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Dong C, Hayashi S. Deep learning applications in vascular dementia using neuroimaging. Curr Opin Psychiatry 2024; 37:101-106. [PMID: 38226547 DOI: 10.1097/yco.0000000000000920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/17/2024]
Abstract
PURPOSE OF REVIEW Vascular dementia (VaD) is the second common cause of dementia after Alzheimer's disease, and deep learning has emerged as a critical tool in dementia research. The aim of this article is to highlight the current deep learning applications in VaD-related imaging biomarkers and diagnosis. RECENT FINDINGS The main deep learning technology applied in VaD using neuroimaging data is convolutional neural networks (CNN). CNN models have been widely used for lesion detection and segmentation, such as white matter hyperintensities (WMH), cerebral microbleeds (CMBs), perivascular spaces (PVS), lacunes, cortical superficial siderosis, and brain atrophy. Applications in VaD subtypes classification also showed excellent results. CNN-based deep learning models have potential for further diagnosis and prognosis of VaD. SUMMARY Deep learning neural networks with neuroimaging data in VaD research represent significant promise for advancing early diagnosis and treatment strategies. Ongoing research and collaboration between clinicians, data scientists, and neuroimaging experts are essential to address challenges and unlock the full potential of deep learning in VaD diagnosis and management.
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Affiliation(s)
- Chao Dong
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry & Mental Health, School of Clinical Medicine, UNSW Sydney, NSW, Australia
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Chen Y, Lu P, Wu S, Yang J, Liu W, Zhang Z, Xu Q. CD163-Mediated Small-Vessel Injury in Alzheimer's Disease: An Exploration from Neuroimaging to Transcriptomics. Int J Mol Sci 2024; 25:2293. [PMID: 38396970 PMCID: PMC10888773 DOI: 10.3390/ijms25042293] [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: 01/10/2024] [Revised: 02/10/2024] [Accepted: 02/11/2024] [Indexed: 02/25/2024] Open
Abstract
Patients with Alzheimer's disease (AD) often present with imaging features indicative of small-vessel injury, among which, white-matter hyperintensities (WMHs) are the most prevalent. However, the underlying mechanism of the association between AD and small-vessel injury is still obscure. The aim of this study is to investigate the mechanism of small-vessel injury in AD. Differential gene expression analyses were conducted to identify the genes related to WMHs separately in mild cognitive impairment (MCI) and cognitively normal (CN) subjects from the ADNI database. The WMH-related genes identified in patients with MCI were considered to be associated with small-vessel injury in early AD. Functional enrichment analyses and a protein-protein interaction (PPI) network were performed to explore the pathway and hub genes related to the mechanism of small-vessel injury in MCI. Subsequently, the Boruta algorithm and support vector machine recursive feature elimination (SVM-RFE) algorithm were performed to identify feature-selection genes. Finally, the mechanism of small-vessel injury was analyzed in MCI from the immunological perspectives; the relationship of feature-selection genes with various immune cells and neuroimaging indices were also explored. Furthermore, 5×FAD mice were used to demonstrate the genes related to small-vessel injury. The results of the logistic regression analyses suggested that WMHs significantly contributed to MCI, the early stage of AD. A total of 276 genes were determined as WMH-related genes in patients with MCI, while 203 WMH-related genes were obtained in CN patients. Among them, only 15 genes overlapped and were thus identified as the crosstalk genes. By employing the Boruta and SVM-RFE algorithms, CD163, ALDH3B1, MIR22HG, DTX2, FOLR2, ALDH2, and ZNF23 were recognized as the feature-selection genes linked to small-vessel injury in MCI. After considering the results from the PPI network, CD163 was finally determined as the critical WMH-related gene in MCI. The expression of CD163 was correlated with fractional anisotropy (FA) values in regions that are vulnerable to small-vessel injury in AD. The immunostaining and RT-qPCR results from the verifying experiments demonstrated that the indicators of small-vessel injury presented in the cortical tissue of 5×FAD mice and related to the upregulation of CD163 expression. CD163 may be the most pivotal candidates related to small-vessel injury in early AD.
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Affiliation(s)
- Yuewei Chen
- Health Management Center, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China; (Y.C.); (P.L.); (W.L.)
- Department of Neurology, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China
- Renji-UNSW CHeBA (Centre for Healthy Brain Ageing of University of New South Wales) Neurocognitive Center, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Peiwen Lu
- Health Management Center, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China; (Y.C.); (P.L.); (W.L.)
