1
|
Marrelec G, Giron A. Multilevel testing of constraints induced by structural equation modeling in fMRI effective connectivity analysis: A proof of concept. Magn Reson Imaging 2024; 109:294-303. [PMID: 38280493 DOI: 10.1016/j.mri.2024.01.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 01/20/2024] [Accepted: 01/20/2024] [Indexed: 01/29/2024]
Abstract
In functional MRI (fMRI), effective connectivity analysis aims at inferring the causal influences that brain regions exert on one another. A common method for this type of analysis is structural equation modeling (SEM). We here propose a novel method to test the validity of a given model of structural equation. Given a structural model in the form of a directed graph, the method extracts the set of all constraints of conditional independence induced by the absence of links between pairs of regions in the model and tests for their validity in a Bayesian framework, either individually (constraint by constraint), jointly (e.g., by gathering all constraints associated with a given missing link), or globally (i.e., all constraints associated with the structural model). This approach has two main advantages. First, it only tests what is testable from observational data and does allow for false causal interpretation. Second, it makes it possible to test each constraint (or group of constraints) separately and, therefore, quantify in what measure each constraint (or, e..g., missing link) is respected in the data. We validate our approach using a simulation study and illustrate its potential benefits through the reanalysis of published data.
Collapse
Affiliation(s)
- Guillaume Marrelec
- Laboratoire d'imagerie biomédicale, LIB, Sorbonne Université, CNRS, INSERM, F-75006 Paris, France.
| | - Alain Giron
- Laboratoire d'imagerie biomédicale, LIB, Sorbonne Université, CNRS, INSERM, F-75006 Paris, France
| |
Collapse
|
2
|
Schott BH, Soch J, Kizilirmak JM, Schütze H, Assmann A, Maass A, Ziegler G, Sauvage M, Richter A. Inhibitory temporo-parietal effective connectivity is associated with explicit memory performance in older adults. iScience 2023; 26:107765. [PMID: 37744028 PMCID: PMC10514462 DOI: 10.1016/j.isci.2023.107765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 06/30/2023] [Accepted: 08/25/2023] [Indexed: 09/26/2023] Open
Abstract
Successful explicit memory encoding is associated with inferior temporal activations and medial parietal deactivations, which are attenuated in aging. Here we used dynamic causal modeling (DCM) of functional magnetic resonance imaging data to elucidate effective connectivity patterns between hippocampus, parahippocampal place area (PPA), and precuneus during encoding of novel visual scenes. In 117 young adults, DCM revealed pronounced activating input from the PPA to the hippocampus and inhibitory connectivity from the PPA to the precuneus during novelty processing, with both being enhanced during successful encoding. This pattern could be replicated in two cohorts (N = 141 and 148) of young and older adults. In both cohorts, older adults selectively exhibited attenuated inhibitory PPA-precuneus connectivity, which correlated negatively with memory performance. Our results provide insight into the network dynamics underlying explicit memory encoding and suggest that age-related differences in memory-related network activity are, at least partly, attributable to altered temporo-parietal neocortical connectivity.
Collapse
Affiliation(s)
- Björn H. Schott
- German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany
- Leibniz Institute for Neurobiology (LIN), Magdeburg, Germany
- Center for Behavioral Brain Sciences (CBBS), Magdeburg, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany
| | - Joram Soch
- German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany
- Bernstein Center for Computational Neuroscience (BCCN), Berlin, Germany
| | - Jasmin M. Kizilirmak
- German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany
- Neurodidactics and NeuroLab, Institute for Psychology, University of Hildesheim, Hildesheim, Germany
| | - Hartmut Schütze
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Otto von Guericke University, Medical Faculty, Magdeburg, Germany
| | - Anne Assmann
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Otto von Guericke University, Medical Faculty, Magdeburg, Germany
| | - Anne Maass
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Gabriel Ziegler
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Otto von Guericke University, Medical Faculty, Magdeburg, Germany
| | | | - Anni Richter
- Leibniz Institute for Neurobiology (LIN), Magdeburg, Germany
- German Center for Mental Health (DZPG), Magdeburg, Germany
- Center for Intervention and Research on adaptive and Maladaptive Brain Circuits Underlying Mental Health (C-I-R-C) Jena-Magdeburg-Halle, Magdeburg, Germany
| |
Collapse
|
3
|
Marxen M, Graff JE, Riedel P, Smolka MN. Observing cognitive processes in time through functional MRI model comparison. Hum Brain Mapp 2023; 44:1359-1370. [PMID: 36288248 PMCID: PMC9921218 DOI: 10.1002/hbm.26114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 08/25/2022] [Accepted: 09/12/2022] [Indexed: 11/06/2022] Open
Abstract
The temporal specificity of functional magnetic resonance imaging (fMRI) is limited by a sluggish and locally variable hemodynamic response trailing the neural activity by seconds. Here, we demonstrate for an attention capture paradigm that it is, never the less, possible to extract information about the relative timing of regional brain activity during cognitive processes on the scale of 100 ms by comparing alternative signal models representing early versus late activation. We demonstrate that model selection is not driven by confounding regional differences in hemodynamic delay. We show, including replication, that the activity in the dorsal anterior insula is an early signal predictive of behavioral performance, while amygdala and ventral anterior insula signals are not. This specific finding provides new insights into how the brain assigns salience to stimuli and emphasizes the role of the dorsal anterior insula in this context. The general analytic approach, named "Cognitive Timing through Model Comparison" (CTMC), offers an exciting and novel method to identify functional brain subunits and their causal interactions.
Collapse
Affiliation(s)
- Michael Marxen
- Department of Psychiatry, Technische Universität Dresden, Dresden, Germany
| | - Johanna E Graff
- Department of Psychiatry, Technische Universität Dresden, Dresden, Germany
| | - Philipp Riedel
- Department of Psychiatry, Technische Universität Dresden, Dresden, Germany
| | - Michael N Smolka
- Department of Psychiatry, Technische Universität Dresden, Dresden, Germany
| |
Collapse
|
4
|
Mills-Finnerty C, Frangos E, Allen K, Komisaruk B, Wise N. Functional Magnetic Resonance Imaging Studies in Sexual Medicine: A Primer. J Sex Med 2022; 19:1073-1089. [DOI: 10.1016/j.jsxm.2022.03.217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 01/27/2022] [Accepted: 03/04/2022] [Indexed: 11/17/2022]
|
5
|
Kvamme TL, Ros T, Overgaard M. Can neurofeedback provide evidence of direct brain-behavior causality? Neuroimage 2022; 258:119400. [PMID: 35728786 DOI: 10.1016/j.neuroimage.2022.119400] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 06/13/2022] [Accepted: 06/17/2022] [Indexed: 01/01/2023] Open
Abstract
Neurofeedback is a procedure that measures brain activity in real-time and presents it as feedback to an individual, thus allowing them to self-regulate brain activity with effects on cognitive processes inferred from behavior. One common argument is that neurofeedback studies can reveal how the measured brain activity causes a particular cognitive process. The causal claim is often made regarding the measured brain activity being manipulated as an independent variable, similar to brain stimulation studies. However, this causal inference is vulnerable to the argument that other upstream brain activities change concurrently and cause changes in the brain activity from which feedback is derived. In this paper, we outline the inference that neurofeedback may causally affect cognition by indirect means. We further argue that researchers should remain open to the idea that the trained brain activity could be part of a "causal network" that collectively affects cognition rather than being necessarily causally primary. This particular inference may provide a better translation of evidence from neurofeedback studies to the rest of neuroscience. We argue that the recent advent of multivariate pattern analysis, when combined with implicit neurofeedback, currently comprises the strongest case for causality. Our perspective is that although the burden of inferring direct causality is difficult, it may be triangulated using a collection of various methods in neuroscience. Finally, we argue that the neurofeedback methodology provides unique advantages compared to other methods for revealing changes in the brain and cognitive processes but that researchers should remain mindful of indirect causal effects.
Collapse
Affiliation(s)
- Timo L Kvamme
- Cognitive Neuroscience Research Unit, CFIN/MINDLab, Aarhus University, Universitetsbyen 3, Aarhus, Denmark; Centre for Alcohol and Drug Research (CRF), Aarhus University, Aarhus, Denmark.
| | - Tomas Ros
- Departments of Neuroscience and Psychiatry, University of Geneva, Campus Biotech, Geneva, Switzerland
| | - Morten Overgaard
- Cognitive Neuroscience Research Unit, CFIN/MINDLab, Aarhus University, Universitetsbyen 3, Aarhus, Denmark
| |
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
Unraveling brain interactions in vision: The example of crowding. Neuroimage 2021; 240:118390. [PMID: 34271157 DOI: 10.1016/j.neuroimage.2021.118390] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 07/09/2021] [Accepted: 07/12/2021] [Indexed: 11/22/2022] Open
Abstract
Crowding, the impairment of target discrimination in clutter, is the standard situation in vision. Traditionally, crowding is explained with (feedforward) models, in which only neighboring elements interact, leading to a "bottleneck" at the earliest stages of vision. It is with this implicit prior that most functional magnetic resonance imaging (fMRI) studies approach the identification of the "neural locus" of crowding, searching for the earliest visual area in which the blood-oxygenation-level-dependent (BOLD) signal is suppressed under crowded conditions. Using this classic approach, we replicated previous findings of crowding-related BOLD suppression starting in V2 and increasing up the visual hierarchy. Surprisingly, under conditions of uncrowding, in which adding flankers improves performance, the BOLD signal was further suppressed. This suggests an important role for top-down connections, which is in line with global models of crowding. To discriminate between various possible models, we used dynamic causal modeling (DCM). We show that recurrent interactions between all visual areas, including higher-level areas like V4 and the lateral occipital complex (LOC), are crucial in crowding and uncrowding. Our results explain the discrepancies in previous findings: in a recurrent visual hierarchy, the crowding effect can theoretically be detected at any stage. Beyond crowding, we demonstrate the need for models like DCM to understand the complex recurrent processing which most likely underlies human perception in general.
Collapse
|
8
|
Auditory cortical micro-networks show differential connectivity during voice and speech processing in humans. Commun Biol 2021; 4:801. [PMID: 34172824 PMCID: PMC8233416 DOI: 10.1038/s42003-021-02328-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 06/09/2021] [Indexed: 02/05/2023] Open
Abstract
The temporal voice areas (TVAs) in bilateral auditory cortex (AC) appear specialized for voice processing. Previous research assumed a uniform functional profile for the TVAs which are broadly spread along the bilateral AC. Alternatively, the TVAs might comprise separate AC nodes controlling differential neural functions for voice and speech decoding, organized as local micro-circuits. To investigate micro-circuits, we modeled the directional connectivity between TVA nodes during voice processing in humans while acquiring brain activity using neuroimaging. Results show several bilateral AC nodes for general voice decoding (speech and non-speech voices) and for speech decoding in particular. Furthermore, non-hierarchical and differential bilateral AC networks manifest distinct excitatory and inhibitory pathways for voice and speech processing. Finally, while voice and speech processing seem to have distinctive but integrated neural circuits in the left AC, the right AC reveals disintegrated neural circuits for both sounds. Altogether, we demonstrate a functional heterogeneity in the TVAs for voice decoding based on local micro-circuits.
