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Valdes G, Scholey J, Nano TF, Gennatas ED, Mohindra P, Mohammed N, Zeng J, Kotecha R, Rosen LR, Chang J, Tsai HK, Urbanic JJ, Vargas CE, Yu NY, Ungar LH, Eaton E, Simone CB. Predicting the Effect of Proton Beam Therapy Technology on Pulmonary Toxicities for Patients With Locally Advanced Lung Cancer Enrolled in the Proton Collaborative Group Prospective Clinical Trial. Int J Radiat Oncol Biol Phys 2024; 119:66-77. [PMID: 38000701 DOI: 10.1016/j.ijrobp.2023.11.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 10/27/2023] [Accepted: 11/13/2023] [Indexed: 11/26/2023]
Abstract
PURPOSE This study aimed to predict the probability of grade ≥2 pneumonitis or dyspnea within 12 months of receiving conventionally fractionated or mildly hypofractionated proton beam therapy for locally advanced lung cancer using machine learning. METHODS AND MATERIALS Demographic and treatment characteristics were analyzed for 965 consecutive patients treated for lung cancer with conventionally fractionated or mildly hypofractionated (2.2-3 Gy/fraction) proton beam therapy across 12 institutions. Three machine learning models (gradient boosting, additive tree, and logistic regression with lasso regularization) were implemented to predict Common Terminology Criteria for Adverse Events version 4 grade ≥2 pulmonary toxicities using double 10-fold cross-validation for parameter hyper-tuning without leak of information. Balanced accuracy and area under the curve were calculated, and 95% confidence intervals were obtained using bootstrap sampling. RESULTS The median age of the patients was 70 years (range, 20-97), and they had predominantly stage IIIA or IIIB disease. They received a median dose of 60 Gy in 2 Gy/fraction, and 46.4% received concurrent chemotherapy. In total, 250 (25.9%) had grade ≥2 pulmonary toxicity. The probability of pulmonary toxicity was 0.08 for patients treated with pencil beam scanning and 0.34 for those treated with other techniques (P = 8.97e-13). Use of abdominal compression and breath hold were highly significant predictors of less toxicity (P = 2.88e-08). Higher total radiation delivered dose (P = .0182) and higher average dose to the ipsilateral lung (P = .0035) increased the likelihood of pulmonary toxicities. The gradient boosting model performed the best of the models tested, and when demographic and dosimetric features were combined, the area under the curve and balanced accuracy were 0.75 ± 0.02 and 0.67 ± 0.02, respectively. After analyzing performance versus the number of data points used for training, we observed that accuracy was limited by the number of observations. CONCLUSIONS In the largest analysis of prospectively enrolled patients with lung cancer assessing pulmonary toxicities from proton therapy to date, advanced machine learning methods revealed that pencil beam scanning, abdominal compression, and lower normal lung doses can lead to significantly lower probability of developing grade ≥2 pneumonitis or dyspnea.
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Affiliation(s)
- Gilmer Valdes
- Department of Radiation Oncology, University of California, San Francisco, California
| | - Jessica Scholey
- Department of Radiation Oncology, University of California, San Francisco, California
| | - Tomi F Nano
- Department of Radiation Oncology, University of California, San Francisco, California.
| | - Efstathios D Gennatas
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California
| | - Pranshu Mohindra
- University of Maryland School of Medicine and Maryland Proton Treatment Center, Baltimore, Maryland
| | - Nasir Mohammed
- Northwestern Medicine Chicago Proton Center, Warrenville, Illinois
| | - Jing Zeng
- University of Washington and Seattle Cancer Care Alliance Proton Therapy Center, Seattle, Washington
| | - Rupesh Kotecha
- Department of Radiation Oncology, Miami Cancer Institute, Baptist Health South Florida, Miami, Florida
| | - Lane R Rosen
- Willis-Knighton Medical Center, Shreveport, Louisiana
| | - John Chang
- Oklahoma Proton Center, Oklahoma City, Oklahoma
| | - Henry K Tsai
- New Jersey Procure Proton Therapy Center, Somerset, New Jersey
| | - James J Urbanic
- Department of Radiation Oncology, California Protons Therapy Center, San Diego, California
| | - Carlos E Vargas
- Department of Radiation Oncology, Mayo Clinic Proton Center, Phoenix, Arizona
| | - Nathan Y Yu
- Department of Radiation Oncology, Mayo Clinic Proton Center, Phoenix, Arizona
| | - Lyle H Ungar
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Eric Eaton
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Charles B Simone
- Department of Radiation Oncology, New York Proton Center, New York, New York
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Ge J, Digitale JC, Fenton C, McCulloch CE, Lai JC, Pletcher MJ, Gennatas ED. Predicting post-liver transplant outcomes in patients with acute-on-chronic liver failure using Expert-Augmented Machine Learning. Am J Transplant 2023; 23:1908-1921. [PMID: 37652176 PMCID: PMC11018271 DOI: 10.1016/j.ajt.2023.08.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 08/04/2023] [Accepted: 08/25/2023] [Indexed: 09/01/2023]
Abstract
Liver transplantation (LT) is a treatment for acute-on-chronic liver failure (ACLF), but high post-LT mortality has been reported. Existing post-LT models in ACLF have been limited. We developed an Expert-Augmented Machine Learning (EAML) model to predict post-LT outcomes. We identified ACLF patients who underwent LT in the University of California Health Data Warehouse. We applied the RuleFit machine learning (ML) algorithm to extract rules from decision trees and create intermediate models. We asked human experts to rate the rules generated by RuleFit and incorporated these ratings to generate final EAML models. We identified 1384 ACLF patients. For death at 1 year, areas under the receiver-operating characteristic curve were 0.707 (confidence interval [CI] 0.625-0.793) for EAML and 0.719 (CI 0.640-0.800) for RuleFit. For death at 90 days, areas under the receiver-operating characteristic curve were 0.678 (CI 0.581-0.776) for EAML and 0.707 (CI 0.615-0.800) for RuleFit. In pairwise comparisons, both EAML and RuleFit models outperformed cross-sectional models. Significant discrepancies between experts and ML occurred in rankings of biomarkers used in clinical practice. EAML may serve as a method for ML-guided hypothesis generation in further ACLF research.
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Affiliation(s)
- Jin Ge
- Division of Gastroenterology and Hepatology, Department of Medicine, University of California-San Francisco, San Francisco, California, USA.
