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Intrinsic Functional Hypoconnectivity in Core Neurocognitive Networks Suggests Central Nervous System Pathology in Patients with Myalgic Encephalomyelitis: A Pilot Study

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Abstract

Exact low resolution electromagnetic tomography (eLORETA) was recorded from nineteen EEG channels in nine patients with myalgic encephalomyelitis (ME) and 9 healthy controls to assess current source density and functional connectivity, a physiological measure of similarity between pairs of distributed regions of interest, between groups. Current source density and functional connectivity were measured using eLORETA software. We found significantly decreased eLORETA source analysis oscillations in the occipital, parietal, posterior cingulate, and posterior temporal lobes in Alpha and Alpha-2. For connectivity analysis, we assessed functional connectivity within Menon triple network model of neuropathology. We found support for all three networks of the triple network model, namely the central executive network (CEN), salience network (SN), and the default mode network (DMN) indicating hypo-connectivity in the Delta, Alpha, and Alpha-2 frequency bands in patients with ME compared to controls. In addition to the current source density resting state dysfunction in the occipital, parietal, posterior temporal and posterior cingulate, the disrupted connectivity of the CEN, SN, and DMN appears to be involved in cognitive impairment for patients with ME. This research suggests that disruptions in these regions and networks could be a neurobiological feature of the disorder, representing underlying neural dysfunction.

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Notes

  1. For the sake of clarity, throughout this article we will use ME even though a number of studies use Chronic Fatigue Syndrome (CFS) to describe their patient samples.

References

  • Afari, N., & Buchwald, D. (2003). Chronic fatigue syndrome: A review. The American Journal of Psychiatry, 160(2), 221–236.

    Article  PubMed  Google Scholar 

  • Anderer, P., Pascual-Marqui, R. D., Semlitsch, H. V., & Saletu, B. (1998). Electrical sources of P300 event-related brain potentials revealed by low resolution electromagnetic tomography. 1. Effects of normal aging. Neuropsychobiology, 37(1), 20–27.

    Article  PubMed  Google Scholar 

  • Angelakis, E., Lubar, J. F., & Stathopoulou, S. (2004). Electroencephalographic peak alpha frequency correlates of cognitive traits. Neuroscience Letters, 371(1), 60–63. doi:10.1016/j.neulet.2004.08.041.

    Article  PubMed  Google Scholar 

  • Babiloni, C., Del Percio, C., Lizio, R., Marzano, N., Infarinato, F., Soricelli, A., & Rossini, P. M. (2014). Cortical sources of resting state electroencephalographic alpha rhythms deteriorate across time in subjects with amnesic mild cognitive impairment. Neurobiology of Aging, 35(1), 130–142. doi:10.1016/j.neurobiolaging.2013.06.019.

    Article  PubMed  Google Scholar 

  • Billiot, K. M., Budzynski, T. H., & Andrasik, F. (1997). EEG patterns and CFS. Journal of Neurotherapy, 2–2(4), 20–30.

    Article  Google Scholar 

  • Bonnelle, V., Ham, T. E., Leech, R., Kinnunen, K. M., Mehta, M. A., Greenwood, R. J., & Sharp, D. J. (2012). Salience network integrity predicts default mode network function after traumatic brain injury. Proceedings of the National Academy of Sciences, 109(12), 4690–4695. doi:10.1073/pnas.1113455109.

    Article  Google Scholar 

  • Bonnelle, V., Leech, R., Kinnunen, K. M., Ham, T. E., Beckmann, C. F., De Boissezon, X., & Sharp, D. J. (2011). Default mode network connectivity predicts sustained attention deficits after traumatic brain injury. The Journal of Neuroscience, 31(38), 13442–13451. doi:10.1523/jneurosci.1163-11.2011.

    Article  PubMed  Google Scholar 

  • Bora, E., Fornito, A., Yucel, M., & Pantelis, C. (2010). Voxelwise meta-analysis of gray matter abnormalities in bipolar disorder. Biological Psychiatry, 67(11), 1097–1105. doi:10.1016/j.biopsych.2010.01.020.

    Article  PubMed  Google Scholar 

  • Bosma, I., Reijneveld, J. C., Klein, M., Douw, L., van Dijk, B. W., Heimans, J. J., & Stam, C. J. (2009). Disturbed functional brain networks and neurocognitive function in low-grade glioma patients: A graph theoretical analysis of resting-state MEG. Nonlinear Biomedical Physics, 3(1), 9. doi:10.1186/1753-4631-3-9.

    Article  PubMed  PubMed Central  Google Scholar 

  • Broderick, G., Fuite, J., Kreitz, A., Vernon, S. D., Klimas, N., & Fletcher, M. A. (2010). A formal analysis of cytokine networks in chronic fatigue syndrome. Brain, Behavior, and Immunity, 24(7), 1209–1217. doi:10.1016/j.bbi.2010.04.012.

    Article  PubMed  PubMed Central  Google Scholar 

  • Broderick, G., Klimas, N., Fletcher, M. A., & Efroni, S. (2011). From cytokines to cells to gene expression: An integrative approach to the study of Gulf War Illness systems biology. Washington, DC. Retrieved from http://www.va.gov/RAC-GWVI/Minutes_June_2011.asp.

  • Burroughs, S. A., Morse, R. P., Mott, S. H., & Holmes, G. L. (2014). Brain connectivity in West syndrome. Seizure, 23(7), 576–579. doi:10.1016/j.seizure.2014.03.016.

    Article  PubMed  PubMed Central  Google Scholar 

  • Buzsaki, G. (2006). Rhythms of the brain. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Canuet, L., Ishii, R., Pascual-Marqui, R. D., Iwase, M., Kurimoto, R., Aoki, Y., & Takeda, M. (2011). Resting-state EEG source localization and functional connectivity in schizophrenia-like psychosis of epilepsy. PLoS ONE, 6(11), e27863. doi:10.1371/journal.pone.0027863.

