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Ebrahimi SM, Tuunanen J, Saarela V, Honkamo M, Huotari N, Raitamaa L, Korhonen V, Helakari H, Järvelä M, Kaakinen M, Eklund L, Kiviniemi V. Synchronous functional magnetic resonance eye imaging, video ophthalmoscopy, and eye surface imaging reveal the human brain and eye pulsation mechanisms. Sci Rep 2024; 14:2250. [PMID: 38278832 PMCID: PMC10817967 DOI: 10.1038/s41598-023-51069-1] [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: 01/31/2023] [Accepted: 12/30/2023] [Indexed: 01/28/2024] Open
Abstract
The eye possesses a paravascular solute transport pathway that is driven by physiological pulsations, resembling the brain glymphatic pathway. We developed synchronous multimodal imaging tools aimed at measuring the driving pulsations of the human eye, using an eye-tracking functional eye camera (FEC) compatible with magnetic resonance imaging (MRI) for measuring eye surface pulsations. Special optics enabled integration of the FEC with MRI-compatible video ophthalmoscopy (MRcVO) for simultaneous retinal imaging along with functional eye MRI imaging (fMREye) of the BOLD (blood oxygen level dependent) contrast. Upon optimizing the fMREye parameters, we measured the power of the physiological (vasomotor, respiratory, and cardiac) eye and brain pulsations by fast Fourier transform (FFT) power analysis. The human eye pulsated in all three physiological pulse bands, most prominently in the respiratory band. The FFT power means of physiological pulsation for two adjacent slices was significantly higher than in one-slice scans (RESP1 vs. RESP2; df = 5, p = 0.045). FEC and MRcVO confirmed the respiratory pulsations at the eye surface and retina. We conclude that in addition to the known cardiovascular pulsation, the human eye also has respiratory and vasomotor pulsation mechanisms, which are now amenable to study using non-invasive multimodal imaging of eye fluidics.
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Affiliation(s)
- Seyed-Mohsen Ebrahimi
- Oulu Functional NeuroImaging (OFNI), Diagnostic Imaging, Medical Research Center (MRC), Finland Oulu University Hospital, 90029, Oulu, Finland.
- Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, 90220, Oulu, Finland.
| | - Johanna Tuunanen
- Oulu Functional NeuroImaging (OFNI), Diagnostic Imaging, Medical Research Center (MRC), Finland Oulu University Hospital, 90029, Oulu, Finland
- Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, 90220, Oulu, Finland
| | - Ville Saarela
- Department of Ophthalmology and Medical Research Center, Oulu University Hospital and Research Unit of Clinical Medicine, University of Oulu, Oulu, Finland
| | - Marja Honkamo
- Department of Ophthalmology and Medical Research Center, Oulu University Hospital and Research Unit of Clinical Medicine, University of Oulu, Oulu, Finland
| | - Niko Huotari
- Oulu Functional NeuroImaging (OFNI), Diagnostic Imaging, Medical Research Center (MRC), Finland Oulu University Hospital, 90029, Oulu, Finland
- Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, 90220, Oulu, Finland
| | - Lauri Raitamaa
- Oulu Functional NeuroImaging (OFNI), Diagnostic Imaging, Medical Research Center (MRC), Finland Oulu University Hospital, 90029, Oulu, Finland
- Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, 90220, Oulu, Finland
| | - Vesa Korhonen
- Oulu Functional NeuroImaging (OFNI), Diagnostic Imaging, Medical Research Center (MRC), Finland Oulu University Hospital, 90029, Oulu, Finland
- Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, 90220, Oulu, Finland
| | - Heta Helakari
- Oulu Functional NeuroImaging (OFNI), Diagnostic Imaging, Medical Research Center (MRC), Finland Oulu University Hospital, 90029, Oulu, Finland
- Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, 90220, Oulu, Finland
| | - Matti Järvelä
- Oulu Functional NeuroImaging (OFNI), Diagnostic Imaging, Medical Research Center (MRC), Finland Oulu University Hospital, 90029, Oulu, Finland
- Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, 90220, Oulu, Finland
| | - Mika Kaakinen
- Oulu Center for Cell-Matrix Research, Faculty of Biochemistry and Molecular Medicine, Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Lauri Eklund
- Oulu Center for Cell-Matrix Research, Faculty of Biochemistry and Molecular Medicine, Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Vesa Kiviniemi
- Oulu Functional NeuroImaging (OFNI), Diagnostic Imaging, Medical Research Center (MRC), Finland Oulu University Hospital, 90029, Oulu, Finland.
- Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, 90220, Oulu, Finland.
- Oulu Center for Cell-Matrix Research, Faculty of Biochemistry and Molecular Medicine, Biocenter Oulu, University of Oulu, Oulu, Finland.
