1. Merikangas, KR, Jin, R, He, J-P, et al. Prevalence and correlates of bipolar spectrum disorder in the world mental health survey initiative. Arch Genl Psychiatry. 2011;68(3):241. doi: 10.1001/archgenpsychiatry.2011.12
Google Scholar | Crossref | Medline2. Martinez-Aran, A, Vieta, E, Torrent, C, et al. Functional outcome in bipolar disorder: the role of clinical and cognitive factors. Bipolar Disord. 2007;9(1-2):103-113. doi: 10.1111/j.1399–5618.2007.00327.x
Google Scholar | Crossref | Medline | ISI3. Schaffer, A, Isometsä, ET, Tondo, L, et al. International society for bipolar disorders task force on suicide: meta-analyses and meta-regression of correlates of suicide attempts and suicide deaths in bipolar disorder. Bipolar Disord. 2015;17(1):1-16. doi: 10.1111/bdi.12271
Google Scholar | Crossref | Medline | ISI4. Matza, LS, Rajagopalan, KS, Thompson, CL, et al. Misdiagnosed patients with bipolar disorder: comorbidities, treatment patterns, and direct treatment costs. J Clin Psychiatry. 2005;66(11):1432-1440. doi: 10.4088/jcp.v66n1114
Google Scholar | Crossref | Medline | ISI5. Meyer, F, Meyer, TD. The misdiagnosis of bipolar disorder as a psychotic disorder: some of its causes and their influence on therapy. J Affect Disord. 2009;112(1-3):174-183. doi: 10.1016/j.jad.2008.04.022
Google Scholar | Crossref | Medline6. Mitchell, PB, Loo, CK, Gould, BM. Diagnosis and monitoring of bipolar disorder in general practice. Med J Aust. 2010;193(S4):S10-S13. doi: 10.5694/j.1326–5377.2010.tb03890.x
Google Scholar | Crossref | Medline7. Kent, L, Craddock, N. Is there a relationship between attention deficit hyperactivity disorder and bipolar disorder? J Affect Disord. 2003;73(3):211-221. doi: 10.1016/s0165–0327(02)00092–7
Google Scholar | Crossref | Medline8. Galanter, CA, Leibenluft, E. Frontiers between attention deficit hyperactivity disorder and bipolar disorder. Child Adolesc Psychiatr Clin N Am. 2008;17(2):325-346. doi: 10.1016/j.chc.2007.11.001 viii-ix.
Google Scholar | Crossref | Medline | ISI9. Gross, J . Magnetoencephalography in cognitive neuroscience: a primer. Neuron. 2019;104(2):189-204. doi: 10.1016/j.neuron.2019.07.001
Google Scholar | Crossref | Medline10. Hironaga, N, Takei, Y, Mitsudo, T, et al. Prospects for future methodological development and application of magnetoencephalography devices in psychiatry. Front Psychiatry. 2020;11:863. doi: 10.3389/fpsyt.2020.00863
Google Scholar | Crossref | Medline11. Kesebir, S, Yosmaoğlu, A. QEEG – spectral power density of brain regions in predicting risk, resistance and resilience for bipolar disorder: a comparison of first degree relatives and unrelated healthy subjects. Heliyon. 2020;6(6):e04100. doi: 10.1016/j.heliyon.2020.e04100
Google Scholar | Crossref | Medline12. Newson, JJ, Thiagarajan, TC. EEG frequency bands in psychiatric disorders: a review of resting state studies. Front Hum Neurosci. 2019;12:521. doi: 10.3389/fnhum.2018.00521
Google Scholar | Crossref | Medline13. Rommel, A-S, Kitsune, GL, Michelini, G, et al. Commonalities in EEG spectral power abnormalities between women with ADHD and women with bipolar disorder during rest and cognitive performance. Brain Topogr. 2016;29(6):856-866. doi: 10.1007/s10548–016–0508–0
Google Scholar | Crossref | Medline14. Moeini, M, Khaleghi, A, Mohammadi, MR. Characteristics of alpha band frequency in adolescents with bipolar II disorder: a resting-state QEEG study. Iran J Psychiatry. 2015;10(1):8-12.
