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Henssen DJHA, Pritsch C, Nazari P, Mulleners W, Vissers K. The non-decussating and decussating trigeminothalamic tracts in humans: A combination of connectome-based tractography and histological validation. Cephalalgia 2024; 44:3331024241235168. [PMID: 38613234 DOI: 10.1177/03331024241235168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/14/2024]
Abstract
BACKGROUND Functional anatomical research proposed the existence of a bilateral trigeminal ascending system although the anatomy trajectories of the trigeminothalamic connections cranial to the pons remain largely elusive. This study therefore aimed to clarify the anatomical distributions of the trigeminothalamic connections in humans. METHODS Advanced deterministic tractography to an averaged template of diffusion tensor imaging data from 1065 subjects from the Human Connectome Project was used. Seedings masks were placed in Montreal Neurological Institute standard space by use of the BigBrain histological dataset. Waypoint masks of the sensory thalamus was obtained from the Brainnetome Atlas. RESULTS Tractography results were validated by use of the BigBrain histological dataset and Polarized Light Imaging microscopy. The trigeminothalamic tract bifurcated into a decussating ventral and a non-decussating dorsal tract. The ventral and dorsal tracts ascended to the contralateral thalamus and ipsilateral thalamus and reflected the ventral trigeminothalamic tract and the dorsal trigeminothalamic tract, respectively. The projection of the ventral trigeminothalamic tract and the dorsal trigeminothalamic tract to both thalami confirm the existence of a bilateral trigeminothalamic system in humans. CONCLUSIONS Because our study is strictly anatomical, no further conclusions can be drawn with regard to physiological functionality. Future research should explore if the dorsal trigeminothalamic tract and the ventral trigeminothalamic tract actually transmit signals from noxious stimuli, this offers potential in understanding and possibly treating neuropathology in the orofacial region.
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Affiliation(s)
- Dylan J H A Henssen
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition & Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Cynthia Pritsch
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Pouyan Nazari
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Wim Mulleners
- Department of Anesthesiology, Pain and Palliative Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Kris Vissers
- Department of Anesthesiology, Pain and Palliative Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
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Hsu CCH, Chong ST, Kung YC, Kuo KT, Huang CC, Lin CP. Integrated diffusion image operator (iDIO): A pipeline for automated configuration and processing of diffusion MRI data. Hum Brain Mapp 2023; 44:2669-2683. [PMID: 36807461 PMCID: PMC10089090 DOI: 10.1002/hbm.26239] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 01/17/2023] [Accepted: 02/09/2023] [Indexed: 02/23/2023] Open
Abstract
The preprocessing of diffusion magnetic resonance imaging (dMRI) data involve numerous steps, including the corrections for head motion, susceptibility distortion, low signal-to-noise ratio, and signal drifting. Researchers or clinical practitioners often need to configure different preprocessing steps depending on disparate image acquisition schemes, which increases the technical threshold for dMRI analysis for nonexpert users. This could cause disparities in data processing approaches and thus hinder the comparability between studies. To make the dMRI data processing steps transparent and adapt to various dMRI acquisition schemes for researchers, we propose a semi-automated pipeline tool for dMRI named integrated diffusion image operator or iDIO. This pipeline integrates features from a wide range of advanced dMRI software tools and targets at providing a one-click solution for dMRI data analysis, via adaptive configuration for a set of suggested processing steps based on the image header of the input data. Additionally, the pipeline provides options for post-processing, such as estimation of diffusion tensor metrics and whole-brain tractography-based connectomes reconstruction using common brain atlases. The iDIO pipeline also outputs an easy-to-interpret quality control report to facilitate users to assess the data quality. To keep the transparency of data processing, the execution log and all the intermediate images produced in the iDIO's workflow are accessible. The goal of iDIO is to reduce the barriers for clinical or nonspecialist users to adopt the state-of-art dMRI processing steps.
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Affiliation(s)
- Chih-Chin Heather Hsu
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Shin Tai Chong
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yi-Chia Kung
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Kuan-Tsen Kuo
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chu-Chung Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China.,Shanghai Changning Mental Health Center, Shanghai, China
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Medical Device Innovation and Translation Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
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3
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Ciavarro M, Grande E, Bevacqua G, Morace R, Ambrosini E, Pavone L, Grillea G, Vangelista T, Esposito V. Structural Brain Network Reorganization Following Anterior Callosotomy for Colloid Cysts: Connectometry and Graph Analysis Results. Front Neurol 2022; 13:894157. [PMID: 35923826 PMCID: PMC9340207 DOI: 10.3389/fneur.2022.894157] [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: 03/11/2022] [Accepted: 06/03/2022] [Indexed: 11/13/2022] Open
Abstract
Introduction:The plasticity of the neural circuits after injuries has been extensively investigated over the last decades. Transcallosal microsurgery for lesions affecting the third ventricle offers an interesting opportunity to investigate the whole-brain white matter reorganization occurring after a selective resection of the genu of the corpus callosum (CC).MethodDiffusion MRI (dMRI) data and neuropsychological testing were collected pre- and postoperatively in six patients with colloid cysts, surgically treated with a transcallosal-transgenual approach. Longitudinal connectometry analysis on dMRI data and graph analysis on structural connectivity matrix were implemented to analyze how white matter pathways and structural network topology reorganize after surgery.ResultsAlthough a significant worsening in cognitive functions (e.g., executive and memory functioning) at early postoperative, a recovery to the preoperative status was observed at 6 months. Connectometry analysis, beyond the decrease of quantitative anisotropy (QA) near the resection cavity, showed an increase of QA in the body and forceps major CC subregions, as well as in the left intra-hemispheric corticocortical associative fibers. Accordingly, a reorganization of structural network topology was observed between centrality increasing in the left hemisphere nodes together with a rise in connectivity strength among mid and posterior CC subregions and cortical nodes.ConclusionA structural reorganization of intra- and inter-hemispheric connective fibers and structural network topology were observed following the resection of the genu of the CC. Beyond the postoperative transient cognitive impairment, it could be argued anterior CC resection does not preclude neural plasticity and may subserve the long-term postoperative cognitive recovery.
