Neuroscience of Reading and its Implications for Education

Post by Kulpreet Cheema

Literacy and Reading

In today's text-reliant society, reading and writing skills are critical to our ability to understand and engage with the world around us. Reading is a process of decoding text to acquire meaning, and while we often engage in it effortlessly and unconsciously, it is a psychologically complex process with various underlying components.

How does reading work in the brain?

The process of reading involves language-specific neural processes that include verbal and text processing, comprehension, and vocabulary. Additionally, general processes like working memory and attention interact with one another to derive meaning from text. Difficulties with any of these processes can cause challenges in reading and writing. For example, in a reading-based disorder like dyslexia, individuals struggle to process a word's distinct sounds and connect them with letters and words. This leads to incorrect decoding at the word level and ultimately results in comprehension breakdown.

While reading can often feel effortless, it is an evolutionarily recent skill to emerge relative to speaking. Therefore, there are no specialized brain regions for reading. Instead, reading re-purposes brain regions intended for other processes. The neural circuitry of reading has been investigated for decades with neuroimaging technologies, with two common technologies being functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI).

fMRI measures changes in blood oxygenation to localize the brain areas involved whilst someone is engaged in a cognitive task. This is possible because neurons in an activated brain region require (and are delivered) more oxygen, and oxygenated blood has different magnetic properties than deoxygenated blood, so activated regions can be detected using the powerful magnets of an MRI scanner.

Cortical brain areas activated by reading are interspersed throughout the brain, and connected with white matter tracts. These tracts enable communication between the brain regions to coordinate the various sub-processes involved in reading and can be identified with another neuroimaging methodology, DTI. DTI leverages the same MRI scanner as fMRI but instead of blood oxygenation, measures the movement of water molecules within white matter tracts to identify the integrity of the tracts. Since white matter tracts are fibrous, lots of unimpeded diffusion of water in the direction of the fibers indicates the tract is intact or well formed.

What circuitry is involved in reading?

Using converging evidence from both fMRI and DTI studies, researchers have mapped the neural network responsible for skilled reading. This network comprises three major components: the anterior network situated around the inferior frontal gyrus, the temporo-parietal region, consisting of supramarginal gyrus and superior temporal gyrus, and the occipito-temporal region, including fusiform gyrus and inferior/middle temporal gyrus. These areas leverage white matter pathways to communicate with each other and accomplish the reading process. Using DTI, various reading-based white matter tracts have been identified, including arcuate fasciculus (connecting temporal areas to inferior frontal region) and inferior longitudinal fasciculus (connecting anterior temporal to occipital regions).

How can we apply neuroscience findings to education?

While we’ve gained significant consensus on the neural basis of reading, leveraging this knowledge to enhance literacy teaching and learning requires further exploration. One field of study that seeks to translate the neuroscience findings about learning to educational practices and policy is known as Educational Neuroscience. This emerging field was initially established with several neuroimaging studies investigating the neural basis of both skilled and disordered reading. As one example, research investigating dyslexia used neuroimaging techniques to reveal disrupted functional activity and structural integrity of neural circuitry important for reading. When individuals with dyslexia read words, researchers identified reduced activity in the superior temporal gyrus, providing evidence for dylexia’s neurobiological basis. Evidence for reduced brain activity in brain regions responsible for sound processing in dyslexia led to interventions that targeted sound awareness that normalized brain activity and had a downstream positive impact on reading behavior. However, such successes are few and far between, with most neuroscience studies merely corroborating behavioral findings, rather than innovating toward new therapeutic measures. In the future, further investigations are needed to explore how neuroscience can better inform the improvement of reading skills. One promising avenue is the use of neuroimaging to identify pre-reading individuals at risk of developing dyslexia, allowing for timely intervention and positive remediation effects.

Looking to the future

In conclusion, neuroscience of reading and its application in educational settings could provide critical clues that inform interventions and help foster literacy. To address the challenges associated with reading difficulties, educators, psychologists, and neuroscientists must collaborate to design and implement effective programs and services. By unraveling the complexities of the reading process and harnessing the potential of educational neuroscience, we can empower individuals to become proficient readers, unlocking a world of knowledge and opportunities.

