1
|
Liang Y, Zhao Q, Neubert JK, Ding M. Causal interactions in brain networks predict pain levels in trigeminal neuralgia. Brain Res Bull 2024; 211:110947. [PMID: 38614409 DOI: 10.1016/j.brainresbull.2024.110947] [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: 06/03/2023] [Revised: 03/13/2024] [Accepted: 04/10/2024] [Indexed: 04/15/2024]
Abstract
Trigeminal neuralgia (TN) is a highly debilitating facial pain condition. Magnetic resonance imaging (MRI) is the main method for generating insights into the central mechanisms of TN pain in humans. Studies have found both structural and functional abnormalities in various brain structures in TN patients as compared with healthy controls. Whereas studies have also examined aberrations in brain networks in TN, no studies have to date investigated causal interactions in these brain networks and related these causal interactions to the levels of TN pain. We recorded fMRI data from 39 TN patients who either rested comfortably in the scanner during the resting state session or tracked their pain levels during the pain tracking session. Applying Granger causality to analyze the data and requiring consistent findings across the two scanning sessions, we found 5 causal interactions, including: (1) Thalamus → dACC, (2) Caudate → Inferior temporal gyrus, (3) Precentral gyrus → Inferior temporal gyrus, (4) Supramarginal gyrus → Inferior temporal gyrus, and (5) Bankssts → Inferior temporal gyrus, that were consistently associated with the levels of pain experienced by the patients. Utilizing these 5 causal interactions as predictor variables and the pain score as the predicted variable in a linear multiple regression model, we found that in both pain tracking and resting state sessions, the model was able to explain ∼36 % of the variance in pain levels, and importantly, the model trained on the 5 causal interaction values from one session was able to predict pain levels using the 5 causal interaction values from the other session, thereby cross-validating the models. These results, obtained by applying novel analytical methods to neuroimaging data, provide important insights into the pathophysiology of TN and could inform future studies aimed at developing innovative therapies for treating TN.
Collapse
Affiliation(s)
- Yun Liang
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Qing Zhao
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - John K Neubert
- Department of Orthodontics, University of Florida, Gainesville, FL, United States
| | - Mingzhou Ding
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States.
| |
Collapse
|
2
|
Celotto M, Bím J, Tlaie A, De Feo V, Lemke S, Chicharro D, Nili H, Bieler M, Hanganu-Opatz IL, Donner TH, Brovelli A, Panzeri S. An information-theoretic quantification of the content of communication between brain regions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.14.544903. [PMID: 37398375 PMCID: PMC10312682 DOI: 10.1101/2023.06.14.544903] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Quantifying the amount, content and direction of communication between brain regions is key to understanding brain function. Traditional methods to analyze brain activity based on the Wiener-Granger causality principle quantify the overall information propagated by neural activity between simultaneously recorded brain regions, but do not reveal the information flow about specific features of interest (such as sensory stimuli). Here, we develop a new information theoretic measure termed Feature-specific Information Transfer (FIT), quantifying how much information about a specific feature flows between two regions. FIT merges the Wiener-Granger causality principle with information-content specificity. We first derive FIT and prove analytically its key properties. We then illustrate and test them with simulations of neural activity, demonstrating that FIT identifies, within the total information flowing between regions, the information that is transmitted about specific features. We then analyze three neural datasets obtained with different recording methods, magneto- and electro-encephalography, and spiking activity, to demonstrate the ability of FIT to uncover the content and direction of information flow between brain regions beyond what can be discerned with traditional anaytical methods. FIT can improve our understanding of how brain regions communicate by uncovering previously hidden feature-specific information flow.
