1
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Chis-Ciure R, Melloni L, Northoff G. A measure centrality index for systematic empirical comparison of consciousness theories. Neurosci Biobehav Rev 2024; 161:105670. [PMID: 38615851 DOI: 10.1016/j.neubiorev.2024.105670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Revised: 03/15/2024] [Accepted: 04/08/2024] [Indexed: 04/16/2024]
Abstract
Consciousness science is marred by disparate constructs and methodologies, making it challenging to systematically compare theories. This foundational crisis casts doubts on the scientific character of the field itself. Addressing it, we propose a framework for systematically comparing consciousness theories by introducing a novel inter-theory classification interface, the Measure Centrality Index (MCI). Recognizing its gradient distribution, the MCI assesses the degree of importance a specific empirical measure has for a given consciousness theory. We apply the MCI to probe how the empirical measures of the Global Neuronal Workspace Theory (GNW), Integrated Information Theory (IIT), and Temporospatial Theory of Consciousness (TTC) would fare within the context of the other two. We demonstrate that direct comparison of IIT, GNW, and TTC is meaningful and valid for some measures like Lempel-Ziv Complexity (LZC), Autocorrelation Window (ACW), and possibly Mutual Information (MI). In contrast, it is problematic for others like the anatomical and physiological neural correlates of consciousness (NCC) due to their MCI-based differential weightings within the structure of the theories. In sum, we introduce and provide proof-of-principle of a novel systematic method for direct inter-theory empirical comparisons, thereby addressing isolated evolution of theories and confirmatory bias issues in the state-of-the-art neuroscience of consciousness.
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Affiliation(s)
- Robert Chis-Ciure
- New York University (NYU), New York, USA; International Center for Neuroscience and Ethics (CINET), Tatiana Foundation, Madrid, Spain; Wolfram Physics Project, USA.
| | - Lucia Melloni
- Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany
| | - Georg Northoff
- University of Ottawa, Institute of Mental Health Research at the Royal Ottawa Hospital, Ottawa, Canada
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2
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Silva Pereira S, Özer EE, Sebastian-Galles N. Complexity of STG signals and linguistic rhythm: a methodological study for EEG data. Cereb Cortex 2024; 34:bhad549. [PMID: 38236741 DOI: 10.1093/cercor/bhad549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 12/29/2023] [Accepted: 12/30/2023] [Indexed: 02/06/2024] Open
Abstract
The superior temporal and the Heschl's gyri of the human brain play a fundamental role in speech processing. Neurons synchronize their activity to the amplitude envelope of the speech signal to extract acoustic and linguistic features, a process known as neural tracking/entrainment. Electroencephalography has been extensively used in language-related research due to its high temporal resolution and reduced cost, but it does not allow for a precise source localization. Motivated by the lack of a unified methodology for the interpretation of source reconstructed signals, we propose a method based on modularity and signal complexity. The procedure was tested on data from an experiment in which we investigated the impact of native language on tracking to linguistic rhythms in two groups: English natives and Spanish natives. In the experiment, we found no effect of native language but an effect of language rhythm. Here, we compare source projected signals in the auditory areas of both hemispheres for the different conditions using nonparametric permutation tests, modularity, and a dynamical complexity measure. We found increasing values of complexity for decreased regularity in the stimuli, giving us the possibility to conclude that languages with less complex rhythms are easier to track by the auditory cortex.
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Affiliation(s)
- Silvana Silva Pereira
- Center for Brain and Cognition, Department of Information and Communications Technologies, Universitat Pompeu Fabra, 08005 Barcelona, Spain
| | - Ege Ekin Özer
- Center for Brain and Cognition, Department of Information and Communications Technologies, Universitat Pompeu Fabra, 08005 Barcelona, Spain
| | - Nuria Sebastian-Galles
- Center for Brain and Cognition, Department of Information and Communications Technologies, Universitat Pompeu Fabra, 08005 Barcelona, Spain
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3
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Rangaprakash D, David O, Barry RL, Deshpande G. Comparison of hemodynamic response functions obtained from resting-state functional MRI and invasive electrophysiological recordings in rats. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.27.530359. [PMID: 37961471 PMCID: PMC10634675 DOI: 10.1101/2023.02.27.530359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Resting-state functional MRI (rs-fMRI) is a popular technology that has enriched our understanding of brain and spinal cord functioning, including how different regions communicate (connectivity). But fMRI is an indirect measure of neural activity capturing blood hemodynamics. The hemodynamic response function (HRF) interfaces between the unmeasured neural activity and measured fMRI time series. The HRF is variable across brain regions and individuals, and is modulated by non-neural factors. Ignoring this HRF variability causes errors in FC estimates. Hence, it is crucial to reliably estimate the HRF from rs-fMRI data. Robust techniques have emerged to estimate the HRF from fMRI time series. Although such techniques have been validated non-invasively using simulated and empirical fMRI data, thorough invasive validation using simultaneous electrophysiological recordings, the gold standard, has been elusive. This report addresses this gap in the literature by comparing HRFs derived from invasive intracranial electroencephalogram recordings with HRFs estimated from simultaneously acquired fMRI data in six epileptic rats. We found that the HRF shape parameters (HRF amplitude, latency and width) were not significantly different (p>0.05) between ground truth and estimated HRFs. In the single pathological region, the HRF width was marginally significantly different (p=0.03). Our study provides preliminary invasive validation for the efficacy of the HRF estimation technique in reliably estimating the HRF non-invasively from rs-fMRI data directly. This has a notable impact on rs-fMRI connectivity studies, and we recommend that HRF deconvolution be performed to minimize HRF variability and improve connectivity estimates.
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Affiliation(s)
- D Rangaprakash
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Olivier David
- Université Grenoble Alpes, Inserm, U1216, Grenoble Institute of Neuroscience, F-38000, Grenoble, France
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Robert L Barry
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
- Harvard-Massachusetts Institute of Technology Division of Health Sciences & Technology, Cambridge, Massachusetts, USA
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
- Department of Psychological Sciences, Auburn University, Auburn, AL, USA
- Center for Neuroscience, Auburn University, Auburn, AL, USA
- Alabama Advanced Imaging Consortium, Birmingham, AL, USA
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4
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Rangaprakash D, Barry RL, Deshpande G. The confound of hemodynamic response function variability in human resting-state functional MRI studies. Front Neurosci 2023; 17:934138. [PMID: 37521709 PMCID: PMC10375034 DOI: 10.3389/fnins.2023.934138] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 04/07/2023] [Indexed: 08/01/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI) is an indirect measure of neural activity with the hemodynamic response function (HRF) coupling it with unmeasured neural activity. The HRF, modulated by several non-neural factors, is variable across brain regions, individuals and populations. Yet, a majority of human resting-state fMRI connectivity studies continue to assume a non-variable HRF. In this article, with supportive prior evidence, we argue that HRF variability cannot be ignored as it substantially confounds within-subject connectivity estimates and between-subjects connectivity group differences. We also discuss its clinical relevance with connectivity impairments confounded by HRF aberrations in several disorders. We present limited data on HRF differences between women and men, which resulted in a 15.4% median error in functional connectivity estimates in a group-level comparison. We also discuss the implications of HRF variability for fMRI studies in the spinal cord. There is a need for more dialogue within the community on the HRF confound, and we hope that our article is a catalyst in the process.
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Affiliation(s)
- D. Rangaprakash
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
| | - Robert L. Barry
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Harvard-Massachusetts Institute of Technology Division of Health Sciences and Technology, Cambridge, MA, United States
| | - Gopikrishna Deshpande
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States
- Department of Psychological Sciences, Auburn University, Auburn, AL, United States
- Center for Neuroscience, Auburn University, Auburn, AL, United States
- Alabama Advanced Imaging Consortium, Birmingham, AL, United States
- Key Laboratory for Learning and Cognition, School of Psychology, Capital Normal University, Beijing, China
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
- Centre for Brain Research, Indian Institute of Science, Bangalore, India
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5
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Nemirovsky IE, Popiel NJM, Rudas J, Caius M, Naci L, Schiff ND, Owen AM, Soddu A. An implementation of integrated information theory in resting-state fMRI. Commun Biol 2023; 6:692. [PMID: 37407655 DOI: 10.1038/s42003-023-05063-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 06/22/2023] [Indexed: 07/07/2023] Open
Abstract
Integrated Information Theory was developed to explain and quantify consciousness, arguing that conscious systems consist of elements that are integrated through their causal properties. This study presents an implementation of Integrated Information Theory 3.0, the latest version of this framework, to functional MRI data. Data were acquired from 17 healthy subjects who underwent sedation with propofol, a short-acting anaesthetic. Using the PyPhi software package, we systematically analyze how Φmax, a measure of integrated information, is modulated by the sedative in different resting-state networks. We compare Φmax to other proposed measures of conscious level, including the previous version of integrated information, Granger causality, and correlation-based functional connectivity. Our results indicate that Φmax presents a variety of sedative-induced behaviours for different networks. Notably, changes to Φmax closely reflect changes to subjects' conscious level in the frontoparietal and dorsal attention networks, which are responsible for higher-order cognitive functions. In conclusion, our findings present important insight into different measures of conscious level that will be useful in future implementations to functional MRI and other forms of neuroimaging.
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Affiliation(s)
- Idan E Nemirovsky
- Western Institute for Neuroscience, Department of Physics and Astronomy, University of Western Ontario, 1151 Richmond St, London, ON, N6A 3K7, Canada.
| | - Nicholas J M Popiel
- Cavendish Laboratory, University of Cambridge, Cambridge, CB3 0HE, United Kingdom
| | - Jorge Rudas
- Institute of Biotechnology, Universidad Nacional de Colombia, Cra 45, Bogotá, Colombia
| | - Matthew Caius
- Western Institute for Neuroscience, Department of Physics and Astronomy, University of Western Ontario, 1151 Richmond St, London, ON, N6A 3K7, Canada
- Department of Medical Biophysics, University of Western Ontario, 1151 Richmond St, London, ON, N6A 3K7, Canada
| | - Lorina Naci
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
| | - Nicholas D Schiff
- Feil Family Brain Mind Research Institute, Weill Cornell Medical College, New York, NY, 10065, USA
| | - Adrian M Owen
- Department of Physiology and Pharmacology and Department of Psychology, University of Western Ontario, 1151 Richmond St, London, ON, N6A 3K7, Canada
| | - Andrea Soddu
- Western Institute for Neuroscience, Department of Physics and Astronomy, University of Western Ontario, 1151 Richmond St, London, ON, N6A 3K7, Canada
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6
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Gandhi SR, Mayner WGP, Marshall W, Billeh YN, Bennett C, Gale SD, Mochizuki C, Siegle JH, Olsen S, Tononi G, Koch C, Arkhipov A. A survey of neurophysiological differentiation across mouse visual brain areas and timescales. Front Comput Neurosci 2023; 17:1040629. [PMID: 36994445 PMCID: PMC10040573 DOI: 10.3389/fncom.2023.1040629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 02/23/2023] [Indexed: 03/18/2023] Open
Abstract
Neurophysiological differentiation (ND), a measure of the number of distinct activity states that a neural population visits over a time interval, has been used as a correlate of meaningfulness or subjective perception of visual stimuli. ND has largely been studied in non-invasive human whole-brain recordings where spatial resolution is limited. However, it is likely that perception is supported by discrete neuronal populations rather than the whole brain. Therefore, here we use Neuropixels recordings from the mouse brain to characterize the ND metric across a wide range of temporal scales, within neural populations recorded at single-cell resolution in localized regions. Using the spiking activity of thousands of simultaneously recorded neurons spanning 6 visual cortical areas and the visual thalamus, we show that the ND of stimulus-evoked activity of the entire visual cortex is higher for naturalistic stimuli relative to artificial ones. This finding holds in most individual areas throughout the visual hierarchy. Moreover, for animals performing an image change detection task, ND of the entire visual cortex (though not individual areas) is higher for successful detection compared to failed trials, consistent with the assumed perception of the stimulus. Together, these results suggest that ND computed on cellular-level neural recordings is a useful tool highlighting cell populations that may be involved in subjective perception.
