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Jafarian A, Assem MK, Kocagoncu E, Lanskey JH, Williams R, Cheng Y, Quinn AJ, Pitt J, Raymont V, Lowe S, Singh KD, Woolrich M, Nobre AC, Henson RN, Friston KJ, Rowe JB. Reliability of dynamic causal modelling of resting-state magnetoencephalography. Hum Brain Mapp 2024; 45:e26782. [PMID: 38989630 PMCID: PMC11237883 DOI: 10.1002/hbm.26782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 06/20/2024] [Accepted: 06/30/2024] [Indexed: 07/12/2024] Open
Abstract
This study assesses the reliability of resting-state dynamic causal modelling (DCM) of magnetoencephalography (MEG) under conductance-based canonical microcircuit models, in terms of both posterior parameter estimates and model evidence. We use resting-state MEG data from two sessions, acquired 2 weeks apart, from a cohort with high between-subject variance arising from Alzheimer's disease. Our focus is not on the effect of disease, but on the reliability of the methods (as within-subject between-session agreement), which is crucial for future studies of disease progression and drug intervention. To assess the reliability of first-level DCMs, we compare model evidence associated with the covariance among subject-specific free energies (i.e., the 'quality' of the models) with versus without interclass correlations. We then used parametric empirical Bayes (PEB) to investigate the differences between the inferred DCM parameter probability distributions at the between subject level. Specifically, we examined the evidence for or against parameter differences (i) within-subject, within-session, and between-epochs; (ii) within-subject between-session; and (iii) within-site between-subjects, accommodating the conditional dependency among parameter estimates. We show that for data acquired close in time, and under similar circumstances, more than 95% of inferred DCM parameters are unlikely to differ, speaking to mutual predictability over sessions. Using PEB, we show a reciprocal relationship between a conventional definition of 'reliability' and the conditional dependency among inferred model parameters. Our analyses confirm the reliability and reproducibility of the conductance-based DCMs for resting-state neurophysiological data. In this respect, the implicit generative modelling is suitable for interventional and longitudinal studies of neurological and psychiatric disorders.
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Affiliation(s)
- Amirhossein Jafarian
- MRC Cognition and Brain Sciences UnitUniversity of CambridgeCambridgeUK
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Foundation TrustCambridge Biomedical CampusCambridgeUK
| | - Melek Karadag Assem
- MRC Cognition and Brain Sciences UnitUniversity of CambridgeCambridgeUK
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Foundation TrustCambridge Biomedical CampusCambridgeUK
| | - Ece Kocagoncu
- MRC Cognition and Brain Sciences UnitUniversity of CambridgeCambridgeUK
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Foundation TrustCambridge Biomedical CampusCambridgeUK
| | - Juliette H. Lanskey
- MRC Cognition and Brain Sciences UnitUniversity of CambridgeCambridgeUK
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Foundation TrustCambridge Biomedical CampusCambridgeUK
| | - Rebecca Williams
- MRC Cognition and Brain Sciences UnitUniversity of CambridgeCambridgeUK
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Foundation TrustCambridge Biomedical CampusCambridgeUK
| | | | - Andrew J. Quinn
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of PsychiatryUniversity of OxfordOxfordUK
- Department of PsychologyUniversity of BirminghamBirminghamUK
| | - Jemma Pitt
- Department of PsychiatryUniversity of OxfordOxfordUK
| | | | - Stephen Lowe
- Lilly Centre for Clinical PharmacologySingaporeSingapore
| | - Krish D. Singh
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUK
| | - Mark Woolrich
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of PsychiatryUniversity of OxfordOxfordUK
| | - Anna C. Nobre
- Department of PsychiatryUniversity of OxfordOxfordUK
- Department of Psychology and Center for Neurocognition and Behavior, Wu Tsai InstituteYale UniversityNew HavenConnecticutUSA
| | - Richard N. Henson
- MRC Cognition and Brain Sciences UnitUniversity of CambridgeCambridgeUK
| | - Karl J. Friston
- Wellcome Centre for Human NeuroimagingUniversity College LondonLondonUK
| | - James B. Rowe
- MRC Cognition and Brain Sciences UnitUniversity of CambridgeCambridgeUK
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Foundation TrustCambridge Biomedical CampusCambridgeUK
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Greaves MD, Novelli L, Razi A. Structurally informed resting-state effective connectivity recapitulates cortical hierarchy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.03.587831. [PMID: 38617335 PMCID: PMC11014588 DOI: 10.1101/2024.04.03.587831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Interregional brain communication is mediated by the brain's physical wiring (i.e., structural connectivity). Yet, it remains unclear whether models describing directed, functional interactions between latent neuronal populations-effective connectivity-benefit from incorporating macroscale structural connectivity. Here, we assess a hierarchical empirical Bayes method: structural connectivity-based priors constrain the inversion of group-level resting-state effective connectivity, using subject-level posteriors as input; subsequently, group-level posteriors serve as empirical priors for re-evaluating subject-level effective connectivity. This approach permits knowledge of the brain's structure to inform inference of (multilevel) effective connectivity. In 17 resting-state brain networks, we find that a positive, monotonic relationship between structural connectivity and the prior probability of group-level effective connectivity generalizes across sessions and samples. Providing further validation, we show that inter-network differences in the coupling between structural and effective connectivity recapitulate a well-known unimodal-transmodal hierarchy. Thus, our results provide support for the use of our method over structurally uninformed alternatives.
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Affiliation(s)
- Matthew D. Greaves
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, 3800, Australia
- Monash Biomedical Imaging, Monash University, Clayton, 3800, Australia
| | - Leonardo Novelli
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, 3800, Australia
- Monash Biomedical Imaging, Monash University, Clayton, 3800, Australia
| | - Adeel Razi
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, 3800, Australia
- Monash Biomedical Imaging, Monash University, Clayton, 3800, Australia
- Wellcome Centre for Human Neuroimaging, University College London, London, WC1N 3AR, United Kingdom
- CIFAR Azrieli Global Scholars Program, CIFAR, Toronto, M5G 1M1, Canada
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Benozzo D, Baron G, Coletta L, Chiuso A, Gozzi A, Bertoldo A. Macroscale coupling between structural and effective connectivity in the mouse brain. Sci Rep 2024; 14:3142. [PMID: 38326324 PMCID: PMC10850485 DOI: 10.1038/s41598-024-51613-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 01/07/2024] [Indexed: 02/09/2024] Open
Abstract
Exploring how the emergent functional connectivity (FC) relates to the underlying anatomy (structural connectivity, SC) is one of the major goals of modern neuroscience. At the macroscale level, no one-to-one correspondence between structural and functional links seems to exist. And we posit that to better understand their coupling, two key aspects should be considered: the directionality of the structural connectome and limitations in explaining networks functions through an undirected measure such as FC. Here, we employed an accurate directed SC of the mouse brain acquired through viral tracers and compared it with single-subject effective connectivity (EC) matrices derived from a dynamic causal model (DCM) applied to whole-brain resting-state fMRI data. We analyzed how SC deviates from EC and quantified their respective couplings by conditioning on the strongest SC links and EC links. We found that when conditioning on the strongest EC links, the obtained coupling follows the unimodal-transmodal functional hierarchy. Whereas the reverse is not true, as there are strong SC links within high-order cortical areas with no corresponding strong EC links. This mismatch is even more clear across networks; only within sensory motor networks did we observe connections that align in terms of both effective and structural strength.
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Affiliation(s)
- Danilo Benozzo
- Department of Information Engineering, University of Padova, Padua, Italy.
| | - Giorgia Baron
- Department of Information Engineering, University of Padova, Padua, Italy
| | - Ludovico Coletta
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems @ UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Alessandro Chiuso
- Department of Information Engineering, University of Padova, Padua, Italy
| | - Alessandro Gozzi
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems @ UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Alessandra Bertoldo
- Department of Information Engineering, University of Padova, Padua, Italy.
- Padova Neuroscience Center (PNC), Padua, Italy.
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Zarghami TS. A new causal centrality measure reveals the prominent role of subcortical structures in the causal architecture of the extended default mode network. Brain Struct Funct 2023; 228:1917-1941. [PMID: 37658184 DOI: 10.1007/s00429-023-02697-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 08/09/2023] [Indexed: 09/03/2023]
Abstract
Network representation has been an incredibly useful concept for understanding the behavior of complex systems in social sciences, biology, neuroscience, and beyond. Network science is mathematically founded on graph theory, where nodal importance is gauged using measures of centrality. Notably, recent work suggests that the topological centrality of a node should not be over-interpreted as its dynamical or causal importance in the network. Hence, identifying the influential nodes in dynamic causal models (DCM) remains an open question. This paper introduces causal centrality for DCM, a dynamics-sensitive and causally-founded centrality measure based on the notion of intervention in graphical models. Operationally, this measure simplifies to an identifiable expression using Bayesian model reduction. As a proof of concept, the average DCM of the extended default mode network (eDMN) was computed in 74 healthy subjects. Next, causal centralities of different regions were computed for this causal graph, and compared against several graph-theoretical centralities. The results showed that the subcortical structures of the eDMN were more causally central than the cortical regions, even though the graph-theoretical centralities unanimously favored the latter. Importantly, model comparison revealed that only the pattern of causal centrality was causally relevant. These results are consistent with the crucial role of the subcortical structures in the neuromodulatory systems of the brain, and highlight their contribution to the organization of large-scale networks. Potential applications of causal centrality-to study causal models of other neurotypical and pathological functional networks-are discussed, and some future lines of research are outlined.
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Affiliation(s)
- Tahereh S Zarghami
- Bio-Electric Department, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
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Bagheri A, Dehshiri M, Bagheri Y, Akhondi-Asl A, Nadjar Araabi B. Brain effective connectome based on fMRI and DTI data: Bayesian causal learning and assessment. PLoS One 2023; 18:e0289406. [PMID: 37594972 PMCID: PMC10437876 DOI: 10.1371/journal.pone.0289406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 07/18/2023] [Indexed: 08/20/2023] Open
Abstract
Neuroscientific studies aim to find an accurate and reliable brain Effective Connectome (EC). Although current EC discovery methods have contributed to our understanding of brain organization, their performances are severely constrained by the short sample size and poor temporal resolution of fMRI data, and high dimensionality of the brain connectome. By leveraging the DTI data as prior knowledge, we introduce two Bayesian causal discovery frameworks -the Bayesian GOLEM (BGOLEM) and Bayesian FGES (BFGES) methods- that offer significantly more accurate and reliable ECs and address the shortcomings of the existing causal discovery methods in discovering ECs based on only fMRI data. Moreover, to numerically assess the improvement in the accuracy of ECs with our method on empirical data, we introduce the Pseudo False Discovery Rate (PFDR) as a new computational accuracy metric for causal discovery in the brain. Through a series of simulation studies on synthetic and hybrid data (combining DTI from the Human Connectome Project (HCP) subjects and synthetic fMRI), we demonstrate the effectiveness of our proposed methods and the reliability of the introduced metric in discovering ECs. By employing the PFDR metric, we show that our Bayesian methods lead to significantly more accurate results compared to the traditional methods when applied to the Human Connectome Project (HCP) data. Additionally, we measure the reproducibility of discovered ECs using the Rogers-Tanimoto index for test-retest data and show that our Bayesian methods provide significantly more reliable ECs than traditional methods. Overall, our study's numerical and visual results highlight the potential for these frameworks to significantly advance our understanding of brain functionality.
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Affiliation(s)
- Abdolmahdi Bagheri
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Mahdi Dehshiri
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Yamin Bagheri
- Department of Psychology, Faculty of Psychology and Education, University of Tehran, Tehran, Iran
| | - Alireza Akhondi-Asl
- Department of Anaesthesia, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Babak Nadjar Araabi
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
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Jafarian A, Hughes LE, Adams NE, Lanskey JH, Naessens M, Rouse MA, Murley AG, Friston KJ, Rowe JB. Neurochemistry-enriched dynamic causal models of magnetoencephalography, using magnetic resonance spectroscopy. Neuroimage 2023; 276:120193. [PMID: 37244323 DOI: 10.1016/j.neuroimage.2023.120193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 05/11/2023] [Accepted: 05/24/2023] [Indexed: 05/29/2023] Open
Abstract
We present a hierarchical empirical Bayesian framework for testing hypotheses about neurotransmitters' concertation as empirical prior for synaptic physiology using ultra-high field magnetic resonance spectroscopy (7T-MRS) and magnetoencephalography data (MEG). A first level dynamic causal modelling of cortical microcircuits is used to infer the connectivity parameters of a generative model of individuals' neurophysiological observations. At the second level, individuals' 7T-MRS estimates of regional neurotransmitter concentration supply empirical priors on synaptic connectivity. We compare the group-wise evidence for alternative empirical priors, defined by monotonic functions of spectroscopic estimates, on subsets of synaptic connections. For efficiency and reproducibility, we used Bayesian model reduction (BMR), parametric empirical Bayes and variational Bayesian inversion. In particular, we used Bayesian model reduction to compare alternative model evidence of how spectroscopic neurotransmitter measures inform estimates of synaptic connectivity. This identifies the subset of synaptic connections that are influenced by individual differences in neurotransmitter levels, as measured by 7T-MRS. We demonstrate the method using resting-state MEG (i.e., task-free recording) and 7T-MRS data from healthy adults. Our results confirm the hypotheses that GABA concentration influences local recurrent inhibitory intrinsic connectivity in deep and superficial cortical layers, while glutamate influences the excitatory connections between superficial and deep layers and connections from superficial to inhibitory interneurons. Using within-subject split-sampling of the MEG dataset (i.e., validation by means of a held-out dataset), we show that model comparison for hypothesis testing can be highly reliable. The method is suitable for applications with magnetoencephalography or electroencephalography, and is well-suited to reveal the mechanisms of neurological and psychiatric disorders, including responses to psychopharmacological interventions.