Collapse
Affiliation(s)
- Marco Celotto
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto (TN), Italy
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Jan Bím
- Datamole, s. r. o, Vitezne namesti 577/2 Dejvice, 160 00 Praha 6, The Czech Republic
| | - Alejandro Tlaie
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto (TN), Italy
| | - Vito De Feo
- Artificial Intelligence Team, Future Health Technology, and Brain-Computer Interfaces laboratories, School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK
| | - Stefan Lemke
- Department of Cell Biology and Physiology, University of North Carolina, Chapel Hill, United States
| | - Daniel Chicharro
- Department of Computer Science, City, University of London, London, UK
| | - Hamed Nili
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Malte Bieler
- Mobile Technology Lab, School of Economics, Innovation and Technology, University College Kristiania, Oslo, Norway
| | - Ileana L. Hanganu-Opatz
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology, University Medical Center, Hamburg-Eppendorf, Hamburg, Germany
| | - Tobias H. Donner
- Section Computational Cognitive Neuroscience, Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Andrea Brovelli
- Institut de Neurosciences de la Timone, UMR 7289, Aix Marseille Université, CNRS, Marseille, France
| | - Stefano Panzeri
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto (TN), Italy
| |
Collapse
|
3
|
Biswas A. Pathway-resolved decomposition demonstrates correlation and noise dependencies of redundant information processing in recurrent feed-forward topologies. Phys Rev E 2022; 105:034406. [PMID: 35428055 DOI: 10.1103/physreve.105.034406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 02/04/2022] [Indexed: 06/14/2023]
Abstract
In a biochemical assay that converts fan-in networks into feed-forward loops (FFLs), we show that the inter-regulator redundant information about the output gene product can be decomposed into finer components, mediated by the constituent pathways. Variance-based information within the linear noise regime facilitates quantifying these submodular redundancies. Contrary to the conventional wisdom on information decomposition, we report that information redundancy depends nontrivially on inter-regulator correlation. For the type-1 coherent (C1) and incoherent (I1) FFLs, the direct regulatory path-mediated redundancy is certainly correlation independent. However, components induced by the indirect regulatory path and interpathway interference are correlation dependent in (non)linear fashion. The trade-off between information redundancy and similarly decomposable extrinsic noise from input to output node has been demonstrated for the pathways and full motifs. Our analyses suggest that the interpathway cross redundancy positively and negatively influences the superposition of elementary redundancies in the C1- and I1-FFLs, respectively. Their corresponding total extrinsic noise is produced by the weighted sum and difference of the pathway-specific components. We find that the I1-FFL is able to manufacture more varied redundancy and extrinsic noise responses compared to the C1-FFL. Underlying the differing characteristics of the composite metrics across FFL variants, there exist uniformly behaving pathway-dependent elements. The decomposition framework has been meticulously explored in biologically rational parametric realizations through analytical estimates and stochastic simulations.
Collapse
Affiliation(s)
- Ayan Biswas
- Department of Chemistry, Bose Institute, 93/1 A P C Road, Kolkata 700009, India
| |
Collapse
|
4
|
Porta A, Gelpi F, Bari V, Cairo B, De Maria B, Tonon D, Rossato G, Ranucci M, Faes L. Categorizing the Role of Respiration in Cardiovascular and Cerebrovascular Variability Interactions. IEEE Trans Biomed Eng 2021; 69:2065-2076. [PMID: 34905489 DOI: 10.1109/tbme.2021.3135313] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Respiration disturbs cardiovascular and cerebrovascular controls but its role is not fully elucidated. METHODS Respiration can be classified as a confounder if its observation reduces the strength of the causal relationship from source to target. Respiration is a suppressor if the opposite situation holds. We prove that a confounding/suppression (C/S) test can be accomplished by evaluating the sign of net redundancy/synergy balance in the predictability framework based on multivariate autoregressive modelling. In addition, we suggest that, under the hypothesis of Gaussian processes, the C/S test can be given in the transfer entropy decomposition framework as well. Experimental protocols: We applied the C/S test to variability series of respiratory movements, heart period, systolic arterial pressure, mean arterial pressure, and mean cerebral blood flow recorded in 17 pathological individuals (age: 648 yrs; 17 males) before and after induction of propofol-based general anesthesia prior to coronary artery bypass grafting, and in 13 healthy subjects (age: 278 yrs; 5 males) at rest in supine position and during head-up tilt with a table inclination of 60. RESULTS Respiration behaved systematically as a confounder for cardiovascular and cerebrovascular controls. In addition, its role was affected by propofol-based general anesthesia but not by a postural stimulus of limited intensity. CONCLUSION The C/S test can be fruitfully exploited to categorize the role of respiration over causal variability interactions. SIGNIFICANCE The application of the C/S test could favor the comprehension of the role of respiration in cardiovascular and cerebrovascular regulations.
Collapse
|
5
|
|
6
|
Allegra M, Favaretto C, Metcalf N, Corbetta M, Brovelli A. Stroke-related alterations in inter-areal communication. NEUROIMAGE-CLINICAL 2021; 32:102812. [PMID: 34544032 PMCID: PMC8453222 DOI: 10.1016/j.nicl.2021.102812] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 08/02/2021] [Accepted: 08/29/2021] [Indexed: 01/03/2023]
Abstract
We used covariance-based Granger Causality on resting-state fMRI of stroke patients. Stroke determines an overall decrease of homotopic Granger causality (GC) Stroke determines a decrease of GC from and within the lesioned hemisphere. Stroke causes imbalances in GC between the lesioned and the healthy hemisphere. GC anomalies correlate with impaired performance in several behavioral domains.
