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Théberge A, Desrosiers C, Boré A, Descoteaux M, Jodoin PM. What matters in reinforcement learning for tractography. Med Image Anal 2024; 93:103085. [PMID: 38219499 DOI: 10.1016/j.media.2024.103085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 12/15/2023] [Accepted: 01/05/2024] [Indexed: 01/16/2024]
Abstract
Recently, deep reinforcement learning (RL) has been proposed to learn the tractography procedure and train agents to reconstruct the structure of the white matter without manually curated reference streamlines. While the performances reported were competitive, the proposed framework is complex, and little is still known about the role and impact of its multiple parts. In this work, we thoroughly explore the different components of the proposed framework, such as the choice of the RL algorithm, seeding strategy, the input signal and reward function, and shed light on their impact. Approximately 7,400 models were trained for this work, totalling nearly 41,000 h of GPU time. Our goal is to guide researchers eager to explore the possibilities of deep RL for tractography by exposing what works and what does not work with the category of approach. As such, we ultimately propose a series of recommendations concerning the choice of RL algorithm, the input to the agents, the reward function and more to help future work using reinforcement learning for tractography. We also release the open source codebase, trained models, and datasets for users and researchers wanting to explore reinforcement learning for tractography.
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Affiliation(s)
- Antoine Théberge
- Faculté des Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada, J1K 2R1.
| | - Christian Desrosiers
- Département de génie logiciel et des TI, École de technologie supérieure, Montréal, QC, Canada, H3C 1K3
| | - Arnaud Boré
- Faculté des Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada, J1K 2R1
| | - Maxime Descoteaux
- Faculté des Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada, J1K 2R1
| | - Pierre-Marc Jodoin
- Faculté des Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada, J1K 2R1
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2
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Becker D, Scherer M, Neher P, Jungk C, Jesser J, Pflüger I, Bendszus M, Maier-Hein K, Unterberg A. Q-ball high-resolution fiber tractography of language associated tracts: quantitative evaluation of applicability for glioma resections. J Neurosurg Sci 2024; 68:1-12. [PMID: 31680507 DOI: 10.23736/s0390-5616.19.04782-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
BACKGROUND To date, fiber tractography (FT) is predominantly based on diffusion tensor imaging (DTI). High angular resolution diffusion imaging (HARDI)-based reconstructions have become a focus of interest, enabling the resolution of intravoxel fiber crossing. However, experience with high resolution tractography (HRFT) for neurosurgical applications is still limited to a few reports. This prospectively designed feasibility study shares our initial experience using an analytical q-ball approach (QBI) for FT of language-associated pathways in comparison with DTI-FT, focussing on a quantitative analysis and evaluation of its applicability in clinical routine. METHODS Probabilistic QBI-, and DTI-FT were performed for the major components of the language-associated fiber bundles (superior longitudinal fasciculus, inferior fronto-occipital fasciculus, medial/inferior longitudinal faciculus) in 11 patients with eloquent gliomas. The data was derived from a routine DWI sequence (b=1000s/mm2, 64 gradient directions). Quantitative analysis evaluated tract volume (TV), tract length (TL) and tract density (TD). Results were correlated to tumor and edema size. RESULTS Quantitative analysis showed larger TV and TL of the overall fiber object using QBI-FT compared with DTI-FT (TV: 16.45±1.85 vs. 10.07±1.15cm3; P<0.0001; TL: 81.95±6.14 vs. 72.06±6.92 mm; P=0.0011). Regarding overall TD, DTI delivered significantly higher values (40.57±6.59 vs. 60.98±15.94 points/voxel; P=0.0118). Bland-Altman analysis illustrated a systematic advantage to yield lager TV and TL via QBI compared with DTI for all reconstructed pathways. The results were independent of tumor or edema volume. CONCLUSIONS QBI proved to be suitable for an application in the neurosurgical setting without additional expense for the patient. Quantitative analysis of FT reveals larger overall TV, longer TL with lower TD using QBI compared with DTI, suggesting the better depiction of marginal and terminal fibers according to neuroanatomical knowledge. This emphasizes the known limitation of DTI to underestimate the dimensions of a pathway. Rather than relying on DTI, sophisticated HRFT techniques should be considered for preoperative planning and intraoperative guidance in selected cases of eloquent glioma surgery.
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Affiliation(s)
- Daniela Becker
- Department of Neurosurgery, University of Heidelberg, Heidelberg, Germany -
| | - Moritz Scherer
- Department of Neurosurgery, University of Heidelberg, Heidelberg, Germany
| | - Peter Neher
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Christine Jungk
- Department of Neurosurgery, University of Heidelberg, Heidelberg, Germany
| | - Jessica Jesser
- Department of Neuroradiology, University of Heidelberg, Heidelberg, Germany
| | - Irada Pflüger
- Department of Neuroradiology, University of Heidelberg, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, University of Heidelberg, Heidelberg, Germany
| | - Klaus Maier-Hein
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Andreas Unterberg
- Department of Neurosurgery, University of Heidelberg, Heidelberg, Germany
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Astolfi P, Verhagen R, Petit L, Olivetti E, Sarubbo S, Masci J, Boscaini D, Avesani P. Supervised tractogram filtering using Geometric Deep Learning. Med Image Anal 2023; 90:102893. [PMID: 37741032 DOI: 10.1016/j.media.2023.102893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 04/18/2023] [Accepted: 07/07/2023] [Indexed: 09/25/2023]
Abstract
A tractogram is a virtual representation of the brain white matter. It is composed of millions of virtual fibers, encoded as 3D polylines, which approximate the white matter axonal pathways. To date, tractograms are the most accurate white matter representation and thus are used for tasks like presurgical planning and investigations of neuroplasticity, brain disorders, or brain networks. However, it is a well-known issue that a large portion of tractogram fibers is not anatomically plausible and can be considered artifacts of the tracking procedure. With Verifyber, we tackle the problem of filtering out such non-plausible fibers using a novel fully-supervised learning approach. Differently from other approaches based on signal reconstruction and/or brain topology regularization, we guide our method with the existing anatomical knowledge of the white matter. Using tractograms annotated according to anatomical principles, we train our model, Verifyber, to classify fibers as either anatomically plausible or non-plausible. The proposed Verifyber model is an original Geometric Deep Learning method that can deal with variable size fibers, while being invariant to fiber orientation. Our model considers each fiber as a graph of points, and by learning features of the edges between consecutive points via the proposed sequence Edge Convolution, it can capture the underlying anatomical properties. The output filtering results highly accurate and robust across an extensive set of experiments, and fast; with a 12GB GPU, filtering a tractogram of 1M fibers requires less than a minute.
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Affiliation(s)
- Pietro Astolfi
- NILab, TeV, Fondazione Bruno Kessler, Trento, Italy; PAVIS, Istituto Italiano di Tecnologia, Geonva, Italy; Center for Mind/Brain Sciences (CiMeC), University of Trento, Rovereto, Italy
| | | | - Laurent Petit
- GIN, IMN, CNRS, CEA, Université de Bordeaux, Bordeaux, France
| | - Emanuele Olivetti
- NILab, TeV, Fondazione Bruno Kessler, Trento, Italy; Center for Mind/Brain Sciences (CiMeC), University of Trento, Rovereto, Italy
| | - Silvio Sarubbo
- Center for Mind/Brain Sciences (CiMeC), University of Trento, Rovereto, Italy; Department of Neurosurgery, Azienda Provinciale per i Servizi Sanitari, "Santa Chiara" Hospital, Trento, Italy
| | | | | | - Paolo Avesani
- NILab, TeV, Fondazione Bruno Kessler, Trento, Italy; Center for Mind/Brain Sciences (CiMeC), University of Trento, Rovereto, Italy.
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4
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He J, Zhang F, Pan Y, Feng Y, Rushmore J, Torio E, Rathi Y, Makris N, Kikinis R, Golby AJ, O'Donnell LJ. Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods. Hum Brain Mapp 2023; 44:6055-6073. [PMID: 37792280 PMCID: PMC10619402 DOI: 10.1002/hbm.26497] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 09/09/2023] [Accepted: 09/13/2023] [Indexed: 10/05/2023] Open
Abstract
The corticospinal tract (CST) is a critically important white matter fiber tract in the human brain that enables control of voluntary movements of the body. The CST exhibits a somatotopic organization, which means that the motor neurons that control specific body parts are arranged in order within the CST. Diffusion magnetic resonance imaging (MRI) tractography is increasingly used to study the anatomy of the CST. However, despite many advances in tractography algorithms over the past decade, modern, state-of-the-art methods still face challenges. In this study, we compare the performance of six widely used tractography methods for reconstructing the CST and its somatotopic organization. These methods include constrained spherical deconvolution (CSD) based probabilistic (iFOD1) and deterministic (SD-Stream) methods, unscented Kalman filter (UKF) tractography methods including multi-fiber (UKF2T) and single-fiber (UKF1T) models, the generalized q-sampling imaging (GQI) based deterministic tractography method, and the TractSeg method. We investigate CST somatotopy by dividing the CST into four subdivisions per hemisphere that originate in the leg, trunk, hand, and face areas of the primary motor cortex. A quantitative and visual comparison is performed using diffusion MRI data (N = 100 subjects) from the Human Connectome Project. Quantitative evaluations include the reconstruction rate of the eight anatomical subdivisions, the percentage of streamlines in each subdivision, and the coverage of the white matter-gray matter (WM-GM) interface. CST somatotopy is further evaluated by comparing the percentage of streamlines in each subdivision to the cortical volumes for the leg, trunk, hand, and face areas. Overall, UKF2T has the highest reconstruction rate and cortical coverage. It is the only method with a significant positive correlation between the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex. However, our experimental results show that all compared tractography methods are biased toward generating many trunk streamlines (ranging from 35.10% to 71.66% of total streamlines across methods). Furthermore, the coverage of the WM-GM interface in the largest motor area (face) is generally low (under 40%) for all compared tractography methods. Different tractography methods give conflicting results regarding the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex, indicating that there is generally no clear relationship, and that reconstruction of CST somatotopy is still a large challenge. Overall, we conclude that while current tractography methods have made progress toward the well-known challenge of improving the reconstruction of the lateral projections of the CST, the overall problem of performing a comprehensive CST reconstruction, including clinically important projections in the lateral (hand and face areas) and medial portions (leg area), remains an important challenge for diffusion MRI tractography.
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Affiliation(s)
- Jianzhong He
- Institution of Information Processing and AutomationZhejiang University of TechnologyHangzhouChina
| | - Fan Zhang
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
- University of Electronic Science and Technology of ChinaChengduSichuanChina
| | - Yiang Pan
- Institution of Information Processing and AutomationZhejiang University of TechnologyHangzhouChina
| | - Yuanjing Feng
- Institution of Information Processing and AutomationZhejiang University of TechnologyHangzhouChina
| | - Jarrett Rushmore
- Departments of Psychiatry, Neurology and RadiologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of Anatomy and NeurobiologyBoston University School of MedicineBostonMassachusettsUSA
| | - Erickson Torio
- Department of NeurosurgeryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Yogesh Rathi
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Department of PsychiatryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Nikos Makris
- Departments of Psychiatry, Neurology and RadiologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of PsychiatryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Ron Kikinis
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Alexandra J. Golby
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Department of NeurosurgeryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Lauren J. O'Donnell
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
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Zhang F, Daducci A, He Y, Schiavi S, Seguin C, Smith RE, Yeh CH, Zhao T, O'Donnell LJ. Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review. Neuroimage 2022; 249:118870. [PMID: 34979249 PMCID: PMC9257891 DOI: 10.1016/j.neuroimage.2021.118870] [Citation(s) in RCA: 84] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 12/03/2021] [Accepted: 12/31/2021] [Indexed: 12/13/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain's structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.
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Affiliation(s)
- Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | | | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Chinese Institute for Brain Research, Beijing, China
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | - Caio Seguin
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia; The University of Sydney, School of Biomedical Engineering, Sydney, Australia
| | - Robert E Smith
- The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia
| | - Chun-Hung Yeh
- Institute for Radiological Research, Chang Gung University, Taoyuan, Taiwan; Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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6
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Yebga Hot R, Siwiaszczyk M, Love SA, Andersson F, Calandreau L, Poupon F, Beaujoin J, Herlin B, Boumezbeur F, Mulot B, Chaillou E, Uszynski I, Poupon C. A novel male Japanese quail structural connectivity atlas using ultra-high field diffusion MRI at 11.7 T. Brain Struct Funct 2022; 227:1577-1597. [PMID: 35355136 PMCID: PMC9098543 DOI: 10.1007/s00429-022-02457-2] [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: 05/27/2021] [Accepted: 01/10/2022] [Indexed: 12/27/2022]
Abstract
The structural connectivity of animal brains can be revealed using post-mortem diffusion-weighted magnetic resonance imaging (MRI). Despite the existence of several structural atlases of avian brains, few of them address the bird’s structural connectivity. In this study, a novel atlas of the structural connectivity is proposed for the male Japanese quail (Coturnix japonica), aiming at investigating two lines divergent on their emotionality trait: the short tonic immobility (STI) and the long tonic immobility (LTI) lines. The STI line presents a low emotionality trait, while the LTI line expresses a high emotionality trait. 21 male Japanese quail brains from both lines were scanned post-mortem for this study, using a preclinical Bruker 11.7 T MRI scanner. Diffusion-weighted MRI was performed using a 3D segmented echo planar imaging (EPI) pulsed gradient spin-echo (PGSE) sequence with a 200 \documentclass[12pt]{minimal}
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\begin{document}$$\upmu$$\end{document}μm isotropic resolution, 75 diffusion-encoding directions and a b-value fixed at 4500 s/mm2. Anatomical MRI was likewise performed using a 2D anatomical T2-weighted spin-echo (SE) sequence with a 150 \documentclass[12pt]{minimal}
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\begin{document}$$\upmu$$\end{document}μm isotropic resolution. This very first anatomical connectivity atlas of the male Japanese quail reveals 34 labeled fiber tracts and the existence of structural differences between the connectivity patterns characterizing the two lines. Thus, the link between the male Japanese quail’s connectivity and its underlying anatomical structures has reached a better understanding.
