1
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Ou Y, Dai P, Zhou X, Xiong T, Li Y, Chen Z, Zou B. A strategy of model space search for dynamic causal modeling in task fMRI data exploratory analysis. Phys Eng Sci Med 2022; 45:867-882. [PMID: 35849323 DOI: 10.1007/s13246-022-01156-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 06/18/2022] [Indexed: 12/01/2022]
Abstract
Dynamic causal modeling (DCM) is a tool used for effective connectivity (EC) estimation in neuroimage analysis. But it is a model-driven analysis method, and the structure of the EC network needs to be determined in advance based on a large amount of prior knowledge. This characteristic makes it difficult to apply DCM to the exploratory brain network analysis. The exploratory analysis of DCM can be realized from two perspectives: one is to reduce the computational cost of the model; the other is to reduce the model space. From the perspective of model space reduction, a model space exploration strategy is proposed, including two algorithms. One algorithm, named GreedyEC, starts with reducing EC from full model, and the other, named GreedyROI, start with adding EC from one node model. Then the two algorithms were applied to the task state functional magnetic resonance imaging (fMRI) data of visual object recognition and selected the best DCM model from the perspective of model comparison based on Bayesian model compare method. Results show that combining the results of the two algorithms can further improve the effect of DCM exploratory analysis. For convenience in application, the algorithms were encapsulated into MATLAB function based on SPM to help neuroscience researchers to analyze the brain causal information flow network. The strategy provides a model space exploration tool that may obtain the best model from the perspective of model comparison and lower the threshold of DCM analysis.
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Affiliation(s)
- Yilin Ou
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | - Peishan Dai
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
- Hunan Engineering Research Center of Machine Vision and Intelligent Medicine, Central South University, Changsha, 410083, China.
| | - Xiaoyan Zhou
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | - Tong Xiong
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | - Yang Li
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
- Hunan Engineering Research Center of Machine Vision and Intelligent Medicine, Central South University, Changsha, 410083, China
| | - Zailiang Chen
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
- Hunan Engineering Research Center of Machine Vision and Intelligent Medicine, Central South University, Changsha, 410083, China
| | - Beiji Zou
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
- Hunan Engineering Research Center of Machine Vision and Intelligent Medicine, Central South University, Changsha, 410083, China
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2
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Ghobadi-Azbari P, Mahdavifar Khayati R, Sangchooli A, Ekhtiari H. Task-Dependent Effective Connectivity of the Reward Network During Food Cue-Reactivity: A Dynamic Causal Modeling Investigation. Front Behav Neurosci 2022; 16:899605. [PMID: 35813594 PMCID: PMC9263922 DOI: 10.3389/fnbeh.2022.899605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 06/08/2022] [Indexed: 11/13/2022] Open
Abstract
Neural reactivity to food cues may play a central role in overeating and excess weight gain. Functional magnetic resonance imaging (fMRI) studies have implicated regions of the reward network in dysfunctional food cue-reactivity, but neural interactions underlying observed patterns of signal change remain poorly understood. Fifty overweight and obese participants with self-reported cue-induced food craving viewed food and neutral cues during fMRI scanning. Regions of the reward network with significantly greater food versus neutral cue-reactivity were used to specify plausible models of task-related neural interactions underlying the observed blood oxygenation level-dependent (BOLD) signal, and a bi-hemispheric winning model was identified in a dynamic causal modeling (DCM) framework. Neuro-behavioral correlations are investigated with group factor analysis (GFA) and Pearson's correlation tests. The ventral tegmental area (VTA), amygdalae, and orbitofrontal cortices (OFC) showed significant food cue-reactivity. DCM suggests these activations are produced by largely reciprocal dynamic signaling between these regions, with food cues causing regional disinhibition and an apparent shifting of activity to the right amygdala. Intrinsic self-inhibition in the VTA and right amygdala is negatively correlated with measures of food craving and hunger and right-amygdalar disinhibition by food cues is associated with the intensity of cue-induced food craving, but no robust cross-unit latent factors were identified between the neural group and behavioral or demographic variable groups. Our results suggest a rich array of dynamic signals drive reward network cue-reactivity, with the amygdalae mediating much of the dynamic signaling between the VTA and OFCs. Neuro-behavioral correlations suggest particularly crucial roles for the VTA, right amygdala, and the right OFC-amygdala connection but the more robust GFA identified no cross-unit factors, so these correlations should be interpreted with caution. This investigation provides novel insights into dynamic circuit mechanisms with etiologic relevance to obesity, suggesting pathways in biomarker development and intervention.