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Affiliation(s)
- Marco Ciavarro
- Mediterranean Neurological Institute Neuromed (IRCCS) Neuromed, Pozzilli, Italy
- *Correspondence: Marco Ciavarro
| | - Eleonora Grande
- Department of Neuroscience, Imaging and Clinical Sciences, Gabriele d'Annunzio University, Chieti, Italy
| | | | - Roberta Morace
- Mediterranean Neurological Institute Neuromed (IRCCS) Neuromed, Pozzilli, Italy
| | - Ettore Ambrosini
- Department of General Psychology, University of Padua, Padua, Italy
- Department of Neuroscience, University of Padua, Padua, Italy
- Padua Neuroscience Center, University of Padua, Padua, Italy
| | - Luigi Pavone
- Mediterranean Neurological Institute Neuromed (IRCCS) Neuromed, Pozzilli, Italy
| | - Giovanni Grillea
- Mediterranean Neurological Institute Neuromed (IRCCS) Neuromed, Pozzilli, Italy
| | - Tommaso Vangelista
- Mediterranean Neurological Institute Neuromed (IRCCS) Neuromed, Pozzilli, Italy
| | - Vincenzo Esposito
- Mediterranean Neurological Institute Neuromed (IRCCS) Neuromed, Pozzilli, Italy
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
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Hokama Y, Nishimura M, Usugi R, Fujiwara K, Katagiri C, Takagi H, Ishiuchi S. Recovery from the damage of cranial radiation modulated by memantine, an NMDA receptor antagonist, combined with hyperbaric oxygen therapy. Neuro Oncol 2022; 25:108-122. [PMID: 35762568 PMCID: PMC9825311 DOI: 10.1093/neuonc/noac162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Radiotherapy is an important treatment option for central nervous system malignancies. However, cranial radiation induces hippocampal dysfunction and white matter injury; this leads to cognitive dysfunction, and results in a reduced quality of life in patients. Excitatory glutamate signaling through N-methyl-d-aspartate receptors (NMDARs) plays a central role both in hippocampal neurogenesis and in the myelination of oligodendrocytes in the cerebrum. METHODS We provide a method for quantifying neurogenesis in human subjects in live brain during cancer therapy. Neuroimaging using originally created behavioral tasks was employed to examine human hippocampal memory pathway in patients with brain disorders. RESULTS Treatment with memantine, a non-competitive NMDAR antagonist, reversed impairment in hippocampal pattern separation networks as detected by functional magnetic resonance imaging. Hyperbaric preconditioning of the patients just before radiotherapy with memantine mostly reversed white matter injury as detected by whole brain analysis with Tract-Based Spatial Statics. Neuromodulation combined with the administration of hyperbaric oxygen therapy and memantine during radiotherapy facilitated the restoration of hippocampal function and white matter integrity, and improved higher cognitive function in patients receiving cranial radiation. CONCLUSIONS The method described herein, for diagnosis of hippocampal dysfunction, and therapeutic intervention can be utilized to restore some of the cognitive decline experienced by patients who have received cranial radiation. The underlying mechanism of restoration is the production of new neurons, which enhances functionality in pattern separation networks in the hippocampi, resulting in an increase in cognitive score, and restoration of microstructural integrity of white matter tracts revealed by Tract-Based Spatial Statics Analysis.
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Affiliation(s)
- Yohei Hokama
- Department of Neurosurgery, Graduate School of Medicine, University of The Ryukyus, 207 Uehara, Nishihara-machi, Okinawa 903-0215, Japan
| | - Masahiko Nishimura
- Department of Neurosurgery, Graduate School of Medicine, University of The Ryukyus, 207 Uehara, Nishihara-machi, Okinawa 903-0215, Japan
| | - Ryoichi Usugi
- Department of Neurosurgery, Graduate School of Medicine, University of The Ryukyus, 207 Uehara, Nishihara-machi, Okinawa 903-0215, Japan
| | - Kyoko Fujiwara
- Department of Neurosurgery, Graduate School of Medicine, University of The Ryukyus, 207 Uehara, Nishihara-machi, Okinawa 903-0215, Japan
| | - Chiaki Katagiri
- Department of Neurosurgery, Graduate School of Medicine, University of The Ryukyus, 207 Uehara, Nishihara-machi, Okinawa 903-0215, Japan
| | - Hiroshi Takagi
- Department of Neurosurgery, Graduate School of Medicine, University of The Ryukyus, 207 Uehara, Nishihara-machi, Okinawa 903-0215, Japan
| | - Shogo Ishiuchi
- Corresponding Author: Dr. Shogo Ishiuchi, Department of Neurosurgery, Graduate School of Medicine, University of The Ryukyus, 207 Uehara, Nishihara-machi, Okinawa 903-0215, Japan ()
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Lai CH. Biomarkers in Panic Disorder. CURRENT PSYCHIATRY RESEARCH AND REVIEWS 2021. [DOI: 10.2174/2666082216999200918163245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Panic disorder (PD) is a kind of anxiety disorder that impacts the life quality
and functional perspectives in patients. However, the pathophysiological study of PD seems still
inadequate and many unresolved issues need to be clarified.
Objectives:
In this review article of biomarkers in PD, the investigator will focus on the findings of
magnetic resonance imaging (MRI) of the brain in the pathophysiology study. The MRI biomarkers
would be divided into several categories, on the basis of structural and functional perspectives.
