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Gabusi I, Battocchio M, Bosticardo S, Schiavi S, Daducci A. Blurred streamlines: A novel representation to reduce redundancy in tractography. Med Image Anal 2024; 93:103101. [PMID: 38325156 DOI: 10.1016/j.media.2024.103101] [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: 10/20/2023] [Revised: 01/23/2024] [Accepted: 01/30/2024] [Indexed: 02/09/2024]
Abstract
Tractography is a powerful tool to study brain connectivity in vivo, but it is well known to suffer from an intrinsic trade-off between sensitivity and specificity. A critical - but usually underrated - parameter to choose that can heavily impact the quality of the estimates is the number of streamlines to be reconstructed for a given data set. In fact, sensitivity can be improved by generating more and more streamlines, as all real anatomical connections are likely reconstructed, but lots of false positives are inevitably introduced, too. Consequently, so-called tractography filtering techniques have become increasingly popular to get rid of these false positives and improve specificity. However, increasing number of streamlines introduces redundancy in tractography reconstructions, which may negatively impact the performance of filtering algorithms, especially those based on linear formulations. To address this problem, we introduce a novel streamlines representation, called "blurred streamlines", which drastically reduces the redundancy among streamlines by (i) clustering similar trajectories and (ii) spatially blurring the corresponding signal contributions. We tested the effectiveness of the blurred streamlines both on synthetic and in vivo data. Our results clearly show that this new representation is as accurate as state-of-the-art methods despite using only 5% of the input streamlines, thus significantly decreasing the computational complexity of filtering algorithms as well as storage requirements of the resulting reconstructions.
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Affiliation(s)
- Ilaria Gabusi
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy.
| | - Matteo Battocchio
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy; Sherbrooke Connectivity Imaging Laboratory (SCIL), Department of Computer Science, University of Sherbrooke, Sherbrooke, Québec, Canada
| | - Sara Bosticardo
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy; Translational Imaging in Neurology (ThINk), Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Simona Schiavi
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy; ASG Superconductors S.p.A., Genova, Italy
| | - Alessandro Daducci
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy
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2
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Cai LT, Moon J, Camacho PB, Anderson AT, Chwa WJ, Sutton BP, Markowitz AJ, Palacios EM, Rodriguez A, Manley GT, Shankar S, Bremer PT, Mukherjee P, Madduri RK. MaPPeRTrac: A Massively Parallel, Portable, and Reproducible Tractography Pipeline. Neuroinformatics 2024; 22:177-191. [PMID: 38446357 DOI: 10.1007/s12021-024-09650-0] [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: 01/29/2024] [Indexed: 03/07/2024]
Abstract
Large-scale diffusion MRI tractography remains a significant challenge. Users must orchestrate a complex sequence of instructions that requires many software packages with complex dependencies and high computational costs. We developed MaPPeRTrac, an edge-centric tractography pipeline that simplifies and accelerates this process in a wide range of high-performance computing (HPC) environments. It fully automates either probabilistic or deterministic tractography, starting from a subject's magnetic resonance imaging (MRI) data, including structural and diffusion MRI images, to the edge density image (EDI) of their structural connectomes. Dependencies are containerized with Singularity (now called Apptainer) and decoupled from code to enable rapid prototyping and modification. Data derivatives are organized with the Brain Imaging Data Structure (BIDS) to ensure that they are findable, accessible, interoperable, and reusable following FAIR principles. The pipeline takes full advantage of HPC resources using the Parsl parallel programming framework, resulting in the creation of connectome datasets of unprecedented size. MaPPeRTrac is publicly available and tested on commercial and scientific hardware, so it can accelerate brain connectome research for a broader user community. MaPPeRTrac is available at: https://github.com/LLNL/mappertrac .
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Affiliation(s)
- Lanya T Cai
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 185 Berry St., San Francisco, CA, 94107, USA
| | - Joseph Moon
- Lawrence Livermore National Laboratory, 7000 East Ave, Livermore, CA, 94550, USA
| | - Paul B Camacho
- Beckman Institute, University of Illinois at Urbana-Champaign, 405 N Mathews Ave, Urbana, IL, 61801, USA
| | - Aaron T Anderson
- Beckman Institute, University of Illinois at Urbana-Champaign, 405 N Mathews Ave, Urbana, IL, 61801, USA
| | - Won Jong Chwa
- Department of Radiology, Washington University in St. Louis, 510 S Kingshighway Blvd, St. Louis, MO, 63110, USA
| | - Bradley P Sutton
- Bioengineering Department, University of Illinois at Urbana-Champaign, 506 S Mathews Ave, Urbana, IL, 61801, USA
| | - Amy J Markowitz
- Department of Neurosurgery, University of California, San Francisco, 400 Parnassus Ave, San Francisco, CA, 94143, USA
| | - Eva M Palacios
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 185 Berry St., San Francisco, CA, 94107, USA
| | - Alexis Rodriguez
- Argonne National Laboratory, 9700 S Cass Ave, Lemont, IL, 60439, USA
| | - Geoffrey T Manley
- Department of Neurosurgery, University of California, San Francisco, 400 Parnassus Ave, San Francisco, CA, 94143, USA
| | - Shivsundaram Shankar
- Lawrence Livermore National Laboratory, 7000 East Ave, Livermore, CA, 94550, USA
| | - Peer-Timo Bremer
- Lawrence Livermore National Laboratory, 7000 East Ave, Livermore, CA, 94550, USA
| | - Pratik Mukherjee
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 185 Berry St., San Francisco, CA, 94107, USA.
| | - Ravi K Madduri
- Argonne National Laboratory, 9700 S Cass Ave, Lemont, IL, 60439, USA.
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3
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Marzetti L, Makkinayeri S, Pieramico G, Guidotti R, D'Andrea A, Roine T, Mutanen TP, Souza VH, Kičić D, Baldassarre A, Ermolova M, Pankka H, Ilmoniemi RJ, Ziemann U, Luca Romani G, Pizzella V. Towards real-time identification of large-scale brain states for improved brain state-dependent stimulation. Clin Neurophysiol 2024; 158:196-203. [PMID: 37827877 DOI: 10.1016/j.clinph.2023.09.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 08/04/2023] [Accepted: 09/13/2023] [Indexed: 10/14/2023]
Affiliation(s)
- Laura Marzetti
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy; Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Abruzzo, Italy.
| | - Saeed Makkinayeri
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Giulia Pieramico
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Roberto Guidotti
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Antea D'Andrea
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Timo Roine
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; Turku Brain and Mind Center, University of Turku, Turku, Finland; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University and Helsinki University Hospital, Helsinki, Finland
| | - Tuomas P Mutanen
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Victor H Souza
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University and Helsinki University Hospital, Helsinki, Finland
| | - Dubravko Kičić
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University and Helsinki University Hospital, Helsinki, Finland
| | - Antonello Baldassarre
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Maria Ermolova
- Hertie-Institute for Clinical Brain Research, Tübingen, Baden-Württemberg, Germany; Department of Neurology & Stroke, University of Tübingen, Tübingen, Baden-Württemberg, Germany
| | - Hanna Pankka
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Risto J Ilmoniemi
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Ulf Ziemann
- Hertie-Institute for Clinical Brain Research, Tübingen, Baden-Württemberg, Germany; Department of Neurology & Stroke, University of Tübingen, Tübingen, Baden-Württemberg, Germany
| | - Gian Luca Romani
- Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Vittorio Pizzella
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy; Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Abruzzo, Italy
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Bröhl T, Rings T, Pukropski J, von Wrede R, Lehnertz K. The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 3:1338864. [PMID: 38293249 PMCID: PMC10825060 DOI: 10.3389/fnetp.2023.1338864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 12/19/2023] [Indexed: 02/01/2024]
Abstract
Epilepsy is now considered a network disease that affects the brain across multiple levels of spatial and temporal scales. The paradigm shift from an epileptic focus-a discrete cortical area from which seizures originate-to a widespread epileptic network-spanning lobes and hemispheres-considerably advanced our understanding of epilepsy and continues to influence both research and clinical treatment of this multi-faceted high-impact neurological disorder. The epileptic network, however, is not static but evolves in time which requires novel approaches for an in-depth characterization. In this review, we discuss conceptual basics of network theory and critically examine state-of-the-art recording techniques and analysis tools used to assess and characterize a time-evolving human epileptic brain network. We give an account on current shortcomings and highlight potential developments towards an improved clinical management of epilepsy.
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Affiliation(s)
- Timo Bröhl
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Thorsten Rings
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Jan Pukropski
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Randi von Wrede
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
- Interdisciplinary Center for Complex Systems, University of Bonn, Bonn, Germany
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Tolonen T, Roine T, Alho K, Leppämäki S, Tani P, Koski A, Laine M, Salmi J. Abnormal wiring of the structural connectome in adults with ADHD. Netw Neurosci 2023; 7:1302-1325. [PMID: 38144696 PMCID: PMC10631790 DOI: 10.1162/netn_a_00326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 06/19/2023] [Indexed: 12/26/2023] Open
Abstract
Current knowledge of white matter changes in large-scale brain networks in adult attention-deficit/hyperactivity disorder (ADHD) is scarce. We collected diffusion-weighted magnetic resonance imaging data in 40 adults with ADHD and 36 neurotypical controls and used constrained spherical deconvolution-based tractography to reconstruct whole-brain structural connectivity networks. We used network-based statistic (NBS) and graph theoretical analysis to investigate differences in these networks between the ADHD and control groups, as well as associations between structural connectivity and ADHD symptoms assessed with the Adult ADHD Self-Report Scale or performance in the Conners Continuous Performance Test 2 (CPT-2). NBS revealed decreased connectivity in the ADHD group compared to the neurotypical controls in widespread unilateral networks, which included subcortical and corticocortical structures and encompassed dorsal and ventral attention networks and visual and somatomotor systems. Furthermore, hypoconnectivity in a predominantly left-frontal network was associated with higher amount of commission errors in CPT-2. Graph theoretical analysis did not reveal topological differences between the groups or associations between topological properties and ADHD symptoms or task performance. Our results suggest that abnormal structural wiring of the brain in adult ADHD is manifested as widespread intrahemispheric hypoconnectivity in networks previously associated with ADHD in functional neuroimaging studies.
