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Zanchi P, Mullier E, Fornari E, Guerrier de Dumast P, Alemán-Gómez Y, Ledoux JB, Beaty R, Hagmann P, Denervaud S. Differences in spatiotemporal brain network dynamics of Montessori and traditionally schooled students. NPJ SCIENCE OF LEARNING 2024; 9:45. [PMID: 38987286 PMCID: PMC11236971 DOI: 10.1038/s41539-024-00254-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 06/12/2024] [Indexed: 07/12/2024]
Abstract
Across development, experience has a strong impact on the way we think and adapt. School experience affects academic and social-emotional outcomes, yet whether differences in pedagogical experience modulate underlying brain network development is still unknown. In this study, we compared the brain network dynamics of students with different pedagogical backgrounds. Specifically, we characterized the diversity and stability of brain activity at rest by combining both resting-state fMRI and diffusion-weighted structural imaging data of 87 4-18 years old students experiencing either the Montessori pedagogy (i.e., student-led, trial-and-error pedagogy) or the traditional pedagogy (i.e., teacher-led, test-based pedagogy). Our results revealed spatiotemporal brain dynamics differences between students as a function of schooling experience at the whole-brain level. Students from Montessori schools showed overall higher functional integration (higher system diversity) and neural stability (lower spatiotemporal diversity) compared to traditionally schooled students. Higher integration was explained mainly through the cerebellar (CBL) functional network. In contrast, higher temporal stability was observed in the ventral attention, dorsal attention, somatomotor, frontoparietal, and CBL functional networks. This study suggests a form of experience-dependent dynamic functional connectivity plasticity, in learning-related networks.
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Affiliation(s)
- Paola Zanchi
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Emeline Mullier
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Eleonora Fornari
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Priscille Guerrier de Dumast
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Yasser Alemán-Gómez
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Jean-Baptiste Ledoux
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Roger Beaty
- Department of Psychology, Pennsylvania State University, University Park, TX, USA
| | - Patric Hagmann
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Solange Denervaud
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland.
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland.
- MRI Animal imaging and technology, Polytechnical School of Lausanne, Swiss Federal Institute of Technology Lausanne (EPFL), 1015, Lausanne, Switzerland.
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2
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Vohryzek J, Cabral J, Timmermann C, Atasoy S, Roseman L, Nutt DJ, Carhart-Harris RL, Deco G, Kringelbach ML. The flattening of spacetime hierarchy of the N,N-dimethyltryptamine brain state is characterized by harmonic decomposition of spacetime (HADES) framework. Natl Sci Rev 2024; 11:nwae124. [PMID: 38778818 PMCID: PMC11110867 DOI: 10.1093/nsr/nwae124] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 02/15/2024] [Accepted: 03/11/2024] [Indexed: 05/25/2024] Open
Abstract
The human brain is a complex system, whose activity exhibits flexible and continuous reorganization across space and time. The decomposition of whole-brain recordings into harmonic modes has revealed a repertoire of gradient-like activity patterns associated with distinct brain functions. However, the way these activity patterns are expressed over time with their changes in various brain states remains unclear. Here, we investigate healthy participants taking the serotonergic psychedelic N,N-dimethyltryptamine (DMT) with the Harmonic Decomposition of Spacetime (HADES) framework that can characterize how different harmonic modes defined in space are expressed over time. HADES demonstrates significant decreases in contributions across most low-frequency harmonic modes in the DMT-induced brain state. When normalizing the contributions by condition (DMT and non-DMT), we detect a decrease specifically in the second functional harmonic, which represents the uni- to transmodal functional hierarchy of the brain, supporting the leading hypothesis that functional hierarchy is changed in psychedelics. Moreover, HADES' dynamic spacetime measures of fractional occupancy, life time and latent space provide a precise description of the significant changes of the spacetime hierarchical organization of brain activity in the psychedelic state.
