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Dagnino PC, Galadí JA, Càmara E, Deco G, Escrichs A. Inducing a meditative state by artificial perturbations: A mechanistic understanding of brain dynamics underlying meditation. Netw Neurosci 2024; 8:517-540. [PMID: 38952817 PMCID: PMC11168722 DOI: 10.1162/netn_a_00366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 01/29/2024] [Indexed: 07/03/2024] Open
Abstract
Contemplative neuroscience has increasingly explored meditation using neuroimaging. However, the brain mechanisms underlying meditation remain elusive. Here, we implemented a mechanistic framework to explore the spatiotemporal dynamics of expert meditators during meditation and rest, and controls during rest. We first applied a model-free approach by defining a probabilistic metastable substate (PMS) space for each condition, consisting of different probabilities of occurrence from a repertoire of dynamic patterns. Moreover, we implemented a model-based approach by adjusting the PMS of each condition to a whole-brain model, which enabled us to explore in silico perturbations to transition from resting-state to meditation and vice versa. Consequently, we assessed the sensitivity of different brain areas regarding their perturbability and their mechanistic local-global effects. Overall, our work reveals distinct whole-brain dynamics in meditation compared to rest, and how transitions can be induced with localized artificial perturbations. It motivates future work regarding meditation as a practice in health and as a potential therapy for brain disorders.
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Affiliation(s)
- Paulina Clara Dagnino
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Javier A. Galadí
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | | | - Gustavo Deco
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avancats (ICREA), Barcelona, Spain
| | - Anira Escrichs
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
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2
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Cardone P, Alnagger N, Annen J, Bicego A, Gosseries O, Martial C. Psychedelics and disorders of consciousness: the current landscape and the path forward. Neurosci Conscious 2024; 2024:niae025. [PMID: 38881630 PMCID: PMC11179162 DOI: 10.1093/nc/niae025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 02/16/2024] [Accepted: 05/20/2024] [Indexed: 06/18/2024] Open
Abstract
Modern medicine has been shaken by the surge of psychedelic science that proposes a new approach to mitigate mental disorders, such as depression and post-traumatic stress disorder. Clinical trials to investigate whether psychedelic substances can treat psychiatric conditions are now underway, yet less discussion gravitates around their use in neurological disorders due to brain injury. One suggested implementation of brain-complexity enhancing psychedelics is to treat people with post-comatose disorders of consciousness (DoC). In this article, we discuss the rationale of this endeavour, examining possible outcomes of such experiments by postulating the existence of an optimal level of complexity. We consider the possible counterintuitive effects of both psychedelics and DoC on the functional connectivity of the default mode network and its possible impact on selfhood. We also elaborate on the role of computational modelling in providing complementary information to experimental studies, both contributing to our understanding of the treatment mechanisms and providing a path towards personalized medicine. Finally, we update the discourse surrounding the ethical considerations, encompassing clinical and scientific values.
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Affiliation(s)
- Paolo Cardone
- Coma Science Group, GIGA-Consciousness, University of Liège, Avenue de l'hôpital 11, Liège 4000, Belgium
- Centre du Cerveau2, University Hospital of Liège, Avenue de l'hôpital 11, Liège 4000, Belgium
| | - Naji Alnagger
- Coma Science Group, GIGA-Consciousness, University of Liège, Avenue de l'hôpital 11, Liège 4000, Belgium
- Centre du Cerveau2, University Hospital of Liège, Avenue de l'hôpital 11, Liège 4000, Belgium
| | - Jitka Annen
- Coma Science Group, GIGA-Consciousness, University of Liège, Avenue de l'hôpital 11, Liège 4000, Belgium
- Centre du Cerveau2, University Hospital of Liège, Avenue de l'hôpital 11, Liège 4000, Belgium
