DNB Pro Compared to Lumosity

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Review of Meta-Analyses of DNB training for IQ and Working Memory

For some quick definitions, general intelligence (g) is how smart we are, a single factor underlying our general cognitive ability. Working memory is our ‘mental workspace’ that stores and processes task-relevant information. It is the interface between the current focus of attention and long-term memories. interface between the current focus of attention and long-term memories. Executive control is our ability to manage our attention and goal focus. Meta-analyses systematically assess all peer-reviewed, published studies meeting relevant criteria for a particular type of training. A meta-analysis, compared to a single peer reviewed paper enables us to draw much stronger conclusions about the effectiveness of brain training interventions.

For a quick primer on experimental design and terms such as ‘active vs passive control’ refer to ‘Some useful definitions’ at the end of this review.

Definitions of general intelligence (g)

Most of the studies that address the issue of whether working memory training improves intelligence use a standard dual n-back working memory training task, and use matrices fluid intelligence tests to measure general intelligence (g). We should first acknowledge that (1) the dual n-back is just one working memory training task, and (2) that matrices (e.g Raven’s) tests are only one type of IQ test.

It is standard for short-term and/or working memory tests to be incorporated in full scale-IQ tests, as one important measure of IQ.

In the well-known WAIS-IV full scale IQ test, matrices tests measure what is labelled the ‘Perceptual Organization Index’. The Working Memory Index is a distinct category in the measurement of general IQ.

 

650px-Wechsler_Adult_Intelligence_Scale_subscores_and_subtests

In an influential theoretical paper in the field of psychometric IQ testing using factor-analytic approaches, Kevin S. McGrew states:

“the Cattell–Horn Gf–Gc and Carroll Three-Stratum models have emerged as the consensus psychometric-based models for understanding the structure of human intelligence. Although the two models differ in a number of ways, the strong correspondence between the two models has resulted in the increased use of a broad umbrella term for a synthesis of the two models (Cattell–Horn–Carroll theory of cognitive abilities—CHC theory).” McGrew, 2009

This model is shown here:

 

CHC theory of intelligence

Adapted from Kevin McGrew. Gwm has also been called ‘Gsm’ in representations of the model.

It’s clear that the construct of IQ embraces more than what is measured by matrices tests, and that it includes working memory.

So if working memory training improves working memory, then it improves IQ. We’ll see there is strong evidence that dual n-back training improves working memory. There’s also evidence (although less conclusive) that dual n-back training improves fluid intelligence (Gf) measured by matrices tests.

In what follows we’ll look in more detail at the following:

  1. Meta-analyses of broad cognitive abilities studies

  2. Meta-analyses of working memory studies

  3. Meta-analyses of matrices (Gf) studies

  4. Specific critiques of the Gf studies (e.g. Bayesian)

  5. Fronto-parietal network neuroplasticity studies


1. Meta-analyses of broad cognitive abilities

The most recently published meta-analysis of the dual n-back / working memory training literature by Schwaighofer and colleagues’ (2015) summarizes the training gains in the following table. The stars indicate statistical significance. Long-term means 5-12 months after training.

training effects from working memory training

 

To convert these effect sizes (both short term and long term) to standardized scores such as points in an IQ test, multiply them by 15 – one standard deviation. So for example, the visuospatial working memory gain of 0.63 is equivalent to 0.63 x 15 = 9.5 points on a standardized scale.

In this meta-analysis  Schwaighofer and colleagues found that

  • Age was not relevant to such gains: they occurred across the full age spectrum.
  • Training duration most likely makes a difference: the more you training, the greater the gains.

And they suggest that

  • More complex activities during working memory brain training – with multiple exercises – may result in more practical advantages after training (what is called ‘wide transfer’).

The average age for this meta-study was relatively young. What about for older adults?

A recent meta-analysis by Karbach and Verhaeghen (Nov 2014) examined the effects of dual n-back/working memory and executive control training (49 studies) in both younger adults and 60+ year olds for a number of general cognitive abilities including attention control, IQ (fluid intelligence), episodic memory (memory for personal experiences), short term and working memory, and processing speed. This study found the following effect sizes with no differences in training-based gains between younger and older adults:
brain training for older adults

Note that a 0.4 effect size is equivalent to 6.5 points on a standardized scale. These effect sizes are considerable, and include a large IQ (fluid intelligence gain) of ~5.5 IQ points.

For older adults, similar results were found in the meta-analyses of Hindin & Zelinksi, 2012, and Karr et al., 2014. The net gain in overall cognitive ability after (on average) 9 hours of DNB / working memory and executive function training is similar in size to the effect of (on average) about five months of  regular 45-minute sessions aerobic training (review).


2. Meta-analyses of studies on training for working memory (Gwm) gains

The latest meta-review of verbal and visuo-spatial short term memory and verbal and visuo-spatial working memory (Schwaighofer et al., 2015) shows there are both short term (within a few days of training) and long-term (6-12 month follow up) training effects, and these effects are considerable (see table below).
dual n-back for working memory

So for example, the visuospatial working memory gain of 0.63 is equivalent to 0.63 x 15 = 9.5 points. Long term visuo-spatial working memory gains from training due to neuroplasticity are 6.5 points.

Melby-Lervag and Hulme’s 2013 meta-analysis, also concluded that working memory training resulted in visuo-spatial and verbal working memory gains.

Effect sizes reported by individual studies for visuo-spatial working memory, with short-term gains on the left and long term (up to 1 year) on the right, is shown in this plot.

working memory training improves working memory

 

Effect sizes reported by individual studies for verbal working memory, with short-term gains on the left and long term (up to 1 year) on the right, is shown in this plot.

brain training for working memory

 

It’s clear from these plots that dual n-back / working memory training improves working memory. Even at 6-12 months after training there is a persisting working memory gain, assessed with standard working memory tests. This demonstrates long-term neuroplasticity change,  and this is consistent with the brain science literature (below).

The controversy surrounding studies looking at the effects of working memory training (e.g. the dual n-back) on Gf measured by matrices tests, fails to address these studies looking at the effects of working memory training on working memory capacity/efficiency – another equally valid measure of IQ. Even the fluid intelligence (Gf) gain skeptics Melby-Lervag and Hulme in their 2013 meta-analysis concluded that working memory training resulted in visuo-spatial and verbal working memory gains.