    Article  PubMed  PubMed Central  Google Scholar 

  • Capotosto, P., Perrucci, M. G., Brunetti, M., Del Gratta, C., Doppelmayr, M., Grabner, R. H., & Babiloni, C. (2009). Is there “neural efficiency” during the processing of visuo-spatial information in male humans? An EEG study. Behavioural Brain Research, 205(2), 468–474. doi:10.1016/j.bbr.2009.07.032.

    Article  PubMed  Google Scholar 

  • Carruthers, B. M., Jain, A. K., De Meirleir, K. L., Peterson, D. L., Klimas, N., Lerner, A. M., et al. (2003). Myalgic encephalomyelitis/chronic fatigue syndrome: Clinical working case definition, diagnostic and treatment protocols. Journal of Chronic Fatigue Syndrome, 11(1), 7–115.

  • Castellanos, N. P., & Makarov, V. A. (2006). Recovering EEG brain signals: Artifact suppression with wavelet enhanced independent component analysis. Journal of Neuroscience Methods, 158(2), 300–312. doi:10.1016/j.jneumeth.2006.05.033.

    Article  PubMed  Google Scholar 

  • Chand, G., & Dhamala, M. (2015). Interactions among the brain default-mode, salience and central-executive networks during perceptual decision-making of moving dots. Brain Connectivity. doi:10.1089/brain.2015.0379.

    Google Scholar 

  • Chiong, W., Wilson, S. M., Esposito, M., Kayser, A. S., Grossman, S. N., Poorzand, P., et al. (2013). The salience network causally influences default mode network activity during moral reasoning. Brain: A Journal of Neurology, 136(Pt 6), 1929–1941. doi:10.1093/brain/awt066.

  • Claypoole, K. H., Noonan, C., Mahurin, R. K., Goldberg, J., Erickson, T., & Buchwald, D. (2007). A twin study of cognitive function in chronic fatigue syndrome: The effects of sudden illness onset. Neuropsychology, 21(4), 507–513. doi:10.1037/0894-4105.21.4.507.

    Article  PubMed  Google Scholar 

  • Cockshell, S. J., & Mathias, J. L. (2014). Cognitive functioning in people with chronic fatigue syndrome: A comparison between subjective and objective measures. Neuropsychology, 28(3), 394–405. doi:10.1037/neu0000025.

    Article  PubMed  Google Scholar 

  • Constant, E. L., Adam, S., Gillain, B., Lambert, M., Masquelier, E., & Seron, X. (2011). Cognitive deficits in patients with chronic fatigue syndrome compared to those with major depressive disorder and healthy controls. Clinical Neurology and Neurosurgery, 113(4), 295–302. doi:10.1016/j.clineuro.2010.12.002.

    Article  PubMed  Google Scholar 

  • Cooray, G. K., Hyllienmark, L., & Brismar, T. (2011). Decreased cortical connectivity and information flow in type 1 diabetes. Clinical Neurophysiology, 122(10), 1943–1950. doi:10.1016/j.clinph.2011.03.007.

    Article  PubMed  Google Scholar 

  • Crone, J. S., Ladurner, G., Holler, Y., Golaszewski, S., Trinka, E., & Kronbichler, M. (2011). Deactivation of the default mode network as a marker of impaired consciousness: An fMRI study. PLoS ONE, 6(10), e26373. doi:10.1371/journal.pone.0026373.

    Article  PubMed  PubMed Central  Google Scholar 

  • Damoiseaux, J. S., Beckmann, C. F., Arigita, E. J., Barkhof, F., Scheltens, P., Stam, C. J., et al. (2008). Reduced resting-state brain activity in the “default network” in normal aging. Cerebral cortex (New York, NY: 1991), 18(8), 1856–1864. doi:10.1093/cercor/bhm207.

  • Damoiseaux, J. S., Rombouts, S. A., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., & Beckmann, C. F. (2006). Consistent resting-state networks across healthy subjects. Proceedings of the National Academy of Sciences of the United States of America, 103(37), 13848–13853. doi:10.1073/pnas.0601417103.

    Article  PubMed  PubMed Central  Google Scholar 

  • Daniels, J. K., McFarlane, A. C., Bluhm, R. L., Moores, K. A., Clark, C. R., Shaw, M. E., & Lanius, R. A. (2010). Switching between executive and default mode networks in posttraumatic stress disorder: Alterations in functional connectivity. Journal of Psychiatry & Neuroscience: JPN, 35(4), 258–266.

    Article  Google Scholar 

  • de Pasquale, F., Della Penna, S., Snyder, A. Z., Marzetti, L., Pizzella, V., Romani, G. L., & Corbetta, M. (2012). A cortical core for dynamic integration of functional networks in the resting human brain. Neuron, 74(4), 753–764. doi:10.1016/j.neuron.2012.03.031.

    Article  PubMed  PubMed Central  Google Scholar 

  • Decker, M. J., Tabassum, H., Lin, J. M., & Reeves, W. C. (2009). Electroencephalographic correlates of Chronic Fatigue Syndrome. Behavioral and Brain Functions, 5, 43. doi:10.1186/1744-9081-5-43.

    Article  PubMed  PubMed Central  Google Scholar 

  • DeLuca, J., Genova, H. M., Capili, E. J., & Wylie, G. R. (2009). Functional neuroimaging of fatigue. Physical Medicine and Rehabilitation Clinics of North America, 20(2), 325–337. doi:10.1016/j.pmr.2008.12.007.

    Article  PubMed  Google Scholar 

  • DeLuca, J., Johnson, S. K., Ellis, S. P., & Natelson, B. H. (1997). Cognitive functioning is impaired in patients with chronic fatigue syndrome devoid of psychiatric disease. Journal of Neurology, Neurosurgery and Psychiatry, 62(2), 151–155.

    Article  PubMed  PubMed Central  Google Scholar 

  • Dickinson, C. J. (1997). Chronic fatigue syndrome–aetiological aspects. European Journal of Clinical Investigation, 27(4), 257–267.