| | - Jean C Digitale
- Department of Epidemiology and Biostatistics, University of California-San Francisco, San Francisco, California, USA
| | - Cynthia Fenton
- Division of Hospital Medicine, Department of Medicine, University of California-San Francisco, San Francisco, California, USA
| | - Charles E McCulloch
- Department of Epidemiology and Biostatistics, University of California-San Francisco, San Francisco, California, USA
| | - Jennifer C Lai
- Division of Gastroenterology and Hepatology, Department of Medicine, University of California-San Francisco, San Francisco, California, USA
| | - Mark J Pletcher
- Department of Epidemiology and Biostatistics, University of California-San Francisco, San Francisco, California, USA
| | - Efstathios D Gennatas
- Department of Epidemiology and Biostatistics, University of California-San Francisco, San Francisco, California, USA
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3
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Valdes G, Friedman JH, Jiang F, Gennatas ED. Representational Gradient Boosting: Backpropagation in the Space of Functions. IEEE Trans Pattern Anal Mach Intell 2022; 44:10186-10195. [PMID: 34941500 DOI: 10.1109/tpami.2021.3137715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The estimation of nested functions (i.e., functions of functions) is one of the central reasons for the success and popularity of machine learning. Today, artificial neural networks are the predominant class of algorithms in this area, known as representational learning. Here, we introduce Representational Gradient Boosting (RGB), a nonparametric algorithm that estimates functions with multi-layer architectures obtained using backpropagation in the space of functions. RGB does not need to assume a functional form in the nodes or output (e.g., linear models or rectified linear units), but rather estimates these transformations. RGB can be seen as an optimized stacking procedure where a meta algorithm learns how to combine different classes of functions (e.g., Neural Networks (NN) and Gradient Boosting (GB)), while building and optimizing them jointly in an attempt to compensate each other's weaknesses. This highlights a stark difference with current approaches to meta-learning that combine models only after they have been built independently. We showed that providing optimized stacking is one of the main advantages of RGB over current approaches. Additionally, due to the nested nature of RGB we also showed how it improves over GB in problems that have several high-order interactions. Finally, we investigate both theoretically and in practice the problem of recovering nested functions and the value of prior knowledge.
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Spinelli MA, Glidden DV, Gennatas ED, Rutherford GW, Gandhi M. Effectiveness of Adding a Mask Recommendation to Other Public Health Measures. Ann Intern Med 2021; 174:1193. [PMID: 34399080 DOI: 10.7326/l21-0397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Affiliation(s)
| | - David V Glidden
- University of California, San Francisco, San Francisco, California
| | | | | | - Monica Gandhi
- University of California, San Francisco, San Francisco, California
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5
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Spinelli MA, Glidden DV, Gennatas ED, Bielecki M, Beyrer C, Rutherford G, Chambers H, Goosby E, Gandhi M. Importance of non-pharmaceutical interventions in lowering the viral inoculum to reduce susceptibility to infection by SARS-CoV-2 and potentially disease severity. Lancet Infect Dis 2021; 21:e296-e301. [PMID: 33631099 PMCID: PMC7906703 DOI: 10.1016/s1473-3099(20)30982-8] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 12/03/2020] [Accepted: 12/09/2020] [Indexed: 01/01/2023]
Abstract
Adherence to non-pharmaceutical interventions to prevent the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been highly variable across settings, particularly in the USA. In this Personal View, we review data supporting the importance of the viral inoculum (the dose of viral particles from an infected source over time) in increasing the probability of infection in respiratory, gastrointestinal, and sexually transmitted viral infections in humans. We also review the available evidence linking the relationship of the viral inoculum to disease severity. Non-pharmaceutical interventions might reduce the susceptibility to SARS-CoV-2 infection by reducing the viral inoculum when there is exposure to an infectious source. Data from physical sciences research suggest that masks protect the wearer by filtering virus from external sources, and others by reducing expulsion of virus by the wearer. Social distancing, handwashing, and improved ventilation also reduce the exposure amount of viral particles from an infectious source. Maintaining and increasing non-pharmaceutical interventions can help to quell SARS-CoV-2 as we enter the second year of the pandemic. Finally, we argue that even as safe and effective vaccines are being rolled out, non-pharmaceutical interventions will continue to play an essential role in suppressing SARS-CoV-2 transmission until equitable and widespread vaccine administration has been completed.
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Affiliation(s)
- Matthew A Spinelli
- Division of HIV, Infectious Diseases, and Global Medicine, Department of Medicine, University of California, San Francisco, CA, USA
| | - David V Glidden
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Efstathios D Gennatas
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Michel Bielecki
- Swiss Armed Forces, Medical Services, Ittigen, Switzerland; Travel Clinic, Institute for Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Chris Beyrer
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - George Rutherford
- Division of HIV, Infectious Diseases, and Global Medicine, Department of Medicine, University of California, San Francisco, CA, USA
| | - Henry Chambers
- Division of HIV, Infectious Diseases, and Global Medicine, Department of Medicine, University of California, San Francisco, CA, USA
| | - Eric Goosby
- Division of HIV, Infectious Diseases, and Global Medicine, Department of Medicine, University of California, San Francisco, CA, USA
| | - Monica Gandhi
- Division of HIV, Infectious Diseases, and Global Medicine, Department of Medicine, University of California, San Francisco, CA, USA.
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Alvarez JB, Bibault JE, Burgun A, Cai J, Cao Z, Chang K, Chen JH, Chen WC, Cho M, Cho PJ, Cornish TC, Costa A, Dekker A, Drukker K, Dunn J, Eminaga O, Erickson BJ, Fournier L, Gambhir SS, Gennatas ED, Giger ML, Halilaj I, Harrison AP, He B, Hong JC, Jin D, Jin MC, Jochems A, Kalpathy-Cramer J, Kapp DS, Karimzadeh M, Karnes W, Lambin P, Langlotz CP, Lee J, Li H, Liao JC, Lin AL, Lin RY, Liu Y, Lu L, Magnus D, McIntosh C, Miao S, Min JK, Neill DB, Oermann EK, Ouyang D, Peng L, Phene S, Poirot MG, Quon JL, Ranti D, Rao A, Raskar R, Rombaoa C, Rubin DL, Samarasena J, Seekins J, Seetharam K, Shearer E, Sibley A, Singh K, Singh P, Sordo M, Suraweera D, Valliani AAA, van Wijk Y, Vepakomma P, Wang B, Wang G, Wang N, Wang Y, Warner E, Welch M, Wong K, Wu Z, Xing F, Xing L, Yan K, Yan P, Yang L, Yeom KW, Zachariah R, Zeng D, Zhang L, Zhang L, Zhang X, Zhou L, Zou J. List of contributors. Artif Intell Med 2021. [DOI: 10.1016/b978-0-12-821259-2.00035-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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8
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Gur RC, Butler ER, Moore TM, Rosen AFG, Ruparel K, Satterthwaite TD, Roalf DR, Gennatas ED, Bilker WB, Shinohara RT, Port A, Elliott MA, Verma R, Davatzikos C, Wolf DH, Detre JA, Gur RE. Structural and Functional Brain Parameters Related to Cognitive Performance Across Development: Replication and Extension of the Parieto-Frontal Integration Theory in a Single Sample. Cereb Cortex 2020; 31:1444-1463. [PMID: 33119049 DOI: 10.1093/cercor/bhaa282] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 07/16/2020] [Accepted: 08/24/2020] [Indexed: 02/06/2023] Open
Abstract
The parieto-frontal integration theory (PFIT) identified a fronto-parietal network of regions where individual differences in brain parameters most strongly relate to cognitive performance. PFIT was supported and extended in adult samples, but not in youths or within single-scanner well-powered multimodal studies. We performed multimodal neuroimaging in 1601 youths age 8-22 on the same 3-Tesla scanner with contemporaneous neurocognitive assessment, measuring volume, gray matter density (GMD), mean diffusivity (MD), cerebral blood flow (CBF), resting-state functional magnetic resonance imaging measures of the amplitude of low frequency fluctuations (ALFFs) and regional homogeneity (ReHo), and activation to a working memory and a social cognition task. Across age and sex groups, better performance was associated with higher volumes, greater GMD, lower MD, lower CBF, higher ALFF and ReHo, and greater activation for the working memory task in PFIT regions. However, additional cortical, striatal, limbic, and cerebellar regions showed comparable effects, hence PFIT needs expansion into an extended PFIT (ExtPFIT) network incorporating nodes that support motivation and affect. Associations of brain parameters became stronger with advancing age group from childhood to adolescence to young adulthood, effects occurring earlier in females. This ExtPFIT network is developmentally fine-tuned, optimizing abundance and integrity of neural tissue while maintaining a low resting energy state.
