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Edelstein R, Gutterman S, Newman B, Van Horn JD. Assessment of Sports Concussion in Female Athletes: A Role for Neuroinformatics? Neuroinformatics 2024:10.1007/s12021-024-09680-8. [PMID: 39078562 DOI: 10.1007/s12021-024-09680-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/02/2024] [Indexed: 07/31/2024]
Abstract
Over the past decade, the intricacies of sports-related concussions among female athletes have become readily apparent. Traditional clinical methods for diagnosing concussions suffer limitations when applied to female athletes, often failing to capture subtle changes in brain structure and function. Advanced neuroinformatics techniques and machine learning models have become invaluable assets in this endeavor. While these technologies have been extensively employed in understanding concussion in male athletes, there remains a significant gap in our comprehension of their effectiveness for female athletes. With its remarkable data analysis capacity, machine learning offers a promising avenue to bridge this deficit. By harnessing the power of machine learning, researchers can link observed phenotypic neuroimaging data to sex-specific biological mechanisms, unraveling the mysteries of concussions in female athletes. Furthermore, embedding methods within machine learning enable examining brain architecture and its alterations beyond the conventional anatomical reference frame. In turn, allows researchers to gain deeper insights into the dynamics of concussions, treatment responses, and recovery processes. This paper endeavors to address the crucial issue of sex differences in multimodal neuroimaging experimental design and machine learning approaches within female athlete populations, ultimately ensuring that they receive the tailored care they require when facing the challenges of concussions. Through better data integration, feature identification, knowledge representation, validation, etc., neuroinformaticists, are ideally suited to bring clarity, context, and explainabilty to the study of sports-related head injuries in males and in females, and helping to define recovery.
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Affiliation(s)
- Rachel Edelstein
- Department of Psychology, University of Virginia, 409 McCormick Road Gilmer Hall Room 304, Charlottesville, VA, 22904, USA.
| | - Sterling Gutterman
- Department of Psychology, University of Virginia, 409 McCormick Road Gilmer Hall Room 304, Charlottesville, VA, 22904, USA
| | - Benjamin Newman
- Department of Psychology, University of Virginia, 409 McCormick Road Gilmer Hall Room 304, Charlottesville, VA, 22904, USA
| | - John Darrell Van Horn
- Department of Psychology, University of Virginia, 409 McCormick Road Gilmer Hall Room 304, Charlottesville, VA, 22904, USA
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Leskinen S, Singha S, Mehta NH, Quelle M, Shah HA, D'Amico RS. Applications of Functional Magnetic Resonance Imaging to the Study of Functional Connectivity and Activation in Neurological Disease: A Scoping Review of the Literature. World Neurosurg 2024; 189:185-192. [PMID: 38843969 DOI: 10.1016/j.wneu.2024.06.003] [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: 05/13/2024] [Accepted: 06/02/2024] [Indexed: 07/09/2024]
Abstract
BACKGROUND Functional magnetic resonance imaging (fMRI) has transformed our understanding of brain's functional architecture, providing critical insights into neurological diseases. This scoping review synthesizes the current landscape of fMRI applications across various neurological domains, elucidating the evolving role of both task-based and resting-state fMRI in different settings. METHODS We conducted a comprehensive scoping review following the Preferred Reporting Items for Systematic Review and Meta-Analyses Extension for Scoping Reviews guidelines. Extensive searches in Medline/PubMed, Embase, and Web of Science were performed, focusing on studies published between 2003 and 2023 that utilized fMRI to explore functional connectivity and regional activation in adult patients with neurological conditions. Studies were selected based on predefined inclusion and exclusion criteria, with data extracted. RESULTS We identified 211 studies, covering a broad spectrum of neurological disorders including mental health, movement disorders, epilepsy, neurodegeneration, traumatic brain injury, cerebrovascular accidents, vascular abnormalities, neurorehabilitation, neuro-critical care, and brain tumors. The majority of studies utilized resting-state fMRI, underscoring its prominence in identifying disease-specific connectivity patterns. Results highlight the potential of fMRI to reveal the underlying pathophysiological mechanisms of various neurological conditions, facilitate diagnostic processes, and potentially guide therapeutic interventions. CONCLUSIONS fMRI serves as a powerful tool for elucidating complex neural dynamics and pathologies associated with neurological diseases. Despite the breadth of applications, further research is required to standardize fMRI protocols, improve interpretative methodologies, and enhance the translation of imaging findings to clinical practice. Advances in fMRI technology and analytics hold promise for improving the precision of neurological assessments and interventions.
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Affiliation(s)
- Sandra Leskinen
- State University of New York Downstate Medical Center, New York, USA
| | - Souvik Singha
- Department of Neurological Surgery, Lenox Hill Hospital/Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, New York, NY, USA.
| | - Neel H Mehta
- Department of Neurosurgery, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA
| | | | - Harshal A Shah
- Department of Neurological Surgery, Lenox Hill Hospital/Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, New York, NY, USA
| | - Randy S D'Amico
- Department of Neurological Surgery, Lenox Hill Hospital/Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, New York, NY, USA
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Falcó-Roget J, Cacciola A, Sambataro F, Crimi A. Functional and structural reorganization in brain tumors: a machine learning approach using desynchronized functional oscillations. Commun Biol 2024; 7:419. [PMID: 38582867 PMCID: PMC10998892 DOI: 10.1038/s42003-024-06119-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 03/28/2024] [Indexed: 04/08/2024] Open
Abstract
Neuroimaging studies have allowed for non-invasive mapping of brain networks in brain tumors. Although tumor core and edema are easily identifiable using standard MRI acquisitions, imaging studies often neglect signals, structures, and functions within their presence. Therefore, both functional and diffusion signals, as well as their relationship with global patterns of connectivity reorganization, are poorly understood. Here, we explore the functional activity and the structure of white matter fibers considering the contribution of the whole tumor in a surgical context. First, we find intertwined alterations in the frequency domain of local and spatially distributed resting-state functional signals, potentially arising within the tumor. Second, we propose a fiber tracking pipeline capable of using anatomical information while still reconstructing bundles in tumoral and peritumoral tissue. Finally, using machine learning and healthy anatomical information, we predict structural rearrangement after surgery given the preoperative brain network. The generative model also disentangles complex patterns of connectivity reorganization for different types of tumors. Overall, we show the importance of carefully designing studies including MR signals within damaged brain tissues, as they exhibit and relate to non-trivial patterns of both structural and functional (dis-)connections or activity.
