Šajā sadaļā iepazīstināsim ar zinātni, kas saistās un ir par pamatu “RigaBrain®  smadzeņu treniņiem". 

Zināšanai –  ”RigaBrain®”   oficiāli Latvijā reģistrēts zīmolsir SIA “RigaBrain”, zem kura tiek piedāvāti inovatīvi autotreniņi smadzeņu darbības uzlabošanai. Tehnoloģiskās pieejas mainās atbilstoši  jaunākajām un progresīvākajām pieejām pasaulē. Kopš 2008. gada, kad “RigaBrain® ” uzsāka savu darbību, tehnoloģiskās pieejas ir nomainījušās vairākārt. Un SIA “RigaBrain” nepārtraukti seko tendencēm pasaulē, izvērtē tās un ieklausās savos sadarbības partneros un klientos, lai pats modernākais būtu sastopams “RigaBrain® ” kabinetā. Šobrīd tiek izmantotas kanādiešu un amerikāņu tehnoloģijas un pašas SIA “RigaBrain” uzkrātā pieredze.

Sniegsim mazu ieskatu par šobrīd izmantojamām tehnoloģijām SIA “RigaBrain” kabinetā.

Kā arī, SIA “RigaBrain” vadītājs Pēteris Urtāns aicina uz semināriem padziļinātāki iepazīties ar RigaBrain®  treniņiem  no dažādiem skatu punktiem un zināšanu līmeņiem. sk. šeit!

Bet iesāksim ar to, ka Neiroloģiski atgriezeniskās saites pieeja (neurofeedback), pie kuras pieder arī RigaBrain®  kabinetā izmantojamās tehnoloģijas, tiek plaši iztirzātas zinātnieku aprindās. Diskusijās nav vienotas nostājas par šīs pieejas efektivitāti un placebo.  Dažāda veida metapētījumi uzrāda dažādus rezultātus. Viens no iemesliem ir tas, ka pasaulē izmanto ļoti dažādas un atšķirīgas neurofeedback tehnoloģijas un pieejas. Piemēram, kaut vai impulsu nolasīšana sekundē var svārstīties no 16 līdz 1000 vienībām, kas katru tehnoloģiju padara atšķirīgu. 

Šobrīd RigaBrain®  kabinetā pieejamā metode, atšķirībā no klasiskajām neurofeedback pieejām, atšķiras ar to, ka netiek balstīta uz fizioloģiskām vai EEG izmaiņām, bet gan uz informācijas apstrādes procesu izmaiņām.

Teorētiskais pamatojums, jeb šīs pieejas paradigma ir balstīta uz neiroķirurga MD. Karl H. Pribram mūža darbu un pētījumiem. Kopskatam var iepazīties ar viņa publikācijām un darbu apkopojumu grāmatā - "The form within".

______________

Video, kurā tiek skaidrots viena no RigaBrain kabinetā  pieejamajām metodēm:

https://www.youtube.com/watch?v=wfPu8wDSzgk

<iframe width="560" height="315" src="https://www.youtube.com/embed/wfPu8wDSzgk" frameborder="0" allowfullscreen></iframe>

_______________

Apraksts:

  EEG Biofeedback In EEG biofeedback (neurofeedback), a real-time display of the brain’s electrical activity, fed back as visual or auditory information, enables the user to modify that brainwave activity. In a 2010 keynote address to the International Society for Neurofeedback and Research, Doidge23 suggested that the misperception that the adult brain was fixed and unchangeable “led scientists to doubt the claims made by the pioneers of neurofeedback.”23 Only with the discovery of neuroplasticity did the work of neurofeedback investigators and clinicians begin to find acceptance among other researchers. Still, for some time, there existed limited evidence as to whether EEG biofeedback directly affects neuroplasticity, resulting in ongoing skepticism about its potential as a restorative therapeutic modality. However, several recent studies utilizing transcranial magnetic stimulation and functional magnetic resonance imaging (fMRI) have demonstrated objective, temporally direct changes in cortical activation and connectivity as a result of neurofeedback.24,25 

In 2000, Clinical EEG and Neuroscience published a special issue on the topic of neurofeedback. The editor introduced the topic by writing, 

" The literature, which lacks any negative study of substance, suggests that EBT (EEG biofeedback therapy) should play a major therapeutic role in many difficult areas. In my opinion, if any medication had demonstrated such a wide spectrum of efficacy, it would be universally accepted and widely used." 26(pv)

Neurofeedback has since been shown to be an effective intervention for traumatic brain injury,27 and there have also been clinical reports (though no formal studies) of neurofeedback providing symptom relief for patients with multiple sclerosis (S. Othmer, PhD, e-mail communication, November 12, 2012). These findings provide a rationale for studying neurofeedback as an intervention for PCCI because traumatic brain injury and multiple sclerosis share features with PCCI: that is, demyelination and an expanded extent of brain activation for short-term memory tasks, suggesting “compensatory recruitment of additional brain regions in order to perform the task successfully” (p. 27).28 

Similar to trends in cognitive neuroscience, current neurofeedback strategies reflect 2 different but complementary directions: one driven by a focus on localization and the other by a focus on global brain function. The more common approach, with its roots in the localization school of neuroscience, could be characterized as a “diagnosis and treatment” approach, in which abnormalities in brainwave frequencies at particular locations are identified, ordinarily by means of a quantitative EEG. Researchers and clinicians have identified EEG patterns commonly associated with particular symptoms, and the neurofeedback equipment can be programmed to reward the brain for shifting its activity away from the symptom-associated patterns. For example, attention-deficit hyperactivity disorder (ADHD) in children frequently is associated with slow (theta) wave to fast (beta) wave ratios greater than 3:1 along the cingulate gyrus, located on the innermost surface of each hemisphere above the corpus callosum. A child with ADHD evidencing this pattern would be trained over a series of sessions to lower his/her theta wave amplitude.29 

The present study used a newer approach to neurofeedback, rooted in the global view of brain function. The NeurOptimal system, developed by the Zengar Institute (www.Zengar.com) is designed to train the brain as a whole, without reference to particular locations or frequencies. Unlike classical neurofeedback approaches, in which the participant engages actively and/or consciously with the software and is rewarded for producing prescribed EEG patterns, the participant in the Zengar approach simply “lets go” and allows the brain to use the feedback—provided as brief interruptions to the music he or she is hearing—to enable its own innate capacity for self-organization.

The feedback delivered by the Zengar system is systemic—based on the whole brain’s dynamic activity over time, not its achievement of prescribed states in prescribed locations. The fundamental assumption is that lowering the amplitude of any specific frequency (eg, 8-12 Hz in the left prefrontal cortex) will, by necessity, affect other frequencies in other parts of the brain in the same way that strengthening a single muscle group will affect alignment in other parts of the body, and so it is more realistic to train the brain as a whole system rather than focus on a single location or set of frequencies. This approach recognizes that the brain has a natural tendency toward self-regulation and resilience, allowing flexible cognitive and behavioral responses to a challenging and changing environment.