3. Meta-analyses of fluid intelligence (Gf) studies

The 2015 Au et al. meta-analysis found a significant IQ increase in fluid intelligence from dual n-back training. The authors conclude:

“We urge that future studies move beyond attempts to answer the simple question of whether or not there is transfer [from training to increases in IQ] and, instead, seek to explore the nature and extent of how these improved test scores may reflect true improvements in intelligence that can translate into practical, real-world settings.” Jacky Au and colleagues, University of California, April 2015 

Here is the plot of effect sizes reported in relevant studies reviewed in this meta-analysis:

Au et al effect sizes

It is the case that many of these studies are underpowered, but the Au et al (2015) estimates are still the best available. (5)

Au and colleagues’ conclusion that working memory training results in IQ gains finds support in the meta-analysis by Karbach and Verhaeghen (Nov 2014) who estimated a  5.5 point IQ increase. And more recently, it is supported by the latest comprehensive 2015 meta-review by Schwaighofer and his colleagues. They found there were significant increases in IQ measured by both non-verbal and verbal ability after training, regardless of control group. In this study they found that these IQ gains did not last without further training when measured between 6 and 12 months later. But in the short term there was a real IQ gain from training, and we can assume these gains could be maintained or improved with continued training.

The effect sizes reported by all studies looking at working memory training’s effect on non-verbal (Gf) IQ are shown in this plot from Schwaighofer and colleagues, 2015:

working memory training for IQ

The effect sizes reported by all studies looking at dual n-back training’s effect on verbal IQ are shown in this plot:

working memory training effect on IQ

We can reasonably conclude from these meta-analyses that Gf gains from training are real – for both younger and older adults.

  • Estimates of the overall training benefit for both non-verbal (Gf) and verbal IQ range between 2.0 – 5.5 standardized points (1,2,3 )
  • An estimate of the overall training benefit for IQ when we only look at experiments in which the comparison/control group is ‘passive’ and does no computer activity is ~7 IQ points (1)

 


4. Specific critiques of the Gf studies

Melby-Lervag and Hulme critique

The conclusion of this meta-analysis has been challenged by Melby-Lervåg and Hulme in a reanalysis of the data. While they do not doubt that there is a Gf gain after brain training of around 7-8 IQ standardized points, they argue that this gain is essentially a placebo effect since when the comparison (‘control’) groups are active (i.e. do other computer tasks such as a simple attention exercises), the effect size is greatly reduced.

But this Oct 2015 response to Melby-Lervag and Hulme counters their arguments and supports Au et al.’s original conclusion that the working memory training does indeed improve IQ:

We demonstrate that there is in fact no evidence that the type of control group per se moderates the effects of working memory training on measures of fluid intelligence and reaffirm the original conclusions in Au et al., which are robust to multiple methods of calculating effect size, including the one proposed by Melby-Lervåg and Hulme. (Au et al., Oct, 2015)

Au et al (2015)  point out that

“…the present direction of effects actually suggests that passive control groups could end up outperforming active control groups which runs opposite to the direction suggested by the idea that Hawthorne or expectancy effects drive improvements in both active control and treatment groups.”

Dougherty and colleagues (2015) Bayesian critique

Dougherty, Hamovitz and Tidwell’s (2015), in their paper Reevaluating the effectiveness of n-back training on transfer through the Bayesian lens: Support for the null, look at the Au et al. meta-analysis using a Bayesian statistical framework. This framework evaluates the relative strength of the evidence for the alternative versus null hypotheses, contingent on the type of control condition used. They argue that a placebo effect accounts for the observed effect, concluding:
“We find that studies using a noncontact (passive) control group strongly favor the alternative hypothesis that training leads to transfer but that studies using active-control groups show modest evidence in favor of the null. We discuss these findings in the context of placebo effects.”

To counter their critique, the first obvious point is that the 7.7 : 1 probability in favor of the null hypothesis in active control studies is not very reassuring. This compares to a 13,241 : 1 factor in favor of the alternative hypothesis in passive control studies – a level of evidence that is certainly convincing. 7.7:1 is one step above ‘weak’ and well below ‘decisive’ in terms of their ‘points of reference’ categories for how to interpret degrees of evidence using the Bayes factor.

Second, cultural differences may be largely driving the hypothesized active vs passive difference. Here is Figure 5 from the Dougherty et al study.

Bayesian analysis of working memory studies

It is clear that it is exclusively the  US x active control studies that support the null. The active control studies in Europe have fairly high g effect sizes (even if the CI crosses zero), and there are a couple of US studies with passive controls that do not support the null hypothesis.

Dougherty and colleagues argue that what is explanatory here is the active vs passive control contrast – not the cultural contrast. But they admit that cultural differences may be the driver: “this leaves open the possibility that cultural differences are driving the difference between the active and passive studies.

There is evidence that in the US particularly  over the last 25 years the difference in effectiveness between real drugs and placebo ones has narrowed considerably, suggesting that Americans are particularly susceptible to the placebo effect more generally (5, 6). Now in the US even many well-established medical drugs would not pass placebo control trials and this is a major concern for medical research.

A graph showing strengthening of placebo response in US studies

Since the ‘susceptibility to placebo’ is a plausible alternative explanation to Dougherty and colleagues’ it needs to be directly addressed in future studies. What is needed before placebo criticisms can be regarded as a serious challenge is direct evidence for placebo effects in the form of experiments where expectations are systematically varied, or adding a third group to the controlled trial set-up, which takes an existing intervention that is known to work – if both that group and the group given the effective intervention fail to beat the placebo, researchers know that their trial design is flawed.

 


 

5. Fronto-parietal network neuroplasticity studies

Studies that look a cortical network and synaptic neuroplasticity effects from dual n-back and cognitive control training are consistent with wide IQ transfer interpretations of the behavioral meta-analyses

There are now many neuroimaging studies showing consistent fronto-parietal network (FPN) neuroplasticity effects from working memory (dual n-back) brain training (e.g. Thompson et al., 2016Metzler-Baddeley et al. 2016Kundu et al., 2015). There is extensive evidence for the recruitment of the fronto-parietal network (FPN) – relative to other cortical networks – in tasks requiring fluid intelligence (IQ) (e.g. Preusse et al., 2011).

FPN  neuroplasticity effects are clearly not ‘placebo’ effects, and they help explain the consistent working memory training gains that are seen in meta-analyses, as well as the consistent emotion-regulation effects that are observed.

Barbey et al. (2012) investigated the neural substrates of the general factor of intelligence (g) and executive function in 182 patients with focal brain damage using MRI brain imaging. They concluded:

Impaired performance on these measures in the WAIS-IV and Delis-Kaplan Executive Function test were associated with damage to a distributed network of left lateralized brain areas, including regions of frontal and parietal cortex and white matter association tracts, which bind these areas into a coordinated system. The observed findings support an integrative framework for understanding the architecture of general intelligence and executive function, supporting their reliance upon a shared fronto-parietal network for the integration and control of cognitive representations.