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Affiliation(s)
- Saurabh R. Gandhi
- MindScope Program, Allen Institute, Seattle, WA, United States
- *Correspondence: Saurabh R. Gandhi,
| | - William G. P. Mayner
- Department of Psychiatry, University of Wisconsin–Madison, Madison, WI, United States
| | - William Marshall
- Department of Mathematics and Statistics, Brock University, St. Catharines, ON, Canada
| | - Yazan N. Billeh
- MindScope Program, Allen Institute, Seattle, WA, United States
| | - Corbett Bennett
- MindScope Program, Allen Institute, Seattle, WA, United States
| | - Samuel D. Gale
- MindScope Program, Allen Institute, Seattle, WA, United States
| | - Chris Mochizuki
- MindScope Program, Allen Institute, Seattle, WA, United States
| | | | - Shawn Olsen
- MindScope Program, Allen Institute, Seattle, WA, United States
| | - Giulio Tononi
- Department of Psychiatry, University of Wisconsin–Madison, Madison, WI, United States
| | - Christof Koch
- MindScope Program, Allen Institute, Seattle, WA, United States
| | - Anton Arkhipov
- MindScope Program, Allen Institute, Seattle, WA, United States
- Anton Arkhipov,
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7
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Orłowski P, Bola M. Sensory modality defines the relation between EEG Lempel-Ziv diversity and meaningfulness of a stimulus. Sci Rep 2023; 13:3453. [PMID: 36859725 PMCID: PMC9977735 DOI: 10.1038/s41598-023-30639-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 02/27/2023] [Indexed: 03/03/2023] Open
Abstract
Diversity of brain activity is a robust neural correlate of global states of consciousness. It has been proposed that diversity measures specifically reflect the temporal variability of conscious experience. Previous studies supported this hypothesis by showing that perception of meaningful visual stimuli causes richer, more-variable experiences than perception of meaningless stimuli, and this is reflected in greater brain signal diversity. To investigate whether this relation is consistent across sensory modalities, to participants we presented three versions of naturalistic visual and auditory stimuli (videos and audiobooks) that varied in the amount of meaning (original, scrambled, and noise), while recording electroencephalographic signals. We report three main findings. First, greater meaningfulness of visual stimuli was related to higher Lempel-Ziv diversity of EEG signals, but the opposite effect was found in the auditory modality. Second, visual perception was related to generally higher EEG diversity than auditory perception. Third, perception of meaningful visual stimuli and auditory stimuli respectively resulted in higher and lower EEG diversity in comparison to the resting state. In conclusion, the signal diversity of continuous brain signals depends on the stimulated sensory modality, therefore it is not a generic index of the variability of conscious experience.
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Affiliation(s)
- Paweł Orłowski
- grid.419305.a0000 0001 1943 2944Laboratory of Brain Imaging, Nencki Institute of Experimental Biology of Polish Academy of Sciences, 3 Pasteur Street, 02-093 Warsaw, Poland
| | - Michał Bola
- Laboratory of Brain Imaging, Nencki Institute of Experimental Biology of Polish Academy of Sciences, 3 Pasteur Street, 02-093, Warsaw, Poland.
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8
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Aamodt A, Sevenius Nilsen A, Markhus R, Kusztor A, HasanzadehMoghadam F, Kauppi N, Thürer B, Storm JF, Juel BE. EEG Lempel-Ziv complexity varies with sleep stage, but does not seem to track dream experience. Front Hum Neurosci 2023; 16:987714. [PMID: 36704096 PMCID: PMC9871639 DOI: 10.3389/fnhum.2022.987714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 12/14/2022] [Indexed: 01/12/2023] Open
Abstract
In a recent electroencephalography (EEG) sleep study inspired by complexity theories of consciousness, we found that multi-channel signal diversity progressively decreased from wakefulness to slow wave sleep, but failed to find any significant difference between dreaming and non-dreaming awakenings within the same sleep stage (NREM2). However, we did find that multi-channel Lempel-Ziv complexity (LZC) measured over the posterior cortex increased with more perceptual ratings of NREM2 dream experience along a thought-perceptual axis. In this follow-up study, we re-tested our previous findings, using a slightly different approach. Partial sleep-deprivation was followed by evening sleep experiments, with repeated awakenings and immediate dream reports. Participants reported whether they had been dreaming, and were asked to rate how diverse, vivid, perceptual, and thought-like the contents of their dreams were. High density (64 channel) EEG was recorded throughout the experiment, and mean single-channel LZC was calculated for each 30 s sleep epoch. LZC progressively decreased with depth of non-REM sleep. Surprisingly, estimated marginal mean LZC was slightly higher for NREM1 than for wakefulness, but the difference did not remain significant after adjusting for multiple comparisons. We found no significant difference in LZC between dream and non-dream awakenings, nor any significant relationship between LZC and subjective ratings of dream experience, within the same sleep stage (NREM2). The failure to reproduce our own previous finding of a positive correlation between posterior LZC and more perceptual dream experiences, or to find any other correlation between brain signal complexity and subjective experience within NREM2 sleep, raises the question of whether EEG LZC is really a reliable correlate of richness of experience as such, within the same sleep stage.
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Affiliation(s)
- Arnfinn Aamodt
- Brain Signalling Lab, Division of Physiology, Faculty of Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway,*Correspondence: Arnfinn Aamodt,
| | - André Sevenius Nilsen
- Brain Signalling Lab, Division of Physiology, Faculty of Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Rune Markhus
- National Centre for Epilepsy, Oslo University Hospital, Oslo, Norway
| | - Anikó Kusztor
- Brain Signalling Lab, Division of Physiology, Faculty of Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway,School of Psychological Sciences, Monash University, Clayton, VIC, Australia
| | - Fatemeh HasanzadehMoghadam
- Brain Signalling Lab, Division of Physiology, Faculty of Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Nils Kauppi
- Brain Signalling Lab, Division of Physiology, Faculty of Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Benjamin Thürer
- Brain Signalling Lab, Division of Physiology, Faculty of Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Johan Frederik Storm
- Brain Signalling Lab, Division of Physiology, Faculty of Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Bjørn Erik Juel
- Brain Signalling Lab, Division of Physiology, Faculty of Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway,Bjørn Erik Juel,
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9
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Srinivasan N, Simione L, Arsiwalla XD, Kleiner J, Raffone A. Editorial: Insights in consciousness research 2021. Front Psychol 2023; 14:1182690. [PMID: 37151321 PMCID: PMC10157188 DOI: 10.3389/fpsyg.2023.1182690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 04/03/2023] [Indexed: 05/09/2023] Open
Affiliation(s)
- Narayanan Srinivasan
- Department of Cognitive Science, Indian Institute of Technology Kanpur, Kanpur, India
- *Correspondence: Narayanan Srinivasan
| | - Luca Simione
- Institute of Cognitive Sciences and Technologies, Consiglio Nazionale delle Ricerche, Rome, Italy
- Faculty of Interpreting and Translation, UNINT, Università degli Studi Internazionali, Rome, Italy
| | - Xerxes D. Arsiwalla
- Department of Information and Communication Technologies, Pompeu Fabra University, Barcelona, Spain
| | - Johannes Kleiner
- Munich Center for Mathematical Philosophy, Ludwig Maximilian University of Munich, Munich, Germany
- Munich Graduate School of Systemic Neurosciences, Ludwig Maximilian University of Munich, Munich, Germany
- Association for Mathematical Consciousness Science, Munich, Germany
| | - Antonino Raffone
- Department of Psychology, Sapienza University of Rome, Rome, Italy
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10
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Coppola P, Allanson J, Naci L, Adapa R, Finoia P, Williams GB, Pickard JD, Owen AM, Menon DK, Stamatakis EA. The complexity of the stream of consciousness. Commun Biol 2022; 5:1173. [PMID: 36329176 PMCID: PMC9633704 DOI: 10.1038/s42003-022-04109-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 10/10/2022] [Indexed: 11/06/2022] Open
Abstract
Typical consciousness can be defined as an individual-specific stream of experiences. Modern consciousness research on dynamic functional connectivity uses clustering techniques to create common bases on which to compare different individuals. We propose an alternative approach by combining modern theories of consciousness and insights arising from phenomenology and dynamical systems theory. This approach enables a representation of an individual's connectivity dynamics in an intrinsically-defined, individual-specific landscape. Given the wealth of evidence relating functional connectivity to experiential states, we assume this landscape is a proxy measure of an individual's stream of consciousness. By investigating the properties of this landscape in individuals in different states of consciousness, we show that consciousness is associated with short term transitions that are less predictable, quicker, but, on average, more constant. We also show that temporally-specific connectivity states are less easily describable by network patterns that are distant in time, suggesting a richer space of possible states. We show that the cortex, cerebellum and subcortex all display consciousness-relevant dynamics and discuss the implication of our results in forming a point of contact between dynamical systems interpretations and phenomenology.
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Affiliation(s)
- Peter Coppola
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
| | - Judith Allanson
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
- Department of Neurosciences, Cambridge University Hospitals NHS Foundation, Addenbrooke's Hospital, Cambridge, UK
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Lloyd Building, Trinity College Dublin, Dublin, Ireland
| | - Ram Adapa
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
| | - Paola Finoia
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
- Division of Neurosurgery, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
| | - Guy B Williams
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - John D Pickard
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
- Division of Neurosurgery, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Adrian M Owen
- The Brain and Mind Institute, Western Interdisciplinary Research Building, N6A 5B7 University of Western Ontario, London, ON, Canada
| | - David K Menon
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK.
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK.
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11
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Arkhipov A. Non-Separability of Physical Systems as a Foundation of Consciousness. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1539. [PMID: 36359629 PMCID: PMC9689906 DOI: 10.3390/e24111539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 10/22/2022] [Accepted: 10/23/2022] [Indexed: 06/16/2023]
Abstract
A hypothesis is presented that non-separability of degrees of freedom is the fundamental property underlying consciousness in physical systems. The amount of consciousness in a system is determined by the extent of non-separability and the number of degrees of freedom involved. Non-interacting and feedforward systems have zero consciousness, whereas most systems of interacting particles appear to have low non-separability and consciousness. By contrast, brain circuits exhibit high complexity and weak but tightly coordinated interactions, which appear to support high non-separability and therefore high amount of consciousness. The hypothesis applies to both classical and quantum cases, and we highlight the formalism employing the Wigner function (which in the classical limit becomes the Liouville density function) as a potentially fruitful framework for characterizing non-separability and, thus, the amount of consciousness in a system. The hypothesis appears to be consistent with both the Integrated Information Theory and the Orchestrated Objective Reduction Theory and may help reconcile the two. It offers a natural explanation for the physical properties underlying the amount of consciousness and points to methods of estimating the amount of non-separability as promising ways of characterizing the amount of consciousness.
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Affiliation(s)
- Anton Arkhipov
- MindScope Program, Allen Institute, Seattle, WA 98109, USA
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12
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Koculak M, Wierzchoń M. How much consciousness is there in complexity? Front Psychol 2022; 13:983315. [PMID: 36204731 PMCID: PMC9530911 DOI: 10.3389/fpsyg.2022.983315] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 08/29/2022] [Indexed: 11/13/2022] Open
Abstract
The notion of complexity currently receives significant attention in neuroscience, mainly through the popularity of the Integrated Information Theory (IIT). It has proven successful in research centred on discriminating states of consciousness, while little theoretical and experimental effort was directed toward studying the content. In this paper, we argue that exploring the relationship between complexity and conscious content is necessary to understand the importance of information-theoretic measures for consciousness research properly. We outline how content could be experimentally operationalised and how rudimental testable hypotheses can be formulated without requiring IIT formalisms. This approach would not only allow for a better understanding of aspects of consciousness captured by complexity but could also facilitate comparison efforts for theories of consciousness.
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Affiliation(s)
- Marcin Koculak
- Consciousness Lab, Institute of Psychology, Jagiellonian University, Kraków, Poland
- Centre for Brain Research, Jagiellonian University, Kraków, Poland
- *Correspondence: Marcin Koculak,
| | - Michał Wierzchoń
- Consciousness Lab, Institute of Psychology, Jagiellonian University, Kraków, Poland
- Centre for Brain Research, Jagiellonian University, Kraków, Poland
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13
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Golesorkhi M, Gomez-Pilar J, Çatal Y, Tumati S, Yagoub MCE, Stamatakis EA, Northoff G. From temporal to spatial topography: hierarchy of neural dynamics in higher- and lower-order networks shapes their complexity. Cereb Cortex 2022; 32:5637-5653. [PMID: 35188968 PMCID: PMC9753094 DOI: 10.1093/cercor/bhac042] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 01/21/2022] [Accepted: 01/22/2022] [Indexed: 01/25/2023] Open
Abstract
The brain shows a topographical hierarchy along the lines of lower- and higher-order networks. The exact temporal dynamics characterization of this lower-higher-order topography at rest and its impact on task states remains unclear, though. Using 2 functional magnetic resonance imaging data sets, we investigate lower- and higher-order networks in terms of the signal compressibility, operationalized by Lempel-Ziv complexity (LZC). As we assume that this degree of complexity is related to the slow-fast frequency balance, we also compute the median frequency (MF), an estimation of frequency distribution. We demonstrate (i) topographical differences at rest between higher- and lower-order networks, showing lower LZC and MF in the former; (ii) task-related and task-specific changes in LZC and MF in both lower- and higher-order networks; (iii) hierarchical relationship between LZC and MF, as MF at rest correlates with LZC rest-task change along the lines of lower- and higher-order networks; and (iv) causal and nonlinear relation between LZC at rest and LZC during task, with MF at rest acting as mediator. Together, results show that the topographical hierarchy of lower- and higher-order networks converges with their temporal hierarchy, with these neural dynamics at rest shaping their range of complexity during task states in a nonlinear way.