We are aware that oncology clinicians will be curious about the biological mechanism by which this form of neurofeedback might ameliorate PCCI. This is particularly true, given that the studies verifying PCCI and attempting to identify its causes have used a different paradigm than the one that underlies this form of neurofeedback. However, the existing evidence that PCCI exhibits variable neuroimaging findings and affects multiple neuropsychological domains (particularly in complex frontal-subcortical networks) does suggest that PCCI is not a clearly localizable phenomenon, which points to the importance of developing management strategies that respect the brain as a complex and highly integrated system. Our suspicion is that the concepts of nonlinear dynamical systems theory (eg, self-organization of complex systems, sensitive dependence on initial conditions, basins of attraction, and the importance of feedback), best understood by theorists and researchers in the fields of complexity science and systems theory,30 ultimately may be more productive in explaining both the changes in cognition seen during and after cancer treatment and also the mechanism underlying this form of neurofeedback.

Some work is already being done to bridge the fields of complexity science and neuroscience. In Modeling Phase Transitions in the Brain, Freeman asserts that “abrupt global reorganizations by phase transition in larger brain systems implement a wide variety of intellectual and intentional brain functions . . . including the switch from prodrome to epilepsy and from from sleep to wake or REM. . . . In each aggregate [of neurons] there are certain conditions that specify a critical point in the phase space at which the system is particularly susceptible to transit from one phase to another, as when the neurons in the sensory cortex transit from a disorganized state of expectation to an organized state of categorization, from noise to signal” (p. v, p. vii).31 Freeman acknowledges that this view of brain function is as yet unproven, but it is currently a focus of study among computational neuroscientists,32 and Freeman advocates the development of a detailed theory of nonlinear neurodynamics.

  The Zengar system is rooted in this view of brain organization. Its software detects phase state changes, the precursors to phase transitions. Alerted by feedback that a phase transition is imminent, the brain is able either to reorganize to return to its prior phase (as when the mind refocuses on a task after wandering) or to transit to a new phase (as in the movement from wakefulness to sleep). Neither phase is preferred, or sought, or avoided by the software. Instead, feedback simply is given when the phase transition is about to occur. Because there is no diagnosis required for this form of neurofeedback, and no specific protocol is developed on the basis of that diagnosis, this approach is considered to be training the brain in flexibility and resilience rather than treating particular symptoms. As a result, the Zengar system is not a controlled medical device and, therefore, the developers have not sought FDA approval. 

23. Doidge N. The brain that changes itself: the neuroplasticity
revolution and film clips of people undergoing plastic
change. Keynote address at: 18th Annual Conference of the
International Society for Neurofeedback and Research; September
27-October 3, 2010; Denver, CO.
24. Ros T, Munneke MA, Ruge D, Gruzelier JH, Rothwell
JC. Endogenous control of waking brain rhythms induces
neuroplasticity in humans. Eur J Neurosci. 2010;31:
770-778.
25. Ros T, Theberge J, Frewen PA, et al. Mind over chatter: plastic
upregulation of the fMRI salience network directly after
EEG neurofeedback. Neuroimage. 2013;65:324-335.
26. Duffy FH. The state of EEG biofeedback therapy (EEG operant
conditioning) in 2000: an editor’s opinion. Clin EEG
Neurosci. 2000;31:v.
27. EEGInfo. Neurofeedback research: head injury. http://
www.eeginfo.com/research/headinjury_main.html. Accessed
December 18, 2012.
28. McDonald BC, Saykin AJ, Ahles TA. Brain imaging investigation
of chemotherapy-induced neurocognitive changes. In:
Meyers CA, Perry JR, eds. Cognition and Cancer. New York,
NY: Cambridge University Press; 2008:19-32.
29. Demos JN. Getting Started With Neurofeedback. New York,
NY: Norton; 2005.
30. Kelso JAS. Dynamic Patterns: The Self-organization of
Brain and Behavior (Complex Adaptive Systems). Boston,
MA: Massachusetts Institute of Technology Press; 1995.
31. Freeman WJ. Foreword. In: Steyn-Ross DA, Steyn-Ross M,
eds. Modeling Phase Transitions in the Brain. New York,
NY: Springer; 2010.

Šie ir pētījumi, kuros tika izmantotas tās pašas tehnoloģijas, ko šobrīd izmanto RigaBrain® . Seansu intensitāte, apmeklējuma biežums un pakalpojuma sniegšanas metodika var atšķirties katrā no pētījumiem, bet izmantojamās tehnoloģijas ir tās pašas!

 ADDICTION

A Family Matter: NeurOptimal® For Fetal Alcohol Syndrome
FAS has a profound impact on both the child and the family. In this pilot study, NeurOptimal® was provided for both child & family members. Standard measures and FMRI were used to assess results.
Dr. Linda Beckett MD
Dr. Janet McCulloch MD
Founders of the Kingston Institute of Psychotherapy & Neurofeedback
In Kingston, Ontario, Canada 


ADHD-ADD (Attention Deficit Disorder)

Does NeurOptimal® Neurofeedback Help To Improve Focus/Attention?
A Kid, An Idea, A Study And A Big Payoff
Jules Ford, Founder of OptiMind Neurofeedback, Focus, Clarity, Serenity

Six Case Studies Examining the Effectiveness of a Comprehensive Adaptive Approach to Neurofeedback for Attention Deficit in an Educational Setting
Shannon Warwick, MA
Union Institute and University at Vermont College,Asheville, North Carolina

Effectiveness of EEG Biofeedback as Compared with Methylphenidate in the Treatment of Attention-Deficit/Hyperactivity Disorder: A Clinical Outcome Study
Mohammad Ali Nazari1, Laurent Querne2, Alain De Broca2, Patrick Berquin2
1Department of Psychology, University of Tabriz, Tabriz, Iran; 2Department of Paediatric Neurology, Lab. Neurosciences Fonction- nelles & Pathologies, Amiens, France.