Beyond causing local ischemia and cell damage at the site of injury, stroke strongly affects long-range anatomical connections, perturbing the functional organization of brain networks. Several studies reported functional connectivity abnormalities parallelling both behavioral deficits and functional recovery across different cognitive domains. FC alterations suggest that long-range communication in the brain is altered after stroke. However, standard FC analyses cannot reveal the directionality and time scale of inter-areal information transfer. We used resting-state fMRI and covariance-based Granger causality analysis to quantify network-level information transfer and its alteration in stroke. Two main large-scale anomalies were observed in stroke patients. First, inter-hemispheric information transfer was significantly decreased with respect to healthy controls. Second, stroke caused inter-hemispheric asymmetries, as information transfer within the affected hemisphere and from the affected to the intact hemisphere was significantly reduced. Both anomalies were more prominent in resting-state networks related to attention and language, and they correlated with impaired performance in several behavioral domains. Overall, our findings support the hypothesis that stroke provokes asymmetries between the affected and spared hemisphere, with different functional consequences depending on which hemisphere is lesioned.
Collapse
Affiliation(s)
- Michele Allegra
- Institut de Neurosciences de la Timone UMR 7289, Aix Marseille Université, CNRS, Marseille 13005, France.
| | - Chiara Favaretto
- Department of Neuroscience, Neurological Clinic, University of Padua, Padua, Italy; Padova Neuroscience Center, University of Padua, Padua, Italy
| | - Nicholas Metcalf
- Department of Neurology, Radiology, and Neuroscience, Washington University in St. Louis, St. Louis, MO, United States
| | - Maurizio Corbetta
- Department of Neuroscience, Neurological Clinic, University of Padua, Padua, Italy; Padova Neuroscience Center, University of Padua, Padua, Italy; Department of Neurology, Radiology, and Neuroscience, Washington University in St. Louis, St. Louis, MO, United States
| | - Andrea Brovelli
- Institut de Neurosciences de la Timone UMR 7289, Aix Marseille Université, CNRS, Marseille 13005, France.
| |
Collapse
|
7
|
Nunez-Ibero M, Camino-Pontes B, Diez I, Erramuzpe A, Martinez-Gutierrez E, Stramaglia S, Alvarez-Cienfuegos JO, Cortes JM. A Controlled Thermoalgesic Stimulation Device for Exploring Novel Pain Perception Biomarkers. IEEE J Biomed Health Inform 2021; 25:2948-2957. [PMID: 33999827 DOI: 10.1109/jbhi.2021.3080935] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE To develop a new device for identifying physiological markers of pain perception by reading the brain's electrical activity and hemodynamic interactions while applying thermoalgesic stimulation. METHODS We designed a compact prototype that generates well-controlled thermal stimuli using a computer-driven Peltier cell while simultaneously capturing electroencephalography (EEG) and photoplethysmography (PPG) signals. The study was performed on 35 healthy subjects (mean age 30.46 years, SD 4.93 years; 20 males, 15 females). We first determined the heat pain threshold (HPT) for each subject, defined as the maximum temperature that the subject can withstand when the Peltier cell gradually increased the temperature. Next, we defined the painful condition as the one occurring at temperature equal to 90% of the HPT, comparing this to the no-pain state (control) in the absence of thermoalgesic stimulation. RESULTS Both the one-dimensional and the two-dimensional spectral entropy (SE) obtained from both the EEG and PPG signals differentiated the condition of pain. In particular, the SE for PPG was significantly reduced in association with pain, while the SE for EEG increased slightly. Moreover, significant discrimination occurred within a specific range of frequencies, 26-30 Hz for EEG and about 5-10 Hz for PPG. CONCLUSION Hemodynamics, brain dynamics and their interactions can discriminate thermal pain perception. SIGNIFICANCE The possibility of monitoring on-line variations in thermal pain perception using a similar device and algorithms may be of interest to study different pathologies that affect the peripheral nervous system, such as small fiber neuropathies, fibromyalgia or painful diabetic neuropathy.
Collapse
|
8
|
Gatica M, Cofré R, Mediano PAM, Rosas FE, Orio P, Diez I, Swinnen SP, Cortes JM. High-Order Interdependencies in the Aging Brain. Brain Connect 2021; 11:734-744. [PMID: 33858199 DOI: 10.1089/brain.2020.0982] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Background: Brain interdependencies can be studied from either a structural/anatomical perspective ("structural connectivity") or by considering statistical interdependencies ("functional connectivity" [FC]). Interestingly, while structural connectivity is by definition pairwise (white-matter fibers project from one region to another), FC is not. However, most FC analyses only focus on pairwise statistics and they neglect higher order interactions. A promising tool to study high-order interdependencies is the recently proposed O-Information, which can quantify the intrinsic statistical synergy and the redundancy in groups of three or more interacting variables. Methods: We analyzed functional magnetic resonance imaging (fMRI) data obtained at rest from 164 healthy subjects with ages ranging in 10 to 80 years and used O-Information to investigate how high-order statistical interdependencies are affected by age. Results: Older participants (from 60 to 80 years old) exhibited a higher predominance of redundant dependencies compared with younger participants, an effect that seems to be pervasive as it is evident for all orders of interaction. In addition, while there is strong heterogeneity across brain regions, we found a "redundancy core" constituted by the prefrontal and motor cortices in which redundancy was evident at all the interaction orders studied. Discussion: High-order interdependencies in fMRI data reveal a dominant redundancy in functions such as working memory, executive, and motor functions. Our methodology can be used for a broad range of applications, and the corresponding code is freely available.