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Affiliation(s)
| | | | - Yasir Çatal
- Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health, Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa ON K1Z 7K4, Canada
| | - Shankar Tumati
- Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health, Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa ON K1Z 7K4, Canada
| | - Mustapha C E Yagoub
- School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa ON K1Z 7K4, Canada
| | - Emanuel A Stamatakis
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge CB1 0SP, United Kingdom
| | - Georg Northoff
- Corresponding author: Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health, Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa, Canada.
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14
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Measuring Stimulus-Evoked Neurophysiological Differentiation in Distinct Populations of Neurons in Mouse Visual Cortex. eNeuro 2022; 9:ENEURO.0280-21.2021. [PMID: 35022186 PMCID: PMC8856714 DOI: 10.1523/eneuro.0280-21.2021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 12/02/2021] [Accepted: 12/08/2021] [Indexed: 11/24/2022] Open
Abstract
Despite significant progress in understanding neural coding, it remains unclear how the coordinated activity of large populations of neurons relates to what an observer actually perceives. Since neurophysiological differences must underlie differences among percepts, differentiation analysis—quantifying distinct patterns of neurophysiological activity—has been proposed as an “inside-out” approach that addresses this question. This methodology contrasts with “outside-in” approaches such as feature tuning and decoding analyses, which are defined in terms of extrinsic experimental variables. Here, we used two-photon calcium imaging in mice of both sexes to systematically survey stimulus-evoked neurophysiological differentiation (ND) in excitatory neuronal populations in layers (L)2/3, L4, and L5 across five visual cortical areas (primary, lateromedial, anterolateral, posteromedial, and anteromedial) in response to naturalistic and phase-scrambled movie stimuli. We find that unscrambled stimuli evoke greater ND than scrambled stimuli specifically in L2/3 of the anterolateral and anteromedial areas, and that this effect is modulated by arousal state and locomotion. By contrast, decoding performance was far above chance and did not vary substantially across areas and layers. Differentiation also differed within the unscrambled stimulus set, suggesting that differentiation analysis may be used to probe the ethological relevance of individual stimuli.
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15
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Walter J. Consciousness as a multidimensional phenomenon: implications for the assessment of disorders of consciousness. Neurosci Conscious 2021; 2021:niab047. [PMID: 34992792 PMCID: PMC8716840 DOI: 10.1093/nc/niab047] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 10/19/2021] [Accepted: 12/10/2021] [Indexed: 01/10/2023] Open
Abstract
Disorders of consciousness (DoCs) pose a significant clinical and ethical challenge because they allow for complex forms of conscious experience in patients where intentional behaviour and communication are highly limited or non-existent. There is a pressing need for brain-based assessments that can precisely and accurately characterize the conscious state of individual DoC patients. There has been an ongoing research effort to develop neural measures of consciousness. However, these measures are challenging to validate not only due to our lack of ground truth about consciousness in many DoC patients but also because there is an open ontological question about consciousness. There is a growing, well-supported view that consciousness is a multidimensional phenomenon that cannot be fully described in terms of the theoretical construct of hierarchical, easily ordered conscious levels. The multidimensional view of consciousness challenges the utility of levels-based neural measures in the context of DoC assessment. To examine how these measures may map onto consciousness as a multidimensional phenomenon, this article will investigate a range of studies where they have been applied in states other than DoC and where more is known about conscious experience. This comparative evidence suggests that measures of conscious level are more sensitive to some dimensions of consciousness than others and cannot be assumed to provide a straightforward hierarchical characterization of conscious states. Elevated levels of brain complexity, for example, are associated with conscious states characterized by a high degree of sensory richness and minimal attentional constraints, but are suboptimal for goal-directed behaviour and external responsiveness. Overall, this comparative analysis indicates that there are currently limitations to the use of these measures as tools to evaluate consciousness as a multidimensional phenomenon and that the relationship between these neural signatures and phenomenology requires closer scrutiny.
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Affiliation(s)
- Jasmine Walter
- Cognition and Philosophy Lab, 21 Chancellor’s Walk, Monash University, Melbourne, VIC 3800, Australia
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16
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Rangaprakash D, Tadayonnejad R, Deshpande G, O'Neill J, Feusner JD. FMRI hemodynamic response function (HRF) as a novel marker of brain function: applications for understanding obsessive-compulsive disorder pathology and treatment response. Brain Imaging Behav 2021; 15:1622-1640. [PMID: 32761566 DOI: 10.1007/s11682-020-00358-8] [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] [Indexed: 11/28/2022]
Abstract
The hemodynamic response function (HRF) represents the transfer function linking neural activity with the functional MRI (fMRI) signal, modeling neurovascular coupling. Since HRF is influenced by non-neural factors, to date it has largely been considered as a confound or has been ignored in many analyses. However, underlying biophysics suggests that the HRF may contain meaningful correlates of neural activity, which might be unavailable through conventional fMRI metrics. Here, we estimated the HRF by performing deconvolution on resting-state fMRI data from a longitudinal sample of 25 healthy controls scanned twice and 44 adults with obsessive-compulsive disorder (OCD) before and after 4-weeks of intensive cognitive-behavioral therapy (CBT). HRF response height, time-to-peak and full-width at half-maximum (FWHM) in OCD were abnormal before treatment and normalized after treatment in regions including the caudate. Pre-treatment HRF predicted treatment outcome (OCD symptom reduction) with 86.4% accuracy, using machine learning. Pre-treatment HRF response height in the caudate head and time-to-peak in the caudate tail were top-predictors of treatment response. Time-to-peak in the caudate tail, a region not typically identified in OCD studies using conventional fMRI activation or connectivity measures, may carry novel importance. Additionally, pre-treatment response height in caudate head predicted post-treatment OCD severity (R = -0.48, P = 0.001), and was associated with treatment-related OCD severity changes (R = -0.44, P = 0.0028), underscoring its relevance. With HRF being a reliable marker sensitive to brain function, OCD pathology, and intervention-related changes, these results could guide future studies towards novel discoveries not possible through conventional fMRI approaches like standard BOLD activation or connectivity.
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Affiliation(s)
- D Rangaprakash
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, 90095, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School and Harvard-MIT Health Sciences and Technology, Cambridge, MA, 02129, USA
| | - Reza Tadayonnejad
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, 90095, USA.,Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, 91125, USA
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, 36849, USA.,Department of Psychological Sciences, Auburn University, Auburn, AL, 36849, USA.,Alabama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, Auburn, AL, USA.,Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, USA.,Center for Neuroscience, Auburn University, Auburn, AL, USA.,School of Psychology, Capital Normal University, Beijing, China.,Key Laboratory for Learning and Cognition, Capital Normal University, Beijing, China.,Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Joseph O'Neill
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Jamie D Feusner
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, 90095, USA.
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17
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Aamodt A, Nilsen AS, Thürer B, Moghadam FH, Kauppi N, Juel BE, Storm JF. EEG Signal Diversity Varies With Sleep Stage and Aspects of Dream Experience. Front Psychol 2021; 12:655884. [PMID: 33967919 PMCID: PMC8102678 DOI: 10.3389/fpsyg.2021.655884] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 03/18/2021] [Indexed: 11/13/2022] Open
Abstract
Several theories link consciousness to complex cortical dynamics, as suggested by comparison of brain signal diversity between conscious states and states where consciousness is lost or reduced. In particular, Lempel-Ziv complexity, amplitude coalition entropy and synchrony coalition entropy distinguish wakefulness and REM sleep from deep sleep and anesthesia, and are elevated in psychedelic states, reported to increase the range and vividness of conscious contents. Some studies have even found correlations between complexity measures and facets of self-reported experience. As suggested by integrated information theory and the entropic brain hypothesis, measures of differentiation and signal diversity may therefore be measurable correlates of consciousness and phenomenological richness. Inspired by these ideas, we tested three hypotheses about EEG signal diversity related to sleep and dreaming. First, diversity should decrease with successively deeper stages of non-REM sleep. Second, signal diversity within the same sleep stage should be higher for periods of dreaming vs. non-dreaming. Third, specific aspects of dream contents should correlate with signal diversity in corresponding cortical regions. We employed a repeated awakening paradigm in sleep deprived healthy volunteers, with immediate dream report and rating of dream content along a thought-perceptual axis, from exclusively thought-like to exclusively perceptual. Generalized linear mixed models were used to assess how signal diversity varied with sleep stage, dreaming and thought-perceptual rating. Signal diversity decreased with sleep depth, but was not significantly different between dreaming and non-dreaming, even though there was a significant positive correlation between Lempel-Ziv complexity of EEG recorded over the posterior cortex and thought-perceptual ratings of dream contents.
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Affiliation(s)
- Arnfinn Aamodt
- Brain Signalling Lab, Division of Physiology, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - André Sevenius Nilsen
- Brain Signalling Lab, Division of Physiology, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Benjamin Thürer
- Brain Signalling Lab, Division of Physiology, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Fatemeh Hasanzadeh Moghadam
- Brain Signalling Lab, Division of Physiology, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Nils Kauppi
- Brain Signalling Lab, Division of Physiology, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Bjørn Erik Juel
- Brain Signalling Lab, Division of Physiology, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Johan Frederik Storm
- Brain Signalling Lab, Division of Physiology, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway
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18
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Canales-Johnson A, Billig AJ, Olivares F, Gonzalez A, Garcia MDC, Silva W, Vaucheret E, Ciraolo C, Mikulan E, Ibanez A, Huepe D, Noreika V, Chennu S, Bekinschtein TA. Dissociable Neural Information Dynamics of Perceptual Integration and Differentiation during Bistable Perception. Cereb Cortex 2020; 30:4563-4580. [PMID: 32219312 PMCID: PMC7325715 DOI: 10.1093/cercor/bhaa058] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
At any given moment, we experience a perceptual scene as a single whole and yet we may distinguish a variety of objects within it. This phenomenon instantiates two properties of conscious perception: integration and differentiation. Integration is the property of experiencing a collection of objects as a unitary percept and differentiation is the property of experiencing these objects as distinct from each other. Here, we evaluated the neural information dynamics underlying integration and differentiation of perceptual contents during bistable perception. Participants listened to a sequence of tones (auditory bistable stimuli) experienced either as a single stream (perceptual integration) or as two parallel streams (perceptual differentiation) of sounds. We computed neurophysiological indices of information integration and information differentiation with electroencephalographic and intracranial recordings. When perceptual alternations were endogenously driven, the integrated percept was associated with an increase in neural information integration and a decrease in neural differentiation across frontoparietal regions, whereas the opposite pattern was observed for the differentiated percept. However, when perception was exogenously driven by a change in the sound stream (no bistability), neural oscillatory power distinguished between percepts but information measures did not. We demonstrate that perceptual integration and differentiation can be mapped to theoretically motivated neural information signatures, suggesting a direct relationship between phenomenology and neurophysiology.
