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Wang L, Wang H, D’Angelo F, Curtin L, Sereduk CP, Leon GD, Singleton KW, Urcuyo J, Hawkins-Daarud A, Jackson PR, Krishna C, Zimmerman RS, Patra DP, Bendok BR, Smith KA, Nakaji P, Donev K, Baxter LC, Mrugała MM, Ceccarelli M, Iavarone A, Swanson KR, Tran NL, Hu LS, Li J. Quantifying intra-tumoral genetic heterogeneity of glioblastoma toward precision medicine using MRI and a data-inclusive machine learning algorithm. PLoS One 2024; 19:e0299267. [PMID: 38568950 PMCID: PMC10990246 DOI: 10.1371/journal.pone.0299267] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 02/06/2024] [Indexed: 04/05/2024] Open
Abstract
BACKGROUND AND OBJECTIVE Glioblastoma (GBM) is one of the most aggressive and lethal human cancers. Intra-tumoral genetic heterogeneity poses a significant challenge for treatment. Biopsy is invasive, which motivates the development of non-invasive, MRI-based machine learning (ML) models to quantify intra-tumoral genetic heterogeneity for each patient. This capability holds great promise for enabling better therapeutic selection to improve patient outcome. METHODS We proposed a novel Weakly Supervised Ordinal Support Vector Machine (WSO-SVM) to predict regional genetic alteration status within each GBM tumor using MRI. WSO-SVM was applied to a unique dataset of 318 image-localized biopsies with spatially matched multiparametric MRI from 74 GBM patients. The model was trained to predict the regional genetic alteration of three GBM driver genes (EGFR, PDGFRA and PTEN) based on features extracted from the corresponding region of five MRI contrast images. For comparison, a variety of existing ML algorithms were also applied. Classification accuracy of each gene were compared between the different algorithms. The SHapley Additive exPlanations (SHAP) method was further applied to compute contribution scores of different contrast images. Finally, the trained WSO-SVM was used to generate prediction maps within the tumoral area of each patient to help visualize the intra-tumoral genetic heterogeneity. RESULTS WSO-SVM achieved 0.80 accuracy, 0.79 sensitivity, and 0.81 specificity for classifying EGFR; 0.71 accuracy, 0.70 sensitivity, and 0.72 specificity for classifying PDGFRA; 0.80 accuracy, 0.78 sensitivity, and 0.83 specificity for classifying PTEN; these results significantly outperformed the existing ML algorithms. Using SHAP, we found that the relative contributions of the five contrast images differ between genes, which are consistent with findings in the literature. The prediction maps revealed extensive intra-tumoral region-to-region heterogeneity within each individual tumor in terms of the alteration status of the three genes. CONCLUSIONS This study demonstrated the feasibility of using MRI and WSO-SVM to enable non-invasive prediction of intra-tumoral regional genetic alteration for each GBM patient, which can inform future adaptive therapies for individualized oncology.
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Affiliation(s)
- Lujia Wang
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Hairong Wang
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Fulvio D’Angelo
- Institute for Cancer Genetics, Columbia University Medical Center, New York City, New York, United States of America
| | - Lee Curtin
- Department of Neurosurgery, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
| | - Christopher P. Sereduk
- Department of Neurosurgery, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
| | - Gustavo De Leon
- Department of Neurosurgery, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
| | - Kyle W. Singleton
- Department of Neurosurgery, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
| | - Javier Urcuyo
- Department of Neurosurgery, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
| | - Andrea Hawkins-Daarud
- Department of Neurosurgery, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
| | - Pamela R. Jackson
- Department of Neurosurgery, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
| | - Chandan Krishna
- Department of Neurosurgery, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
| | - Richard S. Zimmerman
- Department of Neurosurgery, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
| | - Devi P. Patra
- Department of Neurosurgery, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
| | - Bernard R. Bendok
- Department of Neurosurgery, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
| | - Kris A. Smith
- Department of Neurosurgery, Barrow Neurological Institute—St. Joseph’s Hospital and Medical Center, Phoenix, Arizona, United States of America
| | - Peter Nakaji
- Department of Neurosurgery, Barrow Neurological Institute—St. Joseph’s Hospital and Medical Center, Phoenix, Arizona, United States of America
| | - Kliment Donev
- Department of Pathology, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
| | - Leslie C. Baxter
- Department of Neuropsychology, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
| | - Maciej M. Mrugała
- Department of Neuro-Oncology, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
| | - Michele Ceccarelli
- Department of Electrical Engineering and Information Technology, University of Naples “Federico II”, Naples, Italy
| | - Antonio Iavarone
- Institute for Cancer Genetics, Columbia University Medical Center, New York City, New York, United States of America
| | - Kristin R. Swanson
- Department of Neurosurgery, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
| | - Nhan L. Tran
- Department of Neurosurgery, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
- Department of Cancer Biology, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
| | - Leland S. Hu
- Department of Radiology, Mayo Clinic Arizona, Phoenix, Arizona, United States of America
| | - Jing Li
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
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Bergamino M, Burke A, Sabbagh MN, Caselli RJ, Baxter LC, Stokes AM. Altered resting-state functional connectivity and dynamic network properties in cognitive impairment: an independent component and dominant-coactivation pattern analyses study. Front Aging Neurosci 2024; 16:1362613. [PMID: 38562990 PMCID: PMC10982426 DOI: 10.3389/fnagi.2024.1362613] [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] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 03/04/2024] [Indexed: 04/04/2024] Open
Abstract
Introduction Cognitive impairment (CI) due to Alzheimer's disease (AD) encompasses a decline in cognitive abilities and can significantly impact an individual's quality of life. Early detection and intervention are crucial in managing CI, both in the preclinical and prodromal stages of AD prior to dementia. Methods In this preliminary study, we investigated differences in resting-state functional connectivity and dynamic network properties between 23 individual with CI due to AD based on clinical assessment and 15 healthy controls (HC) using Independent Component Analysis (ICA) and Dominant-Coactivation Pattern (d-CAP) analysis. The cognitive status of the two groups was also compared, and correlations between cognitive scores and d-CAP switching probability were examined. Results Results showed comparable numbers of d-CAPs in the Default Mode Network (DMN), Executive Control Network (ECN), and Frontoparietal Network (FPN) between HC and CI groups. However, the Visual Network (VN) exhibited fewer d-CAPs in the CI group, suggesting altered dynamic properties of this network for the CI group. Additionally, ICA revealed significant connectivity differences for all networks. Spatial maps and effect size analyses indicated increased coactivation and more synchronized activity within the DMN in HC compared to CI. Furthermore, reduced switching probabilities were observed for the CI group in DMN, VN, and FPN networks, indicating less dynamic and flexible functional interactions. Discussion The findings highlight altered connectivity patterns within the DMN, VN, ECN, and FPN, suggesting the involvement of multiple functional networks in CI. Understanding these brain processes may contribute to developing targeted diagnostic and therapeutic strategies for CI due to AD.
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Affiliation(s)
- Maurizio Bergamino
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, OK, United States
| | - Anna Burke
- Division of Neurology, Barrow Neurological Institute, Phoenix, OK, United States
| | - Marwan N. Sabbagh
- Division of Neurology, Barrow Neurological Institute, Phoenix, OK, United States
| | - Richard J. Caselli
- Department of Neuropsychology, Mayo Clinic Arizona, Phoenix, AZ, United States
| | - Leslie C. Baxter
- Department of Neurology, Mayo Clinic Arizona, Phoenix, AZ, United States
| | - Ashley M. Stokes
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, OK, United States
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Hu LS, D'Angelo F, Weiskittel TM, Caruso FP, Fortin Ensign SP, Blomquist MR, Flick MJ, Wang L, Sereduk CP, Meng-Lin K, De Leon G, Nespodzany A, Urcuyo JC, Gonzales AC, Curtin L, Lewis EM, Singleton KW, Dondlinger T, Anil A, Semmineh NB, Noviello T, Patel RA, Wang P, Wang J, Eschbacher JM, Hawkins-Daarud A, Jackson PR, Grunfeld IS, Elrod C, Mazza GL, McGee SC, Paulson L, Clark-Swanson K, Lassiter-Morris Y, Smith KA, Nakaji P, Bendok BR, Zimmerman RS, Krishna C, Patra DP, Patel NP, Lyons M, Neal M, Donev K, Mrugala MM, Porter AB, Beeman SC, Jensen TR, Schmainda KM, Zhou Y, Baxter LC, Plaisier CL, Li J, Li H, Lasorella A, Quarles CC, Swanson KR, Ceccarelli M, Iavarone A, Tran NL. Integrated molecular and multiparametric MRI mapping of high-grade glioma identifies regional biologic signatures. Nat Commun 2023; 14:6066. [PMID: 37770427 PMCID: PMC10539500 DOI: 10.1038/s41467-023-41559-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [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: 02/14/2023] [Accepted: 09/06/2023] [Indexed: 09/30/2023] Open
Abstract
Sampling restrictions have hindered the comprehensive study of invasive non-enhancing (NE) high-grade glioma (HGG) cell populations driving tumor progression. Here, we present an integrated multi-omic analysis of spatially matched molecular and multi-parametric magnetic resonance imaging (MRI) profiling across 313 multi-regional tumor biopsies, including 111 from the NE, across 68 HGG patients. Whole exome and RNA sequencing uncover unique genomic alterations to unresectable invasive NE tumor, including subclonal events, which inform genomic models predictive of geographic evolution. Infiltrative NE tumor is alternatively enriched with tumor cells exhibiting neuronal or glycolytic/plurimetabolic cellular states, two principal transcriptomic pathway-based glioma subtypes, which respectively demonstrate abundant private mutations or enrichment in immune cell signatures. These NE phenotypes are non-invasively identified through normalized K2 imaging signatures, which discern cell size heterogeneity on dynamic susceptibility contrast (DSC)-MRI. NE tumor populations predicted to display increased cellular proliferation by mean diffusivity (MD) MRI metrics are uniquely associated with EGFR amplification and CDKN2A homozygous deletion. The biophysical mapping of infiltrative HGG potentially enables the clinical recognition of tumor subpopulations with aggressive molecular signatures driving tumor progression, thereby informing precision medicine targeting.
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Affiliation(s)
- Leland S Hu
- Department of Radiology, Mayo Clinic Arizona, Phoenix, AZ, USA.
- Department of Cancer Biology, Mayo Clinic Arizona, Scottsdale, AZ, USA.
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA.
| | - Fulvio D'Angelo
- Department of Neurological Surgery, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, USA.
| | - Taylor M Weiskittel
- Mayo Clinic Alix School of Medicine Minnesota, Rochester, MN, USA
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Francesca P Caruso
- Department of Electrical Engineering and Information Technologies, University of Naples, "Federico II", I-80128, Naples, Italy
- BIOGEM Institute of Molecular Biology and Genetics, I-83031, Ariano Irpino, Italy
| | - Shannon P Fortin Ensign
- Department of Cancer Biology, Mayo Clinic Arizona, Scottsdale, AZ, USA
- Department of Hematology and Oncology, Mayo Clinic Arizona, Phoenix, AZ, USA
| | - Mylan R Blomquist
- Department of Cancer Biology, Mayo Clinic Arizona, Scottsdale, AZ, USA
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
- Mayo Clinic Alix School of Medicine Arizona, Scottsdale, AZ, USA
| | - Matthew J Flick
- Department of Radiology, Mayo Clinic Arizona, Phoenix, AZ, USA
- Department of Cancer Biology, Mayo Clinic Arizona, Scottsdale, AZ, USA
- Mayo Clinic Alix School of Medicine Arizona, Scottsdale, AZ, USA
| | - Lujia Wang
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Christopher P Sereduk
- Department of Cancer Biology, Mayo Clinic Arizona, Scottsdale, AZ, USA
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Kevin Meng-Lin
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Gustavo De Leon
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Ashley Nespodzany
- Department of Neuroimaging Research, Barrow Neurological Institute, Dignity Health, Phoenix, AZ, USA
| | - Javier C Urcuyo
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Ashlyn C Gonzales
- Department of Neuroimaging Research, Barrow Neurological Institute, Dignity Health, Phoenix, AZ, USA
| | - Lee Curtin
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Erika M Lewis
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Kyle W Singleton
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | | | - Aliya Anil
- Department of Neuroimaging Research, Barrow Neurological Institute, Dignity Health, Phoenix, AZ, USA
| | - Natenael B Semmineh
- Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Teresa Noviello
- Department of Electrical Engineering and Information Technologies, University of Naples, "Federico II", I-80128, Naples, Italy
- BIOGEM Institute of Molecular Biology and Genetics, I-83031, Ariano Irpino, Italy
| | - Reyna A Patel
- Department of Radiology, Mayo Clinic Arizona, Phoenix, AZ, USA
| | - Panwen Wang
- Quantitative Health Sciences, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Junwen Wang
- Division of Applied Oral Sciences & Community Dental Care, The University of Hong Kong, Hong Kong SAR, China
| | - Jennifer M Eschbacher
- Department of Neuropathology, Barrow Neurological Institute, Dignity Health, Phoenix, AZ, USA
| | | | - Pamela R Jackson
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Itamar S Grunfeld
- Department of Psychology, Hunter College, The City University of New York, New York, NY, USA
- Department of Psychology, The Graduate Center, The City University of New York, New York, NY, USA
| | | | - Gina L Mazza
- Quantitative Health Sciences, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Sam C McGee
- Department of Speech and Hearing Science, Arizona State University, Tempe, AZ, USA
| | - Lisa Paulson
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | | | | | - Kris A Smith
- Department of Neurosurgery, Barrow Neurological Institute, Dignity Health, Phoenix, AZ, USA
| | - Peter Nakaji
- Department of Neurosurgery, Banner University Medical Center, University of Arizona, Phoenix, AZ, USA
| | - Bernard R Bendok
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Richard S Zimmerman
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Chandan Krishna
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Devi P Patra
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Naresh P Patel
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Mark Lyons
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Matthew Neal
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Kliment Donev
- Department of Pathology, Mayo Clinic Arizona, Phoenix, AZ, USA
| | | | - Alyx B Porter
- Department of Neurology, Mayo Clinic Arizona, Phoenix, AZ, USA
| | - Scott C Beeman
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, USA
| | | | - Kathleen M Schmainda
- Departments of Biophysics and Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Yuxiang Zhou
- Department of Radiology, Mayo Clinic Arizona, Phoenix, AZ, USA
| | - Leslie C Baxter
- Department of Radiology, Mayo Clinic Arizona, Phoenix, AZ, USA
- Departments of Psychiatry and Psychology, Mayo Clinic, AZ, USA
| | - Christopher L Plaisier
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Jing Li
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Hu Li
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Anna Lasorella
- Department of Biochemistry and Molecular Biology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - C Chad Quarles
- Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Kristin R Swanson
- Department of Cancer Biology, Mayo Clinic Arizona, Scottsdale, AZ, USA
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Michele Ceccarelli
- Department of Public Health Sciences, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, USA.
| | - Antonio Iavarone
- Department of Neurological Surgery, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, USA.
| | - Nhan L Tran
- Department of Cancer Biology, Mayo Clinic Arizona, Scottsdale, AZ, USA.
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA.
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Baxter LC, Limback-Stokin M, Patten KJ, Arreola AC, Locke DEC, Hu L, Zhou Y, Caselli RJ. Hippocampal connectivity and memory decline in cognitively intact APOE ε4 carriers. Alzheimers Dement 2023; 19:3806-3814. [PMID: 36906845 DOI: 10.1002/alz.13023] [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: 08/24/2022] [Revised: 02/06/2023] [Accepted: 02/07/2023] [Indexed: 03/13/2023]
Abstract
INTRODUCTION Resting-state functional magnetic resonance imaging (fMRI) graph theory may help detect subtle functional connectivity changes affecting memory prior to impairment. METHODS Cognitively normal apolipoprotein E (APOE) ε4 carriers/noncarriers underwent longitudinal cognitive assessment and one-time MRI. The relationship of left/right hippocampal connectivity and memory trajectory were compared between carriers/noncarriers. RESULTS Steepness of verbal memory decline correlated with decreased connectivity in the left hippocampus, only among APOE ε4 carriers. Right hippocampal metrics were not correlated with memory and there were no significant correlations in the noncarriers. Verbal memory decline correlated with left hippocampal volume loss for both carriers and noncarriers, with no other significant volumetric findings. DISCUSSION Findings support early hippocampal dysfunction in intact carriers, the AD disconnection hypothesis, and left hippocampal dysfunction earlier than the right. Combining lateralized graph theoretical metrics with a sensitive measure of memory trajectory allowed for detection of early-stage changes in APOE ε4 carriers before symptoms of mild cognitive impairment are present. HIGHLIGHTS Graph theory connectivity detects preclinical hippocampal changes in APOE ε4 carriers. The AD disconnection hypothesis was supported in unimpaired APOE ε4 carriers. Hippocampal dysfunction starts asymmetrically on the left.
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Affiliation(s)
- Leslie C Baxter
- Department of Psychiatry and Psychology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | | | - K Jakob Patten
- Department of Speech and Hearing Sciences, Arizona State University, Tempe, Arizona, USA
| | | | - Dona E C Locke
- Department of Psychiatry and Psychology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Leland Hu
- Department of Radiology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Yuxiang Zhou
- Department of Medical Physics, Mayo Clinic Arizona, Phoenix, Arizona, USA
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Bond KM, Curtin L, Hawkins-Daarud A, Urcuyo JC, De Leon G, Singleton KW, Afshari AE, Paulson LE, Sereduk CP, Smith KA, Nakaji P, Baxter LC, Patra DP, Gustafson MP, Dietz AB, Zimmerman RS, Bendok BR, Tran NL, Hu LS, Parney IF, Rubin JB, Swanson KR. Image-based models of T-cell distribution identify a clinically meaningful response to a dendritic cell vaccine in patients with glioblastoma. medRxiv 2023:2023.07.13.23292619. [PMID: 37503239 PMCID: PMC10370220 DOI: 10.1101/2023.07.13.23292619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
BACKGROUND Glioblastoma is an extraordinarily heterogeneous tumor, yet the current treatment paradigm is a "one size fits all" approach. Hundreds of glioblastoma clinical trials have been deemed failures because they did not extend median survival, but these cohorts are comprised of patients with diverse tumors. Current methods of assessing treatment efficacy fail to fully account for this heterogeneity. METHODS Using an image-based modeling approach, we predicted T-cell abundance from serial MRIs of patients enrolled in the dendritic cell (DC) vaccine clinical trial. T-cell predictions were quantified in both the contrast-enhancing and non-enhancing regions of the imageable tumor, and changes over time were assessed. RESULTS A subset of patients in a DC vaccine clinical trial, who had previously gone undetected, were identified as treatment responsive and benefited from prolonged survival. A mere two months after initial vaccine administration, responsive patients had a decrease in model-predicted T-cells within the contrast-enhancing region, with a simultaneous increase in the T2/FLAIR region. CONCLUSIONS In a field that has yet to see breakthrough therapies, these results highlight the value of machine learning in enhancing clinical trial assessment, improving our ability to prospectively prognosticate patient outcomes, and advancing the pursuit towards individualized medicine.