Collapse
Affiliation(s)
- Marilyn Gatica
- Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Valparaíso, Chile.,Biomedical Research Doctorate Program, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Rodrigo Cofré
- CIMFAV-Ingemat, Facultad de Ingeniería, Universidad de Valparaíso, Valparaíso, Chile
| | - Pedro A M Mediano
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom
| | - Fernando E Rosas
- Centre for Psychedelic Research, Department of Brain Science, Imperial College London, London, United Kingdom.,Data Science Institute, Imperial College London, London, United Kingdom.,Centre for Complexity Science, Imperial College London, London, United Kingdom
| | - Patricio Orio
- Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Valparaíso, Chile.,Instituto de Neurociencia, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Ibai Diez
- Department of Radiology, Gordon Center for Medical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA.,Neurology Department, Harvard Medical School, Boston, Massachusetts, USA.,Neurotechnology Laboratory, Tecnalia Health Department, Derio, Spain
| | - Stephan P Swinnen
- Research Center for Movement Control and Neuroplasticity, Department of Movement Sciences, KU Leuven, Leuven, Belgium.,Leuven Brain Institute (LBI), KU Leuven, Leuven, Belgium
| | - Jesus M Cortes
- Computational Neuroimaging Lab, Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain.,IKERBASQUE: The Basque Foundation for Science, Bilbao, Spain.,Department of Cell Biology and Histology, University of the Basque Country, Leioa, Spain
| |
Collapse
|
9
|
Vecchio F, Miraglia F, Alù F, Judica E, Cotelli M, Pellicciari MC, Rossini PM. Human brain networks in physiological and pathological aging: reproducibility of EEG graph theoretical analysis in cortical connectivity. Brain Connect 2021; 12:41-51. [PMID: 33797981 DOI: 10.1089/brain.2020.0824] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Physiological and pathological brain aging plays a central role in brain networks modulation. The aim of the present paper was to assess the stability of a proposed method for the evaluation of Small World (SW) characteristics for the study of Human Connectome. METHODS 80 subjects were recruited: 36 young healthy controls, 32 elderly healthy controls, and 12 patients affected by Alzheimer's disease. Electroencephalograms (EEG) were recorded during six separate sessions (480 recordings) at an average inter-session interval of 3.8±0.2 days. Graph theory functions were applied to the undirected and weighted networks obtained by the lagged linear coherence evaluated by exact Low Resolution Electromagnetic Tomography (eLORETA). Were explored the following frequency bands: delta (2-4Hz), theta (4-8Hz), alpha1 (8-10.5Hz), alpha2 (10.5-13Hz), beta1 (13-20Hz), beta2 (20-30Hz) and gamma (30-40Hz). RESULTS The proposed method for the evaluation of Small World (SW) characteristics showed good reproducibility and stability. Furthermore, the results showed the pattern Young>Elderly>AD in low frequency delta and theta bands and vice versa in the higher alpha band. Finally, the correlation with age was confirmed in healthy subjects showing that older the age higher the SW values for alpha2. DISCUSSION Evidences from the present study confirm the stability of the Small World index and suggest that graph theory can support the analysis of connectivity patterns estimated from EEG. The proposed method for the evaluation of the characteristics of the Small World (SW) has shown good reproducibility and stability and applied to patient data, this technique could provide more information on the pathophysiological processes underlying the age-related brain disconnection, as well as on the administration of rehabilitation treatments at the right time that could allow to avoid unnecessary interventions.
