Affiliation(s)
- Long Cao
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China; Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Lunxiong Li
- Key Laboratory of Brain, Cognition and Education Science, Ministry of Education, China; Institute for Brain Research and Rehabilitation, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Zitong Huang
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Fengguang Xia
- Key Laboratory of Brain, Cognition and Education Science, Ministry of Education, China; Institute for Brain Research and Rehabilitation, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Ruiwang Huang
- School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Yudan Ma
- School of Public Teaching, Shanwei Institute of Technology, Shanwei 516600, China
| | - Yifan Qin
- College of Physical Education, Shenzhen University, Shenzhen 518060, China
| | - Jinlong Wu
- College of physical education, Southwest University, Chongqing 400715, China
| | - Li Tong
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China
| | - Chi Zhang
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China
| | - Yuanchao Zhang
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Zhanbing Ren
- College of Physical Education, Shenzhen University, Shenzhen 518060, China.
| |
Collapse
|
33
|
Peng Y, Zheng Y, Yuan Z, Guo J, Fan C, Li C, Deng J, Song S, Qiao J, Wang J. The characteristics of brain network in patient with post-stroke depression under cognitive task condition. Front Neurosci 2023; 17:1242543. [PMID: 37655007 PMCID: PMC10467271 DOI: 10.3389/fnins.2023.1242543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 08/04/2023] [Indexed: 09/02/2023] Open
Abstract
Objectives Post-stroke depression (PSD) may be associated with the altered brain network property. This study aimed at exploring the brain network characteristics of PSD under the classic cognitive task, i.e., the oddball task, in order to promote our understanding of the pathogenesis and the diagnosis of PSD. Methods Nineteen stroke survivors with PSD and 18 stroke survivors with no PSD (non-PSD) were recruited. The functional near-infrared spectroscopy (fNIRS) covering the dorsolateral prefrontal cortex was recorded during the oddball task state and the resting state. The brain network characteristics were extracted using the graph theory and compared between the PSD and the non-PSD subjects. In addition, the classification performance between the PSD and non-PSD subjects was evaluated using features in the resting and the task state, respectively. Results Compared with the resting state, more brain network characteristics in the task state showed significant differences between the PSD and non-PSD groups, resulting in better classification performance. In the task state, the assortativity, clustering coefficient, characteristic path length, and local efficiency of the PSD subjects was larger compared with the non-PSD subjects while the global efficiency of the PSD subjects was smaller than that of the non-PSD subjects. Conclusion The altered brain network properties associated with PSD in the cognitive task state were more distinct compared with the resting state, and the ability of the brain network to resist attack and transmit information was reduced in PSD patients in the task state. Significance This study demonstrated the feasibility and superiority of investigating brain network properties in the task state for the exploration of the pathogenesis and new diagnosis methods for PSD.
Collapse
Affiliation(s)
- Yu Peng
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Sciences and Technology, Institute of Biomedical Engineering, Xi’an Jiaotong University, Xi’an, China
- Department of Rehabilitation, First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Yang Zheng
- The State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Institute of Engineering and Medicine Interdisciplinary Studies, Xi’an Jiaotong University, Xi’an, China
| | - Ziwen Yuan
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Sciences and Technology, Institute of Biomedical Engineering, Xi’an Jiaotong University, Xi’an, China
- Department of Rehabilitation, First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Jing Guo
- Department of Rehabilitation, First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Chunyang Fan
- Department of Rehabilitation, First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Chenxi Li
- Department of Military Medical Psychology, Air Force Medical University, Xi’an, China
| | - Jingyuan Deng
- Department of Rehabilitation, First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Siming Song
- Department of Rehabilitation, First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Jin Qiao
- Department of Rehabilitation, First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Jue Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Sciences and Technology, Institute of Biomedical Engineering, Xi’an Jiaotong University, Xi’an, China
| |
Collapse
|
34
|
Voldsbekk I, Kjelkenes R, Dahl A, Holm MC, Lund MJ, Kaufmann T, Tamnes CK, Andreassen OA, Westlye LT, Alnæs D. Delineating disorder-general and disorder-specific dimensions of psychopathology from functional brain networks in a developmental clinical sample. Dev Cogn Neurosci 2023; 62:101271. [PMID: 37348146 PMCID: PMC10439505 DOI: 10.1016/j.dcn.2023.101271] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 06/09/2023] [Accepted: 06/18/2023] [Indexed: 06/24/2023] Open
Abstract
The interplay between functional brain network maturation and psychopathology during development remains elusive. To establish the structure of psychopathology and its neurobiological mechanisms, mapping of both shared and unique functional connectivity patterns across developmental clinical populations is needed. We investigated shared associations between resting-state functional connectivity and psychopathology in children and adolescents aged 5-21 (n = 1689). Specifically, we used partial least squares (PLS) to identify latent variables (LV) between connectivity and both symptom scores and diagnostic information. We also investigated associations between connectivity and each diagnosis specifically, controlling for other diagnosis categories. PLS identified five significant LVs between connectivity and symptoms, mapping onto the psychopathology hierarchy. The first LV resembled a general psychopathology factor, followed by dimensions of internalising- externalising, neurodevelopment, somatic complaints, and thought problems. Another PLS with diagnostic data revealed one significant LV, resembling a cross-diagnostic case-control pattern. The diagnosis-specific PLS identified a unique connectivity pattern for autism spectrum disorder (ASD). All LVs were associated with distinct patterns of functional connectivity. These dimensions largely replicated in an independent sample (n = 420) from the same dataset, as well as to an independent cohort (n = 3504). This suggests that covariance in developmental functional brain networks supports transdiagnostic dimensions of psychopathology.
