Functional EEG connectivity is a neuromarker for adult attention deficit hyperactivity disorder symptoms

Clinical Neurophysiology - Tập 131 - Trang 330-342 - 2020
Hanni Kiiski1, Laura M. Rueda-Delgado1, Marc Bennett1,2, Rachel Knight1, Laura Rai1, Darren Roddy3,4, Katie Grogan5, Jessica Bramham5, Clare Kelly1, Robert Whelan1,6
1Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
2Medical Research Council- Cognition and Brain Sciences Unit, University of Cambridge, United Kingdom
3Department of Psychiatry & trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
4Department of Physiology, School of Medicine, University College Dublin, Dublin 4, Ireland
5UCD School of Psychology, University College Dublin, Dublin, Ireland
6Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland

Tài liệu tham khảo

Ahmadlou, 2010, Wavelet-synchronization methodology: a new approach for EEG-based diagnosis of ADHD, Clin EEG Neurosci, 41, 1, 10.1177/155005941004100103 Ahmadlou, 2011, Functional community analysis of brain: a new approach for EEG-based investigation of the brain pathology, NeuroImage, 58, 401, 10.1016/j.neuroimage.2011.04.070 Alba, 2016, The variability of EEG functional connectivity of young ADHD subjects in different resting states, Clin Neurophysiol, 127, 1321, 10.1016/j.clinph.2015.09.134 American Psychiatric Association, 2000 American Psychiatric Association, 2013, Diagnostic and statistical manual of mental disorders (DSM-5®), American Psychiatric Pub Baez, 2013, Contextual social cognition impairments in schizophrenia and bipolar disorder, PLoS One, 8, e57664, 10.1371/journal.pone.0057664 Barry, 2011, EEG coherence and symptom profiles of children with Attention-Deficit/Hyperactivity Disorder, Clin Neurophysiol, 122, 1327, 10.1016/j.clinph.2011.01.007 Barry, 2003, A review of electrophysiology in attention-deficit/hyperactivity disorder: I. Qualitative and quantitative electroencephalography, Clin Neurophysiol, 114, 171, 10.1016/S1388-2457(02)00362-0 Barry, 2009, EEG differences in children between eyes-closed and eyes-open resting conditions, Clin Neurophysiol, 120, 1806, 10.1016/j.clinph.2009.08.006 Barry, 2007, EEG differences between eyes-closed and eyes-open resting conditions, Clin Neurophysiol, 118, 2765, 10.1016/j.clinph.2007.07.028 Barttfeld, 2014, Functional connectivity and temporal variability of brain connections in adults with attention deficit/hyperactivity disorder and bipolar disorder, Neuropsychobiology., 69, 65, 10.1159/000356964 Brikell, 2015, Heritability of attention-deficit hyperactivity disorder in adults, Am J Med Genet B Neuropsychiatr Genet, 168, 406, 10.1002/ajmg.b.32335 Bzdok, 2019, Exploration, inference, and prediction in neuroscience and biomedicine, Trends Neurosci, 2, 251, 10.1016/j.tins.2019.02.001 Callahan, 2010, Relations between parenting behavior and SES in a clinical sample: validity of SES measures, Child Fam Beh Ther, 32, 125, 10.1080/07317101003776456 Casey, 2013, DSM-5 and RDoC: progress in psychiatry research?, Nat Rev Neurosci, 14, 810, 10.1038/nrn3621 Casey, 2014, A neurodevelopmental perspective on the research domain criteria (RDoC) framework, Biol Psychiatry, 76, 350, 10.1016/j.biopsych.2014.01.006 Castellanos, 2002, Neuroscience of attention-deficit/hyperactivity disorder: the search for endophenotypes, Nat Rev Neurosci, 3, 617, 10.1038/nrn896 Clarke, 2013, Excess beta activity in the EEG of children with attention-deficit/hyperactivity disorder: a disorder of arousal?, Int J Psychophysiol, 89, 314, 10.1016/j.ijpsycho.2013.04.009 Conners, 