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Kwon H, Son S, Morton SU, Wypij D, Cleveland J, Rollins CK, Huang H, Goldmuntz E, Panigrahy A, Thomas NH, Chung WK, Anagnostou E, Norris-Brilliant A, Gelb BD, McQuillen P, Porter GA, Tristani-Firouzi M, Russell MW, Roberts AE, Newburger JW, Grant PE, Lee JM, Im K. Graph-based prototype inverse-projection for identifying cortical sulcal pattern abnormalities in congenital heart disease. Med Image Anal 2025; 102:103538. [PMID: 40121807 PMCID: PMC12049241 DOI: 10.1016/j.media.2025.103538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2024] [Revised: 02/22/2025] [Accepted: 02/27/2025] [Indexed: 03/25/2025]
Abstract
Examining the altered arrangement and patterning of sulcal folds offers insights into the mechanisms of neurodevelopmental differences in psychiatric and neurological disorders. Previous sulcal pattern analysis used spectral graph matching of sulcal pit-based graph structures to assess deviations from normative sulcal patterns. However, challenges exist, including the absence of a standard criterion for defining a typical reference set, time-consuming cost of graph matching, user-defined feature weight sets, and assumptions about uniform node distribution. We developed a deep learning-based sulcal pattern analysis to address these challenges by adapting prototype-based graph neural networks to sulcal pattern graphs. Additionally, we proposed a prototype inverse-projection for better interpretability. Unlike other prototype-based models, our approach inversely projects prototypes onto individual node representations to calculate the inverse-projection weights, enabling efficient visualization of prototypes and focusing the model on selective regions. We evaluated our method through a classification task between healthy controls (n = 174, age = 15.4 ±1.9 [mean ± standard deviation, years]) and patients with congenital heart disease (n = 345, age = 15.8 ±4.7) from four cohort studies and a public dataset. Our approach demonstrated superior classification performance compared to other state-of-the-art models, supported by extensive ablative studies. Furthermore, we visualized and examined the learned prototypes to enhance understanding. We believe our method has the potential to be a sensitive and understandable tool for sulcal pattern analysis.
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Affiliation(s)
- Hyeokjin Kwon
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea; Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA; Division of Newborn Medicine, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Seungyeon Son
- Department of Artificial Intelligence, Hanyang University, Seoul, South Korea
| | - Sarah U Morton
- Division of Newborn Medicine, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - David Wypij
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Cardiology, Boston Children's Hospital, Boston, MA, USA
| | - John Cleveland
- Department of Surgery and Department of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Caitlin K Rollins
- Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Hao Huang
- Department of Radiology, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth Goldmuntz
- Division of Cardiology, Department of Pediatrics, Children's Hospital of Philadelphia, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ashok Panigrahy
- Department of Pediatric Radiology, Children's Hospital of Pittsburgh, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Nina H Thomas
- Department of Child and Adolescent Psychiatry and Behavioral Sciences and Center for Human Phenomic Science, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Wendy K Chung
- Department of Pediatrics and Department of Medicine, Columbia University Medical Center, New York, NY, USA
| | - Evdokia Anagnostou
- Department of Pediatrics, Holland Bloorview Kids Rehabilitation Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Ami Norris-Brilliant
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bruce D Gelb
- Mindich Child Health and Development Institute and Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Patrick McQuillen
- Department of Pediatrics and Department of Neurology, University of California, San Francisco, CA, USA
| | - George A Porter
- Department of Pediatrics, University of Rochester Medical Center, Rochester, NY, USA
| | - Martin Tristani-Firouzi
- Division of Pediatric Cardiology, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Mark W Russell
- Department of Pediatrics, C.S. Mott Children's Hospital, University of Michigan, Ann Arbor, MI, USA
| | - Amy E Roberts
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA; Division of Newborn Medicine, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA; Department of Pediatrics, Boston Children's Hospital, Boston, MA, USA
| | - Jane W Newburger
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA; Department of Cardiology, Boston Children's Hospital, Boston, MA, USA
| | - P Ellen Grant
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA; Division of Newborn Medicine, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Jong-Min Lee
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea; Department of Artificial Intelligence, Hanyang University, Seoul, South Korea; Department of Biomedical Engineering, Hanyang University, Seoul, South Korea.
| | - Kiho Im
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA; Division of Newborn Medicine, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
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Arnone E, Negri L, Panzica F, Sangalli LM. Analyzing data in complicated 3D domains: Smoothing, semiparametric regression, and functional principal component analysis. Biometrics 2023; 79:3510-3521. [PMID: 36807198 DOI: 10.1111/biom.13845] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 01/26/2023] [Indexed: 02/23/2023]
Abstract
In this work, we introduce a family of methods for the analysis of data observed at locations scattered in three-dimensional (3D) domains, with possibly complicated shapes. The proposed family of methods includes smoothing, regression, and functional principal component analysis for functional signals defined over (possibly nonconvex) 3D domains, appropriately complying with the nontrivial shape of the domain. This constitutes an important advance with respect to the literature, because the available methods to analyze data observed in 3D domains rely on Euclidean distances, which are inappropriate when the shape of the domain influences the phenomenon under study. The common building block of the proposed methods is a nonparametric regression model with differential regularization. We derive the asymptotic properties of the methods and show, through simulation studies, that they are superior to the available alternatives for the analysis of data in 3D domains, even when considering domains with simple shapes. We finally illustrate an application to a neurosciences study, with neuroimaging signals from functional magnetic resonance imaging, measuring neural activity in the gray matter, a nonconvex volume with a highly complicated structure.
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Affiliation(s)
- Eleonora Arnone
- Department of Statistical Sciences, University of Padova, Italy
- Department of Management, University of Turin, Italy
| | - Luca Negri
- MOX-Department of Mathematics, Politecnico di Milano, Italy
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3
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Clementi L, Arnone E, Santambrogio MD, Franceschetti S, Panzica F, Sangalli LM. Anatomically compliant modes of variations: New tools for brain connectivity. PLoS One 2023; 18:e0292450. [PMID: 37934760 PMCID: PMC10629624 DOI: 10.1371/journal.pone.0292450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 09/20/2023] [Indexed: 11/09/2023] Open
Abstract
Anatomical complexity and data dimensionality present major issues when analysing brain connectivity data. The functional and anatomical aspects of the connections taking place in the brain are in fact equally relevant and strongly intertwined. However, due to theoretical challenges and computational issues, their relationship is often overlooked in neuroscience and clinical research. In this work, we propose to tackle this problem through Smooth Functional Principal Component Analysis, which enables to perform dimensional reduction and exploration of the variability in functional connectivity maps, complying with the formidably complicated anatomy of the grey matter volume. In particular, we analyse a population that includes controls and subjects affected by schizophrenia, starting from fMRI data acquired at rest and during a task-switching paradigm. For both sessions, we first identify the common modes of variation in the entire population. We hence explore whether the subjects' expressions along these common modes of variation differ between controls and pathological subjects. In each session, we find principal components that are significantly differently expressed in the healthy vs pathological subjects (with p-values < 0.001), highlighting clearly interpretable differences in the connectivity in the two subpopulations. For instance, the second and third principal components for the rest session capture the imbalance between the Default Mode and Executive Networks characterizing schizophrenia patients.
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Affiliation(s)
- Letizia Clementi
- MOX - Department of Mathematics, Politecnico di Milano, Milan, Italy
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
- CHDS, Center for Health Data Science, Human Technopole, Milan, Italy
| | | | - Marco D. Santambrogio
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | | | | | - Laura M. Sangalli
- MOX - Department of Mathematics, Politecnico di Milano, Milan, Italy
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4
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Zheng W, Liu H, Li Z, Li K, Wang Y, Hu B, Dong Q, Wang Z. Classification of Alzheimer's disease based on hippocampal multivariate morphometry statistics. CNS Neurosci Ther 2023; 29:2457-2468. [PMID: 37002795 PMCID: PMC10401169 DOI: 10.1111/cns.14189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 03/07/2023] [Accepted: 03/13/2023] [Indexed: 08/05/2023] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a neurodegenerative disease characterized by progressive cognitive decline, and mild cognitive impairment (MCI) is associated with a high risk of developing AD. Hippocampal morphometry analysis is believed to be the most robust magnetic resonance imaging (MRI) markers for AD and MCI. Multivariate morphometry statistics (MMS), a quantitative method of surface deformations analysis, is confirmed to have strong statistical power for evaluating hippocampus. AIMS We aimed to test whether surface deformation features in hippocampus can be employed for early classification of AD, MCI, and healthy controls (HC). METHODS We first explored the differences in hippocampus surface deformation among these three groups by using MMS analysis. Additionally, the hippocampal MMS features of selective patches and support vector machine (SVM) were used for the binary classification and triple classification. RESULTS By the results, we identified significant hippocampal deformation among the three groups, especially in hippocampal CA1. In addition, the binary classification of AD/HC, MCI/HC, AD/MCI showed good performances, and area under curve (AUC) of triple-classification model achieved 0.85. Finally, positive correlations were found between the hippocampus MMS features and cognitive performances. CONCLUSIONS The study revealed significant hippocampal deformation among AD, MCI, and HC. Additionally, we confirmed that hippocampal MMS can be used as a sensitive imaging biomarker for the early diagnosis of AD at the individual level.
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Affiliation(s)
- Weimin Zheng
- Department of Radiology, Aerospace Center Hospital, Beijing, China
| | - Honghong Liu
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Zhigang Li
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Kuncheng Li
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, Arizona, USA
| | - Bin Hu
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Qunxi Dong
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Zhiqun Wang
- Department of Radiology, Aerospace Center Hospital, Beijing, China
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Robles Aguirre FA, Marrufo-Meléndez ÓR, Carrillo Mezo R, Torres Agustín R, Nuñez Soria M, Arias-Trejo N, Lara Galindo WF, Silva-Pereyra J, Rodríguez-Camacho MA. Neural correlates of semantic matching in indirect priming. COGN SYST RES 2023. [DOI: 10.1016/j.cogsys.2022.10.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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6
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Ponti L, Perotto S, Sangalli LM. A PDE-regularized smoothing method for space-time data over manifolds with application to medical data. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2022; 38:e3650. [PMID: 36127306 PMCID: PMC10078563 DOI: 10.1002/cnm.3650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 05/09/2022] [Accepted: 09/17/2022] [Indexed: 06/15/2023]
Abstract
We propose an innovative statistical-numerical method to model spatio-temporal data, observed over a generic two-dimensional Riemanian manifold. The proposed approach consists of a regression model completed with a regularizing term based on the heat equation. The model is discretized through a finite element scheme set on the manifold, and solved by resorting to a fixed point-based iterative algorithm. This choice leads to a procedure which is highly efficient when compared with a monolithic approach, and which allows us to deal with massive datasets. After a preliminary assessment on simulation study cases, we investigate the performance of the new estimation tool in practical contexts, by dealing with neuroimaging and hemodynamic data.
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Affiliation(s)
| | - Simona Perotto
- MOX‐Department of MathematicsPolitecnico di MilanoMilanItaly
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7
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Nian R, Gao M, Zhang S, Yu J, Gholipour A, Kong S, Wang R, Sui Y, Velasco-Annis C, Tomas-Fernandez X, Li Q, Lv H, Qian Y, Warfield SK. Toward evaluation of multiresolution cortical thickness estimation with FreeSurfer, MaCRUISE, and BrainSuite. Cereb Cortex 2022; 33:5082-5096. [PMID: 36288912 DOI: 10.1093/cercor/bhac401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 09/09/2022] [Accepted: 09/11/2022] [Indexed: 11/12/2022] Open
Abstract
Abstract
Advances in Magnetic Resonance Imaging hardware and methodologies allow for promoting the cortical morphometry with submillimeter spatial resolution. In this paper, we generated 3D self-enhanced high-resolution (HR) MRI imaging, by adapting 1 deep learning architecture, and 3 standard pipelines, FreeSurfer, MaCRUISE, and BrainSuite, have been collectively employed to evaluate the cortical thickness. We systematically investigated the differences in cortical thickness estimation for MRI sequences at multiresolution homologously originated from the native image. It has been revealed that there systematically exhibited the preferences in determining both inner and outer cortical surfaces at higher resolution, yielding most deeper cortical surface placements toward GM/WM or GM/CSF boundaries, which directs a consistent reduction tendency of mean cortical thickness estimation; on the contrary, the lower resolution data will most probably provide a more coarse and rough evaluation in cortical surface reconstruction, resulting in a relatively thicker estimation. Although the differences of cortical thickness estimation at the diverse spatial resolution varied with one another, almost all led to roughly one-sixth to one-fifth significant reduction across the entire brain at the HR, independent to the pipelines we applied, which emphasizes on generally coherent improved accuracy in a data-independent manner and endeavors to cost-efficiency with quantitative opportunities.
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Affiliation(s)
- Rui Nian
- School of Electronic Engineering, Ocean University of China, 238 Songling Road, Qingdao, China
- Harvard Medical School, 25 Shattuck Street, Boston, MA, United States
- Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, United States
| | - Mingshan Gao
- Citigroup Services and Technology Limited, 1000 Chenhi Road, Shanghai, China
| | | | - Junjie Yu
- School of Electronic Engineering, Ocean University of China, 238 Songling Road, Qingdao, China
| | - Ali Gholipour
- Harvard Medical School, 25 Shattuck Street, Boston, MA, United States
- Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, United States
| | - Shuang Kong
- School of Electronic Engineering, Ocean University of China, 238 Songling Road, Qingdao, China
| | - Ruirui Wang
- School of Electronic Engineering, Ocean University of China, 238 Songling Road, Qingdao, China
| | - Yao Sui
- Harvard Medical School, 25 Shattuck Street, Boston, MA, United States
- Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, United States
| | - Clemente Velasco-Annis
- Harvard Medical School, 25 Shattuck Street, Boston, MA, United States
- Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, United States
| | - Xavier Tomas-Fernandez
- Harvard Medical School, 25 Shattuck Street, Boston, MA, United States
- Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, United States
| | - Qiuying Li
- School of Electronic Engineering, Ocean University of China, 238 Songling Road, Qingdao, China
| | - Hangyu Lv
- School of Electronic Engineering, Ocean University of China, 238 Songling Road, Qingdao, China
| | - Yuqi Qian
- School of Electronic Engineering, Ocean University of China, 238 Songling Road, Qingdao, China
| | - Simon K Warfield
- Harvard Medical School, 25 Shattuck Street, Boston, MA, United States
- Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, United States
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8
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Yun HJ, Lee HJ, Lee JY, Tarui T, Rollins CK, Ortinau CM, Feldman HA, Grant PE, Im K. Quantification of sulcal emergence timing and its variability in early fetal life: Hemispheric asymmetry and sex difference. Neuroimage 2022; 263:119629. [PMID: 36115591 PMCID: PMC10011016 DOI: 10.1016/j.neuroimage.2022.119629] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 08/07/2022] [Accepted: 09/12/2022] [Indexed: 12/25/2022] Open
Abstract
Human fetal brains show regionally different temporal patterns of sulcal emergence following a regular timeline, which may be associated with spatiotemporal patterns of gene expression among cortical regions. This study aims to quantify the timing of sulcal emergence and its temporal variability across typically developing fetuses by fitting a logistic curve to presence or absence of sulcus. We found that the sulcal emergence started from the central to the temporo-parieto-occipital lobes and frontal lobe, and the temporal variability of emergence in most of the sulci was similar between 1 and 2 weeks. Small variability (< 1 week) was found in the left central and postcentral sulci and larger variability (>2 weeks) was shown in the bilateral occipitotemporal and left superior temporal sulci. The temporal variability showed a positive correlation with the emergence timing that may be associated with differential contributions between genetic and environmental factors. Our statistical analysis revealed that the right superior temporal sulcus emerged earlier than the left. Female fetuses showed a trend of earlier sulcal emergence in the right superior temporal sulcus, lower temporal variability in the right intraparietal sulcus, and higher variability in the right precentral sulcus compared to male fetuses. Our quantitative and statistical approach quantified the temporal patterns of sulcal emergence in detail that can be a reference for assessing the normality of developing fetal gyrification.
