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Wei W, Zhang K, Chang J, Zhang S, Ma L, Wang H, Zhang M, Zu Z, Yang L, Chen F, Fan C, Li X. Analyzing 20 years of Resting-State fMRI Research: Trends and collaborative networks revealed. Brain Res 2024; 1822:148634. [PMID: 37848120 DOI: 10.1016/j.brainres.2023.148634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 09/19/2023] [Accepted: 10/14/2023] [Indexed: 10/19/2023]
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI), initially proposed by Biswal et al. in 1995, has emerged as a pivotal facet of neuroimaging research. Its ability to examine brain activity during the resting state without the need for explicit tasks or stimuli has made it an integral component of brain imaging studies. In recent years, rs-fMRI has witnessed substantial growth and found widespread application in the investigation of functional connectivity within the brain. To delineate the developmental trajectory of rs-fMRI over the past two decades, we conducted a comprehensive analysis using bibliometric tool Citespace. Our analysis encompassed publication trends, authorship networks, institutional affiliations, international collaborations, as well as emergent themes in references and keywords. Our study reveals a remarkable increase in the volume of rs-fMRI publications over the past two decades, underscoring the burgeoning interest and potential within this field. Harvard University stands out as the institution with the highest number of research papers published in the realm of RS-fMRI, while the United States holds the highest overall influence in this domain. The recent emergence of keywords such as "machine learning" and "default mode," coupled with citation surges in reference to rs-fMRI, have paved new avenues for research within this field. Our study underscores the critical importance of integrating machine learning techniques into rs-fMRI investigations, offering valuable insights into brain function and disease diagnosis. These findings hold profound significance for the field of neuroscience and may furnish insights for future research employing rs-fMRI as a diagnostic tool for a wide array of neurological disorders, thus emphasizing its pivotal role and potential as a tool for investigating brain functionality.
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Affiliation(s)
- Wenzhuo Wei
- Research Centre for Translational Medicine, the Second Affiliated Hospital, Anhui Medical University, Hefei, Anhui, China; Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, Anhui, China
| | - Kaiyuan Zhang
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jin Chang
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, Anhui, China
| | - Shuyu Zhang
- School of Psychology, the Australian National University, Australian
| | - Lijun Ma
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, Anhui, China
| | - Huixue Wang
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, Anhui, China
| | - Mi Zhang
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, Anhui, China
| | - Zhenyue Zu
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, Anhui, China
| | - Linxi Yang
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, Anhui, China
| | - Fenglan Chen
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, Anhui, China
| | - Chuan Fan
- Department of Psychiatry, the First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.
| | - Xiaoming Li
- Research Centre for Translational Medicine, the Second Affiliated Hospital, Anhui Medical University, Hefei, Anhui, China; Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, Anhui, China.
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2
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Di Giovanni DA, Collins DL. A state-of-the-art review on deep learning for estimating eloquent cortex from resting-state fMRI. Neurosurg Rev 2023; 46:249. [PMID: 37725167 DOI: 10.1007/s10143-023-02154-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 09/07/2023] [Accepted: 09/10/2023] [Indexed: 09/21/2023]
Abstract
Deep learning algorithms have greatly improved our ability to estimate eloquent cortex regions from resting-state brain scans for patients about to undergo neurosurgery. The use of deep learning has the potential to fully automate functional mapping of cortex in this context. We present a highly focused state-of-the-art review on current technology for estimating eloquent cortex from resting-state functional magnetic resonance scans and identify potential paths to meet this goal in the future.
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Affiliation(s)
| | - D Louis Collins
- Department of Biomedical Engineering and Department of Neurology and Neurosurgery in McGill University, Montreal, Canada
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3
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Al-Arfaj HK, Al-Sharydah AM, AlSuhaibani SS, Alaqeel S, Yousry T. Task-Based and Resting-State Functional MRI in Observing Eloquent Cerebral Areas Personalized for Epilepsy and Surgical Oncology Patients: A Review of the Current Evidence. J Pers Med 2023; 13:jpm13020370. [PMID: 36836604 PMCID: PMC9964201 DOI: 10.3390/jpm13020370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 01/23/2023] [Accepted: 02/15/2023] [Indexed: 02/22/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI) is among the newest techniques of advanced neuroimaging that offer the opportunity for neuroradiologists, neurophysiologists, neuro-oncologists, and neurosurgeons to pre-operatively plan and manage different types of brain lesions. Furthermore, it plays a fundamental role in the personalized evaluation of patients with brain tumors or patients with an epileptic focus for preoperative planning. While the implementation of task-based fMRI has increased in recent years, the existing resources and evidence related to this technique are limited. We have, therefore, conducted a comprehensive review of the available resources to compile a detailed resource for physicians who specialize in managing patients with brain tumors and seizure disorders. This review contributes to the existing literature because it highlights the lack of studies on fMRI and its precise role and applicability in observing eloquent cerebral areas in surgical oncology and epilepsy patients, which we believe is underreported. Taking these considerations into account would help to better understand the role of this advanced neuroimaging technique and, ultimately, improve patient life expectancy and quality of life.
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Affiliation(s)
| | - Abdulaziz Mohammad Al-Sharydah
- Diagnostic and Interventional Radiology Department, King Fahd Hospital of the University, Imam Abdulrahman Bin Faisal University, Dammam 34221, Saudi Arabia
- Correspondence: ; Fax: +966-013-8676697
| | - Sari Saleh AlSuhaibani
- Diagnostic and Interventional Radiology Department, King Fahd Hospital of the University, Imam Abdulrahman Bin Faisal University, Dammam 34221, Saudi Arabia
| | - Soliman Alaqeel
- Medical Imaging Department, Dammam Medical Complex, Ministry of Health, Dammam 11176, Saudi Arabia
| | - Tarek Yousry
- Division of Neuroradiology and Neurophysics, Lysholm Department of Neuroradiology, UCL IoN, UCLH, London NW1 2BU, UK
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4
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Kaplan S, Meyer D, Miranda-Dominguez O, Perrone A, Earl E, Alexopoulos D, Barch DM, Day TK, Dust J, Eggebrecht AT, Feczko E, Kardan O, Kenley JK, Rogers CE, Wheelock MD, Yacoub E, Rosenberg M, Elison JT, Fair DA, Smyser CD. Filtering respiratory motion artifact from resting state fMRI data in infant and toddler populations. Neuroimage 2022; 247:118838. [PMID: 34942363 PMCID: PMC8803544 DOI: 10.1016/j.neuroimage.2021.118838] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 11/30/2021] [Accepted: 12/18/2021] [Indexed: 11/24/2022] Open
Abstract
The importance of motion correction when processing resting state functional magnetic resonance imaging (rs-fMRI) data is well-established in adult cohorts. This includes adjustments based on self-limited, large amplitude subject head motion, as well as factitious rhythmic motion induced by respiration. In adults, such respiration artifact can be effectively removed by applying a notch filter to the motion trace, resulting in higher amounts of data retained after frame censoring (e.g., "scrubbing") and more reliable correlation values. Due to the unique physiological and behavioral characteristics of infants and toddlers, rs-fMRI processing pipelines, including methods to identify and remove colored noise due to subject motion, must be appropriately modified to accurately reflect true neuronal signal. These younger cohorts are characterized by higher respiration rates and lower-amplitude head movements than adults; thus, the presence and significance of comparable respiratory artifact and the subsequent necessity of applying similar techniques remain unknown. Herein, we identify and characterize the consistent presence of respiratory artifact in rs-fMRI data collected during natural sleep in infants and toddlers across two independent cohorts (aged 8-24 months) analyzed using different pipelines. We further demonstrate how removing this artifact using an age-specific notch filter allows for both improved data quality and data retention in measured results. Importantly, this work reveals the critical need to identify and address respiratory-driven head motion in fMRI data acquired in young populations through the use of age-specific motion filters as a mechanism to optimize the accuracy of measured results in this population.
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Affiliation(s)
- Sydney Kaplan
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.
| | - Dominique Meyer
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Oscar Miranda-Dominguez
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA,Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Anders Perrone
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA,Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Eric Earl
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA,Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Dimitrios Alexopoulos
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Deanna M. Barch
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA,Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA,Department of Psychological and Brain Sciences, Washington University School of Medicine, St. Louis, MO, USA
| | - Trevor K.M. Day
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
| | - Joseph Dust
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Adam T. Eggebrecht
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Eric Feczko
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA,Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Omid Kardan
- Department of Psychology, University of Chicago, Chicago, IL, USA
| | - Jeanette K. Kenley
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Cynthia E. Rogers
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA,Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Muriah D. Wheelock
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Monica Rosenberg
- Department of Psychology, University of Chicago, Chicago, IL, USA
| | - Jed T. Elison
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA,Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA,Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Damien A. Fair
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA,Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA,Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA,Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Christopher D. Smyser
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA,Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA,Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
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5
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Wu C, Ferreira F, Fox M, Harel N, Hattangadi-Gluth J, Horn A, Jbabdi S, Kahan J, Oswal A, Sheth SA, Tie Y, Vakharia V, Zrinzo L, Akram H. Clinical applications of magnetic resonance imaging based functional and structural connectivity. Neuroimage 2021; 244:118649. [PMID: 34648960 DOI: 10.1016/j.neuroimage.2021.118649] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 09/24/2021] [Accepted: 10/10/2021] [Indexed: 12/23/2022] Open
Abstract
Advances in computational neuroimaging techniques have expanded the armamentarium of imaging tools available for clinical applications in clinical neuroscience. Non-invasive, in vivo brain MRI structural and functional network mapping has been used to identify therapeutic targets, define eloquent brain regions to preserve, and gain insight into pathological processes and treatments as well as prognostic biomarkers. These tools have the real potential to inform patient-specific treatment strategies. Nevertheless, a realistic appraisal of clinical utility is needed that balances the growing excitement and interest in the field with important limitations associated with these techniques. Quality of the raw data, minutiae of the processing methodology, and the statistical models applied can all impact on the results and their interpretation. A lack of standardization in data acquisition and processing has also resulted in issues with reproducibility. This limitation has had a direct impact on the reliability of these tools and ultimately, confidence in their clinical use. Advances in MRI technology and computational power as well as automation and standardization of processing methods, including machine learning approaches, may help address some of these issues and make these tools more reliable in clinical use. In this review, we will highlight the current clinical uses of MRI connectomics in the diagnosis and treatment of neurological disorders; balancing emerging applications and technologies with limitations of connectivity analytic approaches to present an encompassing and appropriate perspective.
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Affiliation(s)
- Chengyuan Wu
- Department of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, 909 Walnut Street, Third Floor, Philadelphia, PA 19107, USA; Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, 909 Walnut Street, First Floor, Philadelphia, PA 19107, USA.
| | - Francisca Ferreira
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, 33 Queen Square, London WC1N 3BG, UK; Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, 33 Queen Square, London WC1N 3BG, UK.
| | - Michael Fox
- Center for Brain Circuit Therapeutics, Departments of Neurology, Psychiatry, Radiology, and Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA.
| | - Noam Harel
- Center for Magnetic Resonance Research, University of Minnesota, 2021 Sixth Street S.E., Minneapolis, MN 55455, USA.
| | - Jona Hattangadi-Gluth
- Department of Radiation Medicine and Applied Sciences, Center for Precision Radiation Medicine, University of California, San Diego, 3855 Health Sciences Drive, La Jolla, CA 92037, USA.
| | - Andreas Horn
- Neurology Department, Movement Disorders and Neuromodulation Section, Charité - University Medicine Berlin, Charitéplatz 1, D-10117, Berlin, Germany.
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK.
| | - Joshua Kahan
- Department of Neurology, Weill Cornell Medicine, 525 East 68th Street, New York, NY, 10065, USA.
| | - Ashwini Oswal
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Mansfield Rd, Oxford OX1 3TH, UK.
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, 7200 Cambridge, Ninth Floor, Houston, TX 77030, USA.
| | - Yanmei Tie
- Center for Brain Circuit Therapeutics, Departments of Neurology, Psychiatry, Radiology, and Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA; Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA.
| | - Vejay Vakharia
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, 33 Queen Square, London WC1N 3BG, UK.
| | - Ludvic Zrinzo
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, 33 Queen Square, London WC1N 3BG, UK; Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, 33 Queen Square, London WC1N 3BG, UK.
| | - Harith Akram
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, 33 Queen Square, London WC1N 3BG, UK; Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, 33 Queen Square, London WC1N 3BG, UK.
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6
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Niu C, Wang Y, Cohen AD, Liu X, Li H, Lin P, Chen Z, Min Z, Li W, Ling X, Wen X, Wang M, Thompson HP, Zhang M. Machine learning may predict individual hand motor activation from resting-state fMRI in patients with brain tumors in perirolandic cortex. Eur Radiol 2021; 31:5253-5262. [PMID: 33758954 DOI: 10.1007/s00330-021-07825-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 12/02/2020] [Accepted: 02/22/2021] [Indexed: 01/23/2023]
Abstract
OBJECTIVE The study aimed to evaluate the predictive validity of the neural network (NN) method for presurgical mapping of motor areas using resting-state functional MRI (rs-fMRI) data of patients with brain tumor located in the perirolandic cortex (PRC). METHODS A total of 109 patients with brain tumors occupying PRC underwent rs-fMRI and hand movement task-based fMRI (tb-fMRI) scans. Using a NN model trained on fMRI data of 47 healthy controls, individual task activation maps were predicted from their rs-fMRI data. NN-predicted maps were compared with task activation and independent component analysis (ICA)-derived maps. Spatial Pearson's correlation coefficients (CC) matrices and Dice coefficients (DC) between task activation and predicted activation using NN (DCNN_Act) and ICA (DCICA_Act) were calculated and compared using non-parametric tests. The effects of tumor types and head motion on predicted maps were demonstrated. RESULTS The CC matrix of NN-predicted maps showed higher diagonal values compared with ICA-derived maps (p < 0.001). DCNN_Act were higher than DCICA_Act (p < 0.001) for patients with or without motor deficits. Lower DCs were found in subjects with head motion greater than one voxel. DCs were higher on the nontumor side than on the tumor side (p < 0.001), especially in the glioma group compared with meningioma and metastatic groups. CONCLUSIONS This study indicated that the NN approach could predict individual motor activation using rs-fMRI data and could have promising clinical applications in brain tumor patients with anatomical and functional reorganizations. KEY POINTS • The neural network machine learning approach successfully predicted hand motor activation in patients with a tumor in the perirolandic cortex, despite space-occupying effects and possible functional reorganization. • Compared to the conventional independent component analysis, the neural network approach utilizing resting-state fMRI data yielded a higher correlation to the active task hand activation data. • The Dice coefficient of machine learning-predicted activation vs. task fMRI activation was different between tumor and nontumor side, also between tumor types, which might indicate different effects of possible neurovascular uncoupling on resting-state and task fMRI.