Methods:
The structural category would include the gray matter and white matter tract studies. The
functional category would consist of functional MRI (fMRI), resting-state fMRI (Rs-fMRI), and
magnetic resonance spectroscopy (MRS). The PD biomarkers revealed by the above methodologies
would be discussed in this article.
Results:
For the gray matter perspectives, the PD patients would have alterations in the volumes of
fear network structures, such as the amygdala, parahippocampal gyrus, thalamus, anterior cingulate
cortex, insula, and frontal regions. For the white matter tract studies, the PD patients seemed to have
alterations in the fasciculus linking the fear network regions, such as the anterior thalamic radiation,
uncinate fasciculus, fronto-occipital fasciculus, and superior longitudinal fasciculus. For the fMRI
studies in PD, the significant results also focused on the fear network regions, such as the amygdala,
hippocampus, thalamus, insula, and frontal regions. For the Rs-fMRI studies, PD patients seemed to
have alterations in the regions of the default mode network and fear network model. At last, the
MRS results showed alterations in neuron metabolites of the hippocampus, amygdala, occipital
cortex, and frontal regions.
Conclusion:
The MRI biomarkers in PD might be compatible with the extended fear network model
hypothesis in PD, which included the amygdala, hippocampus, thalamus, insula, frontal regions, and
sensory-related cortex.
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Affiliation(s)
- Chien-Han Lai
- Department of Psychiatry, Institute of Biophotonics, National Yang-Ming University, Taipei, Taiwan
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6
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De Luca A, Guo F, Froeling M, Leemans A. Spherical deconvolution with tissue-specific response functions and multi-shell diffusion MRI to estimate multiple fiber orientation distributions (mFODs). Neuroimage 2020; 222:117206. [DOI: 10.1016/j.neuroimage.2020.117206] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2020] [Revised: 07/20/2020] [Accepted: 07/23/2020] [Indexed: 12/18/2022] Open
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The neural markers of MRI to differentiate depression and panic disorder. Prog Neuropsychopharmacol Biol Psychiatry 2019; 91:72-78. [PMID: 29705713 DOI: 10.1016/j.pnpbp.2018.04.013] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2018] [Revised: 04/05/2018] [Accepted: 04/23/2018] [Indexed: 12/23/2022]
Abstract
Depression and panic disorder (PD) share the common pathophysiology from the perspectives of neurotransmitters. The relatively high comorbidity between depression and PD contributes to the substantial obstacles to differentiate from depression and PD, especially for the brain pathophysiology. There are significant differences in the diagnostic criteria between depression and PD. However, the paradox of similar pathophysiology and different diagnostic criteria in these two disorders were still the issues needing to be addressed. Therefore the clarification of potential difference in the field of neuroscience and pathophysiology between depression and PD can help the clinicians and scientists to understand more comprehensively about significant differences between depression and PD. The researchers should be curious about the underlying difference of pathophysiology beneath the significant distinction of clinical symptoms. In this review article, I tried to find some evidences for the differences between depression and PD, especially for neural markers revealed by magnetic resonance imaging (MRI). The distinctions of structural and functional alterations in depression and PD are reviewed. From the structural perspectives, PD seems to have less severe gray matter alterations in frontal and temporal lobes than depression. The study of white matter microintegrity reveals more widespread alterations in fronto-limbic circuit of depression patients than PD patients, such as the uncinate fasciculus and anterior thalamic radiation. PD might have a more restrictive pattern of structural alterations when compared to depression. For the functional perspectives, the core site of depression pathophysiology is the anterior subnetwork of resting-state network, such as anterior cingulate cortex, which is not significantly altered in PD. A possibly emerging pattern of fronto-limbic distinction between depression and PD has been revealed by these explorative reports. The future trend for machine learning and pattern recognition might confirm the differentiation pattern between depression and PD based on the explorative results.
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8
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O'Donnell LJ, Daducci A, Wassermann D, Lenglet C. Advances in computational and statistical diffusion MRI. NMR IN BIOMEDICINE 2019; 32:e3805. [PMID: 29134716 PMCID: PMC5951736 DOI: 10.1002/nbm.3805] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2016] [Revised: 07/31/2017] [Accepted: 08/14/2017] [Indexed: 06/03/2023]
Abstract
Computational methods are crucial for the analysis of diffusion magnetic resonance imaging (MRI) of the brain. Computational diffusion MRI can provide rich information at many size scales, including local microstructure measures such as diffusion anisotropies or apparent axon diameters, whole-brain connectivity information that describes the brain's wiring diagram and population-based studies in health and disease. Many of the diffusion MRI analyses performed today were not possible five, ten or twenty years ago, due to the requirements for large amounts of computer memory or processor time. In addition, mathematical frameworks had to be developed or adapted from other fields to create new ways to analyze diffusion MRI data. The purpose of this review is to highlight recent computational and statistical advances in diffusion MRI and to put these advances into context by comparison with the more traditional computational methods that are in popular clinical and scientific use. We aim to provide a high-level overview of interest to diffusion MRI researchers, with a more in-depth treatment to illustrate selected computational advances.