1999 Crawford, 1992, Current and premorbid intelligence measures in neuropsychological assessment, 21 Cuthbert, 2013, Toward the future of psychiatric diagnosis: the seven pillars of RDoC, BMC Med, 11, 126, 10.1186/1741-7015-11-126 Delorme, 2004, EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis, J Neurosci Methods, 134, 9, 10.1016/j.jneumeth.2003.10.009 Doyle, 2015, The role of machine learning in neuroimaging for drug discovery and development, Psychopharmacology, 232, 4179, 10.1007/s00213-015-3968-0 Fair, 2010, Atypical default network connectivity in youth with attention-deficit/hyperactivity disorder, Biol Psychiatry, 68, 1084, 10.1016/j.biopsych.2010.07.003 Faraone, 2005, Molecular genetics of attention-deficit/hyperactivity disorder, Biol Psychiatry, 57, 1313, 10.1016/j.biopsych.2004.11.024 Fayyad, 2017, The descriptive epidemiology of DSM-IV Adult ADHD in the world health organization world mental health surveys, ADHD Atten Def Hyp Disord, 9, 47, 10.1007/s12402-016-0208-3 Gillan, 2017, What big data can do for treatment in psychiatry, Curr Opin Behav Sci, 18, 34, 10.1016/j.cobeha.2017.07.003 González JJ, Alba G, Mañas S, González A, Pereda E. Assessment of ADHD through electroencephalographic measures of functional connectivity. ADHD – new directions in diagnosis and treatment; 2015 [cited 2018 Aug 20]. Available from: https://www.intechopen.com/books/adhd-new-directions-in-diagnosis-and-treatment/assessment-of-adhd-through-electroencephalographic-measures-of-functional-connectivity. González, 2013, Performance analysis of univariate and multivariate EEG measurements in the diagnosis of ADHD, Clin Neurophysiol, 124, 1139, 10.1016/j.clinph.2012.12.006 Hobbs, 2007, EEG abnormalities in adolescent males with AD/HD, Clin Neurophysiol, 118, 363, 10.1016/j.clinph.2006.10.013 Hüfner, 2009, Differential effects of eyes open or closed in darkness on brain activation patterns in blind subjects, Neurosci Lett, 466, 30, 10.1016/j.neulet.2009.09.010 Hutchinson, 2013, The endophenotype and the phenotype: Temporal discrimination and adult-onset dystonia, Mov Disord, 28, 1766, 10.1002/mds.25676 Janssen, 2017, Neural network topology in ADHD; evidence for maturational delay and default-mode network alterations, Clin Neurophysiol, 128, 2258, 10.1016/j.clinph.2017.09.004 Jollans, 2016, The clinical added value of imaging: a perspective from outcome prediction, Biol Psychiatry Cogn Neurosci Neuroimaging, 1, 423 Jollans, 2019, Quantifying performance of machine learning methods for neuroimaging data, NeuroImage, 10.1016/j.neuroimage.2019.05.082 Kendler, 2010, Endophenotype: a conceptual analysis, Mol Psychiatry, 15, 789, 10.1038/mp.2010.8 Kessler, 2010, Structure and diagnosis of adult attention-deficit/hyperactivity disorder: analysis of expanded symptom criteria from the adult ADHD clinical diagnostic scale, Arch Gen Psychiatry, 67, 1168, 10.1001/archgenpsychiatry.2010.146 Kiiski, 2018, Machine learning EEG to predict cognitive functioning and processing speed over a 2-year period in multiple sclerosis patients and controls, Brain Topogr, 31, 346, 10.1007/s10548-018-0620-4 Kooij, 2010, European consensus statement on diagnosis and treatment of adult ADHD: the European network adult ADHD, BMC Psychiatry., 10, 67, 10.1186/1471-244X-10-67 Lansbergen, 2011, The increase in theta/beta ratio on resting-state EEG in boys with attention-deficit/hyperactivity disorder is mediated by slow alpha peak frequency, Prog Neuro-Psychopharmacol Biol Psychiatry, 35, 47, 10.1016/j.pnpbp.2010.08.004 Larsson, 2014, The