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Affiliation(s)
- Chen Niu
- Department of Medical Imaging, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi, China.,Department of Computer Science, Technical University of Munich, Munich, Germany
| | - Yang Wang
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA.
| | - Alexander D Cohen
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Xin Liu
- Department of Computer Science, Technical University of Munich, Munich, Germany
| | - Hongwei Li
- Department of Computer Science, Technical University of Munich, Munich, Germany
| | - Pan Lin
- Department of Psychology, Cognition and Human Behavior Key Laboratory of Hunan Province, Hunan Normal University, Changsha, 410081, Hunan, China
| | - Ziyi Chen
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Zhigang Min
- Department of Medical Imaging, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi, China
| | - Wenfei Li
- Department of Medical Imaging, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi, China
| | - Xiao Ling
- Department of Medical Imaging, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi, China
| | - Xin Wen
- Department of Medical Imaging, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi, China
| | - Maode Wang
- Department of Neurosurgery, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi, China
| | - Hannah P Thompson
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Ming Zhang
- Department of Medical Imaging, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi, China.
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7
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Manan HA, Franz EA, Yahya N. The utilisation of resting-state fMRI as a pre-operative mapping tool in patients with brain tumours in comparison to task-based fMRI and intraoperative mapping: A systematic review. Eur J Cancer Care (Engl) 2021; 30:e13428. [PMID: 33592671 DOI: 10.1111/ecc.13428] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Revised: 01/25/2021] [Accepted: 01/28/2021] [Indexed: 12/13/2022]
Abstract
PURPOSE Resting-state functional Magnetic Resonance Imaging (rs-fMRI) is suggested to be a viable option for pre-operative mapping for patients with brain tumours. However, it remains an open issue whether the tool is useful in the clinical setting compared to task-based fMRI (T-fMRI) and intraoperative mapping. Thus, a systematic review was conducted to investigate the usefulness of this technique. METHODS A systematic literature search of rs-fMRI methods applied as a pre-operative mapping tool was conducted using the PubMed/MEDLINE and Cochrane Library electronic databases following PRISMA guidelines. RESULTS Results demonstrated that 50% (six out of twelve) of the studies comparing rs-fMRI and T-fMRI showed good concordance for both language and sensorimotor networks. In comparison to intraoperative mapping, 86% (six out of seven) studies found a good agreement to rs-fMRI. Finally, 87% (twenty out of twenty-three) studies agreed that rs-fMRI is a suitable and useful pre-operative mapping tool. CONCLUSIONS rs-fMRI is a promising technique for pre-operative mapping in assessing the functional brain areas. However, the agreement between rs-fMRI with other techniques, including T-fMRI and intraoperative maps, is not yet optimal. Studies to ascertain and improve the sophistication in pre-processing of rs-fMRI imaging data are needed.
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Affiliation(s)
- Hanani Abdul Manan
- Makmal Pemprosesan Imej Kefungsian (Functional Image Processing Laboratory, Department of Radiology, Universiti Kebangsaan Malaysia Medical Centre, Kuala Lumpur, Malaysia
| | - Elizabeth A Franz
- Department of Psychology and fMRIotago, University of Otago, Dunedin, New Zealand
| | - Noorazrul Yahya
- Diagnostic Imaging & Radiotherapy Program, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
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8
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Zhang J, Zhang Y, Hu L, Huang X, Liu Y, Li J, Hu Q, Xu J, Yu H. Global Trends and Performances of Magnetic Resonance Imaging Studies on Acupuncture: A Bibliometric Analysis. Front Neurosci 2021; 14:620555. [PMID: 33551731 PMCID: PMC7854454 DOI: 10.3389/fnins.2020.620555] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 12/14/2020] [Indexed: 01/17/2023] Open
Abstract
Objectives: To summarize development processes and research hotspots of MRI research on acupuncture and to provide new insights for researchers in future studies. Methods: Publications regarding MRI on acupuncture from inception to 2020 were downloaded from the Web of Science Core Collection. VOSviewer 1.6.15 and CiteSpace V software were used for bibliometric analyses. The main analyses include collaboration analyses between countries/institutions/authors, co-occurrence analysis between keywords, as well as analyses on keyword bursts, citation references, and clusters of references. Results: A total of 829 papers were obtained with a continually increased trend over time. The most productive country and institution in this field were the People's Republic of China (475) and KyungHee University (70), respectively. Evidence-based Complementary and Alternative Medicine (83) was the most productive journal, and Neuroimage (454) was the most co-cited journal. Dhond's et al. (2008) article (co-citation counts: 58) and Napadow's et al. (2005) article (centrality: 0.21) were the most representative and symbolic references, with the highest co-citation number and centrality, respectively. Jie Tian had the highest number of publications (35) and Kathleen K S Hui was the most influential author (280 co-citations). The four hot topics in MRI on acupuncture were acupuncture, fMRI, pain, and stimulation. The three frontier topics were connectivity, modulation, and fMRI. Based on the clustering of co-cited documents, chronic low back pain, sham electro-acupuncture treatment, and clinical research were the main research directions. Conclusion: This study provides an in-depth perspective for MRI research on acupuncture and provides researchers with valuable information to determine the current status, hot spots, and frontier trends of MRI research on acupuncture.
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Affiliation(s)
- Jinhuan Zhang
- The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China.,Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yangxin Zhang
- The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Liyu Hu
- The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Xingxian Huang
- Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, China
| | - Yongfeng Liu
- Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, China
| | - Jiaying Li
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Qingmao Hu
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Jinping Xu
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Haibo Yu
- The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China.,Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, China
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9
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Marawar R, Klinger N, Tarbox-Berry SI, Mittal S, Shah AK. Atypical representation of sensorimotor cortex in a patient with autism and epilepsy confirmed by direct electrocortical stimulation. Epilepsy Behav Rep 2021; 15:100403. [PMID: 33437958 PMCID: PMC7786035 DOI: 10.1016/j.ebr.2020.100403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 10/13/2020] [Accepted: 10/17/2020] [Indexed: 11/30/2022] Open
Abstract
Prior studies have used functional neuroimaging to demonstrate that the organization of the autistic brain is different from that of the non-autistic brain. Similarly, patients with epilepsy have also shown cortical reorganization. We present a case study that provides direct confirmation of disorganized sensorimotor distribution in a patient with autism spectrum disorder and epilepsy. To our knowledge, this is the first time cortical mapping directly showing abnormal cortical organization in a patient with autism spectrum disorder and epilepsy has been reported in the literature.
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Affiliation(s)
- Rohit Marawar
- Department of Neurology, Wayne State University School of Medicine, 4201 St. Antoine, UHC-8D, Detroit, MI 48201, USA
| | - Neil Klinger
- Department of Neurosurgery, Wayne State University School of Medicine, 4160, John R. Street, Suite 930, Detroit, MI 48201, USA
| | - Sarah I Tarbox-Berry
- Department of Neurology, Wayne State University School of Medicine, 4201 St. Antoine, UHC-8D, Detroit, MI 48201, USA
| | - Sandeep Mittal
- Division of Neurosurgery, Virginia Tech Carilion School of Medicine, 2331 Franklin Rd SW, Roanoke, VA 24014, USA
| | - Aashit K Shah
- Division of Neurology, Virginia Tech Carilion School of Medicine, 3 Riverside Circle, Roanoke, VA 24016, USA
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10
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Luckett P, Lee JJ, Park KY, Dierker D, Daniel AGS, Seitzman BA, Hacker CD, Ances BM, Leuthardt EC, Snyder AZ, Shimony JS. Mapping of the Language Network With Deep Learning. Front Neurol 2020; 11:819. [PMID: 32849247 PMCID: PMC7419701 DOI: 10.3389/fneur.2020.00819] [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: 03/02/2020] [Accepted: 06/30/2020] [Indexed: 01/01/2023] Open
Abstract
Background: Pre-surgical functional localization of eloquent cortex with task-based functional MRI (T-fMRI) is part of the current standard of care prior to resection of brain tumors. Resting state fMRI (RS-fMRI) is an alternative method currently under investigation. Here, we compare group level language localization using T-fMRI vs. RS-fMRI analyzed with 3D deep convolutional neural networks (3DCNN). Methods: We analyzed data obtained in 35 patients with brain tumors that had both language T-fMRI and RS-MRI scans during pre-surgical evaluation. The T-fMRI data were analyzed using conventional techniques. The language associated resting state network was mapped using a 3DCNN previously trained with data acquired in >2,700 normal subjects. Group level results obtained by both methods were evaluated using receiver operator characteristic analysis of probability maps of language associated regions, taking as ground truth meta-analytic maps of language T-fMRI responses generated on the Neurosynth platform. Results: Both fMRI methods localized major components of the language system (areas of Broca and Wernicke). Word-stem completion T-fMRI strongly activated Broca's area but also several task-general areas not specific to language. RS-fMRI provided a more specific representation of the language system. Conclusion: 3DCNN was able to accurately localize the language network. Additionally, 3DCNN performance was remarkably tolerant of a limited quantity of RS-fMRI data.
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Affiliation(s)
- Patrick Luckett
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
| | - John J Lee
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Ki Yun Park
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Donna Dierker
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Andy G S Daniel
- Department of Biomedical Engineering, Washington University, St. Louis, MO, United States
| | - Benjamin A Seitzman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
| | - Carl D Hacker
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO, United States
| | - Beau M Ances
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
| | - Eric C Leuthardt
- Department of Biomedical Engineering, Washington University, St. Louis, MO, United States.,Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO, United States
| | - Abraham Z Snyder
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States.,Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Joshua S Shimony
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
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11
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Controlling for the effect of arterial-CO2 fluctuations in resting-state fMRI: Comparing end-tidal CO2 clamping and retroactive CO2 correction. Neuroimage 2020; 216:116874. [DOI: 10.1016/j.neuroimage.2020.116874] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 04/17/2020] [Accepted: 04/21/2020] [Indexed: 01/21/2023] Open
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12
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Gratton C, Kraus BT, Greene DJ, Gordon EM, Laumann TO, Nelson SM, Dosenbach NUF, Petersen SE. Defining Individual-Specific Functional Neuroanatomy for Precision Psychiatry. Biol Psychiatry 2020; 88:28-39. [PMID: 31916942 PMCID: PMC7203002 DOI: 10.1016/j.biopsych.2019.10.026] [Citation(s) in RCA: 83] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Revised: 10/07/2019] [Accepted: 10/25/2019] [Indexed: 12/28/2022]
Abstract
Studies comparing diverse groups have shown that many psychiatric diseases involve disruptions across distributed large-scale networks of the brain. There is hope that functional magnetic resonance imaging (fMRI) functional connectivity techniques will shed light on these disruptions, providing prognostic and diagnostic biomarkers as well as targets for therapeutic interventions. However, to date, progress on clinical translation of fMRI methods has been limited. Here, we argue that this limited translation is driven by a combination of intersubject heterogeneity and the relatively low reliability of standard fMRI techniques at the individual level. We review a potential solution to these limitations: the use of new "precision" fMRI approaches that shift the focus of analysis from groups to single individuals through the use of extended data acquisition strategies. We begin by discussing the potential advantages of fMRI functional connectivity methods for improving our understanding of functional neuroanatomy and disruptions in psychiatric disorders. We then discuss the budding field of precision fMRI and findings garnered from this work. We demonstrate that precision fMRI can improve the reliability of functional connectivity measures, while showing high stability and sensitivity to individual differences. We close by discussing the application of these approaches to clinical settings.
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Affiliation(s)
- Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, Illinois; Department of Neurology, Northwestern University, Evanston, Illinois.
| | - Brian T Kraus
- Department of Psychology, Northwestern University, Evanston, Illinois
| | - Deanna J Greene
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri; Department of Radiology, Washington University in St. Louis, St. Louis, Missouri
| | - Evan M Gordon
- VISN Center of Excellence for Research on Returning War Veterans, Waco, Texas; Department of Psychology and Neuroscience, Baylor University, Waco, Texas; Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas
| | - Timothy O Laumann
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri
| | - Steven M Nelson
- VISN Center of Excellence for Research on Returning War Veterans, Waco, Texas; Department of Psychology and Neuroscience, Baylor University, Waco, Texas; Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas; Department of Psychiatry and Behavioral Science, Texas A&M Health Science Center, College of Medicine, Bryan, Texas
| | - Nico U F Dosenbach
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri; Department of Neurology, Washington University in St. Louis, St. Louis, Missouri; Department of Pediatrics, Washington University in St. Louis, St. Louis, Missouri; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri
| | - Steven E Petersen
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri; Department of Radiology, Washington University in St. Louis, St. Louis, Missouri; Department of Neurology, Washington University in St. Louis, St. Louis, Missouri; Department of Neuroscience, Washington University in St. Louis, St. Louis, Missouri; Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, Missouri
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13
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Voets NL, Plaha P, Parker Jones O, Pretorius P, Bartsch A. Presurgical Localization of the Primary Sensorimotor Cortex in Gliomas : When is Resting State FMRI Beneficial and Sufficient? Clin Neuroradiol 2020; 31:245-256. [PMID: 32274518 PMCID: PMC7943510 DOI: 10.1007/s00062-020-00879-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 01/22/2020] [Indexed: 10/27/2022]
Abstract
PURPOSE Functional magnetic resonance imaging (fMRI) has an established role in neurosurgical planning; however, ambiguity surrounds the comparative value of resting and task-based fMRI relative to anatomical localization of the sensorimotor cortex. This study was carried out to determine: 1) how often fMRI adds to prediction of motor risks beyond expert neuroradiological review, 2) success rates of presurgical resting and task-based sensorimotor mapping, and 3) the impact of accelerated resting fMRI acquisitions on network detectability. METHODS Data were collected at 2 centers from 71 patients with a primary brain tumor (31 women; mean age 41.9 ± 13.9 years) and 14 healthy individuals (6 women; mean age 37.9 ± 12.7 years). Preoperative 3T MRI included anatomical scans and resting fMRI using unaccelerated (TR = 3.5 s), intermediate (TR = 1.56 s) or high temporal resolution (TR = 0.72 s) sequences. Task fMRI finger tapping data were acquired in 45 patients. Group differences in fMRI reproducibility, spatial overlap and success frequencies were assessed with t‑tests and χ2-tests. RESULTS Radiological review identified the central sulcus in 98.6% (70/71) patients. Task-fMRI succeeded in 100% (45/45). Resting fMRI failed to identify a sensorimotor network in up to 10 patients; it succeeded in 97.9% (47/48) of accelerated fMRIs, compared to only 60.9% (14/23) of unaccelerated fMRIs ([Formula: see text](2) = 17.84, p < 0.001). Of the patients 12 experienced postoperative deterioration, largely predicted by anatomical proximity to the central sulcus. CONCLUSION The use of fMRI in patients with residual or intact presurgical motor function added value to uncertain anatomical localization in just a single peri-Rolandic glioma case. Resting fMRI showed high correspondence to task localization when acquired with accelerated sequences but offered limited success at standard acquisitions.
