Multimodal alterations of directed connectivity profiles in patients with attention-deficit/hyperactivity disorders

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55

  • 1.

    Tomasi, D. & Volkow, N. D. Abnormal functional connectivity in children with attention-deficit/hyperactivity disorder. Biol Psychiatry 71, 443–450, https://doi.org/10.1016/j.biopsych.2011.11.003 (2012).

  • 2.

    Friedman, L. A. & Rapoport, J. L. Brain development in ADHD. Current opinion in neurobiology 30, 106–111, https://doi.org/10.1016/j.conb.2014.11.007 (2015).

  • 3.

    Nunez, P. L. & Cutillo, B. A. Neocortical dynamics and human EEG rhythms. (Oxford University Press, USA, 1995).

  • 4.

    Konrad, K. & Eickhoff, S. B. Is the ADHD brain wired differently? A review on structural and functional connectivity in attention deficit hyperactivity disorder. Human brain mapping 31, 904–916, https://doi.org/10.1002/hbm.21058 (2010).

  • 5.

    Murias, M., Swanson, J. M. & Srinivasan, R. Functional connectivity of frontal cortex in healthy and ADHD children reflected in EEG coherence. Cerebral cortex (New York, N.Y.: 1991) 17, 1788–1799, https://doi.org/10.1093/cercor/bhl089 (2007).

  • 6.

    Winter, W. R., Nunez, P. L., Ding, J. & Srinivasan, R. Comparison of the effect of volume conduction on EEG coherence with the effect of field spread on MEG coherence. Statistics in medicine 26, 3946–3957, https://doi.org/10.1002/sim.2978 (2007).

  • 7.

    Muthuraman, M. et al. EEG-MEG Integration Enhances the Characterization of Functional and Effective Connectivity in the Resting State Network. PloS one 10, e0140832, https://doi.org/10.1371/journal.pone.0140832 (2015).

  • 8.

    Muthuraman, M. et al. Beamformer source analysis and connectivity on concurrent EEG and MEG data during voluntary movements. PloS one 9, e91441, https://doi.org/10.1371/journal.pone.0091441 (2014).

  • 9.

    Michels, L. et al. Developmental changes of functional and directed resting-state connectivities associated with neuronal oscillations in EEG. NeuroImage 81, 231–242, https://doi.org/10.1016/j.neuroimage.2013.04.030 (2013).

  • 10.

    Anwar, A. R. et al. Effective Connectivity of Cortical Sensorimotor Networks During Finger Movement Tasks: A Simultaneous fNIRS, fMRI, EEG Study. Brain Topogr 29, 645–660, https://doi.org/10.1007/s10548-016-0507-1 (2016).

  • 11.

    Gross, J. et al. Dynamic imaging of coherent sources: Studying neural interactions in the human brain. Proceedings of the National Academy of Sciences of the United States of America 98, 694–699, https://doi.org/10.1073/pnas.98.2.694 (2001).

  • 12.

    Kujala, J., Gross, J. & Salmelin, R. Localization of correlated network activity at the cortical level with MEG. NeuroImage 39, 1706–1720, https://doi.org/10.1016/j.neuroimage.2007.10.042 (2008).

  • 13.

    Liljestrom, M., Kujala, J., Jensen, O. & Salmelin, R. Neuromagnetic localization of rhythmic activity in the human brain: a comparison of three methods. NeuroImage 25, 734–745, https://doi.org/10.1016/j.neuroimage.2004.11.034 (2005).

  • 14.

    Moeller, F. et al. Representation and propagation of epileptic activity in absences and generalized photoparoxysmal responses. Human brain mapping 34, 1896–1909, https://doi.org/10.1002/hbm.22026 (2013).

  • 15.

    Muthuraman, M. et al. Cortical representation of different motor rhythms during bimanual movements. Exp Brain Res 223, 489–504, https://doi.org/10.1007/s00221-012-3276-4 (2012).

  • 16.

    Schelter, B., Timmer, J. & Eichler, M. Assessing the strength of directed influences among neural signals using renormalized partial directed coherence. J Neurosci Methods 179, 121–130, https://doi.org/10.1016/j.jneumeth.2009.01.006 (2009).