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Affiliation(s)
- Amirhossein Jafarian
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, United Kingdom; MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.
| | - Laura E Hughes
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, United Kingdom; MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.
| | - Natalie E Adams
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, United Kingdom.
| | - Juliette H Lanskey
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.
| | - Michelle Naessens
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, United Kingdom; MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.
| | - Matthew A Rouse
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, United Kingdom; MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.
| | - Alexander G Murley
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, United Kingdom.
| | - Karl J Friston
- The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, United Kingdom.
| | - James B Rowe
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, United Kingdom; MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.
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Edwin Thanarajah S, Hanssen R, Melzer C, Tittgemeyer M. Increased meso-striatal connectivity mediates trait impulsivity in FTO variant carriers. Front Endocrinol (Lausanne) 2023; 14:1130203. [PMID: 37223038 PMCID: PMC10200952 DOI: 10.3389/fendo.2023.1130203] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 03/31/2023] [Indexed: 05/25/2023] Open
Abstract
Objective While variations in the first intron of the fat mass and obesity-associated gene (FTO, rs9939609 T/A variant) have long been identified as a major contributor to polygenic obesity, the mechanisms underlying weight gain in risk allele carriers still remain elusive. On a behavioral level, FTO variants have been robustly linked to trait impulsivity. The regulation of dopaminergic signaling in the meso-striatal neurocircuitry by these FTO variants might represent one mechanism for this behavioral alteration. Notably, recent evidence indicates that variants of FTO also modulate several genes involved in cell proliferation and neuronal development. Hence, FTO polymorphisms might establish a predisposition to heightened trait impulsivity during neurodevelopment by altering structural meso-striatal connectivity. We here explored whether the greater impulsivity of FTO variant carriers was mediated by structural differences in the connectivity between the dopaminergic midbrain and the ventral striatum. Methods Eighty-seven healthy normal-weight volunteers participated in the study; 42 FTO risk allele carriers (rs9939609 T/A variant, FTO + group: AT, AA) and 39 non-carriers (FTO - group: TT) were matched for age, sex and body mass index (BMI). Trait impulsivity was assessed via the Barratt Impulsiveness Scale (BIS-11) and structural connectivity between the ventral tegmental area/substantia nigra (VTA/SN) and the nucleus accumbens (NAc) was measured via diffusion weighted MRI and probabilistic tractography. Results We found that FTO risk allele carriers compared to non-carriers, demonstrated greater motor impulsivity (p = 0.04) and increased structural connectivity between VTA/SN and the NAc (p< 0.05). Increased connectivity partially mediated the effect of FTO genetic status on motor impulsivity. Conclusion We report altered structural connectivity as one mechanism by which FTO variants contribute to increased impulsivity, indicating that FTO variants may exert their effect on obesity-promoting behavioral traits at least partially through neuroplastic alterations in humans.
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Affiliation(s)
- Sharmili Edwin Thanarajah
- Max Planck Institute for Metabolism Research, Cologne, Germany
- Department for Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Ruth Hanssen
- Max Planck Institute for Metabolism Research, Cologne, Germany
- Faculty of Medicine and University Hospital Cologne, Policlinic for Endocrinology, Diabetes and Preventive Medicine (PEPD), University of Cologne, Cologne, Germany
| | - Corina Melzer
- Max Planck Institute for Metabolism Research, Cologne, Germany
| | - Marc Tittgemeyer
- Max Planck Institute for Metabolism Research, Cologne, Germany
- Cluster of Excellence in Cellular Stress Responses in Aging-associated Diseases (CECAD), Cologne, Germany
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Benozzo D, Baron G, Coletta L, Chiuso A, Gozzi A, Bertoldo A. Macroscale coupling between structural and effective connectivity in the mouse brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.22.529400. [PMID: 36865122 PMCID: PMC9980133 DOI: 10.1101/2023.02.22.529400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
How the emergent functional connectivity (FC) relates to the underlying anatomy (structural connectivity, SC) is one of the biggest questions of modern neuroscience. At the macro-scale level, no one-to-one correspondence between structural and functional links seems to exist. And we posit that to better understand their coupling, two key aspects should be taken into account: the directionality of the structural connectome and the limitations of describing network functions in terms of FC. Here, we employed an accurate directed SC of the mouse brain obtained by means of viral tracers, and related it with single-subject effective connectivity (EC) matrices computed by applying a recently developed DCM to whole-brain resting-state fMRI data. We analyzed how SC deviates from EC and quantified their couplings by conditioning both on the strongest SC links and EC links. We found that when conditioning on the strongest EC links, the obtained coupling follows the unimodal-transmodal functional hierarchy. Whereas the reverse is not true, as there are strong SC links within high-order cortical areas with no corresponding strong EC links. This mismatch is even more clear across networks. Only the connections within sensory motor networks align both in terms of effective and structural strength.
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Affiliation(s)
- Danilo Benozzo
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Giorgia Baron
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Ludovico Coletta
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems @ UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Alessandro Chiuso
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Alessandro Gozzi
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems @ UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Alessandra Bertoldo
- Department of Information Engineering, University of Padova, Padova, Italy
- Padova Neuroscience Center, Padova, Italy
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Chen Q, Meng Z, Xu L, Hou Y, Chen A. Effective connectivity analysis reveals the time course of the Stroop effect in manual responding. Biol Psychol 2023; 178:108526. [PMID: 36841469 DOI: 10.1016/j.biopsycho.2023.108526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 02/02/2023] [Accepted: 02/22/2023] [Indexed: 02/27/2023]
Abstract
Investigations of the time course of the Stroop effect have contributed to understanding of the mechanisms of cognitive control. However, previous studies have not reached a consistent conclusion regarding such mechanisms. The current study clarified the controversy by adopting a modified stimulus onset asynchrony (SOA) manual Stroop task combined with effective connectivity analysis based on task-related functional magnetic resonance imaging data. The behavioral results showed a decreasing Stroop effect when the distractor was presented before the target stimulus. In addition, significant effective connectivity related to inhibitory control was observed, which decreased with the time interval between stimuli, including the connection from the right inferior frontal gyrus (rIFG) to the pre-supplementary motor area (pre-SMA) and from the pre-SMA to the primary motor cortex (M1). Diversity of active patterns between different congruency types was also detected. The current results revealed that inhibitory control could be actively performed in response to distractors to reduce their interference and further decrease the Stroop effect, and that inhibition is more efficient the earlier it is started.
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Affiliation(s)
- Qi Chen
- School of Psychology, Inner Mongolia Normal University, Hohhot 010022, China
| | - Zong Meng
- Department of Psychology, Southwest University, Chongqing 400715, China
| | - Liang Xu
- Department of Psychology, Southwest University, Chongqing 400715, China
| | - You Hou
- School of Psychology, Inner Mongolia Normal University, Hohhot 010022, China.
| | - Antao Chen
- School of Psychology, Shanghai University of Sport, Shanghai 200438, China.
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Zhao C, Zhan L, Thompson PM, Huang H. Explainable Contrastive Multiview Graph Representation of Brain, Mind, and Behavior. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2022; 13431:356-365. [PMID: 39051030 PMCID: PMC11267032 DOI: 10.1007/978-3-031-16431-6_34] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/27/2024]
Abstract
Understanding the intrinsic patterns of human brain is important to make inferences about the mind and brain-behavior association. Electrophysiological methods (i.e. MEG/EEG) provide direct measures of neural activity without the effect of vascular confounds. The blood oxygenated level-dependent (BOLD) signal of functional MRI (fMRI) reveals the spatial and temporal brain activity across different brain regions. However, it is unclear how to associate the high temporal resolution Electrophysiological measures with high spatial resolution fMRI signals. Here, we present a novel interpretable model for coupling the structure and function activity of brain based on heterogeneous contrastive graph representation. The proposed method is able to link manifest variables of the brain (i.e. MEG, MRI, fMRI and behavior performance) and quantify the intrinsic coupling strength of different modal signals. The proposed method learns the heterogeneous node and graph representations by contrasting the structural and temporal views through the mind to multimodal brain data. The first experiment with 1200 subjects from Human connectome Project (HCP) shows that the proposed method outperforms the existing approaches in predicting individual gender and enabling the location of the importance of brain regions with sex difference. The second experiment associates the structure and temporal views between the low-level sensory regions and high-level cognitive ones. The experimental results demonstrate that the dependence of structural and temporal views varied spatially through different modal variants. The proposed method enables the heterogeneous biomarkers explanation for different brain measurements.
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Affiliation(s)
- Chongyue Zhao
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Liang Zhan
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Paul M. Thompson
- Imaging Genetics Center, University of Southern California, Los Angeles, CA, USA
| | - Heng Huang
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
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11
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Oliveira R, Pelentritou A, Di Domenicantonio G, De Lucia M, Lutti A. In vivo Estimation of Axonal Morphology From Magnetic Resonance Imaging and Electroencephalography Data. Front Neurosci 2022; 16:874023. [PMID: 35527816 PMCID: PMC9070985 DOI: 10.3389/fnins.2022.874023] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 03/24/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose We present a novel approach that allows the estimation of morphological features of axonal fibers from data acquired in vivo in humans. This approach allows the assessment of white matter microscopic properties non-invasively with improved specificity. Theory The proposed approach is based on a biophysical model of Magnetic Resonance Imaging (MRI) data and of axonal conduction velocity estimates obtained with Electroencephalography (EEG). In a white matter tract of interest, these data depend on (1) the distribution of axonal radius [P(r)] and (2) the g-ratio of the individual axons that compose this tract [g(r)]. P(r) is assumed to follow a Gamma distribution with mode and scale parameters, M and θ, and g(r) is described by a power law with parameters α and β. Methods MRI and EEG data were recorded from 14 healthy volunteers. MRI data were collected with a 3T scanner. MRI-measured g-ratio maps were computed and sampled along the visual transcallosal tract. EEG data were recorded using a 128-lead system with a visual Poffenberg paradigm. The interhemispheric transfer time and axonal conduction velocity were computed from the EEG current density at the group level. Using the MRI and EEG measures and the proposed model, we estimated morphological properties of axons in the visual transcallosal tract. Results The estimated interhemispheric transfer time was 11.72 ± 2.87 ms, leading to an average conduction velocity across subjects of 13.22 ± 1.18 m/s. Out of the 4 free parameters of the proposed model, we estimated θ – the width of the right tail of the axonal radius distribution – and β – the scaling factor of the axonal g-ratio, a measure of fiber myelination. Across subjects, the parameter θ was 0.40 ± 0.07 μm and the parameter β was 0.67 ± 0.02 μm−α. Conclusion The estimates of axonal radius and myelination are consistent with histological findings, illustrating the feasibility of this approach. The proposed method allows the measurement of the distribution of axonal radius and myelination within a white matter tract, opening new avenues for the combined study of brain structure and function, and for in vivo histological studies of the human brain.
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12
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Pascucci D, Rubega M, Rué-Queralt J, Tourbier S, Hagmann P, Plomp G. Structure supports function: Informing directed and dynamic functional connectivity with anatomical priors. Netw Neurosci 2022; 6:401-419. [PMID: 35733424 PMCID: PMC9205420 DOI: 10.1162/netn_a_00218] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 11/23/2021] [Indexed: 12/03/2022] Open
Abstract
The dynamic repertoire of functional brain networks is constrained by the underlying topology of structural connections. Despite this intrinsic relationship between structural connectivity (SC) and functional connectivity (FC), integrative and multimodal approaches to combine the two remain limited. Here, we propose a new adaptive filter for estimating dynamic and directed FC using structural connectivity information as priors. We tested the filter in rat epicranial recordings and human event-related EEG data, using SC priors from a meta-analysis of tracer studies and diffusion tensor imaging metrics, respectively. We show that, particularly under conditions of low signal-to-noise ratio, SC priors can help to refine estimates of directed FC, promoting sparse functional networks that combine information from structure and function. In addition, the proposed filter provides intrinsic protection against SC-related false negatives, as well as robustness against false positives, representing a valuable new tool for multimodal imaging in the context of dynamic and directed FC analysis.