heritability of clinically diagnosed attention deficit hyperactivity disorder across the lifespan, Psychol Med, 44, 2223, 10.1017/S0033291713002493 Liu, 2014, Electroencephalogram synchronization analysis for attention deficit hyperactivity disorder children, Bio-Med Mater Eng, 24, 1035, 10.3233/BME-130901 Loo, 2012, Clinical utility of EEG in attention-deficit/hyperactivity disorder: a research update, Neurotherapeutics, 9, 569, 10.1007/s13311-012-0131-z McLoughlin, 2014, Genetic overlap between evoked frontocentral theta-band phase variability, reaction time variability, and attention-deficit/hyperactivity disorder symptoms in a twin study, Biol Psychiatry, 75, 238, 10.1016/j.biopsych.2013.07.020 Nelson, 1991 Nolan, 2010, FASTER: fully automated statistical thresholding for EEG artifact rejection, J Neurosci Methods, 192, 152, 10.1016/j.jneumeth.2010.07.015 O'Halloran, 2019, Inhibitory-control event-related potentials correlate with individual differences in alcohol use, Addict Biol Oostenveld, 2001, The five percent electrode system for high-resolution EEG and ERP measurements, Clin Neurophysiol, 112, 713, 10.1016/S1388-2457(00)00527-7 Pereda, 2018, The blessing of Dimensionality: Feature Selection outperforms functional connectivity-based feature transformation to classify ADHD subjects from EEG patterns of phase synchronisation, PLoS One, 13, e0201660, 10.1371/journal.pone.0201660 Piantoni, 2013, Disrupted directed connectivity along the cingulate cortex determines vigilance after sleep deprivation, NeuroImage, 79, 213, 10.1016/j.neuroimage.2013.04.103 Pulini, 2018, Classification accuracy of neuroimaging biomarkers in attention-deficit/hyperactivity disorder: effects of sample size and circular analysis, Biol Psychiatry Cogn Neurosci Neuroimaging Rueda-Delgado, 2019, Brain event-related potentials predict individual differences in inhibitory control, Int J Psychophysiol Rutledge, 2019, Machine learning and big data in psychiatry: toward clinical applications, Curr Opin Neurobiol, 55, 152, 10.1016/j.conb.2019.02.006 Snyder, 2006, A meta-analysis of quantitative EEG power associated with attention-deficit hyperactivity disorder, J Clin Neurophysiol, 23, 441, 10.1097/01.wnp.0000221363.12503.78 Strauss, 1998 Tye, 2011, Electrophysiological markers of genetic risk for attention deficit hyperactivity disorder, Expert Rev Mol Med, 13, e9, 10.1017/S1462399411001797 Tye, 2014, Genetic overlap between ADHD symptoms and EEG theta power, Brain Cogn, 87, 168, 10.1016/j.bandc.2014.03.010 van Diessen, 2015, Opportunities and methodological challenges in EEG and MEG resting state functional brain network research, Clin Neurophysiol, 126, 1468, 10.1016/j.clinph.2014.11.018 Vinck, 2011, An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias, NeuroImage, 55, 1548, 10.1016/j.neuroimage.2011.01.055 Willshire, 1991, Estimating WAIS-R IQ from the national adult reading test: a cross-validation, J Clin Exp Neuropsychol, 13, 204, 10.1080/01688639108401038 Woltering, 2012, Resting state EEG oscillatory power differences in ADHD college students and their peers, Behav Brain Funct, 8, 60, 10.1186/1744-9081-8-60 Woo, 2017, Building better biomarkers: brain models in translational neuroimaging, Nature Neurosci, 20, 365, 10.1038/nn.4478 Yarkoni, 2017, Choosing prediction over explanation in psychology: lessons from machine learning, Perspect Psychol Sci, 12, 1100, 10.1177/1745691617693393 Zou, 2005, Regularization and variable selection via the elastic net, J R Stat Soc Ser B, 67, 301, 10.1111/j.1467-9868.2005.00503.x