Collapse
Affiliation(s)
- Irene Voldsbekk
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway.
| | - Rikka Kjelkenes
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway
| | - Andreas Dahl
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway
| | - Madelene C Holm
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway
| | - Martina J Lund
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Tobias Kaufmann
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Psychiatry and Psychotherapy, University of Tübingen, Germany
| | - Christian K Tamnes
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway; PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, & Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Lars T Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, & Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Dag Alnæs
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Kristiania University College, Oslo, Norway
| |
Collapse
|
35
|
Wang F, Zhang H, Wu Z, Hu D, Zhou Z, Girault JB, Wang L, Lin W, Li G. Fine-grained functional parcellation maps of the infant cerebral cortex. eLife 2023; 12:e75401. [PMID: 37526293 PMCID: PMC10393291 DOI: 10.7554/elife.75401] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 07/17/2023] [Indexed: 08/02/2023] Open
Abstract
Resting-state functional MRI (rs-fMRI) is widely used to examine the dynamic brain functional development of infants, but these studies typically require precise cortical parcellation maps, which cannot be directly borrowed from adult-based functional parcellation maps due to the substantial differences in functional brain organization between infants and adults. Creating infant-specific cortical parcellation maps is thus highly desired but remains challenging due to difficulties in acquiring and processing infant brain MRIs. In this study, we leveraged 1064 high-resolution longitudinal rs-fMRIs from 197 typically developing infants and toddlers from birth to 24 months who participated in the Baby Connectome Project to develop the first set of infant-specific, fine-grained, surface-based cortical functional parcellation maps. To establish meaningful cortical functional correspondence across individuals, we performed cortical co-registration using both the cortical folding geometric features and the local gradient of functional connectivity (FC). Then we generated both age-related and age-independent cortical parcellation maps with over 800 fine-grained parcels during infancy based on aligned and averaged local gradient maps of FC across individuals. These parcellation maps reveal complex functional developmental patterns, such as changes in local gradient, network size, and local efficiency, especially during the first 9 postnatal months. Our generated fine-grained infant cortical functional parcellation maps are publicly available at https://www.nitrc.org/projects/infantsurfatlas/ for advancing the pediatric neuroimaging field.
Collapse
Affiliation(s)
- Fan Wang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong UniversityXi'anChina
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Han Zhang
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Dan Hu
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Zhen Zhou
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Jessica B Girault
- Department of Psychiatry, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| |
Collapse
|
36
|
Badke D’Andrea C, Marek S, Van AN, Miller RL, Earl EA, Stewart SB, Dosenbach NUF, Schlaggar BL, Laumann TO, Fair DA, Gordon EM, Greene DJ. Thalamo-cortical and cerebello-cortical functional connectivity in development. Cereb Cortex 2023; 33:9250-9262. [PMID: 37293735 PMCID: PMC10492576 DOI: 10.1093/cercor/bhad198] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 05/09/2023] [Accepted: 05/17/2023] [Indexed: 06/10/2023] Open
Abstract
The thalamus is a critical relay center for neural pathways involving sensory, motor, and cognitive functions, including cortico-striato-thalamo-cortical and cortico-ponto-cerebello-thalamo-cortical loops. Despite the importance of these circuits, their development has been understudied. One way to investigate these pathways in human development in vivo is with functional connectivity MRI, yet few studies have examined thalamo-cortical and cerebello-cortical functional connectivity in development. Here, we used resting-state functional connectivity to measure functional connectivity in the thalamus and cerebellum with previously defined cortical functional networks in 2 separate data sets of children (7-12 years old) and adults (19-40 years old). In both data sets, we found stronger functional connectivity between the ventral thalamus and the somatomotor face cortical functional network in children compared with adults, extending previous cortico-striatal functional connectivity findings. In addition, there was more cortical network integration (i.e. strongest functional connectivity with multiple networks) in the thalamus in children than in adults. We found no developmental differences in cerebello-cortical functional connectivity. Together, these results suggest different maturation patterns in cortico-striato-thalamo-cortical and cortico-ponto-cerebellar-thalamo-cortical pathways.