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Affiliation(s)
- David Pascucci
- Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Perceptual Networks Group, University of Fribourg, Fribourg, Switzerland
| | - Maria Rubega
- Department of Neurosciences, University of Padova, Padova, Italy
| | - Joan Rué-Queralt
- Perceptual Networks Group, University of Fribourg, Fribourg, Switzerland
- Connectomics Lab, Department of Radiology, University Hospital of Lausanne and University of Lausanne (CHUV-SUNIL), Lausanne, Switzerland
| | - Sebastien Tourbier
- Connectomics Lab, Department of Radiology, University Hospital of Lausanne and University of Lausanne (CHUV-SUNIL), Lausanne, Switzerland
| | - Patric Hagmann
- Connectomics Lab, Department of Radiology, University Hospital of Lausanne and University of Lausanne (CHUV-SUNIL), Lausanne, Switzerland
| | - Gijs Plomp
- Perceptual Networks Group, University of Fribourg, Fribourg, Switzerland
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13
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Griffiths JD, Bastiaens SP, Kaboodvand N. Whole-Brain Modelling: Past, Present, and Future. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1359:313-355. [DOI: 10.1007/978-3-030-89439-9_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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14
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Tak YW, Knights E, Henson R, Zeidman P. Ageing and the Ipsilateral M1 BOLD Response: A Connectivity Study. Brain Sci 2021; 11:1130. [PMID: 34573152 PMCID: PMC8470146 DOI: 10.3390/brainsci11091130] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 08/19/2021] [Accepted: 08/23/2021] [Indexed: 02/06/2023] Open
Abstract
Young people exhibit a negative BOLD response in ipsilateral primary motor cortex (M1) when making unilateral movements, such as button presses. This negative BOLD response becomes more positive as people age. In this study, we investigated why this occurs, in terms of the underlying effective connectivity and haemodynamics. We applied dynamic causal modeling (DCM) to task fMRI data from 635 participants aged 18-88 from the Cam-CAN dataset, who performed a cued button pressing task with their right hand. We found that connectivity from contralateral supplementary motor area (SMA) and dorsal premotor cortex (PMd) to ipsilateral M1 became more positive with age, explaining 44% of the variability across people in ipsilateral M1 responses. In contrast, connectivity from contralateral M1 to ipsilateral M1 was weaker and did not correlate with individual differences in rM1 BOLD. Neurovascular and haemodynamic parameters in the model were not able to explain the age-related shift to positive BOLD. Our results add to a body of evidence implicating neural, rather than vascular factors as the predominant cause of negative BOLD-while emphasising the importance of inter-hemispheric connectivity. This study provides a foundation for investigating the clinical and lifestyle factors that determine the sign and amplitude of the M1 BOLD response in ageing, which could serve as a proxy for neural and vascular health, via the underlying neurovascular mechanisms.
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Affiliation(s)
- Yae Won Tak
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK;
| | - Ethan Knights
- MRC Cognition and Brain Sciences Unit, Department of Psychiatry, University of Cambridge, Cambridge CB2 7EF, UK; (E.K.); (R.H.)
| | - Richard Henson
- MRC Cognition and Brain Sciences Unit, Department of Psychiatry, University of Cambridge, Cambridge CB2 7EF, UK; (E.K.); (R.H.)
| | - Peter Zeidman
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK;
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15
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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.
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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
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16
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Koops EA, Eggermont JJ. The thalamus and tinnitus: Bridging the gap between animal data and findings in humans. Hear Res 2021; 407:108280. [PMID: 34175683 DOI: 10.1016/j.heares.2021.108280] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 04/26/2021] [Accepted: 05/27/2021] [Indexed: 12/16/2022]
Abstract
The neuronal mechanisms underlying tinnitus are yet to be revealed. Tinnitus, an auditory phantom sensation, used to be approached as a purely auditory domain symptom. More recently, the modulatory impact of non-auditory brain regions on the percept and burden of tinnitus are explored. The thalamus is uniquely situated to facilitate the communication between auditory and non-auditory subcortical and cortical structures. Traditionally, animal models of tinnitus have focussed on subcortical auditory structures, and research with human participants has been concerned with cortical activity in auditory and non-auditory areas. Recently, both research fields have investigated the connectivity between subcortical and cortical regions and between auditory and non-auditory areas. We show that even though the different fields employ different methods to investigate the activity and connectivity of brain areas, there is consistency in the results on tinnitus between these different approaches. This consistency between human and animals research is observed for tinnitus with peripherally instigated hearing damage, and for results obtained with salicylate and noise-induced tinnitus. The thalamus integrates input from limbic and prefrontal areas and modulates auditory activity via its connections to both subcortical and cortical auditory areas. Reported altered activity and connectivity of the auditory, prefrontal, and limbic regions suggest a more systemic approach is necessary to understand the origins and impact of tinnitus.
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Affiliation(s)
- Elouise A Koops
- Department of Otorhinolaryngology/Head and Neck Surgery, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
| | - Jos J Eggermont
- Departments of Physiology and Pharmacology, and Psychology, University of Calgary, Calgary, Alberta, Canada
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17
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Bajaj S, Raikes AC, Razi A, Miller MA, Killgore WDS. Blue-Light Therapy Strengthens Resting-State Effective Connectivity within Default-Mode Network after Mild TBI. J Cent Nerv Syst Dis 2021; 13:11795735211015076. [PMID: 34104033 PMCID: PMC8145607 DOI: 10.1177/11795735211015076] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 02/08/2021] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND Emerging evidence suggests that post concussive symptoms, including mood changes, may be improved through morning blue-wavelength light therapy (BLT). However, the neurobiological mechanisms underlying these effects remain unknown. We hypothesize that BLT may influence the effective brain connectivity (EC) patterns within the default-mode network (DMN), particularly involving the medial prefrontal cortex (MPFC), which may contribute to improvements in mood. METHODS Resting-state functional MRI data were collected from 41 healthy-controls (HCs) and 28 individuals with mild traumatic brain injury (mTBI). Individuals with mTBI also underwent a diffusion-weighted imaging scan and were randomly assigned to complete either 6 weeks of daily morning BLT (N = 14) or amber light therapy (ALT; N = 14). Advanced spectral dynamic causal modeling (sDCM) and diffusion MRI connectometry were used to estimate EC patterns and structural connectivity strength within the DMN, respectively. RESULTS The sDCM analysis showed dominant connectivity pattern following mTBI (pre-treatment) within the hemisphere contralateral to the one observed for HCs. BLT, but not ALT, resulted in improved directional information flow (ie, EC) from the left lateral parietal cortex (LLPC) to MPFC within the DMN. The improvement in EC from LLPC to MPFC was accompanied by stronger structural connectivity between the 2 areas. For the BLT group, the observed improvements in function and structure were correlated (at a trend level) with changes in self-reported happiness. CONCLUSIONS The current preliminary findings provide empirical evidence that morning short-wavelength light therapy could be used as a novel alternative rehabilitation technique for mTBI. TRIAL REGISTRY The research protocols were registered in the ClinicalTrials.gov database (CT Identifiers NCT01747811 and NCT01721356).
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Affiliation(s)
- Sahil Bajaj
- Social, Cognitive and Affective Neuroscience (SCAN) Laboratory, Department of Psychiatry, College of Medicine, University of Arizona, Tucson, AZ, USA
- Multimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research Hospital, Boys Town, NE, USA
| | - Adam C Raikes
- Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, USA
| | - Adeel Razi
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging at Monash University, Clayton, VIC, Australia
- The Wellcome Centre for Human Neuroimaging, University College London, London, UK
- Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, Pakistan
| | - Michael A Miller
- Social, Cognitive and Affective Neuroscience (SCAN) Laboratory, Department of Psychiatry, College of Medicine, University of Arizona, Tucson, AZ, USA
| | - William DS Killgore
- Social, Cognitive and Affective Neuroscience (SCAN) Laboratory, Department of Psychiatry, College of Medicine, University of Arizona, Tucson, AZ, USA
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18
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Labounek R, Wu Z, Bridwell DA, Brázdil M, Jan J, Nestrašil I. Blind Visualization of Task-Related Networks From Visual Oddball Simultaneous EEG-fMRI Data: Spectral or Spatiospectral Model? Front Neurol 2021; 12:644874. [PMID: 33981283 PMCID: PMC8107237 DOI: 10.3389/fneur.2021.644874] [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: 12/22/2020] [Accepted: 03/22/2021] [Indexed: 02/01/2023] Open
Abstract
Various disease conditions can alter EEG event-related responses and fMRI-BOLD signals. We hypothesized that event-related responses and their clinical alterations are imprinted in the EEG spectral domain as event-related (spatio)spectral patterns (ERSPat). We tested four EEG-fMRI fusion models utilizing EEG power spectra fluctuations (i.e., absolute spectral model - ASM; relative spectral model - RSM; absolute spatiospectral model - ASSM; and relative spatiospectral model - RSSM) for fully automated and blind visualization of task-related neural networks. Two (spatio)spectral patterns (high δ 4 band and low β 1 band) demonstrated significant negative linear relationship (p FWE < 0.05) to the frequent stimulus and three patterns (two low δ 2 and δ 3 bands, and narrow θ 1 band) demonstrated significant positive relationship (p < 0.05) to the target stimulus. These patterns were identified as ERSPats. EEG-fMRI F-map of each δ 4 model showed strong engagement of insula, cuneus, precuneus, basal ganglia, sensory-motor, motor and dorsal part of fronto-parietal control (FPCN) networks with fast HRF peak and noticeable trough. ASM and RSSM emphasized spatial statistics, and the relative power amplified the relationship to the frequent stimulus. For the δ 4 model, we detected a reduced HRF peak amplitude and a magnified HRF trough amplitude in the frontal part of the FPCN, default mode network (DMN) and in the frontal white matter. The frequent-related β 1 patterns visualized less significant and distinct suprathreshold spatial associations. Each θ 1 model showed strong involvement of lateralized left-sided sensory-motor and motor networks with simultaneous basal ganglia co-activations and reduced HRF peak and amplified HRF trough in the frontal part of the FPCN and DMN. The ASM θ 1 model preserved target-related EEG-fMRI associations in the dorsal part of the FPCN. For δ 4, β 1, and θ 1 bands, all models provided high local F-statistics in expected regions. The most robust EEG-fMRI associations were observed for ASM and RSSM.
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Affiliation(s)
- René Labounek
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, United States
| | - Zhuolin Wu
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, United States
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | | | - Milan Brázdil
- Central European Institute of Technology, Masaryk University, Brno, Czechia
| | - Jiří Jan
- Department of Biomedical Engineering, Brno University of Technology, Brno, Czechia
| | - Igor Nestrašil
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, United States
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, United States
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19
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Tao L, Wang G, Zhu M, Cai Q. Bilingualism and domain-general cognitive functions from a neural perspective: A systematic review. Neurosci Biobehav Rev 2021; 125:264-295. [PMID: 33631315 DOI: 10.1016/j.neubiorev.2021.02.029] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 02/11/2021] [Accepted: 02/18/2021] [Indexed: 12/23/2022]
Abstract
A large body of research has indicated that bilingualism - through continual practice in language control - may impact cognitive functions, as well as relevant aspects of brain function and structure. The present review aimed to bring together findings on the relationship between bilingualism and domain-general cognitive functions from a neural perspective. The final sample included 210 studies, covering findings regarding neural responses to bilingual language control and/or domain-general cognitive tasks, as well as findings regarding effects of bilingualism on non-task-related brain function and brain structure. The evidence indicates that a) bilingual language control likely entails neural mechanisms responsible for domain-general cognitive functions; b) bilingual experiences impact neural responses to domain-general cognitive functions; and c) bilingual experiences impact non-task-related brain function (both resting-state and metabolic function) as well as aspects of brain structure (both macrostructure and microstructure), each of which may in turn impact mental processes, including domain-general cognitive functions. Such functional and structural neuroplasticity associated with bilingualism may contribute to both cognitive and neural reserves, producing benefits across the lifespan.
