1
|
Karimi D, Calixto C, Snoussi H, Cortes-Albornoz MC, Velasco-Annis C, Rollins C, Jaimes C, Gholipour A, Warfield SK. Detailed delineation of the fetal brain in diffusion MRI via multi-task learning. ARXIV 2024:arXiv:2409.06716v2. [PMID: 39314513 PMCID: PMC11419175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/25/2024]
Abstract
Diffusion-weighted MRI is increasingly used to study the normal and abnormal development of fetal brain inutero. Recent studies have shown that dMRI can offer invaluable insights into the neurodevelopmental processes in the fetal stage. However, because of the low data quality and rapid brain development, reliable analysis of fetal dMRI data requires dedicated computational methods that are currently unavailable. The lack of automated methods for fast, accurate, and reproducible data analysis has seriously limited our ability to tap the potential of fetal brain dMRI for medical and scientific applications. In this work, we developed and validated a unified computational framework to (1) segment the brain tissue into white matter, cortical/subcortical gray matter, and cerebrospinal fluid, (2) segment 31 distinct white matter tracts, and (3) parcellate the brain's cortex and delineate the deep gray nuclei and white matter structures into 96 anatomically meaningful regions. We utilized a set of manual, semi-automatic, and automatic approaches to annotate 97 fetal brains. Using these labels, we developed and validated a multi-task deep learning method to perform the three computations. Our evaluations show that the new method can accurately carry out all three tasks, achieving a mean Dice similarity coefficient of 0.865 on tissue segmentation, 0.825 on white matter tract segmentation, and 0.819 on parcellation. The proposed method can greatly advance the field of fetal neuroimaging as it can lead to substantial improvements in fetal brain tractography, tract-specific analysis, and structural connectivity assessment.
Collapse
Affiliation(s)
- Davood Karimi
- Boston Children's Hospital and Harvard Medical School, Boston, MA
| | - Camilo Calixto
- Boston Children's Hospital and Harvard Medical School, Boston, MA
- Elmhurst Hospital Center and Icahn School of Medicine at Mount Sinai, New York, NY
| | - Haykel Snoussi
- Boston Children's Hospital and Harvard Medical School, Boston, MA
| | | | | | - Caitlin Rollins
- Boston Children's Hospital and Harvard Medical School, Boston, MA
| | - Camilo Jaimes
- Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Ali Gholipour
- Boston Children's Hospital and Harvard Medical School, Boston, MA
- Department of Radiological Sciences, University of California Irvine, Irvine, CA
| | - Simon K Warfield
- Boston Children's Hospital and Harvard Medical School, Boston, MA
| |
Collapse
|
2
|
Karimi D, Calixto C, Snoussi H, Cortes-Albornoz MC, Velasco-Annis C, Rollins C, Jaimes C, Gholipour A, Warfield SK. Detailed delineation of the fetal brain in diffusion MRI via multi-task learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.29.609697. [PMID: 39257731 PMCID: PMC11383702 DOI: 10.1101/2024.08.29.609697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2024]
Abstract
Diffusion-weighted MRI is increasingly used to study the normal and abnormal development of fetal brain inutero. Recent studies have shown that dMRI can offer invaluable insights into the neurodevelopmental processes in the fetal stage. However, because of the low data quality and rapid brain development, reliable analysis of fetal dMRI data requires dedicated computational methods that are currently unavailable. The lack of automated methods for fast, accurate, and reproducible data analysis has seriously limited our ability to tap the potential of fetal brain dMRI for medical and scientific applications. In this work, we developed and validated a unified computational framework to (1) segment the brain tissue into white matter, cortical/subcortical gray matter, and cerebrospinal fluid, (2) segment 31 distinct white matter tracts, and (3) parcellate the brain's cortex and delineate the deep gray nuclei and white matter structures into 96 anatomically meaningful regions. We utilized a set of manual, semi-automatic, and automatic approaches to annotate 97 fetal brains. Using these labels, we developed and validated a multi-task deep learning method to perform the three computations. Our evaluations show that the new method can accurately carry out all three tasks, achieving a mean Dice similarity coefficient of 0.865 on tissue segmentation, 0.825 on white matter tract segmentation, and 0.819 on parcellation. The proposed method can greatly advance the field of fetal neuroimaging as it can lead to substantial improvements in fetal brain tractography, tract-specific analysis, and structural connectivity assessment.
Collapse
Affiliation(s)
- Davood Karimi
- Boston Children's Hospital and Harvard Medical School, Boston, MA
| | - Camilo Calixto
- Boston Children's Hospital and Harvard Medical School, Boston, MA
- Elmhurst Hospital Center and Icahn School of Medicine at Mount Sinai, New York, NY
| | - Haykel Snoussi
- Boston Children's Hospital and Harvard Medical School, Boston, MA
| | | | | | - Caitlin Rollins
- Boston Children's Hospital and Harvard Medical School, Boston, MA
| | - Camilo Jaimes
- Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Ali Gholipour
- Boston Children's Hospital and Harvard Medical School, Boston, MA
- Department of Radiological Sciences, University of California Irvine, Irvine, CA
| | - Simon K Warfield
- Boston Children's Hospital and Harvard Medical School, Boston, MA
| |
Collapse
|
3
|
Liu W, Calixto C, Warfield SK, Karimi D. Streamline tractography of the fetal brain in utero with machine learning. ARXIV 2024:arXiv:2408.14326v1. [PMID: 39253631 PMCID: PMC11383324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
Abstract
Diffusion-weighted magnetic resonance imaging (dMRI) is the only non-invasive tool for studying white matter tracts and structural connectivity of the brain. These assessments rely heavily on tractography techniques, which reconstruct virtual streamlines representing white matter fibers. Much effort has been devoted to improving tractography methodology for adult brains, while tractography of the fetal brain has been largely neglected. Fetal tractography faces unique difficulties due to low dMRI signal quality, immature and rapidly developing brain structures, and paucity of reference data. To address these challenges, this work presents the first machine learning model, based on a deep neural network, for fetal tractography. The model input consists of five different sources of information: (1) Voxel-wise fiber orientation, inferred from a diffusion tensor fit to the dMRI signal; (2) Directions of recent propagation steps; (3) Global spatial information, encoded as normalized distances to keypoints in the brain cortex; (4) Tissue segmentation information; and (5) Prior information about the expected local fiber orientations supplied with an atlas. In order to mitigate the local tensor estimation error, a large spatial context around the current point in the diffusion tensor image is encoded using convolutional and attention neural network modules. Moreover, the diffusion tensor information at a hypothetical next point is included in the model input. Filtering rules based on anatomically constrained tractography are applied to prune implausible streamlines. We trained the model on manually-refined whole-brain fetal tractograms and validated the trained model on an independent set of 11 test scans with gestational ages between 23 and 36 weeks. Results show that our proposed method achieves superior performance across all evaluated tracts. The new method can significantly advance the capabilities of dMRI for studying normal and abnormal brain development in utero.
Collapse
Affiliation(s)
- Weide Liu
- Boston Children's Hospital and Harvard Medical School, Boston, MA
| | - Camilo Calixto
- Boston Children's Hospital and Harvard Medical School, Boston, MA
- Elmhurst Hospital Center and Icahn School of Medicine at Mount Sinai, New York, NY
| | - Simon K Warfield
- Boston Children's Hospital and Harvard Medical School, Boston, MA
| | - Davood Karimi
- Boston Children's Hospital and Harvard Medical School, Boston, MA
| |
Collapse
|
4
|
Calixto C, Jaimes C, Soldatelli MD, Warfield SK, Gholipour A, Karimi D. Anatomically constrained tractography of the fetal brain. Neuroimage 2024; 297:120723. [PMID: 39029605 PMCID: PMC11382095 DOI: 10.1016/j.neuroimage.2024.120723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Accepted: 07/03/2024] [Indexed: 07/21/2024] Open
Abstract
Diffusion-weighted Magnetic Resonance Imaging (dMRI) is increasingly used to study the fetal brain in utero. An important computation enabled by dMRI is streamline tractography, which has unique applications such as tract-specific analysis of the brain white matter and structural connectivity assessment. However, due to the low fetal dMRI data quality and the challenging nature of tractography, existing methods tend to produce highly inaccurate results. They generate many false streamlines while failing to reconstruct the streamlines that constitute the major white matter tracts. In this paper, we advocate for anatomically constrained tractography based on an accurate segmentation of the fetal brain tissue directly in the dMRI space. We develop a deep learning method to compute the segmentation automatically. Experiments on independent test data show that this method can accurately segment the fetal brain tissue and drastically improve the tractography results. It enables the reconstruction of highly curved tracts such as optic radiations. Importantly, our method infers the tissue segmentation and streamline propagation direction from a diffusion tensor fit to the dMRI data, making it applicable to routine fetal dMRI scans. The proposed method can facilitate the study of fetal brain white matter tracts with dMRI.
Collapse
Affiliation(s)
- Camilo Calixto
- Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115, USA
| | - Camilo Jaimes
- Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114, USA
| | | | - Simon K Warfield
- Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115, USA
| | - Ali Gholipour
- Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115, USA
| | - Davood Karimi
- Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115, USA.
| |
Collapse
|
5
|
Bertò G, Rooks LT, Broglio SP, McAllister TA, McCrea MA, Pasquina PF, Giza C, Brooks A, Mihalik J, Guskiewicz K, Goldman J, Duma S, Rowson S, Port NL, Pestilli F. Diffusion tensor analysis of white matter tracts is prognostic of persisting post-concussion symptoms in collegiate athletes. Neuroimage Clin 2024; 43:103646. [PMID: 39106542 PMCID: PMC11347060 DOI: 10.1016/j.nicl.2024.103646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 06/15/2024] [Accepted: 07/19/2024] [Indexed: 08/09/2024]
Abstract
BACKGROUND AND OBJECTIVES After a concussion diagnosis, the most important issue for patients and loved ones is how long it will take them to recover. The main objective of this study is to develop a prognostic model of concussion recovery. This model would benefit many patients worldwide, allowing for early treatment intervention. METHODS The Concussion Assessment, Research and Education (CARE) consortium study enrolled collegiate athletes from 30 sites (NCAA athletic departments and US Department of Defense service academies), 4 of which participated in the Advanced Research Core, which included diffusion-weighted MRI (dMRI) data collection. We analyzed the dMRI data of 51 injuries of concussed athletes scanned within 48 h of injury. All athletes were cleared to return-to-play by the local medical staff following a standardized, graduated protocol. The primary outcome measure is days to clearance of unrestricted return-to-play. Injuries were divided into early (return-to-play < 28 days) and late (return-to-play >= 28 days) recovery based on the return-to-play clinical records. The late recovery group meets the standard definition of Persisting Post-Concussion Symptoms (PPCS). Data were processed using automated, state-of-the-art, rigorous methods for reproducible data processing using brainlife.io. All processed data derivatives are made available at https://brainlife.io/project/63b2ecb0daffe2c2407ee3c5/dataset. The microstructural properties of 47 major white matter tracts, 5 callosal, 15 subcortical, and 148 cortical structures were mapped. Fractional Anisotropy (FA) and Mean Diffusivity (MD) were estimated for each tract and structure. Correlation analysis and Receiver Operator Characteristic (ROC) analysis were then performed to assess the association between the microstructural properties and return-to-play. Finally, a Logistic Regression binary classifier (LR-BC) was used to classify the injuries between the two recovery groups. RESULTS The mean FA across all white matter volume was negatively correlated with return-to-play (r = -0.38, p = 0.00001). No significant association between mean MD and return-to-play was found, neither for FA nor MD for any other structure. The mean FA of 47 white matter tracts was negatively correlated with return-to-play (rμ = -0.27; rσ = 0.08; rmin = -0.1; rmax = -0.43). Across all tracts, a large mean ROC Area Under the Curve (AUCFA) of 0.71 ± 0.09 SD was found. The top classification performance of the LR-BC was AUC = 0.90 obtained using the 16 statistically significant white matter tracts. DISCUSSION Utilizing a free, open-source, and automated cloud-based neuroimaging pipeline and app (https://brainlife.io/docs/tutorial/using-clairvoy/), a prognostic model has been developed, which predicts athletes at risk for slow recovery (PPCS) with an AUC=0.90, balanced accuracy = 0.89, sensitivity = 1.0, and specificity = 0.79. The small number of participants in this study (51 injuries) is a significant limitation and supports the need for future large concussion dMRI studies and focused on recovery.
Collapse
Affiliation(s)
- Giulia Bertò
- Department of Psychology and Department of Neuroscience, Center for Perceptual Systems, Center for Learning and Memory, The University of Texas at Austin, Austin, TX, USA
| | - Lauren T Rooks
- Indiana University School of Optometry and Program in Neuroscience, Indiana University, Bloomington IN, USA
| | - Steven P Broglio
- Michigan Concussion Center, University of Michigan, Ann Arbor, MI, USA
| | | | - Michael A McCrea
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Paul F Pasquina
- Department of Physical Medicine and Rehabilitation at the Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Christopher Giza
- Pediatric Neurology, University of California, Los Angeles, CA, USA
| | - Alison Brooks
- Department of Orthopaedics and Rehabilitation, University of Wisconsin Madison, Madison WI, USA
| | - Jason Mihalik
- Department of Exercise and Sport Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kevin Guskiewicz
- Department of Exercise and Sport Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Josh Goldman
- Family Medicine & Sports Medicine, UCLA Medical School, Los Angeles, CA, USA
| | - Stefan Duma
- Departmentl of Biomedical Engineering & Mechanics, Virginia Tech, Blacksburg, VA, USA
| | - Steven Rowson
- Departmentl of Biomedical Engineering & Mechanics, Virginia Tech, Blacksburg, VA, USA
| | - Nicholas L Port
- Indiana University School of Optometry and Program in Neuroscience, Indiana University, Bloomington IN, USA.
| | - Franco Pestilli
- Department of Psychology and Department of Neuroscience, Center for Perceptual Systems, Center for Learning and Memory, The University of Texas at Austin, Austin, TX, USA.
| |
Collapse
|
6
|
Calixto C, Soldatelli MD, Li B, Pierotich L, Gholipour A, Warfield SK, Karimi D. White matter tract crossing and bottleneck regions in the fetal brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.20.603804. [PMID: 39091823 PMCID: PMC11291018 DOI: 10.1101/2024.07.20.603804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
There is a growing interest in using diffusion MRI to study the white matter tracts and structural connectivity of the fetal brain. Recent progress in data acquisition and processing suggests that this imaging modality has a unique role in elucidating the normal and abnormal patterns of neurodevelopment in utero. However, there have been no efforts to quantify the prevalence of crossing tracts and bottleneck regions, important issues that have been extensively researched for adult brains. In this work, we determined the brain regions with crossing tracts and bottlenecks between 23 and 36 gestational weeks. We performed probabilistic tractography on 59 fetal brain scans and extracted a set of 51 distinct white tracts, which we grouped into 10 major tract bundle groups. We analyzed the results to determine the patterns of tract crossings and bottlenecks. Our results showed that 20-25% of the white matter voxels included two or three crossing tracts. Bottlenecks were more prevalent. Between 75-80% of the voxels were characterized as bottlenecks, with more than 40% of the voxels involving four or more tracts. The results of this study highlight the challenge of fetal brain tractography and structural connectivity assessment and call for innovative image acquisition and analysis methods to mitigate these problems.
