Deep learning applications in vascular dementia using neuroimaging.

PURPOSE OF REVIEW: Vascular dementia (VaD) is the second common cause of dementia after Alzheimer's disease, and deep learning has emerged as a critical tool in dementia research. The aim of this article is to highlight the current deep learning applications in VaD-related imaging biomarkers and diagnosis. RECENT FINDINGS: The main deep learning technology applied in VaD using neuroimaging data is convolutional neural networks (CNN). CNN models have been widely used for lesion detection and segmentation, such as white matter hyperintensities (WMH), cerebral microbleeds (CMBs), perivascular spaces (PVS), lacunes, cortical superficial siderosis, and brain atrophy. Applications in VaD subtypes classification also showed excellent results. CNN-based deep learning models have potential for further diagnosis and prognosis of VaD. SUMMARY: Deep learning neural networks with neuroimaging data in VaD research represent significant promise for advancing early diagnosis and treatment strategies. Ongoing research and collaboration between clinicians, data scientists, and neuroimaging experts are essential to address challenges and unlock the full potential of deep learning in VaD diagnosis and management.

Neurovascular Correlates of Cobalamin, Folate, and Homocysteine in Dementia.

BACKGROUND: Cobalamin (Cbl) and folate are common supplements clinicians prescribe as an adjuvant therapy for dementia patients, on the presumption of their neurotrophic and/or homocysteine (Hcy) lowering effect. However, the treatment efficacy has been found mixed and the effects of Cbl/folate/Hcy on the human brain remain to be elucidated. OBJECTIVE: To explore the neurovascular correlates of Cbl/folate/Hcy in Alzheimer's disease (AD) and subcortical ischemic vascular dementia (SIVD). METHODS: Sixty-seven AD patients and 57 SIVD patients were prospectively and consecutively recruited from an outpatient clinic. Multimodal 3-Tesla magnetic resonance imaging was performed to quantitatively evaluate cerebral blood flow (CBF) and white matter integrity. The relationship between neuroimaging metrics and the serum levels of Cbl/folate/Hcy was examined by using the Kruskal-Wallis test, partial correlation analysis, and moderation analysis, at a significance level of 0.05. RESULTS: As a whole, CBF mainly associated with Cbl/folate while white matter hyperintensities exclusively associated with Hcy. As compared with AD, SIVD exhibited more noticeable CBF correlates (spatially widespread with Cbl and focal with folate). In SIVD, a bilateral Cbl-moderated CBF coupling was found between medial prefrontal cortex and ipsilateral basal ganglia, while in the fronto-subcortical white matter tracts, elevated Hcy was associated with imaging metrics indicative of increased injury in both axon and myelin sheath. CONCLUSIONS: We identified the neurovascular correlates of previously reported neurotrophic effect of Cbl/folate and neurotoxic effect of Hcy in dementia. The correlates exhibited distinct patterns in AD and SIVD. The findings may help improving the formulation of supplemental Cbl/folate treatment for dementia.

Cerebrovascular Hemodynamics in Cognitive Impairment and Dementia: A Systematic Review and Meta-Analysis of Transcranial Doppler Studies.

INTRODUCTION: Transcranial Doppler (TCD) sonography is a noninvasive tool for measuring cerebrovascular hemodynamics. Studies have reported alterations in cerebrovascular hemodynamics in normal aging, mild cognitive impairment (MCI), and dementia, as well as in different etiologies of dementia. This systematic review and meta-analysis was designed to investigate the relationship between cerebral blood velocity (CBv) and pulsatility index (PI) in the middle cerebral artery (MCA) in persons with MCI and dementia. METHODS: A systematic literature search was conducted in Pubmed, Embase, Cochrane Library, Epistemonikos, PsychINFO, and CINAHL. The search was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. After screening of 33,439 articles, 86 were reviewed in full-text, and 35 fulfilled the inclusion criteria. RESULTS: CBv was significantly lower and PI significantly higher in MCA in vascular dementia (VaD) and Alzheimer's disease (AD) compared to cognitively normal (CN) older persons. Also, CBv was lower in MCI compared to CN. There were no significant differences in CBv in MCA in AD compared with VaD, although PI was higher in VaD compared to AD. CONCLUSION: Alterations in cerebrovascular hemodynamics are seen in AD, VaD, and MCI. While PI was slightly higher in VaD compared to AD, the reduction in CBv appears to be equally pronounced across neurodegenerative and vascular etiologies of dementia.

