a 2023

State analysis of fMRI in amnestic mild cognitive impairment

GAJDOŠ, Martin, Marie NOVÁKOVÁ, Martin LAMOŠ, Pavel ŘÍHA, Irena REKTOROVÁ et. al.

Basic information

Original name

State analysis of fMRI in amnestic mild cognitive impairment

Authors

GAJDOŠ, Martin (203 Czech Republic, guarantor, belonging to the institution), Marie NOVÁKOVÁ (203 Czech Republic, belonging to the institution), Martin LAMOŠ (203 Czech Republic, belonging to the institution), Pavel ŘÍHA (203 Czech Republic, belonging to the institution), Irena REKTOROVÁ (203 Czech Republic, belonging to the institution) and Michal MIKL (203 Czech Republic, belonging to the institution)

Edition

Czech – Austrian Workshop on Magnetic Resonance Imaging and Spectroscopy, 2023, Znojmo, 2023

Other information

Language

English

Type of outcome

Konferenční abstrakt

Field of Study

30103 Neurosciences

Country of publisher

Czech Republic

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

Organization unit

Central European Institute of Technology

Keywords in English

Amnestic mild cognitive impairment; dynamic functional connectivity; sliding window analysis;

Tags

Tags

Reviewed
Změněno: 22/8/2024 14:19, Mgr. Eva Dubská

Abstract

V originále

Amnestic mild cognitive impairment (aMCI) is transitional state between normal aging and early dementia. In this work, we studied dynamic functional connectivity captured with sliding window analysis, whoch was performed on a dataset of 76 subjects (38 HC + 38 aMCI). We found significant differences in coverage in 2 out of 4 identified states. Moreover, with support vector machine, we were able to discriminate between these two groups with approx. 95% accuracy. In future work, we plan to crossvalidate presented classifier. Amnestic mild cognitive impairment (aMCI) is transitional state between normal aging and early dementia. In this work, we studied dynamic functional connectivity captured with sliding window analysis, whoch was performed on a dataset of 76 subjects (38 HC + 38 aMCI). We found significant differences in coverage in 2 out of 4 identified states. Moreover, with support vector machine, we were able to discriminate between these two groups with approx. 95% accuracy. In future work, we plan to crossvalidate presented classifier.

Links

NU21J-04-00077, research and development project
Name: Využití dynamických parametrů funkční konektivity mozku jako diagnostického biomarkeru neurodegenerativních nemocí
Investor: Ministry of Health of the CR, Biomarkers of neurodegenerative diseases based on dynamic functional connectivity, Subprogram 2 - junior
90250, large research infrastructures
Name: Czech-BioImaging III