How is Visual Learning Affected by Social Context?

Post by Meredith McCarty

The takeaway

Altered brain activation and functional connectivity occurs, and performance improves, when people perform a basic visual perception task in a social context versus alone.

What's the science?

Social context and cooperative behaviors are essential features of daily life, and have been found to facilitate learning abilities. However, there is a gap in understanding the neural mechanisms by which social context enhances learning. Prior recent work has revealed that cortical regions important for social cognition, including the dorsolateral prefrontal cortex (dlPFC), show increased activation during high-level learning tasks, such as value-based learning. Additionally, it has been shown that neural responses in early visual cortical (EVC) regions are modulated by visual perceptual learning tasks, though the precise extent of this modulation remains unclear. Therefore, it is essential to understand the dynamics in these early visual and higher cortical areas, and how this activity is modulated by social context and motivation. This week in Current Biology, Zhang and colleagues investigate the role of social context on improved visual perceptual learning, and the neural dynamics correlated with this process.

How did they do it?

To measure changes in visual perceptual learning, the authors had participants perform an orientation discrimination task, where they indicated whether the orientation of the presented visual stimuli differed from each other. Their perceptual accuracy was tested on the first and last day of the experiment, with 6 days of training sessions between them. To assess the influence of social context on performance in this task, participants were separated into two groups: single groups, in which the participants performed the task alone, or dyadic groups, where they were paired with another participant and could monitor their partners’ performance. Of the 135 total participants, three experimental cohorts were selected. For Experiment 1, participants were divided into single and dyadic training groups, and performed the novel orientation discrimination task. This enabled the comparison of task performance accuracy due to social context. For Experiment 2, the performance of one partner in each dyadic training group was altered, either enhanced with additional single training days, or worsened due to visual stimuli being presented in white noise. For Experiment 3, participants performed the single or dyadic tasks while undergoing functional magnetic resonance imaging (fMRI), which quantifies changes in neural activation and connectivity measures across task conditions.

What did they find?

The behavioral results of Experiment 1 revealed a greater performance and faster learning rate for participants in the dyadic training program. This indicates that monitoring their social partner’s performance facilitated individual performance. When the performance of a partner in the dyadic group was either enhanced or worsened in Experiment 2, the authors found this partner manipulation to significantly alter the paired subject’s behavioral performance. When analyzing neural dynamics via fMRI imaging in Experiment 3, authors found several interesting changes across dyadic and single training groups. First, they found significant clustering of neural activity in bilateral parietal cortex (PL), left dorsolateral prefrontal cortex (dlPFC), as well as regions of early visual cortex (EVC). They implemented a decoding analysis to measure how well stimulus orientation could be decoded via neural activations, and found significant increases in the decoding accuracy of EVC activation in the dyadic group, implying a refinement of cortical responses to trained orientations in early visual cortices.

The authors then utilized a Physiological Interaction (PPI) analysis as a measure of functional connectivity between regions, and found that dyadic groups showed enhanced connectivity between EVC and left dlPFC, and EVC and bilateral PL activation. These data suggest that the interplay between early visual and higher order social cortical regions may be responsible for the refined orientation representations in early visual cortex, and subsequent improved behavioral performance.

What's the impact?

The results of this study suggest that social facilitation enhances visual learning, and is correlated with enhanced function connectivity between early visual and frontal cortices. Visual perceptual learning tasks are often utilized in therapeutic contexts to improve long-term visual abilities. The possibility of enhancing this visual learning non-invasively via social facilitation has useful implications in therapeutic contexts for individuals with neuro-ophthalmic disorders (altered function in parts of the brain devoted to vision). 

Access the original scientific publication here.

Predicting Chronic Pain States in Humans

Post by Lani Cupo

The takeaway

The authors developed a neural biomarker to predict chronic pain in patients, with the goal of facilitating diagnosis and treatment of neuropathic pain.

What's the science?

Neuropathic chronic pain (e.g. after a stroke or amputation of a limb) is the cause of great suffering in patients, however it can be difficult to develop objective biomarkers to aid diagnosis and treatment. It is also still not fully clear how brain activity changes with fluctuations in chronic pain levels, and how these changes differ from activity associated with acute pain. This week in Nature Neuroscience, Shirvalkar and colleagues presented a neural biomarker for chronic pain using implanted electrodes in patients, successfully predicting pain ratings.

How did they do it?

The authors enrolled four adult participants in their study (two women), three of whom had post-stroke chronic pain, and one who had phantom limb pain. The authors implanted electrodes into two brain regions important in the processing of pain: the orbitofrontal cortex (OFC) and anterior cingulate cortex (ACC). The study took place over 2.5 - 6 months, during which time participants were asked to record their pain at least 3 times per day. After recording their pain rating (which was inherently subjective, as pain is by definition a subjective, individualized experience), they pushed a button on a remote control which triggered a 30 second recording from the implanted electrodes. This in-depth recording method allowed researchers to track fluctuations in pain over the day as well as over the weeks.

Next, the authors trained a machine learning model to predict subjective pain scores with the neural activity from the implanted electrodes. They compared models trained on data from only one brain region versus models combining data from both electrodes to see which brain region best predicted chronic neuropathic pain.

Finally, the authors sought to compare the neural mechanisms underlying chronic pain with those underlying acute pain in a laboratory experiment. They brought the patients into the lab and presented thermal stimuli (heat at five different temperatures) to both the most painful part and side of the body and the same region on the other side. During the experiment, they recorded neural activity from the electrodes and trained a machine learning algorithm to predict subjective acute pain ratings on the neural activity alone.

What did they find?

First, the authors observed patients had diurnal fluctuations in pain levels (over the 24-hour period), however, they also found cycles of pain in some participants every 3 days. Second, the authors successfully trained an algorithm (linear discriminant analysis) to classify subjective pain states as high vs. low. For three participants, the best prediction resulted in combining data from the ACC and OFC, however, overall the best subregion to predict neuropathic pain was the contralateral OFC — the OFC on the opposite brain hemisphere of the perceived pain. For example, if pain was felt in the left leg, the right OFC was the most effective region to predict pain. The results were stable across the months of the study, suggesting the model was robust in its predictions. Finally, the authors successfully trained a model to distinguish high-vs-low pain states in the acute pain experiment, but importantly, only models that included data from the ACC were successful, unlike the chronic pain state. This suggests the ACC is more centrally involved in acute pain, rather than chronic pain. 

What's the impact?

This study is the first to successfully predict subjective recordings of chronic pain from intracranial recordings over a period of months. In time, their findings may be used to develop patient-specific metrics to aid in diagnosis of chronic pain states. Further, implanted electrodes may be used to stimulate regions integral to chronic pain processing, reducing the pain that patients experience and improving their quality of life.  

Access the original scientific publication here

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.