Collapse
|
9
|
Vaudano AE, Mirandola L, Talami F, Giovannini G, Monti G, Riguzzi P, Volpi L, Michelucci R, Bisulli F, Pasini E, Tinuper P, Di Vito L, Gessaroli G, Malagoli M, Pavesi G, Cardinale F, Tassi L, Lemieux L, Meletti S. fMRI-Based Effective Connectivity in Surgical Remediable Epilepsies: A Pilot Study. Brain Topogr 2021; 34:632-650. [PMID: 34152513 DOI: 10.1007/s10548-021-00857-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 06/13/2021] [Indexed: 11/24/2022]
Abstract
Simultaneous EEG-fMRI can contribute to identify the epileptogenic zone (EZ) in focal epilepsies. However, fMRI maps related to Interictal Epileptiform Discharges (IED) commonly show multiple regions of signal change rather than focal ones. Dynamic causal modeling (DCM) can estimate effective connectivity, i.e. the causal effects exerted by one brain region over another, based on fMRI data. Here, we employed DCM on fMRI data in 10 focal epilepsy patients with multiple IED-related regions of BOLD signal change, to test whether this approach can help the localization process of EZ. For each subject, a family of competing deterministic, plausible DCM models were constructed using IED as autonomous input at each node, one at time. The DCM findings were compared to the presurgical evaluation results and classified as: "Concordant" if the node identified by DCM matches the presumed focus, "Discordant" if the node is distant from the presumed focus, or "Inconclusive" (no statistically significant result). Furthermore, patients who subsequently underwent intracranial EEG recordings or surgery were considered as having an independent validation of DCM results. The effective connectivity focus identified using DCM was Concordant in 7 patients, Discordant in two cases and Inconclusive in one. In four of the 6 patients operated, the DCM findings were validated. Notably, the two Discordant and Invalidated results were found in patients with poor surgical outcome. Our findings provide preliminary evidence to support the applicability of DCM on fMRI data to investigate the epileptic networks in focal epilepsy and, particularly, to identify the EZ in complex cases.
Collapse
Affiliation(s)
- A E Vaudano
- Neurology Unit, OCB Hospital, Azienda Ospedaliero-Universitaria of Modena, Via Giardini 1355, 41100, Modena, Italy. .,Center for Neuroscience and Neurotechnology, Department of Biomedical, Metabolic, and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy.
| | - L Mirandola
- Center for Neuroscience and Neurotechnology, Department of Biomedical, Metabolic, and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - F Talami
- Center for Neuroscience and Neurotechnology, Department of Biomedical, Metabolic, and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - G Giovannini
- Neurology Unit, OCB Hospital, Azienda Ospedaliero-Universitaria of Modena, Via Giardini 1355, 41100, Modena, Italy.,Center for Neuroscience and Neurotechnology, Department of Biomedical, Metabolic, and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - G Monti
- Neurology Unit, AUSL Modena, Ospedale Ramazzini, Carpi, MO, Italy
| | - P Riguzzi
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Unit of Neurology, Bellaria Hospital, Bologna, Italy
| | - L Volpi
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Unit of Neurology, Bellaria Hospital, Bologna, Italy
| | - R Michelucci
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Unit of Neurology, Bellaria Hospital, Bologna, Italy
| | - F Bisulli
- Department of Biomedical and NeuroMotor Sciences (DIBINEM), University of Bologna, Bologna, Italy.,IRCCS Istituto delle Scienze Neurologiche di Bologna, Epilepsy Center (Reference Center for Rare and Complex Epilepsies - EpiCARE), Bologna, Italy
| | - E Pasini
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Unit of Neurology, Bellaria Hospital, Bologna, Italy
| | - P Tinuper
- Department of Biomedical and NeuroMotor Sciences (DIBINEM), University of Bologna, Bologna, Italy.,IRCCS Istituto delle Scienze Neurologiche di Bologna, Epilepsy Center (Reference Center for Rare and Complex Epilepsies - EpiCARE), Bologna, Italy
| | - L Di Vito
- Department of Biomedical and NeuroMotor Sciences (DIBINEM), University of Bologna, Bologna, Italy.,IRCCS Istituto delle Scienze Neurologiche di Bologna, Epilepsy Center (Reference Center for Rare and Complex Epilepsies - EpiCARE), Bologna, Italy
| | - G Gessaroli
- Neurology Unit, OCB Hospital, Azienda Ospedaliero-Universitaria of Modena, Via Giardini 1355, 41100, Modena, Italy
| | - M Malagoli
- Neuroradiology Unit, OCB Hospital, Azienda Ospedaliero-Universitaria of Modena, Modena, Italy
| | - G Pavesi
- Center for Neuroscience and Neurotechnology, Department of Biomedical, Metabolic, and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy.,Neurosurgery Unit, OCB Hospital, Azienda Ospedaliero-Universitaria of Modena, Modena, Italy
| | - F Cardinale
- "Claudio Munari" Epilepsy Surgery Center, Niguarda Hospital, Milan, Italy
| | - L Tassi
- "Claudio Munari" Epilepsy Surgery Center, Niguarda Hospital, Milan, Italy
| | - L Lemieux
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
| | - S Meletti
- Neurology Unit, OCB Hospital, Azienda Ospedaliero-Universitaria of Modena, Via Giardini 1355, 41100, Modena, Italy.,Center for Neuroscience and Neurotechnology, Department of Biomedical, Metabolic, and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| |
Collapse
|
10
|
Gandolla M, Niero L, Molteni F, Guanziroli E, Ward NS, Pedrocchi A. Brain Plasticity Mechanisms Underlying Motor Control Reorganization: Pilot Longitudinal Study on Post-Stroke Subjects. Brain Sci 2021; 11:329. [PMID: 33807679 PMCID: PMC8002039 DOI: 10.3390/brainsci11030329] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 02/24/2021] [Accepted: 03/01/2021] [Indexed: 11/17/2022] Open
Abstract
Functional Electrical Stimulation (FES) has demonstrated to improve walking ability and to induce the carryover effect, long-lasting persisting improvement. Functional magnetic resonance imaging has been used to investigate effective connectivity differences and longitudinal changes in a group of chronic stroke patients that attended a FES-based rehabilitation program for foot-drop correction, distinguishing between carryover effect responders and non-responders, and in comparison with a healthy control group. Bayesian hierarchical procedures were employed, involving nonlinear models at within-subject level-dynamic causal models-and linear models at between-subjects level. Selected regions of interest were primary sensorimotor cortices (M1, S1), supplementary motor area (SMA), and angular gyrus. Our results suggest the following: (i) The ability to correctly plan the movement and integrate proprioception information might be the features to update the motor control loop, towards the carryover effect, as indicated by the reduced sensitivity to proprioception input to S1 of FES non-responders; (ii) FES-related neural plasticity supports the active inference account for motor control, as indicated by the modulation of SMA and M1 connections to S1 area; (iii) SMA has a dual role of higher order motor processing unit responsible for complex movements, and a superintendence role in suppressing standard motor plans as external conditions changes.
Collapse
Affiliation(s)
- Marta Gandolla
- NearLab@Lecco, Polo Territoriale di Lecco, Politecnico di Milano, Via Gaetano Previati, 1/c, 23900 Lecco, Italy; (L.N.); (A.P.)
- Department of Mechanical Engineering, Politecnico di Milano, Via Privata Giuseppe La Masa, 1, 20156 Milano, Italy
| | - Lorenzo Niero
- NearLab@Lecco, Polo Territoriale di Lecco, Politecnico di Milano, Via Gaetano Previati, 1/c, 23900 Lecco, Italy; (L.N.); (A.P.)
| | - Franco Molteni
- Villa Beretta Rehabilitation Center, Valduce Hospital, Via N. Sauro, 17, 23845 Costa Masnaga, Italy; (F.M.); (E.G.)
| | - Elenora Guanziroli
- Villa Beretta Rehabilitation Center, Valduce Hospital, Via N. Sauro, 17, 23845 Costa Masnaga, Italy; (F.M.); (E.G.)
| | - Nick S. Ward
- Department of Movement and Clinical Neuroscience, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK;
- The National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, UK
| | - Alessandra Pedrocchi
- NearLab@Lecco, Polo Territoriale di Lecco, Politecnico di Milano, Via Gaetano Previati, 1/c, 23900 Lecco, Italy; (L.N.); (A.P.)
- NearLab, Department of Electronic Information and Bioengineering, Politecnico di Milano, Via Giuseppe Ponzio, 34/5, 20133 Milano, Italy
| |
Collapse
|
11
|
Sadeghi S, Mier D, Gerchen MF, Schmidt SNL, Hass J. Dynamic Causal Modeling for fMRI With Wilson-Cowan-Based Neuronal Equations. Front Neurosci 2020; 14:593867. [PMID: 33328865 PMCID: PMC7728993 DOI: 10.3389/fnins.2020.593867] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 10/29/2020] [Indexed: 01/26/2023] Open
Abstract
Dynamic causal modeling (DCM) is an analysis technique that has been successfully used to infer about directed connectivity between brain regions based on imaging data such as functional magnetic resonance imaging (fMRI). Most variants of DCM for fMRI rely on a simple bilinear differential equation for neural activation, making it difficult to interpret the results in terms of local neural dynamics. In this work, we introduce a modification to DCM for fMRI by replacing the bilinear equation with a non-linear Wilson-Cowan based equation and use Bayesian Model Comparison (BMC) to show that this modification improves the model evidences. Improved model evidence of the non-linear model is shown for our empirical data (imitation of facial expressions) and validated by synthetic data as well as an empirical test dataset (attention to visual motion) used in previous foundational papers. For our empirical data, we conduct the analysis for a group of 42 healthy participants who performed an imitation task, activating regions putatively containing the human mirror neuron system (MNS). In this regard, we build 540 models as one family for comparing the standard bilinear with the modified Wilson-Cowan models on the family-level. Using this modification, we can interpret the sigmoid transfer function as an averaged f-I curve of many neurons in a single region with a sigmoidal format. In this way, we can make a direct inference from the macroscopic model to detailed microscopic models. The new DCM variant shows superior model evidence on all tested data sets.
Collapse
Affiliation(s)
- Sadjad Sadeghi
- Department of Theoretical Neuroscience, Central Institute of Mental Health, University of Heidelberg/Medical Faculty Mannheim, Mannheim, Germany.,Bernstein Center for Computational Neuroscience (BCCN) Heidelberg/Mannheim, Mannheim, Germany.,Department of Physics and Astronomy, University of Heidelberg, Heidelberg, Germany
| | - Daniela Mier
- Department of Clinical Psychology, Central Institute of Mental Health, University of Heidelberg/Medical Faculty Mannheim, Mannheim, Germany.,Department of Psychology, University of Konstanz, Konstanz, Germany
| | - Martin F Gerchen
- Bernstein Center for Computational Neuroscience (BCCN) Heidelberg/Mannheim, Mannheim, Germany.,Department of Clinical Psychology, Central Institute of Mental Health, University of Heidelberg/Medical Faculty Mannheim, Mannheim, Germany
| | | | - Joachim Hass
- Department of Theoretical Neuroscience, Central Institute of Mental Health, University of Heidelberg/Medical Faculty Mannheim, Mannheim, Germany.,Bernstein Center for Computational Neuroscience (BCCN) Heidelberg/Mannheim, Mannheim, Germany.,Faculty of Applied Psychology, SRH University of Applied Sciences Heidelberg, Heidelberg, Germany
| |
Collapse
|
12
|
Singh MF, Braver TS, Cole MW, Ching S. Estimation and validation of individualized dynamic brain models with resting state fMRI. Neuroimage 2020; 221:117046. [PMID: 32603858 PMCID: PMC7875185 DOI: 10.1016/j.neuroimage.2020.117046] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 05/08/2020] [Accepted: 06/08/2020] [Indexed: 11/25/2022] Open
Abstract
A key challenge for neuroscience is to develop generative, causal models of the human nervous system in an individualized, data-driven manner. Previous initiatives have either constructed biologically-plausible models that are not constrained by individual-level human brain activity or used data-driven statistical characterizations of individuals that are not mechanistic. We aim to bridge this gap through the development of a new modeling approach termed Mesoscale Individualized Neurodynamic (MINDy) modeling, wherein we fit nonlinear dynamical systems models directly to human brain imaging data. The MINDy framework is able to produce these data-driven network models for hundreds to thousands of interacting brain regions in just 1-3 min per subject. We demonstrate that the models are valid, reliable, and robust. We show that MINDy models are predictive of individualized patterns of resting-state brain dynamical activity. Furthermore, MINDy is better able to uncover the mechanisms underlying individual differences in resting state activity than functional connectivity methods.
