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Baghernezhad S, Daliri MR. Age-related changes in human brain functional connectivity using graph theory and machine learning techniques in resting-state fMRI data. GeroScience 2024; 46:5303-5320. [PMID: 38499956 PMCID: PMC11336041 DOI: 10.1007/s11357-024-01128-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] [Received: 11/05/2023] [Accepted: 03/08/2024] [Indexed: 03/20/2024] Open
Abstract
Aging is the basis of neurodegeneration and dementia that affects each endemic in the body. Normal aging in the brain is associated with progressive slowdown and disruptions in various abilities such as motor ability, cognitive impairment, decreasing information processing speed, attention, and memory. With the aggravation of global aging, more research focuses on brain changes in the elderly adult. The graph theory, in combination with functional magnetic resonance imaging (fMRI), makes it possible to evaluate the brain network functional connectivity patterns in different conditions with brain modeling. We have evaluated the brain network communication model changes in three different age groups (including 8 to 15 years, 25 to 35 years, and 45 to 75 years) in lifespan pilot data from the human connectome project (HCP). Initially, Pearson correlation-based connectivity networks were calculated and thresholded. Then, network characteristics were compared between the three age groups by calculating the global and local graph measures. In the resting state brain network, we observed decreasing global efficiency and increasing transitivity with age. Also, brain regions, including the amygdala, putamen, hippocampus, precuneus, inferior temporal gyrus, anterior cingulate gyrus, and middle temporal gyrus, were selected as the most affected brain areas with age through statistical tests and machine learning methods. Using feature selection methods, including Fisher score and Kruskal-Wallis, we were able to classify three age groups using SVM, KNN, and decision-tree classifier. The best classification accuracy is in the combination of Fisher score and decision tree classifier obtained, which was 82.2%. Thus, by examining the measures of functional connectivity using graph theory, we will be able to explore normal age-related changes in the human brain, which can be used as a tool to monitor health with age.
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Affiliation(s)
- Sepideh Baghernezhad
- Neuroscience & Neuroengineering Research Lab, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran
| | - Mohammad Reza Daliri
- Neuroscience & Neuroengineering Research Lab, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran.
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2
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Huang H, Chen Z, Fan B, Huang D, Qiu Z, Luo C, Zheng J. Abnormal global and local connectivity in patients with anti-N-methyl-D-aspartate receptor encephalitis: A resting-state functional MRI study. Brain Res 2024; 1837:148985. [PMID: 38714228 DOI: 10.1016/j.brainres.2024.148985] [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: 11/07/2023] [Revised: 03/27/2024] [Accepted: 05/04/2024] [Indexed: 05/09/2024]
Abstract
OBJECTIVE We decided to investigate the changes of global and local connectivity in anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis patients based on eigenvector centrality (EC) and regional homogeneity (ReHo). We sought new biomarkers to identify the patients based on multivariate pattern analysis (MVPA). METHODS Functional MRI (fMRI) was performed on all participants. EC, ReHo and MVPA were used to analyze the fMRI images. The correlation between the global or local connectivity and neuropsychology tests was detected. RESULTS The MoCA scores of the patients were lower than those of the healthy controls (HCs), while the HAMD24 and HAMA scores of the patients were higher than those of the HCs. Increased EC values in the right calcarine (CAL.R) and decreased EC values in the right putamen (PUT.R) distinguished these subjects with anti-NMDAR encephalitis from HCs. The higher ReHo values in the left postcentral gyrus (PoCG.L) were detected in the patients. The correlation analysis showed that the EC values in the PUT.R were negatively correlated with HAMD24 and HAMA scores, while the ReHo values in the PoCG.L were negatively correlated with MoCA scores. Better classification performance was reached in the EC-based classifier (AUC = 0.80), while weaker classification performance was achieved in the ReHo-based classifier (AUC = 0.74) or the classifier based on EC and ReHo (AUC = 0.74). The brain areas with large weights were located in the frontal lobe, parietal lobe, cerebellum and basal ganglia. CONCLUSION Our findings suggest that abnormal global and local connectivity may play an important part in the pathophysiological mechanism of neuropsychiatric symptoms in the anti-NMDAR encephalitis patients. The EC-based classifier may be better than the ReHo-based classifier in identifying anti-NMDAR encephalitis patients.
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Affiliation(s)
- Huachun Huang
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Zexiang Chen
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Binglin Fan
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Dongying Huang
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Zhuoyan Qiu
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Cuimi Luo
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jinou Zheng
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China.
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3
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Hartung TJ, von Schwanenflug N, Krohn S, Broeders TAA, Prüss H, Schoonheim MM, Finke C. Eigenvector centrality mapping reveals volatility of functional brain dynamics in anti-NMDA receptor encephalitis. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00209-X. [PMID: 39074556 DOI: 10.1016/j.bpsc.2024.07.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 07/18/2024] [Accepted: 07/21/2024] [Indexed: 07/31/2024]
Abstract
BACKGROUND Anti-N-methyl-D-aspartate receptor encephalitis (NMDARE) causes long-lasting cognitive deficits associated with altered functional connectivity. Eigenvector centrality (EC) mapping represents a powerful new method for data-driven voxel-wise and time-resolved estimation of network importance - beyond changes in classical 'static' functional connectivity. METHODS To assess changes in functional brain network organization, we applied EC mapping in 73 patients with NMDARE and 73 matched healthy controls. Areas with significant group differences were further investigated using (i) spatial clustering analyses, (ii) time series correlation to assess synchronicity between the hippocampus and cortical brain regions, and (iii) correlation with cognitive and clinical parameters. RESULTS Dynamic, time-resolved EC showed significantly higher variability in 13 cortical areas (p(FWE)<0.05) in patients with NMDARE compared to HC. Areas with dynamic EC group differences were spatially organized in centrality clusters resembling resting-state networks. Importantly, variability of dynamic EC in the frontotemporal cluster was associated with impaired verbal episodic memory in patients (r=-0.25, p=0.037). EC synchronicity between the hippocampus and the medial prefrontal cortex was reduced in patients compared to HC (p(FWE)<0.05, t(max)=3.76), and associated with verbal episodic memory in patients (r=0.28, p=0.019). Static EC analyses showed group differences in only one brain region (left intracalcarine cortex). CONCLUSIONS Widespread changes in network dynamics and reduced hippocampal-medial prefrontal synchronicity were associated with verbal episodic memory deficits and may thus represent a functional neural correlate of cognitive dysfunction in NMDARE. Importantly, dynamic EC detected substantially more network alterations than traditional static approaches, highlighting the potential of this method to explain long-term deficits in NMDARE.
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Affiliation(s)
- Tim J Hartung
- Charité - Universitätsmedizin Berlin, Department of Neurology and Experimental Neurology, Berlin, Germany
| | - Nina von Schwanenflug
- Charité - Universitätsmedizin Berlin, Department of Neurology and Experimental Neurology, Berlin, Germany; Humboldt-Universität zu Berlin, Berlin School of Mind and Brain, Berlin, Germany
| | - Stephan Krohn
- Charité - Universitätsmedizin Berlin, Department of Neurology and Experimental Neurology, Berlin, Germany
| | - Tommy A A Broeders
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Harald Prüss
- Charité - Universitätsmedizin Berlin, Department of Neurology and Experimental Neurology, Berlin, Germany; German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
| | - Menno M Schoonheim
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Carsten Finke
- Charité - Universitätsmedizin Berlin, Department of Neurology and Experimental Neurology, Berlin, Germany; Humboldt-Universität zu Berlin, Berlin School of Mind and Brain, Berlin, Germany.
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4
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Adami V, Ebadi Z, Nattagh-Najafi M. A dandelion structure of eigenvector preferential attachment networks. Sci Rep 2024; 14:16994. [PMID: 39043773 PMCID: PMC11266672 DOI: 10.1038/s41598-024-67896-9] [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: 05/08/2024] [Accepted: 07/17/2024] [Indexed: 07/25/2024] Open
Abstract
In this paper we introduce a new type of preferential attachment network, the growth of which is based on the eigenvector centrality. In this network, the agents attach most probably to the nodes with larger eigenvector centrality which represents that the agent has stronger connections. A new network is presented, namely a dandelion network, which shares some properties of star-like structure and also a hierarchical network. We show that this network, having hub-and-spoke topology is not generally scale free, and shows essential differences with respect to the Barabási-Albert preferential attachment model. Most importantly, there is a super hub agent in the system (identified by a pronounced peak in the spectrum), and the other agents are classified in terms of the distance to this super-hub. We explore a plenty of statistical centralities like the nodes degree, the betweenness and the eigenvector centrality, along with various measures of structure like the community and hierarchical structures, and the clustering coefficient. Global measures like the shortest path statistics and the self-similarity are also examined.
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Affiliation(s)
- Vadood Adami
- Department of Physics, University of Mohaghegh Ardabili, P.O. Box 179, Ardabil, Iran.
| | - Zahra Ebadi
- Department of Physics, University of Mohaghegh Ardabili, P.O. Box 179, Ardabil, Iran
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Saha P. Eigenvector Centrality Characterization on fMRI Data: Gender and Node Differences in Normal and ASD Subjects : Author name. J Autism Dev Disord 2024; 54:2757-2768. [PMID: 37142901 DOI: 10.1007/s10803-023-05922-x] [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: 02/05/2023] [Indexed: 05/06/2023]
Abstract
With the budding interests of structural and functional network characteristics as potential parameters for abnormal brains, an essential and thus simpler representation and evaluations have become necessary. Eigenvector centrality measure of functional magnetic resonance imaging (fMRI) offer region wise network representations through fMRI diagnostic maps. The article investigates the suitability of network node centrality values to discriminate ASD subject groups compared to typically developing controls following a boxplot formalism and a classification and regression tree model. Region wise differences between normal and ASD subjects primarily belong to the frontoparietal, limbic, ventral attention, default mode and visual networks. The reduced number of regions-of-interests (ROI) clearly suggests the benefit of automated supervised machine learning algorithm over the manual classification method.
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Affiliation(s)
- Papri Saha
- Department of Computer Science, Derozio Memorial College, Rajarhat Road, P.O. - R- Gopalpur, Kolkata, 700136, India.
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6
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Dagnino PC, Galadí JA, Càmara E, Deco G, Escrichs A. Inducing a meditative state by artificial perturbations: A mechanistic understanding of brain dynamics underlying meditation. Netw Neurosci 2024; 8:517-540. [PMID: 38952817 PMCID: PMC11168722 DOI: 10.1162/netn_a_00366] [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: 07/27/2023] [Accepted: 01/29/2024] [Indexed: 07/03/2024] Open
Abstract
Contemplative neuroscience has increasingly explored meditation using neuroimaging. However, the brain mechanisms underlying meditation remain elusive. Here, we implemented a mechanistic framework to explore the spatiotemporal dynamics of expert meditators during meditation and rest, and controls during rest. We first applied a model-free approach by defining a probabilistic metastable substate (PMS) space for each condition, consisting of different probabilities of occurrence from a repertoire of dynamic patterns. Moreover, we implemented a model-based approach by adjusting the PMS of each condition to a whole-brain model, which enabled us to explore in silico perturbations to transition from resting-state to meditation and vice versa. Consequently, we assessed the sensitivity of different brain areas regarding their perturbability and their mechanistic local-global effects. Overall, our work reveals distinct whole-brain dynamics in meditation compared to rest, and how transitions can be induced with localized artificial perturbations. It motivates future work regarding meditation as a practice in health and as a potential therapy for brain disorders.
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Affiliation(s)
- Paulina Clara Dagnino
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Javier A. Galadí
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | | | - Gustavo Deco
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avancats (ICREA), Barcelona, Spain
| | - Anira Escrichs
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
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Fall AB, Preti MG, Eshmawey M, Kagerer SM, Van De Ville D, Unschuld PG. Functional network centrality indicates interactions between APOE4 and age across the clinical spectrum of AD. Neuroimage Clin 2024; 43:103635. [PMID: 38941766 PMCID: PMC11260379 DOI: 10.1016/j.nicl.2024.103635] [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: 04/11/2024] [Revised: 06/07/2024] [Accepted: 06/18/2024] [Indexed: 06/30/2024]
Abstract
Advanced age is the most important risk factor for Alzheimer's disease (AD), and carrier-status of the Apolipoprotein E4 (APOE4) allele is the strongest known genetic risk factor. Many studies have consistently shown a link between APOE4 and synaptic dysfunction, possibly reflecting pathologically accelerated biological aging in persons at risk for AD. To test the hypothesis that distinct functional connectivity patterns characterize APOE4 carriers across the clinical spectrum of AD, we investigated 128 resting state functional Magnetic Resonance Imaging (fMRI) datasets from the Alzheimer's Disease Neuroimaging Initiative database (ADNI), representing all disease stages from cognitive normal to clinical dementia. Brain region centralities within functional networks, computed as eigenvector centrality, were tested for multivariate associations with chronological age, APOE4 carrier status and clinical stage (as well as their interactions) by partial least square analysis (PLSC). By PLSC analysis two distinct brain activity patterns could be identified, which reflected interactive effects of age, APOE4 and clinical disease stage. A first component including sensorimotor regions and parietal regions correlated with age and AD clinical stage (p < 0.001). A second component focused on medial-frontal regions and was specifically related to the interaction between age and APOE4 (p = 0.032). Our findings are consistent with earlier reports on altered network connectivity in APOE4 carriers. Results of our study highlight promise of graph-theory based network centrality to identify brain connectivity linked to genetic risk, clinical stage and age. Our data suggest the existence of brain network activity patterns that characterize APOE4 carriers across clinical stages of AD.
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Affiliation(s)
- Aïda B Fall
- Faculty of Medicine, University of Geneva (UNIGE), Geneva, Switzerland; Geriatric Psychiatry Service, University Hospitals of Geneva (HUG), Thônex, Switzerland; CIBM Center for Biomedical Imaging, Switzerland.
| | - Maria Giulia Preti
- Faculty of Medicine, University of Geneva (UNIGE), Geneva, Switzerland; CIBM Center for Biomedical Imaging, Switzerland; Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Mohamed Eshmawey
- Geriatric Psychiatry Service, University Hospitals of Geneva (HUG), Thônex, Switzerland
| | - Sonja M Kagerer
- Faculty of Medicine, University of Geneva (UNIGE), Geneva, Switzerland; Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland; Psychogeriatric Medicine, Psychiatric University Hospital Zurich (PUK), Zurich, Switzerland
| | - Dimitri Van De Ville
- Faculty of Medicine, University of Geneva (UNIGE), Geneva, Switzerland; CIBM Center for Biomedical Imaging, Switzerland; Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Paul G Unschuld
- Faculty of Medicine, University of Geneva (UNIGE), Geneva, Switzerland; Geriatric Psychiatry Service, University Hospitals of Geneva (HUG), Thônex, Switzerland
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Saha P, Sarkar D. Characterization and Classification of ADHD Subtypes: An Approach Based on the Nodal Distribution of Eigenvector Centrality and Classification Tree Model. Child Psychiatry Hum Dev 2024; 55:622-634. [PMID: 36100839 DOI: 10.1007/s10578-022-01432-6] [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] [Accepted: 08/29/2022] [Indexed: 11/30/2022]
Abstract
In recent times, the complex network theory is increasingly applied to characterize, classify, and diagnose a broad spectrum of neuropathological conditions, including attention deficit hyperactivity disorder (ADHD), Alzheimer's disease, bipolar disorder, and many others. Nevertheless, the diagnosis and associated subtype identification majorly rely on the baseline correlation matrix obtained from the functional MRI scan. Thus, the existing protocols are either full of personalized bias or computationally expensive as network complexity-based simple but deterministic protocols are yet to be developed and formalized. This article proposes a deterministic method to identify and differentiate the common ADHD subtypes, which is based on a single complexity measure, namely the eigenvector centrality. The node-wise centrality differences were explored using a classification tree model (p < 0.05) to diagnose the subtypes. Identification of marker nodes from default mode, visual, frontoparietal, limbic, and cerebellar networks strongly vouch for the involvement of multiple brain regions in ADHD neuropathology.