Collapse
Affiliation(s)
- Carolina Badke D’Andrea
- Department of Cognitive Science, University of California San Diego, La Jolla, CA 92093, United States
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Scott Marek
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Andrew N Van
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, United States
| | - Ryland L Miller
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Eric A Earl
- Data Science and Sharing Team, National Institute of Mental Health, NIH, DHHS, Bethesda, MD 20899, United States
| | - Stephanie B Stewart
- Department of Psychiatry, University of Colorado School of Medicine, Aurora, CO 80045, United States
| | - Nico U F Dosenbach
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, United States
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, United States
- Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO 63110, United States
| | | | - Timothy O Laumann
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Damien A Fair
- Institute of Child Development, College of Education and Human Development, University of Minnesota, Minneapolis, MN 55455, United States
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN 55455, United States
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55455, United States
| | - Evan M Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Deanna J Greene
- Department of Cognitive Science, University of California San Diego, La Jolla, CA 92093, United States
| |
Collapse
|
37
|
He M, Hou X, Ge E, Wang Z, Kang Z, Qiang N, Zhang X, Ge B. Multi-head attention-based masked sequence model for mapping functional brain networks. Front Neurosci 2023; 17:1183145. [PMID: 37214388 PMCID: PMC10192686 DOI: 10.3389/fnins.2023.1183145] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 04/17/2023] [Indexed: 05/24/2023] Open
Abstract
The investigation of functional brain networks (FBNs) using task-based functional magnetic resonance imaging (tfMRI) has gained significant attention in the field of neuroimaging. Despite the availability of several methods for constructing FBNs, including traditional methods like GLM and deep learning methods such as spatiotemporal self-attention mechanism (STAAE), these methods have design and training limitations. Specifically, they do not consider the intrinsic characteristics of fMRI data, such as the possibility that the same signal value at different time points could represent different brain states and meanings. Furthermore, they overlook prior knowledge, such as task designs, during training. This study aims to overcome these limitations and develop a more efficient model by drawing inspiration from techniques in the field of natural language processing (NLP). The proposed model, called the Multi-head Attention-based Masked Sequence Model (MAMSM), uses a multi-headed attention mechanism and mask training approach to learn different states corresponding to the same voxel values. Additionally, it combines cosine similarity and task design curves to construct a novel loss function. The MAMSM was applied to seven task state datasets from the Human Connectome Project (HCP) tfMRI dataset. Experimental results showed that the features acquired by the MAMSM model exhibit a Pearson correlation coefficient with the task design curves above 0.95 on average. Moreover, the model can extract more meaningful networks beyond the known task-related brain networks. The experimental results demonstrated that MAMSM has great potential in advancing the understanding of functional brain networks.
Collapse
Affiliation(s)
- Mengshen He
- Key Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi’an, China
- School of Physics and Information Technology, Shaanxi Normal University, Xi’an, China
| | - Xiangyu Hou
- School of Physics and Information Technology, Shaanxi Normal University, Xi’an, China
| | - Enjie Ge
- School of Physics and Information Technology, Shaanxi Normal University, Xi’an, China
| | - Zhenwei Wang
- School of Physics and Information Technology, Shaanxi Normal University, Xi’an, China
| | - Zili Kang
- School of Physics and Information Technology, Shaanxi Normal University, Xi’an, China
| | - Ning Qiang
- School of Physics and Information Technology, Shaanxi Normal University, Xi’an, China
| | - Xin Zhang
- Institute of Medical Research, Northwestern Polytechnical University, Xi’an, Shaanxi, China
| | - Bao Ge
- Key Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi’an, China
- School of Physics and Information Technology, Shaanxi Normal University, Xi’an, China
| |
Collapse
|
38
|
Chen M, He Y, Hao L, Xu J, Tian T, Peng S, Zhao G, Lu J, Zhao Y, Zhao H, Jiang M, Gao JH, Tan S, He Y, Liu C, Tao S, Uddin LQ, Dong Q, Qin S. Default mode network scaffolds immature frontoparietal network in cognitive development. Cereb Cortex 2023; 33:5251-5263. [PMID: 36320154 PMCID: PMC10152054 DOI: 10.1093/cercor/bhac414] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 09/15/2022] [Accepted: 09/16/2022] [Indexed: 05/03/2023] Open
Abstract
The default mode network (DMN) is a workspace for convergence of internal and external information. The frontal parietal network (FPN) is indispensable to executive functioning. Yet, how they interplay to support cognitive development remains elusive. Using longitudinal developmental fMRI with an n-back paradigm, we show a heterogeneity of maturational changes in multivoxel activity and network connectivity among DMN and FPN nodes in 528 children and 103 young adults. Compared with adults, children exhibited prominent longitudinal improvement but still inferior behavioral performance, which paired with less pronounced DMN deactivation and weaker FPN activation in children, but stronger DMN coupling with FPN regions. Children's DMN reached an adult-like level earlier than FPN at both multivoxel activity pattern and intranetwork connectivity levels. Intrinsic DMN-FPN internetwork coupling in children mediated the relationship between age and working memory-related functional coupling of these networks, with posterior cingulate cortex (PCC)-dorsolateral prefrontal cortex (DLPFC) coupling emerging as most prominent pathway. Coupling of PCC-DLPFC may further work together with task-invoked activity in PCC to account for longitudinal improvement in behavioral performance in children. Our findings suggest that the DMN provides a scaffolding effect in support of an immature FPN that is critical for the development of executive functions in children.