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Affiliation(s)
- Lily Tao
- Key Laboratory of Brain Functional Genomics (MOE & STCSM), Shanghai Changning-ECNU Mental Health Center, Institute of Cognitive Neuroscience, School of Psychology and Cognitive Science, East China Normal University, China
| | - Gongting Wang
- Key Laboratory of Brain Functional Genomics (MOE & STCSM), Shanghai Changning-ECNU Mental Health Center, Institute of Cognitive Neuroscience, School of Psychology and Cognitive Science, East China Normal University, China
| | - Miaomiao Zhu
- Key Laboratory of Brain Functional Genomics (MOE & STCSM), Shanghai Changning-ECNU Mental Health Center, Institute of Cognitive Neuroscience, School of Psychology and Cognitive Science, East China Normal University, China
| | - Qing Cai
- Key Laboratory of Brain Functional Genomics (MOE & STCSM), Shanghai Changning-ECNU Mental Health Center, Institute of Cognitive Neuroscience, School of Psychology and Cognitive Science, East China Normal University, China; Institute of Brain and Education Innovation, East China Normal University, China; NYU-ECNU Institute of Brain and Cognitive Science, New York University Shanghai, China.
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20
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Whole-brain estimates of directed connectivity for human connectomics. Neuroimage 2020; 225:117491. [PMID: 33115664 DOI: 10.1016/j.neuroimage.2020.117491] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Revised: 10/13/2020] [Accepted: 10/21/2020] [Indexed: 02/07/2023] Open
Abstract
Connectomics is essential for understanding large-scale brain networks but requires that individual connection estimates are neurobiologically interpretable. In particular, a principle of brain organization is that reciprocal connections between cortical areas are functionally asymmetric. This is a challenge for fMRI-based connectomics in humans where only undirected functional connectivity estimates are routinely available. By contrast, whole-brain estimates of effective (directed) connectivity are computationally challenging, and emerging methods require empirical validation. Here, using a motor task at 7T, we demonstrate that a novel generative model can infer known connectivity features in a whole-brain network (>200 regions, >40,000 connections) highly efficiently. Furthermore, graph-theoretical analyses of directed connectivity estimates identify functional roles of motor areas more accurately than undirected functional connectivity estimates. These results, which can be achieved in an entirely unsupervised manner, demonstrate the feasibility of inferring directed connections in whole-brain networks and open new avenues for human connectomics.
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21
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Sokolov AA, Zeidman P, Razi A, Erb M, Ryvlin P, Pavlova MA, Friston KJ. Asymmetric high-order anatomical brain connectivity sculpts effective connectivity. Netw Neurosci 2020; 4:871-890. [PMID: 33615094 PMCID: PMC7888488 DOI: 10.1162/netn_a_00150] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 05/18/2020] [Indexed: 12/12/2022] Open
Abstract
Bridging the gap between symmetric, direct white matter brain connectivity and neural dynamics that are often asymmetric and polysynaptic may offer insights into brain architecture, but this remains an unresolved challenge in neuroscience. Here, we used the graph Laplacian matrix to simulate symmetric and asymmetric high-order diffusion processes akin to particles spreading through white matter pathways. The simulated indirect structural connectivity outperformed direct as well as absent anatomical information in sculpting effective connectivity, a measure of causal and directed brain dynamics. Crucially, an asymmetric diffusion process determined by the sensitivity of the network nodes to their afferents best predicted effective connectivity. The outcome is consistent with brain regions adapting to maintain their sensitivity to inputs within a dynamic range. Asymmetric network communication models offer a promising perspective for understanding the relationship between structural and functional brain connectomes, both in normalcy and neuropsychiatric conditions.
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Affiliation(s)
- Arseny A. Sokolov
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
- Department of Neurology, University Neurorehabilitation, University Hospital Inselspital, University of Bern, Bern, Switzerland
- Service de Neurologie and Neuroscape@NeuroTech Platform, Département des Neurosciences Cliniques, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Neuroscape Center, Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Peter Zeidman
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
| | - Adeel Razi
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
- Monash Institute of Cognitive and Clinical Neurosciences & Monash Biomedical Imaging, Monash University, Clayton, Australia
- Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, Pakistan
| | - Michael Erb
- Department of Biomedical Magnetic Resonance, University of Tübingen Medical School, Tübingen, Germany
| | - Philippe Ryvlin
- Service de Neurologie and Neuroscape@NeuroTech Platform, Département des Neurosciences Cliniques, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Marina A. Pavlova
- Department of Psychiatry and Psychotherapy, University of Tübingen Medical School, Tübingen, Germany
| | - Karl J. Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
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22
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Bowling JT, Friston KJ, Hopfinger JB. Top-down versus bottom-up attention differentially modulate frontal-parietal connectivity. Hum Brain Mapp 2020; 41:928-942. [PMID: 31692192 PMCID: PMC7267915 DOI: 10.1002/hbm.24850] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Revised: 08/28/2019] [Accepted: 10/21/2019] [Indexed: 01/01/2023] Open
Abstract
The moment-to-moment focus of our mind's eye results from a complex interplay of voluntary and involuntary influences on attention. Previous neuroimaging studies suggest that the brain networks of voluntary versus involuntary attention can be segregated into a frontal-versus-parietal or a dorsal-versus-ventral partition-although recent work suggests that the dorsal network may be involved in both bottom-up and top-down attention. Research with nonhuman primates has provided evidence that a key distinction between top-down and bottom-up attention may be the direction of connectivity between frontal and parietal areas. Whereas typical fMRI connectivity analyses cannot disambiguate the direction of connections, dynamic causal modeling (DCM) can model directionality. Using DCM, we provide new evidence that directed connections within the dorsal attention network are differentially modulated for voluntary versus involuntary attention. These results suggest that the intraparietal sulcus exerts a baseline inhibitory effect on the frontal eye fields that is strengthened during exogenous orienting and attenuated during endogenous orienting. Furthermore, the attenuation from endogenous attention occurs even with salient peripheral cues when those cues are known to be counter predictive. Thus, directed connectivity between frontal and parietal regions of the dorsal attention network is highly influenced by the type of attention that is engaged.
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Affiliation(s)
- Jake T. Bowling
- School of MedicineUniversity of North Carolina at Chapel HillChapel HillNorth Carolina
| | - Karl J. Friston
- Wellcome Trust Centre for NeuroimagingUniversity College LondonLondonUK
| | - Joseph B. Hopfinger
- Department of Psychology and NeuroscienceUniversity of North Carolina at Chapel HillChapel HillNorth Carolina
- Biomedical Research Imaging CenterUniversity of North Carolina at Chapel HillChapel HillNorth Carolina
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23
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Ji J, Liu J, Zou A, Zhang A. ACOEC-FD: Ant Colony Optimization for Learning Brain Effective Connectivity Networks From Functional MRI and Diffusion Tensor Imaging. Front Neurosci 2020; 13:1290. [PMID: 31920476 PMCID: PMC6920213 DOI: 10.3389/fnins.2019.01290] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Accepted: 11/14/2019] [Indexed: 11/13/2022] Open
Abstract
Identifying brain effective connectivity (EC) networks from neuroimaging data has become an effective tool that can evaluate normal brain functions and the injuries associated with neurodegenerative diseases. So far, there are many methods used to identify EC networks. However, most of the research currently focus on learning EC networks from single modal imaging data such as functional magnetic resonance imaging (fMRI) data. This paper proposes a new method, called ACOEC-FD, to learn EC networks from fMRI and diffusion tensor imaging (DTI) using ant colony optimization (ACO). First, ACOEC-FD uses DTI data to acquire some positively correlated relations among regions of interest (ROI), and takes them as anatomical constraint information to effectively restrict the search space of candidate arcs in an EC network. ACOEC-FD then achieves multi-modal imaging data integration by incorporating anatomical constraint information into the heuristic function of probabilistic transition rules to effectively encourage ants more likely to search for connections between structurally connected regions. Through simulation studies on generated datasets and real fMRI-DTI datasets, we demonstrate that the proposed approach results in improved inference results on EC compared to some methods that only used fMRI data.
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Affiliation(s)
- Junzhong Ji
- Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Artificial Intelligence Institute, Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Jinduo Liu
- Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Artificial Intelligence Institute, Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Aixiao Zou
- Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Artificial Intelligence Institute, Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Aidong Zhang
- Department of Computer Science, University of Virginia, Charlottesville, VA, United States
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24
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Boukrina O, Kucukboyaci NE, Dobryakova E. Considerations of power and sample size in rehabilitation research. Int J Psychophysiol 2019; 154:6-14. [PMID: 31655185 DOI: 10.1016/j.ijpsycho.2019.08.009] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Revised: 05/22/2019] [Accepted: 08/23/2019] [Indexed: 01/26/2023]
Abstract
With the current emphasis on power and reproducibility, pressures are rising to increase sample sizes in rehabilitation research in order to reflect more accurate effect estimation and generalizable results. The conventional way of increasing power by enrolling more participants is less feasible in some fields of research. In particular, rehabilitation research faces considerable challenges in achieving this goal. We describe the specific challenges to increasing power by recruiting large sample sizes and obtaining large effects in rehabilitation research. Specifically, we discuss how variability within clinical populations, lack of common standards for selecting appropriate control groups; potentially reduced reliability of measurements of brain function in individuals recovering from a brain injury; biases involved in a priori effect size estimation, and higher budgetary and staffing requirements can influence considerations of sample and effect size in rehabilitation. We also describe solutions to these challenges, such as increased sampling per participant, improving experimental control, appropriate analyses, transparent result reporting and using innovative ways of harnessing the inherent variability of clinical populations. These solutions can improve statistical power and produce reliable and valid results even in the face of limited availability of large samples.
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Affiliation(s)
- Olga Boukrina
- Center for Stroke Rehabilitation Research, Kessler Foundation, West Orange, NJ, USA; Department of Physical Medicine and Rehabilitation, Rutgers-New Jersey Medical School, Newark, NJ, USA
| | - N Erkut Kucukboyaci
- Center for Traumatic Brain Injury Research, Kessler Foundation, East Hanover, NJ, USA; Department of Physical Medicine and Rehabilitation, Rutgers-New Jersey Medical School, Newark, NJ, USA
| | - Ekaterina Dobryakova
- Center for Traumatic Brain Injury Research, Kessler Foundation, East Hanover, NJ, USA; Department of Physical Medicine and Rehabilitation, Rutgers-New Jersey Medical School, Newark, NJ, USA.
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25
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Preti MG, Van De Ville D. Decoupling of brain function from structure reveals regional behavioral specialization in humans. Nat Commun 2019; 10:4747. [PMID: 31628329 PMCID: PMC6800438 DOI: 10.1038/s41467-019-12765-7] [Citation(s) in RCA: 120] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Accepted: 09/30/2019] [Indexed: 11/09/2022] Open
Abstract
The brain is an assembly of neuronal populations interconnected by structural pathways. Brain activity is expressed on and constrained by this substrate. Therefore, statistical dependencies between functional signals in directly connected areas can be expected higher. However, the degree to which brain function is bound by the underlying wiring diagram remains a complex question that has been only partially answered. Here, we introduce the structural-decoupling index to quantify the coupling strength between structure and function, and we reveal a macroscale gradient from brain regions more strongly coupled, to regions more strongly decoupled, than expected by realistic surrogate data. This gradient spans behavioral domains from lower-level sensory function to high-level cognitive ones and shows for the first time that the strength of structure-function coupling is spatially varying in line with evidence derived from other modalities, such as functional connectivity, gene expression, microstructural properties and temporal hierarchy.
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Affiliation(s)
- Maria Giulia Preti
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Chemin des Mines 9, 1202, Geneva, Switzerland. .,Department of Radiology and Medical Informatics, University of Geneva, Campus Biotech, Chemin des Mines 9, 1202, Geneva, Switzerland.
| | - Dimitri Van De Ville
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Chemin des Mines 9, 1202, Geneva, Switzerland.,Department of Radiology and Medical Informatics, University of Geneva, Campus Biotech, Chemin des Mines 9, 1202, Geneva, Switzerland
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26
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Dang S, Chaudhury S. Novel relative relevance score for estimating brain connectivity from fMRI data using an explainable neural network approach. J Neurosci Methods 2019; 326:108371. [PMID: 31344374 DOI: 10.1016/j.jneumeth.2019.108371] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Revised: 07/14/2019] [Accepted: 07/19/2019] [Indexed: 11/15/2022]
Abstract
BACKGROUND Functional integration or connectivity in brain is directional, non-linear as well as variable in time-lagged dependence. Deep neural networks (DNN) have become an indispensable tool everywhere, by learning higher levels of abstract and complex patterns from raw data. However, in neuroscientific community they generally work as black-boxes, leading to the explanation of results difficult and less intuitive. We aim to propose a brain-connectivity measure based on an explainable NN (xNN) approach. NEW METHOD We build a NN-based predictor for regression problem. Since we aim to determine the contribution/relevance of past data-point from one region i in the prediction of current data-point from another region j, i.e. the higher-order connectivity between two brain-regions, we employ layer-wise relevance propagation (Bach et al., 2015) (LRP, a method for explaining DNN predictions), which has not been done before to the best of our knowledge. Specifically, we propose a novel score depending on weights as a quantitative measure of connectivity, called as relative relevance score (xNN-RRS). The RRS is an intuitive and transparent score. We provide an interpretation of the trained NN weights with-respect-to the brain-connectivity. RESULTS Face validity of our approach is demonstrated with experiments on simulated data, over existing methods. We also demonstrate construct validity of xNN-RRS in a resting-state fMRI experiment. COMPARISON Our approach shows superior performance, in terms of accuracy and computational complexity, over existing state-of-the-art methods for brain-connectivity estimation. CONCLUSION The proposed method is promising to serve as a first post-hoc explainable NN-approach for brain-connectivity analysis in clinical applications.
