1
|
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 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] [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
|
2
|
Puzniak RJ, Prabhakaran GT, McLean RJ, Stober S, Ather S, Proudlock FA, Gottlob I, Dineen RA, Hoffmann MB. CHIASM-Net: Artificial Intelligence-Based Direct Identification of Chiasmal Abnormalities in Albinism. Invest Ophthalmol Vis Sci 2023; 64:14. [PMID: 37815506 PMCID: PMC10573586 DOI: 10.1167/iovs.64.13.14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 09/06/2023] [Indexed: 10/11/2023] Open
Abstract
Purpose Albinism is a congenital disorder affecting pigmentation levels, structure, and function of the visual system. The identification of anatomical changes typical for people with albinism (PWA), such as optic chiasm malformations, could become an important component of diagnostics. Here, we tested an application of convolutional neural networks (CNNs) for this purpose. Methods We established and evaluated a CNN, referred to as CHIASM-Net, for the detection of chiasmal malformations from anatomic magnetic resonance (MR) images of the brain. CHIASM-Net, composed of encoding and classification modules, was developed using MR images of controls (n = 1708) and PWA (n = 32). Evaluation involved 8-fold cross validation involving accuracy, precision, recall, and F1-score metrics and was performed on a subset of controls and PWA samples excluded from the training. In addition to quantitative metrics, we used Explainable AI (XAI) methods that granted insights into factors driving the predictions of CHIASM-Net. Results The results for the scenario indicated an accuracy of 85 ± 14%, precision of 90 ± 14% and recall of 81 ± 18%. XAI methods revealed that the predictions of CHIASM-Net are driven by optic-chiasm white matter and by the optic tracts. Conclusions CHIASM-Net was demonstrated to use relevant regions of the optic chiasm for albinism detection from magnetic resonance imaging (MRI) brain anatomies. This indicates the strong potential of CNN-based approaches for visual pathway analysis and ultimately diagnostics.
Collapse
Affiliation(s)
- Robert J Puzniak
- Visual Processing Lab, Department of Ophthalmology, Otto-von-Guericke-University, Magdeburg, Germany
- Department of Neuroscience, Psychology, and Behaviour, University of Leicester, Leicester, United Kingdom
| | - Gokulraj T Prabhakaran
- Visual Processing Lab, Department of Ophthalmology, Otto-von-Guericke-University, Magdeburg, Germany
| | - Rebecca J McLean
- University of Leicester Ulverscroft Eye Unit, University of Leicester, Leicester Royal Infirmary, Leicester, United Kingdom
| | - Sebastian Stober
- Artificial Intelligence Lab, Institute for Intelligent Cooperating Systems, Otto-von-Guericke-University, Magdeburg, Germany
| | - Sarim Ather
- Department of Radiology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Headington, Oxford, United Kingdom
| | - Frank A Proudlock
- University of Leicester Ulverscroft Eye Unit, University of Leicester, Leicester Royal Infirmary, Leicester, United Kingdom
| | - Irene Gottlob
- University of Leicester Ulverscroft Eye Unit, University of Leicester, Leicester Royal Infirmary, Leicester, United Kingdom
- Cooper Neurological Institute and Cooper Medical School of Rowan University, Camden, New Jersey, United States
| | - Robert A Dineen
- Mental Health and Clinical Neuroscience, Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham, United Kingdom
- NIHR Nottingham Biomedical Research Centre, Nottingham, United Kingdom
| | - Michael B Hoffmann
- Visual Processing Lab, Department of Ophthalmology, Otto-von-Guericke-University, Magdeburg, Germany
- Center for Behavioral Brain Sciences, Otto-von-Guericke-Universität, Magdeburg, Germany
| |
Collapse
|
3
|
Carrozzi A, Gramegna LL, Sighinolfi G, Zoli M, Mazzatenta D, Testa C, Lodi R, Tonon C, Manners DN. Methods of diffusion MRI tractography for localization of the anterior optic pathway: A systematic review of validated methods. Neuroimage Clin 2023; 39:103494. [PMID: 37651845 PMCID: PMC10477810 DOI: 10.1016/j.nicl.2023.103494] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 06/21/2023] [Accepted: 08/07/2023] [Indexed: 09/02/2023]
Abstract
The anterior optic pathway (AOP) is a system of three structures (optic nerves, optic chiasma, and optic tracts) that convey visual stimuli from the retina to the lateral geniculate nuclei. A successful reconstruction of the AOP using tractography could be helpful in several clinical scenarios, from presurgical planning and neuronavigation of sellar and parasellar surgery to monitoring the stage of fiber degeneration both in acute (e.g., traumatic optic neuropathy) or chronic conditions that affect AOP structures (e.g., amblyopia, glaucoma, demyelinating disorders or genetic optic nerve atrophies). However, its peculiar anatomy and course, as well as its surroundings, pose a serious challenge to obtaining successful tractographic reconstructions. Several