Spatially Specific White Matter Tracts are Associated with Reading and Math

Post by Shireen Parimoo

What's the science?

Reading and math involve similar cognitive processes like working memory and verbalization, and math and reading-related disabilities tend to co-occur. Previous research examining reading has identified white matter tracts in the brain that are related to performance on reading tasks, such as the arcuate fasciculus and the inferior longitudinal fasciculus. However, less is known about the white matter tracts associated with math. It is also unclear whether there is an overlap in the structural properties of white matter tracts associated with reading and math ability. This week in Nature Communications, Grotheer and colleagues used multimodal magnetic resonance imaging (MRI) techniques to identify the shared and distinct structural correlates of the cognitive processes involved in reading and addition.

How did they do it?

Twenty adults completed a reading, adding, and a control color task while undergoing functional MRI scanning. The task stimuli were morphs that could be perceived as a number or a letter (e.g. the stimulus for the letter “S” could be perceived as the number “5”), which allowed the visual input to remain constant across the different tasks. A series of four stimuli were presented consecutively. In the reading task, participants had to indicate the word spelled out by the letters; in the addition task, participants had to add up the numbers; in the color task, participants had to indicate the color of the stimuli at the end of the stimulus sequence. The fMRI data was used to determine which brain regions were activated during the reading task, during the addition task, or during both tasks. These brain regions of interest were identified for each participant and co-located white matter tracts were then analyzed.

Diffusion MRI and quantitative MRI scans were performed to investigate the connectivity and microstructure of white matter, respectively. First, the authors used constrained spherical deconvolution (a method used to model the orientation of white matter fibers) on the diffusion MRI data to create a structural connectome, or a map of the white matter pathways connecting brain regions. They then applied an automated algorithm to identify the major white matter pathways (also called fascicles) in the brain, including the arcuate fasciculus (AF), the posterior arcuate fasciculus (pAF) and the superior longitudinal fasciculus (SLF). The white matter fascicles were intersected with the functionally-defined ROIs to localize the tracts associated with reading and math. This allowed the researchers to examine which white matter tracts support the connectivity within and between the reading and math networks. Finally, quantitative MRI was used to estimate the myelination of the white matter tracts connecting regions of the reading and math networks. In general, greater myelination is associated with more efficient neuronal transmission in the brain.  

What did they find?

Reading and math activated largely separate but neighbouring brain regions. For instance, the occipitotemporal sulcus, the superior temporal sulcus, and the inferior frontal gyrus were active during the reading task, whereas the addition task activated the inferior temporal gyrus, the inferior post-central sulcus, and the intraparietal sulcus. Both tasks also activated distinct subregions within the supramarginal gyrus. Across the brain, reading- and math-specific regions were connected to their respective network by the SLF, the AF, and the posterior AF. The SLF and AF connected the prefrontal regions of the math and reading networks, such as the inferior frontal gyrus (reading) and the post-central sulcus (addition), to temporal and parietal regions of each network. Further, the posterior AF connected the temporal regions active during the two tasks, such as the occipitotemporal sulcus (reading) and the inferior temporal gyrus (adding) to the parietal regions of each network. Thus, the same white matter fascicles support the math and reading networks, even though the brain regions themselves are largely distinct. Crucially, though, analogous to distinct lanes in a highway, math and reading-related white matter tracts were found to run in parallel, segregated sub-bundles within the shared fascicles. The specific sub-bundles, or branches, of the SLF and AF involved in reading were located more inferiorly in the brain than those involved in addition. Moreover, the branches of the SLF and AF involved in reading were more heavily myelinated than those associated with the addition network, suggesting greater efficiency of neuronal transmission within the reading network.

What's the impact?

This study is the first to establish that spatially distinct branches of the same white matter fascicles are associated with the reading and math networks in the brain. These findings suggest that the ability to read and perform mathematical operations might develop independently, despite shared cognitive processes and a high rate of comorbidity of their associated learning disorders. This has important implications for future research exploring the relationship between white matter properties and math- and reading-related abilities.  

Grotheer et al. Separate lanes for adding and reading in the white matter highways of the human brain. Nature Communications (2019). Access the original scientific publication here.

Deep Brain Stimulation Normalizes Brain Activity in Parkinson’s Disease

Post by Elisa Guma

What's the science?

Deep brain stimulation (DBS) is an effective and established treatment for Parkinson’s disease, wherein electrodes are implanted in a targeted brain area in order to relieve certain symptoms such as tremor, stiffness and rigidity, and impaired gait. The mechanism by which DBS is thought to improve symptoms is still not fully understood. It was previously thought that improvements were solely due to localized stimulation of specific brain regions, however, they may be due to more global changes in functional brain networks. This week in Brain, Horn and colleagues investigated the effects of DBS on functional brain networks of patients suffering from Parkinson’s disease.

How did they do it?

The authors acquired resting-state functional magnetic resonance imaging (rs-fMRI) data in 20 Parkinson’s patients who underwent surgery to place DBS electrodes in the subthalamic nucleus (STN), and 14 healthy, age-matched controls. RS-fMRI is a technique that measures fluctuations in blood oxygenated level-dependent (BOLD) signals in the brain, which allows for a measure of intrinsic associations between the brain activity of specific regions based on the correlation of signal over time between those brain regions. Patients were first measured with their electrodes turned on, and after a short break, were scanned again with their electrodes turned off in order to see how the electrical stimulation affected global brain activity.