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Affiliation(s)
- Shilpa Dang
- Electrical Engineering Department, Indian Institute of Technology, Delhi, New Delhi, 110016, India.
| | - Santanu Chaudhury
- Electrical Engineering Department, Indian Institute of Technology, Delhi, New Delhi, 110016, India; Indian Institute of Technology Jodhpur, Jodhpur, 342037, India
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27
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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: 13] [Impact Index Per Article: 2.6] [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.
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28
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Isaacs BR, Trutti AC, Pelzer E, Tittgemeyer M, Temel Y, Forstmann BU, Keuken MC. Cortico-basal white matter alterations occurring in Parkinson's disease. PLoS One 2019; 14:e0214343. [PMID: 31425517 PMCID: PMC6699705 DOI: 10.1371/journal.pone.0214343] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 07/17/2019] [Indexed: 01/01/2023] Open
Abstract
Magnetic resonance imaging studies typically use standard anatomical atlases for identification and analyses of (patho-)physiological effects on specific brain areas; these atlases often fail to incorporate neuroanatomical alterations that may occur with both age and disease. The present study utilizes Parkinson's disease and age-specific anatomical atlases of the subthalamic nucleus for diffusion tractography, assessing tracts that run between the subthalamic nucleus and a-priori defined cortical areas known to be affected by Parkinson's disease. The results show that the strength of white matter fiber tracts appear to remain structurally unaffected by disease. Contrary to that, Fractional Anisotropy values were shown to decrease in Parkinson's disease patients for connections between the subthalamic nucleus and the pars opercularis of the inferior frontal gyrus, anterior cingulate cortex, the dorsolateral prefrontal cortex and the pre-supplementary motor, collectively involved in preparatory motor control, decision making and task monitoring. While the biological underpinnings of fractional anisotropy alterations remain elusive, they may nonetheless be used as an index of Parkinson's disease. Moreover, we find that failing to account for structural changes occurring in the subthalamic nucleus with age and disease reduce the accuracy and influence the results of tractography, highlighting the importance of using appropriate atlases for tractography.
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Affiliation(s)
- Bethany. R. Isaacs
- Integrative Model-based Cognitive Neuroscience research unit, University of Amsterdam, Amsterdam, the Netherlands
- Department of Neurosurgery, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Anne. C. Trutti
- Integrative Model-based Cognitive Neuroscience research unit, University of Amsterdam, Amsterdam, the Netherlands
- Cognitive Psychology, University of Leiden, Leiden, the Netherlands
| | - Esther Pelzer
- Translational Neurocircuitry, Max Planck Institute for Metabolism Research, Cologne, Germany
- Department of Neurology, University Clinics, Cologne, Germany
| | - Marc Tittgemeyer
- Translational Neurocircuitry, Max Planck Institute for Metabolism Research, Cologne, Germany
- Department of Neurology, University Clinics, Cologne, Germany
| | - Yasin Temel
- Department of Neurosurgery, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Birte. U. Forstmann
- Integrative Model-based Cognitive Neuroscience research unit, University of Amsterdam, Amsterdam, the Netherlands
| | - Max. C. Keuken
- Integrative Model-based Cognitive Neuroscience research unit, University of Amsterdam, Amsterdam, the Netherlands
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29
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Sotiropoulos SN, Zalesky A. Building connectomes using diffusion MRI: why, how and but. NMR IN BIOMEDICINE 2019; 32:e3752. [PMID: 28654718 PMCID: PMC6491971 DOI: 10.1002/nbm.3752] [Citation(s) in RCA: 163] [Impact Index Per Article: 32.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Revised: 04/05/2017] [Accepted: 05/03/2017] [Indexed: 05/14/2023]
Abstract
Why has diffusion MRI become a principal modality for mapping connectomes in vivo? How do different image acquisition parameters, fiber tracking algorithms and other methodological choices affect connectome estimation? What are the main factors that dictate the success and failure of connectome reconstruction? These are some of the key questions that we aim to address in this review. We provide an overview of the key methods that can be used to estimate the nodes and edges of macroscale connectomes, and we discuss open problems and inherent limitations. We argue that diffusion MRI-based connectome mapping methods are still in their infancy and caution against blind application of deep white matter tractography due to the challenges inherent to connectome reconstruction. We review a number of studies that provide evidence of useful microstructural and network properties that can be extracted in various independent and biologically relevant contexts. Finally, we highlight some of the key deficiencies of current macroscale connectome mapping methodologies and motivate future developments.
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Affiliation(s)
- Stamatios N. Sotiropoulos
- Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
- Sir Peter Mansfield Imaging Centre, School of MedicineUniversity of NottinghamNottinghamUK
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre and Melbourne School of EngineeringUniversity of MelbourneVictoriaAustralia
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30
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Sobczak-Edmans M, Lo YC, Hsu YC, Chen YJ, Kwok FY, Chuang KH, Tseng WYI, Chen SHA. Cerebro-Cerebellar Pathways for Verbal Working Memory. Front Hum Neurosci 2019; 12:530. [PMID: 30670957 PMCID: PMC6333010 DOI: 10.3389/fnhum.2018.00530] [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: 07/30/2018] [Accepted: 12/13/2018] [Indexed: 11/26/2022] Open
Abstract
The current study examined the structural and functional connectivity of the cerebro-cerebellar network of verbal working memory as proposed by Chen and Desmond (2005a). Diffusion spectrum imaging was employed to establish structural connectivity between cerebro-cerebellar regions co-activated during a verbal working memory task. The inferior frontal gyrus, inferior parietal lobule, pons, thalamus, superior cerebellum and inferior cerebellum were used as regions of interest to reconstruct and segment the contralateral white matter cerebro-cerebellar circuitry. The segmented pathways were examined further to establish the relationship between structural and effective connectivity as well as the relationship between structural connectivity and verbal working memory performance. No direct relationship between structural and effective connectivity was found but the results demonstrated that structural connectivity is indirectly related to effective connectivity as DCM models that resembled more closely with underlying white matter pathways had a higher degree of model inference confidence. Additionally, it was demonstrated that the structural connectivity of the ponto-cerebellar tract was associated with individual differences in response time for verbal working memory. The findings of the study contribute to further our understanding of the relationship between structural and functional connectivity and the impact of variability in verbal working memory performance.
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Affiliation(s)
| | - Yu-Chun Lo
- Graduate Institute of Neural Regenerative Medicine, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.,Institute of Medical Device and Imaging, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Yung-Chin Hsu
- Institute of Medical Device and Imaging, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Yu-Jen Chen
- Institute of Medical Device and Imaging, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Fu Yu Kwok
- Centre for Research in Child Development, National Institute of Education, Nanyang Technological University, Singapore, Singapore
| | - Kai-Hsiang Chuang
- The Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia.,The Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD, Australia
| | - Wen-Yih Isaac Tseng
- Institute of Medical Device and Imaging, National Taiwan University College of Medicine, Taipei, Taiwan.,Institute of Brain and Mind Sciences, National Taiwan University College of Medicine, Taipei, Taiwan.,Department of Radiology, National Taiwan University College of Medicine, Taipei, Taiwan.,Molecular Imaging Center, National Taiwan University, Taipei, Taiwan
| | - S H Annabel Chen
- Psychology, School of Social Sciences, Nanyang Technological University, Singapore, Singapore.,Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.,Centre for Research and Development in Learning, Nanyang Technological University, Singapore, Singapore
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31
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Sokolov AA, Zeidman P, Erb M, Ryvlin P, Pavlova MA, Friston KJ. Linking structural and effective brain connectivity: structurally informed Parametric Empirical Bayes (si-PEB). Brain Struct Funct 2019; 224:205-217. [PMID: 30302538 PMCID: PMC6373362 DOI: 10.1007/s00429-018-1760-8] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Accepted: 09/21/2018] [Indexed: 12/13/2022]
Abstract
Despite the potential for better understanding functional neuroanatomy, the complex relationship between neuroimaging measures of brain structure and function has confounded integrative, multimodal analyses of brain connectivity. This is particularly true for task-related effective connectivity, which describes the causal influences between neuronal populations. Here, we assess whether measures of structural connectivity may usefully inform estimates of effective connectivity in larger scale brain networks. To this end, we introduce an integrative approach, capitalising on two recent statistical advances: Parametric Empirical Bayes, which provides group-level estimates of effective connectivity, and Bayesian model reduction, which enables rapid comparison of competing models. Crucially, we show that structural priors derived from high angular resolution diffusion imaging on a dynamic causal model of a 12-region network-based on functional MRI data from the same subjects-substantially improve model evidence (posterior probability 1.00). This provides definitive evidence that structural and effective connectivity depend upon each other in mediating distributed, large-scale interactions in the brain. Furthermore, this work offers novel perspectives for understanding normal brain architecture and its disintegration in clinical conditions.
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Affiliation(s)
- Arseny A Sokolov
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK.
- Service de Neurologie, Département des Neurosciences Cliniques, Centre Hospitalier Universitaire Vaudois (CHUV), 1011, Lausanne, Switzerland.
| | - Peter Zeidman
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK
| | - Michael Erb
- Department of Biomedical Magnetic Resonance, Department of Radiology, University of Tübingen Medical School, 72076, Tübingen, Germany
| | - Philippe Ryvlin
- Service de Neurologie, Département des Neurosciences Cliniques, Centre Hospitalier Universitaire Vaudois (CHUV), 1011, Lausanne, Switzerland
| | - Marina A Pavlova
- Department of Psychiatry and Psychotherapy, University of Tübingen Medical School, 72076, Tübingen, Germany
| | - Karl J Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK
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32
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Perry A, Roberts G, Mitchell PB, Breakspear M. Connectomics of bipolar disorder: a critical review, and evidence for dynamic instabilities within interoceptive networks. Mol Psychiatry 2019; 24:1296-1318. [PMID: 30279458 PMCID: PMC6756092 DOI: 10.1038/s41380-018-0267-2] [Citation(s) in RCA: 81] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Revised: 08/14/2018] [Accepted: 09/07/2018] [Indexed: 12/31/2022]
Abstract
The notion that specific cognitive and emotional processes arise from functionally distinct brain regions has lately shifted toward a connectivity-based approach that emphasizes the role of network-mediated integration across regions. The clinical neurosciences have likewise shifted from a predominantly lesion-based approach to a connectomic paradigm-framing disorders as diverse as stroke, schizophrenia (SCZ), and dementia as "dysconnection syndromes". Here we position bipolar disorder (BD) within this paradigm. We first summarise the disruptions in structural, functional and effective connectivity that have been documented in BD. Not surprisingly, these disturbances show a preferential impact on circuits that support emotional processes, cognitive control and executive functions. Those at high risk (HR) for BD also show patterns of connectivity that differ from both matched control populations and those with BD, and which may thus speak to neurobiological markers of both risk and resilience. We highlight research fields that aim to link brain network disturbances to the phenotype of BD, including the study of large-scale brain dynamics, the principles of network stability and control, and the study of interoception (the perception of physiological states). Together, these findings suggest that the affective dysregulation of BD arises from dynamic instabilities in interoceptive circuits which subsequently impact on fear circuitry and cognitive control systems. We describe the resulting disturbance as a "psychosis of interoception".
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Affiliation(s)
- Alistair Perry
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia. .,Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin/London, Germany. .,Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195, Berlin, Germany.
| | - Gloria Roberts
- 0000 0004 4902 0432grid.1005.4School of Psychiatry, University of New South Wales, Randwick, NSW Australia ,grid.415193.bBlack Dog Institute, Prince of Wales Hospital, Randwick, NSW Australia
| | - Philip B. Mitchell
- 0000 0004 4902 0432grid.1005.4School of Psychiatry, University of New South Wales, Randwick, NSW Australia ,grid.415193.bBlack Dog Institute, Prince of Wales Hospital, Randwick, NSW Australia
| | - Michael Breakspear
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia. .,Metro North Mental Health Service, Brisbane, QLD, Australia.