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Affiliation(s)
- Raïssa Yebga Hot
- Unité BAOBAB, NeuroSpin, Université Paris-Saclay, CNRS, CEA, 91191, Gif-sur-Yvette, France
| | - Marine Siwiaszczyk
- Unité de Physiologie de la Reproduction et des Comportements (PRC), INRAE, CNRS, IFCE, Université de Tours, 37380, Nouzilly, France
| | - Scott A Love
- Unité de Physiologie de la Reproduction et des Comportements (PRC), INRAE, CNRS, IFCE, Université de Tours, 37380, Nouzilly, France
| | | | - Ludovic Calandreau
- Unité de Physiologie de la Reproduction et des Comportements (PRC), INRAE, CNRS, IFCE, Université de Tours, 37380, Nouzilly, France
| | - Fabrice Poupon
- Unité BAOBAB, NeuroSpin, Université Paris-Saclay, CNRS, CEA, 91191, Gif-sur-Yvette, France
| | - Justine Beaujoin
- Unité BAOBAB, NeuroSpin, Université Paris-Saclay, CNRS, CEA, 91191, Gif-sur-Yvette, France
| | - Bastien Herlin
- Unité BAOBAB, NeuroSpin, Université Paris-Saclay, CNRS, CEA, 91191, Gif-sur-Yvette, France
| | - Fawzi Boumezbeur
- Unité BAOBAB, NeuroSpin, Université Paris-Saclay, CNRS, CEA, 91191, Gif-sur-Yvette, France
| | - Baptiste Mulot
- Zooparc de Beauval & Beauval Nature, 41110, Saint-Aignan, France
| | - Elodie Chaillou
- Unité de Physiologie de la Reproduction et des Comportements (PRC), INRAE, CNRS, IFCE, Université de Tours, 37380, Nouzilly, France
| | - Ivy Uszynski
- Unité BAOBAB, NeuroSpin, Université Paris-Saclay, CNRS, CEA, 91191, Gif-sur-Yvette, France
| | - Cyril Poupon
- Unité BAOBAB, NeuroSpin, Université Paris-Saclay, CNRS, CEA, 91191, Gif-sur-Yvette, France.
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Yeh FC, Irimia A, Bastos DCDA, Golby AJ. Tractography methods and findings in brain tumors and traumatic brain injury. Neuroimage 2021; 245:118651. [PMID: 34673247 PMCID: PMC8859988 DOI: 10.1016/j.neuroimage.2021.118651] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 10/05/2021] [Accepted: 10/11/2021] [Indexed: 12/31/2022] Open
Abstract
White matter fiber tracking using diffusion magnetic resonance imaging (dMRI) provides a noninvasive approach to map brain connections, but improving anatomical accuracy has been a significant challenge since the birth of tractography methods. Utilizing tractography in brain studies therefore requires understanding of its technical limitations to avoid shortcomings and pitfalls. This review explores tractography limitations and how different white matter pathways pose different challenges to fiber tracking methodologies. We summarize the pros and cons of commonly-used methods, aiming to inform how tractography and its related analysis may lead to questionable results. Extending these experiences, we review the clinical utilization of tractography in patients with brain tumors and traumatic brain injury, starting from tensor-based tractography to more advanced methods. We discuss current limitations and highlight novel approaches in the context of these two conditions to inform future tractography developments.
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Affiliation(s)
- Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
| | - Andrei Irimia
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, California, USA; Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
| | | | - Alexandra J Golby
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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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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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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10
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Comparison of diffusion signal models for fiber tractography in eloquent glioma surgery - determination of accuracy under awake craniotomy conditions. World Neurosurg 2021; 158:e429-e440. [PMID: 34767992 DOI: 10.1016/j.wneu.2021.11.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 10/30/2021] [Accepted: 11/01/2021] [Indexed: 11/23/2022]
Abstract
OBJECTIVE Fiber tractography(FT) has become an important non-invasive tool to ensure maximal safe tumor resection in eloquent glioma surgery. Intraoperatively applied FT is still predominantly based on Diffusion Tensor Imaging(DTI). However, reconstruction schemes of high angular resolution diffusion imaging(HARDI) data for high resolution fiber tractography(HRFT) are gaining increasing attention. The aim of this prospective study was to compare the accuracy of sophisticated HRFT-models compared with DTI-FT. METHODS Ten patients with eloquent gliomas underwent surgery under awake craniotomy conditions. The localization of acquisition points(AP), representing deteriorations during intraoperative electrostimulation(IOM) and neuropsychological mapping, were documented. The offsets of AP to the respective fiber bundle were calculated. Probabilistic QBI- and CSD-FT were compared to DTI-FT for the major language-associated fiber bundles (superior longitudinal fasciclus (SLF) II-IV, inferior fronto-occipital fasciculus (IFOF), inferior longitudinal fasciculus/medial longitudinal fasciculus (ILF/MLF). RESULTS Among 186 offset values, 46% were located closer than 10mm to the estimated fiber bundle (CSD:36%; DTI:40% and QBI:60%). Moreover, only 10 offsets were further away than 30mm (5%). Lowest mean min-offsets (SLF: 7.7±7.9mm; IFOF: 12.7±8.3mm; ILF/MLF: 17.7±6.7mm) were found for QBI, indicating a significant advantage compared with CSD or DTI (p<0.001), respectively. No significant differences were found between CSD-, and DTI-FT offsets (p=0.105), albeit for the compound SLF exclusively (p<0.001). CONCLUSIONS Comparing HRFT techniques QBI and CSD with DTI, QBI delivered significantly better results with lowest offsets and good correlation to IOM results. Besides, QBI-FT was feasible for neurosurgical pre- and intraoperative applications. Our findings suggest that a combined approach of QBI-FT and IOM under awake craniotomy is considerable for best preservation of neurological function in the presented setting. Overall, the implementation of selected HRFT models into neuronavigation systems seems to be a promising tool in glioma surgery.
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Drobnjak I, Neher P, Poupon C, Sarwar T. Physical and digital phantoms for validating tractography and assessing artifacts. Neuroimage 2021; 245:118704. [PMID: 34748954 DOI: 10.1016/j.neuroimage.2021.118704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 10/01/2021] [Accepted: 11/01/2021] [Indexed: 11/17/2022] Open
Abstract
Fiber tractography is widely used to non-invasively map white-matter bundles in vivo using diffusion-weighted magnetic resonance imaging (dMRI). As it is the case for all scientific methods, proper validation is a key prerequisite for the successful application of fiber tractography, be it in the area of basic neuroscience or in a clinical setting. It is well-known that the indirect estimation of the fiber tracts from the local diffusion signal is highly ambiguous and extremely challenging. Furthermore, the validation of fiber tractography methods is hampered by the lack of a real ground truth, which is caused by the extremely complex brain microstructure that is not directly observable non-invasively and that is the basis of the huge network of long-range fiber connections in the brain that are the actual target of fiber tractography methods. As a substitute for in vivo data with a real ground truth that could be used for validation, a widely and successfully employed approach is the use of synthetic phantoms. In this work, we are providing an overview of the state-of-the-art in the area of physical and digital phantoms, answering the following guiding questions: "What are dMRI phantoms and what are they good for?", "What would the ideal phantom for validation fiber tractography look like?" and "What phantoms, phantom datasets and tools used for their creation are available to the research community?". We will further discuss the limitations and opportunities that come with the use of dMRI phantoms, and what future direction this field of research might take.
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Affiliation(s)
- Ivana Drobnjak
- Center for Medical Image Computing, Department of Computer Science, University College London, UK.
| | - Peter Neher
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Cyril Poupon
- BAOBAB, NeuroSpin, Commissariat à l'Energie Atomique, Institut des Sciences du Vivant Frédéric Joliot, Gif-sur-Yvette, France
| | - Tabinda Sarwar
- School of Computing Technologies, RMIT University, Australia
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12
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Schilling KG, Rheault F, Petit L, Hansen CB, Nath V, Yeh FC, Girard G, Barakovic M, Rafael-Patino J, Yu T, Fischi-Gomez E, Pizzolato M, Ocampo-Pineda M, Schiavi S, Canales-Rodríguez EJ, Daducci A, Granziera C, Innocenti G, Thiran JP, Mancini L, Wastling S, Cocozza S, Petracca M, Pontillo G, Mancini M, Vos SB, Vakharia VN, Duncan JS, Melero H, Manzanedo L, Sanz-Morales E, Peña-Melián Á, Calamante F, Attyé A, Cabeen RP, Korobova L, Toga AW, Vijayakumari AA, Parker D, Verma R, Radwan A, Sunaert S, Emsell L, De Luca A, Leemans A, Bajada CJ, Haroon H, Azadbakht H, Chamberland M, Genc S, Tax CMW, Yeh PH, Srikanchana R, Mcknight CD, Yang JYM, Chen J, Kelly CE, Yeh CH, Cochereau J, Maller JJ, Welton T, Almairac F, Seunarine KK, Clark CA, Zhang F, Makris N, Golby A, Rathi Y, O'Donnell LJ, Xia Y, Aydogan DB, Shi Y, Fernandes FG, Raemaekers M, Warrington S, Michielse S, Ramírez-Manzanares A, Concha L, Aranda R, Meraz MR, Lerma-Usabiaga G, Roitman L, Fekonja LS, Calarco N, Joseph M, Nakua H, Voineskos AN, Karan P, Grenier G, Legarreta JH, Adluru N, Nair VA, Prabhakaran V, Alexander AL, Kamagata K, Saito Y, Uchida W, Andica C, Abe M, Bayrak RG, Wheeler-Kingshott CAMG, D'Angelo E, Palesi F, Savini G, Rolandi N, Guevara P, Houenou J, López-López N, Mangin JF, Poupon C, Román C, Vázquez A, Maffei C, Arantes M, Andrade JP, Silva SM, Calhoun VD, Caverzasi E, Sacco S, Lauricella M, Pestilli F, Bullock D, Zhan Y, Brignoni-Perez E, Lebel C, Reynolds JE, Nestrasil I, Labounek R, Lenglet C, Paulson A, Aulicka S, Heilbronner SR, Heuer K, Chandio BQ, Guaje J, Tang W, Garyfallidis E, Raja R, Anderson AW, Landman BA, Descoteaux M. Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset? Neuroimage 2021; 243:118502. [PMID: 34433094 PMCID: PMC8855321 DOI: 10.1016/j.neuroimage.2021.118502] [Citation(s) in RCA: 65] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 08/10/2021] [Accepted: 08/21/2021] [Indexed: 10/20/2022] Open
Abstract
White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.
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Affiliation(s)
- Kurt G Schilling
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States.
| | | | - Laurent Petit
- Groupe dImagerie Neurofonctionnelle, Institut Des Maladies Neurodegeneratives, CNRS, CEA University of Bordeaux, Bordeaux, France
| | - Colin B Hansen
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Vishwesh Nath
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, United States
| | - Gabriel Girard
- CIBM Center for BioMedical Imaging, Lausanne, Switzerland
| | - Muhamed Barakovic
- Translational Imaging in Neurology (ThINK), Department of Medicine and Biomedical Engineering, University Hospital and University of Basel, Basel, Switzerland
| | - Jonathan Rafael-Patino
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Thomas Yu
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Elda Fischi-Gomez
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Marco Pizzolato
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | | | - Simona Schiavi
- Department of Computer Science, University of Verona, Italy
| | | | | | - Cristina Granziera
- Translational Imaging in Neurology (ThINK), Department of Medicine and Biomedical Engineering, University Hospital and University of Basel, Basel, Switzerland
| | - Giorgio Innocenti
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Jean-Philippe Thiran
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Laura Mancini
- Lysholm Department of Neuroradiology, National Hospital for Neurology & Neurosurgery, UCL Hospitals NHS Foundation Trust, London, United Kingdom
| | - Stephen Wastling
- Lysholm Department of Neuroradiology, National Hospital for Neurology & Neurosurgery, UCL Hospitals NHS Foundation Trust, London, United Kingdom
| | - Sirio Cocozza
- Department of Advanced Biomedical Sciences, University "Federico II", Naples, Italy
| | - Maria Petracca
- Department of Neurosciences and Reproductive and Odontostomatological Sciences, University "Federico II", Naples, Italy
| | - Giuseppe Pontillo
- Department of Advanced Biomedical Sciences, University "Federico II", Naples, Italy
| | - Matteo Mancini
- Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, United Kingdom
| | - Sjoerd B Vos
- Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Vejay N Vakharia
- Department of Clinical and Experimental Epilepsy, University College London, London, United Kingdom
| | - John S Duncan
- Epilepsy Society MRI Unit, Chalfont St Peter, United Kingdom
| | - Helena Melero
- Departamento de Psicobiología y Metodología en Ciencias del Comportamiento - Universidad Complutense de Madrid, Spain Laboratorio de Análisis de Imagen Médica y Biometría (LAIMBIO), Universidad Rey Juan Carlos, Madrid, Spain
| | - Lidia Manzanedo
- Facultad de Ciencias de la Salud, Universidad Rey Juan Carlos, Madrid, Spain
| | - Emilio Sanz-Morales
- Laboratorio de Análisis de Imagen Médica y Biometría (LAIMBIO), Universidad Rey Juan Carlos, Madrid, Spain
| | - Ángel Peña-Melián
- Departamento de Anatomía, Facultad de Medicina, Universidad Complutense de Madrid, Madrid, Spain
| | - Fernando Calamante
- Sydney Imaging and School of Biomedical Engineering, The University of Sydney, Sydney, Australia
| | - Arnaud Attyé
- School of Biomedical Engineering, The University of Sydney, Sydney, Australia
| | - Ryan P Cabeen
- Laboratory of Neuro Imaging, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Laura Korobova
- Center for Integrative Connectomics, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Arthur W Toga
- Laboratory of Neuro Imaging, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | | | - Drew Parker
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Ragini Verma
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Ahmed Radwan
- KU Leuven, Department of Imaging and Pathology, Translational MRI, B-3000, Leuven, Belgium
| | - Stefan Sunaert
- KU Leuven, Department of Imaging and Pathology, Translational MRI, B-3000, Leuven, Belgium
| | - Louise Emsell
- KU Leuven, Department of Imaging and Pathology, Translational MRI, B-3000, Leuven, Belgium
| | | | | | - Claude J Bajada
- Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, University of Malta, Malta
| | - Hamied Haroon
- Division of Neuroscience & Experimental Psychology, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | | | - Maxime Chamberland
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Sila Genc
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Chantal M W Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Ping-Hong Yeh
- National Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Rujirutana Srikanchana
- National Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Colin D Mcknight
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Joseph Yuan-Mou Yang
- Department of Neurosurgery, Neuroscience Advanced Clinical Imaging Suite (NACIS), Royal Children's Hospital, Parkville, Melbourne, Australia
| | - Jian Chen
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia
| | - Claire E Kelly
- Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Australia
| | - Chun-Hung Yeh
- Institute for Radiological Research, Chang Gung University & Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | | | - Jerome J Maller
- MRI Clinical Science Specialist, General Electric Healthcare, Australia
| | | | - Fabien Almairac
- Neurosurgery department, Hôpital Pasteur, University Hospital of Nice, Côte d'Azur University, France
| | - Kiran K Seunarine
- Developmental Imaging and Biophysics Section, UCL GOS Institute of Child Health, London
| | - Chris A Clark
- Developmental Imaging and Biophysics Section, UCL GOS Institute of Child Health, London
| | - Fan Zhang
- Brigham & Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Nikos Makris
- Brigham & Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Alexandra Golby
- Brigham & Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Yogesh Rathi
