Predicting Brain Functional Connectivity Using Mobile Sensing.
Brain Imaging
Mobile Sensing
Neuroscience
Journal
Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies
ISSN: 2474-9567
Titre abrégé: Proc ACM Interact Mob Wearable Ubiquitous Technol
Pays: United States
ID NLM: 101719413
Informations de publication
Date de publication:
Mar 2020
Mar 2020
Historique:
entrez:
21
12
2022
pubmed:
1
3
2020
medline:
1
3
2020
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 36540188
doi: 10.1145/3381001
pmc: PMC9762691
mid: NIHMS1855032
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : NIMH NIH HHS
ID : R01 MH059282
Pays : United States
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