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Affiliation(s)
- Natalie L Voets
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, John Radcliffe Hospital, University of Oxford, OX3 9DU, Headington, Oxford, UK. .,Department of Neurosurgery, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
| | - Puneet Plaha
- Department of Neurosurgery, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Oiwi Parker Jones
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, John Radcliffe Hospital, University of Oxford, OX3 9DU, Headington, Oxford, UK
| | - Pieter Pretorius
- Department of Neuroradiology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Andreas Bartsch
- Department of Neuroradiology, University of Heidelberg, Heidelberg, Germany
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14
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Vakamudi K, Posse S, Jung R, Cushnyr B, Chohan MO. Real-time presurgical resting-state fMRI in patients with brain tumors: Quality control and comparison with task-fMRI and intraoperative mapping. Hum Brain Mapp 2019; 41:797-814. [PMID: 31692177 PMCID: PMC7268088 DOI: 10.1002/hbm.24840] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 10/09/2019] [Accepted: 10/10/2019] [Indexed: 12/11/2022] Open
Abstract
Resting-state functional magnetic resonance imaging (rsfMRI) is a promising task-free functional imaging approach, which may complement or replace task-based fMRI (tfMRI) in patients who have difficulties performing required tasks. However, rsfMRI is highly sensitive to head movement and physiological noise, and validation relative to tfMRI and intraoperative electrocortical mapping is still necessary. In this study, we investigate (a) the feasibility of real-time rsfMRI for presurgical mapping of eloquent networks with monitoring of data quality in patients with brain tumors and (b) rsfMRI localization of eloquent cortex compared with tfMRI and intraoperative electrocortical stimulation (ECS) in retrospective analysis. Five brain tumor patients were studied with rsfMRI and tfMRI on a clinical 3T scanner using MultiBand(8)-echo planar imaging (EPI) with repetition time: 400 ms. Moving-averaged sliding-window correlation analysis with regression of motion parameters and signals from white matter and cerebrospinal fluid was used to map sensorimotor and language resting-state networks. Data quality monitoring enabled rapid optimization of scan protocols, early identification of task noncompliance, and head movement-related false-positive connectivity to determine scan continuation or repetition. Sensorimotor and language resting-state networks were identifiable within 1 min of scan time. The Euclidean distance between ECS and rsfMRI connectivity and task-activation in motor cortex, Broca's, and Wernicke's areas was 5-10 mm, with the exception of discordant rsfMRI and ECS localization of Wernicke's area in one patient due to possible cortical reorganization and/or altered neurovascular coupling. This study demonstrates the potential of real-time high-speed rsfMRI for presurgical mapping of eloquent cortex with real-time data quality control, and clinically acceptable concordance of rsfMRI with tfMRI and ECS localization.
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Affiliation(s)
- Kishore Vakamudi
- Department of Neurology, University of New Mexico, Albuquerque, New Mexico
| | - Stefan Posse
- Department of Neurology, University of New Mexico, Albuquerque, New Mexico.,Department of Physics and Astronomy, University of New Mexico, Albuquerque, New Mexico
| | - Rex Jung
- Department of Neurosurgery, University of New Mexico, Albuquerque, New Mexico
| | - Brad Cushnyr
- Department of Radiology, University of New Mexico, Albuquerque, New Mexico
| | - Muhammad O Chohan
- Department of Neurosurgery, University of New Mexico, Albuquerque, New Mexico
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15
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Ding JR, Zhu F, Hua B, Xiong X, Wen Y, Ding Z, Thompson PM. Presurgical localization and spatial shift of resting state networks in patients with brain metastases. Brain Imaging Behav 2019; 13:408-420. [PMID: 29611075 DOI: 10.1007/s11682-018-9864-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Brain metastases are the most prevalent cerebral tumors. Resting state networks (RSNs) are involved in multiple perceptual and cognitive functions. Therefore, precisely localizing multiple RSNs may be extremely valuable before surgical resection of metastases, to minimize neurocognitive impairments. Here we aimed to investigate the reliability of independent component analysis (ICA) for localizing multiple RSNs from resting-state functional MRI (rs-fMRI) data in individual patients, and further evaluate lesion-related spatial shifts of the RSNs. Twelve patients with brain metastases and 14 healthy controls were recruited. Using an improved automatic component identification method, we successfully identified seven common RSNs, including: the default mode network (DMN), executive control network (ECN), dorsal attention network (DAN), language network (LN), sensorimotor network (SMN), auditory network (AN) and visual network (VN), in both individual patients and controls. Moreover, the RSNs in the patients showed a visible spatial shift compared to those in the controls, and the spatial shift of some regions was related to the tumor location, which may reflect a complicated functional mechanism - functional disruptions and reorganizations - caused by metastases. Besides, higher cognitive networks (DMN, ECN, DAN and LN) showed significantly larger spatial shifts than perceptual networks (SMN, AN and VN), supporting a functional dichotomy between the two network groups even in pathologic alterations associated with metastases. Overall, our findings provide evidence that ICA is a promising approach for presurgical localization of multiple RSNs from rs-fMRI data in individual patients. More attention should be paid to the spatial shifts of the RSNs before surgical resection.
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Affiliation(s)
- Ju-Rong Ding
- School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, People's Republic of China. .,Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging and Informatics, University of Southern California, Marina del Rey, CA, USA.
| | - Fangmei Zhu
- Department of Radiology, Zhejiang Provincial People's Hospital, Hangzhou, People's Republic of China
| | - Bo Hua
- School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, People's Republic of China
| | - Xingzhong Xiong
- School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, People's Republic of China
| | - Yuqiao Wen
- School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, People's Republic of China
| | - Zhongxiang Ding
- Department of Radiology, Zhejiang Provincial People's Hospital, Hangzhou, People's Republic of China.
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging and Informatics, University of Southern California, Marina del Rey, CA, USA.
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16
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Bell CS, Mohd Khairi N, Ding Z, Wilkes DM. Bayesian framework for robust seed-based correlation analysis. Med Phys 2019; 46:3055-3066. [PMID: 30932188 DOI: 10.1002/mp.13522] [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: 05/07/2018] [Revised: 02/07/2019] [Accepted: 03/07/2019] [Indexed: 11/10/2022] Open
Abstract
PURPOSE One popular method of assessing brain functional connectivity (FC) is through seed-based correlation (SCA) analysis. One drawback of this method is when the seed location is varied slightly, the FC can vary dramatically. We propose a method superior to SCA, robust to variations in seed location, which confers a probabilistic interpretation. METHODS We introduce a probabilistic method which generates a cloud of highly connected voxels to determine a stable set of voxels connected to the seed location (SC-SCA). This cloud can generate a correlation map or a probabilistic map. The method is applied to the default mode network (DMN) based on a posterior cingulate cortex (PCC) seed, and the auditory network (AN) as validation on a smaller network. A Bayesian interpretation is demonstrated through performing a maximum a posteriori (MAP) estimation on the DMN. The advantages of the method are tested by performing stability analyses on its influential parameters. The method is extended to region-based SC-SCA, and then comparisons are made based on seed-based vs region-based versions of the methods SC-SCA vs traditional SCA. The statistical significance between the methods is assessed via a bootstrap method using the difference in medians of the standard deviation of the voxels for 16 subjects. RESULTS The proposed method, SC-SCA, is able to identify a set of regions - the DMN - that are known to be associated with and have high correlation with the PCC, and the method is also extensible to smaller networks as shown by its performance on the AN. Based on the certainty of the a priori distribution for MAP analysis, the method is able to identify regions with high probability of belonging to the DMN. The stability analyses demonstrated that substantial deviations from the initial seed locations in the sagittal, posterior transverse, and axial directions by ±10 mm do not cause substantial variation in the correlation network produced. Qualitative inspection of the average correlation maps garnered from the four methods showed that SC-SCA shows a larger amount of detail in FC connectivity as compared to SCA. Seed-based methods show higher detail and contrast in the maps in comparison with region-based methods. Quantitatively, the statistical tests between seed-based vs region-based and SC-SCA vs SCA revealed that there is no significant difference between the following methods: region-based SCA or region-based SC-SCA, and seed-based SC-SCA or region-based SC-SCA. However, there are statistically significant differences and advantages conferred between the following methods: seed-based SC-SCA over seed-based SCA, region-based SC-SCA over seed-based SCA, region-based SCA over seed-based SCA, and region-based SCA over seed-based SC-SCA. Finally, seed-based SC-SCA outperforms sphere-based SCA. CONCLUSIONS The proposed method offers several advantages over traditional SCA: robust single-seed FC estimation, novel Bayesian estimation capabilities, enhanced detail of brain structures, robustness to initial seed location, and enhanced consistency in the correlation maps generated. Region-based SC-SCA is equivalent or superior to all investigated methods, where seed-based SCA is inferior to all methods. The method confers improved single-seed SCA with the additional benefit of Bayesian estimation.
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Affiliation(s)
- Charreau S Bell
- Vanderbilt University Department of Electrical Engineering and Computer Science, 400 24th Avenue S, Featheringill Hall, Room 254, Nashville, TN, 37235, USA
| | - Nazirah Mohd Khairi
- Vanderbilt University Department of Electrical Engineering and Computer Science, 400 24th Avenue S, Featheringill Hall, Room 254, Nashville, TN, 37235, USA
| | - Zhaohua Ding
- Vanderbilt University Department of Electrical Engineering and Computer Science, 400 24th Avenue S, Featheringill Hall, Room 254, Nashville, TN, 37235, USA.,Vanderbilt University Institute of Imaging Science (VUIIS), 1161 21st Avenue S AA-1105, Nashville, TN, 37232, USA
| | - Don Mitchell Wilkes
- Vanderbilt University Department of Electrical Engineering and Computer Science, 400 24th Avenue S, Featheringill Hall, Room 254, Nashville, TN, 37235, USA
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17
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O'Connor EE, Zeffiro TA. Why is Clinical fMRI in a Resting State? Front Neurol 2019; 10:420. [PMID: 31068901 PMCID: PMC6491723 DOI: 10.3389/fneur.2019.00420] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Accepted: 04/05/2019] [Indexed: 12/28/2022] Open
Abstract
While resting state fMRI (rs-fMRI) has gained widespread application in neuroimaging clinical research, its penetration into clinical medicine has been more limited. We surveyed a neuroradiology professional group to ascertain their experience with rs-fMRI, identify perceived barriers to using rs-fMRI clinically and elicit suggestions about ways to facilitate its use in clinical practice. The electronic survey also collected information about demographics and work environment using Likert scales. We found that 90% of the respondents had adequate equipment to conduct rs-fMRI and 82% found rs-fMRI data easy to collect. Fifty-nine percent have used rs-fMRI in their past research and 72% reported plans to use rs-fMRI for research in the next year. Nevertheless, only 40% plan to use rs-fMRI in clinical practice in the next year and 82% agreed that their clinical fMRI use is largely confined to pre-surgical planning applications. To explore the reasons for the persistent low utilization of rs-fMRI in clinical applications, we identified barriers to clinical rs-fMRI use related to the availability of robust denoising procedures, single-subject analysis techniques, demonstration of functional connectivity map reliability, regulatory clearance, reimbursement, and neuroradiologist training opportunities. In conclusion, while rs-fMRI use in clinical neuroradiology practice is limited, enthusiasm appears to be quite high and there are several possible avenues in which further research and development may facilitate its penetration into clinical practice.
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Affiliation(s)
- Erin E O'Connor
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Center, Baltimore, MD, United States
| | - Thomas A Zeffiro
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Center, Baltimore, MD, United States
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18
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Gohel S, Laino ME, Rajeev-Kumar G, Jenabi M, Peck K, Hatzoglou V, Tabar V, Holodny AI, Vachha B. Resting-State Functional Connectivity of the Middle Frontal Gyrus Can Predict Language Lateralization in Patients with Brain Tumors. AJNR Am J Neuroradiol 2019; 40:319-325. [PMID: 30630835 DOI: 10.3174/ajnr.a5932] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2018] [Accepted: 11/12/2018] [Indexed: 01/28/2023]
Abstract
BACKGROUND AND PURPOSE A recent study using task-based fMRI demonstrated that the middle frontal gyrus is comparable with Broca's area in its ability to determine language laterality using a measure of verbal fluency. This study investigated whether the middle frontal gyrus can be used as an indicator for language-hemispheric dominance in patients with brain tumors using task-free resting-state fMRI. We hypothesized that no significant difference in language lateralization would occur between the middle frontal gyrus and Broca area and that the middle frontal gyrus can serve as a simple and reliable means of measuring language laterality. MATERIALS AND METHODS Using resting-state fMRI, we compared the middle frontal gyrus with the Broca area in 51 patients with glial neoplasms for voxel activation, the language laterality index, and the effect of tumor grade on the laterality index. The laterality index derived by resting-state fMRI and task-based fMRI was compared in a subset of 40 patients. RESULTS Voxel activations in the left middle frontal gyrus and left Broca area were positively correlated (r = 0.47, P < .001). Positive correlations were seen between the laterality index of the Broca area and middle frontal gyrus regions (r = 0.56, P < .0005). Twenty-seven of 40 patients (67.5%) showed concordance of the laterality index based on the Broca area using resting-state fMRI and the laterality index based on a language task. Thirty of 40 patients (75%) showed concordance of the laterality index based on the middle frontal gyrus using resting-state fMRI and the laterality index based on a language task. CONCLUSIONS The middle frontal gyrus is comparable with the Broca area in its ability to determine hemispheric dominance for language using resting-state fMRI. Our results suggest the addition of resting-state fMRI of the middle frontal gyrus to the list of noninvasive modalities that could be used in patients with gliomas to evaluate hemispheric dominance of language before tumor resection. In patients who cannot participate in traditional task-based fMRI, resting-state fMRI offers a task-free alternate to presurgically map the eloquent cortex.
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Affiliation(s)
- S Gohel
- From the Department of Health Informatics (S.G.), Rutgers University School of Health Professions, Newark, New Jersey
| | - M E Laino
- Departments of Radiology (M.E.L., M.J., K.P., V.H., A.I.H., B.V.).,Department of Radiology (M.E.L.), Catholic University of the Sacred Heart, Rome, Italy
| | - G Rajeev-Kumar
- Icahn School of Medicine at Mount Sinai (G.R.-K.), New York, New York
| | - M Jenabi
- Departments of Radiology (M.E.L., M.J., K.P., V.H., A.I.H., B.V.)
| | - K Peck
- Departments of Radiology (M.E.L., M.J., K.P., V.H., A.I.H., B.V.).,Medical Physics (K.P.)
| | - V Hatzoglou
- Departments of Radiology (M.E.L., M.J., K.P., V.H., A.I.H., B.V.)
| | - V Tabar
- Neurosurgery (V.T.), Memorial Sloan Kettering Cancer Center, New York, New York
| | - A I Holodny
- Departments of Radiology (M.E.L., M.J., K.P., V.H., A.I.H., B.V.)
| | - B Vachha
- Departments of Radiology (M.E.L., M.J., K.P., V.H., A.I.H., B.V.)
