Gustavo Deco

Institució Catalana de Recerca i Estudis Avançats

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Co-Authors

Morten L. Kringelbach

Royal Academy of Music

236

Yonatan Sanz Perl

Institut du Cerveau

Edmund T. Rolls

Shanghai Center for Brain Science and Brain-Inspired Technology

Enzo Tagliazucchi

University of Buenos Aires

Joana Cabral

University of Minho

Anira Escrichs

Pompeu Fabra University

Matthieu Gilson

Institut de Neurosciences des Systèmes

Adrián Ponce‐Alvarez

Universitat Politècnica de Catalunya

Jakub Vohryzek

University of Oxford

Viktor Jirsa

Aix-Marseille Université

Cooperative Institutions

Pompeu Fabra University

151

Centre National de la Recherche Scientifique

112

University College London

106

University of Oxford

Inserm

Harvard University

Universitat de Barcelona

University of Cambridge

Allen Institute for Brain Science

Consorci Institut D'Investigacions Biomediques August Pi I Sunyer

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Ionization of heavy targets by impact of relativistic projectiles

Nuclear Instruments and Methods in Physics Research Section B Beam Interactions with Materials and Atoms (1988)

Gustavo Deco P D Fainstein R D Rivarola

Relativistic quantum chemistry

10.1016/0168-583x(88)90104-8

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Citations (3)

Brain state identification and neuromodulation to promote recovery of consciousness

Brain Communications (2024)

Glenn J.M. van der Lande Diana Casas-Torremocha Arnau Manasanch Leonardo Dalla Porta Olivia Gosseries

Abstract Experimental and clinical studies of consciousness identify brain states (i.e. quasi-stable functional cerebral organization) in a non-systematic manner and largely independent of the research into brain state modulation. In this narrative review, we synthesize advances in the identification of brain states associated with consciousness in animal models and physiological (sleep), pharmacological (anaesthesia) and pathological (disorders of consciousness) states of altered consciousness in humans. We show that in reduced consciousness the frequencies in which the brain operates are slowed down and that the pattern of functional communication is sparser, less efficient, and less complex. The results also highlight damaged resting-state networks, in particular the default mode network, decreased connectivity in long-range connections and especially in the thalamocortical loops. Next, we show that therapeutic approaches to treat disorders of consciousness, through pharmacology (e.g. amantadine, zolpidem), and (non-) invasive brain stimulation (e.g. transcranial direct current stimulation, deep brain stimulation) have shown partial effectiveness in promoting consciousness recovery. Although some features of conscious brain states may improve in response to neuromodulation, targeting often remains non-specific and does not always lead to (behavioural) improvements. The fields of brain state identification and neuromodulation of brain states in relation to consciousness are showing fascinating developments that, when integrated, might propel the development of new and better-targeted techniques for disorders of consciousness. We here propose a therapeutic framework for the identification and modulation of brain states to facilitate the interaction between the two fields. We propose that brain states should be identified in a predictive setting, followed by theoretical and empirical testing (i.e. in animal models, under anaesthesia and in patients with a disorder of consciousness) of neuromodulation techniques to promote consciousness in line with such predictions. This framework further helps to identify where challenges and opportunities lay for the maturation of brain state research in the context of states of consciousness. It will become apparent that one angle of opportunity is provided through the addition of computational modelling. Finally, it aids in recognizing possibilities and obstacles for the clinical translation of these diagnostic techniques and neuromodulation treatment options across both the multimodal and multi-species approaches outlined throughout the review.

