The role of dopamine in the maintenance of working memory in prefrontal cortex neurons: input-driven versus internally-driven networks

Task demands, tDCS intensity, and the COMT val158met polymorphism impact tDCS-linked working memory training gains

Working memory (WM) training paired with transcranial direct current stimulation (tDCS) can improve executive function in older adults. The unclear mechanism of tDCS likely depends on tDCS intensity, and task relevant genetic factors (e.g., for WM: COMT val¹⁵⁸met, DAT, BDNF val⁶⁶met). Higher tDCS intensity does not always lead to greater cognitive gains, and genetic polymorphisms may modulate tDCS-linked WM improvements. To evaluate these factors, 137 healthy older adults provided DNA samples and received Visual and Spatial WM training paired with tDCS (sham, 1, 1.5, 2 mA). After one session of tDCS, significant group differences in WM performance were predicted by COMT val¹⁵⁸met status. One month after training, there was a significant interaction of tDCS intensity, COMT genotype, and WM task. Specifically, val/val homozygotes benefited most from 1.5 mA tDCS on Visual WM and from 1 mA tDCS on Spatial WM. For met/met homozygotes, 2 mA resulted in significantly poorer performance compared to 1.5 mA on Spatial WM. While this pattern was observed with relatively small sample sizes, these data indicate that variations in COMT val¹⁵⁸met may predict the nature of WM improvement after initial and longitudinal tDCS. This contributes to our understanding of the underlying mechanism by which tDCS affects behaviour.

Older Adults Improve on Everyday Tasks after Working Memory Training and Neurostimulation

BACKGROUND

Aging is associated with decline in executive function (EF), upper-level cognitive abilities such as planning, problem solving, and working memory (WM). This decline is associated with age-related volume loss and reduced functional connectivity in the frontal lobes. Cognitive training interventions aim to counter these losses, but often fail to elicit benefits beyond improvements on trained tasks. Recent interventions pairing WM training with transcranial direct current stimulation (tDCS) have improved WM and elicited transfer to untrained EF tasks. Limitations in previous work include exclusive use of laboratory-based computer training and testing and poor characterization of the mechanism(s) of durable tDCS-linked change.

OBJECTIVE/HYPOTHESIS

To determine if tDCS-linked WM training improves performance on ecologically valid transfer measures administered in participants' homes. To explore intervention-based changes using neuroimaging (fNIRS) and genotyping (COMT val158met).

METHODS

90 healthy older adult participants completed 5 sessions of WM training paired with tDCS (Sham, 1 mA tDCS, 2 mA tDCS; 15 min). At follow-up, we assessed performance change on laboratory-based and ecologically valid tasks.

RESULTS

All participants showed improvement on trained tasks. Importantly, 2 mA of tDCS induced significantly greater far transfer gains after 1 month without contact. Gains were observed on standard far transfer tasks along with ecologically valid far transfer tasks, and stimulation was well tolerated by all participants. FNIRS and genotyping results were less conclusive, but provide promising avenues for future research initiatives.

CONCLUSION

These findings highlight the translational value for tDCS-based interventions in healthy older adults interested in maintaining cognitive function.

A Programmer–Interpreter Neural Network Architecture for Prefrontal Cognitive Control

There is wide consensus that the prefrontal cortex (PFC) is able to exert cognitive control on behavior by biasing processing toward task-relevant information and by modulating response selection. This idea is typically framed in terms of top-down influences within a cortical control hierarchy, where prefrontal-basal ganglia loops gate multiple input–output channels, which in turn can activate or sequence motor primitives expressed in (pre-)motor cortices. Here we advance a new hypothesis, based on the notion of programmability and an interpreter–programmer computational scheme, on how the PFC can flexibly bias the selection of sensorimotor patterns depending on internal goal and task contexts. In this approach, multiple elementary behaviors representing motor primitives are expressed by a single multi-purpose neural network, which is seen as a reusable area of "recycled" neurons (interpreter). The PFC thus acts as a "programmer" that, without modifying the network connectivity, feeds the interpreter networks with specific input parameters encoding the programs (corresponding to network structures) to be interpreted by the (pre-)motor areas. Our architecture is validated in a standard test for executive function: the 1-2-AX task. Our results show that this computational framework provides a robust, scalable and flexible scheme that can be iterated at different hierarchical layers, supporting the realization of multiple goals. We discuss the plausibility of the "programmer–interpreter" scheme to explain the functioning of prefrontal-(pre)motor cortical hierarchies.

BEHIND THE MAGICAL NUMBERS: HIERARCHICAL CHUNKING AND THE HUMAN WORKING MEMORY CAPACITY

To explore the influence of chunking on the capacity limits of working memory, a model for chunking in sequential working memory is proposed, using hierarchical bidirectional inhibition-connected neural networks with winnerless competition. With the assumption of the existence of an upper bound to the inhibitory weights in neurobiological networks, it is shown that chunking increases the number of memorized items in working memory from the "magical number 7" to 16 items. The optimal number of chunks and the number of the memorized items in each chunk are the "magical number 4".