research-article

Predicting Brain Functional Connectivity Using Mobile Sensing

Authors:

Jeremy F. Huckins,

Courtney Rogers,

Paul Holtzheimer,

Shayan Mirjafari, and

Andrew CampbellAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 4, Issue 1

Article No.: 23, Pages 1 - 22

https://doi.org/10.1145/3381001

Published: 18 March 2020 Publication History

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.

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Gopalakrishnan AGururajan RZhou XVenkataraman RChan KHiggins N(2024)A survey of autonomous monitoring systems in mental healthWIREs Data Mining and Knowledge Discovery10.1002/widm.152714:3Online publication date: 24-Jan-2024
https://doi.org/10.1002/widm.1527
Triana ASaramäki JGlerean EHayward N(2024)Neuroscience meets behavior: A systematic literature review on magnetic resonance imaging of the brain combined with real‐world digital phenotypingHuman Brain Mapping10.1002/hbm.2662045:4Online publication date: 4-Mar-2024
https://doi.org/10.1002/hbm.26620
Jianhua TJiangtao GNan GSiwei FShan LChun Y(2023)Human-computer interaction for virtual-real fusionJournal of Image and Graphics10.11834/jig.23002028:6(1513-1542)Online publication date: 2023
https://doi.org/10.11834/jig.230020
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Index Terms

Predicting Brain Functional Connectivity Using Mobile Sensing
1. Human-centered computing
  1. Ubiquitous and mobile computing

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 4, Issue 1

March 2020

1006 pages

EISSN:2474-9567

DOI:10.1145/3388993

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Copyright © 2020 ACM.

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Publication History

Published: 18 March 2020

Published in IMWUT Volume 4, Issue 1

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Cited By

Gopalakrishnan AGururajan RZhou XVenkataraman RChan KHiggins N(2024)A survey of autonomous monitoring systems in mental healthWIREs Data Mining and Knowledge Discovery10.1002/widm.152714:3Online publication date: 24-Jan-2024
https://doi.org/10.1002/widm.1527
Triana ASaramäki JGlerean EHayward N(2024)Neuroscience meets behavior: A systematic literature review on magnetic resonance imaging of the brain combined with real‐world digital phenotypingHuman Brain Mapping10.1002/hbm.2662045:4Online publication date: 4-Mar-2024
https://doi.org/10.1002/hbm.26620
Jianhua TJiangtao GNan GSiwei FShan LChun Y(2023)Human-computer interaction for virtual-real fusionJournal of Image and Graphics10.11834/jig.23002028:6(1513-1542)Online publication date: 2023
https://doi.org/10.11834/jig.230020
Arakawa RAhuja KMak KThompson GShaaban SLindhiem OGoel M(2023)LemurDxProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35962447:2(1-23)Online publication date: 12-Jun-2023
https://dl.acm.org/doi/10.1145/3596244
Assi KMeegahapola LDroz WKun PDe Götzen ABidoglia MStares SGaskell GChagnaa AGanbold AZundui TCaprini CMiorandi DZarza JHume ACernuzzi LBison IRodas Britez MBusso MChenu-Abente RGiunchiglia FGatica-Perez D(2023)Complex Daily Activities, Country-Level Diversity, and Smartphone Sensing: A Study in Denmark, Italy, Mongolia, Paraguay, and UKProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581190(1-23)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581190
Meegahapola LConstantinides MRadivojevic ZLi HQuercia DEggleston M(2023)Quantified Canine: Inferring Dog Personality From WearablesProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581088(1-19)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581088
McGowan ASayed FBoyd ZJovanova MKang YSpeer MCosme DMucha POchsner KBassett DFalk ELydon-Staley D(2023)Dense Sampling Approaches for Psychiatry Research: Combining Scanners and SmartphonesBiological Psychiatry10.1016/j.biopsych.2022.12.01293:8(681-689)Online publication date: Apr-2023
https://doi.org/10.1016/j.biopsych.2022.12.012
Wang WNepal SHuckins JHernandez LVojdanovski VMack DPlomp JPillai AObuchi MdaSilva AMurphy EHedlund ERogers CMeyer MCampbell A(2022)First-Gen LensProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35431946:2(1-32)Online publication date: 7-Jul-2022
https://dl.acm.org/doi/10.1145/3543194
Kalanadhabhatta MSantana AZhang ZGanesan DGrabell ARahman T(2022)EarlyScreenProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35345836:2(1-39)Online publication date: 7-Jul-2022
https://dl.acm.org/doi/10.1145/3534583
Cook DStrickland MSchmitter-Edgecombe M(2022)Detecting Smartwatch-Based Behavior Change in Response to a Multi-Domain Brain Health InterventionACM Transactions on Computing for Healthcare10.1145/35080203:3(1-18)Online publication date: 7-Apr-2022
https://dl.acm.org/doi/10.1145/3508020
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