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.

Does working memory training change neurophysiology in childhood?

The short answer to that question is ‘yes’.

We have known for some time that training particular cognitive skills, like working memory, can produce improvements in cognition. These improvements transfer to other untrained tasks, provided that they are similarly structured. However, we know very little about how these kinds of intensive cognitive training programmes change children’s performance.

The study has been under embargo whilst it awaits publication in the Journal of Neuroscience, but the embargo has just been lifted, and we can now tell you all about it (the paper itself should be published very soon – open access, naturally). We used magnetoencephalography – a technique for measuring electrical brain activity – to explore patterns of brain activity as children rested in the scanner with their eyes closed. We repeated this procedure before and after the children underwent working memory training. Importantly, only half of the children underwent an intensive version of the training, with the other half doing a low intensity version. This latter group acted as a control, and the children were randomly allocated to the high or low intensity conditions.

After the training, children’s working memory performance improved. We used standardised assessments of working memory to show this. Importantly, these improvements were specific to the group of children who had undertaken the intensive training programme, with the control group showing little or no improvement. This pattern could not have resulted from us expecting that the children in the intensive group ought to show bigger improvements than the controls, because the researcher doing the assessments did not know which group the children were assigned to.

The magnetoencephalography data allowed us to explore whether there were any significant changes in children’s brains, and whether these changes mirrored the improvements in performance that we observed. We used the spontaneous electrical activity in the children’s brains to explore network connectivity – that is, how different brain areas are coordinated. After the training, connectivity within networks involved in attentional control were significantly enhanced. Furthermore, the bigger the change in connectivity, the bigger the improvement in the child’s working memory.

We have a lot more data on these children, which we are slowly crunching our way through. So there will be more to come!

A Summary: Working Memory Differences between Children Living in Rural and Urban Poverty – Michele Tine

Working memory (WM) is the process by which we transiently hold and process information required for obtaining a short-term goal. It can be broadly considered to have two main components, verbal WM and visuospatial WM. WM is essential to everyday life, for example, it is used when remembering your friends’ order in a coffee shop or adding up the correct change to pay the man at the checkout counter. WM is essential to higher-processing and cognition including decision-making, learning, complex problem-solving, strategic thinking and sustained attention. In recent years, research has indicated that children from a low socioeconomic background display deficits in WM capacity when compared with children from a high socioeconomic background. (For further reading on this see: Fernald, Weber, Galasso and Ratisfandrihamanana, 2011; Noble, Norman and Farah, 2005.) With the knowledge that a lower WM capacity impacts on attention and learning this finding is of particular significance when considering children’s education. It has been shown that children with a low WM capacity can exhibit poor attention in class, poorer academic performance, learning difficulties and more difficult behaviours. (See our previous post for more insight into the relationship between WM and ADHD https://forgingconnectionsblog.wordpress.com/2015/02/05/adhd-and-low-working-memory-a-comparison/ ).

Given that a low socioeconomic background can impact on WM in children, it is important to explore why this occurs and which socioeconomic factors are the largest contributors. The following paper, by Michele Tine, (http://www.tandfonline.com/doi/full/10.1080/15248372.2013.797906#tabModule) considers how coming from a low-income background can impact on working memory (WM) in children whilst distinguishing between participants based on their ‘developmental context’ (settlement area), either rural or urban. Whilst it has been established that low-income children have deficits in verbal and visuospatial WM when compared with their high-income counterparts, this paper is the first, to my knowledge, that considers whether poverty is associated with a greater deficit in verbal or visuospatial WM and how settlement area influences this relationship.

Considering settlement area is a good way of determining if environmental factors mediate the relationship between low-income and decreased WM capacity. Whilst each settlement area will be unique in it is own way we can assume that some features are more prominent in one environment than another. For example, stress is thought to be a highly important factor when considering the impacts of socioeconomic background on an individual. Grouping via settlement area, takes into account exposure to different stressors in different environments.

