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Barron A. Applications of Microct Imaging to Archaeobotanical Research. J Archaeol Method Theory 2023:1-36. [PMID: 37359278 PMCID: PMC10225294 DOI: 10.1007/s10816-023-09610-z] [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] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 04/05/2023] [Indexed: 06/28/2023]
Abstract
The potential applications of microCT scanning in the field of archaeobotany are only just beginning to be explored. The imaging technique can extract new archaeobotanical information from existing archaeobotanical collections as well as create new archaeobotanical assemblages within ancient ceramics and other artefact types. The technique could aid in answering archaeobotanical questions about the early histories of some of the world's most important food crops from geographical regions with amongst the poorest rates of archaeobotanical preservation and where ancient plant exploitation remains poorly understood. This paper reviews current uses of microCT imaging in the investigation of archaeobotanical questions, as well as in cognate fields of geosciences, geoarchaeology, botany and palaeobotany. The technique has to date been used in a small number of novel methodological studies to extract internal anatomical morphologies and three-dimensional quantitative data from a range of food crops, which includes sexually-propagated cereals and legumes, and asexually-propagated underground storage organs (USOs). The large three-dimensional, digital datasets produced by microCT scanning have been shown to aid in taxonomic identification of archaeobotanical specimens, as well as robustly assess domestication status. In the future, as scanning technology, computer processing power and data storage capacities continue to improve, the possible applications of microCT scanning to archaeobotanical studies will only increase with the development of machine and deep learning networks enabling the automation of analyses of large archaeobotanical assemblages.
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Affiliation(s)
- Aleese Barron
- School of Archaeology and Anthropology, Australian National University, Banks Building, Canberra, Canberra ACT 2601 Australia
- Department of Materials Physics, Research School of Physics, Australian National University, Canberra, Canberra ACT 2601 Australia
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Lefebvre TL, Ciga O, Bhatnagar SR, Ueno Y, Saif S, Winter-Reinhold E, Dohan A, Soyer P, Forghani R, Siddiqi K, Seuntjens J, Reinhold C, Savadjiev P. Predicting histopathology markers of endometrial carcinoma with a quantitative image analysis approach based on spherical harmonics in multiparametric MRI. Diagn Interv Imaging 2023; 104:142-152. [PMID: 36328942 DOI: 10.1016/j.diii.2022.10.007] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 10/07/2022] [Accepted: 10/11/2022] [Indexed: 12/24/2022]
Abstract
PURPOSE Identifying optimal machine learning pipelines for computer-aided diagnosis is key for the development of robust, reproducible, and clinically relevant imaging biomarkers for endometrial carcinoma. The purpose of this study was to introduce the mathematical development of image descriptors computed from spherical harmonics (SPHARM) decompositions as well as the associated machine learning pipeline, and to evaluate their performance in predicting deep myometrial invasion (MI) and histopathological high-grade in preoperative multiparametric magnetic resonance imaging (MRI). PATIENTS AND METHODS This retrospective study included 128 women with histopathology-confirmed endometrial carcinomas who underwent 1.5-T MRI before hysterectomy between January 2011 and July 2015. SPHARM descriptors of each tumor were computed on multiparametric MRI images (T2-weighted, diffusion-weighted, dynamic contrast-enhanced-MRI and apparent diffusion coefficient maps). Tensor-based logistic regression was used to classify two-dimensional SPHARM rotationally-invariant descriptors. Head-to-head comparisons with radiomics analyses were performed with DeLong tests with Bonferroni-Holm correction to compare diagnostic performances. RESULTS With all MRI contrasts, SPHARM analysis resulted in area under the curve, sensitivity, specificity, and balanced accuracy values of 0.94 (95% confidence interval [CI]: 0.85, 1.00), 100% (95% CI: 100, 100), 74% (95% CI: 51, 92), 87% (95% CI: 78, 98), respectively, for predicting deep MI. For predicting high-grade tumor histology, the corresponding values for the same diagnostic metrics were 0.81 (95% CI: 0.64, 0.90), 93% (95% CI: 67, 100), 63% (95% CI: 45, 79) and 78% (95% CI: 64, 86). The corresponding values achieved via radiomics were 0.92 (95% CI: 0.82, 0.95), 82% (95% CI: 65, 93), 80% (95% CI: 51, 94), 81% (95% CI: 70, 91) for deep MI and 0.72 (95% CI: 0.58, 0.83), 93% (95% CI: 65, 100), 55% (95% CI: 41, 69), 74% (95% CI: 52, 88) for high-grade histology. The diagnostic performance of the SPHARM analysis was not significantly different (P = 0.62) from that of radiomics for predicting deep MI but was significantly higher (P = 0.044) for predicting high-grade histology. CONCLUSION The proposed SPHARM analysis yields similar or higher diagnostic performance than radiomics in identifying deep MI and high-grade status in histology-proven endometrial carcinoma.
