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Guo J, He C, Song H, Gao H, Yao S, Dong SS, Yang TL. Unveiling Promising Neuroimaging Biomarkers for Schizophrenia Through Clinical and Genetic Perspectives. Neurosci Bull 2024; 40:1333-1352. [PMID: 38703276 PMCID: PMC11365900 DOI: 10.1007/s12264-024-01214-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 01/08/2024] [Indexed: 05/06/2024] Open
Abstract
Schizophrenia is a complex and serious brain disorder. Neuroscientists have become increasingly interested in using magnetic resonance-based brain imaging-derived phenotypes (IDPs) to investigate the etiology of psychiatric disorders. IDPs capture valuable clinical advantages and hold biological significance in identifying brain abnormalities. In this review, we aim to discuss current and prospective approaches to identify potential biomarkers for schizophrenia using clinical multimodal neuroimaging and imaging genetics. We first described IDPs through their phenotypic classification and neuroimaging genomics. Secondly, we discussed the applications of multimodal neuroimaging by clinical evidence in observational studies and randomized controlled trials. Thirdly, considering the genetic evidence of IDPs, we discussed how can utilize neuroimaging data as an intermediate phenotype to make association inferences by polygenic risk scores and Mendelian randomization. Finally, we discussed machine learning as an optimum approach for validating biomarkers. Together, future research efforts focused on neuroimaging biomarkers aim to enhance our understanding of schizophrenia.
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Affiliation(s)
- Jing Guo
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Changyi He
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Huimiao Song
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Huiwu Gao
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Shi Yao
- Guangdong Key Laboratory of Age-Related Cardiac and Cerebral Diseases, Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524000, China
| | - Shan-Shan Dong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China.
| | - Tie-Lin Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China.
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2
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Watanabe M, Shrivastava RK, Balchandani P. Advanced neuroimaging of the trigeminal nerve and the whole brain in trigeminal neuralgia: a systematic review. Pain 2024:00006396-990000000-00680. [PMID: 39132931 DOI: 10.1097/j.pain.0000000000003365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 06/26/2024] [Indexed: 08/13/2024]
Abstract
ABSTRACT For trigeminal neuralgia (TN), a major role of imaging is to identify the causes, but recent studies demonstrated structural and microstructural changes in the affected nerve. Moreover, an increasing number of studies have reported central nervous system involvement in TN. In this systematic review, recent quantitative magnetic resonance imaging (MRI) studies of the trigeminal nerve and the brain in patients with TN were compiled, organized, and discussed, particularly emphasizing the possible background mechanisms and the interpretation of the results. A systematic search of quantitative MRI studies of the trigeminal nerve and the brain in patients with TN was conducted using PubMed. We included the studies of the primary TN published during 2013 to 2023, conducted for the assessment of the structural and microstructural analysis of the trigeminal nerve, and the structural, diffusion, and functional MRI analysis of the brain. Quantitative MRI studies of the affected trigeminal nerves and the trigeminal pathway demonstrated structural/microstructural alterations and treatment-related changes, which differentiated responders from nonresponders. Quantitative analysis of the brain revealed changes in the brain areas associated with pain processing/modulation and emotional networks. Studies of the affected nerve demonstrated evidence of demyelination and axonal damage, compatible with pathological findings, and have shown its potential value as a tool to assess treatment outcomes. Quantitative MRI has also revealed the possibility of dynamic microstructural, structural, and functional neuronal plasticity of the brain. Further studies are needed to understand these complex mechanisms of neuronal plasticity and to achieve a consensus on the clinical use of quantitative MRI in TN.
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Affiliation(s)
- Memi Watanabe
- Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Raj K Shrivastava
- Department of Neurosurgery, Mount Sinai Medical Center, New York, NY, United States
| | - Priti Balchandani
- Icahn School of Medicine at Mount Sinai, New York, NY, United States
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3
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Park J, Wang J, Guan W, Gjesteby LA, Pollack D, Kamentsky L, Evans NB, Stirman J, Gu X, Zhao C, Marx S, Kim ME, Choi SW, Snyder M, Chavez D, Su-Arcaro C, Tian Y, Park CS, Zhang Q, Yun DH, Moukheiber M, Feng G, Yang XW, Keene CD, Hof PR, Ghosh SS, Frosch MP, Brattain LJ, Chung K. Integrated platform for multiscale molecular imaging and phenotyping of the human brain. Science 2024; 384:eadh9979. [PMID: 38870291 DOI: 10.1126/science.adh9979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 04/22/2024] [Indexed: 06/15/2024]
Abstract
Understanding cellular architectures and their connectivity is essential for interrogating system function and dysfunction. However, we lack technologies for mapping the multiscale details of individual cells and their connectivity in the human organ-scale system. We developed a platform that simultaneously extracts spatial, molecular, morphological, and connectivity information of individual cells from the same human brain. The platform includes three core elements: a vibrating microtome for ultraprecision slicing of large-scale tissues without losing cellular connectivity (MEGAtome), a polymer hydrogel-based tissue processing technology for multiplexed multiscale imaging of human organ-scale tissues (mELAST), and a computational pipeline for reconstructing three-dimensional connectivity across multiple brain slabs (UNSLICE). We applied this platform for analyzing human Alzheimer's disease pathology at multiple scales and demonstrating scalable neural connectivity mapping in the human brain.
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Affiliation(s)
- Juhyuk Park
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Department of Chemical Engineering, MIT, Cambridge, MA 02139, USA
- Picower Institute for Learning and Memory, MIT, Cambridge, MA 02139, USA
- Center for Nanomedicine, Institute for Basic Science, Seoul 03722, Republic of Korea
| | - Ji Wang
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Picower Institute for Learning and Memory, MIT, Cambridge, MA 02139, USA
| | - Webster Guan
- Department of Chemical Engineering, MIT, Cambridge, MA 02139, USA
| | | | | | - Lee Kamentsky
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Picower Institute for Learning and Memory, MIT, Cambridge, MA 02139, USA
| | - Nicholas B Evans
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Picower Institute for Learning and Memory, MIT, Cambridge, MA 02139, USA
| | - Jeff Stirman
- LifeCanvas Technologies, Cambridge, MA 02141, USA
| | - Xinyi Gu
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA 02139, USA
| | - Chuanxi Zhao
- Picower Institute for Learning and Memory, MIT, Cambridge, MA 02139, USA
| | - Slayton Marx
- Picower Institute for Learning and Memory, MIT, Cambridge, MA 02139, USA
| | - Minyoung E Kim
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA
| | - Seo Woo Choi
- Department of Chemical Engineering, MIT, Cambridge, MA 02139, USA
| | | | - David Chavez
- MIT Lincoln Laboratory, Lexington, MA 02421, USA
| | - Clover Su-Arcaro
- Picower Institute for Learning and Memory, MIT, Cambridge, MA 02139, USA
| | - Yuxuan Tian
- Department of Chemical Engineering, MIT, Cambridge, MA 02139, USA
| | - Chang Sin Park
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience, University of California, Los Angeles, CA 90024, USA
| | - Qiangge Zhang
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA
| | - Dae Hee Yun
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA
| | - Mira Moukheiber
- Picower Institute for Learning and Memory, MIT, Cambridge, MA 02139, USA
| | - Guoping Feng
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA
| | - X William Yang
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience, University of California, Los Angeles, CA 90024, USA
| | - C Dirk Keene
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA 98115, USA
| | - Patrick R Hof
- Nash Family Department of Neuroscience, Center for Discovery and Innovation, and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10019, USA
| | - Satrajit S Ghosh
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA
- Department of Otolaryngology, Harvard Medical School, Boston, MA 02114, USA
| | - Matthew P Frosch
- C. S. Kubik Laboratory for Neuropathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | | | - Kwanghun Chung
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Department of Chemical Engineering, MIT, Cambridge, MA 02139, USA
- Picower Institute for Learning and Memory, MIT, Cambridge, MA 02139, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA
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4
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Calixto C, Soldatelli MD, Jaimes C, Warfield SK, Gholipour A, Karimi D. A detailed spatio-temporal atlas of the white matter tracts for the fetal brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.26.590815. [PMID: 38712296 PMCID: PMC11071632 DOI: 10.1101/2024.04.26.590815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
This study presents the construction of a comprehensive spatiotemporal atlas detailing the development of white matter tracts in the fetal brain using diffusion magnetic resonance imaging (dMRI). Our research leverages data collected from fetal MRI scans conducted between 22 and 37 weeks of gestation, capturing the dynamic changes in the brain's microstructure during this critical period. The atlas includes 60 distinct white matter tracts, including commissural, projection, and association fibers. We employed advanced fetal dMRI processing techniques and tractography to map and characterize the developmental trajectories of these tracts. Our findings reveal that the development of these tracts is characterized by complex patterns of fractional anisotropy (FA) and mean diffusivity (MD), reflecting key neurodevelopmental processes such as axonal growth, involution of the radial-glial scaffolding, and synaptic pruning. This atlas can serve as a useful resource for neuroscience research and clinical practice, improving our understanding of the fetal brain and potentially aiding in the early diagnosis of neurodevelopmental disorders. By detailing the normal progression of white matter tract development, the atlas can be used as a benchmark for identifying deviations that may indicate neurological anomalies or predispositions to disorders.
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Affiliation(s)
- Camilo Calixto
- Computational Radiology Laboratory (CRL), Boston Children's Hospital, Harvard Medical School
| | | | - Camilo Jaimes
- Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114, USA
| | - Simon K Warfield
- Computational Radiology Laboratory (CRL), Boston Children's Hospital, Harvard Medical School
| | - Ali Gholipour
- Computational Radiology Laboratory (CRL), Boston Children's Hospital, Harvard Medical School
| | - Davood Karimi
- Computational Radiology Laboratory (CRL), Boston Children's Hospital, Harvard Medical School
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5
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Magondo N, Meintjes EM, Warton FL, Little F, van der Kouwe AJW, Laughton B, Jankiewicz M, Holmes MJ. Distinct alterations in white matter properties and organization related to maternal treatment initiation in neonates exposed to HIV but uninfected. Sci Rep 2024; 14:8822. [PMID: 38627570 PMCID: PMC11021525 DOI: 10.1038/s41598-024-58339-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 03/27/2024] [Indexed: 04/19/2024] Open
Abstract
HIV exposed-uninfected (HEU) infants and children are at risk of developmental delays as compared to HIV uninfected unexposed (HUU) populations. The effects of exposure to in utero HIV and ART regimens on the HEU the developing brain are not well understood. In a cohort of 2-week-old newborns, we used diffusion tensor imaging (DTI) tractography and graph theory to examine the influence of HIV and ART exposure in utero on neonate white matter integrity and organisation. The cohort included HEU infants born to mothers who started ART before conception (HEUpre) and after conception (HEUpost), as well as HUU infants from the same community. We investigated HIV exposure and ART duration group differences in DTI metrics (fractional anisotropy (FA) and mean diffusivity (MD)) and graph measures across white matter. We found increased MD in white matter connections involving the thalamus and limbic system in the HEUpre group compared to HUU. We further identified reduced nodal efficiency in the basal ganglia. Within the HEUpost group, we observed reduced FA in cortical-subcortical and cerebellar connections as well as decreased transitivity in the hindbrain area compared to HUU. Overall, our analysis demonstrated distinct alterations in white matter integrity related to the timing of maternal ART initiation that influence regional brain network properties.
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Affiliation(s)
- Ndivhuwo Magondo
- Division of Biomedical Engineering, Department of Human Biology, Faculty of Health Sciences, Biomedical Engineering Research Centre, University of Cape Town, Cape Town, South Africa.
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa.
| | - Ernesta M Meintjes
- Division of Biomedical Engineering, Department of Human Biology, Faculty of Health Sciences, Biomedical Engineering Research Centre, University of Cape Town, Cape Town, South Africa.
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa.
- Cape Universities Body Imaging Centre, University of Cape Town, Cape Town, South Africa.
| | - Fleur L Warton
- Division of Biomedical Engineering, Department of Human Biology, Faculty of Health Sciences, Biomedical Engineering Research Centre, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Francesca Little
- Department of Statistical Sciences, University of Cape Town, Cape Town, South Africa
| | - Andre J W van der Kouwe
- Division of Biomedical Engineering, Department of Human Biology, Faculty of Health Sciences, Biomedical Engineering Research Centre, University of Cape Town, Cape Town, South Africa
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MI, USA
| | - Barbara Laughton
- Department of Paediatrics and Child Health and Tygerberg Children's Hospital, Faculty of Medicine and Health Sciences, Family Centre for Research with Ubuntu, Stellenbosch University, Stellenbosch, South Africa
| | - Marcin Jankiewicz
- Division of Biomedical Engineering, Department of Human Biology, Faculty of Health Sciences, Biomedical Engineering Research Centre, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- Cape Universities Body Imaging Centre, University of Cape Town, Cape Town, South Africa
- ImageTech, Simon Fraser University, Surrey, BC, Canada
| | - Martha J Holmes
- Division of Biomedical Engineering, Department of Human Biology, Faculty of Health Sciences, Biomedical Engineering Research Centre, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada
- ImageTech, Simon Fraser University, Surrey, BC, Canada
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6
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Brooks SJ, Jones VO, Wang H, Deng C, Golding SGH, Lim J, Gao J, Daoutidis P, Stamoulis C. Community detection in the human connectome: Method types, differences and their impact on inference. Hum Brain Mapp 2024; 45:e26669. [PMID: 38553865 PMCID: PMC10980844 DOI: 10.1002/hbm.26669] [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: 08/25/2023] [Revised: 03/06/2024] [Accepted: 03/12/2024] [Indexed: 04/02/2024] Open
Abstract
Community structure is a fundamental topological characteristic of optimally organized brain networks. Currently, there is no clear standard or systematic approach for selecting the most appropriate community detection method. Furthermore, the impact of method choice on the accuracy and robustness of estimated communities (and network modularity), as well as method-dependent relationships between network communities and cognitive and other individual measures, are not well understood. This study analyzed large datasets of real brain networks (estimated from resting-state fMRI fromn $$ n $$ = 5251 pre/early adolescents in the adolescent brain cognitive development [ABCD] study), andn $$ n $$ = 5338 synthetic networks with heterogeneous, data-inspired topologies, with the goal to investigate and compare three classes of community detection methods: (i) modularity maximization-based (Newman and Louvain), (ii) probabilistic (Bayesian inference within the framework of stochastic block modeling (SBM)), and (iii) geometric (based on graph Ricci flow). Extensive comparisons between methods and their individual accuracy (relative to the ground truth in synthetic networks), and reliability (when applied to multiple fMRI runs from the same brains) suggest that the underlying brain network topology plays a critical role in the accuracy, reliability and agreement of community detection methods. Consistent method (dis)similarities, and their correlations with topological properties, were estimated across fMRI runs. Based on synthetic graphs, most methods performed similarly and had comparable high accuracy only in some topological regimes, specifically those corresponding to developed connectomes with at least quasi-optimal community organization. In contrast, in densely and/or weakly connected networks with difficult to detect communities, the methods yielded highly dissimilar results, with Bayesian inference within SBM having significantly higher accuracy compared to all others. Associations between method-specific modularity and demographic, anthropometric, physiological and cognitive parameters showed mostly method invariance but some method dependence as well. Although method sensitivity to different levels of community structure may in part explain method-dependent associations between modularity estimates and parameters of interest, method dependence also highlights potential issues of reliability and reproducibility. These findings suggest that a probabilistic approach, such as Bayesian inference in the framework of SBM, may provide consistently reliable estimates of community structure across network topologies. In addition, to maximize robustness of biological inferences, identified network communities and their cognitive, behavioral and other correlates should be confirmed with multiple reliable detection methods.
