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Predicting Brain Functional Connectivity Using Mobile Sensing.

Mikio Obuchi1, Jeremy F Huckins2, Weichen Wang1

  • 1Dartmouth College, Computer Science, Hanover, NH, 03755, USA.

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
|December 21, 2022
PubMed
Summary
This summary is machine-generated.

Mobile phone sensing can predict brain functional connectivity, specifically the connection between the ventromedial prefrontal cortex (vmPFC) and amygdala. This breakthrough offers new avenues for understanding mental health conditions like anxiety and depression.

Keywords:
Brain ImagingMobile SensingNeuroscience

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Area of Science:

  • Neuroscience
  • Computational Psychiatry
  • Mobile Health (mHealth)

Background:

  • Human cognition relies on brain circuit functioning and connectivity.
  • Resting-state functional connectivity (RSFC) between the ventromedial prefrontal cortex (vmPFC) and amygdala is crucial and linked to mental health disorders.
  • Existing methods for assessing brain connectivity are often limited in scope and accessibility.

Purpose of the Study:

  • To investigate the potential of mobile sensing data to predict brain functional connectivity.
  • To explore the relationship between behavioral patterns captured by smartphones and vmPFC-amygdala RSFC.
  • To establish a novel, non-invasive method for assessing brain connectivity.

Main Methods:

  • The NeuroSence study involved 105 college students over one semester.
  • Utilized neuroimaging (fMRI) and passive mobile sensing data.
  • Employed support vector classification with 10-fold cross-validation to predict RSFC levels.

Main Results:

  • Significant correlations were found between mobile sensing features (conversation duration, sleep onset time, phone unlocks) and vmPFC-amygdala connectivity.
  • A support vector classifier achieved an F1 score of 0.793 in predicting higher or lower vmPFC-amygdala RSFC using only mobile data.
  • This study demonstrates the first successful prediction of resting-state brain functional connectivity from passive mobile sensing.

Conclusions:

  • Passive mobile sensing data can accurately predict resting-state brain functional connectivity, specifically vmPFC-amygdala connections.
  • This approach offers a promising, scalable, and accessible method for mental health research and monitoring.
  • Future research can leverage mHealth data for early detection and personalized interventions for mental health conditions.