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Chauvin RJ, Newbold DJ, Nielsen AN, Miller RL, Krimmel SR, Metoki A, Wang A, Van AN, Montez DF, Marek S, Suljic V, Baden NJ, Ramirez-Perez N, Scheidter KM, Monk JS, Whiting FI, Adeyemo B, Snyder AZ, Kay BP, Raichle ME, Laumann TO, Gordon EM, Dosenbach NU. Disuse-driven plasticity in the human thalamus and putamen. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.07.566031. [PMID: 37987000 PMCID: PMC10659348 DOI: 10.1101/2023.11.07.566031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Motor adaptation in cortico-striato-thalamo-cortical loops has been studied mainly in animals using invasive electrophysiology. Here, we leverage functional neuroimaging in humans to study motor circuit plasticity in the human subcortex. We employed an experimental paradigm that combined two weeks of upper-extremity immobilization with daily resting-state and motor task fMRI before, during, and after the casting period. We previously showed that limb disuse leads to decreased functional connectivity (FC) of the contralateral somatomotor cortex (SM1) with the ipsilateral somatomotor cortex, increased FC with the cingulo-opercular network (CON) as well as the emergence of high amplitude, fMRI signal pulses localized in the contralateral SM1, supplementary motor area and the cerebellum. From our prior observations, it remains unclear whether the disuse plasticity affects the thalamus and striatum. We extended our analysis to include these subcortical regions and found that both exhibit strengthened cortical FC and spontaneous fMRI signal pulses induced by limb disuse. The dorsal posterior putamen and the central thalamus, mainly CM, VLP and VIM nuclei, showed disuse pulses and FC changes that lined up with fmri task activations from the Human connectome project motor system localizer, acquired before casting for each participant. Our findings provide a novel understanding of the role of the cortico-striato-thalamo-cortical loops in human motor plasticity and a potential link with the physiology of sleep regulation. Additionally, similarities with FC observation from Parkinson Disease (PD) questions a pathophysiological link with limb disuse.
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Affiliation(s)
- Roselyne J. Chauvin
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Dillan J. Newbold
- Department of Neurology, New York University Grossman School of Medicine, New York, New York 10016, USA
| | - Ashley N. Nielsen
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Ryland L. Miller
- Basque Center on Cognition, Brain and Language, Donostia, Gipuzkoa, Spain
| | - Samuel R. Krimmel
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Athanasia Metoki
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Anxu Wang
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Biomedical Engineering, Washington University in St. Louis, MO 63130
| | - Andrew N. Van
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Division of Computation and Data Science, Washington University School of Medicine, St. Louis, MO 63110
| | - David F. Montez
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110
| | - Scott Marek
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Vahdeta Suljic
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Noah J. Baden
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | | | - Kristen M. Scheidter
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Julia S. Monk
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Forrest I. Whiting
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Babatunde Adeyemo
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Abraham Z. Snyder
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Benjamin P. Kay
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Marcus E. Raichle
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St Louis, MO, USA
- Department of Neuroscience, Washington University School of Medicine, St Louis, MO, USA
| | - Timothy O. Laumann
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110
| | - Evan M. Gordon
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Nico U.F. Dosenbach
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Biomedical Engineering, Washington University in St. Louis, MO 63130
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110
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Catanzaro MJ, Rizzo S, Kopchick J, Chowdury A, Rosenberg DR, Bubenik P, Diwadkar VA. Topological Data Analysis Captures Task-Driven fMRI Profiles in Individual Participants: A Classification Pipeline Based on Persistence. Neuroinformatics 2024; 22:45-62. [PMID: 37924429 PMCID: PMC11268454 DOI: 10.1007/s12021-023-09645-3] [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] [Accepted: 10/11/2023] [Indexed: 11/06/2023]
Abstract
BOLD-based fMRI is the most widely used method for studying brain function. The BOLD signal while valuable, is beset with unique vulnerabilities. The most notable of these is the modest signal to noise ratio, and the relatively low temporal and spatial resolution. However, the high dimensional complexity of the BOLD signal also presents unique opportunities for functional discovery. Topological Data Analyses (TDA), a branch of mathematics optimized to search for specific classes of structure within high dimensional data may provide particularly valuable applications. In this investigation, we acquired fMRI data in the anterior cingulate cortex (ACC) using a basic motor control paradigm. Then, for each participant and each of three task conditions, fMRI signals in the ACC were summarized using two methods: a) TDA based methods of persistent homology and persistence landscapes and b) non-TDA based methods using a standard vectorization scheme. Finally, using machine learning (with support vector classifiers), classification accuracy of TDA and non-TDA vectorized data was tested across participants. In each participant, TDA-based classification out-performed the non-TDA based counterpart, suggesting that our TDA analytic pipeline better characterized task- and condition-induced structure in fMRI data in the ACC. Our results emphasize the value of TDA in characterizing task- and condition-induced structure in regional fMRI signals. In addition to providing our analytical tools for other users to emulate, we also discuss the unique role that TDA-based methods can play in the study of individual differences in the structure of functional brain signals in the healthy and the clinical brain.
