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Osmanlıoğlu Y, Alappatt JA, Parker D, Verma R. Analysis of Consistency in Structural and Functional Connectivity of Human Brain. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2020; 2020:1694-1697. [PMID: 33324470 PMCID: PMC7734450 DOI: 10.1109/isbi45749.2020.9098412] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Analysis of structural and functional connectivity of brain has become a fundamental approach in neuroscientific research. Despite several studies reporting consistent similarities as well as differences for structural and resting state (rs) functional connectomes, a comparative investigation of connectomic consistency between the two modalities is still lacking. Nonetheless, connectomic analysis comprising both connectivity types necessitate extra attention as consistency of connectivity differs across modalities, possibly affecting the interpretation of the results. In this study, we present a comprehensive analysis of consistency in structural and rs-functional connectomes obtained from longitudinal diffusion MRI and rs-fMRI data of a single healthy subject. We contrast consistency of deterministic and probabilistic tracking with that of full, positive, and negative functional connectivities across various connectome generation schemes, using correlation as a measure of consistency.
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Affiliation(s)
- Yusuf Osmanlıoğlu
- Diffusion and Connectomics in Precision Healthcare Research Lab, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Jacob A Alappatt
- Diffusion and Connectomics in Precision Healthcare Research Lab, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Drew Parker
- Diffusion and Connectomics in Precision Healthcare Research Lab, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Ragini Verma
- Diffusion and Connectomics in Precision Healthcare Research Lab, Department of Radiology, University of Pennsylvania, Philadelphia, USA
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Wang Y, Qin Y, Li H, Yao D, Sun B, Li Z, Li X, Dai Y, Wen C, Zhang L, Zhang C, Zhu T, Luo C. The Modulation of Reward and Habit Systems by Acupuncture in Adolescents with Internet Addiction. Neural Plast 2020; 2020:7409417. [PMID: 32256558 PMCID: PMC7094193 DOI: 10.1155/2020/7409417] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Revised: 02/15/2020] [Accepted: 02/17/2020] [Indexed: 12/31/2022] Open
Abstract
Purpose Acupuncture is an effective therapy for Internet addiction (IA). However, the underlying mechanisms of acupuncture in relieving compulsive Internet use remain unknown. Neuroimaging studies have demonstrated the role of the ventral striatum (VS) in the progress of IA; hence, the aim of this study was to explore the effects of acupuncture on the resting-state functional connectivity (rsFC) and relevant network of VS in IA. Methods Twenty-seven IA individuals and 30 demographically matched healthy control subjects (HCs) were recruited in this study. We acquired the functional magnetic resonance imaging (fMRI) data in IA subjects before and after 40 days of acupuncture treatment. Seed-to-voxel and ROI-to-ROI analyses were applied to detect the rsFC alterations of the VS and related network in IA subjects and to investigate the modulation effect of acupuncture on the rsFC. Results Compared with HCs, IA subjects exhibited enhanced rsFC of the right ventral rostral putamen (VRP) with the left orbitofrontal cortex (OFC), premotor cortex (PMC), cerebellum, and right ventromedial prefrontal cortex (vmPFC). In the network including these five ROIs, IA also showed increased ROI-to-ROI rsFC. Using a paired t-test in IA subjects before and after 40 days of acupuncture, the increased ROI-to-ROI rsFC was decreased (normalized to HC) with acupuncture, including the rsFC of the right VRP with the left OFC, PMC, and cerebellum, and the rsFC of the left cerebellum with the left OFC, PMC, and right vmPFC. Furthermore, the change in rsFC strength between the right VRP and left cerebellum in IA individuals was found positively correlated with the Internet craving alleviation after acupuncture. Conclusions These findings verified the modulation effect of acupuncture on functional connectivity of reward and habit systems related to the VS in IA individuals, which might partly represent the underlying mechanisms of acupuncture on IA.
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Affiliation(s)
- Yang Wang
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Yun Qin
- Key Laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hui Li
- School of Medicine, Chengdu University, Chengdu, China
| | - Dezhong Yao
- Key Laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Bo Sun
- Key Laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhiliang Li
- Key Laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xin Li
- Key Laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yu Dai
- School of Rehabilitation and Health Preservation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Chao Wen
- Department of Rehabilitation, Zigong Fifth People's Hospital, Zigong, China
| | - Lingrui Zhang
- Department of Medicine, Leshan Vocational and Technical College, Leshan, China
| | - Chenchen Zhang
- Department of Rehabilitation, TCM Hospital of Longquanyi District, Chengdu, China
| | - Tianmin Zhu
- School of Rehabilitation and Health Preservation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Cheng Luo
- Key Laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
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Pasquini L, Toller G, Staffaroni A, Brown JA, Deng J, Lee A, Kurcyus K, Shdo SM, Allen I, Sturm VE, Cobigo Y, Borghesani V, Battistella G, Gorno-Tempini ML, Rankin KP, Kramer J, Rosen HH, Miller BL, Seeley WW. State and trait characteristics of anterior insula time-varying functional connectivity. Neuroimage 2020; 208:116425. [PMID: 31805382 PMCID: PMC7225015 DOI: 10.1016/j.neuroimage.2019.116425] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Revised: 11/28/2019] [Accepted: 11/30/2019] [Indexed: 12/12/2022] Open
Abstract
The human anterior insula (aINS) is a topographically organized brain region, in which ventral portions contribute to socio-emotional function through limbic and autonomic connections, whereas the dorsal aINS contributes to cognitive processes through frontal and parietal connections. Open questions remain, however, regarding how aINS connectivity varies over time. We implemented a novel approach combining seed-to-whole-brain sliding-window functional connectivity MRI and k-means clustering to assess time-varying functional connectivity of aINS subregions. We studied three independent large samples of healthy participants and longitudinal datasets to assess inter- and intra-subject stability, and related aINS time-varying functional connectivity profiles to dispositional empathy. We identified four robust aINS time-varying functional connectivity modes that displayed both "state" and "trait" characteristics: while modes featuring connectivity to sensory regions were modulated by eye closure, modes featuring connectivity to higher cognitive and emotional processing regions were stable over time and related to empathy measures.
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Affiliation(s)
- Lorenzo Pasquini
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Gianina Toller
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Adam Staffaroni
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Jesse A Brown
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Jersey Deng
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Alex Lee
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Katarzyna Kurcyus
- Department of Neuroradiology, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany; Department of Nuclear Medicine, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany
| | | | - Isabel Allen
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Virginia E Sturm
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Yann Cobigo
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Valentina Borghesani
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Giovanni Battistella
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | | | - Katherine P Rankin
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Joel Kramer
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Howard H Rosen
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Bruce L Miller
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - William W Seeley
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA; Memory and Aging Center, Department of Pathology, University of California, San Francisco, CA, USA.
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Lurie DJ, Kessler D, Bassett DS, Betzel RF, Breakspear M, Kheilholz S, Kucyi A, Liégeois R, Lindquist MA, McIntosh AR, Poldrack RA, Shine JM, Thompson WH, Bielczyk NZ, Douw L, Kraft D, Miller RL, Muthuraman M, Pasquini L, Razi A, Vidaurre D, Xie H, Calhoun VD. Questions and controversies in the study of time-varying functional connectivity in resting fMRI. Netw Neurosci 2020; 4:30-69. [PMID: 32043043 PMCID: PMC7006871 DOI: 10.1162/netn_a_00116] [Citation(s) in RCA: 279] [Impact Index Per Article: 69.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Accepted: 11/22/2019] [Indexed: 12/12/2022] Open
Abstract
The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge of the spatiotemporal organization of these interactions is critical for establishing a solid understanding of the brain's functional architecture and the relationship between neural dynamics and cognition in health and disease. The possibility of studying these dynamics through careful analysis of neuroimaging data has catalyzed substantial interest in methods that estimate time-resolved fluctuations in functional connectivity (often referred to as "dynamic" or time-varying functional connectivity; TVFC). At the same time, debates have emerged regarding the application of TVFC analyses to resting fMRI data, and about the statistical validity, physiological origins, and cognitive and behavioral relevance of resting TVFC. These and other unresolved issues complicate interpretation of resting TVFC findings and limit the insights that can be gained from this promising new research area. This article brings together scientists with a variety of perspectives on resting TVFC to review the current literature in light of these issues. We introduce core concepts, define key terms, summarize controversies and open questions, and present a forward-looking perspective on how resting TVFC analyses can be rigorously and productively applied to investigate a wide range of questions in cognitive and systems neuroscience.
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Affiliation(s)
- Daniel J. Lurie
- Department of Psychology, University of California, Berkeley, Berkeley, CA, USA
| | - Daniel Kessler
- Departments of Statistics and Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Danielle S. Bassett
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical & Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Richard F. Betzel
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael Breakspear
- University of Newcastle, Callaghan, NSW, 2308, Australia
- QIMR Berghofer, Brisbane, Australia
| | - Shella Kheilholz
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
| | - Aaron Kucyi
- Department of Neurology and Neurological Sciences, Stanford University, Stanford CA, USA
| | - Raphaël Liégeois
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Switzerland
| | | | - Anthony Randal McIntosh
- Rotman Research Institute - Baycrest Centre, Toronto, Canada
- Department of Psychology, University of Toronto, Toronto, Canada
| | | | - James M. Shine
- Brain and Mind Centre, University of Sydney, NSW, Australia
| | - William Hedley Thompson
- Department of Psychology, Stanford University, Stanford, CA, USA
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | | | - Linda Douw
- Department of Anatomy and Neurosciences, VU University Medical Center, Amsterdam, The Netherlands
| | - Dominik Kraft
- Department of Psychology, Goethe University Frankfurt, Frankfurt am Main, Germany
| | | | - Muthuraman Muthuraman
- Biomedical Statistics and Multimodal Signal Processing Unit, Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience, Johannes-Gutenberg-University Hospital, Mainz, Germany
| | - Lorenzo Pasquini
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Adeel Razi
- Monash Institute of Cognitive and Clinical Neurosciences and Monash Biomedical Imaging, Monash University, Clayton, Australia
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
- Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, Pakistan
| | - Diego Vidaurre
- Wellcome Trust Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, University of Oxford, United Kingdom
| | - Hua Xie
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Vince D. Calhoun
- The Mind Research Network, Albuquerque, NM, USA
- Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, Georgia, USA
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Jalbrzikowski M, Liu F, Foran W, Roeder K, Devlin B, Luna B. Resting-State Functional Network Organization Is Stable Across Adolescent Development for Typical and Psychosis Spectrum Youth. Schizophr Bull 2020; 46:395-407. [PMID: 31424081 PMCID: PMC7442350 DOI: 10.1093/schbul/sbz053] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND Resting-state functional neuroimaging captures large-scale network organization; whether this organization is intact or disrupted during adolescent development across the psychosis spectrum is unresolved. We investigated the integrity of network organization in psychosis spectrum youth and those with first episode psychosis (FEP) from late childhood through adulthood. METHODS We analyzed data from Philadelphia Neurodevelopmental Cohort (PNC; typically developing = 450, psychosis spectrum = 273, 8-22 years), a longitudinal cohort of typically developing youth (LUNA; N = 208, 1-3 visits, 10-25 years), and a sample of FEP (N = 39) and matched controls (N = 34). We extracted individual time series and calculated correlations from brain regions and averaged them for 4 age groups: late childhood, early adolescence, late adolescence, adulthood. Using multiple analytic approaches, we assessed network stability across 4 age groups, compared stability between controls and psychosis spectrum youth, and compared group-level network organization of FEP to controls. We explored whether variability in cognition or clinical symptomatology was related to network organization. RESULTS Network organization was stable across the 4 age groups in the PNC and LUNA typically developing youth and PNC psychosis spectrum youth. Psychosis spectrum and typically developing youth had similar functional network organization during all age ranges. Network organization was intact in PNC youth who met full criteria for psychosis and in FEP. Variability in cognitive functioning or clinical symptomatology was not related to network organization in psychosis spectrum youth or FEP. DISCUSSION These findings provide rigorous evidence supporting intact functional network organization in psychosis risk and psychosis from late childhood through adulthood.
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Affiliation(s)
- Maria Jalbrzikowski
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA,To whom correspondence should be addressed; tel: 201-403-5598, e-mail:
| | - Fuchen Liu
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA
| | - William Foran
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA
| | - Kathryn Roeder
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA,Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA
| | - Bernie Devlin
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA,Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA
| | - Beatriz Luna
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA,Department of Psychology, University of Pittsburgh, Pittsburgh, PA,Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA
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Borsook D, Upadhyay J, Hargreaves R, Wager T. Enhancing Choice and Outcomes for Therapeutic Trials in Chronic Pain: N-of-1 + Imaging (+ i). Trends Pharmacol Sci 2020; 41:85-98. [DOI: 10.1016/j.tips.2019.12.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 11/27/2019] [Accepted: 12/04/2019] [Indexed: 10/25/2022]
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Kurashige H, Kaneko J, Yamashita Y, Osu R, Otaka Y, Hanakawa T, Honda M, Kawabata H. Revealing Relationships Among Cognitive Functions Using Functional Connectivity and a Large-Scale Meta-Analysis Database. Front Hum Neurosci 2020; 13:457. [PMID: 31998102 PMCID: PMC6965330 DOI: 10.3389/fnhum.2019.00457] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Accepted: 12/12/2019] [Indexed: 11/13/2022] Open
Abstract
To characterize each cognitive function per se and to understand the brain as an aggregate of those functions, it is vital to relate dozens of these functions to each other. Knowledge about the relationships among cognitive functions is informative not only for basic neuroscientific research but also for clinical applications and developments of brain-inspired artificial intelligence. In the present study, we propose an exhaustive data mining approach to reveal relationships among cognitive functions based on functional brain mapping and network analysis. We began our analysis with 109 pseudo-activation maps (cognitive function maps; CFM) that were reconstructed from a functional magnetic resonance imaging meta-analysis database, each of which corresponds to one of 109 cognitive functions such as ‘emotion,’ ‘attention,’ ‘episodic memory,’ etc. Based on the resting-state functional connectivity between the CFMs, we mapped the cognitive functions onto a two-dimensional space where the relevant functions were located close to each other, which provided a rough picture of the brain as an aggregate of cognitive functions. Then, we conducted so-called conceptual analysis of cognitive functions using clustering of voxels in each CFM connected to the other 108 CFMs with various strengths. As a result, a CFM for each cognitive function was subdivided into several parts, each of which is strongly associated with some CFMs for a subset of the other cognitive functions, which brought in sub-concepts (i.e., sub-functions) of the cognitive function. Moreover, we conducted network analysis for the network whose nodes were parcels derived from whole-brain parcellation based on the whole-brain voxel-to-CFM resting-state functional connectivities. Since each parcel is characterized by associations with the 109 cognitive functions, network analyses using them are expected to inform about relationships between cognitive and network characteristics. Indeed, we found that informational diversities of interaction between parcels and densities of local connectivity were dependent on the kinds of associated functions. In addition, we identified the homogeneous and inhomogeneous network communities about the associated functions. Altogether, we suggested the effectiveness of our approach in which we fused the large-scale meta-analysis of functional brain mapping with the methods of network neuroscience to investigate the relationships among cognitive functions.
