1
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Nebli A, Schiffer C, Niu M, Palomero-Gallagher N, Amunts K, Dickscheid T. Generative Modelling of Cortical Receptor Distributions from Cytoarchitectonic Images in the Macaque Brain. Neuroinformatics 2024:10.1007/s12021-024-09673-7. [PMID: 38976151 DOI: 10.1007/s12021-024-09673-7] [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] [Accepted: 06/06/2024] [Indexed: 07/09/2024]
Abstract
Neurotransmitter receptor densities are relevant for understanding the molecular architecture of brain regions. Quantitative in vitro receptor autoradiography, has been introduced to map neurotransmitter receptor distributions of brain areas. However, it is very time and cost-intensive, which makes it challenging to obtain whole-brain distributions. At the same time, high-throughput light microscopy and 3D reconstructions have enabled high-resolution brain maps capturing measures of cell density across the whole human brain. Aiming to bridge gaps in receptor measurements for building detailed whole-brain atlases, we study the feasibility of predicting realistic neurotransmitter density distributions from cell-body stainings. Specifically, we utilize conditional Generative Adversarial Networks (cGANs) to predict the density distributions of the M2 receptor of acetylcholine and the kainate receptor for glutamate in the macaque monkey's primary visual (V1) and motor cortex (M1), based on light microscopic scans of cell-body stained sections. Our model is trained on corresponding patches from aligned consecutive sections that display cell-body and receptor distributions, ensuring a mapping between the two modalities. Evaluations of our cGANs, both qualitative and quantitative, show their capability to predict receptor densities from cell-body stained sections while maintaining cortical features such as laminar thickness and curvature. Our work underscores the feasibility of cross-modality image translation problems to address data gaps in multi-modal brain atlases.
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Affiliation(s)
- Ahmed Nebli
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.
- Helmholtz AI, Research Centre Jülich, Jülich, Germany.
| | - Christian Schiffer
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Helmholtz AI, Research Centre Jülich, Jülich, Germany
| | - Meiqi Niu
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Nicola Palomero-Gallagher
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Cécile & Oscar Vogt Institute for Brain Research, University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Cécile & Oscar Vogt Institute for Brain Research, University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Timo Dickscheid
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Helmholtz AI, Research Centre Jülich, Jülich, Germany
- Institute of Computer Science, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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2
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Luppi AI, Gellersen HM, Liu ZQ, Peattie ARD, Manktelow AE, Adapa R, Owen AM, Naci L, Menon DK, Dimitriadis SI, Stamatakis EA. Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics. Nat Commun 2024; 15:4745. [PMID: 38834553 PMCID: PMC11150439 DOI: 10.1038/s41467-024-48781-5] [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: 10/04/2022] [Accepted: 05/10/2024] [Indexed: 06/06/2024] Open
Abstract
Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate brain function through network science. Here, we systematically evaluate 768 data-processing pipelines for network reconstruction from resting-state functional MRI, evaluating the effect of brain parcellation, connectivity definition, and global signal regression. Our criteria seek pipelines that minimise motion confounds and spurious test-retest discrepancies of network topology, while being sensitive to both inter-subject differences and experimental effects of interest. We reveal vast and systematic variability across pipelines' suitability for functional connectomics. Inappropriate choice of data-processing pipeline can produce results that are not only misleading, but systematically so, with the majority of pipelines failing at least one criterion. However, a set of optimal pipelines consistently satisfy all criteria across different datasets, spanning minutes, weeks, and months. We provide a full breakdown of each pipeline's performance across criteria and datasets, to inform future best practices in functional connectomics.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, University of Cambridge, Cambridge, UK.
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
- St John's College, University of Cambridge, Cambridge, UK.
- Montreal Neurological Institute, McGill University, Montreal, Canada.
| | - Helena M Gellersen
- German Center for Neurodegenerative Diseases, Magdeburg, Germany
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Zhen-Qi Liu
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Alexander R D Peattie
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Anne E Manktelow
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Ram Adapa
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Adrian M Owen
- Department of Psychology, Western Institute for Neuroscience (WIN), Western University, London, ON, Canada
- Department of Physiology and Pharmacology, Western Institute for Neuroscience (WIN), Western University, London, ON, Canada
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - David K Menon
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
| | - Stavros I Dimitriadis
- Department of Clinical Psychology and Psychobiology, University of Barcelona, Barcelona, Spain
- Institut de Neurociències, University of Barcelona, Barcelona, Spain
- Neuroinformatics Group, Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff, Wales, UK
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- Neuroscience and Mental Health Research Institute, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- Integrative Neuroimaging Lab, Thessaloniki, Greece
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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3
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Ma L, Zhang Y, Zhang H, Cheng L, Yang Z, Lu Y, Shi W, Li W, Zhuo J, Wang J, Fan L, Jiang T. BAI-Net: Individualized Anatomical Cerebral Cartography Using Graph Neural Network. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:7446-7457. [PMID: 36315537 DOI: 10.1109/tnnls.2022.3213581] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Brain atlas is an important tool in the diagnosis and treatment of neurological disorders. However, due to large variations in the organizational principles of individual brains, many challenges remain in clinical applications. Brain atlas individualization network (BAI-Net) is an algorithm that subdivides individual cerebral cortex into segregated areas using brain morphology and connectomes. The presented method integrates group priors derived from a population atlas, adjusts areal probabilities using the context of connectivity fingerprints derived from the fiber-tract embedding of tractography, and provides reliable and explainable individualized brain areas across multiple sessions and scanners. We demonstrate that BAI-Net outperforms the conventional iterative clustering approach by capturing significantly heritable topographic variations in individualized cartographies. The topographic variability of BAI-Net cartographies has shown strong associations with individual variability in brain morphology, connectivity as well as higher relationship on individual cognitive behaviors and genetics. This study provides an explainable framework for individualized brain cartography that may be useful in the precise localization of neuromodulation and treatments on individual brains.
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4
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Song L, Peng Y, Ouyang M, Peng Q, Feng L, Sotardi S, Yu Q, Kang H, Sindabizera KL, Liu S, Huang H. Diffusion-tensor-imaging 1-year-old and 2-year-old infant brain atlases with comprehensive gray and white matter labels. Hum Brain Mapp 2024; 45:e26695. [PMID: 38727010 PMCID: PMC11083905 DOI: 10.1002/hbm.26695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 03/11/2024] [Accepted: 04/10/2024] [Indexed: 05/13/2024] Open
Abstract
Human infancy is marked by fastest postnatal brain structural changes. It also coincides with the onset of many neurodevelopmental disorders. Atlas-based automated structure labeling has been widely used for analyzing various neuroimaging data. However, the relatively large and nonlinear neuroanatomical differences between infant and adult brains can lead to significant offsets of the labeled structures in infant brains when adult brain atlas is used. Age-specific 1- and 2-year-old brain atlases covering all major gray and white matter (GM and WM) structures with diffusion tensor imaging (DTI) and structural MRI are critical for precision medicine for infant population yet have not been established. In this study, high-quality DTI and structural MRI data were obtained from 50 healthy children to build up three-dimensional age-specific 1- and 2-year-old brain templates and atlases. Age-specific templates include a single-subject template as well as two population-averaged templates from linear and nonlinear transformation, respectively. Each age-specific atlas consists of 124 comprehensively labeled major GM and WM structures, including 52 cerebral cortical, 10 deep GM, 40 WM, and 22 brainstem and cerebellar structures. When combined with appropriate registration methods, the established atlases can be used for highly accurate automatic labeling of any given infant brain MRI. We demonstrated that one can automatically and effectively delineate deep WM microstructural development from 3 to 38 months by using these age-specific atlases. These established 1- and 2-year-old infant brain DTI atlases can advance our understanding of typical brain development and serve as clinical anatomical references for brain disorders during infancy.
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Affiliation(s)
- Limei Song
- Research Center for Sectional and Imaging AnatomyShandong University School of MedicineJinanShandongChina
- Department of RadiologyChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
- School of Medical ImagingWeifang Medical UniversityWeifangChina
| | - Yun Peng
- Department of Radiology, Beijing Children's HospitalCapital Medical UniversityBeijingChina
| | - Minhui Ouyang
- Department of RadiologyChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
- Department of Radiology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Qinmu Peng
- Department of RadiologyChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
| | - Lei Feng
- Research Center for Sectional and Imaging AnatomyShandong University School of MedicineJinanShandongChina
- Department of RadiologyChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
| | - Susan Sotardi
- Department of RadiologyChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
| | - Qinlin Yu
- Department of RadiologyChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
| | - Huiying Kang
- Department of Radiology, Beijing Children's HospitalCapital Medical UniversityBeijingChina
| | - Kay L. Sindabizera
- Department of RadiologyChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
| | - Shuwei Liu
- Research Center for Sectional and Imaging AnatomyShandong University School of MedicineJinanShandongChina
| | - Hao Huang
- Department of RadiologyChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
- Department of Radiology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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5
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Lee DJ, Shin DH, Son YH, Han JW, Oh JH, Kim DH, Jeong JH, Kam TE. Spectral Graph Neural Network-Based Multi-Atlas Brain Network Fusion for Major Depressive Disorder Diagnosis. IEEE J Biomed Health Inform 2024; 28:2967-2978. [PMID: 38363664 DOI: 10.1109/jbhi.2024.3366662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2024]
Abstract
Major Depressive Disorder (MDD) imposes a substantial burden within the healthcare domain, impacting millions of individuals worldwide. Functional Magnetic Resonance Imaging (fMRI) has emerged as a promising tool for the objective diagnosis of MDD, enabling the investigation of functional connectivity patterns in the brain associated with this disorder. However, most existing methods focus on a single brain atlas, which limits their ability to capture the complex, multi-scale nature of functional brain networks. To address these limitations, we propose a novel multi-atlas fusion method that incorporates early and late fusion in a unified framework. Our method introduces the concept of the holistic Functional Connectivity Network (FCN), which captures both intra-atlas relationships within individual atlases and inter-regional relationships between atlases with different brain parcellation scales. This comprehensive representation enables the identification of potential disease-related patterns associated with MDD in the early stage of our framework. Moreover, by decoding the holistic FCN from various perspectives through multiple spectral Graph Convolutional Neural Networks and fusing their results with decision-level ensembles, we further improve the performance of MDD diagnosis. Our approach is easily implemented with minimal modifications to existing model structures and demonstrates a robust performance across different baseline models. Our method, evaluated on public resting-state fMRI datasets, surpasses the current multi-atlas fusion methods, enhancing the accuracy of MDD diagnosis. The proposed novel multi-atlas fusion framework provides a more reliable MDD diagnostic technique. Experimental results show our approach outperforms both single- and multi-atlas-based methods, demonstrating its effectiveness in advancing MDD diagnosis.
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Trišins M, Zdanovskis N, Platkājis A, Šneidere K, Kostiks A, Karelis G, Stepens A. Brodmann Areas, V1 Atlas and Cognitive Impairment: Assessing Cortical Thickness for Cognitive Impairment Diagnostics. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:587. [PMID: 38674233 PMCID: PMC11052167 DOI: 10.3390/medicina60040587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 03/26/2024] [Accepted: 03/29/2024] [Indexed: 04/28/2024]
Abstract
Background and Objectives: Magnetic resonance imaging is vital for diagnosing cognitive decline. Brodmann areas (BA), distinct regions of the cerebral cortex categorized by cytoarchitectural variances, provide insights into cognitive function. This study aims to compare cortical thickness measurements across brain areas identified by BA mapping. We assessed these measurements among patients with and without cognitive impairment, and across groups categorized by cognitive performance levels using the Montreal Cognitive Assessment (MoCA) test. Materials and Methods: In this cross-sectional study, we included 64 patients who were divided in two ways: in two groups with (CI) or without (NCI) impaired cognitive function and in three groups with normal (NC), moderate (MPG) and low (LPG) cognitive performance according to MoCA scores. Scans with a 3T MRI scanner were carried out, and cortical thickness data was acquired using Freesurfer 7.2.0 software. Results: By analyzing differences between the NCI and CI groups cortical thickness of BA3a in left hemisphere (U = 241.000, p = 0.016), BA4a in right hemisphere (U = 269.000, p = 0.048) and BA28 in left hemisphere (U = 584.000, p = 0.005) showed significant differences. In the LPG, MPG and NC cortical thickness in BA3a in left hemisphere (H (2) = 6.268, p = 0.044), in V2 in right hemisphere (H (2) = 6.339, p = 0.042), in BA28 in left hemisphere (H (2) = 23.195, p < 0.001) and in BA28 in right hemisphere (H (2) = 10.015, p = 0.007) showed significant differences. Conclusions: Our study found that cortical thickness in specific Brodmann Areas-BA3a and BA28 in the left hemisphere, and BA4a in the right-differ significantly between NCI and CI groups. Significant differences were also observed in BA3a (left), V2 (right), and BA28 (both hemispheres) across LPG, MPG, NC groups. Despite a small sample size, these findings suggest cortical thickness measurements can serve as effective biomarkers for cognitive impairment diagnosis, warranting further validation with a larger cohort.
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Affiliation(s)
- Maksims Trišins
- Department of Radiology, Riga Stradins University, LV-1007 Riga, Latvia; (M.T.)
| | - Nauris Zdanovskis
- Department of Radiology, Riga Stradins University, LV-1007 Riga, Latvia; (M.T.)
- Department of Radiology, Riga East University Hospital, LV-1038 Riga, Latvia
- Military Medicine Research and Study Centre, Riga Stradins University, LV-1007 Riga, Latvia
| | - Ardis Platkājis
- Department of Radiology, Riga Stradins University, LV-1007 Riga, Latvia; (M.T.)
- Department of Radiology, Riga East University Hospital, LV-1038 Riga, Latvia
| | - Kristīne Šneidere
- Military Medicine Research and Study Centre, Riga Stradins University, LV-1007 Riga, Latvia
- Department of Health Psychology and Pedagogy, Riga Stradins University, LV-1007 Riga, Latvia
| | - Andrejs Kostiks
- Department of Neurology and Neurosurgery, Riga East University Hospital, LV-1038 Riga, Latvia (G.K.)
| | - Guntis Karelis
- Department of Neurology and Neurosurgery, Riga East University Hospital, LV-1038 Riga, Latvia (G.K.)
