Ultrasonic Assessment of the Medial Temporal Lobe Tissue Displacements in Alzheimer’s Disease

Abstract

<jats:p>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.</jats:p>

Diagnostic Ability of Radiofrequency Ultrasound in Parkinson’s Disease Compared to Conventional Transcranial Sonography and Magnetic Resonance Imaging

“…Since this displacement waveform morphology parameter estimate Q3 was the only one from all US RF-based variables differing between HC subjects and PD patients (only Q3 of FreqHP became higher in PD patients), we can hypothesize that the registered micromovements in most parts of the midbrain did not significantly change with the disease, and the changes occurred only in part of the midbrain which had a relatively sharper micromovements pattern. The same FreqHP parameter-captured disease affected other brain structures in our previous study [ 15 ]. There, a set of brain tissue displacement signal parameters (FreqHP maximum, mode and IQR, and strain) worked together well while differentiating the medial temporal lobe of an Alzheimer’s disease patient from that of an HC subject with an excellent diagnostic ability [ 15 ].…”

“…The same FreqHP parameter-captured disease affected other brain structures in our previous study [ 15 ]. There, a set of brain tissue displacement signal parameters (FreqHP maximum, mode and IQR, and strain) worked together well while differentiating the medial temporal lobe of an Alzheimer’s disease patient from that of an HC subject with an excellent diagnostic ability [ 15 ]. The observed tendency that PD has wider RMS dispersion (as evaluated by ex-Gaussian SD and IQR) also suggest increased anisotropy in the midbrain.…”

“…The calculation options used at this step were the same as described in our earlier article [ 14 ]. The displacement signal processing was the same as in our recent study [ 15 ]: the coordinate system was changed into Lagrangian [ 21 ], the median for each scanning frame’s line was subtracted separately and high-pass filtered with cutoff at 0.75 Hz and only confidently repeatable moving spatial points were selected for further analysis.…”

“…The distributions of remaining parameters—amplitude, strain and morphology group parameters of all displacement signals of individual points—were quantified by their minimal and maximal values, median, first (Q1) and third quartiles (Q3), interquartile range (IQR) and the most frequent value (statistical mode); in addition, amplitude group parameters were also evaluated by ex-Gaussian estimates μ , σ , τ , also ex-Gaussian distribution mean and standard deviation (SD) using the DISTRIB toolbox for MATLAB [ 24 ]; as strain has similar to exponential distribution, only the ex-Gaussian distribution τ estimate was added from the ex-Gaussian estimates. Most of above-mentioned parameters and their distributions were described in more a detailed manner in our previous study [ 15 ].…”

“…These brain tissue displacements can be evaluated by detailed analysis of raw ultrasound (US) radiofrequency (RF) signal changes of the same tissue region over time [ 13 ]. We developed such an approach further [ 14 ] and in our recent study [ 15 ] we demonstrated that evaluation of complex interactions between the set of RF TCS-based brain tissue displacement parameters allows us to discern the medial temporal lobe of an Alzheimer’s disease patient from that of a healthy control (HC) subject with excellent diagnostic ability.…”