Proceedings of the 2017 ISNR Conference: Keynotes, Invited, and Student Award Presentations
DOI:
https://doi.org/10.15540/nr.4.3-4.138Abstract
Selected Abstracts of Conference Presentations at the 2017 International Society for Neurofeedback and Research (ISNR) 25th Annual Conference, Mashantucket, CT, USAReferences
-----Functional Neuroimaging as a Window into Human Brain Function: Applications to Better Understand and Optimize Neuromodulatory Therapies
Harris, R. E., Napadow, V., Huggins, J. P., Pauer, L., Kim, J., Hampson, J., … Clauw, D. J. (2013). Pregabalin rectifies aberrant brain chemistry, connectivity, and functional response in chronic pain patients. Anesthesiology, 119(6), 1453–1464. http://dx.doi.org/10.1097/ALN.0000000000000017
Kim, J., Loggia, M. L., Cahalan, C. M., Harris, R. E., Beissner, F., Garcia, R. G., … Napadow, V. (2015). The somatosensory link in fibromyalgia: Functional connectivity of the primary somatosensory cortex is altered by sustained pain and is associated with clinical/autonomic dysfunction. Arthritis & Rheumatology, 67(5), 1395–1405. http://dx.doi.org/10.1002/art.39043
Kim, J., Loggia, M. L., Edwards, R. R., Wasan, A. D., Gollub, R. L., & Napadow, V. (2013). Sustained deep-tissue pain alters functional brain connectivity. Pain, 154(8), 1343–1351. http://dx.doi.org/10.1016/j.pain.2013.04.016
Loggia, M. L., Kim, J., Gollub, R. L., Vangel, M. G., Kirsch, I., Kong, J., … Napadow, V. (2013). Default mode network connectivity encodes clinical pain: An arterial spin labeling study. Pain, 154(1), 24–33. http://dx.doi.org/10.1016/j.pain.2012.07.029
Napadow, V., & Harris, R. E. (2014). What has functional connectivity and chemical neuroimaging in fibromyalgia taught us about the mechanisms and management of “centralized” pain? Arthritis Research & Therapy, 16(4), 425. http://dx.doi.org/10.1186/s13075-014-0425-0
Napadow, V., Kim, J., Clauw, D. J., & Harris, R. E. (2012). Brief report: Decreased intrinsic brain connectivity is associated with reduced clinical pain in fibromyalgia. Arthritis & Rheumatology, 64(7), 2398–2403. http://dx.doi.org/10.1002/art.34412
Napadow, V., LaCount, L., Park, K., As-Sanie, S., Clauw, D. J., & Harris, R. E. (2010). Intrinsic brain connectivity in fibromyalgia is associated with chronic pain intensity. Arthritis & Rheumatology, 62(8), 2545–2555. http://dx.doi.org/10.1002/art.27497
-----Impact of Childhood Maltreatment on Brain Development and the Critical Importance of Distinguishing Between Maltreated and Non-Maltreated Diagnostic Subtypes
Teicher, M. H., & Samson, J. A. (2013). Childhood maltreatment and psychopathology: A case for ecophenotypic variants as clinically and neurobiologically distinct subtypes. The American Journal of Psychiatry, 170(10), 1114–1133. http://dx.doi.org/10.1176/appi.ajp.2013.12070957
Teicher, M. H., & Samson, J. A. (2016). Annual Research Review: Enduring neurobiological effects of childhood abuse and neglect. The Journal of Child Psychology and Psychiatry, 57(3), 241–266. http://dx.doi.org/10.1111/jcpp.12507
Teicher, M. H., Samson, J. A., Anderson, C. M., & Ohashi, K. (2016). The effects of childhood maltreatment on brain structure, function and connectivity. Nature Reviews Neuroscience, 17, 652–666. http://dx.doi.org/10.1038/nrn.2016.111
-----The Evolution of Quantitative EEG: A Perfect Storm
Buckner, R. L., Andrews-Hanna, J. R., & Schacter, D. L. (2008). The Brain's Default Network: Anatomy, Function, and Relevance to Disease. Annals of the New York Academy of Sciences, 1124, 1¬–38. http://dx.doi.org/10.1196 /annals.1440.011
Dohrmann, A.-L., Stengler, K., Jahn, I., & Olbrich, S. (2017). EEG-arousal regulation as predictor of treatment response in patients suffering from obsessive compulsive disorder. Clin Neuropsychol, 128(10), 1906–1914. http://dx.doi.org/10.1016 /j.clinph.2017.07.406
Hanley, D., Prichep, L. S., Bazarian, J., Huff, J. S., Naunheim, R., Garrett, J., … Hack, D. C. (2017). Emergency Department Triage of Traumatic Head Injury Using Brain Electrical Activity Biomarkers: A Multisite Prospective Observational Validation Trial. Academic Emergency Medicine, 24(5), 617–627. http://dx.doi.org/10.1111/acem.13175
Jelic, V., Johansson, S.-E., Almkvist, O., Shigeta, M., Julin, P., Nordberg, A., … Wahlund, L.-O. (2000). Quantitative electroencephalography in mild cognitive impairment: Longitudinal changes and possible prediction of Alzheimer's disease. Neurobiology of Aging, 21(4), 533–540. http://dx.doi.org/10.1016/S0197-4580(00)00153-6
