Small-World Network Analysis of Cortical Connectivity in Chronic Fatigue Syndrome Using Quantitative EEG




chronic fatigue syndrome, myalgic encephalomyelitis, qEEG, eLORETA, graph theory


The aim of this study was to explore the relationship between complex brain networks in people with Chronic Fatigue Syndrome (CFS) and neurocognitive impairment. Quantitative EEG (qEEG) recordings were taken from 14 people with CFS and 15 healthy controls (HCs) during an eye-closed resting condition. Exact low resolution electromagnetic tomography (eLORETA) was used to estimate cortical sources and perform a functional connectivity analysis. The graph theory approach was used to characterize network representations for each participant and derive the “small-worldness” index, a measure of the overall homeostatic balance between local and long-distance connectedness. Results showed that small-worldness for the delta band was significantly lower for patients with CFS compared to HCs. In addition, delta small-worldness was negatively associated with neurocognitive impairment scores on the DePaul Symptom Questionnaire (DSQ). Finally, delta small-worldness indicated a greater risk of complex brain network inefficiency for the CFS group. These results suggest that CFS pathology may be functionally disruptive to small-world networks. In turn, small-world characteristics might serve as a neurophysiological indicator for confirming a biological basis of cognitive symptoms, treatment outcome, and neurophysiological status of people with CFS.

Author Biographies

Mark Alan Zinn, DePaul University

Mark Zinn is pursuing a Ph.D. in Community Psychology at DePaul University.  He specializes in using novel research methods to explore the neural substrates underlying cognition which include quantitative electroencephalography (qEEG) and tomographic EEG inverse solutions. He is currently using qEEG to analyze to characterize neuronal dysregulation in people with chronic fatigue syndrome and myalgic encephalomyelitis. In this manner, exploring the linkage between subtle changes in brain state and specific neuroanatomical regions involved may help elucidate facets within cognitive impairment domains (memory, information processing speed, attention, etc.). Findings may also be implicated in neurocognitive impairments commonly seen in patients diagnosed with neurological disorders such as Parkinson's Disease and Multiple Sclerosis. Mark is currently working to trace those neurological underpinnings with overarching goal of finding new objective, reliable methods which can practically be used to evaluate disease prognosis and treatment outcomes in clinical settings.

Marcie L Zinn, DePaul University

Dr. Marcie Zinn directs the Cognitive Systems Neuroscience lab in the Center for Community Research at DePaul University, Chicago. Dr. Zinn’s multidisciplinary expertise spans data science, psychology, neuroscience, psychiatry and neurology, allowing her to integrate seemingly disparate ideas into novel models. She is especially interested in infectious and rare diseases. She studies both neurologically healthy and diseased individuals using functional and structural network science to characterize disturbances which impact cognition, movement, learning, sensory systems, speech and sleep in neurological disorders. The methods used allow matching the magnitude and nature of patient complaints to functional systems in the brain to understand how these functions appear in real life. The Zinn’s body of research will continue to lead to novel discoveries in brain science of infectious diseases, thereby vastly improving the quality of life of the affected individuals and their families.

Leonard A. Jason, DePaul University

Dr. Leonard Jason is a professor of psychology at DePaul University in Chicago, Illinois, where he also directs the Center for Community Research. He is a former president of the Division of Community Psychology of the American Psychological Association (APA). He has edited or written 27 books, 90 book chapters, and over 700 journal articles. He also published over 150 articles on CFS (Chronic Fatigue Syndrome) and ME (Myalgic Encephalomyelitis), including epidemiology, prospective longitudinal risk factors, scale development, and clinically oriented professional volumes. He has served on review committees of the National Institutes of Health, and was a member of the Chronic Fatigue Syndrome Advisory Committee. He was also a board member and vice-president for the International Association of CFS/ME. He is currently doing NIH supported work on pediatric epidemiology and longitudinal analysis of ME and CFS risk factors.