Collapse
Affiliation(s)
- Fabrizio Vecchio
- IRCCS San Raffaele Pisana, 46729, Via di Val Cannuta, 247, 00166 Roma RM, Roma, Italy, 00163;
| | | | - Francesca Alù
- IRCCS San Raffaele Pisana, 46729, Roma, Lazio, Italy;
| | - Elda Judica
- Casa di Cura del Policlinico SpA, 390725, Milano, Lombardia, Italy;
| | - Maria Cotelli
- IRCCS Centro San Giovanni di Dio Fatebenefratelli, 18518, Brescia, Lombardia, Italy;
| | | | | |
Collapse
|
10
|
Makkeh A, Chicharro D, Theis DO, Vicente R. MAXENT3D_PID: An Estimator for the Maximum-Entropy Trivariate Partial Information Decomposition. ENTROPY 2019; 21:862. [PMCID: PMC7515392 DOI: 10.3390/e21090862] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 08/27/2019] [Indexed: 07/04/2023]
Abstract
Partial information decomposition (PID) separates the contributions of sources about a target into unique, redundant, and synergistic components of information. In essence, PID answers the question of “who knows what” of a system of random variables and hence has applications to a wide spectrum of fields ranging from social to biological sciences. The paper presents MaxEnt3D_Pid, an algorithm that computes the PID of three sources, based on a recently-proposed maximum entropy measure, using convex optimization (cone programming). We describe the algorithm and its associated software utilization and report the results of various experiments assessing its accuracy. Moreover, the paper shows that a hierarchy of bivariate and trivariate PID allows obtaining the finer quantities of the trivariate partial information measure.
Collapse
Affiliation(s)
- Abdullah Makkeh
- Institute of Computer Science, University of Tartu, 51014 Tartu, Estonia
| | - Daniel Chicharro
- Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems@UniTn, Istituto Italiano di Tecnologia, 38068 Rovereto (TN), Italy
| | - Dirk Oliver Theis
- Institute of Computer Science, University of Tartu, 51014 Tartu, Estonia
| | - Raul Vicente
- Institute of Computer Science, University of Tartu, 51014 Tartu, Estonia
| |
Collapse
|
11
|
Biswas A. Multivariate information processing characterizes fitness of a cascaded gene-transcription machinery. CHAOS (WOODBURY, N.Y.) 2019; 29:063108. [PMID: 31266314 DOI: 10.1063/1.5092447] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Accepted: 05/24/2019] [Indexed: 06/09/2023]
Abstract
We report that a genetic two-step activation cascade processes diverse flavors of information, e.g., synergy, redundancy, and unique information. Our computations measuring reduction in Shannon entropies and reduction in variances produce differently behaving absolute magnitudes of these informational flavors. We find that similarity can be brought in if these terms are evaluated in fractions with respect to corresponding total information. Each of the input signal and final gene-product is found to generate common or redundant information fractions (mostly) to predict each other, whereas they also complement one another to harness synergistic information fraction, predicting the intermediate biochemical species. For an optimally growing signal to maintain fixed steady-state abundance of activated downstream gene-products, the interaction information fractions for this cascade module shift from net-redundancy to information-independence.
Collapse
Affiliation(s)
- Ayan Biswas
- Department of Chemistry, Bose Institute, 93/1 A P C Road, Kolkata 700 009, India
| |
Collapse
|
12
|
He B, Astolfi L, Valdés-Sosa PA, Marinazzo D, Palva SO, Bénar CG, Michel CM, Koenig T. Electrophysiological Brain Connectivity: Theory and Implementation. IEEE Trans Biomed Eng 2019; 66:10.1109/TBME.2019.2913928. [PMID: 31071012 PMCID: PMC6834897 DOI: 10.1109/tbme.2019.2913928] [Citation(s) in RCA: 90] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
We review the theory and algorithms of electrophysiological brain connectivity analysis. This tutorial is aimed at providing an introduction to brain functional connectivity from electrophysiological signals, including electroencephalography (EEG), magnetoencephalography (MEG), electrocorticography (ECoG), stereoelectroencephalography (SEEG). Various connectivity estimators are discussed, and algorithms introduced. Important issues for estimating and mapping brain functional connectivity with electrophysiology are discussed.
Collapse
Affiliation(s)
- Bin He
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, USA
| | - Laura Astolfi
- Department of Computer, Control and Management Engineering, University of Rome Sapienza, and with IRCCS Fondazione Santa Lucia, Rome, Italy
| | | | | | | | | | | | | |
Collapse
|
13
|
Paced Breathing Increases the Redundancy of Cardiorespiratory Control in Healthy Individuals and Chronic Heart Failure Patients. ENTROPY 2018; 20:e20120949. [PMID: 33266673 PMCID: PMC7512533 DOI: 10.3390/e20120949] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Revised: 12/04/2018] [Accepted: 12/06/2018] [Indexed: 11/17/2022]
Abstract
Synergy and redundancy are concepts that suggest, respectively, adaptability and fault tolerance of systems with complex behavior. This study computes redundancy/synergy in bivariate systems formed by a target X and a driver Y according to the predictive information decomposition approach and partial information decomposition framework based on the minimal mutual information principle. The two approaches assess the redundancy/synergy of past of X and Y in reducing the uncertainty of the current state of X. The methods were applied to evaluate the interactions between heart and respiration in healthy young subjects (n = 19) during controlled breathing at 10, 15 and 20 breaths/minute and in two groups of chronic heart failure patients during paced respiration at 6 (n = 9) and 15 (n = 20) breaths/minutes from spontaneous beat-to-beat fluctuations of heart period and respiratory signal. Both methods suggested that slowing respiratory rate below the spontaneous frequency increases redundancy of cardiorespiratory control in both healthy and pathological groups, thus possibly improving fault tolerance of the cardiorespiratory control. The two methods provide markers complementary to respiratory sinus arrhythmia and the strength of the linear coupling between heart period variability and respiration in describing the physiology of the cardiorespiratory reflex suitable to be exploited in various pathophysiological settings.
