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1. Simulation-based inference of developmental EEG maturation with the spectral graph model

Author

Danilo Bernardo, Xihe Xie, Parul Verma, Jonathan Kim, Virginia Liu, Adam L. Numis, Ye Wu, Hannah C. Glass, Pew-Thian Yap, Srikantan S. Nagarajan, and Ashish Raj

Subjects
Astrophysics, QB460-466, Physics, QC1-999
Abstract

Abstract The spectral content of macroscopic neural activity evolves throughout development, yet how this maturation relates to underlying brain network formation and dynamics remains unknown. Here, we assess the developmental maturation of electroencephalogram spectra via Bayesian model inversion of the spectral graph model, a parsimonious whole-brain model of spatiospectral neural activity derived from linearized neural field models coupled by the structural connectome. Simulation-based inference was used to estimate age-varying spectral graph model parameter posterior distributions from electroencephalogram spectra spanning the developmental period. This model-fitting approach accurately captures observed developmental electroencephalogram spectral maturation via a neurobiologically consistent progression of key neural parameters: long-range coupling, axonal conduction speed, and excitatory:inhibitory balance. These results suggest that the spectral maturation of macroscopic neural activity observed during typical development is supported by age-dependent functional adaptations in localized neural dynamics and their long-range coupling across the macroscopic structural network.

Published
2024
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2. Impaired long-range excitatory time scale predicts abnormal neural oscillations and cognitive deficits in Alzheimer’s disease

Author

Parul Verma, Kamalini Ranasinghe, Janani Prasad, Chang Cai, Xihe Xie, Hannah Lerner, Danielle Mizuiri, Bruce Miller, Katherine Rankin, Keith Vossel, Steven W. Cheung, Srikantan S. Nagarajan, and Ashish Raj

Subjects
Brain activity, Alzheimer’s disease, Magnetoencephalography, Spectral graph theory, Cognitive decline, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571, Neurology. Diseases of the nervous system, RC346-429
Abstract

Abstract Background Alzheimer’s disease (AD) is the most common form of dementia, progressively impairing cognitive abilities. While neuroimaging studies have revealed functional abnormalities in AD, how these relate to aberrant neuronal circuit mechanisms remains unclear. Using magnetoencephalography imaging we documented abnormal local neural synchrony patterns in patients with AD. To identify global abnormal biophysical mechanisms underlying the spatial and spectral electrophysiological patterns in AD, we estimated the parameters of a biophysical spectral graph model (SGM). Methods SGM is an analytic neural mass model that describes how long-range fiber projections in the brain mediate the excitatory and inhibitory activity of local neuronal subpopulations. Unlike other coupled neuronal mass models, the SGM is linear, available in closed-form, and parameterized by a small set of biophysical interpretable global parameters. This facilitates their rapid and unambiguous inference which we performed here on a well-characterized clinical population of patients with AD (N = 88, age = 62.73 +/- 8.64 years) and a cohort of age-matched controls (N = 88, age = 65.07 +/- 9.92 years). Results Patients with AD showed significantly elevated long-range excitatory neuronal time scales, local excitatory neuronal time scales and local inhibitory neural synaptic strength. The long-range excitatory time scale had a larger effect size, compared to local excitatory time scale and inhibitory synaptic strength and contributed highest for the accurate classification of patients with AD from controls. Furthermore, increased long-range time scale was associated with greater deficits in global cognition. Conclusions These results demonstrate that long-range excitatory time scale of neuronal activity, despite being a global measure, is a key determinant in the local spectral signatures and cognition in the human brain, and how it might be a parsimonious factor underlying altered neuronal activity in AD. Our findings provide new insights into mechanistic links between abnormal local spectral signatures and global connectivity measures in AD.

Published
2024
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4. Mechanically active integrins target lytic secretion at the immune synapse to facilitate cellular cytotoxicity

Author

Mitchell S. Wang, Yuesong Hu, Elisa E. Sanchez, Xihe Xie, Nathan H. Roy, Miguel de Jesus, Benjamin Y. Winer, Elizabeth A. Zale, Weiyang Jin, Chirag Sachar, Joanne H. Lee, Yeonsun Hong, Minsoo Kim, Lance C. Kam, Khalid Salaita, and Morgan Huse

Subjects
Science
Abstract

Abstract Cytotoxic lymphocytes fight pathogens and cancer by forming immune synapses with infected or transformed target cells and then secreting cytotoxic perforin and granzyme into the synaptic space, with potent and specific killing achieved by this focused delivery. The mechanisms that establish the precise location of secretory events, however, remain poorly understood. Here we use single cell biophysical measurements, micropatterning, and functional assays to demonstrate that localized mechanotransduction helps define the position of secretory events within the synapse. Ligand-bound integrins, predominantly the αLβ2 isoform LFA-1, function as spatial cues to attract lytic granules containing perforin and granzyme and induce their fusion with the plasma membrane for content release. LFA-1 is subjected to pulling forces within secretory domains, and disruption of these forces via depletion of the adaptor molecule talin abrogates cytotoxicity. We thus conclude that lymphocytes employ an integrin-dependent mechanical checkpoint to enhance their cytotoxic power and fidelity.

Published
2022
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5. Time-varying dynamic network model for dynamic resting state functional connectivity in fMRI and MEG imaging

Author

Fei Jiang, Huaqing Jin, Yijing Gao, Xihe Xie, Jennifer Cummings, Ashish Raj, and Srikantan Nagarajan

Subjects
Brain state switch, Dynamic resting state functional connectivity, Change point detection, Functional magnetic resonance, Magnetoencephalography, Multi-modality imaging, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Dynamic resting state functional connectivity (RSFC) characterizes fluctuations that occur over time in functional brain networks. Existing methods to extract dynamic RSFCs, such as sliding-window and clustering methods that are inherently non-adaptive, have various limitations such as high-dimensionality, an inability to reconstruct brain signals, insufficiency of data for reliable estimation, insensitivity to rapid changes in dynamics, and a lack of generalizability across multiply functional imaging modalities. To overcome these deficiencies, we develop a novel and unifying time-varying dynamic network (TVDN) framework for examining dynamic resting state functional connectivity. TVDN includes a generative model that describes the relation between a low-dimensional dynamic RSFC and the brain signals, and an inference algorithm that automatically and adaptively learns the low-dimensional manifold of dynamic RSFC and detects dynamic state transitions in data. TVDN is applicable to multiple modalities of functional neuroimaging such as fMRI and MEG/EEG. The estimated low-dimensional dynamic RSFCs manifold directly links to the frequency content of brain signals. Hence we can evaluate TVDN performance by examining whether learnt features can reconstruct observed brain signals. We conduct comprehensive simulations to evaluate TVDN under hypothetical settings. We then demonstrate the application of TVDN with real fMRI and MEG data, and compare the results with existing benchmarks. Results demonstrate that TVDN is able to correctly capture the dynamics of brain activity and more robustly detect brain state switching both in resting state fMRI and MEG data.

Published
2022
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6. Altered excitatory and inhibitory neuronal subpopulation parameters are distinctly associated with tau and amyloid in Alzheimer’s disease

Author

Kamalini G Ranasinghe, Parul Verma, Chang Cai, Xihe Xie, Kiwamu Kudo, Xiao Gao, Hannah Lerner, Danielle Mizuiri, Amelia Strom, Leonardo Iaccarino, Renaud La Joie, Bruce L Miller, Maria Luisa Gorno-Tempini, Katherine P Rankin, William J Jagust, Keith Vossel, Gil D Rabinovici, Ashish Raj, and Srikantan S Nagarajan

Subjects
magnetoencephalogrpahy, tau-PET, Alzheimer's disease, neural mass model, Medicine, Science, Biology (General), QH301-705.5
Abstract

Background: Neuronal- and circuit-level abnormalities of excitation and inhibition are shown to be associated with tau and amyloid-beta (Aβ) in preclinical models of Alzheimer’s disease (AD). These relationships remain poorly understood in patients with AD. Methods: Using empirical spectra from magnetoencephalography and computational modeling (neural mass model), we examined excitatory and inhibitory parameters of neuronal subpopulations and investigated their specific associations to regional tau and Aβ, measured by positron emission tomography, in patients with AD. Results: Patients with AD showed abnormal excitatory and inhibitory time-constants and neural gains compared to age-matched controls. Increased excitatory time-constants distinctly correlated with higher tau depositions while increased inhibitory time-constants distinctly correlated with higher Aβ depositions. Conclusions: Our results provide critical insights about potential mechanistic links between abnormal neural oscillations and cellular correlates of impaired excitatory and inhibitory synaptic functions associated with tau and Aβ in patients with AD. Funding: This study was supported by the National Institutes of Health grants: K08AG058749 (KGR), F32AG050434-01A1 (KGR), K23 AG038357 (KAV), P50 AG023501, P01 AG19724 (BLM), P50-AG023501 (BLM and GDR), R01 AG045611 (GDR); AG034570, AG062542 (WJ); NS100440 (SSN), DC176960 (SSN), DC017091 (SSN), AG062196 (SSN); a grant from John Douglas French Alzheimer’s Foundation (KAV); grants from Larry L. Hillblom Foundation: 2015-A-034-FEL (KGR), 2019-A-013-SUP (KGR); grants from the Alzheimer’s Association: AARG-21-849773 (KGR); PCTRB-13-288476 (KAV), and made possible by Part the CloudTM (ETAC-09-133596); a grant from Tau Consortium (GDR and WJJ), and a gift from the S. D. Bechtel Jr. Foundation.