- Renji-UNSW CHeBA (Centre for Healthy Brain Ageing of University of New South Wales) Neurocognitive Center, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Shengju Wu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Jie Yang
- Health Management Center, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China; (Y.C.); (P.L.); (W.L.)
- Department of Neurology, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China
- Renji-UNSW CHeBA (Centre for Healthy Brain Ageing of University of New South Wales) Neurocognitive Center, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Wanwan Liu
- Health Management Center, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China; (Y.C.); (P.L.); (W.L.)
| | - Zhijun Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Qun Xu
- Health Management Center, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China; (Y.C.); (P.L.); (W.L.)
- Department of Neurology, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China
- Renji-UNSW CHeBA (Centre for Healthy Brain Ageing of University of New South Wales) Neurocognitive Center, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China
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Hakim A, Gallucci L, Sperber C, Rezny-Kasprzak B, Jäger E, Meinel T, Seiffge D, Goeldlin M, Westphalen F, Fischer U, Wiest R, Arnold M, Umarova R. The analysis of association between single features of small vessel disease and stroke outcome shows the independent impact of the number of microbleeds and presence of lacunes. Sci Rep 2024; 14:3402. [PMID: 38336856 PMCID: PMC10858245 DOI: 10.1038/s41598-024-53500-7] [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: 08/30/2023] [Accepted: 02/01/2024] [Indexed: 02/12/2024] Open
Abstract
The impact of small vessel disease (SVD) on stroke outcome was investigated either separately for its single features in isolation or for SVD sum score measuring a qualitative (binary) assessment of SVD-lesions. We aimed to investigate which SVD feature independently impacts the most on stroke outcome and to compare the continuous versus binary SVD assessment that reflects pronouncement and presence correspondingly. Patients with a first-ever anterior circulation ischemic stroke were retrospectively investigated. We performed an ordered logistic regression analysis to predict stroke outcome (mRS 3 months, 0-6) using age, stroke severity, and pre-stroke disability as baseline input variables and adding SVD-features (lacunes, microbleeds, enlarged perivascular spaces, white matter hyperintensities) assessed either continuously (model 1) or binary (model 2). The data of 873 patients (age 67.9 ± 15.4, NIHSS 24 h 4.1 ± 4.8) was analyzed. In model 1 with continuous SVD-features, the number of microbleeds was the only independent predictor of stroke outcome in addition to clinical parameters (OR 1.21; 95% CI 1.07-1.37). In model 2 with the binary SVD assessment, only the presence of lacunes independently improved the prediction of stroke outcome (OR 1.48, 1.1-1.99). In a post hoc analysis, both the continuous number of microbleeds and the presence of lacunes were independent significant predictors. Thus, the number of microbleeds evaluated continuously and the presence of lacunes are associated with stroke outcome independent from age, stroke severity, pre-stroke disability and other SVD-features. Whereas the presence of lacunes is adequately represented in SVD sum score, the microbleeds assessment might require another cutoff and/or gradual scoring, when prediction of stroke outcome is needed.
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Affiliation(s)
- Arsany Hakim
- University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Laura Gallucci
- Department of Neurology, Inselspital, University Hospital Bern, University of Bern, 3010, Bern, Switzerland
| | - Christoph Sperber
- Department of Neurology, Inselspital, University Hospital Bern, University of Bern, 3010, Bern, Switzerland
| | - Beata Rezny-Kasprzak
- University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Eugen Jäger
- University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Thomas Meinel
- Department of Neurology, Inselspital, University Hospital Bern, University of Bern, 3010, Bern, Switzerland
| | - David Seiffge
- Department of Neurology, Inselspital, University Hospital Bern, University of Bern, 3010, Bern, Switzerland
| | - Martina Goeldlin
- Department of Neurology, Inselspital, University Hospital Bern, University of Bern, 3010, Bern, Switzerland
| | - Franziska Westphalen
- Department of Neurology, Inselspital, University Hospital Bern, University of Bern, 3010, Bern, Switzerland
| | - Urs Fischer
- Department of Neurology, Inselspital, University Hospital Bern, University of Bern, 3010, Bern, Switzerland
- Department of Neurology, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Roland Wiest
- University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Marcel Arnold
- Department of Neurology, Inselspital, University Hospital Bern, University of Bern, 3010, Bern, Switzerland
| | - Roza Umarova
- Department of Neurology, Inselspital, University Hospital Bern, University of Bern, 3010, Bern, Switzerland.
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