Amide proton transfer could be a surrogate imaging marker for predicting vascular cognitive impairment.

BACKGROUD: Emerging evidence suggests an overlap in the underlying pathways contributing to both cerebral small vessel disease (CSVD) and the neurodegenerative disease. Studies investigating the progression of CSVD should incorporate markers that reflect neurodegenerative lesions. OBJECTIVE: We aim to investigate whether Amide proton transfer (APT) can serve as a potential marker for reflecting vascular cognitive impairment (VCI). METHOD: Participants were categorized into one of three groups based on their Montreal Cognitive Assessment (MoCA) scores: normal control group (age,54.9 ± 7.9; male, 52.9%), mild cognitive impairment (MCI) group (age,55.7 ± 6.9; male, 42.6%), or vascular dementia (VaD) group (age,57.6 ± 5.5, male, 58.5%). One way analysis of variance was performed to compare the demographic and APT variables between groups. Multiple logistic regression analysis wwas constructed to examine the relationship between APT values and VCI grouping. A hierarchical linear regression model was employed to examine the associations between patients' demographic factors, imaging markers, APT values, and MoCA. RESULTS: The APT values of frontal white matter, hippocampus, amygdala, and thalamus were significantly different among different groups (p < 0.05). The APT values of frontal white matter, amygdala, and thalamus indicate a significant positive effect on MCI grouping. the APT values of frontal white matter, amygdala, and thalamus indicate a significant positive effect on VaD grouping. The demographic data, CSVD imaging markers and APT values can account for 5.1%, 20.1% and 27.7% of the variation in MoCA, respectively. CONCLUSION: APT imaging can partially identifying and predicting the occurrence of VCI.

A Novel Needle Mouse Model of Vascular Cognitive Impairment and Dementia.

Bilateral common carotid artery (CCA) stenosis (BCAS) is a useful model to mimic vascular cognitive impairment and dementia (VCID). However, current BCAS models have the disadvantages of high cost and incompatibility with magnetic resonance imaging (MRI) scanning because of metal implantation. We have established a new low-cost VCID model that better mimics human VCID and is compatible with live-animal MRI. The right and the left CCAs were temporarily ligated to 32- and 34-gauge needles with three ligations, respectively. After needle removal, CCA blood flow, cerebral blood flow, white matter injury (WMI) and cognitive function were measured. In male mice, needle removal led to ∼49.8% and ∼28.2% blood flow recovery in the right and left CCA, respectively. This model caused persistent and long-term cerebral hypoperfusion in both hemispheres (more severe in the left hemisphere), and WMI and cognitive dysfunction in ∼90% of mice, which is more reliable compared with other models. Importantly, these pathologic changes and cognitive impairments lasted for up to 24 weeks after surgery. The survival rate over 24 weeks was 81.6%. Female mice showed similar cognitive dysfunction, but a higher survival rate (91.6%) and relatively milder white matter injury. A novel, low-cost VCID model compatible with live-animal MRI with long-term outcomes was established.SIGNIFICANCE STATEMENT Bilateral common carotid artery (CCA) stenosis (BCAS) is an animal model mimicking carotid artery stenosis to study vascular cognitive impairment and dementia (VCID). However, current BCAS models have the disadvantages of high cost and incompatibility with magnetic resonance imaging (MRI) scanning due to metal implantation. We established a new asymmetric BCAS model by ligating the CCA to various needle gauges followed by an immediate needle removal. Needle removal led to moderate stenosis in the right CCA and severe stenosis in the left CCA. This needle model replicates the hallmarks of VCID well in ∼90% of mice, which is more reliable compared with other models, has ultra-low cost, and is compatible with MRI scanning in live animals. It will provide a new valuable tool and offer new insights for VCID research.

Disrupted Structural White Matter Network in Alzheimer's Disease Continuum, Vascular Dementia, and Mixed Dementia: A Diffusion Tensor Imaging Study.