Published
2022
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7. Emergence of canonical functional networks from the structural connectome

Author

Xihe Xie, Chang Cai, Pablo F. Damasceno, Srikantan S. Nagarajan, and Ashish Raj

Subjects
Structural connectivity, Functional networks, Graph Laplacian, Complex Laplacian, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

How do functional brain networks emerge from the underlying wiring of the brain? We examine how resting-state functional activation patterns emerge from the underlying connectivity and length of white matter fibers that constitute its “structural connectome”. By introducing realistic signal transmission delays along fiber projections, we obtain a complex-valued graph Laplacian matrix that depends on two parameters: coupling strength and oscillation frequency. This complex Laplacian admits a complex-valued eigen-basis in the frequency domain that is highly tunable and capable of reproducing the spatial patterns of canonical functional networks without requiring any detailed neural activity modeling. Specific canonical functional networks can be predicted using linear superposition of small subsets of complex eigenmodes. Using a novel parameter inference procedure we show that the complex Laplacian outperforms the real-valued Laplacian in predicting functional networks. The complex Laplacian eigenmodes therefore constitute a tunable yet parsimonious substrate on which a rich repertoire of realistic functional patterns can emerge. Although brain activity is governed by highly complex nonlinear processes and dense connections, our work suggests that simple extensions of linear models to the complex domain effectively approximate rich macroscopic spatial patterns observable on BOLD fMRI.

Published
2021
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8. Autologous tumor lysate-pulsed dendritic cell immunotherapy with cytokine-induced killer cells improves survival in gastric and colorectal cancer patients.

Author

Daiqing Gao, Changyou Li, Xihe Xie, Peng Zhao, Xiaofang Wei, Weihong Sun, Hsin-Chen Liu, Aris T Alexandrou, Jennifer Jones, Ronghua Zhao, and Jian Jian Li

Subjects
Medicine, Science
Abstract

Gastric and colorectal cancers (GC and CRC) have poor prognosis and are resistant to chemo- and/or radiotherapy. In the present study, the prophylactic effects of dendritic cell (DC) vaccination are evaluated on disease progression and clinical benefits in a group of 54 GC and CRC patients treated with DC immunotherapy combined with cytokine-induced killer (CIK) cells after surgery with or without chemo-radiotherapy. DCs were prepared from the mononuclear cells isolated from patients using IL-2/GM-CSF and loaded with tumor antigens; CIK cells were prepared by incubating peripheral blood lymphocytes with IL-2, IFN-γ, and CD3 antibodies. The DC/CIK therapy started 3 days after low-dose chemotherapy and was repeated 3-5 times in 2 weeks as one cycle with a total of 188.3 ± 79.8 × 10(6) DCs and 58.8 ± 22.3 × 10(8) CIK cells. Cytokine levels in patients' sera before and after treatments were measured and the follow-up was conducted for 98 months to determine disease-free survival (DFS) and overall survival (OS). The results demonstrate that all cytokines tested were elevated with significantly higher levels of IFN-γ and IL-12 in both GC and CRC cohorts of DC/CIK treated patients. By Cox regression analysis, DC/CIK therapy reduced the risk of post-operative disease progression (p

Published
2014
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9. Spectral graph modeling reveals global slowing of neurophysiological network transmission in Alzheimer's disease

Author

Parul Verma, Kamalini Ranasinghe, Janani Prasad, Chang Cai, Xihe Xie, Hannah Lerner, Danielle Mizuiri, Bruce Miller, Katherine Rankin, Keith Vossel, Steven Cheung, Srikantan Nagarajan, and Ashish Raj

Abstract

Alzheimer’s disease (AD) is the most common form of dementia, progressively impairing memory and cognition. While neuroimaging studies have revealed functional abnormalities in AD, how these relate to aberrant neuronal circuit mechanisms remains unclear. We employed a spectral graph-theory model (SGM) to identify abnormal biophysical markers of neuronal activity in AD. SGM is an analytic model that describes how long-range fiber projections in the brain mediate excitatory and inhibitory activity of local neuronal subpopulations. We estimated SGM parameters that captured the regional power spectra obtained from magnetoencephalography imaging of a well-characterized population of patients with AD and controls. The long-range excitatory time constant was the most important feature for the accurate classification of AD and controls and was associated with global cognitive deficits in AD. These results indicate that a global impairment in the long-range excitatory neurons might be a sufficient factor underlying spatiotemporal alterations of neuronal activity in AD.

Published
2023

11. Abnormal neural oscillations depicting excitatory‐inhibitory imbalance are distinctly associated with amyloid and tau depositions in Alzheimer's disease

Author

Kamalini G. Ranasinghe, Parul Verma, Chang Cai, Xihe Xie, Kiwamu Kudo, Xiao Gao, Hannah M. Lerner, Danielle Mizuiri, Amelia Strom, Leonardo Iaccarino, Renaud La Joie, Bruce L. Miller, Maria Luisa Gorno Tempini, Katherine P. Rankin, William J. Jagust, Keith A. Vossel, Gil D. Rabinovici, Ashish Raj, and Srikantan S. Nagarajan

Subjects
Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Epidemiology, Health Policy, Neurology (clinical), Geriatrics and Gerontology
Published
2021

12. Time-varying dynamic network model for dynamic resting state functional connectivity in fMRI and MEG imaging

Author

Fei Jiang, Huaqing Jin, Yijing Gao, Xihe Xie, Jennifer Cummings, Ashish Raj, and Srikantan Nagarajan

Subjects
Brain Mapping, Neurology & Neurosurgery, Cognitive Neuroscience, Functional Neuroimaging, Psychology and Cognitive Sciences, Neurosciences, Change point detection, Magnetoencephalography, Brain, Bioengineering, Medical and Health Sciences, Magnetic Resonance Imaging, Multi-modality imaging, Neurology, Clinical Research, Neurological, Biomedical Imaging, Cluster Analysis, Humans, Nerve Net, Dynamic resting state functional connectivity, Brain state switch, Functional magnetic resonance, Algorithms
Abstract

Dynamic resting state functional connectivity (RSFC) characterizes fluctuations that occur over time in functional brain networks. Existing methods to extract dynamic RSFCs, such as sliding-window and clustering methods that are inherently non-adaptive, have various limitations such as high-dimensionality, an inability to reconstruct brain signals, insufficiency of data for reliable estimation, insensitivity to rapid changes in dynamics, and a lack of generalizability across multiply functional imaging modalities. To overcome these deficiencies, we develop a novel and unifying time-varying dynamic network (TVDN) framework for examining dynamic resting state functional connectivity. TVDN includes a generative model that describes the relation between a low-dimensional dynamic RSFC and the brain signals, and an inference algorithm that automatically and adaptively learns the low-dimensional manifold of dynamic RSFC and detects dynamic state transitions in data. TVDN is applicable to multiple modalities of functional neuroimaging such as fMRI and MEG/EEG. The estimated low-dimensional dynamic RSFCs manifold directly links to the frequency content of brain signals. Hence we can evaluate TVDN performance by examining whether learnt features can reconstruct observed brain signals. We conduct comprehensive simulations to evaluate TVDN under hypothetical settings. We then demonstrate the application of TVDN with real fMRI and MEG data, and compare the results with existing benchmarks. Results demonstrate that TVDN is able to correctly capture the dynamics of brain activity and more robustly detect brain state switching both in resting state fMRI and MEG data.