Adebimpe, A., Aarabi, A., Bourel-Ponchel, E., Mahmoudzadeh, M., & Wallois, F. (2016). EEG Resting State Functional Connectivity Analysis in Children with Benign Epilepsy with Centrotemporal Spikes. Frontiers in Neuroscience, 10, 143. doi:10.3389/fnins.2016.00143

Babiloni, C., Carducci, F., Lizio, R., Vecchio, F., Baglieri, A., Bernardini, S., . . . Frisoni, G. B. (2013). Resting state cortical electroencephalographic rhythms are related to gray matter volume in subjects with mild cognitive impairment and Alzheimer's disease. Human Brain Mapping, 34(6), 1427-1446. doi:10.1002/hbm.22005

Babiloni, C., De Pandis, M. F., Vecchio, F., Buffo, P., Sorpresi, F., Frisoni, G. B., & Rossini, P. M. (2011). Cortical sources of resting state electroencephalographic rhythms in Parkinson's disease related dementia and Alzheimer's disease. Clinical Neurophysiology, 122(12), 2355-2364. doi:10.1016/j.clinph.2011.03.029

Babiloni, C., Del Percio, C., Capotosto, P., Noce, G., Infarinato, F., Muratori, C., . . . Lupattelli, T. (2016). Cortical sources of resting state electroencephalographic rhythms differ in relapsing-remitting and secondary progressive multiple sclerosis. Clinical Neurophysiology, 127(1), 581-590. doi:10.1016/j.clinph.2015.05.029

Barnden, L. R., Crouch, B., Kwiatek, R., Burnet, R., & Del Fante, P. (2015). Evidence in chronic fatigue syndrome for severity-dependent upregulation of prefrontal myelination that is independent of anxiety and depression. NMR in Biomedicine, 28(3), 404-413. doi:10.1002/nbm.3261

Barnden, L. R., Crouch, B., Kwiatek, R., Burnet, R., Mernone, A., Chryssidis, S., . . . Del Fante, P. (2011). A brain MRI study of chronic fatigue syndrome: Evidence of brainstem dysfunction and altered homeostasis. NMR in Biomedicine, 24(10), 1302-1312. doi:10.1002/nbm.1692

Bassett, D. S., & Bullmore, E. (2006). Small-world brain networks. Neuroscientist, 12(6), 512-523. doi:10.1177/1073858406293182

Boissoneault, Jeff, Letzen, Janelle, Lai, Song, O'Shea, Andrew, Craggs, Jason, Robinson, Michael E., & Staud, Roland. (2016). Abnormal resting state functional connectivity in patients with chronic fatigue syndrome: An arterial spin-labeling fMRI study. Magnetic Resonance Imaging, 34(4), 603-608. doi:10.1016/j.mri.2015.12.008

Bullmore, E., & Sporns, O. (2012). The economy of brain network organization. Nature Reviews. Neuroscience, 13(5), 336-349. doi:10.1038/nrn3214

Busichio, K., Tiersky, L. A., Deluca, J., & Natelson, B. H. (2004). Neuropsychological deficits in patients with chronic fatigue syndrome. Journal of the International Neuropsychological Society, 10(2), 278-285. doi:10.1017/s1355617704102178

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

Buzsaki, G., & Watson, B. O. (2012). Brain rhythms and neural syntax: Implications for efficient coding of cognitive content and neuropsychiatric disease. Dialogues in Clinical Neuroscience, 14(4), 345-367.

Buzsáki, György, & Freeman, Walter. (2015). Editorial overview: Brain rhythms and dynamic coordination. Current Opinion in Neurobiology, 31, v-ix. doi:10.1016/j.conb.2015.01.016

Carruthers, B.M., Jain, A. K., de Meirleir, K., Peterson, D. L., Klimas, N. G., Lerner, A. M., . . . Van de Sande, M. I. (2003). Myalgic encephalomyelitits/chronic fatigue syndrome: Clinical working case definition, diagnostic, and treatment protocols Journal of Chronic Fatigue Syndrome, 11(1).