BACKGROUND: Dementia presents a significant burden to patients and healthcare systems worldwide. Early and accurate diagnosis, as well as differential diagnosis of various types of dementia, are crucial for timely intervention and management. However, there is currently a lack of clinical tools for accurately distinguishing between these types. OBJECTIVE: This study aimed to investigate the differences in the structural white matter (WM) network among different types of cognitive impairment/dementia using diffusion tensor imaging, and to explore the clinical relevance of the structural network. METHODS: A total of 21 normal control, 13 subjective cognitive decline (SCD), 40 mild cognitive impairment (MCI), 22 Alzheimer's disease (AD), 13 mixed dementia (MixD), and 17 vascular dementia (VaD) participants were recruited. Graph theory was utilized to construct the brain network. RESULTS: Our findings revealed a monotonic trend of disruption in the brain WM network (VaD > MixD > AD > MCI > SCD) in terms of decreased global efficiency, local efficiency, and average clustering coefficient, as well as increased characteristic path length. These network measurements were significantly associated with the clinical cognition index in each disease group separately. CONCLUSION: These findings suggest that structural WM network measurements can be utilized to differentiate between different types of cognitive impairment/dementia, and these measurements can provide valuable cognition-related information.

Imaging, Genetic, and Pathological Features of Vascular Dementia.

BACKGROUND: Over the past decades, marked progress has been made in detecting vascular dementia (VD) both through maturation of diagnostic concepts and advances in brain imaging, especially MRI. We summarized the imaging, genetic, and pathological features of VD in this review. SUMMARY: It is a challenge for the diagnosis and treatment of VD, particularly in patients where there is no evident temporal relation between cerebrovascular events and cognitive dysfunction. In patients with cognitive dysfunction with poststroke onset, the etiological classification is still complicated. KEY MESSAGES: In this review, we summarized the clinical, imaging, and genetic as well as pathological features of VD. We hope to offer a framework to translate diagnostic criteria to daily practice, address treatment, and highlight some future perspectives.

Neuroinflammation and amyloid deposition in the progression of mixed Alzheimer and vascular dementia.

BACKGROUND: Alzheimer's disease (AD) and vascular contributions to cognitive impairment and dementia (VCID) pathologies coexist in patients with cognitive impairment. Abnormal amyloid beta (Aß) deposition is the hallmark pathologic biomarker for AD. Neuroinflammation may be a pathophysiological mechanism in both AD and VCID. In this study, we aimed to understand the role of neuroinflammation and Aß deposition in white matter hyperintensities (WMH) progression and cognitive decline over a decade in patients with mixed AD and VCID pathologies. METHODS: Twenty-four elderly participants (median [interquartile range] age 78 [64.8, 83] years old, 14 female) were recruited from the Knight Alzheimer Disease Research Center. 11C-PK11195 standard uptake value ratio (SUVR) and 11C-PiB mean cortical binding potential (MCBP) were used to evaluate neuroinflammation and Aß deposition in-vivo, respectively. Fluid-attenuated inversion recovery MR images were acquired to obtain baseline WMH volume and its progression over 11.5 years. Composite cognitive scores (global, processing speed and memory) were computed at baseline and follow-up over 7.5 years. Multiple linear regression models evaluated the association between PET biomarkers (11C-PK11195 SUVR and 11C-PiB MCBP) and baseline WMH volume and cognitive function. Moreover, linear mixed-effects models evaluated whether PET biomarkers predicted greater WMH progression or cognitive decline over a decade. RESULTS: Fifteen participants (62.5%) had mixed AD (positive PiB) and VCID (at least one vascular risk factor) pathologies. Elevated 11C-PK11195 SUVR, but not 11C-PiB MCBP, was associated with greater baseline WMH volume and predicted greater WMH progression. Elevated 11C-PiB MCBP was associated with baseline memory and global cognition. Elevated 11C-PK11195 SUVR and elevated 11C-PiB MCBP independently predicted greater global cognition and processing speed declines. No association was found between 11C-PK11195 SUVR and 11C-PiB MCBP. CONCLUSIONS: Neuroinflammation and Aß deposition may represent two distinct pathophysiological pathways, and both independently contributed to the progression of cognitive impairment in mixed AD and VCID pathologies. Neuroinflammation, but not Aß deposition, contributed to WMH volume and progression.

