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Ihalainen R, Annen J, Gosseries O, Cardone P, Panda R, Martial C, Thibaut A, Laureys S, Chennu S. Lateral frontoparietal effective connectivity differentiates and predicts state of consciousness in a cohort of patients with traumatic disorders of consciousness. PLoS One 2024; 19:e0298110. [PMID: 38968195 PMCID: PMC11226086 DOI: 10.1371/journal.pone.0298110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 01/13/2024] [Indexed: 07/07/2024] Open
Abstract
Neuroimaging studies have suggested an important role for the default mode network (DMN) in disorders of consciousness (DoC). However, the extent to which DMN connectivity can discriminate DoC states-unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS)-is less evident. Particularly, it is unclear whether effective DMN connectivity, as measured indirectly with dynamic causal modelling (DCM) of resting EEG can disentangle UWS from healthy controls and from patients considered conscious (MCS+). Crucially, this extends to UWS patients with potentially "covert" awareness (minimally conscious star, MCS*) indexed by voluntary brain activity in conjunction with partially preserved frontoparietal metabolism as measured with positron emission tomography (PET+ diagnosis; in contrast to PET- diagnosis with complete frontoparietal hypometabolism). Here, we address this gap by using DCM of EEG data acquired from patients with traumatic brain injury in 11 UWS (6 PET- and 5 PET+) and in 12 MCS+ (11 PET+ and 1 PET-), alongside with 11 healthy controls. We provide evidence for a key difference in left frontoparietal connectivity when contrasting UWS PET- with MCS+ patients and healthy controls. Next, in a leave-one-subject-out cross-validation, we tested the classification performance of the DCM models demonstrating that connectivity between medial prefrontal and left parietal sources reliably discriminates UWS PET- from MCS+ patients and controls. Finally, we illustrate that these models generalize to an unseen dataset: models trained to discriminate UWS PET- from MCS+ and controls, classify MCS* patients as conscious subjects with high posterior probability (pp > .92). These results identify specific alterations in the DMN after severe brain injury and highlight the clinical utility of EEG-based effective connectivity for identifying patients with potential covert awareness.
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Affiliation(s)
- Riku Ihalainen
- Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Boston, MA, United States of America
- School of Computing, University of Kent, Canterbury, United Kingdom
| | - Jitka Annen
- Coma Science Group, GIGA Consciousness Research Unit, University and University Hospital of Liège, Liège, Belgium
- Centre du Cerveau, University Hospital of Liège, Liège, Belgium
- Department of Data Analysis, University of Ghent, Ghent, Belgium
| | - Olivia Gosseries
- Coma Science Group, GIGA Consciousness Research Unit, University and University Hospital of Liège, Liège, Belgium
- Centre du Cerveau, University Hospital of Liège, Liège, Belgium
| | - Paolo Cardone
- Coma Science Group, GIGA Consciousness Research Unit, University and University Hospital of Liège, Liège, Belgium
- Centre du Cerveau, University Hospital of Liège, Liège, Belgium
| | - Rajanikant Panda
- Coma Science Group, GIGA Consciousness Research Unit, University and University Hospital of Liège, Liège, Belgium
- Centre du Cerveau, University Hospital of Liège, Liège, Belgium
| | - Charlotte Martial
- Coma Science Group, GIGA Consciousness Research Unit, University and University Hospital of Liège, Liège, Belgium
- Centre du Cerveau, University Hospital of Liège, Liège, Belgium
| | - Aurore Thibaut
- Coma Science Group, GIGA Consciousness Research Unit, University and University Hospital of Liège, Liège, Belgium
- Centre du Cerveau, University Hospital of Liège, Liège, Belgium
| | - Steven Laureys
- Coma Science Group, GIGA Consciousness Research Unit, University and University Hospital of Liège, Liège, Belgium
- CERVO Brain Research Centre, de la Canardière, Québec, Canada
- Consciousness Science Institute, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Srivas Chennu
- School of Computing, University of Kent, Canterbury, United Kingdom
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Yang C, Luo Q, Shu H, Le Bouquin Jeannès R, Li J, Xiang W. Exploration of interictal to ictal transition in epileptic seizures using a neural mass model. Cogn Neurodyn 2024; 18:1215-1225. [PMID: 38826671 PMCID: PMC11143138 DOI: 10.1007/s11571-023-09976-6] [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: 08/27/2022] [Revised: 02/16/2023] [Accepted: 04/19/2023] [Indexed: 06/04/2024] Open
Abstract
An epileptic seizure can usually be divided into three stages: interictal, preictal, and ictal. However, the seizure underlying the transition from interictal to ictal activities in the brain involves complex interactions between inhibition and excitation in groups of neurons. To explore this mechanism at the level of a single population, this paper employed a neural mass model, named the complete physiology-based model (cPBM), to reconstruct electroencephalographic (EEG) signals and to infer the changes in excitatory/inhibitory connections related to excitation-inhibition (E-I) balance based on an open dataset recorded for ten epileptic patients. Since epileptic signals display spectral characteristics, spectral dynamic causal modelling (DCM) was applied to quantify these frequency characteristics by maximizing the free energy in the framework of power spectral density (PSD) and estimating the cPBM parameters. In addition, to address the local maximum problem that DCM may suffer from, a hybrid deterministic DCM (H-DCM) approach was proposed, with a deterministic annealing-based scheme applied in two directions. The H-DCM approach adjusts the temperature introduced in the objective function by gradually decreasing the temperature to obtain relatively good initialization and then gradually increasing the temperature to search for a better estimation after each maximization. The results showed that (i) reconstructed EEG signals belonging to the three stages together with their PSDs can be reproduced from the estimated parameters of the cPBM; (ii) compared to DCM, traditional D-DCM and anti D-DCM, the proposed H-DCM shows higher free energies and lower root mean square error (RMSE), and it provides the best performance for all stages (e.g., the RMSEs between the reconstructed PSD computed from the reconstructed EEG signal and the sample PSD obtained from the real EEG signal are 0.33 ± 0.08, 0.67 ± 0.37 and 0.78 ± 0.57 in the interictal, preictal and ictal stages, respectively); and (iii) the transition from interictal to ictal activity can be explained by an increase in the connections between pyramidal cells and excitatory interneurons and between pyramidal cells and fast inhibitory interneurons, as well as a decrease in the self-loop connection of the fast inhibitory interneurons in the cPBM. Moreover, the E-I balance, defined as the ratio between the excitatory connection from pyramidal cells to fast inhibitory interneurons and the inhibitory connection with the self-loop of fast inhibitory interneurons, is also significantly increased during the epileptic seizure transition. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-023-09976-6.
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Affiliation(s)
- Chunfeng Yang
- Key Laboratory of Computer Network and Information Integration of Ministry of Education, Southeast University, Nanjing, 210096 China
- Jiangsu Provincal Joint International Research Laboratory of Medical Information Processing, Southeast University, Nanjing, 210096 China
- Centre de Recherche en Information Biomédicale Sino-français, Southeast University & Université de Rennes 1, Nanjing, 210096 China
| | - Qingbo Luo
- Key Laboratory of Computer Network and Information Integration of Ministry of Education, Southeast University, Nanjing, 210096 China
- Jiangsu Provincal Joint International Research Laboratory of Medical Information Processing, Southeast University, Nanjing, 210096 China
- Centre de Recherche en Information Biomédicale Sino-français, Southeast University & Université de Rennes 1, Nanjing, 210096 China
| | - Huazhong Shu
- Key Laboratory of Computer Network and Information Integration of Ministry of Education, Southeast University, Nanjing, 210096 China
- Jiangsu Provincal Joint International Research Laboratory of Medical Information Processing, Southeast University, Nanjing, 210096 China
- Centre de Recherche en Information Biomédicale Sino-français, Southeast University & Université de Rennes 1, Nanjing, 210096 China
| | - Régine Le Bouquin Jeannès
- Centre de Recherche en Information Biomédicale Sino-français, Southeast University & Université de Rennes 1, Nanjing, 210096 China
- Univ Rennes, Inserm, LTSI, UMR 1099, Rennes, 35000 France
| | - Jianqing Li
- Jiangsu Province Engineering Research Center for Smart Wearable and Rehabilitation Devices, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, 211166 China
| | - Wentao Xiang
- Jiangsu Province Engineering Research Center for Smart Wearable and Rehabilitation Devices, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, 211166 China
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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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Sumner RL, McMillan RL, Forsyth A, Muthukumaraswamy SD, Shaw AD. Neurophysiological evidence that frontoparietal connectivity and GABA-A receptor changes underpin the antidepressant response to ketamine. Transl Psychiatry 2024; 14:116. [PMID: 38402231 PMCID: PMC10894245 DOI: 10.1038/s41398-024-02738-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 12/20/2023] [Accepted: 01/05/2024] [Indexed: 02/26/2024] Open
Abstract
Revealing the acute cortical pharmacodynamics of an antidepressant dose of ketamine in humans with depression is key to determining the specific mechanism(s) of action for alleviating symptoms. While the downstream effects are characterised by increases in plasticity and reductions in depressive symptoms-it is the acute response in the brain that triggers this cascade of events. Computational modelling of cortical interlaminar and cortico-cortical connectivity and receptor dynamics provide the opportunity to interrogate this question using human electroencephalography (EEG) data recorded during a ketamine infusion. Here, resting-state EEG was recorded in a group of 30 patients with major depressive disorder (MDD) at baseline and during a 0.44 mg/kg ketamine dose comprising a bolus and infusion. Fronto-parietal connectivity was assessed using dynamic causal modelling to fit a thalamocortical model to hierarchically connected nodes in the medial prefrontal cortex and superior parietal lobule. We found a significant increase in parietal-to-frontal AMPA-mediated connectivity and a significant decrease in the frontal GABA time constant. Both parameter changes were correlated across participants with the antidepressant response to ketamine. Changes to the NMDA receptor time constant and inhibitory intraneuronal input into superficial pyramidal cells did not survive correction for multiple comparisons and were not correlated with the antidepressant response. These results provide evidence that the antidepressant effects of ketamine may be mediated by acute fronto-parietal connectivity and GABA receptor dynamics. Furthermore, it supports the large body of literature suggesting the acute mechanism underlying ketamine's antidepressant properties is related to GABA-A and AMPA receptors rather than NMDA receptor antagonism.
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Affiliation(s)
- Rachael L Sumner
- School of Pharmacy, University of Auckland, Auckland, New Zealand.
| | | | - Anna Forsyth
- School of Pharmacy, University of Auckland, Auckland, New Zealand
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Hall CV, Radford-Smith G, Savage E, Robinson C, Cocchi L, Moran RJ. Brain signatures of chronic gut inflammation. Front Psychiatry 2023; 14:1250268. [PMID: 38025434 PMCID: PMC10661239 DOI: 10.3389/fpsyt.2023.1250268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 10/09/2023] [Indexed: 12/01/2023] Open
Abstract
Gut inflammation is thought to modify brain activity and behaviour via modulation of the gut-brain axis. However, how relapsing and remitting exposure to peripheral inflammation over the natural history of inflammatory bowel disease (IBD) contributes to altered brain dynamics is poorly understood. Here, we used electroencephalography (EEG) to characterise changes in spontaneous spatiotemporal brain states in Crohn's Disease (CD) (n = 40) and Ulcerative Colitis (UC) (n = 30), compared to healthy individuals (n = 28). We first provide evidence of a significantly perturbed and heterogeneous microbial profile in CD, consistent with previous work showing enduring and long-standing dysbiosis in clinical remission. Results from our brain state assessment show that CD and UC exhibit alterations in the temporal properties of states implicating default-mode network, parietal, and visual regions, reflecting a shift in the predominance from externally to internally-oriented attentional modes. We investigated these dynamics at a finer sub-network resolution, showing a CD-specific and highly selective enhancement of connectivity between the insula and medial prefrontal cortex (mPFC), regions implicated in cognitive-interoceptive appraisal mechanisms. Alongside overall higher anxiety scores in CD, we also provide preliminary support to suggest that the strength of chronic interoceptive hyper-signalling in the brain co-occurs with disease duration. Together, our results demonstrate that a long-standing diagnosis of CD is, in itself, a key factor in determining the risk of developing altered brain network signatures.