The data was processed using state of the art software, Lead-DBS, which allowed for the localization of the DBS electrodes, as well as analysis of brain volume and activation, with careful regard for artefacts due to motion during scans and metal from the electrodes. This allowed the authors to analyze how the electric field of the DBS-electrodes modulated brain activity in a key motor network referred to as the basal ganglia-cerebellar-cortical loops. These loops include the sensorimotor functional zones of the cortex, striatum, thalamus, internal and external globus pallidus, substantia nigra, and cerebellum. They compared functional brain networks in the DBS-on and -off conditions to those of healthy controls. Further, they investigated how electrode placement modulated the patterns in brain activity they observed.

What did they find?

First, the authors found that the accuracy of the electrode placement within the STN determined the strength of connectivity between the STN and the supplementary motor area (a motor network region); the better the placement, the stronger the connectivity between these two regions. Next, they found that connectivity maps between the volume of tissue activated around the STN and the motor network were most similar between DBS-on conditions and healthy controls, suggesting that DBS electrode activity might normalize brain networks towards healthy controls. This was also affected by the electrode placement. Finally, the authors found that connectivity in the DBS-on group was increased in the motor network (between the thalamus and cortex), with a decrease in basal ganglia connectivity (striatum to cerebellum, STN, and STN to globus pallidus).

What's the impact?

This study is one of the first to demonstrate the feasibility of conducting rs-fMRI in DBS implanted patients. They identify that DBS has a significant effect on brain connectivity throughout the motor network and that these changes were strongly dependent on correct electrode placement. The findings are promising evidence for the use of invasive neuromodulation. Further, DBS provides a framework within which to study how brain networks change in response to targeted stimulation, which could be applied to other populations undergoing DBS treatment, such as those with depression, obsessive-compulsive disorder, or eating disorders.

Andreas Horn et al. Deep brain stimulation induced normalization of the human functional connectome in Parkinson’s disease. Brain (2019). Access the original scientific publication here.

A National Experiment Reveals Where a Growth Mindset Improves Achievement

Post by Stephanie Williams

What's the science?

The term “growth mindset” reflects the belief that abilities are not hard-wired into the brain, but can be developed and improved over time. Growth mindset interventions can be effective in changing the beliefs of students who think their academic abilities are fixed. Recently, experimental work in educational settings has investigated how interventions can use ideas related to a growth mindset to intervene in cases of academic underachievement. This week in Nature, Yeager, Hanselman, and colleagues demonstrate that a growth-mindset intervention can effectively improve grades and enrollment in advanced math courses in particular student populations and school environments.

How did they do it?

The authors analyzed the effects of a validated growth-mindset intervention in a nationally representative sample of high schools. Ninth grade students were randomized to a particular condition (intervention vs. control activity), and teachers, as well as researchers, were blind to the condition of each student. The intervention consisted of two 25-minute online modules. In 25-minute self-administered online sessions, students heard stories from both older students and adults, interacted with guided exercises, and were asked to reflect on their own learning. At the end of the second session, students participated in a math task with two options: an easy but low learning task, and a challenging but high learning task. The authors used the percentage of students in the control condition that chose the challenging problem as a metric of challenge-seeking norms for a particular school. The authors monitored the fidelity of the intervention implementation by analyzing the percentage of the module screens students viewed (97%) and analyzing the number of open-ended questions that students responded to (96%).

The authors were interested in identifying which contexts were most conducive to the growth mindset intervention. They analyzed treatment effect heterogeneity by looking at the types of school contexts (availability of resources, motivation to follow an intervention, etc.), and peer contexts (supportive vs. unsupportive of taking intellectual risks) The authors point out that schools on both ends of the resource spectrum could fail to see an effect of the intervention: schools with low-quality curricula don’t offer as many learning opportunities for students, and schools with ample resources may have less of a need for the intervention. The authors assessed the effect of their intervention on the mindset of students, as well as the effect on their GPAs and advanced mathematics course enrollment. They looked at GPAs of all core courses, and also at math and science GPAs in their secondary analysis. These subjects are of particular interest because of the commonly held belief that success in math or science is innate. The authors also re-analyzed the data using a Bayesian machine learning algorithm and compared the findings from both sets of analyses.  

What did they find?

The authors found that lower-achieving students who received the intervention performed better than lower-achieving students who did not receive the intervention. This was true when the effect was quantified by assessing overall GPA as well as math-science GPA. The intervention was more effective in low and medium-achieving schools than in high-achieving schools. The authors found no significant difference in the treatment’s effectiveness in low vs. medium achieving schools, but point out that there is high variability in student performance in low-achievement schools. The intervention did still have an effect on students in high-achieving schools. For the top 25% of high achieving schools, the intervention increased the rate at which students enrolled in advanced math courses the following year by 4 percentage points. Students in school environments with peer norms that supported the adoption of intellectual challenges showed larger GPA benefits from the treatment. The authors interpret this result as suggesting that students in unsupportive environments may feel social pressure not to take intellectual risks in front of their peers. They also suggest that beliefs can affect how students react to ongoing academic challenges. The results from the Bayesian machine learning analysis confirmed these findings. 

What's the impact?

A low-cost growth mindset intervention can have a substantial effect on grades and enrollment rates in advanced mathematics courses. The findings around context-specific treatment effects (e.g. school resources or peer environment) can be used to inform future types of interventions. Future interventions should target the messages students receive about learning ability from schools and assess additional challenges faced by adolescents, including social and interpersonal difficulties.

Yeager, D, Hanselman, P., et al. A national experiment reveals where a growth mindset improves achievement. Nature (2019). Access the original scientific publication here.