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Affiliation(s)
- Hyuk Jin Yun
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States; Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Hyun Ju Lee
- Department of Pediatrics, Hanyang University College of Medicine, Seoul 04763, Korea (the Republic of)
| | - Joo Young Lee
- Department of Pediatrics, Hanyang University College of Medicine, Seoul 04763, Korea (the Republic of)
| | - Tomo Tarui
- Mother Infant Research Institute, Tufts Medical Center, Boston, MA 02115, United States
| | - Caitlin K Rollins
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Cynthia M Ortinau
- Department of Pediatrics, Washington University in St. Louis, St. Louis, MO 63130, United States
| | - Henry A Feldman
- Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, United States; Institutional Centers for Clinical and Translational Research, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - P Ellen Grant
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States; Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, United States; Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Kiho Im
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States; Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, United States.
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9
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Demirci N, Holland MA. Cortical thickness systematically varies with curvature and depth in healthy human brains. Hum Brain Mapp 2022; 43:2064-2084. [PMID: 35098606 PMCID: PMC8933257 DOI: 10.1002/hbm.25776] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 11/30/2021] [Accepted: 01/05/2022] [Indexed: 12/30/2022] Open
Abstract
Cortical thickness varies throughout the cortex in a systematic way. However, it is challenging to investigate the patterns of cortical thickness due to the intricate geometry of the cortex. The cortex has a folded nature both in radial and tangential directions which forms not only gyri and sulci but also tangential folds and intersections. In this article, cortical curvature and depth are used to characterize the spatial distribution of the cortical thickness with much higher resolution than conventional regional atlases. To do this, a computational pipeline was developed that is capable of calculating a variety of quantitative measures such as surface area, cortical thickness, curvature (mean curvature, Gaussian curvature, shape index, intrinsic curvature index, and folding index), and sulcal depth. By analyzing 501 neurotypical adult human subjects from the ABIDE-I dataset, we show that cortex has a very organized structure and cortical thickness is strongly correlated with local shape. Our results indicate that cortical thickness consistently increases along the gyral-sulcal spectrum from concave to convex shape, encompassing the saddle shape along the way. Additionally, tangential folds influence cortical thickness in a similar way as gyral and sulcal folds; outer folds are consistently thicker than inner.
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Affiliation(s)
- Nagehan Demirci
- Bioengineering Graduate ProgramUniversity of Notre DameNotre DameIndianaUSA
| | - Maria A. Holland
- Bioengineering Graduate ProgramUniversity of Notre DameNotre DameIndianaUSA
- Department of Aerospace and Mechanical EngineeringUniversity of Notre DameNotre DameIndianaUSA
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10
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Wu J, Su Y, Reiman EM, Caselli RJ, Chen K, Thompson PM, Wang J, Wang Y. Investigating the Effect of Tau Deposition and Apoe on Hippocampal Morphometry in Alzheimer’s Disease: A Federated Chow Test Model. 2022 IEEE 19TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI) 2022; 2022. [PMID: 36147309 PMCID: PMC9491515 DOI: 10.1109/isbi52829.2022.9761576] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Alzheimer's disease (AD) affects more than 1 in 9 people age 65 and older and becomes an urgent public health concern as the global population ages. Tau tangle is the specific protein pathological hallmark of AD and plays a crucial role in leading to dementia-related structural deformations observed in magnetic resonance imaging (MRI) scans. The volume loss of hippocampus is mainly related to the development of AD. Besides, apolipoprotein E (APOE) also has significant effects on the risk of developing AD. However, few studies focus on integrating genotypes, MRI, and tau deposition to infer multimodal relationships. In this paper, we proposed a federated chow test model to study the synergistic effects of APOE and tau on hippocampal morphometry. Our experimental results demonstrate our model can detect the difference of tau deposition and hippocampal atrophy among the cohorts with different genotypes and subiculum and cornu ammonis 1 (CA1 subfield) were identified as hippocampal subregions where atrophy is strongly associated with abnormal tau in the homozygote cohort. Our model will provide novel insight into the neural mechanisms about the individual impact of APOE and tau deposition on brain imaging.
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Affiliation(s)
| | | | | | | | | | | | - Junwen Wang
- Mayo Clinic,Dept. of Health Sciences Research & Center for Individualized Medicine,Scottsdale,AZ,USA
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11
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Chen Y, Cha YH, Gleghorn D, Doudican BC, Shou G, Ding L, Yuan H. Brain network effects by continuous theta burst stimulation in mal de débarquement syndrome: simultaneous EEG and fMRI study. J Neural Eng 2021; 18. [PMID: 34670201 DOI: 10.1088/1741-2552/ac314b] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 10/20/2021] [Indexed: 01/01/2023]
Abstract
Objective. Heterogeneous clinical responses to treatment with non-invasive brain stimulation are commonly observed, making it necessary to determine personally optimized stimulation parameters. We investigated neuroimaging markers of effective brain targets of treatment with continuous theta burst stimulation (cTBS) in mal de débarquement syndrome (MdDS), a balance disorder of persistent oscillating vertigo previously shown to exhibit abnormal intrinsic functional connectivity.Approach.Twenty-four right-handed, cTBS-naive individuals with MdDS received single administrations of cTBS over one of three stimulation targets in randomized order. The optimal target was determined based on the assessment of acute changes after the administration of cTBS over each target. Repetitive cTBS sessions were delivered on three consecutive days with the optimal target chosen by the participant. Electroencephalography (EEG) was recorded at single-administration test sessions of cTBS. Simultaneous EEG and functional MRI data were acquired at baseline and after completion of 10-12 sessions. Network connectivity changes after single and repetitive stimulations of cTBS were analyzed.Main results.Using electrophysiological source imaging and a data-driven method, we identified network-level connectivity changes in EEG that correlated with symptom responses after completion of multiple sessions of cTBS. We further determined that connectivity changes demonstrated by EEG during test sessions of single administrations of cTBS were signatures that could predict optimal targets.Significance.Our findings demonstrate the effect of cTBS on resting state brain networks and suggest an imaging-based, closed-loop stimulation paradigm that can identify optimal targets during short-term test sessions of stimulation.ClinicalTrials.gov Identifier:NCT02470377.
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Affiliation(s)
- Yafen Chen
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, United States of America
| | - Yoon-Hee Cha
- University of Minnesota, Minneapolis, MN, United States of America
| | - Diamond Gleghorn
- Missouri State University, Springfield, MO, United States of America
| | | | - Guofa Shou
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, United States of America
| | - Lei Ding
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, United States of America.,Institute for Biomedical Engineering, Science, and Technology, University of Oklahoma, 3100 Monitor Ave Suite 125Norman, OK, 73019, United States of America
| | - Han Yuan
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, United States of America.,Institute for Biomedical Engineering, Science, and Technology, University of Oklahoma, 3100 Monitor Ave Suite 125Norman, OK, 73019, United States of America
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12
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Wu J, Dong Q, Zhang J, Su Y, Wu T, Caselli RJ, Reiman EM, Ye J, Lepore N, Chen K, Thompson PM, Wang Y. Federated Morphometry Feature Selection for Hippocampal Morphometry Associated Beta-Amyloid and Tau Pathology. Front Neurosci 2021; 15:762458. [PMID: 34899166 PMCID: PMC8655732 DOI: 10.3389/fnins.2021.762458] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Accepted: 11/01/2021] [Indexed: 12/03/2022] Open
Abstract
Amyloid-β (Aβ) plaques and tau protein tangles in the brain are now widely recognized as the defining hallmarks of Alzheimer's disease (AD), followed by structural atrophy detectable on brain magnetic resonance imaging (MRI) scans. One of the particular neurodegenerative regions is the hippocampus to which the influence of Aβ/tau on has been one of the research focuses in the AD pathophysiological progress. This work proposes a novel framework, Federated Morphometry Feature Selection (FMFS) model, to examine subtle aspects of hippocampal morphometry that are associated with Aβ/tau burden in the brain, measured using positron emission tomography (PET). FMFS is comprised of hippocampal surface-based feature calculation, patch-based feature selection, federated group LASSO regression, federated screening rule-based stability selection, and region of interest (ROI) identification. FMFS was tested on two Alzheimer's Disease Neuroimaging Initiative (ADNI) cohorts to understand hippocampal alterations that relate to Aβ/tau depositions. Each cohort included pairs of MRI and PET for AD, mild cognitive impairment (MCI), and cognitively unimpaired (CU) subjects. Experimental results demonstrated that FMFS achieves an 89× speedup compared to other published state-of-the-art methods under five independent hypothetical institutions. In addition, the subiculum and cornu ammonis 1 (CA1 subfield) were identified as hippocampal subregions where atrophy is strongly associated with abnormal Aβ/tau. As potential biomarkers for Aβ/tau pathology, the features from the identified ROIs had greater power for predicting cognitive assessment and for survival analysis than five other imaging biomarkers. All the results indicate that FMFS is an efficient and effective tool to reveal associations between Aβ/tau burden and hippocampal morphometry.
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Affiliation(s)
- Jianfeng Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States
| | - Qunxi Dong
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States
- Institute of Engineering Medicine, Beijing Institute of Technology, Beijing, China
| | - Jie Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States
| | - Yi Su
- Banner Alzheimer’s Institute, Phoenix, AZ, United States
| | - Teresa Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States
| | - Richard J. Caselli
- Department of Neurology, Mayo Clinic Arizona, Scottsdale, AZ, United States
| | - Eric M. Reiman
- Banner Alzheimer’s Institute, Phoenix, AZ, United States
| | - Jieping Ye
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Natasha Lepore
- CIBORG Lab, Department of Radiology, Children’s Hospital Los Angeles, Los Angeles, CA, United States
| | - Kewei Chen
- Banner Alzheimer’s Institute, Phoenix, AZ, United States
| | - Paul M. Thompson
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, CA, United States
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States
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13
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Gata-Garcia A, Porat A, Brimberg L, Volpe BT, Huerta PT, Diamond B. Contributions of Sex Chromosomes and Gonadal Hormones to the Male Bias in a Maternal Antibody-Induced Model of Autism Spectrum Disorder. Front Neurol 2021; 12:721108. [PMID: 34721260 PMCID: PMC8548617 DOI: 10.3389/fneur.2021.721108] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Accepted: 09/14/2021] [Indexed: 11/29/2022] Open
Abstract
Autism Spectrum Disorder (ASD) is a group of neurodevelopmental conditions that is four times more commonly diagnosed in males than females. While susceptibility genes located in the sex chromosomes have been identified in ASD, it is unclear whether they are sufficient to explain the male bias or whether gonadal hormones also play a key role. We evaluated the sex chromosomal and hormonal influences on the male bias in a murine model of ASD, in which mice are exposed in utero to a maternal antibody reactive to contactin-associated protein-like 2 (Caspr2), which was originally cloned from a mother of a child with ASD (termed C6 mice henceforth). In this model, only male mice are affected. We used the four-core-genotypes (FCG) model in which the Sry gene is deleted from the Y chromosome (Y−) and inserted into autosome 3 (TgSry). Thus, by combining the C6 and FCG models, we were able to differentiate the contributions of sex chromosomes and gonadal hormones to the development of fetal brain and adult behavioral phenotypes. We show that the presence of the Y chromosome, or lack of two X chromosomes, irrespective of gonadal sex, increased the susceptibility to C6-induced phenotypes including the abnormal growth of the developing fetal cerebral cortex, as well as a behavioral pattern of decreased open-field exploration in adult mice. Our results indicate that sex chromosomes are the main determinant of the male bias in the maternal C6-induced model of ASD. The less dominant hormonal effect may be due to modulation by sex chromosome genes of factors involved in gonadal hormone pathways in the brain.
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Affiliation(s)
- Adriana Gata-Garcia
- Center for Autoimmune, Musculoskeletal and Hematopoietic Diseases, Institute of Molecular Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States.,Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Amit Porat
- Elmezzi Graduate School of Molecular Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States
| | - Lior Brimberg
- Center for Autoimmune, Musculoskeletal and Hematopoietic Diseases, Institute of Molecular Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States
| | - Bruce T Volpe
- Center for Autoimmune, Musculoskeletal and Hematopoietic Diseases, Institute of Molecular Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States
| | - Patricio T Huerta
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States.,Laboratory of Immune and Neural Networks, Institute of Molecular Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States
| | - Betty Diamond
- Center for Autoimmune, Musculoskeletal and Hematopoietic Diseases, Institute of Molecular Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States.,Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
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14
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Nastase SA, Liu YF, Hillman H, Zadbood A, Hasenfratz L, Keshavarzian N, Chen J, Honey CJ, Yeshurun Y, Regev M, Nguyen M, Chang CHC, Baldassano C, Lositsky O, Simony E, Chow MA, Leong YC, Brooks PP, Micciche E, Choe G, Goldstein A, Vanderwal T, Halchenko YO, Norman KA, Hasson U. The "Narratives" fMRI dataset for evaluating models of naturalistic language comprehension. Sci Data 2021; 8:250. [PMID: 34584100 PMCID: PMC8479122 DOI: 10.1038/s41597-021-01033-3] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 08/18/2021] [Indexed: 02/08/2023] Open
Abstract
The "Narratives" collection aggregates a variety of functional MRI datasets collected while human subjects listened to naturalistic spoken stories. The current release includes 345 subjects, 891 functional scans, and 27 diverse stories of varying duration totaling ~4.6 hours of unique stimuli (~43,000 words). This data collection is well-suited for naturalistic neuroimaging analysis, and is intended to serve as a benchmark for models of language and narrative comprehension. We provide standardized MRI data accompanied by rich metadata, preprocessed versions of the data ready for immediate use, and the spoken story stimuli with time-stamped phoneme- and word-level transcripts. All code and data are publicly available with full provenance in keeping with current best practices in transparent and reproducible neuroimaging.