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Bergamino M, Burke A, Baxter LC, Caselli RJ, Sabbagh MN, Talboom JS, Huentelman MJ, Stokes AM. Longitudinal Assessment of Intravoxel Incoherent Motion Diffusion-Weighted MRI Metrics in Cognitive Decline. J Magn Reson Imaging 2022; 56:1845-1862. [PMID: 35319142 DOI: 10.1002/jmri.28172] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 03/09/2022] [Accepted: 03/11/2022] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Advanced diffusion-based MRI biomarkers may provide insight into microstructural and perfusion changes associated with neurodegeneration and cognitive decline. PURPOSE To assess longitudinal microstructural and perfusion changes using apparent diffusion coefficient (ADC) and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) parameters in cognitively impaired (CI) and healthy control (HC) groups. STUDY TYPE Prospective/longitudinal. POPULATION Twelve CI patients (75% female) and 13 HC subjects (69% female). FIELD STRENGTH/SEQUENCE 3 T; Spin-Echo-IVIM-DWI. ASSESSMENT Two MRI scans were performed with a 12-month interval. ADC and IVIM-DWI metrics (diffusion coefficient [D] and perfusion fraction [f]) were generated from monoexponential and biexponential fits, respectively. Additionally, voxel-based correlations were evaluated between change in Montreal Cognitive Assessment (ΔMoCA) and baseline imaging parameters. STATISTICAL TESTS Analysis of covariance with sex and age as covariates was performed for main effects of group and time (false discovery rate [FDR] corrected) with post hoc comparisons using Bonferroni correction. Partial-η2 and Hedges' g were used for effect-size analysis. Spearman's correlations (FDR corrected) were used for the relationship between ΔMoCA score and imaging. P < 0.05 was considered statistically significant. RESULTS Significant differences were found for the main effects of group (HC vs. CI) and time. For group effects, higher ADC, IVIM-D, and IVIM-f were observed in the CI group compared to HC (ADC: 1.23 ± 0.08. 10-3 vs. 1.09 ± 0.07. 10-3 mm2 /sec; IVIM-D: 0.82 ± 0.01. 10-3 vs. 0.73 ± 0.01. 10-3 mm2 /sec; and IVIM-f: 0.317 ± 0.008 vs. 0.253 ± 0.009). Significantly higher ADC, IVIM-D, and IVIM-f values were observed in the CI group after 12 months (ADC: 1.45 ± 0.05. 10-3 vs. 1.50 ± 0.07. 10-3 mm2 /sec; IVIM-D: 0.87 ± 0.01. 10-3 vs. 0.94 ± 0.02. 10-3 mm2 /sec; and IVIM-f: 0.303 ± 0.007 vs. 0.332 ± 0.008), but not in the HC group at large effect size. ADC, IVIM-D, and IVIM-f negatively correlated with ΔMoCA score (ρ = -0.49, -0.51, and -0.50, respectively). DATA CONCLUSION These findings demonstrate that longitudinal differences between CI and HC cohorts can be measured using IVIM-based metrics. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Maurizio Bergamino
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Anna Burke
- Department of Neurology, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Leslie C Baxter
- Department of Neurology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Richard J Caselli
- Department of Psychiatry and Psychology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Marwan N Sabbagh
- Department of Neurology, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Joshua S Talboom
- Neurogenomics Division, Translational Genomics Research Institute, Phoenix, Arizona, USA
| | - Matthew J Huentelman
- Neurogenomics Division, Translational Genomics Research Institute, Phoenix, Arizona, USA
| | - Ashley M Stokes
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, Arizona, USA
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Bergamino M, Keeling EG, Baxter LC, Sisco NJ, Walsh RR, Stokes AM. Sex Differences in Alzheimer's Disease Revealed by Free-Water Diffusion Tensor Imaging and Voxel-Based Morphometry. J Alzheimers Dis 2022; 85:395-414. [PMID: 34842185 PMCID: PMC9015709 DOI: 10.3233/jad-210406] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
BACKGROUND Imaging biomarkers are increasingly used in Alzheimer's disease (AD), and the identification of sex differences using neuroimaging may provide insight into disease heterogeneity, progression, and therapeutic targets. OBJECTIVE The purpose of this study was to investigate differences in grey matter (GM) volume and white matter (WM) microstructural disorganization between males and females with AD using voxel-based morphometry (VBM) and free-water-corrected diffusion tensor imaging (FW-DTI). METHODS Data were downloaded from the OASIS-3 database, including 158 healthy control (HC; 86 females) and 46 mild AD subjects (24 females). VBM and FW-DTI metrics (fractional anisotropy (FA), axial and radial diffusivities (AxD and RD, respectively), and FW index) were compared using effect size for the main effects of group, sex, and their interaction. RESULTS Significant group and sex differences were observed, with no significant interaction. Post-hoc comparisons showed that AD is associated with reduced GM volume, reduced FW-FA, and higher FW-RD/FW-index, consistent with neurodegeneration. Females in both groups exhibited higher GM volume than males, while FW-DTI metrics showed sex differences only in the AD group. Lower FW, lower FW-FA and higher FW-RD were observed in females relative to males in the AD group. CONCLUSION The combination of VBM and DTI may reveal complementary sex-specific changes in GM and WM associated with AD and aging. Sex differences in GM volume were observed for both groups, while FW-DTI metrics only showed significant sex differences in the AD group, suggesting that WM tract disorganization may play a differential role in AD pathophysiology between females and males.
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Affiliation(s)
| | - Elizabeth G. Keeling
- Neuroimaging Research, Barrow Neurological Institute,School of Life Sciences, Arizona State University
| | | | | | - Ryan R. Walsh
- Muhammad Ali Parkinson Center at Barrow Neurological
Institute
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Stokes AM, Bergamino M, Alhilali L, Hu LS, Karis JP, Baxter LC, Bell LC, Quarles CC. Evaluation of single bolus, dual-echo dynamic susceptibility contrast MRI protocols in brain tumor patients. J Cereb Blood Flow Metab 2021; 41:3378-3390. [PMID: 34415211 PMCID: PMC8669280 DOI: 10.1177/0271678x211039597] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [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: 11/16/2022]
Abstract
Relative cerebral blood volume (rCBV) obtained from dynamic susceptibility contrast (DSC) MRI is adversely impacted by contrast agent leakage in brain tumors. Using simulations, we previously demonstrated that multi-echo DSC-MRI protocols provide improvements in contrast agent dosing, pulse sequence flexibility, and rCBV accuracy. The purpose of this study is to assess the in-vivo performance of dual-echo acquisitions in patients with brain tumors (n = 59). To verify pulse sequence flexibility, four single-dose dual-echo acquisitions were tested with variations in contrast agent dose, flip angle, and repetition time, and the resulting dual-echo rCBV was compared to standard single-echo rCBV obtained with preload (double-dose). Dual-echo rCBV was comparable to standard double-dose single-echo protocols (mean (standard deviation) tumor rCBV 2.17 (1.28) vs. 2.06 (1.20), respectively). High rCBV similarity was observed (CCC = 0.96), which was maintained across both flip angle (CCC = 0.98) and repetition time (CCC = 0.96) permutations, demonstrating that dual-echo acquisitions provide flexibility in acquisition parameters. Furthermore, a single dual-echo acquisition was shown to enable quantification of both perfusion and permeability metrics. In conclusion, single-dose dual-echo acquisitions provide similar rCBV to standard double-dose single-echo acquisitions, suggesting contrast agent dose can be reduced while providing significant pulse sequence flexibility and complementary tumor perfusion and permeability metrics.
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Affiliation(s)
- Ashley M Stokes
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Maurizio Bergamino
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Lea Alhilali
- Neuroradiology, Southwest Neuroimaging at Barrow Neurological Institute, Phoenix, AZ, USA
| | - Leland S Hu
- Department of Radiology, Division of Neuroradiology, Mayo Clinic Arizona, Phoenix, AZ, USA
| | - John P Karis
- Neuroradiology, Southwest Neuroimaging at Barrow Neurological Institute, Phoenix, AZ, USA
| | - Leslie C Baxter
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, USA.,Department of Radiology, Division of Neuroradiology, Mayo Clinic Arizona, Phoenix, AZ, USA
| | - Laura C Bell
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, USA
| | - C Chad Quarles
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, USA
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9
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LaCroix AN, Baxter LC, Rogalsky C. Auditory attention following a left hemisphere stroke: comparisons of alerting, orienting, and executive control performance using an auditory Attention Network Test. Audit Percept Cogn 2021; 3:238-251. [PMID: 34671722 PMCID: PMC8525781 DOI: 10.1080/25742442.2021.1922988] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 04/22/2021] [Indexed: 06/13/2023]
Abstract
INTRODUCTION Auditory attention is a critical foundation for successful language comprehension, yet is rarely studied in individuals with acquired language disorders. METHODS We used an auditory version of the well-studied Attention Network Test to study alerting, orienting, and executive control in 28 persons with chronic stroke (PWS). We further sought to characterize the neurobiology of each auditory attention measure in our sample using exploratory lesion-symptom mapping analyses. RESULTS PWS exhibited the expected executive control effect (i.e., decreased accuracy for incongruent compared to congruent trials), but their alerting and orienting attention were disrupted. PWS did not exhibit an alerting effect and they were actually distracted by the auditory spatial orienting cue compared to the control cue. Lesion-symptom mapping indicated that poorer alerting and orienting were associated with damage to the left retrolenticular part of the internal capsule (adjacent to the thalamus) and left posterior middle frontal gyrus (overlapping with the frontal eye fields), respectively. DISCUSSION The behavioral findings correspond to our previous work investigating alerting and spatial orienting attention in persons with aphasia in the visual modality and suggest that auditory alerting and spatial orienting attention may be impaired in PWS due to stroke lesions damaging multi-modal attention resources.
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Affiliation(s)
| | | | - Corianne Rogalsky
- College of Health Solutions, Arizona State University, Tempe, AZ USA
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10
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Hu LS, Wang L, Hawkins-Daarud A, Eschbacher JM, Singleton KW, Jackson PR, Clark-Swanson K, Sereduk CP, Peng S, Wang P, Wang J, Baxter LC, Smith KA, Mazza GL, Stokes AM, Bendok BR, Zimmerman RS, Krishna C, Porter AB, Mrugala MM, Hoxworth JM, Wu T, Tran NL, Swanson KR, Li J. Uncertainty quantification in the radiogenomics modeling of EGFR amplification in glioblastoma. Sci Rep 2021; 11:3932. [PMID: 33594116 PMCID: PMC7886858 DOI: 10.1038/s41598-021-83141-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 01/18/2021] [Indexed: 12/13/2022] Open
Abstract
Radiogenomics uses machine-learning (ML) to directly connect the morphologic and physiological appearance of tumors on clinical imaging with underlying genomic features. Despite extensive growth in the area of radiogenomics across many cancers, and its potential role in advancing clinical decision making, no published studies have directly addressed uncertainty in these model predictions. We developed a radiogenomics ML model to quantify uncertainty using transductive Gaussian Processes (GP) and a unique dataset of 95 image-localized biopsies with spatially matched MRI from 25 untreated Glioblastoma (GBM) patients. The model generated predictions for regional EGFR amplification status (a common and important target in GBM) to resolve the intratumoral genetic heterogeneity across each individual tumor-a key factor for future personalized therapeutic paradigms. The model used probability distributions for each sample prediction to quantify uncertainty, and used transductive learning to reduce the overall uncertainty. We compared predictive accuracy and uncertainty of the transductive learning GP model against a standard GP model using leave-one-patient-out cross validation. Additionally, we used a separate dataset containing 24 image-localized biopsies from 7 high-grade glioma patients to validate the model. Predictive uncertainty informed the likelihood of achieving an accurate sample prediction. When stratifying predictions based on uncertainty, we observed substantially higher performance in the group cohort (75% accuracy, n = 95) and amongst sample predictions with the lowest uncertainty (83% accuracy, n = 72) compared to predictions with higher uncertainty (48% accuracy, n = 23), due largely to data interpolation (rather than extrapolation). On the separate validation set, our model achieved 78% accuracy amongst the sample predictions with lowest uncertainty. We present a novel approach to quantify radiogenomics uncertainty to enhance model performance and clinical interpretability. This should help integrate more reliable radiogenomics models for improved medical decision-making.
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Affiliation(s)
- Leland S Hu
- Department of Radiology, Mayo Clinic Arizona, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA. .,School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ, 85281, USA. .,Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic Arizona, 5777 East Mayo Blvd, Support Services Building Suite 2-700, Phoenix, AZ, 85054, USA.
| | - Lujia Wang
- Department of Radiology, Mayo Clinic Arizona, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA.,School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ, 85281, USA.,Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic Arizona, 5777 East Mayo Blvd, Support Services Building Suite 2-700, Phoenix, AZ, 85054, USA
| | - Andrea Hawkins-Daarud
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic Arizona, 5777 East Mayo Blvd, Support Services Building Suite 2-700, Phoenix, AZ, 85054, USA
| | - Jennifer M Eschbacher
- Department of Pathology, Barrow Neurological Institute-St. Joseph's Hospital and Medical Center, Phoenix, AZ, 85013, USA
| | - Kyle W Singleton
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic Arizona, 5777 East Mayo Blvd, Support Services Building Suite 2-700, Phoenix, AZ, 85054, USA
| | - Pamela R Jackson
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic Arizona, 5777 East Mayo Blvd, Support Services Building Suite 2-700, Phoenix, AZ, 85054, USA
| | - Kamala Clark-Swanson
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic Arizona, 5777 East Mayo Blvd, Support Services Building Suite 2-700, Phoenix, AZ, 85054, USA
| | - Christopher P Sereduk
- Department of Neurosurgery, Mayo Clinic Arizona, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA.,Department of Cancer Biology, Mayo Clinic Arizona, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA
| | - Sen Peng
- Cancer and Cell Biology Division, Translational Genomics Research Institute, Phoenix, AZ, 85004, USA
| | - Panwen Wang
- Department of Quantitative Health Sciences, Center for Individualized Medicine, Mayo Clinic Arizona, Scottsdale, AZ, 85259, USA
| | - Junwen Wang
- Department of Quantitative Health Sciences, Center for Individualized Medicine, Mayo Clinic Arizona, Scottsdale, AZ, 85259, USA
| | - Leslie C Baxter
- Department of Neuropsychology, Mayo Clinic Arizona, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA
| | - Kris A Smith
- Department of Neurosurgery, Barrow Neurological Institute-St. Joseph's Hospital and Medical Center, Phoenix, AZ, 85013, USA
| | - Gina L Mazza
- Department of Quantitative Health Sciences, Mayo Clinic Arizona, Scottsdale, AZ, 85259, USA
| | - Ashley M Stokes
- Department of Imaging Research, Barrow Neurological Institute-St. Joseph's Hospital and Medical Center, Phoenix, AZ, 85013, USA
| | - Bernard R Bendok
- Department of Neurosurgery, Mayo Clinic Arizona, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA
| | - Richard S Zimmerman
- Department of Neurosurgery, Mayo Clinic Arizona, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA
| | - Chandan Krishna
- Department of Neurosurgery, Mayo Clinic Arizona, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA
| | - Alyx B Porter
- Department of Neuro-Oncology, Mayo Clinic Arizona, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA
| | - Maciej M Mrugala
- Department of Neuro-Oncology, Mayo Clinic Arizona, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA
| | - Joseph M Hoxworth
- Department of Radiology, Mayo Clinic Arizona, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA
| | - Teresa Wu
- Department of Radiology, Mayo Clinic Arizona, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA.,School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ, 85281, USA
| | - Nhan L Tran
- Department of Neurosurgery, Mayo Clinic Arizona, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA.,Department of Cancer Biology, Mayo Clinic Arizona, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA
| | - Kristin R Swanson
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic Arizona, 5777 East Mayo Blvd, Support Services Building Suite 2-700, Phoenix, AZ, 85054, USA.,Department of Neurosurgery, Mayo Clinic Arizona, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA
| | - Jing Li
- Department of Radiology, Mayo Clinic Arizona, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA.,School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ, 85281, USA.,Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic Arizona, 5777 East Mayo Blvd, Support Services Building Suite 2-700, Phoenix, AZ, 85054, USA
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11
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Bergamino M, Nespodzany A, Baxter LC, Burke A, Caselli RJ, Sabbagh MN, Walsh RR, Stokes AM. Preliminary Assessment of Intravoxel Incoherent Motion
Diffusion‐Weighted MRI
(
IVIM‐DWI
) Metrics in Alzheimer's Disease. J Magn Reson Imaging 2020. [DOI: 10.1002/jmri.27445] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
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12
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Fitzhugh MC, Schaefer SY, Baxter LC, Rogalsky C. Cognitive and neural predictors of speech comprehension in noisy backgrounds in older adults. Lang Cogn Neurosci 2020; 36:269-287. [PMID: 34250179 PMCID: PMC8261331 DOI: 10.1080/23273798.2020.1828946] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Accepted: 09/18/2020] [Indexed: 06/13/2023]
Abstract
Older adults often experience difficulties comprehending speech in noisy backgrounds, which hearing loss does not fully explain. It remains unknown how cognitive abilities, brain networks, and age-related hearing loss may uniquely contribute to speech in noise comprehension at the sentence level. In 31 older adults, using cognitive measures and resting-state fMRI, we investigated the cognitive and neural predictors of speech comprehension with energetic (broadband noise) and informational masking (multi-speakers) effects. Better hearing thresholds and greater working memory abilities were associated with better speech comprehension with energetic masking. Conversely, faster processing speed and stronger functional connectivity between frontoparietal and language networks were associated with better speech comprehension with informational masking. Our findings highlight the importance of the frontoparietal network in older adults' ability to comprehend speech in multi-speaker backgrounds, and that hearing loss and working memory in older adults contributes to speech comprehension abilities related to energetic, but not informational masking.