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19
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Lee JY, Choi Y, Ahn KJ, Nam Y, Jang JH, Choi HS, Jung SL, Kim BS. Seed-Based Resting-State Functional MRI for Presurgical Localization of the Motor Cortex: A Task-Based Functional MRI-Determined Seed Versus an Anatomy-Determined Seed. Korean J Radiol 2018; 20:171-179. [PMID: 30627033 PMCID: PMC6315064 DOI: 10.3348/kjr.2018.0004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Accepted: 08/23/2018] [Indexed: 01/25/2023] Open
Abstract
Objective For localization of the motor cortex, seed-based resting-state functional MRI (rsfMRI) uses the contralateral motor cortex as a seed. However, research has shown that the location of the motor cortex could differ according to anatomical variations. The purpose of this study was to compare the results of rsfMRI using two seeds: a template seed (the anatomically expected location of the contralateral motor cortex) and a functional seed (the actual location of the contralateral motor cortex determined by task-based functional MRI [tbfMRI]). Materials and Methods Eight patients (4 with glioma, 3 with meningioma, and 1 with arteriovenous malformation) and 9 healthy volunteers participated. For the patients, tbfMRI was performed unilaterally to activate the healthy contralateral motor cortex. The affected ipsilateral motor cortices were mapped with rsfMRI using seed-based and independent component analysis (ICA). In the healthy volunteer group, both motor cortices were mapped with both-hands tbfMRI and rsfMRI. We compared the results between template and functional seeds, and between the seed-based analysis and ICA with visual and quantitative analysis. Results For the visual analysis, the functional seed showed significantly higher scores compared to the template seed in both the patients (p = 0.002) and healthy volunteers (p < 0.001). Although no significant difference was observed between the functional seed and ICA, the ICA results showed significantly higher scores than the template seed in both the patients (p = 0.01) and healthy volunteers (p = 0.005). In the quantitative analysis, the functional seed exhibited greater similarity to tbfMRI than the template seed and ICA. Conclusion Using the contralateral motor cortex determined by tbfMRI as a seed could enhance visual delineation of the motor cortex in seed-based rsfMRI.
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Affiliation(s)
- Ji Young Lee
- Department of Radiology, Hanyang University Hospital, Hanyang University College of Medicine, Seoul, Korea.,Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Yangsean Choi
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Kook Jin Ahn
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Yoonho Nam
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Jin Hee Jang
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Hyun Seok Choi
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - So Lyung Jung
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Bum Soo Kim
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
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Karpiel I, Klose U, Drzazga Z. Optimization of rs-fMRI parameters in the Seed Correlation Analysis (SCA) in DPARSF toolbox: A preliminary study. J Neurosci Res 2018; 97:433-443. [PMID: 30575101 DOI: 10.1002/jnr.24364] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Revised: 11/20/2018] [Accepted: 11/21/2018] [Indexed: 11/12/2022]
Abstract
There are a number of various methods of resting-state functional magnetic resonance imaging (rs-fMRI) analysis such as independent component analysis, multivariate autoregressive models, or seed correlation analysis however their results depend on arbitrary choice of parameters. Therefore, the aim of this work was to optimize the parameters in the seed correlation analysis using the Data Processing Assistant for Resting-State fMRI (DPARSF) toolbox for rs-fMRI data received from a Siemens Magnetom Skyra 3-Tesla scanner using a whole-brain, gradient-echo echo planar sequence with a 32-channel head coil. Different ranges of the following parameters: amplitude of low-frequency fluctuation (ALFF), Gaussian kernel at FWHM and radius of spherical ROI for 109 regions were tested for 20 healthy volunteers. The highest values of functional connectivity (FC) correlations were found for ALFF 0.01-0.08, spherical ROIs with the 8-mm radius and Gaussian kernel 8 mm at FWHM in all the studied areas that is, Auditory, Sensimotor, Visual, and Default Mode Network. The dominating influence of ALFF and smoothing on values of FC correlations was noted.
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Affiliation(s)
- Ilona Karpiel
- Department of Medical Physics, A. Chełkowski Institute of Physics, University of Silesia, Chorzów, Poland.,Department of Interventional and Diagnostic Neuroradiology at the University Hospital, University of Tuebingen, Tuebingen, Germany
| | - Uwe Klose
- Department of Interventional and Diagnostic Neuroradiology at the University Hospital, University of Tuebingen, Tuebingen, Germany
| | - Zofia Drzazga
- Department of Medical Physics, A. Chełkowski Institute of Physics, University of Silesia, Chorzów, Poland
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21
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Yordanova YN, Cochereau J, Duffau H, Herbet G. Combining resting state functional MRI with intraoperative cortical stimulation to map the mentalizing network. Neuroimage 2018; 186:628-636. [PMID: 30500423 DOI: 10.1016/j.neuroimage.2018.11.046] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Revised: 11/02/2018] [Accepted: 11/26/2018] [Indexed: 10/27/2022] Open
Abstract
OBJECTIVE To infer the face-based mentalizing network from resting-state functional MRI (rsfMRI) using a seed-based correlation analysis with regions of interest identified during intraoperative cortical electrostimulation. METHODS We retrospectively included 23 patients in whom cortical electrostimulation induced transient face-based mentalizing impairment during 'awake' craniotomy for resection of a right-sided diffuse low-grade glioma. Positive stimulation sites were recorded and transferred to the patients' preoperative normalized MRI, and then used as seeds for subsequent seed-to-voxel functional connectivity analyses. The analyses, conducted with an uncorrected voxel-level p-value of 0.001 and a false-discovery-rate cluster-level p-value of 0.05, allowed identification of the cortical structures, functionally coupled with the mentalizing-related sites. RESULTS Two clusters of responsive stimulations were identified intraoperatively - one in the right dorsolateral prefrontal cortex (dlPFC, n = 13) and the other in the right inferior frontal gyrus (IFG, n = 10). A whole group level analysis revealed that stimulation sites correlated mainly with voxels located in the pars triangularis of the IFG, the dorsolateral and dorsomedial prefrontal cortices, the temporo-parietal junction, the posterior superior temporal sulcus, and the posterior inferior temporal/fusiform gyrus. Other analyses, taking into consideration the location of the responsive sites (IFG versus dlPFC cluster), highlighted only minor differences between both groups. CONCLUSIONS The present study successfully demonstrated the involvement of a large-scale neural network in the face-based mentalizing that strongly matches networks, classically identified using task-based fMRI paradigms. We thus validated the combination of rsfMRI and stimulation mapping as a powerful approach to identify functional networks in brain-damaged patients.
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Affiliation(s)
- Yordanka Nikolova Yordanova
- Department of Neurosurgery, 'Percy' Military Hospital, 101 avenue Henri Barbusse, 92140, Clamart, France; National Institute for Health and Medical Research (INSERM), U1051, Team "Plasticity of the Central Nervous System, Human Stem Cells and Glial Tumors", Institute for Neurosciences of Montpellier, France.
| | - Jérôme Cochereau
- Department of Neurosurgery, Gui de Chauliac Hospital, 80 avenue Augustin Fliche, 34295, France; National Institute for Health and Medical Research (INSERM), U1051, Team "Plasticity of the Central Nervous System, Human Stem Cells and Glial Tumors", Institute for Neurosciences of Montpellier, France.
| | - Hugues Duffau
- Department of Neurosurgery, Gui de Chauliac Hospital, 80 avenue Augustin Fliche, 34295, France; National Institute for Health and Medical Research (INSERM), U1051, Team "Plasticity of the Central Nervous System, Human Stem Cells and Glial Tumors", Institute for Neurosciences of Montpellier, France; University of Montpellier, France.
| | - Guillaume Herbet
- Department of Neurosurgery, Gui de Chauliac Hospital, 80 avenue Augustin Fliche, 34295, France; National Institute for Health and Medical Research (INSERM), U1051, Team "Plasticity of the Central Nervous System, Human Stem Cells and Glial Tumors", Institute for Neurosciences of Montpellier, France; University of Montpellier, France.
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22
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Ulloa A, Horwitz B. Quantifying Differences Between Passive and Task-Evoked Intrinsic Functional Connectivity in a Large-Scale Brain Simulation. Brain Connect 2018; 8:637-652. [PMID: 30430844 DOI: 10.1089/brain.2018.0620] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Establishing a connection between intrinsic and task-evoked brain activities is critical because it would provide a way to map task-related brain regions in patients unable to comply with such tasks. A crucial question within this realm is to what extent the execution of a cognitive task affects the intrinsic activity of brain regions not involved in the task. Computational models can be useful to answer this question because they allow us to distinguish task from nontask neural elements while giving us the effects of task execution on nontask regions of interest at the neuroimaging level. The quantification of those effects in a computational model would represent a step toward elucidating the intrinsic versus task-evoked connection. In this study we used computational modeling and graph theoretical metrics to quantify changes in intrinsic functional brain connectivity due to task execution. We used our large-scale neural modeling framework to embed a computational model of visual short-term memory into an empirically derived connectome. We simulated a neuroimaging study consisting of 10 subjects performing passive fixation (PF), passive viewing (PV), and delayed match-to-sample (DMS) tasks. We used the simulated blood oxygen level-dependent functional magnetic resonance imaging time series to calculate functional connectivity (FC) matrices and used those matrices to compute several graph theoretical measures. After determining that the simulated graph theoretical measures were largely consistent with experiments, we were able to quantify the differences between the graph metrics of the PF condition and those of the PV and DMS conditions. Thus, we show that we can use graph theoretical methods applied to simulated brain networks to aid in the quantification of changes in intrinsic brain FC during task execution. Our results represent a step toward establishing a connection between intrinsic and task-related brain activities.
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Affiliation(s)
- Antonio Ulloa
- Brain Imaging and Modeling Section, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, Maryland.,Neural Bytes, Washington, District of Columbia
| | - Barry Horwitz
- Brain Imaging and Modeling Section, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, Maryland
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23
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Pasquali S, Sommariva A, Mahteme H, Suo T, Ma H, Tropea S, Steenberg JL, Mocellin S. Cytoreductive surgery alone or combined with hyperthermic intraperitoneal chemotherapy (HIPEC) for pseudomyxoma peritonei. Hippokratia 2018. [DOI: 10.1002/14651858.cd005659.pub3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Sandro Pasquali
- Fondazione IRCCS Istituto Nazionale dei Tumori; Sarcoma Service; Via G. Venezian 1 Milano Italy 20133
| | - Antonio Sommariva
- Istituto Oncologico Veneto, IOV-IRCCS; Surgical Oncology Unit; Via Gattamelata 64 Padova Veneto Italy 35128
| | - Haile Mahteme
- Uppsala Cancer Clinic; Uppsala Cancer Clinic; Box 833 Uppsala Sweden 75108
| | - Tao Suo
- Zhongshan Hospital, Fudan University; Department of General Surgery, Institute of General Surgery; 180 Fenglin Road, Xuhui District Shanghai Shanghai China 200032
| | - Huaixing Ma
- Anhui Provincial Cancer Hospital, the First Affiliated Hospital of University of Science and Technology of China (The Western Area); Department of Medical Oncology; Hefei Anhui China
| | - Saveria Tropea
- Istituto Oncologico Veneto, IOV-IRCCS; Surgical Oncology Unit; Via Gattamelata 64 Padova Veneto Italy 35128
| | - Josephine L Steenberg
- Bispebjerg Hospital, Region H; Cochrane Colorectal Cancer Group; Bispebjerg Bakke 23 Building 39N Copenhagen NV Denmark 2400
| | - Simone Mocellin
- Istituto Oncologico Veneto, IOV-IRCCS; Surgical Oncology Unit; Via Gattamelata 64 Padova Veneto Italy 35128
- University of Padova; Department of Surgery Oncology and Gastroenterology; Padova Veneto Italy 35128
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24
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Pasquali S, Sommariva A, Mahteme H, Suo T, Ma H, Tropea S, Steenberg JL, Mocellin S. Cytoreductive surgery alone or combined with hyperthermic intraperitoneal chemotherapy (HIPEC) for pseudomyxoma peritonei. Hippokratia 2018. [DOI: 10.1002/14651858.cd005659.pub2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Sandro Pasquali
- Fondazione IRCCS Istituto Nazionale dei Tumori; Sarcoma Service; Via G. Venezian 1 Milano Italy 20133
| | - Antonio Sommariva
- Istituto Oncologico Veneto, IOV-IRCCS; Surgical Oncology Unit; Via Gattamelata 64 Padova Veneto Italy 35128
| | - Haile Mahteme
- Uppsala Cancer Clinic; Uppsala Cancer Clinic; Box 833 Uppsala Sweden 75108
| | - Tao Suo
- Zhongshan Hospital, Fudan University; Department of General Surgery, Institute of General Surgery; 180 Fenglin Road, Xuhui District Shanghai Shanghai China 200032
| | - Huaixing Ma
- Anhui Provincial Cancer Hospital, the First Affiliated Hospital of University of Science and Technology of China (The Western Area); Department of Medical Oncology; Hefei Anhui China
| | - Saveria Tropea
- Istituto Oncologico Veneto, IOV-IRCCS; Surgical Oncology Unit; Via Gattamelata 64 Padova Veneto Italy 35128
| | - Josephine L Steenberg
- Bispebjerg Hospital, Region H; Cochrane Colorectal Cancer Group; Bispebjerg Bakke 23 Building 39N Copenhagen NV Denmark 2400
| | - Simone Mocellin
- Istituto Oncologico Veneto, IOV-IRCCS; Surgical Oncology Unit; Via Gattamelata 64 Padova Veneto Italy 35128
- IOV-IRCCS; Istituto Oncologico Veneto; Padova Italy 35100
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25
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Liouta E, Katsaros VK, Stranjalis G, Leks E, Klose U, Bisdas S. Motor and language deficits correlate with resting state functional magnetic resonance imaging networks in patients with brain tumors. J Neuroradiol 2018; 46:199-206. [PMID: 30179690 DOI: 10.1016/j.neurad.2018.08.002] [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: 11/06/2017] [Revised: 07/10/2018] [Accepted: 08/15/2018] [Indexed: 11/29/2022]
Abstract
BACKGROUND AND PURPOSE Evidence of pre-operative resting state functional magnetic resonance (RS-fMRI) validation by correlating it with clinical pre-operative status in brain tumor patients is scarce. Our aim was to validate the functional relevance of RS-fMRI by investigating the association between RS-fMRI and pre-operative motor and language function performance in patients with brain tumor. MATERIALS AND METHODS Sixty-nine patients with brain tumors were prospectively recruited. Patients with tumors near precentral gyrus (n = 49) underwent assessment for apparent (paresis) and subtle (finger tapping) deficits. Patients with left frontal tumors in the vicinity of the inferior frontal gyrus (n = 29) underwent assessment for gross (aphasia) and mild language (phonological verbal fluency) deficits. RS-fMRI results were extracted by spatial independent component analysis (ICA). RESULTS Motor group: paretic patients showed significantly (P = 0.01) decreased BOLD signal in ipsilesional precentral gyrus when compared to contralesional one. Significantly (P < 0.01) lower BOLD signal was also observed in ipsilesional precentral gyrus of paretics when compared with the non-paretics. In asymptomatic patients, a strong positive correlation (r = 0.68, P < 0.01) between ipsilesional motor cortex BOLD signal and contralesional finger tapping performance was observed. Language group: patients with aphasia showed significantly (P = 0.01) decreased RS-fMRI BOLD signal in left BA 44 when compared with non- aphasics. In asymptomatic patients, a strong positive correlation (r = 0.72, P < 0.01) between BA 44 BOLD signal and phonological fluency performance was observed. CONCLUSIONS Our results showed that RS-fMRI BOLD signal of motor and language networks were significantly affected by the tumors implying the usefulness of the method for assessment of the underlying functions in brain tumors patients.
