GF
Guillén Fernández
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2 records found
1
Journal article
(2026)
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W. Dziarnowska, M. Orhun, Yannan Zhu, Nils Kohn, Guillén Fernández, M. Jafarian
Objectives: The interplay between emotion and memory is a central topic in cognitive neuroscience, with open questions about the underlying neuronal mechanisms. This article aims to study the effects of order and intensity of emotional information on associative memory encoding. To this aim, we employ dynamic causal modeling to model the dynamic network composed of the hippocampus, amygdala, and orbitofrontal cortex during an fMRI associative memory encoding task and apply graph and control theory tools to obtain novel insights.
Methods: Participants were clustered into three condition groups, neutral–neutral, neutral–emotional, or emotional–emotional, and viewed image pairs associated with their assigned condition. Using the dynamic causal modeling framework, we explore several dynamic models and show that a stochastic bilinear state-space model best describes the neuronal dynamics in all conditions. Furthermore, we use graph and control theory techniques to both validate and analyze the model. Particularly, we analyze the network dynamics of each condition using tools from graph theory and stability theory and discuss the differences in the strength and direction of connectivity as well as stability of each of these networks.
Results: We confirm the prior finding that memory is enhanced in the neutral-emotional condition. In our work, this enhanced memory is associated with the increased hippocampus–amygdala coupling strength in this condition. Moreover, we show that in the emotional–emotional condition, coupling of hippocampus and amygdala, as well as the whole network connectivity increases. We further predict that the hippocampus–amygdala connectivity in this condition increases, when the first image's valence is substantially less negative rated than the second image, but decreases otherwise. This pattern mirrors the neutral–emotional condition, where the first image is emotionally neutral compared with the second. Moreover, our model-based analyses suggest that the amygdala predominantly influences the other two regions in the neutral–emotional condition.
Conclusion: Combined data-driven DCM modeling, stability analyses, and graph-theory tools led to new insights and enhanced the mechanistic understanding of dynamics of emotional associative memory. We discuss these insights, utilize these analytical tools to generalize our findings to some unmeasured conditions, and highlight the potential of these techniques to inform the development of future technological or pharmacological approaches targeting regulatory mechanisms. ...
Methods: Participants were clustered into three condition groups, neutral–neutral, neutral–emotional, or emotional–emotional, and viewed image pairs associated with their assigned condition. Using the dynamic causal modeling framework, we explore several dynamic models and show that a stochastic bilinear state-space model best describes the neuronal dynamics in all conditions. Furthermore, we use graph and control theory techniques to both validate and analyze the model. Particularly, we analyze the network dynamics of each condition using tools from graph theory and stability theory and discuss the differences in the strength and direction of connectivity as well as stability of each of these networks.
Results: We confirm the prior finding that memory is enhanced in the neutral-emotional condition. In our work, this enhanced memory is associated with the increased hippocampus–amygdala coupling strength in this condition. Moreover, we show that in the emotional–emotional condition, coupling of hippocampus and amygdala, as well as the whole network connectivity increases. We further predict that the hippocampus–amygdala connectivity in this condition increases, when the first image's valence is substantially less negative rated than the second image, but decreases otherwise. This pattern mirrors the neutral–emotional condition, where the first image is emotionally neutral compared with the second. Moreover, our model-based analyses suggest that the amygdala predominantly influences the other two regions in the neutral–emotional condition.
Conclusion: Combined data-driven DCM modeling, stability analyses, and graph-theory tools led to new insights and enhanced the mechanistic understanding of dynamics of emotional associative memory. We discuss these insights, utilize these analytical tools to generalize our findings to some unmeasured conditions, and highlight the potential of these techniques to inform the development of future technological or pharmacological approaches targeting regulatory mechanisms. ...
Objectives: The interplay between emotion and memory is a central topic in cognitive neuroscience, with open questions about the underlying neuronal mechanisms. This article aims to study the effects of order and intensity of emotional information on associative memory encoding. To this aim, we employ dynamic causal modeling to model the dynamic network composed of the hippocampus, amygdala, and orbitofrontal cortex during an fMRI associative memory encoding task and apply graph and control theory tools to obtain novel insights.
Methods: Participants were clustered into three condition groups, neutral–neutral, neutral–emotional, or emotional–emotional, and viewed image pairs associated with their assigned condition. Using the dynamic causal modeling framework, we explore several dynamic models and show that a stochastic bilinear state-space model best describes the neuronal dynamics in all conditions. Furthermore, we use graph and control theory techniques to both validate and analyze the model. Particularly, we analyze the network dynamics of each condition using tools from graph theory and stability theory and discuss the differences in the strength and direction of connectivity as well as stability of each of these networks.