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Affiliation(s)
- Hiroki Kurashige
- Institute of Innovative Science and Technology, Tokai University, Tokyo, Japan.,National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Jun Kaneko
- Institute of Innovative Science and Technology, Tokai University, Tokyo, Japan
| | - Yuichi Yamashita
- National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Rieko Osu
- Faculty of Human Sciences, Waseda University, Tokyo, Japan
| | - Yohei Otaka
- Department of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Aichi, Japan.,Department of Rehabilitation Medicine, Tokyo Bay Rehabilitation Hospital, Chiba, Japan
| | - Takashi Hanakawa
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Manabu Honda
- National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan
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Dinis Fernandes C, Varsou O, Stringer M, Macleod MJ, Schwarzbauer C. Scanning Conditions in Functional Connectivity Magnetic Resonance Imaging: How to Standardise Resting-State for Optimal Data Acquisition and Visualisation? ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2020; 1235:35-52. [PMID: 32488635 DOI: 10.1007/978-3-030-37639-0_3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Functional connectivity magnetic resonance imaging (fcMRI), performed during resting wakefulness without tasks or stimulation, is a non-invasive technique to assess and visualise functional brain networks in vivo. Acquisition of resting-state imaging data has become increasingly common in longitudinal studies to investigate brain health and disease. However, the scanning protocols vary considerably across different institutions creating challenges for comparability especially for the interpretation of findings in patient cohorts and establishment of diagnostic or prognostic imaging biomarkers. The aim of this chapter is to discuss the effect of two experimental conditions (i.e. a low cognitive demand paradigm and a pure resting-state fcMRI) on the reproducibility of brain networks between a baseline and a follow-up session, 30 (±5) days later, acquired from 12 right-handed volunteers (29 ± 5 yrs). A novel method was developed and used for a direct statistical comparison of the test-retest reliability using 28 well-established functional brain networks. Overall, both scanning conditions produced good levels of test-retest reliability. While the pure resting-state condition showed higher test-retest reliability for 18 of the 28 analysed networks, the low cognitive demand paradigm produced higher test-retest reliability for 8 of the 28 brain networks (i.e. visual, sensorimotor and frontal areas); in 2 of the 28 brain networks no significant changes could be detected. These results are relevant to planning of longitudinal studies, as higher test-retest reliability generally increases statistical power. This work also makes an important contribution to neuroimaging where optimising fcMRI experimental scanning conditions, and hence data visualisation of brain function, remains an on-going topic of interest. In this chapter, we provide a full methodological explanation of the two paradigms and our analysis so that readers can apply them to their own scanning protocols.
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Affiliation(s)
| | - Ourania Varsou
- School of Life Sciences, Anatomy Facility, University of Glasgow, Glasgow, Scotland, UK
| | - Michael Stringer
- Edinburgh Imaging, University of Edinburgh, Edinburgh, Scotland, UK
| | - Mary Joan Macleod
- The Institute of Medical Sciences, King's College, University of Aberdeen, Aberdeen, Scotland, UK
| | - Christian Schwarzbauer
- Faculty of Applied Sciences & Mechatronics, Munich University of Applied Sciences, Munich, Germany
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Li X, Pan Y, Fang Z, Lei H, Zhang X, Shi H, Ma N, Raine P, Wetherill R, Kim JJ, Wan Y, Rao H. Test-retest reliability of brain responses to risk-taking during the balloon analogue risk task. Neuroimage 2019; 209:116495. [PMID: 31887425 PMCID: PMC7061333 DOI: 10.1016/j.neuroimage.2019.116495] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 12/20/2019] [Accepted: 12/23/2019] [Indexed: 12/24/2022] Open
Abstract
The Balloon Analogue Risk Task (BART) provides a reliable and ecologically valid model for the assessment of individual risk-taking propensity and is frequently used in neuroimaging and developmental research. Although the test-retest reliability of risk-taking behavior during the BART is well established, the reliability of brain activation patterns in response to risk-taking during the BART remains elusive. In this study, we used functional magnetic resonance imaging (fMRI) and evaluated the test-retest reliability of brain responses in 34 healthy adults during a modified BART by calculating the intraclass correlation coefficients (ICC) and Dice’s similarity coefficients (DSC). Analyses revealed that risk-induced brain activation patterns showed good test-retest reliability (median ICC = 0.62) and moderate to high spatial consistency, while brain activation patterns associated with win or loss outcomes only had poor to fair reliability (median ICC = 0.33 for win and 0.42 for loss). These findings have important implications for future utility of the BART in fMRI to examine brain responses to risk-taking and decision-making.
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Affiliation(s)
- Xiong Li
- School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China
| | - Yu Pan
- School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China; Key Laboratory of Applied Brain and Cognitive Sciences, School of Business and Management, Shanghai International Studies University, Shanghai, China
| | - Zhuo Fang
- Key Laboratory of Applied Brain and Cognitive Sciences, School of Business and Management, Shanghai International Studies University, Shanghai, China; Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Hui Lei
- Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Xiaocui Zhang
- Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Hui Shi
- Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Ning Ma
- Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Philip Raine
- Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Reagan Wetherill
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Junghoon J Kim
- Department of Molecular, Cellular, and Biomedical Sciences, CUNY School of Medicine, The City College of New York, New York, NY, USA
| | - Yan Wan
- School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China
| | - Hengyi Rao
- Key Laboratory of Applied Brain and Cognitive Sciences, School of Business and Management, Shanghai International Studies University, Shanghai, China; Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
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Kundu S, Lukemire J, Wang Y, Guo Y. A Novel Joint Brain Network Analysis Using Longitudinal Alzheimer's Disease Data. Sci Rep 2019; 9:19589. [PMID: 31863067 PMCID: PMC6925181 DOI: 10.1038/s41598-019-55818-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Accepted: 11/26/2019] [Indexed: 12/14/2022] Open
Abstract
There is well-documented evidence of brain network differences between individuals with Alzheimer's disease (AD) and healthy controls (HC). To date, imaging studies investigating brain networks in these populations have typically been cross-sectional, and the reproducibility of such findings is somewhat unclear. In a novel study, we use the longitudinal ADNI data on the whole brain to jointly compute the brain network at baseline and one-year using a state of the art approach that pools information across both time points to yield distinct visit-specific networks for the AD and HC cohorts, resulting in more accurate inferences. We perform a multiscale comparison of the AD and HC networks in terms of global network metrics as well as at the more granular level of resting state networks defined under a whole brain parcellation. Our analysis illustrates a decrease in small-worldedness in the AD group at both the time points and also identifies more local network features and hub nodes that are disrupted due to the progression of AD. We also obtain high reproducibility of the HC network across visits. On the other hand, a separate estimation of the networks at each visit using standard graphical approaches reveals fewer meaningful differences and lower reproducibility.
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Affiliation(s)
- Suprateek Kundu
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Ga, 30322, USA.
| | - Joshua Lukemire
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Ga, 30322, USA
| | - Yikai Wang
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Ga, 30322, USA
| | - Ying Guo
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Ga, 30322, USA
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Noble S, Scheinost D, Constable RT. A decade of test-retest reliability of functional connectivity: A systematic review and meta-analysis. Neuroimage 2019; 203:116157. [PMID: 31494250 PMCID: PMC6907736 DOI: 10.1016/j.neuroimage.2019.116157] [Citation(s) in RCA: 295] [Impact Index Per Article: 59.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Revised: 08/30/2019] [Accepted: 09/02/2019] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Once considered mere noise, fMRI-based functional connectivity has become a major neuroscience tool in part due to early studies demonstrating its reliability. These fundamental studies revealed only the tip of the iceberg; over the past decade, many test-retest reliability studies have continued to add nuance to our understanding of this complex topic. A summary of these diverse and at times contradictory perspectives is needed. OBJECTIVES We aimed to summarize the existing knowledge regarding test-retest reliability of functional connectivity at the most basic unit of analysis: the individual edge level. This entailed (1) a meta-analytic estimate of reliability and (2) a review of factors influencing reliability. METHODS A search of Scopus was conducted to identify studies that estimated edge-level test-retest reliability. To facilitate comparisons across studies, eligibility was restricted to studies measuring reliability via the intraclass correlation coefficient (ICC). The meta-analysis included a random effects pooled estimate of mean edge-level ICC, with studies nested within datasets. The review included a narrative summary of factors influencing edge-level ICC. RESULTS From an initial pool of 212 studies, 44 studies were identified for the qualitative review and 25 studies for quantitative meta-analysis. On average, individual edges exhibited a "poor" ICC of 0.29 (95% CI = 0.23 to 0.36). The most reliable measurements tended to involve: (1) stronger, within-network, cortical edges, (2) eyes open, awake, and active recordings, (3) more within-subject data, (4) shorter test-retest intervals, (5) no artifact correction (likely due in part to reliable artifact), and (6) full correlation-based connectivity with shrinkage. CONCLUSION This study represents the first meta-analysis and systematic review investigating test-retest reliability of edge-level functional connectivity. Key findings suggest there is room for improvement, but care should be taken to avoid promoting reliability at the expense of validity. By pooling existing knowledge regarding this key facet of accuracy, this study supports broader efforts to improve inferences in the field.
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Affiliation(s)
- Stephanie Noble
- Interdepartmental Neuroscience Program, Yale University, USA.
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale University, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA; Department of Statistics and Data Science, Yale University, USA; Child Study Center, Yale School of Medicine, USA
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale University, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA; Department of Neurosurgery, Yale School of Medicine, USA
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Avery EW, Yoo K, Rosenberg MD, Greene AS, Gao S, Na DL, Scheinost D, Constable TR, Chun MM. Distributed Patterns of Functional Connectivity Predict Working Memory Performance in Novel Healthy and Memory-impaired Individuals. J Cogn Neurosci 2019; 32:241-255. [PMID: 31659926 DOI: 10.1162/jocn_a_01487] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Individual differences in working memory relate to performance differences in general cognitive ability. The neural bases of such individual differences, however, remain poorly understood. Here, using a data-driven technique known as connectome-based predictive modeling, we built models to predict individual working memory performance from whole-brain functional connectivity patterns. Using n-back or rest data from the Human Connectome Project, connectome-based predictive models significantly predicted novel individuals' 2-back accuracy. Model predictions also correlated with measures of fluid intelligence and, with less strength, sustained attention. Separate fluid intelligence models predicted working memory score, as did sustained attention models, again with less strength. Anatomical feature analysis revealed significant overlap between working memory and fluid intelligence models, particularly in utilization of prefrontal and parietal regions, and less overlap in predictive features between working memory and sustained attention models. Furthermore, showing the generality of these models, the working memory model developed from Human Connectome Project data generalized to predict memory in an independent data set of 157 older adults (mean age = 69 years; 48 healthy, 54 amnestic mild cognitive impairment, 55 Alzheimer disease). The present results demonstrate that distributed functional connectivity patterns predict individual variation in working memory capability across the adult life span, correlating with constructs including fluid intelligence and sustained attention.
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Affiliation(s)
| | | | | | | | | | - Duk L Na
- Samsung Medical Center, Seoul, South Korea
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Meier TB, Giraldo-Chica M, España LY, Mayer AR, Harezlak J, Nencka AS, Wang Y, Koch KM, Wu YC, Saykin AJ, Giza CC, Goldman J, DiFiori JP, Guskiewicz KM, Mihalik JP, Brooks A, Broglio SP, McAllister T, McCrea MA. Resting-State fMRI Metrics in Acute Sport-Related Concussion and Their Association with Clinical Recovery: A Study from the NCAA-DOD CARE Consortium. J Neurotrauma 2019; 37:152-162. [PMID: 31407610 DOI: 10.1089/neu.2019.6471] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
There has been a recent call for longitudinal cohort studies to track the physiological recovery of sport-related concussion (SRC) and its relationship with clinical recovery. Resting-state functional magnetic resonance imaging (rs-fMRI) has shown potential for detecting subtle changes in brain function after SRC. We investigated the effects of SRC on rs-fMRI metrics assessing local connectivity (regional homogeneity; REHO), global connectivity (average nodal strength), and the relative amplitude of slow oscillations of rs-fMRI (fractional amplitude of low-frequency fluctuations; fALFF). Athletes diagnosed with SRC (n = 92) completed visits with neuroimaging at 24-48 h post-injury (24 h), after clearance to begin the return-to-play (RTP) progression (asymptomatic), and 7 days following unrestricted RTP (post-RTP). Non-injured athletes (n = 82) completed visits yoked to the schedule of matched injured athletes and served as controls. Concussed athletes had elevated symptoms, worse neurocognitive performance, greater balance deficits, and elevated psychological symptoms at the 24-h visit relative to controls. These deficits were largely recovered by the asymptomatic visit. Concussed athletes still reported elevated psychological symptoms at the asymptomatic visit relative to controls. Concussed athletes also had elevated REHO in the right middle and superior frontal gyri at the 24-h visit that returned to normal levels by the asymptomatic visit. Additionally, REHO in these regions at 24 h predicted psychological symptoms at the asymptomatic visit in concussed athletes. Current results suggest that SRC is associated with an acute alteration in local connectivity that follows a similar time course as clinical recovery. Our results do not indicate strong evidence that concussion-related alterations in rs-fMRI persist beyond clinical recovery.
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Affiliation(s)
- Timothy B Meier
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin.,Department of Cell Biology, Neurobiology and Anatomy, Medical College of Wisconsin, Milwaukee, Wisconsin.,Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, Wisconsin
| | | | - Lezlie Y España
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Andrew R Mayer
- The Mind Research Network/Lovelace Biomedical and Environmental Research Institute, Neurology and Psychiatry Departments, University of New Mexico School of Medicine, Department of Psychology, University of New Mexico, Albuquerque, New Mexico
| | - Jaroslaw Harezlak
- Department of Epidemiology and Biostatistics, Indiana University, Bloomington, Indiana
| | - Andrew S Nencka
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Yang Wang
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Kevin M Koch
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Yu-Chien Wu
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana
| | - Christopher C Giza
- Departments of Pediatrics and Neurosurgery, University of California Los Angeles, Los Angeles, California
| | - Joshua Goldman
- Departments of Family Medicine and Orthopaedic Surgery, University of California Los Angeles, Los Angeles, California.,Center for Sports Medicine, Orthopaedic Institute for Children, Los Angeles, California
| | - John P DiFiori
- Hospital for Special Surgery, Primary Sports Medicine Service, New York, New York
| | - Kevin M Guskiewicz
- Department of Exercise and Sport Science, University of North Carolina, Chapel Hill, North Carolina
| | - Jason P Mihalik
- Department of Exercise and Sport Science, University of North Carolina, Chapel Hill, North Carolina
| | - Alison Brooks
- Department of Orthopedics and Rehabilitation, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Steven P Broglio
- School of Kinesiology, University of Michigan, Ann Arbor, Michigan
| | - Thomas McAllister
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, Indiana
| | - Michael A McCrea
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin
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Tuleasca C, Régis J, Najdenovska E, Witjas T, Girard N, Bolton T, Delaire F, Vincent M, Faouzi M, Thiran JP, Bach Cuadra M, Levivier M, Van de Ville D. Pretherapeutic resting-state fMRI profiles are associated with MR signature volumes after stereotactic radiosurgical thalamotomy for essential tremor. J Neurosurg 2019; 129:63-71. [PMID: 30544321 DOI: 10.3171/2018.7.gks18752] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Accepted: 07/24/2018] [Indexed: 11/06/2022]
Abstract
OBJECTIVEEssential tremor (ET) is the most common movement disorder. Drug-resistant ET can benefit from standard stereotactic deep brain stimulation or radiofrequency thalamotomy or, alternatively, minimally invasive techniques, including stereotactic radiosurgery (SRS) and high-intensity focused ultrasound, at the level of the ventral intermediate nucleus (Vim). The aim of the present study was to evaluate potential correlations between pretherapeutic interconnectivity (IC), as depicted on resting-state functional MRI (rs-fMRI), and MR signature volume at 1 year after Vim SRS for tremor, to be able to potentially identify hypo- and hyperresponders based only on pretherapeutic neuroimaging data.METHODSSeventeen consecutive patients with ET were included, who benefitted from left unilateral SRS thalamotomy (SRS-T) between September 2014 and August 2015. Standard tremor assessment and rs-fMRI were acquired pretherapeutically and 1 year after SRS-T. A healthy control group was also included (n = 12). Group-level independent component analysis (ICA; only n = 17 for pretherapeutic rs-fMRI) was applied. The mean MR signature volume was 0.125 ml (median 0.063 ml, range 0.002-0.600 ml). The authors correlated baseline IC with 1-year MR signatures within all networks. A 2-sample t-test at the level of each component was first performed in two groups: group 1 (n = 8, volume < 0.063 ml) and group 2 (n = 9, volume ≥ 0.063 ml). These groups did not statistically differ by age, duration of symptoms, baseline ADL score, ADL point decrease at 1 year, time to tremor arrest, or baseline tremor score on the treated hand (TSTH; p > 0.05). An ANOVA was then performed on each component, using individual subject-level maps and continuous values of 1-year MR signatures, correlated with pretherapeutic IC.RESULTSUsing 2-sample t-tests, two networks were found to be statistically significant: network 3, including the brainstem, motor cerebellum, bilateral thalamus, and left supplementary motor area (SMA) (pFWE = 0.004, cluster size = 94), interconnected with the red nucleus (MNI -2, -22, -32); and network 9, including the brainstem, posterior insula, bilateral thalamus, and left SMA (pFWE = 0.002, cluster size = 106), interconnected with the left SMA (MNI 24, -28, 44). Higher pretherapeutic IC was associated with higher MR volumes, in a network including the anterior default-mode network and bilateral thalamus (ANOVA, pFWE = 0.004, cluster size = 73), interconnected with cerebellar lobule V (MNI -12, -70, -22). Moreover, in the same network, radiological hyporesponders presented with negative IC values.CONCLUSIONSThese findings have clinical implications for predicting MR signature volumes after SRS-T. Here, using pretherapeutic MRI and data processing without prior hypothesis, the authors showed that pretherapeutic network interconnectivity strength predicts 1-year MR signature volumes following SRS-T.
