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Russo C, Pirozzi MA, Mazio F, Cascone D, Cicala D, De Liso M, Nastro A, Covelli EM, Cinalli G, Quarantelli M. Fully automated measurement of intracranial CSF and brain parenchyma volumes in pediatric hydrocephalus by segmentation of clinical MRI studies. Med Phys 2023; 50:7921-7933. [PMID: 37166045 DOI: 10.1002/mp.16445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 03/29/2023] [Accepted: 04/18/2023] [Indexed: 05/12/2023] Open
Abstract
BACKGROUND Brain parenchyma (BP) and intracranial cerebrospinal fluid (iCSF) volumes measured by fully automated segmentation of clinical brain MRI studies may be useful for the diagnosis and follow-up of pediatric hydrocephalus. However, previously published segmentation techniques either rely on dedicated sequences, not routinely used in clinical practice, or on spatial normalization, which has limited accuracy when severe brain distortions, such as in hydrocephalic patients, are present. PURPOSE We developed a fully automated method to measure BP and iCSF volumes from clinical brain MRI studies of pediatric hydrocephalus patients, exploiting the complementary information contained in T2- and T1-weighted images commonly used in clinical practice. METHODS The proposed procedure, following skull-stripping of the combined volumes, performed using a multiparametric method to obtain a reliable definition of the inner skull profile, maximizes the CSF-to-parenchyma contrast by dividing the T2w- by the T1w- volume after full-scale dynamic rescaling, thus allowing separation of iCSF and BP through a simple thresholding routine. RESULTS Validation against manual tracing on 23 studies (four controls and 19 hydrocephalic patients) showed excellent concordance (ICC > 0.98) and spatial overlap (Dice coefficients ranging from 77.2% for iCSF to 96.8% for intracranial volume). Accuracy was comparable to the intra-operator reproducibility of manual segmentation, as measured in 14 studies processed twice by the same experienced neuroradiologist. Results of the application of the algorithm to a dataset of 63 controls and 57 hydrocephalic patients (19 with parenchymal damage), measuring volumes' changes with normal development and in hydrocephalic patients, are also reported for demonstration purposes. CONCLUSIONS The proposed approach allows fully automated segmentation of BP and iCSF in clinical studies, also in severely distorted brains, enabling to assess age- and disease-related changes in intracranial tissue volume with an accuracy comparable to expert manual segmentation.
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Affiliation(s)
- Carmela Russo
- Neuroradiology Unit, Department of Neuroscience, Santobono-Pausilipon Children's Hospital, Naples, Italy
| | - Maria Agnese Pirozzi
- Institute of Biostructures and Bioimaging, National Research Council, Naples, Italy
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Federica Mazio
- Neuroradiology Unit, Department of Neuroscience, Santobono-Pausilipon Children's Hospital, Naples, Italy
| | - Daniele Cascone
- Neuroradiology Unit, Department of Neuroscience, Santobono-Pausilipon Children's Hospital, Naples, Italy
| | - Domenico Cicala
- Neuroradiology Unit, Department of Neuroscience, Santobono-Pausilipon Children's Hospital, Naples, Italy
| | - Maria De Liso
- Neuroradiology Unit, Department of Neuroscience, Santobono-Pausilipon Children's Hospital, Naples, Italy
| | - Anna Nastro
- Neuroradiology Unit, Department of Neuroscience, Santobono-Pausilipon Children's Hospital, Naples, Italy
| | - Eugenio Maria Covelli
- Neuroradiology Unit, Department of Neuroscience, Santobono-Pausilipon Children's Hospital, Naples, Italy
| | - Giuseppe Cinalli
- Pediatric Neurosurgery Unit, Department of Neuroscience, Santobono-Pausilipon Children's Hospital, Naples, Italy
| | - Mario Quarantelli
- Institute of Biostructures and Bioimaging, National Research Council, Naples, Italy
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Tamminga CA, Clementz BA, Pearlson G, Keshavan M, Gershon ES, Ivleva EI, McDowell J, Meda SA, Keedy S, Calhoun VD, Lizano P, Bishop JR, Hudgens-Haney M, Alliey-Rodriguez N, Asif H, Gibbons R. Biotyping in psychosis: using multiple computational approaches with one data set. Neuropsychopharmacology 2021; 46:143-155. [PMID: 32979849 PMCID: PMC7689458 DOI: 10.1038/s41386-020-00849-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 08/24/2020] [Accepted: 08/26/2020] [Indexed: 12/15/2022]
Abstract
Focusing on biomarker identification and using biomarkers individually or in clusters to define biological subgroups in psychiatry requires a re-orientation from behavioral phenomenology to quantifying brain features, requiring big data approaches for data integration. Much still needs to be accomplished, not only to refine but also to build support for the application and customization of such an analytical phenotypic approach. In this review, we present some of what Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) has learned so far to guide future applications of multivariate phenotyping and their analyses to understanding psychosis. This paper describes several B-SNIP projects that use phenotype data and big data computations to generate novel outcomes and glimpse what phenotypes contribute to disease understanding and, with aspiration, to treatment. The source of the phenotypes varies from genetic data, structural neuroanatomic localization, immune markers, brain physiology, and cognition. We aim to see guiding principles emerge and areas of commonality revealed. And, we will need to demonstrate not only data stability but also the usefulness of biomarker information for subgroup identification enhancing target identification and treatment development.