Collapse
Affiliation(s)
- Menglu Chen
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Ying He
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Lei Hao
- College of Teacher Education, Southwest University, Chongqing 400715, China
- Qiongtai Normal University Key Laboratory of Child Cognition & Behavior Development of Hainan Province, Haikou 571127, China
| | - Jiahua Xu
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Ting Tian
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Siya Peng
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Gai Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Jing Lu
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Yuyao Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Hui Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Min Jiang
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
- McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Shuping Tan
- Beijing HuiLongGuan Hospital, Peking University, Beijing 100036, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Chao Liu
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Lucina Q Uddin
- Department of Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, Los Angeles, CA 90095, USA
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Chinese Institute for Brain Research, Beijing 100069, China
| |
Collapse
|
39
|
Abrol A, Fu Z, Du Y, Wilson TW, Wang Y, Stephen JM, Calhoun VD. Developmental and aging resting functional magnetic resonance imaging brain state adaptations in adolescents and adults: A large N (>47K) study. Hum Brain Mapp 2023; 44:2158-2175. [PMID: 36629328 PMCID: PMC10028673 DOI: 10.1002/hbm.26200] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 12/02/2022] [Accepted: 12/28/2022] [Indexed: 01/12/2023] Open
Abstract
The brain's functional architecture and organization undergo continual development and modification throughout adolescence. While it is well known that multiple factors govern brain maturation, the constantly evolving patterns of time-resolved functional connectivity are still unclear and understudied. We systematically evaluated over 47,000 youth and adult brains to bridge this gap, highlighting replicable time-resolved developmental and aging functional brain patterns. The largest difference between the two life stages was captured in a brain state that indicated coherent strengthening and modularization of functional coupling within the auditory, visual, and motor subdomains, supplemented by anticorrelation with other subdomains in adults. This distinctive pattern, which we replicated in independent data, was consistently less modular or absent in children and presented a negative association with age in adults, thus indicating an overall inverted U-shaped trajectory. This indicates greater synchrony, strengthening, modularization, and integration of the brain's functional connections beyond adolescence, and gradual decline of this pattern during the healthy aging process. We also found evidence that the developmental changes may also bring along a departure from the canonical static functional connectivity pattern in favor of more efficient and modularized utilization of the vast brain interconnections. State-based statistical summary measures presented robust and significant group differences that also showed significant age-related associations. The findings reported in this article support the idea of gradual developmental and aging brain state adaptation processes in different phases of life and warrant future research via lifespan studies to further authenticate the projected time-resolved brain state trajectories.
Collapse
Affiliation(s)
- Anees Abrol
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - Zening Fu
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - Yuhui Du
- School of Computer & Information TechnologyShanxi UniversityTaiyuanChina
| | - Tony W. Wilson
- Boys Town National Research HospitalInstitute for Human NeuroscienceBoys TownNebraskaUSA
| | - Yu‐Ping Wang
- Department of Biomedical EngineeringTulane UniversityNew OrleansLouisianaUSA
- Department of Global Biostatistics and Data ScienceTulane UniversityNew OrleansLouisianaUSA
| | | | - Vince D. Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| |
Collapse
|
40
|
Schettini E, Hiersche KJ, Saygin ZM. Individual Variability in Performance Reflects Selectivity of the Multiple Demand Network among Children and Adults. J Neurosci 2023; 43:1940-1951. [PMID: 36750368 PMCID: PMC10027032 DOI: 10.1523/jneurosci.1460-22.2023] [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: 07/30/2022] [Revised: 12/19/2022] [Accepted: 01/25/2023] [Indexed: 02/09/2023] Open
Abstract
Executive function (EF) is essential for humans to effectively engage in cognitively demanding tasks. In adults, EF is subserved by frontoparietal regions in the multiple demand (MD) network, which respond to various cognitively demanding tasks. However, children initially show poor EF and prolonged development. Do children recruit the same network as adults? Is it functionally and connectionally distinct from adjacent language cortex, as in adults? And is this activation or connectivity dependent on age or ability? We examine task-dependent (spatial working memory and passive language tasks) and resting state functional data in 44 adults (18-38 years, 68% female) and 37 children (4-12 years, 35% female). Subject-specific functional ROIs (ss-fROIs) show bilateral MD network activation in children. In both children and adults, these MD ss-fROIs are not recruited for linguistic processing and are connectionally distinct from language ss-fROIs. While MD activation was lower in children than in adults (even in motion- and performance-matched groups), both showed increasing MD activation with better performance, especially in right hemisphere ss-fROIs. We observe this relationship even when controlling for age, cross-sectionally and in a small longitudinal sample of children. These data suggest that the MD network is selective to cognitive demand in children, is distinct from adjacent language cortex, and increases in selectivity as performance improves. These findings show that neural structures subserving domain-general EF emerge early and are sensitive to ability even in children. This research advances understanding of how high-level human cognition emerges and could inform interventions targeting cognitive control.SIGNIFICANCE STATEMENT This study provides evidence that young children already show differentiated brain network organization between regions that process cognitive demand and language. These data support the hypothesis that children recruit a similar network as adults to process cognitive demand; and despite immature characteristics, children's selectivity looks more adult-like as their executive function ability increases. Mapping early stages of network organization furthers our understanding of the functional architecture underlying domain-general executive function. Determining typical variability underlying cognitive processing across developmental periods helps establish a threshold for executive dysfunction. Early markers of dysfunction are necessary for effective early identification, prevention, and intervention efforts for individuals struggling with deficits in processing cognitive demand.
