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Gaire BP, Koronyo Y, Fuchs DT, Shi H, Rentsendorj A, Danziger R, Vit JP, Mirzaei N, Doustar J, Sheyn J, Hampel H, Vergallo A, Davis MR, Jallow O, Baldacci F, Verdooner SR, Barron E, Mirzaei M, Gupta VK, Graham SL, Tayebi M, Carare RO, Sadun AA, Miller CA, Dumitrascu OM, Lahiri S, Gao L, Black KL, Koronyo-Hamaoui M. Alzheimer's disease pathophysiology in the Retina. Prog Retin Eye Res 2024; 101:101273. [PMID: 38759947 DOI: 10.1016/j.preteyeres.2024.101273] [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: 02/11/2023] [Revised: 04/23/2024] [Accepted: 05/10/2024] [Indexed: 05/19/2024]
Abstract
The retina is an emerging CNS target for potential noninvasive diagnosis and tracking of Alzheimer's disease (AD). Studies have identified the pathological hallmarks of AD, including amyloid β-protein (Aβ) deposits and abnormal tau protein isoforms, in the retinas of AD patients and animal models. Moreover, structural and functional vascular abnormalities such as reduced blood flow, vascular Aβ deposition, and blood-retinal barrier damage, along with inflammation and neurodegeneration, have been described in retinas of patients with mild cognitive impairment and AD dementia. Histological, biochemical, and clinical studies have demonstrated that the nature and severity of AD pathologies in the retina and brain correspond. Proteomics analysis revealed a similar pattern of dysregulated proteins and biological pathways in the retina and brain of AD patients, with enhanced inflammatory and neurodegenerative processes, impaired oxidative-phosphorylation, and mitochondrial dysfunction. Notably, investigational imaging technologies can now detect AD-specific amyloid deposits, as well as vasculopathy and neurodegeneration in the retina of living AD patients, suggesting alterations at different disease stages and links to brain pathology. Current and exploratory ophthalmic imaging modalities, such as optical coherence tomography (OCT), OCT-angiography, confocal scanning laser ophthalmoscopy, and hyperspectral imaging, may offer promise in the clinical assessment of AD. However, further research is needed to deepen our understanding of AD's impact on the retina and its progression. To advance this field, future studies require replication in larger and diverse cohorts with confirmed AD biomarkers and standardized retinal imaging techniques. This will validate potential retinal biomarkers for AD, aiding in early screening and monitoring.
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Affiliation(s)
- Bhakta Prasad Gaire
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Yosef Koronyo
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Dieu-Trang Fuchs
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Haoshen Shi
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Altan Rentsendorj
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ron Danziger
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jean-Philippe Vit
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Nazanin Mirzaei
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jonah Doustar
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Julia Sheyn
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Harald Hampel
- Sorbonne University, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Paris, France
| | - Andrea Vergallo
- Sorbonne University, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Paris, France
| | - Miyah R Davis
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ousman Jallow
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Filippo Baldacci
- Sorbonne University, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Paris, France; Department of Clinical and Experimental Medicine, Neurology Unit, University of Pisa, Pisa, Italy
| | | | - Ernesto Barron
- Department of Ophthalmology, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA; Doheny Eye Institute, Los Angeles, CA, USA
| | - Mehdi Mirzaei
- Department of Clinical Medicine, Health and Human Sciences, Macquarie Medical School, Macquarie University, Sydney, NSW, Australia
| | - Vivek K Gupta
- Department of Clinical Medicine, Health and Human Sciences, Macquarie Medical School, Macquarie University, Sydney, NSW, Australia
| | - Stuart L Graham
- Department of Clinical Medicine, Health and Human Sciences, Macquarie Medical School, Macquarie University, Sydney, NSW, Australia; Department of Clinical Medicine, Macquarie University, Sydney, NSW, Australia
| | - Mourad Tayebi
- School of Medicine, Western Sydney University, Campbelltown, NSW, Australia
| | - Roxana O Carare
- Department of Clinical Neuroanatomy, University of Southampton, Southampton, UK
| | - Alfredo A Sadun
- Department of Ophthalmology, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA; Doheny Eye Institute, Los Angeles, CA, USA
| | - Carol A Miller
- Department of Pathology Program in Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | | | - Shouri Lahiri
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Liang Gao
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA
| | - Keith L Black
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Maya Koronyo-Hamaoui
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Biomedical Sciences, Division of Applied Cell Biology and Physiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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2
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Zhao Y, Sun B, Fu X, Zuo Z, Qin H, Yao K. YAP in development and disease: Navigating the regulatory landscape from retina to brain. Biomed Pharmacother 2024; 175:116703. [PMID: 38713948 DOI: 10.1016/j.biopha.2024.116703] [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: 01/17/2024] [Revised: 04/30/2024] [Accepted: 05/01/2024] [Indexed: 05/09/2024] Open
Abstract
The distinctive role of Yes-associated protein (YAP) in the nervous system has attracted widespread attention. This comprehensive review strategically uses the retina as a vantage point, embarking on an extensive exploration of YAP's multifaceted impact from the retina to the brain in development and pathology. Initially, we explore the crucial roles of YAP in embryonic and cerebral development. Our focus then shifts to retinal development, examining in detail YAP's regulatory influence on the development of retinal pigment epithelium (RPE) and retinal progenitor cells (RPCs), and its significant effects on the hierarchical structure and functionality of the retina. We also investigate the essential contributions of YAP in maintaining retinal homeostasis, highlighting its precise regulation of retinal cell proliferation and survival. In terms of retinal-related diseases, we explore the epigenetic connections and pathophysiological regulation of YAP in diabetic retinopathy (DR), glaucoma, and proliferative vitreoretinopathy (PVR). Lastly, we broaden our exploration from the retina to the brain, emphasizing the research paradigm of "retina: a window to the brain." Special focus is given to the emerging studies on YAP in brain disorders such as Alzheimer's disease (AD) and Parkinson's disease (PD), underlining its potential therapeutic value in neurodegenerative disorders and neuroinflammation.