John, E. R., Ahn, H., Prichep, L. S., Trepetin, M., Brown, D., & Kaye, H. (1980). Developmental equations for the electroencephalogram. Science, 210(4475), 1255–1258. http://dx.doi.org/10.1126/science.7434026
Pascual-Marqui, R. D., Esslen, M., Kochi, K., & Lehman, D. (2002). Functional imaging with low resolution brain electromagnetic tomography (LORETA): A review. Methods & Findings in Experimental & Clinical Pharmacology, 24C, 91–95. http://www.brainm.com/software/pubs/brain/loreta /LORETA-ReviewPaper03.pdf
Prichep, L. S., John, E. R., Ferris, S. H., Rausch, L., Fang, Z., Cancro, R., … Reisberg, B. (2006). Prediction of longitudinal cognitive decline in normal elderly with subjective complaints using electrophysiological imaging. Neurobiology of Aging, 27(3), 471–481. http://dx.doi.org/10.1016 /j.neurobiolaging.2005.07.021
Prichep, L. S., Shah, J., Merkin, H., et al. (in press, 2017). Identification of Chronic Pain Matrix Using Quantitative EEG Source Localization. Clinical EEG and Neuroscience.
-----Early Detection and Treatment of Attention Deficits in Preterm Infants
Gomes, H., Molholm, S., Christodoulou, C., Ritter, W., & Cowan, N. (2000). The development of auditory attention in children. Frontiers in Bioscience, 5, 108–120. https://www.bioscience.org/2000/v5/d/gomes/fulltext.htm
Gutiérrez-Hernández, C. C., Harmony, T., Avecilla-Ramírez, G. N., Barrón-Quiroz, I., Guillén-Gasca, V., Trejo-Bautista, G., & Bautista-Olvera, M. M. (2017). Infant Scale of Selective Attention: A Proposal to Assess Cognitive Abilities. Evaluar, 17(1), 94–106. Retrieved from https://revistas.unc.edu.ar /index.php/revaluar/article/download/17077/16708
Polich, J. (2007). Updating P300: An integrative theory of P3a and P3b. Clinical Neurophysiology, 118(10), 2128–2148. http://dx.doi.org/10.1016/j.clinph.2007.04.019
Reynolds, G. D., & Romano, A. C. (2016). The development of attention systems and working memory in infancy. Frontiers in Systems Neuroscience, 10, 15. http://dx.doi.org/10.3389 /fnsys.2016.00015
-----Functional Neuromarkers for Psychiatry and Neurology: Defining Brain Dysfunctions and Constructing Protocols of Neuromodulation
Kropotov, J. D. (2016). Functional neuromarkers for psychiatry: Applications for diagnosis and treatment. Amsterdam, Netherlands: Academic Press, Elsevier.
-----Is A/T Neurofeedback Training (NFT) a Successful Treatment Method for Women with Moderate to Severe Trait Anxiety: A Clinical Trial and Methodological Considerations
Aliño, M., Gadea, M., & Espert, R. (2016). A critical view of neurofeedback experimental designs: Sham and control as necessary conditions. International Journal of Neurology and Neurotherapy, 3(1), 041. http://dx.doi.org/10.23937/2378-3001/3/1/1041
Alkoby, O., Abu-Rmileh, A., Shriki, O., & Todder, D. (2017). Can we predict who will respond to neurofeedback? A review of the inefficacy problem and existing predictors for successful EEG neurofeedback learning. Neuroscience. http://dx.doi.org /10.1016/j.neuroscience.2016.12.050
Bates, D., Maechler, M., & Bolker, B. (2013). lme4: Linear mixed-effects models using S4 classes. R package version 0.999999-0. 2012. Retrieved from http://CRAN.R-project.org/package=lme4
Baxter, A. J., Scott, K. M., Vos, T., & Whiteford, H. A. (2013). Global prevalence of anxiety disorders: A systematic review and meta-regression. Psychological Medicine, 43(5), 897–910. http://dx.doi.org/10.1017/S003329171200147X
Éismont, E. V., Lutsyuk, N. V., & Pavlenko, V. B. (2011). Moderation of increased anxiety in children and teenagers with the use of neurotherapy: estimation of efficacy. Neurophysiology, 43(1), 53–61. http://dx.doi.org/10.1007/s11062-011-9185-5
Ferreira, A., Celeste, W. C., Cheein, F. A., Bastos-Filho, T. F., Sarcinelli-Filho, M., & Carelli, R. (2008). Human-machine interfaces based on EMG and EEG applied to robotic systems. Journal of NeuroEngineering and Rehabilitation, 5, 10. http://dx.doi.org/10.1186/1743-0003-5-10
Gidron, Y. (2013). State Anxiety. In M. D. Gellman & J. R. Turner (Eds.), Encyclopedia of Behavioral Medicine (p. 1877). New York, NY: Springer. http://dx.doi.org10.1007/978-1-4419-1005-9