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Affiliation(s)
- Tuija Tolonen
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
| | - Timo Roine
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
- Turku Brain and Mind Center, University of Turku, Turku, Finland
| | - Kimmo Alho
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
- AMI Centre, Aalto Neuroimaging, Aalto University, Espoo, Finland
| | | | - Pekka Tani
- Department of Psychiatry, Helsinki University Hospital, Helsinki, Finland
| | - Anniina Koski
- Department of Psychiatry, Helsinki University Hospital, Helsinki, Finland
| | - Matti Laine
- Turku Brain and Mind Center, University of Turku, Turku, Finland
- Department of Psychology, Åbo Akademi University, Turku, Finland
| | - Juha Salmi
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
- AMI Centre, Aalto Neuroimaging, Aalto University, Espoo, Finland
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Chen Q, Abrigo J, Deng M, Shi L, Wang YX, Chu WC. Structural Network Topology Reveals Higher Brain Resilience in Individuals with Preclinical Alzheimer's Disease. Brain Connect 2023; 13:553-562. [PMID: 37551987 PMCID: PMC10771874 DOI: 10.1089/brain.2023.0013] [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] [Indexed: 08/09/2023] Open
Abstract
Introduction: The diagnosis of Alzheimer's disease (AD) requires the presence of amyloid and tau pathology, but it remains unclear how they affect the structural network in the pre-clinical stage. We aimed to assess differences in topological properties in cognitively normal (CN) individuals with varying levels of amyloid and tau pathology, as well as their association with AD pathology burden. Methods: A total of 68 CN individuals were included and stratified by normal/abnormal (-/+) amyloid (A) and tau (T) status based on positron emission tomography results, yielding three groups: A-T- (n = 19), A+T- (n = 28), and A+T+ (n = 21). Topological properties were measured from structural connectivity. Group differences and correlations with A and T were evaluated. Results: Compared with the A-T- group, the A+T+ group exhibited changes in the structural network topology. At the global level, higher assortativity was shown in the A+T+ group and was correlated with greater tau burden (r = 0.29, p = 0.02), while no difference in global efficiency was found across the three groups. At the local level, the A+T+ group showed disrupted topological properties in the left hippocampus compared with the A-T- group, characterized by lower local efficiency (p < 0.01) and a lower clustering coefficient (p = 0.014). Conclusions: The increased linkage in the higher level architecture of the white matter network reflected by assortativity may indicate increased brain resilience in the early pathological state. Our results encourage further investigation of the topological properties of the structural network in pre-clinical AD.
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Affiliation(s)
- Qianyun Chen
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Jill Abrigo
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Min Deng
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Lin Shi
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Yi-Xiang Wang
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Winnie C.W. Chu
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
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Borrelli P, Savini G, Cavaliere C, Palesi F, Grazia Bruzzone M, Aquino D, Biagi L, Bosco P, Carne I, Ferraro S, Giulietti G, Napolitano A, Nigri A, Pavone L, Pirastru A, Redolfi A, Tagliavini F, Tosetti M, Salvatore M, Gandini Wheeler-Kingshott CAM, Aiello M. Normative values of the topological metrics of the structural connectome: A multi-site reproducibility study across the Italian Neuroscience network. Phys Med 2023; 112:102610. [PMID: 37331082 DOI: 10.1016/j.ejmp.2023.102610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 03/20/2023] [Accepted: 05/30/2023] [Indexed: 06/20/2023] Open
Abstract
PURPOSE The use of topological metrics to derive quantitative descriptors from structural connectomes is receiving increasing attention but deserves specific studies to investigate their reproducibility and variability in the clinical context. This work exploits the harmonization of diffusion-weighted acquisition for neuroimaging data performed by the Italian Neuroscience and Neurorehabilitation Network initiative to obtain normative values of topological metrics and to investigate their reproducibility and variability across centers. METHODS Different topological metrics, at global and local level, were calculated on multishell diffusion-weighted data acquired at high-field (e.g. 3 T) Magnetic Resonance Imaging scanners in 13 different centers, following the harmonization of the acquisition protocol, on young and healthy adults. A "traveling brains" dataset acquired on a subgroup of subjects at 3 different centers was also analyzed as reference data. All data were processed following a common processing pipeline that includes data pre-processing, tractography, generation of structural connectomes and calculation of graph-based metrics. The results were evaluated both with statistical analysis of variability and consistency among sites with the traveling brains range. In addition, inter-site reproducibility was assessed in terms of intra-class correlation variability. RESULTS The results show an inter-center and inter-subject variability of <10%, except for "clustering coefficient" (variability of 30%). Statistical analysis identifies significant differences among sites, as expected given the wide range of scanners' hardware. CONCLUSIONS The results show low variability of connectivity topological metrics across sites running a harmonised protocol.
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Affiliation(s)
| | | | | | - Fulvia Palesi
- Department of Brain and Behavioral Sciences, Università degli Studi di Pavia, Pavia, Italy
| | - Maria Grazia Bruzzone
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Domenico Aquino
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Laura Biagi
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris Foundation, Pisa, Italy
| | - Paolo Bosco
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris Foundation, Pisa, Italy
| | - Irene Carne
- Neuroradiology Unit, IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy
| | - Stefania Ferraro
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Giovanni Giulietti
- Neuroimaging Laboratory, IRCCS Santa Lucia Foundation, Rome, Italy; SAIMLAL Department, Sapienza University of Rome, Rome, Italy
| | - Antonio Napolitano
- Medical Physics, IRCCS Istituto Ospedale Pediatrico Bambino Gesù, Rome, Italy
| | - Anna Nigri
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | | | | | - Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Fabrizio Tagliavini
- Scientific Direction, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Michela Tosetti
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris Foundation, Pisa, Italy
| | | | - Claudia A M Gandini Wheeler-Kingshott
- Department of Brain and Behavioral Sciences, Università degli Studi di Pavia, Pavia, Italy; NMR Research Unit, Department of Neuroinflammation, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
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8
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Liu R, Li M, Dunson DB. PPA: Principal parcellation analysis for brain connectomes and multiple traits. Neuroimage 2023; 276:120214. [PMID: 37286151 DOI: 10.1016/j.neuroimage.2023.120214] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 05/31/2023] [Indexed: 06/09/2023] Open
Abstract
Our understanding of the structure of the brain and its relationships with human traits is largely determined by how we represent the structural connectome. Standard practice divides the brain into regions of interest (ROIs) and represents the connectome as an adjacency matrix having cells measuring connectivity between pairs of ROIs. Statistical analyses are then heavily driven by the (largely arbitrary) choice of ROIs. In this article, we propose a human trait prediction framework utilizing a tractography-based representation of the brain connectome, which clusters fiber endpoints to define a data-driven white matter parcellation targeted to explain variation among individuals and predict human traits. This leads to Principal Parcellation Analysis (PPA), representing individual brain connectomes by compositional vectors building on a basis system of fiber bundles that captures the connectivity at the population level. PPA eliminates the need to choose atlases and ROIs a priori, and provides a simpler, vector-valued representation that facilitates easier statistical analysis compared to the complex graph structures encountered in classical connectome analyses. We illustrate the proposed approach through applications to data from the Human Connectome Project (HCP) and show that PPA connectomes improve power in predicting human traits over state-of-the-art methods based on classical connectomes, while dramatically improving parsimony and maintaining interpretability. Our PPA package is publicly available on GitHub, and can be implemented routinely for diffusion image data.
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Affiliation(s)
- Rongjie Liu
- Department of Statistics, Florida State University, Tallahassee, FL, USA
| | - Meng Li
- Department of Statistics, Rice University, Houston, TX, USA.
| | - David B Dunson
- Department of Statistical Science, Duke University, Durham, NC, USA
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9
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Lloyd EC, Foerde KE, Muratore AF, Aw N, Semanek D, Steinglass JE, Posner J. Large-Scale Exploration of Whole-Brain Structural Connectivity in Anorexia Nervosa: Alterations in the Connectivity of Frontal and Subcortical Networks. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:864-873. [PMID: 35714857 PMCID: PMC11060509 DOI: 10.1016/j.bpsc.2022.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 05/27/2022] [Accepted: 06/01/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Anorexia nervosa (AN) is characterized by disturbances in cognition and behavior surrounding eating and weight. The severity of AN combined with the absence of localized brain abnormalities suggests distributed, systemic underpinnings that may be identified using diffusion-weighted magnetic resonance imaging and tractography to reconstruct white matter pathways. METHODS Diffusion-weighted magnetic resonance imaging data acquired from female patients with AN (n= 147) and female healthy control (HC) participants (n = 119), ages 12 to 40 years, were combined across 5 studies. Probabilistic tractography was completed, and full-cortex connectomes describing streamline counts between 84 brain regions were generated and harmonized. Graph theory methods were used to describe alterations in network organization in AN. The network-based statistic tested between-group differences in brain subnetwork connectivity. The metrics strength and efficiency indexed the connectivity of brain regions (network nodes) and were compared between groups using multiple linear regression. RESULTS Individuals with AN, relative to HC peers, had reduced connectivity in a network comprising subcortical regions and greater connectivity between frontal cortical regions (p < .05, familywise error corrected). Node-based analyses indicated reduced connectivity of the left hippocampus in patients relative to HC peers (p < .05, permutation corrected). Severity of illness, assessed by body mass index, was associated with subcortical connectivity (p < .05, uncorrected). CONCLUSIONS Analyses identified reduced structural connectivity of subcortical networks and regions, and stronger cortical network connectivity, among individuals with AN relative to HC peers. These findings are consistent with alterations in feeding, emotion, and executive control circuits in AN, and may direct hypothesis-driven research into mechanisms of persistent restrictive eating behavior.
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Affiliation(s)
- E Caitlin Lloyd
- Department of Psychiatry, Columbia University Irving Medical Center, New York, New York; New York State Psychiatric Institute, New York, New York.
| | - Karin E Foerde
- Department of Psychiatry, Columbia University Irving Medical Center, New York, New York; New York State Psychiatric Institute, New York, New York; Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands
| | - Alexandra F Muratore
- Department of Psychiatry, Columbia University Irving Medical Center, New York, New York; New York State Psychiatric Institute, New York, New York
| | - Natalie Aw
- Department of Psychiatry, Columbia University Irving Medical Center, New York, New York; New York State Psychiatric Institute, New York, New York
| | - David Semanek
- Department of Psychiatry, Columbia University Irving Medical Center, New York, New York; New York State Psychiatric Institute, New York, New York
| | - Joanna E Steinglass
- Department of Psychiatry, Columbia University Irving Medical Center, New York, New York; New York State Psychiatric Institute, New York, New York
| | - Jonathan Posner
- Department of Psychiatry, Duke University, Durham, North Carolina
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10
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Madole JW, Buchanan CR, Rhemtulla M, Ritchie SJ, Bastin ME, Deary IJ, Cox SR, Tucker-Drob EM. Strong intercorrelations among global graph-theoretic indices of structural connectivity in the human brain. Neuroimage 2023; 275:120160. [PMID: 37169117 DOI: 10.1016/j.neuroimage.2023.120160] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 04/06/2023] [Accepted: 05/08/2023] [Indexed: 05/13/2023] Open
Abstract
Graph-theoretic metrics derived from neuroimaging data have been heralded as powerful tools for uncovering neural mechanisms of psychological traits, psychiatric disorders, and neurodegenerative diseases. In N = 8,185 human structural connectomes from UK Biobank, we examined the extent to which 11 commonly-used global graph-theoretic metrics index distinct versus overlapping information with respect to interindividual differences in brain organization. Using unthresholded, FA-weighted networks we found that all metrics other than Participation Coefficient were highly intercorrelated, both with each other (mean |r| = 0.788) and with a topologically-naïve summary index of brain structure (mean edge weight; mean |r| = 0.873). In a series of sensitivity analyses, we found that overlap between metrics is influenced by the sparseness of the network and the magnitude of variation in edge weights. Simulation analyses representing a range of population network structures indicated that individual differences in global graph metrics may be intrinsically difficult to separate from mean edge weight. In particular, Closeness, Characteristic Path Length, Global Efficiency, Clustering Coefficient, and Small Worldness were nearly perfectly collinear with one another (mean |r| = 0.939) and with mean edge weight (mean |r| = 0.952) across all observed and simulated conditions. Global graph-theoretic measures are valuable for their ability to distill a high-dimensional system of neural connections into summary indices of brain organization, but they may be of more limited utility when the goal is to index separable components of interindividual variation in specific properties of the human structural connectome.
