Deep Learning for Classifying and Cognitive Profiling of Subcortical Vascular Cognitive Impairment

Vascular cognitive impairment (VCI) describes all forms of cognitive impairment caused by any type of cerebrovascular disease. Early identification of VCI is quite difficult due to the lack of both...

It is challenging to predict which patients who meet criteria for subcortical ischemic vascular disease (SIVD) will ultimately progress to subcortical vascular cognitive impairment (SVCI). We collected clinical information, neuropsychological assessments, T1 imaging, diffusion tensor imaging, and resting-state functional magnetic resonance imaging from 83 patients with SVCI and 53 age-matched patients with SIVD without cognitive impairment. We built an unsupervised machine learning model to isolate patients with SVCI. The model was validated using multimodal data from an external cohort comprising 45 patients with SVCI and 32 patients with SIVD without cognitive impairment. The accuracy, sensitivity, and specificity of the unsupervised machine learning model were 86.03%, 79.52%, and 96.23% and 80.52%, 71.11%, and 93.75% for internal and external cohort, respectively. We developed an accurate and accessible clinical tool which requires only data from routine imaging to predict patients at risk of progressing from SIVD to SVCI. Our unsupervised machine learning model provides an accurate and accessible clinical tool to predict patients at risk of progressing from subcortical ischemic vascular disease (SIVD) to subcortical vascular cognitive impairment (SVCI) and requires only data from imaging routinely used during the diagnosis of suspected SVCI. The model yields good accuracy, sensitivity, and specificity and is portable to other cohorts and to clinical practice to distinguish patients with SIVD at risk for progressing to SVCI. The model combines assessment of diffusion tensor imaging and functional magnetic resonance imaging measures in patients with SVCI to analyze whether the "disconnection hypothesis" contributes to functional and structural changes and to the clinical presentation of SVCI.

Objective To investigate the intracranial vascular lesions in patients with subcortical infarction-induced vascular cognitive impairment(VCI),and to investigate the etiology and mechanisms of VCI.Methods Inpatients with subcortical infarction in our hospital were enrolled in this study from Nov.2012 to Feb.2014,with those unable to complete the cognitive evaluation eliminated.According to the diagnostic criteria of VCI the patients were divided into two groups:49with cognitive impairment(VCI group)and 42 without cognitive impairment(NVCI group).The clinical data,physical examinations,laboratory tests,and the Montreal Cognitive Assessment(MoCA)scale scores were collected.Cerebral blood vessels were assessed by CT angiography or magnetic resonance angiography(MRA).Results The results showed that,according to TOAST classification,VCI group had 24(48.98%)patients with large artery atherosclerosis(LAA)and NVCI group had 22(52.38%),showing no significant difference between the two groups.CTA or MRA indicated that 37(75.51%)patients in VCI group had vascular stenosis,with 75.25% of the 37 patients having intracranial vascular stenosis and 28.71%with middle cerebral artery stenosis.Patients with single cerebral artery stenosis accounted for 18.37% and those with the multiple artery stenosis accounted for 57.14%.CTA or MRA indicated that 34(80.95%)patients in NVCI group had vascular stenosis,with 60% having intracranial vascular stenosis,including 32% with middle cerebral artery stenosis,26.19% with single cerebral artery stenosis,and 54.76% with the multiple artery stenosis,with the latter two data being significantly different from the VCI group(P0.05).In VCI group,the number of intracranial vascular stenosis branches ranged 0-6,with a mean of(1.51±1.67),and a negative relation was found between numbers of stenosis arteries and MoCA scores in patients of VCI group(rs=-0.283,P0.05).Conclusion Different from the common causes of small blood vessels,LAA is the most common etiology of subcortical VCI,which implied that exploring the LAA causes of small lesions is crucial for the prevention of VCI in Chinese patients.

The genetic basis of amyloid β (Aβ) deposition in subcortical vascular cognitive impairment (SVCI) is still unknown. Here, we investigated genetic variants involved in Aβ deposition in patients with SVCI. We recruited a total of 110 patients with SVCI and 424 patients with Alzheimer's disease-related cognitive impairment (ADCI), who underwent Aβ positron emission tomography and genetic testing. Using candidate AD-associated single nucleotide polymorphisms (SNPs) that were previously identified, we investigated Aβ-associated SNPs that were shared or distinct between patients with SVCI and those with ADCI. Replication analyses were performed using the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Religious Orders Study and Rush Memory and Aging Project cohorts (ROS/MAP). We identified a novel SNP, rs4732728, which showed distinct associations with Aβ positivity in patients with SVCI (P interaction = 1.49 × 10-5); rs4732728 was associated with increased Aβ positivity in SVCI but decreased Aβ positivity in ADCI. This pattern was also observed in ADNI and ROS/MAP cohorts. Prediction performance for Aβ positivity in patients with SVCI increased (area under the receiver operating characteristic curve = 0.780; 95% confidence interval = 0.757-0.803) when rs4732728 was included. Cis-expression quantitative trait loci analysis demonstrated that rs4732728 was associated with EPHX2 expression in the brain (normalized effect size = -0.182, P = 0.005). The novel genetic variants associated with EPHX2 showed a distinct effect on Aβ deposition between SVCI and ADCI. This finding may provide a potential pre-screening marker for Aβ positivity and a candidate therapeutic target for SVCI.