Collapse
|
14
|
Camino-Pontes B, Diez I, Jimenez-Marin A, Rasero J, Erramuzpe A, Bonifazi P, Stramaglia S, Swinnen S, Cortes JM. Interaction Information Along Lifespan of the Resting Brain Dynamics Reveals a Major Redundant Role of the Default Mode Network. ENTROPY 2018; 20:e20100742. [PMID: 33265831 PMCID: PMC7512305 DOI: 10.3390/e20100742] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Revised: 09/07/2018] [Accepted: 09/24/2018] [Indexed: 01/06/2023]
Abstract
Interaction Information (II) generalizes the univariate Shannon entropy to triplets of variables, allowing the detection of redundant (R) or synergetic (S) interactions in dynamical networks. Here, we calculated II from functional magnetic resonance imaging data and asked whether R or S vary across brain regions and along lifespan. Preserved along lifespan, we found high overlapping between the pattern of high R and the default mode network, whereas high values of S were overlapping with different cognitive domains, such as spatial and temporal memory, emotion processing and motor skills. Moreover, we have found a robust balance between R and S among different age intervals, indicating informational compensatory mechanisms in brain networks.
Collapse
Affiliation(s)
- Borja Camino-Pontes
- Computational Neuroimaging Lab, Biocruces Health Research Institute, 48903 Barakaldo, Spain
| | - Ibai Diez
- Functional Neurology Research Group, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA
- Gordon Center, Department of Nuclear Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA
- Neurotechnology Laboratory, Tecnalia Health Department, 48160 Derio, Spain
| | - Antonio Jimenez-Marin
- Computational Neuroimaging Lab, Biocruces Health Research Institute, 48903 Barakaldo, Spain
| | - Javier Rasero
- Computational Neuroimaging Lab, Biocruces Health Research Institute, 48903 Barakaldo, Spain
| | - Asier Erramuzpe
- Computational Neuroimaging Lab, Biocruces Health Research Institute, 48903 Barakaldo, Spain
| | - Paolo Bonifazi
- Computational Neuroimaging Lab, Biocruces Health Research Institute, 48903 Barakaldo, Spain
- IKERBASQUE: The Basque Foundation for Science, 48013 Bilbao, Spain
| | | | - Stephan Swinnen
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, 3001 Leuven, Belgium
- Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
| | - Jesus M. Cortes
- Computational Neuroimaging Lab, Biocruces Health Research Institute, 48903 Barakaldo, Spain
- IKERBASQUE: The Basque Foundation for Science, 48013 Bilbao, Spain
- Department of Cell Biology and Histology, University of the Basque Country, 48940 Leioa, Spain
- Correspondence: ; Tel.: +34-94600600 (ext. 5199)
| |
Collapse
|
15
|
Bonifazi P, Erramuzpe A, Diez I, Gabilondo I, Boisgontier MP, Pauwels L, Stramaglia S, Swinnen SP, Cortes JM. Structure-function multi-scale connectomics reveals a major role of the fronto-striato-thalamic circuit in brain aging. Hum Brain Mapp 2018; 39:4663-4677. [PMID: 30004604 DOI: 10.1002/hbm.24312] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Revised: 06/27/2018] [Accepted: 06/28/2018] [Indexed: 12/15/2022] Open
Abstract
Physiological aging affects brain structure and function impacting morphology, connectivity, and performance. However, whether some brain connectivity metrics might reflect the age of an individual is still unclear. Here, we collected brain images from healthy participants (N = 155) ranging from 10 to 80 years to build functional (resting state) and structural (tractography) connectivity matrices, both data sets combined to obtain different connectivity features. We then calculated the brain connectome age-an age estimator resulting from a multi-scale methodology applied to the structure-function connectome, and compared it to the chronological age (ChA). Our results were twofold. First, we found that aging widely affects the connectivity of multiple structures, such as anterior cingulate and medial prefrontal cortices, basal ganglia, thalamus, insula, cingulum, hippocampus, parahippocampus, occipital cortex, fusiform, precuneus, and temporal pole. Second, we found that the connectivity between basal ganglia and thalamus to frontal areas, also known as the fronto-striato-thalamic (FST) circuit, makes the major contribution to age estimation. In conclusion, our results highlight the key role played by the FST circuit in the process of healthy aging. Notably, the same methodology can be generally applied to identify the structural-functional connectivity patterns correlating to other biomarkers than ChA.