- Department of Data Analysis, University of Ghent, Henri Dunantlaan 1, Ghent 9000, Belgium
| | - Aminata Bicego
- Sensation and Perception Research Group, GIGA-Consciousness, University of Liège, Avenue de l'hôpital 11, Liège 4000, Belgium
| | - Olivia Gosseries
- Coma Science Group, GIGA-Consciousness, University of Liège, Avenue de l'hôpital 11, Liège 4000, Belgium
- Centre du Cerveau2, University Hospital of Liège, Avenue de l'hôpital 11, Liège 4000, Belgium
- Sensation and Perception Research Group, GIGA-Consciousness, University of Liège, Avenue de l'hôpital 11, Liège 4000, Belgium
| | - Charlotte Martial
- Coma Science Group, GIGA-Consciousness, University of Liège, Avenue de l'hôpital 11, Liège 4000, Belgium
- Centre du Cerveau2, University Hospital of Liège, Avenue de l'hôpital 11, Liège 4000, Belgium
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3
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Ibanez A, Kringelbach ML, Deco G. A synergetic turn in cognitive neuroscience of brain diseases. Trends Cogn Sci 2024; 28:319-338. [PMID: 38246816 DOI: 10.1016/j.tics.2023.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 12/15/2023] [Accepted: 12/27/2023] [Indexed: 01/23/2024]
Abstract
Despite significant improvements in our understanding of brain diseases, many barriers remain. Cognitive neuroscience faces four major challenges: complex structure-function associations; disease phenotype heterogeneity; the lack of transdiagnostic models; and oversimplified cognitive approaches restricted to the laboratory. Here, we propose a synergetics framework that can help to perform the necessary dimensionality reduction of complex interactions between the brain, body, and environment. The key solutions include low-dimensional spatiotemporal hierarchies for brain-structure associations, whole-brain modeling to handle phenotype diversity, model integration of shared transdiagnostic pathophysiological pathways, and naturalistic frameworks balancing experimental control and ecological validity. Creating whole-brain models with reduced manifolds combined with ecological measures can improve our understanding of brain disease and help identify novel interventions. Synergetics provides an integrated framework for future progress in clinical and cognitive neuroscience, pushing the boundaries of brain health and disease toward more mature, naturalistic approaches.
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Affiliation(s)
- Agustin Ibanez
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibanez, Santiago, Chile; Global Brain Health Institute (GBHI), University California San Francisco (UCSF), San Francisco, CA, USA; Global Brain Health Institute (GBHI), Trinity College Dublin, Dublin, Ireland; Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina; Department of Psychiatry, University of Oxford, Oxford, UK.
| | - Morten L Kringelbach
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona 08010, Spain.
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4
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Zdybał K, Parente A, Sutherland JC. Improving reduced-order models through nonlinear decoding of projection-dependent outputs. PATTERNS (NEW YORK, N.Y.) 2023; 4:100859. [PMID: 38035196 PMCID: PMC10682754 DOI: 10.1016/j.patter.2023.100859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 07/12/2023] [Accepted: 09/14/2023] [Indexed: 12/02/2023]
Abstract
A fundamental hindrance to building data-driven reduced-order models (ROMs) is the poor topological quality of a low-dimensional data projection. This includes behavior such as overlapping, twisting, or large curvatures or uneven data density that can generate nonuniqueness and steep gradients in quantities of interest (QoIs). Here, we employ an encoder-decoder neural network architecture for dimensionality reduction. We find that nonlinear decoding of projection-dependent QoIs, when embedded in a dimensionality reduction technique, promotes improved low-dimensional representations of complex multiscale and multiphysics datasets. When data projection (encoding) is affected by forcing accurate nonlinear reconstruction of the QoIs (decoding), we minimize nonuniqueness and gradients in representing QoIs on a projection. This in turn leads to enhanced predictive accuracy of a ROM. Our findings are relevant to a variety of disciplines that develop data-driven ROMs of dynamical systems such as reacting flows, plasma physics, atmospheric physics, or computational neuroscience.