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Affiliation(s)
- Ruben C Gur
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.,Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.,Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Ellyn R Butler
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Tyler M Moore
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Adon F G Rosen
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Kosha Ruparel
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Theodore D Satterthwaite
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - David R Roalf
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Efstathios D Gennatas
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Warren B Bilker
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.,Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Allison Port
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Mark A Elliott
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.,Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Ragini Verma
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Christos Davatzikos
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Daniel H Wolf
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - John A Detre
- Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Raquel E Gur
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.,Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.,Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
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9
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Morin O, Chen WC, Nassiri F, Susko M, Magill ST, Vasudevan HN, Wu A, Vallières M, Gennatas ED, Valdes G, Pekmezci M, Alcaide-Leon P, Choudhury A, Interian Y, Mortezavi S, Turgutlu K, Bush NAO, Solberg TD, Braunstein SE, Sneed PK, Perry A, Zadeh G, McDermott MW, Villanueva-Meyer JE, Raleigh DR. Integrated models incorporating radiologic and radiomic features predict meningioma grade, local failure, and overall survival. Neurooncol Adv 2019; 1:vdz011. [PMID: 31608329 PMCID: PMC6777505 DOI: 10.1093/noajnl/vdz011] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Background We investigated prognostic models based on clinical, radiologic, and radiomic feature to preoperatively identify meningiomas at risk for poor outcomes. Methods Retrospective review was performed for 303 patients who underwent resection of 314 meningiomas (57% World Health Organization grade I, 35% grade II, and 8% grade III) at two independent institutions, which comprised primary and external datasets. For each patient in the primary dataset, 16 radiologic and 172 radiomic features were extracted from preoperative magnetic resonance images, and prognostic features for grade, local failure (LF) or overall survival (OS) were identified using the Kaplan–Meier method, log-rank tests and recursive partitioning analysis. Regressions and random forests were used to generate and test prognostic models, which were validated using the external dataset. Results Multivariate analysis revealed that apparent diffusion coefficient hypointensity (HR 5.56, 95% CI 2.01–16.7, P = .002) was associated with high grade meningioma, and low sphericity was associated both with increased LF (HR 2.0, 95% CI 1.1–3.5, P = .02) and worse OS (HR 2.94, 95% CI 1.47–5.56, P = .002). Both radiologic and radiomic predictors of adverse meningioma outcomes were significantly associated with molecular markers of aggressive meningioma biology, such as somatic mutation burden, DNA methylation status, and FOXM1 expression. Integrated prognostic models combining clinical, radiologic, and radiomic features demonstrated improved accuracy for meningioma grade, LF, and OS (area under the curve 0.78, 0.75, and 0.78, respectively) compared to models based on clinical features alone. Conclusions Preoperative radiologic and radiomic features such as apparent diffusion coefficient and sphericity can predict tumor grade, LF, and OS in patients with meningioma.
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Affiliation(s)
- Olivier Morin
- Department of Radiation Oncology, University of California San Francisco, California
| | - William C Chen
- Department of Radiation Oncology, University of California San Francisco, California
| | - Farshad Nassiri
- Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Matthew Susko
- Department of Radiation Oncology, University of California San Francisco, California
| | - Stephen T Magill
- Department of Neurological Surgery, University of California San Francisco, California
| | - Harish N Vasudevan
- Department of Radiation Oncology, University of California San Francisco, California
| | - Ashley Wu
- Department of Radiation Oncology, University of California San Francisco, California
| | - Martin Vallières
- Department of Radiation Oncology, University of California San Francisco, California
| | - Efstathios D Gennatas
- Department of Radiation Oncology, University of California San Francisco, California
| | - Gilmer Valdes
- Department of Radiation Oncology, University of California San Francisco, California
| | - Melike Pekmezci
- Department of Pathology, University of California San Francisco, California
| | - Paula Alcaide-Leon
- Department of Radiology and Biomedical Imaging, University of California San Francisco, California
| | - Abrar Choudhury
- Department of Radiation Oncology, University of California San Francisco, California.,Department of Neurological Surgery, University of California San Francisco, California
| | - Yannet Interian
- Department of Radiation Oncology, University of California San Francisco, California
| | - Siavash Mortezavi
- Department of Radiation Oncology, University of California San Francisco, California
| | - Kerem Turgutlu
- Department of Radiation Oncology, University of California San Francisco, California
| | | | - Timothy D Solberg
- Department of Radiation Oncology, University of California San Francisco, California
| | - Steve E Braunstein
- Department of Radiation Oncology, University of California San Francisco, California
| | - Penny K Sneed
- Department of Radiation Oncology, University of California San Francisco, California
| | - Arie Perry
- Department of Pathology, University of California San Francisco, California.,Department of Neurological Surgery, University of California San Francisco, California
| | - Gelareh Zadeh
- Department of Radiation Oncology, University of California San Francisco, California
| | - Michael W McDermott
- Department of Neurological Surgery, University of California San Francisco, California
| | | | - David R Raleigh
- Department of Radiation Oncology, University of California San Francisco, California.,Department of Neurological Surgery, University of California San Francisco, California
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Butler PM, Chiong W, Perry DC, Miller ZA, Gennatas ED, Brown JA, Pasquini L, Karydas A, Dokuru D, Coppola G, Sturm VE, Boxer AL, Gorno-Tempini ML, Rosen HJ, Kramer JH, Miller BL, Seeley WW. Dopamine receptor D 4 (DRD 4) polymorphisms with reduced functional potency intensify atrophy in syndrome-specific sites of frontotemporal dementia. Neuroimage Clin 2019; 23:101822. [PMID: 31003069 PMCID: PMC6475809 DOI: 10.1016/j.nicl.2019.101822] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Revised: 04/04/2019] [Accepted: 04/09/2019] [Indexed: 11/23/2022]
Abstract
OBJECTIVE We aimed to understand the impact of dopamine receptor D4 (DRD4) polymorphisms on neurodegeneration in patients with dementia. We hypothesized that DRD4dampened-variants with reduced functional potency would be associated with greater atrophy in regions with higher receptor density. Given that DRD4 is concentrated in anterior regions of the limbic and cortical forebrain we anticipated genotype effects in patients with a more rostral pattern of neurodegeneration. METHODS 337 subjects, including healthy controls, patients with Alzheimer's disease (AD) and frontotemporal dementia (FTD) underwent genotyping, structural MRI, and cognitive/behavioral testing. We conducted whole-brain voxel-based morphometry to examine the relationship between DRD4 genotypes and brain atrophy patterns within and across groups. General linear modeling was used to evaluate relationships between genotype and cognitive/behavioral measures. RESULTS DRD4 dampened-variants predicted gray matter atrophy in disease-specific regions of FTD in anterior cingulate, ventromedial prefrontal, orbitofrontal and insular cortices on the right greater than the left. Genotype predicted greater apathy and repetitive motor disturbance in patients with FTD. These results covaried with frontoinsular cortical atrophy. Peak atrophy patterned along regions of neuroanatomic vulnerability in FTD-spectrum disorders. In AD subjects and controls, genotype did not impact gray matter intensity. CONCLUSIONS We conclude that DRD4 polymorphisms with reduced functional potency exacerbate neuronal injury in sites of higher receptor density, which intersect with syndrome-specific regions undergoing neurodegeneration in FTD.
