Structural network efficiency predicts conversion to dementia

Tuladhar, Anil M.; Uden, Ingeborg W.M. van; Rutten-Jacobs, Loes C.A.; Lawrence, Andrew J.; Holst, Helena van der; Norden, Anouk van; Laat, Karlijn de; Dijk, Ewoud van; Claassen, Jurgen A.H.R.; Kessels, Roy P. C.; Markus, Hugh S.; Norris, David G.; Leeuw, Frank‐Erik de

doi:10.1212/wnl.0000000000002502

Cited by 99 publications

(94 citation statements)

References 25 publications

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“…Consistent with earlier studies, the global and local efficiency of the connectome was associated with WMH volume (Lawrence et al, 2014;Tuladhar, van Dijk, et al, 2016;Tuladhar, van Uden, et al, 2016;Valenti et al, 2017;Wiseman et al, 2018;Xu et al, 2018). Consistent with earlier studies, the global and local efficiency of the connectome was associated with WMH volume (Lawrence et al, 2014;Tuladhar, van Dijk, et al, 2016;Tuladhar, van Uden, et al, 2016;Valenti et al, 2017;Wiseman et al, 2018;Xu et al, 2018).…”

Section: Discussionsupporting

confidence: 88%

“…DTI tractography was used to generate white matter tracts (Behrens & Jbabdi, 2009). As a control, we assessed the association between WMH volume with global and local efficiency which has been described in prior studies (Lawrence et al, 2014;Tuladhar, van Dijk, et al, 2016;Tuladhar, van Uden, et al, 2016;Valenti et al, 2017;Wiseman et al, 2018;Xu et al, 2018). For the first time, we correlated the CVR of gray and white matter with the global and local efficiency of the connectome while adjusting for age, gender, WMH volume, and gray matter volume.…”

Section: Introductionmentioning

confidence: 99%

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The efficiency of the brain connectome is associated with cerebrovascular reactivity in persons with white matter hyperintensities

Reginold

Sam²,

Poublanc

et al. 2019

Human Brain Mapping

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The purpose of this study was to determine the relationship between the organization of the brain connectome and cerebrovascular reactivity (CVR) in persons with white matter hyperintensities. Diffusion tensor and CVR mapping 3T MRI scans were acquired in 31 participants with white matter hyperintensities. In each participant, the connectome was assessed by reconstructing all white matter tracts with tractography and segmenting the whole brain into multiple regions. Graph theory analysis was performed to quantify how effectively tracts connected brain regions by measuring the global and local efficiency of the connectome. CVR in white matter and gray matter was correlated with the global and local efficiency of the connectome, while adjusting for age, gender, and gray matter volume. For comparison, white matter hyperintensity volume was also correlated with global and local efficiency. White matter CVR was positively correlated with the global efficiency (coefficient: 23.3, p = .005) and local efficiency (coefficient: 2850, p = .004) of the connectome. Gray matter CVR was positively correlated with the global efficiency (coefficient: 21.3, p < .001) and local efficiency (coefficient: 2670, p < .001) of the connectome. White matter hyperintensity volume was negatively correlated with global efficiency (coefficient: −0.0002, p = .003) and local efficiency (coefficient: −0.024, p = .003) of the connectome. The association between CVR and the brain connectome suggests that impaired cerebrovascular function may be part of the pathophysiology of the disruption of the brain connectome in persons with white matter hyperintensities.

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Section: Discussionsupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

The efficiency of the brain connectome is associated with cerebrovascular reactivity in persons with white matter hyperintensities

Reginold

Sam²,

Poublanc

et al. 2019

Human Brain Mapping

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“…Indeed, it has been demonstrated in both sporadic SVD and CADASIL patients that the forceps minor and the thalamic radiation are strategic WM tracts for processing speed. 29,30 O'Sullivan et al 13 demonstrated that in SVD patients DTI indices, especially in NAWM, correlated more strongly with cognitive function than T2-lesion volume, after controlling for conventional MRI parameters. Similarly, diffusion changes predict faster decline in psychomotor speed, executive functions, and working memory regardless of conventional MRI findings.…”

Section: June 2016mentioning

confidence: 99%

White Matter Microstructural Damage on Diffusion Tensor Imaging in Cerebral Small Vessel Disease

