Vascular cognitive impairment

Flier, Wiesje M. van der; Skoog, Ingmar; Schneider, Julie A.; Pantoni, Leonardo; Mok, Vincent; Chen, Christopher; Scheltens, Philip

doi:10.1038/nrdp.2018.3

Cited by 392 publications

(295 citation statements)

References 207 publications

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“…Together, such neurovascular system dysfunction and altered cerebral blood flow are likely significant elements of the pathophysiological processes underlying vascular cognitive impairment [37], and may be a mechanism of cognitive dysfunction in patients with HF. Not surprisingly, treating vascular diseases is the primary method to prevent vascular cognitive impairment [40]. …”

Section: Discussionmentioning

confidence: 99%

Differential Peripheral Inflammatory Factors Associated with Cognitive Function in Patients with Heart Failure

Redwine

Pung

Wilson

et al. 2018

Neuroimmunomodulation

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Objective: Cognitive deficits are common in patients with heart failure (HF), and can negatively affect self-care, predict rehospitalizations, and increase mortality rates 5-fold. Inflammation can produce vascular pathology, reducing cerebral blood flow to brain regions necessary for optimal cognitive function. The purpose of the investigation was to identify a pattern of peripheral blood inflammation-related biomarkers associated with cognitive impairment in patients with HF. Methods: Forty-five outpatients (median age = 67 years, SD = 9.9) were recruited from University of California, San Diego (UCSD) and Veterans Affairs San Diego Healthcare Systems (VASDHS), diagnosed with New York Heart Association Stages I–III HF. Participants were administered the Montreal Cognitive Assessment (MoCA) as a measure of global cognitive impairment, and blood was analyzed for plasma biomarkers, interferon-γ, tumor necrosis factor-α (TNFα), soluble intercellular adhesion molecule-1 (sICAM-1), soluble vascular cell adhesion molecule-1 (sVCAM-1), brain-derived neurotrophic factor (BDNF), interleukin-8 (IL-8), matrix metallopeptidase-9 (MMP-9), IL-6, C-reactive protein (CRP), and serum amyloid-A (SAA). Results: Almost half the patients scored below the threshold on the MoCA, indicating at least mild cognitive impairment. A factor analysis produced three biomarker factors: vascular inflammatory factor-1: TNFα, sICAM1, sVCAM1; neuroinflammatory factor-2: BDNF, MMP-9, IL-8; peripheral inflammatory factor-3: IL-6, CRP, SAA. Only vascular inflammatory factor-1 was significantly associated with cognitive function (MoCA) (ΔR² = 0.214, beta = –0.468, p = 0.008). Conclusions: In this cohort with HF, vascular inflammation appears related to poorer cognitive function. This could indicate targets for treatment to reduce cognitive deficits in HF. However, this is a preliminary study, and further research is needed.

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Section: Discussionmentioning

confidence: 99%

Differential Peripheral Inflammatory Factors Associated with Cognitive Function in Patients with Heart Failure

Redwine

Pung

Wilson

et al. 2018

Neuroimmunomodulation

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“…This is particularly important in neurodegenerative disease populations (e.g. Alzheimer's disease and Parkinson's disease), where WMHs are very common findings (van der Flier et al, 2018) and have been shown to interact with neurodegeneration and contribute to cognitive deficits (Dadar et al, 2018c(Dadar et al, , 2019 and therefore their misclassification as GM may systematically bias the findings of such studies.…”

Section: Introductionmentioning

confidence: 99%

“…cases with dementia(van der Flier et al, 2018). Since they contribute to cognitive deficits and neurodegeneration, incorrectly segmenting them as GM can introduce a systematic error into studies in such populations.…”

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confidence: 99%

BISON: Brain tISue segmentatiON pipeline using T1-weighted magnetic resonance images and a random forests classifier

