Elsevier

NeuroImage

Volume 134, 1 July 2016, Pages 236-249
NeuroImage

Neural correlates of training and transfer effects in working memory in older adults

https://doi.org/10.1016/j.neuroimage.2016.03.068Get rights and content

Highlights

  • Working memory training leads to neural activation decrease in the right MFG in aging.

  • Neural changes in transfer task are related to updating (compared to maintenance).

  • Neural correlates of training and transfer effects overlap in the right MFG.

  • Behavioral transfer to executive functions, processing speed, fluid intelligence

  • Results advance understanding of neural mechanisms of training and transfer.

Abstract

As indicated by previous research, aging is associated with a decline in working memory (WM) functioning, related to alterations in fronto-parietal neural activations. At the same time, previous studies showed that WM training in older adults may improve the performance in the trained task (training effect), and more importantly, also in untrained WM tasks (transfer effects). However, neural correlates of these transfer effects that would improve understanding of its underlying mechanisms, have not been shown in older participants as yet. In this study, we investigated blood-oxygen-level-dependent (BOLD) signal changes during n-back performance and an untrained delayed recognition (Sternberg) task following 12 sessions (45 min each) of adaptive n-back training in older adults. The Sternberg task used in this study allowed to test for neural training effects independent of specific task affordances of the trained task and to separate maintenance from updating processes. Thirty-two healthy older participants (60–75 years) were assigned either to an n-back training or a no-contact control group. Before (t1) and after (t2) training/waiting period, both the n-back task and the Sternberg task were conducted while BOLD signal was measured using functional Magnetic Resonance Imaging (fMRI) in all participants. In addition, neuropsychological tests were performed outside the scanner. WM performance improved with training and behavioral transfer to tests measuring executive functions, processing speed, and fluid intelligence was found. In the training group, BOLD signal in the right lateral middle frontal gyrus/caudal superior frontal sulcus (Brodmann area, BA 6/8) decreased in both the trained n-back and the updating condition of the untrained Sternberg task at t2, compared to the control group. fMRI findings indicate a training-related increase in processing efficiency of WM networks, potentially related to the process of WM updating. Performance gains in untrained tasks suggest that transfer to other cognitive tasks remains possible in aging.

Introduction

Age-related reduction in working memory (WM) performance has been related to changes in brain functioning (for reviews, see Eyler et al., 2011, Nyberg et al., 2012, Rajah and D’Esposito, 2005). Studies comparing neural activity during verbal WM tasks between older and younger participants have found that younger adults asymmetrically recruited areas in the left fronto-parietal areas, while older adults showed greater activity also in homologous regions in the right hemisphere (Cabeza et al., 2002, Cabeza et al., 2004, Reuter-Lorenz et al., 2000). More recent studies (Heinzel et al., 2014a, Nagel et al., 2011) have indicated that age-associated activation differences depend largely on task difficulty. Compared to younger adults, older adults showed similar performance but greater activation in WM-related brain areas at low WM load but reduced performance and lesser activation at high WM load in a recent sample of younger and older adults that included a subsample of the current study (Heinzel et al., 2014a). These findings can be explained by the notion of reduced neural efficiency and capacity in older adults (Barulli and Stern, 2013) and the compensation-related utilization of neural circuits hypothesis (CRUNCH, Reuter-Lorenz and Cappell, 2008). The CRUNCH model suggests that older adults recruit more neural resources to achieve a similar performance as younger adults at relatively low task demands (reduced processing efficiency in older adults). It has been argued that older adults utilize cognitive control strategies already at low task difficulty levels to compensate for structural and functional decline (Grady, 2012). However, according to the CRUNCH model, attempted compensation fails at high task difficulty because older adults are unable to further exceed their neural activation level and performance collapses due to a reduced capacity in older adults (Nyberg et al., 2009, Schneider-Garces et al., 2010). Cognitive training is thought to enable older adults to perform low and medium levels of WM tasks below their capacity limit due to the development of more efficient processing strategies. Therefore, WM training is expected to lead to activation decreases only at low and medium difficulty, but not at high difficulty levels (Lustig et al., 2009).

