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Pannunzi M, Hindriks R, Bettinardi RG, Wenger E, Lisofsky N, Martensson J, Butler O, Filevich E, Becker M, Lochstet M, Kühn S, Deco G. Resting-state fMRI correlations: From link-wise unreliability to whole brain stability. Neuroimage 2017; 157:250-262. [PMID: 28599964 DOI: 10.1016/j.neuroimage.2017.06.006] [Citation(s) in RCA: 55] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2016] [Revised: 05/30/2017] [Accepted: 06/01/2017] [Indexed: 12/23/2022] Open
Abstract
The functional architecture of spontaneous BOLD fluctuations has been characterized in detail by numerous studies, demonstrating its potential relevance as a biomarker. However, the systematic investigation of its consistency is still in its infancy. Here, we analyze within- and between-subject variability and test-retest reliability of resting-state functional connectivity (FC) in a unique data set comprising multiple fMRI scans (42) from 5 subjects, and 50 single scans from 50 subjects. We adopt a statistical framework that enables us to identify different sources of variability in FC. We show that the low reliability of single links can be significantly improved by using multiple scans per subject. Moreover, in contrast to earlier studies, we show that spatial heterogeneity in FC reliability is not significant. Finally, we demonstrate that despite the low reliability of individual links, the information carried by the whole-brain FC matrix is robust and can be used as a functional fingerprint to identify individual subjects from the population.
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Affiliation(s)
- Mario Pannunzi
- Universitat Pompeu Fabra, Theoretical and Computational Neuroscience, Center for Brain and Cognition, Roc Boronat, 138, 08018 Barcelona, Spain.
| | - Rikkert Hindriks
- Universitat Pompeu Fabra, Theoretical and Computational Neuroscience, Center for Brain and Cognition, Roc Boronat, 138, 08018 Barcelona, Spain
| | - Ruggero G Bettinardi
- Universitat Pompeu Fabra, Theoretical and Computational Neuroscience, Center for Brain and Cognition, Roc Boronat, 138, 08018 Barcelona, Spain
| | - Elisabeth Wenger
- Max Planck Institute for Human Development, Center for Lifespan Psychology, Lentzeallee 94, 14195 Berlin, Germany
| | - Nina Lisofsky
- Max Planck Institute for Human Development, Center for Lifespan Psychology, Lentzeallee 94, 14195 Berlin, Germany; University Clinic Hamburg-Eppendorf, Clinic and Policlinic for Psychiatry and Psychotherapy, Martinistraße 52, 20246 Hamburg, Germany
| | - Johan Martensson
- Max Planck Institute for Human Development, Center for Lifespan Psychology, Lentzeallee 94, 14195 Berlin, Germany; Department of psychology, Lund University, Box 117, 221 00 Lund, Sweden
| | - Oisin Butler
- Max Planck Institute for Human Development, Center for Lifespan Psychology, Lentzeallee 94, 14195 Berlin, Germany
| | - Elisa Filevich
- Department of Psychology, Humboldt Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany; Bernstein Center for Computational Neuroscience Berlin, Philippstr. 13 Haus 6, 10115 Berlin, Germany
| | - Maxi Becker
- Max Planck Institute for Human Development, Center for Lifespan Psychology, Lentzeallee 94, 14195 Berlin, Germany; University Clinic Hamburg-Eppendorf, Clinic and Policlinic for Psychiatry and Psychotherapy, Martinistraße 52, 20246 Hamburg, Germany
| | - Martyna Lochstet
- Max Planck Institute for Human Development, Center for Lifespan Psychology, Lentzeallee 94, 14195 Berlin, Germany
| | - Simone Kühn
- Max Planck Institute for Human Development, Center for Lifespan Psychology, Lentzeallee 94, 14195 Berlin, Germany; University Clinic Hamburg-Eppendorf, Clinic and Policlinic for Psychiatry and Psychotherapy, Martinistraße 52, 20246 Hamburg, Germany
| | - Gustavo Deco
- Universitat Pompeu Fabra, Theoretical and Computational Neuroscience, Center for Brain and Cognition, Roc Boronat, 138, 08018 Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Theoretical and Computational Neuroscience, Center for Brain and Cognition, Roc Boronat, 138, 08018 Barcelona, Spain
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Bettinardi RG, Deco G, Karlaftis VM, Van Hartevelt TJ, Fernandes HM, Kourtzi Z, Kringelbach ML, Zamora-López G. How structure sculpts function: Unveiling the contribution of anatomical connectivity to the brain's spontaneous correlation structure. Chaos 2017; 27:047409. [PMID: 28456160 DOI: 10.1063/1.4980099] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Intrinsic brain activity is characterized by highly organized co-activations between different regions, forming clustered spatial patterns referred to as resting-state networks. The observed co-activation patterns are sustained by the intricate fabric of millions of interconnected neurons constituting the brain's wiring diagram. However, as for other real networks, the relationship between the connectional structure and the emergent collective dynamics still evades complete understanding. Here, we show that it is possible to estimate