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Affiliation(s)
- James W Madole
- Department of Psychology, University of Texas at Austin, Austin, TX, USA; VA Puget Sound Health Care System, Seattle Division, Seattle, WA, USA.
| | - Colin R Buchanan
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK; Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| | - Mijke Rhemtulla
- Department of Psychology, University of California, Davis, CA, USA
| | - Stuart J Ritchie
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK
| | - Mark E Bastin
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK; Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Ian J Deary
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Simon R Cox
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK; Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| | - Elliot M Tucker-Drob
- Department of Psychology, University of Texas at Austin, Austin, TX, USA; Population Research Center and Center on Aging and Population Sciences, University of Texas at Austin, Austin, TX, USA
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11
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Cai LY, Lee HH, Newlin NR, Kerley CI, Kanakaraj P, Yang Q, Johnson GW, Moyer D, Schilling KG, Rheault FC, Landman BA. Convolutional-recurrent neural networks approximate diffusion tractography from T1-weighted MRI and associated anatomical context. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.25.530046. [PMID: 36909466 PMCID: PMC10002661 DOI: 10.1101/2023.02.25.530046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/04/2023]
Abstract
Diffusion MRI (dMRI) streamline tractography is the gold-standard for in vivo estimation of white matter (WM) pathways in the brain. However, the high angular resolution dMRI acquisitions capable of fitting the microstructural models needed for tractography are often time-consuming and not routinely collected clinically, restricting the scope of tractography analyses. To address this limitation, we build on recent advances in deep learning which have demonstrated that streamline propagation can be learned from dMRI directly without traditional model fitting. Specifically, we propose learning the streamline propagator from T1w MRI to facilitate arbitrary tractography analyses when dMRI is unavailable. To do so, we present a novel convolutional-recurrent neural network (CoRNN) trained in a teacher-student framework that leverages T1w MRI, associated anatomical context, and streamline memory from data acquired for the Human Connectome Project. We characterize our approach under two common tractography paradigms, WM bundle analysis and structural connectomics, and find approximately a 5-15% difference between measures computed from streamlines generated with our approach and those generated using traditional dMRI tractography. When placed in the literature, these results suggest that the accuracy of WM measures computed from T1w MRI with our method is on the level of scan-rescan dMRI variability and raise an important question: is tractography truly a microstructural phenomenon, or has dMRI merely facilitated its discovery and implementation?
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Affiliation(s)
- Leon Y Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Ho Hin Lee
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Nancy R Newlin
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Cailey I Kerley
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | | | - Qi Yang
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Graham W Johnson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Daniel Moyer
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Kurt G Schilling
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Fran Cois Rheault
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Bennett A Landman
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
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12
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Gao Z, Xiao Y, Zhu F, Tao B, Yu W, Lui S. The whole-brain connectome landscape in patients with schizophrenia: a systematic review and meta-analysis of graph theoretical characteristics. Neurosci Biobehav Rev 2023; 148:105144. [PMID: 36990373 DOI: 10.1016/j.neubiorev.2023.105144] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 03/14/2023] [Accepted: 03/24/2023] [Indexed: 03/29/2023]
Abstract
The alterations of connectome in schizophrenia have been reported, but the results remain inconsistent. We conducted a systematic review and random-effects meta-analysis on structural or functional connectome MRI studies comparing global graph theoretical characteristics between schizophrenia and healthy controls. Meta-regression and subgroup analyses were performed to examine confounding effects. Based on the included 48 studies, Structural connectome in schizophrenia showed a significant decrease in segregation (lower clustering coefficient and local efficiency, Hedge's g= -0.352 and -0.864, respectively) and integration (higher characteristic path length and lower global efficiency, Hedge's g= 0.532 and -0.577 respectively). The functional connectome showed no difference between groups except γ. Moderator analysis indicated that clinical and methodological factors exerted a potential effect on the graph theoretical characteristics. Our analysis revealed a weaker small-worldization trend in structural connectome of schizophrenia. For the relatively unchanged functional connectome, more homogenous and high-quality studies are warranted to elucidate whether the change was blurred by heterogeneity or the presentation of pathophysiological reconfiguration.
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13
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Roine U, Tokola AM, Autti T, Roine T. Topological Structural Brain Connectivity Alterations in Aspartylglucosaminuria: A Case-Control Study. AJNR Am J Neuroradiol 2023; 44:40-46. [PMID: 36549851 PMCID: PMC9835915 DOI: 10.3174/ajnr.a7745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 11/16/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND AND PURPOSE We investigated global and local properties of the structural brain connectivity networks in aspartylglucosaminuria, an autosomal recessive and progressive neurodegenerative lysosomal storage disease. Brain connectivity in aspartylglucosaminuria has not been investigated before, but previous structural MR imaging studies have shown brain atrophy, delayed myelination, and decreased thalamic and increased periventricular WM T2 signal intensity. MATERIALS AND METHODS We acquired diffusion MR imaging and T1-weighted data from 12 patients with aspartylglucosaminuria (mean age, 23 [SD, 8] years; 5 men), and 30 healthy controls (mean age, 25 [SD, 10] years; 13 men). We performed whole-brain constrained spherical deconvolution tractography, which enables the reconstruction of neural tracts through regions with complex fiber configurations, and used graph-theoretical analysis to investigate the structural brain connectivity networks. RESULTS The integration of the networks was decreased, as demonstrated by a decreased normalized global efficiency and an increased normalized characteristic path length. In addition, the average strength of the networks was decreased. In the local analyses, we found decreased strength in 11 nodes, including, for example, the right thalamus, right putamen, and, bilaterally, several occipital and temporal regions. CONCLUSIONS We found global and local structural connectivity alterations in aspartylglucosaminuria. Biomarkers related to the treatment efficacy are needed, and brain network properties may provide the means for long term follow-up.
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Affiliation(s)
- U Roine
- From the Department of Radiology (U.R., A.M.T., T.A., T.R.), HUS Medical Imaging Center
- Department of Pediatric Neurology (U.R.), Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - A M Tokola
- From the Department of Radiology (U.R., A.M.T., T.A., T.R.), HUS Medical Imaging Center
| | - T Autti
- From the Department of Radiology (U.R., A.M.T., T.A., T.R.), HUS Medical Imaging Center
| | - T Roine
- From the Department of Radiology (U.R., A.M.T., T.A., T.R.), HUS Medical Imaging Center
- Department of Neuroscience and Biomedical Engineering (T.R.), Aalto University School of Science, Espoo, Finland
- Turku Brain and Mind Center (T.R.), University of Turku, Turku, Finland
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14
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Mirza-Davies A, Foley S, Caseras X, Baker E, Holmans P, Escott-Price V, Jones DK, Harrison JR, Messaritaki E. The impact of genetic risk for Alzheimer's disease on the structural brain networks of young adults. Front Neurosci 2022; 16:987677. [PMID: 36532292 PMCID: PMC9748570 DOI: 10.3389/fnins.2022.987677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 11/09/2022] [Indexed: 12/02/2022] Open
Abstract
Introduction We investigated the structural brain networks of 562 young adults in relation to polygenic risk for Alzheimer's disease, using magnetic resonance imaging (MRI) and genotype data from the Avon Longitudinal Study of Parents and Children. Methods Diffusion MRI data were used to perform whole-brain tractography and generate structural brain networks for the whole-brain connectome, and for the default mode, limbic and visual subnetworks. The mean clustering coefficient, mean betweenness centrality, characteristic path length, global efficiency and mean nodal strength were calculated for these networks, for each participant. The connectivity of the rich-club, feeder and local connections was also calculated. Polygenic risk scores (PRS), estimating each participant's genetic risk, were calculated at genome-wide level and for nine specific disease pathways. Correlations were calculated between the PRS and (a) the graph theoretical metrics of the structural networks and (b) the rich-club, feeder and local connectivity of the whole-brain networks. Results In the visual subnetwork, the mean nodal strength was negatively correlated with the genome-wide PRS (r = -0.19, p = 1.4 × 10-3), the mean betweenness centrality was positively correlated with the plasma lipoprotein particle assembly PRS (r = 0.16, p = 5.5 × 10-3), and the mean clustering coefficient was negatively correlated with the tau-protein binding PRS (r = -0.16, p = 0.016). In the default mode network, the mean nodal strength was negatively correlated with the genome-wide PRS (r = -0.14, p = 0.044). The rich-club and feeder connectivities were negatively correlated with the genome-wide PRS (r = -0.16, p = 0.035; r = -0.15, p = 0.036). Discussion We identified small reductions in brain connectivity in young adults at risk of developing Alzheimer's disease in later life.
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Affiliation(s)
- Anastasia Mirza-Davies
- School of Medicine, University Hospital Wales, Cardiff University, Cardiff, United Kingdom
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Sonya Foley
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Xavier Caseras
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom
| | - Emily Baker
- UK Dementia Research Institute, Cardiff University, Cardiff, United Kingdom
| | - Peter Holmans
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom
| | - Valentina Escott-Price
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom
- UK Dementia Research Institute, Cardiff University, Cardiff, United Kingdom
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Judith R. Harrison
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
- Institute for Translational and Clinical Research, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Eirini Messaritaki
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
- BRAIN Biomedical Research Unit, School of Medicine, Cardiff University, Cardiff, United Kingdom
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15
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Seider NA, Adeyemo B, Miller R, Newbold DJ, Hampton JM, Scheidter KM, Rutlin J, Laumann TO, Roland JL, Montez DF, Van AN, Zheng A, Marek S, Kay BP, Bretthorst GL, Schlaggar BL, Greene DJ, Wang Y, Petersen SE, Barch DM, Gordon EM, Snyder AZ, Shimony JS, Dosenbach NUF. Accuracy and reliability of diffusion imaging models. Neuroimage 2022; 254:119138. [PMID: 35339687 PMCID: PMC9841915 DOI: 10.1016/j.neuroimage.2022.119138] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 03/01/2022] [Accepted: 03/22/2022] [Indexed: 01/19/2023] Open
Abstract
Diffusion imaging aims to non-invasively characterize the anatomy and integrity of the brain's white matter fibers. We evaluated the accuracy and reliability of commonly used diffusion imaging methods as a function of data quantity and analysis method, using both simulations and highly sampled individual-specific data (927-1442 diffusion weighted images [DWIs] per individual). Diffusion imaging methods that allow for crossing fibers (FSL's BedpostX [BPX], DSI Studio's Constant Solid Angle Q-Ball Imaging [CSA-QBI], MRtrix3's Constrained Spherical Deconvolution [CSD]) estimated excess fibers when insufficient data were present and/or when the data did not match the model priors. To reduce such overfitting, we developed a novel Bayesian Multi-tensor Model-selection (BaMM) method and applied it to the popular ball-and-stick model used in BedpostX within the FSL software package. BaMM was robust to overfitting and showed high reliability and the relatively best crossing-fiber accuracy with increasing amounts of diffusion data. Thus, sufficient data and an overfitting resistant analysis method enhance precision diffusion imaging. For potential clinical applications of diffusion imaging, such as neurosurgical planning and deep brain stimulation (DBS), the quantities of data required to achieve diffusion imaging reliability are lower than those needed for functional MRI.
