Loss of network efficiency associated with cognitive decline in chronic epilepsy

Vlooswijk, Mariëlle C.G.; Vaessen, Maarten J.; Jansen, Jacobus F. A.; Krom, Marc C.F.T.M. de; Majoie, Marian; Hofman, Paul; Aldenkamp, Albert P.; Backes, Walter H.

doi:10.1212/wnl.0b013e31822cfc2f

Cited by 150 publications

(157 citation statements)

References 36 publications

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“…Next, the individual anatomical T1 segmentations were registered to the fMRI images (24) to calculate the average time series for the cortical and subcortical gray and white matter areas and CSF. To reduce the effect of physiological noise (respiratory and cardiac artifacts), the fMRI data were filtered by using a band pass filter (0.01-0.1 Hz) and linear regression with the averaged time series of white matter, CSF, and movement parameters acquired in the previous realignment step as nuisance regressors (25,26). Finally, the averaged time series of the cortical and subcortical segmented areas (27,28) (a total of 82 areas, 41 for each hemisphere) was used to form a correlation matrix by calculating the Pearson correlation coefficients between all pairs of the segmented brain areas.…”

Section: Image Preprocessingmentioning

confidence: 99%

Functional Brain Networks Are Altered in Type 2 Diabetes and Prediabetes: Signs for Compensation of Cognitive Decrements? The Maastricht Study

et al. 2016

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Type 2 diabetes is associated with cognitive decrements, accelerated cognitive decline, and increased risk for dementia. Patients with the metabolic syndrome, a major risk factor for diabetes, may display comparable cognitive decrements as seen in type 2 diabetes. Currently, the impact of diabetes and prediabetes on cognition and the underlying organization of functional brain networks still remain to be elucidated. This study investigated whether functional brain networks are affected in type 2 diabetes and prediabetes. Forty-seven participants with diabetes, 47 participants with prediabetes, and 45 control participants underwent detailed cognitive testing and 3-Tesla resting state functional MRI. Graph theoretical network analysis was performed to investigate alterations in functional cerebral networks. Participants with diabetes displayed altered network measures, characterized by a higher normalized cluster coefficient and higher local efficiency, compared with control participants. The network measures of the participants with prediabetes fell between those with diabetes and control participants. Lower processing speed was associated with shorter path length and higher global efficiency. Participants with type 2 diabetes have altered functional brain networks. This alteration is already apparent in the prediabetic stage to a somewhat lower level, hinting at functional reorganization of the cerebral networks as a compensatory mechanism for cognitive decrements.The worldwide prevalence of diabetes is increasing rapidly, with the majority of patients having type 2 diabetes (1). Along with cardiovascular risk factors, type 2 diabetes is associated with cognitive decrements, accelerated cognitive decline, and an increased risk for dementia and Alzheimer disease (2,3). A broad range of cognitive domains are affected in type 2 diabetes, and one of the most commonly affected is processing speed (4,5). However, the underlying pathological mechanism is not yet clear.The progression of normal glucose metabolism to type 2 diabetes is a gradual process in which insulin resistance plays a crucial role. Before the clinical presentation of type 2 diabetes, insulin resistance often is accompanied by other metabolic and vascular abnormalities. The cluster of these cardiovascular risk factors is referred to as the metabolic syndrome and is considered a major risk factor for diabetes (6). Patients with the metabolic syndrome have a high likelihood for diabetes and may display comparable cognitive decrements as seen in patients with type 2 diabetes (5). Furthermore, the cardiovascular risk factors are associated with an increased risk of late-life dementia (7).