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Affiliation(s)
- Joan Falcó-Roget
- Brain and More Lab, Computer Vision, Sano Centre for Computational Medicine, Kraków, Poland.
| | - Alberto Cacciola
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Imaging, University of Messina, Messina, Italy
| | - Fabio Sambataro
- Department of Neuroscience, University of Padova, Padua, Italy
| | - Alessandro Crimi
- Brain and More Lab, Computer Vision, Sano Centre for Computational Medicine, Kraków, Poland.
- Faculty of Computer Science, AGH University of Krakow, Kraków, Poland.
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Facchini S, Favaretto C, Castellaro M, Zangrossi A, Zannin M, Bisogno AL, Baro V, Anglani MG, Vallesi A, Baracchini C, D'Avella D, Della Puppa A, Semenza C, Corbetta M. A common low dimensional structure of cognitive impairment in stroke and brain tumors. Neuroimage Clin 2023; 40:103518. [PMID: 37778195 PMCID: PMC10562193 DOI: 10.1016/j.nicl.2023.103518] [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: 01/20/2023] [Revised: 09/23/2023] [Accepted: 09/25/2023] [Indexed: 10/03/2023]
Abstract
INTRODUCTION Neuropsychological studies infer brain-behavior relationships from focal lesions like stroke and tumors. However, these pathologies impair brain function through different mechanisms even when they occur at the same brain's location. The aim of this study was to compare the profile of cognitive impairment in patients with brain tumors vs. stroke and examine the correlation with lesion location in each pathology. METHODS Patients with first time stroke (n = 77) or newly diagnosed brain tumors (n = 76) were assessed with a neuropsychological battery. Their lesions were mapped with MRI scans. Test scores were analyzed using principal component analysis (PCA) to measure their correlation, and logistic regression to examine differences between pathologies. Next, with ridge regression we examined whether lesion features (location, volume) were associated with behavioral performance. RESULTS The PCA showed a similar cognitive impairment profile in tumors and strokes with three principal components (PCs) accounting for about half of the individual variance. PC1 loaded on language, verbal memory, and executive/working memory; PC2 loaded on general performance, visuo-spatial attention and memory, and executive functions; and, PC3 loaded on calculation, reading and visuo-spatial attention. The average lesion distribution was different, and lesion location was correlated with cognitive deficits only in stroke. Logistic regression found language and calculation more affected in stroke, and verbal memory and verbal fluency more affected in tumors. CONCLUSIONS A similar low dimensional set of behavioral impairments was found both in stroke and brain tumors, even though each pathology caused some specific deficits in different domains. The lesion distribution was different for stroke and tumors and correlated with behavioral impairment only in stroke.
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Affiliation(s)
- Silvia Facchini
- Clinica Neurologica, Azienda Ospedale Università Padova, and Department of Neuroscience, University of Padua, Italy
| | | | - Marco Castellaro
- Department of Information Engineering, University of Padua, Italy
| | | | - Margherita Zannin
- Clinica Neurologica, Azienda Ospedale Università Padova, and Department of Neuroscience, University of Padua, Italy
| | - Antonio Luigi Bisogno
- Clinica Neurologica, Azienda Ospedale Università Padova, and Department of Neuroscience, University of Padua, Italy
| | - Valentina Baro
- Paediatric and Functional Neurosurgery Unit, Azienda Ospedale Università Padova, and Department of Neuroscience, University of Padua, Italy
| | | | - Antonio Vallesi
- Clinica Neurologica, Azienda Ospedale Università Padova, and Department of Neuroscience, University of Padua, Italy; Padova Neuroscience Center (PNC), University of Padua, Italy
| | - Claudio Baracchini
- Stroke Unit and Neurosonology Laboratory, Azienda Ospedale Università Padova, Padua, Italy
| | - Domenico D'Avella
- Paediatric and Functional Neurosurgery Unit, Azienda Ospedale Università Padova, and Department of Neuroscience, University of Padua, Italy
| | - Alessandro Della Puppa
- Neurosurgery Unit, Department of Neuroscience, Psychology, Pharmacology and Child Health, Careggi University Hospital and University of Florence, Italy
| | - Carlo Semenza
- Clinica Neurologica, Azienda Ospedale Università Padova, and Department of Neuroscience, University of Padua, Italy; Padova Neuroscience Center (PNC), University of Padua, Italy
| | - Maurizio Corbetta
- Clinica Neurologica, Azienda Ospedale Università Padova, and Department of Neuroscience, University of Padua, Italy; Padova Neuroscience Center (PNC), University of Padua, Italy; Venetian Institute of Molecular Medicine, VIMM, Padua, Italy.