Pasi

Uden

Tuladhar

et al. 2016

Stroke

Self Cite

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C erebral small vessel disease (SVD) is a major health problem for its contribution to ≈45% of dementias, and about a fifth of all strokes worldwide, representing one of the most important causes of disabilities.1 The term SVD refers to a group of pathological processes with various etiologies that affect the small arteries, arterioles, venules, and capillaries of the brain. The most common forms are age-and hypertensionrelated SVD and cerebral amyloid angiopathy (CAA).2 Vessel wall changes may lead to both ischemic and hemorrhagic consequences: (1) a state of chronic hypoperfusion or vascular dysfunction responsible for incomplete infarction, 3,4 (2) acute focal necrosis (lacunar infarct), or (3) vessel rupture manifesting as hemorrhagic SVD. The clinical consequences of SVD are various and mainly consist of cognitive, mood, and motor dysfunctions leading to functional disability in the late stages of the disease.Magnetic resonance imaging (MRI) has become crucial in the diagnosis of SVD enabling the evaluation of the disease progression both in the clinical and research settings. However, correlations between clinical features of SVD and conventional MRI measures have been partially discordant. Some authors suggested that the cumulative effect of SVD lesions, rather than the individual lesions themselves determines the clinical impact, 5 whereas others suggested that the presence and severity of alterations nonvisible on conventional MRI might also be an explanation. 6 In the past decade, diffusion tensor imaging (DTI) has been increasingly used for the evaluation of SVD patients because it is sensitive to tissue damage and can show abnormalities in both areas of white matter hyperintensities (WMH) and in normal appearing WM (NAWM). Despite the high sensitivity in detecting cerebral damage, DTI has a low specificity in detecting the underlying cause. In fact, we can only infer that DTI changes reflect a loss of WM integrity because of damage to structures that restrict molecular movement along the primary axis of the axons, such as axonal cell membranes, myelin sheaths, and neurofilaments. Growing evidences indicate definite structural vascular abnormalities associated with WMH, strengthening the argument that WMH have a vascular pathogenesis.7 DTI is suited to study cortical disconnection because it provides indices of microstructural integrity within interconnected neural networks. Most DTI studies evaluated WM microstructural damage in aging, Alzheimer disease and mild cognitive impairment patients, but recently, studies in SVD patients have documented a significant association between WM microstructural damage and clinical features, providing new insight in the biological basis of this condition. 8-10The aim of this review is to analyze the evidence of the role of WM microstructural damage beyond the standard structural MRI sequences, evaluated with DTI, in the clinical consequences of cerebral SVD. Although some definitions have been proposed, 11 it is explicitly not our intention to clinically define...

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“…3) [45], [46], [44]. Lower connectivity within cerebral networks was associated with cognitive impairment and dementia [47], [48]; WMHs were associated with less brain activation in the frontal cortex on fludeoxyglucose-positron emission tomography [49], [50], [51]. Mood disorders and apathy are common after stroke, affect cognitive test performance (and more fundamentally, patient participation in research), and may reflect impaired connectivity.…”

Section: Resultsmentioning

confidence: 99%

METACOHORTS for the study of vascular disease and its contribution to cognitive decline and neurodegeneration: An initiative of the Joint Programme for Neurodegenerative Disease Research

Dichgans¹,

Wardlaw²,

Smith³

et al. 2016

Alzheimer's & Dementia

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Dementia is a global problem and major target for health care providers. Although up to 45% of cases are primarily or partly due to cerebrovascular disease, little is known of these mechanisms or treatments because most dementia research still focuses on pure Alzheimer's disease. An improved understanding of the vascular contributions to neurodegeneration and dementia, particularly by small vessel disease, is hampered by imprecise data, including the incidence and prevalence of symptomatic and clinically “silent” cerebrovascular disease, long-term outcomes (cognitive, stroke, or functional), and risk factors. New large collaborative studies with long follow-up are expensive and time consuming, yet substantial data to advance the field are available. In an initiative funded by the Joint Programme for Neurodegenerative Disease Research, 55 international experts surveyed and assessed available data, starting with European cohorts, to promote data sharing to advance understanding of how vascular disease affects brain structure and function, optimize methods for cerebrovascular disease in neurodegeneration research, and focus future research on gaps in knowledge. Here, we summarize the results and recommendations from this initiative. We identified data from over 90 studies, including over 660,000 participants, many being additional to neurodegeneration data initiatives. The enthusiastic response means that cohorts from North America, Australasia, and the Asia Pacific Region are included, creating a truly global, collaborative, data sharing platform, linked to major national dementia initiatives. Furthermore, the revised World Health Organization International Classification of Diseases version 11 should facilitate recognition of vascular-related brain damage by creating one category for all cerebrovascular disease presentations and thus accelerate identification of targets for dementia prevention.

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Structural network efficiency predicts conversion to dementia

Abstract: These results support a role of network disruption playing a pivotal role in the genesis of dementia in SVD, and suggest network analysis of the connectivity of white matter has potential as a predictive marker in the disease.

Cited by 99 publications

References 25 publications

The efficiency of the brain connectome is associated with cerebrovascular reactivity in persons with white matter hyperintensities

The efficiency of the brain connectome is associated with cerebrovascular reactivity in persons with white matter hyperintensities

White Matter Microstructural Damage on Diffusion Tensor Imaging in Cerebral Small Vessel Disease

METACOHORTS for the study of vascular disease and its contribution to cognitive decline and neurodegeneration: An initiative of the Joint Programme for Neurodegenerative Disease Research

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