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Affiliation(s)
- Thierry L Lefebvre
- Medical Physics Unit, McGill University, Montreal, QC H4A 3J1, Canada; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, United Kingdom
| | - Ozan Ciga
- School of Computer Science and Centre for Intelligent Machines, McGill University, Montreal, QC H3A 2A7, Canada; Department of Medical Biophysics, University of Toronto, Toronto ON M5G 1L7, Canada
| | - Sahir Rai Bhatnagar
- Department of Diagnostic Radiology, McGill University, Montreal, QC H4A 3J1, Canada; Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC H3A 1G1, Canada; Augmented Intelligence & Precision Health Laboratory (AIPHL), Research Institute of McGill University Health Centre (RI-MUHC), Montreal, QC H4A 3J1, Canada
| | - Yoshiko Ueno
- Department of Diagnostic Radiology, McGill University, Montreal, QC H4A 3J1, Canada; Department of Radiology, Kobe University Graduate School of Medicine, Kobe City, Hyogo, 650-0017, Japan
| | - Sameh Saif
- Department of Diagnostic Radiology, McGill University, Montreal, QC H4A 3J1, Canada
| | - Eric Winter-Reinhold
- Augmented Intelligence & Precision Health Laboratory (AIPHL), Research Institute of McGill University Health Centre (RI-MUHC), Montreal, QC H4A 3J1, Canada
| | - Anthony Dohan
- Department of Radiology, Hopital Cochin, AP-HP, 75014, Paris, France; Université Paris Cité, Faculté de Médecine, 75006, Paris, France
| | - Philippe Soyer
- Department of Radiology, Hopital Cochin, AP-HP, 75014, Paris, France; Université Paris Cité, Faculté de Médecine, 75006, Paris, France
| | - Reza Forghani
- Department of Diagnostic Radiology, McGill University, Montreal, QC H4A 3J1, Canada; Augmented Intelligence & Precision Health Laboratory (AIPHL), Research Institute of McGill University Health Centre (RI-MUHC), Montreal, QC H4A 3J1, Canada
| | - Kaleem Siddiqi
- School of Computer Science and Centre for Intelligent Machines, McGill University, Montreal, QC H3A 2A7, Canada
| | - Jan Seuntjens
- Medical Physics Unit, McGill University, Montreal, QC H4A 3J1, Canada
| | - Caroline Reinhold
- Department of Diagnostic Radiology, McGill University, Montreal, QC H4A 3J1, Canada; Augmented Intelligence & Precision Health Laboratory (AIPHL), Research Institute of McGill University Health Centre (RI-MUHC), Montreal, QC H4A 3J1, Canada; Montreal Imaging Experts Inc., Montreal, QC H9R 5K3, Canada
| | - Peter Savadjiev
- School of Computer Science and Centre for Intelligent Machines, McGill University, Montreal, QC H3A 2A7, Canada; Department of Diagnostic Radiology, McGill University, Montreal, QC H4A 3J1, Canada; Augmented Intelligence & Precision Health Laboratory (AIPHL), Research Institute of McGill University Health Centre (RI-MUHC), Montreal, QC H4A 3J1, Canada.