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Affiliation(s)
- Megan C. Fitzhugh
- Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA
- College of Health Solutions, Arizona State University, Tempe, AZ
| | - Sydney Y. Schaefer
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ
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13
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Dong Q, Zhang W, Stonnington CM, Wu J, Gutman BA, Chen K, Su Y, Baxter LC, Thompson PM, Reiman EM, Caselli RJ, Wang Y. Applying surface-based morphometry to study ventricular abnormalities of cognitively unimpaired subjects prior to clinically significant memory decline. Neuroimage Clin 2020; 27:102338. [PMID: 32683323 PMCID: PMC7371915 DOI: 10.1016/j.nicl.2020.102338] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 06/15/2020] [Accepted: 07/02/2020] [Indexed: 12/31/2022]
Abstract
A completely automated surface-based ventricular morphometry system. Generate a whole connected 3D ventricular shape model. Test-retest the system in two independent CU subject cohorts. Subregional ventricular abnormalities prior to clinically memory decline.
Ventricular volume (VV) is a widely used structural magnetic resonance imaging (MRI) biomarker in Alzheimer’s disease (AD) research. Abnormal enlargements of VV can be detected before clinically significant memory decline. However, VV does not pinpoint the details of subregional ventricular expansions. Here we introduce a ventricular morphometry analysis system (VMAS) that generates a whole connected 3D ventricular shape model and encodes a great deal of ventricular surface deformation information that is inaccessible by VV. VMAS contains an automated segmentation approach and surface-based multivariate morphometry statistics. We applied VMAS to two independent datasets of cognitively unimpaired (CU) groups. To our knowledge, it is the first work to detect ventricular abnormalities that distinguish normal aging subjects from those who imminently progress to clinically significant memory decline. Significant bilateral ventricular morphometric differences were first shown in 38 members of the Arizona APOE cohort, which included 18 CU participants subsequently progressing to the clinically significant memory decline within 2 years after baseline visits (progressors), and 20 matched CU participants with at least 4 years of post-baseline cognitive stability (non-progressors). VMAS also detected significant differences in bilateral ventricular morphometry in 44 Alzheimer’s Disease Neuroimaging Initiative (ADNI) subjects (18 CU progressors vs. 26 CU non-progressors) with the same inclusion criterion. Experimental results demonstrated that the ventricular anterior horn regions were affected bilaterally in CU progressors, and more so on the left. VMAS may track disease progression at subregional levels and measure the effects of pharmacological intervention at a preclinical stage.
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Affiliation(s)
- Qunxi Dong
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Wen Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | | | - Jianfeng Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Boris A Gutman
- Armour College of Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ, USA
| | - Yi Su
- Banner Alzheimer's Institute, Phoenix, AZ, USA
| | - Leslie C Baxter
- Human Brain Imaging Laboratory, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | | | | | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
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14
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Bergamino M, Nespodzany A, Baxter LC, Burke A, Caselli RJ, Sabbagh MN, Walsh RR, Stokes AM. Preliminary Assessment of Intravoxel Incoherent Motion
Diffusion‐Weighted MRI
(
IVIM‐DWI
) Metrics in Alzheimer's Disease. J Magn Reson Imaging 2020; 52:1811-1826. [DOI: 10.1002/jmri.27272] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 06/15/2020] [Accepted: 06/16/2020] [Indexed: 01/25/2023] Open
Affiliation(s)
- Maurizio Bergamino
- Division of Neuroimaging Research Barrow Neurological Institute Phoenix Arizona USA
| | - Ashley Nespodzany
- Division of Neuroimaging Research Barrow Neurological Institute Phoenix Arizona USA
| | - Leslie C. Baxter
- Division of Neuroimaging Research Barrow Neurological Institute Phoenix Arizona USA
- Department of Neurology Mayo Clinic Arizona Phoenix Arizona USA
| | - Anna Burke
- Division of Neurology Barrow Neurological Institute Phoenix Arizona USA
| | | | - Marwan N. Sabbagh
- Lou Ruvo Center for Brain Health, Cleveland Clinic Las Vegas Nevada USA
| | - Ryan R. Walsh
- Division of Neurology Barrow Neurological Institute Phoenix Arizona USA
| | - Ashley M. Stokes
- Division of Neuroimaging Research Barrow Neurological Institute Phoenix Arizona USA
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15
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LaCroix AN, Blumenstein N, Tully M, Baxter LC, Rogalsky C. Effects of prosody on the cognitive and neural resources supporting sentence comprehension: A behavioral and lesion-symptom mapping study. Brain Lang 2020; 203:104756. [PMID: 32032865 PMCID: PMC7064294 DOI: 10.1016/j.bandl.2020.104756] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Revised: 12/03/2019] [Accepted: 01/19/2020] [Indexed: 05/29/2023]
Abstract
Non-canonical sentence comprehension impairments are well-documented in aphasia. Studies of neurotypical controls indicate that prosody can aid comprehension by facilitating attention towards critical pitch inflections and phrase boundaries. However, no studies have examined how prosody may engage specific cognitive and neural resources during non-canonical sentence comprehension in persons with left hemisphere damage. Experiment 1 examines the relationship between comprehension of non-canonical sentences spoken with typical and atypical prosody and several cognitive measures in 25 persons with chronic left hemisphere stroke and 20 matched controls. Experiment 2 explores the neural resources critical for non-canonical sentence comprehension with each prosody type using region-of-interest-based multiple regressions. Lower orienting attention abilities and greater inferior frontal and parietal damage predicted lower comprehension, but only for sentences with typical prosody. Our results suggest that typical sentence prosody may engage attention resources to support non-canonical sentence comprehension, and this relationship may be disrupted following left hemisphere stroke.
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Affiliation(s)
- Arianna N LaCroix
- College of Health Solutions, Arizona State University, Tempe, AZ, USA; College of Health Sciences, Midwestern University, Glendale, AZ, USA
| | | | - McKayla Tully
- College of Health Solutions, Arizona State University, Tempe, AZ, USA
| | | | - Corianne Rogalsky
- College of Health Solutions, Arizona State University, Tempe, AZ, USA.
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16
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Hoxworth JM, Eschbacher JM, Gonzales AC, Singleton KW, Leon GD, Smith KA, Stokes AM, Zhou Y, Mazza GL, Porter AB, Mrugala MM, Zimmerman RS, Bendok BR, Patra DP, Krishna C, Boxerman JL, Baxter LC, Swanson KR, Quarles CC, Schmainda KM, Hu LS. Performance of Standardized Relative CBV for Quantifying Regional Histologic Tumor Burden in Recurrent High-Grade Glioma: Comparison against Normalized Relative CBV Using Image-Localized Stereotactic Biopsies. AJNR Am J Neuroradiol 2020; 41:408-415. [PMID: 32165359 DOI: 10.3174/ajnr.a6486] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 12/23/2019] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Perfusion MR imaging measures of relative CBV can distinguish recurrent tumor from posttreatment radiation effects in high-grade gliomas. Currently, relative CBV measurement requires normalization based on user-defined reference tissues. A recently proposed method of relative CBV standardization eliminates the need for user input. This study compares the predictive performance of relative CBV standardization against relative CBV normalization for quantifying recurrent tumor burden in high-grade gliomas relative to posttreatment radiation effects. MATERIALS AND METHODS We recruited 38 previously treated patients with high-grade gliomas (World Health Organization grades III or IV) undergoing surgical re-resection for new contrast-enhancing lesions concerning for recurrent tumor versus posttreatment radiation effects. We recovered 112 image-localized biopsies and quantified the percentage of histologic tumor content versus posttreatment radiation effects for each sample. We measured spatially matched normalized and standardized relative CBV metrics (mean, median) and fractional tumor burden for each biopsy. We compared relative CBV performance to predict tumor content, including the Pearson correlation (r), against histologic tumor content (0%-100%) and the receiver operating characteristic area under the curve for predicting high-versus-low tumor content using binary histologic cutoffs (≥50%; ≥80% tumor). RESULTS Across relative CBV metrics, fractional tumor burden showed the highest correlations with tumor content (0%-100%) for normalized (r = 0.63, P < .001) and standardized (r = 0.66, P < .001) values. With binary cutoffs (ie, ≥50%; ≥80% tumor), predictive accuracies were similar for both standardized and normalized metrics and across relative CBV metrics. Median relative CBV achieved the highest area under the curve (normalized = 0.87, standardized = 0.86) for predicting ≥50% tumor, while fractional tumor burden achieved the highest area under the curve (normalized = 0.77, standardized = 0.80) for predicting ≥80% tumor. CONCLUSIONS Standardization of relative CBV achieves similar performance compared with normalized relative CBV and offers an important step toward workflow optimization and consensus methodology.
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Affiliation(s)
- J M Hoxworth
- From the Departments of Radiology (J.M.H., Y.Z., L.S.H.)
| | | | | | - K W Singleton
- Precision Neurotherapeutics Lab (K.W.S., G.D.L., B.R.B., K.R.S.), Mayo Clinic in Arizona, Phoenix, Arizona
| | - G D Leon
- Precision Neurotherapeutics Lab (K.W.S., G.D.L., B.R.B., K.R.S.), Mayo Clinic in Arizona, Phoenix, Arizona
| | - K A Smith
- Keller Center for Imaging Innovation (A.M.S.), Barrow Neurological Institute, Phoenix, Arizona
| | - A M Stokes
- Keller Center for Imaging Innovation (A.M.S.), Barrow Neurological Institute, Phoenix, Arizona
| | - Y Zhou
- From the Departments of Radiology (J.M.H., Y.Z., L.S.H.)
| | - G L Mazza
- Department of Health Sciences Research (G.L.M.), Division of Biomedical Statistics and Informatics, Mayo Clinic Scottsdale, Scottsdale, Arizona
| | | | | | | | - B R Bendok
- Precision Neurotherapeutics Lab (K.W.S., G.D.L., B.R.B., K.R.S.), Mayo Clinic in Arizona, Phoenix, Arizona
| | - D P Patra
- Departments of Neurosurgery (D.P.P.)
| | | | - J L Boxerman
- Department of Diagnostic Imaging (J.L.B.), Rhode Island Hospital, Providence, Rhode Island
| | - L C Baxter
- Neuropsychology (L.C.B.), Mayo Clinic Hospital, Phoenix, Arizona
| | - K R Swanson
- Precision Neurotherapeutics Lab (K.W.S., G.D.L., B.R.B., K.R.S.), Mayo Clinic in Arizona, Phoenix, Arizona
| | | | - K M Schmainda
- Department of Radiology (K.M.S.), Medical College of Wisconsin, Milwaukee, Wisconsin
| | - L S Hu
- From the Departments of Radiology (J.M.H., Y.Z., L.S.H.)
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17
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Fitzhugh MC, Hemesath A, Schaefer SY, Baxter LC, Rogalsky C. Functional Connectivity of Heschl's Gyrus Associated With Age-Related Hearing Loss: A Resting-State fMRI Study. Front Psychol 2019; 10:2485. [PMID: 31780994 PMCID: PMC6856672 DOI: 10.3389/fpsyg.2019.02485] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [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] [Received: 04/26/2019] [Accepted: 10/21/2019] [Indexed: 12/23/2022] Open
Abstract
A large proportion of older adults experience hearing loss. Yet, the impact of hearing loss on the aging brain, particularly on large-scale brain networks that support cognition and language, is relatively unknown. We used resting-state functional magnetic resonance imaging (fMRI) to identify hearing loss-related changes in the functional connectivity of primary auditory cortex to determine if these changes are distinct from age and cognitive measures known to decline with age (e.g., working memory and processing speed). We assessed the functional connectivity of Heschl's gyrus in 31 older adults (60-80 years) who expressed a range of hearing abilities from normal hearing to a moderate hearing loss. Our results revealed that both left and right Heschl's gyri were significantly connected to regions within auditory, sensorimotor, and visual cortices, as well as to regions within the cingulo-opercular network known to support attention. Participant age, working memory, and processing speed did not significantly correlate with any connectivity measures once variance due to hearing loss was removed. However, hearing loss was associated with increased connectivity between right Heschl's gyrus and the dorsal anterior cingulate in the cingulo-opercular network even once variance due to age, working memory, and processing speed was removed. This greater connectivity was not driven by high frequency hearing loss, but rather by hearing loss measured in the 0.5-2 kHz range, particularly in the left ear. We conclude that hearing loss-related differences in functional connectivity in older adults are distinct from other aging-related differences and provide insight into a possible neural mechanism of compensation for hearing loss in older adults.
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Affiliation(s)
- Megan C Fitzhugh
- College of Health Solutions, Arizona State University, Tempe, AZ, United States
| | - Angela Hemesath
- College of Health Solutions, Arizona State University, Tempe, AZ, United States
| | - Sydney Y Schaefer
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, United States
| | - Leslie C Baxter
- Department of Psychology, Mayo Clinic, Scottsdale, AZ, United States
| | - Corianne Rogalsky
- College of Health Solutions, Arizona State University, Tempe, AZ, United States
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18
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Gaw N, Hawkins-Daarud A, Hu LS, Yoon H, Wang L, Xu Y, Jackson PR, Singleton KW, Baxter LC, Eschbacher J, Gonzales A, Nespodzany A, Smith K, Nakaji P, Mitchell JR, Wu T, Swanson KR, Li J. Integration of machine learning and mechanistic models accurately predicts variation in cell density of glioblastoma using multiparametric MRI. Sci Rep 2019; 9:10063. [PMID: 31296889 PMCID: PMC6624304 DOI: 10.1038/s41598-019-46296-4] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [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: 02/11/2019] [Accepted: 06/26/2019] [Indexed: 01/30/2023] Open
Abstract
Glioblastoma (GBM) is a heterogeneous and lethal brain cancer. These tumors are followed using magnetic resonance imaging (MRI), which is unable to precisely identify tumor cell invasion, impairing effective surgery and radiation planning. We present a novel hybrid model, based on multiparametric intensities, which combines machine learning (ML) with a mechanistic model of tumor growth to provide spatially resolved tumor cell density predictions. The ML component is an imaging data-driven graph-based semi-supervised learning model and we use the Proliferation-Invasion (PI) mechanistic tumor growth model. We thus refer to the hybrid model as the ML-PI model. The hybrid model was trained using 82 image-localized biopsies from 18 primary GBM patients with pre-operative MRI using a leave-one-patient-out cross validation framework. A Relief algorithm was developed to quantify relative contributions from the data sources. The ML-PI model statistically significantly outperformed (p < 0.001) both individual models, ML and PI, achieving a mean absolute predicted error (MAPE) of 0.106 ± 0.125 versus 0.199 ± 0.186 (ML) and 0.227 ± 0.215 (PI), respectively. Associated Pearson correlation coefficients for ML-PI, ML, and PI were 0.838, 0.518, and 0.437, respectively. The Relief algorithm showed the PI model had the greatest contribution to the result, emphasizing the importance of the hybrid model in achieving the high accuracy.
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Affiliation(s)
- Nathan Gaw
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ, 85281, USA
| | - Andrea Hawkins-Daarud
- Precision NeuroTherapeutics (PNT) Lab, Mayo Clinic Arizona, 5777 E Mayo Blvd, Phoenix, Arizona, 85054, USA.
| | - Leland S Hu
- Department of Radiology, Mayo Clinic Arizona, 5777 E Mayo Blvd, Phoenix, Arizona, 85054, USA
| | - Hyunsoo Yoon
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ, 85281, USA
| | - Lujia Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ, 85281, USA
| | - Yanzhe Xu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ, 85281, USA
| | - Pamela R Jackson
- Precision NeuroTherapeutics (PNT) Lab, Mayo Clinic Arizona, 5777 E Mayo Blvd, Phoenix, Arizona, 85054, USA
| | - Kyle W Singleton
- Precision NeuroTherapeutics (PNT) Lab, Mayo Clinic Arizona, 5777 E Mayo Blvd, Phoenix, Arizona, 85054, USA
| | - Leslie C Baxter
- Department of Radiology, Mayo Clinic Arizona, 5777 E Mayo Blvd, Phoenix, Arizona, 85054, USA
| | - Jennifer Eschbacher
- Department of Pathology, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Ashlyn Gonzales
- Department of Radiology, Mayo Clinic Arizona, 5777 E Mayo Blvd, Phoenix, Arizona, 85054, USA
| | - Ashley Nespodzany
- Department of Radiology, Mayo Clinic Arizona, 5777 E Mayo Blvd, Phoenix, Arizona, 85054, USA
| | - Kris Smith
- Department of Neurosurgery, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Peter Nakaji
- Department of Neurosurgery, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - J Ross Mitchell
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, Florida, 33612, USA
| | - Teresa Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ, 85281, USA
| | - Kristin R Swanson
- Precision NeuroTherapeutics (PNT) Lab, Mayo Clinic Arizona, 5777 E Mayo Blvd, Phoenix, Arizona, 85054, USA
- Department of Neurosurgery, Mayo Clinic Arizona, 5777 E Mayo Blvd, Phoenix, Arizona, 85054, USA
| | - Jing Li
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ, 85281, USA
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Baxter LC, Nespodzany A, Wood E, Stoeckmann M, Smith CJ, Braden BB. The influence of age and ASD on verbal fluency networks. Res Autism Spectr Disord 2019; 63:52-62. [PMID: 32565886 PMCID: PMC7304570 DOI: 10.1016/j.rasd.2019.03.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
BACKGROUND The integrity and connectivity of the frontal lobe, which subserves fluency, may be compromised by both ASD and aging. Alternate networks often integrate to help compensate for compromised functions during aging. We used network analyses to study how compensation may overcome age-related compromised in individuals with ASD. METHOD Participants consisted of middle-aged (40-60; n=24) or young (18-25; n=18) right-handed males who have a diagnosis of ASD, and age- and IQ-matched control participants (n=20, 14, respectively). All performed tests of language and executive functioning and a fluency functional MRI task. We first used group individual component analysis (ICA) for each of the 4 groups to determine whether different networks were engaged. An SPM analysis was used to compare activity detected in the network nodes from the ICA analyses. RESULTS The individuals with ASD performed more slowly on two cognitive tasks (Stroop word reading and Trailmaking Part A). The 4 groups engaged different networks during the fluency fMRI task despite equivalent performance. Comparisons of specific regions within these networks indicated younger individuals had greater engagement of the thalamus and supplementary speech area, while older adults engaged the superior temporal gyrus. Individuals with ASD did not disengage from the Default Mode Network during word generation. CONCLUSION Interactions between diagnosis and aging were not found in this study of young and middle-aged men, but evidence for differential engagement of compensatory networks was observed.