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Affiliation(s)
- Kamila Zdybał
- Université Libre de Bruxelles, École Polytechnique de Bruxelles, Aero-Thermo-Mechanics Laboratory, Brussels, Belgium
- BRITE: Brussels Institute for Thermal-Fluid Systems and Clean Energy, Brussels, Belgium
| | - Alessandro Parente
- Université Libre de Bruxelles, École Polytechnique de Bruxelles, Aero-Thermo-Mechanics Laboratory, Brussels, Belgium
- BRITE: Brussels Institute for Thermal-Fluid Systems and Clean Energy, Brussels, Belgium
| | - James C. Sutherland
- Department of Chemical Engineering, University of Utah, Salt Lake City, UT, USA
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Zhang J, Yang Y, Liu T, Shi Z, Pei G, Wang L, Wu J, Funahashi S, Suo D, Wang C, Yan T. Functional connectivity in people at clinical and familial high risk for schizophrenia. Psychiatry Res 2023; 328:115464. [PMID: 37690192 DOI: 10.1016/j.psychres.2023.115464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 09/03/2023] [Accepted: 09/04/2023] [Indexed: 09/12/2023]
Abstract
Patients diagnosed with schizophrenia (SZ) exhibit compromised functional connectivity within extensive brain networks. However, the precise development of this impairment during disease progression in the clinical high-risk (CHR) population and their relatives remains unclear. Our study leveraged data from 128 resting electroencephalography (EEG) channels acquired from 30 SZ patients, 21 CHR individuals, 17 unaffected healthy relatives (RSs; those at heightened SZ risk due to family history), and 31 healthy controls (HCs). These data were harnessed to establish functional connectivity patterns. By calculating the geometric distance between EEG sequences, we unveiled local and global nonlinear relationships within the entire brain. The process of dimensionality reduction led to low-dimensional representations, providing insights into high-dimensional EEG data. Our findings indicated that CHR participants exhibited aberrant functional connectivity across hemispheres, whereas RS individuals showcased anomalies primarily concentrated within hemispheres. In the realm of low-dimensional analysis, RS participants' third-dimensional occipital lobe values lay between those of the CHR individuals and HCs, significantly correlating with scale scores. This low-dimensional approach facilitated the visualization of brain states, potentially offering enhanced comprehension of brain structure, function, and early-stage functional impairment, such as occipital visual deficits, in RS individuals before cognitive decline onset.
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Affiliation(s)
- Jian Zhang
- School of Medical Technology, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Yaxin Yang
- School of Life Science, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Tiantian Liu
- School of Medical Technology, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Zhongyan Shi
- School of Medical Technology, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Guangying Pei
- School of Medical Technology, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Li Wang
- School of Medical Technology, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Jinglong Wu
- School of Medical Technology, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Shintaro Funahashi
- Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Dingjie Suo
- School of Medical Technology, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Changming Wang
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
| | - Tianyi Yan
- School of Medical Technology, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China.
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Idesis S, Allegra M, Vohryzek J, Sanz Perl Y, Faskowitz J, Sporns O, Corbetta M, Deco G. A low dimensional embedding of brain dynamics enhances diagnostic accuracy and behavioral prediction in stroke. Sci Rep 2023; 13:15698. [PMID: 37735201 PMCID: PMC10514061 DOI: 10.1038/s41598-023-42533-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 09/11/2023] [Indexed: 09/23/2023] Open
Abstract
Large-scale brain networks reveal structural connections as well as functional synchronization between distinct regions of the brain. The latter, referred to as functional connectivity (FC), can be derived from neuroimaging techniques such as functional magnetic resonance imaging (fMRI). FC studies have shown that brain networks are severely disrupted by stroke. However, since FC data are usually large and high-dimensional, extracting clinically useful information from this vast amount of data is still a great challenge, and our understanding of the functional consequences of stroke remains limited. Here, we propose a dimensionality reduction approach to simplify the analysis of this complex neural data. By using autoencoders, we find a low-dimensional representation encoding the fMRI data which preserves the typical FC anomalies known to be present in stroke patients. By employing the latent representations emerging from the autoencoders, we enhanced patients' diagnostics and severity classification. Furthermore, we showed how low-dimensional representation increased the accuracy of recovery prediction.
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Affiliation(s)
- Sebastian Idesis
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Carrer Trias i Fargas 25-27, 08005, Barcelona, Catalonia, Spain.
| | - Michele Allegra
- Padova Neuroscience Center (PNC), University of Padova, via Orus 2/B, 35129, Padua, Italy
- Department of Physics and Astronomy "G. Galilei", University of Padova, via Marzolo 8, 35131, Padua, Italy
| | - Jakub Vohryzek
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Carrer Trias i Fargas 25-27, 08005, Barcelona, Catalonia, Spain
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK
| | - Yonatan Sanz Perl
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Carrer Trias i Fargas 25-27, 08005, Barcelona, Catalonia, Spain
- Universidad de San Andrés, Buenos Aires, Argentina
- National Scientific and Technical Research Council, Buenos Aires, Argentina
- Institut du Cerveau et de la Moelle Épinière, ICM, Paris, France
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47405, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47405, USA
| | - Maurizio Corbetta
- Padova Neuroscience Center (PNC), University of Padova, via Orus 2/B, 35129, Padua, Italy
- Department of Neuroscience, University of Padova, via Giustiniani 5, 35128, Padua, Italy
- Veneto Institute of Molecular Medicine (VIMM), via Orus 2/B, 35129, Padua, Italy
| | - Gustavo Deco
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Carrer Trias i Fargas 25-27, 08005, Barcelona, Catalonia, Spain
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