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Luckett PH, Olufawo M, Lamichhane B, Park KY, Dierker D, Verastegui GT, Yang P, Kim AH, Chheda MG, Snyder AZ, Shimony JS, Leuthardt EC. Predicting survival in glioblastoma with multimodal neuroimaging and machine learning. J Neurooncol 2023; 164:309-320. [PMID: 37668941 PMCID: PMC10522528 DOI: 10.1007/s11060-023-04439-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 08/26/2023] [Indexed: 09/06/2023]
Abstract
PURPOSE Glioblastoma (GBM) is the most common and aggressive malignant glioma, with an overall median survival of less than two years. The ability to predict survival before treatment in GBM patients would lead to improved disease management, clinical trial enrollment, and patient care. METHODS GBM patients (N = 133, mean age 60.8 years, median survival 14.1 months, 57.9% male) were retrospectively recruited from the neurosurgery brain tumor service at Washington University Medical Center. All patients completed structural neuroimaging and resting state functional MRI (RS-fMRI) before surgery. Demographics, measures of cortical thickness (CT), and resting state functional network connectivity (FC) were used to train a deep neural network to classify patients based on survival (< 1y, 1-2y, >2y). Permutation feature importance identified the strongest predictors of survival based on the trained models. RESULTS The models achieved a combined cross-validation and hold out accuracy of 90.6% in classifying survival (< 1y, 1-2y, >2y). The strongest demographic predictors were age at diagnosis and sex. The strongest CT predictors of survival included the superior temporal sulcus, parahippocampal gyrus, pericalcarine, pars triangularis, and middle temporal regions. The strongest FC features primarily involved dorsal and inferior somatomotor, visual, and cingulo-opercular networks. CONCLUSION We demonstrate that machine learning can accurately classify survival in GBM patients based on multimodal neuroimaging before any surgical or medical intervention. These results were achieved without information regarding presentation symptoms, treatments, postsurgical outcomes, or tumor genomic information. Our results suggest GBMs have a global effect on the brain's structural and functional organization, which is predictive of survival.
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Affiliation(s)
- Patrick H Luckett
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, 63110, USA.
| | - Michael Olufawo
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Bidhan Lamichhane
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Center for Health Sciences, Oklahoma State University, Tulsa, OK, 74136, USA
| | - Ki Yun Park
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Donna Dierker
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Peter Yang
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Albert H Kim
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Brain Tumor Center at Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Milan G Chheda
- Brain Tumor Center at Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Abraham Z Snyder
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Joshua S Shimony
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Brain Tumor Center at Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Eric C Leuthardt
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Brain Tumor Center at Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Biomedical Engineering, Washington University in Saint Louis, St. Louis, MO, 63130, USA
- Department of Mechanical Engineering and Materials Science, Washington University in Saint Louis, St. Louis, MO, 63130, USA
- Center for Innovation in Neuroscience and Technology, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Brain Laser Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
- National Center for Adaptive Neurotechnologies, Albany, USA
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Mandal AS, Brem S, Suckling J. Brain network mapping and glioma pathophysiology. Brain Commun 2023; 5:fcad040. [PMID: 36895956 PMCID: PMC9989143 DOI: 10.1093/braincomms/fcad040] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 12/23/2022] [Accepted: 02/18/2023] [Indexed: 02/25/2023] Open
Abstract
Adult diffuse gliomas are among the most difficult brain disorders to treat in part due to a lack of clarity regarding the anatomical origins and mechanisms of migration of the tumours. While the importance of studying networks of glioma spread has been recognized for at least 80 years, the ability to carry out such investigations in humans has emerged only recently. Here, we comprehensively review the fields of brain network mapping and glioma biology to provide a primer for investigators interested in merging these areas of inquiry for the purposes of translational research. Specifically, we trace the historical development of ideas in both brain network mapping and glioma biology, highlighting studies that explore clinical applications of network neuroscience, cells-of-origin of diffuse glioma and glioma-neuronal interactions. We discuss recent research that has merged neuro-oncology and network neuroscience, finding that the spatial distribution patterns of gliomas follow intrinsic functional and structural brain networks. Ultimately, we call for more contributions from network neuroimaging to realize the translational potential of cancer neuroscience.
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Affiliation(s)
- Ayan S Mandal
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.,Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Steven Brem
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.,Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA 19104, USA.,Glioblastoma Translational Center of Excellence, Abramson Cancer Center, Philadelphia, PA 19104, USA
| | - John Suckling
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
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Connectome Analysis in an Individual with SETD1B -Related Neurodevelopmental Disorder and Epilepsy. J Dev Behav Pediatr 2022; 43:e419-e422. [PMID: 35385430 DOI: 10.1097/dbp.0000000000001079] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 02/08/2022] [Indexed: 11/27/2022]
Abstract
OBJECTIVE Causative variants in SETD1B , encoding a lysine-specific methyltransferase, have recently been associated with a neurodevelopmental phenotype encompassing intellectual disability, autistic features, pronounced language delay, and epilepsy. It has been noted that long-term and deep phenotype data are needed to further delineate this rare condition. METHODS In this study, we provide an in-depth clinical characterization with long-term follow-up and trio exome sequencing findings to describe one additional individual affected by SETD1B -related disorder. The diagnostic workup was complemented by a functional magnetic resonance imaging (fMRI) study. RESULTS We report a 24-year-old male individual with an early-onset neurodevelopmental disorder with epilepsy due to the de novo missense variant c.5699A>G, p.(Tyr1900Cys) in SETD1B (NM_015048.1). He exhibited delayed speech development, autism spectrum disorder, and early-onset epilepsy with absence and generalized tonic-clonic seizures. Despite profoundly impaired communication skills, ongoing improvements regarding language production have been noted in adulthood. fMRI findings demonstrate abnormal language activation and resting-state connectivity structure. CONCLUSION Our report expands the previously delineated phenotype of SETD1B -related disorder and provides novel insights into underlying disease mechanisms.