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Affiliation(s)
- Andrés Canales-Johnson
- Department of Psychology, University of Cambridge, CB2 3EB Cambridge, UK
- Vicerectoria de Investigacion y Posgrado, Universidad Catolica del Maule, Talca 3480112, Chile
| | - Alexander J Billig
- Brain and Mind Institute, University of Western Ontario, London, N6A 3K7, Canada
- UCL Ear Institute, University College London, London, UK
| | - Francisco Olivares
- Facultad de Psicologia, Universidad Diego Portales, Santiago 8370076, Chile
| | - Andrés Gonzalez
- Facultad de Psicologia, Universidad Diego Portales, Santiago 8370076, Chile
| | - María del Carmen Garcia
- Programa de Cirugía de Epilepsia, Hospital Italiano de Buenos Aires, Buenos Aires C1199ABB, Argentina
| | - Walter Silva
- Programa de Cirugía de Epilepsia, Hospital Italiano de Buenos Aires, Buenos Aires C1199ABB, Argentina
| | - Esteban Vaucheret
- Programa de Cirugía de Epilepsia, Hospital Italiano de Buenos Aires, Buenos Aires C1199ABB, Argentina
| | - Carlos Ciraolo
- Programa de Cirugía de Epilepsia, Hospital Italiano de Buenos Aires, Buenos Aires C1199ABB, Argentina
| | - Ezequiel Mikulan
- Laboratory of Experimental Psychology and Neuroscience (LPEN), Institute of Cognitive and Translational Neuroscience (INCyT), INECO Foundation, Favaloro University, Buenos Aires 1126, Argentina
| | - Agustín Ibanez
- Laboratory of Experimental Psychology and Neuroscience (LPEN), Institute of Cognitive and Translational Neuroscience (INCyT), INECO Foundation, Favaloro University, Buenos Aires 1126, Argentina
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
- School of Psychology, Center for Social and Cognitive Neuroscience (CSCN), Universidad Adolfo Ibáñez, Santiago 2485, Chile
| | - David Huepe
- School of Psychology, Center for Social and Cognitive Neuroscience (CSCN), Universidad Adolfo Ibáñez, Santiago 2485, Chile
| | - Valdas Noreika
- Department of Psychology, University of Cambridge, CB2 3EB Cambridge, UK
| | - Srivas Chennu
- School of Computing, University of Kent, ME4 4AG Chatham, UK
- Department of Clinical Neurosciences, University of Cambridge, CB2 3EB Cambridge, UK
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19
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Cohen D, Sasai S, Tsuchiya N, Oizumi M. A general spectral decomposition of causal influences applied to integrated information. J Neurosci Methods 2019; 330:108443. [PMID: 31732159 DOI: 10.1016/j.jneumeth.2019.108443] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 09/06/2019] [Accepted: 09/25/2019] [Indexed: 11/17/2022]
Abstract
BACKGROUND Quantifying interactions among many neurons is fundamental to understanding system-level phenomena such as attention, learning and even conscious experience. Causal influences in the brain, quantified as integrated information, are thought to support subjective conscious experience. Recent empirical work has shown that the spectral decomposition of causal influences, for example using Granger causality, can reveal frequency-specific influences that are not observed in the time domain. However, a spectral decomposition of integrated information has not been put forward, limiting its adoption for analyzing neural data. NEW METHOD We present a general and flexible framework for deriving the spectral decomposition of causal influences in autoregressive processes. RESULTS We use the framework to derive a spectral decomposition of integrated information. We show that other well-known measures, including Granger causality, can be derived using the same framework. Using simulations, we demonstrate a complex interplay between the spectral decomposition of integrated information and other measures that is not observed in the time domain. COMPARISON WITH EXISTING METHODS This paper provides a spectral decomposition of integrated information for the first time. Although a spectral decomposition of Granger causality has been derived, that approach is only applicable to uni-directional causal influences, not multi-directional causal influences as required for integrated information. CONCLUSIONS Our novel framework can be used to derive the spectral decomposition of uni- and multi-directional measures of causal influences. We use this framework to derive a spectral decomposition of integrated information, paving the way for better understanding how frequency-specific causal influences in the brain relate to cognition.
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Affiliation(s)
- Dror Cohen
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), Suita, Osaka 565-0871, Japan; School of Psychological Sciences, Monash University, Clayton Campus, Victoria 3800, Australia.
| | - Shuntaro Sasai
- University of Wisconsin - Madison, 6001 Research Park Blvd, Madison, WI 53719, United States
| | - Naotsugu Tsuchiya
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), Suita, Osaka 565-0871, Japan; School of Psychological Sciences, Monash University, Clayton Campus, Victoria 3800, Australia; Turner Institute for Brain and Mental Health, Monash University, Clayton Campus, Victoria 3800, Australia; ATR Computational Neuroscience Laboratories, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan
| | - Masafumi Oizumi
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), Suita, Osaka 565-0871, Japan; School of Psychological Sciences, Monash University, Clayton Campus, Victoria 3800, Australia; Graduate School of Arts and Sciences, The University of Tokyo, Tokyo 153-8092, Japan; RIKEN Brain Science Institute, Wako, Saitama 351-0198, Japan.
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20
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Rangaprakash D, Dretsch MN, Katz JS, Denney TS, Deshpande G. Dynamics of Segregation and Integration in Directional Brain Networks: Illustration in Soldiers With PTSD and Neurotrauma. Front Neurosci 2019; 13:803. [PMID: 31507353 PMCID: PMC6716456 DOI: 10.3389/fnins.2019.00803] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Accepted: 07/17/2019] [Indexed: 01/08/2023] Open
Abstract
Brain functioning relies on various segregated/specialized neural regions functioning as an integrated-interconnected network (i.e., metastability). Various psychiatric and neurologic disorders are associated with aberrant functioning of these brain networks. In this study, we present a novel framework integrating the strength and temporal variability of metastability in brain networks. We demonstrate that this approach provides novel mechanistic insights which enables better imaging-based predictions. Using whole-brain resting-state fMRI and a graph-theoretic framework, we integrated strength and temporal-variability of complex-network properties derived from effective connectivity networks, obtained from 87 U.S. Army soldiers consisting of healthy combat controls (n = 28), posttraumatic stress disorder (PTSD; n = 17), and PTSD with comorbid mild-traumatic brain injury (mTBI; n = 42). We identified prefrontal dysregulation of key subcortical and visual regions in PTSD/mTBI, with all network properties exhibiting lower variability over time, indicative of poorer flexibility. Larger impairment in the prefrontal-subcortical pathway but not prefrontal-visual pathway differentiated comorbid PTSD/mTBI from the PTSD group. Network properties of the prefrontal-subcortical pathway also had significant association (R 2 = 0.56) with symptom severity and neurocognitive performance; and were also found to possess high predictive ability (81.4% accuracy in classifying the disorders, explaining 66-72% variance in symptoms), identified through machine learning. Our framework explained 13% more variance in behaviors compared to the conventional framework. These novel insights and better predictions were made possible by our novel framework using static and time-varying network properties in our three-group scenario, advancing the mechanistic understanding of PTSD and comorbid mTBI. Our contribution has wide-ranging applications for network-level characterization of healthy brains as well as mental disorders.
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Affiliation(s)
- D Rangaprakash
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States.,Departments of Radiology and Biomedical Engineering, Northwestern University, Chicago, IL, United States
| | - Michael N Dretsch
- U.S. Army Aeromedical Research Laboratory, Fort Rucker, AL, United States.,U.S. Army Medical Research Directorate-West, Walter Reed Army Institute for Research, Joint Base Lewis-McChord, WA, United States.,Department of Psychology, Auburn University, Auburn, AL, United States
| | - Jeffrey S Katz
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States.,Department of Psychology, Auburn University, Auburn, AL, United States.,Alabama Advanced Imaging Consortium, Auburn, AL, United States.,Center for Neuroscience, Auburn University, Auburn, AL, United States
| | - Thomas S Denney
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States.,Department of Psychology, Auburn University, Auburn, AL, United States.,Alabama Advanced Imaging Consortium, Auburn, AL, United States.,Center for Neuroscience, Auburn University, Auburn, AL, United States
| | - Gopikrishna Deshpande
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States.,Department of Psychology, Auburn University, Auburn, AL, United States.,Alabama Advanced Imaging Consortium, Auburn, AL, United States.,Center for Neuroscience, Auburn University, Auburn, AL, United States.,Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, United States.,Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
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21
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Dretsch MN, Rangaprakash D, Katz JS, Daniel TA, Goodman AM, Denney TS, Deshpande G. Strength and Temporal Variance of the Default Mode Network to Investigate Chronic Mild Traumatic Brain Injury in Service Members with Psychological Trauma. J Exp Neurosci 2019; 13:1179069519833966. [PMID: 30911222 PMCID: PMC6423682 DOI: 10.1177/1179069519833966] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Accepted: 02/05/2019] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND There is a significant number of military personnel with a history of mild traumatic brain injury (mTBI) who suffer from comorbid posttraumatic stress symptoms (PTS). Although there is evidence of disruptions of the default mode network (DMN) associated with PTS and mTBI, previous studies have only studied static connectivity while ignoring temporal variability of connectivity. OBJECTIVE To assess DMN disrupted or dysregulated neurocircuitry, cognitive functioning, and psychological health of active-duty military with mTBI and PTS. METHOD U.S. Army soldiers with PTS (n = 14), mTBI + PTS (n = 25), and healthy controls (n = 21) voluntarily completed a cognitive and symptom battery. In addition, participants had magnetic resonance imaging (MRI) to assess both static functional connectivity (SFC) and variance of dynamic functional connectivity (vDFC) of the DMN. RESULTS Both the PTS and mTBI + PTS groups had significant symptoms, but only the comorbid group had significant decrements in cognitive functioning. Both groups showed less stable and disrupted neural signatures of the DMN, mainly constituting the cingulate-frontal-temporal-parietal attention network. Specifically, the PTS group showed a combination of both reduced contralateral strength and reduced unilateral variability of frontal-cingulate-temporal connectivities, as well as increased variability of frontal-parietal connectivities. The mTBI + PTS group had fewer abnormal connectives than the PTS group, all of which included reduced strength of frontal-temporal regions and reduced variability frontal-cingulate-temporal regions. Greater SFC and vDFC connectivity of the left dorsolateral prefrontal cortex (dlPFC) ↔ precuneus was associated with higher cognitive scores and lower symptom scores. CONCLUSIONS Findings suggest that individuals with PTS and mTBI + PTS have a propensity for accentuated generation of thoughts, feelings, sensations, and/or images while in a resting state. Compared with controls, only the PTS group was associated with accentuated variability of the frontal-parietal attention network. While there were no significant differences in DMN connectivity strength between the mTBI + PTS and PTS groups, variability of connectivity was able to distinguish them.
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Affiliation(s)
- Michael N Dretsch
- U.S. Army Aeromedical Research
Laboratory, Fort Rucker, AL, USA
- Department of Psychology, Auburn
University, Auburn, AL, USA
- U.S. Army Medical Research
Directorate-West, Walter Reed Army Institute for Research, Joint Base Lewis-McChord,
WA, USA
| | - D Rangaprakash
- Department of Psychiatry and
Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA,
USA
- AU MRI Research Center, Department of
Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
| | - Jeffrey S Katz
- Department of Psychology, Auburn
University, Auburn, AL, USA
- AU MRI Research Center, Department of
Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
- Alabama Advanced Imaging Consortium,
Auburn University and The University of Alabama at Birmingham, Birmingham, AL,
USA
| | - Thomas A Daniel
- Department of Psychology, Auburn
University, Auburn, AL, USA
| | - Adam M Goodman
- Department of Psychology, Auburn
University, Auburn, AL, USA
- Alabama Advanced Imaging Consortium,
Auburn University and The University of Alabama at Birmingham, Birmingham, AL,
USA
| | - Thomas S Denney
- AU MRI Research Center, Department of
Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
- Alabama Advanced Imaging Consortium,
Auburn University and The University of Alabama at Birmingham, Birmingham, AL,
USA
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of
Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
- Alabama Advanced Imaging Consortium,
Auburn University and The University of Alabama at Birmingham, Birmingham, AL,
USA
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22
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Human cognition involves the dynamic integration of neural activity and neuromodulatory systems. Nat Neurosci 2019; 22:289-296. [PMID: 30664771 DOI: 10.1038/s41593-018-0312-0] [Citation(s) in RCA: 233] [Impact Index Per Article: 46.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Accepted: 11/26/2018] [Indexed: 12/21/2022]
Abstract
The human brain integrates diverse cognitive processes into a coherent whole, shifting fluidly as a function of changing environmental demands. Despite recent progress, the neurobiological mechanisms responsible for this dynamic system-level integration remain poorly understood. Here we investigated the spatial, dynamic, and molecular signatures of system-wide neural activity across a range of cognitive tasks. We found that neuronal activity converged onto a low-dimensional manifold that facilitates the execution of diverse task states. Flow within this attractor space was associated with dissociable cognitive functions, unique patterns of network-level topology, and individual differences in fluid intelligence. The axes of the low-dimensional neurocognitive architecture aligned with regional differences in the density of neuromodulatory receptors, which in turn relate to distinct signatures of network controllability estimated from the structural connectome. These results advance our understanding of functional brain organization by emphasizing the interface between neural activity, neuromodulatory systems, and cognitive function.