Collapse
Affiliation(s)
- Paolo Bonifazi
- Biocruces Health Research Institute, Barakaldo, Spain.,IKERBASQUE: The Basque Foundation for Science, Bilbao, Spain
| | | | - Ibai Diez
- Biocruces Health Research Institute, Barakaldo, Spain
| | | | - Matthieu P Boisgontier
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
| | - Lisa Pauwels
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
| | - Sebastiano Stramaglia
- Dipartimento Interateneo di Fisica, Universita di Bari, and INFN, Sezione di Bari, Italy
| | - Stephan P Swinnen
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, Leuven, Belgium.,Leuven Brain Institute (LBI), KU Leuven, Leuven, Belgium
| | - Jesus M Cortes
- Biocruces Health Research Institute, Barakaldo, Spain.,IKERBASQUE: The Basque Foundation for Science, Bilbao, Spain.,Department of Cell Biology and Histology, University of the Basque Country, Leioa, Spain
| |
Collapse
|
16
|
Pointwise Partial Information Decomposition Using the Specificity and Ambiguity Lattices. ENTROPY 2018; 20:e20040297. [PMID: 33265388 PMCID: PMC7512814 DOI: 10.3390/e20040297] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Revised: 04/06/2018] [Accepted: 04/10/2018] [Indexed: 11/17/2022]
Abstract
What are the distinct ways in which a set of predictor variables can provide information about a target variable? When does a variable provide unique information, when do variables share redundant information, and when do variables combine synergistically to provide complementary information? The redundancy lattice from the partial information decomposition of Williams and Beer provided a promising glimpse at the answer to these questions. However, this structure was constructed using a much criticised measure of redundant information, and despite sustained research, no completely satisfactory replacement measure has been proposed. In this paper, we take a different approach, applying the axiomatic derivation of the redundancy lattice to a single realisation from a set of discrete variables. To overcome the difficulty associated with signed pointwise mutual information, we apply this decomposition separately to the unsigned entropic components of pointwise mutual information which we refer to as the specificity and ambiguity. This yields a separate redundancy lattice for each component. Then based upon an operational interpretation of redundancy, we define measures of redundant specificity and ambiguity enabling us to evaluate the partial information atoms in each lattice. These atoms can be recombined to yield the sought-after multivariate information decomposition. We apply this framework to canonical examples from the literature and discuss the results and the various properties of the decomposition. In particular, the pointwise decomposition using specificity and ambiguity satisfies a chain rule over target variables, which provides new insights into the so-called two-bit-copy example.
Collapse
|
17
|
Chicharro D, Pica G, Panzeri S. The Identity of Information: How Deterministic Dependencies Constrain Information Synergy and Redundancy. ENTROPY (BASEL, SWITZERLAND) 2018; 20:e20030169. [PMID: 33265260 PMCID: PMC7512685 DOI: 10.3390/e20030169] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Revised: 02/26/2018] [Accepted: 02/28/2018] [Indexed: 06/12/2023]
Abstract
Understanding how different information sources together transmit information is crucial in many domains. For example, understanding the neural code requires characterizing how different neurons contribute unique, redundant, or synergistic pieces of information about sensory or behavioral variables. Williams and Beer (2010) proposed a partial information decomposition (PID) that separates the mutual information that a set of sources contains about a set of targets into nonnegative terms interpretable as these pieces. Quantifying redundancy requires assigning an identity to different information pieces, to assess when information is common across sources. Harder et al. (2013) proposed an identity axiom that imposes necessary conditions to quantify qualitatively common information. However, Bertschinger et al. (2012) showed that, in a counterexample with deterministic target-source dependencies, the identity axiom is incompatible with ensuring PID nonnegativity. Here, we study systematically the consequences of information identity criteria that assign identity based on associations between target and source variables resulting from deterministic dependencies. We show how these criteria are related to the identity axiom and to previously proposed redundancy measures, and we characterize how they lead to negative PID terms. This constitutes a further step to more explicitly address the role of information identity in the quantification of redundancy. The implications for studying neural coding are discussed.