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Affiliation(s)
- Samuel A Nastase
- Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, NJ, USA.
| | - Yun-Fei Liu
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Hanna Hillman
- Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, NJ, USA
| | - Asieh Zadbood
- Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, NJ, USA
| | - Liat Hasenfratz
- Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, NJ, USA
| | - Neggin Keshavarzian
- Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, NJ, USA
| | - Janice Chen
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Christopher J Honey
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Yaara Yeshurun
- School of Psychological Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Mor Regev
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Mai Nguyen
- Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, NJ, USA
| | - Claire H C Chang
- Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, NJ, USA
| | | | - Olga Lositsky
- Department of Cognitive, Linguistic and Psychological Sciences, Brown University, Providence, RI, USA
| | - Erez Simony
- Faculty of Electrical Engineering, Holon Institute of Technology, Holon, Israel
- Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel
| | | | - Yuan Chang Leong
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - Paula P Brooks
- Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, NJ, USA
| | - Emily Micciche
- Peabody College, Vanderbilt University, Nashville, TN, USA
| | - Gina Choe
- Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, NJ, USA
| | - Ariel Goldstein
- Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, NJ, USA
| | - Tamara Vanderwal
- Department of Psychiatry, University of British Columbia, and BC Children's Hospital Research Institute, Vancouver, BC, Canada
| | - Yaroslav O Halchenko
- Department of Psychological and Brain Sciences and Department of Computer Science, Dartmouth College, Hanover, NH, USA
| | - Kenneth A Norman
- Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, NJ, USA
| | - Uri Hasson
- Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, NJ, USA
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15
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Fan Y, Wang G, Dong Q, Liu Y, Leporé N, Wang Y. Tetrahedral spectral feature-Based bayesian manifold learning for grey matter morphometry: Findings from the Alzheimer's disease neuroimaging initiative. Med Image Anal 2021; 72:102123. [PMID: 34214958 PMCID: PMC8316398 DOI: 10.1016/j.media.2021.102123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 03/30/2021] [Accepted: 05/26/2021] [Indexed: 11/17/2022]
Abstract
Structural and anatomical analyses of magnetic resonance imaging (MRI) data often require a reconstruction of the three-dimensional anatomy to a statistical shape model. Our prior work demonstrated the usefulness of tetrahedral spectral features for grey matter morphometry. However, most of the current methods provide a large number of descriptive shape features, but lack an unsupervised scheme to automatically extract a concise set of features with clear biological interpretations and that also carries strong statistical power. Here we introduce a new tetrahedral spectral feature-based Bayesian manifold learning framework for effective statistical analysis of grey matter morphology. We start by solving the technical issue of generating tetrahedral meshes which preserve the details of the grey matter geometry. We then derive explicit weak-form tetrahedral discretizations of the Hamiltonian operator (HO) and the Laplace-Beltrami operator (LBO). Next, the Schrödinger's equation is solved for constructing the scale-invariant wave kernel signature (SIWKS) as the shape descriptor. By solving the heat equation and utilizing the SIWKS, we design a morphometric Gaussian process (M-GP) regression framework and an active learning strategy to select landmarks as concrete shape descriptors. We evaluate the proposed system on publicly available data from the Alzheimers Disease Neuroimaging Initiative (ADNI), using subjects structural MRI covering the range from cognitively unimpaired (CU) to full blown Alzheimer's disease (AD). Our analyses suggest that the SIWKS and M-GP compare favorably with seven other baseline algorithms to obtain grey matter morphometry-based diagnoses. Our work may inspire more tetrahedral spectral feature-based Bayesian learning research in medical image analysis.
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Affiliation(s)
- Yonghui Fan
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Gang Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA; School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Qunxi Dong
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Yuxiang Liu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Natasha Leporé
- CIBORG Lab, Department of Radiology Children's Hospital Los Angeles, Los Angeles, CA, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
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16
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Squarcina L, Nosari G, Marin R, Castellani U, Bellani M, Bonivento C, Fabbro F, Molteni M, Brambilla P. Automatic classification of autism spectrum disorder in children using cortical thickness and support vector machine. Brain Behav 2021; 11:e2238. [PMID: 34264004 PMCID: PMC8413814 DOI: 10.1002/brb3.2238] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 05/10/2021] [Accepted: 05/23/2021] [Indexed: 12/11/2022] Open
Abstract
OBJECTIVE Autism spectrum disorder (ASD) is a neurodevelopmental condition with a heterogeneous phenotype. The role of biomarkers in ASD diagnosis has been highlighted; cortical thickness has proved to be involved in the etiopathogenesis of ASD core symptoms. We apply support vector machine, a supervised machine learning method, in order to identify specific cortical thickness alterations in ASD subjects. METHODS A sample of 76 subjects (9.5 ± 3.4 years old) has been selected, 40 diagnosed with ASD and 36 typically developed subjects. All children underwent a magnetic resonance imaging (MRI) examination; T1-MPRAGE sequences were analyzed to extract features for the characterization and parcellation of regions of interests (ROI); average cortical thickness (CT) has been measured for each ROI. For the classification process, the extracted features were used as input for a classifier to identify ASD subjects through a "learning by example" procedure; the features with best performance was then selected by "greedy forward-feature selection." Finally, this model underwent a leave-one-out cross-validation approach. RESULTS From the training set of 68 ROIs, five ROIs reached accuracies of over 70%. After this phase, we used a recursive feature selection process in order to identify the eight features with the best accuracy (84.2%). CT resulted higher in ASD compared to controls in all the ROIs identified at the end of the process. CONCLUSION We found increased CT in various brain regions in ASD subjects, confirming their role in the pathogenesis of this condition. Considering the brain development curve during ages, these changes in CT may normalize during development. Further validation on a larger sample is required.
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Affiliation(s)
- Letizia Squarcina
- Department of Pathophysiology and TransplantationUniversity of MilanVia Festa del Perdono, 7, 20122 MilanItaly
| | - Guido Nosari
- Department of Pathophysiology and TransplantationUniversity of MilanVia Festa del Perdono, 7, 20122 MilanItaly
| | - Riccardo Marin
- Department of InformaticsUniversity of VeronaVeronaItaly
| | | | - Marcella Bellani
- Department of NeurosciencesBiomedicine and Movement SciencesSection of PsychiatryUniversity of VeronaVeronaItaly
| | - Carolina Bonivento
- IRCCS “E. Medea”, Polo Friuli Venezia GiuliaSan Vito al Tagliamento (PN)Italy
| | | | - Massimo Molteni
- IRCCS “E. Medea”, Polo Friuli Venezia GiuliaSan Vito al Tagliamento (PN)Italy
| | - Paolo Brambilla
- Department of Pathophysiology and TransplantationUniversity of MilanVia Festa del Perdono, 7, 20122 MilanItaly
- Department of Neurosciences and Mental Health Department of Neurosciences and Mental HealthFondazione IRCCS Ca' Granda Ospedale Maggiore Policlinicovia Francesco Sforza 28, 20122 MilanItaly
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17
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Zoltowski AR, Lyu I, Failla M, Mash LE, Dunham K, Feldman JI, Woynaroski TG, Wallace MT, Barquero LA, Nguyen TQ, Cutting LE, Kang H, Landman BA, Cascio CJ. Cortical Morphology in Autism: Findings from a Cortical Shape-Adaptive Approach to Local Gyrification Indexing. Cereb Cortex 2021; 31:5188-5205. [PMID: 34195789 DOI: 10.1093/cercor/bhab151] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 04/09/2021] [Accepted: 05/04/2021] [Indexed: 11/14/2022] Open
Abstract
It has been challenging to elucidate the differences in brain structure that underlie behavioral features of autism. Prior studies have begun to identify patterns of changes in autism across multiple structural indices, including cortical thickness, local gyrification, and sulcal depth. However, common approaches to local gyrification indexing used in prior studies have been limited by low spatial resolution relative to functional brain topography. In this study, we analyze the aforementioned structural indices, utilizing a new method of local gyrification indexing that quantifies this index adaptively in relation to specific sulci/gyri, improving interpretation with respect to functional organization. Our sample included n = 115 autistic and n = 254 neurotypical participants aged 5-54, and we investigated structural patterns by group, age, and autism-related behaviors. Differing structural patterns by group emerged in many regions, with age moderating group differences particularly in frontal and limbic regions. There were also several regions, particularly in sensory areas, in which one or more of the structural indices of interest either positively or negatively covaried with autism-related behaviors. Given the advantages of this approach, future studies may benefit from its application in hypothesis-driven examinations of specific brain regions and/or longitudinal studies to assess brain development in autism.
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Affiliation(s)
- Alisa R Zoltowski
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN 37232, USA
| | - Ilwoo Lyu
- Department of Computer Science and Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, South Korea
| | - Michelle Failla
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN 37212, USA.,College of Nursing, Ohio State University, Columbus, OH 43210, USA
| | - Lisa E Mash
- San Diego Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California, San Diego, CA 92120, USA
| | - Kacie Dunham
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN 37232, USA.,Department of Hearing and Speech Sciences, Vanderbilt University, Nashville, TN 37232, USA
| | - Jacob I Feldman
- Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA.,Frist Center for Autism and Innovation, Vanderbilt University, Nashville, TN 37212, USA
| | - Tiffany G Woynaroski
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN 37232, USA.,Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA.,Frist Center for Autism and Innovation, Vanderbilt University, Nashville, TN 37212, USA.,Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | - Mark T Wallace
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN 37232, USA.,Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN 37212, USA.,Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA.,Frist Center for Autism and Innovation, Vanderbilt University, Nashville, TN 37212, USA.,Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN 37203, USA.,Department of Pharmacology, Vanderbilt University, Nashville, TN 37232, USA.,Department of Psychology and Human Development, Vanderbilt University, Nashville, TN 37203, USA
| | - Laura A Barquero
- Department of Psychology and Human Development, Vanderbilt University, Nashville, TN 37203, USA
| | - Tin Q Nguyen
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN 37232, USA.,Department of Special Education, Vanderbilt University, Nashville, TN 37203, USA
| | - Laurie E Cutting
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN 37232, USA.,Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN 37203, USA.,Department of Psychology and Human Development, Vanderbilt University, Nashville, TN 37203, USA.,Department of Special Education, Vanderbilt University, Nashville, TN 37203, USA.,Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37232, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Hakmook Kang
- Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN 37203, USA.,Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | - Bennett A Landman
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN 37232, USA.,Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN 37212, USA.,Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN 37203, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA.,Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37212, USA
| | - Carissa J Cascio
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN 37232, USA.,Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN 37212, USA.,Frist Center for Autism and Innovation, Vanderbilt University, Nashville, TN 37212, USA.,Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN 37203, USA.,Department of Psychology and Human Development, Vanderbilt University, Nashville, TN 37203, USA
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18
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Abstract
The attention schema theory posits a specific relationship between subjective awareness and attention, in which awareness is the control model that the brain uses to aid in the endogenous control of attention. In previous experiments, we developed a behavioral paradigm in human subjects to manipulate awareness and attention. The paradigm involved a visual cue that could be used to guide attention to a target stimulus. In task 1, subjects were aware of the cue, but not aware that it provided information about the target. The cue measurably drew exogenous attention to itself. In addition, implicitly, the subjects' endogenous attention mechanism used the cue to help shift attention to the target. In task 2, subjects were no longer aware of the cue. The cue still measurably drew exogenous attention to itself, yet without awareness of the cue, the subjects' endogenous control mechanism was no longer able to use the cue to control attention. Thus, the control of attention depended on awareness. Here, we tested the two tasks while scanning brain activity in human volunteers. We predicted that the right temporoparietal junction (TPJ) would be active in relation to the process in which awareness helps control attention. This prediction was confirmed. The right TPJ was active in relation to the effect of the cue on attention in task 1; it was not measurably active in task 2. The difference was significant. In our interpretation, the right TPJ is involved in an interaction in which awareness permits the control of attention.
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19
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Khan AF, Zhang F, Yuan H, Ding L. Brain-wide functional diffuse optical tomography of resting state networks. J Neural Eng 2021; 18. [PMID: 33946052 DOI: 10.1088/1741-2552/abfdf9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Accepted: 05/04/2021] [Indexed: 02/07/2023]
Abstract
Objective.Diffuse optical tomography (DOT) has the potential in reconstructing resting state networks (RSNs) in human brains with high spatio-temporal resolutions and multiple contrasts. While several RSNs have been reported and successfully reconstructed using DOT, its full potential in recovering a collective set of distributed brain-wide networks with the number of RSNs close to those reported using functional magnetic resonance imaging (fMRI) has not been demonstrated.Approach.The present study developed a novel brain-wide DOT (BW-DOT) framework that integrates a cap-based whole-head optode placement system with multiple computational approaches, i.e. finite-element modeling, inverse source reconstruction, data-driven pattern recognition, and statistical correlation tomography, to reconstruct RSNs in dual contrasts of oxygenated (HbO) and deoxygenated hemoglobins (HbR).Main results.Our results from the proposed framework revealed a comprehensive set of RSNs and their subnetworks, which collectively cover almost the entire neocortical surface of the human brain, both at the group level and individual participants. The spatial patterns of these DOT RSNs suggest statistically significant similarities to fMRI RSN templates. Our results also reported the networks involving the medial prefrontal cortex and precuneus that had been missed in previous DOT studies. Furthermore, RSNs obtained from HbO and HbR suggest similarity in terms of both the number of RSN types reconstructed and their corresponding spatial patterns, while HbR RSNs show statistically more similarity to fMRI RSN templates and HbO RSNs indicate more bilateral patterns over two hemispheres. In addition, the BW-DOT framework allowed consistent reconstructions of RSNs across individuals and across recording sessions, indicating its high robustness and reproducibility, respectively.Significance.Our present results suggest the feasibility of using the BW-DOT, as a neuroimaging tool, in simultaneously mapping multiple RSNs and its potential values in studying RSNs, particularly in patient populations under diverse conditions and needs, due to its advantages in accessibility over fMRI.
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Affiliation(s)
- Ali F Khan
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, United States of America
| | - Fan Zhang
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, United States of America
| | - Han Yuan
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, United States of America.,Institute for Biomedical Engineering, Science, and Technology, University of Oklahoma, Norman, OK, United States of America
| | - Lei Ding
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, United States of America.,Institute for Biomedical Engineering, Science, and Technology, University of Oklahoma, Norman, OK, United States of America
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20
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Zhang J, Dong Q, Shi J, Li Q, Stonnington CM, Gutman BA, Chen K, Reiman EM, Caselli RJ, Thompson PM, Ye J, Wang Y. Predicting future cognitive decline with hyperbolic stochastic coding. Med Image Anal 2021; 70:102009. [PMID: 33711742 PMCID: PMC8049149 DOI: 10.1016/j.media.2021.102009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2019] [Revised: 08/10/2020] [Accepted: 02/16/2021] [Indexed: 01/18/2023]
Abstract
Hyperbolic geometry has been successfully applied in modeling brain cortical and subcortical surfaces with general topological structures. However, such approaches, similar to other surface-based brain morphology analysis methods, usually generate high dimensional features. It limits their statistical power in cognitive decline prediction research, especially in datasets with limited subject numbers. To address the above limitation, we propose a novel framework termed as hyperbolic stochastic coding (HSC). We first compute diffeomorphic maps between general topological surfaces by mapping them to a canonical hyperbolic parameter space with consistent boundary conditions and extracts critical shape features. Secondly, in the hyperbolic parameter space, we introduce a farthest point sampling with breadth-first search method to obtain ring-shaped patches. Thirdly, stochastic coordinate coding and max-pooling algorithms are adopted for feature dimension reduction. We further validate the proposed system by comparing its classification accuracy with some other methods on two brain imaging datasets for Alzheimer's disease (AD) progression studies. Our preliminary experimental results show that our algorithm achieves superior results on various classification tasks. Our work may enrich surface-based brain imaging research tools and potentially result in a diagnostic and prognostic indicator to be useful in individualized treatment strategies.
