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Relevance of structural brain connectivity to learning and recovery from stroke.

Johansen-Berg H, Scholz J, Stagg CJ - Front Syst Neurosci (2010)

Bottom Line: The physical structure of white matter fiber bundles constrains their function.Any behavior that relies on transmission of signals along a particular pathway will therefore be influenced by the structural condition of that pathway.We provide examples of ways in which imaging measures of structural brain connectivity can inform our study of motor behavior and effects of motor training in three different domains: (1) to assess network degeneration or damage with healthy aging and following stroke, (2) to identify a structural basis for individual differences in behavioral responses, and (3) to test for dynamic changes in structural connectivity with learning or recovery.

View Article: PubMed Central - PubMed

Affiliation: Department of Clinical Neurology, University of Oxford Oxford, UK.

ABSTRACT
The physical structure of white matter fiber bundles constrains their function. Any behavior that relies on transmission of signals along a particular pathway will therefore be influenced by the structural condition of that pathway. Diffusion-weighted magnetic resonance imaging provides localized measures that are sensitive to white matter microstructure. In this review, we discuss imaging evidence on the relevance of white matter microstructure to behavior. We focus in particular on motor behavior and learning in healthy individuals and in individuals who have suffered a stroke. We provide examples of ways in which imaging measures of structural brain connectivity can inform our study of motor behavior and effects of motor training in three different domains: (1) to assess network degeneration or damage with healthy aging and following stroke, (2) to identify a structural basis for individual differences in behavioral responses, and (3) to test for dynamic changes in structural connectivity with learning or recovery.

No MeSH data available.


Related in: MedlinePlus

Network analysis detects changes in contralesional structural connectivity following stroke. (A,B) Results of reordering of participants using structural connectivity data. Chronic stroke patients are indicated by red circles and age-matched healthy controls by blue stars. Ordering of participants is achieved using spectral reordering and is based on the right singular vector, v[2], which is plotted on the y-axis (see Crofts et al., 2010 for details). Individuals are ordered along the x-axis based on increasing values of [v]2. Clear separation between patients and controls is apparent when using communicability information from both the lesioned (A) and the contralesional hemisphere (B). (C) Areas driving the separation between patients and controls are shown in blue. These regions have significantly lower communicability in patients compared to controls. They tend to be clustered around the lesion location (overlap map of lesions is shown in red to yellow). Based on data presented in Crofts et al. (2010).
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Figure 3: Network analysis detects changes in contralesional structural connectivity following stroke. (A,B) Results of reordering of participants using structural connectivity data. Chronic stroke patients are indicated by red circles and age-matched healthy controls by blue stars. Ordering of participants is achieved using spectral reordering and is based on the right singular vector, v[2], which is plotted on the y-axis (see Crofts et al., 2010 for details). Individuals are ordered along the x-axis based on increasing values of [v]2. Clear separation between patients and controls is apparent when using communicability information from both the lesioned (A) and the contralesional hemisphere (B). (C) Areas driving the separation between patients and controls are shown in blue. These regions have significantly lower communicability in patients compared to controls. They tend to be clustered around the lesion location (overlap map of lesions is shown in red to yellow). Based on data presented in Crofts et al. (2010).

Mentions: One promising approach for detecting subtle or spatially variable changes in structural connectivity is to use complex network analysis methods. These are a class of techniques that have been employed to interrogate network structure in a variety of contexts such as protein interactions, social networks or the internet (Barabasi, 2009), and that have proved powerful in exploring the network structure of the brain (Bullmore and Sporns, 2009). We recently used a novel network measure of weighted communicability (Estrada and Hatano, 2008; Crofts and Higham, 2009) to assess differences in structural connectivity between stroke patients and age-matched healthy controls using probabilistic tractography on diffusion data to generate estimates of structural connectivity between brain regions (Crofts et al., 2010). Communicability measures the ease with which information can travel between brain regions by considering not only the direct path between them but also all possible indirect paths. We used clustering methods to test whether or not this measure could differentiate between structural brain networks of chronic stroke patients and controls. When considering data from the stroke hemisphere (Figure 3A) we found a clear separation between patients and controls – as expected given the presence of a lesion and widespread degeneration in this hemisphere (Werring et al., 2000; Pierpaoli et al., 2001). However, more surprisingly, we also found that clustering differentiated between patients and controls even when considering only the structural connections of the contralesional hemisphere (Figure 3B). This suggests that subtle changes in structural connectivity, that are not apparent on conventional MRI or maps of FA (Liang et al., 2007), are present bilaterally following stroke and potentially provide a structural correlate of transhemispheric diaschisis (Andrews, 1991). The separation between groups depended on communicability changes in a few brain regions (Figure 3C). Our patients all had left hemisphere subcortical strokes around the basal ganglia/internal capsule and regions of reduced communicability clustered around this area in the stroke hemisphere and around remote, but interconnected, mirror locations in the contralesional hemisphere. This pattern of change is consistent with the idea that, in addition to direct ischemic damage to white matter, secondary degeneration occurs along distributed white matter pathways.