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Advanced Neuroimaging Approaches to Pediatric Brain Tumors. Cancers (Basel) 2022; 14:cancers14143401. [PMID: 35884462 PMCID: PMC9318188 DOI: 10.3390/cancers14143401] [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: 06/26/2022] [Accepted: 07/08/2022] [Indexed: 12/10/2022] Open
Abstract
Simple Summary After leukemias, brain tumors are the most common cancers in children, and early, accurate diagnosis is critical to improve patient outcomes. Beyond the conventional imaging methods of computed tomography (CT) and magnetic resonance imaging (MRI), advanced neuroimaging techniques capable of both structural and functional imaging are moving to the forefront to improve the early detection and differential diagnosis of tumors of the central nervous system. Here, we review recent developments in neuroimaging techniques for pediatric brain tumors. Abstract Central nervous system tumors are the most common pediatric solid tumors; they are also the most lethal. Unlike adults, childhood brain tumors are mostly primary in origin and differ in type, location and molecular signature. Tumor characteristics (incidence, location, and type) vary with age. Children present with a variety of symptoms, making early accurate diagnosis challenging. Neuroimaging is key in the initial diagnosis and monitoring of pediatric brain tumors. Conventional anatomic imaging approaches (computed tomography (CT) and magnetic resonance imaging (MRI)) are useful for tumor detection but have limited utility differentiating tumor types and grades. Advanced MRI techniques (diffusion-weighed imaging, diffusion tensor imaging, functional MRI, arterial spin labeling perfusion imaging, MR spectroscopy, and MR elastography) provide additional and improved structural and functional information. Combined with positron emission tomography (PET) and single-photon emission CT (SPECT), advanced techniques provide functional information on tumor metabolism and physiology through the use of radiotracer probes. Radiomics and radiogenomics offer promising insight into the prediction of tumor subtype, post-treatment response to treatment, and prognostication. In this paper, a brief review of pediatric brain cancers, by type, is provided with a comprehensive description of advanced imaging techniques including clinical applications that are currently utilized for the assessment and evaluation of pediatric brain tumors.
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Aabedi AA, Young JS, Chang EF, Berger MS, Hervey-Jumper SL. Involvement of White Matter Language Tracts in Glioma: Clinical Implications, Operative Management, and Functional Recovery After Injury. Front Neurosci 2022; 16:932478. [PMID: 35898410 PMCID: PMC9309688 DOI: 10.3389/fnins.2022.932478] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 06/14/2022] [Indexed: 11/13/2022] Open
Abstract
To achieve optimal survival and quality of life outcomes in patients with glioma, the extent of tumor resection must be maximized without causing injury to eloquent structures. Preservation of language function is of particular importance to patients and requires careful mapping to reveal the locations of cortical language hubs and their structural and functional connections. Within this language network, accurate mapping of eloquent white matter tracts is critical, given the high risk of permanent neurological impairment if they are injured during surgery. In this review, we start by describing the clinical implications of gliomas involving white matter language tracts. Next, we highlight the advantages and limitations of methods commonly used to identify these tracts during surgery including structural imaging techniques, functional imaging, non-invasive stimulation, and finally, awake craniotomy. We provide a rationale for combining these complementary techniques as part of a multimodal mapping paradigm to optimize postoperative language outcomes. Next, we review local and long-range adaptations that take place as the language network undergoes remodeling after tumor growth and surgical resection. We discuss the probable cellular mechanisms underlying this plasticity with emphasis on the white matter, which until recently was thought to have a limited role in adults. Finally, we provide an overview of emerging developments in targeting the glioma-neuronal network interface to achieve better disease control and promote recovery after injury.
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Affiliation(s)
| | | | | | | | - Shawn L. Hervey-Jumper
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
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Pasquini L, Jenabi M, Yildirim O, Silveira P, Peck KK, Holodny AI. Brain Functional Connectivity in Low- and High-Grade Gliomas: Differences in Network Dynamics Associated with Tumor Grade and Location. Cancers (Basel) 2022; 14:cancers14143327. [PMID: 35884387 PMCID: PMC9324249 DOI: 10.3390/cancers14143327] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 07/06/2022] [Accepted: 07/07/2022] [Indexed: 12/27/2022] Open
Abstract
Brain tumors lead to modifications of brain networks. Graph theory plays an important role in clarifying the principles of brain connectivity. Our objective was to investigate network modifications related to tumor grade and location using resting-state functional magnetic resonance imaging (fMRI) and graph theory. We retrospectively studied 30 low-grade (LGG), 30 high-grade (HGG) left-hemispheric glioma patients and 20 healthy controls (HC) with rs-fMRI. Tumor location was labeled as: frontal, temporal, parietal, insular or occipital. We collected patients’ clinical data from records. We analyzed whole-brain and hemispheric networks in all patients and HC. Subsequently, we studied lobar networks in subgroups of patients divided by tumor location. Seven graph-theoretical metrics were calculated (FDR p < 0.05). Connectograms were computed for significant nodes. The two-tailed Student t-test or Mann−Whitney U-test (p < 0.05) were used to compare graph metrics and clinical data. The hemispheric network analysis showed increased ipsilateral connectivity for LGG (global efficiency p = 0.03) and decreased contralateral connectivity for HGG (degree/cost p = 0.028). Frontal and temporal tumors showed bilateral modifications; parietal and insular tumors showed only local effects. Temporal tumors led to a bilateral decrease in all graph metrics. Tumor grade and location influence the pattern of network reorganization. LGG may show more favorable network changes than HGG, reflecting fewer clinical deficits.