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Affiliation(s)
- Lauren J O'Donnell
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Alessandro Daducci
- Computer Science department, University of Verona, Verona, Italy
- Radiology department, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Demian Wassermann
- Athena Team, Inria Sophia Antipolis-Méditerranée, 2004 route des Lucioles, 06902 Biot, France
| | - Christophe Lenglet
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
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9
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Caspers S, Axer M. Decoding the microstructural correlate of diffusion MRI. NMR IN BIOMEDICINE 2019; 32:e3779. [PMID: 28858413 DOI: 10.1002/nbm.3779] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2016] [Revised: 06/28/2017] [Accepted: 07/05/2017] [Indexed: 06/07/2023]
Abstract
Diffusion imaging has evolved considerably over the past decade. While it provides valuable information about the structural connectivity at the macro- and mesoscopic scale, bridging the gap to the microstructure at the level of single nerve fibers poses an enormous challenge. This is particularly true for the human brain with its large size, its large white-matter volume and availability of histological techniques for studying human whole-brain sections and subsequent 3D reconstruction. Classic post-mortem techniques for studying the fiber architecture of the brain, such as myeloarchitectonic staining or dye tracing, are complemented by novel histological approaches, such as 3D polarized light imaging or optical coherence tomography, enabling unique insight into the fiber architecture from large fiber bundles within deep white matter to single nerve fibers in the cortex. The present review discusses the benefits and challenges of these latest developments in comparison with the classic techniques, with particular focus on the mutual exchange between in vivo and post-mortem diffusion imaging and post-mortem microstructural approaches for understanding the wiring of the brain across different scales.
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Affiliation(s)
- Svenja Caspers
- C. and O. Vogt Institute for Brain Research, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- JARA-BRAIN, Jülich-Aachen Research Alliance, Jülich, Germany
| | - Markus Axer
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
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10
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Bastiani M, Cottaar M, Fitzgibbon SP, Suri S, Alfaro-Almagro F, Sotiropoulos SN, Jbabdi S, Andersson JLR. Automated quality control for within and between studies diffusion MRI data using a non-parametric framework for movement and distortion correction. Neuroimage 2018; 184:801-812. [PMID: 30267859 PMCID: PMC6264528 DOI: 10.1016/j.neuroimage.2018.09.073] [Citation(s) in RCA: 160] [Impact Index Per Article: 26.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Revised: 09/21/2018] [Accepted: 09/25/2018] [Indexed: 11/24/2022] Open
Abstract
Diffusion MRI data can be affected by hardware and subject-related artefacts that can adversely affect downstream analyses. Therefore, automated quality control (QC) is of great importance, especially in large population studies where visual QC is not practical. In this work, we introduce an automated diffusion MRI QC framework for single subject and group studies. The QC is based on a comprehensive, non-parametric approach for movement and distortion correction: FSL EDDY, which allows us to extract a rich set of QC metrics that are both sensitive and specific to different types of artefacts. Two different tools are presented: QUAD (QUality Assessment for DMRI), for single subject QC and SQUAD (Study-wise QUality Assessment for DMRI), which is designed to enable group QC and facilitate cross-studies harmonisation efforts. Two tools to automatically perform QC of diffusion MRI data. Automated generation of single subject reports for visual inspection and database. Group databases and reports allow to compare subjects within and between studies. Categorical and continuous variables can be used to update the reports.
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Affiliation(s)
- Matteo Bastiani
- Wellcome Centre for Integrative Neuroimaging - Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK; Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK.
| | - Michiel Cottaar
- Wellcome Centre for Integrative Neuroimaging - Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
| | - Sean P Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging - Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
| | - Sana Suri
- Department of Psychiatry, University of Oxford, UK; Wellcome Centre for Integrative Neuroimaging - Oxford Centre for Human Brain Activity (OHBA), University of Oxford, UK
| | - Fidel Alfaro-Almagro
- Wellcome Centre for Integrative Neuroimaging - Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
| | - Stamatios N Sotiropoulos
- Wellcome Centre for Integrative Neuroimaging - Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK; Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK; National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Queens Medical Centre, Nottingham, UK
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging - Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
| | - Jesper L R Andersson
- Wellcome Centre for Integrative Neuroimaging - Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
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11
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Probing the reproducibility of quantitative estimates of structural connectivity derived from global tractography. Neuroimage 2018; 175:215-229. [PMID: 29438843 DOI: 10.1016/j.neuroimage.2018.01.086] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Revised: 01/12/2018] [Accepted: 01/30/2018] [Indexed: 11/20/2022] Open
Abstract
As quantitative measures derived from fiber tractography are increasingly being used to characterize the structural connectivity of the brain, it is important to establish their reproducibility. However, no such information is as yet available for global tractography. Here we provide the first comprehensive analysis of the reproducibility of streamline counts derived from global tractography as quantitative estimates of structural connectivity. In a sample of healthy young adults scanned twice within one week, within-session and between-session test-retest reproducibility was estimated for streamline counts of connections based on regions of the AAL atlas using the intraclass correlation coefficient (ICC) for absolute agreement. We further evaluated the influence of the type of head-coil (12 versus 32 channels) and the number of reconstruction repetitions (reconstructing streamlines once or aggregated over ten repetitions). Factorial analyses demonstrated that reproducibility was significantly greater for within- than between-session reproducibility and significantly increased by aggregating streamline counts over ten reconstruction repetitions. Using a high-resolution head-coil incurred only small beneficial effects. Overall, ICC values were positively correlated with the streamline count of a connection. Additional analyses assessed the influence of different selection variants (defining fuzzy versus no fuzzy borders of the seed mask; selecting streamlines that end in versus pass through a seed) showing that an endpoint-based variant using fuzzy selection provides the best compromise between reproducibility and anatomical specificity. In sum, aggregating quantitative indices over repeated estimations and higher numbers of streamlines are important determinants of test-retest reproducibility. If these factors are taken into account, streamline counts derived from global tractography provide an adequately reproducible quantitative measure that can be used to gauge the structural connectivity of the brain in health and disease.