Collapse
Affiliation(s)
- Matthew F Singh
- Department of Neuroscience, Washington University in St. Louis, St. Louis, MO, USA; Department of Psychology, Washington University in St. Louis, St. Louis, MO, USA; Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA.
| | - Todd S Braver
- Department of Psychology, Washington University in St. Louis, St. Louis, MO, USA
| | - Michael W Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA
| | - ShiNung Ching
- Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| |
Collapse
|
13
|
Aberrant Cortical Connectivity During Ambiguous Object Recognition Is Associated With Schizophrenia. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 6:1193-1201. [PMID: 33359154 DOI: 10.1016/j.bpsc.2020.09.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 09/25/2020] [Accepted: 09/28/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Dysfunctional connectivity within the perceptual hierarchy is proposed to be an integral component of psychosis. The fragmented ambiguous object task was implemented to investigate neural connectivity during object recognition in patients with schizophrenia (SCZ) and bipolar disorder and first-degree relatives of patients with SCZ (SREL). METHODS We analyzed 3T functional magnetic resonance imaging data collected from 27 patients with SCZ, 23 patients with bipolar disorder, 24 control subjects, and 19 SREL during the administration of the fragmented ambiguous object task. Fragmented ambiguous object task stimuli were line-segmented versions of objects and matched across a number of low-level features. Images were categorized as meaningful or meaningless based on ratings assigned by the participants. RESULTS An a priori region of interest was defined in the primary visual cortex (V1). In addition, the lateral occipital complex/ventral visual areas, intraparietal sulcus (IPS), and middle frontal gyrus (MFG) were identified functionally via the contrast of cortical responses to stimuli judged as meaningful or meaningless. SCZ was associated with altered neural activations at V1, IPS, and MFG. Psychophysiological interaction analyses revealed negative connectivity between V1 and MFG in patient groups and altered modulation of connectivity between conditions from right IPS to left IPS and right IPS to left MFG in patients with SCZ and SREL. CONCLUSIONS Results provide evidence that SCZ is associated with inefficient processing of ambiguous visual objects at V1, which is likely attributable to altered feedback from higher-level visual areas. We also observed distinct patterns of aberrant connectivity among low-level, mid-level, and high-level visual areas in patients with SCZ, patients with bipolar disorder, and SREL.
Collapse
|
14
|
Weichwald S, Peters J. Causality in Cognitive Neuroscience: Concepts, Challenges, and Distributional Robustness. J Cogn Neurosci 2020; 33:226-247. [PMID: 32812827 DOI: 10.1162/jocn_a_01623] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Whereas probabilistic models describe the dependence structure between observed variables, causal models go one step further: They predict, for example, how cognitive functions are affected by external interventions that perturb neuronal activity. In this review and perspective article, we introduce the concept of causality in the context of cognitive neuroscience and review existing methods for inferring causal relationships from data. Causal inference is an ambitious task that is particularly challenging in cognitive neuroscience. We discuss two difficulties in more detail: the scarcity of interventional data and the challenge of finding the right variables. We argue for distributional robustness as a guiding principle to tackle these problems. Robustness (or invariance) is a fundamental principle underlying causal methodology. A (correctly specified) causal model of a target variable generalizes across environments or subjects as long as these environments leave the causal mechanisms of the target intact. Consequently, if a candidate model does not generalize, then either it does not consist of the target variable's causes or the underlying variables do not represent the correct granularity of the problem. In this sense, assessing generalizability may be useful when defining relevant variables and can be used to partially compensate for the lack of interventional data.
Collapse
|
15
|
Deshpande G, Jia H. Multi-Level Clustering of Dynamic Directional Brain Network Patterns and Their Behavioral Relevance. Front Neurosci 2020; 13:1448. [PMID: 32116487 PMCID: PMC7017718 DOI: 10.3389/fnins.2019.01448] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2019] [Accepted: 12/27/2019] [Indexed: 11/18/2022] Open
Abstract
Dynamic functional connectivity (DFC) obtained from resting state functional magnetic resonance imaging (fMRI) data has been shown to provide novel insights into brain function which may be obscured by static functional connectivity (SFC). Further, DFC, and by implication how different brain regions may engage or disengage with each other over time, has been shown to be behaviorally relevant and more predictive than SFC of behavioral performance and/or diagnostic status. DFC is not a directional entity and may capture neural synchronization. However, directional interactions between different brain regions is another putative mechanism by which neural populations communicate. Accordingly, static effective connectivity (SEC) has been explored as a means of characterizing such directional interactions. But investigation of its dynamic counterpart, i.e., dynamic effective connectivity (DEC), is still in its infancy. Of particular note are methodological insufficiencies in identifying DEC configurations that are reproducible across time and subjects as well as a lack of understanding of the behavioral relevance of DEC obtained from resting state fMRI. In order to address these issues, we employed a dynamic multivariate autoregressive (MVAR) model to estimate DEC. The method was first validated using simulations and then applied to resting state fMRI data obtained in-house (N = 21), wherein we performed dynamic clustering of DEC matrices across multiple levels [using adaptive evolutionary clustering (AEC)] – spatial location, time, and subjects. We observed a small number of directional brain network configurations alternating between each other over time in a quasi-stable manner akin to brain microstates. The dominant and consistent DEC network patterns involved several regions including inferior and mid temporal cortex, motor and parietal cortex, occipital cortex, as well as part of frontal cortex. The functional relevance of these DEC states were determined using meta-analyses and pertained mainly to memory and emotion, but also involved execution and language. Finally, a larger cohort of resting-state fMRI and behavioral data from the Human Connectome Project (HCP) (N = 232, Q1–Q3 release) was used to demonstrate that metrics derived from DEC can explain larger variance in 70 behaviors across different domains (alertness, cognition, emotion, and personality traits) compared to SEC in healthy individuals.
Collapse
Affiliation(s)
- Gopikrishna Deshpande
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States.,Department of Psychology, Auburn University, Auburn, AL, United States.,Center for Neuroscience, Auburn University, Auburn, AL, United States.,Center for Health Ecology and Equity Research, Auburn, AL, United States.,Alabama Advanced Imaging Consortium, Birmingham, AL, United States.,Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India.,School of Psychology, Capital Normal University, Beijing, China.,Key Laboratory for Learning and Cognition, Capital Normal University, Beijing, China
| | - Hao Jia
- Department of Automation, College of Information Engineering, Taiyuan University of Technology, Taiyuan, China
| |
Collapse
|
16
|
Reid AT, Headley DB, Mill RD, Sanchez-Romero R, Uddin LQ, Marinazzo D, Lurie DJ, Valdés-Sosa PA, Hanson SJ, Biswal BB, Calhoun V, Poldrack RA, Cole MW. Advancing functional connectivity research from association to causation. Nat Neurosci 2019; 22:1751-1760. [PMID: 31611705 PMCID: PMC7289187 DOI: 10.1038/s41593-019-0510-4] [Citation(s) in RCA: 142] [Impact Index Per Article: 28.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Accepted: 09/06/2019] [Indexed: 11/09/2022]
Abstract
Cognition and behavior emerge from brain network interactions, such that investigating causal interactions should be central to the study of brain function. Approaches that characterize statistical associations among neural time series-functional connectivity (FC) methods-are likely a good starting point for estimating brain network interactions. Yet only a subset of FC methods ('effective connectivity') is explicitly designed to infer causal interactions from statistical associations. Here we incorporate best practices from diverse areas of FC research to illustrate how FC methods can be refined to improve inferences about neural mechanisms, with properties of causal neural interactions as a common ontology to facilitate cumulative progress across FC approaches. We further demonstrate how the most common FC measures (correlation and coherence) reduce the set of likely causal models, facilitating causal inferences despite major limitations. Alternative FC measures are suggested to immediately start improving causal inferences beyond these common FC measures.
Collapse
Affiliation(s)
- Andrew T Reid
- School of Psychology, University of Nottingham, Nottingham, UK
| | - Drew B Headley
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, USA
| | - Ravi D Mill
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, USA
| | - Ruben Sanchez-Romero
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, USA
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, FL, USA
- Neuroscience Program, University of Miami Miller School of Medicine, Miami, FL, USA
| | | | - Daniel J Lurie
- Department of Psychology, University of California, Berkeley, Berkeley, CA, USA
| | - Pedro A Valdés-Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- Cuban Neuroscience Center, La Habana, Cuba
| | | | - Bharat B Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, GA, USA
| | | | - Michael W Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, USA.
| |
Collapse
|
17
|
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).
Collapse
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
| |
Collapse
|
18
|
Ventromedial prefrontal cortex contributes to performance success by controlling reward-driven arousal representation in amygdala. Neuroimage 2019; 202:116136. [PMID: 31470123 DOI: 10.1016/j.neuroimage.2019.116136] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 08/23/2019] [Accepted: 08/27/2019] [Indexed: 11/21/2022] Open
Abstract
When preparing for a challenging task, potential rewards can cause physiological arousal that may impair performance. In this case, it is important to control reward-driven arousal while preparing for task execution. We recently examined neural representations of physiological arousal and potential reward magnitude during preparation, and found that performance failure was explained by relatively increased reward representation in the left caudate nucleus and arousal representation in the right amygdala (Watanabe, et al., 2019). Here we examine how prefrontal cortex influences the amygdala and caudate to control reward-driven arousal. Ventromedial prefrontal cortex (VMPFC) exhibited activity that was negatively correlated with trial-wise physiological arousal change, which identified this region as a potential modulator of amygdala and caudate. Next we tested the VMPFC - amygdala - caudate effective network using dynamic causal modeling (Friston et al., 2003). Post-hoc Bayesian model selection (Friston and Penny, 2011) identified a model that best fit data, in which amygdala activation was suppressively controlled by the VMPFC only in success trials. Furthermore, fixed connectivity strength from VMPFC to amygdala explained individual task performance. These findings highlight the role of effective connectivity from VMPFC to amygdala in order to control arousal during preparation for successful performance.