Carruthers, B.M., van de Sande, M.I., De Meirleir, K.L., Klimas, N.G., Broderick, G., Mitchell, T., . . . Stevens, S. . (2011). Myalgic Encephalomyelitis: International Consensus Criteria. Journal of Internal Medicine doi:10.1111/j.1365- 2796.2011.02428.x

Caseras, X., Mataix-Cols, D., Giampietro, V., Rimes, K. A., Brammer, M., Zelaya, F., . . . Godfrey, E. L. (2006). Probing the working memory system in chronic fatigue syndrome: A functional magnetic resonance imaging study using the n-back task. Psychosomatic Medicine, 68(6), 947-955. doi:10.1097/01.psy.0000242770.50979.5f

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

Cockshell, S. J., & Mathias, J. L. (2010). Cognitive functioning in chronic fatigue syndrome: A meta-analysis. Psychological medicine, 40(8), 1253-1267. doi:10.1017/s0033291709992054

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

Cook, D. B., O'Connor, P. J., Lange, G., & Steffener, J. (2007). Functional neuroimaging correlates of mental fatigue induced by cognition among chronic fatigue syndrome patients and controls. Neuroimage, 36(1), 108-122. doi:10.1016/j.neuroimage.2007.02.033

Crossley, N. A., Mechelli, A., Scott, J., Carletti, F., Fox, P. T., McGuire, P., & Bullmore, E. T. (2014). The hubs of the human connectome are generally implicated in the anatomy of brain disorders. Brain, 137(Pt 8), 2382-2395. doi:10.1093/brain/awu132

Deco, G., Jirsa, V., & Friston, K. J. (2012). The dynamical structural basis of brain activity. In M. I. Rabinovich, K. Friston, & P. Varona (Eds.), Principles of Brain Dynamics: Global State Interactions (pp. 1-23). Cambridge, MA: MIT Press.

DeLuca, J., Johnson, S. K., & Natelson, B. H. (1994). Neuropsychiatric status of patients with chronic fatigue syndrome: An overview. Toxicology and Industrial Health, 10(4-5), 513-522.

Dobbs, B. M., Dobbs, A. R., & Kiss, I. (2001). Working memory deficits associated with chronic fatigue syndrome. Journal of the International Neuropsychological Society, 7(3), 285-293.

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

Fukuda, K., Straus, S. E., Hickie, I., Sharpe, M. C., Dobbins, J. G., & Komaroff, A. (1994). The chronic fatigue syndrome: A comprehensive approach to its definition and study. Annals of Internal Medicine, 121(12), 953-959.

Gay, C., Robinson, M. E., Lai, S., O'Shea, A., Craggs, J., Price, D. D., & Staud, R. (2015). Abnormal Resting-State Functional Connectivity in Patients with Chronic Fatigue Syndrome: Results of Seed and Data-Driven Analyses. Brain Connectivity. doi:10.1089/brain.2015.0366

Gloor, P., Ball, G., & Schaul, N. (1977). Brain lesions that produce delta waves in the EEG. Neurology, 27(4), 326-333.

Grafman, J., Schwartz, V., Dale, J. K., Scheffers, M., Houser, C., & Straus, S. E. (1993). Analysis of neuropsychological functioning in patients with chronic fatigue syndrome. Journal of Neurology, Neurosurgery, and Psychiatry, 56(6), 684-689.

