Multimodal magnetic resonance imaging reveals distinct sensitivity of hippocampal subfields in asymptomatic stage of Alzheimer’s disease

Wu, Junjie; Shahid, Syed Salman; Lin, Qixiang; Hone‐Blanchet, Antoine; Smith, Jeremy L; Risk, Benjamin B.; Bisht, Aditya; Loring, David W.; Goldstein, Felicia C.; Levey, Aĺlan I.; Lah, James J.; Qiu, Deqiang

doi:10.3389/fnagi.2022.901140

Front. Aging Neurosci.

2022

DOI: 10.3389/fnagi.2022.901140

|View full text |Cite

Multimodal magnetic resonance imaging reveals distinct sensitivity of hippocampal subfields in asymptomatic stage of Alzheimer’s disease

Junjie Wu

Syed Salman Shahid

Qixiang Lin

et al.

Abstract: While hippocampal atrophy and its regional susceptibility to Alzheimer’s disease (AD) are well reported at late stages of AD, studies of the asymptomatic stage of AD are limited but could elucidate early stage pathophysiology as well as provide predictive biomarkers. In this study, we performed multi-modal magnetic resonance imaging (MRI) to estimate morphometry, functional connectivity, and tissue microstructure of hippocampal subfields in cognitively normal adults including those with asymptomatic AD. High-r… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

(1 citation statement)

References 66 publications

(76 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Известно, что атрофические изменения гиппокампальной формации происходят на ранних стадиях БА, при этом часть исследователей описывают изменения определенных субполей даже на доклинических стадиях [7]. По данным патоморфологических и нейровизуализационных исследований, наиболее уязвимыми субполями гиппокампальной формации являются субикулюм, СА1 и энторинальная кора [8].…”

unclassified

Method of differential diagnosis of mild cognitive impairment of various origins: cross sectional study

Shilova¹,

Ananyeva

Лукина³

et al. 2023

Lučevaâ diagn. ter.

View full text Add to dashboard Cite

INTRODUCTION: The differential diagnosis of mild cognitive impairment (MCI), due to the high prevalence in the population and the rapid increase in incidence, is an urgent task. The most common causes leading to the development of cognitive impairment are Alzheimer’s disease (AD), cerebrovascular pathology, and their combination. AD usually manifests as an amnestic type of mild cognitive impairment (aMCI) at the pre-dementia stage. Subcortical vascular mild cognitive impairment (svMCI) is considered as the prodromal stage of subcortical vascular dementia. According to the results of pathomorphological studies, it was found that subfields of the hippocampal formation are selective vulnerability to AD, ischemia/hypoxia, and aging.Currently, using the FreeSurfer 6.0 software, it is possible to obtain quantitative indicators of the hippocampal formation subfields in vivo.The current trend in medicine is the development and implementation of new diagnostic solutions based on artificial intelligence and machine learning. One of the machine learning algorithms is binary logistic regression, which we used in the course of the study for the differential diagnosis of MCI of various origins.OBJECTIVE: To develop a method for the differential diagnosis of mil cognitive impairment of various origins.MATERIALS AND METHODS: The study included patients with the syndrome of mild cognitive impairment who were examined in the department of geriatric psychiatry of the V.M.Bekhterev National Medical Research Center for Psychiatry and Neurology, from which two groups were formed: group 1 — patients with aMCI, group 2 — patients with svMCI. Conditionally healthy volunteers, comparable in age, sex and level of education, made up the 3rd group (control). MRI examination was performed using a Excelart Vantage Atlas XGV magnetic resonance imaging system (Toshiba, Japan) with a magnetic field strength of 1.5 T, followed by MR morphometry of the subfields of the hippocampal formation.Statistics: Statistical analysis was carried out using data that was converted from a database in Microsoft Excel to the statistical package IBM SPSS 21. To develop a differential diagnosis method, based on the data obtained, the binary regression method and ROC analysis were used.RESULTS: Based on the obtained MR-morphometry data, a method was developed using the binary logistic regression equation. The value of p≥0.5 makes it possible to classify the patient to the aMCI group, and the value of p<0.5 — to the svMCI. The sensitivity of the method is 90%, the specificity is 80%, and the accuracy is 85%.DISCUSSION: Using binary logistic regression, the selection of variants of sets of variables (quantitative indicators) was carried out, for which ROC curves were constructed. The selection criterion was the area under the ROC curve — the AUC criterion (Area Under the Curve). The largest area under the curve (AUC=0.824) in the differential diagnosis of aMCI from svMCI was determined for the combination of the volume of the left subiculum and the thickness of the right entorhinal cortex.Since patients in the aMCI group have a significantly lower number of vascular foci than in the svMCI group (p<0.05), at the next stage, one more variable, the volume fraction, was added to the selected combination of two variables (volume of the left subiculum and thickness of the right entorhinal cortex) hypointense foci. When conducting an ROC analysis with a combination of three variables, an increase in AUC to 0.892 was noted. Further, using a combination of three variables and a binary logistic regression equation, a method for differential diagnosis of aMCI from svMCI was developed.CONCLUSION: The method of differential diagnosis based on binary logistic regression using MR morphometry data allows to distinguish patients with aMCI from patients with svMCI with high sensitivity and specificity.

show abstract

unclassified

Method of differential diagnosis of mild cognitive impairment of various origins: cross sectional study

Shilova¹,

Ananyeva

Лукина³

et al. 2023

Lučevaâ diagn. ter.

View full text Add to dashboard Cite

show abstract

Possibilities and prospects of the MR morphometry method in the diagnosis of dementia

Ternovykh,

Vorobyev,

Yanishevskiy

et al. 2024

Medicinskij sovet

View full text Add to dashboard Cite

Dementia is one variant severe disorder’s higher cortical function and is currently considered as one most important cause a decrease in the quality household, social adaptation patients, and a violation their ability function in the world around them. It can be formed in a wide range disease, main which are neurodegenerative processes, in particular Alzheimer’s disease. In the diagnosis cognitive disorders, in addition to clinical and neuropsychological, as well as laboratory examination, modern methods computer neuroimaging plays an essential role. Same time, significant importance is attached research based on magnetic resonance, which has achieved great progress in recent years. Changes recorded by structural magnetic resonance imaging (MR tomography) often “lag behind” recorded clinical symptoms and are determined advanced disease. New MR methods have been proposed to verify early manifestations of neurodegenerative changes observed in the brain. These include, in particular, MR morphometry. It allows you to determine absolute values severity of atrophic changes, to establish their exact topical localization. Its use not only improves the possibilities of diagnosis pre-stage stages, but also improves quality of differential diagnosis various pathogenetic variants observed cognitive disorders. In addition, use special postprocessing data processing programs reduce the likelihood errors in interpretation received data. Currently, three options have been proposed for evaluating the results of MR morphometry, based on the analysis volume individual brain structures, thickness gray matter, as well as the spatial shape certain formations. Integrated use these options contributes highest quality MR morphometric examination of the brain during formation neurodegenerative process.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Multimodal magnetic resonance imaging reveals distinct sensitivity of hippocampal subfields in asymptomatic stage of Alzheimer’s disease

Cited by 2 publications

References 66 publications

Method of differential diagnosis of mild cognitive impairment of various origins: cross sectional study

Method of differential diagnosis of mild cognitive impairment of various origins: cross sectional study

Possibilities and prospects of the MR morphometry method in the diagnosis of dementia

Contact Info

Product

Resources

About