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23
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Bola M, Orłowski P, Baranowska K, Schartner M, Marchewka A. Informativeness of Auditory Stimuli Does Not Affect EEG Signal Diversity. Front Psychol 2018; 9:1820. [PMID: 30319513 PMCID: PMC6168660 DOI: 10.3389/fpsyg.2018.01820] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Accepted: 09/06/2018] [Indexed: 11/13/2022] Open
Abstract
Brain signal diversity constitutes a robust neuronal marker of the global states of consciousness. It has been demonstrated that, in comparison to the resting wakefulness, signal diversity is lower during unconscious states, and higher during psychedelic states. A plausible interpretation of these findings is that the neuronal diversity corresponds to the diversity of subjective conscious experiences. Therefore, in the present study we varied an information rate processed by the subjects and hypothesized that greater information rate will be related to richer and more differentiated phenomenology and, consequently, to greater signal diversity. To test this hypothesis speech recordings (excerpts from an audio-book) were presented to subjects at five different speeds (65, 83, 100, 117, and 135% of the original speed). By increasing or decreasing speed of the recordings we were able to, respectively, increase or decrease the presented information rate. We also included a backward (unintelligible) speech presentation and a resting-state condition (no auditory stimulation). We tested 19 healthy subjects and analyzed the recorded EEG signal (64 channels) in terms of Lempel-Ziv diversity (LZs). We report the following findings. First, our main hypothesis was not confirmed, as Bayes Factor indicates evidence for no effect when comparing LZs among five presentation speeds. Second, we found that LZs during the resting-state was greater than during processing of both meaningful and unintelligible speech. Third, an additional analysis uncovered a gradual decrease of diversity over the time-course of the experiment, which might reflect a decrease in vigilance. We thus speculate that higher signal diversity during the unconstrained resting-state might be due to a greater variety of experiences, involving spontaneous attention switching and mind wandering.
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Affiliation(s)
- Michał Bola
- Laboratory of Brain Imaging, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
| | - Paweł Orłowski
- Laboratory of Brain Imaging, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland.,Institute of Philosophy, University of Warsaw, Warsaw, Poland.,Faculty of Electronics and Information Technology, Warsaw University of Technology, Warsaw, Poland
| | - Karolina Baranowska
- Laboratory of Brain Imaging, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland.,Faculty of Physics, Warsaw University of Technology, Warsaw, Poland
| | - Michael Schartner
- Département des Neurosciences Fondamentales, Université de Genève, Geneva, Switzerland
| | - Artur Marchewka
- Laboratory of Brain Imaging, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
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Cavanna F, Vilas MG, Palmucci M, Tagliazucchi E. Dynamic functional connectivity and brain metastability during altered states of consciousness. Neuroimage 2018; 180:383-395. [DOI: 10.1016/j.neuroimage.2017.09.065] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Revised: 09/01/2017] [Accepted: 09/29/2017] [Indexed: 11/16/2022] Open
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25
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Dolan D, Jensen HJ, Mediano PAM, Molina-Solana M, Rajpal H, Rosas F, Sloboda JA. The Improvisational State of Mind: A Multidisciplinary Study of an Improvisatory Approach to Classical Music Repertoire Performance. Front Psychol 2018; 9:1341. [PMID: 30319469 PMCID: PMC6167963 DOI: 10.3389/fpsyg.2018.01341] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Accepted: 07/12/2018] [Indexed: 11/13/2022] Open
Abstract
The recent re-introduction of improvisation as a professional practice within classical music, however cautious and still rare, allows direct and detailed contemporary comparison between improvised and "standard" approaches to performances of the same composition, comparisons which hitherto could only be inferred from impressionistic historical accounts. This study takes an interdisciplinary multi-method approach to discovering the contrasting nature and effects of prepared and improvised approaches during live chamber-music concert performances of a movement from Franz Schubert's "Shepherd on the Rock," given by a professional trio consisting of voice, flute, and piano, in the presence of an invited audience of 22 adults with varying levels of musical experience and training. The improvised performances were found to differ systematically from prepared performances in their timing, dynamic, and timbral features as well as in the degree of risk-taking and "mind reading" between performers, which included moments of spontaneously exchanging extemporized notes. Post-performance critical reflection by the performers characterized distinct mental states underlying the two modes of performance. The amount of overall body movements was reduced in the improvised performances, which showed less unco-ordinated movements between performers when compared to the prepared performance. Audience members, who were told only that the two performances would be different, but not how, rated the improvised version as more emotionally compelling and musically convincing than the prepared version. The size of this effect was not affected by whether or not the audience could see the performers, or by levels of musical training. EEG measurements from 19 scalp locations showed higher levels of Lempel-Ziv complexity (associated with awareness and alertness) in the improvised version in both performers and audience. Results are discussed in terms of their potential support for an "improvisatory state of mind" which may have aspects of flow (as characterized by Csikszentmihalyi, 1997) and primary states (as characterized by the Entropic Brain Hypothesis of Carhart-Harris et al., 2014). In a group setting, such as a live concert, our evidence suggests that this state of mind is communicable between performers and audience thus contributing to a heightened quality of shared experience.
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Affiliation(s)
- David Dolan
- Guildhall School of Music and Drama, London, United Kingdom
| | - Henrik J. Jensen
- Department of Mathematics, Centre of Complexity Science, Imperial College London, London, United Kingdom
- Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan
| | | | - Miguel Molina-Solana
- Department of Computing, Imperial College London, London, United Kingdom
- Data Science Institute, Imperial College London, London, United Kingdom
| | - Hardik Rajpal
- Department of Mathematics, Centre of Complexity Science, Imperial College London, London, United Kingdom
| | - Fernando Rosas
- Department of Mathematics, Centre of Complexity Science, Imperial College London, London, United Kingdom
- Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom
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26
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Haugg A, Cusack R, Gonzalez-Lara LE, Sorger B, Owen AM, Naci L. Do Patients Thought to Lack Consciousness Retain the Capacity for Internal as Well as External Awareness? Front Neurol 2018; 9:492. [PMID: 29997565 PMCID: PMC6030833 DOI: 10.3389/fneur.2018.00492] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Accepted: 06/06/2018] [Indexed: 01/04/2023] Open
Abstract
It is well established that some patients, who are deemed to have disorders of consciousness, remain entirely behaviorally non-responsive and are diagnosed as being in a vegetative state, yet can nevertheless demonstrate covert awareness of their external environment by modulating their brain activity, a phenomenon known as cognitive-motor dissociation. However, the extent to which these patients retain internal awareness remains unknown. To investigate the potential for internal and external awareness in patients with chronic disorders of consciousness (DoC), we asked whether the pattern of juxtaposition between the functional time-courses of the default mode (DMN) and fronto-parietal networks, shown in healthy individuals to mediate the naturally occurring dominance switching between internal and external aspects of consciousness, was present in these patients. We used a highly engaging movie by Alfred Hitchcock to drive the recruitment of the fronto-parietal networks, including the dorsal attention (DAN) and executive control (ECN) networks, and their maximal juxtaposition to the DMN in response to the complex stimulus, relative to rest and a scrambled, meaningless movie baseline condition. We tested a control group of healthy participants (N = 13/12) and two groups of patients with disorders of consciousness, one comprised of patients who demonstrated independent, neuroimaging-based evidence of covert external awareness (N = 8), and the other of those who did not (N = 8). Similarly to the healthy controls, only the group of patients with overt and, critically, covert external awareness showed significantly heightened differentiation between the DMN and the DAN in response to movie viewing relative to their resting state time-courses, which was driven by the movie's narrative. This result suggested the presence of functional integrity in the DMN and fronto-parietal networks and their relationship to one another in patients with covert external awareness. Similar to the effect in healthy controls, these networks became more strongly juxtaposed to one another in response to movie viewing relative to the baseline conditions, suggesting the potential for internal and external awareness during complex stimulus processing. Furthermore, our results suggest that naturalistic paradigms can dissociate between groups of DoC patients with and without covert awareness based on the functional integrity of brain networks.
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Affiliation(s)
- Amelie Haugg
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland.,Neuroscience Center Zurich, Swiss Federal Institute of Technology, University of Zurich, Zurich, Switzerland
| | - Rhodri Cusack
- School of Psychology, Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | | | - Bettina Sorger
- Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Adrian M Owen
- The Brain and Mind Institute, University of Western Ontario, London, ON, Canada
| | - Lorina Naci
- School of Psychology, Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
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27
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Mensen A, Marshall W, Sasai S, Tononi G. Differentiation Analysis of Continuous Electroencephalographic Activity Triggered by Video Clip Contents. J Cogn Neurosci 2018; 30:1108-1118. [PMID: 29762103 DOI: 10.1162/jocn_a_01278] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
While viewing a video clip, we experience a wide variety of contents, from low-level features of the images to high-level ideas such as the storyline. Each change in our experience must be supported by some corresponding change in neurophysiological activity. Differentiation analysis, which quantifies the differences in brain activity by measuring the distances between observed brain states, was applied here to continuous high-density electroencephalographic data recorded while participants watched short video clips. These clips were manipulated in various ways to change the degree of meaningfulness of their contents. We found that neurophysiological differentiation mirrored that of phenomenal differentiation, being higher for meaningful clips and lower for phase-scrambled versions or random noise. The distinction between meaningful and meaningless clips was present even at the individual level, and moreover, differentiation values correlated with individual subjective reports of meaningfulness. Spatial and spectral breakdowns of the overall effect showed frontal and posterior ROIs and highlighted specific roles for different spectral bands. Comparing the results with a multivariate decoding approach reveals that the two methods are capturing different aspects of brain activity and highlights a crucial theoretical distinction between the level and pattern of activity. In future applications, differentiation analysis may be used to evaluate the subjective meaningfulness of stimuli when behavioral responses may be inadequate, as with disorders of consciousness.
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Affiliation(s)
- Armand Mensen
- University of Liège.,University of Wisconsin at Madison
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28
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Estimated hemodynamic response function parameters obtained from resting state BOLD fMRI signals in subjects with autism spectrum disorder and matched healthy subjects. Data Brief 2018; 19:1305-1309. [PMID: 30225289 PMCID: PMC6139368 DOI: 10.1016/j.dib.2018.04.126] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Revised: 04/27/2018] [Accepted: 04/30/2018] [Indexed: 11/20/2022] Open
Abstract
In Functional magnetic resonance imaging (fMRI), the blood oxygen level dependent (BOLD) signal is modeled as a convolution of the hemodynamic response function (HRF) and the unmeasured latent neural signal. Although most cortical and subcortical brain regions share the canonical shape of the HRF, the temporal structure of HRFs are variable across brain regions and subjects. This variability is induced by both neural and non-neural factors. The variability between subjects can be examined by three parameters that characterize the HRF: response height (RH), time-to-peak (TTP) and full-width at half-max (FWHM). This data provides three HRF parameters at every voxel, obtained from Autism Spectrum Disorder (ASD) patients (N = 531), and matched healthy controls (N = 571). Since ongoing studies suggest that non-standard populations have important differences in their HRFs when compared with healthy control, this data set is valuable in studying variability of HRF in ASD group and inferring the underlying pathology that also affects the HRF. It also has implications for fMRI analyses like resting-sate connectivity analysis.