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Affiliation(s)
- Jie Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, 85287 USA
| | - Qunxi Dong
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, 85287 USA
| | - Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, 85287 USA
| | - Qingyang Li
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, 85287 USA
| | | | - Boris A Gutman
- Armour College of Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ, USA
| | | | | | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Jieping Ye
- Department of Computational Medicine and Bioinformatics & Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, 85287 USA.
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21
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Richard N, Desmurget M, Teillac A, Beuriat PA, Bardi L, Coudé G, Szathmari A, Mottolese C, Sirigu A, Hiba B. Anatomical bases of fast parietal grasp control in humans: A diffusion-MRI tractography study. Neuroimage 2021; 235:118002. [PMID: 33789136 DOI: 10.1016/j.neuroimage.2021.118002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 01/26/2021] [Accepted: 03/24/2021] [Indexed: 11/26/2022] Open
Abstract
The dorso-posterior parietal cortex (DPPC) is a major node of the grasp/manipulation control network. It is assumed to act as an optimal forward estimator that continuously integrates efferent outflows and afferent inflows to modulate the ongoing motor command. In agreement with this view, a recent per-operative study, in humans, identified functional sites within DPPC that: (i) instantly disrupt hand movements when electrically stimulated; (ii) receive short-latency somatosensory afferences from intrinsic hand muscles. Based on these results, it was speculated that DPPC is part of a rapid grasp control loop that receives direct inputs from the hand-territory of the primary somatosensory cortex (S1) and sends direct projections to the hand-territory of the primary motor cortex (M1). However, evidence supporting this hypothesis is weak and partial. To date, projections from DPPC to M1 grasp zone have been identified in monkeys and have been postulated to exist in humans based on clinical and transcranial magnetic studies. This work uses diffusion-MRI tractography in two samples of right- (n = 50) and left-handed (n = 25) subjects randomly selected from the Human Connectome Project. It aims to determine whether direct connections exist between DPPC and the hand control sectors of the primary sensorimotor regions. The parietal region of interest, related to hand control (hereafter designated DPPChand), was defined permissively as the 95% confidence area of the parietal sites that were found to disrupt hand movements in the previously evoked per-operative study. In both hemispheres, irrespective of handedness, we found dense ipsilateral connections between a restricted part of DPPChand and focal sectors within the pre and postcentral gyrus. These sectors, corresponding to the hand territories of M1 and S1, targeted the same parietal zone (spatial overlap > 92%). As a sensitivity control, we searched for potential connections between the angular gyrus (AG) and the pre and postcentral regions. No robust pathways were found. Streamline densities identified using AG as the starting seed represented less than 5 % of the streamline densities identified from DPPChand. Together, these results support the existence of a direct sensory-parietal-motor loop suited for fast manual control and more generally, for any task requiring rapid integration of distal sensorimotor signals.
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Affiliation(s)
- Nathalie Richard
- Institute of Cognitive Neuroscience Marc Jeannerod, CNRS / UMR 5229, 69500 Bron, France; Université Claude Bernard, Lyon 1, 69100 Villeurbanne, France
| | - Michel Desmurget
- Institute of Cognitive Neuroscience Marc Jeannerod, CNRS / UMR 5229, 69500 Bron, France; Université Claude Bernard, Lyon 1, 69100 Villeurbanne, France
| | - Achille Teillac
- Institute of Cognitive Neuroscience Marc Jeannerod, CNRS / UMR 5229, 69500 Bron, France; Université Claude Bernard, Lyon 1, 69100 Villeurbanne, France; Institut de neurosciences cognitives et intégratives d'Aquitaine, CNRS / UMR 5287, 33076 Bordeaux, France
| | - Pierre-Aurélien Beuriat
- Institute of Cognitive Neuroscience Marc Jeannerod, CNRS / UMR 5229, 69500 Bron, France; Université Claude Bernard, Lyon 1, 69100 Villeurbanne, France; Department of Pediatric Neurosurgery, Hôpital Femme Mère Enfant, 69500, Bron, France
| | - Lara Bardi
- Institute of Cognitive Neuroscience Marc Jeannerod, CNRS / UMR 5229, 69500 Bron, France; Université Claude Bernard, Lyon 1, 69100 Villeurbanne, France
| | - Gino Coudé
- Institute of Cognitive Neuroscience Marc Jeannerod, CNRS / UMR 5229, 69500 Bron, France; Université Claude Bernard, Lyon 1, 69100 Villeurbanne, France
| | - Alexandru Szathmari
- Institute of Cognitive Neuroscience Marc Jeannerod, CNRS / UMR 5229, 69500 Bron, France; Université Claude Bernard, Lyon 1, 69100 Villeurbanne, France; Department of Pediatric Neurosurgery, Hôpital Femme Mère Enfant, 69500, Bron, France
| | - Carmine Mottolese
- Institute of Cognitive Neuroscience Marc Jeannerod, CNRS / UMR 5229, 69500 Bron, France; Université Claude Bernard, Lyon 1, 69100 Villeurbanne, France; Department of Pediatric Neurosurgery, Hôpital Femme Mère Enfant, 69500, Bron, France
| | - Angela Sirigu
- Institute of Cognitive Neuroscience Marc Jeannerod, CNRS / UMR 5229, 69500 Bron, France; Université Claude Bernard, Lyon 1, 69100 Villeurbanne, France
| | - Bassem Hiba
- Institute of Cognitive Neuroscience Marc Jeannerod, CNRS / UMR 5229, 69500 Bron, France; Université Claude Bernard, Lyon 1, 69100 Villeurbanne, France.
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22
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23
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Ahtam B, Turesky TK, Zöllei L, Standish J, Grant PE, Gaab N, Im K. Intergenerational Transmission of Cortical Sulcal Patterns from Mothers to their Children. Cereb Cortex 2021; 31:1888-1897. [PMID: 33230560 PMCID: PMC7945013 DOI: 10.1093/cercor/bhaa328] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Revised: 10/09/2020] [Accepted: 10/10/2020] [Indexed: 12/23/2022] Open
Abstract
Intergenerational effects are described as the genetic, epigenetic, as well as pre- and postnatal environmental influence parents have on their offspring's behavior, cognition, and brain. During fetal brain development, the primary cortical sulci emerge with a distinctive folding pattern that are under strong genetic influence and show little change of this pattern throughout postnatal brain development. We examined intergenerational transmission of cortical sulcal patterns by comparing primary sulcal patterns between children (N = 16, age 5.5 ± 0.81 years, 8 males) and their biological mothers (N = 15, age 39.72 ± 4.68 years) as well as between children and unrelated adult females. Our graph-based sulcal pattern comparison method detected stronger sulcal pattern similarity for child-mother pairs than child-unrelated pairs, where higher similarity between child-mother pairs was observed mostly for the right lobar regions. Our results also show that child-mother versus child-unrelated pairs differ for daughters and sons with a trend toward significance, particularly for the left hemisphere lobar regions. This is the first study to reveal significant intergenerational transmission of cortical sulcal patterns, and our results have important implications for the study of the heritability of complex behaviors, brain-based disorders, the identification of biomarkers, and targets for interventions.
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Affiliation(s)
- Banu Ahtam
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Division of Newborn Medicine, Department of Pediatrics, Boston Children’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Department of Pediatrics, Boston, MA 02115, USA
| | - Ted K Turesky
- Harvard Medical School, Department of Pediatrics, Boston, MA 02115, USA
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of Medicine, Boston Children’s Hospital, Boston, MA 02115, USA
| | - Lilla Zöllei
- A.A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA 02129, USA
| | - Julianna Standish
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Division of Newborn Medicine, Department of Pediatrics, Boston Children’s Hospital, Boston, MA 02115, USA
| | - P Ellen Grant
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Division of Newborn Medicine, Department of Pediatrics, Boston Children’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Department of Pediatrics, Boston, MA 02115, USA
| | - Nadine Gaab
- Harvard Medical School, Department of Pediatrics, Boston, MA 02115, USA
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of Medicine, Boston Children’s Hospital, Boston, MA 02115, USA
| | - Kiho Im
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Division of Newborn Medicine, Department of Pediatrics, Boston Children’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Department of Pediatrics, Boston, MA 02115, USA
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24
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Pyrzowski J, Le Douget JE, Fouad A, Siemiński M, Jędrzejczak J, Le Van Quyen M. Zero-crossing patterns reveal subtle epileptiform discharges in the scalp EEG. Sci Rep 2021; 11:4128. [PMID: 33602954 PMCID: PMC7892826 DOI: 10.1038/s41598-021-83337-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 12/14/2020] [Indexed: 11/08/2022] Open
Abstract
Clinical diagnosis of epilepsy depends heavily on the detection of interictal epileptiform discharges (IEDs) from scalp electroencephalographic (EEG) signals, which by purely visual means is far from straightforward. Here, we introduce a simple signal analysis procedure based on scalp EEG zero-crossing patterns which can extract the spatiotemporal structure of scalp voltage fluctuations. We analyzed simultaneous scalp and intracranial EEG recordings from patients with pharmacoresistant temporal lobe epilepsy. Our data show that a large proportion of intracranial IEDs manifest only as subtle, low-amplitude waveforms below scalp EEG background and could, therefore, not be detected visually. We found that scalp zero-crossing patterns allow detection of these intracranial IEDs on a single-trial level with millisecond temporal precision and including some mesial temporal discharges that do not propagate to the neocortex. Applied to an independent dataset, our method discriminated accurately between patients with epilepsy and normal subjects, confirming its practical applicability.
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Affiliation(s)
- Jan Pyrzowski
- Bioelectrics Lab, Institute of Brain and Spine (ICM), (UMRS 1127, CNRS UMR 7225), Pitié-Salpêtriere Hospital, 47 Boulevard de l'Hôpital, 75013, Paris, France
| | | | - Amal Fouad
- Bioelectrics Lab, Institute of Brain and Spine (ICM), (UMRS 1127, CNRS UMR 7225), Pitié-Salpêtriere Hospital, 47 Boulevard de l'Hôpital, 75013, Paris, France
- Department of Neurology, Ain-Shams University, Cairo, Egypt
| | - Mariusz Siemiński
- Department of Emergency Medicine, Medical University of Gdańsk, Gdańsk, Poland
| | - Joanna Jędrzejczak
- Department of Neurology and Epileptology, Medical Centre for Postgraduate Education, Warsaw, Poland
| | - Michel Le Van Quyen
- Bioelectrics Lab, Institute of Brain and Spine (ICM), (UMRS 1127, CNRS UMR 7225), Pitié-Salpêtriere Hospital, 47 Boulevard de l'Hôpital, 75013, Paris, France.
- Sorbonne University, UPMC Univ, Paris 06, 75005, Paris, France.
- Laboratoire D'Imagerie Biomédicale, (INSERM U1146UMR7371 CNRS, Sorbonne université), Campus des Cordeliers, 15 rue de l'Ecole de Médecine, 75006, Paris, France.
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25
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Shishegar R, Pizzagalli F, Georgiou-Karistianis N, Egan GF, Jahanshad N, Johnston LA. A gyrification analysis approach based on Laplace Beltrami eigenfunction level sets. Neuroimage 2021; 229:117751. [PMID: 33460799 DOI: 10.1016/j.neuroimage.2021.117751] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Revised: 12/22/2020] [Accepted: 01/07/2021] [Indexed: 10/22/2022] Open
Abstract
An accurate measure of the complexity of patterns of cortical folding or gyrification is necessary for understanding normal brain development and neurodevelopmental disorders. Conventional gyrification indices (GIs) are calculated based on surface curvature (curvature-based GI) or an outer hull surface of the cortex (outer surface-based GI). The latter is dependent on the definition of the outer hull surface and a corresponding function between surfaces. In the present study, we propose the Laplace Beltrami-based gyrification index (LB-GI). This is a new curvature-based local GI computed using the first three Laplace Beltrami eigenfunction level sets. As with outer surface-based GI methods, this method is based on the hypothesis that gyrification stems from a flat surface during development. However, instead of quantifying gyrification with reference to corresponding points on an outer hull surface, LB-GI quantifies the gyrification at each point on the cortical surface with reference to their surrounding gyral points, overcoming several shortcomings of existing methods. The LB-GI was applied to investigate the cortical maturation profile of the human brain from preschool to early adulthood using the PING database. The results revealed more detail in patterns of cortical folding than conventional curvature-based methods, especially on frontal and posterior tips of the brain, such as the frontal pole, lateral occipital, lateral cuneus, and lingual. Negative associations of cortical folding with age were observed at cortical regions, including bilateral lingual, lateral occipital, precentral gyrus, postcentral gyrus, and superior frontal gyrus. The results also indicated positive significant associations between age and the LB-GI of bilateral insula, the medial orbitofrontal, frontal pole and rostral anterior cingulate regions. It is anticipated that the LB-GI will be advantageous in providing further insights in the understanding of brain development and degeneration in large clinical neuroimaging studies.
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Affiliation(s)
- Rosita Shishegar
- School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia; Monash Biomedical Imaging, Monash University, Melbourne, Australia; Department of Biomedical Engineering, University of Melbourne, Melbourne, Australia; The Australian e-Health Research Centre, CSIRO, Melbourne, Australia.
| | - Fabrizio Pizzagalli
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA; Department of Neurosciences, University of Turin, Italy
| | - Nellie Georgiou-Karistianis
- School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | - Gary F Egan
- School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia; Monash Biomedical Imaging, Monash University, Melbourne, Australia
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Leigh A Johnston
- Department of Biomedical Engineering, University of Melbourne, Melbourne, Australia; Melbourne Brain Centre Imaging Unit, University of Melbourne, Melbourne, Australia
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26
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Sanabria-Diaz G, Melie-Garcia L, Draganski B, Demonet JF, Kherif F. Apolipoprotein E4 effects on topological brain network organization in mild cognitive impairment. Sci Rep 2021; 11:845. [PMID: 33436948 PMCID: PMC7804004 DOI: 10.1038/s41598-020-80909-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 12/30/2020] [Indexed: 01/29/2023] Open
Abstract
The Apolipoprotein E isoform E4 (ApoE4) is consistently associated with an elevated risk of developing late-onset Alzheimer's Disease (AD); however, less is known about the potential genetic modulation of the brain networks organization during prodromal stages like Mild Cognitive Impairment (MCI). To investigate this issue during this critical stage, we used a dataset with a cross-sectional sample of 253 MCI patients divided into ApoE4-positive (‛Carriers') and ApoE4-negative ('non-Carriers'). We estimated the cortical thickness (CT) from high-resolution T1-weighted structural magnetic images to calculate the correlation among anatomical regions across subjects and build the CT covariance networks (CT-Nets). The topological properties of CT-Nets were described through the graph theory approach. Specifically, our results showed a significant decrease in characteristic path length, clustering-index, local efficiency, global connectivity, modularity, and increased global efficiency for Carriers compared to non-Carriers. Overall, we found that ApoE4 in MCI shaped the topological organization of CT-Nets. Our results suggest that in the MCI stage, the ApoE4 disrupting the CT correlation between regions may be due to adaptive mechanisms to sustain the information transmission across distant brain regions to maintain the cognitive and behavioral abilities before the occurrence of the most severe symptoms.