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Affiliation(s)
- Skylar J. Brooks
- Boston Children's HospitalDepartment of PediatricsBostonMassachusettsUSA
- University of California BerkeleyHelen Wills Neuroscience InstituteBerkeleyCaliforniaUSA
| | - Victoria O. Jones
- University of MinnesotaDepartment of Chemical Engineering and Material ScienceMinneapolisMinnesotaUSA
| | - Haotian Wang
- Rutgers UniversityDepartment of Computer SciencePiscatawayNew JerseyUSA
| | - Chengyuan Deng
- Rutgers UniversityDepartment of Computer SciencePiscatawayNew JerseyUSA
| | | | - Jethro Lim
- Boston Children's HospitalDepartment of PediatricsBostonMassachusettsUSA
| | - Jie Gao
- Rutgers UniversityDepartment of Computer SciencePiscatawayNew JerseyUSA
| | - Prodromos Daoutidis
- University of MinnesotaDepartment of Chemical Engineering and Material ScienceMinneapolisMinnesotaUSA
| | - Catherine Stamoulis
- Boston Children's HospitalDepartment of PediatricsBostonMassachusettsUSA
- Harvard Medical SchoolDepartment of PediatricsBostonMassachusettsUSA
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7
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Mecklenbrauck F, Gruber M, Siestrup S, Zahedi A, Grotegerd D, Mauritz M, Trempler I, Dannlowski U, Schubotz RI. The significance of structural rich club hubs for the processing of hierarchical stimuli. Hum Brain Mapp 2024; 45:e26543. [PMID: 38069537 PMCID: PMC10915744 DOI: 10.1002/hbm.26543] [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: 06/09/2023] [Revised: 10/17/2023] [Accepted: 11/09/2023] [Indexed: 03/07/2024] Open
Abstract
The brain's structural network follows a hierarchy that is described as rich club (RC) organization, with RC hubs forming the well-interconnected top of this hierarchy. In this study, we tested whether RC hubs are involved in the processing of hierarchically higher structures in stimulus sequences. Moreover, we explored the role of previously suggested cortical gradients along anterior-posterior and medial-lateral axes throughout the frontal cortex. To this end, we conducted a functional magnetic resonance imaging (fMRI) experiment and presented participants with blocks of digit sequences that were structured on different hierarchically nested levels. We additionally collected diffusion weighted imaging data of the same subjects to identify RC hubs. This classification then served as the basis for a region of interest analysis of the fMRI data. Moreover, we determined structural network centrality measures in areas that were found as activation clusters in the whole-brain fMRI analysis. Our findings support the previously found anterior and medial shift for processing hierarchically higher structures of stimuli. Additionally, we found that the processing of hierarchically higher structures of the stimulus structure engages RC hubs more than for lower levels. Areas involved in the functional processing of hierarchically higher structures were also more likely to be part of the structural RC and were furthermore more central to the structural network. In summary, our results highlight the potential role of the structural RC organization in shaping the cortical processing hierarchy.
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Affiliation(s)
- Falko Mecklenbrauck
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
| | - Marius Gruber
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
- Department for Psychiatry, Psychosomatic Medicine and PsychotherapyUniversity Hospital Frankfurt, Goethe UniversityFrankfurtGermany
| | - Sophie Siestrup
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
| | - Anoushiravan Zahedi
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
| | - Dominik Grotegerd
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | - Marco Mauritz
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
- Institute for Computational and Applied MathematicsUniversity of MünsterMünsterGermany
| | - Ima Trempler
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
| | - Udo Dannlowski
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | - Ricarda I. Schubotz
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
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8
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Park CH, Durand-Ruel M, Moyne M, Morishita T, Hummel FC. Brain connectome correlates of short-term motor learning in healthy older subjects. Cortex 2024; 171:247-256. [PMID: 38043242 DOI: 10.1016/j.cortex.2023.09.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 03/28/2023] [Accepted: 09/25/2023] [Indexed: 12/05/2023]
Abstract
The motor learning process entails plastic changes in the brain, especially in brain network reconfigurations. In the current study, we sought to characterize motor learning by determining changes in the coupling behaviour between the brain functional and structural connectomes on a short timescale. 39 older subjects (age: mean (SD) = 69.7 (4.7) years, men:women = 15:24) were trained on a visually guided sequential hand grip learning task. The brain structural and functional connectomes were constructed from diffusion-weighted MRI and resting-state functional MRI, respectively. The association of motor learning ability with changes in network topology of the brain functional connectome and changes in the correspondence between the brain structural and functional connectomes were assessed. Motor learning ability was related to decreased efficiency and increased modularity in the visual, somatomotor, and frontoparietal networks of the brain functional connectome. Between the brain structural and functional connectomes, reduced correspondence in the visual, ventral attention, and frontoparietal networks as well as the whole-brain network was related to motor learning ability. In addition, structure-function correspondence in the dorsal attention, ventral attention, and frontoparietal networks before motor learning was predictive of motor learning ability. These findings indicate that, in the view of brain connectome changes, short-term motor learning is represented by a detachment of the brain functional from the brain structural connectome. The structure-function uncoupling accompanied by the enhanced segregation into modular structures over the core functional networks involved in the learning process may suggest that facilitation of functional flexibility is associated with successful motor learning.
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Affiliation(s)
- Chang-Hyun Park
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland; Defitech Chair for Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne EPFL Valais, Clinique Romande de Réadaptation Sion, Switzerland
| | - Manon Durand-Ruel
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland; Defitech Chair for Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne EPFL Valais, Clinique Romande de Réadaptation Sion, Switzerland
| | - Maëva Moyne
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland; Defitech Chair for Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne EPFL Valais, Clinique Romande de Réadaptation Sion, Switzerland; Clinical Neuroscience, University of Geneva Medical School, Geneva, Switzerland
| | - Takuya Morishita
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland; Defitech Chair for Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne EPFL Valais, Clinique Romande de Réadaptation Sion, Switzerland
| | - Friedhelm C Hummel
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland; Defitech Chair for Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne EPFL Valais, Clinique Romande de Réadaptation Sion, Switzerland; Clinical Neuroscience, University of Geneva Medical School, Geneva, Switzerland.
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9
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Magondo N, Meintjes EM, Warton FL, Little F, van der Kouwe AJ, Laughton B, Jankiewicz M, Holmes MJ. Distinct alterations in white matter properties and organization related to maternal treatment initiation in neonates exposed to HIV but uninfected. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.11.575169. [PMID: 38260347 PMCID: PMC10802593 DOI: 10.1101/2024.01.11.575169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
HIV exposed-uninfected (HEU) infants and children are at risk of developmental delays as compared to uninfected unexposed (HUU) populations. The effects of exposure to in utero HIV and ART regimens on the HEU the developing brain are not well understood. In a cohort of 2-week-old newborns, we used diffusion tensor imaging (DTI) tractography and graph theory to examine the influence of HIV and ART exposure in utero on neonate white matter integrity and organisation. The cohort included HEU infants born to mothers who started ART before conception (HEUpre) and after conception (HEUpost), as well as HUU infants from the same community. We investigated HIV exposure and ART duration group differences in DTI metrics (fractional anisotropy (FA) and mean diffusivity (MD)) and graph measures across white matter. We found increased MD in white matter connections involving the thalamus and limbic system in the HEUpre group compared to HUU. We further identified reduced nodal efficiency in the basal ganglia. Within the HEUpost group, we observed reduced FA in cortical-subcortical and cerebellar connections as well as decreased transitivity in the hindbrain area compared to HUU. Overall, our analysis demonstrated distinct alterations in white matter integrity related to the timing of maternal ART initiation that influence regional brain network properties.
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Affiliation(s)
- Ndivhuwo Magondo
- Biomedical Engineering Research Centre, Division of Biomedical Engineering, Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Ernesta M. Meintjes
- Biomedical Engineering Research Centre, Division of Biomedical Engineering, Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- Cape Universities Body Imaging Centre, University of Cape Town, Cape Town, South Africa
| | - Fleur L. Warton
- Biomedical Engineering Research Centre, Division of Biomedical Engineering, Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Francesca Little
- Department of Statistical Sciences, University of Cape Town, Cape Town, South Africa
| | - Andre J.W. van der Kouwe
- Biomedical Engineering Research Centre, Division of Biomedical Engineering, Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA,USA
- Department of Radiology, Harvard Medical School, Boston, MI, USA
| | - Barbara Laughton
- Family Centre for Research with Ubuntu, Department of Paediatrics and Child Health and Tygerberg Children’s Hospital, Faculty of Medicine and Health Sciences, Stellenbosch University, Stellenbosch,South Africa
| | - Marcin Jankiewicz
- Biomedical Engineering Research Centre, Division of Biomedical Engineering, Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- Cape Universities Body Imaging Centre, University of Cape Town, Cape Town, South Africa
- ImageTech, Simon Fraser University, Surrey, BC, Canada
| | - Martha J. Holmes
- Biomedical Engineering Research Centre, Division of Biomedical Engineering, Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada
- ImageTech, Simon Fraser University, Surrey, BC, Canada
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10
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Kotlarz P, Lankinen K, Hakonen M, Turpin T, Polimeni JR, Ahveninen J. Multilayer Network Analysis across Cortical Depths in Resting-State 7T fMRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.23.573208. [PMID: 38187540 PMCID: PMC10769454 DOI: 10.1101/2023.12.23.573208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
In graph theory, "multilayer networks" represent systems involving several interconnected topological levels. A neuroscience example is the hierarchy of connections between different cortical depths or "lamina". This hierarchy is becoming non-invasively accessible in humans using ultra-high-resolution functional MRI (fMRI). Here, we applied multilayer graph theory to examine functional connectivity across different cortical depths in humans, using 7T fMRI (1-mm3 voxels; 30 participants). Blood oxygenation level dependent (BOLD) signals were derived from five depths between the white matter and pial surface. We then compared networks where the inter-regional connections were limited to a single cortical depth only ("layer-by-layer matrices") to those considering all possible connections between regions and cortical depths ("multilayer matrix"). We utilized global and local graph theory features that quantitatively characterize network attributes such as network composition, nodal centrality, path-based measures, and hub segregation. Detecting functional differences between cortical depths was improved using multilayer connectomics compared to the layer-by-layer versions. Superficial aspects of the cortex dominated information transfer and deeper aspects clustering. These differences were largest in frontotemporal and limbic brain regions. fMRI functional connectivity across different cortical depths may contain neurophysiologically relevant information. Multilayer connectomics could provide a methodological framework for studies on how information flows across this hierarchy.
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Affiliation(s)
- Parker Kotlarz
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Kaisu Lankinen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Maria Hakonen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | | | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jyrki Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
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11
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Zuo C, Suo X, Lan H, Pan N, Wang S, Kemp GJ, Gong Q. Global Alterations of Whole Brain Structural Connectome in Parkinson's Disease: A Meta-analysis. Neuropsychol Rev 2023; 33:783-802. [PMID: 36125651 PMCID: PMC10770271 DOI: 10.1007/s11065-022-09559-y] [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: 10/29/2021] [Accepted: 06/14/2022] [Indexed: 10/14/2022]
Abstract
Recent graph-theoretical studies of Parkinson's disease (PD) have examined alterations in the global properties of the brain structural connectome; however, reported alterations are not consistent. The present study aimed to identify the most robust global metric alterations in PD via a meta-analysis. A comprehensive literature search was conducted for all available diffusion MRI structural connectome studies that compared global graph metrics between PD patients and healthy controls (HC). Hedges' g effect sizes were calculated for each study and then pooled using a random-effects model in Comprehensive Meta-Analysis software, and the effects of potential moderator variables were tested. A total of 22 studies met the inclusion criteria for review. Of these, 16 studies reporting 10 global graph metrics (916 PD patients; 560 HC) were included in the meta-analysis. In the structural connectome of PD patients compared with HC, we found a significant decrease in clustering coefficient (g = -0.357, P = 0.005) and global efficiency (g = -0.359, P < 0.001), and a significant increase in characteristic path length (g = 0.250, P = 0.006). Dopaminergic medication, sex and age of patients were potential moderators of global brain network changes in PD. These findings provide evidence of decreased global segregation and integration of the structural connectome in PD, indicating a shift from a balanced small-world network to 'weaker small-worldization', which may provide useful markers of the pathophysiological mechanisms underlying PD.
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Affiliation(s)
- Chao Zuo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Xueling Suo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Huan Lan
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Nanfang Pan
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Song Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
| | - Graham J Kemp
- Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China.
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China.
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12
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Shahbodaghy F, Shafaghi L, Rostampour M, Rostampour A, Kolivand P, Gharaylou Z. Symmetry differences of structural connectivity in multiple sclerosis and healthy state. Brain Res Bull 2023; 205:110816. [PMID: 37972899 DOI: 10.1016/j.brainresbull.2023.110816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 10/27/2023] [Accepted: 11/13/2023] [Indexed: 11/19/2023]
Abstract
Focal and diffuse cerebral damages occur in Multiple Sclerosis (MS) that promotes profound shifts in local and global structural connectivity parameters, mainly derived from diffusion tensor imaging. Most of the reconstruction analyses have applied conventional tracking algorithms largely based on the controversial streamline count. For a more credible explanation of the diffusion MRI signal, we used convex optimization modeling for the microstructure-informed tractography2 (COMMIT2) framework. All multi-shell diffusion data from 40 healthy controls (HCs) and 40 relapsing-remitting MS (RRMS) patients were transformed into COMMIT2-weighted matrices based on the Schefer-200 parcels atlas (7 networks) and 14 bilateral subcortical regions. The success of the classification process between MS and healthy state was efficiently predicted by the left DMN-related structures and visual network-associated pathways. Additionally, the lesion volume and age of onset were remarkably correlated with the components of the left DMN. Using complementary approaches such as global metrics revealed differences in WM microstructural integrity between MS and HCs (efficiency, strength). Our findings demonstrated that the cutting-edge diffusion MRI biomarkers could hold the potential for interpreting brain abnormalities in a more distinctive way.
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Affiliation(s)
- Fatemeh Shahbodaghy
- Biomedical Engineering Department, Amirkabir University of Technology, Tehran, Iran
| | - Lida Shafaghi
- Department of Neuroscience, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Massoumeh Rostampour
- Sleep Disorders Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Ali Rostampour
- Department of Computer Engineering and Information Technology, Payame Noor University, Tehran, Iran
| | - Pirhossein Kolivand
- Department of Health Economics, School of Medicine, Shahed University, Tehran, Iran
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13
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Seguin C, Sporns O, Zalesky A. Brain network communication: concepts, models and applications. Nat Rev Neurosci 2023; 24:557-574. [PMID: 37438433 DOI: 10.1038/s41583-023-00718-5] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/08/2023] [Indexed: 07/14/2023]
Abstract
Understanding communication and information processing in nervous systems is a central goal of neuroscience. Over the past two decades, advances in connectomics and network neuroscience have opened new avenues for investigating polysynaptic communication in complex brain networks. Recent work has brought into question the mainstay assumption that connectome signalling occurs exclusively via shortest paths, resulting in a sprawling constellation of alternative network communication models. This Review surveys the latest developments in models of brain network communication. We begin by drawing a conceptual link between the mathematics of graph theory and biological aspects of neural signalling such as transmission delays and metabolic cost. We organize key network communication models and measures into a taxonomy, aimed at helping researchers navigate the growing number of concepts and methods in the literature. The taxonomy highlights the pros, cons and interpretations of different conceptualizations of connectome signalling. We showcase the utility of network communication models as a flexible, interpretable and tractable framework to study brain function by reviewing prominent applications in basic, cognitive and clinical neurosciences. Finally, we provide recommendations to guide the future development, application and validation of network communication models.
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Affiliation(s)
- Caio Seguin
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia.
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA.