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Affiliation(s)
- Nicole A Seider
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Babatunde Adeyemo
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Ryland Miller
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Dillan J Newbold
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Neurology, New York University Langone Medical Center, New York, NY 10016, United States of America
| | - Jacqueline M Hampton
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Kristen M Scheidter
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Jerrel Rutlin
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Timothy O Laumann
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Jarod L Roland
- Department of Neurological Surgery, Washington University School of Medicine, St Louis, MO 63110 United States of America
| | - David F Montez
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Andrew N Van
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Biomedical Engineering, Washington University in St Louis, St. Louis, MO 63110, United States of America
| | - Annie Zheng
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Scott Marek
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Benjamin P Kay
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - G Larry Bretthorst
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Chemistry, Washington University in St Louis, St. Louis, MO 63110, United States of America
| | - Bradley L Schlaggar
- Kennedy Krieger Institute, Baltimore, MD 21205, United States of America; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, United States of America; Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD 21287, United States of America
| | - Deanna J Greene
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, United States of America
| | - Yong Wang
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Biomedical Engineering, Washington University in St Louis, St. Louis, MO 63110, United States of America
| | - Steven E Petersen
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Biomedical Engineering, Washington University in St Louis, St. Louis, MO 63110, United States of America; Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Psychological and Brain Sciences, Washington University in St. Louis, MO 63110, United States of America
| | - Deanna M Barch
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Psychological and Brain Sciences, Washington University in St. Louis, MO 63110, United States of America
| | - Evan M Gordon
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Abraham Z Snyder
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Joshua S Shimony
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Nico U F Dosenbach
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Biomedical Engineering, Washington University in St Louis, St. Louis, MO 63110, United States of America; Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, United States of America
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16
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Koch PJ, Girard G, Brügger J, Cadic-Melchior AG, Beanato E, Park CH, Morishita T, Wessel MJ, Pizzolato M, Canales-Rodríguez EJ, Fischi-Gomez E, Schiavi S, Daducci A, Piredda GF, Hilbert T, Kober T, Thiran JP, Hummel FC. Evaluating reproducibility and subject-specificity of microstructure-informed connectivity. Neuroimage 2022; 258:119356. [PMID: 35659995 DOI: 10.1016/j.neuroimage.2022.119356] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 05/01/2022] [Accepted: 06/01/2022] [Indexed: 11/30/2022] Open
Abstract
Tractography enables identifying and evaluating the healthy and diseased brain's white matter pathways from diffusion-weighted magnetic resonance imaging data. As previous evaluation studies have reported significant false-positive estimation biases, recent microstructure-informed tractography algorithms have been introduced to improve the trade-off between specificity and sensitivity. However, a major limitation for characterizing the performance of these techniques is the lack of ground truth brain data. In this study, we compared the performance of two relevant microstructure-informed tractography methods, SIFT2 and COMMIT, by assessing the subject specificity and reproducibility of their derived white matter pathways. Specifically, twenty healthy young subjects were scanned at eight different time points at two different sites. Subject specificity and reproducibility were evaluated using the whole-brain connectomes and a subset of 29 white matter bundles. Our results indicate that although the raw tractograms are more vulnerable to the presence of false-positive connections, they are highly reproducible, suggesting that the estimation bias is subject-specific. This high reproducibility was preserved when microstructure-informed tractography algorithms were used to filter the raw tractograms. Moreover, the resulting track-density images depicted a more uniform coverage of streamlines throughout the white matter, suggesting that these techniques could increase the biological meaning of the estimated fascicles. Notably, we observed an increased subject specificity by employing connectivity pre-processing techniques to reduce the underlaying noise and the data dimensionality (using principal component analysis), highlighting the importance of these tools for future studies. Finally, no strong bias from the scanner site or time between measurements was found. The largest intraindividual variance originated from the sole repetition of data measurements (inter-run).
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Affiliation(s)
- Philipp J Koch
- Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland; Department of Neurology, University of Lübeck, 23562 Lübeck, Germany; Center of Brain, Behavior and Metabolism (CBBM), University of Lübeck, 23562 Lübeck, Germany
| | - Gabriel Girard
- CIBM Center for Biomedical Imaging, Switzerland; Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland.
| | - Julia Brügger
- Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland
| | - Andéol G Cadic-Melchior
- Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland
| | - Elena Beanato
- Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland
| | - Chang-Hyun Park
- Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland
| | - Takuya Morishita
- Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland
| | - Maximilian J Wessel
- Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland; Department of Neurology, Julius-Maximilians-University Würzburg, Würzburg, Germany
| | - Marco Pizzolato
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark
| | | | - Elda Fischi-Gomez
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland; Translational Machine Learning Lab, Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
| | - Simona Schiavi
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy; Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Alessandro Daducci
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy
| | - Gian Franco Piredda
- Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland; Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
| | - Tom Hilbert
- Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland; Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
| | - Tobias Kober
- Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland; Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
| | - Jean-Philippe Thiran
- CIBM Center for Biomedical Imaging, Switzerland; Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland
| | - Friedhelm C Hummel
- Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland; Clinical Neuroscience, University Hospital of Geneva (HUG), Geneva, Switzerland
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17
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De Brito Robalo BM, Vlegels N, Leemans A, Reijmer YD, Biessels GJ. Impact of thresholding on the consistency and sensitivity of diffusion MRI-based brain networks in patients with cerebral small vessel disease. Brain Behav 2022; 12:e2523. [PMID: 35413156 PMCID: PMC9120729 DOI: 10.1002/brb3.2523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 12/21/2021] [Accepted: 01/25/2022] [Indexed: 11/10/2022] Open
Abstract
INTRODUCTION Thresholding of low-weight connections of diffusion MRI-based brain networks has been proposed to remove false-positive connections. It has been previously established that this yields more reproducible scan-rescan network architecture in healthy subjects. In patients with brain disease, network measures are applied to assess inter-individual variation and changes over time. Our aim was to investigate whether thresholding also achieves improved consistency in network architecture in patients, while maintaining sensitivity to disease effects for these applications. METHODS We applied fixed-density and absolute thresholding on brain networks in patients with cerebral small vessel disease (SVD, n = 86; ≈24 months follow-up), as a clinically relevant exemplar condition. In parallel, we applied the same methods in healthy young subjects (n = 44; scan-rescan interval ≈4 months) as a frame of reference. Consistency of network architecture was assessed with dice similarity of edges and intraclass correlation coefficient (ICC) of edge-weights and hub-scores. Sensitivity to disease effects in patients was assessed by evaluating interindividual variation, changes over time, and differences between those with high and low white matter hyperintensity burden, using correlation analyses and mixed ANOVA. RESULTS Compared to unthresholded networks, both thresholding methods generated more consistent architecture over time in patients (unthresholded: dice = .70; ICC: .70-.78; thresholded: dice = .77; ICC: .73-.83). However, absolute thresholding created fragmented nodes. Similar observations were made in the reference group. Regarding sensitivity to disease effects in patients, fixed-density thresholds that were optimal in terms of consistency (densities: .10-.30) preserved interindividual variation in global efficiency and node strength as well as the sensitivity to detect effects of time and group. Absolute thresholding produced larger fluctuations of interindividual variation. CONCLUSIONS Our results indicate that thresholding of low-weight connections, particularly when using fixed-density thresholding, results in more consistent network architecture in patients with longer rescan intervals, while preserving sensitivity to disease effects.
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Affiliation(s)
- Bruno M De Brito Robalo
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Naomi Vlegels
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Alexander Leemans
- PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Yael D Reijmer
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Geert Jan Biessels
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
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18
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Fekonja LS, Wang Z, Cacciola A, Roine T, Aydogan DB, Mewes D, Vellmer S, Vajkoczy P, Picht T. Network analysis shows decreased ipsilesional structural connectivity in glioma patients. Commun Biol 2022; 5:258. [PMID: 35322812 PMCID: PMC8943189 DOI: 10.1038/s42003-022-03190-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 02/22/2022] [Indexed: 11/15/2022] Open
Abstract
Gliomas that infiltrate networks and systems, such as the motor system, often lead to substantial functional impairment in multiple systems. Network-based statistics (NBS) allow to assess local network differences and graph theoretical analyses enable investigation of global and local network properties. Here, we used network measures to characterize glioma-related decreases in structural connectivity by comparing the ipsi- with the contralesional hemispheres of patients and correlated findings with neurological assessment. We found that lesion location resulted in differential impairment of both short and long connectivity patterns. Network analysis showed reduced global and local efficiency in the ipsilesional hemisphere compared to the contralesional hemispheric networks, which reflect the impairment of information transfer across different regions of a network. Tumors and their location distinctly alter both local and global brain connectivity within the ipsilesional hemisphere of glioma patients.
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Affiliation(s)
- Lucius S Fekonja
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Berlin, Germany. .,Cluster of Excellence: "Matters of Activity. Image Space Material", Humboldt University, Berlin, Germany.
| | - Ziqian Wang
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Alberto Cacciola
- Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy
| | - Timo Roine
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland.,Turku Brain and Mind Center, University of Turku, Turku, Finland
| | - D Baran Aydogan
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland.,Department of Psychiatry, Helsinki University and Helsinki University Hospital, Helsinki, Finland.,A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Darius Mewes
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Sebastian Vellmer
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Peter Vajkoczy
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Thomas Picht
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Cluster of Excellence: "Matters of Activity. Image Space Material", Humboldt University, Berlin, Germany
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19
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Roine T, Mohammadian M, Hirvonen J, Kurki T, Posti JP, Takala RS, Newcombe V, Tallus J, Katila AJ, Maanpää HR, Frantzen J, Menon D, Tenovuo O. Structural brain connectivity correlates with outcome in mild traumatic brain injury. J Neurotrauma 2022; 39:336-347. [PMID: 35018829 DOI: 10.1089/neu.2021.0093] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
We investigated the topology of structural brain connectivity networks and its association to outcome following mild traumatic brain injury, a major cause of permanent disability. Eighty-five patients with mild traumatic brain injury underwent MRI twice, about three weeks and eight months after injury, and 30 age-matched orthopedic trauma control subjects were scanned. Outcome was assessed with Extended Glasgow Outcome Scale on average eight months after injury. We performed constrained spherical deconvolution based probabilistic streamlines tractography on diffusion MRI data and parcellated cortical and subcortical gray matter into 84 regions based on T1-weighted data to reconstruct structural brain connectivity networks weighted by the number of streamlines. Graph theoretical methods were employed to measure network properties in both patients and controls, and correlations between these properties and outcome were calculated. We found no global differences in the network properties between patients with mild traumatic brain injury and orthopedic control subjects at either stage. However, we found significantly increased betweenness centrality of the right pars opercularis in the chronic stage compared to control subjects. Furthermore, both global and local network properties correlated significantly with outcome. Higher normalized global efficiency, degree, and strength as well as lower small-worldness were associated with better outcome. Correlations between the outcome and the local network properties were the most prominent in the left putamen and the left postcentral gyrus. Our results indicate that both global and local network properties provide valuable information about the outcome already in the acute/subacute stage, and therefore, are promising biomarkers for prognostic purposes in mild traumatic brain injury.
