Background/Aims: To investigate whether diffusion tensor imaging (DTI) is more sensitive than conventional MRI at detecting cognitive deterioration in patients with subcortical ischemic vascular disease (SIVD). Methods: Forty-two SIVD patients had a diagnosis of no cognitive impairment (NCI), vascular cognitive impairment/no dementia or vascular dementia (VaD). Whole-brain DTI histography and routine MRI were performed on these participants. Results: There were significant differences between cognitively impaired patients and NCI subjects in mean diffusivity and fractional anisotropy in either whole-brain white matter (WBWM) or in normal-appearing white matter (NAWM). All DTI indices within either WBWM or NAWM were found to be significantly correlated with both the attention-executive and memory measures in SIVD subjects. Lacune numbers and T₂-weighted lesions correlated only with attention-executive measures, whereas hippocampal volumes correlated only weakly with memory measures. Whole-brain gray matter volumes correlated with Z scores for all cognitive domains but language. After VaD patients had been excluded from the analysis, cognitive measures remained significantly correlated with some of the DTI indices, but not with conventional MRI findings. Conclusions: Compared with conventional MRI, whole-brain DTI is a more reliable and sensitive technique for the early detection of cognitive impairment in SIVD patients.

Objectives In this cross-sectional study, we aimed to explore the mechanisms of early cognitive impairment in a post stroke non-dementia cerebral small vessel disease (SVD) cohort by comparing the SVD score with the structural brain network measures. Method 127 SVD patients were recruited consecutively from a stroke clinic, comprising 76 individuals with mild cognitive impairment (MCI) and 51 with no cognitive impairment (NCI). Detailed neuropsychological assessments and multimodal MRI were performed. SVD scores were calculated on a standard scale, and structural brain network measures were analyzed by diffusion tensor imaging (DTI). Between-group differences were analyzed, and logistic regression was applied to determine the predictive value of SVD and network measures for cognitive status. Mediation analysis with structural equation modeling (SEM) was used to better understand the interactions of SVD burden, brain networks and cognitive deficits. Results Group difference was found on all global brain network measures. After adjustment for age, gender, education and depression, significant correlations were found between global brain network measures and diverse neuropsychological tests, including TMT-B ( r = −0.209, p < .05), DSST ( r = 0.206, p < .05), AVLT short term free recall ( r = 0.233, p < .05), AVLT long term free recall ( r = 0.264, p < .05) and Rey-O copy ( r = 0.272, p < .05). SVD score showed no group difference and was not correlated with cognition tests. Network global efficiency (E Global ) was significantly related to cognitive state ( p < .01) but not the SVD score. Mediation analysis showed that the standardized total effect ( p = .013) and the standardized indirect effect ( p = .016) of SVD score on cognition was significant, but the direct effect was not. Conclusions Brain network measures, but not the SVD score, are significantly correlated with cognition in post-stroke SVD patients. Mediation analysis showed that the cerebral vascular lesions produce cognitive dysfunction by interfering with the structural brain network in SVD patients. The brain network measures may be regarded as direct and independent surrogate markers of cognitive impairment in SVD.

Abnormal reductions in cortical cerebral blood flow (CBF) have been identified in subcortical vascular cognitive impairment (SVCI). However, little is known about the pattern of CBF reduction in relation with the degree of cognitive impairment. CBF measured with three-dimensional (3D) Arterial Spin Labeling (ASL) perfusion magnetic resonance imaging (MRI) helps detect functional changes in subjects with SVCI. We aimed to compare CBF maps in subcortical ischemic vascular disease (SIVD) subjects with and without cognitive impairment and to detect the relationship of the regions of CBF reduction in the brain with the degree of cognitive impairment according to the z-score. A total of 53 subjects with SVCI and 23 matched SIVD subjects without cognitive impairment (controls), underwent a whole-brain 3D ASL MRI in the resting state. Regional CBF (rCBF) was compared voxel wise by using an analysis of variance design in a statistical parametric mapping program, with patient age and sex as covariates. Correlations were calculated between the rCBF value in the whole brain and the z-score in the 53 subjects with SVCI. Compared with the control subjects, SVCI group demonstrated diffuse decreased CBF in the brain. Significant positive correlations were determined in the rCBF values in the left hippocampus, left superior temporal pole gyrus, right superior frontal orbital lobe, right medial frontal orbital lobe, right middle temporal lobe, left thalamus and right insula with the z-scores in SVCI group. The noninvasively quantified resting CBF demonstrated altered CBF distributions in the SVCI brain. The deficit brain perfusions in the temporal and frontal lobe, hippocampus, thalamus and insula was related to the degree of cognitive impairment. Its relationship to cognition indicates the clinical relevance of this functional marker. Thus, our results provide further evidence for the mechanisms underlying the cognitive deficit in patients with SVCI.

Background and Purpose: To assess the validity of the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE) in the detection of vascular mild cognitive impairment (VaMCI) in patients with subcortical ischemic vascular disease (SIVD). Methods: Among 102 SIVD patients, both cutoff scores of the MMSE and MoCA for differentiating VaMCI from no cognitive impairment (NCI) or differentiating VaMCI from vascular dementia (VaD) were determined by the receiver operator characteristic (ROC) analysis. Optimal sensitivity with specificity of cutoff scores was obtained after the raw scores were adjusted for education. Results: After adjusting for education, the MoCA cutoff score for differentiating VaMCI from NCI was at 24/25 and that for differentiating VaMCI from VaD was at 18/19. After applying the adjusted MoCA scores from 19 to 24 to identify VaMCI in all SIVD patients, sensitivity was at 76.7% and specificity was at 81.4% (κ = 0.579). The adjusted cutoff score of the MMSE for differentiating VaMCI from NCI was at 28/29 and that for differentiating VaMCI from VaD was at 25/26. The sensitivity and specificity of the adjusted MMSE was at 58.1 and 71.2%, respectively, when using the score from 26 to 28 to identify VaMCI in SIVD patients (κ = 0.294). Conclusions: The MoCA detected subcortical VaMCI better than the MMSE.