Carotid artery stenosis and brain connectivity: the role of white matter hyperintensities

Porcu, Michele; Garofalo, Paolo; Craboledda, Davide; Suri, Jasjit S.; Suri, Harman S.; Montisci, Roberto; Sanfilippo, Roberto; Saba, Luca

doi:10.1007/s00234-019-02327-5

Cited by 16 publications

(25 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To the best of our knowledges, no similar studies have been conducted to date on this topic, except for the one by Porcu et al. 14 mentioned earlier. In that study, the authors analysed the effects of WMLs on patients with cICA stenosis eligible for CEA by using resting-state functional connectivity and applying graph theory in order to study the effects of both WMLB and TNWMLs.…”

Section: Discussionmentioning

confidence: 94%

See 1 more Smart Citation

White-matter hyperintensities in patients with carotid artery stenosis: An exploratory connectometry study

et al. 2020

Self Cite

View full text Add to dashboard Cite

Background White-matter lesions (WMLs) are frequently found in magnetic resonance imaging (MRi), and the WML load tends to be higher in patients affected by cervical internal carotid artery (cICA) stenosis. Purpose This study aimed to investigate whether and how WMLs influence cerebral networking in patients with asymptomatic cICA stenosis eligible for carotid endarterectomy (CEA) by exploiting the connectometry technique. Methods The study was designed as a cross-sectional exploratory investigation, and 28 patients with cICA stenosis eligible for CEA were enrolled. All patients received an MRI scan, including a T1-weighted, a FLAIR and a diffusion-weighted (DW) sequence. The T1 and FLAIR sequences were analysed for quantification of WML burden (WMLB) and total number of WMLs (TNWMLs). The DW data were reconstructed in the MNI space using q-space diffeomorphic reconstruction, and were grouped to create a connectometry database. The connectometry analysis evaluated the influence of both the WMLB and TNWMLs to local connectivity in a multiple regression model that included age, WMLB and TNWMLs, adopting three different T-score thresholds (1, 2 and 3). A p-value corrected for false discovery rate of <0.05 was adopted as a threshold to identify statistically significant results. Results The connectometry analysis identified several white-matter bundles negatively correlated with WMLB; no statistically significant correlation was found for TNWMLs. Conclusion Results of our study suggest that WMLs influence brain connectivity measured by the connectometry technique in patients with cICA stenosis eligible for CEA. Further studies are warranted to understand the role of WMLs better as a marker of disease in patients with cICA stenosis.

show abstract

Section: Discussionmentioning

confidence: 94%

“…A first step in this direction was made by Porcu et al. 14 who analysed the effects of WMLs by resting-state functional connectivity, evidencing that the total number of WMLs (TNWMLs) and the WML burden (WMLB) influence cerebral networking differently.…”

Section: Introductionmentioning

confidence: 99%

White-matter hyperintensities in patients with carotid artery stenosis: An exploratory connectometry study

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…The rs-fc group analysis was performed exploiting the CONN-fMRI (functional connectivity magnetic resonance imaging) fc toolbox v18b (Whitfield-Gabrieli and Nieto-Castanon, 2012) and SPM 12 tool (Wellcome Department of Imaging Neuroscience, London, United Kingdom) on the MATLAB platform vR2019b (MathWorks, Inc.). Similarly to previous studies (Porcu et al, 2019(Porcu et al, , 2020a, all the structural MP2RAGE T1-weighted and functional SE-EPI sequences were preprocessed using the CONN's default pipeline for volume-based analysis. The pipeline followed these steps: (1) functional label current functional files as a part list of secondary data sets (''original data label''); (2) functional realignment and unwarping; (3) functional center to (0, 0, 0) coordinates; (4) functional slice-timing correction; (5) functional outlier detection using intermediate settings (97th percentile in normative sample in functional outlier detection system, adopting globalsignal z-value threshold = 5 and subject-motion mm threshold = 0.9); (6) functional direct simultaneous segmentation of FIG.…”

