Classification of Symptomatic and Asymptomatic Patients with and without Cognitive Decline Using Non-invasive Carotid Plaque Strain Indices as Biomarkers

Abstract

Vascular cognitive decline may be caused by micro-emboli generated from carotid plaque instability. We have previously shown that maximum strain indices in carotid plaque were significantly correlated with cognitive function. In this paper, we examine these associations with a larger sample size, along with performance evaluation of these maximum strain indices to predict cognitive impairment. Ultrasound-based strain imaging and cognition assessment were conducted on 75 human subjects. Patients underwent one of two standardized cognitive test batteries, using either the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) or the National Institute of Neurological Disorder and Stroke-Canadian Stroke Network (NINDS-CSN) Vascular Cognitive Impairment Harmonization Standards (60-minute). Scores were standardized within each battery to allow these data to be combined across all participants. Radiofrequency signals for ultrasound strain imaging were acquired on the carotid arteries using either a Siemens Antares with a VFX 13-5 linear array transducer or a Siemens S2000 with a 18L6 linear array transducer. The same hierarchical block-matching motion tracking algorithm developed in our laboratory was utilized to estimate accumulated axial, lateral, and shear strain indices in carotid plaque with inclusion of adventitia regardless of the ultrasound system and transducer used. Associations between cognitive z-scores and maximum strain indices were examined using Pearson’s correlation coefficients. Maximum strain indices were also utilized to predict cognitive impairment using ROC analysis. All correlations between maximum strain indices and total cognition were statistically significant (p<0.05), indicating that these indices have good utility for predicting cognitive impairment. Maximum lateral strain indices provide an AUC of 0.85 for symptomatic patients, and 0.68 for asymptomatic patients. Our results demonstrate the important relationship of maximum strain indices and cognitive function, and the feasibility of using maximum strain indices to predict cognitive decline with inclusion of adventitia layer into the segmentation of plaque.

Histopathologic Validation of Grayscale Carotid Plaque Characteristics Related to Plaque Vulnerability

“…Ultrasound methods used to assess carotid plaque for features of vulnerability include; integrated backscatter (IBC) (Bridal et al 2000; Kawasaki et al 2001; Nagano et al 2008), midband, slope and intercept values of straight-line fit (MBF) to the apparent backscatter transfer function, (Waters et al 2003) carotid strain imaging, (Maurice et al 2005; Shi et al 2008; Shi et al 2009; Wang et al 2013; Wang et al 2016a; Wang et al 2016b), acoustic radiation impulse force imaging (ARFI) based approaches (Czernuszewicz et al 2015), shearwave elastography (Garrard et al 2015) and grayscale analyses of plaque features (El-Barghouty et al 1996; Tegos et al 2000; Grogan et al 2005; Salem et al 2014). IBC, MBF, zero frequency intercept, and carotid strain imaging utilize the raw radiofrequency echo signal to determine associations between scattering properties of the plaque tissues and plaque composition.…”

Quantification of carotid artery plaque stability with multiple region of interest based ultrasound strain indices and relationship with cognition

“…However, clinical management is largely determined by the percent stenosis as derived from medical imaging tests (Dempsey et al , 2010; Brott et al , 2011; NASCET, 1999) and therefore does not include comprehensive evaluation of the composition and mechanical properties associated with plaque instability. Our research utilizes structurally-based, noninvasive strain indices as vascular biomarkers for early diagnosis of vascular sites in the carotid at risk for embolization due to unstable plaque (Wang et al , 2016; Wang et al , 2015; Berman et al , 2015; Wang et al , 2014; Rocque et al , 2012; Dempsey et al , 2010). …”

“…Strain imaging has been used in the assessment of unstable carotid artery plaque based on axial, lateral and shear strain variations over a cardiac cycle (Wang et al , 2016; Wang et al , 2014; McCormick et al , 2012; Shi et al , 2008). Other groups have also proposed metrics based on strain to assess plaque instability (Widman et al , 2015; Majdouline et al , 2014; Ribbers et al , 2007).…”

“…This algorithm uses a hierarchical framework to calculate accumulated axial, lateral and shear strain. In (Wang et al , 2014; Wang et al , 2016) plaque with adventitia were segmented over a cardiac cycle, and a region of interest (ROI) selected around the location where strain value was maximum. The mean or average value in this region over a cardiac cycle was plotted and used to calculate strain indices for the accumulated temporal strain curves to obtain the maximum accumulated strain and peak to trough strain indices.…”

“…The mean or average value in this region over a cardiac cycle was plotted and used to calculate strain indices for the accumulated temporal strain curves to obtain the maximum accumulated strain and peak to trough strain indices. These strain indices have been shown to be correlated with cognitive function (Wang et al , 2014; Wang et al , 2016). …”