Title Ultrasonic assessment of the medial temporal lobe tissue displacements in Alzheimer's disease /
Authors Baranauskas, Mindaugas ; Jurkonis, Rytis ; Lukoševičius, Arūnas ; Makūnaitė, Monika ; Matijošaitis, Vaidas ; Gleiznienė, Rymantė ; Rastenytė, Daiva
DOI 10.3390/diagnostics10070452
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Is Part of Diagnostics.. Basel : MDPI. 2020, vol. 10, iss. 7, art. no. 452, p. 1-14.. ISSN 2075-4418
Keywords [eng] Alzheimer’s disease ; brain pulsation ; diagnostic ; radiofrequency ultrasound ; sonography ; strain
Abstract [eng] We aim to estimate brain tissue displacements in the medial temporal lobe (MTL) using backscattered ultrasound radiofrequency (US RF) signals, and to assess the diagnostic ability of brain tissue displacement parameters for the differentiation of patients with Alzheimer's disease (AD) from healthy controls (HC). Standard neuropsychological evaluation and transcranial sonography (TCS) for endogenous brain tissue motion data collection are performed for 20 patients with AD and for 20 age- and sex-matched HC in a prospective manner. Essential modifications of our previous method in US waveform parametrization, raising the confidence of micrometer-range displacement signals in the presence of noise, are done. Four logistic regression models are constructed, and receiver operating characteristic (ROC) curve analyses are applied. All models have cut-offs from 61.0 to 68.5% and separate AD patients from HC with a sensitivity of 89.5% and a specificity of 100%. The area under a ROC curve of predicted probability in all models is excellent (from 95.2 to 95.7%). According to our models, AD patients can be differentiated from HC by a sharper morphology of some individual MTL spatial point displacements (i.e., by spreading the spectrum of displacements to the high-end frequencies with higher variability across spatial points within a region), by lower displacement amplitude differences between adjacent spatial points (i.e., lower strain), and by a higher interaction of these attributes.
Published Basel : MDPI
Type Journal article
Language English
Publication date 2020
CC license CC license description