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Affiliation(s)
- Leslie C. Baxter
- Department of Neuroimaging Research, Barrow Neurological Institute, St. Joseph’s Hospital and Medical Center, 350 W Thomas Rd, Phoenix, AZ 85013
| | - Ashley Nespodzany
- Department of Neuroimaging Research, Barrow Neurological Institute, St. Joseph’s Hospital and Medical Center, 350 W Thomas Rd, Phoenix, AZ 85013
| | - Emily Wood
- Barrow Neurological Institute, Radiology, 85013, Phoenix, AZ, United States
| | - Melissa Stoeckmann
- Barrow Neurological Institute, Radiology, 85013, Phoenix, AZ, United States
| | - Christopher J. Smith
- Southwest Autism Research & Resource Center, 2225 N 16th Street, Phoenix, AZ 85006
| | - B. Blair Braden
- Department of Speech and Hearing Science, Arizona State University, 976 S Forest Mall, Tempe, AZ 85281
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20
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Walsh MJM, Baxter LC, Smith CJ, Braden BB. Age Group Differences in Executive Network Functional Connectivity and Relationships with Social Behavior in Men with Autism Spectrum Disorder. Res Autism Spectr Disord 2019; 63:63-77. [PMID: 32405319 PMCID: PMC7220036 DOI: 10.1016/j.rasd.2019.02.008] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
BACKGROUND Research suggests adults with autism spectrum disorder (ASD) may use executive functions to compensate for social difficulties. Given hallmark age-related declines in executive functioning and the executive brain network in normal aging, there is concern that older adults with ASD may experience further declines in social functioning as they age. In a male-only sample, we hypothesized: 1) older adults with ASD would demonstrate greater ASD-related social behavior than young adults with ASD, 2) adults with ASD would demonstrate a greater age group reduction in connectivity of the executive brain network than neurotypical (NT) adults, and 3) that behavioral and neural mechanisms of executive functioning would predict ASD-related social difficulties in adults with ASD. METHODS Participants were a cross-sectional sample of non-intellectually disabled young (ages 18-25) and middle-aged (ages 40-70) adult men with ASD and NT development (young adult ASD: n=24; middle-age ASD: n=25; young adult NT: n=15; middle-age NT: n=21). We assessed ASD-related social behavior via the self-report Social Responsiveness Scale-2 (SRS-2) Total Score, with exploratory analyses of the Social Cognition Subscale. We assessed neural executive function via connectivity of the resting-state executive network (EN) as measured by independent component analysis. Correlations were investigated between SRS-2 Total Scores (with exploratory analyses of the Social Cognition Subscale), EN functional connectivity of the dorsolateral prefrontal cortex (dlPFC), and a behavioral measure of executive function, Tower of London (ToL) Total Moves. RESULTS We did not confirm a significant age group difference for adults with ASD on the SRS-2 Total Score; however, exploratory analysis revealed middle-age men with ASD had higher scores on the SRS-2 Social Cognition Subscale than young adult men with ASD. Exacerbated age group reductions in EN functional connectivity were confirmed (left dlPFC) in men with ASD compared to NT, such that older adults with ASD demonstrated the greatest levels of hypoconnectivity. A significant correlation was confirmed between dlPFC connectivity and the SRS-2 Total Score in middle-age men with ASD, but not young adult men with ASD. Furthermore, exploratory analysis revealed a significant correlation with the SRS-2 Social Cognition Subscale for young and middle-aged ASD groups and ToL Total Moves. CONCLUSIONS Our findings suggest that ASD-related difficulties in social cognition and EN hypoconnectivity may get worse with age in men with ASD and is related to executive functioning. Further, exacerbated EN hypoconnectivity associated with older age in ASD may be a mechanism of increased ASD-related social cognition difficulties in older adults with ASD. Given the cross-sectional nature of this sample, longitudinal replication is needed.
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Affiliation(s)
- Melissa J. M. Walsh
- Department of Speech and Hearing Science, Arizona State University, 976 S Forest Mall, Tempe, AZ 85281
| | - Leslie C. Baxter
- Department of Neuroimaging Research, Barrow Neurological Institute, St. Joseph’s Hospital and Medical Center, 350 W Thomas Rd, Phoenix, AZ 85013
| | - Christopher J. Smith
- Southwest Autism Research & Resource Center, 2225 N 16th Street, Phoenix, AZ 85006
| | - B. Blair Braden
- Department of Speech and Hearing Science, Arizona State University, 976 S Forest Mall, Tempe, AZ 85281
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21
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Dong Q, Zhang W, Wu J, Li B, Schron EH, McMahon T, Shi J, Gutman BA, Chen K, Baxter LC, Thompson PM, Reiman EM, Caselli RJ, Wang Y. Applying surface-based hippocampal morphometry to study APOE-E4 allele dose effects in cognitively unimpaired subjects. Neuroimage Clin 2019; 22:101744. [PMID: 30852398 PMCID: PMC6411498 DOI: 10.1016/j.nicl.2019.101744] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Revised: 02/02/2019] [Accepted: 03/02/2019] [Indexed: 11/30/2022]
Abstract
Apolipoprotein E (APOE) e4 is the major genetic risk factor for late-onset Alzheimer's disease (AD). The dose-dependent impact of this allele on hippocampal volumes has been documented, but its influence on general hippocampal morphology in cognitively unimpaired individuals is still elusive. Capitalizing on the study of a large number of cognitively unimpaired late middle aged and older adults with two, one and no APOE-e4 alleles, the current study aims to characterize the ability of our automated surface-based hippocampal morphometry algorithm to distinguish between these three levels of genetic risk for AD and demonstrate its superiority to a commonly used hippocampal volume measurement. We examined the APOE-e4 dose effect on cross-sectional hippocampal morphology analysis in a magnetic resonance imaging (MRI) database of 117 cognitively unimpaired subjects aged between 50 and 85 years (mean = 57.4, SD = 6.3), including 36 heterozygotes (e3/e4), 37 homozygotes (e4/e4) and 44 non-carriers (e3/e3). The proposed automated framework includes hippocampal surface segmentation and reconstruction, higher-order hippocampal surface correspondence computation, and hippocampal surface deformation analysis with multivariate statistics. In our experiments, the surface-based method identified APOE-e4 dose effects on the left hippocampal morphology. Compared to the widely-used hippocampal volume measure, our hippocampal morphometry statistics showed greater statistical power by distinguishing cognitively unimpaired subjects with two, one, and no APOE-e4 alleles. Our findings mirrored previous studies showing that APOE-e4 has a dose effect on the acceleration of brain structure deformities. The results indicated that the proposed surface-based hippocampal morphometry measure is a potential preclinical AD imaging biomarker for cognitively unimpaired individuals. Applied surface-based hippocampal morphometry on cognitively unimpaired subjects. Our study identified APOE-e4 dose effects on cognitively unimpaired subjects. Surface-based hippocampal morphometry outperformed the hippocampal volume measure. Surface-based hippocampal morphometry may be a potential preclinical AD biomarker.
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Affiliation(s)
- Qunxi Dong
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Wen Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Jianfeng Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Bolun Li
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | | | - Travis McMahon
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Boris A Gutman
- Armour College of Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ, USA
| | - Leslie C Baxter
- Human Brain Imaging Laboratory, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | | | | | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
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22
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Hu LS, Yoon H, Eschbacher JM, Baxter LC, Dueck AC, Nespodzany A, Smith KA, Nakaji P, Xu Y, Wang L, Karis JP, Hawkins-Daarud AJ, Singleton KW, Jackson PR, Anderies BJ, Bendok BR, Zimmerman RS, Quarles C, Porter-Umphrey AB, Mrugala MM, Sharma A, Hoxworth JM, Sattur MG, Sanai N, Koulemberis PE, Krishna C, Mitchell JR, Wu T, Tran NL, Swanson KR, Li J. Accurate Patient-Specific Machine Learning Models of Glioblastoma Invasion Using Transfer Learning. AJNR Am J Neuroradiol 2019; 40:418-425. [PMID: 30819771 DOI: 10.3174/ajnr.a5981] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Accepted: 12/13/2018] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE MR imaging-based modeling of tumor cell density can substantially improve targeted treatment of glioblastoma. Unfortunately, interpatient variability limits the predictive ability of many modeling approaches. We present a transfer learning method that generates individualized patient models, grounded in the wealth of population data, while also detecting and adjusting for interpatient variabilities based on each patient's own histologic data. MATERIALS AND METHODS We recruited patients with primary glioblastoma undergoing image-guided biopsies and preoperative imaging, including contrast-enhanced MR imaging, dynamic susceptibility contrast MR imaging, and diffusion tensor imaging. We calculated relative cerebral blood volume from DSC-MR imaging and mean diffusivity and fractional anisotropy from DTI. Following image coregistration, we assessed tumor cell density for each biopsy and identified corresponding localized MR imaging measurements. We then explored a range of univariate and multivariate predictive models of tumor cell density based on MR imaging measurements in a generalized one-model-fits-all approach. We then implemented both univariate and multivariate individualized transfer learning predictive models, which harness the available population-level data but allow individual variability in their predictions. Finally, we compared Pearson correlation coefficients and mean absolute error between the individualized transfer learning and generalized one-model-fits-all models. RESULTS Tumor cell density significantly correlated with relative CBV (r = 0.33, P < .001), and T1-weighted postcontrast (r = 0.36, P < .001) on univariate analysis after correcting for multiple comparisons. With single-variable modeling (using relative CBV), transfer learning increased predictive performance (r = 0.53, mean absolute error = 15.19%) compared with one-model-fits-all (r = 0.27, mean absolute error = 17.79%). With multivariate modeling, transfer learning further improved performance (r = 0.88, mean absolute error = 5.66%) compared with one-model-fits-all (r = 0.39, mean absolute error = 16.55%). CONCLUSIONS Transfer learning significantly improves predictive modeling performance for quantifying tumor cell density in glioblastoma.
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Affiliation(s)
- L S Hu
- From the Department of Radiology (L.S.H., J.M.H., J.R.M., T.W., J.L.)
| | - H Yoon
- Arizona State University (H.Y., Y.X., L.W., T.W., J.L.), Tempe, Arizona
| | | | | | - A C Dueck
- Department of Biostatistics (A.C.D.), Mayo Clinic in Arizona, Scottsdale, Arizona
| | | | | | - P Nakaji
- Neurosurgery (K.A.S., P.N., N.S.)
| | - Y Xu
- Arizona State University (H.Y., Y.X., L.W., T.W., J.L.), Tempe, Arizona
| | - L Wang
- Arizona State University (H.Y., Y.X., L.W., T.W., J.L.), Tempe, Arizona
| | | | - A J Hawkins-Daarud
- Precision Neurotherapeutics Lab (A.J.H.-D., K.W.S., P.R.J, B.R.B., K.R.S.)
| | - K W Singleton
- Precision Neurotherapeutics Lab (A.J.H.-D., K.W.S., P.R.J, B.R.B., K.R.S.)
| | - P R Jackson
- Precision Neurotherapeutics Lab (A.J.H.-D., K.W.S., P.R.J, B.R.B., K.R.S.)
| | - B J Anderies
- Department of Neurosurgery (B.J.A., B.R.B., R.S.Z., M.G.S., P.E.K., C.K., K.R.S.)
| | - B R Bendok
- Precision Neurotherapeutics Lab (A.J.H.-D., K.W.S., P.R.J, B.R.B., K.R.S.).,Department of Neurosurgery (B.J.A., B.R.B., R.S.Z., M.G.S., P.E.K., C.K., K.R.S.)
| | - R S Zimmerman
- Department of Neurosurgery (B.J.A., B.R.B., R.S.Z., M.G.S., P.E.K., C.K., K.R.S.)
| | - C Quarles
- Neuroimaging Research (C.Q.), Barrow Neurological Institute, Phoenix, Arizona
| | | | - M M Mrugala
- Department of Neuro-Oncology (A.B.P.-U., M.M.M., A.S.)
| | - A Sharma
- Department of Neuro-Oncology (A.B.P.-U., M.M.M., A.S.)
| | - J M Hoxworth
- From the Department of Radiology (L.S.H., J.M.H., J.R.M., T.W., J.L.)
| | - M G Sattur
- Department of Neurosurgery (B.J.A., B.R.B., R.S.Z., M.G.S., P.E.K., C.K., K.R.S.)
| | - N Sanai
- Neurosurgery (K.A.S., P.N., N.S.)
| | - P E Koulemberis
- Department of Neurosurgery (B.J.A., B.R.B., R.S.Z., M.G.S., P.E.K., C.K., K.R.S.)
| | - C Krishna
- Department of Neurosurgery (B.J.A., B.R.B., R.S.Z., M.G.S., P.E.K., C.K., K.R.S.)
| | - J R Mitchell
- From the Department of Radiology (L.S.H., J.M.H., J.R.M., T.W., J.L.).,H. Lee Moffitt Cancer Center and Research Institute (J.R.M.), Tampa, Florida
| | - T Wu
- From the Department of Radiology (L.S.H., J.M.H., J.R.M., T.W., J.L.).,Arizona State University (H.Y., Y.X., L.W., T.W., J.L.), Tempe, Arizona
| | - N L Tran
- Department of Cancer Biology (N.L.T.), Mayo Clinic in Arizona, Phoenix, Arizona
| | - K R Swanson
- Precision Neurotherapeutics Lab (A.J.H.-D., K.W.S., P.R.J, B.R.B., K.R.S.).,Department of Neurosurgery (B.J.A., B.R.B., R.S.Z., M.G.S., P.E.K., C.K., K.R.S.)
| | - J Li
- From the Department of Radiology (L.S.H., J.M.H., J.R.M., T.W., J.L.).,Arizona State University (H.Y., Y.X., L.W., T.W., J.L.), Tempe, Arizona
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23
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Hu L, Gaw N, Yoon H, Eschbacher J, C. Baxter L, A. Smith K, Nakaji P, P. Karis J, Whitmire P, Hawkins-Daarud A, Singleton K, Jackson P, Christine Massey S, Bendok B, Mitchell J, Wu T, Tran N, Rubin J, Swanson K, Li J. NIMG-12. RADIOGENOMICS ON VENUS AND MARS: IMPACT OF SEX-DIFFERENCES ON MRI AND GENETIC CORRELATIONS IN GLIOBLASTOMA. Neuro Oncol 2018. [DOI: 10.1093/neuonc/noy148.739] [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: 11/12/2022] Open
Affiliation(s)
- Leland Hu
- Radiology, Mayo Clinic, Phoenix, AZ, USA
| | - Nathan Gaw
- Arizona State University, Tempe, AZ, USA
| | | | | | | | - Kris A. Smith
- Neurosurgery, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Peter Nakaji
- Neurosurgery, Barrow Neurological Institute, Phoenix, AZ, USA
| | - John P. Karis
- Radiology, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Paula Whitmire
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic Arizona, Phoenix, AZ, Phoenix, AZ, USA
| | | | - Kyle Singleton
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic Arizona, Phoenix, AZ, Phoenix, AZ, USA
| | - Pamela Jackson
- Mathematical Neuro-Oncology Lab, Precision Neurotherapeutics Innovation Program, Neurological Surgery, Mayo Clinic, Phoenix, AZ, USA
| | - Susan Christine Massey
- Mathematical Neuro-Oncology Lab, Precision Neurotherapeutics Innovation Program, Neurological Surgery, Mayo Clinic, Phoenix, AZ, USA
| | | | | | - Teresa Wu
- Arizona State University, Tempe, AZ, USA
| | - Nhan Tran
- Departments of Cancer Biology and Neurosurgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Joshua Rubin
- Washington University School of Medicine, St. Louis, MO, USA
| | - Kristin Swanson
- Mathematical Neuro-Oncology Lab, Neurological Surgery, Mayo Clinic, Phoenix, AZ, USA
| | - Jing Li
- Arizona State University, Tempe, AZ, USA
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24
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Johnson L, Fitzhugh MC, Yi Y, Mickelsen S, Baxter LC, Howard P, Rogalsky C. Functional Neuroanatomy of Second Language Sentence Comprehension: An fMRI Study of Late Learners of American Sign Language. Front Psychol 2018; 9:1626. [PMID: 30237778 PMCID: PMC6136263 DOI: 10.3389/fpsyg.2018.01626] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [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] [Received: 03/13/2018] [Accepted: 08/14/2018] [Indexed: 01/16/2023] Open
Abstract
The neurobiology of sentence comprehension is well-studied but the properties and characteristics of sentence processing networks remain unclear and highly debated. Sign languages (i.e., visual-manual languages), like spoken languages, have complex grammatical structures and thus can provide valuable insights into the specificity and function of brain regions supporting sentence comprehension. The present study aims to characterize how these well-studied spoken language networks can adapt in adults to be responsive to sign language sentences, which contain combinatorial semantic and syntactic visual-spatial linguistic information. Twenty native English-speaking undergraduates who had completed introductory American Sign Language (ASL) courses viewed videos of the following conditions during fMRI acquisition: signed sentences, signed word lists, English sentences and English word lists. Overall our results indicate that native language (L1) sentence processing resources are responsive to ASL sentence structures in late L2 learners, but that certain L1 sentence processing regions respond differently to L2 ASL sentences, likely due to the nature of their contribution to language comprehension. For example, L1 sentence regions in Broca's area were significantly more responsive to L2 than L1 sentences, supporting the hypothesis that Broca's area contributes to sentence comprehension as a cognitive resource when increased processing is required. Anterior temporal L1 sentence regions were sensitive to L2 ASL sentence structure, but demonstrated no significant differences in activation to L1 than L2, suggesting its contribution to sentence processing is modality-independent. Posterior superior temporal L1 sentence regions also responded to ASL sentence structure but were more activated by English than ASL sentences. An exploratory analysis of the neural correlates of L2 ASL proficiency indicates that ASL proficiency is positively correlated with increased activations in response to ASL sentences in L1 sentence processing regions. Overall these results suggest that well-established fronto-temporal spoken language networks involved in sentence processing exhibit functional plasticity with late L2 ASL exposure, and thus are adaptable to syntactic structures widely different than those in an individual's native language. Our findings also provide valuable insights into the unique contributions of the inferior frontal and superior temporal regions that are frequently implicated in sentence comprehension but whose exact roles remain highly debated.
