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Idesis S, Geli S, Faskowitz J, Vohryzek J, Sanz Perl Y, Pieper F, Galindo-Leon E, Engel AK, Deco G. Functional hierarchies in brain dynamics characterized by signal reversibility in ferret cortex. PLoS Comput Biol 2024; 20:e1011818. [PMID: 38241383 PMCID: PMC10836715 DOI: 10.1371/journal.pcbi.1011818] [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: 06/30/2023] [Revised: 02/02/2024] [Accepted: 01/09/2024] [Indexed: 01/21/2024] Open
Abstract
Brain signal irreversibility has been shown to be a promising approach to study neural dynamics. Nevertheless, the relation with cortical hierarchy and the influence of different electrophysiological features is not completely understood. In this study, we recorded local field potentials (LFPs) during spontaneous behavior, including awake and sleep periods, using custom micro-electrocorticographic (μECoG) arrays implanted in ferrets. In contrast to humans, ferrets remain less time in each state across the sleep-wake cycle. We deployed a diverse set of metrics in order to measure the levels of complexity of the different behavioral states. In particular, brain irreversibility, which is a signature of non-equilibrium dynamics, captured by the arrow of time of the signal, revealed the hierarchical organization of the ferret's cortex. We found different signatures of irreversibility and functional hierarchy of large-scale dynamics in three different brain states (active awake, quiet awake, and deep sleep), showing a lower level of irreversibility in the deep sleep stage, compared to the other. Irreversibility also allowed us to disentangle the influence of different cortical areas and frequency bands in this process, showing a predominance of the parietal cortex and the theta band. Furthermore, when inspecting the embedded dynamic through a Hidden Markov Model, the deep sleep stage was revealed to have a lower switching rate and lower entropy production. These results suggest functional hierarchies in organization that can be revealed through thermodynamic features and information theory metrics.
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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, Barcelona, Catalonia, Spain
| | - Sebastián Geli
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Barcelona, Catalonia, Spain
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, Indiana, United States of America
| | - Jakub Vohryzek
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Barcelona, Catalonia, Spain
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, United Kingdom
| | - Yonatan Sanz Perl
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Barcelona, Catalonia, Spain
- National Scientific and Technical Research Council, Buenos Aires, Argentina
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, France
| | - Florian Pieper
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Edgar Galindo-Leon
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Andreas K. Engel
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Gustavo Deco
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Barcelona, Catalonia, Spain
- Institució Catalana de Recerca I Estudis Avançats (ICREA), Barcelona, Catalonia, Spain
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2
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Liu R, Li M, Dunson DB. PPA: Principal parcellation analysis for brain connectomes and multiple traits. Neuroimage 2023; 276:120214. [PMID: 37286151 DOI: 10.1016/j.neuroimage.2023.120214] [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: 05/04/2023] [Accepted: 05/31/2023] [Indexed: 06/09/2023] Open
Abstract
Our understanding of the structure of the brain and its relationships with human traits is largely determined by how we represent the structural connectome. Standard practice divides the brain into regions of interest (ROIs) and represents the connectome as an adjacency matrix having cells measuring connectivity between pairs of ROIs. Statistical analyses are then heavily driven by the (largely arbitrary) choice of ROIs. In this article, we propose a human trait prediction framework utilizing a tractography-based representation of the brain connectome, which clusters fiber endpoints to define a data-driven white matter parcellation targeted to explain variation among individuals and predict human traits. This leads to Principal Parcellation Analysis (PPA), representing individual brain connectomes by compositional vectors building on a basis system of fiber bundles that captures the connectivity at the population level. PPA eliminates the need to choose atlases and ROIs a priori, and provides a simpler, vector-valued representation that facilitates easier statistical analysis compared to the complex graph structures encountered in classical connectome analyses. We illustrate the proposed approach through applications to data from the Human Connectome Project (HCP) and show that PPA connectomes improve power in predicting human traits over state-of-the-art methods based on classical connectomes, while dramatically improving parsimony and maintaining interpretability. Our PPA package is publicly available on GitHub, and can be implemented routinely for diffusion image data.