  • 17.

    Anwar, A. R. et al. Comparison of causality analysis on simultaneously measured fMRI and NIRS signals during motor tasks. Conference proceedings:… Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference 2013, 2628–2631, https://doi.org/10.1109/embc.2013.6610079 (2013).

  • 18.

    Chiosa, V. et al. Breakdown of Thalamo-Cortical Connectivity Precedes Spike Generation in Focal Epilepsies. Brain connectivity 7, 309–320, https://doi.org/10.1089/brain.2017.0487 (2017).

  • 19.

    American Psychiatric Association. Anxiety Disorders. In Diagnostic and statistical manual of mental disorders (5th ed.) (2013).

  • 20.

    Cortese, S. et al. White matter alterations at 33-year follow-up in adults with childhood attention-deficit/hyperactivity disorder. Biol Psychiatry 74, 591–598, https://doi.org/10.1016/j.biopsych.2013.02.025 (2013).

  • 21.

    van Ewijk, H. et al. Different mechanisms of white matter abnormalities in attention-deficit/hyperactivity disorder: a diffusion tensor imaging study. Journal of the American Academy of Child and Adolescent Psychiatry 53, 790–799.e793, https://doi.org/10.1016/j.jaac.2014.05.001 (2014).

  • 22.

    Bush, G. Attention-deficit/hyperactivity disorder and attention networks. Neuropsychopharmacology: official publication of the American College of Neuropsychopharmacology 35, 278–300, https://doi.org/10.1038/npp.2009.120 (2010).

  • 23.

    Castellanos, F. X. & Proal, E. Large-scale brain systems in ADHD: beyond the prefrontal-striatal model. Trends in cognitive sciences 16, 17–26, https://doi.org/10.1016/j.tics.2011.11.007 (2012).

  • 24.

    Sripada, C. et al. Disrupted network architecture of the resting brain in attention-deficit/hyperactivity disorder. Human brain mapping 35, 4693–4705, https://doi.org/10.1002/hbm.22504 (2014).

  • 25.

    Gonzalez-Escamilla, G., Muthuraman, M., Chirumamilla, V. C., Vogt, J. & Groppa, S. Brain Networks Reorganization During Maturation and Healthy Aging-Emphases for Resilience. Frontiers in psychiatry 9, 601, https://doi.org/10.3389/fpsyt.2018.00601 (2018).

  • 26.

    Baroni, A. & Castellanos, F. X. Neuroanatomic and cognitive abnormalities in attention-deficit/hyperactivity disorder in the era of ‘high definition’ neuroimaging. Current opinion in neurobiology 30, 1–8, https://doi.org/10.1016/j.conb.2014.08.005 (2015).

  • 27.

    Geurts, H. M. et al. Intra-individual variability in ADHD, autism spectrum disorders and Tourette’s syndrome. Neuropsychologia 46, 3030–3041 (2008).

  • 28.

    Sergeant, J. A., Geurts, H. & Oosterlaan, J. How specific is a deficit of executive functioning for attention-deficit/hyperactivity disorder? Behav Brain Res 130, 3–28, https://doi.org/10.1016/s0166-4328(01)00430-2 (2002).

  • 29.

    Schneider, S. Kinder-DIPS: diagnostisches Interview bei psychischen Störungen im Kindes-und Jugendalter. (Springer, 2009).

  • 30.

    Weiß, R. H. Grundintelligenztest CFT 20-R. Göttingen: Hogrefe (2006).

  • 31.

    Dopfner, M. et al. Effectiveness of an adaptive multimodal treatment in children with Attention-Deficit Hyperactivity Disorder–global outcome. Eur Child Adolesc Psychiatry 13(Suppl 1), I117–129, https://doi.org/10.1007/s00787-004-1011-9 (2004).

  • 32.

    Döpfner, M. et al. How often do children meet ICD-10/DSM-IV criteria of attention deficit-/hyperactivity disorder and hyperkinetic disorder? Parent-based prevalence rates in a national sample–results of the BELLA study. European child & adolescent psychiatry 17, 59–70 (2008).