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

Harris, P. A., Taylor, R., Thielke, R., Payne, J., Gonzalez, N., & Conde, J. G. (2009). Research electronic data capture (REDCap)--A metadata-driven methodology and workflow process for providing translational research informatics support. Journal of Biomedical Informatics, 42(2), 377-381. doi:10.1016/j.jbi.2008.08.010

Hata, M., Kazui, H., Tanaka, T., Ishii, R., Canuet, L., Pascual-Marqui, R. D., . . . Takeda, M. (2016). Functional connectivity assessed by resting state EEG correlates with cognitive decline of Alzheimer's disease: An eLORETA study. Clinical Neurophysiology, 127(2), 1269-1278. doi:10.1016/j.clinph.2015.10.030

Hopkins, R. O., & Jackson, J. C. (2006). Long-term neurocognitive function after critical illness. Chest, 130(3), 869-878. doi:10.1378/chest.130.3.869

Humphries, M. D., & Gurney, K. (2008). Network 'small-world-ness': a quantitative method for determining canonical network equivalence. PloS One, 3(4), e0002051. doi:10.1371/journal.pone.0002051

Jason, L. A., So, S., Brown, A., Sunnquist, M., & Evans, M. (2015). Test-retest reliability of the DePaul Symptom Questionnaire. Fatigue Biomedicine Health & Behavior, 3(1), 16-32.

Jason, L. A., Sunnquist, M., Brown, A., Furst, J., Cid, M., Farietta, J., . . . Strand, E. B. (2015). Factor analysis of the DePaul Symptom Questionnaire: Identifying core domains. Journal of Neurology and Neurobiology, 1(4). doi:10.16966/2379-7150.114

Jason, L. A., Zinn, M. L., & Zinn, M. A. (2015). Myalgic encephalomyelitis: symptoms and biomarkers. Current Neuropharmacology, 13(5), 701-734.

John, E. R. (2005). From synchronous neuronal discharges to subjective awareness? In S. Laureys (Ed.), Progress in Brain Research (Vol. 150): Elsevier.

Johnson, S. K., DeLuca, J., & Natelson, B. H. (1996). Assessing somatization disorder in the chronic fatigue syndrome. Psychosomatic Medicine, 58(1), 50-57.

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

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 Biomedical Engineering, 53(2), 246-253. doi:10.1109/tbme.2005.862533

Kim, B. H., Namkoong, K., Kim, J. J., Lee, S., Yoon, K. J., Choi, M., & Jung, Y. C. (2015). Altered resting-state functional connectivity in women with chronic fatigue syndrome. Psychiatry Research, 234(3), 292-297. doi:10.1016/j.pscychresns.2015.10.014

Kirk, R. E. (2013). Experimental design: Procedures for the behavioral sciences (4th ed.). Los Angeles: Sage.

Klimesch, W., Freunberger, R., Sauseng, P., & Gruber, W. (2008). A short review of slow phase synchronization and memory: evidence for control processes in different memory systems? Brain Research, 1235, 31-44. doi:10.1016/j.brainres.2008.06.049

Komaroff, A. L., & Buchwald, D. (1991). Symptoms and signs of chronic fatigue syndrome. Reviews of Infectious Diseases, 13 Suppl 1, S8-11.

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

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

Le Van Quyen, M. (2011). The brainweb of cross-scale interactions. New Ideas in Psychology, 29(2), 57-63. doi:10.1016/j.newideapsych.2010.11.001

Lehmann, D., Faber, P. L., Gianotti, L. R., Kochi, K., & Pascual-Marqui, R. D. (2006). Coherence and phase locking in the scalp EEG and between LORETA model sources, and microstates as putative mechanisms of brain temporo-spatial functional organization. Journal of Physiology, Paris, 99(1), 29-36. doi:10.1016/j.jphysparis.2005.06.005

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

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

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.

Minati, L., Varotto, G., D'Incerti, L., Panzica, F., & Chan, D. (2013). From brain topography to brain topology: Relevance of graph theory to functional neuroscience. Neuroreport, 24(10), 536-543. doi:10.1097/WNR.0b013e3283621234

Murdock, K. W., Wang, X. S., Shi, Q., Cleeland, C. S., Fagundes, C. P., & Vernon, S. D. (2016). The utility of patient-reported outcome measures among patients with myalgic encephalomyelitis/chronic fatigue syndrome. Quality of Life Research. doi:10.1007/s11136-016-1406-3

Nakatomi, Y., Mizuno, K., Ishii, R., Wada, Y., Tanaka, M., Tazawa, S., . . . Watanabe, Y. (2014). Neuroinflammation in patients with Chronic Fatigue Syndrome/Myalgic Encephalomyelitis: An 11C-(R)-PK11195 PET study. Journal of Nuclear Medicine, 55(6), 945-950.