Published
2021

13. Mechanically active integrins direct cytotoxic secretion at the immune synapse

Author

Khalid Salaita, M. De Jesus, E. Sanchez, Xihe Xie, M. S. Wang, Yi-Juan Hu, Y. Hong, N. H. Roy, Weiyang Jin, Morgan Huse, Minsoo Kim, JinHyung Lee, and Lance C. Kam

Subjects
biology, Perforin, Granzyme, Chemistry, Integrin, biology.protein, Cytotoxic T cell, Secretion, Mechanotransduction, Pore forming protein, Immunological synapse, Cell biology
Abstract

The secretory output of cell-cell interfaces must be tightly controlled in space and time to ensure functional efficacy. This is particularly true for the cytotoxic immune synapse (IS), the stereotyped junction formed between a cytotoxic lymphocyte and the infected or transformed target cell it aims to destroy1. Cytotoxic lymphocytes kill their targets by channeling a mixture of granzyme proteases and the pore forming protein perforin directly into the IS2, 3. The synaptic secretion of these toxic molecules constrains their deleterious effects to the target cell alone, thereby protecting innocent bystander cells in the surrounding tissue from collateral damage. Despite the importance of this process for immune specificity, the molecular and cellular mechanisms that establish secretory sites within the IS remain poorly understood. Here, we identified an essential role for integrin mechanotransduction in cytotoxic secretion using a combination of single cell biophysical measurements, ligand micropatterning, and functional assays. Upon ligand-binding, the αLβ2integrin LFA-1 functioned as a spatial cue, attracting lytic granules containing perforin and granzyme and inducing their fusion at closely adjacent sites within the synaptic membrane. LFA-1 molecules were subjected to pulling forces within these secretory domains, and genetic or pharmacological suppression of these forces abrogated cytotoxicity. We conclude that lymphocytes employ an integrin-dependent mechanical checkpoint to enhance both the potency and the security of their cytotoxic output.

Published
2021

14. Centering inclusivity in the design of online conferences - An OHBM - Open Science perspective

Author

Elizabeth Levitis, Cassandra Doris Gould van Praag, Remi Gau, Stephan Heunis, Elizabeth DuPre, Greg Kiar, Katherine L Bottenhorn, Tristan Glatard, Aki Nikolaidis, Kirstie Jane Whitaker, Matteo Mancini, Guiomar Niso, Soroosh Afyouni, Eva Alonso Ortiz, Stefan Appelhoff, Aurina Arnatkeviciute, Melvin Selim ATAY, Tibor Auer, Giulia Baracchini, Johanna Margarete Marianne Bayer, Michael J. S. Beauvais, Janine Diane Bijsterbosch, Isil Poyraz Bilgin, Saskia Bollmann, Steffen Bollmann, Rotem Botvinik-Nezer, Molly G Bright, Vince D Calhoun, Xiao Chen, Sidhant Chopra, Hu Chuan-Peng, Thomas Close, Savannah Cookson, Cameron Craddock, Alejandro De La Vega, Benjamin De Leener, Damion Demeter, Paola Di Maio, Erin W Dickie, Simon B Eickhoff, Oscar Esteban, Karolina Finc, Matteo Frigo, Saampras Ganesan, Melanie Ganz, Kelly Garner, Eduardo A. Garza-Villarreal, Gabriel Gonzalez-Escamilla, Rohit Goswami, John David Griffiths, Tijl Grootswagers, Samuel Guay, Olivia Guest, Daniel A. Handwerker, Peer Herholz, Katja Heuer, Dorien Huijser, Vittorio Iacovella, Michael Joseph, Agah Karakuzu, David Keator, Xenia Kobeleva, Manoj Kumar, Angie Laird, Linda J. Larson-Prior, Alexandra Lautarescu, Alberto Lazari, Jon Haitz Legarreta Gorroño, Jeff jeffers, Jinglei Lv, Sina Mansour L., David Meunier, Dustin Moraczewski, Tulika Nandi, Samuel A. Nastase, Matthias Nau, Stephanie Noble, Martin Norgaard, Johnes Obungoloch, Robert Oostenveld, Edwina R Orchard, Ana Luísa Pinho, Russell Poldrack, Anqi Qiu, Pradeep Reddy Raamana, Ariel Rokem, Saige Rutherford, Malvika Sharan, Thomas Shaw, Warda T Syeda, Meghan M. Testerman, Roberto Toro, Sofie L. Valk, Sofie Van Den Bossche, Gael P. Varoquaux, Frantisek Vasa, Michele Veldsman, Jakub Vohryzek, Adina Svenja Wagner, Reubs J Walsh, Tonya White, null zuxfoucault, Xihe Xie, Chao-Gan Yan, Yu-Fang Yang, Yohan Yee, Gaston E Zanitti, Ana Van Gulick, Eugene Duff, Camille MAUMET, National Institute of Mental Health (NIMH), University College of London [London] (UCL), University of Oxford, Université Catholique de Louvain = Catholic University of Louvain (UCL), Eindhoven University of Technology [Eindhoven] (TU/e), McGill University = Université McGill [Montréal, Canada], Florida International University [Miami] (FIU), Concordia University [Montreal], Child Mind Institute, The Alan Turing Institute, University of Sussex, Cardiff University, École Polytechnique de Montréal (EPM), Indiana University [Bloomington], Indiana University System, Universidad Politécnica de Madrid (UPM), Max Planck Institute for Human Development, Max-Planck-Gesellschaft, Monash University [Melbourne], Middle East Technical University [Ankara] (METU), University of Surrey (UNIS), Montreal Neurological Institute and Hospital, University of Melbourne, Orygen Youth Health Research Centre [Melbourne], Washington University School of Medicine in St. Louis, Washington University in Saint Louis (WUSTL), University of Reading (UOR), University of Queensland [Brisbane], Dartmouth College [Hanover], Northwestern University [Evanston], Georgia State University, University System of Georgia (USG), Institute of Psychology [Beijing], Chinese Academy of Sciences [Changchun Branch] (CAS), University of Chinese Academy of Sciences [Beijing] (UCAS), Nanjing Normal University (NNU), The University of Sydney, University of California (UC), University of Texas at Austin [Austin], CHU Sainte Justine [Montréal], Institute for Globally Distributed Open Research and Education (IGDORE ), Centre for Addiction and Mental Health [Toronto] (CAMH), University of Toronto, Heinrich Heine Universität Düsseldorf = Heinrich Heine University [Düsseldorf], Jülich Research Centre, Université de Lausanne = University of Lausanne (UNIL), Nicolaus Copernicus University [Toruń], Université Côte d'Azur (UCA), Computational Imaging of the Central Nervous System (ATHENA), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Rigshospitalet [Copenhagen], Copenhagen University Hospital, University of Copenhagen = Københavns Universitet (UCPH), University of Birmingham [Birmingham], Universidad Nacional Autónoma de México = National Autonomous University of Mexico (UNAM), Johannes Gutenberg - Universität Mainz = Johannes Gutenberg University (JGU), University of Iceland [Reykjavik], Indian Institute of Technology Kanpur (IIT Kanpur), Western Sydney University, Université de Montréal (UdeM), Radboud University [Nijmegen], Centre de Recherche Interdisciplinaire / Center for Research and Interdisciplinarity [Paris, France] (CRI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig] (IMPNSC), Erasmus University Rotterdam, Universiteit Leiden, University of Trento [Trento], Université du Québec à Montréal = University of Québec in Montréal (UQAM), University Hospital Bonn, German Research Center for Neurodegenerative Diseases - Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Princeton University, University of Arkansas for Medical Sciences (UAMS), Arkansas Children's Research Institute, King‘s College London, Université de Sherbrooke (UdeS), Aarhus University [Aarhus], Institut de Neurosciences de la Timone (INT), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Yale University [New Haven], Stanford University, Mbarara University of Science and Technology [Mbarara] (MUST), Karolinska Institutet [Stockholm], Université Paris-Saclay, National University of Singapore (NUS), Johns Hopkins University (JHU), University of Pittsburgh (PITT), Pennsylvania Commonwealth System of Higher Education (PCSHE), University of Washington [Seattle], Radboud University Medical Center [Nijmegen], University of Michigan [Ann Arbor], University of Michigan System, Institut Pasteur [Paris] (IP), Universiteit Gent = Ghent University (UGENT), Vrije Universiteit Amsterdam [Amsterdam] (VU), Department of Public Health, Erasmus University Medical Centre, Rotterdam, Erasmus University Medical Centre, Rotterdam, Academia Sinica, Weill Medical College of Cornell University [New York], Chinese Academy of Sciences [Beijing] (CAS), University of Würzburg, The Hospital for sick children [Toronto] (SickKids), Carnegie Mellon University [Pittsburgh] (CMU), Neuroimagerie: méthodes et applications (Empenn), Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), R25-DA051675, National Institutes of Health, 1631325, National Science Foundation, 5R21MH118556-02, National Institute of Mental Health, BB/S008314/1, Biotechnology and Biological Sciences Research Council, G036716N, Fonds Wetenschappelijk Onderzoek, University of Oxford [Oxford], University of California, University of Lausanne (UNIL), University of Copenhagen = Københavns Universitet (KU), Universidad Nacional Autónoma de México (UNAM), Johannes Gutenberg - Universität Mainz (JGU), Radboud university [Nijmegen], Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP), Leiden University, Institut Pasteur [Paris], Universiteit Gent = Ghent University [Belgium] (UGENT), Empenn, Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), and Maumet, Camille