ALZHEIMERS

The Latest News on The Continuing Research of Neuroptimal®'s Effects on Alzheimers Patients in France
Daniel Wagner, Zengar NeurOptimal® French Representative & Instructor
Founder of France Neurofeedback NeurOptimum®

Experiences with Alzheimer's Clients From Referring Hospitals in France
Daniel Wagner, Zengar NeurOptimal® French Representative & Instructor
Founder of France Neurofeedback NeurOptimum®


ANXIETY-DEPRESSION

Effects of NeurOptimal® Neurofeedback on Symptoms of Depression and Anxiety
Dr. Linda Beckett MD
Dr. Janet McCulloch MD
Founders of the Kingston Institute of Psychotherapy & Neurofeedback
In Kingston, Ontario, Canada 

Neurofeedback with Anxiety and Affective Disorders
D. Corydon Hammond, PhD, ABEN/ECNS
Physical Medicine and Rehabilitation, University of Utah School of Medicine, PM&R 30 No 1900 East, Salt Lake City, UT 84132-2119, USA


CHEMO BRAIN (Cancer therapy)

The Effect of EEG Biofeedback on Reducing Postcancer Cognitive Impairment
Jean Alvarez, EdD, Fremonta L. Meyer, David L. Granoff and Allan Lundy
Integr Cancer Ther published online 12 April 2013, DOI: 10.1177/1534735413477192
The abstract is available here where the full article is available for purchase.  Alternatively you may email Jean Alvarez directly for a full copy.

Efficacy of EEG Biofeedback in Addressing Cognitive Dysfunction in Cancer Survivors
This study examined the efficacy of EEG biofeedback (neurofeedback) in addressing the cognitive sequelae of cancer therapy, commonly known as “chemobrain” or “chemofog.” Approximately fifty percent of breast cancer survivors exhibit cognitive impairment within three weeks of beginning chemotherapy, and half of those have not recovered one year later.  Neurofeedback, unlike compensatory strategies currently recommended by the National Cancer Institute and major cancer centers, has the potential to restore cognitive function.
Jean Alvarez, EdD, David L. Granoff, Allan Lundy


FAMILY 

The Role of NeurOptimal® in Learning:  
A program for leaders in family enterprises and the professionals who serve them
NeurOptimal® training is shown to make a difference in an education-training course for professionals as they learn family systems theory. From the capable professionals employed in family firms, managing financial assets, to consulting complicated family enterprises. NeurOptimal® affects the ability to be more of a functioning self in anxious systems.
Kathy Wiseman (Bowen faculty member), Priscilla Friesen & Andrea Maloney Schara, LCSWA

Working With Extrinsic Constraints: 
Clinical Case Study From the Perspective of Bowen Family Systems Theory and NeurOptimal®. This study demonstrates the evident changes brought on through NeurOptimal® training
Lois Walker, B.N., M. Div.  

Observations of Change in a Family System Using NeurOptimal®
Someone dear to you, someone you deeply care about, has fallen through the cracks in the mental health system.  The medical system has not been able to interrupt a downward spiral in the person’s life. If you are interested in how one person in a family might respond differently in a crisis, you may be interested in this story of how an older sister learns to mange self using family systems theory and NeurOptimal® (www.zengar.com). It is a story of learning to be more aware and objective about mental illness and how thoughtful relationships changing, throughout the family, can make a tremendous difference in enabling better functioning (for everyone, not just the symptomatic one). Overall it is a story of how one begins to redirect anxiety in a system. 
Andrea Maloney Schara, LCSWA (www.ideastoaction.wordpress.com)

GENERAL

Five Year Clinical Outcome Study using NeurOptimal®
Outcome Analysis 2013 using NeurOptimal® Neurofeedback -------- A retrospective chart review
NeurOptimal® neurofeedback is designed to interface with the nervous system as a Non-Linear Complex Dynamical System, whereas traditional 'evidence based' research tends to measure linearly.  Therefore, it seems reasonable to measure outcomes because that's what prospective clients and referral sources really want to know. Since NeurOptimal® is training at the source of complaints, the nervous system's turbulence, rather than treating diagnostic categories, it also seemed reasonable to look at outcomes across diagnostic categories rather than try to compare medically defined groups where fallible diagnostic judgments create more issues.
Joan Cross, BSc, PT, MPH from the InnerWave Center

Comprehensive Neurofeedback Training in the Context of Psychotherapy for Tranformational Change
A doctoral dissertation submitted to the Dean of Behavioural Health, July 2010.
Karen Cochrane, Ph.D.

Brain-Mind Operational Architectonics Imaging: Technical and Methodological Aspects
Andrew A. Fingelkurts, Alexander A. Fingelkurts
BM-Science – Brain & Mind Technologies Research Centre, P.O. Box 77, FI-02601, Espoo, Finland


PEAK PERFORMANCE

The Effects of EEG Neurofeedback and Neuro-Cognitive Processing in the
Educational Environment of an Arts-Based Private Elementary/Middle School
The purpose of this study was to ascertain measurable differences in academic outcomes among two groups of learners within a creative arts curriculum that employed a selfdirected, stress-reduced, and individualized curriculum. One group was provided with EEG neurofeedback and neuro-cognitive coaching for stress reduction, while the other was not. This study measured the rate of improvement in academic progress that a learner can achieve when formalized learning takes place in a stress-reduced arts-based learning environment with EEG neurofeedback and neurocognitive coaching.
Lise' D. Spurlock-DeLong, Ph.D., CPCRT, CCCM 

PTSD (Post Traumatic Stress Disorder) 

Overcoming Extreme Challenges: How NeurOptimal® can be an Answer for our Veterans
Marie-Laure Wagner, M.S. Zengar NeurOptimal® Representative & Founder of  InnerOptimal


SLEEP DISORDERS

Natural Sleep Rhythms: From Cells to Behavior  
How NeurOptimal® potentially interfaces with, and restores, natural sleep rhythms.  New evidence mapping cellular networks underlying the brain’s circadian timing nucleus has provided a potential locus of control for the human circadian rhythm (HCR). This network provides a biologically plausible regulatory site for external influence of the HCR-notably pharmacologic agents, environmental influence, and particularly salient to this presentation, NeurOptimal®.
Dr. Ed O'Malley, Ph.D, FAASM (PhD in Neurobiology at Cornell University Graduate School of Medical Sciences; postdoc in EEG brain-mapping and clinical fellowship for board certification in Sleep Medicine (NYU School of Medicine); Director, Norwalk Hospital Sleep Disorders Center for 12 years-pioneered neurofeedback for insomnia).

Optimal Sleep Using NeurOptimal®: Insomnia Studies
Dr. Ed O'Malley, Ph.D, FAASM

Global qEEG Changes Associated With Non-frequency & Non-site Specific Neurofeedback Training
Dr. Ed O'Malley, Ph.D., FAASM  and Merlyn Hurd, PhD, BCIAC/EEG Fellow, New York, NY

Neurofeedback COPD Abstract Submitted by Ed O'Malley PhD, FAASM
Gleeson S, Saad T, Haggerty M, O’Malley M, O’Malley E, Weddle A, Winter S. Efficacy of Neurofeedback as an Adjunct Therapy to Pulmonary Rehabilitation. Am J Respir Crit Care Med, 2009; 179: A2366.