Kundu and colleagues (2015) have recently proposed that the transfer of working memory training to other cognitive abilities is supported by changes in connectivity in frontoparietal and parieto-occipital networks – active in  both the trained and transfer tasks.  The frontoparietal network is part of the Cognitive Control Network (9) involved in attention control and goal maintenance.

working memory training mechanisms

Consistent with this, in a recent MIT, Harvard and Stanford neuroimaging study (Jan, 2016), Thompson and colleagues, found:

[Dual n-back] training differentially affected activations in two large-scale frontoparietal networks thought to underlie working memory: the executive control network and the dorsal attention network. …Load-dependent functional connectivity both within and between these two networks increased following training, and the magnitudes of increased connectivity were positively correlated with improvements in task performance. These results provide insight into the adaptive neural systems that underlie large gains in working memory capacity through training.

And in their recent paper Task complexity and location specific changes of cortical thickness in executive and salience networks after working memory training, Metzler-Baddeley and colleagues (2016) found that working memory training resulted in increases of cortical thickness in right parieto-frontal cortex. (They also found that training led to a reduction of thickness in the right insula and that these changes were related to changes in working memory span.)

Interference Control

There is an important overlap in brain circuitry between interference control, working memory capacity and IQ (Gf). Brain imaging studies reveal that neural mechanisms of interference control underlie the relationship between fluid intelligence and working memory span.

Greenwood and Parasuraman (2015) hypothesize that training-related increases in control of attention in the frontoparietal control network and circuits underlying interference control underlie ‘far transfer’ of cognitive training to untrained abilities, notably to fluid intelligence.

Interference control allows us to suppress distractions. There is compelling evidence that distraction suppression (evident in behavior, neuronal firing, scalp electroencephalography, and hemodynamic change) is important for protecting target processing during perception and holding information in working memory. Consistent with this evidence, forms of cognitive training that increase the ability to ignore distractions (e.g., working memory training and perceptual training) not only affect the frontoparietal control network but also affect transfer to fluid intelligence.

Working memory training games that incorporate systematic interference control and distraction shielding may be expected to enhance IQ gains.

Summary

This brief review of the 2014-2015 scientific meta-analysis literature supports the conclusion that dual n-back (working memory) and executive control training  increases general cognitive performance – whether IQ (verbal or non-verbal ability), short-term memory or working memory. This training benefit is mediated in part by neuroplastic changes in the frontoparietal network.

Based on the available evidence, we can conclude that there are not sufficient grounds to discredit the claim that working memory training is an effective and efficient strategy for improving IQ.

.


References

Au, J., Buschkuehl, M., Duncan, G. J., & Jaeggi, S. M. (2015). There is no convincing evidence that working memory training is NOT effective: A reply to Melby-Lervåg and Hulme.  Psychonomic Bulletin & Review. Oct, 2015. Abstract

Au, J., Sheehan, E., Tsai, N., Duncan, G. J., Buschkuehl, M., & Jaeggi, S. M. (2015). Improving fluid intelligence with training on working memory: a meta-analysis. Psychonomic Bulletin & Review, 22(2), 366-377. Abstract

Barbey, A. K., Colom, R., Paul, E. J., Grafman, J. (2013). Architecture of fluid intelligence and working memory revealed by lesion mapping. Brain Structure and Function, 219, 2. 485-94. Article.

Barbey, A. K., Colom, R., Solomon, J., Krueger, F., Forbes, C., & Grafman, J. (2012). An integrative architecture for general intelligence and executive function revealed by lesion mapping. Brain, 135(4), 1154–1164. http://doi.org/10.1093/brain/aws021

Bogg, T., & Lasecki, L. (2014). Reliable gains? Evidence for substantially underpowered designs in studies of working memory training transfer to fluid intelligence. Frontiers in Psychology, 5, 1589. Abstract.

Greenwood, P. M., & Parasuraman, R. (2015). The Mechanisms of Far Transfer From Cognitive Training: Review and Hypothesis.Neuropsychology. [Ahead of print].

Hindin S.B., Zelinski E.M. Extended Practice and Aerobic Exercise Interventions Benefit Untrained Cognitive Outcomes in Older Adults: A Meta-Analysis. Journal of the American Geriatrics Society.2012;60(1):136–141. [Article]

Karbach, J., & Verhaeghen, P. (2014). Making working memory work: A meta-analysis of executive-control and working memory training in older adults. Psychological Science, 25, 2027–2037. Abstract.

Karr J.E., Areshenkoff C.N, Rast P, Garcia-Barrera M.A.  (2014). An Empirical Comparison of the Therapeutic Benefits of Physical Exercise and Cognitive Training on the Executive Functions of Older Adults: A Meta-Analysis of Controlled Trials. Neuropsychology, 28(6):829-45. [Article]

Jaeggi, S.M., Buschkuehl, M., Jonides, J., & Perrig, W.J. (2008). Improving fluid intelligence with training on working memory. Proceedings of the National Academy of Sciences of the United States of America, 105(19), 6829-6833. Abstract / Article

Metzler-Baddeley, C., Caeyenberghs, K., Foley, S., & Jones, D. K. (n.d.). Task complexity and location specific changes of cortical thickness in executive and salience networks after working memory training.NeuroImage. http://doi.org/10.1016/j.neuroimage.2016.01.007

McGrew, K. S. (2009). CHC theory and the human cognitive abilities project: Standing on the shoulders of the giants of psychometric intelligence research. Intelligence, 37(1), 1–10. http://doi.org/10.1016/j.intell.2008.08.004

Melby-Lervag, M., & Hulme, C. (2013). Is working-memory training effective? A meta-analytic review. Developmental Psychology, 49, 270– 291. Abstract.

Preusse, F., Elke, van der M., Deshpande, G., Krueger, F., & Wartenburger, I. (2011). Fluid Intelligence Allows Flexible Recruitment of the Parieto-Frontal Network in Analogical Reasoning. Frontiers in Human Neuroscience, 5. http://doi.org/Abstract

Thompson, T. W., Waskom, M. L., & Gabrieli, J. D. E. (2016). Intensive Working Memory Training Produces Functional Changes in Large-scale Frontoparietal Networks.Journal of Cognitive Neuroscience, 1–14. http://doi.org/Abstract

Schwaighofer, M., Fischer, F., Buhner, M. (2015) Does Working Memory Training Transfer? A Meta-Analysis Including Training Conditions as Moderators. Educational Psychologist 50, 2. Abstract, Article.