Some of the differing stressors identified in the paper for those from a low-socioeconomic  background  include:

For an URBAN environment	For a RURAL environment
Substandard and crowded housing	Isolation from people
Excessive noise	Isolation due to reduced access to technologies the internet and reduced computer ownership
Inadequate health care, education and inadequate health care	Isolation from institutions and services
Increased crime rate	Decreased social support*
Increased physiological disorders	Less everyday visual stimulation
Increased divorce rates
Exposure to chronic aircraft noise

* Social support is a known buffer for stressors

Prior research has determined that the relationship between socioeconomic status and visuospatial WM is ‘fully mediated by allostatic load’, where allostatic load is the strain on the individual that accumulates over time with chronic stress. Thus, we would expect that the higher the amount of stress experienced, the greater the reduction in visuospatial WM capacity. On this basis we would perhaps expect there to be a difference in WM deficits once settlement area is considered as a result of exposure to differing stressors. Whilst the relationship between stress, socioeconomic status and visuospatial WM has been investigated, there has been little definitive research into how this compares to the relationship between stress, socioeconomic status and verbal WM.

 The individuals that participated in this study were recruited via their schools and their income measures (high or low) were determined by the following thresholds:

Low-income:

The individual attends a school that ‘serves a community with a median family income below the national median family income’. The school must have >75% of students qualified for free or reduced school meals and the individual themselves must qualify for free school meals.

High-income:

The individual attends a school that ‘serves a community with a median family income above the national median family income’. The school must have <25% of students qualified for free or reduced school meals and the individual must not qualify for reduced or free school meals.

To determine if an area was rural or urban The US Census Bureau (2010) and McCracken and Barcina’s (1991) work was utilised along with population per county and the average number of students per class in the county.

With these thresholds, four groups of individuals could be defined:

1) Urban and high-income

2) Rural and high-income

3) Urban and low-income

4) Rural and low-income

There were no significant differences in gender between the four groups but there was, however, a significant difference in ethnicity distribution across the groups.

Each individual completed four WM tasks from the Automated Working Memory Assessment battery. Two of the subtests measured verbal WM and two measured visuospatial WM.  As expected, the low-income children were found to have significantly lower composite WM percentile scores than the high-income students and there were no differences between the urban high-income and the rural high-income groups. The urban low-income group had significantly lower WM scores than both the high-income groups and the rural low-income group showed the same trend, having significantly lower WM scores than both of the high-income groups.

However, a comparison of the two low-income groups showed that they had differing WM profiles. The rural low-income group had a significantly higher verbal WM profile than the urban low-income group but also a significantly lower visuospatial WM profile than the urban low-income group. The urban low-income group showed no significant differences across the verbal and visuospatial WM scores, whilst the rural low-income group had significantly different verbal and visuospatial WM scores.

Based on past literature, Tine speculates that the differences in verbal WM between the two low-income groups may be accounted for by chronic noise pollution amongst the low-income urban population, for example through living close to airports and train tracks. However, the reason for this difference and for the decreased visuospatial WM capacity in the rural low-income group needs further investigation before any conclusion can be drawn.

Whilst this paper presents solid and much needed research into how income and settlement area/developmental context impacts on WM capacity in children, the author states that caution must be taken in inferring any causal relationships from studies such as this one. Due to a lack of information on the participants’ parents, although unlikely, it is possible that ‘low-income parents (and in turn, children) with particularly weak verbal WM abilities tend to seek out urban low-income areas, or on the contrary, low-income parents (and children) with relatively weak visuospatial WM capacity self-select into rural low-income areas.

Additionally, as previously mentioned, there was a significant difference in racial distribution across the groups and thus it cannot be ruled out that some of the differences seen in the WM profiles are a result of cultural differences and the ‘experience of race-based stereotype threat’ which has been shown to reduce WM capacity (Schmader and Johns, 2003). The study also does not consider language ability amongst the populations or the proportion of non-native English speakers, which is also known to be related to WM ability. Whilst the insights this paper provides are unique, the research could be furthered still by taking into account factors other than income and developmental context, for example, maternal education, parental employment or family structure, other well recognised and substantial components of what it means to be from a low-socioeconomic background.

So, in conclusion, whist we cannot assume causality from the findings, this paper presents, in my opinion, a very good first step into developing a much deeper understanding of the impact of SES on cognitive ability in children.  The hope is that this kind of research will eventually allow us to develop a successful intervention to aid those children from disadvantaged backgrounds so that they do not experience the cognitive deficits and disadvantages in education that their high-socioeconomic peers evade.