Collapse
Affiliation(s)
- Elana Schettini
- Department of Psychology, Ohio State University, Columbus, Ohio 43212
- Center for Cognitive and Behavioral Brain Imaging, Ohio State University, Columbus, Ohio 43212
| | - Kelly J Hiersche
- Department of Psychology, Ohio State University, Columbus, Ohio 43212
- Center for Cognitive and Behavioral Brain Imaging, Ohio State University, Columbus, Ohio 43212
| | - Zeynep M Saygin
- Department of Psychology, Ohio State University, Columbus, Ohio 43212
- Center for Cognitive and Behavioral Brain Imaging, Ohio State University, Columbus, Ohio 43212
| |
Collapse
|
41
|
Social navigation modulates the anterior and posterior hippocampal circuits in the resting brain. Brain Struct Funct 2023; 228:799-813. [PMID: 36813907 DOI: 10.1007/s00429-023-02622-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 02/13/2023] [Indexed: 02/24/2023]
Abstract
Social navigation is a dynamic and complex process that requires the collaboration of multiple brain regions. However, the neural networks for navigation in a social space remain largely unknown. This study aimed to investigate the role of hippocampal circuit in social navigation from a resting-state fMRI data. Here, resting-state fMRI data were acquired before and after participants performed a social navigation task. By taking the anterior and posterior hippocampus (HPC) as the seeds, we calculated their connectivity with the whole brain using the seed-based static functional connectivity (sFC) and dynamic FC (dFC) approaches. We found that the sFC and dFC between the anterior HPC and supramarginal gyrus, sFC or dFC between posterior HPC and middle cingulate cortex, inferior parietal gyrus, angular gyrus, posterior cerebellum, medial superior frontal gyrus were increased after the social navigation task. These alterations were related to social cognition of tracking location in the social navigation. Moreover, participants who had more social support or less neuroticism showed a greater increase in hippocampal connectivity. These findings may highlight a more important role of the posterior hippocampal circuit in the social navigation, which is crucial for social cognition.
Collapse
|
42
|
Chinichian N, Kruschwitz JD, Reinhardt P, Palm M, Wellan SA, Erk S, Heinz A, Walter H, Veer IM. A fast and intuitive method for calculating dynamic network reconfiguration and node flexibility. Front Neurosci 2023; 17:1025428. [PMID: 36845440 PMCID: PMC9949291 DOI: 10.3389/fnins.2023.1025428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 01/04/2023] [Indexed: 02/11/2023] Open
Abstract
Dynamic interactions between brain regions, either during rest or performance of cognitive tasks, have been studied extensively using a wide variance of methods. Although some of these methods allow elegant mathematical interpretations of the data, they can easily become computationally expensive or difficult to interpret and compare between subjects or groups. Here, we propose an intuitive and computationally efficient method to measure dynamic reconfiguration of brain regions, also termed flexibility. Our flexibility measure is defined in relation to an a-priori set of biologically plausible brain modules (or networks) and does not rely on a stochastic data-driven module estimation, which, in turn, minimizes computational burden. The change of affiliation of brain regions over time with respect to these a-priori template modules is used as an indicator of brain network flexibility. We demonstrate that our proposed method yields highly similar patterns of whole-brain network reconfiguration (i.e., flexibility) during a working memory task as compared to a previous study that uses a data-driven, but computationally more expensive method. This result illustrates that the use of a fixed modular framework allows for valid, yet more efficient estimation of whole-brain flexibility, while the method additionally supports more fine-grained (e.g. node and group of nodes scale) flexibility analyses restricted to biologically plausible brain networks.
Collapse
Affiliation(s)
- Narges Chinichian
- Institute for Theoretical Physics, Technical University of Berlin, Berlin, Germany
- Department of Psychiatry and Psychotherapy, Charité Campus Mitte (CCM), Charité-Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Johann D. Kruschwitz
- Department of Psychiatry and Psychotherapy, Charité Campus Mitte (CCM), Charité-Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Research Centre (SFB 940) “Volition and Cognitive Control”, Technische Universität Dresden, Dresden, Germany
| | - Pablo Reinhardt
- Department of Psychiatry and Psychotherapy, Charité Campus Mitte (CCM), Charité-Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Maximilian Palm
- Department of Philosophy and Humanities, Freie Universität Berlin, Berlin, Germany
- Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany
| | - Sarah A. Wellan
- Department of Psychiatry and Psychotherapy, Charité Campus Mitte (CCM), Charité-Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Faculty of Philosophy, Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Susanne Erk
- Department of Psychiatry and Psychotherapy, Charité Campus Mitte (CCM), Charité-Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy, Charité Campus Mitte (CCM), Charité-Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy, Charité Campus Mitte (CCM), Charité-Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Ilya M. Veer
- Department of Psychiatry and Psychotherapy, Charité Campus Mitte (CCM), Charité-Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Developmental Psychology, University of Amsterdam, Amsterdam, Netherlands
| |
Collapse
|
43
|
Jacobs NPT, Pouwels PJW, van der Krogt MM, Meyns P, Zhu K, Nelissen L, Schoonmade LJ, Buizer AI, van de Pol LA. Brain structural and functional connectivity and network organization in cerebral palsy: A scoping review. Dev Med Child Neurol 2023. [PMID: 36750309 DOI: 10.1111/dmcn.15516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 12/10/2022] [Accepted: 12/13/2022] [Indexed: 02/09/2023]
Abstract
AIM To explore altered structural and functional connectivity and network organization in cerebral palsy (CP), by clinical CP subtype (unilateral spastic, bilateral spastic, dyskinetic, and ataxic CP). METHOD PubMed and Embase databases were systematically searched. Extracted data included clinical characteristics, analyses, outcome measures, and results. RESULTS Sixty-five studies were included, of which 50 investigated structural connectivity, and 20 investigated functional connectivity using functional magnetic resonance imaging (14 studies) or electroencephalography (six studies). Five of the 50 studies of structural connectivity and one of 14 of functional connectivity investigated whole-brain network organization. Most studies included patients with unilateral spastic CP; none included ataxic CP. INTERPRETATION Differences in structural and functional connectivity were observed between investigated clinical CP subtypes and typically developing individuals on a wide variety of measures, including efferent, afferent, interhemispheric, and intrahemispheric connections. Directions for future research include extending knowledge in underrepresented CP subtypes and methodologies, evaluating the prognostic potential of specific connectivity and network measures in neonates, and understanding therapeutic effects on brain connectivity.