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Affiliation(s)
- Michael J Catanzaro
- Iowa State University, Ames, IA, USA.
- Geometric Data Analytics, 343 West Main Street, Durham, NC, 27701, USA.
| | - Sam Rizzo
- Vanderbilt University, Nashville, TN, USA
| | - John Kopchick
- Wayne State University School of Medicine, Detroit, MI, USA
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Ji J, Zou A, Liu J, Yang C, Zhang X, Song Y. A Survey on Brain Effective Connectivity Network Learning. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:1879-1899. [PMID: 34469315 DOI: 10.1109/tnnls.2021.3106299] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Human brain effective connectivity characterizes the causal effects of neural activities among different brain regions. Studies of brain effective connectivity networks (ECNs) for different populations contribute significantly to the understanding of the pathological mechanism associated with neuropsychiatric diseases and facilitate finding new brain network imaging markers for the early diagnosis and evaluation for the treatment of cerebral diseases. A deeper understanding of brain ECNs also greatly promotes brain-inspired artificial intelligence (AI) research in the context of brain-like neural networks and machine learning. Thus, how to picture and grasp deeper features of brain ECNs from functional magnetic resonance imaging (fMRI) data is currently an important and active research area of the human brain connectome. In this survey, we first show some typical applications and analyze existing challenging problems in learning brain ECNs from fMRI data. Second, we give a taxonomy of ECN learning methods from the perspective of computational science and describe some representative methods in each category. Third, we summarize commonly used evaluation metrics and conduct a performance comparison of several typical algorithms both on simulated and real datasets. Finally, we present the prospects and references for researchers engaged in learning ECNs.
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Sjuls GS, Specht K. Variability in Resting-State Functional Magnetic Resonance Imaging: The Effect of Body Mass, Blood Pressure, Hematocrit, and Glycated Hemoglobin on Hemodynamic and Neuronal Parameters. Brain Connect 2022; 12:870-882. [PMID: 35473334 PMCID: PMC9807254 DOI: 10.1089/brain.2021.0125] [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] [Indexed: 01/13/2023] Open
Abstract
Introduction: Replicability has become an increasing focus within the scientific communities with the ongoing "replication crisis." One area that appears to struggle with unreliable results is resting-state functional magnetic resonance imaging (rs-fMRI). Therefore, the current study aimed at improving the knowledge of endogenous factors that contribute to inter-individual variability. Methods: Arterial blood pressure (BP), body mass, hematocrit, and glycated hemoglobin were investigated as potential sources of between-subject variability in rs-fMRI, in healthy individuals. Whether changes in resting-state networks (rs-networks) could be attributed to variability in the blood-oxygen-level-dependent (BOLD)-signal, changes in neuronal activity, or both was of special interest. Within-subject parameters were estimated by utilizing dynamic-causal modeling, as it allows to make inferences on the estimated hemodynamic (BOLD-signal dynamics) and neuronal parameters (effective connectivity) separately. Results: The results of the analyses imply that BP and body mass can cause between-subject and between-group variability in the BOLD-signal and that all the included factors can affect the underlying connectivity. Discussion: Given the results of the current and previous studies, rs-fMRI results appear to be susceptible to a range of factors, which is likely to contribute to the low degree of replicability of these studies. Interestingly, the highest degree of variability seems to appear within the much-studied default mode network and its connections to other networks. Impact statement We believe that thanks to the evidence that we have collected by analyzing the well-controlled data of the Human Connectome Project with dynamic-causal modeling (DCM) and by focusing not only on the effective connectivity, which is the typical way of using DCM, but also by analyzing the underlying hemodynamic parameters, we were able to explore the underlying vascular dependencies in a much broader perspective. Our results challenge the premise for studying changes in the default mode network as a clinical marker of disease, and we add to the growing list of factors that contribute to resting-state network variability.