The full paper can be accessed here: http://www.tandfonline.com/doi/full/10.1080/15248372.2013.797906#tabModule)

References

Noble, K.G. Norman, M.F. and Farah, M.J. (2005) Neurocognitive correlates of socioeconomic status in kindergarten children. Developmental Science, 8, 74-87.

Fernald, L.C.H. Weber, A. Galasso, E. and Ratisfandrihamanana, L. (2011) Socioeconomic gradients and child development in a very low income population: Evidence from Madagascar. Developmental Science, 14, 832-847.

McCracken, J.D. and Barcinas, J.D. (1991) Differences between rural and urban schools, student characteristics, and student aspirations in Ohio. Journal or Research in Rural Education, 7, 29-40.

Schmader, T. and Johns, M. (2003) Converging evidence that stereotype threat reduces working memory capacity. Journal of Personality and Social Psychology, 85, 440-452.

ADHD and Low Working Memory: A comparison

I was recently directed to a paper titled ‘Children with low working memory and children with ADHD: same or different?’ From the title alone this paper piqued my curiosity. As scientists we often focus on collecting large amounts of data, and in some cases forget the importance of forming new theories and ideas. For me, this paper casts the profiles of an ADHD diagnosis and a deficit in working memory, a common childhood issue, in a new light. What does it mean (if anything) to have a clinical diagnosis? And what is the best way of characterising children with problems of attention and memory?

Whilst I will summarise the main findings below, for those that want to read the paper for themselves it can be accessed here:

http://journal.frontiersin.org/Journal/10.3389/fnhum.2014.00976/full

This paper provides a novel comparison of cognitive skills, executive function, educational attainment and behaviour between those with a low WM and those with a diagnosis of ADHD. It utilised three groups of children (8-11yrs): one group identified by routine screening as having a low WM, one group with diagnoses of ADHD and one group of typically developing children.

Age was controlled for across the groups and for each group the following measures were taken: Working memory (Automated working memory assessment); Executive function (Delis-Kaplan Executive Function System, The K-test of the Continuous Performance Test and Walk/Don’t Walk from the Test of Everyday Attention for Children); Academic ability (Wechsler reading, spelling, reading comprehension, mathematical reasoning and number operations and the four WASI subtests); And classroom behaviour problems (Connors teacher rating and the Behaviour rating inventory of executive function). Those children taking medication for their ADHD stopped taking it at least 24hrs prior to the session to ensure the effects of the drugs were eliminated by the time of testing.

The theory that makes up the basis for the research, includes the fact that both ADHD and low WM in childhood are significantly associated with poor educational attainment. It has also been established in the literature that there are associations between poor WM and inattentive behaviour and poor WM and executive function deficits. These elements combined suggest a high degree of overlap between the profiles of those with a deficit in WM and those with an ADHD diagnosis. In ADHD, we see executive function problems (deficits in response inhibition, attentional switching, planning and sustained attention) and also excessively high levels of motor activity and impulsive behaviour.

Some have argued that the executive function deficits in ADHD are a result of an underlying low WM, whilst others have proposed that ADHD involves two distinct neurodevelopmental systems:

1) a ‘cool/cognitive system’ that affects executive functions and WM

and

2) a ‘hot/affective system’ that leads to ‘aversion to delay that manifests as impulsive behaviour’.

It is this impulsive behaviour that manifests as excessive motor activity and problems in impulse control that are core characteristics of ADHD and are not associated with low WM. Thus, this study hypothesised that the two groups share a common deficit in the ‘cool executive function system’ reflected in the fact that both profiles exhibit lack of attentional control, but only those with the ADHD diagnosis have impairments in the ‘hot executive function system’ that results in hyperactivity and impulsivity.

The ADHD and low WM group, as expected, performed more poorly than the typically developing children on the tests of working memory (necessarily so for the latter group, because they were selected on this basis). Performance was significantly decreased for the low WM group in the two WM subtests that they were initially screened on. This suggests a selection artefact as ‘the two groups did not differ on the verbal WM task that was not used at screening’. In the executive function switching task no significant difference was seen between the low WM and ADHD group when considering accuracy, however, the low WM group showed a significantly slower performance than the ADHD and typical group. The sustained attention executive function task showed that both the ADHD and low WM children were significantly less accurate than the typically developing group and that they both made a significantly greater number of omissions than the typically developing group. The ADHD group also further made a significantly greater number of commission errors than the other two groups. No other significant differences were seen between the low WM group and the ADHD group on any other test of executive function. It is also worth noting that although there are a number of different models proposed for the functional structure of WM, the findings of this paper remain consistent across models on the basis that the ‘storage-only capacities of short-term memory (STM) and the capacity-limited attentional control functions of WM can be distinguished’.