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Affiliation(s)
- Evangelia Liouta
- Department of Neurosurgery, University of Athens, "Evangelismos" Hospital, Athens, Greece; Department of Neuroradiology, University Hospital of Tübingen, Tübingen, Germany.
| | - Vasileios K Katsaros
- Department of Radiology, General Anti-Cancer and Oncological Hospital of Athens "St. Savvas", Athens, Greece; Department of Neurosurgery, University of Athens, "Evangelismos" Hospital, Athens, Greece; Department of Neuroradiology, University Hospital of Tübingen, Tübingen, Germany
| | - George Stranjalis
- Department of Neurosurgery, University of Athens, "Evangelismos" Hospital, Athens, Greece
| | - Edyta Leks
- Department of Biomedical Magnetic Resonance, University Hospital of Tübingen, Tübingen, Germany
| | - Uwe Klose
- Department of Neuroradiology, University Hospital of Tübingen, Tübingen, Germany
| | - Sotirios Bisdas
- Department of Neuroradiology, University Hospital of Tübingen, Tübingen, Germany; Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals, London, UK; Institute of Neurology, University College London, London, UK
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26
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Wongsripuemtet J, Tyan AE, Carass A, Agarwal S, Gujar SK, Pillai JJ, Sair HI. Preoperative Mapping of the Supplementary Motor Area in Patients with Brain Tumor Using Resting-State fMRI with Seed-Based Analysis. AJNR Am J Neuroradiol 2018; 39:1493-1498. [PMID: 30002054 DOI: 10.3174/ajnr.a5709] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Accepted: 05/08/2018] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE The supplementary motor area can be a critical region in the preoperative planning of patients undergoing brain tumor resection because it plays a role in both language and motor function. While primary motor regions have been successfully identified using resting-state fMRI, there is variability in the literature regarding the identification of the supplementary motor area for preoperative planning. The purpose of our study was to compare resting-state fMRI to task-based fMRI for localization of the supplementary motor area in a large cohort of patients with brain tumors presenting for preoperative brain mapping. MATERIALS AND METHODS Sixty-six patients with brain tumors were evaluated with resting-state fMRI using seed-based analysis of hand and orofacial motor regions. Rates of supplementary motor area localization were compared with those in healthy controls and with localization results by task-based fMRI. RESULTS Localization of the supplementary motor area using hand motor seed regions was more effective than seeding using orofacial motor regions for both patients with brain tumor (95.5% versus 34.8%, P < .001) and controls (95.2% versus 45.2%, P < .001). Bilateral hand motor seeding was superior to unilateral hand motor seeding in patients with brain tumor for either side (95.5% versus 75.8%/75.8% for right/left, P < .001). No difference was found in the ability to identify the supplementary motor area between patients with brain tumors and controls. CONCLUSIONS In addition to task-based fMRI, seed-based analysis of resting-state fMRI represents an equally effective method for supplementary motor area localization in patients with brain tumors, with the best results obtained with bilateral hand motor region seeding.
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Affiliation(s)
- J Wongsripuemtet
- From the Russell H. Morgan Department of Radiology and Radiological Sciences (J.W., A.E.T., S.A., S.K.G., J.J.P., H.I.S.).,Department of Radiology (J.W.), Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - A E Tyan
- From the Russell H. Morgan Department of Radiology and Radiological Sciences (J.W., A.E.T., S.A., S.K.G., J.J.P., H.I.S.)
| | - A Carass
- Department of Computer Science and Department of Electrical and Computer Engineering (A.C.), Johns Hopkins University, Baltimore, Maryland
| | - S Agarwal
- From the Russell H. Morgan Department of Radiology and Radiological Sciences (J.W., A.E.T., S.A., S.K.G., J.J.P., H.I.S.)
| | - S K Gujar
- From the Russell H. Morgan Department of Radiology and Radiological Sciences (J.W., A.E.T., S.A., S.K.G., J.J.P., H.I.S.)
| | - J J Pillai
- From the Russell H. Morgan Department of Radiology and Radiological Sciences (J.W., A.E.T., S.A., S.K.G., J.J.P., H.I.S.).,Department of Neurosurgery (J.J.P.), Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - H I Sair
- From the Russell H. Morgan Department of Radiology and Radiological Sciences (J.W., A.E.T., S.A., S.K.G., J.J.P., H.I.S.)
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27
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Agarwal S, Sair HI, Pillai JJ. Limitations of Resting-State Functional MR Imaging in the Setting of Focal Brain Lesions. Neuroimaging Clin N Am 2018; 27:645-661. [PMID: 28985935 DOI: 10.1016/j.nic.2017.06.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Methods of image acquisition and analysis for resting-state functional MR imaging (rsfMR imaging) are still evolving. Neurovascular uncoupling and susceptibility artifact are important confounds of rsfMR imaging in the setting of focal brain lesions such as brain tumors. This article reviews the detection of these confounds using rsfMR imaging metrics in the setting of focal brain lesions. In the near future, with the wide range of ongoing research in rsfMR imaging, these issues likely will be overcome and will open new windows into brain function and connectivity.
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Affiliation(s)
- Shruti Agarwal
- Division of Neuroradiology, The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Phipps B-100, 1800 Orleans Street, Baltimore, MD 21287, USA
| | - Haris I Sair
- Division of Neuroradiology, The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Phipps B-100, 1800 Orleans Street, Baltimore, MD 21287, USA
| | - Jay J Pillai
- Division of Neuroradiology, The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Phipps B-100, 1800 Orleans Street, Baltimore, MD 21287, USA.
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28
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Connectivity changes after laser ablation: Resting-state fMRI. Epilepsy Res 2018; 142:156-160. [DOI: 10.1016/j.eplepsyres.2017.09.015] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Revised: 08/18/2017] [Accepted: 09/20/2017] [Indexed: 11/21/2022]
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29
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Shah MN, Mitra A, Goyal MS, Snyder AZ, Zhang J, Shimony JS, Limbrick DD, Raichle ME, Smyth MD. Resting state signal latency predicts laterality in pediatric medically refractory temporal lobe epilepsy. Childs Nerv Syst 2018; 34:901-910. [PMID: 29511809 PMCID: PMC5897166 DOI: 10.1007/s00381-018-3770-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2017] [Accepted: 02/27/2018] [Indexed: 11/25/2022]
Abstract
PURPOSE Temporal lobe epilepsy (TLE) affects resting state brain networks in adults. This study aims to correlate resting state functional MRI (rsMRI) signal latency in pediatric TLE patients with their laterality. METHODS From 2006 to 2016, 26 surgical TLE patients (12 left, 14 right) with a mean age of 10.7 years (range 0.9-18) were prospectively studied. Preoperative rsMRI was obtained in patients with concordant lateralizing structural MRI, EEG, and PET studies. Standard preprocessing techniques and seed-based rsMRI analyses were performed. Additionally, the latency in rsMRI signal between each 6 mm voxel sampled was examined, compared to the global mean signal, and projected onto standard atlas space for individuals and the cohort. RESULTS All but one of the 26 patients improved seizure frequency postoperatively with a mean follow-up of 2.9 years (range 0-7.7), with 21 patients seizure-free. When grouped for epileptogenic laterality, the latency map qualitatively demonstrated that the right TLE patients had a relatively early signal pattern, whereas the left TLE patients had a relatively late signal pattern compared to the global mean signal in the right temporal lobe. Quantitatively, the two groups had significantly different signal latency clusters in the bilateral temporal lobes (p < 0.001). CONCLUSION There are functional MR signal latency changes in medical refractory pediatric TLE patients. Qualitatively, signal latency in the right temporal lobe precedes the mean signal in right TLE patients and is delayed in left TLE patients. With larger confirmatory studies, preoperative rsMRI latency analysis may offer an inexpensive, noninvasive adjunct modality to lateralize pediatric TLE.
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Affiliation(s)
- Manish N Shah
- Departments of Pediatric Surgery and Neurosurgery, McGovern Medical School at UTHealth, 6431 Fannin St, MSB 5.144, Houston, TX, 77030, USA.
| | - Anish Mitra
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Manu S Goyal
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Abraham Z Snyder
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Jing Zhang
- Departments of Pediatric Surgery and Neurosurgery, McGovern Medical School at UTHealth, 6431 Fannin St, MSB 5.144, Houston, TX, 77030, USA
| | - Joshua S Shimony
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - David D Limbrick
- Department of Neurological Surgery, St. Louis Children's Hospital, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Marcus E Raichle
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Department of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Matthew D Smyth
- Department of Neurological Surgery, St. Louis Children's Hospital, Washington University School of Medicine, St. Louis, MO, 63110, USA
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30
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Agarwal S, Lu H, Pillai JJ. Value of Frequency Domain Resting-State Functional Magnetic Resonance Imaging Metrics Amplitude of Low-Frequency Fluctuation and Fractional Amplitude of Low-Frequency Fluctuation in the Assessment of Brain Tumor-Induced Neurovascular Uncoupling. Brain Connect 2018; 7:382-389. [PMID: 28657344 DOI: 10.1089/brain.2016.0480] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The aim of this study was to explore whether the phenomenon of brain tumor-related neurovascular uncoupling (NVU) in resting-state blood oxygen level-dependent functional magnetic resonance imaging (BOLD fMRI) (rsfMRI) may also affect the resting-state fMRI (rsfMRI) frequency domain metrics the amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF). Twelve de novo brain tumor patients, who underwent clinical fMRI examinations, including task-based fMRI (tbfMRI) and rsfMRI, were included in this Institutional Review Board-approved study. Each patient displayed decreased/absent tbfMRI activation in the primary ipsilesional (IL) sensorimotor cortex in the absence of a corresponding motor deficit or suboptimal task performance, consistent with NVU. Z-score maps for the motor tasks were obtained from general linear model analysis (reflecting motor activation vs. rest). Seed-based correlation analysis (SCA) maps of sensorimotor network, ALFF, and fALFF were calculated from rsfMRI data. Precentral and postcentral gyri in contralesional (CL) and IL hemispheres were parcellated using an automated anatomical labeling template for each patient. Region of interest (ROI) analysis was performed on four maps: tbfMRI, SCA, ALFF, and fALFF. Voxel values in the CL and IL ROIs of each map were divided by the corresponding global mean of ALFF and fALFF in the cortical brain tissue. Group analysis revealed significantly decreased IL ALFF (p = 0.02) and fALFF (p = 0.03) metrics compared with CL ROIs, consistent with similar findings of significantly decreased IL BOLD signal for tbfMRI (p = 0.0005) and SCA maps (p = 0.0004). The frequency domain metrics ALFF and fALFF may be markers of lesion-induced NVU in rsfMRI similar to previously reported alterations in tbfMRI activation and SCA-derived resting-state functional connectivity maps.
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Affiliation(s)
- Shruti Agarwal
- 1 Division of Neuroradiology, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine , Baltimore, Maryland
| | - Hanzhang Lu
- 1 Division of Neuroradiology, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine , Baltimore, Maryland.,2 Division of MR Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine , Baltimore, Maryland
| | - Jay J Pillai
- 1 Division of Neuroradiology, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine , Baltimore, Maryland.,3 Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland
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31
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Hsu AL, Hou P, Johnson JM, Wu CW, Noll KR, Prabhu SS, Ferguson SD, Kumar VA, Schomer DF, Hazle JD, Chen JH, Liu HL. IClinfMRI Software for Integrating Functional MRI Techniques in Presurgical Mapping and Clinical Studies. Front Neuroinform 2018; 12:11. [PMID: 29593520 PMCID: PMC5854683 DOI: 10.3389/fninf.2018.00011] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2017] [Accepted: 02/23/2018] [Indexed: 01/25/2023] Open
Abstract
Task-evoked and resting-state (rs) functional magnetic resonance imaging (fMRI) techniques have been applied to the clinical management of neurological diseases, exemplified by presurgical localization of eloquent cortex, to assist neurosurgeons in maximizing resection while preserving brain functions. In addition, recent studies have recommended incorporating cerebrovascular reactivity (CVR) imaging into clinical fMRI to evaluate the risk of lesion-induced neurovascular uncoupling (NVU). Although each of these imaging techniques possesses its own advantage for presurgical mapping, a specialized clinical software that integrates the three complementary techniques and promptly outputs the analyzed results to radiology and surgical navigation systems in a clinical format is still lacking. We developed the Integrated fMRI for Clinical Research (IClinfMRI) software to facilitate these needs. Beyond the independent processing of task-fMRI, rs-fMRI, and CVR mapping, IClinfMRI encompasses three unique functions: (1) supporting the interactive rs-fMRI mapping while visualizing task-fMRI results (or results from published meta-analysis) as a guidance map, (2) indicating/visualizing the NVU potential on analyzed fMRI maps, and (3) exporting these advanced mapping results in a Digital Imaging and Communications in Medicine (DICOM) format that are ready to export to a picture archiving and communication system (PACS) and a surgical navigation system. In summary, IClinfMRI has the merits of efficiently translating and integrating state-of-the-art imaging techniques for presurgical functional mapping and clinical fMRI studies.
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Affiliation(s)
- Ai-Ling Hsu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.,Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
| | - Ping Hou
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Jason M Johnson
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Changwei W Wu
- Graduate Institute of Humanities in Medicine, Taipei Medical University, Taipei, Taiwan
| | - Kyle R Noll
- Section of Neuropsychology, Department of Neuro-Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Sujit S Prabhu
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Sherise D Ferguson
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Vinodh A Kumar
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Donald F Schomer
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - John D Hazle
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Jyh-Horng Chen
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
| | - Ho-Ling Liu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
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32
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Huang H, Ding Z, Mao D, Yuan J, Zhu F, Chen S, Xu Y, Lou L, Feng X, Qi L, Qiu W, Zhang H, Zang YF. PreSurgMapp: a MATLAB Toolbox for Presurgical Mapping of Eloquent Functional Areas Based on Task-Related and Resting-State Functional MRI. Neuroinformatics 2018; 14:421-38. [PMID: 27221107 DOI: 10.1007/s12021-016-9304-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
The main goal of brain tumor surgery is to maximize tumor resection while minimizing the risk of irreversible postoperative functional sequelae. Eloquent functional areas should be delineated preoperatively, particularly for patients with tumors near eloquent areas. Functional magnetic resonance imaging (fMRI) is a noninvasive technique that demonstrates great promise for presurgical planning. However, specialized data processing toolkits for presurgical planning remain lacking. Based on several functions in open-source software such as Statistical Parametric Mapping (SPM), Resting-State fMRI Data Analysis Toolkit (REST), Data Processing Assistant for Resting-State fMRI (DPARSF) and Multiple Independent Component Analysis (MICA), here, we introduce an open-source MATLAB toolbox named PreSurgMapp. This toolbox can reveal eloquent areas using comprehensive methods and various complementary fMRI modalities. For example, PreSurgMapp supports both model-based (general linear model, GLM, and seed correlation) and data-driven (independent component analysis, ICA) methods and processes both task-based and resting-state fMRI data. PreSurgMapp is designed for highly automatic and individualized functional mapping with a user-friendly graphical user interface (GUI) for time-saving pipeline processing. For example, sensorimotor and language-related components can be automatically identified without human input interference using an effective, accurate component identification algorithm using discriminability index. All the results generated can be further evaluated and compared by neuro-radiologists or neurosurgeons. This software has substantial value for clinical neuro-radiology and neuro-oncology, including application to patients with low- and high-grade brain tumors and those with epilepsy foci in the dominant language hemisphere who are planning to undergo a temporal lobectomy.