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Affiliation(s)
- Timo Roine
- University of Turku, 8058, Turku Brain and Mind Center, Turku, Finland.,Aalto University School of Science, 313201, Department of Neuroscience and Biomedical Engineering, Espoo, Finland;
| | - Mehrbod Mohammadian
- University of Turku Faculty of Medicine, 60654, Department of Clinical Neurosciences, Turku, Finland.,Turku University Hospital, 60652, Turku Brain Injury Center, Neurocenter, Turku, Finland;
| | - Jussi Hirvonen
- TYKS Turku University Hospital, 60652, Department of Radiology, Turku, Varsinais-Suomi, Finland;
| | - Timo Kurki
- University of Turku Faculty of Medicine, 60654, Department of Clinical Neurosciences, Turku, Finland.,Turku University Hospital, 60652, Turku Brain Injury Center, Neurocenter, Turku, Finland.,TYKS Turku University Hospital, 60652, Department of Radiology, Turku, Varsinais-Suomi, Finland;
| | - Jussi P Posti
- University of Turku Faculty of Medicine, 60654, Department of Clinical Neurosciences, Turku, Finland.,Turku University Hospital, 60652, Turku Brain Injury Center, Neurocenter, Turku, Varsinais-Suomi, Finland.,TYKS Turku University Hospital, 60652, Department of Neurosurgery. Neurocenter, Turku, Varsinais-Suomi, Finland;
| | - Riikka Sk Takala
- Turku University Hospital, Perioperative Services, Intensive Care Medicine and Pain Management, Turku, Finland.,University of Turku, 8058, Anaesthesiology, Intensive Care, Emergency Care and Pain Medicine, Turku, Varsinais-Suomi, Finland;
| | - Virginia Newcombe
- University of Cambridge, Division of Anaesthesia, Addenbrooke's Hospital, Cambridge, United Kingdom of Great Britain and Northern Ireland;
| | - Jussi Tallus
- Turku University Hospital, 60652, Turku Brain Injury Center, Neurocenter, Turku, Varsinais-Suomi, Finland;
| | - Ari J Katila
- Turku University Hospital, Perioperative Services, Intensive Care Medicine and Pain Management, Turku, Varsinais-Suomi, Finland;
| | - Henna-Riikka Maanpää
- Turku University Hospital, 60652, Turku Brain Injury Center, Neurocenter, Turku, Varsinais-Suomi, Finland.,Turku University Hospital, Department of Neurosurgery, Neurocenter, Turku, Varsinais-Suomi, Finland;
| | - Janek Frantzen
- Turku University Hospital, Turku Brain Injury Center, Neurocenter, Turku, Finland.,Turku University Hospital, Department of Neurosurgery, Neurocenter, Turku, Varsinais-Suomi, Finland.,University of Turku Faculty of Medicine, 60654, Department of Clinical Neurosciences, Turku, Finland;
| | - David Menon
- University of Cambridge, Division of Anaesthesia, Addenbrooke's Hospital, Cambridge, United Kingdom of Great Britain and Northern Ireland;
| | - Olli Tenovuo
- University of Turku Faculty of Medicine, 60654, Department of Clinical Neurosciences, Turku, Finland.,Turku University Hospital, 60652, Turku Brain Injury Center, Neurocenter, Turku, Finland;
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20
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Cox SR, Deary IJ. Brain and cognitive ageing: The present, and some predictions (…about the future). AGING BRAIN 2022; 2:100032. [PMID: 36908875 PMCID: PMC9997131 DOI: 10.1016/j.nbas.2022.100032] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 01/18/2022] [Accepted: 01/31/2022] [Indexed: 11/26/2022] Open
Abstract
Experiencing decline in one's cognitive abilities is among the most feared aspects of growing old [53]. Age-related cognitive decline carries a huge personal, societal, and financial cost both in pathological ageing (such as dementias) and also within the non-clinical majority of the population. A projected 152 million people worldwide will suffer from dementia by 2050 [3]. The early stages of cognitive decline are much more prevalent than dementia, and can still impose serious limitations of performance on everyday activities, independence, and quality of life in older age [5], [60], [80]. Cognitive decline also predicts poorer health, adherence to medical regimens, and financial decision-making, and can herald dementia, illness, and death [6], [40]. Of course, when seeking to understand why some people experience more severe cognitive ageing than others, researchers have turned to the organ of thinking for clues about the nature, possible mechanisms, and determinants that might underpin more and less successful cognitive agers. However, that organ is relatively inaccessible, a limitation partly alleviated by advances in neuroimaging. Here we discuss lessons for cognitive and brain ageing that have come from neuroimaging research (especially structural brain imaging), what neuroimaging still has left to teach us, and our views on possible ways forward in this multidisciplinary field.
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Affiliation(s)
- Simon R Cox
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, UK.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| | - Ian J Deary
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, UK
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21
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Borrelli P, Cavaliere C, Salvatore M, Jovicich J, Aiello M. Structural Brain Network Reproducibility: Influence of Different Diffusion Acquisition and Tractography Reconstruction Schemes on Graph Metrics. Brain Connect 2021; 12:754-767. [PMID: 34605673 DOI: 10.1089/brain.2021.0123] [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] [Indexed: 11/13/2022] Open
Abstract
Background: Graph metrics of structural brain networks demonstrate to be a powerful tool for investigating brain topology at a large scale. However, the variability of the results related to applying different magnetic resonance acquisition schemes and tractography reconstruction techniques is not fully characterized. Materials and Methods: The present work aims to evaluate the influence of different combinations of diffusion acquisition schemes (single and multishell), diffusion models (tensor and spherical deconvolution), and tractography reconstruction approaches (deterministic and probabilistic) on the reproducibility of graph metrics derived from structural connectome on test/retest (TRT) data released by the Human Connectome Project. From each implemented experimental setup, both global and local graph metrics were evaluated and their reproducibility was estimated by the intraclass correlation coefficient (ICC). Moreover, the percentage relative standard deviation (pRSD) from the ICC values of local graph metrics was calculated to quantify how much the reproducibility varied across nodes within each experimental setup. Results: The presented results show that different combinations of diffusion acquisition schemes, diffusion models, and tractography algorithms can strongly affect the reproducibility of global and local graph metrics. The combination of constrained spherical deconvolution (CSD) and deterministic tractography gave generally high reproducibility (ICCs >0.75) and lowest pRSD for the considered graph metrics, meanwhile probabilistic CSD with a high b-value returned the highest reproducibility. Notably, the introduction of streamline selection filters on CSD can substantially affect the reproducibility. Discussion: This work demonstrates that the TRT reproducibility of graph metrics is generally high but can vary substantially with different combinations of acquisition and reconstruction schemes. Impact statement This work demonstrates the influence of different diffusion acquisition schemes, diffusion models, and tractography reconstruction approaches on the reproducibility of graph metrics derived from structural connectome. The presented findings impact on the choice of both acquisition protocol and processing pipeline for topological analyses to produce reproducible measurements for brain network studies.
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Affiliation(s)
| | | | | | - Jorge Jovicich
- Center for Mind/Brain Sciences, University of Trento, Rovereto, Italy
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22
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Cai LY, Yang Q, Kanakaraj P, Nath V, Newton AT, Edmonson HA, Luci J, Conrad BN, Price GR, Hansen CB, Kerley CI, Ramadass K, Yeh FC, Kang H, Garyfallidis E, Descoteaux M, Rheault F, Schilling KG, Landman BA. MASiVar: Multisite, multiscanner, and multisubject acquisitions for studying variability in diffusion weighted MRI. Magn Reson Med 2021; 86:3304-3320. [PMID: 34270123 PMCID: PMC9087815 DOI: 10.1002/mrm.28926] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 06/21/2021] [Accepted: 06/23/2021] [Indexed: 12/18/2022]
Abstract
PURPOSE Diffusion-weighted imaging allows investigators to identify structural, microstructural, and connectivity-based differences between subjects, but variability due to session and scanner biases is a challenge. METHODS To investigate DWI variability, we present MASiVar, a multisite data set consisting of 319 diffusion scans acquired at 3 T from b = 1000 to 3000 s/mm2 across 14 healthy adults, 83 healthy children (5 to 8 years), three sites, and four scanners as a publicly available, preprocessed, and de-identified data set. With the adult data, we demonstrate the capacity of MASiVar to simultaneously quantify the intrasession, intersession, interscanner, and intersubject variability of four common DWI processing approaches: (1) a tensor signal representation, (2) a multi-compartment neurite orientation dispersion and density model, (3) white-matter bundle segmentation, and (4) structural connectomics. Respectively, we evaluate region-wise fractional anisotropy, mean diffusivity, and principal eigenvector; region-wise CSF volume fraction, intracellular volume fraction, and orientation dispersion index; bundle-wise shape, volume, fractional anisotropy, and length; and whole connectome correlation and maximized modularity, global efficiency, and characteristic path length. RESULTS We plot the variability in these measures at each level and find that it consistently increases with intrasession to intersession to interscanner to intersubject effects across all processing approaches and that sometimes interscanner variability can approach intersubject variability. CONCLUSIONS This study demonstrates the potential of MASiVar to more globally investigate DWI variability across multiple levels and processing approaches simultaneously and suggests harmonization between scanners for multisite analyses should be considered before inference of group differences on subjects.
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Affiliation(s)
- Leon Y. Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Qi Yang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Praitayini Kanakaraj
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Vishwesh Nath
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Allen T. Newton
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA
| | | | - Jeffrey Luci
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, USA
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, New Jersey, USA
| | - Benjamin N. Conrad
- Neuroscience Graduate Program, Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Psychology and Human Development, Peabody College, Vanderbilt University, Nashville, Tennessee, USA
| | - Gavin R. Price
- Department of Psychology and Human Development, Peabody College, Vanderbilt University, Nashville, Tennessee, USA
| | - Colin B. Hansen
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Cailey I. Kerley
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Karthik Ramadass
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | | | - Maxime Descoteaux
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Francois Rheault
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Kurt G. Schilling
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Bennett A. Landman
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA
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23
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Identifying brain regions supporting amygdalar functionality: Application of a novel graph theory technique. Neuroimage 2021; 244:118614. [PMID: 34571162 DOI: 10.1016/j.neuroimage.2021.118614] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 09/21/2021] [Indexed: 11/22/2022] Open
Abstract
Effective amygdalar functionality depends on the concerted activity of a complex network of regions. Thus, the role of the amygdala cannot be fully understood without identifying the set of brain structures that allow the processes performed by the amygdala to emerge. However, this identification has yet to occur, hampering our ability to understand both normative and pathological processes that rely on the amygdala. We developed and applied novel graph theory methods to diffusion-based anatomical networks in a large sample (n = 1,052, 54.28% female, mean age=28.75) to identify nodes that critically support amygdalar interactions with the larger brain network. We examined three graph properties, each indexing a different emergent aspect of amygdalar network communication: current-flow betweenness centrality (amygdalar influence on information flowing between other pairs of nodes), node communicability (clarity of communication between the amygdala and other nodes), and subgraph centrality (amygdalar influence over local network processing). Findings demonstrate that each of these aspects of amygdalar communication is associated with separable sets of regions and, in some cases, these sets map onto previously identified sub-circuits. For example, betweenness and communicability were each associated with different sub-circuits that have been identified in previous work as supporting distinct aspects of memory-guided behavior. Other regions identified span basic (e.g., visual cortex) to higher-order (e.g., insula) sensory processing and executive functions (e.g., dorsolateral prefrontal cortex). Present findings expand our current understanding of amygdalar function by showing that there is no single 'amygdala network', but rather multiple networks, each supporting different modes of amygdalar interaction with the larger brain network. Additionally, our novel method allowed for the identification of how such regions support the amygdala, which has not been previously explored.