References +

  1. Hung, C. O. Y. (2021). The role of executive function in reading comprehension among beginning readers. British Journal of Educational Psychology, 91(2), 600-616.
  2. Introduction to FMRI. Nuffield Department of Clinical Neurosciences. (n.d.). https://www.ndcn.ox.ac.uk/divisions/fmrib/what-is-fmri/introduction-to-fmri
  3. Kwok, F. Y., & Ansari, D. (2019). The promises of educational neuroscience: examples from literacy and numeracy. Learning: Research and Practice, 5(2), 189-200.
  4. Ozernov-Palchik, O., & Gabrieli, J. D. (2018). Neuroimaging, early identification, and personalized intervention for developmental dyslexia. Perspectives on Language and Literacy, 44(3), 15-20.
  5. Richlan, F., Kronbichler, M., & Wimmer, H. (2011). Meta-analyzing brain dysfunctions in dyslexic children and adults. Neuroimage, 56(3), 1735-1742.
  6. Shaywitz, S. E., Morris, R., & Shaywitz, B. A. (2008). The education of dyslexic children from childhood to young adulthood. Annu. Rev. Psychol., 59, 451-475.
  7. Soares, J. M., Marques, P., Alves, V., & Sousa, N. (2013). A hitchhiker's guide to diffusion tensor imaging. Frontiers in neuroscience, 7, 31.
  8. Thomas, M. S., Ansari, D., & Knowland, V. C. (2019). Annual research review: Educational neuroscience: Progress and prospects. Journal of Child Psychology and Psychiatry, 60(4), 477-492.

Neurons and Astrocytes Interact to Create Day-Night Cycles

Post by Anastasia Sares

The takeaway

Recent work shows how a partnership between neurons and surrounding cells called astrocytes helps to regulate our body’s central clock. This highlights the importance of non-neuronal cells in brain function, and adds a piece to the puzzle of how the brain manages day/night cycles.

What's the science?

The body’s circadian rhythm includes cycles of wake and sleep, hunger and digestion, blood pressure, hormones, and many other daily patterns. The brain region responsible for this is the suprachiasmatic nucleus, which maintains a circadian rhythm even in the absence of any light. But how do all the cells in this nucleus stay synchronized with each other and avoid sending out contradictory signals? This mystery becomes even more puzzling given that the main neurotransmitter in this region, GABA, is inhibitory, which should inhibit activity across the whole network instead of creating the cycling behavior we actually see.

This week in PNAS, Patton and colleagues demonstrated that support cells called astrocytes help to regulate the activity of neurons in this area by “vacuuming up” the GABA floating around outside of cells during the day and letting it accumulate at night.

How did they do it?

The authors obtained the brains of mice and extracted the suprachiasmatic nucleus, slicing it so it was only micrometers thick and mounting these slices on membranes. The slices were kept in a solution that would allow the cells to live and the neurons to keep firing. Each of these slices was then infected with adeno-associated viral vectors (AAV), which introduce genetic material so that the cell itself produces a custom molecule. In this case, the inserted gene encoded for a fluorescent protein that would latch on to GABA molecules. With the fluorescent molecules active, the brain slices would glow when there was GABA present, and go dark when the GABA disappeared. The authors observed that GABA concentrations were low during the day and peaked at night, even though the neurons that should release the GABA were firing more during the day.

The authors then re-analyzed their previously published single-cell RNA-sequencing studies of suprachiasmatic nucleus slices harvested in daytime vs nighttime. Some of the genes being transcribed differently in day and night were involved in GABA transport by astrocytes, which are support cells present in brain tissue. Using the same fluorescent tagging method, they investigated the activity of these GABA transporters, and what happens when they are chemically blocked.

What did they find?

GABA transport proteins in astrocytes were up-regulated during the day, meaning that the astrocytes are likely “installing” them in their membranes and using them to move GABA out of the intercellular space. At night, the opposite is true: there are fewer GABA transport proteins, and thus GABA builds up in the intercellular space. This cycle, in turn, influences how often neurons in the suprachiasmatic nucleus fire, and how often secondary transmitter molecules called neuropeptides are released—these neuropeptides go on to influence circadian behavior.

Inhibiting the activity of GABA transport proteins disrupted the circadian rhythm in the brain slices, and initiating the clock of the astrocytes was able to restore circadian rhythm to “clock-less” neurons in slices genetically engineered to lack certain proteins that would help the circadian clock function. So, although it was previously thought that GABA control of neuronal activity was not important, it is now thought that astrocytes actively remove it during the day instead, and allow it to accumulate at night supporting daily cycles of neuronal activity.

