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Moretto M, Silvestri E, Facchini S, Anglani M, Cecchin D, Corbetta M, Bertoldo A. The dynamic functional connectivity fingerprint of high-grade gliomas. Sci Rep 2023; 13:10389. [PMID: 37369744 DOI: 10.1038/s41598-023-37478-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 06/22/2023] [Indexed: 06/29/2023] Open
Abstract
Resting state fMRI has been used in many studies to investigate the impact of brain tumours on functional connectivity (FC). However, these studies have so far assumed that FC is stationary, disregarding the fact that the brain fluctuates over dynamic states. Here we utilised resting state fMRI data from 33 patients with high-grade gliomas and 33 healthy controls to examine the dynamic interplay between resting-state networks and to gain insights into the impact of brain tumours on functional dynamics. By employing Hidden Markov Models, we demonstrated that functional dynamics persist even in the presence of a high-grade glioma, and that patients exhibited a global decrease of connections strength, as well as of network segregation. Furthermore, through a multivariate analysis, we demonstrated that patients' cognitive scores are highly predictive of pathological dynamics, thus supporting our hypothesis that functional dynamics could serve as valuable biomarkers for better understanding the traits of high-grade gliomas.
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Affiliation(s)
- Manuela Moretto
- Padova Neuroscience Center, University of Padova, 35131, Padova, Italy
- Department of Information Engineering, University of Padova, Via G. Gradenigo 6/B, 35131, Padova, Italy
| | - Erica Silvestri
- Padova Neuroscience Center, University of Padova, 35131, Padova, Italy
- Department of Information Engineering, University of Padova, Via G. Gradenigo 6/B, 35131, Padova, Italy
| | - Silvia Facchini
- Padova Neuroscience Center, University of Padova, 35131, Padova, Italy
- Department of Neuroscience, University of Padova, 35121, Padova, Italy
| | | | - Diego Cecchin
- Padova Neuroscience Center, University of Padova, 35131, Padova, Italy
- Unit of Nuclear Medicine, University of Padova, 35121, Padova, Italy
| | - Maurizio Corbetta
- Padova Neuroscience Center, University of Padova, 35131, Padova, Italy
- Department of Neuroscience, University of Padova, 35121, Padova, Italy
- Venetian Institute of Molecular Medicine, 35131, Padova, Italy
| | - Alessandra Bertoldo
- Padova Neuroscience Center, University of Padova, 35131, Padova, Italy.
- Department of Information Engineering, University of Padova, Via G. Gradenigo 6/B, 35131, Padova, Italy.
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Chang WH, Wei KC, Chen PY, Chen YC, Wu YY, Tsai HC, Chen MH, Chao YP, Chen KT. The impact of patient factors and tumor characteristics on language neuroplasticity in left hemispheric diffuse gliomas prior to surgical resection. J Neurooncol 2023; 163:95-104. [PMID: 37093525 DOI: 10.1007/s11060-023-04311-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 04/07/2023] [Indexed: 04/25/2023]
Abstract
PURPOSE Language networks are reorganized during glioma growth, leading to varying language performance in patients with gliomas located in or around language-eloquent areas. Therefore, pre-treated language performance reflects the neuroplasticity potential. Different domains of language processing, such as speech expression, repetition, and comprehension, involving different neural networks. We analyzed the effects of patient factors and tumor characteristics on the pre-treated performance to investigate neuroplastic potential of different language domains. METHODS Patient age, sex, education level, tumor grade, language pathway involvement, T1 contrast enhanced (C+), and FLAIR (T2) volume were selected as variables. The correlation with abnormal language performance was verified using univariate and multivariate logistic regression. RESULTS In total, 104 left hemispheric glioma patients were enrolled in this study. 44% of patients had repetitive abnormalities, 34.9% had comprehensive abnormalities, and 32.1% had expressive abnormalities. The proportion of normal language performance was 60% in grade 2 and 3 gliomas and 16% in grade 4 gliomas. Tumor grade (p = 0.006) and T2 volume (p = 0.008) were associated with abnormal performance in the expressive domain, education level (p = 0.004) and T1 C+ volume (p = 0.049) in the repetitive domain, and education level (p = 0.013), T2 volume (p = 0.011), and tumor grade (p = 0.089) in the comprehensive domain. CONCLUSION Different clinical and radiological factors affected the abnormal performance of the three language domains, indicating their functional connectivity and neuroplastic potential are inherently varied. The dynamic interactions between patient factors, tumor characteristics, and language processing should be considered when resecting left hemispheric gliomas.
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Affiliation(s)
- Wei-Han Chang
- School of Traditional Chinese Medicine, Chang Gung University, Taoyuan, Taiwan
- Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital at Keelung, New Taipei, Taiwan
- Department of Nutrition and Health Sciences, College of Human Ecology, Chang Gung University of Science and Technology, Taoyuan, Taiwan
| | - Kuo-Chen Wei
- Department of Neurosurgery, New Taipei Municipal TuCheng Hospital, Chang Gung Medical Foundation, New Taipei, Taiwan
- School of Medicine, Chang Gung University, Taoyuan, Taiwan
- Neuroscience Research Center, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Pin-Yuan Chen
- School of Medicine, Chang Gung University, Taoyuan, Taiwan
- Department of Neurosurgery, Chang Gung Memorial Hospital at Keelung, New Taipei, Taiwan
- Neuroscience Research Center, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Yi-Chun Chen
- Department of Neurology, Chang Gung Memorial Hospital Linkou Medical Center and College of Medicine, Chang-Gung University, Taoyuan, Taiwan
- Dementia Center, Taoyuan Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Yah-Yuan Wu
- Department of Neurology, Chang Gung Memorial Hospital Linkou Medical Center and College of Medicine, Chang-Gung University, Taoyuan, Taiwan
- Dementia Center, Taoyuan Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Hong-Chieh Tsai
- Department of Neurosurgery, Chang Gung Memorial Hospital at Linkou, No. 5, Fuxing Street, Guishan Dist., Taoyuan, 33305, Taiwan
| | - Mei-Hui Chen
- Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Yi-Ping Chao
- Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan
| | - Ko-Ting Chen
- School of Traditional Chinese Medicine, Chang Gung University, Taoyuan, Taiwan.
