[Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.] Relation Between MRI Markers for SVD, Network Efficiency, and Cognition Higher global efficiency was related to higher scores on cognitive index and psychomotor velocity ( em P /em ? ?0.05, Bonferroni\corrected), after adjusting for the MRI markers for SVD (Table ?(Table2,2, model 2; Fig. and microbleeds, and lower total brain volume) was related to networks with lower density, connection strengths, and network efficiency, and to lower scores on cognitive overall performance. In multiple regressions Riociguat (BAY 63-2521) models, network efficiency remained significantly associated with cognitive index and psychomotor velocity, impartial of MRI markers for SVD and mediated the associations between these markers and cognition. This study provides evidence that network (in)efficiency might drive the association between SVD and cognitive overall performance. This hightlights the importance of network analysis in our understanding of SVD\related cognitive impairment in addition to standard MRI markers for SVD and might provide an useful tool as disease marker. em Hum Brain Mapp 37:300C310, 2016 /em . ? 2015 Wiley Periodicals, Inc. strong class=”kwd-title” Keywords: cognition, graph\theory, cerebral small vessel disease, structural brain networks INTRODUCTION Cerebral small vessel disease (SVD)\related brain lesions include white matter hyperintensities (WMH), lacunes of presumed vascular origin, microbleeds, and brain atrophy [Pantoni, 2010; Wardlaw et al., 2013]. SVD is Riociguat (BAY 63-2521) frequently observed in elderly individuals [de Leeuw et al., 1999] and is an important cause of cognitive and motor impairment [de Laat et al., 2011; Prins et al., 2005]. Despite several studies using standard magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) [Roman et al., 2002; Tuladhar et al., 2015a, 2015b; van Norden Riociguat (BAY 63-2521) et al., 2012a], it is still incompletely comprehended how SVD relates to these clinical symptoms. A presumed mechanism is usually that SVD disrupts the structural connectivity within a large\scale brain network, thereby impairing the brain’s ability to integrate the neural processes efficiently. Graph theory is usually a mathematical tool that allows for the analysis and quantification of these large\scale brain networks [Bullmore and Sporns, 2009] and their relationship with cognitive function. Structural connectivity can be derived from imaging techniques such as DTI followed by whole\brain tractography [Gong et al., 2009; Shu et al., 2009; Verstraete et al., 2011; Zalesky et al., 2011]. In graph\theoretical framework, a structural network consists of a set of nodes (brain regions) connected by edges (white matter tracts). Recently, several studies showed that this network efficiency is associated with cognition Riociguat (BAY 63-2521) in various diseases with white matter abnormalities [Lawrence et al., 2014; Reijmer et al., 2013, 2015; Wen et al., 2011b]. However, it is not yet obvious, how network efficiency relates to the conventional MRI markers for SVD markers (WMH, lacunes, microbleeds, and brain atrophy) and to cognitive overall performance in cognitively rather healthy participants with numerous degrees of SVD, while taking these MRI markers for SVD into account. We hypothesized that this SVD\severity, indicated by MRI markers for SVD, is related to network efficiency, and on its change with cognitive overall performance, independent of these MRI markers. To this end, we measured the degree of the structural connectivity using DTI and whole\brain tractography in participants with SVD. Graph\theoretical analyses were then conducted to examine the relation between MRI markers for SVD, network efficiency, and cognitive overall performance from a network perspective. MATERIAL AND METHODS Study Population The study sample is part of the Radboud University or college Nijmegen Diffusion tensor and MRI Cohort (RUN DMC) study [van Norden et al., 2011], a prospective study that was designed to investigate risk factors and cognitive, motor, and mood effects of functional and structural brain changes as assessed by MRI among elderly with cerebral SVD. The primary study end result of the longitudinal part of this study is the development of dementia or parkinsonism. Cerebral SVD is usually characterized on neuroimaging by either WMH and/or lacunes of presumed vascular origin. Symptoms of SVD can be acute, such as transient ischemic attacks (TIAs) or lacunar syndromes, or subacute manifestations, such as cognitive, motor and/or mood disturbances [Roman et al., 2002]. Because the onset of cerebral SVD is usually often insidious, clinically.Density of a network is defined as the number of connections in a network divided by the total possible connections within the network. hightlights the importance of network analysis in our understanding of SVD\related cognitive impairment in addition to standard MRI markers for SVD and might provide an useful tool as disease marker. em Hum Brain Mapp 37:300C310, 2016 /em . ? 2015 Wiley Periodicals, Inc. strong class=”kwd-title” Keywords: cognition, graph\theory, cerebral small vessel disease, structural brain networks INTRODUCTION Cerebral small vessel disease (SVD)\related brain lesions include white matter hyperintensities (WMH), lacunes of presumed vascular origin, microbleeds, and brain atrophy [Pantoni, 2010; Wardlaw et al., 2013]. SVD is frequently observed in elderly individuals [de Leeuw et al., 1999] and is an important cause of cognitive and motor impairment [de Laat et al., 2011; Prins et al., 2005]. Despite several studies using standard magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) [Roman et al., 2002; Tuladhar et al., 2015a, 2015b; van Norden et al., 2012a], it is still incompletely comprehended how SVD relates to these clinical symptoms. A presumed mechanism is usually that SVD disrupts the structural connectivity within a large\scale brain network, thereby impairing the brain’s ability to integrate the neural processes efficiently. Graph theory is usually a mathematical tool Mouse monoclonal to GAPDH that allows for the analysis and quantification of these large\scale brain networks [Bullmore and Sporns, 2009] and their relationship with cognitive function. Structural connectivity can be derived from imaging techniques such as DTI followed by whole\brain tractography [Gong et al., 2009; Shu et al., 2009; Verstraete et al., 2011; Zalesky et al., 2011]. In graph\theoretical framework, a structural network consists of a set of nodes (brain regions) connected by edges (white matter tracts). Recently, several studies showed that this network efficiency is associated with cognition in various diseases with white matter abnormalities [Lawrence et al., 2014; Reijmer et al., 2013, 2015; Wen et al., 2011b]. However, it is not yet obvious, how network efficiency relates to the conventional MRI markers for SVD markers (WMH, lacunes, microbleeds, and brain atrophy) and to cognitive overall performance in cognitively rather healthy participants with numerous degrees of SVD, while taking these MRI markers for SVD into account. We hypothesized that this SVD\severity, indicated by MRI markers for SVD, is related to network efficiency, and on its change with cognitive overall performance, independent of these MRI markers. To this end, we measured the degree of the structural connectivity using DTI and whole\brain tractography in participants with SVD. Graph\theoretical analyses were then conducted to examine the relation between MRI markers for SVD, network efficiency, and cognitive overall performance from a network perspective. MATERIAL AND METHODS Study Population The study sample is part of the Radboud University or college Nijmegen Diffusion tensor and MRI Cohort (RUN DMC) study [van Norden et al., 2011], a prospective study that was designed to investigate risk factors and cognitive, motor, and mood consequences of functional and structural brain changes as assessed by MRI among elderly with cerebral SVD. The primary study outcome of the longitudinal part of this study is the development of dementia or parkinsonism. Cerebral SVD is characterized on neuroimaging by either WMH and/or lacunes of presumed vascular origin. Symptoms of SVD can be acute, such as transient ischemic attacks (TIAs) or lacunar syndromes, or subacute manifestations, such as cognitive, motor and/or mood disturbances [Roman et al., 2002]. Because the onset of cerebral SVD is often insidious, clinically heterogeneous, and Riociguat (BAY 63-2521) typically with mild symptoms, it has been suggested that the selection of participants with cerebral SVD in.