  • 33.

    Delorme, A. & Makeig, S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 134, 9–21, https://doi.org/10.1016/j.jneumeth.2003.10.009 (2004).

  • 34.

    Lehmann, D. & Skrandies, W. Reference-free identification of components of checkerboard-evoked multichannel potential fields. Electroencephalogr Clin Neurophysiol 48, 609–621, https://doi.org/10.1016/0013-4694(80)90419-8 (1980).

  • 35.

    Muthuraman, M. et al. Cerebello-cortical network fingerprints differ between essential, Parkinson’s and mimicked tremors. Brain: a journal of neurology 141, 1770–1781, https://doi.org/10.1093/brain/awy098 (2018).

  • 36.

    Van Veen, B. D., van Drongelen, W., Yuchtman, M. & Suzuki, A. Localization of brain electrical activity via linearly constrained minimum variance spatial filtering. IEEE transactions on bio-medical engineering 44, 867–880, https://doi.org/10.1109/10.623056 (1997).

  • 37.

    Fuchs, M., Kastner, J., Wagner, M., Hawes, S. & Ebersole, J. S. A standardized boundary element method volume conductor model. Clin Neurophysiol 113, 702–712, https://doi.org/10.1016/s1388-2457(02)00030-5 (2002).

  • 38.

    Richards, J. E., Sanchez, C., Phillips-Meek, M. & Xie, W. A database of age-appropriate average MRI templates. NeuroImage 124, 1254–1259, https://doi.org/10.1016/j.neuroimage.2015.04.055 (2016).

  • 39.

    Oostenveld, R., Fries, P., Maris, E. & Schoffelen, J. M. FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput Intell Neurosci 2011, 156869, https://doi.org/10.1155/2011/156869 (2011).

  • 40.

    Muthuraman, M., Heute, U., Deuschl, G. & Raethjen, J. The central oscillatory network of essential tremor. Conference proceedings:… Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference 2010, 154–157, https://doi.org/10.1109/iembs.2010.5627211 (2010).

  • 41.

    Amjad, A., Halliday, D., Rosenberg, J. & Conway, B. An extended difference of coherence test for comparing and combining several independent coherence estimates: theory and application to the study of motor units and physiological tremor. Journal of neuroscience methods 73, 69–79 (1997).

  • 42.

    Rosenberg, J. R., Amjad, A. M., Breeze, P., Brillinger, D. R. & Halliday, D. M. The Fourier approach to the identification of functional coupling between neuronal spike trains. Progress in biophysics and molecular biology 53, 1–31 (1989).

  • 43.

    Neumaier, A. & Schneider, T. Estimation of parameters and eigenmodes of multivariate autoregressive models. ACM Transactions on Mathematical Software (TOMS) 27, 27–57 (2001).

  • 44.

    Akaike, H. In Selected Papers of Hirotugu Akaike 215–222 (Springer, 1974).

  • 45.

    Ding, M., Bressler, S. L., Yang, W. & Liang, H. Short-window spectral analysis of cortical event-related potentials by adaptive multivariate autoregressive modeling: data preprocessing, model validation, and variability assessment. Biol Cybern 83, 35–45, https://doi.org/10.1007/s004229900137 (2000).

  • 46.

    Haykin, S. Kalman filtering and neural networks. Vol. 47 (John Wiley & Sons, 2004).

  • 47.

    Wan, E. A. & Nelson, A. T. Dual extended Kalman filter methods. Kalman filtering and neural networks 123 (2001).

  • 48.

    Kaminski, M., Ding, M., Truccolo, W. A. & Bressler, S. L. Evaluating causal relations in neural systems: granger causality, directed transfer function and statistical assessment of significance. Biol Cybern 85, 145–157, https://doi.org/10.1007/s004220000235 (2001).

  • 49.

    Nolte, G. et al. Identifying true brain interaction from EEG data using the imaginary part of coherency. Clin Neurophysiol 115, 2292–2307, https://doi.org/10.1016/j.clinph.2004.04.029 (2004).