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Affiliation(s)
- Papri Saha
- Department of Computer Science, Derozio Memorial College, Kolkata, 700136, India.
| | - Debasish Sarkar
- Department of Chemical Engineering, University of Calcutta, Kolkata, 700009, India
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9
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Claaß LV, Hedrich A, Reinelt J, Sehm B, Villringer A, Schlagenhauf F, Kaminski J. Influence of noninvasive brain stimulation on connectivity and local activation: a combined tDCS and fMRI study. Eur Arch Psychiatry Clin Neurosci 2024; 274:827-835. [PMID: 37597023 PMCID: PMC11127864 DOI: 10.1007/s00406-023-01666-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 07/31/2023] [Indexed: 08/21/2023]
Abstract
The effect of transcranial direct current stimulation (tDCS) on neurobiological mechanisms underlying executive function in the human brain remains elusive. This study aims at examining the effect of anodal and cathodal tDCS over the left dorsolateral prefrontal cortex (DLPFC) in comparison with sham stimulation on resting-state connectivity as well as functional activation and working memory performance. We hypothesized perturbed fronto-parietal resting-state connectivity during stimulation and altered working memory performance combined with modified functional working memory-related activation. We applied tDCS with 1 mA for 21 min over the DLPFC inside an fMRI scanner. During stimulation, resting-state fMRI was acquired and task-dependent fMRI during working memory task performance was acquired directly after stimulation. N = 36 healthy subjects were studied in a within-subject design with three different experimental conditions (anodal, cathodal and sham) in a double-blind design. Seed-based functional connectivity analyses and dynamic causal modeling were conducted for the resting-state fMRI data. We found a significant stimulation by region interaction in the seed-based ROI-to-ROI resting-state connectivity, but no effect on effective connectivity. We also did not find an effect of stimulation on task-dependent signal alterations in working memory activation in our regions of interest and no effect on working memory performance parameters. We found effects on measures of seed-based resting-state connectivity, while measures of effective connectivity and task-based connectivity did not show any stimulation effect. We could not replicate previous findings of tDCS stimulation effects on behavioral outcomes. We critically discuss possible methodological limitations and implications for future studies.
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Affiliation(s)
- Luise Victoria Claaß
- Department of Neurology, Max-Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1, 04103, Leipzig, Germany
| | - Annika Hedrich
- Department of Neurology, Max-Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1, 04103, Leipzig, Germany
- Department of Psychiatry and Neurosciences CCM, Charité-Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany
| | - Janis Reinelt
- Department of Neurology, Max-Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1, 04103, Leipzig, Germany
| | - Bernhard Sehm
- Department of Neurology, Max-Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1, 04103, Leipzig, Germany
- Department of Neurology, Martin Luther University of Halle-Wittenberg, Ernst-Grube-Str. 40, 06120, Halle, Germany
| | - Arno Villringer
- Department of Neurology, Max-Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1, 04103, Leipzig, Germany
- Day Clinic for Cognitive Neurology, University Hospital at the University of Leipzig, Liebigstraße 16, 04103, Leipzig, Germany
- Berlin School of Mind and Brain, MindBrainBody Institute, Humboldt-Universität zu Berlin, Unter den Linden 6, 10999, Berlin, Germany
| | - Florian Schlagenhauf
- Department of Neurology, Max-Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1, 04103, Leipzig, Germany
- Department of Psychiatry and Neurosciences CCM, Charité-Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099, Berlin, Germany
| | - Jakob Kaminski
- Department of Neurology, Max-Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1, 04103, Leipzig, Germany.
- Department of Psychiatry and Neurosciences CCM, Charité-Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany.
- Bernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099, Berlin, Germany.
- Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, Campus Mitte, Charitéplatz 1, 10117, Berlin, Germany.
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Sanda P, Hlinka J, van den Berg M, Skoch A, Bazhenov M, Keliris GA, Krishnan GP. Cholinergic modulation supports dynamic switching of resting state networks through selective DMN suppression. PLoS Comput Biol 2024; 20:e1012099. [PMID: 38843298 PMCID: PMC11185486 DOI: 10.1371/journal.pcbi.1012099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 06/18/2024] [Accepted: 04/23/2024] [Indexed: 06/19/2024] Open
Abstract
Brain activity during the resting state is widely used to examine brain organization, cognition and alterations in disease states. While it is known that neuromodulation and the state of alertness impact resting-state activity, neural mechanisms behind such modulation of resting-state activity are unknown. In this work, we used a computational model to demonstrate that change in excitability and recurrent connections, due to cholinergic modulation, impacts resting-state activity. The results of such modulation in the model match closely with experimental work on direct cholinergic modulation of Default Mode Network (DMN) in rodents. We further extended our study to the human connectome derived from diffusion-weighted MRI. In human resting-state simulations, an increase in cholinergic input resulted in a brain-wide reduction of functional connectivity. Furthermore, selective cholinergic modulation of DMN closely captured experimentally observed transitions between the baseline resting state and states with suppressed DMN fluctuations associated with attention to external tasks. Our study thus provides insight into potential neural mechanisms for the effects of cholinergic neuromodulation on resting-state activity and its dynamics.
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Affiliation(s)
- Pavel Sanda
- Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic
| | - Jaroslav Hlinka
- Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic
- National Institute of Mental Health, Klecany, Czech Republic
| | - Monica van den Berg
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium
- μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Antonin Skoch
- National Institute of Mental Health, Klecany, Czech Republic
- MR Unit, Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Maxim Bazhenov
- Department of Medicine, University of California San Diego, La Jolla, California, United States of America
| | - Georgios A. Keliris
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium
- Institute of Computer Science, Foundation for Research and Technology - Hellas, Heraklion, Crete, Greece
| | - Giri P. Krishnan
- Department of Medicine, University of California San Diego, La Jolla, California, United States of America
- Georgia Institute of Technology, Atlanta, Georgia, United States of America
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Dagnino PC, Escrichs A, López-González A, Gosseries O, Annen J, Sanz Perl Y, Kringelbach ML, Laureys S, Deco G. Re-awakening the brain: Forcing transitions in disorders of consciousness by external in silico perturbation. PLoS Comput Biol 2024; 20:e1011350. [PMID: 38701063 PMCID: PMC11068192 DOI: 10.1371/journal.pcbi.1011350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 03/31/2024] [Indexed: 05/05/2024] Open
Abstract
A fundamental challenge in neuroscience is accurately defining brain states and predicting how and where to perturb the brain to force a transition. Here, we investigated resting-state fMRI data of patients suffering from disorders of consciousness (DoC) after coma (minimally conscious and unresponsive wakefulness states) and healthy controls. We applied model-free and model-based approaches to help elucidate the underlying brain mechanisms of patients with DoC. The model-free approach allowed us to characterize brain states in DoC and healthy controls as a probabilistic metastable substate (PMS) space. The PMS of each group was defined by a repertoire of unique patterns (i.e., metastable substates) with different probabilities of occurrence. In the model-based approach, we adjusted the PMS of each DoC group to a causal whole-brain model. This allowed us to explore optimal strategies for promoting transitions by applying off-line in silico probing. Furthermore, this approach enabled us to evaluate the impact of local perturbations in terms of their global effects and sensitivity to stimulation, which is a model-based biomarker providing a deeper understanding of the mechanisms underlying DoC. Our results show that transitions were obtained in a synchronous protocol, in which the somatomotor network, thalamus, precuneus and insula were the most sensitive areas to perturbation. This motivates further work to continue understanding brain function and treatments of disorders of consciousness.
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Affiliation(s)
- Paulina Clara Dagnino
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
| | - Anira Escrichs
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
| | - Ane López-González
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
| | - Olivia Gosseries
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
- Centre du Cerveau 2, University Hospital of Liège, Liège, Belgium
| | - Jitka Annen
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
- Centre du Cerveau 2, University Hospital of Liège, Liège, Belgium
| | - Yonatan Sanz Perl
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, France
| | - Morten L. Kringelbach
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, United Kingdom
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Steven Laureys
- Joint International Research Unit on Consciousness, CERVO Brain Research Centre, University of Laval, Québec, Québec, Canada
| | - Gustavo Deco
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain
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12
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Ji Y, Cai M, Zhou Y, Ma J, Zhang Y, Zhang Z, Zhao J, Wang Y, Jiang Y, Zhai Y, Xu J, Lei M, Xu Q, Liu H, Liu F. Exploring functional dysconnectivity in schizophrenia: alterations in eigenvector centrality mapping and insights into related genes from transcriptional profiles. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2024; 10:37. [PMID: 38491019 PMCID: PMC10943118 DOI: 10.1038/s41537-024-00457-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 03/05/2024] [Indexed: 03/18/2024]
Abstract
Schizophrenia is a mental health disorder characterized by functional dysconnectivity. Eigenvector centrality mapping (ECM) has been employed to investigate alterations in functional connectivity in schizophrenia, yet the results lack consistency, and the genetic mechanisms underlying these changes remain unclear. In this study, whole-brain voxel-wise ECM analyses were conducted on resting-state functional magnetic resonance imaging data. A cohort of 91 patients with schizophrenia and 91 matched healthy controls were included during the discovery stage. Additionally, in the replication stage, 153 individuals with schizophrenia and 182 healthy individuals participated. Subsequently, a comprehensive analysis was performed using an independent transcriptional database derived from six postmortem healthy adult brains to explore potential genetic factors influencing the observed functional dysconnectivity, and to investigate the roles of identified genes in neural processes and pathways. The results revealed significant and reliable alterations in the ECM across multiple brain regions in schizophrenia. Specifically, there was a significant decrease in ECM in the bilateral superior and middle temporal gyrus, and an increase in the bilateral thalamus in both the discovery and replication stages. Furthermore, transcriptional analysis revealed 420 genes whose expression patterns were related to changes in ECM, and these genes were enriched mainly in biological processes associated with synaptic signaling and transmission. Together, this study enhances our knowledge of the neural processes and pathways involved in schizophrenia, shedding light on the genetic factors that may be linked to functional dysconnectivity in this disorder.
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Affiliation(s)
- Yuan Ji
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Mengjing Cai
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yujing Zhou
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Juanwei Ma
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yijing Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Zhihui Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Jiaxuan Zhao
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Ying Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yurong Jiang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Ying Zhai
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Jinglei Xu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Minghuan Lei
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Qiang Xu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.
| | - Huaigui Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.
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13
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Zhang X, Zeng Q, Wang Y, Jin Y, Qiu T, Li K, Luo X, Wang S, Xu X, Liu X, Zhao S, Li Z, Hong L, Li J, Zhong S, Zhang T, Huang P, Zhang B, Zhang M, Chen Y. Alteration of functional connectivity network in population of objectively-defined subtle cognitive decline. Brain Commun 2024; 6:fcae033. [PMID: 38425749 PMCID: PMC10903975 DOI: 10.1093/braincomms/fcae033] [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: 09/06/2023] [Revised: 01/10/2024] [Accepted: 02/08/2024] [Indexed: 03/02/2024] Open
Abstract
The objectively-defined subtle cognitive decline individuals had higher progression rates of cognitive decline and pathological deposition than healthy elderly, indicating a higher risk of progressing to Alzheimer's disease. However, little is known about the brain functional alterations during this stage. Thus, we aimed to investigate the functional network patterns in objectively-defined subtle cognitive decline cohort. Forty-two cognitive normal, 29 objectively-defined subtle cognitive decline and 55 mild cognitive impairment subjects were included based on neuropsychological measures from the Alzheimer's disease Neuroimaging Initiative dataset. Thirty cognitive normal, 22 objectively-defined subtle cognitive declines and 48 mild cognitive impairment had longitudinal MRI data. The degree centrality and eigenvector centrality for each participant were calculated by using resting-state functional MRI. For cross-sectional data, analysis of covariance was performed to detect between-group differences in degree centrality and eigenvector centrality after controlling age, sex and education. For longitudinal data, repeated measurement analysis of covariance was used for comparing the alterations during follow-up period among three groups. In order to classify the clinical significance, we correlated degree centrality and eigenvector centrality values to Alzheimer's disease biomarkers and cognitive function. The results of analysis of covariance showed significant between-group differences in eigenvector centrality and degree centrality in left superior temporal gyrus and left precuneus, respectively. Across groups, the eigenvector centrality value of left superior temporal gyrus was positively related to recognition scores in auditory verbal learning test, whereas the degree centrality value of left precuneus was positively associated with mini-mental state examination total score. For longitudinal data, the results of repeated measurement analysis of covariance indicated objectively-defined subtle cognitive decline group had the highest declined rate of both eigenvector centrality and degree centrality values than other groups. Our study showed an increased brain functional connectivity in objectively-defined subtle cognitive decline individuals at both local and global level, which were associated with Alzheimer's disease pathology and neuropsychological assessment. Moreover, we also observed a faster declined rate of functional network matrix in objectively-defined subtle cognitive decline individuals during the follow-ups.
