The UCSF Learning Success Program is an exciting opportunity to stay up-to-date with your child's academic (reading and math) and cognitive (self-control, attention, memory) skills, which are critical to future success, every year for three years from Kindergarten. Click to learn more.
brainLENS (Laboratory for Educational NeuroScience) at the University of California, San Francisco combines cutting-edge, cross-disciplinary research methods with a deep passion for maximizing children’s potential in life, particularly stemming from the academic domain.
With a firm belief in interdisciplinary research, we integrate the latest brain imaging techniques, genetic analysis, and computational approaches to examine processes of learning, including acquisition of skills, such as reading, socio-emotional processing, motivation and resilience.
In the spirit of our lab, we aim to share the love of neuroscience with children of all ages, rapidly translate research findings to practice, and collaborate with teachers, clinicians, and families.
We feel that it is imperative as an educational neuroscience laboratory to translate insights from the laboratory to educational practice and theory. Some examples of our research include:
Neuroscience of internal environment such as grit, motivation, mindset, and resilience.
How do we inherit brain networks, cognitive and character traits? Research on intergenerational transmission patterns using a natural cross-fostering design, genetics, & imaging.
Genes to cognition. Research to understand auditory processing, dyslexia & related disabilities using a comprehensive approach from genes, neurochemicals, oscillations, connectomes, to cognition.
Innovative growth charts for brain networks.Brain version of those for height & weight received from a pediatrician’s office. Collaboration with 20+ sites.
Early identification & personalized education. Development of a comprehensive school-readiness app from reading, math, executive function, character traits and creativity. Development of neuroimaging-based models to predict learning profiles and risk for developing disorders before they can be identified using conventional methods.
Identification of novel subtypes of and examination of relative strength of dyslexia using data-driven approaches.
For more information regarding the lab, contact Professor Fumiko Hoeft at firstname.lastname@example.org.