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Section: Image Preprocessingmentioning

confidence: 99%

Functional Brain Networks Are Altered in Type 2 Diabetes and Prediabetes: Signs for Compensation of Cognitive Decrements? The Maastricht Study

et al. 2016

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“…Previous studies have examined brain efficiency in both functional networks (Vlooswijk et al, 2011;Wang et al, 2014;Zhang et al, 2011) and structural networks in epilepsy (Bernhardt et al, 2011;Liu et al, 2014). However, these findings have remained conflicting potentially due to various factors such as different data processing approach and heterogeneous study population.…”

Section: Discussionmentioning

confidence: 99%

Disrupted Brain Functional Organization in Epilepsy Revealed by Graph Theory Analysis

et al. 2015

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The human brain is a complex and dynamic system that can be modeled as a large-scale brain network to better understand the reorganizational changes secondary to epilepsy. In this study, we developed a brain functional network model using graph theory methods applied to resting-state fMRI data acquired from a group of epilepsy patients and age-and gender-matched healthy controls. A brain functional network model was constructed based on resting-state functional connectivity. A minimum spanning tree combined with proportional thresholding approach was used to obtain sparse connectivity matrices for each subject, which formed the basis of brain networks. We examined the brain reorganizational changes in epilepsy thoroughly at the level of the whole brain, the functional network, and individual brain regions. At the whole-brain level, local efficiency was significantly decreased in epilepsy patients compared with the healthy controls. However, global efficiency was significantly increased in epilepsy due to increased number of functional connections between networks (although weakly connected). At the functional network level, there were significant proportions of newly formed connections between the default mode network and other networks and between the subcortical network and other networks. There was a significant proportion of decreasing connections between the cingulo-opercular task control network and other networks. Individual brain regions from different functional networks, however, showed a distinct pattern of reorganizational changes in epilepsy. These findings suggest that epilepsy alters brain efficiency in a consistent pattern at the whole-brain level, yet alters brain functional networks and individual brain regions differently.

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“…Various studies have reported differences in clustering, path length, and efficiency from a range of diseases and disorders including Alzheimer's disease (Lo et al, 2010;SanzArigita et al, 2010;Yao et al, 2010), multiple sclerosis (He et al, 2009b;Shu et al, 2011), depression (Zhang et al, 2011a), obsessive compulsive disorder (Zhang et al, 2011b), and synesthesia (Hä nggi et al, 2011). In two separate studies on epilepsy, one group reported lower path length between patients with epilepsy and the control group (Liao et al, 2010); whereas another study showed an increase (Vlooswijk et al, 2011). Further inquiry to determine the direction of global changes in the network neglects the bigger question: Does the direction of change imply that the network has entered a particular disease state?…”

Section: Discussionmentioning

confidence: 99%

The Brain as a Complex System: Using Network Science as a Tool for Understanding the Brain

et al. 2011

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Although graph theory has been around since the 18th century, the field of network science is more recent and continues to gain popularity, particularly in the field of neuroimaging. The field was propelled forward when Watts and Strogatz introduced their small-world network model, which described a network that provided regional specialization with efficient global information transfer. This model is appealing to the study of brain connectivity, as the brain can be viewed as a system with various interacting regions that produce complex behaviors. In practice, graph metrics such as clustering coefficient, path length, and efficiency measures are often used to characterize system properties. Centrality metrics such as degree, betweenness, closeness, and eigenvector centrality determine critical areas within the network. Community structure is also essential for understanding network organization and topology. Network science has led to a paradigm shift in the neuroscientific community, but it should be viewed as more than a simple ''tool du jour.'' To fully appreciate the utility of network science, a greater understanding of how network models apply to the brain is needed. An integrated appraisal of multiple network analyses should be performed to better understand network structure rather than focusing on univariate comparisons to find significant group differences; indeed, such comparisons, popular with traditional functional magnetic resonance imaging analyses, are arguably no longer relevant with graph-theory based approaches. These methods necessitate a philosophical shift toward complexity science. In this context, when correctly applied and interpreted, network scientific methods have a chance to revolutionize the understanding of brain function.

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Loss of network efficiency associated with cognitive decline in chronic epilepsy

Cited by 150 publications

References 36 publications

Functional Brain Networks Are Altered in Type 2 Diabetes and Prediabetes: Signs for Compensation of Cognitive Decrements? The Maastricht Study

Functional Brain Networks Are Altered in Type 2 Diabetes and Prediabetes: Signs for Compensation of Cognitive Decrements? The Maastricht Study

Disrupted Brain Functional Organization in Epilepsy Revealed by Graph Theory Analysis

The Brain as a Complex System: Using Network Science as a Tool for Understanding the Brain

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