 


Some Useful Definitions

For those of you without training in experimental design, here are some useful definitions that will equip you to understood some of the more technical content of this review, and help you evaluate it for yourself.

Experiments / Randomized Control Trials involve randomly assigning participants in the study to receive one of a number of cognitive interventions. One of these interventions is the computerized cognitive training (brain training) program. One of these interventions is the standard of comparison or control. The control may be an active control (e.g. playing a simple game or doing cross-words for the same duration as the brain training), or a passive control where there is no intervention at all.

Peer-reviewed journal articles.  These are published articles of randomized control trials (studies) on brain training that have been submitted to the scrutiny of experts in the same field, and judged to acceptable for publication.

Meta-analyses systematically assess all peer-reviewed studies meeting adequate standards of experimental design and relevance criteria for a particular type of brain training. A meta-analysis uses a statistical approach to combine the results from multiple trials to improve estimates of the size of the effect and resolve uncertainty when reports disagree – for example when one study concludes there is an effect and another study does not. It can also correct for publication bias – the tendency to only publish reports when there is a positive result. A meta-analysis, compared to a single peer-reviewed journal article, enables us to draw much stronger conclusions about the effectiveness of brain training interventions.

If there is statistical significance in a brain training study, it means that the difference in tested outcomes such as average IQ score between training group and the control group is very unlikely (p < 0.05) to have occurred by chance. If the study is well-designed, this gives us confidence that the difference in IQ scores between the brain training and placebo group is due to the training itself, and not some fluke.

The effect size is a measure of the magnitude of the outcome difference between the two groupswhich can be measured in standardized scores. Effect size is typically measured in  ‘standard deviation’ units (g). When  SD = 1.0, this is equivalent to 15 points in a standardized IQ test. If SD = 0.5 this would be 7.5 points. And so on. As a reference, antidepressant drugs typically have an effect size (compared to placebo) of 0.3 – 0.5 – i.e. 4.5 – 7.5 points.


 

Neuroplasticity Mechanisms of Dual N-Back Pro Training Transfer

dual n-back pro brain mechanisms

Brain training neuroplasticity is the ability of the brain to reorganize its structure, function and connections as a result of training. 

General principles

Transfer can occur if the criterion and transfer tasks depend on shared networks.

  • Working memory, attention control and fluid reasoning depend on shared executive control networks.
  • Working memory training targets the fronto-parietal network (FPN) which is a neural substrate of fluid intelligence (Thompson et al., 2016; Preusse et al., 2011). The FPN’s brain-wide functional connectivity patterns are more flexible than those of other networks across a variety of tasks.. These patterns are consistent across practiced and novel tasks, suggesting that reuse of flexible hub connectivity patterns facilitates adaptive (novel) task performance. The FPN consists of flexible hubs in cognitive control and adaptive implementation of novel task demands (such as novel problem solving). (Cole et al., 2013).
  • Cognitive training that increases ability to ignore distractions (e.g., working memory training) not only affects the dorsal attention network (DAN) but by the same mechanism may result in transfer to Gf. (Greenwood & Parasuraman, 2015).

There is evidence for working memory training related effects at the synaptic, neuronal, neural tissue and network levels.

Synapses, Neurons and Neural Tissue

  • Neurotransmitter efficiency – e.g. changes in dopaminergic receptor density and dopamine release at synapses. (McNab et al., 2009; Tan et al., 2013)
  • Increased thickness of cortical grey matter (e.g. synaptogenesis, microglia proliferation, angiogenesis, neurogenesis, capillaries). (Takeuchi et al., 2013)
  • Adaptive hormesis response: increased production BDNF growth factor > neurogenesis and synapse formation and protecting existing neurons from cell death. (Mattson, 2014)

 

hormesis for brain function

Mattson, 2014

Cortical Networks

  • Changes in dynamic functional connectivity by e.g. changes in neural (oscillatory) synchrony (tuning effect). (Kundu et al., 2013; Uhlhaas et al., 2009)
  • Increased capillary density and blood flow in networks (e.g. Takeuchi et al., 2013).
  • Increases in white matter structural integrity connecting areas (Takeuchi et al., 2010)
  • Changed network organization. Higher small-world connectivity in the fronto-parietal network resulting in more optimal information transfer. (Langer et al., 2013)

small world network.png


WM Training Related Brain Changes:  Data

Frontoparietal control network (FPN)

  • Training differentially affects activity in two large-scale frontoparietal networks: the executive control network and the dorsal attention network. Load-dependent functional connectivity both within and between these two networks increased following training, and the magnitudes of increased connectivity were positively correlated with improvements in task performance. (Thompson et al., 2016).
  • Increased resting rCBF in right dorso-lateral prefrontal cortex (Takeuchi et al., 2012)
  • Changes in task-related effective connectivity in frontoparietal and parieto-occipital networks that are engaged by both the trained and transfer tasks (EEG and TMS). (Kundu et al., 2013).
  • Training on updating tasks has been shown to decrease functional MRI activation in frontoparietal brain areas (Dahlin et al., 2008).
  • Increased grey matter volume in medial and lateral rostral PFC (Tackeuchi et al., 2012).
  • Reduced D1 binding potential / receptor density in right dorsolateral frontal, and both posterior parietal cortices (McNab et al., 2009). (also EAS?)
  • Increased small-worldness (network efficiency) within a distributed fronto-parietal network (Langer et al., 2013).

External Attention System (dorsal and ventral attention networks)

  • Load-dependent functional connectivity within the dorsal attention network increased following training, and the magnitudes of increased connectivity were positively correlated with improvements in task performance. (Thompson et al., 2016).
  • Increased resting functional connectivity between lateral prefrontal cortex and posterior parietal cortex (inferior/superior parietal lobule) – part of the EAS (Takeuchi et al, 2012)
  • Change of grey matter volume in dorso-lateral PFC, left ventro-lateral PFC, superior parietal cortices (Tackeuchi et al., 2012).
  • Reduced D1 binding potential / receptor density in right ventrolateral frontal, right dorsolateral frontal, and both posterior parietal cortices (McNab et al., 2009). (Also EAS?)

Opercular-cingulate network (OCN)

  • Increased grey matter in anterior cingulate cortex and left perisylvian cortex (Tackeuchi et al., 2012).