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Affiliation(s)
- Constantin Tuleasca
- 1Neurosurgery Service and Gamma Knife Center.,4Faculty of Biology and Medicine, University of Lausanne, Switzerland
| | - Jean Régis
- 5Stereotactic and Functional Neurosurgery Service and Gamma Knife Unit, and
| | - Elena Najdenovska
- 2Medical Image Analysis Laboratory (MIAL) and Department of Radiology, Centre d'Imagerie BioMédicale (CIBM), and
| | | | - Nadine Girard
- 7AMU, CRMBM UMR CNRS 7339, Faculté de Médecine et APHM, Hôpital Timone, Department of Diagnostic and Interventional Neuroradiology, Marseille, France
| | - Thomas Bolton
- 8Medical Image Processing Laboratory, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland
| | - Francois Delaire
- 5Stereotactic and Functional Neurosurgery Service and Gamma Knife Unit, and
| | - Marion Vincent
- 5Stereotactic and Functional Neurosurgery Service and Gamma Knife Unit, and
| | - Mohamed Faouzi
- 9Institute of Social and Preventive Medicine, Lausanne, Switzerland; and
| | - Jean-Philippe Thiran
- 3Signal Processing Laboratory (LTS 5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland.,4Faculty of Biology and Medicine, University of Lausanne, Switzerland.,10Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Meritxell Bach Cuadra
- 2Medical Image Analysis Laboratory (MIAL) and Department of Radiology, Centre d'Imagerie BioMédicale (CIBM), and.,3Signal Processing Laboratory (LTS 5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland
| | - Marc Levivier
- 1Neurosurgery Service and Gamma Knife Center.,4Faculty of Biology and Medicine, University of Lausanne, Switzerland
| | - Dimitri Van de Ville
- 8Medical Image Processing Laboratory, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland.,11University of Geneva, Faculty of Medicine, Geneva, Switzerland
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Mayer EA, Labus J, Aziz Q, Tracey I, Kilpatrick L, Elsenbruch S, Schweinhardt P, Van Oudenhove L, Borsook D. Role of brain imaging in disorders of brain-gut interaction: a Rome Working Team Report. Gut 2019; 68:1701-1715. [PMID: 31175206 PMCID: PMC6999847 DOI: 10.1136/gutjnl-2019-318308] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Revised: 03/18/2019] [Accepted: 03/24/2019] [Indexed: 12/12/2022]
Abstract
Imaging of the living human brain is a powerful tool to probe the interactions between brain, gut and microbiome in health and in disorders of brain-gut interactions, in particular IBS. While altered signals from the viscera contribute to clinical symptoms, the brain integrates these interoceptive signals with emotional, cognitive and memory related inputs in a non-linear fashion to produce symptoms. Tremendous progress has occurred in the development of new imaging techniques that look at structural, functional and metabolic properties of brain regions and networks. Standardisation in image acquisition and advances in computational approaches has made it possible to study large data sets of imaging studies, identify network properties and integrate them with non-imaging data. These approaches are beginning to generate brain signatures in IBS that share some features with those obtained in other often overlapping chronic pain disorders such as urological pelvic pain syndromes and vulvodynia, suggesting shared mechanisms. Despite this progress, the identification of preclinical vulnerability factors and outcome predictors has been slow. To overcome current obstacles, the creation of consortia and the generation of standardised multisite repositories for brain imaging and metadata from multisite studies are required.
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Affiliation(s)
- Emeran A Mayer
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, Vatche and Tamar Manoukian Division of Digestive Diseases David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Jennifer Labus
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, Vatche and Tamar Manoukian Division of Digestive Diseases David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Qasim Aziz
- Neurogastroenterology Group, Queen Mary University of London, London, UK
| | - Irene Tracey
- Departments of Anaesthetics and Clinical Neurology, Pembroke College, Oxford, UK
| | - Lisa Kilpatrick
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, Vatche and Tamar Manoukian Division of Digestive Diseases David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Sigrid Elsenbruch
- Institute of Medical Psychology & Behavioral Immunobiology, University Hospital Essen, University of Duisburg, Duisburg, Germany
| | | | - Lukas Van Oudenhove
- Translational Research in GastroIntestinal Disorders, KU Leuven Department of Clinical and Experimental Medicine, University of Leuven, Leuven, Belgium
| | - David Borsook
- Center for Pain and the Brain, Boston Children's, Massachusetts General and McLean Hospitals, Harvard Medical School, Boston, Massachusetts, USA
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66
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Diekfuss JA, Grooms DR, Nissen KS, Schneider DK, Foss KDB, Thomas S, Bonnette S, Dudley JA, Yuan W, Reddington DL, Ellis JD, Leach J, Gordon M, Lindsey C, Rushford K, Shafer C, Myer GD. Alterations in knee sensorimotor brain functional connectivity contributes to ACL injury in male high-school football players: a prospective neuroimaging analysis. Braz J Phys Ther 2019; 24:415-423. [PMID: 31377125 DOI: 10.1016/j.bjpt.2019.07.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Revised: 07/02/2019] [Accepted: 07/03/2019] [Indexed: 01/13/2023] Open
Abstract
OBJECTIVE This study's purpose was to utilize a prospective dataset to examine differences in functional brain connectivity in male high school athletes who suffered an anterior cruciate ligament (ACL) injury relative to their non-injured peers. METHODS Sixty-two male high school football players were evaluated using functional magnetic resonance imaging prior to their competitive season to evaluate resting-state functional brain connectivity. Three athletes later experienced an ACL injury and were matched to 12 teammates who did not go on to sustain an ACL injury (controls) based on school, age, height, weight, and year in school. Twenty-five knee-motor regions of interest (ROIs) were created to identify differences in connectivity between the two groups. Between-subject F and t tests were used to identify significant ROI differences using a false discovery rate correction for multiple comparisons. RESULTS There was significantly less connectivity between the left secondary somatosensory cortex and the left supplementary motor area (p = 0.025), right pre-motor cortex (p = 0.026), right supplementary motor area (p = 0.026), left primary somatosensory cortex (superior division; p = 0.026), left primary somatosensory cortex (inferior division; p = 0.026), and left primary motor cortex (p = 0.048) for the ACL-injured compared to the control subjects. No other ROI-to-ROI comparisons were significantly different between the groups (all p > 0.05). CONCLUSION Our preliminary data indicate a potential sensorimotor disruption for male football players who go on to experience an ACL injury. Future studies with larger sample sizes and complementary measures of neuromuscular control are needed to support these findings.
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Affiliation(s)
- Jed A Diekfuss
- The SPORT Center, Division of Sports Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
| | - Dustin R Grooms
- Ohio Musculoskeletal & Neurological Institute and Division of Athletic Training, School of Applied Health Sciences and Wellness, College of Health Sciences and Professions, Ohio University, Athens, OH, USA
| | - Katharine S Nissen
- The SPORT Center, Division of Sports Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Daniel K Schneider
- The SPORT Center, Division of Sports Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Riverside Methodist Hospital, Columbus, OH, USA
| | - Kim D Barber Foss
- The SPORT Center, Division of Sports Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Staci Thomas
- The SPORT Center, Division of Sports Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Scott Bonnette
- The SPORT Center, Division of Sports Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Jonathan A Dudley
- Pediatric Neuroimaging Research Consortium, Cincinnati Children's Hospital Medical Center, Ohio, USA
| | - Weihong Yuan
- Pediatric Neuroimaging Research Consortium, Cincinnati Children's Hospital Medical Center, Ohio, USA; University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Danielle L Reddington
- The SPORT Center, Division of Sports Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Jonathan D Ellis
- The SPORT Center, Division of Sports Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; University of Cincinnati Medical Center, Department of Orthopaedic Surgery, Cincinnati, OH, USA
| | - James Leach
- University of Cincinnati College of Medicine, Cincinnati, OH, USA; Division of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH
| | | | | | | | | | - Gregory D Myer
- The SPORT Center, Division of Sports Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Departments of Pediatrics and Orthopaedic Surgery, University of Cincinnati, Cincinnati, OH, USA; The Micheli Center for Sports Injury Prevention, Waltham, MA, USA
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Lu W, Dong K, Cui D, Jiao Q, Qiu J. Quality assurance of human functional magnetic resonance imaging: a literature review. Quant Imaging Med Surg 2019; 9:1147-1162. [PMID: 31367569 DOI: 10.21037/qims.2019.04.18] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Functional magnetic resonance imaging (fMRI) has been a popular approach in brain research over the past 20 years. It offers a noninvasive method to probe the brain and uses blood oxygenation level dependent (BOLD) signal changes to access brain function. However, the BOLD signal only represents a small fraction of the total MR signal. System instability and various noise have a strong impact on the BOLD signal. Additionally, fMRI applies fast imaging technique to record brain cognitive process over time, requiring high temporal stability of MR scanners. Furthermore, data acquisition, image quality, processing, and statistical analysis methods also have a great effect on the results of fMRI studies. Quality assurance (QA) programs for fMRI can test the stability of MR scanners, evaluate the quality of fMRI and help to find errors during fMRI scanning, thereby greatly enhancing the success rate of fMRI. In this review, we focus on previous studies which developed QA programs and methods in SCI/SCIE citation peer-reviewed publications over the last 20 years, including topics on existing fMRI QA programs, QA phantoms, image QA metrics, quality evaluation of existing preprocessing pipelines and fMRI statistical analysis methods. The summarized studies were classified into four categories: QA of fMRI systems, QA of fMRI data, quality evaluation of data processing pipelines and statistical methods and QA of task-related fMRI. Summary tables and figures of QA programs and metrics have been developed based on the comprehensive review of the literature.
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Affiliation(s)
- Weizhao Lu
- Medical Engineering and Technical Center, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China.,Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Kejiang Dong
- Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Dong Cui
- Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Qing Jiao
- Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Jianfeng Qiu
- Medical Engineering and Technical Center, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China.,Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
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68
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Krishnamurthy V, Krishnamurthy LC, Schwam DM, Ealey A, Shin J, Greenberg D, Morris RD. Retrospective Correction of Physiological Noise: Impact on Sensitivity, Specificity, and Reproducibility of Resting-State Functional Connectivity in a Reading Network Model. Brain Connect 2019; 8:94-105. [PMID: 29226700 DOI: 10.1089/brain.2017.0513] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
It is well accepted that physiological noise (PN) obscures the detection of neural fluctuations in resting-state functional connectivity (rsFC) magnetic resonance imaging. However, a clear consensus for an optimal PN correction (PNC) methodology and how it can impact the rsFC signal characteristics is still lacking. In this study, we probe the impact of three PNC methods: RETROICOR: (Glover et al., 2000 ), ANATICOR: (Jo et al., 2010 ), and RVTMBPM: (Bianciardi et al., 2009 ). Using a reading network model, we systematically explore the effects of PNC optimization on sensitivity, specificity, and reproducibility of rsFC signals. In terms of specificity, ANATICOR was found to be effective in removing local white matter (WM) fluctuations and also resulted in aggressive removal of expected cortical-to-subcortical functional connections. The ability of RETROICOR to remove PN was equivalent to removal of simulated random PN such that it artificially inflated the connection strength, thereby decreasing sensitivity. RVTMBPM maintained specificity and sensitivity by balanced removal of vasodilatory PN and local WM nuisance edges. Another aspect of this work was exploring the effects of PNC on identifying reading group differences. Most PNC methods accounted for between-subject PN variability resulting in reduced intersession reproducibility. This effect facilitated the detection of the most consistent group differences. RVTMBPM was most effective in detecting significant group differences due to its inherent sensitivity to removing spatially structured and temporally repeating PN arising from dense vasculature. Finally, results suggest that combining all three PNC resulted in "overcorrection" by removing signal along with noise.
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Affiliation(s)
- Venkatagiri Krishnamurthy
- 1 Department of Neurology, Emory University , Atlanta, Georgia .,2 Center for Visual and Neurocognitive Rehabilitation , Atlanta VAMC, Decatur, Georgia .,3 Center for Advanced Brain Imaging, Georgia State University and Georgia Institute of Technology , Atlanta, Georgia
| | - Lisa C Krishnamurthy
- 2 Center for Visual and Neurocognitive Rehabilitation , Atlanta VAMC, Decatur, Georgia .,3 Center for Advanced Brain Imaging, Georgia State University and Georgia Institute of Technology , Atlanta, Georgia .,4 Department of Physics and Astronomy, Georgia State University , Atlanta, Georgia
| | - Dina M Schwam
- 5 Department of Educational Psychology, Special Education, and Communication Disorders, Georgia State University , Atlanta, Georgia
| | - Ashley Ealey
- 6 Department of Biology, Neuroscience Program, Agnes Scott College , Decatur, Georgia
| | - Jaemin Shin
- 3 Center for Advanced Brain Imaging, Georgia State University and Georgia Institute of Technology , Atlanta, Georgia
| | - Daphne Greenberg
- 5 Department of Educational Psychology, Special Education, and Communication Disorders, Georgia State University , Atlanta, Georgia
| | - Robin D Morris
- 3 Center for Advanced Brain Imaging, Georgia State University and Georgia Institute of Technology , Atlanta, Georgia .,7 Department of Psychology, Georgia State University , Atlanta, Georgia
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69
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Hu R, Qiu D, Guo Y, Zhao Y, Leatherday C, Wu J, Allen JW. Variability of Resting-State Functional MRI Graph Theory Metrics across 3T Platforms. J Neuroimaging 2019; 29:344-347. [PMID: 30702182 PMCID: PMC6506355 DOI: 10.1111/jon.12603] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2018] [Revised: 01/19/2019] [Accepted: 01/21/2019] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND AND PURPOSE Graph theory analysis of brain connectivity data is a promising tool for studying the function of the healthy and diseased brain. The consistency of resting-state functional MRI (rsfMRI) connectivity measures across multiple scanner types is an important factor in designing multi-institutional research studies and has important implications for the potential use of this technique in a heterogeneous clinical setting. We sought to quantitatively study the interscanner variability of rsfMRI graph theory metrics obtained from healthy volunteers scanned on three different scanner platforms. METHODS In this prospective Institutional Review Board approved study, 9 healthy volunteers were enrolled for brain MRI on three 3T scanners (Magnetom Prisma, Skyra, and Trio, Siemens, Erlangen, Germany) in three separate scan sessions within approximately 1 week. Standard preprocessing of rsfMRI was performed with SPM12. Subject scans were normalized to Montreal Neurologic Institute (MNI) space, and connectivity of 116 regions-of-interests based on the automated anatomic labeling (AAL) atlas was calculated using Conn toolbox. Whole-network graph theory metrics were calculated using Brain Connectivity Toolbox, and intraclass correlation (ICC) across three scan sessions was assessed. RESULTS A total of 25 rsfMRI exams were completed in 9 subjects with a median-intersession time of 3 days. Among all three sessions, there was good to excellent agreement in characteristic path length and global efficiency (ICC: .79, .79) and good agreement in the transitivity, local efficiency, and clustering coefficient (ICC = .72, .69, .62). CONCLUSIONS There was high consistency of graph theory metrics of rsfMRI connectivity networks among healthy volunteers scanned on three different generation 3T MRI scanners.