Collapse
Affiliation(s)
- Camilo Calixto
- Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Matheus D Soldatelli
- Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Bo Li
- Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Lana Pierotich
- Department of Pediatrics, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Ali Gholipour
- Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Simon K Warfield
- Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Davood Karimi
- Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| |
Collapse
|
7
|
Takemura H, Kruper JA, Miyata T, Rokem A. Tractometry of Human Visual White Matter Pathways in Health and Disease. Magn Reson Med Sci 2024; 23:316-340. [PMID: 38866532 PMCID: PMC11234945 DOI: 10.2463/mrms.rev.2024-0007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2024] Open
Abstract
Diffusion-weighted MRI (dMRI) provides a unique non-invasive view of human brain tissue properties. The present review article focuses on tractometry analysis methods that use dMRI to assess the properties of brain tissue within the long-range connections comprising brain networks. We focus specifically on the major white matter tracts that convey visual information. These connections are particularly important because vision provides rich information from the environment that supports a large range of daily life activities. Many of the diseases of the visual system are associated with advanced aging, and tractometry of the visual system is particularly important in the modern aging society. We provide an overview of the tractometry analysis pipeline, which includes a primer on dMRI data acquisition, voxelwise model fitting, tractography, recognition of white matter tracts, and calculation of tract tissue property profiles. We then review dMRI-based methods for analyzing visual white matter tracts: the optic nerve, optic tract, optic radiation, forceps major, and vertical occipital fasciculus. For each tract, we review background anatomical knowledge together with recent findings in tractometry studies on these tracts and their properties in relation to visual function and disease. Overall, we find that measurements of the brain's visual white matter are sensitive to a range of disorders and correlate with perceptual abilities. We highlight new and promising analysis methods, as well as some of the current barriers to progress toward integration of these methods into clinical practice. These barriers, such as variability in measurements between protocols and instruments, are targets for future development.
Collapse
Affiliation(s)
- Hiromasa Takemura
- Division of Sensory and Cognitive Brain Mapping, Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki, Aichi, Japan
- Graduate Institute for Advanced Studies, SOKENDAI, Hayama, Kanagawa, Japan
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology, Suita, Osaka, Japan
| | - John A Kruper
- Department of Psychology and eScience Institute, University of Washington, Seattle, WA, USA
| | - Toshikazu Miyata
- Division of Sensory and Cognitive Brain Mapping, Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki, Aichi, Japan
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology, Suita, Osaka, Japan
| | - Ariel Rokem
- Department of Psychology and eScience Institute, University of Washington, Seattle, WA, USA
| |
Collapse
|
8
|
Cocozza S, Bosticardo S, Battocchio M, Corben L, Delatycki M, Egan G, Georgiou‐Karistianis N, Monti S, Palma G, Pane C, Saccà F, Schiavi S, Selvadurai L, Tranfa M, Daducci A, Brunetti A, Harding IH. Gradient of microstructural damage along the dentato-thalamo-cortical tract in Friedreich ataxia. Ann Clin Transl Neurol 2024; 11:1691-1702. [PMID: 38952134 PMCID: PMC11251475 DOI: 10.1002/acn3.52048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 02/02/2024] [Accepted: 02/29/2024] [Indexed: 07/03/2024] Open
Abstract
OBJECTIVE The dentato-thalamo-cortical tract (DTT) is the main cerebellar efferent pathway. Degeneration of the DTT is a core feature of Friedreich ataxia (FRDA). However, it remains unclear whether DTT disruption is spatially specific, with some segments being more impacted than others. This study aimed to investigate microstructural integrity along the DTT in FRDA using a profilometry diffusion MRI (dMRI) approach. METHODS MRI data from 45 individuals with FRDA (mean age: 33.2 ± 13.2, Male/Female: 26/19) and 37 healthy controls (mean age: 36.5 ± 12.7, Male/Female:18/19) were included in this cross-sectional multicenter study. A profilometry analysis was performed on dMRI data by first using tractography to define the DTT as the white matter pathway connecting the dentate nucleus to the contralateral motor cortex. The tract was then divided into 100 segments, and dMRI metrics of microstructural integrity (fractional anisotropy, mean diffusivity and radial diffusivity) at each segment were compared between groups. The process was replicated on the arcuate fasciculus for comparison. RESULTS Across all diffusion metrics, the region of the DTT connecting the dentate nucleus and thalamus was more impacted in FRDA than downstream cerebral sections from the thalamus to the cortex. The arcuate fasciculus was minimally impacted. INTERPRETATION Our study further expands the current knowledge about brain involvement in FRDA, showing that microstructural abnormalities within the DTT are weighted to early segments of the tract (i.e., the superior cerebellar peduncle). These findings are consistent with the hypothesis of DTT undergoing anterograde degeneration arising from the dentate nuclei and progressing to the primary motor cortex.
Collapse
Affiliation(s)
- Sirio Cocozza
- Department of Advanced Biomedical SciencesUniversity of Naples “Federico II”NaplesItaly
| | - Sara Bosticardo
- Department of Computer Science, Diffusion Imaging and Connectivity Estimation (DICE) LabUniversity of VeronaVeronaItaly
| | - Matteo Battocchio
- Department of Computer Science, Diffusion Imaging and Connectivity Estimation (DICE) LabUniversity of VeronaVeronaItaly
| | - Louise Corben
- Bruce Lefroy Centre for Genetic Health ResearchMurdoch Children's Research InstituteParkvilleVictoriaAustralia
- Department of PediatricsUniversity of MelbourneParkvilleVictoriaAustralia
- School of Psychological Sciences, The Turner Institute for Brain and Mental HealthMonash UniversityClaytonVictoriaAustralia
| | - Martin Delatycki
- Bruce Lefroy Centre for Genetic Health ResearchMurdoch Children's Research InstituteParkvilleVictoriaAustralia
- Department of PediatricsUniversity of MelbourneParkvilleVictoriaAustralia
- School of Psychological Sciences, The Turner Institute for Brain and Mental HealthMonash UniversityClaytonVictoriaAustralia
| | - Gary Egan
- Turner Institute for Brain and Mental Health, School of Psychological SciencesMonash UniversityClaytonVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityClaytonVictoriaAustralia
| | - Nellie Georgiou‐Karistianis
- School of Psychological Sciences, The Turner Institute for Brain and Mental HealthMonash UniversityClaytonVictoriaAustralia
| | - Serena Monti
- Institute of Biostructures and BioimagingNational Research CouncilNapoliItaly
| | - Giuseppe Palma
- Institute of NanotechnologyNational Research CouncilLecceItaly
| | - Chiara Pane
- Department of Neurosciences Reproductive and Odontostomatological SciencesUniversity of Naples “Federico II”NaplesItaly
| | - Francesco Saccà
- Department of Neurosciences Reproductive and Odontostomatological SciencesUniversity of Naples “Federico II”NaplesItaly
| | - Simona Schiavi
- Department of Computer Science, Diffusion Imaging and Connectivity Estimation (DICE) LabUniversity of VeronaVeronaItaly
- ASG Superconductors SpAGenoaItaly
| | - Louisa Selvadurai
- School of Psychological Sciences, The Turner Institute for Brain and Mental HealthMonash UniversityClaytonVictoriaAustralia
| | - Mario Tranfa
- Department of Advanced Biomedical SciencesUniversity of Naples “Federico II”NaplesItaly
| | - Alessandro Daducci
- Department of Computer Science, Diffusion Imaging and Connectivity Estimation (DICE) LabUniversity of VeronaVeronaItaly
| | - Arturo Brunetti
- Department of Advanced Biomedical SciencesUniversity of Naples “Federico II”NaplesItaly
| | - Ian H. Harding
- Monash Biomedical ImagingMonash UniversityClaytonVictoriaAustralia
- Department of Neuroscience, Central Clinical SchoolMonash UniversityMelbourneVictoriaAustralia
| |
Collapse
|
9
|
Calixto C, Soldatelli MD, Jaimes C, Warfield SK, Gholipour A, Karimi D. A detailed spatio-temporal atlas of the white matter tracts for the fetal brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.26.590815. [PMID: 38712296 PMCID: PMC11071632 DOI: 10.1101/2024.04.26.590815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
This study presents the construction of a comprehensive spatiotemporal atlas detailing the development of white matter tracts in the fetal brain using diffusion magnetic resonance imaging (dMRI). Our research leverages data collected from fetal MRI scans conducted between 22 and 37 weeks of gestation, capturing the dynamic changes in the brain's microstructure during this critical period. The atlas includes 60 distinct white matter tracts, including commissural, projection, and association fibers. We employed advanced fetal dMRI processing techniques and tractography to map and characterize the developmental trajectories of these tracts. Our findings reveal that the development of these tracts is characterized by complex patterns of fractional anisotropy (FA) and mean diffusivity (MD), reflecting key neurodevelopmental processes such as axonal growth, involution of the radial-glial scaffolding, and synaptic pruning. This atlas can serve as a useful resource for neuroscience research and clinical practice, improving our understanding of the fetal brain and potentially aiding in the early diagnosis of neurodevelopmental disorders. By detailing the normal progression of white matter tract development, the atlas can be used as a benchmark for identifying deviations that may indicate neurological anomalies or predispositions to disorders.
Collapse
Affiliation(s)
- Camilo Calixto
- Computational Radiology Laboratory (CRL), Boston Children's Hospital, Harvard Medical School
| | | | - Camilo Jaimes
- Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114, USA
| | - Simon K Warfield
- Computational Radiology Laboratory (CRL), Boston Children's Hospital, Harvard Medical School
| | - Ali Gholipour
- Computational Radiology Laboratory (CRL), Boston Children's Hospital, Harvard Medical School
| | - Davood Karimi
- Computational Radiology Laboratory (CRL), Boston Children's Hospital, Harvard Medical School
| |
Collapse
|
10
|
Mendoza C, Román C, Mangin JF, Hernández C, Guevara P. Short fiber bundle filtering and test-retest reproducibility of the Superficial White Matter. Front Neurosci 2024; 18:1394681. [PMID: 38737100 PMCID: PMC11088237 DOI: 10.3389/fnins.2024.1394681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 04/08/2024] [Indexed: 05/14/2024] Open
Abstract
In recent years, there has been a growing interest in studying the Superficial White Matter (SWM). The SWM consists of short association fibers connecting near giry of the cortex, with a complex organization due to their close relationship with the cortical folding patterns. Therefore, their segmentation from dMRI tractography datasets requires dedicated methodologies to identify the main fiber bundle shape and deal with spurious fibers. This paper presents an enhanced short fiber bundle segmentation based on a SWM bundle atlas and the filtering of noisy fibers. The method was tuned and evaluated over HCP test-retest probabilistic tractography datasets (44 subjects). We propose four fiber bundle filters to remove spurious fibers. Furthermore, we include the identification of the main fiber fascicle to obtain well-defined fiber bundles. First, we identified four main bundle shapes in the SWM atlas, and performed a filter tuning in a subset of 28 subjects. The filter based on the Convex Hull provided the highest similarity between corresponding test-retest fiber bundles. Subsequently, we applied the best filter in the 16 remaining subjects for all atlas bundles, showing that filtered fiber bundles significantly improve test-retest reproducibility indices when removing between ten and twenty percent of the fibers. Additionally, we applied the bundle segmentation with and without filtering to the ABIDE-II database. The fiber bundle filtering allowed us to obtain a higher number of bundles with significant differences in fractional anisotropy, mean diffusivity, and radial diffusivity of Autism Spectrum Disorder patients relative to controls.
Collapse
Affiliation(s)
- Cristóbal Mendoza
- Department of Electrical Engineering, Faculty of Engineering, Universidad de Concepción, Concepción, Chile
| | - Claudio Román
- Centro de Investigación y Desarrollo en Ingeniería en Salud, Universidad de Valparaíso, Valparaíso, Chile
| | | | - Cecilia Hernández
- Department of Computer Science, Faculty of Engineering, Universidad de Concepción, Concepción, Chile
- Center for Biotechnology and Bioengineering (CeBiB), Santiago, Chile
| | - Pamela Guevara
- Department of Electrical Engineering, Faculty of Engineering, Universidad de Concepción, Concepción, Chile
| |
Collapse
|
11
|
Joshi A, Li H, Parikh NA, He L. A systematic review of automated methods to perform white matter tract segmentation. Front Neurosci 2024; 18:1376570. [PMID: 38567281 PMCID: PMC10985163 DOI: 10.3389/fnins.2024.1376570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 03/04/2024] [Indexed: 04/04/2024] Open
Abstract
White matter tract segmentation is a pivotal research area that leverages diffusion-weighted magnetic resonance imaging (dMRI) for the identification and mapping of individual white matter tracts and their trajectories. This study aims to provide a comprehensive systematic literature review on automated methods for white matter tract segmentation in brain dMRI scans. Articles on PubMed, ScienceDirect [NeuroImage, NeuroImage (Clinical), Medical Image Analysis], Scopus and IEEEXplore databases and Conference proceedings of Medical Imaging Computing and Computer Assisted Intervention Society (MICCAI) and International Symposium on Biomedical Imaging (ISBI), were searched in the range from January 2013 until September 2023. This systematic search and review identified 619 articles. Adhering to the specified search criteria using the query, "white matter tract segmentation OR fiber tract identification OR fiber bundle segmentation OR tractography dissection OR white matter parcellation OR tract segmentation," 59 published studies were selected. Among these, 27% employed direct voxel-based methods, 25% applied streamline-based clustering methods, 20% used streamline-based classification methods, 14% implemented atlas-based methods, and 14% utilized hybrid approaches. The paper delves into the research gaps and challenges associated with each of these categories. Additionally, this review paper illuminates the most frequently utilized public datasets for tract segmentation along with their specific characteristics. Furthermore, it presents evaluation strategies and their key attributes. The review concludes with a detailed discussion of the challenges and future directions in this field.