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Affiliation(s)
- Yaqin Zhao
- Institute of Visual Neuroscience and Stem Cell Engineering, Wuhan University of Science and Technology, Wuhan 430065, China; College of Life Sciences and Health, Wuhan University of Science and Technology, Wuhan 430065, China
| | - Bin Sun
- Institute of Visual Neuroscience and Stem Cell Engineering, Wuhan University of Science and Technology, Wuhan 430065, China; College of Life Sciences and Health, Wuhan University of Science and Technology, Wuhan 430065, China
| | - Xuefei Fu
- Institute of Visual Neuroscience and Stem Cell Engineering, Wuhan University of Science and Technology, Wuhan 430065, China; College of Life Sciences and Health, Wuhan University of Science and Technology, Wuhan 430065, China
| | - Zhuan Zuo
- Institute of Visual Neuroscience and Stem Cell Engineering, Wuhan University of Science and Technology, Wuhan 430065, China; College of Life Sciences and Health, Wuhan University of Science and Technology, Wuhan 430065, China
| | - Huan Qin
- Institute of Visual Neuroscience and Stem Cell Engineering, Wuhan University of Science and Technology, Wuhan 430065, China; College of Life Sciences and Health, Wuhan University of Science and Technology, Wuhan 430065, China.
| | - Kai Yao
- Institute of Visual Neuroscience and Stem Cell Engineering, Wuhan University of Science and Technology, Wuhan 430065, China; College of Life Sciences and Health, Wuhan University of Science and Technology, Wuhan 430065, China.
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3
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Hasan MM, Phu J, Sowmya A, Meijering E, Kalloniatis M. Artificial intelligence in the diagnosis of glaucoma and neurodegenerative diseases. Clin Exp Optom 2024; 107:130-146. [PMID: 37674264 DOI: 10.1080/08164622.2023.2235346] [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: 02/23/2023] [Accepted: 07/07/2023] [Indexed: 09/08/2023] Open
Abstract
Artificial Intelligence is a rapidly expanding field within computer science that encompasses the emulation of human intelligence by machines. Machine learning and deep learning - two primary data-driven pattern analysis approaches under the umbrella of artificial intelligence - has created considerable interest in the last few decades. The evolution of technology has resulted in a substantial amount of artificial intelligence research on ophthalmic and neurodegenerative disease diagnosis using retinal images. Various artificial intelligence-based techniques have been used for diagnostic purposes, including traditional machine learning, deep learning, and their combinations. Presented here is a review of the literature covering the last 10 years on this topic, discussing the use of artificial intelligence in analysing data from different modalities and their combinations for the diagnosis of glaucoma and neurodegenerative diseases. The performance of published artificial intelligence methods varies due to several factors, yet the results suggest that such methods can potentially facilitate clinical diagnosis. Generally, the accuracy of artificial intelligence-assisted diagnosis ranges from 67-98%, and the area under the sensitivity-specificity curve (AUC) ranges from 0.71-0.98, which outperforms typical human performance of 71.5% accuracy and 0.86 area under the curve. This indicates that artificial intelligence-based tools can provide clinicians with useful information that would assist in providing improved diagnosis. The review suggests that there is room for improvement of existing artificial intelligence-based models using retinal imaging modalities before they are incorporated into clinical practice.
