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High B-value diffusion tensor imaging for early detection of hippocampal microstructural alteration in a mouse model of multiple sclerosis. Sci Rep 2022; 12:12008. [PMID: 35835801 PMCID: PMC9283448 DOI: 10.1038/s41598-022-15511-0] [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: 12/01/2021] [Accepted: 06/24/2022] [Indexed: 11/23/2022] Open
Abstract
Several studies have highlighted the value of diffusion tensor imaging (DTI) with strong diffusion weighting to reveal white matter microstructural lesions, but data in gray matter (GM) remains scarce. Herein, the effects of b-values combined with different numbers of diffusion-encoding directions (NDIRs) on DTI metrics to capture the normal hippocampal microstructure and its early alterations were investigated in a mouse model of multiple sclerosis (experimental autoimmune encephalomyelitis [EAE]). Two initial DTI datasets (B2700-43Dir acquired with b = 2700 s.mm−2 and NDIR = 43; B1000-22Dir acquired with b = 1000 s.mm−2 and NDIR = 22) were collected from 18 normal and 18 EAE mice at 4.7 T. Three additional datasets (B2700-22Dir, B2700-12Dir and B1000-12Dir) were extracted from the initial datasets. In healthy mice, we found a significant influence of b-values and NDIR on all DTI metrics. Confronting unsupervised hippocampal layers classification to the true anatomical classification highlighted the remarkable discrimination of the molecular layer with B2700-43Dir compared with the other datasets. Only DTI from the B2700 datasets captured the dendritic loss occurring in the molecular layer of EAE mice. Our findings stress the needs for both high b-values and sufficient NDIR to achieve a GM DTI with more biologically meaningful correlations, though DTI-metrics should be interpreted with caution in these settings.
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Seider NA, Adeyemo B, Miller R, Newbold DJ, Hampton JM, Scheidter KM, Rutlin J, Laumann TO, Roland JL, Montez DF, Van AN, Zheng A, Marek S, Kay BP, Bretthorst GL, Schlaggar BL, Greene DJ, Wang Y, Petersen SE, Barch DM, Gordon EM, Snyder AZ, Shimony JS, Dosenbach NUF. Accuracy and reliability of diffusion imaging models. Neuroimage 2022; 254:119138. [PMID: 35339687 PMCID: PMC9841915 DOI: 10.1016/j.neuroimage.2022.119138] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 03/01/2022] [Accepted: 03/22/2022] [Indexed: 01/19/2023] Open
Abstract
Diffusion imaging aims to non-invasively characterize the anatomy and integrity of the brain's white matter fibers. We evaluated the accuracy and reliability of commonly used diffusion imaging methods as a function of data quantity and analysis method, using both simulations and highly sampled individual-specific data (927-1442 diffusion weighted images [DWIs] per individual). Diffusion imaging methods that allow for crossing fibers (FSL's BedpostX [BPX], DSI Studio's Constant Solid Angle Q-Ball Imaging [CSA-QBI], MRtrix3's Constrained Spherical Deconvolution [CSD]) estimated excess fibers when insufficient data were present and/or when the data did not match the model priors. To reduce such overfitting, we developed a novel Bayesian Multi-tensor Model-selection (BaMM) method and applied it to the popular ball-and-stick model used in BedpostX within the FSL software package. BaMM was robust to overfitting and showed high reliability and the relatively best crossing-fiber accuracy with increasing amounts of diffusion data. Thus, sufficient data and an overfitting resistant analysis method enhance precision diffusion imaging. For potential clinical applications of diffusion imaging, such as neurosurgical planning and deep brain stimulation (DBS), the quantities of data required to achieve diffusion imaging reliability are lower than those needed for functional MRI.
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Affiliation(s)
- Nicole A Seider
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Babatunde Adeyemo
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Ryland Miller
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Dillan J Newbold
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Neurology, New York University Langone Medical Center, New York, NY 10016, United States of America
| | - Jacqueline M Hampton
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Kristen M Scheidter
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Jerrel Rutlin
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Timothy O Laumann
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Jarod L Roland
- Department of Neurological Surgery, Washington University School of Medicine, St Louis, MO 63110 United States of America
| | - David F Montez
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Andrew N Van
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Biomedical Engineering, Washington University in St Louis, St. Louis, MO 63110, United States of America
| | - Annie Zheng
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Scott Marek
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Benjamin P Kay
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - G Larry Bretthorst
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Chemistry, Washington University in St Louis, St. Louis, MO 63110, United States of America
| | - Bradley L Schlaggar
- Kennedy Krieger Institute, Baltimore, MD 21205, United States of America; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, United States of America; Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD 21287, United States of America
| | - Deanna J Greene
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, United States of America
| | - Yong Wang
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Biomedical Engineering, Washington University in St Louis, St. Louis, MO 63110, United States of America
| | - Steven E Petersen
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Biomedical Engineering, Washington University in St Louis, St. Louis, MO 63110, United States of America; Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Psychological and Brain Sciences, Washington University in St. Louis, MO 63110, United States of America
| | - Deanna M Barch
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Psychological and Brain Sciences, Washington University in St. Louis, MO 63110, United States of America
| | - Evan M Gordon
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Abraham Z Snyder
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Joshua S Shimony
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Nico U F Dosenbach
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Biomedical Engineering, Washington University in St Louis, St. Louis, MO 63110, United States of America; Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, United States of America
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53
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Zhang J, Sun Z, Duan F, Shi L, Zhang Y, Solé‐Casals J, Caiafa CF. Cerebral cortex layer segmentation using diffusion magnetic resonance imaging in vivo with applications to laminar connections and working memory analysis. Hum Brain Mapp 2022; 43:5220-5234. [PMID: 35778791 PMCID: PMC9812233 DOI: 10.1002/hbm.25998] [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: 03/27/2022] [Revised: 06/10/2022] [Accepted: 06/15/2022] [Indexed: 01/15/2023] Open
Abstract
Understanding the laminar brain structure is of great help in further developing our knowledge of the functions of the brain. However, since most layer segmentation methods are invasive, it is difficult to apply them to the human brain in vivo. To systematically explore the human brain's laminar structure noninvasively, the K-means clustering algorithm was used to automatically segment the left hemisphere into two layers, the superficial and deep layers, using a 7 Tesla (T) diffusion magnetic resonance imaging (dMRI)open dataset. The obtained layer thickness was then compared with the layer thickness of the BigBrain reference dataset, which segmented the neocortex into six layers based on the von Economo atlas. The results show a significant correlation not only between our automatically segmented superficial layer thickness and the thickness of layers 1-3 from the reference histological data, but also between our automatically segmented deep layer thickness and the thickness of layers 4-6 from the reference histological data. Second, we constructed the laminar connections between two pairs of unidirectional connected regions, which is consistent with prior research. Finally, we conducted the laminar analysis of the working memory, which was challenging to do in the past, and explained the conclusions of the functional analysis. Our work successfully demonstrates that it is possible to segment the human cortex noninvasively into layers using dMRI data and further explores the mechanisms of the human brain.
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Affiliation(s)
- Jie Zhang
- College of Artificial IntelligenceNankai UniversityTianjinChina
| | - Zhe Sun
- Computational Engineering Applications UnitHead Office for Information Systems and Cybersecurity, RIKENSaitamaJapan
| | - Feng Duan
- College of Artificial IntelligenceNankai UniversityTianjinChina
| | - Liang Shi
- College of Artificial IntelligenceNankai UniversityTianjinChina
| | - Yu Zhang
- Department of Bioengineering and Department of Electrical and Computer EngineeringLehigh UniversityBethlehemPennsylvaniaUSA
| | - Jordi Solé‐Casals
- College of Artificial IntelligenceNankai UniversityTianjinChina,Department of PsychiatryUniversity of CambridgeCambridgeUK,Data and Signal Processing Research GroupUniversity of Vic‐Central University of CataloniaVicCataloniaSpain
| | - Cesar F. Caiafa
- College of Artificial IntelligenceNankai UniversityTianjinChina,Instituto Argentino de Radioastronomía‐ CCT La Plata, CONICET/CIC‐PBA/UNLP, 1894 V.ElisaArgentina
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Bayrakçı A, Zorlu N, Karakılıç M, Gülyüksel F, Yalınçetin B, Oral E, Gelal F, Bora E. Negative symptoms are associated with modularity and thalamic connectivity in schizophrenia. Eur Arch Psychiatry Clin Neurosci 2022; 273:565-574. [PMID: 35661912 DOI: 10.1007/s00406-022-01433-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 05/15/2022] [Indexed: 11/30/2022]
Abstract
Negative symptoms, including avolition, anhedonia, asociality, blunted affect and alogia are associated with poor long-term outcome and functioning. However, treatment options for negative symptoms are limited and neurobiological mechanisms underlying negative symptoms in schizophrenia are still poorly understood. Diffusion-weighted magnetic resonance imaging scans were acquired from 64 patients diagnosed with schizophrenia and 35 controls. Global and regional network properties and rich club organization were investigated using graph analytical methods. We found that the schizophrenia group had higher modularity, clustering coefficient and characteristic path length, and lower rich connections compared to controls, suggesting highly connected nodes within modules but less integrated with nodes in other modules in schizophrenia. We also found a lower nodal degree in the left thalamus and left putamen in schizophrenia relative to the control group. Importantly, higher modularity was associated with greater negative symptoms but not with cognitive deficits in patients diagnosed with schizophrenia suggesting an alteration in modularity might be specific to overall negative symptoms. The nodal degree of the left thalamus was associated with both negative and cognitive symptoms. Our findings are important for improving our understanding of abnormal white-matter network topology underlying negative symptoms in schizophrenia.
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Affiliation(s)
- Adem Bayrakçı
- Department of Psychiatry, Katip Celebi University, Ataturk Education and Research Hospital, Izmir, Turkey
| | - Nabi Zorlu
- Department of Psychiatry, Katip Celebi University, Ataturk Education and Research Hospital, Izmir, Turkey.
| | - Merve Karakılıç
- Department of Psychiatry, Katip Celebi University, Ataturk Education and Research Hospital, Izmir, Turkey
| | - Funda Gülyüksel
- Department of Psychiatry, Katip Celebi University, Ataturk Education and Research Hospital, Izmir, Turkey
| | - Berna Yalınçetin
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, Izmir, Turkey
| | - Elif Oral
- Department of Psychiatry, Katip Celebi University, Ataturk Education and Research Hospital, Izmir, Turkey
| | - Fazıl Gelal
- Department of Radiodiagnostics, Katip Celebi University, Ataturk Education and Research Hospital, Izmir, Turkey
| | - Emre Bora
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, Izmir, Turkey.,Faculty of Medicine, Department of Psychiatry, Dokuz Eylul University, Izmir, Turkey.,Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne, Melbourne, Australia
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Radhakrishnan H, Bennett IJ, Stark CE. Higher-order multi-shell diffusion measures complement tensor metrics and volume in gray matter when predicting age and cognition. Neuroimage 2022; 253:119063. [PMID: 35272021 PMCID: PMC10538083 DOI: 10.1016/j.neuroimage.2022.119063] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 03/02/2022] [Accepted: 03/07/2022] [Indexed: 12/13/2022] Open
Abstract
Recent advances in diffusion-weighted imaging have enabled us to probe the microstructure of even gray matter non-invasively. However, these advanced multi-shell protocols are often not included in large-scale studies as they significantly increase scan time. In this study, we investigated whether one set of multi-shell diffusion metrics commonly used in gray matter (as derived from Neurite Orientation Dispersion and Density Imaging, NODDI) provide enough additional information over typical tensor and volume metrics to justify the increased acquisition time, using the cognitive aging framework in the human hippocampus as a testbed. We first demonstrated that NODDI metrics are robust and reliable by replicating previous findings from our lab in a larger population of 79 younger (20.41 ± 1.89 years, 46 females) and 75 older (73.56 ± 6.26 years, 45 females) adults, showing that these metrics in the hippocampal subfields are sensitive to age and memory performance. We then asked how these subfield specific hippocampal NODDI metrics compared with standard tensor metrics and volume in predicting age and memory ability. We discovered that both NODDI and tensor measures separately predicted age and cognition in comparable capacities. However, integrating these modalities together considerably increased the predictive power of our logistic models, indicating that NODDI and tensor measures may be capturing independent microstructural information. We use these findings to encourage neuroimaging data collection consortiums to include a multi-shell diffusion sequence in their protocols since existing NODDI measures (and potential future multi-shell measures) may be able to capture microstructural variance that is missed by traditional approaches, even in studies exclusively examining gray matter.
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Affiliation(s)
- Hamsanandini Radhakrishnan
- Mathematical, Computational and Systems Biology, University of California, Postal Address: 1400 Biological Sciences III, Irvine, CA 92697, United States
| | - Ilana J Bennett
- Department of Psychology, University of California Riverside, Riverside, California, United States
| | - Craig El Stark
- Mathematical, Computational and Systems Biology, University of California, Postal Address: 1400 Biological Sciences III, Irvine, CA 92697, United States; Department of Neurobiology and Behavior, University of California, Irvine, California 92697, United States.
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Brumer I, De Vita E, Ashmore J, Jarosz J, Borri M. Reproducibility of MRI-based white matter tract estimation using multi-fiber probabilistic tractography: effect of user-defined parameters and regions. MAGNETIC RESONANCE MATERIALS IN PHYSICS, BIOLOGY AND MEDICINE 2022; 35:365-373. [PMID: 34661789 PMCID: PMC9188621 DOI: 10.1007/s10334-021-00965-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 08/31/2021] [Accepted: 10/01/2021] [Indexed: 11/21/2022]
Abstract
Objective There is a pressing need to assess user-dependent reproducibility of multi-fibre probabilistic tractography in order to encourage clinical implementation of these advanced and relevant approaches. The goal of this study was to evaluate both intrinsic and inter-user reproducibility of corticospinal tract estimation. Materials and methods Six clinical datasets including motor functional and diffusion MRI were used. Three users performed an independent tractography analysis following identical instructions. Dice indices were calculated to quantify the reproducibility of seed region, fMRI-based end region, and streamline maps. Results The inter-user reproducibility ranged 41–93%, 29–94%, and 50–92%, for seed regions, end regions, and streamline maps, respectively. Differences in streamline maps correlated with differences in seed and end regions. Good inter-user agreement in seed and end regions, yielded inter-user reproducibility close to the intrinsic reproducibility (92–97%) and in most cases higher than 80%. Discussion Uncertainties related to user-dependent decisions and the probabilistic nature of the analysis should be considered when interpreting probabilistic tractography data. The standardization of the methods used to define seed and end regions is a necessary step to improve the accuracy and robustness of multi-fiber probabilistic tractography in a clinical setting. Clinical users should choose a feasible compromise between reproducibility and analysis duration.
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57
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Damatac CG, Soheili-Nezhad S, Blazquez Freches G, Zwiers MP, de Bruijn S, Ikde S, Portengen CM, Abelmann AC, Dammers JT, van Rooij D, Akkermans SEA, Naaijen J, Franke B, Buitelaar JK, Beckmann CF, Sprooten E. Longitudinal changes of ADHD symptoms in association with white matter microstructure: A tract-specific fixel-based analysis. Neuroimage Clin 2022; 35:103057. [PMID: 35644111 PMCID: PMC9144034 DOI: 10.1016/j.nicl.2022.103057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 05/09/2022] [Accepted: 05/21/2022] [Indexed: 11/19/2022]
Abstract
HI symptom remission is associated with more follow-up lCST FD. Combined symptom remission is associated with more follow-up lCST FC. Altered white matter development may be moderated by preceding symptom trajectory.