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Affiliation(s)
- Caitlin V. Hall
- Clinical Brain Networks Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, United Kingdom
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Graham Radford-Smith
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- Gut Health Research Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Department of Gastroenterology, Royal Brisbane and Women’s Hospital, Brisbane, QLD, Australia
| | - Emma Savage
- Clinical Brain Networks Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Conor Robinson
- Clinical Brain Networks Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Luca Cocchi
- Clinical Brain Networks Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Rosalyn J. Moran
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, United Kingdom
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Zeidman P, Friston K, Parr T. A primer on Variational Laplace (VL). Neuroimage 2023; 279:120310. [PMID: 37544417 PMCID: PMC10951963 DOI: 10.1016/j.neuroimage.2023.120310] [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: 09/30/2022] [Revised: 07/13/2023] [Accepted: 08/04/2023] [Indexed: 08/08/2023] Open
Abstract
This article details a scheme for approximate Bayesian inference, which has underpinned thousands of neuroimaging studies since its introduction 15 years ago. Variational Laplace (VL) provides a generic approach to fitting linear or non-linear models, which may be static or dynamic, returning a posterior probability density over the model parameters and an approximation of log model evidence, which enables Bayesian model comparison. VL applies variational Bayesian inference in conjunction with quadratic or Laplace approximations of the evidence lower bound (free energy). Importantly, update equations do not need to be derived for each model under consideration, providing a general method for fitting a broad class of models. This primer is intended for experimenters and modellers who may wish to fit models to data using variational Bayesian methods, without assuming previous experience of variational Bayes or machine learning. Accompanying code demonstrates how to fit different kinds of model using the reference implementation of the VL scheme in the open-source Statistical Parametric Mapping (SPM) software package. In addition, we provide a standalone software function that does not require SPM, in order to ease translation to other fields, together with detailed pseudocode. Finally, the supplementary materials provide worked derivations of the key equations.
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Affiliation(s)
- Peter Zeidman
- Wellcome Centre for Human Neuroimaging, UCL, 12 Queen Square, London WC1N 3AR, United Kingdom.
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, UCL, 12 Queen Square, London WC1N 3AR, United Kingdom
| | - Thomas Parr
- Wellcome Centre for Human Neuroimaging, UCL, 12 Queen Square, London WC1N 3AR, United Kingdom
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Ehrlich SK, Battistella G, Simonyan K. Temporal Signature of Task-Specificity in Isolated Focal Laryngeal Dystonia. Mov Disord 2023; 38:1925-1935. [PMID: 37489600 PMCID: PMC10615685 DOI: 10.1002/mds.29557] [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: 04/10/2023] [Revised: 06/06/2023] [Accepted: 06/28/2023] [Indexed: 07/26/2023] Open
Abstract
BACKGROUND AND OBJECTIVE Laryngeal dystonia (LD) is focal task-specific dystonia, predominantly affecting speech but not whispering or emotional vocalizations. Prior neuroimaging studies identified brain regions forming a dystonic neural network and contributing to LD pathophysiology. However, the underlying temporal dynamics of these alterations and their contribution to the task-specificity of LD remain largely unknown. The objective of the study was to identify the temporal-spatial signature of altered cortical oscillations associated with LD pathophysiology. METHODS We used high-density 128-electrode electroencephalography (EEG) recordings during symptomatic speaking and two asymptomatic tasks, whispering and writing, in 24 LD patients and 22 healthy individuals to investigate the spectral dynamics, spatial localization, and interregional effective connectivity of aberrant cortical oscillations within the dystonic neural network, as well as their relationship with LD symptomatology. RESULTS Symptomatic speaking in LD patients was characterized by significantly increased gamma synchronization in the middle/superior frontal gyri, primary somatosensory cortex, and superior parietal lobule, establishing the altered prefrontal-parietal loop. Hyperfunctional connectivity from the left middle frontal gyrus to the right superior parietal lobule was significantly correlated with the age of onset and the duration of LD symptoms. Asymptomatic whisper in LD patients had not no statistically significant changes in any frequency band, whereas asymptomatic writing was characterized by significantly decreased synchronization of beta-band power localized in the right superior frontal gyrus. CONCLUSION Task-specific oscillatory activity of prefrontal-parietal circuitry is likely one of the underlying mechanisms of aberrant heteromodal integration of information processing and transfer within the neural network leading to dystonic motor output. © 2023 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Stefan K. Ehrlich
- Department of Otolaryngology - Head & Neck Surgery, Harvard Medical School and Massachusetts Eye and Ear, 243 Charles Street, Boston, MA 02114, USA
| | - Giovanni Battistella
- Department of Otolaryngology - Head & Neck Surgery, Harvard Medical School and Massachusetts Eye and Ear, 243 Charles Street, Boston, MA 02114, USA
| | - Kristina Simonyan
- Department of Otolaryngology - Head & Neck Surgery, Harvard Medical School and Massachusetts Eye and Ear, 243 Charles Street, Boston, MA 02114, USA
- Department of Neurology - Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
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Brændholt M, Kluger DS, Varga S, Heck DH, Gross J, Allen MG. Breathing in waves: Understanding respiratory-brain coupling as a gradient of predictive oscillations. Neurosci Biobehav Rev 2023; 152:105262. [PMID: 37271298 DOI: 10.1016/j.neubiorev.2023.105262] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 05/03/2023] [Accepted: 05/24/2023] [Indexed: 06/06/2023]
Abstract
Breathing plays a crucial role in shaping perceptual and cognitive processes by regulating the strength and synchronisation of neural oscillations. Numerous studies have demonstrated that respiratory rhythms govern a wide range of behavioural effects across cognitive, affective, and perceptual domains. Additionally, respiratory-modulated brain oscillations have been observed in various mammalian models and across diverse frequency spectra. However, a comprehensive framework to elucidate these disparate phenomena remains elusive. In this review, we synthesise existing findings to propose a neural gradient of respiratory-modulated brain oscillations and examine recent computational models of neural oscillations to map this gradient onto a hierarchical cascade of precision-weighted prediction errors. By deciphering the computational mechanisms underlying respiratory control of these processes, we can potentially uncover new pathways for understanding the link between respiratory-brain coupling and psychiatric disorders.
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Affiliation(s)
- Malthe Brændholt
- Center of Functionally Integrative Neuroscience, Aarhus University, Denmark
| | - Daniel S Kluger
- Institute for Biomagnetism and Biosignal Analysis, University of Münster, Germany.
| | - Somogy Varga
- School of Culture and Society, Aarhus University, Denmark; The Centre for Philosophy of Epidemiology, Medicine and Public Health, University of Johannesburg, South Africa
| | - Detlef H Heck
- Department of Biomedical Sciences, University of Minnesota Medical School, Duluth, MN
| | - Joachim Gross
- Institute for Biomagnetism and Biosignal Analysis, University of Münster, Germany; Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Germany
| | - Micah G Allen
- Center of Functionally Integrative Neuroscience, Aarhus University, Denmark; Cambridge Psychiatry, University of Cambridge, UK
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Nag S, Uludag K. Dynamic Effective Connectivity using Physiologically informed Dynamic Causal Model with Recurrent Units: A functional Magnetic Resonance Imaging simulation study. Front Hum Neurosci 2023; 17:1001848. [PMID: 36936613 PMCID: PMC10014816 DOI: 10.3389/fnhum.2023.1001848] [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: 07/24/2022] [Accepted: 01/25/2023] [Indexed: 03/05/2023] Open
Abstract
Functional MRI (fMRI) is an indirect reflection of neuronal activity. Using generative biophysical model of fMRI data such as Dynamic Causal Model (DCM), the underlying neuronal activities of different brain areas and their causal interactions (i.e., effective connectivity) can be calculated. Most DCM studies typically consider the effective connectivity to be static for a cognitive task within an experimental run. However, changes in experimental conditions during complex tasks such as movie-watching might result in temporal variations in the connectivity strengths. In this fMRI simulation study, we leverage state-of-the-art Physiologically informed DCM (P-DCM) along with a recurrent window approach and discretization of the equations to infer the underlying neuronal dynamics and concurrently the dynamic (time-varying) effective connectivities between various brain regions for task-based fMRI. Results from simulation studies on 3- and 10-region models showed that functional magnetic resonance imaging (fMRI) blood oxygenation level-dependent (BOLD) responses and effective connectivity time-courses can be accurately predicted and distinguished from faulty graphical connectivity models representing cognitive hypotheses. In summary, we propose and validate a novel approach to determine dynamic effective connectivity between brain areas during complex cognitive tasks by combining P-DCM with recurrent units.
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Affiliation(s)
- Sayan Nag
- Techna Institute & Koerner Scientist in MR Imaging, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- *Correspondence: Sayan Nag,
| | - Kamil Uludag
- Techna Institute & Koerner Scientist in MR Imaging, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
- Kamil Uludag,
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Van de Steen F, Pinotsis D, Devos W, Colenbier N, Bassez I, Friston K, Marinazzo D. Dynamic causal modelling shows a prominent role of local inhibition in alpha power modulation in higher visual cortex. PLoS Comput Biol 2022; 18:e1009988. [PMID: 36574458 PMCID: PMC9829170 DOI: 10.1371/journal.pcbi.1009988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 01/09/2023] [Accepted: 12/16/2022] [Indexed: 12/29/2022] Open
Abstract
During resting-state EEG recordings, alpha activity is more prominent over the posterior cortex in eyes-closed (EC) conditions compared to eyes-open (EO). In this study, we characterized the difference in spectra between EO and EC conditions using dynamic causal modelling. Specifically, we investigated the role of intrinsic and extrinsic connectivity-within the visual cortex-in generating EC-EO alpha power differences over posterior electrodes. The primary visual cortex (V1) and the bilateral middle temporal visual areas (V5) were equipped with bidirectional extrinsic connections using a canonical microcircuit. The states of four intrinsically coupled subpopulations-within each occipital source-were also modelled. Using Bayesian model selection, we tested whether modulations of the intrinsic connections in V1, V5 or extrinsic connections (or a combination thereof) provided the best evidence for the data. In addition, using parametric empirical Bayes (PEB), we estimated group averages under the winning model. Bayesian model selection showed that the winning model contained both extrinsic connectivity modulations, as well as intrinsic connectivity modulations in all sources. The PEB analysis revealed increased extrinsic connectivity during EC. Overall, we found a reduction in the inhibitory intrinsic connections during EC. The results suggest that the intrinsic modulations in V5 played the most important role in producing EC-EO alpha differences, suggesting an intrinsic disinhibition in higher order visual cortex, during EC resting state.
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Affiliation(s)
- Frederik Van de Steen
- Department of Data Analysis, Ghent University, Ghent, Belgium
- Vrije Universiteit Brussel, AIMS laboratory, Brussel, Belgium
- The Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom
- * E-mail:
| | - Dimitris Pinotsis
- Centre for Mathematical Neuroscience and Psychology and Department of Psychology, City—University of London, London, United Kingdom
- The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Wouter Devos
- Department of Data Analysis, Ghent University, Ghent, Belgium
| | | | - Iege Bassez
- Department of Data Analysis, Ghent University, Ghent, Belgium
| | - Karl Friston
- The Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom
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Xiang W, Karfoul A, Yang C, Shu H, Le Bouquin Jeannès R. Investigation of two neural mass models for DCM-based effective connectivity inference in temporal epilepsy. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 221:106840. [PMID: 35550455 DOI: 10.1016/j.cmpb.2022.106840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 04/13/2022] [Accepted: 04/25/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Recently, spectral Dynamic Causal Modelling (DCM) has been used increasingly to infer effective connectivity from epileptic intracranial electroencephalographic (iEEG) signals. In this context, the Physiology-Based Model (PBM), a neural mass model, is used as a generative model. However, previous studies have highlighted out the inability of PBM to properly describe iEEG signals with specific power spectral densities (PSDs). More precisely, PSDs that have multiple peaks around β and γ rhythms (i.e. spectral characteristics at seizure onset) are concerned. METHODS To cope with this limitation, an alternative neural mass model, called the complete PBM (cPBM), is investigated. The spectral DCM and two recent variants are used to evaluate the relevance of cPBM over PBM. RESULTS The study is conducted on both simulated signals and real epileptic iEEG recordings. Our results confirm that, compared to PBM, cPBM shows (i) more ability to model the desired PSDs and (ii) lower numerical complexity whatever the method. CONCLUSIONS Thanks to its intrinsic and extrinsic connectivity parameters as well as the input coming into the fast inhibitory subpopulation, the cPBM provides a more expressive model of PSDs, leading to a better understanding of epileptic patterns and DCM-based effective connectivity inference.