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Affiliation(s)
- Md Mahmudul Hasan
- School of Computer Science and Engineering, University of New South Wales, Kensington, New South Wales, Australia
| | - Jack Phu
- School of Optometry and Vision Science, University of New South Wales, Kensington, Australia
- Centre for Eye Health, University of New South Wales, Sydney, New South Wales, Australia
- School of Medicine (Optometry), Deakin University, Waurn Ponds, Victoria, Australia
| | - Arcot Sowmya
- School of Computer Science and Engineering, University of New South Wales, Kensington, New South Wales, Australia
| | - Erik Meijering
- School of Computer Science and Engineering, University of New South Wales, Kensington, New South Wales, Australia
| | - Michael Kalloniatis
- School of Optometry and Vision Science, University of New South Wales, Kensington, Australia
- School of Medicine (Optometry), Deakin University, Waurn Ponds, Victoria, Australia
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Ashayeri H, Jafarizadeh A, Yousefi M, Farhadi F, Javadzadeh A. Retinal imaging and Alzheimer's disease: a future powered by Artificial Intelligence. Graefes Arch Clin Exp Ophthalmol 2024:10.1007/s00417-024-06394-0. [PMID: 38358524 DOI: 10.1007/s00417-024-06394-0] [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: 08/03/2023] [Revised: 01/22/2024] [Accepted: 01/30/2024] [Indexed: 02/16/2024] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative condition that primarily affects brain tissue. Because the retina and brain share the same embryonic origin, visual deficits have been reported in AD patients. Artificial Intelligence (AI) has recently received a lot of attention due to its immense power to process and detect image hallmarks and make clinical decisions (like diagnosis) based on images. Since retinal changes have been reported in AD patients, AI is being proposed to process images to predict, diagnose, and prognosis AD. As a result, the purpose of this review was to discuss the use of AI trained on retinal images of AD patients. According to previous research, AD patients experience retinal thickness and retinal vessel density changes, which can occasionally occur before the onset of the disease's clinical symptoms. AI and machine vision can detect and use these changes in the domains of disease prediction, diagnosis, and prognosis. As a result, not only have unique algorithms been developed for this condition, but also databases such as the Retinal OCTA Segmentation dataset (ROSE) have been constructed for this purpose. The achievement of high accuracy, sensitivity, and specificity in the classification of retinal images between AD and healthy groups is one of the major breakthroughs in using AI based on retinal images for AD. It is fascinating that researchers could pinpoint individuals with a positive family history of AD based on the properties of their eyes. In conclusion, the growing application of AI in medicine promises its future position in processing different aspects of patients with AD, but we need cohort studies to determine whether it can help to follow up with healthy persons at risk of AD for a quicker diagnosis or assess the prognosis of patients with AD.
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Affiliation(s)
- Hamidreza Ashayeri
- Neuroscience Research Center (NSRC), Tabriz University of Medical Sciences, Tabriz, Iran
| | - Ali Jafarizadeh
- Nikookari Eye Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Milad Yousefi
- Faculty of Mathematics, Statistics and Computer Sciences, University of Tabriz, Tabriz, Iran
| | - Fereshteh Farhadi
- Nikookari Eye Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Alireza Javadzadeh
- Department of Ophthalmology, Nikookari Eye Center, Tabriz University of Medical Sciences, Tabriz, Iran.
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Shi XH, Ju L, Dong L, Zhang RH, Shao L, Yan YN, Wang YX, Fu XF, Chen YZ, Ge ZY, Wei WB. Deep Learning Models for the Screening of Cognitive Impairment Using Multimodal Fundus Images. Ophthalmol Retina 2024:S2468-6530(24)00045-9. [PMID: 38280426 DOI: 10.1016/j.oret.2024.01.019] [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: 07/31/2023] [Revised: 01/03/2024] [Accepted: 01/19/2024] [Indexed: 01/29/2024]
Abstract
OBJECTIVE We aimed to develop a deep learning system capable of identifying subjects with cognitive impairment quickly and easily based on multimodal ocular images. DESIGN Cross sectional study. SUBJECTS Participants of Beijing Eye Study 2011 and patients attending Beijing Tongren Eye Center and Beijing Tongren Hospital Physical Examination Center. METHODS We trained and validated a deep learning algorithm to assess cognitive impairment using retrospectively collected data from the Beijing Eye Study 2011. Cognitive impairment was defined as a Mini-Mental State Examination score < 24. Based on fundus photographs and OCT images, we developed 5 models based on the following sets of images: macula-centered fundus photographs, optic disc-centered fundus photographs, fundus photographs of both fields, OCT images, and fundus photographs of both fields with OCT (multimodal). The performance of the models was evaluated and compared in an external validation data set, which was collected from patients attending Beijing Tongren Eye Center and Beijing Tongren Hospital Physical Examination Center. MAIN OUTCOME MEASURES Area under the curve (AUC). RESULTS A total of 9424 retinal photographs and 4712 OCT images were used to develop the model. The external validation sets from each center included 1180 fundus photographs and 590 OCT