Subjects
SDG 16 - Peace, collaborative events, [SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.NEUR] Cognitive science/Neuroscience, SDG 16 - Peace, Justice and Strong Institutions, Review, Justice and Strong Institutions, online conferences, diversity, World Wide Web, inclusivity, open science, ddc:610, Sociology
Abstract

As the global health crisis unfolded throughout the world, many academic conferences moved online in 2020. This move has been hailed as a positive step towards inclusivity in its attenuation of economic, physical and legal barriers and effectively enabled many individuals who have traditionally been underrepresented to join and participate. A number of studies have outlined how moving online made it possible to gather a more global community and has increased opportunities for individuals with various constraints, e.g. caregiving responsibilities. Yet, the mere existence of online conferences is unfortunately no guarantee that everyone can attend and participate meaningfully. In fact, many elements of an online conference are still significant barriers to truly diverse participation: the tools used can be inaccessible for some individuals; the scheduling choices can favour some geographical locations; the setup of the conference can provide more visibility to well-established researchers and reduce opportunities for early career researchers. While acknowledging the benefits of an online setting, especially for individuals who have traditionally been underrepresented or excluded, we recognize that fostering social justice requires inclusivity to actively be centered in every aspect of online conference design.Here, we draw from the literature and from our own experiences to identify practices that purposefully encourage a diverse community to: attend, participate in, and lead online conferences. Reflecting on how to design more inclusive online events is especially important as multiple scientific organizations have announced that they will continue offering an online version of their event when in-person conferences can resume.

Published
2021

15. Emergence of canonical functional networks from the structural connectome

Author

Pablo F. Damasceno, Srikantan S. Nagarajan, Chang Cai, Ashish Raj, and Xihe Xie

Subjects
Computer science, Cognitive Neuroscience, 1.1 Normal biological development and functioning, Neuroimaging, Neurosciences. Biological psychiatry. Neuropsychiatry, Topology, Medical and Health Sciences, 050105 experimental psychology, Domain (mathematical analysis), Article, Complex Laplacian, White matter, 03 medical and health sciences, Superposition principle, Matrix (mathematics), 0302 clinical medicine, Theoretical, Models, Underpinning research, medicine, Humans, Graph Laplacian, 0501 psychology and cognitive sciences, Neurology & Neurosurgery, Structural connectivity, 05 social sciences, Psychology and Cognitive Sciences, Linear model, Neurosciences, Brain, Default Mode Network, Models, Theoretical, Magnetic Resonance Imaging, Nonlinear system, medicine.anatomical_structure, Neurology, Functional networks, Frequency domain, Neurological, Laplacian matrix, Nerve Net, Laplace operator, 030217 neurology & neurosurgery, RC321-571
Abstract

How do functional brain networks emerge from the underlying wiring of the brain? We examine how resting-state functional activation patterns emerge from the underlying connectivity and length of white matter fibers that constitute its “structural connectome”. By introducing realistic signal transmission delays along fiber projections, we obtain a complex-valued graph Laplacian matrix that depends on two parameters: coupling strength and oscillation frequency. This complex Laplacian admits a complex-valued eigen-basis in the frequency domain that is highly tunable and capable of reproducing the spatial patterns of canonical functional networks without requiring any detailed neural activity modeling. Specific canonical functional networks can be predicted using linear superposition of small subsets of complex eigenmodes. Using a novel parameter inference procedure we show that the complex Laplacian outperforms the real-valued Laplacian in predicting functional networks. The complex Laplacian eigenmodes therefore constitute a tunable yet parsimonious substrate on which a rich repertoire of realistic functional patterns can emerge. Although brain activity is governed by highly complex nonlinear processes and dense connections, our work suggests that simple extensions of linear models to the complex domain effectively approximate rich macroscopic spatial patterns observable on BOLD fMRI.

Published
2021

16. Altered excitatory and inhibitory neuronal subpopulation parameters are distinctly associated with tau and amyloid in Alzheimer's disease.

Author

Ranasinghe, Kamalini G., Verma, Parul, Chang Cai, Xihe Xie, Kiwamu Kudo, Xiao Gao, Lerner, Hannah, Mizuiri, Danielle, Strom, Amelia, Iaccarino, Leonardo, Joie, Renaud La, Miller, Bruce L., Gorno Tempini, Maria Luisa, Rankin, Katherine P., Jagust, William J., Vossel, Keith, Rabinovici, Gil D., Raj, Ashish, and Nagarajan, Srikantan S.

Published
2022
Full Text
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17. Spectral graph theory of brain oscillations

Author

Srikantan S. Nagarajan, Ashish Raj, Xihe Xie, Eva M. Palacios, Pratik Mukherjee, Chang Cai, and Julia P. Owen

Subjects
Male, magnetoencephalography, Computer science, spectral graph theory, 0302 clinical medicine, Theoretical, Models, brain activity, Statistical physics, Child, Research Articles, Cerebral Cortex, 0303 health sciences, Mesoscopic physics, Radiological and Ultrasound Technology, medicine.diagnostic_test, Spectral graph theory, 05 social sciences, Experimental Psychology, Middle Aged, Magnetic Resonance Imaging, Graph, Neurology, Neurological, Connectome, Graph (abstract data type), Female, Cognitive Sciences, Anatomy, Laplace operator, Linear equation, Research Article, Adult, alpha rhythm, Adolescent, 1.1 Normal biological development and functioning, connectomes, Network topology, 050105 experimental psychology, Young Adult, 03 medical and health sciences, medicine, Humans, 0501 psychology and cognitive sciences, Radiology, Nuclear Medicine and imaging, 030304 developmental biology, Computational neuroscience, Quantitative Biology::Neurons and Cognition, Neurosciences, Magnetoencephalography, Models, Theoretical, Brain Waves, Nonlinear system, Diffusion Magnetic Resonance Imaging, Neurology (clinical), Nerve Net, 030217 neurology & neurosurgery
Abstract

The relationship between the brain’s structural wiring and the functional patterns of neural activity is of fundamental interest in computational neuroscience. We examine a hierarchical, linear graph spectral model of brain activity at mesoscopic and macroscopic scales. The model formulation yields an elegant closed-form solution for the structure-function problem, specified by the graph spectrum of the structural connectome’s Laplacian, with simple, universal rules of dynamics specified by a minimal set of global parameters. The resulting parsimonious and analytical solution stands in contrast to complex numerical simulations of high dimensional coupled non-linear neural field models. This spectral graph model accurately predicts spatial and spectral features of neural oscillatory activity across the brain and was successful in simultaneously reproducing empirically observed spatial and spectral patterns of alpha-band (8-12 Hz) and beta-band (15-30Hz) activity estimated from source localized scalp magneto-encephalography (MEG). This spectral graph model demonstrates that certain brain oscillations are emergent properties of the graph structure of the structural connectome and provides important insights towards understanding the fundamental relationship between network topology and macroscopic whole-brain dynamics.Significance StatementThe relationship between the brain’s structural wiring and the functional patterns of neural activity is of fundamental interest in computational neuroscience. We examine a hierarchical, linear graph spectral model of brain activity at mesoscopic and macroscopic scales. The model formulation yields an elegant closed-form solution for the structure-function problem, specified by the graph spectrum of the structural connectome’s Laplacian, with simple, universal rules of dynamics specified by a minimal set of global parameters. This spectral graph model demonstrates that certain brain oscillations are emergent properties of the graph structure of the structural connectome and provides important insights towards understanding the fundamental relationship between network topology and macroscopic whole-brain dynamics.