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Affiliation(s)
- P M Butler
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA.
| | - W Chiong
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - D C Perry
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - Z A Miller
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - E D Gennatas
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - J A Brown
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - L Pasquini
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - A Karydas
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - D Dokuru
- Departments of Psychiatry and Neurology, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA
| | - G Coppola
- Departments of Psychiatry and Neurology, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA
| | - V E Sturm
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - A L Boxer
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - M L Gorno-Tempini
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - H J Rosen
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - J H Kramer
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - B L Miller
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - W W Seeley
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
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Vandekar SN, Shou H, Satterthwaite TD, Shinohara RT, Merikangas AK, Roalf DR, Ruparel K, Rosen A, Gennatas ED, Elliott MA, Davatzikos C, Gur RC, Gur RE, Detre JA. Sex differences in estimated brain metabolism in relation to body growth through adolescence. J Cereb Blood Flow Metab 2019; 39:524-535. [PMID: 29072856 PMCID: PMC6421255 DOI: 10.1177/0271678x17737692] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
The human brain consumes a disproportionate amount of the body's overall metabolic resources, and evidence suggests that brain and body may compete for substrate during development. Using perfusion MRI from a large cross-sectional cohort, we examined developmental changes of MRI-derived estimates of brain metabolism, in relation to weight change. Nonlinear models demonstrated that, in childhood, changes in body weight were inversely related to developmental age-related changes in brain metabolism. This inverse relationship persisted through early adolescence, after which body and brain metabolism began to decline. Females achieved maximum body growth approximately two years earlier than males, with a correspondingly earlier stabilization of brain metabolism to adult levels. These findings confirm prior findings with positron emission tomography performed in a much smaller cohort, demonstrate that relative brain metabolism can be inferred from noninvasive MRI data, and extend observations on the associations between body growth and brain metabolism to sex differences through adolescence.
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Affiliation(s)
- Simon N Vandekar
- 1 Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Haochang Shou
- 1 Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Russell T Shinohara
- 1 Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Alison K Merikangas
- 2 Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - David R Roalf
- 2 Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Kosha Ruparel
- 2 Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Adon Rosen
- 2 Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Mark A Elliott
- 3 Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- 3 Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Ruben C Gur
- 2 Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA.,3 Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.,4 Philadelphia Veterans Administration Medical Center, Philadelphia, PA, USA.,5 Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Raquel E Gur
- 2 Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA.,3 Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.,5 Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - John A Detre
- 3 Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.,5 Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
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12
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Rosen AFG, Roalf DR, Ruparel K, Blake J, Seelaus K, Villa LP, Ciric R, Cook PA, Davatzikos C, Elliott MA, Garcia de La Garza A, Gennatas ED, Quarmley M, Schmitt JE, Shinohara RT, Tisdall MD, Craddock RC, Gur RE, Gur RC, Satterthwaite TD. Quantitative assessment of structural image quality. Neuroimage 2017; 169:407-418. [PMID: 29278774 DOI: 10.1016/j.neuroimage.2017.12.059] [Citation(s) in RCA: 187] [Impact Index Per Article: 26.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Revised: 12/12/2017] [Accepted: 12/19/2017] [Indexed: 12/21/2022] Open
Abstract
Data quality is increasingly recognized as one of the most important confounding factors in brain imaging research. It is particularly important for studies of brain development, where age is systematically related to in-scanner motion and data quality. Prior work has demonstrated that in-scanner head motion biases estimates of structural neuroimaging measures. However, objective measures of data quality are not available for most structural brain images. Here we sought to identify quantitative measures of data quality for T1-weighted volumes, describe how these measures relate to cortical thickness, and delineate how this in turn may bias inference regarding associations with age in youth. Three highly-trained raters provided manual ratings of 1840 raw T1-weighted volumes. These images included a training set of 1065 images from Philadelphia Neurodevelopmental Cohort (PNC), a test set of 533 images from the PNC, as well as an external test set of 242 adults acquired on a different scanner. Manual ratings were compared to automated quality measures provided by the Preprocessed Connectomes Project's Quality Assurance Protocol (QAP), as well as FreeSurfer's Euler number, which summarizes the topological complexity of the reconstructed cortical surface. Results revealed that the Euler number was consistently correlated with manual ratings across samples. Furthermore, the Euler number could be used to identify images scored "unusable" by human raters with a high degree of accuracy (AUC: 0.98-0.99), and out-performed proxy measures from functional timeseries acquired in the same scanning session. The Euler number also was significantly related to cortical thickness in a regionally heterogeneous pattern that was consistent across datasets and replicated prior results. Finally, data quality both inflated and obscured associations with age during adolescence. Taken together, these results indicate that reliable measures of data quality can be automatically derived from T1-weighted volumes, and that failing to control for data quality can systematically bias the results of studies of brain maturation.
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Affiliation(s)
- Adon F G Rosen
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA, USA
| | - David R Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA, USA
| | - Kosha Ruparel
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA, USA
| | - Jason Blake
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA, USA
| | - Kevin Seelaus
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA, USA
| | - Lakshmi P Villa
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA, USA
| | - Rastko Ciric
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA, USA
| | - Philip A Cook
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA, USA
| | - Christos Davatzikos
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia PA, USA
| | - Mark A Elliott
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA, USA
| | - Angel Garcia de La Garza
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA, USA
| | - Efstathios D Gennatas
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA, USA
| | - Megan Quarmley
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA, USA
| | - J Eric Schmitt
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia PA, USA
| | - M Dylan Tisdall
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA, USA
| | - R Cameron Craddock
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA; Department of Diagnostic Medicine, University of Texas at Austin, Austin TX, USA
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA, USA
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA, USA.
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13
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Kaczkurkin AN, Moore TM, Ruparel K, Ciric R, Calkins ME, Shinohara RT, Elliott MA, Hopson R, Roalf DR, Vandekar SN, Gennatas ED, Wolf DH, Scott JC, Pine DS, Leibenluft E, Detre JA, Foa EB, Gur RE, Gur RC, Satterthwaite TD. Elevated Amygdala Perfusion Mediates Developmental Sex Differences in Trait Anxiety. Biol Psychiatry 2016; 80:775-785. [PMID: 27395327 PMCID: PMC5074881 DOI: 10.1016/j.biopsych.2016.04.021] [Citation(s) in RCA: 74] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2015] [Revised: 04/15/2016] [Accepted: 04/18/2016] [Indexed: 01/04/2023]
Abstract
BACKGROUND Adolescence is a critical period for emotional maturation and is a time when clinically significant symptoms of anxiety and depression increase, particularly in females. However, few studies relate developmental differences in symptoms of anxiety and depression to brain development. Cerebral blood flow is one brain phenotype that is known to have marked developmental sex differences. METHODS We investigated whether developmental sex differences in cerebral blood flow mediated sex differences in anxiety and depression symptoms by capitalizing on a large sample of 875 youths who completed cross-sectional imaging as part of the Philadelphia Neurodevelopmental Cohort. Perfusion was quantified on a voxelwise basis using arterial spin-labeled magnetic resonance imaging at 3T. Perfusion images were related to trait and state anxiety using general additive models with penalized splines, while controlling for gray matter density on a voxelwise basis. Clusters found to be related to anxiety were evaluated for interactions with age, sex, and puberty. RESULTS Trait anxiety was associated with elevated perfusion in a network of regions including the amygdala, anterior insula, and fusiform cortex, even after accounting for prescan state anxiety. Notably, these relationships strengthened with age and the transition through puberty. Moreover, higher trait anxiety in postpubertal females was mediated by elevated perfusion of the left amygdala. CONCLUSIONS Taken together, these results demonstrate that differences in the evolution of cerebral perfusion during adolescence may be a critical element of the affective neurobiological characteristics underlying sex differences in anxiety and mood symptoms.
