Article
Neurosciences
Andrew A. Chen, Dhivya Srinivasan, Raymond Pomponio, Yong Fan, Ilya M. Nasrallah, Susan M. Resnick, Lori L. Beason-Held, Christos Davatzikos, Theodore D. Satterthwaite, Dani S. Bassett, Russell T. Shinohara, Haochang Shou
Summary: Community detection on graphs constructed from functional magnetic resonance imaging (fMRI) data has provided important insights into brain functional organization. However, differences in scanners can introduce variability into the results, known as scanner effects. In this study, new methodology for harmonizing functional connectivity is proposed to reduce these effects.
Article
Multidisciplinary Sciences
Abdullah Karaaslanli, Meiby Ortiz-Bouza, Tamanna T. K. Munia, Selin Aviyente
Summary: This paper proposes a method to study functional connectivity of the human brain using multilayer networks, where each layer represents a different frequency band. The study shows that following an error response, the brain forms communities across frequencies, particularly between theta and gamma bands, while this is not observed following a correct response.
SCIENTIFIC REPORTS
(2023)
Article
Mathematical & Computational Biology
Changwei Gong, Bing Xue, Changhong Jing, Chun-Hui He, Guo-Cheng Wu, Baiying Lei, Shuqiang Wang
Summary: This paper proposes a time-sequential graph adversarial learning (TGAL) framework for detecting brain communities and characterizing the structure of communities in brain networks. The framework utilizes a novel time-sequential graph neural network as an encoder to extract efficient graph representations using spatio-temporal attention mechanism. The effectiveness of the framework is demonstrated through experiments on real-world brain network datasets, showcasing its advantage in brain community detection.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2022)
Article
Psychiatry
Mariana N. Castro, Hernan Bocaccio, Gabriela De Pino, Stella M. Sanchez, Agustina E. Wainsztein, Lucas Drucaroff, Elsa Y. Costanzo, Nicolas A. Crossley, Mirta F. Villarreal, Salvador M. Guinjoan
Summary: Recent functional imaging studies have shown that schizophrenia is associated with disrupted brain connectivity, especially during stress conditions. Patients with schizophrenia exhibit abnormal community structure and under-connected reconfiguration network during stress, suggesting deficits in integration dynamic with greater compromise of the right hemisphere. These findings provide evidence for altered functional brain dynamics in schizophrenia, potentially underlying the hyper-sensitivity to stress that characterizes the disorder.
SCHIZOPHRENIA RESEARCH
(2023)
Article
Physics, Multidisciplinary
Daniel Gamermann, Jose Antonio Pellizzaro
Summary: This paper proposes a new benchmark and approach based on surprise metric for identifying community structures in networks. The surprise-based methods outperform modularity-based methods, especially for heterogeneous graphs with different community sizes.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2022)
Article
Chemistry, Physical
Neda Zarayeneh, Nitesh Kumar, Ananth Kalyanaraman, Aurora E. Clark
Summary: This article introduces an algorithm called Delta-screening for identifying temporal communities. The algorithm is flexible in handling the evolving compositions, merging, and splitting behaviors within chemical networks, and is able to resolve multiple time scales.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Atefeh Moradan, Andrew Draganov, Davide Mottin, Ira Assent
Summary: UCoDe is a unified method for community detection in attributed graphs that can detect both overlapping and non-overlapping communities, capturing node similarity using a novel contrastive loss. Experimental results show that UCoDe performs well in both overlapping and non-overlapping detection without requiring extensive hyper-parameter tuning.
Article
Multidisciplinary Sciences
Jenna N. Adams, Miranda G. Chappel-Farley, Jessica L. Yaros, Lisa Taylor, Alyssa L. Harris, Abanoub Mikhail, Liv McMillan, David B. Keator, Michael A. Yassa
Summary: Older adults with high levels of Aβ pathology can still perform at age-normal levels on memory assessments, and this may be associated with the presence of functional brain networks that are modular and efficient.
SCIENTIFIC REPORTS
(2023)
Article
Multidisciplinary Sciences
Kai Qi, Heng Zhang, Yang Zhou, Yifan Liu, Qingxiang Li
Summary: This study introduces an algorithm called PR-LFM, which combines an improved local fitness maximization (LFM) algorithm with the PageRank (PR) algorithm for community partitioning on cyberspace resources. The experimental data demonstrate good results in the resource division of cyberspace.