the expected pair-wise correlations that a network tends to generate thanks to the underlying path structure. We start from the assumption that in order for two nodes to exhibit correlated activity, they must be exposed to similar input patterns from the entire network. We then acknowledge that information rarely spreads only along a unique route but rather travels along all possible paths. In real networks, the strength of local perturbations tends to decay as they propagate away from the sources, leading to a progressive attenuation of the original information content and, thus, of their influence. Accordingly, we define a novel graph measure, topological similarity, which quantifies the propensity of two nodes to dynamically correlate as a function of the resemblance of the overall influences they are expected to receive due to the underlying structure of the network. Applied to the human brain, we find that the similarity of whole-network inputs, estimated from the topology of the anatomical connectome, plays an important role in sculpting the backbone pattern of time-average correlations observed at rest.
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Affiliation(s)
- R G Bettinardi
- Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | - G Deco
- Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | - V M Karlaftis
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom
| | - T J Van Hartevelt
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - H M Fernandes
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Z Kourtzi
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom
| | - M L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - G Zamora-López
- Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
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Bettinardi RG, Tort-Colet N, Ruiz-Mejias M, Sanchez-Vives MV, Deco G. Gradual emergence of spontaneous correlated brain activity during fading of general anesthesia in rats: Evidences from fMRI and local field potentials. Neuroimage 2015; 114:185-98. [PMID: 25804643 PMCID: PMC4461308 DOI: 10.1016/j.neuroimage.2015.03.037] [Citation(s) in RCA: 59] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2014] [Revised: 03/05/2015] [Accepted: 03/14/2015] [Indexed: 12/11/2022] Open
Abstract
Intrinsic brain activity is characterized by the presence of highly structured networks of correlated fluctuations between different regions of the brain. Such networks encompass different functions, whose properties are known to be modulated by the ongoing global brain state and are altered in several neurobiological disorders. In the present study, we induced a deep state of anesthesia in rats by means of a ketamine/medetomidine peritoneal injection, and analyzed the time course of the correlation between the brain activity in different areas while anesthesia spontaneously decreased over time. We compared results separately obtained from fMRI and local field potentials (LFPs) under the same anesthesia protocol, finding that while most profound phases of anesthesia can be described by overall sparse connectivity, stereotypical activity and poor functional integration, during lighter states different frequency-specific functional networks emerge, endowing the gradual restoration of structured large-scale activity seen during rest. Noteworthy, our in vivo results show that those areas belonging to the same functional network (the default-mode) exhibited sustained correlated oscillations around 10 Hz throughout the protocol, suggesting the presence of a specific functional backbone that is preserved even during deeper phases of anesthesia. Finally, the overall pattern of results obtained from both imaging and in vivo-recordings suggests that the progressive emergence from deep anesthesia is reflected by a corresponding gradual increase of organized correlated oscillations across the cortex. Rat brain activity was recorded while anesthesia spontaneously decreased over time. Fading of anesthesia modulates the overall pattern of brain structured co-activations. Areas of the same network show frequency-specific coupling even in deep anesthesia. Light anesthesia is characterized by the gradual emergence of large-scale connectivity. Correlated band-limited oscillations distinguish between states and network properties.
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Affiliation(s)
- Ruggero G Bettinardi
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona 08018, Spain.
| | - Núria Tort-Colet
- Institut D' Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona 08036, Spain
| | - Marcel Ruiz-Mejias
- Institut D' Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona 08036, Spain
| | - Maria V Sanchez-Vives
- Institut D' Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona 08036, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona 08010, Spain
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona 08018, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona 08010, Spain
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