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Affiliation(s)
- Antonia N Kaczkurkin
- Departments of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Tyler M Moore
- Departments of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Kosha Ruparel
- Departments of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Rastko Ciric
- Departments of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Monica E Calkins
- Departments of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Russell T Shinohara
- Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Mark A Elliott
- Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Ryan Hopson
- Departments of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - David R Roalf
- Departments of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Simon N Vandekar
- Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Efstathios D Gennatas
- Departments of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Daniel H Wolf
- Departments of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - J Cobb Scott
- Departments of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania; Philadelphia Veterans Administration Medical Center, Philadelphia, Pennsylvania
| | - Daniel S Pine
- Emotion and Development Branch, Intramural Research Program, National Institute of Mental Health, Bethesda, Maryland
| | - Ellen Leibenluft
- Emotion and Development Branch, Intramural Research Program, National Institute of Mental Health, Bethesda, Maryland
| | - John A Detre
- Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania; Neurology,University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Edna B Foa
- Departments of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Raquel E Gur
- Departments of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania; Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Ruben C Gur
- Departments of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania; Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania; Emotion and Development Branch, Intramural Research Program, National Institute of Mental Health, Bethesda, Maryland
| | - Theodore D Satterthwaite
- Departments of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.
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14
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Aguirre GK, Datta R, Benson NC, Prasad S, Jacobson SG, Cideciyan AV, Bridge H, Watkins KE, Butt OH, Dain AS, Brandes L, Gennatas ED. Patterns of Individual Variation in Visual Pathway Structure and Function in the Sighted and Blind. PLoS One 2016; 11:e0164677. [PMID: 27812129 PMCID: PMC5094697 DOI: 10.1371/journal.pone.0164677] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2016] [Accepted: 09/28/2016] [Indexed: 11/18/2022] Open
Abstract
Many structural and functional brain alterations accompany blindness, with substantial individual variation in these effects. In normally sighted people, there is correlated individual variation in some visual pathway structures. Here we examined if the changes in brain anatomy produced by blindness alter the patterns of anatomical variation found in the sighted. We derived eight measures of central visual pathway anatomy from a structural image of the brain from 59 sighted and 53 blind people. These measures showed highly significant differences in mean size between the sighted and blind cohorts. When we examined the measurements across individuals within each group we found three clusters of correlated variation, with V1 surface area and pericalcarine volume linked, and independent of the thickness of V1 cortex. These two clusters were in turn relatively independent of the volumes of the optic chiasm and lateral geniculate nucleus. This same pattern of variation in visual pathway anatomy was found in the sighted and the blind. Anatomical changes within these clusters were graded by the timing of onset of blindness, with those subjects with a post-natal onset of blindness having alterations in brain anatomy that were intermediate to those seen in the sighted and congenitally blind. Many of the blind and sighted subjects also contributed functional MRI measures of cross-modal responses within visual cortex, and a diffusion tensor imaging measure of fractional anisotropy within the optic radiations and the splenium of the corpus callosum. We again found group differences between the blind and sighted in these measures. The previously identified clusters of anatomical variation were also found to be differentially related to these additional measures: across subjects, V1 cortical thickness was related to cross-modal activation, and the volume of the optic chiasm and lateral geniculate was related to fractional anisotropy in the visual pathway. Our findings show that several of the structural and functional effects of blindness may be reduced to a smaller set of dimensions. It also seems that the changes in the brain that accompany blindness are on a continuum with normal variation found in the sighted.
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Affiliation(s)
- Geoffrey K. Aguirre
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- * E-mail:
| | - Ritobrato Datta
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Noah C. Benson
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Sashank Prasad
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Samuel G. Jacobson
- Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Artur V. Cideciyan
- Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Holly Bridge
- FMRIB Centre, Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, United Kingdom
| | - Kate E. Watkins
- Department of Experimental Psychology, University of Oxford, Oxford, OX1 3UD, United Kingdom
| | - Omar H. Butt
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Aleksandra S. Dain
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Lauren Brandes
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Efstathios D. Gennatas
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
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15
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Shanmugan S, Wolf DH, Calkins ME, Moore TM, Ruparel K, Hopson RD, Vandekar SN, Roalf DR, Elliott MA, Jackson C, Gennatas ED, Leibenluft E, Pine DS, Shinohara RT, Hakonarson H, Gur RC, Gur RE, Satterthwaite TD. Common and Dissociable Mechanisms of Executive System Dysfunction Across Psychiatric Disorders in Youth. Am J Psychiatry 2016; 173:517-26. [PMID: 26806874 PMCID: PMC4886342 DOI: 10.1176/appi.ajp.2015.15060725] [Citation(s) in RCA: 148] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
OBJECTIVE Disruption of executive function is present in many neuropsychiatric disorders. However, determining the specificity of executive dysfunction across these disorders is challenging given high comorbidity of conditions. Here the authors investigate executive system deficits in association with dimensions of psychiatric symptoms in youth using a working memory paradigm. The authors hypothesize that common and dissociable patterns of dysfunction would be present. METHOD The authors studied 1,129 youths who completed a fractal n-back task during functional magnetic resonance imaging at 3-T as part of the Philadelphia Neurodevelopmental Cohort. Factor scores of clinical psychopathology were calculated using an item-wise confirmatory bifactor model, describing overall psychopathology as well as four orthogonal dimensions of symptoms: anxious-misery (mood and anxiety), behavioral disturbance (attention deficit hyperactivity disorder and conduct disorder), psychosis-spectrum symptoms, and fear (phobias). The effect of psychopathology dimensions on behavioral performance and executive system recruitment (2-back > 0-back) was examined using both multivariate (matrix regression) and mass-univariate (linear regression) analyses. RESULTS Overall psychopathology was associated with both abnormal multivariate patterns of activation and a failure to activate executive regions within the cingulo-opercular control network, including the frontal pole, cingulate cortex, and anterior insula. In addition, psychosis-spectrum symptoms were associated with hypoactivation of the left dorsolateral prefrontal cortex, whereas behavioral symptoms were associated with hypoactivation of the frontoparietal cortex and cerebellum. In contrast, anxious-misery symptoms were associated with widespread hyperactivation of the executive network. CONCLUSIONS These findings provide novel evidence that common and dissociable deficits within the brain's executive system are present in association with dimensions of psychopathology in youth.