SCIENTIFIC REPORTS
(2023)
Article
Neuroimaging
Evgeny J. Chumin, Shannon L. Risacher, John D. West, Liana G. Apostolova, Martin R. Farlow, Brenna C. McDonald, Yu-Chien Wu, Andrew J. Saykin, Olaf Sporns
Summary: The degradation of resting-state networks in Alzheimer's disease (AD) has been observed when functional connectivity is estimated over the entire scan. However, the temporal dynamics of these networks are less studied. Group differences in temporal stability within and between multiple resting state networks were observed in time-varying connectivity analysis.
NEUROIMAGE-CLINICAL
(2021)
Article
Computer Science, Artificial Intelligence
Furkan Oztemiz, Ali Karci
Summary: This study proposes a modularity optimization algorithm to increase clustering success in any network without being dependent on any community detection algorithm. The algorithm transfers nodes at the community boundary to neighboring communities to improve the modularity value. Experimental results show that the proposed method significantly enhances the modularity values of community detection algorithms.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Multidisciplinary Sciences
Teuntje A. D. Pelgrim, Matthijs G. Bossong, Analia Cuiza, Luz Maria Alliende, Carlos Mena, Angeles Tepper, Juan Pablo Ramirez-Mahaluf, Barbara Iruretagoyena, Claudia Ornstein, Rosemarie Fritsch, Juan Pablo Cruz, Cristian Tejos, Gabriela Repetto, Nicolas Crossley
Summary: 22q11 deletion syndrome is a genetic disorder associated with a high risk of developing psychosis, and studies have shown abnormal functional brain connectivity similar to schizophrenia. Graph theory analysis of fMRI data revealed globally reduced connectivity strength and abnormal local topological properties in 22q11 deletion syndrome patients, indicating a vulnerability factor to psychosis.
SCIENTIFIC REPORTS
(2021)
Article
Neurosciences
Blake R. Neyland, Christina E. Hugenschmidt, Robert G. Lyday, Jonathan H. Burdette, Laura D. Baker, W. Jack Rejeski, Michael E. Miller, Stephen B. Kritchevsky, Paul J. Laurienti
Summary: This study is the first to use graph theory and network community structure to characterize the effects of a motor imagery task on overall brain network organization in older adults. The results show that the task may lead to decreased consistency in the default mode network and sensorimotor network, but increased consistency in the dorsal attention network.
Article
Physics, Multidisciplinary
Stefano Benati, Justo Puerto, Antonio M. Rodriguez-Chia, Francisco Temprano
Summary: In this article, a new optimization model is proposed to detect overlapping communities in networks. The model not only addresses the biases of previous models but also reveals additional structural properties. Furthermore, two heuristic algorithms are introduced to handle larger instances, which show favorable performance compared to other methodologies.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2022)
Article
Physics, Multidisciplinary
Jing Xiao, Xiao-ke Xu
Summary: This review provides an overview of two emerging research directions in complex network analysis - fuzzy and higher-order community detection. It covers related concepts, mathematical descriptions, latest advancements, current challenges, and future directions.