Cognitive training research has shown that WM training might have the potential to slow down or even restore some aspects of age-related decline in WM functioning (e.g. (Brehmer et al., 2011, Dahlin et al., 2008, Heinzel et al., 2014a, Heinzel et al., 2014c, Li et al., 2008, Richmond et al., 2011). While several studies have investigated neural correlates of WM training gains in younger, there are only few studies in older adults (Buschkuehl et al., 2012, Klingberg, 2010). Erickson et al. (2007) found that dual-task performance gains following training were related to decreased activity mainly in the right dorsolateral prefrontal cortex (DLPFC) in older adults. Similarly, Brehmer et al. (2011) reported an activity decrease in WM-associated areas in the right fronto-parietal regions in response to behavioral training gains in a delayed recognition task. If training enables older participants to increase the efficiency of their WM processing as suggested by the CRUNCH model (Reuter-Lorenz and Cappell, 2008), activity is expected to decrease particularly at 1-back (low difficulty) and 2-back (medium difficulty) following training. Taken together, behavioral training gains seem to be associated with changes in neural activity during the performance of the trained task. However, results might be relatively task-specific since neural correlates of transfer to other WM tasks have only been reported in one study in younger but not in older adults as yet (Dahlin et al., 2008). Furthermore, it is not known which components of WM are associated with training-related activity changes.

According to the WM model of Baddeley (2000), WM refers to “a limited capacity system allowing the temporary storage and manipulation of information” (Baddeley, 2000, p. 418). Although there has been a proliferation of cognitive psychological theories on WM since Baddeley and Hitch's (1974) model (for a comparison of models, see e.g. Chein and Fiez, 2010), most WM models suggest a distinction between at least two components of WM, namely maintenance and executive control of information. Both components are thought to be involved in the performance of the n-back WM task. While n-back training was found to be effective in older adults (Heinzel et al., 2014a, Heinzel et al., 2014c, Li et al., 2008), as yet, it is unclear whether training gains in the n-back task refer to an improved ability to maintain or to update information or both. If activity changes related to training gains within a cognitive process (e.g. updating) are not just task-specific, common activity changes in similar tasks could be expected (Buschkuehl et al., 2012, Dahlin et al., 2008, Gray et al., 2003, Jonides, 2004). Thus, training-related changes in brain activity during n-back performance might overlap with activity changes in the maintenance or the updating component of WM in an untrained task depending on the specific process component mediating training gains.

There has been a debate whether subcomponents of WM such as updating or maintenance may be associated with specific brain areas. Modality-specific activity patterns in ventral vs. dorsal posterior lateral frontal cortex and more posterior brain regions have been shown consistently for the maintenance of verbal vs. spatial information (Curtis and D'Esposito, 2003). In line with current conceptions of PFC function (Frank et al., 2001, Fuster, 2004, Miller and Cohen, 2001), WM updating can be understood as a highly interactive executive control process, not just involving DLPFC, which has been most prominently associated with executive control (Baddeley, 2003, Collette and Van der Linden, 2002, D’Esposito et al., 1995, D’Esposito et al., 2000, Mohr et al., 2006). Instead, WM updating seems to rely on a distributed network also including ventrolateral PFC and areas of the dorsal attention system located in lateral premotor cortex (LPMC)/caudal superior frontal sulcus (cSFS), in posterior parietal cortex (PPC, for recent reviews, see Linden, 2007, Nee et al., 2013) and subcortical regions (Frank et al., 2001), which partly overlap with regions related to maintenance. Therefore, activity changes within these areas after n-back training might be either specific to maintenance or updating or common to both.

Finally, an important question in cognitive training research concerns the degree to which training effects are transferable to tasks in other cognitive domains (“far transfer”, Klingberg, 2010, Lustig et al., 2009, Noack et al., 2009, Zelinski, 2009). In the past decade, WM has gained attention in transfer research, presumably because WM is believed to be a central mental capacity that has been shown to be closely linked to other cognitive domains, such as executive functions (e.g. Chen and Li, 2007, Conway et al., 2003), processing speed (e.g. Burgaleta and Colom, 2008, Clay et al., 2009, Salthouse, 1996), and fluid intelligence (e.g. Ackerman et al., 2005, Kyllonen et al., 1990).