Collapse
Affiliation(s)
- Ankita Joshi
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Hailong Li
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Nehal A. Parikh
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Lili He
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
- Computer Science, Biomedical Informatics, and Biomedical Engineering, University of Cincinnati, Cincinnati, OH, United States
| |
Collapse
|
12
|
González Rodríguez LL, Osorio I, Cofre G. A, Hernandez Larzabal H, Román C, Poupon C, Mangin JF, Hernández C, Guevara P. Phybers: a package for brain tractography analysis. Front Neurosci 2024; 18:1333243. [PMID: 38529266 PMCID: PMC10962387 DOI: 10.3389/fnins.2024.1333243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 02/09/2024] [Indexed: 03/27/2024] Open
Abstract
We present a Python library (Phybers) for analyzing brain tractography data. Tractography datasets contain streamlines (also called fibers) composed of 3D points representing the main white matter pathways. Several algorithms have been proposed to analyze this data, including clustering, segmentation, and visualization methods. The manipulation of tractography data is not straightforward due to the geometrical complexity of the streamlines, the file format, and the size of the datasets, which may contain millions of fibers. Hence, we collected and structured state-of-the-art methods for the analysis of tractography and packed them into a Python library, to integrate and share tools for tractography analysis. Due to the high computational requirements, the most demanding modules were implemented in C/C++. Available functions include brain Bundle Segmentation (FiberSeg), Hierarchical Fiber Clustering (HClust), Fast Fiber Clustering (FFClust), normalization to a reference coordinate system, fiber sampling, calculation of intersection between sets of brain fibers, tools for cluster filtering, calculation of measures from clusters, and fiber visualization. The library tools were structured into four principal modules: Segmentation, Clustering, Utils, and Visualization (Fibervis). Phybers is freely available on a GitHub repository under the GNU public license for non-commercial use and open-source development, which provides sample data and extensive documentation. In addition, the library can be easily installed on both Windows and Ubuntu operating systems through the pip library.
Collapse
Affiliation(s)
| | - Ignacio Osorio
- Department of Electrical Engineering, Faculty of Engineering, Universidad de Concepción, Concepción, Chile
| | - Alejandro Cofre G.
- Department of Electrical Engineering, Faculty of Engineering, Universidad de Concepción, Concepción, Chile
| | - Hernan Hernandez Larzabal
- Department of Electrical Engineering, Faculty of Engineering, Universidad de Concepción, Concepción, Chile
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago, Chile
| | - Claudio Román
- Centro de Investigación y Desarrollo en Ingeniería en Salud, Universidad de Valparaíso, Valparaíso, Chile
| | - Cyril Poupon
- CEA, CNRS, Baobab, Neurospin, Université Paris-Saclay, Gif-sur-Yvette, France
| | | | - Cecilia Hernández
- Department of Computer Science, Faculty of Engineering, Universidad de Concepción, Concepción, Chile
- Center for Biotechnology and Bioengineering (CeBiB), Santiago, Chile
| | - Pamela Guevara
- Department of Electrical Engineering, Faculty of Engineering, Universidad de Concepción, Concepción, Chile
| |
Collapse
|
13
|
Zhang F, Cho KIK, Seitz-Holland J, Ning L, Legarreta JH, Rathi Y, Westin CF, O'Donnell LJ, Pasternak O. DDParcel: Deep Learning Anatomical Brain Parcellation From Diffusion MRI. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:1191-1202. [PMID: 37943635 PMCID: PMC10994696 DOI: 10.1109/tmi.2023.3331691] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
Parcellation of anatomically segregated cortical and subcortical brain regions is required in diffusion MRI (dMRI) analysis for region-specific quantification and better anatomical specificity of tractography. Most current dMRI parcellation approaches compute the parcellation from anatomical MRI (T1- or T2-weighted) data, using tools such as FreeSurfer or CAT12, and then register it to the diffusion space. However, the registration is challenging due to image distortions and low resolution of dMRI data, often resulting in mislabeling in the derived brain parcellation. Furthermore, these approaches are not applicable when anatomical MRI data is unavailable. As an alternative we developed the Deep Diffusion Parcellation (DDParcel), a deep learning method for fast and accurate parcellation of brain anatomical regions directly from dMRI data. The input to DDParcel are dMRI parameter maps and the output are labels for 101 anatomical regions corresponding to the FreeSurfer Desikan-Killiany (DK) parcellation. A multi-level fusion network leverages complementary information in the different input maps, at three network levels: input, intermediate layer, and output. DDParcel learns the registration of diffusion features to anatomical MRI from the high-quality Human Connectome Project data. Then, to predict brain parcellation for a new subject, the DDParcel network no longer requires anatomical MRI data but only the dMRI data. Comparing DDParcel's parcellation with T1w-based parcellation shows higher test-retest reproducibility and a higher regional homogeneity, while requiring much less computational time. Generalizability is demonstrated on a range of populations and dMRI acquisition protocols. Utility of DDParcel's parcellation is demonstrated on tractography analysis for fiber tract identification.
Collapse
|
14
|
Deferm W, Tang T, Moerkerke M, Daniels N, Steyaert J, Alaerts K, Ortibus E, Naulaers G, Boets B. Subtle microstructural alterations in white matter tracts involved in socio-emotional processing after very preterm birth. Neuroimage Clin 2024; 41:103580. [PMID: 38401459 PMCID: PMC10944182 DOI: 10.1016/j.nicl.2024.103580] [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: 12/18/2023] [Revised: 02/10/2024] [Accepted: 02/10/2024] [Indexed: 02/26/2024]
Abstract
Children born very preterm (VPT, < 32 weeks of gestation) have an increased risk of developing socio-emotional difficulties. Possible neural substrates for these socio-emotional difficulties are alterations in the structural connectivity of the social brain due to premature birth. The objective of the current study was to study microstructural white matter integrity in VPT versus full-term (FT) born school-aged children along twelve white matter tracts involved in socio-emotional processing. Diffusion MRI scans were obtained from a sample of 35 VPT and 38 FT 8-to-12-year-old children. Tractography was performed using TractSeg, a state-of-the-art neural network-based approach, which offers investigation of detailed tract profiles of fractional anisotropy (FA). Group differences in FA along the tracts were investigated using both a traditional and complementary functional data analysis approach. Exploratory correlations were performed between the Social Responsiveness Scale (SRS-2), a parent-report questionnaire assessing difficulties in social functioning, and FA along the tract. Both analyses showed significant reductions in FA for the VPT group along the middle portion of the right SLF I and an anterior portion of the left SLF II. These group differences possibly indicate altered white matter maturation due to premature birth and may contribute to altered functional connectivity in the Theory of Mind network which has been documented in earlier work with VPT samples. Apart from reduced social motivation in the VPT group, there were no significant group differences in reported social functioning, as assessed by SRS-2. We found that in the VPT group higher FA values in segments of the left SLF I and right SLF II were associated with better social functioning. Surprisingly, the opposite was found for segments in the right IFO, where higher FA values were associated with worse reported social functioning. Since no significant correlations were found for the FT group, this relationship may be specific for VPT children. The current study overcomes methodological limitations of previous studies by more accurately segmenting white matter tracts using constrained spherical deconvolution based tractography, by applying complementary tractometry analysis approaches to estimate changes in FA more accurately, and by investigating the FA profile along the three components of the SLF.
Collapse
Affiliation(s)
- Ward Deferm
- Center for Developmental Psychiatry, KU Leuven, Belgium.
| | - Tiffany Tang
- Center for Developmental Psychiatry, KU Leuven, Belgium
| | | | - Nicky Daniels
- Neuromotor Rehabilitation Research Group, KU Leuven, Belgium
| | - Jean Steyaert
- Center for Developmental Psychiatry, KU Leuven, Belgium; Child Psychiatry, UZ Leuven, Belgium
| | - Kaat Alaerts
- Neuromotor Rehabilitation Research Group, KU Leuven, Belgium
| | | | - Gunnar Naulaers
- Neonatal Intensive Care Unit - Neonatology, UZ Leuven, Belgium; UZ Leuven & Center for Developmental Disorders, Belgium
| | - Bart Boets
- Center for Developmental Psychiatry, KU Leuven, Belgium
| |
Collapse
|
15
|
Young F, Aquilina K, Seunarine KK, Mancini L, Clark CA, Clayden JD. Fibre orientation atlas guided rapid segmentation of white matter tracts. Hum Brain Mapp 2024; 45:e26578. [PMID: 38339907 PMCID: PMC10826637 DOI: 10.1002/hbm.26578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 12/14/2023] [Accepted: 12/19/2023] [Indexed: 02/12/2024] Open
Abstract
Fibre tract delineation from diffusion magnetic resonance imaging (MRI) is a valuable clinical tool for neurosurgical planning and navigation, as well as in research neuroimaging pipelines. Several popular methods are used for this task, each with different strengths and weaknesses making them more or less suited to different contexts. For neurosurgical imaging, priorities include ease of use, computational efficiency, robustness to pathology and ability to generalise to new tracts of interest. Many existing methods use streamline tractography, which may require expert neuroimaging operators for setting parameters and delineating anatomical regions of interest, or suffer from as a lack of generalisability to clinical scans involving deforming tumours and other pathologies. More recently, data-driven approaches including deep-learning segmentation models and streamline clustering methods have improved reproducibility and automation, although they can require large amounts of training data and/or computationally intensive image processing at the point of application. We describe an atlas-based direct tract mapping technique called 'tractfinder', utilising tract-specific location and orientation priors. Our aim was to develop a clinically practical method avoiding streamline tractography at the point of application while utilising prior anatomical knowledge derived from only 10-20 training samples. Requiring few training samples allows emphasis to be placed on producing high quality, neuro-anatomically accurate training data, and enables rapid adaptation to new tracts of interest. Avoiding streamline tractography at the point of application reduces computational time, false positives and vulnerabilities to pathology such as tumour deformations or oedema. Carefully filtered training streamlines and track orientation distribution mapping are used to construct tract specific orientation and spatial probability atlases in standard space. Atlases are then transformed to target subject space using affine registration and compared with the subject's voxel-wise fibre orientation distribution data using a mathematical measure of distribution overlap, resulting in a map of the tract's likely spatial distribution. This work includes extensive performance evaluation and comparison with benchmark techniques, including streamline tractography and the deep-learning method TractSeg, in two publicly available healthy diffusion MRI datasets (from TractoInferno and the Human Connectome Project) in addition to a clinical dataset comprising paediatric and adult brain tumour scans. Tract segmentation results display high agreement with established techniques while requiring less than 3 min on average when applied to a new subject. Results also display higher robustness than compared methods when faced with clinical scans featuring brain tumours and resections. As well as describing and evaluating a novel proposed tract delineation technique, this work continues the discussion on the challenges surrounding the white matter segmentation task, including issues of anatomical definitions and the use of quantitative segmentation comparison metrics.
Collapse
Affiliation(s)
- Fiona Young
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child HealthUniversity College LondonLondonUK
- Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
| | - Kristian Aquilina
- Department of NeurosurgeryGreat Ormond Street Hospital for ChildrenLondonUK
| | - Kiran K. Seunarine
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child HealthUniversity College LondonLondonUK
- Department of RadiologyGreat Ormond Street Hospital for ChildrenLondonUK
| | - Laura Mancini
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and NeurosurgeryUniversity College London Hospitals NHS Foundation TrustLondonUK
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Chris A. Clark
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child HealthUniversity College LondonLondonUK
| | - Jonathan D. Clayden
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child HealthUniversity College LondonLondonUK
| |
Collapse
|
16
|
Meesters S, Landers M, Rutten GJ, Florack L. Subject-Specific Automatic Reconstruction of White Matter Tracts. J Digit Imaging 2023; 36:2648-2661. [PMID: 37537513 PMCID: PMC10584769 DOI: 10.1007/s10278-023-00883-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 07/05/2023] [Accepted: 07/05/2023] [Indexed: 08/05/2023] Open
Abstract
MRI-based tractography is still underexploited and unsuited for routine use in brain tumor surgery due to heterogeneity of methods and functional-anatomical definitions and above all, the lack of a turn-key system. Standardization of methods is therefore desirable, whereby an objective and reliable approach is a prerequisite before the results of any automated procedure can subsequently be validated and used in neurosurgical practice. In this work, we evaluated these preliminary but necessary steps in healthy volunteers. Specifically, we evaluated the robustness and reliability (i.e., test-retest reproducibility) of tractography results of six clinically relevant white matter tracts by using healthy volunteer data (N = 136) from the Human Connectome Project consortium. A deep learning convolutional network-based approach was used for individualized segmentation of regions of interest, combined with an evidence-based tractography protocol and appropriate post-tractography filtering. Robustness was evaluated by estimating the consistency of tractography probability maps, i.e., averaged tractograms in normalized space, through the use of a hold-out cross-validation approach. No major outliers were found, indicating a high robustness of the tractography results. Reliability was evaluated at the individual level. First by examining the overlap of tractograms that resulted from repeatedly processed identical MRI scans (N = 10, 10 iterations) to establish an upper limit of reliability of the pipeline. Second, by examining the overlap for subjects that were scanned twice at different time points (N = 40). Both analyses indicated high reliability, with the second analysis showing a reliability near the upper limit. The robust and reliable subject-specific generation of white matter tracts in healthy subjects holds promise for future validation of our pipeline in a clinical population and subsequent implementation in brain tumor surgery.
Collapse
Affiliation(s)
- Stephan Meesters
- Department of Mathematics & Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
- Department of Neurosurgery, Elisabeth-Tweesteden Hospital, Tilburg, The Netherlands
| | - Maud Landers
- Department of Neurosurgery, Elisabeth-Tweesteden Hospital, Tilburg, The Netherlands
| | - Geert-Jan Rutten
- Department of Neurosurgery, Elisabeth-Tweesteden Hospital, Tilburg, The Netherlands.
| | - Luc Florack
- Department of Mathematics & Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
| |
Collapse
|
17
|
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.
Collapse
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
| |
Collapse
|
18
|
Dumais F, Legarreta JH, Lemaire C, Poulin P, Rheault F, Petit L, Barakovic M, Magon S, Descoteaux M, Jodoin PM. FIESTA: Autoencoders for accurate fiber segmentation in tractography. Neuroimage 2023; 279:120288. [PMID: 37495198 DOI: 10.1016/j.neuroimage.2023.120288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 07/20/2023] [Indexed: 07/28/2023] Open
Abstract
White matter bundle segmentation is a cornerstone of modern tractography to study the brain's structural connectivity in domains such as neurological disorders, neurosurgery, and aging. In this study, we present FIESTA (FIbEr Segmentation in Tractography using Autoencoders), a reliable and robust, fully automated, and easily semi-automatically calibrated pipeline based on deep autoencoders that can dissect and fully populate white matter bundles. This pipeline is built upon previous works that demonstrated how autoencoders can be used successfully for streamline filtering, bundle segmentation, and streamline generation in tractography. Our proposed method improves bundle segmentation coverage by recovering hard-to-track bundles with generative sampling through the latent space seeding of the subject bundle and the atlas bundle. A latent space of streamlines is learned using autoencoder-based modeling combined with contrastive learning. Using an atlas of bundles in standard space (MNI), our proposed method segments new tractograms using the autoencoder latent distance between each tractogram streamline and its closest neighbor bundle in the atlas of bundles. Intra-subject bundle reliability is improved by recovering hard-to-track streamlines, using the autoencoder to generate new streamlines that increase the spatial coverage of each bundle while remaining anatomically correct. Results show that our method is more reliable than state-of-the-art automated virtual dissection methods such as RecoBundles, RecoBundlesX, TractSeg, White Matter Analysis and XTRACT. Our framework allows for the transition from one anatomical bundle definition to another with marginal calibration efforts. Overall, these results show that our framework improves the practicality and usability of current state-of-the-art bundle segmentation framework.