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29
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Rangaprakash D, Wu GR, Marinazzo D, Hu X, Deshpande G. Hemodynamic response function (HRF) variability confounds resting-state fMRI functional connectivity. Magn Reson Med 2018; 80:1697-1713. [DOI: 10.1002/mrm.27146] [Citation(s) in RCA: 70] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Revised: 02/01/2018] [Accepted: 02/02/2018] [Indexed: 01/26/2023]
Affiliation(s)
- D. Rangaprakash
- Auburn University MRI Research Center, Department of Electrical and Computer Engineering; Auburn University; Auburn Alabama
- Department of Psychiatry and Biobehavioral Sciences; University of California Los Angeles; Los Angeles California
| | - Guo-Rong Wu
- Department of Data Analysis; University of Ghent; Ghent Belgium
- Key Laboratory of Cognition and Personality, Southwest University; Chongqing China
| | | | - Xiaoping Hu
- Department of Bioengineering; University of California Riverside; Riverside California
| | - Gopikrishna Deshpande
- Auburn University MRI Research Center, Department of Electrical and Computer Engineering; Auburn University; Auburn Alabama
- Department of Psychology; Auburn University; Auburn Alabama
- Center for Health Ecology and Equity Research, Auburn University; Auburn Alabama
- Alabama Advanced Imaging Consortium, Auburn University, University of South Alabama and University of Alabama at Tuscaloosa and Birmingham; Alabama
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30
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Betti V, Corbetta M, de Pasquale F, Wens V, Della Penna S. Topology of Functional Connectivity and Hub Dynamics in the Beta Band As Temporal Prior for Natural Vision in the Human Brain. J Neurosci 2018; 38:3858-3871. [PMID: 29555851 PMCID: PMC6705907 DOI: 10.1523/jneurosci.1089-17.2018] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2017] [Revised: 02/20/2018] [Accepted: 02/22/2018] [Indexed: 12/30/2022] Open
Abstract
Networks hubs represent points of convergence for the integration of information across many different nodes and systems. Although a great deal is known on the topology of hub regions in the human brain, little is known about their temporal dynamics. Here, we examine the static and dynamic centrality of hub regions when measured in the absence of a task (rest) or during the observation of natural or synthetic visual stimuli. We used Magnetoencephalography (MEG) in humans (both sexes) to measure static and transient regional and network-level interaction in α- and β-band limited power (BLP) in three conditions: visual fixation (rest), viewing of movie clips (natural vision), and time-scrambled versions of the same clips (scrambled vision). Compared with rest, we observed in both movie conditions a robust decrement of α-BLP connectivity. Moreover, both movie conditions caused a significant reorganization of connections in the α band, especially between networks. In contrast, β-BLP connectivity was remarkably similar between rest and natural vision. Not only the topology did not change, but the joint dynamics of hubs in a core network during natural vision was predicted by similar fluctuations in the resting state. We interpret these findings by suggesting that slow-varying fluctuations of integration occurring in higher-order regions in the β band may be a mechanism to anticipate and predict slow-varying temporal patterns of the visual environment.SIGNIFICANCE STATEMENT A fundamental question in neuroscience concerns the function of spontaneous brain connectivity. Here, we tested the hypothesis that topology of intrinsic brain connectivity and its dynamics might predict those observed during natural vision. Using MEG, we tracked the static and time-varying brain functional connectivity when observers were either fixating or watching different movie clips. The spatial distribution of connections and the dynamics of centrality of a set of regions were similar during rest and movie in the β band, but not in the α band. These results support the hypothesis that the intrinsic β-rhythm integration occurs with a similar temporal structure during natural vision, possibly providing advanced information about incoming stimuli.
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Affiliation(s)
- Viviana Betti
- Department of Psychology, Sapienza University of Rome, Rome 00185, Italy,
- Fondazione Santa Lucia, Istituto Di Ricovero e Cura a Carattere Scientifico, Rome 00142, Italy
| | - Maurizio Corbetta
- Department of Neuroscience and Padova Neuroscience Center, University of Padua, Padua 35128, Italy
- Department of Neurology, Radiology, and Anatomy and Neurobiology, Washington University, St. Louis, Missouri 63101
| | | | - Vincent Wens
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neurosciences Institute, Université libre de Bruxelles (ULB) and Magnetoencephalography Unit, Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB-Hôpital Erasme, Brussels 1070, Belgium, and
| | - Stefania Della Penna
- Department of Neuroscience, Imaging and Clinical Sciences and Institute for Advanced Biomedical Technologies, G. d'Annunzio University Chieti-Pescara, Chieti 66013, Italy
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31
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Pregenual Anterior Cingulate Dysfunction Associated with Depression in OCD: An Integrated Multimodal fMRI/ 1H MRS Study. Neuropsychopharmacology 2018; 43:1146-1155. [PMID: 29052616 PMCID: PMC5854805 DOI: 10.1038/npp.2017.249] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2017] [Revised: 09/18/2017] [Accepted: 10/15/2017] [Indexed: 11/08/2022]
Abstract
Depression is a commonly occurring symptom in obsessive-compulsive disorder (OCD), and is associated with worse functional impairment, poorer quality of life, and poorer treatment response. Understanding the underlying neurochemical and connectivity-based brain mechanisms of this important symptom domain in OCD is necessary for development of novel, more globally effective treatments. To investigate biopsychological mechanisms of comorbid depression in OCD, we examined effective connectivity and neurochemical signatures in the pregenual anterior cingulate cortex (pACC), a structure known to be involved in both OCD and depression. Resting-state functional magnetic resonance imaging (fMRI) and 1H magnetic resonance spectroscopy (MRS) data were obtained from participants with OCD (n=49) and healthy individuals of equivalent age and sex (n=25). Granger causality-based effective (directed) connectivity was used to define causal networks involving the right and left pACC. The interplay between fMRI connectivity, 1H MRS and clinical data was explored by applying moderation and mediation analyses. We found that the causal influence of the right dorsal anterior midcingulate cortex (daMCC) on the right pACC was significantly lower in the OCD group and showed significant correlation with depressive symptom severity in the OCD group. Lower and moderate levels of glutamate (Glu) in the right pACC significantly moderated the interaction between right daMCC-pACC connectivity and depression severity. Our results suggest a biochemical-connectivity-psychological model of pACC dysfunction contributing to depression in OCD, particularly involving intracingulate connectivity and glutamate levels in the pACC. These findings have implications for potential molecular and network targets for treatment of this multi-faceted psychiatric condition.
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32
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Rangaprakash D, Wu GR, Marinazzo D, Hu X, Deshpande G. Parameterized hemodynamic response function data of healthy individuals obtained from resting-state functional MRI in a 7T MRI scanner. Data Brief 2018; 17:1175-1179. [PMID: 29876476 PMCID: PMC5988211 DOI: 10.1016/j.dib.2018.01.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2017] [Revised: 12/22/2017] [Accepted: 01/02/2018] [Indexed: 01/10/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI), being an indirect measure of brain activity, is mathematically defined as a convolution of the unmeasured latent neural signal and the hemodynamic response function (HRF). The HRF is known to vary across the brain and across individuals, and it is modulated by neural as well as non-neural factors. Three parameters characterize the shape of the HRF, which is obtained by performing deconvolution on resting-state fMRI data: response height, time-to-peak and full-width at half-max. The data provided here, obtained from 47 healthy adults, contains these three HRF parameters at every voxel in the brain, as well as HRF parameters from the default-mode network (DMN). In addition, we have provided functional connectivity (FC) data from the same DMN regions, obtained for two cases: data with deconvolution (HRF variability minimized) and data with no deconvolution (HRF variability corrupted). This would enable researchers to compare regional changes in HRF with corresponding FC differences, to assess the impact of HRF variability on FC. Importantly, the data was obtained in a 7T MRI scanner. While most fMRI studies are conducted at lower field strengths, like 3T, ours is the first study to report HRF data obtained at 7T. FMRI data at ultra-high fields contains larger contributions from small vessels, consequently HRF variability is lower for small vessels at higher field strengths. This implies that findings made from this data would be more conservative than from data acquired at lower fields, such as 3T. Results obtained with this data and further interpretations are available in our recent research study (Rangaprakash et al., in press) [1]. This is a valuable dataset for studying HRF variability in conjunction with FC, and for developing the HRF profile in healthy individuals, which would have direct implications for fMRI data analysis, especially resting-state connectivity modeling. This is the first public HRF data at 7T.
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Affiliation(s)
- D. Rangaprakash
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | - Guo-Rong Wu
- Department of Data Analysis, University of Ghent, Ghent, Belgium
- Key Laboratory of Cognition and Personality, Southwest University, Chongqing, China
| | | | - Xiaoping Hu
- Department of Bioengineering, University of California Riverside, Riverside, CA, USA
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
- Department of Psychology, Auburn University, Auburn, AL, USA
- Alabama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, AL, USA
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Kitazono J, Kanai R, Oizumi M. Efficient Algorithms for Searching the Minimum Information Partition in Integrated Information Theory. ENTROPY 2018; 20:e20030173. [PMID: 33265264 PMCID: PMC7512690 DOI: 10.3390/e20030173] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2017] [Revised: 02/26/2018] [Accepted: 02/27/2018] [Indexed: 11/23/2022]
Abstract
The ability to integrate information in the brain is considered to be an essential property for cognition and consciousness. Integrated Information Theory (IIT) hypothesizes that the amount of integrated information (Φ) in the brain is related to the level of consciousness. IIT proposes that, to quantify information integration in a system as a whole, integrated information should be measured across the partition of the system at which information loss caused by partitioning is minimized, called the Minimum Information Partition (MIP). The computational cost for exhaustively searching for the MIP grows exponentially with system size, making it difficult to apply IIT to real neural data. It has been previously shown that, if a measure of Φ satisfies a mathematical property, submodularity, the MIP can be found in a polynomial order by an optimization algorithm. However, although the first version of Φ is submodular, the later versions are not. In this study, we empirically explore to what extent the algorithm can be applied to the non-submodular measures of Φ by evaluating the accuracy of the algorithm in simulated data and real neural data. We find that the algorithm identifies the MIP in a nearly perfect manner even for the non-submodular measures. Our results show that the algorithm allows us to measure Φ in large systems within a practical amount of time.
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Affiliation(s)
- Jun Kitazono
- Araya, Inc., Toranomon 15 Mori Building, 2-8-10 Toranomon, Minato-ku, Tokyo 105-0001, Japan
- Graduate School of Engineering, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe-shi, Hyogo 657-8501, Japan
- Correspondence: (J.K.); (M.O.); Tel.: +81-3-6550-9977 (J.K. & M.O.)
| | - Ryota Kanai
- Araya, Inc., Toranomon 15 Mori Building, 2-8-10 Toranomon, Minato-ku, Tokyo 105-0001, Japan
| | - Masafumi Oizumi
- Araya, Inc., Toranomon 15 Mori Building, 2-8-10 Toranomon, Minato-ku, Tokyo 105-0001, Japan
- RIKEN Brain Science Institute, 2-1 Hirosawa Wako City, Saitama 351-0198, Japan
- Correspondence: (J.K.); (M.O.); Tel.: +81-3-6550-9977 (J.K. & M.O.)
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34
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Borges AFT, Giraud AL, Mansvelder HD, Linkenkaer-Hansen K. Scale-Free Amplitude Modulation of Neuronal Oscillations Tracks Comprehension of Accelerated Speech. J Neurosci 2018; 38:710-722. [PMID: 29217685 PMCID: PMC6596185 DOI: 10.1523/jneurosci.1515-17.2017] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Revised: 10/24/2017] [Accepted: 11/20/2017] [Indexed: 01/17/2023] Open
Abstract
Speech comprehension is preserved up to a threefold acceleration, but deteriorates rapidly at higher speeds. Current models posit that perceptual resilience to accelerated speech is limited by the brain's ability to parse speech into syllabic units using δ/θ oscillations. Here, we investigated whether the involvement of neuronal oscillations in processing accelerated speech also relates to their scale-free amplitude modulation as indexed by the strength of long-range temporal correlations (LRTC). We recorded MEG while 24 human subjects (12 females) listened to radio news uttered at different comprehensible rates, at a mostly unintelligible rate and at this same speed interleaved with silence gaps. δ, θ, and low-γ oscillations followed the nonlinear variation of comprehension, with LRTC rising only at the highest speed. In contrast, increasing the rate was associated with a monotonic increase in LRTC in high-γ activity. When intelligibility was restored with the insertion of silence gaps, LRTC in the δ, θ, and low-γ oscillations resumed the low levels observed for intelligible speech. Remarkably, the lower the individual subject scaling exponents of δ/θ oscillations, the greater the comprehension of the fastest speech rate. Moreover, the strength of LRTC of the speech envelope decreased at the maximal rate, suggesting an inverse relationship with the LRTC of brain dynamics when comprehension halts. Our findings show that scale-free amplitude modulation of cortical oscillations and speech signals are tightly coupled to speech uptake capacity.SIGNIFICANCE STATEMENT One may read this statement in 20-30 s, but reading it in less than five leaves us clueless. Our minds limit how much information we grasp in an instant. Understanding the neural constraints on our capacity for sensory uptake is a fundamental question in neuroscience. Here, MEG was used to investigate neuronal activity while subjects listened to radio news played faster and faster until becoming unintelligible. We found that speech comprehension is related to the scale-free dynamics of δ and θ bands, whereas this property in high-γ fluctuations mirrors speech rate. We propose that successful speech processing imposes constraints on the self-organization of synchronous cell assemblies and their scale-free dynamics adjusts to the temporal properties of spoken language.
