Proceedings of the 2020 ISNR Annual Conference: Keynote and Plenary Sessions

Authors

  • International Society of Neurofeedback and Research (ISNR)

DOI:

https://doi.org/10.15540/nr.7.4.158

Keywords:

Neurofeedback, qEEG, neuroregulation, self-regulation, brain health

Abstract

Selected Keynote and Plenary session Abstracts of Conference Presentations at the 2020 International Society for Neurofeedback and Research (ISNR) 28th Conference, Miami, Florida, USA

References

---Recent Psychophysiological Advances of “Spontaneous” Reading

Bode, E. (1973). Developmental dyslexia: A diagnostic approach based on three atypical reading–spelling patterns. Developmental Medicine & Child Neurology, 15(5), 663–687. https://doi.org/10.1111/j.1469-8749.1973.tb05180.x

Chiarenza, A. C. (2010). Word searches in L1 and L2 Italian conversation: Re-establishing intersubjectivity (Unpublished doctoral dissertation). University of Illinois, Urbana-Champaign.

---Automatic De-artifacting in Normative qEEG Databases: Does Reality Follow the Hype?

Blum, S., Jacobsen, N. S. J., Bleichner, M. G., & Debener, S. (2019). A Riemannian modification of artifact subspace reconstruction for EEG artifact handling. Frontiers in Human Neuroscience, 13, 141. https://doi.org/10.3389/fnhum.2019.00141

Delorme, A., Sejnowski, T., & Makeig, S. (2007). Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis. NeuroImage, 34(4), 1443–1449. https://doi.org/10.1016/j.neuroimage.2006.11.004

Keizer, A. W. (2018). A systematic comparison and evaluation of four major QEEG databases. International Journal of Psychophysiology, 131(Suppl.), S2–S3. https://doi.org/10.1016/j.ijpsycho.2018.07.011

Makeig, S., Bell, A. J., Jung, T.-P., & Sejnowski, T. J. (1996). Independent component analysis of electroencephalographic data. In D. Touretzky, M. Mozer, & M. Hasselmo (Eds.), Advances in neural information processing systems (pp. 145–151). Cambridge MA: MIT Press.

Mullen, T. R., Kothe, C. A. E., Chi, Y. M., Ojeda, A., Kerth, T., Makeig, S., ... & Cauwenberghs, G. (2015). Real-time neuroimaging and cognitive monitoring using wearable dry EEG. IEEE Transactions on Biomedical Engineering, 62(11), 2553–2567. https://doi.org/10.1109/tbme.2015.2481482

Nolan, H., Whelan, R., & Reilly, R. B. (2010). FASTER: Fully automated statistical thresholding for EEG artifact rejection. Journal of Neuroscience Methods, 192(1), 152–162. https://doi.org/10.1016/j.jneumeth.2010.07.015

Thatcher, R. W., & Lubar, J. F. (2009). History of the scientific standards of QEEG normative databases. Introduction to Quantitative EEG Neurofeedback, 2009, 29–59. https://doi.org/10.1016/b978-0-12-374534-7.00002-2

White, J. N. (2003). Comparison of QEEG reference databases in basic signal analysis and in the evaluation of adult ADHD. Journal of Neurotherapy, 7(3–4), 123–169. https://doi.org/10.1300/j184v07n03_06

---Combining Bio- and Neurofeedback with Virtual Reality in the Treatment of Anxiety: Current Applications, Research Findings, and Future Directions

Chirico, A., Cipresso, P., Yaden, D. B., Biassoi, F., Riva, G., & Gaggioli, A. (2017). Effectiveness of immersive videos in inducing awe: An experimental study. Scientific Reports, 7, 1218. https://doi.org/10.1038/s41598-017-01242-0

Lamson, R. J. (1994). Virtual therapy of anxiety disorders. CyberEdge Journal, 4(2), 1, 6–8.