Collapse
|
19
|
Teckentrup V, van der Meer JN, Borchardt V, Fan Y, Neuser MP, Tempelmann C, Herrmann L, Walter M, Kroemer NB. The anterior insula channels prefrontal expectancy signals during affective processing. Neuroimage 2019; 200:414-424. [PMID: 31229657 DOI: 10.1016/j.neuroimage.2019.06.041] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Revised: 06/06/2019] [Accepted: 06/17/2019] [Indexed: 12/16/2022] Open
Abstract
Expectancy shapes our perception of impending events. Although such an interplay between cognitive and affective processes is often impaired in mental disorders, it is not well understood how top-down expectancy signals modulate future affect. We therefore track the information flow in the brain during cognitive and affective processing segregated in time using task-specific cross-correlations. Participants in two independent fMRI studies (N1 = 37 & N2 = 55) were instructed to imagine a situation with affective content as indicated by a cue, which was then followed by an emotional picture congruent with expectancy. To correct for intrinsic covariance of brain function, we calculate resting-state cross-correlations analogous to the task. First, using factorial modeling of delta cross-correlations (task-rest) of the first study, we find that the magnitude of expectancy signals in the anterior insula cortex (AIC) modulates the BOLD response to emotional pictures in the anterior cingulate and dorsomedial prefrontal cortex in opposite directions. Second, using hierarchical linear modeling of lagged connectivity, we demonstrate that expectancy signals in the AIC indeed foreshadow this opposing pattern in the prefrontal cortex. Third, we replicate the results in the second study using a higher temporal resolution, showing that our task-specific cross-correlation approach robustly uncovers the dynamics of information flow. We conclude that the AIC arbitrates the recruitment of distinct prefrontal networks during cued picture processing according to triggered expectations. Taken together, our study provides new insights into neuronal pathways channeling cognition and affect within well-defined brain networks. Better understanding of such dynamics could lead to new applications tracking aberrant information processing in mental disorders.
Collapse
Affiliation(s)
- Vanessa Teckentrup
- University of Tübingen, Department of Psychiatry and Psychotherapy, Tübingen, Germany
| | - Johan N van der Meer
- Queensland Institute of Medical Research, Brisbane, Australia; University of Magdeburg, Department of Psychiatry and Psychotherapy, Germany; Clinical Affective Neuroimaging Laboratory, Magdeburg, Germany; Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Viola Borchardt
- Clinical Affective Neuroimaging Laboratory, Magdeburg, Germany; Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Yan Fan
- Leibniz Research Centre for Working Environment and Human Factors, Department of Psychology and Neurosciences Dortmund, Germany
| | - Monja P Neuser
- University of Tübingen, Department of Psychiatry and Psychotherapy, Tübingen, Germany
| | | | - Luisa Herrmann
- University of Tübingen, Department of Psychiatry and Psychotherapy, Tübingen, Germany
| | - Martin Walter
- University of Tübingen, Department of Psychiatry and Psychotherapy, Tübingen, Germany; University of Magdeburg, Department of Psychiatry and Psychotherapy, Germany; Clinical Affective Neuroimaging Laboratory, Magdeburg, Germany; Leibniz Institute for Neurobiology, Magdeburg, Germany.
| | - Nils B Kroemer
- University of Tübingen, Department of Psychiatry and Psychotherapy, Tübingen, Germany.
| |
Collapse
|
20
|
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: 34] [Impact Index Per Article: 6.8] [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.
Collapse
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
| |
Collapse
|
21
|
Grimm O, Löffler M, Kamping S, Hartmann A, Rohleder C, Leweke M, Flor H. Probing the endocannabinoid system in healthy volunteers: Cannabidiol alters fronto-striatal resting-state connectivity. Eur Neuropsychopharmacol 2018; 28:841-849. [PMID: 29887287 DOI: 10.1016/j.euroneuro.2018.04.004] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Revised: 04/25/2018] [Accepted: 04/30/2018] [Indexed: 01/08/2023]
Abstract
Tetrahydrocannabinol (THC) and Cannabidiol (CBD) are two substances from cannabis sativa that have beenimplicated in the treatment of mental and neurological disorders. We concentrated on a previously validated neuroimaging phenotype, fronto-striatal connectivity across different striatal seeds, because of this loop's relevance to executive functioning, decision making, salience generation and motivation and its link to various neuropsychiatric conditions. Therefore, we studied the effect of THC and CBD on fronto-striatal circuitry by a seed-voxel connectivity approach using seeds from the caudate and the putamen. We conducted a cross-over pharmaco-fMRI study in 16 healthy male volunteers with placebo, 10 mg oral THC and 600 mg oral CBD. Resting state was measured in a 3 T scanner. CBD lead to an increase of fronto-striatal connectivity in comparison to placebo. In contrast to our expectation that THC and CBD show opposing effects, THC versus placebo did not show any significant effects, probably due to insufficient concentration of THC during scanning. The effect of CBD on enhancing fronto-striatal connectivity is of interest because it might be a neural correlate of its anti-psychotic effect in patients.
Collapse
Affiliation(s)
- Oliver Grimm
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany; Department of Psychiatry, Psychosomatics and Psychotherapy, Goethe University, Frankfurt am Main, Germany
| | - Martin Löffler
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
| | - Sandra Kamping
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany; Department of Anesthesiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Aljoscha Hartmann
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
| | - Cathrin Rohleder
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
| | - Markus Leweke
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
| | - Herta Flor
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
| |
Collapse
|
22
|
Oestreich LKL, Whitford TJ, Garrido MI. Prediction of Speech Sounds Is Facilitated by a Functional Fronto-Temporal Network. Front Neural Circuits 2018; 12:43. [PMID: 29875638 PMCID: PMC5975240 DOI: 10.3389/fncir.2018.00043] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Accepted: 05/02/2018] [Indexed: 11/13/2022] Open
Abstract
Predictive coding postulates that the brain continually predicts forthcoming sensory events based on past experiences in order to process sensory information and respond to unexpected events in a fast and efficient manner. Predictive coding models in the context of overt speech are believed to operate along auditory white matter pathways such as the arcuate fasciculus and the frontal aslant. The aim of this study was to investigate whether brain regions that are structurally connected via these white matter pathways are also effectively engaged when listening to externally-generated, temporally-predicable speech sounds. Using Electroencephalography (EEG) and Dynamic Causal Modeling (DCM) we investigated network models that are structurally connected via the arcuate fasciculus from primary auditory cortex to Wernicke’s and via Geschwind’s territory to Broca’s area. Connections between Broca’s and supplementary motor area, which are structurally connected by the frontal aslant, were also included. The results revealed that bilateral areas interconnected by indirect and direct pathways of the arcuate fasciculus, in addition to regions interconnected by the frontal aslant best explain the EEG responses to speech that is externally-generated but temporally predictable. These findings indicate that structurally connected brain regions involved in the production and processing of auditory stimuli are also effectively connected.
Collapse
Affiliation(s)
- Lena K L Oestreich
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia.,Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD, Australia
| | - Thomas J Whitford
- School of Psychology, University of New South Wales, Sydney, NSW, Australia
| | - Marta I Garrido
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia.,Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD, Australia.,Australian Centre of Excellence for Integrative Brain Function, The University of Queensland, Brisbane, QLD, Australia.,School of Mathematics and Physics, The University of Queensland, Brisbane, QLD, Australia
| |
Collapse
|
23
|
Beltz AM. Connecting Theory and Methods in Adolescent Brain Research. JOURNAL OF RESEARCH ON ADOLESCENCE : THE OFFICIAL JOURNAL OF THE SOCIETY FOR RESEARCH ON ADOLESCENCE 2018; 28:10-25. [PMID: 29460359 DOI: 10.1111/jora.12366] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Networks are often implicated in theories of adolescent brain development, but they are not regularly examined in empirical studies. The aim of this article is to address this disconnect between theory and quantitative methodology, using the dual systems model of adolescent decision making as a prototype. After reviewing the key task-related connectivity methods that have been applied in the adolescent neuroimaging literature (seed-based correlations, psychophysiological interactions, and dynamic causal modeling), a novel connectivity method is introduced (extended unified structural equation modeling). The potential of this method for understanding adolescent brain development is showcased with a simulation study: It creates person-specific networks that have direct and time-lagged connections that can be modulated by behavior.
Collapse
|
24
|
Morawetz C, Bode S, Baudewig J, Heekeren HR. Effective amygdala-prefrontal connectivity predicts individual differences in successful emotion regulation. Soc Cogn Affect Neurosci 2018; 12:569-585. [PMID: 27998996 PMCID: PMC5390747 DOI: 10.1093/scan/nsw169] [Citation(s) in RCA: 116] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2016] [Accepted: 11/14/2016] [Indexed: 11/21/2022] Open
Abstract
The ability to voluntarily regulate our emotional response to threatening and highly arousing stimuli by using cognitive reappraisal strategies is essential for our mental and physical well-being. This might be achieved by prefrontal brain regions (e.g. inferior frontal gyrus, IFG) down-regulating activity in the amygdala. It is unknown, to which degree effective connectivity within the emotion-regulation network is linked to individual differences in reappraisal skills. Using psychophysiological interaction analyses of functional magnetic resonance imaging data, we examined changes in inter-regional connectivity between the amygdala and IFG with other brain regions during reappraisal of emotional responses and used emotion regulation success as an explicit regressor. During down-regulation of emotion, reappraisal success correlated with effective connectivity between IFG with dorsolateral, dorsomedial and ventromedial prefrontal cortex (PFC). During up-regulation of emotion, effective coupling between IFG with anterior cingulate cortex, dorsomedial and ventromedial PFC as well as the amygdala correlated with reappraisal success. Activity in the amygdala covaried with activity in lateral and medial prefrontal regions during the up-regulation of emotion and correlated with reappraisal success. These results suggest that successful reappraisal is linked to changes in effective connectivity between two systems, prefrontal cognitive control regions and regions crucially involved in emotional evaluation.
Collapse
Affiliation(s)
- Carmen Morawetz
- Department of Education and Psychology, Freie Universität Berlin, Germany.,Center for Cognitive Neuroscience Berlin, Freie Universität Berlin, Germany
| | - Stefan Bode
- Melbourne School of Psychological Sciences, The University of Melbourne, Australia
| | | | - Hauke R Heekeren
- Department of Education and Psychology, Freie Universität Berlin, Germany.,Center for Cognitive Neuroscience Berlin, Freie Universität Berlin, Germany
| |
Collapse
|
25
|
Steine-Hanson Z, Koh N, Stocco A. Refining the Common Model of Cognition Through Large Neuroscience Data. ACTA ACUST UNITED AC 2018. [DOI: 10.1016/j.procs.2018.11.026] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
|
26
|
Park CH, Choi YS, Kim HJ, Chung HK, Jung AR, Yoo JH, Lee HW. Interactive effects of seizure frequency and lateralization on intratemporal effective connectivity in temporal lobe epilepsy. Epilepsia 2017; 59:215-225. [PMID: 29205291 DOI: 10.1111/epi.13951] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/17/2017] [Indexed: 01/22/2023]
Abstract
OBJECTIVE Patients with temporal lobe epilepsy (TLE) show brain connectivity changes in association with cognitive impairment. Seizure frequency and lateralization are 2 important clinical factors that characterize epileptic seizures. In this study, we sought to examine an interactive effect of the 2 seizure factors on intratemporal effective connectivity based on resting-state functional magnetic resonance imaging (rsfMRI) in patients with TLE. METHODS For rsfMRI data acquired from 48 TLE patients and 45 healthy controls, we applied stochastic dynamical causal modeling to infer effective connectivity between 3 medial temporal lobe (MTL) regions, including the hippocampus (Hipp), parahippocampal gyrus (PHG), and amygdala (Amyg), ipsilateral to the seizure focus. We searched for the effect of the 2 seizure factors, seizure frequency (good vs poor seizure control) and lateralization (left vs right TLE), on connection strengths and their relationship with the level of verbal memory and language impairment. RESULTS Impairment of verbal memory and language function was mainly affected by seizure lateralization, consistent with preferential involvement of the left MTL in verbal mnemonic processing. For the fully connected model, which was selected as the effective connectivity structure that best explained the observed rsfMRI time series, alterations in connection strengths were primarily influenced by seizure frequency; there was an increase in the strength of the Hipp to PHG connection in TLE patients with poor seizure control, whereas the strength of the Amyg to PHG connection increased in those with good seizure control. Furthermore, the association between connection strength alterations and cognitive impairment was interactively affected by both seizure frequency and lateralization. SIGNIFICANCE These findings suggest an interactive effect as well as an individual effect of seizure frequency and lateralization on neuroimaging features and cognitive function. This potential interaction needs to be evaluated in the consideration of multiple seizure factors.