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Affiliation(s)
- Luca Pasquini
- Neuroradiology Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (M.J.); (O.Y.); (K.K.P.); (A.I.H.)
- Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, 00189 Rome, Italy
- Correspondence:
| | - Mehrnaz Jenabi
- Neuroradiology Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (M.J.); (O.Y.); (K.K.P.); (A.I.H.)
| | - Onur Yildirim
- Neuroradiology Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (M.J.); (O.Y.); (K.K.P.); (A.I.H.)
| | - Patrick Silveira
- Molecular Imaging and Therapy Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA;
| | - Kyung K. Peck
- Neuroradiology Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (M.J.); (O.Y.); (K.K.P.); (A.I.H.)
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Andrei I. Holodny
- Neuroradiology Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (M.J.); (O.Y.); (K.K.P.); (A.I.H.)
- Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Department of Radiology, Weill Medical College of Cornell University, New York, NY 10065, USA
- Department of Neuroscience, Weill-Cornell Graduate School of the Medical Sciences, New York, NY 10065, USA
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Yajun F, Limin T, Ruizhi Z, Qi X, Haisong C. Differentiation of microinfiltration and simple-edema areas in VX2 bone tumors by diffusion kurtosis imaging in animal experiments: a preliminary study. Acta Radiol 2022; 63:794-801. [PMID: 34000825 DOI: 10.1177/02841851211017519] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
BACKGROUND Dual-energy computed tomography, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can be used to distinguish microinvasion areas of malignant bone tumors. However, reports of diffusion kurtosis imaging (DKI) to determine the extent of intramedullary infiltration are relatively rare. PURPOSE To assess the application value of MR-DKI in differentiating areas of microinfiltration and simple edema in rabbit bone VX2 tumor models. MATERIAL AND METHODS Conventional MRI and DKI were performed on 25 successfully constructed rabbit VX2 bone tumor models. We acquired a midline sagittal section of the tumor for hematoxylin and eosin staining. Using pathological findings as the gold standard and combining them with MRI data, strict point-to-point control was performed to delineate regions of interest (ROIs) in the microinfiltration and simple-edema areas of bone tumors for quantitative measurement of mean diffusivity (MD) and mean kurtosis (MK). MD and MK values between microinfiltration and simple-edema areas were compared using an independent sample t-test, and the diagnostic values were evaluated by receiver operating characteristic (ROC) curve analysis. RESULTS In comparison with the simple-edema area, the micro-infiltration area demonstrated significantly smaller MD values and larger MK values (P < 0.05), and MD showed a better area under the curve (AUC) than MK (AUC = 0.884 vs. AUC = 0.690) for distinguishing the microinfiltration area from the simple-edema area. The optimal cutoff MD value was 1108.5 mm2/s with a sensitivity of 84% and specificity of 84%. CONCLUSION DKI can distinguish the microinfiltration and simple-edema areas of malignant bone tumors in animal experiments.
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Affiliation(s)
- Fang Yajun
- Department of Radiology, the Affiliated Hospital of Qingdao University,Qingdao, China
| | - Tang Limin
- Department of Radiology, the Affiliated Hospital of Qingdao University,Qingdao, China
| | - Zhou Ruizhi
- Department of Radiology, the Affiliated Hospital of Qingdao University,Qingdao, China
| | - Xu Qi
- Department of Radiology, the Affiliated Hospital of Qingdao University,Qingdao, China
| | - Chen Haisong
- Department of Radiology, the Affiliated Hospital of Qingdao University,Qingdao, China
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Rammohan N, Ho A, Saxena M, Bajaj A, Kruser TJ, Horbinski C, Korutz A, Tate M, Sachdev S. Tumor-associated alterations in white matter connectivity have prognostic significance in MGMT-unmethylated glioblastoma. J Neurooncol 2022; 158:331-339. [PMID: 35525907 DOI: 10.1007/s11060-022-04018-3] [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: 03/22/2022] [Accepted: 04/16/2022] [Indexed: 11/29/2022]
Abstract
PURPOSE We investigated the prognostic significance of tumor-associated white matter (TA-WM) tracts in glioblastoma (GBM) using magnetic resonance-diffusion tensor imaging (MR-DTI). We hypothesized that (1) TA-WM tracts harbor microscopic disease not targeted through surgery or radiotherapy (RT), and (2) the greater the extent of TA-WM involvement, the worse the survival outcomes. METHODS We studied a retrospective cohort of 76 GBM patients. TA-WM tracts were identified by MR-DTI fractional anisotropy (FA) maps. For each patient, 22 TA-WM tracts were analyzed and each tract was graded 1-3 based on FA. A TA-WM score (TA-WMS) was computed based on number of involved tracts and corresponding FA grade of involvement. Kaplan-Meier statistics were utilized to determine survival outcomes, log-rank test was used to compare survival between groups, and Cox regression was utilized to determine prognostic variables. RESULTS For the MGMT-unmethylated cohort, there was a decrease in OS for increasing TA-WMS (median OS 16.5 months for TA-WMS 0-4; 13.6 months for TA-WMS 5-8; 7.3 months for TA-WMS > 9; p = 0.0002). This trend was not observed in the MGMT-methylated cohort. For MGMT-unmethylated patients with TA-WMS > 6 and involvement of tracts passing through brainstem or contralateral hemisphere, median OS was 8.3 months versus median OS 14.1 months with TA-WMS > 6 but not involving aforementioned critical tracts (p = 0.003 log-rank test). For MGMT-unmethylated patients, TA-WMS was predictive of overall survival in multivariate analysis (HR = 1.14, 95% CI 1.03-1.27, p = 0.012) while age, gender, and largest tumor dimension were non-significant. CONCLUSION Increased TA-WMS and involvement of critical tracts are associated with decreased overall survival in MGMT-unmethylated GBM.
