Vascular Cognitive Impairment and cognitive decline; a longitudinal study comparing different types of vascular brain injury - The TRACE-VCI study

Machine learning based on the EEG and structural MRI can predict different stages of vascular cognitive impairment

Background

Vascular cognitive impairment (VCI) is a major cause of cognitive impairment in the elderly and a co-factor in the development and progression of most neurodegenerative diseases. With the continuing development of neuroimaging, multiple markers can be combined to provide richer biological information, but little is known about their diagnostic value in VCI.

Methods

A total of 83 subjects participated in our study, including 32 patients with vascular cognitive impairment with no dementia (VCIND), 21 patients with vascular dementia (VD), and 30 normal controls (NC). We utilized resting-state quantitative electroencephalography (qEEG) power spectra, structural magnetic resonance imaging (sMRI) for feature screening, and combined them with support vector machines to predict VCI patients at different disease stages.

Results

The classification performance of sMRI outperformed qEEG when distinguishing VD from NC (AUC of 0.90 vs. 0,82), and sMRI also outperformed qEEG when distinguishing VD from VCIND (AUC of 0.8 vs. 0,0.64), but both underperformed when distinguishing VCIND from NC (AUC of 0.58 vs. 0.56). In contrast, the joint model based on qEEG and sMRI features showed relatively good classification accuracy (AUC of 0.72) to discriminate VCIND from NC, higher than that of either qEEG or sMRI alone.

Conclusion

Patients at varying stages of VCI exhibit diverse levels of brain structure and neurophysiological abnormalities. EEG serves as an affordable and convenient diagnostic means to differentiate between different VCI stages. A machine learning model that utilizes EEG and sMRI as composite markers is highly valuable in distinguishing diverse VCI stages and in individually tailoring the diagnosis.

Distinct spatiotemporal patterns of cortical thinning in Alzheimer's disease-type cognitive impairment and subcortical vascular cognitive impairment

Previous studies on Alzheimer's disease-type cognitive impairment (ADCI) and subcortical vascular cognitive impairment (SVCI) has rarely explored spatiotemporal heterogeneity. This study aims to identify distinct spatiotemporal cortical atrophy patterns in ADCI and SVCI. 1,338 participants (713 ADCI, 208 SVCI, and 417 cognitively unimpaired elders) underwent brain magnetic resonance imaging (MRI), amyloid positron emission tomography, and neuropsychological tests. Using MRI, this study measures cortical thickness in five brain regions (medial temporal, inferior temporal, posterior medial parietal, lateral parietal, and frontal areas) and utilizes the Subtype and Stage Inference (SuStaIn) model to predict the most probable subtype and stage for each participant. SuStaIn identifies two distinct cortical thinning patterns in ADCI (medial temporal: 65.8%, diffuse: 34.2%) and SVCI (frontotemporal: 47.1%, parietal: 52.9%) patients. The medial temporal subtype of ADCI shows a faster decline in attention, visuospatial, visual memory, and frontal/executive domains than the diffuse subtype (p-value < 0.01). However, there are no significant differences in longitudinal cognitive outcomes between the two subtypes of SVCI. Our study provides valuable insights into the distinct spatiotemporal patterns of cortical thinning in patients with ADCI and SVCI, suggesting the potential for individualized therapeutic and preventive strategies to improve clinical outcomes.

Examination of brain area volumes based on voxel-based morphometry and multidomain cognitive impairment in asymptomatic unilateral carotid artery stenosis

Objective

Recent evidence has demonstrated that unilateral carotid artery stenosis (CAS) can contribute to the development of cognitive impairment. However, the features of cognitive dysfunction induced by unilateral CAS remain unclear.

Methods

Sixty asymptomatic patients with unilateral CAS were divided into mild, moderate and severe stenosis groups. These patients and 20 healthy controls provided clinical data and serum, which was used to assess the levels of certain vascular risk factors. Then, they participated in a battery of neuropsychological tests. Additionally, all participants underwent a 3.0 T magnetic resonance imaging (MRI) scan of the brain. Chi-square tests and one-way ANOVA were used to determine significant differences in the risk factors and cognitive test scores between groups. Multiple logistic regression analysis and the receiver operating characteristic (ROC) curve analysis were performed to identify the independent risk factors for cognitive impairment in patients with CAS. Finally, fluid attenuated inversion recovery (FLAIR) T1-weighted MRI images were processed by voxel-based morphometry (VBM) analysis using the Statistical Parametric Mapping (SPM) 8 software.

Results

Compared with healthy controls, the scores of the Mini-Mental State Examination, Digital Span Test backward, and Rapid Verbal Retrieve were significantly reduced in patients with left CAS. The scores in all cognitive scales were significantly lower in patients with right CAS than in controls. Logistic regression analysis demonstrated that the degree of carotid stenosis was an independent risk factor for cognitive impairment in asymptomatic patients with unilateral CAS. Furthermore, VBM analysis showed that, compared with those in healthy controls, gray matter and white matter volumes in specific brain areas were markedly decreased in patients with severe unilateral CAS. However, in patients with moderate right CAS, there was a significant decline in the volume of gray matter in the left parahippocampal gyrus and supplementary motor area. Additionally, the volume of white matter in the left insula was obviously lower in patients with moderate right CAS than in healthy controls.

Conclusion

Unilateral asymptomatic CAS, especially on the right side, contributed to cognitive impairment, including memory, language, attention, executive function and visuospatial function. In addition, based on VBM analysis, both gray matter atrophy and white matter lesions were found in patients with unilateral asymptomatic CAS.