    Article  PubMed  Google Scholar 

  • Dinkel, K., Ogle, W. O., & Sapolsky, R. M. (2002). Glucocorticoids and central nervous system inflammation. Journal of Neurovirology, 8(6), 513–528. doi:10.1080/13550280290100914.

    Article  PubMed  Google Scholar 

  • Fisher, R. A. (1971). The design of experiments. New York: Hafner Publishing Company.

    Google Scholar 

  • Flor-Henry, P., Lind, J. C., & Koles, Z. J. (2004). A source-imaging (low-resolution electromagnetic tomography) study of the EEGs from unmedicated males with depression. Psychiatry Research, 130(2), 191–207. doi:10.1016/j.pscychresns.2003.08.006.

    Article  PubMed  Google Scholar 

  • Flor-Henry, P., Lind, J. C., & Koles, Z. J. (2010). EEG source analysis of chronic fatigue syndrome. Psychiatry Research, 181(2), 155–164. doi:10.1016/j.pscychresns.2009.10.007.

    Article  PubMed  Google Scholar 

  • Friston, K. (2002). Beyond phrenology: What can neuroimaging tell us about distributed circuitry? Annual Review of Neuroscience, 25, 221–250. doi:10.1146/annurev.neuro.25.112701.142846.

    Article  PubMed  Google Scholar 

  • Friston, K. (2012). Prediction, perception and agency. International Journal of Psychophysiology, 83(2), 248–252. doi:10.1016/j.ijpsycho.2011.11.014.

    Article  PubMed  PubMed Central  Google Scholar 

  • Fuster, J. M. (2009). The prefrontal cortex (4th ed.). New York: Elsevier.

    Google Scholar 

  • Fuster, J. M., & Bressler, S. L. (2012). Cognit activation: A mechanism enabling temporal integration in working memory. Trends in Cognitive Sciences, 16(4), 207–218. doi:10.1016/j.tics.2012.03.005.

    Article  PubMed  PubMed Central  Google Scholar 

  • Gaudino, E. A., Coyle, P. K., & Krupp, L. B. (1997). Post-Lyme syndrome and chronic fatigue syndrome. Neuropsychiatric similarities and differences. Archives of Neurology, 54(11), 1372–1376.

    Article  PubMed  Google Scholar 

  • George, D. N., & Pearce, J. M. (2012). A configural theory of attention and associative learning. Learning & Behavior, 40(3), 241–254. doi:10.3758/s13420-012-0078-2.

    Article  Google Scholar 

  • Grech, R., Cassar, T., Muscat, J., Camilleri, K. P., Fabri, S. G., Zervakis, M., & Vanrumste, B. (2008). Review on solving the inverse problem in EEG source analysis. Journal of Neuroengineering and Rehabilitation, 5, 25. doi:10.1186/1743-0003-5-25.

    Article  PubMed  PubMed Central  Google Scholar 

  • Greicius, M. D., Kiviniemi, V., Tervonen, O., Vainionpaa, V., Alahuhta, S., Reiss, A. L., & Menon, V. (2008). Persistent default-mode network connectivity during light sedation. Human Brain Mapping, 29(7), 839–847. doi:10.1002/hbm.20537.

    Article  PubMed  PubMed Central  Google Scholar 

  • Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003). Functional connectivity in the resting brain: A network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences, 100(1), 253–258. doi:10.1073/pnas.0135058100.

    Article  Google Scholar 

  • Greicius, M. D., & Menon, V. (2004). Default-mode activity during a passive sensory task: Uncoupled from deactivation but impacting activation. Journal of Cognitive Neuroscience, 16(9), 1484–1492. doi:10.1162/0898929042568532.

    Article  PubMed  Google Scholar 

  • Greicius, M. D., Srivastava, G., Reiss, A. L., & Menon, V. (2004). Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proceedings of the National Academy of Sciences, 101(13), 4637–4642. doi:10.1073/pnas.0308627101.

    Article  Google Scholar 

  • Greicius, M. D., Supekar, K., Menon, V., & Dougherty, R. F. (2009). Resting-state functional connectivity reflects structural connectivity in the default mode network. Cerebral Cortex (New York, NY: 1991), 19(1), 72–78. doi:10.1093/cercor/bhn059.

  • Haase, L., Thom, N. J., Shukla, A., Davenport, P. W., Simmons, A. N., Stanley, E. A., & Johnson, D. C. (2016). Mindfulness-based training attenuates insula response to an aversive interoceptive challenge. Social Cognitive and Affective Neuroscience, 11(1), 182–190. doi:10.1093/scan/nsu042.

    Article  PubMed  Google Scholar 

  • Hacker, C. D., Laumann, T. O., Szrama, N. P., Baldassarre, A., Snyder, A. Z., Leuthardt, E. C., & Corbetta, M. (2013). Resting state network estimation in individual subjects. NeuroImage. doi:10.1016/j.neuroimage.2013.05.108.

    PubMed  PubMed Central  Google Scholar 

  • Hammond, D. C. (2001). Treatment of chronic fatigue with neurofeedback and self-hypnosis. NeuroRehabilitation, 16(4), 295–300.

    PubMed  Google Scholar 

  • Hawk, C., Jason, L. A., & Torres-Harding, S. (2006). Differential diagnosis of chronic fatigue syndrome and major depressive disorder. International Journal of Behavioral Medicine, 13(3), 244–251. doi:10.1207/s15327558ijbm1303_8.

    Article  PubMed  Google Scholar 

  • Holmes, A. P., Blair, R. C., Watson, J. D., & Ford, I. (1996). Nonparametric analysis of statistic images from functional mapping experiments. Journal of Cerebral Blood Flow and Metabolism, 16(1), 7–22. doi:10.1097/00004647-199601000-00002.