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Affiliation(s)
- Rongjie Liu
- Department of Statistics, Florida State University, Tallahassee, FL, USA
| | - Meng Li
- Department of Statistics, Rice University, Houston, TX, USA.
| | - David B Dunson
- Department of Statistical Science, Duke University, Durham, NC, USA
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3
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Deco G, Perl YS, Ponce-Alvarez A, Tagliazucchi E, Whybrow P, Fuster J, Kringelbach ML. One ring to rule them all: The unifying role of prefrontal cortex in steering task-related brain dynamics. Prog Neurobiol 2023:102468. [PMID: 37301532 DOI: 10.1016/j.pneurobio.2023.102468] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 05/10/2023] [Accepted: 05/18/2023] [Indexed: 06/12/2023]
Abstract
Surviving and thriving in a complex world require intricate balancing of higher order brain functions with essential survival-related behaviours. Exactly how this is achieved is not fully understood but a large body of work has shown that different regions in the prefrontal cortex (PFC) play key roles for diverse cognitive and emotional tasks including emotion, control, response inhibition, mental set shifting and working memory. We hypothesised that the key regions are hierarchically organised and we developed a framework for discovering the driving brain regions at the top of the hierarchy, responsible for steering the brain dynamics of higher brain function. We fitted a time-dependent whole-brain model to the neuroimaging data from large-scale Human Connectome Project with over 1,000 participants and computed the entropy production for rest and seven tasks (covering the main domains of cognition). This thermodynamics framework allowed us to identify the main common, unifying drivers steering the orchestration of brain dynamics during difficult tasks; located in key regions of the PFC (inferior frontal gyrus, lateral orbitofrontal cortex, rostral and caudal frontal cortex and rostral anterior cingulate cortex). Selectively lesioning these regions in the whole-brain model demonstrated their causal mechanistic importance. Overall, this shows the existence of a 'ring' of specific PFC regions ruling over the orchestration of higher brain function.
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Affiliation(s)
- 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
| | - Yonatan Sanz Perl
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona, 08018, Spain; Department of Physics, University of Buenos Aires, Buenos Aires, Argentina
| | - Adrián Ponce-Alvarez
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona, 08018, Spain
| | - Enzo Tagliazucchi
- Department of Physics, University of Buenos Aires, Buenos Aires, Argentina; Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibanez, Santiago, Chile
| | - Peter Whybrow
- University of California, Los Angeles, CA 90024, USA; Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK
| | - Joaquín Fuster
- University of California, Los Angeles, CA 90024, USA; Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK
| | - Morten L Kringelbach
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK; Department of Psychiatry, University of Oxford, Oxford, UK; Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, DK
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4
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Kringelbach ML, Perl YS, Tagliazucchi E, Deco G. Toward naturalistic neuroscience: Mechanisms underlying the flattening of brain hierarchy in movie-watching compared to rest and task. SCIENCE ADVANCES 2023; 9:eade6049. [PMID: 36638163 PMCID: PMC9839335 DOI: 10.1126/sciadv.ade6049] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 12/13/2022] [Indexed: 06/04/2023]
Abstract
Identifying the functional specialization of the brain has moved from using cognitive tasks and resting state to using ecological relevant, naturalistic movies. We leveraged a large-scale neuroimaging dataset to directly investigate the hierarchical reorganization of functional brain activity when watching naturalistic films compared to performing seven cognitive tasks and resting. A thermodynamics-inspired whole-brain model paradigm revealed the generative underlying mechanisms for changing the balance in causal interactions between brain regions in different conditions. Paradoxically, the hierarchy is flatter for movie-watching, and the level of nonreversibility is significantly smaller in comparison to both rest and tasks, where the latter in turn have the highest levels of hierarchy and nonreversibility. The underlying mechanisms were revealed by the model-based generative effective connectivity (GEC). Naturalistic films could therefore provide a fast and convenient way to measure important changes in GEC (integrating functional and anatomical connectivity) found in, for example, neuropsychiatric disorders. Overall, this study demonstrates the benefits of moving toward a more naturalistic neuroscience.