  • 50.

    Haufe, S., Nikulin, V. V., Muller, K. R. & Nolte, G. A critical assessment of connectivity measures for EEG data: a simulation study. NeuroImage 64, 120–133, https://doi.org/10.1016/j.neuroimage.2012.09.036 (2013).

  • 51.

    Dubovik, S. et al. The behavioral significance of coherent resting-state oscillations after stroke. NeuroImage 61, 249–257, https://doi.org/10.1016/j.neuroimage.2012.03.024 (2012).

  • 52.

    Cortes, C. & Vapnik, V. Support-vector networks. Machine learning 20, 273–297 (1995).

  • 53.

    Muthuraman, M. et al. Structural brain network characteristics can differentiate CIS from early RRMS. Frontiers in neuroscience 10, 14 (2016).

  • 54.

    Doppelmayr, M., Klimesch, W., Pachinger, T. & Ripper, B. Individual differences in brain dynamics: important implications for the calculation of event-related band power. Biol Cybern 79, 49–57, https://doi.org/10.1007/s004220050457 (1998).

  • 55.

    Michels, L. et al. Changes of Functional and Directed Resting-State Connectivity Are Associated with Neuronal Oscillations, ApoE Genotype and Amyloid Deposition in Mild Cognitive Impairment. Frontiers in aging neuroscience 9, 304, https://doi.org/10.3389/fnagi.2017.00304 (2017).

  • 56.

    Srinivasan, R., Winter, W. R., Ding, J. & Nunez, P. L. EEG and MEG coherence: measures of functional connectivity at distinct spatial scales of neocortical dynamics. J Neurosci Methods 166, 41–52, https://doi.org/10.1016/j.jneumeth.2007.06.026 (2007).

  • 57.

    Huang, Y. et al. Sensor Level Functional Connectivity Topography Comparison Between Different References Based EEG and MEG. Frontiers in behavioral neuroscience 12, 96, https://doi.org/10.3389/fnbeh.2018.00096 (2018).

  • 58.

    Gomez, C., Poza, J., Garcia, M., Fernandez, A. & Hornero, R. Regularity analysis of spontaneous MEG activity in Attention-Deficit/Hyperactivity Disorder. Conference proceedings:… Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference 2011, 1765–1768, https://doi.org/10.1109/iembs.2011.6090504 (2011).

  • 59.

    Khadmaoui, A. et al. MEG Analysis of Neural Interactions in Attention-Deficit/Hyperactivity Disorder. Comput Intell Neurosci 2016, 8450241, https://doi.org/10.1155/2016/8450241 (2016).

  • 60.

    Monge, J. et al. MEG analysis of neural dynamics in attention-deficit/hyperactivity disorder with fuzzy entropy. Medical engineering & physics 37, 416–423, https://doi.org/10.1016/j.medengphy.2015.02.006 (2015).

  • 61.

    Mulas, F. et al. Shifting-related brain magnetic activity in attention-deficit/hyperactivity disorder. Biol Psychiatry 59, 373–379, https://doi.org/10.1016/j.biopsych.2005.06.031 (2006).

  • 62.

    Fair, D. et al. Maturing thalamocortical functional connectivity across development. Frontiers in systems neuroscience 4, 10 (2010).

  • 63.

    Mills, K. L. et al. Altered cortico-striatal–thalamic connectivity in relation to spatial working memory capacity in children with ADHD. Frontiers in psychiatry 3, 2 (2012).

  • 64.

    Jensen, O. & Mazaheri, A. Shaping functional architecture by oscillatory alpha activity: gating by inhibition. Frontiers in human neuroscience 4, 186, https://doi.org/10.3389/fnhum.2010.00186 (2010).

  • 65.

    Lorincz, M. L., Crunelli, V. & Hughes, S. W. Cellular dynamics of cholinergically induced alpha (8–13 Hz) rhythms in sensory thalamic nuclei in vitro. J Neurosci 28, 660–671, https://doi.org/10.1523/jneurosci.4468-07.2008 (2008).