- Department of Infectology, Riga Stradins University, LV-1007 Riga, Latvia
| | - Ainārs Stepens
- Military Medicine Research and Study Centre, Riga Stradins University, LV-1007 Riga, Latvia
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Wu Y, Ridwan AR, Niaz MR, Bennett DA, Arfanakis K. High resolution 0.5mm isotropic T 1-weighted and diffusion tensor templates of the brain of non-demented older adults in a common space for the MIITRA atlas. Neuroimage 2023; 282:120387. [PMID: 37783362 PMCID: PMC10625170 DOI: 10.1016/j.neuroimage.2023.120387] [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: 08/10/2023] [Accepted: 09/22/2023] [Indexed: 10/04/2023] Open
Abstract
High quality, high resolution T1-weighted (T1w) and diffusion tensor imaging (DTI) brain templates located in a common space can enhance the sensitivity and precision of template-based neuroimaging studies. However, such multimodal templates have not been constructed for the older adult brain. The purpose of this work which is part of the MIITRA atlas project was twofold: (A) to develop 0.5 mm isotropic resolution T1w and DTI templates that are representative of the brain of non-demented older adults and are located in the same space, using advanced multimodal template construction techniques and principles of super resolution on data from a large, diverse, community cohort of 400 non-demented older adults, and (B) to systematically compare the new templates to other standardized templates. It was demonstrated that the new MIITRA-0.5mm T1w and DTI templates are well-matched in space, exhibit good definition of brain structures, including fine structures, exhibit higher image sharpness than other standardized templates, and are free of artifacts. The MIITRA-0.5mm T1w and DTI templates allowed higher intra-modality inter-subject spatial normalization precision as well as higher inter-modality intra-subject spatial matching of older adult T1w and DTI data compared to other available templates. Consequently, MIITRA-0.5mm templates allowed detection of smaller inter-group differences for older adult data compared to other templates. The MIITRA-0.5mm templates were also shown to be most representative of the brain of non-demented older adults compared to other templates with submillimeter resolution. The new templates constructed in this work constitute two of the final products of the MIITRA atlas project and are anticipated to have important implications for the sensitivity and precision of studies on older adults.
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Affiliation(s)
- Yingjuan Wu
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States
| | - Abdur Raquib Ridwan
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States
| | - Mohammad Rakeen Niaz
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, United States
| | - Konstantinos Arfanakis
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States; Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, United States.
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8
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Sjöholm T, Tarai S, Malmberg F, Strand R, Korenyushkin A, Enblad G, Ahlström H, Kullberg J. A whole-body diffusion MRI normal atlas: development, evaluation and initial use. Cancer Imaging 2023; 23:87. [PMID: 37710346 PMCID: PMC10503210 DOI: 10.1186/s40644-023-00603-5] [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: 03/09/2023] [Accepted: 08/28/2023] [Indexed: 09/16/2023] Open
Abstract
BACKGROUND Statistical atlases can provide population-based descriptions of healthy volunteers and/or patients and can be used for region- and voxel-based analysis. This work aims to develop whole-body diffusion atlases of healthy volunteers scanned at 1.5T and 3T. Further aims include evaluating the atlases by establishing whole-body Apparent Diffusion Coefficient (ADC) values of healthy tissues and including healthy tissue deviations in an automated tumour segmentation task. METHODS Multi-station whole-body Diffusion Weighted Imaging (DWI) and water-fat Magnetic Resonance Imaging (MRI) of healthy volunteers (n = 45) were acquired at 1.5T (n = 38) and/or 3T (n = 29), with test-retest imaging for five subjects per scanner. Using deformable image registration, whole-body MRI data was registered and composed into normal atlases. Healthy tissue ADCmean was manually measured for ten tissues, with test-retest percentage Repeatability Coefficient (%RC), and effect of age, sex and scanner assessed. Voxel-wise whole-body analyses using the normal atlases were studied with ADC correlation analyses and an automated tumour segmentation task. For the latter, lymphoma patient MRI scans (n = 40) with and without information about healthy tissue deviations were entered into a 3D U-Net architecture. RESULTS Sex- and Body Mass Index (BMI)-stratified whole-body high b-value DWI and ADC normal atlases were created at 1.5T and 3T. %RC of healthy tissue ADCmean varied depending on tissue assessed (4-48% at 1.5T, 6-70% at 3T). Scanner differences in ADCmean were visualised in Bland-Altman analyses of dually scanned subjects. Sex differences were measurable for liver, muscle and bone at 1.5T, and muscle at 3T. Volume of Interest (VOI)-based multiple linear regression, and voxel-based correlations in normal atlas space, showed that age and ADC were negatively associated for liver and bone at 1.5T, and positively associated with brain tissue at 1.5T and 3T. Adding voxel-wise information about healthy tissue deviations in an automated tumour segmentation task gave numerical improvements in the segmentation metrics Dice score, sensitivity and precision. CONCLUSIONS Whole-body DWI and ADC normal atlases were created at 1.5T and 3T, and applied in whole-body voxel-wise analyses.
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Affiliation(s)
- Therese Sjöholm
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Sambit Tarai
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Filip Malmberg
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Robin Strand
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | | | - Gunilla Enblad
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Håkan Ahlström
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
- Antaros Medical AB, Mölndal, Sweden
| | - Joel Kullberg
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
- Antaros Medical AB, Mölndal, Sweden.
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9
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Zhu H, Li T, Zhao B. Statistical Learning Methods for Neuroimaging Data Analysis with Applications. Annu Rev Biomed Data Sci 2023; 6:73-104. [PMID: 37127052 DOI: 10.1146/annurev-biodatasci-020722-100353] [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] [Indexed: 05/03/2023]
Abstract
The aim of this review is to provide a comprehensive survey of statistical challenges in neuroimaging data analysis, from neuroimaging techniques to large-scale neuroimaging studies and statistical learning methods. We briefly review eight popular neuroimaging techniques and their potential applications in neuroscience research and clinical translation. We delineate four themes of neuroimaging data and review major image processing analysis methods for processing neuroimaging data at the individual level. We briefly review four large-scale neuroimaging-related studies and a consortium on imaging genomics and discuss four themes of neuroimaging data analysis at the population level. We review nine major population-based statistical analysis methods and their associated statistical challenges and present recent progress in statistical methodology to address these challenges.
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Affiliation(s)
- Hongtu Zhu
- Department of Biostatistics, Department of Statistics, Department of Genetics, and Department of Computer Science, University of North Carolina, Chapel Hill, North Carolina, USA;
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Tengfei Li
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, North Carolina, USA
- Department of Radiology, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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10
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Conti A, Gambadauro NM, Mantovani P, Picciano CP, Rosetti V, Magnani M, Lucerna S, Tuleasca C, Cortelli P, Giannini G. A Brief History of Stereotactic Atlases: Their Evolution and Importance in Stereotactic Neurosurgery. Brain Sci 2023; 13:brainsci13050830. [PMID: 37239302 DOI: 10.3390/brainsci13050830] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 05/05/2023] [Accepted: 05/19/2023] [Indexed: 05/28/2023] Open
Abstract
Following the recent acquisition of unprecedented anatomical details through state-of-the-art neuroimaging, stereotactic procedures such as microelectrode recording (MER) or deep brain stimulation (DBS) can now rely on direct and accurately individualized topographic targeting. Nevertheless, both modern brain atlases derived from appropriate histological techniques involving post-mortem studies of human brain tissue and the methods based on neuroimaging and functional information represent a valuable tool to avoid targeting errors due to imaging artifacts or insufficient anatomical details. Hence, they have thus far been considered a reference guide for functional neurosurgical procedures by neuroscientists and neurosurgeons. In fact, brain atlases, ranging from the ones based on histology and histochemistry to the probabilistic ones grounded on data derived from large clinical databases, are the result of a long and inspiring journey made possible thanks to genial intuitions of great minds in the field of neurosurgery and to the technical advancement of neuroimaging and computational science. The aim of this text is to review the principal characteristics highlighting the milestones of their evolution.
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Affiliation(s)
- Alfredo Conti
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Via Altura 3, 40123 Bologna, Italy
- Dipartimento di Biomorfologia e. Scienze Neuromotorie (DIBINEM), Alma Mater Studiorum Università di Bologna, Via Altura 3, 40123 Bologna, Italy
| | - Nicola Maria Gambadauro
- Stroke Unit- Barking, Havering and Redbrige University Hospitals NHS Trust, Queen's Hospital, Rom Valley Way, London RM7 0AG, UK
| | - Paolo Mantovani
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Via Altura 3, 40123 Bologna, Italy
| | - Canio Pietro Picciano
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Via Altura 3, 40123 Bologna, Italy
- Dipartimento di Biomorfologia e. Scienze Neuromotorie (DIBINEM), Alma Mater Studiorum Università di Bologna, Via Altura 3, 40123 Bologna, Italy
| | - Vittoria Rosetti
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Via Altura 3, 40123 Bologna, Italy
- Dipartimento di Biomorfologia e. Scienze Neuromotorie (DIBINEM), Alma Mater Studiorum Università di Bologna, Via Altura 3, 40123 Bologna, Italy
| | - Marcello Magnani
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Via Altura 3, 40123 Bologna, Italy
- Dipartimento di Biomorfologia e. Scienze Neuromotorie (DIBINEM), Alma Mater Studiorum Università di Bologna, Via Altura 3, 40123 Bologna, Italy
| | - Sebastiano Lucerna
- Department of Neurosurgery, AOU "G. Martino", Via Consolare Valeria 1, 98125 Messina, Italy
| | - Constantin Tuleasca
- Neurosurgery Service and Gamma Knife Center, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011 Lausanne, Switzerland
- Faculty of Biology and Medicine (FBM), University of Lausanne (UNIL), Rue du Bugnon 21 CH-1011, 1015 Lausanne, Switzerland
- Ecole Polytechnique Fédérale de Lausanne (EPFL, LTS-5), Rte Cantonale, 1015 Lausanne, Switzerland
| | - Pietro Cortelli
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Via Altura 3, 40123 Bologna, Italy
- Dipartimento di Biomorfologia e. Scienze Neuromotorie (DIBINEM), Alma Mater Studiorum Università di Bologna, Via Altura 3, 40123 Bologna, Italy
| | - Giulia Giannini
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Via Altura 3, 40123 Bologna, Italy
- Dipartimento di Biomorfologia e. Scienze Neuromotorie (DIBINEM), Alma Mater Studiorum Università di Bologna, Via Altura 3, 40123 Bologna, Italy
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11
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Wu J, Yang Q, Zhou S. Latent shape image learning via disentangled representation for cross-sequence image registration and segmentation. Int J Comput Assist Radiol Surg 2023; 18:621-628. [PMID: 36346499 DOI: 10.1007/s11548-022-02788-9] [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: 05/13/2022] [Accepted: 10/27/2022] [Indexed: 11/11/2022]
Abstract
PURPOSE Cross-sequence magnetic resonance image (MRI) registration and segmentation are two essential steps in a variety of medical image analysis tasks. And have attracted considerable research interest. However, they remain challenging due to domain shifts between different sequences. This study is aiming at proposing a novel method via disentangled representations, latent shape image learning (LSIL), for cross-sequence image registration and segmentation. METHODS Images from different sequences were firstly decomposed into a shared domain-invariant shape space and a domain-specific appearance space via an unsupervised image-to-image translation approach. A latent shape image learning model is then built on the disentangled shape representations to generate latent shape images. A series of experiments including cross-sequence image registration and segmentation were performed to qualitatively and quantitatively verify the validity of our method. Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95) were adopted as our evaluation metrics. RESULTS The performance of our method was evaluated based on 2 datasets total of 50 MRIs. The experimental results showed the superiority of the proposed framework over the state-of-the-art cross-sequence registration and segmentation approaches. The proposed method shows the mean DSCs of 0.711 and 0.867, respectively, in cross-sequence registration and segmentation. CONCLUSION We proposed a novel method based on representation disentangling to solve the cross-sequence registration and segmentation problem. Experimental results prove the feasibility and generalization of the generated latent shape images. The proposed method demonstrates significant potential for use in clinical environments of missing sequences. The source code is available at https://github.com/wujiong-hub/LSIL .
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Affiliation(s)
- Jiong Wu
- School of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde, 415000, Hunan, China.
| | - Qi Yang
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China
| | - Shuang Zhou
- Furong College, Hunan University of Arts and Science, Changde, 415000, Hunan, China
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12
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Li M, Xu X, Cao Z, Chen R, Zhao R, Zhao Z, Dang X, Oishi K, Wu D. Multi-modal multi-resolution atlas of the human neonatal cerebral cortex based on microstructural similarity. Neuroimage 2023; 272:120071. [PMID: 37003446 DOI: 10.1016/j.neuroimage.2023.120071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 03/13/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023] Open
Abstract
The neonatal period is a critical window for the development of the human brain and may hold implications for the long-term development of cognition and disorders. Multi-modal connectome studies have revealed many important findings underlying the adult brain but related studies were rare in the early human brain. One potential challenge is the lack of an appropriate and unbiased parcellation that combines structural and functional information in this population. Using 348 multi-modal MRI datasets from the developing human connectome project, we found that the information fused from the structural, diffusion, and functional MRI was relatively stable across MRI features and showed high reproducibility at the group level. Therefore, we generated automated multi-resolution parcellations (300 - 500 parcels) based on the similarity across multi-modal features using a gradient-based parcellation algorithm. In addition, to acquire a parcellation with high interpretability, we provided a manually delineated parcellation (210 parcels), which was approximately symmetric, and the adjacent areas around each boundary were statistically different in terms of the integrated similarity metric and at least one kind of original features. Overall, the present study provided multi-resolution and neonate-specific parcellations of the cerebral cortex based on multi-modal MRI properties, which may facilitate future studies of the human connectome in the early development period.
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Affiliation(s)
- Mingyang Li
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou 310027, China
| | - Xinyi Xu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou 310027, China
| | - Zuozhen Cao
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou 310027, China
| | - Ruike Chen
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou 310027, China
| | - Ruoke Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou 310027, China
| | - Zhiyong Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou 310027, China
| | - Xixi Dang
- Department of Psychology, Zhejiang Sci-Tech University, Hangzhou 310018, China
| | - Kenichi Oishi
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore 21205, United States
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou 310027, China.