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Panahi A, Rezaee A, Hajati F, Shariflou S, Agar A, Golzan SM. Autonomous assessment of spontaneous retinal venous pulsations in fundus videos using a deep learning framework. Sci Rep 2023; 13:14445. [PMID: 37660115 PMCID: PMC10475061 DOI: 10.1038/s41598-023-41110-8] [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] [Accepted: 08/22/2023] [Indexed: 09/04/2023] Open
Abstract
The presence or absence of spontaneous retinal venous pulsations (SVP) provides clinically significant insight into the hemodynamic status of the optic nerve head. Reduced SVP amplitudes have been linked to increased intracranial pressure and glaucoma progression. Currently, monitoring for the presence or absence of SVPs is performed subjectively and is highly dependent on trained clinicians. In this study, we developed a novel end-to-end deep model, called U3D-Net, to objectively classify SVPs as present or absent based on retinal fundus videos. The U3D-Net architecture consists of two distinct modules: an optic disc localizer and a classifier. First, a fast attention recurrent residual U-Net model is applied as the optic disc localizer. Then, the localized optic discs are passed on to a deep convolutional network for SVP classification. We trained and tested various time-series classifiers including 3D Inception, 3D Dense-ResNet, 3D ResNet, Long-term Recurrent Convolutional Network, and ConvLSTM. The optic disc localizer achieved a dice score of 95% for locating the optic disc in 30 milliseconds. Amongst the different tested models, the 3D Inception model achieved an accuracy, sensitivity, and F1-Score of 84 ± 5%, 90 ± 8%, and 81 ± 6% respectively, outperforming the other tested models in classifying SVPs. To the best of our knowledge, this research is the first study that utilizes a deep neural network for an autonomous and objective classification of SVPs using retinal fundus videos.
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Affiliation(s)
- Amirhossein Panahi
- Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
| | - Alireza Rezaee
- Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran.
| | - Farshid Hajati
- Intelligent Technology Innovation Lab (ITIL) Group, Institute for Sustainable Industries and Liveable Cities, Victoria University, Footscray, Australia
| | - Sahar Shariflou
- Vision Science Group, Graduate School of Health, University of Technology Sydney, Ultimo, Australia
| | - Ashish Agar
- Ophthalmology Department, Prince of Wales Hospital, Sydney, NSW, Australia
- Department of Ophthalmology, University of New South Wales, Sydney, NSW, Australia
- Marsden Eye Specialists, Sydney, NSW, Australia
| | - S Mojtaba Golzan
- Vision Science Group, Graduate School of Health, University of Technology Sydney, Ultimo, Australia
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Abstract
Theoretical models of retinal hemodynamics showed the modulation of retinal pulsatile patterns (RPPs) by heart rate (HR), yet in-vivo validation and scientific merit of this biological process is lacking. Such evidence is critical for result interpretation, study design, and (patho-)physiological modeling of human biology spanning applications in various medical specialties. In retinal hemodynamic video-recordings, we characterize the morphology of RPPs and assess the impact of modulation by HR or other variables. Principal component analysis isolated two RPPs, i.e., spontaneous venous pulsation (SVP) and optic cup pulsation (OCP). Heart rate modulated SVP and OCP morphology (pFDR < 0.05); age modulated SVP morphology (pFDR < 0.05). In addition, age and HR demonstrated the effect on between-group differences. This knowledge greatly affects future study designs, analyses of between-group differences in RPPs, and biophysical models investigating relationships between RPPs, intracranial, intraocular pressures, and cardiovascular physiology. Video-recordings of retinal pulsatile patterns in human eye are modulated by heart rate and age. This invivo evidence fundamentally impacts modeling of retinal function and clinical design in wide range of medical specialties.
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Labounkova I, Labounek R, Nestrasil I, Odstrcilik J, Tornow RP, Kolar R. Blind Source Separation of Retinal Pulsatile Patterns in Optic Nerve Head Video-Recordings. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:852-864. [PMID: 33232226 DOI: 10.1109/tmi.2020.3039917] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Dynamic optical imaging of retinal hemodynamics is a rapidly evolving technique in vision and eye-disease research. Video-recording, which may be readily accessible and affordable, captures several distinct functional phenomena such as the spontaneous venous pulsations (SVP) of central vein or local arterial blood supply etc. These phenomena display specific dynamic patterns that have been detected using manual or semi-automated methods. We propose a pioneering concept in retina video-imaging using blind source separation (BSS) serving as an automated localizer of distinct areas with temporally synchronized hemodynamics. The feasibility of BSS techniques (such as spatial principal component analysis and spatial independent component analysis) and K-means based post-processing method were successfully tested on the monocular and binocular video-ophthalmoscopic (VO) recordings of optic nerve head (ONH) in healthy subjects. BSSs automatically detected three spatially distinct reproducible areas, i.e. SVP, optic cup pulsations (OCP) that included areas of larger vessels in the nasal part of ONH, and "other" pulsations (OP). The K-means post-processing reduced a spike noise from the patterns' dynamics while high linear dependence between the non-filtered and post-processed signals was preserved. Although the dynamics of all patterns were heart rate related, the morphology analysis demonstrated significant phase shifts between SVP and OCP, and between SVP and OP. In addition, we detected low frequency oscillations that may represent respiratory-induced effects in time-courses of the VO recordings.
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