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Affiliation(s)
- Jakub Vohryzek
- Centre for Eudaimonia and Human Flourishing, Linacre College, Department of Psychiatry, University of Oxford, Oxford OX3 9BX, UK
- Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK
- Center for Music in the Brain, Aarhus University, Aarhus 8000, Denmark
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona 08005, Spain
| | - Joana Cabral
- Centre for Eudaimonia and Human Flourishing, Linacre College, Department of Psychiatry, University of Oxford, Oxford OX3 9BX, UK
- Life and Health Sciences Research Institute, School of Medicine, University of Minho, Braga 4710-057, Portugal
- ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães 4710-057, Portugal
| | - Christopher Timmermann
- Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London SW7 2AZ, UK
| | - Selen Atasoy
- Centre for Eudaimonia and Human Flourishing, Linacre College, Department of Psychiatry, University of Oxford, Oxford OX3 9BX, UK
- Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK
| | - Leor Roseman
- Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London SW7 2AZ, UK
| | - David J Nutt
- Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London SW7 2AZ, UK
| | - Robin L Carhart-Harris
- Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London SW7 2AZ, UK
- Departments of Neurology and Psychiatry, University of California San Francisco, San Francisco 94143, USA
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona 08005, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona 08010, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Morten L Kringelbach
- Centre for Eudaimonia and Human Flourishing, Linacre College, Department of Psychiatry, University of Oxford, Oxford OX3 9BX, UK
- Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK
- Center for Music in the Brain, Aarhus University, Aarhus 8000, Denmark
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3
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Vohryzek J, Cabral J, Lord LD, Fernandes HM, Roseman L, Nutt DJ, Carhart-Harris RL, Deco G, Kringelbach ML. Brain dynamics predictive of response to psilocybin for treatment-resistant depression. Brain Commun 2024; 6:fcae049. [PMID: 38515439 PMCID: PMC10957168 DOI: 10.1093/braincomms/fcae049] [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: 03/06/2023] [Revised: 10/16/2023] [Accepted: 02/14/2024] [Indexed: 03/23/2024] Open
Abstract
Psilocybin therapy for depression has started to show promise, yet the underlying causal mechanisms are not currently known. Here, we leveraged the differential outcome in responders and non-responders to psilocybin (10 and 25 mg, 7 days apart) therapy for depression-to gain new insights into regions and networks implicated in the restoration of healthy brain dynamics. We used large-scale brain modelling to fit the spatiotemporal brain dynamics at rest in both responders and non-responders before treatment. Dynamic sensitivity analysis of systematic perturbation of these models enabled us to identify specific brain regions implicated in a transition from a depressive brain state to a healthy one. Binarizing the sample into treatment responders (>50% reduction in depressive symptoms) versus non-responders enabled us to identify a subset of regions implicated in this change. Interestingly, these regions correlate with in vivo density maps of serotonin receptors 5-hydroxytryptamine 2a and 5-hydroxytryptamine 1a, which psilocin, the active metabolite of psilocybin, has an appreciable affinity for, and where it acts as a full-to-partial agonist. Serotonergic transmission has long been associated with depression, and our findings provide causal mechanistic evidence for the role of brain regions in the recovery from depression via psilocybin.
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Affiliation(s)
- Jakub Vohryzek
- Department of Psychiatry, University of Oxford, Oxford, UK
- Center for Music in the Brain, Aarhus University, Aarhus, Denmark
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Joana Cabral
- Department of Psychiatry, University of Oxford, Oxford, UK
- Center for Music in the Brain, Aarhus University, Aarhus, Denmark
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B’s—PT Government Associate Laboratory, Braga/Guimarães, University of Minho, Portugal
| | - Louis-David Lord
- Department of Psychiatry, University of Oxford, Oxford, UK
- Center for Music in the Brain, Aarhus University, Aarhus, Denmark
| | - Henrique M Fernandes
- Department of Psychiatry, University of Oxford, Oxford, UK
- Center for Music in the Brain, Aarhus University, Aarhus, Denmark
| | - Leor Roseman
- Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, UK
| | - David J Nutt
- Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, UK
| | - Robin L Carhart-Harris
- Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, UK
- Psychedelics Division, Neuroscape, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, UK
- Center for Music in the Brain, Aarhus University, Aarhus, Denmark
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
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4
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Bessadok A, Mahjoub MA, Rekik I. Brain multigraph prediction using topology-aware adversarial graph neural network. Med Image Anal 2021; 72:102090. [PMID: 34004494 DOI: 10.1016/j.media.2021.102090] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Revised: 03/21/2021] [Accepted: 04/20/2021] [Indexed: 12/21/2022]
Abstract
Brain graphs (i.e, connectomes) constructed from medical scans such as magnetic resonance imaging (MRI) have become increasingly important tools to characterize the abnormal changes in the human brain. Due to the high acquisition cost and processing time of multimodal MRI, existing deep learning frameworks based on Generative Adversarial Network (GAN) focused on predicting the missing multimodal medical images from a few existing modalities. While brain graphs help better understand how a particular disorder can change the connectional facets of the brain, synthesizing a target brain multigraph (i.e, multiple brain graphs) from a single source brain graph is strikingly lacking. Additionally, existing graph generation works mainly learn one model for each target domain which limits their scalability in jointly predicting multiple target domains. Besides, while they consider the global topological scale of a graph (i.e., graph connectivity structure), they overlook the local topology at the node scale (e.g., how central a node is in the graph). To address these limitations, we introduce topology-aware graph GAN architecture (topoGAN), which jointly predicts multiple brain graphs from a single brain graph while preserving the topological structure of each target graph. Its three key innovations are: (i) designing a novel graph adversarial auto-encoder for predicting multiple brain graphs from a single one, (ii) clustering the encoded source graphs in order to handle the mode collapse issue of GAN and proposing a cluster-specific decoder, (iii) introducing a topological loss to force the prediction of topologically sound target brain graphs. The experimental results using five target domains demonstrated the outperformance of our method in brain multigraph prediction from a single graph in comparison with baseline approaches.