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Affiliation(s)
- Nikhil Rammohan
- Department of Radiation Oncology, Northwestern University Feinberg School of Medicine, 676 N St. Clair St, Ste 1820, Chicago, IL, 60611, USA
| | - Alexander Ho
- Department of Radiation Oncology, Northwestern University Feinberg School of Medicine, 676 N St. Clair St, Ste 1820, Chicago, IL, 60611, USA
| | - Mohit Saxena
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Amishi Bajaj
- Department of Radiation Oncology, Northwestern University Feinberg School of Medicine, 676 N St. Clair St, Ste 1820, Chicago, IL, 60611, USA
| | - Tim J Kruser
- Turville Bay Radiation Oncology Center, SSM Health Dean Medical Group, Madison, WI, USA
| | - Craig Horbinski
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Alexander Korutz
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Matthew Tate
- Department of Neurologic Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Sean Sachdev
- Department of Radiation Oncology, Northwestern University Feinberg School of Medicine, 676 N St. Clair St, Ste 1820, Chicago, IL, 60611, USA.
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Silvestri E, Moretto M, Facchini S, Castellaro M, Anglani M, Monai E, D’Avella D, Della Puppa A, Cecchin D, Bertoldo A, Corbetta M. Widespread cortical functional disconnection in gliomas: an individual network mapping approach. Brain Commun 2022; 4:fcac082. [PMID: 35474856 PMCID: PMC9034119 DOI: 10.1093/braincomms/fcac082] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 02/04/2022] [Accepted: 04/04/2022] [Indexed: 11/14/2022] Open
Abstract
Abstract
Assessment of impaired/preserved cortical regions in brain tumours is typically performed via intraoperative direct brain stimulation of eloquent areas or task-based functional MRI. One main limitation is that they overlook distal brain regions or networks that could be functionally impaired by the tumour.
This study aims: 1) to investigate the impact of brain tumours on the cortical synchronization of brain networks measured with resting-state functional magnetic resonance imaging (resting-state networks) both near the lesion and remotely; 2) to test whether potential changes in resting state networks correlate with cognitive status.
The sample included twenty-four glioma patients (mean age 58.1 ± 16.4y) with different pathological staging. We developed a new method for single subject localization of resting state networks abnormalities. First, we derived the spatial pattern of the main resting state networks by means of the group guided independent component analysis. This was informed by a high-resolution resting state networks template derived from an independent sample of healthy controls. Second, we developed a spatial similarity index to measure differences in network topography and strength between healthy controls and individual brain tumour patients. Next, we investigated the spatial relationship between altered networks and tumour location. Finally, multivariate analyses related cognitive scores across multiple cognitive domains (attention, language, memory, decision making) with patterns of multi-network abnormality.
We found that brain gliomas cause broad alterations of resting state networks topography that occurred mainly in structurally normal regions outside the tumour and oedema region. Cortical regions near the tumour often showed normal synchronization. Finally, multi-network abnormalities predicted attention deficits.
Overall, we present a novel method for the functional localization of resting state networks abnormalities in individual glioma patients. These abnormalities partially explain cognitive disabilities and shall be carefully navigated during surgery.
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Affiliation(s)
- Erica Silvestri
- Department of Information Engineering, University of Padova, 35131 Padova, Italy
- Padova Neuroscience Center, University of Padova, 35129 Padova, Italy
| | - Manuela Moretto
- Department of Information Engineering, University of Padova, 35131 Padova, Italy
- Padova Neuroscience Center, University of Padova, 35129 Padova, Italy
| | - Silvia Facchini
- Padova Neuroscience Center, University of Padova, 35129 Padova, Italy
- Department of Neuroscience, University of Padova, 35128 Padova, Italy
| | - Marco Castellaro
- Department of Information Engineering, University of Padova, 35131 Padova, Italy
- Padova Neuroscience Center, University of Padova, 35129 Padova, Italy
| | | | - Elena Monai
- Padova Neuroscience Center, University of Padova, 35129 Padova, Italy
- Department of Neuroscience, University of Padova, 35128 Padova, Italy
| | - Domenico D’Avella
- Department of Neuroscience, University of Padova, 35128 Padova, Italy
| | - Alessandro Della Puppa
- Neurosurgery, Department of NEUROFARBA, University Hospital of Careggi, University of Florence, 50139 Florence, Italy
| | - Diego Cecchin
- Padova Neuroscience Center, University of Padova, 35129 Padova, Italy
- Department of Medicine, Unit of Nuclear Medicine, University of Padova, 35128 Padova, Italy
| | - Alessandra Bertoldo
- Department of Information Engineering, University of Padova, 35131 Padova, Italy
- Padova Neuroscience Center, University of Padova, 35129 Padova, Italy
| | - Maurizio Corbetta
- Padova Neuroscience Center, University of Padova, 35129 Padova, Italy
- Department of Neuroscience, University of Padova, 35128 Padova, Italy
- Venetian Institute of Molecular Medicine, 35129 Padova, Italy
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14
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Dadario NB, Sughrue ME. Should Neurosurgeons Try to Preserve Non-Traditional Brain Networks? A Systematic Review of the Neuroscientific Evidence. J Pers Med 2022; 12:jpm12040587. [PMID: 35455703 PMCID: PMC9029431 DOI: 10.3390/jpm12040587] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/30/2022] [Accepted: 04/03/2022] [Indexed: 12/25/2022] Open
Abstract
The importance of large-scale brain networks in higher-order human functioning is well established in neuroscience, but has yet to deeply penetrate neurosurgical thinking due to concerns of clinical relevance. Here, we conducted the first systematic review examining the clinical importance of non-traditional, large-scale brain networks, including the default mode (DMN), central executive (CEN), salience (SN), dorsal attention (DAN), and ventral attention (VAN) networks. Studies which reported evidence of neurologic, cognitive, or emotional deficits in relation to damage or dysfunction in these networks were included. We screened 22,697 articles on PubMed, and 551 full-text articles were included and examined. Cognitive deficits were the most common symptom of network disturbances in varying amounts (36–56%), most frequently related to disruption of the DMN (n = 213) or some combination of DMN, CEN, and SN networks (n = 182). An increased proportion of motor symptoms was seen with CEN disruption (12%), and emotional (35%) or language/speech deficits (24%) with SN disruption. Disruption of the attention networks (VAN/DAN) with each other or the other networks mostly led to cognitive deficits (56%). A large body of evidence is available demonstrating the clinical importance of non-traditional, large-scale brain networks and suggests the need to preserve these networks is relevant for neurosurgical patients.