    Article  PubMed  Google Scholar 

  • Holz, E. M., Doppelmayr, M., Klimesch, W., & Sauseng, P. (2008). EEG correlates of action observation in humans. Brain Topography, 21(2), 93–99. doi:10.1007/s10548-008-0066-1.

    Article  PubMed  Google Scholar 

  • Hu, L., Zhang, L., Chen, R., Yu, H., Li, H., & Mouraux, A. (2015). The primary somatosensory cortex and the insula contribute differently to the processing of transient and sustained nociceptive and non-nociceptive somatosensory inputs. Human Brain Mapping, 36(11), 4346–4360. doi:10.1002/hbm.22922.

    Article  PubMed  Google Scholar 

  • Hughes, J. R., & John, E. R. (1999). Conventional and quantitative electroencephalography in psychiatry. The Journal of Neuropsychiatry and Clinical Neurosciences, 11(2), 190–208.

    Article  PubMed  Google Scholar 

  • Ishii, R., Canuet, L., Kurimoto, R., Ikezawa, K., Aoki, Y., Azechi, M., & Takeda, M. (2010). Frontal shift of posterior alpha activity is correlated with cognitive impairment in early Alzheimer’s disease: A magnetoencephalography-beamformer study. Psychogeriatrics, 10(3), 138–143. doi:10.1111/j.1479-8301.2010.00326.x.

    Article  PubMed  Google Scholar 

  • James, L. C., & Folen, R. A. (1996). EEG biofeedback as a treatment for chronic fatigue syndrome: A controlled case report. Behavioral Medicine (Washington, DC), 22(2), 77–81. doi:10.1080/08964289.1996.9933767.

  • Jann, K., Federspiel, A., Giezendanner, S., Andreotti, J., Kottlow, M., Dierks, T., & Koenig, T. (2012). Linking brain connectivity across different time scales with electroencephalogram, functional magnetic resonance imaging, and diffusion tensor imaging. Brain Connectivity, 2(1), 11–20. doi:10.1089/brain.2011.0063.

    Article  PubMed  PubMed Central  Google Scholar 

  • Jason, L. A., Evans, M., Porter, N., Brown, A., Brown, M., Hunnell, J., & Friedberg, F. (2010a). The development of a revised Canadian myalgic encephalomyelitis-chronic fatigue syndrome case definition. American Journal of Biochemistry and Biotechnology, 6(2), 120–135.

    Article  Google Scholar 

  • Jason, L., Porter, N., Shelleby, E., Till, L., Bell, D. S., Lap, C. W., et al. (2010b). Examining criteria to diagnose ME/CFS in pediatric samples. Journal of Behavioral Health and Medicine, 3(3), 186–195.

    Article  Google Scholar 

  • Jason, L. A., Sorenson, M., Evans, M., Brown, A., Flores, S., Sunnquist, M., & Schafer, C. (2013). The implications of sensitization and kindling for chronic fatigue syndrome. In N. Gotsiridze-Columbus (Ed.), Encephalopathies: Symptoms, causes and potential complications (pp. 73–94). New York: Nova Science.

    Google Scholar 

  • Jason, L. A., Zinn, M. L., & Zinn, M. A. (2015). Myalgic Encephalomyelitis: Symptoms and Biomarkers. Current Neuropharmacology, 13(5), 701–734.

    Article  PubMed  Google Scholar 

  • Jurcak, V., Tsuzuki, D., & Dan, I. (2007). 10/20, 10/10, and 10/5 systems revisited: Their validity as relative head-surface-based positioning systems. NeuroImage, 34(4), 1600–1611. doi:10.1016/j.neuroimage.2006.09.024.

    Article  PubMed  Google Scholar 

  • Kierkels, J. J., van Boxtel, G. J., & Vogten, L. L. (2006). A model-based objective evaluation of eye movement correction in EEG recordings. IEEE Transactions on Bio-Medical Engineering, 53(2), 246–253. doi:10.1109/tbme.2005.862533.

    Article  PubMed  Google Scholar 

  • Kim, H. J., Cha, J., Lee, J. M., Shin, J. S., Jung, N. Y., Kim, Y. J., & Seo, S. W. (2016). Distinctive resting state network disruptions among alzheimer’s disease, subcortical vascular dementia, and mixed dementia patients. Journal of Alzheimer’s Disease,. doi:10.3233/jad-150637.

    Google Scholar 

  • Kishi, A., Natelson, B. H., Togo, F., Struzik, Z. R., Rapoport, D. M., & Yamamoto, Y. (2011). Sleep-stage dynamics in patients with chronic fatigue syndrome with or without fibromyalgia. Sleep, 34(11), 1551–1560. doi:10.5665/sleep.1396.

    PubMed  PubMed Central  Google Scholar 

  • Klimesch, W. (1996). Memory processes, brain oscillations and EEG synchronization. International Journal of Psychophysiology, 24(1–2), 61–100.

    Article  PubMed  Google Scholar 

  • Klimesch, W. (1997). EEG-alpha rhythms and memory processes. International Journal of Psychophysiology, 26(1–3), 319–340.

    Article  PubMed  Google Scholar 

  • Klimesch, W. (1999). EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Research Reviews, 29(2–3), 169–195.

    Article  PubMed  Google Scholar 

  • Klimesch, W. (2012). alpha-band oscillations, attention, and controlled access to stored information. Trends in Cognitive Sciences, 16(12), 606–617. doi:10.1016/j.tics.2012.10.007.

    Article  PubMed  PubMed Central  Google Scholar 

  • Klimesch, W., Doppelmayr, M., Pachinger, T., & Ripper, B. (1997). Brain oscillations and human memory: EEG correlates in the upper alpha and theta band. Neuroscience Letters, 238(1–2), 9–12.

    Article  PubMed  Google Scholar 

  • Klimesch, W., Fellinger, R., & Freunberger, R. (2011). Alpha oscillations and early stages of visual encoding. Frontiers in Psychology, 2, 118. doi:10.3389/fpsyg.2011.00118.