Google Scholar | Medline15. Oldfield, RC . The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia. 1971;9(1):97-113.
Google Scholar | Crossref | Medline | ISI16. First MB, SR, Gibbon, M, Williams, JBW. Structured Clinical Interview for DSM-IV Axis I Disorders, Clinician Version 2.0. New York State Psychiatric Institute; 1996.
Google Scholar17. First MB, GM, Spitzer, RL, Williams, JBW, Benjamin, LS. Structured Clinical Interview for DSM-IV Axis II Personality Disorders (SCID-II) User's Guide and Interview. American Psychiatric Press; 1997.
Google Scholar18. Inada, T, Inagaki, A. Psychotropic dose equivalence in Japan. Psychiatry Clin Neurosci. 2015;69(8):440-447. doi: 10.1111/pcn.12275
Google Scholar | Crossref | Medline19. Hoddes E, DW, Zarcone, V. The history and use of the Stanford sleepiness scale. Psychophysiology. 1971;9:150.
Google Scholar20. Hironaga, N, Hagiwara, K, Ogata, K, et al. Proposal for a new MEG-MRI co-registration: a 3D laser scanner system. Clin Neurophysiol. 2014;125(12):2404-2412. doi: 10.1016/j.clinph.2014.03.029
Google Scholar | Crossref | Medline21. Larson, E, Taulu, S. Reducing sensor noise in MEG and EEG recordings using oversampled temporal projection. IEEE Trans Biomed Eng. 2018;65(5):1002-1013. doi: 10.1109/TBME.2017.2734641
Google Scholar | Crossref | Medline22. Taulu, S, Simola, J. Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements. Phys Med Biol. 2006;51(7):1759-1768. doi: 10.1088/0031–9155/51/7/008
Google Scholar | Crossref | Medline | ISI23. Taulu, S, Kajola, M, Simola, J. Suppression of interference and artifacts by the signal space separation method. Brain Topogr. 2004;16(4):269-275. Comparative Study 2004/09/24.
Google Scholar | Crossref | Medline | ISI24. Hironaga, N, Ioannides, AA. Localization of individual area neuronal activity. Neuroimage. 2007;34(4):1519-1534. doi: 10.1016/j.neuroimage.2006.10.030
Google Scholar | Crossref | Medline25. Dale, AM, Fischl, B, Sereno, MI. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage. 1999;9(2):179-194. doi: 10.1006/nimg.1998.0395
Google Scholar | Crossref | Medline | ISI26. Fischl, B, Sereno, MI, Dale, AM. Cortical surface-based analysis. II: inflation, flattening, and a surface-based coordinate system. Neuroimage. 1999;9(2):195-207. doi: 10.1006/nimg.1998.0396
Google Scholar | Crossref | Medline | ISI27. Reuter, M, Schmansky, NJ, Rosas, HD, et al. Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage. 2012;61(4):1402-1418. doi: 10.1016/j.neuroimage.2012.02.084
Google Scholar | Crossref | Medline | ISI28. Gramfort, A, Luessi, M, Larson, E, et al. MNE Software for processing MEG and EEG data. Neuroimage. 2014;86:446-460. doi: 10.1016/j.neuroimage.2013.10.027
Google Scholar | Crossref | Medline29. Hamalainen, MS, Sarvas, J. Realistic conductivity geometry model of the human head for interpretation of neuromagnetic data. IEEE Trans Biomed Eng. 1989;36(2):165-171. doi: 10.1109/10.16463
Google Scholar | Crossref | Medline | ISI30. Hashizume, A, Hironaga, N. Principles of magnetoencephalography. In: Tobimatsu, S, Kakigi, R, eds. Clin Appl Mag. Springer Japan; 2016:3-32.