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Affiliation(s)
- Guro Stensby Sjuls
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway.,Mohn Medical and Imaging Visualization Centre, Haukeland University Hospital, Bergen, Norway.,Language Acquisition and Language Processing Lab, Department of Language and Literature, Norwegian University of Science and Technology, Trondheim, Norway.,Address correspondence to: Guro Stensby Sjuls, Language Acquisition and Language Processing Lab, Department of Language and Literature, Norwegian University of Science and Technology, Trondheim 7491, Norway
| | - Karsten Specht
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway.,Mohn Medical and Imaging Visualization Centre, Haukeland University Hospital, Bergen, Norway.,Department of Education, UiT/The Arctic University of Norway, Tromsø, Norway
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Fetscher L, Batra M, Klose U. Improved localization of language areas using single voxel signal analysis of unprocessed fMRI data. FRONTIERS IN RADIOLOGY 2022; 2:997330. [PMID: 37492663 PMCID: PMC10365080 DOI: 10.3389/fradi.2022.997330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 09/06/2022] [Indexed: 07/27/2023]
Abstract
Activated brain regions can be visualized and localized with the use of fMRI (functional magnetic imaging). This is based on changes in the blood flow in activated regions, or more precisely on the hemodynamic response function (HRF) and the Blood-Oxygen-Level-Dependent (BOLD) effect. This study used a task-based fMRI examination with language paradigms in order to stimulate the language areas. The measured fMRI data are frequently altered by different preprocessing steps for the analysis and the display of activations. These changes can lead to discrepancies between the displayed and the truly measured location of the activations. Simple t-maps were created with unprocessed fMRI data, to provide a more realistic representation of the language areas. HRF-dependent single-voxel fMRI signal analysis was performed to improve the analyzability of these activation maps.
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Bielczyk NZ, Llera A, Buitelaar JK, Glennon JC, Beckmann CF. Increasing robustness of pairwise methods for effective connectivity in magnetic resonance imaging by using fractional moment series of BOLD signal distributions. Netw Neurosci 2019; 3:1009-1037. [PMID: 31637336 PMCID: PMC6779268 DOI: 10.1162/netn_a_00099] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2018] [Accepted: 06/03/2019] [Indexed: 12/20/2022] Open
Abstract
Estimating causal interactions in the brain from functional magnetic resonance imaging (fMRI) data remains a challenging task. Multiple studies have demonstrated that all current approaches to determine direction of connectivity perform poorly when applied to synthetic fMRI datasets. Recent advances in this field include methods for pairwise inference, which involve creating a sparse connectome in the first step, and then using a classifier in order to determine the directionality of connection between every pair of nodes in the second step. In this work, we introduce an advance to the second step of this procedure, by building a classifier based on fractional moments of the BOLD distribution combined into cumulants. The classifier is trained on datasets generated under the dynamic causal modeling (DCM) generative model. The directionality is inferred based on statistical dependencies between the two-node time series, for example, by assigning a causal link from time series of low variance to time series of high variance. Our approach outperforms or performs as well as other methods for effective connectivity when applied to the benchmark datasets. Crucially, it is also more resilient to confounding effects such as differential noise level across different areas of the connectome.