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Affiliation(s)
| | | | - Arshiya Sangchooli
- Iranian National Center for Addiction Studies, Tehran University of Medical Sciences, Tehran, Iran
| | - Hamed Ekhtiari
- Department of Psychiatry, University of Minnesota, Minnesota, MN, United States
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3
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Sladky R, Hahn A, Karl IL, Geissberger N, Kranz GS, Tik M, Kraus C, Pfabigan DM, Gartus A, Lanzenberger R, Lamm C, Windischberger C. Dynamic Causal Modeling of the Prefrontal/Amygdala Network During Processing of Emotional Faces. Brain Connect 2021; 12:670-682. [PMID: 34605671 DOI: 10.1089/brain.2021.0073] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Introduction: The importance of the amygdala/medial orbitofrontal cortex (OFC) network during processing of emotional stimuli, emotional faces in particular, is well established. This premise is supported by converging evidence from animal models, human neuroanatomical results, and neuroimaging studies. However, there is missing evidence from human brain connectivity studies that the OFC and no other prefrontal brain areas such as the dorsolateral prefrontal cortex (DLPFC) or ventrolateral prefrontal cortex (VLPFC) are responsible for amygdala regulation in the functional context of emotional face stimuli. Methods: Dynamic causal modeling of ultrahigh-field functional magnetic resonance imaging data acquired at 7 Tesla in 38 healthy subjects and a well-established paradigm for emotional face processing were used to assess the central role of the OFC to provide empirical validation for the assumed network architecture. Results: Using Bayesian model selection, it is demonstrated that indeed the OFC, and not the VLPFC and the DLPFC, downregulates amygdala activation during the emotion discrimination task. In addition, Bayesian model averaging group results were rigorously tested using bootstrapping, further corroborating these findings and providing an estimator for robustness and optimal sample sizes. Discussion: While it is true that VLPFC and DLPFC are relevant for the processing of emotional faces and are connected to the OFC, the OFC appears to be a central hub for the prefrontal/amygdala interaction.
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Affiliation(s)
- Ronald Sladky
- MR Center of Excellence, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria.,Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Inga-Lisa Karl
- Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Nicole Geissberger
- MR Center of Excellence, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Georg S Kranz
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria.,Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China.,The State Key Laboratory of Brain and Cognitive Science, The University of Hong Kong, Hong Kong, China
| | - Martin Tik
- MR Center of Excellence, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Christoph Kraus
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Daniela M Pfabigan
- Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Andreas Gartus
- Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Claus Lamm
- Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Christian Windischberger
- MR Center of Excellence, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
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4
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Uscătescu LC, Kronbichler L, Stelzig-Schöler R, Pearce BG, Said-Yürekli S, Reich LA, Weber S, Aichhorn W, Kronbichler M. Effective Connectivity of the Hippocampus Can Differentiate Patients with Schizophrenia from Healthy Controls: A Spectral DCM Approach. Brain Topogr 2021; 34:762-778. [PMID: 34482503 PMCID: PMC8556208 DOI: 10.1007/s10548-021-00868-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 08/22/2021] [Indexed: 12/01/2022]
Abstract
We applied spectral dynamic causal modelling (Friston et al. in Neuroimage 94:396–407. 10.1016/j.neuroimage.2013.12.009, 2014) to analyze the effective connectivity differences between the nodes of three resting state networks (i.e. default mode network, salience network and dorsal attention network) in a dataset of 31 male healthy controls (HC) and 25 male patients with a diagnosis of schizophrenia (SZ). Patients showed increased directed connectivity from the left hippocampus (LHC) to the: dorsal anterior cingulate cortex (DACC), right anterior insula (RAI), left frontal eye fields and the bilateral inferior parietal sulcus (LIPS & RIPS), as well as increased connectivity from the right hippocampus (RHC) to the: bilateral anterior insula (LAI & RAI), right frontal eye fields and RIPS. In SZ, negative symptoms predicted the connectivity strengths from the LHC to: the DACC, the left inferior parietal sulcus (LIPAR) and the RHC, while positive symptoms predicted the connectivity strengths from the LHC to the LIPAR and from the RHC to the LHC. These results reinforce the crucial role of hippocampus dysconnectivity in SZ pathology and its potential as a biomarker of disease severity.
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Affiliation(s)
- Lavinia Carmen Uscătescu
- Centre for Cognitive Neuroscience and Department of Psychology, University of Salzburg, Salzburg, Austria
| | - Lisa Kronbichler
- Centre for Cognitive Neuroscience and Department of Psychology, University of Salzburg, Salzburg, Austria
- Neuroscience Institute, Christian-Doppler Medical Centre, Paracelsus Medical University, Salzburg, Austria
| | - Renate Stelzig-Schöler
- Department of Psychiatry, Psychotherapy and Psychosomatics, Christian-Doppler Medical Centre, Paracelsus Medical University, Salzburg, Austria
| | - Brandy-Gale Pearce
- Department of Psychiatry, Psychotherapy and Psychosomatics, Christian-Doppler Medical Centre, Paracelsus Medical University, Salzburg, Austria
| | - Sarah Said-Yürekli
- Centre for Cognitive Neuroscience and Department of Psychology, University of Salzburg, Salzburg, Austria
- Neuroscience Institute, Christian-Doppler Medical Centre, Paracelsus Medical University, Salzburg, Austria
| | | | - Stefanie Weber
- Department of Psychiatry, Psychotherapy and Psychosomatics, Christian-Doppler Medical Centre, Paracelsus Medical University, Salzburg, Austria
| | - Wolfgang Aichhorn
- Department of Psychiatry, Psychotherapy and Psychosomatics, Christian-Doppler Medical Centre, Paracelsus Medical University, Salzburg, Austria
| | - Martin Kronbichler
- Centre for Cognitive Neuroscience and Department of Psychology, University of Salzburg, Salzburg, Austria
- Neuroscience Institute, Christian-Doppler Medical Centre, Paracelsus Medical University, Salzburg, Austria
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5
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Cai W, Ryali S, Pasumarthy R, Talasila V, Menon V. Dynamic causal brain circuits during working memory and their functional controllability. Nat Commun 2021; 12:3314. [PMID: 34188024 PMCID: PMC8241851 DOI: 10.1038/s41467-021-23509-x] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 04/30/2021] [Indexed: 02/04/2023] Open
Abstract
Control processes associated with working memory play a central role in human cognition, but their underlying dynamic brain circuit mechanisms are poorly understood. Here we use system identification, network science, stability analysis, and control theory to probe functional circuit dynamics during working memory task performance. Our results show that dynamic signaling between distributed brain areas encompassing the salience (SN), fronto-parietal (FPN), and default mode networks can distinguish between working memory load and predict performance. Network analysis of directed causal influences suggests the anterior insula node of the SN and dorsolateral prefrontal cortex node of the FPN are causal outflow and inflow hubs, respectively. Network controllability decreases with working memory load and SN nodes show the highest functional controllability. Our findings reveal dissociable roles of the SN and FPN in systems control and provide novel insights into dynamic circuit mechanisms by which cognitive control circuits operate asymmetrically during cognition.
