Fusing Mobile Phone Sensing and Brain Imaging to Assess Depression in College Students

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, we identified a positive relationship 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, such that increased phone usage was related to stronger connectivity between these regions. We then used this cluster to constrain subsequent analyses looking at depressive symptoms in the same cohort and observed partial replication in a separate cohort. We believe the data and analyses presented here provide relatively simplistic initial analyses which replicate and provide a first 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.

[1]  Evan M. Gordon,et al.  Reward-related regions form a preferentially coupled system at rest , 2018, bioRxiv.

[2]  J. Pell,et al.  Association of disrupted circadian rhythmicity with mood disorders, subjective wellbeing, and cognitive function: a cross-sectional study of 91 105 participants from the UK Biobank. , 2018, The lancet. Psychiatry.

[3]  Timothy O. Laumann,et al.  Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation , 2018, Neuron.

[4]  Rui Wang,et al.  Tracking Depression Dynamics in College Students Using Mobile Phone and Wearable Sensing , 2018, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[5]  T. Joiner,et al.  Increases in Depressive Symptoms, Suicide-Related Outcomes, and Suicide Rates Among U.S. Adolescents After 2010 and Links to Increased New Media Screen Time , 2018 .

[6]  N. Volkow,et al.  The conception of the ABCD study: From substance use to a broad NIH collaboration , 2017, Developmental Cognitive Neuroscience.

[7]  Mirco Musolesi,et al.  MyTraces , 2017, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[8]  D. Jaalouk,et al.  Depression, anxiety, and smartphone addiction in university students- A cross sectional study , 2017, PloS one.

[9]  L. Lorenzo-Luaces,et al.  History of Depression , 2017 .

[10]  Gang Chen,et al.  fMRI clustering and false-positive rates , 2017, Proceedings of the National Academy of Sciences.

[11]  Evan M. Gordon,et al.  Individual-specific features of brain systems identified with resting state functional correlations , 2017, NeuroImage.

[12]  Andrew T. Drysdale,et al.  Resting-state connectivity biomarkers define neurophysiological subtypes of depression , 2016, Nature Medicine.

[13]  J. Khubchandani,et al.  The Psychometric Properties of PHQ-4 Depression and Anxiety Screening Scale Among College Students. , 2016, Archives of psychiatric nursing.

[14]  Satrajit S. Ghosh,et al.  The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments , 2016, Scientific Data.

[15]  Runze Li,et al.  FEATURE SCREENING FOR TIME-VARYING COEFFICIENT MODELS WITH ULTRAHIGH DIMENSIONAL LONGITUDINAL DATA. , 2016, The annals of applied statistics.

[16]  J. S. Guntupalli,et al.  A Model of Representational Spaces in Human Cortex , 2016, Cerebral cortex.

[17]  Timothy O. Laumann,et al.  Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations. , 2016, Cerebral cortex.

[18]  Evan M. Gordon,et al.  Long-term neural and physiological phenotyping of a single human , 2015, Nature Communications.

[19]  Paul M. Thompson,et al.  Heritability of the network architecture of intrinsic brain functional connectivity , 2015, NeuroImage.

[20]  Mirco Musolesi,et al.  Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis , 2015, UbiComp.

[21]  Konrad Paul Kording,et al.  Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study , 2015, Journal of medical Internet research.

[22]  M. Rietschel,et al.  Correlated gene expression supports synchronous activity in brain networks , 2015, Science.

[23]  J. Andrews-Hanna,et al.  Large-Scale Network Dysfunction in Major Depressive Disorder: A Meta-analysis of Resting-State Functional Connectivity. , 2015, JAMA psychiatry.

[24]  Andrew T. Campbell,et al.  Next-generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health. , 2015, Psychiatric rehabilitation journal.

[25]  Brian Caffo,et al.  Longitudinal High-Dimensional Principal Components Analysis with Application to Diffusion Tensor Imaging of Multiple Sclerosis. , 2015, The annals of applied statistics.