  • 66.

    Mazaheri, A., Nieuwenhuis, I. L., van Dijk, H. & Jensen, O. Prestimulus alpha and mu activity predicts failure to inhibit motor responses. Human brain mapping 30, 1791–1800 (2009).

  • 67.

    Woltering, S., Jung, J., Liu, Z. & Tannock, R. Resting state EEG oscillatory power differences in ADHD college students and their peers. Behav Brain Funct 8, 60, https://doi.org/10.1186/1744-9081-8-60 (2012).

  • 68.

    Barry, R. J., Clarke, A. R. & Johnstone, S. J. A review of electrophysiology in attention-deficit/hyperactivity disorder: I. Qualitative and quantitative electroencephalography. Clin Neurophysiol 114, 171–183, https://doi.org/10.1016/s1388-2457(02)00362-0 (2003).

  • 69.

    Klimesch, W. EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain research. Brain research reviews 29, 169–195 (1999).

  • 70.

    Clarke, A. R., Barry, R. J., McCarthy, R. & Selikowitz, M. EEG-defined subtypes of children with attention-deficit/hyperactivity disorder. Clin Neurophysiol 112, 2098–2105, https://doi.org/10.1016/s1388-2457(01)00668-x (2001).

  • 71.

    Sonuga-Barke, E. J. & Castellanos, F. X. Spontaneous attentional fluctuations in impaired states and pathological conditions: a neurobiological hypothesis. Neuroscience and biobehavioral reviews 31, 977–986, https://doi.org/10.1016/j.neubiorev.2007.02.005 (2007).

  • 72.

    El-Sayed, E., Larsson, J. O., Persson, H. E., Santosh, P. J. & Rydelius, P. A. “Maturational lag” hypothesis of attention deficit hyperactivity disorder: an update. Acta paediatrica (Oslo, Norway: 1992) 92, 776–784 (2003).

  • 73.

    Baijot, S. et al. EEG Dynamics of a Go/Nogo Task in Children with ADHD. Brain Sci 7, https://doi.org/10.3390/brainsci7120167 (2017).

  • 74.

    Satterfield, J. H., Cantwell, D. P. & Satterfield, B. T. Pathophysiology of the hyperactive child syndrome. Arch Gen Psychiatry 31, 839–844, https://doi.org/10.1001/archpsyc.1974.01760180079010 (1974).

  • 75.

    Mazaheri, A. et al. Differential oscillatory electroencephalogram between attention-deficit/hyperactivity disorder subtypes and typically developing adolescents. Biol Psychiatry 76, 422–429, https://doi.org/10.1016/j.biopsych.2013.08.023 (2014).

  • 76.

    Heinrich, H. et al. EEG spectral analysis of attention in ADHD: implications for neurofeedback training? Frontiers in human neuroscience 8, 611, https://doi.org/10.3389/fnhum.2014.00611 (2014).

  • 77.

    Vorwerk, J., Engwer, C., Pursiainen, S. & Wolters, C. H. A Mixed Finite Element Method to Solve the EEG Forward Problem. IEEE transactions on medical imaging 36, 930–941, https://doi.org/10.1109/tmi.2016.2624634 (2017).

  • 78.

    Aydin, U. et al. Combined EEG/MEG can outperform single modality EEG or MEG source reconstruction in presurgical epilepsy diagnosis. PloS one 10, e0118753, https://doi.org/10.1371/journal.pone.0118753 (2015).

  • 79.

    Schnitzler, A., Munks, C., Butz, M., Timmermann, L. & Gross, J. Synchronized brain network associated with essential tremor as revealed by magnetoencephalography. Movement disorders: official journal of the Movement Disorder Society 24, 1629–1635, https://doi.org/10.1002/mds.22633 (2009).

  • 80.

    Pedrosa, D. J. et al. The differentiated networks related to essential tremor onset and its amplitude modulation after alcohol intake. Exp Neurol 297, 50–61, https://doi.org/10.1016/j.expneurol.2017.07.013 (2017).

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