Objectives: Subcortical ischemic vascular disease (SIVD) is frequently associated with cognitive impairment. Rating scales to grade cerebrovascular disease are available, but their sensitivity to the clinical features of mild SIVD is unclear. The aim of the study is to devise and validate a computed tomography (CT)-based visual rating scale sensitive to SIVD in patients with mild cognitive deterioration. Methods: Subjects were 122 consecutive outpatients of a memory clinic (mean age ± SD 77 ± 8 years, 71% females, mean ± SD score of mental state exam 22 ± 3, 61% with clinical dementia rating of 0.5 and 39% of 1). Diffuse leukoaraiosis, fuzzy and patchy lesions, and lacunes were assessed on ordinary CT films and weights were computed based on clinical indicators of SIVD. A continuous score and an ordinal class (0–3) with higher values indicating higher vascular damage were derived. Results: Intraclass correlation coefficients for intra- and interrater reliability of the subcortical vascular score and class ranged from 0.84 to 0.88. Convergent validity versus the scale of Wahlund et al. was good (Spearman’s correlation coefficient between 0.53 and 0.73, p < 0.0005). Known-group validity was assessed versus clinical diagnoses of degenerative (mild cognitive impairment determined by Petersen et al. and NINCDS-ADRDA probable Alzheimer’s disease; n = 58), mixed (possible Alzheimer’s disease with cerebrovascular disease; n = 21), and vascular cognitive impairment (criteria of Erkinjuntti et al. for subcortical vascular dementia and cognitive impairment; n = 43). Patients with degenerative cognitive impairment were more often in the lower subcortical vascular classes (43% in class 0, 43% in class 1, 14% in class 2, and 0% in class 3), while patients with mixed (0, 24, 24, and 52%) and vascular cognitive impairment were in increasingly higher subcortical vascular classes (0, 12, 35, 53%; p < 0.00005). Criterion-related validity was assessed versus clinical indicators of cerebrovascular disease. Hypertension, platelet aggregation inhibitor use, balance, gait, and bradykinesia increased linearly with increasing subcortical vascular class (p for trend <0.02). Conclusions: This rating scale is valid and sensitive to capture different degrees of SIVD associated with mild cognitive deterioration.

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.

Departments of Neurology*, Radiology†, Inha University School of Medicine, Incheon; Department of Biomedical Engineering‡, Hanyang University, Seoul, Korea Background: Cerebral small vessel disease (SVD) induces vascular cognitive impairment (VCI) such as subcortical vascular dementia (SVaD) and subcortical vascular mild cognitive impairment (svMCI). We compared MRI parameters between SVaD and svMCI and determined which MRI parameters best correlated with cognitive function and disability on cross-sectional and longitudinal analyses within them. Methods: Twenty-four patients with SVaD and twelve with svMCI were recruited. They underwent multimodal MRIs including fluid-attenuated inversion recovery lesion load, lacunar infarct number, and fractional anisotropy (FA) and mean diffusivity (MD) from diffusion tensor imaging (DTI), neuropsychological testing, Sum of Boxes of Clinical Dementia Rating Scale (CDR-SB), Barthel Index, and the Korean version of a Geriatric Depression Scale (GDS-K). Seventeen patients were retested after 20 months for a brain MRI and clinical evaluation. Results: There were significant differences in average MD and peak height of MD histograms within normal-appearing brain tissue (NABT) between SVaD and svMCI patients. In the cross-sectional analysis, average MD within NABT significantly correlated with the composite neuropsychology score (r = -0.80, p< 0.001), the composite executive function score (r = -0.67, p< 0.001), and the CDR-SB (r = 0.54, p= 0.001), and the Barthel Index correlated with peak heights of the MD histograms (r = 0.37, p= 0.03) in NABT. Changes of CDR-SB was associated with changes of average MD within WMH (r = 0.57, p= 0.02), and changes of GDS-K was associated with changes of WMH volume (r = 0.51, p= 0.04) on a longitudinal scale. Conclusions: DTI parameters in NABT correlated with cognitive impairment and disability in VCI associated with SVD. Clinical progression of SVD was associated with some increment of WML volume and ultrastructural changes in WMH. Received: July 12, 2013 Revision received: September 9, 2013 Accepted: September 9, 2013

