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Marrelec G, Giron A. Multilevel testing of constraints induced by structural equation modeling in fMRI effective connectivity analysis: A proof of concept. Magn Reson Imaging 2024; 109:294-303. [PMID: 38280493 DOI: 10.1016/j.mri.2024.01.010] [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: 08/14/2023] [Revised: 01/20/2024] [Accepted: 01/20/2024] [Indexed: 01/29/2024]
Abstract
In functional MRI (fMRI), effective connectivity analysis aims at inferring the causal influences that brain regions exert on one another. A common method for this type of analysis is structural equation modeling (SEM). We here propose a novel method to test the validity of a given model of structural equation. Given a structural model in the form of a directed graph, the method extracts the set of all constraints of conditional independence induced by the absence of links between pairs of regions in the model and tests for their validity in a Bayesian framework, either individually (constraint by constraint), jointly (e.g., by gathering all constraints associated with a given missing link), or globally (i.e., all constraints associated with the structural model). This approach has two main advantages. First, it only tests what is testable from observational data and does allow for false causal interpretation. Second, it makes it possible to test each constraint (or group of constraints) separately and, therefore, quantify in what measure each constraint (or, e..g., missing link) is respected in the data. We validate our approach using a simulation study and illustrate its potential benefits through the reanalysis of published data.
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Affiliation(s)
- Guillaume Marrelec
- Laboratoire d'imagerie biomédicale, LIB, Sorbonne Université, CNRS, INSERM, F-75006 Paris, France.
| | - Alain Giron
- Laboratoire d'imagerie biomédicale, LIB, Sorbonne Université, CNRS, INSERM, F-75006 Paris, France
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2
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Novelli L, Friston K, Razi A. Spectral dynamic causal modeling: A didactic introduction and its relationship with functional connectivity. Netw Neurosci 2024; 8:178-202. [PMID: 38562289 PMCID: PMC10898785 DOI: 10.1162/netn_a_00348] [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: 06/23/2023] [Accepted: 10/23/2023] [Indexed: 04/04/2024] Open
Abstract
We present a didactic introduction to spectral dynamic causal modeling (DCM), a Bayesian state-space modeling approach used to infer effective connectivity from noninvasive neuroimaging data. Spectral DCM is currently the most widely applied DCM variant for resting-state functional MRI analysis. Our aim is to explain its technical foundations to an audience with limited expertise in state-space modeling and spectral data analysis. Particular attention will be paid to cross-spectral density, which is the most distinctive feature of spectral DCM and is closely related to functional connectivity, as measured by (zero-lag) Pearson correlations. In fact, the model parameters estimated by spectral DCM are those that best reproduce the cross-correlations between all measurements-at all time lags-including the zero-lag correlations that are usually interpreted as functional connectivity. We derive the functional connectivity matrix from the model equations and show how changing a single effective connectivity parameter can affect all pairwise correlations. To complicate matters, the pairs of brain regions showing the largest changes in functional connectivity do not necessarily coincide with those presenting the largest changes in effective connectivity. We discuss the implications and conclude with a comprehensive summary of the assumptions and limitations of spectral DCM.
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Affiliation(s)
- Leonardo Novelli
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Australia
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
| | - Adeel Razi
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Australia
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
- CIFAR Azrieli Global Scholars Program, Toronto, Canada
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Nunez MD, Fernandez K, Srinivasan R, Vandekerckhove J. A tutorial on fitting joint models of M/EEG and behavior to understand cognition. Behav Res Methods 2024:10.3758/s13428-023-02331-x. [PMID: 38409458 DOI: 10.3758/s13428-023-02331-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/21/2023] [Indexed: 02/28/2024]
Abstract
We present motivation and practical steps necessary to find parameter estimates of joint models of behavior and neural electrophysiological data. This tutorial is written for researchers wishing to build joint models of human behavior and scalp and intracranial electroencephalographic (EEG) or magnetoencephalographic (MEG) data, and more specifically those researchers who seek to understand human cognition. Although these techniques could easily be applied to animal models, the focus of this tutorial is on human participants. Joint modeling of M/EEG and behavior requires some knowledge of existing computational and cognitive theories, M/EEG artifact correction, M/EEG analysis techniques, cognitive modeling, and programming for statistical modeling implementation. This paper seeks to give an introduction to these techniques as they apply to estimating parameters from neurocognitive models of M/EEG and human behavior, and to evaluate model results and compare models. Due to our research and knowledge on the subject matter, our examples in this paper will focus on testing specific hypotheses in human decision-making theory. However, most of the motivation and discussion of this paper applies across many modeling procedures and applications. We provide Python (and linked R) code examples in the tutorial and appendix. Readers are encouraged to try the exercises at the end of the document.
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Affiliation(s)
- Michael D Nunez
- Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands.
| | - Kianté Fernandez
- Department of Psychology, University of California, Los Angeles, CA, USA
| | - Ramesh Srinivasan
- Department of Cognitive Sciences, University of California, Irvine, CA, USA
- Department of Biomedical Engineering, University of California, Irvine, CA, USA
- Institute of Mathematical Behavioral Sciences, University of California, Irvine, CA, USA
| | - Joachim Vandekerckhove
- Department of Cognitive Sciences, University of California, Irvine, CA, USA
- Institute of Mathematical Behavioral Sciences, University of California, Irvine, CA, USA
- Department of Statistics, University of California, Irvine, CA, USA
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Giorgio J, Adams JN, Maass A, Jagust WJ, Breakspear M. Amyloid induced hyperexcitability in default mode network drives medial temporal hyperactivity and early tau accumulation. Neuron 2024; 112:676-686.e4. [PMID: 38096815 PMCID: PMC10922797 DOI: 10.1016/j.neuron.2023.11.014] [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: 03/24/2023] [Revised: 09/01/2023] [Accepted: 11/14/2023] [Indexed: 02/24/2024]
Abstract
In early Alzheimer's disease (AD) β-amyloid (Aβ) deposits throughout association cortex and tau appears in the entorhinal cortex (EC). Why these initially appear in disparate locations is not understood. Using task-based fMRI and multimodal PET imaging, we assess the impact of local AD pathology on network-to-network interactions. We show that AD pathologies flip interactions between the default mode network (DMN) and the medial temporal lobe (MTL) from inhibitory to excitatory. The DMN is hyperexcited with increasing levels of Aβ, which drives hyperexcitability within the MTL and this directed hyperexcitation of the MTL by the DMN predicts the rate of tau accumulation within the EC. Our results support a model whereby Aβ induces disruptions to local excitatory-inhibitory balance in the DMN, driving hyperexcitability in the MTL, leading to tau accumulation. We propose that Aβ-induced disruptions to excitatory-inhibitory balance is a candidate causal route between Aβ and remote EC-tau accumulation.
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Affiliation(s)
- Joseph Giorgio
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA; School of Psychological Sciences, College of Engineering, Science, and the Environment, University of Newcastle, Newcastle, NSW 2305, Australia.
| | - Jenna N Adams
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA 92697, USA
| | - Anne Maass
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg 39120, Germany
| | - William J Jagust
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Michael Breakspear
- School of Psychological Sciences, College of Engineering, Science, and the Environment, University of Newcastle, Newcastle, NSW 2305, Australia; Discipline of Psychiatry, College of Health, Medicine, and Wellbeing, The University of Newcastle, Newcastle, NSW 2305, Australia
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Chase HW. A novel technique for delineating the effect of variation in the learning rate on the neural correlates of reward prediction errors in model-based fMRI. Front Psychol 2023; 14:1211528. [PMID: 38187436 PMCID: PMC10768009 DOI: 10.3389/fpsyg.2023.1211528] [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: 06/19/2023] [Accepted: 11/28/2023] [Indexed: 01/09/2024] Open
Abstract
Introduction Computational models play an increasingly important role in describing variation in neural activation in human neuroimaging experiments, including evaluating individual differences in the context of psychiatric neuroimaging. In particular, reinforcement learning (RL) techniques have been widely adopted to examine neural responses to reward prediction errors and stimulus or action values, and how these might vary as a function of clinical status. However, there is a lack of consensus around the importance of the precision of free parameter estimation for these methods, particularly with regard to the learning rate. In the present study, I introduce a novel technique which may be used within a general linear model (GLM) to model the effect of mis-estimation of the learning rate on reward prediction error (RPE)-related neural responses. Methods Simulations employed a simple RL algorithm, which was used to generate hypothetical neural activations that would be expected to be observed in functional magnetic resonance imaging (fMRI) studies of RL. Similar RL models were incorporated within a GLM-based analysis method including derivatives, with individual differences in the resulting GLM-derived beta parameters being evaluated with respect to the free parameters of the RL model or being submitted to other validation analyses. Results Initial simulations demonstrated that the conventional approach to fitting RL models to RPE responses is more likely to reflect individual differences in a reinforcement efficacy construct (lambda) rather than learning rate (alpha). The proposed method, adding a derivative regressor to the GLM, provides a second regressor which reflects the learning rate. Validation analyses were performed including examining another comparable method which yielded highly similar results, and a demonstration of sensitivity of the method in presence of fMRI-like noise. Conclusion Overall, the findings underscore the importance of the lambda parameter for interpreting individual differences in RPE-coupled neural activity, and validate a novel neural metric of the modulation of such activity by individual differences in the learning rate. The method is expected to find application in understanding aberrant reinforcement learning across different psychiatric patient groups including major depression and substance use disorder.
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Affiliation(s)
- Henry W. Chase
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
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Novitskaya Y, Dümpelmann M, Schulze-Bonhage A. Physiological and pathological neuronal connectivity in the living human brain based on intracranial EEG signals: the current state of research. FRONTIERS IN NETWORK PHYSIOLOGY 2023; 3:1297345. [PMID: 38107334 PMCID: PMC10723837 DOI: 10.3389/fnetp.2023.1297345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 11/17/2023] [Indexed: 12/19/2023]
Abstract
Over the past decades, studies of human brain networks have received growing attention as the assessment and modelling of connectivity in the brain is a topic of high impact with potential application in the understanding of human brain organization under both physiological as well as various pathological conditions. Under specific diagnostic settings, human neuronal signal can be obtained from intracranial EEG (iEEG) recording in epilepsy patients that allows gaining insight into the functional organisation of living human brain. There are two approaches to assess brain connectivity in the iEEG-based signal: evaluation of spontaneous neuronal oscillations during ongoing physiological and pathological brain activity, and analysis of the electrophysiological cortico-cortical neuronal responses, evoked by single pulse electrical stimulation (SPES). Both methods have their own advantages and limitations. The paper outlines available methodological approaches and provides an overview of current findings in studies of physiological and pathological human brain networks, based on intracranial EEG recordings.
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Affiliation(s)
- Yulia Novitskaya
- Epilepsy Center, Department of Neurosurgery, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Matthias Dümpelmann
- Epilepsy Center, Department of Neurosurgery, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Department of Microsystems Engineering (IMTEK), University of Freiburg, Freiburg, Germany
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Department of Neurosurgery, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Center for Basics in NeuroModulation, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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Antono JE, Dang S, Auksztulewicz R, Pooresmaeili A. Distinct Patterns of Connectivity between Brain Regions Underlie the Intra-Modal and Cross-Modal Value-Driven Modulations of the Visual Cortex. J Neurosci 2023; 43:7361-7375. [PMID: 37684031 PMCID: PMC10621764 DOI: 10.1523/jneurosci.0355-23.2023] [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: 02/24/2023] [Revised: 07/30/2023] [Accepted: 08/26/2023] [Indexed: 09/10/2023] Open
Abstract
Past reward associations may be signaled from different sensory modalities; however, it remains unclear how different types of reward-associated stimuli modulate sensory perception. In this human fMRI study (female and male participants), a visual target was simultaneously presented with either an intra- (visual) or a cross-modal (auditory) cue that was previously associated with rewards. We hypothesized that, depending on the sensory modality of the cues, distinct neural mechanisms underlie the value-driven modulation of visual processing. Using a multivariate approach, we confirmed that reward-associated cues enhanced the target representation in early visual areas and identified the brain valuation regions. Then, using an effective connectivity analysis, we tested three possible patterns of connectivity that could underlie the modulation of the visual cortex: a direct pathway from the frontal valuation areas to the visual areas, a mediated pathway through the attention-related areas, and a mediated pathway that additionally involved sensory association areas. We found evidence for the third model demonstrating that the reward-related information in both sensory modalities is communicated across the valuation and attention-related brain regions. Additionally, the superior temporal areas were recruited when reward was cued cross-modally. The strongest dissociation between the intra- and cross-modal reward-driven effects was observed at the level of the feedforward and feedback connections of the visual cortex estimated from the winning model. These results suggest that, in the presence of previously rewarded stimuli from different sensory modalities, a combination of domain-general and domain-specific mechanisms are recruited across the brain to adjust the visual perception.SIGNIFICANCE STATEMENT Reward has a profound effect on perception, but it is not known whether shared or disparate mechanisms underlie the reward-driven effects across sensory modalities. In this human fMRI study, we examined the reward-driven modulation of the visual cortex by visual (intra-modal) and auditory (cross-modal) reward-associated cues. Using a model-based approach to identify the most plausible pattern of inter-regional effective connectivity, we found that higher-order areas involved in the valuation and attentional processing were recruited by both types of rewards. However, the pattern of connectivity between these areas and the early visual cortex was distinct between the intra- and cross-modal rewards. This evidence suggests that, to effectively adapt to the environment, reward signals may recruit both domain-general and domain-specific mechanisms.