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Affiliation(s)
- Ana Filipa Teixeira Borges
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, VU University Amsterdam, Amsterdam, Netherlands
- Amsterdam Neuroscience, Amsterdam, Netherlands, and
| | - Anne-Lise Giraud
- Department of Neuroscience, University of Geneva, Biotech Campus, Geneva 1211, Switzerland
| | - Huibert D Mansvelder
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, VU University Amsterdam, Amsterdam, Netherlands
- Amsterdam Neuroscience, Amsterdam, Netherlands, and
| | - Klaus Linkenkaer-Hansen
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, VU University Amsterdam, Amsterdam, Netherlands,
- Amsterdam Neuroscience, Amsterdam, Netherlands, and
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35
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Measuring complexity to infer changes in the dynamics of ecological systems under stress. ECOLOGICAL COMPLEXITY 2017. [DOI: 10.1016/j.ecocom.2016.08.005] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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36
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Rangaprakash D, Dretsch MN, Venkataraman A, Katz JS, Denney TS, Deshpande G. Identifying disease foci from static and dynamic effective connectivity networks: Illustration in soldiers with trauma. Hum Brain Mapp 2017; 39:264-287. [PMID: 29058357 DOI: 10.1002/hbm.23841] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Revised: 08/29/2017] [Accepted: 10/01/2017] [Indexed: 12/15/2022] Open
Abstract
Brain connectivity studies report group differences in pairwise connection strengths. While informative, such results are difficult to interpret since our understanding of the brain relies on region-based properties, rather than on connection information. Given that large disruptions in the brain are often caused by a few pivotal sources, we propose a novel framework to identify the sources of functional disruption from effective connectivity networks. Our approach integrates static and time-varying effective connectivity modeling in a probabilistic framework, to identify aberrant foci and the corresponding aberrant connectomics network. Using resting-state fMRI, we illustrate the utility of this novel approach in U.S. Army soldiers (N = 87) with posttraumatic stress disorder (PTSD), mild traumatic brain injury (mTBI) and combat controls. Additionally, we employed machine-learning classification to identify those significant connectivity features that possessed high predictive ability. We identified three disrupted foci (middle frontal gyrus [MFG], insula, hippocampus), and an aberrant prefrontal-subcortical-parietal network of information flow. We found the MFG to be the pivotal focus of network disruption, with aberrant strength and temporal-variability of effective connectivity to the insula, amygdala and hippocampus. These connectivities also possessed high predictive ability (giving a classification accuracy of 81%); and they exhibited significant associations with symptom severity and neurocognitive functioning. In summary, dysregulation originating in the MFG caused elevated and temporally less-variable connectivity in subcortical regions, followed by a similar effect on parietal memory-related regions. This mechanism likely contributes to the reduced control over traumatic memories leading to re-experiencing, hyperarousal and flashbacks observed in soldiers with PTSD and mTBI. Hum Brain Mapp 39:264-287, 2018. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- D Rangaprakash
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - Michael N Dretsch
- U.S. Army Aeromedical Research Laboratory, Fort Rucker, Alabama.,Human Dimension Division, HQ TRADOC, Fort Eustis, Virgina
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Jeffrey S Katz
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychology, Auburn University, Auburn, Alabama.,Alabama Advanced Imaging Consortium, USA
| | - Thomas S Denney
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychology, Auburn University, Auburn, Alabama.,Alabama Advanced Imaging Consortium, USA
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychology, Auburn University, Auburn, Alabama.,Alabama Advanced Imaging Consortium, USA
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37
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Mensen A, Marshall W, Tononi G. EEG Differentiation Analysis and Stimulus Set Meaningfulness. Front Psychol 2017; 8:1748. [PMID: 29056921 PMCID: PMC5635725 DOI: 10.3389/fpsyg.2017.01748] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Accepted: 09/21/2017] [Indexed: 11/13/2022] Open
Abstract
A set of images can be considered as meaningfully different for an observer if they can be distinguished phenomenally from one another. Each phenomenal difference must be supported by some neurophysiological differences. Differentiation analysis aims to quantify neurophysiological differentiation evoked by a given set of stimuli to assess its meaningfulness to the individual observer. As a proof of concept using high-density EEG, we show increased neurophysiological differentiation for a set of natural, meaningfully different images in contrast to another set of artificially generated, meaninglessly different images in nine participants. Stimulus-evoked neurophysiological differentiation (over 257 channels, 800 ms) was systematically greater for meaningful vs. meaningless stimulus categories both at the group level and for individual subjects. Spatial breakdown showed a central-posterior peak of differentiation, consistent with the visual nature of the stimulus sets. Temporal breakdown revealed an early peak of differentiation around 110 ms, prominent in the central-posterior region; and a later, longer-lasting peak at 300-500 ms that was spatially more distributed. The early peak of differentiation was not accompanied by changes in mean ERP amplitude, whereas the later peak was associated with a higher amplitude ERP for meaningful images. An ERP component similar to visual-awareness-negativity occurred during the nadir of differentiation across all image types. Control stimulus sets and further analysis indicate that changes in neurophysiological differentiation between meaningful and meaningless stimulus sets could not be accounted for by spatial properties of the stimuli or by stimulus novelty and predictability.
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Affiliation(s)
- Armand Mensen
- Center for Sleep and Consciousness, University of Wisconsin-Madison, Madison, WI, United States.,Department of Neurology, Inselspital Bern, Bern, Switzerland
| | - William Marshall
- Center for Sleep and Consciousness, University of Wisconsin-Madison, Madison, WI, United States
| | - Giulio Tononi
- Center for Sleep and Consciousness, University of Wisconsin-Madison, Madison, WI, United States
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38
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Rangaprakash D, Dretsch MN, Yan W, Katz JS, Denney TS, Deshpande G. Hemodynamic response function parameters obtained from resting-state functional MRI data in soldiers with trauma. Data Brief 2017; 14:558-562. [PMID: 28861454 PMCID: PMC5567973 DOI: 10.1016/j.dib.2017.07.072] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Revised: 07/12/2017] [Accepted: 07/27/2017] [Indexed: 11/21/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) is an indirect measure of brain activity, i.e. it is a convolution of the latent (unmeasured) neural signal and the hemodynamic response function (HRF). As such, the HRF has been shown to vary across brain regions and individuals. The shape of the HRF is controlled by both neural and non-neural factors. The shape of the HRF can be characterized by three parameters (response height, time-to-peak and full-width at half-max). The data presented here provides the three HRF parameters at every voxel, obtained from U.S. Army soldiers (N=87) diagnosed with posttraumatic stress disorder (PTSD), with comorbid PTSD and mild-traumatic brain injury (mTBI), and matched healthy combat controls. Findings from this data and further interpretations are available in our recent research study (Rangaprakash et al., 2017) [1]. This data is a valuable asset in studying the impact of HRF variability on fMRI data analysis, specifically resting state functional connectivity.
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Affiliation(s)
- D Rangaprakash
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - Michael N Dretsch
- Human Dimension Division, HQ TRADOC, Fort Eustis, VA, USA.,US Army Aeromedical Research Laboratory, Fort Rucker, AL, USA
| | - Wenjing Yan
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
| | - Jeffrey S Katz
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychology, Auburn University, Auburn, AL, USA.,Alabama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, AL, USA
| | - Thomas S Denney
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychology, Auburn University, Auburn, AL, USA.,Alabama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, AL, USA
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychology, Auburn University, Auburn, AL, USA.,Alabama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, AL, USA
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Rangaprakash D, Dretsch MN, Yan W, Katz JS, Denney TS, Deshpande G. Hemodynamic variability in soldiers with trauma: Implications for functional MRI connectivity studies. NEUROIMAGE-CLINICAL 2017; 16:409-417. [PMID: 28879082 PMCID: PMC5574840 DOI: 10.1016/j.nicl.2017.07.016] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Revised: 06/29/2017] [Accepted: 07/22/2017] [Indexed: 01/01/2023]
Abstract
Functional MRI (fMRI) is an indirect measure of neural activity as a result of the convolution of the hemodynamic response function (HRF) and latent (unmeasured) neural activity. Recent studies have shown variability of HRF across brain regions (intra-subject spatial variability) and between subjects (inter-subject variability). Ignoring this HRF variability during data analysis could impair the reliability of such fMRI results. Using whole-brain resting-state fMRI (rs-fMRI), we employed hemodynamic deconvolution to estimate voxel-wise HRF. Studying the impact of mental disorders on HRF variability, we identified HRF aberrations in soldiers (N = 87) with posttraumatic stress disorder (PTSD) and mild-traumatic brain injury (mTBI) compared to combat controls. Certain subcortical and default-mode regions were found to have significant HRF aberrations in the clinical groups. These brain regions have been previously associated with neurochemical alterations in PTSD, which are known to impact the shape of the HRF. We followed-up these findings with seed-based functional connectivity (FC) analysis using regions-of-interest (ROIs) whose HRFs differed between the groups. We found that part of the connectivity group differences reported from traditional FC analysis (no deconvolution) were attributable to HRF variability. These findings raise the question of the degree of reliability of findings from conventional rs-fMRI studies (especially in psychiatric populations like PTSD and mTBI), which are corrupted by HRF variability. We also report and discus, for the first time, voxel-level HRF alterations in PTSD and mTBI. To the best of our knowledge, this is the first study to report evidence for the impact of HRF variability on connectivity group differences. Our work has implications for rs-fMRI connectivity studies. We encourage researchers to incorporate hemodynamic deconvolution during pre-processing to minimize the impact of HRF variability.
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Affiliation(s)
- D Rangaprakash
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - Michael N Dretsch
- Human Dimension Division, HQ TRADOC, Fort Eustis, VA, USA.,U.S. Army Aeromedical Research Laboratory, Fort Rucker, AL, USA
| | - Wenjing Yan
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
| | - Jeffrey S Katz
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychology, Auburn University, Auburn, AL, USA.,Alabama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, AL, USA
| | - Thomas S Denney
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychology, Auburn University, Auburn, AL, USA.,Alabama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, AL, USA
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychology, Auburn University, Auburn, AL, USA.,Alabama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, AL, USA
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Are There Multiple Kinds of Episodic Memory? An fMRI Investigation Comparing Autobiographical and Recognition Memory Tasks. J Neurosci 2017; 37:2764-2775. [PMID: 28179554 DOI: 10.1523/jneurosci.1534-16.2017] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2016] [Revised: 01/19/2017] [Accepted: 01/26/2017] [Indexed: 01/28/2023] Open
Abstract
What brain regions underlie retrieval from episodic memory? The bulk of research addressing this question with fMRI has relied upon recognition memory for materials encoded within the laboratory. Another, less dominant tradition has used autobiographical methods, whereby people recall events from their lifetime, often after being cued with words or pictures. The current study addresses how the neural substrates of successful memory retrieval differed as a function of the targeted memory when the experimental parameters were held constant in the two conditions (except for instructions). Human participants studied a set of scenes and then took two types of memory test while undergoing fMRI scanning. In one condition (the picture memory test), participants reported for each scene (32 studied, 64 nonstudied) whether it was recollected from the prior study episode. In a second condition (the life memory test), participants reported for each scene (32 studied, 64 nonstudied) whether it reminded them of a specific event from their preexperimental lifetime. An examination of successful retrieval (yes responses) for recently studied scenes for the two test types revealed pronounced differences; that is, autobiographical retrieval instantiated with the life memory test preferentially activated the default mode network, whereas hits in the picture memory test preferentially engaged the parietal memory network as well as portions of the frontoparietal control network. When experimental cueing parameters are held constant, the neural underpinnings of successful memory retrieval differ when remembering life events and recently learned events.SIGNIFICANCE STATEMENT Episodic memory is often discussed as a solitary construct. However, experimental traditions examining episodic memory use very different approaches, and these are rarely compared to one another. When the neural correlates associated with each approach have been directly contrasted, results have varied considerably and at times contradicted each other. The present experiment was designed to match the two primary approaches to studying episodic memory in an unparalleled manner. Results suggest a clear separation of systems supporting memory as it is typically tested in the laboratory and memory as assessed under autobiographical retrieval conditions. These data provide neurobiological evidence that episodic memory is not a single construct, challenging the degree to which different experimental traditions are studying the same construct.