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33
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Ren Y, Nguyen VT, Sonkusare S, Lv J, Pang T, Guo L, Eickhoff SB, Breakspear M, Guo CC. Effective connectivity of the anterior hippocampus predicts recollection confidence during natural memory retrieval. Nat Commun 2018; 9:4875. [PMID: 30451864 PMCID: PMC6242820 DOI: 10.1038/s41467-018-07325-4] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2018] [Accepted: 10/26/2018] [Indexed: 11/25/2022] Open
Abstract
Human interactions with the world are influenced by memories of recent events. This effect, often triggered by perceptual cues, occurs naturally and without conscious effort. However, the neuroscience of involuntary memory in a dynamic milieu has received much less attention than the mechanisms of voluntary retrieval with deliberate purpose. Here, we investigate the neural processes driven by naturalistic cues that relate to, and presumably trigger the retrieval of recent experiences. Viewing the continuation of recently viewed clips evokes greater bilateral activation in anterior hippocampus, precuneus and angular gyrus than naïve clips. While these regions manifest reciprocal connectivity, continued viewing specifically modulates the effective connectivity from the anterior hippocampus to the precuneus. The strength of this modulation predicts participants' confidence in later voluntary recall of news details. Our study reveals network mechanisms of dynamic, involuntary memory retrieval and its relevance to metacognition in a rich context resembling everyday life.
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Affiliation(s)
- Yudan Ren
- School of Automation, Northwestern Polytechnical University, 710072, Xi'an, China
- QIMR Berghofer Medical Research Institute, Brisbane, 4006, Australia
| | - Vinh T Nguyen
- QIMR Berghofer Medical Research Institute, Brisbane, 4006, Australia
| | - Saurabh Sonkusare
- QIMR Berghofer Medical Research Institute, Brisbane, 4006, Australia
- School of Medicine, The University of Queensland, Brisbane, 4072, Australia
| | - Jinglei Lv
- QIMR Berghofer Medical Research Institute, Brisbane, 4006, Australia
| | - Tianji Pang
- School of Automation, Northwestern Polytechnical University, 710072, Xi'an, China
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, 710072, Xi'an, China
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, 52425, Germany
| | | | - Christine C Guo
- School of Automation, Northwestern Polytechnical University, 710072, Xi'an, China.
- QIMR Berghofer Medical Research Institute, Brisbane, 4006, Australia.
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Bi K, Luo G, Tian S, Zhang S, Liu X, Wang Q, Lu Q, Yao Z. An enriched granger causal model allowing variable static anatomical constraints. Neuroimage Clin 2018; 21:101592. [PMID: 30448217 PMCID: PMC6411584 DOI: 10.1016/j.nicl.2018.11.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Revised: 10/08/2018] [Accepted: 11/03/2018] [Indexed: 01/08/2023]
Abstract
The anatomical connectivity constrains but does not fully determine functional connectivity, especially when one explores into the dynamics over the course of a trial. Therefore, an enriched granger causal model (GCM) integrated with anatomical prior information is proposed in this study, to describe the dynamic effective connectivity to distinguish the depression and explore the pathogenesis of depression. In the proposed frame, the anatomical information was converted via an optimized transformation model, which was then integrated into the normal GCM by variational bayesian model. Magnetoencephalography (MEG) signals and diffusion tensor imaging (DTI) of 24 depressive patients and 24 matched controls were utilized for performance comparison. Together with the sliding windowed MEG signals under sad facial stimuli, the enriched GCM was applied to calculate the regional-pair dynamic effective connectivity, which were repeatedly sifted via feature selection and fed into different classifiers. From the aspects of model errors and recognition accuracy rates, results supported the superiority of the enriched GCM with anatomical priors over the normal GCM. For the effective connectivity with anatomical priors, the best subject discrimination accuracy of SVM was 85.42% (the sensitivity was 87.50% and the specificity was 83.33%). Furthermore, discriminative feature analysis suggested that the enriched GCM that detect the variable anatomical constraint on function could better detect more stringent and less dynamic brain function in depression. The proposed approach is valuable in dynamic functional dysfunction exploration in depression and could be useful for depression recognition.
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Affiliation(s)
- Kun Bi
- Key Laboratory of Child Development and Learning Science, School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China
| | - Guoping Luo
- Key Laboratory of Child Development and Learning Science, School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China
| | - Shui Tian
- Key Laboratory of Child Development and Learning Science, School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China
| | - Siqi Zhang
- Key Laboratory of Child Development and Learning Science, School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China
| | - Xiaoxue Liu
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210029, China
| | - Qiang Wang
- Medical School of Nanjing University, Nanjing University, Nanjing 210093, China
| | - Qing Lu
- Key Laboratory of Child Development and Learning Science, School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China.
| | - Zhijian Yao
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210029, China; Medical School of Nanjing University, Nanjing University, Nanjing 210093, China.
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35
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Frässle S, Lomakina EI, Kasper L, Manjaly ZM, Leff A, Pruessmann KP, Buhmann JM, Stephan KE. A generative model of whole-brain effective connectivity. Neuroimage 2018; 179:505-529. [DOI: 10.1016/j.neuroimage.2018.05.058] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Revised: 05/16/2018] [Accepted: 05/24/2018] [Indexed: 12/17/2022] Open
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Kleineberg NN, Dovern A, Binder E, Grefkes C, Eickhoff SB, Fink GR, Weiss PH. Action and semantic tool knowledge - Effective connectivity in the underlying neural networks. Hum Brain Mapp 2018; 39:3473-3486. [PMID: 29700893 PMCID: PMC6866288 DOI: 10.1002/hbm.24188] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2017] [Revised: 02/27/2018] [Accepted: 04/11/2018] [Indexed: 12/14/2022] Open
Abstract
Evidence from neuropsychological and imaging studies indicate that action and semantic knowledge about tools draw upon distinct neural substrates, but little is known about the underlying interregional effective connectivity. With fMRI and dynamic causal modeling (DCM) we investigated effective connectivity in the left-hemisphere (LH) while subjects performed (i) a function knowledge and (ii) a value knowledge task, both addressing semantic tool knowledge, and (iii) a manipulation (action) knowledge task. Overall, the results indicate crosstalk between action nodes and semantic nodes. Interestingly, effective connectivity was weakened between semantic nodes and action nodes during the manipulation task. Furthermore, pronounced modulations of effective connectivity within the fronto-parietal action system of the LH (comprising lateral occipito-temporal cortex, intraparietal sulcus, supramarginal gyrus, inferior frontal gyrus) were observed in a bidirectional manner during the processing of action knowledge. In contrast, the function and value knowledge tasks resulted in a significant strengthening of the effective connectivity between visual cortex and fusiform gyrus. Importantly, this modulation was present in both semantic tasks, indicating that processing different aspects of semantic knowledge about tools evokes similar effective connectivity patterns. Data revealed that interregional effective connectivity during the processing of tool knowledge occurred in a bidirectional manner with a weakening of connectivity between areas engaged in action and semantic knowledge about tools during the processing of action knowledge. Moreover, different semantic tool knowledge tasks elicited similar effective connectivity patterns.
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Affiliation(s)
- Nina N. Kleineberg
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM‐3), Research Center JülichGermany
- Department of NeurologyUniversity Hospital CologneGermany
| | - Anna Dovern
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM‐3), Research Center JülichGermany
| | - Ellen Binder
- Department of NeurologyUniversity Hospital CologneGermany
| | - Christian Grefkes
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM‐3), Research Center JülichGermany
- Department of NeurologyUniversity Hospital CologneGermany
| | - Simon B. Eickhoff
- Institute for Systems Neuroscience, Heinrich Heine University DüsseldorfGermany
- Brain and BehaviourInstitute of Neuroscience and Medicine (INM‐7), Research Center JülichGermany
| | - Gereon R. Fink
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM‐3), Research Center JülichGermany
- Department of NeurologyUniversity Hospital CologneGermany
| | - Peter H. Weiss
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM‐3), Research Center JülichGermany
- Department of NeurologyUniversity Hospital CologneGermany
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Hadida J, Sotiropoulos SN, Abeysuriya RG, Woolrich MW, Jbabdi S. Bayesian Optimisation of Large-Scale Biophysical Networks. Neuroimage 2018; 174:219-236. [PMID: 29518570 PMCID: PMC6324723 DOI: 10.1016/j.neuroimage.2018.02.063] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Revised: 02/27/2018] [Accepted: 02/28/2018] [Indexed: 01/08/2023] Open
Abstract
The relationship between structure and function in the human brain is well established, but not yet well characterised. Large-scale biophysical models allow us to investigate this relationship, by leveraging structural information (e.g. derived from diffusion tractography) in order to couple dynamical models of local neuronal activity into networks of interacting regions distributed across the cortex. In practice however, these models are difficult to parametrise, and their simulation is often delicate and computationally expensive. This undermines the experimental aspect of scientific modelling, and stands in the way of comparing different parametrisations, network architectures, or models in general, with confidence. Here, we advocate the use of Bayesian optimisation for assessing the capabilities of biophysical network models, given a set of desired properties (e.g. band-specific functional connectivity); and in turn the use of this assessment as a principled basis for incremental modelling and model comparison. We adapt an optimisation method designed to cope with costly, high-dimensional, non-convex problems, and demonstrate its use and effectiveness. Using five parameters controlling key aspects of our model, we find that this method is able to converge to regions of high functional similarity with real MEG data, with very few samples given the number of parameters, without getting stuck in local extrema, and while building and exploiting a map of uncertainty defined smoothly across the parameter space. We compare the results obtained using different methods of structural connectivity estimation from diffusion tractography, and find that one method leads to better simulations.
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Affiliation(s)
- J Hadida
- Wellcome Centre for Integrative Neuroimaging (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Wellcome Centre for Integrative Neuroimaging (OHBA), Department of Psychiatry, University of Oxford, UK.
| | - S N Sotiropoulos
- Wellcome Centre for Integrative Neuroimaging (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Sir Peter Mansfield Imaging Centre (SPMIC), School of Medicine, University of Nottingham, UK
| | - R G Abeysuriya
- Wellcome Centre for Integrative Neuroimaging (OHBA), Department of Psychiatry, University of Oxford, UK
| | - M W Woolrich
- Wellcome Centre for Integrative Neuroimaging (OHBA), Department of Psychiatry, University of Oxford, UK
| | - S Jbabdi
- Wellcome Centre for Integrative Neuroimaging (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, UK
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Tak S, Noh J, Cheong C, Zeidman P, Razi A, Penny W, Friston K. A validation of dynamic causal modelling for 7T fMRI. J Neurosci Methods 2018; 305:36-45. [DOI: 10.1016/j.jneumeth.2018.05.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Revised: 04/16/2018] [Accepted: 05/03/2018] [Indexed: 01/12/2023]
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Kale P, Zalesky A, Gollo LL. Estimating the impact of structural directionality: How reliable are undirected connectomes? Netw Neurosci 2018; 2:259-284. [PMID: 30234180 PMCID: PMC6135560 DOI: 10.1162/netn_a_00040] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Accepted: 12/19/2017] [Indexed: 11/30/2022] Open
Abstract
Directionality is a fundamental feature of network connections. Most structural brain networks are intrinsically directed because of the nature of chemical synapses, which comprise most neuronal connections. Because of the limitations of noninvasive imaging techniques, the directionality of connections between structurally connected regions of the human brain cannot be confirmed. Hence, connections are represented as undirected, and it is still unknown how this lack of directionality affects brain network topology. Using six directed brain networks from different species and parcellations (cat, mouse, C. elegans, and three macaque networks), we estimate the inaccuracies in network measures (degree, betweenness, clustering coefficient, path length, global efficiency, participation index, and small-worldness) associated with the removal of the directionality of connections. We employ three different methods to render directed brain networks undirected: (a) remove unidirectional connections, (b) add reciprocal connections, and (c) combine equal numbers of removed and added unidirectional connections. We quantify the extent of inaccuracy in network measures introduced through neglecting connection directionality for individual nodes and across the network. We find that the coarse division between core and peripheral nodes remains accurate for undirected networks. However, hub nodes differ considerably when directionality is neglected. Comparing the different methods to generate undirected networks from directed ones, we generally find that the addition of reciprocal connections (false positives) causes larger errors in graph-theoretic measures than the removal of the same number of directed connections (false negatives). These findings suggest that directionality plays an essential role in shaping brain networks and highlight some limitations of undirected connectomes.