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Affiliation(s)
- Wentao Xiang
- Key Laboratory of Clinical and Medical Engineering, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China; Univ Rennes, Inserm, LTSI, UMR 1099, Rennes F-35000, France; Univ Rennes, Inserm, SEU, LIA - Centre de Recherche en Information Biomédicale Sino-français (CRIBs), Rennes F-35000, France
| | - Ahmad Karfoul
- Univ Rennes, Inserm, LTSI, UMR 1099, Rennes F-35000, France; Univ Rennes, Inserm, SEU, LIA - Centre de Recherche en Information Biomédicale Sino-français (CRIBs), Rennes F-35000, France
| | - Chunfeng Yang
- Univ Rennes, Inserm, SEU, LIA - Centre de Recherche en Information Biomédicale Sino-français (CRIBs), Rennes F-35000, France; Laboratory of Image Science and Technology (LIST), School of Computer Science and Engineering, Southeast University, Nanjing 210096, China
| | - Huazhong Shu
- Univ Rennes, Inserm, SEU, LIA - Centre de Recherche en Information Biomédicale Sino-français (CRIBs), Rennes F-35000, France; Laboratory of Image Science and Technology (LIST), School of Computer Science and Engineering, Southeast University, Nanjing 210096, China
| | - Régine Le Bouquin Jeannès
- Univ Rennes, Inserm, LTSI, UMR 1099, Rennes F-35000, France; Univ Rennes, Inserm, SEU, LIA - Centre de Recherche en Information Biomédicale Sino-français (CRIBs), Rennes F-35000, France.
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12
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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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13
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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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14
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Multimodal electrophysiological analyses reveal that reduced synaptic excitatory neurotransmission underlies seizures in a model of NMDAR antibody-mediated encephalitis. Commun Biol 2021; 4:1106. [PMID: 34545200 PMCID: PMC8452639 DOI: 10.1038/s42003-021-02635-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 09/02/2021] [Indexed: 12/15/2022] Open
Abstract
Seizures are a prominent feature in N-Methyl-D-Aspartate receptor antibody (NMDAR antibody) encephalitis, a distinct neuro-immunological disorder in which specific human autoantibodies bind and crosslink the surface of NMDAR proteins thereby causing internalization and a state of NMDAR hypofunction. To further understand ictogenesis in this disorder, and to test a potential treatment compound, we developed an NMDAR antibody mediated rat seizure model that displays spontaneous epileptiform activity in vivo and in vitro. Using a combination of electrophysiological and dynamic causal modelling techniques we show that, contrary to expectation, reduction of synaptic excitatory, but not inhibitory, neurotransmission underlies the ictal events through alterations in the dynamical behaviour of microcircuits in brain tissue. Moreover, in vitro application of a neurosteroid, pregnenolone sulphate, that upregulates NMDARs, reduced established ictal activity. This proof-of-concept study highlights the complexity of circuit disturbances that may lead to seizures and the potential use of receptor-specific treatments in antibody-mediated seizures and epilepsy.
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15
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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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16
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West TO, Berthouze L, Farmer SF, Cagnan H, Litvak V. Inference of brain networks with approximate Bayesian computation - assessing face validity with an example application in Parkinsonism. Neuroimage 2021; 236:118020. [PMID: 33839264 PMCID: PMC8270890 DOI: 10.1016/j.neuroimage.2021.118020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 03/16/2021] [Accepted: 03/21/2021] [Indexed: 11/21/2022] Open
Abstract
This paper describes and validates a novel framework using the Approximate Bayesian Computation (ABC) algorithm for parameter estimation and model selection in models of mesoscale brain network activity. We provide a proof of principle, first pass validation of this framework using a set of neural mass models of the cortico-basal ganglia thalamic circuit inverted upon spectral features from experimental, in vivo recordings. This optimization scheme relaxes an assumption of fixed-form posteriors (i.e. the Laplace approximation) taken in previous approaches to inverse modelling of spectral features. This enables the exploration of model dynamics beyond that approximated from local linearity assumptions and so fit to explicit, numerical solutions of the underlying non-linear system of equations. In this first paper, we establish a face validation of the optimization procedures in terms of: (i) the ability to approximate posterior densities over parameters that are plausible given the known causes of the data; (ii) the ability of the model comparison procedures to yield posterior model probabilities that can identify the model structure known to generate the data; and (iii) the robustness of these procedures to local minima in the face of different starting conditions. Finally, as an illustrative application we show (iv) that model comparison can yield plausible conclusions given the known neurobiology of the cortico-basal ganglia-thalamic circuit in Parkinsonism. These results lay the groundwork for future studies utilizing highly nonlinear or brittle models that can explain time dependant dynamics, such as oscillatory bursts, in terms of the underlying neural circuits.
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Affiliation(s)
- Timothy O West
- Nuffield Department of Clinical Neurosciences, Medical Sciences Division, University of Oxford, Oxford OX3 9DU, United Kingdom; Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford OX1 3TH, United Kingdom; Wellcome Trust Centre for Human Neuroimaging, UCL Institute of Neurology, Queen Square, London WC1N 3BG, United Kingdom.
| | - Luc Berthouze
- Centre for Computational Neuroscience and Robotics, University of Sussex, Falmer, United Kingdom; UCL Great Ormond Street Institute of Child Health, Guildford St., London WC1N 1EH, United Kingdom
| | - Simon F Farmer
- Department of Neurology, National Hospital for Neurology & Neurosurgery, Queen Square, London WC1N 3BG, United Kingdom; Department of Clinical and Movement Neurosciences, Institute of Neurology, Queen Square, UCL, London WC1N 3BG, United Kingdom
| | - Hayriye Cagnan
- Nuffield Department of Clinical Neurosciences, Medical Sciences Division, University of Oxford, Oxford OX3 9DU, United Kingdom; Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford OX1 3TH, United Kingdom; Wellcome Trust Centre for Human Neuroimaging, UCL Institute of Neurology, Queen Square, London WC1N 3BG, United Kingdom
| | - Vladimir Litvak
- Wellcome Trust Centre for Human Neuroimaging, UCL Institute of Neurology, Queen Square, London WC1N 3BG, United Kingdom
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17
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Beck MM, Spedden ME, Dietz MJ, Karabanov AN, Christensen MS, Lundbye-Jensen J. Cortical signatures of precision grip force control in children, adolescents, and adults. eLife 2021; 10:61018. [PMID: 34121656 PMCID: PMC8216716 DOI: 10.7554/elife.61018] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 06/04/2021] [Indexed: 11/13/2022] Open
Abstract
Human dexterous motor control improves from childhood to adulthood, but little is known about the changes in cortico-cortical communication that support such ontogenetic refinement of motor skills. To investigate age-related differences in connectivity between cortical regions involved in dexterous control, we analyzed electroencephalographic data from 88 individuals (range 8-30 years) performing a visually guided precision grip task using dynamic causal modelling and parametric empirical Bayes. Our results demonstrate that bidirectional coupling in a canonical 'grasping network' is associated with precision grip performance across age groups. We further demonstrate greater backward coupling from higher-order to lower-order sensorimotor regions from late adolescence in addition to differential associations between connectivity strength in a premotor-prefrontal network and motor performance for different age groups. We interpret these findings as reflecting greater use of top-down and executive control processes with development. These results expand our understanding of the cortical mechanisms that support dexterous abilities through development.
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Affiliation(s)
- Mikkel Malling Beck
- Department of Nutrition, Exercise and Sports (NEXS), University of Copenhagen, Copenhagen, Denmark
| | | | - Martin Jensen Dietz
- Center of Functionally Integrative Neuroscience, Institute of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Anke Ninija Karabanov
- Department of Nutrition, Exercise and Sports (NEXS), University of Copenhagen, Copenhagen, Denmark.,Danish Research Centre for Magnetic Resonance (DRCMR), Hvidovre Hospital, Hvidovre, Denmark
| | | | - Jesper Lundbye-Jensen
- Department of Nutrition, Exercise and Sports (NEXS), University of Copenhagen, Copenhagen, Denmark.,Department of Neuroscience, University of Copenhagen, Copenhagen, Denmark
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18
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Chan JS, Wibral M, Stawowsky C, Brandl M, Helbling S, Naumer MJ, Kaiser J, Wollstadt P. Predictive Coding Over the Lifespan: Increased Reliance on Perceptual Priors in Older Adults-A Magnetoencephalography and Dynamic Causal Modeling Study. Front Aging Neurosci 2021; 13:631599. [PMID: 33897405 PMCID: PMC8062739 DOI: 10.3389/fnagi.2021.631599] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 03/04/2021] [Indexed: 12/17/2022] Open
Abstract
Aging is accompanied by unisensory decline. To compensate for this, two complementary strategies are potentially relied upon increasingly: first, older adults integrate more information from different sensory organs. Second, according to the predictive coding (PC) model, we form “templates” (internal models or “priors”) of the environment through our experiences. It is through increased life experience that older adults may rely more on these templates compared to younger adults. Multisensory integration and predictive coding would be effective strategies for the perception of near-threshold stimuli, which may however come at the cost of integrating irrelevant information. Both strategies can be studied in multisensory illusions because these require the integration of different sensory information, as well as an internal model of the world that can take precedence over sensory input. Here, we elicited a classic multisensory illusion, the sound-induced flash illusion, in younger (mean: 27 years, N = 25) and older (mean: 67 years, N = 28) adult participants while recording the magnetoencephalogram. Older adults perceived more illusions than younger adults. Older adults had increased pre-stimulus beta-band activity compared to younger adults as predicted by microcircuit theories of predictive coding, which suggest priors and predictions are linked to beta-band activity. Transfer entropy analysis and dynamic causal modeling of pre-stimulus magnetoencephalography data revealed a stronger illusion-related modulation of cross-modal connectivity from auditory to visual cortices in older compared to younger adults. We interpret this as the neural correlate of increased reliance on a cross-modal predictive template in older adults leading to the illusory percept.