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Affiliation(s)
- Xinyi Zhang
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Qingze Zeng
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Yanbo Wang
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Yu Jin
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Tiantian Qiu
- Department of Radiology, Linyi People’s Hospital, 276003, Linyi, China
| | - Kaicheng Li
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Xiao Luo
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Shuyue Wang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Xiaopei Xu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Xiaocao Liu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Shuai Zhao
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Zheyu Li
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Luwei Hong
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Jixuan Li
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Siyan Zhong
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Tianyi Zhang
- Department of Neurology, The First Affiliated Hospital of Zhejiang University School of Medicine, 310003, Hangzhou, China
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Baorong Zhang
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Yanxing Chen
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
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14
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Zimmermann MLM, Breedt LC, Centeno EGZ, Reijneveld JC, Santos FAN, Stam CJ, van Lingen MR, Schoonheim MM, Hillebrand A, Douw L. The relationship between pathological brain activity and functional network connectivity in glioma patients. J Neurooncol 2024; 166:523-533. [PMID: 38308803 PMCID: PMC10876827 DOI: 10.1007/s11060-024-04577-7] [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: 10/03/2023] [Accepted: 01/17/2024] [Indexed: 02/05/2024]
Abstract
PURPOSE Glioma is associated with pathologically high (peri)tumoral brain activity, which relates to faster progression. Functional connectivity is disturbed locally and throughout the entire brain, associating with symptomatology. We, therefore, investigated how local activity and network measures relate to better understand how the intricate relationship between the tumor and the rest of the brain may impact disease and symptom progression. METHODS We obtained magnetoencephalography in 84 de novo glioma patients and 61 matched healthy controls. The offset of the power spectrum, a proxy of neuronal activity, was calculated for 210 cortical regions. We calculated patients' regional deviations in delta, theta and lower alpha network connectivity as compared to controls, using two network measures: clustering coefficient (local connectivity) and eigenvector centrality (integrative connectivity). We then tested group differences in activity and connectivity between (peri)tumoral, contralateral homologue regions, and the rest of the brain. We also correlated regional offset to connectivity. RESULTS As expected, patients' (peri)tumoral activity was pathologically high, and patients showed higher clustering and lower centrality than controls. At the group-level, regionally high activity related to high clustering in controls and patients alike. However, within-patient analyses revealed negative associations between regional deviations in brain activity and clustering, such that pathologically high activity coincided with low network clustering, while regions with 'normal' activity levels showed high network clustering. CONCLUSION Our results indicate that pathological activity and connectivity co-localize in a complex manner in glioma. This insight is relevant to our understanding of disease progression and cognitive symptomatology.
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Affiliation(s)
- Mona L M Zimmermann
- Anatomy and Neurosciences, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
- Amsterdam Neuroscience, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
- Cancer Center Amsterdam, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| | - Lucas C Breedt
- Anatomy and Neurosciences, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Eduarda G Z Centeno
- Anatomy and Neurosciences, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Univ. Bordeaux, CNRS, IMN, UMR 5293, Bordeaux, France
| | - Jaap C Reijneveld
- Department of Neurology, Stichting Epilepsie Instellingen Nederland, Heemstede, The Netherlands
| | - Fernando A N Santos
- Anatomy and Neurosciences, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Dutch Institute for Emergent Phenomena (DIEP), Institute for Advanced Studies, University of Amsterdam, Amsterdam, The Netherlands
| | - Cornelis J Stam
- Clinical Neurophysiology and MEG Center, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Marike R van Lingen
- Anatomy and Neurosciences, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Menno M Schoonheim
- Anatomy and Neurosciences, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Amsterdam Neuroscience, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Linda Douw
- Anatomy and Neurosciences, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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15
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Yang YC, Wei XY, Zhang YY, Xu CY, Cheng JM, Gong ZG, Chen H, Huang YW, Yuan J, Xu HH, Wang H, Zhan SH, Tan WL. Modulation of temporal and occipital cortex by acupuncture in non-menstrual MWoA patients: a rest BOLD fMRI study. BMC Complement Med Ther 2024; 24:43. [PMID: 38245739 PMCID: PMC10799457 DOI: 10.1186/s12906-024-04349-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] [Received: 05/22/2023] [Accepted: 01/12/2024] [Indexed: 01/22/2024] Open
Abstract
OBJECTIVE To investigate the changes in amplitude of low-frequency fluctuation (ALFF) and degree centrality (DC) values before and after acupuncture in young women with non-menstrual migraine without aura (MWoA) through rest blood-oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI). METHODS Patients with non-menstrual MWoA (Group 1, n = 50) and healthy controls (Group 2, n = 50) were recruited. fMRI was performed in Group 1 at 2 time points: before acupuncture (time point 1, TP1); and after the end of all acupuncture sessions (time point 2, TP2), and performed in Group 2 as a one-time scan. Patients in Group 1 were assessed with the Migraine Disability Assessment Questionnaire (MIDAS) and the Short-Form McGill Pain Questionnaire (SF-MPQ) at TP1 and TP2 after fMRI was performed. The ALFF and DC values were compared within Group 1 at two time points and between Group 1 and Group2. The correlation between ALFF and DC values with the statistical differences and the clinical scales scores were analyzed. RESULTS Brain activities increased in the left fusiform gyrus and right angular gyrus, left middle occipital gyrus, and bilateral prefrontal cortex and decreased in left inferior parietal lobule in Group 1, which had different ALFF values compared with Group 2 at TP1. The bilateral fusiform gyrus, bilateral inferior temporal gyrus and right middle temporal gyrus increased and right angular gyrus, right superior marginal gyrus, right inferior parietal lobule, right middle occipital gyrus, right superior frontal gyrus, right middle frontal gyrus, right anterior central gyrus, and right supplementary motor area decreased in activity in Group 1 had different DC values compared with Group 2 at TP1. ALFF and DC values of right inferior temporal gyrus, right fusiform gyrus and right middle temporal gyrus were decreased in Group1 at TP1 compared with TP2. ALFF values in the left middle occipital area were positively correlated with the pain degree at TP1 in Group1 (correlation coefficient r, r = 0.827, r = 0.343; P < 0.01, P = 0.015). The DC values of the right inferior temporal area were positively correlated with the pain degree at TP1 in Group 1 (r = 0.371; P = 0.008). CONCLUSION Spontaneous brain activity and network changes in young women with non-menstrual MwoA were altered by acupuncture. The right temporal area may be an important target for acupuncture modulated brain function in young women with non-menstrual MwoA.
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Affiliation(s)
- Yu-Chan Yang
- Department of Radiology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Xiang-Yu Wei
- Institute of Acupuncture and Anesthesia, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Ying-Ying Zhang
- Department of Radiology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Chun-Yang Xu
- Department of Radiology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Jian-Ming Cheng
- Department of Radiology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Zhi-Gang Gong
- Department of Radiology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Hui Chen
- Department of Radiology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Yan-Wen Huang
- Department of Radiology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Jie Yuan
- Department of Radiology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Hui-Hui Xu
- Department of Radiology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Hui Wang
- Department of Radiology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Song-Hua Zhan
- Department of Radiology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
| | - Wen-Li Tan
- Department of Radiology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
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16
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Wang H, Zhang T, Zu M, Fan S, Kai Y, Zhang J, Ji Y, Pang X, Tian Y. Electroconvulsive therapy enhances degree centrality in the orbitofrontal cortex in depressive rumination. Psychiatry Res Neuroimaging 2024; 337:111765. [PMID: 38104485 DOI: 10.1016/j.pscychresns.2023.111765] [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: 09/30/2023] [Revised: 11/13/2023] [Accepted: 12/02/2023] [Indexed: 12/19/2023]
Abstract
Depressive rumination has been implicated in the onset, duration, and treatment response of refractory depression. Electroconvulsive therapy (ECT) is remarkably effective in treatment of refractory depression by modulating the functional coordination between brain hubs. However, the mechanisms by which ECT regulates depressive rumination remain unsolved. We investigated degree centrality (DC) in 32 pre- and post-ECT depression patients as well as 38 matched healthy controls. An identified brain region was defined as the seed to calculate functional connectivity (FC) in whole brains. Rumination was measured by the Ruminative Response Scale (RRS) and its relationships with identified DC and FC alterations were examined. We found a significant negative correlation between DC of the right orbitofrontal cortex (rOFC) before ECT and brooding level before and after treatment. Moreover, rOFC DC increased after ECT. DC of the left superior temporal gyrus (lSTG) was positively correlated with reflective level before intervention, while lSTG DC decreased after ECT. Patients showed elevated FC in the rOFC with default mode network. No significant association was found between decreased RRS scores and changes in DC and FC. Our findings suggest that functional changes in rOFC and lSTG may be associated with the beneficial effects of ECT on depressive rumination.
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Affiliation(s)
- Hongping Wang
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Ting Zhang
- Department of Psychiatry, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Meidan Zu
- Department of Neurology, Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Siyu Fan
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Yiao Kai
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Jiahua Zhang
- School of Mental Health and Psychological Science, Anhui Medical University, Hefei, Anhui, China
| | - Yang Ji
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Xiaonan Pang
- Department of Neurology, Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
| | - Yanghua Tian
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China; Department of Neurology, Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
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17
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Chappel-Farley MG, Adams JN, Betzel RF, Janecek JC, Sattari NS, Berisha DE, Meza NJ, Niknazar H, Kim S, Dave A, Chen IY, Lui KK, Neikrug AB, Benca RM, Yassa MA, Mander BA. Medial temporal lobe functional network architecture supports sleep-related emotional memory processing in older adults. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.27.564260. [PMID: 37961192 PMCID: PMC10634911 DOI: 10.1101/2023.10.27.564260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Memory consolidation occurs via reactivation of a hippocampal index during non-rapid eye movement slow-wave sleep (NREM SWS) which binds attributes of an experience existing within cortical modules. For memories containing emotional content, hippocampal-amygdala dynamics facilitate consolidation over a sleep bout. This study tested if modularity and centrality-graph theoretical measures that index the level of segregation/integration in a system and the relative import of its nodes-map onto central tenets of memory consolidation theory and sleep-related processing. Findings indicate that greater network integration is tied to overnight emotional memory retention via NREM SWS expression. Greater hippocampal and amygdala influence over network organization supports emotional memory retention, and hippocampal or amygdala control over information flow are differentially associated with distinct stages of memory processing. These centrality measures are also tied to the local expression and coupling of key sleep oscillations tied to sleep-dependent memory consolidation. These findings suggest that measures of intrinsic network connectivity may predict the capacity of brain functional networks to acquire, consolidate, and retrieve emotional memories.
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Affiliation(s)
- Miranda G. Chappel-Farley
- Department of Neurobiology and Behavior, University of California Irvine, Irvine CA, 92697, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine CA, 92697, USA
| | - Jenna N. Adams
- Department of Neurobiology and Behavior, University of California Irvine, Irvine CA, 92697, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine CA, 92697, USA
| | - Richard F. Betzel
- Department of Psychological and Brain Sciences, University of Indiana Bloomington, Bloomington IN, 47405
| | - John C. Janecek
- Department of Neurobiology and Behavior, University of California Irvine, Irvine CA, 92697, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine CA, 92697, USA
| | - Negin S. Sattari
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine CA, 92697, USA
| | - Destiny E. Berisha
- Department of Neurobiology and Behavior, University of California Irvine, Irvine CA, 92697, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine CA, 92697, USA
| | - Novelle J. Meza
- Department of Neurobiology and Behavior, University of California Irvine, Irvine CA, 92697, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine CA, 92697, USA
| | - Hamid Niknazar
- Department of Cognitive Sciences, University of California Irvine, Irvine CA, 92697, USA
| | - Soyun Kim
- Department of Neurobiology and Behavior, University of California Irvine, Irvine CA, 92697, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine CA, 92697, USA
| | - Abhishek Dave
- Department of Cognitive Sciences, University of California Irvine, Irvine CA, 92697, USA
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine CA, 92697, USA
| | - Ivy Y. Chen
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine CA, 92697, USA
| | - Kitty K. Lui
- San Diego State University/University of California San Diego, Joint Doctoral Program in Clinical Psychology, San Diego, CA, 92093, USA
| | - Ariel B. Neikrug
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine CA, 92697, USA
| | - Ruth M. Benca
- Department of Neurobiology and Behavior, University of California Irvine, Irvine CA, 92697, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine CA, 92697, USA
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine CA, 92697, USA
- Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Department of Psychiatry, University of Wisconsin-Madison, Madison, 53706, WI, USA
- Department of Psychiatry and Behavioral Medicine, Wake Forest University, Winston-Salem, NC, 27109, USA
- Institute for Memory Impairments and Neurological Disorders, University of California Irvine, Irvine CA, 92697, USA
| | - Michael A. Yassa
- Department of Neurobiology and Behavior, University of California Irvine, Irvine CA, 92697, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine CA, 92697, USA
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine CA, 92697, USA
- Institute for Memory Impairments and Neurological Disorders, University of California Irvine, Irvine CA, 92697, USA
- Department of Neurology, University of California Irvine, Irvine CA, 92697, USA
| | - Bryce A. Mander
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine CA, 92697, USA
- Department of Cognitive Sciences, University of California Irvine, Irvine CA, 92697, USA
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine CA, 92697, USA
- Institute for Memory Impairments and Neurological Disorders, University of California Irvine, Irvine CA, 92697, USA
- Department of Pathology and Laboratory Medicine, University of California Irvine, Irvine CA, 92697, USA
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18
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Lin CS, Chang WJ, Fuh JL. Lower masticatory function relates to cognitive health and intrinsic brain network in older adults. Oral Dis 2023; 29:2895-2906. [PMID: 36577658 DOI: 10.1111/odi.14487] [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: 09/19/2022] [Revised: 11/23/2022] [Accepted: 12/20/2022] [Indexed: 12/30/2022]
Abstract
OBJECTIVES Mastication is associated with brain activation at the primary somatosensory cortex (S1) and the primary motor cortex (M1). Masticatory functions differ between patients with cognitive impairment (CI) and cognitively healthy older adults (non-CI). The association between cognitive health, brain network of functional connectivity, and mastication has remained unknown. The study investigated the association between masticatory performance (MP) and the topological feature of the functional network at the M1 and S1 in the CI and non-CI groups. SUBJECTS AND METHODS Forty-nine non-CI and 15 CI subjects received resting-state (rs) fMRI and assessment of MP. The topological feature of the M1 and S1 was quantified by eigenvector centrality (EC), an index that reflects a brain region as a functional "hub" of brain network. RESULTS In the non-CI group, MP was significantly correlated with EC of the left M1 and the right M1. The correlation was not statistically significant in the CI group. Cognitive status (CI or non-CI) and EC of the left M1 and the right M1, respectively, were statistically significant predictors to individual MP. CONCLUSION Cognitive status and the topological feature of the M1 in the intrinsic functional network may contribute to the individual difference in masticatory function.