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Affiliation(s)
- Huiyuan Huang
- Center for Cognition and Brain Disorders, Hangzhou Normal University, 58 Haishu Road, Hangzhou, 311121, People's Republic of China.,School of Education Science, Hangzhou Normal University, Hangzhou, Zhejiang, 311121, People's Republic of China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, 310015, People's Republic of China
| | - Zhongxiang Ding
- Department of Radiology, Zhejiang Provincial People's Hospital, Hangzhou, Zhejiang, 310014, People's Republic of China
| | - Dewang Mao
- Department of Radiology, Zhejiang Provincial People's Hospital, Hangzhou, Zhejiang, 310014, People's Republic of China
| | - Jianhua Yuan
- Department of Radiology, Zhejiang Provincial People's Hospital, Hangzhou, Zhejiang, 310014, People's Republic of China
| | - Fangmei Zhu
- Department of Radiology, Zhejiang Provincial People's Hospital, Hangzhou, Zhejiang, 310014, People's Republic of China
| | - Shuda Chen
- Department of Neurosurgery, Zhejiang Provincial People's Hospital, Hangzhou, Zhejiang, 310014, People's Republic of China
| | - Yan Xu
- Department of Neurosurgery, Zhejiang Provincial People's Hospital, Hangzhou, Zhejiang, 310014, People's Republic of China
| | - Lin Lou
- Department of Neurosurgery, Zhejiang Provincial People's Hospital, Hangzhou, Zhejiang, 310014, People's Republic of China
| | - Xiaoyan Feng
- Department of Neurosurgery, Zhejiang Provincial People's Hospital, Hangzhou, Zhejiang, 310014, People's Republic of China
| | - Le Qi
- Department of Radiology, Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, 310015, People's Republic of China
| | - Wusi Qiu
- Department of Neurosurgery, Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, 310015, People's Republic of China
| | - Han Zhang
- Center for Cognition and Brain Disorders, Hangzhou Normal University, 58 Haishu Road, Hangzhou, 311121, People's Republic of China. .,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, 310015, People's Republic of China.
| | - Yu-Feng Zang
- Center for Cognition and Brain Disorders, Hangzhou Normal University, 58 Haishu Road, Hangzhou, 311121, People's Republic of China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, 310015, People's Republic of China
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33
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Huang H, Lu J, Wu J, Ding Z, Chen S, Duan L, Cui J, Chen F, Kang D, Qi L, Qiu W, Lee SW, Qiu S, Shen D, Zang YF, Zhang H. Tumor Tissue Detection using Blood-Oxygen-Level-Dependent Functional MRI based on Independent Component Analysis. Sci Rep 2018; 8:1223. [PMID: 29352123 PMCID: PMC5775317 DOI: 10.1038/s41598-017-18453-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2015] [Accepted: 12/12/2017] [Indexed: 11/09/2022] Open
Abstract
Accurate delineation of gliomas from the surrounding normal brain areas helps maximize tumor resection and improves outcome. Blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) has been routinely adopted for presurgical mapping of the surrounding functional areas. For completely utilizing such imaging data, here we show the feasibility of using presurgical fMRI for tumor delineation. In particular, we introduce a novel method dedicated to tumor detection based on independent component analysis (ICA) of resting-state fMRI (rs-fMRI) with automatic tumor component identification. Multi-center rs-fMRI data of 32 glioma patients from three centers, plus the additional proof-of-concept data of 28 patients from the fourth center with non-brain musculoskeletal tumors, are fed into individual ICA with different total number of components (TNCs). The best-fitted tumor-related components derived from the optimized TNCs setting are automatically determined based on a new template-matching algorithm. The success rates are 100%, 100% and 93.75% for glioma tissue detection for the three centers, respectively, and 85.19% for musculoskeletal tumor detection. We propose that the high success rate could come from the previously overlooked ability of BOLD rs-fMRI in characterizing the abnormal vascularization, vasomotion and perfusion caused by tumors. Our findings suggest an additional usage of the rs-fMRI for comprehensive presurgical assessment.
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Affiliation(s)
- Huiyuan Huang
- Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang, 311121, China
- School of Psychology, South China Normal University, Guangzhou, 510631, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, 310015, China
| | - Junfeng Lu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Jinsong Wu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Zhongxiang Ding
- Department of Radiology, Zhejiang Provincial People's Hospital, Hangzhou, Zhejiang, 310014, China
| | - Shuda Chen
- Department of Neurosurgery, Zhejiang Provincial People's Hospital, Hangzhou, Zhejiang, 310014, China
| | - Lisha Duan
- Department of Radiology, The Third Hospital of Hebei Medical University, Shijiazhuang, Hebei, 050051, China
| | - Jianling Cui
- Department of Radiology, The Third Hospital of Hebei Medical University, Shijiazhuang, Hebei, 050051, China
| | - Fuyong Chen
- Department of Neurosurgery, No.1 Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, 350000, China
| | - Dezhi Kang
- Department of Neurosurgery, No.1 Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, 350000, China
| | - Le Qi
- Department of Radiology, Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, 310015, China
| | - Wusi Qiu
- Department of Neurosurgery, Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, 310015, China
| | - Seong-Whan Lee
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - ShiJun Qiu
- Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea.
| | - Yu-Feng Zang
- Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang, 311121, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, 310015, China
| | - Han Zhang
- Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang, 311121, China.
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, 310015, China.
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
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Silva MA, See AP, Essayed WI, Golby AJ, Tie Y. Challenges and techniques for presurgical brain mapping with functional MRI. NEUROIMAGE-CLINICAL 2017; 17:794-803. [PMID: 29270359 PMCID: PMC5735325 DOI: 10.1016/j.nicl.2017.12.008] [Citation(s) in RCA: 75] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2017] [Revised: 11/10/2017] [Accepted: 12/05/2017] [Indexed: 01/22/2023]
Abstract
Functional magnetic resonance imaging (fMRI) is increasingly used for preoperative counseling and planning, and intraoperative guidance for tumor resection in the eloquent cortex. Although there have been improvements in image resolution and artifact correction, there are still limitations of this modality. In this review, we discuss clinical fMRI's applications, limitations and potential solutions. These limitations depend on the following parameters: foundations of fMRI, physiologic effects of the disease, distinctions between clinical and research fMRI, and the design of the fMRI study. We also compare fMRI to other brain mapping modalities which should be considered as alternatives or adjuncts when appropriate, and discuss intraoperative use and validation of fMRI. These concepts direct the clinical application of fMRI in neurosurgical patients. fMRI is increasingly used for presurgical brain mapping for surgical planning. Understanding of the limitations of fMRI is critical for its clinical use. Clinical fMRI's challenges and potential solutions are discussed. Intraoperative use and validation of fMRI are discussed.
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Affiliation(s)
- Michael A Silva
- Harvard Medical School, Boston, MA, USA; Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, USA
| | - Alfred P See
- Harvard Medical School, Boston, MA, USA; Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, USA
| | - Walid I Essayed
- Harvard Medical School, Boston, MA, USA; Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, USA
| | - Alexandra J Golby
- Harvard Medical School, Boston, MA, USA; Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, USA; Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Yanmei Tie
- Harvard Medical School, Boston, MA, USA; Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, USA.
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35
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Ghinda DC, Wu JS, Duncan NW, Northoff G. How much is enough-Can resting state fMRI provide a demarcation for neurosurgical resection in glioma? Neurosci Biobehav Rev 2017; 84:245-261. [PMID: 29198588 DOI: 10.1016/j.neubiorev.2017.11.019] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Revised: 11/20/2017] [Accepted: 11/27/2017] [Indexed: 01/09/2023]
Abstract
This study represents a systematic review of the insights provided by resting state functional MRI (rs-fMRI) use in the glioma population. Following PRISMA guidelines, 45 studies were included in the review and were classified in glioma-related neuronal changes (n=28) and eloquent area localization (n=17). Despite the heterogeneous nature of the studies, there is considerable evidence of diffuse functional reorganization occurring in the setting of gliomas with local and interhemispheric functional connectivity alterations involving different functional networks. The studies showed evidence of decreased long distance functional connectivity and increased global local efficiency occurring in the setting of gliomas. The tumour grade seems to correlate with distinct functional connectivity changes. Overall, there is a potential clinical utility of rs-fMRI for identifying the functional brain network disruptions occurring in the setting of gliomas. Further studies utilizing standardized analytical methods are required to elucidate the mechanism through which gliomas induce global changes in brain connectivity.
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Affiliation(s)
- Diana C Ghinda
- Ottawa Hospital Research Institute, University of Ottawa, Division of Neurosurgery, The Ottawa Hospital, 1053 Carling Avenue, Ottawa, ON, K1Y 4E9, Canada; Mind, Brain Imaging and Neuroethics, Canada Research Chair, EJLB-Michael Smith Chair for Neuroscience and Mental Health, Royal Ottawa Mental Health Centre, University of Ottawa Institute of Mental Health Research, 1145 Carling Avenue, Rm. 6435, Ottawa, ON, K1Z 7K4, Canada.
| | - Jin-Song Wu
- Glioma Surgery Division, Department of Neurological Surgery, Huashan Hospital, Fudan University, 518 Wuzhong E Rd, Shanghai, China.
| | - Niall W Duncan
- Brain and Consciousness Research Center, Taipei Medical University-Shuang Ho Hospital, 250 Wu-Xing Street, Taipei, 11031, Taiwan.
| | - Georg Northoff
- Mind, Brain Imaging and Neuroethics, Canada Research Chair, EJLB-Michael Smith Chair for Neuroscience and Mental Health, Royal Ottawa Mental Health Centre, University of Ottawa Institute of Mental Health Research, 1145 Carling Avenue, Rm. 6435, Ottawa, ON, K1Z 7K4, Canada; Mental Health Center/7th Hospital, Zhejiang University School of Medicine, 305 Tianmu Road, Hangzhou, Zhejiang Province, 310013, China.
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36
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Dierker D, Roland JL, Kamran M, Rutlin J, Hacker CD, Marcus DS, Milchenko M, Miller-Thomas MM, Benzinger TL, Snyder AZ, Leuthardt EC, Shimony JS. Resting-state Functional Magnetic Resonance Imaging in Presurgical Functional Mapping: Sensorimotor Localization. Neuroimaging Clin N Am 2017; 27:621-633. [PMID: 28985933 PMCID: PMC5773116 DOI: 10.1016/j.nic.2017.06.011] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
This article compares resting-state functional magnetic resonance (fMR) imaging with task fMR imaging for presurgical functional mapping of the sensorimotor (SM) region. Before tumor resection, 38 patients were scanned using both methods. The SM area was anatomically defined using 2 different software tools. Overlap of anatomic regions of interest with task activation maps and resting-state networks was measured in the SM region. A paired t-test showed higher overlap between resting-state maps and anatomic references compared with task activation when using a maximal overlap criterion. Resting state-derived maps are more comprehensive than those derived from task fMR imaging.
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Affiliation(s)
- Donna Dierker
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, 4525 Scott Avenue, Saint Louis, MO 63110, USA
| | - Jarod L Roland
- Department of Neurological Surgery, Washington University School of Medicine, 4525 Scott Avenue, Saint Louis, MO 63110, USA
| | - Mudassar Kamran
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, 4525 Scott Avenue, Saint Louis, MO 63110, USA
| | - Jerrel Rutlin
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, 4525 Scott Avenue, Saint Louis, MO 63110, USA
| | - Carl D Hacker
- Department of Neurological Surgery, Washington University School of Medicine, 4525 Scott Avenue, Saint Louis, MO 63110, USA
| | - Daniel S Marcus
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, 4525 Scott Avenue, Saint Louis, MO 63110, USA
| | - Mikhail Milchenko
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, 4525 Scott Avenue, Saint Louis, MO 63110, USA
| | - Michelle M Miller-Thomas
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, 4525 Scott Avenue, Saint Louis, MO 63110, USA
| | - Tammie L Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, 4525 Scott Avenue, Saint Louis, MO 63110, USA; Department of Neurological Surgery, Washington University School of Medicine, 4525 Scott Avenue, Saint Louis, MO 63110, USA
| | - Abraham Z Snyder
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, 4525 Scott Avenue, Saint Louis, MO 63110, USA; Department of Neurology, Washington University School of Medicine, 4525 Scott Avenue, Saint Louis, MO 63110, USA
| | - Eric C Leuthardt
- Department of Neurological Surgery, Washington University School of Medicine, 4525 Scott Avenue, Saint Louis, MO 63110, USA; Department of Biomedical Imaging, Washington University School of Medicine, 4525 Scott Avenue, Saint Louis, MO 63110, USA
| | - Joshua S Shimony
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, 4525 Scott Avenue, Saint Louis, MO 63110, USA.
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37
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Pre-surgical mapping of eloquent cortex for paediatric epilepsy surgery candidates: Evidence from a review of advanced functional neuroimaging. Seizure 2017; 52:136-146. [DOI: 10.1016/j.seizure.2017.09.024] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Revised: 06/16/2017] [Accepted: 09/29/2017] [Indexed: 11/19/2022] Open
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Lu J, Zhang H, Hameed NUF, Zhang J, Yuan S, Qiu T, Shen D, Wu J. An automated method for identifying an independent component analysis-based language-related resting-state network in brain tumor subjects for surgical planning. Sci Rep 2017; 7:13769. [PMID: 29062010 PMCID: PMC5653800 DOI: 10.1038/s41598-017-14248-5] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2016] [Accepted: 10/09/2017] [Indexed: 02/08/2023] Open
Abstract
As a noninvasive and “task-free” technique, resting-state functional magnetic resonance imaging (rs-fMRI) has been gradually applied to pre-surgical functional mapping. Independent component analysis (ICA)-based mapping has shown advantage, as no a priori information is required. We developed an automated method for identifying language network in brain tumor subjects using ICA on rs-fMRI. In addition to standard processing strategies, we applied a discriminability-index-based component identification algorithm to identify language networks in three different groups. The results from the training group were validated in an independent group of healthy human subjects. For the testing group, ICA and seed-based correlation were separately computed and the detected language networks were assessed by intra-operative stimulation mapping to verify reliability of application in the clinical setting. Individualized language network mapping could be automatically achieved for all subjects from the two healthy groups except one (19/20, success rate = 95.0%). In the testing group (brain tumor patients), the sensitivity of the language mapping result was 60.9%, which increased to 87.0% (superior to that of conventional seed-based correlation [47.8%]) after extending to a radius of 1 cm. We established an automatic and practical component identification method for rs-fMRI-based pre-surgical mapping and successfully applied it to brain tumor patients.