Fronto-striatal network (FSN)

  • Training-related gains in working memory are associated with increased functional activity in striatum (Backman et al., 2011)
  • Greater release of striatal dopamine is observed following working-memory training (Backman et al., 2011)..
  • Changes in D2 receptor binding potential in the striatum after 5 weeks of updating training. (Backman et al., 2011)

Default mode network (DMN)

  • Increases in resting functional connectivity between medial prefrontal cortex and precuneus (nodes of the DMN). (Takeuchi et al., 2012)
  • Left middle temporal gyrus. (Takeuchi et al., 2012)
working memory training brain imaging

Left: brain regions activated during 2-back task. Right: resting-FC change from WM training. Below – resting-FC change from WM training (DMN) (Tacheuchi et al., 2013)

 

resting blood flow WM training

(FPN region – Tacheuchi et al., 2013)

 

wm training brain image

Buschkuehl et al., 2014

 

frontal and parietal activity working memory training

Increases in frontal and parietal activity after training of WM (Olsen et al., 2004)

 


Functional Networks: Graph-Theoretic Approach

Fronto-Parietal Network

  • Anterior dorso-lateral prefrontal cortex (aDLPFC)
  • Intra-parietal sulcus IIPS) and inferior parietal lobule (IPL)
  • Middle cingulate cortex (MCC)
  • Rostral inferior temporal cortex (rITC)

Functions:  Top ­down signals for current task goals exert control by flexibly biasing information flow across multiple large-­scale functional networks overcoming conflict from previous habits. Also allows for novel task control. Part of the ‘task positive’ cognitive control network (CCN).

Dorsal Attention Network 

  • pDLPFC / frontal eye fields
  • Posterior parietal cortex: Superior parietal lobule (SPL) / Intra-parietal sulcus (IPS)
  • rITC (above FPN region)

Functions: Selective attention.

Ventral Attention Network (VAN)

  • Ventro-lateral prefrontal cortex – middle frontal gyrus (MFG) and inferior frontal gyrus (IFG)
  • Temporal parietal junction (TPJ) – inferior parietal lobule (IPL) and superior temporal gyrus (STG)

Functions: Bottom-up attentional processing.

External Attention System (EAS) : DAN and VAN

dorsal and ventral attention networks

Dorsal attention network (DAN) orange; ventral attention network (VAN) blue. From Aboitiz et al (2014)

.

Functions: Control of attention through flexible interaction between both systems enables the dynamic control of attention in relation to top-down goals and bottom-up sensory stimulation.  Part of the ‘task positive’ cognitive control network (CCN).

Cingulo-Opercular Network (CON)

  • DLPFC
  • Anterior Insula
  • Dorsal anterior cingulate cortex (dACC)
  • Thalamus

Functions: Vigilance and sustained attention. Tonic alertness for working memory. Set maintenance in working memory related tasks. Response override  after conflict detection.

Default Mode Network

  • Medial prefrontal cortex (mPFC)
  • Lateral parietal cortex (LPC)
  • Precuneus & Posterior cingulate cortex (PCC)
  • Subgenual anterior cingulate cortex (sACC)
  • Middle temporal gyrus (MTG)
  • Inferior temporal cortex (IT)

Functions: Recall of the past (autobiographical memory) and imagination of the future, reflection on present mental states (esp. affective) and ‘mind-reading’ (social cognition).

networks.PNG

brain networks for brain training

From Sylvester et al., 2012

 


Cortico-Striatal (Sub-Cortical) Networks

 

fronto-striatal circuits

Internal and external segments of the globus pallidus (GPi and GPe); Sub-thalamic nucleus (STN). From Jahanshahi et al., 2015

 

  • Bilateral putamen of the basal ganglia have negative modulatory interactions with the anterior DMN and salience networks.
  • Medial portions of the basal ganglia (mainly the globus pallidus) and the thalamus have positive modulatory interactions with the salience and dorsal attention networks.

Functions: These circuits may be important for WM for information selection and updating/manipulation in WM (Dahlin et al., 2008; Klingberg, 2010), or task switching and attention shifting (Di & Biswal, 2014). May be critical in producing automatic (habitual) and goal-directed behaviours – and inhibiting these classes of behaviours (Jahanshahi et al., 2015). May help mediate the inhibition of the DMN (anterior) when the task-positive dorsal attention network is active (Di & Biswal, 2014).


References

Bäckman, L., Nyberg, L., Soveri, A., Johansson, J., Andersson, M., Dahlin, E., … Rinne, J. O. (2011). Effects of working-memory training on striatal dopamine release. Science (New York, N.Y.), 333(6043), 718.

Bäckman, L., & Nyberg, L. (2013). Dopamine and training-related working-memory improvement. Neuroscience & Biobehavioral Reviews,37(9, Part B), 2209–2219.

Buschkuehl, M., Hernandez-Garcia, L., Jaeggi, S. M., Bernard, J. A., & Jonides, J. (2014). Neural effects of short-term training on working memory. Cognitive, Affective & Behavioral Neuroscience, 14(1), 147–160.

Dahlin, E., Neely, A. S., Larsson, A., Bäckman, L., & Nyberg, L. (2008). Transfer of learning after updating training mediated by the striatum.Science (New York, N.Y.), 320(5882), 1510–1512.

Di, X., & Biswal, B. B. (2014). Modulatory interactions between the default mode network and task positive networks in resting-state. PeerJ, 2.

Dosenbach, N. U. F., Fair, D. A., Miezin, F. M., Cohen, A. L., Wenger, K. K., Dosenbach, R. A. T., … Petersen, S. E. (2007). Distinct brain networks for adaptive and stable task control in humans. Proceedings of the National Academy of Sciences of the United States of America,104(26), 11073–11078.

Greenwood, P. M., & Parasuraman, R. (2015). The Mechanisms of Far Transfer From Cognitive Training: Review and Hypothesis.Neuropsychology. [Ahead of print].

Jaeggi, S. M., & Buschkuehl, M. (2014). Working Memory Training and Transfer: Theoretical and Practical Considerations. In B. Toni (Ed.),New Frontiers of Multidisciplinary Research in STEAM-H (Science, Technology, Engineering, Agriculture, Mathematics, and Health) (pp. 19–43). Springer International Publishing.

Jahanshahi, M., Obeso, I., Rothwell, J. C., & Obeso, J. A. (2015). A fronto-striato-subthalamic-pallidal network for goal-directed and habitual inhibition. Nature Reviews Neuroscience, 16(12), 719–732.

Klingberg, T. (2010). Training and plasticity of working memory. Trends in Cognitive Sciences, 14(7), 317–324.