Riva, G., & Waterworth, J. A. (2014). Being present in a virtual world. In M. Grimshaw (Ed.), The Oxford Handbook of Virtuality (pp. 205–221). New York, NY: Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199826162.013.015

Riva, G., Waterworth, J. A., Waterworth, E. L., & Mantovani, F. (2011). From intention to action: The role of presence. New Ideas in Psychology, 29(1), 24–37. https://doi.org/10.1016/j.newideapsych.2009.11.002

Rizzo, A. S., & Koenig, S. T. (2017). Is clinical virtual reality ready for primetime? Neuropsychology, 31(8), 877–899. https://doi.org/10.1037/neu0000405

Rothbaum, B. O., Hodges, L., Kooper, R., Opdyke, D., Williford, J. S., & North, M. (1995). Effectiveness of computer-generated (virtual reality) graded exposure in the treatment of acrophobia. The American Journal of Psychiatry, 152(4), 626–628. https://doi.org/10.1176/ajp.152.4.626

Tarrant, J., Viczko, J., & Cope, H. (2018). Virtual reality for anxiety reduction demonstrated by quantitative EEG: A pilot study. Frontiers in Psychology, 9, 1280. https://doi.org/10.3389/fpsyg.2018.01280

Tarrant, J., & Cope, H. (2018). Combining frontal gamma asymmetry neurofeedback with virtual reality: A proof-of-concept case study. NeuroRegulation, 5(2), 57–67. https://doi.org/10.15540/nr.5.2.57

Waterworth, J. A., Waterworth, E. L., Mantovani, F., & Riva, G. (2010). On feeling (the) present: An evolutionary account of the sense of presence in physical and electronically-mediated environments. Journal of Consciousness Studies, 17(1–2), 167–188.

Waterworth, J., & Riva, G., (2014). Feeling present in the physical world and in computer-mediated environments. Basingstoke: London, UK: Palgrave Macmillan. https://doi.org/10.1057/9781137431677

---Counteracting Our Current Happiness Deficit by "Happitation" Via Neurofeedback

Cowan, J., D., & Rubik, B. (2009, September). Positive subjective experiences related to clarified gamma brainwave neurofeedback from the prefrontal cortical region of meditators and non-meditators. Presented at the International Society for Neurofeedback and Research (ISNR) 17th Annual Conference, Indianapolis, IN.

Cowan, J., & Sokhadze, E. (2010, September). Happiness specifically increases a clarified 40 Hz EEG rhythm used for neurofeedback. Presented at the International Society Neurofeedback Research (ISNR) 18th Annual Conference, Denver, CO.

Cowan, J. D., & Sokhadze, E. (2011). Prefrontal gamma neurofeedback improves emotional state and cognitive function. Applied Psychophysiology & Biofeedback, 36, 220.

Cowan, J., & Blum, K. Finding happiness and activating the Neureka! neurofeedback way: We can return to Neverland. Retrieved from www.thesoberworld.com , pp. 28–29.

Cowan, J., & Sokhadze, E. (2018). Understanding of mysterious 40 Hz brain system for attention, learning, and feeling good. Proceedings of the 2018 ISNR Conference. NeuroRegulation 5(4), 159–160. https://doi.org/10.15540/nr.5.4.150

Rubik, B. N. (2011). Neurofeedback-enhanced gamma brainwaves from the prefrontal cortical region of meditators and non-meditators and associated subjective experiences. The Journal of Alternative and Complementary Medicine, 17(2), 109–115. https://doi.org/10.1089/acm.2009.0191

Sokhadze, E. M., & Daniels, R. (2016) Effects of prefrontal 40 Hz-centered EEG band neurofeedback on emotional state and cognitive functions in adolescents. Adolescent Psychiatry, 6(2), 116–129. https://doi.org/10.2174/2210676606666161025115616

Sokhadze, E. M., Cowan, J., Wang, Y., Casanova, M., Lamina, E., & Tasman, A. (2016) Effects of prefrontal neurofeedback on perceived emotional state and cognitive functioning in adolescents with drug abuse history. NeuroRegulation, 3(4), 191. https://doi.org/10.15540/nr.3.4

Sokhadze, E. (2012). Peak performance training using prefrontal EEG biofeedback. Biofeedback, 39, 7–15.