Results: We confirm the prior finding that memory is enhanced in the neutral-emotional condition. In our work, this enhanced memory is associated with the increased hippocampus–amygdala coupling strength in this condition. Moreover, we show that in the emotional–emotional condition, coupling of hippocampus and amygdala, as well as the whole network connectivity increases. We further predict that the hippocampus–amygdala connectivity in this condition increases, when the first image's valence is substantially less negative rated than the second image, but decreases otherwise. This pattern mirrors the neutral–emotional condition, where the first image is emotionally neutral compared with the second. Moreover, our model-based analyses suggest that the amygdala predominantly influences the other two regions in the neutral–emotional condition.
Conclusion: Combined data-driven DCM modeling, stability analyses, and graph-theory tools led to new insights and enhanced the mechanistic understanding of dynamics of emotional associative memory. We discuss these insights, utilize these analytical tools to generalize our findings to some unmeasured conditions, and highlight the potential of these techniques to inform the development of future technological or pharmacological approaches targeting regulatory mechanisms.
Methods: Participants were clustered into three condition groups, neutral–neutral, neutral–emotional, or emotional–emotional, and viewed image pairs associated with their assigned condition. Using the dynamic causal modeling framework, we explore several dynamic models and show that a stochastic bilinear state-space model best describes the neuronal dynamics in all conditions. Furthermore, we use graph and control theory techniques to both validate and analyze the model. Particularly, we analyze the network dynamics of each condition using tools from graph theory and stability theory and discuss the differences in the strength and direction of connectivity as well as stability of each of these networks.
Results: We confirm the prior finding that memory is enhanced in the neutral-emotional condition. In our work, this enhanced memory is associated with the increased hippocampus–amygdala coupling strength in this condition. Moreover, we show that in the emotional–emotional condition, coupling of hippocampus and amygdala, as well as the whole network connectivity increases. We further predict that the hippocampus–amygdala connectivity in this condition increases, when the first image's valence is substantially less negative rated than the second image, but decreases otherwise. This pattern mirrors the neutral–emotional condition, where the first image is emotionally neutral compared with the second. Moreover, our model-based analyses suggest that the amygdala predominantly influences the other two regions in the neutral–emotional condition.
Conclusion: Combined data-driven DCM modeling, stability analyses, and graph-theory tools led to new insights and enhanced the mechanistic understanding of dynamics of emotional associative memory. We discuss these insights, utilize these analytical tools to generalize our findings to some unmeasured conditions, and highlight the potential of these techniques to inform the development of future technological or pharmacological approaches targeting regulatory mechanisms.
Journal article
(2016)
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Hieab H H Adams, Derrek P. Hibar, Ashley H. Beecham, Lianne Schmaal, Andrew J. Schork, Li Shen, Jean Shin, Elena Shumskaya, Albert V. Smith, Emma Sprooten, Lachlan T. Strike, Alexander Teumer, Russell Thomson, Neda Jahanshad, Diana Tordesillas-Gutierrez, Roberto Toro, Daniah Trabzuni, Dhananjay Vaidya, Jeroen Van Der Grond, Dennis Van Der Meer, Marjolein M J Van Donkelaar, Kristel R. Van Eijk, Theo G M Van Erp, Daan Van Rooij, Katharina Wittfeld, Esther Walton, Lars T. Westlye, Christopher D. Whelan, Beverly G. Windham, Anderson M. Winkler, Girma Woldehawariat, Christiane Wolf, Thomas Wolfers, Bing Xu, Lisa R. Yanek, Sven J. Van Der Lee, Jingyun Yang, Alex Zijdenbos, Marcel P. Zwiers, Ingrid Agartz, Neelum T. Aggarwal, Laura Almasy, David Ames, Philippe Amouyel, Ole A. Andreassen, Sampath Arepalli, Lucija Abramovic, Amelia A. Assareh, Sandra Barral, Mark E. Bastin, Diane M. Becker, James T. Becker, David A. Bennett, John Blangero, Hans Van Bokhoven, Dorret I. Boomsma, Henry Brodaty, Saud Alhusaini, Rachel M. Brouwer, Han G. Brunner, Randy L. Buckner, Jan K. Buitelaar, Kazima B. Bulayeva, Wiepke Cahn, Vince D. Calhoun, Dara M. Cannon, Gianpiero L. Cavalleri, Christopher Chen, Najaf Amin, Ching Yu Cheng, Sven Cichon, Mark R. Cookson, Aiden Corvin, Benedicto Crespo-Facorro, Joanne E. Curran, Michael Czisch, Anders M. Dale, Gareth E. Davies, Eco J C De Geus, Micael Andersson, Philip