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Solders SK, Galinsky VL, Clark AL, Sorg SF, Weigand AJ, Bondi MW, Frank LR. Diffusion MRI tractography of the locus coeruleus-transentorhinal cortex connections using GO-ESP. Magn Reson Med 2022; 87:1816-1831. [PMID: 34792198 PMCID: PMC8810611 DOI: 10.1002/mrm.29088] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [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: 08/10/2021] [Revised: 10/29/2021] [Accepted: 11/01/2021] [Indexed: 01/11/2023]
Abstract
PURPOSE The locus coeruleus (LC) is implicated as an early site of protein pathogenesis in Alzheimer's disease (AD). Tau pathology is hypothesized to propagate in a prion-like manner along the LC-transentorhinal cortex (TEC) white matter (WM) pathway, leading to atrophy of the entorhinal cortex and adjacent cortical regions in a progressive and stereotypical manner. However, WM damage along the LC-TEC pathway may be an earlier observable change that can improve detection of preclinical AD. THEORY AND METHODS Diffusion-weighted MRI (dMRI) allows reconstruction of WM pathways in vivo, offering promising potential to examine this pathway and enhance our understanding of neural mechanisms underlying the preclinical phase of AD. However, standard dMRI analysis tools have generally been unable to reliably reconstruct this pathway. We apply a novel method, geometric-optics based entropy spectrum pathways (GO-ESP) and produce a new measure of connectivity: the equilibrium probability (EP). RESULTS We demonstrated reliable reconstruction of LC-TEC pathways in 50 cognitively normal older adults and showed a negative association between LC-TEC EP and cerebrospinal fluid tau. Using Human Connectome Project data, we demonstrated replicability of the method across acquisition schemes and scanners. Finally, we compared our findings with the only other existing LC-TEC tractography template, and replicated their pathway as well as investigated the source of these discrepant findings. CONCLUSIONS AD-related tau pathology may be detectable within GO-ESP-identified LC-TEC pathways. Furthermore, there may be multiple possible routes from LC to TEC, raising important questions for future research on the LC-TEC connectome and its role in AD pathogenesis.
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Affiliation(s)
- Seraphina K. Solders
- Neuroscience Graduate ProgramUniversity of California at San DiegoLa JollaCaliforniaUSA
- Center for Scientific Computation in ImagingUniversity of California at San DiegoLa JollaCaliforniaUSA
| | - Vitaly L. Galinsky
- Center for Scientific Computation in ImagingUniversity of California at San DiegoLa JollaCaliforniaUSA
| | | | - Scott F. Sorg
- Department of PsychiatrySchool of MedicineUniversity of California San DiegoLa JollaCaliforniaUSA
- Research and Psychology ServicesVA San Diego Healthcare SystemSan DiegoCaliforniaUSA
| | - Alexandra J. Weigand
- San Diego State University/University of California at San Diego Joint Doctoral Program in Clinical PsychologySan DiegoCaliforniaUSA
| | - Mark W. Bondi
- Department of PsychiatrySchool of MedicineUniversity of California San DiegoLa JollaCaliforniaUSA
- Research and Psychology ServicesVA San Diego Healthcare SystemSan DiegoCaliforniaUSA
| | - Lawrence R. Frank
- Center for Scientific Computation in ImagingUniversity of California at San DiegoLa JollaCaliforniaUSA
- Department of RadiologyUniversity of California at San DiegoLa JollaCaliforniaUSA
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Frank LR, Rowe TB, Boyer DM, Witmer LM, Galinsky VL. Unveiling the third dimension in morphometry with automated quantitative volumetric computations. Sci Rep 2021; 11:14438. [PMID: 34262066 PMCID: PMC8280169 DOI: 10.1038/s41598-021-93490-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [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: 02/07/2021] [Accepted: 06/25/2021] [Indexed: 02/06/2023] Open
Abstract
As computed tomography and related technologies have become mainstream tools across a broad range of scientific applications, each new generation of instrumentation produces larger volumes of more-complex 3D data. Lagging behind are step-wise improvements in computational methods to rapidly analyze these new large, complex datasets. Here we describe novel computational methods to capture and quantify volumetric information, and to efficiently characterize and compare shape volumes. It is based on innovative theoretical and computational reformulation of volumetric computing. It consists of two theoretical constructs and their numerical implementation: the spherical wave decomposition (SWD), that provides fast, accurate automated characterization of shapes embedded within complex 3D datasets; and symplectomorphic registration with phase space regularization by entropy spectrum pathways (SYMREG), that is a non-linear volumetric registration method that allows homologous structures to be correctly warped to each other or a common template for comparison. Together, these constitute the Shape Analysis for Phenomics from Imaging Data (SAPID) method. We demonstrate its ability to automatically provide rapid quantitative segmentation and characterization of single unique datasets, and both inter-and intra-specific comparative analyses. We go beyond pairwise comparisons and analyze collections of samples from 3D data repositories, highlighting the magnified potential our method has when applied to data collections. We discuss the potential of SAPID in the broader context of generating normative morphologies required for meaningfully quantifying and comparing variations in complex 3D anatomical structures and systems.