Dadar

Collins

2019

Preprint

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Introduction: Accurate differentiation of brain tissue types from T1-weighted magnetic resonance images (MRIs) is a critical requirement in many neuroscience and clinical applications. Accurate automated tissue segmentation is challenging due to the variabilities in the tissue intensity profiles caused by differences in scanner models and acquisition protocols, in addition to the varying age of the subjects and potential presence of pathology.In this paper, we present BISON (Brain tISue segmentatiON), a new pipeline for tissue segmentation.Methods: BISON performs tissue segmentation using a random forests classifier and a set of intensity and location priors obtained based on T1-weighted images. The proposed method has been developed and cross-validated based on multi-center and multi-scanner manual labels of 72 subjects aging from 5-96 years old, ensuring the generalizability of the results to new data from various age ranges. In addition, we assessed the test-retest reliability of BISON on 2 datasets; a. using 20 subjects that had scan/re-scan MRIs and manual segmentations available, and b. using a human phantom dataset including 90 scans from a single individual acquired across 10 years. Results:The results of the proposed method were compared against Atropos, a commonly used tissue classification method from ANTs. The proposed method yielded cross-validation Dice Kappa values of κGM = 0.88 ± 0.03, κWM = 0.85 ± 0.03, κCSF = 0.77 ± 0.11, outperforming ANTs Atropos (κGM = 0.79 ± 0.05, κWM = 0.84 ± 0.05, κCSF = 0.64 ± 0.22) as well as test-retest Dice Kappa values of κGM = 0.94 ± 0.006, κWM = 0.92 ± 0.006, κCSF = 0.77 ± 0.11 outperforming both manual (κGM = 0.92 ± 0.01, κWM = 0.91 ± 0.01, κCSF = 0.74 ± 0.03) andANTs Atropos (κGM = 0.87 ± 0.001, κWM = 0.92 ± 0.001, κCSF = 0.79 ± 0.05). The human phantom dataset validations showed high generalizability for both Atropos (κGM = 0.97 ± 0.01, κWM = 0.96 ± 0.01, κCSF = 0.93 ± 0.02) and BISON (κGM = 0.95 ± 0.01, κWM = 0.94 ± 0.01, κCSF = 0.85 ± 0.03), while Atropos tended to consistently under-segment the cortical CSF. Finally, our assessment of BISON, Atropos, FAST from FSL, and SPM12 segmentations in presence of white matter hyperintensities (WMHs) showed that BISON outperforms the other three methods, correctly detecting WMHs as WM. Conclusion:Our results show that BISON can provide accurate and robust segmentations in data from different age ranges and various scanner models, making it ideal for performing tissue classification in large multi-center and multi-scanner databases.

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“…Several mechanisms link vascular disease to cognitive impairment. Historically, VaD has been associated with large vessel infarcts, however small vessel disease can also lead to cognitive decline and dementia [7]. A previous prospective study showed that at least seven different pathologies can predict VCI: large infarcts, lacunar infarcts, microinfarcts, myelin loss, arteriolosclerosis, cerebral amyloid angiopathy (CAA), and perivascular space dilation [8].…”

Section: Introductionmentioning

confidence: 99%

Update on the Neurobiology of Vascular Cognitive Impairment: From Lab to Clinic

Vinciguerra

Lanza

Puglisi

et al. 2020

IJMS

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In the last years, there has been a significant growth in the literature exploring the pathophysiology of vascular cognitive impairment (VCI). As an "umbrella term" encompassing any degree of vascular-related cognitive decline, VCI is deemed to be the most common cognitive disorder in the elderly, with a significant impact on social and healthcare expenses. Interestingly, some of the molecular, biochemical, and electrophysiological abnormalities detected in VCI seem to correlate with disease process and progression, eventually promoting an adaptive plasticity in some patients and a maladaptive, dysfunctional response in others. However, the exact relationships between vascular lesion, cognition, and neuroplasticity are not completely understood. Recent findings point out also the possibility to identify a panel of markers able to predict cognitive deterioration in the so-called "brain at risk" for vascular or mixed dementia. This will be of pivotal importance when designing trials of disease-modifying drugs or non-pharmacological approaches, including non-invasive neuromodulatory techniques. Taken together, these advances could make VCI a potentially preventable cause of both vascular and degenerative dementia in late life. This review provides a timely update on the recent serological, cerebrospinal fluid, histopathological, imaging, and neurophysiological studies on this "cutting-edge" topic, including the limitations, future perspectives and translational implications in the diagnosis and management of VCI patients.

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Vascular cognitive impairment

Cited by 392 publications

References 207 publications

Differential Peripheral Inflammatory Factors Associated with Cognitive Function in Patients with Heart Failure

Differential Peripheral Inflammatory Factors Associated with Cognitive Function in Patients with Heart Failure

BISON: Brain tISue segmentatiON pipeline using T1-weighted magnetic resonance images and a random forests classifier

Update on the Neurobiology of Vascular Cognitive Impairment: From Lab to Clinic

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