    Article  PubMed  PubMed Central  Google Scholar 

  • Klimesch, W., Freunberger, R., & Sauseng, P. (2010). Oscillatory mechanisms of process binding in memory. Neuroscience and Biobehavioral Reviews, 34(7), 1002–1014. doi:10.1016/j.neubiorev.2009.10.004.

    Article  PubMed  Google Scholar 

  • Klimesch, W., Schimke, H., & Pfurtscheller, G. (1993). Alpha frequency, cognitive load and memory performance. Brain Topography, 5(3), 241–251.

    Article  PubMed  Google Scholar 

  • Koziol, L. F., & Budding, D. E. (2009). Subcortical Structures and Cognition: Implications for Neuropsychological Assessment. New York: Springer.

    Book  Google Scholar 

  • Laird, A. R., Fox, P. M., Eickhoff, S. B., Turner, J. A., Ray, K. L., McKay, D. R., & Fox, P. T. (2011). Behavioral interpretations of intrinsic connectivity networks. Journal of Cognitive Neuroscience, 23(12), 4022–4037. doi:10.1162/jocn_a_00077.

    Article  PubMed  PubMed Central  Google Scholar 

  • Lancaster, J. L., Woldorff, M. G., Parsons, L. M., Liotti, M., Freitas, C. S., Rainey, L., & Fox, P. T. (2000). Automated Talairach atlas labels for functional brain mapping. Human Brain Mapping, 10(3), 120–131.

    Article  PubMed  Google Scholar 

  • Lange, G., Steffener, J., Cook, D. B., Bly, B. M., Christodoulou, C., Liu, W. C., & Natelson, B. H. (2005). Objective evidence of cognitive complaints in Chronic Fatigue Syndrome: A BOLD fMRI study of verbal working memory. NeuroImage, 26(2), 513–524. doi:10.1016/j.neuroimage.2005.02.011.

    Article  PubMed  Google Scholar 

  • Lange, G., Wang, S., DeLuca, J., & Natelson, B. H. (1998). Neuroimaging in chronic fatigue syndrome. The American Journal of Medicine, 105(3A), 50S–53S.

    Article  PubMed  Google Scholar 

  • Le Bon, O., Neu, D., Berquin, Y., Lanquart, J. P., Hoffmann, R., Mairesse, O., & Armitage, R. (2012). Ultra-slow delta power in chronic fatigue syndrome. Psychiatry Research, 200(2–3), 742–747. doi:10.1016/j.psychres.2012.06.027.

    Article  PubMed  Google Scholar 

  • Lehmann, D., Faber, P. L., Tei, S., Pascual-Marqui, R. D., Milz, P., & Kochi, K. (2012). Reduced functional connectivity between cortical sources in five meditation traditions detected with lagged coherence using EEG tomography. NeuroImage, 60(2), 1574–1586. doi:10.1016/j.neuroimage.2012.01.042.

    Article  PubMed  Google Scholar 

  • Light, A. R., Bateman, L., Jo, D., Hughen, R. W., Vanhaitsma, T. A., White, A. T., & Light, K. C. (2012). Gene expression alterations at baseline and following moderate exercise in patients with Chronic Fatigue Syndrome and Fibromyalgia Syndrome. Journal of Internal Medicine, 271(1), 64–81. doi:10.1111/j.1365-2796.2011.02405.x.

    Article  PubMed  Google Scholar 

  • Maes, M., Twisk, F. N., & Johnson, C. (2012). Myalgic Encephalomyelitis (ME), Chronic Fatigue Syndrome (CFS), and Chronic Fatigue (CF) are distinguished accurately: Results of supervised learning techniques applied on clinical and inflammatory data. Psychiatry Research, 200(2–3), 754–760. doi:10.1016/j.psychres.2012.03.031.

    Article  PubMed  Google Scholar 

  • Majer, M., Welberg, L. A., Capuron, L., Miller, A. H., Pagnoni, G., & Reeves, W. C. (2008). Neuropsychological performance in persons with chronic fatigue syndrome: Results from a population-based study. Psychosomatic Medicine, 70(7), 829–836. doi:10.1097/PSY.0b013e31817b9793.

    Article  PubMed  Google Scholar 

  • Makovac, E., Meeten, F., Watson, D. R., Garfinkel, S. N., Critchley, H. D., & Ottaviani, C. (2016). Neurostructural abnormalities associated with axes of emotion dysregulation in generalized anxiety. NeuroImage, Clinical, 10, 172–181. doi:10.1016/j.nicl.2015.11.022.

    Article  Google Scholar 

  • Mazziotta, J. C. (2001). Brain mapping: Its use in patients with neurological disorders. Revue Neurologique, 157(8–9 Pt 1), 863–871.

    PubMed  Google Scholar 

  • McCormick, C., Quraan, M., Cohn, M., Valiante, T. A., & McAndrews, M. P. (2013). Default mode network connectivity indicates episodic memory capacity in mesial temporal lobe epilepsy. Epilepsia, 54(5), 809–818. doi:10.1111/epi.12098.

    Article  PubMed  Google Scholar 

  • Menon, V. (2011). Large-scale brain networks and psychopathology: A unifying triple network model. Trends in cognitive sciences, 15(10), 483–506. doi:10.1016/j.tics.2011.08.003.

    Article  PubMed  Google Scholar 

  • Menon, V. (2012). Functional connectivity, neurocognitive networks, and brain dynamics. In M. I. Rabinovich, K. J. Friston, & P. Varona (Eds.), Principles of brain dynamics: Global state interactions (pp. 27–47). Cambridge, MA: MIT Press.

    Google Scholar 

  • Menon, V., & Uddin, L. Q. (2010). Saliency, switching, attention and control: A network model of insula function. Brain Structure & Function, 214(5–6), 655–667. doi:10.1007/s00429-010-0262-0.

    Article  Google Scholar 

  • Naglieri, J. A., & Das, J. P. (1997). Cognitive assessment system. Rolling Meadows, Illinois: Riverside Publlishing.