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Affiliation(s)
- Ranliang Hu
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA
| | - Deqiang Qiu
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA
| | - Ying Guo
- Department of Biostatistics and Bioinformatics, Emory University Rollins School of Public Health, Atlanta, GA
| | - Yujie Zhao
- Department of Biostatistics and Bioinformatics, Emory University Rollins School of Public Health, Atlanta, GA
| | - Christopher Leatherday
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA
| | - Junjie Wu
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA
| | - Jason W Allen
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA
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70
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van der Velde B, Haartsen R, Kemner C. Test-retest reliability of EEG network characteristics in infants. Brain Behav 2019; 9:e01269. [PMID: 30912271 PMCID: PMC6520303 DOI: 10.1002/brb3.1269] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Revised: 01/27/2019] [Accepted: 02/03/2019] [Indexed: 12/18/2022] Open
Abstract
INTRODUCTION Functional Electroencephalography (EEG) networks in infants have been proposed as useful biomarkers for developmental brain disorders. However, the reliability of these networks and their characteristics has not been established. We evaluated the reliability of these networks and their characteristics in 10-month-old infants. METHODS Data were obtained during two EEG sessions 1 week apart and was subsequently analyzed at delta (0.5-3 Hz), theta (3-6 Hz), alpha1 (6-9 Hz), alpha2 (9-12 Hz), beta (12-25 Hz), and low gamma (25-45 Hz) frequency bands. Connectivity matrices were created by calculating the phase lag index between all channel pairs at given frequency bands. To determine the reliability of these connectivity matrices, intra-class correlations were calculated of global connectivity, local connectivity, and several graph characteristics. RESULTS Comparing both sessions, global connectivity, as well as global graph characteristics (characteristic path length and average clustering coefficient) are highly reliable across multiple frequency bands; the alpha1 and theta band having the highest reliability in general. In contrast, local connectivity characteristics were less reliable across all frequency bands. CONCLUSIONS We conclude that global connectivity measures are highly reliable over sessions. Local connectivity measures show lower reliability over sessions. This research therefore underlines the possibility of these global network characteristics to be used both as biomarkers of neurodevelopmental disorders, but also as important factors explaining development of typical behavior.
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Affiliation(s)
- Bauke van der Velde
- Department of Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, the Netherlands.,Department of Developmental Psychology, Utrecht University, Utrecht, the Netherlands
| | - Rianne Haartsen
- Centre for Brain and Cognitive Development, Department of Psychological Science, University of London, Birkbeck, London, UK
| | - Chantal Kemner
- Department of Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, the Netherlands.,Department of Developmental Psychology, Utrecht University, Utrecht, the Netherlands
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71
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Sakhare AR, Barisano G, Pa J. Assessing test-retest reliability of phase contrast MRI for measuring cerebrospinal fluid and cerebral blood flow dynamics. Magn Reson Med 2019; 82:658-670. [PMID: 31020721 DOI: 10.1002/mrm.27752] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Revised: 03/05/2019] [Accepted: 03/06/2019] [Indexed: 02/06/2023]
Abstract
PURPOSE Pathological states occur when cerebrospinal fluid (CSF) and cerebral blood flow (CBF) dynamics become dysregulated in the brain. Phase-contrast MRI (PC-MRI) is a noninvasive imaging technique that enables quantitative measurements of CSF and CBF flow. While studies have validated PC-MRI as an imaging technique for flow, few studies have evaluated its reliability for CSF and CBF flow parameters commonly associated with neurological disease. The purpose of this study was to evaluate test-retest reliability at the cerebral aqueduct (CA) and C2-C3 area using PC-MRI to assess the feasibility of investigating CSF and CBF flow dynamics. METHODS This study was performed on 27 cognitively normal young adults (ages 20-35 years). Flow data was acquired on a 3T Siemens Prisma using a 2D cine-PC pulse sequence. Three consecutive flow measurements were acquired at the CA and C2-C3 area. Intraclass correlation coefficient (ICC) and coefficient of variance (CV) were used to evaluate intrarater, inter-rater, and test-retest reliability. RESULTS Among the 26 flow parameters analyzed, 22 had excellent reliability (ICC > 0.80), including measurements of CSF stroke volume, flush peak, and fill peak, and 4 parameters had good reliability (ICC 0.60-0.79). 16 flow parameters had a mean CV ≤ 10%, 7 had a CV ≤ 15%, and 3 had a CV ≤ 30%. All CSF and CBF flow measurements had excellent inter-rater and intrarater reliability (ICC > 0.80). CONCLUSION This study shows that CSF and CBF flow can be reliably measured at the CA and C2-C3 area using PC-MRI, making it a promising tool for studying flow dynamics in the central nervous system.
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Affiliation(s)
- Ashwin R Sakhare
- Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California.,Department of Neurology, Laboratory of Neuro Imaging, Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, California
| | - Giuseppe Barisano
- Department of Neurology, Laboratory of Neuro Imaging, Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, California.,Neuroscience Graduate Program, University of Southern California, Los Angeles, California
| | - Judy Pa
- Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California.,Department of Neurology, Laboratory of Neuro Imaging, Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, California.,Neuroscience Graduate Program, University of Southern California, Los Angeles, California
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72
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Casimo K, Madhyastha TM, Ko AL, Brown AB, Grassia F, Ojemann JG, Weaver KE. Spontaneous Variation in Electrocorticographic Resting-State Connectivity. Brain Connect 2019; 9:488-499. [PMID: 31002014 DOI: 10.1089/brain.2018.0596] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Prior studies using functional magnetic resonance imaging, electroencephalography, and magnetoencephalography have observed both structured patterns in resting-state functional connectivity and spontaneous longitudinal variation in connectivity patterns independent of a task. In this first study using electrocorticography (ECoG), we characterized spontaneous, intersession variation in resting-state functional connectivity not linked to a task. We evaluated pairwise connectivity between electrodes using three measures (phase locking value [PLV], amplitude correlation, and coherence) for six canonical frequency bands, capturing different characteristics of time-evolving signals. We grouped electrodes into 10 functional regions and used intraclass correlation (ICC) to estimate pairwise longitudinal stability. We found that stronger PLV (PLV ≥0.4) in theta through gamma bands and strong correlation in all bands (R2's ≥0.6) are linked to substantial stability (ICC ≥0.6), but that stability does not imply strong phase locking or amplitude correlation. There was no notable link between strong coherence and high ICC. All within-region PLVs are markedly stable across frequencies. In addition, we highlight interaction patterns across several regions: parahippocampal/entorhinal cortex is characterized by stable, weak functional connectivity except self-connections. Dorsolateral prefrontal cortex connectivity is weak and unstable, except self-connections. Inferior parietal lobule has little stability despite narrow connectivity bounds. We confirm prior studies linking functional connectivity strength and intersession variability, extending into higher frequencies than other modalities, with greater spatial specificity than scalp electrophysiology. We suggest further studies quantitatively compare ECoG to other modalities and/or use these findings as a baseline to capture functional connectivity and dynamics linked to perturbations with a task or disease state.
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Affiliation(s)
- Kaitlyn Casimo
- 1 Graduate Program in Neuroscience, Center for Neurotechnology, University of Washington, Seattle, Washington
| | - Tara M Madhyastha
- 2 Integrated Brain Imaging Center, Department of Radiology, University of Washington, Seattle, Washington
| | - Andrew L Ko
- 3 Department of Neurological Surgery, Graduate Program in Neuroscience, University of Washington, Seattle, Washington
| | - Alainna B Brown
- 4 Graduate Program in Neuroscience, School of Medicine, University of Washington, Seattle, Washington
| | - Fabio Grassia
- 5 Department of Neurosurgery, University of Milan, San Gerardo Hospital, Monza, Italy
| | - Jeffrey G Ojemann
- 6 Division of Neurosurgery, Seattle Children's Hospital, Seattle, Washington.,7 Department of Neurological Surgery, Graduate Program in Neuroscience, Center for Neurotechnology, University of Washington, Seattle, Washington
| | - Kurt E Weaver
- 1 Graduate Program in Neuroscience, Center for Neurotechnology, University of Washington, Seattle, Washington.,2 Integrated Brain Imaging Center, Department of Radiology, University of Washington, Seattle, Washington
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73
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Salience network connectivity is reduced by a meal and influenced by genetic background and hypothalamic gliosis. Int J Obes (Lond) 2019; 44:167-177. [PMID: 30967608 PMCID: PMC6785381 DOI: 10.1038/s41366-019-0361-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 03/04/2019] [Accepted: 03/10/2019] [Indexed: 01/30/2023]
Abstract
Background/Objectives: The salience network (SN) comprises brain regions that evaluate cues in the external environment in light of internal signals. We examined the SN response to meal intake and potential genetic and acquired influences on SN function. Subjects/Methods: Monozygotic (MZ; 40 pairs) and dizygotic (15 pairs) twins had body composition and plasma metabolic profile evaluated (glucose, insulin, leptin, ghrelin and GLP-1). Twins underwent resting-state functional magnetic resonance imaging (fMRI) scans before and after a standardized meal. The strength of SN connectivity was analyzed pre- and post-meal and the percentage change elicited by a meal was calculated. A multi-echo T2 MRI scan measured T2 relaxation time, a radiologic index of gliosis, in the mediobasal hypothalamus (MBH) and control regions. Statistical approaches included intraclass correlations (ICC) to investigate genetic influences and within-pair analyses to exclude genetic confounders. Results: SN connectivity was reduced by meal ingestion (β=−0.20; P<0.001). Inherited influences on both pre- and post-meal connectivity were present (ICC MZ twins 26%, P<0.05 and 47%, P<0.001, respectively), but not percentage change in response to the meal. SN connectivity in response to a meal did not differ between participants with obesity and of normal weight (χ2(1)=0.93; P=0.33). However, when participants were classified as having high or low signs of MBH gliosis, the high MBH gliosis group failed to reduce the connectivity in response to a meal (z=−1.32; P=0.19). Excluding genetic confounders, the percentage change in SN connectivity by a meal correlated to body fat percentage (r=0.24; P<0.01). Conclusions: SN connectivity was reduced by a meal, indicating potential participation of the SN in control of feeding. The strength of SN connectivity is inherited, but the degree to which SN connectivity is reduced by eating appears to be influenced by adiposity and the presence of hypothalamic gliosis.
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74
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Prognostic Value of CT Radiomic Features in Resectable Pancreatic Ductal Adenocarcinoma. Sci Rep 2019; 9:5449. [PMID: 30931954 PMCID: PMC6443807 DOI: 10.1038/s41598-019-41728-7] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Accepted: 03/14/2019] [Indexed: 12/14/2022] Open
Abstract
In this work, we assess the reproducibility and prognostic value of CT-derived radiomic features for resectable pancreatic ductal adenocarcinoma (PDAC). Two radiologists contoured tumour regions on pre-operative CT of two cohorts from two institutions undergoing curative-intent surgical resection for PDAC. The first (n = 30) and second cohorts (n = 68) were used for training and validation of proposed prognostic model for overall survival (OS), respectively. Radiomic features were extracted using PyRadiomics library and those with weak inter-reader reproducibility were excluded. Through Cox regression models, significant features were identified in the training cohort and retested in the validation cohort. Significant features were then fused via Cox regression to build a single radiomic signature in the training cohort, which was validated across readers in the validation cohort. Two radiomic features derived from Sum Entropy and Cluster Tendency features were both robust to inter-reader reproducibility and prognostic of OS across cohorts and readers. The radiomic signature showed prognostic value for OS in the validation cohort with hazard ratios of 1.56 (P = 0.005) and 1.35 (P = 0.022), for the first and second reader, respectively. CT-based radiomic features were shown to be prognostic in patients with resectable PDAC. These features may help stratify patients for neoadjuvant or alternative therapies.
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75
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Elliott ML, Knodt AR, Cooke M, Kim MJ, Melzer TR, Keenan R, Ireland D, Ramrakha S, Poulton R, Caspi A, Moffitt TE, Hariri AR. General functional connectivity: Shared features of resting-state and task fMRI drive reliable and heritable individual differences in functional brain networks. Neuroimage 2019; 189:516-532. [PMID: 30708106 PMCID: PMC6462481 DOI: 10.1016/j.neuroimage.2019.01.068] [Citation(s) in RCA: 163] [Impact Index Per Article: 32.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Revised: 01/22/2019] [Accepted: 01/27/2019] [Indexed: 01/15/2023] Open
Abstract
Intrinsic connectivity, measured using resting-state fMRI, has emerged as a fundamental tool in the study of the human brain. However, due to practical limitations, many studies do not collect enough resting-state data to generate reliable measures of intrinsic connectivity necessary for studying individual differences. Here we present general functional connectivity (GFC) as a method for leveraging shared features across resting-state and task fMRI and demonstrate in the Human Connectome Project and the Dunedin Study that GFC offers better test-retest reliability than intrinsic connectivity estimated from the same amount of resting-state data alone. Furthermore, at equivalent scan lengths, GFC displayed higher estimates of heritability than resting-state functional connectivity. We also found that predictions of cognitive ability from GFC generalized across datasets, performing as well or better than resting-state or task data alone. Collectively, our work suggests that GFC can improve the reliability of intrinsic connectivity estimates in existing datasets and, subsequently, the opportunity to identify meaningful correlates of individual differences in behavior. Given that task and resting-state data are often collected together, many researchers can immediately derive more reliable measures of intrinsic connectivity through the adoption of GFC rather than solely using resting-state data. Moreover, by better capturing heritable variation in intrinsic connectivity, GFC represents a novel endophenotype with broad applications in clinical neuroscience and biomarker discovery.
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Affiliation(s)
- Maxwell L Elliott
- Department of Psychology & Neuroscience, Duke University, Box 104410, Durham, NC, 27708, USA.
| | - Annchen R Knodt
- Department of Psychology & Neuroscience, Duke University, Box 104410, Durham, NC, 27708, USA
| | - Megan Cooke
- Department of Psychology & Neuroscience, Duke University, Box 104410, Durham, NC, 27708, USA
| | - M Justin Kim
- Department of Psychology, University of Hawaii at Manoa, Honolulu, HI, 96822, USA
| | - Tracy R Melzer
- New Zealand Brain Research Institute, Christchurch, New Zealand; Department of Medicine, University of Otago, Christchurch, New Zealand
| | - Ross Keenan
- New Zealand Brain Research Institute, Christchurch, New Zealand; Christchurch Radiology Group, Christchurch, New Zealand
| | - David Ireland
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, 163 Union St E, Dunedin, 9016, New Zealand
| | - Sandhya Ramrakha
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, 163 Union St E, Dunedin, 9016, New Zealand
| | - Richie Poulton
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, 163 Union St E, Dunedin, 9016, New Zealand
| | - Avshalom Caspi
- Department of Psychology & Neuroscience, Duke University, Box 104410, Durham, NC, 27708, USA; Social, Genetic, & Developmental Psychiatry Research Centre, Institute of Psychiatry, Psychology, & Neuroscience, King's College London, De Crespigny Park, Denmark Hill, London, SE5 8AF, UK; Department of Psychiatry & Behavioral Sciences, Duke University School of Medicine, Durham, NC, 27708, USA; Center for Genomic and Computational Biology, Duke University, Box 90338, Durham, NC, 27708, USA
| | - Terrie E Moffitt
- Department of Psychology & Neuroscience, Duke University, Box 104410, Durham, NC, 27708, USA; Social, Genetic, & Developmental Psychiatry Research Centre, Institute of Psychiatry, Psychology, & Neuroscience, King's College London, De Crespigny Park, Denmark Hill, London, SE5 8AF, UK; Department of Psychiatry & Behavioral Sciences, Duke University School of Medicine, Durham, NC, 27708, USA; Center for Genomic and Computational Biology, Duke University, Box 90338, Durham, NC, 27708, USA
| | - Ahmad R Hariri
- Department of Psychology & Neuroscience, Duke University, Box 104410, Durham, NC, 27708, USA
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76
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Iraji A, Deramus TP, Lewis N, Yaesoubi M, Stephen JM, Erhardt E, Belger A, Ford JM, McEwen S, Mathalon DH, Mueller BA, Pearlson GD, Potkin SG, Preda A, Turner JA, Vaidya JG, van Erp TGM, Calhoun VD. The spatial chronnectome reveals a dynamic interplay between functional segregation and integration. Hum Brain Mapp 2019; 40:3058-3077. [PMID: 30884018 DOI: 10.1002/hbm.24580] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Revised: 02/26/2019] [Accepted: 03/07/2019] [Indexed: 12/21/2022] Open
Abstract
The brain is highly dynamic, reorganizing its activity at different interacting spatial and temporal scales, including variation within and between brain networks. The chronnectome is a model of the brain in which nodal activity and connectivity patterns change in fundamental and recurring ways over time. Most literature assumes fixed spatial nodes/networks, ignoring the possibility that spatial nodes/networks may vary in time. Here, we introduce an approach to calculate a spatially fluid chronnectome (called the spatial chronnectome for clarity), which focuses on the variations of networks coupling at the voxel level, and identify a novel set of spatially dynamic features. Results reveal transient spatially fluid interactions between intra- and internetwork relationships in which brain networks transiently merge and separate, emphasizing dynamic segregation and integration. Brain networks also exhibit distinct spatial patterns with unique temporal characteristics, potentially explaining a broad spectrum of inconsistencies in previous studies that assumed static networks. Moreover, we show anticorrelative connections to brain networks are transient as opposed to constant across the entire scan. Preliminary assessments using a multi-site dataset reveal the ability of the approach to obtain new information and nuanced alterations that remain undetected during static analysis. Patients with schizophrenia (SZ) display transient decreases in voxel-wise network coupling within visual and auditory networks, and higher intradomain coupling variability. In summary, the spatial chronnectome represents a new direction of research enabling the study of functional networks which are transient at the voxel level, and the identification of mechanisms for within- and between-subject spatial variability.