A neuropsychological profile and its correlation with neuroimaging markers in patients with subcortical ischaemic vascular dementia.

OBJECTIVES: Cognitive and neuroimaging assessments are still the main clinical practice methods for screening and diagnosing vascular dementia (VaD) patients. This study aimed to establish the neuropsychological characteristics of mild-to-moderate subcortical ischaemic vascular dementia (SIVD) patients, find an optimal cognitive marker for differentiating them from Alzheimer's disease (AD) patients, and explore the correlation between cognitive function and total small vessel disease (SVD) burden. METHODS: SIVD (n = 60) and AD (n = 30) patients and cognitively unimpaired healthy controls (HCs; n = 30) were recruited from our longitudinal MRI AD and SIVD study (ChiCTR1900027943) and received a comprehensive neuropsychological assessment and a multimodal MRI scan. Cognitive performance and MRI SVD markers were compared between groups. Combined cognitive scores were established for differentiating between SIVD and AD patients. Correlations between cognitive function and total SVD scores were analysed in dementia patients. RESULTS: SIVD patients showed poorer performance in information processing speed and better performance in memory, language, and visuospatial function than AD patients, although all cognitive domains were impaired in both groups compared with HCs. Combined cognitive scores showed an area under the curve of 0.727 (95%CI 0.62-0.84, p < 0.001) for differentiating SIVD and AD patients. Auditory Verbal Learning Test recognition scores were negatively correlated with total SVD scores in SIVD patients. CONCLUSIONS: Our results suggested that neuropsychological assessments, specifically combined tests including episodic memory, information processing speed, language and visuospatial ability, are useful in the clinical differentiation between SIVD and AD patients. Moreover, cognitive dysfunction was partly correlated with MRI SVD burden in SIVD patients.

Brain Imaging Abnormalities in Mixed Alzheimer's and Subcortical Vascular Dementia.

BACKGROUND: A large proportion of Alzheimer's disease (AD) patients have coexisting subcortical vascular dementia (SVaD), a condition referred to as mixed dementia (MixD). Brain imaging features of MixD presumably include those of cerebrovascular disease and AD pathology, but are difficult to characterize due to their heterogeneity. OBJECTIVE: To perform an exploratory analysis of conventional and non-conventional structural magnetic resonance imaging (MRI) abnormalities in MixD and to compare them to those observed in AD and SVaD. METHODS: We conducted a cross-sectional, region-of-interest-based analysis of 1) hyperintense white-matter signal abnormalities (WMSA) on T2-FLAIR and hypointense WMSA on T1-weighted MRI; 2) diffusion tensor imaging; 3) quantitative susceptibility mapping; and 4) effective transverse relaxation rate (R2*) in N = 17 participants (AD:5, SVaD:5, MixD:7). General linear model was used to explore group differences in these brain imaging measures. RESULTS: Model findings suggested imaging characteristics specific to our MixD group, including 1) higher burden of WMSAs on T1-weighted MRI (versus both AD and SVaD); 2) frontal lobar preponderance of WMSAs on both T2-FLAIR and T1-weighted MRI; 3) higher fractional anisotropy values within normal-appear white-matter tissues (versus SVaD, but not AD); and 4) lower R2* values within the T2-FLAIR WMSA areas (versus both AD and SVaD). CONCLUSION: These findings suggest a preliminary picture of the location and type of brain imaging characteristics associated with MixD. Future imaging studies may employ region-specific hypotheses to distinguish MixD more rigorously from AD or SVaD.

Diffusion Tensor Imaging as a Tool to Evaluate the Cognitive Function of Patients With Vascular Dementia: A Meta-Analysis.