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12
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Grinberg F, Maximov II, Farrher E, Shah NJ. Microstructure-informed slow diffusion tractography in humans enhances visualisation of fibre pathways. Magn Reson Imaging 2017; 45:7-17. [PMID: 28870514 DOI: 10.1016/j.mri.2017.08.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Revised: 08/15/2017] [Accepted: 08/30/2017] [Indexed: 11/26/2022]
Abstract
Conventional fibre tractography methods based on diffusion tensor imaging exploit diffusion anisotropy and directionality in the range of low diffusion weightings (b-values). High b-value Biexponential Diffusion Tensor Analysis reported previously has demonstrated that fractional anisotropy of the slow diffusion component is essentially higher than that of conventional diffusion tensor imaging whereas popular compartment models associate this slow diffusion component with axonal water fraction. One of the primary aims of this study is to elucidate the feasibility and potential benefits of "microstructure-informed" whole-brain slow-diffusion fibre tracking (SDIFT) in humans. In vivo diffusion-weighted images in humans were acquired in the extended range of diffusion weightings≤6000smm-2 at 3T. Fast and slow diffusion tensors were reconstructed using the bi-exponential tensor decomposition, and a detailed statistical analysis of the relevant whole-brain tensor metrics was performed. We visualised three-dimensional fibre tracts in in vivo human brains using deterministic streamlining via the major eigenvector of the slow diffusion tensor. In particular, we demonstrated that slow-diffusion fibre tracking provided considerably higher fibre counts of long association fibres and allowed one to reconstruct more short association fibres than conventional diffusion tensor imaging. SDIFT is suggested to be useful as a complimentary method capable to enhance reliability and visualisation of the evaluated fibre pathways. It is especially informative in precortical areas where the uncertainty of the mono-exponential tensor evaluation becomes too high due to decreased anisotropy of low b-value diffusion in these areas. Benefits can be expected in assessment of the residual axonal integrity in tissues affected by various pathological conditions, in surgical planning, and in evaluation of cortical connectivity, in particular, between Brodmann's areas.
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Affiliation(s)
- Farida Grinberg
- Institute of Neuroscience and Medicine - 4, Forschungszentrum Juelich GmbH, Juelich, Germany,; Department of Neurology, Faculty of Medicine, RWTH Aachen University, JARA, Aachen, Germany.
| | - Ivan I Maximov
- Institute of Neuroscience and Medicine - 4, Forschungszentrum Juelich GmbH, Juelich, Germany
| | - Ezequiel Farrher
- Institute of Neuroscience and Medicine - 4, Forschungszentrum Juelich GmbH, Juelich, Germany
| | - N Jon Shah
- Institute of Neuroscience and Medicine - 4, Forschungszentrum Juelich GmbH, Juelich, Germany,; Department of Neurology, Faculty of Medicine, RWTH Aachen University, JARA, Aachen, Germany
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Noristani HN, Boukhaddaoui H, Saint-Martin G, Auzer P, Sidiboulenouar R, Lonjon N, Alibert E, Tricaud N, Goze-Bac C, Coillot C, Perrin FE. A Combination of Ex vivo Diffusion MRI and Multiphoton to Study Microglia/Monocytes Alterations after Spinal Cord Injury. Front Aging Neurosci 2017; 9:230. [PMID: 28769787 PMCID: PMC5515855 DOI: 10.3389/fnagi.2017.00230] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Accepted: 07/04/2017] [Indexed: 12/11/2022] Open
Abstract
Central nervous system (CNS) injury has been observed to lead to microglia activation and monocytes infiltration at the lesion site. Ex vivo diffusion magnetic resonance imaging (diffusion MRI or DWI) allows detailed examination of CNS tissues, and recent advances in clearing procedures allow detailed imaging of fluorescent-labeled cells at high resolution. No study has yet combined ex vivo diffusion MRI and clearing procedures to establish a possible link between microglia/monocytes response and diffusion coefficient in the context of spinal cord injury (SCI). We carried out ex vivo MRI of the spinal cord at different time-points after spinal cord transection followed by tetrahydrofuran based clearing and examined the density and morphology of microglia/monocytes using two-photon microscopy. Quantitative analysis revealed an early marked increase in microglial/monocytes density that is associated with an increase in the extension of the lesion measured using diffusion MRI. Morphological examination of microglia/monocytes somata at the lesion site revealed a significant increase in their surface area and volume as early as 72 hours post-injury. Time-course analysis showed differential microglial/monocytes response rostral and caudal to the lesion site. Microglia/monocytes showed a decrease in reactivity over time caudal to the lesion site, but an increase was observed rostrally. Direct comparison of microglia/monocytes morphology, obtained through multiphoton, and the longitudinal apparent diffusion coefficient (ADC), measured with diffusion MRI, highlighted that axonal integrity does not correlate with the density of microglia/monocytes or their somata morphology. We emphasize that differential microglial/monocytes reactivity rostral and caudal to the lesion site may thus coincide, at least partially, with reported temporal differences in debris clearance. Our study demonstrates that the combination of ex vivo diffusion MRI and two-photon microscopy may be used to follow structural tissue alteration. Lesion extension coincides with microglia/monocytes density; however, a direct relationship between ADC and microglia/monocytes density and morphology was not observed. We highlighted a differential rostro-caudal microglia/monocytes reactivity that may correspond to a temporal difference in debris clearance and axonal integrity. Thus, potential therapeutic strategies targeting microglia/monocytes after SCI may need to be adjusted not only with the time after injury but also relative to the location to the lesion site.