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Affiliation(s)
- Weidong Cai
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.
- Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, CA, USA.
| | - Srikanth Ryali
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Ramkrishna Pasumarthy
- Department of Electrical Engineering, Robert Bosch Center of Data Sciences and Artificial Intelligence, Indian Institute of Technology Madras, Chennai, India
| | - Viswanath Talasila
- Department of Electronics and Telecommunication Engineering, Center for Imaging Technologies, M.S. Ramaiah Institute of Technology, Bengaluru, India
| | - Vinod Menon
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.
- Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.
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6
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Kuhnke P, Kiefer M, Hartwigsen G. Task-Dependent Functional and Effective Connectivity during Conceptual Processing. Cereb Cortex 2021; 31:3475-3493. [PMID: 33677479 PMCID: PMC8196308 DOI: 10.1093/cercor/bhab026] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 01/21/2021] [Accepted: 01/22/2021] [Indexed: 11/13/2022] Open
Abstract
Conceptual knowledge is central to cognition. Previous neuroimaging research indicates that conceptual processing involves both modality-specific perceptual-motor areas and multimodal convergence zones. For example, our previous functional magnetic resonance imaging (fMRI) study revealed that both modality-specific and multimodal regions respond to sound and action features of concepts in a task-dependent fashion (Kuhnke P, Kiefer M, Hartwigsen G. 2020b. Task-dependent recruitment of modality-specific and multimodal regions during conceptual processing. Cereb Cortex. 30:3938–3959.). However, it remains unknown whether and how modality-specific and multimodal areas interact during conceptual tasks. Here, we asked 1) whether multimodal and modality-specific areas are functionally coupled during conceptual processing, 2) whether their coupling depends on the task, 3) whether information flows top-down, bottom-up or both, and 4) whether their coupling is behaviorally relevant. We combined psychophysiological interaction analyses with dynamic causal modeling on the fMRI data of our previous study. We found that functional coupling between multimodal and modality-specific areas strongly depended on the task, involved both top-down and bottom-up information flow, and predicted conceptually guided behavior. Notably, we also found coupling between different modality-specific areas and between different multimodal areas. These results suggest that functional coupling in the conceptual system is extensive, reciprocal, task-dependent, and behaviorally relevant. We propose a new model of the conceptual system that incorporates task-dependent functional interactions between modality-specific and multimodal areas.
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Affiliation(s)
- Philipp Kuhnke
- Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04103, Germany
| | - Markus Kiefer
- Department of Psychiatry, Ulm University, Ulm 89081, Germany
| | - Gesa Hartwigsen
- Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04103, Germany
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7
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Han J, Wu X, Wu H, Wang D, She X, Xie M, Zhang F, Zhang D, Zhang X, Qin P. Eye-Opening Alters the Interaction Between the Salience Network and the Default-Mode Network. Neurosci Bull 2020; 36:1547-1551. [PMID: 32676974 DOI: 10.1007/s12264-020-00546-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 05/09/2020] [Indexed: 01/23/2023] Open
Affiliation(s)
- Junrong Han
- Centre for Studies of Psychological Applications, Guangdong Key Laboratory of Mental Health and Cognitive Science, School of Psychology, South China Normal University, Guangzhou, 510631, China
| | - Xuehai Wu
- Neurosurgical Department, Shanghai Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Hang Wu
- Centre for Studies of Psychological Applications, Guangdong Key Laboratory of Mental Health and Cognitive Science, School of Psychology, South China Normal University, Guangzhou, 510631, China
| | - Dong Wang
- Centre for Studies of Psychological Applications, Guangdong Key Laboratory of Mental Health and Cognitive Science, School of Psychology, South China Normal University, Guangzhou, 510631, China
| | - Xuan She
- Centre for Studies of Psychological Applications, Guangdong Key Laboratory of Mental Health and Cognitive Science, School of Psychology, South China Normal University, Guangzhou, 510631, China
| | - Musi Xie
- Centre for Studies of Psychological Applications, Guangdong Key Laboratory of Mental Health and Cognitive Science, School of Psychology, South China Normal University, Guangzhou, 510631, China
| | - Fang Zhang
- Department of Rehabilitation Medicine, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510630, China
| | - Delong Zhang
- Centre for Studies of Psychological Applications, Guangdong Key Laboratory of Mental Health and Cognitive Science, School of Psychology, South China Normal University, Guangzhou, 510631, China
| | - Xilin Zhang
- Centre for Studies of Psychological Applications, Guangdong Key Laboratory of Mental Health and Cognitive Science, School of Psychology, South China Normal University, Guangzhou, 510631, China.