Published
2020

18. Brainhack: Developing a culture of open, inclusive, community-driven neuroscience

Author

Selim Melvin Atay, Iska Moxon-Emre, Anders Eklund, Paula P. Brooks, Nandita Vijayakumar, Anibal Sólon Heinsfeld, Jean-Baptiste Poline, Yaroslav O. Halchenko, David Meunier, Daniela M. Hohmann, Martin Szinte, Arshitha Basavaraj, Andrija Štajduhar, Link Tejavibulya, Michael Dayan, Anisha Keshavan, Chao Jiang, Felix Hoffstaedter, Michael P. Milham, Davide Momi, Hua Xie, Ai Wern Chung, Georg Langs, Hao-Ting Wang, Xenia Kobeleva, Robert Oostenveld, Thomas G. Close, Lena Dorfschmidt, Jesse A. Brown, Serge Koudoro, J.P. Manzano-Patron, Isil P. Bilgin, Shreyas Fadnavis, Sylvain Takerkart, R. Cameron Craddock, Stephan Heunis, Scott Peltier, Kathryn Berluti, Hayli Spence, Pablo F. Damasceno, David H. O’Connor, Eleftherios Garyfallidis, Eugene P. Duff, Rudolph Pienaar, Nicolas Traut, AmanPreet Badhwar, Ali R. Khan, Marissa Laws, Abigail S. Greene, Richard A. I. Bethlehem, Angela R. Laird, Krista DeStasio, Ina Thome, Camille Maumet, Alexandru D. Iordan, P. Christiaan Klink, Damion V. Demeter, John A. Onofrey, Cassandra D. Gould van Praag, Jakub Vohryzek, Suzanne T. Witt, Aysha Motala, Valerie Hayot-Sasson, Geetika Gupta, Alexandre Rosa Franco, John W. VanMeter, Michel Thiebaut de Schotten, James M. Shine, Lucina Q. Uddin, Thomas S. Hartmann, Guillaume Flandin, Clara Moreau, Tomislav Lipic, Claire Bradley, Fernando A. Barrios, Daniel S. Margulies, R. Austin Benn, Sofie Van Den Bossche, Lydia Riedl, Xihe Xie, Christopher R. Madan, Roberto Toro, Viviana Siless, Fabrizio De Vico Fallani, Nikoloz Sirmpilatze, Emily Olafson, Anqi Qiu, Theresa W. Cheng, Valentina Borghesani, Sidhant Chopra, Claire Cury, Giorgio Marinato, Horea-Ioan Ioanas, Giorgia Cona, Michael Joseph, Angela Tam, Mathias Scharinger, Daniel A. Handwerker, Katherine L. Bottenhorn, Sina Mansour L, Kathryn L. Mills, Christopher J. Markiewicz, Elizabeth Levitis, Cyril Pernet, Stephanie J. Forkel, Agah Karakuzu, Edwina R Orchard, Sarah L. Dziura, Saige Rutherford, Kamalaker Dadi, Enrico Glerean, Desiree Lussier, Davide Poggiali, Molly Simmonite, Jason Kai, Jessica Flannery, Ting Xu, Jon Haitz Legarreta, Nasim Anousheh, Marco Bedini, Tristan Glatard, Thomas E. Nichols, Joscelin Rocha-Hidalgo, Erin W. Dickie, Dipankar Bachar, Malin Sandström, Xi-Nian Zuo, Pedro Pinheiro-Chagas, Laura C. Rice, Jakša Vukojević, Javier Gonzalez-Castillo, José C. García Alanis, Lorenzo Pasquini, Yu-Fang Yang, Matteo Mancini, Deena Shariq, Chao-Gan Yan, Laura Tomaz da Silva, César Caballero-Gaudes, Yasmine Bassil, Aurina Arnatkeviciute, Dustin Scheinost, Satrajit S. Ghosh, Gaël Varoquaux, Etienne Combrisson, Bramsh Qamar Chandio, Kelly Garner, Tiago Quendera, Patrick Friedrich, Shawn A. Rhoads, Roxane Licandro, Elizabeth DuPre, Aki Nikolaidis, Simon Schwab, Stephanie Noble, Guillaume Auzias, Daniel J. Lurie, Mahboobeh Parsapoor, Eneko Uruñuela, Andrew Doyle, Peer Herholz, Saampras Ganesan, Vincent Koppelmans, Corey Horien, Samir Das, Junaid S. Merchant, Siyuan Gao, Matheus Marcon, Nathalia Bianchini Esper, B.T. Thomas Yeo, Katja Heuer, Caroline O’Brien, Micaela Y. Chan, Sook-Lei Liew, Lindsay D. Oliver, Kirstie Whitaker, Christoph Vogelbacher, Dylan M. Nielson, Krisanne Litinas, Dorien C. Huijser, Pierre Bellec, R. Todd Constable, David N. Kennedy, Julia Sprenger, Lea-Theresa Mais, Oscar Esteban, Patrick J. Park, Patrick Callahan, Christopher R. Nolan, Johanna Bayer, Guillaume Dumas, Elise Bannier, Elizabeth A. McDevitt, Ariel Rokem, Samuel A. Nastase, Olivia W. Stanley, Ruggero Basanisi, Daniele Marinazzo, Gregory Kiar, Lisa Novello, Samuel Guay, John C. Flournoy, Stefano Moia, Kendra Oudyk, Fang-Cheng Yeh, Gustav Nilsonne, Thomas B. Shaw, Steve Wideman, Saskia Bollmann, Steffen Bollmann, Julia M. Huntenburg, Augusto Buchweitz, Matteo Visconti di Oleggio Castello, Dimitra Maoutsa, Lucy B. Whitmore, Catherine Alice Hahn, Antonino Vallesi, Remi Gau, Felipe Meneguzzi, Université Catholique de Louvain = Catholic University of Louvain (UCL), Yale University [New Haven], Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig] (IMPNSC), Max-Planck-Gesellschaft, Florida International University [Miami] (FIU), University of Reading (UOR), University of Würzburg, Champalimaud Centre for the Unknown [Lisbon], University of Melbourne, University of Cambridge [UK] (CAM), Georgetown University [Washington] (GU), Philipps Universität Marburg = Philipps University of Marburg, Université de Montréal (UdeM), University College of London [London] (UCL), University of Sussex, Universiteit Gent = Ghent University (UGENT), German Research Center for Neurodegenerative Diseases - Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Université de Sherbrooke (UdeS), Middle East Technical University [Ankara] (METU), McGill University = Université McGill [Montréal, Canada], Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Universiteit Leiden, Karolinska Institutet [Stockholm], Princeton University, Universität Zürich [Zürich] = University of Zurich (UZH), University of the Basque Country/Euskal Herriko Unibertsitatea (UPV/EHU), University medical center and campus biotech Geneva, Emory University [Atlanta, GA], Cardiff University's Brain Research Imaging Centre [Cardiff] (CUBRIC), School of Psychology [Cardiff University], Cardiff University-Cardiff University, The University of Sydney, Weill Cornell Medicine [Cornell University], Cornell University [New York], Università degli Studi di Padova = University of Padua (Unipd), Jülich Research Centre, University of Texas at Austin [Austin], Institut Pasteur [Paris] (IP), Centre de Recherche Interdisciplinaire / Center for Research and Interdisciplinarity [Paris, France] (CRI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Basque Center on Cognition Brain and Language [Gipuzkoa, Espagne] (BCBL), Linköping University (LIU), University of Queensland [Brisbane], University of New South Wales [Sydney] (UNSW), Universidad Nacional Autónoma de México = National Autonomous University of Mexico (UNAM), University of Maryland [College Park], University of Maryland System, University of Washington [Seattle], Harvard Medical School [Boston] (HMS), Child Mind Institute, University of Western Ontario (UWO), Indiana University [Bloomington], Indiana University System, Monash university, Institut de Neurosciences de la Timone (INT), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Neuroimagerie: méthodes et applications (Empenn), Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), National Institute of Mental Health (NIMH), Università degli Studi di Trento (UNITN), Centro Nacional de Investigaciones Cardiovasculares Carlos III [Madrid, Spain] (CNIC), Instituto de Salud Carlos III [Madrid] (ISC), Queensland University of Technology [Brisbane] (QUT), University of California [San Francisco] (UC San Francisco), University of California (UC), Universidade Federal do Rio Grande do Sul [Porto Alegre] (UFRGS), University of Texas at Dallas [Richardson] (UT Dallas), University of Oregon [Eugene], Monash University [Melbourne], Boston Children's Hospital, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Neuroimagerie: méthodes et applications (EMPENN), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAL, IMAGE ET LANGAGE (IRISA-D6), Chinese Academy of Sciences [Beijing] (CAS), Beijing Normal University (BNU), This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 867458 awarded to Julia M. Huntenburg, from ANR-19-DATA-0025 NeuroWebLab for Katja Heuer, Roberto Toro, and Nicholas Traut, and from NIH K00MH122372 for Stephanie Noble., Brainhack Community: Nasim Anousheh, Aurina Arnatkeviciute, Guillaume Auzias, Dipankar Bachar, Elise Bannier, Ruggero Basanisi, Arshitha Basavaraj, Marco Bedini, Pierre Bellec, R Austin Benn, Kathryn Berluti, Steffen Bollmann, Saskia Bollmann, Claire Bradley, Jesse Brown, Augusto Buchweitz, Patrick Callahan, Micaela Y Chan, Bramsh Q Chandio, Theresa Cheng, Sidhant Chopra, Ai Wern Chung, Thomas G Close, Etienne Combrisson, Giorgia Cona, R Todd Constable, Claire Cury, Kamalaker Dadi, Pablo F Damasceno, Samir Das, Fabrizio De Vico Fallani, Krista DeStasio, Erin W Dickie, Lena Dorfschmidt, Eugene P Duff, Elizabeth DuPre, Sarah Dziura, Nathalia B Esper, Oscar Esteban, Shreyas Fadnavis, Guillaume Flandin, Jessica E Flannery, John Flournoy, Stephanie J Forkel, Alexandre R Franco, Saampras Ganesan, Siyuan Gao, José C García Alanis, Eleftherios Garyfallidis, Tristan Glatard, Enrico Glerean, Javier Gonzalez-Castillo, Cassandra D Gould van Praag, Abigail S Greene, Geetika Gupta, Catherine Alice Hahn, Yaroslav O Halchenko, Daniel Handwerker, Thomas S Hartmann, Valérie Hayot-Sasson, Stephan Heunis, Felix Hoffstaedter, Daniela M Hohmann, Corey Horien, Horea-Ioan Ioanas, Alexandru Iordan, Chao Jiang, Michael Joseph, Jason Kai, Agah Karakuzu, David N Kennedy, Anisha Keshavan, Ali R Khan, Gregory Kiar, P Christiaan Klink, Vincent Koppelmans, Serge Koudoro, Angela R Laird, Georg Langs, Marissa Laws, Roxane Licandro, Sook-Lei Liew, Tomislav Lipic, Krisanne Litinas, Daniel J Lurie, Désirée Lussier, Christopher R Madan, Lea-Theresa Mais, Sina Mansour L, J P Manzano-Patron, Dimitra Maoutsa, Matheus Marcon, Daniel S Margulies, Giorgio Marinato, Daniele Marinazzo, Christopher J Markiewicz, Camille Maumet, Felipe Meneguzzi, David Meunier, Michael P Milham, Kathryn L Mills, Davide Momi, Clara A Moreau, Aysha Motala, Iska Moxon-Emre, Thomas E Nichols, Dylan M Nielson, Gustav Nilsonne, Lisa Novello, Caroline O'Brien, Emily Olafson, Lindsay D Oliver, John A Onofrey, Edwina R Orchard, Kendra Oudyk, Patrick J Park, Mahboobeh Parsapoor, Lorenzo Pasquini, Scott Peltier, Cyril R Pernet, Rudolph Pienaar, Pedro Pinheiro-Chagas, Jean-Baptiste Poline, Anqi Qiu, Tiago Quendera, Laura C Rice, Joscelin Rocha-Hidalgo, Saige Rutherford, Mathias Scharinger, Dustin Scheinost, Deena Shariq, Thomas B Shaw, Viviana Siless, Molly Simmonite, Nikoloz Sirmpilatze, Hayli Spence, Julia Sprenger, Andrija Stajduhar, Martin Szinte, Sylvain Takerkart, Angela Tam, Link Tejavibulya, Michel Thiebaut de Schotten, Ina Thome, Laura Tomaz da Silva, Nicolas Traut, Lucina Q Uddin, Antonino Vallesi, John W VanMeter, Nandita Vijayakumar, Matteo Visconti di Oleggio Castello, Jakub Vohryzek, Jakša Vukojević, Kirstie Jane Whitaker, Lucy Whitmore, Steve Wideman, Suzanne T Witt, Hua Xie, Ting Xu, Chao-Gan Yan, Fang-Cheng Yeh, B T Thomas Yeo, Xi-Nian Zuo, ANR-19-DATA-0025,NeuroWebLab,Un laboratoire de neuroscience collectif: Au delà de FAIR(2019), European Project: 867458,LC-FMR, Maumet, Camille, Un laboratoire de neuroscience collectif: Au delà de FAIR - - NeuroWebLab2019 - ANR-19-DATA-0025 - DONNEES - VALID, Brainhack: Developing a culture of open, inclusive, community-driven neuroscience - LC-FMR - 867458 - INCOMING, Leiden University, Weill Cornell Medicine [New York], Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPC), Universiteit Gent = Ghent University [Belgium] (UGENT), University Hospital Bonn, Sherbrooke Connectivity Imaging Lab [Sherbrooke] (SCIL), Département d'informatique [Sherbrooke] (UdeS), Faculté des sciences [Sherbrooke] (UdeS), Université de Sherbrooke (UdeS)-Université de Sherbrooke (UdeS)-Faculté des sciences [Sherbrooke] (UdeS), Université de Sherbrooke (UdeS)-Université de Sherbrooke (UdeS), Videos and Images Theory and Analytics Laboratory (VITAL), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Erasmus University Rotterdam, Origami (Origami), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Princeton Neuroscience Institute [Princeton], Human Neuroscience Platform, Brighton and Sussex Medical School (BSMS), Azienda Ospedale Università di Padova = Hospital-University of Padua (AOUP), Institute of Neuroscience and Medicine [Jülich] (INM-1), Département de Neuroscience - Department of Neuroscience, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPC), Queensland Brain Institute, Universidad Nacional Autónoma de México (UNAM), Radboud university [Nijmegen], McGovern Institute for Brain Research [Cambridge], Massachusetts Institute of Technology (MIT), The Brainhack Community - https://pubmed.ncbi.nlm.nih.gov/33932337, Philipps University of Marburg, University of the Basque Country [Bizkaia] (UPV/EHU), Universita degli Studi di Padova, Philipps Universität Marburg, Institut Pasteur [Paris], Empenn, Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), University of California [San Francisco] (UCSF), University of California, Geochemistry, and University of Padova [Padova, Italy]