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, USA
- Program in Cognitive Science, Indiana University, Bloomington, IN, USA
- Indiana University Network Science Institute, Indiana University, Bloomington, IN, USA
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia
- Department of Biomedical Engineering, Melbourne School of Engineering, University of Melbourne, Melbourne, Victoria, Australia
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14
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Borrelli P, Savini G, Cavaliere C, Palesi F, Grazia Bruzzone M, Aquino D, Biagi L, Bosco P, Carne I, Ferraro S, Giulietti G, Napolitano A, Nigri A, Pavone L, Pirastru A, Redolfi A, Tagliavini F, Tosetti M, Salvatore M, Gandini Wheeler-Kingshott CAM, Aiello M. Normative values of the topological metrics of the structural connectome: A multi-site reproducibility study across the Italian Neuroscience network. Phys Med 2023; 112:102610. [PMID: 37331082 DOI: 10.1016/j.ejmp.2023.102610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 03/20/2023] [Accepted: 05/30/2023] [Indexed: 06/20/2023] Open
Abstract
PURPOSE The use of topological metrics to derive quantitative descriptors from structural connectomes is receiving increasing attention but deserves specific studies to investigate their reproducibility and variability in the clinical context. This work exploits the harmonization of diffusion-weighted acquisition for neuroimaging data performed by the Italian Neuroscience and Neurorehabilitation Network initiative to obtain normative values of topological metrics and to investigate their reproducibility and variability across centers. METHODS Different topological metrics, at global and local level, were calculated on multishell diffusion-weighted data acquired at high-field (e.g. 3 T) Magnetic Resonance Imaging scanners in 13 different centers, following the harmonization of the acquisition protocol, on young and healthy adults. A "traveling brains" dataset acquired on a subgroup of subjects at 3 different centers was also analyzed as reference data. All data were processed following a common processing pipeline that includes data pre-processing, tractography, generation of structural connectomes and calculation of graph-based metrics. The results were evaluated both with statistical analysis of variability and consistency among sites with the traveling brains range. In addition, inter-site reproducibility was assessed in terms of intra-class correlation variability. RESULTS The results show an inter-center and inter-subject variability of <10%, except for "clustering coefficient" (variability of 30%). Statistical analysis identifies significant differences among sites, as expected given the wide range of scanners' hardware. CONCLUSIONS The results show low variability of connectivity topological metrics across sites running a harmonised protocol.
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Affiliation(s)
| | | | | | - Fulvia Palesi
- Department of Brain and Behavioral Sciences, Università degli Studi di Pavia, Pavia, Italy
| | - Maria Grazia Bruzzone
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Domenico Aquino
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Laura Biagi
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris Foundation, Pisa, Italy
| | - Paolo Bosco
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris Foundation, Pisa, Italy
| | - Irene Carne
- Neuroradiology Unit, IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy
| | - Stefania Ferraro
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Giovanni Giulietti
- Neuroimaging Laboratory, IRCCS Santa Lucia Foundation, Rome, Italy; SAIMLAL Department, Sapienza University of Rome, Rome, Italy
| | - Antonio Napolitano
- Medical Physics, IRCCS Istituto Ospedale Pediatrico Bambino Gesù, Rome, Italy
| | - Anna Nigri
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | | | | | - Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Fabrizio Tagliavini
- Scientific Direction, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Michela Tosetti
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris Foundation, Pisa, Italy
| | | | - Claudia A M Gandini Wheeler-Kingshott
- Department of Brain and Behavioral Sciences, Università degli Studi di Pavia, Pavia, Italy; NMR Research Unit, Department of Neuroinflammation, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
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15
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Sone D. White Matter Structural Connectivity and Its Impact on Psychogenic Non-Epileptic Seizures: An Evidence-Based Review. Neuropsychiatr Dis Treat 2023; 19:1573-1579. [PMID: 37457838 PMCID: PMC10349606 DOI: 10.2147/ndt.s402378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 07/06/2023] [Indexed: 07/18/2023] Open
Abstract
Psychiatric non-epileptic seizure (PNES), also known as a form of functional neurological disorders (FND), is a common but still underrecognized disorder presenting seizure-like symptoms and no electrophysiological abnormality. Despite the significant burden of this disorder, the neurobiological mechanisms are not clearly understood, which hinders the development of better diagnosis and treatment. In the recent neuroimaging research on PNES, brain network analysis has become a relevant topic beyond conventional methodologies. The human brain is a highly intricate system of interconnected regions that collaborate to facilitate a wide range of cognitive and behavioral functions. White matter tracts, which are comprised of bundles of axonal fibers, are the primary means by which information is transmitted between different brain regions. As such, comprehending the organization and structure of the brain's white matter network is critical for gaining insight into its functional architecture. This review article aims to provide an overview of the brain mechanisms underlying PNES, with a special focus on analyzing brain networks.
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Affiliation(s)
- Daichi Sone
- Department of Psychiatry, Jikei University School of Medicine, Tokyo, Japan
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16
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Nabulsi L, Chandio BQ, Dhinagar N, Laltoo E, McPhilemy G, Martyn FM, Hallahan B, McDonald C, Thompson PM, Cannon DM. Along-Tract Statistical Mapping of Microstructural Abnormalities in Bipolar Disorder: A Pilot Study. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-7. [PMID: 38083303 DOI: 10.1109/embc40787.2023.10339964] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Investigating brain circuitry involved in bipolar disorder (BD) is key to discovering brain biomarkers for genetic and interventional studies of the disorder. Even so, prior research has not provided a fine-scale spatial mapping of brain microstructural differences in BD. In this pilot diffusion MRI dataset, we used BUndle ANalytics (BUAN)-a recently developed analytic approach for tractography-to extract, map, and visualize the profile of microstructural abnormalities on a 3D model of fiber tracts in people with BD (N=38) and healthy controls (N=49), and investigate along-tract white matter (WM) microstructural differences between these groups. Using the BUAN pipeline, BD was associated with lower mean fractional anisotropy (FA) in fronto-limbic and interhemispheric pathways and higher mean FA in posterior bundles relative to controls.Clinical Relevance- BUAN combines tractography and anatomical information to capture distinct along-tract effects on WM microstructure that may aid in classifying diseases based on anatomical differences.
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17
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Schmithorst V, Ceschin R, Lee V, Wallace J, Sahel A, Chenevert TL, Parmar H, Berman JI, Vossough A, Qiu D, Kadom N, Grant PE, Gagoski B, LaViolette PS, Maheshwari M, Sleeper LA, Bellinger DC, Ilardi D, O’Neil S, Miller TA, Detterich J, Hill KD, Atz AM, Richmond ME, Cnota J, Mahle WT, Ghanayem NS, Gaynor JW, Goldberg CS, Newburger JW, Panigrahy A. Single Ventricle Reconstruction III: Brain Connectome and Neurodevelopmental Outcomes: Design, Recruitment, and Technical Challenges of a Multicenter, Observational Neuroimaging Study. Diagnostics (Basel) 2023; 13:1604. [PMID: 37174995 PMCID: PMC10178603 DOI: 10.3390/diagnostics13091604] [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: 04/05/2023] [Revised: 04/25/2023] [Accepted: 04/27/2023] [Indexed: 05/15/2023] Open
Abstract
Patients with hypoplastic left heart syndrome who have been palliated with the Fontan procedure are at risk for adverse neurodevelopmental outcomes, lower quality of life, and reduced employability. We describe the methods (including quality assurance and quality control protocols) and challenges of a multi-center observational ancillary study, SVRIII (Single Ventricle Reconstruction Trial) Brain Connectome. Our original goal was to obtain advanced neuroimaging (Diffusion Tensor Imaging and Resting-BOLD) in 140 SVR III participants and 100 healthy controls for brain connectome analyses. Linear regression and mediation statistical methods will be used to analyze associations of brain connectome measures with neurocognitive measures and clinical risk factors. Initial recruitment challenges occurred that were related to difficulties with: (1) coordinating brain MRI for participants already undergoing extensive testing in the parent study, and (2) recruiting healthy control subjects. The COVID-19 pandemic negatively affected enrollment late in the study. Enrollment challenges were addressed by: (1) adding additional study sites, (2) increasing the frequency of meetings with site coordinators, and (3) developing additional healthy control recruitment strategies, including using research registries and advertising the study to community-based groups. Technical challenges that emerged early in the study were related to the acquisition, harmonization, and transfer of neuroimages. These hurdles were successfully overcome with protocol modifications and frequent site visits that involved human and synthetic phantoms.
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Affiliation(s)
- Vanessa Schmithorst
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Avenue, Floor 2, Pittsburgh, PA 15224, USA
| | - Rafael Ceschin
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Avenue, Floor 2, Pittsburgh, PA 15224, USA
- Department of Biomedical Informatics, University of Pittsburgh School, 5607 Baum Blvd., Pittsburgh, PA 15206, USA
| | - Vincent Lee
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Avenue, Floor 2, Pittsburgh, PA 15224, USA
| | - Julia Wallace
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Avenue, Floor 2, Pittsburgh, PA 15224, USA
| | - Aurelia Sahel
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Avenue, Floor 2, Pittsburgh, PA 15224, USA
| | - Thomas L. Chenevert
- Michigan Medicine Department of Radiology, University of Michigan, 1500 E Medical Center Dr., Ann Arbor, MI 48109, USA
| | - Hemant Parmar
- Michigan Medicine Department of Radiology, University of Michigan, 1500 E Medical Center Dr., Ann Arbor, MI 48109, USA
| | - Jeffrey I. Berman
- Department of Radiology, Children’s Hospital of Philadelphia, 3401 Civic Center Blvd., Philadelphia, PA 19104, USA
| | - Arastoo Vossough
- Department of Radiology, Children’s Hospital of Philadelphia, 3401 Civic Center Blvd., Philadelphia, PA 19104, USA
| | - Deqiang Qiu
- Department of Radiology and Imaging Sciences, Children’s Healthcare of Atlanta, Emory University, 1364 Clifton Rd, Atlanta, GA 30322, USA
| | - Nadja Kadom
- Department of Radiology and Imaging Sciences, Children’s Healthcare of Atlanta, Emory University, 1364 Clifton Rd, Atlanta, GA 30322, USA
| | - Patricia Ellen Grant
- Children’s Hospital Boston, Fetal-Neonatal Neuroimaging and Developmental Science Center (FNNDSC), 300 Longwood Avenue, Boston, MA 02115, USA
| | - Borjan Gagoski
- Department of Radiology, Children’s Hospital Boston, 300 Longwood Avenue, Boston, MA 02115, USA
| | - Peter S. LaViolette
- Department of Radiology, Medical College of Wisconsin, 9200 W Wisconsin Avenue, Milwaukee, WI 53226, USA
| | - Mohit Maheshwari
- Department of Radiology, Medical College of Wisconsin, 9200 W Wisconsin Avenue, Milwaukee, WI 53226, USA
| | - Lynn A. Sleeper
- Department of Cardiology, Boston Children’s Hospital, 300 Longwood Avenue, Boston, MA 02115, USA
- Department of Pediatrics, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - David C. Bellinger
- Cardiac Neurodevelopmental Program, Department of Neurology, Boston Children’s Hospital, 300 Longwood Avenue, Boston, MA 02115, USA
| | - Dawn Ilardi
- Department of Neuropsychology, Children’s Healthcare of Atlanta, 1400 Tullie Road NE, Atlanta, GA 30329, USA
| | - Sharon O’Neil
- Children’s Hospital Los Angeles, Neuropsychology Core of the Saban Research Institute, 4661 Sunset Blvd., Los Angeles, CA 90027, USA
| | - Thomas A. Miller
- Division of Pediatric Cardiology, Department of Pediatrics, University of Utah School of Medicine, 30 N 1900 E, Salt Lake City, UT 84132, USA
| | - Jon Detterich
- Division of Pediatric Cardiology, Children’s Hospital Los Angeles, 4650 Sunset Blvd., Los Angeles, CA 90027, USA
| | - Kevin D. Hill
- Division of Pediatric Cardiology, Department of Pediatrics, Duke University School of Medicine, 7506 Hospital North, DUMC Box 3090, Durham, NC 27710, USA
| | - Andrew M. Atz
- Division of Pediatric Cardiology, Medical University of South Carolina, 96 Jonathan Lucas St. Ste. 601, MSC 617, Charleston, SC 29425, USA
| | - Marc E. Richmond
- Program for Pediatric Cardiomyopathy, Heart Failure, and Transplantation, New York-Presbyterian Morgan Stanley Children’s Hospital, 3959 Broadway MSCH North, 2nd Floor, New York, NY 10032, USA
| | - James Cnota
- Fetal Heart Program, Cincinnati Children’s, 3333 Burnet Avenue, Cincinnati, OH 45229, USA
| | - William T. Mahle
- Division of Pediatric Cardiology, Children’s Healthcare of Atlanta, 1400 Tullie Rd NE Suite 630, Atlanta, GA 30329, USA
| | - Nancy S. Ghanayem
- Section of Pediatric Critical Care, Department of Pediatrics, Comer Children’s Hospital, University of Chicago Medicine, 5721 S. Maryland Avenue, Chicago, IL 60637, USA
- Department of Pediatrics, Medical College of Wisconsin Section of Pediatric Critical Care, 9000 W. Wisconsin Avenue MS 681, Milwaukee, WI 53226, USA
| | - J. William Gaynor
- Heart Failure and Transplant Program, Children’s Hospital of Philadelphia, 3401 Civic Center Blvd., Philadelphia, PA 19104, USA
| | - Caren S. Goldberg
- Department of Pediatrics, Division of Cardiology, C.S. Mott Children’s Hospital, 1540 E Hospital Dr #4204, Ann Arbor, MI 48109, USA
| | - Jane W. Newburger
- Department of Cardiology, Boston Children’s Hospital, 300 Longwood Avenue, Boston, MA 02115, USA
| | - Ashok Panigrahy
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Avenue, Floor 2, Pittsburgh, PA 15224, USA
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18
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Cheng P, Li Y, Wang G, Dong H, Liu H, Shen W, Zhou W. Aberrant topology of white matter networks in patients with methamphetamine dependence and its application in support vector machine-based classification. Sci Rep 2023; 13:6958. [PMID: 37117256 PMCID: PMC10147725 DOI: 10.1038/s41598-023-33199-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Accepted: 04/08/2023] [Indexed: 04/30/2023] Open
Abstract
Brain white matter (WM) networks have been widely studied in neuropsychiatric disorders. However, few studies have evaluated alterations in WM network topological organization in patients with methamphetamine (MA) dependence. Therefore, using machine learning classification methods to analyze WM network topological attributes may give new insights into patients with MA dependence. In the study, diffusion tensor imaging-based probabilistic tractography was used to map the weighted WM networks in 46 MA-dependent patients and 46 control subjects. Using graph-theoretical analyses, the global and regional topological attributes of WM networks for both groups were calculated and compared to determine inter-group differences using a permutation-based general linear model. In addition, the study used a support vector machine (SVM) learning approach to construct a classifier for discriminating subjects with MA dependence from control subjects. Relative to the control group, the MA-dependent group exhibited abnormal topological organization, as evidenced by decreased small-worldness and modularity, and increased nodal efficiency in the right medial superior temporal gyrus, right pallidum, and right ventromedial putamen; the MA-dependent group had the higher hubness scores in 25 regions, which were mainly located in the default mode network. An SVM trained with topological attributes achieved classification accuracy, sensitivity, specificity, and kappa values of 98.09% ± 2.59%, 98.24% ± 4.00%, 97.94% ± 4.26%, and 96.18% ± 5.19% for patients with MA dependence. Our results may suggest altered global WM structural networks in MA-dependent patients. Furthermore, the abnormal WM network topological attributes may provide promising features for the construction of high-efficacy classification models.
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Affiliation(s)
- Ping Cheng
- Department of Radiology, Ningbo Medical Treatment Center Lihuili Hospital, Ningbo University, 57# Xing Ning Road, Ningbo, Zhejiang, China
| | - Yadi Li
- Department of Radiology, Ningbo Medical Treatment Center Lihuili Hospital, Ningbo University, 57# Xing Ning Road, Ningbo, Zhejiang, China.
| | - Gaoyan Wang
- Department of Radiology, Ningbo Medical Treatment Center Lihuili Hospital, Ningbo University, 57# Xing Ning Road, Ningbo, Zhejiang, China
| | - Haibo Dong
- Department of Radiology, Ningbo Medical Treatment Center Lihuili Hospital, Ningbo University, 57# Xing Ning Road, Ningbo, Zhejiang, China
| | - Huifen Liu
- Department of Psychiatry, Ningbo Kangning Hospital, Ningbo University, 1# Zhuangyu South Road, Ningbo, Zhejiang, China
| | - Wenwen Shen
- Department of Psychiatry, Ningbo Kangning Hospital, Ningbo University, 1# Zhuangyu South Road, Ningbo, Zhejiang, China
| | - Wenhua Zhou
- Department of Psychiatry, Ningbo Kangning Hospital, Ningbo University, 1# Zhuangyu South Road, Ningbo, Zhejiang, China.