Collapse
Affiliation(s)
- Nina P T Jacobs
- Department of Rehabilitation Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.,Amsterdam Movement Sciences, Rehabilitation & Development, Amsterdam, the Netherlands
| | - Petra J W Pouwels
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Marjolein M van der Krogt
- Department of Rehabilitation Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.,Amsterdam Movement Sciences, Rehabilitation & Development, Amsterdam, the Netherlands
| | - Pieter Meyns
- REVAL Rehabilitation Research, Faculty of Rehabilitation Sciences, Hasselt University, Diepenbeek, Belgium
| | - Kangdi Zhu
- Department of Rehabilitation Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Loïs Nelissen
- Department of Pediatric Neurology, Emma Children's Hospital, Amsterdam UMC, location Vrije Universiteit, Amsterdam, the Netherlands
| | - Linda J Schoonmade
- Medical Library, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Annemieke I Buizer
- Department of Rehabilitation Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.,Amsterdam Movement Sciences, Rehabilitation & Development, Amsterdam, the Netherlands.,Emma Children's Hospital, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Laura A van de Pol
- Department of Pediatric Neurology, Emma Children's Hospital, Amsterdam UMC, location Vrije Universiteit, Amsterdam, the Netherlands
| |
Collapse
|
44
|
Wilkey ED, Gupta I, Peiris A, Ansari D. The mathematical brain at rest. Curr Opin Behav Sci 2023. [DOI: 10.1016/j.cobeha.2022.101246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
|
45
|
Lineham A, Avila-Quintero VJ, Bloch MH, Dwyer J. The Relationship Between Acute Dissociative Effects Induced by Ketamine and Treatment Response in Adolescent Patients with Treatment-Resistant Depression. J Child Adolesc Psychopharmacol 2023; 33:20-26. [PMID: 36799961 DOI: 10.1089/cap.2022.0086] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
Objective: Ketamine has proven effective as a rapid-acting antidepressant agent. Several adult studies have investigated the association between ketamine's acute dissociative effects and depression response, but no studies have examined the association in adolescents with treatment-resistant depression (TRD). Methods: We conducted a secondary data analysis of 16 adolescent participants who participated in a randomized, single-dose, midazolam-controlled crossover trial of ketamine in adolescents with depression. We examined the association between the acute dissociative symptoms (measured at 60 minutes following start of infusion using the Clinician-Administered Dissociative States Scale [CADSS], and its three subscales: depersonalization, derealization, amnesia) and response and depression symptom improvement at 1'day (using the Montgomery-Åsberg Depression Rating Scale). Results: Within the ketamine group, there were no significant associations between dissociation symptoms or CADSS subscale scores and magnitude of depression symptom improvement or likelihood of ketamine response. When receiving midazolam, there was no significant association between overall dissociation symptoms and magnitude or likelihood of response of depressive symptoms. Higher levels of symptoms on the 'depersonalization' CADSS subscale when receiving midazolam were associated with less improvement in depression symptoms at 1 day following infusion. Conclusions: In contrast to some adult literature, the current data do not show a relationship between acute dissociative effects and antidepressant response to ketamine in pediatric patients with TRD. Interpretation may be limited by the small sample size, reducing the power to detect small or medium associations. Future research should utilize larger samples to more definitively measure the magnitude of association between acute dissociative symptoms and later antidepressant response to ketamine and to assess the relationship to trial design (e.g., crossover vs. parallel trial, comparison condition utilized and number of infusions) within both adult and pediatric populations. ClinicalTrials.gov identifier: NCT02579928.