- School of Medicine, Chang Gung University, Taoyuan, Taiwan.
- Department of Neurosurgery, Chang Gung Memorial Hospital at Linkou, No. 5, Fuxing Street, Guishan Dist., Taoyuan, 33305, Taiwan.
- Neuroscience Research Center, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.
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What Can Resting-State fMRI Data Analysis Explain about the Functional Brain Connectivity in Glioma Patients? Tomography 2022; 8:267-280. [PMID: 35202187 PMCID: PMC8878995 DOI: 10.3390/tomography8010021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 01/12/2022] [Accepted: 01/14/2022] [Indexed: 11/24/2022] Open
Abstract
Resting-state functional MRI has been increasingly implemented in imaging protocols for the study of functional connectivity in glioma patients as a sequence able to capture the activity of brain networks and to investigate their properties without requiring the patients’ cooperation. The present review aims at describing the most recent results obtained through the analysis of resting-state fMRI data in different contexts of interest for brain gliomas: the identification and localization of functional networks, the characterization of altered functional connectivity, and the evaluation of functional plasticity in relation to the resection of the glioma. An analysis of the literature showed that significant and promising results could be achieved through this technique in all the aspects under investigation. Nevertheless, there is room for improvement, especially in terms of stability and generalizability of the outcomes. Further research should be conducted on homogeneous samples of glioma patients and at fixed time points to reduce the considerable variability in the results obtained across and within studies. Future works should also aim at establishing robust metrics for the assessment of the disruption of functional connectivity and its recovery at the single-subject level.
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Wolthuis N, Satoer D, Veenstra W, Smits M, Wagemakers M, Vincent A, Bastiaanse R, Cherian PJ, Bosma I. Resting-State Electroencephalography Functional Connectivity Networks Relate to Pre- and Postoperative Language Functioning in Low-Grade Glioma and Meningioma Patients. Front Neurosci 2021; 15:785969. [PMID: 34955732 PMCID: PMC8693574 DOI: 10.3389/fnins.2021.785969] [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: 09/29/2021] [Accepted: 10/29/2021] [Indexed: 11/13/2022] Open
Abstract
Introduction: Preservation of language functioning in patients undergoing brain tumor surgery is essential because language impairments negatively impact the quality of life. Brain tumor patients have alterations in functional connectivity (FC), the extent to which brain areas functionally interact. We studied FC networks in relation to language functioning in glioma and meningioma patients. Method: Patients with a low-grade glioma (N = 15) or meningioma (N = 10) infiltrating into/pressing on the language-dominant hemisphere underwent extensive language testing before and 1 year after surgery. Resting-state EEG was registered preoperatively, postoperatively (glioma patients only), and once in healthy individuals. After analyzing FC in theta and alpha frequency bands, weighted networks and Minimum Spanning Trees were quantified by various network measures. Results: Pre-operative FC network characteristics did not differ between glioma patients and healthy individuals. However, hub presence and higher local and global FC are associated with poorer language functioning before surgery in glioma patients and predict worse language performance at 1 year after surgery. For meningioma patients, a greater small worldness was related to worse language performance and hub presence; better average clustering and global integration were predictive of worse outcome on language function 1 year after surgery. The average eccentricity, diameter and tree hierarchy seem to be the network metrics with the more pronounced relation to language performance. Discussion: In this exploratory study, we demonstrated that preoperative FC networks are informative for pre- and postoperative language functioning in glioma patients and to a lesser extent in meningioma patients.
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Affiliation(s)
- Nienke Wolthuis
- Center for Language and Cognition Groningen, University of Groningen, Groningen, Netherlands
| | - Djaina Satoer
- Department of Neurosurgery, Erasmus MC - University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Wencke Veenstra
- Department of Rehabilitation Medicine, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Marion Smits
- Department of Radiology & Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, Rotterdam, Netherlands.,Brain Tumour Centre, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Michiel Wagemakers
- Department of Neurosurgery, University Medical Center Groningen, Groningen, Netherlands
| | - Arnaud Vincent
- Department of Neurosurgery, Erasmus MC - University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Roelien Bastiaanse
- Center for Language and Cognition Groningen, University of Groningen, Groningen, Netherlands.,National Research University Higher School of Economics, Moscow, Russia
| | - Perumpillichira J Cherian
- Department of Neurology, University Medical Center Rotterdam, Rotterdam, Netherlands.,Division of Neurology, Department of Medicine, McMaster University and Hamilton Health Sciences, Hamilton, ON, Canada
| | - Ingeborg Bosma
- Department of Neurology, University Medical Center Groningen, Groningen, Netherlands
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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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