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24
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Domhof JWM, Jung K, Eickhoff SB, Popovych OV. Parcellation-induced variation of empirical and simulated brain connectomes at group and subject levels. Netw Neurosci 2021; 5:798-830. [PMID: 34746628 PMCID: PMC8567834 DOI: 10.1162/netn_a_00202] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 05/27/2021] [Indexed: 11/13/2022] Open
Abstract
Recent developments of whole-brain models have demonstrated their potential when investigating resting-state brain activity. However, it has not been systematically investigated how alternating derivations of the empirical structural and functional connectivity, serving as the model input, from MRI data influence modeling results. Here, we study the influence from one major element: the brain parcellation scheme that reduces the dimensionality of brain networks by grouping thousands of voxels into a few hundred brain regions. We show graph-theoretical statistics derived from the empirical data and modeling results exhibiting a high heterogeneity across parcellations. Furthermore, the network properties of empirical brain connectomes explain the lion’s share of the variance in the modeling results with respect to the parcellation variation. Such a clear-cut relationship is not observed at the subject-resolved level per parcellation. Finally, the graph-theoretical statistics of the simulated connectome correlate with those of the empirical functional connectivity across parcellations. However, this relation is not one-to-one, and its precision can vary between models. Our results imply that network properties of both empirical connectomes can explain the goodness-of-fit of whole-brain models to empirical data at a global group level but not at a single-subject level, which provides further insights into the personalization of whole-brain models. The structural and functional connectivities of the brain, which reflect the anatomical connections of axonal bundles and the amount of coactivation between brain regions, respectively, only weakly correlate with each other. In order to enhance and investigate this relationship, large-scale whole-brain dynamical models were involved in this branch of research. However, how the definitions of the brain regions parcellated according to a so-called brain atlas influence these models has so far not been systematically assessed. In this article, we show that this influence can be large, and link group-averaged, atlas-induced deviations to network properties extracted from both types of connectivity. Additionally, we demonstrate that the same association does not apply to subject-specific variations. These results may contribute to the further personalization of the whole-brain models.
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Affiliation(s)
- Justin W M Domhof
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Kyesam Jung
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Oleksandr V Popovych
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
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25
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Kurokawa R, Kamiya K, Koike S, Nakaya M, Uematsu A, Tanaka SC, Kamagata K, Okada N, Morita K, Kasai K, Abe O. Cross-scanner reproducibility and harmonization of a diffusion MRI structural brain network: A traveling subject study of multi-b acquisition. Neuroimage 2021; 245:118675. [PMID: 34710585 DOI: 10.1016/j.neuroimage.2021.118675] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 09/26/2021] [Accepted: 10/21/2021] [Indexed: 01/18/2023] Open
Abstract
Characterization of brain networks by diffusion MRI (dMRI) has rapidly evolved, and there are ongoing movements toward data sharing and multi-center studies. To extract meaningful information from multi-center data, methods to correct for the bias caused by scanner differences, that is, harmonization, are urgently needed. In this work, we report the cross-scanner differences in structural network analyses using data from nine traveling subjects (four males and five females, 21-49 years-old) who underwent scanning using four 3T scanners (public database available from the Brain/MINDS Beyond Human Brain MRI project (http://mriportal.umin.jp/)). The reliability and reproducibility were compared to those of data from another set of four subjects (all males, 29-42 years-old) who underwent scan-rescan (interval, 105-147 days) with the same scanner as well as scan-rescan data from the Human Connectome Project database. The results demonstrated that the reliability of the edge weights and graph theory metrics was lower for data including different scanners, compared to the scan-rescan with the same scanner. Besides, systematic differences between scanners were observed, indicating the risk of bias in comparing networks obtained from different scanners directly. We further demonstrate that it is feasible to reduce inter-scanner variabilities while preserving the inter-subject differences among healthy individuals by modeling the scanner effects at the level of network matrices, when traveling-subject data are available for calibration between scanners. The present data and results are expected to serve as a basis for developing and evaluating novel harmonization methods.
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Affiliation(s)
- Ryo Kurokawa
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
| | - Kouhei Kamiya
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; Department of Radiology, Toho University, Tokyo, Japan; Department of Radiology, Juntendo University, Tokyo, Japan.
| | - Shinsuke Koike
- Center for Evolutionary Cognitive Sciences (ECS), Graduate School of Art and Sciences, The University of Tokyo, Tokyo, Japan; University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), Tokyo, Japan; University of Tokyo Center for Integrative Science of Human Behavior (CiSHuB), Tokyo, Japan; The International Research Center for Neurointelligence (WPI-IRCN), Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, Japan.
| | - Moto Nakaya
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
| | - Akiko Uematsu
- Center for Evolutionary Cognitive Sciences (ECS), Graduate School of Art and Sciences, The University of Tokyo, Tokyo, Japan.
| | - Saori C Tanaka
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International (ATR), Kyoto, Japan.
| | - Koji Kamagata
- Department of Radiology, Juntendo University, Tokyo, Japan.
| | - Naohiro Okada
- University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), Tokyo, Japan; The International Research Center for Neurointelligence (WPI-IRCN), Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, Japan; Department of Neuropsychiatry, The University of Tokyo, Tokyo, Japan.
| | - Kentaro Morita
- Department of Neuropsychiatry, The University of Tokyo, Tokyo, Japan.
| | - Kiyoto Kasai
- University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), Tokyo, Japan; University of Tokyo Center for Integrative Science of Human Behavior (CiSHuB), Tokyo, Japan; The International Research Center for Neurointelligence (WPI-IRCN), Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, Japan; Department of Neuropsychiatry, The University of Tokyo, Tokyo, Japan.
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
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Kurokawa R, Kamiya K, Inui S, Kato S, Suzuki F, Amemiya S, Shinozaki T, Takanezawa D, Kohashi R, Abe O. Structural connectivity changes in the cerebral pain matrix in burning mouth syndrome: a multi-shell, multi-tissue-constrained spherical deconvolution model analysis. Neuroradiology 2021; 63:2005-2012. [PMID: 34142212 DOI: 10.1007/s00234-021-02732-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 05/03/2021] [Indexed: 01/15/2023]
Abstract
PURPOSE Burning mouth syndrome (BMS) is a chronic intraoral pain syndrome. Previous studies have attempted to determine the brain connectivity features in BMS using functional and structural magnetic resonance imaging. However, no study has investigated the structural connectivity using multi-shell, multi-tissue-constrained spherical deconvolution (MSMT-CSD), anatomically constrained tractography (ACT), and spherical deconvolution informed filtering of tractograms (SIFT). Therefore, this study aimed to assess the differences in brain structural connectivity of patients with BMS and healthy controls using probabilistic tractography with these methods, and graph analysis. METHODS Fourteen patients with BMS and 11 age- and sex-matched healthy volunteers underwent 3-T magnetic resonance imaging. MSMT-CSD-based probabilistic structural connectivity was computed using the second-order integration over fiber orientation distributions algorithm based on nodes set in 84 anatomical cortical regions with ACT and SIFT. A t-test was performed for comparisons between the BMS and healthy control brain networks. RESULTS The betweenness centrality was significantly higher in the left insula, right amygdala, and right lateral orbitofrontal cortex and significantly lower in the right inferotemporal cortex in the BMS group than that in healthy controls. However, no significant difference was found in the clustering coefficient, node degree, and small-worldness between the two groups. CONCLUSION Graph analysis of brain probabilistic structural connectivity, based on diffusion imaging using an MSMT-CSD model with ACT and SIFT, revealed alterations in the regions comprising the pain matrix and medial pain ascending pathway. These results highlight the emotional-affective profile of BMS, which is a type of chronic pain syndrome.
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Affiliation(s)
- Ryo Kurokawa
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
| | - Kouhei Kamiya
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.,Department of Radiology, Toho University, Tokyo, Japan
| | - Shohei Inui
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Shimpei Kato
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Fumio Suzuki
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Shiori Amemiya
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Takahiro Shinozaki
- Department of Oral Diagnostic Sciences, Nihon University School of Dentistry, Tokyo, Japan
| | - Daiki Takanezawa
- Department of Oral Diagnostic Sciences, Nihon University School of Dentistry, Tokyo, Japan
| | - Ryutarou Kohashi
- Department of Oral and Maxillofacial Radiology, Nihon University School of Dentistry, Tokyo, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
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Yeh CH, Jones DK, Liang X, Descoteaux M, Connelly A. Mapping Structural Connectivity Using Diffusion MRI: Challenges and Opportunities. J Magn Reson Imaging 2021; 53:1666-1682. [PMID: 32557893 PMCID: PMC7615246 DOI: 10.1002/jmri.27188] [Citation(s) in RCA: 72] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 04/21/2020] [Accepted: 04/21/2020] [Indexed: 12/13/2022] Open
Abstract
Diffusion MRI-based tractography is the most commonly-used technique when inferring the structural brain connectome, i.e., the comprehensive map of the connections in the brain. The utility of graph theory-a powerful mathematical approach for modeling complex network systems-for analyzing tractography-based connectomes brings important opportunities to interrogate connectome data, providing novel insights into the connectivity patterns and topological characteristics of brain structural networks. When applying this framework, however, there are challenges, particularly regarding methodological and biological plausibility. This article describes the challenges surrounding quantitative tractography and potential solutions. In addition, challenges related to the calculation of global network metrics based on graph theory are discussed.Evidence Level: 5Technical Efficacy: Stage 1.
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Affiliation(s)
- Chun-Hung Yeh
- Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital, Taoyuan, Taiwan
- Department of Child and Adolescent Psychiatry, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
- Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
- Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- Mary Mackillop Institute for Health Research, Australian Catholic University, Melbourne, Victoria, Australia
| | - Xiaoyun Liang
- Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
- Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia
- Mary Mackillop Institute for Health Research, Australian Catholic University, Melbourne, Victoria, Australia
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Alan Connelly
- Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
- Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia
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Jung K, Eickhoff SB, Popovych OV. Tractography density affects whole-brain structural architecture and resting-state dynamical modeling. Neuroimage 2021; 237:118176. [PMID: 34000399 DOI: 10.1016/j.neuroimage.2021.118176] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 05/09/2021] [Accepted: 05/13/2021] [Indexed: 11/24/2022] Open
Abstract
Dynamical modeling of the resting-state brain dynamics essentially relies on the empirical neuroimaging data utilized for the model derivation and validation. There is however still no standardized data processing for magnetic resonance imaging pipelines and the structural and functional connectomes involved in the models. In this study, we thus address how the parameters of diffusion-weighted data processing for structural connectivity (SC) can influence the validation results of the whole-brain mathematical models informed by SC. For this, we introduce a set of simulation conditions including the varying number of total streamlines of the whole-brain tractography (WBT) used for extraction of SC, cortical parcellations based on functional and anatomical brain properties and distinct model fitting modalities. The main objective of this study is to explore how the quality of the model validation can vary across the considered simulation conditions. We observed that the graph-theoretical network properties of structural connectome can be affected by varying tractography density and strongly relate to the model performance. We also found that the optimal number of the total streamlines of WBT can vary for different brain atlases. Consequently, we suggest a way how to improve the model performance based on the network properties and the optimal parameter configurations from multiple WBT conditions. Furthermore, the population of subjects can be stratified into subgroups with divergent behaviors induced by the varying WBT density such that different recommendations can be made with respect to the data processing for individual subjects and brain parcellations.