[26]  Bing Chen,et al.  An open science resource for establishing reliability and reproducibility in functional connectomics , 2014, Scientific Data.

[27]  Fanglin Chen,et al.  StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones , 2014, UbiComp.

[28]  Timothy O. Laumann,et al.  Methods to detect, characterize, and remove motion artifact in resting state fMRI , 2014, NeuroImage.

[29]  Mary E. Meyerand,et al.  The effect of scan length on the reliability of resting-state fMRI connectivity estimates , 2013, NeuroImage.

[30]  Vivek Prabhakaran,et al.  The effect of resting condition on resting-state fMRI reliability and consistency: A comparison between resting with eyes open, closed, and fixated , 2013, NeuroImage.

[31]  Jialiang Li,et al.  Nonparametric independence screening and structure identification for ultra-high dimensional longitudinal data , 2013, 1308.3942.

[32]  J. Jonides,et al.  Facebook Use Predicts Declines in Subjective Well-Being in Young Adults , 2013, PloS one.

[33]  M. Lindquist,et al.  An fMRI-based neurologic signature of physical pain. , 2013, The New England journal of medicine.

[34]  Daniel Eisenberg,et al.  Mental Health in American Colleges and Universities: Variation Across Student Subgroups and Across Campuses , 2013, The Journal of nervous and mental disease.

[35]  Ethan Kross,et al.  Dimensionality of brain networks linked to life-long individual differences in self-control , 2012, Nature Communications.

[36]  A. Donald,et al.  Supplemental Material to , 2013 .

[37]  Timothy S. Coalson,et al.  Parcellations and hemispheric asymmetries of human cerebral cortex analyzed on surface-based atlases. , 2012, Cerebral cortex.

[38]  Annie Qu,et al.  Penalized Generalized Estimating Equations for High‐Dimensional Longitudinal Data Analysis , 2012, Biometrics.

[39]  Ciprian M Crainiceanu,et al.  Longitudinal penalized functional regression for cognitive outcomes on neuronal tract measurements , 2012, Journal of the Royal Statistical Society. Series C, Applied statistics.

[40]  Alan R. Ellis,et al.  Risk of adverse events in treatment-resistant depression: propensity-score-matched comparison of antidepressant augment and switch strategies. , 2012, General hospital psychiatry.

[41]  Megan M. Filkowski,et al.  Subcallosal cingulate deep brain stimulation for treatment-resistant unipolar and bipolar depression. , 2012, Archives of general psychiatry.

[42]  Abraham Z. Snyder,et al.  Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion , 2012, NeuroImage.

[43]  R. Buckner,et al.  The organization of the human striatum estimated by intrinsic functional connectivity. , 2012, Journal of neurophysiology.

[44]  Timothy O. Laumann,et al.  Functional Network Organization of the Human Brain , 2011, Neuron.

[45]  Vidhura Tennekoon,et al.  Measuring bias in self-reported data. , 2011, International journal of behavioural & healthcare research.

[46]  D. Mohr,et al.  Harnessing Context Sensing to Develop a Mobile Intervention for Depression , 2011, Journal of medical Internet research.

[47]  Marisa O. Hollinshead,et al.  The organization of the human cerebral cortex estimated by intrinsic functional connectivity. , 2011, Journal of neurophysiology.

[48]  Russell A. Poldrack,et al.  Large-scale automated synthesis of human functional neuroimaging data , 2011, Nature Methods.

[49]  J. M. Hughes,et al.  Nonparametric Sparsification of Complex Multiscale Networks , 2011, PloS one.

[50]  John G. Csernansky,et al.  Open Access Series of Imaging Studies: Longitudinal MRI Data in Nondemented and Demented Older Adults , 2010, Journal of Cognitive Neuroscience.

[51]  N. Bolger,et al.  Brain Mediators of Predictive Cue Effects on Perceived Pain , 2010, The Journal of Neuroscience.