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19
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Schmithorst V, Ceschin R, Lee V, Wallace J, Sahel A, Chenevert T, Parmar H, Berman JI, Vossough A, Qiu D, Kadom N, Grant PE, Gagoski B, LaViolette P, Maheshwari M, Sleeper LA, Bellinger D, Ilardi D, O’Neil S, Miller TA, Detterich J, Hill KD, Atz AM, Richmond M, Cnota J, Mahle WT, Ghanayem N, Gaynor W, Goldberg CS, Newburger JW, Panigrahy A. Single Ventricle Reconstruction III: Brain Connectome and Neurodevelopmental Outcomes: Design, Recruitment, and Technical Challenges of a Multicenter, Observational Neuroimaging Study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.12.23288433. [PMID: 37131744 PMCID: PMC10153324 DOI: 10.1101/2023.04.12.23288433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Patients with hypoplastic left heart syndrome who have been palliated with the Fontan procedure are at risk for adverse neurodevelopmental outcomes, lower quality of life, and reduced employability. We describe the methods (including quality assurance and quality control protocols) and challenges of a multi-center observational ancillary study, SVRIII (Single Ventricle Reconstruction Trial) Brain Connectome. Our original goal was to obtain advanced neuroimaging (Diffusion Tensor Imaging and Resting-BOLD) in 140 SVR III participants and 100 healthy controls for brain connectome analyses. Linear regression and mediation statistical methods will be used to analyze associations of brain connectome measures with neurocognitive measures and clinical risk factors. Initial recruitment challenges occurred related to difficulties with: 1) coordinating brain MRI for participants already undergoing extensive testing in the parent study, and 2) recruiting healthy control subjects. The COVID-19 pandemic negatively affected enrollment late in the study. Enrollment challenges were addressed by 1) adding additional study sites, 2) increasing the frequency of meetings with site coordinators and 3) developing additional healthy control recruitment strategies, including using research registries and advertising the study to community-based groups. Technical challenges that emerged early in the study were related to the acquisition, harmonization, and transfer of neuroimages. These hurdles were successfully overcome with protocol modifications and frequent site visits that involved human and synthetic phantoms. Trial registration number ClinicalTrials.gov Registration Number: NCT02692443.
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Affiliation(s)
- Vanessa Schmithorst
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave, Floor 2, Pittsburgh, PA 15224 USA
| | - Rafael Ceschin
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave, Floor 2, Pittsburgh, PA 15224 USA
- Department of Biomedical Informatics, University of Pittsburgh School, 5607 Baum Blvd, Pittsburgh, PA 15206-3701 USA
| | - Vince Lee
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave, Floor 2, Pittsburgh, PA 15224 USA
| | - Julia Wallace
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave, Floor 2, Pittsburgh, PA 15224 USA
| | - Aurelia Sahel
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave, Floor 2, Pittsburgh, PA 15224 USA
| | - Thomas Chenevert
- Department of Radiology, Michigan Medicine, University of Michigan, University of Michigan, 1500 E Medical Center Dr, Ann Arbor, MI 48109 USA
| | - Hemant Parmar
- Department of Radiology, Michigan Medicine, University of Michigan, University of Michigan, 1500 E Medical Center Dr, Ann Arbor, MI 48109 USA
| | - Jeffrey I. Berman
- Department of Radiology, Children’s Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, USA
| | - Arastoo Vossough
- Department of Radiology, Children’s Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, USA
| | - Deqiang Qiu
- Department of Radiology and Imaging Sciences, Children’s Healthcare of Atlanta, Emory University, 1364 Clifton Rd, Atlanta, GA 30322 USA
| | - Nadja Kadom
- Department of Radiology and Imaging Sciences, Children’s Healthcare of Atlanta, Emory University, 1364 Clifton Rd, Atlanta, GA 30322 USA
| | - Patricia Ellen Grant
- Fetal-Neonatal Neuroimaging and Developmental Science Center (FNNDSC), Children’s Hospital Boston, 300 Longwood Avenue, Boston, MA 02115 USA
| | - Borjan Gagoski
- Department of Radiology, Children’s Hospital Boston, 300 Longwood Ave, Boston, MA 02115 USA
| | - Peter LaViolette
- Department of Radiology, Medical College of Wisconsin, 9200 W Wisconsin Ave, Milwaukee, WI 53226 USA
| | - Mohit Maheshwari
- Department of Radiology, Medical College of Wisconsin, 9200 W Wisconsin Ave, Milwaukee, WI 53226 USA
| | - Lynn A. Sleeper
- Department of Cardiology, Boston Children’s Hospital, 300 Longwood Avenue, Boston, MA 02115
- Department of Pediatrics, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115 USA
| | - David Bellinger
- Cardiac Neurodevelopmental Program, Department of Neurology, Boston, Children’s Hospital, 300 Longwood Avenue, Boston, MA 02115 USA
| | - Dawn Ilardi
- Department of Neuropsychology, Children’s Healthcare of Atlanta, 1400 Tullie Road NE, Atlanta, GA 30329
| | - Sharon O’Neil
- Neuropsychology Core of the Saban Research Institute, Children’s Hospital Los Angeles, 4661 Sunset Blvd., Los Angeles, CA 90027 USA
| | - Thomas A. Miller
- Division of Pediatric Cardiology, Department of Pediatrics, University of Utah, School of Medicine, 30 N 1900 E, Salt Lake City, UT 84132 USA
| | - Jon Detterich
- Division of Pediatric Cardiology, Children’s Hospital Los Angeles, 4650 Sunset Blvd, Los Angeles, CA 90027 USA
| | - Kevin D. Hill
- Division of Pediatric Cardiology, Department of Pediatrics, Duke University, School of Medicine, 7506 Hospital North, DUMC Box 3090, Durham, NC 27710 USA
| | - Andrew M. Atz
- Division of Pediatric Cardiology, Medical University of South Carolina, 96 Jonathan Lucas St. Ste. 601, MSC 617, Charleston, SC 29425 USA
| | - Marc Richmond
- Program for Pediatric Cardiomyopathy, Heart Failure, and Transplantation, New York-Presbyterian Morgan Stanley Children’s Hospital, 3959 Broadway MSCH North, 2 Floor, New York, NY 10032 USA
| | - James Cnota
- Fetal Heart Program, Cincinnati Children’s, 3333 Burnet Avenue, Cincinnati, Ohio 45229-3026 USA
| | - William T. Mahle
- Division of Pediatric Cardiology, Children’s Healthcare of Atlanta, 1400 Tullie Rd NE Suite 630, Atlanta, GA 30329
| | - Nancy Ghanayem
- Section of Pediatric Critical Care, Department of Pediatrics, University of Chicago Medicine, Comer Children’s Hospital, 5721 S. Maryland Ave., Chicago, IL 60637 USA
- Section of Pediatric Critical Care, Department of Pediatrics, Medical College of Wisconsin, 9000 W. Wisconsin Ave. MS 681, Milwaukee, WI 53226 USA
| | - William Gaynor
- Heart Failure and Transplant Program, Children’s Hospital of Philadelphia, 3401 Civic Center Blvd., Philadelphia, PA 19104 USA
| | - Caren S. Goldberg
- Department of Pediatrics, Division of Cardiology, C.S. Mott Children’s Hospital, 1540 E Hospital Dr #4204, Ann Arbor, MI 48109 USA
| | - Jane W. Newburger
- Department of Cardiology, Boston Children’s Hospital, 300 Longwood Avenue, Boston, MA 02115
| | - Ashok Panigrahy
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave, Floor 2, Pittsburgh, PA 15224 USA
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20
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Yeung HW, Stolicyn A, Buchanan CR, Tucker‐Drob EM, Bastin ME, Luz S, McIntosh AM, Whalley HC, Cox SR, Smith K. Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes. Hum Brain Mapp 2023; 44:1913-1933. [PMID: 36541441 PMCID: PMC9980898 DOI: 10.1002/hbm.26182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 11/11/2022] [Accepted: 11/30/2022] [Indexed: 12/24/2022] Open
Abstract
There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population-wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different ML algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. We modelled N = 8183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI. Streamline count generally provided the highest prediction accuracies in all prediction tasks. DL did not improve on prediction accuracies from simpler linear models. Further, high correlations between gradient attribution coefficients from DL and model coefficients from linear models suggested the models ranked the importance of features in similar ways, which indirectly suggested the similarity in models' strategies for making predictive decision to some extent. This highlights that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size.
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Affiliation(s)
- Hon Wah Yeung
- Department of PsychiatryUniversity of EdinburghEdinburghUK
| | - Aleks Stolicyn
- Department of PsychiatryUniversity of EdinburghEdinburghUK
| | - Colin R. Buchanan
- Department of PsychologyUniversity of EdinburghEdinburghUK
- Lothian Birth Cohorts, University of EdinburghEdinburghUK
- Scottish Imaging Network, A Platform for Scientific Excellence Collaboration (SINAPSE)EdinburghUK
| | - Elliot M. Tucker‐Drob
- Department of PsychologyUniversity of TexasAustinTexasUSA
- Population Research Center and Center on Aging and Population SciencesUniversity of Texas at AustinAustinTexasUSA
| | - Mark E. Bastin
- Lothian Birth Cohorts, University of EdinburghEdinburghUK
- Scottish Imaging Network, A Platform for Scientific Excellence Collaboration (SINAPSE)EdinburghUK
- Centre for Clinical Brain ScienceUniversity of EdinburghEdinburghUK
| | - Saturnino Luz
- Edinburgh Medical SchoolUsher Institute, The University of EdinburghEdinburghUK
| | - Andrew M. McIntosh
- Department of PsychiatryUniversity of EdinburghEdinburghUK
- Centre for Genomic and Experimental MedicineInstitute of Genetics and Molecular Medicine, University of EdinburghEdinburghUK
| | | | - Simon R. Cox
- Department of PsychologyUniversity of EdinburghEdinburghUK
- Lothian Birth Cohorts, University of EdinburghEdinburghUK
- Scottish Imaging Network, A Platform for Scientific Excellence Collaboration (SINAPSE)EdinburghUK
| | - Keith Smith
- Department of Physics and MathematicsNottingham Trent UniversityNottinghamUK
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21
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Bosma MJ, Cox SR, Ziermans T, Buchanan CR, Shen X, Tucker-Drob EM, Adams MJ, Whalley HC, Lawrie SM. White matter, cognition and psychotic-like experiences in UK Biobank. Psychol Med 2023; 53:2370-2379. [PMID: 37310314 PMCID: PMC10123836 DOI: 10.1017/s0033291721004244] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 09/09/2021] [Accepted: 09/29/2021] [Indexed: 11/05/2022]
Abstract
BACKGROUND Psychotic-like experiences (PLEs) are risk factors for the development of psychiatric conditions like schizophrenia, particularly if associated with distress. As PLEs have been related to alterations in both white matter and cognition, we investigated whether cognition (g-factor and processing speed) mediates the relationship between white matter and PLEs. METHODS We investigated two independent samples (6170 and 19 891) from the UK Biobank, through path analysis. For both samples, measures of whole-brain fractional anisotropy (gFA) and mean diffusivity (gMD), as indications of white matter microstructure, were derived from probabilistic tractography. For the smaller sample, variables whole-brain white matter network efficiency and microstructure were also derived from structural connectome data. RESULTS The mediation of cognition on the relationships between white matter properties and PLEs was non-significant. However, lower gFA was associated with having PLEs in combination with distress in the full available sample (standardized β = -0.053, p = 0.011). Additionally, lower gFA/higher gMD was associated with lower g-factor (standardized β = 0.049, p < 0.001; standardized β = -0.027, p = 0.003), and partially mediated by processing speed with a proportion mediated of 7% (p = < 0.001) for gFA and 11% (p < 0.001) for gMD. CONCLUSIONS We show that lower global white matter microstructure is associated with having PLEs in combination with distress, which suggests a direction of future research that could help clarify how and why individuals progress from subclinical to clinical psychotic symptoms. Furthermore, we replicated that processing speed mediates the relationship between white matter microstructure and g-factor.
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Affiliation(s)
- M. J. Bosma
- Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
| | - S. R. Cox
- School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, UK
| | - T. Ziermans
- Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
| | - C. R. Buchanan
- School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, UK
| | - X. Shen
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, Scotland, UK
| | - E. M. Tucker-Drob
- Department of Psychology, University of Texas at Austin, Austin, USA
| | - M. J. Adams
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, Scotland, UK
| | - H. C. Whalley
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, Scotland, UK
| | - S. M. Lawrie
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, Scotland, UK
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22
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Wu Y, Gholipour A, Vasung L, Karimi D. A computational framework for characterizing normative development of structural brain connectivity in the perinatal stage. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.10.532142. [PMID: 36945435 PMCID: PMC10029005 DOI: 10.1101/2023.03.10.532142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2023]
Abstract
Quantitative assessment of the brain's structural connectivity in the perinatal stage is useful for studying normal and abnormal neurodevelopment. However, estimation of the structural connectome from diffusion MRI data involves a series of complex and ill-posed computations. For the perinatal period, this analysis is further challenged by the rapid brain development and difficulties of imaging subjects at this stage. These factors, along with high inter-subject variability, have made it difficult to chart the normative development of the structural connectome. Hence, there is a lack of baseline trends in connectivity metrics that can be used as reliable references for assessing normal and abnormal brain development at this critical stage. In this paper we propose a computational framework, based on spatio-temporal atlases, for determining such baselines. We apply the framework on data from 169 subjects between 33 and 45 postmenstrual weeks. We show that this framework can unveil clear and strong trends in the development of structural connectivity in the perinatal stage. Some of our interesting findings include that connection weighting based on neurite density produces more consistent trends and that the trends in global efficiency, local efficiency, and characteristic path length are more consistent than in other metrics.
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Affiliation(s)
- Yihan Wu
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ali Gholipour
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Lana Vasung
- Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Davood Karimi
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
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23
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Nabulsi L, Chandio BQ, Dhinagar N, Laltoo E, McPhilemy G, Martyn FM, Hallahan B, McDonald C, Thompson PM, Cannon DM. Along-Tract Statistical Mapping of Microstructural Abnormalities in Bipolar Disorder: A Pilot Study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.07.531585. [PMID: 36945403 PMCID: PMC10028925 DOI: 10.1101/2023.03.07.531585] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
Investigating brain circuitry involved in bipolar disorder (BD) is key to discovering brain biomarkers for genetic and interventional studies of the disorder. Even so, prior research has not provided a fine-scale spatial mapping of brain microstructural differences in BD. In this pilot diffusion MRI dataset, we used BUndle ANalytics (BUAN), a recently developed analytic approach for tractography, to extract, map, and visualize the profile of microstructural abnormalities on a 3D model of fiber tracts in people with BD (N=38) and healthy controls (N=49), and investigate along-tract white matter (WM) microstructural differences between these groups. Using the BUAN pipeline, BD was associated with lower mean Fractional Anisotropy (FA) in fronto-limbic and interhemispheric pathways and higher mean FA in posterior bundles relative to controls. BUAN combines tractography and anatomical information to capture distinct along-tract effects on WM microstructure that may aid in classifying diseases based on anatomical differences.