Background Variation in the longitudinal course of childhood attention deficit/hyperactivity disorder (ADHD) coincides with neurodevelopmental maturation of brain structure and function. Prior work has attempted to determine how alterations in white matter (WM) relate to changes in symptom severity, but much of that work has been done in smaller cross-sectional samples using voxel-based analyses. Using standard diffusion-weighted imaging (DWI) methods, we previously showed WM alterations were associated with ADHD symptom remission over time in a longitudinal sample of probands, siblings, and unaffected individuals. Here, we extend this work by further assessing the nature of these changes in WM microstructure by including an additional follow-up measurement (aged 18 – 34 years), and using the more physiologically informative fixel-based analysis (FBA). Methods Data were obtained from 139 participants over 3 clinical and 2 follow-up DWI waves, and analyzed using FBA in regions-of-interest based on prior findings. We replicated previously reported significant models and extended them by adding another time-point, testing whether changes in combined ADHD and hyperactivity-impulsivity (HI) continuous symptom scores are associated with fixel metrics at follow-up. Results Clinical improvement in HI symptoms over time was associated with more fiber density at follow-up in the left corticospinal tract (lCST) (tmax = 1.092, standardized effect[SE] = 0.044, pFWE = 0.016). Improvement in combined ADHD symptoms over time was associated with more fiber cross-section at follow-up in the lCST (tmax = 3.775, SE = 0.051, pFWE = 0.019). Conclusions Aberrant white matter development involves both lCST micro- and macrostructural alterations, and its path may be moderated by preceding symptom trajectory.
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Affiliation(s)
- Christienne G Damatac
- Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands; Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands.
| | - Sourena Soheili-Nezhad
- Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands; Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands.
| | - Guilherme Blazquez Freches
- Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands; Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands.
| | - Marcel P Zwiers
- Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands; Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands.
| | - Sanne de Bruijn
- Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands; Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands
| | - Seyma Ikde
- Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands; Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands
| | - Christel M Portengen
- Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands; Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands.
| | - Amy C Abelmann
- Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands; Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands.
| | - Janneke T Dammers
- Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands; Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands.
| | - Daan van Rooij
- Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands; Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands.
| | - Sophie E A Akkermans
- Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands; Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands
| | - Jilly Naaijen
- Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands; Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands.
| | - Barbara Franke
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, The Netherlands; Department of Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, The Netherlands.
| | - Jan K Buitelaar
- Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands; Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands; Karakter Child and Adolescent Psychiatry University Centre, Reiner Postlaan 12, 6525 GC Nijmegen, The Netherlands.
| | - Christian F Beckmann
- Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands; Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands; Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain, Nufeld Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, OX3 9DU Oxford, United Kingdom.
| | - Emma Sprooten
- Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands; Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands.
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Yan M, Yu W, Lv Q, Lv Q, Bo T, Chen X, Liu Y, Zhan Y, Yan S, Shen X, Yang B, Hu Q, Yu J, Qiu Z, Feng Y, Zhang XY, Wang H, Xu F, Wang Z. Mapping brain-wide excitatory projectome of primate prefrontal cortex at submicron resolution and comparison with diffusion tractography. eLife 2022; 11:72534. [PMID: 35593765 PMCID: PMC9122499 DOI: 10.7554/elife.72534] [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: 07/27/2021] [Accepted: 04/07/2022] [Indexed: 11/13/2022] Open
Abstract
Resolving trajectories of axonal pathways in the primate prefrontal cortex remains crucial to gain insights into higher-order processes of cognition and emotion, which requires a comprehensive map of axonal projections linking demarcated subdivisions of prefrontal cortex and the rest of brain. Here, we report a mesoscale excitatory projectome issued from the ventrolateral prefrontal cortex (vlPFC) to the entire macaque brain by using viral-based genetic axonal tracing in tandem with high-throughput serial two-photon tomography, which demonstrated prominent monosynaptic projections to other prefrontal areas, temporal, limbic, and subcortical areas, relatively weak projections to parietal and insular regions but no projections directly to the occipital lobe. In a common 3D space, we quantitatively validated an atlas of diffusion tractography-derived vlPFC connections with correlative green fluorescent protein-labeled axonal tracing, and observed generally good agreement except a major difference in the posterior projections of inferior fronto-occipital fasciculus. These findings raise an intriguing question as to how neural information passes along long-range association fiber bundles in macaque brains, and call for the caution of using diffusion tractography to map the wiring diagram of brain circuits. In the brain is a web of interconnected nerve cells that send messages to one another via spindly projections called axons. These axons join together at junctions called synapses to create circuits of nerve cells which connect neighboring or distant brain regions. Notably, long-range neural connections underpin higher-order cognitive skills (such as planning and emotion regulation) which make humans distinct from our primate relatives. Only by untangling these far-reaching networks can researchers begin to delineate what sets the human brain apart from other species. Researchers deploy a range of imaging techniques to map neural networks: scanning entire brains using MRI machines, or imaging thin slices of fluorescently labelled brain tissue using powerful microscopes. However, tracing long-range axons at a high resolution is challenging, and has stirred up debate about whether some neural tracts, such as the inferior fronto-occipital fasciculus, are present in all primates or only humans. To address these discrepancies, Yan, Yu et al. employed a two-pronged approach to map neural circuits in the brains of macaques. First, two techniques – called viral tracing and two-photon microscopy – were used to create a three-dimensional, fine-grain map showing how the ventrolateral prefrontal cortex (vlPFC), which regulates complex behaviors, connects to the rest of the brain. This revealed prominent axons from the vlPFC projecting via a single synapse to distant brain regions involved in higher-order functions, such as encoding memories and processing emotion. However, there were no direct, monosynaptic connections between the vlPFC and the occipital lobe, the brain’s visual processing center at the back of the head. Next, Yan, Yu et al. used a specialized MRI scanner to create an atlas of neural circuits connected to the vlPFC, and compared these results to a technique tracing axons stained with a fluorescent dye. In general, there was good agreement between the two methods, except for major differences in the rear-end projections that typically form the inferior fronto-occipital fasciculus. This suggests that this long-range neural pathway exists in monkeys, but it connects via multiple synapses instead of a single junction as was previously thought. The findings of Yan, Yu et al. provide new insights on the far-reaching neural pathways connecting distant parts of the macaque brain. It also suggests that atlases of neural circuits from whole brain scans should be taken with caution and validated using neural tracing experiments.
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Affiliation(s)
- Mingchao Yan
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Wenwen Yu
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Qian Lv
- School of Psychological and Cognitive Sciences; Beijing Key Laboratory of Behavior and Mental Health; IDG/McGovern Institute for Brain Research; Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Qiming Lv
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Tingting Bo
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoyu Chen
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Yilin Liu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Yafeng Zhan
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Shengyao Yan
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Xiangyu Shen
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Baofeng Yang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Qiming Hu
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Jiangli Yu
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Zilong Qiu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Yuanjing Feng
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Xiao-Yong Zhang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - He Wang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Fuqiang Xu
- Shenzhen Key Lab of Neuropsychiatric Modulation and Collaborative Innovation Center for Brain Science, Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, CAS Key Laboratory of Brain Connectome and Manipulation, Brain Cognition and Brain Disease Institute (BCBDI), Shenzhen Institutes of Advanced Technology, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China
| | - Zheng Wang
- School of Psychological and Cognitive Sciences; Beijing Key Laboratory of Behavior and Mental Health; IDG/McGovern Institute for Brain Research; Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
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Chandwani R, Harpster K, Kline JE, Mehta V, Wang H, Merhar SL, Schwartz TL, Parikh NA. Brain microstructural antecedents of visual difficulties in infants born very preterm. Neuroimage Clin 2022; 34:102987. [PMID: 35290855 PMCID: PMC8918861 DOI: 10.1016/j.nicl.2022.102987] [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: 11/02/2021] [Revised: 02/12/2022] [Accepted: 03/07/2022] [Indexed: 11/29/2022]
Abstract
Infants born very preterm (VPT) are at risk of later visual problems. Although neonatal screening can identify ophthalmologic abnormalities, subtle perinatal brain injury and/or delayed brain maturation may be significant contributors to complex visual-behavioral problems. Our aim was to assess the micro and macrostructural antecedents of early visual-behavioral difficulties in VPT infants by using diffusion MRI (dMRI) at term-equivalent age. We prospectively recruited a cohort of 262 VPT infants (≤32 weeks gestational age [GA]) from five neonatal intensive care units. We obtained structural and diffusion MRI at term-equivalent age and administered the Preverbal Visual Assessment (PreViAs) questionnaire to parents at 3-4 months corrected age. We used constrained spherical deconvolution to reconstruct nine white matter tracts of the visual pathways with high reliability and performed fixel-based analysis to derive fiber density (FD), fiber-bundle cross-section (FC), and combined fiber density and cross-section (FDC). In multiple logistic regression analyses, we related these tract metrics to visual-behavioral function. Of 262 infants, 191 had both high-quality dMRI and completed PreViAs, constituting the final cohort: mean (SD) GA was 29.3 (2.4) weeks, 90 (47.1%) were males, and postmenstrual age (PMA) at MRI was 42.8 (1.3) weeks. FD and FC of several tracts were altered in infants with (N = 59) versus those without retinopathy of prematurity (N = 132). FDC of the left posterior thalamic radiations (PTR), left inferior longitudinal fasciculus (ILF), right superior longitudinal fasciculus (SLF), and left inferior fronto-occipital fasciculus (IFOF) were significantly associated with visual attention scores, prior to adjusting for confounders. After adjustment for PMA at MRI, GA, severe retinopathy of prematurity, and total brain volume, FDC of the left PTR, left ILF, and left IFOF remained significantly associated with visual attention. Early visual-behavioral difficulties in VPT infants are preceded by micro and macrostructural abnormalities in several major visual pathways at term-equivalent age.
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Affiliation(s)
- Rahul Chandwani
- Center for Prevention of Neurodevelopmental Disorders, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Karen Harpster
- Center for Prevention of Neurodevelopmental Disorders, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Division of Occupational Therapy and Physical Therapy, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Rehabilitation, Exercise, and Nutrition Sciences, College of Allied Health Sciences, University of Cincinnati, Cincinnati, OH, United States
| | - Julia E Kline
- Center for Prevention of Neurodevelopmental Disorders, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Ved Mehta
- Center for Prevention of Neurodevelopmental Disorders, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Hui Wang
- Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; MR Clinical Science, Philips, Cincinnati, OH, United States
| | - Stephanie L Merhar
- Center for Prevention of Neurodevelopmental Disorders, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Terry L Schwartz
- Division of Pediatric Ophthalmology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Ophthalmology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Nehal A Parikh
- Center for Prevention of Neurodevelopmental Disorders, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States.
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60
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Pur DR, Preti MG, de Ribaupierre A, Van De Ville D, Eagleson R, Mella N, de Ribaupierre S. Mapping of Structure-Function Age-Related Connectivity Changes on Cognition Using Multimodal MRI. Front Aging Neurosci 2022; 14:757861. [PMID: 35663581 PMCID: PMC9158434 DOI: 10.3389/fnagi.2022.757861] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 04/21/2022] [Indexed: 11/13/2022] Open
Abstract
The relationship between age-related changes in brain structural connectivity (SC) and functional connectivity (FC) with cognition is not well understood. Furthermore, it is not clear whether cognition is represented via a similar spatial pattern of FC and SC or instead is mapped by distinct sets of distributed connectivity patterns. To this end, we used a longitudinal, within-subject, multimodal approach aiming to combine brain data from diffusion-weighted MRI (DW-MRI), and functional MRI (fMRI) with behavioral evaluation, to better understand how changes in FC and SC correlate with changes in cognition in a sample of older adults. FC and SC measures were derived from the multimodal scans acquired at two time points. Change in FC and SC was correlated with 13 behavioral measures of cognitive function using Partial Least Squares Correlation (PLSC). Two of the measures indicate an age-related change in cognition and the rest indicate baseline cognitive performance. FC and SC—cognition correlations were expressed across several cognitive measures, and numerous structural and functional cortical connections, mainly cingulo-opercular, dorsolateral prefrontal, somatosensory and motor, and temporo-parieto-occipital, contributed both positively and negatively to the brain-behavior relationship. Whole-brain FC and SC captured distinct and independent connections related to the cognitive measures. Overall, we examined age-related function-structure associations of the brain in a comprehensive and integrated manner, using a multimodal approach. We pointed out the behavioral relevance of age-related changes in FC and SC. Taken together, our results highlight that the heterogeneity in distributed FC and SC connectivity patterns provide unique information about the variable nature of healthy cognitive aging.
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Affiliation(s)
- Daiana Roxana Pur
- Schulich School of Medicine & Dentistry, Western University, London, ON, Canada
- *Correspondence: Daiana Roxana Pur
| | - Maria Giulia Preti
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Institute of Bioengineering, Center for Neuroprosthetics, EPFL, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
| | | | - Dimitri Van De Ville
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Institute of Bioengineering, Center for Neuroprosthetics, EPFL, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
| | - Roy Eagleson
- Department of Electrical and Computer Engineering, Western University, London, ON, Canada
- The Brain and Mind Institute, Western University, London, ON, Canada
| | - Nathalie Mella
- Department of Psychology, University of Geneva, Geneva, Switzerland
| | - Sandrine de Ribaupierre
- Schulich School of Medicine & Dentistry, Western University, London, ON, Canada
- The Brain and Mind Institute, Western University, London, ON, Canada
- Department of Clinical Neurological Sciences, Schulich School of Medicine, Western University, London, ON, Canada
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61
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Taoudi-Benchekroun Y, Christiaens D, Grigorescu I, Gale-Grant O, Schuh A, Pietsch M, Chew A, Harper N, Falconer S, Poppe T, Hughes E, Hutter J, Price AN, Tournier JD, Cordero-Grande L, Counsell SJ, Rueckert D, Arichi T, Hajnal JV, Edwards AD, Deprez M, Batalle D. Predicting age and clinical risk from the neonatal connectome. Neuroimage 2022; 257:119319. [PMID: 35589001 DOI: 10.1016/j.neuroimage.2022.119319] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 02/28/2022] [Accepted: 05/12/2022] [Indexed: 12/12/2022] Open
Abstract
The development of perinatal brain connectivity underpins motor, cognitive and behavioural abilities in later life. Diffusion MRI allows the characterisation of subtle inter-individual differences in structural brain connectivity. Individual brain connectivity maps (connectomes) are by nature high in dimensionality and complex to interpret. Machine learning methods are a powerful tool to uncover properties of the connectome which are not readily visible and can give us clues as to how and why individual developmental trajectories differ. In this manuscript we used Deep Neural Networks and Random Forests to predict demographic and neurodevelopmental characteristics from neonatal structural connectomes in a large sample of babies (n = 524) from the developing Human Connectome Project. We achieved an accurate prediction of post menstrual age (PMA) at scan in term-born infants (mean absolute error (MAE) = 0.72 weeks, r = 0.83 and p<0.001). We also achieved good accuracy when predicting gestational age at birth in a cohort of term and preterm babies scanned at term equivalent age (MAE = 2.21 weeks, r = 0.82, p<0.001). We subsequently used sensitivity analysis to obtain feature relevance from our prediction models, with the most important connections for prediction of PMA and GA found to predominantly involve frontal and temporal regions, thalami, and basal ganglia. From our models of PMA at scan for infants born at term, we computed a brain maturation index (predicted age minus actual age) of individual preterm neonates and found a significant correlation between this index and motor outcome at 18 months corrected age. Our results demonstrate the applicability of machine learning techniques in analyses of the neonatal connectome and suggest that a neural substrate of brain maturation with implications for future neurodevelopment is detectable at term equivalent age from the neonatal connectome.