Collapse
Affiliation(s)
- Félix Dumais
- Sherbrooke Connectivity Imaging Lab (SCIL), Department of Computer Science, Université de Sherbrooke, Canada; Videos & Images Theory and Analytics Lab (VITAL), Department of Computer Science, Université de Sherbrooke, Canada.
| | - Jon Haitz Legarreta
- Department of Radiology, Brigham and Women's Hospital, Mass General Brigham/Harvard Medical School, USA
| | - Carl Lemaire
- Centre de Calcul Scientifique, Université de Sherbrooke, Canada
| | - Philippe Poulin
- Sherbrooke Connectivity Imaging Lab (SCIL), Department of Computer Science, Université de Sherbrooke, Canada; Videos & Images Theory and Analytics Lab (VITAL), Department of Computer Science, Université de Sherbrooke, Canada
| | - François Rheault
- Medical Imaging and Neuroinformatic (MINi) Lab, Department of Computer Science, Université de Sherbrooke, Canada
| | - Laurent Petit
- Groupe d'Imagerie Neurofonctionnelle (GIN), CNRS, CEA, IMN, GIN, UMR 5293, F-33000 Bordeaux, Université de Bordeaux, France
| | - Muhamed Barakovic
- Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Stefano Magon
- Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab (SCIL), Department of Computer Science, Université de Sherbrooke, Canada; Imeka Solutions inc, Sherbrooke, Canada
| | - Pierre-Marc Jodoin
- Videos & Images Theory and Analytics Lab (VITAL), Department of Computer Science, Université de Sherbrooke, Canada; Imeka Solutions inc, Sherbrooke, Canada
| |
Collapse
|
19
|
Hain A, Jörgens D, Moreno R. Randomized iterative spherical-deconvolution informed tractogram filtering. Neuroimage 2023; 278:120248. [PMID: 37423271 DOI: 10.1016/j.neuroimage.2023.120248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Revised: 06/15/2023] [Accepted: 06/23/2023] [Indexed: 07/11/2023] Open
Abstract
Tractography has become an indispensable part of brain connectivity studies. However, it is currently facing problems with reliability. In particular, a substantial amount of nerve fiber reconstructions (streamlines) in tractograms produced by state-of-the-art tractography methods are anatomically implausible. To address this problem, tractogram filtering methods have been developed to remove faulty connections in a postprocessing step. This study takes a closer look at one such method, Spherical-deconvolution Informed Filtering of Tractograms (SIFT), which uses a global optimization approach to improve the agreement between the remaining streamlines after filtering and the underlying diffusion magnetic resonance imaging data. SIFT is not suitable for judging the compliance of individual streamlines with the acquired data since its results depend on the size and composition of the surrounding tractogram. To tackle this problem, we propose applying SIFT to randomly selected tractogram subsets in order to retrieve multiple assessments for each streamline. This approach makes it possible to identify streamlines with very consistent filtering results, which were used as pseudo-ground truths for training classifiers. The trained classifier is able to distinguish the obtained groups of complying and non-complying streamlines with the acquired data with an accuracy above 80%.
Collapse
Affiliation(s)
- Antonia Hain
- Saarland University, Faculty of Mathematics and Computer Science, Campus E1.7, Saarbruecken, 66041, Saarland, Germany.
| | - Daniel Jörgens
- Division of Brain, Imaging, and Behaviour, Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada; KTH Royal Institute of Technology, Department of Biomedical Engineering and Health Systems, Hälsovägen 11C, Huddinge, 14157, Stockholm, Sweden.
| | - Rodrigo Moreno
- KTH Royal Institute of Technology, Department of Biomedical Engineering and Health Systems, Hälsovägen 11C, Huddinge, 14157, Stockholm, Sweden.
| |
Collapse
|
20
|
Lucena O, Lavrador JP, Irzan H, Semedo C, Borges P, Vergani F, Granados A, Sparks R, Ashkan K, Ourselin S. Assessing informative tract segmentation and nTMS for pre-operative planning. J Neurosci Methods 2023; 396:109933. [PMID: 37524245 PMCID: PMC10861808 DOI: 10.1016/j.jneumeth.2023.109933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 07/15/2023] [Accepted: 07/28/2023] [Indexed: 08/02/2023]
Abstract
BACKGROUND Deep learning-based (DL) methods are the best-performing methods for white matter tract segmentation in anatomically healthy subjects. However, tract annotations are variable or absent in clinical data and manual annotations are especially difficult in patients with tumors where normal anatomy may be distorted. Direct cortical and subcortical stimulation is the gold standard ground truth to determine the cortical and sub-cortical lo- cation of motor-eloquent areas intra-operatively. Nonetheless, this technique is invasive, prolongs the surgical procedure, and may cause patient fatigue. Navigated Transcranial Magnetic Stimulation (nTMS) has a well-established correlation to direct cortical stimulation for motor mapping and the added advantage of being able to be acquired pre-operatively. NEW METHOD In this work, we evaluate the feasibility of using nTMS motor responses as a method to assess corticospinal tract (CST) binary masks and estimated uncertainty generated by a DL-based tract segmentation in patients with diffuse gliomas. RESULTS Our results show CST binary masks have a high overlap coefficient (OC) with nTMS response masks. A strong negative correlation is found between estimated uncertainty and nTMS response mask distance to the CST binary mask. COMPARISON WITH EXISTING METHODS We compare our approach (UncSeg) with the state-of-the-art TractSeg in terms of OC between the CST binary masks and nTMS response masks. CONCLUSIONS In this study, we demonstrate that estimated uncertainty from UncSeg is a good measure of the agreement between the CST binary masks and nTMS response masks distance to the CST binary mask boundary.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | | | - Keyoumars Ashkan
- King's College London, London, UK; King's College Hospital Foundation Trust, London, UK
| | | |
Collapse
|
21
|
Ghazi N, Aarabi MH, Soltanian-Zadeh H. Deep Learning Methods for Identification of White Matter Fiber Tracts: Review of State-of-the-Art and Future Prospective. Neuroinformatics 2023; 21:517-548. [PMID: 37328715 DOI: 10.1007/s12021-023-09636-4] [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] [Accepted: 05/20/2023] [Indexed: 06/18/2023]
Abstract
Quantitative analysis of white matter fiber tracts from diffusion Magnetic Resonance Imaging (dMRI) data is of great significance in health and disease. For example, analysis of fiber tracts related to anatomically meaningful fiber bundles is highly demanded in pre-surgical and treatment planning, and the surgery outcome depends on accurate segmentation of the desired tracts. Currently, this process is mainly done through time-consuming manual identification performed by neuro-anatomical experts. However, there is a broad interest in automating the pipeline such that it is fast, accurate, and easy to apply in clinical settings and also eliminates the intra-reader variabilities. Following the advancements in medical image analysis using deep learning techniques, there has been a growing interest in using these techniques for the task of tract identification as well. Recent reports on this application show that deep learning-based tract identification approaches outperform existing state-of-the-art methods. This paper presents a review of current tract identification approaches based on deep neural networks. First, we review the recent deep learning methods for tract identification. Next, we compare them with respect to their performance, training process, and network properties. Finally, we end with a critical discussion of open challenges and possible directions for future works.
Collapse
Affiliation(s)
- Nayereh Ghazi
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, 14399, Iran
| | - Mohammad Hadi Aarabi
- Department of Neuroscience, University of Padova, Padova, Italy
- Padova Neuroscience Center (PNC), University of Padova, Padova, Italy
| | - Hamid Soltanian-Zadeh
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, 14399, Iran.
- Medical Image Analysis Laboratory, Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, MI, 48202, USA.
| |
Collapse
|
22
|
Delinte N, Dricot L, Macq B, Gosse C, Van Reybroeck M, Rensonnet G. Unraveling multi-fixel microstructure with tractography and angular weighting. Front Neurosci 2023; 17:1199568. [PMID: 37351427 PMCID: PMC10282555 DOI: 10.3389/fnins.2023.1199568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 05/15/2023] [Indexed: 06/24/2023] Open
Abstract
Recent advances in MRI technology have enabled richer multi-shell sequences to be implemented in diffusion MRI, allowing the investigation of both the microscopic and macroscopic organization of the brain white matter and its complex network of neural fibers. The emergence of advanced diffusion models has enabled a more detailed analysis of brain microstructure by estimating the signal received from a voxel as the combination of responses from multiple fiber populations. However, disentangling the individual microstructural properties of different macroscopic white matter tracts where those pathways intersect remains a challenge. Several approaches have been developed to assign microstructural properties to macroscopic streamlines, but often present shortcomings. ROI-based heuristics rely on averages that are not tract-specific. Global methods solve a computationally-intensive global optimization but prevent the use of microstructural properties not included in the model and often require restrictive hypotheses. Other methods use atlases that might not be adequate in population studies where the shape of white matter tracts varies significantly between patients. We introduce UNRAVEL, a framework combining the microscopic and macroscopic scales to unravel multi-fixel microstructure by utilizing tractography. The framework includes commonly-used heuristics as well as a new algorithm, estimating the microstructure of a specific white matter tract with angular weighting. Our framework grants considerable freedom as the inputs required, a set of streamlines defining a tract and a multi-fixel diffusion model estimated in each voxel, can be defined by the user. We validate our approach on synthetic data and in vivo data, including a repeated scan of a subject and a population study of children with dyslexia. In each case, we compare the estimation of microstructural properties obtained with angular weighting to other commonly-used approaches. Our framework provides estimations of the microstructure at the streamline level, volumetric maps for visualization and mean microstructural values for the whole tract. The angular weighting algorithm shows increased accuracy, robustness to uncertainties in its inputs and maintains similar or better reproducibility compared to commonly-used analysis approaches. UNRAVEL will provide researchers with a flexible and open-source tool enabling them to study the microstructure of specific white matter pathways with their diffusion model of choice.
Collapse
Affiliation(s)
- Nicolas Delinte
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
- Institute of NeuroScience, Université Catholique de Louvain, Brussels, Belgium
| | - Laurence Dricot
- Institute of NeuroScience, Université Catholique de Louvain, Brussels, Belgium
| | - Benoit Macq
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Claire Gosse
- Institute of NeuroScience, Université Catholique de Louvain, Brussels, Belgium
- Psychological Sciences Research Institute, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Marie Van Reybroeck
- Institute of NeuroScience, Université Catholique de Louvain, Brussels, Belgium
- Psychological Sciences Research Institute, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Gaetan Rensonnet
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| |
Collapse
|
23
|
Wang Z, He M, Lv Y, Ge E, Zhang S, Qiang N, Liu T, Zhang F, Li X, Ge B. Accurate corresponding fiber tract segmentation via FiberGeoMap learner with application to autism. Cereb Cortex 2023:7133663. [PMID: 37083279 DOI: 10.1093/cercor/bhad125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 03/16/2023] [Accepted: 03/17/2023] [Indexed: 04/22/2023] Open
Abstract
Fiber tract segmentation is a prerequisite for tract-based statistical analysis. Brain fiber streamlines obtained by diffusion magnetic resonance imaging and tractography technology are usually difficult to be leveraged directly, thus need to be segmented into fiber tracts. Previous research mainly consists of two steps: defining and computing the similarity features of fiber streamlines, then adopting machine learning algorithms for fiber clustering or classification. Defining the similarity feature is the basic premise and determines its potential reliability and application. In this study, we adopt geometric features for fiber tract segmentation and develop a novel descriptor (FiberGeoMap) for the corresponding representation, which can effectively depict fiber streamlines' shapes and positions. FiberGeoMap can differentiate fiber tracts within the same subject, meanwhile preserving the shape and position consistency across subjects, thus can identify common fiber tracts across brains. We also proposed a Transformer-based encoder network called FiberGeoMap Learner, to perform segmentation based on the geometric features. Experimental results showed that the proposed method can differentiate the 103 various fiber tracts, which outperformed the existing methods in both the number of categories and segmentation accuracy. Furthermore, the proposed method identified some fiber tracts that were statistically different on fractional anisotropy (FA), mean diffusion (MD), and fiber number ration in autism.
Collapse
Affiliation(s)
- Zhenwei Wang
- Key Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi'an, China
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Mengshen He
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Yifan Lv
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Enjie Ge
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Shu Zhang
- Center for Brain and Brain-Inspired Computing Research, Department of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Ning Qiang
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
- Center for Brain and Brain-Inspired Computing Research, Department of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Tianming Liu
- Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, United States
| | - Fan Zhang
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Xiang Li
- Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Bao Ge
- Key Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi'an, China
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
- Center for Brain and Brain-Inspired Computing Research, Department of Computer Science, Northwestern Polytechnical University, Xi'an, China
| |
Collapse
|
24
|
Andica C, Kamagata K, Aoki S. Automated three-dimensional major white matter bundle segmentation using diffusion magnetic resonance imaging. Anat Sci Int 2023:10.1007/s12565-023-00715-9. [PMID: 37017902 DOI: 10.1007/s12565-023-00715-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 03/09/2023] [Indexed: 04/06/2023]
Abstract
White matter bundle segmentation using diffusion magnetic resonance imaging fiber tractography enables detailed evaluation of individual white matter tracts three-dimensionally, and plays a crucial role in studying human brain anatomy, function, development, and diseases. Manual extraction of streamlines utilizing a combination of the inclusion and exclusion of regions of interest can be considered the current gold standard for extracting white matter bundles from whole-brain tractograms. However, this is a time-consuming and operator-dependent process with limited reproducibility. Several automated approaches using different strategies to reconstruct the white matter tracts have been proposed to address the issues of time, labor, and reproducibility. In this review, we discuss few of the most well-validated approaches that automate white matter bundle segmentation with an end-to-end pipeline, including TRActs Constrained by UnderLying Anatomy (TRACULA), Automated Fiber Quantification, and TractSeg.
Collapse
Affiliation(s)
- Christina Andica
- Faculty of Health Data Science, Juntendo University, 6-8-1 Hinode, Urayasu, Chiba, 279-0013, Japan.