What's the impact?

These findings call attention to the often-forgotten “support” cells that can be found throughout neural tissue, showing that they may in fact be orchestrating important brain functions. It also brings us closer to understanding how our day/night cycles work, how they might be disrupted, and what might be the consequences of that disruption.

Neural Replay as a Proposed Explanation for the Experience of Dreams

Post by Megan McCullough

What is neural replay?

Hippocampal neurons have been observed to spontaneously increase their firing rate during sleep. Recent studies have linked this display of brain activity with prior experiences; neurons that were active during an activity in an awake state are more likely to be reactivated during sleep. This is known as hippocampal neural replay. Neural replay, more broadly known as memory reactivation, occurs when there is a sequence of neuronal activity during rest or sleep that echoes the sequence of activity that occurred in an awake state. The evidence for this phenomenon was first discovered in maze exploration experiments with rodents; brain cells that were active when the rodents were exploring the maze also showed similar activity patterns during sleep. Recent technological advances in neuroimaging and electrical recordings have provided the first evidence for neural replay in humans. Neural replay during NREM has been shown to relay new information to the larger neural network, thus playing a key role in memory consolidation during sleep.  Interestingly, dreams share some features with neural replays, which has led to the idea that neural replays may be one mechanism underlying dreaming.

What is the link between neural replay and dreaming?

One proposed explanation for the purpose of dreams is that they support memory processes like consolidation, the process of transforming short-term memories into long-term ones. Since neural replays have also been shown to support memory consolidation, one hypothesis proposes that dreams are the subjective experience of neural replays that facilitate memory consolidation. Like dreaming, neural replays represent fragments of experiences, can combine multiple memories, and occur in both the hippocampus and cortical regions. Interestingly, neural replay has been shown to occur during sleep onset and NREM stages. These memory reactivations tend to occur for spatial memories, but can also occur for  motor, visual, and social memories.

Neural replay shares some features with the neural correlates of dreaming, but current research shows that memory activation is probably not the main explanation for dreams. Most neural replay events occur in earlier sleep stages, whereas dreams become most vivid in later sleep cycles. The timescales also differ; studies show that dreams occur on a timeline of seconds to minutes and are experienced at "life-like" timescales whereas neural replay occurs in the range of hundreds of milliseconds. These differences suggest that dreaming relies on other mechanisms than neural replay. Because of the number of shared features however, neural replay may relate to dreams in different ways. Dreams that include memories may rely on neural replay to an extent or neural replays could trigger dreaming. But since dreams most vividly occur in the REM stage, don't always include events that the dreamer experienced, and happen at a different timescale than neural replay events, memory activation events alone do not explain the neural basis of dreaming. 

Are there other possible explanations for the basis of dreams?

Beyond memory consolidation, there are other proposed explanations for why we dream, such as improved emotional regulation, future preparation, and the idea that dreams may have evolved to help us adapt to new sets of data. Although there are many hypotheses for why we dream, the neural correlates of dreaming remain unknown. Dreaming is a subjective experience and although new advances in electrical recordings and brain scanning have allowed scientists to monitor brain activity during sleep, the content of dreams is still studied through subjective measures such as dream journaling. More research is needed as we move into the future to further understand the reasons why humans dream, and its neural basis.

References +

Aleman-Zapata et al. Sleep deprivation and hippocampal ripple disruption after one-session learning eliminate memory expression the next day. PNAS (2022). Access the original scientific publication here

Freyja Olasfsdottir et al. The role of hippocampal replay in memory and planning. Current Biology (2018). Access the original scientific publication here

Hoel. The overfitted brain: Dreams evolved to assist generalization. Patterns: Cell Press (2021). Access the original scientific publication here

Mutz et al. Exploring the neural correlates of dream phenomenology and altered states of consciousness during sleep. Neuroscience of Consciousness (2017). Access the original scientific publication here

Picard-Deland et al. Memory reactivations during sleep: A neural basis of dream experiences. Trends in Cognitive Sciences: Cell Press (2023). Access the original scientific publication here

Ruby PM (2020) The Neural Correlates of Dreaming Have Not Been Identified Yet. Commentary on “The Neural Correlates of Dreaming. Nat Neurosci. 2017”. Front. Neurosci. 14:585470. doi: 10.3389/fnins.2020.585470