Collapse
Affiliation(s)
- Daniel Chicharro
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA
- Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems@UniTn, Istituto Italiano di Tecnologia, Rovereto (TN) 38068, Italy
| | - Giuseppe Pica
- Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems@UniTn, Istituto Italiano di Tecnologia, Rovereto (TN) 38068, Italy
| | - Stefano Panzeri
- Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems@UniTn, Istituto Italiano di Tecnologia, Rovereto (TN) 38068, Italy
| |
Collapse
|
18
|
Partial and Entropic Information Decompositions of a Neuronal Modulatory Interaction. ENTROPY 2017. [DOI: 10.3390/e19110560] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
|
19
|
Quantifying Information Modification in Developing Neural Networks via Partial Information Decomposition. ENTROPY 2017. [DOI: 10.3390/e19090494] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
20
|
Invariant Components of Synergy, Redundancy, and Unique Information among Three Variables. ENTROPY 2017. [DOI: 10.3390/e19090451] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
21
|
Multiscale Information Decomposition: Exact Computation for Multivariate Gaussian Processes. ENTROPY 2017. [DOI: 10.3390/e19080408] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
22
|
Diez I, Drijkoningen D, Stramaglia S, Bonifazi P, Marinazzo D, Gooijers J, Swinnen SP, Cortes JM. Enhanced prefrontal functional-structural networks to support postural control deficits after traumatic brain injury in a pediatric population. Netw Neurosci 2017; 1:116-142. [PMID: 29911675 PMCID: PMC5988395 DOI: 10.1162/netn_a_00007] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2016] [Accepted: 01/28/2017] [Indexed: 11/04/2022] Open
Abstract
Traumatic brain injury (TBI) affects structural connectivity, triggering the reorganization of structural-functional circuits in a manner that remains poorly understood. We focus here on brain network reorganization in relation to postural control deficits after TBI. We enrolled young participants who had suffered moderate to severe TBI, comparing them to young, typically developing control participants. TBI patients (but not controls) recruited prefrontal regions to interact with two separated networks: (1) a subcortical network, including parts of the motor network, basal ganglia, cerebellum, hippocampus, amygdala, posterior cingulate gyrus, and precuneus; and (2) a task-positive network, involving regions of the dorsal attention system, together with dorsolateral and ventrolateral prefrontal regions. We also found that the increased prefrontal connectivity in TBI patients was correlated with some postural control indices, such as the amount of body sway, whereby patients with worse balance increased their connectivity in frontal regions more strongly. The increased prefrontal connectivity found in TBI patients may provide the structural scaffolding for stronger cognitive control of certain behavioral functions, consistent with the observations that various motor tasks are performed less automatically following TBI and that more cognitive control is associated with such actions.
Collapse
Affiliation(s)
- Ibai Diez
- Biocruces Health Research Institute, Cruces University Hospital, Barakaldo, Spain
| | - David Drijkoningen
- KU Leuven, Movement Control and Neuroplasticity Research Group, Group Biomedical Sciences, Leuve, Belgium
| | - Sebastiano Stramaglia
- Dipartimento di Fisica, Universita degli Studi di Bari and INFN, Bari, Italy.,Basque Center for Applied Mathematics (BCAM), Bilbao, Spain
| | - Paolo Bonifazi
- Biocruces Health Research Institute, Cruces University Hospital, Barakaldo, Spain.,Ikerbasque: The Basque Foundation for Science, Bilbao, Spain
| | - Daniele Marinazzo
- Department of Data Analysis, Faculty of Psychological and Pedagogical Sciences, University of Ghent, Ghent, Belgium
| | - Jolien Gooijers
- KU Leuven, Movement Control and Neuroplasticity Research Group, Group Biomedical Sciences, Leuve, Belgium
| | - Stephan P Swinnen
- KU Leuven, Movement Control and Neuroplasticity Research Group, Group Biomedical Sciences, Leuve, Belgium.,KU Leuven, Leuven Research Institute for Neuroscience & Disease (LIND), Leuven, Belgium
| | - Jesus M Cortes
- Biocruces Health Research Institute, Cruces University Hospital, Barakaldo, Spain.,Ikerbasque: The Basque Foundation for Science, Bilbao, Spain.,Department of Cell Biology and Histology, University of the Basque Country, Leioa, Spain
| |
Collapse
|
23
|
Babiloni F, Gee J. The Power of Connecting Dots: Advanced Techniques to Evaluate Brain Functional Connectivity in Humans. IEEE Trans Biomed Eng 2016; 63:2447-2449. [PMID: 27810794 DOI: 10.1109/tbme.2016.2621727] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Brain functional connectivity estimation allows us to depict patterns of cerebral activity not understandable otherwise with the standard brain imaging techniques such as functional magnetic resonance imaging (fMRI) as well as electro or magnetoencephalography (hr-EEG, MEG). This special issue of the IEEE Transactions on Biomedical Engineering reports a range of methodological innovations toward the estimation of functional connectivity from brain activity data, with emphasis on neuroelectric and hemodynamic imaging modalities. Functional connectivity methodologies enable "connecting of the dots" derived from brain activity observations over multiple distributed sites, as depicted by such fMRI and hr-EEG/MEG devices.
Collapse
|