Working Memory Brain Training Mechanisms

Neuroplasticity Mechanisms of Working Memory (WM) Training Transfer: How Brain Training Works

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)

 

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)

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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)

 

 

 

 

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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

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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).

 

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Cortico-Striatal (Sub-Cortical) Networks

 

 

• 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).

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References

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