Lesion topography and microscopic white matter tract damage contribute to cognitive impairment in symptomatic carotid artery disease

Purpose: To investigate associations between neuroimaging markers of cerebrovascular disease, including lesion topography and extent and severity of strategic and global cerebral tissue injury, and cognition in carotid artery disease (CAD). Materials and Methods: All participants gave written informed consent to undergo brain magnetic resonance imaging and the Addenbrooke’s Cognitive Examination–Revised. One hundred eight patients with symptomatic CAD but no dementia were included, and a score less than 82 represented cognitive impairment. Group comparison and interrelations between global cognitive and fluency performance, lesion topography, and ultrastructural damage were assessed with voxel-based statistics. Associations between cognition, medial temporal lobe atrophy (MTA), lesion volumes, and global white matter ultrastructural damage indexed as increased mean diffusivity were tested with regression analysis by controlling for age. Diagnostic accuracy of imaging markers selected from a multivariate prediction model was tested with receiver operating characteristic analysis. Results: Cognitively impaired patients (n = 53 [49.1%], classified as having probable vascular cognitive disorder) were older than nonimpaired patients (P = .027) and had more frequent MTA (P<.001), more cortical infarctions (P = .016), and larger volumes of acute (P = .028) and chronic (P = .009) subcortical ischemic lesions. Lesion volumes did not correlate with global cognitive performance (lacunar infarctions, P = .060; acute lesions, P = .088; chronic subcortical ischemic lesions, P = .085). In contrast, cognitive performance correlated with presence of chronic ischemic lesions within the interhemispheric tracts and thalamic radiation (P< .05, false discovery rate corrected). Skeleton mean diffusivity showed the closest correlation with cognition (R2 = 0.311, P< .001) and promising diagnostic accuracy for vascular cognitive disorder (area under the curve, 0.82 [95% confidence interval: 0.75, 0.90]). Findings were confirmed in subjects with a low risk of preclinical Alzheimer disease indexed by the absence of MTA (n = 85). Conclusion: Subcortical white matter ischemic lesion locations and severity of ultrastructural tract damage contribute to cognitive impairment in symptomatic CAD, which suggests that subcortical disconnection within large-scale cognitive neural networks is a key mechanism of vascular cognitive disorder

Diffusion tensor imaging (DTI) in the detection of white matter lesions in patients with mild cognitive impairment (MCI)

Mild cognitive impairment (MCI) is recognized as a precursor to dementia. The amnestic MCI progresses usually to Alzheimer disease. Amnestic MCI multiple domain (md-MCI) seems to progress more rapidly than amnestic MCI single domain (a-MCI). In an attempt to identify patients at risk, we examined white matter changes in MCI subtypes using diffusion tensor imaging (DTI). We also tried to correlate DTI findings to neuropsychological tests. Forty-four amnestic single domain (a-MCI) patients, 19 amnestic multi domain (md-MCI), and 25 cognitively normal (NC) controls were included in the present study. All participants were assessed clinically using a battery of cognitive tests. DTI was performed to measure fractional anisotropy (FA) and apparent diffusion coefficient (ADC). Areas studied were corpus callosum, posterior cingulum (PC), anterior cingulum (AC), and superior longitudinal fasciculus (SLF). ADC and FA of the above areas were related to the scores of certain neuropsychological tests that evaluate visual and verbal memory. No difference in DTI measurements was found between the two MCI subtypes. ADC in MCI cases was increased in comparison with NC in the genu, PC, right SLF, and left AC. FA was spared. Verbal memory was related to ADC of the genu, PC, right AC and right SLF, and to FA of the left SLF. Visual memory was related to ADC of the genu, PC, right AC, and SLF. The strongest correlation found was between the visual memory and the ADC of the right PC (Spearman rho = 0.45, p < 0.001). DTI revealed that ADC was increased in certain brain areas in MCI patients. No difference in DTI measurements was found between the two MCI subtypes. DTI indices correlate with cognitive performance

Memory Concerns, Memory Performance and Risk of Dementia in Patients with Mild Cognitive Impairment

Background: Concerns about worsening memory (“memory concerns”; MC) and impairment in memory performance are both predictors of Alzheimer's dementia (AD). The relationship of both in dementia prediction at the pre-dementia disease stage, however, is not well explored. Refined understanding of the contribution of both MC and memory performance in dementia prediction is crucial for defining at-risk populations. We examined the risk of incident AD by MC and memory performance in patients with mild cognitive impairment (MCI). Methods: We analyzed data of 417 MCI patients from a longitudinal multicenter observational study. Patients were classified based on presence (n = 305) vs. absence (n = 112) of MC. Risk of incident AD was estimated with Cox Proportional-Hazards regression models. Results: Risk of incident AD was increased by MC (HR = 2.55, 95%CI: 1.33–4.89), lower memory performance (HR = 0.63, 95%CI: 0.56–0.71) and ApoE4-genotype (HR = 1.89, 95%CI: 1.18–3.02). An interaction effect between MC and memory performance was observed. The predictive power of MC was greatest for patients with very mild memory impairment and decreased with increasing memory impairment. Conclusions: Our data suggest that the power of MC as a predictor of future dementia at the MCI stage varies with the patients' level of cognitive impairment. While MC are predictive at early stage MCI, their predictive value at more advanced stages of MCI is reduced. This suggests that loss of insight related to AD may occur at the late stage of MCI

Sparse Multivariate Autoregressive Modeling for Mild Cognitive Impairment Classification

Brain connectivity network derived from functional magnetic resonance imaging (fMRI) is becoming increasingly prevalent in the researches related to cognitive and perceptual processes. The capability to detect causal or effective connectivity is highly desirable for understanding the cooperative nature of brain network, particularly when the ultimate goal is to obtain good performance of control-patient classification with biological meaningful interpretations. Understanding directed functional interactions between brain regions via brain connectivity network is a challenging task. Since many genetic and biomedical networks are intrinsically sparse, incorporating sparsity property into connectivity modeling can make the derived models more biologically plausible. Accordingly, we propose an effective connectivity modeling of resting-state fMRI data based on the multivariate autoregressive (MAR) modeling technique, which is widely used to characterize temporal information of dynamic systems. This MAR modeling technique allows for the identification of effective connectivity using the Granger causality concept and reducing the spurious causality connectivity in assessment of directed functional interaction from fMRI data. A forward orthogonal least squares (OLS) regression algorithm is further used to construct a sparse MAR model. By applying the proposed modeling to mild cognitive impairment (MCI) classification, we identify several most discriminative regions, including middle cingulate gyrus, posterior cingulate gyrus, lingual gyrus and caudate regions, in line with results reported in previous findings. A relatively high classification accuracy of 91.89 % is also achieved, with an increment of 5.4 % compared to the fully-connected, non-directional Pearson-correlation-based functional connectivity approach