Effectiveness of Neurofeedback Training In Chronic Insomnia
Okunola O,1 O'Malley E,2 O'Malley M2
(1) Norwalk Hospital, Norwalk, Connecticut, Norwalk, CT, USA, (2) Norwalk Hospital, Norwalk, CT, USA


TBI (Traumatic Brain Injury)

A CASE STUDY …. Physical, Psychological and Cognitive Trauma with Recovery through NeurOptimal® Neurofeedback.
Personal case study with pre and post NeurOptimal® psychometric testing and SPECT Scan results.
Denise M. Cull, M.Psych, Forensic

The Use of NeurOptimal® With a Young Adult With an Acute TBI - A Case Study
This is a case study using clinical, NeurOptimal® baseline and anecdotal data. Dr. Lartin’s 22 year old son Brian survived a life-threatening TBI in April of 2012 and had more than 70 sessions of NeurOptimal® in a year. The contribution of NeurOptimal® to his recovery is discussed. Brian made an essentially complete recovery.
Dr. Joan-Marie Lartin, Ph.D., RN and Alan Bachers, Ph.D

NeuroCognitive Approaches to Brain Injury 
Independent case studies focusing on the cognitive aspects of remediation when coupled with NeurOptimal®. 
A trio of case studies, protocols and progress:
1. 24 year old boy with Concussive Syndrome
2. 68 year old woman with primary ischemic stroke 
3. 30 month old girl with a rare chromosomal disorder;
Dr. Lise' D. Spurlock-DeLong, Ph.D., CPCRT, CCCM   
Founder of Cognitive Connections, a cognitive rehabilitation clinic focusing on the use of neurofeedback and assistive therapies to promote increased neuroactivity and processing in all people. 

Systemic EEG Biofeedback for Traumatic Brain Injury
Since NeurOptimal® helped ChemoBrain patients, will it also help people with TBI with similar symptoms? 
Dr. Jean Alvarez, Ed.D     

Director of research at the newly incorporated Cleveland-based Applied Brain

Iesākumā rekomendēja iepazīties ar šo rakstu latviešu valodā: 

http://rigabraininfo.uservoice.com/knowledgebase/articles/96200-rigabrain-smadze%C5%86u-treni%C5%86%C5%A1-un-neiroplasticit%C4%81te

un šo angļu valodā prezentāciju:

The Science Behind NeurOptimal® by Valdeane W. Brown, Ph.D.

______

RigaBrain®  treniņu pamatā ir nelineārā neiroloģiskās atgriezeniskās saites metode NeurOptimal, un RigaBrain®  treniņu ietvaros smadzenes tiek izprastas kā kompleksa, dinamiska sistēma.

Neuroptimal ir izstrādāta Zengar Institūtā Kanādā. Vairāk kā 3 000 000 stundas, pārbaudīts uz cilvēkiem, 20 gadi pētījumi un eksperimenti. www.zengar.com

Raksti par “nonlinear brain” – Spied šeit!

Lai izprastu pieeju, jums ir jābūt izpratnei par:

- Control of Nonlinear Dynamic Systems

- Joint Time-Frequency Analysis (JTFA)

RigaBrain®  metode un cilvēka prāta spējas –  pilotpētījums

Rezumējums

Šī pilotpētījuma mērķis bija noskaidrot, vai RigaBrain®  kabinetā pieejamā neiroloģiskās atgriezeniskās saites metode uzlabo kognitīvās funkcijas – verbālo atmiņu, vizuālo atmiņu, psihomotoro ātrumu, reakcijas laiku, kognitīvo elastību, dažādas uzmanības modalitātes, apstrādes ātrumu, vadības funkcijas un spriestspēju – normatīvi veselu pieaugušu indivīdu vidū. Pētījuma dalībniekiem tika veikts sākotnējais un atkārtotais kognitīvo spēju mērījums; starp tiem katrs no viņiem apmeklēja vienu RigaBrain®  procedūru (parasti rekomendējam apmeklēt aptuveni 10 seansus). Rezultātu analīzē izmaiņas tika konstatētas šādās izziņas spējās: kognitīvajā elastībā, vadības funkcijās un emociju atpazīšanā.

Atslēgas vārdi: neiroloģiskā atgriezeniskā saite, kognitīvās spējas, prāta spējas, izziņas spējas, RigaBrain

Ievads

Arvien pieaugošu aktualitāti Latvijā, tāpat kā citviet pasaulē, gūst neiroloģiskās atgriezeniskās saites metodes smadzeņu darbības līdzsvarošanai – procedūras, ar kuru palīdzību cilvēka centrālā nervu sistēma un smadzeņu darbība tiek tuvināta iespējami optimālākam stāvoklim. Tā kā šīs ir salīdzinoši jaunas metodes, daļai no tām līdz šim nav rasts pilnvērtīgs zinātnisks pamatojums (Duffy, 2000). Aktuālākais jautājums pētnieku vidū šobrīd – cik noturīgi laikā ir šajās procedūrās iegūtie rezultāti.

Sabiedrībā popularitāti gūst arī cilvēku tieksme pilnveidot un uzturēt relatīvi nemainīgu prāta spēju kvalitāti, – lielā mērā augstās konkurences darba tirgū dēļ. Viena no iespējām, kā to varētu panākt, ir tieši neiroloģiskās atgriezeniskās saites procedūru izmantošana.

Lai arī kā atsevišķi konstrukti gan kognitīvo spēju uzlabošana, gan neiroloģiskās atgriezeniskās saites metodes tiek pētīti diezgan bieži, tomēr pētījumu par neiroloģiskās atgriezeniskās saites metožu lietošanas ietekmi uz kognitīvo spēju attīstību šobrīd vēl ir salīdzinoši maz, un iegūtie rezultāti nereti iezīmē pretrunīgu ainu. Šis pilotpētījums ir Latvijā pirmais mēģinājums noskaidrot, vai klasiskām neiroloģiskās atgriezeniskās saites metodēm līdzvērtīga procedūra, kādu iespējams veikt RigaBrain®  smadzeņu darbības uzlabošanas centrā, nozīmīgi ietekmē cilvēka izziņas spēju uzlabošanos.

Neiroloģiskās atgriezeniskā saites metodes

Neiroloģiskās atgriezeniskās saites (NAS) treniņi smadzeņu darbības līdzsvarošanai ir metodes, kas veicina smadzeņu viļņu ierastā modeļa izmaiņas, tuvinot to pēc iespējas optimālākam, dabiskākam stāvoklim. Tas ietver individuāli atšķirīgus psihofizioloģiskus rezultātus personām, kas izmanto šīs metodes. Lai gan kopš 20. gs. 70. gadiem veikti vairāk kā 1000 pētījumu, kuros vairumā gadījumu pierādīta neiroloģiskās atgriezeniskās saites metožu efektivitāte (Duffy, 2000), tomēr pētījumu respondentu unikālo un individuāli atšķirīgo rezultātu dēļ NAS arvien pieskaitāmas eksperimentālo metožu grupai, turklāt vairums veikto pētījumu sniedz kvalitatīvus datus, kas vairāk pakļauti subjektīvai interpretācijai, salīdzinot ar kvantitatīvām pieejām.

