Fusing Mobile Phone Sensing and Brain Imaging to Assess Depression in College Students
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Abstract
As smartphone usage has become increasingly prevalent in our society, so have rates of depression, particularly among young adults. Individual differences in smartphone usage patterns have been shown to reflect individual differences in underlying affective processes such as depression (Wang et al., 2018). In the current study, a positive relationship was identified between smartphone screen time (e.g., phone unlock duration) and resting-state functional connectivity (RSFC) between the subgenual cingulate cortex (sgCC), a brain region implicated in depression and antidepressant treatment response, and regions of the ventromedial/orbitofrontal cortex (OFC), such that increased phone usage was related to stronger connectivity between these regions. This cluster was subsequently used to constrain subsequent analyses looking at individual differences in depressive symptoms in the same cohort and observed partial replication in a separate cohort. Similar analyses were subsequently performed on metrics of circadian rhythm consistency showing a negative relationship between connectivity of the sgCC and OFC. The data and analyses presented here provide relatively simplistic preliminary analyses which replicate and provide an initial step in combining functional brain activity and smartphone usage patterns to better understand issues related to mental health. Smartphones are a prevalent part of modern life and the usage of mobile sensing data from smartphones promises to be an important tool for mental health diagnostics and neuroscience research.
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Huckins et al. found connections between mobile sensing and fMRI scans, including correlations between smartphone usage (unlock duration) and RSFC for the subgenual cingulate cortex.
“Huckins et al. [44] present connections between mobile sensing technology and fMRI scans.”
From Predicting Brain Functional Connectivity Using Mobile Sensing (2020) · cites this paper as [44]
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- Authors
- Huckins, J. F., daSilva, A. W., Wang, R., Wang, W., Hedlund, E. L., Murphy, E. I., Lopez, R. B., Rogers, C., Holtzheimer, P. E., Kelley, W. M., Heatherton, T. F., Wagner, D. D., Haxby, J. V., & Campbell, A. T.
- Affiliations
- Dartmouth College (US)
- Dartmouth–Hitchcock Medical Center (US)
- The Ohio State University (US)
- Corresponding author
- Huckins, Jeremy F.
- ORCID
- Venue
- Frontiers in Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2019-03-21
- Volume
- 13
- Pages
- 248-248
- ISSN
- 1662-453X (ISSN-L)
- PMC
- PMC6437560
- OpenAlex
- W2924823958
- Cited by
- 51
- References
- 81
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 6 · 2023: 7 · 2022: 11 · 2021: 11 · 2020: 7 · 2019: 1
- FWCI
- 4.33
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Neuroscience (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute of Mental Health (5R01MH059282-12, 5R01DA022582-10)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
- Keywords
- depression, smartphone screen time, smartphone sensing, resting-state functional connectivity, subgenual anterior cingulate cortex, orbitofrontal cortex, ventromedial prefrontal cortex, university students, individual differences, psychiatric diagnosis, smartphone usage patterns
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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