title: Predictability of depression severity based on posterior alpha oscillations.
abstract: OBJECTIVE
  We aimed to integrate neural data and an advanced machine learning technique to predict individual major depressive disorder (MDD) patient severity.
  
  
  METHODS
  MEG data was acquired from 22 MDD patients and 22 healthy controls (HC) resting awake with eyes closed. Individual power spectra were calculated by a Fourier transform. Sources were reconstructed via beamforming technique. Bayesian linear regression was applied to predict depression severity based on the spatial distribution of oscillatory power.
  
  
  RESULTS
  In MDD patients, decreased theta (4-8 Hz) and alpha (8-14 Hz) power was observed in fronto-central and posterior areas respectively, whereas increased beta (14-30 Hz) power was observed in fronto-central regions. In particular, posterior alpha power was negatively related to depression severity. The Bayesian linear regression model showed significant depression severity prediction performance based on the spatial distribution of both alpha (r=0.68, p=0.0005) and beta power (r=0.56, p=0.007) respectively.
  
  
  CONCLUSIONS
  Our findings point to a specific alteration of oscillatory brain activity in MDD patients during rest as characterized from MEG data in terms of spectral and spatial distribution.
  
  
  CONCLUSIONS
  The proposed model yielded a quantitative and objective estimation for the depression severity, which in turn has a potential for diagnosis and monitoring of the recovery process.
authors.affiliation: Academic Department of Psychiatry, Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210029, China; Radboud University, Donders Institute for Brain, Cognition and Behaviour, 6500 HE Nijmegen, The Netherlands.
authors.forename: H
authors.initials: H
authors.lastname: Jiang
authors.affiliation: Radboud University, Donders Institute for Brain, Cognition and Behaviour, 6500 HE Nijmegen, The Netherlands.
authors.forename: T
authors.initials: T
authors.lastname: Popov
authors.affiliation: Radboud University, Donders Institute for Brain, Cognition and Behaviour, 6500 HE Nijmegen, The Netherlands.
authors.forename: P
authors.initials: P
authors.lastname: Jylänki
authors.affiliation: Research Centre for Learning Science, Key Lab of Child Development and Learning Science, Southeast University, Nanjing 210096, China.
authors.forename: K
authors.initials: K
authors.lastname: Bi
authors.affiliation: Academic Department of Psychiatry, Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210029, China; Medical College of Nanjing University, 22 Hankou Road, Nanjing 210093, China. Electronic address: zjyao@njmu.edu.cn.
authors.forename: Z
authors.initials: Z
authors.lastname: Yao
authors.affiliation: Research Centre for Learning Science, Key Lab of Child Development and Learning Science, Southeast University, Nanjing 210096, China. Electronic address: luq@seu.edu.cn.
authors.forename: Q
authors.initials: Q
authors.lastname: Lu
authors.affiliation: Radboud University, Donders Institute for Brain, Cognition and Behaviour, 6500 HE Nijmegen, The Netherlands.
authors.forename: O
authors.initials: O
authors.lastname: Jensen
authors.affiliation: Radboud University, Donders Institute for Brain, Cognition and Behaviour, 6500 HE Nijmegen, The Netherlands.
authors.forename: M A J
authors.initials: MA
authors.lastname: van Gerven
mesh_terms: D000328:Adult; D000513:Alpha Rhythm / Q000502:physiology*; D001921:Brain / Q000503:physiopathology; D003865:Major Depressive Disorder / Q000175:diagnosis* / Q000503:physiopathology / Q000523:psychology; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D015225:Magnetoencephalography / Q000379:methods*; D008297:Male; D011237:Predictive Value of Tests; D012720:Severity of Illness Index; D055815:Young Adult
keywords: Bayesian linear regression; MEG; Major depressive disorder; Resting state