| | - Pengmin Qin
- Centre for Studies of Psychological Applications, Guangdong Key Laboratory of Mental Health and Cognitive Science, School of Psychology, South China Normal University, Guangzhou, 510631, China. .,Ministry of Education, Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Guangzhou, 510631, China. .,Pazhou Lab, Guangzhou, 510335, China.
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8
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GABA-ergic Dynamics in Human Frontotemporal Networks Confirmed by Pharmaco-Magnetoencephalography. J Neurosci 2020; 40:1640-1649. [PMID: 31915255 DOI: 10.1523/jneurosci.1689-19.2019] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 11/25/2019] [Accepted: 12/25/2019] [Indexed: 12/15/2022] Open
Abstract
To bridge the gap between preclinical cellular models of disease and in vivo imaging of human cognitive network dynamics, there is a pressing need for informative biophysical models. Here we assess dynamic causal models (DCM) of cortical network responses, as generative models of magnetoencephalographic observations during an auditory oddball roving paradigm in healthy adults. This paradigm induces robust perturbations that permeate frontotemporal networks, including an evoked 'mismatch negativity' response and transiently induced oscillations. Here, we probe GABAergic influences in the networks using double-blind placebo-controlled randomized-crossover administration of the GABA reuptake inhibitor, tiagabine (oral, 10 mg) in healthy older adults. We demonstrate the facility of conductance-based neural mass mean-field models, incorporating local synaptic connectivity, to investigate laminar-specific and GABAergic mechanisms of the auditory response. The neuronal model accurately recapitulated the observed magnetoencephalographic data. Using parametric empirical Bayes for optimal model inversion across both drug sessions, we identify the effect of tiagabine on GABAergic modulation of deep pyramidal and interneuronal cell populations. We found a transition of the main GABAergic drug effects from auditory cortex in standard trials to prefrontal cortex in deviant trials. The successful integration of pharmaco- magnetoencephalography with dynamic causal models of frontotemporal networks provides a potential platform on which to evaluate the effects of disease and pharmacological interventions.SIGNIFICANCE STATEMENT Understanding human brain function and developing new treatments require good models of brain function. We tested a detailed generative model of cortical microcircuits that accurately reproduced human magnetoencephalography, to quantify network dynamics and connectivity in frontotemporal cortex. This approach identified the effect of a test drug (GABA-reuptake inhibitor, tiagabine) on neuronal function (GABA-ergic dynamics), opening the way for psychopharmacological studies in health and disease with the mechanistic precision afforded by generative models of the brain.
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9
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Jovellar DB, Doudet DJ. fMRI in Non-human Primate: A Review on Factors That Can Affect Interpretation and Dynamic Causal Modeling Application. Front Neurosci 2019; 13:973. [PMID: 31619951 PMCID: PMC6759819 DOI: 10.3389/fnins.2019.00973] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Accepted: 08/30/2019] [Indexed: 11/13/2022] Open
Abstract
Dynamic causal modeling (DCM)-a framework for inferring hidden neuronal states from brain activity measurements (e. g., fMRI) and their context-dependent modulation-was developed for human neuroimaging, and has not been optimized for non-human primate (NHP) studies, which are usually done under anesthesia. Animal neuroimaging studies offer the potential to improve effective connectivity modeling using DCM through combining functional imaging with invasive procedures such as in vivo optogenetic or electrical stimulation. Employing a Bayesian approach, model parameters are estimated based on prior knowledge of conditions that might be related to neural and BOLD dynamics (e.g., requires empirical knowledge about the range of plausible parameter values). As such, we address the following questions in this review: What factors need to be considered when applying DCM to NHP data? What differences in functional networks, cerebrovascular architecture and physiology exist between human and NHPs that are relevant for DCM application? How do anesthetics affect vascular physiology, BOLD contrast, and neural dynamics-particularly, effective communication within, and between networks? Considering the factors that are relevant for DCM application to NHP neuroimaging, we propose a strategy for modeling effective connectivity under anesthesia using an integrated physiologic-stochastic DCM (IPS-DCM).