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Affiliation(s)
- Leila Nabulsi
- Imaging Genetics Center, Stevens Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, 90292 USA
| | - Bramsh Q Chandio
- Imaging Genetics Center, Stevens Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, 90292 USA
| | - Nikhil Dhinagar
- Imaging Genetics Center, Stevens Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, 90292 USA
| | - Emily Laltoo
- Imaging Genetics Center, Stevens Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, 90292 USA
| | - Genevieve McPhilemy
- Clinical Neuroimaging Lab, Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, University of Galway, Galway, Ireland
| | - Fiona M Martyn
- Clinical Neuroimaging Lab, Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, University of Galway, Galway, Ireland
| | - Brian Hallahan
- Clinical Neuroimaging Lab, Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, University of Galway, Galway, Ireland
| | - Colm McDonald
- Clinical Neuroimaging Lab, Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, University of Galway, Galway, Ireland
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, 90292 USA
| | - Dara M Cannon
- Clinical Neuroimaging Lab, Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, University of Galway, Galway, Ireland
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24
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Neuronal Cultures: Exploring Biophysics, Complex Systems, and Medicine in a Dish. BIOPHYSICA 2023. [DOI: 10.3390/biophysica3010012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
Neuronal cultures are one of the most important experimental models in modern interdisciplinary neuroscience, allowing to investigate in a control environment the emergence of complex behavior from an ensemble of interconnected neurons. Here, I review the research that we have conducted at the neurophysics laboratory at the University of Barcelona over the last 15 years, describing first the neuronal cultures that we prepare and the associated tools to acquire and analyze data, to next delve into the different research projects in which we actively participated to progress in the understanding of open questions, extend neuroscience research on new paradigms, and advance the treatment of neurological disorders. I finish the review by discussing the drawbacks and limitations of neuronal cultures, particularly in the context of brain-like models and biomedicine.
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25
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Sha Z, Schijven D, Fisher SE, Francks C. Genetic architecture of the white matter connectome of the human brain. SCIENCE ADVANCES 2023; 9:eadd2870. [PMID: 36800424 PMCID: PMC9937579 DOI: 10.1126/sciadv.add2870] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 01/13/2023] [Indexed: 06/18/2023]
Abstract
White matter tracts form the structural basis of large-scale brain networks. We applied brain-wide tractography to diffusion images from 30,810 adults (U.K. Biobank) and found significant heritability for 90 node-level and 851 edge-level network connectivity measures. Multivariate genome-wide association analyses identified 325 genetic loci, of which 80% had not been previously associated with brain metrics. Enrichment analyses implicated neurodevelopmental processes including neurogenesis, neural differentiation, neural migration, neural projection guidance, and axon development, as well as prenatal brain expression especially in stem cells, astrocytes, microglia, and neurons. The multivariate association profiles implicated 31 loci in connectivity between core regions of the left-hemisphere language network. Polygenic scores for psychiatric, neurological, and behavioral traits also showed significant multivariate associations with structural connectivity, each implicating distinct sets of brain regions with trait-relevant functional profiles. This large-scale mapping study revealed common genetic contributions to variation in the structural connectome of the human brain.
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Affiliation(s)
- Zhiqiang Sha
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
| | - Dick Schijven
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
| | - Simon E. Fisher
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Clyde Francks
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, Netherlands
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26
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Roine U, Tokola AM, Autti T, Roine T. Topological Structural Brain Connectivity Alterations in Aspartylglucosaminuria: A Case-Control Study. AJNR Am J Neuroradiol 2023; 44:40-46. [PMID: 36549851 PMCID: PMC9835915 DOI: 10.3174/ajnr.a7745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 11/16/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND AND PURPOSE We investigated global and local properties of the structural brain connectivity networks in aspartylglucosaminuria, an autosomal recessive and progressive neurodegenerative lysosomal storage disease. Brain connectivity in aspartylglucosaminuria has not been investigated before, but previous structural MR imaging studies have shown brain atrophy, delayed myelination, and decreased thalamic and increased periventricular WM T2 signal intensity. MATERIALS AND METHODS We acquired diffusion MR imaging and T1-weighted data from 12 patients with aspartylglucosaminuria (mean age, 23 [SD, 8] years; 5 men), and 30 healthy controls (mean age, 25 [SD, 10] years; 13 men). We performed whole-brain constrained spherical deconvolution tractography, which enables the reconstruction of neural tracts through regions with complex fiber configurations, and used graph-theoretical analysis to investigate the structural brain connectivity networks. RESULTS The integration of the networks was decreased, as demonstrated by a decreased normalized global efficiency and an increased normalized characteristic path length. In addition, the average strength of the networks was decreased. In the local analyses, we found decreased strength in 11 nodes, including, for example, the right thalamus, right putamen, and, bilaterally, several occipital and temporal regions. CONCLUSIONS We found global and local structural connectivity alterations in aspartylglucosaminuria. Biomarkers related to the treatment efficacy are needed, and brain network properties may provide the means for long term follow-up.
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Affiliation(s)
- U Roine
- From the Department of Radiology (U.R., A.M.T., T.A., T.R.), HUS Medical Imaging Center
- Department of Pediatric Neurology (U.R.), Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - A M Tokola
- From the Department of Radiology (U.R., A.M.T., T.A., T.R.), HUS Medical Imaging Center
| | - T Autti
- From the Department of Radiology (U.R., A.M.T., T.A., T.R.), HUS Medical Imaging Center
| | - T Roine
- From the Department of Radiology (U.R., A.M.T., T.A., T.R.), HUS Medical Imaging Center
- Department of Neuroscience and Biomedical Engineering (T.R.), Aalto University School of Science, Espoo, Finland
- Turku Brain and Mind Center (T.R.), University of Turku, Turku, Finland
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27
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Srivishagan S, Kumaralingam L, Thanikasalam K, Pinidiyaarachchi UAJ, Ratnarajah N. Discriminative patterns of white matter changes in Alzheimer's. Psychiatry Res Neuroimaging 2023; 328:111576. [PMID: 36495726 DOI: 10.1016/j.pscychresns.2022.111576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 11/12/2022] [Accepted: 11/22/2022] [Indexed: 12/02/2022]
Abstract
Changes in structural connectivity of the Alzheimer's brain have not been widely studied utilizing cutting-edge methodologies. This study develops an efficient structural connectome-based convolutional neural network (CNN) to classify the AD and uses explanations of CNNs' choices in classification to pinpoint the discriminative changes in white matter connectivity in AD. A CNN architecture has been developed to classify normal control (NC) and AD subjects from the weighted structural connectome. Then, the CNN classification decision is visually analyzed using gradient-based localization techniques to identify the discriminative changes in white matter connectivity in Alzheimer's. The cortical regions involved in the identified discriminative structural connectivity changes in AD are highly covered in the temporal/subcortical regions. A specific pattern is identified in the discriminative changes in structural connectivity of AD, where the white matter changes are revealed within the temporal/subcortical regions and from the temporal/subcortical regions to the frontal and parietal regions in both left and right hemispheres. The proposed approach has the potential to comprehensively analyze the discriminative structural connectivity differences in AD, change the way of detecting biomarkers, and help clinicians better understand the structural changes in AD and provide them with more confidence in automated diagnostic systems.
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Affiliation(s)
- Subaramya Srivishagan
- Department of Physical Science, Faculty of Applied Science, University of Vavuniya, Vavuniya, Sri Lanka; PGIS, University of Peradeniya, Peradeniya, Sri Lanka
| | - Logiraj Kumaralingam
- Department of Computer Science, Faculty of Science, University of Jaffna, Jaffna, Sri Lanka
| | - Kokul Thanikasalam
- Department of Computer Science, Faculty of Science, University of Jaffna, Jaffna, Sri Lanka
| | - U A J Pinidiyaarachchi
- Department of Statistics and Computer Science, Faculty of Science, University of Peradeniya, Peradeniya, Sri Lanka
| | - Nagulan Ratnarajah
- Department of Physical Science, Faculty of Applied Science, University of Vavuniya, Vavuniya, Sri Lanka.
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28
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Zhao Y, Lin X, Zhang Z, Wang X, He X, Yang L. STDP-based adaptive graph convolutional networks for automatic sleep staging. Front Neurosci 2023; 17:1158246. [PMID: 37152593 PMCID: PMC10157055 DOI: 10.3389/fnins.2023.1158246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 04/03/2023] [Indexed: 05/09/2023] Open
Abstract
Automatic sleep staging is important for improving diagnosis and treatment, and machine learning with neuroscience explainability of sleep staging is shown to be a suitable method to solve this problem. In this paper, an explainable model for automatic sleep staging is proposed. Inspired by the Spike-Timing-Dependent Plasticity (STDP), an adaptive Graph Convolutional Network (GCN) is established to extract features from the Polysomnography (PSG) signal, named STDP-GCN. In detail, the channel of the PSG signal can be regarded as a neuron, the synapse strength between neurons can be constructed by the STDP mechanism, and the connection between different channels of the PSG signal constitutes a graph structure. After utilizing GCN to extract spatial features, temporal convolution is used to extract transition rules between sleep stages, and a fully connected neural network is used for classification. To enhance the strength of the model and minimize the effect of individual physiological signal discrepancies on classification accuracy, STDP-GCN utilizes domain adversarial training. Experiments demonstrate that the performance of STDP-GCN is comparable to the current state-of-the-art models.
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Mijalkov M, Veréb D, Jamialahmadi O, Canal-Garcia A, Gómez-Ruiz E, Vidal-Piñeiro D, Romeo S, Volpe G, Pereira JB. Sex differences in multilayer functional network topology over the course of aging in 37543 UK Biobank participants. Netw Neurosci 2023; 7:351-376. [PMID: 37334001 PMCID: PMC10275214 DOI: 10.1162/netn_a_00286] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 10/06/2022] [Indexed: 07/27/2023] Open
Abstract
Aging is a major risk factor for cardiovascular and neurodegenerative disorders, with considerable societal and economic implications. Healthy aging is accompanied by changes in functional connectivity between and within resting-state functional networks, which have been associated with cognitive decline. However, there is no consensus on the impact of sex on these age-related functional trajectories. Here, we show that multilayer measures provide crucial information on the interaction between sex and age on network topology, allowing for better assessment of cognitive, structural, and cardiovascular risk factors that have been shown to differ between men and women, as well as providing additional insights into the genetic influences on changes in functional connectivity that occur during aging. In a large cross-sectional sample of 37,543 individuals from the UK Biobank cohort, we demonstrate that such multilayer measures that capture the relationship between positive and negative connections are more sensitive to sex-related changes in the whole-brain connectivity patterns and their topological architecture throughout aging, when compared to standard connectivity and topological measures. Our findings indicate that multilayer measures contain previously unknown information on the relationship between sex and age, which opens up new avenues for research into functional brain connectivity in aging.
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Affiliation(s)
- Mite Mijalkov
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Dániel Veréb
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Oveis Jamialahmadi
- Department of Molecular and Clinical Medicine, Goteborg University, Goteborg, Sweden
| | - Anna Canal-Garcia
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | | | | | - Stefano Romeo
- Department of Molecular and Clinical Medicine, Goteborg University, Goteborg, Sweden
- Cardiology Department, Sahlgrenska University Hospital, Gothenburg, Sweden
- Clinical Nutrition Unit, University Magna Graecia, Catanzaro, Italy
| | - Giovanni Volpe
- Department of Physics, Goteborg University, Goteborg, Sweden
| | - Joana B. Pereira
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
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Ismail L, Karwowski W, Farahani FV, Rahman M, Alhujailli A, Fernandez-Sumano R, Hancock PA. Modeling Brain Functional Connectivity Patterns during an Isometric Arm Force Exertion Task at Different Levels of Perceived Exertion: A Graph Theoretical Approach. Brain Sci 2022; 12:1575. [PMID: 36421899 PMCID: PMC9688629 DOI: 10.3390/brainsci12111575] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 11/09/2022] [Accepted: 11/13/2022] [Indexed: 09/29/2023] Open
Abstract
The perception of physical exertion is the cognitive sensation of work demands associated with voluntary muscular actions. Measurements of exerted force are crucial for avoiding the risk of overexertion and understanding human physical capability. For this purpose, various physiological measures have been used; however, the state-of-the-art in-force exertion evaluation lacks assessments of underlying neurophysiological signals. The current study applied a graph theoretical approach to investigate the topological changes in the functional brain network induced by predefined force exertion levels for twelve female participants during an isometric arm task and rated their perceived physical comfort levels. The functional connectivity under predefined force exertion levels was assessed using the coherence method for 84 anatomical brain regions of interest at the electroencephalogram (EEG) source level. Then, graph measures were calculated to quantify the network topology for two frequency bands. The results showed that high-level force exertions are associated with brain networks characterized by more significant clustering coefficients (6%), greater modularity (5%), higher global efficiency (9%), and less distance synchronization (25%) under alpha coherence. This study on the neurophysiological basis of physical exertions with various force levels suggests that brain regions communicate and cooperate higher when muscle force exertions increase to meet the demands of physically challenging tasks.
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Affiliation(s)
- Lina Ismail
- Department of Industrial and Management Engineering, Arab Academy for Science Technology & Maritime Transport, Alexandria 2913, Egypt
| | - Waldemar Karwowski
- Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA
| | - Farzad V. Farahani
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Mahjabeen Rahman
- Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA
| | - Ashraf Alhujailli
- Department of Management Science, Yanbu Industrial College, Yanbu 46452, Saudi Arabia
| | - Raul Fernandez-Sumano
- Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA
| | - P. A. Hancock
- Department of Psychology, University of Central Florida, Orlando, FL 32816, USA
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Sun R, Zhang SY, Cheng X, Xie S, Qiao PG, Li GJ. White matter structural and network topological changes in moyamoya disease with limb paresthesia: A study based on diffusion kurtosis imaging. Front Neurosci 2022; 16:1029388. [PMID: 36389234 PMCID: PMC9659737 DOI: 10.3389/fnins.2022.1029388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Accepted: 10/17/2022] [Indexed: 11/22/2022] Open
Abstract
Purpose To investigate the structural and network topological changes in the white matter (WM) in MMD patients with limb paresthesia by performing diffusion kurtosis imaging (DKI). Materials and methods A total of 151 MMD patients, including 46 with left-limb paresthesia (MLP), 52 with right-limb paresthesia (MRP), and 53 without paresthesia (MWP), and 28 healthy controls (HCs) underwent whole-brain DKI, while the surgical patients were reexamined 3-4 months after revascularization. The data were preprocessed to calculate the fractional anisotropy (FA) and mean kurtosis (MK) values. Voxel-wise statistics for FA and MK images were obtained by using tract-based spatial statistics (TBSS). Next, the whole-brain network was constructed, and global and local network parameters were analyzed using graph theory. All parameters were compared among the HC, MWP, MLP, and MRP groups, and changes in the MMD patients before and after revascularization were also compared. Results The TBSS analysis revealed significant reductions in FA and MK in extensive WM regions in the three patient groups. In comparison with the MWP group, the MLP group showed reductions in FA and MK in both right and left WM, mainly in the right WM, while the MRP group mainly showed a reduction in FA in the left WM region and demonstrated no significant change in MK. The graph theoretical analysis showed decreased global network efficiency, increased characteristic path length, and increased sigma in the MWP, MRP, and MLP groups in comparison with the HC group. Among local network parameters, the nodal efficiency decreased in the bilateral MFG and IFGtriang, while the degree decreased in the MFG.L and bilateral IFGtriang. Patients with right-limb paresthesia showed the lowest nodal efficiency and degree in MFG.L and IFGtriang.L, while those with left-limb paresthesia showed the lowest nodal efficiency in MFG.R and IFGtriang.R and the lowest degree in IFGtriang.R. Conclusion A DKI-based whole-brain structural and network analysis can be used to detect changes in WM damage and network topological changes in MMD patients with limb paresthesia. FA is more sensitive than MK in detecting WM injury, while MFG and IFGtriang are the key nodes related to the development of acroparesthesia.
