ArticleProceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies2020
Predicting Brain Functional Connectivity Using Mobile Sensing.
Article in Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- Neuroscience meets behavior: A systematic literature review on magnetic resonance imaging of the brain combined with real-world digital phenotyping.Human brain mapping · 2024Pooled it
- Personalised modelling of routine variability and affective states.NPJ digital medicine · 2025Article
- Current challenges and opportunities in active and passive data collection for mobile health sensing: a scoping review.JAMIA open · 2025Review
- Dense Sampling Approaches for Psychiatry Research: Combining Scanners and Smartphones.Biological psychiatry · 2023Review
- Detecting Smartwatch-based Behavior Change in Response to a Multi-domain Brain Health Intervention.ACM transactions on computing for healthcare · 2022Article
- First-Gen Lens: Assessing Mental Health of First-Generation Students across Their First Year at College Using Mobile Sensing.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2022Article
- COVID Student Study: A Year in the Life of College Students during the COVID-19 Pandemic Through the Lens of Mobile Phone Sensing.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2022Article
- On the Transition of Social Interaction from In-Person to Online: Predicting Changes in Social Media Usage of College Students during the COVID-19 Pandemic based on Pre-COVID-19 On-Campus Colocation.Proceedings of the ... ACM International Conference on Multimodal Interaction. ICMI (Conference) · 2021Article
- Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study.JMIR mHealth and uHealth · 2021Article
- On Blurry Boundaries When Defining Digital Biomarkers: How Much Biology Needs to Be in a Digital Biomarker?Frontiers in psychiatry · 2021Review
- Impact of dynamic greenspace exposure on symptomatology in individuals with schizophrenia.PloS one · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 2 institutions in 1 country.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.