images. Model comparison revealed that the multimodal performed best, achieving an AUC of 0.820 in the internal validation set, 0.786 in external validation set 1, and 0.784 in external validation set 2. We evaluated the performance of the multi-model in different sexes and different age groups; there were no significant differences. The heatmap analysis showed that signals around the optic disc in fundus photographs and the retina and choroid around the macular and optic disc regions in OCT images were used by the multimodal to identify participants with cognitive impairment. CONCLUSIONS Fundus photographs and OCT can provide valuable information on cognitive function. Multimodal models provide richer information compared with single-mode models. Deep learning algorithms based on multimodal retinal images may be capable of screening cognitive impairment. This technique has potential value for broader implementation in community-based screening or clinic settings. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Xu Han Shi
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Lie Ju
- Beijing Airdoc Technology Co., Ltd., Beijing, China; Augmented Intelligence and Multimodal Analytics (AIM) for Health Lab, Faculty of Information Technology, Monash University, Clayton, Australia; Faculty of Engineering, Monash University, Clayton, Australia
| | - Li Dong
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Rui Heng Zhang
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Lei Shao
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Yan Ni Yan
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Ya Xing Wang
- Beijing Ophthalmology and Visual Science Key Laboratory, Beijing Tongren Eye Center, Beijing Tongren Hospital, Beijing Institute of Ophthalmology, Capital Medical University, Beijing, China
| | - Xue Fei Fu
- Beijing Airdoc Technology Co., Ltd., Beijing, China
| | | | - Zong Yuan Ge
- Beijing Airdoc Technology Co., Ltd., Beijing, China; Augmented Intelligence and Multimodal Analytics (AIM) for Health Lab, Faculty of Information Technology, Monash University, Clayton, Australia; Faculty of Engineering, Monash University, Clayton, Australia
| | - Wen Bin Wei
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
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Garcia-Martin E, Jimeno-Huete D, Dongil-Moreno FJ, Boquete L, Sánchez-Morla EM, Miguel-Jiménez JM, López-Dorado A, Vilades E, Fuertes MI, Pueyo A, Ortiz del Castillo M. Differential Study of Retinal Thicknesses in the Eyes of Alzheimer's Patients, Multiple Sclerosis Patients and Healthy Subjects. Biomedicines 2023; 11:3126. [PMID: 38137347 PMCID: PMC10740772 DOI: 10.3390/biomedicines11123126] [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: 09/30/2023] [Revised: 11/18/2023] [Accepted: 11/22/2023] [Indexed: 12/24/2023] Open
Abstract
Multiple sclerosis (MS) and Alzheimer's disease (AD) cause retinal thinning that is detectable in vivo using optical coherence tomography (OCT). To date, no papers have compared the two diseases in terms of the structural differences they produce in the retina. The purpose of this study is to analyse and compare the neuroretinal structure in MS patients, AD patients and healthy subjects using OCT. Spectral domain OCT was performed on 21 AD patients, 33 MS patients and 19 control subjects using the Posterior Pole protocol. The area under the receiver operating characteristic (AUROC) curve was used to analyse the differences between the cohorts in nine regions of the retinal nerve fibre layer (RNFL), ganglion cell layer (GCL), inner plexiform layer (IPL) and outer nuclear layer (ONL). The main differences between MS and AD are found in the ONL, in practically all the regions analysed (AUROCFOVEAL = 0.80, AUROCPARAFOVEAL = 0.85, AUROCPERIFOVEAL = 0.80, AUROC_PMB = 0.77, AUROCPARAMACULAR = 0.85, AUROCINFERO_NASAL = 0.75, AUROCINFERO_TEMPORAL = 0.83), and in the paramacular zone (AUROCPARAMACULAR = 0.75) and infero-temporal quadrant (AUROCINFERO_TEMPORAL = 0.80) of the GCL. In conclusion, our findings suggest that OCT data analysis could facilitate the differential diagnosis of MS and AD.
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Affiliation(s)
- Elena Garcia-Martin
- Department of Ophthalmology, Miguel Servet University Hospital, 50009 Zaragoza, Spain; (E.V.); (M.I.F.); (A.P.)
- Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), Aragon Institute for Health Research (IIS Aragon), Biotech Vision SLP (Spin-Off Company), University of Zaragoza, 50009 Zaragoza, Spain
| | - Daniel Jimeno-Huete
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, 28871 Alcalá de Henares, Spain; (D.J.-H.); (F.J.D.-M.); (J.M.M.-J.); (A.L.-D.)
| | - Francisco J. Dongil-Moreno
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, 28871 Alcalá de Henares, Spain; (D.J.-H.); (F.J.D.-M.); (J.M.M.-J.); (A.L.-D.)
| | - Luciano Boquete
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, 28871 Alcalá de Henares, Spain; (D.J.-H.); (F.J.D.-M.); (J.M.M.-J.); (A.L.-D.)
| | - Eva M. Sánchez-Morla
- Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, 28007 Madrid, Spain
- School of Medicine, Universidad Complutense, 28040 Madrid, Spain
| | - Juan M. Miguel-Jiménez
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, 28871 Alcalá de Henares, Spain; (D.J.-H.); (F.J.D.-M.); (J.M.M.-J.); (A.L.-D.)
| | - Almudena López-Dorado
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, 28871 Alcalá de Henares, Spain; (D.J.-H.); (F.J.D.-M.); (J.M.M.-J.); (A.L.-D.)
| | - Elisa Vilades
- Department of Ophthalmology, Miguel Servet University Hospital, 50009 Zaragoza, Spain; (E.V.); (M.I.F.); (A.P.)
- Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), Aragon Institute for Health Research (IIS Aragon), Biotech Vision SLP (Spin-Off Company), University of Zaragoza, 50009 Zaragoza, Spain
| | - Maria I. Fuertes
- Department of Ophthalmology, Miguel Servet University Hospital, 50009 Zaragoza, Spain; (E.V.); (M.I.F.); (A.P.)
- Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), Aragon Institute for Health Research (IIS Aragon), Biotech Vision SLP (Spin-Off Company), University of Zaragoza, 50009 Zaragoza, Spain
| | - Ana Pueyo
- Department of Ophthalmology, Miguel Servet University Hospital, 50009 Zaragoza, Spain; (E.V.); (M.I.F.); (A.P.)
- Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), Aragon Institute for Health Research (IIS Aragon), Biotech Vision SLP (Spin-Off Company), University of Zaragoza, 50009 Zaragoza, Spain
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Ibrahim Y, Xie J, Macerollo A, Sardone R, Shen Y, Romano V, Zheng Y. A Systematic Review on Retinal Biomarkers to Diagnose Dementia from OCT/OCTA Images. J Alzheimers Dis Rep 2023; 7:1201-1235. [PMID: 38025800 PMCID: PMC10657718 DOI: 10.3233/adr-230042] [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: 06/06/2023] [Accepted: 09/22/2023] [Indexed: 12/01/2023] Open
Abstract
Background Traditional methods for diagnosing dementia are costly, time-consuming, and somewhat invasive. Since the retina shares significant anatomical similarities with the brain, retinal abnormalities detected via optical coherence tomography (OCT) and OCT angiography (OCTA) have been studied as a potential non-invasive diagnostic tool for neurodegenerative disorders; however, the most effective retinal changes remain a mystery to be unraveled in this review. Objective This study aims to explore the relationship between retinal abnormalities in OCT/OCTA images and cognitive decline as well as evaluating biomarkers' effectiveness in detecting neurodegenerative diseases. Methods A systematic search was conducted on PubMed, Web of Science, and Scopus until December 2022, resulted in 64 papers using agreed search keywords, and inclusion/exclusion criteria. Results The superior peripapillary retinal nerve fiber layer (pRNFL) is a trustworthy biomarker to identify most Alzheimer's disease (AD) cases; however, it is inefficient when dealing with mild AD and mild cognitive impairment (MCI). The global pRNFL (pRNFL-G) is another reliable biomarker to discriminate frontotemporal dementia from mild AD and healthy controls (HCs), moderate AD and MCI from HCs, as well as identifing pathological Aβ42/tau in cognitively healthy individuals. Conversely, pRNFL-G fails to realize mild AD and the progression of AD. The average pRNFL thickness variation is considered a viable biomarker to monitor the progression of AD. Finally, the superior and average pRNFL thicknesses are considered consistent for advanced AD but not for early/mild AD. Conclusions Retinal changes may indicate dementia, but further research is needed to confirm the most effective biomarkers for early and mild AD.
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Affiliation(s)
- Yehia Ibrahim
- Department of Eye and Vision Sciences, University of Liverpool, Liverpool, UK
| | - Jianyang Xie
- Department of Eye and Vision Sciences, University of Liverpool, Liverpool, UK
| | - Antonella Macerollo
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
- Department of Neurology, The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Rodolfo Sardone
- Department of Eye and Vision Sciences, University of Liverpool, Liverpool, UK
- Statistics and Epidemiology Unit, Local Healthcare Authority of Taranto, Taranto, Italy
| | - Yaochun Shen
- Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, UK
| | - Vito Romano
- Department of Eye and Vision Sciences, University of Liverpool, Liverpool, UK
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
| | - Yalin Zheng
- Department of Eye and Vision Sciences, University of Liverpool, Liverpool, UK
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart and Chest Hospital, Liverpool, UK
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Yi F, Yang H, Chen D, Qin Y, Han H, Cui J, Bai W, Ma Y, Zhang R, Yu H. XGBoost-SHAP-based interpretable diagnostic framework for alzheimer's disease. BMC Med Inform Decis Mak 2023; 23:137. [PMID: 37491248 PMCID: PMC10369804 DOI: 10.1186/s12911-023-02238-9] [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: 10/09/2022] [Accepted: 07/13/2023] [Indexed: 07/27/2023] Open
Abstract
BACKGROUND Due to the class imbalance issue faced when Alzheimer's disease (AD) develops from normal cognition (NC) to mild cognitive impairment (MCI), present clinical practice is met with challenges regarding the auxiliary diagnosis of AD using machine learning (ML). This leads to low diagnosis performance. We aimed to construct an interpretable framework, extreme gradient boosting-Shapley additive explanations (XGBoost-SHAP), to handle the imbalance among different AD progression statuses at the algorithmic level. We also sought to achieve multiclassification of NC, MCI, and AD. METHODS We obtained patient data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including clinical information, neuropsychological test results, neuroimaging-derived biomarkers, and APOE-ε4 gene statuses. First, three feature selection algorithms were applied, and they were then included in the XGBoost algorithm. Due to the imbalance among the three classes, we changed the sample weight distribution to achieve multiclassification of NC, MCI, and AD. Then, the SHAP method was linked to XGBoost to form an interpretable framework. This framework utilized attribution ideas that quantified the impacts of model predictions into numerical values and analysed them based on their directions and sizes. Subsequently, the top 10 features (optimal subset) were used to simplify the clinical decision-making process, and their