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Affiliation(s)
- Junfeng Lu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Han Zhang
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - N U Farrukh Hameed
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Jie Zhang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Shiwen Yuan
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Tianming Qiu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. .,Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea.
| | - Jinsong Wu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.
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Yahyavi-Firouz-Abadi N, Pillai JJ, Lindquist MA, Calhoun VD, Agarwal S, Airan RD, Caffo B, Gujar SK, Sair HI. Presurgical Brain Mapping of the Ventral Somatomotor Network in Patients with Brain Tumors Using Resting-State fMRI. AJNR Am J Neuroradiol 2017; 38:1006-1012. [PMID: 28364005 DOI: 10.3174/ajnr.a5132] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2016] [Accepted: 12/25/2016] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Resting-state fMRI readily identifies the dorsal but less consistently the ventral somatomotor network. Our aim was to assess the relative utility of resting-state fMRI in the identification of the ventral somatomotor network via comparison with task-based fMRI in patients with brain tumor. MATERIALS AND METHODS We identified 26 surgically naïve patients referred for presurgical fMRI brain mapping who had undergone both satisfactory ventral motor activation tasks and resting-state fMRI. Following standard preprocessing for task-based fMRI and resting-state fMRI, general linear model analysis of the ventral motor tasks and independent component analysis of resting-state fMRI were performed with the number of components set to 20, 30, 40, and 50. Visual overlap of task-based fMRI and resting-state fMRI at different component levels was assessed and categorized as full match, partial match, or no match. Rest-versus-task-fMRI concordance was calculated with Dice coefficients across varying fMRI thresholds before and after noise removal. Multithresholded Dice coefficient volume under the surface was calculated. RESULTS The ventral somatomotor network was identified in 81% of patients. At the subject level, better matches between resting-state fMRI and task-based fMRI were seen with an increasing order of components (53% of cases for 20 components versus 73% for 50 components). Noise-removed group-mean volume under the surface improved as component numbers increased from 20 to 50, though ANOVA demonstrated no statistically significant difference among the 4 groups. CONCLUSIONS In most patients, the ventral somatomotor network can be identified with an increase in the probability of a better match at a higher component number. There is variable concordance of the ventral somatomotor network at the single-subject level between resting-state and task-based fMRI.
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Affiliation(s)
- N Yahyavi-Firouz-Abadi
- From the Department of Radiology (N.Y.-F.-A.), Mid-Atlantic Permanente Medical Group of Kaiser Permanente, Kensington, Maryland .,Division of Neuroradiology, (N.Y.-F.-A., J.J.P., S.A., R.D.A., S.K.G., H.I.S.), The Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - J J Pillai
- Division of Neuroradiology, (N.Y.-F.-A., J.J.P., S.A., R.D.A., S.K.G., H.I.S.), The Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - M A Lindquist
- Department of Biostatistics (M.A.L., B.C.), Johns Hopkins University, Baltimore, Maryland
| | - V D Calhoun
- The Mind Research Network (S.A., V.D.C.), Departments of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico
| | - S Agarwal
- Division of Neuroradiology, (N.Y.-F.-A., J.J.P., S.A., R.D.A., S.K.G., H.I.S.), The Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland.,The Mind Research Network (S.A., V.D.C.), Departments of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico
| | - R D Airan
- Division of Neuroradiology, (N.Y.-F.-A., J.J.P., S.A., R.D.A., S.K.G., H.I.S.), The Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - B Caffo
- Department of Biostatistics (M.A.L., B.C.), Johns Hopkins University, Baltimore, Maryland
| | - S K Gujar
- Division of Neuroradiology, (N.Y.-F.-A., J.J.P., S.A., R.D.A., S.K.G., H.I.S.), The Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - H I Sair
- Division of Neuroradiology, (N.Y.-F.-A., J.J.P., S.A., R.D.A., S.K.G., H.I.S.), The Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
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40
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Teghipco A, Hussain A, Tivarus ME. Disrupted functional connectivity affects resting state based language lateralization. NEUROIMAGE-CLINICAL 2016; 12:910-927. [PMID: 27882297 PMCID: PMC5114586 DOI: 10.1016/j.nicl.2016.10.015] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2016] [Revised: 09/10/2016] [Accepted: 10/20/2016] [Indexed: 12/01/2022]
Abstract
Pre-operative assessment of language localization and lateralization is critical to preserving brain function after lesion or epileptogenic tissue resection. Task fMRI (t-fMRI) has been extensively and reliably used to this end, but resting state fMRI (rs-fMRI) is emerging as an alternative pre-operative brain mapping method that is independent of a patient's ability to comply with a task. We sought to evaluate if language lateralization obtained from rs-fMRI can replace standard assessment using t-fMRI. In a group of 43 patients scheduled for pre-operative fMRI brain mapping and 17 healthy controls, we found that existing methods of determining rs-fMRI lateralization by considering interhemispheric and intrahemispheric functional connectivity are inadequate compared to t-fMRI when applied to the language network. We determined that this was attributable to widespread but nuanced disturbances in the functional connectivity of the language network in patients. We found changes in interhemispheric and intrahemispheric functional connectivity that were dependent on lesion location, and particularly impacted patients with lesions in the left temporal lobe. We then tested whether a simpler measure of functional connectivity to the language network has a better relation to t-fMRI based language lateralization. Remarkably, we found that functional connectivity between the language network and the frontal pole, and superior frontal gyrus, as well as the supramarginal gyrus, significantly correlated to task based language lateralization indices in both patients and healthy controls. These findings are consistent with prior work with epilepsy patients, and provide a framework for evaluating language lateralization at rest. Existing methods of determining rs-fMRI lateralization are inadequate for language. Functional connectivity to language network correlates with task lateralization. Lesion location affects functional connectivity. Lesions exhibit some interhemispheric hyperconnectivity within language network.
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Affiliation(s)
- Alex Teghipco
- Rochester Center for Brain Imaging, University of Rochester, USA
| | - Ali Hussain
- Department of Imaging Sciences, University of Rochester, USA
| | - Madalina E Tivarus
- Rochester Center for Brain Imaging, University of Rochester, USA; Department of Imaging Sciences, University of Rochester, USA
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Vergun S, Gaggl W, Nair VA, Suhonen JI, Birn RM, Ahmed AS, Meyerand ME, Reuss J, DeYoe EA, Prabhakaran V. Classification and Extraction of Resting State Networks Using Healthy and Epilepsy fMRI Data. Front Neurosci 2016; 10:440. [PMID: 27729846 PMCID: PMC5037187 DOI: 10.3389/fnins.2016.00440] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2016] [Accepted: 09/09/2016] [Indexed: 12/14/2022] Open
Abstract
Functional magnetic resonance imaging studies have significantly expanded the field's understanding of functional brain activity of healthy and patient populations. Resting state (rs-) fMRI, which does not require subjects to perform a task, eliminating confounds of task difficulty, allows examination of neural activity and offers valuable functional mapping information. The purpose of this work was to develop an automatic resting state network (RSN) labeling method which offers value in clinical workflow during rs-fMRI mapping by organizing and quickly labeling spatial maps into functional networks. Here independent component analysis (ICA) and machine learning were applied to rs-fMRI data with the goal of developing a method for the clinically oriented task of extracting and classifying spatial maps into auditory, visual, default-mode, sensorimotor, and executive control RSNs from 23 epilepsy patients (and for general comparison, separately for 30 healthy subjects). ICA revealed distinct and consistent functional network components across patients and healthy subjects. Network classification was successful, achieving 88% accuracy for epilepsy patients with a naïve Bayes algorithm (and 90% accuracy for healthy subjects with a perceptron). The method's utility to researchers and clinicians is the provided RSN spatial maps and their functional labeling which offer complementary functional information to clinicians' expert interpretation.
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Affiliation(s)
- Svyatoslav Vergun
- Medical Physics, University of Wisconsin-MadisonMadison, WI, USA; Radiology, University of Wisconsin-MadisonMadison, WI, USA
| | - Wolfgang Gaggl
- Radiology, University of Wisconsin-MadisonMadison, WI, USA; Prism Clinical Imaging, Inc.,Elm Grove, WI, USA
| | - Veena A Nair
- Radiology, University of Wisconsin-Madison Madison, WI, USA
| | | | - Rasmus M Birn
- Medical Physics, University of Wisconsin-MadisonMadison, WI, USA; Psychiatry, University of Wisconsin-MadisonMadison, WI, USA
| | - Azam S Ahmed
- Neurological Surgery, University of Wisconsin-Madison Madison, WI, USA
| | - M Elizabeth Meyerand
- Medical Physics, University of Wisconsin-MadisonMadison, WI, USA; Radiology, University of Wisconsin-MadisonMadison, WI, USA; Biomedical Engineering, University of Wisconsin-MadisonMadison, WI, USA
| | - James Reuss
- Prism Clinical Imaging, Inc., Elm Grove, WI, USA
| | - Edgar A DeYoe
- Radiology, Medical College of WisconsinMilwaukee, WI, USA; Cell Biology, Neurobiology and Anatomy, Medical College of WisconsinMilwaukee, WI, USA; Biophysics, Medical College of WisconsinMilwaukee, WI, USA
| | - Vivek Prabhakaran
- Medical Physics, University of Wisconsin-MadisonMadison, WI, USA; Radiology, University of Wisconsin-MadisonMadison, WI, USA; Psychiatry, University of Wisconsin-MadisonMadison, WI, USA; Biomedical Engineering, University of Wisconsin-MadisonMadison, WI, USA; Psychology, University of Wisconsin-MadisonMadison, WI, USA
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Hart MG, Price SJ, Suckling J. Functional connectivity networks for preoperative brain mapping in neurosurgery. J Neurosurg 2016; 126:1941-1950. [PMID: 27564466 DOI: 10.3171/2016.6.jns1662] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Resection of focal brain lesions involves maximizing the resection while preserving brain function. Mapping brain function has entered a new era focusing on distributed connectivity networks at "rest," that is, in the absence of a specific task or stimulus, requiring minimal participant engagement. Central to this frame shift has been the development of methods for the rapid assessment of whole-brain connectivity with functional MRI (fMRI) involving blood oxygenation level-dependent imaging. The authors appraised the feasibility of fMRI-based mapping of a repertoire of functional connectivity networks in neurosurgical patients with focal lesions and the potential benefits of resting-state connectivity mapping for surgical planning. METHODS Resting-state fMRI sequences with a 3-T scanner and multiecho echo-planar imaging coupled to independent component analysis were acquired preoperatively from 5 study participants who had a right temporoparietooccipital glioblastoma. Seed-based functional connectivity analysis was performed with InstaCorr. Network identification focused on 7 major functional connectivity networks described in the literature and a putative language network centered on Broca's area. RESULTS All 8 functional connectivity networks were identified in each participant. Tumor-related topological changes to the default mode network were observed in all participants. In addition, each participant had at least 1 other abnormal network, and each network was abnormal in at least 1 participant. Individual patterns of network irregularities were identified with a qualitative approach and included local displacement due to mass effect, loss of a functional network component, and recruitment of new regions. CONCLUSIONS Resting-state fMRI can reliably and rapidly detect common functional connectivity networks in patients with glioblastoma and also has sufficient sensitivity for identifying patterns of network alterations. Mapping of functional connectivity networks offers the possibility to expand investigations to less commonly explored neuropsychological processes, such as executive control, attention, and salience. Changes in these networks may allow insights into mechanisms underlying the functional consequences of tumor growth, surgical intervention, and patient rehabilitation.
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Affiliation(s)
- Michael G Hart
- Brain Mapping Unit, Department of Psychiatry, and.,Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, United Kingdom
| | - Stephen J Price
- Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, United Kingdom
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DeSalvo MN, Tanaka N, Douw L, Leveroni CL, Buchbinder BR, Greve DN, Stufflebeam SM. Resting-State Functional MR Imaging for Determining Language Laterality in Intractable Epilepsy. Radiology 2016; 281:264-9. [PMID: 27467465 DOI: 10.1148/radiol.2016141010] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Purpose To measure the accuracy of resting-state functional magnetic resonance (MR) imaging in determining hemispheric language dominance in patients with medically intractable focal epilepsies against the results of an intracarotid amobarbital procedure (IAP). Materials and Methods This study was approved by the institutional review board, and all subjects gave signed informed consent. Data in 23 patients with medically intractable focal epilepsy were retrospectively analyzed. All 23 patients were candidates for epilepsy surgery and underwent both IAP and resting-state functional MR imaging as part of presurgical evaluation. Language dominance was determined from functional MR imaging data by calculating a laterality index (LI) after using independent component analysis. The accuracy of this method was assessed against that of IAP by using a variety of thresholds. Sensitivity and specificity were calculated by using leave-one-out cross validation. Spatial maps of language components were qualitatively compared among each hemispheric language dominance group. Results Measurement of hemispheric language dominance with resting-state functional MR imaging was highly concordant with IAP results, with up to 96% (22 of 23) accuracy, 96% (22 of 23) sensitivity, and 96% (22 of 23) specificity. Composite language component maps in patients with typical language laterality consistently included classic language areas such as the inferior frontal gyrus, the posterior superior temporal gyrus, and the inferior parietal lobule, while those of patients with atypical language laterality also included non-classical language areas such as the superior and middle frontal gyri, the insula, and the occipital cortex. Conclusion Resting-state functional MR imaging can be used to measure language laterality in patients with medically intractable focal epilepsy. (©) RSNA, 2016 Online supplemental material is available for this article.