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Affiliation(s)
- Rujing Sun
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Shi-Yu Zhang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Xu Cheng
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Sangma Xie
- School of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Peng-Gang Qiao
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
- Department of Radiology, Affiliated Hospital of Academy of Military Medical Sciences, Beijing, China
- *Correspondence: Peng-Gang Qiao,
| | - Gong-Jie Li
- Department of Radiology, Affiliated Hospital of Academy of Military Medical Sciences, Beijing, China
- Gong-Jie Li,
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Schnellbächer GJ, Rajkumar R, Veselinović T, Ramkiran S, Hagen J, Shah NJ, Neuner I. Structural alterations of the insula in depression patients - A 7-Tesla-MRI study. Neuroimage Clin 2022; 36:103249. [PMID: 36451355 PMCID: PMC9668670 DOI: 10.1016/j.nicl.2022.103249] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 09/26/2022] [Accepted: 10/23/2022] [Indexed: 11/11/2022]
Abstract
INTRODUCTION The insular cortex is part of a network of highly connected cerebral "rich club" - regions and has been implicated in the pathophysiology of various psychiatric and neurological disorders, of which major depressive disease is one of the most prevalent. "Rich club" vulnerability can be a contributing factor in disease development. High-resolution structural subfield analysis of insular volume in combination with cortical thickness measurements and psychological testing might elucidate the way in which the insula is changed in depression. MATERIAL AND METHODS High-resolution structural images of the brain were acquired using a 7T-MRI scanner. The mean grey matter volume and cortical thickness within the insular subfields were analysed using voxel-based morphometry (VBM) and surface analysis techniques respectively. Insular subfields were defined according to the Brainnetome Atlas for VBM - and the Destrieux-Atlas for cortical thickness - analysis. Thirty-three patients with confirmed major depressive disease, as well as thirty-one healthy controls matched for age and gender, were measured. The severity of depression in MDD patients was measured via a BDI-II score and objective clinical assessment (AMDP). Intergroup statistical analysis was performed using ANCOVA. An intragroup multivariate regression analysis of patient psychological test results was calculated. Corrections for multiple comparisons was performed using FDR. RESULTS Significant differences between groups were observed in the left granular dorsal insula according to VBM-analysis. AMDP-scores positively correlated with cortical thickness in the right superior segment of the circular insular sulcus. CONCLUSIONS The combination of differences in grey matter volume between healthy controls and patients with a positive correlation of cortical thickness with disease severity underscores the insula's role in the pathogeneses of MDD. The connectivity hub insular cortex seems vulnerable to disruption in context of affective disease.
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Affiliation(s)
- Gereon J. Schnellbächer
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany
| | - Ravichandran Rajkumar
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany,Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Germany,JARA-BRAIN, 52074 Aachen, Germany
| | - Tanja Veselinović
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany,Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Germany
| | - Shukti Ramkiran
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany,Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Germany
| | - Jana Hagen
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany
| | - N. Jon Shah
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Germany,JARA-BRAIN, 52074 Aachen, Germany,Department of Neurology, RWTH Aachen University, 52074 Aachen, Germany,Institute of Neuroscience and Medicine 11, INM-11, Forschungszentrum Jülich, Germany
| | - Irene Neuner
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany,Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Germany,JARA-BRAIN, 52074 Aachen, Germany,Corresponding author.
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Alemán-Gómez Y, Griffa A, Houde JC, Najdenovska E, Magon S, Cuadra MB, Descoteaux M, Hagmann P. A multi-scale probabilistic atlas of the human connectome. Sci Data 2022; 9:516. [PMID: 35999243 PMCID: PMC9399115 DOI: 10.1038/s41597-022-01624-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 08/03/2022] [Indexed: 11/10/2022] Open
Abstract
The human brain is a complex system that can be efficiently represented as a network of structural connectivity. Many imaging studies would benefit from such network information, which is not always available. In this work, we present a whole-brain multi-scale structural connectome atlas. This tool has been derived from a cohort of 66 healthy subjects imaged with optimal technology in the setting of the Human Connectome Project. From these data we created, using extensively validated diffusion-data processing, tractography and gray-matter parcellation tools, a multi-scale probabilistic atlas of the human connectome. In addition, we provide user-friendly and accessible code to match this atlas to individual brain imaging data to extract connection-specific quantitative information. This can be used to associate individual imaging findings, such as focal white-matter lesions or regional alterations, to specific connections and brain circuits. Accordingly, network-level consequences of regional changes can be analyzed even in absence of diffusion and tractography data. This method is expected to broaden the accessibility and lower the yield for connectome research.
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Affiliation(s)
- Yasser Alemán-Gómez
- Connectomics Lab, Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.
- Center for Psychiatric Neuroscience, Department of Psychiatry, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Prilly, Switzerland.
| | - Alessandra Griffa
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
- Medical Image Processing Laboratory, Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Leenaards Memory Centre, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | | | - Elena Najdenovska
- Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- Medical Image Analysis Laboratory (MIAL), Centre d'Imagerie BioMédicale (CIBM), Lausanne, Switzerland
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Stefano Magon
- Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Meritxell Bach Cuadra
- Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- Medical Image Analysis Laboratory (MIAL), Centre d'Imagerie BioMédicale (CIBM), Lausanne, Switzerland
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab, Sherbrooke University, Sherbrooke, Canada
| | - Patric Hagmann
- Connectomics Lab, Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
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Human brain structural connectivity matrices-ready for modelling. Sci Data 2022; 9:486. [PMID: 35945231 PMCID: PMC9363436 DOI: 10.1038/s41597-022-01596-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 07/25/2022] [Indexed: 11/10/2022] Open
Abstract
The human brain represents a complex computational system, the function and structure of which may be measured using various neuroimaging techniques focusing on separate properties of the brain tissue and activity. We capture the organization of white matter fibers acquired by diffusion-weighted imaging using probabilistic diffusion tractography. By segmenting the results of tractography into larger anatomical units, it is possible to draw inferences about the structural relationships between these parts of the system. This pipeline results in a structural connectivity matrix, which contains an estimate of connection strength among all regions. However, raw data processing is complex, computationally intensive, and requires expert quality control, which may be discouraging for researchers with less experience in the field. We thus provide brain structural connectivity matrices in a form ready for modelling and analysis and thus usable by a wide community of scientists. The presented dataset contains brain structural connectivity matrices together with the underlying raw diffusion and structural data, as well as basic demographic data of 88 healthy subjects. Measurement(s) | Diffusion Weighted Imaging | Technology Type(s) | Magnetic Resonance Imaging | Sample Characteristic - Organism | Homo sapiens | Sample Characteristic - Location | Czech Republic |
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Nigro S, Filardi M, Tafuri B, De Blasi R, Cedola A, Gigli G, Logroscino G. The Role of Graph Theory in Evaluating Brain Network Alterations in Frontotemporal Dementia. Front Neurol 2022; 13:910054. [PMID: 35837233 PMCID: PMC9275562 DOI: 10.3389/fneur.2022.910054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 06/02/2022] [Indexed: 11/21/2022] Open
Abstract
Frontotemporal dementia (FTD) is a spectrum of clinical syndromes that affects personality, behavior, language, and cognition. The current diagnostic criteria recognize three main clinical subtypes: the behavioral variant of FTD (bvFTD), the semantic variant of primary progressive aphasia (svPPA), and the non-fluent/agrammatic variant of PPA (nfvPPA). Patients with FTD display heterogeneous clinical and neuropsychological features that highly overlap with those presented by psychiatric syndromes and other types of dementia. Moreover, up to now there are no reliable disease biomarkers, which makes the diagnosis of FTD particularly challenging. To overcome this issue, different studies have adopted metrics derived from magnetic resonance imaging (MRI) to characterize structural and functional brain abnormalities. Within this field, a growing body of scientific literature has shown that graph theory analysis applied to MRI data displays unique potentialities in unveiling brain network abnormalities of FTD subtypes. Here, we provide a critical overview of studies that adopted graph theory to examine the topological changes of large-scale brain networks in FTD. Moreover, we also discuss the possible role of information arising from brain network organization in the diagnostic algorithm of FTD-spectrum disorders and in investigating the neural correlates of clinical symptoms and cognitive deficits experienced by patients.
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Affiliation(s)
- Salvatore Nigro
- Institute of Nanotechnology (NANOTEC), National Research Council, Lecce, Italy
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico”, Tricase, Italy
- Salvatore Nigro
| | - Marco Filardi
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico”, Tricase, Italy
- Department of Basic Medicine, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Benedetta Tafuri
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico”, Tricase, Italy
- Department of Basic Medicine, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Roberto De Blasi
- Department of Radiology, “Pia Fondazione Cardinale G. Panico”, Tricase, Lecce, Italy
| | - Alessia Cedola
- Institute of Nanotechnology (NANOTEC), National Research Council, Lecce, Italy
| | - Giuseppe Gigli
- Institute of Nanotechnology (NANOTEC), National Research Council, Lecce, Italy
- Department of Mathematics and Physics “Ennio De Giorgi”, University of Salento, Lecce, Italy
| | - Giancarlo Logroscino
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico”, Tricase, Italy
- Department of Basic Medicine, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Bari, Italy
- *Correspondence: Giancarlo Logroscino
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Yang Z, Cieri F, Kinney JW, Cummings JL, Cordes D, Caldwell JZK. Brain functional topology differs by sex in cognitively normal older adults. Cereb Cortex Commun 2022; 3:tgac023. [PMID: 35795479 PMCID: PMC9252274 DOI: 10.1093/texcom/tgac023] [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: 04/11/2022] [Revised: 05/27/2022] [Accepted: 05/31/2022] [Indexed: 11/14/2022] Open
Abstract
Introduction Late onset Alzheimer's disease (AD) is the most common form of dementia, in which almost 70% of patients are women. Hypothesis We hypothesized that women show worse global FC metrics compared to men, and further hypothesized a sex-specific positive correlation between FC metrics and cognitive scores in women. Methods We studied cognitively healthy individuals from the Alzheimer's Disease Neuroimaging Initiative cohort, with resting-state functional Magnetic Resonance Imaging. Metrics derived from graph theoretical analysis and functional connectomics were used to assess the global/regional sex differences in terms of functional integration and segregation, considering the amyloid status and the contributions of APOE E4. Linear mixed effect models with covariates (education, handedness, presence of apolipoprotein [APOE] E4 and intra-subject effect) were utilized to evaluate sex differences. The associations of verbal learning and memory abilities with topological network properties were assessed. Result Women had a significantly lower magnitude of the global and regional functional network metrics compared to men. Exploratory association analysis showed that higher global clustering coefficient was associated with lower percent forgetting in women and worse cognitive scores in men. Conclusion Women overall show lower magnitude on measures of resting state functional network topology and connectivity. This factor can play a role in their different vulnerability to AD. Significance statement Two thirds of AD patients are women but the reasons for these sex difference are not well understood. When this late onset form dementia arises is too late to understand the potential causes of this sex disparities. Studies on cognitively healthy elderly population are a fundamental approach to explore in depth this different vulnerability to the most common form of dementia, currently affecting 6.2 million Americans aged 65 and older are, which means that >1 in 9 people (11.3%) 65 and older are affected by AD. Approaches such as resting-state functional network topology and connectivity may play a key role in understanding and elucidate sex-dependent differences relevant to late-onset dementia syndromes.
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Affiliation(s)
| | - Filippo Cieri
- Corresponding author: Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV 89106, United States.
| | - Jefferson W Kinney
- Department of Brain Health, University of Nevada, Mail Stop: 4022; 4505 S. Maryland Pkwy. Room 1172, Las Vegas, NV 89154, United States,Chambers-Grundy Center for Transformative Neuroscience, University of Nevada, Box 454022, 4505 S. Maryland Pkwy, Las Vegas, NV 89154-4022, United States
| | - Jeffrey L Cummings
- Department of Brain Health, University of Nevada, Mail Stop: 4022; 4505 S. Maryland Pkwy. Room 1172, Las Vegas, NV 89154, United States,Chambers-Grundy Center for Transformative Neuroscience, University of Nevada, Box 454022, 4505 S. Maryland Pkwy, Las Vegas, NV 89154-4022, United States
| | - Dietmar Cordes
- Department of Neurology, Cleveland Clinic Lou Ruvo Center for Brain Health, 888 W Bonneville Ave, Las Vegas, NV 89106, United States,Department of Brain Health, University of Nevada, Mail Stop: 4022; 4505 S. Maryland Pkwy. Room 1172, Las Vegas, NV 89154, United States,Department of Psychology and Neuroscience, University of Colorado, 3100 Marine St., Boulder, CO 80309, United States
| | - Jessica Z K Caldwell
- Department of Neurology, Cleveland Clinic Lou Ruvo Center for Brain Health, 888 W Bonneville Ave, Las Vegas, NV 89106, United States
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Baldi S, Michielse S, Vriend C, van den Heuvel MP, van den Heuvel OA, Schruers KRJ, Goossens L. Abnormal white-matter rich-club organization in obsessive-compulsive disorder. Hum Brain Mapp 2022; 43:4699-4709. [PMID: 35735129 PMCID: PMC9491289 DOI: 10.1002/hbm.25984] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/24/2022] [Accepted: 06/03/2022] [Indexed: 11/09/2022] Open
Abstract
Rich‐club organization is key to efficient global neuronal signaling and integration of information. Alterations interfere with higher‐order cognitive processes, and are common to several psychiatric and neurological conditions. A few studies examining the structural connectome in obsessive–compulsive disorder (OCD) suggest lower efficiency of information transfer across the brain. However, it remains unclear whether this is due to alterations in rich‐club organization. In the current study, the structural connectome of 28 unmedicated OCD patients, 8 of their unaffected siblings and 28 healthy controls was reconstructed by means of diffusion‐weighted imaging and probabilistic tractography. Topological and weighted measures of rich‐club organization and connectivity were computed, alongside global and nodal measures of network integration and segregation. The relationship between clinical scores and network properties was explored. Compared to healthy controls, OCD patients displayed significantly lower topological and weighted rich‐club organization, allocating a smaller fraction of all connection weights to the rich‐club core. Global clustering coefficient, local efficiency, and clustering of nonrich club nodes were significantly higher in OCD patients. Significant three‐group differences emerged, with siblings displaying highest and lowest values in different measures. No significant correlation with any clinical score was found. Our results suggest weaker structural connectivity between rich‐club nodes in OCD patients, possibly resulting in lower network integration in favor of higher network segregation. We highlight the need of looking at network‐based alterations in brain organization and function when investigating the neurobiological basis of this disorder, and stimulate further research into potential familial protective factors against the development of OCD.
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Affiliation(s)
- Samantha Baldi
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Stijn Michielse
- Department of Neurosurgery, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Chris Vriend
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, Netherlands.,Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Odile A van den Heuvel
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, Netherlands.,Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Koen R J Schruers
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Liesbet Goossens
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
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38
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Rheault F, Schilling KG, Valcourt‐Caron A, Théberge A, Poirier C, Grenier G, Guberman GI, Begnoche J, Legarreta JH, Y. Cai L, Roy M, Edde M, Caceres MP, Ocampo‐Pineda M, Al‐Sharif N, Karan P, Bontempi P, Obaid S, Bosticardo S, Schiavi S, Sairanen V, Daducci A, Cutting LE, Petit L, Descoteaux M, Landman BA. Tractostorm 2: Optimizing tractography dissection reproducibility with segmentation protocol dissemination. Hum Brain Mapp 2022; 43:2134-2147. [PMID: 35141980 PMCID: PMC8996349 DOI: 10.1002/hbm.25777] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 11/19/2021] [Accepted: 12/31/2021] [Indexed: 11/29/2022] Open
Abstract
The segmentation of brain structures is a key component of many neuroimaging studies. Consistent anatomical definitions are crucial to ensure consensus on the position and shape of brain structures, but segmentations are prone to variation in their interpretation and execution. White-matter (WM) pathways are global structures of the brain defined by local landmarks, which leads to anatomical definitions being difficult to convey, learn, or teach. Moreover, the complex shape of WM pathways and their representation using tractography (streamlines) make the design and evaluation of dissection protocols difficult and time-consuming. The first iteration of Tractostorm quantified the variability of a pyramidal tract dissection protocol and compared results between experts in neuroanatomy and nonexperts. Despite virtual dissection being used for decades, in-depth investigations of how learning or practicing such protocols impact dissection results are nonexistent. To begin to fill the gap, we evaluate an online educational tractography course and investigate the impact learning and practicing a dissection protocol has on interrater (groupwise) reproducibility. To generate the required data to quantify reproducibility across raters and time, 20 independent raters performed dissections of three bundles of interest on five Human Connectome Project subjects, each with four timepoints. Our investigation shows that the dissection protocol in conjunction with an online course achieves a high level of reproducibility (between 0.85 and 0.90 for the voxel-based Dice score) for the three bundles of interest and remains stable over time (repetition of the protocol). Suggesting that once raters are familiar with the software and tasks at hand, their interpretation and execution at the group level do not drastically vary. When compared to previous work that used a different method of communication for the protocol, our results show that incorporating a virtual educational session increased reproducibility. Insights from this work may be used to improve the future design of WM pathway dissection protocols and to further inform neuroanatomical definitions.