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Affiliation(s)
- Lawrence R Frank
- Institute for Engineering in Medicine, Center for Scientific Computation in Imaging, University of California San Diego, 8950 Villa La Jolla Dr., Suite B227, La Jolla, CA, 92037, USA.
- Department of Radiology, Center for Functional MRI, University of California San Diego, 9500 Gilman Dr., #0677, La Jolla, CA, 92093-0677, USA.
| | - Timothy B Rowe
- Department of Geological Sciences, Jackson School of Geosciences, University of Texas, Austin, TX, 78712, USA
| | - Doug M Boyer
- Department of Evolutionary Anthropology, Duke University, Chapel Hill, NC, USA
| | - Lawrence M Witmer
- Department of Biomedical Sciences, Heritage College of Osteopathic Medicine, Ohio University, Athens, OH, USA
| | - Vitaly L Galinsky
- Institute for Engineering in Medicine, Center for Scientific Computation in Imaging, University of California San Diego, 8950 Villa La Jolla Dr., Suite B227, La Jolla, CA, 92037, USA
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Abstract
An inhomogeneous anisotropic physical model of the brain cortex is presented that predicts the emergence of nonevanescent (weakly damped) wave-like modes propagating in the thin cortex layers transverse to both the mean neural fiber direction and the cortex spatial gradient. Although the amplitude of these modes stays below the typically observed axon spiking potential, the lifetime of these modes may significantly exceed the spiking potential inverse decay constant. Full-brain numerical simulations based on parameters extracted from diffusion and structural MRI confirm the existence and extended duration of these wave modes. Contrary to the commonly agreed paradigm that the neural fibers determine the pathways for signal propagation in the brain, the signal propagation because of the cortex wave modes in the highly folded areas will exhibit no apparent correlation with the fiber directions. Nonlinear coupling of those linear weakly evanescent wave modes then provides a universal mechanism for the emergence of synchronized brain wave field activity. The resonant and nonresonant terms of nonlinear coupling between multiple modes produce both synchronous spiking-like high-frequency wave activity as well as low-frequency wave rhythms. Numerical simulation of forced multiple-mode dynamics shows that, as forcing increases, there is a transition from damped to oscillatory regime that can then transition quickly to a nonoscillatory state when a critical excitation threshold is reached. The resonant nonlinear coupling results in the emergence of low-frequency rhythms with frequencies that are several orders of magnitude below the linear frequencies of modes taking part in the coupling. The localization and persistence of these weakly evanescent cortical wave modes have significant implications in particular for neuroimaging methods that detect electromagnetic physiological activity, such as EEG and magnetoencephalography, and for the understanding of brain activity in general, including mechanisms of memory.
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Galinsky VL, Frank LR. Universal theory of brain waves: from linear loops to nonlinear synchronized spiking and collective brain rhythms. Phys Rev Res 2020; 2:023061. [PMID: 33718881 PMCID: PMC7951957 DOI: 10.1103/physrevresearch.2.023061] [Citation(s) in RCA: 8] [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: 06/12/2023]
Abstract
An inhomogeneous anisotropic physical model of the brain cortex is presented that predicts the emergence of non-evanescent (weakly damped) wave-like modes propagating in the thin cortex layers transverse to both the mean neural fiber direction and to the cortex spatial gradient. Although the amplitude of these modes stays below the typically observed axon spiking potential, the lifetime of these modes may significantly exceed the spiking potential inverse decay constant. Full brain numerical simulations based on parameters extracted from diffusion and structural MRI confirm the existence and extended duration of these wave modes. Contrary to the standard paradigm that the neural fibers determine the pathways for signal propagation in the brain, the signal propagation due to the cortex wave modes in highly folded areas will exhibit no apparent correlation with the fiber directions. The results are consistent with numerous recent experimental animal and human brain studies demonstrating the existence of electrostatic field activity in the form of traveling waves (including studies where neuronal connections were severed) and with wave loop induced peaks observed in EEG spectra. In addition, we demonstrate that the resonant and non-resonant terms of the nonlinear coupling between multiple modes produce both synchronous spiking-like high frequency wave activity as well as low frequency wave rhythms as a result of their unique dispersion properties. Numerical simulation of forced multiple mode dynamics shows that as forcing increases there is a transition from damped to oscillatory regime that subsequently decays away as over-excitation is reached. The resonant nonlinear coupling results in the emergence of low frequency rhythms with frequencies that are several orders of magnitude below the linear frequencies of modes taking part in the coupling. The localization and persistence of these cortical wave modes, and this new mechanism for understanding the nature of spiking behavior, have significant implications in particular for neuroimaging methods that detect electromagnetic physiological activity, such as EEG and MEG, and in general for the understanding of brain activity, including mechanisms of memory.