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Affiliation(s)
- Armin Iraji
- The Mind Research Network, Albuquerque, New Mexico
| | | | - Noah Lewis
- The Mind Research Network, Albuquerque, New Mexico
| | | | | | - Erik Erhardt
- Department of Mathematics and Statistics, University of New Mexico, Albuquerque, New Mexico
| | - Aysneil Belger
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina
| | - Judith M Ford
- Department of Psychiatry, University of California San Francisco, San Francisco, California.,Psychiatry Service, San Francisco VA Medical Center, San Francisco, California
| | - Sarah McEwen
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California
| | - Daniel H Mathalon
- Department of Psychiatry, University of California San Francisco, San Francisco, California.,Psychiatry Service, San Francisco VA Medical Center, San Francisco, California
| | - Bryon A Mueller
- Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota
| | - Godfrey D Pearlson
- Department of Psychiatry, Yale University, School of Medicine, New Haven, Connecticut
| | - Steven G Potkin
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California
| | - Jessica A Turner
- Department of Psychology, Georgia State University, Atlanta, Georgia
| | - Jatin G Vaidya
- Department of Psychiatry, University of Iowa, Iowa City, Iowa
| | - Theo G M van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, New Mexico.,Department of Psychiatry, Yale University, School of Medicine, New Haven, Connecticut.,Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico
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77
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Xiang J, Xue J, Guo H, Li D, Cui X, Niu Y, Yan T, Cao R, Ma Y, Yang Y, Wang B. Graph-based network analysis of resting-state fMRI: test-retest reliability of binarized and weighted networks. Brain Imaging Behav 2019; 14:1361-1372. [DOI: 10.1007/s11682-019-00042-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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78
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Hansen MS, Becerra L, Dahl JB, Borsook D, Mårtensson J, Christensen A, Nybing JD, Havsteen I, Boesen M, Asghar MS. Brain resting-state connectivity in the development of secondary hyperalgesia in healthy men. Brain Struct Funct 2019; 224:1119-1139. [PMID: 30631932 DOI: 10.1007/s00429-018-01819-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Accepted: 12/16/2018] [Indexed: 01/25/2023]
Abstract
Central sensitization is a condition in which there is an abnormal responsiveness to nociceptive stimuli. As such, the process may contribute to the development and maintenance of pain. Factors influencing the propensity for development of central sensitization have been a subject of intense debate and remain elusive. Injury-induced secondary hyperalgesia can be elicited by experimental pain models in humans, and is believed to be a result of central sensitization. Secondary hyperalgesia may thus reflect the individual level of central sensitization. The objective of this study was to investigate possible associations between increasing size of secondary hyperalgesia area and brain connectivity in known resting-state networks. We recruited 121 healthy participants (male, age 22, SD 3.35) who underwent resting-state functional magnetic resonance imaging. Prior to the scan session, areas of secondary hyperalgesia following brief thermal sensitization (3 min. 45 °C heat stimulation) were evaluated in all participants. 115 participants were included in the final analysis. We found a positive correlation (increasing connectivity) with increasing area of secondary hyperalgesia in the sensorimotor- and default mode networks. We also observed a negative correlation (decreasing connectivity) with increasing secondary hyperalgesia area in the sensorimotor-, fronto-parietal-, and default mode networks. Our findings indicate that increasing area of secondary hyperalgesia is associated with increasing and decreasing connectivity in multiple networks, suggesting that differences in the propensity for central sensitization, assessed as secondary hyperalgesia areas, may be expressed as differences in the resting-state central neuronal activity.
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Affiliation(s)
- Morten Sejer Hansen
- Department of Anaesthesiology, 4231, Centre of Head and Orthopaedics, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark.
- Department of Radiology, Copenhagen University Hospital Bispebjerg and Frederiksberg, Bispebjerg Bakke 23, 2400, Copenhagen, NV, Denmark.
| | - Lino Becerra
- Invicro, A Konica Minolta Company, 27 Drydock Avenue, 7th Floor West, Boston, MA, 02210, USA
| | - Jørgen Berg Dahl
- Department of Anaesthesiology, Copenhagen University Hospital Bispebjerg and Frederiksberg, Bispebjerg Bakke 23, 2400, Copenhagen, NV, Denmark
| | - David Borsook
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Psychiatry, McLean Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Johan Mårtensson
- Department of Clinical Sciences, Faculty of Medicine, Lund University, Box 213, 221 00, Lund, Sweden
| | - Anders Christensen
- Department of Radiology, Copenhagen University Hospital Bispebjerg and Frederiksberg, Bispebjerg Bakke 23, 2400, Copenhagen, NV, Denmark
| | - Janus Damm Nybing
- Department of Radiology, Copenhagen University Hospital Bispebjerg and Frederiksberg, Bispebjerg Bakke 23, 2400, Copenhagen, NV, Denmark
| | - Inger Havsteen
- Department of Radiology, Copenhagen University Hospital Bispebjerg and Frederiksberg, Bispebjerg Bakke 23, 2400, Copenhagen, NV, Denmark
| | - Mikael Boesen
- Department of Radiology and the Parker Institute, Copenhagen University Hospital Bispebjerg and Frederiksberg, Bispebjerg Hospital, Bispebjerg Bakke 23, 2400, Copenhagen, NV, Denmark
| | - Mohammad Sohail Asghar
- Department of Neuroanaesthesiology, Neurocentre, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark
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Fredericks CA, Brown JA, Deng J, Kramer A, Ossenkoppele R, Rankin K, Kramer JH, Miller BL, Rabinovici GD, Seeley WW. Intrinsic connectivity networks in posterior cortical atrophy: A role for the pulvinar? Neuroimage Clin 2018; 21:101628. [PMID: 30528957 PMCID: PMC6411779 DOI: 10.1016/j.nicl.2018.101628] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Revised: 11/26/2018] [Accepted: 12/01/2018] [Indexed: 12/12/2022]
Abstract
BACKGROUND Posterior cortical atrophy (PCA) is a clinical variant of Alzheimer's disease (AD) that presents with progressive visuospatial symptoms. While amnestic AD is characterized by disrupted default mode network (DMN) connectivity with corresponding increases in salience network (SN) connectivity, a visuospatial network appears to be disrupted early in PCA. Based on PCA patients' clinical features, we hypothesized that, in addition to early decreased integrity within the visuospatial network, patients with PCA would show increases in SN connectivity despite relative preservation of DMN. As the lateral pulvinar nucleus of the thalamus has direct anatomical connections with striate and extrastriate cortex and DMN, and the medial pulvinar is anatomically interconnected with SN, we further hypothesized that lateral and medial pulvinar nuclei might be implicated in intrinsic connectivity changes in PCA. METHODS 26 patients with PCA and 64 matched controls were recruited through UCSF Memory and Aging Center research programs. Each completed a standardized neuropsychological battery, structural MRI, and task-free fMRI. Seed-based functional correlations were used to probe networks of interest, including those seeded by the medial and lateral pulvinar thalamic nuclei, across the whole brain, and functional data analyses were adjusted for brain atrophy. RESULTS Patients with PCA showed disproportionate deficits in the visuospatial domain; they also showed preserved social sensitivity and endorsed more depressive symptoms than HCs. PCA patients had significant parietooccipital atrophy accompanied by widespread connectivity decreases within the visuospatial network, enhanced connectivity between some structures in SN, and enhanced connectivity between key nodes of the DMN compared to controls. Increased SN connectivity correlated with a measure of social sensitivity, and increased DMN connectivity correlated with short-term memory performance. Medial pulvinar connectivity increases in PCA were topographically similar to SN (anterior insula) connectivity increases, while lateral pulvinar connectivity increases were similar to DMN (posterior cingulate) connectivity increases. CONCLUSIONS PCA is characterized by preserved to heightened connectivity in the SN and DMN despite decreased visuospatial network connectivity. The spatial similarity of medial and lateral pulvinar connectivity changes to those seen in the SN and DMN suggests a role for the pulvinar in intrinsic connectivity network changes in PCA.
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Affiliation(s)
- Carolyn A Fredericks
- Memory and Aging Center, University of California, 675 Nelson Rising Lane, San Francisco, CA 94143, USA.
| | - Jesse A Brown
- Memory and Aging Center, University of California, 675 Nelson Rising Lane, San Francisco, CA 94143, USA.
| | - Jersey Deng
- Memory and Aging Center, University of California, 675 Nelson Rising Lane, San Francisco, CA 94143, USA
| | - Abigail Kramer
- Memory and Aging Center, University of California, 675 Nelson Rising Lane, San Francisco, CA 94143, USA.
| | - Rik Ossenkoppele
- Memory and Aging Center, University of California, 675 Nelson Rising Lane, San Francisco, CA 94143, USA.
| | - Katherine Rankin
- Memory and Aging Center, University of California, 675 Nelson Rising Lane, San Francisco, CA 94143, USA.
| | - Joel H Kramer
- Memory and Aging Center, University of California, 675 Nelson Rising Lane, San Francisco, CA 94143, USA.
| | - Bruce L Miller
- Memory and Aging Center, University of California, 675 Nelson Rising Lane, San Francisco, CA 94143, USA.
| | - Gil D Rabinovici
- Memory and Aging Center, University of California, 675 Nelson Rising Lane, San Francisco, CA 94143, USA.
| | - William W Seeley
- Memory and Aging Center, University of California, 675 Nelson Rising Lane, San Francisco, CA 94143, USA.
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80
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Billings JCW, Thompson GJ, Pan WJ, Magnuson ME, Medda A, Keilholz S. Disentangling Multispectral Functional Connectivity With Wavelets. Front Neurosci 2018; 12:812. [PMID: 30459548 PMCID: PMC6232345 DOI: 10.3389/fnins.2018.00812] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Accepted: 10/18/2018] [Indexed: 02/01/2023] Open
Abstract
The field of brain connectomics develops our understanding of the brain's intrinsic organization by characterizing trends in spontaneous brain activity. Linear correlations in spontaneous blood-oxygen level dependent functional magnetic resonance imaging (BOLD-fMRI) fluctuations are often used as measures of functional connectivity (FC), that is, as a quantity describing how similarly two brain regions behave over time. Given the natural spectral scaling of BOLD-fMRI signals, it may be useful to represent BOLD-fMRI as multiple processes occurring over multiple scales. The wavelet domain presents a transform space well suited to the examination of multiscale systems as the wavelet basis set is constructed from a self-similar rescaling of a time and frequency delimited kernel. In the present study, we utilize wavelet transforms to examine fluctuations in whole-brain BOLD-fMRI connectivity as a function of wavelet spectral scale in a sample (N = 31) of resting healthy human volunteers. Information theoretic criteria measure relatedness between spectrally-delimited FC graphs. Voxelwise comparisons of between-spectra graph structures illustrate the development of preferential functional networks across spectral bands.
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Affiliation(s)
- Jacob C W Billings
- Graduate Division of Biological and Biomedical Sciences - Program in Neuroscience, Emory University, Atlanta, GA, United States.,Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Garth J Thompson
- Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States.,iHuman Institute, ShanghaiTech University, Pudong, China
| | - Wen-Ju Pan
- Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Matthew E Magnuson
- Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Alessio Medda
- Aerospace Transportation and Advanced Systems, Georgia Tech Research Institute, Atlanta, GA, United States
| | - Shella Keilholz
- Graduate Division of Biological and Biomedical Sciences - Program in Neuroscience, Emory University, Atlanta, GA, United States.,Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
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81
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Sturm VE, Brown JA, Hua AY, Lwi SJ, Zhou J, Kurth F, Eickhoff SB, Rosen HJ, Kramer JH, Miller BL, Levenson RW, Seeley WW. Network Architecture Underlying Basal Autonomic Outflow: Evidence from Frontotemporal Dementia. J Neurosci 2018; 38:8943-8955. [PMID: 30181137 PMCID: PMC6191520 DOI: 10.1523/jneurosci.0347-18.2018] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Revised: 08/23/2018] [Accepted: 08/27/2018] [Indexed: 12/22/2022] Open
Abstract
The salience network is a distributed neural system that maintains homeostasis by regulating autonomic nervous system activity and social-emotional function. Here we examined how within-network connectivity relates to individual differences in human (including males and females) baseline parasympathetic and sympathetic nervous activity. We measured resting autonomic nervous system physiology in 24 healthy controls and 23 patients with behavioral variant frontotemporal dementia (bvFTD), a neurodegenerative disease characterized by baseline autonomic deficits. Participants also underwent structural and task-free fMRI. First, we used voxel-based morphometry to determine whether salience network atrophy was associated with lower baseline respiratory sinus arrhythmia (a parasympathetic measure) and skin conductance level (a sympathetic measure) in bvFTD. Next, we examined whether functional connectivity deficits in 21 autonomic-relevant, salience network node-pairs related to baseline autonomic dysfunction. Lower baseline respiratory sinus arrhythmia was associated with smaller volume in left ventral anterior insula (vAI), weaker connectivity between bilateral vAI and bilateral anterior cingulate cortex (ACC), and stronger connectivity between bilateral ACC and bilateral hypothalamus/amygdala. Lower baseline skin conductance level, in contrast, was associated with smaller volume in inferior temporal gyrus, dorsal mid-insula, and hypothalamus; weaker connectivity between bilateral ACC and right hypothalamus/amygdala; and stronger connectivity between bilateral dorsal anterior insula and periaqueductal gray. Our results suggest that baseline parasympathetic and sympathetic tone depends on the integrity of lateralized salience network hubs (left vAI for parasympathetic and right hypothalamus/amygdala for sympathetic) and highly calibrated ipsilateral and contralateral network connections. In bvFTD, deficits in this system may underlie resting parasympathetic and sympathetic disruption.SIGNIFICANCE STATEMENT The salience network maintains homeostasis and regulates autonomic nervous system activity. Whether within-network connectivity patterns underlie individual differences in resting parasympathetic and sympathetic nervous system activity, however, is not well understood. We measured baseline autonomic nervous system activity in healthy controls and patients with behavioral variant frontotemporal dementia, a neurodegenerative disease characterized by resting autonomic deficits, and probed how salience network dysfunction relates to diminished parasympathetic and sympathetic outflow. Our results indicate that baseline parasympathetic and sympathetic tone are the product of complex, opposing intranetwork nodal interactions and depend on the integrity of highly tuned, lateralized salience network hubs (i.e., left ventral anterior insula for parasympathetic activity and right hypothalamus/amygdala for sympathetic activity).