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Affiliation(s)
- Chia-Shu Lin
- Department of Dentistry, College of Dentistry, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Wen-Ju Chang
- Department of Dentistry, College of Dentistry, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Jong-Ling Fuh
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
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19
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Huck J, Jäger A, Schneider U, Grahl S, Fan AP, Tardif C, Villringer A, Bazin P, Steele CJ, Gauthier CJ. Modeling venous bias in resting state functional MRI metrics. Hum Brain Mapp 2023; 44:4938-4955. [PMID: 37498014 PMCID: PMC10472917 DOI: 10.1002/hbm.26431] [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: 06/30/2022] [Revised: 04/12/2023] [Accepted: 05/11/2023] [Indexed: 07/28/2023] Open
Abstract
Resting-state (rs) functional magnetic resonance imaging (fMRI) is used to detect low-frequency fluctuations in the blood oxygen-level dependent (BOLD) signal across brain regions. Correlations between temporal BOLD signal fluctuations are commonly used to infer functional connectivity. However, because BOLD is based on the dilution of deoxyhemoglobin, it is sensitive to veins of all sizes, and its amplitude is biased by draining veins. These biases affect local BOLD signal location and amplitude, and may also influence BOLD-derived connectivity measures, but the magnitude of this venous bias and its relation to vein size and proximity is unknown. Here, veins were identified using high-resolution quantitative susceptibility maps and utilized in a biophysical model to investigate systematic venous biases on common local rsfMRI-derived measures. Specifically, we studied the impact of vein diameter and distance to veins on the amplitude of low-frequency fluctuations (ALFF), fractional ALFF (fALFF), Hurst exponent (HE), regional homogeneity (ReHo), and eigenvector centrality values in the grey matter. Values were higher across all distances in smaller veins, and decreased with increasing vein diameter. Additionally, rsfMRI values associated with larger veins decrease with increasing distance from the veins. ALFF and ReHo were the most biased by veins, while HE and fALFF exhibited the smallest bias. Across all metrics, the amplitude of the bias was limited in voxel-wise data, confirming that venous structure is not the dominant source of contrast in these rsfMRI metrics. Finally, the models presented can be used to correct this venous bias in rsfMRI metrics.
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Affiliation(s)
- Julia Huck
- Department of PhysicsConcordia UniversityMontrealQuebecCanada
- PERFORM CenterMontrealQuebecCanada
| | - Anna‐Thekla Jäger
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Center for Stroke Research Berlin (CSB)Charité ‐ Universitätsmedizin BerlinBerlinGermany
| | - Uta Schneider
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Sophia Grahl
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Audrey P. Fan
- Department of Biomedical EngineeringUniversity of CaliforniaDavisCaliforniaUSA
- Department of NeurologyUniversity of CaliforniaDavisCaliforniaUSA
| | - Christine Tardif
- Faculty of Medicine and Health Sciences, Department of Biomedical EngineeringMcGill UniversityMontrealQuebecCanada
- McConnell Brain Imaging CentreMontreal Neurological InstituteMontrealQuebecCanada
| | - Arno Villringer
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Center for Stroke Research Berlin (CSB)Charité ‐ Universitätsmedizin BerlinBerlinGermany
- Clinic for Cognitive NeurologyUniversity of LeipzigLeipzigGermany
- IFB Adiposity DiseasesLeipzig University Medical CentreLeipzigGermany
| | - Pierre‐Louis Bazin
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Faculty of Social and Behavioural SciencesUniversity of AmsterdamAmsterdamThe Netherlands
| | - Christopher J. Steele
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Department of PsychologyConcordia UniversityMontrealQuebecCanada
| | - Claudine J. Gauthier
- Department of PhysicsConcordia UniversityMontrealQuebecCanada
- PERFORM CenterMontrealQuebecCanada
- Montreal Heart InstituteMontrealQuebecCanada
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20
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Bae CH, Kim HY, Seo JE, Lee H, Kim S. In Silico Analysis of Pyeongwi-San Involved in Inflammatory Bowel Disease Treatment Using Network Pharmacology, Molecular Docking, and Molecular Dynamics. Biomolecules 2023; 13:1322. [PMID: 37759722 PMCID: PMC10526905 DOI: 10.3390/biom13091322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 08/25/2023] [Accepted: 08/25/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGOUND Pyeongwi-san (PWS) is a widely used formula for treating digestive disorders in Korea and China. Inflammatory bowel disease (IBD) is characterized by progressive inflammation of the gastrointestinal tract. Emerging evidence supports the protective effect of PWS against IBD, but specific mechanisms are still elusive. METHODS Active compounds of PWS were screened from the medicinal materials and chemical compounds in Northeast Asian traditional medicine (TM-MC) in the consideration of drug-likeness and oral bioavailability. Target candidates of active compounds were predicted using the ChEMBL database. IBD-related targets were obtained from the GeneCards and DisGeNET databases. The network of composition-targets-disease was constructed. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were analyzed. Molecular docking was used to simulate the binding affinity of active compounds on target proteins and molecular dynamics was used to validate the molecular docking result. RESULTS A total of 26 core target proteins of PWS were related to IBD. Enrichment analysis suggested that PWS is highly associated with tumor necrosis factor signaling pathway, apoptosis, and the collapse of tight junctions. Moreover, molecular docking and molecular dynamics simulation proposed β-eudesmol and (3R,6R,7S)-1,10-bisaboladien-3-ol to ameliorate IBD through the binding to TNF and MMP9, respectively. CONCLUSION Present in silico analysis revealed potential pathways and insight of PWS to regulate IBD. These results imply that the therapeutic effect of PWS might be achieved via an inhibitory effect.
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Affiliation(s)
- Chang-Hwan Bae
- Department of Korean Medical Science, School of Korean Medicine, Pusan National University, Yangsan 50612, Republic of Korea; (C.-H.B.); (J.E.S.); (H.L.)
| | - Hee-Young Kim
- Korean Medicine Research Center for Healthy Aging, Pusan National University, Yangsan 50612, Republic of Korea;
| | - Ji Eun Seo
- Department of Korean Medical Science, School of Korean Medicine, Pusan National University, Yangsan 50612, Republic of Korea; (C.-H.B.); (J.E.S.); (H.L.)
| | - Hanul Lee
- Department of Korean Medical Science, School of Korean Medicine, Pusan National University, Yangsan 50612, Republic of Korea; (C.-H.B.); (J.E.S.); (H.L.)
| | - Seungtae Kim
- Department of Korean Medical Science, School of Korean Medicine, Pusan National University, Yangsan 50612, Republic of Korea; (C.-H.B.); (J.E.S.); (H.L.)
- Korean Medicine Research Center for Healthy Aging, Pusan National University, Yangsan 50612, Republic of Korea;
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21
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Inoue R, Nishimune H. Neuronal Plasticity and Age-Related Functional Decline in the Motor Cortex. Cells 2023; 12:2142. [PMID: 37681874 PMCID: PMC10487126 DOI: 10.3390/cells12172142] [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: 07/16/2023] [Revised: 08/16/2023] [Accepted: 08/23/2023] [Indexed: 09/09/2023] Open
Abstract
Physiological aging causes a decline of motor function due to impairment of motor cortex function, losses of motor neurons and neuromuscular junctions, sarcopenia, and frailty. There is increasing evidence suggesting that the changes in motor function start earlier in the middle-aged stage. The mechanism underlining the middle-aged decline in motor function seems to relate to the central nervous system rather than the peripheral neuromuscular system. The motor cortex is one of the responsible central nervous systems for coordinating and learning motor functions. The neuronal circuits in the motor cortex show plasticity in response to motor learning, including LTP. This motor cortex plasticity seems important for the intervention method mechanisms that revert the age-related decline of motor function. This review will focus on recent findings on the role of plasticity in the motor cortex for motor function and age-related changes. The review will also introduce our recent identification of an age-related decline of neuronal activity in the primary motor cortex of middle-aged mice using electrophysiological recordings of brain slices.
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Affiliation(s)
- Ritsuko Inoue
- Laboratory of Neurobiology of Aging, Tokyo Metropolitan Institute for Geriatrics and Gerontology, 35-2 Sakaecho, Itabashi-ku, Tokyo 173-0015, Japan;
| | - Hiroshi Nishimune
- Laboratory of Neurobiology of Aging, Tokyo Metropolitan Institute for Geriatrics and Gerontology, 35-2 Sakaecho, Itabashi-ku, Tokyo 173-0015, Japan;
- Department of Applied Biological Science, Tokyo University of Agriculture and Technology, 3-8-1 Harumicho, Fuchu-shi, Tokyo 183-8538, Japan
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22
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Spetsieris PG, Eidelberg D. Parkinson's disease progression: Increasing expression of an invariant common core subnetwork. Neuroimage Clin 2023; 39:103488. [PMID: 37660556 PMCID: PMC10491857 DOI: 10.1016/j.nicl.2023.103488] [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: 06/06/2022] [Revised: 07/31/2023] [Accepted: 08/02/2023] [Indexed: 09/05/2023]
Abstract
Notable success has been achieved in the study of neurodegenerative conditions using reduction techniques such as principal component analysis (PCA) and sparse inverse covariance estimation (SICE) in positron emission tomography (PET) data despite their widely differing approach. In a recent study of SICE applied to metabolic scans from Parkinson's disease (PD) patients, we showed that by using PCA to prespecify disease-related partition layers, we were able to optimize maps of functional metabolic connectivity within the relevant networks. Here, we show the potential of SICE, enhanced by disease-specific subnetwork partitions, to identify key regional hubs and their connections, and track their associations in PD patients with increasing disease duration. This approach enabled the identification of a core zone that included elements of the striatum, pons, cerebellar vermis, and parietal cortex and provided a deeper understanding of progressive changes in their connectivity. This subnetwork constituted a robust invariant disease feature that was unrelated to phenotype. Mean expression levels for this subnetwork increased steadily in a group of 70 PD patients spanning a range of symptom durations between 1 and 21 years. The findings were confirmed in a validation sample of 69 patients with up to 32 years of symptoms. The common core elements represent possible targets for disease modification, while their connections to external regions may be better suited for symptomatic treatment.
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Affiliation(s)
- Phoebe G Spetsieris
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, United States
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, United States; Molecular Medicine and Neurology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11549, United States.
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23
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van Lingen MR, Breedt LC, Geurts JJG, Hillebrand A, Klein M, Kouwenhoven MCM, Kulik SD, Reijneveld JC, Stam CJ, De Witt Hamer PC, Zimmermann MLM, Santos FAN, Douw L. The longitudinal relation between executive functioning and multilayer network topology in glioma patients. Brain Imaging Behav 2023; 17:425-435. [PMID: 37067658 PMCID: PMC10435610 DOI: 10.1007/s11682-023-00770-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] [Accepted: 03/28/2023] [Indexed: 04/18/2023]
Abstract
Many patients with glioma, primary brain tumors, suffer from poorly understood executive functioning deficits before and/or after tumor resection. We aimed to test whether frontoparietal network centrality of multilayer networks, allowing for integration across multiple frequencies, relates to and predicts executive functioning in glioma. Patients with glioma (n = 37) underwent resting-state magnetoencephalography and neuropsychological tests assessing word fluency, inhibition, and set shifting before (T1) and one year after tumor resection (T2). We constructed binary multilayer networks comprising six layers, with each layer representing frequency-specific functional connectivity between source-localized time series of 78 cortical regions. Average frontoparietal network multilayer eigenvector centrality, a measure for network integration, was calculated at both time points. Regression analyses were used to investigate associations with executive functioning. At T1, lower multilayer integration (p = 0.017) and epilepsy (p = 0.006) associated with poorer set shifting (adj. R2 = 0.269). Decreasing multilayer integration (p = 0.022) and not undergoing chemotherapy at T2 (p = 0.004) related to deteriorating set shifting over time (adj. R2 = 0.283). No significant associations were found for word fluency or inhibition, nor did T1 multilayer integration predict changes in executive functioning. As expected, our results establish multilayer integration of the frontoparietal network as a cross-sectional and longitudinal correlate of executive functioning in glioma patients. However, multilayer integration did not predict postoperative changes in executive functioning, which together with the fact that this correlate is also found in health and other diseases, limits its specific clinical relevance in glioma.
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Affiliation(s)
- Marike R van Lingen
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, de Boelelaan 1108, Amsterdam, the Netherlands.
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands.
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands.
- Cancer Center Amsterdam, Amsterdam, the Netherlands.
| | - Lucas C Breedt
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, de Boelelaan 1108, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands
| | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, de Boelelaan 1108, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands
| | - Arjan Hillebrand
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Martin Klein
- Department of Medical Psychology, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Mathilde C M Kouwenhoven
- Department of Neurology, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Shanna D Kulik
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, de Boelelaan 1108, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands
| | - Jaap C Reijneveld
- Department of Neurology, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, the Netherlands
- Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Cornelis J Stam
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Philip C De Witt Hamer
- Department of Neurosurgery, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Mona L M Zimmermann
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, de Boelelaan 1108, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Fernando A N Santos
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, de Boelelaan 1108, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands
- Institute of Advanced Studies, University of Amsterdam, Amsterdam, the Netherlands
| | - Linda Douw
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, de Boelelaan 1108, Amsterdam, the Netherlands.
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands.
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands.
- Cancer Center Amsterdam, Amsterdam, the Netherlands.
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24
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Invernizzi A, Renzetti S, van Thriel C, Rechtman E, Patrono A, Ambrosi C, Mascaro L, Cagna G, Gasparotti R, Reichenberg A, Tang CY, Lucchini RG, Wright RO, Placidi D, Horton MK. Covid-19 related cognitive, structural and functional brain changes among Italian adolescents and young adults: a multimodal longitudinal case-control study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.07.19.23292909. [PMID: 37503251 PMCID: PMC10371098 DOI: 10.1101/2023.07.19.23292909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Coronavirus disease 2019 (COVID-19) has been associated with brain functional, structural, and cognitive changes that persist months after infection. Most studies of the neurologic outcomes related to COVID-19 focus on severe infection and aging populations. Here, we investigated the neural activities underlying COVID-19 related outcomes in a case-control study of mildly infected youth enrolled in a longitudinal study in Lombardy, Italy, a global hotspot of COVID-19. All participants (13 cases, 27 controls, mean age 24 years) completed resting state functional (fMRI), structural MRI, cognitive assessments (CANTAB spatial working memory) at baseline (pre-COVID) and follow-up (post-COVID). Using graph theory eigenvector centrality (EC) and data-driven statistical methods, we examined differences in ECdelta (i.e., the difference in EC values pre- and post-COVID-19) and volumetricdelta (i.e., the difference in cortical volume of cortical and subcortical areas pre- and post-COVID) between COVID-19 cases and controls. We found that ECdeltasignificantly between COVID-19 and healthy participants in five brain regions; right intracalcarine cortex, right lingual gyrus, left hippocampus, left amygdala, left frontal orbital cortex. The left hippocampus showed a significant decrease in volumetricdelta between groups (p=0.041). The reduced ECdelta in the right amygdala associated with COVID-19 status mediated the association between COVID-19 and disrupted spatial working memory. Our results show persistent structural, functional and cognitive brain changes in key brain areas associated with olfaction and cognition. These results may guide treatment efforts to assess the longevity, reversibility and impact of the observed brain and cognitive changes following COVID-19.