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Affiliation(s)
- Carol A Tamminga
- Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, 75390, USA.
| | - Brett A Clementz
- Departments of Psychology, Neuroscience, and BioImaging Research Center, University of Georgia, Athens, GA, 30602, USA
| | - Godfrey Pearlson
- Olin Neuropsychiatry Research Center, Institute of Living at Hartford Hospital, Hartford, CT, USA
- Departments of Psychiatry & Neuroscience, Yale University, New Haven, CT, USA
| | - Macheri Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, United States
| | - Elliot S Gershon
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, 60637, USA
| | - Elena I Ivleva
- Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Jennifer McDowell
- Departments of Psychology, Neuroscience, and BioImaging Research Center, University of Georgia, Athens, GA, 30602, USA
| | - Shashwath A Meda
- Olin Neuropsychiatry Research Center, Institute of Living at Hartford Hospital, Hartford, CT, USA
- Departments of Psychiatry & Neuroscience, Yale University, New Haven, CT, USA
| | - Sarah Keedy
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, 60637, USA
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Paulo Lizano
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, United States
| | - Jeffrey R Bishop
- Department of Experimental and Clinical Pharmacology, University of Minnesota College of Pharmacy, Minneapolis, United States
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, 55455, USA
| | | | - Ney Alliey-Rodriguez
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, 60637, USA
| | - Huma Asif
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, 60637, USA
| | - Robert Gibbons
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, 60637, USA
- Departments of Medicine and Public Health Sciences, University of Chicago, Chicago, Ill, USA
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Zhang H, Shen D, Lin W. Resting-state functional MRI studies on infant brains: A decade of gap-filling efforts. Neuroimage 2019; 185:664-684. [PMID: 29990581 PMCID: PMC6289773 DOI: 10.1016/j.neuroimage.2018.07.004] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 05/19/2018] [Accepted: 07/02/2018] [Indexed: 12/16/2022] Open
Abstract
Resting-state functional MRI (rs-fMRI) is one of the most prevalent brain functional imaging modalities. Previous rs-fMRI studies have mainly focused on adults and elderly subjects. Recently, infant rs-fMRI studies have become an area of active research. After a decade of gap filling studies, many facets of the brain functional development from early infancy to toddler has been uncovered. However, infant rs-fMRI is still in its infancy. The image analysis tools for neonates and young infants can be quite different from those for adults. From data analysis to result interpretation, more questions and issues have been raised, and new hypotheses have been formed. With the anticipated availability of unprecedented high-resolution rs-fMRI and dedicated analysis pipelines from the Baby Connectome Project (BCP), it is important now to revisit previous findings and hypotheses, discuss and comment existing issues and problems, and make a "to-do-list" for the future studies. This review article aims to comprehensively review a decade of the findings, unveiling hidden jewels of the fields of developmental neuroscience and neuroimage computing. Emphases will be given to early infancy, particularly the first few years of life. In this review, an end-to-end summary, from infant rs-fMRI experimental design to data processing, and from the development of individual functional systems to large-scale brain functional networks, is provided. A comprehensive summary of the rs-fMRI findings in developmental patterns is highlighted. Furthermore, an extensive summary of the neurodevelopmental disorders and the effects of other hazardous factors is provided. Finally, future research trends focusing on emerging dynamic functional connectivity and state-of-the-art functional connectome analysis are summarized. In next decade, early infant rs-fMRI and developmental connectome study could be one of the shining research topics.
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Affiliation(s)
- Han Zhang
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, 27599, USA
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, 27599, USA; Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea.
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, 27599, USA.