AOP tractography strategies have been adopted but no standard procedure has been agreed upon. We performed a systematic review of the literature according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) 2020 guidelines in order to find the combinations of acquisition and reconstruction parameters that have been performed previously and have provided the highest rate of successful reconstruction of the AOP, in order to promote their routine implementation in clinical practice. For this purpose, we reviewed data regarding how the process of anatomical validation of the tractographies was performed. The Cochrane Handbook for Systematic Reviews of Interventions was used to assess the risk of bias and thus the study quality We identified thirty-nine studies that met our inclusion criteria, and only five were considered at low risk of bias and achieved over 80% of successful reconstructions. We found a high degree of heterogeneity in the acquisition and analysis parameters used to perform AOP tractography and different combinations of them can achieve satisfactory levels of anterior optic tractographic reconstruction both in real-life research and clinical scenarios. One thousand s/mm2 was the most frequently used b value, while both deterministic and probabilistic tractography algorithms performed morphological reconstruction of the tract satisfactorily, although probabilistic algorithms estimated a more realistic percentage of crossing fibers (45.6%) in healthy subjects. A wide heterogeneity was also found regarding the method used to assess the anatomical fidelity of the AOP reconstructions. Three main strategies can be found: direct visual direct visual assessment of the tractography superimposed to a conventional MR image, surgical evaluation, and computational methods. Because the latter is less dependent on a priori knowledge of the anatomy by the operator, computational methods of validation of the anatomy should be considered whenever possible.
Collapse
Affiliation(s)
- Alessandro Carrozzi
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy
| | - Laura Ludovica Gramegna
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy; IRCCS Istituto delle Scienze Neurologiche di Bologna, Functional and Molecular Neuroimaging Unit, Bologna, Italy.
| | - Giovanni Sighinolfi
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy
| | - Matteo Zoli
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy; IRCCS Istituto delle Scienze Neurologiche di Bologna, Pituitary Unit, Bologna, Italy
| | - Diego Mazzatenta
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy; IRCCS Istituto delle Scienze Neurologiche di Bologna, Pituitary Unit, Bologna, Italy
| | - Claudia Testa
- Department of Physics and Astronomy, University of Bologna, Bologna, Italy
| | - Raffaele Lodi
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy; IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Caterina Tonon
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy; IRCCS Istituto delle Scienze Neurologiche di Bologna, Functional and Molecular Neuroimaging Unit, Bologna, Italy
| | - David Neil Manners
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Functional and Molecular Neuroimaging Unit, Bologna, Italy; Department for Life Quality Studies (QUVI), University of Bologna, Bologna, Italy
| |
Collapse
|
4
|
Jin R, Cai Y, Zhang S, Yang T, Feng H, Jiang H, Zhang X, Hu Y, Liu J. Computational approaches for the reconstruction of optic nerve fibers along the visual pathway from medical images: a comprehensive review. Front Neurosci 2023; 17:1191999. [PMID: 37304011 PMCID: PMC10250625 DOI: 10.3389/fnins.2023.1191999] [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: 03/22/2023] [Accepted: 05/09/2023] [Indexed: 06/13/2023] Open
Abstract
Optic never fibers in the visual pathway play significant roles in vision formation. Damages of optic nerve fibers are biomarkers for the diagnosis of various ophthalmological and neurological diseases; also, there is a need to prevent the optic nerve fibers from getting damaged in neurosurgery and radiation therapy. Reconstruction of optic nerve fibers from medical images can facilitate all these clinical applications. Although many computational methods are developed for the reconstruction of optic nerve fibers, a comprehensive review of these methods is still lacking. This paper described both the two strategies for optic nerve fiber reconstruction applied in existing studies, i.e., image segmentation and fiber tracking. In comparison to image segmentation, fiber tracking can delineate more detailed structures of optic nerve fibers. For each strategy, both conventional and AI-based approaches were introduced, and the latter usually demonstrates better performance than the former. From the review, we concluded that AI-based methods are the trend for optic nerve fiber reconstruction and some new techniques like generative AI can help address the current challenges in optic nerve fiber reconstruction.