---Exploratory Study of Loreta Z-Scored Neurofeedback and Homeostatic Learning in a Group with Mild Traumatic Brain Injury (MTBI) and Post-Concussion Syndrome (PCS)

Cannon, R., Lubar, J., Congedo, M., Thornton, K., Towler, K., & Hutchens, T. (2007). The effects of neurofeedback training in the cognitive division of the anterior cingulate gyrus. International Journal of Neuroscience, 117(3), 337–357. https://doi.org/10.1080/00207450500514003

Cannon, R. L., Baldwin, D. R., Diloreto, D. J., Phillips, S. T., Shaw, T. L., & Levy, J. J. (2014). LORETA neurofeedback in the precuneus: Operant conditioning in basic mechanisms of self-regulation. Clinical EEG and Neuroscience, 45(4), 238–248. https://doi.org/10.1177/1550059413512796

D'Souza, M. M., Trivedi, R., Singh, K., Grover, H., Choudhury, A., Kaur, P., ... Tripathi, R. P. (2015). Traumatic brain injury and the post-concussion syndrome: A diffusion tensor tractography study. Indian Journal of Radiology and Imaging, 25(4), 404–414. https://doi.org/10.4103/0971-3026.169445

Datta, S. G. S., Pillai, S. V., Rao, S. L., Kovoor, J. M. E., & Chandramouli, B. A. (2009). Post-concussion syndrome: Correlation of neuropsychological deficits, structural lesions on magnetic resonance imaging and symptoms. Neurology India, 57(5), 594–598. https://doi.org/10.4103/0028-3886.57810

Duff, J. (2004). The usefulness of quantitative EEG (QEEG) and neurotherapy in the assessment and treatment of post-concussion syndrome. Clinical EEG and Neuroscience, 35(4), 198–209. https://doi.org/10.1177/155005940403500410

Iverson, G. L. (2019). Network analysis and precision rehabilitation for the post-concussion syndrome. Frontiers in Neurology, 10, 489. https://doi.org/10.3389/fneur.2019.00489

Kennedy, E., Quinn, D., Tumilty, S., & Chapple, C. M. (2017). Clinical characteristics and outcomes of treatment of the cervical spine in patients with persistent post-concussion symptoms: A retrospective analysis. Musculoskeletal Science and Practice, 29, 91–98. https://doi.org/10.1016/j.msksp.2017.03.002

Kenzie, E. S., Parks, E. L., Bigler, E. D., Wright, D. W., Lim, M. M., Chesnutt, J. C., ... Wakeland, W. (2018). The dynamics of concussion: Mapping pathophysiology, persistence, and recovery with causal-loop diagramming. Frontiers in Neurology, 9, 203. https://doi.org/10.3389/fneur.2018.00203

Khong, E., Odenwald, N., Hashim, E., & Cusimano, M. D. (2016). Diffusion tensor imaging findings in post-concussion syndrome patients after mild traumatic brain injury: A systematic review. Frontiers in Neurology, 7, 156. https://doi.org/10.3389/fneur.2016.00156

Thatcher, R. W. (2000). EEG operant conditioning (biofeedback) and traumatic brain injury. Clinical Electroencephalography, 31(1), 38–44. https://doi.org/10.1177/155005940003100110

Thatcher, R. W., Biver, C., Gomez, J. F., North, D., Curtin, R., Walker, R. A., & Salazar, A. (2001). Estimation of the EEG power spectrum using MRI T(2) relaxation time in traumatic brain injury. Clinical Neurophysiology, 112(9), 1729–1745. https://doi.org/10.1016/s1388-2457(01)00609-5

World Health Organization. (1992). The ICD-10 classification of mental and behavioral disorders: Clinical descriptions and guidelines. Geneva, Switzerland: World Health Organization.