Kundu, B., Sutterer, D. W., Emrich, S. M., & Postle, B. R. (2013). Strengthened effective connectivity underlies transfer of working memory training to tests of short-term memory and attention. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 33(20), 8705–8715.

Langer, N., von Bastian, C. C., Wirz, H., Oberauer, K., & Jäncke, L. (2013). The effects of working memory training on functional brain network efficiency. Cortex, 49(9), 2424–2438.

Mattson, M. P. (2014). Challenging Oneself Intermittently to Improve Health. Dose-Response,12(4), 600–618.

McNab, F., Varrone, A., Farde, L., Jucaite, A., Bystritsky, P., Forssberg, H., & Klingberg, T. (2009). Changes in cortical dopamine D1 receptor binding associated with cognitive training. Science ,323( 5915), 800–802.  

Olesen, P. et al. (2004) Increased prefrontal and parietal brain activity after training of working memory. Nat. Neurosci. 7, 75–79.

Preusse, F., Elke, van der M., Deshpande, G., Krueger, F., & Wartenburger, I. (2011). Fluid Intelligence Allows Flexible Recruitment of the Parieto-Frontal Network in Analogical Reasoning. Frontiers in Human Neuroscience, 5. 22.

Takeuchi, H., et al. (2010). Training of working memory impacts structural connectivity. Journal of Neuroscience, 30(9), 3297–3303.

Takeuchi, H., Sekiguchi, A., Taki, Y., Yokoyama, S., Yomogida, Y., Komuro, N., … Kawashima, R. (2010). Training of Working Memory Impacts Structural Connectivity. The Journal of Neuroscience, 30(9), 3297–3303.

Takeuchi, H., Taki, Y., & Kawashima, R. (2010). Effects of working memory training on cognitive functions and neural systems. Reviews in the Neurosciences, 21(6), 427–449.

Takeuchi, H., Taki, Y., Nouchi, R., Hashizume, H., Sekiguchi, A., Kotozaki, Y., … Kawashima, R. (2013). Effects of working memory training on functional connectivity and cerebral blood flow during rest. Cortex, 49(8), 2106–2125.

Tan, H. Y., Chen, A. G., Kolachana, B., Apud, J. A., Mattay, V. S., Callicott, J. H., … Weinberger, D. R. (2012). Effective connectivity of AKT1-mediated dopaminergic working memory networks and pharmacogenetics of anti-dopaminergic treatment. Brain, 135(5), 1436–1445.

Thompson, T. W., Waskom, M. L., & Gabrieli, J. D. E. (2016). Intensive Working Memory Training Produces Functional Changes in Large-scale Frontoparietal Networks. Journal of Cognitive Neuroscience, 1–14. http://doi.org/10.1162/jocn_a_00916

Uhlhaas, P., Pipa, G., Lima, B., Melloni, L., Neuenschwander, S., Nikolić, D., … Singer, W. (2009). Neural synchrony in cortical networks: history, concept and current status. Frontiers in Integrative Neuroscience, 3, 17.

Vossel, S., Geng, J. J., & Fink, G. R. (2014). Dorsal and Ventral Attention Systems: Distinct Neural Circuits but Collaborative Roles. The Neuroscientist, 20(2), 150–159.


Review of 2015-2016 Studies on Brain Training for Depression, Anxiety and Burnout

Here I review the latest peer-reviewed studies and meta-analyses that look at the benefits of working memory and executive control brain training for depression and anxiety.

For some quick definitions, working memory is our ‘mental workspace’ that stores and processes task-relevant information. It is the interface between the current focus of attention and long-term memories. Executive control is our ability to manage our attention and goal focus. Meta-analyses systematically assess all peer-reviewed, published studies meeting relevant criteria for a particular type of training. A meta-analysis, compared to a single peer reviewed paper enables us to draw much stronger conclusions about the effectiveness of brain training interventions. The most effective brain training interventions capitalize on the brain’s dynamic capacity to recover from brain disease – its neuroplasticity.

Depression

Depression is a condition in which a person feels discouraged, sad, hopeless, unmotivated or disinterested in life in general. Depressed individuals often suffer from insomnia. Loss of interest, apathy, and insomnia make it difficult to complete daily tasks.

Major depressive disorder is a common disorder among adults with a lifetime prevalence of 16.6% in the United States (1). In the UK a quarter of the population will experience some kind of mental health problem in the course of a year, with mixed anxiety and depression the most common disorder (2). The World Health Organisation forecasts that by 2020 depression will be the second leading contributor to the global burden of disease (3). It is estimated that only 37.5% of adults receive minimally adequate treatment (4).

Depression is associated with (5):

  • greater psychosocial disability
  • low productivity
  • missed work days,
  • greater risk of developing anxiety disorders
  • greater risk of developing cardiovascular diseases
  • higher rates of mortality

An estimated 7% of depressed adults commit suicide., largely in part due to lack of treatment or low treatment efficacy (6).

Cognitive Impairments from Depression

Depressed individuals have cognitive deficits associated with reduced quality of life. There are deficits in (7):

  • verbal fluency
  • working memory
  • attention
  • executive function
  • processing speed

While psychotherapy and antidepressants have proven efficacy for improving mood, cognitive deficits often remain when mood improves (8). And cognitive impairments are a risk factor for development of dementia.

Computerized Cognitive Training for Depression

Development of computerized cognitive training (CCT) to improve cognition in depressed people, and possibly improve antidepressant responses to other treatments can have a major public health benefit. Advantages of CCT as a treatment include:

  • Inexpensive
  • Noninvasive
  • Convenient (can be completed at home)
  • Can be tailored to meet specific cognitive needs of patient
  • No concern for medical side effects

 

Evidence for the Efficacy of CCT for Depression

Jeffrey Motter and his colleagues at Queens College/University of Columbia conducted a meta-analysis to look at the efficacy of CCT for depression, and have pre-published the results in their paper Computerized cognitive training and functional recovery in major depressive disorder: A meta-analysis (Jan 2016).

This meta-study systematically analysed 9 randomized trials of CCT for depression and found.