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Frank LR, Zahneisen B, Galinsky VL. JEDI: Joint Estimation Diffusion Imaging of macroscopic and microscopic tissue properties. Magn Reson Med 2020; 84:966-990. [PMID: 31916626 DOI: 10.1002/mrm.28141] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Revised: 11/12/2019] [Accepted: 11/30/2019] [Indexed: 11/07/2022]
Abstract
PURPOSE A new method for enhancing the sensitivity of diffusion MRI (dMRI) by combining the data from single (sPFG) and double (dPFG) pulsed field gradient experiments is presented. METHODS This method uses our JESTER framework to combine microscopic anisotropy information from dFPG experiments using a new method called diffusion tensor subspace imaging (DiTSI) to augment the macroscopic anisotropy information from sPFG data analyzed using our guided by entropy spectrum pathways method. This new method, called joint estimation diffusion imaging (JEDI), combines the sensitivity to macroscopic diffusion anisotropy of sPFG with the sensitivity to microscopic diffusion anisotropy of dPFG methods. RESULTS Its ability to produce significantly more detailed anisotropy maps and more complete fiber tracts than existing methods within both brain white matter (WM) and gray matter (GM) is demonstrated on normal human subjects on data collected using a novel fast, robust, and clinically feasible sPFG/dPFG acquisition. CONCLUSIONS The potential utility of this method is suggested by an initial demonstration of its ability to mitigate the problem of gyral bias. The capability of more completely characterizing the tissue structure and connectivity throughout the entire brain has broad implications for the utility and scope of dMRI in a wide range of research and clinical applications.
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Affiliation(s)
- Lawrence R Frank
- Center for Scientific Computation in Imaging, University of California at San Diego, La Jolla, CA, USA
- Center for Functional MRI, University of California at San Diego, La Jolla, CA, USA
| | | | - Vitaly L Galinsky
- Center for Scientific Computation in Imaging, University of California at San Diego, La Jolla, CA, USA
- Electrical and Computer Engineering Department, University of California at San Diego, La Jolla, CA, USA
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Galinsky VL, Frank LR. Symplectomorphic registration with phase space regularization by entropy spectrum pathways. Magn Reson Med 2018; 81:1335-1352. [PMID: 30230014 DOI: 10.1002/mrm.27402] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [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: 12/04/2017] [Revised: 04/19/2018] [Accepted: 05/22/2018] [Indexed: 12/25/2022]
Abstract
PURPOSE The ability to register image data to a common coordinate system is a critical feature of virtually all imaging studies. However, in spite of the abundance of literature on the subject and the existence of several variants of registration algorithms, their practical utility remains problematic, as commonly acknowledged even by developers of these methods. METHODS A new registration method is presented that utilizes a Hamiltonian formalism and constructs registration as a sequence of symplectomorphic maps in conjunction with a novel phase space regularization. For validation of the framework a panel of deformations expressed in analytical form is developed that includes deformations based on known physical processes in MRI and reproduces various distortions and artifacts typically present in images collected using these different MRI modalities. RESULTS The method is demonstrated on the three different magnetic resonance imaging (MRI) modalities by mapping between high resolution anatomical (HRA) volumes, medium resolution diffusion weighted MRI (DW-MRI) and HRA volumes, and low resolution functional MRI (fMRI) and HRA volumes. CONCLUSIONS The method has shown an excellent performance and the panel of deformations was instrumental to quantify its repeatability and reproducibility in comparison to several available alternative approaches.