NAS ietver vairākas formas. „RigaBrain® ” kabinetā pieejamā nelineārā NAS metode, kas pirms 12 gadiem tika radīta “Zengar NeuroCARE/NeurOptimal” institūtā Kanādā, ir konstrukts, kas veicina kontroli pār elektroķīmiskiem procesiem smadzenēs un centrālajā nervu sistēmā (CNS). Teju neskaitāmos pētījumos atrunāta smadzeņu izteikti adaptīvā pielāgoties spēja organisma iekšējai un ārējai videi (neiroplasticitāte) – smadzenes un CNS mūsdienās tiek aplūkota kā nelineāra, dinamiska, kompleksa sistēma (Hammond, 2009). Ar šīs NAS metodes palīdzību (pašatspoguļojošas terapijas forma) smadzenes pašas spēj monitorēt jeb novērot savu darbību un fiksēt disfunkcionālus smadzeņu viļņu paternus, veicinot to izmaiņas (Cochrane, 2010). Līdz ar to no klasiskajām, invazīvajām NAS metodēm (to laikā klientam tiek piedāvāts veikt noteiktas darbības, kontrolēt un vadīt noteiktas norises, lai panāktu noteiktu smadzeņu darbības stāvokli) „Zengar NeuroCARE/NeurOptimal” atšķiras ar neinvazīvu pieeju, kas tādējādi veicina CNS pašorganizējošus procesus. Metodes pamatā ir vienotā laika – biežumu analīze (Joint Time-Frequency Analysis  (JTFA)), t. i., fiksēto signālu apstrāde notiek vienlaicīgi gan laikā, gan biežumu ziņā (Cohren, 1995).

Svarīgākās pētījumā minētās kognitīvās spējas – terminu skaidrojums

Kognitīvās (izziņas, prāta) spējas (cognitive abilities) ir indivīda kapacitātes atspoguļojums, apstrādājot un integrējot informāciju, darbojoties un risinot pieredzē nebijušas problēmsituācijas. Tās ir saistītas ar vispārējo intelektu (general intelligence), un to dažādība un stabilitāte indivīdam lielākoties’ ir ģenētiski noteikta (Stawski et al., 2010). Kognitīvās spējas izsaka indivīda potenciālu (Caroll, 1993).

Saskaņā ar pētnieku Pīteru Andersonu (Peter Anderson), vadības funkcijas parasti tiek iedalītas šādi: uzmanības kontrole, kognitīvā elastība, mērķu plānošana, apstrādes ātrums un efektivitāte (Anderson, 2002). Citi autori uzsver, ka ar vadības funkcijām tiek saprasti tādi izziņas procesi kā plānošana, uz mērķi orientētas uzvedības un domu apzinātība un kontrole. Vadības funkcijas ir svarīgas jaunas informācijas apguvē un emociju kontrolē, piemēram, atklāts, ka pirmsskolas vecuma bērnu vidū vadības funkcijas ir saistītas ar gatavību skolai pat ciešāk, kā vispārējais intelekts un matemātiskās iemaņas (Enriquez-Geppert et.al., 2013).

Emociju atpazīšana ir spēja izjust un piedzīvot cita cilvēka psiholoģisko stāvokli un vadīt savu uzvedību saskaņā ar šo pieredzi (Jaramillo, 2004).

Savukārt kognitīvā elastība ir mentāla spēja manipulēt ar domām par diviem dažādiem jēdzieniem, kā arī domāt par vairākiem jēdzieniem vienlaicīgi (Scott, 1962).

Līdzīgi pētījumi

Mūsdienās (saskaņā ar 193 pētījumu meta-analīzi) ir skaidrs, ka vadības funkcijas smadzenēs lokalizējas vairākos reģionos galvenokārt smadzeņu garozā, kā arī subkortikālās zonās, piemēram, talāmā, bazālajos ganglijos un smadzenītēs. Interesanti, ka neiroloģiskās atgriezeniskās saites metodes nozīmīgi ietekmē neirālo plasticitāti (neironu savienojumu elastību) tieši smadzeņu garozas reģionos (Ros et al., 2010), kas šādā saistībā daļēji varētu skaidrot izziņas spēju uzlabojumu NAS procedūru ietekmē. Vēl kādā pētījumā atklāts, ka ar NAS metožu palīdzību iespējams uzlabot alfa un teta smadzeņu viļņu darbību (Zaehle et al., 2010, kā minēts Enriquez-Geppert et.al., 2013).

Savukārt vēl citā pētījumā desmit bērniem, kas cieš no Aspergera sindroma, tika veiktas NAS procedūras (kontroles grupā ietilpa desmit bērnu ar minēto sindromu, kas neapmeklēja procedūras). Tika atklāta kognitīvās elastības spēju uzlabošanās attiecībā uz ciparu un tēlu kombinēšanu un veidošanu (set shifting), bet ne uz jēdzienu veidošanu (concept generation) (Kouijzer et.al., 2009).

Jaunākajos pētījumos secināts, ka NAS metodēm ir potenciāls, tomēr nav skaidrs, cik ilgstoša ir procedūrās sasniegtā rezultāta noturība laikā. Tiek uzsvērta šo metožu papildināšanas un adaptācijas nepieciešamība atbilstoši vides un fenotipiskiem apstākļiem (Enriquez-Geppert et.al., 2013).

Lai saglabātu ilgtermiņa vadības funkciju uzlabojumu noturību, tiek ieteikta arī šo metožu kombinēšana ar uzvedības trenēšanas jeb kognitīvi – biheiviorālās terapijas metodēm (Enriquez-Geppert et.al., 2013).

Metode

Šī pilotpētījuma ietvaros piedalījās deviņi dalībnieki* (septiņas sievietes un divi vīrieši; vidējais vecums – 30,1 gads), kuri iepriekš tika iepazīstināti ar pētījuma mērķi – noskaidrot, kā mainās cilvēka prāta spējas smadzeņu atgriezeniskās saites treniņu rezultātā. Dalībniekiem tika veikts sākotnējais un atkārtotais kognitīvo spēju mērījums (kopskaitā tika pētītas 14 izziņas spējas); starp tiem katrs no viņiem apmeklēja vienu RigaBrain®  procedūru. Starp pirmo un otro kognitīvo novērtējumu bija astoņas līdz 21 diena (vidēji 16,6); atkārtotais izziņas spēju novērtējums pēc neiroloģiskās atgriezeniskās saites norisinājās pēc vienas līdz divām dienām. Kognitīvais novērtējums tika veikts ar datorizētu testu CNS Vital signs, kas ir plaši izmantota neirokognitīvo testu baterija un tiek raksturota kā skrīninga, ierindas novērtējuma instruments klīniskos pētījumos (Gualtieri, Johnson, 2006) ar augstiem validitātes rādītājiem.