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Affiliation(s)
- Jason S Chan
- Institute of Medical Psychology, Goethe-University, Frankfurt, Germany.,School of Applied Psychology, University College Cork, Cork, Ireland
| | - Michael Wibral
- Brain Imaging Center, Goethe-University, Frankfurt am Main, Germany
| | - Cerisa Stawowsky
- Brain Imaging Center, Goethe-University, Frankfurt am Main, Germany
| | - Mareike Brandl
- Institute of Medical Psychology, Goethe-University, Frankfurt, Germany
| | - Saskia Helbling
- Institute of Medical Psychology, Goethe-University, Frankfurt, Germany.,Brain Imaging Center, Goethe-University, Frankfurt am Main, Germany
| | - Marcus J Naumer
- Institute of Medical Psychology, Goethe-University, Frankfurt, Germany
| | - Jochen Kaiser
- Institute of Medical Psychology, Goethe-University, Frankfurt, Germany
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19
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How hot is the hot zone? Computational modelling clarifies the role of parietal and frontoparietal connectivity during anaesthetic-induced loss of consciousness. Neuroimage 2021; 231:117841. [PMID: 33577934 DOI: 10.1016/j.neuroimage.2021.117841] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 01/28/2021] [Accepted: 02/03/2021] [Indexed: 02/04/2023] Open
Abstract
In recent years, specific cortical networks have been proposed to be crucial for sustaining consciousness, including the posterior hot zone and frontoparietal resting state networks (RSN). Here, we computationally evaluate the relative contributions of three RSNs - the default mode network (DMN), the salience network (SAL), and the central executive network (CEN) - to consciousness and its loss during propofol anaesthesia. Specifically, we use dynamic causal modelling (DCM) of 10 min of high-density EEG recordings (N = 10, 4 males) obtained during behavioural responsiveness, unconsciousness and post-anaesthetic recovery to characterise differences in effective connectivity within frontal areas, the posterior 'hot zone', frontoparietal connections, and between-RSN connections. We estimate - for the first time - a large DCM model (LAR) of resting EEG, combining the three RSNs into a rich club of interconnectivity. Consistent with the hot zone theory, our findings demonstrate reductions in inter-RSN connectivity in the parietal cortex. Within the DMN itself, the strongest reductions are in feed-forward frontoparietal and parietal connections at the precuneus node. Within the SAL and CEN, loss of consciousness generates small increases in bidirectional connectivity. Using novel DCM leave-one-out cross-validation, we show that the most consistent out-of-sample predictions of the state of consciousness come from a key set of frontoparietal connections. This finding also generalises to unseen data collected during post-anaesthetic recovery. Our findings provide new, computational evidence for the importance of the posterior hot zone in explaining the loss of consciousness, highlighting also the distinct role of frontoparietal connectivity in underpinning conscious responsiveness, and consequently, suggest a dissociation between the mechanisms most prominently associated with explaining the contrast between conscious awareness and unconsciousness, and those maintaining consciousness.
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20
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Shaw AD, Hughes LE, Moran R, Coyle-Gilchrist I, Rittman T, Rowe JB. In Vivo Assay of Cortical Microcircuitry in Frontotemporal Dementia: A Platform for Experimental Medicine Studies. Cereb Cortex 2021; 31:1837-1847. [PMID: 31216360 PMCID: PMC7869085 DOI: 10.1093/cercor/bhz024] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Revised: 01/07/2019] [Indexed: 11/13/2022] Open
Abstract
The analysis of neural circuits can provide crucial insights into the mechanisms of neurodegeneration and dementias, and offer potential quantitative biological tools to assess novel therapeutics. Here we use behavioral variant frontotemporal dementia (bvFTD) as a model disease. We demonstrate that inversion of canonical microcircuit models to noninvasive human magnetoencephalography, using dynamic causal modeling, can identify the regional- and laminar-specificity of bvFTD pathophysiology, and their parameters can accurately differentiate patients from matched healthy controls. Using such models, we show that changes in local coupling in frontotemporal dementia underlie the failure to adequately establish sensory predictions, leading to altered prediction error responses in a cortical information-processing hierarchy. Using machine learning, this model-based approach provided greater case-control classification accuracy than conventional evoked cortical responses. We suggest that this approach provides an in vivo platform for testing mechanistic hypotheses about disease progression and pharmacotherapeutics.
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Affiliation(s)
- Alexander D Shaw
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, UK
| | - Laura E Hughes
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Medical Research Council, Cognition and Brain, Sciences Unit, Cambridge, UK
| | - Rosalyn Moran
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | - Ian Coyle-Gilchrist
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Tim Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - James B Rowe
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Medical Research Council, Cognition and Brain, Sciences Unit, Cambridge, UK
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21
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Noise induced quiescence of epileptic spike generation in patients with epilepsy. J Comput Neurosci 2021; 49:57-67. [PMID: 33420615 PMCID: PMC7875857 DOI: 10.1007/s10827-020-00772-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 11/01/2020] [Accepted: 11/17/2020] [Indexed: 11/29/2022]
Abstract
Clinical scalp electroencephalographic recordings from patients with epilepsy are distinguished by the presence of epileptic discharges i.e. spikes or sharp waves. These often occur randomly on a background of fluctuating potentials. The spike rate varies between different brain states (sleep and awake) and patients. Epileptogenic tissue and regions near these often show increased spike rates in comparison to other cortical regions. Several studies have shown a relation between spike rate and background activity although the underlying reason for this is still poorly understood. Both these processes, spike occurrence and background activity show evidence of being at least partly stochastic processes. In this study we show that epileptic discharges seen on scalp electroencephalographic recordings and background activity are driven at least partly by a common biological noise. Furthermore, our results indicate noise induced quiescence of spike generation which, in analogy with computational models of spiking, indicate spikes to be generated by transitions between semi-stable states of the brain, similar to the generation of epileptic seizure activity. The deepened physiological understanding of spike generation in epilepsy that this study provides could be useful in the electrophysiological assessment of different therapies for epilepsy including the effect of different drugs or electrical stimulation.
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22
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Zheng L, Yan W, Yu L, Gao B, Yu S, Chen L, Hao X, Liu H, Lin Z. Altered Effective Brain Connectivity During Habituation in First Episode Schizophrenia With Auditory Verbal Hallucinations: A Dichotic Listening EEG Study. Front Psychiatry 2021; 12:731387. [PMID: 35046846 PMCID: PMC8761615 DOI: 10.3389/fpsyt.2021.731387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Accepted: 12/07/2021] [Indexed: 11/17/2022] Open
Abstract
Background: Habituation is considered to have protective and filtering mechanisms. The present study is aim to find the casual relationship and mechanisms of excitatory-inhibitory (E/I) dysfunctions in schizophrenia (SCZ) via habituation. Methods: A dichotic listening paradigm was performed with simultaneous EEG recording on 22 schizophrenia patients and 22 gender- and age-matched healthy controls. Source reconstruction and dynamic causal modeling (DCM) analysis were performed to estimate the effective connectivity and casual relationship between frontal and temporal regions before and after habituation. Results: The schizophrenia patients expressed later habituation onset (p < 0.01) and hyper-activity in both lateral frontal-temporal cortices than controls (p = 0.001). The patients also showed decreased top-down and bottom-up connectivity in bilateral frontal-temporal regions (p < 0.01). The contralateral frontal-frontal and temporal-temporal connectivity showed a left to right decreasing (p < 0.01) and right to left strengthening (p < 0.01). Conclusions: The results give causal evidence for E/I imbalance in schizophrenia during dichotic auditory processing. The altered effective connectivity in frontal-temporal circuit could represent the trait bio-marker of schizophrenia with auditory hallucinations.
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Affiliation(s)
- Leilei Zheng
- Department of Psychiatry, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Weizheng Yan
- Department of Psychiatry, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.,Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Linzhen Yu
- Department of Psychiatry, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Bin Gao
- Department of Psychiatry, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Shaohua Yu
- Department of Psychiatry, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Lili Chen
- Department of Psychiatry, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Xiaoyi Hao
- Department of Psychiatry, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Han Liu
- Department of Psychiatry, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Zheng Lin
- Department of Psychiatry, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
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23
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Monk AM, Dalton MA, Barnes GR, Maguire EA. The Role of Hippocampal-Ventromedial Prefrontal Cortex Neural Dynamics in Building Mental Representations. J Cogn Neurosci 2021; 33:89-103. [PMID: 32985945 PMCID: PMC7116437 DOI: 10.1162/jocn_a_01634] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
The hippocampus and ventromedial prefrontal cortex (vmPFC) play key roles in numerous cognitive domains including mind-wandering, episodic memory, and imagining the future. Perspectives differ on precisely how they support these diverse functions, but there is general agreement that it involves constructing representations composed of numerous elements. Visual scenes have been deployed extensively in cognitive neuroscience because they are paradigmatic multielement stimuli. However, it remains unclear whether scenes, rather than other types of multifeature stimuli, preferentially engage hippocampus and vmPFC. Here, we leveraged the high temporal resolution of magnetoencephalography to test participants as they gradually built scene imagery from three successive auditorily presented object descriptions and an imagined 3-D space. This was contrasted with constructing mental images of nonscene arrays that were composed of three objects and an imagined 2-D space. The scene and array stimuli were, therefore, highly matched, and this paradigm permitted a closer examination of step-by-step mental construction than has been undertaken previously. We observed modulation of theta power in our two regions of interest-anterior hippocampus during the initial stage and vmPFC during the first two stages, of scene relative to array construction. Moreover, the scene-specific anterior hippocampal activity during the first construction stage was driven by the vmPFC, with mutual entrainment between the two brain regions thereafter. These findings suggest that hippocampal and vmPFC neural activity is especially tuned to scene representations during the earliest stage of their formation, with implications for theories of how these brain areas enable cognitive functions such as episodic memory.
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24
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Pinotsis DA, Miller EK. Differences in visually induced MEG oscillations reflect differences in deep cortical layer activity. Commun Biol 2020; 3:707. [PMID: 33239652 PMCID: PMC7688644 DOI: 10.1038/s42003-020-01438-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Accepted: 10/22/2020] [Indexed: 11/09/2022] Open
Abstract
Neural activity is organized at multiple scales, ranging from the cellular to the whole brain level. Connecting neural dynamics at different scales is important for understanding brain pathology. Neurological diseases and disorders arise from interactions between factors that are expressed in multiple scales. Here, we suggest a new way to link microscopic and macroscopic dynamics through combinations of computational models. This exploits results from statistical decision theory and Bayesian inference. To validate our approach, we used two independent MEG datasets. In both, we found that variability in visually induced oscillations recorded from different people in simple visual perception tasks resulted from differences in the level of inhibition specific to deep cortical layers. This suggests differences in feedback to sensory areas and each subject's hypotheses about sensations due to differences in their prior experience. Our approach provides a new link between non-invasive brain imaging data, laminar dynamics and top-down control.
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Affiliation(s)
- Dimitris A Pinotsis
- Centre for Mathematical Neuroscience and Psychology and Department of Psychology, City -University of London, London, EC1V 0HB, UK.
- The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| | - Earl K Miller
- The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
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25
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Limanowski J, Litvak V, Friston K. Cortical beta oscillations reflect the contextual gating of visual action feedback. Neuroimage 2020; 222:117267. [PMID: 32818621 PMCID: PMC7779369 DOI: 10.1016/j.neuroimage.2020.117267] [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: 06/02/2020] [Revised: 08/08/2020] [Accepted: 08/12/2020] [Indexed: 11/26/2022] Open
Abstract
We decouple seen and felt hand postures during action via virtual reality. Vision of the hand is either task-relevant or a distractor. Task-relevance of vision is reflected by in- or decreases of occipital beta power. DCM suggests underlying changes in cortical (visual) excitability. Occipital beta may indicate the contextual gating of visual action feedback.
In sensorimotor integration, the brain needs to decide how its predictions should accommodate novel evidence by ‘gating’ sensory data depending on the current context. Here, we examined the oscillatory correlates of this process by recording magnetoencephalography (MEG) data during a new task requiring action under intersensory conflict. We used virtual reality to decouple visual (virtual) and proprioceptive (real) hand postures during a task in which the phase of grasping movements tracked a target (in either modality). Thus, we rendered visual information either task-relevant or a (to-be-ignored) distractor. Under visuo-proprioceptive incongruence, occipital beta power decreased (relative to congruence) when vision was task-relevant but increased when it had to be ignored. Dynamic causal modeling (DCM) revealed that this interaction was best explained by diametrical, task-dependent changes in visual gain. These results suggest a crucial role for beta oscillations in the contextual gating (i.e., gain or precision control) of visual vs proprioceptive action feedback, depending on current behavioral demands.