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Affiliation(s)
- Lisa Johnson
- Department of Speech and Hearing Science, Arizona State University, Tempe, AZ, United States
| | - Megan C Fitzhugh
- Department of Speech and Hearing Science, Arizona State University, Tempe, AZ, United States.,Interdisciplinary Graduate Neuroscience Program, Arizona State University, Tempe, AZ, United States
| | - Yuji Yi
- Department of Speech and Hearing Science, Arizona State University, Tempe, AZ, United States
| | - Soren Mickelsen
- Department of Speech and Hearing Science, Arizona State University, Tempe, AZ, United States
| | - Leslie C Baxter
- Barrow Neurological Institute and St. Joseph's Hospital and Medical Center, Phoenix, AZ, United States
| | - Pamela Howard
- Department of Speech and Hearing Science, Arizona State University, Tempe, AZ, United States
| | - Corianne Rogalsky
- Department of Speech and Hearing Science, Arizona State University, Tempe, AZ, United States
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25
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Berisha V, Gilton D, Baxter LC, Corman SR, Blais C, Brewer G, Ruston S, Hunter Ball B, Wingert KM, Peter B, Rogalsky C. Structural neural predictors of Farsi-English bilingualism. Brain Lang 2018; 180-182:42-49. [PMID: 29723828 DOI: 10.1016/j.bandl.2018.04.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2017] [Revised: 03/24/2018] [Accepted: 04/14/2018] [Indexed: 06/08/2023]
Abstract
The neurobiology of bilingualism is hotly debated. The present study examines whether normalized cortical measurements can be used to reliably classify monolinguals versus bilinguals in a structural MRI dataset of Farsi-English bilinguals and English monolinguals. A decision tree classifier classified bilinguals with an average correct classification rate of 85%, and monolinguals with a rate of 71.4%. The most relevant regions for classification were the right supramarginal gyrus, left inferior temporal gyrus and left inferior frontal gyrus. Larger studies with carefully matched monolingual and bilingual samples are needed to confirm that features of these regions can reliably categorize monolingual and bilingual brains. Nonetheless, the present findings suggest that a single structural MRI scan, analyzed with measures readily available using default procedures in a free open-access software (Freesurfer), can be used to reliably predict an individual's language experience using a decision tree classifier, and that Farsi-English bilingualism implicates regions identified in previous group-level studies of bilingualism in other languages.
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Affiliation(s)
- Visar Berisha
- Department of Speech and Hearing Science, Arizona State University, Tempe, AZ 85287, USA; School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85287, USA
| | - Davis Gilton
- School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85287, USA
| | - Leslie C Baxter
- Barrow Neurological Institute and St. Joseph's Medical Center and Hospital, Phoenix, AZ 85013, USA
| | - Steven R Corman
- The Hugh Downs School of Human Communication, Arizona State University, Tempe, AZ 85281, USA
| | - Chris Blais
- Department of Psychology, Arizona State University, Tempe, AZ 85287, USA
| | - Gene Brewer
- Department of Psychology, Arizona State University, Tempe, AZ 85287, USA
| | - Scott Ruston
- The Hugh Downs School of Human Communication, Arizona State University, Tempe, AZ 85281, USA
| | - B Hunter Ball
- Department of Psychology, Arizona State University, Tempe, AZ 85287, USA
| | - Kimberly M Wingert
- Department of Psychology, Arizona State University, Tempe, AZ 85287, USA
| | - Beate Peter
- Department of Speech and Hearing Science, Arizona State University, Tempe, AZ 85287, USA; Department of Communication Sciences and Disorders, Saint Louis University, Saint Louis, MO 63101, USA
| | - Corianne Rogalsky
- Department of Speech and Hearing Science, Arizona State University, Tempe, AZ 85287, USA.
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Braden BB, Smith CJ, Thompson A, Glaspy TK, Wood E, Vatsa D, Abbott AE, McGee SC, Baxter LC. Executive function and functional and structural brain differences in middle-age adults with autism spectrum disorder. Autism Res 2017; 10:1945-1959. [PMID: 28940848 DOI: 10.1002/aur.1842] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [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: 01/30/2017] [Revised: 07/06/2017] [Accepted: 07/12/2017] [Indexed: 11/11/2022]
Abstract
There is a rapidly growing group of aging adults with autism spectrum disorder (ASD) who may have unique needs, yet cognitive and brain function in older adults with ASD is understudied. We combined functional and structural neuroimaging and neuropsychological tests to examine differences between middle-aged men with ASD and matched neurotypical (NT) men. Participants (ASD, n = 16; NT, n = 17) aged 40-64 years were well-matched according to age, IQ (range: 83-131), and education (range: 9-20 years). Middle-age adults with ASD made more errors on an executive function task (Wisconsin Card Sorting Test) but performed similarly to NT adults on tests of delayed verbal memory (Rey Auditory Verbal Learning Test) and local visual search (Embedded Figures Task). Independent component analysis of a functional MRI working memory task (n-back) completed by most participants (ASD = 14, NT = 17) showed decreased engagement of a cortico-striatal-thalamic-cortical neural network in older adults with ASD. Structurally, older adults with ASD had reduced bilateral hippocampal volumes, as measured by FreeSurfer. Findings expand our understanding of ASD as a lifelong condition with persistent cognitive and functional and structural brain differences evident at middle-age. Autism Res 2017, 10: 1945-1959. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY We compared cognitive abilities and brain measures between 16 middle-age men with high-functioning autism spectrum disorder (ASD) and 17 typical middle-age men to better understand how aging affects an older group of adults with ASD. Men with ASD made more errors on a test involving flexible thinking, had less activity in a flexible thinking brain network, and had smaller volume of a brain structure related to memory than typical men. We will follow these older adults over time to determine if aging changes are greater for individuals with ASD.
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Affiliation(s)
- B Blair Braden
- Department of Speech and Hearing Science, Arizona State University, Tempe, Arizona
| | | | - Amiee Thompson
- Department of Neuroimaging Research, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, Arizona
| | - Tyler K Glaspy
- Department of Neuroimaging Research, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, Arizona
| | - Emily Wood
- Department of Neuroimaging Research, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, Arizona
| | - Divya Vatsa
- Department of Neuroimaging Research, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, Arizona
| | - Angela E Abbott
- Department of Neuroimaging Research, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, Arizona
| | - Samuel C McGee
- Department of Neuroimaging Research, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, Arizona
| | - Leslie C Baxter
- Department of Neuroimaging Research, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, Arizona
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Bell LC, Does MD, Stokes AM, Baxter LC, Schmainda KM, Dueck AC, Quarles CC. Optimization of DSC MRI Echo Times for CBV Measurements Using Error Analysis in a Pilot Study of High-Grade Gliomas. AJNR Am J Neuroradiol 2017; 38:1710-1715. [PMID: 28684456 PMCID: PMC5591773 DOI: 10.3174/ajnr.a5295] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2016] [Accepted: 05/07/2017] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE The optimal TE must be calculated to minimize the variance in CBV measurements made with DSC MR imaging. Simulations can be used to determine the influence of the TE on CBV, but they may not adequately recapitulate the in vivo heterogeneity of precontrast T2*, contrast agent kinetics, and the biophysical basis of contrast agent-induced T2* changes. The purpose of this study was to combine quantitative multiecho DSC MRI T2* time curves with error analysis in order to compute the optimal TE for a traditional single-echo acquisition. MATERIALS AND METHODS Eleven subjects with high-grade gliomas were scanned at 3T with a dual-echo DSC MR imaging sequence to quantify contrast agent-induced T2* changes in this retrospective study. Optimized TEs were calculated with propagation of error analysis for high-grade glial tumors, normal-appearing white matter, and arterial input function estimation. RESULTS The optimal TE is a weighted average of the T2* values that occur as a contrast agent bolus transverses a voxel. The mean optimal TEs were 30.0 ± 7.4 ms for high-grade glial tumors, 36.3 ± 4.6 ms for normal-appearing white matter, and 11.8 ± 1.4 ms for arterial input function estimation (repeated-measures ANOVA, P < .001). CONCLUSIONS Greater heterogeneity was observed in the optimal TE values for high-grade gliomas, and mean values of all 3 ROIs were statistically significant. The optimal TE for the arterial input function estimation is much shorter; this finding implies that quantitative DSC MR imaging acquisitions would benefit from multiecho acquisitions. In the case of a single-echo acquisition, the optimal TE prescribed should be 30-35 ms (without a preload) and 20-30 ms (with a standard full-dose preload).
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Affiliation(s)
- L C Bell
- From the Division of Imaging Research (L.C. Bell, A.M.S., L.C. Baxter, C.C.Q.), Barrow Neurological Institute, Phoenix, Arizona
| | - M D Does
- Department of Biomedical Engineering (M.D.D.), Vanderbilt University Institute of Imaging Science, Nashville, Tennessee
| | - A M Stokes
- From the Division of Imaging Research (L.C. Bell, A.M.S., L.C. Baxter, C.C.Q.), Barrow Neurological Institute, Phoenix, Arizona
| | - L C Baxter
- From the Division of Imaging Research (L.C. Bell, A.M.S., L.C. Baxter, C.C.Q.), Barrow Neurological Institute, Phoenix, Arizona
| | - K M Schmainda
- Departments of Biophysics and Radiology (K.M.S.), Medical College of Wisconsin, Milwaukee, Wisconsin
| | - A C Dueck
- Division of Health Sciences Research (A.C.D.), Section of Biostatistics, Mayo Clinic, Scottsdale, Arizona
| | - C C Quarles
- From the Division of Imaging Research (L.C. Bell, A.M.S., L.C. Baxter, C.C.Q.), Barrow Neurological Institute, Phoenix, Arizona
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Bell LC, Hu LS, Stokes AM, McGee SC, Baxter LC, Quarles CC. Characterizing the Influence of Preload Dosing on Percent Signal Recovery (PSR) and Cerebral Blood Volume (CBV) Measurements in a Patient Population With High-Grade Glioma Using Dynamic Susceptibility Contrast MRI. ACTA ACUST UNITED AC 2017; 3:89-95. [PMID: 28825039 PMCID: PMC5557059 DOI: 10.18383/j.tom.2017.00004] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [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: 11/24/2022]
Abstract
With DSC-MRI, contrast agent leakage effects in brain tumors can either be leveraged for percent signal recovery (PSR) measurements or be adequately resolved for accurate relative cerebral blood volume (rCBV) measurements. Leakage effects can be dimished by administration of a preload dose before imaging and/or specific postprocessing steps. This study compares the consistency of both PSR and rCBV measurements as a function of varying preload doses in a retrospective analysis of 14 subjects with high-grade gliomas. The scans consisted of 6 DSC-MRI scans during 6 sequential bolus injections (0.05 mmol/kg). Mean PSR was calculated for tumor and normal-appearing white matter regions of interest. DSC-MRI data were corrected for leakage effects before computing mean tumor rCBV. Statistical differences were seen across varying preloads for tumor PSR (P value = 4.57E-24). Tumor rCBV values did not exhibit statistically significant differences across preloads (P value = .14) and were found to be highly consistent for clinically relevant preloads (intraclass correlation coefficient = 0.93). For a 0.05 mmol/kg injection bolus and pulse sequence parameters used, the highest PSR contrast between normal-appearing white matter and tumor occurs when no preload is used. This suggests that studies using PSR as a biomarker should acquire DSC-MRI data without preload. The finding that leakage-corrected rCBV values do not depend on the presence or dose of preload contradicts that of previous studies with dissimilar acquisition protocols. This further confirms the sensitivity of rCBV to preload dosing schemes and pulse sequence parameters and highlights the importance of standardization efforts for achieving multisite rCBV consistency.
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Affiliation(s)
- Laura C Bell
- Division of Imaging Research, Barrow Neurological Institute, Phoenix, Arizona
| | - Leland S Hu
- Department of Radiology, Mayo Clinic Arizona, Scottsdale, Arizona
| | - Ashley M Stokes
- Division of Imaging Research, Barrow Neurological Institute, Phoenix, Arizona
| | - Samuel C McGee
- Division of Imaging Research, Barrow Neurological Institute, Phoenix, Arizona
| | - Leslie C Baxter
- Division of Imaging Research, Barrow Neurological Institute, Phoenix, Arizona
| | - C Chad Quarles
- Division of Imaging Research, Barrow Neurological Institute, Phoenix, Arizona
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Benjamin CF, Walshaw PD, Hale K, Gaillard WD, Baxter LC, Berl MM, Polczynska M, Noble S, Alkawadri R, Hirsch LJ, Constable RT, Bookheimer SY. Presurgical language fMRI: Mapping of six critical regions. Hum Brain Mapp 2017; 38:4239-4255. [PMID: 28544168 PMCID: PMC5518223 DOI: 10.1002/hbm.23661] [Citation(s) in RCA: 73] [Impact Index Per Article: 10.4] [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] [Received: 12/20/2016] [Revised: 05/12/2017] [Accepted: 05/15/2017] [Indexed: 02/01/2023] Open
Abstract
Language mapping is a key goal in neurosurgical planning. fMRI mapping typically proceeds with a focus on Broca's and Wernicke's areas, although multiple other language‐critical areas are now well‐known. We evaluated whether clinicians could use a novel approach, including clinician‐driven individualized thresholding, to reliably identify six language regions, including Broca's Area, Wernicke's Area (inferior, superior), Exner's Area, Supplementary Speech Area, Angular Gyrus, and Basal Temporal Language Area. We studied 22 epilepsy and tumor patients who received Wada and fMRI (age 36.4[12.5]; Wada language left/right/mixed in 18/3/1). fMRI tasks (two × three tasks) were analyzed by two clinical neuropsychologists who flexibly thresholded and combined these to identify the six regions. The resulting maps were compared to fixed threshold maps. Clinicians generated maps that overlapped significantly, and were highly consistent, when at least one task came from the same set. Cases diverged when clinicians prioritized different language regions or addressed noise differently. Language laterality closely mirrored Wada data (85% accuracy). Activation consistent with all six language regions was consistently identified. In blind review, three external, independent clinicians rated the individualized fMRI language maps as superior to fixed threshold maps; identified the majority of regions significantly more frequently; and judged language laterality to mirror Wada lateralization more often. These data provide initial validation of a novel, clinician‐based approach to localizing language cortex. They also demonstrate clinical fMRI is superior when analyzed by an experienced clinician and that when fMRI data is of low quality judgments of laterality are unreliable and should be withheld. Hum Brain Mapp 38:4239–4255, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Christopher F Benjamin
- Department of Neurology, Comprehensive Epilepsy Center, Yale School of Medicine, New Haven, Connecticut.,Department of Neurosurgery, Yale School of Medicine, New Haven, Connecticut
| | - Patricia D Walshaw
- UCLA Department of Psychiatry and Biobehavioral Sciences, Los Angeles, California
| | - Kayleigh Hale
- U.S. Department of Veterans Affairs, War Related Illness and Injury Study Center, Washington, DC
| | - William D Gaillard
- Center for Neuroscience Research, Children's National Health System, Washington, DC
| | - Leslie C Baxter
- Department of Neuroimaging Research, Barrow Neurological Institute, Phoenix, Arizona
| | - Madison M Berl
- Center for Neuroscience Research, Children's National Health System, Washington, DC
| | - Monika Polczynska
- UCLA Department of Psychiatry and Biobehavioral Sciences, Los Angeles, California.,Faculty of English, Adam Mickiewicz University, Poznań, Poland
| | - Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Rafeed Alkawadri
- Department of Neurology, Comprehensive Epilepsy Center, Yale School of Medicine, New Haven, Connecticut
| | - Lawrence J Hirsch
- Department of Neurology, Comprehensive Epilepsy Center, Yale School of Medicine, New Haven, Connecticut
| | - R Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Susan Y Bookheimer
- UCLA Department of Psychiatry and Biobehavioral Sciences, Los Angeles, California
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Hawkins-Daarud A, DeGirolamo L, Jacobs J, Clark-Swanson K, Eschbacher JM, Smith KA, Nakaji P, Baxter LC, Karis JP, Wu T, Mitchell JR, Li J, Hu L, Swanson KR. Abstract A08: Histologic evidence for a bio-mathematical model of glioblastoma invasion. Cancer Res 2017. [DOI: 10.1158/1538-7445.epso16-a08] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Purpose: Investigate the utility of patient-specific spatial predictions of tumor cell density from a bio-mathematical model.
Introduction: Glioblastomas (GBMs) are the most malignant of all primary brain tumors. While it is known there is always a non-detectable portion of the tumor, current techniques of monitoring GBM progression, imaging and initial histological assessment, are not able to reliably estimate the tumor invasion past the enhancing region on T2-Weighted (T2W) imaging. Over the last two decades, a large effort has been made to create a simple patient-specific mathematical model of gliomas. The resulting model, referred to as the Proliferation-Invasion (PI) model, is based on two key parameters, the net growth rate, ρ, and the dispersal coefficient, D. In this model, the ratio of D/ρ is related to degree of invasion and the product D*ρ, is related to the speed of growth.
The intuitive understanding provided by this model has been able to provide patient-specific understanding of disease kinetics enabling prediction of outcomes following surgical resection, radiation and the development of a prognostic response metric. Previous literature utilizing this model has been based on the assumption that what is seen on the pretreatment T1-Weighted contrast-enhanced (T1Gd) and T2W, images correspond to an 80% and 16% tumor cell density threshold respectively. This assumption allows for an estimate of D/ρ from a single time point of imaging. While these values were based on extensive experience, for ethical and technical reasons, they have never been rigorously investigated histologically. Recent technological advances have made it possible for surgeons to use an MRI to guide the acquisition of tissue making it possible to know with a good degree of accuracy where on the MR image the histological specimen comes from.
Methods: Model Calibration : To estimate D/ρ for each patient, we assume abnormalities on the T1Gd and T2W images correspond to an 80% and 16% tumor cell density threshold respectively. We then utilize a Bayesian calibration approach based on adaptive grid refinement while holding the velocity constant to find the most likely value of D/ρ to match the observed radial measurements. Three-Dimensional Density Maps : Given a gray/white segmentation and an estimate for D/ρ, we can build a tumor cell density prediction in the patient's anatomy using the Eikonal equations and the modified Fast Marching Method (FMM) algorithm presented by Konukoglu et al. Patient Cohort : Eighteen patients were recruited with clinically suspected GBM undergoing preoperative stereotactic MRI for surgical resection with IRB approval Barrow Neurological Institute and Mayo Clinic in Arizona. Surgical Biopsy : Pre-operative conventional MRI, including T1Gd and T2W, was utilized to guide stereotactic biopsies. An average of 5–6 tissue specimens were acquired from each tumor by using stereotactic surgical localization, following the smallest possible diameter craniotomies to minimize brain shift. Histological Analysis : 4 μm tissue sections were stained with hematoxylin and eosin (H&E) for neuropathology review. H&E slides were reviewed by a neuropathologist. The percent tumor nuclei in the field was estimated.