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Affiliation(s)
- Gretel Sanabria-Diaz
- Laboratoire de Recherche en Neuroimagerie (LREN), Département des neurosciences cliniques, Centre Hospitalier Universitaire Vaudois (CHUV), Mont Paisible 16, 1011, Lausanne, Switzerland.
| | - Lester Melie-Garcia
- Laboratoire de Recherche en Neuroimagerie (LREN), Département des neurosciences cliniques, Centre Hospitalier Universitaire Vaudois (CHUV), Mont Paisible 16, 1011, Lausanne, Switzerland
| | - Bogdan Draganski
- Laboratoire de Recherche en Neuroimagerie (LREN), Département des neurosciences cliniques, Centre Hospitalier Universitaire Vaudois (CHUV), Mont Paisible 16, 1011, Lausanne, Switzerland
| | | | - Ferath Kherif
- Laboratoire de Recherche en Neuroimagerie (LREN), Département des neurosciences cliniques, Centre Hospitalier Universitaire Vaudois (CHUV), Mont Paisible 16, 1011, Lausanne, Switzerland
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27
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Liao X, Sun J, Jin Z, Wu D, Liu J. Cortical Morphological Changes in Congenital Amusia: Surface-Based Analyses. Front Psychiatry 2021; 12:721720. [PMID: 35095585 PMCID: PMC8794692 DOI: 10.3389/fpsyt.2021.721720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 12/07/2021] [Indexed: 11/25/2022] Open
Abstract
Background: Congenital amusia (CA) is a rare disorder characterized by deficits in pitch perception, and many structural and functional magnetic resonance imaging studies have been conducted to better understand its neural bases. However, a structural magnetic resonance imaging analysis using a surface-based morphology method to identify regions with cortical features abnormalities at the vertex-based level has not yet been performed. Methods: Fifteen participants with CA and 13 healthy controls underwent structural magnetic resonance imaging. A surface-based morphology method was used to identify anatomical abnormalities. Then, the surface parameters' mean value of the identified clusters with statistically significant between-group differences were extracted and compared. Finally, Pearson's correlation analysis was used to assess the correlation between the Montreal Battery of Evaluation of Amusia (MBEA) scores and surface parameters. Results: The CA group had significantly lower MBEA scores than the healthy controls (p = 0.000). The CA group exhibited a significant higher fractal dimension in the right caudal middle frontal gyrus and a lower sulcal depth in the right pars triangularis gyrus (p < 0.05; false discovery rate-corrected at the cluster level) compared to healthy controls. There were negative correlations between the mean fractal dimension values in the right caudal middle frontal gyrus and MBEA score, including the mean MBEA score (r = -0.5398, p = 0.0030), scale score (r = -0.5712, p = 0.0015), contour score (r = -0.4662, p = 0.0124), interval score (r = -0.4564, p = 0.0146), rhythmic score (r = -0.5133, p = 0.0052), meter score (r = -0.3937, p = 0.0382), and memory score (r = -0.3879, p = 0.0414). There was a significant positive correlation between the mean sulcal depth in the right pars triangularis gyrus and the MBEA score, including the mean score (r = 0.5130, p = 0.0052), scale score (r = 0.5328, p = 0.0035), interval score (r = 0.4059, p = 0.0321), rhythmic score (r = 0.5733, p = 0.0014), meter score (r = 0.5061, p = 0.0060), and memory score (r = 0.4001, p = 0.0349). Conclusion: Individuals with CA exhibit cortical morphological changes in the right hemisphere. These findings may indicate that the neural basis of speech perception and memory impairments in individuals with CA is associated with abnormalities in the right pars triangularis gyrus and middle frontal gyrus, and that these cortical abnormalities may be a neural marker of CA.
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Affiliation(s)
- Xuan Liao
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Junjie Sun
- Department of Radiology, The Sir Run Run Shaw Hospital Affiliated to Zhejiang University School of Medicine, Hangzhou, China
| | - Zhishuai Jin
- Medical Psychological Center, The Second Xiangya Hospital of Central South University, Changsha, China
| | - DaXing Wu
- Medical Psychological Center, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Jun Liu
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, China.,Clinical Research Center for Medical Imaging in Hunan Province, Changsha, China.,Department of Radiology Quality Control Center, The Second Xiangya Hospital of Central South University, Changsha, China
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28
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Wang G, Dong Q, Wu J, Su Y, Chen K, Su Q, Zhang X, Hao J, Yao T, Liu L, Zhang C, Caselli RJ, Reiman EM, Wang Y. Developing univariate neurodegeneration biomarkers with low-rank and sparse subspace decomposition. Med Image Anal 2021; 67:101877. [PMID: 33166772 PMCID: PMC7725891 DOI: 10.1016/j.media.2020.101877] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Revised: 08/24/2020] [Accepted: 10/13/2020] [Indexed: 01/01/2023]
Abstract
Cognitive decline due to Alzheimer's disease (AD) is closely associated with brain structure alterations captured by structural magnetic resonance imaging (sMRI). It supports the validity to develop sMRI-based univariate neurodegeneration biomarkers (UNB). However, existing UNB work either fails to model large group variances or does not capture AD dementia (ADD) induced changes. We propose a novel low-rank and sparse subspace decomposition method capable of stably quantifying the morphological changes induced by ADD. Specifically, we propose a numerically efficient rank minimization mechanism to extract group common structure and impose regularization constraints to encode the original 3D morphometry connectivity. Further, we generate regions-of-interest (ROI) with group difference study between common subspaces of Aβ+AD and Aβ-cognitively unimpaired (CU) groups. A univariate morphometry index (UMI) is constructed from these ROIs by summarizing individual morphological characteristics weighted by normalized difference between Aβ+AD and Aβ-CU groups. We use hippocampal surface radial distance feature to compute the UMIs and validate our work in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. With hippocampal UMIs, the estimated minimum sample sizes needed to detect a 25% reduction in the mean annual change with 80% power and two-tailed P=0.05are 116, 279 and 387 for the longitudinal Aβ+AD, Aβ+mild cognitive impairment (MCI) and Aβ+CU groups, respectively. Additionally, for MCI patients, UMIs well correlate with hazard ratio of conversion to AD (4.3, 95% CI = 2.3-8.2) within 18 months. Our experimental results outperform traditional hippocampal volume measures and suggest the application of UMI as a potential UNB.
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Affiliation(s)
- Gang Wang
- Ulsan Ship and Ocean College, Ludong University, Yantai, China.
| | - Qunxi Dong
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, P.O. Box 878809 Tempe, AZ 85287, USA
| | - Jianfeng Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, P.O. Box 878809 Tempe, AZ 85287, USA
| | - Yi Su
- Banner Alzheimer's Institute and Banner Good Samaritan Pet Center, Phoenix, AZ, USA
| | - Kewei Chen
- Banner Alzheimer's Institute and Banner Good Samaritan Pet Center, Phoenix, AZ, USA
| | - Qingtang Su
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Xiaofeng Zhang
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Jinguang Hao
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Tao Yao
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Li Liu
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Caiming Zhang
- Shandong Province Key Lab of Digital Media Technology, Shandong University of Finance and Economics, Jinan, China
| | | | - 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, P.O. Box 878809 Tempe, AZ 85287, USA.
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29
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Wei GX, Ge L, Chen LZ, Cao B, Zhang X. Structural abnormalities of cingulate cortex in patients with first-episode drug-naïve schizophrenia comorbid with depressive symptoms. Hum Brain Mapp 2020; 42:1617-1625. [PMID: 33296139 PMCID: PMC7978138 DOI: 10.1002/hbm.25315] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 11/27/2020] [Accepted: 11/30/2020] [Indexed: 11/07/2022] Open
Abstract
Depressive symptoms are common in patients with first-episode psychosis. However, the neural mechanisms underlying the comorbid depression in schizophrenia are still unknown. The main purpose of this study was to characterize the structural abnormalities of first-episodes drug-naïve (FEDN) schizophrenia comorbid with depression by utilizing both volume-based and surface-based morphometric measurements. Forty-two patients with FEDN schizophrenia and 29 healthy controls were recruited. The 24-item Hamilton Depression Rating Scale (HAMD-24) was administrated to divide all patients into depressive patients (DP) and non-depressive patients (NDP). Compared with NDP, DP had a significantly larger volume and surface area in the left isthmus cingulate cortex and also had a greater volume in the left posterior cingulate cortex. Correlation analysis showed that HAMD total score was positively correlated with the surface area of the left isthmus cingulate and gray matter volume of the left isthmus cingulate cortex. In addition, gray matter volume of the left isthmus cingulate was also correlated with the PANSS general psychopathology or total score. The findings suggest that prominent structural abnormalities of gray matter are mainly concentrated on the cingulate cortex in FEDN schizophrenia patients comorbid with depression, which may contribute to depressive symptoms and psychopathological symptoms.
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Affiliation(s)
- Gao-Xia Wei
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China.,CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Likun Ge
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Li-Zhen Chen
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Bo Cao
- Department of Psychiatry, Faculty of Medicine & Dentistry, University of Alberta, Alberta, Canada
| | - Xiangyang Zhang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China
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30
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Wu J, Zhang J, Li Q, Su Y, Chen K, Reiman EM, Wang J, Lepore N, Ye J, Thompson PM, Wang Y. Patch-Based Surface Morphometry Feature Selection with Federated Group Lasso Regression. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2020; 11583. [PMID: 33250550 DOI: 10.1117/12.2575984] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Collectively, vast quantities of brain imaging data exist across hospitals and research institutions, providing valuable resources to study brain disorders such as Alzheimer's disease (AD). However, in practice, putting all these distributed datasets into a centralized platform is infeasible due to patient privacy concerns, data restrictions and legal regulations. In this study, we propose a novel federated feature selection framework that can analyze the data at each individual institution without data-sharing or accessing private patient information. In this framework, we first propose a federated group lasso optimization method based on block coordinate descent. We employ stability selection to determine statistically significant features, by solving the group lasso problem with a sequence of regularization parameters. To accelerate the stability selection, we further propose a federated screening rule, which can identify and exclude the irrelevant features before solving the group lasso. Here, we use this framework for patch based feature selection on hippocampal morphometry. Shape is characterized through two different kinds of local measures, the radial distance and the surface area determined via tensor-based morphometry (TBM). The method is tested on 1,127 T1-weighted brain magnetic resonance images (MRI) of AD, mild cognitive impairment (MCI) and elderly control subjects, randomly assigned to five independent hypothetical institutions for testing purpose. We examine the association of MRI-based anatomical measures with general cognitive assessment and amyloid burden to identify the morphometry changes related to AD deterioration and plaque accumulation. Finally, we visualize the significance of the association on the hippocampal surfaces. Our experimental results successfully demonstrate the efficiency and effectiveness of our method.
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Affiliation(s)
- Jianfeng Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, USA
| | - Jie Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, USA
| | - Qingyang Li
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, USA
| | - Yi Su
- Banner Alzheimer's Institute, 100 Washtenaw Avenue, Phoenix, USA
| | - Kewei Chen
- Banner Alzheimer's Institute, 100 Washtenaw Avenue, Phoenix, USA
| | - Eric M Reiman
- Banner Alzheimer's Institute, 100 Washtenaw Avenue, Phoenix, USA
| | - Jie Wang
- Department of Electronic Engineering and Information Science, University of Science and Technology of China, 1129 Huizhou Ave, Baohe District, Hefei, China
| | - Natasha Lepore
- CIBORG Lab, Department of Radiology, Children's Hospital Los Angeles, 4650 Sunset Blvd. MS 81, Los Angeles, USA
| | - Jieping Ye
- Department of Computational Medicine and Bioinformatics, University of Michigan, 1301 Beal Avenue, Ann Arbor, USA
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, 4676 Admiralty Way, Los Angeles, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, USA
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31
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Mostapha M, Kim SH, Evans AC, Dager SR, Estes AM, McKinstry RC, Botteron KN, Gerig G, Pizer SM, Schultz RT, Hazlett HC, Piven J, Girault JB, Shen MD, Styner MA. A Novel Method for High-Dimensional Anatomical Mapping of Extra-Axial Cerebrospinal Fluid: Application to the Infant Brain. Front Neurosci 2020; 14:561556. [PMID: 33132824 PMCID: PMC7561674 DOI: 10.3389/fnins.2020.561556] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 08/21/2020] [Indexed: 12/21/2022] Open
Abstract
Cerebrospinal fluid (CSF) plays an essential role in early postnatal brain development. Extra-axial CSF (EA-CSF) volume, which is characterized by CSF in the subarachnoid space surrounding the brain, is a promising marker in the early detection of young children at risk for neurodevelopmental disorders. Previous studies have focused on global EA-CSF volume across the entire dorsal extent of the brain, and not regionally-specific EA-CSF measurements, because no tools were previously available for extracting local EA-CSF measures suitable for localized cortical surface analysis. In this paper, we propose a novel framework for the localized, cortical surface-based analysis of EA-CSF. The proposed processing framework combines probabilistic brain tissue segmentation, cortical surface reconstruction, and streamline-based local EA-CSF quantification. The quantitative analysis of local EA-CSF was applied to a dataset of typically developing infants with longitudinal MRI scans from 6 to 24 months of age. There was a high degree of consistency in the spatial patterns of local EA-CSF across age using the proposed methods. Statistical analysis of local EA-CSF revealed several novel findings: several regions of the cerebral cortex showed reductions in EA-CSF from 6 to 24 months of age, and specific regions showed higher local EA-CSF in males compared to females. These age-, sex-, and anatomically-specific patterns of local EA-CSF would not have been observed if only a global EA-CSF measure were utilized. The proposed methods are integrated into a freely available, open-source, cross-platform, user-friendly software tool, allowing neuroimaging labs to quantify local extra-axial CSF in their neuroimaging studies to investigate its role in typical and atypical brain development.