- Brigham & Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Lauren J O'Donnell
- Brigham & Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Yihao Xia
- University of Southern California, Keck School of Medicine, Neuroimaging and Informatics Institute, Los Angeles, California, United States
| | - Dogu Baran Aydogan
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
| | - Yonggang Shi
- University of Southern California, Keck School of Medicine, Neuroimaging and Informatics Institute, Los Angeles, California, United States
| | | | - Mathijs Raemaekers
- UMC Utrecht Brain Center, Department of Neurology&Neurosurgery, Utrecht, the Netherlands
| | - Shaun Warrington
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK
| | - Stijn Michielse
- Department of Neurosurgery, School for Mental Health and Neuroscience, Maastricht University
| | | | - Luis Concha
- Universidad Nacional Autonoma de Mexico, Institute of Neurobiology, Mexico City, Mexico
| | - Ramón Aranda
- Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE-UT3), Cátedras-CONACyT, Ensenada, Mexico
| | | | | | - Lucas Roitman
- Department of Psychology, Stanford University, Stanford, California, USA
| | - Lucius S Fekonja
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Navona Calarco
- Kimel Family Translational Imaging-Genetics Laboratory, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario
| | - Michael Joseph
- Kimel Family Translational Imaging-Genetics Laboratory, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario
| | - Hajer Nakua
- Kimel Family Translational Imaging-Genetics Laboratory, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario
| | - Aristotle N Voineskos
- Kimel Family Translational Imaging-Genetics Laboratory, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario
| | | | | | | | | | - Veena A Nair
- University of Wisconsin-Madison, Madison, WI, USA
| | | | | | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
| | - Yuya Saito
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
| | - Wataru Uchida
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
| | - Christina Andica
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
| | - Masahiro Abe
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
| | - Roza G Bayrak
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Italy
| | - Fulvia Palesi
- Department of Brain and Behavioral Sciences, University of Pavia, Italy
| | - Giovanni Savini
- Brain MRI 3T Research Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Nicolò Rolandi
- Department of Brain and Behavioral Sciences, University of Pavia, Italy
| | - Pamela Guevara
- Universidad de Concepción, Faculty of Engineering, Concepción, Chile
| | - Josselin Houenou
- Université Paris-Saclay, CEA, CNRS, Neurospin, Gif-sur-Yvette, France
| | | | | | - Cyril Poupon
- Université Paris-Saclay, CEA, CNRS, Neurospin, Gif-sur-Yvette, France
| | - Claudio Román
- Universidad de Concepción, Faculty of Engineering, Concepción, Chile
| | - Andrea Vázquez
- Universidad de Concepción, Faculty of Engineering, Concepción, Chile
| | - Chiara Maffei
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Mavilde Arantes
- Department of Biomedicine, Unit of Anatomy, Faculty of Medicine of the University of Porto, Al. Professor Hernâni Monteiro, Porto, Portugal
| | - José Paulo Andrade
- Department of Biomedicine, Unit of Anatomy, Faculty of Medicine of the University of Porto, Al. Professor Hernâni Monteiro, Porto, Portugal
| | - Susana Maria Silva
- Department of Biomedicine, Unit of Anatomy, Faculty of Medicine of the University of Porto, Al. Professor Hernâni Monteiro, Porto, Portugal
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303, United States
| | - Eduardo Caverzasi
- Neurology Department UCSF Weill Institute for Neurosciences, University of California, San Francisco
| | - Simone Sacco
- Neurology Department UCSF Weill Institute for Neurosciences, University of California, San Francisco
| | - Michael Lauricella
- Memory and Aging Center. UCSF Weill Institute for Neurosciences, University of California, San Francisco, USA
| | - Franco Pestilli
- Department of Psychology, The University of Texas at Austin, TX 78731, USA
| | - Daniel Bullock
- Department of Psychology, The University of Texas at Austin, TX 78731, USA
| | - Yang Zhan
- Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Edith Brignoni-Perez
- Developmental-Behavioral Pediatrics Division, Department of Pediatrics, Stanford School of Medicine, Stanford, CA, United States
| | - Catherine Lebel
- Department of Radiology, University of Calgary, 2500 University Drive NW, Calgary, AB, Canada, T2N 1N4
| | - Jess E Reynolds
- Department of Radiology, University of Calgary, 2500 University Drive NW, Calgary, AB, Canada, T2N 1N4
| | - Igor Nestrasil
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - René Labounek
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Christophe Lenglet
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Amy Paulson
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Stefania Aulicka
- Department of Paediatric Neurology, University Hospital and Medicine Faculty, Masaryk University, Brno, Czech Republic
| | | | - Katja Heuer
- Center for Research and Interdisciplinarity (CRI), INSERM U1284, Université de Paris, Paris, France
| | - Bramsh Qamar Chandio
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Javier Guaje
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Wei Tang
- Department of Computer Science, Indiana University, Bloomington, IN, USA
| | | | - Rajikha Raja
- University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Adam W Anderson
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bennett A Landman
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
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Wasserthal J, Maier-Hein KH, Neher PF, Wolf RC, Northoff G, Waddington JL, Kubera KM, Fritze S, Harneit A, Geiger LS, Tost H, Hirjak D. White matter microstructure alterations in cortico-striatal networks are associated with parkinsonism in schizophrenia spectrum disorders. Eur Neuropsychopharmacol 2021; 50:64-74. [PMID: 33984810 DOI: 10.1016/j.euroneuro.2021.04.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 03/15/2021] [Accepted: 04/10/2021] [Indexed: 12/17/2022]
Abstract
The specific role of white matter (WM) microstructure in parkinsonism among patients with schizophrenia spectrum disorders (SSD) is largely unknown. To determine whether topographical alterations of WM microstructure contribute to parkinsonism in SSD patients, we examined healthy controls (HC, n=16) and SSD patients with and without parkinsonism, as defined by Simpson-Angus Scale total score of ≥4 (SSD-P, n=33) or <4 (SSD-nonP, n=62). We used whole brain tract-based spatial statistics (TBSS), tractometry (along tract statistics using TractSeg) and graph analytics (clustering coefficient (CCO), local betweenness centrality (BC)) to provide a framework of specific WM microstructural changes underlying parkinsonism in SSD. Using these methods, post hoc analyses showed (a) decreased fractional anisotrophy (FA), as measured via tractometry, in the corpus callosum, corticospinal tract and striato-fronto-orbital tract, and (b) increased CCO, as derived by graph analytics, in the left orbitofrontal cortex (OFC) and left superior frontal gyrus (SFG), in SSD-P patients when compared to SSD-nonP patients. Increased CCO in the left OFC and SFG was associated with SAS scores. These findings indicate the prominence of OFC alterations and aberrant connectivity with fronto-parietal regions and striatum in the pathogenesis of parkinsonism in SSD. This study further supports the notion of altered "bottom-up modulation" between basal ganglia and fronto-parietal regions in the pathobiology of parkinsonism, which may reflect an interaction between movement disorder intrinsic to SSD and antipsychotic drug-induced sensorimotor dysfunction.
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Affiliation(s)
- Jakob Wasserthal
- Division of Medical Imaging Computing (MIC), German Cancer Research Center (DKFZ), Heidelberg, Germany; Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany
| | - Klaus H Maier-Hein
- Division of Medical Imaging Computing (MIC), German Cancer Research Center (DKFZ), Heidelberg, Germany; Section of Automated Image Analysis, Heidelberg University Hospital, Heidelberg, Germany
| | - Peter F Neher
- Division of Medical Imaging Computing (MIC), German Cancer Research Center (DKFZ), Heidelberg, Germany; Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany
| | - Robert C Wolf
- Center for Psychosocial Medicine, Department of General Psychiatry, University of Heidelberg, Germany
| | - Georg Northoff
- Mind, Brain Imaging and Neuroethics Research Unit, The Royal's Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada
| | - John L Waddington
- School of Pharmacy and Biomolecular Sciences, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Katharina M Kubera
- Center for Psychosocial Medicine, Department of General Psychiatry, University of Heidelberg, Germany
| | - Stefan Fritze
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Anais Harneit
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Lena S Geiger
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Heike Tost
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Dusan Hirjak
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
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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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15
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Trigeminal neuralgia diffusivities using Gaussian process classification and merged group tractography. Pain 2021; 162:361-371. [PMID: 32701655 DOI: 10.1097/j.pain.0000000000002023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Accepted: 07/17/2020] [Indexed: 11/26/2022]
Abstract
ABSTRACT Imaging of trigeminal neuralgia (TN) has demonstrated key diffusion tensor imaging-based diffusivity alterations in the trigeminal nerve; however, imaging has primarily focused on the peripheral nerve segment because of previous limitations in reliably segmenting small fiber bundles across multiple subjects. We used Selective Automated Group Integrated Tractography to study 36 subjects with TN (right-sided pain) and 36 sex-matched controls to examine the trigeminal nerve (fifth cranial nerve [CN V]), pontine decussation (TPT), and thalamocortical fibers (S1). Gaussian process classifiers were trained by scrolling a moving window over CN V, TPT, and S1 tractography centroids. Fractional anisotropy (FA), generalized FA, radial diffusivity, axial diffusivity, and mean diffusivity metrics were evaluated for both groups, analyzing TN vs control groups and affected vs unaffected sides. Classifiers that performed at greater-than-or-equal-to 70% accuracy were included. Gaussian process classifier consistently demonstrated bilateral trigeminal changes, differentiating them from controls with an accuracy of 80%. Affected and unaffected sides could be differentiated from each other with 75% accuracy. Bilateral TPT could be distinguished from controls with at least 85% accuracy. TPT left-right classification achieved 98% accuracy. Bilateral S1 could be differentiated from controls, where the affected S1 radial diffusivity classifier achieved 87% accuracy. This is the first TN study that combines group-wise merged tractography, machine learning classification, and analysis of the complete trigeminal pathways from the peripheral fibers to S1 cortex. This analysis demonstrates that TN is characterized by bilateral abnormalities throughout the trigeminal pathway compared with controls and abnormalities between affected and unaffected sides. This full pathway tractography study of TN demonstrates bilateral changes throughout the trigeminal pathway and changes between affected and unaffected sides.
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16
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Bopp MHA, Emde J, Carl B, Nimsky C, Saß B. Diffusion Kurtosis Imaging Fiber Tractography of Major White Matter Tracts in Neurosurgery. Brain Sci 2021; 11:brainsci11030381. [PMID: 33802710 PMCID: PMC8002557 DOI: 10.3390/brainsci11030381] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 03/08/2021] [Accepted: 03/14/2021] [Indexed: 01/31/2023] Open
Abstract
Diffusion tensor imaging (DTI)-based fiber tractography is routinely used in clinical applications to visualize major white matter tracts, such as the corticospinal tract (CST), optic radiation (OR), and arcuate fascicle (AF). Nevertheless, DTI is limited due to its capability of resolving intra-voxel multi-fiber populations. Sophisticated models often require long acquisition times not applicable in clinical practice. Diffusion kurtosis imaging (DKI), as an extension of DTI, combines sophisticated modeling of the diffusion process with short acquisition times but has rarely been investigated in fiber tractography. In this study, DTI- and DKI-based fiber tractography of the CST, OR, and AF was investigated in healthy volunteers and glioma patients. For the CST, significantly larger tract volumes were seen in DKI-based fiber tractography. Similar results were obtained for the OR, except for the right OR in patients. In the case of the AF, results of both models were comparable with DTI-based fiber tractography showing even significantly larger tract volumes in patients. In the case of the CST and OR, DKI-based fiber tractography contributes to advanced visualization under clinical time constraints, whereas for the AF, other models should be considered.
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Affiliation(s)
- Miriam H. A. Bopp
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (J.E.); (B.C.); (C.N.); (B.S.)
- Center for Mind, Brain and Behavior (CMBB), 35043 Marburg, Germany
- Correspondence:
| | - Julia Emde
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (J.E.); (B.C.); (C.N.); (B.S.)
| | - Barbara Carl
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (J.E.); (B.C.); (C.N.); (B.S.)
- Department of Neurosurgery, Helios Dr. Horst Schmidt Kliniken, Ludwig-Erhard-Strasse 100, 65199 Wiesbaden, Germany
| | - Christopher Nimsky
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (J.E.); (B.C.); (C.N.); (B.S.)
- Center for Mind, Brain and Behavior (CMBB), 35043 Marburg, Germany
| | - Benjamin Saß
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (J.E.); (B.C.); (C.N.); (B.S.)
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17
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Wasserthal J, Maier-Hein KH, Neher PF, Northoff G, Kubera KM, Fritze S, Harneit A, Geiger LS, Tost H, Wolf RC, Hirjak D. Multiparametric mapping of white matter microstructure in catatonia. Neuropsychopharmacology 2020; 45:1750-1757. [PMID: 32369829 PMCID: PMC7419514 DOI: 10.1038/s41386-020-0691-2] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Revised: 04/20/2020] [Accepted: 04/22/2020] [Indexed: 12/13/2022]
Abstract
Catatonia is characterized by motor, affective and behavioral abnormalities. To date, the specific role of white matter (WM) abnormalities in schizophrenia spectrum disorders (SSD) patients with catatonia is largely unknown. In this study, diffusion magnetic resonance imaging (dMRI) data were collected from 111 right-handed SSD patients and 28 healthy controls. Catatonic symptoms were examined on the Northoff Catatonia Rating Scale (NCRS). We used whole-brain tract-based spatial statistics (TBSS), tractometry (along tract statistics using TractSeg) and graph analytics (clustering coefficient-CCO, local betweenness centrality-BC) to provide a framework of specific WM microstructural abnormalities underlying catatonia in SSD. Following a categorical approach, post hoc analyses showed differences in fractional anisotrophy (FA) measured via tractometry in the corpus callosum, corticospinal tract and thalamo-premotor tract as well as increased CCO as derived by graph analytics of the right superior parietal cortex (SPC) and left caudate nucleus in catatonic patients (NCRS total score ≥ 3; n = 30) when compared to non-catatonic patients (NCRS total score = 0; n = 29). In catatonic patients according to DSM-IV-TR (n = 43), catatonic symptoms were associated with FA variations (tractometry) of the left corticospinal tract and CCO of the left orbitofrontal cortex, primary motor cortex, supplementary motor area and putamen. This study supports the notion that structural reorganization of WM bundles connecting orbitofrontal/parietal, thalamic and striatal regions contribute to catatonia in SSD patients.