Section: Rs-fc Group Analysismentioning

confidence: 99%

“…The effects of global WMH load on brain network have been studied by resting-state functional connectivity (rs-fc) both in healthy subjects (HS) (Benson et al, 2018;De Marco et al, 2017;Leuchter et al, 1994;Reijmer et al, 2015) and in subjects with different pathologies, including individuals with carotid artery stenosis (Porcu et al, 2020a) and with Alzheimer's disease (Chong et al, 2017). It has been suggested that the WMH interfere in brain networking by decreasing tract-specific functional connectivity, both directly and indirectly (Langen et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Effects of White Matter Hyperintensities on Brain Connectivity and Hippocampal Volume in Healthy Subjects According to Their Localization

et al. 2020

Self Cite

View full text Add to dashboard Cite

Purpose: To investigate the relationships between white matter hyperintensities (WMH) and hippocampal volume and their influence on brain networks by using resting-state functional connectivity (rs-fc) magnetic resonance (MR) according to their localization. Methods: In this exploratory cross-sectional study, 38 subjects from the public ''Leipzig Study for Mind/Body/ Emotion Interactions'' (LEMON) data set were selected. Morphometric analyses of both WMH burden and the total hippocampal relative volume (tHRV) were performed for each subject with two automated software. The WMH were then categorized as total (tWMH), periventricular (pvWMH), deep (dWMH), and juxtacortical (jcWMH). Spearman's correlation analyses were performed to evaluate the relationships between the following variables: age, tWMH, pvWMH, dWMH, jcWMH, and tHRV. Subsequently, three different rs-fc MR group analyses were performed using a multiple regression model that included age, pvWMH, dWMH, and jcWMH as second-level covariates. The graph theoretical analysis was applied to evaluate the effects of pvWMH (analysis 1), jcWMH (analysis 2), and dWMH (analysis 3). Results: Spearman's correlation analysis revealed several statistically significant (p < 0.05) positive and negative correlations, in particular positive between age and tWMH, and negative between dWMH and tHRV. rs-fc MR analysis 1 and 2 did not reveal statistically significant results; analysis 3 revealed that dWMH influenced network properties of several cerebral regions, in particular global and local efficiency of both the hippocampi. Conclusion: The localization of WMH influences brain activity in healthy subjects. In particular, dWMH are inversely correlated with tHRV and influence several properties of different cerebral areas, included both the hippocampi.

show abstract

“…16,17 Further, the use of imaging tools, such as resting-state functional connectivity (rs-fc) magnetic resonance (MR), has allowed the scientific community to study the effects of ECAS on brain circuits. [19][20][21][22][23][24] The aim of this narrative review is to discuss the current knowledge on the effects of ECAS on brain and cognition by analyzing the main evidence from animal and clinical studies, with a special focus on rs-fc MR studies.…”

Section: Introductionmentioning

confidence: 99%

Extracranial Carotid Artery Stenosis: The Effects on Brain and Cognition with a Focus on Resting‐State Functional Connectivity

Porcu

Cocco

Saloner

et al. 2020

Journal of Neuroimaging

Self Cite

View full text Add to dashboard Cite

Extracranial carotid artery stenosis (ECAS) due to the presence of atherosclerotic plaque is a well-known risk factor for stroke. Several structural imaging studies have investigated the effect of ECAS on the brain, focusing on structural damage (in particular cerebral small vessel disease) and on the rearrangement of the cerebral circulation. For example, it is known from the literature an association between carotid stenosis and cerebral small vessel disease, and it is also noted that a series of compensatory mechanisms are activated by the cerebrovascular system in order to overcome the cerebral hypoperfusion that is induced by the ECAS. Several neurocognitive studies have also investigated the association of ECAS with cognitive deficits. While the evidence tends to favor a direct association between ECAS and cognition, the actual relationship remains controversial. In the last decade, various resting-state functional connectivity (rs-fc) magnetic resonance (MR) studies have tried to elucidate the "in vivo" mechanisms underlying the cognitive impairment observed in these patients. Although these studies have been performed on small cohorts of patients without standardized protocols, it is plausible that in the future, such studies will help find early stage markers of cognitive impairment. This could permit an extension of indications for revascularization for the treatment of cognitive impairment in selected patients. The aim of this narrative review is to discuss the current knowledge on the effects of ECAS on brain and cognition by analyzing the main evidence from animal and clinical studies, with a special focus on rs-fc MR studies.

show abstract

Carotid artery stenosis and brain connectivity: the role of white matter hyperintensities

Cited by 16 publications

References 53 publications

White-matter hyperintensities in patients with carotid artery stenosis: An exploratory connectometry study

White-matter hyperintensities in patients with carotid artery stenosis: An exploratory connectometry study

Effects of White Matter Hyperintensities on Brain Connectivity and Hippocampal Volume in Healthy Subjects According to Their Localization

Extracranial Carotid Artery Stenosis: The Effects on Brain and Cognition with a Focus on Resting‐State Functional Connectivity

Contact Info

Product

Resources

About