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41
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Unified framework for information integration based on information geometry. Proc Natl Acad Sci U S A 2016; 113:14817-14822. [PMID: 27930289 DOI: 10.1073/pnas.1603583113] [Citation(s) in RCA: 61] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Assessment of causal influences is a ubiquitous and important subject across diverse research fields. Drawn from consciousness studies, integrated information is a measure that defines integration as the degree of causal influences among elements. Whereas pairwise causal influences between elements can be quantified with existing methods, quantifying multiple influences among many elements poses two major mathematical difficulties. First, overestimation occurs due to interdependence among influences if each influence is separately quantified in a part-based manner and then simply summed over. Second, it is difficult to isolate causal influences while avoiding noncausal confounding influences. To resolve these difficulties, we propose a theoretical framework based on information geometry for the quantification of multiple causal influences with a holistic approach. We derive a measure of integrated information, which is geometrically interpreted as the divergence between the actual probability distribution of a system and an approximated probability distribution where causal influences among elements are statistically disconnected. This framework provides intuitive geometric interpretations harmonizing various information theoretic measures in a unified manner, including mutual information, transfer entropy, stochastic interaction, and integrated information, each of which is characterized by how causal influences are disconnected. In addition to the mathematical assessment of consciousness, our framework should help to analyze causal relationships in complex systems in a complete and hierarchical manner.
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42
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Hoel EP, Albantakis L, Marshall W, Tononi G. Can the macro beat the micro? Integrated information across spatiotemporal scales. Neurosci Conscious 2016; 2016:niw012. [PMID: 30788150 PMCID: PMC6367968 DOI: 10.1093/nc/niw012] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2016] [Revised: 05/27/2016] [Accepted: 07/09/2016] [Indexed: 11/13/2022] Open
Abstract
Causal interactions within complex systems such as the brain can be analyzed at multiple spatiotemporal levels. It is widely assumed that the micro level is causally complete, thus excluding causation at the macro level. However, by measuring effective information-how much a system's mechanisms constrain its past and future states-we recently showed that causal power can be stronger at macro rather than micro levels. In this work, we go beyond effective information and consider additional requirements of a proper measure of causal power from the intrinsic perspective of a system: composition (the cause-effect power of the parts), state-dependency (the cause-effect power of the system in a specific state); integration (the causal irreducibility of the whole to its parts), and exclusion (the causal borders of the system). A measure satisfying these requirements, called Φ Max, was developed in the context of integrated information theory. Here, we evaluate Φ Max systematically at micro and macro levels in space and time using simplified neuronal-like systems. We show that for systems characterized by indeterminism and/or degeneracy, Φ can indeed peak at a macro level. This happens if coarse-graining micro elements produces macro mechanisms with high irreducible causal selectivity. These results are relevant to a theoretical account of consciousness, because for integrated information theory the spatiotemporal maximum of integrated information fixes the spatiotemporal scale of consciousness. More generally, these results show that the notions of macro causal emergence and micro causal exclusion hold when causal power is assessed in full and from the intrinsic perspective of a system.
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Affiliation(s)
- Erik P. Hoel
- Department of Psychiatry, University of Wisconsin, Madison, 6001
Research Park Blvd, WI 53703, USA
| | - Larissa Albantakis
- Department of Psychiatry, University of Wisconsin, Madison, 6001
Research Park Blvd, WI 53703, USA
| | - William Marshall
- Department of Psychiatry, University of Wisconsin, Madison, 6001
Research Park Blvd, WI 53703, USA
| | - Giulio Tononi
- Department of Psychiatry, University of Wisconsin, Madison, 6001
Research Park Blvd, WI 53703, USA
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43
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Marshall W, Gomez-Ramirez J, Tononi G. Integrated Information and State Differentiation. Front Psychol 2016; 7:926. [PMID: 27445896 PMCID: PMC4923128 DOI: 10.3389/fpsyg.2016.00926] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2016] [Accepted: 06/03/2016] [Indexed: 11/15/2022] Open
Abstract
Integrated information (Φ) is a measure of the cause-effect power of a physical system. This paper investigates the relationship between Φ as defined in Integrated Information Theory and state differentiation (D), the number of, and difference between potential system states. Here we provide theoretical justification of the relationship between Φ and D, then validate the results using a simulation study. First, we show that a physical system in a state with high Φ necessarily has many elements and specifies many causal relationships. Furthermore, if the average value of integrated information across all states is high, the system must also have high differentiation. Next, we explore the use of D as a proxy for Φ using artificial networks, evolved to have integrated structures. The results show a positive linear relationship between Φ and D for multiple network sizes and connectivity patterns. Finally we investigate the differentiation evoked by sensory inputs and show that, under certain conditions, it is possible to estimate integrated information without a direct perturbation of its internal elements. In concluding, we discuss the need for further validation on larger networks and explore the potential applications of this work to the empirical study of consciousness, especially concerning the practical estimation of Φ from neuroimaging data.
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Affiliation(s)
- William Marshall
- Department of Psychiatry, Center for Sleep and Consciousness, University of Wisconsin Madison, WI, USA
| | - Jaime Gomez-Ramirez
- Department of Psychiatry, Center for Sleep and Consciousness, University of Wisconsin Madison, WI, USA
| | - Giulio Tononi
- Department of Psychiatry, Center for Sleep and Consciousness, University of Wisconsin Madison, WI, USA
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44
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Hudetz AG, Liu X, Pillay S, Boly M, Tononi G. Propofol anesthesia reduces Lempel-Ziv complexity of spontaneous brain activity in rats. Neurosci Lett 2016; 628:132-5. [PMID: 27291459 DOI: 10.1016/j.neulet.2016.06.017] [Citation(s) in RCA: 58] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2016] [Revised: 05/30/2016] [Accepted: 06/08/2016] [Indexed: 11/28/2022]
Abstract
Consciousness is thought to scale with brain complexity, and it may be diminished in anesthesia. Lempel-Ziv complexity (LZC) of field potentials has been shown to be a promising measure of the level of consciousness in anesthetized human subjects, neurological patients, and across the sleep-wake states in rats. Whether this relationship holds for intrinsic networks obtained by functional brain imaging has not been tested. To fill this gap of knowledge, we estimated LZC from large-scale dynamic analysis of functional magnetic resonance images (fMRI) in conscious sedated and unconscious anesthetized rats. Blood oxygen dependent (BOLD) signals were obtained from 30-min whole-brain resting-state scans while the anesthetic propofol was infused intravenously at constant infusion rates of 20mg/kg/h (conscious sedated) and 40mg/kg/h (unconscious). Dynamic brain networks were defined at voxel level by sliding window analysis of regional homogeneity (ReHo) of the BOLD signal. From scans performed at low to high propofol dose, the LZC was significantly reduced by 110%. The results suggest that the difference in LZC between conscious sedated and anesthetized unconscious subjects is conserved in rats and this effect is detectable in large-scale brain network obtained from fMRI.
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Affiliation(s)
- Anthony G Hudetz
- Department of Anesthesiology and Center for Consciousness Science, University of Michigan, Ann Arbor, MI, United States.
| | - Xiping Liu
- Department of Anesthesiology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Siveshigan Pillay
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States
| | - Melanie Boly
- Department of Neurology, University of Wisconsin-Madison, Madison, WI, United States
| | - Giulio Tononi
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States
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45
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Integrated information theory: from consciousness to its physical substrate. Nat Rev Neurosci 2016; 17:450-61. [DOI: 10.1038/nrn.2016.44] [Citation(s) in RCA: 644] [Impact Index Per Article: 80.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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46
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Kringelbach ML, McIntosh AR, Ritter P, Jirsa VK, Deco G. The Rediscovery of Slowness: Exploring the Timing of Cognition. Trends Cogn Sci 2016; 19:616-628. [PMID: 26412099 DOI: 10.1016/j.tics.2015.07.011] [Citation(s) in RCA: 69] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2015] [Revised: 07/24/2015] [Accepted: 07/27/2015] [Indexed: 01/03/2023]
Abstract
Slowness of thought is not necessarily a handicap but could be a signature of optimal brain function. Emerging evidence shows that neuroanatomical and dynamical constraints of the human brain shape its functionality in optimal ways, characterized by slowness during task-based cognition in the context of spontaneous resting-state activity. This activity can be described mechanistically by whole-brain computational modeling that relates directly to optimality in the context of theories arguing for metastability in the brain. We discuss the role for optimal processing of information in the context of cognitive, task-related activity, and propose that combining multi-modal neuroimaging and explicit whole-brain models focused on the timing of functional dynamics can help to uncover fundamental rules of brain function in health and disease.
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Affiliation(s)
- Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK; Center of Functionally Integrative Neuroscience (CFIN), Aarhus University, Denmark
| | - Anthony R McIntosh
- Rotman Research Institute of Baycrest Center, University of Toronto, Toronto M6A 2E1, Canada
| | - Petra Ritter
- Max-Planck Institute for Cognitive and Brain Sciences, Leipzig, Germany; Department of Neurology, Charité, Charitéplatz 1, 10117 Berlin, Germany
| | - Viktor K Jirsa
- Institut de Neurosciences des Systèmes, Institut National de la Santé et de la Recherche Médicale (INSERM) Unité Mixte de Recherche (UMR) 1106, Aix-Marseille Université, Faculté de Médecine, 27, Boulevard Jean Moulin, 13005 Marseille, France
| | - 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), Universitat Pompeu Fabra, Passeig Lluís Companys 23, Barcelona 08010, Spain.
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47
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Abstract
The human brain is a particularly demanding system to infer its nature from observations. Thus, there is on one hand plenty of room for theorizing and on the other hand a pressing need for a rigorous theory. We apply statistical mechanics of open systems to describe the brain as a hierarchical system in consuming free energy in least time. This holistic tenet accounts for cellular metabolism, neuronal signaling, cognitive processes all together, or any other process by a formal equation of motion that extends down to the ultimate precision of one quantum of action. According to this general thermodynamic theory cognitive processes are no different by their operational and organizational principle from other natural processes. Cognition too will emerge and evolve along path-dependent and non-determinate trajectories by consuming free energy in least time to attain thermodynamic balance within the nervous system itself and with its surrounding systems. Specifically, consciousness can be ascribed to a natural process that integrates various neural networks for coherent consumption of free energy, i.e., for meaningful deeds. The whole hierarchy of integrated systems can be formally summed up to thermodynamic entropy. The holistic tenet provides insight to the character of consciousness also by acknowledging awareness in other systems at other levels of nature's hierarchy.
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Affiliation(s)
- Arto Annila
- Department of Physics, University of HelsinkiHelsinki, Finland; Department of Biosciences, University of HelsinkiHelsinki, Finland
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48
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Oizumi M, Amari SI, Yanagawa T, Fujii N, Tsuchiya N. Measuring Integrated Information from the Decoding Perspective. PLoS Comput Biol 2016; 12:e1004654. [PMID: 26796119 PMCID: PMC4721632 DOI: 10.1371/journal.pcbi.1004654] [Citation(s) in RCA: 70] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2015] [Accepted: 11/13/2015] [Indexed: 11/19/2022] Open
Abstract
Accumulating evidence indicates that the capacity to integrate information in the brain is a prerequisite for consciousness. Integrated Information Theory (IIT) of consciousness provides a mathematical approach to quantifying the information integrated in a system, called integrated information, Φ. Integrated information is defined theoretically as the amount of information a system generates as a whole, above and beyond the amount of information its parts independently generate. IIT predicts that the amount of integrated information in the brain should reflect levels of consciousness. Empirical evaluation of this theory requires computing integrated information from neural data acquired from experiments, although difficulties with using the original measure Φ precludes such computations. Although some practical measures have been previously proposed, we found that these measures fail to satisfy the theoretical requirements as a measure of integrated information. Measures of integrated information should satisfy the lower and upper bounds as follows: The lower bound of integrated information should be 0 and is equal to 0 when the system does not generate information (no information) or when the system comprises independent parts (no integration). The upper bound of integrated information is the amount of information generated by the whole system. Here we derive the novel practical measure Φ* by introducing a concept of mismatched decoding developed from information theory. We show that Φ* is properly bounded from below and above, as required, as a measure of integrated information. We derive the analytical expression of Φ* under the Gaussian assumption, which makes it readily applicable to experimental data. Our novel measure Φ* can generally be used as a measure of integrated information in research on consciousness, and also as a tool for network analysis on diverse areas of biology.
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Affiliation(s)
- Masafumi Oizumi
- RIKEN Brain Science Institute, Wako, Saitama, Japan
- School of Psychological Sciences, Faculty of Biomedical and Psychological Sciences, Monash University, Clayton, Victoria, Australia
| | | | | | | | - Naotsugu Tsuchiya
- School of Psychological Sciences, Faculty of Biomedical and Psychological Sciences, Monash University, Clayton, Victoria, Australia
- Japan Science and Technology Agency, Kawaguchi, Saitama, Japan
- Monash Institute of Cognitive and Clinical Neurosciences, Monash University, Clayton, Victoria, Australia
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