Connectivity features for identifying cognitive impairment in presymptomatic carotid stenosis

Abstract

Severe asymptomatic stenosis of the internal carotid artery (ICA) leads to increased incidence of mild cognitive impairment (MCI) likely through silent embolic infarcts and/or chronic hypoperfusion, but the brain dysfunction is poorly understood and difficult to diagnose. Thirty cognitively intact subjects with asymptomatic, severe (≥ 70%), unilateral stenosis of the ICA were compared with 30 healthy controls, matched for age, sex, cardiovascular risk factors and education level, on a battery of neuropsychiatric tests, voxel-based morphometry of magnetic resonance imaging (MRI), diffusion tensor imaging and brain-wise, seed-based analysis of resting-state functional MRI. Multivariate regression models and multivariate pattern classification (support vector machines) were computed to assess the relationship between connectivity measures and neurocognitive performance. The patients had worse dizziness scores and poorer verbal memory, executive function and complex visuo-spatial performance than controls. Twelve out of the 30 patients (40%) were considered to have MCI. Nonetheless, the leukoaraiosis Sheltens scores, hippocampal and brain volumes were not different between groups. Their whole-brain mean fractional anisotropy (FA) was significantly reduced and regional functional connectivity (Fc) was significantly impaired in the dorsal attention network (DAN), frontoparietal network, sensorimotor network and default mode network. In particular, the Fc strength at the insula of the DAN and the mean FA were linearly related with attention performance and dizziness severity, respectively. The multivariate pattern classification gave over 90% predictive accuracy of individuals with MCI or severe dizziness. Cognitive decline in stroke-free individuals with severe carotid stenosis may arise from nonselective widespread disconnections of long-range, predominantly interhemispheric non-hippocampal pathways. Connectivity measures may serve as both predictors for cases at risk and therapeutic targets for mitigating vascular cognitive impairment.

Figure 1. Horizontal (left three) and coronal (right) fractional anisotropy (FA) maps.

The carotid stenotic side is set to the left in all patients. The white matter skeleton derived from the controls is shown in green. Note the significant decrements of FA (red-yellow in the color bar) in the patients, particularly at the splenium, the lateral basal ganglion and frontoparietal regions ipsilateral to the stenosis (red arrows). There was no notable increment of FA compared to controls.

Figure 2. Comparisons of six resting-state functional networks between healthy controls and patients.

(A) A within-group analysis of resting-state networks in healthy controls and patients. Hollow circles indicate the predefined ROIs for individual networks. (B) Group comparisons of respective resting-state networks. Clusters with significant decrements of functional connectivity in the patients are shown in red-yellow; increments of functional connectivity are shown in blue. The carotid stenotic side was flipped to the left. Color bars represent T values. DAN: dorsal attention network; FPN: frontoparietal network; SMN: sensorimotor network; DMN: default mode network; SN: salience network; VN: visual network.

Figure 3. Simple linear regression relationships between connectivity strength and neurocognitive presentation.

(A) The functional connectivity between the left frontal eye field (the seed in the dorsal attention network ipsilateral to carotid stenosis; the carotid stenotic side was flipped to the left in the patients) and the right insula (contralateral to carotid stenosis in the patients) is positively correlated with symbol digit test scores. (B) Mean fractional anisotropy is negatively correlated with dizziness handicap inventory scores. HC: healthy controls; PA: patients; L_FEF: left frontal eye field; R_INS: right insula.