Relevance of structural brain connectivity to learning and recovery from stroke.

Johansen-Berg H, Scholz J, Stagg CJ - Front Syst Neurosci (2010)

Network analysis detects changes in contralesional structural connectivity following stroke. (A,B) Results of reordering of participants using structural connectivity data. Chronic stroke patients are indicated by red circles and age-matched healthy controls by blue stars. Ordering of participants is achieved using spectral reordering and is based on the right singular vector, v[2], which is plotted on the y-axis (see Crofts et al., 2010 for details). Individuals are ordered along the x-axis based on increasing values of [v]2. Clear separation between patients and controls is apparent when using communicability information from both the lesioned (A) and the contralesional hemisphere (B). (C) Areas driving the separation between patients and controls are shown in blue. These regions have significantly lower communicability in patients compared to controls. They tend to be clustered around the lesion location (overlap map of lesions is shown in red to yellow). Based on data presented in Crofts et al. (2010).
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Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC2990506&req=5

Figure 3: Network analysis detects changes in contralesional structural connectivity following stroke. (A,B) Results of reordering of participants using structural connectivity data. Chronic stroke patients are indicated by red circles and age-matched healthy controls by blue stars. Ordering of participants is achieved using spectral reordering and is based on the right singular vector, v[2], which is plotted on the y-axis (see Crofts et al., 2010 for details). Individuals are ordered along the x-axis based on increasing values of [v]2. Clear separation between patients and controls is apparent when using communicability information from both the lesioned (A) and the contralesional hemisphere (B). (C) Areas driving the separation between patients and controls are shown in blue. These regions have significantly lower communicability in patients compared to controls. They tend to be clustered around the lesion location (overlap map of lesions is shown in red to yellow). Based on data presented in Crofts et al. (2010).
Mentions: One promising approach for detecting subtle or spatially variable changes in structural connectivity is to use complex network analysis methods. These are a class of techniques that have been employed to interrogate network structure in a variety of contexts such as protein interactions, social networks or the internet (Barabasi, 2009), and that have proved powerful in exploring the network structure of the brain (Bullmore and Sporns, 2009). We recently used a novel network measure of weighted communicability (Estrada and Hatano, 2008; Crofts and Higham, 2009) to assess differences in structural connectivity between stroke patients and age-matched healthy controls using probabilistic tractography on diffusion data to generate estimates of structural connectivity between brain regions (Crofts et al., 2010). Communicability measures the ease with which information can travel between brain regions by considering not only the direct path between them but also all possible indirect paths. We used clustering methods to test whether or not this measure could differentiate between structural brain networks of chronic stroke patients and controls. When considering data from the stroke hemisphere (Figure 3A) we found a clear separation between patients and controls – as expected given the presence of a lesion and widespread degeneration in this hemisphere (Werring et al., 2000; Pierpaoli et al., 2001). However, more surprisingly, we also found that clustering differentiated between patients and controls even when considering only the structural connections of the contralesional hemisphere (Figure 3B). This suggests that subtle changes in structural connectivity, that are not apparent on conventional MRI or maps of FA (Liang et al., 2007), are present bilaterally following stroke and potentially provide a structural correlate of transhemispheric diaschisis (Andrews, 1991). The separation between groups depended on communicability changes in a few brain regions (Figure 3C). Our patients all had left hemisphere subcortical strokes around the basal ganglia/internal capsule and regions of reduced communicability clustered around this area in the stroke hemisphere and around remote, but interconnected, mirror locations in the contralesional hemisphere. This pattern of change is consistent with the idea that, in addition to direct ischemic damage to white matter, secondary degeneration occurs along distributed white matter pathways.

Bottom Line: The physical structure of white matter fiber bundles constrains their function.Any behavior that relies on transmission of signals along a particular pathway will therefore be influenced by the structural condition of that pathway.We provide examples of ways in which imaging measures of structural brain connectivity can inform our study of motor behavior and effects of motor training in three different domains: (1) to assess network degeneration or damage with healthy aging and following stroke, (2) to identify a structural basis for individual differences in behavioral responses, and (3) to test for dynamic changes in structural connectivity with learning or recovery.

View Article: PubMed Central - PubMed

Affiliation: Department of Clinical Neurology, University of Oxford Oxford, UK.

ABSTRACT
The physical structure of white matter fiber bundles constrains their function. Any behavior that relies on transmission of signals along a particular pathway will therefore be influenced by the structural condition of that pathway. Diffusion-weighted magnetic resonance imaging provides localized measures that are sensitive to white matter microstructure. In this review, we discuss imaging evidence on the relevance of white matter microstructure to behavior. We focus in particular on motor behavior and learning in healthy individuals and in individuals who have suffered a stroke. We provide examples of ways in which imaging measures of structural brain connectivity can inform our study of motor behavior and effects of motor training in three different domains: (1) to assess network degeneration or damage with healthy aging and following stroke, (2) to identify a structural basis for individual differences in behavioral responses, and (3) to test for dynamic changes in structural connectivity with learning or recovery.

No MeSH data available.


Related in: MedlinePlus