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Affiliation(s)
- Natalia Z. Bielczyk
- Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
| | - Alberto Llera
- Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
- Radboud University Nijmegen, Nijmegen, the Netherlands
| | - Jan K. Buitelaar
- Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
| | - Jeffrey C. Glennon
- Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
| | - Christian F. Beckmann
- Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
- Radboud University Nijmegen, Nijmegen, the Netherlands
- Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, UK
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Teckentrup V, van der Meer JN, Borchardt V, Fan Y, Neuser MP, Tempelmann C, Herrmann L, Walter M, Kroemer NB. The anterior insula channels prefrontal expectancy signals during affective processing. Neuroimage 2019; 200:414-424. [PMID: 31229657 DOI: 10.1016/j.neuroimage.2019.06.041] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Revised: 06/06/2019] [Accepted: 06/17/2019] [Indexed: 12/16/2022] Open
Abstract
Expectancy shapes our perception of impending events. Although such an interplay between cognitive and affective processes is often impaired in mental disorders, it is not well understood how top-down expectancy signals modulate future affect. We therefore track the information flow in the brain during cognitive and affective processing segregated in time using task-specific cross-correlations. Participants in two independent fMRI studies (N1 = 37 & N2 = 55) were instructed to imagine a situation with affective content as indicated by a cue, which was then followed by an emotional picture congruent with expectancy. To correct for intrinsic covariance of brain function, we calculate resting-state cross-correlations analogous to the task. First, using factorial modeling of delta cross-correlations (task-rest) of the first study, we find that the magnitude of expectancy signals in the anterior insula cortex (AIC) modulates the BOLD response to emotional pictures in the anterior cingulate and dorsomedial prefrontal cortex in opposite directions. Second, using hierarchical linear modeling of lagged connectivity, we demonstrate that expectancy signals in the AIC indeed foreshadow this opposing pattern in the prefrontal cortex. Third, we replicate the results in the second study using a higher temporal resolution, showing that our task-specific cross-correlation approach robustly uncovers the dynamics of information flow. We conclude that the AIC arbitrates the recruitment of distinct prefrontal networks during cued picture processing according to triggered expectations. Taken together, our study provides new insights into neuronal pathways channeling cognition and affect within well-defined brain networks. Better understanding of such dynamics could lead to new applications tracking aberrant information processing in mental disorders.
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Affiliation(s)
- Vanessa Teckentrup
- University of Tübingen, Department of Psychiatry and Psychotherapy, Tübingen, Germany
| | - Johan N van der Meer
- Queensland Institute of Medical Research, Brisbane, Australia; University of Magdeburg, Department of Psychiatry and Psychotherapy, Germany; Clinical Affective Neuroimaging Laboratory, Magdeburg, Germany; Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Viola Borchardt
- Clinical Affective Neuroimaging Laboratory, Magdeburg, Germany; Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Yan Fan
- Leibniz Research Centre for Working Environment and Human Factors, Department of Psychology and Neurosciences Dortmund, Germany
| | - Monja P Neuser
- University of Tübingen, Department of Psychiatry and Psychotherapy, Tübingen, Germany
| | | | - Luisa Herrmann
- University of Tübingen, Department of Psychiatry and Psychotherapy, Tübingen, Germany
| | - Martin Walter
- University of Tübingen, Department of Psychiatry and Psychotherapy, Tübingen, Germany; University of Magdeburg, Department of Psychiatry and Psychotherapy, Germany; Clinical Affective Neuroimaging Laboratory, Magdeburg, Germany; Leibniz Institute for Neurobiology, Magdeburg, Germany.
| | - Nils B Kroemer
- University of Tübingen, Department of Psychiatry and Psychotherapy, Tübingen, Germany.
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Bielczyk NZ, Uithol S, van Mourik T, Anderson P, Glennon JC, Buitelaar JK. Disentangling causal webs in the brain using functional magnetic resonance imaging: A review of current approaches. Netw Neurosci 2019; 3:237-273. [PMID: 30793082 PMCID: PMC6370462 DOI: 10.1162/netn_a_00062] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Accepted: 06/08/2018] [Indexed: 01/05/2023] Open
Abstract
In the past two decades, functional Magnetic Resonance Imaging (fMRI) has been used to relate neuronal network activity to cognitive processing and behavior. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this paper, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, Linear Non-Gaussian Acyclic Models, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area.