Vascular cognitive impairment (VCI) is the second leading cause of dementia with limited treatment options, characterised by cerebral hypoperfusion-induced white matter rarefaction (WMR). Subcortical VCI is the most common form of VCI, but the underlying reasons for region susceptibility remain elusive. Recent studies employing the bilateral cortical artery stenosis (BCAS) method demonstrate that various inflammasomes regulate white matter injury and blood-brain barrier dysfunction but whether caspase-1 inhibition will be beneficial remains unclear. To address this, we performed BCAS on C57/BL6 mice to study the effects of Ac-YVAD-cmk, a caspase-1 inhibitor, on the subcortical and cortical regions. Cerebral blood flow (CBF), WMR, neuroinflammation and the expression of tight junction-related proteins associated with blood-brain barrier integrity were assessed 15 days post BCAS. We observed that Ac-YVAD-cmk restored CBF, attenuated BCAS-induced WMR and restored subcortical myelin expression. Within the subcortical region, BCAS activated the NLRP3/caspase-1/interleukin-1beta axis only within the subcortical region, which was attenuated by Ac-YVAD-cmk. Although we observed that BCAS induced significant increases in VCAM-1 expression in both brain regions that were attenuated with Ac-YVAD-cmk, only ZO-1 and occludin were observed to be significantly altered in the subcortical region. Here we show that caspase-1 may contribute to subcortical regional susceptibility in a mouse model of VCI. In addition, our results support further investigations into the potential of Ac-YVAD-cmk as a novel treatment strategy against subcortical VCI and other conditions exhibiting cerebral hypoperfusion-induced WMR.

Donepezil in patients with subcortical vascular cognitive impairment: a randomised double-blind trial in CADASIL

Alzheimer’s disease (AD) is characterized by the presence of β-amyloid (Aβ) and tau, and subcortical vascular cognitive impairment (SVCI) is characterized by cerebral small vessel disease (CSVD). They are the most common causes of cognitive impairment in the elderly population. Concurrent CSVD burden is more commonly observed in AD-type dementia than in other neurodegenerative diseases. Recent developments in Aβ and tau positron emission tomography (PET) have enabled the investigation of the relationship between AD biomarkers and CSVD in vivo. In this review, we focus on the interaction between AD and CSVD markers and the clinical effects of these two markers based on molecular imaging studies. First, we cover the frequency of AD imaging markers, including Aβ and tau, in patients with SVCI. Second, we discuss the relationship between AD and CSVD markers and the potential distinct pathobiology of AD markers in SVCI compared to AD-type dementia. Next, we discuss the clinical effects of AD and CSVD markers in SVCI, and hemorrhagic markers in cerebral amyloid angiopathy. Finally, this review provides both the current challenges and future perspectives for SVCI.

Background: Behavioral and psychological symptoms of dementia (BPSD) are common in vascular cognitive impairment (VCI). Objective: Analyze the prevalence of BPSD in different VCI subtypes. Methods: We conducted a systematic review and meta-analysis per PRISMA guidelines. Eligible studies were retrieved from PubMed, Embase, and Web of Science and they used the Neuropsychiatric Inventory (NPI) to assess symptoms. A random-effects model calculated pooled prevalence rates for each NPI domain. Results: Thirty-five studies encompassing 5,805 patients were included. In unspecified VCI, apathy (54.29%), depression (43.48%), and irritability (38.76%) were the most common BPSD. Subcortical VCI showed a similar profile, with more frequent apathy (62.01%), depression (52.11%), and irritability (44.73%). Mixed dementia presented distinctively high apathy (61.65%), depression (45.68%), and sleep disturbances (44.63%), with elevated hallucinations (26.64%) compared to other VCI types. VCI non-dementia (VCI-ND) presented depression (44.97%), irritability (32.75%), and anxiety (30.07%) as the most common symptoms. Discussion: Hallucinations and sleep disturbances were more common in mixed cases, likely reflecting overlapping vascular and neurodegenerative pathology. High prevalences of apathy and agitation in subcortical VCI may reflect vascular burden. Depression was common across all groups. In VCI-ND, apathy, disinhibition, hallucinations, and agitation/aggression rates were significantly lower compared to other groups, suggesting an association of these symptoms with dementia progression and possibly increased vascular burden. Conclusion: BPSD are prevalent across VCI subtypes, with distinct profiles suggesting varying underlying mechanisms.

The study by Lu et al. highlights progressive structural changes in cerebral small vessel disease, spanning subcortical vascular mild cognitive impairment to vascular dementia. Using voxel-based morphometry, diffusion tensor imaging, and fixel-based analysis, the authors demonstrate converging gray and white matter alterations associated with both cognitive and motor decline. This commentary discusses the significance of their multimodal approach, its clinical implications for early diagnosis and disease monitoring, and the methodological challenges of generalizability and mixed pathology. We situate these findings within the broader literature on vascular cognitive impairment and emphasize directions for biomarker-driven translational research.