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Affiliation(s)
- D Blair Jovellar
- Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, BC, Canada.,Center of Neurology, Hertie Institute for Clinical Brain Research, University Hospital, Tuebingen, Germany
| | - Doris J Doudet
- Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, BC, Canada
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Zeidman P, Jafarian A, Corbin N, Seghier ML, Razi A, Price CJ, Friston KJ. A guide to group effective connectivity analysis, part 1: First level analysis with DCM for fMRI. Neuroimage 2019; 200:174-190. [PMID: 31226497 PMCID: PMC6711459 DOI: 10.1016/j.neuroimage.2019.06.031] [Citation(s) in RCA: 178] [Impact Index Per Article: 35.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 05/13/2019] [Accepted: 06/16/2019] [Indexed: 12/05/2022] Open
Abstract
Dynamic Causal Modelling (DCM) is the predominant method for inferring effective connectivity from neuroimaging data. In the 15 years since its introduction, the neural models and statistical routines in DCM have developed in parallel, driven by the needs of researchers in cognitive and clinical neuroscience. In this guide, we step through an exemplar fMRI analysis in detail, reviewing the current implementation of DCM and demonstrating recent developments in group-level connectivity analysis. In the appendices, we detail the theory underlying DCM and the assumptions (i.e., priors) in the models. In the first part of the guide (current paper), we focus on issues specific to DCM for fMRI. This is accompanied by all the necessary data and instructions to reproduce the analyses using the SPM software. In the second part (in a companion paper), we move from subject-level to group-level modelling using the Parametric Empirical Bayes framework, and illustrate how to test for commonalities and differences in effective connectivity across subjects, based on imaging data from any modality. This guide walks through a group effective connectivity study using DCM and PEB. Part 1, presented here, covers first level analysis using DCM for fMRI. It clarifies the specific neural and haemodynamic models in DCM and their priors. An accompanying dataset is provided with step-by-step analysis instructions.
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Affiliation(s)
- Peter Zeidman
- Wellcome Centre for Human Neuroimaging, 12 Queen Square, London, WC1N 3AR, UK.
| | | | - Nadège Corbin
- Wellcome Centre for Human Neuroimaging, 12 Queen Square, London, WC1N 3AR, UK
| | | | - Adeel Razi
- Monash Institute of Cognitive & Clinical Neuroscience, Monash Biomedical Imaging, 18 Innovation Walk, Monash University, Clayton, VIC, 3800, Australia; Wellcome Centre for Human Neuroimaging, 12 Queen Square, London, WC1N 3AR, UK
| | - Cathy J Price
- Wellcome Centre for Human Neuroimaging, 12 Queen Square, London, WC1N 3AR, UK
| | - Karl J Friston
- Wellcome Centre for Human Neuroimaging, 12 Queen Square, London, WC1N 3AR, UK
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Bielczyk NZ, Uithol S, van Mourik T, Anderson P, Glennon JC, Buitelaar JK. Disentangling causal webs in the brain using functional magnetic resonance imaging: A review of current approaches. Netw Neurosci 2019; 3:237-273. [PMID: 30793082 PMCID: PMC6370462 DOI: 10.1162/netn_a_00062] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Accepted: 06/08/2018] [Indexed: 01/05/2023] Open
Abstract
In the past two decades, functional Magnetic Resonance Imaging (fMRI) has been used to relate neuronal network activity to cognitive processing and behavior. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this paper, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, Linear Non-Gaussian Acyclic Models, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area.
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Affiliation(s)
- Natalia Z. Bielczyk
- Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
| | - Sebo Uithol
- Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
- Bernstein Centre for Computational Neuroscience, Charité Universitätsmedizin, Berlin, Germany
| | - Tim van Mourik
- Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
| | - Paul Anderson
- Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
- Faculty of Science, Radboud University Nijmegen, Nijmegen, the Netherlands
| | - Jeffrey C. Glennon
- Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
| | - Jan K. Buitelaar
- Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
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Roy A, Bernier RA, Wang J, Benson M, French JJ, Good DC, Hillary FG. The evolution of cost-efficiency in neural networks during recovery from traumatic brain injury. PLoS One 2017; 12:e0170541. [PMID: 28422992 PMCID: PMC5396850 DOI: 10.1371/journal.pone.0170541] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2015] [Accepted: 01/06/2017] [Indexed: 02/08/2023] Open
Abstract
A somewhat perplexing finding in the systems neuroscience has been the observation that physical injury to neural systems may result in enhanced functional connectivity (i.e., hyperconnectivity) relative to the typical network response. The consequences of local or global enhancement of functional connectivity remain uncertain and this is particularly true for the overall metabolic cost of the network. We examine the hyperconnectivity hypothesis in a sample of 14 individuals with TBI with data collected at approximately 3, 6, and 12 months following moderate and severe TBI. As anticipated, individuals with TBI showed increased network strength and cost early after injury, but by one-year post injury hyperconnectivity was more circumscribed to frontal DMN and temporal-parietal attentional control regions. Cost in these subregions was a significant predictor of cognitive performance. Cost-efficiency analysis in the Power 264 data parcellation suggested that at 6 months post injury the network requires higher cost connections to achieve high efficiency as compared to the network 12 months post injury. These results demonstrate that networks self-organize to re-establish connectivity while balancing cost-efficiency trade-offs.