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Affiliation(s)
- Francois Rheault
- Electrical and Computer EngineeringVanderbilt UniversityNashvilleTennesseeUSA
| | - Kurt G. Schilling
- Vanderbilt University Institute of ImagingNashvilleTennesseeUSA
- Department of Radiology and Radiological ScienceVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Alex Valcourt‐Caron
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Département d'InformatiqueUniversité de SherbrookeSherbrookeQuébecCanada
| | - Antoine Théberge
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Département d'InformatiqueUniversité de SherbrookeSherbrookeQuébecCanada
- Videos and Images Theory and Analytics Laboratory (VITAL), Département d'InformatiqueUniversité de SherbrookeSherbrookeQuébecCanada
| | - Charles Poirier
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Département d'InformatiqueUniversité de SherbrookeSherbrookeQuébecCanada
| | - Gabrielle Grenier
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Département d'InformatiqueUniversité de SherbrookeSherbrookeQuébecCanada
| | - Guido I. Guberman
- Department of Neurology and Neurosurgery, Faculty of MedicineMcGill UniversityMontrealQuébecCanada
| | - John Begnoche
- The Center for Cognitive Medicine, Department of PsychiatryVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Jon Haitz Legarreta
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Département d'InformatiqueUniversité de SherbrookeSherbrookeQuébecCanada
- Videos and Images Theory and Analytics Laboratory (VITAL), Département d'InformatiqueUniversité de SherbrookeSherbrookeQuébecCanada
| | - Leon Y. Cai
- Department of Biomedical EngineeringVanderbilt UniversityNashvilleTennesseeUSA
| | - Maggie Roy
- Research Center on AgingUniversité de SherbrookeSherbrookeQuébecCanada
| | - Manon Edde
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Département d'InformatiqueUniversité de SherbrookeSherbrookeQuébecCanada
| | - Marco Perez Caceres
- Département de Radiologie DiagnostiqueUniversité de SherbrookeSherbrookeQuébecCanada
| | - Mario Ocampo‐Pineda
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, Children's HospitalHelsinki University Hospital and University of HelsinkiHelsinkiFinland
| | - Noor Al‐Sharif
- McGill Centre for Integrative Neuroscience (MCIN)McGill UniversityMontrealQuébecCanada
| | - Philippe Karan
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Département d'InformatiqueUniversité de SherbrookeSherbrookeQuébecCanada
| | - Pietro Bontempi
- Department of Neurosciences, Biomedicine and Movement SciencesUniversity of VeronaVeronaItaly
| | - Sami Obaid
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Département d'InformatiqueUniversité de SherbrookeSherbrookeQuébecCanada
- University of Montreal, Health Center Research CenterMontrealCanada
| | - Sara Bosticardo
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, Children's HospitalHelsinki University Hospital and University of HelsinkiHelsinkiFinland
| | - Simona Schiavi
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, Children's HospitalHelsinki University Hospital and University of HelsinkiHelsinkiFinland
| | - Viljami Sairanen
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, Children's HospitalHelsinki University Hospital and University of HelsinkiHelsinkiFinland
| | - Alessandro Daducci
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, Children's HospitalHelsinki University Hospital and University of HelsinkiHelsinkiFinland
| | - Laurie E. Cutting
- Vanderbilt Kennedy CenterVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Laurent Petit
- Groupe d'imagerie neurofonctionnelleCNRS, CEA, IMN, University of BordeauxBordeauxFrance
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Département d'InformatiqueUniversité de SherbrookeSherbrookeQuébecCanada
| | - Bennett A. Landman
- Electrical and Computer EngineeringVanderbilt UniversityNashvilleTennesseeUSA
- Vanderbilt University Institute of ImagingNashvilleTennesseeUSA
- Department of Radiology and Radiological ScienceVanderbilt University Medical CenterNashvilleTennesseeUSA
- Computer ScienceVanderbilt UniversityNashvilleTennesseeUSA
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39
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Zhang F, Daducci A, He Y, Schiavi S, Seguin C, Smith RE, Yeh CH, Zhao T, O'Donnell LJ. Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review. Neuroimage 2022; 249:118870. [PMID: 34979249 PMCID: PMC9257891 DOI: 10.1016/j.neuroimage.2021.118870] [Citation(s) in RCA: 99] [Impact Index Per Article: 49.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 12/03/2021] [Accepted: 12/31/2021] [Indexed: 12/13/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain's structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.
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Affiliation(s)
- Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | | | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Chinese Institute for Brain Research, Beijing, China
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | - Caio Seguin
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia; The University of Sydney, School of Biomedical Engineering, Sydney, Australia
| | - Robert E Smith
- The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia
| | - Chun-Hung Yeh
- Institute for Radiological Research, Chang Gung University, Taoyuan, Taiwan; Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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40
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Fekonja LS, Wang Z, Cacciola A, Roine T, Aydogan DB, Mewes D, Vellmer S, Vajkoczy P, Picht T. Network analysis shows decreased ipsilesional structural connectivity in glioma patients. Commun Biol 2022; 5:258. [PMID: 35322812 PMCID: PMC8943189 DOI: 10.1038/s42003-022-03190-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 02/22/2022] [Indexed: 11/15/2022] Open
Abstract
Gliomas that infiltrate networks and systems, such as the motor system, often lead to substantial functional impairment in multiple systems. Network-based statistics (NBS) allow to assess local network differences and graph theoretical analyses enable investigation of global and local network properties. Here, we used network measures to characterize glioma-related decreases in structural connectivity by comparing the ipsi- with the contralesional hemispheres of patients and correlated findings with neurological assessment. We found that lesion location resulted in differential impairment of both short and long connectivity patterns. Network analysis showed reduced global and local efficiency in the ipsilesional hemisphere compared to the contralesional hemispheric networks, which reflect the impairment of information transfer across different regions of a network. Tumors and their location distinctly alter both local and global brain connectivity within the ipsilesional hemisphere of glioma patients.
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Affiliation(s)
- Lucius S Fekonja
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Berlin, Germany. .,Cluster of Excellence: "Matters of Activity. Image Space Material", Humboldt University, Berlin, Germany.
| | - Ziqian Wang
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Alberto Cacciola
- Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy
| | - Timo Roine
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland.,Turku Brain and Mind Center, University of Turku, Turku, Finland
| | - D Baran Aydogan
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland.,Department of Psychiatry, Helsinki University and Helsinki University Hospital, Helsinki, Finland.,A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Darius Mewes
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Sebastian Vellmer
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Peter Vajkoczy
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Thomas Picht
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Cluster of Excellence: "Matters of Activity. Image Space Material", Humboldt University, Berlin, Germany
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41
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Naro A, Pignolo L, Calabrò RS. Brain Network Organization Following Post-Stroke Neurorehabilitation. Int J Neural Syst 2022; 32:2250009. [PMID: 35139774 DOI: 10.1142/s0129065722500095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Brain network analysis can offer useful information to guide the rehabilitation of post-stroke patients. We applied functional network connection models based on multiplex-multilayer network analysis (MMN) to explore functional network connectivity changes induced by robot-aided gait training (RAGT) using the Ekso, a wearable exoskeleton, and compared it to conventional overground gait training (COGT) in chronic stroke patients. We extracted the coreness of individual nodes at multiple locations in the brain from EEG recordings obtained before and after gait training in a resting state. We found that patients provided with RAGT achieved a greater motor function recovery than those receiving COGT. This difference in clinical outcome was paralleled by greater changes in connectivity patterns among different brain areas central to motor programming and execution, as well as a recruitment of other areas beyond the sensorimotor cortices and at multiple frequency ranges, contemporarily. The magnitude of these changes correlated with motor function recovery chances. Our data suggest that the use of RAGT as an add-on treatment to COGT may provide post-stroke patients with a greater modification of the functional brain network impairment following a stroke. This might have potential clinical implications if confirmed in large clinical trials.
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Affiliation(s)
- Antonino Naro
- IRCCS Centro Neurolesi Bonino Pulejo, Messina, Italy. Via Palermo, SS 113, Ctr. Casazza, 98124, Messina, Italy
| | - Loris Pignolo
- Sant'Anna Institute, Via Siris, 11, 88900 Crotone, Italy
| | - Rocco Salvatore Calabrò
- IRCCS Centro Neurolesi Bonino Pulejo, Messina, Italy. Via Palermo, SS 113, Ctr. Casazza, 98124, Messina, Italy
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42
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Gu S, Fotiadis P, Parkes L, Xia CH, Gur RC, Gur RE, Roalf DR, Satterthwaite TD, Bassett DS. Network controllability mediates the relationship between rigid structure and flexible dynamics. Netw Neurosci 2022; 6:275-297. [PMID: 36605890 PMCID: PMC9810281 DOI: 10.1162/netn_a_00225] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 12/15/2021] [Indexed: 01/07/2023] Open
Abstract
Precisely how the anatomical structure of the brain supports a wide range of complex functions remains a question of marked importance in both basic and clinical neuroscience. Progress has been hampered by the lack of theoretical frameworks explaining how a structural network of relatively rigid interareal connections can produce a diverse repertoire of functional neural dynamics. Here, we address this gap by positing that the brain's structural network architecture determines the set of accessible functional connectivity patterns according to predictions of network control theory. In a large developmental cohort of 823 youths aged 8 to 23 years, we found that the flexibility of a brain region's functional connectivity was positively correlated with the proportion of its structural links extending to different cognitive systems. Notably, this relationship was mediated by nodes' boundary controllability, suggesting that a region's strategic location on the boundaries of modules may underpin the capacity to integrate information across different cognitive processes. Broadly, our study provides a mechanistic framework that illustrates how temporal flexibility observed in functional networks may be mediated by the controllability of the underlying structural connectivity.
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Affiliation(s)
- Shi Gu
- Brain and Intelligence Group, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Panagiotis Fotiadis
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Linden Parkes
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Cedric H. Xia
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ruben C. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Raquel E. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - David R. Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D. Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dani S. Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, NM, USA
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43
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Zekelman LR, Zhang F, Makris N, He J, Chen Y, Xue T, Liera D, Drane DL, Rathi Y, Golby AJ, O'Donnell LJ. White matter association tracts underlying language and theory of mind: An investigation of 809 brains from the Human Connectome Project. Neuroimage 2022; 246:118739. [PMID: 34856375 PMCID: PMC8862285 DOI: 10.1016/j.neuroimage.2021.118739] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 10/20/2021] [Accepted: 11/15/2021] [Indexed: 12/24/2022] Open
Abstract
Language and theory of mind (ToM) are the cognitive capacities that allow for the successful interpretation and expression of meaning. While functional MRI investigations are able to consistently localize language and ToM to specific cortical regions, diffusion MRI investigations point to an inconsistent and sometimes overlapping set of white matter tracts associated with these two cognitive domains. To further examine the white matter tracts that may underlie these domains, we use a two-tensor tractography method to investigate the white matter microstructure of 809 participants from the Human Connectome Project. 20 association white matter tracts (10 in each hemisphere) are uniquely identified by leveraging a neuroanatomist-curated automated white matter tract atlas. The fractional anisotropy (FA), mean diffusivity (MD), and number of streamlines (NoS) are measured for each white matter tract. Performance on neuropsychological assessments of semantic memory (NIH Toolbox Picture Vocabulary Test, TPVT) and emotion perception (Penn Emotion Recognition Test, PERT) are used to measure critical subcomponents of the language and ToM networks, respectively. Regression models are constructed to examine how structural measurements of left and right white matter tracts influence performance across these two assessments. We find that semantic memory performance is influenced by the number of streamlines of the left superior longitudinal fasciculus III (SLF-III), and emotion perception performance is influenced by the number of streamlines of the right SLF-III. Additionally, we find that performance on both semantic memory & emotion perception is influenced by the FA of the left arcuate fasciculus (AF). The results point to multiple, overlapping white matter tracts that underlie the cognitive domains of language and ToM. Results are discussed in terms of hemispheric dominance and concordance with prior investigations.
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Affiliation(s)
- Leo R Zekelman
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Speech and Hearing Bioscience and Technology, Harvard Medical School, Boston, USA.
| | - Fan Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Nikos Makris
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, USA; Center for Morphometric Analysis, Department of Psychiatry and Neurology, A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Psychiatric Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Jianzhong He
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Institution of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, China
| | - Yuqian Chen
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; School of Computer Science, University of Sydney, NSW, Australia
| | - Tengfei Xue
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; School of Computer Science, University of Sydney, NSW, Australia
| | | | - Daniel L Drane
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA; Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA; Department of Neurology, University of Washington School of Medicine, Seattle, WA, US
| | - Yogesh Rathi
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Alexandra J Golby
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Lauren J O'Donnell
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
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44
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Canal-Garcia A, Gómez-Ruiz E, Mijalkov M, Chang YW, Volpe G, Pereira JB. Multiplex Connectome Changes across the Alzheimer’s Disease Spectrum Using Gray Matter and Amyloid Data. Cereb Cortex 2022; 32:3501-3515. [PMID: 35059722 PMCID: PMC9376877 DOI: 10.1093/cercor/bhab429] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 10/27/2021] [Accepted: 10/28/2021] [Indexed: 11/25/2022] Open
Abstract
The organization of the Alzheimer’s disease (AD) connectome has been studied using graph theory using single neuroimaging modalities such as positron emission tomography (PET) or structural magnetic resonance imaging (MRI). Although these modalities measure distinct pathological processes that occur in different stages in AD, there is evidence that they are not independent from each other. Therefore, to capture their interaction, in this study we integrated amyloid PET and gray matter MRI data into a multiplex connectome and assessed the changes across different AD stages. We included 135 cognitively normal (CN) individuals without amyloid-β pathology (Aβ−) in addition to 67 CN, 179 patients with mild cognitive impairment (MCI) and 132 patients with AD dementia who all had Aβ pathology (Aβ+) from the Alzheimer’s Disease Neuroimaging Initiative. We found widespread changes in the overlapping connectivity strength and the overlapping connections across Aβ-positive groups. Moreover, there was a reorganization of the multiplex communities in MCI Aβ + patients and changes in multiplex brain hubs in both MCI Aβ + and AD Aβ + groups. These findings offer a new insight into the interplay between amyloid-β pathology and brain atrophy over the course of AD that moves beyond traditional graph theory analyses based on single brain networks.
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Affiliation(s)
- Anna Canal-Garcia
- Address correspondence to Department of NVS, Division of Clinical Geriatrics, NEO seventh floor, Blickagången 16, 141 52 Huddinge, Sweden. ; 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
| | | | - Mite Mijalkov
- Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden
| | - Yu-Wei Chang
- Department of Physics, University of Gothenburg, Gothenburg, Sweden
| | - Giovanni Volpe
- Department of Physics, University of Gothenburg, Gothenburg, Sweden
| | - Joana B Pereira
- Address correspondence to Department of NVS, Division of Clinical Geriatrics, NEO seventh floor, Blickagången 16, 141 52 Huddinge, Sweden. ; 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
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45
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Roine T, Mohammadian M, Hirvonen J, Kurki T, Posti JP, Takala RS, Newcombe V, Tallus J, Katila AJ, Maanpää HR, Frantzen J, Menon D, Tenovuo O. Structural brain connectivity correlates with outcome in mild traumatic brain injury. J Neurotrauma 2022; 39:336-347. [PMID: 35018829 DOI: 10.1089/neu.2021.0093] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
We investigated the topology of structural brain connectivity networks and its association to outcome following mild traumatic brain injury, a major cause of permanent disability. Eighty-five patients with mild traumatic brain injury underwent MRI twice, about three weeks and eight months after injury, and 30 age-matched orthopedic trauma control subjects were scanned. Outcome was assessed with Extended Glasgow Outcome Scale on average eight months after injury. We performed constrained spherical deconvolution based probabilistic streamlines tractography on diffusion MRI data and parcellated cortical and subcortical gray matter into 84 regions based on T1-weighted data to reconstruct structural brain connectivity networks weighted by the number of streamlines. Graph theoretical methods were employed to measure network properties in both patients and controls, and correlations between these properties and outcome were calculated. We found no global differences in the network properties between patients with mild traumatic brain injury and orthopedic control subjects at either stage. However, we found significantly increased betweenness centrality of the right pars opercularis in the chronic stage compared to control subjects. Furthermore, both global and local network properties correlated significantly with outcome. Higher normalized global efficiency, degree, and strength as well as lower small-worldness were associated with better outcome. Correlations between the outcome and the local network properties were the most prominent in the left putamen and the left postcentral gyrus. Our results indicate that both global and local network properties provide valuable information about the outcome already in the acute/subacute stage, and therefore, are promising biomarkers for prognostic purposes in mild traumatic brain injury.