BACKGROUND: Vascular dementia (VaD) is the most common type of dementia secondary to Alzheimer's disease. The pathologic mechanism of VaD is complex, and VaD still lacks a more objective diagnosis and evaluation method. Diffusion tensor imaging (DTI) can better detect the organizational structure and functional characteristics compared with any other diagnosis methods. Therefore, DTI has broad application in evaluating the severity and prognosis of VaD. This study aimed to assess the value of DTI in evaluating the cognitive function of patients with VaD. METHODS: Authors searched Pubmed, Embase, and Cochrane Library, using the search terms, such as "diffusion tensor imaging," "DTI," "Vascular Dementia," "Arteriosclerotic Dementia," "Cognition," and "Cognitive." A voxel-based meta-analysis combined with quality statistics was performed, using the anisotropic effect-size version of the signed differential mapping method. RESULTS: A total of 8 case-control studies were included in this meta-analysis. The sample size of patients ranged from 35 to 60, including 166 patients in the VaD group and 177 healthy individuals. The DTI imaging of the brain tissue of VaD patients was significantly different from that of healthy individuals. CONCLUSIONS: DTI imaging of the brain tissue of VaD patients was clearly different from that of healthy controls. Therefore it may be feasible to use DTI imaging as a diagnostic method for VaD.

Effects of Carotid Artery Stiffness on Cerebral Small-Vessel Disease and Cognition.

Background Carotid artery stiffness is associated with cognitive impairment and dementia, but the underlying mechanisms remain unknown. We examined the associations of carotid artery stiffness with cerebral small-vessel disease markers, cognition, and dementia subtypes in a memory clinic cohort. Methods and Results A total of 272 participants underwent carotid ultrasonography, 3 Tesla brain magnetic resonance imaging, and neuropsychological assessment. Carotid ultrasonography was used to assess ß-index, pressure-strain elastic modulus, and pulse-wave velocity-ß. Brain magnetic resonance images were graded for cerebral small-vessel disease markers, including white matter hyperintensities, lacunes, and cerebral microbleeds. Participants were classified as having no cognitive impairment, cognitive impairment and no dementia, or dementia subtyped as Alzheimer disease and vascular dementia. Cognition was assessed using National Institute of Neurological Disorders and Stroke-Canadian Stroke Network harmonization battery. After adjusting for age, sex, cardiovascular risk factors, and diseases, multivariable models showed that ß-index (ß=0.69; P=0.002), elastic modulus (ß=0.78; P<0.001), and pulse-wave velocity-ß (ß=0.80; P<0.001) were associated with white matter hyperintensities, and elastic modulus (odds ratio [OR], 1.39 [95% CI, 1.04-1.85]) and pulse-wave velocity-ß (OR, 1.47 [95% CI, 1.10-1.98]) were independently associated with lacunes. Similarly, ß-index (OR, 2.04 [95% CI, 1.14-4.13]), elastic modulus (OR, 2.22 [95% CI, 1.25-4.42]), and pulse-wave velocity-ß (OR, 2.50 [95% CI, 1.36-5.18]) were independently associated with vascular dementia. Carotid stiffness measures were independently associated with worse performance in global cognition, visuomotor speed, visuospatial function, and executive function. These associations became largely nonsignificant after further adjusting for cerebral small-vessel disease markers. Conclusions In memory clinic patients, carotid artery stiffness was associated with white matter hyperintensities and lacunes, impairment in global and domain-specific cognition, and causative subtypes of dementia, particularly vascular. The effects of carotid stiffness on cognition were not independent of, and were partially mediated by, cerebral small-vessel disease.

Diagnostic performance of deep learning-based automatic white matter hyperintensity segmentation for classification of the Fazekas scale and differentiation of subcortical vascular dementia.

PURPOSE: To validate the diagnostic performance of commercially available, deep learning-based automatic white matter hyperintensity (WMH) segmentation algorithm for classifying the grades of the Fazekas scale and differentiating subcortical vascular dementia. METHODS: This retrospective, observational, single-institution study investigated the diagnostic performance of a deep learning-based automatic WMH volume segmentation to classify the grades of the Fazekas scale and differentiate subcortical vascular dementia. The VUNO Med-DeepBrain was used for the WMH segmentation system. The system for segmentation of WMH was designed with convolutional neural networks, in which the input image was comprised of a pre-processed axial FLAIR image, and the output was a segmented WMH mask and its volume. Patients presented with memory complaint between March 2017 and June 2018 were included and were split into training (March 2017-March 2018, n = 596) and internal validation test set (April 2018-June 2018, n = 204). RESULTS: Optimal cut-off values to categorize WMH volume as normal vs. mild/moderate/severe, normal/mild vs. moderate/severe, and normal/mild/moderate vs. severe were 3.4 mL, 9.6 mL, and 17.1 mL, respectively, and the AUC were 0.921, 0.956 and 0.960, respectively. When differentiating normal/mild vs. moderate/severe using WMH volume in the test set, sensitivity, specificity, and accuracy were 96.4%, 89.9%, and 91.7%, respectively. For distinguishing subcortical vascular dementia from others using WMH volume, sensitivity, specificity, and accuracy were 83.3%, 84.3%, and 84.3%, respectively. CONCLUSION: Deep learning-based automatic WMH segmentation may be an accurate and promising method for classifying the grades of the Fazekas scale and differentiating subcortical vascular dementia.