Subjects
0301 basic medicine, Open science, Community building, Best practice, Neuroscience(all), Brainhack, Article, neuroscience, 03 medical and health sciences, 0302 clinical medicine, best practices, collaboration, community building, hackathon, inclusivity, open science, reproducibility, training, Congresses as Topic, Neurosciences, Practice Guidelines as Topic, Communication, Internet, Research community, Medical Bioscience, Psychology, ddc:610, Sociology, Brainhack Community, Training, Neurology & Neurosurgery, Scientific progress, organization & administration [Neurosciences], General Neuroscience, [SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.NEUR] Cognitive science/Neuroscience, Medicinsk biovetenskap, 030104 developmental biology, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Cognitive Sciences, Engineering ethics, 030217 neurology & neurosurgery
Abstract

Available online 30 April 2021. Brainhack is an innovative meeting format that promotes scientific collaboration and education in an open, inclusive environment. This NeuroView describes the myriad benefits for participants and the research community and how Brainhacks complement conventional formats to augment scientific progress. The present manuscript is part of a growing community effort to collate Brainhack-related insights and expertise into a Jupyter Book (http://brainhack.org/brainhack_jupyter_book/) that will serve as a centralized set of resources for the community; we acknowledge all the individuals who contributed and will make ongoing contributions to these resources. A pre-print version of the present manuscript is available as part of the Jupyter Book. Moreover, we would like to acknowledge all Brainhack organizers, supporters, presenters, and participants for their contribution to growing and maintaining this community. The benefits described in this manuscript would not be possible without them. We also thank all institutions, labs, and organizations who have helped this community grow, meet in stimulating environments, and add an excellent educational resource pool and agenda. With an expanding community, Brainhack’s support network keeps growing, and we thank all labs and individual researchers for their dedication and expertise offered to this community (see http://brainhack.org/brainhack_jupyter_book/acknowledgments.html for a full list of individual acknowledgments; an updated list will be maintained in the Jupyter Book). Grants and funding bodies: This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 867458 awarded to Julia M. Huntenburg; from ANR-19-DATA-0025 NeuroWebLab for Katja Heuer, Roberto Toro, and Nicholas Traut; and from NIH K00MH122372 for Stephanie Noble. The Brainhack Community member list and contributions of the different authors are detailed at http://brainhack.org/brainhack_jupyter_book/contributors.html. Our crediting system is described here: http://brainhack.org/brainhack_jupyter_book/neuroview_authorship-agreement.html.