Kohn, P. M., Kantor, L., DeCicco, T. L., & Beck, A. T. (2008). The Beck Anxiety Inventory-Trait (BAI): A measure of dispositional anxiety not contaminated by dispositional depression. Journal of Personality Assessment, 90(5), 499–506. http://dx.doi.org /10.1080/00223890802248844
Marzbani, H., Marateb, H. R., & Mansourian, M. (2016). Neurofeedback: A comprehensive review on system design, methodology and clinical applications. Basic and Clinical Neuroscience, 7(2), 143–158. http://dx.doi.org/10.15412 /J.BCN.03070208
Mennella, R., Patron, E., & Palomba, D. (2017). Frontal alpha asymmetry neurofeedback for the reduction of negative affect and anxiety. Behaviour Research and Therapy, 92, 32–40. http://dx.doi.org/10.1016/j.brat.2017.02.002
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Mirman, D., Dixon, J. A., & Magnuson, J. S. (2008). Statistical and computational models of the visual world paradigm: Growth curves and individual differences. Journal of Memory and Language, 59(4), 475–494. http://dx.doi.org/10.1016 /j.jml.2007.11.006
Monastra, V. J., Lynn, S., Linden, M., Lubar, J. F., Gruzelier, J., & LaVaque, T. J. (2005). Electroencephalographic biofeedback in the treatment of Attention-Deficit/Hyperactivity Disorder. Applied Psychophysiology and Biofeedback, 30(2), 95–114. http://dx.doi.org/10.1300/J184v09n04_02
Moore, N. C. (2000). A review of EEG biofeedback treatment of anxiety disorders. Clinical Electroencephalography, 31(1), 1–6. http://dx.doi.org/10.1177/155005940003100105
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Paluch, K., Jurewicz, K., Rogala, J., Krauz, R., Szczypińska, M., Mikicin, M., ... & Kublik, E. (2017). Beware: Recruitment of Muscle Activity by the EEG-Neurofeedback Trainings of High Frequencies. Frontiers in Human Neuroscience, 11, 119. http://dx.doi.org/10.3389/fnhum.2017.00119
Phneah, S. W., & Nisar, H. (2017). EEG-based alpha neurofeedback training for mood enhancement. Australasian Physical & Engineering Sciences in Medicine, 40(2), 325–336. http://dx.doi.org/10.1007/s13246-017-0538-2
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Raymond, J., Varney, C., Parkinson, L. A., & Gruzelier, J. H. (2005). The effects of alpha/theta neurofeedback on personality and mood. Cognitive Brain Research, 23(2), 287–292. http://dx.doi.org/10.1016/j.cogbrainres.2004.10.023
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Zuberer, A., Brandeis, D., & Drechsler, R. (2015). Are treatment effects of neurofeedback training in children with ADHD related to the successful regulation of brain activity? A review on the learning of regulation of brain activity and a contribution to the discussion on specificity. Frontiers in Human Neuroscience, 9, 135. http://dx.doi.org/10.3389/fnhum.2015.00135
-----The Effect of Slow Breathing Training on Electroencephalogram
Lehrer, P. M., Vaschillo, E., & Vaschillo, B. (2000). Resonant frequency biofeedback training to increase cardiac variability: Rationale and manual for training. Applied Psychophysiology and Biofeedback, 25(3), 177–191. http://dx.doi.org/10.1023 /A:1009554825745
Prinsloo, G. E., Rauch, H. G. L., Karpul, D., & Derman, W. E. (2013). The effect of a single session of short duration heart rate variability biofeedback on EEG: A pilot study. Applied Psychophysiology and Biofeedback, 38(1), 45–56. http://dx.doi.org/10.1007/s10484-012-9207-0
-----The Effects of Personalized EEG-Neurofeedback in College Students with ADHD
Arns, M., de Ridder, S., Strehl, U., Breteler, M., & Coenen, A. (2009). Efficacy of neurofeedback treatment in ADHD: The effects on inattention, impulsivity and hyperactivity: A meta-analysis. Clinical EEG and Neuroscience, 40(3), 180–189. http://dx.doi.org/10.1177/155005940904000311
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-----The Differences Between Frontal Alpha Asymmetry Among Healthy Participants and Patients with Major Depressive Disorder
Baehr, E., Rosenfeld, J. P., & Baehr, R. (1997). The clinical use of an alpha asymmetry protocol in the neurofeedback treatment of depression: Two case studies. Journal of Neurotherapy, 2(3), 10–23. http://dx.doi.org/10.1300/J184v02n03_02
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-----Neurostructural Predictors of Cognitive Behavioral Therapy (CBT) for Obsessive-Compulsive Disorder: Implications for the Integration of Neurofeedback Training and CBT
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