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Affiliation(s)
- Sheila Shanmugan
- Department of Psychiatry, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Daniel H. Wolf
- Department of Psychiatry, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Monica E. Calkins
- Department of Psychiatry, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Tyler M. Moore
- Department of Psychiatry, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Kosha Ruparel
- Department of Psychiatry, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Ryan D. Hopson
- Department of Psychiatry, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Simon N. Vandekar
- Department of Psychiatry, University of Pennsylvania, Philadelphia PA 19104, USA,Department of Biostatistics and Epidemiology, University of Pennsylvania
| | - David R. Roalf
- Department of Psychiatry, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Mark A. Elliott
- Department of Radiology, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Chad Jackson
- Department of Psychiatry, University of Pennsylvania, Philadelphia PA 19104, USA
| | | | - Ellen Leibenluft
- National Institutes of Mental Health Mood and Anxiety Disorders Program,5 Bethesda, MD 20892
| | - Daniel S. Pine
- National Institutes of Mental Health Mood and Anxiety Disorders Program,5 Bethesda, MD 20892
| | | | - Hakon Hakonarson
- Center for Applied Genomics, Children’s Hospital of Philadelphia 19104,Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ruben C. Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia PA 19104, USA,Department of Biostatistics and Epidemiology, University of Pennsylvania,Philadelphia Veterans Administration Medical Center, Philadelphia PA 19104, USA
| | - Raquel E. Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia PA 19104, USA,Department of Biostatistics and Epidemiology, University of Pennsylvania
| | - Theodore D. Satterthwaite
- Department of Psychiatry, University of Pennsylvania, Philadelphia PA 19104, USA,Corresponding Author: Theodore D. Satterthwaite, 10th Floor Gates Bldg., Hospital of the University of Pennsylvania, 3400 Spruce St., Philadelphia, PA 19104. , Voice: (215) 662-7119; Fax: (215) 662-7903
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16
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Roalf DR, Quarmley M, Elliott MA, Satterthwaite TD, Vandekar SN, Ruparel K, Gennatas ED, Calkins ME, Moore TM, Hopson R, Prabhakaran K, Jackson CT, Verma R, Hakonarson H, Gur RC, Gur RE. The impact of quality assurance assessment on diffusion tensor imaging outcomes in a large-scale population-based cohort. Neuroimage 2016; 125:903-919. [PMID: 26520775 PMCID: PMC4753778 DOI: 10.1016/j.neuroimage.2015.10.068] [Citation(s) in RCA: 148] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2015] [Revised: 10/19/2015] [Accepted: 10/24/2015] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Diffusion tensor imaging (DTI) is applied in investigation of brain biomarkers for neurodevelopmental and neurodegenerative disorders. However, the quality of DTI measurements, like other neuroimaging techniques, is susceptible to several confounding factors (e.g., motion, eddy currents), which have only recently come under scrutiny. These confounds are especially relevant in adolescent samples where data quality may be compromised in ways that confound interpretation of maturation parameters. The current study aims to leverage DTI data from the Philadelphia Neurodevelopmental Cohort (PNC), a sample of 1601 youths with ages of 8-21 who underwent neuroimaging, to: 1) establish quality assurance (QA) metrics for the automatic identification of poor DTI image quality; 2) examine the performance of these QA measures in an external validation sample; 3) document the influence of data quality on developmental patterns of typical DTI metrics. METHODS All diffusion-weighted images were acquired on the same scanner. Visual QA was performed on all subjects completing DTI; images were manually categorized as Poor, Good, or Excellent. Four image quality metrics were automatically computed and used to predict manual QA status: Mean voxel intensity outlier count (MEANVOX), Maximum voxel intensity outlier count (MAXVOX), mean relative motion (MOTION) and temporal signal-to-noise ratio (TSNR). Classification accuracy for each metric was calculated as the area under the receiver-operating characteristic curve (AUC). A threshold was generated for each measure that best differentiated visual QA status and applied in a validation sample. The effects of data quality on sensitivity to expected age effects in this developmental sample were then investigated using the traditional MRI diffusion metrics: fractional anisotropy (FA) and mean diffusivity (MD). Finally, our method of QA is compared with DTIPrep. RESULTS TSNR (AUC=0.94) best differentiated Poor data from Good and Excellent data. MAXVOX (AUC=0.88) best differentiated Good from Excellent DTI data. At the optimal threshold, 88% of Poor data and 91% Good/Excellent data were correctly identified. Use of these thresholds on a validation dataset (n=374) indicated high accuracy. In the validation sample 83% of Poor data and 94% of Excellent data was identified using thresholds derived from the training sample. Both FA and MD were affected by the inclusion of poor data in an analysis of an age, sex and race matched comparison sample. In addition, we show that the inclusion of poor data results in significant attenuation of the correlation between diffusion metrics (FA and MD) and age during a critical neurodevelopmental period. We find higher correspondence between our QA method and DTIPrep for Poor data, but we find our method to be more robust for apparently high-quality images. CONCLUSION Automated QA of DTI can facilitate large-scale, high-throughput quality assurance by reliably identifying both scanner and subject induced imaging artifacts. The results present a practical example of the confounding effects of artifacts on DTI analysis in a large population-based sample, and suggest that estimates of data quality should not only be reported but also accounted for in data analysis, especially in studies of development.
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Affiliation(s)
- David R Roalf
- Neuropsychiatry Section, Department of Psychiatry, USA.
| | | | - Mark A Elliott
- Department of Radiology, University of Pennsylvania, Perelman School of Medicine, USA
| | | | - Simon N Vandekar
- Neuropsychiatry Section, Department of Psychiatry, USA; Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kosha Ruparel
- Neuropsychiatry Section, Department of Psychiatry, USA
| | | | | | - Tyler M Moore
- Neuropsychiatry Section, Department of Psychiatry, USA
| | - Ryan Hopson
- Neuropsychiatry Section, Department of Psychiatry, USA
| | | | | | - Ragini Verma
- Department of Radiology, University of Pennsylvania, Perelman School of Medicine, USA; Section of Biomedical Image Analysis, University of Pennsylvania, USA
| | - Hakon Hakonarson
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Ruben C Gur
- Neuropsychiatry Section, Department of Psychiatry, USA; Department of Radiology, University of Pennsylvania, Perelman School of Medicine, USA
| | - Raquel E Gur
- Neuropsychiatry Section, Department of Psychiatry, USA; Department of Radiology, University of Pennsylvania, Perelman School of Medicine, USA
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17
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Satterthwaite TD, Wolf DH, Roalf DR, Ruparel K, Erus G, Vandekar S, Gennatas ED, Elliott MA, Smith A, Hakonarson H, Verma R, Davatzikos C, Gur RE, Gur RC. Linked Sex Differences in Cognition and Functional Connectivity in Youth. Cereb Cortex 2015; 25:2383-94. [PMID: 24646613 PMCID: PMC4537416 DOI: 10.1093/cercor/bhu036] [Citation(s) in RCA: 202] [Impact Index Per Article: 22.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Sex differences in human cognition are marked, but little is known regarding their neural origins. Here, in a sample of 674 human participants ages 9-22, we demonstrate that sex differences in cognitive profiles are related to multivariate patterns of resting-state functional connectivity MRI (rsfc-MRI). Males outperformed females on motor and spatial cognitive tasks; females were faster in tasks of emotion identification and nonverbal reasoning. Sex differences were also prominent in the rsfc-MRI data at multiple scales of analysis, with males displaying more between-module connectivity, while females demonstrated more within-module connectivity. Multivariate pattern analysis using support vector machines classified subject sex on the basis of their cognitive profile with 63% accuracy (P < 0.001), but was more accurate using functional connectivity data (71% accuracy; P < 0.001). Moreover, the degree to which a given participant's cognitive profile was "male" or "female" was significantly related to the masculinity or femininity of their pattern of brain connectivity (P = 2.3 × 10(-7)). This relationship was present even when considering males and female separately. Taken together, these results demonstrate for the first time that sex differences in patterns of cognition are in part represented on a neural level through divergent patterns of brain connectivity.