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Shigeki Aoki
- Faculty of Health Data Science, Juntendo University, 6-8-1 Hinode, Urayasu, Chiba, 279-0013, Japan
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| |
Collapse
|
25
|
Makris N, Rushmore R, Kaiser J, Albaugh M, Kubicki M, Rathi Y, Zhang F, O’Donnell LJ, Yeterian E, Caviness VS, Kennedy DN. A Proposed Human Structural Brain Connectivity Matrix in the Center for Morphometric Analysis Harvard-Oxford Atlas Framework: A Historical Perspective and Future Direction for Enhancing the Precision of Human Structural Connectivity with a Novel Neuroanatomical Typology. Dev Neurosci 2023; 45:161-180. [PMID: 36977393 PMCID: PMC10526721 DOI: 10.1159/000530358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 03/02/2023] [Indexed: 03/30/2023] Open
Abstract
A complete structural definition of the human nervous system must include delineation of its wiring diagram (e.g., Swanson LW. Brain architecture: understanding the basic plan, 2012). The complete formulation of the human brain circuit diagram (BCD [Front Neuroanat. 2020;14:18]) has been hampered by an inability to determine connections in their entirety (i.e., not only pathway stems but also origins and terminations). From a structural point of view, a neuroanatomic formulation of the BCD should include the origins and terminations of each fiber tract as well as the topographic course of the fiber tract in three dimensions. Classic neuroanatomical studies have provided trajectory information for pathway stems and their speculative origins and terminations [Dejerine J and Dejerine-Klumpke A. Anatomie des Centres Nerveux, 1901; Dejerine J and Dejerine-Klumpke A. Anatomie des Centres Nerveux: Méthodes générales d'étude-embryologie-histogénèse et histologie. Anatomie du cerveau, 1895; Ludwig E and Klingler J. Atlas cerebri humani, 1956; Makris N. Delineation of human association fiber pathways using histologic and magnetic resonance methodologies; 1999; Neuroimage. 1999 Jan;9(1):18-45]. We have summarized these studies previously [Neuroimage. 1999 Jan;9(1):18-45] and present them here in a macroscale-level human cerebral structural connectivity matrix. A matrix in the present context is an organizational construct that embodies anatomical knowledge about cortical areas and their connections. This is represented in relation to parcellation units according to the Harvard-Oxford Atlas neuroanatomical framework established by the Center for Morphometric Analysis at Massachusetts General Hospital in the early 2000s, which is based on the MRI volumetrics paradigm of Dr. Verne Caviness and colleagues [Brain Dev. 1999 Jul;21(5):289-95]. This is a classic connectional matrix based mainly on data predating the advent of DTI tractography, which we refer to as the "pre-DTI era" human structural connectivity matrix. In addition, we present representative examples that incorporate validated structural connectivity information from nonhuman primates and more recent information on human structural connectivity emerging from DTI tractography studies. We refer to this as the "DTI era" human structural connectivity matrix. This newer matrix represents a work in progress and is necessarily incomplete due to the lack of validated human connectivity findings on origins and terminations as well as pathway stems. Importantly, we use a neuroanatomical typology to characterize different types of connections in the human brain, which is critical for organizing the matrices and the prospective database. Although substantial in detail, the present matrices may be assumed to be only partially complete because the sources of data relating to human fiber system organization are limited largely to inferences from gross dissections of anatomic specimens or extrapolations of pathway tracing information from nonhuman primate experiments [Front Neuroanat. 2020;14:18, Front Neuroanat. 2022;16:1035420, and Brain Imaging Behav. 2021;15(3):1589-1621]. These matrices, which embody a systematic description of cerebral connectivity, can be used in cognitive and clinical studies in neuroscience and, importantly, to guide research efforts for further elucidating, validating, and completing the human BCD [Front Neuroanat. 2020;14:18].
Collapse
Affiliation(s)
- Nikos Makris
- Center for Morphometric Analysis, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
- Psychiatry Neuroimaging Laboratory, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA
| | - Richard Rushmore
- Center for Morphometric Analysis, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
- Psychiatry Neuroimaging Laboratory, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA
| | - Jonathan Kaiser
- Center for Morphometric Analysis, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
| | - Matthew Albaugh
- Department of Psychiatry, University of Vermont Larner College of Medicine, Burlington, VT, USA
| | - Marek Kubicki
- Center for Morphometric Analysis, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
- Psychiatry Neuroimaging Laboratory, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA, USA
| | - Yogesh Rathi
- Center for Morphometric Analysis, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
- Psychiatry Neuroimaging Laboratory, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA, USA
| | - Fan Zhang
- Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Lauren J. O’Donnell
- Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Edward Yeterian
- Center for Morphometric Analysis, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychology, Colby College, Waterville, ME, USA
| | - Verne S. Caviness
- Center for Morphometric Analysis, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
| | - David N. Kennedy
- Department of Psychiatry, University of Massachusetts Chan Medical School, Worcester, MA, USA
| |
Collapse
|
26
|
Prediction of the Topography of the Corticospinal Tract on T1-Weighted MR Images Using Deep-Learning-Based Segmentation. Diagnostics (Basel) 2023; 13:diagnostics13050911. [PMID: 36900055 PMCID: PMC10000710 DOI: 10.3390/diagnostics13050911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 02/23/2023] [Accepted: 02/27/2023] [Indexed: 03/04/2023] Open
Abstract
INTRODUCTION Tractography is an invaluable tool in the planning of tumor surgery in the vicinity of functionally eloquent areas of the brain as well as in the research of normal development or of various diseases. The aim of our study was to compare the performance of a deep-learning-based image segmentation for the prediction of the topography of white matter tracts on T1-weighted MR images to the performance of a manual segmentation. METHODS T1-weighted MR images of 190 healthy subjects from 6 different datasets were utilized in this study. Using deterministic diffusion tensor imaging, we first reconstructed the corticospinal tract on both sides. After training a segmentation model on 90 subjects of the PIOP2 dataset using the nnU-Net in a cloud-based environment with graphical processing unit (Google Colab), we evaluated its performance using 100 subjects from 6 different datasets. RESULTS Our algorithm created a segmentation model that predicted the topography of the corticospinal pathway on T1-weighted images in healthy subjects. The average dice score was 0.5479 (0.3513-0.7184) on the validation dataset. CONCLUSIONS Deep-learning-based segmentation could be applicable in the future to predict the location of white matter pathways in T1-weighted scans.
Collapse
|
27
|
Zhylka A, Leemans A, Pluim JPW, De Luca A. Anatomically informed multi-level fiber tractography for targeted virtual dissection. MAGMA (NEW YORK, N.Y.) 2023; 36:79-93. [PMID: 35904612 PMCID: PMC9992235 DOI: 10.1007/s10334-022-01033-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 06/30/2022] [Accepted: 07/15/2022] [Indexed: 12/01/2022]
Abstract
OBJECTIVES Diffusion-weighted MRI can assist preoperative planning by reconstructing the trajectory of eloquent fiber pathways, such as the corticospinal tract (CST). However, accurate reconstruction of the full extent of the CST remains challenging with existing tractography methods. We suggest a novel tractography algorithm exploiting unused fiber orientations to produce more complete and reliable results. METHODS Our novel approach, referred to as multi-level fiber tractography (MLFT), reconstructs fiber pathways by progressively considering previously unused fiber orientations at multiple levels of tract propagation. Anatomical priors are used to minimize the number of false-positive pathways. The MLFT method was evaluated on synthetic data and in vivo data by reconstructing the CST while compared to conventional tractography approaches. RESULTS The radial extent of MLFT reconstructions is comparable to that of probabilistic reconstruction: [Formula: see text] for the left and [Formula: see text] for the right hemisphere according to Wilcoxon test, while achieving significantly higher topography preservation compared to probabilistic tractography: [Formula: see text]. DISCUSSION MLFT provides a novel way to reconstruct fiber pathways by adding the capability of including branching pathways in fiber tractography. Thanks to its robustness, feasible reconstruction extent and topography preservation, our approach may assist in clinical practice as well as in virtual dissection studies.
Collapse
Affiliation(s)
- Andrey Zhylka
- Biomedical Engineering, Eindhoven University of Technology, Rondom 70, 5612 AP, Eindhoven, The Netherlands.
| | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Josien P W Pluim
- Biomedical Engineering, Eindhoven University of Technology, Rondom 70, 5612 AP, Eindhoven, The Netherlands
| | - Alberto De Luca
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.,Neurology Department, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| |
Collapse
|
28
|
Bartha-Doering L, Kollndorfer K, Schwartz E, Fischmeister FP, Langs G, Weber M, Lackner-Schmelz S, Kienast P, Stümpflen M, Taymourtash A, Mandl S, Alexopoulos J, Prayer D, Seidl R, Kasprian G. Fetal temporal sulcus depth asymmetry has prognostic value for language development. Commun Biol 2023; 6:109. [PMID: 36707693 PMCID: PMC9883513 DOI: 10.1038/s42003-023-04503-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 01/18/2023] [Indexed: 01/28/2023] Open
Abstract
In most humans, the superior temporal sulcus (STS) shows a rightward depth asymmetry. This asymmetry can not only be observed in adults, but is already recognizable in the fetal brain. As the STS lies adjacent to brain areas important for language, STS depth asymmetry may represent an anatomical marker for language abilities. This study investigated the prognostic value of STS depth asymmetry in healthy fetuses for later language abilities, language localization, and language-related white matter tracts. Less right lateralization of the fetal STS depth was significantly associated with better verbal abilities, with fetal STS depth asymmetry explaining more than 40% of variance in verbal skills 6-13 years later. Furthermore, less right fetal STS depth asymmetry correlated with increased left language localization during childhood. We hypothesize that earlier and/or more localized fetal development of the left temporal cortex is accompanied by an earlier development of the left STS and is favorable for early language learning. If the findings of this pilot study hold true in larger samples of healthy children and in different clinical populations, fetal STS asymmetry has the potential to become a diagnostic biomarker of the maturity and integrity of neural correlates of language.
Collapse
Affiliation(s)
- Lisa Bartha-Doering
- grid.22937.3d0000 0000 9259 8492Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University of Vienna, Vienna, Austria
| | - Kathrin Kollndorfer
- grid.22937.3d0000 0000 9259 8492Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University of Vienna, Vienna, Austria ,grid.22937.3d0000 0000 9259 8492Division of Neuroradiology and Muscoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Ernst Schwartz
- grid.22937.3d0000 0000 9259 8492Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Florian Ph.S. Fischmeister
- grid.22937.3d0000 0000 9259 8492Division of Neuroradiology and Muscoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria ,grid.5110.50000000121539003Institute of Psychology, University of Graz, Graz, Austria ,grid.452216.6BioTechMed-Graz, Graz, Austria
| | - Georg Langs
- grid.22937.3d0000 0000 9259 8492Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Michael Weber
- grid.22937.3d0000 0000 9259 8492Division of Neuroradiology and Muscoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Sonja Lackner-Schmelz
- grid.22937.3d0000 0000 9259 8492Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University of Vienna, Vienna, Austria ,grid.22937.3d0000 0000 9259 8492Division of Neuroradiology and Muscoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Patric Kienast
- grid.22937.3d0000 0000 9259 8492Division of Neuroradiology and Muscoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Marlene Stümpflen
- grid.22937.3d0000 0000 9259 8492Division of Neuroradiology and Muscoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Athena Taymourtash
- grid.22937.3d0000 0000 9259 8492Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Sophie Mandl
- grid.22937.3d0000 0000 9259 8492Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University of Vienna, Vienna, Austria
| | - Johanna Alexopoulos
- grid.22937.3d0000 0000 9259 8492Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University of Vienna, Vienna, Austria ,grid.22937.3d0000 0000 9259 8492Department of Psychoanalysis and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Daniela Prayer
- grid.22937.3d0000 0000 9259 8492Division of Neuroradiology and Muscoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Rainer Seidl
- grid.22937.3d0000 0000 9259 8492Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University of Vienna, Vienna, Austria
| | - Gregor Kasprian
- grid.22937.3d0000 0000 9259 8492Division of Neuroradiology and Muscoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| |
Collapse
|
29
|
Alves PN, Forkel SJ, Corbetta M, Thiebaut de Schotten M. The subcortical and neurochemical organization of the ventral and dorsal attention networks. Commun Biol 2022; 5:1343. [PMID: 36477440 PMCID: PMC9729227 DOI: 10.1038/s42003-022-04281-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 11/18/2022] [Indexed: 12/12/2022] Open
Abstract
Attention is a core cognitive function that filters and selects behaviourally relevant information in the environment. The cortical mapping of attentional systems identified two segregated networks that mediate stimulus-driven and goal-driven processes, the Ventral and the Dorsal Attention Networks (VAN, DAN). Deep brain electrophysiological recordings, behavioral data from phylogenetic distant species, and observations from human brain pathologies challenge purely corticocentric models. Here, we used advanced methods of functional alignment applied to resting-state functional connectivity analyses to map the subcortical architecture of the Ventral and Dorsal Attention Networks. Our investigations revealed the involvement of the pulvinar, the superior colliculi, the head of caudate nuclei, and a cluster of brainstem nuclei relevant to both networks. These nuclei are densely connected structural network hubs, as revealed by diffusion-weighted imaging tractography. Their projections establish interrelations with the acetylcholine nicotinic receptor as well as dopamine and serotonin transporters, as demonstrated in a spatial correlation analysis with a normative atlas of neurotransmitter systems. This convergence of functional, structural, and neurochemical evidence provides a comprehensive framework to understand the neural basis of attention across different species and brain diseases.
Collapse
Affiliation(s)
- Pedro Nascimento Alves
- Laboratório de Estudos de Linguagem, Centro de Estudos Egas Moniz, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal.
- Serviço de Neurologia, Departmento de Neurociências e Saúde Mental, Hospital de Santa Maria, CHULN, Lisboa, Portugal.
| | - Stephanie J Forkel
- Brain Connectivity and Behaviour Laboratory, Sorbonne University, Paris, France
- Donders Institute for Brain Cognition Behaviour, Radboud University, Thomas van Aquinostraat 4, 6525GD, Nijmegen, the Netherlands
- Centre for Neuroimaging Sciences, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Departments of Neurosurgery, Technical University of Munich School of Medicine, Munich, Germany
| | - Maurizio Corbetta
- Clinica Neurologica, Department of Neuroscience, University of Padova, Padova, Italy
- Padova Neuroscience Center (PNC), University of Padova, Padova, Italy
- Venetian Institute of Molecular Medicine, VIMM, Padova, Italy
- Department of Neurology, Radiology, Neuroscience Washington University School of Medicine, St.Louis, MO, USA
| | - Michel Thiebaut de Schotten
- Brain Connectivity and Behaviour Laboratory, Sorbonne University, Paris, France.