Niedermeyer, E., & Lopez da Silva, F. (2005). Electroencephalography: Basic principles, clinical applications and related fields (5th ed. ed.). Philadelphia: Lippincott Williams and Wilkins.

Nunez, P. L., Srinivasan, R., & Fields, R. D. (2014). EEG functional connectivity, axon delays and white matter disease. Clinical Neurophysiology(0). doi:10.1016/j.clinph.2014.04.003

Ocon, A. J. (2013). Caught in the thickness of brain fog: Exploring the cognitive symptoms of chronic fatigue syndrome. Frontiers in Physiology, 4, 63. doi:10.3389/fphys.2013.00063

Pascual-Marqui, R. D. (2007a). Coherence and phase synchronization: Generalization to pairs of multivariate time series, and removal of zero-lag contribution (arXiv:0706.1776v3 [stat.ME]). Retrieved from

Pascual-Marqui, R. D. (2007b). Discrete, 3D distributed linear imaging methods of electric neuronal activity. Part 1: Exact, zero error localization ( arXiv:0710.3341 [math-ph]). Retrieved from

Pascual-Marqui, R. D. (2007c). Instantaneous and lagged measurements of linear and nonlinear dependence between groups of multivariate time series: Frequency decomposition (arXiv:0711.1455[stat.ME]). Retrieved from

Pascual-Marqui, R. D. (2015). LORETA-KEY software (Version 2015-12-22). Zurich, Switzerland: KEY Institute for Brain-Mind Research. Retrieved from

Pascual-Marqui, R. D., Lehmann, D., Koukkou, M., Kochi, K., Anderer, P., Saletu, B., . . . Kinoshita, T. (2011). 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

Puri, B. K., Jakeman, P. M., Agour, M., Gunatilake, K. D., Fernando, K. A., Gurusinghe, A. I., . . . Gishen, P. (2012). Regional grey and white matter volumetric changes in myalgic encephalomyelitis (chronic fatigue syndrome): A voxel-based morphometry 3 T MRI study. British Journal of Radiology, 85(1015), e270-273. doi:10.1259/bjr/93889091

Rossini, P. M., Rossi, S., Babiloni, C., & Polich, J. (2007). Clinical neurophysiology of aging brain: From normal aging to neurodegeneration. Progress in Neurobiology, 83(6), 375-400. doi:10.1016/j.pneurobio.2007.07.010

Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. Neuroimage, 52(3), 1059-1069. doi:10.1016/j.neuroimage.2009.10.003

Sauseng, P., & Klimesch, W. (2008). What does phase information of oscillatory brain activity tell us about cognitive processes? Neuroscience and Biobehavioral Reviews, 32(5), 1001-1013. doi:10.1016/j.neubiorev.2008.03.014

Schaul, N., Gloor, P., & Gotman, J. (1981). The EEG in deep midline lesions. Neurology, 31(2), 157-167.

Sepulcre, J. (2014). Functional Streams and Cortical Integration in the Human Brain. The Neuroscientist, 20(5), 499-508. doi:10.1177/1073858414531657

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

Sporns, Olaf, & Honey, Christopher J. (2006). Small worlds inside big brains. Proceedings of the National Academy of Sciences, 103(51), 19219-19220. doi:10.1073/pnas.0609523103

Stam, C. J. (2010). Characterization of anatomical and functional connectivity in the brain: a complex networks perspective. International Journal of Psychophysiology, 77(3), 186-194. doi:10.1016/j.ijpsycho.2010.06.024

Stam, C. J. (2014). Modern network science of neurological disorders. Nature Reviews. Neuroscience, 15(10), 683-695. doi:10.1038/nrn3801

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 (pp. 31-83). New York: Lippincott Williams & Wilkins.