Collapse
Affiliation(s)
- Alice Lineham
- Child Study Center, Yale School of Medicine, New Haven, Connecticut, USA
| | | | - Michael H Bloch
- Child Study Center, Yale School of Medicine, New Haven, Connecticut, USA
- Department of Psychiatry and Yale School of Medicine, New Haven, Connecticut, USA
| | - Jennifer Dwyer
- Child Study Center, Yale School of Medicine, New Haven, Connecticut, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| |
Collapse
|
46
|
Keller AS, Sydnor VJ, Pines A, Fair DA, Bassett DS, Satterthwaite TD. Hierarchical functional system development supports executive function. Trends Cogn Sci 2023; 27:160-174. [PMID: 36437189 PMCID: PMC9851999 DOI: 10.1016/j.tics.2022.11.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 11/01/2022] [Accepted: 11/03/2022] [Indexed: 11/26/2022]
Abstract
In this perspective, we describe how developmental improvements in youth executive function (EF) are supported by hierarchically organized maturational changes in functional brain systems. We first highlight evidence that functional brain systems are embedded within a hierarchical sensorimotor-association axis of cortical organization. We then review data showing that functional system developmental profiles vary along this axis: systems near the associative end become more functionally segregated, while those in the middle become more integrative. Developmental changes that strengthen the hierarchical organization of the cortex may support EF by facilitating top-down information flow and balancing within- and between-system communication. We propose a central role for attention and frontoparietal control systems in the maturation of healthy EF and suggest that reduced functional system differentiation across the sensorimotor-association axis contributes to transdiagnostic EF deficits.
Collapse
Affiliation(s)
- Arielle S Keller
- Penn Lifespan Informatics and Neuroimaging Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Valerie J Sydnor
- Penn Lifespan Informatics and Neuroimaging Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Adam Pines
- Penn Lifespan Informatics and Neuroimaging Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, 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
| | - Dani S Bassett
- Department of Psychiatry, Perelman School of Medicine, 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; Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA; Santa Fe Institute, Santa Fe, NM 87501, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
| |
Collapse
|
47
|
Pinto CB, Bielefeld J, Barroso J, Yip B, Huang L, Schnitzer T, Apkarian AV. Chronic pain domains and their relationship to personality, abilities, and brain networks. Pain 2023; 164:59-71. [PMID: 35612403 PMCID: PMC9582040 DOI: 10.1097/j.pain.0000000000002657] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 02/23/2022] [Indexed: 01/09/2023]
Abstract
Abstract
Chronic pain is a multidimensional pathological state. Recent evidence suggests that specific brain properties and patients' psychological and physical traits are distorted in chronic pain patients. However, the relationship between these alterations and pain dimensions remains poorly understood. Here, we first evaluated multiple dimensions of chronic pain by assessing a broad battery of pain-related questionnaire scores (23 outcomes) of 107 chronic low back pain patients and identified 3 distinct chronic pain domains: magnitude, affect & disability, and quality. Second, we investigated the pain domains relationship with measures of personality, social interaction, psychological traits, and ability traits (77 biopsychosocial & ability [biopsy&ab] outcomes). Pain magnitude (out-of-sample [OOS]
) is associated with emotional control, attention, and working memory, with higher pain scores showing lower capacity to regulate and adapt behaviorally. Pain affect & disability (OOS
associated with anxiety, catastrophizing and social relationships dysfunction. Pain quality did not relate significantly to biopsy&ab variables. Third, we mapped these 3 pain domains to brain functional connectivity. Pain magnitude mainly associated with the sensorimotor and the cingulo-opercular networks (OOS
). Pain affect & disability related to frontoparietal and default mode networks (OOS
. Pain quality integrated sensorimotor, auditory, and cingulo-opercular networks (OOS
). Mediation analysis could link functional connectivity and biopsy&ab models to respective pain domains. Our results provide a global overview of the complexity of chronic pain, showing how underlying distinct domains of the experience map to different biopsy&ab correlates and underlie unique brain network signatures.
Collapse
Affiliation(s)
- Camila Bonin Pinto
- Department of Neuroscience, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
- Center for Translational Pain Research, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Jannis Bielefeld
- Department of Neuroscience, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
- Center for Translational Pain Research, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Joana Barroso
- Department of Neuroscience, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
- Center for Translational Pain Research, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Byron Yip
- Departments of Physical Medicine and Rehabilitation
| | - Lejian Huang
- Department of Neuroscience, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
- Center for Translational Pain Research, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Thomas Schnitzer
- Departments of Physical Medicine and Rehabilitation
- Anesthesiology, and
- Medicine (Rheumatology), Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - A Vania Apkarian
- Department of Neuroscience, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
- Center for Translational Pain Research, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Departments of Physical Medicine and Rehabilitation
- Anesthesiology, and
| |
Collapse
|
48
|
Moyano S, Conejero Á, Fernández M, Serrano F, Rueda MR. Development of visual attention control in early childhood: Associations with temperament and home environment. Front Psychol 2022; 13:1069478. [PMID: 36619065 PMCID: PMC9811174 DOI: 10.3389/fpsyg.2022.1069478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 11/29/2022] [Indexed: 12/24/2022] Open
Abstract
Endogenous visual attention orienting is early available from infancy. It shows a steady development during the preschool period towards monitoring and managing executive attention to optimize the interplay between environmental contingencies and internal goals. The current study aims at understanding this transition from basic forms of endogenous control of visual orienting towards the engagement of executive attention, as well as their association with individual differences in temperament and home environment. A total of 150 children between 2 and 4 years of age were evaluated in a Visual Sequence Learning task, measuring visual anticipations in easy (context-free) and complex (context-dependent) stimuli transitions. Results showed age to be a predictor of a reduction in exogenous attention, as well as increased abilities to attempt to anticipate and to correctly anticipate in complex transitions. Home chaos predicted more complex correct anticipations, suggesting that the exposure to more unpredictable environments could benefit learning in context-dependent settings. Finally, temperamental surgency was found to be positively related to sustained attention in the task. Results are informative of age differences in visual attention control during toddlerhood and early childhood, and their association with temperament and home environment.