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA, University of Bordeaux, Bordeaux, France.
| |
Collapse
|
30
|
Bullock DN, Hayday EA, Grier MD, Tang W, Pestilli F, Heilbronner SR. A taxonomy of the brain's white matter: twenty-one major tracts for the 21st century. Cereb Cortex 2022; 32:4524-4548. [PMID: 35169827 PMCID: PMC9574243 DOI: 10.1093/cercor/bhab500] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 12/06/2021] [Accepted: 12/07/2021] [Indexed: 01/26/2023] Open
Abstract
The functional and computational properties of brain areas are determined, in large part, by their connectivity profiles. Advances in neuroimaging and network neuroscience allow us to characterize the human brain noninvasively, but a comprehensive understanding of the human brain demands an account of the anatomy of brain connections. Long-range anatomical connections are instantiated by white matter, which itself is organized into tracts. These tracts are often disrupted by central nervous system disorders, and they can be targeted by neuromodulatory interventions, such as deep brain stimulation. Here, we characterized the connections, morphology, traversal, and functions of the major white matter tracts in the brain. There are major discrepancies across different accounts of white matter tract anatomy, hindering our attempts to accurately map the connectivity of the human brain. However, we are often able to clarify the source(s) of these discrepancies through careful consideration of both histological tract-tracing and diffusion-weighted tractography studies. In combination, the advantages and disadvantages of each method permit novel insights into brain connectivity. Ultimately, our synthesis provides an essential reference for neuroscientists and clinicians interested in brain connectivity and anatomy, allowing for the study of the association of white matter's properties with behavior, development, and disorders.
Collapse
Affiliation(s)
- Daniel N Bullock
- Department of Psychological and Brain Sciences, Program in Neuroscience, Indiana University Bloomington, Bloomington, IN 47405, USA
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | - Elena A Hayday
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | - Mark D Grier
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | - Wei Tang
- Department of Psychological and Brain Sciences, Program in Neuroscience, Indiana University Bloomington, Bloomington, IN 47405, USA
- Department of Computer Science, Indiana University Bloomington, Bloomington, IN 47408, USA
| | - Franco Pestilli
- Department of Psychology, The University of Texas at Austin, Austin, TX 78712, USA
| | - Sarah R Heilbronner
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| |
Collapse
|
31
|
Therriault J, Pascoal TA, Savard M, Mathotaarachchi S, Benedet AL, Chamoun M, Tissot C, Lussier FZ, Rahmouni N, Stevenson J, Qureshi MNI, Kang MS, Thomas É, Vitali P, Soucy JP, Massarweh G, Saha-Chaudhuri P, Gauthier S, Rosa-Neto P. Intrinsic connectivity of the human brain provides scaffold for tau aggregation in clinical variants of Alzheimer's disease. Sci Transl Med 2022; 14:eabc8693. [PMID: 36001678 DOI: 10.1126/scitranslmed.abc8693] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Alzheimer's disease (AD) phenotypes might result from differences in selective vulnerability. Evidence from preclinical models suggests that tau pathology has cell-to-cell propagation properties. Therefore, here, we tested the cell-to-cell propagation framework in the amnestic, visuospatial, language, and behavioral/dysexecutive phenotypes of AD. We report that each AD phenotype is associated with a distinct network-specific pattern of tau aggregation, where tau aggregation is concentrated in brain network hubs. In all AD phenotypes, regional tau load could be predicted by connectivity patterns of the human brain. Furthermore, regions with greater connectivity displayed similar rates of longitudinal tau accumulation in an independent cohort. Connectivity-based tau deposition was not restricted to a specific vulnerable network but was rather a general property of brain organization, linking selective vulnerability and transneuronal spreading models of neurodegeneration. Together, this study indicates that intrinsic brain connectivity provides a framework for tau aggregation across diverse phenotypic manifestations of AD.
Collapse
Affiliation(s)
- Joseph Therriault
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada.,Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada
| | - Tharick A Pascoal
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada.,Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada
| | - Mélissa Savard
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Canada
| | - Sulantha Mathotaarachchi
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Canada
| | - Andréa L Benedet
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada.,Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada
| | - Mira Chamoun
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada
| | - Cécile Tissot
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada.,Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada
| | - Firoza Z Lussier
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada.,Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada
| | - Nesrine Rahmouni
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada.,Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada
| | - Jenna Stevenson
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada.,Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada
| | - Muhammad Naveed Iqbal Qureshi
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada.,Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada
| | - Min Su Kang
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada.,Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada
| | - Émilie Thomas
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada
| | - Paolo Vitali
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada
| | - Jean-Paul Soucy
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada.,Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada
| | - Gassan Massarweh
- Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada.,Department of Radiochemistry, McGill University, Montreal, Quebec H3A 2B4, Canada
| | | | - Serge Gauthier
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada.,Department of Psychiatry, McGill University, Montreal, Quebec H3A 1G1, Canada
| | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada.,Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada.,Department of Psychiatry, McGill University, Montreal, Quebec H3A 1G1, Canada
| |
Collapse
|
32
|
Kai J, Khan AR, Haast RA, Lau JC. Mapping the subcortical connectome using in vivo diffusion MRI: Feasibility and reliability. Neuroimage 2022; 262:119553. [PMID: 35961469 DOI: 10.1016/j.neuroimage.2022.119553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 07/15/2022] [Accepted: 08/08/2022] [Indexed: 10/31/2022] Open
Abstract
Tractography combined with regions of interest (ROIs) has been used to non-invasively study the structural connectivity of the cortex as well as to assess the reliability of these connections. However, the subcortical connectome (subcortex to subcortex) has not been comprehensively examined, in part due to the difficulty of performing tractography in this complex and compact region. In this study, we performed an in vivo investigation using tractography to assess the feasibility and reliability of mapping known connections between structures of the subcortex using the test-retest dataset from the Human Connectome Project (HCP). We further validated our observations using a separate unrelated subjects dataset from the HCP. Quantitative assessment was performed by computing tract densities and spatial overlap of identified connections between subcortical ROIs. Further, known connections between structures of the basal ganglia and thalamus were identified and visually inspected, comparing tractography reconstructed trajectories with descriptions from tract-tracing studies. Our observations demonstrate both the feasibility and reliability of using a data-driven tractography-based approach to map the subcortical connectome in vivo.
Collapse
Affiliation(s)
- Jason Kai
- Department of Medical Biophysics, Schulich School of Medicine & Dentistry, The University of Western Ontario, London, Ontario, Canada; Centre for Functional and Metabolic Mapping, Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada
| | - Ali R Khan
- Department of Medical Biophysics, Schulich School of Medicine & Dentistry, The University of Western Ontario, London, Ontario, Canada; Centre for Functional and Metabolic Mapping, Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada
| | - Roy Am Haast
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada; Aix-Marseille University, CNRS, CRMBM, UMR 7339, Marseille, France
| | - Jonathan C Lau
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada; Department of Clinical Neurological Sciences, Division of Neurosurgery, Schulich School of Medicine & Dentistry, The University of Western Ontario, London, Ontario, Canada.
| |
Collapse
|
33
|
Jarret J, Ferré P, Chedid G, Bedetti C, Bore A, Joanette Y, Rouleau I, Maria Brambati S. Functional network and structural connections involved in picture naming. BRAIN AND LANGUAGE 2022; 231:105146. [PMID: 35709592 DOI: 10.1016/j.bandl.2022.105146] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 04/14/2022] [Accepted: 06/03/2022] [Indexed: 06/15/2023]
Abstract
We mapped the left hemisphere cortical regions and fiber bundles involved in picture naming in adults by integrating task-based fMRI with dMRI tractography. We showed that a ventral pathway that "maps image and sound to meaning" involves the middle occipital, inferior temporal, superior temporal, inferior frontal gyri, and the temporal pole where a signal exchange is made possible by the inferior fronto-occipital, inferior longitudinal, middle longitudinal, uncinate fasciculi, and the extreme capsule. A dorsal pathway that "maps sound to speech" implicates the inferior temporal, superior temporal, inferior frontal, precentral gyri, and the supplementary motor area where the arcuate fasciculus and the frontal aslant ensure intercommunication. This study provides a neurocognitive model of picture naming and supports the hypothesis that the ventral indirect route passes through the temporal pole. This further supports the idea that the inferior and superior temporal gyri may play pivotal roles within the dual-stream framework of language.
Collapse
Affiliation(s)
- Julien Jarret
- Département de psychologie, Université de Montréal, Montréal, QC, Canada; Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, QC, Canada
| | - Perrine Ferré
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, QC, Canada
| | - Georges Chedid
- Département de psychologie, Université de Montréal, Montréal, QC, Canada; Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, QC, Canada
| | - Christophe Bedetti
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, QC, Canada
| | - Arnaud Bore
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, QC, Canada
| | - Yves Joanette
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, QC, Canada
| | - Isabelle Rouleau
- Département de psychologie, Université du Québec à Montréal (UQÀM), QC, Canada
| | - Simona Maria Brambati
- Département de psychologie, Université de Montréal, Montréal, QC, Canada; Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, QC, Canada.
| |
Collapse
|
34
|
Akeret K, Forkel SJ, Buzzi RM, Vasella F, Amrein I, Colacicco G, Serra C, Krayenbühl N. Multimodal anatomy of the human forniceal commissure. Commun Biol 2022; 5:742. [PMID: 35879431 PMCID: PMC9314404 DOI: 10.1038/s42003-022-03692-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 07/07/2022] [Indexed: 11/25/2022] Open
Abstract
Ambiguity surrounds the existence and morphology of the human forniceal commissure. We combine advanced in-vivo tractography, multidirectional ex-vivo fiber dissection, and multiplanar histological analysis to characterize this structure’s anatomy. Across all 178 subjects, in-vivo fiber dissection based on the Human Connectome Project 7 T MRI data identifies no interhemispheric connections between the crura fornicis. Multidirectional ex-vivo fiber dissection under the operating microscope demonstrates the psalterium as a thin soft-tissue membrane spanning between the right and left crus fornicis, but exposes no commissural fibers. Multiplanar histological analysis with myelin and Bielchowsky silver staining, however, visualizes delicate cruciform fibers extending between the crura fornicis, enclosed by connective tissue, the psalterium. The human forniceal commissure is therefore much more delicate than previously described and presented in anatomical textbooks. This finding is consistent with the observed phylogenetic trend of a reduction of the forniceal commissure in non-human primates compared to non-primate eutherian mammals. Anatomical dissection and tractography elucidate the delicate nature of the human forniceal commissure, an interhemispheric white matter circuit.
Collapse
Affiliation(s)
- Kevin Akeret
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Stephanie J Forkel
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France.,Donders Centre for Cognition, Radboud University, Thomas van Aquinostraat 4, 6525 GD, Nijmegen, the Netherlands.,Centre for Neuroimaging Sciences, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Departments of Neurosurgery, Technical University of Munich School of Medicine, Munich, Germany
| | - Raphael M Buzzi
- Division of Internal Medicine, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Flavio Vasella
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Irmgard Amrein
- Institute of Anatomy, University of Zurich, Zurich, Switzerland.,Department of Health Sciences and Technology, ETH, Zurich, Switzerland
| | | | - Carlo Serra
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Niklaus Krayenbühl
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland. .,Division of Pediatric Neurosurgery, University Children's Hospital, Zurich, Switzerland.
| |
Collapse
|
35
|
Rheault F, Schilling KG, Obaid S, Begnoche JP, Cutting LE, Descoteaux M, Landman BA, Petit L. The influence of regions of interest on tractography virtual dissection protocols: general principles to learn and to follow. Brain Struct Funct 2022; 227:2191-2207. [PMID: 35672532 PMCID: PMC9884471 DOI: 10.1007/s00429-022-02518-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 05/22/2022] [Indexed: 01/31/2023]
Abstract
Efficient communication across fields of research is challenging, especially when they are at opposite ends of the physical and digital spectrum. Neuroanatomy and neuroimaging may seem close to each other. When neuroimaging studies try to isolate structures of interest, according to a specific anatomical definition, a variety of challenges emerge. It is a non-trivial task to convert the neuroanatomical knowledge to instructions and rules to be executed in neuroimaging software. In the process called "virtual dissection" used to isolate coherent white matter structure in tractography, each white matter pathway has its own set of landmarks (regions of interest) used as inclusion and exclusion criteria. The ability to segment and study these pathways is critical for scientific progress, yet, variability may depend on region placement, and be influenced by the person positioning the region (i.e., a rater). When raters' variability is taken into account, the impact made by each region of interest becomes even more difficult to interpret. A delicate balance between anatomical validity, impact on the virtual dissection and raters' reproducibility emerge. In this work, we investigate this balance by leveraging manual delineation data of a group of raters from a previous study to quantify which set of landmarks and criteria contribute most to variability in virtual dissection. To supplement our analysis, the variability of each pathway with a region-by-region exploration was performed. We present a detailed exploration and description of each region, the causes of variability and its impacts. Finally, we provide a brief overview of the lessons learned from our previous virtual dissection projects and propose recommendations for future virtual dissection protocols as well as perspectives to reach better community agreement when it comes to anatomical definitions of white matter pathways.
Collapse
Affiliation(s)
- Francois Rheault
- Electrical and Computer Engineering, Vanderbilt University, Nashville, USA
| | - Kurt G. Schilling
- Vanderbilt University Institute of Imaging, Nashville, USA,Department of Radiology and Radiological Science, Vanderbilt University Medical Center, Nashville, USA
| | - Sami Obaid
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Département d’Informatique, Université de Sherbrooke, Sherbrooke, Canada,Health Center Research Center, University of Montreal, Montreal, Canada
| | - John P. Begnoche
- The Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, USA
| | - Laurie E. Cutting
- Vanderbilt Kennedy Center, University Medical Center, VanderbiltNashville, USA
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Département d’Informatique, Université de Sherbrooke, Sherbrooke, Canada
| | - Bennett A. Landman
- Electrical and Computer Engineering, Vanderbilt University, Nashville, USA,Vanderbilt University Institute of Imaging, Nashville, USA,Department of Radiology and Radiological Science, Vanderbilt University Medical Center, Nashville, USA,Computer Science, Vanderbilt University, Nashville, USA
| | - Laurent Petit
- Groupe d’Imagerie Neurofonctionnelle, Institut Des Maladies Neurodégénératives, CNRS, CEA University of Bordeaux, Bordeaux, France
| |
Collapse
|
36
|
Rocha RP, Koçillari L, Suweis S, De Filippo De Grazia M, de Schotten MT, Zorzi M, Corbetta M. Recovery of neural dynamics criticality in personalized whole-brain models of stroke. Nat Commun 2022; 13:3683. [PMID: 35760787 PMCID: PMC9237050 DOI: 10.1038/s41467-022-30892-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 05/16/2022] [Indexed: 01/13/2023] Open
Abstract
The critical brain hypothesis states that biological neuronal networks, because of their structural and functional architecture, work near phase transitions for optimal response to internal and external inputs. Criticality thus provides optimal function and behavioral capabilities. We test this hypothesis by examining the influence of brain injury (strokes) on the criticality of neural dynamics estimated at the level of single participants using directly measured individual structural connectomes and whole-brain models. Lesions engender a sub-critical state that recovers over time in parallel with behavior. The improvement of criticality is associated with the re-modeling of specific white-matter connections. We show that personalized whole-brain dynamical models poised at criticality track neural dynamics, alteration post-stroke, and behavior at the level of single participants.