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Affiliation(s)
- Jakob Wasserthal
- Division of Medical Imaging Computing (MIC), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany
| | - Klaus H Maier-Hein
- Division of Medical Imaging Computing (MIC), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Section of Automated Image Analysis, Heidelberg University Hospital, Heidelberg, Germany
| | - Peter F Neher
- Division of Medical Imaging Computing (MIC), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany
| | - Georg Northoff
- Mind, Brain Imaging and Neuroethics Research Unit, The Royal's Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada
| | - Katharina M Kubera
- Center for Psychosocial Medicine, Department of General Psychiatry, Heidelberg University, Heidelberg, Germany
| | - Stefan Fritze
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Anais Harneit
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Lena S Geiger
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Heike Tost
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Robert C Wolf
- Center for Psychosocial Medicine, Department of General Psychiatry, Heidelberg University, Heidelberg, Germany
| | - Dusan Hirjak
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
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18
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Lerma-Usabiaga G, Benson N, Winawer J, Wandell BA. A validation framework for neuroimaging software: The case of population receptive fields. PLoS Comput Biol 2020; 16:e1007924. [PMID: 32584808 PMCID: PMC7343185 DOI: 10.1371/journal.pcbi.1007924] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 07/08/2020] [Accepted: 05/03/2020] [Indexed: 12/29/2022] Open
Abstract
Neuroimaging software methods are complex, making it a near certainty that some implementations will contain errors. Modern computational techniques (i.e., public code and data repositories, continuous integration, containerization) enable the reproducibility of the analyses and reduce coding errors, but they do not guarantee the scientific validity of the results. It is difficult, nay impossible, for researchers to check the accuracy of software by reading the source code; ground truth test datasets are needed. Computational reproducibility means providing software so that for the same input anyone obtains the same result, right or wrong. Computational validity means obtaining the right result for the ground-truth test data. We describe a framework for validating and sharing software implementations, and we illustrate its usage with an example application: population receptive field (pRF) methods for functional MRI data. The framework is composed of three main components implemented with containerization methods to guarantee computational reproducibility. In our example pRF application, those components are: (1) synthesis of fMRI time series from ground-truth pRF parameters, (2) implementation of four public pRF analysis tools and standardization of inputs and outputs, and (3) report creation to compare the results with the ground truth parameters. The framework was useful in identifying realistic conditions that lead to imperfect parameter recovery in all four pRF implementations, that would remain undetected using classic validation methods. We provide means to mitigate these problems in future experiments. A computational validation framework supports scientific rigor and creativity, as opposed to the oft-repeated suggestion that investigators rely upon a few agreed upon packages. We hope that the framework will be helpful to validate other critical neuroimaging algorithms, as having a validation framework helps (1) developers to build new software, (2) research scientists to verify the software's accuracy, and (3) reviewers to evaluate the methods used in publications and grants.
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Affiliation(s)
- Garikoitz Lerma-Usabiaga
- Department of Psychology, Stanford University, Stanford, California, United States of America
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, California, United States of America
- BCBL, Basque Center on Cognition, Brain and Language, Mikeletegi Pasealekua, Donostia—San Sebastián, Gipuzkoa, Spain
| | - Noah Benson
- Department of Psychology and Center for Neural Science, New York University, Washington Pl, New York, New York, United States of America
| | - Jonathan Winawer
- Department of Psychology and Center for Neural Science, New York University, Washington Pl, New York, New York, United States of America
| | - Brian A. Wandell
- Department of Psychology, Stanford University, Stanford, California, United States of America
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, California, United States of America
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19
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Jordan KM, Keshavan A, Caverzasi E, Osorio J, Papinutto N, Amirbekian B, Berger MS, Henry RG. Longitudinal Disconnection Tractograms to Investigate the Functional Consequences of White Matter Damage: An Automated Pipeline. J Neuroimaging 2020; 30:443-457. [PMID: 32436352 DOI: 10.1111/jon.12713] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 03/27/2020] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND AND PURPOSE Neurosurgical resection is one of the few opportunities researchers have to image the human brain pre- and postfocal damage. A major challenge associated with brains undergoing surgical resection is that they often do not fit brain templates most image-processing methodologies are based on. Manual intervention is required to reconcile the pathology, requiring time investment and introducing reproducibility concerns, and extreme cases must be excluded. METHODS We propose an automatic longitudinal pipeline based on High Angular Resolution Diffusion Imaging acquisitions to facilitate a Pathway Lesion Symptom Mapping analysis relating focal white matter injury to functional deficits. This two-part approach includes (i) automatic segmentation of focal white matter injury from anisotropic power differences, and (ii) modeling disconnection using tractography on the single-subject level, which specifically identifies the disconnections associated with focal white matter damage. RESULTS The advantages of this approach stem from (1) objective and automatic lesion segmentation and tractogram generation, (2) objective and precise segmentation of affected tissue likely to be associated with damage to long-range white matter pathways (defined by anisotropic power), (3) good performance even in the cases of anatomical distortions by use of nonlinear tensor-based registration, which aligns images using an approach sensitive to white matter microstructure. CONCLUSIONS Mapping a system as variable and complex as the human brain requires sample sizes much larger than the current technology can support. This pipeline can be used to execute large-scale, sufficiently powered analyses by meeting the need for an automatic approach to objectively quantify white matter disconnection.
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Affiliation(s)
- Kesshi M Jordan
- UCSF-UC Berkeley Graduate Group in Bioengineering, San Francisco, CA.,Department of Neurology, University of California, San Francisco, CA
| | - Anisha Keshavan
- UCSF-UC Berkeley Graduate Group in Bioengineering, San Francisco, CA.,Department of Neurology, University of California, San Francisco, CA
| | - Eduardo Caverzasi
- Department of Neurology, University of California, San Francisco, CA
| | - Joseph Osorio
- Division of Neurosurgery, Department of Surgery, University of California, San Diego, CA
| | - Nico Papinutto
- Department of Neurology, University of California, San Francisco, CA
| | - Bagrat Amirbekian
- UCSF-UC Berkeley Graduate Group in Bioengineering, San Francisco, CA.,Department of Neurology, University of California, San Francisco, CA
| | - Mitchel S Berger
- Department of Neurosurgery, University of California, San Francisco, CA
| | - Roland G Henry
- UCSF-UC Berkeley Graduate Group in Bioengineering, San Francisco, CA.,Department of Neurology, University of California, San Francisco, CA.,Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA
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20
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Sarwar T, Seguin C, Ramamohanarao K, Zalesky A. Towards deep learning for connectome mapping: A block decomposition framework. Neuroimage 2020; 212:116654. [PMID: 32068163 DOI: 10.1016/j.neuroimage.2020.116654] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 02/09/2020] [Accepted: 02/13/2020] [Indexed: 12/13/2022] Open
Abstract
We propose a new framework to map structural connectomes using deep learning and diffusion MRI. We show that our framework not only enables connectome mapping with a convolutional neural network (CNN), but can also be straightforwardly incorporated into conventional connectome mapping pipelines to enhance accuracy. Our framework involves decomposing the entire brain volume into overlapping blocks. Blocks are sufficiently small to ensure that a CNN can be efficiently trained to predict each block's internal connectivity architecture. We develop a block stitching algorithm to rebuild the full brain volume from these blocks and thereby map end-to-end connectivity matrices. To evaluate our block decomposition and stitching (BDS) framework independent of CNN performance, we first map each block's internal connectivity using conventional streamline tractography. Performance is evaluated using simulated diffusion MRI data generated from numerical connectome phantoms with known ground truth connectivity. Due to the redundancy achieved by allowing blocks to overlap, we find that our block decomposition and stitching steps per se can enhance the accuracy of probabilistic and deterministic tractography algorithms by up to 20-30%. Moreover, we demonstrate that our framework can improve the strength of structure-function coupling between in vivo diffusion and functional MRI data. We find that structural brain networks mapped with deep learning correlate more strongly with functional brain networks (r = 0.45) than those mapped with conventional tractography (r = 0.36). In conclusion, our BDS framework not only enables connectome mapping with deep learning, but its two constituent steps can be straightforwardly incorporated as part of conventional connectome mapping pipelines to enhance accuracy.
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Affiliation(s)
- Tabinda Sarwar
- Department of Computing and Information Systems, The University of Melbourne, Victoria, 3010, Australia.
| | - Caio Seguin
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Victoria, 3010, Australia
| | - Kotagiri Ramamohanarao
- Department of Computing and Information Systems, The University of Melbourne, Victoria, 3010, Australia
| | - Andrew Zalesky
- Department of Biomedical Engineering, The University of Melbourne, Victoria, 3010, Australia; Melbourne Neuropsychiatry Centre, The University of Melbourne, Victoria, 3010, Australia.
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21
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Vanderweyen DC, Theaud G, Sidhu J, Rheault F, Sarubbo S, Descoteaux M, Fortin D. The role of diffusion tractography in refining glial tumor resection. Brain Struct Funct 2020; 225:1413-1436. [PMID: 32180019 DOI: 10.1007/s00429-020-02056-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2019] [Accepted: 02/28/2020] [Indexed: 12/14/2022]
Abstract
Primary brain tumors are notoriously hard to resect surgically. Due to their infiltrative nature, finding the optimal resection boundary without damaging healthy tissue can be challenging. One potential tool to help make this decision is diffusion-weighted magnetic resonance imaging (dMRI) tractography. dMRI exploits the diffusion of water molecule along axons to generate a 3D modelization of the white matter bundles in the brain. This feature is particularly useful to visualize how a tumor affects its surrounding white matter and plan a surgical path. This paper reviews the different ways in which dMRI can be used to improve brain tumor resection, its benefits and also its limitations. We expose surgical tools that can be paired with dMRI to improve its impact on surgical outcome, such as loading the 3D tractography in the neuronavigation system and direct electrical stimulation to validate the position of the white matter bundles of interest. We also review articles validating dMRI findings using other anatomical investigation techniques, such as postmortem dissections, manganese-enhanced MRI, electrophysiological stimulations, and phantom studies with known ground truth. We will be discussing the areas of the brain where dMRI performs well and where the future challenges are. We will conclude this review with suggestions and take home messages for neurosurgeons, tractographers, and vendors for advancing the field and on how to benefit from tractography's use in clinical practice.
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Affiliation(s)
- Davy Charles Vanderweyen
- Department of Surgery, Division of Neurosurgery, Faculty of Medicine, University of Sherbrooke, 3001 12 Ave N, Sherbrooke, QC, J1H 5H3, Canada.
| | - Guillaume Theaud
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, University of Sherbrooke, 2500 Boulevard Université, Sherbrooke, QC, J1K2R1, Canada
| | - Jasmeen Sidhu
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, University of Sherbrooke, 2500 Boulevard Université, Sherbrooke, QC, J1K2R1, Canada
| | - François Rheault
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, University of Sherbrooke, 2500 Boulevard Université, Sherbrooke, QC, J1K2R1, Canada
| | - Silvio Sarubbo
- Division of Neurosurgery, Emergency Area, Structural and Functional Connectivity Lab Project, "S. Chiara" Hospital, Azienda Provinciale Per I Servizi Sanitari (APSS), Trento, Italy
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, University of Sherbrooke, 2500 Boulevard Université, Sherbrooke, QC, J1K2R1, Canada
| | - David Fortin
- Department of Surgery, Division of Neurosurgery, Faculty of Medicine, University of Sherbrooke, 3001 12 Ave N, Sherbrooke, QC, J1H 5H3, Canada
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22
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Going Beyond Diffusion Tensor Imaging Tractography in Eloquent Glioma Surgery–High-Resolution Fiber Tractography: Q-Ball or Constrained Spherical Deconvolution? World Neurosurg 2020; 134:e596-e609. [DOI: 10.1016/j.wneu.2019.10.138] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 10/21/2019] [Accepted: 10/22/2019] [Indexed: 11/22/2022]
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23
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Garcia-Garcia S, Kakaizada S, Oleaga L, Benet A, Rincon-Toroella J, González-Sánchez JJ. Presurgical simulation for neuroendoscopic procedures: Virtual study of the integrity of neurological pathways using diffusion tensor imaging tractography. Neurol India 2019; 67:763-769. [PMID: 31347551 PMCID: PMC6746412 DOI: 10.4103/0028-3886.263199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Background: White matter (WM) transgression is an unexplored concept in neuroendoscopy. Diffusion tensor image (DTI) tractography could be implemented as a planning and postoperative evaluation tool in functional disconnection procedures (FDPs), which are, currently, the subject of technological innovations. We intend to prove the usefulness of this planning method focused on the assessment of WM injury that is suitable for planning FDPs. Methods: Ten cranial magnetic resonance studies (20 sides) without pathological findings were processed. Fascicles were defined by two regions of interest (ROIs) using the fiber assignment method by the continuous tracking approach. Using three-dimensional (3D) simulation and DTI tractography, we created an 8-mm virtual endoscope and an uninjured inferior fronto-occipital fasciculus (IFOF) from two ROIs. The injured tract was generated using a third ROI built from the 3D model of the intersection of the oriented trajectory of the endoscope with the fascicle. Data and images were quantitatively and qualitatively analyzed. Results: The average percentage of the injured fibers was 32.0% (range: 12.4%–70%). The average intersected volume was 1.1 cm3 (range: 0.3–2.3 cm3). Qualitative analysis showed the inferior medial quadrant of the inferior fronto-occipital fasciculus (IFOF) as the most frequently injured region. No hemispherical asymmetry was found (P > 0.5). Conclusion: DTI tractography is a useful surgical planning tool that could be implemented in several endoscopic procedures. Together with a functional atlas, the presented technique provides a noninvasive method to assess the potential sequelae and thus to optimize the surgical route. The suggested method could be implemented to analyze pathological WM fascicles and to assess the surgical results of FDP such as hemispherotomy or amygdalohippocampectomy. More studies are needed to overcome the limitations of the tractography based information and to develop more anatomically and functionally reliable planning systems.