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Affiliation(s)
- Mahmoud Mostapha
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, United States
| | - Sun Hyung Kim
- Department of Psychiatry, UNC School of Medicine, University of North Carolina, Chapel Hill, NC, United States
| | - Alan C. Evans
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Stephen R. Dager
- Department of Radiology, University of Washington, Seattle, WA, United States
| | - Annette M. Estes
- Department of Speech and Hearing Sciences, University of Washington, Seattle, WA, United States
| | - Robert C. McKinstry
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, United States
| | - Kelly N. Botteron
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, United States
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
| | - Guido Gerig
- Department of Computer Science and Engineering, New York University, New York, NY, United States
| | - Stephen M. Pizer
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, United States
| | - Robert T. Schultz
- Department of Pediatrics, Center for Autism Research, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, United States
| | - Heather C. Hazlett
- Department of Psychiatry, UNC School of Medicine, University of North Carolina, Chapel Hill, NC, United States
- Carolina Institute for Developmental Disabilities, UNC School of Medicine, University of North Carolina-Chapel Hill, Chapel Hill, NC, United States
| | - Joseph Piven
- Department of Psychiatry, UNC School of Medicine, University of North Carolina, Chapel Hill, NC, United States
- Carolina Institute for Developmental Disabilities, UNC School of Medicine, University of North Carolina-Chapel Hill, Chapel Hill, NC, United States
| | - Jessica B. Girault
- Department of Psychiatry, UNC School of Medicine, University of North Carolina, Chapel Hill, NC, United States
- Carolina Institute for Developmental Disabilities, UNC School of Medicine, University of North Carolina-Chapel Hill, Chapel Hill, NC, United States
| | - Mark D. Shen
- Department of Psychiatry, UNC School of Medicine, University of North Carolina, Chapel Hill, NC, United States
- Carolina Institute for Developmental Disabilities, UNC School of Medicine, University of North Carolina-Chapel Hill, Chapel Hill, NC, United States
- UNC Neuroscience Center, University of North Carolina-Chapel Hill, Chapel Hill, NC, United States
| | - Martin A. Styner
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, United States
- Department of Psychiatry, UNC School of Medicine, University of North Carolina, Chapel Hill, NC, United States
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32
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Grant PE, Im K, Ahtam B, Laurentys CT, Chan WM, Brainard M, Chew S, Drottar M, Robson CD, Drmic I, Engle EC. Altered White Matter Organization in the TUBB3 E410K Syndrome. Cereb Cortex 2020; 29:3561-3576. [PMID: 30272120 DOI: 10.1093/cercor/bhy231] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Revised: 08/20/2018] [Indexed: 01/25/2023] Open
Abstract
Seven unrelated individuals (four pediatric, three adults) with the TUBB3 E410K syndrome, harboring identical de novo heterozygous TUBB3 c.1228 G>A mutations, underwent neuropsychological testing and neuroimaging. Despite the absence of cortical malformations, they have intellectual and social disabilities. To search for potential etiologies for these deficits, we compared their brain's structural and white matter organization to 22 controls using structural and diffusion magnetic resonance imaging. Diffusion images were processed to calculate fractional anisotropy (FA) and perform tract reconstructions. Cortical parcellation-based network analysis and gyral topology-based FA analyses were performed. Major interhemispheric, projection and intrahemispheric tracts were manually segmented. Subjects had decreased corpus callosum volume and decreased network efficiency. While only pediatric subjects had diffuse decreases in FA predominantly affecting mid- and long-range tracts, only adult subjects had white matter volume loss associated with decreased cortical surface area. All subjects showed aberrant corticospinal tract trajectory and bilateral absence of the dorsal language network long segment. Furthermore, pediatric subjects had more tracts with decreased FA compared with controls than did adult subjects. These findings define a TUBB3 E410K neuroimaging endophenotype and lead to the hypothesis that the age-related changes are due to microscopic intrahemispheric misguided axons that are pruned during maturation.
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Affiliation(s)
- P Ellen Grant
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA.,Department of Pediatrics, Boston Children's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Kiho Im
- Department of Pediatrics, Boston Children's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Banu Ahtam
- Department of Pediatrics, Boston Children's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Cynthia T Laurentys
- Department of Pediatrics, Boston Children's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Wai-Man Chan
- Harvard Medical School, Boston, MA, USA.,Department of Neurology, Boston Children's Hospital, Boston, MA, USA.,F.M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA, USA.,Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Maya Brainard
- Harvard Medical School, Boston, MA, USA.,Department of Neurology, Boston Children's Hospital, Boston, MA, USA.,F.M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA, USA
| | - Sheena Chew
- Harvard Medical School, Boston, MA, USA.,Department of Neurology, Boston Children's Hospital, Boston, MA, USA.,F.M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA, USA.,Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Marie Drottar
- Department of Pediatrics, Boston Children's Hospital, Boston, MA, USA
| | - Caroline D Robson
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Irene Drmic
- Hamilton Health Sciences, Ron Joyce Children's Health Centre, Hamilton, Ontario L8L 0A4, Canada
| | - Elizabeth C Engle
- Harvard Medical School, Boston, MA, USA.,Department of Neurology, Boston Children's Hospital, Boston, MA, USA.,F.M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA, USA.,Howard Hughes Medical Institute, Chevy Chase, MD, USA.,Department of Ophthalmology, Boston Children's Hospital, Boston, MA, USA
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33
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Ortinau CM, Rollins CK, Gholipour A, Yun HJ, Marshall M, Gagoski B, Afacan O, Friedman K, Tworetzky W, Warfield SK, Newburger JW, Inder TE, Grant PE, Im K. Early-Emerging Sulcal Patterns Are Atypical in Fetuses with Congenital Heart Disease. Cereb Cortex 2020; 29:3605-3616. [PMID: 30272144 DOI: 10.1093/cercor/bhy235] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Revised: 08/28/2018] [Indexed: 12/30/2022] Open
Abstract
Fetuses with congenital heart disease (CHD) have third trimester alterations in cortical development on brain magnetic resonance imaging (MRI). However, the intersulcal relationships contributing to global sulcal pattern remain unknown. This study applied a novel method for examining the geometric and topological relationships between sulci to fetal brain MRIs from 21-30 gestational weeks in CHD fetuses (n = 19) and typically developing (TD) fetuses (n = 17). Sulcal pattern similarity index (SI) to template fetal brain MRIs was determined for the position, area, and depth for corresponding sulcal basins and intersulcal relationships for each subject. CHD fetuses demonstrated altered global sulcal patterns in the left hemisphere compared with TD fetuses (TD [SI, mean ± SD]: 0.822 ± 0.023, CHD: 0.795 ± 0.030, P = 0.002). These differences were present in the earliest emerging sulci and were driven by differences in the position of corresponding sulcal basins (TD: 0.897 ± 0.024, CHD: 0.878 ± 0.019, P = 0.006) and intersulcal relationships (TD: 0.876 ± 0.031, CHD: 0.857 ± 0.018, P = 0.033). No differences in cortical gyrification index, mean curvature, or surface area were present. These data suggest our methods may be more sensitive than traditional measures for evaluating cortical developmental alterations early in gestation.
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Affiliation(s)
- Cynthia M Ortinau
- Department of Pediatrics, Washington University in St. Louis, St. Louis, MO, USA.,Department of Pediatric Newborn Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Caitlin K Rollins
- Department of Neurology, Boston Children's Hospital, Boston, MA, USA.,Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Ali Gholipour
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA.,Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Hyuk Jin Yun
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, USA.,Department of Pediatrics, Harvard Medical School, Boston, MA, USA.,Division of Newborn Medicine, Boston Children's Hospital Boston, MA, USA
| | - Mackenzie Marshall
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, USA
| | - Borjan Gagoski
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA.,Department of Radiology, Harvard Medical School, Boston, MA, USA.,Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, USA
| | - Onur Afacan
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA.,Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Kevin Friedman
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA.,Department of Cardiology, Boston Children's Hospital Boston, MA, USA
| | - Wayne Tworetzky
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA.,Department of Cardiology, Boston Children's Hospital Boston, MA, USA
| | - Simon K Warfield
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA.,Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Jane W Newburger
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA.,Department of Cardiology, Boston Children's Hospital Boston, MA, USA
| | - Terrie E Inder
- Department of Pediatric Newborn Medicine, Brigham and Women's Hospital, Boston, MA, USA.,Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - P Ellen Grant
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA.,Department of Radiology, Harvard Medical School, Boston, MA, USA.,Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, USA.,Division of Newborn Medicine, Boston Children's Hospital Boston, MA, USA
| | - Kiho Im
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, USA.,Department of Pediatrics, Harvard Medical School, Boston, MA, USA.,Division of Newborn Medicine, Boston Children's Hospital Boston, MA, USA
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34
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Nunes AS, Vakorin VA, Kozhemiako N, Peatfield N, Ribary U, Doesburg SM. Atypical age-related changes in cortical thickness in autism spectrum disorder. Sci Rep 2020; 10:11067. [PMID: 32632150 PMCID: PMC7338512 DOI: 10.1038/s41598-020-67507-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Accepted: 06/08/2020] [Indexed: 01/17/2023] Open
Abstract
Recent longitudinal neuroimaging and neurophysiological studies have shown that tracking relative age-related changes in neural signals, rather than a static snapshot of a neural measure, could offer higher sensitivity for discriminating typically developing (TD) individuals from those with autism spectrum disorder (ASD). It is not clear, however, which aspects of age-related changes (trajectories) would be optimal for identifying atypical brain development in ASD. Using a large cross-sectional data set (Autism Brain Imaging Data Exchange [ABIDE] repository; releases I and II), we aimed to explore age-related changes in cortical thickness (CT) in TD and ASD populations (age range 6–30 years old). Cortical thickness was estimated from T1-weighted MRI images at three scales of spatial coarseness (three parcellations with different numbers of regions of interest). For each parcellation, three polynomial models of age-related changes in CT were tested. Specifically, to characterize alterations in CT trajectories, we compared the linear slope, curvature, and aberrancy of CT trajectories across experimental groups, which was estimated using linear, quadratic, and cubic polynomial models, respectively. Also, we explored associations between age-related changes with ASD symptomatology quantified as the Autism Diagnostic Observation Schedule (ADOS) scores. While no overall group differences in cortical thickness were observed across the entire age range, ASD and TD populations were different in terms of age-related changes, which were located primarily in frontal and tempo-parietal areas. These atypical age-related changes were also associated with ADOS scores in the ASD group and used to predict ASD from TD development. These results indicate that the curvature is the most reliable feature for localizing brain areas developmentally atypical in ASD with a more pronounced effect with symptomatology and is the most sensitive in predicting ASD development.
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Affiliation(s)
- Adonay S Nunes
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, 8888 University Dr, Burnaby, BC, V5A 1S6, Canada.
| | - Vasily A Vakorin
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, 8888 University Dr, Burnaby, BC, V5A 1S6, Canada.,Behavioral & Cognitive Neuroscience Institute, Simon Fraser University, Burnaby, Canada
| | - Nataliia Kozhemiako
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, 8888 University Dr, Burnaby, BC, V5A 1S6, Canada
| | - Nicholas Peatfield
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, 8888 University Dr, Burnaby, BC, V5A 1S6, Canada
| | - Urs Ribary
- Behavioral & Cognitive Neuroscience Institute, Simon Fraser University, Burnaby, Canada.,Department Pediatrics and Psychiatry, University of British Columbia, Vancouver, Canada.,B.C. Children's Hospital Research Institute, Vancouver, Canada.,Department Psychology, Simon Fraser University, Burnaby, Canada
| | - Sam M Doesburg
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, 8888 University Dr, Burnaby, BC, V5A 1S6, Canada.,Behavioral & Cognitive Neuroscience Institute, Simon Fraser University, Burnaby, Canada
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35
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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-CLINICAL 2020; 27:102338. [PMID: 32683323 PMCID: PMC7371915 DOI: 10.1016/j.nicl.2020.102338] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [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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36
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Vogt NM, Hunt JF, Adluru N, Dean DC, Johnson SC, Asthana S, Yu JPJ, Alexander AL, Bendlin BB. Cortical Microstructural Alterations in Mild Cognitive Impairment and Alzheimer's Disease Dementia. Cereb Cortex 2020; 30:2948-2960. [PMID: 31833550 PMCID: PMC7197091 DOI: 10.1093/cercor/bhz286] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
In Alzheimer's disease (AD), neurodegenerative processes are ongoing for years prior to the time that cortical atrophy can be reliably detected using conventional neuroimaging techniques. Recent advances in diffusion-weighted imaging have provided new techniques to study neural microstructure, which may provide additional information regarding neurodegeneration. In this study, we used neurite orientation dispersion and density imaging (NODDI), a multi-compartment diffusion model, in order to investigate cortical microstructure along the clinical continuum of mild cognitive impairment (MCI) and AD dementia. Using gray matter-based spatial statistics (GBSS), we demonstrated that neurite density index (NDI) was significantly lower throughout temporal and parietal cortical regions in MCI, while both NDI and orientation dispersion index (ODI) were lower throughout parietal, temporal, and frontal regions in AD dementia. In follow-up ROI analyses comparing microstructure and cortical thickness (derived from T1-weighted MRI) within the same brain regions, differences in NODDI metrics remained, even after controlling for cortical thickness. Moreover, for participants with MCI, gray matter NDI-but not cortical thickness-was lower in temporal, parietal, and posterior cingulate regions. Taken together, our results highlight the utility of NODDI metrics in detecting cortical microstructural degeneration that occurs prior to measurable macrostructural changes and overt clinical dementia.
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Affiliation(s)
- Nicholas M Vogt
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53792 USA
| | - Jack F Hunt
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53792 USA
| | - Nagesh Adluru
- Waisman Laboratory for Brain Imaging and Behavior, Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705 USA
| | - Douglas C Dean
- Waisman Laboratory for Brain Imaging and Behavior, Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705 USA
- Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53792 USA
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705 USA
| | - Sterling C Johnson
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53792 USA
- Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI, 53705 USA
| | - Sanjay Asthana
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53792 USA
- Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI, 53705 USA
| | - John-Paul J Yu
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53792 USA
- Department of Biomedical Engineering, College of Engineering, University of Wisconsin-Madison, Madison, WI, 53706 USA
- Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53719 USA
| | - Andrew L Alexander
- Waisman Laboratory for Brain Imaging and Behavior, Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705 USA
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705 USA
| | - Barbara B Bendlin
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53792 USA
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37
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Vasung L, Yun HJ, Feldman HA, Grant PE, Im K. An Atypical Sulcal Pattern in Children with Disorders of the Corpus Callosum and Its Relation to Behavioral Outcomes. Cereb Cortex 2020; 30:4790-4799. [PMID: 32307538 DOI: 10.1093/cercor/bhaa067] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 01/27/2020] [Accepted: 02/20/2020] [Indexed: 01/05/2023] Open
Abstract
Hypogenesis (hCC) and dysgenesis (dCC) of the corpus callosum (CC) are characterized by its smaller size or absence. The outcomes of these patients vary considerably and are unrelated to the size of the CC abnormality. The aim of the current study was to characterize the sulcal pattern in children with hCC and dCC and to explore its relation to clinical outcome. We used quantitative sulcal pattern analysis that measures deviation (similarity index, SI) of the composite or individual sulcal features (position, depth, area, and graph topology) compared to the control group. We calculated SI for each hemisphere and lobe in 11 children with CC disorder (hCC = 4, dCC = 7) and 15 controls. hCC and dCC had smaller hemispheric SI compared to controls. dCC subjects had smaller regional SI in the frontal and occipital lobes, which were driven by a smaller SI in a position or a graph topology. The significantly decreased SI gradient was found across groups only in the sulcal graph topology of the temporal lobes (controls > hCC > dCC) and was related to clinical outcome. Our results suggest that careful examination of sulcal pattern in hCC and dCC patients could be a useful biomarker of outcome.