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Affiliation(s)
- Penelope Kale
- QIMR Berghofer Medical Research Institute, Australia
- University of Queensland, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre and Department of Biomedical Engineering, University of Melbourne, Australia
| | - Leonardo L. Gollo
- QIMR Berghofer Medical Research Institute, Australia
- University of Queensland, Australia
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Tittgemeyer M, Rigoux L, Knösche TR. Cortical parcellation based on structural connectivity: A case for generative models. Neuroimage 2018; 173:592-603. [PMID: 29407457 DOI: 10.1016/j.neuroimage.2018.01.077] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2016] [Revised: 01/26/2018] [Accepted: 01/29/2018] [Indexed: 12/14/2022] Open
Abstract
One of the major challenges in systems neuroscience is to identify brain networks and unravel their significance for brain function -this has led to the concept of the 'connectome'. Connectomes are currently extensively studied in large-scale international efforts at multiple scales, and follow different definitions with respect to their connections as well as their elements. Perhaps the most promising avenue for defining the elements of connectomes originates from the notion that individual brain areas maintain distinct (long-range) connection profiles. These connectivity patterns determine the areas' functional properties and also allow for their anatomical delineation and mapping. This rationale has motivated the concept of connectivity-based cortex parcellation. In the past ten years, non-invasive mapping of human brain connectivity has led to immense advances in the development of parcellation techniques and their applications. Unfortunately, many of these approaches primarily aim for confirmation of well-known, existing architectonic maps and, to that end, unsuitably incorporate prior knowledge and frequently build on circular argumentation. Often, current approaches also tend to disregard the specific apertures of connectivity measurements, as well as the anatomical specificities of cortical areas, such as spatial compactness, regional heterogeneity, inter-subject variability, the multi-scaling nature of connectivity information, and potential hierarchical organisation. From a methodological perspective, however, a useful framework that regards all of these aspects in an unbiased way is technically demanding. In this commentary, we first outline the concept of connectivity-based cortex parcellation and discuss its prospects and limitations in particular with respect to structural connectivity. To improve reliability and efficiency, we then strongly advocate for connectivity-based cortex parcellation as a modelling approach; that is, an approximation of the data based on (model) parameter inference. As such, a parcellation algorithm can be formally tested for robustness -the precision of its predictions can be quantified and statistics about potential generalization of the results can be derived. Such a framework also allows the question of model constraints to be reformulated in terms of hypothesis testing through model selection and offers a formative way to integrate anatomical knowledge in terms of prior distributions.
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Affiliation(s)
| | - Lionel Rigoux
- Max-Planck-Institute for Metabolism Research, Cologne, Germany
| | - Thomas R Knösche
- Max-Planck-Institute for Cognitive and Brain Sciences, Leipzig, Germany
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Ma L, Steinberg JL, Cunningham KA, Bjork JM, Lane SD, Schmitz JM, Burroughs T, Narayana PA, Kosten TR, Bechara A, Moeller FG. Altered anterior cingulate cortex to hippocampus effective connectivity in response to drug cues in men with cocaine use disorder. Psychiatry Res 2018; 271:59-66. [PMID: 29108734 PMCID: PMC5741507 DOI: 10.1016/j.pscychresns.2017.10.012] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Revised: 08/24/2017] [Accepted: 10/22/2017] [Indexed: 11/26/2022]
Abstract
Drug-related attentional bias may have significant implications for the treatment of cocaine use disorder (CocUD). However, the neurobiology of attentional bias is not completely understood. This study employed dynamic causal modeling (DCM) to conduct an analysis of effective (directional) connectivity involved in drug-related attentional bias in treatment-seeking CocUD subjects. The DCM analysis was conducted based on functional magnetic resonance imaging (fMRI) data acquired from fifteen CocUD subjects while performing a cocaine-word Stroop task, during which blocks of Cocaine Words (CW) and Neutral Words (NW) alternated. There was no significant attentional bias at group level. Although no significant brain activation was found, the DCM analysis found that, relative to the NW, the CW caused a significant increase in the strength of the right (R) anterior cingulate cortex (ACC) to R hippocampus effective connectivity. Greater increase of this connectivity was associated with greater CW reaction time (relative to NW reaction time). The increased strength of R ACC to R hippocampus connectivity may reflect ACC activation of hippocampal memories related to drug use, which was triggered by the drug cues. This circuit could be a potential target for therapeutics in CocUD patients. No significant change was found in the other modeled connectivities.
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Affiliation(s)
- Liangsuo Ma
- Institute for Drug and Alcohol Studies, Richmond, VA, USA; Department of Radiology, Richmond, VA, USA.
| | - Joel L Steinberg
- Institute for Drug and Alcohol Studies, Richmond, VA, USA; Department of Psychiatry, Virginia Commonwealth University (VCU), Richmond, VA, USA
| | - Kathryn A Cunningham
- Center for Addiction Research and Department of Pharmacology and Toxicology, University of Texas Medical Branch, Galveston, TX, USA
| | - James M Bjork
- Institute for Drug and Alcohol Studies, Richmond, VA, USA; Department of Psychiatry, Virginia Commonwealth University (VCU), Richmond, VA, USA
| | - Scott D Lane
- Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center (UTHSC), Houston, TX, USA
| | - Joy M Schmitz
- Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center (UTHSC), Houston, TX, USA
| | | | - Ponnada A Narayana
- Department of Diagnostic and Interventional Imaging, UTHSC, Houston, TX, USA
| | - Thomas R Kosten
- Department of Psychiatry and Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Antoine Bechara
- Brain and Creativity Institute, and Department of Psychology, University of Southern California, Los Angeles, CA, USA
| | - F Gerard Moeller
- Institute for Drug and Alcohol Studies, Richmond, VA, USA; Department of Psychiatry, Virginia Commonwealth University (VCU), Richmond, VA, USA; Department of Pharmacology and Toxicology, Richmond, VA, USA; Department of Neurology, VCU, Richmond, VA, USA
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42
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Medaglia JD, Huang W, Karuza EA, Kelkar A, Thompson-Schill SL, Ribeiro A, Bassett DS. Functional Alignment with Anatomical Networks is Associated with Cognitive Flexibility. Nat Hum Behav 2017; 2:156-164. [PMID: 30498789 PMCID: PMC6258039 DOI: 10.1038/s41562-017-0260-9] [Citation(s) in RCA: 89] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Cognitive flexibility describes the human ability to switch between modes of mental function to achieve goals. Mental switching is accompanied by transient changes in brain activity, which must occur atop an anatomical architecture that bridges disparate cortical and subcortical regions by underlying white matter tracts. However, an integrated perspective regarding how white matter networks might constrain brain dynamics during cognitive processes requiring flexibility has remained elusive. To address this challenge, we applied emerging tools from graph signal processing to examine whether BOLD signals measured at each point in time correspond to complex underlying anatomical networks in 28 individuals performing a perceptual task that probed cognitive flexibility. We found that the alignment between functional signals and the architecture of the underlying white matter network was associated with greater cognitive flexibility across subjects. By computing a concise measure using multi-modal neuroimaging data, we uncovered an integrated structure-function correlate of human behavior.
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Affiliation(s)
- John D Medaglia
- Department of Psychology, Drexel University, Philadelphia, PA, 19104 USA.,Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104 USA
| | - Weiyu Huang
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104 USA
| | - Elisabeth A Karuza
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, 19104 USA
| | - Apoorva Kelkar
- Department of Psychology, Drexel University, Philadelphia, PA, 19104 USA
| | | | - Alejandro Ribeiro
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104 USA
| | - Danielle S Bassett
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104 USA.,Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104 USA
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Colombo M, Weinberger N. Discovering Brain Mechanisms Using Network Analysis and Causal Modeling. Minds Mach (Dordr) 2017; 28:265-286. [PMID: 30996522 PMCID: PMC6438494 DOI: 10.1007/s11023-017-9447-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2016] [Accepted: 09/16/2017] [Indexed: 01/29/2023]
Abstract
Mechanist philosophers have examined several strategies scientists use for discovering causal mechanisms in neuroscience. Findings about the anatomical organization of the brain play a central role in several such strategies. Little attention has been paid, however, to the use of network analysis and causal modeling techniques for mechanism discovery. In particular, mechanist philosophers have not explored whether and how these strategies incorporate information about the anatomical organization of the brain. This paper clarifies these issues in the light of the distinction between structural, functional and effective connectivity. Specifically, we examine two quantitative strategies currently used for causal discovery from functional neuroimaging data: dynamic causal modeling and probabilistic graphical modeling. We show that dynamic causal modeling uses findings about the brain's anatomical organization to improve the statistical estimation of parameters in an already specified causal model of the target brain mechanism. Probabilistic graphical modeling, in contrast, makes no appeal to the brain's anatomical organization, but lays bare the conditions under which correlational data suffice to license reliable inferences about the causal organization of a target brain mechanism. The question of whether findings about the anatomical organization of the brain can and should constrain the inference of causal networks remains open, but we show how the tools supplied by graphical modeling methods help to address it.
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Affiliation(s)
- Matteo Colombo
- Tilburg Center for Logic, Ethics and Philosophy of Science, Tilburg University, P.O. Box 90153, 5000 LE Tilburg, The Netherlands
| | - Naftali Weinberger
- Tilburg Center for Logic, Ethics and Philosophy of Science, Tilburg University, P.O. Box 90153, 5000 LE Tilburg, The Netherlands
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Bogdanov P, Dereli N, Dang XH, Bassett DS, Wymbs NF, Grafton ST, Singh AK. Learning about learning: Mining human brain sub-network biomarkers from fMRI data. PLoS One 2017; 12:e0184344. [PMID: 29016686 PMCID: PMC5634545 DOI: 10.1371/journal.pone.0184344] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2017] [Accepted: 08/22/2017] [Indexed: 01/24/2023] Open
Abstract
Modeling the brain as a functional network can reveal the relationship between distributed neurophysiological processes and functional interactions between brain structures. Existing literature on functional brain networks focuses mainly on a battery of network properties in "resting state" employing, for example, modularity, clustering, or path length among regions. In contrast, we seek to uncover functionally connected subnetworks that predict or correlate with cohort differences and are conserved within the subjects within a cohort. We focus on differences in both the rate of learning as well as overall performance in a sensorimotor task across subjects and develop a principled approach for the discovery of discriminative subgraphs of functional connectivity based on imaging acquired during practice. We discover two statistically significant subgraph regions: one involving multiple regions in the visual cortex and another involving the parietal operculum and planum temporale. High functional coherence in the former characterizes sessions in which subjects take longer to perform the task, while high coherence in the latter is associated with high learning rate (performance improvement across trials). Our proposed methodology is general, in that it can be applied to other cognitive tasks, to study learning or to differentiate between healthy patients and patients with neurological disorders, by revealing the salient interactions among brain regions associated with the observed global state. The discovery of such significant discriminative subgraphs promises a better data-driven understanding of the dynamic brain processes associated with high-level cognitive functions.
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Affiliation(s)
- Petko Bogdanov
- Department of Computer Science, University at Albany—SUNY, 1400 Washington Ave, Albany, NY 12222, United States of America
| | - Nazli Dereli
- Ticketmaster, Los Angeles, CA, United States of America
| | - Xuan-Hong Dang
- Department of Computer Science, University of California Santa Barbara, Santa Barbara, CA 93106-5110, United States of America
| | - Danielle S. Bassett
- Complex Systems Group, Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, United States of America
- Department of Electrical Engineering, University of Pennsylvania, Philadelphia, PA, 19104, United States of America
| | - Nicholas F. Wymbs
- Department of Physical Medicine and Rehabilitation, Johns Hopkins Medical Institutions, Baltimore, MD 21205, United States of America
| | - Scott T. Grafton
- Department of Psychology and UCSB Brain Imaging Center, University of California Santa Barbara, Santa Barbara, CA, United States of America
| | - Ambuj K. Singh
- Department of Computer Science, University of California Santa Barbara, Santa Barbara, CA 93106-5110, United States of America
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Dang S, Chaudhury S, Lall B, Roy PK. Tractography-Based Score for Learning Effective Connectivity From Multimodal Imaging Data Using Dynamic Bayesian Networks. IEEE Trans Biomed Eng 2017; 65:1057-1068. [PMID: 28809668 DOI: 10.1109/tbme.2017.2738035] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Effective connectivity (EC) is the methodology for determining functional-integration among the functionally active segregated regions of the brain. By definition EC is "the causal influence exerted by one neuronal group on another" which is constrained by anatomical connectivity (AC) (axonal connections). AC is necessary for EC but does not fully determine it, because synaptic communication occurs dynamically in a context-dependent fashion. Although there is a vast emerging evidence of structure-function relationship using multimodal imaging studies, till date only a few studies have done joint modeling of the two modalities: functional MRI (fMRI) and diffusion tensor imaging (DTI). We aim to propose a unified probabilistic framework that combines information from both sources to learn EC using dynamic Bayesian networks (DBNs). METHOD DBNs are probabilistic graphical temporal models that learn EC in an exploratory fashion. Specifically, we propose a novel anatomically informed (AI) score that evaluates fitness of a given connectivity structure to both DTI and fMRI data simultaneously. The AI score is employed in structure learning of DBN given the data. RESULTS Experiments with synthetic-data demonstrate the face validity of structure learning with our AI score over anatomically uninformed counterpart. Moreover, real-data results are cross-validated by performing classification-experiments. CONCLUSION EC inferred on real fMRI-DTI datasets is found to be consistent with previous literature and show promising results in light of the AC present as compared to other classically used techniques such as Granger-causality. SIGNIFICANCE Multimodal analyses provide a more reliable basis for differentiating brain under abnormal/diseased conditions than the single modality analysis.