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Affiliation(s)
- Jakub Limanowski
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3BG, United Kingdom; Centre for Tactile Internet with Human-in-the-Loop, Technische Universität Dresden, Dresden, Germany.
| | - Vladimir Litvak
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3BG, United Kingdom
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3BG, United Kingdom
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26
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Monk AM, Barnes GR, Maguire EA. The Effect of Object Type on Building Scene Imagery-an MEG Study. Front Hum Neurosci 2020; 14:592175. [PMID: 33240069 PMCID: PMC7683518 DOI: 10.3389/fnhum.2020.592175] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 10/09/2020] [Indexed: 12/28/2022] Open
Abstract
Previous studies have reported that some objects evoke a sense of local three-dimensional space (space-defining; SD), while others do not (space-ambiguous; SA), despite being imagined or viewed in isolation devoid of a background context. Moreover, people show a strong preference for SD objects when given a choice of objects with which to mentally construct scene imagery. When deconstructing scenes, people retain significantly more SD objects than SA objects. It, therefore, seems that SD objects might enjoy a privileged role in scene construction. In the current study, we leveraged the high temporal resolution of magnetoencephalography (MEG) to compare the neural responses to SD and SA objects while they were being used to build imagined scene representations, as this has not been examined before using neuroimaging. On each trial, participants gradually built a scene image from three successive auditorily-presented object descriptions and an imagined 3D space. We then examined the neural dynamics associated with the points during scene construction when either SD or SA objects were being imagined. We found that SD objects elicited theta changes relative to SA objects in two brain regions, the right ventromedial prefrontal cortex (vmPFC) and the right superior temporal gyrus (STG). Furthermore, using dynamic causal modeling, we observed that the vmPFC drove STG activity. These findings may indicate that SD objects serve to activate schematic and conceptual knowledge in vmPFC and STG upon which scene representations are then built.
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Affiliation(s)
- Anna M Monk
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Gareth R Barnes
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Eleanor A Maguire
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
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27
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Min BK, Kim HS, Pinotsis DA, Pantazis D. Thalamocortical inhibitory dynamics support conscious perception. Neuroimage 2020; 220:117066. [PMID: 32565278 DOI: 10.1016/j.neuroimage.2020.117066] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 05/25/2020] [Accepted: 06/14/2020] [Indexed: 11/28/2022] Open
Abstract
Whether thalamocortical interactions play a decisive role in conscious perception remains an open question. We presented rapid red/green color flickering stimuli, which induced the mental perception of either an illusory orange color or non-fused red and green colors. Using magnetoencephalography, we observed 6-Hz thalamic activity associated with thalamocortical inhibitory coupling only during the conscious perception of the illusory orange color. This sustained thalamic disinhibition was temporally coupled with higher visual cortical activation during the conscious perception of the orange color, providing neurophysiological evidence of the role of thalamocortical synchronization in conscious awareness of mental representation. Bayesian model comparison consistently supported the thalamocortical model in conscious perception. Taken together, experimental and theoretical evidence established the thalamocortical inhibitory network as a gateway to conscious mental representations.
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Affiliation(s)
- Byoung-Kyong Min
- Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| | - Hyun Seok Kim
- Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea
| | - Dimitris A Pinotsis
- Center for Mathematical Neuroscience and Psychology, Department of Psychology, City-University of London, London, EC1V 0HB, UK; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Dimitrios Pantazis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
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28
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McCormick C, Barry DN, Jafarian A, Barnes GR, Maguire EA. vmPFC Drives Hippocampal Processing during Autobiographical Memory Recall Regardless of Remoteness. Cereb Cortex 2020; 30:5972-5987. [PMID: 32572443 PMCID: PMC7899055 DOI: 10.1093/cercor/bhaa172] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 05/29/2020] [Accepted: 05/31/2020] [Indexed: 12/25/2022] Open
Abstract
Our ability to recall past experiences, autobiographical memories (AMs), is crucial to cognition, endowing us with a sense of self and underwriting our capacity for autonomy. Traditional views assume that the hippocampus orchestrates event recall, whereas recent accounts propose that the ventromedial prefrontal cortex (vmPFC) instigates and coordinates hippocampal-dependent processes. Here we sought to characterize the dynamic interplay between the hippocampus and vmPFC during AM recall to adjudicate between these perspectives. Leveraging the high temporal resolution of magnetoencephalography, we found that the left hippocampus and the vmPFC showed the greatest power changes during AM retrieval. Moreover, responses in the vmPFC preceded activity in the hippocampus during initiation of AM recall, except during retrieval of the most recent AMs. The vmPFC drove hippocampal activity during recall initiation and also as AMs unfolded over subsequent seconds, and this effect was evident regardless of AM age. These results recast the positions of the hippocampus and the vmPFC in the AM retrieval hierarchy, with implications for theoretical accounts of memory processing and systems-level consolidation.
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Affiliation(s)
- Cornelia McCormick
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK
- Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, 53127 Bonn, Germany
| | - Daniel N Barry
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK
| | - Amirhossein Jafarian
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK
| | - Gareth R Barnes
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK
| | - Eleanor A Maguire
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK
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29
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Spedden ME, Beck MM, Christensen MS, Dietz MJ, Karabanov AN, Geertsen SS, Nielsen JB, Lundbye-Jensen J. Directed connectivity between primary and premotor areas underlying ankle force control in young and older adults. Neuroimage 2020; 218:116982. [DOI: 10.1016/j.neuroimage.2020.116982] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2019] [Revised: 03/31/2020] [Accepted: 05/19/2020] [Indexed: 11/29/2022] Open
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30
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Pinotsis DA. Statistical decision theory and multiscale analyses of human brain data. J Neurosci Methods 2020; 346:108912. [PMID: 32835705 DOI: 10.1016/j.jneumeth.2020.108912] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 08/11/2020] [Accepted: 08/12/2020] [Indexed: 02/02/2023]
Abstract
BACKGROUND In the era of Big Data, large scale electrophysiological data from animal and human studies are abundant. These data contain information at multiple spatiotemporal scales. However, current approaches for the analysis of electrophysiological data often focus on a single spatiotemporal scale only. NEW METHOD We discuss a multiscale approach for the analysis of electrophysiological data. This is based on combining neural models that describe brain data at different scales. It allows us to make laminar-specific inferences about neurobiological properties of cortical sources using non invasive human electrophysiology data. RESULTS We provide a mathematical proof of this approach using statistical decision theory. We also consider its extensions to brain imaging studies including data from the same subjects performing different tasks. As an illustration, we show that changes in gamma oscillations between different people might originate from differences in recurrent connection strengths of inhibitory interneurons in layers 5/6. COMPARISON WITH EXISTING METHODS This is a new approach that follows up on our recent work. It is different from other approaches where the scale of spatiotemporal dynamics is fixed. CONCLUSIONS We discuss a multiscale approach for the analysis of human MEG data. This uses a neural mass model that includes constraints informed by a compartmental model. This has two advantages. First, it allows us to find differences in cortical laminar dynamics and understand neurobiological properties like neuromodulation, excitation to inhibition balance etc. using non invasive data. Second, it allows us to validate macroscale models by exploiting animal data.
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Affiliation(s)
- D A Pinotsis
- Centre for Mathematical Neuroscience and Psychology and Department of Psychology, City -University of London, London EC1V 0HB, United Kingdom; The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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31
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Seymour RA, Rippon G, Gooding-Williams G, Sowman PF, Kessler K. Reduced auditory steady state responses in autism spectrum disorder. Mol Autism 2020; 11:56. [PMID: 32611372 PMCID: PMC7329477 DOI: 10.1186/s13229-020-00357-y] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Accepted: 06/10/2020] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Auditory steady state responses (ASSRs) are elicited by clicktrains or amplitude-modulated tones, which entrain auditory cortex at their specific modulation rate. Previous research has reported reductions in ASSRs at 40 Hz for autism spectrum disorder (ASD) participants and first-degree relatives of people diagnosed with ASD (Mol Autism. 2011;2:11, Biol Psychiatry. 2007;62:192-197). METHODS Using a 1.5 s-long auditory clicktrain stimulus, designed to elicit an ASSR at 40 Hz, this study attempted to replicate and extend these findings. Magnetencephalography (MEG) data were collected from 18 adolescent ASD participants and 18 typically developing controls. RESULTS The ASSR localised to bilateral primary auditory regions. Regions of interest were thus defined in left and right primary auditory cortex (A1). While the transient gamma-band response (tGBR) from 0-0.1 s following presentation of the clicktrain stimulus was not different between groups, for either left or right A1, the ASD group had reduced oscillatory power at 40 Hz from 0.5 to 1.5 s post-stimulus onset, for both left and right A1. Additionally, the ASD group had reduced inter-trial coherence (phase consistency over trials) at 40 Hz from 0.64-0.82 s for right A1 and 1.04-1.22 s for left A1. LIMITATIONS In this study, we did not conduct a clinical autism assessment (e.g. the ADOS), and therefore, it remains unclear whether ASSR power and/or ITC are associated with the clinical symptoms of ASD. CONCLUSION Overall, our results support a specific reduction in ASSR oscillatory power and inter-trial coherence in ASD, rather than a generalised deficit in gamma-band responses. We argue that this could reflect a developmentally relevant reduction in non-linear neural processing.
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Affiliation(s)
- R A Seymour
- Aston Neuroscience Institute, School of Life and Health Sciences, Aston University, Birmingham, B4 7ET, UK.
- Department of Cognitive Science, Macquarie University, Sydney, 2109, Australia.
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, 12 Queen Square, London, WC1N 3AR, UK.
| | - G Rippon
- Aston Neuroscience Institute, School of Life and Health Sciences, Aston University, Birmingham, B4 7ET, UK
| | - G Gooding-Williams
- Aston Neuroscience Institute, School of Life and Health Sciences, Aston University, Birmingham, B4 7ET, UK
| | - P F Sowman
- Department of Cognitive Science, Macquarie University, Sydney, 2109, Australia
| | - K Kessler
- Aston Neuroscience Institute, School of Life and Health Sciences, Aston University, Birmingham, B4 7ET, UK.
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32
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Hamburg S, Rosch R, Startin CM, Friston KJ, Strydom A. Dynamic Causal Modeling of the Relationship between Cognition and Theta-alpha Oscillations in Adults with Down Syndrome. Cereb Cortex 2020; 29:2279-2290. [PMID: 30877793 PMCID: PMC6458903 DOI: 10.1093/cercor/bhz043] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 02/09/2019] [Indexed: 01/17/2023] Open
Abstract
Individuals with Down syndrome (DS) show high inter-subject variability in cognitive ability and have an ultra-high risk of developing dementia (90% lifetime prevalence). Elucidating factors underlying variability in cognitive function can inform us about intellectual disability (ID) and may improve our understanding of factors associated with later cognitive decline. Increased neuronal inhibition has been posited to contribute to ID in DS. Combining electroencephalography (EEG) with dynamic causal modeling (DCM) provides a non-invasive method for investigating excitatory/inhibitory mechanisms. Resting-state EEG recordings were obtained from 36 adults with DS with no evidence of cognitive decline. Theta–alpha activity (4–13 Hz) was characterized in relation to general cognitive ability (raw Kaufmann’s Brief Intelligence Test second Edition (KBIT-2) score). Higher KBIT-2 was associated with higher frontal alpha peak amplitude and higher theta–alpha band power across distributed regions. Modeling this association with DCM revealed intrinsic self-inhibition was the key network parameter underlying observed differences in 4–13 Hz power in relation to KBIT-2 and age. In particular, intrinsic self-inhibition in right V1 was negatively correlated with KBIT-2. Results suggest intrinsic self-inhibition within the alpha network is associated with individual differences in cognitive ability in adults with DS, and may provide a potential therapeutic target for cognitive enhancement.