*Laika posmā starp pirmo un otro kognitīvo novērtējumu viens līdz divi pētījuma respondenti uzsāka medikamentu lietošanas kursu.

Rezultāti

Kognitīvo spēju novērtējumu salīdzinājums pirms un pēc neiroloģiskā stimulmateriāla ietekmes, izmantojot T – Stjūdenta kritēriju atkarīgām grupām, redzams pirmajā tabulā.

1.tabula. Kognitīvo spēju novērtējumu salīdzinājums pirms un pēc RigaBrain®  procedūras.

* p<0,05, **p<0,01

Statistiskā aprēķina rezultātā iespējams secināt, ka nozīmīgas izmaiņas konstatētas šādās izziņas spējās: kognitīvajā elastībā, vadības funkcijās un emociju atpazīšanā. Tas nozīmē, ka laika posmā starp pirmo un otro kognitīvo spēju novērtējumu, kad norisinājās arī RigaBrain®  procedūras, noticis statistiski nozīmīgs šo spēju uzlabojums.

Zemāk arī vizuāli attēlotas izmaiņas visās kognitīvajās spējās pēc stimulmateriāla ietekmes.

 1.grafiks. Kognitīvo spēju novērtējumu vizuāls salīdzinājums pirms un pēc RigaBrain®  procedūras.

Turpmākā pētnieciskā darbība

Pētījuma pilnveidojamās puses būtu izlases reprezentivitātes uzlabošana (mazs respondentu skaits; kontrolgrupas neesamība) un papildu sajaukto mainīgo kontrole (ārējās un iekšējās vides apstākļi starp kognitīvo spēju mērījumiem, mācāmības un placebo efektu kontrole), lai nodrošinātu augstāku rezultātu ticamību.

Būtu svarīgi veikt arī trešo kognitīvo spēju novērtējumu, lai noskaidrotu, vai iegūtie rezultāti ir noturīgi laikā.

Pētījuma autori: Edmunds Vanags, Linda Tīruma, Pēteris Urtāns

Izmantotā literatūra:

Anderson, P. (2002). Assessment and development of executive function (EF) during childhood. Child Neuropsychology, 8, 71–82.

Carroll, J. B. (1993). Human Cognitive Abilities. Cambridge: Cambridge University Press.

Cohen, l. (1995). Time–Frequency Analysis. Prentice-Hall: New York.

Duffy, F. R. (2000). The State of EEG Biofeedback Therapy (EEG Operant  Conditioning) in 2000: An Editor’s Opinion.  Clinical  Electroencephalography, 31 (1), 5 – 13.

Enriquez-Geppert S., Huster R. J., Herrmann C. S. (2013). Boosting brain functions: improving executive functions with behavioral training, neurostimulation, and neurofeedback. International Journal of Psychophysiology, 88, 1–16.

Gualtieri, C. T., Johnson, L. G. (2006). Reliability and validity of a computerized neurocognitivetest battery, CNS Vital Signs. Clinical Neuropsychology, 21,  623–643.

Hammond, D. C. (2009). Comprehensive Neurofeedback Bibliography (pp. 30). Retrieved from http://www.isnr.org/ComprehensiveBibliography.cfm

Jaramillo, R. R. (2004). Differences Between African Americans and White Americans on Social Acuity. California State University Press: San Bernardino.

Kouijzer, M. E. J., van Schie, H. T., de Moor J., Gerrits, B., J., L., & Buitelaar, J. K. (2009). Neurofeedback treatment in autism. Preliminary findings in behavioral, cognitive, and neurophysiological functioning. Research in Autism Spectrum Disorders, 216, 2 – 14.

Ros, T., Munneke, M.A., Ruge, D., Gruzelier, J.H., Rothwell, J.C., 2010. Endogenous control of waking brain rhythms induces neuroplasticity in humans. The European Journal of Neuroscience 31, 770–778.

Scott, W. A. (1962). Cognitive complexity and cognitive flexibility. American Sociological Association, 25, 405–414.

The Mean of the median: A New Metric for Targeting in Clinical Neurofeedback?

Valdeane W. Brown

Zengar Institute, Port Jefferson, NY

In order to explore the role of “variability” around targets during Neurofeedback, NeuroCare Pro® software was used to measure the emergent median values of each of the targets employed during Period 3 training, as well as in post-hoc analysis of other data. These median values were concurrently subjected to a 16 times per second averaging procedure to derive a dynamic measure of the “variability” of the emergent “central tendency” of each of the targets during clinical training. This “Mean of the Median” (or MoM) measure was used to derive the actual triggering of feedback events in re: to calculated divergence within a neighborhood around this dynamic measure. An 80% “inclusion” criterion was used for determining the size of the neighborhood for each target. For inhibit targets, feedback was enabled and/or produced when the emergent median remained within that neighborhood, whereas feedback for all targets was disabled by excursions beyond this neighborhood for any inhibit. The same basic procedure was used for augment targets except that excursions outside of an augment’s neighborhood had no effect on any other target. This procedure was used for active training of clients (N > 50), as well as for post-hoc analysis of pre-existing data obtained from other, successful Neurofeedback cases (N > 200) using different feedback paradigms, equipment and/or software. Several interesting results have begun to emerge from these preliminary investigations:

1- Clinical improvements appear to be correlated with decrease in both negative and positive divergence for all inhibit targets. This reflects a kind of “regression to the mean” re: inhibits during renormalization.

2- Renormalization of the inhibits may be an indicator of resilience in the CNS, or what Pribram refers to as efficiency.

3- Clinical improvements appear to also be correlated with a decrease in the number, duration and intensity of negative divergences for all augment targets but do not seem to be particularly correlated with any form of positive divergence for the same targets. Thus, it appears that it is not increases in augments per se that are important, but lack of decreases.

4- Renormalization of the augments may be an indicator of flexibility in the CSN, or what Pribram refers to as effectiveness.

5- This procedure may yield some useful metrics for successful training regardless of the approach used.