Google Scholar | Crossref31. Hayamizu, M, Hagiwara, K, Hironaga, N, et al. A spatiotemporal signature of cortical pain relief by tactile stimulation: an MEG study. Neuroimage. 2016;130:175-183. doi: 10.1016/j.neuroimage.2016.01.065
Google Scholar | Crossref | Medline32. Hironaga, N, Mitsudo, T, Hayamizu, M, et al. Spatiotemporal brain dynamics of auditory temporal assimilation. Sci Rep. 2017;7(1). doi: 10.1038/s41598–017–11631–0
Google Scholar | Crossref | Medline33. Lin, FH, Witzel, T, Ahlfors, SP, et al. Assessing and improving the spatial accuracy in MEG source localization by depth-weighted minimum-norm estimates. Neuroimage. 2006;31(1):160-171. doi: 10.1016/j.neuroimage.2005.11.054 Comparative Study. Research Support, N.I.H., Extramural.
Google Scholar | Crossref | Medline34. Percival, DB, Walden, AT. Spectral Analysis for Physical Applications. Cambridge: Cambridge University Press. 1993;7:331-377.
Google Scholar35. Jones, E, Oliphant, T, Peterson, P. Scipy: open source scientific tools for python. 2001.
Google Scholar36. Basar, E, Guntekin, B, Atagun, I, et al. Brain's alpha activity is highly reduced in euthymic bipolar disorder patients. Cogn Neurodyn. 2012;6(1):11-20. doi: 10.1007/s11571–011–9172-y
Google Scholar | Crossref | Medline | ISI37. Clementz, BA, Sponheim, SR, Iacono, WG, et al. Resting EEG in first-episode schizophrenia patients, bipolar psychosis patients, and their first-degree relatives. Psychophysiology. 1994;31(5):486-494. doi: 10.1111/j.1469–8986.1994.tb01052.x
Google Scholar | Crossref | Medline | ISI38. El-Badri, SM, Ashton, CH, Moore, PB, et al. Electrophysiological and cognitive function in young euthymic patients with bipolar affective disorder. Bipolar Disord. 2001;3(2):79-87. doi: 10.1034/j.1399-5618.2001.030206.x
Google Scholar | Crossref | Medline | ISI39. Kim, DJ, Bolbecker, AR, Howell, J, et al. Disturbed resting state EEG synchronization in bipolar disorder: a graph-theoretic analysis. Neuroimage Clin. 2013;2:414-423. doi: 10.1016/j.nicl.2013.03.007
Google Scholar | Crossref | Medline40. Nusslock, R, Harmon-Jones, E, Alloy, LB, et al. Elevated left mid-frontal cortical activity prospectively predicts conversion to bipolar I disorder. J Abnorm Psychol. 2012;121(3):592-601. doi: 10.1037/a0028973
Google Scholar | Crossref | Medline | ISI41. Raichle, ME . The brain's default mode network. Annu Rev Neurosci. 2015;38:433-447. doi: 10.1146/annurev-neuro-071013–014030
Google Scholar | Crossref | Medline | ISI42. Zovetti, N, Rossetti, MG, Perlini, C, et al. Default mode network activity in bipolar disorder. Epidemiol Psychiatr Sci. 2020;29. doi: 10.1017/s2045796020000803
Google Scholar | Crossref | Medline43. Solé, B, Jiménez, E, Torrent, C, et al. Cognitive impairment in bipolar disorder: treatment and prevention strategies. Int J Neuropsuchopharmacolog. 2017;20(8):670-680. doi: 10.1093/ijnp/pyx032
Google Scholar | Crossref | Medline44. Öngür, D, Lundy, M, Greenhouse, I, et al. Default mode network abnormalities in bipolar disorder and schizophrenia. Psychiatry Res Neuroimaging. 2010;183(1):59-68. doi: 10.1016/j.pscychresns.2010.04.008
Google Scholar | Crossref | Medline45. Hiser, J, Koenigs, M. The multifaceted role of the ventromedial prefrontal cortex in emotion, decision making, social cognition, and psychopathology. Biol Psychiatry. 2018;83(3):638-647. doi: 10.1016/j.biopsych.2017.10.030
Google Scholar | Crossref | Medline46. Utevsky, AV, Smith, DV, Huettel, SA. Precuneus is a functional core of the default-mode network. J Neurosci. 2014;34(3):932-940. doi: 10.1523/jneurosci.4227–13.2014