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Affiliation(s)
- Yassine Taoudi-Benchekroun
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Daan Christiaens
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom; Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Irina Grigorescu
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Oliver Gale-Grant
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom; Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Maximilian Pietsch
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Andrew Chew
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Nicholas Harper
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Shona Falconer
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Tanya Poppe
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Emer Hughes
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Anthony N Price
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - J-Donald Tournier
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom; Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid & CIBER-BBN, Madrid, Spain
| | - Serena J Counsell
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom; Institute for Artificial Intelligence and Informatics in Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Tomoki Arichi
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom; Department of Bioengineering, Imperial College London, London, United Kingdom; Children's Neurosciences, Evelina London Children's Hospital, Guy's and St Thomas' NHS Trust, London, United Kingdom
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - A David Edwards
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom; MRC Centre for Neurodevelopmental Disorders, King's College London, London, United Kingdom
| | - Maria Deprez
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Dafnis Batalle
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom; Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
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Moon JY, Mukherjee P, Madduri RK, Markowitz AJ, Cai LT, Palacios EM, Manley GT, Bremer PT. The Case for Optimized Edge-Centric Tractography at Scale. Front Neuroinform 2022; 16:752471. [PMID: 35651721 PMCID: PMC9148990 DOI: 10.3389/fninf.2022.752471] [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: 08/03/2021] [Accepted: 04/22/2022] [Indexed: 11/13/2022] Open
Abstract
The anatomic validity of structural connectomes remains a significant uncertainty in neuroimaging. Edge-centric tractography reconstructs streamlines in bundles between each pair of cortical or subcortical regions. Although edge bundles provides a stronger anatomic embedding than traditional connectomes, calculating them for each region-pair requires exponentially greater computation. We observe that major speedup can be achieved by reducing the number of streamlines used by probabilistic tractography algorithms. To ensure this does not degrade connectome quality, we calculate the identifiability of edge-centric connectomes between test and re-test sessions as a proxy for information content. We find that running PROBTRACKX2 with as few as 1 streamline per voxel per region-pair has no significant impact on identifiability. Variation in identifiability caused by streamline count is overshadowed by variation due to subject demographics. This finding even holds true in an entirely different tractography algorithm using MRTrix. Incidentally, we observe that Jaccard similarity is more effective than Pearson correlation in calculating identifiability for our subject population.
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Affiliation(s)
- Joseph Y. Moon
- Lawrence Livermore National Laboratory, Livermore, CA, United States
- *Correspondence: Joseph Y. Moon
| | - Pratik Mukherjee
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
- Pratik Mukherjee
| | | | - Amy J. Markowitz
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Lanya T. Cai
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Eva M. Palacios
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Geoffrey T. Manley
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Peer-Timo Bremer
- Lawrence Livermore National Laboratory, Livermore, CA, United States
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63
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Hwang YE, Kim YB, Son YD. Finding Cortical Subregions Regarding the Dorsal Language Pathway Based on the Structural Connectivity. Front Hum Neurosci 2022; 16:784340. [PMID: 35585994 PMCID: PMC9108242 DOI: 10.3389/fnhum.2022.784340] [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: 09/27/2021] [Accepted: 04/01/2022] [Indexed: 11/13/2022] Open
Abstract
Although the language-related fiber pathways in the human brain, such as the superior longitudinal fasciculus (SLF) and arcuate fasciculus (AF), are already well-known, understanding more sophisticated cortical regions connected by the fiber tracts is essential to scrutinize the structural connectivity of language circuits. With the regions of interest that were selected based on the Brainnetome atlas, the fiber orientation distribution estimation method for tractography was used to produce further elaborate connectivity information. The results indicated that both fiber bundles had two distinct connections with the prefrontal cortex (PFC). The SLF-II and dorsal AF are mainly connected to the rostrodorsal part of the inferior parietal cortex (IPC) and lateral part of the fusiform gyrus with the inferior frontal junction (IFJ), respectively. In contrast, the SLF-III and ventral AF were primarily linked to the anterior part of the supramarginal gyrus and superior part of the temporal cortex with the inferior frontal cortex, including the Broca's area. Moreover, the IFJ in the PFC, which has rarely been emphasized as a language-related subregion, also had the strongest connectivity with the previously known language-related subregions among the PFC; consequently, we proposed that these specific regions are interconnected via the SLF and AF within the PFC, IPC, and temporal cortex as language-related circuitry.
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Affiliation(s)
- Young-Eun Hwang
- Neuroscience Convergence Center, Korea University, Seoul, South Korea
- Department of Health Sciences and Technology, Gachion Advanced Institute for Health Sciences & Technology (GAHIST), Gachon University, Incheon, South Korea
- Department of Biomedical Engineering, Gachon University, Incheon, South Korea
| | - Young-Bo Kim
- Department of Neurosurgery, Gil Medical Center, College of Medicine, Gachon University, Incheon, South Korea
| | - Young-Don Son
- Department of Health Sciences and Technology, Gachion Advanced Institute for Health Sciences & Technology (GAHIST), Gachon University, Incheon, South Korea
- Department of Biomedical Engineering, Gachon University, Incheon, South Korea
- *Correspondence: Young-Don Son
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Raja R, Na X, Moore A, Otoo R, Glasier CM, Badger TM, Ou X. Associations Between White Matter Microstructures and Cognitive Functioning in 8-Year-Old Children: A Track-Weighted Imaging Study. J Child Neurol 2022; 37:471-490. [PMID: 35254148 PMCID: PMC9149064 DOI: 10.1177/08830738221083487] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
PURPOSE Quantitative tractography using diffusion-weighted magnetic resonance imaging data is widely used in characterizing white matter microstructure throughout childhood, but more studies are still needed to investigate comprehensive brain-behavior relationships between tract-specific white matter measures and multiple cognitive functions in children. METHODS In this study, we analyzed diffusion-weighted MRI data of 71 healthy 8-year-old children utilizing white matter tract-specific quantitative measures derived from diffusion-weighted MRI tractography based on a novel track-weighted imaging approach. Track density imaging, average path length map and 4 track-weighted diffusion tensor imaging measures including: mean diffusivity, fractional anisotropy, axial diffusivity, and radial diffusivity were computed for 63 white matter tracts. The track-weighted imaging measures were then correlated with a comprehensive set of neuropsychological test scores in different cognitive domains including intelligence, language, memory, academic skills, and executive functions to identify tract-specific brain-behavior relationships. RESULTS Significant correlations (P < .05, false discovery rate corrected; r = 0.27-0.57) were found in multiple white matter tracts, with a total of 40 correlations identified between various track-weighted imaging measures including average path length map, track-weighted imaging-fractional anisotropy, and neuropsychological test scores and subscales. Specifically, track-weighted imaging measures indicative of better white matter connectivity and/or microstructural development significantly correlated with higher IQ and better language abilities. CONCLUSION Our findings demonstrate the ability of track-weighted imaging measures in establishing associations between white matter and cognitive functioning in healthy children and can serve as a reference for normal brain/cognition relationships in young school-age children and further aid in identifying imaging biomarkers predictive of adverse neurodevelopmental outcomes.
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Affiliation(s)
- Rajikha Raja
- Department of Radiology, University of Arkansas for Medical Sciences
| | - Xiaoxu Na
- Department of Radiology, University of Arkansas for Medical Sciences
| | - Alexandra Moore
- College of Medicine, University of Arkansas for Medical Sciences
| | - Raymond Otoo
- College of Medicine, University of Arkansas for Medical Sciences
| | - Charles M. Glasier
- Department of Radiology, University of Arkansas for Medical Sciences,Department of Pediatrics, University of Arkansas for Medical Sciences
| | - Thomas M. Badger
- Department of Pediatrics, University of Arkansas for Medical Sciences,Arkansas Children’s Nutrition Center
| | - Xiawei Ou
- Department of Radiology, University of Arkansas for Medical Sciences,Department of Pediatrics, University of Arkansas for Medical Sciences
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65
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Neonatal encephalopathy prediction of poor outcome with diffusion-weighted imaging connectome and fixel-based analysis. Pediatr Res 2022; 91:1505-1515. [PMID: 33966055 PMCID: PMC9053106 DOI: 10.1038/s41390-021-01550-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 04/01/2021] [Accepted: 04/08/2021] [Indexed: 02/03/2023]
Abstract
BACKGROUND Better biomarkers of eventual outcome are needed for neonatal encephalopathy. To identify the most potent neonatal imaging marker associated with 2-year outcomes, we retrospectively performed diffusion-weighted imaging connectome (DWIC) and fixel-based analysis (FBA) on magnetic resonance imaging (MRI) obtained in the first 4 weeks of life in term neonatal encephalopathy newborns. METHODS Diffusion tractography was available in 15 out of 24 babies with MRI, five each with normal, abnormal motor outcome, or death. All 15 except one underwent hypothermia as initial treatment. In abnormal motor and death groups, DWIC found 19 white matter pathways with severely disrupted fiber orientation distributions. RESULTS Using random forest classification, these disruptions predicted the follow-up outcomes with 89-99% accuracy. These pathways showed reduced integrity in abnormal motor and death vs. normal tone groups (p < 10-6). Using ranked supervised multi-view canonical correlation and depicting just three of the five dimensions of the analysis, the abnormal motor and death were clearly differentiated from each other and the normal tone group. CONCLUSIONS This study suggests that a machine-learning model for prediction using early DWIC and FBA could be a possible way of developing biomarkers in large MRI datasets having clinical outcomes. IMPACT Early connectome and FBA of clinically acquired DWI provide a new noninvasive imaging tool to predict the long-term motor outcomes after birth, based on the severity of white matter injury. Disrupted white matter connectivity as a novel neonatal marker achieves high accuracy of 89-99% to predict 2-year motor outcomes using conventional machine-learning classification. The proposed neonatal marker may allow better prognostication that is important to elucidate neural repair mechanisms and evaluate treatment modalities in neonatal encephalopathy.
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Resolution and b value dependent Structural Connectome in ex vivo Mouse Brain. Neuroimage 2022; 255:119199. [PMID: 35417754 PMCID: PMC9195912 DOI: 10.1016/j.neuroimage.2022.119199] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Revised: 04/05/2022] [Accepted: 04/07/2022] [Indexed: 12/24/2022] Open
Abstract
Diffusion magnetic resonance imaging has been widely used in both clinical and preclinical studies to characterize tissue microstructure and structural connectivity. The diffusion MRI protocol for the Human Connectome Project (HCP) has been developed and optimized to obtain high-quality, high-resolution diffusion MRI (dMRI) datasets. However, such efforts have not been fully explored in preclinical studies, especially for rodents. In this study, high quality dMRI datasets of mouse brains were acquired at 9.4T system from two vendors. In particular, we acquired a high-spatial resolution dMRI dataset (25 μm isotropic with 126 diffusion encoding directions), which we believe to be the highest spatial resolution yet obtained; and a high-angular resolution dMRI dataset (50 μm isotropic with 384 diffusion encoding directions), which we believe to be the highest angular resolution compared to the dMRI datasets at the microscopic resolution. We systematically investigated the effects of three important parameters that affect the final outcome of the connectome: b value (1000s/mm2 to 8000 s/mm2), angular resolution (10 to 126), and spatial resolution (25 μm to 200 μm). The stability of tractography and connectome increase with the angular resolution, where more than 50 angles is necessary to achieve consistent results. The connectome and quantitative parameters derived from graph theory exhibit a linear relationship to the b value (R2 > 0.99); a single-shell acquisition with b value of 3000 s/mm2 shows comparable results to the multi-shell high angular resolution dataset. The dice coefficient decreases and both false positive rate and false negative rate gradually increase with coarser spatial resolution. Our study provides guidelines and foundations for exploration of tradeoffs among acquisition parameters for the structural connectome in ex vivo mouse brain.
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67
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O'Hara NB, Lee MH, Juhász C, Asano E, Jeong JW. Diffusion tractography predicts propagated high-frequency activity during epileptic spasms. Epilepsia 2022; 63:1787-1798. [PMID: 35388455 DOI: 10.1111/epi.17251] [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: 01/09/2022] [Revised: 03/28/2022] [Accepted: 04/05/2022] [Indexed: 12/01/2022]
Abstract
OBJECTIVE Determine the structural networks that constrain propagation of ictal oscillations during epileptic spasm events, and compare observed propagation patterns across patients with successful or unsuccessful surgical outcomes. METHODS Subdural electrode recordings of 18 young patients (age 1-11 years) were analyzed during epileptic spasm events to determine ictal networks and quantify the amplitude and onset time of ictal oscillations across the cortical surface. Corresponding structural networks were generated with diffusion MRI tractography by seeding the cortical region associated with the earliest average oscillation onset time, and white matter pathways connecting active electrode regions within the ictal network were isolated. Properties of this structural network were used to predict oscillation onset times and amplitudes, and this relationship was compared across patients who did and did not achieve seizure freedom following resective surgery. RESULTS Onset propagation patterns were relatively consistent across each patients' spasm events. An electrode's average ictal oscillation onset latency was most significantly associated with the length of direct corticocortical tracts connecting to the area with the earliest average oscillation onset (p < .001, model R2 = 0.54). Moreover, patients demonstrating a faster propagation of ictal oscillation signals within the corticocortical network were more likely to have seizure recurrence following resective surgery (p = .039). Ictal oscillation amplitude was also associated with connecting tractography length and weighted fractional anisotropy (FA) measures along these pathways (p = .002/.030, model R2 = 0.31/0.25). Characteristics of analogous corticothalamic pathways did not show significant associations with ictal oscillation onset latency or amplitude. SIGNIFICANCE Spatiotemporal propagation patterns of high-frequency activity in epileptic spasms align with length and FA measures from onset-originating corticocortical pathways. Considering data in this individualized framework may help inform surgical decision making and expectations of surgical outcomes.