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Affiliation(s)
- Alaa Bessadok
- BASIRA lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey; Higher Institute of Informatics and Communication Technologies, University of Sousse, Tunisia; National Engineering School of Sousse, University of Sousse, LATIS- Laboratory of Advanced Technology and Intelligent Systems, Sousse, 4023, Tunisia
| | - Mohamed Ali Mahjoub
- Higher Institute of Informatics and Communication Technologies, University of Sousse, Tunisia; National Engineering School of Sousse, University of Sousse, LATIS- Laboratory of Advanced Technology and Intelligent Systems, Sousse, 4023, Tunisia
| | - Islem Rekik
- BASIRA lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey; School of Science and Engineering, Computing, University of Dundee, UK.
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5
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Hinault T, Mijalkov M, Pereira JB, Volpe G, Bakke A, Courtney SM. Age-related differences in network structure and dynamic synchrony of cognitive control. Neuroimage 2021; 236:118070. [PMID: 33887473 DOI: 10.1016/j.neuroimage.2021.118070] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 04/09/2021] [Accepted: 04/10/2021] [Indexed: 12/18/2022] Open
Abstract
Cognitive trajectories vary greatly across older individuals, and the neural mechanisms underlying these differences remain poorly understood. Here, we investigate the cognitive variability in older adults by linking the influence of white matter microstructure on the task-related organization of fast and effective communications between brain regions. Using diffusion tensor imaging and electroencephalography, we show that individual differences in white matter network organization are associated with network clustering and efficiency in the alpha and high-gamma bands, and that functional network dynamics partly explain individual differences in cognitive control performance in older adults. We show that older individuals with high versus low structural network clustering differ in task-related network dynamics and cognitive performance. These findings were corroborated by investigating magnetoencephalography networks in an independent dataset. This multimodal (fMRI and biological markers) brain connectivity framework of individual differences provides a holistic account of how differences in white matter microstructure underlie age-related variability in dynamic network organization and cognitive performance.
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Affiliation(s)
- T Hinault
- U1077 Inserm-Ephe-unicaen, Caen 14032, France; Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD 21218, United States.
| | - M Mijalkov
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm 17177, Sweden
| | - J B Pereira
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm 17177, Sweden; Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmo 47700, Sweden
| | - Giovanni Volpe
- Department of Physics, Goteborg University, Goteborg 41296, Sweden
| | - A Bakke
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21287, United States; F.M. Kirby Research Center, Kennedy Krieger Institute, Baltimore, MD 21287, United States
| | - S M Courtney
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD 21218, United States; F.M. Kirby Research Center, Kennedy Krieger Institute, Baltimore, MD 21287, United States; Department of Neuroscience, Johns Hopkins University, MD 21287, United States
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6
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Angulo-Ruiz BY, Muñoz V, Rodríguez-Martínez EI, Gómez CM. Absolute and relative variability changes of the resting state brain rhythms from childhood and adolescence to young adulthood. Neurosci Lett 2021; 749:135747. [PMID: 33610662 DOI: 10.1016/j.neulet.2021.135747] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 01/26/2021] [Accepted: 02/14/2021] [Indexed: 10/22/2022]
Abstract
The present report aimed to analyze the possible relationship of spontaneous EEG power variability across epochs in individual subjects (absolute and relative) with age. For this purpose, the resting state EEG of a sample of 258 healthy subjects (6-29 years old) in open and closed eyes experimental conditions were recorded. The power spectral density (PSD) was calculated from 0.5-45 Hz. Three electrodes with the highest PSD in each band were selected, and linear and inverse regression of the mean, standard deviation (SD), and coefficient of variation CV of the PSD vs age were computed. The results showed that the EEG absolute variability (SD) decreases with age, and in contrast, the relative variability (CV) increased, except for high frequencies in which it remains stable during maturation. We conclude that the variability in the EEG PSD when is not influenced by the mean PSD tends to increase from childhood and adolescence to young adulthood. Present results complement the extensive literature on changes of EEG power in different brain rhythms with the changes in EEG power variability during maturation.