Published
2021

19. Gemcitabine treatment enhanced the anti-tumor effect of cytokine induced killer cells by depletion of CD4+CD25bri regulatory T cells

Author

Changyou Li, Weihong Sun, Danni Zhu, Qingqing Wang, Bin Han, Daiqing Gao, Qingming Guo, Peng Zhao, Xihe Xie, Zhen Zhang, and Xiaofang Wei

Subjects
Interleukin 2, Cytokine-induced killer cell, Cell growth, medicine.medical_treatment, Immunology, Immunotherapy, Biology, Pharmacology, Peripheral blood mononuclear cell, 03 medical and health sciences, 0302 clinical medicine, Cytokine, Cell culture, 030220 oncology & carcinogenesis, medicine, Immunology and Allergy, Adjuvant, 030215 immunology, medicine.drug
Abstract

Cytokine induced killer (CIK) cells have a powerful tumor cells killing activity both in vitro and in vivo and transfusion of these cells have become an adjuvant treatment for tumors. CIK cells are induced and amplified from peripheral blood mononuclear cells (PBMCs) with multiple cytokines. As CD4+CD25bri regulatory T cells can be also induced by high dose of interleukin 2 (IL-2) which is used for CIK cells amplification in the CIK cell culture system, the anti-tumor activity of CIK cells was suppressed to some extent. In order to overcome this unwanted suppressive factor, we found that low dose of gemcitabine could reduce the proportion of CD4+CD25bri regulatory T cells in the CIK cell culture system and significantly enhance the anti-tumor activity of CIK cells in vitro. The levels of interleukin-10 (IL-10) and transforming growth factor-β (TGF-β) were also reduced significantly following the depletion of CD4+CD25bri regulatory T cells in gemcitabine treated CIK cell culture system. In vivo experiment showed that low dose of gemcitabine treated CIK cells significantly suppressed tumor growth and prolonged their lifespan in tumor-bearing nude mice, with the proportion of CD4+CD25bri regulatory T cells reduced. Meanwhile, we detected lower levels of IL-10, TGF-β and a higher level of interferon-γ (IFN-γ) in tumor-bearing nude mice that received gemcitabine treated CIK cells transfusion than those in other groups. The possible mechanism involved in the enhanced anti-tumor activity in vivo was that gemcitabine treated CIK cells created a strengthened anti-tumor immune microenvironment with the changed levels of cytokines such as IL-10, TGF-β and IFN-γ. These results suggested a strategy to improve the adoptive immune therapy in recent use by removing the suppressive factors and a more effective tumor treatment combining chemotherapy and immunotherapy.

Published
2017

20. Parameter Identifiability and Non-Uniqueness In Connectome Based Neural Mass Models

Author

Sudhin A. Shah, Amy Kuceyeski, Nicholas D. Schiff, Ashish Raj, Srikantan S. Nagarajan, and Xihe Xie

Subjects
medicine.diagnostic_test, Quantitative Biology::Neurons and Cognition, business.industry, Computer science, Functional connectivity, Pattern recognition, Electroencephalography, Reduction (complexity), Transmission (telecommunications), Temporal resolution, medicine, Connectome, Identifiability, Artificial intelligence, Functional magnetic resonance imaging, business, Network model
Abstract

The spatial-temporal patterns of neuronal dynamics emerge from the network of coordinated brain regions, this structure-function relationship of the brain can be described mathematically by biophysical models of coupled brain regions connected by white matter tractography. Implementations of such models have focused on reproducing functional connectivity extracted from functional magnetic resonance imaging (fMRI), but these efforts are limited by the temporal resolution of fMRI data and the reduction of time course recordings into phenomenological functional connectivity maps. Here, we optimize parameters of a neural mass model (NMM) to best fit region-wise power spectra across the whole brain estimated from source localized electroencephalography (EEG). NMM models with global parameters were not able to fully reproduce region-wise power spectra, with or without the inclusion of structural connectivity information. In contrast, without the inclusion of structural connectivity information, independent oscillators at each brain region are able to reproduce region-wise power spectra. But the addition of structural connectivity and transmission delays to the NMM does not improve overall power spectra fit. Connectome-based NMM implementations with regional parameters lead to high dimensional network models that produce non-unique results. Inherent parameter identifiability problem in network models poses challenges for using such models as diagnostic tools for neurological diseases.

Published
2018
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21. Clinician Accessible Tools for GUI Computational Models of Transcranial Electrical Stimulation: BONSAI and SPHERES

Author

Marom Bikson, Xihe Xie, Asif Rahman, Jacek P. Dmochowski, Dennis Q. Truong, Abhishek Datta, Mathias Hüber, and Lucas C. Parra

Subjects
Computer science, Interface (computing), medicine.medical_treatment, Biophysics, Cloud computing, Article, lcsh:RC321-571, Access to Information, User-Computer Interface, Software, Human–computer interaction, medicine, Humans, Computer Simulation, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, Transcranial alternating current stimulation, Graphical user interface, Flexibility (engineering), Internet, Computational model, Transcranial direct-current stimulation, business.industry, General Neuroscience, Modeling, Brain, Open access, Electric Stimulation, Dose, Transcranial direct current stimulation, Neurology (clinical), business, Transcranial electrical stimulation
Abstract

Computational models of brain current flow during transcranial electrical stimulation (tES), including transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS), are increasingly used to understand and optimize clinical trials. We propose that broad dissemination requires a simple graphical user interface (GUI) software that allows users to explore and design montages in real-time, based on their own clinical/experimental experience and objectives. We introduce two complimentary open-source platforms for this purpose: BONSAI and SPHERES. BONSAI is a web (cloud) based application (available at neuralengr.com/bonsai) that can be accessed through any flash-supported browser interface. SPHERES (available at neuralengr.com/spheres) is a stand-alone GUI application that allow consideration of arbitrary montages on a concentric sphere model by leveraging an analytical solution. These open-source tES modeling platforms are designed go be upgraded and enhanced. Trade-offs between open-access approaches that balance ease of access, speed, and flexibility are discussed.

Published
2014

22. Gemcitabine treatment enhanced the anti-tumor effect of cytokine induced killer cells by depletion of CD4

Author

Peng, Zhao, Danni, Zhu, Zhen, Zhang, Bin, Han, Daiqing, Gao, Xiaofang, Wei, Xihe, Xie, Changyou, Li, Weihong, Sun, Qingqing, Wang, and Qingming, Guo

Subjects
Cytotoxicity, Immunologic, Male, Antimetabolites, Antineoplastic, Dose-Response Relationship, Drug, Cell Survival, Apoptosis, Deoxycytidine, T-Lymphocytes, Regulatory, Gemcitabine, Lymphocyte Depletion, Immunomodulation, Disease Models, Animal, Mice, Cytokine-Induced Killer Cells, Phenotype, T-Lymphocyte Subsets, Cell Line, Tumor, Neoplasms, Disease Progression, Animals, Cytokines, Humans, Biomarkers, Immunosuppressive Agents, Cell Proliferation
Abstract

Cytokine induced killer (CIK) cells have a powerful tumor cells killing activity both in vitro and in vivo and transfusion of these cells have become an adjuvant treatment for tumors. CIK cells are induced and amplified from peripheral blood mononuclear cells (PBMCs) with multiple cytokines. As CD4

Published
2016

23. Dendritic cell immunotherapy combined with cytokine-induced killer cells promotes skewing toward Th2 cytokine profile in patients with metastatic non-small cell lung cancer

Author

Changyou Li, Danni Zhu, Xiaofang Wei, Qingming Guo, Xiaocui Bu, Daiqing Gao, Weihong Sun, Peng Zhao, Xiaoqiang Wei, and Xihe Xie

Subjects
Adult, Male, Vascular Endothelial Growth Factor A, Lung Neoplasms, medicine.medical_treatment, Immunology, Immunotherapy, Adoptive, Cell therapy, chemistry.chemical_compound, Immune system, Cytokine-Induced Killer Cells, Th2 Cells, Carcinoma, Non-Small-Cell Lung, medicine, Immunology and Allergy, Humans, Lung cancer, Aged, Pharmacology, Cytokine-induced killer cell, business.industry, Immunotherapy, Dendritic cell, Dendritic Cells, Middle Aged, medicine.disease, Vascular endothelial growth factor, chemistry, Cytokines, Female, Interleukin-4, business, CD8
Abstract

