ASAF: altered spontaneous activity fingerprinting in Alzheimer’s disease based on multisite fMRI

Li, Jiachen; Jin, Depeng; Li, Ang; Liu, Bing; Song, Chengyuan; Wang, Pan; Wang, Dawei; Xu, Kaibin; Yang, Hongwei; Yao, Hongxiang; Zhou, Bo; Bejanin, Alexandre; Chételat, Gaël; Han, Tong; Lu, Jie; Wang, Qing; Yu, Chunshui; Zhang, Xinqing; Zhou, Yuying; Zhang, Xi; Jiang, Tianzi; Liu, Yong; Han, Ying

doi:10.1016/j.scib.2019.04.034

Cited by 27 publications

(12 citation statements)

References 104 publications

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“…We should notice that the image acquisition scanning protocols differ across different sites. Various fMRI scanning parameters (echo time, voxel size, scan time duration, etc.,) in different sites may result in the inconsistency of fALFF and ReHo (Li, Jin, et al, 2019); and various PET scanning parameters (injected FDG amount, dynamic scan time, etc.,) may also result in the inconsistency of FDG SUVR. Therefore, the deviations may appear when we calculated SUVR/fALFF (ρ) or SUVR/ReHo (ρ) in different sites.…”

Section: Discussionmentioning

confidence: 99%

Coupling relationship between glucose and oxygen metabolisms to differentiate preclinical Alzheimer's disease and normal individuals

Ding

Zhang

et al. 2021

Human Brain Mapping

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The discovery of preclinical Alzheimer's disease (preAD) provides a wide time window for the early intervention of AD. The coupling relationships between glucose and oxygen metabolisms from hybrid PET/MRI can provide complementary information on the brain's physiological state for preAD. In this study, we purpose to explore the change of coupling relationship among 27 normal controls (NCs), 20 preADs, and 15 cognitive impairments (CIs). For each subject, we calculated the Spearman partial correlation between the fractional amplitude of low-frequency fluctuations (fALFF) and the regional homogeneity (ReHo) from functional image (fMRI), and the standard uptake value ratio (SUVR) from [18F] fluorodeoxyglucose positron emission tomography ( 18 F-FDG PET), in the whole-brain and default mode network (DMN) as a novel potential biomarker. The diagnostic performance of this biomarker was evaluated by the receiver operating characteristic analysis. Significant Spearman correlations between the FDG SUVR and the fALFF/ReHo were found in 98% of subjects. For the DMN-based biomarker, there was a significant decreasing trend for the preAD and CI groups compared to the NC group, whereas no significant difference in preAD based on whole-brain. The correlation ρ value for the FDG SUVR/ReHo showed the highest area under curve of the preAD classification (0.787). The results imply the coupling relationship changed during the preAD stage in the DMN area.

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Section: Discussionmentioning

confidence: 99%

Coupling relationship between glucose and oxygen metabolisms to differentiate preclinical Alzheimer's disease and normal individuals

Ding

Zhang

et al. 2021

Human Brain Mapping

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“…Machine learning is an application of artificial intelligence that allows computers to learn automatically and improve from experience. It is one of today's most rapidly growing technical fields (13), which performs throughout science including health care (14) such as identification and classification for diseases like AD (15)(16)(17), traffic programming (18), and marketing apps designing (19), which allows us to process largescale, multidimensional, complex datasets in this information explosion of an era. Machine learning-based analysis of connectomic data created from neuroimaging studies in patients AD has been extensively studied in the literature (5,9,12,20,21).…”

Section: Introductionmentioning

confidence: 99%

Application of Structural and Functional Connectome Mismatch for Classification and Individualized Therapy in Alzheimer Disease