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Knight MJ, Smith-Collins A, Newell S, Denbow M, Kauppinen RA. Cerebral White Matter Maturation Patterns in Preterm Infants: An MRI T2 Relaxation Anisotropy and Diffusion Tensor Imaging Study. J Neuroimaging 2017; 28:86-94. [PMID: 29205635 DOI: 10.1111/jon.12486] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2017] [Accepted: 11/01/2017] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND AND PURPOSE Preterm birth is associated with worse neurodevelopmental outcome, but brain maturation in preterm infants is poorly characterized with standard methods. We evaluated white matter (WM) of infant brains at term-equivalent age, as a function of gestational age at birth, using multimodal magnetic resonance imaging (MRI). METHODS Infants born very preterm (<32 weeks gestation) and late preterm (33-36 weeks gestation) were scanned at 3 T at term-equivalent age using diffusion tensor imaging (DTI) and T2 relaxometry. MRI data were analyzed using tract-based spatial statistics, and anisotropy of T2 relaxation was also determined. Principal component analysis and linear discriminant analysis were applied to seek the variables best distinguishing very preterm and late preterm groups. RESULTS Across widespread regions of WM, T2 is longer in very preterm infants than in late preterm ones. These effects are more prevalent in regions of WM that myelinate earlier and faster. Similar effects are obtained from DTI, showing that fractional anisotropy (FA) is lower and radial diffusivity higher in the very preterm group, with a bias toward earlier myelinating regions. Discriminant analysis shows high sensitivity and specificity of combined T2 relaxometry and DTI for the detection of a distinct WM development pathway in very preterm infants. T2 relaxation is anisotropic, depending on the angle between WM fiber and magnetic field, and this effect is modulated by FA. CONCLUSIONS Combined T2 relaxometry and DTI characterizes specific patterns of retarded WM maturation, at term equivalent age, in infants born very preterm relative to late preterm.
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Affiliation(s)
| | - Adam Smith-Collins
- Clinical Research and Imaging Centre, University of Bristol, UK.,Fetal Medicine Unit, St Michael's Hospital, University Hospitals Bristol NHS Foundation Trust, UK
| | - Sarah Newell
- Fetal Medicine Unit, St Michael's Hospital, University Hospitals Bristol NHS Foundation Trust, UK
| | - Mark Denbow
- Fetal Medicine Unit, St Michael's Hospital, University Hospitals Bristol NHS Foundation Trust, UK
| | - Risto A Kauppinen
- School of Experimental Psychology, University of Bristol, UK.,Clinical Research and Imaging Centre, University of Bristol, UK
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Franke K, Gaser C, Roseboom TJ, Schwab M, de Rooij SR. Premature brain aging in humans exposed to maternal nutrient restriction during early gestation. Neuroimage 2017; 173:460-471. [PMID: 29074280 DOI: 10.1016/j.neuroimage.2017.10.047] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Revised: 10/16/2017] [Accepted: 10/22/2017] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Prenatal exposure to undernutrition is widespread in both developing and industrialized countries, causing irreversible damage to the developing brain, resulting in altered brain structure and decreased cognitive function during adulthood. The Dutch famine in 1944/45 was a humanitarian disaster, now enabling studies of the effects of prenatal undernutrition during gestation on brain aging in late adulthood. METHODS We hypothesized that study participants prenatally exposed to maternal nutrient restriction (MNR) would demonstrate altered brain structure resembling premature brain aging in late adulthood, expecting the effect being stronger in men. Utilizing the Dutch famine birth cohort (n = 118; mean age: 67.5 ± 0.9 years), this study implements an innovative biomarker for individual brain aging, using structural neuroimaging. BrainAGE was calculated using state-of-the-art pattern recognition methods, trained on an independent healthy reference sample, then applied to the Dutch famine MRI sample, to evaluate the effects of prenatal undernutrition during early gestation on individual brain aging in late adulthood. RESULTS Exposure to famine in early gestation was associated with BrainAGE scores indicative of an older-appearing brain in the male sample (mean difference to subjects born before famine: 4.3 years, p < 0.05). Furthermore, in explaining the observed variance in individual BrainAGE scores in the male sample, maternal age at birth, head circumference at birth, medical treatment of hypertension, history of cerebral incidences, actual heart rate, and current alcohol intake emerged to be the most influential variables (adjusted R2 = 0.63, p < 0.01). INTERPRETATION The findings of our study on exposure to prenatal undernutrition being associated with a status of premature brain aging during late adulthood, as well as individual brain structure being shaped by birth- and late-life health characteristics, are strongly supporting the critical importance of sufficient nutrient supply during pregnancy. Interestingly, the status of premature brain aging in participants exposed to the Dutch famine during early gestation occurred in the absence of fetal growth restriction at birth as well as vascular pathology in late-life. Additionally, the neuroimaging brain aging biomarker presented in this study will further enable tracking effects of environmental influences or (preventive) treatments on individual brain maturation and aging in epidemiological and clinical studies.
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Affiliation(s)
- Katja Franke
- Structural Brain Mapping Group, Department of Neurology, Jena University Hospital, Jena, Germany.
| | - Christian Gaser
- Structural Brain Mapping Group, Department of Neurology, Jena University Hospital, Jena, Germany; Department of Psychiatry, Jena University Hospital, Jena, Germany
| | - Tessa J Roseboom
- Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands; Department of Obstetrics and Gynaecology, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands
| | - Matthias Schwab
- Department of Neurology, Jena University Hospital, Jena, Germany
| | - Susanne R de Rooij
- Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands
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Forsting M. Machine Learning Will Change Medicine. J Nucl Med 2017; 58:357-358. [DOI: 10.2967/jnumed.117.190397] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2017] [Accepted: 01/24/2017] [Indexed: 11/16/2022] Open
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