---Infraslow Neurofeedback, the Latest Research

Aladjalova, N. A. (1957). Infra-slow rhythmic oscillations of the steady potential of the cerebral cortex. Nature, 179(4567), 957–959. https://doi.org/10.1038/179957a0

Alshelh, Z., Di Pietro, F., Youssef, A. M., Reeves, J. M., Macey, P. M., Vickers, E. R., … Henderson, L. A. (2016). Chronic neuropathic pain: It's about the rhythm. The Journal of Neuroscience, 36(3), 1008–1018. https://doi.org/10.1523/jneurosci.2768-15.2016

Beissner, F., Meissner, K., Bär, K.-J., & Napadow, V. (2013). The autonomic brain: An activation likelihood estimation meta-analysis for central processing of autonomic function. The Journal of Neuroscience, 33(25), 10503–10511. https://doi.org/10.1523/jneurosci.1103-13.2013

Broyd, S. J., Helps, S. K., & Sonuga-Barke, E. J. S. (2011). Attention-induced deactivations in very low frequency EEG oscillations: Differential localisation according to ADHD symptom status. PLoS ONE, 6(3), e17325. https://doi.org/10.1371/journal.pone.0017325

Lecci, S., Fernandez, L. M. J., Weber, F. D., Cardis, R., Chatton, J.-Y., Born, J., & Lüthi, A. (2017). Coordinated infraslow neural and cardiac oscillations mark fragility and offline periods in mammalian sleep. Science Advances, 3(2) ), e1602026. https://doi.org/10.1126/sciadv.1602026

Leong, S. L., Vanneste, S., Lim, J., Smith, M., Manning, P., & De Ridder, D. (2018). A randomised, double-blind, placebo-controlled parallel trial of closed-loop infraslow brain training in food addiction. Scientific Reports, 8(1), 11659. https://doi.org/10.1038/s41598-018-30181-7

Mathew, J., Adhia, D. B., Smith, M. L., De Ridder, D., & Mani, R. (2020). Protocol for a pilot randomized sham-controlled clinical trial evaluating the feasibility, safety, and acceptability of infraslow electroencephalography neurofeedback training on experimental and clinical pain outcomes in people with chronic painful knee osteoarthritis. NeuroRegulation, 7(1), 30–44. https://doi.org/10.15540/nr.7.1.30

Palva, J. M., & Palva, S. (2012). Infra-slow fluctuations in electrophysiological recordings, blood-oxygenation-level-dependent signals, and psychophysical time series. NeuroImage, 62(4), 2201–2211. https://doi.org/10.1016/j.neuroimage.2012.02.060

Smith, M. L., Collura, T. F., Ferrera, J., & de Vries, J. (2014). Infra-slow fluctuation training in clinical practice: A technical history. NeuroRegulation, 1(2), 187–207. https://doi.org/10.15540/nr.1.2.187

Thayer, J. F., & Lane, R. D. (2000). A model of neurovisceral integration in emotion regulation and dysregulation. Journal of Affective Disorders, 61(3), 201–216. https://doi.org/10.1016/S0165-0327(00)00338-4

---Neurofeedback in Healthy Elderly at Risk of Cognitive Decline

Alatorre-Cruz, G. C., Silva-Pereyra, J., Fernández, T., Rodríguez-Camacho, M. A., Castro-Chavira, S. A., & Sanchez-Lopez, J. (2018). Effects of age and working memory load on syntactic processing: An event-related potential study. Frontiers in Human Neuroscience, 12, 185. https://doi.org/10.3389/fnhum.2018.00185

Becerra, J., Fernández, T., Roca-Stappung, M., Díaz-Comas, L., Galán, L., Bosch, J., ... Harmony, T. (2012). Neurofeedback in healthy elderly human subjects with electroencephalographic risk for cognitive disorder. Journal of Alzheimer's Disease, 28(2), 357–367. https://doi.org/10.3233/jad-2011-111055

Castro-Chavira, S. A., Barrios, F. A., Pasaye, E. H., Alatorre-Cruz, G. C., & Fernández, T. (2016). Compensatory larger cortical thickness in healthy elderly individuals with electroencephalographic risk for cognitive decline. NeuroReport, 27(9), 710–715.