Collapse
Affiliation(s)
- Chang-Hyun Park
- Department of Neurology, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, South Korea.,Department of Medical Science, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, South Korea.,Department of Psychiatry, Catholic University of Korea College of Medicine, Seoul, South Korea
| | - Yun Seo Choi
- Department of Neurology, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, South Korea.,Department of Medical Science, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, South Korea
| | - Hyeon Jin Kim
- Department of Neurology, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, South Korea.,Department of Medical Science, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, South Korea
| | - Hwa-Kyung Chung
- Department of Neurology, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, South Korea
| | - A-Reum Jung
- Department of Neurology, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, South Korea
| | - Jeong Hyun Yoo
- Department of Radiology, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, South Korea
| | - Hyang Woon Lee
- Department of Neurology, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, South Korea.,Department of Medical Science, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, South Korea
| |
Collapse
|
27
|
Rangaprakash D, Dretsch MN, Venkataraman A, Katz JS, Denney TS, Deshpande G. Identifying disease foci from static and dynamic effective connectivity networks: Illustration in soldiers with trauma. Hum Brain Mapp 2017; 39:264-287. [PMID: 29058357 DOI: 10.1002/hbm.23841] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Revised: 08/29/2017] [Accepted: 10/01/2017] [Indexed: 12/15/2022] Open
Abstract
Brain connectivity studies report group differences in pairwise connection strengths. While informative, such results are difficult to interpret since our understanding of the brain relies on region-based properties, rather than on connection information. Given that large disruptions in the brain are often caused by a few pivotal sources, we propose a novel framework to identify the sources of functional disruption from effective connectivity networks. Our approach integrates static and time-varying effective connectivity modeling in a probabilistic framework, to identify aberrant foci and the corresponding aberrant connectomics network. Using resting-state fMRI, we illustrate the utility of this novel approach in U.S. Army soldiers (N = 87) with posttraumatic stress disorder (PTSD), mild traumatic brain injury (mTBI) and combat controls. Additionally, we employed machine-learning classification to identify those significant connectivity features that possessed high predictive ability. We identified three disrupted foci (middle frontal gyrus [MFG], insula, hippocampus), and an aberrant prefrontal-subcortical-parietal network of information flow. We found the MFG to be the pivotal focus of network disruption, with aberrant strength and temporal-variability of effective connectivity to the insula, amygdala and hippocampus. These connectivities also possessed high predictive ability (giving a classification accuracy of 81%); and they exhibited significant associations with symptom severity and neurocognitive functioning. In summary, dysregulation originating in the MFG caused elevated and temporally less-variable connectivity in subcortical regions, followed by a similar effect on parietal memory-related regions. This mechanism likely contributes to the reduced control over traumatic memories leading to re-experiencing, hyperarousal and flashbacks observed in soldiers with PTSD and mTBI. Hum Brain Mapp 39:264-287, 2018. © 2017 Wiley Periodicals, Inc.
Collapse
Affiliation(s)
- D Rangaprakash
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - Michael N Dretsch
- U.S. Army Aeromedical Research Laboratory, Fort Rucker, Alabama.,Human Dimension Division, HQ TRADOC, Fort Eustis, Virgina
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Jeffrey S Katz
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychology, Auburn University, Auburn, Alabama.,Alabama Advanced Imaging Consortium, USA
| | - Thomas S Denney
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychology, Auburn University, Auburn, Alabama.,Alabama Advanced Imaging Consortium, USA
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychology, Auburn University, Auburn, Alabama.,Alabama Advanced Imaging Consortium, USA
| |
Collapse
|
28
|
A study of problems encountered in Granger causality analysis from a neuroscience perspective. Proc Natl Acad Sci U S A 2017; 114:E7063-E7072. [PMID: 28778996 DOI: 10.1073/pnas.1704663114] [Citation(s) in RCA: 130] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Granger causality methods were developed to analyze the flow of information between time series. These methods have become more widely applied in neuroscience. Frequency-domain causality measures, such as those of Geweke, as well as multivariate methods, have particular appeal in neuroscience due to the prevalence of oscillatory phenomena and highly multivariate experimental recordings. Despite its widespread application in many fields, there are ongoing concerns regarding the applicability of Granger causality methods in neuroscience. When are these methods appropriate? How reliably do they recover the system structure underlying the observed data? What do frequency-domain causality measures tell us about the functional properties of oscillatory neural systems? In this paper, we analyze fundamental properties of Granger-Geweke (GG) causality, both computational and conceptual. Specifically, we show that (i) GG causality estimates can be either severely biased or of high variance, both leading to spurious results; (ii) even if estimated correctly, GG causality estimates alone are not interpretable without examining the component behaviors of the system model; and (iii) GG causality ignores critical components of a system's dynamics. Based on this analysis, we find that the notion of causality quantified is incompatible with the objectives of many neuroscience investigations, leading to highly counterintuitive and potentially misleading results. Through the analysis of these problems, we provide important conceptual clarification of GG causality, with implications for other related causality approaches and for the role of causality analyses in neuroscience as a whole.
Collapse
|
29
|
Gow DW, Ahlfors SP. Tracking reorganization of large-scale effective connectivity in aphasia following right hemisphere stroke. BRAIN AND LANGUAGE 2017; 170:12-17. [PMID: 28364641 PMCID: PMC5472378 DOI: 10.1016/j.bandl.2017.03.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2015] [Revised: 12/22/2016] [Accepted: 03/15/2017] [Indexed: 06/07/2023]
Abstract
In this paper we demonstrate the application of new effective connectivity analyses to characterize changing patterns of task-related directed interaction in large (25-55 node) cortical networks following the onset of aphasia. The subject was a left-handed woman who became aphasic following a right-hemisphere stroke. She was tested on an auditory word-picture verification task administered one and seven months after the onset of aphasia. MEG/EEG and anatomical MRI data were used to create high spatiotemporal resolution estimates of task-related cortical activity. Effective connectivity analyses of those data showed a reduction of bilateral network influences on preserved right-hemisphere structures, and an increase in intra-hemispheric left-hemisphere influences. She developed a connectivity pattern that was more left lateralized than that of right-handed control subjects. Her emergent left hemisphere network showed a combination of increased functional subdivision of perisylvian language areas and recruitment of medial structures.
Collapse
Affiliation(s)
- David W Gow
- Neuropsychology Laboratory, Massachusetts General Hospital, 175 Cambridge St., CPZ S340, Boston, MA 02114, United States; Department of Psychology, Salem State University, 352 Lafayette St., Salem, MA 01970, United States; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 Thirteenth St., S2301, Charlestown, MA 02129, United States; Harvard-MIT Division of Health Sciences and Technology, 77 Massachusetts Ave., E25-519, Cambridge, MA 02139, United States.
| | - Seppo P Ahlfors
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 Thirteenth St., S2301, Charlestown, MA 02129, United States
| |
Collapse
|
30
|
Tuning the Brake While Raising the Stake: Network Dynamics during Sequential Decision-Making. J Neurosci 2017; 36:5417-26. [PMID: 27170137 DOI: 10.1523/jneurosci.3191-15.2016] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2015] [Accepted: 04/07/2016] [Indexed: 11/21/2022] Open
Abstract
UNLABELLED When gathering valued goods, risk and reward are often coupled and escalate over time, for instance, during foraging, trading, or gambling. This escalating frame requires agents to continuously balance expectations of reward against those of risk. To address how the human brain dynamically computes these tradeoffs, we performed whole-brain fMRI while healthy young individuals engaged in a sequential gambling task. Participants were repeatedly confronted with the option to continue with throwing a die to accumulate monetary reward under escalating risk, or the alternative option to stop to bank the current balance. Within each gambling round, the accumulation of gains gradually increased reaction times for "continue" choices, indicating growing uncertainty in the decision to continue. Neural activity evoked by "continue" choices was associated with growing activity and connectivity of a cortico-subcortical "braking" network that positively scaled with the accumulated gains, including pre-supplementary motor area (pre-SMA), inferior frontal gyrus, caudate, and subthalamic nucleus (STN). The influence of the STN on continue-evoked activity in the pre-SMA was predicted by interindividual differences in risk-aversion attitudes expressed during the gambling task. Furthermore, activity in dorsal anterior cingulate cortex (ACC) reflected individual choice tendencies by showing increased activation when subjects made nondefault "continue" choices despite an increasing tendency to stop, but ACC activity did not change in proportion with subjective choice uncertainty. Together, the results implicate a key role of dorsal ACC, pre-SMA, inferior frontal gyrus, and STN in computing the trade-off between escalating reward and risk in sequential decision-making. SIGNIFICANCE STATEMENT Using a paradigm where subjects experienced increasing potential rewards coupled with increasing risk, this study addressed two unresolved questions in the field of decision-making: First, we investigated an "inhibitory" network of regions that has so far been investigated with externally cued action inhibition. In this study, we show that the dynamics in this network under increasingly risky decisions are predictive of subjects' risk attitudes. Second, we contribute to a currently ongoing debate about the anterior cingulate cortex's role in sequential foraging decisions by showing that its activity is related to making nondefault choices rather than to choice uncertainty.
Collapse
|
31
|
Jin C, Jia H, Lanka P, Rangaprakash D, Li L, Liu T, Hu X, Deshpande G. Dynamic brain connectivity is a better predictor of PTSD than static connectivity. Hum Brain Mapp 2017; 38:4479-4496. [PMID: 28603919 DOI: 10.1002/hbm.23676] [Citation(s) in RCA: 76] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2017] [Accepted: 05/23/2017] [Indexed: 12/24/2022] Open
Abstract
Using resting-state functional magnetic resonance imaging, we test the hypothesis that subjects with post-traumatic stress disorder (PTSD) are characterized by reduced temporal variability of brain connectivity compared to matched healthy controls. Specifically, we test whether PTSD is characterized by elevated static connectivity, coupled with decreased temporal variability of those connections, with the latter providing greater sensitivity toward the pathology than the former. Static functional connectivity (FC; nondirectional zero-lag correlation) and static effective connectivity (EC; directional time-lagged relationships) were obtained over the entire brain using conventional models. Dynamic FC and dynamic EC were estimated by letting the conventional models to vary as a function of time. Statistical separation and discriminability of these metrics between the groups and their ability to accurately predict the diagnostic label of a novel subject were ascertained using separate support vector machine classifiers. Our findings support our hypothesis that PTSD subjects have stronger static connectivity, but reduced temporal variability of connectivity. Further, machine learning classification accuracy obtained with dynamic FC and dynamic EC was significantly higher than that obtained with static FC and static EC, respectively. Furthermore, results also indicate that the ease with which brain regions engage or disengage with other regions may be more sensitive to underlying pathology than the strength with which they are engaged. Future studies must examine whether this is true only in the case of PTSD or is a general organizing principle in the human brain. Hum Brain Mapp 38:4479-4496, 2017. © 2017 Wiley Periodicals, Inc.