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Affiliation(s)
- Sarah Hamburg
- Division of Psychiatry, Faculty of Brain Sciences, University College London, 149 Tottenham Court Road, London, UK.,Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK.,The London Down Syndrome Consortium (LonDownS), London, UK
| | - Richard Rosch
- MRC Centre for Neurodevelopmental Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London, UK
| | - Carla Marie Startin
- Division of Psychiatry, Faculty of Brain Sciences, University College London, 149 Tottenham Court Road, London, UK.,Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK.,The London Down Syndrome Consortium (LonDownS), London, UK
| | - Karl John Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London, UK
| | - André Strydom
- Division of Psychiatry, Faculty of Brain Sciences, University College London, 149 Tottenham Court Road, London, UK.,Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK.,The London Down Syndrome Consortium (LonDownS), London, UK
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33
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Sonkusare S, Nguyen VT, Moran R, van der Meer J, Ren Y, Koussis N, Dionisio S, Breakspear M, Guo C. Intracranial-EEG evidence for medial temporal pole driving amygdala activity induced by multi-modal emotional stimuli. Cortex 2020; 130:32-48. [PMID: 32640373 DOI: 10.1016/j.cortex.2020.05.018] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 05/13/2020] [Accepted: 05/29/2020] [Indexed: 12/13/2022]
Abstract
The temporal pole (TP) is an associative cortical region required for complex cognitive functions such as social and emotional cognition. However, mapping the TP with functional magnetic resonance imaging is technically challenging and thus understanding its interaction with other key emotional circuitry, such as the amygdala, remains elusive. We exploited the unique advantages of stereo-electroencephalography (sEEG) to assess the responses of the TP and the amygdala during the perception of emotionally salient stimuli of pictures, music and movies. These stimuli consistently elicited high gamma responses (70-140 Hz) in both the TP and the amygdala, accompanied by functional connectivity in the low frequency range (2-12 Hz). Computational analyses suggested that the TP drove this effect in the theta frequency range, modulated by the emotional valence of the stimuli. Notably, cross-frequency analysis indicated the phase of theta oscillations in the TP modulated the amplitude of high gamma activity in the amygdala. These results were reproducible across three types of sensory inputs including naturalistic stimuli. Our results suggest that multimodal emotional stimuli induce a hierarchical influence of the TP over the amygdala.
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Affiliation(s)
- Saurabh Sonkusare
- QIMR Berghofer Medical Research Institute, Brisbane, Australia; School of Medicine, The University of Queensland, Brisbane, Australia.
| | - Vinh T Nguyen
- QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Rosalyn Moran
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | | | - Yudan Ren
- QIMR Berghofer Medical Research Institute, Brisbane, Australia; School of Information Science and Technology, Northwest University, Xi'an, China
| | - Nikitas Koussis
- QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Sasha Dionisio
- Mater Advanced Epilepsy Unit, Mater Hospital, Brisbane, Australia
| | - Michael Breakspear
- QIMR Berghofer Medical Research Institute, Brisbane, Australia; Hunter Medical Research Institute, University of Newcastle, Newcastle, Australia.
| | - Christine Guo
- QIMR Berghofer Medical Research Institute, Brisbane, Australia
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Kube T, Berg M, Kleim B, Herzog P. Rethinking post-traumatic stress disorder - A predictive processing perspective. Neurosci Biobehav Rev 2020; 113:448-460. [PMID: 32315695 DOI: 10.1016/j.neubiorev.2020.04.014] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 04/07/2020] [Accepted: 04/09/2020] [Indexed: 12/15/2022]
Abstract
Predictive processing has become a popular framework in neuroscience and computational psychiatry, where it has provided a new understanding of various mental disorders. Here, we apply the predictive processing account to post-traumatic stress disorder (PTSD). We argue that the experience of a traumatic event in Bayesian terms can be understood as a perceptual hypothesis that is subsequently given a very high a-priori likelihood due to its (life-) threatening significance; thus, this hypothesis is re-selected although it does not fit the actual sensory input. Based on this account, we re-conceptualise the symptom clusters of PTSD through the lens of a predictive processing model. We particularly focus on re-experiencing symptoms as the hallmark symptoms of PTSD, and discuss the occurrence of flashbacks in terms of perceptual and interoceptive inference. This account provides not only a new understanding of the clinical profile of PTSD, but also a unifying framework for the corresponding pathologies at the neurobiological level. Finally, we derive directions for future research and discuss implications for psychological and pharmacological interventions.
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Affiliation(s)
- Tobias Kube
- Harvard Medical School, Program in Placebo Studies, Beth Israel Deaconess Medical Center, Brookline Avenue 330, Boston, MA, 02115, USA; University of Koblenz-Landau, Pain and Psychotherapy Research Lab, Ostbahnstr. 10, 76829 Landau, Germany.
| | - Max Berg
- Philipps-University of Marburg, Department of Psychology, Division of Clinical Psychology and Psychological Treatment Gutenbergstraße 18, D-35032, Marburg, Germany
| | - Birgit Kleim
- University of Zurich, Department of Psychology, Binzmühlestrasse 14, Box 8, CH-8050, Zurich, Switzerland; Psychiatric University Hospital (PUK), Lenggstrasse 31, CH-8032, Zurich, Switzerland
| | - Philipp Herzog
- Philipps-University of Marburg, Department of Psychology, Division of Clinical Psychology and Psychological Treatment Gutenbergstraße 18, D-35032, Marburg, Germany; University of Greifswald, Department of Psychology, Clinical Psychology and Psychotherapy, Franz-Mehring-Straße 47, D-17489, Greifswald, Germany; Department of Psychiatry and Psychotherapy, University of Lübeck, Ratzeburger Allee 160, D-23562, Lübeck, Germany
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35
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Li X, Yang X, Sun Z. Alpha rhythm slowing in a modified thalamo-cortico-thalamic model related with Alzheimer's disease. PLoS One 2020; 15:e0229950. [PMID: 32163454 PMCID: PMC7067465 DOI: 10.1371/journal.pone.0229950] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Accepted: 02/18/2020] [Indexed: 12/21/2022] Open
Abstract
A decrease in alpha band power is defined as a hallmark of electroencephalogram (EEG) in Alzheimer’s disease (AD). This study devotes to understanding the neuronal correlates of alpha rhythm slowing associated with AD from the view of neurocomputation. Firstly, a modified computational model of thalamo-cortico-thalamic (TCT) circuitry is constructed by incorporating two important biologically plausible ingredients. One is the disinhibition property between different inhibitory interneurons in the cortical module. The other is the full relay function of thalamic relay nucleus (TCR) to the cortical module. Then, by decreasing synaptic connectivity parameters to mimic the neuropathological condition of synapse loss in AD, the correlation between neuronal synaptic behavior and abnormal alpha rhythm is simulated by means of power spectral analysis. The results indicate that these decreases of synaptic activity, i.e., not only the excitatory synaptic connections from TCR to fast inhibitory interneurons Cfte and from excitatory interneurons to pyramidal neurons Cpxe but also the inhibitory synaptic connections from fast inhibitory interneurons to slow inhibitory interneurons Clfi and from inhibitory interneurons to TCR Ctii, can significantly diminish the peak power density over the alpha band of the thalamic output, which implies that there is a slowing of alpha band. Furthermore, the underlying mechanism behind the alpha rhythmic changes is analyzed using nonlinear dynamical technique. The results reveal that decreases of Cfte, Cpxe, Clfi and Ctii can make the thalamic module transfer from a limit cycle mode to a point attractor mode, which may lead to the alpha rhythm slowing in the modified TCT model. We expect this work can be helpful in identifying early biomarkers of AD’s EEG and understanding potential pathogenesis of AD.
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Affiliation(s)
- XiaoYuan Li
- College of Mathematics and Information Science, Shaanxi Normal University, Xi’an, PR China
| | - XiaoLi Yang
- College of Mathematics and Information Science, Shaanxi Normal University, Xi’an, PR China
- * E-mail:
| | - ZhongKui Sun
- Department of Applied Mathematics, Northwestern Polytechnical University, Xi’an, PR China
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MCMC for Bayesian Uncertainty Quantification from Time-Series Data. LECTURE NOTES IN COMPUTER SCIENCE 2020. [PMCID: PMC7304777 DOI: 10.1007/978-3-030-50436-6_52] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In computational neuroscience, Neural Population Models (NPMs) are mechanistic models that describe brain physiology in a range of different states. Within computational neuroscience there is growing interest in the inverse problem of inferring NPM parameters from recordings such as the EEG (Electroencephalogram). Uncertainty quantification is essential in this application area in order to infer the mechanistic effect of interventions such as anaesthesia. This paper presents
software for Bayesian uncertainty quantification in the parameters of NPMs from approximately stationary data using Markov Chain Monte Carlo (MCMC). Modern MCMC methods require first order (and in some cases higher order) derivatives of the posterior density. The software presented offers two distinct methods of evaluating derivatives: finite differences and exact derivatives obtained through Algorithmic Differentiation (AD). For AD, two different implementations are used: the open source Stan Math Library and the commercially licenced
tool distributed by NAG (Numerical Algorithms Group). The use of derivative information in MCMC sampling is demonstrated through a simple example, the noise-driven harmonic oscillator. And different methods for computing derivatives are compared. The software is written in a modular object-oriented way such that it can be extended to derivative based MCMC for other scientific domains.
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Jafarian A, Zeidman P, Litvak V, Friston K. Structure learning in coupled dynamical systems and dynamic causal modelling. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2019; 377:20190048. [PMID: 31656140 PMCID: PMC6833995 DOI: 10.1098/rsta.2019.0048] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/18/2019] [Indexed: 05/03/2023]
Abstract
Identifying a coupled dynamical system out of many plausible candidates, each of which could serve as the underlying generator of some observed measurements, is a profoundly ill-posed problem that commonly arises when modelling real-world phenomena. In this review, we detail a set of statistical procedures for inferring the structure of nonlinear coupled dynamical systems (structure learning), which has proved useful in neuroscience research. A key focus here is the comparison of competing models of network architectures-and implicit coupling functions-in terms of their Bayesian model evidence. These methods are collectively referred to as dynamic causal modelling. We focus on a relatively new approach that is proving remarkably useful, namely Bayesian model reduction, which enables rapid evaluation and comparison of models that differ in their network architecture. We illustrate the usefulness of these techniques through modelling neurovascular coupling (cellular pathways linking neuronal and vascular systems), whose function is an active focus of research in neurobiology and the imaging of coupled neuronal systems. This article is part of the theme issue 'Coupling functions: dynamical interaction mechanisms in the physical, biological and social sciences'.
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Affiliation(s)
- Amirhossein Jafarian
- The Wellcome Centre for Human Neuroimaging, Institute of Neurology, 12 Queen Square, London WC1N 3AR, UK
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The Neural Dynamics of Novel Scene Imagery. J Neurosci 2019; 39:4375-4386. [PMID: 30902867 PMCID: PMC6538850 DOI: 10.1523/jneurosci.2497-18.2019] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Revised: 03/06/2019] [Accepted: 03/07/2019] [Indexed: 12/16/2022] Open
Abstract
Retrieval of long-term episodic memories is characterized by synchronized neural activity between hippocampus and ventromedial prefrontal cortex (vmPFC), with additional evidence that vmPFC activity leads that of the hippocampus. It has been proposed that the mental generation of scene imagery is a crucial component of episodic memory processing. If this is the case, then a comparable interaction between the two brain regions should exist during the construction of novel scene imagery. To address this question, we leveraged the high temporal resolution of MEG to investigate the construction of novel mental imagery. We tasked male and female humans with imagining scenes and single isolated objects in response to one-word cues. We performed source-level power, coherence, and causality analyses to characterize the underlying interregional interactions. Both scene and object imagination resulted in theta power changes in the anterior hippocampus. However, higher theta coherence was observed between the hippocampus and vmPFC in the scene compared with the object condition. This interregional theta coherence also predicted whether imagined scenes were subsequently remembered. Dynamic causal modeling of this interaction revealed that vmPFC drove activity in hippocampus during novel scene construction. Additionally, theta power changes in the vmPFC preceded those observed in the hippocampus. These results constitute the first evidence in humans that episodic memory retrieval and scene imagination rely on similar vmPFC–hippocampus neural dynamics. Furthermore, they provide support for theories emphasizing similarities between both cognitive processes and perspectives that propose the vmPFC guides the construction of context-relevant representations in the hippocampus. SIGNIFICANCE STATEMENT Episodic memory retrieval is characterized by a dialog between hippocampus and ventromedial prefrontal cortex (vmPFC). It has been proposed that the mental generation of scene imagery is a crucial component of episodic memory processing. An ensuing prediction would be of a comparable interaction between the two brain regions during the construction of novel scene imagery. Here, we leveraged the high temporal resolution of MEG and combined it with a scene imagination task. We found that a hippocampal–vmPFC dialog existed and that it took the form of vmPFC driving the hippocampus. We conclude that episodic memory and scene imagination share fundamental neural dynamics and the process of constructing vivid, spatially coherent, contextually appropriate scene imagery is strongly modulated by vmPFC.