    Google Scholar 

  • Nguyen, V. T., Breakspear, M., Hu, X., & Guo, C. C. (2016). The integration of the internal and external milieu in the insula during dynamic emotional experiences. NeuroImage, 124(Pt A), 455–463. doi:10.1016/j.neuroimage.2015.08.078.

  • Nichols, T. E., & Holmes, A. P. (2002). Nonparametric permutation tests for functional neuroimaging: A primer with examples. Human Brain Mapping, 15(1), 1–25.

    Article  PubMed  Google Scholar 

  • Nickl-Jockschat, T., Kleiman, A., Schulz, J. B., Schneider, F., Laird, A. R., Fox, P. T., & Reetz, K. (2012). Neuroanatomic changes and their association with cognitive decline in mild cognitive impairment: A meta-analysis. Brain Structure & Function, 217(1), 115–125. doi:10.1007/s00429-011-0333-x.

    Article  Google Scholar 

  • Osaka, M. (1984). Peak alpha frequency of EEG during a mental task: Task difficulty and hemispheric differences. Psychophysiology, 21(1), 101–105.

    Article  PubMed  Google Scholar 

  • Osaka, M., Osaka, N., Koyama, S., Okusa, T., & Kakigi, R. (1999). Individual differences in working memory and the peak alpha frequency shift on magnetoencephalography. Brain Research. Cognitive Brain Research, 8(3), 365–368.

    Article  PubMed  Google Scholar 

  • Pascual-Marqui, R. D. (2002). Standardized low-resolution brain electromagnetic tomography (sLORETA): Technical details. Methods and Findings in Experimental and Clinical Pharmacology, 24(Suppl D), 5–12.

    PubMed  Google Scholar 

  • Pascual-Marqui, R. D. (2007). Discrete, 3D distributed linear imaging methods of electric neuronal activity. Part 1: exact, zero error localization. eprint arXiv, 16. Retrieved from http://arxiv.org/abs/0710.3341v2.

  • Pascual-Marqui, R. (2015). LORETA-KEY (Version 12/22/15). Institute for Brain-Mind Research: University Hospital of Psychiatry, Zurich. Retrieved from http://www.uzh.ch/keyinst/loreta.htm.

  • Pascual-Marqui, R. D., Kochi, K., Lehmann, D., Koukkou, M., & Kinoshita, T. (2011a). Functional independent components: Revealing cortico-cortical, cross-frequency interactions. Japanese Journal of Pharmaco-EEG, 12, 53–58.

    Google Scholar 

  • Pascual-Marqui, R. D., Lehmann, D., Koenig, T., Kochi, K., Merlo, M. C., Hell, D., & Koukkou, M. (1999). Low resolution brain electromagnetic tomography (LORETA) functional imaging in acute, neuroleptic-naive, first-episode, productive schizophrenia. Psychiatry Research, 90(3), 169–179.

    Article  PubMed  Google Scholar 

  • Pascual-Marqui, R. D., Lehmann, D., Koukkou, M., Kochi, K., Anderer, P., Saletu, B., & Kinoshita, T. (2011b). Assessing interactions in the brain with exact low-resolution electromagnetic tomography. Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences, 369(1952), 3768–3784. doi:10.1098/rsta.2011.0081.

    Article  PubMed  Google Scholar 

  • Pascual-Marqui, R. D., Michel, C. M., & Lehmann, D. (1994). Low resolution electromagnetic tomography: A new method for localizing electrical activity in the brain. International Journal of Psychophysiology, 18(1), 49–65.

    Article  PubMed  Google Scholar 

  • Perlovsky, L. (2012). Nonlinear dynamics and higher cognitive mental functions: Comment on “Information flow dynamics in the brain” by M. I. Rabinovich et al. Physics of Life Reviews, 9(1), 74–75; discussion 80–73. doi:10.1016/j.plrev.2011.12.004.

  • Poldrack, R. A., Mumford, J. A., & Nichols, T. E. (2012). Handbook of functional MRI data analysis. Cambridge: Cambridge University Press.

    Google Scholar 

  • Prasher, D., Smith, A., & Findley, L. (1990). Sensory and cognitive event-related potentials in myalgic encephalomyelitis. Journal of Neurology, Neurosurgery and Psychiatry, 53(3), 247–253.

    Article  PubMed  PubMed Central  Google Scholar 

  • Putcha, D., Ross, R. S., Cronin-Golomb, A., Janes, A. C., & Stern, C. E. (2015). Altered intrinsic functional coupling between core neurocognitive networks in Parkinson’s disease. NeuroImage Clinical, 7, 449–455. doi:10.1016/j.nicl.2015.01.012.

    Article  PubMed  PubMed Central  Google Scholar 

  • Qin, P., Wu, X., Huang, Z., Duncan, N. W., Tang, W., Wolff, A., & Northoff, G. (2015). How are different neural networks related to consciousness? Annals of Neurology, 78(4), 594–605. doi:10.1002/ana.24479.

    Article  PubMed  Google Scholar 

  • Rabinovich, M. I., Afraimovich, V. S., Bick, C., & Varona, P. (2012a). Information flow dynamics in the brain. Physics of Life Reviews, 9(1), 51–73. doi:10.1016/j.plrev.2011.11.002.

    Article  PubMed  Google Scholar 

  • Rabinovich, M. I., Friston, K., & Varona, P. (Eds.). (2012b). Principles of brain dynamics: Global state interactions. Cambridge, MA: The MIT Press.

    Google Scholar 

  • Raichle, M. E. (2010). Two views of brain function. Trends in Cognitive Sciences, 14(4), 180–190. doi:10.1016/j.tics.2010.01.008.

    Article  PubMed  Google Scholar 

  • Raichle, M. E. (2011). The restless brain. Brain Connectivity, 1(1), 3–12. doi:10.1089/brain.2011.0019.