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Affiliation(s)
- Arnab Roy
- Department of Psychology, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Rachel A. Bernier
- Department of Psychology, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Jianli Wang
- Department of Radiology, Hershey Medical Center, Hershey, Pennsylvania, United States of America
| | - Monica Benson
- Department of Psychology, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Jerry J. French
- Department of Psychology, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - David C. Good
- Department of Neurology, Hershey Medical Center, Hershey, Pennsylvania, United States of America
| | - Frank G. Hillary
- Department of Psychology, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- Department of Neurology, Hershey Medical Center, Hershey, Pennsylvania, United States of America
- Social, Life and Engineering Sciences Imaging Center, University Park, Pennsylvania, United States of America
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13
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Bernal-Casas D, Lee HJ, Weitz AJ, Lee JH. Studying Brain Circuit Function with Dynamic Causal Modeling for Optogenetic fMRI. Neuron 2017; 93:522-532.e5. [PMID: 28132829 DOI: 10.1016/j.neuron.2016.12.035] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2016] [Revised: 10/30/2016] [Accepted: 12/20/2016] [Indexed: 12/12/2022]
Abstract
Defining the large-scale behavior of brain circuits with cell type specificity is a major goal of neuroscience. However, neuronal circuit diagrams typically draw upon anatomical and electrophysiological measurements acquired in isolation. Consequently, a dynamic and cell-type-specific connectivity map has never been constructed from simultaneous measurements across the brain. Here, we introduce dynamic causal modeling (DCM) for optogenetic fMRI experiments-which uniquely allow cell-type-specific, brain-wide functional measurements-to parameterize the causal relationships among regions of a distributed brain network with cell type specificity. Strikingly, when applied to the brain-wide basal ganglia-thalamocortical network, DCM accurately reproduced the empirically observed time series, and the strongest connections were key connections of optogenetically stimulated pathways. We predict that quantitative and cell-type-specific descriptions of dynamic connectivity, as illustrated here, will empower novel systems-level understanding of neuronal circuit dynamics and facilitate the design of more effective neuromodulation therapies.
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Affiliation(s)
- David Bernal-Casas
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA
| | - Hyun Joo Lee
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA
| | - Andrew J Weitz
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Jin Hyung Lee
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA; Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.
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14
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Rifkin-Graboi A, Kong L, Sim LW, Sanmugam S, Broekman BFP, Chen H, Wong E, Kwek K, Saw SM, Chong YS, Gluckman PD, Fortier MV, Pederson D, Meaney MJ, Qiu A. Maternal sensitivity, infant limbic structure volume and functional connectivity: a preliminary study. Transl Psychiatry 2015; 5:e668. [PMID: 26506054 PMCID: PMC4930120 DOI: 10.1038/tp.2015.133] [Citation(s) in RCA: 59] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2015] [Revised: 07/02/2015] [Accepted: 07/22/2015] [Indexed: 11/30/2022] Open
Abstract
Mechanisms underlying the profound parental effects on cognitive, emotional and social development in humans remain poorly understood. Studies with nonhuman models suggest variations in parental care affect the limbic system, influential to learning, autobiography and emotional regulation. In some research, nonoptimal care relates to decreases in neurogenesis, although other work suggests early-postnatal social adversity accelerates the maturation of limbic structures associated with emotional learning. We explored whether maternal sensitivity predicts human limbic system development and functional connectivity patterns in a small sample of human infants. When infants were 6 months of age, 20 mother-infant dyads attended a laboratory-based observational session and the infants underwent neuroimaging at the same age. After considering age at imaging, household income and postnatal maternal anxiety, regression analyses demonstrated significant indirect associations between maternal sensitivity and bilateral hippocampal volume at six months, with the majority of associations between sensitivity and the amygdala demonstrating similar indirect, but not significant results. Moreover, functional analyses revealed direct associations between maternal sensitivity and connectivity between the hippocampus and areas important for emotional regulation and socio-emotional functioning. Sensitivity additionally predicted indirect associations between limbic structures and regions related to autobiographical memory. Our volumetric results are consistent with research indicating accelerated limbic development in response to early social adversity, and in combination with our functional results, if replicated in a larger sample, may suggest that subtle, but important, variations in maternal care influence neuroanatomical trajectories important to future cognitive and emotional functioning.