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Affiliation(s)
- Nicholas B. Dadario
- Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ 08901, USA;
| | - Michael E. Sughrue
- Centre for Minimally Invasive Neurosurgery, Prince of Wales Private Hospital, Randwick, NSW 2031, Australia
- Omniscient Neurotechnology, Sydney, NSW 2000, Australia
- Correspondence:
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15
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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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16
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Nenning KH, Langs G. Machine learning in neuroimaging: from research to clinical practice. RADIOLOGIE (HEIDELBERG, GERMANY) 2022; 62:1-10. [PMID: 36044070 PMCID: PMC9732070 DOI: 10.1007/s00117-022-01051-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 07/07/2022] [Indexed: 12/14/2022]
Abstract
Neuroimaging is critical in clinical care and research, enabling us to investigate the brain in health and disease. There is a complex link between the brain's morphological structure, physiological architecture, and the corresponding imaging characteristics. The shape, function, and relationships between various brain areas change during development and throughout life, disease, and recovery. Like few other areas, neuroimaging benefits from advanced analysis techniques to fully exploit imaging data for studying the brain and its function. Recently, machine learning has started to contribute (a) to anatomical measurements, detection, segmentation, and quantification of lesions and disease patterns, (b) to the rapid identification of acute conditions such as stroke, or (c) to the tracking of imaging changes over time. As our ability to image and analyze the brain advances, so does our understanding of its intricate relationships and their role in therapeutic decision-making. Here, we review the current state of the art in using machine learning techniques to exploit neuroimaging data for clinical care and research, providing an overview of clinical applications and their contribution to fundamental computational neuroscience.
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Affiliation(s)
- Karl-Heinz Nenning
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA
- Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Georg Langs
- Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.
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17
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Jütten K, Weninger L, Mainz V, Gauggel S, Binkofski F, Wiesmann M, Merhof D, Clusmann H, Na CH. Dissociation of structural and functional connectomic coherence in glioma patients. Sci Rep 2021; 11:16790. [PMID: 34408195 PMCID: PMC8373888 DOI: 10.1038/s41598-021-95932-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 07/31/2021] [Indexed: 01/21/2023] Open
Abstract
With diffuse infiltrative glioma being increasingly recognized as a systemic brain disorder, the macroscopically apparent tumor lesion is suggested to impact on cerebral functional and structural integrity beyond the apparent lesion site. We investigated resting-state functional connectivity (FC) and diffusion-MRI-based structural connectivity (SC) (comprising edge-weight (EW) and fractional anisotropy (FA)) in isodehydrogenase mutated (IDHmut) and wildtype (IDHwt) patients and healthy controls. SC and FC were determined for whole-brain and the Default-Mode Network (DMN), mean intra- and interhemispheric SC and FC were compared across groups, and partial correlations were analyzed intra- and intermodally. With interhemispheric EW being reduced in both patient groups, IDHwt patients showed FA decreases in the ipsi- and contralesional hemisphere, whereas IDHmut patients revealed FA increases in the contralesional hemisphere. Healthy controls showed strong intramodal connectivity, each within the structural and functional connectome. Patients however showed a loss in structural and reductions in functional connectomic coherence, which appeared to be more pronounced in IDHwt glioma patients. Findings suggest a relative dissociation of structural and functional connectomic coherence in glioma patients at the time of diagnosis, with more structural connectomic aberrations being encountered in IDHwt glioma patients. Connectomic profiling may aid in phenotyping and monitoring prognostically differing tumor types.
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Affiliation(s)
- Kerstin Jütten
- Department of Neurosurgery, RWTH Aachen University, Pauwelsstraße 30, 52074, Aachen, Germany.