    Article  PubMed  PubMed Central  Google Scholar 

  • Ramos Reis, P. M., Eckhardt, H., Denise, P., Bodem, F., & Lochmann, M. (2013). Localization of scopolamine induced electrocortical brain activity changes, in healthy humans at rest. Journal of Clinical Pharmacology, 53(6), 619–625. doi:10.1002/jcph.83.

    Article  PubMed  Google Scholar 

  • Romero-Grimaldi, C., Berrocoso, E., Alba-Delgado, C., Madrigal, J. L., Perez-Nievas, B. G., Leza, J. C., & Mico, J. A. (2015). Stress increases the negative effects of chronic pain on hippocampal neurogenesis. Anesthesia and Analgesia, 121(4), 1078–1088. doi:10.1213/ane.0000000000000838.

    Article  PubMed  Google Scholar 

  • Ropper, A. H., & Samiuels, M. A. (2014). In M. A. Samuels Allan, H. Ropper, J. P. Klein (Eds.) Principles of neurology (10 ed.). Chicago, Illinois: McGraw Hill.

  • Sandman, C. A., Barron, J. L., Nackoul, K., Goldstein, J., & Fidler, F. (1993). Memory deficits associated with chronic fatigue immune dysfunction syndrome. Biological Psychiatry, 33(8–9), 618–623.

    Article  PubMed  Google Scholar 

  • Sauseng, P., Klimesch, W., Gerloff, C., & Hummel, F. C. (2009). Spontaneous locally restricted EEG alpha activity determines cortical excitability in the motor cortex. Neuropsychologia, 47(1), 284–288. doi:10.1016/j.neuropsychologia.2008.07.021.

    Article  PubMed  Google Scholar 

  • Schabus, M., Pelikan, C., Chwala-Schlegel, N., Weilhart, K., Roehm, D., Donis, J., & Klimesch, W. (2011). Oscillatory brain activity in vegetative and minimally conscious state during a sentence comprehension task. Functional Neurology, 26(1), 31–36.

    PubMed  PubMed Central  Google Scholar 

  • Seo, E. H., & Choo, I. L. (2015). Amyloid-independent functional neural correlates of episodic memory in amnestic mild cognitive impairment. European Journal of Nuclear Medicine and Molecular Imaging. doi:10.1007/s00259-015-3261-9.

    PubMed  Google Scholar 

  • Sherlin, L., Budzynski, T., Kogan Budzynski, H., Congedo, M., Fischer, M. E., & Buchwald, D. (2007). Low-resolution electromagnetic brain tomography (LORETA) of monozygotic twins discordant for chronic fatigue syndrome. NeuroImage, 34(4), 1438–1442. doi:10.1016/j.neuroimage.2006.11.007.

    Article  PubMed  Google Scholar 

  • Siemionow, V., Fang, Y., Calabrese, L., Sahgal, V., & Yue, G. H. (2004). Altered central nervous system signal during motor performance in chronic fatigue syndrome. Clinical Neurophysiology, 115(10), 2372–2381. doi:10.1016/j.clinph.2004.05.012.

    Article  PubMed  Google Scholar 

  • Simkin, D. R., Thatcher, R. W., & Lubar, J. (2014). Quantitative EEG and neurofeedback in children and adolescents: Anxiety disorders, depressive disorders, comorbid addiction and attention-deficit/hyperactivity disorder, and brain injury. Child and Adolescent Psychiatric Clinics of North America, 23(3), 427–464. doi:10.1016/j.chc.2014.03.001.

    Article  PubMed  Google Scholar 

  • Sporns, O. (2013). Structure and function of complex brain networks. Dialogues in Clinical Neuroscience, 15(3), 247–262.

    PubMed  PubMed Central  Google Scholar 

  • Sridharan, D., Levitin, D. J., & Menon, V. (2008). A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proceedings of the National Academy of Sciences of the United States of America, 105(34), 12569–12574. doi:10.1073/pnas.0800005105.

    Article  PubMed  PubMed Central  Google Scholar 

  • Steriade, M. (2005). Cellular substrates of brain rhythms. In E. Niedermeyer & F. H. Lopes de Silva (Eds.), Electroencephalography: Basic principles, clinical applications and related fields. New York: Lippincott Williams & Wilkins.

    Google Scholar 

  • Sterman, M. B., & Kaiser, D. A. (2000). Automatic artifact detection, overlapping windows, and state transitions. Jounal of Neurotherapy, 4(3), 85–92.

    Article  Google Scholar 

  • Supekar, K., & Menon, V. (2012). Developmental maturation of dynamic causal control signals in higher-order cognition: A neurocognitive network model. PLoS Computational Biology, 8(2), e1002374. doi:10.1371/journal.pcbi.1002374.

    Article  PubMed  PubMed Central  Google Scholar 

  • Talairach, J., & Tournoux, P. (1988). Co-polanar stereotaxic atlas of the human brain. New York: Thieme.

    Google Scholar 

  • Thatcher, R. W. (2012). Handbook of quantitative electroencephalography and EEG biofeedback. St. Petersburg, FL: ANI Publishing.

    Google Scholar 

  • Thatcher, R. W., North, D. M., & Biver, C. J. (2008). Intelligence and EEG phase reset: A two compartmental model of phase shift and lock. NeuroImage, 42(4), 1639–1653. doi:10.1016/j.neuroimage.2008.06.009.

    Article  PubMed  Google Scholar 

  • Thomas, M., & Smith, A. (2009). An investigation into the cognitive deficits associated with chronic fatigue syndrome. The Open Neurology Journal, 3, 13–23. doi:10.2174/1874205x00903010013.

    Article  PubMed  PubMed Central  Google Scholar 

  • Tiersky, L. A., Johnson, S. K., Lange, G., Natelson, B. H., & DeLuca, J. (1997). Neuropsychology of chronic fatigue syndrome: A critical review. Journal of Clinical and Experimental Neuropsychology, 19(4), 560–586. doi:10.1080/01688639708403744.