[52]  N. Kerse,et al.  Validation of PHQ-2 and PHQ-9 to Screen for Major Depression in the Primary Care Population , 2010, The Annals of Family Medicine.

[53]  T. Insel,et al.  Wesleyan University From the SelectedWorks of Charles A . Sanislow , Ph . D . 2010 Research Domain Criteria ( RDoC ) : Toward a New Classification Framework for Research on Mental Disorders , 2018 .

[54]  Elizabeth A Stuart,et al.  Matching methods for causal inference: A review and a look forward. , 2010, Statistical science : a review journal of the Institute of Mathematical Statistics.

[55]  B. Löwe,et al.  An ultra-brief screening scale for anxiety and depression: the PHQ-4. , 2009, Psychosomatics.

[56]  B. Biswal,et al.  The resting brain: unconstrained yet reliable. , 2009, Cerebral cortex.

[57]  Marián Boguñá,et al.  Extracting the multiscale backbone of complex weighted networks , 2009, Proceedings of the National Academy of Sciences.

[58]  P. Harris,et al.  Research electronic data capture (REDCap) - A metadata-driven methodology and workflow process for providing translational research informatics support , 2009, J. Biomed. Informatics.

[59]  T. Strine,et al.  The PHQ-8 as a measure of current depression in the general population. , 2009, Journal of affective disorders.

[60]  S. Shiffman,et al.  Ecological momentary assessment. , 2008, Annual review of clinical psychology.

[61]  Martin Rosvall,et al.  Maps of random walks on complex networks reveal community structure , 2007, Proceedings of the National Academy of Sciences.

[62]  A. Lozano,et al.  Deep Brain Stimulation for Treatment-Resistant , 2008 .

[63]  J. Crawford,et al.  Psychometric comparison of PHQ-9 and HADS for measuring depression severity in primary care. , 2008, The British journal of general practice : the journal of the Royal College of General Practitioners.

[64]  G. Glover,et al.  Resting-State Functional Connectivity in Major Depression: Abnormally Increased Contributions from Subgenual Cingulate Cortex and Thalamus , 2007, Biological Psychiatry.

[65]  R. Spitzer,et al.  The PHQ-9 , 2001, Journal of General Internal Medicine.

[66]  William M. Kelley,et al.  Neuroanatomical Evidence for Distinct Cognitive and Affective Components of Self , 2006, Journal of Cognitive Neuroscience.

[67]  T. Heatherton,et al.  Anterior cingulate cortex responds differentially to expectancy violation and social rejection , 2006, Nature Neuroscience.

[68]  Anders M. Dale,et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.

[69]  A. Lozano,et al.  Deep Brain Stimulation for Treatment-Resistant Depression , 2005, Neuron.

[70]  D. Seese,et al.  Algorithms for Spectral Analysis of Irregularly Sampled Time Series , 2004 .

[71]  T. B. Üstün,et al.  Global burden of depressive disorders in the year 2000 , 2004, British Journal of Psychiatry.

[72]  Ronald C Kessler,et al.  The economic burden of depression in the United States: how did it change between 1990 and 2000? , 2003, The Journal of clinical psychiatry.

[73]  N. Logothetis,et al.  Neurophysiological investigation of the basis of the fMRI signal , 2001, Nature.

[74]  R. Spitzer,et al.  The PHQ-9: validity of a brief depression severity measure. , 2001, Journal of general internal medicine.

[75]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[76]  Karl J. Friston,et al.  Movement‐Related effects in fMRI time‐series , 1996, Magnetic resonance in medicine.

[77]  P. Rogers,et al.  Nutrition and mental performance , 1994, Proceedings of the Nutrition Society.

[78]  D. Blazer,et al.  The economic burden of depression. , 1986, General hospital psychiatry.

[79]  J. Fleiss,et al.  Intraclass correlations: uses in assessing rater reliability. , 1979, Psychological bulletin.

[80]  N. Lomb Least-squares frequency analysis of unequally spaced data , 1976 .