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Affiliation(s)
| | - Daniel H Wolf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - David R Roalf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kosha Ruparel
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Guray Erus
- Department of Radiology, Perelman Scholl of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Simon Vandekar
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | | | - Mark A Elliott
- Department of Radiology, Perelman Scholl of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Alex Smith
- Department of Radiology, Perelman Scholl of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Hakon Hakonarson
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Ragini Verma
- Department of Radiology, Perelman Scholl of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Christos Davatzikos
- Department of Radiology, Perelman Scholl of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA Department of Radiology, Perelman Scholl of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ruben C Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA Department of Radiology, Perelman Scholl of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA Philadelphia Veterans Administration Medical Center, Philadelphia, PA 19104, USA
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Zhou J, Miller BL, Gennatas ED, Kramer JH, Seeley WW. F2‐02‐02: PREDICTING REGIONAL NEURODEGENERATION FROM THE HEALTHY BRAIN CONNECTOME. Alzheimers Dement 2014. [DOI: 10.1016/j.jalz.2014.04.142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Juan Zhou
- Duke‐NUS Graduate Medical School, National University of SingaporeSingaporeSingapore
| | - Bruce L. Miller
- Memory & Aging Center, Department of NeurologyUniversity of California, San FranciscoSan FranciscoCaliforniaUnited States
| | - Efstathios D. Gennatas
- Memory & Aging Center, Department of NeurologyUniversity of California, San FranciscoSan FranciscoCaliforniaUnited States
| | - Joel H. Kramer
- Memory & Aging Center, Department of NeurologyUniversity of California, San FranciscoSan FranciscoCaliforniaUnited States
| | - William W. Seeley
- Memory & Aging Center, Department of NeurologyUniversity of California, San FranciscoSan FranciscoCaliforniaUnited States
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Satterthwaite TD, Wolf DH, Ruparel K, Erus G, Elliott MA, Eickhoff SB, Gennatas ED, Jackson C, Prabhakaran K, Smith A, Hakonarson H, Verma R, Davatzikos C, Gur RE, Gur RC. Heterogeneous impact of motion on fundamental patterns of developmental changes in functional connectivity during youth. Neuroimage 2013; 83:45-57. [PMID: 23792981 DOI: 10.1016/j.neuroimage.2013.06.045] [Citation(s) in RCA: 179] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2013] [Revised: 06/10/2013] [Accepted: 06/12/2013] [Indexed: 10/26/2022] Open
Abstract
Several independent studies have demonstrated that small amounts of in-scanner motion systematically bias estimates of resting-state functional connectivity. This confound is of particular importance for studies of neurodevelopment in youth because motion is strongly related to subject age during this period. Critically, the effects of motion on connectivity mimic major findings in neurodevelopmental research, specifically an age-related strengthening of distant connections and weakening of short-range connections. Here, in a sample of 780 subjects ages 8-22, we re-evaluate patterns of change in functional connectivity during adolescent development after rigorously controlling for the confounding influences of motion at both the subject and group levels. We find that motion artifact inflates both overall estimates of age-related change as well as specific distance-related changes in connectivity. When motion is more fully accounted for, the prevalence of age-related change as well as the strength of distance-related effects is substantially reduced. However, age-related changes remain highly significant. In contrast, motion artifact tends to obscure age-related changes in connectivity associated with segregation of functional brain modules; improved preprocessing techniques allow greater sensitivity to detect increased within-module connectivity occurring with development. Finally, we show that subject's age can still be accurately estimated from the multivariate pattern of functional connectivity even while controlling for motion. Taken together, these results indicate that while motion artifact has a marked and heterogeneous impact on estimates of connectivity change during adolescence, functional connectivity remains a valuable phenotype for the study of neurodevelopment.
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20
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Gardner RC, Boxer AL, Trujillo A, Mirsky JB, Guo CC, Gennatas ED, Heuer HW, Fine E, Zhou J, Kramer JH, Miller BL, Seeley WW. Intrinsic connectivity network disruption in progressive supranuclear palsy. Ann Neurol 2013; 73:603-16. [PMID: 23536287 DOI: 10.1002/ana.23844] [Citation(s) in RCA: 81] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2012] [Revised: 12/17/2012] [Accepted: 12/21/2012] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Progressive supranuclear palsy (PSP) has been conceptualized as a large-scale network disruption, but the specific network targeted has not been fully characterized. We sought to delineate the affected network in patients with clinical PSP. METHODS Using task-free functional magnetic resonance imaging, we mapped intrinsic connectivity to the dorsal midbrain tegmentum (dMT), a region that shows focal atrophy in PSP. Two healthy control groups (1 young, 1 older) were used to define and replicate the normal connectivity pattern, and patients with PSP were compared to an independent matched healthy control group on measures of network connectivity. RESULTS Healthy young and older subjects showed a convergent pattern of connectivity to the dMT, including brainstem, cerebellar, diencephalic, basal ganglia, and cortical regions involved in skeletomotor, oculomotor, and executive control. Patients with PSP showed significant connectivity disruptions within this network, particularly within corticosubcortical and cortico-brainstem interactions. Patients with more severe functional impairment showed lower mean dMT network connectivity scores. INTERPRETATION This study defines a PSP-related intrinsic connectivity network in the healthy brain and demonstrates the sensitivity of network-based imaging methods to PSP-related physiological and clinical changes.
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Affiliation(s)
- Raquel C Gardner
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
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21
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Gennatas ED, Cholfin JA, Zhou J, Crawford RK, Sasaki DA, Karydas A, Boxer AL, Bonasera SJ, Rankin KP, Gorno-Tempini ML, Rosen HJ, Kramer JH, Weiner M, Miller BL, Seeley WW. COMT Val158Met genotype influences neurodegeneration within dopamine-innervated brain structures. Neurology 2012; 78:1663-9. [PMID: 22573634 DOI: 10.1212/wnl.0b013e3182574fa1] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE We sought to determine whether the Val(158)Met polymorphism in the catechol-O-methyltransferase (COMT) gene influences neurodegeneration within dopamine-innervated brain regions. METHODS A total of 252 subjects, including healthy controls and patients with Alzheimer disease, behavioral variant frontotemporal dementia, and semantic dementia, underwent COMT genotyping and structural MRI. RESULTS Whole-brain voxel-wise regression analyses revealed that COMT Val(158)Met Val allele dosage, known to produce a dose-dependent decrease in synaptic dopamine (DA) availability, correlated with decreased gray matter in the region of the ventral tegmental area (VTA), ventromedial prefrontal cortex, bilateral dorsal midinsula, left dorsolateral prefrontal cortex, and right ventral striatum. Unexpectedly, patients carrying a Met allele showed greater VTA volumes than age-matched controls. Gray matter intensities within COMT-related brain regions correlated with cognitive and behavioral deficits. CONCLUSIONS The results are consistent with the hypothesis that increased synaptic DA catabolism promotes neurodegeneration within DA-innervated brain regions.