Article
Neurosciences
Cecile Bordier, Jean-Michel Hupe, Michel Dojat
FRONTIERS IN HUMAN NEUROSCIENCE
(2015)
Article
Neurosciences
Cecile Bordier, Emiliano Macaluso
HUMAN BRAIN MAPPING
(2015)
Article
Neurosciences
Cecile Bordier, Carlo Nicolini, Angelo Bifone
FRONTIERS IN NEUROSCIENCE
(2017)
Article
Neuroimaging
Cecile Bordier, Carlo Nicolini, Giulia Forcellini, Angelo Bifone
NEUROIMAGE-CLINICAL
(2018)
Article
Neurosciences
Benoit Musel, Cecile Bordier, Michel Dojat, Cedric Pichat, Sylvie Chokron, Jean-Francois Le Bas, Carole Peyrin
JOURNAL OF COGNITIVE NEUROSCIENCE
(2013)
Article
Neurosciences
Jean-Michel Hupe, Cecile Bordier, Michel Dojat
Article
Neurosciences
Cecile Bordier, Francesco Puja, Emiliano Macaluso
Article
Multidisciplinary Sciences
Akitoshi Ogawa, Cecile Bordier, Emiliano Macaluso
Article
Clinical Neurology
Salvatore Nigro, Cecile Bordier, Antonio Cerasa, Rita Nistico, Giuseppe Olivadese, Basilio Vescio, Maria Giovanna Bianco, Antonino Fiorillo, Gaetano Barbagallo, Marianna Crasa, Andrea Quattrone, Maurizio Morelli, Gennarina Arabia, Antonio Augimeri, Carlo Nicolini, Angelo Bifone, Aldo Quattrone
PARKINSONISM & RELATED DISORDERS
(2019)
Article
Biochemistry & Molecular Biology
Cecile Bordier, Georg Weil, Patrick Bach, Giulia Scuppa, Carlo Nicolini, Giulia Forcellini, Ursula Perez-Ramirez, David Moratal, Santiago Canals, Sabine Hoffmann, Derik Hermann, Sabine Vollstadt-Klein, Falk Kiefer, Peter Kirsch, Wolfgang H. Sommer, Angelo Bifone
Summary: The study found that recently detoxified alcohol dependent patients showed significant reductions in global connectivity and region-specific disruption in network topology compared to healthy controls. An increase in centrality of the anterior insula was strongly associated with increased risk of relapse. Exploratory analysis suggested partial recovery of modular structure and insular connectivity in patients after 2 weeks.
Article
Clinical Neurology
Marianne Pollet, Emilie Skrobala, Renaud Lopes, Gregory Kuchcinski, Cecile Bordier, Adeline Rollin-Sillaire, Stephanie Bombois, Florence Pasquier, Xavier Delbeuck
Summary: By applying a k-means clustering analysis, two clusters of EOAD patients were identified, which differed in certain clinical, imaging, and laboratory characteristics. This clustering procedure may be useful for managing EOAD patients and identifying those at risk of faster decline.
EUROPEAN JOURNAL OF NEUROLOGY
(2021)
Article
Clinical Neurology
Quentin Devignes, Cecile Bordier, Romain Viard, Luc Defebvre, Gregory Kuchcinski, Albert F. G. Leentjens, Renaud Lopes, Kathy Dujardin
Summary: The dual syndrome hypothesis in mild cognitive impairment (MCI) in Parkinson's disease distinguishes between frontostriatal and posterior cortical subtypes, and this study found specific changes in resting-state functional connectivity associated with these subtypes. Patients with posterior cortical deficits showed increased functional connectivity within the basal ganglia network, while patients with frontostriatal deficits showed reduced inter-network connectivity compared to healthy controls and patients with normal cognition or a posterior cortical subtype.
MOVEMENT DISORDERS
(2022)
Article
Geriatrics & Gerontology
Morgan Gautherot, Gregory Kuchcinski, Cecile Bordier, Adeline Rollin Sillaire, Xavier Delbeuck, Melanie Leroy, Xavier Leclerc, Jean-Pierre Pruvo, Florence Pasquier, Renaud Lopes
Summary: The study evaluated PAD as a marker of phenotypic heterogeneity and severity among early-onset Alzheimer's disease (EOAD) patients, showing that PAD could distinguish between amnestic and non-amnestic sporadic forms.
FRONTIERS IN AGING NEUROSCIENCE
(2021)
Article
Psychology, Multidisciplinary
Valerio Santangelo, Cecile Bordier
FRONTIERS IN PSYCHOLOGY
(2019)
Article
Neurosciences
Jose Sanchez-Bornot, Roberto C. Sotero, J. A. Scott Kelso, Ozguer Simsek, Damien Coyle
Summary: This study proposes a multi-penalized state-space model for analyzing unobserved dynamics, using a data-driven regularization method. Novel algorithms are developed to solve the model, and a cross-validation method is introduced to evaluate regularization parameters. The effectiveness of this method is validated through simulations and real data analysis, enabling a more accurate exploration of cognitive brain functions.