Reprogramming Minds: New Technique Using Electrical Stimulation Improves Math Learning

This study shows that the ability to learn math is linked to how different regions of the brain work together, and that it is possible to improve this process with brain stimulation, especially in people with less efficient connectivity patterns. By integrating stimulation, brain imaging, and chemical measurements, the researchers offered a more complete view of how the brain learns math and how personalized interventions can help those who struggle.

School learning, especially in areas like math, has a significant impact on both individual lives and societal development. However, not everyone benefits from educational opportunities equally.

A phenomenon known as the Matthew effect describes this inequality: those who start with an advantage tend to accumulate even more knowledge, while those who initially struggle tend to fall behind.

This inequality is especially evident in math, an area in which many adults in developed countries demonstrate a similar level of ability to young children. This poor performance can lead to worse working conditions, lower income, health problems, and even reduced political participation.

Studies indicate that math skills tend to remain stable from childhood to adulthood. This suggests that, in addition to environment and upbringing, biological factors such as brain function and genetics play an important role.

Research shows that certain genetic variations related to neuronal function are linked to learning difficulties, affecting the brain's ability to adapt and learn (called brain plasticity).

However, even with these advances, the exact mechanisms that explain how we learn math are still poorly understood. Most previous studies only observe what happens in the brain, without directly testing what causes these changes.

To better understand how the brain learns math, researchers at the University of Oxford, UK, used a technique called transcranial random noise stimulation (tRNS), which sends weak, safe electrical signals to the brain with the aim of improving its activity.

Transcranial Random Noise Stimulation (tRNS) Technique

They applied this technique for five days to two groups of young adults while they were learning math. The stimulation targeted two specific brain regions: the dorsolateral prefrontal cortex (dlPFC), which is important for decision-making and memory, and the posterior parietal cortex (PPC), which helps with spatial reasoning and numerical operations.

To study the effects of this stimulation, the scientists also used brain imaging and chemical analyses to observe how the brain changed over time.

Previous research has shown that the brain processes math using multiple regions simultaneously, such as the frontal cortex, parietal cortex, and even areas related to vision. Initially, solving math relies heavily on working memory and other executive functions, such as attention and self-control, all controlled by the frontal part of the brain.

Over time and with practice, the brain begins to solve problems more automatically, shifting effort to more specialized parietal areas. This means that as someone learns, the brain reorganizes itself, making the process faster and more efficient.

The study's hypothesis was that brain stimulation of the dorsolateral prefrontal cortex would be more helpful in reasoning- and calculation-based learning (e.g., using a method to solve 23 × 8), while stimulation of the posterior parietal cortex would be more helpful in repetition and memorization exercises.

The idea was to see if, with the help of stimulation, the brain could improve its ability to learn, especially in people who, because of their brain connections, would learn less efficiently.

In addition to studying the brain regions involved, the scientists also investigated the role of the balance between excitation (activation) and inhibition (control) between neurons. This balance is controlled by substances such as glutamate (which excites neurons) and GABA (which inhibits them).

When there is more excitation than inhibition, the brain is more "plastic," meaning it is more likely to learn new things. Then, inhibition must kick in to consolidate what has been learned and avoid confusion with new learning. In the study, researchers measured the levels of these substances and observed how they influenced the effectiveness of mathematical learning after brain stimulation.

The results showed that people with a strong initial connection between the frontal and parietal regions of the brain (called frontoparietal connectivity) learned calculations better. However, even those with weaker connections benefited from stimulation of the dorsolateral prefrontal cortex.

This figure shows how different brain regions connect and influence performance on a math task after different types of mild brain stimulation. In panel A, we see a brain model with four areas linked to cognition (two frontal and two parietal), used to measure frontoparietal connectivity, a communication between regions important for reasoning. Graphs (B to E) show how this connectivity relates to response time (RT) in the task, with lower RT values ​​indicating better performance. Panels B and C show that the higher the connectivity (from -1 to +1 standard deviation from the mean), the better the performance. Panels D and E show this effect with different types of stimulation: tRNS in the prefrontal cortex (dlPFC) and parietal cortex (PPC), compared to a sham simulation. The results suggest that real stimulation improves performance.

The improvement in learning was more significant when stimulation reduced GABA levels (less inhibition), facilitating brain plasticity. Interestingly, for people who already had good brain connections, stimulation can even hinder them, likely by "over-stimulating" them.

This study shows that the ability to learn math is linked to how different brain regions work together, and that brain stimulation can improve this process, especially in people with less efficient connectivity patterns.

By integrating stimulation, brain imaging, and chemical measurements, the researchers offered a more complete view of how the brain learns math and how personalized interventions can help those who struggle.

In short, this research shows that mathematical learning depends as much on practice as it does on the biological structure of the brain. Modern techniques like tRNS can help improve this learning, especially if adapted to each person's brain profile. These findings may be useful in the future for developing more effective and personalized teaching methods, reducing educational inequalities and improving opportunities for all.

READ MORE:

Functional connectivity and GABAergic signaling modulate the enhancement effect of neurostimulation on mathematical learning

George Zacharopoulos, Masoumeh Dehghani, Beatrix Krause-Sorio,

Jamie Near, and Roi Cohen Kadosh 

PLoS Biol 23(7): e3003200. July 1, 2025

https://doi.org/10.1371/journal.pbio.3003200

Abstract: 

Effortful learning and practice are integral to academic attainment in areas like reading, language, and mathematics, shaping future career prospects, socioeconomic status, and health outcomes. However, academic learning outcomes often exhibit disparities, with initial cognitive advantages leading to further advantages (the Matthew effect). One of the areas in which learners frequently exhibit difficulties is mathematical learning. Neurobiological research has underscored the involvement of the dorsolateral prefrontal cortex (dlPFC), the posterior parietal cortex (PPC), and the hippocampus in mathematical learning. However, their causal contributions remain unclear. Moreover, recent findings have highlighted the potential role of excitation/inhibition (E/I) balance in neuroplasticity and learning. To deepen our understanding of the mechanisms driving mathematical learning, we employed a novel approach integrating double-blind excitatory neurostimulation—high-frequency transcranial random noise stimulation (tRNS)—and examined its effect at the behavioral, functional, and neurochemical levels. During a 5-day mathematical learning paradigm (n = 72) active tRNS was applied over the dlPFC or the PPC, and we compared the effects versus sham tRNS. Individuals exhibiting stronger positive baseline frontoparietal connectivity demonstrated greater improvement in calculation learning. Subsequently, utilizing tRNS to modulate frontoparietal connectivity, we found that participants with weaker positive baseline frontoparietal connectivity, typically associated with poorer learning performance, experienced enhanced learning outcomes following dlPFC-tRNS only. Further analyses revealed that dlPFC-tRNS improved learning outcomes for participants who showed reductions in dlPFC GABA when it was accompanied by a reduced positive frontoparietal connectivity, but this effect was reversed for participants who showed increased positive frontoparietal connectivity. Our multimodal approach elucidates the causal role of the dlPFC and frontoparietal network in a critical academic learning skill, shedding light on the interplay between functional connectivity and GABAergic modulation in the efficacy of brain-based interventions to augment learning outcomes, particularly benefiting individuals who would learn less optimally based on their neurobiological profile.