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Affiliation(s)
- E D Gennatas
- Memory and Aging Center, Department of Neurology, University of California-San Francisco, CA, USA
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22
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Zhou J, Gennatas ED, Kramer JH, Miller BL, Seeley WW. Predicting regional neurodegeneration from the healthy brain functional connectome. Neuron 2012; 73:1216-27. [PMID: 22445348 DOI: 10.1016/j.neuron.2012.03.004] [Citation(s) in RCA: 499] [Impact Index Per Article: 41.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/05/2012] [Indexed: 11/25/2022]
Abstract
Neurodegenerative diseases target large-scale neural networks. Four competing mechanistic hypotheses have been proposed to explain network-based disease patterning: nodal stress, transneuronal spread, trophic failure, and shared vulnerability. Here, we used task-free fMRI to derive the healthy intrinsic connectivity patterns seeded by brain regions vulnerable to any of five distinct neurodegenerative diseases. These data enabled us to investigate how intrinsic connectivity in health predicts region-by-region vulnerability to disease. For each illness, specific regions emerged as critical network "epicenters" whose normal connectivity profiles most resembled the disease-associated atrophy pattern. Graph theoretical analyses in healthy subjects revealed that regions with higher total connectional flow and, more consistently, shorter functional paths to the epicenters, showed greater disease-related vulnerability. These findings best fit a transneuronal spread model of network-based vulnerability. Molecular pathological approaches may help clarify what makes each epicenter vulnerable to its targeting disease and how toxic protein species travel between networked brain structures.
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Affiliation(s)
- Juan Zhou
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA 94143, USA
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23
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Rohrer JD, Geser F, Zhou J, Gennatas ED, Sidhu M, Trojanowski JQ, Dearmond SJ, Miller BL, Seeley WW. TDP-43 subtypes are associated with distinct atrophy patterns in frontotemporal dementia. Neurology 2011; 75:2204-11. [PMID: 21172843 DOI: 10.1212/wnl.0b013e318202038c] [Citation(s) in RCA: 127] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND We sought to describe the antemortem clinical and neuroimaging features among patients with frontotemporal lobar degeneration with TDP-43 immunoreactive inclusions (FTLD-TDP). METHODS Subjects were recruited from a consecutive series of patients with a primary neuropathologic diagnosis of FTLD-TDP and antemortem MRI. Twenty-eight patients met entry criteria: 9 with type 1, 5 with type 2, and 10 with type 3 FTLD-TDP. Four patients had too sparse FTLD-TDP pathology to be subtyped. Clinical, neuropsychological, and neuroimaging features of these cases were reviewed. Voxel-based morphometry was used to assess regional gray matter atrophy in relation to a group of 50 cognitively normal control subjects. RESULTS Clinical diagnosis varied between the groups: semantic dementia was only associated with type 1 pathology, whereas progressive nonfluent aphasia and corticobasal syndrome were only associated with type 3. Behavioral variant frontotemporal dementia and frontotemporal dementia with motor neuron disease were seen in type 2 or type 3 pathology. The neuroimaging analysis revealed distinct patterns of atrophy between the pathologic subtypes: type 1 was associated with asymmetric anterior temporal lobe atrophy (either left- or right-predominant) with involvement also of the orbitofrontal lobes and insulae; type 2 with relatively symmetric atrophy of the medial temporal, medial prefrontal, and orbitofrontal-insular cortices; and type 3 with asymmetric atrophy (either left- or right-predominant) involving more dorsal areas including frontal, temporal, and inferior parietal cortices as well as striatum and thalamus. No significant atrophy was seen among patients with too sparse pathology to be subtyped. CONCLUSIONS FTLD-TDP subtypes have distinct clinical and neuroimaging features, highlighting the relevance of FTLD-TDP subtyping to clinicopathologic correlation.
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Affiliation(s)
- J D Rohrer
- Dementia Research Centre, UCL Institute of Neurology, University College London, Queen Square, London, UK
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Zhou J, Greicius MD, Gennatas ED, Growdon ME, Jang JY, Rabinovici GD, Kramer JH, Weiner M, Miller BL, Seeley WW. Divergent network connectivity changes in behavioural variant frontotemporal dementia and Alzheimer's disease. ACTA ACUST UNITED AC 2010; 133:1352-67. [PMID: 20410145 DOI: 10.1093/brain/awq075] [Citation(s) in RCA: 719] [Impact Index Per Article: 51.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Resting-state or intrinsic connectivity network functional magnetic resonance imaging provides a new tool for mapping large-scale neural network function and dysfunction. Recently, we showed that behavioural variant frontotemporal dementia and Alzheimer's disease cause atrophy within two major networks, an anterior 'Salience Network' (atrophied in behavioural variant frontotemporal dementia) and a posterior 'Default Mode Network' (atrophied in Alzheimer's disease). These networks exhibit an anti-correlated relationship with each other in the healthy brain. The two diseases also feature divergent symptom-deficit profiles, with behavioural variant frontotemporal dementia undermining social-emotional function and preserving or enhancing visuospatial skills, and Alzheimer's disease showing the inverse pattern. We hypothesized that these disorders would exert opposing connectivity effects within the Salience Network (disrupted in behavioural variant frontotemporal dementia but enhanced in Alzheimer's disease) and the Default Mode Network (disrupted in Alzheimer's disease but enhanced in behavioural variant frontotemporal dementia). With task-free functional magnetic resonance imaging, we tested these ideas in behavioural variant frontotemporal dementia, Alzheimer's disease and healthy age-matched controls (n = 12 per group), using independent component analyses to generate group-level network contrasts. As predicted, behavioural variant frontotemporal dementia attenuated Salience Network connectivity, most notably in frontoinsular, cingulate, striatal, thalamic and brainstem nodes, but enhanced connectivity within the Default Mode Network. Alzheimer's disease, in contrast, reduced Default Mode Network connectivity to posterior hippocampus, medial cingulo-parieto-occipital regions and the dorsal raphe nucleus, but intensified Salience Network connectivity. Specific regions of connectivity disruption within each targeted network predicted intrinsic connectivity enhancement within the reciprocal network. In behavioural variant frontotemporal dementia, clinical severity correlated with loss of right frontoinsular Salience Network connectivity and with biparietal Default Mode Network connectivity enhancement. Based on these results, we explored whether a combined index of Salience Network and Default Mode Network connectivity might discriminate between the three groups. Linear discriminant analysis achieved 92% clinical classification accuracy, including 100% separation of behavioural variant frontotemporal dementia and Alzheimer's disease. Patients whose clinical diagnoses were supported by molecular imaging, genetics, or pathology showed 100% separation using this method, including four diagnostically equivocal 'test' patients not used to train the algorithm. Overall, the findings suggest that behavioural variant frontotemporal dementia and Alzheimer's disease lead to divergent network connectivity patterns, consistent with known reciprocal network interactions and the strength and deficit profiles of the two disorders. Further developed, intrinsic connectivity network signatures may provide simple, inexpensive, and non-invasive biomarkers for dementia differential diagnosis and disease monitoring.
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Affiliation(s)
- Juan Zhou
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA 94117, USA
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