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Affiliation(s)
| | - Sofia Kakaizada
- Department of Neurosurgery, University of California, San Francisco, California, USA
| | - Laura Oleaga
- Department of Radiology, Hospital Clinic, Barcelona, Spain
| | - Arnau Benet
- Department of Neurosurgery, Barrow Neurological Institute, Phoenix, Arizona, USA
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Brun L, Pron A, Sein J, Deruelle C, Coulon O. Diffusion MRI: Assessment of the Impact of Acquisition and Preprocessing Methods Using the BrainVISA-Diffuse Toolbox. Front Neurosci 2019; 13:536. [PMID: 31275091 PMCID: PMC6593278 DOI: 10.3389/fnins.2019.00536] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Accepted: 05/08/2019] [Indexed: 12/28/2022] Open
Abstract
Diffusion MR images are prone to severe geometric distortions induced by head movement, eddy-current and inhomogeneity of magnetic susceptibility. Various correction methods have been proposed that depend on the choice of the acquisition settings and potentially provide highly different data quality. However, the impact of this choice has not been evaluated in terms of the ratio between scan time and preprocessed data quality. This study aims at investigating the impact of six well-known preprocessing methods, each associated to specific acquisition settings, on the outcome of diffusion analyses. For this purpose, we developed a comprehensive toolbox called Diffuse which automatically guides the user to the best preprocessing pipeline according to the input data. Using MR images of 20 subjects from the HCP dataset, we compared the six pre-processing pipelines regarding the following criteria: the ability to recover brain’s true geometry, the tensor model estimation and derived indices in the white matter, and finally the spatial dispersion of six well known connectivity pathways. As expected the pipeline associated to the longer acquisition fully repeated with reversed phase-encoding (RPE) yielded the higher data quality and was used as a reference to evaluate the other pipelines. In this way, we highlighted several significant aspects of other pre-processing pipelines. Our results first established that eddy-current correction improves the tensor-fitting performance with a localized impact especially in the corpus callosum. Concerning susceptibility distortions, we showed that the use of a field map is not sufficient and involves additional smoothing, yielding to an artificial decrease of tensor-fitting error. Of most importance, our findings demonstrate that, for an equivalent scan time, the acquisition of a b0 volume with RPE ensures a better brain’s geometry reconstruction and local improvement of tensor quality, without any smoothing of the image. This was found to be the best scan time/data quality compromise. To conclude, this study highlights and attempts to quantify the strong dependence of diffusion metrics on acquisition settings and preprocessing methods.
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Affiliation(s)
- Lucile Brun
- Institut de Neurosciences de La Timone, Aix-Marseille University, CNRS, UMR 7289, Marseille, France
| | - Alexandre Pron
- Institut de Neurosciences de La Timone, Aix-Marseille University, CNRS, UMR 7289, Marseille, France
| | - Julien Sein
- Institut de Neurosciences de La Timone, Aix-Marseille University, CNRS, UMR 7289, Marseille, France
| | - Christine Deruelle
- Institut de Neurosciences de La Timone, Aix-Marseille University, CNRS, UMR 7289, Marseille, France
| | - Olivier Coulon
- Institut de Neurosciences de La Timone, Aix-Marseille University, CNRS, UMR 7289, Marseille, France
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25
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Mancini M, Vos SB, Vakharia VN, O'Keeffe AG, Trimmel K, Barkhof F, Dorfer C, Soman S, Winston GP, Wu C, Duncan JS, Sparks R, Ourselin S. Automated fiber tract reconstruction for surgery planning: Extensive validation in language-related white matter tracts. Neuroimage Clin 2019; 23:101883. [PMID: 31163386 PMCID: PMC6545442 DOI: 10.1016/j.nicl.2019.101883] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Revised: 04/18/2019] [Accepted: 05/25/2019] [Indexed: 12/30/2022]
Abstract
Diffusion MRI and tractography hold great potential for surgery planning, especially to preserve eloquent white matter during resections. However, fiber tract reconstruction requires an expert with detailed understanding of neuroanatomy. Several automated approaches have been proposed, using different strategies to reconstruct the white matter tracts in a supervised fashion. However, validation is often limited to comparison with manual delineation by overlap-based measures, which is limited in characterizing morphological and topological differences. In this work, we set up a fully automated pipeline based on anatomical criteria that does not require manual intervention, taking advantage of atlas-based criteria and advanced acquisition protocols available on clinical-grade MRI scanners. Then, we extensively validated it on epilepsy patients with specific focus on language-related bundles. The validation procedure encompasses different approaches, including simple overlap with manual segmentations from two experts, feasibility ratings from external multiple clinical raters and relation with task-based functional MRI. Overall, our results demonstrate good quantitative agreement between automated and manual segmentation, in most cases better performances of the proposed method in qualitative terms, and meaningful relationships with task-based fMRI. In addition, we observed significant differences between experts in terms of both manual segmentation and external ratings. These results offer important insights on how different levels of validation complement each other, supporting the idea that overlap-based measures, although quantitative, do not offer a full perspective on the similarities and differences between automated and manual methods.
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Affiliation(s)
- Matteo Mancini
- Centre for Medical Image Computing, University College London, London, United Kingdom.
| | - Sjoerd B Vos
- Centre for Medical Image Computing, University College London, London, United Kingdom; Epilepsy Society MRI Unit, Chalfont St Peter, United Kingdom
| | - Vejay N Vakharia
- Department of Clinical and Experimental Epilepsy, University College London, London, United Kingdom; National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
| | - Aidan G O'Keeffe
- Department of Statistical Science, University College London, London, UK
| | - Karin Trimmel
- Epilepsy Society MRI Unit, Chalfont St Peter, United Kingdom; Department of Clinical and Experimental Epilepsy, University College London, London, United Kingdom; National Hospital for Neurology and Neurosurgery, Queen Square, London, UK; Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Frederik Barkhof
- Centre for Medical Image Computing, University College London, London, United Kingdom; Brain Repair and Rehabilitation, University College London, London, UK; Radiology & Nuclear Medicine, VU University Medical Centre, Amsterdam, Netherlands
| | - Christian Dorfer
- Department of Neurosurgery, Vienna General Hospital, Medical University of Vienna, Vienna, Austria
| | - Salil Soman
- Harvard Medical School, Beth Israel Deaconess Medical Center, Department of Radiology, Boston, MA 00215, United States
| | - Gavin P Winston
- Epilepsy Society MRI Unit, Chalfont St Peter, United Kingdom; Department of Clinical and Experimental Epilepsy, University College London, London, United Kingdom; Department of Medicine, Division of Neurology, Queen's University, Kingston, Ontario, Canada
| | - Chengyuan Wu
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, USA
| | - John S Duncan
- Epilepsy Society MRI Unit, Chalfont St Peter, United Kingdom; Department of Clinical and Experimental Epilepsy, University College London, London, United Kingdom; National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
| | - Rachel Sparks
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
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26
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Avesani P, McPherson B, Hayashi S, Caiafa CF, Henschel R, Garyfallidis E, Kitchell L, Bullock D, Patterson A, Olivetti E, Sporns O, Saykin AJ, Wang L, Dinov I, Hancock D, Caron B, Qian Y, Pestilli F. The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services. Sci Data 2019; 6:69. [PMID: 31123325 PMCID: PMC6533280 DOI: 10.1038/s41597-019-0073-y] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Accepted: 04/11/2019] [Indexed: 12/31/2022] Open
Abstract
We describe the Open Diffusion Data Derivatives (O3D) repository: an integrated collection of preserved brain data derivatives and processing pipelines, published together using a single digital-object-identifier. The data derivatives were generated using modern diffusion-weighted magnetic resonance imaging data (dMRI) with diverse properties of resolution and signal-to-noise ratio. In addition to the data, we publish all processing pipelines (also referred to as open cloud services). The pipelines utilize modern methods for neuroimaging data processing (diffusion-signal modelling, fiber tracking, tractography evaluation, white matter segmentation, and structural connectome construction). The O3D open services can allow cognitive and clinical neuroscientists to run the connectome mapping algorithms on new, user-uploaded, data. Open source code implementing all O3D services is also provided to allow computational and computer scientists to reuse and extend the processing methods. Publishing both data-derivatives and integrated processing pipeline promotes practices for scientific reproducibility and data upcycling by providing open access to the research assets for utilization by multiple scientific communities.
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Affiliation(s)
- Paolo Avesani
- Neuroinformatics Laboratory, Center for Information Technology, Fondazione Bruno Kessler, via Sommarive 18, 38123, Trento, Italy
- Center for Mind/Brain Sciences (CIMeC), University of Trento, via Delle Regole 101, 38123, Trento, Italy
| | - Brent McPherson
- Pestilli Lab. Department of Psychological and Brain Sciences, Program in Cognitive Science, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Soichi Hayashi
- Department of Psychological and Brain Sciences and Pervasive Technology Institute, University Information Technology Services, Indiana University, 1101 E 10th Street, Bloomington, IN, 47405, USA
| | - Cesar F Caiafa
- Pestilli Lab. Department of Psychological and Brain Sciences, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
- Instituto Argentino de Radioastronomía (CCT-La Plata, CONICET; CICPBA), CC5 V, Elisa, 1894, Argentina
- Facultad de Ingeniería, Universidad de Buenos Aires, Buenos Aires, C1063ACV, Argentina
| | - Robert Henschel
- Pervasive Technology Institute, Indiana University Bloomington, 2709 E 10th Street, Bloomington, IN, 47408, USA
| | - Eleftherios Garyfallidis
- Department of Intelligent Systems Engineering, Programs in Neuroscience and Cognitive Science, Indiana University Bloomington, 700N Woodlawn Ave, Bloomington, Indiana, 47408, USA
| | - Lindsey Kitchell
- Pestilli Lab. Department of Psychological and Brain Sciences, Program in Cognitive Science, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Daniel Bullock
- Pestilli Lab. Department of Psychological and Brain Sciences, Program in Neuroscience, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Andrew Patterson
- Pestilli Lab. Department of Psychological and Brain Sciences, Program in Neuroscience, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Emanuele Olivetti
- Neuroinformatics Laboratory, Center for Information Technology, Fondazione Bruno Kessler, via Sommarive 18, 38123, Trento, Italy
- Center for Mind/Brain Sciences (CIMeC), University of Trento, via Delle Regole 101, 38123, Trento, Italy
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Programs in Neuroscience and Cognitive Science, and Indiana Network Science Institute, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Andrew J Saykin
- Indiana University School of Medicine, Departments of Radiology and Imaging Sciences and Medical and Molecular Genetics, and the Indiana Alzheimer Disease Center, Indiana University, 355 W 16th St., Indianapolis, Indiana, 46202, USA
| | - Lei Wang
- Departments of Psychiatry and Behavioral Sciences and Radiology, Northwestern University Feinberg School of Medicine, 710N. Lake Shore Drive, Abbott Hall 1322, Chicago, IL, 60611, USA
| | - Ivo Dinov
- Statistics Online Computational Resource (SOCR), Center for Complexity of Self-Management in Chronic Disease (CSCD), Health Behavior and Biological Sciences, Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI, 49109, USA
| | - David Hancock
- Pervasive Technology Institute, Indiana University Bloomington, 2709 E 10th Street, Bloomington, IN, 47408, USA
| | - Bradley Caron
- Pestilli Lab. Indiana University School of Optometry and Program in Neuroscience, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, USA
| | - Yiming Qian
- Pestilli Lab. Department of Psychological and Brain Sciences, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Franco Pestilli
- Pestilli Lab. Department of Psychological and Brain Sciences, Engineering, Computer Science, Programs in Neuroscience and Cognitive Science, School of Optometry, and Indiana Network Science Institute, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA.
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Roebroeck A, Miller KL, Aggarwal M. Ex vivo diffusion MRI of the human brain: Technical challenges and recent advances. NMR IN BIOMEDICINE 2019; 32:e3941. [PMID: 29863793 PMCID: PMC6492287 DOI: 10.1002/nbm.3941] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2016] [Revised: 04/17/2018] [Accepted: 04/17/2018] [Indexed: 05/23/2023]
Abstract
This review discusses ex vivo diffusion magnetic resonance imaging (dMRI) as an important research tool for neuroanatomical investigations and the validation of in vivo dMRI techniques, with a focus on the human brain. We review the challenges posed by the properties of post-mortem tissue, and discuss state-of-the-art tissue preparation methods and recent advances in pulse sequences and acquisition techniques to tackle these. We then review recent ex vivo dMRI studies of the human brain, highlighting the validation of white matter orientation estimates and the atlasing and mapping of large subcortical structures. We also give particular emphasis to the delineation of layered gray matter structure with ex vivo dMRI, as this application illustrates the strength of its mesoscale resolution over large fields of view. We end with a discussion and outlook on future and potential directions of the field.
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Affiliation(s)
- Alard Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology & NeuroscienceMaastricht UniversityMaastrichtthe Netherlands
| | | | - Manisha Aggarwal
- Department of RadiologyJohns Hopkins University School of MedicineBaltimoreMDUSA
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28
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From ideas to action: The prefrontal–premotor connections that shape motor behavior. HANDBOOK OF CLINICAL NEUROLOGY 2019; 163:237-255. [DOI: 10.1016/b978-0-12-804281-6.00013-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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29
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Sarwar T, Ramamohanarao K, Zalesky A. Mapping connectomes with diffusion MRI: deterministic or probabilistic tractography? Magn Reson Med 2018; 81:1368-1384. [PMID: 30303550 DOI: 10.1002/mrm.27471] [Citation(s) in RCA: 113] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2018] [Revised: 06/11/2018] [Accepted: 07/09/2018] [Indexed: 12/21/2022]
Abstract
PURPOSE Human connectomics necessitates high-throughput, whole-brain reconstruction of multiple white matter fiber bundles. Scaling up tractography to meet these high-throughput demands yields new fiber tracking challenges, such as minimizing spurious connections and controlling for gyral biases. The aim of this study is to determine which of the two broadest classes of tractography algorithms-deterministic or probabilistic-is most suited to mapping connectomes. METHODS This study develops numerical connectome phantoms that feature realistic network topologies and that are matched to the fiber complexity of in vivo diffusion MRI (dMRI) data. The phantoms are utilized to evaluate the performance of tensor-based and multi-fiber implementations of deterministic and probabilistic tractography. RESULTS For connectome phantoms that are representative of the fiber complexity of in vivo dMRI, multi-fiber deterministic tractography yields the most accurate connectome reconstructions (F-measure = 0.35). Probabilistic algorithms are hampered by an abundance of false-positive connections, leading to lower specificity (F = 0.19). While omitting connections with the fewest number of streamlines (thresholding) improves the performance of probabilistic algorithms (F = 0.38), multi-fiber deterministic tractography remains optimal when it benefits from thresholding (F = 0.42). CONCLUSIONS Multi-fiber deterministic tractography is well suited to connectome mapping, while connectome thresholding is essential when using probabilistic algorithms.