Collapse
Affiliation(s)
- Sebastián Moyano
- Department of Experimental Psychology, University of Granada, Granada, Spain
- Mind, Brain and Behavior Research Center (CIMCYC), University of Granada, Granada, Spain
| | - Ángela Conejero
- Department of Developmental and Educational Psychology, University of Granada, Granada, Spain
| | - María Fernández
- Department of Experimental Psychology, University of Granada, Granada, Spain
| | - Francisca Serrano
- Mind, Brain and Behavior Research Center (CIMCYC), University of Granada, Granada, Spain
- Department of Developmental and Educational Psychology, University of Granada, Granada, Spain
| | - M. Rosario Rueda
- Department of Experimental Psychology, University of Granada, Granada, Spain
- Mind, Brain and Behavior Research Center (CIMCYC), University of Granada, Granada, Spain
| |
Collapse
|
49
|
Altered Functional Connectivity and Complexity in Major Depressive Disorder after Musical Stimulation. Brain Sci 2022; 12:brainsci12121680. [PMID: 36552139 PMCID: PMC9775252 DOI: 10.3390/brainsci12121680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 11/24/2022] [Accepted: 11/30/2022] [Indexed: 12/13/2022] Open
Abstract
Major depressive disorder (MDD) is a common mental illness. This study used electroencephalography (EEG) to explore the effects of music therapy on brain networks in MDD patients and to elucidate changes in functional brain connectivity in subjects before and after musical stimulation. EEG signals were collected from eight MDD patients and eight healthy controls. The phase locking value was adopted to calculate the EEG correlation of different channels in different frequency bands. Correlation matrices and network topologies were studied to analyze changes in functional connectivity between brain regions. The results of the experimental analysis found that the connectivity of the delta and beta bands decreased, while the connectivity of the alpha band increased. Regarding the characteristics of the EEG functional network, the average clustering coefficient, characteristic path length and degree of each node in the delta band decreased significantly after musical stimulation, while the characteristic path length in the beta band increased significantly. Characterized by the average clustering coefficient and characteristic path length, the classification of depression and healthy controls reached 93.75% using a support vector machine.
Collapse
|
50
|
Ye F, Kohler R, Serio B, Lichenstein S, Yip SW. Task-based co-activation patterns reliably predict resting state canonical network engagement during development. Dev Cogn Neurosci 2022; 58:101160. [PMID: 36270101 PMCID: PMC9583448 DOI: 10.1016/j.dcn.2022.101160] [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: 05/16/2022] [Revised: 09/30/2022] [Accepted: 10/07/2022] [Indexed: 01/13/2023] Open
Abstract
Neurodevelopmental research has traditionally focused on development of individual structures, yet multiple lines of evidence indicate parallel development of large-scale systems, including canonical neural networks (i.e., default mode, frontoparietal). However, the relationship between region- vs. network-level development remains poorly understood. The current study tests the ability of a recently developed multi-task coactivation matrix approach to predict canonical resting state network engagement at baseline and at two-year follow-up in a large and cohort of young adolescents. Pre-processed tabulated neuroimaging data were obtained from the Adolescent Brain and Cognitive Development (ABCD) study, assessing youth at baseline (N = 6073, age = 10.0 ± 0.6 years, 3056 female) and at two-year follow-up (N = 3539, age = 11.9 ± 0.6 years, 1726 female). Individual multi-task co-activation matrices were constructed from the beta weights of task contrasts from the stop signal task, the monetary incentive delay task, and emotional N-back task. Activation-based predictive modeling, a cross-validated machine learning approach, was adopted to predict resting-state canonical network engagement from multi-task co-activation matrices at baseline. Note that the tabulated data used different parcellations of the task fMRI data ("ASEG" and Desikan) and the resting-state fMRI data (Gordon). Despite this, the model successfully predicted connectivity within the default mode network (DMN, rho = 0.179 ± 0.002, p < 0.001) across participants and identified a subset of co-activations within parietal and occipital macroscale brain regions as key contributors to model performance, suggesting an underlying common brain functional architecture across cognitive domains. Notably, predictive features for resting-state connectivity within the DMN identified at baseline also predicted DMN connectivity at two-year follow-up (rho = 0.258). These results indicate that multi-task co-activation matrices are functionally meaningful and can be used to predict resting-state connectivity. Interestingly, given that predictive features within the co-activation matrices identified at baseline can be extended to predictions at a future time point, our results suggest that task-based neural features and models are valid predictors of resting state network level connectivity across the course of development. Future work is encouraged to verify these findings with more consistent parcellations between task-based and resting-state fMRI, and with longer developmental trajectories.
Collapse
Affiliation(s)
- Fengdan Ye
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511, USA
| | - Robert Kohler
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511, USA
| | - Bianca Serio
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511, USA
| | - Sarah Lichenstein
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511, USA
| | - Sarah W Yip
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511, USA; Child Study Center, Yale School of Medicine, New Haven, CT 06519, USA.
| |
Collapse
|