Neuromodulation

Persistent vegetative state

Brain stimulation

Transcranial Direct Current Stimulation

Consciousness Disorders

10.1093/braincomms/fcae362

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Citations (0)

List of Contributors

Elsevier eBooks (2019)

Niels Birbaumer Ricky Burns Henk Cremers Lauren Dahl Gustavo Deco

10.1016/b978-0-12-816179-1.00032-3

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The effective connectivity of the human hippocampal memory system

Cerebral Cortex (2021)

Edmund T. Rolls Gustavo Deco Chu‐Chung Huang Jianfeng Feng

Abstract Effective connectivity measurements in the human hippocampal memory system based on the resting-state blood oxygenation-level dependent signal were made in 172 participants in the Human Connectome Project to reveal the directionality and strength of the connectivity. A ventral “what” hippocampal stream involves the temporal lobe cortex, perirhinal and parahippocampal TF cortex, and entorhinal cortex. A dorsal “where” hippocampal stream connects parietal cortex with posterior and retrosplenial cingulate cortex, and with parahippocampal TH cortex, which, in turn, project to the presubiculum, which connects to the hippocampus. A third stream involves the orbitofrontal and ventromedial-prefrontal cortex with effective connectivity with the hippocampal, entorhinal, and perirhinal cortex. There is generally stronger forward connectivity to the hippocampus than backward. Thus separate “what,” “where,” and “reward” streams can converge in the hippocampus, from which back projections return to the sources. However, unlike the simple dual stream hippocampal model, there is a third stream related to reward value; there is some cross-connectivity between these systems before the hippocampus is reached; and the hippocampus has some effective connectivity with earlier stages of processing than the entorhinal cortex and presubiculum. These findings complement diffusion tractography and provide a foundation for new concepts on the operation of the human hippocampal memory system.

Perirhinal cortex

Entorhinal cortex

Posterior cingulate

Parahippocampal gyrus

Orbitofrontal cortex

10.1093/cercor/bhab442

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Citations (50)

Bridging the gap between single receptor type activity and whole‐brain dynamics

FEBS Journal (2021)

Dirk Jancke Stefan Herlitze Morten L. Kringelbach Gustavo Deco

What is the effect of activating a single modulatory neuronal receptor type on entire brain network dynamics? Can such effect be isolated at all? These are important questions because characterizing elementary neuronal processes that influence network activity across the given anatomical backbone is fundamental to guide theories of brain function. Here, we introduce the concept of the cortical ‘receptome’ taking into account the distribution and densities of expression of different modulatory receptor types across the brain's anatomical connectivity matrix. By modelling whole‐brain dynamics in silico , we suggest a bidirectional coupling between modulatory neurotransmission and neuronal connectivity hardware exemplified by the impact of single serotonergic (5‐HT) receptor types on cortical dynamics. As experimental support of this concept, we show how optogenetic tools enable specific activation of a single 5‐HT receptor type across the cortex as well as in vivo measurement of its distinct effects on cortical processing. Altogether, we demonstrate how the structural neuronal connectivity backbone and its modulation by a single neurotransmitter system allow access to a rich repertoire of different brain states that are fundamental for flexible behaviour. We further propose that irregular receptor expression patterns—genetically predisposed or acquired during a lifetime—may predispose for neuropsychiatric disorders like addiction, depression and anxiety along with distinct changes in brain state. Our long‐term vision is that such diseases could be treated through rationally targeted therapeutic interventions of high specificity to eventually recover natural transitions of brain states.

Human brain

10.1111/febs.15855

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Citations (16)

Independent Component Analysis: General Formulation and Linear Case

Perspectives in neural computing (1996)

Gustavo Deco Dragan Obradović

This chapter formulates the problem of Independent Component Analysis (ICA) as a search for an information-preserving linear mapping which results in statistically independent output components. The mathematical tools used herein originate from information theory and statistical analysis.

Component (thermodynamics)

Component analysis

Statistical Analysis

10.1007/978-1-4612-4016-7_4

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Citations (4)

Using The Virtual Brain to study the relationship between structural and functional connectivity in patients with multiple sclerosis: a multicenter study

Cerebral Cortex (2023)

Gerard Martí-Juan Jaume Sastre‐Garriga Eloy Martínez‐Heras Ángela Vidal‐Jordana Sara Llufriú

