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Do children with stronger functional connectivity at rest have better working memory capacity?

You are driving to the house of a friend, and you are getting directions from a GPS. “Drive to the end of the road, turn right, drive straight ahead and turn down the third road on your left, and the house is at the end of the road.”  As you are driving you need to remember each instruction, and hold that information in mind whilst completing each step. This is an example of a task that requires working memory. Working memory is the ability to hold relevant information in mind for a short period of time, whilst processing or using other material to complete a task. Working memory is critically important for learning at school, and failures of working memory can be a key predictor of children showing poor academic performance.

One way in which we can start to think about why some children seem to do far better on working memory tasks than others is by exploring the underlying neurophysiological processes that support working memory. With a technique called magnetoencephalography (MEG) we are able to look at how and when different brain areas are communicating, by seeing whether activity between different brain regions co-occurs in time. Importantly, it is possible to observe the strength of communication – functional connectivity – between different brain areas at rest.

In a new paper, we found that a child’s spatial working memory capacity can be predicted on the basis of particular patterns of brain activity at rest. We measured the resting-state functional connectivity of 8 to 11-year-olds, and assessed whether the strength of functional connectivity could be predicted by the child’s capacity. To measure working memory, we used well-validated behavioural measures of spatial and verbal working memory, taken outside the MEG scanner. These measures are routinely used in educational settings and are closely related to children’s literacy and numeracy levels. To examine functional connectivity, we measured the extent to which the activity of different brain regions was coordinated whilst the children were at rest inside the scanner. We then selected specific networks of coordinated regions that we considered ought to relate to working memory, based on adult networks responsible for cognitive control, because this is thought to be a key component of working memory. We reasoned that better these control areas can become coordinated with other brain systems, the better the child ought to do at control-demanding working memory exercises.

And this was what we found. We identified that the strength of connectivity between areas of the frontal and parietal lobe, and a portion of the temporal lobe,  was related to children’s spatial working memory capacity. The stronger the coordination between these areas, the higher the child’s capacity. Importantly, this relationship could not be explained simply by differences in motivation or strategy during the spatial working memory tasks, as children were at rest when the MEG was measured. Interestingly, we did not find a relationship between verbal working memory scores and any functional networks at rest – this may have been because the brain networks involved in verbal working memory tasks are less discrete or easily detectible with MEG.

Why is this important? Working memory is a key predictor of educational outcomes, and being able to relate individual differences in working memory to the functional coordination of different brain regions helps us understand the mechanisms supporting successful (and less successful) performance. The specific network related to children’s spatial working memory was the bilateral superior parietal cortex and middle frontal gyri, areas often associated with spatial attentional control, and lower-level processing areas. This suggests that children with better spatial working memory may be those with stronger connections between these frontal control regions and lower-level sensory areas. We can use this knowledge to begin to ask questions such as whether training can strengthen this connectivity (yes, as we have seen in a previous blog post), and to identify and redress the ways in which it can impact on learning.

References

Barnes, J. K., Woolrich, M. W., Baker, K., Colclough, G. L., & Astle, D. E. (2015). Electrophysiological measures of resting state functional connectivity and their relationship with working memory capacity in childhood. Developmental Science, doi: 10.1111/desc.12297.

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