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Affiliation(s)
- Lana Vasung
- Fetal-Neonatal Neuroimaging & Developmental Science Center (FNNDSC), Boston, MA 02115, USA.,Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Hyuk Jin Yun
- Fetal-Neonatal Neuroimaging & Developmental Science Center (FNNDSC), Boston, MA 02115, USA.,Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Henry A Feldman
- Fetal-Neonatal Neuroimaging & Developmental Science Center (FNNDSC), Boston, MA 02115, USA.,Institutional Centers for Clinical and Translational Research, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Patricia Ellen Grant
- Fetal-Neonatal Neuroimaging & Developmental Science Center (FNNDSC), Boston, MA 02115, USA.,Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Kiho Im
- Fetal-Neonatal Neuroimaging & Developmental Science Center (FNNDSC), Boston, MA 02115, USA.,Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
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38
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Shou G, Yuan H, Li C, Chen Y, Chen Y, Ding L. Whole-brain electrophysiological functional connectivity dynamics in resting-state EEG. J Neural Eng 2020; 17:026016. [PMID: 32106106 DOI: 10.1088/1741-2552/ab7ad3] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
OBJECTIVE Functional connectivity (FC) dynamics have been studied in functional magnetic resonance imaging (fMRI) data, while it is largely unknown in electrophysiological data, e.g. EEG. APPROACH The present study proposed a novel analytic framework to study spatiotemporal dynamics of FC (dFC) in resting-state human EEG data, including independent component analysis, cortical source imaging, sliding-window correlation analysis, and k-means clustering. MAIN RESULTS Our results confirm that major fMRI intrinsic connectivity networks (ICNs) can be successfully reconstructed from EEG using our analytic framework. Prominent spatial and temporal variability were revealed in these ICNs. The mean dFC spatial patterns of individual ICNs resemble their corresponding static FC (sFC) patterns but show fewer cross-talks among distinct ICNs. Our investigation unveils evidences of time-domain variations in individual ICNs comparable to their mean FC level in terms of magnitude. The major contributors to these variations are from the frequency below 0.0156 Hz, in the similar range of FC dynamics from fMRI data. Among different ICNs, larger temporal variabilities are observed in the frontal attention and auditory/visual ICNs, while sensorimotor, salience, and default model networks showed less. Our analytic framework for the first time revealed quasi-stable states within individual EEG ICNs, with various strengths or spatial patterns that were reliably detected at both group and individual levels. These states all together reveal a more complete picture of EEG ICNs: (1) quasi-stable state spatial patterns as a whole for each EEG ICN are more consistent with the corresponding fMRI ICN in terms of the bilateral distribution and multi-nodes structure; (2) EEG ICNs reveal more transient patterns about within-ICN between-node communications than fMRI ICNs. SIGNIFICANCE The present findings highlight the fact that rich temporal and spatial dynamics exist in ICN that can be detected from EEG data. Future studies might extend investigations towards spectral dynamics of EEG ICNs.
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Affiliation(s)
- Guofa Shou
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, United States of America
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Ma X, Wu G, Kim WH. ENRICHING STATISTICAL INFERENCES ON BRAIN CONNECTIVITY FOR ALZHEIMER'S DISEASE ANALYSIS VIA LATENT SPACE GRAPH EMBEDDING. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2020; 2020:1685-1689. [PMID: 32922658 PMCID: PMC7482999 DOI: 10.1109/isbi45749.2020.9098641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
We develop a graph node embedding Deep Neural Network that leverages statistical outcome measure and graph structure given in the data. The objective is to identify regions of interests (ROIs) in the brain that are affected by topological changes of brain connectivity due to specific neurodegenerative diseases by enriching statistical group analysis. We tackle this problem by learning a latent space where statistical inference can be made more effectively. Our experiments on a large-scale Alzheimer's Disease dataset show promising result identifying ROIs that show statistically significant group differences separating even early and late Mild Cognitive Impairment (MCI) groups whose effect sizes are very subtle.
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Affiliation(s)
- Xin Ma
- Department of Computer Science and Engineering, University of Texas at Arlington
| | - Guorong Wu
- Department of Psychiatry, University of North Carolina - Chapel Hill
- Department of Computer Science, University of North Carolina - Chapel Hill
| | - Won Hwa Kim
- Department of Computer Science and Engineering, University of Texas at Arlington
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40
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Dong Q, Zhang J, Li Q, Wang J, Leporé N, Thompson PM, Caselli RJ, Ye J, Wang Y, Alzheimer’s Disease Neuroimaging Initiative. Integrating Convolutional Neural Networks and Multi-Task Dictionary Learning for Cognitive Decline Prediction with Longitudinal Images. J Alzheimers Dis 2020; 75:971-992. [PMID: 32390615 PMCID: PMC7427104 DOI: 10.3233/jad-190973] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND Disease progression prediction based on neuroimaging biomarkers is vital in Alzheimer's disease (AD) research. Convolutional neural networks (CNN) have been proved to be powerful for various computer vision research by refining reliable and high-level feature maps from image patches. OBJECTIVE A key challenge in applying CNN to neuroimaging research is the limited labeled samples with high dimensional features. Another challenge is how to improve the prediction accuracy by joint analysis of multiple data sources (i.e., multiple time points or multiple biomarkers). To address these two challenges, we propose a novel multi-task learning framework based on CNN. METHODS First, we pre-trained CNN on the ImageNet dataset and transferred the knowledge from the pre-trained model to neuroimaging representation. We used this deep model as feature extractor to generate high-level feature maps of different tasks. Then a novel unsupervised learning method, termed Multi-task Stochastic Coordinate Coding (MSCC), was proposed for learning sparse features of multi-task feature maps by using shared and individual dictionaries. Finally, Lasso regression was performed on these multi-task sparse features to predict AD progression measured by the Mini-Mental State Examination (MMSE) and the Alzheimer's Disease Assessment Scale cognitive subscale (ADAS-Cog). RESULTS We applied this novel CNN-MSCC system on the Alzheimer's Disease Neuroimaging Initiative dataset to predict future MMSE/ADAS-Cog scales. We found our method achieved superior performances compared with seven other methods. CONCLUSION Our work may add new insights into data augmentation and multi-task deep model research and facilitate the adoption of deep models in neuroimaging research.
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Affiliation(s)
- Qunxi Dong
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Jie Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Qingyang Li
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Junwen Wang
- Department of Health Sciences Research, Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ, 85259, USA
| | - Natasha Leporé
- Department of Radiology, Children’s Hospital Los Angeles, Los Angeles, CA, USA
| | - Paul M. Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | | | - Jieping Ye
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
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41
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Rohleder C, Koethe D, Fritze S, Topor CE, Leweke FM, Hirjak D. Neural correlates of binocular depth inversion illusion in antipsychotic-naïve first-episode schizophrenia patients. Eur Arch Psychiatry Clin Neurosci 2019; 269:897-910. [PMID: 29556734 DOI: 10.1007/s00406-018-0886-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2017] [Accepted: 03/13/2018] [Indexed: 12/26/2022]
Abstract
OBJECTIVES Binocular depth inversion illusion (BDII), a visual, 'top-down'-driven information process, is impaired in schizophrenia and particularly in its early stages. BDII is a sensitive measure of impaired visual information processing and represents a valid diagnostic tool for schizophrenia and other psychotic disorders. However, neurobiological underpinnings of aberrant BDII in first-episode schizophrenia are largely unknown at present. METHODS In this study, 22 right-handed, first-episode, antipsychotic-naïve schizophrenia patients underwent BDII assessment and MRI scanning at 1.5 T. The surface-based analysis via new version of Freesurfer (6.0) enabled calculation of cortical thickness and surface area. BDII total and faces scores were related to the two distinct cortical measurements. RESULTS We found a significant correlation between BDII performance and cortical thickness in the inferior frontal gyrus and middle temporal gyrus (p < 0.003, Bonferroni corr.), as well as superior parietal gyrus, postcentral gyrus, supramarginal gyrus, and precentral gyrus (p < 0.05, CWP corr.), respectively. BDII performance was significantly correlated with surface area in the superior parietal gyrus and right postcentral gyrus (p < 0.003, Bonferroni corr.). CONCLUSION BDII performance may be linked to cortical thickness and surface area variations in regions involved in "adaptive" or "top-down" modulation and stimulus processing, i.e., frontal and parietal lobes. Our results suggest that cortical features of distinct evolutionary and genetic origin differently contribute to BDII performance in first-episode, antipsychotic-naïve schizophrenia patients.
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Affiliation(s)
- Cathrin Rohleder
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany.,Institute of Radiochemistry and Experimental Molecular Imaging, University Hospital of Cologne, Cologne, Germany
| | - Dagmar Koethe
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.,Brain and Mind Centre, University of Sydney, Sydney, Australia
| | - Stefan Fritze
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany
| | - Cristina E Topor
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany
| | - F Markus Leweke
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany.,Brain and Mind Centre, University of Sydney, Sydney, Australia
| | - Dusan Hirjak
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany.
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42
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Tarui T, Madan N, Farhat N, Kitano R, Ceren Tanritanir A, Graham G, Gagoski B, Craig A, Rollins CK, Ortinau C, Iyer V, Pienaar R, Bianchi DW, Grant PE, Im K. Disorganized Patterns of Sulcal Position in Fetal Brains with Agenesis of Corpus Callosum. Cereb Cortex 2019; 28:3192-3203. [PMID: 30124828 DOI: 10.1093/cercor/bhx191] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2017] [Accepted: 07/11/2017] [Indexed: 12/22/2022] Open
Abstract
Fetuses with isolated agenesis of the corpus callosum (ACC) are associated with a broad spectrum of neurodevelopmental disability that cannot be specifically predicted in prenatal neuroimaging. We hypothesized that ACC may be associated with aberrant cortical folding. In this study, we determined altered patterning of early primary sulci development in fetuses with isolated ACC using novel quantitative sulcal pattern analysis which measures deviations of regional sulcal features (position, depth, and area) and their intersulcal relationships in 7 fetuses with isolated ACC (27.1 ± 3.8 weeks of gestation, mean ± SD) and 17 typically developing (TD) fetuses (25.7 ± 2.0 weeks) from normal templates. Fetuses with ACC showed significant alterations in absolute sulcal positions and relative intersulcal positional relationship compared to TD fetuses, which were not detected by traditional gyrification index. Our results reveal altered sulcal positional development even in isolated ACC that is present as early as the second trimester and continues throughout the fetal period. It might originate from altered white matter connections and portend functional variances in later life.
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Affiliation(s)
- Tomo Tarui
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Division of Newborn Medicine, Boston Children's Hospital,Harvard Medical School, Boston, MA, USA.,Mother Infant Research Institute, Tufts Medical Center, Tufts University School of Medicine, Boston, MA, USA.,Department of Pediatrics, Tufts Medical Center, Tufts University School of Medicine, Boston, MA, USA
| | - Neel Madan
- Department of Radiology, Tufts Medical Center, Tufts University School of Medicine, Boston, MA, USA
| | - Nabgha Farhat
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Division of Newborn Medicine, Boston Children's Hospital,Harvard Medical School, Boston, MA, USA
| | - Rie Kitano
- Mother Infant Research Institute, Tufts Medical Center, Tufts University School of Medicine, Boston, MA, USA
| | - Asye Ceren Tanritanir
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Division of Newborn Medicine, Boston Children's Hospital,Harvard Medical School, Boston, MA, USA
| | - George Graham
- Department of Obstetrics and Gynecology, Tufts Medical Center, Tufts University School of Medicine, Boston, MA, USA
| | - Borjan Gagoski
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Alexa Craig
- Department of Pediatrics, Maine Medical Center, ME, USA
| | - Caitlin K Rollins
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Cynthia Ortinau
- Department of Pediatrics Newborn Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
| | - Vidya Iyer
- Mother Infant Research Institute, Tufts Medical Center, Tufts University School of Medicine, Boston, MA, USA
| | - Rudolph Pienaar
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Diana W Bianchi
- Medical Genetics Branch, National Human Genome Research Institute, Bethesda, MD, USA
| | - P Ellen Grant
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Division of Newborn Medicine, Boston Children's Hospital,Harvard Medical School, Boston, MA, USA.,Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kiho Im
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Division of Newborn Medicine, Boston Children's Hospital,Harvard Medical School, Boston, MA, USA
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43
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Shishegar R, Rajapakse S, Georgiou-Karistianis N. Altered Cortical Morphometry in Pre-manifest Huntington's Disease: Cross-sectional Data from the IMAGE-HD Study. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2019:2844-2847. [PMID: 31946485 DOI: 10.1109/embc.2019.8857240] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Huntington's disease (HD) is an inherited progressive neurodegenerative disease mainly associated with subcortical striatal atrophy. There is also strong evidence showing cerebral atrophy and cortical thinning; however, limited research has investigated altered patterns of cortical folding in this disease. Here, we investigated cortical morphometry via both gyrification index (GI, a measure of cortical folding) and cortical thinning. The localized GI was examined using a novel GI, namely LB-GI. As part of a cross-sectional study, pre-manifest (pre-HD) individuals (n = 29) and matched controls (n = 29) underwent T1-MRI using data from the IMAGE-HD study. Compared to controls, pre-HD individuals demonstrated significantly lower GI in the left superior parietal and the right superior temporal regions and greater cortical thinning in the bilateral pre-central and the superior frontal gyri and left caudal middle frontal gyrus, as well as the superior parietal region. For the first time, we report evidence of abnormal localized cortical folding in pre-HD. We also provide evidence that cortical folding impacts different regions of the cortical surface more so than cortical thickness. As a result, we propose a potential new biological marker that may increase our understanding of the neuropathology of HD. Greater understanding of brain changes could inform new therapeutic approaches and target points for clinical trials.
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44
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Tokariev A, Roberts JA, Zalesky A, Zhao X, Vanhatalo S, Breakspear M, Cocchi L. Large-scale brain modes reorganize between infant sleep states and carry prognostic information for preterms. Nat Commun 2019; 10:2619. [PMID: 31197175 PMCID: PMC6565810 DOI: 10.1038/s41467-019-10467-8] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 05/06/2019] [Indexed: 12/18/2022] Open
Abstract
Sleep architecture carries vital information about brain health across the lifespan. In particular, the ability to express distinct vigilance states is a key physiological marker of neurological wellbeing in the newborn infant although systems-level mechanisms remain elusive. Here, we demonstrate that the transition from quiet to active sleep in newborn infants is marked by a substantial reorganization of large-scale cortical activity and functional brain networks. This reorganization is attenuated in preterm infants and predicts visual performance at two years. We find a striking match between these empirical effects and a computational model of large-scale brain states which uncovers fundamental biophysical mechanisms not evident from inspection of the data. Active sleep is defined by reduced energy in a uniform mode of neural activity and increased energy in two more complex anteroposterior modes. Preterm-born infants show a deficit in this sleep-related reorganization of modal energy that carries novel prognostic information.