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Affiliation(s)
- Tabinda Sarwar
- School of Computing and Information Systems, The University of Melbourne, Parkville, Victoria, Australia
| | - Kotagiri Ramamohanarao
- School of Computing and Information Systems, The University of Melbourne, Parkville, Victoria, Australia
| | - Andrew Zalesky
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Parkville, Victoria, Australia
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30
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Aydogan DB, Jacobs R, Dulawa S, Thompson SL, Francois MC, Toga AW, Dong H, Knowles JA, Shi Y. When tractography meets tracer injections: a systematic study of trends and variation sources of diffusion-based connectivity. Brain Struct Funct 2018; 223:2841-2858. [PMID: 29663135 PMCID: PMC5997540 DOI: 10.1007/s00429-018-1663-8] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2017] [Accepted: 04/08/2018] [Indexed: 12/23/2022]
Abstract
Tractography is a powerful technique capable of non-invasively reconstructing the structural connections in the brain using diffusion MRI images, but the validation of tractograms is challenging due to lack of ground truth. Owing to recent developments in mapping the mouse brain connectome, high-resolution tracer injection-based axonal projection maps have been created and quickly adopted for the validation of tractography. Previous studies using tracer injections mainly focused on investigating the match in projections and optimal tractography protocols. Being a complicated technique, however, tractography relies on multiple stages of operations and parameters. These factors introduce large variabilities in tractograms, hindering the optimization of protocols and making the interpretation of results difficult. Based on this observation, in contrast to previous studies, in this work we focused on quantifying and ranking the amount of performance variation introduced by these factors. For this purpose, we performed over a million tractography experiments and studied the variability across different subjects, injections, anatomical constraints and tractography parameters. By using N-way ANOVA analysis, we show that all tractography parameters are significant and importantly performance variations with respect to the differences in subjects are comparable to the variations due to tractography parameters, which strongly underlines the importance of fully documenting the tractography protocols in scientific experiments. We also quantitatively show that inclusion of anatomical constraints is the most significant factor for improving tractography performance. Although this critical factor helps reduce false positives, our analysis indicates that anatomy-informed tractography still fails to capture a large portion of axonal projections.
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Affiliation(s)
- Dogu Baran Aydogan
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90032, USA.
| | - Russell Jacobs
- Department of Physiology and Biophysics, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90032, USA
| | - Stephanie Dulawa
- Department of Psychiatry, University of California at San Diego, San Diego, CA, 90089, USA
| | - Summer L Thompson
- Department of Psychiatry, University of California at San Diego, San Diego, CA, 90089, USA
- Committee on Neurobiology, University of Chicago, Chicago, IL, 60637, USA
| | - Maite Christi Francois
- Department of Psychiatry, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
| | - Arthur W Toga
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90032, USA
| | - Hongwei Dong
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90032, USA
| | - James A Knowles
- Department of Psychiatry, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
| | - Yonggang Shi
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90032, USA
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31
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Chamberland M, Girard G, Bernier M, Fortin D, Descoteaux M, Whittingstall K. On the Origin of Individual Functional Connectivity Variability: The Role of White Matter Architecture. Brain Connect 2018; 7:491-503. [PMID: 28825322 DOI: 10.1089/brain.2017.0539] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Fingerprint patterns derived from functional connectivity (FC) can be used to identify subjects across groups and sessions, indicating that the topology of the brain substantially differs between individuals. However, the source of FC variability inferred from resting-state functional magnetic resonance imaging remains unclear. One possibility is that these variations are related to individual differences in white matter structural connectivity (SC). However, directly comparing FC with SC is challenging given the many potential biases associated with quantifying their respective strengths. In an attempt to circumvent this, we employed a recently proposed test-retest approach that better quantifies inter-subject variability by first correcting for intra-subject nuisance variability (i.e., head motion, physiological differences in brain state, etc.) that can artificially influence FC and SC measures. Therefore, rather than directly comparing the strength of FC with SC, we asked whether brain regions with, for example, low inter-subject FC variability also exhibited low SC variability. From this, we report two main findings: First, at the whole-brain level, SC variability was significantly lower than FC variability, indicating that an individual's structural connectome is far more similar to another relative to their functional counterpart even after correcting for noise. Second, although FC and SC variability were mutually low in some brain areas (e.g., primary somatosensory cortex) and high in others (e.g., memory and language areas), the two were not significantly correlated across all cortical and sub-cortical regions. Taken together, these results indicate that even after correcting for factors that may differently affect FC and SC, the two, nonetheless, remain largely independent of one another. Further work is needed to understand the role that direct anatomical pathways play in supporting vascular-based measures of FC and to what extent these measures are dictated by anatomical connectivity.
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Affiliation(s)
- Maxime Chamberland
- 1 Department of Nuclear Medicine and Radiobiology, Faculty of Medicine and Health Science, University of Sherbrooke , Sherbrooke, Canada .,2 Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University , Cardiff, United Kingdom
| | - Gabriel Girard
- 3 Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Faculty of Science, University of Sherbrooke , Sherbrooke, Canada .,4 Signal Processing Lab (LTS5) , Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Michaël Bernier
- 1 Department of Nuclear Medicine and Radiobiology, Faculty of Medicine and Health Science, University of Sherbrooke , Sherbrooke, Canada
| | - David Fortin
- 5 Division of Neurosurgery and Neuro-Oncology, Faculty of Medicine and Health Science, University of Sherbrooke , Sherbrooke, Canada
| | - Maxime Descoteaux
- 3 Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Faculty of Science, University of Sherbrooke , Sherbrooke, Canada
| | - Kevin Whittingstall
- 1 Department of Nuclear Medicine and Radiobiology, Faculty of Medicine and Health Science, University of Sherbrooke , Sherbrooke, Canada
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32
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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: 23] [Impact Index Per Article: 3.8] [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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33
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Network connectivity determines cortical thinning in early Parkinson's disease progression. Nat Commun 2018; 9:12. [PMID: 29295991 PMCID: PMC5750227 DOI: 10.1038/s41467-017-02416-0] [Citation(s) in RCA: 133] [Impact Index Per Article: 22.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Accepted: 11/23/2017] [Indexed: 12/22/2022] Open
Abstract
Here we test the hypothesis that the neurodegenerative process in Parkinson’s disease (PD) moves stereotypically along neural networks, possibly reflecting the spread of toxic alpha-synuclein molecules. PD patients (n = 105) and matched controls (n = 57) underwent T1-MRI at entry and 1 year later as part of the Parkinson’s Progression Markers Initiative. Over this period, PD patients demonstrate significantly greater cortical thinning than controls in parts of the left occipital and bilateral frontal lobes and right somatomotor-sensory cortex. Cortical thinning is correlated to connectivity (measured functionally or structurally) to a “disease reservoir” evaluated by MRI at baseline. The atrophy pattern in the ventral frontal lobes resembles one described in certain cases of Alzheimer’s disease. Our findings suggest that disease propagation to the cortex in PD follows neuronal connectivity and that disease spread to the cortex may herald the onset of cognitive impairment. In Parkinson’s disease (PD), neurodegeneration spreads from the brainstem to the cerebral cortex. Here, in a longitudinal study of PD patients, the authors found that cortical thinning followed neural connectivity from a “disease reservoir”.
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34
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Maier-Hein KH, Neher PF, Houde JC, Côté MA, Garyfallidis E, Zhong J, Chamberland M, Yeh FC, Lin YC, Ji Q, Reddick WE, Glass JO, Chen DQ, Feng Y, Gao C, Wu Y, Ma J, He R, Li Q, Westin CF, Deslauriers-Gauthier S, González JOO, Paquette M, St-Jean S, Girard G, Rheault F, Sidhu J, Tax CMW, Guo F, Mesri HY, Dávid S, Froeling M, Heemskerk AM, Leemans A, Boré A, Pinsard B, Bedetti C, Desrosiers M, Brambati S, Doyon J, Sarica A, Vasta R, Cerasa A, Quattrone A, Yeatman J, Khan AR, Hodges W, Alexander S, Romascano D, Barakovic M, Auría A, Esteban O, Lemkaddem A, Thiran JP, Cetingul HE, Odry BL, Mailhe B, Nadar MS, Pizzagalli F, Prasad G, Villalon-Reina JE, Galvis J, Thompson PM, Requejo FDS, Laguna PL, Lacerda LM, Barrett R, Dell'Acqua F, Catani M, Petit L, Caruyer E, Daducci A, Dyrby TB, Holland-Letz T, Hilgetag CC, Stieltjes B, Descoteaux M. The challenge of mapping the human connectome based on diffusion tractography. Nat Commun 2017; 8:1349. [PMID: 29116093 PMCID: PMC5677006 DOI: 10.1038/s41467-017-01285-x] [Citation(s) in RCA: 721] [Impact Index Per Article: 103.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2016] [Accepted: 09/01/2017] [Indexed: 01/14/2023] Open
Abstract
Tractography based on non-invasive diffusion imaging is central to the study of human brain connectivity. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain data set with ground truth tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. Here, we report the encouraging finding that most state-of-the-art algorithms produce tractograms containing 90% of the ground truth bundles (to at least some extent). However, the same tractograms contain many more invalid than valid bundles, and half of these invalid bundles occur systematically across research groups. Taken together, our results demonstrate and confirm fundamental ambiguities inherent in tract reconstruction based on orientation information alone, which need to be considered when interpreting tractography and connectivity results. Our approach provides a novel framework for estimating reliability of tractography and encourages innovation to address its current limitations. Though tractography is widely used, it has not been systematically validated. Here, authors report results from 20 groups showing that many tractography algorithms produce both valid and invalid bundles.
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Affiliation(s)
- Klaus H Maier-Hein
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, 69120, Germany.
| | - Peter F Neher
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, 69120, Germany
| | - Jean-Christophe Houde
- Sherbrooke Connectivity Imaging Lab (SCIL), Université de Sherbrooke, Sherbrooke, QC J1K 0A5, QC, Canada
| | - Marc-Alexandre Côté
- Sherbrooke Connectivity Imaging Lab (SCIL), Université de Sherbrooke, Sherbrooke, QC J1K 0A5, QC, Canada
| | - Eleftherios Garyfallidis
- Sherbrooke Connectivity Imaging Lab (SCIL), Université de Sherbrooke, Sherbrooke, QC J1K 0A5, QC, Canada.,Department of Intelligent Systems Engineering, School of Informatics and Computing, Indiana University, Bloomington, IN 47408, USA
| | - Jidan Zhong
- Krembil Research Institute, University Health Network, Toronto, Canada, M5G 2C4
| | - Maxime Chamberland
- Sherbrooke Connectivity Imaging Lab (SCIL), Université de Sherbrooke, Sherbrooke, QC J1K 0A5, QC, Canada
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Ying-Chia Lin
- IMT-Institute for Advanced Studies, Lucca, 55100, Italy
| | - Qing Ji
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Wilburn E Reddick
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - John O Glass
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - David Qixiang Chen
- University of Toronto Institute of Medical Science, Toronto, Canada, M5S 1A8
| | - Yuanjing Feng
- Institute of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, 310023, Zhejiang, China
| | - Chengfeng Gao
- Institute of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, 310023, Zhejiang, China
| | - Ye Wu
- Institute of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, 310023, Zhejiang, China
| | - Jieyan Ma
- United Imaging Healthcare Co., Shanghai, 201807, China
| | - Renjie He
- United Imaging Healthcare Co., Shanghai, 201807, China
| | - Qiang Li
- United Imaging Healthcare Co., Shanghai, 201807, China.,Shanghai Advanced Research Institute, Shanghai, 201210, China
| | - Carl-Fredrik Westin
- Laboratory of Mathematics in Imaging, Harvard Medical School, Boston, MA, 02215, USA
| | | | | | - Michael Paquette
- Sherbrooke Connectivity Imaging Lab (SCIL), Université de Sherbrooke, Sherbrooke, QC J1K 0A5, QC, Canada
| | - Samuel St-Jean
- Sherbrooke Connectivity Imaging Lab (SCIL), Université de Sherbrooke, Sherbrooke, QC J1K 0A5, QC, Canada
| | - Gabriel Girard
- Sherbrooke Connectivity Imaging Lab (SCIL), Université de Sherbrooke, Sherbrooke, QC J1K 0A5, QC, Canada
| | - François Rheault
- Sherbrooke Connectivity Imaging Lab (SCIL), Université de Sherbrooke, Sherbrooke, QC J1K 0A5, QC, Canada
| | - Jasmeen Sidhu
- Sherbrooke Connectivity Imaging Lab (SCIL), Université de Sherbrooke, Sherbrooke, QC J1K 0A5, QC, Canada
| | - Chantal M W Tax
- PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht, 3508, The Netherlands.,Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
| | - Fenghua Guo
- PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht, 3508, The Netherlands
| | - Hamed Y Mesri
- PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht, 3508, The Netherlands
| | - Szabolcs Dávid
- PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht, 3508, The Netherlands
| | - Martijn Froeling
- Department of Radiology, University Medical Center Utrecht, Utrecht, 3508, The Netherlands
| | - Anneriet M Heemskerk
- PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht, 3508, The Netherlands
| | - Alexander Leemans
- PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht, 3508, The Netherlands
| | - Arnaud Boré
- Centre de recherche institut universitaire de geriatrie de Montreal (CRIUGM), Université de Montréal, Montreal, QC, Canada, H3W 1W5
| | - Basile Pinsard
- Centre de recherche institut universitaire de geriatrie de Montreal (CRIUGM), Université de Montréal, Montreal, QC, Canada, H3W 1W5.,Sorbonne Universités, UPMC Univ Paris 06, CNRS, INSERM, Laboratoire d'Imagerie Biomédicale (LIB), 75013, Paris, France
| | - Christophe Bedetti
- Centre de recherche institut universitaire de geriatrie de Montreal (CRIUGM), Université de Montréal, Montreal, QC, Canada, H3W 1W5.,Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Montreal, Canada, H4J 1C5
| | - Matthieu Desrosiers
- Centre de recherche institut universitaire de geriatrie de Montreal (CRIUGM), Université de Montréal, Montreal, QC, Canada, H3W 1W5
| | - Simona Brambati
- Centre de recherche institut universitaire de geriatrie de Montreal (CRIUGM), Université de Montréal, Montreal, QC, Canada, H3W 1W5