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13
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Wu Y, Ridwan AR, Niaz MR, Qi X, Zhang S, Alzheimer's Disease Neuroimaging Initiative, Bennett DA, Arfanakis K. Development of high quality T 1-weighted and diffusion tensor templates of the older adult brain in a common space. Neuroimage 2022; 260:119417. [PMID: 35793748 PMCID: PMC9437946 DOI: 10.1016/j.neuroimage.2022.119417] [Citation(s) in RCA: 1] [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: 02/10/2022] [Revised: 05/27/2022] [Accepted: 06/27/2022] [Indexed: 01/23/2023] Open
Abstract
High-quality T1-weighted (T1w) and diffusion tensor imaging (DTI) brain templates that are representative of the individuals under study enhance the accuracy of template-based neuroimaging investigations, and when they are also located in a common space they facilitate optimal integration of information on brain morphometry and diffusion characteristics. However, such multimodal templates have not been constructed for the brain of older adults. The purpose of this work was threefold: (A) to introduce an iterative method for construction of multimodal T1w and DTI templates that aims at maximizing the quality of each template separately as well as the spatial matching between templates, (B) to use this method to develop T1w and DTI templates of the older adult brain in a common space, and (C) to evaluate the performance of the method across iterations and compare it to the performance of state-of-the-art approaches based on multichannel registration. It was demonstrated that more iterations of the proposed method enhanced the characteristics and spatial matching of the resulting T1w and DTI templates. The templates of the older adult brain generated by the final iteration of the proposed method provided better delineation of brain structures, higher discriminability between tissues, and higher image sharpness near the cortex compared to templates generated with approaches employing multichannel registration. In addition, the spatial matching between the T1w and DTI templates constructed by the proposed method approximated the template alignment achieved with methods employing multichannel registration. Finally, when using the templates generated by the proposed method as references for spatial normalization of older adult T1w and DTI data, both the intra-modality inter-subject normalization precision and the inter-modality spatial matching were higher in most metrics than those achieved with templates constructed with other methods. Overall, the present work brought new insights into multimodal template construction, generated much-needed high quality T1w and DTI templates of the older adult brain in a common space, and conducted a thorough, quantitative evaluation of available multimodal template construction methods.
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Affiliation(s)
- Yingjuan Wu
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL USA
| | - Abdur Raquib Ridwan
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL USA
| | - Mohammad Rakeen Niaz
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL USA
| | - Xiaoxiao Qi
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL USA
| | - Shengwei Zhang
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois USA
| | - Alzheimer's Disease Neuroimaging Initiative
- A portion of the data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois USA
| | - Konstantinos Arfanakis
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL USA; Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois USA.
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14
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Damigos G, Zacharaki EI, Zerva N, Pavlopoulos A, Chatzikyrkou K, Koumenti A, Moustakas K, Pantos C, Mourouzis I, Lourbopoulos A. Machine learning based analysis of stroke lesions on mouse tissue sections. J Cereb Blood Flow Metab 2022; 42:1463-1477. [PMID: 35209753 PMCID: PMC9274860 DOI: 10.1177/0271678x221083387] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
An unbiased, automated and reliable method for analysis of brain lesions in tissue after ischemic stroke is missing. Manual infarct volumetry or by threshold-based semi-automated approaches is laborious, and biased to human error or biased by many false -positive and -negative data, respectively. Thereby, we developed a novel machine learning, atlas-based method for fully automated stroke analysis in mouse brain slices stained with 2% Triphenyltetrazolium-chloride (2% TTC), named "StrokeAnalyst", which runs on a user-friendly graphical interface. StrokeAnalyst registers subject images on a common spatial domain (a novel mouse TTC- brain atlas of 80 average mathematical images), calculates pixel-based, tissue-intensity statistics (z-scores), applies outlier-detection and machine learning (Random-Forest) models to increase accuracy of lesion detection, and produces volumetry data and detailed neuroanatomical information per lesion. We validated StrokeAnalyst in two separate experimental sets using the filament stroke model. StrokeAnalyst detects stroke lesions in a rater-independent and reproducible way, correctly detects hemispheric volumes even in presence of post-stroke edema and significantly minimizes false-positive errors compared to threshold-based approaches (false-positive rate 1.2-2.3%, p < 0.05). It can process scanner-acquired, and even smartphone-captured or pdf-retrieved images. Overall, StrokeAnalyst surpasses all previous TTC-volumetry approaches and increases quality, reproducibility and reliability of stroke detection in relevant preclinical models.
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Affiliation(s)
- Gerasimos Damigos
- Department of Pharmacology, Medical School of Athens, National and Kapodistrian University of Athens, Athens, Greece.,Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | - Evangelia I Zacharaki
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | - Nefeli Zerva
- Department of Pharmacology, Medical School of Athens, National and Kapodistrian University of Athens, Athens, Greece
| | - Angelos Pavlopoulos
- Department of Pharmacology, Medical School of Athens, National and Kapodistrian University of Athens, Athens, Greece
| | - Konstantina Chatzikyrkou
- Department of Pharmacology, Medical School of Athens, National and Kapodistrian University of Athens, Athens, Greece
| | - Argyro Koumenti
- Department of Pharmacology, Medical School of Athens, National and Kapodistrian University of Athens, Athens, Greece
| | | | - Constantinos Pantos
- Department of Pharmacology, Medical School of Athens, National and Kapodistrian University of Athens, Athens, Greece
| | - Iordanis Mourouzis
- Department of Pharmacology, Medical School of Athens, National and Kapodistrian University of Athens, Athens, Greece
| | - Athanasios Lourbopoulos
- Department of Pharmacology, Medical School of Athens, National and Kapodistrian University of Athens, Athens, Greece.,Institute for Stroke and Dementia Research (ISD), University of Munich Medical Center, Munich, Germany.,Neurointensive Care Unit, Schoen Klinik Bad Aibling, Germany
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15
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Quabs J, Caspers S, Schöne C, Mohlberg H, Bludau S, Dickscheid T, Amunts K. Cytoarchitecture, probability maps and segregation of the human insula. Neuroimage 2022; 260:119453. [PMID: 35809885 DOI: 10.1016/j.neuroimage.2022.119453] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 06/09/2022] [Accepted: 07/04/2022] [Indexed: 10/17/2022] Open
Abstract
The human insular cortex supports multifunctional integration including interoceptive, sensorimotor, cognitive and social-emotional processing. Different concepts of the underlying microstructure have been proposed over more than a century. However, a 3D map of the cytoarchitectonic segregation of the insula in standard reference space, that could be directly linked to neuroimaging experiments addressing different cognitive tasks, is not yet available. Here we analyzed the middle posterior and dorsal anterior insula with image analysis and a statistical mapping procedure to delineate cytoarchitectonic areas in ten human postmortem brains. 3D-probability maps of seven new areas with granular (Ig3, posterior), agranular (Ia1, posterior) and dysgranular (Id2-Id6, middle to dorsal anterior) cytoarchitecture have been calculated to represent the new areas in stereotaxic space. A hierarchical cluster analysis based on cytoarchitecture resulted in three distinct clusters in the superior posterior, inferior posterior and dorsal anterior insula, providing deeper insights into the structural organization of the insula. The maps are openly available to support future studies addressing relations between structure and function in the human insula.
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Affiliation(s)
- Julian Quabs
- C. and O. Vogt Institute for Brain Research, Medical Faculty, University Hospital Düsseldorf, Heinrich Heine University of Düsseldorf, Germany; Institute for Anatomy I, Medical Faculty, Heinrich Heine University of Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Germany.
| | - Svenja Caspers
- Institute for Anatomy I, Medical Faculty, Heinrich Heine University of Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Germany
| | - Claudia Schöne
- C. and O. Vogt Institute for Brain Research, Medical Faculty, University Hospital Düsseldorf, Heinrich Heine University of Düsseldorf, Germany
| | - Hartmut Mohlberg
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Germany
| | - Sebastian Bludau
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Germany
| | - Timo Dickscheid
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Germany
| | - Katrin Amunts
- C. and O. Vogt Institute for Brain Research, Medical Faculty, University Hospital Düsseldorf, Heinrich Heine University of Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Germany
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16
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de Jager EJ, Risser L, Mescam M, Fonta C, Beaudet A. Sulci 3D mapping from human cranial endocasts: A powerful tool to study hominin brain evolution. Hum Brain Mapp 2022; 43:4433-4443. [PMID: 35661328 PMCID: PMC9435008 DOI: 10.1002/hbm.25964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 03/14/2022] [Accepted: 05/09/2022] [Indexed: 11/29/2022] Open
Abstract
Key questions in paleoneurology concern the timing and emergence of derived cerebral features within the human lineage. Endocasts are replicas of the internal table of the bony braincase that are widely used in paleoneurology as a proxy for reconstructing a timeline for hominin brain evolution in the fossil record. The accurate identification of cerebral sulci imprints in endocasts is critical for assessing the topographic extension and structural organisation of cortical regions in fossil hominins. High‐resolution imaging techniques combined with established methods based on population‐specific brain atlases offer new opportunities for tracking detailed endocranial characteristics. This study provides the first documentation of sulcal pattern imprints from the superolateral surface of the cerebrum using a population‐based atlas technique on extant human endocasts. Human crania from the Pretoria Bone Collection (South Africa) were scanned using micro‐CT. Endocasts were virtually extracted, and sulci were automatically detected and manually labelled. A density map method was applied to project all the labels onto an averaged endocast to visualise the mean distribution of each identified sulcal imprint. This method allowed for the visualisation of inter‐individual variation of sulcal imprints, for example, frontal lobe sulci, correlating with previous brain‐MRI studies and for the first time the extensive overlapping of imprints in historically debated areas of the endocast (e.g. occipital lobe). In providing an innovative, non‐invasive, observer‐independent method to investigate human endocranial structural organisation, our analytical protocol introduces a promising perspective for future research in paleoneurology and for discussing critical hypotheses on the evolution of cognitive abilities among hominins.
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Affiliation(s)
- Edwin John de Jager
- Department of Archaeology, University of Cambridge, Cambridge, UK.,Department of Anatomy, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa
| | - Laurent Risser
- Institute de Mathématiques de Toulouse, Université de Toulouse, UPS, Toulouse, France
| | - Muriel Mescam
- Centre de Recherche Cerveau et Cognition (CerCo), CNRS, Université de Toulouse, UPS, Toulouse, France
| | - Caroline Fonta
- Centre de Recherche Cerveau et Cognition (CerCo), CNRS, Université de Toulouse, UPS, Toulouse, France
| | - Amélie Beaudet
- Department of Archaeology, University of Cambridge, Cambridge, UK.,School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg, South Africa.,Institut Català de Paleontologia Miquel Crusafont, Universitat Autònoma de Barcelona, Barcelona, Spain
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17
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Hermann KL, Singh SR, Rosenthal IA, Pantazis D, Conway BR. Temporal dynamics of the neural representation of hue and luminance polarity. Nat Commun 2022; 13:661. [PMID: 35115511 PMCID: PMC8814185 DOI: 10.1038/s41467-022-28249-0] [Citation(s) in RCA: 3] [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: 03/16/2021] [Accepted: 01/12/2022] [Indexed: 11/09/2022] Open
Abstract
Hue and luminance contrast are basic visual features. Here we use multivariate analyses of magnetoencephalography data to investigate the timing of the neural computations that extract them, and whether they depend on common neural circuits. We show that hue and luminance-contrast polarity can be decoded from MEG data and, with lower accuracy, both features can be decoded across changes in the other feature. These results are consistent with the existence of both common and separable neural mechanisms. The decoding time course is earlier and more temporally precise for luminance polarity than hue, a result that does not depend on task, suggesting that luminance contrast is an updating signal that separates visual events. Meanwhile, cross-temporal generalization is slightly greater for representations of hue compared to luminance polarity, providing a neural correlate of the preeminence of hue in perceptual grouping and memory. Finally, decoding of luminance polarity varies depending on the hues used to obtain training and testing data. The pattern of results is consistent with observations that luminance contrast is mediated by both L-M and S cone sub-cortical mechanisms.
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Affiliation(s)
- Katherine L Hermann
- Laboratory of Sensorimotor Research, National Eye Institute, Bethesda, MD, 20892, USA
- Department of Psychology, Stanford University, Stanford, CA, 94305, USA
| | - Shridhar R Singh
- Laboratory of Sensorimotor Research, National Eye Institute, Bethesda, MD, 20892, USA
| | - Isabelle A Rosenthal
- Laboratory of Sensorimotor Research, National Eye Institute, Bethesda, MD, 20892, USA
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
| | - Dimitrios Pantazis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Bevil R Conway
- Laboratory of Sensorimotor Research, National Eye Institute, Bethesda, MD, 20892, USA.
- National Institute of Mental Health, Bethesda, MD, 20892, USA.
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18
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Brun G, Testud B, Girard OM, Lehmann P, de Rochefort L, Besson P, Massire A, Ridley B, Girard N, Guye M, Ranjeva JP, Le Troter A. Automatic segmentation of Deep Grey Nuclei using a high-resolution 7T MRI Atlas - quantification of T1 values in healthy volunteers. Eur J Neurosci 2021; 55:438-460. [PMID: 34939245 DOI: 10.1111/ejn.15575] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 12/17/2021] [Accepted: 12/18/2021] [Indexed: 11/30/2022]
Abstract
We present a new consensus atlas of deep grey nuclei obtained by shape-based averaging of manual segmentation of two experienced neuroradiologists and optimized from 7T MP2RAGE images acquired at (0.6mm)3 in 60 healthy subjects. A group-wise normalization method was used to build a high-contrast and high-resolution T1 -weighted brain template (0.5mm)3 using data from 30 out of the 60 controls. Delineation of 24 deep grey nuclei per hemisphere, including the claustrum and twelve thalamic nuclei, was then performed by two expert neuroradiologists and reviewed by a third neuroradiologist according to tissue contrast and external references based on the Morel atlas. Corresponding deep grey matter structures were also extracted from the Morel and CIT168 atlases. The data-derived, Morel and CIT168 atlases were all applied at the individual level using non-linear registration to fit the subject reference and to extract absolute mean quantitative T1 values derived from the 3D-MP2RAGE volumes, after correction for residual B1 + biases. Three metrics (The Dice and the volumetric similarity coefficients, and a novel Hausdorff distance) were used to estimate the inter-rater agreement of manual MRI segmentation and inter-atlas variability, and these metrics were measured to quantify biases due to image registration and their impact on the measurements of the quantitative T1 values was highlighted. This represents a fully-automated segmentation process permitting the extraction of unbiased normative T1 values in a population of young healthy controls as a reference for characterizing subtle structural alterations of deep grey nuclei relevant to a range of neurological diseases.