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Affiliation(s)
- Anton Tokariev
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia. .,Department of Clinical Neurophysiology, Clinicum, University of Helsinki, 00014, Helsinki, Finland. .,BABA center, Pediatric Research Center, Clinical Neurophysiology, Children's Hospital, Helsinki University Central Hospital, 00029, Helsinki, Finland.
| | - James A Roberts
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, VIC, 3053, Australia.,Department of Biomedical Engineering, University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Xuelong Zhao
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sampsa Vanhatalo
- Department of Clinical Neurophysiology, Clinicum, University of Helsinki, 00014, Helsinki, Finland.,BABA center, Pediatric Research Center, Clinical Neurophysiology, Children's Hospital, Helsinki University Central Hospital, 00029, Helsinki, Finland
| | - Michael Breakspear
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia.,Hunter Medical Research Institute, University of Newcastle, Newcastle, NSW, 2305, Australia
| | - Luca Cocchi
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia. .,School of Medicine, University of Queensland, Brisbane, QLD, 4006, Australia.
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Kohli JS, Kinnear MK, Fong CH, Fishman I, Carper RA, Müller RA. Local Cortical Gyrification is Increased in Children With Autism Spectrum Disorders, but Decreases Rapidly in Adolescents. Cereb Cortex 2019; 29:2412-2423. [PMID: 29771286 PMCID: PMC6519693 DOI: 10.1093/cercor/bhy111] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Revised: 04/19/2018] [Indexed: 01/03/2023] Open
Abstract
Extensive MRI evidence indicates early brain overgrowth in autism spectrum disorders (ASDs). Local gyrification may reflect the distribution and timing of aberrant cortical expansion in ASDs. We examined MRI data from (Study 1) 64 individuals with ASD and 64 typically developing (TD) controls (7-19 years), and from (Study 2) an independent sample from the Autism Brain Imaging Data Exchange (n = 31/group). Local Gyrification Index (lGI), cortical thickness (CT), and surface area (SA) were measured. In Study 1, differences in lGI (ASD > TD) were found in left parietal and temporal and right frontal and temporal regions. lGI decreased bilaterally with age, but more steeply in ASD in left precentral, right lateral occipital, and middle frontal clusters. CT differed between groups in right perisylvian cortex (TD > ASD), but no differences were found for SA. Partial correlations between lGI and CT were generally negative, but associations were weaker in ASD in several clusters. Study 2 results were consistent, though less extensive. Altered gyrification may reflect unique information about the trajectory of cortical development in ASDs. While early overgrowth tends to be undetectable in later childhood in ASDs, findings may indicate that a trace of this developmental abnormality could remain in a disorder-specific pattern of gyrification.
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Affiliation(s)
- Jiwandeep S Kohli
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, CA, USA,San Diego State University/University of California, San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA
| | - Mikaela K Kinnear
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, CA, USA
| | - Christopher H Fong
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, CA, USA
| | - Inna Fishman
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, CA, USA
| | - Ruth A Carper
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, CA, USA,Address correspondence to Ruth A. Carper, Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, 6363 Alvarado Ct., Suite 200, San Diego, CA 92120, USA. E-mail:
| | - Ralph-Axel Müller
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, CA, USA
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46
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Papadelis C, Ahtam B, Feldman HA, AlHilani M, Tamilia E, Nimec D, Snyder B, Ellen Grant P, Im K. Altered White Matter Connectivity Associated with Intergyral Brain Disorganization in Hemiplegic Cerebral Palsy. Neuroscience 2019; 399:146-160. [PMID: 30593919 PMCID: PMC10716912 DOI: 10.1016/j.neuroscience.2018.12.028] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Revised: 12/08/2018] [Accepted: 12/17/2018] [Indexed: 01/05/2023]
Abstract
Despite extensive literature showing damages in the sensorimotor projection fibers of children with hemiplegic cerebral palsy (HCP), little is known about how these damages affect the global brain network. In this study, we assess the relationship between the structural integrity of sensorimotor projection fibers and the integrity of intergyral association white matter connections in children with HCP. Diffusion tensor imaging was performed in 10 children with HCP and 16 typically developing children. We estimated the regional and global white-matter connectivity using a region-of-interest (ROI)-based approach and a whole-brain gyrus-based parcellation method. Using the ROI-based approach, we tracked the spinothalamic (STh), thalamocortical (ThC), corticospinal (CST), and sensorimotor U- (SMU) fibers. Using the whole-brain parcellation method, we tracked the short-, middle-, and long-range association fibers. We observed for the more affected hemisphere of children with HCP: (i) an increase in axial diffusivity (AD), mean diffusivity (MD), and radial diffusivity (RD) for the STh and ThC fibers; (ii) a decrease in fractional anisotropy (FA) and an increase in MD and RD for the CST and SMU fibers; in (iii) a decrease in FA and an increase in AD, MD, and RD for the middle- and long-range association fibers; and (iv) an association between the integrity of sensorimotor projection and intergyral association fibers. Our findings indicate that altered structural integrity of the sensorimotor projection fibers disorganizes the intergyral association white matter connections among local and distant regions in children with HCP.
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Affiliation(s)
- Christos Papadelis
- Laboratory of Children's Brain Dynamics, Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Banu Ahtam
- Laboratory of Children's Brain Dynamics, Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Henry A Feldman
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Institutional Centers for Clinical and Translational Research, Boston Children's Hospital, Boston, MA, USA
| | - Michel AlHilani
- Laboratory of Children's Brain Dynamics, Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Eleonora Tamilia
- Laboratory of Children's Brain Dynamics, Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Donna Nimec
- Department of Orthopedic Surgery, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, USA
| | - Brian Snyder
- Department of Orthopedic Surgery, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, USA
| | - P Ellen Grant
- Laboratory of Children's Brain Dynamics, Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Radiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, USA
| | - Kiho Im
- Laboratory of Children's Brain Dynamics, Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
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47
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Blazejewska AI, Fischl B, Wald LL, Polimeni JR. Intracortical smoothing of small-voxel fMRI data can provide increased detection power without spatial resolution losses compared to conventional large-voxel fMRI data. Neuroimage 2019; 189:601-614. [PMID: 30690157 DOI: 10.1016/j.neuroimage.2019.01.054] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 12/17/2018] [Accepted: 01/19/2019] [Indexed: 10/27/2022] Open
Abstract
Continued improvement in MRI acquisition technology has made functional MRI (fMRI) with small isotropic voxel sizes down to 1 mm and below more commonly available. Although many conventional fMRI studies seek to investigate regional patterns of cortical activation for which conventional voxel sizes of 3 mm and larger provide sufficient spatial resolution, smaller voxels can help avoid contamination from adjacent white matter (WM) and cerebrospinal fluid (CSF), and thereby increase the specificity of fMRI to signal changes within the gray matter. Unfortunately, temporal signal-to-noise ratio (tSNR), a metric of fMRI sensitivity, is reduced in high-resolution acquisitions, which offsets the benefits of small voxels. Here we introduce a framework that combines small, isotropic fMRI voxels acquired at 7 T field strength with a novel anatomically-informed, surface mesh-navigated spatial smoothing that can provide both higher detection power and higher resolution than conventional voxel sizes. Our smoothing approach uses a family of intracortical surface meshes and allows for kernels of various shapes and sizes, including curved 3D kernels that adapt to and track the cortical folding pattern. Our goal is to restrict smoothing to the cortical gray matter ribbon and avoid noise contamination from CSF and signal dilution from WM via partial volume effects. We found that the intracortical kernel that maximizes tSNR does not maximize percent signal change (ΔS/S), and therefore the kernel configuration that optimizes detection power cannot be determined from tSNR considerations alone. However, several kernel configurations provided a favorable balance between boosting tSNR and ΔS/S, and allowed a 1.1-mm isotropic fMRI acquisition to have higher performance after smoothing (in terms of both detection power and spatial resolution) compared to an unsmoothed 3.0-mm isotropic fMRI acquisition. Overall, the results of this study support the strategy of acquiring voxels smaller than the cortical thickness, even for studies not requiring high spatial resolution, and smoothing them down within the cortical ribbon with a kernel of an appropriate shape to achieve the best performance-thus decoupling the choice of fMRI voxel size from the spatial resolution requirements of the particular study. The improvement of this new intracortical smoothing approach over conventional surface-based smoothing is expected to be modest for conventional resolutions, however the improvement is expected to increase with higher resolutions. This framework can also be applied to anatomically-informed intracortical smoothing of higher-resolution data (e.g. along columns and layers) in studies with prior information about the spatial structure of activation.
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Affiliation(s)
- Anna I Blazejewska
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA.
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
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48
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Prigge MBD, Bigler ED, Travers BG, Froehlich A, Abildskov T, Anderson JS, Alexander AL, Lange N, Lainhart JE, Zielinski BA. Social Responsiveness Scale (SRS) in Relation to Longitudinal Cortical Thickness Changes in Autism Spectrum Disorder. J Autism Dev Disord 2018; 48:3319-3329. [PMID: 29728946 DOI: 10.1007/s10803-018-3566-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
The relationship between brain development and clinical heterogeneity in autism (ASD) is unknown. This study examines the Social Responsiveness Scale (SRS) in relation to the longitudinal development of cortical thickness. Participants (N = 91 ASD, N = 56 TDC; 3-39 years at first scan) were scanned up to three times over a 7-year period. Mixed-effects models examined cortical thickness in relation to SRS score. ASD participants with higher SRS scores showed regionally increased age-related cortical thinning. Regional thickness differences and reduced age-related cortical thinning were found in predominantly right lateralized regions in ASD with decreasing SRS scores over time. Our findings emphasize the importance of examining clinical phenotypes in brain-based studies of ASD.
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Affiliation(s)
- Molly B D Prigge
- Department of Pediatrics, University of Utah, Salt Lake City, UT, USA. .,Department of Radiology, University of Utah, Salt Lake City, UT, USA. .,Waisman Center, University of Wisconsin-Madison, Madison, WI, USA. .,University of Utah, 417 Wakara Way, Suite 3111, Salt Lake City, UT, 84108, USA.
| | - Erin D Bigler
- Departments of Psychology and Neuroscience, Brigham Young University, Provo, UT, USA
| | - Brittany G Travers
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA.,Occupational Therapy Program in Kinesiology, University of Wisconsin-Madison, Madison, WI, USA
| | - Alyson Froehlich
- Department of Psychology, University of Utah, Salt Lake City, UT, USA
| | - Tracy Abildskov
- Departments of Psychology and Neuroscience, Brigham Young University, Provo, UT, USA
| | - Jeffrey S Anderson
- Department of Radiology, University of Utah, Salt Lake City, UT, USA.,Department of Bioengineering, University of Utah, Salt Lake City, UT, USA
| | - Andrew L Alexander
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA.,Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | - Nicholas Lange
- McLean Hospital and Department of Psychiatry, Harvard University, Cambridge, MA, USA
| | - Janet E Lainhart
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA.,Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | - Brandon A Zielinski
- Department of Pediatrics, University of Utah, Salt Lake City, UT, USA.,Department of Neurology, University of Utah, Salt Lake City, UT, USA
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Chung MK, Wang Y, Wu G. Discrete Heat Kernel Smoothing in Irregular Image Domains. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:5101-5104. [PMID: 30441488 DOI: 10.1109/embc.2018.8513450] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
We present the discrete version of heat kernel smoothing on graph data structure. The method is used to smooth data in an irregularly shaped domains in 3D images. New statistical properties of heat kernel smoothing are derived. As an application, we show how to filter out noisy data in the lung blood vessel trees obtained from computed tomography. The method can be further used in representing the complex vessel trees parametrically as a linear combination of basis functions and extracting the skeleton representation of the trees.
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50
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Chen Y, Cichy RM, Stannat W, Haynes JD. Scale-specific analysis of fMRI data on the irregular cortical surface. Neuroimage 2018; 181:370-381. [PMID: 30033391 DOI: 10.1016/j.neuroimage.2018.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Revised: 05/21/2018] [Accepted: 07/02/2018] [Indexed: 11/25/2022] Open
Abstract
To fully characterize the activity patterns on the cerebral cortex as measured with fMRI, the spatial scale of the patterns must be ascertained. Here we address this problem by constructing steerable bandpass filters on the discrete, irregular cortical mesh, using an improved Gaussian smoothing in combination with differential operators of directional derivatives. We demonstrate the utility of the algorithm in two ways. First, using modelling we show that our algorithm yields superior results in numerical precision and spatial uniformity of filter kernels compared to the most widely adopted approach for cortical smoothing. As the effective scales of information differ from the nominal filter sizes applied to extract them, we evaluated the effective scales empirically for different filters to make subsequent comparisons well calibrated. Second, we applied the algorithm to an fMRI dataset to assess the scale and pattern form of cortical encoding of information about visual objects in the ventral visual pathway. We found that filtering by our method improved the detection of discriminant information about experimental conditions over previous methods, that the level of categorization (subordinate versus superordinate) of objects was differentially related to the spatial scale of fMRI patterns, and that the spatial scale at which information was encoded increased along the ventral visual pathway. In sum, our results indicate that the proposed algorithm is particularly suited to assess and detect scale-specific information encoding in cortex, and promises further insight into the topography of cortical encoding in the human brain.
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Affiliation(s)
- Yi Chen
- Bernstein Center for Computational Neuroscience, Berlin Center of Advanced Neuroimaging & Clinic of Neurology, Charité-Universitätsmedizin Berlin, Corporate Member of Humboldt-Universität zu Berlin, Freie Universität Berlin, Berlin Institute of Health, Berlin, Germany; Institute of Cognitive Neurology and Dementia Research, University Hospital Magdeburg, Magdeburg, Germany.
| | - Radoslaw Martin Cichy
- Bernstein Center for Computational Neuroscience, Berlin Center of Advanced Neuroimaging & Clinic of Neurology, Charité-Universitätsmedizin Berlin, Corporate Member of Humboldt-Universität zu Berlin, Freie Universität Berlin, Berlin Institute of Health, Berlin, Germany; Department of Education and Psychology, Free University Berlin, Berlin, Germany
| | - Wilhelm Stannat
- Institute for Mathematics, Technical University Berlin, Berlin, Germany
| | - John-Dylan Haynes
- Bernstein Center for Computational Neuroscience, Berlin Center of Advanced Neuroimaging & Clinic of Neurology, Charité-Universitätsmedizin Berlin, Corporate Member of Humboldt-Universität zu Berlin, Freie Universität Berlin, Berlin Institute of Health, Berlin, Germany
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