Research in neuroscience has great potential for transforming education. However, the brain systems that support academic and cognitive skills are poorly understood in comparison to the systems that support sensory processing. Decades of basic research have examined the role that brain plasticity plays in the genesis and treatment of developmental visual disorders, which may help to inform how cognitive training approaches can be tailored for students who experience environmental disadvantage. In this review, we draw parallels between visual and cognitive intervention approaches, and suggest research avenues that could inform educational practice in the future.

Understanding the relationship between changes in sensory perception and functional/structural changes in the brain is a major endeavor in the field of systems neuroscience. Progress in this area holds the potential to reveal how the brain adapts to the demands of a complex and changing environment, as well as to assist with the development of therapeutic interventions to reverse the negative effects of abnormal experience. The cells and circuits that make up the mammalian visual system provide a unique scientific test-bed for studying brain plasticity, thanks to the rich literature on their basic organization and similarity across a range of species. In this minireview, we highlight recent advances in the study of plasticity in adult binocular vision, emphasizing the importance of considering changes that occur over different timescales. We discuss key new insights, significant open questions, and how this research is leading to a broader understanding of the ways that the adult brain maintains a robust ability for adaptation and change.

Diffusion tensor imaging (DTI) techniques have made it possible to investigate white matter plasticity in humans. Changes in DTI measures, principally increases in fractional anisotropy (FA), have been observed following training programs as diverse as juggling, meditation, and working memory. Here, we sought to test whether three months of reasoning training could alter white matter microstructure. We recruited participants (n = 23) who were enrolled in a course to prepare for the Law School Admission Test (LSAT), a test that places strong demands on reasoning skills, as well as age- and IQ-matched controls planning to take the LSAT in the future (n = 22). DTI data were collected at two scan sessions scheduled three months apart. In trained participants but not controls, we observed decreases in radial diffusivity (RD) in white matter connecting frontal cortices, and in mean diffusivity (MD) within frontal and parietal lobe white matter. Further, participants exhibiting larger gains on the LSAT exhibited greater decreases in MD in the right internal capsule. In summary, reasoning training altered multiple measures of white matter structure in young adults. While the cellular underpinnings are unknown, these results provide evidence of experience-dependent white matter changes that may not be limited to myelination.

Patterns of correlated activity among brain regions reflect functionally relevant networks that are widely assumed to be stable over time. We hypothesized that if these correlations reflect the prior history of coactivation of brain regions, then a marked shift in cognition could alter the strength of coupling between these regions. We sought to test whether intensive reasoning training in humans would result in tighter coupling among regions in the lateral frontoparietal network, as measured with resting-state fMRI (rs-fMRI). Rather than designing an artificial training program, we studied individuals who were preparing for a standardized test that places heavy demands on relational reasoning, the Law School Admissions Test (LSAT). LSAT questions require test takers to group or sequence items according to a set of complex rules. We recruited young adults who were enrolled in an LSAT course that offers 70 h of reasoning instruction (n = 25), and age- and IQ-matched controls intending to take the LSAT in the future (n = 24). rs-fMRI data were collected for all subjects during two scanning sessions separated by 90 d. An analysis of pairwise correlations between brain regions implicated in reasoning showed that fronto-parietal connections were strengthened, along with parietal-striatal connections. These findings provide strong evidence for neural plasticity at the level of large-scale networks supporting high-level cognition.

Although brain plasticity is greatest in the first few years of life, the brain continues to be shaped by experience throughout adulthood. Advances in fMRI have enabled us to examine the plasticity of large-scale networks using blood oxygen level-dependent (BOLD) correlations measured at rest. Resting-state functional connectivity analysis makes it possible to measure task-independent changes in brain function and therefore could provide unique insights into experience-dependent brain plasticity in humans. Here, we evaluate the hypothesis that resting-state functional connectivity reflects the repeated history of co-activation between brain regions. To this end, we review resting-state fMRI studies in the sensory, motor, and cognitive learning literature. This body of research provides evidence that the brain's resting-state functional architecture displays dynamic properties in young adulthood.

We have reported previously that intensive preparation for a standardized test that taxes reasoning leads to changes in structural and functional connectivity within the frontoparietal network. Here, we investigated whether reasoning instruction transfers to improvement on unpracticed tests of reasoning, and whether these improvements are associated with changes in neural recruitment during reasoning task performance. We found behavioral evidence for transfer to a transitive inference task, but no evidence for transfer to a rule generation task. Across both tasks, we observed reduced lateral prefrontal activation in the trained group relative to the control group, consistent with other studies of practice-related changes in brain activation. In the transitive inference task, we observed enhanced suppression of task-negative, or default-mode, regions, consistent with work suggesting that better cognitive skills are associated with more efficient switching between networks. In the rule generation task, we found a pattern consistent with a training-related shift in the balance between phonological and visuospatial processing. Broadly, we discuss general methodological considerations related to the analysis and interpretation of training-related changes in brain activation. In summary, we present preliminary evidence for changes in brain activation associated with practice of high-level cognitive skills.

Tracking one’s performance over time is essential to efficientself-guided learning but it is not clear whether young childrencan accurately monitor their past performance. Here, welooked at whether 4-6-year-olds can use the trajectory of theirpast performance to allocate future resources. Across fourexperiments (N = 274), we found that children were sensitiveto their rate of change in past performance: Children assignedto a condition in which they got better over time were morelikely to take on challenges and teach others than children inconditions where they got worse or stayed the same.Furthermore, children privileged their rate and direction ofchange more than their total or final score. These resultssuggest that young children monitor their rate of improvementand can use this information to guide their future efforts.

In the United States, the difference in academic achievement between higher- and lower-income students (i.e., the income-achievement gap) is substantial and growing. In the research reported here, we investigated neuroanatomical correlates of this gap in adolescents (N = 58) in whom academic achievement was measured by statewide standardized testing. Cortical gray-matter volume was significantly greater in students from higher-income backgrounds (n = 35) than in students from lower-income backgrounds (n = 23), but cortical white-matter volume and total cortical surface area did not differ significantly between groups. Cortical thickness in all lobes of the brain was greater in students from higher-income than lower-income backgrounds. Greater cortical thickness, particularly in temporal and occipital lobes, was associated with better test performance. These results represent the first evidence that cortical thickness in higher- and lower-income students differs across broad swaths of the brain and that cortical thickness is related to scores on academic-achievement tests.

Diffusion-weighted magnetic resonance imaging (dMRI) is the primary method for noninvasively studying the organization of white matter in the human brain. Here we introduce QSIPrep, an integrative software platform for the processing of diffusion images that is compatible with nearly all dMRI sampling schemes. Drawing on a diverse set of software suites to capitalize on their complementary strengths, QSIPrep facilitates the implementation of best practices for processing of diffusion images.