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Reis C, Sharott A, Magill PJ, van Wijk BCM, Parr T, Zeidman P, Friston KJ, Cagnan H. Thalamocortical dynamics underlying spontaneous transitions in beta power in Parkinsonism. Neuroimage 2019; 193:103-114. [PMID: 30862535 PMCID: PMC6503152 DOI: 10.1016/j.neuroimage.2019.03.009] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Revised: 02/01/2019] [Accepted: 03/05/2019] [Indexed: 01/03/2023] Open
Abstract
Parkinson's disease (PD) is a neurodegenerative condition in which aberrant oscillatory synchronization of neuronal activity at beta frequencies (15–35 Hz) across the cortico-basal ganglia-thalamocortical circuit is associated with debilitating motor symptoms, such as bradykinesia and rigidity. Mounting evidence suggests that the magnitude of beta synchrony in the parkinsonian state fluctuates over time, but the mechanisms by which thalamocortical circuitry regulates the dynamic properties of cortical beta in PD are poorly understood. Using the recently developed generic Dynamic Causal Modelling (DCM) framework, we recursively optimized a set of plausible models of the thalamocortical circuit (n = 144) to infer the neural mechanisms that best explain the transitions between low and high beta power states observed in recordings of field potentials made in the motor cortex of anesthetized Parkinsonian rats. Bayesian model comparison suggests that upregulation of cortical rhythmic activity in the beta-frequency band results from changes in the coupling strength both between and within the thalamus and motor cortex. Specifically, our model indicates that high levels of cortical beta synchrony are mainly achieved by a delayed (extrinsic) input from thalamic relay cells to deep pyramidal cells and a fast (intrinsic) input from middle pyramidal cells to superficial pyramidal cells. From a clinical perspective, our study provides insights into potential therapeutic strategies that could be utilized to modulate the network mechanisms responsible for the enhancement of cortical beta in PD. Specifically, we speculate that cortical stimulation aimed to reduce the enhanced excitatory inputs to either the superficial or deep pyramidal cells could be a potential non-invasive therapeutic strategy for PD. Coupling changes within and between circuit nodes lead to cortical beta enhancement. Input propagation delays play a crucial role in the up-regulation of cortical beta. Beta power could be modulated by altering lamina specific inputs.
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Affiliation(s)
- Carolina Reis
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Andrew Sharott
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, UK
| | - Peter J Magill
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, UK; Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
| | - Bernadette C M van Wijk
- Wellcome Centre for Human Neuroimaging, University College London, UK; Integrative Model-based Cognitive Neuroscience Research Unit, Department of Psychology, University of Amsterdam, the Netherlands
| | - Thomas Parr
- Wellcome Centre for Human Neuroimaging, University College London, UK
| | - Peter Zeidman
- Wellcome Centre for Human Neuroimaging, University College London, UK
| | - Karl J Friston
- Wellcome Centre for Human Neuroimaging, University College London, UK
| | - Hayriye Cagnan
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK.
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Yeldesbay A, Fink GR, Daun S. Reconstruction of effective connectivity in the case of asymmetric phase distributions. J Neurosci Methods 2019; 317:94-107. [PMID: 30786248 DOI: 10.1016/j.jneumeth.2019.02.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Revised: 02/13/2019] [Accepted: 02/15/2019] [Indexed: 11/16/2022]
Abstract
BACKGROUND The interaction of different brain regions is supported by transient synchronization between neural oscillations at different frequencies. Different measures based on synchronization theory are used to assess the strength of the interactions from experimental data. One method of estimating the effective connectivity between brain regions, within the framework of the theory of weakly coupled phase oscillators, was implemented in Dynamic Causal Modelling (DCM) for phase coupling (Penny et al., 2009). However, the results of such an approach strongly depend on the observables used to reconstruct the equations (Kralemann et al., 2008). In particular, an asymmetric distribution of the observables could result in a false estimation of the effective connectivity between the network nodes. NEW METHOD In this work we built a new modelling part into DCM for phase coupling, and extended it with a distortion function that accommodates departures from purely sinusoidal oscillations. RESULTS By analysing numerically generated data sets with an asymmetric phase distribution, we demonstrated that the extended DCM for phase coupling with the additional modelling component, correctly estimates the coupling functions. COMPARISON WITH EXISTING METHODS The new method allows for different intrinsic frequencies among coupled neuronal populations and provides results that do not depend on the distribution of the observables. CONCLUSIONS The proposed method can be used to analyse effective connectivity between brain regions within and between different frequency bands, to characterize m:n phase coupling, and to unravel underlying mechanisms of the transient synchronization.
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Affiliation(s)
- Azamat Yeldesbay
- University of Cologne, Institute of Zoology, Heisenberg Research Group of Computational Neuroscience - Modeling Neural Network Function, Zülpicher Str. 47b, 50674 Cologne, Germany; Research Centre Jülich, Institute of Neuroscience and Medicine (INM-3), Cognitive Neuroscience, 52425 Jülich, Germany.
| | - Gereon R Fink
- Research Centre Jülich, Institute of Neuroscience and Medicine (INM-3), Cognitive Neuroscience, 52425 Jülich, Germany; University of Cologne, Department of Neurology, Medical Faculty and University Hospital Cologne, Kerpener Str. 62, 50937 Cologne, Germany
| | - Silvia Daun
- University of Cologne, Institute of Zoology, Heisenberg Research Group of Computational Neuroscience - Modeling Neural Network Function, Zülpicher Str. 47b, 50674 Cologne, Germany; Research Centre Jülich, Institute of Neuroscience and Medicine (INM-3), Cognitive Neuroscience, 52425 Jülich, Germany.
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Generic dynamic causal modelling: An illustrative application to Parkinson's disease. Neuroimage 2018; 181:818-830. [PMID: 30130648 PMCID: PMC7343527 DOI: 10.1016/j.neuroimage.2018.08.039] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Revised: 08/15/2018] [Accepted: 08/16/2018] [Indexed: 12/26/2022] Open
Abstract
We present a technical development in the dynamic causal modelling of
electrophysiological responses that combines qualitatively different neural mass
models within a single network. This affords the option to couple various
cortical and subcortical nodes that differ in their form and dynamics. Moreover,
it enables users to implement new neural mass models in a straightforward and
standardized way. This generic framework hence supports flexibility and
facilitates the exploration of increasingly plausible models. We illustrate this
by coupling a basal ganglia-thalamus model to a (previously validated) cortical
model developed specifically for motor cortex. The ensuing DCM is used to infer
pathways that contribute to the suppression of beta oscillations induced by
dopaminergic medication in patients with Parkinson's disease.
Experimental recordings were obtained from deep brain stimulation electrodes
(implanted in the subthalamic nucleus) and simultaneous magnetoencephalography.
In line with previous studies, our results indicate a reduction of synaptic
efficacy within the circuit between the subthalamic nucleus and external
pallidum, as well as reduced efficacy in connections of the hyperdirect and
indirect pathway leading to this circuit. This work forms the foundation for a
range of modelling studies of the synaptic mechanisms (and pathophysiology)
underlying event-related potentials and cross-spectral densities.
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A Blind Module Identification Approach for Predicting Effective Connectivity Within Brain Dynamical Subnetworks. Brain Topogr 2018; 32:28-65. [PMID: 30076488 DOI: 10.1007/s10548-018-0666-3] [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: 11/08/2017] [Accepted: 07/28/2018] [Indexed: 10/28/2022]
Abstract
Model-based network discovery measures, such as the brain effective connectivity, require fitting of generative process models to measurements obtained from key areas across the network. For distributed dynamic phenomena, such as generalized seizures and slow-wave sleep, studying effective connectivity from real-time recordings is significantly complicated since (i) outputs from only a subnetwork can be practically measured, and (ii) exogenous subnetwork inputs are unobservable. Model fitting, therefore, constitutes a challenging blind module identification or model inversion problem for finding both the parameters and the many unknown inputs of the subnetwork. We herein propose a novel estimation framework for identifying nonlinear dynamic subnetworks in the case of slowly-varying, otherwise unknown local inputs. Starting with approximate predictions obtained using Cubature Kalman filtering, residuals of local output predictions are utilized to improve upon local input estimates. The algorithm performance is tested on both simulated and clinical EEG of induced seizures under electroconvulsive therapy (ECT). For the simulated network, the algorithm significantly boosted the estimation accuracy for inputs and connections from noisy EEG. For the clinical data, the algorithm predicted increased subnetwork inputs during the pre-stimulus anesthesia condition. Importantly, it predicted an increased frontocentral connectivity during the generalized seizure that is commensurate with electrode placement and that corroborates the clinical hypothesis of increased frontal focality of therapeutic ECT seizures. The proposed framework can be extended to account for several input configurations and can in principle be applied to study effective connectivity within brain subnetworks defined at the microscale (cortical lamina interaction) or at the macroscale (sensory integration).
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Sumner RL, McMillan RL, Shaw AD, Singh KD, Sundram F, Muthukumaraswamy SD. Peak visual gamma frequency is modified across the healthy menstrual cycle. Hum Brain Mapp 2018; 39:3187-3202. [PMID: 29665216 PMCID: PMC6055613 DOI: 10.1002/hbm.24069] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2017] [Revised: 03/20/2018] [Accepted: 03/22/2018] [Indexed: 12/14/2022] Open
Abstract
Fluctuations in gonadal hormones over the course of the menstrual cycle are known to cause functional brain changes and are thought to modulate changes in the balance of cortical excitation and inhibition. Animal research has shown this occurs primarily via the major metabolite of progesterone, allopregnanolone, and its action as a positive allosteric modulator of the GABAA receptor. Our study used EEG to record gamma oscillations induced in the visual cortex using stationary and moving gratings. Recordings took place during twenty females' mid-luteal phase when progesterone and estradiol are highest, and early follicular phase when progesterone and estradiol are lowest. Significantly higher (∼5 Hz) gamma frequency was recorded during the luteal compared to the follicular phase for both stimuli types. Using dynamic causal modeling, these changes were linked to stronger self-inhibition of superficial pyramidal cells in the luteal compared to the follicular phase. In addition, the connection from inhibitory interneurons to deep pyramidal cells was found to be stronger in the follicular compared to the luteal phase. These findings show that complex functional changes in synaptic microcircuitry occur across the menstrual cycle and that menstrual cycle phase should be taken into consideration when including female participants in research into gamma-band oscillations.