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Affiliation(s)
- Gilles Brun
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,AP-HM, CHU Timone, Pôle d'Imagerie Médicale, CEMEREM, Marseille, France.,AP-HM, CHU Timone, Pôle d'Imagerie Médicale, Service de Neuroradiologie, Marseille, France
| | - Benoit Testud
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,AP-HM, CHU Timone, Pôle d'Imagerie Médicale, CEMEREM, Marseille, France.,AP-HM, CHU Timone, Pôle d'Imagerie Médicale, Service de Neuroradiologie, Marseille, France
| | - Olivier M Girard
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,AP-HM, CHU Timone, Pôle d'Imagerie Médicale, CEMEREM, Marseille, France
| | - Pierre Lehmann
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,AP-HM, CHU Timone, Pôle d'Imagerie Médicale, CEMEREM, Marseille, France.,AP-HM, CHU Timone, Pôle d'Imagerie Médicale, Service de Neuroradiologie, Marseille, France
| | - Ludovic de Rochefort
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,AP-HM, CHU Timone, Pôle d'Imagerie Médicale, CEMEREM, Marseille, France
| | - Pierre Besson
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,AP-HM, CHU Timone, Pôle d'Imagerie Médicale, CEMEREM, Marseille, France
| | - Aurélien Massire
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,AP-HM, CHU Timone, Pôle d'Imagerie Médicale, CEMEREM, Marseille, France
| | - Ben Ridley
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,AP-HM, CHU Timone, Pôle d'Imagerie Médicale, CEMEREM, Marseille, France.,IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italia
| | - Nadine Girard
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,AP-HM, CHU Timone, Pôle d'Imagerie Médicale, CEMEREM, Marseille, France.,AP-HM, CHU Timone, Pôle d'Imagerie Médicale, Service de Neuroradiologie, Marseille, France
| | - Maxime Guye
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,AP-HM, CHU Timone, Pôle d'Imagerie Médicale, CEMEREM, Marseille, France
| | - Jean-Philippe Ranjeva
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,AP-HM, CHU Timone, Pôle d'Imagerie Médicale, CEMEREM, Marseille, France
| | - Arnaud Le Troter
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,AP-HM, CHU Timone, Pôle d'Imagerie Médicale, CEMEREM, Marseille, France
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19
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Towards an Architecture of a Multi-purpose, User-Extendable Reference Human Brain Atlas. Neuroinformatics 2021; 20:405-426. [PMID: 34825350 PMCID: PMC9546954 DOI: 10.1007/s12021-021-09555-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/09/2021] [Indexed: 11/29/2022]
Abstract
Human brain atlas development is predominantly research-oriented and the use of atlases in clinical practice is limited. Here I introduce a new definition of a reference human brain atlas that serves education, research and clinical applications, and is extendable by its user. Subsequently, an architecture of a multi-purpose, user-extendable reference human brain atlas is proposed and its implementation discussed. The human brain atlas is defined as a vehicle to gather, present, use, share, and discover knowledge about the human brain with highly organized content, tools enabling a wide range of its applications, massive and heterogeneous knowledge database, and means for content and knowledge growing by its users. The proposed architecture determines major components of the atlas, their mutual relationships, and functional roles. It contains four functional units, core cerebral models, knowledge database, research and clinical data input and conversion, and toolkit (supporting processing, content extension, atlas individualization, navigation, exploration, and display), all united by a user interface. Each unit is described in terms of its function, component modules and sub-modules, data handling, and implementation aspects. This novel architecture supports brain knowledge gathering, presentation, use, sharing, and discovery and is broadly applicable and useful in student- and educator-oriented neuroeducation for knowledge presentation and communication, research for knowledge acquisition, aggregation and discovery, and clinical applications in decision making support for prevention, diagnosis, treatment, monitoring, and prediction. It establishes a backbone for designing and developing new, multi-purpose and user-extendable brain atlas platforms, serving as a potential standard across labs, hospitals, and medical schools.
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Sichko S, Bui TQ, Vinograd M, Shields GS, Saha K, Devkota S, Olvera-Alvarez HA, Carroll JE, Cole SW, Irwin MR, Slavich GM. Psychobiology of Stress and Adolescent Depression (PSY SAD) Study: Protocol overview for an fMRI-based multi-method investigation. Brain Behav Immun Health 2021; 17:100334. [PMID: 34595481 PMCID: PMC8478351 DOI: 10.1016/j.bbih.2021.100334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 08/17/2021] [Accepted: 08/21/2021] [Indexed: 11/22/2022] Open
Abstract
Depression is a common, often recurrent disorder that causes substantial disease burden worldwide, and this is especially true for women following the pubertal transition. According to the Social Signal Transduction Theory of Depression, stressors involving social stress and rejection, which frequently precipitate major depressive episodes, induce depressive symptoms in vulnerable individuals in part by altering the activity and connectivity of stress-related neural pathways, and by upregulating components of the immune system involved in inflammation. To test this theory, we recruited adolescent females at high and low risk for depression and assessed their psychological, neural, inflammatory, and genomic responses to a brief (10 minute) social stress task, in addition to trait psychological and microbial factors affecting these responses. We then followed these adolescents longitudinally to investigate how their multi-level stress responses at baseline were related to their biological aging at baseline, and psychosocial and clinical functioning over one year. In this protocol paper, we describe the theoretical motivations for conducting this study as well as the sample, study design, procedures, and measures. Ultimately, our aim is to elucidate how social adversity influences the brain and immune system to cause depression, one of the most common and costly of all disorders.
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Affiliation(s)
- Stassja Sichko
- Department of Psychology, University of California, Los Angeles, CA, USA
| | - Theresa Q. Bui
- Tulane University School of Medicine, New Orleans, LA, USA
| | - Meghan Vinograd
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, and Department of Psychiatry, University of California, San Diego, CA, USA
| | - Grant S. Shields
- Department of Psychological Science, University of Arkansas, Fayetteville, AR, USA
| | - Krishanu Saha
- Wisconsin Institute for Discovery and Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Suzanne Devkota
- Department of Medicine, F. Widjaja Foundation Inflammatory Bowel and Immunobiology Research Institute, Cedars-Sinai Medical Center, and David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | | | - Judith E. Carroll
- Cousins Center for Psychoneuroimmunology and Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | - Steven W. Cole
- Cousins Center for Psychoneuroimmunology and Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | - Michael R. Irwin
- Department of Psychology, University of California, Los Angeles, CA, USA
- Cousins Center for Psychoneuroimmunology and Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | - George M. Slavich
- Cousins Center for Psychoneuroimmunology and Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
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Wu J, Zhou S, Yang Q, Zhang Y, Tang X. Multi-modality Large Deformation Diffeomorphic Metric Mapping Driven by Single-modality Images. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2610-2613. [PMID: 34891788 DOI: 10.1109/embc46164.2021.9630617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Multi-modality magnetic resonance image (MRI) registration is an essential step in various MRI analysis tasks. However, it is challenging to have all required modalities in clinical practice, and thus the application of multi-modality registration is limited. This paper tackles such problem by proposing a novel unsupervised deep learning based multi-modality large deformation diffeomorphic metric mapping (LDDMM) framework which is capable of performing multi-modality registration only using single-modality MRIs. Specifically, an unsupervised image-to-image translation model is trained and used to synthesize the missing modality MRIs from the available ones. Multi-modality LDDMM is then performed in a multi-channel manner. Experimental results obtained on one publicly- accessible datasets confirm the superior performance of the proposed approach.Clinical relevance-This work provides a tool for multi-modality MRI registration with solely single-modality images, which addresses the very common issue of missing modalities in clinical practice.
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22
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Morales H. Current and Future Challenges of Functional MRI and Diffusion Tractography in the Surgical Setting: From Eloquent Brain Mapping to Neural Plasticity. Semin Ultrasound CT MR 2021; 42:474-489. [PMID: 34537116 DOI: 10.1053/j.sult.2021.07.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Decades ago, Spetzler (1986) and Sawaya (1998) provided a rough brain segmentation of the eloquent areas of the brain, aimed to help surgical decisions in cases of vascular malformations and tumors, respectively. Currently in clinical use, their criteria are in need of revision. Defining functions (eg, sensorimotor, language and visual) that should be preserved during surgery seems a straightforward task. In practice, locating the specific areas that could cause a permanent vs transient deficit is not an easy task. This is particularly true for the associative cortex and cognitive domains such as language. The old model, with Broca's and Wernicke's areas at the forefront, has been superseded by a dual-stream model of parallel language processing; named ventral and dorsal pathways. This complicated network of cortical hubs and subcortical white matter pathways needing preservation during surgery is a work in progress. Preserving not only cortical regions but most importantly preserving the connections, or white matter fiber bundles, of core regions in the brain is the new paradigm. For instance, the arcuate fascicululs and inferior fronto-occipital fasciculus are key components of the dorsal and ventral language pathways, respectively; and their damage result in permanent language deficits. Interestedly, the damage of the temporal portions of these bundles -where there is a crossroad with other multiple bundles-, appears to be more important (permanent) than the damage of the frontal portions - where plasticity and contralateral activation could help. Although intraoperative direct cortical and subcortical stimulation have contributed largely, advanced MR techniques such as functional MRI (fMRI) and diffusion tractography (DT), are at the epi-center of our current understanding. Nevertheless, these techniques posse important challenges: such as neurovascular uncoupling or venous bias on fMRI; and appropriate anatomical validation or accurate representation of crossing fibers on DT. These limitations should be well understood and taken into account in clinical practice. Unifying multidisciplinary research and clinical efforts is desirable, so these techniques could contribute more efficiently not only to locate eloquent areas but to improve outcomes and our understanding of neural plasticity. Finally, although there are constant anatomical and functional regions at the individual level, there is a known variability at the inter-individual level. This concept should strengthen the importance of a personalized approach when evaluating these regions on fMRI and DT. It should strengthen the importance of personalized treatments as well, aimed to meet tailored needs and expectations.
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Affiliation(s)
- Humberto Morales
- Section of Neuroradiology, University of Cincinnati Medical Center, Cincinnati, OH.
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23
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Kusano T, Kurashige H, Nambu I, Moriguchi Y, Hanakawa T, Wada Y, Osu R. Wrist and finger motor representations embedded in the cerebral and cerebellar resting-state activation. Brain Struct Funct 2021; 226:2307-2319. [PMID: 34236531 PMCID: PMC8354910 DOI: 10.1007/s00429-021-02330-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 06/22/2021] [Indexed: 11/02/2022]
Abstract
Several functional magnetic resonance imaging (fMRI) studies have demonstrated that resting-state brain activity consists of multiple components, each corresponding to the spatial pattern of brain activity induced by performing a task. Especially in a movement task, such components have been shown to correspond to the brain activity pattern of the relevant anatomical region, meaning that the voxels of pattern that are cooperatively activated while using a body part (e.g., foot, hand, and tongue) also behave cooperatively in the resting state. However, it is unclear whether the components involved in resting-state brain activity correspond to those induced by the movement of discrete body parts. To address this issue, in the present study, we focused on wrist and finger movements in the hand, and a cross-decoding technique trained to discriminate between the multi-voxel patterns induced by wrist and finger movement was applied to the resting-state fMRI. We found that the multi-voxel pattern in resting-state brain activity corresponds to either wrist or finger movements in the motor-related areas of each hemisphere of the cerebrum and cerebellum. These results suggest that resting-state brain activity in the motor-related areas consists of the components corresponding to the elementary movements of individual body parts. Therefore, the resting-state brain activity possibly has a finer structure than considered previously.
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Affiliation(s)
- Toshiki Kusano
- Nagaoka University of Technology, 1603-1 Kamitomioka, Nagaoka, Niigata, 940-2188, Japan.
| | - Hiroki Kurashige
- Research and Information Center, Tokai University, 2-3-23 Takanawa, Minato-ku, Tokyo, 108-8619, Japan.
| | - Isao Nambu
- Nagaoka University of Technology, 1603-1 Kamitomioka, Nagaoka, Niigata, 940-2188, Japan.
| | - Yoshiya Moriguchi
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, 4-1-1 Ogawa-Higashi, Kodaira, Tokyo, 187-8551, Japan
| | - Takashi Hanakawa
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, 4-1-1 Ogawa-Higashi, Kodaira, Tokyo, 187-8551, Japan.,Department of Integrated Neuroanatomy and Neuroimaging, Kyoto University Graduate School of Medicine, Yoshida Konoe-cho, Sakyo-ku, Kyoto, 606-8501, Japan
| | - Yasuhiro Wada
- Nagaoka University of Technology, 1603-1 Kamitomioka, Nagaoka, Niigata, 940-2188, Japan
| | - Rieko Osu
- The Advanced Telecommunications Research Institute International, 2-2-2 Hikaridai Seika, Soraku, Kyoto, 619-0288, Japan.,Faculty of Human Sciences, Waseda University, 2-579-15 Mikajima, Tokorozawa, Saitama, 359-1192, Japan
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24
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Lu S, Ortiz C, Fürth D, Fischer S, Meletis K, Zador A, Gillis J. Assessing the replicability of spatial gene expression using atlas data from the adult mouse brain. PLoS Biol 2021; 19:e3001341. [PMID: 34280183 PMCID: PMC8321401 DOI: 10.1371/journal.pbio.3001341] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 07/29/2021] [Accepted: 06/29/2021] [Indexed: 11/19/2022] Open
Abstract
High-throughput, spatially resolved gene expression techniques are poised to be transformative across biology by overcoming a central limitation in single-cell biology: the lack of information on relationships that organize the cells into the functional groupings characteristic of tissues in complex multicellular organisms. Spatial expression is particularly interesting in the mammalian brain, which has a highly defined structure, strong spatial constraint in its organization, and detailed multimodal phenotypes for cells and ensembles of cells that can be linked to mesoscale properties such as projection patterns, and from there, to circuits generating behavior. However, as with any type of expression data, cross-dataset benchmarking of spatial data is a crucial first step. Here, we assess the replicability, with reference to canonical brain subdivisions, between the Allen Institute's in situ hybridization data from the adult mouse brain (Allen Brain Atlas (ABA)) and a similar dataset collected using spatial transcriptomics (ST). With the advent of tractable spatial techniques, for the first time, we are able to benchmark the Allen Institute's whole-brain, whole-transcriptome spatial expression dataset with a second independent dataset that similarly spans the whole brain and transcriptome. We use regularized linear regression (LASSO), linear regression, and correlation-based feature selection in a supervised learning framework to classify expression samples relative to their assayed location. We show that Allen Reference Atlas labels are classifiable using transcription in both data sets, but that performance is higher in the ABA than in ST. Furthermore, models trained in one dataset and tested in the opposite dataset do not reproduce classification performance bidirectionally. While an identifying expression profile can be found for a given brain area, it does not generalize to the opposite dataset. In general, we found that canonical brain area labels are classifiable in gene expression space within dataset and that our observed performance is not merely reflecting physical distance in the brain. However, we also show that cross-platform classification is not robust. Emerging spatial datasets from the mouse brain will allow further characterization of cross-dataset replicability ultimately providing a valuable reference set for understanding the cell biology of the brain.