Collapse
Affiliation(s)
- Changfeng Jin
- The Mental Health Institute, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Hao Jia
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, Alabama.,Department of Automation, College of Information Engineering, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Pradyumna Lanka
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, Alabama
| | - D Rangaprakash
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, Alabama.,Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California
| | - Lingjiang Li
- The Mental Health Institute, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, University of Georgia, Athens, Georgia
| | - Xiaoping Hu
- Center for Advanced Neuroimaging, Department of Bioengineering, University of California, Riverside, California
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, Alabama.,Department of Psychology, Auburn University, Auburn, Alabama.,Alabama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, Alabama
| |
Collapse
|
32
|
Mueller K, Jech R, Hoskovcová M, Ulmanová O, Urgošík D, Vymazal J, Růžička E. General and selective brain connectivity alterations in essential tremor: A resting state fMRI study. Neuroimage Clin 2017; 16:468-476. [PMID: 28913163 PMCID: PMC5587870 DOI: 10.1016/j.nicl.2017.06.004] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2017] [Revised: 05/22/2017] [Accepted: 06/01/2017] [Indexed: 12/17/2022]
Abstract
Although essential tremor is the most common movement disorder, there is little knowledge about the pathophysiological mechanisms of this disease. Therefore, we explored brain connectivity based on slow spontaneous fluctuations of blood oxygenation level dependent (BOLD) signal in patients with essential tremor (ET). A cohort of 19 ET patients and 23 healthy individuals were scanned in resting condition using functional magnetic resonance imaging (fMRI). General connectivity was assessed by eigenvector centrality (EC) mapping. Selective connectivity was analyzed by correlations of the BOLD signal between the preselected seed regions and all the other brain areas. These measures were then correlated with the tremor severity evaluated by the Fahn-Tolosa-Marin Tremor Rating Scale (FTMTS). Compared to healthy subjects, ET patients were found to have lower EC in the cerebellar hemispheres and higher EC in the anterior cingulate and in the primary motor cortices bilaterally. In patients, the FTMTS score correlated positively with the EC in the putamen. In addition, the FTMTS score correlated positively with selective connectivity between the thalamus and other structures (putamen, pre-supplementary motor area (pre-SMA), parietal cortex), and between the pre-SMA and the putamen. We observed a selective coupling between a number of areas in the sensorimotor network including the basal ganglia and the ventral intermediate nucleus of thalamus, which is widely used as neurosurgical target for tremor treatment. Finally, ET was marked by suppression of general connectivity in the cerebellum, which is in agreement with the concept of ET as a disorder with cerebellar damage.
Collapse
Affiliation(s)
- Karsten Mueller
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Robert Jech
- Department of Neurology, Charles University, First Faculty of Medicine and General University Hospital in Prague, Czech Republic
| | - Martina Hoskovcová
- Department of Neurology, Charles University, First Faculty of Medicine and General University Hospital in Prague, Czech Republic
| | - Olga Ulmanová
- Department of Neurology, Charles University, First Faculty of Medicine and General University Hospital in Prague, Czech Republic
| | | | | | - Evžen Růžička
- Department of Neurology, Charles University, First Faculty of Medicine and General University Hospital in Prague, Czech Republic
| |
Collapse
|
33
|
Turner R, De Haan D. Bridging the gap between system and cell: The role of ultra-high field MRI in human neuroscience. PROGRESS IN BRAIN RESEARCH 2017; 233:179-220. [DOI: 10.1016/bs.pbr.2017.05.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
|
34
|
Network interactions underlying mirror feedback in stroke: A dynamic causal modeling study. NEUROIMAGE-CLINICAL 2016; 13:46-54. [PMID: 27920978 PMCID: PMC5126151 DOI: 10.1016/j.nicl.2016.11.012] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Revised: 10/04/2016] [Accepted: 11/11/2016] [Indexed: 01/22/2023]
Abstract
Mirror visual feedback (MVF) is potentially a powerful tool to facilitate recovery of disordered movement and stimulate activation of under-active brain areas due to stroke. The neural mechanisms underlying MVF have therefore been a focus of recent inquiry. Although it is known that sensorimotor areas can be activated via mirror feedback, the network interactions driving this effect remain unknown. The aim of the current study was to fill this gap by using dynamic causal modeling to test the interactions between regions in the frontal and parietal lobes that may be important for modulating the activation of the ipsilesional motor cortex during mirror visual feedback of unaffected hand movement in stroke patients. Our intent was to distinguish between two theoretical neural mechanisms that might mediate ipsilateral activation in response to mirror-feedback: transfer of information between bilateral motor cortices versus recruitment of regions comprising an action observation network which in turn modulate the motor cortex. In an event-related fMRI design, fourteen chronic stroke subjects performed goal-directed finger flexion movements with their unaffected hand while observing real-time visual feedback of the corresponding (veridical) or opposite (mirror) hand in virtual reality. Among 30 plausible network models that were tested, the winning model revealed significant mirror feedback-based modulation of the ipsilesional motor cortex arising from the contralesional parietal cortex, in a region along the rostral extent of the intraparietal sulcus. No winning model was identified for the veridical feedback condition. We discuss our findings in the context of supporting the latter hypothesis, that mirror feedback-based activation of motor cortex may be attributed to engagement of a contralateral (contralesional) action observation network. These findings may have important implications for identifying putative cortical areas, which may be targeted with non-invasive brain stimulation as a means of potentiating the effects of mirror training.
Collapse
|
35
|
Turner R. Uses, misuses, new uses and fundamental limitations of magnetic resonance imaging in cognitive science. Philos Trans R Soc Lond B Biol Sci 2016; 371:20150349. [PMID: 27574303 PMCID: PMC5003851 DOI: 10.1098/rstb.2015.0349] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/24/2016] [Indexed: 11/29/2022] Open
Abstract
When blood oxygenation level-dependent (BOLD) contrast functional magnetic resonance imaging (fMRI) was discovered in the early 1990s, it provoked an explosion of interest in exploring human cognition, using brain mapping techniques based on MRI. Standards for data acquisition and analysis were rapidly put in place, in order to assist comparison of results across laboratories. Recently, MRI data acquisition capabilities have improved dramatically, inviting a rethink of strategies for relating functional brain activity at the systems level with its neuronal substrates and functional connections. This paper reviews the established capabilities of BOLD contrast fMRI, the perceived weaknesses of major methods of analysis, and current results that may provide insights into improved brain modelling. These results have inspired the use of in vivo myeloarchitecture for localizing brain activity, individual subject analysis without spatial smoothing and mapping of changes in cerebral blood volume instead of BOLD activation changes. The apparent fundamental limitations of all methods based on nuclear magnetic resonance are also discussed.This article is part of the themed issue 'Interpreting BOLD: a dialogue between cognitive and cellular neuroscience'.
Collapse
Affiliation(s)
- Robert Turner
- Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1A, 04103 Leipzig, Germany
| |
Collapse
|
36
|
Influence of Resting Venous Blood Volume Fraction on Dynamic Causal Modeling and System Identifiability. Sci Rep 2016; 6:29426. [PMID: 27389074 PMCID: PMC4937422 DOI: 10.1038/srep29426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2016] [Accepted: 06/17/2016] [Indexed: 11/18/2022] Open
Abstract
Changes in BOLD signals are sensitive to the regional blood content associated with the vasculature, which is known as V0 in hemodynamic models. In previous studies involving dynamic causal modeling (DCM) which embodies the hemodynamic model to invert the functional magnetic resonance imaging signals into neuronal activity, V0 was arbitrarily set to a physiolog-ically plausible value to overcome the ill-posedness of the inverse problem. It is interesting to investigate how the V0 value influences DCM. In this study we addressed this issue by using both synthetic and real experiments. The results show that the ability of DCM analysis to reveal information about brain causality depends critically on the assumed V0 value used in the analysis procedure. The choice of V0 value not only directly affects the strength of system connections, but more importantly also affects the inferences about the network architecture. Our analyses speak to a possible refinement of how the hemody-namic process is parameterized (i.e., by making V0 a free parameter); however, the conditional dependencies induced by a more complex model may create more problems than they solve. Obtaining more realistic V0 information in DCM can improve the identifiability of the system and would provide more reliable inferences about the properties of brain connectivity.
Collapse
|
37
|
Task-Related Edge Density (TED)-A New Method for Revealing Dynamic Network Formation in fMRI Data of the Human Brain. PLoS One 2016; 11:e0158185. [PMID: 27341204 PMCID: PMC4920409 DOI: 10.1371/journal.pone.0158185] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2016] [Accepted: 06/10/2016] [Indexed: 12/22/2022] Open
Abstract
The formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges that show differing levels of synchrony between two distinct task conditions and that occur in dense packs with similar characteristics. Hence, we call this approach "Task-related Edge Density" (TED). TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping and an emotion processing task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function.
Collapse
|
38
|
Zhang Y, Wang Z, Cai Z, Lin Q, Hu Z. Nonlinear estimation of BOLD signals with the aid of cerebral blood volume imaging. Biomed Eng Online 2016; 15:22. [PMID: 26897355 PMCID: PMC4761419 DOI: 10.1186/s12938-016-0137-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2015] [Accepted: 02/04/2016] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND The hemodynamic balloon model describes the change in coupling from underlying neural activity to observed blood oxygen level dependent (BOLD) response. It plays an increasing important role in brain research using magnetic resonance imaging (MRI) techniques. However, changes in the BOLD signal are sensitive to the resting blood volume fraction (i.e., [Formula: see text]) associated with the regional vasculature. In previous studies the value was arbitrarily set to a physiologically plausible value to circumvent the ill-posedness of the inverse problem. These approaches fail to explore actual [Formula: see text] value and could yield inaccurate model estimation. METHODS The present study represents the first empiric attempt to derive the actual [Formula: see text] from data obtained using cerebral blood volume imaging, with the aim of augmenting the existing estimation schemes. Bimanual finger tapping experiments were performed to determine how [Formula: see text] influences the model estimation of BOLD signals within a single-region and multiple-regions (i.e., dynamic causal modeling). In order to show the significance of applying the true [Formula: see text], we have presented the different results obtained when using the real [Formula: see text] and assumed [Formula: see text] in terms of single-region model estimation and dynamic causal modeling. RESULTS The results show that [Formula: see text] significantly influences the estimation results within a single-region and multiple-regions. Using the actual [Formula: see text] might yield more realistic and physiologically meaningful model estimation results. CONCLUSION Incorporating regional venous information in the analysis of the hemodynamic model can provide more reliable and accurate parameter estimations and model predictions, and improve the inference about brain connectivity based on fMRI data.