Ren

Zhu

et al. 2020

Front. Public Health

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While machine learning approaches to analyzing Alzheimer disease connectome neuroimaging data have been studied, many have limited ability to provide insight in individual patterns of disease and lack the ability to provide actionable information about where in the brain a specific patient's disease is located. We studied a cohort of patients with Alzheimer disease who underwent resting state functional magnetic resonance imaging and diffusion tractography imaging. These images were processed, and a structural and functional connectivity matrix was generated using the HCP cortical and subcortical atlas. By generating a machine learning model, individual-level structural and functional anomalies detection and characterization were explored in this study. Our study found that structural disease burden in Alzheimer's patients is mainly focused in the subcortical structures and the Default mode network (DMN). Interestingly, functional anomalies were less consistent between individuals and less common in general in these patients. More intriguing was that some structural anomalies were noted in all patients in the study, namely a reduction in fibers involving parcellations in the right anterior cingulate. Alternately, the functional consequences of connectivity loss were cortical and variable. Integrated structural/functional connectomics might provide a useful tool for assessing AD progression, while few concerns have been made for analyzing the mismatch between these two. We performed a preliminary exploration into a set of Alzheimer disease data, intending to improve a personalized approach to understanding individual connectomes in an actionable manner. Specifically, we found that there were consistent patterns of white matter fiber loss, mainly focused around the DMN and deep subcortical structures, which were present in nearly all patients with clinical AD. Functional magnetic resonance imaging shows abnormal functional connectivity different within the patients, which may be used as the individual target for further therapeutic strategies making, like non-invasive stimulation technology.

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“…As discussed in several previous studies [31,[45][46][47][48][49], cross-site validation is crucial for optimizing valid biomarkers and is particularly important for translational medicine. Hence, the classification analysis with independent site cross-validations to predict diagnostic status provides shreds of evidence that tdNCD can provide additional information as imaging biomarkers for AD.…”

Section: Discussionmentioning

confidence: 99%

Impaired time-distance reconfiguration patterns in Alzheimer's disease: a dynamic functional connectivity study with 809 individuals from 7 sites

Chen²,

Zhao³

et al. 2022

BMC Bioinformatics

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Background The dynamic functional connectivity (dFC) has been used successfully to investigate the dysfunction of Alzheimer's disease (AD) patients. The reconfiguration intensity of nodal dFC, which means the degree of alteration between FCs at different time scales, could provide additional information for understanding the reconfiguration of brain connectivity. Results In this paper, we introduced a feature named time distance nodal connectivity diversity (tdNCD), and then evaluated the network reconfiguration intensity in every specific brain region in AD using a large multicenter dataset (N = 809 from 7 independent sites). Our results showed that the dysfunction involved in three subnetworks in AD, including the default mode network (DMN), the subcortical network (SCN), and the cerebellum network (CBN). The nodal tdNCD inside the DMN increased in AD compared to normal controls, and the nodal dynamic FC of the SCN and the CBN decreased in AD. Additionally, the classification analysis showed that the classification performance was better when combined tdNCD and FC to classify AD from normal control (ACC = 81%, SEN = 83.4%, SPE = 80.6%, and F1-score = 79.4%) than that only using FC (ACC = 78.2%, SEN = 76.2%, SPE = 76.5%, and F1-score = 77.5%) with a leave-one-site-out cross-validation. Besides, the performance of the three classes classification was improved from 50% (only using FC) to 53.3% (combined FC and tdNCD) (macro F1-score accuracy from 46.8 to 48.9%). More importantly, the classification model showed significant clinically predictive correlations (two classes classification: R = −0.38, P < 0.001; three classes classification: R = −0.404, P < 0.001). More importantly, several commonly used machine learning models confirmed that the tdNCD would provide additional information for classifying AD from normal controls. Conclusions The present study demonstrated dynamic reconfiguration of nodal FC abnormities in AD. The tdNCD highlights the potential for further understanding core mechanisms of brain dysfunction in AD. Evaluating the tdNCD FC provides a promising way to understand AD processes better and investigate novel diagnostic brain imaging biomarkers for AD.

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ASAF: altered spontaneous activity fingerprinting in Alzheimer’s disease based on multisite fMRI

Cited by 27 publications

References 104 publications

Coupling relationship between glucose and oxygen metabolisms to differentiate preclinical Alzheimer's disease and normal individuals

Coupling relationship between glucose and oxygen metabolisms to differentiate preclinical Alzheimer's disease and normal individuals

Application of Structural and Functional Connectome Mismatch for Classification and Individualized Therapy in Alzheimer Disease

Impaired time-distance reconfiguration patterns in Alzheimer's disease: a dynamic functional connectivity study with 809 individuals from 7 sites

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