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Affiliation(s)
- Nolan B O'Hara
- Wayne State University (WSU) Translational Neuroscience Program.,Children's Hospital of Michigan Translational Imaging Laboratory
| | - Min-Hee Lee
- Children's Hospital of Michigan Translational Imaging Laboratory
| | - Csaba Juhász
- Wayne State University (WSU) Translational Neuroscience Program.,Children's Hospital of Michigan Translational Imaging Laboratory.,WSU Department of Pediatrics.,WSU Department of Neurology
| | - Eishi Asano
- Wayne State University (WSU) Translational Neuroscience Program.,Children's Hospital of Michigan Translational Imaging Laboratory.,WSU Department of Pediatrics.,WSU Department of Neurology
| | - Jeong-Won Jeong
- Wayne State University (WSU) Translational Neuroscience Program.,Children's Hospital of Michigan Translational Imaging Laboratory.,WSU Department of Pediatrics.,WSU Department of Neurology
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68
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Zeng R, Lv J, Wang H, Zhou L, Barnett M, Calamante F, Wang C. FOD-Net: A deep learning method for fiber orientation distribution angular super resolution. Med Image Anal 2022; 79:102431. [PMID: 35397471 DOI: 10.1016/j.media.2022.102431] [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: 03/22/2021] [Revised: 03/16/2022] [Accepted: 03/21/2022] [Indexed: 10/18/2022]
Abstract
Mapping the human connectome using fiber-tracking permits the study of brain connectivity and yields new insights into neuroscience. However, reliable connectome reconstruction using diffusion magnetic resonance imaging (dMRI) data acquired by widely available clinical protocols remains challenging, thus limiting the connectome/tractography clinical applications. Here we develop fiber orientation distribution (FOD) network (FOD-Net), a deep-learning-based framework for FOD angular super-resolution. Our method enhances the angular resolution of FOD images computed from common clinical-quality dMRI data, to obtain FODs with quality comparable to those produced from advanced research scanners. Super-resolved FOD images enable superior tractography and structural connectome reconstruction from clinical protocols. The method was trained and tested with high-quality data from the Human Connectome Project (HCP) and further validated with a local clinical 3.0T scanner as well as with another public available multicenter-multiscanner dataset. Using this method, we improve the angular resolution of FOD images acquired with typical single-shell low-angular-resolution dMRI data (e.g., 32 directions, b=1000s/mm2) to approximate the quality of FODs derived from time-consuming, multi-shell high-angular-resolution dMRI research protocols. We also demonstrate tractography improvement, removing spurious connections and bridging missing connections. We further demonstrate that connectomes reconstructed by super-resolved FODs achieve comparable results to those obtained with more advanced dMRI acquisition protocols, on both HCP and clinical 3.0T data. Advances in deep-learning approaches used in FOD-Net facilitate the generation of high quality tractography/connectome analysis from existing clinical MRI environments. Our code is freely available at https://github.com/ruizengalways/FOD-Net.
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Affiliation(s)
- Rui Zeng
- School of Biomedical Engineering, The University of Sydney, Sydney 2050, Australia; Brain and Mind Centre, The University of Sydney, Sydney 2050, Australia
| | - Jinglei Lv
- School of Biomedical Engineering, The University of Sydney, Sydney 2050, Australia; Brain and Mind Centre, The University of Sydney, Sydney 2050, Australia
| | - He Wang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China; Human Phenome Institute, Fudan University, Shanghai, China
| | - Luping Zhou
- School of Computer Science, The University of Sydney, Sydney 2050, Australia
| | - Michael Barnett
- Brain and Mind Centre, The University of Sydney, Sydney 2050, Australia; Sydney Neuroimaging Analysis Centre, Sydney 2050, Australia
| | - Fernando Calamante
- School of Biomedical Engineering, The University of Sydney, Sydney 2050, Australia; Brain and Mind Centre, The University of Sydney, Sydney 2050, Australia; Sydney Imaging, The University of Sydney, Sydney 2050, Australia
| | - Chenyu Wang
- Brain and Mind Centre, The University of Sydney, Sydney 2050, Australia; Sydney Neuroimaging Analysis Centre, Sydney 2050, Australia.
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69
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Jha RR, Pathak SK, Nath V, Schneider W, Kumar BVR, Bhavsar A, Nigam A. VRfRNet: Volumetric ROI fODF reconstruction network for estimation of multi-tissue constrained spherical deconvolution with only single shell dMRI. Magn Reson Imaging 2022; 90:1-16. [PMID: 35341904 DOI: 10.1016/j.mri.2022.03.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 02/19/2022] [Accepted: 03/19/2022] [Indexed: 10/18/2022]
Abstract
Diffusion MRI (dMRI) is one of the most popular techniques for studying the brain structure, mainly the white matter region. Among several sampling methods in dMRI, the high angular resolution diffusion imaging (HARDI) technique has attracted researchers due to its more accurate fiber orientation estimation. However, the current single-shell HARDI makes the intravoxel structure challenging to estimate accurately. While multi-shell acquisition can address this problem, it takes a longer scanning time, restricting its use in clinical applications. In addition, most existing dMRI scanners with low gradient-strengths often acquire single-shell up to b=1000s/mm2 because of signal-to-noise ratio issues and severe image artefacts. Hence, we propose a novel generative adversarial network, VRfRNet, for the reconstruction of multi-shell multi-tissue fiber orientation distribution function from single-shell HARDI volumes. Such a transformation learning is performed in the spherical harmonics (SH) space, as raw input HARDI volume is transformed to SH coefficients to soften gradient directions. The proposed VRfRNet consists of several modules, such as multi-context feature enrichment module, feature level attention, and softmax level attention. In addition, three loss functions have been used to optimize network learning, including L1, adversarial, and total variation. The network is trained and tested using standard qualitative and quantitative performance metrics on the publicly available HCP data-set.
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Affiliation(s)
- Ranjeet Ranjan Jha
- MANAS Lab, School of Computing and Electrical Engineering (SCEE), Indian Institute of Technology (IIT) Mandi, India.
| | - Sudhir K Pathak
- Learning Research and Development Center, University of Pittsburgh, USA
| | - Vishwesh Nath
- Vanderbilt Institute for Surgery and Engineering, Nashville, Tennessee, USA
| | - Walter Schneider
- Learning Research and Development Center, University of Pittsburgh, USA
| | - B V Rathish Kumar
- Department of Mathematics and Statistics, Indian Institute of Technology Kanpur, India
| | - Arnav Bhavsar
- MANAS Lab, School of Computing and Electrical Engineering (SCEE), Indian Institute of Technology (IIT) Mandi, India
| | - Aditya Nigam
- MANAS Lab, School of Computing and Electrical Engineering (SCEE), Indian Institute of Technology (IIT) Mandi, India
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70
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Singh K, García-Gomar MG, Cauzzo S, Staab JP, Indovina I, Bianciardi M. Structural connectivity of autonomic, pain, limbic, and sensory brainstem nuclei in living humans based on 7 Tesla and 3 Tesla MRI. Hum Brain Mapp 2022; 43:3086-3112. [PMID: 35305272 PMCID: PMC9188976 DOI: 10.1002/hbm.25836] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 02/09/2022] [Accepted: 03/06/2022] [Indexed: 11/18/2022] Open
Abstract
Autonomic, pain, limbic, and sensory processes are mainly governed by the central nervous system, with brainstem nuclei as relay centers for these crucial functions. Yet, the structural connectivity of brainstem nuclei in living humans remains understudied. These tiny structures are difficult to locate using conventional in vivo MRI, and ex vivo brainstem nuclei atlases lack precise and automatic transformability to in vivo images. To fill this gap, we mapped our recently developed probabilistic brainstem nuclei atlas developed in living humans to high‐spatial resolution (1.7 mm isotropic) and diffusion weighted imaging (DWI) at 7 Tesla in 20 healthy participants. To demonstrate clinical translatability, we also acquired 3 Tesla DWI with conventional resolution (2.5 mm isotropic) in the same participants. Results showed the structural connectome of 15 autonomic, pain, limbic, and sensory (including vestibular) brainstem nuclei/nuclei complex (superior/inferior colliculi, ventral tegmental area‐parabrachial pigmented, microcellular tegmental–parabigeminal, lateral/medial parabrachial, vestibular, superior olivary, superior/inferior medullary reticular formation, viscerosensory motor, raphe magnus/pallidus/obscurus, parvicellular reticular nucleus‐alpha part), derived from probabilistic tractography computation. Through graph measure analysis, we identified network hubs and demonstrated high intercommunity communication in these nuclei. We found good (r = .5) translational capability of the 7 Tesla connectome to clinical (i.e., 3 Tesla) datasets. Furthermore, we validated the structural connectome by building diagrams of autonomic/pain/limbic connectivity, vestibular connectivity, and their interactions, and by inspecting the presence of specific links based on human and animal literature. These findings offer a baseline for studies of these brainstem nuclei and their functions in health and disease, including autonomic dysfunction, chronic pain, psychiatric, and vestibular disorders.
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Affiliation(s)
- Kavita Singh
- Brainstem Imaging Laboratory, Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - María Guadalupe García-Gomar
- Brainstem Imaging Laboratory, Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.,Escuela Nacional de Estudios Superiores, Juriquilla, Universidad Nacional Autónoma de México, Querétaro, Mexico
| | - Simone Cauzzo
- Brainstem Imaging Laboratory, Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.,Life Sciences Institute, Sant'Anna School of Advanced Studies, Pisa, Italy.,Research Center E. Piaggio, University of Pisa, Pisa, Italy
| | - Jeffrey P Staab
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA.,Department of Otorhinolaryngology - Head and Neck Surgery, Mayo Clinic, Rochester, Minnesota, USA
| | - Iole Indovina
- Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Italy.,Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Marta Bianciardi
- Brainstem Imaging Laboratory, Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.,Division of Sleep Medicine, Harvard University, Boston, Massachusetts, USA
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71
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Roque M, de Souza DAR, Rangel-Sosa MM, Altounian M, Hocine M, Deloulme JC, Barbier EL, Mann F, Chauvet S. VPS35 deficiency in the embryonic cortex leads to prenatal cell loss and abnormal development of axonal connectivity. Mol Cell Neurosci 2022; 120:103726. [DOI: 10.1016/j.mcn.2022.103726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 03/25/2022] [Accepted: 03/27/2022] [Indexed: 10/18/2022] Open
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Beaumont J, Gambarota G, Prior M, Fripp J, Reid LB. Avoiding data loss: Synthetic MRIs generated from diffusion imaging can replace corrupted structural acquisitions for freesurfer-seeded tractography. PLoS One 2022; 17:e0247343. [PMID: 35180211 PMCID: PMC8856573 DOI: 10.1371/journal.pone.0247343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 11/30/2021] [Indexed: 11/18/2022] Open
Abstract
Magnetic Resonance Imaging (MRI) motion artefacts frequently complicate structural and diffusion MRI analyses. While diffusion imaging is easily ‘scrubbed’ of motion affected volumes, the same is not true for T1w or T2w ‘structural’ images. Structural images are critical to most diffusion-imaging pipelines thus their corruption can lead to disproportionate data loss. To enable diffusion-image processing when structural images are missing or have been corrupted, we propose a means by which synthetic structural images can be generated from diffusion MRI. This technique combines multi-tissue constrained spherical deconvolution, which is central to many existing diffusion analyses, with the Bloch equations that allow simulation of MRI intensities for given scanner parameters and magnetic resonance (MR) tissue properties. We applied this technique to 32 scans, including those acquired on different scanners, with different protocols and with pathology present. The resulting synthetic T1w and T2w images were visually convincing and exhibited similar tissue contrast to acquired structural images. These were also of sufficient quality to drive a Freesurfer-based tractographic analysis. In this analysis, probabilistic tractography connecting the thalamus to the primary sensorimotor cortex was delineated with Freesurfer, using either real or synthetic structural images. Tractography for real and synthetic conditions was largely identical in terms of both voxels encountered (Dice 0.88–0.95) and mean fractional anisotropy (intrasubject absolute difference 0.00–0.02). We provide executables for the proposed technique in the hope that these may aid the community in analysing datasets where structural image corruption is common, such as studies of children or cognitively impaired persons.
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Affiliation(s)
- Jeremy Beaumont
- The Australian e-Health Research Centre, CSIRO, Queensland, Australia
- Univ Rennes, INSERM, LTSI-UMR1099, Rennes, France
- * E-mail:
| | | | - Marita Prior
- Department of Medical Imaging, Royal Brisbane and Women’s Hospital, Herston, Queensland, Australia
| | - Jurgen Fripp
- The Australian e-Health Research Centre, CSIRO, Queensland, Australia
| | - Lee B. Reid
- The Australian e-Health Research Centre, CSIRO, Queensland, Australia
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73
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Rao B, Cheng H, Xu H, Peng Y. Random Network and Non-rich-club Organization Tendency in Children With Non-syndromic Cleft Lip and Palate After Articulation Rehabilitation: A Diffusion Study. Front Neurol 2022; 13:790607. [PMID: 35185761 PMCID: PMC8847279 DOI: 10.3389/fneur.2022.790607] [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: 10/07/2021] [Accepted: 01/03/2022] [Indexed: 11/30/2022] Open
Abstract
Objective The neuroimaging pattern in brain networks after articulation rehabilitation can be detected using graph theory and multivariate pattern analysis (MVPA). In this study, we hypothesized that the characteristics of the topology pattern of brain structural network in articulation-rehabilitated children with non-syndromic cleft lip and palate (NSCLP) were similar to that in healthy comparisons. Methods A total of 28 children with NSCLP and 28 controls with typical development were scanned for diffusion tensor imaging on a 3T MRI scanner. Structural networks were constructed, and their topological properties were obtained. Besides, the Chinese language clear degree scale (CLCDS) scores were used for correlation analysis with topological features in patients with NSCLP. Results The NSCLP group showed a similar rich-club connection pattern, but decreased small-world index, normalized rich-club coefficient, and increased connectivity strength of connections compared to controls. The univariate and multivariate patterns of the structural network in articulation-rehabilitated children were primarily in the feeder and local connections, covering sensorimotor, visual, frontoparietal, default mode, salience, and language networks, and orbitofrontal cortex. In addition, the connections that were significantly correlated with the CLCDS scores, as well as the weighted regions for classification, were chiefly distributed in the dorsal and ventral stream associated with the language networks of the non-dominant hemisphere. Conclusion The average level rich-club connection pattern and the compensatory of the feeder and local connections mainly covering language networks may be related to the CLCDS in articulation-rehabilitated children with NSCLP. However, the patterns of small-world and rich-club structural organization in the articulation-rehabilitated children exhibited a random network and non-rich-club organization tendency. These findings enhanced the understanding of neuroimaging patterns in children with NSCLP after articulation rehabilitation.
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Affiliation(s)
- Bo Rao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, China
| | - Hua Cheng
- Department of Radiology, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, China
| | - Haibo Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, China
- *Correspondence: Haibo Xu
| | - Yun Peng
- Department of Radiology, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, China
- Yun Peng
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74
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A methodological scoping review of the integration of fMRI to guide dMRI tractography. What has been done and what can be improved: A 20-year perspective. J Neurosci Methods 2022; 367:109435. [PMID: 34915047 DOI: 10.1016/j.jneumeth.2021.109435] [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: 04/07/2021] [Revised: 11/30/2021] [Accepted: 12/02/2021] [Indexed: 11/23/2022]
Abstract
Combining MRI modalities is a growing trend in neurosciences. It provides opportunities to investigate the brain architecture supporting cognitive functions. Integrating fMRI activation to guide dMRI tractography offers potential advantages over standard tractography methods. A quick glimpse of the literature on this topic reveals that this technique is challenging, and no consensus or "best practices" currently exist, at least not within a single document. We present the first attempt to systematically analyze and summarize the literature of 80 studies that integrated task-based fMRI results to guide tractography, over the last two decades. We report 19 findings that cover challenges related to sample size, microstructure modelling, seeding methods, multimodal space registration, false negatives/positives, specificity/validity, gray/white matter interface and more. These findings will help the scientific community (1) understand the strengths and limitations of the approaches, (2) design studies using this integrative framework, and (3) motivate researchers to fill the gaps identified. We provide references toward best practices, in order to improve the overall result's replicability, sensitivity, specificity, and validity.