The clinical effects of β-amyloid positivity (Aβ+) on copathologies in various dementias remain relatively underexamined. Thus, the present study was conducted to investigate the prevalence and clinical effects of Aβ+ in subcortical vascular cognitive impairment (SVCI) and frontotemporal dementia (FTD). We enrolled SVCI (n = 583), FTD (n = 152), and cognitively unimpaired (CU) participants (n = 1,249) who underwent Aβ PET scans. The odds of having Aβ+ were subsequently compared among the diagnostic groups (CU, SVCI, and FTD) according to age and apolipoprotein E genotype. Additionally, a linear mixed-effects model was used to investigate the effects of Aβ+ on cognitive trajectories in SVCI and FTD. Compared with CU, the SVCI group had a higher prevalence of Aβ+ in the 75 to 90 years age group (adjusted odds ratio, 1.97; 95% confidence interval, 1.36-2.85; P < 0.001), as well as within the apolipoprotein E ε3/ε3 group (adjusted odds ratio, 1.78; 95% confidence interval, 1.20-2.63; P = 0.001), whereas the FTD group showed no difference in Aβ+ prevalence. Aβ+ was associated with a worse cognitive trajectory in SVCI (adjusted β-coefficient = -0.6424; P < 0.001), but not in FTD. These findings contribute to our understanding of Aβ biomarker traits in various dementias in Korea.

White matter hyperintensities (WMH) are a common cause of subcortical vascular cognitive impairment (SVCI). The silent yet progressive nature of WMH in cognitive decline underscores the need for reliable biomarkers for early detection and monitoring of its progression. This study aims to investigate the association between functional near-infrared spectroscopy (fNIRS) signals during mental and physical activities and WMH volume. Additionally, it explores the relationship between fNIRS signals and WMH progression. We recruited 27 patients with mild cognitive impairment (MCI) presenting WMH clinical characteristics. Data from fNIRS and MRI scans were collected during their first visit. Ten of them underwent fNIRS and MRI scans in a second visit two years later. WMH volume analysis used volBrain lesionBrain 1.0 (https://www.volbrain.net). ROC curve analysis was applied to the normalized WMH volume to determine a cut-off value for distinguishing between the subcortical vascular MCI (svMCI) and amnestic MCI (aMCI) groups. We compared fNIRS data during cognitive tests and physical activities between svMCI and aMCI groups at the first visit and in the two-year follow-up. While cognitive profiles were similar between groups, svMCI patients showed significantly reduced fNIRS signals, particularly in the left orbitofrontal cortex (OFC) during verbal fluency tasks (P = 0.005), with further reductions in the left dorsolateral prefrontal cortex (P = 0.049), left OFC (P = 0.012), and right OFC (P = 0.02) over two years. Baseline WMH volume correlated negatively with fNIRS signals during the Stroop test (r = -0.837, P = 0.005). Changes in WMH volume over two years correlated positively with changes in fNIRS signals in the right ventrolateral prefrontal cortex during memory tasks (r = 0.886, P = 0.033) and left OFC during balance tasks (r = 0.786, P = 0.028). Our results suggest that fNIRS signals have the potential to serve as biomarkers for WMH progression.

Subcortical vascular cognitive impairment (SVCI) is characterized by the presence of cerebral small vessel disease (CSVD) markers. Some SVCI patients also show Alzheimer's disease and cerebral amyloid angiopathy markers. However, the effects of these imaging markers on long-term clinical outcomes have not yet been established. The present study, therefore, aimed to determine how these imaging markers influence functional disability and/or mortality. We recruited 194 participants with SVCI from the memory clinic and followed them up. All participants underwent brain magnetic resonance imaging at baseline, and 177 (91.2%) participants underwent beta-amyloid (Aβ) positron emission tomography. We examined the occurrence of ischemic or hemorrhagic strokes. We also evaluated functional disability and mortality using the modified Rankin scale. To determine the effects of imaging markers on functional disability or mortality, we used Fine and Gray competing regression or Cox regression analysis. During a 8.6-year follow-up period, 46 of 194 patients (23.7%) experienced a stroke, 110 patients (56.7%) developed functional disabilities and 75 (38.6%) died. Aβ positivity (subdistribution hazard ratio [SHR]=2.73), greater white matter hyperintensity (WMH) volume (SHR=3.11) and ≥3microbleeds (SHR=2.29) at baseline were independent predictors of functional disability regardless of the occurrence of stroke. Greater WMH volume (hazard ratio =2.07) was an independent predictor of mortality. Our findings suggest that diverse imaging markers may predict long-term functional disability and mortality in patients with SVCI, which in turn may provide clinicians with a more insightful understanding of the long-term outcomes of SVCI.