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Affiliation(s)
- Shaina Lu
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | - Cantin Ortiz
- Department of Neuroscience, Karolinska Institutet, Solna, Sweden
| | - Daniel Fürth
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | - Stephan Fischer
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | | | - Anthony Zador
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | - Jesse Gillis
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
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25
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Better the devil you know than the devil you don't: Neural processing of risk and ambiguity. Neuroimage 2021; 236:118109. [PMID: 33940147 DOI: 10.1016/j.neuroimage.2021.118109] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 04/14/2021] [Accepted: 04/18/2021] [Indexed: 11/23/2022] Open
Abstract
Risk and ambiguity are inherent in virtually all human decision-making. Risk refers to a situation in which we know the precise probability of potential outcomes of each option, whereas ambiguity refers to a situation in which outcome probabilities are not known. A large body of research has shown that individuals prefer known risks to ambiguity, a phenomenon known as ambiguity aversion. One heated debate concerns whether risky and ambiguous decisions rely on the same or distinct neural circuits. In the current meta-analyses, we integrated the results of neuroimaging research on decision-making under risk (n = 69) and ambiguity (n = 31). Our results showed that both processing of risk and ambiguity showed convergence in anterior insula, indicating a key role of anterior insula in encoding uncertainty. Risk additionally engaged dorsomedial prefrontal cortex (dmPFC) and ventral striatum, whereas ambiguity specifically recruited the dorsolateral prefrontal cortex (dlPFC), inferior parietal lobe (IPL) and right anterior insula. Our findings demonstrate overlapping and distinct neural substrates underlying different types of uncertainty, guiding future neuroimaging research on risk-taking and ambiguity aversion.
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26
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Hodology of the superior longitudinal system of the human brain: a historical perspective, the current controversies, and a proposal. Brain Struct Funct 2021; 226:1363-1384. [PMID: 33881634 DOI: 10.1007/s00429-021-02265-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Accepted: 03/23/2021] [Indexed: 02/07/2023]
Abstract
The description of human white matter pathways experienced a tremendous improvement, thanks to the advancement of neuroimaging and dissection techniques. The downside of this progress is the production of redundant and conflicting literature, bound by specific studies' methods and aims. The Superior Longitudinal System (SLS), encompassing the arcuate (AF) and the superior longitudinal fasciculi (SLF), becomes an illustrative example of this fundamental issue, being one of the most studied white matter association pathways of the brain. Herein, we provide a complete illustration of this white matter fiber system's current definition, from its early descriptions in the nineteenth century to its most recent characterizations. We propose a review of both in vivo diffusion magnetic resonance imaging-based tractography and anatomical dissection studies, enclosing all the information available up to date. Based on these findings, we reconstruct the wiring diagram of the SLS, highlighting a substantial variability in the description of its cortical sites of termination and the taxonomy and partonomy that characterize the system. We aim to level up discrepancies in the literature by proposing a parallel across the various nomenclature. Consistent with the topographical arrangement already documented for commissural and projection pathways, we suggest approaching the SLS organization as an orderly and continuous wiring diagram, respecting a medio-lateral palisading topography between the different frontal, parietal, occipital, and temporal gyri rather than in terms of individualized fascicles. A better and complete description of the fine organization of white matter association pathways' connectivity is fundamental for a better understanding of brain function and their clinical and neurosurgical applications.
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27
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Wason TD. A model integrating multiple processes of synchronization and coherence for information instantiation within a cortical area. Biosystems 2021; 205:104403. [PMID: 33746019 DOI: 10.1016/j.biosystems.2021.104403] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 03/05/2021] [Indexed: 12/14/2022]
Abstract
What is the form of dynamic, e.g., sensory, information in the mammalian cortex? Information in the cortex is modeled as a coherence map of a mixed chimera state of synchronous, phasic, and disordered minicolumns. The theoretical model is built on neurophysiological evidence. Complex spatiotemporal information is instantiated through a system of interacting biological processes that generate a synchronized cortical area, a coherent aperture. Minicolumn elements are grouped in macrocolumns in an array analogous to a phased-array radar, modeled as an aperture, a "hole through which radiant energy flows." Coherence maps in a cortical area transform inputs from multiple sources into outputs to multiple targets, while reducing complexity and entropy. Coherent apertures can assume extremely large numbers of different information states as coherence maps, which can be communicated among apertures with corresponding very large bandwidths. The coherent aperture model incorporates considerable reported research, integrating five conceptually and mathematically independent processes: 1) a damped Kuramoto network model, 2) a pumped area field potential, 3) the gating of nearly coincident spikes, 4) the coherence of activity across cortical lamina, and 5) complex information formed through functions in macrocolumns. Biological processes and their interactions are described in equations and a functional circuit such that the mathematical pieces can be assembled the same way the neurophysiological ones are. The model can be conceptually convolved over the specifics of local cortical areas within and across species. A coherent aperture becomes a node in a graph of cortical areas with a corresponding distribution of information.
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Affiliation(s)
- Thomas D Wason
- North Carolina State University, Department of Biological Sciences, Meitzen Laboratory, Campus Box 7617, 128 David Clark Labs, Raleigh, NC 27695-7617, USA.
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28
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Padawer-Curry JA, Jahnavi J, Breimann JS, Licht DJ, Yodh AG, Cohen AS, White BR. Variability in atlas registration of optical intrinsic signal imaging and its effect on functional connectivity analysis. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA. A, OPTICS, IMAGE SCIENCE, AND VISION 2021; 38:245-252. [PMID: 33690536 PMCID: PMC7993363 DOI: 10.1364/josaa.410447] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 12/22/2020] [Indexed: 05/25/2023]
Abstract
To compare neuroimaging data between subjects, images from individual sessions need to be aligned to a common reference or "atlas." Atlas registration of optical intrinsic signal imaging of mice, for example, is commonly performed using affine transforms with parameters determined by manual selection of canonical skull landmarks. Errors introduced by such procedures have not previously been investigated. We quantify the variability that arises from this process and consequent errors from misalignment that affect interpretation of functional neuroimaging data. We propose an improved method, using separately acquired high-resolution images and demonstrate improvements in variability and alignment using this method.
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Affiliation(s)
- Jonah A. Padawer-Curry
- Department of Pediatrics, The Children’s Hospital of Philadelphia and the Perelman School of Medicine at the University of Pennsylvania. 3401 Civic Center Blvd., Philadelphia, PA 19104 USA
| | - Jharna Jahnavi
- Department of Pediatrics, The Children’s Hospital of Philadelphia and the Perelman School of Medicine at the University of Pennsylvania. 3401 Civic Center Blvd., Philadelphia, PA 19104 USA
| | - Jake S. Breimann
- Department of Pediatrics, The Children’s Hospital of Philadelphia and the Perelman School of Medicine at the University of Pennsylvania. 3401 Civic Center Blvd., Philadelphia, PA 19104 USA
| | - Daniel J. Licht
- Department of Pediatrics, The Children’s Hospital of Philadelphia and the Perelman School of Medicine at the University of Pennsylvania. 3401 Civic Center Blvd., Philadelphia, PA 19104 USA
| | - Arjun G. Yodh
- Department of Physics and Astronomy, University of Pennsylvania. 3231 Walnut St., Philadelphia, PA 19104, USA
| | - Akiva S. Cohen
- Department of Anesthesiology and Critical Care Medicine, The Children’s Hospital of Philadelphia and the Perelman School of Medicine at the University of Pennsylvania. 3615 Civic Center Blvd., Philadelphia, PA 19104 USA
| | - Brian R. White
- Department of Pediatrics, The Children’s Hospital of Philadelphia and the Perelman School of Medicine at the University of Pennsylvania. 3401 Civic Center Blvd., Philadelphia, PA 19104 USA
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Abstract
Human brain atlases have been evolving tremendously, propelled recently by brain big projects, and driven by sophisticated imaging techniques, advanced brain mapping methods, vast data, analytical strategies, and powerful computing. We overview here this evolution in four categories: content, applications, functionality, and availability, in contrast to other works limited mostly to content. Four atlas generations are distinguished: early cortical maps, print stereotactic atlases, early digital atlases, and advanced brain atlas platforms, and 5 avenues in electronic atlases spanning the last two generations. Content-wise, new electronic atlases are categorized into eight groups considering their scope, parcellation, modality, plurality, scale, ethnicity, abnormality, and a mixture of them. Atlas content developments in these groups are heading in 23 various directions. Application-wise, we overview atlases in neuroeducation, research, and clinics, including stereotactic and functional neurosurgery, neuroradiology, neurology, and stroke. Functionality-wise, tools and functionalities are addressed for atlas creation, navigation, individualization, enabling operations, and application-specific. Availability is discussed in media and platforms, ranging from mobile solutions to leading-edge supercomputers, with three accessibility levels. The major application-wise shift has been from research to clinical practice, particularly in stereotactic and functional neurosurgery, although clinical applications are still lagging behind the atlas content progress. Atlas functionality also has been relatively neglected until recently, as the management of brain data explosion requires powerful tools. We suggest that the future human brain atlas-related research and development activities shall be founded on and benefit from a standard framework containing the core virtual brain model cum the brain atlas platform general architecture.
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Affiliation(s)
- Wieslaw L Nowinski
- John Paul II Center for Virtual Anatomy and Surgical Simulation, University of Cardinal Stefan Wyszynski, Woycickiego 1/3, Block 12, room 1220, 01-938, Warsaw, Poland.
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Abstract
Stroke is a leading cause of death and a major cause of permanent disability. Its management is demanding because of variety of protocols, imaging modalities, pulse sequences, hemodynamic maps, criteria for treatment, and time constraints to promptly evaluate and treat. To cope with some of these issues, we propose novel, patented solutions in stroke management by employing multiple brain atlases for diagnosis, treatment, and prediction. Numerous and diverse CT and MRI scans are used: ARIC cohort, ischemic and hemorrhagic stroke CT cases, MRI cases with multiple pulse sequences, and 128 stroke CT patients, each with 170 variables and one year follow-up. The method employs brain atlases of anatomy, blood supply territories, and probabilistic stroke atlas. It rapidly maps an atlas to scan and provides atlas-assisted scan processing. Atlas-to-scan mapping is application-dependent and handles three types of regions of interest (ROIs): atlas-defined ROIs, atlas-quantified ROIs, and ROIs creating an atlas. An ROI is defined by atlas-guided anatomy or scan-derived pathology. The atlas defines ROI or quantifies it. A brain atlas potential has been illustrated in four atlas-assisted applications for stroke occurrence prediction and screening, rapid and automatic stroke diagnosis in emergency room, quantitative decision support in thrombolysis in ischemic stroke, and stroke outcome prediction and treatment assessment. The use of brain atlases in stroke has many potential advantages, including rapid processing, automated and robust handling, wide range of applications, and quantitative assessment. Further work is needed to enhance the developed prototypes, clinically validate proposed solutions, and introduce them to clinical practice.
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Affiliation(s)
- Wieslaw L Nowinski
- John Paul II Center for Virtual Anatomy and Surgical Simulation, University of Cardinal Stefan Wyszynski, Woycickiego 1/3, Block 12, room 1220, 01-938, Warsaw, Poland.
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31
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Li L, Li L, Zuo Y. A Hands-On Organ-Slicing Activity to Teach the Cross-Sectional Anatomy. ANATOMICAL SCIENCES EDUCATION 2020; 13:732-742. [PMID: 32034876 DOI: 10.1002/ase.1947] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2019] [Revised: 01/22/2020] [Accepted: 02/05/2020] [Indexed: 06/10/2023]
Abstract
The presentation of pre-sliced specimens is a frequently used method in the laboratory teaching of cross-sectional anatomy. In the present study, a new teaching method based on a hands-on slicing activity was introduced into the teaching of brain, heart, and liver cross-sectional anatomy. A randomized, controlled trial was performed. A total of 182 third-year medical students were randomized into a control group taught with the prosection mode (pre-sliced organ viewing) and an experimental group taught with the dissection mode (hands-on organ slicing). These teaching methods were assessed by testing the students' knowledge of cross-sectional specimens and cross-sectional radiological images, and analyzing students' feedback. Using a specimen test on three organs (brain, heart, and liver), significant differences were observed in the mean scores of the control and experimental groups: for brain 59.6% (±14.2) vs. 70.1% (±15.5), (P < 0.001, Cohen's d = 0.17); for heart: 57.6% (±12.5) vs. 75.6% (±15.3), (P < 0.001, d = 0.30); and for liver: 60.4% (±14.5) vs. 81.7% (±14.2), (P < 0.001, d = 0.46). In a cross-sectional radiological image test, better performance was also found in the experimental group (P < 0.001). The mean scores of the control vs. experimental groups were as follows: for brain imaging 63.9% (±15.1) vs. 71.1% (±16.1); for heart imaging 64.7% (±14.5) vs. 75.2% (±15.5); and for liver imaging 61.1% (±15.5) vs. 81.2% (±14.6), respectively. The effect sizes (Cohen's d) were 0.05, 0.23, and 0.52, respectively. Students in the lower tertile benefited the most from the slicing experiences. Students' feedback was generally positive. Hands-on slicing activity can increase the effectiveness of anatomy teaching and increase students' ability to interpret radiological images.
