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| Using smartphones and wearable devices to monitor behavioral changes during COVID-19 S Sun, AA Folarin, Y Ranjan, Z Rashid, P Conde, C Stewart, N Cummins, ... Journal of medical Internet research 22 (9), e19992, 2020 | 195 | 2020 |
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| An improved model for disease progression in patients from the Alzheimer's disease neuroimaging initiative MN Samtani, M Farnum, V Lobanov, E Yang, N Raghavan, A DiBernardo, ... The Journal of Clinical Pharmacology 52 (5), 629-644, 2012 | 105 | 2012 |
| Remote assessment of disease and relapse in major depressive disorder (RADAR-MDD): a multi-centre prospective cohort study protocol F Matcham, C Barattieri di San Pietro, V Bulgari, G De Girolamo, ... BMC psychiatry 19, 1-11, 2019 | 95 | 2019 |
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| Quantifying the pathophysiological timeline of Alzheimer's disease E Yang, M Farnum, V Lobanov, T Schultz, N Raghavan, MN Samtani, ... Journal of Alzheimer's Disease 26 (4), 745-753, 2011 | 75 | 2011 |
| Analysis of 23andMe antidepressant efficacy survey data: implication of circadian rhythm and neuroplasticity in bupropion response QS Li, C Tian, GR Seabrook, WC Drevets, VA Narayan Translational psychiatry 6 (9), e889-e889, 2016 | 73 | 2016 |
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| Real-time assessment of COVID-19 prevalence among multiple sclerosis patients: a multicenter European study G Dalla Costa, L Leocani, X Montalban, AI Guerrero, PS Sørensen, ... Neurological Sciences 41, 1647-1650, 2020 | 57 | 2020 |
| Identifying amyloid pathology–related cerebrospinal fluid biomarkers for Alzheimer's disease in a multicohort study YY Leung, JB Toledo, A Nefedov, R Polikar, N Raghavan, SX Xie, ... Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring 1 (3 …, 2015 | 57 | 2015 |
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