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Affiliation(s)
- Jessica Emily Antono
- Perception and Cognition Lab, European Neuroscience Institute Goettingen-A Joint Initiative of the University Medical Center Goettingen and the Max-Planck-Society, Germany, Goettingen, 37077, Germany
| | - Shilpa Dang
- Perception and Cognition Lab, European Neuroscience Institute Goettingen-A Joint Initiative of the University Medical Center Goettingen and the Max-Planck-Society, Germany, Goettingen, 37077, Germany
- School of Artificial Intelligence and Data Science, Indian Institute of Technology Jodhpur, Karwar, Jodhpur 342030, India
| | - Ryszard Auksztulewicz
- Center for Cognitive Neuroscience Berlin, Free University Berlin, Berlin, 14195, Germany
| | - Arezoo Pooresmaeili
- Perception and Cognition Lab, European Neuroscience Institute Goettingen-A Joint Initiative of the University Medical Center Goettingen and the Max-Planck-Society, Germany, Goettingen, 37077, Germany
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Schott BH, Soch J, Kizilirmak JM, Schütze H, Assmann A, Maass A, Ziegler G, Sauvage M, Richter A. Inhibitory temporo-parietal effective connectivity is associated with explicit memory performance in older adults. iScience 2023; 26:107765. [PMID: 37744028 PMCID: PMC10514462 DOI: 10.1016/j.isci.2023.107765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 06/30/2023] [Accepted: 08/25/2023] [Indexed: 09/26/2023] Open
Abstract
Successful explicit memory encoding is associated with inferior temporal activations and medial parietal deactivations, which are attenuated in aging. Here we used dynamic causal modeling (DCM) of functional magnetic resonance imaging data to elucidate effective connectivity patterns between hippocampus, parahippocampal place area (PPA), and precuneus during encoding of novel visual scenes. In 117 young adults, DCM revealed pronounced activating input from the PPA to the hippocampus and inhibitory connectivity from the PPA to the precuneus during novelty processing, with both being enhanced during successful encoding. This pattern could be replicated in two cohorts (N = 141 and 148) of young and older adults. In both cohorts, older adults selectively exhibited attenuated inhibitory PPA-precuneus connectivity, which correlated negatively with memory performance. Our results provide insight into the network dynamics underlying explicit memory encoding and suggest that age-related differences in memory-related network activity are, at least partly, attributable to altered temporo-parietal neocortical connectivity.
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Affiliation(s)
- Björn H. Schott
- German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany
- Leibniz Institute for Neurobiology (LIN), Magdeburg, Germany
- Center for Behavioral Brain Sciences (CBBS), Magdeburg, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany
| | - Joram Soch
- German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany
- Bernstein Center for Computational Neuroscience (BCCN), Berlin, Germany
| | - Jasmin M. Kizilirmak
- German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany
- Neurodidactics and NeuroLab, Institute for Psychology, University of Hildesheim, Hildesheim, Germany
| | - Hartmut Schütze
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Otto von Guericke University, Medical Faculty, Magdeburg, Germany
| | - Anne Assmann
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Otto von Guericke University, Medical Faculty, Magdeburg, Germany
| | - Anne Maass
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Gabriel Ziegler
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Otto von Guericke University, Medical Faculty, Magdeburg, Germany
| | | | - Anni Richter
- Leibniz Institute for Neurobiology (LIN), Magdeburg, Germany
- German Center for Mental Health (DZPG), Magdeburg, Germany
- Center for Intervention and Research on adaptive and Maladaptive Brain Circuits Underlying Mental Health (C-I-R-C) Jena-Magdeburg-Halle, Magdeburg, Germany
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Jin J, Zeidman P, Friston KJ, Kotov R. Inferring Trajectories of Psychotic Disorders Using Dynamic Causal Modeling. COMPUTATIONAL PSYCHIATRY (CAMBRIDGE, MASS.) 2023; 7:60-75. [PMID: 38774642 PMCID: PMC11104383 DOI: 10.5334/cpsy.94] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 06/27/2023] [Indexed: 05/24/2024]
Abstract
Introduction Illness course plays a crucial role in delineating psychiatric disorders. However, existing nosologies consider only its most basic features (e.g., symptom sequence, duration). We developed a Dynamic Causal Model (DCM) that characterizes course patterns more fully using dense timeseries data. This foundational study introduces the new modeling approach and evaluates its validity using empirical and simulated data. Methods A three-level DCM was constructed to model how latent dynamics produce symptoms of depression, mania, and psychosis. This model was fit to symptom scores of nine patients collected prospectively over four years, following first hospitalization. Simulated subjects based on these empirical data were used to evaluate model parameters at the subject-level. At the group-level, we tested the accuracy with which the DCM can estimate the latent course patterns using Parametric Empirical Bayes (PEB) and leave-one-out cross-validation. Results Analyses of empirical data showed that DCM accurately captured symptom trajectories for all nine subjects. Simulation results showed that parameters could be estimated accurately (correlations between generative and estimated parameters >= 0.76). Moreover, the model could distinguish different latent course patterns, with PEB correctly assigning simulated patients for eight of nine course patterns. When testing any pair of two specific course patterns using leave-one-out cross-validation, 30 out of 36 pairs showed a moderate or high out-of-samples correlation between the true group-membership and the estimated group-membership values. Conclusion DCM has been widely used in neuroscience to infer latent neuronal processes from neuroimaging data. Our findings highlight the potential of adopting this methodology for modeling symptom trajectories to explicate nosologic entities, temporal patterns that define them, and facilitate personalized treatment.
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Affiliation(s)
- Jingwen Jin
- Department of Psychology, The University of Hong Kong, Hong Kong SAR, China
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, China
| | - Peter Zeidman
- Wellcome Centre for Human Neuroimaging, University College London, UK
| | - Karl J. Friston
- Wellcome Centre for Human Neuroimaging, University College London, UK
| | - Roman Kotov
- Department of Psychiatry, Renaissance School of Medicine, Stony Brook University, USA
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Vargas G, Araya D, Sepulveda P, Rodriguez-Fernandez M, Friston KJ, Sitaram R, El-Deredy W. Self-regulation learning as active inference: dynamic causal modeling of an fMRI neurofeedback task. Front Neurosci 2023; 17:1212549. [PMID: 37650101 PMCID: PMC10465165 DOI: 10.3389/fnins.2023.1212549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 07/12/2023] [Indexed: 09/01/2023] Open
Abstract
Introduction Learning to self-regulate brain activity by neurofeedback has been shown to lead to changes in the brain and behavior, with beneficial clinical and non-clinical outcomes. Neurofeedback uses a brain-computer interface to guide participants to change some feature of their brain activity. However, the neural mechanism of self-regulation learning remains unclear, with only 50% of the participants succeeding in achieving it. To bridge this knowledge gap, our study delves into the neural mechanisms of self-regulation learning via neurofeedback and investigates the brain processes associated with successful brain self-regulation. Methods We study the neural underpinnings of self-regulation learning by employing dynamical causal modeling (DCM) in conjunction with real-time functional MRI data. The study involved a cohort of 18 participants undergoing neurofeedback training targeting the supplementary motor area. A critical focus was the comparison between top-down hierarchical connectivity models proposed by Active Inference and alternative bottom-up connectivity models like reinforcement learning. Results Our analysis revealed a crucial distinction in brain connectivity patterns between successful and non-successful learners. Particularly, successful learners evinced a significantly stronger top-down effective connectivity towards the target area implicated in self-regulation. This heightened top-down network engagement closely resembles the patterns observed in goal-oriented and cognitive control studies, shedding light on the intricate cognitive processes intertwined with self-regulation learning. Discussion The findings from our investigation underscore the significance of cognitive mechanisms in the process of self-regulation learning through neurofeedback. The observed stronger top-down effective connectivity in successful learners indicates the involvement of hierarchical cognitive control, which aligns with the tenets of Active Inference. This study contributes to a deeper understanding of the neural dynamics behind successful self-regulation learning and provides insights into the potential cognitive architecture underpinning this process.
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Affiliation(s)
- Gabriela Vargas
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Catolica de Chile, Santiago, Chile
- Brain Dynamics Lab, Universidad de Valparaíso, Valparaiso, Chile
| | - David Araya
- Brain Dynamics Lab, Universidad de Valparaíso, Valparaiso, Chile
- Instituto de Tecnología para la Innovación en Salud y Bienestar, Facultad de Ingeniería, Universidad Andrés Bello, Viña del Mar, Chile
| | - Pradyumna Sepulveda
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom
- Department of Psychiatry, Columbia University, New York, NY, United States
| | - Maria Rodriguez-Fernandez
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - Karl J. Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
| | | | - Wael El-Deredy
- Brain Dynamics Lab, Universidad de Valparaíso, Valparaiso, Chile
- Valencian Graduate School and Research Network of Artificial Intelligence, Valencia, Spain
- Department of Electronic Engineering, School of Engineering, Universitat de València, Valencia, Spain
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Uludağ K. Physiological modeling of the BOLD signal and implications for effective connectivity: A primer. Neuroimage 2023; 277:120249. [PMID: 37356779 DOI: 10.1016/j.neuroimage.2023.120249] [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: 02/15/2023] [Revised: 06/12/2023] [Accepted: 06/23/2023] [Indexed: 06/27/2023] Open
Abstract
In this primer, I provide an overview of the physiological processes that contribute to the observed BOLD signal (i.e., the generative biophysical model), including their time course properties within the framework of the physiologically-informed dynamic causal modeling (P-DCM). The BOLD signal is primarily determined by the change in paramagnetic deoxygenated hemoglobin, which results from combination of changes in oxygen metabolism, and cerebral blood flow and volume. Specifically, the physiological origin of the so-called BOLD signal "transients" will be discussed, including the initial overshoot, steady-state activation and the post-stimulus undershoot. I argue that incorrect physiological assumptions in the generative model of the BOLD signal can lead to incorrect inferences pertaining to both local neuronal activity and effective connectivity between brain regions. In addition, I introduce the recent laminar BOLD signal model, which extends P-DCM to cortical depths-resolved BOLD signals, allowing for laminar neuronal activity to be determined using high-resolution fMRI data.
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Affiliation(s)
- Kâmil Uludağ
- Krembil Brain Institute, University Health Network Toronto, Ontario, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Center for Neuroscience Imaging Research, Institute for Basic Science & Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea.
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12
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Tolley N, Rodrigues PLC, Gramfort A, Jones S. Methods and considerations for estimating parameters in biophysically detailed neural models with simulation based inference. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.17.537118. [PMID: 37131818 PMCID: PMC10153146 DOI: 10.1101/2023.04.17.537118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Biophysically detailed neural models are a powerful technique to study neural dynamics in health and disease with a growing number of established and openly available models. A major challenge in the use of such models is that parameter inference is an inherently difficult and unsolved problem. Identifying unique parameter distributions that can account for observed neural dynamics, and differences across experimental conditions, is essential to their meaningful use. Recently, simulation based inference (SBI) has been proposed as an approach to perform Bayesian inference to estimate parameters in detailed neural models. SBI overcomes the challenge of not having access to a likelihood function, which has severely limited inference methods in such models, by leveraging advances in deep learning to perform density estimation. While the substantial methodological advancements offered by SBI are promising, their use in large scale biophysically detailed models is challenging and methods for doing so have not been established, particularly when inferring parameters that can account for time series waveforms. We provide guidelines and considerations on how SBI can be applied to estimate time series waveforms in biophysically detailed neural models starting with a simplified example and extending to specific applications to common MEG/EEG waveforms using the the large scale neural modeling framework of the Human Neocortical Neurosolver. Specifically, we describe how to estimate and compare results from example oscillatory and event related potential simulations. We also describe how diagnostics can be used to assess the quality and uniqueness of the posterior estimates. The methods described provide a principled foundation to guide future applications of SBI in a wide variety of applications that use detailed models to study neural dynamics.