L. De Jager, Greig I. De Zubicaray, Norman Delanty, Chantal Depondt, Anita L. Destefano, Allissa Dillman, Srdjan Djurovic, Gary Donohoe, Wayne C. Drevets, Ravi Duggirala, Konstantinos Arfanakis, Thomas D. Dyer, Susanne Erk, Thomas Espeseth, Denis A. Evans, Iryna O. Fedko, Guillén Fernández, Luigi Ferrucci, Simon E. Fisher, Debra A. Fleischman, Ian Ford, Benjamin S. Aribisala, Tatiana M. Foroud, Peter T. Fox, Clyde Francks, Masaki Fukunaga, J. Raphael Gibbs, David C. Glahn, Randy L. Gollub, Harald H H Göring, Hans J. Grabe, Robert C. Green, Vincent Chouraki, Nicola J. Armstrong, Oliver Gruber, Vilmundur Gudnason, Sebastian Guelfi, Narelle K. Hansell, John Hardy, Catharina A. Hartman, Ryota Hashimoto, Katrin Hegenscheid, Andreas Heinz, Stephanie Le Hellard, Lavinia Athanasiu, Dena G. Hernandez, Dirk J. Heslenfeld, Beng Choon Ho, Pieter J. Hoekstra, Wolfgang Hoffmann, Albert Hofman, Florian Holsboer, Georg Homuth, Norbert Hosten, Jouke Jan Hottenga, Tomas Axelsson, Hilleke E Hulshoff Pol, Masashi Ikeda, M. Kamran Ikram, Clifford R. Jack, Mark Jenkinson, Robert Johnson, Erik G. Jönsson, J. Wouter Jukema, René S. Kahn, Derek W. Morris, Alexa Beiser, Wiro J. Niessen, More Authors..., Manon Bernard, Joshua C. Bis, Laura M E Blanken, Susan H. Blanton, Marc M. Bohlken, Marco P. Boks, Jason L. Stein, Janita Bralten, Adam M. Brickman, Owen Carmichael, M. Mallar Chakravarty, Ganesh Chauhan, Qiang Chen, Christopher R K Ching, Gabriel Cuellar-Partida, Anouk Den Braber, Nhat Trung Doan, Paul A. Nyquist, Stefan Ehrlich, Irina Filippi, Tian Ge, Sudheer Giddaluru, Aaron L. Goldman, Rebecca F. Gottesman, Corina U. Greven, Oliver Grimm, Michael E. Griswold, Tulio Guadalupe, Miguel E. Rentería, Johanna Hass, Unn K. Haukvik, Saima Hilal, Edith Hofer, David Hoehn, Avram J. Holmes, Martine Hoogman, Deborah Janowitz, Tianye Jia, Dalia Kasperaviciute, Stella Trompet, Sungeun Kim, Marieke Klein, Bernd Kraemer, Phil H. Lee, Jiemin Liao, David C M Liewald, Lorna M. Lopez, Michelle Luciano, Christine Macare, Andre Marquand, Alejandro Arias-Vasquez, Mar Matarin, Karen A. Mather, Manuel Mattheisen, Bernard Mazoyer, David R. McKay, Rebekah McWhirter, Yuri Milaneschi, Nazanin Mirza-Schreiber, Ryan L. Muetzel, Susana Muñoz Maniega, Sudha Seshadri, Kwangsik Nho, Allison C. Nugent, Loes M Olde Loohuis, Jaap Oosterlaan, Martina Papmeyer, Irene Pappa, Lukas Pirpamer, Sara Pudas, Benno Pütz, Kumar B. Rajan, Sylvane Desrivières, Adaikalavan Ramasamy, Jennifer S. Richards, Shannon L. Risacher, Roberto Roiz-Santiañez, Nanda Rommelse, Emma J. Rose, Natalie A. Royle, Tatjana Rundek, Philipp G. Sämann, Claudia L. Satizabal
Intracranial volume reflects the maximally attained brain size during development, and remains stable with loss of tissue in late life. It is highly heritable, but the underlying genes remain largely undetermined. In a genome-wide association study of 32,438 adults, we discovered five previously unknown loci for intracranial volume and confirmed two known signals. Four of the loci were also associated with adult human stature, but these remained associated with intracranial volume after adjusting for height. We found a high genetic correlation with child head circumference (genetic = 0.748), which indicates a similar genetic background and allowed us to identify four additional loci through meta-analysis (N combined = 37,345). Variants for intracranial volume were also related to childhood and adult cognitive function, and Parkinson's disease, and were enriched near genes involved in growth pathways, including PI3K-AKT signaling. These findings identify the biological underpinnings of intracranial volume and their link to physiological and pathological traits.
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Intracranial volume reflects the maximally attained brain size during development, and remains stable with loss of tissue in late life. It is highly heritable, but the underlying genes remain largely undetermined. In a genome-wide association study of 32,438 adults, we discovered five previously unknown loci for intracranial volume and confirmed two known signals. Four of the loci were also associated with adult human stature, but these remained associated with intracranial volume after adjusting for height. We found a high genetic correlation with child head circumference (genetic = 0.748), which indicates a similar genetic background and allowed us to identify four additional loci through meta-analysis (N combined = 37,345). Variants for intracranial volume were also related to childhood and adult cognitive function, and Parkinson's disease, and were enriched near genes involved in growth pathways, including PI3K-AKT signaling. These findings identify the biological underpinnings of intracranial volume and their link to physiological and pathological traits.