Collapse
Affiliation(s)
- Yan Zhang
- College of Optical and Electronic Technology, China Jiliang University, Xueyuan Street 258, Hangzhou, 310018, China.
| | - Zuli Wang
- College of Optical and Electronic Technology, China Jiliang University, Xueyuan Street 258, Hangzhou, 310018, China.
| | - Zhongzhou Cai
- College of Optical Science and Engineering, Zhejiang University, Zheda Road 38, Hangzhou, 310027, China.
| | - Qiang Lin
- Center for Optics and Optoelectronics Research, College of Science, Zhejiang University of Technology, Liuhe Road 288, Hangzhou, 310023, China.
| | - Zhenghui Hu
- Center for Optics and Optoelectronics Research, College of Science, Zhejiang University of Technology, Liuhe Road 288, Hangzhou, 310023, China.
| |
Collapse
|
39
|
Allen M, Fardo F, Dietz MJ, Hillebrandt H, Friston KJ, Rees G, Roepstorff A. Anterior insula coordinates hierarchical processing of tactile mismatch responses. Neuroimage 2016; 127:34-43. [PMID: 26584870 PMCID: PMC4758822 DOI: 10.1016/j.neuroimage.2015.11.030] [Citation(s) in RCA: 74] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2015] [Revised: 10/13/2015] [Accepted: 11/09/2015] [Indexed: 11/24/2022] Open
Abstract
The body underlies our sense of self, emotion, and agency. Signals arising from the skin convey warmth, social touch, and the physical characteristics of external stimuli. Surprising or unexpected tactile sensations can herald events of motivational salience, including imminent threats (e.g., an insect bite) and hedonic rewards (e.g., a caressing touch). Awareness of such events is thought to depend upon the hierarchical integration of body-related mismatch responses by the anterior insula. To investigate this possibility, we measured brain activity using functional magnetic resonance imaging, while healthy participants performed a roving tactile oddball task. Mass-univariate analysis demonstrated robust activations in limbic, somatosensory, and prefrontal cortical areas previously implicated in tactile deviancy, body awareness, and cognitive control. Dynamic Causal Modelling revealed that unexpected stimuli increased the strength of forward connections along a caudal to rostral hierarchy-projecting from thalamic and somatosensory regions towards insula, cingulate and prefrontal cortices. Within this ascending flow of sensory information, the AIC was the only region to show increased backwards connectivity to the somatosensory cortex, augmenting a reciprocal exchange of neuronal signals. Further, participants who rated stimulus changes as easier to detect showed stronger modulation of descending PFC to AIC connections by deviance. These results suggest that the AIC coordinates hierarchical processing of tactile prediction error. They are interpreted in support of an embodied predictive coding model where AIC mediated body awareness is involved in anchoring a global neuronal workspace.
Collapse
Affiliation(s)
- Micah Allen
- Institute of Cognitive Neuroscience, University College London, London WC1N 3AR, United Kingdom; Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, United Kingdom.
| | - Francesca Fardo
- Center of Functionally Integrative Neuroscience, Aarhus University Hospital, Aarhus 8000, Denmark
| | - Martin J Dietz
- Center of Functionally Integrative Neuroscience, Aarhus University Hospital, Aarhus 8000, Denmark
| | - Hauke Hillebrandt
- Institute of Cognitive Neuroscience, University College London, London WC1N 3AR, United Kingdom; Harvard University, Cambridge, MA, 02138, United States
| | - Karl J Friston
- Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, United Kingdom
| | - Geraint Rees
- Institute of Cognitive Neuroscience, University College London, London WC1N 3AR, United Kingdom; Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, United Kingdom
| | - Andreas Roepstorff
- Center of Functionally Integrative Neuroscience, Aarhus University Hospital, Aarhus 8000, Denmark; Interacting Minds Centre, Aarhus University, DK-8000 Aarhus C, Denmark
| |
Collapse
|
40
|
Hampstead BM, Khoshnoodi M, Yan W, Deshpande G, Sathian K. Patterns of effective connectivity during memory encoding and retrieval differ between patients with mild cognitive impairment and healthy older adults. Neuroimage 2016; 124:997-1008. [PMID: 26458520 PMCID: PMC5619652 DOI: 10.1016/j.neuroimage.2015.10.002] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2015] [Revised: 09/09/2015] [Accepted: 10/01/2015] [Indexed: 01/04/2023] Open
Abstract
Previous research has shown that there is considerable overlap in the neural networks mediating successful memory encoding and retrieval. However, little is known about how the relevant human brain regions interact during these distinct phases of memory or how such interactions are affected by memory deficits that characterize mild cognitive impairment (MCI), a condition that often precedes dementia due to Alzheimer's disease. Here we employed multivariate Granger causality analysis using autoregressive modeling of inferred neuronal time series obtained by deconvolving the hemodynamic response function from measured blood oxygenation level-dependent (BOLD) time series data, in order to examine the effective connectivity between brain regions during successful encoding and/or retrieval of object location associations in MCI patients and comparable healthy older adults. During encoding, healthy older adults demonstrated a left hemisphere dominant pattern where the inferior frontal junction, anterior intraparietal sulcus (likely involving the parietal eye fields), and posterior cingulate cortex drove activation in most left hemisphere regions and virtually every right hemisphere region tested. These regions are part of a frontoparietal network that mediates top-down cognitive control and is implicated in successful memory formation. In contrast, in the MCI patients, the right frontal eye field drove activation in every left hemisphere region examined, suggesting reliance on more basic visual search processes. Retrieval in the healthy older adults was primarily driven by the right hippocampus with lesser contributions of the right anterior thalamic nuclei and right inferior frontal sulcus, consistent with theoretical models holding the hippocampus as critical for the successful retrieval of memories. The pattern differed in MCI patients, in whom the right inferior frontal junction and right anterior thalamus drove successful memory retrieval, reflecting the characteristic hippocampal dysfunction of these patients. These findings demonstrate that neural network interactions differ markedly between MCI patients and healthy older adults. Future efforts will investigate the impact of cognitive rehabilitation of memory on these connectivity patterns.
Collapse
Affiliation(s)
- B M Hampstead
- Rehabilitation R&D Center for Visual and Neurocognitive Rehabilitation, Atlanta VAMC, Decatur, GA 30033, USA; Department of Rehabilitation Medicine, Emory University, Atlanta, GA 30322, USA; VA Ann Arbor Healthcare System, Ann Arbor, MI 48105, USA; Department of Psychiatry, University of Michigan, Ann Arbor, MI 48105, USA.
| | - M Khoshnoodi
- Department of Neurology, Emory University, Atlanta, GA 30322, USA
| | - W Yan
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL 36830, USA
| | - G Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL 36830, USA; Department of Psychology, Auburn University, Auburn, AL 36830, USA; Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, AL, USA
| | - K Sathian
- Rehabilitation R&D Center for Visual and Neurocognitive Rehabilitation, Atlanta VAMC, Decatur, GA 30033, USA; Department of Rehabilitation Medicine, Emory University, Atlanta, GA 30322, USA; Department of Neurology, Emory University, Atlanta, GA 30322, USA; Department of Psychology, Emory University, Atlanta, GA 30322, USA
| |
Collapse
|
41
|
Feng C, Deshpande G, Liu C, Gu R, Luo YJ, Krueger F. Diffusion of responsibility attenuates altruistic punishment: A functional magnetic resonance imaging effective connectivity study. Hum Brain Mapp 2015; 37:663-77. [PMID: 26608776 DOI: 10.1002/hbm.23057] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2015] [Revised: 10/16/2015] [Accepted: 11/06/2015] [Indexed: 11/11/2022] Open
Abstract
Humans altruistically punish violators of social norms to enforce cooperation and pro-social behaviors. However, such altruistic behaviors diminish when others are present, due to a diffusion of responsibility. We investigated the neural signatures underlying the modulations of diffusion of responsibility on altruistic punishment, conjoining a third-party punishment task with event-related functional magnetic resonance imaging and multivariate Granger causality mapping. In our study, participants acted as impartial third-party decision-makers and decided how to punish norm violations under two different social contexts: alone (i.e., full responsibility) or in the presence of putative other third-party decision makers (i.e., diffused responsibility). Our behavioral results demonstrated that the diffusion of responsibility served as a mediator of context-dependent punishment. In the presence of putative others, participants who felt less responsible also punished less severely in response to norm violations. Our neural results revealed that underlying this behavioral effect was a network of interconnected brain regions. For unfair relative to fair splits, the presence of others led to attenuated responses in brain regions implicated in signaling norm violations (e.g., AI) and to increased responses in brain regions implicated in calculating values of norm violations (e.g., vmPFC, precuneus) and mentalizing about others (dmPFC). The dmPFC acted as the driver of the punishment network, modulating target regions, such as AI, vmPFC, and precuneus, to adjust altruistic punishment behavior. Our results uncovered the neural basis of the influence of diffusion of responsibility on altruistic punishment and highlighted the role of the mentalizing network in this important phenomenon. Hum Brain Mapp 37:663-677, 2016. © 2015 Wiley Periodicals, Inc.
Collapse
Affiliation(s)
- Chunliang Feng
- Institute of Affective and Social Neuroscience, School of Psychology and Sociology, Shenzhen University, Shenzhen, China.,State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Gopikrishna Deshpande
- Department of Electrical and Computer Engineering, Auburn University MRI Research Center, Auburn University, Auburn, Alabama.,Department of Psychology, Auburn University, Auburn, Alabama.,labama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, Alabama
| | - Chao Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Ruolei Gu
- Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Yue-Jia Luo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.,Collaborative Innovation Center of Sichuan for Elder Care and Health, Chengdu Medical College, Chengdu, China
| | - Frank Krueger
- Molecular Neuroscience Department, George Mason University, Fairfax, Virginia.,Department of Psychology, George Mason University, Fairfax, Virginia
| |
Collapse
|
42
|
Hierarchical Organization of Frontotemporal Networks for the Prediction of Stimuli across Multiple Dimensions. J Neurosci 2015; 35:9255-64. [PMID: 26109651 DOI: 10.1523/jneurosci.5095-14.2015] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Brain function can be conceived as a hierarchy of generative models that optimizes predictions of sensory inputs and minimizes "surprise." Each level of the hierarchy makes predictions of neural events at a lower level in the hierarchy, which returns a prediction error when these expectations are violated. We tested the generalization of this hypothesis to multiple sequential deviations, and we identified the most likely organization of the network that accommodates deviations in temporal structure of stimuli. Magnetoencephalography of healthy human participants during an auditory paradigm identified prediction error responses in bilateral primary auditory cortex, superior temporal gyrus, and lateral prefrontal cortex for deviation by frequency, intensity, location, duration, and silent gap. We examined the connectivity between cortical sources using a set of 21 generative models that embedded alternate hypotheses of frontotemporal network dynamics. Bayesian model selection provided evidence for two new features of functional network organization. First, an expectancy signal provided input to the prefrontal cortex bilaterally, related to the temporal structure of stimuli. Second, there are functionally significant lateral connections between superior temporal and/or prefrontal cortex. The results support a predictive coding hypothesis but go beyond previous work in demonstrating the generalization to multiple concurrent stimulus dimensions and the evidence for a temporal expectancy input at the higher level of the frontotemporal hierarchy. We propose that this framework for studying the brain's response to unexpected events is not limited to simple sensory tasks but may also apply to the neurocognitive mechanisms of higher cognitive functions and their disorders.
Collapse
|
43
|
Chen JE, Glover GH. Functional Magnetic Resonance Imaging Methods. Neuropsychol Rev 2015; 25:289-313. [PMID: 26248581 PMCID: PMC4565730 DOI: 10.1007/s11065-015-9294-9] [Citation(s) in RCA: 88] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2015] [Accepted: 07/28/2015] [Indexed: 12/11/2022]
Abstract
Since its inception in 1992, Functional Magnetic Resonance Imaging (fMRI) has become an indispensible tool for studying cognition in both the healthy and dysfunctional brain. FMRI monitors changes in the oxygenation of brain tissue resulting from altered metabolism consequent to a task-based evoked neural response or from spontaneous fluctuations in neural activity in the absence of conscious mentation (the "resting state"). Task-based studies have revealed neural correlates of a large number of important cognitive processes, while fMRI studies performed in the resting state have demonstrated brain-wide networks that result from brain regions with synchronized, apparently spontaneous activity. In this article, we review the methods used to acquire and analyze fMRI signals.