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Affiliation(s)
- Azzurra Invernizzi
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stefano Renzetti
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
| | - Christoph van Thriel
- Leibniz Research Centre for Working Environment and Human Factors, Dortmund, Germany
| | - Elza Rechtman
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alessandra Patrono
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Claudia Ambrosi
- Department of Neuroscience, Neuroradiology Unit, ASST Cremona
| | | | - Giuseppa Cagna
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
| | - Roberto Gasparotti
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
| | - Abraham Reichenberg
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Cheuk Y Tang
- Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Roberto G Lucchini
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
- Department of Environmental Health Sciences, Robert Stempel College of Public Health and Social Work, Florida International University, Miami, FL, United States
| | - Robert O Wright
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Donatella Placidi
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
| | - Megan K Horton
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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25
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Invernizzi A, Rechtman E, Curtin P, Papazaharias DM, Jalees M, Pellecchia AC, Santiago-Michels S, Bromet EJ, Lucchini RG, Luft BJ, Clouston SA, Tang CY, Horton MK. Functional changes in neural mechanisms underlying post-traumatic stress disorder in World Trade Center responders. Transl Psychiatry 2023; 13:239. [PMID: 37429850 DOI: 10.1038/s41398-023-02526-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 06/07/2023] [Accepted: 06/13/2023] [Indexed: 07/12/2023] Open
Abstract
World Trade Center (WTC) responders exposed to traumatic and environmental stressors during rescue and recovery efforts have a high prevalence of chronic WTC-related post-traumatic stress disorder (WTC-PTSD). We investigated neural mechanisms underlying WTC-PTSD by applying eigenvector centrality (EC) metrics and data-driven methods on resting state functional magnetic resonance (fMRI). We identified how EC differences relate to WTC-exposure and behavioral symptoms. We found that connectivity differentiated significantly between WTC-PTSD and non-PTSD responders in nine brain regions, as these differences allowed an effective discrimination of PTSD and non-PTSD responders based solely on analysis of resting state data. Further, we found that WTC exposure duration (months on site) moderates the association between PTSD and EC values in two of the nine brain regions; the right anterior parahippocampal gyrus and the left amygdala (p = 0.010; p = 0.005, respectively, adjusted for multiple comparisons). Within WTC-PTSD, a dimensional measure of symptom severity was positively associated with EC values in the right anterior parahippocampal gyrus and brainstem. Functional neuroimaging can provide effective tools to identify neural correlates of diagnostic and dimensional indicators of PTSD.
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Affiliation(s)
- Azzurra Invernizzi
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Elza Rechtman
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Paul Curtin
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Demetrios M Papazaharias
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Maryam Jalees
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alison C Pellecchia
- World Trade Center Health and Wellness Program, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Stephanie Santiago-Michels
- World Trade Center Health and Wellness Program, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Evelyn J Bromet
- Department of Psychiatry, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Roberto G Lucchini
- Department of Environmental Health Sciences, Robert Stempel School of Public Health, Florida International University, Miami, FL, USA
- Department of Medical Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
| | - Benjamin J Luft
- World Trade Center Health and Wellness Program, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
- Department of Medicine, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Sean A Clouston
- Program in Public Health and Department of Family, Population, and Preventive Medicine, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Cheuk Y Tang
- Department of Radiology and Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Megan K Horton
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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26
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Schlögl H, Villringer A, Miehle K, Fasshauer M, Stumvoll M, Mueller K. Metreleptin Robustly Increases Resting-state Brain Connectivity in Treatment-naïve Female Patients With Lipodystrophy. J Endocr Soc 2023; 7:bvad072. [PMID: 37404242 PMCID: PMC10315645 DOI: 10.1210/jendso/bvad072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Indexed: 07/06/2023] Open
Abstract
Context Research in lipodystrophy (LD) and its treatment with metreleptin has not only helped patients with LD but has opened new directions in investigating leptin's role in metabolism and the regulation of eating behavior. Previously, in a study with patients with LD undergoing metreleptin treatment using functional magnetic resonance imaging (MRI), we found significantly increased resting-state brain connectivity in 3 brain areas including the hypothalamus. Objective In this study, we aimed to reproduce our functional MRI findings in an independent sample and compare results to healthy participants. Design Measurements in 4 female patients with LD undergoing metreleptin treatment and 3 healthy untreated controls were performed at 4 different time points over 12 weeks. To identify treatment-related brain connectivity alterations, eigenvector centrality was computed from resting-state functional MRI data for each patient and each session. Thereafter, analysis aimed at detecting consistent brain connectivity changes over time across all patients. Results In parallel to metreleptin treatment of the patients with LD, we found a significant brain connectivity increase in the hypothalamus and bilaterally in posterior cingulate gyrus. Using a 3-factorial model, a significant interaction between group and time was found in the hypothalamus. Conclusions Investigating brain connectivity alterations with metreleptin treatment using an independent sample of patients with LD, we have reproduced an increase of brain connectivity in hedonic and homeostatic central nervous networks observed previously with metreleptin treatment. These results are an important contribution to ascertain brain leptin action and help build a foundation for further research of central nervous effects of this important metabolic hormone.
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Affiliation(s)
- Haiko Schlögl
- Department of Endocrinology, Nephrology, Rheumatology, Division of Endocrinology, University Hospital Leipzig, 04103 Leipzig, Germany
- Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Zentrum München at the University of Leipzig and University Hospital Leipzig, 04103 Leipzig, Germany
| | - Arno Villringer
- Max-Planck-Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
- Day Clinic of Cognitive Neurology, University of Leipzig, 04103 Leipzig, Germany
| | - Konstanze Miehle
- Department of Endocrinology, Nephrology, Rheumatology, Division of Endocrinology, University Hospital Leipzig, 04103 Leipzig, Germany
| | - Mathias Fasshauer
- Institute of Nutritional Sciences, Justus-Liebig-University, 35392 Giessen, Germany
| | - Michael Stumvoll
- Department of Endocrinology, Nephrology, Rheumatology, Division of Endocrinology, University Hospital Leipzig, 04103 Leipzig, Germany
- Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Zentrum München at the University of Leipzig and University Hospital Leipzig, 04103 Leipzig, Germany
| | - Karsten Mueller
- Max-Planck-Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
- Department of Neurology, Charles University, First Faculty of Medicine and General University Hospital, 120 00 Prague, Czech Republic
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27
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Nakuci J, Wasylyshyn N, Cieslak M, Elliott JC, Bansal K, Giesbrecht B, Grafton ST, Vettel JM, Garcia JO, Muldoon SF. Within-subject reproducibility varies in multi-modal, longitudinal brain networks. Sci Rep 2023; 13:6699. [PMID: 37095180 PMCID: PMC10126005 DOI: 10.1038/s41598-023-33441-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 04/12/2023] [Indexed: 04/26/2023] Open
Abstract
Network neuroscience provides important insights into brain function by analyzing complex networks constructed from diffusion Magnetic Resonance Imaging (dMRI), functional MRI (fMRI) and Electro/Magnetoencephalography (E/MEG) data. However, in order to ensure that results are reproducible, we need a better understanding of within- and between-subject variability over long periods of time. Here, we analyze a longitudinal, 8 session, multi-modal (dMRI, and simultaneous EEG-fMRI), and multiple task imaging data set. We first confirm that across all modalities, within-subject reproducibility is higher than between-subject reproducibility. We see high variability in the reproducibility of individual connections, but observe that in EEG-derived networks, during both rest and task, alpha-band connectivity is consistently more reproducible than connectivity in other frequency bands. Structural networks show a higher reliability than functional networks across network statistics, but synchronizability and eigenvector centrality are consistently less reliable than other network measures across all modalities. Finally, we find that structural dMRI networks outperform functional networks in their ability to identify individuals using a fingerprinting analysis. Our results highlight that functional networks likely reflect state-dependent variability not present in structural networks, and that the type of analysis should depend on whether or not one wants to take into account state-dependent fluctuations in connectivity.
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Affiliation(s)
- Johan Nakuci
- Neuroscience Program, University at Buffalo, SUNY, Buffalo, NY, 14260, USA.
- School of Psychology, Georgia Institute of Technology, Atlanta, GA, 14260, USA.
| | - Nick Wasylyshyn
- U.S. CCDC Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD, 21005, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Matthew Cieslak
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, 93106, USA
| | - James C Elliott
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, 93106, USA
| | - Kanika Bansal
- U.S. CCDC Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD, 21005, USA
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA
| | - Barry Giesbrecht
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, 93106, USA
- Institute for Collaborative Biotechnologies, University of California, Santa Barbara, CA, 93106, USA
| | - Scott T Grafton
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, 93106, USA
- Institute for Collaborative Biotechnologies, University of California, Santa Barbara, CA, 93106, USA
| | - Jean M Vettel
- U.S. CCDC Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD, 21005, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, 93106, USA
| | - Javier O Garcia
- U.S. CCDC Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD, 21005, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sarah F Muldoon
- Neuroscience Program, University at Buffalo, SUNY, Buffalo, NY, 14260, USA.
- Department of Mathematics and CDSE Program, University at Buffalo, SUNY, Buffalo, NY, 14260, USA.
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28
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Asatani K, Oki S, Momma T, Sakata I. Quantifying progress in research topics across nations. Sci Rep 2023; 13:4759. [PMID: 36959309 PMCID: PMC10036561 DOI: 10.1038/s41598-023-31452-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 03/12/2023] [Indexed: 03/25/2023] Open
Abstract
A scientist's choice of research topic affects the impact of their work and future career. While the disparity between nations in scientific information, funding, and facilities has decreased, scientists on the cutting edge of their fields are not evenly distributed across nations. Here, we quantify relative progress in research topics of a nation from the time-series comparison of reference lists from papers, using 71 million published papers from Scopus. We discover a steady leading-following relationship in research topics between Western nations or Asian city-states and others. Furthermore, we find that a nation's share of information-rich scientists in co-authorship networks correlates highly with that nation's progress in research topics. These results indicate that scientists' relationships continue to dominate scientific evolution in the age of open access to information and explain the failure or success of nations' investments in science.
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Affiliation(s)
| | - Sumihiro Oki
- Amsterdam School of Historical Studies, Universiteit van Amsterdam, Amsterdam, The Netherlands
| | - Takuya Momma
- School of Humanities, Kwansei Gakuin University, Nishinomiya, Japan
- Japan Society for the Promotion of Science, Tokyo, Japan
| | - Ichiro Sakata
- Department of Engineering, University of Tokyo, Tokyo, Japan
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Wang A, Fan Z, Zhang Y, Wang J, Zhang X, Wang P, Mu W, Zhan G, Wang M, Zhang L, Gan Z, Kang X. Resting-state SEEG-based brain network analysis for the detection of epileptic area. J Neurosci Methods 2023; 390:109839. [PMID: 36933706 DOI: 10.1016/j.jneumeth.2023.109839] [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: 09/23/2022] [Revised: 03/10/2023] [Accepted: 03/14/2023] [Indexed: 03/18/2023]
Abstract
BACKGROUND Most epilepsy research is based on interictal or ictal functional connectivity. However, prolonged electrode implantation may affect patients' health and the accuracy of epileptic zone identification. Brief resting-state SEEG recordings reduce the observation of epileptic discharges by reducing electrode implantation and other seizure-inducing interventions. NEW METHOD The location coordinates of SEEG in the brain were identified using CT and MRI. Based on undirected brain network connectivity, five functional connectivity measures and data feature vector centrality were calculated. Network connectivity was calculated from multiple perspectives of linear correlation, information theory, phase, and frequency, and the relative influence of nodes on network connectivity was considered. We investigated the potential value of resting-state SEEG for epileptic zone identification by comparing the differences between epileptic and non-epileptic zones, as well as the differences between patients with different surgical outcomes. RESULTS By comparing the centrality of brain network connectivity between epileptic and non-epileptic zones, we found significant differences in the distribution of brain networks between the two zones. There was a significant difference in brain network between patients with good surgical outcomes and those with poor surgical outcomes (p < 0.01). By combining support vector machines with static node importance, we predicted an AUC of 0.94 ± 0.08 for the epilepsy zone. CONCLUSIONS AND SIGNIFICANCE The results illustrated that nodes in epileptic zones are distinct from those in non-epileptic zones. Analysis of resting-state SEEG data and the importance of nodes in the brain network may contribute to identifying the epileptic zone and predicting the outcome.
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Affiliation(s)
- Aiping Wang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, 200433 Shanghai, China
| | - Zhen Fan
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, China
| | - Yuan Zhang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, 200433 Shanghai, China
| | - Junkongshuai Wang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, 200433 Shanghai, China
| | - Xueze Zhang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, 200433 Shanghai, China
| | - Pengchao Wang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, 200433 Shanghai, China
| | - Wei Mu
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, 200433 Shanghai, China
| | - Gege Zhan
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, 200433 Shanghai, China
| | - Minjie Wang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, 200433 Shanghai, China
| | - Lihua Zhang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, 200433 Shanghai, China; Ji Hua Laboratory, 28 Island Ring South Rd., Foshan City 528200, China
| | - Zhongxue Gan
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, 200433 Shanghai, China; Ji Hua Laboratory, 28 Island Ring South Rd., Foshan City 528200, China
| | - Xiaoyang Kang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, 200433 Shanghai, China; Yiwu Research Institute of Fudan University, Chengbei Road, Yiwu City, 322000 Zhejiang, China; Ji Hua Laboratory, 28 Island Ring South Rd., Foshan City 528200, China; Research Center for Intelligent Sensing, Zhejiang Lab, Hangzhou 311100, China.
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30
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Wang M, Zhao G, Jiang Y, Lu T, Wang Y, Zhu Y, Zhang Z, Xie C, Wang Z, Ren Q. Disconnection of Network Hubs Underlying the Executive Function Deficit in Patients with Ischemic Leukoaraiosis. J Alzheimers Dis 2023; 94:1577-1586. [PMID: 37458032 DOI: 10.3233/jad-230048] [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] [Indexed: 07/18/2023]
Abstract
BACKGROUND Cognitive impairment is the most common clinical manifestation of ischemic leukoaraiosis (ILA), but the underlying neurobiological pathways have not been well elucidated. Recently, it was thought that ILA is a "disconnection syndrome". Disorganized brain connectome were considered the key neuropathology underlying cognitive deficits in ILA patients. OBJECTIVE We aimed to detect the disruption of network hubs in ILA patients using a new analytical method called voxel-based eigenvector centrality (EC) mapping. METHODS Subjects with moderate to severe white matters hyperintensities (Fazekas score ≥3) and healthy controls (HCs) (Fazekas score = 0) were included in the study. The resting-state functional magnetic resonance imaging and the EC mapping approach were performed to explore the alteration of whole-brain network connectivity in ILA patients. RESULTS Relative to the HCs, the ILA patients exhibited poorer cognitive performance in episodic memory, information processing speed, and executive function (all ps < 0.0125). Additionally, compared with HCs, the ILA patients had lower functional connectivity (i.e., EC values) in the medial parts of default-mode network (i.e., bilateral posterior cingulate gyrus and ventral medial prefrontal cortex [vMPFC]). Intriguingly, the functional connectivity strength at the right vMPFC was positively correlated with executive function deficit in the ILA patients. CONCLUSION The findings suggested disorganization of the hierarchy of the default-mode regions within the whole-brain network in patients with ILA and advanced our understanding of the neurobiological mechanism underlying executive function deficit in ILA.