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. https://doi.org/10.1016/j.neurobiolaging.2005.07.021

Prichep, L. S. (2007). Quantitative EEG and electromagnetic brain imaging in aging and in the evolution of dementia. Annals of the New York Academy of Sciences, 1097(1), 156–167. https://doi.org/10.1196/annals.1379.008

Sánchez-Moguel, S. M., Alatorre-Cruz, G. C., Silva-Pereyra, J., González-Salinas, S., Sanchez-Lopez, J., Otero-Ojeda, G. A., & Fernández, T. (2018). Two different populations within the healthy elderly: Lack of conflict detection in those at risk of cognitive decline. Frontiers in Human Neuroscience, 11, 658. https://doi.org/10.3389/fnhum.2017.00658

---Noninvasive Brain Stimulation Interventions to Elevate Neurofeedback Outcomes – PBM, TDCS, TACS, TMS, and Others

Giordano, J., Bikson, M., Kappenman, E. S., Clark, V. P., Coslett, H. B., Hamblin, M. R., … Calabrese, E. (2017). Mechanisms and effects of transcranial direct current stimulation. Dose-Response, 15(1), 1–22. https://doi.org/10.1177%2F1559325816685467

Lefaucheur, J. P., André-Obadia, N., Antal, A., Ayache, S. S., Baeken, C., Benninger, D. H., …, Garcia-Larrea, L. (2014). Evidence-based guidelines on the therapeutic use of repetitive transcranial magnetic stimulation (rTMS). Clinical Neurophysiology. 125(11), 2150–2206. https://doi.org/10.1016/j.clinph.2014.05.021

Saltmarche, A. E., Naeser, M. A., Ho, K. F., Hamblin, M. R., & Lim, L. (2017). Significant improvement in cognition in mild to moderately severe dementia cases treated with transcranial plus intranasal photobiomodulation: Case series report. Photomedicine and Laser Surgery, 35(8), 432–441, https://doi.org/10.1089/pho.2016.4227

Terranova, C., Rizzo, V., Cacciola, A., Chillemi, G., Calamuneri, A., Milardi, D. & Quartarone A. (2019). Is there a future for non-invasive brain stimulation as a therapeutic tool? Frontiers in Neurology, 9, 1146. https://doi.org/10.3389/fneur.2018.01146

Zomorrodi, R., Loheswaran, G., Pushparaj, A., & Lim, L. (2019). Pulsed near infrared transcranial and intranasal photobiomodulation significantly modulates neural oscillations: A pilot exploratory study. Scientific Reports, 9, 6309. https://doi.org/10.1038/s41598-019-42693-x

---Solving the Mystery: When Children and Adolescents Fail Treatment, Parents Want Answers

Chez, M. G., Chang, M., Krasne, V., Coughlan, C., Kominsky, M., & Schwartz, A. (2006). Frequency of epileptiform EEG abnormalities in a sequential screening of autistic patients with no known clinical epilepsy from 1996 to 2005. Epilepsy & Behavior, 8(1), 267–271. https://doi.org/10.1016/j.yebeh.2005.11.001

Kanazawa, O. (2014). Reappraisal of abnormal EEG findings in children with ADHD: On the relationship between ADHD and epileptiform discharges. Epilepsy & Behavior, 41, 251–256. https://doi.org/10.1016/j.yebeh.2014.09.078

Lee, E. H., Choi, Y. S., Yoon, H. S., & Bhan, G. H. (2015). Clinical impact of epileptiform discharge in children with attention-deficit/hyperactivity disorder (ADHD). Journal of Child Neurology, 31(5), 584–588. https://doi.org/10.1177/0883073815604223

Millichap, J. J., Stack, C. V., & Millichap, J. G. (2011). Frequency of epileptiform discharges in the sleep-deprived electroencephalogram in children evaluated for attention-deficit disorders. Journal of Child Neurology, 26(1), 6–11. https://doi.org/10.1177/0883073810371228.