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Affiliation(s)
- Matthew N DeSalvo
- From the Athinoula A. Martinos Center for Biomedical Imaging, 149 Thirteenth St, Suite 2301, Charlestown, MA 02129 (M.N.D., N.T., L.D., D.N.G., S.M.S.); and Departments of Neurology (C.L.L.) and Radiology (B.R.B., S.M.S.), Massachusetts General Hospital, Boston, Mass
| | - Naoaki Tanaka
- From the Athinoula A. Martinos Center for Biomedical Imaging, 149 Thirteenth St, Suite 2301, Charlestown, MA 02129 (M.N.D., N.T., L.D., D.N.G., S.M.S.); and Departments of Neurology (C.L.L.) and Radiology (B.R.B., S.M.S.), Massachusetts General Hospital, Boston, Mass
| | - Linda Douw
- From the Athinoula A. Martinos Center for Biomedical Imaging, 149 Thirteenth St, Suite 2301, Charlestown, MA 02129 (M.N.D., N.T., L.D., D.N.G., S.M.S.); and Departments of Neurology (C.L.L.) and Radiology (B.R.B., S.M.S.), Massachusetts General Hospital, Boston, Mass
| | - Catherine L Leveroni
- From the Athinoula A. Martinos Center for Biomedical Imaging, 149 Thirteenth St, Suite 2301, Charlestown, MA 02129 (M.N.D., N.T., L.D., D.N.G., S.M.S.); and Departments of Neurology (C.L.L.) and Radiology (B.R.B., S.M.S.), Massachusetts General Hospital, Boston, Mass
| | - Bradley R Buchbinder
- From the Athinoula A. Martinos Center for Biomedical Imaging, 149 Thirteenth St, Suite 2301, Charlestown, MA 02129 (M.N.D., N.T., L.D., D.N.G., S.M.S.); and Departments of Neurology (C.L.L.) and Radiology (B.R.B., S.M.S.), Massachusetts General Hospital, Boston, Mass
| | - Douglas N Greve
- From the Athinoula A. Martinos Center for Biomedical Imaging, 149 Thirteenth St, Suite 2301, Charlestown, MA 02129 (M.N.D., N.T., L.D., D.N.G., S.M.S.); and Departments of Neurology (C.L.L.) and Radiology (B.R.B., S.M.S.), Massachusetts General Hospital, Boston, Mass
| | - Steven M Stufflebeam
- From the Athinoula A. Martinos Center for Biomedical Imaging, 149 Thirteenth St, Suite 2301, Charlestown, MA 02129 (M.N.D., N.T., L.D., D.N.G., S.M.S.); and Departments of Neurology (C.L.L.) and Radiology (B.R.B., S.M.S.), Massachusetts General Hospital, Boston, Mass
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Chang TY, Huang KL, Ho MY, Ho PS, Chang CH, Liu CH, Chang YJ, Wong HF, Hsieh IC, Lee TH, Liu HL. Graph theoretical analysis of functional networks and its relationship to cognitive decline in patients with carotid stenosis. J Cereb Blood Flow Metab 2016; 36:808-18. [PMID: 26661184 PMCID: PMC4820004 DOI: 10.1177/0271678x15608390] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2014] [Accepted: 06/19/2015] [Indexed: 11/17/2022]
Abstract
Significant carotid stenosis compromises hemodynamics and impairs cognitive functions. The interplay between these changes and brain connectivity has rarely been investigated. We aimed to discover the changes of functional connectivity and its relation to cognitive decline in carotid stenosis patients. Twenty-seven patients with unilateral carotid stenosis (≥60%) and 20 age- and sex-matched controls underwent neuropsychological tests and resting-state functional magnetic resonance imaging. The patients also received perfusion magnetic resonance imaging. The relationships between cognitive function and functional networks among the patients and controls were evaluated. Graph theory was applied on resting-state functional magnetic resonance imaging network analysis, which revealed that the hemispheres ipsilateral to the stenosis were significantly impaired in "degree" and "global efficiency." The neuropsychological performances were positively correlated with degree, clustering coefficient, local efficiency, and global efficiency, and negatively correlated with characteristic path length, modularity, and small-worldness in the patients, whereas these relationships were not observed in the controls. In this study, we identified the networks that were impaired in the affected hemispheres in patients with carotid stenosis. Specific indices (global efficiency, characteristic path length, and modularity) were highly correlated with neuropsychological performance in our patients. Analysis of brain connectivity may help to elucidate the relationship between hemodynamic impairment and cognitive decline.
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Affiliation(s)
- Ting-Yu Chang
- Department of Neurology, Stroke Section, Chang Gung Memorial Hospital, Linkou Medical Center and College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Kuo-Lun Huang
- Department of Neurology, Stroke Section, Chang Gung Memorial Hospital, Linkou Medical Center and College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Meng-Yang Ho
- Clinical Psychology Program, c/o Department of Occupational Therapy, Chang Gung University, Taoyuan, Taiwan
| | - Pei-Shan Ho
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, Taiwan
| | - Chien-Hung Chang
- Department of Neurology, Stroke Section, Chang Gung Memorial Hospital, Linkou Medical Center and College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Chi-Hung Liu
- Department of Neurology, Stroke Section, Chang Gung Memorial Hospital, Linkou Medical Center and College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Yeu-Jhy Chang
- Department of Neurology, Stroke Section, Chang Gung Memorial Hospital, Linkou Medical Center and College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Ho-Fai Wong
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - I-Chang Hsieh
- Department of Cardiology, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Tsong-Hai Lee
- Department of Neurology, Stroke Section, Chang Gung Memorial Hospital, Linkou Medical Center and College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Ho-Ling Liu
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, Taiwan Department of Imaging Physics, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA
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Hou BL, Bhatia S, Carpenter JS. Quantitative comparisons on hand motor functional areas determined by resting state and task BOLD fMRI and anatomical MRI for pre-surgical planning of patients with brain tumors. NEUROIMAGE-CLINICAL 2016; 11:378-387. [PMID: 27069871 PMCID: PMC4810013 DOI: 10.1016/j.nicl.2016.03.003] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2015] [Revised: 02/17/2016] [Accepted: 03/01/2016] [Indexed: 11/24/2022]
Abstract
For pre-surgical planning we present quantitative comparison of the location of the hand motor functional area determined by right hand finger tapping BOLD fMRI, resting state BOLD fMRI, and anatomically using high resolution T1 weighted images. Data were obtained on 10 healthy subjects and 25 patients with left sided brain tumors. Our results show that there are important differences in the locations (i.e., > 20 mm) of the determined hand motor voxels by these three MR imaging methods. This can have significant effect on the pre-surgical planning of these patients depending on the modality used. In 13 of the 25 cases (i.e., 52%) the distances between the task-determined and the rs-fMRI determined hand areas were more than 20 mm; in 13 of 25 cases (i.e., 52%) the distances between the task-determined and anatomically determined hand areas were > 20 mm; and in 16 of 25 cases (i.e., 64%) the distances between the rs-fMRI determined and anatomically determined hand areas were more than 20 mm. In just three cases, the distances determined by all three modalities were within 20 mm of each other. The differences in the location or fingerprint of the hand motor areas, as determined by these three MR methods result from the different underlying mechanisms of these three modalities and possibly the effects of tumors on these modalities.
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Affiliation(s)
- Bob L Hou
- Department of Radiology, WVU, Morgantown, WV 26506, USA.
| | - Sanjay Bhatia
- Department of Neurosurgery, WVU, Morgantown, WV 26506, USA
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Agarwal S, Sair HI, Airan R, Hua J, Jones CK, Heo HY, Olivi A, Lindquist MA, Pekar JJ, Pillai JJ. Demonstration of Brain Tumor-Induced Neurovascular Uncoupling in Resting-State fMRI at Ultrahigh Field. Brain Connect 2016; 6:267-72. [PMID: 26918887 DOI: 10.1089/brain.2015.0402] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
To demonstrate in a small case series for the first time the phenomenon of brain tumor-related neurovascular uncoupling (NVU) in resting-state blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) at ultrahigh field (7T). Two de novo (i.e., untreated) brain tumor patients underwent both BOLD resting-state fMRI (rsfMRI) on a 7T MRI system and motor task-based BOLD fMRI at 3T. Ipsilesional (i.e., ipsilateral to tumor or IL) and contralesional (i.e., contralateral to tumor or CL) region of interest (ROI) analysis was performed on both 3T motor task-related general linear model-derived activation maps and on 7T rsfMRI independent component analysis (ICA)-derived sensorimotor network maps for each case. Asymmetry scores (ASs) were computed based on numbers of suprathreshold voxels in the IL and CL ROIs. In each patient, ASs derived from ROI analysis of suprathreshold voxels in IL and CL ROIs in task-related activation maps and rsfMRI ICA-derived sensorimotor component maps indicate greater number of suprathreshold voxels in contralesional than ipsilesional sensorimotor cortex in both maps. In patient 1, an AS of 0.2 was obtained from the suprathreshold Z-score spectrum (voxels with Z-scores >5.0) of the task-based activation map and AS of 1.0 was obtained from the suprathreshold Z-score spectrum (Z-scores >5.0) of the ICA-derived sensorimotor component map. Similarly, in patient 2, an AS of 1.0 was obtained from the suprathreshold Z-score spectrum (Z-scores >5.0) of the task-based activation map and an AS of 1.0 was obtained from the suprathreshold Z-score spectrum (Z-scores >5.0) of the ICA-derived sensorimotor component map. Overall, decreased BOLD signal was noted in IL compared with CL ROIs on both task-based activation maps and ultrahigh field resting-state maps, indicating the presence of NVU. We have demonstrated evidence of NVU on ultrahigh field 7T rsfMRI comparable with the findings on standard 3T motor task-based fMRI in both cases.
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Affiliation(s)
- Shruti Agarwal
- 1 Division of Neuroradiology, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine , Baltimore, Maryland
| | - Haris I Sair
- 1 Division of Neuroradiology, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine , Baltimore, Maryland
| | - Raag Airan
- 1 Division of Neuroradiology, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine , Baltimore, Maryland
| | - Jun Hua
- 2 Division of MR Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine , Baltimore, Maryland
- 3 F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute , Baltimore, Maryland
| | - Craig K Jones
- 2 Division of MR Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine , Baltimore, Maryland
- 3 F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute , Baltimore, Maryland
| | - Hye-Young Heo
- 2 Division of MR Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine , Baltimore, Maryland
- 3 F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute , Baltimore, Maryland
| | - Alessandro Olivi
- 4 Department of Neurosurgery, Johns Hopkins University School of Medicine , Baltimore, Maryland
| | - Martin A Lindquist
- 5 Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health , Baltimore, Maryland
| | - James J Pekar
- 2 Division of MR Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine , Baltimore, Maryland
- 3 F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute , Baltimore, Maryland
| | - Jay J Pillai
- 1 Division of Neuroradiology, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine , Baltimore, Maryland
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Batra P, Bandt SK, Leuthardt EC. Resting state functional connectivity magnetic resonance imaging integrated with intraoperative neuronavigation for functional mapping after aborted awake craniotomy. Surg Neurol Int 2016; 7:13. [PMID: 26958419 PMCID: PMC4766807 DOI: 10.4103/2152-7806.175885] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2015] [Accepted: 12/29/2015] [Indexed: 11/04/2022] Open
Abstract
BACKGROUND Awake craniotomy is currently the gold standard for aggressive tumor resections in eloquent cortex. However, a significant subset of patients is unable to tolerate this procedure, particularly the very young or old or those with psychiatric comorbidities, cardiopulmonary comorbidities, or obesity, among other conditions. In these cases, typical alternative procedures include biopsy alone or subtotal resection, both of which are associated with diminished surgical outcomes. CASE DESCRIPTION Here, we report the successful use of a preoperatively obtained resting state functional connectivity magnetic resonance imaging (MRI) integrated with intraoperative neuronavigation software in order to perform functional cortical mapping in the setting of an aborted awake craniotomy due to loss of airway. CONCLUSION Resting state functional connectivity MRI integrated with intraoperative neuronavigation software can provide an alternative option for functional cortical mapping in the setting of an aborted awake craniotomy.
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Affiliation(s)
- Prag Batra
- Department of Computer Science, Washington University, St. Louis, Missouri, USA
| | - S Kathleen Bandt
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Eric C Leuthardt
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, Missouri, USA; Department of Biomedical Engineering, Washington University, St. Louis, Missouri, USA; Center for Innovation in Neuroscience and Technology, Washington University School of Medicine, St. Louis, Missouri, USA
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Leuthardt EC, Allen M, Kamran M, Hawasli AH, Snyder AZ, Hacker CD, Mitchell TJ, Shimony JS. Resting-State Blood Oxygen Level-Dependent Functional MRI: A Paradigm Shift in Preoperative Brain Mapping. Stereotact Funct Neurosurg 2016; 93:427-39. [PMID: 26784290 DOI: 10.1159/000442424] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2015] [Accepted: 11/12/2015] [Indexed: 11/19/2022]
Abstract
Currently, functional magnetic resonance imaging (fMRI) facilitates a preoperative awareness of an association of an eloquent region with a tumor. This information gives the neurosurgeon helpful information that can aid in creating a surgical strategy. Typically, task-based fMRI has been employed to preoperatively localize speech and motor function. Task-based fMRI depends on the patient's ability to comply with the task paradigm, which often is impaired in the setting of a brain tumor. This problem is overcome by using resting-state fMRI (rs-fMRI) to localize function. rs-fMRI measures spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal, representing the brain's functional organization. In a neurosurgical context, it allows noninvasive simultaneous assessment of multiple large-scale distributed networks. Compared with task-related fMRI, rs-fMRI provides more comprehensive information on the functional architecture of the brain and is applicable in settings where task-related fMRI may provide inadequate information or could not be performed. Taken together, rs-fMRI substantially expands the preoperative mapping capability in efficiency, effectiveness, and scope. In this article, a brief introduction into rs-fMRI processing methods is followed by a detailed discussion on the role rs-fMRI plays in presurgical planning.
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Abstract
Functional magnetic resonance imaging (fMRI) maps the spatiotemporal distribution of neural activity in the brain under varying cognitive conditions. Since its inception in 1991, blood oxygen level-dependent (BOLD) fMRI has rapidly become a vital methodology in basic and applied neuroscience research. In the clinical realm, it has become an established tool for presurgical functional brain mapping. This chapter has three principal aims. First, we review key physiologic, biophysical, and methodologic principles that underlie BOLD fMRI, regardless of its particular area of application. These principles inform a nuanced interpretation of the BOLD fMRI signal, along with its neurophysiologic significance and pitfalls. Second, we illustrate the clinical application of task-based fMRI to presurgical motor, language, and memory mapping in patients with lesions near eloquent brain areas. Integration of BOLD fMRI and diffusion tensor white-matter tractography provides a road map for presurgical planning and intraoperative navigation that helps to maximize the extent of lesion resection while minimizing the risk of postoperative neurologic deficits. Finally, we highlight several basic principles of resting-state fMRI and its emerging translational clinical applications. Resting-state fMRI represents an important paradigm shift, focusing attention on functional connectivity within intrinsic cognitive networks.
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Affiliation(s)
- Bradley R Buchbinder
- Department of Radiology, Division of Neuroradiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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50
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Wang D, Ma D, Wong ML, Wáng YXJ. Recent advances in surgical planning & navigation for tumor biopsy and resection. Quant Imaging Med Surg 2015; 5:640-8. [PMID: 26682133 DOI: 10.3978/j.issn.2223-4292.2015.10.03] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
This paper highlights recent advancements in imaging technologies for surgical planning and navigation in tumor biopsy and resection which need high-precision in detection and characterization of lesion margin in preoperative planning and intraoperative navigation. Multimodality image-guided surgery platforms brought great benefits in surgical planning and operation accuracy via registration of various data sets with information on morphology [X-ray, magnetic resonance (MR), computed tomography (CT)], function connectivity [functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), rest-status fMRI], or molecular activity [positron emission tomography (PET)]. These image-guided platforms provide a correspondence between the pre-operative surgical planning and intra-operative procedure. We envisage that the combination of advanced multimodal imaging, three-dimensional (3D) printing, and cloud computing will play increasingly important roles in planning and navigation of surgery for tumor biopsy and resection in the coming years.
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Affiliation(s)
- Defeng Wang
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Diya Ma
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Matthew Lun Wong
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Yì Xiáng J Wáng
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong SAR, China
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