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Affiliation(s)
- Nicholas Tolley
- Department of Neuroscience, Brown University, Providence, RI, United States
| | | | | | - Stephanie Jones
- Department of Neuroscience, Brown University, Providence, RI, United States
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Paz-Linares D, Gonzalez-Moreira E, Areces-Gonzalez A, Wang Y, Li M, Vega-Hernandez M, Wang Q, Bosch-Bayard J, Bringas-Vega ML, Martinez-Montes E, Valdes-Sosa MJ, Valdes-Sosa PA. Minimizing the distortions in electrophysiological source imaging of cortical oscillatory activity via Spectral Structured Sparse Bayesian Learning. Front Neurosci 2023; 17:978527. [PMID: 37008210 PMCID: PMC10050575 DOI: 10.3389/fnins.2023.978527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 02/07/2023] [Indexed: 03/17/2023] Open
Abstract
Oscillatory processes at all spatial scales and on all frequencies underpin brain function. Electrophysiological Source Imaging (ESI) is the data-driven brain imaging modality that provides the inverse solutions to the source processes of the EEG, MEG, or ECoG data. This study aimed to carry out an ESI of the source cross-spectrum while controlling common distortions of the estimates. As with all ESI-related problems under realistic settings, the main obstacle we faced is a severely ill-conditioned and high-dimensional inverse problem. Therefore, we opted for Bayesian inverse solutions that posited a priori probabilities on the source process. Indeed, rigorously specifying both the likelihoods and a priori probabilities of the problem leads to the proper Bayesian inverse problem of cross-spectral matrices. These inverse solutions are our formal definition for cross-spectral ESI (cESI), which requires a priori of the source cross-spectrum to counter the severe ill-condition and high-dimensionality of matrices. However, inverse solutions for this problem were NP-hard to tackle or approximated within iterations with bad-conditioned matrices in the standard ESI setup. We introduce cESI with a joint a priori probability upon the source cross-spectrum to avoid these problems. cESI inverse solutions are low-dimensional ones for the set of random vector instances and not random matrices. We achieved cESI inverse solutions through the variational approximations via our Spectral Structured Sparse Bayesian Learning (ssSBL) algorithm https://github.com/CCC-members/Spectral-Structured-Sparse-Bayesian-Learning. We compared low-density EEG (10-20 system) ssSBL inverse solutions with reference cESIs for two experiments: (a) high-density MEG that were used to simulate EEG and (b) high-density macaque ECoG that were recorded simultaneously with EEG. The ssSBL resulted in two orders of magnitude with less distortion than the state-of-the-art ESI methods. Our cESI toolbox, including the ssSBL method, is available at https://github.com/CCC-members/BC-VARETA_Toolbox.
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Affiliation(s)
- Deirel Paz-Linares
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba
| | - Eduardo Gonzalez-Moreira
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States
- Research Unit for Neurodevelopment, Institute of Neurobiology, Autonomous University of Mexico, Querétaro, Mexico
- Faculty of Electrical Engineering, Central University “Marta Abreu” of Las Villas, Santa Clara, Cuba
| | - Ariosky Areces-Gonzalez
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Faculty of Technical Sciences, University of Pinar del Río “Hermanos Saiz Montes de Oca”, Pinar del Rio, Cuba
| | - Ying Wang
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Min Li
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | | | - Qing Wang
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- McGill Centre for Integrative Neurosciences MCIN, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Ludmer Centre for Mental Health, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Jorge Bosch-Bayard
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- McGill Centre for Integrative Neurosciences MCIN, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Ludmer Centre for Mental Health, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Maria L. Bringas-Vega
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba
| | | | - Mitchel J. Valdes-Sosa
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba
| | - Pedro A. Valdes-Sosa
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba
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14
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Human cerebellum and corticocerebellar connections involved in emotional memory enhancement. Proc Natl Acad Sci U S A 2022; 119:e2204900119. [PMID: 36191198 PMCID: PMC9564100 DOI: 10.1073/pnas.2204900119] [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] [Indexed: 01/27/2023] Open
Abstract
Enhanced memory for emotional stimuli is crucial for survival, but it may also contribute to the development and maintenance of fear-related disorders in case of highly aversive experiences. This large-scale functional brain imaging study identifies the cerebellum and cerebellar–cerebral connections involved in the phenomenon of superior memory for emotionally arousing visual information. These findings expand knowledge on the role of the cerebellum in complex cognitive and emotional processes and may be relevant for the understanding of psychiatric disorders with aberrant emotional circuitry, such as posttraumatic stress disorder or autism spectrum disorder. Emotional information is better remembered than neutral information. Extensive evidence indicates that the amygdala and its interactions with other cerebral regions play an important role in the memory-enhancing effect of emotional arousal. While the cerebellum has been found to be involved in fear conditioning, its role in emotional enhancement of episodic memory is less clear. To address this issue, we used a whole-brain functional MRI approach in 1,418 healthy participants. First, we identified clusters significantly activated during enhanced memory encoding of negative and positive emotional pictures. In addition to the well-known emotional memory–related cerebral regions, we identified a cluster in the cerebellum. We then used dynamic causal modeling and identified several cerebellar connections with increased connection strength corresponding to enhanced emotional memory, including one to a cluster covering the amygdala and hippocampus, and bidirectional connections with a cluster covering the anterior cingulate cortex. The present findings indicate that the cerebellum is an integral part of a network involved in emotional enhancement of episodic memory.
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15
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Zhao Y, Boley M, Pelentritou A, Karoly PJ, Freestone DR, Liu Y, Muthukumaraswamy S, Woods W, Liley D, Kuhlmann L. Space-time resolved inference-based neurophysiological process imaging: application to resting-state alpha rhythm. Neuroimage 2022; 263:119592. [PMID: 36031185 DOI: 10.1016/j.neuroimage.2022.119592] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 07/28/2022] [Accepted: 08/24/2022] [Indexed: 11/28/2022] Open
Abstract
Neural processes are complex and difficult to image. This paper presents a new space-time resolved brain imaging framework, called Neurophysiological Process Imaging (NPI), that identifies neurophysiological processes within cerebral cortex at the macroscopic scale. By fitting uncoupled neural mass models to each electromagnetic source time-series using a novel nonlinear inference method, population averaged membrane potentials and synaptic connection strengths are efficiently and accurately inferred and imaged across the whole cerebral cortex at a resolution afforded by source imaging. The efficiency of the framework enables return of the augmented source imaging results overnight using high performance computing. This suggests it can be used as a practical and novel imaging tool. To demonstrate the framework, it has been applied to resting-state magnetoencephalographic source estimates. The results suggest that endogenous inputs to cingulate, occipital, and inferior frontal cortex are essential modulators of resting-state alpha power. Moreover, endogenous input and inhibitory and excitatory neural populations play varied roles in mediating alpha power in different resting-state sub-networks. The framework can be applied to arbitrary neural mass models and has broad applicability to image neural processes in different brain states.
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Affiliation(s)
- Yun Zhao
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia
| | - Mario Boley
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia
| | - Andria Pelentritou
- Swinburne University of Technology, Hawthorn, Australia; Laboratoire de Recherche en Neuroimagerie (LREN), University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Philippa J Karoly
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Australia; Department of Medicine-St Vincent's Hospital, The University of Melbourne, Parkville, Australia
| | - Dean R Freestone
- Department of Medicine-St Vincent's Hospital, The University of Melbourne, Parkville, Australia; Seer Medical Pty Ltd, Melbourne, Australia
| | - Yueyang Liu
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia
| | | | - William Woods
- School of Health Sciences, Swinburne University of Technology, Hawthorn, Australia
| | - David Liley
- Swinburne University of Technology, Hawthorn, Australia; Department of Medicine-St Vincent's Hospital, The University of Melbourne, Parkville, Australia; School of Health Sciences, Swinburne University of Technology, Hawthorn, Australia
| | - Levin Kuhlmann
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia; Department of Medicine-St Vincent's Hospital, The University of Melbourne, Parkville, Australia.
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16
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Li G, Yap PT. From descriptive connectome to mechanistic connectome: Generative modeling in functional magnetic resonance imaging analysis. Front Hum Neurosci 2022; 16:940842. [PMID: 36061504 PMCID: PMC9428697 DOI: 10.3389/fnhum.2022.940842] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 07/28/2022] [Indexed: 01/28/2023] Open
Abstract
As a newly emerging field, connectomics has greatly advanced our understanding of the wiring diagram and organizational features of the human brain. Generative modeling-based connectome analysis, in particular, plays a vital role in deciphering the neural mechanisms of cognitive functions in health and dysfunction in diseases. Here we review the foundation and development of major generative modeling approaches for functional magnetic resonance imaging (fMRI) and survey their applications to cognitive or clinical neuroscience problems. We argue that conventional structural and functional connectivity (FC) analysis alone is not sufficient to reveal the complex circuit interactions underlying observed neuroimaging data and should be supplemented with generative modeling-based effective connectivity and simulation, a fruitful practice that we term "mechanistic connectome." The transformation from descriptive connectome to mechanistic connectome will open up promising avenues to gain mechanistic insights into the delicate operating principles of the human brain and their potential impairments in diseases, which facilitates the development of effective personalized treatments to curb neurological and psychiatric disorders.
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Affiliation(s)
- Guoshi Li
- Department of Radiology, University of North Carolina, Chapel Hill, NC, United States,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, United States,*Correspondence: Guoshi Li,
| | - Pew-Thian Yap
- Department of Radiology, University of North Carolina, Chapel Hill, NC, United States,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, United States
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17
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Ramezanian-Panahi M, Abrevaya G, Gagnon-Audet JC, Voleti V, Rish I, Dumas G. Generative Models of Brain Dynamics. Front Artif Intell 2022; 5:807406. [PMID: 35910192 PMCID: PMC9335006 DOI: 10.3389/frai.2022.807406] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 06/10/2022] [Indexed: 01/28/2023] Open
Abstract
This review article gives a high-level overview of the approaches across different scales of organization and levels of abstraction. The studies covered in this paper include fundamental models in computational neuroscience, nonlinear dynamics, data-driven methods, as well as emergent practices. While not all of these models span the intersection of neuroscience, AI, and system dynamics, all of them do or can work in tandem as generative models, which, as we argue, provide superior properties for the analysis of neuroscientific data. We discuss the limitations and unique dynamical traits of brain data and the complementary need for hypothesis- and data-driven modeling. By way of conclusion, we present several hybrid generative models from recent literature in scientific machine learning, which can be efficiently deployed to yield interpretable models of neural dynamics.
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Affiliation(s)
| | - Germán Abrevaya
- Mila-Quebec AI Institute, Montréal, QC, Canada
- Departamento de Física, Facultad de Ciencias Exactas y Naturales, Instituto de Física de Buenos Aires (IFIBA), CONICET, Universidad de Buenos Aires, Buenos Aires, Argentina
| | | | - Vikram Voleti
- Mila-Quebec AI Institute, Montréal, QC, Canada
- Université de Montréal, Montréal, QC, Canada
| | - Irina Rish
- Mila-Quebec AI Institute, Montréal, QC, Canada
- Université de Montréal, Montréal, QC, Canada
| | - Guillaume Dumas
- Mila-Quebec AI Institute, Montréal, QC, Canada
- Université de Montréal, Montréal, QC, Canada
- Department of Psychiatry, CHU Sainte-Justine Research Center, Mila-Quebec AI Institute, Université de Montréal, Montréal, QC, Canada
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18
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Abstract
Recent advances in imaging and tracing technology provide increasingly detailed reconstructions of brain connectomes. Concomitant analytic advances enable rigorous identification and quantification of functionally important features of brain network architecture. Null models are a flexible tool to statistically benchmark the presence or magnitude of features of interest, by selectively preserving specific architectural properties of brain networks while systematically randomizing others. Here we describe the logic, implementation and interpretation of null models of connectomes. We introduce randomization and generative approaches to constructing null networks, and outline a taxonomy of network methods for statistical inference. We highlight the spectrum of null models - from liberal models that control few network properties, to conservative models that recapitulate multiple properties of empirical networks - that allow us to operationalize and test detailed hypotheses about the structure and function of brain networks. We review emerging scenarios for the application of null models in network neuroscience, including for spatially embedded networks, annotated networks and correlation-derived networks. Finally, we consider the limits of null models, as well as outstanding questions for the field.
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19
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Morris RW, Dezfouli A, Griffiths KR, Le Pelley ME, Balleine BW. The Neural Bases of Action-Outcome Learning in Humans. J Neurosci 2022; 42:3636-3647. [PMID: 35296548 PMCID: PMC9053851 DOI: 10.1523/jneurosci.1079-21.2022] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 02/17/2022] [Accepted: 02/22/2022] [Indexed: 11/21/2022] Open
Abstract
From an associative perspective the acquisition of new goal-directed actions requires the encoding of specific action-outcome (AO) associations and, therefore, sensitivity to the validity of an action as a predictor of a specific outcome relative to other events. Although competitive architectures have been proposed within associative learning theory to achieve this kind of identity-based selection, whether and how these architectures are implemented by the brain is still a matter of conjecture. To investigate this issue, we trained human participants to encode various AO associations while undergoing functional neuroimaging (fMRI). We then degraded one AO contingency by increasing the probability of the outcome in the absence of its associated action while keeping other AO contingencies intact. We found that this treatment selectively reduced performance of the degraded action. Furthermore, when a signal predicted the unpaired outcome, performance of the action was restored, suggesting that the degradation effect reflects competition between the action and the context for prediction of the specific outcome. We used a Kalman filter to model the contribution of different causal variables to AO learning and found that activity in the medial prefrontal cortex (mPFC) and the dorsal anterior cingulate cortex (dACC) tracked changes in the association of the action and context, respectively, with regard to the specific outcome. Furthermore, we found the mPFC participated in a network with the striatum and posterior parietal cortex to segregate the influence of the various competing predictors to establish specific AO associations.SIGNIFICANCE STATEMENT Humans and other animals learn the consequences of their actions, allowing them to control their environment in a goal-directed manner. Nevertheless, it is unknown how we parse environmental causes from the effects of our own actions to establish these specific action-outcome (AO) relationships. Here, we show that the brain learns the causal structure of the environment by segregating the unique influence of actions from other causes in the medial prefrontal and anterior cingulate cortices and, through a network of structures, including the caudate nucleus and posterior parietal cortex, establishes the distinct causal relationships from which specific AO associations are formed.
