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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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Blank IA, Kiran S, Fedorenko E. Can neuroimaging help aphasia researchers? Addressing generalizability, variability, and interpretability. Cogn Neuropsychol 2017; 34:377-393. [PMID: 29188746 PMCID: PMC6157596 DOI: 10.1080/02643294.2017.1402756] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Neuroimaging studies of individuals with brain damage seek to link brain structure and activity to cognitive impairments, spontaneous recovery, or treatment outcomes. To date, such studies have relied on the critical assumption that a given anatomical landmark corresponds to the same functional unit(s) across individuals. However, this assumption is fallacious even across neurologically healthy individuals. Here, we discuss the severe implications of this issue, and argue for an approach that circumvents it, whereby: (i) functional brain regions are defined separately for each subject using fMRI, allowing for inter-individual variability in their precise location; (ii) the response profile of these subject-specific regions are characterized using various other tasks; and (iii) the results are averaged across individuals, guaranteeing generalizabliity. This method harnesses the complementary strengths of single-case studies and group studies, and it eliminates the need for post hoc "reverse inference" from anatomical landmarks back to cognitive operations, thus improving data interpretability.
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Affiliation(s)
- Idan A Blank
- a McGovern Institute for Brain Research , Massachusetts Institute of Technology , Cambridge , MA , USA
| | - Swathi Kiran
- b Department of Speech Language and Hearing Sciences, Aphasia Research Laboratory , Sargent College, Boston University , Boston , MA , USA
| | - Evelina Fedorenko
- c Department of Psychiatry , Massachusetts General Hospital , Charlestown , MA , USA
- d Department of Psychiatry , Harvard Medical School , Boston , MA , USA
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Sohn WS, Lee TY, Yoo K, Kim M, Yun JY, Hur JW, Yoon YB, Seo SW, Na DL, Jeong Y, Kwon JS. Node Identification Using Inter-Regional Correlation Analysis for Mapping Detailed Connections in Resting State Networks. Front Neurosci 2017; 11:238. [PMID: 28507502 PMCID: PMC5410606 DOI: 10.3389/fnins.2017.00238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2016] [Accepted: 04/11/2017] [Indexed: 11/13/2022] Open
Abstract
Brain function is often characterized by the connections and interactions between highly interconnected brain regions. Pathological disruptions in these networks often result in brain dysfunction, which manifests as brain disease. Typical analysis investigates disruptions in network connectivity based correlations between large brain regions. To obtain a more detailed description of disruptions in network connectivity, we propose a new method where functional nodes are identified in each region based on their maximum connectivity to another brain region in a given network. Since this method provides a unique approach to identifying functionally relevant nodes in a given network, we can provide a more detailed map of brain connectivity and determine new measures of network connectivity. We applied this method to resting state fMRI of Alzheimer's disease patients to validate our method and found decreased connectivity within the default mode network. In addition, new measure of network connectivity revealed a more detailed description of how the network connections deteriorate with disease progression. This suggests that analysis using key relative network hub regions based on regional correlation can be used to detect detailed changes in resting state network connectivity.
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Affiliation(s)
- William S Sohn
- Institute of Human Behavioral Medicine, Medical Research Center, Seoul National UniversitySeoul, South Korea
| | - Tae Young Lee
- Department of Psychiatry, Seoul National University College of MedicineSeoul, South Korea
| | - Kwangsun Yoo
- Department of Bio and Brain Engineering, KAISTDaejeon, South Korea
| | - Minah Kim
- Department of Psychiatry, Seoul National University College of MedicineSeoul, South Korea
| | - Je-Yeon Yun
- Department of Psychiatry, Seoul National University College of MedicineSeoul, South Korea
| | - Ji-Won Hur
- Department of Psychology, Chung-Ang UniversitySeoul, South Korea
| | - Youngwoo Bryan Yoon
- Department of Brain and Cognitive Sciences, Seoul National UniversitySeoul, South Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sunkyunkwan UniversitySeoul, South Korea.,Neuroscience Center, Samsung Medical CenterSeoul, South Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sunkyunkwan UniversitySeoul, South Korea.,Neuroscience Center, Samsung Medical CenterSeoul, South Korea
| | - Yong Jeong
- Department of Bio and Brain Engineering, KAISTDaejeon, South Korea
| | - Jun Soo Kwon
- Institute of Human Behavioral Medicine, Medical Research Center, Seoul National UniversitySeoul, South Korea.,Department of Psychiatry, Seoul National University College of MedicineSeoul, South Korea.,Department of Brain and Cognitive Sciences, Seoul National UniversitySeoul, South Korea
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