The Consortium for Interacting Minds is committed to developing open source tools to aid in the study of behavior and neural activity associated with social interactions.
A Python toolbox for automatically detecting, preprocessing, analyzing, and visualizing facial expressions from images and videos.
StudentLife is the first study that uses passive and automatic sensing data from the phones of a class of 48 Dartmouth students over a 10 week term to assess their mental health (e.g., depression, loneliness, stress), academic performance (grades across all their classes, term GPA and cumulative GPA) and behavioral trends (e.g., how stress, sleep, visits to the gym, etc. change in response to college workload - i.e., assignments, midterms, finals - as the term progresses).
In this dataset, participants (n=35) watched the pilot episode of the NBC television show Friday Night Lights while undergoing fMRI. There is an additional dataset, in which participants (n=13) watched two episodes of the same show and two additional datasets in which participants rated their subjective feelings (n=192) and had their facial expressions recorded (n=20) while watching the pilot episode.
The Paranoia dataset contains 23 participants who listened to a 22 minute original narrative that describes an ambiguous social scenario. It was written such that some individuals might find it highly suspicious.
In the naturalistic conversation dataset, participants (n=66) completed ten 10-minute unstructured conversations within sex same-gendered round-robin groups (322 conversations). A subset of the participants (n=22) completed additional conversations with 3 close friends (65 conversations).
Dartbrains is an online textbook introducing the basics of neuroimaging data analysis using the Python programming language. It covers the basic signal processing and statistical principles used in univariate neuroimaging analyses. The format includes short lectures, interactive code, and short homework questions.
This course introduces the state of the art neuroimaging analysis methods using Python for analyzing naturalistic experimental designs such as passively watching movies. It covers functional alignment, intersubject correlations, event segmentation, and dynamic connectivity. The format includes short interactive tutorials using open datasets.
The MIND computational summer school is a 10 day intensive experience to learn cutting edge analysis methods to study a particular research topic that integrates the disciplines of cognitive, social, and systems neuroscience. The topics rotate every year and have previously included network dynamics, narratives and natural contexts, and cognitive maps. The format includes lectures from leaders in the field, hand on tutorials on specific techniques, and a hackathon environment for applying the techniques to answer a question using real data.