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Affiliation(s)
- Harun N Noristani
- Institut National de la Santé et de la Recherche Médicale, U1051Montpellier, France.,University of Montpellier, Montpellier; Institut National de la Santé et de la Recherche Médicale, U1198, Montpellier; École Pratique des Hautes ÉtudesParis, France
| | - Hassan Boukhaddaoui
- Institut National de la Santé et de la Recherche Médicale, U1051Montpellier, France
| | - Guillaume Saint-Martin
- University of Montpellier, Montpellier; Institut National de la Santé et de la Recherche Médicale, U1198, Montpellier; École Pratique des Hautes ÉtudesParis, France.,Charles Coulomb Laboratory, UMR 5221 Centre National de la Recherche ScientifiqueMontpellier, France
| | - Pauline Auzer
- Institut National de la Santé et de la Recherche Médicale, U1051Montpellier, France
| | - Rahima Sidiboulenouar
- Charles Coulomb Laboratory, UMR 5221 Centre National de la Recherche ScientifiqueMontpellier, France
| | - Nicolas Lonjon
- University of Montpellier, Montpellier; Institut National de la Santé et de la Recherche Médicale, U1198, Montpellier; École Pratique des Hautes ÉtudesParis, France.,Centre Hospitalier Universitaire de Montpellier (CHRU), Gui de Chauliac HospitalMontpellier, France
| | - Eric Alibert
- Charles Coulomb Laboratory, UMR 5221 Centre National de la Recherche ScientifiqueMontpellier, France
| | - Nicolas Tricaud
- Institut National de la Santé et de la Recherche Médicale, U1051Montpellier, France
| | - Christophe Goze-Bac
- Charles Coulomb Laboratory, UMR 5221 Centre National de la Recherche ScientifiqueMontpellier, France
| | - Christophe Coillot
- Charles Coulomb Laboratory, UMR 5221 Centre National de la Recherche ScientifiqueMontpellier, France
| | - Florence E Perrin
- Institut National de la Santé et de la Recherche Médicale, U1051Montpellier, France.,University of Montpellier, Montpellier; Institut National de la Santé et de la Recherche Médicale, U1198, Montpellier; École Pratique des Hautes ÉtudesParis, France
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14
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Bastiani M, Cottaar M, Dikranian K, Ghosh A, Zhang H, Alexander DC, Behrens TE, Jbabdi S, Sotiropoulos SN. Improved tractography using asymmetric fibre orientation distributions. Neuroimage 2017; 158:205-218. [PMID: 28669902 PMCID: PMC6318223 DOI: 10.1016/j.neuroimage.2017.06.050] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Revised: 05/27/2017] [Accepted: 06/21/2017] [Indexed: 12/13/2022] Open
Abstract
Diffusion MRI allows us to make inferences on the structural organisation of the brain by mapping water diffusion to white matter microstructure. However, such a mapping is generally ill-defined; for instance, diffusion measurements are antipodally symmetric (diffusion along x and -x are equal), whereas the distribution of fibre orientations within a voxel is generally not symmetric. Therefore, different sub-voxel patterns such as crossing, fanning, or sharp bending, cannot be distinguished by fitting a voxel-wise model to the signal. However, asymmetric fibre patterns can potentially be distinguished once spatial information from neighbouring voxels is taken into account. We propose a neighbourhood-constrained spherical deconvolution approach that is capable of inferring asymmetric fibre orientation distributions (A-fods). Importantly, we further design and implement a tractography algorithm that utilises the estimated A-fods, since the commonly used streamline tractography paradigm cannot directly take advantage of the new information. We assess performance using ultra-high resolution histology data where we can compare true orientation distributions against sub-voxel fibre patterns estimated from down-sampled data. Finally, we explore the benefits of A-fods-based tractography using in vivo data by evaluating agreement of tractography predictions with connectivity estimates made using different in-vivo modalities. The proposed approach can reliably estimate complex fibre patterns such as sharp bending and fanning, which voxel-wise approaches cannot estimate. Moreover, histology-based and in-vivo results show that the new framework allows more accurate tractography and reconstruction of maps quantifying (symmetric and asymmetric) fibre complexity.
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Affiliation(s)
- Matteo Bastiani
- Wellcome Centre for Integrative Neuroscience (WIN) - Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK.
| | - Michiel Cottaar
- Wellcome Centre for Integrative Neuroscience (WIN) - Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
| | - Krikor Dikranian
- Department of Neuroscience, Washington University, St. Louis, MO, USA
| | - Aurobrata Ghosh
- Department of Computer Science & Centre for Medical Image Computing, University College London, UK
| | - Hui Zhang
- Department of Computer Science & Centre for Medical Image Computing, University College London, UK
| | - Daniel C Alexander
- Department of Computer Science & Centre for Medical Image Computing, University College London, UK
| | - Timothy E Behrens
- Wellcome Centre for Integrative Neuroscience (WIN) - Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroscience (WIN) - Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
| | - Stamatios N Sotiropoulos
- Wellcome Centre for Integrative Neuroscience (WIN) - Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK; Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK
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15
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Rokem A, Takemura H, Bock AS, Scherf KS, Behrmann M, Wandell BA, Fine I, Bridge H, Pestilli F. The visual white matter: The application of diffusion MRI and fiber tractography to vision science. J Vis 2017; 17:4. [PMID: 28196374 PMCID: PMC5317208 DOI: 10.1167/17.2.4] [Citation(s) in RCA: 59] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2016] [Accepted: 12/12/2016] [Indexed: 12/19/2022] Open
Abstract
Visual neuroscience has traditionally focused much of its attention on understanding the response properties of single neurons or neuronal ensembles. The visual white matter and the long-range neuronal connections it supports are fundamental in establishing such neuronal response properties and visual function. This review article provides an introduction to measurements and methods to study the human visual white matter using diffusion MRI. These methods allow us to measure the microstructural and macrostructural properties of the white matter in living human individuals; they allow us to trace long-range connections between neurons in different parts of the visual system and to measure the biophysical properties of these connections. We also review a range of findings from recent studies on connections between different visual field maps, the effects of visual impairment on the white matter, and the properties underlying networks that process visual information supporting visual face recognition. Finally, we discuss a few promising directions for future studies. These include new methods for analysis of MRI data, open datasets that are becoming available to study brain connectivity and white matter properties, and open source software for the analysis of these data.