___________________________

Non – linear Dynamics Panel: EEG, Neurofeedback and Non – linear, Dynamical Approaches: Explorations into the Chaos at the Cutting Edge of the Clinical Practice and Research

Valdeane W. Brown

Zengar Institute

Data acquisition and “real-time” analysis continue to be a central issue in the rapidly emerging field of Neurofeedback. Fundamental questions concerning the characteristics of the EEG signal itself directly affect equipment manufacturers, researchers and clinicians. These questions can not be avoided any longer – with the easy availability of advanced computing platforms and sophisticated statistical packages, the average practitioner can reasonably address these concerns within the confines of his/her own office. In this panel we will discuss many of the current issues concerning the role that Non-Linear, Dynamical or NLD approaches to data analysis play in the field of Neurofeedback. Chaos theory, as these approaches are also known, has been applied successfully to many other scientific domains including biology, economics, hydraulics, aerodynamics, cognitive science and meteorology. In fact, virtually every other field of inquiry has benefited greatly from the insights and techniques afforded by this revolutionary and essentially interdisciplinary approach to scientific inquiry. Thus, there is a clear difference in paradigm implied by a shift to NLD or Chaos Theory and the question really is: Is there any reason to make this shift in paradigm?

+

Because of all of this there isn’t a specific “target” or “profile” towards which (or away from which) we hope to direct the CNS.  We don’t have a concept of the “normal” brain and try to get the brain to produce a “more normal” profile — that would be to base what we do on presumed normative data that actually doesn’t exist at this time.

All we do is to present real-time information to the CNS, in its own language (viz that of time-frequency atoms), and that allows the CNS to do what it does best: viz, process information and respond to that information.

__________________

Brain lives at “edge of chaos”

March 18, 2009
Courtesy Public Library of Science
and World Science staff

U.K. re­search­ers are of­fer­ing new ev­i­dence that the hu­man brain lives “on the edge of chaos,” at a crit­i­cal tran­si­tion point be­tween ran­dom­ness and or­der.

The stu­dy, pub­lished March 20 in the re­search jour­nal PLoS Com­puta­t­ional Bi­ol­o­gy, pro­vides ex­pe­ri­men­tal da­ta on an idea pre­vi­ously fraught with the­o­ret­i­cal specula­t­ion.

Sci­en­tists have iden­ti­fied a phe­nom­e­non they call self-or­gan­ized crit­i­cal­ity—where sys­tems spon­ta­ne­ously or­gan­ize them­selves to op­er­ate at the bor­derline be­tween or­der and chaos—in many dif­fer­ent phys­i­cal sys­tems, in­clud­ing avalanches, for­est fires, earth­quakes, and heart rhythms.

Ac­cord­ing to the stu­dy, by a team from the Uni­ver­s­ity of Cam­bridge, the Med­i­cal Re­search Coun­cil Cog­ni­tion & Brain Sci­ences Un­it, and the Glax­o­SmithK­line Clin­i­cal Un­it Cam­bridge, hu­man brain net­work dy­nam­ics have some­thing im­por­tant in com­mon with some su­per­fi­cially very dif­fer­ent sys­tems in na­ture.

Com­puta­t­ional net­works show­ing these char­ac­ter­is­tics have al­so been shown to have the best mem­o­ry and in­forma­t­ion-processing ca­pacity, re­search­ers say: crit­i­cal sys­tems can re­spond quickly and ex­ten­sively to small changes in their in­puts.

“Due to these char­ac­ter­is­tics, self-or­gan­ized crit­i­cal­ity is in­tu­i­tively at­trac­tive as a mod­el for brain func­tions such as per­cep­tion and ac­tion, be­cause it would al­low us to switch quickly be­tween men­tal states in or­der to re­spond to chang­ing en­vi­ron­men­tal con­di­tions,” said co-author Man­fred Kitzbich­ler of Cam­bridge.

The re­search­ers used brain im­ag­ing tech­niques to meas­ure dy­nam­ic changes in the syn­chron­iz­a­tion of ac­ti­vity be­tween dif­fer­ent re­gions of the func­tion­al net­work in the hu­man brain. They al­so in­ves­t­i­gated the syn­chron­iz­a­tion of ac­ti­vity in com­puta­t­ional mod­els, and found that the “dy­nam­ic pro­file” they had iden­ti­fied in the brain was ex­actly re­flected in the mod­els.

“A nat­u­ral next ques­tion we plan to ad­dress in fu­ture re­search will be: How do meas­ures of crit­i­cal dy­nam­ics re­late to cog­ni­tive per­for­mance or neu­ro­psy­chi­atric disor­ders and their treat­ments?” said Kitzbich­ler.

http://www.ploscompbiol.org/home.action
Kitzbichler et al. Broadband Criticality of Human Brain Network Synchronization. PLoS Computational Biology, March 20, 2009; 5 (3): e1000314 DOI:10.1371/journal.pcbi.1000314

http://www.sciencedaily.com/releases/2009/03/090319224532.htm

___________________________

The Science of Neurofeedback

The positive effects of neurofeedback were first noted by accident. Dr. Barry Sterman, a research scientist, was studying the levels at which toxic fumes caused cats to experience seizures. Dr. Sterman noticed that one group of cats was able to withstand much greater levels of toxic fumes prior to seizuring. Puzzling over the causes, Dr. Sterman noted that these cats had been reused from a prior experiment in which they had undergone neurofeedback training. Further studies revealed what Dr. Sterman had begun to suspect: neurofeedback training had a preventative effect on seizuring in both cats and humans. Many studies later, the evidence suggested that neurofeedback training was helpful for many conditions, such as attention deficit disorder (ADD), depression, improving cognitive focus, alleviating sleep disorders, etc.

Neurofeedback began to be used by psychologists for training the brain to alleviate symptoms such as seizures in epileptics and attention in those with ADD. Typically, sensors would be hooked up to a client’s brain in order to read and map brain activity. The client’s brain map would be compared to a database of average people of the client’s age and gender to determine areas of over- or under-activity, and then a course of treatment would be provided to reward activity in some areas and punish it in others.(1) Typically, rewards and punishments are focused on a few highly specific frequencies of brainwave activity. This means of conducting neurofeedback is still in use by most neurofeedback providers today.

In the 1990s, Dr. Val Brown began to doubt this mode of neurofeedback, feeling that rewarding and punishing a limited set of frequencies of brain activity might have more adverse than beneficial effects. He and Dr. Susan Brown, both clinical psychologists and neurofeedback experts, began to experiment with new ways of conducting neurofeedback. With highly sophisticated mathematics programmed into their neurofeedback system, the Drs. Brown found that clients’ brains, given information about their own functioning, would dynamically adjust themselves over time to more optimal functioning across a broad spectrum of brain frequencies. No diagnosis was necessary. Because no reward or punishment is administered in this system, and because a comprehensive set of frequencies is used rather than a few specific frequencies, the risk of side effects is low. Yet because the human brain responds automatically to information about its own functioning, clients undergoing the neurofeedback training provided by the Drs. Brown experienced significant enhancements in brain and central nervous system functioning. “Everyone can benefit from NeurOptimal™ because everyone has a brain and a central nervous system”, according to Dr. Susan Brown. Trainers claim that NeurOptimal™ is highly effective for a wide variety of disorders and people, and has been most effective with anxiety problems, stress related sleep disorders, depression, ADD/ADHD, headaches and migraines, and focus issues.