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75
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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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76
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Heijmans M, Wolters AF, Temel Y, Kuijf ML, Michielse S. Comparison of Olfactory Tract Diffusion Measures Between Early Stage Parkinson's Disease Patients and Healthy Controls Using Ultra-High Field MRI. JOURNAL OF PARKINSON'S DISEASE 2022; 12:2161-2170. [PMID: 36093714 PMCID: PMC9661345 DOI: 10.3233/jpd-223349] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 08/16/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND MRI is a valuable method to assist in the diagnostic work-up of Parkinson's disease (PD). The olfactory tract (OT) has been proposed as a potential MRI biomarker for distinguishing PD patients from healthy controls. OBJECTIVE This study aims to further investigate whether diffusion measures of the OT differ between early stage PD patients and healthy controls. METHODS Twenty hyposmic/anosmic PD patients, 65 normosmic PD patients, and 36 normosmic healthy controls were evaluated and a 7T diffusion weighted image scan was acquired. Manual seed regions of interest were drawn in the OT region. Tractography of the OT was performed using a deterministic streamlines algorithm. Diffusion measures (fractional anisotropy and mean- radial- and axial diffusivity) of the generated streamlines were compared between groups. RESULTS Diffusion measures did not differ between PD patients compared to healthy controls and between hyposmic/anosmic PD patients, normosmic PD patients, and normosmic healthy controls. A positive correlation was found between age and mean- and axial diffusivity within the hyposmic/anosmic PD subgroup, but not in the normosmic groups. A positive correlation was found between MDS-UPDRSIII scores and fractional anisotropy. CONCLUSION This study showed that fiber tracking of the OT was feasible in both early stage PD and healthy controls using 7T diffusion weighted imaging data. However, 7T MRI diffusion measures of the OT are not useful as an early clinical biomarker for PD. Future work is needed to clarify the role of other OT measurements as a biomarker for PD and its different subgroups.
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Affiliation(s)
- Margot Heijmans
- School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Amée F. Wolters
- School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
- Department of Neurology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Yasin Temel
- School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
- Department of Neurosurgery, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Mark L. Kuijf
- School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
- Department of Neurology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Stijn Michielse
- School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
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Sang T, He J, Wang J, Zhang C, Zhou W, Zeng Q, Yuan Y, Yu L, Feng Y. Alterations in white matter fiber in Parkinson's disease across different cognitive stages. Neurosci Lett 2021; 769:136424. [PMID: 34958911 DOI: 10.1016/j.neulet.2021.136424] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 12/21/2021] [Accepted: 12/22/2021] [Indexed: 02/03/2023]
Affiliation(s)
- Tian Sang
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China
| | - Jianzhong He
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China
| | - Jingqiang Wang
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China
| | - Chengzhe Zhang
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China
| | - Wenyang Zhou
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China
| | - Qingrun Zeng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China
| | - Yuan Yuan
- Department of Neurology, the First Affiliated Hospital, Zhejiang University School of Medicine, #79 Qingchun Road, Hangzhou 310003, China
| | - Lihua Yu
- Department of Neurology, the First Affiliated Hospital, Zhejiang University School of Medicine, #79 Qingchun Road, Hangzhou 310003, China
| | - Yuanjing Feng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China.
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78
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An S, Fousek J, Kiss ZHT, Cortese F, van der Wijk G, McAusland LB, Ramasubbu R, Jirsa VK, Protzner AB. High-resolution Virtual Brain Modeling Personalizes Deep Brain Stimulation for Treatment-Resistant Depression: Spatiotemporal Response Characteristics Following Stimulation of Neural Fiber Pathways. Neuroimage 2021; 249:118848. [PMID: 34954330 DOI: 10.1016/j.neuroimage.2021.118848] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 11/25/2021] [Accepted: 12/21/2021] [Indexed: 02/07/2023] Open
Abstract
Over the past 15 years, deep brain stimulation (DBS) has been actively investigated as a groundbreaking therapy for patients with treatment-resistant depression (TRD); nevertheless, outcomes have varied from patient to patient, with an average response rate of ∼50%. The engagement of specific fiber tracts at the stimulation site has been hypothesized to be an important factor in determining outcomes, however, the resulting individual network effects at the whole-brain scale remain largely unknown. Here we provide a computational framework that can explore each individual's brain response characteristics elicited by selective stimulation of fiber tracts. We use a novel personalized in-silico approach, the Virtual Big Brain, which makes use of high-resolution virtual brain models at a mm-scale and explicitly reconstructs more than 100 000 fiber tracts for each individual. Each fiber tract is active and can be selectively stimulated. Simulation results demonstrate distinct stimulus-induced event-related potentials as a function of stimulation location, parametrized by the contact positions of the electrodes implanted in each patient, even though validation against empirical patient data reveals some limitations (i.e., the need for individual parameter adjustment, and differential accuracy across stimulation locations). This study provides evidence for the capacity of personalized high-resolution virtual brain models to investigate individual network effects in DBS for patients with TRD and opens up novel avenues in the personalized optimization of brain stimulation.
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Affiliation(s)
- Sora An
- Department of Communication Disorders, Ewha Womans University, 03760, Seoul, Republic of Korea.
| | - Jan Fousek
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, 13005, Marseille, France
| | - Zelma H T Kiss
- Hotchkiss Brain Institute, University of Calgary, T2N 1N4, Calgary, Alberta, Canada; Mathison Centre for Mental Health, University of Calgary, T2N 1N4, Calgary, Alberta, Canada; Department of Clinical Neurosciences and Psychiatry, Cumming School of Medicine, University of Calgary, T2N 1N4, Calgary, Alberta, Canada
| | - Filomeno Cortese
- Hotchkiss Brain Institute, University of Calgary, T2N 1N4, Calgary, Alberta, Canada; Seaman Family MR Centre, Foothills Medical Centre, University of Calgary, T2N 1N4, Calgary, Alberta, Canada
| | - Gwen van der Wijk
- Department of Psychology, University of Calgary, T2N 1N4, Calgary, Alberta, Canada
| | - Laina Beth McAusland
- Department of Clinical Neurosciences and Psychiatry, Cumming School of Medicine, University of Calgary, T2N 1N4, Calgary, Alberta, Canada
| | - Rajamannar Ramasubbu
- Hotchkiss Brain Institute, University of Calgary, T2N 1N4, Calgary, Alberta, Canada; Mathison Centre for Mental Health, University of Calgary, T2N 1N4, Calgary, Alberta, Canada; Department of Clinical Neurosciences and Psychiatry, Cumming School of Medicine, University of Calgary, T2N 1N4, Calgary, Alberta, Canada
| | - Viktor K Jirsa
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, 13005, Marseille, France.
| | - Andrea B Protzner
- Hotchkiss Brain Institute, University of Calgary, T2N 1N4, Calgary, Alberta, Canada; Mathison Centre for Mental Health, University of Calgary, T2N 1N4, Calgary, Alberta, Canada; Department of Psychology, University of Calgary, T2N 1N4, Calgary, Alberta, Canada.
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79
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Reid LB, Martínez‐Heras E, Manjón JV, Jeffree RL, Alexander H, Trinder J, Solana E, Llufriu S, Rose S, Prior M, Fripp J. Fully automated delineation of the optic radiation for surgical planning using clinically feasible sequences. Hum Brain Mapp 2021; 42:5911-5926. [PMID: 34547147 PMCID: PMC8596983 DOI: 10.1002/hbm.25658] [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: 03/31/2021] [Revised: 07/21/2021] [Accepted: 08/23/2021] [Indexed: 11/30/2022] Open
Abstract
Quadrantanopia caused by inadvertent severing of Meyer's Loop of the optic radiation is a well-recognised complication of temporal lobectomy for conditions such as epilepsy. Dissection studies indicate that the anterior extent of Meyer's Loop varies considerably between individuals. Quantifying this for individual patients is thus an important step to improve the safety profile of temporal lobectomies. Previous attempts to delineate Meyer's Loop using diffusion MRI tractography have had difficulty estimating its full anterior extent, required manual ROI placement, and/or relied on advanced diffusion sequences that cannot be acquired routinely in most clinics. Here we present CONSULT: a pipeline that can delineate the optic radiation from raw DICOM data in a completely automated way via a combination of robust pre-processing, segmentation, and alignment stages, plus simple improvements that bolster the efficiency and reliability of standard tractography. We tested CONSULT on 696 scans of predominantly healthy participants (539 unique brains), including both advanced acquisitions and simpler acquisitions that could be acquired in clinically acceptable timeframes. Delineations completed without error in 99.4% of the scans. The distance between Meyer's Loop and the temporal pole closely matched both averages and ranges reported in dissection studies for all tested sequences. Median scan-rescan error of this distance was 1 mm. When tested on two participants with considerable pathology, delineations were successful and realistic. Through this, we demonstrate not only how to identify Meyer's Loop with clinically feasible sequences, but also that this can be achieved without fundamental changes to tractography algorithms or complex post-processing methods.
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Affiliation(s)
- Lee B. Reid
- The Australian e‐Health Research CentreCSIROBrisbaneQueenslandAustralia
| | - Eloy Martínez‐Heras
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic BarcelonaInstitut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de BarcelonaBarcelonaSpain
| | - Jose V. Manjón
- Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de ValènciaValenciaSpain
| | - Rosalind L. Jeffree
- Royal Brisbane and Women's HospitalMetro NorthQueenslandAustralia
- School of Clinical MedicineUniversity of QueenslandHerstonQueenslandAustralia
| | - Hamish Alexander
- Royal Brisbane and Women's HospitalMetro NorthQueenslandAustralia
| | - Julie Trinder
- The Australian e‐Health Research CentreCSIROBrisbaneQueenslandAustralia
| | - Elisabeth Solana
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic BarcelonaInstitut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de BarcelonaBarcelonaSpain
| | - Sara Llufriu
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic BarcelonaInstitut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de BarcelonaBarcelonaSpain
| | - Stephen Rose
- The Australian e‐Health Research CentreCSIROBrisbaneQueenslandAustralia
| | - Marita Prior
- Royal Brisbane and Women's HospitalMetro NorthQueenslandAustralia
| | - Jurgen Fripp
- The Australian e‐Health Research CentreCSIROBrisbaneQueenslandAustralia
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80
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Tozzi L, Anene ET, Gotlib IH, Wintermark M, Kerr AB, Wu H, Seok D, Narr KL, Sheline YI, Whitfield-Gabrieli S, Williams LM. Convergence, preliminary findings and future directions across the four human connectome projects investigating mood and anxiety disorders. Neuroimage 2021; 245:118694. [PMID: 34732328 PMCID: PMC8727513 DOI: 10.1016/j.neuroimage.2021.118694] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 10/11/2021] [Accepted: 10/29/2021] [Indexed: 12/31/2022] Open
Abstract
In this paper we provide an overview of the rationale, methods, and preliminary results of the four Connectome Studies Related to Human Disease investigating mood and anxiety disorders. The first study, "Dimensional connectomics of anxious misery" (HCP-DAM), characterizes brain-symptom relations of a transdiagnostic sample of anxious misery disorders. The second study, "Human connectome Project for disordered emotional states" (HCP-DES), tests a hypothesis-driven model of brain circuit dysfunction in a sample of untreated young adults with symptoms of depression and anxiety. The third study, "Perturbation of the treatment resistant depression connectome by fast-acting therapies" (HCP-MDD), quantifies alterations of the structural and functional connectome as a result of three fast-acting interventions: electroconvulsive therapy, serial ketamine therapy, and total sleep deprivation. Finally, the fourth study, "Connectomes related to anxiety and depression in adolescents" (HCP-ADA), investigates developmental trajectories of subtypes of anxiety and depression in adolescence. The four projects use comparable and standardized Human Connectome Project magnetic resonance imaging (MRI) protocols, including structural MRI, diffusion-weighted MRI, and both task and resting state functional MRI. All four projects also conducted comprehensive and convergent clinical and neuropsychological assessments, including (but not limited to) demographic information, clinical diagnoses, symptoms of mood and anxiety disorders, negative and positive affect, cognitive function, and exposure to early life stress. The first round of analyses conducted in the four projects offered novel methods to investigate relations between functional connectomes and self-reports in large datasets, identified new functional correlates of symptoms of mood and anxiety disorders, characterized the trajectory of connectome-symptom profiles over time, and quantified the impact of novel treatments on aberrant connectivity. Taken together, the data obtained and reported by the four Connectome Studies Related to Human Disease investigating mood and anxiety disorders describe a rich constellation of convergent biological, clinical, and behavioral phenotypes that span the peak ages for the onset of emotional disorders. These data are being prepared for open sharing with the scientific community following screens for quality by the Connectome Coordinating Facility (CCF). The CCF also plans to release data from all projects that have been pre-processed using identical state-of-the-art pipelines. The resultant dataset will give researchers the opportunity to pool complementary data across the four projects to study circuit dysfunctions that may underlie mood and anxiety disorders, to map cohesive relations among circuits and symptoms, and to probe how these relations change as a function of age and acute interventions. This large and combined dataset may also be ideal for using data-driven analytic approaches to inform neurobiological targets for future clinical trials and interventions focused on clinical or behavioral outcomes.
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Affiliation(s)
- Leonardo Tozzi
- Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Esther T Anene
- Psychiatry, Neurology, Radiology, University of Pennsylvania, Philadelphia PA, USA
| | | | | | - Adam B Kerr
- Center for Cognitive and Neurobiological Imaging, Stanford University, CA, USA; Electrical Engineering, Stanford University, CA, USA
| | - Hua Wu
- Electrical Engineering, Stanford University, CA, USA
| | - Darsol Seok
- Department of Psychiatry, University of Pennsylvania, Philadelphia PA, USA
| | - Katherine L Narr
- Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA
| | - Yvette I Sheline
- Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA.
| | | | - Leanne M Williams
- Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA; Sierra-Pacific Mental Illness Research, Education, and Clinical Center (MIRECC) Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA.
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81
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Zhao Q, Ridout RP, Shen J, Wang N. Effects of Angular Resolution and b Value on Diffusion Tensor Imaging in Knee Joint. Cartilage 2021; 13:295S-303S. [PMID: 33843284 PMCID: PMC8804734 DOI: 10.1177/19476035211007909] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
OBJECTIVE To investigate the influences of the diffusion gradient directions (angular resolution) and the strength of the diffusion gradient (b value) on diffusion tensor imaging (DTI) metrics and tractography of various connective tissues in knee joint. DESIGN Two rat knee joints were scanned on a preclinical 9.4-T system using a 3-dimensional diffusion-weighted spin echo pulse sequence. One protocol with b value of 500, 1500, and 2500 s/mm2 were acquired separately using 43 diffusion gradient directions. The other protocol with b value of 1000 s/mm2 was performed using 147 diffusion gradient directions. The in-plane resolution was 45 µm isotropic. Fractional anisotropy (FA) and mean diffusivity (MD) were compared at different angular resolution. Tractography was quantitatively evaluated at different b values and angular resolutions in cartilage, ligament, meniscus, and growth plate. RESULTS The ligament showed higher FA value compared with growth plate and cartilage. The FA values were largely overestimated at the angular resolution of 6. Compared with FA, MD showed less sensitivity to the angular resolution. The fiber tracking was failed at low angular resolution (6 diffusion gradient directions) or high b value (2500 s/mm2). The quantitative measurements of tract length and track volume were strongly dependent on angular resolution and b value. CONCLUSIONS To obtain consistent DTI outputs and tractography in knee joint, the scan may require a proper b value (ranging from 500 to 1500 s/mm2) and sufficient angular resolution (>14) with signal-to-noise ratio >10.