Mulligan, C. K., & Trauner, D. A. (2013). Incidence and behavioral correlates of epileptiform abnormalities in autism spectrum disorders. Journal of Autism and Developmental Disorders, 44(2), 452–458. https://doi.org/10.1007/s10803-013-1888-6

Reinhold, J. A., Molloy, C. A., & Manning-Courtney, P. (2005). Electroencephalogram abnormalities in children with autism spectrum disorders. Journal of Neuroscience Nursing, 37(3), 136¬–139. https://doi.org/10.1097/01376517-200506000-00003

Swatzyna, R. J., Tarnow, J. D., Roark, A., & Mardick, J. (2017a). The utility of EEG in attention deficit hyperactivity disorder: A replication study. Clinical EEG and Neuroscience, 48(4), 243–245. https://doi.org/10.1177/1550059416640441

Swatzyna, R. J., Tarnow, J. D., Turner, R. P., Roark, A. J., MacInerney, E. K., & Kozlowski, G. P. (2017b). Integration of eeg into psychiatric practice: A step toward precision medicine in autism spectrum disorder. Journal of Clinical Neurophysiology, 34(3), 230–235. https://doi.org/10.1097/wnp.0000000000000365

Yasuhara, A. (2010). Correlation between EEG abnormalities and symptoms of autism spectrum disorder (ASD). Brain & Development, 32(10), 791–798. https://doi.org/10.1016/j.braindev.2010.08.010

Zimmerman, E. M., & Konopka, L. M. (2014). Preliminary findings of single- and multifocused epileptiform discharges in nonepileptic psychiatric patients. Clinical EEG and Neuroscience, 45(4), 285–292. https://doi.org/10.1177/1550059413506001

---Standardization and Personalized Medicine Using Quantitative EEG in Clinical Settings

Alyass, A., Turcotte, M., & Meyre, D. (2015). From big data analyses to personalized medicine for all: Challenges and opportunities. BMC Medical Genomics, 8, 33. https://doi.org/10.1186/s12920-015-0108-y

Hamburg, M., A., & Collins, F. C. S. (2010). The path to personalized medicine. The New England Journal of Medicine, 363(4), 301–304. https://doi.org/10.1056/nejmp1006304

Jameson, J. L., & Longo, D. L. (2015). Precision medicine—personalized, problematic, and promising. The New England Journal of Medicine, 372(23), 2229–2234. https://doi.org/10.1056/NEJMsb1503104

---The Reality of the Unreal: Dissociation and Dissociative Disorders

Blihar, D., Delgado, E., Buryak, M., Gonzalez, M., & Waechter, R. (2020). A systematic review of the neuroanatomy of dissociative identity disorder. European Journal of Trauma & Dissociation, 4(3), 100148. https://doi.org/10.1016/j.ejtd.2020.100148

Liotti, G. (2006). A model of dissociation based on attachment theory and research. Journal of Trauma & Dissociation. 7(4), 55–73. https://doi.org/10.1300/J229v07n04_04

Loewenstein, R. J. (2018). Dissociation debates: Everything you know is wrong. Dialogues in Clinical Neuroscience, 20(3), 229–242. https://doi.org/10.31887/dcns.2018.20.3/rloewenstein

Parry, S., Lloyd, M., & Simpson, J. (2018). “It's not like you have PSTD with a touch of dissociation”: Understanding dissociative identity disorder through first person accounts. European Journal of Trauma & Dissociation, 2(1), 31–38. https://doi.org/10.1016/j.ejtd.2017.08.002

Spitzer, C., Barnow, S., Freyberger, H. J., & Grabe, H. J. (2006). Recent developments in the theory of dissociation. World Psychiatry, 5(2), 82–86.

Steele, K., Boon, S., & van der Hart, O. (2017). Norton series on interpersonal neurobiology. Treating trauma-related dissociation: A practical, integrative approach. New York, NY: W. W. Norton & Company.