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Affiliation(s)
- Virginia E Sturm
- Department of Neurology, University of California-San Francisco, Sandler Neurosciences Center, San Francisco, California 94158
| | - Jesse A Brown
- Department of Neurology, University of California-San Francisco, Sandler Neurosciences Center, San Francisco, California 94158
| | - Alice Y Hua
- Department of Psychology, University of California, Berkeley, California 94720-1650
| | - Sandy J Lwi
- Department of Psychology, University of California, Berkeley, California 94720-1650
| | - Juan Zhou
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Medical School, Singapore 169857
| | - Florian Kurth
- Cousins Center for Psychoneuroimmunology, Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, University of California-Los Angeles School of Medicine, Los Angeles, California 90095
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine Universität, 40225 Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour, Research Centre Jülich, Jülich, 52425, Germany, and
| | - Howard J Rosen
- Department of Neurology, University of California-San Francisco, Sandler Neurosciences Center, San Francisco, California 94158
| | - Joel H Kramer
- Department of Neurology, University of California-San Francisco, Sandler Neurosciences Center, San Francisco, California 94158
| | - Bruce L Miller
- Department of Neurology, University of California-San Francisco, Sandler Neurosciences Center, San Francisco, California 94158
| | - Robert W Levenson
- Department of Psychology, University of California, Berkeley, California 94720-1650
| | - William W Seeley
- Department of Neurology, University of California-San Francisco, Sandler Neurosciences Center, San Francisco, California 94158,
- Department of Pathology, University of California, San Francisco, California 94143
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82
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Fan Y, Borchardt V, von Düring F, Leutritz AL, Dietz M, Herrera-Meléndez AL, Bajbouj M, Li M, Grimm S, Walter M. Dorsal and Ventral Posterior Cingulate Cortex Switch Network Assignment via Changes in Relative Functional Connectivity Strength to Noncanonical Networks. Brain Connect 2018; 9:77-94. [PMID: 30255708 DOI: 10.1089/brain.2018.0602] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
The posterior cingulate cortex (PCC) is often used as a seed region for probing default-mode network (DMN) connectivity. However, there is evidence for a functional segregation between its dorsal (dPCC) and ventral (vPCC) subregions, which suggests differential involvements of d-/vPCC in regulating cognitive demands. Our paradigm included functional magnetic resonance imaging measures for baseline resting state, affective or cognitive tasks, and post-task resting states. We investigated the effect of task demands on intra-PCC coupling and d-/vPCC network assignment to major intrinsic connectivity networks (ICNs), which was estimated via edge weights of a graph network encompassing DMN, dorsal-attention network, and central-executive network (CEN). Although PCC subregions were functionally coupled during both resting-state conditions and cognitive tasks, they decoupled during affective stimulation. For dPCC, functional connectivity strength (FCS) to CEN was higher than to the other two ICNs; whereas for vPCC, FCS to DMN was the highest. We, hence, defined CEN and DMN as the canonical networks at rest for dPCC and vPCC, respectively. Switching from rest to affective stimulation, however, induced the strongest effects to relative network assignments between non-canonical networks of dPCC and vPCC. Although vPCC showed a durable functional connectivity (FC) to DMN, dPCC played a crucial role during switches of between-network FC depending on cognitive versus affective task requirements. Our results underline that it is crucial for future seed-based FC studies to consider these two subregions separately in terms of seed location and discussion of findings. Finally, our findings highlight the functional importance of connectivity changes toward regions outside the canonical networks.
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Affiliation(s)
- Yan Fan
- 1 Clinical Affective Neuroimaging Laboratory, Magdeburg, Germany.,2 Department of Psychiatry, Campus Benjamin Franklin, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-University of Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Viola Borchardt
- 1 Clinical Affective Neuroimaging Laboratory, Magdeburg, Germany.,3 Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Felicia von Düring
- 1 Clinical Affective Neuroimaging Laboratory, Magdeburg, Germany.,4 Clinic for Psychiatry and Psychotherapy, Otto-von Guericke University Magdeburg, Magdeburg, Germany
| | - Anna Linda Leutritz
- 1 Clinical Affective Neuroimaging Laboratory, Magdeburg, Germany.,4 Clinic for Psychiatry and Psychotherapy, Otto-von Guericke University Magdeburg, Magdeburg, Germany
| | - Marie Dietz
- 2 Department of Psychiatry, Campus Benjamin Franklin, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-University of Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Ana Lucía Herrera-Meléndez
- 2 Department of Psychiatry, Campus Benjamin Franklin, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-University of Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Malek Bajbouj
- 2 Department of Psychiatry, Campus Benjamin Franklin, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-University of Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Meng Li
- 1 Clinical Affective Neuroimaging Laboratory, Magdeburg, Germany.,3 Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Simone Grimm
- 2 Department of Psychiatry, Campus Benjamin Franklin, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-University of Berlin, and Berlin Institute of Health, Berlin, Germany.,5 Department of Psychiatry, Psychotherapy and Psychosomatics, Therapy and Process Research, University Hospital of Psychiatry Zurich, Zurich, Switzerland.,6 MSB Medical School Berlin, Berlin, Germany
| | - Martin Walter
- 1 Clinical Affective Neuroimaging Laboratory, Magdeburg, Germany.,3 Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Magdeburg, Germany.,4 Clinic for Psychiatry and Psychotherapy, Otto-von Guericke University Magdeburg, Magdeburg, Germany.,7 Clinic for Psychiatry and Psychotherapy, Eberhard-Karls University Tuebingen, Tuebingen, Germany
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83
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Huang X, Long Z, Lei X. Electrophysiological signatures of the resting-state fMRI global signal: A simultaneous EEG-fMRI study. J Neurosci Methods 2018; 311:351-359. [PMID: 30236777 DOI: 10.1016/j.jneumeth.2018.09.017] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Revised: 09/10/2018] [Accepted: 09/14/2018] [Indexed: 12/24/2022]
Abstract
BACKGROUND The global signal of resting-state functional magnetic resonance imaging (fMRI) constitutes an intrinsic fluctuation and presents an opportunity to characterize and understand the activity of the whole brain. Recently, evidence that the global signal contains neurophysiologic information has been growing, but the global signal of electroencephalography (EEG) has never been determined. NEW METHODS We developed a new method to obtain the EEG global signal. The EEG global signal was reconstructed by the reference electrode standardization technique and represented the outer cortical electrophysiological activity. To investigate its relationship with the global signal of resting-state fMRI, a simultaneous EEG-fMRI signal was recorded, and this was analyzed in 24 subjects. RESULTS We found that the global signal of resting-state fMRI showed a positive correlation with power fluctuations of the EEG global signal in the γ band (30-45 Hz) and a negative correlation in the low-frequency band (4-20 Hz). COMPARISON WITH EXISTING METHOD(S) Compared with the global signal of fMRI, the global signal of EEG provides more temporal information about outer cortical neural activity. CONCLUSIONS These results provide new evidence for the electrophysiology information of the global signal of resting-state fMRI. More importantly, due to its high correlation with the fMRI global signal, the EEG global signal may serve as a new biomarker for neurological disorders.
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Affiliation(s)
- Xiaoli Huang
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China; Key Laboratory of Cognition and Personality of Ministry of Education, Chongqing, 400715, China
| | - Zhiliang Long
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China; Key Laboratory of Cognition and Personality of Ministry of Education, Chongqing, 400715, China
| | - Xu Lei
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China; Key Laboratory of Cognition and Personality of Ministry of Education, Chongqing, 400715, China; Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 610054, China; Chongqing Collaborative Innovation Center for Brain Science, Chongqing, 400715, China.
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84
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Hernandez-Castillo CR, Diedrichsen J, Aguilar-Castañeda E, Iglesias M. Decoupling between the hand territory and the default mode network after bilateral arm transplantation: four-year follow-up case study. Brain Imaging Behav 2018; 12:296-302. [PMID: 28185062 DOI: 10.1007/s11682-017-9683-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Several studies have suggested both a local and network reorganization of the sensorimotor system following amputation. Transplantation of a new limb results in a new shifting of cortical activity in the local territory of the transplanted limb. However, there is a lack of information about the reversibility of the abnormalities at the network level. The objective of this study was to characterize the functional connectivity changes between the cortical territory of the new hand and two intrinsic network of interest: the sensorimotor network (SMN) and the default mode network (DMN) of one patient whom received bilateral forearm transplants. Using resting-state fMRI these two networks were identified across four different time points, starting four months after the transplantation surgery and during three consecutive years while the patient underwent physical rehabilitation. The topology of the SMN was disrupted at the first acquisition and over the years returned to its canonical pattern. Analysis of the DMN showed the normal topology with no significant changes across acquisitions. Functional connectivity between the missing hand's cortical territory and the SMN increased over time. Accordingly, functional connectivity between the missing hand's cortical territory and the DMN became anticorrelated over time. Our results suggest that after transplantation a new reorganization occurs at the network level, supporting the idea that extreme behavioral changes can affect not only the local rewiring but also the intrinsic network organization in neurologically healthy subjects. Overall this study provides new insight on the complex dynamics of brain organization.
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Affiliation(s)
- Carlos R Hernandez-Castillo
- CONACYT - Instituto de Neuroetologia, Universidad Veracruzana, Av. Luis Cartelazo Ayala s/n, Col. Industrial Ánimas, Xalapa, Veracruz, Mexico. .,The Brain and Mind Institute, Western University, London, Canada.
| | - Jörn Diedrichsen
- The Brain and Mind Institute, Western University, London, Canada
| | - Erika Aguilar-Castañeda
- Instituto Nacional de Neurología y Neurocirugía "Manuel Velasco Suarez", Ciudad de México, Mexico
| | - Martin Iglesias
- Instituto Nacional de Ciencias Médicas y Nutrición "Salvador Zubirán", Ciudad de México, Mexico
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85
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Zhang C, Baum SA, Adduru VR, Biswal BB, Michael AM. Test-retest reliability of dynamic functional connectivity in resting state fMRI. Neuroimage 2018; 183:907-918. [PMID: 30120987 DOI: 10.1016/j.neuroimage.2018.08.021] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 07/29/2018] [Accepted: 08/10/2018] [Indexed: 10/28/2022] Open
Abstract
While static functional connectivity (sFC) of resting state fMRI (rfMRI) measures the average functional connectivity (FC) over the entire rfMRI scan, dynamic FC (dFC) captures the temporal variations of FC at shorter time windows. Although numerous studies have implemented dFC analyses, only a few studies have investigated the reliability of dFC and this limits the biological interpretation of dFC. Here, we used a large cohort (N = 820) of subjects and four rfMRI scans from the Human Connectome Project to systematically explore the relationship between sFC, dFC and their test-retest reliabilities through intra-class correlation (ICC). dFC ICC was explored through the sliding window approach with three dFC statistics (standard deviation, ALFF, and excursion). Excursion demonstrated the highest dFC ICC and the highest age prediction accuracy. dFC ICC was generally higher at window sizes less than 40 s. sFC and dFC were negatively correlated. Compared to sFC, dFC was less reliable. While sFC and sFC ICC were positively correlated, dFC and dFC ICC were negatively correlated, indicating that FC that was more dynamic was less reliable. Intra-network FCs in the frontal-parietal, default mode, sensorimotor and visual networks demonstrated high sFC and low dFC. Moreover, ICCs of both sFC and dFC in these regions were higher. The above results were consistent across two brain atlases and independent component analysis-based networks, multiple window sizes and all three dFC statistics. In summary, dFC is less reliable than sFC and additional experiments are required to better understand the neurophysiological relevance of dFC.
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Affiliation(s)
- Chao Zhang
- Autism & Developmental Medicine Institute, Geisinger, Lewisburg, PA, USA; Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, USA
| | - Stefi A Baum
- Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, USA; Faculty of Science, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Viraj R Adduru
- Autism & Developmental Medicine Institute, Geisinger, Lewisburg, PA, USA; Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, USA
| | - Bharat B Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
| | - Andrew M Michael
- Autism & Developmental Medicine Institute, Geisinger, Lewisburg, PA, USA; Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, USA; Duke Institute for Brain Sciences, Duke University, Durham, NC, USA.
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86
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Mandelli ML, Welch AE, Vilaplana E, Watson C, Battistella G, Brown JA, Possin KL, Hubbard HI, Miller ZA, Henry ML, Marx GA, Santos-Santos MA, Bajorek LP, Fortea J, Boxer A, Rabinovici G, Lee S, Deleon J, Rosen HJ, Miller BL, Seeley WW, Gorno-Tempini ML. Altered topology of the functional speech production network in non-fluent/agrammatic variant of PPA. Cortex 2018; 108:252-264. [PMID: 30292076 DOI: 10.1016/j.cortex.2018.08.002] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2017] [Revised: 03/07/2018] [Accepted: 08/02/2018] [Indexed: 12/13/2022]
Abstract
Non-fluent/agrammatic primary progressive aphasia (nfvPPA) is caused by neurodegeneration within the left fronto-insular speech and language production network (SPN). Graph theory is a branch of mathematics that studies network architecture (topology) by quantifying features based on its elements (nodes and connections). This approach has been recently applied to neuroimaging data to explore the complex architecture of the brain connectome, though few studies have exploited this technique in PPA. Here, we used graph theory on functional MRI resting state data from a group of 20 nfvPPA patients and 20 matched controls to investigate topological changes in response to focal neurodegeneration. We hypothesized that changes in the network architecture would be specific to the affected SPN in nfvPPA, while preserved in the spared default mode network (DMN). Topological configuration was quantified by hub location and global network metrics. Our findings showed a less efficiently wired and less optimally clustered SPN, while no changes were detected in the DMN. The SPN in the nfvPPA group showed a loss of hubs in the left fronto-parietal-temporal area and new critical nodes in the anterior left inferior-frontal and right frontal regions. Behaviorally, speech production score and rule violation errors correlated with the strength of functional connectivity of the left (lost) and right (new) regions respectively. This study shows that focal neurodegeneration within the SPN in nfvPPA is associated with network-specific topological alterations, with the loss and gain of crucial hubs and decreased global efficiency that were better accounted for through functional rather than structural changes. These findings support the hypothesis of selective network vulnerability in nfvPPA and may offer biomarkers for future behavioral intervention.