Abstract The relationship between structural connectivity (SC) and functional connectivity (FC) captured from magnetic resonance imaging, as well as its interaction with disability and cognitive impairment, is not well understood in people with multiple sclerosis (pwMS). The Virtual Brain (TVB) is an open-source brain simulator for creating personalized brain models using SC and FC. The aim of this study was to explore SC–FC relationship in MS using TVB. Two different model regimes have been studied: stable and oscillatory, with the latter including conduction delays in the brain. The models were applied to 513 pwMS and 208 healthy controls (HC) from 7 different centers. Models were analyzed using structural damage, global diffusion properties, clinical disability, cognitive scores, and graph-derived metrics from both simulated and empirical FC. For the stable model, higher SC–FC coupling was associated with pwMS with low Single Digit Modalities Test (SDMT) score (F=3.48, P$\lt$0.05), suggesting that cognitive impairment in pwMS is associated with a higher SC–FC coupling. Differences in entropy of the simulated FC between HC, high and low SDMT groups (F=31.57, P$\lt$1e-5), show that the model captures subtle differences not detected in the empirical FC, suggesting the existence of compensatory and maladaptive mechanisms between SC and FC in MS.

10.1093/cercor/bhad041

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Citations (7)

Computational Neuroscience for Cognitive Brain Functions

Springer eBooks (2006)

Marco Loh Miruna Szabo Rita Almeida M. Stetter Gustavo Deco

Computational neuroscience

10.1007/1-4020-2865-2_11

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Redundancy reduction with information-preserving nonlinear maps

Network Computation in Neural Systems (1995)

Lucas C. Parra Gustavo Deco Stefan Miesbach

AbstractThe basic idea of linear principal component analysis (PCA) involves decorrelating coordinates by an orthogonal linear transformation. In this paper we generalize this idea to the nonlinear case. Simultaneously we shall drop the usual restriction to Gaussian distributions. The linearity and orthogonality condition of linear PCA is replaced by the condition of volume conservation in order to avoid spurious information generated by the nonlinear transformation. This leads us to another very general class of nonlinear transformations, called symplectic maps. Later, instead of minimizing the correlation, we minimize the redundancy measured at the output coordinates. This generalizes second-order statistics, being only valid for Gaussian output distributions, to higher-order statistics. The proposed paradigm implements Barlow's redundancy-reduction principle for unsupervised feature extraction. The resulting factorial representation of the joint probability distribution presumably facilitates density estimation and is applied in particular to novelty detection.

Linear map

Higher-order statistics

10.1088/0954-898x_6_1_004

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Citations (37)

Attention and working memory: a dynamical model of neuronal activity in the prefrontal cortex

European Journal of Neuroscience (2003)

Gustavo Deco Edmund T. Rolls

Abstract Cognitive behaviour requires complex context‐dependent mapping between sensory stimuli and actions. The same stimulus can lead to different behaviours depending on the situation, or the same behaviour may be elicited by different cueing stimuli. Neurons in the primate prefrontal cortex show task‐specific firing activity during working memory delay periods. These neurons provide a neural substrate for mapping stimulus and response in a flexible, context‐ or rule‐dependent, fashion. We describe here an integrate‐and‐fire network model to explain and investigate the different types of working‐memory‐related neuronal activity observed. The model contains different populations (or pools) of neurons (as found neurophysiologically) in attractor networks which respond in the delay period to the stimulus object, the stimulus position (‘sensory pools’), to combinations of the stimulus sensory properties (e.g. the object identity or object location) and the response (‘intermediate pools’), and to the response required (left or right) (‘premotor pools’). The pools are arranged hierarchically, are linked by associative synaptic connections, and have global inhibition through inhibitory interneurons to implement competition. It is shown that a biasing attentional input to define the current rule applied to the intermediate pools enables the system to select the correct response in what is a biased competition model of attention. The integrate‐and‐fire model not only produces realistic spiking dynamicals very similar to the neuronal data but also shows how dopamine could weaken and shorten the persistent neuronal activity in the delay period; and allows us to predict more response errors when dopamine is elevated because there is less different activity in the different pools of competing neurons, resulting in more conflict.

Stimulus (psychology)

10.1046/j.1460-9568.2003.02956.x

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Citations (192)