    Article  PubMed  Google Scholar 

  • Towle, V. L., Bolanos, J., Suarez, D., Tan, K., Grzeszczuk, R., Levin, D. N., & Spire, J. P. (1993). The spatial location of EEG electrodes: Locating the best-fitting sphere relative to cortical anatomy. Electroencephalography and Clinical Neurophysiology, 86(1), 1–6.

    Article  PubMed  Google Scholar 

  • Twisk, F. N. (2014). The status of and future research into Myalgic Encephalomyelitis and Chronic Fatigue Syndrome: The need of accurate diagnosis, objective assessment, and acknowledging biological and clinical subgroups. Frontiers in Physiology, 5, 109. doi:10.3389/fphys.2014.00109.

    Article  PubMed  PubMed Central  Google Scholar 

  • Uddin, L. Q., Supekar, K., Lynch, C. J., Khouzam, A., Phillips, J., Feinstein, C., et al. (2013). Salience network-based classification and prediction of symptom severity in children with autism. JAMA Psychiatry (Chicago, Ill.), 1–11. doi:10.1001/jamapsychiatry.2013.104.

  • Uddin, L. Q., Supekar, K. S., Ryali, S., & Menon, V. (2011). Dynamic reconfiguration of structural and functional connectivity across core neurocognitive brain networks with development. The Journal of Neuroscience, 31(50), 18578–18589. doi:10.1523/jneurosci.4465-11.2011.

    Article  PubMed  PubMed Central  Google Scholar 

  • Van Den Eede, F., Moorkens, G., Hulstijn, W., Maas, Y., Schrijvers, D., Stevens, S. R., & Sabbe, B. G. (2011). Psychomotor function and response inhibition in chronic fatigue syndrome. Psychiatry Research, 186(2–3), 367–372. doi:10.1016/j.psychres.2010.07.022.

    Article  Google Scholar 

  • Van Hoof, E., De Becker, P., Lapp, C., Cluydts, R., & De Meirleir, K. (2007). Defining the occurrence and influence of alpha-delta sleep in chronic fatigue syndrome. The American Journal of the Medical Sciences, 333(2), 78–84.

    Article  PubMed  Google Scholar 

  • Varela, C. (2014). Thalamic neuromodulation and its implications for executive networks. Frontiers in Neural Circuits, 8, 69. doi:10.3389/fncir.2014.00069.

    Article  PubMed  PubMed Central  Google Scholar 

  • Vogt, F., Klimesch, W., & Doppelmayr, M. (1998). High-frequency components in the alpha band and memory performance. Journal of Clinical Neurophysiology, 15(2), 167–172.

    Article  PubMed  Google Scholar 

  • Wallstrom, G. L., Kass, R. E., Miller, A., Cohn, J. F., & Fox, N. A. (2004). Automatic correction of ocular artifacts in the EEG: A comparison of regression-based and component-based methods. International Journal of Psychophysiology, 53(2), 105–119. doi:10.1016/j.ijpsycho.2004.03.007.

    Article  PubMed  Google Scholar 

  • Westmoreland, B. (2005). The EEG in Cerebral Inflammatory Processes. In E. Niedermeyer & F. Lopez da Silva (Eds.), Electroencephalography: Basic principles, clinical applications and related fields (5th ed., pp. 323–337). Philadelphia: Lippincott Williams and Wilkins.

    Google Scholar 

  • Wilson, C. J., Finch, C. E., & Cohen, H. J. (2002). Cytokines and cognition: The case for a head-to-toe inflammatory paradigm. Journal of the American Geriatrics Society, 50(12), 2041–2056.

    Article  PubMed  Google Scholar 

  • Yang, X. F., Bossmann, J., Schiffhauer, B., Jordan, M., & Immordino-Yang, M. H. (2012). Intrinsic default mode network connectivity predicts spontaneous verbal descriptions of autobiographical memories during social processing. Frontiers in Psychology, 3, 592. doi:10.3389/fpsyg.2012.00592.

    PubMed  Google Scholar 

  • Zeng, K., Wang, Y., Ouyang, G., Bian, Z., Wang, L., & Li, X. (2015). Complex network analysis of resting state EEG in amnestic mild cognitive impairment patients with type 2 diabetes. Frontiers in Computational Neuroscience, 9, 133. doi:10.3389/fncom.2015.00133.

    Article  PubMed  PubMed Central  Google Scholar 

  • Zinn, M. L., Zinn, M. A., & Jason, L. A. (2016). qEEG/LORETA in assessment of neurocognitive impairment in a patient with chronic fatigue syndrome: A case report. Clinical Research, 2(1). doi:10.16966/2469-6714.110.

  • Zinn, M. A., Zinn, M. L., Norris, J. L., Valencia, I., Montoya, J. G., & Maldonado, J. R. (2014a). Cortical hypoactivation during resting EEG suggests central nervous system pathology in patients with Chronic Fatigue Syndrome. Paper presented at the symposium conducted at the meeting of IACFS, ME 2014. San Francisco, CA, USA: Biennial Conference.

    Google Scholar 

  • Zinn, M. L., Zinn, M. A., Norris, J. L., Valencia, I., Montoya, J. G., & Maldonado, J. R. (2014). EEG peak alpha frequency correlates in chronic fatigue syndrome: A case-control observational study. Paper presented at the IACFS/ME. Biennial Conference. San Francisco, CA, USA.

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Financial Disclosure: This study was supported by Linda Clark.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This study was approved by the Institutional Review Board at DePaul University in Chicago, Protocol # LJ052615 PSY.

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Zinn, M.L., Zinn, M.A. & Jason, L.A. Intrinsic Functional Hypoconnectivity in Core Neurocognitive Networks Suggests Central Nervous System Pathology in Patients with Myalgic Encephalomyelitis: A Pilot Study. Appl Psychophysiol Biofeedback 41, 283–300 (2016). https://doi.org/10.1007/s10484-016-9331-3

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