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Affiliation(s)
- Lei Li
- Department of Anatomy, Nanjing Medical University, Nanjing, People's Republic of China
| | - Lin Li
- Department of Anatomy, Nanjing Medical University, Nanjing, People's Republic of China
| | - Yizhi Zuo
- Department of Anatomy, Nanjing Medical University, Nanjing, People's Republic of China
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32
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Zheng W, Minama Reddy GK, Dai F, Chandramani A, Brang D, Hunter S, Kohrman MH, Rose S, Rossi M, Tao J, Wu S, Byrne R, Frim DM, Warnke P, Towle VL. Chasing language through the brain: Successive parallel networks. Clin Neurophysiol 2020; 132:80-93. [PMID: 33360179 DOI: 10.1016/j.clinph.2020.10.007] [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: 06/07/2020] [Revised: 10/06/2020] [Accepted: 10/11/2020] [Indexed: 11/29/2022]
Abstract
OBJECTIVE To describe the spatio-temporal dynamics and interactions during linguistic and memory tasks. METHODS Event-related electrocorticographic (ECoG) spectral patterns obtained during cognitive tasks from 26 epilepsy patients (aged: 9-60 y) were analyzed in order to examine the spatio-temporal patterns of activation of cortical language areas. ECoGs (1024 Hz/channel) were recorded from 1567 subdural electrodes and 510 depth electrodes chronically implanted over or within the frontal, parietal, occipital and/or temporal lobes as part of their surgical work-up for intractable seizures. Six language/memory tasks were performed, which required responding verbally to auditory or visual word stimuli. Detailed analysis of electrode locations allowed combining results across patients. RESULTS Transient increases in induced ECoG gamma power (70-100 Hz) were observed in response to hearing words (central superior temporal gyrus), reading text and naming pictures (occipital and fusiform cortex) and speaking (pre-central, post-central and sub-central cortex). CONCLUSIONS Between these activations there was widespread spatial divergence followed by convergence of gamma activity that reliably identified cortical areas associated with task-specific processes. SIGNIFICANCE The combined dataset supports the concept of functionally-specific locally parallel language networks that are widely distributed, partially interacting in succession to serve the cognitive and behavioral demands of the tasks.
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Affiliation(s)
- Weili Zheng
- Department of Engineering, The University of Illinois, Chicago, IL, USA
| | | | - Falcon Dai
- Department of Neurology, The University of Chicago, Chicago, IL, USA
| | | | - David Brang
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
| | - Scott Hunter
- Department of Psychiatry and Behavioral Neuroscience, The University of Chicago, Chicago, IL, USA
| | - Michael H Kohrman
- Department of Pediatrics, The University of Chicago, Chicago, IL 60487, USA
| | - Sandra Rose
- Department of Neurology, The University of Chicago, Chicago, IL, USA
| | - Marvin Rossi
- Department of Neurology, Rush University, Chicago, IL, USA
| | - James Tao
- Department of Neurology, The University of Chicago, Chicago, IL, USA
| | - Shasha Wu
- Department of Neurology, The University of Chicago, Chicago, IL, USA
| | - Richard Byrne
- Department of Surgery, Rush University, Chicago, IL, USA
| | - David M Frim
- Department of Surgery, The University of Chicago, 5841 S. Maryland Ave, 60487 Chicago, IL, USA
| | - Peter Warnke
- Department of Surgery, The University of Chicago, 5841 S. Maryland Ave, 60487 Chicago, IL, USA
| | - Vernon L Towle
- Department of Neurology, The University of Chicago, Chicago, IL, USA.
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33
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Sokolov AA, Zeidman P, Razi A, Erb M, Ryvlin P, Pavlova MA, Friston KJ. Asymmetric high-order anatomical brain connectivity sculpts effective connectivity. Netw Neurosci 2020; 4:871-890. [PMID: 33615094 PMCID: PMC7888488 DOI: 10.1162/netn_a_00150] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 05/18/2020] [Indexed: 12/12/2022] Open
Abstract
Bridging the gap between symmetric, direct white matter brain connectivity and neural dynamics that are often asymmetric and polysynaptic may offer insights into brain architecture, but this remains an unresolved challenge in neuroscience. Here, we used the graph Laplacian matrix to simulate symmetric and asymmetric high-order diffusion processes akin to particles spreading through white matter pathways. The simulated indirect structural connectivity outperformed direct as well as absent anatomical information in sculpting effective connectivity, a measure of causal and directed brain dynamics. Crucially, an asymmetric diffusion process determined by the sensitivity of the network nodes to their afferents best predicted effective connectivity. The outcome is consistent with brain regions adapting to maintain their sensitivity to inputs within a dynamic range. Asymmetric network communication models offer a promising perspective for understanding the relationship between structural and functional brain connectomes, both in normalcy and neuropsychiatric conditions.
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Affiliation(s)
- Arseny A. Sokolov
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
- Department of Neurology, University Neurorehabilitation, University Hospital Inselspital, University of Bern, Bern, Switzerland
- Service de Neurologie and Neuroscape@NeuroTech Platform, Département des Neurosciences Cliniques, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Neuroscape Center, Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Peter Zeidman
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
| | - Adeel Razi
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
- Monash Institute of Cognitive and Clinical Neurosciences & Monash Biomedical Imaging, Monash University, Clayton, Australia
- Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, Pakistan
| | - Michael Erb
- Department of Biomedical Magnetic Resonance, University of Tübingen Medical School, Tübingen, Germany
| | - Philippe Ryvlin
- Service de Neurologie and Neuroscape@NeuroTech Platform, Département des Neurosciences Cliniques, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Marina A. Pavlova
- Department of Psychiatry and Psychotherapy, University of Tübingen Medical School, Tübingen, Germany
| | - Karl J. Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
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Dinkelbach L, Südmeyer M, Hartmann CJ, Roeber S, Arzberger T, Felsberg J, Ferrea S, Moldovan AS, Amunts K, Schnitzler A, Caspers S. Somatosensory area 3b is selectively unaffected in corticobasal syndrome: combining MRI and histology. Neurobiol Aging 2020; 94:89-100. [PMID: 32593032 DOI: 10.1016/j.neurobiolaging.2020.05.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 04/04/2020] [Accepted: 05/14/2020] [Indexed: 10/24/2022]
Abstract
An increasing number of neuroimaging studies addressing patients with corticobasal syndrome use macroscopic definitions of brain regions. As a closer link to functionally relevant units, we aimed at identifying magnetic resonance-based atrophy patterns in regions defined by probability maps of cortical microstructure. For this purpose, three analyses were conducted: (1) Whole-brain cortical thickness was compared between 36 patients with corticobasal syndrome and 24 controls. A pattern of pericentral atrophy was found, covering primary motor area 4, premotor area 6, and primary somatosensory areas 1, 2, and 3a. Within the central region, only area 3b was without atrophy. (2) In 18 patients, longitudinal measures with follow-ups of up to 59 months (mean 21.3 ± 15.4) were analyzed. Areas 1, 2, and 6 showed significantly faster atrophy rates than primary somatosensory area 3b. (3) In an individual autopsy case, longitudinal in vivo morphometry and postmortem pathohistology were conducted. The rate of magnetic resonance-based atrophy was significantly correlated with tufted-astrocyte load in those cytoarchitectonically defined regions also seen in the group study, with area 3b being selectively unaffected.
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Affiliation(s)
- Lars Dinkelbach
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany; Institute for Anatomy I, Medical Faculty, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany
| | - Martin Südmeyer
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany; Department of Neurology, Ernst von Bergmann Klinikum, Potsdam, Germany
| | - Christian Johannes Hartmann
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany; Department of Neurology, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany
| | - Sigrun Roeber
- Center for Neuropathology and Prion Research, Ludwig Maximilian University of Munich, Munich, Germany
| | - Thomas Arzberger
- Center for Neuropathology and Prion Research, Ludwig Maximilian University of Munich, Munich, Germany; Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Jörg Felsberg
- Department of Neuropathology, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany
| | - Stefano Ferrea
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany
| | - Alexia-Sabine Moldovan
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany; Department of Neurology, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; JARA-BRAIN, Jülich-Aachen Research Alliance, Research Centre Jülich, Jülich, Germany; C. & O. Vogt Institute for Brain Research, Medical Faculty, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany
| | - Alfons Schnitzler
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany; Department of Neurology, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany
| | - Svenja Caspers
- Institute for Anatomy I, Medical Faculty, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; JARA-BRAIN, Jülich-Aachen Research Alliance, Research Centre Jülich, Jülich, Germany.
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35
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Luo N, Sui J, Abrol A, Lin D, Chen J, Vergara VM, Fu Z, Du Y, Damaraju E, Xu Y, Turner JA, Calhoun VD. Age-related structural and functional variations in 5,967 individuals across the adult lifespan. Hum Brain Mapp 2020; 41:1725-1737. [PMID: 31876339 PMCID: PMC7267948 DOI: 10.1002/hbm.24905] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 11/24/2019] [Accepted: 12/10/2019] [Indexed: 12/13/2022] Open
Abstract
Exploring brain changes across the human lifespan is becoming an important topic in neuroscience. Though there are multiple studies which investigated the relationship between age and brain imaging, the results are heterogeneous due to small sample sizes and relatively narrow age ranges. Here, based on year-wise estimation of 5,967 subjects from 13 to 72 years old, we aimed to provide a more precise description of adult lifespan variation trajectories of gray matter volume (GMV), structural network correlation (SNC), and functional network connectivity (FNC) using independent component analysis and multivariate linear regression model. Our results revealed the following relationships: (a) GMV linearly declined with age in most regions, while parahippocampus showed an inverted U-shape quadratic relationship with age; SNC presented a U-shape quadratic relationship with age within cerebellum, and inverted U-shape relationship primarily in the default mode network (DMN) and frontoparietal (FP) related correlation. (b) FNC tended to linearly decrease within resting-state networks (RSNs), especially in the visual network and DMN. Early increase was revealed between RSNs, primarily in FP and DMN, which experienced a decrease at older ages. U-shape relationship was also revealed to compensate for the cognition deficit in attention and subcortical related connectivity at late years. (c) The link between middle occipital gyrus and insula, as well as precuneus and cerebellum, exhibited similar changing trends between SNC and FNC across the adult lifespan. Collectively, these results highlight the benefit of lifespan study and provide a precise description of age-related regional variation and SNC/FNC changes based on a large dataset.
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Affiliation(s)
- Na Luo
- Brainnetome Center and National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
- CAS Center for Excellence in Brain Science and Intelligence TechnologyInstitute of Automation, Chinese Academy of SciencesBeijingChina
| | - Anees Abrol
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
| | - Dongdong Lin
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
| | - Jiayu Chen
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
| | - Victor M. Vergara
- CAS Center for Excellence in Brain Science and Intelligence TechnologyInstitute of Automation, Chinese Academy of SciencesBeijingChina
| | - Zening Fu
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
| | - Yuhui Du
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
- School of Computer and Information TechnologyShanxi UniversityTaiyuanChina
| | - Eswar Damaraju
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
| | - Yong Xu
- Department of PsychiatryFirst Clinical Medical College/ First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Jessica A. Turner
- Department of PsychologyNeuroscience Institute, Georgia State UniversityAtlantaGeorgia
| | - Vince D. Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
- Department of PsychiatryYale University, School of MedicineNew HavenConnecticut
- Department of Psychology, Computer ScienceNeuroscience Institute, and Physics, Georgia State UniversityAtlantaGeorgia
- Department of Electrical and Computer EngineeringGeorgia Institute of TechnologyAtlantaGeorgia
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36
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Johnson PJ, Pascalau R, Luh WM, Raj A, Cerda-Gonzalez S, Barry EF. Stereotaxic Diffusion Tensor Imaging White Matter Atlas for the in vivo Domestic Feline Brain. Front Neuroanat 2020; 14:1. [PMID: 32116572 PMCID: PMC7026623 DOI: 10.3389/fnana.2020.00001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 01/16/2020] [Indexed: 02/02/2023] Open
Abstract
The cat brain is a useful model for neuroscientific research and with the increasing use of advanced neuroimaging techniques there is a need for an open-source stereotaxic white matter brain atlas to accompany the cortical gray matter atlas, currently available. A stereotaxic white matter atlas would facilitate anatomic registration and segmentation of the white matter to aid in lesion localization or standardized regional analysis of specific regions of the white matter. In this article, we document the creation of a stereotaxic feline white matter atlas from diffusion tensor imaging (DTI) data obtained from a population of eight mesaticephalic felines. Deterministic tractography reconstructions were performed to create tract priors for the major white matter projections of Corpus callosum (CC), fornix, cingulum, uncinate, Corona Radiata (CR), Corticospinal tract (CST), inferior longitudinal fasciculus (ILF), Superior Longitudinal Fasciculus (SLF), and the cerebellar tracts. T1-weighted, fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD) population maps were generated. The volume, mean tract length and mean FA, MD, AD and RD values for each tract prior were documented. A structural connectome was then created using previously published cortical priors and the connectivity metrics for all cortical regions documented. The provided white matter atlas, diffusivity maps, tract priors and connectome will be a valuable resource for anatomical, pathological and translational neuroimaging research in the feline model. Multi-atlas population maps and segmentation priors are available at Cornell’s digital repository: https://ecommons.cornell.edu/handle/1813/58775.2.
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Affiliation(s)
- Philippa J Johnson
- Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, United States
| | - Raluca Pascalau
- Faculty of Medicine, "Iuliu Hatieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Wen-Ming Luh
- National Institute on Aging, National Institutes of Health, Baltimore, MD, United States
| | - Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | | | - Erica F Barry
- Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, United States
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37
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Schurr R, Zelman A, Mezer AA. Subdividing the superior longitudinal fasciculus using local quantitative MRI. Neuroimage 2019; 208:116439. [PMID: 31821870 DOI: 10.1016/j.neuroimage.2019.116439] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Revised: 11/06/2019] [Accepted: 12/03/2019] [Indexed: 01/17/2023] Open
Abstract
The association fibers of the superior longitudinal fasciculus (SLF) connect parietal and frontal cortical regions in the human brain. The SLF comprises of three distinct sub-bundles, each presenting a different anatomical trajectory, and specific functional roles. Nevertheless, in vivo studies of the SLF often consider the entire SLF complex as a single entity. In this work, we suggest a data-driven approach that relies on microstructure measurements for separating SLF-III from the rest of the SLF. We apply the SLF-III separation procedure in three independent datasets using parameters of diffusion MRI (fractional anisotropy), as well as relaxometry-based parameters (T1, T2, T2* and T2-weighted/T1-weighted). We show that the proposed procedure is reproducible across datasets and tractography algorithms. Finally, we suggest that differential crossing with different white-matter tracts is the source of the distinct MRI signatures of SLF-II and SLF-III.