Since n-back is considered to be an executively demanding WM task (Veltman et al., 2003), we expected training gains in n-back to be related to the executive component of WM and thus find overlapping activity changes between n-back and the updating component of an untrained WM task. Furthermore, we expected to detect behavioral transfer to tests of executive functions and short-term memory outside the MRI scanner. By including additional executive neuropsychological tests (such as the Stroop word/color interference test, Stroop, 1935), we will investigate if transfer effects are specific to updating or if they are also found in other domains of executive functions (such as “inhibition” according to the model by Miyake et al., 2000). To test these hypotheses in the present study, a sample of healthy older adults was assigned to either an n-back training group or to a no-contact control group. Before and after training/waiting period, a parametric n-back task was performed during fMRI measurement and a set of cognitive tasks was administered. An additional WM paradigm (Sternberg delayed match to sample task) for which WM component processes (i.e. maintenance vs. updating) could separately be analyzed was administered before and after training to investigate common activity changes between n-back and Sternberg task. That way, it could be tested whether the neural correlates of transfer effects in WM are associated with maintenance or updating processes.

Taken together, the specific aims of this study were to (i) investigate whether training-induced changes in neural activation in older adults reflect increases in processing efficiency as postulated by the CRUNCH model; (ii) test whether these neuronal activation changes vary with WM load as predicted by task demand to activation functions. Regarding transfer effects, we aimed to investigate (iii) which components (updating versus maintenance) of the working memory system are involved in transfer effects, (iv) whether transfer effects go along with neuronal activation overlap, and (v) whether such overlap is related to near and far behavioral transfer effects.

Section snippets

Participants

Sixteen control participants were carefully matched one by one to sixteen training participants according to their age (+/− 2 years), sex, and education level (+/− 3 years of education) to assure parallelization of both groups. The first half of the control group was recruited after the first half of the training group was included, the second half of the control group after testing the second half of the training group. The age range was 60 to 75 years. All participants were recruited via newspaper

N-back performance and training gains

The groups did not differ in n-back performance at t1 (all p's > .18). To test whether improvements in n-back performance (% correct, defined as hit rate minus false alarm rate) can be related to the training, and whether training gains differ between different WM load levels, a group by time by WM load repeated measures GLM was conducted and revealed a significant 3-way interaction (F(3,81) = 5.13, p = .003, partial η2 = .16). See Table 1, panel A and Fig. 1, panel A for means and standard errors per

Discussion

In the present study, we investigated training-related gains in two verbal WM tasks and associated changes in BOLD response in older participants. Our results indicate that after four weeks of WM training, participants in the training group improve in their performance in the trained n-back task (see also previously published results of a subsample of the current study (Heinzel et al., 2014a)). This was accompanied by training-related decreases in BOLD signal in lateral prefrontal cortex

Conclusion

After 4 weeks of n-back training, BOLD signal in the right MFG/cSFS decreased in the training group in both the trained n-back and the untrained Sternberg updating task. Behavioral transfer to processing speed, executive functions, and figural relations (fluid intelligence) was found. Performance gains in these untrained tasks suggest that transfer to other cognitive domains may remain possible throughout the lifespan. fMRI findings indicate a training-related increase in processing efficiency

Disclosure statement

All authors and their institutions declare to have no actual or potential conflicts of interest to disclose.

Acknowledgment

This work was supported in part by German National Academic Foundation scholarships to S.H. and R.C.L.; the German Ministry for Education and Research (BMBF 01QG87164, 01GS08195 and 01GQ0914), the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG, FOR 1617: grant RA1047/2-1; and in part by the DFG Priority Program, SPP 1772, grants RA1047/4-1 and HE 7464/1-1), and by a MaxNetAging award to M.A.R. The authors wish to thank Ulrike Basten and Christian J. Fiebach for making the

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