Steriade, M., & Pare, D. (2006). Gating in cerebral networks. Cambridge: Cambridge University Press.

Telesford, Q. K., Simpson, S. L., Burdette, J. H., Hayasaka, S., & Laurienti, P. J. (2011). The brain as a complex system: using network science as a tool for understanding the brain. Brain Connectivity, 1(4), 295-308. doi:10.1089/brain.2011.0055

Thatcher, R. W. (2016). Handbook of Quantitattive Electroencephalography and EEG Biofeedback. St. Petersburg, FLA: ANI Publishing.

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

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

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

van den Heuvel, M. P., & Sporns, O. (2013). An anatomical substrate for integration among functional networks in human cortex. The Journal of Neuroscience, 33(36), 14489-14500. doi:10.1523/jneurosci.2128-13.2013

van Straaten, E. C., & Stam, C. J. (2013). Structure out of chaos: Functional brain network analysis with EEG, MEG, and functional MRI. European Neuropsychopharmacology, 23(1), 7-18. doi:10.1016/j.euroneuro.2012.10.010

Vecchio, F., Miraglia, F., Curcio, G., Altavilla, R., Scrascia, F., Giambattistelli, F., . . . Rossini, P. M. (2015). Cortical brain connectivity evaluated by graph theory in dementia: A correlation study between functional and structural data. Journal of Alzheimer's Disease, 45(3), 745-756. doi:10.3233/jad-142484

Vecchio, F., Miraglia, F., Curcio, G., Della Marca, G., Vollono, C., Mazzucchi, E., . . . Rossini, P. M. (2015). Cortical connectivity in fronto-temporal focal epilepsy from EEG analysis: A study via graph theory. Clinical Neurophysiology, 126(6), 1108-1116. doi:10.1016/j.clinph.2014.09.019

Vecchio, F., Miraglia, F., Porcaro, C., Cottone, C., Cancelli, A., Rossini, P. M., & Tecchio, F. (2017). Electroencephalography-Derived Sensory and Motor Network Topology in Multiple Sclerosis Fatigue. Neurorehabilitation and Neural Repair, 31(1), 56-64. doi:10.1177/1545968316656055

Vecchio, F., Miraglia, F., Quaranta, D., Granata, G., Romanello, R., Marra, C., . . . Rossini, P. M. (2016). Cortical connectivity and memory performance in cognitive decline: A study via graph theory from EEG data. Neuroscience, 316, 143-150. doi:10.1016/j.neuroscience.2015.12.036

Vysata, O., Kukal, J., Prochazka, A., Pazdera, L., Simko, J., & Valis, M. (2014). Age-related changes in EEG coherence. Neurologia i Neurochirurgia Polska, 48(1), 35-38. doi:10.1016/j.pjnns.2013.09.001

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

Watts, Duncan J., & Strogatz, Steven H. (1998). Collective dynamics of "small-world" networks. Nature, 393(6684), 440-442.

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. ed., pp. 323-337). Philadelphia: Lippincott Williams and Wilkins.

Wig, G. S., Schlaggar, B. L., & Petersen, S. E. (2011). Concepts and principles in the analysis of brain networks. Annals of the New York Academy of Sciences, 1224, 126-146. doi:10.1111/j.1749-6632.2010.05947.x

Wortinger, L. A., Endestad, T., Melinder, A. M., Oie, M. G., Sevenius, A., & Bruun Wyller, V. (2016). Aberrant Resting-State Functional Connectivity in the Salience Network of Adolescent Chronic Fatigue Syndrome. PLoS One, 11(7), e0159351. doi:10.1371/journal.pone.0159351

Zinn, M. A., Zinn, M. L., Norris, J. L., Valencia, I., Montoya, J. G., & Maldonado, J. R. (2014). 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 Biennial Conference, San Francisco, CA, USA.

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


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