  • Improvement in Depressive Symptoms
  • Improved Global Functioning (including measures of IQ)
  • Improved Working Memory and Attention

The prevalent CCT training type was either executive control or working memory training such as dual n-back or ‘cognitive control’ training (see Table 1)

The outcome measures in this review were:

  • Symptom Severity (measures of mood and anxiety, such as the Beck Depression Inventory)
  • Daily Functioning (measures of social skills, work ability, and mobility)
  • Attention (tests for ability to maintain focus)
  • Working Memory (measures of the ability to maintain and update information while performing tasks)
  • Verbal Memory (the Hopkins Verbal Learning Test)
  • Executive Functioning (measures of task switching, inhibition and verbal fluency)
  • Global Functioning (general cognition measure, includes IQ test measures – the WAIS Verbal and Performance IQs)

The effect sizes for individual measures by study and domain (type of measure) are shown in this table below. ‘Favors CCT’ means the study supported the effectiveness of the brain training (CCT) for depression. ‘Favors CG’ means the study did not support brain training effectiveness, with no difference from the control group (CG). Standardized scores can be calculated by multiplying the ‘Hedges’ g’ by 15 (one standard deviation). So the first study reported a ~15 point improvement in Symptom Severity for depression (compared to the control group who did not do the brain training).
.

working memory and executive control training for depression

 

Effect sizes for different outcome measures are summarized in this table below:
.

brain training for depression

The mean effect sizes that were statistically significant are

  • Symptom Severity (0.43 = 6.5 points),
  • Daily Functioning (0.48 = 7 points),
  • Attention (0.67 = 10 points),
  • Working Memory (0.72 = 11 points)
  • Global Functioning (1.05 = 16 points)

(Note. The mean effect size for executive functioning was 0.20 was trending to significance. Executive Functioning was targeting in one study during the last two weeks of the training, and for only one session of psychoeducation in another, so the data are not sufficient to draw strong conclusions here. Verbal memory, targeted in half the studies, did not significantly improve with training.)

These are impressive results.

Not only is their cognitive improvement and reduction in the symptoms of depression, there is apparently good transfer to Daily Functioning – everyday life activities.

Compare symptom reduction from brain training with the effect sizes reported in FDA studies on the efficacy of antidepressant medication (compared to placebo) for symptom severity using the Hamilton Rating Scale of Depression (HRSD). Effect size is also Hedges g, as in the brain training studies (Figure 3).

Antidepressant drug effect sizes

The average overall estimated effect size for both published and unpublished studies is 0.26, compared to 0.43 for the brain training interventions. 

For potential limitations of this meta-analysis see Notes.

 

Brain Networks

Poor treatment response may be due in part to reduced functioning of the Cognitive Control Network (CCN) (9) a neural pathway that regulates higher-order cognitive processes (10). Brain training may enhance executive functioning, and strengthen functional connectivity in the CCN, in turn producing improvements in antidepressant response.

cognitive control network

The Cognitive Control Network

 


Building Resilience

It is known that both stress reactivity and brooding (a counterproductive type of rumination) are risk factors for developing depression.

In a recent study by Kristof Hoorelbeke and colleagues (2015) at-risk students performed 10 sessions of cognitive control or placebo (visual search) training. They found:

  • Increase in cognitive control was related to increased resilience.
  • The CCT group was more resilient when confronted with a lab stressor.
  • CCT reduced brooding in confrontation with a naturalistic stressor at follow-up.

Their conclusion was that cognitive control training forms a preventive intervention for depression.

The current experimental study provides evidence for the effectiveness of a working memory based cognitive control training (CCT) in increasing resilience to depression in an at risk population.

 


 

Anxiety

Anxiety disorders are characterized by exaggerated worry and tension, often expecting the worst even when there is no apparent reason for concern particularly with respect to money, work, health, and relationships. Individuals suffering from anxiety may suffer sleep disorders, feel on edge, and be constantly monitoring for potential threats.

Lifetime prevalence of anxiety disorders in the US is around 30% – almost a third of the population. Half of all lifetime cases start by age 14 years and three fourths by age 24 years (1).

Brain Networks

Impairment of attentional control in the face of threat-related distractors is well established for high-anxiety individuals. And high trait anxiety impairs cognitive processes that need attentional control  even in the absence of threat-related stimuli. In high-anxious individuals there is reduced coupling between regions of the Cognitive Control Network  (CCN) including the pre-frontal cortex and parietal cortex resulting in less neural efficiency (2). Even in non-anxious individuals negative distraction leads to relative deactivation in the CCN (also known as Dorsal Executive System) and its decoupling from emotional response centres involving the amygdala called the Ventral Affective System (3). These two systems are also known as the ‘cold executive’ and ‘hot emotional’ systems. In normal functioning the CNN maintains attentional focus by regulating the emotional reactivity of the VAS  in a tightly coupled way (4).

 

ventral affective system

Figure 1. Neural systems involved in cognitive/executive (dorsal) vs. emotional (ventral) processing. The dorsal system includes brain regions typically associated with “cold” executive (ColdEx; color-coded in blue) functions, such as the dorsolateral prefrontal cortex (dlPFC) and the lateral parietal cortex (LPC), which are critical to the active maintenance of goal-relevant information in working memory (WM). The ventral system includes brain regions involved in “hot” emotional (HotEmo; color-coded in red) processing, such as the amygdala (AMY), the ventrolateral PFC (vlPFC), and the medial PFC.  MTL MS, medial temporal lobe memory system; OFC, orbitofrontal cortex; OTC, occipitotemporal cortex.

 

Dual n-back (working memory) and executive control brain training can result in functional strengthening of the ‘cold executive’ CCN and its regulation of the ‘hot emotional’ VAS system. This in turn can reduce symptoms of anxiety.

Evidence for Efficacy of Brain Training for Anxiety Disorders

There have been no systematic meta-reviews of the efficacy of working memory and executive control brain training for anxiety and anxiety disorders. But several studies suggest that this kind of training programmes can be a promising new approach for the treatment of various anxiety disorders (5).

Working Memory Training

This recently published study by Dr Sari and colleagues looked at the effects of just three weeks of working memory (n-back) training on attention control in high anxiety individuals.

They found that:

  • Training resulted in better attention control measured by a resting state EEG (theta/beta ratio – an index of attention control).
  • There was better inhibition of emotionally charged distractions.
  • These training-related gains were related to lower levels of trait anxiety after the brain training.

As with the depression studies, training effects were substantial:

“Our results showed that …training related gains were associated with lower levels of trait anxiety at post (vs pre) intervention. Our results demonstrate that adaptive working memory training in anxiety can have beneficial effects on attentional control and cognitive performance that may protect against emotional vulnerability in individuals at risk of developing clinical anxiety.”

More recently, Hadwin and Richards (2016) showed that for adolescents, working memory training (CogMed) led to positive changes in symptoms of trait and test anxiety, increased inhibitory control and reduced attention to threat. Results were maintained at follow-up 3-4 months after the intervention.