| | - Julien Doyon
- Centre de recherche institut universitaire de geriatrie de Montreal (CRIUGM), Université de Montréal, Montreal, QC, Canada, H3W 1W5
| | - Alessia Sarica
- Neuroimaging Unit, Institute of Bioimaging and Molecular Physiology (IBFM), National Research Council (CNR), Policlinico Magna Graecia, Germaneto, 88100, CZ, Italy
| | - Roberta Vasta
- Neuroimaging Unit, Institute of Bioimaging and Molecular Physiology (IBFM), National Research Council (CNR), Policlinico Magna Graecia, Germaneto, 88100, CZ, Italy
| | - Antonio Cerasa
- Neuroimaging Unit, Institute of Bioimaging and Molecular Physiology (IBFM), National Research Council (CNR), Policlinico Magna Graecia, Germaneto, 88100, CZ, Italy
| | - Aldo Quattrone
- Neuroimaging Unit, Institute of Bioimaging and Molecular Physiology (IBFM), National Research Council (CNR), Policlinico Magna Graecia, Germaneto, 88100, CZ, Italy.,Institute of Neurology, University Magna Graecia, Germaneto, 88100, CZ, Italy
| | - Jason Yeatman
- Institute for Learning & Brain Sciences and Department of Speech & Hearing Sciences, University of Washington, Seattle, WA, 98195, USA
| | - Ali R Khan
- Departments of Medical Biophysics & Medical Imaging, Schulich School of Medicine and Dentistry, Western University, 1151 Richmond St N, London, ON, Canada, N6A 5C1
| | - Wes Hodges
- Synaptive Medical Inc., MaRS Discovery District, 101 College Street, Suite 200, Toronto, ON, Canada, M5V 3B1
| | - Simon Alexander
- Synaptive Medical Inc., MaRS Discovery District, 101 College Street, Suite 200, Toronto, ON, Canada, M5V 3B1
| | - David Romascano
- Signal Processing Lab (LTS5), Ecole Polytechnique Federale de Lausanne, Lausanne, 1015, Switzerland
| | - Muhamed Barakovic
- Signal Processing Lab (LTS5), Ecole Polytechnique Federale de Lausanne, Lausanne, 1015, Switzerland
| | - Anna Auría
- Signal Processing Lab (LTS5), Ecole Polytechnique Federale de Lausanne, Lausanne, 1015, Switzerland
| | - Oscar Esteban
- Biomedical Image Technologies (BIT), ETSI Telecom., U. Politécnica de Madrid and CIBER-BBN, Madrid, 28040, Spain
| | - Alia Lemkaddem
- Signal Processing Lab (LTS5), Ecole Polytechnique Federale de Lausanne, Lausanne, 1015, Switzerland
| | - Jean-Philippe Thiran
- Signal Processing Lab (LTS5), Ecole Polytechnique Federale de Lausanne, Lausanne, 1015, Switzerland.,Department of Radiology, University Hospital Center (CHUV) and University of Lausanne (UNIL), Lausanne, 1011, Switzerland
| | - H Ertan Cetingul
- Medical Imaging Technologies, Siemens Healthcare, Princeton, NJ, 08540, USA
| | - Benjamin L Odry
- Medical Imaging Technologies, Siemens Healthcare, Princeton, NJ, 08540, USA
| | - Boris Mailhe
- Medical Imaging Technologies, Siemens Healthcare, Princeton, NJ, 08540, USA
| | - Mariappan S Nadar
- Medical Imaging Technologies, Siemens Healthcare, Princeton, NJ, 08540, USA
| | - Fabrizio Pizzagalli
- Imaging Genetics Center, Stevens Neuro Imaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, 90033, USA
| | - Gautam Prasad
- Imaging Genetics Center, Stevens Neuro Imaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, 90033, USA
| | - Julio E Villalon-Reina
- Imaging Genetics Center, Stevens Neuro Imaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, 90033, USA
| | - Justin Galvis
- Imaging Genetics Center, Stevens Neuro Imaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, 90033, USA
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuro Imaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, 90033, USA
| | | | - Pedro Luque Laguna
- NatBrainLab, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK
| | - Luis Miguel Lacerda
- NatBrainLab, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK
| | - Rachel Barrett
- NatBrainLab, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK
| | - Flavio Dell'Acqua
- NatBrainLab, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK
| | - Marco Catani
- NatBrainLab, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK
| | - Laurent Petit
- Groupe d'imagerie Neurofonctionnelle-Institut des Maladies Neurodégénératives (GIN-IMN), UMR5293 CNRS, CEA, University of Bordeaux, Bordeaux, 33000, France
| | - Emmanuel Caruyer
- Centre national de la recherche scientifique (CNRS), Institute for Research in IT and Random Systems (IRISA), UMR 6074 VISAGES Project-Team, Rennes, 35042, France
| | - Alessandro Daducci
- Signal Processing Lab (LTS5), Ecole Polytechnique Federale de Lausanne, Lausanne, 1015, Switzerland.,Department of Radiology, University Hospital Center (CHUV) and University of Lausanne (UNIL), Lausanne, 1011, Switzerland
| | - Tim B Dyrby
- Danish Research Centre for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, 2650, Denmark.,Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, 2800, Denmark
| | - Tim Holland-Letz
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, 69120, Germany
| | - Claus C Hilgetag
- Department of Computational Neuroscience, University Medical Center Eppendorf, Hamburg, 20246, Germany
| | - Bram Stieltjes
- University Hospital Basel, Radiology & Nuclear Medicine Clinic, Basel, 4031, Switzerland
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab (SCIL), Université de Sherbrooke, Sherbrooke, QC J1K 0A5, QC, Canada.
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Neher PF, Côté MA, Houde JC, Descoteaux M, Maier-Hein KH. Fiber tractography using machine learning. Neuroimage 2017; 158:417-429. [PMID: 28716716 DOI: 10.1016/j.neuroimage.2017.07.028] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2017] [Revised: 05/13/2017] [Accepted: 07/14/2017] [Indexed: 11/19/2022] Open
Abstract
We present a fiber tractography approach based on a random forest classification and voting process, guiding each step of the streamline progression by directly processing raw diffusion-weighted signal intensities. For comparison to the state-of-the-art, i.e. tractography pipelines that rely on mathematical modeling, we performed a quantitative and qualitative evaluation with multiple phantom and in vivo experiments, including a comparison to the 96 submissions of the ISMRM tractography challenge 2015. The results demonstrate the vast potential of machine learning for fiber tractography.
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Affiliation(s)
- Peter F Neher
- Medical Image Computing (MIC), German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Marc-Alexandre Côté
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, Québec, Canada.
| | - Jean-Christophe Houde
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, Québec, Canada.
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, Québec, Canada.
| | - Klaus H Maier-Hein
- Medical Image Computing (MIC), German Cancer Research Center (DKFZ), Heidelberg, Germany.
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Liao R, Ning L, Chen Z, Rigolo L, Gong S, Pasternak O, Golby AJ, Rathi Y, O'Donnell LJ. Performance of unscented Kalman filter tractography in edema: Analysis of the two-tensor model. NEUROIMAGE-CLINICAL 2017; 15:819-831. [PMID: 28725549 PMCID: PMC5506885 DOI: 10.1016/j.nicl.2017.06.027] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2016] [Revised: 06/01/2017] [Accepted: 06/19/2017] [Indexed: 11/30/2022]
Abstract
Diffusion MRI tractography is increasingly used in pre-operative neurosurgical planning to visualize critical fiber tracts. However, a major challenge for conventional tractography, especially in patients with brain tumors, is tracing fiber tracts that are affected by vasogenic edema, which increases water content in the tissue and lowers diffusion anisotropy. One strategy for improving fiber tracking is to use a tractography method that is more sensitive than the traditional single-tensor streamline tractography. We performed experiments to assess the performance of two-tensor unscented Kalman filter (UKF) tractography in edema. UKF tractography fits a diffusion model to the data during fiber tracking, taking advantage of prior information from the previous step along the fiber. We studied UKF performance in a synthetic diffusion MRI digital phantom with simulated edema and in retrospective data from two neurosurgical patients with edema affecting the arcuate fasciculus and corticospinal tracts. We compared the performance of several tractography methods including traditional streamline, UKF single-tensor, and UKF two-tensor. To provide practical guidance on how the UKF method could be employed, we evaluated the impact of using various seed regions both inside and outside the edematous regions, as well as the impact of parameter settings on the tractography sensitivity. We quantified the sensitivity of different methods by measuring the percentage of the patient-specific fMRI activation that was reached by the tractography. We expected that diffusion anisotropy threshold parameters, as well as the inclusion of a free water model, would significantly influence the reconstruction of edematous WM fiber tracts, because edema increases water content in the tissue and lowers anisotropy. Contrary to our initial expectations, varying the fractional anisotropy threshold and including a free water model did not affect the UKF two-tensor tractography output appreciably in these two patient datasets. The most effective parameter for increasing tracking sensitivity was the generalized anisotropy (GA) threshold, which increased the length of tracked fibers when reduced to 0.075. In addition, the most effective seeding strategy was seeding in the whole brain or in a large region outside of the edema. Overall, the main contribution of this study is to provide insight into how UKF tractography can work, using a two-tensor model, to begin to address the challenge of fiber tract reconstruction in edematous regions near brain tumors. Reconstruction of edematous white matter from diffusion MRI is investigated. The performance of two–tensor unscented Kalman filter (UKF) tractography is assessed. The two–tensor model in UKF is analyzed in phantom and patient data experiments. Practical guidance on employing the UKF method in neurosurgical patients is provided
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Affiliation(s)
- Ruizhi Liao
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lipeng Ning
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Zhenrui Chen
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Laura Rigolo
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Shun Gong
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Shanghai Changzheng Hospital, Shanghai, China
| | - Ofer Pasternak
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Alexandra J Golby
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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Barbagallo G, Caligiuri ME, Arabia G, Cherubini A, Lupo A, Nisticò R, Salsone M, Novellino F, Morelli M, Cascini GL, Galea D, Quattrone A. Structural connectivity differences in motor network between tremor-dominant and nontremor Parkinson's disease. Hum Brain Mapp 2017. [PMID: 28631404 DOI: 10.1002/hbm.23697] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Motor phenotypes of Parkinson's disease (PD) are recognized to have different prognosis and therapeutic response, but the neural basis for this clinical heterogeneity remains largely unknown. The main aim of this study was to compare differences in structural connectivity metrics of the main motor network between tremor-dominant and nontremor PD phenotypes (TD-PD and NT-PD, respectively) using probabilistic tractography-based network analysis. A total of 63 PD patients (35 TD-PD patients and 28 NT-PD patients) and 30 healthy controls underwent a 3 T MRI. Next, probabilistic tractography-based network analysis was performed to assess structural connectivity in cerebello-thalamo-basal ganglia-cortical circuits, by measuring the connectivity indices of each tract and the efficiency of each node. Furthermore, dopamine transporter single-photon emission computed tomography (DAT-SPECT) with 123 I-ioflupane was used to assess dopaminergic striatal depletion in all PD patients. Both PD phenotypes showed nodal abnormalities in the substantia nigra, in agreement with DAT-SPECT evaluation. In addition, NT-PD patients displayed connectivity alterations in nigro-pallidal and fronto-striatal pathways, compared with both controls and TD-PD patients, in which the same motor connections seemed to be relatively spared. Of note, in NT-PD group, rigidity-bradykinesia score correlated with fronto-striatal connectivity abnormalities. These findings demonstrate that structural connectivity alterations occur in the cortico-basal ganglia circuit of NT-PD patients, but not in TD-PD patients, suggesting that these anatomical differences may underlie different motor phenotypes of PD. Hum Brain Mapp 38:4716-4729, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
| | - Maria Eugenia Caligiuri
- Neuroimaging Research Unit, Institute of Molecular Bioimaging and Physiology, National Research Council, Catanzaro, Italy
| | - Gennarina Arabia
- Institute of Neurology, University "Magna Graecia", Catanzaro, Italy
| | - Andrea Cherubini
- Neuroimaging Research Unit, Institute of Molecular Bioimaging and Physiology, National Research Council, Catanzaro, Italy
| | - Angela Lupo
- Institute of Neurology, University "Magna Graecia", Catanzaro, Italy
| | - Rita Nisticò
- Neuroimaging Research Unit, Institute of Molecular Bioimaging and Physiology, National Research Council, Catanzaro, Italy
| | - Maria Salsone
- Neuroimaging Research Unit, Institute of Molecular Bioimaging and Physiology, National Research Council, Catanzaro, Italy
| | - Fabiana Novellino
- Neuroimaging Research Unit, Institute of Molecular Bioimaging and Physiology, National Research Council, Catanzaro, Italy
| | - Maurizio Morelli
- Institute of Neurology, University "Magna Graecia", Catanzaro, Italy
| | - Giuseppe Lucio Cascini
- Institute of Radiology, Nuclear Medicine Unit, University "Magna Graecia", Catanzaro, Italy
| | - Domenico Galea
- Institute of Radiology, Nuclear Medicine Unit, University "Magna Graecia", Catanzaro, Italy
| | - Aldo Quattrone
- Institute of Neurology, University "Magna Graecia", Catanzaro, Italy.,Neuroimaging Research Unit, Institute of Molecular Bioimaging and Physiology, National Research Council, Catanzaro, Italy
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38
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Hirjak D, Thomann PA, Wolf RC, Kubera KM, Goch C, Hering J, Maier-Hein KH. White matter microstructure variations contribute to neurological soft signs in healthy adults. Hum Brain Mapp 2017; 38:3552-3565. [PMID: 28429448 DOI: 10.1002/hbm.23609] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2017] [Revised: 03/26/2017] [Accepted: 03/29/2017] [Indexed: 11/07/2022] Open
Abstract
OBJECTIVE Neurological soft signs (NSS) are core features of psychiatric disorders with significant neurodevelopmental origin. However, it is unclear whether NSS correlates are associated with neuropathological processes underlying the disease or if they are confounded by medication. Given that NSS are also present in healthy persons (HP), investigating HP could reveal NSS correlates, which are not biased by disease-specific processes or drug treatment. Therefore, we used a combination of diffusion MRI analysis tools to provide a framework of specific white matter (WM) microstructure variations underlying NSS in HP. METHOD NSS of 59 HP were examined on the Heidelberg Scale and related to diffusion associated metrics. Using tract-based spatial statistics (TBSS), we studied WM variations in fractional anisotropy (FA) as well as radial (RD), axial (AD), and mean diffusivity (MD). Using graph analytics (clustering coefficient-CC, local betweenness centrality -BC), we then explored DTI-derived structural network variations in regions identified by previous MRI studies on NSS. RESULTS NSS scores were negatively associated with RD, AD and MD in corpus callosum, brainstem and cerebellum (P < 0.05, corr.). NSS scores were negatively associated with CC and BC of the pallidum, the superior parietal gyrus, the precentral sulcus, the insula, and the cingulate gyrus (P < 0.05, uncorr.). CONCLUSION The present study supports the notion that WM microstructure variations in subcortical and cortical sensorimotor regions contribute to NSS expression in young HP. Hum Brain Mapp 38:3552-3565, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Dusan Hirjak