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Affiliation(s)
- Roey Schurr
- Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.
| | - Ady Zelman
- Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Aviv A Mezer
- Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
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38
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Iranmehr E, Shouraki SB, Faraji MM, Bagheri N, Linares-Barranco B. Bio-Inspired Evolutionary Model of Spiking Neural Networks in Ionic Liquid Space. Front Neurosci 2019; 13:1085. [PMID: 31787863 PMCID: PMC6856051 DOI: 10.3389/fnins.2019.01085] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2019] [Accepted: 09/25/2019] [Indexed: 11/27/2022] Open
Abstract
One of the biggest struggles while working with artificial neural networks is being able to come up with models which closely match biological observations. Biological neural networks seem to capable of creating and pruning dendritic spines, leading to synapses being changed, which results in higher learning capability. The latter forms the basis of the present study in which a new ionic model for reservoir-like networks, consisting of spiking neurons, is introduced. High plasticity of this model makes learning possible with a fewer number of neurons. In order to study the effect of the applied stimulus in an ionic liquid space through time, a diffusion operator is used which somehow compensates for the separation between spatial and temporal coding in spiking neural networks and therefore, makes the mentioned model suitable for spatiotemporal patterns. Inspired by partial structural changes in the human brain over the years, the proposed model evolves during the learning process. The effect of topological evolution on the proposed model's performance for some classification problems is studied in this paper. Several datasets have been used to evaluate the performance of the proposed model compared to the original LSM. Classification results via separation and accuracy values have shown that the proposed ionic liquid outperforms the original LSM.
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Affiliation(s)
- Ensieh Iranmehr
- Artificial Creatures Laboratory, Electrical Engineering Department, Sharif University of Technology, Tehran, Iran
| | - Saeed Bagheri Shouraki
- Artificial Creatures Laboratory, Electrical Engineering Department, Sharif University of Technology, Tehran, Iran
| | - Mohammad Mahdi Faraji
- Artificial Creatures Laboratory, Electrical Engineering Department, Sharif University of Technology, Tehran, Iran
| | - Nasim Bagheri
- Artificial Creatures Laboratory, Electrical Engineering Department, Sharif University of Technology, Tehran, Iran
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39
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Hong SJ, Valk SL, Di Martino A, Milham MP, Bernhardt BC. Multidimensional Neuroanatomical Subtyping of Autism Spectrum Disorder. Cereb Cortex 2019; 28:3578-3588. [PMID: 28968847 DOI: 10.1093/cercor/bhx229] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Accepted: 08/23/2017] [Indexed: 12/15/2022] Open
Abstract
Autism spectrum disorder (ASD) is a group of neurodevelopmental disorders with multiple biological etiologies and highly variable symptoms. Using a novel analytical framework that integrates cortex-wide MRI markers of vertical (i.e., thickness, tissue contrast) and horizontal (i.e., surface area, geodesic distance) cortical organization, we could show that a large multi-centric cohort of individuals with ASD falls into 3 distinctive anatomical subtypes (ASD-I: cortical thickening, increased surface area, tissue blurring; ASD-II: cortical thinning, decreased distance; ASD-III: increased distance). Bootstrap analysis indicated a high consistency of these biotypes across thousands of simulations, while analysis of behavioral phenotypes and resting-state fMRI showed differential symptom load (i.e., Autism Diagnostic Observation Schedule; ADOS) and instrinsic connectivity anomalies in communication and social-cognition networks. Notably, subtyping improved supervised learning approaches predicting ADOS score in single subjects, with significantly increased performance compared to a subtype-blind approach. The existence of different subtypes may reconcile previous results so far not converging on a consistent pattern of anatomical anomalies in autism, and possibly relate the presence of diverging corticogenic and maturational anomalies. The high accuracy for symptom severity prediction indicates benefits of MRI biotyping for personalized diagnostics and may guide the development of targeted therapeutic strategies.
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Affiliation(s)
- Seok-Jun Hong
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, QC, Canada
| | - Sofie L Valk
- Department of Social Neuroscience, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstrasse 1a, Leipzig, Germany
| | - Adriana Di Martino
- Department of Child and Adolescent Psychiatry, Child Study Center at NYU Langone Health, 1 Park Avenue, New York, NY, USA
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, 445 Park Avenue, New York, NY, USA.,Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, 140 Old Orangeburg Rd, Orangeburg, New York, NY, USA
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, QC, Canada
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40
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Sarubbo S, Tate M, De Benedictis A, Merler S, Moritz-Gasser S, Herbet G, Duffau H. Mapping critical cortical hubs and white matter pathways by direct electrical stimulation: an original functional atlas of the human brain. Neuroimage 2019; 205:116237. [PMID: 31626897 DOI: 10.1016/j.neuroimage.2019.116237] [Citation(s) in RCA: 114] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 09/25/2019] [Accepted: 09/30/2019] [Indexed: 01/19/2023] Open
Abstract
OBJECTIVE The structural and functional organization of brain networks subserving basic daily activities (i.e. language, visuo-spatial cognition, movement, semantics, etc.) are not completely understood to date. Here, we report the first probabilistic cortical and subcortical atlas of critical structures mediating human brain functions based on direct electrical stimulation (DES), a well-validated tool for the exploration of cerebral processing and for performing safe surgical interventions in eloquent areas. METHODS We collected 1162 cortical and 659 subcortical DES responses during testing of 16 functional domains in 256 patients undergoing awake surgery. Spatial coordinates for each functional response were calculated, and probability distributions for the entire patient cohort were mapped onto a standardized three-dimensional brain template using a multinomial statistical analysis. In addition, matching analyses were performed against prior established anatomy-based cortical and white matter (WM) atlases. RESULTS The probabilistic maps for each functional domain were provided. The topographical analysis demonstrated a wide spatial distribution of cortical functional responses, while subcortical responses were more restricted, localizing to known WM pathways. These DES-derived data showed reliable matching with existing cortical and WM atlases as well as recent neuroimaging and neurophysiological data. CONCLUSIONS We present the first integrated and comprehensive cortical-subcortical atlas of structures essential for humans' neural functions based on highly-specific DES mapping during real-time neuropsychological testing. This novel atlas can serve as a complementary tool for neuroscientists, along with data obtained from other modalities, to improve and refine our understanding of the functional anatomy of critical brain networks.
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Affiliation(s)
- Silvio Sarubbo
- Division of Neurosurgery, Structural and Functional Connectivity Lab Project, Azienda Provinciale per i Servizi Sanitari (APSS), 9 Largo Medaglie d'Oro, 38122, Trento, Italy.
| | - Matthew Tate
- Departments of Neurosurgery and Neurology, Northwestern University, Feinberg School of Medicine, 420 E Superior St, 60611, Chicago, IL, USA
| | - Alessandro De Benedictis
- Neurosurgery Unit, Department of Neuroscience and Neurorehabilitation, Bambino Gesù Children's Hospital IRCCS, 4 Piazza Sant'Onofrio, 00165, Rome, Italy
| | | | - Sylvie Moritz-Gasser
- Department of Neurosurgery, Gui de Chauliac Hospital, Montpellier University Medical Center, 80 Avenue Augustin Fliche, Montpellier, France; National Institute for Health and Medical Research (INSERM), U1051, Team ''Plasticity of the Central Nervous System, Human Stem Cells and Glial Tumors'', Institute for Neurosciences of Montpellier, Montpellier University Medical Center, 80 Av Augustin Fliche, Montpellier, France
| | - Guillaume Herbet
- Department of Neurosurgery, Gui de Chauliac Hospital, Montpellier University Medical Center, 80 Avenue Augustin Fliche, Montpellier, France; National Institute for Health and Medical Research (INSERM), U1051, Team ''Plasticity of the Central Nervous System, Human Stem Cells and Glial Tumors'', Institute for Neurosciences of Montpellier, Montpellier University Medical Center, 80 Av Augustin Fliche, Montpellier, France
| | - Hugues Duffau
- Department of Neurosurgery, Gui de Chauliac Hospital, Montpellier University Medical Center, 80 Avenue Augustin Fliche, Montpellier, France; National Institute for Health and Medical Research (INSERM), U1051, Team ''Plasticity of the Central Nervous System, Human Stem Cells and Glial Tumors'', Institute for Neurosciences of Montpellier, Montpellier University Medical Center, 80 Av Augustin Fliche, Montpellier, France
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41
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Ambrosen KS, Eskildsen SF, Hinne M, Krug K, Lundell H, Schmidt MN, van Gerven MAJ, Mørup M, Dyrby TB. Validation of structural brain connectivity networks: The impact of scanning parameters. Neuroimage 2019; 204:116207. [PMID: 31539592 DOI: 10.1016/j.neuroimage.2019.116207] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 08/20/2019] [Accepted: 09/17/2019] [Indexed: 12/20/2022] Open
Abstract
Evaluation of the structural connectivity (SC) of the brain based on tractography has mainly focused on the choice of diffusion model, tractography algorithm, and their respective parameter settings. Here, we systematically validate SC derived from a post mortem monkey brain, while varying key acquisition parameters such as the b-value, gradient angular resolution and image resolution. As gold standard we use the connectivity matrix obtained invasively with histological tracers by Markov et al. (2014). As performance metric, we use cross entropy as a measure that enables comparison of the relative tracer labeled neuron counts to the streamline counts from tractography. We find that high angular resolution and high signal-to-noise ratio are important to estimate SC, and that SC derived from low image resolution (1.03 mm3) are in better agreement with the tracer network, than those derived from high image resolution (0.53 mm3) or at an even lower image resolution (2.03 mm3). In contradiction, sensitivity and specificity analyses suggest that if the angular resolution is sufficient, the balanced compromise in which sensitivity and specificity are identical remains 60-64% regardless of the other scanning parameters. Interestingly, the tracer graph is assumed to be the gold standard but by thresholding, the balanced compromise increases to 70-75%. Hence, by using performance metrics based on binarized tracer graphs, one risks losing important information, changing the performance of SC graphs derived by tractography and their dependence of different scanning parameters.
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Affiliation(s)
- Karen S Ambrosen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark; Danish Research Centre for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | - Simon F Eskildsen
- Center of Functionally Integrative Neuroscience (CFIN), Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Max Hinne
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Kristine Krug
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK; Institute of Biology, Otto-von-Guericke-Universität Magdeburg, Magdeburg, Germany; Leibniz-Insitute for Neurobiology, Magdeburg, Germany
| | - Henrik Lundell
- Danish Research Centre for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | - Mikkel N Schmidt
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Marcel A J van Gerven
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Morten Mørup
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Tim B Dyrby
- Danish Research Centre for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark.
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42
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Shi D, Levina A, Noori HR. Refined parcellation of the nervous system by algorithmic detection of hidden features within communities. Phys Rev E 2019; 100:012301. [PMID: 31499866 DOI: 10.1103/physreve.100.012301] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Indexed: 11/07/2022]
Abstract
The nervous system can be represented as a multiscale network comprised by single cells or ensembles that are linked by physical or functional connections. Groups of morphologically and physiologically diverse neurons are wired as connectivity patterns with a certain degree of universality across species and individual variability. Thereby, community detection approaches are often used to characterize how neural units cluster into such densely interconnected groups. However, the communities may possess deeper structural features that remain undetected by current algorithms. We present a scheme for refined parcellation of neuronal networks, by identifying local integrator units (LU) that are contained in network communities. An LU is defined as a connected subnetwork in which all neuronal connections are constrained within this unit, and can be formed for instance by a set of interneurons. Our method uses the Louvain algorithm to detect communities and participation coefficients to discriminate local neurons from global hubs. The sensitivity of the algorithm for discovering LUs with respect to the choice of community detection algorithm and network parameters was tested by simulations of different synthetic networks. The appropriateness of the algorithm for real-world scenarios was demonstrated on weighted and binary Caenorhabditis elegans connectomes. The detected LUs are distinctly localized within the worm body and clearly define functional groups. This approach provides a robust, observer-independent parcellation strategy that is useful for functional structure confirmation and potentially contributes to the current efforts in quantitative whole-brain architectonics of different species as well as the analysis of functional connectivity networks.
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Affiliation(s)
- Dongmei Shi
- Max Planck Institute for Biological Cybernetics, Tübingen 72076, Germany
| | - Anna Levina
- Max Planck Institute for Biological Cybernetics, Tübingen 72076, Germany.,Department of Computer Science, University of Tübingen, Tübingen 72076, Germany
| | - Hamid R Noori
- Max Planck Institute for Biological Cybernetics, Tübingen 72076, Germany.,Courant Institute for Mathematical Sciences, New York University, New York 10012, USA
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43
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Abstract
A defining aspect of brain organization is its spatial heterogeneity, which gives rise to multiple topographies at different scales. Brain parcellation - defining distinct partitions in the brain, be they areas or networks that comprise multiple discontinuous but closely interacting regions - is thus fundamental for understanding brain organization and function. The past decade has seen an explosion of in vivo MRI-based approaches to identify and parcellate the brain on the basis of a wealth of different features, ranging from local properties of brain tissue to long-range connectivity patterns, in addition to structural and functional markers. Given the high diversity of these various approaches, assessing the convergence and divergence among these ensuing maps is a challenge. Inter-individual variability adds to this challenge but also provides new opportunities when coupled with cross-species and developmental parcellation studies.