Collapse
Affiliation(s)
- Rodrigo P Rocha
- Departamento de Física, Centro de Ciências Físicas e Matemáticas, Universidade Federal de Santa Catarina, 88040-900, Florianópolis, SC, Brazil.
- Department of Physics, School of Philosophy, Sciences and Letters of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP, Brazil.
- Padova Neuroscience Center, Università di Padova, Padova, Italy.
| | - Loren Koçillari
- Padova Neuroscience Center, Università di Padova, Padova, Italy
- Laboratory of Neural Computation, Istituto Italiano di Tecnologia, 38068, Rovereto, Italy
- Dipartimento di Fisica e Astronomia, Università di Padova and INFN, via Marzolo 8, I-35131, Padova, Italy
| | - Samir Suweis
- Padova Neuroscience Center, Università di Padova, Padova, Italy
- Dipartimento di Fisica e Astronomia, Università di Padova and INFN, via Marzolo 8, I-35131, Padova, Italy
| | | | - Michel Thiebaut de Schotten
- Brain Connectivity and Behaviour Laboratory, BCBlab, Sorbonne Universities, Paris, France
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA University of Bordeaux, Bordeaux, France
| | - Marco Zorzi
- IRCCS San Camillo Hospital, Venice, Italy
- Dipartimento di Psicologia Generale, Università di Padova, Padova, Italy
| | - Maurizio Corbetta
- Padova Neuroscience Center, Università di Padova, Padova, Italy
- Dipartimento di Neuroscienze, Università di Padova, Padova, Italy
- Venetian Institute of Molecular Medicine (VIMM), Fondazione Biomedica, Padova, Italy
| |
Collapse
|
37
|
Lucena O, Borges P, Cardoso J, Ashkan K, Sparks R, Ourselin S. Informative and Reliable Tract Segmentation for Preoperative Planning. FRONTIERS IN RADIOLOGY 2022; 2:866974. [PMID: 37492653 PMCID: PMC10365092 DOI: 10.3389/fradi.2022.866974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 03/31/2022] [Indexed: 07/27/2023]
Abstract
Identifying white matter (WM) tracts to locate eloquent areas for preoperative surgical planning is a challenging task. Manual WM tract annotations are often used but they are time-consuming, suffer from inter- and intra-rater variability, and noise intrinsic to diffusion MRI may make manual interpretation difficult. As a result, in clinical practice direct electrical stimulation is necessary to precisely locate WM tracts during surgery. A measure of WM tract segmentation unreliability could be important to guide surgical planning and operations. In this study, we use deep learning to perform reliable tract segmentation in combination with uncertainty quantification to measure segmentation unreliability. We use a 3D U-Net to segment white matter tracts. We then estimate model and data uncertainty using test time dropout and test time augmentation, respectively. We use a volume-based calibration approach to compute representative predicted probabilities from the estimated uncertainties. In our findings, we obtain a Dice of ≈0.82 which is comparable to the state-of-the-art for multi-label segmentation and Hausdorff distance <10mm. We demonstrate a high positive correlation between volume variance and segmentation errors, which indicates a good measure of reliability for tract segmentation ad uncertainty estimation. Finally, we show that calibrated predicted volumes are more likely to encompass the ground truth segmentation volume than uncalibrated predicted volumes. This study is a step toward more informed and reliable WM tract segmentation for clinical decision-making.
Collapse
Affiliation(s)
- Oeslle Lucena
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Pedro Borges
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Jorge Cardoso
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Keyoumars Ashkan
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- King's College Hospital Foundation Trust, London, United Kingdom
| | - Rachel Sparks
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| |
Collapse
|
38
|
Raja R, Na X, Moore A, Otoo R, Glasier CM, Badger TM, Ou X. Associations Between White Matter Microstructures and Cognitive Functioning in 8-Year-Old Children: A Track-Weighted Imaging Study. J Child Neurol 2022; 37:471-490. [PMID: 35254148 PMCID: PMC9149064 DOI: 10.1177/08830738221083487] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
PURPOSE Quantitative tractography using diffusion-weighted magnetic resonance imaging data is widely used in characterizing white matter microstructure throughout childhood, but more studies are still needed to investigate comprehensive brain-behavior relationships between tract-specific white matter measures and multiple cognitive functions in children. METHODS In this study, we analyzed diffusion-weighted MRI data of 71 healthy 8-year-old children utilizing white matter tract-specific quantitative measures derived from diffusion-weighted MRI tractography based on a novel track-weighted imaging approach. Track density imaging, average path length map and 4 track-weighted diffusion tensor imaging measures including: mean diffusivity, fractional anisotropy, axial diffusivity, and radial diffusivity were computed for 63 white matter tracts. The track-weighted imaging measures were then correlated with a comprehensive set of neuropsychological test scores in different cognitive domains including intelligence, language, memory, academic skills, and executive functions to identify tract-specific brain-behavior relationships. RESULTS Significant correlations (P < .05, false discovery rate corrected; r = 0.27-0.57) were found in multiple white matter tracts, with a total of 40 correlations identified between various track-weighted imaging measures including average path length map, track-weighted imaging-fractional anisotropy, and neuropsychological test scores and subscales. Specifically, track-weighted imaging measures indicative of better white matter connectivity and/or microstructural development significantly correlated with higher IQ and better language abilities. CONCLUSION Our findings demonstrate the ability of track-weighted imaging measures in establishing associations between white matter and cognitive functioning in healthy children and can serve as a reference for normal brain/cognition relationships in young school-age children and further aid in identifying imaging biomarkers predictive of adverse neurodevelopmental outcomes.
Collapse
Affiliation(s)
- Rajikha Raja
- Department of Radiology, University of Arkansas for Medical Sciences
| | - Xiaoxu Na
- Department of Radiology, University of Arkansas for Medical Sciences
| | - Alexandra Moore
- College of Medicine, University of Arkansas for Medical Sciences
| | - Raymond Otoo
- College of Medicine, University of Arkansas for Medical Sciences
| | - Charles M. Glasier
- Department of Radiology, University of Arkansas for Medical Sciences
- Department of Pediatrics, University of Arkansas for Medical Sciences
| | - Thomas M. Badger
- Department of Pediatrics, University of Arkansas for Medical Sciences
- Arkansas Children’s Nutrition Center
| | - Xiawei Ou
- Department of Radiology, University of Arkansas for Medical Sciences
- Department of Pediatrics, University of Arkansas for Medical Sciences
| |
Collapse
|
39
|
Vinci-Booher S, Caron B, Bullock D, James K, Pestilli F. Development of white matter tracts between and within the dorsal and ventral streams. Brain Struct Funct 2022; 227:1457-1477. [PMID: 35267078 DOI: 10.1007/s00429-021-02414-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Accepted: 10/12/2021] [Indexed: 01/11/2023]
Abstract
The degree of interaction between the ventral and dorsal visual streams has been discussed in multiple scientific domains for decades. Recently, several white matter tracts that directly connect cortical regions associated with the dorsal and ventral streams have become possible to study due to advancements in automated and reproducible methods. The developmental trajectory of this set of tracts, here referred to as the posterior vertical pathway (PVP), has yet to be described. We propose an input-driven model of white matter development and provide evidence for the model by focusing on the development of the PVP. We used reproducible, cloud-computing methods and diffusion imaging from adults and children (ages 5-8 years) to compare PVP development to that of tracts within the ventral and dorsal pathways. PVP microstructure was more adult-like than dorsal stream microstructure, but less adult-like than ventral stream microstructure. Additionally, PVP microstructure was more similar to the microstructure of the ventral than the dorsal stream and was predicted by performance on a perceptual task in children. Overall, results suggest a potential role for the PVP in the development of the dorsal visual stream that may be related to its ability to facilitate interactions between ventral and dorsal streams during learning. Our results are consistent with the proposed model, suggesting that the microstructural development of major white matter pathways is related, at least in part, to the propagation of sensory information within the visual system.
Collapse
Affiliation(s)
- S Vinci-Booher
- Indiana University, 1101 E. 10th Street, Bloomington, IN, 47405, USA.
| | - B Caron
- Indiana University, 1101 E. 10th Street, Bloomington, IN, 47405, USA
| | - D Bullock
- Indiana University, 1101 E. 10th Street, Bloomington, IN, 47405, USA
| | - K James
- Indiana University, 1101 E. 10th Street, Bloomington, IN, 47405, USA
| | - F Pestilli
- Indiana University, 1101 E. 10th Street, Bloomington, IN, 47405, USA.
- The University of Texas, 108 E Dean Keeton St, Austin, TX, 78712, USA.
| |
Collapse
|
40
|
Zhang F, Daducci A, He Y, Schiavi S, Seguin C, Smith RE, Yeh CH, Zhao T, O'Donnell LJ. Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review. Neuroimage 2022; 249:118870. [PMID: 34979249 PMCID: PMC9257891 DOI: 10.1016/j.neuroimage.2021.118870] [Citation(s) in RCA: 99] [Impact Index Per Article: 49.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 12/03/2021] [Accepted: 12/31/2021] [Indexed: 12/13/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain's structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.
Collapse
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
| | | |
Collapse
|
41
|
Lu Q, Liu W, Zhuo Z, Li Y, Duan Y, Yu P, Qu L, Ye C, Liu Y. A Transfer Learning Approach to Few-shot Segmentation of Novel White Matter Tracts. Med Image Anal 2022; 79:102454. [DOI: 10.1016/j.media.2022.102454] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Revised: 03/19/2022] [Accepted: 04/08/2022] [Indexed: 12/20/2022]
|
42
|
Zhang Y, Furst AJ. Brainstem Diffusion Tensor Tractography and Clinical Applications in Pain. FRONTIERS IN PAIN RESEARCH (LAUSANNE, SWITZERLAND) 2022; 3:840328. [PMID: 35399154 PMCID: PMC8989264 DOI: 10.3389/fpain.2022.840328] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 02/15/2022] [Indexed: 11/13/2022]
Abstract
The brainstem is one of the most vulnerable brain structures in many neurological conditions, such as pain, sleep problems, autonomic dysfunctions, and neurodegenerative disorders. Diffusion tensor imaging and tractography provide structural details and quantitative measures of brainstem fiber pathways. Until recently, diffusion tensor tractographic studies have mainly focused on whole-brain MRI acquisition. Due to the brainstem's spatial localization, size, and tissue characteristics, and limits of imaging techniques, brainstem diffusion MRI poses particular challenges in tractography. We provide a brief overview on recent advances in diffusion tensor tractography in revealing human pathways connecting the brainstem to the subcortical regions (e.g., basal ganglia, mesolimbic, basal forebrain), and cortical regions. Each of these pathways contains different distributions of fiber tracts from known neurotransmitter-specific nuclei in the brainstem. We compare the brainstem tractographic approaches in literature and our in-lab developed automated brainstem tractography in terms of atlas building, technical advantages, and neuroanatomical implications on neurotransmitter systems. Lastly, we summarize recent investigations of using brainstem tractography as a promising tool in association with pain.
Collapse
Affiliation(s)
- Yu Zhang
- War Related Illness and Injury Study Center (WRIISC), VA Palo Alto Health Care System, Palo Alto, CA, United States,*Correspondence: Yu Zhang ;
| | - Ansgar J. Furst
- War Related Illness and Injury Study Center (WRIISC), VA Palo Alto Health Care System, Palo Alto, CA, United States,Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Palo Alto, CA, United States,Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Palo Alto, CA, United States,Polytrauma System of Care (PSC), VA Palo Alto Health Care System, Palo Alto, CA, United States
| |
Collapse
|
43
|
Ning L, Rathi Y, Barbour T, Makris N, Camprodon JA. White matter markers and predictors for subject-specific rTMS response in major depressive disorder. J Affect Disord 2022; 299:207-214. [PMID: 34875281 PMCID: PMC8766915 DOI: 10.1016/j.jad.2021.12.005] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/30/2021] [Accepted: 12/02/2021] [Indexed: 10/19/2022]
Abstract
Repetitive transcranial magnetic stimulation (rTMS) has established therapeutic efficacy for major depressive disorder (MDD). While translational research has focused primarily on understanding the mechanism of action of TMS on functional activation and connectivity, the effects on structural connectivity remain largely unknown especially when rTMS is applied using subject-specific brain targets. This study aims to use novel diffusion magnetic resonance imaging (dMRI) analysis to examine microstructural changes related to rTMS treatment response using a unique cohort of 21 patients with MDD treated using rTMS with subject-specific targets. White matter dMRI microstructural measures and clinical scores were captured before and after the full course of treatment. We defined disease-relevant fiber bundles connected to different subregions of the left prefrontal cortex and analyzed changes in diffusion properties as well as correlations between the changes of dMRI measures and the changes in Hamilton Depression Rating Scale (HAMD). No significant changes were observed in tracts connected to the TMS targets. rTMS significantly increased the extra-axonal free-water volume, fractional anisotropy and decreased the radial diffusivity in anterior-medial prefrontal fiber bundles but did not lead to raw changes in lateral prefrontal tracts. That said, the microstructural changes in the lateral prefrontal white matter were significantly correlated with treatment response. Moreover, pre-rTMS dMRI measures of the dorsal anterior cingulate cortex and lateral prefrontal cortex connections are correlated with changes in HAMD scores. Microstructural changes in the anterior-medial and lateral prefrontal white matter are potentially involved in treatment response to TMS, though further investigation is needed using larger datasets.