Collapse
Affiliation(s)
- Jingyuan E Chen
- Department of Radiology, Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA,
| | | |
Collapse
|
44
|
Vanni S, Sharifian F, Heikkinen H, Vigário R. Modeling fMRI signals can provide insights into neural processing in the cerebral cortex. J Neurophysiol 2015; 114:768-80. [PMID: 25972586 DOI: 10.1152/jn.00332.2014] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2014] [Accepted: 05/04/2015] [Indexed: 12/16/2022] Open
Abstract
Every stimulus or task activates multiple areas in the mammalian cortex. These distributed activations can be measured with functional magnetic resonance imaging (fMRI), which has the best spatial resolution among the noninvasive brain imaging methods. Unfortunately, the relationship between the fMRI activations and distributed cortical processing has remained unclear, both because the coupling between neural and fMRI activations has remained poorly understood and because fMRI voxels are too large to directly sense the local neural events. To get an idea of the local processing given the macroscopic data, we need models to simulate the neural activity and to provide output that can be compared with fMRI data. Such models can describe neural mechanisms as mathematical functions between input and output in a specific system, with little correspondence to physiological mechanisms. Alternatively, models can be biomimetic, including biological details with straightforward correspondence to experimental data. After careful balancing between complexity, computational efficiency, and realism, a biomimetic simulation should be able to provide insight into how biological structures or functions contribute to actual data processing as well as to promote theory-driven neuroscience experiments. This review analyzes the requirements for validating system-level computational models with fMRI. In particular, we study mesoscopic biomimetic models, which include a limited set of details from real-life networks and enable system-level simulations of neural mass action. In addition, we discuss how recent developments in neurophysiology and biophysics may significantly advance the modelling of fMRI signals.
Collapse
Affiliation(s)
- Simo Vanni
- Clinical Neurosciences, Neurology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland;
| | - Fariba Sharifian
- Clinical Neurosciences, Neurology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland; Advanced Magnetic Imaging Centre, Aalto Neuroimaging, School of Science, Aalto University, Espoo, Finland; and
| | - Hanna Heikkinen
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland; Advanced Magnetic Imaging Centre, Aalto Neuroimaging, School of Science, Aalto University, Espoo, Finland; and
| | - Ricardo Vigário
- Department Computer Science, School of Science, Aalto University, Espoo, Finland
| |
Collapse
|
45
|
Craddock RC, Tungaraza RL, Milham MP. Connectomics and new approaches for analyzing human brain functional connectivity. Gigascience 2015; 4:13. [PMID: 25810900 PMCID: PMC4373299 DOI: 10.1186/s13742-015-0045-x] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2014] [Accepted: 01/18/2015] [Indexed: 11/10/2022] Open
Abstract
Estimating the functional interactions between brain regions and mapping those connections to corresponding inter-individual differences in cognitive, behavioral and psychiatric domains are central pursuits for understanding the human connectome. The number and complexity of functional interactions within the connectome and the large amounts of data required to study them position functional connectivity research as a “big data” problem. Maximizing the degree to which knowledge about human brain function can be extracted from the connectome will require developing a new generation of neuroimaging analysis algorithms and tools. This review describes several outstanding problems in brain functional connectomics with the goal of engaging researchers from a broad spectrum of data sciences to help solve these problems. Additionally it provides information about open science resources consisting of raw and preprocessed data to help interested researchers get started.
Collapse
Affiliation(s)
- R Cameron Craddock
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, 10962 New York USA ; Center for the Developing Brain, Child Mind Institute, 445 Park Ave, New York, 10022 New York USA
| | - Rosalia L Tungaraza
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, 10962 New York USA
| | - Michael P Milham
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, 10962 New York USA ; Center for the Developing Brain, Child Mind Institute, 445 Park Ave, New York, 10022 New York USA
| |
Collapse
|
46
|
Klamer S, Rona S, Elshahabi A, Lerche H, Braun C, Honegger J, Erb M, Focke NK. Multimodal effective connectivity analysis reveals seizure focus and propagation in musicogenic epilepsy. Neuroimage 2015; 113:70-7. [PMID: 25797835 DOI: 10.1016/j.neuroimage.2015.03.027] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2014] [Revised: 03/08/2015] [Accepted: 03/12/2015] [Indexed: 11/20/2022] Open
Abstract
Dynamic causal modeling (DCM) is a method to non-invasively assess effective connectivity between brain regions. 'Musicogenic epilepsy' is a rare reflex epilepsy syndrome in which seizures can be elicited by musical stimuli and thus represents a unique possibility to investigate complex human brain networks and test connectivity analysis tools. We investigated effective connectivity in a case of musicogenic epilepsy using DCM for fMRI, high-density (hd-) EEG and MEG and validated results with intracranial EEG recordings. A patient with musicogenic seizures was examined using hd-EEG/fMRI and simultaneous '256-channel hd-EEG'/'whole head MEG' to characterize the epileptogenic focus and propagation effects using source analysis techniques and DCM. Results were validated with invasive EEG recordings. We recorded one seizure with hd-EEG/fMRI and four auras with hd-EEG/MEG. During the seizures, increases of activity could be observed in the right mesial temporal region as well as bilateral mesial frontal regions. Effective connectivity analysis of fMRI and hd-EEG/MEG indicated that right mesial temporal neuronal activity drives changes in the frontal areas consistently in all three modalities, which was confirmed by the results of invasive EEG recordings. Seizures thus seem to originate in the right mesial temporal lobe and propagate to mesial frontal regions. Using DCM for fMRI, hd-EEG and MEG we were able to correctly localize focus and propagation of epileptic activity and thereby characterize the underlying epileptic network in a patient with musicogenic epilepsy. The concordance between all three functional modalities validated by invasive monitoring is noteworthy, both for epileptic activity spread as well as for effective connectivity analysis in general.
Collapse
Affiliation(s)
- Silke Klamer
- Department of Neurology and Epileptology, Hertie-Institute for Clinical Brain Research, University of Tuebingen, Tuebingen, Germany; MEG Center, University of Tuebingen, Tuebingen, Germany.
| | - Sabine Rona
- Department of Neurosurgery, University of Tuebingen, Tuebingen, Germany
| | - Adham Elshahabi
- Department of Neurology and Epileptology, Hertie-Institute for Clinical Brain Research, University of Tuebingen, Tuebingen, Germany; MEG Center, University of Tuebingen, Tuebingen, Germany
| | - Holger Lerche
- Department of Neurology and Epileptology, Hertie-Institute for Clinical Brain Research, University of Tuebingen, Tuebingen, Germany; Werner Reichardt Centre for Integrative Neuroscience, Tuebingen, Germany
| | - Christoph Braun
- MEG Center, University of Tuebingen, Tuebingen, Germany; Werner Reichardt Centre for Integrative Neuroscience, Tuebingen, Germany; CIMeC, Center for Mind/Brain Sciences, University of Trento, Trento, Italy
| | - Jürgen Honegger
- Department of Neurosurgery, University of Tuebingen, Tuebingen, Germany
| | - Michael Erb
- Department of Biomedical Magnetic Resonance, University of Tuebingen, Tuebingen, Germany
| | - Niels K Focke
- Department of Neurology and Epileptology, Hertie-Institute for Clinical Brain Research, University of Tuebingen, Tuebingen, Germany; Werner Reichardt Centre for Integrative Neuroscience, Tuebingen, Germany
| |
Collapse
|
47
|
Domhoff GW, Fox KCR. Dreaming and the default network: A review, synthesis, and counterintuitive research proposal. Conscious Cogn 2015; 33:342-53. [PMID: 25723600 DOI: 10.1016/j.concog.2015.01.019] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2014] [Revised: 01/26/2015] [Accepted: 01/28/2015] [Indexed: 11/28/2022]
Abstract
This article argues that the default network, augmented by secondary visual and sensorimotor cortices, is the likely neural correlate of dreaming. This hypothesis is based on a synthesis of work on dream content, the findings on the contents and neural correlates of mind-wandering, and the results from EEG and neuroimaging studies of REM sleep. Relying on studies in the 1970s that serendipitously discovered episodes of dreaming during waking mind-wandering, this article presents the seemingly counterintuitive hypothesis that the neural correlates for dreaming could be further specified in the process of carrying out EEG/fMRI studies of mind-wandering and default network activity. This hypothesis could be tested by asking participants for experiential reports during moments of differentially high levels of default network activation, as indicated by mixed EEG/fMRI criteria. Evidence from earlier EEG/fMRI studies of mind-wandering and from laboratory studies of dreaming during the sleep-onset process is used to support the argument.
Collapse
Affiliation(s)
- G William Domhoff
- Department of Psychology, University of California, Santa Cruz, Santa Cruz, CA 95064, USA.
| | - Kieran C R Fox
- Department of Psychology, University of British Columbia, Vancouver, British Columbia, Canada
| |
Collapse
|
48
|
Abstract
Positive and negative emotional events are better remembered than neutral events. Studies in animals suggest that this phenomenon depends on the influence of the amygdala upon the hippocampus. In humans, however, it is largely unknown how these two brain structures functionally interact and whether these interactions are similar between positive and negative information. Using dynamic causal modeling of fMRI data in 586 healthy subjects, we show that the strength of the connection from the amygdala to the hippocampus was rapidly and robustly increased during the encoding of both positive and negative pictures in relation to neutral pictures. We also observed an increase in connection strength from the hippocampus to the amygdala, albeit at a smaller scale. These findings indicate that, during encoding, emotionally arousing information leads to a robust increase in effective connectivity from the amygdala to the hippocampus, regardless of its valence.
Collapse
|
49
|
Morawetz C, Bode S, Baudewig J, Kirilina E, Heekeren HR. Changes in Effective Connectivity Between Dorsal and Ventral Prefrontal Regions Moderate Emotion Regulation. Cereb Cortex 2015; 26:1923-1937. [DOI: 10.1093/cercor/bhv005] [Citation(s) in RCA: 85] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
|
50
|
Ernst M, Torrisi S, Balderston N, Grillon C, Hale EA. fMRI functional connectivity applied to adolescent neurodevelopment. Annu Rev Clin Psychol 2015; 11:361-77. [PMID: 25581237 PMCID: PMC4990783 DOI: 10.1146/annurev-clinpsy-032814-112753] [Citation(s) in RCA: 76] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The exponential rise in the number of functional brain connectivity studies, particularly those examining intrinsic functional connectivity (iFC) at rest, and the promises of this work for unraveling the ontogeny of functional neural systems motivate this review. Shortly before this explosion in functional connectivity research, developmental neuroscientists had proposed theories based on neural systems models to explain behavioral changes, particularly in adolescence. The current review presents recent advances in imaging in brain connectivity research, which provides a unique tool for the study of neural systems. Understanding the potential of neuroimaging for refining neurodevelopmental models of brain function requires a description of various functional connectivity approaches. In this review, we describe task-based and resting-state functional magnetic resonance imaging (fMRI) analytic strategies, but we focus on iFC findings from resting-state data to describe general developmental trajectories of brain network organization. Finally, we use the example of drug addiction to frame a discussion of psychopathology that emerges in adolescence.
Collapse
Affiliation(s)
- Monique Ernst
- National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892
| | - Salvatore Torrisi
- National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892
| | - Nicholas Balderston
- National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892
| | - Christian Grillon
- National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892
| | - Elizabeth A. Hale
- National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892
| |
Collapse
|