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Kalinosky BT, Berrios Barillas R, Schmit BD. Structurofunctional resting-state networks correlate with motor function in chronic stroke. NEUROIMAGE-CLINICAL 2017; 16:610-623. [PMID: 28971011 PMCID: PMC5619927 DOI: 10.1016/j.nicl.2017.07.002] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Revised: 06/12/2017] [Accepted: 07/03/2017] [Indexed: 12/26/2022]
Abstract
Purpose Motor function and recovery after stroke likely rely directly on the residual anatomical connections in the brain and its resting-state functional connectivity. Both structural and functional properties of cortical networks after stroke are revealed using multimodal magnetic resonance imaging (MRI). Specifically, functional connectivity MRI (fcMRI) can extract functional networks of the brain at rest, while structural connectivity can be estimated from white matter fiber orientations measured with high angular-resolution diffusion imaging (HARDI). A model that marries these two techniques may be the key to understanding functional recovery after stroke. In this study, a novel set of voxel-level measures of structurofunctional correlations (SFC) was developed and tested in a group of chronic stroke subjects. Methods A fully automated method is presented for modeling the structure-function relationship of brain connectivity in individuals with stroke. Brains from ten chronic stroke subjects and nine age-matched controls were imaged with a structural T1-weighted scan, resting-state fMRI, and HARDI. Each subject's T1-weighted image was nonlinearly registered to a T1-weighted 152-brain MNI template using a local histogram-matching technique that alleviates distortions caused by brain lesions. Fractional anisotropy maps and mean BOLD images of each subject were separately registered to the individual's T1-weighted image using affine transformations. White matter fiber orientations within each voxel were estimated with the q-ball model, which approximates the orientation distribution function (ODF) from the diffusion-weighted measurements. Deterministic q-ball tractography was performed in order to obtain a set of fiber trajectories. The new structurofunctional correlation method assigns each voxel a new BOLD time course based on a summation of its structural connections with a common fiber length interval. Then, the voxel's original time-course was correlated with this fiber-distance BOLD signal to derive a novel structurofunctional correlation coefficient. These steps were repeated for eight fiber distance intervals, and the maximum of these correlations was used to define an intrinsic structurofunctional correlation (iSFC) index. A network-based SFC map (nSFC) was also developed in order to enhance resting-state functional networks derived from conventional functional connectivity analyses. iSFC and nSFC maps were individually compared between stroke subjects and controls using a voxel-based two-tailed Student's t-test (alpha = 0.01). A linear regression was also performed between the SFC metrics and the Box and Blocks Score, a clinical measure of arm motor function. Results Significant decreases (p < 0.01) in iSFC were found in stroke subjects within functional hubs of the brain, including the precuneus, prefrontal cortex, posterior parietal cortex, and cingulate gyrus. Many of these differences were significantly correlated with the Box and Blocks Score. The nSFC maps of prefrontal networks in control subjects revealed localized increases within the cerebellum, and these enhancements were diminished in stroke subjects. This finding was further supported by a reduction in functional connectivity between the prefrontal cortex and cerebellum. Default-mode network nSFC maps were higher in the contralesional hemisphere of lower-functioning stroke subjects. Conclusion The results demonstrate that changes after a stroke in both intrinsic and network-based structurofunctional correlations at rest are correlated with motor function, underscoring the importance of residual structural connectivity in cortical networks.
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Affiliation(s)
| | | | - Brian D. Schmit
- Department of Biomedical Engineering, Marquette University, Milwaukee, WI, USA
- Corresponding author at: Department of Biomedical Engineering, Marquette University, PO Box 1881, Milwaukee, WI 53201-1881, USA.
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Frässle S, Lomakina EI, Razi A, Friston KJ, Buhmann JM, Stephan KE. Regression DCM for fMRI. Neuroimage 2017; 155:406-421. [PMID: 28259780 DOI: 10.1016/j.neuroimage.2017.02.090] [Citation(s) in RCA: 77] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2016] [Revised: 01/25/2017] [Accepted: 02/28/2017] [Indexed: 12/13/2022] Open
Abstract
The development of large-scale network models that infer the effective (directed) connectivity among neuronal populations from neuroimaging data represents a key challenge for computational neuroscience. Dynamic causal models (DCMs) of neuroimaging and electrophysiological data are frequently used for inferring effective connectivity but are presently restricted to small graphs (typically up to 10 regions) in order to keep model inversion computationally feasible. Here, we present a novel variant of DCM for functional magnetic resonance imaging (fMRI) data that is suited to assess effective connectivity in large (whole-brain) networks. The approach rests on translating a linear DCM into the frequency domain and reformulating it as a special case of Bayesian linear regression. This paper derives regression DCM (rDCM) in detail and presents a variational Bayesian inversion method that enables extremely fast inference and accelerates model inversion by several orders of magnitude compared to classical DCM. Using both simulated and empirical data, we demonstrate the face validity of rDCM under different settings of signal-to-noise ratio (SNR) and repetition time (TR) of fMRI data. In particular, we assess the potential utility of rDCM as a tool for whole-brain connectomics by challenging it to infer effective connection strengths in a simulated whole-brain network comprising 66 regions and 300 free parameters. Our results indicate that rDCM represents a computationally highly efficient approach with promising potential for inferring whole-brain connectivity from individual fMRI data.
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Affiliation(s)
- Stefan Frässle
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland.
| | - Ekaterina I Lomakina
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland; Department of Computer Science, ETH Zurich, 8032 Zurich, Switzerland
| | - Adeel Razi
- Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, United Kingdom; Department of Electronic Engineering, NED University of Engineering & Technology, Karachi, Pakistan
| | - Karl J Friston
- Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, United Kingdom
| | - Joachim M Buhmann
- Department of Computer Science, ETH Zurich, 8032 Zurich, Switzerland
| | - Klaas E Stephan
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland; Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, United Kingdom
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Torrisi S, Nord CL, Balderston NL, Roiser JP, Grillon C, Ernst M. Resting state connectivity of the human habenula at ultra-high field. Neuroimage 2017; 147:872-879. [PMID: 27780778 PMCID: PMC5303669 DOI: 10.1016/j.neuroimage.2016.10.034] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2016] [Revised: 10/03/2016] [Accepted: 10/20/2016] [Indexed: 11/24/2022] Open
Abstract
The habenula, a portion of the epithalamus, is implicated in the pathophysiology of depression, anxiety and addiction disorders. Its small size and connection to other small regions prevent standard human imaging from delineating its structure and connectivity with confidence. Resting state functional connectivity is an established method for mapping connections across the brain from a seed region of interest. The present study takes advantage of 7T fMRI to map, for the first time, the habenula resting state network with very high spatial resolution in 32 healthy human participants. Results show novel functional connections in humans, including functional connectivity with the septum and bed nucleus of the stria terminalis (BNST). Results also show many habenula connections previously described only in animal research, such as with the nucleus basalis of Meynert, dorsal raphe, ventral tegmental area (VTA), and periaqueductal grey (PAG). Connectivity with caudate, thalamus and cortical regions such as the anterior cingulate, retrosplenial cortex and auditory cortex are also reported. This work, which demonstrates the power of ultra-high field for mapping human functional connections, is a valuable step toward elucidating subcortical and cortical regions of the habenula network.
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Affiliation(s)
- Salvatore Torrisi
- Section on the Neurobiology of Fear and Anxiety, National Institute of Mental Health, Bethesda, MD, United States
| | - Camilla L Nord
- Neuroscience and Cognitive Neuropsychiatry group, University of College, London, UK
| | - Nicholas L Balderston
- Section on the Neurobiology of Fear and Anxiety, National Institute of Mental Health, Bethesda, MD, United States
| | - Jonathan P Roiser
- Neuroscience and Cognitive Neuropsychiatry group, University of College, London, UK
| | - Christian Grillon
- Section on the Neurobiology of Fear and Anxiety, National Institute of Mental Health, Bethesda, MD, United States
| | - Monique Ernst
- Section on the Neurobiology of Fear and Anxiety, National Institute of Mental Health, Bethesda, MD, United States
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Ziegler G, Ridgway GR, Blakemore SJ, Ashburner J, Penny W. Multivariate dynamical modelling of structural change during development. Neuroimage 2017; 147:746-762. [PMID: 27979788 PMCID: PMC5315058 DOI: 10.1016/j.neuroimage.2016.12.017] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2016] [Revised: 10/28/2016] [Accepted: 12/08/2016] [Indexed: 01/07/2023] Open
Abstract
Here we introduce a multivariate framework for characterising longitudinal changes in structural MRI using dynamical systems. The general approach enables modelling changes of states in multiple imaging biomarkers typically observed during brain development, plasticity, ageing and degeneration, e.g. regional gray matter volume of multiple regions of interest (ROIs). Structural brain states follow intrinsic dynamics according to a linear system with additional inputs accounting for potential driving forces of brain development. In particular, the inputs to the system are specified to account for known or latent developmental growth/decline factors, e.g. due to effects of growth hormones, puberty, or sudden behavioural changes etc. Because effects of developmental factors might be region-specific, the sensitivity of each ROI to contributions of each factor is explicitly modelled. In addition to the external effects of developmental factors on regional change, the framework enables modelling and inference about directed (potentially reciprocal) interactions between brain regions, due to competition for space, or structural connectivity, and suchlike. This approach accounts for repeated measures in typical MRI studies of development and aging. Model inversion and posterior distributions are obtained using earlier established variational methods enabling Bayesian evidence-based comparisons between various models of structural change. Using this approach we demonstrate dynamic cortical changes during brain maturation between 6 and 22 years of age using a large openly available longitudinal paediatric dataset with 637 scans from 289 individuals. In particular, we model volumetric changes in 26 bilateral ROIs, which cover large portions of cortical and subcortical gray matter. We account for (1) puberty-related effects on gray matter regions; (2) effects of an early transient growth process with additional time-lag parameter; (3) sexual dimorphism by modelling parameter differences between boys and girls. There is evidence that the regional pattern of sensitivity to dynamic hidden growth factors in late childhood is similar across genders and shows a consistent anterior-posterior gradient with strongest impact to prefrontal cortex (PFC) brain changes. Finally, we demonstrate the potential of the framework to explore the coupling of structural changes across a priori defined subnetworks using an example of previously established resting state functional connectivity.
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Affiliation(s)
- Gabriel Ziegler
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke-University Magdeburg, 39120 Magdeburg, Germany; German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany.
| | - Gerard R Ridgway
- FMRIB Centre, University of Oxford, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK; Wellcome Trust Centre for Neuroimaging, University College, London WC1N 3BG, UK
| | | | - John Ashburner
- Wellcome Trust Centre for Neuroimaging, University College, London WC1N 3BG, UK
| | - Will Penny
- Wellcome Trust Centre for Neuroimaging, University College, London WC1N 3BG, UK
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50
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Chiang S, Guindani M, Yeh HJ, Haneef Z, Stern JM, Vannucci M. Bayesian vector autoregressive model for multi-subject effective connectivity inference using multi-modal neuroimaging data. Hum Brain Mapp 2016; 38:1311-1332. [PMID: 27862625 DOI: 10.1002/hbm.23456] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2016] [Revised: 10/13/2016] [Accepted: 10/25/2016] [Indexed: 11/05/2022] Open
Abstract
In this article a multi-subject vector autoregressive (VAR) modeling approach was proposed for inference on effective connectivity based on resting-state functional MRI data. Their framework uses a Bayesian variable selection approach to allow for simultaneous inference on effective connectivity at both the subject- and group-level. Furthermore, it accounts for multi-modal data by integrating structural imaging information into the prior model, encouraging effective connectivity between structurally connected regions. They demonstrated through simulation studies that their approach resulted in improved inference on effective connectivity at both the subject- and group-level, compared with currently used methods. It was concluded by illustrating the method on temporal lobe epilepsy data, where resting-state functional MRI and structural MRI were used. Hum Brain Mapp 38:1311-1332, 2017. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Sharon Chiang
- Department of Statistics, Rice University, Houston, Texas
| | - Michele Guindani
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Hsiang J Yeh
- Department of Neurology, University of California Los Angeles, Los Angeles, California
| | - Zulfi Haneef
- Department of Neurology, Baylor College of Medicine, Houston, Texas
| | - John M Stern
- Department of Neurology, University of California Los Angeles, Los Angeles, California
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