They concluded that the ADHD and low WM groups have very similar profiles of WM ability and executive function impairment. They did however find two important differences: the low WM group were ‘slower to respond on several tasks’ and the ADHD group were ‘more hyperactive and exhibited more difficulties in controlling impulsivity in sustained attention’.  The processing speed impairments in the low WM group were an unexpected finding. They state that ‘it does not appear to be a part of a broader problem in fluid intelligence, as controlling statistically for performance IQ had little impact’. They present the idea that the impairment in response time may be a result of sluggish cognitive tempo (SCT). Sluggish cognitive tempo is a ‘set of symptoms strongly associated with the predominantly inattentive form of ADHD that includes high levels of daydreaming, slow response times, poor mental alertness and hypoactivity.’ It could perhaps be the case that those with a low WM may correspond to those with SCT, a predominantly inattentive subtype of ADHD. At present SCT is not a commonly utilised diagnosis in the UK. The more hyperactive behaviour in the ADHD children however was expected. ‘They violated rules more frequently during a planning task, and made more commission errors on the Continuous Performance Test of sustained attention’. This elevated hyperactivity was also shown in the Connor’s teacher rating as increased impulsivity and oppositional behaviour.

Interestingly they found the two groups to have equivalently high levels of inattentive behaviour. Whilst it has been shown before that children with low WM often exhibit inattentiveness, this paper is the first to show that this inattentive behaviour is of a very similar degree to that of  children with ADHD. In terms of the deficits in working memory, both groups exhibited deficits in the same regions and of a similar magnitude. They had impairment in ‘visuospatial short term memory, verbal working memory and visuo-spatial working memory’; but ‘their verbal short term memory scores fell within the typical age range’. The novel finding here is that there is such a high degree of correspondence between the profile and severity of the WM impairments in the two groups.

Previous research has also suggested a close relationship between ‘controlled cognitive attention and working memory’. This paper takes this understanding one step further suggesting that it is now looking more likely that there is a ‘link between poor working memory and overt inattentive and distractible behaviours’ as seen through the inattentiveness in the behaviour of those children with a low WM. In addition to being comparable in inattentiveness and working memory impairment, the two groups both exhibited ‘high rates of problem behaviours across a wide range of executive function behaviours’. Previous studies have also indicated that problem behaviours associated with executive function are seen in children with a low WM. This paper supports and extends this idea by showing that both groups ‘performed poorly on direct measures of switching, inhibition, sorting, planning, sustained attention and response suppression’.

When considering again, the cool (cognitively based) and the hot (affective) model of executive function, the pattern found in the results from this study suggest that there is some shared general executive deficit between the ADHD and low WM groups. The cool deficits manifest as inattention and low WM in both groups whilst the hot deficits, only present in the ADHD group, manifests as the hyperactivity. The additional delay in response times in the low WM group is the only component that does not fit with this theory, but the authors have identified that it ‘may be symptomatic of a subgroup of children with the predominantly inattentive form of ADHD who are characterised by SCT’.

In conclusion, this paper shows some very interesting preliminary findings into two conditions that are generally considered distinct. Both groups showed equal levels of underachievement when IQ differences were taken into account (‘they are indistinguishable in terms of their poor learning progress in mathematics and reading’). This is of great significance as those with a diagnosis of ADHD will almost certainly have learning support provided to them whilst those with a low WM will not. The educational needs of the group of children with a low WM are being overlooked as they do not show the disruptive hyperactivity of ADHD, even though in many respects the groups are indistinguishable.

Holmes J, Hilton KA, Place M, Alloway TP, Elliott JG and Gathercole SE (2014) Children with low working memory and children with ADHD: same or different? Front. Hum. Neurosci. 8:976. doi: 10.3389/fnhum.2014.00976