Collapse
Affiliation(s)
- Richu Jin
- Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, China
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Yongning Cai
- Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, China
| | - Shiyang Zhang
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Ting Yang
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Haibo Feng
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Hongyang Jiang
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Xiaoqing Zhang
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Yan Hu
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Jiang Liu
- Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, China
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
- Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| |
Collapse
|
5
|
Al-Nosairy KO, Quanz EV, Biermann J, Hoffmann MB. Optical Coherence Tomography as a Biomarker for Differential Diagnostics in Nystagmus: Ganglion Cell Layer Thickness Ratio. J Clin Med 2022; 11:jcm11174941. [PMID: 36078871 PMCID: PMC9456294 DOI: 10.3390/jcm11174941] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 08/12/2022] [Accepted: 08/13/2022] [Indexed: 11/25/2022] Open
Abstract
In albinism, with the use of optical coherence tomography (OCT), a thinning of the macular ganglion cell layer was recently reported. As a consequence, the relevant OCT measure, i.e., a reduction of the temporal/nasal ganglion cell layer thickness quotient (GCLTQ), is a strong candidate for a novel biomarker of albinism. However, nystagmus is a common trait in albinism and is known as a potential confound of imaging techniques. Therefore, there is a need to determine the impact of nystagmus without albinism on the GCLTQ. In this bi-center study, the retinal GCLTQ was determined (OCT Spectralis, Heidelberg Engineering, Heidelberg, Germany) for healthy controls (n = 5, 10 eyes) vs. participants with nystagmus and albinism (Nalbinism, n = 8, 15 eyes), and with nystagmus of other origins (Nother, n = 11, 17 eyes). Macular OCT with 25 horizontal B scans 20 × 20° with 9 automated real time tracking (ART) frames centered on the retina was obtained for each group. From the sectoral GCLTs of the early treatment diabetic retinopathy study (ETDRS) circular thickness maps, i.e., 3 mm and 6 mm ETDRS rings, GCLTQ I and GCLTQ II were determined. Both GCLTQs were reduced in Nalbinism (GCLTQ I and II: 0.78 and 0.77, p < 0.001) compared to Nother (0.91 and 0.93) and healthy controls (0.89 and 0.95). The discrimination of Nalbinism from Nother via GCLTQ I and II had an area under the curve of 80 and 82% with an optimal cutoff point of 0.86 and 0.88, respectively. In conclusion, lower GCLTQ in Nalbinism appears as a distinguished feature in albinism-related nystagmus as opposed to other causes of nystagmus.
Collapse
Affiliation(s)
- Khaldoon O. Al-Nosairy
- Department of Ophthalmology, Faculty of Medicine, Otto-von-Guericke University, 39120 Magdeburg, Germany
| | - Elisabeth V. Quanz
- Department of Ophthalmology, Faculty of Medicine, Otto-von-Guericke University, 39120 Magdeburg, Germany
| | - Julia Biermann
- Department of Ophthalmology, University of Muenster Medical Centre, 48149 Muenster, Germany
| | - Michael B. Hoffmann
- Department of Ophthalmology, Faculty of Medicine, Otto-von-Guericke University, 39120 Magdeburg, Germany
- Center for Behavioral Brain Sciences, 39118 Magdeburg, Germany
- Correspondence:
| |
Collapse
|
6
|
Abstract
We describe a collection of T1-, diffusion- and functional T2*-weighted magnetic resonance imaging data from human individuals with albinism and achiasma. This repository can be used as a test-bed to develop and validate tractography methods like diffusion-signal modeling and fiber tracking as well as to investigate the properties of the human visual system in individuals with congenital abnormalities. The MRI data is provided together with tools and files allowing for its preprocessing and analysis, along with the data derivatives such as manually curated masks and regions of interest for performing tractography.
Collapse
|