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Affiliation(s)
- Richard W Morris
- Centre for Translational Data Science, University of Sydney, Sydney, NSW 2006, Australia
| | - Amir Dezfouli
- Data61, Commonwealth Scientific and Industrial Research Organisation, Sydney, NSW 2015, Australia
| | - Kristi R Griffiths
- Brain Dynamics Centre, Westmead Institute for Medical Research, University of Sydney, Sydney, NSW 2145, Australia
| | - Mike E Le Pelley
- School of Psychology, University of New South Wales Sydney, Sydney, NSW 2052, Australia
| | - Bernard W Balleine
- School of Psychology, University of New South Wales Sydney, Sydney, NSW 2052, Australia
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20
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Aponte EA, Yao Y, Raman S, Frässle S, Heinzle J, Penny WD, Stephan KE. An introduction to thermodynamic integration and application to dynamic causal models. Cogn Neurodyn 2022; 16:1-15. [PMID: 35116083 PMCID: PMC8807794 DOI: 10.1007/s11571-021-09696-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 06/03/2021] [Accepted: 07/01/2021] [Indexed: 11/25/2022] Open
Abstract
In generative modeling of neuroimaging data, such as dynamic causal modeling (DCM), one typically considers several alternative models, either to determine the most plausible explanation for observed data (Bayesian model selection) or to account for model uncertainty (Bayesian model averaging). Both procedures rest on estimates of the model evidence, a principled trade-off between model accuracy and complexity. In the context of DCM, the log evidence is usually approximated using variational Bayes. Although this approach is highly efficient, it makes distributional assumptions and is vulnerable to local extrema. This paper introduces the use of thermodynamic integration (TI) for Bayesian model selection and averaging in the context of DCM. TI is based on Markov chain Monte Carlo sampling which is asymptotically exact but orders of magnitude slower than variational Bayes. In this paper, we explain the theoretical foundations of TI, covering key concepts such as the free energy and its origins in statistical physics. Our aim is to convey an in-depth understanding of the method starting from its historical origin in statistical physics. In addition, we demonstrate the practical application of TI via a series of examples which serve to guide the user in applying this method. Furthermore, these examples demonstrate that, given an efficient implementation and hardware capable of parallel processing, the challenge of high computational demand can be overcome successfully. The TI implementation presented in this paper is freely available as part of the open source software TAPAS.
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Affiliation(s)
- Eduardo A. Aponte
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Present Address: Roche Innovation Center, Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Yu Yao
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Sudhir Raman
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Stefan Frässle
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Jakob Heinzle
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Will D. Penny
- School of Psychology, University of East Anglia, Norwich, UK
| | - Klaas E. Stephan
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Max Planck Institute for Metabolism Research, Cologne, Germany
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21
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Altered effective connectivity within the cingulo-frontal-parietal cognitive attention networks in chronic low back pain: a dynamic causal modeling study. Brain Imaging Behav 2022; 16:1516-1527. [PMID: 35080703 DOI: 10.1007/s11682-021-00623-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/16/2021] [Indexed: 11/02/2022]
Abstract
Dysfunction of the cingulo-frontal-parietal (CFP) cognitive attention network has been associated with the pathophysiology of chronic low back pain (cLBP). However, the direction of information processing within this network remains largely unknown. We aimed to study the effective connectivity among the CFP regions in 36 cLBP patients and 36 healthy controls by dynamic causal modeling (DCM). Both the resting-state and task-related (Multi-Source Interference Task, MSIT) functional magnetic resonance imaging (fMRI) data were collected and analyzed. The relationship between the effective connectivity of the CFP regions and clinical measures was also examined. Our results suggested that cLBP had significantly altered resting-state effective connectivity of the prefrontal cortex (PFC)-to-mid-cingulate cortex (MCC) (increased) and MCC-to-left superior parietal cortex (LPC) (decreased) pathways as compared with healthy controls. MSIT-related DCM suggested that the interference task could significantly increase the effective connectivity of the right superior parietal cortex (RPC)-to-PFC and RPC-to-MCC pathways in cLBP than that in healthy controls. The control task could significantly decrease the effective connectivity of the MCC-to-LPC and MCC-to-RPC pathways in cLBP than that in healthy controls. The endogenous connectivity of the PFC-to-RPC pathway in cLBP was significantly lower than that in healthy controls. No significant correlations were found between the effective connectivity within CFP networks and pain/depression scores in patients with cLBP. In summary, our findings suggested altered effective connectivity in multiple pathways within the CFP network in both resting-state and performing attention-demanding tasks in patients with cLBP, which extends our understanding of attention dysfunction in patients with cLBP.
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22
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Safron A, Klimaj V, Hipólito I. On the Importance of Being Flexible: Dynamic Brain Networks and Their Potential Functional Significances. Front Syst Neurosci 2022; 15:688424. [PMID: 35126062 PMCID: PMC8814434 DOI: 10.3389/fnsys.2021.688424] [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: 03/30/2021] [Accepted: 11/10/2021] [Indexed: 12/14/2022] Open
Abstract
In this theoretical review, we begin by discussing brains and minds from a dynamical systems perspective, and then go on to describe methods for characterizing the flexibility of dynamic networks. We discuss how varying degrees and kinds of flexibility may be adaptive (or maladaptive) in different contexts, specifically focusing on measures related to either more disjoint or cohesive dynamics. While disjointed flexibility may be useful for assessing neural entropy, cohesive flexibility may potentially serve as a proxy for self-organized criticality as a fundamental property enabling adaptive behavior in complex systems. Particular attention is given to recent studies in which flexibility methods have been used to investigate neurological and cognitive maturation, as well as the breakdown of conscious processing under varying levels of anesthesia. We further discuss how these findings and methods might be contextualized within the Free Energy Principle with respect to the fundamentals of brain organization and biological functioning more generally, and describe potential methodological advances from this paradigm. Finally, with relevance to computational psychiatry, we propose a research program for obtaining a better understanding of ways that dynamic networks may relate to different forms of psychological flexibility, which may be the single most important factor for ensuring human flourishing.
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Affiliation(s)
- Adam Safron
- Center for Psychedelic and Consciousness Research, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Kinsey Institute, Indiana University, Bloomington, IN, United States
- Cognitive Science Program, Indiana University, Bloomington, IN, United States
| | - Victoria Klimaj
- Cognitive Science Program, Indiana University, Bloomington, IN, United States
- Complex Networks and Systems, Informatics Department, Indiana University, Bloomington, IN, United States
| | - Inês Hipólito
- Department of Philosophy, Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
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23
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Hipólito I. Cognition Without Neural Representation: Dynamics of a Complex System. Front Psychol 2022; 12:643276. [PMID: 35095629 PMCID: PMC8789682 DOI: 10.3389/fpsyg.2021.643276] [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: 12/17/2020] [Accepted: 10/31/2021] [Indexed: 12/26/2022] Open
Abstract
This paper proposes an account of neurocognitive activity without leveraging the notion of neural representation. Neural representation is a concept that results from assuming that the properties of the models used in computational cognitive neuroscience (e.g., information, representation, etc.) must literally exist the system being modelled (e.g., the brain). Computational models are important tools to test a theory about how the collected data (e.g., behavioural or neuroimaging) has been generated. While the usefulness of computational models is unquestionable, it does not follow that neurocognitive activity should literally entail the properties construed in the model (e.g., information, representation). While this is an assumption present in computationalist accounts, it is not held across the board in neuroscience. In the last section, the paper offers a dynamical account of neurocognitive activity with Dynamical Causal Modelling (DCM) that combines dynamical systems theory (DST) mathematical formalisms with the theoretical contextualisation provided by Embodied and Enactive Cognitive Science (EECS).
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Affiliation(s)
- Inês Hipólito
- Berlin School of Mind and Brain, Institut für Philosophie, Humboldt-Universität zu Berlin, Berlin, Germany
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24
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Balderston NL, Beer JC, Seok D, Makhoul W, Deng ZD, Girelli T, Teferi M, Smyk N, Jaskir M, Oathes DJ, Sheline YI. Proof of concept study to develop a novel connectivity-based electric-field modelling approach for individualized targeting of transcranial magnetic stimulation treatment. Neuropsychopharmacology 2022; 47:588-598. [PMID: 34321597 PMCID: PMC8674270 DOI: 10.1038/s41386-021-01110-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 07/09/2021] [Accepted: 07/13/2021] [Indexed: 11/08/2022]
Abstract
Resting state functional connectivity (rsFC) offers promise for individualizing stimulation targets for transcranial magnetic stimulation (TMS) treatments. However, current targeting approaches do not account for non-focal TMS effects or large-scale connectivity patterns. To overcome these limitations, we propose a novel targeting optimization approach that combines whole-brain rsFC and electric-field (e-field) modelling to identify single-subject, symptom-specific TMS targets. In this proof of concept study, we recruited 91 anxious misery (AM) patients and 25 controls. We measured depression symptoms (MADRS/HAMD) and recorded rsFC. We used a PCA regression to predict symptoms from rsFC and estimate the parameter vector, for input into our e-field augmented model. We modeled 17 left dlPFC and 7 M1 sites using 24 equally spaced coil orientations. We computed single-subject predicted ΔMADRS/HAMD scores for each site/orientation using the e-field augmented model, which comprises a linear combination of the following elementwise products (1) the estimated connectivity/symptom coefficients, (2) a vectorized e-field model for site/orientation, (3) rsFC matrix, scaled by a proportionality constant. In AM patients, our connectivity-based model predicted a significant decrease depression for sites near BA9, but not M1 for coil orientations perpendicular to the cortical gyrus. In control subjects, no site/orientation combination showed a significant predicted change. These results corroborate previous work suggesting the efficacy of left dlPFC stimulation for depression treatment, and predict better outcomes with individualized targeting. They also suggest that our novel connectivity-based e-field modelling approach may effectively identify potential TMS treatment responders and individualize TMS targeting to maximize the therapeutic impact.
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Affiliation(s)
- Nicholas L Balderston
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA.
| | - Joanne C Beer
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Darsol Seok
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Walid Makhoul
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Zhi-De Deng
- Noninvasive Neuromodulation Unit, Experimental Therapeutics & Pathophysiology Branch, National Institute of Mental Health, Bethesda, MD, USA
| | - Tommaso Girelli
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Marta Teferi
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Nathan Smyk
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Marc Jaskir
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Desmond J Oathes
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Yvette I Sheline
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
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25
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Sladky R, Hahn A, Karl IL, Geissberger N, Kranz GS, Tik M, Kraus C, Pfabigan DM, Gartus A, Lanzenberger R, Lamm C, Windischberger C. Dynamic Causal Modeling of the Prefrontal/Amygdala Network During Processing of Emotional Faces. Brain Connect 2021; 12:670-682. [PMID: 34605671 DOI: 10.1089/brain.2021.0073] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Introduction: The importance of the amygdala/medial orbitofrontal cortex (OFC) network during processing of emotional stimuli, emotional faces in particular, is well established. This premise is supported by converging evidence from animal models, human neuroanatomical results, and neuroimaging studies. However, there is missing evidence from human brain connectivity studies that the OFC and no other prefrontal brain areas such as the dorsolateral prefrontal cortex (DLPFC) or ventrolateral prefrontal cortex (VLPFC) are responsible for amygdala regulation in the functional context of emotional face stimuli. Methods: Dynamic causal modeling of ultrahigh-field functional magnetic resonance imaging data acquired at 7 Tesla in 38 healthy subjects and a well-established paradigm for emotional face processing were used to assess the central role of the OFC to provide empirical validation for the assumed network architecture. Results: Using Bayesian model selection, it is demonstrated that indeed the OFC, and not the VLPFC and the DLPFC, downregulates amygdala activation during the emotion discrimination task. In addition, Bayesian model averaging group results were rigorously tested using bootstrapping, further corroborating these findings and providing an estimator for robustness and optimal sample sizes. Discussion: While it is true that VLPFC and DLPFC are relevant for the processing of emotional faces and are connected to the OFC, the OFC appears to be a central hub for the prefrontal/amygdala interaction.