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44
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Bloss EB, Hunt DL. Revealing the Synaptic Hodology of Mammalian Neural Circuits With Multiscale Neurocartography. Front Neuroinform 2019; 13:52. [PMID: 31427940 PMCID: PMC6690003 DOI: 10.3389/fninf.2019.00052] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Accepted: 07/02/2019] [Indexed: 11/20/2022] Open
Abstract
The functional features of neural circuits are determined by a combination of properties that range in scale from projections systems across the whole brain to molecular interactions at the synapse. The burgeoning field of neurocartography seeks to map these relevant features of brain structure—spanning a volume ∼20 orders of magnitude—to determine how neural circuits perform computations supporting cognitive function and complex behavior. Recent technological breakthroughs in tissue sample preparation, high-throughput electron microscopy imaging, and automated image analyses have produced the first visualizations of all synaptic connections between neurons of invertebrate model systems. However, the sheer size of the central nervous system in mammals implies that reconstruction of the first full brain maps at synaptic scale may not be feasible for decades. In this review, we outline existing and emerging technologies for neurocartography that complement electron microscopy-based strategies and are beginning to derive some basic organizing principles of circuit hodology at the mesoscale, microscale, and nanoscale. Specifically, we discuss how a host of light microscopy techniques including array tomography have been utilized to determine both long-range and subcellular organizing principles of synaptic connectivity. In addition, we discuss how new techniques, such as two-photon serial tomography of the entire mouse brain, have become attractive approaches to dissect the potential connectivity of defined cell types. Ultimately, principles derived from these techniques promise to facilitate a conceptual understanding of how connectomes, and neurocartography in general, can be effectively utilized toward reaching a mechanistic understanding of circuit function.
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Affiliation(s)
- Erik B Bloss
- Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, VA, United States
| | - David L Hunt
- Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, VA, United States
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45
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Diedrichsen J, King M, Hernandez-Castillo C, Sereno M, Ivry RB. Universal Transform or Multiple Functionality? Understanding the Contribution of the Human Cerebellum across Task Domains. Neuron 2019; 102:918-928. [PMID: 31170400 PMCID: PMC6686189 DOI: 10.1016/j.neuron.2019.04.021] [Citation(s) in RCA: 138] [Impact Index Per Article: 27.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Revised: 04/10/2019] [Accepted: 04/11/2019] [Indexed: 01/18/2023]
Abstract
An impressive body of research over the past 30 years has implicated the human cerebellum in a broad range of functions, including motor control, perception, language, working memory, cognitive control, and social cognition. The relatively uniform anatomy and physiology of the cerebellar cortex has given rise to the idea that this structure performs the same computational function across diverse domains. Here we highlight evidence from the human neuroimaging literature that documents the striking functional heterogeneity of the cerebellum, both in terms of task-evoked activity patterns and, as measured under task-free conditions, functional connectivity with the neocortex. Building on these observations, we discuss the theoretical challenges these results present to the idea of a universal cerebellar computation and consider the alternative concept of multiple functionality, the idea that the same underlying circuit implements functionally distinct computations.
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Affiliation(s)
- Jörn Diedrichsen
- Brain and Mind Institute, Western University London, ON N6A 5B7, Canada; Department of Statistical and Actuarial Sciences, Western University, London, ON N6A 5B7, Canada; Department of Computer Science, Western University, London, ON N6A 5B7, Canada.
| | - Maedbh King
- Department of Psychology, University of California, Berkeley, CA 94720, USA
| | | | - Marty Sereno
- Department of Psychology, San Diego State University, San Diego, CA 92182, USA
| | - Richard B Ivry
- Department of Psychology, University of California, Berkeley, CA 94720, USA
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46
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Hess A, Hinz R, Keliris GA, Boehm-Sturm P. On the Usage of Brain Atlases in Neuroimaging Research. Mol Imaging Biol 2019; 20:742-749. [PMID: 30094652 DOI: 10.1007/s11307-018-1259-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Brain atlases play a key role in modern neuroimaging analysis of brain structure and function. We review available atlas databases for humans and animals and illustrate common state-of-the-art workflows in neuroimaging research based on image registration. Advances in noninvasive imaging methods, 3D ex vivo microscopy, and image processing are summarized which will eventually close the current resolution gap between brain atlases based on conventional 2D histology and those based on 3D in vivo imaging.
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Affiliation(s)
- Andreas Hess
- Institute for Experimental Pharmacology, Friedrich Alexander University Erlangen Nuremberg, Fahrstraße 17, 91054, Erlangen, Germany.
| | - Rukun Hinz
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium
| | | | - Philipp Boehm-Sturm
- Department of Experimental Neurology and Center for Stroke Research Berlin, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany. .,NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Charité - Universitätsmedizin Berlin, Berlin, Germany.
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47
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Feng L, Li H, Oishi K, Mishra V, Song L, Peng Q, Ouyang M, Wang J, Slinger M, Jeon T, Lee L, Heyne R, Chalak L, Peng Y, Liu S, Huang H. Age-specific gray and white matter DTI atlas for human brain at 33, 36 and 39 postmenstrual weeks. Neuroimage 2019; 185:685-698. [PMID: 29959046 PMCID: PMC6289605 DOI: 10.1016/j.neuroimage.2018.06.069] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2017] [Revised: 05/21/2018] [Accepted: 06/25/2018] [Indexed: 01/24/2023] Open
Abstract
During the 3rd trimester, dramatic structural changes take place in the human brain, underlying the neural circuit formation. The survival rate of premature infants has increased significantly in recent years. The large morphological differences of the preterm brain at 33 or 36 postmenstrual weeks (PMW) from the brain at 40PMW (full term) make it necessary to establish age-specific atlases for preterm brains. In this study, with high quality (1.5 × 1.5 × 1.6 mm3 imaging resolution) diffusion tensor imaging (DTI) data obtained from 84 healthy preterm and term-born neonates, we established age-specific preterm and term-born brain templates and atlases at 33, 36 and 39PMW. Age-specific DTI templates include a single-subject template, a population-averaged template with linear transformation and a population-averaged template with nonlinear transformation. Each of the age-specific DTI atlases includes comprehensive labeling of 126 major gray matter (GM) and white matter (WM) structures, specifically 52 cerebral cortical structures, 40 cerebral WM structures, 22 brainstem and cerebellar structures and 12 subcortical GM structures. From 33 to 39 PMW, dramatic morphological changes of delineated individual neural structures such as ganglionic eminence and uncinate fasciculus were revealed. The evaluation based on measurements of Dice ratio and L1 error suggested reliable and reproducible automated labels from the age-matched atlases compared to labels from manual delineation. Applying these atlases to automatically and effectively delineate microstructural changes of major WM tracts during the 3rd trimester was demonstrated. The established age-specific DTI templates and atlases of 33, 36 and 39 PMW brains may be used for not only understanding normal functional and structural maturational processes but also detecting biomarkers of neural disorders in the preterm brains.
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Affiliation(s)
- Lei Feng
- Department of Radiology, Children's Hospital of Philadelphia, PA, USA; Research Center for Sectional and Imaging Anatomy, Shandong University Cheeloo College of Medicine, Shandong, China
| | - Hang Li
- Department of Radiology, Children's Hospital of Philadelphia, PA, USA; Department of Radiology, Beijing Children's Hospital Affiliated to Capital Medical University, National Center for Children's Health, Beijing, China
| | - Kenichi Oishi
- Department of Radiology, Johns Hopkins University, MD, USA
| | - Virendra Mishra
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, TX, USA
| | - Limei Song
- Department of Radiology, Children's Hospital of Philadelphia, PA, USA; Research Center for Sectional and Imaging Anatomy, Shandong University Cheeloo College of Medicine, Shandong, China
| | - Qinmu Peng
- Department of Radiology, Children's Hospital of Philadelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, PA, USA
| | - Minhui Ouyang
- Department of Radiology, Children's Hospital of Philadelphia, PA, USA; Advanced Imaging Research Center, University of Texas Southwestern Medical Center, TX, USA
| | - Jiaojian Wang
- Department of Radiology, Children's Hospital of Philadelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, PA, USA
| | - Michelle Slinger
- Department of Radiology, Children's Hospital of Philadelphia, PA, USA
| | - Tina Jeon
- Department of Radiology, Children's Hospital of Philadelphia, PA, USA
| | - Lizette Lee
- Department of Pediatrics, University of Texas Southwestern Medical Center, TX, USA
| | - Roy Heyne
- Department of Pediatrics, University of Texas Southwestern Medical Center, TX, USA
| | - Lina Chalak
- Department of Pediatrics, University of Texas Southwestern Medical Center, TX, USA
| | - Yun Peng
- Department of Radiology, Beijing Children's Hospital Affiliated to Capital Medical University, National Center for Children's Health, Beijing, China
| | - Shuwei Liu
- Research Center for Sectional and Imaging Anatomy, Shandong University Cheeloo College of Medicine, Shandong, China
| | - Hao Huang
- Department of Radiology, Children's Hospital of Philadelphia, PA, USA; Advanced Imaging Research Center, University of Texas Southwestern Medical Center, TX, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, PA, USA.
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48
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Lützkendorf R, Heidemann RM, Feiweier T, Luchtmann M, Baecke S, Kaufmann J, Stadler J, Budinger E, Bernarding J. Mapping fine-scale anatomy of gray matter, white matter, and trigeminal-root region applying spherical deconvolution to high-resolution 7-T diffusion MRI. MAGMA (NEW YORK, N.Y.) 2018; 31:701-713. [PMID: 30225801 DOI: 10.1007/s10334-018-0705-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2017] [Revised: 08/29/2018] [Accepted: 09/03/2018] [Indexed: 11/29/2022]
Abstract
OBJECTIVES We assessed the use of high-resolution ultra-high-field diffusion magnetic resonance imaging (dMRI) to determine neuronal fiber orientation density functions (fODFs) throughout the human brain, including gray matter (GM), white matter (WM), and small intertwined structures in the cerebellopontine region. MATERIALS AND METHODS We acquired 7-T whole-brain dMRI data of 23 volunteers with 1.4-mm isotropic resolution; fODFs were estimated using constrained spherical deconvolution. RESULTS High-resolution fODFs enabled a detailed view of the intravoxel distributions of fiber populations in the whole brain. In the brainstem region, the fODF of the extra- and intrapontine parts of the trigeminus could be resolved. Intrapontine trigeminal fiber populations were crossed in a network-like fashion by fiber populations of the surrounding cerebellopontine tracts. In cortical GM, additional evidence was found that in parts of primary somatosensory cortex, fODFs seem to be oriented less perpendicular to the cortical surface than in GM of motor, premotor, and secondary somatosensory cortices. CONCLUSION With 7-T MRI being introduced into clinical routine, high-resolution dMRI and derived measures such as fODFs can serve to characterize fine-scale anatomic structures as a prerequisite to detecting pathologies in GM and small or intertwined WM tracts.
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Affiliation(s)
- Ralf Lützkendorf
- Institute for Biometry and Medical Informatics, Otto-von-Guericke-University, Magdeburg, Germany.
| | | | | | - Michael Luchtmann
- Department of Neurosurgery, Otto-von-Guericke-University, Magdeburg, Germany
| | - Sebastian Baecke
- Institute for Biometry and Medical Informatics, Otto-von-Guericke-University, Magdeburg, Germany
| | - Jörn Kaufmann
- Department of Neurology, Otto-von-Guericke-University, Magdeburg, Germany
| | - Jörg Stadler
- Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Eike Budinger
- Leibniz Institute for Neurobiology, Magdeburg, Germany.,Center of Behavioral Brain Sciences, Magdeburg, Germany
| | - Johannes Bernarding
- Institute for Biometry and Medical Informatics, Otto-von-Guericke-University, Magdeburg, Germany.,Center of Behavioral Brain Sciences, Magdeburg, Germany
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49
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Zilles K. Brodmann: a pioneer of human brain mapping-his impact on concepts of cortical organization. Brain 2018; 141:3262-3278. [PMID: 30358817 PMCID: PMC6202576 DOI: 10.1093/brain/awy273] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 09/24/2018] [Accepted: 09/26/2018] [Indexed: 11/13/2022] Open
Affiliation(s)
- Karl Zilles
- Institute of Neuroscience and Medicine INM-1, Research Centre Jülich, Jülich, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Germany
- JARA–Translational Brain Medicine, Aachen, Germany
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50
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Schaefer A, Kong R, Gordon EM, Laumann TO, Zuo XN, Holmes AJ, Eickhoff SB, Yeo BTT. Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cereb Cortex 2018; 28:3095-3114. [PMID: 28981612 PMCID: PMC6095216 DOI: 10.1093/cercor/bhx179] [Citation(s) in RCA: 1379] [Impact Index Per Article: 229.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2016] [Revised: 04/26/2017] [Accepted: 06/23/2017] [Indexed: 12/17/2022] Open
Abstract
A central goal in systems neuroscience is the parcellation of the cerebral cortex into discrete neurobiological "atoms". Resting-state functional magnetic resonance imaging (rs-fMRI) offers the possibility of in vivo human cortical parcellation. Almost all previous parcellations relied on 1 of 2 approaches. The local gradient approach detects abrupt transitions in functional connectivity patterns. These transitions potentially reflect cortical areal boundaries defined by histology or visuotopic fMRI. By contrast, the global similarity approach clusters similar functional connectivity patterns regardless of spatial proximity, resulting in parcels with homogeneous (similar) rs-fMRI signals. Here, we propose a gradient-weighted Markov Random Field (gwMRF) model integrating local gradient and global similarity approaches. Using task-fMRI and rs-fMRI across diverse acquisition protocols, we found gwMRF parcellations to be more homogeneous than 4 previously published parcellations. Furthermore, gwMRF parcellations agreed with the boundaries of certain cortical areas defined using histology and visuotopic fMRI. Some parcels captured subareal (somatotopic and visuotopic) features that likely reflect distinct computational units within known cortical areas. These results suggest that gwMRF parcellations reveal neurobiologically meaningful features of brain organization and are potentially useful for future applications requiring dimensionality reduction of voxel-wise fMRI data. Multiresolution parcellations generated from 1489 participants are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal).
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Affiliation(s)
- Alexander Schaefer
- Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore
| | - Ru Kong
- Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore
| | - Evan M Gordon
- VISN 17 Center of Excellence for Research on Returning War Veterans, Waco, TX, USA
| | - Timothy O Laumann
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Xi-Nian Zuo
- CAS Key Laboratory of Behavioral Sciences, Institute of Psychology, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | | | - Simon B Eickhoff
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Centre for Cognitive Neuroscience, Duke-NUS Medical School, Singapore, Singapore
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