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Affiliation(s)
- Brenda Y Angulo-Ruiz
- University of Sevilla, Experimental Psychology Department, Human Psychobiology Lab., Sevilla, Spain.
| | - Vanesa Muñoz
- University of Sevilla, Experimental Psychology Department, Human Psychobiology Lab., Sevilla, Spain.
| | | | - Carlos M Gómez
- University of Sevilla, Experimental Psychology Department, Human Psychobiology Lab., Sevilla, Spain.
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7
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Maallo AMS, Granovetter MC, Freud E, Kastner S, Pinsk MA, Glen D, Patterson C, Behrmann M. Large-scale resculpting of cortical circuits in children after surgical resection. Sci Rep 2020; 10:21589. [PMID: 33299002 PMCID: PMC7725819 DOI: 10.1038/s41598-020-78394-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 11/24/2020] [Indexed: 11/09/2022] Open
Abstract
Despite the relative successes in the surgical treatment of pharmacoresistant epilepsy, there is rather little research on the neural (re)organization that potentially subserves behavioral compensation. Here, we examined the post-surgical functional connectivity (FC) in children and adolescents who have undergone unilateral cortical resection and, yet, display remarkably normal behavior. Conventionally, FC has been investigated in terms of the mean correlation of the BOLD time courses extracted from different brain regions. Here, we demonstrated the value of segregating the voxel-wise relationships into mutually exclusive populations that were either positively or negatively correlated. While, relative to controls, the positive correlations were largely normal, negative correlations among networks were increased. Together, our results point to reorganization in the contralesional hemisphere, possibly suggesting competition for cortical territory due to the demand for representation of function. Conceivably, the ubiquitous negative correlations enable the differentiation of function in the reduced cortical volume following a unilateral resection.
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Affiliation(s)
- Anne Margarette S Maallo
- Department of Psychology and Neuroscience Institute, Carnegie Mellon University, Pittsburgh, USA
| | - Michael C Granovetter
- Department of Psychology and Neuroscience Institute, Carnegie Mellon University, Pittsburgh, USA.,School of Medicine, University of Pittsburgh, Pittsburgh, USA
| | - Erez Freud
- Department of Psychology, The Centre for Vision Research, York University, Toronto, Canada
| | - Sabine Kastner
- Princeton Neuroscience Institute, Princeton University, Princeton, USA.,Department of Psychology, Princeton University, Princeton, USA
| | - Mark A Pinsk
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Daniel Glen
- Scientific and Statistical Computing Core, National Institute of Mental Health, Bethesda, USA
| | | | - Marlene Behrmann
- Department of Psychology and Neuroscience Institute, Carnegie Mellon University, Pittsburgh, USA.
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8
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Vohryzek J, Deco G, Cessac B, Kringelbach ML, Cabral J. Ghost Attractors in Spontaneous Brain Activity: Recurrent Excursions Into Functionally-Relevant BOLD Phase-Locking States. Front Syst Neurosci 2020; 14:20. [PMID: 32362815 PMCID: PMC7182014 DOI: 10.3389/fnsys.2020.00020] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Accepted: 03/25/2020] [Indexed: 12/20/2022] Open
Abstract
Functionally relevant network patterns form transiently in brain activity during rest, where a given subset of brain areas exhibits temporally synchronized BOLD signals. To adequately assess the biophysical mechanisms governing intrinsic brain activity, a detailed characterization of the dynamical features of functional networks is needed from the experimental side to constrain theoretical models. In this work, we use an open-source fMRI dataset from 100 healthy participants from the Human Connectome Project and analyze whole-brain activity using Leading Eigenvector Dynamics Analysis (LEiDA), which serves to characterize brain activity at each time point by its whole-brain BOLD phase-locking pattern. Clustering these BOLD phase-locking patterns into a set of k states, we demonstrate that the cluster centroids closely overlap with reference functional subsystems. Borrowing tools from dynamical systems theory, we characterize spontaneous brain activity in the form of trajectories within the state space, calculating the Fractional Occupancy and the Dwell Times of each state, as well as the Transition Probabilities between states. Finally, we demonstrate that within-subject reliability is maximized when including the high frequency components of the BOLD signal (>0.1 Hz), indicating the existence of individual fingerprints in dynamical patterns evolving at least as fast as the temporal resolution of acquisition (here TR = 0.72 s). Our results reinforce the mechanistic scenario that resting-state networks are the expression of erratic excursions from a baseline synchronous steady state into weakly-stable partially-synchronized states - which we term ghost attractors. To better understand the rules governing the transitions between ghost attractors, we use methods from dynamical systems theory, giving insights into high-order mechanisms underlying brain function.
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Affiliation(s)
- Jakub Vohryzek
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia
| | - Bruno Cessac
- Biovision Team, Université Côte d’Azur, Inria, France
| | - Morten L. Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Joana Cabral
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Life and Health Sciences Research Institute, School of Medicine, University of Minho, Braga, Portugal
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