Sar, V. (2011). Epidemiology of dissociative disorders: An overview. Epidemiology Research International, 2011, 404538. https://doi.org/10.1155/2011/404538

van der Kolk, B. A. (2014). The body keeps the score: Brain, mind, and body in the healing of trauma. London, U.K.: Penguin Books.

van der Hart, O., Steele, K., & Nijenhuis, E. (2017). The treatment of traumatic memories in patients with complex dissociative disorders. European Journal of Trauma & Dissociation, 1(1), 25–35. https://doi.org/10.1016/j.ejtd.2017.01.008

Zucker, M., Spinazzola, J., Blaustein, M., & van der Kolk, B. (2006). Dissociative symptomatology in posttraumatic stress disorder and disorders of extreme stress. Journal of Trauma & Dissociation, 7(1), 19–31. https://doi.org/10.1300/J229v07n01_03

---Visualizing Neurological Decision-Making Pathways to Help Clients Understand Self

Anderson, J. R. (1991). The adaptive nature of human categorization. Psychological Review, 98(3), 409–429. https://doi.org/10.1037/0033-295X.98.3.409

Baranov, S. (2018). Finite state machines and algorithmic state machines. ISBN-13: 978-1-7750917-07

Bonnstetter, R. J., Collura, T., Hebets, D., & Bonnstetter, B. J. (2013, Winter). Uncovering the belief behind the action. NeuroConnections. Newsletter of The International Society of Neurofeedback and Research and Applied Psychophysiology and Biofeedback Neurofeedback Division. McLean, VA

Collura, T. F., Zalaquett, C., & Bonnstetter, R. J. (2014). Seeing inside the client's mind. Counseling Today, 57(6), 24–27.

Collura, T. F., Zalaquett, C. P., Bonnstetter, R., Chatters, S. J. (2014). Toward an operational model of decision making, emotional regulation, and mental health impact. Advances in Mind-Body Medicine, 28(4), 4–19.

Davidson, R. J., & Begley, S. (2012). The emotional life of your brain. New York, NY: Hudson Street Press.

Haidt, J. (2001). The emotional dog and its rational tail: A social intuitionist approach to moral judgment. Psychological Review, 108(4), 814–834. https://doi.org/10.1037/0033-295X.108.4.814

Ivey, A. E., Ivey, M. B., & Zalaquett, C. P. (2014). Intentional interviewing and counseling: Facilitating client development. Belmont, CA: Brooks-Cole.

Michel, C. M., & Koenig, T. (2018). EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review. NeuroImage, 180(Part B), 577–593. https://doi.org/10.1016/j.neuroimage.2017.11.062

Wiener, N. (1961). Cybernetics—Control and communication in man and machine. Boston, MA: MIT Press

---Neurofeedback in ADHD: Rating the Evidence, APA Guidelines, and a Multicenter Replication Study of qEEG-informed Neurofeedback

Arns, M., Clark, C. R., Trullinger, M., deBeus, R., Mack, M. & Aniftos, M. (2020). Neurofeedback and attention-deficit/hyperactivity-disorder (ADHD) in children: Rating the evidence and proposed guidelines. Applied Psychophysiol ogy and Biofeedback, 45(2), 39–48. https://doi.org/10.1007/s10484-020-09455-2

Arns, M., Drinkenburg, W., & Kenemans, J. L. (2012). The effects of QEEG-informed neurofeedback in ADHD: An open-label pilot study. Applied Psychophysiology and Biofeedback, 37(3), 171–180. https://doi.org/10.1007/s10484-012-9191-4

Arns, M., Gunkelman, J., Breteler, M., & Spronk, D. (2008). EEG phenotypes predict treatment outcome to stimulants in children with ADHD. Journal of Integrative Neuroscience, 7(03), 421–438. https://doi.org/10.1142/s0219635208001897

Krepel, N., Egtberts, T., Sack, A. T., Heinrich, H., Ryan, M., & Arns, M. (2020). A multicenter effectiveness trial of QEEG-informed neurofeedback in ADHD: Replication and treatment prediction. NeuroImage: Clinical, 28, 102399. https://doi.org/10.1016/j.nicl.2020.102399

Wu, W., Zhang, Y., Jiang, J., Lucas, M. V., Fonzo, G. A., Rolle, C. E., … Etkin, A. (2020). An electroencephalographic signature predicts antidepressant response in major depression. Nature Biotechnology, 38, 439–447. https://doi.org/10.1038/s41587-019-0397-3

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2020-12-28

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Vol. 7 No. 4 (2020)

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Proceedings

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