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Affiliation(s)
- Timo Roine
- University of Turku, 8058, Turku Brain and Mind Center, Turku, Finland.,Aalto University School of Science, 313201, Department of Neuroscience and Biomedical Engineering, Espoo, Finland;
| | - Mehrbod Mohammadian
- University of Turku Faculty of Medicine, 60654, Department of Clinical Neurosciences, Turku, Finland.,Turku University Hospital, 60652, Turku Brain Injury Center, Neurocenter, Turku, Finland;
| | - Jussi Hirvonen
- TYKS Turku University Hospital, 60652, Department of Radiology, Turku, Varsinais-Suomi, Finland;
| | - Timo Kurki
- University of Turku Faculty of Medicine, 60654, Department of Clinical Neurosciences, Turku, Finland.,Turku University Hospital, 60652, Turku Brain Injury Center, Neurocenter, Turku, Finland.,TYKS Turku University Hospital, 60652, Department of Radiology, Turku, Varsinais-Suomi, Finland;
| | - Jussi P Posti
- University of Turku Faculty of Medicine, 60654, Department of Clinical Neurosciences, Turku, Finland.,Turku University Hospital, 60652, Turku Brain Injury Center, Neurocenter, Turku, Varsinais-Suomi, Finland.,TYKS Turku University Hospital, 60652, Department of Neurosurgery. Neurocenter, Turku, Varsinais-Suomi, Finland;
| | - Riikka Sk Takala
- Turku University Hospital, Perioperative Services, Intensive Care Medicine and Pain Management, Turku, Finland.,University of Turku, 8058, Anaesthesiology, Intensive Care, Emergency Care and Pain Medicine, Turku, Varsinais-Suomi, Finland;
| | - Virginia Newcombe
- University of Cambridge, Division of Anaesthesia, Addenbrooke's Hospital, Cambridge, United Kingdom of Great Britain and Northern Ireland;
| | - Jussi Tallus
- Turku University Hospital, 60652, Turku Brain Injury Center, Neurocenter, Turku, Varsinais-Suomi, Finland;
| | - Ari J Katila
- Turku University Hospital, Perioperative Services, Intensive Care Medicine and Pain Management, Turku, Varsinais-Suomi, Finland;
| | - Henna-Riikka Maanpää
- Turku University Hospital, 60652, Turku Brain Injury Center, Neurocenter, Turku, Varsinais-Suomi, Finland.,Turku University Hospital, Department of Neurosurgery, Neurocenter, Turku, Varsinais-Suomi, Finland;
| | - Janek Frantzen
- Turku University Hospital, Turku Brain Injury Center, Neurocenter, Turku, Finland.,Turku University Hospital, Department of Neurosurgery, Neurocenter, Turku, Varsinais-Suomi, Finland.,University of Turku Faculty of Medicine, 60654, Department of Clinical Neurosciences, Turku, Finland;
| | - David Menon
- University of Cambridge, Division of Anaesthesia, Addenbrooke's Hospital, Cambridge, United Kingdom of Great Britain and Northern Ireland;
| | - Olli Tenovuo
- University of Turku Faculty of Medicine, 60654, Department of Clinical Neurosciences, Turku, Finland.,Turku University Hospital, 60652, Turku Brain Injury Center, Neurocenter, Turku, Finland;
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46
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Elsheikh SSM, Chimusa ER, Mulder NJ, Crimi A. Relating Global and Local Connectome Changes to Dementia and Targeted Gene Expression in Alzheimer's Disease. Front Hum Neurosci 2022; 15:761424. [PMID: 35002653 PMCID: PMC8734427 DOI: 10.3389/fnhum.2021.761424] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 11/25/2021] [Indexed: 01/01/2023] Open
Abstract
Networks are present in many aspects of our lives, and networks in neuroscience have recently gained much attention leading to novel representations of brain connectivity. The integration of neuroimaging characteristics and genetics data allows a better understanding of the effects of the gene expression on brain structural and functional connections. The current work uses whole-brain tractography in a longitudinal setting, and by measuring the brain structural connectivity changes studies the neurodegeneration of Alzheimer's disease. This is accomplished by examining the effect of targeted genetic risk factors on the most common local and global brain connectivity measures. Furthermore, we examined the extent to which Clinical Dementia Rating relates to brain connections longitudinally, as well as to gene expression. For instance, here we show that the expression of PLAU gene increases the change over time in betweenness centrality related to the fusiform gyrus. We also show that the betweenness centrality metric impact dementia-related changes in distinct brain regions. Our findings provide insights into the complex longitudinal interplay between genetics and brain characteristics and highlight the role of Alzheimer's genetic risk factors in the estimation of regional brain connectivity alterations.
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Affiliation(s)
- Samar S M Elsheikh
- Pharmacogenetic Research Clinic, Centre for Addiction and Mental Health, Campbell Family Mental Health Research Institute, Toronto, ON, Canada.,Computational Biology Division, Department of Integrative Biomedical Sciences, Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Emile R Chimusa
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | | | - Nicola J Mulder
- Computational Biology Division, Department of Integrative Biomedical Sciences, Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Alessandro Crimi
- Computer Vision Group, Sano Centre for Computational Medicine, Kraków, Poland.,Institute for Neuropathology, University Hospital of Zurich, Zurich, Switzerland.,Department of Mathematics, African Institute for Mathematical Sciences, Cape Coast, Ghana
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47
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Sairanen V, Ocampo-Pineda M, Granziera C, Schiavi S, Daducci A. Incorporating outlier information into diffusion-weighted MRI modeling for robust microstructural imaging and structural brain connectivity analyses. Neuroimage 2021; 247:118802. [PMID: 34896584 DOI: 10.1016/j.neuroimage.2021.118802] [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: 06/10/2021] [Revised: 11/01/2021] [Accepted: 12/09/2021] [Indexed: 11/28/2022] Open
Abstract
The white matter structures of the human brain can be represented using diffusion-weighted MRI tractography. Unfortunately, tractography is prone to find false-positive streamlines causing a severe decline in its specificity and limiting its feasibility in accurate structural brain connectivity analyses. Filtering algorithms have been proposed to reduce the number of invalid streamlines but the currently available filtering algorithms are not suitable to process data that contains motion artefacts which are typical in clinical research. We augmented the Convex Optimization Modelling for Microstructure Informed Tractography (COMMIT) algorithm to adjust for these signals drop-out motion artefacts. We demonstrate with comprehensive Monte-Carlo whole brain simulations and in vivo infant data that our robust algorithm is capable of properly filtering tractography reconstructions despite these artefacts. We evaluated the results using parametric and non-parametric statistics and our results demonstrate that if not accounted for, motion artefacts can have severe adverse effects in human brain structural connectivity analyses as well as in microstructural property mappings. In conclusion, the usage of robust filtering methods to mitigate motion related errors in tractogram filtering is highly beneficial, especially in clinical studies with uncooperative patient groups such as infants. With our presented robust augmentation and open-source implementation, robust tractogram filtering is readily available.
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Affiliation(s)
- Viljami Sairanen
- Department of Computer Science, University of Verona, Verona, Italy; Translational Imaging in Neurology, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Neurologic Clinic and Policlinic, Basel, Switzerland; BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland.
| | | | - Cristina Granziera
- Translational Imaging in Neurology, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Neurologic Clinic and Policlinic, Basel, Switzerland
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
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48
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Borrelli P, Cavaliere C, Salvatore M, Jovicich J, Aiello M. Structural Brain Network Reproducibility: Influence of Different Diffusion Acquisition and Tractography Reconstruction Schemes on Graph Metrics. Brain Connect 2021; 12:754-767. [PMID: 34605673 DOI: 10.1089/brain.2021.0123] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Background: Graph metrics of structural brain networks demonstrate to be a powerful tool for investigating brain topology at a large scale. However, the variability of the results related to applying different magnetic resonance acquisition schemes and tractography reconstruction techniques is not fully characterized. Materials and Methods: The present work aims to evaluate the influence of different combinations of diffusion acquisition schemes (single and multishell), diffusion models (tensor and spherical deconvolution), and tractography reconstruction approaches (deterministic and probabilistic) on the reproducibility of graph metrics derived from structural connectome on test/retest (TRT) data released by the Human Connectome Project. From each implemented experimental setup, both global and local graph metrics were evaluated and their reproducibility was estimated by the intraclass correlation coefficient (ICC). Moreover, the percentage relative standard deviation (pRSD) from the ICC values of local graph metrics was calculated to quantify how much the reproducibility varied across nodes within each experimental setup. Results: The presented results show that different combinations of diffusion acquisition schemes, diffusion models, and tractography algorithms can strongly affect the reproducibility of global and local graph metrics. The combination of constrained spherical deconvolution (CSD) and deterministic tractography gave generally high reproducibility (ICCs >0.75) and lowest pRSD for the considered graph metrics, meanwhile probabilistic CSD with a high b-value returned the highest reproducibility. Notably, the introduction of streamline selection filters on CSD can substantially affect the reproducibility. Discussion: This work demonstrates that the TRT reproducibility of graph metrics is generally high but can vary substantially with different combinations of acquisition and reconstruction schemes. Impact statement This work demonstrates the influence of different diffusion acquisition schemes, diffusion models, and tractography reconstruction approaches on the reproducibility of graph metrics derived from structural connectome. The presented findings impact on the choice of both acquisition protocol and processing pipeline for topological analyses to produce reproducible measurements for brain network studies.
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Affiliation(s)
| | | | | | - Jorge Jovicich
- Center for Mind/Brain Sciences, University of Trento, Rovereto, Italy
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49
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Trinkle S, Foxley S, Wildenberg G, Kasthuri N, La Rivière P. The role of spatial embedding in mouse brain networks constructed from diffusion tractography and tracer injections. Neuroimage 2021; 244:118576. [PMID: 34520833 PMCID: PMC8611903 DOI: 10.1016/j.neuroimage.2021.118576] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 09/07/2021] [Accepted: 09/10/2021] [Indexed: 11/24/2022] Open
Abstract
Diffusion MRI tractography is the only noninvasive method to measure the structural connectome in humans. However, recent validation studies have revealed limitations of modern tractography approaches, which lead to significant mistracking caused in part by local uncertainties in fiber orientations that accumulate to produce larger errors for longer streamlines. Characterizing the role of this length bias in tractography is complicated by the true underlying contribution of spatial embedding to brain topology. In this work, we compare graphs constructed with ex vivo tractography data in mice and neural tracer data from the Allen Mouse Brain Connectivity Atlas to random geometric surrogate graphs which preserve the low-order distance effects from each modality in order to quantify the role of geometry in various network properties. We find that geometry plays a substantially larger role in determining the topology of graphs produced by tractography than graphs produced by tracers. Tractography underestimates weights at long distances compared to neural tracers, which leads tractography to place network hubs close to the geometric center of the brain, as do corresponding tractography-derived random geometric surrogates, while tracer graphs place hubs further into peripheral areas of the cortex. We also explore the role of spatial embedding in modular structure, network efficiency and other topological measures in both modalities. Throughout, we compare the use of two different tractography streamline node assignment strategies and find that the overall differences between tractography approaches are small relative to the differences between tractography- and tracer-derived graphs. These analyses help quantify geometric biases inherent to tractography and promote the use of geometric benchmarking in future tractography validation efforts.
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Affiliation(s)
- Scott Trinkle
- Department of Radiology, University of Chicago, Chicago, IL, USA.
| | - Sean Foxley
- Department of Radiology, University of Chicago, Chicago, IL, USA
| | - Gregg Wildenberg
- Department of Neurobiology, University of Chicago, Chicago, IL, USA
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50
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Kurokawa R, Kamiya K, Koike S, Nakaya M, Uematsu A, Tanaka SC, Kamagata K, Okada N, Morita K, Kasai K, Abe O. Cross-scanner reproducibility and harmonization of a diffusion MRI structural brain network: A traveling subject study of multi-b acquisition. Neuroimage 2021; 245:118675. [PMID: 34710585 DOI: 10.1016/j.neuroimage.2021.118675] [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: 05/17/2021] [Revised: 09/26/2021] [Accepted: 10/21/2021] [Indexed: 01/18/2023] Open
Abstract
Characterization of brain networks by diffusion MRI (dMRI) has rapidly evolved, and there are ongoing movements toward data sharing and multi-center studies. To extract meaningful information from multi-center data, methods to correct for the bias caused by scanner differences, that is, harmonization, are urgently needed. In this work, we report the cross-scanner differences in structural network analyses using data from nine traveling subjects (four males and five females, 21-49 years-old) who underwent scanning using four 3T scanners (public database available from the Brain/MINDS Beyond Human Brain MRI project (http://mriportal.umin.jp/)). The reliability and reproducibility were compared to those of data from another set of four subjects (all males, 29-42 years-old) who underwent scan-rescan (interval, 105-147 days) with the same scanner as well as scan-rescan data from the Human Connectome Project database. The results demonstrated that the reliability of the edge weights and graph theory metrics was lower for data including different scanners, compared to the scan-rescan with the same scanner. Besides, systematic differences between scanners were observed, indicating the risk of bias in comparing networks obtained from different scanners directly. We further demonstrate that it is feasible to reduce inter-scanner variabilities while preserving the inter-subject differences among healthy individuals by modeling the scanner effects at the level of network matrices, when traveling-subject data are available for calibration between scanners. The present data and results are expected to serve as a basis for developing and evaluating novel harmonization methods.
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Affiliation(s)
- Ryo Kurokawa
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
| | - Kouhei Kamiya
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; Department of Radiology, Toho University, Tokyo, Japan; Department of Radiology, Juntendo University, Tokyo, Japan.
| | - Shinsuke Koike
- Center for Evolutionary Cognitive Sciences (ECS), Graduate School of Art and Sciences, The University of Tokyo, Tokyo, Japan; University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), Tokyo, Japan; University of Tokyo Center for Integrative Science of Human Behavior (CiSHuB), Tokyo, Japan; The International Research Center for Neurointelligence (WPI-IRCN), Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, Japan.
| | - Moto Nakaya
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
| | - Akiko Uematsu
- Center for Evolutionary Cognitive Sciences (ECS), Graduate School of Art and Sciences, The University of Tokyo, Tokyo, Japan.
| | - Saori C Tanaka
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International (ATR), Kyoto, Japan.
| | - Koji Kamagata
- Department of Radiology, Juntendo University, Tokyo, Japan.
| | - Naohiro Okada
- University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), Tokyo, Japan; The International Research Center for Neurointelligence (WPI-IRCN), Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, Japan; Department of Neuropsychiatry, The University of Tokyo, Tokyo, Japan.
| | - Kentaro Morita
- Department of Neuropsychiatry, The University of Tokyo, Tokyo, Japan.
| | - Kiyoto Kasai
- University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), Tokyo, Japan; University of Tokyo Center for Integrative Science of Human Behavior (CiSHuB), Tokyo, Japan; The International Research Center for Neurointelligence (WPI-IRCN), Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, Japan; Department of Neuropsychiatry, The University of Tokyo, Tokyo, Japan.
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
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