Collapse
Affiliation(s)
- Lipeng Ning
- Brigham and Women's Hospital, Boston, MA, United States of America; Massachusetts General Hospital, Boston, MA, United States of America; Harvard Medical School, Boston, MA, United States of America.
| | - Yogesh Rathi
- Brigham and Women’s Hospital, Boston, MA, USA,Harvard Medical School, Boston, MA, USA
| | - Tracy Barbour
- Massachusetts General Hospital, Boston, MA, USA,Harvard Medical School, Boston, MA, USA
| | - Nikos Makris
- Massachusetts General Hospital, Boston, MA, USA,Harvard Medical School, Boston, MA, USA
| | - Joan A. Camprodon
- Massachusetts General Hospital, Boston, MA, USA,Harvard Medical School, Boston, MA, USA
| |
Collapse
|
44
|
Huang J, Li M, Zeng Q, Xie L, He J, Chen G, Liang J, Li M, Feng Y. Automatic oculomotor nerve identification based on
data‐driven
fiber clustering. Hum Brain Mapp 2022; 43:2164-2180. [PMID: 35092135 PMCID: PMC8996358 DOI: 10.1002/hbm.25779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 12/09/2021] [Accepted: 12/26/2021] [Indexed: 11/10/2022] Open
Abstract
The oculomotor nerve (OCN) is the main motor nerve innervating eye muscles and can be involved in multiple flammatory, compressive, or pathologies. The diffusion magnetic resonance imaging (dMRI) tractography is now widely used to describe the trajectory of the OCN. However, the complex cranial structure leads to difficulties in fiber orientation distribution (FOD) modeling, fiber tracking, and region of interest (ROI) selection. Currently, the identification of OCN relies on expert manual operation, resulting in challenges, such as the carries high clinical, time‐consuming, and labor costs. Thus, we propose a method that can automatically identify OCN from dMRI tractography. First, we choose the multi‐shell multi‐tissue constraint spherical deconvolution (MSMT‐CSD) FOD estimation model and deterministic tractography to describe the 3D trajectory of the OCN. Then, we rely on the well‐established computational pipeline and anatomical expertise to create a data‐driven OCN tractography atlas from 40 HCP data. We identify six clusters belonging to the OCN from the atlas, including the structures of three kinds of positional relationships (pass between, pass through, and go around) with the red nuclei and two kinds of positional relationships with medial longitudinal fasciculus. Finally, we apply the proposed OCN atlas to identify the OCN automatically from 40 new HCP subjects and two patients with brainstem cavernous malformation. In terms of spatial overlap and visualization, experiment results show that the automatically and manually identified OCN fibers are consistent. Our proposed OCN atlas provides an effective tool for identifying OCN by avoiding the traditional selection strategy of ROIs.
Collapse
Affiliation(s)
- Jiahao Huang
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology Hangzhou China
- Zhejiang Provincial United Key Laboratory of Embedded Systems Hangzhou China
| | - Mengjun Li
- Department of Radiology, Second Xiangya Hospital Central South University Hunan China
- Department of Neurosurgery Capital Medical University Xuanwu Hospital Beijing China
| | - Qingrun Zeng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology Hangzhou China
- Zhejiang Provincial United Key Laboratory of Embedded Systems Hangzhou China
| | - Lei Xie
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology Hangzhou China
- Zhejiang Provincial United Key Laboratory of Embedded Systems Hangzhou China
| | - Jianzhong He
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology Hangzhou China
- Zhejiang Provincial United Key Laboratory of Embedded Systems Hangzhou China
| | - Ge Chen
- Department of Neurosurgery Capital Medical University Xuanwu Hospital Beijing China
| | - Jiantao Liang
- Department of Neurosurgery Capital Medical University Xuanwu Hospital Beijing China
| | - Mingchu Li
- Department of Neurosurgery Capital Medical University Xuanwu Hospital Beijing China
| | - Yuanjing Feng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology Hangzhou China
- Zhejiang Provincial United Key Laboratory of Embedded Systems Hangzhou China
| |
Collapse
|
45
|
Volumetric Segmentation of White Matter Tracts with Label Embedding. Neuroimage 2022; 250:118934. [PMID: 35091078 DOI: 10.1016/j.neuroimage.2022.118934] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Revised: 01/04/2022] [Accepted: 01/24/2022] [Indexed: 11/23/2022] Open
Abstract
Convolutional neural networks have achieved state-of-the-art performance for white matter (WM) tract segmentation based on diffusion magnetic resonance imaging (dMRI). However, the segmentation can still be difficult for challenging WM tracts with thin bodies or complicated shapes; the segmentation is even more problematic in challenging scenarios with reduced data quality or domain shift between training and test data, which can be easily encountered in clinical settings. In this work, we seek to improve the segmentation of WM tracts, especially for challenging WM tracts in challenging scenarios. In particular, our method is based on volumetric WM tract segmentation, where voxels are directly labeled without performing tractography. To improve the segmentation, we exploit the characteristics of WM tracts that different tracts can cross or overlap and revise the network design accordingly. Specifically, because multiple tracts can co-exist in a voxel, we hypothesize that the different tract labels can be correlated. The tract labels at a single voxel are concatenated as a label vector, the length of which is the number of tract labels. Due to the tract correlation, this label vector can be projected into a lower-dimensional space-referred to as the embedded space-for each voxel, which allows the segmentation network to solve a simpler problem. By predicting the coordinate in the embedded space for the tracts at each voxel and subsequently mapping the coordinate to the label vector with a reconstruction module, the segmentation result can be achieved. To facilitate the learning of the embedded space, an auxiliary label reconstruction loss is integrated with the segmentation accuracy loss during network training, and network training and inference are end-to-end. Our method was validated on two dMRI datasets under various settings. The results show that the proposed method improves the accuracy of WM tract segmentation, and the improvement is more prominent for challenging tracts in challenging scenarios.
Collapse
|
46
|
Auto-encoded Latent Representations of White Matter Streamlines for Quantitative Distance Analysis. Neuroinformatics 2022; 20:1105-1120. [PMID: 35731372 PMCID: PMC9588484 DOI: 10.1007/s12021-022-09593-4] [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] [Accepted: 06/10/2022] [Indexed: 12/31/2022]
Abstract
Parcellation of whole brain tractograms is a critical step to study brain white matter structures and connectivity patterns. The existing methods based on supervised classification of streamlines into predefined streamline bundle types are not designed to explore sub-bundle structures, and methods with manually designed features are expensive to compute streamline-wise similarities. To resolve these issues, we propose a novel atlas-free method that learns a latent space using a deep recurrent auto-encoder trained in an unsupervised manner. The method efficiently embeds any length of streamlines to fixed-size feature vectors, named streamline embedding, for tractogram parcellation using non-parametric clustering in the latent space. The method was evaluated on the ISMRM 2015 tractography challenge dataset with discrimination of major bundles using clustering algorithms and streamline querying based on similarity, as well as real tractograms of 102 subjects Human Connectome Project. The learnt latent streamline and bundle representations open the possibility of quantitative studies of arbitrary granularity of sub-bundle structures using generic data mining techniques.
Collapse
|
47
|
Moshe YH, Ben Bashat D, Hananis Z, Teicher M, Artzi M. Utilizing the TractSeg Tool for Automatic Corticospinal Tract Segmentation in Patients With Brain Pathology. Technol Cancer Res Treat 2022; 21:15330338221131387. [PMID: 36320179 PMCID: PMC9630891 DOI: 10.1177/15330338221131387] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
Purpose: White-matter tract segmentation in patients with brain
pathology can guide surgical planning and can be used for tissue integrity
assessment. Recently, TractSeg was proposed for automatic tract segmentation in
healthy subjects. The aim of this study was to assess the use of TractSeg for
corticospinal-tract (CST) segmentation in a large cohort of patients with brain
pathology and to evaluate its consistency in repeated measurements.
Methods: A total of 649 diffusion-tensor-imaging scans were
included, of them: 625 patients and 24 scans from 12 healthy controls (scanned
twice for consistency assessment). Manual CST labeling was performed in all
cases, and by 2 raters for the healthy subjects. Segmentation results were
evaluated based on the Dice score. In order to evaluate consistency in repeated
measurements, volume, Fractional Anisotropy (FA), and Mean Diffusivity (MD)
values were extracted and correlated for the manual versus automatic methods.
Results: For the automatic CST segmentation Dice scores of 0.63
and 0.64 for the training and testing datasets were obtained. Higher consistency
between measurements was detected for the automatic segmentation, with between
measurements correlations of volume = 0.92/0.65, MD = 0.94/0.75 for the
automatic versus manual segmentation. Conclusions: The TractSeg
method enables automatic CST segmentation in patients with brain pathology.
Superior measurements consistency was detected for the automatic in comparison
to manual fiber segmentation, which indicates an advantage when using this
method for clinical and longitudinal studies.
Collapse
Affiliation(s)
- Yael H Moshe
- Sagol Brain Institute, 26738Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,196686Department of Mathematics, Bar Ilan University, Ramat Gan, Israel
| | - Dafna Ben Bashat
- Sagol Brain Institute, 26738Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,58408Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Zeev Hananis
- Sagol Brain Institute, 26738Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,205204Gonda Brain Research Center, Bar-Ilan University, Ramat Gan, Israel
| | - Mina Teicher
- 196686Department of Mathematics, Bar Ilan University, Ramat Gan, Israel.,205204Gonda Brain Research Center, Bar-Ilan University, Ramat Gan, Israel
| | - Moran Artzi
- Sagol Brain Institute, 26738Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,58408Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| |
Collapse
|
48
|
Joo SW, Kim H, Jo YT, Ahn S, Choi YJ, Park S, Kang Y, Lee J. White matter impairments in patients with schizophrenia: A multisite diffusion MRI study. Prog Neuropsychopharmacol Biol Psychiatry 2021; 111:110381. [PMID: 34111494 DOI: 10.1016/j.pnpbp.2021.110381] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 05/31/2021] [Accepted: 06/04/2021] [Indexed: 10/21/2022]
Abstract
There is a lack of convincing and replicative findings regarding white matter abnormalities in schizophrenia. Several multisite diffusion magnetic resonance imaging (dMRI) studies have been conducted to increase statistical power and reveal subtle white matter changes. Data pooling methods are crucial in joint analysis to compensate for the use of different scanners and image acquisition parameters. A harmonization method using raw dMRI data was developed to overcome the limited generalizability of previous data pooling methods. We obtained dMRI data of 242 healthy controls and 190 patients with schizophrenia from four different study sites. After applying the harmonization method to the raw dMRI data, a two-tensor whole-brain tractography was performed, and diffusion measures were compared between the two groups. The correlation of fractional anisotropy (FA) with the positive and negative symptoms was evaluated, and the interaction effect of diagnosis-by-age, age-squared, and sex was examined. The following white matter tracts showed significant group differences in the FA: the right superior longitudinal fascicle (SLF), the left-to-right lateral orbitofrontal commissural tract, pars orbitalis (pOr-pOr) commissural tract, and pars triangularis (pTr-pTr) commissural tract. The FA of the right SLF and pTr-pTr commissural tract were significantly associated with the Positive and Negative Syndrome Scale (PANSS) positive and negative scores. No significant interaction effect was observed. These findings add to the evidence on structural brain abnormalities in schizophrenia and can aid in obtaining a better understanding of the biological foundations of schizophrenia.
Collapse
Affiliation(s)
- Sung Woo Joo
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Harin Kim
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Young Tak Jo
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Soojin Ahn
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Young Jae Choi
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Soyeon Park
- Department of Psychiatry, Medical Foundation Yongin Mental Hospital, Yongin, Republic of Korea
| | - Yuree Kang
- Department of Psychiatry, Medical Foundation Yongin Mental Hospital, Yongin, Republic of Korea
| | - Jungsun Lee
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
| |
Collapse
|
49
|
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: 28] [Impact Index Per Article: 9.3] [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.
Collapse
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
| |
Collapse
|
50
|
Liu J, Wang W, Zhou Y, Gan C, Wang T, Hu Z, Lou J, Wang H, Yang LZ, Wong STC, Li H. Early-Onset Micromorphological Changes of Neuronal Fiber Bundles During Radiotherapy. J Magn Reson Imaging 2021; 56:210-218. [PMID: 34854521 DOI: 10.1002/jmri.28018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 11/18/2021] [Accepted: 11/18/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Patients receiving cranial radiation face the risk of delayed brain dysfunction. However, an early medical imaging marker is not available until irreversible morphological changes emerge. PURPOSE To explore the micromorphological white matter changes during the radiotherapy session by utilizing an along-tract analysis framework. STUDY TYPE Prospective. POPULATION Eighteen nasopharyngeal carcinoma (two female) patients receiving cranial radiation. FIELD STRENGTH/SEQUENCE 3.0 T; Diffusion tensor imaging (DTI) and T1- and T2-weighted images (T1W, T2W); computed tomography (CT). ASSESSMENT Patients received three DTI imaging scans during the radiotherapy (RT), namely the baseline scan (1-2 days before RT began), the middle scan (the middle of the RT session), and the end scan (1-2 days after RT ended). Twelve fibers were segmented after whole-brain tractography. Then, the fractional anisotropy (FA) values and the cumulative radiation dose received for each fiber streamline were resampled and projected into their center fiber. STATISTICAL TESTS The contrast among the three scans (P1: middle scan-baseline scan; P2: end scan-middle scan; P3: end scan-baseline scan) were compared using the linear mixed model for each of the 12 center fibers. Then, a dose-responsiveness relationship was performed using Pearson correlation. P < 0.05 was considered statistically significant. RESULTS Six of the 12 center fibers showed significant changes of FA values during the RT but with heterogeneous patterns. The significant changes along a specific center fiber were associated with their cumulative dose received (Genu: P1 r = -0.6182, P2 r = -0.5907; Splenium: P1 r = 0.4055, P = 0.1063, P2 r = 0.6742; right uncinate fasciculus: P1 r = -0.3865, P2 r = -0.4912, P = 0.0533; right corticospinal tract: P1 r = 0.4273, P = 0.1122, P2 r = -0.6885). DATA CONCLUSION The along-tract analysis might provide sensitive measures on the early-onset micromorphological changes. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 3.
Collapse
Affiliation(s)
- Jin Liu
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.,University of Science and Technology of China, Hefei, China.,Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, China
| | - Wenjuan Wang
- University of Science and Technology of China, Hefei, China.,Center for Biomedical Engineering, Department of Electronic Science and Technology, University of Science and Technology of China, Hefei, China.,School of Science, Anhui Agricultural University, Hefei, China
| | - Yanfei Zhou
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.,Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, China
| | - Chen Gan
- Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, China
| | - Tengfei Wang
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.,Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, China
| | - Zongtao Hu
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.,Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, China
| | - Jianjun Lou
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.,Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, China
| | - Hongzhi Wang
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.,Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, China
| | - Li-Zhuang Yang
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.,Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, China
| | - Stephen T C Wong
- Department of Systems Medicine and Bioengineering, Houston Methodist Cancer Center, Houston, Texas, USA.,Department of Radiology and Neurosciences, Weill Cornell Medical College, Cornell University, Houston, Texas, USA
| | - Hai Li
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.,Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, China
| |
Collapse
|