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Affiliation(s)
- Mengxue Wang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Guofeng Zhao
- Center of Interventional Radiology and Vascular Surgery, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China
- Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Suzhou, China
| | - Ying Jiang
- Department of Neurology, The 962nd Hospital of the PLA Joint Logistic Support Force, Harbin, China
| | - Tong Lu
- Department of Radiology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Yanjuan Wang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Yixin Zhu
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Zhengsheng Zhang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Chunming Xie
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Zan Wang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Qingguo Ren
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
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31
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Skouras S. Eigenvector centrality and its variability over time are promising indicators of alterations in brain function due to early amyloid deposition. Brain Commun 2023; 5:fcad104. [PMID: 37151224 PMCID: PMC10156142 DOI: 10.1093/braincomms/fcad104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 02/23/2023] [Accepted: 04/03/2023] [Indexed: 05/09/2023] Open
Abstract
This scientific commentary refers to 'Eigenvector centrality dynamics are related to Alzheimer's disease pathological changes in non-demented individuals', by Lorenzini et al. (https://doi.org/10.1093/braincomms/fcad088).
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Affiliation(s)
- Stavros Skouras
- Correspondence to: Stavros Skouras, PhD, Department of Neurology, University Hospital Bern, Freiburgstrasse 18, 3010 Bern, Switzerland E-mail:
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32
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Perez TM, Glue P, Adhia DB, Navid MS, Zeng J, Dillingham P, Smith M, Niazi IK, Young CK, De Ridder D. Infraslow closed-loop brain training for anxiety and depression (ISAD): a protocol for a randomized, double-blind, sham-controlled pilot trial in adult females with internalizing disorders. Trials 2022; 23:949. [DOI: 10.1186/s13063-022-06863-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 10/22/2022] [Indexed: 11/18/2022] Open
Abstract
Abstract
Background
The core intrinsic connectivity networks (core-ICNs), encompassing the default-mode network (DMN), salience network (SN) and central executive network (CEN), have been shown to be dysfunctional in individuals with internalizing disorders (IDs, e.g. major depressive disorder, MDD; generalized anxiety disorder, GAD; social anxiety disorder, SOC). As such, source-localized, closed-loop brain training of electrophysiological signals, also known as standardized low-resolution electromagnetic tomography (sLORETA) neurofeedback (NFB), targeting key cortical nodes within these networks has the potential to reduce symptoms associated with IDs and restore normal core ICN function. We intend to conduct a randomized, double-blind (participant and assessor), sham-controlled, parallel-group (3-arm) trial of sLORETA infraslow (<0.1 Hz) fluctuation neurofeedback (sLORETA ISF-NFB) 3 times per week over 4 weeks in participants (n=60) with IDs. Our primary objectives will be to examine patient-reported outcomes (PROs) and neurophysiological measures to (1) compare the potential effects of sham ISF-NFB to either genuine 1-region ISF-NFB or genuine 2-region ISF-NFB, and (2) assess for potential associations between changes in PRO scores and modifications of electroencephalographic (EEG) activity/connectivity within/between the trained regions of interest (ROIs). As part of an exploratory analysis, we will investigate the effects of additional training sessions and the potential for the potentiation of the effects over time.
Methods
We will randomly assign participants who meet the criteria for MDD, GAD, and/or SOC per the MINI (Mini International Neuropsychiatric Interview for DSM-5) to one of three groups: (1) 12 sessions of posterior cingulate cortex (PCC) ISF-NFB up-training (n=15), (2) 12 sessions of concurrent PCC ISF up-training and dorsal anterior cingulate cortex (dACC) ISF-NFB down-training (n=15), or (3) 6 sessions of yoked-sham training followed by 6 sessions genuine ISF-NFB (n=30). Transdiagnostic PROs (Hospital Anxiety and Depression Scale, HADS; Inventory of Depression and Anxiety Symptoms – Second Version, IDAS-II; Multidimensional Emotional Disorder Inventory, MEDI; Intolerance of Uncertainty Scale – Short Form, IUS-12; Repetitive Thinking Questionnaire, RTQ-10) as well as resting-state neurophysiological measures (full-band EEG and ECG) will be collected from all subjects during two baseline sessions (approximately 1 week apart) then at post 6 sessions, post 12 sessions, and follow-up (1 month later). We will employ Bayesian methods in R and advanced source-localisation software (i.e. exact low-resolution brain electromagnetic tomography; eLORETA) in our analysis.
Discussion
This protocol will outline the rationale and research methodology for a clinical pilot trial of sLORETA ISF-NFB targeting key nodes within the core-ICNs in a female ID population with the primary aims being to assess its potential efficacy via transdiagnostic PROs and relevant neurophysiological measures.
Trial registration
Our study was prospectively registered with the Australia New Zealand Clinical Trials Registry (ANZCTR; Trial ID: ACTRN12619001428156). Registered on October 15, 2019.
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Chen Z, Zhang R, Xie J, Liu P, Zhang C, Zhao J, Laplante JP, Feng T. Hybrid brain model accurately predict human procrastination behavior. Cogn Neurodyn 2022; 16:1107-1121. [PMID: 36237406 PMCID: PMC9508313 DOI: 10.1007/s11571-021-09765-z] [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: 10/15/2021] [Revised: 11/20/2021] [Accepted: 12/05/2021] [Indexed: 11/03/2022] Open
Abstract
Procrastination behavior is quite ubiquitous, and should warrant cautions to us owing to its significant influences in poor mental health, low subjective well-beings and bad academic performance. However, how to identify this behavioral problem have not yet to be fully elucidated. 1132 participants were recruited as distribution of benchmark. 81 high trait procrastinators (HP) and matched low trait procrastinators (LP) were screened. To address this issue, we have built upon the hybrid brain model by using hierarchical machine learning techniques to classify HP and LP with multi-modalities neuroimaging data (i.e., grey matter volume, fractional anisotropy, static/dynamic amplitude of low frequency fluctuation and static/dynamic degree centrality). Further, we capitalized on the multiple Canonical Correlation Analysis (mCCA) and joint Independent Component Analysis algorithm (mCCA + jICA) to clarify its fusion neural components as well. The hybrid brain model showed high accuracy to discriminate HP and LP (accuracy rate = 87.04%, sensitivity rate = 86.42%, specificity rate = 85.19%). Moreover, results of mCCA + jICA model revealed several joint-discriminative neural independent components (ICs) of this classification, showing wider co-variants of frontoparietal cortex and hippocampus networks. In addition, this study demonstrated three modal-specific discriminative ICs for classification, highlighting the temporal variants of brain local and global natures in ventromedial prefrontal cortex (vmPFC) and PHC in HP. To sum-up, this research developed a hybrid brain model to identify trait procrastination with high accuracy, and further revealed the neural hallmarks of this trait by integrating neuroimaging fusion data. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-021-09765-z.
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Affiliation(s)
- Zhiyi Chen
- Faculty of Psychology, School of Psychology, Southwest University, Tian Sheng RD, No.2, Beibei, ChongQing, 400715 China
- Key Laboratory of Cognition and Personality, Ministry of Education, ChongQing, China
| | - Rong Zhang
- Faculty of Psychology, School of Psychology, Southwest University, Tian Sheng RD, No.2, Beibei, ChongQing, 400715 China
- Key Laboratory of Cognition and Personality, Ministry of Education, ChongQing, China
| | - Jiawei Xie
- Department of Psychology, The University of Sheffield, Sheffield, UK
| | - Peiwei Liu
- Department of Psychology, University of Florida, Gainesville, USA
| | - Chenyan Zhang
- Cognitive Psychology Unit, Faculty of Social and Behavioural Sciences, The Institute of Psychology, Leiden University, Leiden, Netherlands
| | - Jia Zhao
- Faculty of Psychology, School of Psychology, Southwest University, Tian Sheng RD, No.2, Beibei, ChongQing, 400715 China
- Key Laboratory of Cognition and Personality, Ministry of Education, ChongQing, China
| | | | - Tingyong Feng
- Faculty of Psychology, School of Psychology, Southwest University, Tian Sheng RD, No.2, Beibei, ChongQing, 400715 China
- Key Laboratory of Cognition and Personality, Ministry of Education, ChongQing, China
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Zhu XW, Zhang LL, Zhu ZM, Wang LY, Ding ZX, Fang XM. Altered intrinsic brain activity and connectivity in unaffected parents of individuals with autism spectrum disorder: a resting-state fMRI study. Front Hum Neurosci 2022; 16:997150. [PMID: 36248683 PMCID: PMC9563234 DOI: 10.3389/fnhum.2022.997150] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 09/16/2022] [Indexed: 11/16/2022] Open
Abstract
Objectives: Autism spectrum disorder (ASD) is a juvenile onset neurodevelopmental disorder with social impairment and stereotyped behavior as the main symptoms. Unaffected relatives may also exhibit similar ASD features due to genetic factors. Although previous studies have demonstrated atypical brain morphological features as well as task-state brain function abnormalities in unaffected parents with ASD children, it remains unclear the pattern of brain function in the resting state. Methods: A total of 42 unaffected parents of ASD children (pASD) and 39 age-, sex-, and handedness-matched controls were enrolled. Multiple resting-state fMRI (rsfMRI) analyzing methods were applied, including amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), degree centrality (DC), and functional connectivity (FC), to reveal the functional abnormalities of unaffected parents in ASD-related brain regions. Spearman Rho correlation analysis between imaging metric values and the severity of ASD traits were evaluated as well. Results: ALFF, ReHo, and DC methods all revealed abnormal brain regions in the pASD group, such as the left medial orbitofrontal cortex (mOFC) and rectal gyrus (ROI-1), bilateral supplementary motor area (ROI-2), right caudate nucleus head and right amygdala/para-hippocampal gyrus (ROI-3). FC decreasing was observed between ROI-1 and right anterior cingulate cortex (ACC), ROI-2, and bilateral precuneus. FC enhancing was observed between ROI-3 and right anterior cerebellar lobe, left medial temporal gyrus, left superior temporal gyrus, left medial frontal gyrus, left precentral gyrus, right postcentral gyrus in pASD. In addition, ALFF values in ROI-1, DC values in ROI-3 were positively correlated with AQ scores in pASD (ρ1 = 0.298, P1 = 0.007; ρ2 = 0.220, P2 = 0.040), while FC values between ROI-1 and right ACC were negatively correlated with AQ scores (ρ3 = −0.334, P3 = 0.002). Conclusion: rsfMRI metrics could be used as biomarkers to reveal the underlying neurobiological feature of ASD for unaffected parents.
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Affiliation(s)
- Xiang-Wen Zhu
- Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Li-Li Zhang
- Department of Child Health Care, Wuxi Children’s Hospital, Wuxi, China
| | - Zong-Ming Zhu
- Department of Radiology, Affiliated Wuxi People’s Hospital, Nanjing Medical University, Wuxi, China
| | - Luo-Yu Wang
- Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhong-Xiang Ding
- Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Zhong-Xiang Ding Xiang-Ming Fang
| | - Xiang-Ming Fang
- Department of Radiology, Affiliated Wuxi People’s Hospital, Nanjing Medical University, Wuxi, China
- *Correspondence: Zhong-Xiang Ding Xiang-Ming Fang
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Zhang Y, Xue Y, Wu X, Qiao L, Wang Z, Shen D. Selecting Multiple Node Statistics Jointly from Functional Connectivity Networks for Brain Disorders Identification. Brain Topogr 2022; 35:559-571. [PMID: 36138188 DOI: 10.1007/s10548-022-00914-z] [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: 02/06/2022] [Accepted: 08/27/2022] [Indexed: 11/29/2022]
Abstract
Functional connectivity networks (FCN) analysis is instructive for the diagnosis of brain diseases, such as mild cognitive impairment (MCI) and major depressive disorder (MDD) at their early stages. As the critical step of FCN analysis, feature representation provides the basis for finding potential biomarkers of brain diseases. In previous studies, different node statistics (e.g. local efficiency and local clustering coefficients) are usually extracted from FCNs as features for the diagnosis/classification task, which can specifically locate disease-related regions on the node level, so as to help us understand the neurodevelopmental roots of brain disorders. However, each node statistic is proposed only considering a kind of specific network property, which has one-sidedness and limitations. As a result, it is incomplete to represent a node with only one statistic. To resolve this issue, we put forward a novel scheme to select multiple node statistics jointly from the estimated FCNs for automated classification, called multiple node statistics feature selection (MNSFS). Specifically, we first extract multiple statistics from the same nodes and assign each kind of statistic into a group. Then, sparse group least absolute shrinkage and selection operator (sgLASSO) is used to select groups (nodes) and statistics in the groups towards a better classification performance. Such a technique enables us to simultaneously locate the discriminative brain regions, as well as the specific statistics associated with these brain regions, making the classification results more interpretable. We conducted our scheme on two public databases for identifying subjects with MCI and MDD from normal controls. Experimental results show that the proposed scheme achieves superior classification accuracy and features interpreted on the benchmark datasets.
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Affiliation(s)
- Yangyang Zhang
- School of Mathematics Science, Liaocheng University, Liaocheng, China.,School of Computer Science and Cyberspace Security, Hainan University, Haikou, Hainan, China
| | - Yanfang Xue
- School of Mathematics Science, Liaocheng University, Liaocheng, China
| | - Xiao Wu
- School of Mathematics Science, Liaocheng University, Liaocheng, China
| | - Lishan Qiao
- School of Mathematics Science, Liaocheng University, Liaocheng, China.
| | - Zhengxia Wang
- School of Computer Science and Cyberspace Security, Hainan University, Haikou, Hainan, China
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.,Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.,Department of Artificial Intelligence, Korea University, Seoul, South Korea
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Dynamic Functional Connectivity of Emotion Processing in Beta Band with Naturalistic Emotion Stimuli. Brain Sci 2022; 12:brainsci12081106. [PMID: 36009166 PMCID: PMC9405988 DOI: 10.3390/brainsci12081106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 08/11/2022] [Accepted: 08/16/2022] [Indexed: 11/17/2022] Open
Abstract
While naturalistic stimuli, such as movies, better represent the complexity of the real world and are perhaps crucial to understanding the dynamics of emotion processing, there is limited research on emotions with naturalistic stimuli. There is a need to understand the temporal dynamics of emotion processing and their relationship to different dimensions of emotion experience. In addition, there is a need to understand the dynamics of functional connectivity underlying different emotional experiences that occur during or prior to such experiences. To address these questions, we recorded the EEG of participants and asked them to mark the temporal location of their emotional experience as they watched a video. We also obtained self-assessment ratings for emotional multimedia stimuli. We calculated dynamic functional the connectivity (DFC) patterns in all the frequency bands, including information about hubs in the network. The change in functional networks was quantified in terms of temporal variability, which was then used in regression analysis to evaluate whether temporal variability in DFC (tvDFC) could predict different dimensions of emotional experience. We observed that the connectivity patterns in the upper beta band could differentiate emotion categories better during or prior to the reported emotional experience. The temporal variability in functional connectivity dynamics is primarily related to emotional arousal followed by dominance. The hubs in the functional networks were found across the right frontal and bilateral parietal lobes, which have been reported to facilitate affect, interoception, action, and memory-related processing. Since our study was performed with naturalistic real-life resembling emotional videos, the study contributes significantly to understanding the dynamics of emotion processing. The results support constructivist theories of emotional experience and show that changes in dynamic functional connectivity can predict aspects of our emotional experience.