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Affiliation(s)
- Ronald Sladky
- MR Center of Excellence, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria.,Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Inga-Lisa Karl
- Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Nicole Geissberger
- MR Center of Excellence, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Georg S Kranz
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria.,Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China.,The State Key Laboratory of Brain and Cognitive Science, The University of Hong Kong, Hong Kong, China
| | - Martin Tik
- MR Center of Excellence, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Christoph Kraus
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Daniela M Pfabigan
- Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Andreas Gartus
- Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Claus Lamm
- Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Christian Windischberger
- MR Center of Excellence, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
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26
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Bellot E, Kauffmann L, Coizet V, Meoni S, Moro E, Dojat M. Effective connectivity in subcortical visual structures in de novo Patients with Parkinson's Disease. Neuroimage Clin 2021; 33:102906. [PMID: 34891045 PMCID: PMC8670854 DOI: 10.1016/j.nicl.2021.102906] [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: 08/04/2021] [Revised: 10/26/2021] [Accepted: 12/01/2021] [Indexed: 10/19/2022]
Abstract
BACKGROUND Parkinson's disease (PD) manifests with the appearance of non-motor symptoms before motor symptoms onset. Among these, dysfunctioning visual structures have recently been reported to occur at early disease stages. OBJECTIVE This study addresses effective connectivity in the visual network of PD patients. METHODS Using functional MRI and dynamic causal modeling analysis, we evaluated the connectivity between the superior colliculus, the lateral geniculate nucleus and the primary visual area V1 in de novo untreated PD patients (n = 22). A subset of the PD patients (n = 8) was longitudinally assessed two times at two months and at six months after starting dopaminergic treatment. Results were compared to those of age-matched healthy controls (n = 22). RESULTS Our results indicate that the superior colliculus drives cerebral activity for luminance contrast processing both in healthy controls and untreated PD patients. The same effective connectivity was observed with neuromodulatory differences in terms of neuronal dynamic interactions. Our main findings were that the modulation induced by luminance contrast changes of the superior colliculus connectivity (self-connectivity and connectivity to the lateral geniculate nucleus) was inhibited in PD patients (effect of contrast: p = 0.79 and p = 0.77 respectively). The introduction of dopaminergic medication in a subset (n = 8) of the PD patients failed to restore the effective connectivity modulation observed in the healthy controls. INTERPRETATION The deficits in luminance contrast processing in PD was associated with a deficiency in connectivity adjustment from the superior colliculus to the lateral geniculate nucleus and to V1. No differences in cerebral blood flow were observed between controls and PD patients suggesting that the deficiency was at the neuronal level. Administration of a dopaminergic treatment over six months was not able to normalize the observed alterations in inter-regional coupling. These findings highlight the presence of early dysfunctions in primary visual areas, which might be used as early markers of the disease.
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Affiliation(s)
- Emmanuelle Bellot
- University Grenoble Alpes, Inserm U1216, Centre Hospitalier Universitaire de Grenoble, Grenoble Institute of Neurosciences, Grenoble, France
| | - Louise Kauffmann
- Laboratory of Psychology and Neurocognition, CNRS UMR 5105, Grenoble, France
| | - Véronique Coizet
- University Grenoble Alpes, Inserm U1216, Centre Hospitalier Universitaire de Grenoble, Grenoble Institute of Neurosciences, Grenoble, France
| | - Sara Meoni
- University Grenoble Alpes, Inserm U1216, Centre Hospitalier Universitaire de Grenoble, Grenoble Institute of Neurosciences, Grenoble, France; Laboratory of Psychology and Neurocognition, CNRS UMR 5105, Grenoble, France; Movement Disorders Unit, Division of Neurology, CHU Grenoble Alpes, Grenoble, France
| | - Elena Moro
- University Grenoble Alpes, Inserm U1216, Centre Hospitalier Universitaire de Grenoble, Grenoble Institute of Neurosciences, Grenoble, France; Laboratory of Psychology and Neurocognition, CNRS UMR 5105, Grenoble, France
| | - Michel Dojat
- University Grenoble Alpes, Inserm U1216, Centre Hospitalier Universitaire de Grenoble, Grenoble Institute of Neurosciences, Grenoble, France.
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27
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Wang M, Zheng H, Zhou W, Jiang Q, Dong G. Persistent dependent behaviour is accompanied by dynamic switching between the ventral and dorsal striatal connections in internet gaming disorder. Addict Biol 2021; 26:e13046. [PMID: 33957705 DOI: 10.1111/adb.13046] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 02/11/2021] [Accepted: 04/12/2021] [Indexed: 01/02/2023]
Abstract
Cross-sectional studies have suggested that functional heterogeneity within the striatum in individuals with addictive behaviours may involve the transition from ventral to dorsal partitions; however, due to limitations of the cross-sectional design, whether the contribution of this transition to addiction was confused by individual differences remains unclear, especially for internet gaming disorder (IGD). Longitudinal functional magnetic resonance imaging (fMRI) data from 22 IGD subjects and 18 healthy controls were collected at baseline and more than 6 months later. We examined the connectivity features of subregions within the striatum between these two scans. Based on the results, we further performed dynamic causal modelling to explore the directional effect between regions and used these key features for data classification in machine learning to test the replicability of the results. Compared with controls, IGD subjects exhibited decreased functional connectivity between the left dorsal striatum (putamen) and the left insula, whereas connectivity between the right ventral striatum (nucleus accumbens [Nacc]) and the left insula was relatively stable over time. An inhibitory effective connectivity from the left putamen to the right Nacc was found in IGD subjects during the follow-up scan. Using the above features, the classification accuracy of the training model developed with the follow-up was better than that of the model based on the initial scan. Persistent IGD status was accompanied by a switch in the locus of control within the striatum, which provided new insights into association between IGD and drug addiction.
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Affiliation(s)
- Min Wang
- Center for Cognition and Brain Disorders The Affiliated Hospital of Hangzhou Normal University Hangzhou China
| | - Hui Zheng
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center Shanghai Jiaotong University School of Medicine Shanghai China
| | - Weiran Zhou
- Center for Cognition and Brain Disorders The Affiliated Hospital of Hangzhou Normal University Hangzhou China
| | - Qing Jiang
- Department of Psychology Zhejiang Normal University Jinhua China
| | - Guang‐Heng Dong
- Center for Cognition and Brain Disorders The Affiliated Hospital of Hangzhou Normal University Hangzhou China
- Institute of Psychological Science Hangzhou Normal University Hangzhou China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Hangzhou China
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28
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Wang D, Liang S. Dynamic Causal Modeling on the Identification of Interacting Networks in the Brain: A Systematic Review. IEEE Trans Neural Syst Rehabil Eng 2021; 29:2299-2311. [PMID: 34714747 DOI: 10.1109/tnsre.2021.3123964] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Dynamic causal modeling (DCM) has long been used to characterize effective connectivity within networks of distributed neuronal responses. Previous reviews have highlighted the understanding of the conceptual basis behind DCM and its variants from different aspects. However, no detailed summary or classification research on the task-related effective connectivity of various brain regions has been made formally available so far, and there is also a lack of application analysis of DCM for hemodynamic and electrophysiological measurements. This review aims to analyze the effective connectivity of different brain regions using DCM for different measurement data. We found that, in general, most studies focused on the networks between different cortical regions, and the research on the networks between other deep subcortical nuclei or between them and the cerebral cortex are receiving increasing attention, but far from the same scale. Our analysis also reveals a clear bias towards some task types. Based on these results, we identify and discuss several promising research directions that may help the community to attain a clear understanding of the brain network interactions under different tasks.
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29
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Pereira I, Frässle S, Heinzle J, Schöbi D, Do CT, Gruber M, Stephan KE. Conductance-based dynamic causal modeling: A mathematical review of its application to cross-power spectral densities. Neuroimage 2021; 245:118662. [PMID: 34687862 DOI: 10.1016/j.neuroimage.2021.118662] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 10/12/2021] [Accepted: 10/17/2021] [Indexed: 11/19/2022] Open
Abstract
Dynamic Causal Modeling (DCM) is a Bayesian framework for inferring on hidden (latent) neuronal states, based on measurements of brain activity. Since its introduction in 2003 for functional magnetic resonance imaging data, DCM has been extended to electrophysiological data, and several variants have been developed. Their biophysically motivated formulations make these models promising candidates for providing a mechanistic understanding of human brain dynamics, both in health and disease. However, due to their complexity and reliance on concepts from several fields, fully understanding the mathematical and conceptual basis behind certain variants of DCM can be challenging. At the same time, a solid theoretical knowledge of the models is crucial to avoid pitfalls in the application of these models and interpretation of their results. In this paper, we focus on one of the most advanced formulations of DCM, i.e. conductance-based DCM for cross-spectral densities, whose components are described across multiple technical papers. The aim of the present article is to provide an accessible exposition of the mathematical background, together with an illustration of the model's behavior. To this end, we include step-by-step derivations of the model equations, point to important aspects in the software implementation of those models, and use simulations to provide an intuitive understanding of the type of responses that can be generated and the role that specific parameters play in the model. Furthermore, all code utilized for our simulations is made publicly available alongside the manuscript to allow readers an easy hands-on experience with conductance-based DCM.
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Affiliation(s)
- Inês Pereira
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Wilfriedstrasse 6, Zurich 8032, Switzerland.
| | - Stefan Frässle
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Wilfriedstrasse 6, Zurich 8032, Switzerland
| | - Jakob Heinzle
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Wilfriedstrasse 6, Zurich 8032, Switzerland
| | - Dario Schöbi
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Wilfriedstrasse 6, Zurich 8032, Switzerland
| | - Cao Tri Do
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Wilfriedstrasse 6, Zurich 8032, Switzerland
| | - Moritz Gruber
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Wilfriedstrasse 6, Zurich 8032, Switzerland
| | - Klaas E Stephan
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Wilfriedstrasse 6, Zurich 8032, Switzerland; Max Planck Institute for Metabolism Research, Cologne, Germany
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30
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Muzik O, Baajour S, Chowdury A, Diwadkar VA. Effective connectivity of brain networks controlling human thermoregulation. Brain Struct Funct 2021; 227:299-312. [PMID: 34605996 DOI: 10.1007/s00429-021-02401-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 09/26/2021] [Indexed: 12/01/2022]
Abstract
Homeostatic centers in the mammalian brainstem are critical in responding to thermal challenges. These centers play a prominent role in human thermoregulation, but humans also respond to thermal challenges through behavior modification. Behavioral modifications are presumably sub served by interactions between the brainstem and interoceptive, cognitive and affective elements in human brain networks. Prior evidence suggests that interoceptive regions such as the insula, and cognitive/affective regions such as the orbitofrontal cortex and anterior cingulate cortex are crucial. Here we used dynamic causal modeling (DCM) to discover likely generative network architectures and estimate changes in the effective connectivity between nodes in a hierarchically organized thermoregulatory network (homeostatic-interoceptive-cognitive/affective). fMRI data were acquired while participants (N = 20) were subjected to a controlled whole body thermal challenge that alternatingly evoked sympathetic and parasympathetic responses. Using a competitive modeling framework (ten competing modeling architectures), we demonstrated that sympathetic responses (evoked by whole-body cooling) resulted in more complex network interactions along two ascending pathways: (i) homeostatic interoceptive and (ii) homeostatic cognitive/affective. Analyses of estimated connectivity coefficients demonstrated that sympathetic responses evoked greater network connectivity in key pathways compared to parasympathetic responses. These results reveal putative mechanisms by which human thermoregulatory networks evince a high degree of contextual sensitivity to thermoregulatory challenges. The patterns of the discovered interactions also reveal how information propagation from homeostatic regions to both interoceptive and cognitive/affective regions sub serves the behavioral repertoire that is an important aspect of thermoregulatory defense in humans.
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Affiliation(s)
- Otto Muzik
- Departments of Pediatrics, Wayne State University School of Medicine, Detroit, MI, 48201, USA. .,Departments of Radiology, Wayne State University School of Medicine, Detroit, MI, 48201, USA. .,KCI PET Center, Children's Hospital of Michigan, 3901 Beaubien Blvd, Detroit, MI, 48201, USA.
| | - Shahira Baajour
- Departments of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, 48201, USA
| | - Asadur Chowdury
- Departments of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, 48201, USA
| | - Vaibhav A Diwadkar
- Departments of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, 48201, USA
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31
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Hippmann B, Tzvi E, Göttlich M, Weiblen R, Münte TF, Jessen S. Effective connectivity underlying reward-based executive control. Hum Brain Mapp 2021; 42:4555-4567. [PMID: 34173997 PMCID: PMC8410574 DOI: 10.1002/hbm.25564] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 06/01/2021] [Accepted: 06/02/2021] [Indexed: 12/18/2022] Open
Abstract
Motivational influences on cognitive control play an important role in shaping human behavior. Cognitive facilitation through motivators such as prospective reward or punishment is thought to depend on regions from the dopaminergic mesocortical network, primarily the ventral tegmental area (VTA), inferior frontal junction (IFJ), and anterior cingulate cortex (ACC). However, how interactions between these regions relate to motivated control remains elusive. In the present functional magnetic resonance imaging study, we used dynamic causal modeling (DCM) to investigate effective connectivity between left IFJ, ACC, and VTA in a task-switching paradigm comprising three distinct motivational conditions (prospective monetary reward or punishment and a control condition). We found that while prospective punishment significantly facilitated switching between tasks on a behavioral level, interactions between IFJ, ACC, and VTA were characterized by modulations through prospective reward but not punishment. Our DCM results show that IFJ and VTA modulate ACC activity in parallel rather than by interaction to serve task demands in reward-based cognitive control. Our findings further demonstrate that prospective reward and punishment differentially affect neural control mechanisms to initiate decision-making.