Hemodynamic changes and neuronal damage detected by 9.4 T MRI in rats with chronic cerebral ischemia and cognitive impairment.

INTRODUCTION: The bilateral common carotid artery occlusion (BCCAO) rat model is an ideal animal model for simulating the pathology of chronic brain hypoperfusion in humans. However, dynamic changes in neuronal activity, cellular edema, and neuronal structural integrity in vivo after BCCAO have rarely been reported. The purpose of this study is to use a 9.4 T MRI to explore the pathophysiological mechanisms of vascular dementia. MATERIALS AND METHODS: Twelve Sprague-Dawley (SD) rats were randomly divided into two groups: the sham group and the model group (n = 6 for each group). Rats were subjected to MRI using T2*WI, diffusion tensor imaging (DTI), and DWI sequences by MRI at the following six time points: presurgery and 6 h, 3 days, 7 days, 21 days, and 28 days postsurgery. Then, the T2*, fractional anisotropy (FA), and average apparent diffusion coefficient (ADC) values were measured in the bilateral cortices and hippocampi. After MRI scanning, all rats in both groups were subjected to the Y-maze test, novel object recognition test, and open-field test to assess their learning, memory, cognition, and locomotor activity. RESULTS: The T2*, FA, and ADC values in the cerebral cortex and hippocampus decreased sharply at 6 h after BCCAO in the model group compared with those of the sham group. By Day 28, the T2* and ADC values gradually increased to close to those in the sham group, but the FA values changed little, and the rats in the model group had worse learning, memory, and cognition and less locomotor activity than the rats in the sham group. CONCLUSIONS: The BCCAO is an ideal rat model for studying the pathophysiological mechanisms of vascular dementia.

Vascular Lesions and Brain Atrophy in Alzheimer's, Vascular and Mixed Dementia: An Optimized 3T MRI Protocol Reveals Distinctive Radiological Profiles.

BACKGROUND: Vascular lesions may be a common finding also in Alzheimer's dementia, but their role on cognitive status is uncertain. OBJECTIVE: The study aims to investigate their distribution in patients with Alzheimer's, vascular or mixed dementia and detect any distinctive neuroradiological profiles. METHODS: Seventy-six subjects received a diagnosis of Alzheimer's (AD=32), vascular (VD=26) and mixed (MD=18) dementia. Three independent raters assessed the brain images acquired with an optimized 3T MRI protocol (including (3D FLAIR, T1, SWI, and 2D coronal T2 sequences) using semiquantitative scales for vascular lesions (periventricular lesions (PVL), deep white matter lesions (DWML), deep grey matter lesions (DGML), enlarged perivascular spaces (PVS), and microbleeds (MB)) and brain atrophy (medial temporal atrophy (MTA), posterior atrophy (PA), global cortical atrophy- frontal (GCA-F) and Evans' index). RESULTS: Raters reached a good-to-excellent agreement for all scales (ICC ranging from 0.78-0.96). A greater number of PVL (p<0.001), DWML (p<0.001), DGML (p=0.010), and PVS (p=0.001) was observed in VD compared to AD, while MD showed a significant greater number of PVL (p=0.001), DWML (p=0.002), DGML (p=0.018), and deep and juxtacortical MB (p=0.006 and p<0.001, respectively). Comparing VD and MD, VD showed a higher number of PVS in basal ganglia and centrum semiovale (p=0.040), while MD showed more deep and juxtacortical MB (p=0.042 and p=0.022, respectively). No significant difference was observed in scores of cortical atrophy scales and Evans' index among the three groups. CONCLUSION: The proposed MRI protocol represents a useful advancement in the diagnostic assessment of patients with cognitive impairment by more accurately detecting vascular lesions, mainly microbleeds, without a significant increase in time and resource expenditure. Our findings confirm that white and grey matter lesions predominate in vascular and mixed dementia, whereas deep and juxtacortical microbleeds predominate in mixed dementia, suggesting that cerebral amyloid angiopathy could be the main underlying pathology.