| | - Leon Weninger
- Imaging and Computer Vision, RWTH Aachen University, Templergraben 55, 52074, Aachen, Germany
| | - Verena Mainz
- Institute of Medical Psychology and Medical Sociology, RWTH Aachen University, Pauwelsstraße 19, 52074, Aachen, Germany
| | - Siegfried Gauggel
- Institute of Medical Psychology and Medical Sociology, RWTH Aachen University, Pauwelsstraße 19, 52074, Aachen, Germany
| | - Ferdinand Binkofski
- Division of Clinical Cognitive Sciences, RWTH Aachen University, Pauwelsstraße 17, 52074, Aachen, Germany
| | - Martin Wiesmann
- Department of Diagnostic and Interventional Neuroradiology, RWTH Aachen University, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Dorit Merhof
- Imaging and Computer Vision, RWTH Aachen University, Templergraben 55, 52074, Aachen, Germany
| | - Hans Clusmann
- Department of Neurosurgery, RWTH Aachen University, Pauwelsstraße 30, 52074, Aachen, Germany.,Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf (CIO ABCD), Aachen, Germany
| | - Chuh-Hyoun Na
- Department of Neurosurgery, RWTH Aachen University, Pauwelsstraße 30, 52074, Aachen, Germany.,Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf (CIO ABCD), Aachen, Germany
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18
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Wang C, Van Dyk K, Cho N, Raymond C, Choi J, Salamon N, Pope WB, Lai A, Cloughesy TF, Nghiemphu PL, Ellingson BM. Characterization of cognitive function in survivors of diffuse gliomas using resting-state functional MRI (rs-fMRI). Brain Imaging Behav 2021; 16:239-251. [PMID: 34350525 PMCID: PMC8825610 DOI: 10.1007/s11682-021-00497-6] [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] [Accepted: 07/05/2021] [Indexed: 11/29/2022]
Abstract
As treatments for diffuse gliomas have advanced, survival for patients with gliomas has also increased. However, there remains limited knowledge on the relationships between brain connectivity and the lasting changes to cognitive function that glioma survivors often experience long after completing treatment. This resting-state functional magnetic resonance imaging (rs-fMRI) study explored functional connectivity (FC) alterations associated with cognitive function in survivors of gliomas. In this pilot study, 22 patients (mean age 43.8 ± 11.9) with diffuse gliomas who completed treatment within the past 10 years were evaluated using rs-fMRI and neuropsychological measures. Novel rs-fMRI analysis methods were used to account for missing brain in the resection cavity. FC relationships were assessed between cognitively impaired and non-impaired glioma patients, along with self-reported cognitive impairment, non-work daily functioning, and time with surgery. In the cognitively non-impaired patients, FC was stronger in the medial prefrontal cortex, rostral prefrontal cortex, and intraparietal sulcus compared to the impaired survivors. When examining non-work daily functioning, a positive correlation with FC was observed between the accumbens and the intracalcarine cortices, while a negative correlation with FC was observed between the parietal operculum cortex and the cerebellum. Additionally, worse self-reported cognitive impairment and worse non-work daily functioning were associated with increased FC between regions involved in cognition and sensorimotor processing. These preliminary findings suggest that neural correlates for cognitive and daily functioning in glioma patients can be revealed using rs-fMRI. Resting-state network alterations may serve as a biomarker for patients’ cognition and functioning.
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Affiliation(s)
- Chencai Wang
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA.,Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Kathleen Van Dyk
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, Semel Institute, University of California Los Angeles, Los Angeles, CA, USA
| | - Nicholas Cho
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA.,Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.,Medical Scientist Training Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA.,Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Justin Choi
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Noriko Salamon
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Whitney B Pope
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Albert Lai
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Timothy F Cloughesy
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Phioanh L Nghiemphu
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA. .,Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA. .,Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, Semel Institute, University of California Los Angeles, Los Angeles, CA, USA.
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19
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Daniel AGS, Hacker CD, Lee JJ, Dierker D, Humphries JB, Shimony JS, Leuthardt EC. Homotopic functional connectivity disruptions in glioma patients are associated with tumor malignancy and overall survival. Neurooncol Adv 2021; 3:vdab176. [PMID: 34988455 PMCID: PMC8694208 DOI: 10.1093/noajnl/vdab176] [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] [Indexed: 11/21/2022] Open
Abstract
Background Gliomas exhibit widespread bilateral functional connectivity (FC) alterations that may be associated with tumor grade. Limited studies have examined the connection-level mechanisms responsible for these effects. Given the typically strong FC observed between mirroring/homotopic brain regions in healthy subjects, we hypothesized that homotopic connectivity (HC) is altered in low-grade and high-grade glioma patients and the extent of disruption is associated with tumor grade and predictive of overall survival (OS) in a cohort of de novo high-grade glioma (World Health Organization [WHO] grade 4) patients. Methods We used a mirrored FC-derived cortical parcellation to extract blood-oxygen-level-dependent (BOLD) signals and to quantify FC differences between homotopic pairs in normal-appearing brain in a retrospective cohort of glioma patients and healthy controls. Results Fifty-nine glioma patients (WHO grade 2, n = 9; grade 4 = 50; mean age, 57.5 years) and 30 healthy subjects (mean age, 65.9 years) were analyzed. High-grade glioma patients showed lower HC compared with low-grade glioma patients and healthy controls across several cortical locations and resting-state networks. Connectivity disruptions were also strongly correlated with hemodynamic lags between homotopic regions. Finally, in high-grade glioma patients with known survival times (n = 42), HC in somatomotor and dorsal attention networks were significantly correlated with OS. Conclusions These findings demonstrate an association between tumor grade and HC alterations that may underlie global FC changes and provide prognostic information.
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Affiliation(s)
- Andy G S Daniel
- Department of Biomedical Engineering, McKelvey School of Engineering, Washington University, St. Louis, MO 63130, USA
| | - Carl D Hacker
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - John J Lee
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Donna Dierker
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Joseph B Humphries
- Department of Biomedical Engineering, McKelvey School of Engineering, Washington University, St. Louis, MO 63130, USA
| | - Joshua S Shimony
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Eric C Leuthardt
- Department of Biomedical Engineering, McKelvey School of Engineering, Washington University, St. Louis, MO 63130, USA
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Mechanical Engineering and Materials Science, McKelvey School of Engineering, Washington University, St. Louis, MO 63130, USA
- Center for Innovation in Neuroscience and Technology, Washington University School of Medicine, St. Louis, MO 63110, USA
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