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Affiliation(s)
- A Rifkin-Graboi
- Integrative Neuroscience Program, Singapore Institute for Clinical Sciences, Singapore, Singapore,Integrative Neuroscience Program, Singapore Institute for Clinical Sciences, Brenner Centre for Molecular Medicine 30 Medical Drive, Singapore 117609, Singapore. E-mail:
| | - L Kong
- Department of Biomedical Engineering and Clinical Imaging Research Center, National University of Singapore, Singapore, Singapore
| | - L W Sim
- Integrative Neuroscience Program, Singapore Institute for Clinical Sciences, Singapore, Singapore
| | - S Sanmugam
- Integrative Neuroscience Program, Singapore Institute for Clinical Sciences, Singapore, Singapore
| | - B F P Broekman
- Integrative Neuroscience Program, Singapore Institute for Clinical Sciences, Singapore, Singapore,Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore, Singapore
| | - H Chen
- Department of Psychological Medicine, KK Women's and Children's Hospital, Duke-National University of Singapore, Singapore, Singapore
| | - E Wong
- Integrative Neuroscience Program, Singapore Institute for Clinical Sciences, Singapore, Singapore
| | - K Kwek
- Department of Maternal Fetal Medicine, KK Women's and Children's Hospital, Singapore, Singapore
| | - S-M Saw
- Department of Epidemiology, Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Y-S Chong
- Integrative Neuroscience Program, Singapore Institute for Clinical Sciences, Singapore, Singapore,Department of Obstetrics & Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore, Singapore
| | - P D Gluckman
- Human Development, Singapore Institute for Clinical Sciences, Singapore, Singapore,Liggins Institute, University of Auckland, Auckland, New Zealand
| | - M V Fortier
- Department of Diagnostic and Interventional Imaging, KK Women's and Children's Hospital, Singapore, Singapore
| | - D Pederson
- Department of Psychology, University of Western Ontario, London, Ontario, Canada
| | - M J Meaney
- Integrative Neuroscience Program, Singapore Institute for Clinical Sciences, Singapore, Singapore,Department of Neurosciences, Ludmer Centre for Neuroinformatics and Mental Health, Douglas Mental Health University Institute, McGill University, Montreal, Quebec, Canada,Sackler Program for Epigenetics and Psychobiology, Douglas Mental Health University Institute, McGill University, Montreal, Quebec, Canada
| | - A Qiu
- Integrative Neuroscience Program, Singapore Institute for Clinical Sciences, Singapore, Singapore,Department of Biomedical Engineering and Clinical Imaging Research Center, National University of Singapore, Singapore, Singapore,Department of Biomedical Engineering, National University of Singapore, 9 Engineering Drive 1, Block EA #03-12, Singapore 117576, Singapore. E-mail:
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15
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Gee DG, McEwen SC, Forsyth JK, Haut KM, Bearden CE, Addington J, Goodyear B, Cadenhead KS, Mirzakhanian H, Cornblatt BA, Olvet D, Mathalon DH, McGlashan TH, Perkins DO, Belger A, Seidman LJ, Thermenos H, Tsuang MT, van Erp TGM, Walker EF, Hamann S, Woods SW, Constable T, Cannon TD. Reliability of an fMRI paradigm for emotional processing in a multisite longitudinal study. Hum Brain Mapp 2015; 36:2558-79. [PMID: 25821147 DOI: 10.1002/hbm.22791] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2014] [Revised: 03/03/2015] [Accepted: 03/06/2015] [Indexed: 12/14/2022] Open
Abstract
Multisite neuroimaging studies can facilitate the investigation of brain-related changes in many contexts, including patient groups that are relatively rare in the general population. Though multisite studies have characterized the reliability of brain activation during working memory and motor functional magnetic resonance imaging tasks, emotion processing tasks, pertinent to many clinical populations, remain less explored. A traveling participants study was conducted with eight healthy volunteers scanned twice on consecutive days at each of the eight North American Longitudinal Prodrome Study sites. Tests derived from generalizability theory showed excellent reliability in the amygdala ( Eρ2 = 0.82), inferior frontal gyrus (IFG; Eρ2 = 0.83), anterior cingulate cortex (ACC; Eρ2 = 0.76), insula ( Eρ2 = 0.85), and fusiform gyrus ( Eρ2 = 0.91) for maximum activation and fair to excellent reliability in the amygdala ( Eρ2 = 0.44), IFG ( Eρ2 = 0.48), ACC ( Eρ2 = 0.55), insula ( Eρ2 = 0.42), and fusiform gyrus ( Eρ2 = 0.83) for mean activation across sites and test days. For the amygdala, habituation ( Eρ2 = 0.71) was more stable than mean activation. In a second investigation, data from 111 healthy individuals across sites were aggregated in a voxelwise, quantitative meta-analysis. When compared with a mixed effects model controlling for site, both approaches identified robust activation in regions consistent with expected results based on prior single-site research. Overall, regions central to emotion processing showed strong reliability in the traveling participants study and robust activation in the aggregation study. These results support the reliability of blood oxygen level-dependent signal in emotion processing areas across different sites and scanners and may inform future efforts to increase efficiency and enhance knowledge of rare conditions in the population through multisite neuroimaging paradigms.