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Affiliation(s)
| | - Elinor Tzvi
- Department of NeurologyUniversity of LeipzigLeipzigGermany
| | | | - Ronja Weiblen
- Department of NeurologyUniversity of LübeckLübeckGermany
| | | | - Sarah Jessen
- Department of NeurologyUniversity of LübeckLübeckGermany
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32
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Sabahi Y, Setarehdan SK, Nasrabadi AM. Dynamic causal modeling of evoked responses during emergency braking: an ERP study. Cogn Neurodyn 2021; 16:353-363. [PMID: 35401862 PMCID: PMC8934904 DOI: 10.1007/s11571-021-09716-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 07/12/2021] [Accepted: 09/02/2021] [Indexed: 11/28/2022] Open
Abstract
Describing a neural activity map based on observed responses in emergency situations, especially during driving, is a challenging issue that would help design driver-assistant devices and a better understanding of the brain. This study aimed to investigate which regions were involved during emergency braking, measuring the interactions and strength of the connections and describing coupling among these brain regions by dynamic causal modeling (DCM) parameters that we extracted from event-related potential signals, which were then estimated based on emergency braking data with visual stimulation. The data were reanalyzed from a simulator study, which was designed to create emergency situations for participants during a simple driving task. The experimental protocol includes driving a virtual reality car, and the subjects were exposed to emergency situations in a simulator system, while electroencephalogram, electro-oculogram, and electromyogram signals were recorded. In this research, locations of active brain regions in montreal neurological institute coordinates from event-related responses were identified using multiple sparse priors method, in which sensor space was allocated to resource space. Source localization results revealed nine active regions. After applying DCM on data, a proposed model during emergency braking for all people was obtained. The braking response time was defined based on the first noticeable (above noise-level) braking pedal deflection after an induced braking maneuver. The result revealed a significant difference in response time between subjects who have the lateral connection between visual cortex, visual processing, and detecting objects areas have shorter response time (p-value = 0.05) than the subjects who do not have such connections.
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Affiliation(s)
- Yasaman Sabahi
- Department of Biomedical Engineering-Bioelectric, Faculty of Medical Science and Technologies, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Seyed Kamaledin Setarehdan
- Control and Intelligent Processing Centre of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Ali Motie Nasrabadi
- Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
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33
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Csukly G, Szabó Á, Polgár P, Farkas K, Gyebnár G, Kozák LR, Stefanics G. Fronto-thalamic structural and effective connectivity and delusions in schizophrenia: a combined DTI/DCM study. Psychol Med 2021; 51:2083-2093. [PMID: 32329710 PMCID: PMC8426148 DOI: 10.1017/s0033291720000859] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Revised: 02/07/2020] [Accepted: 03/20/2020] [Indexed: 12/11/2022]
Abstract
BACKGROUND Schizophrenia (SZ) is a complex disorder characterized by a range of behavioral and cognitive symptoms as well as structural and functional alterations in multiple cortical and subcortical structures. SZ is associated with reduced functional network connectivity involving core regions such as the anterior cingulate cortex (ACC) and the thalamus. However, little is known whether effective coupling, the directed influence of one structure over the other, is altered during rest in the ACC-thalamus network. METHODS We collected resting-state fMRI and diffusion-weighted MRI data from 18 patients and 20 healthy controls. We analyzed fronto-thalamic effective connectivity using dynamic causal modeling for cross-spectral densities in a network consisting of the ACC and the left and right medio-dorsal thalamic regions. We studied structural connectivity using fractional anisotropy (FA). RESULTS We found decreased coupling strength from the right thalamus to the ACC and from the right thalamus to the left thalamus, as well as increased inhibitory intrinsic connectivity in the right thalamus in patients relative to controls. ACC-to-left thalamus coupling strength correlated with the Positive and Negative Syndrome Scale (PANSS) total positive syndrome score and with delusion score. Whole-brain structural analysis revealed several tracts with reduced FA in patients, with a maximum decrease in white matter tracts containing fronto-thalamic and cingulo-thalamic fibers. CONCLUSIONS We found altered effective and structural connectivity within the ACC-thalamus network in SZ. Our results indicate that ACC-thalamus network activity at rest is characterized by reduced thalamus-to-ACC coupling. We suggest that positive symptoms may arise as a consequence of compensatory measures to imbalanced fronto-thalamic coupling.
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Affiliation(s)
- Gábor Csukly
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Ádám Szabó
- Magnetic Resonance Research Centre, Semmelweis University, Budapest, Hungary
| | - Patrícia Polgár
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Kinga Farkas
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Gyula Gyebnár
- Magnetic Resonance Research Centre, Semmelweis University, Budapest, Hungary
| | - Lajos R. Kozák
- Magnetic Resonance Research Centre, Semmelweis University, Budapest, Hungary
| | - Gábor Stefanics
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Wilfriedstrasse 6, 8032, Zurich, Switzerland
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34
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Unraveling brain interactions in vision: The example of crowding. Neuroimage 2021; 240:118390. [PMID: 34271157 DOI: 10.1016/j.neuroimage.2021.118390] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 07/09/2021] [Accepted: 07/12/2021] [Indexed: 11/22/2022] Open
Abstract
Crowding, the impairment of target discrimination in clutter, is the standard situation in vision. Traditionally, crowding is explained with (feedforward) models, in which only neighboring elements interact, leading to a "bottleneck" at the earliest stages of vision. It is with this implicit prior that most functional magnetic resonance imaging (fMRI) studies approach the identification of the "neural locus" of crowding, searching for the earliest visual area in which the blood-oxygenation-level-dependent (BOLD) signal is suppressed under crowded conditions. Using this classic approach, we replicated previous findings of crowding-related BOLD suppression starting in V2 and increasing up the visual hierarchy. Surprisingly, under conditions of uncrowding, in which adding flankers improves performance, the BOLD signal was further suppressed. This suggests an important role for top-down connections, which is in line with global models of crowding. To discriminate between various possible models, we used dynamic causal modeling (DCM). We show that recurrent interactions between all visual areas, including higher-level areas like V4 and the lateral occipital complex (LOC), are crucial in crowding and uncrowding. Our results explain the discrepancies in previous findings: in a recurrent visual hierarchy, the crowding effect can theoretically be detected at any stage. Beyond crowding, we demonstrate the need for models like DCM to understand the complex recurrent processing which most likely underlies human perception in general.
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35
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Li G, Liu Y, Zheng Y, Wu Y, Li D, Liang X, Chen Y, Cui Y, Yap PT, Qiu S, Zhang H, Shen D. Multiscale neural modeling of resting-state fMRI reveals executive-limbic malfunction as a core mechanism in major depressive disorder. Neuroimage Clin 2021; 31:102758. [PMID: 34284335 PMCID: PMC8313604 DOI: 10.1016/j.nicl.2021.102758] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 06/30/2021] [Accepted: 07/03/2021] [Indexed: 11/15/2022]
Abstract
Major depressive disorder (MDD) represents a grand challenge to human health and society, but the underlying pathophysiological mechanisms remain elusive. Previous neuroimaging studies have suggested that MDD is associated with abnormal interactions and dynamics in two major neural systems including the default mode - salience (DMN-SAL) network and the executive - limbic (EXE-LIM) network, but it is not clear which network plays a central role and which network plays a subordinate role in MDD pathophysiology. To address this question, we refined a newly developed Multiscale Neural Model Inversion (MNMI) framework and applied it to test whether MDD is more affected by impaired circuit interactions in the DMN-SAL network or the EXE-LIM network. The model estimates the directed connection strengths between different neural populations both within and between brain regions based on resting-state fMRI data collected from normal healthy subjects and patients with MDD. Results show that MDD is primarily characterized by abnormal circuit interactions in the EXE-LIM network rather than the DMN-SAL network. Specifically, we observe reduced frontoparietal effective connectivity that potentially contributes to hypoactivity in the dorsolateral prefrontal cortex (dlPFC), and decreased intrinsic inhibition combined with increased excitation from the superior parietal cortex (SPC) that potentially lead to amygdala hyperactivity, together resulting in activation imbalance in the PFC-amygdala circuit that pervades in MDD. Moreover, the model reveals reduced PFC-to-hippocampus excitation but decreased SPC-to-thalamus inhibition in MDD population that potentially lead to hypoactivity in the hippocampus and hyperactivity in the thalamus, consistent with previous experimental data. Overall, our findings provide strong support for the long-standing limbic-cortical dysregulation model in major depression but also offer novel insights into the multiscale pathophysiology of this debilitating disease.
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Affiliation(s)
- Guoshi Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Yujie Liu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC USA; The First School of Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China; Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China; Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
| | - Yanting Zheng
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC USA; The First School of Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China; Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China; Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
| | - Ye Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Danian Li
- Cerebropathy Center, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Xinyu Liang
- The First School of Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Yaoping Chen
- The First School of Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China; Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Ying Cui
- Cerebropathy Center, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Pew-Thian Yap
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Shijun Qiu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
| | - Han Zhang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC USA.
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC USA.
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Dong GH, Wang M, Zheng H, Wang Z, Du X, Potenza MN. Disrupted prefrontal regulation of striatum-related craving in Internet gaming disorder revealed by dynamic causal modeling: results from a cue-reactivity task. Psychol Med 2021; 51:1549-1561. [PMID: 32102722 DOI: 10.1017/s003329172000032x] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
BACKGROUND Studies of Internet gaming disorder (IGD) suggest an imbalanced relationship between cognitive control and reward processing in people with IGD. However, it remains unclear how these two systems interact with each other, and whether they could serve as neurobiological markers for IGD. METHODS Fifty IGD subjects and matched individuals with recreational game use (RGU) were selected and compared when they were performing a cue-craving task. Regions of interests [anterior cingulate cortex (ACC), lentiform nucleus] were selected based on the comparison between brain responses to gaming-related cues and neutral cues. Directional connectivities among these brain regions were determined using Bayesian estimation. We additionally examined the posterior cingulate cortex (PCC) in a separate analysis based on data implicating the PCC in craving in addiction. RESULTS During fixed-connectivity analyses, IGD subjects showed blunted ACC-to-lentiform and lentiform-to-ACC connectivity relative to RGU subjects, especially in the left hemisphere. When facing gaming cues, IGD subjects trended toward lower left-hemispheric modulatory effects in ACC-to-lentiform connectivity than RGU subjects. Self-reported cue-related craving prior to scanning correlated inversely with left-hemispheric modulatory effects in ACC-to-lentiform connectivity. CONCLUSIONS The results suggesting that prefrontal-to-lentiform connectivity is impaired in IGD provides a possible neurobiological mechanism for difficulties in controlling gaming-cue-elicited cravings. Reduced connectivity ACC-lentiform connectivity may be a useful neurobiological marker for IGD.
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Affiliation(s)
- Guang-Heng Dong
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, PR China
| | - Min Wang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
| | - Hui Zheng
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, PR China
| | - Ziliang Wang
- School of Psychology, Beijing Normal University, Beijing10010, PR China
| | - Xiaoxia Du
- Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, PR China
| | - Marc N Potenza
- Connecticut Mental Health Center, New Haven, CT, USA
- Connecticut Council on Problem Gambling, Wethersfield, CT, USA
- Department of Psychiatry, Department of Neurobiology, and Child Study Center, Yale University School of Medicine, New Haven, CT, USA
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Kuhnke P, Kiefer M, Hartwigsen G. Task-Dependent Functional and Effective Connectivity during Conceptual Processing. Cereb Cortex 2021; 31:3475-3493. [PMID: 33677479 PMCID: PMC8196308 DOI: 10.1093/cercor/bhab026] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 01/21/2021] [Accepted: 01/22/2021] [Indexed: 11/13/2022] Open
Abstract
Conceptual knowledge is central to cognition. Previous neuroimaging research indicates that conceptual processing involves both modality-specific perceptual-motor areas and multimodal convergence zones. For example, our previous functional magnetic resonance imaging (fMRI) study revealed that both modality-specific and multimodal regions respond to sound and action features of concepts in a task-dependent fashion (Kuhnke P, Kiefer M, Hartwigsen G. 2020b. Task-dependent recruitment of modality-specific and multimodal regions during conceptual processing. Cereb Cortex. 30:3938–3959.). However, it remains unknown whether and how modality-specific and multimodal areas interact during conceptual tasks. Here, we asked 1) whether multimodal and modality-specific areas are functionally coupled during conceptual processing, 2) whether their coupling depends on the task, 3) whether information flows top-down, bottom-up or both, and 4) whether their coupling is behaviorally relevant. We combined psychophysiological interaction analyses with dynamic causal modeling on the fMRI data of our previous study. We found that functional coupling between multimodal and modality-specific areas strongly depended on the task, involved both top-down and bottom-up information flow, and predicted conceptually guided behavior. Notably, we also found coupling between different modality-specific areas and between different multimodal areas. These results suggest that functional coupling in the conceptual system is extensive, reciprocal, task-dependent, and behaviorally relevant. We propose a new model of the conceptual system that incorporates task-dependent functional interactions between modality-specific and multimodal areas.