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Affiliation(s)
- Rachael L. Sumner
- School of PsychologyThe University of AucklandAuckland1142New Zealand
| | | | | | - Krish D. Singh
- CUBRIC, School of PsychologyCardiff UniversityCardiffCF24 4HQUK
| | - Fred Sundram
- Department of Psychological MedicineThe University of AucklandAuckland1142New Zealand
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Madi MK, Karameh FN. Adaptive optimal input design and parametric estimation of nonlinear dynamical systems: application to neuronal modeling. J Neural Eng 2018; 15:046028. [PMID: 29749350 DOI: 10.1088/1741-2552/aac3f7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Many physical models of biological processes including neural systems are characterized by parametric nonlinear dynamical relations between driving inputs, internal states, and measured outputs of the process. Fitting such models using experimental data (data assimilation) is a challenging task since the physical process often operates in a noisy, possibly non-stationary environment; moreover, conducting multiple experiments under controlled and repeatable conditions can be impractical, time consuming or costly. The accuracy of model identification, therefore, is dictated principally by the quality and dynamic richness of collected data over single or few experimental sessions. Accordingly, it is highly desirable to design efficient experiments that, by exciting the physical process with smart inputs, yields fast convergence and increased accuracy of the model. APPROACH We herein introduce an adaptive framework in which optimal input design is integrated with square root cubature Kalman filters (OID-SCKF) to develop an online estimation procedure that first, converges significantly quicker, thereby permitting model fitting over shorter time windows, and second, enhances model accuracy when only few process outputs are accessible. The methodology is demonstrated on common nonlinear models and on a four-area neural mass model with noisy and limited measurements. Estimation quality (speed and accuracy) is benchmarked against high-performance SCKF-based methods that commonly employ dynamically rich informed inputs for accurate model identification. MAIN RESULTS For all the tested models, simulated single-trial and ensemble averages showed that OID-SCKF exhibited (i) faster convergence of parameter estimates and (ii) lower dependence on inter-trial noise variability with gains up to around 1000 ms in speed and 81% increase in variability for the neural mass models. In terms of accuracy, OID-SCKF estimation was superior, and exhibited considerably less variability across experiments, in identifying model parameters of (a) systems with challenging model inversion dynamics and (b) systems with fewer measurable outputs that directly relate to the underlying processes. SIGNIFICANCE Fast and accurate identification therefore carries particular promise for modeling of transient (short-lived) neuronal network dynamics using a spatially under-sampled set of noisy measurements, as is commonly encountered in neural engineering applications.
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Affiliation(s)
- Mahmoud K Madi
- Department of Electrical and Computer Engineering, American University of Beirut, Beirut, Lebanon
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45
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Starke L, Ostwald D. Variational Bayesian Parameter Estimation Techniques for the General Linear Model. Front Neurosci 2017; 11:504. [PMID: 28966572 PMCID: PMC5605759 DOI: 10.3389/fnins.2017.00504] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Accepted: 08/24/2017] [Indexed: 12/05/2022] Open
Abstract
Variational Bayes (VB), variational maximum likelihood (VML), restricted maximum likelihood (ReML), and maximum likelihood (ML) are cornerstone parametric statistical estimation techniques in the analysis of functional neuroimaging data. However, the theoretical underpinnings of these model parameter estimation techniques are rarely covered in introductory statistical texts. Because of the widespread practical use of VB, VML, ReML, and ML in the neuroimaging community, we reasoned that a theoretical treatment of their relationships and their application in a basic modeling scenario may be helpful for both neuroimaging novices and practitioners alike. In this technical study, we thus revisit the conceptual and formal underpinnings of VB, VML, ReML, and ML and provide a detailed account of their mathematical relationships and implementational details. We further apply VB, VML, ReML, and ML to the general linear model (GLM) with non-spherical error covariance as commonly encountered in the first-level analysis of fMRI data. To this end, we explicitly derive the corresponding free energy objective functions and ensuing iterative algorithms. Finally, in the applied part of our study, we evaluate the parameter and model recovery properties of VB, VML, ReML, and ML, first in an exemplary setting and then in the analysis of experimental fMRI data acquired from a single participant under visual stimulation.
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Affiliation(s)
- Ludger Starke
- Arbeitsbereich Computational Cognitive Neuroscience, Department of Education and Psychology, Freie Universität BerlinBerlin, Germany
| | - Dirk Ostwald
- Arbeitsbereich Computational Cognitive Neuroscience, Department of Education and Psychology, Freie Universität BerlinBerlin, Germany.,Center for Cognitive Neuroscience Berlin, Freie Universität BerlinBerlin, Germany.,Center for Adaptive Rationality, Max Planck Institute for Human DevelopmentBerlin, Germany
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46
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Shaw AD, Moran RJ, Muthukumaraswamy SD, Brealy J, Linden DE, Friston KJ, Singh KD. Neurophysiologically-informed markers of individual variability and pharmacological manipulation of human cortical gamma. Neuroimage 2017; 161:19-31. [PMID: 28807873 PMCID: PMC5692925 DOI: 10.1016/j.neuroimage.2017.08.034] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2016] [Revised: 08/08/2017] [Accepted: 08/10/2017] [Indexed: 12/13/2022] Open
Abstract
The ability to quantify synaptic function at the level of cortical microcircuits from non-invasive data would be enormously useful in the study of neuronal processing in humans and the pathophysiology that attends many neuropsychiatric disorders. Here, we provide proof of principle that one can estimate inter-and intra-laminar interactions among specific neuronal populations using induced gamma responses in the visual cortex of human subjects - using dynamic causal modelling based upon the canonical microcircuit (CMC; a simplistic model of a cortical column). Using variability in induced (spectral) responses over a large cohort of normal subjects, we find that the predominant determinants of gamma responses rest on recurrent and intrinsic connections between superficial pyramidal cells and inhibitory interneurons. Furthermore, variations in beta responses were mediated by inter-subject differences in the intrinsic connections between deep pyramidal cells and inhibitory interneurons. Interestingly, we also show that increasing the self-inhibition of superficial pyramidal cells suppresses the amplitude of gamma activity, while increasing its peak frequency. This systematic and nonlinear relationship was only disclosed by modelling the causes of induced responses. Crucially, we were able to validate this form of neurophysiological phenotyping by showing a selective effect of the GABA re-uptake inhibitor tiagabine on the rate constants of inhibitory interneurons. Remarkably, we were able to recover the pharmacodynamics of this effect over the course of several hours on a per subject basis. These findings speak to the possibility of measuring population specific synaptic function - and its response to pharmacological intervention - to provide subject-specific biomarkers of mesoscopic neuronal processes using non-invasive data. Finally, our results demonstrate that, using the CMC as a proxy, the synaptic mechanisms that underlie the gain control of neuronal message passing within and between different levels of cortical hierarchies may now be amenable to quantitative study using non-invasive (MEG) procedures.
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Affiliation(s)
- A D Shaw
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, UK
| | - R J Moran
- Department of Engineering Mathematics, Merchant Venturers School of Engineering, University of Bristol, UK
| | - S D Muthukumaraswamy
- School of Pharmacy, The University of Auckland, Auckland, New Zealand; School of Psychology, The University of Auckland, Auckland, New Zealand
| | - J Brealy
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, UK
| | - D E Linden
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, UK
| | - K J Friston
- Wellcome Trust Centre for Neuroimaging, University College London, UK
| | - K D Singh
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, UK.
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Xiang W, Karfoul A, Shu H, Le Bouquin Jeannès R. A local adjustment strategy for the initialization of dynamic causal modelling to infer effective connectivity in brain epileptic structures. Comput Biol Med 2017; 84:30-44. [PMID: 28340406 DOI: 10.1016/j.compbiomed.2017.03.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Revised: 03/03/2017] [Accepted: 03/04/2017] [Indexed: 01/22/2023]
Abstract
This paper addresses the question of effective connectivity in the human cerebral cortex in the context of epilepsy. Among model based approaches to infer brain connectivity, spectral Dynamic Causal Modelling is a conventional technique for which we propose an alternative to estimate cross spectral density. The proposed strategy we investigated tackles the sub-estimation of the free energy using the well-known variational Expectation-Maximization algorithm highly sensitive to the initialization of the parameters vector by a permanent local adjustment of the initialization process. The performance of the proposed strategy in terms of effective connectivity identification is assessed using simulated data generated by a neuronal mass model (simulating unidirectional and bidirectional flows) and real epileptic intracerebral Electroencephalographic signals. Results show the efficiency of proposed approach compared to the conventional Dynamic Causal Modelling and the one wherein a deterministic annealing scheme is employed.
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Affiliation(s)
- Wentao Xiang
- INSERM, U1099, Rennes 35000, France; Université de Rennes 1, LTSI, Rennes 35000, France; Centre de Recherche en Information Biomédicale sino-français (CRIBs), 35000, France
| | - Ahmad Karfoul
- INSERM, U1099, Rennes 35000, France; Université de Rennes 1, LTSI, Rennes 35000, France
| | - Huazhong Shu
- Laboratory of Image Science and Technology (LIST), School of Computer Science and Engineering, Southeast University, Nanjing 210096, China; Centre de Recherche en Information Biomédicale sino-français (CRIBs), 35000, France
| | - Régine Le Bouquin Jeannès
- INSERM, U1099, Rennes 35000, France; Université de Rennes 1, LTSI, Rennes 35000, France; Centre de Recherche en Information Biomédicale sino-français (CRIBs), 35000, France.
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Zheng L, Liu W, He W, Yu S, Zhong G. Altered effective brain connectivity at early response of antipsychotics in first-episode schizophrenia with auditory hallucinations. Clin Neurophysiol 2017; 128:867-874. [PMID: 28399440 DOI: 10.1016/j.clinph.2017.02.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2016] [Revised: 02/02/2017] [Accepted: 02/03/2017] [Indexed: 12/17/2022]
Abstract
OBJECTIVE This study aimed to examine the alterations of cortical connectivity in first-episode schizophrenia (FES) with auditory hallucinations at early response of antipsychotics. METHODS This was a nonexperimental control of medication study. We measured the cortical activity of 20 medicated patients with FES (medicated group), 19 nonmedicated patients with FES (nonmedicated group), and 22 healthy controls using electroencephalogram during eye-open resting state. Source reconstruction analysis was performed to determine the brain regions that showed significant group difference. A dynamic causal modelling (DCM) analysis was used to estimate the effective connectivity between sources. RESULT Both FES groups expressed increased activity in the right middle frontal gyrus (RMFG) and left/right superior temporal gyrus (L/RSTG) relative to that in the controls (p<0.05), and the nonmedicated group presented even higher activity than the medicated group (p<0.05). The effective connectivity from RMFG to LSTG was weaker in the nonmedicated group relative to that in the medicated group (p<0.01), although patients in the medicated group showed no difference with healthy controls in RMFG to L/RSTG connections. The Bayesian model selection analysis found modulatory lateralization in the nonmedicated group. CONCLUSION The patients with FES showed frontotemporal hyperactivity and disconnectivity. The effective connections accompanied with modulation were improved when hallucination diminished at early response of routine medication. SIGNIFICANCE This study provided the first evidence of early drug response-related alterations in effective brain connectivity.
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Affiliation(s)
- Leilei Zheng
- Department of Psychiatry, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Weibo Liu
- Department of Psychiatry, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
| | - Wei He
- Department of Cognitive Science, Macquaire University, New South Wales, Australia; Australian Research Council Centre of Excellence in Cognition and Its Disorders, Macquaire University, New South Wales, Australia
| | - Shaohua Yu
- Department of Psychiatry, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Guodong Zhong
- Electroencephalogram Laboratory, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
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Xiang W, Yang C, Karfoul A, Le Bouquin Jeannes R. Quantifying connectivity in a physiology based model using adaptive dynamic causal modelling. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2016:2818-2821. [PMID: 28268904 DOI: 10.1109/embc.2016.7591316] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This paper proposes an Adaptive Dynamic Causal Modelling based approach to detect and quantify effective connectivity in human brain structures injured by epileptic activities. The identification of the parameters in the physiology based model subtended the Electroencephalographic observations is performed by improving the optimization step in the Expectation Maximization algorithm. Considering unidirectional flow propagation, we show the efficiency of our proposed approach compared to the conventional technique.
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Loehrer PA, Nettersheim FS, Jung F, Weber I, Huber C, Dembek TA, Pelzer EA, Fink GR, Tittgemeyer M, Timmermann L. Ageing changes effective connectivity of motor networks during bimanual finger coordination. Neuroimage 2016; 143:325-342. [DOI: 10.1016/j.neuroimage.2016.09.014] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2016] [Revised: 07/13/2016] [Accepted: 09/06/2016] [Indexed: 10/21/2022] Open
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