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Thams F, Külzow N, Flöel A, Antonenko D. Modulation of network centrality and gray matter microstructure using multi-session brain stimulation and memory training. Hum Brain Mapp 2022; 43:3416-3426. [PMID: 35373873 PMCID: PMC9248322 DOI: 10.1002/hbm.25857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 02/15/2022] [Accepted: 03/24/2022] [Indexed: 11/07/2022] Open
Abstract
Neural mechanisms of behavioral improvement induced by repeated transcranial direct current stimulation (tDCS) combined with cognitive training are yet unclear. Previously, we reported behavioral effects of a 3-day visuospatial memory training with concurrent anodal tDCS over the right temporoparietal cortex in older adults. To investigate intervention-induced neural alterations we here used functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) datasets available from 35 participants of this previous study, acquired before and after the intervention. To delineate changes in whole-brain functional network architecture, we employed eigenvector centrality mapping. Gray matter alterations were analyzed using DTI-derived mean diffusivity (MD). Network centrality in the bilateral posterior temporooccipital cortex was reduced after anodal compared to sham stimulation. This focal effect is indicative of decreased functional connectivity of the brain region underneath the anodal electrode and its left-hemispheric homolog with other "relevant" (i.e., highly connected) brain regions, thereby providing evidence for reorganizational processes within the brain's network architecture. Examining local MD changes in these clusters, an interaction between stimulation condition and training success indicated a decrease of MD in the right (stimulated) temporooccipital cluster in individuals who showed superior behavioral training benefits. Using a data-driven whole-brain network approach, we provide evidence for targeted neuromodulatory effects of a combined tDCS-and-training intervention. We show for the first time that gray matter alterations of microstructure (assessed by DTI-derived MD) may be involved in tDCS-enhanced cognitive training. Increased knowledge on how combined interventions modulate neural networks in older adults, will help the development of specific therapeutic interventions against age-associated cognitive decline.
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Affiliation(s)
- Friederike Thams
- Department of Neurology, Universitätsmedizin Greifswald, Greifswald, Germany
| | - Nadine Külzow
- Charité - Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Neurocure Cluster of Excellence, Berlin, Germany.,Neurological Rehabilitation Clinic, Kliniken Beelitz GmbH, Beelitz, Germany
| | - Agnes Flöel
- Department of Neurology, Universitätsmedizin Greifswald, Greifswald, Germany.,German Centre for Neurodegenerative Diseases (DZNE) Standort Greifswald, Greifswald, Germany
| | - Daria Antonenko
- Department of Neurology, Universitätsmedizin Greifswald, Greifswald, Germany
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Óskarsdóttir M, Ahmed W, Antonio K, Baesens B, Dendievel R, Donas T, Reynkens T. Social Network Analytics for Supervised Fraud Detection in Insurance. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2022; 42:1872-1890. [PMID: 33547691 DOI: 10.1111/risa.13693] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Insurance fraud occurs when policyholders file claims that are exaggerated or based on intentional damages. This contribution develops a fraud detection strategy by extracting insightful information from the social network of a claim. First, we construct a network by linking claims with all their involved parties, including the policyholders, brokers, experts, and garages. Next, we establish fraud as a social phenomenon in the network and use the BiRank algorithm with a fraud-specific query vector to compute a fraud score for each claim. From the network, we extract features related to the fraud scores as well as the claims' neighborhood structure. Finally, we combine these network features with the claim-specific features and build a supervised model with fraud in motor insurance as the target variable. Although we build a model for only motor insurance, the network includes claims from all available lines of business. Our results show that models with features derived from the network perform well when detecting fraud and even outperform the models using only the classical claim-specific features. Combining network and claim-specific features further improves the performance of supervised learning models to detect fraud. The resulting model flags highly suspicions claims that need to be further investigated. Our approach provides a guided and intelligent selection of claims and contributes to a more effective fraud investigation process.
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Affiliation(s)
- María Óskarsdóttir
- Department of Computer Science, Reykjavik University, Reykjavik, Iceland
| | | | - Katrien Antonio
- Faculty of Economics and Business, KU Leuven, Leuven, Belgium
- Faculty of Economics and Business, University of Amsterdam, Amsterdam, The Netherlands
- LRisk, Leuven Research Center on Insurance and Financial Risk Analysis, KU Leuven, Leuven, Belgium
- LStat, Leuven Statistics Research Center, KU Leuven, Leuven, Belgium
| | - Bart Baesens
- Faculty of Economics and Business, KU Leuven, Leuven, Belgium
- Department of Decision Analytics and Risk, University of Southampton, Southampton, UK
| | | | | | - Tom Reynkens
- Faculty of Economics and Business, KU Leuven, Leuven, Belgium
- LRisk, Leuven Research Center on Insurance and Financial Risk Analysis, KU Leuven, Leuven, Belgium
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Tarchi L, Damiani S, Fantoni T, Pisano T, Castellini G, Politi P, Ricca V. Centrality and interhemispheric coordination are related to different clinical/behavioral factors in attention deficit/hyperactivity disorder: a resting-state fMRI study. Brain Imaging Behav 2022; 16:2526-2542. [PMID: 35859076 DOI: 10.1007/s11682-022-00708-8] [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: 07/10/2022] [Indexed: 11/26/2022]
Abstract
Eigenvector-Centrality (EC) has shown promising results in the field of Psychiatry, with early results also pertaining to ADHD. Parallel efforts have focused on the description of aberrant interhemispheric coordination in ADHD, as measured by Voxel-Mirrored-Homotopic-Connectivity (VMHC), with early evidence of altered Resting-State fMRI. A sample was collected from the ADHD200-NYU initiative: 86 neurotypicals and 89 participants with ADHD between 7 and 18 years old were included after quality control for motion. After preprocessing, voxel-wise EC and VMHC values between diagnostic groups were compared, and network-level values from 15 functional networks extracted. Age, ADHD severity (Connor's Parent Rating-Scale), IQ (Wechsler-Abbreviated-Scale), and right-hand dominance were correlated with EC/VMHC values in the whole sample and within groups, both at the voxel-wise and network-level. Motion was controlled by censoring time-points with Framewise-Displacement > 0.5 mm, as well as controlling for group differences in mean Framewise-Displacement values. EC was significantly higher in ADHD compared to neurotypicals in the left inferior Frontal lobe, Lingual gyri, Peri-Calcarine cortex, superior and middle Occipital lobes, right inferior Occipital lobe, right middle Temporal gyrus, Fusiform gyri, bilateral Cuneus, right Precuneus, and Cerebellum (FDR-corrected-p = 0.05). No differences were observed between groups in voxel-wise VMHC. EC was positively correlated with ADHD severity scores at the network level (at p-value < 0.01, Inattentive: Cerebellum rho = 0.273; Hyper/Impulsive: High-Visual Network rho = 0.242, Cerebellum rho = 0.273; Global Index Severity: High-Visual Network rho = 0.241, Cerebellum rho = 0.293). No differences were observed between groups for motion (p = 0.443). While EC was more related to ADHD psychopathology, VMHC was consistently and negatively correlated with age across all networks.
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Affiliation(s)
- Livio Tarchi
- Psychiatry Unit, Department of Health Sciences, University of Florence, Florence, FI, Italy.
| | - Stefano Damiani
- Department of Brain and Behavioral Science, University of Pavia, 27100, Pavia, Italy
| | - Teresa Fantoni
- Pediatric Neurology, Neurogenetics and Neurobiology Unit and Laboratories, Neuroscience Department, Meyer Children's Hospital, University of Florence, Florence, Italy
| | - Tiziana Pisano
- Pediatric Neurology, Neurogenetics and Neurobiology Unit and Laboratories, Neuroscience Department, Meyer Children's Hospital, University of Florence, Florence, Italy
| | - Giovanni Castellini
- Psychiatry Unit, Department of Health Sciences, University of Florence, Florence, FI, Italy
| | - Pierluigi Politi
- Department of Brain and Behavioral Science, University of Pavia, 27100, Pavia, Italy
| | - Valdo Ricca
- Psychiatry Unit, Department of Health Sciences, University of Florence, Florence, FI, Italy
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von Wrede R, Rings T, Bröhl T, Pukropski J, Schach S, Helmstaedter C, Lehnertz K. Transcutaneous Auricular Vagus Nerve Stimulation Differently Modifies Functional Brain Networks of Subjects With Different Epilepsy Types. Front Hum Neurosci 2022; 16:867563. [PMID: 35814953 PMCID: PMC9260140 DOI: 10.3389/fnhum.2022.867563] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 05/24/2022] [Indexed: 11/15/2022] Open
Abstract
Epilepsy types differ by pathophysiology and prognosis. Transcutaneous auricular vagus nerve stimulation (taVNS) is a non-invasive treatment option in epilepsy. Nevertheless, its mode of action and impact on different types of epilepsy are still unknown. We investigated whether short-term taVNS differently affects local and global characteristics of EEG-derived functional brain networks in different types of epilepsy. Thirty subjects (nine with focal epilepsy, 11 with generalized epilepsy, and 10 without epilepsy or seizures) underwent a 3-h continuous EEG-recording (1 h pre-stimulation, 1 h taVNS stimulation, 1 h post-stimulation) from which we derived evolving functional brain networks. We assessed—in a time-resolved manner—important global (topological, robustness, and stability properties) and local (centralities of vertices and edges) network characteristics. Compared to the subjects with focal epilepsies and without epilepsy, those with generalized epilepsies clearly presented with different topological properties of their functional brain network already at rest. Furthermore, subjects with focal and generalized epilepsies reacted differently to the stimulation, expressed as different taVNS-induced immediate and enduring reorganization of global network characteristics. On the local network scale, no discernible spatial pattern could be detected, which points to a rather unspecific and generalized modification of brain activity. Assessing functional brain network characteristics can provide additional information for differentiating between focal and generalized epilepsy. TaVNS-related modifications of global network characteristics clearly differ between epilepsy types. Impact of such a non–pharmaceutical intervention on clinical decision-making in the treatment of different epilepsy types needs to be assessed in future studies.
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Affiliation(s)
- Randi von Wrede
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
- *Correspondence: Randi von Wrede,
| | - Thorsten Rings
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
- Helmholtz-Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Timo Bröhl
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
- Helmholtz-Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Jan Pukropski
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | - Sophia Schach
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | | | - Klaus Lehnertz
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
- Helmholtz-Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
- Interdisciplinary Center for Complex Systems, University of Bonn, Bonn, Germany
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41
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Mueller K, Růžička F, Slovák M, Forejtová Z, Dušek P, Dušek P, Jech R, Serranová T. Symptom-severity-related brain connectivity alterations in functional movement disorders. Neuroimage Clin 2022; 34:102981. [PMID: 35287089 PMCID: PMC8921488 DOI: 10.1016/j.nicl.2022.102981] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 03/01/2022] [Accepted: 03/02/2022] [Indexed: 01/21/2023]
Abstract
Brain connectivity alterations were found in functional movement disorders. Hyperconnectivity in temporoparietal junction and precuneus in functional weakness. Consistent brain connectivity differences with four different centrality measures. Motor symptom severity correlates positively with connectivity in functional weakness.
Background Functional movement disorders, a common cause of neurological disabilities, can occur with heterogeneous motor manifestations including functional weakness. However, the underlying mechanisms related to brain function and connectivity are unknown. Objective To identify brain connectivity alterations related to functional weakness we assessed network centrality changes in a group of patients with heterogeneous motor manifestations using task-free functional MRI in combination with different network centrality approaches. Methods Task-free functional MRI was performed in 48 patients with heterogeneous motor manifestations including 28 patients showing functional weakness and 65 age- and sex-matched healthy controls. Functional connectivity differences were assessed using different network centrality approaches, i.e. global correlation, eigenvector centrality, and intrinsic connectivity. Motor symptom severity was assessed using The Simplified Functional Movement Disorders Rating Scale and correlated with network centrality. Results Comparing patients with and without functional weakness showed significant network centrality differences in the left temporoparietal junction and precuneus. Patients with functional weakness showed increased centrality in the same anatomical regions when comparing functional weakness with healthy controls. Moreover, in the same regions, patients with functional weakness showed a positive correlation between motor symptom severity and network centrality. This correlation was shown to be specific to functional weakness with an interaction analysis, confirming a significant difference between patients with and without functional weakness. Conclusions We identified the temporoparietal junction and precuneus as key regions involved in brain connectivity alterations related to functional weakness. We propose that both regions may be promising targets for phenotype-specific non-invasive brain stimulation.
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Affiliation(s)
- Karsten Mueller
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| | - Filip Růžička
- Department of Neurology and Center of Clinical Neuroscience, Charles University in Prague, First Faculty of Medicine and General University Hospital in Prague, Czech Republic
| | - Matěj Slovák
- Department of Neurology and Center of Clinical Neuroscience, Charles University in Prague, First Faculty of Medicine and General University Hospital in Prague, Czech Republic
| | - Zuzana Forejtová
- Department of Neurology and Center of Clinical Neuroscience, Charles University in Prague, First Faculty of Medicine and General University Hospital in Prague, Czech Republic
| | - Petr Dušek
- Department of Neurology and Center of Clinical Neuroscience, Charles University in Prague, First Faculty of Medicine and General University Hospital in Prague, Czech Republic
| | - Pavel Dušek
- Department of Neurology and Center of Clinical Neuroscience, Charles University in Prague, First Faculty of Medicine and General University Hospital in Prague, Czech Republic
| | - Robert Jech
- Department of Neurology and Center of Clinical Neuroscience, Charles University in Prague, First Faculty of Medicine and General University Hospital in Prague, Czech Republic
| | - Tereza Serranová
- Department of Neurology and Center of Clinical Neuroscience, Charles University in Prague, First Faculty of Medicine and General University Hospital in Prague, Czech Republic.