The Association Between Standard Electrocardiography and Cerebral Small Vessel Disease in a Memory Clinic Study.

BACKGROUND: P-wave terminal force in lead V1 (PTFV1) on electrocardiography has been associated with atrial fibrillation and ischemic stroke. OBJECTIVE: To investigate whether PTFV1 is associated with cerebral small vessel disease (CSVD) markers and etiological subtypes of cognitive impairment and dementia. METHODS: Participants were recruited from ongoing memory clinic study between August 2010 to January 2019. All participants underwent physical and medical evaluation along with an electrocardiography and 3 T brain magnetic resonance imaging. Participants were classified as no cognitive impairment, cognitive impairment no dementia, vascular cognitive impairment no dementia, and dementia subtypes (Alzheimer's disease and vascular dementia). Elevated PTFV1 was defined as > 4,000µV×ms and measured manually on ECG. RESULTS: Of 408 participants, 78 (19.1%) had elevated PTFV1 (37 women [47%]; mean [SD] age, 73.8 [7.2] years). The participants with elevated PTFV1 had higher burden of lacunes, cerebral microbleeds (CMB), and cortical microinfarcts. As for the CMB location, persons with strictly deep CMB and mixed CMB had significantly higher PTFV1 than those with no CMB (p = 0.005, p = 0.007). Regardless of adjustment for cardiovascular risk factors and/or heart diseases, elevated PTFV1 was significantly associated with presence of CMB (odds ratio, 2.26; 95% CI,1.33-3.91). CONCLUSION: Elevated PTFV1 was associated with CSVD, especially deep CMB. PTFV1 in vascular dementia was also higher compared to Alzheimer's disease. Thus, PTFV1 might be a potential surrogate marker of brain-heart connection and vascular brain damage.

Long-Term Connectome Analysis Reveals Reshaping of Visual, Spatial Networks in a Model With Vascular Dementia Features.

BACKGROUND: Connectome analysis of neuroimaging data is a rapidly expanding field that offers the potential to diagnose, characterize, and predict neurological disease. Animal models provide insight into biological mechanisms that underpin disease, but connectivity approaches are currently lagging in the rodent. METHODS: We present a pipeline adapted for structural and functional connectivity analysis of the mouse brain, and we tested it in a mouse model of vascular dementia. RESULTS: We observed lacunar infarctions, microbleeds, and progressive white matter change across 6 months. For the first time, we report that default mode network activity is disrupted in the mouse model. We also identified specific functional circuitry that was vulnerable to vascular stress, including perturbations in a sensorimotor, visual resting state network that were accompanied by deficits in visual and spatial memory tasks. CONCLUSIONS: These findings advance our understanding of the mouse connectome and provide insight into how it can be altered by vascular insufficiency.

Evaluation of cerebrovascular hemodynamics in vascular dementia patients with a new individual computational fluid dynamics algorithm.