performance was compared with that of a random forest (RF), Bagging, AdaBoost, and a naive Bayes (NB) classifier. Finally, the National Alzheimer's Coordinating Center (NACC) dataset was employed to assess the impact path consistency of the features within the optimal subset. RESULTS Compared to the RF, Bagging, AdaBoost, NB and XGBoost (unweighted), the interpretable framework had higher classification performance with accuracy improvements of 0.74%, 0.74%, 1.46%, 13.18%, and 0.83%, respectively. The framework achieved high sensitivity (81.21%/74.85%), specificity (92.18%/89.86%), accuracy (87.57%/80.52%), area under the receiver operating characteristic curve (AUC) (0.91/0.88), positive clinical utility index (0.71/0.56), and negative clinical utility index (0.75/0.68) on the ADNI and NACC datasets, respectively. In the ADNI dataset, the top 10 features were found to have varying associations with the risk of AD onset based on their SHAP values. Specifically, the higher SHAP values of CDRSB, ADAS13, ADAS11, ventricle volume, ADASQ4, and FAQ were associated with higher risks of AD onset. Conversely, the higher SHAP values of LDELTOTAL, mPACCdigit, RAVLT_immediate, and MMSE were associated with lower risks of AD onset. Similar results were found for the NACC dataset. CONCLUSIONS The proposed interpretable framework contributes to achieving excellent performance in imbalanced AD multiclassification tasks and provides scientific guidance (optimal subset) for clinical decision-making, thereby facilitating disease management and offering new research ideas for optimizing AD prevention and treatment programs.
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Affiliation(s)
- Fuliang Yi
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, 030001 P.R. China
| | - Hui Yang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, 030001 P.R. China
| | - Durong Chen
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, 030001 P.R. China
| | - Yao Qin
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, 030001 P.R. China
| | - Hongjuan Han
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, 030001 P.R. China
| | - Jing Cui
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, 030001 P.R. China
| | - Wenlin Bai
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, 030001 P.R. China
| | - Yifei Ma
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, 030001 P.R. China
| | - Rong Zhang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, 030001 P.R. China
| | - Hongmei Yu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, 030001 P.R. China
- Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan, China
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Li H, Cao J, Grzybowski A, Jin K, Lou L, Ye J. Diagnosing Systemic Disorders with AI Algorithms Based on Ocular Images. Healthcare (Basel) 2023; 11:1739. [PMID: 37372857 DOI: 10.3390/healthcare11121739] [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: 04/15/2023] [Revised: 06/07/2023] [Accepted: 06/08/2023] [Indexed: 06/29/2023] Open
Abstract
The advent of artificial intelligence (AI), especially the state-of-the-art deep learning frameworks, has begun a silent revolution in all medical subfields, including ophthalmology. Due to their specific microvascular and neural structures, the eyes are anatomically associated with the rest of the body. Hence, ocular image-based AI technology may be a useful alternative or additional screening strategy for systemic diseases, especially where resources are scarce. This review summarizes the current applications of AI related to the prediction of systemic diseases from multimodal ocular images, including cardiovascular diseases, dementia, chronic kidney diseases, and anemia. Finally, we also discuss the current predicaments and future directions of these applications.
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Affiliation(s)
- Huimin Li
- Eye Center, The Second Affiliated Hospital School of Medicine Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou 310009, China
| | - Jing Cao
- Eye Center, The Second Affiliated Hospital School of Medicine Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou 310009, China
| | - Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, 60-836 Poznan, Poland
| | - Kai Jin
- Eye Center, The Second Affiliated Hospital School of Medicine Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou 310009, China
| | - Lixia Lou
- Eye Center, The Second Affiliated Hospital School of Medicine Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou 310009, China
| | - Juan Ye
- Eye Center, The Second Affiliated Hospital School of Medicine Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou 310009, China
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10
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Hussain A, Sheikh Z, Subramanian M. The Eye as a Diagnostic Tool for Alzheimer’s Disease. Life (Basel) 2023; 13:life13030726. [PMID: 36983883 PMCID: PMC10052959 DOI: 10.3390/life13030726] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 02/23/2023] [Accepted: 03/04/2023] [Indexed: 03/10/2023] Open
Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder impacting cognition, function, and behavior in the elderly population. While there are currently no disease-modifying agents capable of curing AD, early diagnosis and management in the preclinical stage can significantly improve patient morbidity and life expectancy. Currently, the diagnosis of Alzheimer’s disease is a clinical one, often supplemented by invasive and expensive biomarker testing. Over the last decade, significant advancements have been made in our understanding of AD and the role of ocular tissue as a potential biomarker. Ocular biomarkers hold the potential to provide noninvasive and easily accessible diagnostic and monitoring capabilities. This review summarizes current research for detecting biomarkers of Alzheimer’s disease in ocular tissue.