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Affiliation(s)
- Natalia Z. Bielczyk
- Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
| | - Sebo Uithol
- Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
- Bernstein Centre for Computational Neuroscience, Charité Universitätsmedizin, Berlin, Germany
| | - Tim van Mourik
- Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
| | - Paul Anderson
- Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
- Faculty of Science, Radboud University Nijmegen, Nijmegen, the Netherlands
| | - Jeffrey C. Glennon
- Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
| | - Jan K. Buitelaar
- Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
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Billings JCW, Thompson GJ, Pan WJ, Magnuson ME, Medda A, Keilholz S. Disentangling Multispectral Functional Connectivity With Wavelets. Front Neurosci 2018; 12:812. [PMID: 30459548 PMCID: PMC6232345 DOI: 10.3389/fnins.2018.00812] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Accepted: 10/18/2018] [Indexed: 02/01/2023] Open
Abstract
The field of brain connectomics develops our understanding of the brain's intrinsic organization by characterizing trends in spontaneous brain activity. Linear correlations in spontaneous blood-oxygen level dependent functional magnetic resonance imaging (BOLD-fMRI) fluctuations are often used as measures of functional connectivity (FC), that is, as a quantity describing how similarly two brain regions behave over time. Given the natural spectral scaling of BOLD-fMRI signals, it may be useful to represent BOLD-fMRI as multiple processes occurring over multiple scales. The wavelet domain presents a transform space well suited to the examination of multiscale systems as the wavelet basis set is constructed from a self-similar rescaling of a time and frequency delimited kernel. In the present study, we utilize wavelet transforms to examine fluctuations in whole-brain BOLD-fMRI connectivity as a function of wavelet spectral scale in a sample (N = 31) of resting healthy human volunteers. Information theoretic criteria measure relatedness between spectrally-delimited FC graphs. Voxelwise comparisons of between-spectra graph structures illustrate the development of preferential functional networks across spectral bands.
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Affiliation(s)
- Jacob C W Billings
- Graduate Division of Biological and Biomedical Sciences - Program in Neuroscience, Emory University, Atlanta, GA, United States.,Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Garth J Thompson
- Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States.,iHuman Institute, ShanghaiTech University, Pudong, China
| | - Wen-Ju Pan
- Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Matthew E Magnuson
- Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Alessio Medda
- Aerospace Transportation and Advanced Systems, Georgia Tech Research Institute, Atlanta, GA, United States
| | - Shella Keilholz
- Graduate Division of Biological and Biomedical Sciences - Program in Neuroscience, Emory University, Atlanta, GA, United States.,Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
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Bielczyk NZ, Walocha F, Ebel PW, Haak KV, Llera A, Buitelaar JK, Glennon JC, Beckmann CF. Thresholding functional connectomes by means of mixture modeling. Neuroimage 2018; 171:402-414. [PMID: 29309896 PMCID: PMC5981009 DOI: 10.1016/j.neuroimage.2018.01.003] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2017] [Revised: 12/30/2017] [Accepted: 01/02/2018] [Indexed: 12/19/2022] Open
Abstract
Functional connectivity has been shown to be a very promising tool for studying the large-scale functional architecture of the human brain. In network research in fMRI, functional connectivity is considered as a set of pair-wise interactions between the nodes of the network. These interactions are typically operationalized through the full or partial correlation between all pairs of regional time series. Estimating the structure of the latent underlying functional connectome from the set of pair-wise partial correlations remains an open research problem though. Typically, this thresholding problem is approached by proportional thresholding, or by means of parametric or non-parametric permutation testing across a cohort of subjects at each possible connection. As an alternative, we propose a data-driven thresholding approach for network matrices on the basis of mixture modeling. This approach allows for creating subject-specific sparse connectomes by modeling the full set of partial correlations as a mixture of low correlation values associated with weak or unreliable edges in the connectome and a sparse set of reliable connections. Consequently, we propose to use alternative thresholding strategy based on the model fit using pseudo-False Discovery Rates derived on the basis of the empirical null estimated as part of the mixture distribution. We evaluate the method on synthetic benchmark fMRI datasets where the underlying network structure is known, and demonstrate that it gives improved performance with respect to the alternative methods for thresholding connectomes, given the canonical thresholding levels. We also demonstrate that mixture modeling gives highly reproducible results when applied to the functional connectomes of the visual system derived from the n-back Working Memory task in the Human Connectome Project. The sparse connectomes obtained from mixture modeling are further discussed in the light of the previous knowledge of the functional architecture of the visual system in humans. We also demonstrate that with use of our method, we are able to extract similar information on the group level as can be achieved with permutation testing even though these two methods are not equivalent. We demonstrate that with both of these methods, we obtain functional decoupling between the two hemispheres in the higher order areas of the visual cortex during visual stimulation as compared to the resting state, which is in line with previous studies suggesting lateralization in the visual processing. However, as opposed to permutation testing, our approach does not require inference at the cohort level and can be used for creating sparse connectomes at the level of a single subject.