Results and Conclusion: Our analysis showed that the intuitive ordering of diffuse to nodular tumor profiles from the PI model is consistent with the histologic data. Further, utilizing the histologic data, limitations to a universal threshold cutoff of the T2/FLAIR region are specified and a better threshold cutoff value is suggested. We hope that this paper will lead to more studies of this kind resulting in more accurate ways to predict the spatial distribution of GBM tumor cells from medical imaging. Such advances could revolutionize surgical procedures and radiation therapy and may enable additional insights into tumor kinetic differences meaningful for treatment.
Citation Format: Andrea Hawkins-Daarud, Lauren DeGirolamo, Joshua Jacobs, Kamala Clark-Swanson, Jennifer M. Eschbacher, Kris A. Smith, Peter Nakaji, Leslie C. Baxter, John P. Karis, Teresa Wu, J. Ross Mitchell, Jing Li, Leland Hu, Kristin R. Swanson. Histologic evidence for a bio-mathematical model of glioblastoma invasion. [abstract]. In: Proceedings of the AACR Special Conference on Engineering and Physical Sciences in Oncology; 2016 Jun 25-28; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2017;77(2 Suppl):Abstract nr A08.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Teresa Wu
- 4Arizona State University, Tempe, AZ
| | | | - Jing Li
- 4Arizona State University, Tempe, AZ
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Hu LS, Ning S, Eschbacher JM, Baxter LC, Gaw N, Ranjbar S, Plasencia J, Dueck AC, Peng S, Smith KA, Nakaji P, Karis JP, Quarles CC, Wu T, Loftus JC, Jenkins RB, Sicotte H, Kollmeyer TM, O'Neill BP, Elmquist W, Hoxworth JM, Frakes D, Sarkaria J, Swanson KR, Tran NL, Li J, Mitchell JR. Radiogenomics to characterize regional genetic heterogeneity in glioblastoma. Neuro Oncol 2016; 19:128-137. [PMID: 27502248 DOI: 10.1093/neuonc/now135] [Citation(s) in RCA: 137] [Impact Index Per Article: 17.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Glioblastoma (GBM) exhibits profound intratumoral genetic heterogeneity. Each tumor comprises multiple genetically distinct clonal populations with different therapeutic sensitivities. This has implications for targeted therapy and genetically informed paradigms. Contrast-enhanced (CE)-MRI and conventional sampling techniques have failed to resolve this heterogeneity, particularly for nonenhancing tumor populations. This study explores the feasibility of using multiparametric MRI and texture analysis to characterize regional genetic heterogeneity throughout MRI-enhancing and nonenhancing tumor segments. METHODS We collected multiple image-guided biopsies from primary GBM patients throughout regions of enhancement (ENH) and nonenhancing parenchyma (so called brain-around-tumor, [BAT]). For each biopsy, we analyzed DNA copy number variants for core GBM driver genes reported by The Cancer Genome Atlas. We co-registered biopsy locations with MRI and texture maps to correlate regional genetic status with spatially matched imaging measurements. We also built multivariate predictive decision-tree models for each GBM driver gene and validated accuracies using leave-one-out-cross-validation (LOOCV). RESULTS We collected 48 biopsies (13 tumors) and identified significant imaging correlations (univariate analysis) for 6 driver genes: EGFR, PDGFRA, PTEN, CDKN2A, RB1, and TP53. Predictive model accuracies (on LOOCV) varied by driver gene of interest. Highest accuracies were observed for PDGFRA (77.1%), EGFR (75%), CDKN2A (87.5%), and RB1 (87.5%), while lowest accuracy was observed in TP53 (37.5%). Models for 4 driver genes (EGFR, RB1, CDKN2A, and PTEN) showed higher accuracy in BAT samples (n = 16) compared with those from ENH segments (n = 32). CONCLUSION MRI and texture analysis can help characterize regional genetic heterogeneity, which offers potential diagnostic value under the paradigm of individualized oncology.
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Affiliation(s)
- Leland S Hu
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Shuluo Ning
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Jennifer M Eschbacher
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Leslie C Baxter
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Nathan Gaw
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Sara Ranjbar
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Jonathan Plasencia
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Amylou C Dueck
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Sen Peng
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Kris A Smith
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Peter Nakaji
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - John P Karis
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - C Chad Quarles
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Teresa Wu
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Joseph C Loftus
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Robert B Jenkins
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Hugues Sicotte
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Thomas M Kollmeyer
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Brian P O'Neill
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - William Elmquist
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Joseph M Hoxworth
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - David Frakes
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Jann Sarkaria
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Kristin R Swanson
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Nhan L Tran
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Jing Li
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - J Ross Mitchell
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
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Abstract
The neural processes associated with becoming aware of sad mood are not fully understood. We examined the dynamic process of becoming aware of sad mood and recovery from sad mood. Sixteen healthy subjects underwent fMRI while participating in a sadness induction task designed to allow for variable mood induction times. Individualized regressors linearly modeled the time periods during the attainment of self-reported sad and baseline "neutral" mood states, and the validity of the linearity assumption was further tested using independent component analysis. During sadness induction the dorsomedial and ventrolateral prefrontal cortices, and anterior insula exhibited a linear increase in the blood oxygen level-dependent (BOLD) signal until subjects became aware of a sad mood and then a subsequent linear decrease as subjects transitioned from sadness back to the non-sadness baseline condition. These findings extend understanding of the neural basis of conscious emotional experience.
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Affiliation(s)
- Ryan Smith
- Department of Neuroimaging, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, 222 West Thomas Rd, Phoenix, AZ, 85259, USA. .,Departments of Psychiatry and Psychology, University of Arizona, 1501 N. Campbell Ave, Tucson, AZ, 85724, USA. .,Division of Neuroimaging Research, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, 222 West Thomas Rd, Phoenix, AZ, 85013, USA.
| | - B Blair Braden
- Department of Neuroimaging, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, 222 West Thomas Rd, Phoenix, AZ, 85259, USA
| | - Kewei Chen
- Department of Neuroimaging, Banner Alzheimer Institute and Banner Good Samaritan PET Center, 1111 E. McDowell Road, Phoenix, AZ, 85006, USA
| | - Francisco A Ponce
- Department of Neurosurgery, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, 222 West Thomas Rd, Phoenix, AZ, 85259, USA
| | - Richard D Lane
- Departments of Psychiatry and Psychology, University of Arizona, 1501 N. Campbell Ave, Tucson, AZ, 85724, USA
| | - Leslie C Baxter
- Department of Neuroimaging, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, 222 West Thomas Rd, Phoenix, AZ, 85259, USA
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Braden BB, Dassel KB, Bimonte-Nelson HA, O'Rourke HP, Connor DJ, Moorhous S, Sabbagh MN, Caselli RJ, Baxter LC. Sex and post-menopause hormone therapy effects on hippocampal volume and verbal memory. Neuropsychol Dev Cogn B Aging Neuropsychol Cogn 2016; 24:227-246. [PMID: 27263667 DOI: 10.1080/13825585.2016.1182962] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Many studies suggest sex differences in memory and hippocampal size, and that hormone therapy (HT) may positively affect these measures in women; however, the parameters of HT use that most likely confer benefits are debated. We evaluated the impact of sex and postmenopausal HT use on verbal learning and memory and hippocampal size in 94 cognitively intact women and 49 men. Using analysis of covariance that controlled for age and education, women had better total word learning and delayed verbal memory performance than men. HT analyses showed that non-HT users performed similarly to men, while HT users performed better than men in Delayed Memory regardless of whether use was current or in the past. Women had larger hippocampal volumes than men regardless of whether they were HT users. Using univariate linear models, we assessed group differences in the predictive value of hippocampal volumes for verbal learning and memory. Hippocampal size significantly predicted memory performance for men and non-HT users, but not for HT users. This lack of relationship between hippocampal size and verbal learning and memory performance in HT users suggests HT use may impact memory through extra-hippocampal neural systems.
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Affiliation(s)
- B Blair Braden
- a Department of Neuropsychology , Barrow Neurological Institute , Phoenix , AZ , USA
| | - Kara B Dassel
- a Department of Neuropsychology , Barrow Neurological Institute , Phoenix , AZ , USA
| | | | - Holly P O'Rourke
- b Department of Psychology , Arizona State University , Tempe , AZ , USA
| | - Donald J Connor
- c The Cleo Roberts Center for Clinical Research, Banner Sun Health Research Institute , Sun City , AZ , USA
| | - Sallie Moorhous
- a Department of Neuropsychology , Barrow Neurological Institute , Phoenix , AZ , USA
| | - Marwan N Sabbagh
- c The Cleo Roberts Center for Clinical Research, Banner Sun Health Research Institute , Sun City , AZ , USA
| | - Richard J Caselli
- d Department of Neurology , Mayo Clinic Arizona , Scottsdale , AZ , USA
| | - Leslie C Baxter
- a Department of Neuropsychology , Barrow Neurological Institute , Phoenix , AZ , USA
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Braden BB, Pipe TB, Smith R, Glaspy TK, Deatherage BR, Baxter LC. Brain and behavior changes associated with an abbreviated 4-week mindfulness-based stress reduction course in back pain patients. Brain Behav 2016; 6:e00443. [PMID: 26925304 PMCID: PMC4754498 DOI: 10.1002/brb3.443] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/17/2015] [Revised: 12/17/2015] [Accepted: 12/19/2015] [Indexed: 11/29/2022] Open
Abstract
INTRODUCTION Mindfulness-based stress reduction (MBSR) reduces depression, anxiety, and pain for people suffering from a variety of illnesses, and there is a growing need to understand the neurobiological networks implicated in self-reported psychological change as a result of training. Combining complementary and alternative treatments such as MBSR with other therapies is helpful; however, the time commitment of the traditional 8-week course may impede accessibility. This pilot study aimed to (1) determine if an abbreviated MBSR course improves symptoms in chronic back pain patients and (2) examine the neural and behavioral correlates of MBSR treatment. METHODS Participants were assigned to 4 weeks of weekly MBSR training (n = 12) or a control group (stress reduction reading; n = 11). Self-report ratings and task-based functional MRI were obtained prior to, and after, MBSR training, or at a yoked time point in the control group. RESULTS While both groups showed significant improvement in total depression symptoms, only the MBSR group significantly improved in back pain and somatic-affective depression symptoms. The MBSR group also uniquely showed significant increases in regional frontal lobe hemodynamic activity associated with gaining awareness to changes in one's emotional state. CONCLUSIONS An abbreviated MBSR course may be an effective complementary intervention that specifically improves back pain symptoms and frontal lobe regulation of emotional awareness, while the traditional 8-week course may be necessary to detect unique improvements in total anxiety and cognitive aspects of depression.
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Affiliation(s)
- B Blair Braden
- Department of Neuroimaging Barrow Neurological Institute St. Joseph's Hospital and Medical Center Phoenix Arizona
| | - Teri B Pipe
- College of Nursing and Health Innovation Arizona State University Phoenix Arizona
| | - Ryan Smith
- Department of Neuroimaging Barrow Neurological Institute St. Joseph's Hospital and Medical Center Phoenix Arizona; Departments of Psychiatry and Psychology University of Arizona Tucson Arizona
| | - Tyler K Glaspy
- Department of Neuroimaging Barrow Neurological Institute St. Joseph's Hospital and Medical Center Phoenix Arizona
| | - Brandon R Deatherage
- Department of Neuroimaging Barrow Neurological Institute St. Joseph's Hospital and Medical Center Phoenix Arizona
| | - Leslie C Baxter
- Department of Neuroimaging Barrow Neurological Institute St. Joseph's Hospital and Medical Center Phoenix Arizona
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Smith R, Baxter LC, Thayer JF, Lane RD. Disentangling introspective and exteroceptive attentional control from emotional appraisal in depression using fMRI: A preliminary study. Psychiatry Res Neuroimaging 2016; 248:39-47. [PMID: 26805672 DOI: 10.1016/j.pscychresns.2016.01.009] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2014] [Revised: 10/08/2015] [Accepted: 01/03/2016] [Indexed: 01/30/2023]
Abstract
The neurocognitive abnormalities in affective experience associated with depression remain incompletely understood. We examined BOLD activity in 10 healthy and 10 depressed subjects while they viewed emotional picture sets and categorized their experience in the moment as pleasant, unpleasant or neutral (introspective attention), as well as when they viewed matched pictures and judged whether they depicted indoor or outdoor scenes (exteroceptive attention). Contrasts permitted investigation of differences in neural activity between groups associated with (1) attentional control, and (2) appraisal of valence. Introspective attentional control (compared to exteroceptive attentional control) activated a common pregenual anterior cingulate (pACC) region in depressed and control subjects. Contrasts between appraised valences of attended emotional responses revealed a consistent pattern of increased BOLD activity to unpleasant emotional responses and decreased BOLD activity to pleasant emotional responses in depressed subjects relative to controls in ventromedial prefrontal cortex and insula. These findings support the conclusion that mechanisms for conscious attention to emotional experiences are intact in depressed subjects and that the affective disturbance in MDD is related to altered reactivity to pleasant vs. unpleasant stimuli.
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Affiliation(s)
- Ryan Smith
- Department of Psychiatry, University of Arizona, Tucson, AZ, USA; Department of Psychology, University of Arizona, Tucson, AZ, USA.
| | - Leslie C Baxter
- Department of Neuroimaging, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Julian F Thayer
- Department of Psychology, Ohio State University, Columbus, OH, USA
| | - Richard D Lane
- Department of Psychiatry, University of Arizona, Tucson, AZ, USA; Department of Psychology, University of Arizona, Tucson, AZ, USA
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Hu LS, Ning S, Eschbacher JM, Gaw N, Dueck AC, Smith KA, Nakaji P, Plasencia J, Ranjbar S, Price SJ, Tran N, Loftus J, Jenkins R, O’Neill BP, Elmquist W, Baxter LC, Gao F, Frakes D, Karis JP, Zwart C, Swanson KR, Sarkaria J, Wu T, Mitchell JR, Li J. Multi-Parametric MRI and Texture Analysis to Visualize Spatial Histologic Heterogeneity and Tumor Extent in Glioblastoma. PLoS One 2015; 10:e0141506. [PMID: 26599106 PMCID: PMC4658019 DOI: 10.1371/journal.pone.0141506] [Citation(s) in RCA: 82] [Impact Index Per Article: 9.1] [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: 07/31/2015] [Accepted: 10/08/2015] [Indexed: 01/14/2023] Open
Abstract
Background Genetic profiling represents the future of neuro-oncology but suffers from inadequate biopsies in heterogeneous tumors like Glioblastoma (GBM). Contrast-enhanced MRI (CE-MRI) targets enhancing core (ENH) but yields adequate tumor in only ~60% of cases. Further, CE-MRI poorly localizes infiltrative tumor within surrounding non-enhancing parenchyma, or brain-around-tumor (BAT), despite the importance of characterizing this tumor segment, which universally recurs. In this study, we use multiple texture analysis and machine learning (ML) algorithms to analyze multi-parametric MRI, and produce new images indicating tumor-rich targets in GBM. Methods We recruited primary GBM patients undergoing image-guided biopsies and acquired pre-operative MRI: CE-MRI, Dynamic-Susceptibility-weighted-Contrast-enhanced-MRI, and Diffusion Tensor Imaging. Following image coregistration and region of interest placement at biopsy locations, we compared MRI metrics and regional texture with histologic diagnoses of high- vs low-tumor content (≥80% vs <80% tumor nuclei) for corresponding samples. In a training set, we used three texture analysis algorithms and three ML methods to identify MRI-texture features that optimized model accuracy to distinguish tumor content. We confirmed model accuracy in a separate validation set. Results We collected 82 biopsies from 18 GBMs throughout ENH and BAT. The MRI-based model achieved 85% cross-validated accuracy to diagnose high- vs low-tumor in the training set (60 biopsies, 11 patients). The model achieved 81.8% accuracy in the validation set (22 biopsies, 7 patients). Conclusion Multi-parametric MRI and texture analysis can help characterize and visualize GBM’s spatial histologic heterogeneity to identify regional tumor-rich biopsy targets.
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Affiliation(s)
- Leland S. Hu
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Radiology, Barrow Neurological Institute, Phoenix, Arizona, United States of America
- * E-mail:
| | - Shuluo Ning
- School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona, United States of America
| | - Jennifer M. Eschbacher
- Department of Pathology, Barrow Neurological Institute, Phoenix, Arizona, United States of America
| | - Nathan Gaw
- School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona, United States of America
| | - Amylou C. Dueck
- Department of Biostatistics, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Kris A. Smith
- Department of Neurosurgery, Barrow Neurological Institute, Phoenix, Arizona, United States of America
| | - Peter Nakaji
- Department of Neurosurgery, Barrow Neurological Institute, Phoenix, Arizona, United States of America
| | - Jonathan Plasencia
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona, United States of America
| | - Sara Ranjbar
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Stephen J. Price
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Nhan Tran
- Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona, United States of America
| | - Joseph Loftus
- Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, AZ, United States of America
| | - Robert Jenkins
- Department of Pathology, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Brian P. O’Neill
- Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota, United States of America
| | - William Elmquist
- Department of Pharmacology, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Leslie C. Baxter
- Department of Radiology, Barrow Neurological Institute, Phoenix, Arizona, United States of America
| | - Fei Gao
- School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona, United States of America
| | - David Frakes
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona, United States of America
| | - John P. Karis
- Department of Radiology, Barrow Neurological Institute, Phoenix, Arizona, United States of America
| | - Christine Zwart
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Kristin R. Swanson
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Jann Sarkaria
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Teresa Wu
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, United States of America
- School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona, United States of America
| | - J. Ross Mitchell
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Jing Li
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, United States of America
- School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona, United States of America
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Hu LS, Kelm Z, Korfiatis P, Dueck AC, Elrod C, Ellingson BM, Kaufmann TJ, Eschbacher JM, Karis JP, Smith K, Nakaji P, Brinkman D, Pafundi D, Baxter LC, Erickson BJ. Impact of Software Modeling on the Accuracy of Perfusion MRI in Glioma. AJNR Am J Neuroradiol 2015; 36:2242-9. [PMID: 26359151 DOI: 10.3174/ajnr.a4451] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2015] [Accepted: 04/30/2015] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Relative cerebral blood volume, as measured by T2*-weighted dynamic susceptibility-weighted contrast-enhanced MRI, represents the most robust and widely used perfusion MR imaging metric in neuro-oncology. Our aim was to determine whether differences in modeling implementation will impact the correction of leakage effects (from blood-brain barrier disruption) and the accuracy of relative CBV calculations as measured on T2*-weighted dynamic susceptibility-weighted contrast-enhanced MR imaging at 3T field strength. MATERIALS AND METHODS This study included 52 patients with glioma undergoing DSC MR imaging. Thirty-six patients underwent both non-preload dose- and preload dose-corrected DSC acquisitions, with 16 patients undergoing preload dose-corrected acquisitions only. For each acquisition, we generated 2 sets of relative CBV metrics by using 2 separate, widely published, FDA-approved commercial software packages: IB Neuro and nordicICE. We calculated 4 relative CBV metrics within tumor volumes: mean relative CBV, mode relative CBV, percentage of voxels with relative CBV > 1.75, and percentage of voxels with relative CBV > 1.0 (fractional tumor burden). We determined Pearson (r) and Spearman (ρ) correlations between non-preload dose- and preload dose-corrected metrics. In a subset of patients with recurrent glioblastoma (n = 25), we determined receiver operating characteristic area under the curve for fractional tumor burden accuracy to predict the tissue diagnosis of tumor recurrence versus posttreatment effect. We also determined correlations between rCBV and microvessel area from stereotactic biopsies (n = 29) in 12 patients. RESULTS With IB Neuro, relative CBV metrics correlated highly between non-preload dose- and preload dose-corrected conditions for fractional tumor burden (r = 0.96, ρ = 0.94), percentage > 1.75 (r = 0.93, ρ = 0.91), mean (r = 0.87, ρ = 0.86), and mode (r = 0.78, ρ = 0.76). These correlations dropped substantially with nordicICE. With fractional tumor burden, IB Neuro was more accurate than nordicICE in diagnosing tumor versus posttreatment effect (area under the curve = 0.85 versus 0.67) (P < .01). The highest relative CBV-microvessel area correlations required preload dose and IB Neuro (r = 0.64, ρ = 0.58, P = .001). CONCLUSIONS Different implementations of perfusion MR imaging software modeling can impact the accuracy of leakage correction, relative CBV calculation, and correlations with histologic benchmarks.