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Affiliation(s)
- Vitaly L Galinsky
- Center for Scientific Computation in Imaging, University of California at San Diego, La Jolla, California.,Electrical and Computer Engineering Department, University of California at San Diego, La Jolla, California
| | - Lawrence R Frank
- Center for Scientific Computation in Imaging, University of California at San Diego, La Jolla, California.,Center for Functional MRI, University of California at San Diego, La Jolla, California
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Galinsky VL, Martinez A, Paulus MP, Frank LR. Joint Estimation of Effective Brain Wave Activation Modes Using EEG/MEG Sensor Arrays and Multimodal MRI Volumes. Neural Comput 2018; 30:1725-1749. [PMID: 29652588 PMCID: PMC6031448 DOI: 10.1162/neco_a_01087] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
In this letter, we present a new method for integration of sensor-based multifrequency bands of electroencephalography and magnetoencephalography data sets into a voxel-based structural-temporal magnetic resonance imaging analysis by utilizing the general joint estimation using entropy regularization (JESTER) framework. This allows enhancement of the spatial-temporal localization of brain function and the ability to relate it to morphological features and structural connectivity. This method has broad implications for both basic neuroscience research and clinical neuroscience focused on identifying disease-relevant biomarkers by enhancing the spatial-temporal resolution of the estimates derived from current neuroimaging modalities, thereby providing a better picture of the normal human brain in basic neuroimaging experiments and variations associated with disease states.
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Affiliation(s)
- Vitaly L Galinsky
- Center for Scientific Computation in Imaging and Electrical and Computer Engineering Department, University of California at San Diego, La Jolla, CA 92093, U.S.A
| | - Antigona Martinez
- Division of Experimental Therapeutics, Department of Psychiatry, Columbia University, New York, NY 10032, and Schizophrenia Research Division, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962, U.S.A
| | - Martin P Paulus
- Laureate Institute for Brain Research, Tulsa, OK 74136-3326, U.S.A
| | - Lawrence R Frank
- Center for Scientific Computation in Imaging and Department of Radiology, University of California at San Diego, La Jolla, CA 92093, and VA San Diego Healthcare System, San Diego, CA 92161, U.S.A
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Zeinali R, Keshtkar A, Zamani A, Gharehaghaji N. Brain Volume Estimation Enhancement by Morphological Image Processing Tools. J Biomed Phys Eng 2017; 7:379-388. [PMID: 29445714 PMCID: PMC5809931] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2016] [Accepted: 06/13/2016] [Indexed: 11/14/2022]
Abstract
BACKGROUND Volume estimation of brain is important for many neurological applications. It is necessary in measuring brain growth and changes in brain in normal/abnormal patients. Thus, accurate brain volume measurement is very important. Magnetic resonance imaging (MRI) is the method of choice for volume quantification due to excellent levels of image resolution and between-tissue contrast. Stereology method is a good method for estimating volume but it requires to segment enough MRI slices and have a good resolution. In this study, it is desired to enhance stereology method for volume estimation of brain using less MRI slices with less resolution. METHODS In this study, a program for calculating volume using stereology method has been introduced. After morphologic method, dilation was applied and the stereology method enhanced. For the evaluation of this method, we used T1-wighted MR images from digital phantom in BrainWeb which had ground truth. RESULTS The volume of 20 normal brain extracted from BrainWeb, was calculated. The volumes of white matter, gray matter and cerebrospinal fluid with given dimension were estimated correctly. Volume calculation from Stereology method in different cases was made. In three cases, Root Mean Square Error (RMSE) was measured. Case I with T=5, d=5, Case II with T=10, D=10 and Case III with T=20, d=20 (T=slice thickness, d=resolution as stereology parameters). By comparing these results of two methods, it is obvious that RMSE values for our proposed method are smaller than Stereology method. CONCLUSION Using morphological operation, dilation allows to enhance the estimation volume method, Stereology. In the case with less MRI slices and less test points, this method works much better compared to Stereology method.