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Affiliation(s)
- Qi Zhao
- School of Psychology, Shanghai
University of Sport, Shanghai, China
| | - Rees P. Ridout
- Pratt School of Engineering, Duke
University, Durham, NC, USA
| | - Jikai Shen
- Pratt School of Engineering, Duke
University, Durham, NC, USA
| | - Nian Wang
- Department of Radiology, Duke
University School of Medicine, Durham, NC, USA,Department of Radiology and Imaging
Sciences, Indiana University School of Medicine, Indianapolis, IN, USA,Nian Wang, Department of Radiology and
Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202,
USA.
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82
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Sarwar T, Ramamohanarao K, Zalesky A. A critical review of connectome validation studies. NMR IN BIOMEDICINE 2021; 34:e4605. [PMID: 34516016 DOI: 10.1002/nbm.4605] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 07/22/2021] [Accepted: 08/01/2021] [Indexed: 06/13/2023]
Abstract
Diffusion MRI tractography is the most widely used macroscale method for mapping connectomes in vivo. However, tractography is prone to various errors and biases, and thus tractography-derived connectomes require careful validation. Here, we critically review studies that have developed or utilized phantoms and tracer maps to validate tractography-derived connectomes, either quantitatively or qualitatively. We identify key factors impacting connectome reconstruction accuracy, including streamline seeding, propagation and filtering methods, and consider the strengths and limitations of state-of-the-art connectome phantoms and associated validation studies. These studies demonstrate the inherent limitations of current fiber orientation models and tractography algorithms and their impact on connectome reconstruction accuracy. Reconstructing connectomes with both high sensitivity and high specificity is challenging, given that some tractography methods can generate an abundance of spurious connections, while others can overlook genuine fiber bundles. We argue that streamline filtering can minimize spurious connections and potentially improve the biological plausibility of connectomes derived from tractography. We find that algorithmic choices such as the tractography seeding methodology, angular threshold, and streamline propagation method can substantially impact connectome reconstruction accuracy. Hence, careful application of tractography is necessary to reconstruct accurate connectomes. Improvements in diffusion MRI acquisition techniques will not necessarily overcome current tractography limitations without accompanying modeling and algorithmic advances.
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Affiliation(s)
- Tabinda Sarwar
- School of Computing Technologies, RMIT University, Melbourne, Victoria, Australia
| | - Kotagiri Ramamohanarao
- Department of Computing and Information Systems, The University of Melbourne, Melbourne, Victoria, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Victoria, Australia
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia
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83
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Abstract
We describe a collection of T1-, diffusion- and functional T2*-weighted magnetic resonance imaging data from human individuals with albinism and achiasma. This repository can be used as a test-bed to develop and validate tractography methods like diffusion-signal modeling and fiber tracking as well as to investigate the properties of the human visual system in individuals with congenital abnormalities. The MRI data is provided together with tools and files allowing for its preprocessing and analysis, along with the data derivatives such as manually curated masks and regions of interest for performing tractography.
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84
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Chary K, Narvaez O, Salo RA, San Martín Molina I, Tohka J, Aggarwal M, Gröhn O, Sierra A. Microstructural Tissue Changes in a Rat Model of Mild Traumatic Brain Injury. Front Neurosci 2021; 15:746214. [PMID: 34899158 PMCID: PMC8662623 DOI: 10.3389/fnins.2021.746214] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 10/27/2021] [Indexed: 12/31/2022] Open
Abstract
Our study investigates the potential of diffusion MRI (dMRI), including diffusion tensor imaging (DTI), fixel-based analysis (FBA) and neurite orientation dispersion and density imaging (NODDI), to detect microstructural tissue abnormalities in rats after mild traumatic brain injury (mTBI). The brains of sham-operated and mTBI rats 35 days after lateral fluid percussion injury were imaged ex vivo in a 11.7-T scanner. Voxel-based analyses of DTI-, fixel- and NODDI-based metrics detected extensive tissue changes in directly affected brain areas close to the primary injury, and more importantly, also in distal areas connected to primary injury and indirectly affected by the secondary injury mechanisms. Histology revealed ongoing axonal abnormalities and inflammation, 35 days after the injury, in the brain areas highlighted in the group analyses. Fractional anisotropy (FA), fiber density (FD) and fiber density and fiber bundle cross-section (FDC) showed similar pattern of significant areas throughout the brain; however, FA showed more significant voxels in gray matter areas, while FD and FDC in white matter areas, and orientation dispersion index (ODI) in areas most damage based on histology. Region-of-interest (ROI)-based analyses on dMRI maps and histology in selected brain regions revealed that the changes in MRI parameters could be attributed to both alterations in myelinated fiber bundles and increased cellularity. This study demonstrates that the combination of dMRI methods can provide a more complete insight into the microstructural alterations in white and gray matter after mTBI, which may aid diagnosis and prognosis following a mild brain injury.
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Affiliation(s)
- Karthik Chary
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Omar Narvaez
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Raimo A. Salo
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | | | - Jussi Tohka
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Manisha Aggarwal
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Olli Gröhn
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Alejandra Sierra
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
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85
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Arefin TM, Lee CH, White JD, Zhang J, Kaffman A. Macroscopic Structural and Connectome Mapping of the Mouse Brain Using Diffusion Magnetic Resonance Imaging. Bio Protoc 2021; 11:e4221. [PMID: 34909442 PMCID: PMC8635841 DOI: 10.21769/bioprotoc.4221] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 08/11/2021] [Accepted: 08/16/2021] [Indexed: 01/08/2023] Open
Abstract
Translational work in rodents elucidates basic mechanisms that drive complex behaviors relevant to psychiatric and neurological conditions. Nonetheless, numerous promising studies in rodents later fail in clinical trials, highlighting the need for improving the translational utility of preclinical studies in rodents. Imaging of small rodents provides an important strategy to address this challenge, as it enables a whole-brain unbiased search for structural and dynamic changes that can be directly compared to human imaging. The functional significance of structural changes identified using imaging can then be further investigated using molecular and genetic tools available for the mouse. Here, we describe a pipeline for unbiased search and characterization of structural changes and network properties, based on diffusion MRI data covering the entire mouse brain at an isotropic resolution of 100 µm. We first used unbiased whole-brain voxel-based analyses to identify volumetric and microstructural alterations in the brain of adult mice exposed to unpredictable postnatal stress (UPS), which is a mouse model of complex early life stress (ELS). Brain regions showing structural abnormalities were used as nodes to generate a grid for assessing structural connectivity and network properties based on graph theory. The technique described here can be broadly applied to understand brain connectivity in other mouse models of human disorders, as well as in genetically modified mouse strains. Graphic abstract: Pipeline for characterizing structural connectome in the mouse brain using diffusion magnetic resonance imaging. Scale bar = 1 mm.
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Affiliation(s)
- Tanzil Mahmud Arefin
- Bernard Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, USA
| | - Choong Heon Lee
- Bernard Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, USA
| | - Jordon D. White
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Jiangyang Zhang
- Bernard Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, USA
| | - Arie Kaffman
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
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86
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Schilling KG, Tax CM, Rheault F, Hansen C, Yang Q, Yeh FC, Cai L, Anderson AW, Landman BA. Fiber tractography bundle segmentation depends on scanner effects, vendor effects, acquisition resolution, diffusion sampling scheme, diffusion sensitization, and bundle segmentation workflow. Neuroimage 2021; 242:118451. [PMID: 34358660 PMCID: PMC9933001 DOI: 10.1016/j.neuroimage.2021.118451] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 07/08/2021] [Accepted: 08/03/2021] [Indexed: 01/08/2023] Open
Abstract
When investigating connectivity and microstructure of white matter pathways of the brain using diffusion tractography bundle segmentation, it is important to understand potential confounds and sources of variation in the process. While cross-scanner and cross-protocol effects on diffusion microstructure measures are well described (in particular fractional anisotropy and mean diffusivity), it is unknown how potential sources of variation effect bundle segmentation results, which features of the bundle are most affected, where variability occurs, nor how these sources of variation depend upon the method used to reconstruct and segment bundles. In this study, we investigate six potential sources of variation, or confounds, for bundle segmentation: variation (1) across scan repeats, (2) across scanners, (3) across vendors (4) across acquisition resolution, (5) across diffusion schemes, and (6) across diffusion sensitization. We employ four different bundle segmentation workflows on two benchmark multi-subject cross-scanner and cross-protocol databases, and investigate reproducibility and biases in volume overlap, shape geometry features of fiber pathways, and microstructure features within the pathways. We find that the effects of acquisition protocol, in particular acquisition resolution, result in the lowest reproducibility of tractography and largest variation of features, followed by vendor-effects, scanner-effects, and finally diffusion scheme and b-value effects which had similar reproducibility as scan-rescan variation. However, confounds varied both across pathways and across segmentation workflows, with some bundle segmentation workflows more (or less) robust to sources of variation. Despite variability, bundle dissection is consistently able to recover the same location of pathways in the deep white matter, with variation at the gray matter/ white matter interface. Next, we show that differences due to the choice of bundle segmentation workflows are larger than any other studied confound, with low-to-moderate overlap of the same intended pathway when segmented using different methods. Finally, quantifying microstructure features within a pathway, we show that tractography adds variability over-and-above that which exists due to noise, scanner effects, and acquisition effects. Overall, these confounds need to be considered when harmonizing diffusion datasets, interpreting or combining data across sites, and when attempting to understand the successes and limitations of different methodologies in the design and development of new tractography or bundle segmentation methods.
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Affiliation(s)
- Kurt G. Schilling
- Department of Radiology & Radiological Science, Vanderbilt University Medical Center, Nashville, TN, United States,Vanderbilt Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Chantal M.W. Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Francois Rheault
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Colin Hansen
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Qi Yang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh, United States
| | - Leon Cai
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Adam W. Anderson
- Department of Radiology & Radiological Science, Vanderbilt University Medical Center, Nashville, TN, United States,Vanderbilt Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
| | - Bennett A. Landman
- Department of Radiology & Radiological Science, Vanderbilt University Medical Center, Nashville, TN, United States,Vanderbilt Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States,Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
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87
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Ponticorvo S, Manara R, Cassandro E, Canna A, Scarpa A, Troisi D, Cassandro C, Cuoco S, Cappiello A, Pellecchia MT, Salle FD, Esposito F. Cross-modal connectivity effects in age-related hearing loss. Neurobiol Aging 2021; 111:1-13. [PMID: 34915240 DOI: 10.1016/j.neurobiolaging.2021.09.024] [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: 05/27/2021] [Revised: 08/31/2021] [Accepted: 09/01/2021] [Indexed: 10/19/2022]
Abstract
Age-related sensorineural hearing loss (HL) leads to localized brain changes in the primary auditory cortex, long-range functional alterations, and is considered a risk factor for dementia. Nonhuman studies have repeatedly highlighted cross-modal brain plasticity in sensorial brain networks other than those primarily involved in the peripheral damage, thus in this study, the possible cortical alterations associated with HL have been analyzed using a whole-brain multimodal connectomic approach. Fifty-two HL and 30 normal hearing participants were examined in a 3T MRI study along with audiological and neurological assessments. Between-regions functional connectivity and whole-brain probabilistic tractography were calculated in a connectome-based manner and graph theory was used to obtain low-dimensional features for the analysis of brain connectivity at global and local levels. The HL condition was associated with a different functional organization of the visual subnetwork as revealed by a significant increase in global efficiency, density, and clustering coefficient. These functional effects were mirrored by similar (but more subtle) structural effects suggesting that a functional repurposing of visual cortical centers occurs to compensate for age-related loss of hearing abilities.
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Affiliation(s)
- Sara Ponticorvo
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Baronissi, Italy
| | - Renzo Manara
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Baronissi, Italy; Department of Neuroscience, University of Padova, Padova, Italy
| | - Ettore Cassandro
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Baronissi, Italy; University Hospital "San Giovanni di Dio e Ruggi D'Aragona", Scuola Medica Salernitana, Salerno, Italy
| | - Antonietta Canna
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Napoli, Italy
| | - Alfonso Scarpa
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Baronissi, Italy; University Hospital "San Giovanni di Dio e Ruggi D'Aragona", Scuola Medica Salernitana, Salerno, Italy
| | - Donato Troisi
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Baronissi, Italy; University Hospital "San Giovanni di Dio e Ruggi D'Aragona", Scuola Medica Salernitana, Salerno, Italy
| | - Claudia Cassandro
- University Hospital "San Giovanni di Dio e Ruggi D'Aragona", Scuola Medica Salernitana, Salerno, Italy
| | - Sofia Cuoco
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Baronissi, Italy; University Hospital "San Giovanni di Dio e Ruggi D'Aragona", Scuola Medica Salernitana, Salerno, Italy
| | - Arianna Cappiello
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Baronissi, Italy; University Hospital "San Giovanni di Dio e Ruggi D'Aragona", Scuola Medica Salernitana, Salerno, Italy
| | - Maria Teresa Pellecchia
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Baronissi, Italy; University Hospital "San Giovanni di Dio e Ruggi D'Aragona", Scuola Medica Salernitana, Salerno, Italy
| | - Francesco Di Salle
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Baronissi, Italy; University Hospital "San Giovanni di Dio e Ruggi D'Aragona", Scuola Medica Salernitana, Salerno, Italy
| | - Fabrizio Esposito
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Napoli, Italy.
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88
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Changes in white matter microstructure and MRI-derived cerebral blood flow after 1-week of exercise training. Sci Rep 2021; 11:22061. [PMID: 34764358 PMCID: PMC8586229 DOI: 10.1038/s41598-021-01630-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 05/31/2021] [Indexed: 11/23/2022] Open
Abstract
Exercise is beneficial for brain health, inducing neuroplasticity and vascular plasticity in the hippocampus, which is possibly mediated by brain-derived neurotrophic factor (BDNF) levels. Here we investigated the short-term effects of exercise, to determine if a 1-week intervention is sufficient to induce brain changes. Fifteen healthy young males completed five supervised exercise training sessions over seven days. This was preceded and followed by a multi-modal magnetic resonance imaging (MRI) scan (diffusion-weighted MRI, perfusion-weighted MRI, dual-calibrated functional MRI) acquired 1 week apart, and blood sampling for BDNF. A diffusion tractography analysis showed, after exercise, a significant reduction relative to baseline in restricted fraction-an axon-specific metric-in the corpus callosum, uncinate fasciculus, and parahippocampal cingulum. A voxel-based approach found an increase in fractional anisotropy and reduction in radial diffusivity symmetrically, in voxels predominantly localised in the corpus callosum. A selective increase in hippocampal blood flow was found following exercise, with no change in vascular reactivity. BDNF levels were not altered. Thus, we demonstrate that 1 week of exercise is sufficient to induce microstructural and vascular brain changes on a group level, independent of BDNF, providing new insight into the temporal dynamics of plasticity, necessary to exploit the therapeutic potential of exercise.