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Affiliation(s)
- Kyesam Jung
- Institute of Neuroscience and Medicine, Brain and Behavior (INM-7), Research Center Jülich, Germany; Institute for Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Germany.
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain and Behavior (INM-7), Research Center Jülich, Germany; Institute for Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Germany.
| | - Oleksandr V Popovych
- Institute of Neuroscience and Medicine, Brain and Behavior (INM-7), Research Center Jülich, Germany; Institute for Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Germany.
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Morez J, Sijbers J, Vanhevel F, Jeurissen B. Constrained spherical deconvolution of nonspherically sampled diffusion MRI data. Hum Brain Mapp 2020; 42:521-538. [PMID: 33169880 PMCID: PMC7776001 DOI: 10.1002/hbm.25241] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2020] [Revised: 08/30/2020] [Accepted: 09/29/2020] [Indexed: 12/18/2022] Open
Abstract
Constrained spherical deconvolution (CSD) of diffusion-weighted MRI (DW-MRI) is a popular analysis method that extracts the full white matter (WM) fiber orientation density function (fODF) in the living human brain, noninvasively. It assumes that the DW-MRI signal on the sphere can be represented as the spherical convolution of a single-fiber response function (RF) and the fODF, and recovers the fODF through the inverse operation. CSD approaches typically require that the DW-MRI data is sampled shell-wise, and estimate the RF in a purely spherical manner using spherical basis functions, such as spherical harmonics (SH), disregarding any radial dependencies. This precludes analysis of data acquired with nonspherical sampling schemes, for example, Cartesian sampling. Additionally, nonspherical sampling can also arise due to technical issues, for example, gradient nonlinearities, resulting in a spatially dependent bias of the apparent tissue densities and connectivity information. Here, we adopt a compact model for the RFs that also describes their radial dependency. We demonstrate that the proposed model can accurately predict the tissue response for a wide range of b-values. On shell-wise data, our approach provides fODFs and tissue densities indistinguishable from those estimated using SH. On Cartesian data, fODF estimates and apparent tissue densities are on par with those obtained from shell-wise data, significantly broadening the range of data sets that can be analyzed using CSD. In addition, gradient nonlinearities can be accounted for using the proposed model, resulting in much more accurate apparent tissue densities and connectivity metrics.
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Affiliation(s)
- Jan Morez
- Imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium
| | - Jan Sijbers
- Imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium
| | - Floris Vanhevel
- Department of Radiology, University Hospital Antwerp, Antwerp, Belgium
| | - Ben Jeurissen
- Imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium
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Estevez-Fraga C, Scahill R, Rees G, Tabrizi SJ, Gregory S. Diffusion imaging in Huntington's disease: comprehensive review. J Neurol Neurosurg Psychiatry 2020; 92:jnnp-2020-324377. [PMID: 33033167 PMCID: PMC7803908 DOI: 10.1136/jnnp-2020-324377] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 09/07/2020] [Accepted: 09/07/2020] [Indexed: 12/31/2022]
Abstract
Huntington's disease (HD) is a monogenic disorder with 100% penetrance. With the advent of genetic testing in adults, disease-related, structural brain changes can be investigated from the earliest, premorbid stages of HD. While examining macrostructural change characterises global neuronal damage, investigating microstructural alterations provides information regarding brain organisation and its underlying biological properties. Diffusion MRI can be used to track the progression of microstructural anomalies in HD decades prior to clinical disease onset, providing a greater understanding of neurodegeneration. Multiple approaches, including voxelwise, region of interest and tractography, have been used in HD cohorts, showing a centrifugal pattern of white matter (WM) degeneration starting from deep brain areas, which is consistent with neuropathological studies. The corpus callosum, longer WM tracts and areas that are more densely connected, in particular the sensorimotor network, also tend to be affected early during premanifest stages. Recent evidence supports the routine inclusion of diffusion analyses within clinical trials principally as an additional measure to improve understanding of treatment effects, while the advent of novel techniques such as multitissue compartment models and connectomics can help characterise the underpinnings of progressive functional decline in HD.
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Affiliation(s)
- Carlos Estevez-Fraga
- Huntington's Disease Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Rachael Scahill
- Huntington's Disease Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Geraint Rees
- Wellcome Centre for Neuroimaging, University College London, London, UK
- Institute of Cognitive Neuroscience, University College London, London, UK
| | - Sarah J Tabrizi
- Huntington's Disease Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sarah Gregory
- Huntington's Disease Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
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Newman BT, Dhollander T, Reynier KA, Panzer MB, Druzgal TJ. Test-retest reliability and long-term stability of three-tissue constrained spherical deconvolution methods for analyzing diffusion MRI data. Magn Reson Med 2020; 84:2161-2173. [PMID: 32112479 PMCID: PMC7329572 DOI: 10.1002/mrm.28242] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 02/10/2020] [Accepted: 02/11/2020] [Indexed: 12/13/2022]
Abstract
PURPOSE Several recent studies have used a three-tissue constrained spherical deconvolution pipeline to obtain quantitative metrics of brain tissue microstructure from diffusion-weighted MRI data. The three tissue compartments, consisting of white matter, gray matter, and CSF-like (free water) signals, are potentially useful in the evaluation of brain microstructure in a range of pathologies. However, the reliability and long-term stability of these metrics have not yet been evaluated. METHODS This study examined estimates of whole-brain microstructure for the three tissue compartments, in three separate test-retest cohorts. Each cohort had different lengths of time between baseline and retest, ranging from within the same scanning session in the shortest interval to 3 months in the longest interval. Each cohort was also collected with different acquisition parameters. RESULTS The CSF-like compartment displayed the greatest reliability across all cohorts, with intraclass correlation coefficient (ICC) values being above 0.95 in each cohort. White matter-like and gray matter-like compartments both demonstrated very high reliability in the immediate cohort (both ICC > 0.90); however, this declined in the 3-month interval cohort to both compartments having ICC > 0.80. Regional CSF-like signal fraction was examined in bilateral hippocampus and had an ICC > 0.80 in each cohort. CONCLUSION The three-tissue constrained spherical deconvolution techniques provide reliable and stable estimates of tissue-microstructure composition, up to 3 months longitudinally in a control population. This forms an important basis for further investigations using three-tissue constrained spherical deconvolution techniques to track changes in microstructure across a variety of brain pathologies.
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Affiliation(s)
- Benjamin T. Newman
- Department of Radiology and Medical Imaging, School of Medicine, University of Virginia, Charlottesville, USA
- Brain Institute, University of Virginia, Charlottesville, USA
| | - Thijs Dhollander
- Developmental Imaging, Murdoch Children’s Research Institute, Melbourne, Australia
| | - Kristen A. Reynier
- Center for Applied Biomechanics, Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, USA
| | - Matthew B. Panzer
- Center for Applied Biomechanics, Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, USA
| | - T. Jason Druzgal
- Department of Radiology and Medical Imaging, School of Medicine, University of Virginia, Charlottesville, USA
- Brain Institute, University of Virginia, Charlottesville, USA
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Abstract
AbstractThe prospect of improving or maintaining cognitive functioning has provoked a steadily increasing number of cognitive training interventions over the last years, especially for clinical and elderly populations. However, there are discrepancies between the findings of the studies. One of the reasons behind these heterogeneous findings is that there are vast inter-individual differences in how people benefit from the training and in the extent that training-related gains are transferred to other untrained tasks and domains. In this paper, we address the value of incorporating neural measures to cognitive training studies in order to fully understand the mechanisms leading to inter-individual differences in training gains and their generalizability to other tasks. Our perspective is that it is necessary to collect multimodal neural measures in the pre- and post-training phase, which can enable us to understand the factors contributing to successful training outcomes. More importantly, this understanding can enable us to predict who will benefit from different types of interventions, thereby allowing the development of individually tailored intervention programs.
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33
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TractoFlow: A robust, efficient and reproducible diffusion MRI pipeline leveraging Nextflow & Singularity. Neuroimage 2020; 218:116889. [DOI: 10.1016/j.neuroimage.2020.116889] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 03/08/2020] [Accepted: 04/27/2020] [Indexed: 12/13/2022] Open
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de Brito Robalo BM, Vlegels N, Meier J, Leemans A, Biessels GJ, Reijmer YD. Effect of Fixed-Density Thresholding on Structural Brain Networks: A Demonstration in Cerebral Small Vessel Disease. Brain Connect 2020; 10:121-133. [PMID: 32103679 DOI: 10.1089/brain.2019.0686] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
A popular solution to control for edge density variability in structural brain network analysis is to threshold the networks to a fixed density across all subjects. However, it remains unclear how this type of thresholding affects the basic network architecture in terms of edge weights, hub location, and hub connectivity and, especially, how it affects the sensitivity to detect disease-related abnormalities. We investigated these two questions in a cohort of patients with cerebral small vessel disease and age-matched controls. Brain networks were reconstructed from diffusion magnetic resonance imaging data using deterministic fiber tractography. Networks were thresholded to a fixed density by removing edges with the lowest number of streamlines. We compared edge length (mm), fractional anisotropy (FA), proportion of hub connections, and hub location between the unthresholded and the thresholded networks of each subject. Moreover, we compared weighted graph measures of global and local connectivity obtained from the (un)thresholded networks between patients and controls. We performed these analyses over a range of densities (2-20%). Results indicate that fixed-density thresholding disproportionally removes edges composed of long streamlines, but is independent of FA. The edges removed were not preferentially connected to hub or nonhub nodes. Over half of the original hubs were reproducible when networks were thresholded to a density ≥10%. Furthermore, the between-group differences in graph measures observed in the unthresholded network remained present after thresholding, irrespective of the chosen density. We therefore conclude that moderate fixed-density thresholds can successfully be applied to control for the effects of density in structural brain network analysis.
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Affiliation(s)
- Bruno M de Brito Robalo
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Naomi Vlegels
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jil Meier
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Alexander Leemans
- PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Geert Jan Biessels
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Yael D Reijmer
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
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Rodríguez-Cruces R, Bernhardt BC, Concha L. Multidimensional associations between cognition and connectome organization in temporal lobe epilepsy. Neuroimage 2020; 213:116706. [PMID: 32151761 DOI: 10.1016/j.neuroimage.2020.116706] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 01/14/2020] [Accepted: 03/03/2020] [Indexed: 11/25/2022] Open
Abstract
OBJECTIVE Temporal lobe epilepsy (TLE) is known to affect large-scale structural networks and cognitive function in multiple domains. The study of complex relations between structural network organization and cognition requires comprehensive analytical methods and a shift towards multivariate techniques. Here, we sought to identify multidimensional associations between cognitive performance and structural network topology in TLE. METHODS We studied 34 drug-resistant adult TLE patients and 24 age- and sex-matched healthy controls. Participants underwent a comprehensive neurocognitive battery and multimodal MRI, allowing for large-scale connectomics, and morphological evaluation of subcortical and neocortical regions. Using canonical correlation analysis, we identified a multivariate mode that links cognitive performance to a brain structural network. Our approach was complemented by bootstrap-based hierarchical clustering to derive cognitive subtypes and associated patterns of macroscale connectome anomalies. RESULTS Both methodologies provided converging evidence for a close coupling between cognitive impairments across multiple domains and large-scale structural network compromise. Cognitive classes presented with an increasing gradient of abnormalities (increasing cortical and subcortical atrophy and less efficient white matter connectome organization in patients with increasing degrees of cognitive impairments). Notably, network topology characterized cognitive performance better than morphometric measures did. CONCLUSIONS Our multivariate approach emphasized a close coupling of cognitive dysfunction and large-scale network anomalies in TLE. Our findings contribute to understand the complexity of structural connectivity regulating the heterogeneous cognitive deficits found in epilepsy.