Dendritic cell (DC) vaccination and cytokine-induced killer (CIK) cell therapy (DC/CIK) have shown limited success in the treatment of advanced non-small cell lung cancer (NSCLC). To investigate the reason for this limited success, the effects of DC/CIK cell therapy on the immune responses of tumor-bearing patients and patients with resected NSCLC were evaluated. In the total 50 patients studied, the serum concentrations of the Th2 cytokines (IL-4 and IL-10) in tumor-bearing patients were significantly higher than those with resected NSCLC before immunotherapy. The post-therapy Th1 cytokine (IFN-γ) level in patients with resected NSCLC significantly increased from the pre-therapy level. In contrast, significantly enhanced post-therapy Th2 cytokine (IL-4 and IL-10) levels were found in tumor-bearing patients. The intracellular staining assay revealed that DC/CIK cell therapy increased the IFN-γ-producing T lymphocyte (CD8(+)IFN-γ(+)) frequency in patients with resected NSCLC, but these lymphocytes were not found in tumor-bearing patients. Furthermore, overproduction of vascular endothelial growth factor (VEGF) in tumor-bearing patients showed a statistically positive correlation with IL-4, suggesting that VEGF might be responsible for the predominance of serum Th2 cytokines. In a word, tumor-bearing patients developed a Th2-dominant status that could not be reversed toward Th1 following immunotherapy. A combined regiment of DC vaccination and CIK cell therapy with other treatments to overcome systemic Th2-dominant immune response might improve the current clinical benefit.

Published
2014

24. Autologous tumor lysate-pulsed dendritic cell immunotherapy with cytokine-induced killer cells improves survival in gastric and colorectal cancer patients

Author

Xihe Xie, Weihong Sun, Aris T. Alexandrou, Jennifer Jones, Hsin-Chen Liu, Jian Jian Li, Daiqing Gao, Xiaofang Wei, Peng Zhao, Changyou Li, Ronghua Zhao, and Lim, Michael

Subjects
Male, Colorectal cancer, medicine.medical_treatment, Cancer Treatment, Cytokine-Induced Killer Cells, Cancer immunotherapy, Adenocarcinomas, Medicine and Health Sciences, Cancer, Multidisciplinary, Cytokine-induced killer cell, Colon Adenocarcinoma, Middle Aged, Prognosis, Colo-Rectal Cancer, Survival Rate, Cytokine, Treatment Outcome, Oncology, Research Design, 6.1 Pharmaceuticals, Disease Progression, Medicine, Female, Immunotherapy, Colorectal Neoplasms, Research Article, General Science & Technology, Clinical Research Design, Science, Immunology, Radiation Therapy, Gastroenterology and Hepatology, Adenocarcinoma, Research and Analysis Methods, Peripheral blood mononuclear cell, Cancer Vaccines, Carcinomas, Disease-Free Survival, Clinical Research, Stomach Neoplasms, Gastrointestinal Tumors, medicine, Humans, Aged, Retrospective Studies, Chemotherapy, business.industry, Evaluation of treatments and therapeutic interventions, Biology and Life Sciences, Cancers and Neoplasms, Dendritic cell, Dendritic Cells, medicine.disease, Gastric Cancer, Cancer research, Immunization, Clinical Immunology, business, Digestive Diseases
Abstract

Gastric and colorectal cancers (GC and CRC) have poor prognosis and are resistant to chemo- and/or radiotherapy. In the present study, the prophylactic effects of dendritic cell (DC) vaccination are evaluated on disease progression and clinical benefits in a group of 54 GC and CRC patients treated with DC immunotherapy combined with cytokine-induced killer (CIK) cells after surgery with or without chemo-radiotherapy. DCs were prepared from the mononuclear cells isolated from patients using IL-2/GM-CSF and loaded with tumor antigens; CIK cells were prepared by incubating peripheral blood lymphocytes with IL-2, IFN-γ, and CD3 antibodies. The DC/CIK therapy started 3 days after low-dose chemotherapy and was repeated 3-5 times in 2 weeks as one cycle with a total of 188.3±79.8×106 DCs and 58.8±22. 3×108 CIK cells. Cytokine levels in patients' sera before and after treatments were measured and the follow-up was conducted for 98 months to determine disease-free survival (DFS) and overall survival (OS). The results demonstrate that all cytokines tested were elevated with significantly higher levels of IFN-c and IL-12 in both GC and CRC cohorts of DC/CIK treated patients. By Cox regression analysis, DC/CIK therapy reduced the risk of post-operative disease progression (p

Published
2013

25. Abstract 2812: The clinical effects of dendritic cell and cytokine-induced killer cell therapy for lung cancer after surgery

Author

Xihe Xie, Qingming Guo, Jian Jian Li, Changyou Li, Xiaofang Wei, Aris T. Alexandrou, Weihong Sun, Peng Zhao, Danni Zhu, Daiqing Gao, and Gao Tianyi

Subjects
Cancer Research, medicine.medical_specialty, Cytokine-induced killer cell, business.industry, T cell, Therapeutic effect, Cancer, Treatment of lung cancer, medicine.disease, Peripheral blood mononuclear cell, Surgery, Cell therapy, medicine.anatomical_structure, Oncology, medicine, business, Lung cancer
Abstract

Objective:The aim of the study was to evaluate the safety and therapeutic efficacy of autologous dendritic cells co-cultured with cytokine-induced killer cells (DC-CIK) for the treatment of lung cancer patients. Methods:In the present study, we recruited a group of 40 lung cancer patients after surgery registered in the Biotherapy Center during 2005-2011 and randomly divided patients into two groups: Twenty patients treated with DC-CIK therapy as the treatment group and the other twenty patients served as controls.Subjects were administrated with 1×106 units of IL-2 daily for 5 days before mononuclear cell isolation from peripheral blood. For the treatment group, after primary tumors were removed by surgery, 1-3 doses of DCs and CIKs were perfused via peripheral veins. The immunologic function,short-term therapeutic effects, 5-years overall survival rate (OS), and disease free survival rate (DFS) were compared between the two groups.In addition, the peripheral blood samples were extracted from all patients before and after DC-CIK therapy,analyzed for T cell subsets by flow cytometry,and the intracellular cytokines of T cells were detected simultaneously. Results:OS for 1, 3, and 5 years of DC-CIK therapy group were 100%, 88%, and 88% respectively which contrasted with 78%,66%, and 25% in the control group. DFS 1, 3, and 5 years of DC-CIK therapy groups were 100%, 85%,65% respectively compared to 73%,58%, 23% in the control groups. Although there were no changes in the subsets of T cells in peripheral blood before and after DC-CIK therapy, the intrace1lular cytokines IFN-γ,IL-2,IL-4, and IL-6 of the immune cells were noticeably improved in the treatment group. Conclusion:DC-CIK therapy is a potential effective therapeutic modality to improve OS and DFS in post-surgical lung cancer patients. Citation Format: Daiqing Gao, Changyou Li, Peng Zhao, Xiaofang Wei, Xihe Xie, Weihong Sun, Qingming Guo, Danni Zhu, Tianyi Gao, Aris T. Alexandrou, Jian Jian Li. The clinical effects of dendritic cell and cytokine-induced killer cell therapy for lung cancer after surgery. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2812. doi:10.1158/1538-7445.AM2014-2812

Published
2014

26. Clinician Accessible Tools for GUI Computational Models of Transcranial Electrical Stimulation: BONSAI and SPHERES.

Author

Truong, Dennis Q., Hüber, Mathias, Xihe Xie, Datta, Abhishek, Rahman, Asif, Parra, Lucas C., Dmochowski, Jacek P., and Bikson, Marom

Abstract

Computational models of brain current flow during transcranial electrical stimulation (tES), including transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS), are increasingly used to understand and optimize clinical trials. We propose that broad dissemination requires a simple graphical user interface (GUI) software that allows users to explore and design montages in real-time, based on their own clinical/experimental experience and objectives. We introduce two complimentary open-source platforms for this purpose: BONSAI and SPHERES. BONSAI is a web (cloud) based application (available at neuralengr.com/bonsai) that can be accessed through any flash-supported browser interface. SPHERES (available at neuralengr.com/spheres) is a stand-alone GUI application that allow consideration of arbitrary montages on a concentric sphere model by leveraging an analytical solution. These open-source tES modeling platforms are designed go be upgraded and enhanced. Trade-offs between open-access approaches that balance ease of access, speed, and flexibility are discussed. [ABSTRACT FROM AUTHOR]

Published
2014
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