CALC Study

We are looking for current kindergarteners with and without a family history of reading or math difficulties to join our CALC Study. If you are interested in getting involved, please fill out this interest form and a member of our team will contact you soon! Please email gaablab@gse.harvard.edu if you have any questions.

what’s involved?

The study will last about four years, following your child from kindergarten to third grade. Each year, there will be one behavioral session with reading and math activities and one brain imaging session with Magnetic Resonance Imaging (MRI). Participants will receive $50 per session as well as yearly progress reports on their child’s reading and math development.

Behavioral sessions can take place either virtually or at the Gaab Lab, located in Cambridge, MA. Brain imaging sessions will take place at our neuroimaging facility in Cambridge. For all sessions, we can help you with transportation by providing free parking, assistance with public transportation, and free rideshares.

what are we researching?

Research suggests a strong neurocognitive and genetic link between reading and math, with high co-occurrence rates of reading and math learning difficulties. However, the developmental trajectories of typical and atypical math and reading skills have mostly been studied apart and potential shared mechanisms are unknown.

The CALC Study seeks to further explore this link through Magnetic Resonance Imaging (MRI) as well as language, math, and cognitive assessments. MRI is a safe and completely non-invasive method that we use to take pictures of the brain! To learn more about MRI, please visit our MRI Safety page. The purpose of the study is to use these methods to track the development of the reading and math networks in children over four years, beginning in kindergarten, to investigate how early brain differences in people at familial risk for reading and math difficulties manifest.

This study has the potential to provide a model for understanding developmental learning disabilities, their underlying mechanisms, and their co-occurrence. This, in turn, could inform more effective development of early screening, diagnostic, and intervention tools.