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Affiliation(s)
- Philipp Kuhnke
- Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04103, Germany
| | - Markus Kiefer
- Department of Psychiatry, Ulm University, Ulm 89081, Germany
| | - Gesa Hartwigsen
- Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04103, Germany
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Frässle S, Aponte EA, Bollmann S, Brodersen KH, Do CT, Harrison OK, Harrison SJ, Heinzle J, Iglesias S, Kasper L, Lomakina EI, Mathys C, Müller-Schrader M, Pereira I, Petzschner FH, Raman S, Schöbi D, Toussaint B, Weber LA, Yao Y, Stephan KE. TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry. Front Psychiatry 2021; 12:680811. [PMID: 34149484 PMCID: PMC8206497 DOI: 10.3389/fpsyt.2021.680811] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 05/10/2021] [Indexed: 12/26/2022] Open
Abstract
Psychiatry faces fundamental challenges with regard to mechanistically guided differential diagnosis, as well as prediction of clinical trajectories and treatment response of individual patients. This has motivated the genesis of two closely intertwined fields: (i) Translational Neuromodeling (TN), which develops "computational assays" for inferring patient-specific disease processes from neuroimaging, electrophysiological, and behavioral data; and (ii) Computational Psychiatry (CP), with the goal of incorporating computational assays into clinical decision making in everyday practice. In order to serve as objective and reliable tools for clinical routine, computational assays require end-to-end pipelines from raw data (input) to clinically useful information (output). While these are yet to be established in clinical practice, individual components of this general end-to-end pipeline are being developed and made openly available for community use. In this paper, we present the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) software package, an open-source collection of building blocks for computational assays in psychiatry. Collectively, the tools in TAPAS presently cover several important aspects of the desired end-to-end pipeline, including: (i) tailored experimental designs and optimization of measurement strategy prior to data acquisition, (ii) quality control during data acquisition, and (iii) artifact correction, statistical inference, and clinical application after data acquisition. Here, we review the different tools within TAPAS and illustrate how these may help provide a deeper understanding of neural and cognitive mechanisms of disease, with the ultimate goal of establishing automatized pipelines for predictions about individual patients. We hope that the openly available tools in TAPAS will contribute to the further development of TN/CP and facilitate the translation of advances in computational neuroscience into clinically relevant computational assays.
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Affiliation(s)
- Stefan Frässle
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Eduardo A. Aponte
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Saskia Bollmann
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
- Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD, Australia
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
- Department of Radiology, Harvard Medical School, Charlestown, MA, United States
| | - Kay H. Brodersen
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Department of Computer Science, ETH Zurich, Zurich, Switzerland
| | - Cao T. Do
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Olivia K. Harrison
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- School of Pharmacy, University of Otago, Dunedin, New Zealand
| | - Samuel J. Harrison
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Jakob Heinzle
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Sandra Iglesias
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Lars Kasper
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Techna Institute, University Health Network, Toronto, ON, Canada
| | - Ekaterina I. Lomakina
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Department of Computer Science, ETH Zurich, Zurich, Switzerland
| | - Christoph Mathys
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Interacting Minds Center, Aarhus University, Aarhus, Denmark
| | - Matthias Müller-Schrader
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Inês Pereira
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Frederike H. Petzschner
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Sudhir Raman
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Dario Schöbi
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Birte Toussaint
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Lilian A. Weber
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Yu Yao
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Klaas E. Stephan
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
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Wein S, Malloni WM, Tomé AM, Frank SM, Henze GI, Wüst S, Greenlee MW, Lang EW. A graph neural network framework for causal inference in brain networks. Sci Rep 2021; 11:8061. [PMID: 33850173 PMCID: PMC8044149 DOI: 10.1038/s41598-021-87411-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 03/26/2021] [Indexed: 02/02/2023] Open
Abstract
A central question in neuroscience is how self-organizing dynamic interactions in the brain emerge on their relatively static structural backbone. Due to the complexity of spatial and temporal dependencies between different brain areas, fully comprehending the interplay between structure and function is still challenging and an area of intense research. In this paper we present a graph neural network (GNN) framework, to describe functional interactions based on the structural anatomical layout. A GNN allows us to process graph-structured spatio-temporal signals, providing a possibility to combine structural information derived from diffusion tensor imaging (DTI) with temporal neural activity profiles, like that observed in functional magnetic resonance imaging (fMRI). Moreover, dynamic interactions between different brain regions discovered by this data-driven approach can provide a multi-modal measure of causal connectivity strength. We assess the proposed model's accuracy by evaluating its capabilities to replicate empirically observed neural activation profiles, and compare the performance to those of a vector auto regression (VAR), like that typically used in Granger causality. We show that GNNs are able to capture long-term dependencies in data and also computationally scale up to the analysis of large-scale networks. Finally we confirm that features learned by a GNN can generalize across MRI scanner types and acquisition protocols, by demonstrating that the performance on small datasets can be improved by pre-training the GNN on data from an earlier study. We conclude that the proposed multi-modal GNN framework can provide a novel perspective on the structure-function relationship in the brain. Accordingly this approach appears to be promising for the characterization of the information flow in brain networks.
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Affiliation(s)
- S Wein
- CIML, Biophysics, University of Regensburg, 93040, Regensburg, Germany.
- Experimental Psychology, University of Regensburg, 93040, Regensburg, Germany.
| | - W M Malloni
- Experimental Psychology, University of Regensburg, 93040, Regensburg, Germany
| | - A M Tomé
- IEETA/DETI, Universidade de Aveiro, 3810-193, Aveiro, Portugal
| | - S M Frank
- Department of Cognitive, Linguistic,and Psychological Sciences, Brown University, Providence, RI, 02912, USA
| | - G -I Henze
- Experimental Psychology, University of Regensburg, 93040, Regensburg, Germany
| | - S Wüst
- Experimental Psychology, University of Regensburg, 93040, Regensburg, Germany
| | - M W Greenlee
- Experimental Psychology, University of Regensburg, 93040, Regensburg, Germany
| | - E W Lang
- CIML, Biophysics, University of Regensburg, 93040, Regensburg, Germany
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40
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Martínez-Cañada P, Ness TV, Einevoll GT, Fellin T, Panzeri S. Computation of the electroencephalogram (EEG) from network models of point neurons. PLoS Comput Biol 2021; 17:e1008893. [PMID: 33798190 PMCID: PMC8046357 DOI: 10.1371/journal.pcbi.1008893] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 04/14/2021] [Accepted: 03/18/2021] [Indexed: 12/28/2022] Open
Abstract
The electroencephalogram (EEG) is a major tool for non-invasively studying brain function and dysfunction. Comparing experimentally recorded EEGs with neural network models is important to better interpret EEGs in terms of neural mechanisms. Most current neural network models use networks of simple point neurons. They capture important properties of cortical dynamics, and are numerically or analytically tractable. However, point neurons cannot generate an EEG, as EEG generation requires spatially separated transmembrane currents. Here, we explored how to compute an accurate approximation of a rodent's EEG with quantities defined in point-neuron network models. We constructed different approximations (or proxies) of the EEG signal that can be computed from networks of leaky integrate-and-fire (LIF) point neurons, such as firing rates, membrane potentials, and combinations of synaptic currents. We then evaluated how well each proxy reconstructed a ground-truth EEG obtained when the synaptic currents of the LIF model network were fed into a three-dimensional network model of multicompartmental neurons with realistic morphologies. Proxies based on linear combinations of AMPA and GABA currents performed better than proxies based on firing rates or membrane potentials. A new class of proxies, based on an optimized linear combination of time-shifted AMPA and GABA currents, provided the most accurate estimate of the EEG over a wide range of network states. The new linear proxies explained 85-95% of the variance of the ground-truth EEG for a wide range of network configurations including different cell morphologies, distributions of presynaptic inputs, positions of the recording electrode, and spatial extensions of the network. Non-linear EEG proxies using a convolutional neural network (CNN) on synaptic currents increased proxy performance by a further 2-8%. Our proxies can be used to easily calculate a biologically realistic EEG signal directly from point-neuron simulations thus facilitating a quantitative comparison between computational models and experimental EEG recordings.
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Affiliation(s)
- Pablo Martínez-Cañada
- Neural Coding Laboratory, Istituto Italiano di Tecnologia, Genova, Italy
- Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
- Optical Approaches to Brain Function Laboratory, Istituto Italiano di Tecnologia, Genova, Italy
| | - Torbjørn V. Ness
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
| | - Gaute T. Einevoll
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
- Department of Physics, University of Oslo, Oslo, Norway
| | - Tommaso Fellin
- Neural Coding Laboratory, Istituto Italiano di Tecnologia, Genova, Italy
- Optical Approaches to Brain Function Laboratory, Istituto Italiano di Tecnologia, Genova, Italy
| | - Stefano Panzeri
- Neural Coding Laboratory, Istituto Italiano di Tecnologia, Genova, Italy
- Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
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Hodkinson DJ, Bungert A, Bowtell R, Jackson SR, Jung J. Operculo-insular and anterior cingulate plasticity induced by transcranial magnetic stimulation in the human motor cortex: a dynamic casual modeling study. J Neurophysiol 2021; 125:1180-1190. [PMID: 33625934 DOI: 10.1152/jn.00670.2020] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 02/12/2021] [Indexed: 11/22/2022] Open
Abstract
The ability to induce neuroplasticity with noninvasive brain stimulation techniques offers a unique opportunity to examine the human brain systems involved in pain modulation. In experimental and clinical settings, the primary motor cortex (M1) is commonly targeted to alleviate pain, but its mechanism of action remains unclear. Using dynamic causal modeling (DCM) and Bayesian model selection (BMS), we tested seven competing hypotheses about how transcranial magnetic stimulation (TMS) modulates the directed influences (or effective connectivity) between M1 and three distinct cortical areas of the medial and lateral pain systems, including the insular cortex (INS), anterior cingulate cortex (ACC), and parietal operculum cortex (PO). The data set included a novel fMRI acquisition collected synchronously with M1 stimulation during rest and while performing a simple hand motor task. DCM and BMS showed a clear preference for the fully connected model in which all cortical areas receive input directly from M1, with facilitation of the connections INS→M1, PO→M1, and ACC→M1, plus increased inhibition of their reciprocal connections. An additional DCM analysis comparing the reduced models only corresponding to networks with a sparser connectivity within the full model showed that M1 input into the INS is the second-best model of plasticity following TMS manipulations. The results reported here provide a starting point for investigating whether pathway-specific targeting involving M1↔INS improves analgesic response beyond conventional targeting. We eagerly await future empirical data and models that tests this hypothesis.NEW & NOTEWORTHY Transcranial magnetic stimulation of the primary motor cortex (M1) is a promising treatment for chronic pain, but its mechanism of action remains unclear. Competing dynamic causal models of effective connectivity between M1 and medial and lateral pain systems suggest direct input into the insular, anterior cingulate cortex, and parietal operculum. This supports the hypothesis that analgesia produced from M1 stimulation most likely acts through the activation of top-down processes associated with intracortical modulation.
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Affiliation(s)
- Duncan J Hodkinson
- Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- National Institute for Health Research, Nottingham Biomedical Research Centre, Queens Medical Center, Nottingham, United Kingdom
- Versus Arthritis Pain Centre, University of Nottingham, Nottingham, United Kingdom
| | - Andreas Bungert
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Richard Bowtell
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Stephen R Jackson
- School of Psychology, University of Nottingham, Nottingham, United Kingdom
| | - JeYoung Jung
- School of Psychology, University of Nottingham, Nottingham, United Kingdom
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42
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Sip V, Scholly J, Guye M, Bartolomei F, Jirsa V. Evidence for spreading seizure as a cause of theta-alpha activity electrographic pattern in stereo-EEG seizure recordings. PLoS Comput Biol 2021; 17:e1008731. [PMID: 33635864 PMCID: PMC7946361 DOI: 10.1371/journal.pcbi.1008731] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 03/10/2021] [Accepted: 01/21/2021] [Indexed: 02/07/2023] Open
Abstract
Intracranial electroencephalography is a standard tool in clinical evaluation of patients with focal epilepsy. Various early electrographic seizure patterns differing in frequency, amplitude, and waveform of the oscillations are observed. The pattern most common in the areas of seizure propagation is the so-called theta-alpha activity (TAA), whose defining features are oscillations in the θ - α range and gradually increasing amplitude. A deeper understanding of the mechanism underlying the generation of the TAA pattern is however lacking. In this work we evaluate the hypothesis that the TAA patterns are caused by seizures spreading across the cortex. To do so, we perform simulations of seizure dynamics on detailed patient-derived cortical surfaces using the spreading seizure model as well as reference models with one or two homogeneous sources. We then detect the occurrences of the TAA patterns both in the simulated stereo-electroencephalographic signals and in the signals of recorded epileptic seizures from a cohort of fifty patients, and we compare the features of the groups of detected TAA patterns to assess the plausibility of the different models. Our results show that spreading seizure hypothesis is qualitatively consistent with the evidence available in the seizure recordings, and it can explain the features of the detected TAA groups best among the examined models.