BACKGROUND: Cerebral hemodynamic disorders are involved in the occurrence and progression of vascular dementia (VaD), but the methods to detect hemodynamics remainmultifarious and uncertain nowadays. We aim to exploit a computational fluid dynamics (CFD) approach by static and dynamic parameters, which can be used to detect individual cerebrovascular hemodynamics quantitatively. METHODS: A patient-specific CFD model was constructed with geometrical arteries on the magnetic resonance angiography (MRA) and hemodynamic parameters on ultrasound Doppler, by which, the structural and simulated hemodynamic indexes could be obtained, mainly including the cerebral arterial volume (CAV), the number of visible arterial outlets, the total cerebral blood flow (tCBF) index and the total cerebrovascular resistance (tCVR) index. The hemodynamics were detected in subcortical vascular dementia (SVaD) patients (n = 38) and cognitive normal controls (CNCs; n = 40). RESULTS: Compared with CNCs, the SVaD patients had reduced outlets, CAV and tCBF index (all P ≤ 0.001), increased volume of white matter hyperintensity (WMH) and tCVR index (both P ≤ 0.01). The fewer outlets (OR = 0.77), higher Hachinski ischemic score (HIS) (OR = 3.65), increased tCVR index (OR = 1.98) and volume of WMH (OR = 1.12) were independently associated with SVaD. All hemodynamic parameters could differentiate the SVaD patinets and CNCs, especially the composite index calculated by outlets, tCVR index and HIS (AUC = 0.943). Fewer outlets and more WMH increased the odds of SVaD, which were partly mediated by the tCBF index (14.4% and 13.0%, respectively). CONCLUSION: The reduced outlets, higher HIS and tCVR index may be independent risk factors for the SVaD, and a combination of these indexes can differentiate SVaD patients and CNCs reliably. The tCBF index potentially mediates the relationships between hemodynamic indexes and SVaD. Although all simulated indexes only represented the true hemodynamics indirectly, this CFD model can provide patient-specific hemodynamic alterations non-invasively and conveniently.

Role of White Matter Hyperintensities and Related Risk Factors in Vascular Cognitive Impairment: A Review.

White matter hyperintensities (WMHs) of presumed vascular origin are one of the imaging markers of cerebral small-vessel disease, which is prevalent in older individuals and closely associated with the occurrence and development of cognitive impairment. The heterogeneous nature of the imaging manifestations of WMHs creates difficulties for early detection and diagnosis of vascular cognitive impairment (VCI) associated with WMHs. Because the underlying pathological processes and biomarkers of WMHs and their development in cognitive impairment remain uncertain, progress in prevention and treatment is lagging. For this reason, this paper reviews the status of research on the features of WMHs related to VCI, as well as mediators associated with both WMHs and VCI, and summarizes potential treatment strategies for the prevention and intervention in WMHs associated with VCI.

Imaging features associated with idiopathic normal pressure hydrocephalus have high specificity even when comparing with vascular dementia and atypical parkinsonism.

BACKGROUND: Vascular dementia (VaD) and atypical parkinsonism often present with symptoms that can resemble idiopathic normal pressure hydrocephalus (iNPH) and enlarged cerebral ventricles, and can be challenging differential diagnoses. The aim was to investigate frequencies of imaging features usually associated with iNPH and their radiological diagnostic accuracy in a sample containing the relevant differential diagnoses VaD, progressive supranuclear palsy (PSP), multiple system atrophy parkinsonian type (MSA-P), and healthy controls. METHODS: Nine morphological imaging features usually associated with iNPH were retrospectively investigated in MR images of 55 patients with shunt-responsive iNPH, 32 patients with VaD, 30 patients with PSP, 27 patients with MSA-P, and 39 age-matched healthy controls. Logistic regression and receiver operating characteristic curves were used to assess diagnostic accuracy, sensitivity, and specificity for each imaging finding. RESULTS: In a logistic regression model using iNPH diagnosis as a dependent variable, the following imaging features contributed significantly to the model: callosal angle (OR = 0.95 (0.92-0.99), p = 0.012), Evans' index * 100 (OR = 1.51 (1.23-1.86), p < 0.001), enlarged Sylvian fissures (OR = 6.01 (1.42-25.40), p = 0.015), and focally enlarged sulci (OR = 10.18 (1.89-55.02), p = 0.007). Imaging features with 95% specificity for iNPH were: callosal angle ≤ 71°, temporal horns ≥ 7 mm, Evans' index ≥ 0.37, iNPH Radscale ≥ 9, and presence of DESH, bilateral ventricular roof bulgings or focally enlarged sulci. A simplified version of the iNPH Radscale with only four features resulted in equally high diagnostic accuracy as the original iNPH Radscale. CONCLUSIONS: There is a notable overlap between some of the commonly used imaging markers regarding iNPH, VaD and atypical parkinsonism, such as PSP. However, this study shows that the specificity of imaging markers usually associated with iNPH was high even when comparing with these challenging differential diagnoses. The callosal angle was the single imaging feature with highest diagnostic accuracy to discriminate iNPH from its mimics. A simplified rating scale using only a few selected features could be used with retained specificity.