We are a multidisciplinary team studying the neural architecture and dynamics of human intelligence, with a focus on cognitive symbolic systems such as mathematics and language. Our research program aims at understanding how these systems develop and decline, and how we can help.

We combine machine learning computational modeling, electrophysiological recordings (intracranial EEG, MEG), neuroimaging (fMRI), continuous behavioral measures (trajectory-tracking), and neuropsychology to study the processing stages and representational codes underlying cognitive operations. Complementarily, we use intracranial electrical stimulation to modulate brain activity and behavior.

• Our core members are:
• Pedro Pinheiro-Chagas, PhD - PI Assistant Professor
• Betinna Pedemonte, PhD - Full Specialist in Math Education
• Mariah Pospisil, MEd - Learning Interventions Applied Research Manager
• Margo Kersey - Research Data Analyst
• Rian Bogley - Database Architect
• Daniel Quintana - UCSF Medical Student

We are part of UCSF Neurology (Memory and Aging Center) and Neurosurgery departments and we collaborate with several labs and initiatives:

• UCSF Dyslexia Center (UCSF)
• ALBA Lab (UCSF)
• Multitudes (UCSF)
• Chang Lab (UCSF)
• CAN Lab (UCSF)
• Computational and Language Lab (UC Berkeley)
• NoCe Lab (Université de Genève)

Our current projects are:

• Logical primitives of symbolic reasoning
• Neurocognitive mechanisms underlying the co-occurence of Dyslexia and Dyscalculia
• NeuroCausal - an open data sharing and metadata synthesis platform for clinical data
• A machine learning framework for classifying and predicting neurodevelopmental disorders
• Behavioral and neurological signatures of math deficits in primary progressive aphasia
• A novel approach to subtypes of developmental dyscalculia
• Neurophysiological basis of math anxiety

We are active participants of initiatives to promote open, inclusive, and reproducible science:

• Brainhack: Developing a culture of open, inclusive, community-driven neuroscience.
• Gender bias in academia: A lifetime problem that needs solutions

Because of the interdisciplinary nature of our research program, we have developed and improved methods to measure behavior and model brain activity. All methods are open source and publicly available, notably:

• Trajectory tracking
• iEEG preprocessing and analysis toolbox
• Methods for MEG multivariate decoding
• Toolbox to convert iEEG data into the Neurodata Without Borders data standard