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Affiliation(s)
- R. Zeinali
- M.Sc. Student of Medical Physics, Tabriz University of Medical Science,Tabriz, Iran
| | - A. Keshtkar
- Professor of Medical Physics and Engineering, Medical Physics Department, School of Medicine, Tabriz, Iran
| | - A. Zamani
- Assistant Professor of Biomedical Engineering, Biomedical Physics and Biomedical Engineering Dept., Shiraz University of Medical Sciences, Shiraz, Iran
| | - N. Gharehaghaji
- Associate Professor of Medical Physics, Radiology Department, School of Paramedical, Tabriz, Iran
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Abstract
A primary goal of many neuroimaging studies that use magnetic resonance imaging (MRI) is to deduce the structure-function relationships in the human brain using data from the three major neuro-MRI modalities: high-resolution anatomical, diffusion tensor imaging, and functional MRI. To date, the general procedure for analyzing these data is to combine the results derived independently from each of these modalities. In this article, we develop a new theoretical and computational approach for combining these different MRI modalities into a powerful and versatile framework that combines our recently developed methods for morphological shape analysis and segmentation, simultaneous local diffusion estimation and global tractography, and nonlinear and nongaussian spatial-temporal activation pattern classification and ranking, as well as our fast and accurate approach for nonlinear registration between modalities. This joint analysis method is capable of extracting new levels of information that is not achievable from any of those single modalities alone. A theoretical probabilistic framework based on a reformulation of prior information and available interdependencies between modalities through a joint coupling matrix and an efficient computational implementation allows construction of quantitative functional, structural, and effective brain connectivity modes and parcellation. This new method provides an overall increase of resolution, accuracy, level of detail, and information content and has the potential to be instrumental in the clinical adaptation of neuro-MRI modalities, which, when jointly analyzed, provide a more comprehensive view of a subject's structure-function relations, while the current standard, wherein single-modality methods are analyzed separately, leaves a critical gap in an integrated view of a subject's neuorphysiological state. As one example of this increased sensitivity, we demonstrate that the jointly estimated structural and functional dependencies of mode power follow the same power law decay with the same exponent.
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Affiliation(s)
- Vitaly L Galinsky
- Center for Scientific Computation in Imaging, University of California at San Diego, La Jolla, CA 92093-0854, U.S.A., and Electrical and Computer Engineering Department, University of California at San Diego, La Jolla, CA 92093-0407, U.S.A.
| | - Lawrence R Frank
- Center for Scientific Computation in Imaging, University of California at San Diego, La Jolla, CA 92093-0854, U.S.A.; Department of Radiology, University of California at San Diego, La Jolla, CA 92093-0854, U.S.A.; and VA San Diego Healthcare System, San Diego, CA 92161, U.S.A.
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Yopak K, Galinsky VL, Berquist R, Frank LR. Quantitative Classification of Cerebellar Foliation in Cartilaginous Fishes (Class: Chondrichthyes) Using Three-Dimensional Shape Analysis and Its Implications for Evolutionary Biology. Brain Behav Evol 2016; 87:252-64. [PMID: 27450795 PMCID: PMC5023489 DOI: 10.1159/000446904] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [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: 11/19/2015] [Accepted: 05/13/2016] [Indexed: 11/19/2022]
Abstract
A true cerebellum appeared at the onset of the chondrichthyan (sharks, batoids, and chimaerids) radiation and is known to be essential for executing fast, accurate, and efficient movement. In addition to a high degree of variation in size, the corpus cerebellum in this group has a high degree of variation in convolution (or foliation) and symmetry, which ranges from a smooth cerebellar surface to deep, branched convexities and folds, although the functional significance of this trait is unclear. As variation in the degree of foliation similarly exists throughout vertebrate evolution, it becomes critical to understand this evolutionary process in a wide variety of species. However, current methods are either qualitative and lack numerical rigor or they are restricted to two dimensions. In this paper, a recently developed method for the characterization of shapes embedded within noisy, three-dimensional data called spherical wave decomposition (SWD) is applied to the problem of characterizing cerebellar foliation in cartilaginous fishes. The SWD method provides a quantitative characterization of shapes in terms of well-defined mathematical functions. An additional feature of the SWD method is the construction of a statistical criterion for the optimal fit, which represents the most parsimonious choice of parameters that fits to the data without overfitting to background noise. We propose that this optimal fit can replace a previously described qualitative visual foliation index (VFI) in cartilaginous fishes with a quantitative analog, i.e. the cerebellar foliation index (CFI). The capability of the SWD method is demonstrated in a series of volumetric images of brains from different chondrichthyan species that span the range of foliation gradings currently described for this group. The CFI is consistent with the qualitative grading provided by the VFI, delivers a robust measure of cerebellar foliation, and can provide a quantitative basis for brain shape characterization across taxa.