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Affiliation(s)
- Natalia Z Bielczyk
- Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands; Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Geert Groteplein Zuid 10, 6525GA Nijmegen, The Netherlands.
| | - Fabian Walocha
- Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands; University of Osnabrück, Neuer Graben 29/Schloss, 49074 Osnabrück, Germany
| | - Patrick W Ebel
- Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands; Radboud University Nijmegen, Comeniuslaan 4, 6525 HP Nijmegen, The Netherlands
| | - Koen V Haak
- Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands; Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Geert Groteplein Zuid 10, 6525GA Nijmegen, The Netherlands
| | - Alberto Llera
- Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands; Radboud University Nijmegen, Comeniuslaan 4, 6525 HP Nijmegen, The Netherlands
| | - Jan K Buitelaar
- Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands; Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Geert Groteplein Zuid 10, 6525GA Nijmegen, The Netherlands
| | - Jeffrey C Glennon
- Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands; Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Geert Groteplein Zuid 10, 6525GA Nijmegen, The Netherlands
| | - Christian F Beckmann
- Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands; Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Geert Groteplein Zuid 10, 6525GA Nijmegen, The Netherlands; Oxford Centre for Functional MRI of the Brain, John Radcliffe Hospital, Oxford OX3 9DU, United Kingdom
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Bielczyk NZ, Llera A, Buitelaar JK, Glennon JC, Beckmann CF. The impact of hemodynamic variability and signal mixing on the identifiability of effective connectivity structures in BOLD fMRI. Brain Behav 2017; 7:e00777. [PMID: 28828228 PMCID: PMC5561328 DOI: 10.1002/brb3.777] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2017] [Accepted: 06/07/2017] [Indexed: 01/03/2023] Open
Abstract
PURPOSE Multiple computational studies have demonstrated that essentially all current analytical approaches to determine effective connectivity perform poorly when applied to synthetic functional Magnetic Resonance Imaging (fMRI) datasets. In this study, we take a theoretical approach to investigate the potential factors facilitating and hindering effective connectivity research in fMRI. MATERIALS AND METHODS In this work, we perform a simulation study with use of Dynamic Causal Modeling generative model in order to gain new insights on the influence of factors such as the slow hemodynamic response, mixed signals in the network and short time series, on the effective connectivity estimation in fMRI studies. RESULTS First, we perform a Linear Discriminant Analysis study and find that not the hemodynamics itself but mixed signals in the neuronal networks are detrimental to the signatures of distinct connectivity patterns. This result suggests that for statistical methods (which do not involve lagged signals), deconvolving the BOLD responses is not necessary, but at the same time, functional parcellation into Regions of Interest (ROIs) is essential. Second, we study the impact of hemodynamic variability on the inference with use of lagged methods. We find that the local hemodynamic variability provide with an upper bound on the success rate of the lagged methods. Furthermore, we demonstrate that upsampling the data to TRs lower than the TRs in state-of-the-art datasets does not influence the performance of the lagged methods. CONCLUSIONS Factors such as background scale-free noise and hemodynamic variability have a major impact on the performance of methods for effective connectivity research in functional Magnetic Resonance Imaging.
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Affiliation(s)
- Natalia Z. Bielczyk
- Donders Institute for Brain, Cognition and BehaviorNijmegenThe Netherlands
- Radboud University Nijmegen Medical CentreNijmegenThe Netherlands
| | - Alberto Llera
- Oxford Centre for Functional MRI of the BrainJohn Radcliffe HospitalOxfordUK
| | - Jan K. Buitelaar
- Donders Institute for Brain, Cognition and BehaviorNijmegenThe Netherlands
- Radboud University Nijmegen Medical CentreNijmegenThe Netherlands
| | - Jeffrey C. Glennon
- Donders Institute for Brain, Cognition and BehaviorNijmegenThe Netherlands
- Radboud University Nijmegen Medical CentreNijmegenThe Netherlands
| | - Christian F. Beckmann
- Donders Institute for Brain, Cognition and BehaviorNijmegenThe Netherlands
- Radboud University Nijmegen Medical CentreNijmegenThe Netherlands
- Oxford Centre for Functional MRI of the BrainJohn Radcliffe HospitalOxfordUK
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