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11
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Hao X, Zhang W, Jiao B, Yang Q, Zhang X, Chen R, Wang X, Xiao X, Zhu Y, Liao W, Wang D, Shen L. Correlation between retinal structure and brain multimodal magnetic resonance imaging in patients with Alzheimer's disease. Front Aging Neurosci 2023; 15:1088829. [PMID: 36909943 PMCID: PMC9992546 DOI: 10.3389/fnagi.2023.1088829] [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: 11/03/2022] [Accepted: 02/06/2023] [Indexed: 02/24/2023] Open
Abstract
Background The retina imaging and brain magnetic resonance imaging (MRI) can both reflect early changes in Alzheimer's disease (AD) and may serve as potential biomarker for early diagnosis, but their correlation and the internal mechanism of retinal structural changes remain unclear. This study aimed to explore the possible correlation between retinal structure and visual pathway, brain structure, intrinsic activity changes in AD patients, as well as to build a classification model to identify AD patients. Methods In the study, 49 AD patients and 48 healthy controls (HCs) were enrolled. Retinal images were obtained by optical coherence tomography (OCT). Multimodal MRI sequences of all subjects were collected. Spearman correlation analysis and multiple linear regression models were used to assess the correlation between OCT parameters and multimodal MRI findings. The diagnostic value of combination of retinal imaging and brain multimodal MRI was assessed by performing a receiver operating characteristic (ROC) curve. Results Compared with HCs, retinal thickness and multimodal MRI findings of AD patients were significantly altered (p < 0.05). Significant correlations were presented between the fractional anisotropy (FA) value of optic tract and mean retinal thickness, macular volume, macular ganglion cell layer (GCL) thickness, inner plexiform layer (IPL) thickness in AD patients (p < 0.01). The fractional amplitude of low frequency fluctuations (fALFF) value of primary visual cortex (V1) was correlated with temporal quadrant peripapillary retinal nerve fiber layer (pRNFL) thickness (p < 0.05). The model combining thickness of GCL and temporal quadrant pRNFL, volume of hippocampus and lateral geniculate nucleus, and age showed the best performance to identify AD patients [area under the curve (AUC) = 0.936, sensitivity = 89.1%, specificity = 87.0%]. Conclusion Our study demonstrated that retinal structure change was related to the loss of integrity of white matter fiber tracts in the visual pathway and the decreased LGN volume and functional metabolism of V1 in AD patients. Trans-synaptic axonal retrograde lesions may be the underlying mechanism. Combining retinal imaging and multimodal MRI may provide new insight into the mechanism of retinal structural changes in AD and may serve as new target for early auxiliary diagnosis of AD.
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Affiliation(s)
- Xiaoli Hao
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, China
| | - Weiwei Zhang
- Department of Radiology, Xiangya Hospital of Central South University, Changsha, China
| | - Bin Jiao
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China.,Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China.,Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, China.,Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China
| | - Qijie Yang
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, China
| | - Xinyue Zhang
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, China
| | - Ruiting Chen
- Department of Radiology, Xiangya Hospital of Central South University, Changsha, China
| | - Xin Wang
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, China
| | - Xuewen Xiao
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, China
| | - Yuan Zhu
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, China
| | - Weihua Liao
- Department of Radiology, Xiangya Hospital of Central South University, Changsha, China
| | - Dongcui Wang
- Department of Radiology, Xiangya Hospital of Central South University, Changsha, China
| | - Lu Shen
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China.,Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China.,Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, China.,Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China.,Key Laboratory of Organ Injury, Aging and Regenerative Medicine of Hunan Province, Changsha, China
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Shah J, Siddiquee MMR, Krell-Roesch J, Syrjanen JA, Kremers WK, Vassilaki M, Forzani E, Wu T, Geda YE. Neuropsychiatric Symptoms and Commonly Used Biomarkers of Alzheimer's Disease: A Literature Review from a Machine Learning Perspective. J Alzheimers Dis 2023; 92:1131-1146. [PMID: 36872783 PMCID: PMC11102734 DOI: 10.3233/jad-221261] [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] [Indexed: 03/06/2023]
Abstract
There is a growing interest in the application of machine learning (ML) in Alzheimer's disease (AD) research. However, neuropsychiatric symptoms (NPS), frequent in subjects with AD, mild cognitive impairment (MCI), and other related dementias have not been analyzed sufficiently using ML methods. To portray the landscape and potential of ML research in AD and NPS studies, we present a comprehensive literature review of existing ML approaches and commonly studied AD biomarkers. We conducted PubMed searches with keywords related to NPS, AD biomarkers, machine learning, and cognition. We included a total of 38 articles in this review after excluding some irrelevant studies from the search results and including 6 articles based on a snowball search from the bibliography of the relevant studies. We found a limited number of studies focused on NPS with or without AD biomarkers. In contrast, multiple statistical machine learning and deep learning methods have been used to build predictive diagnostic models using commonly known AD biomarkers. These mainly included multiple imaging biomarkers, cognitive scores, and various omics biomarkers. Deep learning approaches that combine these biomarkers or multi-modality datasets typically outperform single-modality datasets. We conclude ML may be leveraged to untangle the complex relationships of NPS and AD biomarkers with cognition. This may potentially help to predict the progression of MCI or dementia and develop more targeted early intervention approaches based on NPS.