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Affiliation(s)
- Kara Yopak
- UWA Oceans Institute and the School of Animal Biology, University of Western Australia, Crawley, WA 6009
| | - Vitaly L. Galinsky
- Center for Scientific Computation in Imaging, University of California, San Diego
| | - Rachel Berquist
- Center for Scientific Computation in Imaging, University of California, San Diego
| | - Lawrence R. Frank
- Center for Scientific Computation in Imaging, University of California, San Diego
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Abstract
Managing, processing and understanding big healthcare data is challenging, costly and demanding. Without a robust fundamental theory for representation, analysis and inference, a roadmap for uniform handling and analyzing of such complex data remains elusive. In this article, we outline various big data challenges, opportunities, modeling methods and software techniques for blending complex healthcare data, advanced analytic tools, and distributed scientific computing. Using imaging, genetic and healthcare data we provide examples of processing heterogeneous datasets using distributed cloud services, automated and semi-automated classification techniques, and open-science protocols. Despite substantial advances, new innovative technologies need to be developed that enhance, scale and optimize the management and processing of large, complex and heterogeneous data. Stakeholder investments in data acquisition, research and development, computational infrastructure and education will be critical to realize the huge potential of big data, to reap the expected information benefits and to build lasting knowledge assets. Multi-faceted proprietary, open-source, and community developments will be essential to enable broad, reliable, sustainable and efficient data-driven discovery and analytics. Big data will affect every sector of the economy and their hallmark will be 'team science'.
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Affiliation(s)
- Ivo D. Dinov
- Statistics Online Computational Resource (SOCR), Health Behavior and Biological Sciences, Michigan Institute for Data Science, University of Michigan, 426 N. Ingalls, Ann Arbor, MI 49109 USA
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Galinsky VL, Frank LR. Simultaneous multi-scale diffusion estimation and tractography guided by entropy spectrum pathways. IEEE Trans Med Imaging 2015; 34:1177-1193. [PMID: 25532167 PMCID: PMC4417445 DOI: 10.1109/tmi.2014.2380812] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
We have developed a method for the simultaneous estimation of local diffusion and the global fiber tracts based upon the information entropy flow that computes the maximum entropy trajectories between locations and depends upon the global structure of the multi-dimensional and multi-modal diffusion field. Computation of the entropy spectrum pathways requires only solving a simple eigenvector problem for the probability distribution for which efficient numerical routines exist, and a straight forward integration of the probability conservation through ray tracing of the convective modes guided by a global structure of the entropy spectrum coupled with a small scale local diffusion. The intervoxel diffusion is sampled by multi b-shell multi q-angle diffusion weighted imaging data expanded in spherical waves. This novel approach to fiber tracking incorporates global information about multiple fiber crossings in every individual voxel and ranks it in the most scientifically rigorous way. This method has potential significance for a wide range of applications, including studies of brain connectivity.
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Berquist RM, Galinsky VL, Kajiura SM, Frank LR. The coelacanth rostral organ is a unique low-resolution electro-detector that facilitates the feeding strike. Sci Rep 2015; 5:8962. [PMID: 25758410 DOI: 10.1038/srep08962] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2014] [Accepted: 02/03/2015] [Indexed: 11/08/2022] Open
Abstract
The cartilaginous and non-neopterygian bony fishes have an electric sense typically comprised of hundreds or thousands of sensory canals distributed in broad clusters over the head. This morphology facilitates neural encoding of local electric field intensity, orientation, and polarity, used for determining the position of nearby prey. The coelacanth rostral organ electric sense, however, is unique in having only three paired sensory canals with distribution restricted to the dorsal snout, raising questions about its function. To address this, we employed magnetic resonance imaging methods to map electrosensory canal morphology in the extant coelacanth, Latimeria chalumnae, and a simple dipole 'rabbit ears' antennae model with toroidal gain function to approximate their directional sensitivity. This identified a unique focal region of electrosensitivity directly in front of the mouth, and is the first evidence of a low-resolution electro-detector that solely facilitates prey ingestion.
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