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Sagulkoo P, Suratanee A, Plaimas K. Immune-Related Protein Interaction Network in Severe COVID-19 Patients toward the Identification of Key Proteins and Drug Repurposing. Biomolecules 2022; 12:biom12050690. [PMID: 35625619 PMCID: PMC9138873 DOI: 10.3390/biom12050690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 05/07/2022] [Accepted: 05/09/2022] [Indexed: 02/05/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) is still an active global public health issue. Although vaccines and therapeutic options are available, some patients experience severe conditions and need critical care support. Hence, identifying key genes or proteins involved in immune-related severe COVID-19 is necessary to find or develop the targeted therapies. This study proposed a novel construction of an immune-related protein interaction network (IPIN) in severe cases with the use of a network diffusion technique on a human interactome network and transcriptomic data. Enrichment analysis revealed that the IPIN was mainly associated with antiviral, innate immune, apoptosis, cell division, and cell cycle regulation signaling pathways. Twenty-three proteins were identified as key proteins to find associated drugs. Finally, poly (I:C), mitomycin C, decitabine, gemcitabine, hydroxyurea, tamoxifen, and curcumin were the potential drugs interacting with the key proteins to heal severe COVID-19. In conclusion, IPIN can be a good representative network for the immune system that integrates the protein interaction network and transcriptomic data. Thus, the key proteins and target drugs in IPIN help to find a new treatment with the use of existing drugs to treat the disease apart from vaccination and conventional antiviral therapy.
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Affiliation(s)
- Pakorn Sagulkoo
- Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok 10330, Thailand;
- Center of Biomedical Informatics, Department of Family Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand
| | - Apichat Suratanee
- Department of Mathematics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand;
- Intelligent and Nonlinear Dynamics Innovations Research Center, Science and Technology Research Institute, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand
| | - Kitiporn Plaimas
- Advance Virtual and Intelligent Computing (AVIC) Center, Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand
- Omics Science and Bioinformatics Center, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand
- Correspondence:
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Palombit A, Silvestri E, Volpi T, Aiello M, Cecchin D, Bertoldo A, Corbetta M. Variability of regional glucose metabolism and the topology of functional networks in the human brain. Neuroimage 2022; 257:119280. [PMID: 35525522 DOI: 10.1016/j.neuroimage.2022.119280] [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: 01/20/2022] [Revised: 04/04/2022] [Accepted: 05/02/2022] [Indexed: 11/17/2022] Open
Abstract
The brain consumes the most energy per relative mass amongst the organs in the human body. Theoretical and empirical studies have shown that behavioral processes are relatively inexpensive metabolically, and that most energy goes to maintaining the status quo, i.e., the balance of cell membranes' resting potentials and subthreshold spontaneous activity. Spontaneous activity fluctuates across brain regions in a correlated fashion that defines multi-scale hierarchical networks called resting-state networks (RSNs). Different regions of the brain display different metabolic consumption, but the relationship between regional brain metabolism and RSNs is still under investigation. Here, we examine the variability of glucose metabolism across brain regions, measured with the relative standard uptake value (SUVR) using 18F-FDG PET, and the topology of RSNs, measured through graph analysis applied to fMRI resting-state functional connectivity (FC). We found a moderate linear relationship between the strength (STR) of pairwise regional FC and metabolism. Moreover, the linear correlation between SUVR and STR grew stronger as we considered more connected regions (hubs). Regions connecting different RSNs, or connector hubs, showed higher SUVR than regions connecting nodes within the same RSN, or provincial hubs. Our results show that functional connections as probed by fMRI are related to glucose metabolism, especially in a system of provincial and connector hubs.
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Affiliation(s)
- Alessandro Palombit
- Department of Information Engineering, University of Padova, 35131 Padova, Italy; Padova Neuroscience Center (PNC), University of Padova, 35131 Padova, Italy
| | - Erica Silvestri
- Department of Information Engineering, University of Padova, 35131 Padova, Italy; Padova Neuroscience Center (PNC), University of Padova, 35131 Padova, Italy
| | - Tommaso Volpi
- Padova Neuroscience Center (PNC), University of Padova, 35131 Padova, Italy; Department of Neuroscience, University of Padova, 35128 Padova, Italy
| | | | - Diego Cecchin
- Unit of Nuclear Medicine, Department of Medicine, University of Padova, Padova, Italy; Padova Neuroscience Center (PNC), University of Padova, 35131 Padova, Italy
| | - Alessandra Bertoldo
- Department of Information Engineering, University of Padova, 35131 Padova, Italy; Padova Neuroscience Center (PNC), University of Padova, 35131 Padova, Italy
| | - Maurizio Corbetta
- Padova Neuroscience Center (PNC), University of Padova, 35131 Padova, Italy; Department of Neuroscience, University of Padova, 35128 Padova, Italy; Venetian Institute of Molecular Medicine (VIMM) Biomedical Foundation, 35128 Padova, Italy.
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FUJIMOTO UTA, OGAWA AKITOSHI, OSADA TAKAHIRO, TANAKA MASAKI, SUDA AKIMITSU, HATTORI NOBUTAKA, KAMAGATA KOJI, AOKI SHIGEKI, KONISHI SEIKI. Network Centrality Analysis Characterizes Brain Activity during Response Inhibition in Right Ventral Inferior Frontal Cortex. JUNTENDO IJI ZASSHI = JUNTENDO MEDICAL JOURNAL 2022; 68:208-211. [PMID: 39021727 PMCID: PMC11250004 DOI: 10.14789/jmj.jmj21-0055-ot] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 01/21/2022] [Indexed: 07/20/2024]
Affiliation(s)
| | | | | | | | | | | | | | | | - SEIKI KONISHI
- Corresponding author: Seiki Konishi, Department of Neurophysiology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan, TEL: +81-3-5802-1026 E-mail:
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Node capability dependency importance evaluation of heterogeneous target operational network. EVOLUTIONARY INTELLIGENCE 2022. [DOI: 10.1007/s12065-022-00712-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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46
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Mori K, Haruno M. Resting functional connectivity of the left inferior frontal gyrus with the dorsomedial prefrontal cortex and temporoparietal junction reflects the social network size for active interactions. Hum Brain Mapp 2022; 43:2869-2879. [PMID: 35261111 PMCID: PMC9120559 DOI: 10.1002/hbm.25822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 02/03/2022] [Accepted: 02/16/2022] [Indexed: 11/08/2022] Open
Abstract
The size of an individual active social network is a key parameter of human social behavior and is correlated with subjective well-being. However, it remains unknown how the social network size of active interactions is represented in the brain. Here, we examined whether resting-state functional magnetic resonance imaging (fMRI) connectivity is associated with the social network size of active interactions using behavioral data of a large sample (N = 222) on Twitter. Region of interest (ROI)-to-ROI analysis, graph theory analysis, seed-based analysis, and decoding analysis together provided compelling evidence that people who have a large social network size of active interactions, as measured by "reply," show higher fMRI connectivity of the left inferior frontal gyrus with the dorsomedial prefrontal cortex and temporoparietal junction, which represents the core of the theory of mind network. These results demonstrated that people who have a large social network size of active interactions maintain activity of the identified functional connectivity in daily life, possibly providing a mechanism for efficient information transmission between the brain networks related to language and theory-of-mind.
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Affiliation(s)
- Kazuma Mori
- Center for Information and Neural Networks, National Institute of Information and Communications Technology (NICT), Suita, Osaka, Japan.,Graduate School of Information Science and Technology, Osaka University, Suita, Osaka, Japan
| | - Masahiko Haruno
- Center for Information and Neural Networks, National Institute of Information and Communications Technology (NICT), Suita, Osaka, Japan.,Grauduate School of Frontier Biosciences, Osaka University, Suita, Osaka, Japan
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Bosch-Bayard J, Biscay RJ, Fernandez T, Otero GA, Ricardo-Garcell J, Aubert-Vazquez E, Evans AC, Harmony T. EEG effective connectivity during the first year of life mirrors brain synaptogenesis, myelination, and early right hemisphere predominance. Neuroimage 2022; 252:119035. [PMID: 35218932 DOI: 10.1016/j.neuroimage.2022.119035] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 11/25/2021] [Accepted: 02/22/2022] [Indexed: 10/19/2022] Open
Abstract
INTRODUCTION The maturation of electroencephalogram (EEG) effective connectivity in healthy infants during the first year of life is described. METHODS Participants: A cross-sectional sample of 125 healthy at-term infants, from 0 to 12 months of age, underwent EEG in a state of quiet sleep. PROCEDURES The EEG primary currents at the source were described with the sLoreta method. An unmixing algorithm was applied to reduce the leakage, and the isolated effective coherence, a direct and directed measurement of information flow, was calculated. RESULTS AND DISCUSSION Initially, the highest indices of connectivity are at the subcortical nuclei, continuing to the parietal lobe, predominantly the right hemisphere, then expanding to temporal, occipital, and finally the frontal areas, which is consistent with the myelination process. Age-related connectivity changes were mostly long-range and bilateral. Connections increased with age, mainly in the right hemisphere, while they mainly decreased in the left hemisphere. Increased connectivity from 20 to 30 Hz, mostly at the right hemisphere. These findings were consistent with right hemisphere predominance during the first three years of life. Theta and alpha connections showed the greatest changes with age. Strong connectivity was found between the parietal, temporal, and occipital regions to the frontal lobes, responsible for executive functions and consistent with behavioral development during the first year. The thalamus exchanges information bidirectionally with all cortical regions and frequency bands. CONCLUSIONS The maturation of EEG connectivity during the first year in healthy infants is very consistent with synaptogenesis, reductions in synaptogenesis, myelination, and functional and behavioral development.
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Affiliation(s)
- Jorge Bosch-Bayard
- McGill Center for Integrative Neuroscience (MCIN), Ludmer Center for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal H3A2B4, Canada
| | - Rolando J Biscay
- Centro de Investigación en Matemáticas, Guanajuato 36023, Mexico
| | - Thalia Fernandez
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Boulevard Juriquilla 3001, Querétaro 76230, Mexico
| | - Gloria A Otero
- Facultad de Medicina, Universidad Autónoma del Estado de México, Toluca de Lerdo 50180, Mexico
| | - Josefina Ricardo-Garcell
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Boulevard Juriquilla 3001, Querétaro 76230, Mexico
| | | | - Alan C Evans
- McGill Center for Integrative Neuroscience (MCIN), Ludmer Center for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal H3A2B4, Canada
| | - Thalia Harmony
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Boulevard Juriquilla 3001, Querétaro 76230, Mexico.
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A survey of brain network analysis by electroencephalographic signals. Cogn Neurodyn 2022; 16:17-41. [PMID: 35126769 PMCID: PMC8807775 DOI: 10.1007/s11571-021-09689-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 04/25/2021] [Accepted: 05/31/2021] [Indexed: 02/03/2023] Open
Abstract
Brain network analysis is one efficient tool in exploring human brain diseases and can differentiate the alterations from comparative networks. The alterations account for time, mental states, tasks, individuals, and so forth. Furthermore, the changes determine the segregation and integration of functional networks that lead to network reorganization (or reconfiguration) to extend the neuroplasticity of the brain. Exploring related brain networks should be of interest that may provide roadmaps for brain research and clinical diagnosis. Recent electroencephalogram (EEG) studies have revealed the secrets of the brain networks and diseases (or disorders) within and between subjects and have provided instructive and promising suggestions and methods. This review summarized the corresponding algorithms that had been used to construct functional or effective networks on the scalp and cerebral cortex. We reviewed EEG network analysis that unveils more cognitive functions and neural disorders of the human and then explored the relationship between brain science and artificial intelligence which may fuel each other to accelerate their advances, and also discussed some innovations and future challenges in the end.
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An examination of the relationships between attention/deficit hyperactivity disorder symptoms and functional connectivity over time. Neuropsychopharmacology 2022; 47:704-710. [PMID: 33558680 PMCID: PMC8782893 DOI: 10.1038/s41386-021-00958-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 11/10/2020] [Accepted: 12/23/2020] [Indexed: 01/30/2023]
Abstract
Previous cross-sectional work has demonstrated resting-state connectivity abnormalities in children and adolescents with attention/deficit hyperactivity disorder (ADHD) relative to typically developing controls. However, it is unclear to what extent these neural abnormalities confer risk for later symptoms of the disorder, or represent the downstream effects of symptoms on functional connectivity. Here, we studied 167 children and adolescents (mean age at baseline = 10.74 years (SD = 2.54); mean age at follow-up = 13.3 years (SD = 2.48); 56 females) with varying levels of ADHD symptoms, all of whom underwent resting-state functional magnetic resonance imaging and ADHD symptom assessments on two occasions during development. Resting-state functional connectivity was quantified using eigenvector centrality mapping. Using voxelwise cross-lag modeling, we found that less connectivity at baseline within right inferior frontal gyrus was associated with more follow-up symptoms of inattention (significant at an uncorrected cluster-forming threshold of p ≤ 0.001 and a cluster-level familywise error corrected threshold of p < 0.05). Findings suggest that previously reported cross-sectional abnormalities in functional connectivity within inferior frontal gyrus in patients with ADHD may represent a longitudinal risk factor for the disorder, in line with efforts to target this region with novel therapeutic methods.
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Koelsch S, Andrews‐Hanna JR, Skouras S. Tormenting thoughts: The posterior cingulate sulcus of the default mode network regulates valence of thoughts and activity in the brain's pain network during music listening. Hum Brain Mapp 2022; 43:773-786. [PMID: 34652882 PMCID: PMC8720190 DOI: 10.1002/hbm.25686] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 09/06/2021] [Accepted: 10/04/2021] [Indexed: 01/08/2023] Open
Abstract
Many individuals spend a significant amount of their time "mind-wandering". Mind-wandering often includes spontaneous, nonintentional thought, and a neural correlate of this kind of thought is the default mode network (DMN). Thoughts during mind-wandering can have positive or negative valence, but only little is known about the neural correlates of positive or negative thoughts. We used resting-state functional magnetic resonance imaging (fMRI) and music to evoke mind-wandering in n = 33 participants, with positive-sounding music eliciting thoughts with more positive valence and negative-sounding music eliciting thoughts with more negative valence. Applying purely data-driven analysis methods, we show that medial orbitofrontal cortex (mOFC, part of the ventromedial prefrontal cortex) and the posterior cingulate sulcus (likely area 23c of the posterior cingulate cortex), two sub-regions of the DMN, modulate the valence of thought-contents during mind-wandering. In addition, across two independent experiments, we observed that the posterior cingulate sulcus, a region involved in pain, shows valence-specific functional connectivity with core regions of the brain's putative pain network. Our results suggest that two DMN regions (mOFC and posterior cingulate sulcus) support the formation of negative spontaneous, nonintentional thoughts, and that the interplay between these structures with regions of the putative pain network forms a neural mechanism by which thoughts can become painful.
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Affiliation(s)
- Stefan Koelsch
- Department of Biological and Medical PsychologyUniversity of BergenBergen
| | | | - Stavros Skouras
- Department of Biological and Medical PsychologyUniversity of BergenBergen
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