Paper Info
Title
Predicting Brain Functional Connectivity Using Mobile Sensing
Abstract
Brain circuit functioning and connectivity between specific regions allow us to learn, remember, recognize and think as humans. In this paper, we ask the question if mobile sensing from phones can predict brain functional connectivity. We study the brain resting-state functional connectivity (RSFC) between the ventromedial prefrontal cortex (vmPFC) and the amygdala, which has been shown by neuroscientists to be associated with mental illness such as anxiety and depression. We discuss initial results and insights from the NeuroSence study, an exploratory study of 105 first year college students using neuroimaging and mobile sensing across one semester. We observe correlations between several behavioral features from students' mobile phones and connectivity between vmPFC and amygdala, including conversation duration (r=0.365, p<0.001), sleep onset time (r=0.299, p<0.001) and the number of phone unlocks (r=0.253, p=0.029). We use a support vector classifier and 10-fold cross validation and show that we can classify whether students have higher (i.e., stronger) or lower (i.e., weaker) vmPFC-amygdala RSFC purely based on mobile sensing data with an F1 score of 0.793. To the best of our knowledge, this is the first paper to report that resting-state brain functional connectivity can be predicted using passive sensing data from mobile phones.
Year
DOI
Venue
2020
10.1145/3381001
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Keywords
DocType
Volume
Brain Imaging,Mobile Sensing,Neuroscience
Journal
4
Issue
ISSN
Citations 
1
2474-9567
0
PageRank 
References 
Authors
0.34
24
10
Name
Order
Citations
PageRank
Mikio Obuchi1112.94
Jeremy Huckins2515.03
Weichen Wang3224.66
Alex daSilva400.68
Courtney Rogers500.68
Eilis Murphy601.01
Elin Hedlund700.68
Paul Holtzheimer800.34
Shayan Mirjafari9173.99
Andrew T. Campbell108958759.66