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89
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Spencer APC, Brooks JCW, Masuda N, Byrne H, Lee-Kelland R, Jary S, Thoresen M, Goodfellow M, Cowan FM, Chakkarapani E. Motor function and white matter connectivity in children cooled for neonatal encephalopathy. Neuroimage Clin 2021; 32:102872. [PMID: 34749285 PMCID: PMC8578038 DOI: 10.1016/j.nicl.2021.102872] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 10/13/2021] [Accepted: 10/30/2021] [Indexed: 11/24/2022]
Abstract
Therapeutic hypothermia reduces the incidence of severe motor disability, such as cerebral palsy, following neonatal hypoxic-ischaemic encephalopathy. However, cooled children without cerebral palsy at school-age demonstrate motor deficits and altered white matter connectivity. In this study, we used diffusion-weighted imaging to investigate the relationship between white matter connectivity and motor performance, measured using the Movement Assessment Battery for Children-2, in children aged 6-8 years treated with therapeutic hypothermia for neonatal hypoxic-ischaemic encephalopathy at birth, who did not develop cerebral palsy (cases), and matched typically developing controls. Correlations between total motor scores and diffusion properties in major white matter tracts were assessed in 33 cases and 36 controls. In cases, significant correlations (FDR-corrected P < 0.05) were found in the anterior thalamic radiation bilaterally (left: r = 0.513; right: r = 0.488), the cingulate gyrus part of the left cingulum (r = 0.588), the hippocampal part of the left cingulum (r = 0.541), and the inferior fronto-occipital fasciculus bilaterally (left: r = 0.445; right: r = 0.494). No significant correlations were found in controls. We then constructed structural connectivity networks, for 22 cases and 32 controls, in which nodes represent brain regions and edges were determined by probabilistic tractography and weighted by fractional anisotropy. Analysis of whole-brain network metrics revealed correlations (FDR-corrected P < 0.05), in cases, between total motor scores and average node strength (r = 0.571), local efficiency (r = 0.664), global efficiency (r = 0.677), clustering coefficient (r = 0.608), and characteristic path length (r = -0.652). No significant correlations were found in controls. We then investigated edge-level association with motor function using the network-based statistic. This revealed subnetworks which exhibited group differences in the association between motor outcome and edge weights, for total motor scores (P = 0.0109) as well as for balance (P = 0.0245) and manual dexterity (P = 0.0233) domain scores. All three of these subnetworks comprised numerous frontal lobe regions known to be associated with motor function, including the superior frontal gyrus and middle frontal gyrus. The subnetwork associated with total motor scores was highly left-lateralised. These findings demonstrate an association between impaired motor function and brain organisation in school-age children treated with therapeutic hypothermia for neonatal hypoxic-ischaemic encephalopathy.
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Affiliation(s)
- Arthur P C Spencer
- Clinical Research and Imaging Centre, University of Bristol, Bristol, UK; Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jonathan C W Brooks
- Clinical Research and Imaging Centre, University of Bristol, Bristol, UK; School of Psychology, University of East Anglia, Norwich, UK
| | - Naoki Masuda
- Department of Mathematics, State University of New York at Buffalo, Buffalo, NY, USA; Computational and Data-Enabled Science and Engineering Program, State University of New York at Buffalo, Buffalo, NY, USA
| | - Hollie Byrne
- Clinical Research and Imaging Centre, University of Bristol, Bristol, UK; Department of Paediatrics, University of Melbourne, Melbourne, Australia
| | - Richard Lee-Kelland
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Sally Jary
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Marianne Thoresen
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; Faculty of Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Marc Goodfellow
- Living Systems Institute, University of Exeter, Exeter, UK; Wellcome Trust Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, UK; EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, UK; College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
| | - Frances M Cowan
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; Department of Paediatrics, Imperial College London, London, UK
| | - Ela Chakkarapani
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; Neonatal Intensive Care Unit, St Michael's Hospital, University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK.
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90
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Vattikonda AN, Hashemi M, Sip V, Woodman MM, Bartolomei F, Jirsa VK. Identifying spatio-temporal seizure propagation patterns in epilepsy using Bayesian inference. Commun Biol 2021; 4:1244. [PMID: 34725441 PMCID: PMC8560929 DOI: 10.1038/s42003-021-02751-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 10/04/2021] [Indexed: 01/24/2023] Open
Abstract
Focal drug resistant epilepsy is a neurological disorder characterized by seizures caused by abnormal activity originating in one or more regions together called as epileptogenic zone. Treatment for such patients involves surgical resection of affected regions. Epileptogenic zone is typically identified using stereotactic EEG recordings from the electrodes implanted into the patient's brain. Identifying the epileptogenic zone is a challenging problem due to the spatial sparsity of electrode implantation. We propose a probabilistic hierarchical model of seizure propagation patterns, based on a phenomenological model of seizure dynamics called Epileptor. Using Bayesian inference, the Epileptor model is optimized to build patient specific virtual models that best fit to the log power of intracranial recordings. First, accuracy of the model predictions and identifiability of the model are investigated using synthetic data. Then, model predictions are evaluated against a retrospective patient cohort of 25 patients with varying surgical outcomes. In the patients who are seizure free after surgery, model predictions showed good match with the clinical hypothesis. In patients where surgery failed to achieve seizure freedom model predictions showed a strong mismatch. Our results demonstrate that proposed probabilistic model could be a valuable tool to aid the clinicians in identifying the seizure focus.
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Affiliation(s)
- Anirudh N Vattikonda
- Aix Marseille Univ, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Meysam Hashemi
- Aix Marseille Univ, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Viktor Sip
- Aix Marseille Univ, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Marmaduke M Woodman
- Aix Marseille Univ, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Fabrice Bartolomei
- Aix Marseille Univ, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille, France
- Epileptology Department and Clinical Neurophysiology Department, Assistance publique des Hopitaux de Marseille, Marseille, France
| | - Viktor K Jirsa
- Aix Marseille Univ, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille, France.
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91
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Gorman BDA, Calamante F, Civier O, DeMayo MM, Demetriou EA, Hickie IB, Guastella AJ. Investigating white matter structure in social anxiety disorder using fixel-based analysis. J Psychiatr Res 2021; 143:30-37. [PMID: 34438201 DOI: 10.1016/j.jpsychires.2021.08.028] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 08/09/2021] [Accepted: 08/18/2021] [Indexed: 10/20/2022]
Abstract
Social anxiety disorder (SAD) is one of the most common mental health disorders in youth, defined by a persistent and intense fear of negative evaluation by others. Recent research has examined its neurological underpinnings, including structural connectivity changes in the brain. This has been examined through measurement of the white matter (WM) structure of fibre pathways. Previous studies have shown inconsistent results. This study attempts to resolve these inconsistencies by utilising a recently proposed, advanced method for diffusion MRI analysis, known as fixel based analysis (FBA). This technique enables examination of WM macro- and micro-structure with measures of fibre density (FD), fibre bundle cross-section (FC) and fibre density-cross-section (FDC). This study evidenced increased FDC in a region of the right superior longitudinal fasciculus (SLF) from a whole brain FBA, along with increased FC and FDC from an analysis restricted to a-priori tracts of interest, in regions of the right inferior longitudinal fasciculus (R-ILF). The average FDC of the left uncinate fasciculus (L-UF) was also increased. To examine the relationship between WM structure and severity of symptoms, these FBA metrics were correlated with Leibowitz Social Anxiety Scale (LSAS) scores. From the tract-restricted analysis an inverse correlation between FC and LSAS scores was found in the R-ILF. The average FC of the R-ILF was also inversely correlated with symptom severity. By utilising a more sensitive and fibre-specific method of analysis than previous studies, these findings highlight innovative outcomes relating to white matter in numerous fibre tracts.
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Affiliation(s)
- Ben D A Gorman
- Autism Clinic for Translational Research, Brain and Mind Centre, Children's Hospital Westmead Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Fernando Calamante
- School of Biomedical Engineering, The University of Sydney, Sydney, New South Wales, Australia; Sydney Imaging, The University of Sydney, Sydney, New South Wales, Australia
| | - Oren Civier
- School of Biomedical Engineering, The University of Sydney, Sydney, New South Wales, Australia; Swinburne University of Technology, Swinburne Neuroimaging, Melbourne, Victoria, Australia
| | - Marilena M DeMayo
- Autism Clinic for Translational Research, Brain and Mind Centre, Children's Hospital Westmead Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia; Brain and Mind Centre, Central Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia; School of Biomedical Engineering, The University of Sydney, Sydney, New South Wales, Australia
| | - Eleni Andrea Demetriou
- Autism Clinic for Translational Research, Brain and Mind Centre, Children's Hospital Westmead Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Ian B Hickie
- Brain and Mind Centre, Central Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Adam J Guastella
- Autism Clinic for Translational Research, Brain and Mind Centre, Children's Hospital Westmead Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.
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92
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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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93
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Fiori S, Pannek K, Podda I, Cipriani P, Lorenzoni V, Franchi B, Pasquariello R, Guzzetta A, Cioni G, Chilosi A. Neural Changes Induced by a Speech Motor Treatment in Childhood Apraxia of Speech: A Case Series. J Child Neurol 2021; 36:958-967. [PMID: 34315296 PMCID: PMC8461047 DOI: 10.1177/08830738211015800] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
We report a case series of children with childhood apraxia of speech, by describing behavioral and white matter microstructural changes following 2 different treatment approaches.Five children with childhood apraxia of speech were assigned to a motor speech treatment (PROMPT) and 5 to a language, nonspeech oral motor treatment. Speech assessment and brain MRI were performed pre- and post-treatment. The ventral (tongue/larynx) and dorsal (lips) corticobulbar tracts were reconstructed in each subject. Mean fractional anisotropy and mean diffusivity were extracted. The hand corticospinal tract was assessed as a control pathway. In both groups speech improvements paralleled changes in the left ventral corticobulbar tract fractional anisotropy. The PROMPT treated group also showed fractional anisotropy increase and mean diffusivity decrease in the left dorsal corticobulbar tract. No changes were detected in the hand tract. Our results may provide preliminary support to the possible neurobiologic effect of a multimodal speech motor treatment in childhood apraxia of speech.
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Affiliation(s)
- Simona Fiori
- Department of Developmental Neuroscience, IRCCS Stella Maris Foundation, Pisa, Italy,Simona Fiori, MD, PhD, Department of Developmental Neuroscience, Stella Maris Foundation, Viale del Tirreno 331, 56128, Pisa, Italy.
| | - Kerstin Pannek
- CSIRO, Health and Biosecurity, Australian E-Health Research Centre, Brisbane, Australia
| | - Irina Podda
- Parole al Centro, Studio di Logopedia e Neuropsicomotricità, Genova, Italy
| | - Paola Cipriani
- Department of Developmental Neuroscience, IRCCS Stella Maris Foundation, Pisa, Italy
| | - V. Lorenzoni
- Institute of Management, Scuola Superiore Sant’Anna, Pisa, Italy
| | - Beatrice Franchi
- Department of Developmental Neuroscience, IRCCS Stella Maris Foundation, Pisa, Italy
| | - Rosa Pasquariello
- Department of Developmental Neuroscience, IRCCS Stella Maris Foundation, Pisa, Italy
| | - Andrea Guzzetta
- Department of Developmental Neuroscience, IRCCS Stella Maris Foundation, Pisa, Italy,Department of Clinical and Experimental Medicine, University of Pisa, Italy
| | - Giovanni Cioni
- Department of Developmental Neuroscience, IRCCS Stella Maris Foundation, Pisa, Italy,Department of Clinical and Experimental Medicine, University of Pisa, Italy
| | - Anna Chilosi
- Department of Developmental Neuroscience, IRCCS Stella Maris Foundation, Pisa, Italy
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94
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Collins SE, Thompson DK, Kelly CE, Yang JYM, Pascoe L, Inder TE, Doyle LW, Cheong JLY, Burnett AC, Anderson PJ. Development of brain white matter and math computation ability in children born very preterm and full-term. Dev Cogn Neurosci 2021; 51:100987. [PMID: 34273749 PMCID: PMC8319459 DOI: 10.1016/j.dcn.2021.100987] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 07/07/2021] [Accepted: 07/11/2021] [Indexed: 11/08/2022] Open
Abstract
Children born very preterm (VPT; <32 weeks' gestation) have alterations in brain white matter and poorer math ability than full-term (FT) peers. Diffusion-weighted magnetic resonance imaging studies suggest a link between white matter microstructure and math in VPT and FT children, although longitudinal studies using advanced modelling are lacking. In a prospective longitudinal cohort of VPT and FT children we used Fixel-Based Analysis to investigate associations between maturation of white matter fibre density (FD), fibre-bundle cross-section (FC), and combined fibre density and cross-section (FDC) and math computation ability at 7 (n = 136 VPT; n = 32 FT) and 13 (n = 130 VPT; n = 44 FT) years, as well as between change in white matter and math computation ability from 7 to 13 years (n = 103 VPT; n = 21 FT). In both VPT and FT children, higher FD, FC and FDC in visual, sensorimotor and cortico-thalamic/thalamo-cortical white matter tracts were associated with better math computation ability at 7 and 13 years. Longitudinally, accelerated maturation of the posterior body of the corpus callosum (FDC) was associated with greater math computation development. White matter-math associations were similar for VPT and FT children. In conclusion, white matter maturation is associated with math computation ability across late childhood, irrespective of birth group.
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Affiliation(s)
- Simonne E Collins
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia; Victorian Infant Brain Study (VIBeS), Murdoch Children's Research Institute, Melbourne, Australia; Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia.
| | - Deanne K Thompson
- Victorian Infant Brain Study (VIBeS), Murdoch Children's Research Institute, Melbourne, Australia; Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia; Department of Paediatrics, The University of Melbourne, Melbourne, Australia; Florey Institute of Neuroscience and Mental Health, Melbourne, Australia
| | - Claire E Kelly
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia; Victorian Infant Brain Study (VIBeS), Murdoch Children's Research Institute, Melbourne, Australia; Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia
| | - Joseph Y M Yang
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia; Department of Paediatrics, The University of Melbourne, Melbourne, Australia; Neuroscience Advanced Clinical Imaging Suite (NACIS), Department of Neurosurgery, The Royal Children's Hospital, Melbourne, Australia; Neuroscience Research, Murdoch Children's Research Institute, Melbourne, Australia
| | - Leona Pascoe
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia; Victorian Infant Brain Study (VIBeS), Murdoch Children's Research Institute, Melbourne, Australia
| | - Terrie E Inder
- Victorian Infant Brain Study (VIBeS), Murdoch Children's Research Institute, Melbourne, Australia; Department of Pediatric Newborn Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Lex W Doyle
- Victorian Infant Brain Study (VIBeS), Murdoch Children's Research Institute, Melbourne, Australia; Department of Paediatrics, The University of Melbourne, Melbourne, Australia; Newborn Research, The Royal Women's Hospital, Melbourne, Australia; Department of Obstetrics and Gynaecology, The University of Melbourne, Melbourne, Australia; Premature Infant Follow-Up Program, Royal Women's Hospital, Melbourne, Australia
| | - Jeanie L Y Cheong
- Victorian Infant Brain Study (VIBeS), Murdoch Children's Research Institute, Melbourne, Australia; Newborn Research, The Royal Women's Hospital, Melbourne, Australia; Department of Obstetrics and Gynaecology, The University of Melbourne, Melbourne, Australia; Premature Infant Follow-Up Program, Royal Women's Hospital, Melbourne, Australia
| | - Alice C Burnett
- Victorian Infant Brain Study (VIBeS), Murdoch Children's Research Institute, Melbourne, Australia; Department of Paediatrics, The University of Melbourne, Melbourne, Australia; Premature Infant Follow-Up Program, Royal Women's Hospital, Melbourne, Australia; Neonatal Medicine, Royal Children's Hospital, Melbourne, Australia
| | - Peter J Anderson
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia; Victorian Infant Brain Study (VIBeS), Murdoch Children's Research Institute, Melbourne, Australia.