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Affiliation(s)
- Dylan G Gee
- Departments of Psychology and Psychiatry, University of California, Los Angeles, California
| | - Sarah C McEwen
- Departments of Psychology and Psychiatry, University of California, Los Angeles, California
| | - Jennifer K Forsyth
- Departments of Psychology and Psychiatry, University of California, Los Angeles, California
| | - Kristen M Haut
- Department of Psychology, Yale University, New Haven, Connecticut
| | - Carrie E Bearden
- Departments of Psychology and Psychiatry, University of California, Los Angeles, California
| | - Jean Addington
- Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada
| | - Bradley Goodyear
- Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada
| | - Kristin S Cadenhead
- Department of Psychiatry, University of California, San Diego, La Jolla, California
| | - Heline Mirzakhanian
- Department of Psychiatry, University of California, San Diego, La Jolla, California
| | - Barbara A Cornblatt
- Department of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, New York
| | - Doreen Olvet
- Department of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, New York
| | - Daniel H Mathalon
- Department of Psychiatry, University of California, San Francisco, California
| | | | - Diana O Perkins
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina
| | - Aysenil Belger
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina
| | - Larry J Seidman
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
| | - Heidi Thermenos
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
| | - Ming T Tsuang
- Department of Psychiatry, University of California, San Diego, La Jolla, California
| | - Theo G M van Erp
- Department of Psychiatry and Human Behavior, University of California, Irvine, California
| | - Elaine F Walker
- Department of Psychology, Emory University, Atlanta, Georgia
| | - Stephan Hamann
- Department of Psychology, Emory University, Atlanta, Georgia
| | - Scott W Woods
- Department of Psychiatry, Yale University, New Haven, Connecticut
| | - Todd Constable
- Department of Psychiatry, Yale University, New Haven, Connecticut
| | - Tyrone D Cannon
- Department of Psychology, Yale University, New Haven, Connecticut.,Department of Psychiatry, Yale University, New Haven, Connecticut
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16
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Chase HW, Clos M, Dibble S, Fox P, Grace AA, Phillips ML, Eickhoff SB. Evidence for an anterior-posterior differentiation in the human hippocampal formation revealed by meta-analytic parcellation of fMRI coordinate maps: focus on the subiculum. Neuroimage 2015; 113:44-60. [PMID: 25776219 DOI: 10.1016/j.neuroimage.2015.02.069] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2014] [Revised: 12/17/2014] [Accepted: 02/25/2015] [Indexed: 02/05/2023] Open
Abstract
Previous studies, predominantly in experimental animals, have suggested the presence of a differentiation of function across the hippocampal formation. In rodents, ventral regions are thought to be involved in emotional behavior while dorsal regions mediate cognitive or spatial processes. Using a combination of modeling the co-occurrence of significant activations across thousands of neuroimaging experiments and subsequent data-driven clustering of these data we were able to provide evidence of distinct subregions within a region corresponding to the human subiculum, a critical hub within the hippocampal formation. This connectivity-based model consists of a bilateral anterior region, as well as separate posterior and intermediate regions on each hemisphere. Functional connectivity assessed both by meta-analytic and resting fMRI approaches revealed that more anterior regions were more strongly connected to the default mode network, and more posterior regions were more strongly connected to 'task positive' regions. In addition, our analysis revealed that the anterior subregion was functionally connected to the ventral striatum, midbrain and amygdala, a circuit that is central to models of stress and motivated behavior. Analysis of a behavioral taxonomy provided evidence for a role for each subregion in mnemonic processing, as well as implication of the anterior subregion in emotional and visual processing and the right posterior subregion in reward processing. These findings lend support to models which posit anterior-posterior differentiation of function within the human hippocampal formation and complement other early steps toward a comparative (cross-species) model of the region.
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Affiliation(s)
- Henry W Chase
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
| | - Mareike Clos
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Germany; Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Germany
| | - Sofia Dibble
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA
| | - Peter Fox
- Research Imaging Center, University of Texas Health Science Center San Antonio, San Antonio, TX, USA; South Texas Veterans Administration Medical Center, San Antonio, TX, USA
| | - Anthony A Grace
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA; Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mary L Phillips
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Germany; Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Germany
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17
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Balaguer-Ballester E, Tabas-Diaz A, Budka M. Can we identify non-stationary dynamics of trial-to-trial variability? PLoS One 2014; 9:e95648. [PMID: 24769735 PMCID: PMC4000201 DOI: 10.1371/journal.pone.0095648] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2014] [Accepted: 03/28/2014] [Indexed: 11/19/2022] Open
Abstract
Identifying sources of the apparent variability in non-stationary scenarios is a fundamental problem in many biological data analysis settings. For instance, neurophysiological responses to the same task often vary from each repetition of the same experiment (trial) to the next. The origin and functional role of this observed variability is one of the fundamental questions in neuroscience. The nature of such trial-to-trial dynamics however remains largely elusive to current data analysis approaches. A range of strategies have been proposed in modalities such as electro-encephalography but gaining a fundamental insight into latent sources of trial-to-trial variability in neural recordings is still a major challenge. In this paper, we present a proof-of-concept study to the analysis of trial-to-trial variability dynamics founded on non-autonomous dynamical systems. At this initial stage, we evaluate the capacity of a simple statistic based on the behaviour of trajectories in classification settings, the trajectory coherence, in order to identify trial-to-trial dynamics. First, we derive the conditions leading to observable changes in datasets generated by a compact dynamical system (the Duffing equation). This canonical system plays the role of a ubiquitous model of non-stationary supervised classification problems. Second, we estimate the coherence of class-trajectories in empirically reconstructed space of system states. We show how this analysis can discern variations attributable to non-autonomous deterministic processes from stochastic fluctuations. The analyses are benchmarked using simulated and two different real datasets which have been shown to exhibit attractor dynamics. As an illustrative example, we focused on the analysis of the rat's frontal cortex ensemble dynamics during a decision-making task. Results suggest that, in line with recent hypotheses, rather than internal noise, it is the deterministic trend which most likely underlies the observed trial-to-trial variability. Thus, the empirical tool developed within this study potentially allows us to infer the source of variability in in-vivo neural recordings.
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Affiliation(s)
- Emili Balaguer-Ballester
- Faculty of Science and Technology, Bournemouth University, United Kingdom
- Bernstein Center for Computational Neuroscience, Medical Faculty Mannheim and Heidelberg University, Mannheim, Germany
- * E-mail:
| | | | - Marcin Budka
- Faculty of Science and Technology, Bournemouth University, United Kingdom
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