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Affiliation(s)
- Viktor Sip
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Julia Scholly
- Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, CEMEREM, Pôle d’Imagerie Médicale, CHU, Marseille, France
- Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, Service de Neurophysiologie Clinique, CHU, Marseille, France
| | - Maxime Guye
- Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, CEMEREM, Pôle d’Imagerie Médicale, CHU, Marseille, France
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
| | - Fabrice Bartolomei
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
- Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, Service de Neurophysiologie Clinique, CHU, Marseille, France
| | - Viktor Jirsa
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
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Tsvetanov KA, Henson RNA, Rowe JB. Separating vascular and neuronal effects of age on fMRI BOLD signals. Philos Trans R Soc Lond B Biol Sci 2021; 376:20190631. [PMID: 33190597 PMCID: PMC7741031 DOI: 10.1098/rstb.2019.0631] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/19/2020] [Indexed: 12/14/2022] Open
Abstract
Accurate identification of brain function is necessary to understand the neurobiology of cognitive ageing, and thereby promote well-being across the lifespan. A common tool used to investigate neurocognitive ageing is functional magnetic resonance imaging (fMRI). However, although fMRI data are often interpreted in terms of neuronal activity, the blood oxygenation level-dependent (BOLD) signal measured by fMRI includes contributions of both vascular and neuronal factors, which change differentially with age. While some studies investigate vascular ageing factors, the results of these studies are not well known within the field of neurocognitive ageing and therefore vascular confounds in neurocognitive fMRI studies are common. Despite over 10 000 BOLD-fMRI papers on ageing, fewer than 20 have applied techniques to correct for vascular effects. However, neurovascular ageing is not only a confound in fMRI, but an important feature in its own right, to be assessed alongside measures of neuronal ageing. We review current approaches to dissociate neuronal and vascular components of BOLD-fMRI of regional activity and functional connectivity. We highlight emerging evidence that vascular mechanisms in the brain do not simply control blood flow to support the metabolic needs of neurons, but form complex neurovascular interactions that influence neuronal function in health and disease. This article is part of the theme issue 'Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity'.
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Affiliation(s)
- Kamen A. Tsvetanov
- Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0SZ, UK
- Department of Psychology, University of Cambridge, Cambridge CB2 3EB, UK
| | - Richard N. A. Henson
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SP, UK
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - James B. Rowe
- Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0SZ, UK
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
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Sadeghi S, Mier D, Gerchen MF, Schmidt SNL, Hass J. Dynamic Causal Modeling for fMRI With Wilson-Cowan-Based Neuronal Equations. Front Neurosci 2020; 14:593867. [PMID: 33328865 PMCID: PMC7728993 DOI: 10.3389/fnins.2020.593867] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 10/29/2020] [Indexed: 01/26/2023] Open
Abstract
Dynamic causal modeling (DCM) is an analysis technique that has been successfully used to infer about directed connectivity between brain regions based on imaging data such as functional magnetic resonance imaging (fMRI). Most variants of DCM for fMRI rely on a simple bilinear differential equation for neural activation, making it difficult to interpret the results in terms of local neural dynamics. In this work, we introduce a modification to DCM for fMRI by replacing the bilinear equation with a non-linear Wilson-Cowan based equation and use Bayesian Model Comparison (BMC) to show that this modification improves the model evidences. Improved model evidence of the non-linear model is shown for our empirical data (imitation of facial expressions) and validated by synthetic data as well as an empirical test dataset (attention to visual motion) used in previous foundational papers. For our empirical data, we conduct the analysis for a group of 42 healthy participants who performed an imitation task, activating regions putatively containing the human mirror neuron system (MNS). In this regard, we build 540 models as one family for comparing the standard bilinear with the modified Wilson-Cowan models on the family-level. Using this modification, we can interpret the sigmoid transfer function as an averaged f-I curve of many neurons in a single region with a sigmoidal format. In this way, we can make a direct inference from the macroscopic model to detailed microscopic models. The new DCM variant shows superior model evidence on all tested data sets.
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Affiliation(s)
- Sadjad Sadeghi
- Department of Theoretical Neuroscience, Central Institute of Mental Health, University of Heidelberg/Medical Faculty Mannheim, Mannheim, Germany.,Bernstein Center for Computational Neuroscience (BCCN) Heidelberg/Mannheim, Mannheim, Germany.,Department of Physics and Astronomy, University of Heidelberg, Heidelberg, Germany
| | - Daniela Mier
- Department of Clinical Psychology, Central Institute of Mental Health, University of Heidelberg/Medical Faculty Mannheim, Mannheim, Germany.,Department of Psychology, University of Konstanz, Konstanz, Germany
| | - Martin F Gerchen
- Bernstein Center for Computational Neuroscience (BCCN) Heidelberg/Mannheim, Mannheim, Germany.,Department of Clinical Psychology, Central Institute of Mental Health, University of Heidelberg/Medical Faculty Mannheim, Mannheim, Germany
| | | | - Joachim Hass
- Department of Theoretical Neuroscience, Central Institute of Mental Health, University of Heidelberg/Medical Faculty Mannheim, Mannheim, Germany.,Bernstein Center for Computational Neuroscience (BCCN) Heidelberg/Mannheim, Mannheim, Germany.,Faculty of Applied Psychology, SRH University of Applied Sciences Heidelberg, Heidelberg, Germany
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45
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The potential for complex computational models of aging. Mech Ageing Dev 2020; 193:111403. [PMID: 33220267 DOI: 10.1016/j.mad.2020.111403] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 10/24/2020] [Accepted: 11/11/2020] [Indexed: 12/15/2022]
Abstract
The gradual accumulation of damage and dysregulation during the aging of living organisms can be quantified. Even so, the aging process is complex and has multiple interacting physiological scales - from the molecular to cellular to whole tissues. In the face of this complexity, we can significantly advance our understanding of aging with the use of computational models that simulate realistic individual trajectories of health as well as mortality. To do so, they must be systems-level models that incorporate interactions between measurable aspects of age-associated changes. To incorporate individual variability in the aging process, models must be stochastic. To be useful they should also be predictive, and so must be fit or parameterized by data from large populations of aging individuals. In this perspective, we outline where we have been, where we are, and where we hope to go with such computational models of aging. Our focus is on data-driven systems-level models, and on their great potential in aging research.
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46
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Isomura T, Friston K. Reverse-Engineering Neural Networks to Characterize Their Cost Functions. Neural Comput 2020; 32:2085-2121. [PMID: 32946704 DOI: 10.1162/neco_a_01315] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
This letter considers a class of biologically plausible cost functions for neural networks, where the same cost function is minimized by both neural activity and plasticity. We show that such cost functions can be cast as a variational bound on model evidence under an implicit generative model. Using generative models based on partially observed Markov decision processes (POMDP), we show that neural activity and plasticity perform Bayesian inference and learning, respectively, by maximizing model evidence. Using mathematical and numerical analyses, we establish the formal equivalence between neural network cost functions and variational free energy under some prior beliefs about latent states that generate inputs. These prior beliefs are determined by particular constants (e.g., thresholds) that define the cost function. This means that the Bayes optimal encoding of latent or hidden states is achieved when the network's implicit priors match the process that generates its inputs. This equivalence is potentially important because it suggests that any hyperparameter of a neural network can itself be optimized-by minimization with respect to variational free energy. Furthermore, it enables one to characterize a neural network formally, in terms of its prior beliefs.
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Affiliation(s)
- Takuya Isomura
- Brain Intelligence Theory Unit, RIKEN Center for Brain Science, Wako, Saitama 351-0198, Japan
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, WC1N 3AR, U.K.
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47
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Al-Zubaidi A, Iglesias S, Stephan KE, Buades-Rotger M, Heldmann M, Nolde JM, Kirchner H, Mertins A, Jauch-Chara K, Münte TF. Effects of hunger, satiety and oral glucose on effective connectivity between hypothalamus and insular cortex. Neuroimage 2020; 217:116931. [DOI: 10.1016/j.neuroimage.2020.116931] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Revised: 04/12/2020] [Accepted: 05/07/2020] [Indexed: 12/16/2022] Open
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48
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Raj A, Cai C, Xie X, Palacios E, Owen J, Mukherjee P, Nagarajan S. Spectral graph theory of brain oscillations. Hum Brain Mapp 2020; 41:2980-2998. [PMID: 32202027 PMCID: PMC7336150 DOI: 10.1002/hbm.24991] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 02/13/2020] [Accepted: 02/16/2020] [Indexed: 11/10/2022] Open
Abstract
The relationship between the brain's structural wiring and the functional patterns of neural activity is of fundamental interest in computational neuroscience. We examine a hierarchical, linear graph spectral model of brain activity at mesoscopic and macroscopic scales. The model formulation yields an elegant closed-form solution for the structure-function problem, specified by the graph spectrum of the structural connectome's Laplacian, with simple, universal rules of dynamics specified by a minimal set of global parameters. The resulting parsimonious and analytical solution stands in contrast to complex numerical simulations of high dimensional coupled nonlinear neural field models. This spectral graph model accurately predicts spatial and spectral features of neural oscillatory activity across the brain and was successful in simultaneously reproducing empirically observed spatial and spectral patterns of alpha-band (8-12 Hz) and beta-band (15-30 Hz) activity estimated from source localized magnetoencephalography (MEG). This spectral graph model demonstrates that certain brain oscillations are emergent properties of the graph structure of the structural connectome and provides important insights towards understanding the fundamental relationship between network topology and macroscopic whole-brain dynamics. .
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Affiliation(s)
- Ashish Raj
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCalifornia
- Department of Bioengineering and Therapeutic SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Chang Cai
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCalifornia
| | - Xihe Xie
- Department of Neuroscience, Weill Cornell Graduate School of Medical SciencesWeill Cornell MedicineNew YorkNew YorkUSA
| | - Eva Palacios
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCalifornia
| | - Julia Owen
- Department of RadiologyUniversity of WashingtonSeattleWashingtonUSA
| | - Pratik Mukherjee
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCalifornia
- Department of Bioengineering and Therapeutic SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Srikantan Nagarajan
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCalifornia
- Department of Bioengineering and Therapeutic SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
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49
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Esménio S, Soares JM, Oliveira-Silva P, Gonçalves ÓF, Friston K, Fernandes Coutinho J. Changes in the Effective Connectivity of the Social Brain When Making Inferences About Close Others vs. the Self. Front Hum Neurosci 2020; 14:151. [PMID: 32410974 PMCID: PMC7202326 DOI: 10.3389/fnhum.2020.00151] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 04/06/2020] [Indexed: 11/16/2022] Open
Abstract
Previous research showed that the ability to make inferences about our own and other’s mental states rely on common brain pathways; particularly in the case of close relationships (e.g., romantic relationships). Despite the evidence for shared neural representations of self and others, less is known about the distributed processing within these common neural networks, particularly whether there are specific patterns of internode communication when focusing on other vs. self. This study aimed to characterize context-sensitive coupling among social brain regions involved in self and other understanding. Participants underwent an fMRI while watching emotional video vignettes of their romantic partner and elaborated on their partner’s (other-condition) or on their own experience (self-condition). We used dynamic causal modeling (DCM) to quantify the associated changes in effective connectivity (EC) in a network of brain regions involved in social cognition including the temporoparietal junction (TPJ), the posterior cingulate (PCC)/precuneus and middle temporal gyrus (MTG). DCM revealed that: the PCC plays a central coordination role within this network, the bilateral MTG receives driving inputs from other nodes suggesting that social information is first processed in language comprehension regions; the right TPJ evidenced a selective increase in its sensitivity when focusing on the other’s experience, relative to focusing on oneself.
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Affiliation(s)
- Sofia Esménio
- Psychological Neuroscience Laboratory, CIPsi, School of Psychology, University of Minho, Campus Gualtar, Braga, Portugal
| | - José Miguel Soares
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus Gualtar, Braga, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center, Braga, Portugal
| | - Patrícia Oliveira-Silva
- Human Neurobehavioral Laboratory, CEDH-Research Centre for Human Development, Faculdade de Educação e Psicologia, Universidade Católica Portuguesa, Porto, Portugal
| | - Óscar F Gonçalves
- Psychological Neuroscience Laboratory, CIPsi, School of Psychology, University of Minho, Campus Gualtar, Braga, Portugal.,Spaulding Center for Neuromodulation, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA, United States
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
| | - Joana Fernandes Coutinho
- Psychological Neuroscience Laboratory, CIPsi, School of Psychology, University of Minho, Campus Gualtar, Braga, Portugal
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50
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Silva PHRD, Secchinato KF, Rondinoni C, Leoni RF. Brain Structural–Functional Connectivity Relationship Underlying the Information Processing Speed. Brain Connect 2020; 10:143-154. [DOI: 10.1089/brain.2019.0726] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Affiliation(s)
| | | | - Carlo Rondinoni
- InBrain, Department of Physics, FFCLRP, University of São Paulo, Ribeirão Preto, Brazil
| | - Renata Ferranti Leoni
- InBrain, Department of Physics, FFCLRP, University of São Paulo, Ribeirão Preto, Brazil
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