Elsevier

NeuroImage

Volume 54, Issue 1, 1 January 2011, Pages 161-169
NeuroImage

Network analysis detects changes in the contralesional hemisphere following stroke

https://doi.org/10.1016/j.neuroimage.2010.08.032Get rights and content

Abstract

Changes in brain structure occur in remote regions following focal damage such as stroke. Such changes could disrupt processing of information across widely distributed brain networks. We used diffusion MRI tractography to assess connectivity between brain regions in 9 chronic stroke patients and 18 age-matched controls. We applied complex network analysis to calculate ‘communicability’, a measure of the ease with which information can travel across a network. Clustering individuals based on communicability separated patient and control groups, not only in the lesioned hemisphere but also in the contralesional hemisphere, despite the absence of gross structural pathology in the latter. In our highly selected patient group, lesions were localised to the left basal ganglia/internal capsule. We found reduced communicability in patients in regions surrounding the lesions in the affected hemisphere. In addition, communicability was reduced in homologous locations in the contralesional hemisphere for a subset of these regions. We interpret this as evidence for secondary degeneration of fibre pathways which occurs in remote regions interconnected, directly or indirectly, with the area of primary damage. We also identified regions with increased communicability in patients that could represent adaptive, plastic changes post-stroke. Network analysis provides new and powerful tools for understanding subtle changes in interactions across widely distributed brain networks following stroke.

Research Highlights

►Using diffusion MRI tractography we assess structural connectivity between brain regions in chronic unilateral stroke patients. ►Complex network analysis enabled us to separate patient and control groups, not only in the lesioned hemisphere but also in the contralesional hemisphere. ►We found evidence for secondary degeneration of fibre pathways which occur in remote regions interconnected, directly or indirectly, with the primary area of damage.

Introduction

Following a focal stroke, there are multiple ways in which the structure and function of the rest of the brain may change. The region immediately surrounding a stroke undergoes potentially reversible structural change and anterograde or retrograde degeneration of axons intersecting or connecting with a lesion site may occur. In addition to these degenerative structural changes, animal studies suggest that the brain has the capacity for potentially adaptive structural change in response to injury, including dendritic branching and synaptogenesis (Biernaskie & Corbett, 2001, Jones et al., 1996) and even growth of new long-range connections (Dancause et al., 2005).

Diffusion tensor imaging (DTI) (Basser et al., 1994) and tractography (Jones et al., 1999, Mori et al., 1999) provide methods for interrogating white matter structure in vivo. Reductions in fractional anisotropy (FA), a DTI-derived measure of white matter microstructure (Beaulieu, 2002, Beaulieu, 2009), have been found above and below a stroke location, consistent with patterns of Wallerian and retrograde degeneration (Pierpaoli et al., 2001, Werring et al., 2000). DTI-based measures have been used not only to detect degeneration but also to pinpoint potentially beneficial white matter change. A recent study found that, while poorly recovered patients had reduced FA in both corticospinal tracts relative to healthy controls, well-recovered stroke patients had elevated FA relative to controls in the same regions (Schaechter et al., 2009). This observation is striking as it shows not only that white matter microstructure can be apparently improved following stroke, but also that such changes occur not just in the stroke hemisphere but also in the contralesional hemisphere. This result complements previous demonstrations of functional plasticity in the contralesional hemisphere in stroke patients (Johansen-Berg et al., 2002, Lotze et al., 2006) and of adaptive white matter plasticity in healthy subjects (Keller & Just, 2009, Scholz et al., 2009).

However, one difficulty with voxel-based assessments of structural change following stroke is the underlying assumption that regions of change will be highly co-localised across individuals. Given the heterogeneity in even carefully selected stroke populations, this approach may miss potentially interesting findings if they occur with a less consistent topography. An alternative approach to assessing structural connectivity is provided by complex network analysis. This refers to a class of mathematical tools that have been used to understand networks present in contexts as diverse as the internet, disease spread, scientific citations or protein interactions (Barabasi, 2009). These approaches have proved exceptionally powerful in characterising structural and functional brain networks (Bullmore and Sporns, 2009). Briefly, the brain is divided up according to some parcellation scheme (e.g., into cortical areas) to form the nodes of the network, then some measure of connectivity is derived between nodes (e.g., correlation in functional responses (Salvador et al., 2005), co-variation in cortical thickness (He et al., 2007) or probability of anatomical connectivity (Gong et al., 2009, Iturria-Medina et al., 2008) to characterise network ‘edges’. Once a network has been defined in this way, various measures can be derived to describe the organisation of the network. Such measures can be relatively global, capturing network organisation by a summary value describing the connectivity of a whole brain or hemisphere, or can be related back to thenetwork in order to determine which brain regions are driving observed differences in network measures.

Complex network analysis approaches have recently been shown to be sensitive to subtle pathology in a number of neurological and neuropsychiatric disorders including Alzheimer's Disease (He et al., 2008) and Schizophrenia (Bassett et al., 2008). In general, theaim has been to identify a network measure that allows separation of patients from healthy controls with a high degree of sensitivity and specificity. In stroke, there is less need for an imaging measure to assist with diagnosis, as conventional imaging does well in this regard. Rather, we wished to assess whether complex network analysis could be used to test the hypothesis that regions remote from the lesion site (including those in the contralesional hemisphere) undergo structural change, both degenerative and potentially adaptive, following unilateral stroke.

Here, we provide the first application of complex network analysis to the study of structural white matter changes following stroke. We used probabilistic diffusion weighted imaging tractography (Behrens et al., 2003, Behrens et al., 2007) to study a group of chronic stroke patients following left hemisphere subcortical stroke. We derived connectivity estimates between cortical and subcortical brain regions of the left (lesioned) and right (contralesional) hemisphere and use these to derive weighted measures of ‘communicability’, a novel network measure that measures the ease with which information can flow between network nodes, using both direct and indirect paths (Crofts and Higham, 2009). By comparing connectivity and communicability between patients and controls we can test the hypothesis that changes in global or local network structure occur following stroke.

Section snippets

Subjects

9 chronic stroke patients (mean age 64, range: 41-83, 1 female) and 18 controls (mean age 58 years, range: 30-81 years, 7 females) participated in the study (Table 1). Patients were at least 6 months post first ischaemic or haemorrhagic left hemisphere subcortical stroke without concurrence of any other neurological condition. Healthy controls were recruited via advertisements and word of mouth. All subjects gave written informed consent to participate in accordance with the Declaration of

Spectral Clustering Using Connectivity and Communicability Measures

Measures of connectivity (‘direct’ connections) and communicability (direct plus indirect connections) between all network nodes were stored in matrices of 1540 values by 27 subjects (Aconn and Cconn). Matrices of measures of degree (Adeg and Cdeg) summarise connectivity or communicability measures for each of the 56 brain regions for each subject. We applied spectral clustering to Aconn, Cconn, Adeg and Cdeg (Fig. 2). Most measures broadly separated stroke patients from healthy control

Discussion

Using novel network analysis methods, we found evidence for altered structural connectivity not only in the lesioned hemisphere but also in the contralesional hemisphere of chronic stroke patients. Clustering of individuals according to measures of structural connectivity scores broadly separated patients with sub-cortical left hemisphere strokes from age-matched controls using data from the left (lesioned) hemisphere or the right (contralesional) hemisphere, particularly when both direct and

Acknowledgments

We are grateful for funding from the UK Medical Research Council (to DJH, HJB, SJ, TEJB); Wellcome Trust (to TEJB, HJB); EPSRC (to DJH) and NIHR Biomedical Reseach Centre, Oxford (to HJB). PMM is a full time employee of GlaxoSmithKline.

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