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95
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Spencer APC, Byrne H, Lee-Kelland R, Jary S, Thoresen M, Cowan FM, Chakkarapani E, Brooks JCW. An Age-Specific Atlas for Delineation of White Matter Pathways in Children Aged 6-8 Years. Brain Connect 2021; 12:402-416. [PMID: 34210166 PMCID: PMC7612846 DOI: 10.1089/brain.2021.0058] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Introduction Diffusion MRI allows non-invasive assessment of white matter connectivity in typical development and of changes due to brain injury or pathology. Probabilistic white matter atlases allow diffusion metrics to be measured in specific white matter pathways, and are a critical component in spatial normalisation for group analysis. However, given the known developmental changes in white matter it may be sub-optimal to use an adult template when assessing data acquired from children. Methods By averaging subject-specific fibre bundles from 28 children aged from 6 to 8 years, we created an age-specific probabilistic white matter atlas for 12 major white matter tracts. Using both the newly developed and Johns Hopkins adult atlases, we compared the atlas to subject-specific fibre bundles in two independent validation cohorts, assessing accuracy in terms of volumetric overlap and measured diffusion metrics. Results Our age-specific atlas gave better overall performance than the adult atlas, achieving higher volumetric overlap with subject-specific fibre tracking and higher correlation of FA measurements with those measured from subject-specific fibre bundles. Specifically, estimates of FA values for cortico-spinal tract, uncinate fasciculus, forceps minor, cingulate gyrus part of the cingulum and anterior thalamic radiation were all significantly more accurate when estimated with an age-specific atlas. Discussion The age-specific atlas allows delineation of white matter tracts in children aged 6-8 years, without the need for tractography, more accurately than when normalising to an adult atlas. To our knowledge, this is the first publicly available probabilistic atlas of white matter tracts for this age group.
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Affiliation(s)
- Arthur P C Spencer
- Clinical Research and Imaging Centre, University of Bristol, Bristol, United Kingdom.,Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Hollie Byrne
- Clinical Research and Imaging Centre, University of Bristol, Bristol, United Kingdom
| | - Richard Lee-Kelland
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Sally Jary
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Marianne Thoresen
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom.,Faculty of Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Frances M Cowan
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom.,Department of Paediatrics, Imperial College London, London, United Kingdom
| | - Ela Chakkarapani
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Jonathan C W Brooks
- Clinical Research and Imaging Centre, University of Bristol, Bristol, United Kingdom.,School of Psychology, University of East Anglia, Norwich, United Kingdom
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96
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Bender AR, Brandmaier AM, Düzel S, Keresztes A, Pasternak O, Lindenberger U, Kühn S. Hippocampal Subfields and Limbic White Matter Jointly Predict Learning Rate in Older Adults. Cereb Cortex 2021; 30:2465-2477. [PMID: 31800016 DOI: 10.1093/cercor/bhz252] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 08/20/2019] [Accepted: 10/01/2019] [Indexed: 12/21/2022] Open
Abstract
Age-related memory impairments have been linked to differences in structural brain parameters, including cerebral white matter (WM) microstructure and hippocampal (HC) volume, but their combined influences are rarely investigated. In a population-based sample of 337 older participants aged 61-82 years (Mage = 69.66, SDage = 3.92 years), we modeled the independent and joint effects of limbic WM microstructure and HC subfield volumes on verbal learning. Participants completed a verbal learning task of recall over five repeated trials and underwent magnetic resonance imaging (MRI), including structural and diffusion scans. We segmented three HC subregions on high-resolution MRI data and sampled mean fractional anisotropy (FA) from bilateral limbic WM tracts identified via deterministic fiber tractography. Using structural equation modeling, we evaluated the associations between learning rate and latent factors representing FA sampled from limbic WM tracts, and HC subfield volumes, and their latent interaction. Results showed limbic WM and the interaction of HC and WM-but not HC volume alone-predicted verbal learning rates. Model decomposition revealed HC volume is only positively associated with learning rate in individuals with higher WM anisotropy. We conclude that the structural characteristics of limbic WM regions and HC volume jointly contribute to verbal learning in older adults.
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Affiliation(s)
- Andrew R Bender
- Departments of Epidemiology and Biostatistics, Neurology and Ophthalmology, College of Human Medicine, Michigan State University, East Lansing, MI 48824, USA.,Center for Lifespan Psychology, Max Planck Institute for Human Development, D-14195 Berlin, Germany
| | - Andreas M Brandmaier
- Center for Lifespan Psychology, Max Planck Institute for Human Development, D-14195 Berlin, Germany.,Max Planck UCL Centre for Computational Psychiatry and Ageing Research, D-14195 Berlin, Germany and London, UK WC1B 5EH
| | - Sandra Düzel
- Center for Lifespan Psychology, Max Planck Institute for Human Development, D-14195 Berlin, Germany
| | - Attila Keresztes
- Center for Lifespan Psychology, Max Planck Institute for Human Development, D-14195 Berlin, Germany.,Research Centre for Natural Sciences, Hungarian Academy of Sciences, H-1117 Budapest, Hungary.,Faculty of Education and Psychology, Eötvös Loránd University, H-1053 Budapest, Hungary
| | - Ofer Pasternak
- Departments of Psychiatry and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Ulman Lindenberger
- Center for Lifespan Psychology, Max Planck Institute for Human Development, D-14195 Berlin, Germany.,Max Planck UCL Centre for Computational Psychiatry and Ageing Research, D-14195 Berlin, Germany and London, UK WC1B 5EH.,European University Institute, I-50014. San Domenico di Fiesole, Italy
| | - Simone Kühn
- Center for Lifespan Psychology, Max Planck Institute for Human Development, D-14195 Berlin, Germany.,Department of Psychiatry and Psychotherapy, University Clinic Hamburg-Eppendorf, 20246 Hamburg, Germany
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97
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Navas-Sánchez FJ, Martín De Blas D, Fernández-Pena A, Alemán-Gómez Y, Lage-Castellanos A, Marcos-Vidal L, Guzmán-De-Villoria JA, Catalina I, Lillo L, Muñoz-Blanco JL, -Ugalde AO, Quintáns B, Sobrido MJ, Carmona S, Grandas F, Desco M. Corticospinal tract and motor cortex degeneration in pure hereditary spastic paraparesis type 4 (SPG4). Amyotroph Lateral Scler Frontotemporal Degener 2021; 23:25-34. [PMID: 34396852 DOI: 10.1080/21678421.2021.1962353] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Objective: SPG4 is an autosomal dominant pure form of hereditary spastic paraplegia (HSP) caused by mutations in the SPAST gene. HSP is considered an upper motor neuron disorder characterized by progressive retrograde degeneration, or "dying-back" phenomenon, of the corticospinal tract's longest axons. Neuroimaging studies mainly focus on white matter changes and, although previous studies reported cortical thinning in complicated HSP forms, cortical changes remain unclear in SPG4 patients. This work aimed to compare changes in white matter microstructure and cortical thickness between 12 SPG4 patients and 22 healthy age-matched controls. We also explore whether white matter alterations are related to cortical thickness and their correlation with clinical symptoms. Methods: we used fixel-based analysis, an advanced diffusion-weighted imaging technique, and probabilistic tractography of the corticospinal tracts. We also analyzed cortical morphometry using whole-brain surface-based and atlas-based methods in sensorimotor areas. Results: SPG4 patients showed bilateral involvement in the corticospinal tracts; this was more intense in the distal portion than in the upper segments and was associated with the degree of clinical impairment. We found a significant correlation between disease severity and fiber density and cross-section of the corticospinal tracts. Furthermore, corticospinal tract changes were significantly correlated with bilateral cortical thinning in the precentral gyrus in SPG4 patients. Conclusions: Our data point to axonal damage of the corticospinal motor neurons in SPG4 patients might be related to cortical thinning in motor regions.
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Affiliation(s)
- Francisco J Navas-Sánchez
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.,Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
| | | | | | - Yasser Alemán-Gómez
- Department of Psychiatry, Centre Hospitalier Universitaire Vaudois, Prilly, Switzerland.,Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Centre d'Imagerie BioMédicale (CIBM), Medical Image Analysis Laboratory (MIAL), Lausanne, Switzerland
| | | | - Luis Marcos-Vidal
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.,Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain.,Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain
| | - Juan A Guzmán-De-Villoria
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.,Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain.,Servicio de Radiodiagnóstico, Hospital General Universitario Gregorio Marañón, Madrid, Spain
| | - Irene Catalina
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain.,Servicio de Neurología, Hospital General Universitario Gregorio Marañón, Madrid, Spain
| | - Laura Lillo
- Servicio de Neurología, Hospital Ruber Internacional, Madrid, Spain.,Servicio de Neurología, Hospital Universitario Fundación Alcorcón, Madrid, Spain
| | - José L Muñoz-Blanco
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain.,Servicio de Neurología, Hospital General Universitario Gregorio Marañón, Madrid, Spain
| | - Andrés Ordoñez -Ugalde
- Laboratorio Biomolecular, Cuenca, Ecuador.,Unidad de Genética y Molecular, Hospital de Especialidades José Carrasco Arteaga, Cuenca, Ecuador.,Neurogenetics Group, FPGMX-IDIS, Santiago de Compostela, Spain
| | - Beatriz Quintáns
- Instituto de Investigación Sanitaria, Santiago de Compostela, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER-U711), Madrid, Spain.,Fundación Pública Galega de Medicina Xenómica, Santiago de Compostela, Spain
| | - María-Jesús Sobrido
- Instituto de Investigación Sanitaria, Santiago de Compostela, Spain.,Hospital Clínico Universitario de A Coruña, SERGAS, A Coruña, Spain and
| | - Susanna Carmona
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.,Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
| | - Francisco Grandas
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain.,Servicio de Neurología, Hospital General Universitario Gregorio Marañón, Madrid, Spain
| | - Manuel Desco
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.,Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain.,Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain.,Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain
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98
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Trinkle S, Foxley S, Kasthuri N, Rivière PL. Synchrotron X-ray micro-CT as a validation dataset for diffusion MRI in whole mouse brain. Magn Reson Med 2021; 86:1067-1076. [PMID: 33768633 PMCID: PMC8076078 DOI: 10.1002/mrm.28776] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Revised: 01/26/2021] [Accepted: 02/28/2021] [Indexed: 12/13/2022]
Abstract
PURPOSE To introduce synchrotron X-ray microcomputed tomography (microCT) and demonstrate its use as a natively isotropic, nondestructive, 3D validation modality for diffusion MRI in whole, fixed mouse brain. METHODS Postmortem diffusion MRI and microCT data were acquired of the same whole mouse brain. Diffusion data were processed using constrained spherical deconvolution. Synchrotron data were acquired at an isotropic voxel size of 1.17 μm. Structure tensor analysis was used to calculate fiber orientation distribution functions from the microCT data. A pipeline was developed to spatially register the 2 datasets in order to perform qualitative comparisons of fiber geometries represented by fiber orientation distribution functions. Fiber orientations from both modalities were used to perform whole-brain deterministic tractography to demonstrate validation of long-range white matter pathways. RESULTS Fiber orientation distribution functions were able to be extracted throughout the entire microCT dataset, with spatial registration to diffusion MRI simplified due to the whole brain extent of the microCT data. Fiber orientations and tract pathways showed good agreement between modalities. CONCLUSION Synchrotron microCT is a potentially valuable new tool for future multi-scale diffusion MRI validation studies, providing comparable value to optical histology validation methods while addressing some key limitations in data acquisition and ease of processing.
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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
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99
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Yang JYM, Yeh CH, Poupon C, Calamante F. Diffusion MRI tractography for neurosurgery: the basics, current state, technical reliability and challenges. Phys Med Biol 2021; 66. [PMID: 34157706 DOI: 10.1088/1361-6560/ac0d90] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 06/22/2021] [Indexed: 01/20/2023]
Abstract
Diffusion magnetic resonance imaging (dMRI) tractography is currently the only imaging technique that allows for non-invasive delineation and visualisation of white matter (WM) tractsin vivo,prompting rapid advances in related fields of brain MRI research in recent years. One of its major clinical applications is for pre-surgical planning and intraoperative image guidance in neurosurgery, where knowledge about the location of WM tracts nearby the surgical target can be helpful to guide surgical resection and optimise post-surgical outcomes. Surgical injuries to these WM tracts can lead to permanent neurological and functional deficits, making the accuracy of tractography reconstructions paramount. The quality of dMRI tractography is influenced by many modifiable factors, ranging from MRI data acquisition through to the post-processing of tractography output, with the potential of error propagation based on decisions made at each and subsequent processing steps. Research over the last 25 years has significantly improved the anatomical accuracy of tractography. An updated review about tractography methodology in the context of neurosurgery is now timely given the thriving research activities in dMRI, to ensure more appropriate applications in the clinical neurosurgical realm. This article aims to review the dMRI physics, and tractography methodologies, highlighting recent advances to provide the key concepts of tractography-informed neurosurgery, with a focus on the general considerations, the current state of practice, technical challenges, potential advances, and future demands to this field.
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Affiliation(s)
- Joseph Yuan-Mou Yang
- Department of Neurosurgery, The Royal Children's Hospital, Melbourne, Australia.,Neuroscience Research, Murdoch Children's Research Institute, Melbourne, Australia.,Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia.,Department of Paediatrics, The University of Melbourne, Melbourne, Australia
| | - Chun-Hung Yeh
- Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital, Taoyuan, Taiwan.,Department of Child and Adolescent Psychiatry, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
| | - Cyril Poupon
- NeuroSpin, Frédéric Joliot Life Sciences Institute, CEA, CNRS, Paris-Saclay University, Gif-sur-Yvette, France
| | - Fernando Calamante
- The University of Sydney, Sydney Imaging, Sydney, Australia.,The University of Sydney, School of Biomedical Engineering, Sydney, Australia
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100
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Fixel-based Analysis of Diffusion MRI: Methods, Applications, Challenges and Opportunities. Neuroimage 2021; 241:118417. [PMID: 34298083 DOI: 10.1016/j.neuroimage.2021.118417] [Citation(s) in RCA: 87] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Revised: 07/11/2021] [Accepted: 07/20/2021] [Indexed: 12/13/2022] Open
Abstract
Diffusion MRI has provided the neuroimaging community with a powerful tool to acquire in-vivo data sensitive to microstructural features of white matter, up to 3 orders of magnitude smaller than typical voxel sizes. The key to extracting such valuable information lies in complex modelling techniques, which form the link between the rich diffusion MRI data and various metrics related to the microstructural organization. Over time, increasingly advanced techniques have been developed, up to the point where some diffusion MRI models can now provide access to properties specific to individual fibre populations in each voxel in the presence of multiple "crossing" fibre pathways. While highly valuable, such fibre-specific information poses unique challenges for typical image processing pipelines and statistical analysis. In this work, we review the "Fixel-Based Analysis" (FBA) framework, which implements bespoke solutions to this end. It has recently seen a stark increase in adoption for studies of both typical (healthy) populations as well as a wide range of clinical populations. We describe the main concepts related to Fixel-Based Analyses, as well as the methods and specific steps involved in a state-of-the-art FBA pipeline, with a focus on providing researchers with practical advice on how to interpret results. We also include an overview of the scope of all current FBA studies, categorized across a broad range of neuro-scientific domains, listing key design choices and summarizing their main results and conclusions. Finally, we critically discuss several aspects and challenges involved with the FBA framework, and outline some directions and future opportunities.
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