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Affiliation(s)
- Morten L. Kringelbach
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK
- Department of Psychiatry, University of Oxford, Oxford, UK
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Yonatan Sanz Perl
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain
- Department of Physics, University of Buenos Aires, Buenos Aires, Argentina
| | - Enzo Tagliazucchi
- Department of Physics, University of Buenos Aires, Buenos Aires, Argentina
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibanez, Santiago, Chile
| | - 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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5
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Kobeleva X, Varoquaux G, Dagher A, Adhikari M, Grefkes C, Gilson M. Advancing brain network models to reconcile functional neuroimaging and clinical research. Neuroimage Clin 2022; 36:103262. [PMID: 36451365 PMCID: PMC9723311 DOI: 10.1016/j.nicl.2022.103262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 10/26/2022] [Accepted: 11/06/2022] [Indexed: 11/09/2022]
Abstract
Functional magnetic resonance imaging (fMRI) captures information on brain function beyond the anatomical alterations that are traditionally visually examined by neuroradiologists. However, the fMRI signals are complex in addition to being noisy, so fMRI still faces limitations for clinical applications. Here we review methods that have been proposed as potential solutions so far, namely statistical, biophysical and decoding models, with their strengths and weaknesses. We especially evaluate the ability of these models to directly predict clinical variables from their parameters (predictability) and to extract clinically relevant information regarding biological mechanisms and relevant features for classification and prediction (interpretability). We then provide guidelines for useful applications and pitfalls of such fMRI-based models in a clinical research context, looking beyond the current state of the art. In particular, we argue that the clinical relevance of fMRI calls for a new generation of models for fMRI data, which combine the strengths of both biophysical and decoding models. This leads to reliable and biologically meaningful model parameters, which thus fulfills the need for simultaneous interpretability and predictability. In our view, this synergy is fundamental for the discovery of new pharmacological and interventional targets, as well as the use of models as biomarkers in neurology and psychiatry.
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Affiliation(s)
- Xenia Kobeleva
- Department of Neurology, University of Bonn, Bonn, Germany; German Center for Neurodegenerative Diseases (DZNE) Bonn, Bonn, Germany
| | | | - Alain Dagher
- Montreal Neurological Institute, McGill University, Montréal, Canada
| | - Mohit Adhikari
- Bio-imaging Lab, University of Antwerp, Antwerp, Belgium
| | - Christian Grefkes
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany; Institute of Neuroscience and Medicine (INM-1, INM-3), Research Centre Juelich, Juelich, Germany; Department of Neurology, Goethe University Frankfurt, Frankfurt, Germany
| | - Matthieu Gilson
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany; Center for Brain and Cognition, Department of Information and Telecommunication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; Institut de Neurosciences des Systèmes, Aix-Marseille University, Marseille, France.
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6
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Olafson E, Russello G, Jamison KW, Liu H, Wang D, Bruss JE, Boes AD, Kuceyeski A. Frontoparietal network activation is associated with motor recovery in ischemic stroke patients. Commun Biol 2022; 5:993. [PMID: 36131012 PMCID: PMC9492673 DOI: 10.1038/s42003-022-03950-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 09/06/2022] [Indexed: 11/30/2022] Open
Abstract
Strokes cause lesions that damage brain tissue, disrupt normal brain activity patterns and can lead to impairments in motor function. Although modulation of cortical activity is central to stimulation-based rehabilitative therapies, aberrant and adaptive patterns of brain activity after stroke have not yet been fully characterized. Here, we apply a brain dynamics analysis approach to study longitudinal brain activity patterns in individuals with ischemic pontine stroke. We first found 4 commonly occurring brain states largely characterized by high amplitude activations in the visual, frontoparietal, default mode, and motor networks. Stroke subjects spent less time in the frontoparietal state compared to controls. For individuals with dominant-hand CST damage, more time spent in the frontoparietal state from 1 week to 3-6 months post-stroke was associated with better motor recovery over the same time period, an association which was independent of baseline impairment. Furthermore, the amount of time spent in brain states was linked empirically to functional connectivity. This work suggests that when the dominant-hand CST is compromised in stroke, resting state configurations may include increased activation of the frontoparietal network, which may facilitate compensatory neural pathways that support recovery of motor function when traditional motor circuits of the dominant-hemisphere are compromised.
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Affiliation(s)
- Emily Olafson
- Department of Radiology, Weill Cornell Medicine, New York City, NY, 10021, USA.
| | - Georgia Russello
- Pelham Memorial High School, 575 Colonial Ave, Village of Pelham, NY, 10803, USA
| | - Keith W Jamison
- Department of Radiology, Weill Cornell Medicine, New York City, NY, 10021, USA
| | - Hesheng Liu
- Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Danhong Wang
- Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Joel E Bruss
- Department of Neurology, University of Iowa, Iowa City, IA, 52242, USA
| | - Aaron D Boes
- Department of Neurology, University of Iowa, Iowa City, IA, 52242, USA
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York City, NY, 10021, USA
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