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Affiliation(s)
- Raúl Rodríguez-Cruces
- Universidad Nacional Autónoma de México, Instituto de Neurobiología, Querétaro, Querétaro, Mexico; MICA Laboratory, Montreal Neurological Institute and Hospital, Montreal, Canada.
| | - Boris C Bernhardt
- MICA Laboratory, Montreal Neurological Institute and Hospital, Montreal, Canada.
| | - Luis Concha
- Universidad Nacional Autónoma de México, Instituto de Neurobiología, Querétaro, Querétaro, Mexico.
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Reid LB, Cespedes MI, Pannek K. How many streamlines are required for reliable probabilistic tractography? Solutions for microstructural measurements and neurosurgical planning. Neuroimage 2020; 211:116646. [PMID: 32084566 DOI: 10.1016/j.neuroimage.2020.116646] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Accepted: 02/12/2020] [Indexed: 10/25/2022] Open
Abstract
Diffusion MRI tractography is commonly used to delineate white matter tracts. These delineations can be used for planning neurosurgery or for identifying regions of interest from which microstructural measurements can be taken. Probabilistic tractography produces different delineations each time it is run, potentially leading to microstructural measurements or anatomical delineations that are not reproducible. Generating a sufficiently large number of streamlines is required to avoid this scenario, but what constitutes "sufficient" is difficult to assess and so streamline counts are typically chosen in an arbitrary or qualitative manner. This work explores several factors influencing tractography reliability and details two methods for estimating this reliability. The first method automatically estimates the number of streamlines required to achieve reliable microstructural measurements, whilst the second estimates the number of streamlines required to achieve a reliable binarised trackmap than can be used clinically. Using these methods, we calculated the number of streamlines required to achieve a range of quantitative reproducibility criteria for three anatomical tracts in 40 Human Connectome Project datasets. Actual reproducibility was checked by repeatedly generating the tractograms with the calculated numbers of streamlines. We found that the required number of streamlines varied strongly by anatomical tract, image resolution, number of diffusion directions, the degree of reliability desired, the microstructural measurement of interest, and/or the specifics on how the tractogram was converted to a binary volume. The proposed methods consistently predicted streamline counts that achieved the target reproducibility. Implementations are made available to enable the scientific community to more-easily achieve reproducible tractography.
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Affiliation(s)
- Lee B Reid
- The Australian e-Health Research Centre, The Commonwealth Scientific and Industrial Research Organisation (CSIRO), Brisbane, Australia.
| | - Marcela I Cespedes
- The Australian e-Health Research Centre, The Commonwealth Scientific and Industrial Research Organisation (CSIRO), Brisbane, Australia
| | - Kerstin Pannek
- The Australian e-Health Research Centre, The Commonwealth Scientific and Industrial Research Organisation (CSIRO), Brisbane, Australia
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Challenges and Opportunities in Connectome Construction and Quantification in the Developing Human Fetal Brain. Top Magn Reson Imaging 2020; 28:265-273. [PMID: 31592993 DOI: 10.1097/rmr.0000000000000212] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The white matter structure of the human brain undergoes critical developmental milestones in utero, which we can observe noninvasively using diffusion-weighted magnetic resonance imaging. In order to understand this fascinating developmental process, we must establish the variability inherent in such a challenging imaging environment and how measurable quantities can be transformed into meaningful connectomes. We review techniques for reconstructing and studying the brain connectome and explore promising opportunities for in utero studies that could lead to more accurate measurement of structural properties and allow for more refined and insightful analyses of the fetal brain. Opportunities for more sophisticated analyses of the properties of the brain and its dynamic changes have emerged in recent years, based on the development of iterative techniques to reconstruct motion-corrupted diffusion-weighted data. Although reconstruction quality is greatly improved, the treatment of fundamental quantities like edge strength requires careful treatment because of the specific challenges of imaging in utero. There are intriguing challenges to overcome, from those in analysis due to both imaging limitations and the significant changes in structural connectivity, to further image processing to address the specific properties of the target anatomy and quantification into a developmental connectome.
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Roine T, Roine U, Tokola A, Balk MH, Mannerkoski M, Åberg L, Lönnqvist T, Autti T. Topological Alterations of the Structural Brain Connectivity Network in Children with Juvenile Neuronal Ceroid Lipofuscinosis. AJNR Am J Neuroradiol 2019; 40:2146-2153. [PMID: 31727742 DOI: 10.3174/ajnr.a6306] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Accepted: 09/18/2019] [Indexed: 12/13/2022]
Abstract
BACKGROUND AND PURPOSE We used diffusion MR imaging to investigate the structural brain connectivity networks in juvenile neuronal ceroid lipofuscinosis, a neurodegenerative lysosomal storage disease of childhood. Although changes in conventional MR imaging are typically not visually apparent in children aged <10 years, we previously found significant microstructural abnormalities by using diffusion MR imaging. Therefore, we hypothesized that the structural connectivity networks would also be affected in the disease. MATERIALS AND METHODS We acquired diffusion MR imaging data from 14 children with juvenile neuronal ceroid lipofuscinosis (mean ± SD age, 9.6 ± 3.4 years; 10 boys) and 14 control subjects (mean ± SD age, 11.2 ± 2.3 years; 7 boys). A follow-up MR imaging was performed for 12 of the patients (mean ± SD age, 11.4 ± 3.2 years; 8 boys). We used graph theoretical analysis to investigate the global and local properties of the structural brain connectivity networks reconstructed with constrained spherical deconvolution-based whole-brain probabilistic tractography. RESULTS We found significantly increased characteristic path length (P = .003) and decreased degree (P = .003), which indicated decreased network integration and centrality in children with juvenile neuronal ceroid lipofuscinosis. The findings were similar for the follow-up MR imaging, and there were no significant differences between the two acquisitions of the patients. In addition, we found that the disease severity correlated negatively (P < .007) with integration, segregation, centrality, and small-worldness of the networks. Moreover, we found significantly (P < .0003) decreased local efficiency in the left supramarginal gyrus and temporal plane, and decreased strength in the right lingual gyrus. CONCLUSIONS We found significant global and local network alterations in juvenile neuronal ceroid lipofuscinosis that correlated with the disease severity and in areas related to the symptomatology.
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Affiliation(s)
- T Roine
- Radiology, Child Psychiatry (M.M.)
- Turku Brain and Mind Center (T.R.), University of Turku, Turku, Finland
- Department of Neuroscience and Biomedical Engineering (T.R.), Aalto University School of Science, Espoo, Finland
| | - U Roine
- Radiology, Child Psychiatry (M.M.)
| | - A Tokola
- Radiology, Child Psychiatry (M.M.)
| | - M H Balk
- Radiology, Child Psychiatry (M.M.)
| | | | - L Åberg
- Department of Psychiatry (L.Å.), University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - T Lönnqvist
- Department of Child Neurology (T.L.), Children's Hospital, University of Helsinki and Helsinki University, Helsinki, Finland
| | - T Autti
- Radiology, Child Psychiatry (M.M.)
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Brain structural connectivity network alterations in insomnia disorder reveal a central role of the right angular gyrus. NEUROIMAGE-CLINICAL 2019; 24:102019. [PMID: 31678910 PMCID: PMC6839281 DOI: 10.1016/j.nicl.2019.102019] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Revised: 08/05/2019] [Accepted: 09/27/2019] [Indexed: 12/31/2022]
Abstract
People with insomnia show widespread brain structural hyperconnectivity. The right angular gyrus is central to the structural connectivity alterations. Connectivity of this angular gyrus subnetwork correlates with reactive hyperarousal. Brain structural hyperconnectivity may mark vulnerability to insomnia.
Insomnia Disorder (ID) is a prevalent and persistent condition, yet its neural substrate is not well understood. The cognitive, emotional, and behavioral characteristics of ID suggest that vulnerability involves distributed brain networks rather than a single brain area or connection. The present study utilized probabilistic diffusion tractography to compare the whole-brain structural connectivity networks of people with ID and those of matched controls without sleep complaints. Diffusion-weighted images and T1-weighed images were acquired in 51 people diagnosed with ID (21–69 years of age, 37 female) and 48 matched controls without sleep complaints (22–70 years of age, 31 female). Probabilistic tractography was performed to construct the whole-brain structural connectivity network of each participant. Case–control differences in connectivity strength and network efficiency were evaluated with permutation tests. People with ID showed structural hyperconnectivity within a subnetwork that spread over frontal, parietal, temporal, and subcortical regions and was anchored at the right angular gyrus. The result was robust across different edge-weighting strategies. Moreover, converging support was given by the finding of heightened right angular gyrus nodal efficiency (harmonic centrality) across varying graph density in people with ID. Follow-up correlation analyses revealed that subnetwork connectivity was associated with self-reported reactive hyperarousal. The findings demonstrate that the right angular gyrus is a hub of enhanced structural connectivity in ID. Hyperconnectivity within the identified subnetwork may contribute to increased reactivity to stimuli and may signify vulnerability to ID.
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Optimization of graph construction can significantly increase the power of structural brain network studies. Neuroimage 2019; 199:495-511. [PMID: 31176831 PMCID: PMC6693529 DOI: 10.1016/j.neuroimage.2019.05.052] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Revised: 04/08/2019] [Accepted: 05/19/2019] [Indexed: 12/31/2022] Open
Abstract
Structural brain networks derived from diffusion magnetic resonance imaging data have been used extensively to describe the human brain, and graph theory has allowed quantification of their network properties. Schemes used to construct the graphs that represent the structural brain networks differ in the metrics they use as edge weights and the algorithms they use to define the network topologies. In this work, twenty graph construction schemes were considered. The schemes use the number of streamlines, the fractional anisotropy, the mean diffusivity or other attributes of the tracts to define the edge weights, and either an absolute threshold or a data-driven algorithm to define the graph topology. The test-retest data of the Human Connectome Project were used to compare the reproducibility of the graphs and their various attributes (edges, topologies, graph theoretical metrics) derived through those schemes, for diffusion images acquired with three different diffusion weightings. The impact of the scheme on the statistical power of the study and on the number of participants required to detect a difference between populations or an effect of an intervention was also calculated. The reproducibility of the graphs and their attributes depended heavily on the graph construction scheme. Graph reproducibility was higher for schemes that used thresholding to define the graph topology, while data-driven schemes performed better at topology reproducibility (mean similarities of 0.962 and 0.984 respectively, for graphs derived from diffusion images with b=2000 s/mm2). Additionally, schemes that used thresholding resulted in better reproducibility for local graph theoretical metrics (intra-class correlation coefficients (ICC) of the order of 0.8), compared to data-driven schemes. Thresholded and data-driven schemes resulted in high (0.86 or higher) ICCs only for schemes that use exclusively the number of streamlines to construct the graphs. Crucially, the number of participants required to detect a difference between populations or an effect of an intervention could change by a factor of two or more depending on the scheme used, affecting the power of studies to reveal the effects of interest.
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