Multivariate Deep Learning Classification of Alzheimer’s Disease Based on Hierarchical Partner Matching Independent Component Analysis

Qiao, Jianping; Lv, Yubao; Cao, Chongfeng; Wang, Zhishun; Li, Anning

doi:10.3389/fnagi.2018.00417

Cited by 34 publications

(19 citation statements)

References 83 publications

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“…Granger causality was carried out to analyze causal influences across the ICs identified by HPM-ICA method. The granger causality indices (GCIs) were computed using the time courses of the identified ICs (Wang et al, 2011;Qiao et al, 2018). The two-sample t-tests were finally used to detect group difference in GCIs between patients and normal controls in which age and sex were applied as covariates.…”

Section: The Functional Mri Image Analysismentioning

confidence: 99%

Functional and Structural Brain Alterations in Encephalitis With LGI1 Antibodies

Qiao

Wang

et al. 2020

Front. Neurosci.

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The purpose of this study was to examine the neural substrates and mechanisms that generate memory deficits, seizures and neuropsychiatric abnormalities in encephalitis with LGI1 antibodies using a data-driven, multimodal magnetic resonance imaging (MRI) approach. Methods: Functional MRI data were acquired from 14 anti-LGI1 encephalitis patients and 14 age and gender matched normal controls. Independent component analysis with hierarchical partner matching (HPM-ICA) was used to assess the whole-brain intrinsic functional connectivity. Granger causality (GC) was applied to investigate the effective connectivity among the brain regions that identified by HPM-ICA. Diffusion tensor imaging (DTI) was utilized to investigate white matter microstructural changes of the patients. Results: Participants with LGI1 antibodies encephalitis presented reduced functional connectivity in the brain areas associated with memory, cognition and motion circuits, while increased functional connectivity in putamen and caudate in comparison to the normal controls. Moreover, the effective connectivity in patients was decreased from the frontal cortex to supplementary motor area. Finally, patients had significant reductions in fractional anisotropy (FA) for the corpus callosum, internal capsule, corona radiata and superior longitudinal fasciculus, accompanied by increases in mean diffusivity (MD) for these regions as compared to controls. Conclusion: Our findings suggest that the neural disorder and behavioral deficits of anti-LGI1 encephalitis may be associated with extensive changes in brain connectivity and microstructure. These pathological alterations affect the basal ganglia and limbic system besides the temporal and frontal lobe.

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Section: The Functional Mri Image Analysismentioning

confidence: 99%

Functional and Structural Brain Alterations in Encephalitis With LGI1 Antibodies

Qiao

Wang

et al. 2020

Front. Neurosci.

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“…Internationally, automatic analysis with the aid of artificial intelligence has covered a variety of diseases, ranging from "benign" conditions such as diabetic retinopathy and Alzheimer's disease [7], to malignant tumors such as breast cancer [26][27][28], lung cancer [29], liver cancer [30], skin cancer [31], osteosarcoma [32], and lymphoma [33,34], with an accuracy rate of 89.4-97.8%, and an AUC score of 0.85-0.94 [7,27,31]. In addition, various AI systems related to breast cancer have penetrated through different levels of IDC, such as histology-assisted and cytology-assisted diagnosis, mitotic cell count, lymph node metastasis assessment [9,10,18,22], breast cancer drug development and others [8], with an accuracy rate of 82.7-92.4% and an AUC score of 0.97 [27,28].…”

Section: Discussionmentioning

confidence: 99%

Automated quantitative analysis of Ki-67 staining and HE images recognition and registration based on whole tissue sections in breast carcinoma

et al. 2020

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Background: The scoring of Ki-67 is highly relevant for the diagnosis, classification, prognosis, and treatment in breast invasive ductal carcinoma (IDC). Traditional scoring method of Ki-67 staining followed by manual counting, is time-consumption and inter−/intra observer variability, which may limit its clinical value. Although more and more algorithms and individual platforms have been developed for the assessment of Ki-67 stained images to improve its accuracy level, most of them lack of accurate registration of immunohistochemical (IHC) images and their matched hematoxylin-eosin (HE) images, or did not accurately labelled each positive and negative cell with Ki-67 staining based on whole tissue sections (WTS). In view of this, we introduce an accurate image registration method and an automatic identification and counting software of Ki-67 based on WTS by deep learning. Methods: We marked 1017 breast IDC whole slide imaging (WSI), established a research workflow based on the (i) identification of IDC area, (ii) registration of HE and IHC slides from the same anatomical region, and (iii) counting of positive Ki-67 staining. Results: The accuracy, sensitivity, and specificity levels of identifying breast IDC regions were 89.44, 85.05, and 95.23%, respectively, and the contiguous HE and Ki-67 stained slides perfectly registered. We counted and labelled each cell of 10 Ki-67 slides as standard for testing on WTS, the accuracy by automatic calculation of Ki-67 positive rate in attained IDC was 90.2%. In the human-machine competition of Ki-67 scoring, the average time of 1 slide was 2.3 min with 1 GPU by using this software, and the accuracy was 99.4%, which was over 90% of the results provided by participating doctors.

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“…The study detected the underlying biomarkers of AD by analysing functional MRI. Intrinsic functional connectivity in AD patients was noted to be significantly reduced in subcortical brain regions of the hippocampus, amygdala, insula and putam [80].…”

Section: • Alzheimer's Disease (Ad): a Common Disease In Aging Peoplementioning

confidence: 98%

“…For the functional connectivity studies of the human brain, independent components analysis (ICA) has been widely used for analysing neuroimaging data [79]. In the study by Qiao et al [80], a DL-based method was developed to distinguish AD from controls by fusing the functional connectivity. The study detected the underlying biomarkers of AD by analysing functional MRI.…”

Section: • Alzheimer's Disease (Ad): a Common Disease In Aging Peoplementioning

confidence: 99%

Role of Deep Learning in Predicting Aging-Related Diseases: A Scoping Review

Wassan

Zheng

Wang

2021

Cells

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Aging refers to progressive physiological changes in a cell, an organ, or the whole body of an individual, over time. Aging-related diseases are highly prevalent and could impact an individual’s physical health. Recently, artificial intelligence (AI) methods have been used to predict aging-related diseases and issues, aiding clinical providers in decision-making based on patient’s medical records. Deep learning (DL), as one of the most recent generations of AI technologies, has embraced rapid progress in the early prediction and classification of aging-related issues. In this paper, a scoping review of publications using DL approaches to predict common aging-related diseases (such as age-related macular degeneration, cardiovascular and respiratory diseases, arthritis, Alzheimer’s and lifestyle patterns related to disease progression), was performed. Google Scholar, IEEE and PubMed are used to search DL papers on common aging-related issues published between January 2017 and August 2021. These papers were reviewed, evaluated, and the findings were summarized. Overall, 34 studies met the inclusion criteria. These studies indicate that DL could help clinicians in diagnosing disease at its early stages by mapping diagnostic predictions into observable clinical presentations; and achieving high predictive performance (e.g., more than 90% accurate predictions of diseases in aging).

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Multivariate Deep Learning Classification of Alzheimer’s Disease Based on Hierarchical Partner Matching Independent Component Analysis

Cited by 34 publications

References 83 publications

Functional and Structural Brain Alterations in Encephalitis With LGI1 Antibodies

Functional and Structural Brain Alterations in Encephalitis With LGI1 Antibodies

Automated quantitative analysis of Ki-67 staining and HE images recognition and registration based on whole tissue sections in breast carcinoma

Role of Deep Learning in Predicting Aging-Related Diseases: A Scoping Review

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