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.,Center for Psychosocial Medicine, Department of General Psychiatry, University of Heidelberg, Germany
| | - Philipp A Thomann
- Center for Psychosocial Medicine, Department of General Psychiatry, University of Heidelberg, Germany.,Center for Mental Health, Odenwald District Healthcare Center, Albert-Schweitzer-Straße 10-20, 64711, Erbach, Germany
| | - Robert C Wolf
- Center for Psychosocial Medicine, Department of General Psychiatry, University of Heidelberg, Germany
| | - Katharina M Kubera
- Center for Psychosocial Medicine, Department of General Psychiatry, University of Heidelberg, Germany
| | - Caspar Goch
- Medical Image Computing Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jan Hering
- Medical Image Computing Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Klaus H Maier-Hein
- Medical Image Computing Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
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Chamberland M, Scherrer B, Prabhu SP, Madsen J, Fortin D, Whittingstall K, Descoteaux M, Warfield SK. Active delineation of Meyer's loop using oriented priors through MAGNEtic tractography (MAGNET). Hum Brain Mapp 2017; 38:509-527. [PMID: 27647682 PMCID: PMC5333642 DOI: 10.1002/hbm.23399] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2016] [Revised: 08/04/2016] [Accepted: 08/31/2016] [Indexed: 12/19/2022] Open
Abstract
Streamline tractography algorithms infer connectivity from diffusion MRI (dMRI) by following diffusion directions which are similarly aligned between neighboring voxels. However, not all white matter (WM) fascicles are organized in this manner. For example, Meyer's loop is a highly curved portion of the optic radiation (OR) that exhibits a narrow turn, kissing and crossing pathways, and changes in fascicle dispersion. From a neurosurgical perspective, damage to Meyer's loop carries a potential risk of inducing vision deficits to the patient, especially during temporal lobe resection surgery. To prevent such impairment, achieving an accurate delineation of Meyer's loop with tractography is thus of utmost importance. However, current algorithms tend to under-estimate the full extent of Meyer's loop, mainly attributed to the aforementioned rule for connectivity which requires a direction to be chosen across a field of orientations. In this article, it was demonstrated that MAGNEtic Tractography (MAGNET) can benefit Meyer's loop delineation by incorporating anatomical knowledge of the expected fiber orientation to overcome local ambiguities. A new ROI-mechanism was proposed which supplies additional information to streamline reconstruction algorithms by the means of oriented priors. Their results showed that MAGNET can accurately generate Meyer's loop in all of our 15 child subjects (8 males; mean age 10.2 years ± 3.1). It effectively improved streamline coverage when compared with deterministic tractography, and significantly reduced the distance between the anterior-most portion of Meyer's loop and the temporal pole by 16.7 mm on average, a crucial landmark used for preoperative planning of temporal lobe surgery. Hum Brain Mapp 38:509-527, 2017. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Maxime Chamberland
- Centre de Recherche CHUSUniversity of SherbrookeSherbrookeCanada
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Faculty of ScienceUniversity of SherbrookeSherbrookeCanada
- Department of Nuclear Medicine and Radiobiology, Faculty of Medicine and Health ScienceUniversity of SherbrookeSherbrookeCanada
| | - Benoit Scherrer
- Department of RadiologyBoston Children's Hospital and Harvard Medical School300 Longwood AvenueBostonMassachusettsUSA
| | - Sanjay P. Prabhu
- Department of RadiologyBoston Children's Hospital and Harvard Medical School300 Longwood AvenueBostonMassachusettsUSA
| | - Joseph Madsen
- Department of RadiologyBoston Children's Hospital and Harvard Medical School300 Longwood AvenueBostonMassachusettsUSA
| | - David Fortin
- Centre de Recherche CHUSUniversity of SherbrookeSherbrookeCanada
- Division of Neurosurgery and Neuro‐Oncology, Faculty of Medicine and Health ScienceUniversity of SherbrookeSherbrookeCanada
| | - Kevin Whittingstall
- Centre de Recherche CHUSUniversity of SherbrookeSherbrookeCanada
- Department of Nuclear Medicine and Radiobiology, Faculty of Medicine and Health ScienceUniversity of SherbrookeSherbrookeCanada
- Department of Diagnostic Radiology, Faculty of Medicine and Health ScienceUniversity of SherbrookeSherbrookeCanada
| | - Maxime Descoteaux
- Centre de Recherche CHUSUniversity of SherbrookeSherbrookeCanada
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Faculty of ScienceUniversity of SherbrookeSherbrookeCanada
| | - Simon K. Warfield
- Department of RadiologyBoston Children's Hospital and Harvard Medical School300 Longwood AvenueBostonMassachusettsUSA
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García S, Rincon-Torroella J, Benet A, Oleaga L, González Sánchez JJ. Assessment of White Matter Transgression During Neuroendoscopic Procedures Using Diffusion Tensor Image Fiber Tracking. World Neurosurg 2016; 99:232-240. [PMID: 27915065 DOI: 10.1016/j.wneu.2016.11.112] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2016] [Revised: 11/20/2016] [Accepted: 11/21/2016] [Indexed: 11/28/2022]
Abstract
BACKGROUND Presurgical planning allows anticipating intraoperative difficulties, increasing efficiency, and reducing risks. Neuroendoscopy is a minimally invasive technique whose related complications have been focused on cortical function and surface vessels injury. However, white matter disruption has been insufficiently acknowledged. OBJECTIVE To present a new surgical planning method based on diffusion tensor image that allows quantifying subcortical transgression and optimizing neuroendoscopic trajectories. METHODS Ten cranial magnetic resonance studies (20 sides) without pathologic findings were anonymized and processed. A standard transcortical approach to the frontal horn was used to study the transgression of the corpus callosum (CC) and cingulum (Ci) caused by a virtual endoscope (VE) oriented from the Kocher point to the foramen of Monro. An 8-mm VE model was created, oriented, and coregistered. VE-CC and VE-Ci intersections were segmented. The number and volume of injured fibers were measured, intersections were quantified, and the percentage of tract transgression was calculated. The areas damaged by the VE were also recorded. RESULTS Among the CC fibers, 16.4% were injured (range: 3.3%-37%) and 26.7% of fibers on Ci (rank: 0%-73.4%). The average intersected volumes were 19.1% (range: 4.2%-53.2%) for CC and 33.2% for Ci (range: 0%-73.7%). Qualitative analysis showed the lateral aspect of both tracts as the most frequently injured region. No hemispherical asymmetry was found (P > 0.05). CONCLUSION This method using tractography and oriented models of surgical instruments allows assessing white matter transgression, both qualitatively and quantitatively, for a deep brain trajectory. Thus our method permits surgeons to optimize safety and avoid transgression of eloquent tracts during surgical planning. Nevertheless, more studies are necessary.
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Affiliation(s)
- Sergio García
- Department of Neurosurgery, Hospital Clinic Barcelona, Barcelona, Spain
| | | | - Arnau Benet
- Neurosurgery and OHNS University of California, Skull Base and Cerebrovascular Laboratory, San Francisco, California, USA
| | - Laura Oleaga
- Department of Radiology, Hospital Clinic, Barcelona, Spain
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41
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Farquharson S, Tournier JD, Calamante F, Mandelstam S, Burgess R, Schneider ME, Berkovic SF, Scheffer IE, Jackson GD, Connelly A. Periventricular Nodular Heterotopia: Detection of Abnormal Microanatomic Fiber Structures with Whole-Brain Diffusion MR Imaging Tractography. Radiology 2016; 281:896-906. [DOI: 10.1148/radiol.2016150852] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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42
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O'Donnell LJ, Suter Y, Rigolo L, Kahali P, Zhang F, Norton I, Albi A, Olubiyi O, Meola A, Essayed WI, Unadkat P, Ciris PA, Wells WM, Rathi Y, Westin CF, Golby AJ. Automated white matter fiber tract identification in patients with brain tumors. NEUROIMAGE-CLINICAL 2016; 13:138-153. [PMID: 27981029 PMCID: PMC5144756 DOI: 10.1016/j.nicl.2016.11.023] [Citation(s) in RCA: 90] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2016] [Revised: 10/13/2016] [Accepted: 11/22/2016] [Indexed: 01/06/2023]
Abstract
We propose a method for the automated identification of key white matter fiber tracts for neurosurgical planning, and we apply the method in a retrospective study of 18 consecutive neurosurgical patients with brain tumors. Our method is designed to be relatively robust to challenges in neurosurgical tractography, which include peritumoral edema, displacement, and mass effect caused by mass lesions. The proposed method has two parts. First, we learn a data-driven white matter parcellation or fiber cluster atlas using groupwise registration and spectral clustering of multi-fiber tractography from healthy controls. Key fiber tract clusters are identified in the atlas. Next, patient-specific fiber tracts are automatically identified using tractography-based registration to the atlas and spectral embedding of patient tractography. Results indicate good generalization of the data-driven atlas to patients: 80% of the 800 fiber clusters were identified in all 18 patients, and 94% of the 800 fiber clusters were found in 16 or more of the 18 patients. Automated subject-specific tract identification was evaluated by quantitative comparison to subject-specific motor and language functional MRI, focusing on the arcuate fasciculus (language) and corticospinal tracts (motor), which were identified in all patients. Results indicate good colocalization: 89 of 95, or 94%, of patient-specific language and motor activations were intersected by the corresponding identified tract. All patient-specific activations were within 3mm of the corresponding language or motor tract. Overall, our results indicate the potential of an automated method for identifying fiber tracts of interest for neurosurgical planning, even in patients with mass lesions. Spectral clustering machine learning approach for white matter tract identification Data-driven white matter parcellation learned from healthy subjects tractography White matter parcellation applied to 18 consecutive patients with brain tumors Arcuate fasciculus and corticospinal tracts identified in all patients All tracts within 3 mm of corresponding patient-specific functional activations
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Affiliation(s)
- Lauren J O'Donnell
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Yannick Suter
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Institute for Surgical Technology and Biomechanics, University of Bern, Switzerland
| | - Laura Rigolo
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Pegah Kahali
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Fan Zhang
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Isaiah Norton
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Angela Albi
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Center for Mind/Brain Sciences (CIMEC), University of Trento, Rovereto, Italy
| | - Olutayo Olubiyi
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Antonio Meola
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Walid I Essayed
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Prashin Unadkat
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Pelin Aksit Ciris
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Department of Biomedical Engineering, Akdeniz University, Antalya, Turkey
| | - William M Wells
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Yogesh Rathi
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Alexandra J Golby
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
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43
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Chen DQ, Zhong J, Hayes DJ, Behan B, Walker M, Hung PSP, Hodaie M. Merged Group Tractography Evaluation with Selective Automated Group Integrated Tractography. Front Neuroanat 2016; 10:96. [PMID: 27790095 PMCID: PMC5061742 DOI: 10.3389/fnana.2016.00096] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Accepted: 09/27/2016] [Indexed: 11/17/2022] Open
Abstract
Introduction: Tractography analysis in group-based studies across large populations has been difficult to implement. We propose Selective Automated Group Integrated Tractography (SAGIT), an automated group tractography software platform that incorporates multiple diffusion magnetic resonance imaging (dMRI) practices which will allow great accessibility to group-wise dMRI. We use a merged tractography approach that permits evaluation of tractography datasets at the group level. We also introduce an image normalized overlap score (NOS) that measures the quality of the group tractography results. We deploy SAGIT to evaluate deterministic and probabilistic constrained spherical deconvolution (CST det , CST prob ) tractography, eXtended Streamline Tractography (XST), and diffusion tensor tractography (DTT) in their ability to delineate different neuroanatomy, as well as validating NOS across these different brain regions. Materials and methods: Magnetic resonance sequences were acquired from 42 healthy adults. Anatomical and group registrations were performed using Automated Normalization Tools. Cortical segmentation was performed using FreeSurfer. Four tractography algorithms were used to delineate six sets of neuroanatomy: fornix, facial/vestibular-cochlear cranial nerve complex, vagus nerve, rubral-cerebellar decussation, optic radiation, and auditory radiation. The tracts were generated both with and without region of interest filters. The generated visual reports were then evaluated by five neuroscientists. Results: At a group level, merged tractography demonstrated that different methods have different fiber distribution characteristics. CST prob is prone to false-positives, and thereby suitable in anatomy with strong priors. CST det and XST are more conservative, but have greater difficulty resolving hemispherical decussation and distant crossing projections. DTT consistently shows the worst reproducibility across the anatomies. Linear regression of rater scores against NOS shows significant (p < 0.05) correlation of the two sets of scores in filtered tractography. However, correlations are not significant (p > 0.05) for unfiltered tractography. Conclusion: The tractography results demonstrated reliable and consistent performance of SAGIT across multiple subjects and techniques. Through SAGIT, we quantifiably demonstrated that different algorithms showed different strengths and weaknesses at a group level. While no single algorithm seems to be suitable for all anatomical tasks, it is useful to consider the use of a mix of algorithms for different anatomical segments. SAGIT appears to be a promising group-wise tractography analysis approach for this purpose.
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Affiliation(s)
- David Q. Chen
- Institute of Medical Science, Faculty of Medicine, University of Toronto, TorontoON, Canada
| | - Jidan Zhong
- Krembil Research Institute, University Health Network, TorontoON, Canada
| | - David J. Hayes
- Krembil Research Institute, University Health Network, TorontoON, Canada
| | - Brendan Behan
- Krembil Research Institute, University Health Network, TorontoON, Canada
| | - Matthew Walker
- Institute of Medical Science, Faculty of Medicine, University of Toronto, TorontoON, Canada
| | - Peter S.-P. Hung
- Institute of Medical Science, Faculty of Medicine, University of Toronto, TorontoON, Canada
| | - Mojgan Hodaie
- Institute of Medical Science, Faculty of Medicine, University of Toronto, TorontoON, Canada
- Krembil Research Institute, University Health Network, TorontoON, Canada
- Division of Neurosurgery, Toronto Western Hospital and University of Toronto, TorontoON, Canada
- Joint Department of Medical Imaging, University Health Network, TorontoON, Canada
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