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Affiliation(s)
- Maria Luisa Mandelli
- Department of Neurology, Memory and Aging Center, University of California San Francisco, CA, USA.
| | - Ariane E Welch
- Department of Neurology, Memory and Aging Center, University of California San Francisco, CA, USA
| | - Eduard Vilaplana
- Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau - Biomedical Research Institute Sant Pau - Universitat Autonoma de Barcelona, Spain; Centro de Investigacion Biomedica en Red de Enfermedades Neurodegenerativas - CIBERNED, Spain
| | - Christa Watson
- Department of Neurology, Memory and Aging Center, University of California San Francisco, CA, USA
| | - Giovanni Battistella
- Department of Neurology, Memory and Aging Center, University of California San Francisco, CA, USA
| | - Jesse A Brown
- Department of Neurology, Memory and Aging Center, University of California San Francisco, CA, USA
| | - Katherine L Possin
- Department of Neurology, Memory and Aging Center, University of California San Francisco, CA, USA
| | - Honey I Hubbard
- Department of Communication Science and Disorders, Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, AB, Canada
| | - Zachary A Miller
- Department of Neurology, Memory and Aging Center, University of California San Francisco, CA, USA
| | - Maya L Henry
- Department of Communication Sciences and Disorders, University of Texas, Austin, USA
| | - Gabe A Marx
- Department of Neurology, Memory and Aging Center, University of California San Francisco, CA, USA
| | - Miguel A Santos-Santos
- Cognition and Brain Plasticity Group [Bellvitge Biomedical Research Institute-IDIBELL], L'Hospitalet de Llobregat, Barcelona, Spain; Fundació ACE Memory Clinic and Research Center, Institut Catalá de Neurociències Aplicades, Barcelona, Spain
| | - Lynn P Bajorek
- Department of Neurology, Memory and Aging Center, University of California San Francisco, CA, USA
| | - Juan Fortea
- Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau - Biomedical Research Institute Sant Pau - Universitat Autonoma de Barcelona, Spain
| | - Adam Boxer
- Department of Neurology, Memory and Aging Center, University of California San Francisco, CA, USA
| | - Gil Rabinovici
- Department of Neurology, Memory and Aging Center, University of California San Francisco, CA, USA
| | - Suzee Lee
- Department of Neurology, Memory and Aging Center, University of California San Francisco, CA, USA
| | - Jessica Deleon
- Department of Neurology, Memory and Aging Center, University of California San Francisco, CA, USA
| | - Howard J Rosen
- Department of Neurology, Memory and Aging Center, University of California San Francisco, CA, USA
| | - Bruce L Miller
- Department of Neurology, Memory and Aging Center, University of California San Francisco, CA, USA
| | - William W Seeley
- Department of Neurology, Memory and Aging Center, University of California San Francisco, CA, USA; Department of Pathology, University of California San Francisco, CA, USA
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87
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Higgins IA, Kundu S, Guo Y. Integrative Bayesian analysis of brain functional networks incorporating anatomical knowledge. Neuroimage 2018; 181:263-278. [PMID: 30017786 DOI: 10.1016/j.neuroimage.2018.07.015] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2018] [Revised: 07/04/2018] [Accepted: 07/05/2018] [Indexed: 12/31/2022] Open
Abstract
Recently, there has been increased interest in fusing multimodal imaging to better understand brain organization by integrating information on both brain structure and function. In particular, incorporating anatomical knowledge leads to desirable outcomes such as increased accuracy in brain network estimates and greater reproducibility of topological features across scanning sessions. Despite the clear advantages, major challenges persist in integrative analyses including an incomplete understanding of the structure-function relationship and inaccuracies in mapping anatomical structures due to inherent deficiencies in existing imaging technology. This calls for the development of advanced network modeling tools that appropriately incorporate anatomical structure in constructing brain functional networks. We propose a hierarchical Bayesian Gaussian graphical modeling approach which models the brain functional networks via sparse precision matrices whose degree of edge specific shrinkage is a random variable that is modeled using both anatomical structure and an independent baseline component. The proposed approach adaptively shrinks functional connections and flexibly identifies functional connections supported by structural connectivity knowledge. This enables robust brain network estimation even in the presence of misspecified anatomical knowledge, while accommodating heterogeneity in the structure-function relationship. We implement the approach via an efficient optimization algorithm which yields maximum a posteriori estimates. Extensive numerical studies involving multiple functional network structures reveal the clear advantages of the proposed approach over competing methods in accurately estimating brain functional connectivity, even when the anatomical knowledge is misspecified up to a certain degree. An application of the approach to data from the Philadelphia Neurodevelopmental Cohort (PNC) study reveals gender based connectivity differences across multiple age groups, and higher reproducibility in the estimation of network metrics compared to alternative methods.
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Affiliation(s)
- Ixavier A Higgins
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, 30322, USA
| | - Suprateek Kundu
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, 30322, USA.
| | - Ying Guo
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, 30322, USA
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88
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Conwell K, von Reutern B, Richter N, Kukolja J, Fink GR, Onur OA. Test-retest variability of resting-state networks in healthy aging and prodromal Alzheimer's disease. NEUROIMAGE-CLINICAL 2018; 19:948-962. [PMID: 30003032 PMCID: PMC6039839 DOI: 10.1016/j.nicl.2018.06.016] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Revised: 06/11/2018] [Accepted: 06/13/2018] [Indexed: 12/03/2022]
Abstract
In recent years, changes in resting-state networks (RSN), identified by functional magnetic resonance imaging (fMRI), have gained increasing attention as potential biomarkers and trackers of neurological disorders such as Alzheimer's disease (AD). Intersession reliability of RSN is fundamental to this approach. In this study, we investigated the test-retest reliability of three memory related RSN (i.e., the default mode, salience, and executive control network) in 15 young, 15 healthy seniors (HS), and 15 subjects affected by mild cognitive impairment (MCI) with positive biomarkers suggestive of incipient AD (6 females each). FMRI was conducted on three separate occasions. Independent Component Analysis decomposed the resting-state data into RSNs. Comparisons of variation in functional connectivity between groups were made applying different thresholds in an explorative approach. Intersession test-retest reliability was evaluated by intraclass correlation coefficient (ICC) comparisons. To assess the effect of gray matter volume loss, motion, cerebrospinal fluid based biomarkers and the time gap between sessions on intersession variation, the former four were correlated separately with the latter. Data showed that i) young subjects ICCs (relative to HS/MCI-subjects) had higher intersession reliability, ii) stringent statistical thresholds need to be applied to prevent false-positives, iii) both HS and MCI-subjects (relative to young) showed significantly more clusters of intersession variation in all three RSN, iv) while intersession variation was highly correlated with head motion, it was also correlated with biomarkers (especially phospho-tau), the time gap between sessions and local GMV. Results indicate that time gaps between sessions should be kept constant and that head motion must be taken into account when using RSN to assess aging and neurodegeneration. In patients with prodromal AD, re-test reliability may be increased by accouting for overall disease burden by including biomarkers of neuronal injury (especially phospho-tau) in statistical analyses. Local atrophy however, does not seem to play a major role in regards to reliability, but should be used as covariate depending on the research question. Intersession reliability of resting state networks is highest in young subjects. Test-Retest Variability increases with aging and in MCI. Motion and csf-biomarkers correlate with increased variability. Motion and biomarkers should be included as confounders in the statistical models. Stringent statistical thresholds should be applied to prevent type I-errors.
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Affiliation(s)
- K Conwell
- Department of Neurology, University Hospital of Cologne, Cologne 50937, Germany; Department of General, Abdominal, Endocrine and Minimally Invasive Surgery, Academic Hospital Bogenhausen, 81925 Munich, Germany
| | - B von Reutern
- Department of Neurology, University Hospital of Cologne, Cologne 50937, Germany; Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre, Jülich 52428, Germany
| | - N Richter
- Department of Neurology, University Hospital of Cologne, Cologne 50937, Germany; Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre, Jülich 52428, Germany
| | - J Kukolja
- Department of Neurology, University Hospital of Cologne, Cologne 50937, Germany; Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre, Jülich 52428, Germany
| | - G R Fink
- Department of Neurology, University Hospital of Cologne, Cologne 50937, Germany; Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre, Jülich 52428, Germany
| | - O A Onur
- Department of Neurology, University Hospital of Cologne, Cologne 50937, Germany; Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre, Jülich 52428, Germany.
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89
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Ye Q, Su F, Shu H, Gong L, Xie C, Zhang Z, Bai F. The apolipoprotein E gene affects the three-year trajectories of compensatory neural processes in the left-lateralized hippocampal network. Brain Imaging Behav 2018; 11:1446-1458. [PMID: 27734307 DOI: 10.1007/s11682-016-9623-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Previous cross-sectional studies that investigated the effects of apolipoprotein E (ApoE) ε4 status on hippocampal networks have shown inconsistent results. Aging is a well-known risk factor for Alzheimer's disease (AD) and could strongly interact with ApoE-related vulnerabilities to affect AD risk. However, no longitudinal data have been published regarding the interaction of the ApoE genotype and aging on hippocampal networks. Fifty-one patients with amnestic-type mild cognitive impairment (aMCI) and 64 matched cognitively normal elderly subjects underwent resting-state fMRI scans and neuropsychological tests at baseline and at a 35-month follow-up. Hippocampal resting-state functional connectivity (FC) data were analyzed utilizing a mixed analysis of covariance with ApoE genotype, time points and disease as fixed factors, controlling for age, sex and years of education. The notable finding was that the FC between the left hippocampus and right frontal regions for ε4 carriers longitudinally increased in the normal subjects, but decreased in aMCI patients, whereas the FC for non-carriers was maintained in normal subjects but increased in aMCI patients. Specifically, the longitudinal increases in hippocampal FC with the right inferior frontal gyrus were positively correlated with the changes in episodic memory test scores in non-carriers with aMCI. The interaction between the ApoE genotype, aging and disease suggested that aging should be considered a key regulator of the impact of the ApoE genotype on the phenotypic variants of AD. These findings also demonstrated that compensatory neural processes were accelerated in genetically high risk individuals, but could be subsequently exhausted with the onset of cognitive impairment.
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Affiliation(s)
- Qing Ye
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, 210009, China
| | - Fan Su
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, 210009, China
| | - Hao Shu
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, 210009, China
| | - Liang Gong
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, 210009, China
| | - Chunming Xie
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, 210009, China
| | - Zhijun Zhang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, 210009, China
| | - Feng Bai
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, 210009, China.
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90
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Bagarinao E, Tsuzuki E, Yoshida Y, Ozawa Y, Kuzuya M, Otani T, Koyama S, Isoda H, Watanabe H, Maesawa S, Naganawa S, Sobue G. Effects of Gradient Coil Noise and Gradient Coil Replacement on the Reproducibility of Resting State Networks. Front Hum Neurosci 2018; 12:148. [PMID: 29725294 PMCID: PMC5917444 DOI: 10.3389/fnhum.2018.00148] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2017] [Accepted: 04/03/2018] [Indexed: 11/24/2022] Open
Abstract
The stability of the MRI scanner throughout a given study is critical in minimizing hardware-induced variability in the acquired imaging data set. However, MRI scanners do malfunction at times, which could generate image artifacts and would require the replacement of a major component such as its gradient coil. In this article, we examined the effect of low intensity, randomly occurring hardware-related noise due to a faulty gradient coil on brain morphometric measures derived from T1-weighted images and resting state networks (RSNs) constructed from resting state functional MRI. We also introduced a method to detect and minimize the effect of the noise associated with a faulty gradient coil. Finally, we assessed the reproducibility of these morphometric measures and RSNs before and after gradient coil replacement. Our results showed that gradient coil noise, even at relatively low intensities, could introduce a large number of voxels exhibiting spurious significant connectivity changes in several RSNs. However, censoring the affected volumes during the analysis could minimize, if not completely eliminate, these spurious connectivity changes and could lead to reproducible RSNs even after gradient coil replacement.
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Affiliation(s)
| | - Erina Tsuzuki
- Department of Radiological Technology, School of Health Sciences, Nagoya University, Nagoya, Japan
| | - Yukina Yoshida
- Department of Radiological Technology, School of Health Sciences, Nagoya University, Nagoya, Japan
| | - Yohei Ozawa
- Department of Radiological Technology, School of Health Sciences, Nagoya University, Nagoya, Japan
| | - Maki Kuzuya
- Department of Radiological Technology, School of Health Sciences, Nagoya University, Nagoya, Japan
| | - Takashi Otani
- Department of Radiological Technology, School of Health Sciences, Nagoya University, Nagoya, Japan
| | - Shuji Koyama
- Brain and Mind Research Center, Nagoya University, Nagoya, Japan
| | - Haruo Isoda
- Brain and Mind Research Center, Nagoya University, Nagoya, Japan
| | | | - Satoshi Maesawa
- Brain and Mind Research Center, Nagoya University, Nagoya, Japan
| | - Shinji Naganawa
- Brain and Mind Research Center, Nagoya University, Nagoya, Japan.,Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Gen Sobue
- Brain and Mind Research Center, Nagoya University, Nagoya, Japan
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91
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Tuleasca C, Najdenovska E, Régis J, Witjas T, Girard N, Champoudry J, Faouzi M, Thiran JP, Cuadra MB, Levivier M, Van De Ville D. Ventrolateral Motor Thalamus Abnormal Connectivity in Essential Tremor Before and After Thalamotomy: A Resting-State Functional Magnetic Resonance Imaging Study. World Neurosurg 2018; 113:e453-e464. [DOI: 10.1016/j.wneu.2018.02.055] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Revised: 02/08/2018] [Accepted: 02/09/2018] [Indexed: 01/30/2023]
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92
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Alarcón G, Pfeifer JH, Fair DA, Nagel BJ. Adolescent Gender Differences in Cognitive Control Performance and Functional Connectivity Between Default Mode and Fronto-Parietal Networks Within a Self-Referential Context. Front Behav Neurosci 2018; 12:73. [PMID: 29740292 PMCID: PMC5924772 DOI: 10.3389/fnbeh.2018.00073] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2018] [Accepted: 04/03/2018] [Indexed: 11/13/2022] Open
Abstract
Ineffective reduction of functional connectivity between the default mode network (DMN) and frontoparietal network (FPN) during cognitive control can interfere with performance in healthy individuals—a phenomenon present in psychiatric disorders, such as depression. Here, this mechanism is studied in healthy adolescents by examining gender differences in task-regressed functional connectivity using functional magnetic resonance imaging (MRI) and a novel task designed to place the DMN—supporting self-referential processing (SRP)—and FPN—supporting cognitive control—into conflict. Compared to boys, girls showed stronger functional connectivity between DMN and FPN during cognitive control in an SRP context (n = 40; boys = 20), a context that also elicited more errors of omission in girls. The gender difference in errors of omission was mediated by higher self-reported co-rumination—the extensive and repetitive discussion of problems and focus on negative feelings with a same-gender peer—by girls, compared to boys. These findings indicate that placing internal and external attentional demands in conflict lead to persistent functional connectivity between FPN and DMN in girls, but not boys; however, deficits in performance during this context were explained by co-rumination, such that youth with higher co-rumination displayed the largest performance deficits. Previous research shows that co-rumination predicts depressive symptoms during adolescence; thus, gender differences in the mechanisms involved with transitioning from internal to external processing may be relevant for understanding heightened vulnerability for depression in adolescent girls.
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Affiliation(s)
- Gabriela Alarcón
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States
| | - Jennifer H Pfeifer
- Department of Psychology, University of Oregon, Eugene, OR, United States
| | - Damien A Fair
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, United States.,Department of Psychiatry, Oregon Health & Science University, Portland, OR, United States.,Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR, United States
| | - Bonnie J Nagel
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, United States.,Department of Psychiatry, Oregon Health & Science University, Portland, OR, United States
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93
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de Bézenac CE, Sluming V, Alhazmi F, Corcoran R. Agency performance modulates resting-state variation in prefrontal brain regions. Neuropsychologia 2018; 111:16-25. [DOI: 10.1016/j.neuropsychologia.2017.12.035] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Revised: 11/30/2017] [Accepted: 12/22/2017] [Indexed: 11/16/2022]
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94
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Clinical response to Vim's thalamic stereotactic radiosurgery for essential tremor is associated with distinctive functional connectivity patterns. Acta Neurochir (Wien) 2018; 160:611-624. [PMID: 29335882 DOI: 10.1007/s00701-017-3456-x] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Accepted: 12/26/2017] [Indexed: 10/18/2022]
Abstract
INTRODUCTION Essential tremor (ET) is the most common movement disorder. Drug-resistant ET can benefit from standard surgical stereotactic procedures (deep brain stimulation, thalamotomy) or minimally invasive high-intensity focused ultrasound (HIFU) or stereotactic radiosurgical thalamotomy (SRS-T). Resting-state fMRI (rs-fMRI) is a non-invasive imaging method acquired in absence of a task. We examined whether rs-fMRI correlates with tremor score on the treated hand (TSTH) improvement 1 year after SRS-T. METHODS We included 17 consecutive patients treated with left unilateral SRS-T in Marseille, France. Tremor score evaluation and rs-fMRI were acquired at baseline and 1 year after SRS-T. Resting-state data (34 scans) were analyzed without a priori hypothesis, in Lausanne, Switzerland. Based on degree of improvement in TSTH, to consider SRS-T at least as effective as medication, we separated two groups: 1, ≤ 50% (n = 6, 35.3%); 2, > 50% (n = 11, 64.7%). They did not differ statistically by age (p = 0.86), duration of symptoms (p = 0.41), or lesion volume at 1 year (p = 0.06). RESULTS We report TSTH improvement correlated with interconnectivity strength between salience network with the left claustrum and putamen, as well as between bilateral motor cortices, frontal eye fields and left cerebellum lobule VI with right visual association area (the former also with lesion volume). Longitudinal changes showed additional associations in interconnectivity strength between right dorsal attention network with ventro-lateral prefrontal cortex and a reminiscent salience network with fusiform gyrus. CONCLUSIONS Brain connectivity measured by resting-state fMRI relates to clinical response after SRS-T. Relevant networks are visual, motor, and attention. Interconnectivity between visual and motor areas is a novel finding, revealing implication in movement sensory guidance.