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Affiliation(s)
- Ariel Rokem
- The University of Washington eScience Institute, Seattle, WA, ://arokem.org
| | - Hiromasa Takemura
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, and Osaka University, Suita-shi, JapanGraduate School of Frontier Biosciences, Osaka University, Suita-shi,
| | | | | | | | | | - Ione Fine
- University of Washington, Seattle, WA,
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16
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Axer M, Strohmer S, Gräßel D, Bücker O, Dohmen M, Reckfort J, Zilles K, Amunts K. Estimating Fiber Orientation Distribution Functions in 3D-Polarized Light Imaging. Front Neuroanat 2016; 10:40. [PMID: 27147981 PMCID: PMC4835454 DOI: 10.3389/fnana.2016.00040] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2015] [Accepted: 03/29/2016] [Indexed: 11/13/2022] Open
Abstract
Research of the human brain connectome requires multiscale approaches derived from independent imaging methods ideally applied to the same object. Hence, comprehensible strategies for data integration across modalities and across scales are essential. We have successfully established a concept to bridge the spatial scales from microscopic fiber orientation measurements based on 3D-Polarized Light Imaging (3D-PLI) to meso- or macroscopic dimensions. By creating orientation distribution functions (pliODFs) from high-resolution vector data via series expansion with spherical harmonics utilizing high performance computing and supercomputing technologies, data fusion with Diffusion Magnetic Resonance Imaging has become feasible, even for a large-scale dataset such as the human brain. Validation of our approach was done effectively by means of two types of datasets that were transferred from fiber orientation maps into pliODFs: simulated 3D-PLI data showing artificial, but clearly defined fiber patterns and real 3D-PLI data derived from sections through the human brain and the brain of a hooded seal.
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Affiliation(s)
- Markus Axer
- Research Centre Jülich, Institute of Neuroscience and Medicine Jülich, Germany
| | - Sven Strohmer
- Jülich Supercomputing Centre, Institute for Advanced Simulation, Research Centre JülichJülich, Germany; Research Centre Jülich, Simulation Lab Neuroscience, Bernstein Facility for Simulation and Database Technology, Institute for Advanced SimulationJülich, Germany
| | - David Gräßel
- Research Centre Jülich, Institute of Neuroscience and Medicine Jülich, Germany
| | - Oliver Bücker
- Jülich Supercomputing Centre, Institute for Advanced Simulation, Research Centre Jülich Jülich, Germany
| | - Melanie Dohmen
- Research Centre Jülich, Institute of Neuroscience and Medicine Jülich, Germany
| | - Julia Reckfort
- Research Centre Jülich, Institute of Neuroscience and Medicine Jülich, Germany
| | - Karl Zilles
- Research Centre Jülich, Institute of Neuroscience and MedicineJülich, Germany; Department of Psychiatry, Psychotherapy, and Psychosomatics, RWTH Aachen UniversityAachen, Germany; JARA Jülich-Aachen Research Alliance, Translational Brain MedicineAachen, Germany
| | - Katrin Amunts
- Research Centre Jülich, Institute of Neuroscience and MedicineJülich, Germany; C. and O. Vogt Institute for Brain Research, Heinrich-Heine-University DüsseldorfDüsseldorf, Germany
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17
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Qi S, Meesters S, Nicolay K, Ter Haar Romeny BM, Ossenblok P. Structural Brain Network: What is the Effect of LiFE Optimization of Whole Brain Tractography? Front Comput Neurosci 2016; 10:12. [PMID: 26909034 PMCID: PMC4754446 DOI: 10.3389/fncom.2016.00012] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2015] [Accepted: 01/29/2016] [Indexed: 01/21/2023] Open
Abstract
Structural brain networks constructed based on diffusion-weighted MRI (dMRI) have provided a systems perspective to explore the organization of the human brain. Some redundant and nonexistent fibers, however, are inevitably generated in whole brain tractography. We propose to add one critical step while constructing the networks to remove these fibers using the linear fascicle evaluation (LiFE) method, and study the differences between the networks with and without LiFE optimization. For a cohort of nine healthy adults and for 9 out of the 35 subjects from Human Connectome Project, the T 1-weighted images and dMRI data are analyzed. Each brain is parcellated into 90 regions-of-interest, whilst a probabilistic tractography algorithm is applied to generate the original connectome. The elimination of redundant and nonexistent fibers from the original connectome by LiFE creates the optimized connectome, and the random selection of the same number of fibers as the optimized connectome creates the non-optimized connectome. The combination of parcellations and these connectomes leads to the optimized and non-optimized networks, respectively. The optimized networks are constructed with six weighting schemes, and the correlations of different weighting methods are analyzed. The fiber length distributions of the non-optimized and optimized connectomes are compared. The optimized and non-optimized networks are compared with regard to edges, nodes and networks, within a sparsity range of 0.75-0.95. It has been found that relatively more short fibers exist in the optimized connectome. About 24.0% edges of the optimized network are significantly different from those in the non-optimized network at a sparsity of 0.75. About 13.2% of edges are classified as false positives or the possible missing edges. The strength and betweenness centrality of some nodes are significantly different for the non-optimized and optimized networks, but not the node efficiency. The normalized clustering coefficient, the normalized characteristic path length and the small-worldness are higher in the optimized network weighted by the fiber number than in the non-optimized network. These observed differences suggest that LiFE optimization can be a crucial step for the construction of more reasonable and more accurate structural brain networks.