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Affiliation(s)
- L S Hu
- From the Department of Radiology (L.S.H.) Keller Center for Imaging Innovation (L.S.H., C.E., J.P.K., L.C.B.)
| | - Z Kelm
- the Department of Radiology (Z.K., P.K., T.J.K., B.J.E.), Mayo Clinic, Rochester, Minnesota
| | - P Korfiatis
- the Department of Radiology (Z.K., P.K., T.J.K., B.J.E.), Mayo Clinic, Rochester, Minnesota
| | - A C Dueck
- Biostatistics (A.C.D.), Mayo Clinic, Phoenix/Scottsdale, Arizona
| | - C Elrod
- Keller Center for Imaging Innovation (L.S.H., C.E., J.P.K., L.C.B.)
| | - B M Ellingson
- the Department of Radiological Sciences (B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, California
| | - T J Kaufmann
- the Department of Radiology (Z.K., P.K., T.J.K., B.J.E.), Mayo Clinic, Rochester, Minnesota
| | | | - J P Karis
- Keller Center for Imaging Innovation (L.S.H., C.E., J.P.K., L.C.B.) Neuroradiology (J.P.K.)
| | - K Smith
- Neurosurgery (K.S., P.N.), Barrow Neurological Institute, Phoenix, Arizona
| | - P Nakaji
- Neurosurgery (K.S., P.N.), Barrow Neurological Institute, Phoenix, Arizona
| | - D Brinkman
- the Department of Radiation Oncology (D.B., D.P.), Mayo Clinic, Rochester, Minnesota
| | - D Pafundi
- the Department of Radiation Oncology (D.B., D.P.), Mayo Clinic, Rochester, Minnesota
| | - L C Baxter
- Keller Center for Imaging Innovation (L.S.H., C.E., J.P.K., L.C.B.)
| | - B J Erickson
- the Department of Radiology (Z.K., P.K., T.J.K., B.J.E.), Mayo Clinic, Rochester, Minnesota
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Mennenga SE, Gerson JE, Koebele SV, Kingston ML, Tsang CWS, Engler-Chiurazzi EB, Baxter LC, Bimonte-Nelson HA. Understanding the cognitive impact of the contraceptive estrogen Ethinyl Estradiol: tonic and cyclic administration impairs memory, and performance correlates with basal forebrain cholinergic system integrity. Psychoneuroendocrinology 2015; 54:1-13. [PMID: 25679306 PMCID: PMC4433884 DOI: 10.1016/j.psyneuen.2015.01.002] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2014] [Revised: 12/30/2014] [Accepted: 01/02/2015] [Indexed: 11/23/2022]
Abstract
Ethinyl Estradiol (EE), a synthetic, orally bio-available estrogen, is the most commonly prescribed form of estrogen in oral contraceptives, and is found in at least 30 different contraceptive formulations currently prescribed to women as well as hormone therapies prescribed to menopausal women. Thus, EE is prescribed clinically to women at ages ranging from puberty to reproductive senescence. Here, in two separate studies, the cognitive effects of cyclic or tonic EE administration following ovariectomy (Ovx) were evaluated in young female rats. Study I assessed the cognitive effects of low and high doses of EE, delivered tonically via a subcutaneous osmotic pump. Study II evaluated the cognitive effects of low, medium, and high doses of EE administered via a daily subcutaneous injection, modeling the daily rise and fall of serum EE levels with oral regimens. Study II also investigated the impact of low, medium and high doses of EE on the basal forebrain cholinergic system. The low and medium doses utilized here correspond to the range of doses currently used in clinical formulations, and the high dose corresponds to doses prescribed to a generation of women between 1960 and 1970, when oral contraceptives first became available. We evaluate cognition using a battery of maze tasks tapping several domains of spatial learning and memory as well as basal forebrain cholinergic integrity using immunohistochemistry and unbiased stereology to estimate the number of choline acetyltransferase (ChAT)-producing cells in the medial septum and vertical/diagonal bands. At the highest dose, EE treatment impaired multiple domains of spatial memory relative to vehicle treatment, regardless of administration method. When given cyclically at the low and medium doses, EE did not impact working memory, but transiently impaired reference memory during the learning phase of testing. Of the doses and regimens tested here, only EE at the highest dose impaired several domains of memory; tonic delivery of low EE, a dose that corresponds to the most popular doses used in the clinic today, did not impact cognition on any measure. Both medium and high injection doses of EE reduced the number of ChAt-immunoreactive cells in the basal forebrain, and cell population estimates in the vertical/diagonal bands negatively correlated with working memory errors.
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Affiliation(s)
- Sarah E Mennenga
- Department of Psychology, Arizona State University, Tempe, AZ 85287, USA; Arizona Alzheimer's Consortium, Phoenix, AZ 85006, USA
| | - Julia E Gerson
- Department of Psychology, Arizona State University, Tempe, AZ 85287, USA; Arizona Alzheimer's Consortium, Phoenix, AZ 85006, USA
| | - Stephanie V Koebele
- Department of Psychology, Arizona State University, Tempe, AZ 85287, USA; Arizona Alzheimer's Consortium, Phoenix, AZ 85006, USA
| | - Melissa L Kingston
- Department of Psychology, Arizona State University, Tempe, AZ 85287, USA; Arizona Alzheimer's Consortium, Phoenix, AZ 85006, USA
| | - Candy W S Tsang
- Department of Psychology, Arizona State University, Tempe, AZ 85287, USA; Arizona Alzheimer's Consortium, Phoenix, AZ 85006, USA
| | - Elizabeth B Engler-Chiurazzi
- Department of Psychology, Arizona State University, Tempe, AZ 85287, USA; Arizona Alzheimer's Consortium, Phoenix, AZ 85006, USA
| | - Leslie C Baxter
- Arizona Alzheimer's Consortium, Phoenix, AZ 85006, USA; Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, AZ 85013, USA
| | - Heather A Bimonte-Nelson
- Department of Psychology, Arizona State University, Tempe, AZ 85287, USA; Arizona Alzheimer's Consortium, Phoenix, AZ 85006, USA.
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Abstract
Education and related proxies for cognitive reserve (CR) are confounded by associations with environmental factors that correlate with cerebrovascular disease possibly explaining discrepancies between studies examining their relationships to cognitive aging and dementia. In contrast, sex-related memory differences may be a better proxy. Since they arise developmentally, they are less likely to reflect environmental confounds. Women outperform men on verbal and men generally outperform women on visuospatial memory tasks. Furthermore, memory declines during the preclinical stage of AD, when it is clinically indistinguishable from normal aging. To determine whether CR mitigates age-related memory decline, we examined the effects of gender and APOE genotype on longitudinal memory performances. Memory decline was assessed in a cohort of healthy men and women enriched for APOE ɛ4 who completed two verbal [Rey Auditory Verbal Learning Test (AVLT), Buschke Selective Reminding Test (SRT)] and two visuospatial [Rey-Osterrieth Complex Figure Test (CFT), and Benton Visual Retention Test (VRT)] memory tests, as well as in a separate larger and older cohort [National Alzheimer's Coordinating Center (NACC)] who completed a verbal memory test (Logical Memory). Age-related memory decline was accelerated in APOE ɛ4 carriers on all verbal memory measures (AVLT, p=.03; SRT p<.001; logical memory p<.001) and on the VRT p=.006. Baseline sex associated differences were retained over time, but no sex differences in rate of decline were found for any measure in either cohort. Sex-based memory advantage does not mitigate age-related memory decline in either APOE ɛ4 carriers or non-carriers.
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Affiliation(s)
| | - Amylou C. Dueck
- Department of Biostatistics, Mayo Clinic Arizona, Scottsdale, Arizona
| | - Dona E.C. Locke
- Division of Psychology, Mayo Clinic Arizona, Scottsdale, Arizona
| | - Leslie C. Baxter
- Division of Psychology, Barrow Neurological Institute, Phoenix, Arizona
| | | | - Yonas E. Geda
- Department of Neurology, Mayo Clinic Arizona, Scottsdale, Arizona
- Department of Psychiatry, Mayo Clinic Arizona, Scottsdale, Arizona
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Shi J, Stonnington CM, Thompson PM, Chen K, Gutman B, Reschke C, Baxter LC, Reiman EM, Caselli RJ, Wang Y. Studying ventricular abnormalities in mild cognitive impairment with hyperbolic Ricci flow and tensor-based morphometry. Neuroimage 2014; 104:1-20. [PMID: 25285374 DOI: 10.1016/j.neuroimage.2014.09.062] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [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: 05/03/2014] [Revised: 09/20/2014] [Accepted: 09/29/2014] [Indexed: 11/29/2022] Open
Abstract
Mild Cognitive Impairment (MCI) is a transitional stage between normal aging and dementia and people with MCI are at high risk of progression to dementia. MCI is attracting increasing attention, as it offers an opportunity to target the disease process during an early symptomatic stage. Structural magnetic resonance imaging (MRI) measures have been the mainstay of Alzheimer's disease (AD) imaging research, however, ventricular morphometry analysis remains challenging because of its complicated topological structure. Here we describe a novel ventricular morphometry system based on the hyperbolic Ricci flow method and tensor-based morphometry (TBM) statistics. Unlike prior ventricular surface parameterization methods, hyperbolic conformal parameterization is angle-preserving and does not have any singularities. Our system generates a one-to-one diffeomorphic mapping between ventricular surfaces with consistent boundary matching conditions. The TBM statistics encode a great deal of surface deformation information that could be inaccessible or overlooked by other methods. We applied our system to the baseline MRI scans of a set of MCI subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI: 71 MCI converters vs. 62 MCI stable). Although the combined ventricular area and volume features did not differ between the two groups, our fine-grained surface analysis revealed significant differences in the ventricular regions close to the temporal lobe and posterior cingulate, structures that are affected early in AD. Significant correlations were also detected between ventricular morphometry, neuropsychological measures, and a previously described imaging index based on fluorodeoxyglucose positron emission tomography (FDG-PET) scans. This novel ventricular morphometry method may offer a new and more sensitive approach to study preclinical and early symptomatic stage AD.
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Affiliation(s)
- Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | | | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Kewei Chen
- Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix, AZ, USA
| | - Boris Gutman
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Cole Reschke
- Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix, AZ, USA
| | - Leslie C Baxter
- Human Brain Imaging Laboratory, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Eric M Reiman
- Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix, AZ, USA
| | | | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
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Mennenga SE, Baxter LC, Grunfeld IS, Brewer GA, Aiken LS, Engler-Chiurazzi EB, Camp BW, Acosta JI, Braden BB, Schaefer KR, Gerson JE, Lavery CN, Tsang CWS, Hewitt LT, Kingston ML, Koebele SV, Patten KJ, Ball BH, McBeath MK, Bimonte-Nelson HA. Navigating to new frontiers in behavioral neuroscience: traditional neuropsychological tests predict human performance on a rodent-inspired radial-arm maze. Front Behav Neurosci 2014; 8:294. [PMID: 25249951 PMCID: PMC4158810 DOI: 10.3389/fnbeh.2014.00294] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [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] [Received: 06/01/2014] [Accepted: 08/11/2014] [Indexed: 11/13/2022] Open
Abstract
We constructed an 11-arm, walk-through, human radial-arm maze (HRAM) as a translational instrument to compare existing methodology in the areas of rodent and human learning and memory research. The HRAM, utilized here, serves as an intermediary test between the classic rat radial-arm maze (RAM) and standard human neuropsychological and cognitive tests. We show that the HRAM is a useful instrument to examine working memory ability, explore the relationships between rodent and human memory and cognition models, and evaluate factors that contribute to human navigational ability. One-hundred-and-fifty-seven participants were tested on the HRAM, and scores were compared to performance on a standard cognitive battery focused on episodic memory, working memory capacity, and visuospatial ability. We found that errors on the HRAM increased as working memory demand became elevated, similar to the pattern typically seen in rodents, and that for this task, performance appears similar to Miller's classic description of a processing-inclusive human working memory capacity of 7 ± 2 items. Regression analysis revealed that measures of working memory capacity and visuospatial ability accounted for a large proportion of variance in HRAM scores, while measures of episodic memory and general intelligence did not serve as significant predictors of HRAM performance. We present the HRAM as a novel instrument for measuring navigational behavior in humans, as is traditionally done in basic science studies evaluating rodent learning and memory, thus providing a useful tool to help connect and translate between human and rodent models of cognitive functioning.
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Affiliation(s)
- Sarah E Mennenga
- Department of Psychology, Arizona State University , Tempe, AZ, USA ; Arizona Alzheimer's Consortium , Phoenix, AZ, USA
| | - Leslie C Baxter
- Arizona Alzheimer's Consortium , Phoenix, AZ, USA ; Barrow Neurological Institute, St. Joseph's Hospital and Medical Center , Phoenix, AZ, USA
| | - Itamar S Grunfeld
- Department of Psychology, Arizona State University , Tempe, AZ, USA ; Arizona Alzheimer's Consortium , Phoenix, AZ, USA
| | - Gene A Brewer
- Department of Psychology, Arizona State University , Tempe, AZ, USA
| | - Leona S Aiken
- Department of Psychology, Arizona State University , Tempe, AZ, USA
| | - Elizabeth B Engler-Chiurazzi
- Department of Psychology, Arizona State University , Tempe, AZ, USA ; Arizona Alzheimer's Consortium , Phoenix, AZ, USA
| | - Bryan W Camp
- Department of Psychology, Arizona State University , Tempe, AZ, USA ; Arizona Alzheimer's Consortium , Phoenix, AZ, USA
| | - Jazmin I Acosta
- Department of Psychology, Arizona State University , Tempe, AZ, USA ; Arizona Alzheimer's Consortium , Phoenix, AZ, USA
| | - B Blair Braden
- Department of Psychology, Arizona State University , Tempe, AZ, USA ; Arizona Alzheimer's Consortium , Phoenix, AZ, USA ; Barrow Neurological Institute, St. Joseph's Hospital and Medical Center , Phoenix, AZ, USA
| | - Keley R Schaefer
- Department of Psychology, Arizona State University , Tempe, AZ, USA ; Arizona Alzheimer's Consortium , Phoenix, AZ, USA
| | - Julia E Gerson
- Department of Psychology, Arizona State University , Tempe, AZ, USA ; Arizona Alzheimer's Consortium , Phoenix, AZ, USA
| | - Courtney N Lavery
- Department of Psychology, Arizona State University , Tempe, AZ, USA ; Arizona Alzheimer's Consortium , Phoenix, AZ, USA
| | - Candy W S Tsang
- Department of Psychology, Arizona State University , Tempe, AZ, USA ; Arizona Alzheimer's Consortium , Phoenix, AZ, USA
| | - Lauren T Hewitt
- Department of Psychology, Arizona State University , Tempe, AZ, USA ; Arizona Alzheimer's Consortium , Phoenix, AZ, USA
| | - Melissa L Kingston
- Department of Psychology, Arizona State University , Tempe, AZ, USA ; Arizona Alzheimer's Consortium , Phoenix, AZ, USA
| | - Stephanie V Koebele
- Department of Psychology, Arizona State University , Tempe, AZ, USA ; Arizona Alzheimer's Consortium , Phoenix, AZ, USA
| | - K Jakob Patten
- Department of Psychology, Arizona State University , Tempe, AZ, USA
| | - B Hunter Ball
- Department of Psychology, Arizona State University , Tempe, AZ, USA
| | | | - Heather A Bimonte-Nelson
- Department of Psychology, Arizona State University , Tempe, AZ, USA ; Arizona Alzheimer's Consortium , Phoenix, AZ, USA
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Han P, Liang W, Baxter LC, Yin J, Tang Z, Beach TG, Caselli RJ, Reiman EM, Shi J. Pituitary adenylate cyclase-activating polypeptide is reduced in Alzheimer disease. Neurology 2014; 82:1724-8. [PMID: 24719484 DOI: 10.1212/wnl.0000000000000417] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [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
OBJECTIVES There is growing evidence that pituitary adenylate cyclase-activating polypeptide (PACAP) is associated with Alzheimer disease (AD) pathology in animal models, but human studies are needed. METHODS We studied the brains of patients with pathologically confirmed late-onset AD and age-matched cognitively normal (CN) subjects to investigate the expression of PACAP messenger RNA (34 AD and 14 CN) and protein (12 AD and 11 CN) in a case-control study. RESULTS We report that PACAP levels are reduced in multiple brain regions, including the entorhinal cortex, the middle temporal gyrus, the superior frontal gyrus, and the primary visual cortex. This reduction is correlated with higher amyloid burden (CERAD plaque density) in the entorhinal cortex and superior frontal gyrus but not in the primary visual cortex, a region spared in most cases of AD. PACAP expression is lower in advanced Braak stages (V and VI) than in moderate stages (III and IV). Increased PACAP levels are associated with decreased scores on the Dementia Rating Scale, a global cognitive measure. Finally, CSF levels paralleled brain levels in AD but not in Parkinson dementia or frontotemporal dementia brains. CONCLUSIONS The close relationship between PACAP reduction and the severity of AD pathology suggests that downregulation of PACAP may contribute to AD pathogenesis.