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95
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Chen G, Taylor PA, Haller SP, Kircanski K, Stoddard J, Pine DS, Leibenluft E, Brotman MA, Cox RW. Intraclass correlation: Improved modeling approaches and applications for neuroimaging. Hum Brain Mapp 2018; 39:1187-1206. [PMID: 29218829 PMCID: PMC5807222 DOI: 10.1002/hbm.23909] [Citation(s) in RCA: 79] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Revised: 11/20/2017] [Accepted: 11/29/2017] [Indexed: 12/21/2022] Open
Abstract
Intraclass correlation (ICC) is a reliability metric that gauges similarity when, for example, entities are measured under similar, or even the same, well-controlled conditions, which in MRI applications include runs/sessions, twins, parent/child, scanners, sites, and so on. The popular definitions and interpretations of ICC are usually framed statistically under the conventional ANOVA platform. Here, we provide a comprehensive overview of ICC analysis in its prior usage in neuroimaging, and we show that the standard ANOVA framework is often limited, rigid, and inflexible in modeling capabilities. These intrinsic limitations motivate several improvements. Specifically, we start with the conventional ICC model under the ANOVA platform, and extend it along two dimensions: first, fixing the failure in ICC estimation when negative values occur under degenerative circumstance, and second, incorporating precision information of effect estimates into the ICC model. These endeavors lead to four modeling strategies: linear mixed-effects (LME), regularized mixed-effects (RME), multilevel mixed-effects (MME), and regularized multilevel mixed-effects (RMME). Compared to ANOVA, each of these four models directly provides estimates for fixed effects and their statistical significances, in addition to the ICC estimate. These new modeling approaches can also accommodate missing data and fixed effects for confounding variables. More importantly, we show that the MME and RMME approaches offer more accurate characterization and decomposition among the variance components, leading to more robust ICC computation. Based on these theoretical considerations and model performance comparisons with a real experimental dataset, we offer the following general-purpose recommendations. First, ICC estimation through MME or RMME is preferable when precision information (i.e., weights that more accurately allocate the variances in the data) is available for the effect estimate; when precision information is unavailable, ICC estimation through LME or the RME is the preferred option. Second, even though the absolute agreement version, ICC(2,1), is presently more popular in the field, the consistency version, ICC(3,1), is a practical and informative choice for whole-brain ICC analysis that achieves a well-balanced compromise when all potential fixed effects are accounted for. Third, approaches for clear, meaningful, and useful result reporting in ICC analysis are discussed. All models, ICC formulations, and related statistical testing methods have been implemented in an open source program 3dICC, which is publicly available as part of the AFNI suite. Even though our work here focuses on the whole-brain level, the modeling strategy and recommendations can be equivalently applied to other situations such as voxel, region, and network levels.
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Affiliation(s)
- Gang Chen
- Scientific and Statistical Computing CoreNational Institute of Mental Health, National Institutes of HealthBethesdaMD
| | - Paul A. Taylor
- Scientific and Statistical Computing CoreNational Institute of Mental Health, National Institutes of HealthBethesdaMD
| | - Simone P. Haller
- Section on Mood Dysregulation and Neuroscience, Emotion and Development BranchNational Institute of Mental HealthBethesdaMD
| | - Katharina Kircanski
- Section on Mood Dysregulation and Neuroscience, Emotion and Development BranchNational Institute of Mental HealthBethesdaMD
| | - Joel Stoddard
- Division of Child and Adolescent Psychiatry, Department of PsychiatryUniversity of Colorado School of MedicineAuroraColorado
| | - Daniel S. Pine
- Section on Development and Affective Neuroscience, Emotion and Development BranchNational Institute of Mental HealthBethesdaMD
| | - Ellen Leibenluft
- Section on Mood Dysregulation and Neuroscience, Emotion and Development BranchNational Institute of Mental HealthBethesdaMD
| | - Melissa A. Brotman
- Section on Mood Dysregulation and Neuroscience, Emotion and Development BranchNational Institute of Mental HealthBethesdaMD
| | - Robert W. Cox
- Scientific and Statistical Computing CoreNational Institute of Mental Health, National Institutes of HealthBethesdaMD
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96
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Gargouri F, Kallel F, Delphine S, Ben Hamida A, Lehéricy S, Valabregue R. The Influence of Preprocessing Steps on Graph Theory Measures Derived from Resting State fMRI. Front Comput Neurosci 2018; 12:8. [PMID: 29497372 PMCID: PMC5819575 DOI: 10.3389/fncom.2018.00008] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2017] [Accepted: 01/23/2018] [Indexed: 12/17/2022] Open
Abstract
Resting state functional MRI (rs-fMRI) is an imaging technique that allows the spontaneous activity of the brain to be measured. Measures of functional connectivity highly depend on the quality of the BOLD signal data processing. In this study, our aim was to study the influence of preprocessing steps and their order of application on small-world topology and their efficiency in resting state fMRI data analysis using graph theory. We applied the most standard preprocessing steps: slice-timing, realign, smoothing, filtering, and the tCompCor method. In particular, we were interested in how preprocessing can retain the small-world economic properties and how to maximize the local and global efficiency of a network while minimizing the cost. Tests that we conducted in 54 healthy subjects showed that the choice and ordering of preprocessing steps impacted the graph measures. We found that the csr (where we applied realignment, smoothing, and tCompCor as a final step) and the scr (where we applied realignment, tCompCor and smoothing as a final step) strategies had the highest mean values of global efficiency (eg). Furthermore, we found that the fscr strategy (where we applied realignment, tCompCor, smoothing, and filtering as a final step), had the highest mean local efficiency (el) values. These results confirm that the graph theory measures of functional connectivity depend on the ordering of the processing steps, with the best results being obtained using smoothing and tCompCor as the final steps for global efficiency with additional filtering for local efficiency.
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Affiliation(s)
- Fatma Gargouri
- Institut du Cerveau et de la Moelle Épinière, Centre de NeuroImagerie de Recherche, Paris, France.,Sorbonne Universités, UPMC Univ Paris 06, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique UMR 7225, Paris, France.,Advanced Technologies for Medicine and Signals, ENIS, Université de Sfax, Sfax, Tunisia
| | - Fathi Kallel
- Advanced Technologies for Medicine and Signals, ENIS, Université de Sfax, Sfax, Tunisia
| | - Sebastien Delphine
- Institut du Cerveau et de la Moelle Épinière, Centre de NeuroImagerie de Recherche, Paris, France
| | - Ahmed Ben Hamida
- Advanced Technologies for Medicine and Signals, ENIS, Université de Sfax, Sfax, Tunisia
| | - Stéphane Lehéricy
- Institut du Cerveau et de la Moelle Épinière, Centre de NeuroImagerie de Recherche, Paris, France.,Sorbonne Universités, UPMC Univ Paris 06, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique UMR 7225, Paris, France
| | - Romain Valabregue
- Institut du Cerveau et de la Moelle Épinière, Centre de NeuroImagerie de Recherche, Paris, France.,Sorbonne Universités, UPMC Univ Paris 06, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique UMR 7225, Paris, France
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97
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Tuleasca C, Régis J, Najdenovska E, Witjas T, Girard N, Champoudry J, Faouzi M, Thiran JP, Cuadra MB, Levivier M, Van De Ville D. Pretherapeutic Functional Imaging Allows Prediction of Head Tremor Arrest After Thalamotomy for Essential Tremor: The Role of Altered Interconnectivity Between Thalamolimbic and Supplementary Motor Circuits. World Neurosurg 2018; 112:e479-e488. [PMID: 29410136 DOI: 10.1016/j.wneu.2018.01.063] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Revised: 01/05/2018] [Accepted: 01/11/2018] [Indexed: 11/24/2022]
Abstract
OBJECTIVE To correlate pretherapeutic resting-state functional magnetic resonance imaging (rs-fMRI) measures with pretherapeutic head tremor presence and/or further improvement 1 year after stereotactic radiosurgical thalamotomy (SRS-T) for essential tremor (ET). METHODS We prospectively collected head tremor scores (range, 0-3) and rs-fMRI data for a cohort of 17 consecutive ET patients in pretherapeutic and 1 year after SRS-T states. We additionally acquired rs-fMRI data for a healthy control (HC) group (n = 12). Group-level independent component analysis (n = 17 for pretherapeutic rs-fMRI) was applied to decompose neuroimaging data into 20 large-scale brain networks using a standard approach. Through spatial regression, we projected 1 year after SRS-T and HC rs-fMRI time points, on the same 20 brain networks. RESULTS Pretherapeutic interconnectivity (IC) strength between the network including bilateral thalamus and limbic system with left supplementary motor area predicted head tremor improvement at 1 year after SRS-T (family-wise corrected P < 0.001, cluster size Kc = 146). For the statistically significant cluster, IC strength was strongest in HCs (mean, 4.6; median, 3.8) compared with pre- (mean, 0.1; median, 0.2) or posttherapeutic (mean, -0.2; median, 0.09) states. CONCLUSIONS Baseline measures of IC between bilateral thalamus and limbic system with left supplementary motor area may predict head tremor arrest after thalamotomy. However, procedures such as SRS-T, for this particular clinical feature, do not align patients to HCs in terms of functional brain connectivity. We postulate that supplementary motor area is modulating head tremor appearance, by abnormal connectivity with the thalamolimbic system.
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Affiliation(s)
- Constantin Tuleasca
- Centre Hospitalier Universitaire Vaudois, Neurosurgery Service and Gamma Knife Center, Lausanne, Switzerland; Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.
| | - Jean Régis
- Stereotactic and Functional Neurosurgery Service and Gamma Knife Unit, CHU Timone, Marseille, France
| | - Elena Najdenovska
- Medical Image Analysis Laboratory and Department of Radiology-Center of Biomedical Imaging, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | | | - Nadine Girard
- Department of Diagnostic and Interventional Neuroradiology, AMU, CRMBM UMR CNRS 7339, Faculté de Médecine et APHM, Hopital Timone, Marseille, France
| | - Jérôme Champoudry
- Stereotactic and Functional Neurosurgery Service and Gamma Knife Unit, CHU Timone, Marseille, France
| | - Mohamed Faouzi
- Centre for Clinical Epidemiology, Institute of Social and Preventive Medicine, Lausanne, Switzerland
| | - Jean-Philippe Thiran
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland; Department of Radiology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Meritxell Bach Cuadra
- Medical Image Analysis Laboratory and Department of Radiology-Center of Biomedical Imaging, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland; Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Marc Levivier
- Centre Hospitalier Universitaire Vaudois, Neurosurgery Service and Gamma Knife Center, Lausanne, Switzerland; Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Dimitri Van De Ville
- Faculty of Medicine, University of Geneva, Geneva, Switzerland; Medical Image Processing Laboratory, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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98
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Engman J, Sundström Poromaa I, Moby L, Wikström J, Fredrikson M, Gingnell M. Hormonal Cycle and Contraceptive Effects on Amygdala and Salience Resting-State Networks in Women with Previous Affective Side Effects on the Pill. Neuropsychopharmacology 2018; 43:555-563. [PMID: 28741624 PMCID: PMC5770753 DOI: 10.1038/npp.2017.157] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2017] [Revised: 06/22/2017] [Accepted: 07/18/2017] [Indexed: 12/16/2022]
Abstract
The mechanisms linking ovarian hormones to negative affect are poorly characterized, but important clues may come from the examination of the brain's intrinsic organization. Here, we studied the effects of both the menstrual cycle and oral contraceptives (OCs) on amygdala and salience network resting-state functional connectivity using a double-blind, randomized, and placebo-controlled design. Hormone levels, depressive symptoms, and resting-state functional connectivity were measured in 35 healthy women (24.9±4.2 years) who had previously experienced OC-related negative affect. All participants were examined in the follicular phase of a baseline cycle and in the third week of the subsequent cycle during treatment with either a combined OC (30 μg ethinyl estradiol/0.15 mg levonorgestrel) or placebo. The latter time point targeted the midluteal phase in placebo users and steady-state ethinyl estradiol and levonorgestrel concentrations in OC users. Amygdala and salience network connectivity generally increased with both higher endogenous and synthetic hormone levels, although amygdala-parietal cortical connectivity decreased in OC users. When in the luteal phase, the naturally cycling placebo users demonstrated higher connectivity in both networks compared with the women receiving OCs. Our results support a causal link between the exogenous administration of synthetic hormones and amygdala and salience network connectivity. Furthermore, they suggest a similar, potentially stronger, association between the natural hormonal variations across the menstrual cycle and intrinsic network connectivity.
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Affiliation(s)
- Jonas Engman
- Department of Psychology, Uppsala University, Uppsala, Sweden,Department of Psychology, Uppsala University, Box 1225, SE-751 42 Uppsala, Sweden, Tel: +46 18 471 21 07, Fax: +46 18 471 21 23, E-mail:
| | | | - Lena Moby
- Department of Women’s and Children’s Health, Uppsala University, Uppsala, Sweden
| | - Johan Wikström
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Mats Fredrikson
- Department of Psychology, Uppsala University, Uppsala, Sweden,Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Malin Gingnell
- Department of Psychology, Uppsala University, Uppsala, Sweden,Department of Women’s and Children’s Health, Uppsala University, Uppsala, Sweden
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99
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Yousaf T, Dervenoulas G, Politis M. Advances in MRI Methodology. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2018; 141:31-76. [DOI: 10.1016/bs.irn.2018.08.008] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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100
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Ren Y, Lv J, Guo L, Fang J, Guo CC. Sparse coding reveals greater functional connectivity in female brains during naturalistic emotional experience. PLoS One 2017; 12:e0190097. [PMID: 29272294 PMCID: PMC5741239 DOI: 10.1371/journal.pone.0190097] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2017] [Accepted: 12/10/2017] [Indexed: 11/19/2022] Open
Abstract
Functional neuroimaging is widely used to examine changes in brain function associated with age, gender or neuropsychiatric conditions. FMRI (functional magnetic resonance imaging) studies employ either laboratory-designed tasks that engage the brain with abstracted and repeated stimuli, or resting state paradigms with little behavioral constraint. Recently, novel neuroimaging paradigms using naturalistic stimuli are gaining increasing attraction, as they offer an ecologically-valid condition to approximate brain function in real life. Wider application of naturalistic paradigms in exploring individual differences in brain function, however, awaits further advances in statistical methods for modeling dynamic and complex dataset. Here, we developed a novel data-driven strategy that employs group sparse representation to assess gender differences in brain responses during naturalistic emotional experience. Comparing to independent component analysis (ICA), sparse coding algorithm considers the intrinsic sparsity of neural coding and thus could be more suitable in modeling dynamic whole-brain fMRI signals. An online dictionary learning and sparse coding algorithm was applied to the aggregated fMRI signals from both groups, which was subsequently factorized into a common time series signal dictionary matrix and the associated weight coefficient matrix. Our results demonstrate that group sparse representation can effectively identify gender differences in functional brain network during natural viewing, with improved sensitivity and reliability over ICA-based method. Group sparse representation hence offers a superior data-driven strategy for examining brain function during naturalistic conditions, with great potential for clinical application in neuropsychiatric disorders.
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Affiliation(s)
- Yudan Ren
- School of Automation, Northwestern Polytechnical University, Xi’an, Shaanxi, China
- QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia
| | - Jinglei Lv
- QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi’an, Shaanxi, China
| | - Jun Fang
- School of Automation, Northwestern Polytechnical University, Xi’an, Shaanxi, China
| | - Christine Cong Guo
- QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia
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