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Affiliation(s)
- Shouliang Qi
- Sino-Dutch Biomedical and Information Engineering School, Northeastern UniversityShenyang, China; Academic Center for Epileptology Kempenhaeghe and Maastricht UMC+Heeze, Netherlands; Department of Biomedical Engineering, Eindhoven University of TechnologyEindhoven, Netherlands
| | - Stephan Meesters
- Academic Center for Epileptology Kempenhaeghe and Maastricht UMC+Heeze, Netherlands; Department of Mathematics and Computer Science, Eindhoven University of TechnologyEindhoven, Netherlands
| | - Klaas Nicolay
- Department of Biomedical Engineering, Eindhoven University of Technology Eindhoven, Netherlands
| | - Bart M Ter Haar Romeny
- Sino-Dutch Biomedical and Information Engineering School, Northeastern UniversityShenyang, China; Department of Biomedical Engineering, Eindhoven University of TechnologyEindhoven, Netherlands
| | - Pauly Ossenblok
- Academic Center for Epileptology Kempenhaeghe and Maastricht UMC+Heeze, Netherlands; Department of Biomedical Engineering, Eindhoven University of TechnologyEindhoven, Netherlands
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18
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Ohno N, Katoh M, Saitoh Y, Saitoh S. Recent advancement in the challenges to connectomics. Microscopy (Oxf) 2015; 65:97-107. [PMID: 26671942 DOI: 10.1093/jmicro/dfv371] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2015] [Accepted: 11/12/2015] [Indexed: 01/23/2023] Open
Abstract
Advancement of microscopic technologies established significant progress in our understanding of the brain. In the recent effort to elucidate the complete wiring map of the brain circuitry termed 'connectome', the different modalities of imaging technology, including those of light and electron microscopy, have started providing essential contribution in multiple organisms. The contribution would be impossible without the recent innovation in both acquisition and analyses of the big connectomic data. The current data demonstrated complicated networks with unidirectional and reciprocal connections of the cerebral circuits at the macroscopic and light microscopic ('mesoscopic') levels, and the unimaginable complexity of synaptic connections between axons and dendrites at the electron microscopic ('microscopic') level. At the same time, the data highlighted the necessity to make substantial advancement in methodology of the connectomic studies, including efficient handling and automated analyses of the acquired dataset. Further understanding about structural and functional connectome seems to be facilitated by combinations of the different imaging modalities. Such multidisciplinary approaches will give us the clues to address whether the complete connectome can elucidate fundamental mechanisms processing the basic and higher functions of human brains.
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Affiliation(s)
- Nobuhiko Ohno
- Department of Anatomy and Molecular Histology, Interdisciplinary Graduate School of Medicine and Engineering, University of Yamanashi, 1110 Shimokato, Chuo-city, Yamanashi 409-3898, Japan
| | - Mitsuhiko Katoh
- Department of Anatomy and Molecular Histology, Interdisciplinary Graduate School of Medicine and Engineering, University of Yamanashi, 1110 Shimokato, Chuo-city, Yamanashi 409-3898, Japan
| | - Yurika Saitoh
- Department of Anatomy and Molecular Histology, Interdisciplinary Graduate School of Medicine and Engineering, University of Yamanashi, 1110 Shimokato, Chuo-city, Yamanashi 409-3898, Japan
| | - Sei Saitoh
- Department of Anatomy and Molecular Histology, Interdisciplinary Graduate School of Medicine and Engineering, University of Yamanashi, 1110 Shimokato, Chuo-city, Yamanashi 409-3898, Japan
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19
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Ma L, Steinberg JL, Moeller FG, Johns SE, Narayana PA. Effect of cocaine dependence on brain connections: clinical implications. Expert Rev Neurother 2015; 15:1307-19. [PMID: 26512421 DOI: 10.1586/14737175.2015.1103183] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Cocaine dependence (CD) is associated with several cognitive deficits. Accumulating evidence, based on human and animal studies, has led to models for interpreting the neural basis of cognitive functions as interactions between functionally related brain regions. In this review, we focus on magnetic resonance imaging (MRI) studies using brain connectivity techniques as related to CD. The majority of these brain connectivity studies indicated that cocaine use is associated with altered brain connectivity between different structures, including cortical-striatal regions and default mode network. In cocaine users some of the altered brain connectivity measures are associated with behavioral performance, history of drug use, and treatment outcome. The implications of these brain connectivity findings to the treatment of CD and the pros and cons of the major brain connectivity techniques are discussed. Finally potential future directions in cocaine use disorder research using brain connectivity techniques are briefly described.
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Affiliation(s)
- Liangsuo Ma
- a Institute for Drug and Alcohol Studies , Virginia Commonwealth University (VCU) , Richmond , VA , USA.,b Department of Radiology , VCU , Richmond , VA , USA
| | - Joel L Steinberg
- a Institute for Drug and Alcohol Studies , Virginia Commonwealth University (VCU) , Richmond , VA , USA.,c Department of Psychiatry , VCU , Richmond , VA , USA
| | - F Gerard Moeller
- a Institute for Drug and Alcohol Studies , Virginia Commonwealth University (VCU) , Richmond , VA , USA.,c Department of Psychiatry , VCU , Richmond , VA , USA.,d Department of Pharmacology and Toxicology , VCU , Richmond , VA , USA.,e Department of Neurology , VCU , Richmond , VA , USA
| | - Sade E Johns
- a Institute for Drug and Alcohol Studies , Virginia Commonwealth University (VCU) , Richmond , VA , USA.,c Department of Psychiatry , VCU , Richmond , VA , USA
| | - Ponnada A Narayana
- f Department of Diagnostic and Interventional Imaging , University of Texas Health Science Center at Houston (UTHealth) , Houston , TX , USA
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