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Affiliation(s)
- Pengcheng Han
- From the Barrow Neurological Institute (P.H., L.C.B., J.Y., Z.T., J.S.), St. Joseph Hospital and Medical Center, Dignity Health Organization, Phoenix; Translational Genomics Research Institute (W.L.), Phoenix, AZ; Department of Neurosurgery (Z.T.), The First Hospital of Kunming Medical University, Kunming, China; Civin Laboratory for Neuropathology (T.G.B.), Banner Sun Health Research Institute, Sun City; Department of Neurology (R.J.C.), Mayo Clinic Arizona, Scottsdale; and Banner Alzheimer's Institute (E.M.R.), Phoenix, AZ
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Shi J, Leporé N, Gutman BA, Thompson PM, Baxter LC, Caselli RJ, Wang Y. Genetic influence of apolipoprotein E4 genotype on hippocampal morphometry: An N = 725 surface-based Alzheimer's disease neuroimaging initiative study. Hum Brain Mapp 2014; 35:3903-18. [PMID: 24453132 DOI: 10.1002/hbm.22447] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.1] [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: 09/12/2013] [Revised: 11/23/2013] [Accepted: 11/26/2013] [Indexed: 01/12/2023] Open
Abstract
The apolipoprotein E (APOE) e4 allele is the most prevalent genetic risk factor for Alzheimer's disease (AD). Hippocampal volumes are generally smaller in AD patients carrying the e4 allele compared to e4 noncarriers. Here we examined the effect of APOE e4 on hippocampal morphometry in a large imaging database-the Alzheimer's Disease Neuroimaging Initiative (ADNI). We automatically segmented and constructed hippocampal surfaces from the baseline MR images of 725 subjects with known APOE genotype information including 167 with AD, 354 with mild cognitive impairment (MCI), and 204 normal controls. High-order correspondences between hippocampal surfaces were enforced across subjects with a novel inverse consistent surface fluid registration method. Multivariate statistics consisting of multivariate tensor-based morphometry (mTBM) and radial distance were computed for surface deformation analysis. Using Hotelling's T(2) test, we found significant morphological deformation in APOE e4 carriers relative to noncarriers in the entire cohort as well as in the nondemented (pooled MCI and control) subjects, affecting the left hippocampus more than the right, and this effect was more pronounced in e4 homozygotes than heterozygotes. Our findings are consistent with previous studies that showed e4 carriers exhibit accelerated hippocampal atrophy; we extend these findings to a novel measure of hippocampal morphometry. Hippocampal morphometry has significant potential as an imaging biomarker of early stage AD.
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Affiliation(s)
- Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, Arizona
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Hu LS, Eschbacher JM, Heiserman JE, Dueck AC, Shapiro WR, Liu S, Karis JP, Smith KA, Coons SW, Nakaji P, Spetzler RF, Feuerstein BG, Debbins J, Baxter LC. Reevaluating the imaging definition of tumor progression: perfusion MRI quantifies recurrent glioblastoma tumor fraction, pseudoprogression, and radiation necrosis to predict survival. Neuro Oncol 2012; 14:919-30. [PMID: 22561797 PMCID: PMC3379799 DOI: 10.1093/neuonc/nos112] [Citation(s) in RCA: 157] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
INTRODUCTION: Contrast-enhanced MRI (CE-MRI) represents the current mainstay for monitoring treatment response in glioblastoma multiforme (GBM), based on the premise that enlarging lesions reflect increasing tumor burden, treatment failure, and poor prognosis. Unfortunately, irradiating such tumors can induce changes in CE-MRI that mimic tumor recurrence, so called post treatment radiation effect (PTRE), and in fact, both PTRE and tumor re-growth can occur together. Because PTRE represents treatment success, the relative histologic fraction of tumor growth versus PTRE affects survival. Studies suggest that Perfusion MRI (pMRI)–based measures of relative cerebral blood volume (rCBV) can noninvasively estimate histologic tumor fraction to predict clinical outcome. There are several proposed pMRI-based analytic methods, although none have been correlated with overall survival (OS). This study compares how well histologic tumor fraction and OS correlate with several pMRI-based metrics. METHODS: We recruited previously treated patients with GBM undergoing surgical re-resection for suspected tumor recurrence and calculated preoperative pMRI-based metrics within CE-MRI enhancing lesions: rCBV mean, mode, maximum, width, and a new thresholding metric called pMRI–fractional tumor burden (pMRI-FTB). We correlated all pMRI-based metrics with histologic tumor fraction and OS. RESULTS: Among 25 recurrent patients with GBM, histologic tumor fraction correlated most strongly with pMRI-FTB (r = 0.82; P < .0001), which was the only imaging metric that correlated with OS (P<.02). CONCLUSION: The pMRI-FTB metric reliably estimates histologic tumor fraction (i.e., tumor burden) and correlates with OS in the context of recurrent GBM. This technique may offer a promising biomarker of tumor progression and clinical outcome for future clinical trials.
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Affiliation(s)
- Leland S Hu
- Department of Radiology, Mayo Clinic in Arizona, Phoenix, AZ 85054, USA.
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Hu LS, Eschbacher JM, Dueck AC, Heiserman JE, Liu S, Karis JP, Smith KA, Shapiro WR, Pinnaduwage DS, Coons SW, Nakaji P, Debbins J, Feuerstein BG, Baxter LC. Correlations between perfusion MR imaging cerebral blood volume, microvessel quantification, and clinical outcome using stereotactic analysis in recurrent high-grade glioma. AJNR Am J Neuroradiol 2012; 33:69-76. [PMID: 22095961 PMCID: PMC7966183 DOI: 10.3174/ajnr.a2743] [Citation(s) in RCA: 92] [Impact Index Per Article: 7.7] [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: 12/30/2010] [Accepted: 05/09/2011] [Indexed: 01/14/2023]
Abstract
BACKGROUND AND PURPOSE Quantifying MVA rather than MVD provides better correlation with survival in HGG. This is attributed to a specific "glomeruloid" vascular pattern, which is better characterized by vessel area than number. Despite its prognostic value, MVA quantification is laborious and clinically impractical. The DSC-MR imaging measure of rCBV offers the advantages of speed and convenience to overcome these limitations; however, clinical use of this technique depends on establishing accurate correlations between rCBV, MVA, and MVD, particularly in the setting of heterogeneous vascular size inherent to human HGG. MATERIALS AND METHODS We obtained preoperative 3T DSC-MR imaging in patients with HGG before stereotactic surgery. We histologically quantified MVA, MVD, and vascular size heterogeneity from CD34-stained 10-μm sections of stereotactic biopsies, and we coregistered biopsy locations with localized rCBV measurements. We statistically correlated rCBV, MVA, and MVD under conditions of high and low vascular-size heterogeneity and among tumor grades. We correlated all parameters with OS by using Cox regression. RESULTS We analyzed 38 biopsies from 24 subjects. rCBV correlated strongly with MVA (r = 0.83, P < .0001) but weakly with MVD (r = 0.32, P = .05), due to microvessel size heterogeneity. Among samples with more homogeneous vessel size, rCBV correlation with MVD improved (r = 0.56, P = .01). OS correlated with both rCBV (P = .02) and MVA (P = .01) but not with MVD (P = .17). CONCLUSIONS rCBV provides a reliable estimation of tumor MVA as a biomarker of glioma outcome. rCBV poorly estimates MVD in the presence of vessel size heterogeneity inherent to human HGG.
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Affiliation(s)
- L S Hu
- Department of Radiology, Mayo Clinic, Phoenix/Scottsdale, Arizona, USA.
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Caselli RJ, Dueck AC, Locke DEC, Sabbagh MN, Ahern GL, Rapcsak SZ, Baxter LC, Yaari R, Woodruff BK, Hoffman-Snyder C, Rademakers R, Findley S, Reiman EM. Cerebrovascular risk factors and preclinical memory decline in healthy APOE ε4 homozygotes. Neurology 2011; 76:1078-84. [PMID: 21325652 DOI: 10.1212/wnl.0b013e318211c3ae] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
OBJECTIVE To characterize the effects of cerebrovascular (CV) risk factors on preclinical memory decline in cognitively normal individuals at 3 levels of genetic risk for Alzheimer disease (AD) based on APOE genotype. METHODS We performed longitudinal neuropsychological testing on an APOE ε4 enriched cohort, ages 21-97. The long-term memory (LTM) score of the Auditory Verbal Learning Test (AVLT) was the primary outcome measure. Any of 4 CV risk factors (CVany), including hypercholesterolemia (CHOL), prior cigarette use (CIG), diabetes mellitus (DM), and hypertension (HTN), was treated as a dichotomized variable. We estimated the longitudinal effect of age using statistical models that simultaneously modeled the cross-sectional and longitudinal effects of age on AVLT LTM by APOE genotype, CVany, and the interaction between the two. RESULTS A total of 74 APOE ε4 homozygotes (HMZ), 239 ε4 heterozygotes (HTZ), and 494 ε4 noncarriers were included. APOE ε4 carrier status showed a significant quadratic effect with age-related LTM decline in all models as previously reported. CVany was associated with further longitudinal AVLT LTM decline in APOE ε4 carriers (p=0.02), but had no effect in noncarriers. When ε4 HTZ and HMZ were considered separately, there was a striking effect in HMZ (p<0.001) but not in HTZ. In exploratory analyses, significant deleterious effects were found for CIG (p=0.001), DM (p=0.03), and HTN (p=0.05) in APOE ε4 carriers only that remained significant only for CIG after correction for multiple comparisons. CONCLUSION CV risk factors influence age-related memory decline in APOE ε4 HMZ.
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Affiliation(s)
- R J Caselli
- Department of Neurology, Mayo Clinic Arizona, 13400 East Shea Boulevard, Scottsdale, AZ 85259, USA.
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Little AS, Liu S, Beeman S, Sankar T, Preul MC, Hu LS, Smith KA, Baxter LC. Brain Retraction and Thickness of Cerebral Neocortex: An Automated Technique for Detecting Retraction-Induced Anatomic Changes Using Magnetic Resonance Imaging. Oper Neurosurg (Hagerstown) 2010; 67:ons277-82; discussion ons282. [DOI: 10.1227/01.neu.0000374699.12150.0] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Abstract
BACKGROUND:
Treating deep-seated cerebral lesions often requires retracting the brain. Retraction, however, causes clinically significant postoperative neurological deficits in 3% to 9% of intracranial cases.
OBJECTIVE:
This pilot study used automated analysis of postoperative magnetic resonance images (MRIs) to determine whether brain retraction caused local anatomic changes to the cerebral neocortex and whether such changes represented sensitive markers for detecting brain retraction injury.
METHODS:
Pre- and postoperative maps of whole-brain cortical thickness were generated from 3-dimensional MRIs of 6 patients who underwent selective amygdalohippocam-pectomy for temporal lobe epilepsy (5 left hemispheres, 1 right hemisphere). Mean cortical thickness was determined in the inferior temporal gyrus (ITG test), where a retractor was placed during surgery, and in 2 control gyri—the posterior portion of the inferior temporal gyrus (ITG control) and motor cortex control. Regions of cortical thinning were also compared with signs of retraction injury on early postoperative MRIs.
RESULTS:
Postoperative maps of cortical thickness showed thinning in the inferior temporal gyrus where the retractor was placed in 5 patients. Postoperatively, mean cortical thickness declined from 4.1 ± 0.4 mm to 2.9 ± 0.9 mm in ITG test (P = .03) and was unchanged in the control regions. Anatomically, the region of neocortical thinning correlated with postoperative edema on MRIs obtained within 48 hours of surgery.
CONCLUSION:
Postoperative MRIs can be successfully interrogated for information on cortical thickness. Brain retraction is associated with chronic local thinning of the neocortex. This automated technique may be sensitive enough to detect regions at risk for functional impairment during craniotomy that cannot be easily detected on postoperative structural imaging.
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Affiliation(s)
- Andrew S. Little
- Division of Neurological Surgery, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, Arizona
| | - Seban Liu
- Division of Neuropsychology, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, Arizona
| | - Scott Beeman
- Division of Neuropsychology, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, Arizona
| | - Tejas Sankar
- Neurosurgery Research Laboratory, Division of Neurological Surgery Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, Arizona
| | - Mark C. Preul
- Neurosurgery Research Laboratory, Division of Neurological Surgery Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, Arizona
| | - Leland S. Hu
- Adjunct, Division of Neuroradiology, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Department of Radiology, Mayo Clinic, Phoenix, Arizona
| | - Kris A. Smith
- Division of Neurological Surgery, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, Arizona
| | - Leslie C. Baxter
- Division of Neuropsychology, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, Arizona
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Hu LS, Eschbacher JM, Liu S, Baxter LC, Heiserman JE, Smith KA, Karis JP, Coons SW, Nakaji P, Dueck A, Feuerstein BG. Abstract 3748: Perfusion MRI estimation of glioma microvascular density to predict tumor recurrence and treatment response: Validation study through stereotactic tissue analysis. Cancer Res 2010. [DOI: 10.1158/1538-7445.am10-3748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Purpose: This study uses spatially accurate image-guided tissue analysis to evaluate the correlation between Perfusion MRI (pMRI) and tissue microvascular density parameters as markers for tumor recurrence and treatment response in high-grade glioma (HGG).
Methods: Following Institutional Review Board approval, we recruited previously treated (including chemo-radiation therapy) HGG patients undergoing surgical re-resection of new enhancing lesions on surveillance MRI. Preoperatively, we acquired pMRI and contrast-enhanced stereotactic T1-Weighted (T1W) MRI data sets. Intraoperatively, we recorded stereotactic locations of multiple biopsies based on T1W data sets that guided surgery. Following surgery, we coregistered pMRI and T1W data sets to calculate localized relative cerebral blood volume (rCBV) for each stereotactic specimen location. For each specimen, we performed 1) standard H&E staining to diagnose tumor recurrence or post-treatment radiation effect (PTRE); and 2) immunohistochemical analysis with CD34 to highlight tissue vessels. We analyzed CD-34 stained slides with Axiovision Automeasure 3.4 (Zeiss, Germany) to calculate both total microvessel number (MVN) and total microvessel area (MVA), each normalized to total slide specimen area (μm2). We calculated Pearson correlations to establish relationships between a) rCBV and MVN; and b) rCBV and MVA. We also performed t-test to compare MVN, MVA, and rCBV values between tumor and PTRE samples. A neuroradiologist performed all coregistration and pMRI calculations, and a neuropathologist analyzed all tissue specimens, without knowledge of corresponding data. A biostatistician performed all statistical comparisons.
Results: In this preliminary study, we included 16 tissue specimens (from 8 subjects), each diagnosed as either tumor (n=7) or PTRE (n=9). We successfully calculated localized rCBV and determined both MVA and MVN for each specimen. The rCBV values showed highest correlation with total vessel area (MVA) (r=0.65, p=0.007) and slightly less correlation with vessel number (MVN) (r=0.52, p=0.04). Tumor showed significantly higher values than PTRE for all parameters: rCBV (1.87 + 0.82 versus 0.78 + 0.2, p=.002); MVA (0.22 + 0.03 versus 0.04 + 0.007, p=0.0001); and MVN (0.0068 + 0.001 versus. 0.0016 + 0.0006, p=0.0004).
Conclusion: These preliminary results show the promise of Perfusion MRI to non-invasively estimate tissue microvessel density and distinguish tumor recurrence from treatment effects. The current study is ongoing to confirm results in a larger patient population.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 3748.
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Affiliation(s)
| | | | - Seban Liu
- 2Barrow Neurological Institute, Phoenix, AZ
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Hu LS, Baxter LC, Pinnaduwage DS, Paine TL, Karis JP, Feuerstein BG, Schmainda KM, Dueck AC, Debbins J, Smith KA, Nakaji P, Eschbacher JM, Coons SW, Heiserman JE. Optimized preload leakage-correction methods to improve the diagnostic accuracy of dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging in posttreatment gliomas. AJNR Am J Neuroradiol 2010; 31:40-8. [PMID: 19749223 DOI: 10.3174/ajnr.a1787] [Citation(s) in RCA: 118] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Relative cerebral blood volume (rCBV) accuracy can vary substantially depending on the dynamic susceptibility-weighted contrast-enhanced (DSC) acquisition and postprocessing methods, due to blood-brain barrier disruption and resulting T1-weighted leakage and T2- and/or T2*-weighted imaging (T2/T2*WI) residual effects. We set out to determine optimal DSC conditions that address these errors and maximize rCBV accuracy in differentiating posttreatment radiation effect (PTRE) and tumor. MATERIALS AND METHODS We recruited patients with previously treated high-grade gliomas undergoing image-guided re-resection of recurrent contrast-enhancing MR imaging lesions. Thirty-six surgical tissue samples were collected from 11 subjects. Preoperative 3T DSC used 6 sequential evenly timed acquisitions, each by using a 0.05-mmol/kg gadodiamide bolus. Preload dosing (PLD) and baseline subtraction (BLS) techniques corrected T1-weighted leakage and T2/T2*WI residual effects, respectively. PLD amount and incubation time increased with each sequential acquisition. Corresponding tissue specimen stereotactic locations were coregistered to DSC to measure localized rCBV under varying PLD amounts, incubation times, and the presence of BLS. rCBV thresholds were determined to maximize test accuracy (average of sensitivity and specificity) in distinguishing tumor (n = 21) and PTRE (n = 15) samples under the varying conditions. Receiver operator characteristic (ROC) areas under the curve (AUCs) were statistically compared. RESULTS The protocol that combined PLD (0.1-mmol/kg amount, 6-minute incubation time) and BLS correction methods maximized test AUC (0.99) and accuracy (95.2%) compared with uncorrected rCBV AUC (0.85) and accuracy (81.0%) measured without PLD and BLS (P = .01). CONCLUSIONS Combining PLD and BLS correction methods for T1-weighted and T2/T2*WI errors, respectively, enables highly accurate differentiation of PTRE and tumor growth.
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Affiliation(s)
- L S Hu
- Department of Radiology, Mayo Clinic, Phoenix/Scottsdale, Arizona 85259, USA.
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