 

Executive Control Training

Cohen and her colleagues, in a study published on Oct 28, 2015, in the journal Neuroimage, found that executive control training can reduce the brain’s emotional reactivity by changing the brain’s ‘hot emotional’ VAS circuitry (see figure above) to make it less responsive to threatening information.

‘Emotional reactivity’ results when emotionally charged information grabs attention and slows down higher order cognitive functioning such as decision-making. Emotional reactivity is known to be heightened in individuals suffering from stress-related emotional dysregulation such as depression or anxiety.

We can measure how emotionally reactive someone is by flashing a picture of a threatening picture just before a colored square, and measuring the reaction time for naming the color. The more emotionally reactive, the slower the reaction time – because the ‘threat’ information draws attention and slows down the decision-making.

mj green

Cohen and her colleagues looked at how 6 days of brain training (45 minutes per day) affected emotional reactivity in this reaction time task. Using fMRI they also looked at brain activity doing this task – both before and after the brain training.

They found that participants who did the interference training showed reduced activation in the amygdala – a brain structure strongly linked to emotional reactivity and conditioning.

And the more the amygdala was calmed, the less decision-making was slowed by threatening pictures. In other words, the less participants were distracted by threatening information, the better their emotional regulation while getting on with the task at hand.

There was also evidence that, after the training there was increased connectivity between the amygdala and regions of the frontal cortex (vlPFC in diagram above) that interact strongly with the Cognitive Control Network (Dorsal Executive System).

The evidence suggests that executive control training can dampen emotional reactivity and serve as a short-term and easy-to-implement treatment for individuals suffering from disorders characterised by emotion dysregulation – including both anxiety and depression.

 


Burnout

Stress-related exhaustion – which can be associated with depression and/or anxiety – has been linked to  cognitive impairments including:

  • executive functioning
  • attention 
  • episodic memory (memory for specific personal experiences)

Gavelin and colleagues (2015) have recently looked at the additional benefits of cognitive control brain training to a standard stress-rehabilitation program for patients diagnosed with exhaustion disorder (ED).

Results showed pronounced training-related improvements on:

  • Updating task (executive functioning)
  • Episodic memory
  • Subjective memory complaints
  • Levels of burnout

Effect sizes for everyday memory problems on three measures are shown below:

brain training for burnout

The findings suggest that process-based cognitive training may be a cost-effective intervention for burn-out.

 


Mood & Prevention

It is known that dorso-lateral prefrontal cortex  (part of the frontoparietal control network) and other widespread lateral prefrontal and medial prefrontal regions are activated to control negative emotions (Phan et al., 2005Belden et al., 2014).

Tacheuchi and colleagues (2014) have looked at the effects of working memory training on emotional states and related brain mechanisms. They found that compared with controls who did no working memory training, those who did WM training for 4 weeks showed reduced anger, fatigue, and depression. And they found WM training reduced activity in the left frontoparietal network. They also found reduced activity in the left posterior insula during tasks evoking negative emotion, which was related to anger.

They concluded that WMT can reduce negative mood and provide new insight into the clinical applications of WMT, at least among subjects with preclinical-level conditions.


 References

Cohen, N. et al. (2015). Using Executive Control Training to Suppress Amygdala Reactivity to Aversive Information. NeuroImage. Online publication date: Oct-2015. Abstract.

Gavelin, H. M. (2015).  Effects of a process-based cognitive training intervention for patients with stress-related exhaustion. 18, 5. 578-588. Abstract.

Hadwin, Julie A and Field, Andy P, eds. (2010) Information processing biases and anxiety: a developmental perspective.Wiley-Blackwell.

Hadwin, J. A., & Richards, H. J. (2016). Working Memory Training and CBT Reduces Anxiety Symptoms and Attentional Biases to Threat: A Preliminary Study. Psychopathology, 47. Full article.

Hoorelbeke, K. et al. (2015). The influence of cognitive control training on stress reactivity and rumination in response to a lab stressor and naturalistic stress. Behaviour Research and Therapy, 69. 1-1. Full article.

Iordan, A. D., Dolcos, S. & Dolcos, F. (2013). Neural signatures of the response to emotional distraction: a review of evidence from brain imaging investigations. Front. Hum. Neurosci. 7, 200. Full article.

Motter, J. N. et al. (2016). Computerized cognitive training and functional recovery in major depressive disorder: A meta-analysis. Journal of Affective Disorders , 189 , 184 – 191. Full article.

Sari, B. A. et al. (2015) Training working memory to improve attentional control in anxiety: A proof-of-principle study using behavioral and electrophysiological measures. Biological Psychology. Online publication date: Sep-2015. Abstract.

Takeuchi, H., Taki, Y., Nouchi, R., Hashizume, H., Sekiguchi, A., Kotozaki, Y., … Kawashima, R. (2014). Working memory training improves emotional states of healthy individuals. Frontiers in Systems Neuroscience, 8, 200. Article.

Turner, E. H., Matthews, A. M., Linardatos, E., Tell, R. A., & Rosenthal, R. (2008). Selective Publication of Antidepressant Trials and Its Influence on Apparent Efficacy. New England Journal of Medicine, 358(3), 252–260. Article.

 

 


Notes

There are three limitations in Motter and colleagues’ meta-analysis, aside from the relatively small number of studies (9) with only partially overlapping outcome measures.

One – the similarity of the brain training in some cases to the outcome measures. Do we get ‘near transfer’ or ‘far transfer’ from training? But while this may be of concern for cognitive-ability outcomes such as attention, this is not relevant to depression Symptom Severity, Daily Functioning and Global Functioning measures. These are all ‘far transfer’ effects.

Two – the problem of ‘placebo effects’ (expectancy or motivation effects) due to lack of active controls as the comparison groups against which to judge the effectiveness of the brain training. Expectancy and motivation can contribute to differential performance between the training and control group if there is no equivalent to a ‘placebo group’ in antidepressant medication studies. However, only 4 of thee 9 studies had no intervening task or treatment (Table 1). Others were administered peripheral vision tasks, or a non-adaptive n-back tasks, or Transcranial Direct Current Stimulation or antidepressant medication and so on. Brain training was effective in these studies.

Three – in some of the studies additional antidepressant treatments (medication, psychotherapy, tDCS) were administered alongside the brain training. But these were also administered to the the control groups, and concurrent treatment did not account for a significant amount of variance in the results. Interactions with other treatments may be at work that partly explain the effectiveness of the brain training. As the authors state: “it is possible that positive changes in cognitive functioning directly improves mood, or indirectly by enhancing the effects of ongoing treatments”.