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Affiliation(s)
- Jay Shah
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
- ASU-Mayo Center for Innovative Imaging, Tempe, AZ, USA
| | - Md Mahfuzur Rahman Siddiquee
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
- ASU-Mayo Center for Innovative Imaging, Tempe, AZ, USA
| | - Janina Krell-Roesch
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Jeremy A. Syrjanen
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Walter K. Kremers
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Maria Vassilaki
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Erica Forzani
- Biodesign Institute, Arizona State University, Tempe, AZ, USA
| | - Teresa Wu
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
- ASU-Mayo Center for Innovative Imaging, Tempe, AZ, USA
| | - Yonas E. Geda
- Department of Neurology and the Franke Global Neuroscience Education Center, Barrow Neurological Institute, Phoenix, AZ, USA
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13
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Liu X, Zeng Q, Luo X, Li K, Xu X, Hong L, Li J, Guan X, Xu X, Huang P, Zhang M. Effects of APOE ε2 allele on basal forebrain functional connectivity in mild cognitive impairment. CNS Neurosci Ther 2022; 29:597-608. [PMID: 36468416 PMCID: PMC9873529 DOI: 10.1111/cns.14038] [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: 06/09/2022] [Revised: 10/27/2022] [Accepted: 11/10/2022] [Indexed: 12/10/2022] Open
Abstract
BACKGROUND Basal forebrain cholinergic system (BFCS) dysfunction is associated with cognitive decline in Alzheimer's disease (AD) and mild cognitive impairment (MCI). Apolipoprotein E (APOE) ε2 is a protective genetic factor in AD and MCI, and cholinergic sprouting depends on APOE. OBJECTIVE We investigated the effect of the APOE ε2 allele on BFCS functional connectivity (FC) in cognitively normal (CN) subjects and MCI patients. METHOD We included 60 MCI patients with APOE ε3/ε3, 18 MCI patients with APOE ε2/ε3, 73 CN subjects with APOE ε3/ε3, and 36 CN subjects with APOE ε2/ε3 genotypes who had resting-state functional magnetic resonance imaging data from the Alzheimer's disease Neuroimaging Initiative. We used BFCS subregions (Ch1-3 and Ch4) as seeds and calculated the FC with other brain areas. Using a mixed-effect analysis, we explored the interaction effects of APOE ε2 allele × cognitive status on BFCS-FC. Furthermore, we examined the relationships between imaging metrics, cognitive abilities, and AD pathology markers, controlling for sex, age, and education as covariates. RESULTS An interaction effect on functional connectivity was found between the right Ch4 (RCh4) and left insula (p < 0.05, corrected), and between the RCh4 and left Rolandic operculum (p < 0.05, corrected). Among all subjects and APOE ε2 carriers, RCh4-left Insula FC was associated with early tau deposition. Furthermore, no correlation was found between imaging metrics and amyloid burden. Among all subjects and APOE ε2 carriers, FC metrics were associated with cognitive performance. CONCLUSION The APOE ε2 genotype may play a protective role during BFCS degeneration in MCI.
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Affiliation(s)
- Xiaocao Liu
- Department of RadiologyThe 2nd Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Qingze Zeng
- Department of RadiologyThe 2nd Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Xiao Luo
- Department of RadiologyThe 2nd Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Kaicheng Li
- Department of RadiologyThe 2nd Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Xiaopei Xu
- Department of RadiologyThe 2nd Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Luwei Hong
- Department of RadiologyThe 2nd Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Jixuan Li
- Department of RadiologyThe 2nd Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Xiaojun Guan
- Department of RadiologyThe 2nd Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Xiaojun Xu
- Department of RadiologyThe 2nd Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Peiyu Huang
- Department of RadiologyThe 2nd Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Min‐Ming Zhang
- Department of RadiologyThe 2nd Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
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