(1) http://en.wikipedia.org/wiki/Neurofeedback, accessed June 29, 2009.

_____

Viens uzskatāms piemērs par Neurofeedback metodes efektivitāti. Spied šeit!

_______

Probācija un cietumnieki - spied šeit!

Laimas slimība un CNS – spied šeit!

Neiroreakcija nemiera un afektīvu traucējumu ārstēšanai

D. Corydon Hammond, PhD, ABEN/ECNS , Physical Medicine and Rehabilitation, University of Utah School of Medicine, PM&R 30 No 1900 East, Salt Lake City, UT 84132-2119, USA

spied šeit!

Evidence-Based Practice in Biofeedback and Neurofeedback 2008; grāmata – spied šeit!

____

http://www.mrc-cbu.cam.ac.uk/

___

Pētījumi, kuros ir izmantotas tehnoloģijas, kuras izmanto RigaBrain®  kabinets:

Comprehensive Neurofeedback Training in the Context of Psychotherapy for Tranformational Change, By Karen Cochrane

A doctoral dissertation submitted to the Dean of Behavioural Health, July 2010.

Global qEEG Changes Associated With Non-frequency & Non-site Specific Neurofeedback Training

A powerpoint presentation submitted by Edward B. O'Malley, PhD, D,ABSM (Director, Norwalk Hospital Sleep Disorders Center, Norwalk CT) and Merlyn Hurd, PhD, BCIAC/EEG Fellow, New York, NY

The Effects of EEG Neurofeedback and Neuro-Cognitive Processing in the Educational Environment of an Arts-Based Private Elementary/Middle School By Lise’ DeAndre’ DeLong

The purpose of this study was to ascertain measurable differences in academic outcomes among two groups of learners within a creative arts curriculum that employed a selfdirected, stress-reduced, and individualized curriculum. One group was provided with EEG neurofeedback and neuro-cognitive coaching for stress reduction, while the other was not. This study measured the rate of improvement in academic progress that a learner can achieve when formalized learning takes place in a stress-reduced arts-based learning environment with EEG neurofeedback and neurocognitive coaching.

Six Case Studies Examining the Effectiveness of a Comprehensive Adaptive Approach to Neurofeedback for Attention Deficit in an Educational Setting

By Shannon Warwick, MA

Union Institute and University at Vermont College,Asheville, North Carolina

Neurofeedback COPD Abstract Submitted by Ed O'Malley PhD, FAASM

Gleeson S, Saad T, Haggerty M, O’Malley M, O’Malley E, Weddle A, Winter S. Efficacy of Neurofeedback as an Adjunct Therapy to Pulmonary Rehabilitation. Am J Respir Crit Care Med, 2009; 179: A2366.

Neurofeedback with Anxiety and Affective Disorders

By D. Corydon Hammond, PhD, ABEN/ECNS

Physical Medicine and Rehabilitation, University of Utah School of Medicine, PM&R 30 No 1900 East, Salt Lake City, UT 84132-2119, USA

Brain-Mind Operational Architectonics Imaging: Technical and Methodological Aspects

Andrew A. Fingelkurts, Alexander A. Fingelkurts

BM-Science – Brain & Mind Technologies Research Centre, P.O. Box 77, FI-02601, Espoo, Finland

Efficacy of EEG Biofeedback in Addressing Cognitive Dysfunction in Cancer Survivors By Jean Alvarez, David L. Granoff, Allan Lundy

This study examined the efficacy of EEG biofeedback (neurofeedback) in addressing the cognitive sequelae of cancer therapy, commonly known as “chemobrain” or “chemofog.” Approximately fifty percent of breast cancer survivors exhibit cognitive impairment within three weeks of beginning chemotherapy, and half of those have not recovered one year later.  Neurofeedback, unlike compensatory strategies currently recommended by the National Cancer Institute and major cancer centers, has the potential to restore cognitive function.

Effectiveness of EEG Biofeedback as Compared with Methylphenidate in the Treatment of Attention-Deficit/Hyperactivity Disorder: A Clinical Outcome Study

Mohammad Ali Nazari1, Laurent Querne2, Alain De Broca2, Patrick Berquin2

1Department of Psychology, University of Tabriz, Tabriz, Iran; 2Department of Paediatric Neurology, Lab. Neurosciences Fonction- nelles & Pathologies, Amiens, France.

Effectiveness of Neurofeedback Training In Chronic Insomnia

Okunola O,1 O'Malley E,2 O'Malley M2

(1) Norwalk Hospital, Norwalk, Connecticut, Norwalk, CT, USA, (2) Norwalk Hospital, Norwalk, CT, USA

u.c.

______________________________

Citi saistošie pētījumi:

Brain Waves' Challenge Area-Specific View of Brain Activity

Mar. 20, 2013 — Our understanding of brain activity has traditionally been linked to brain areas – when we speak, the speech area of the brain is active. New research by an international team of psychologists led by David Alexander and Cees van Leeuwen (KU Leuven – University of Leuven) shows that this view may be overly rigid. The entire cortex, not just the area responsible for a certain function, is activated when a given task is initiated. Furthermore, activity occurs in a pattern: waves of activity roll from one side of the brain to the other.

In this study, the psychologists explore uncharted territory: "We are examining the activity in the cerebral cortex as a whole. The brain is a non-stop, always-active system. When we perceive something, the information does not end up in a specific part of our brain. Rather, it is added to the brain's existing activity. If we measure the electrochemical activity of the whole cortex, we find wave-like patterns. This shows that brain activity is not local but rather that activity constantly moves from one part of the brain to another. The local activity in the Brodmann areas only appears when you average over many such waves.”The brain can be studied on various scales, researcher David Alexander explains: "You have the neurons, the circuits between the neurons, the Brodmann areas – brain areas that correspond to a certain function – and the entire cortex. Traditionally, scientists looked at local activity when studying brain activity, for example, activity in the Brodmann areas. To do this, you take EEG's (electroencephalograms) to measure the brain’s electrical activity while a subject performs a task and then you try to trace that activity back to one or more brain areas."

Each activity wave in the cerebral cortex is unique. "When someone repeats the same action, such as drumming their fingers, the motor centre in the brain is stimulated. But with each individual action, you still get a different wave across the cortex as a whole. Perhaps the person was more engaged in the action the first time than he was the second time, or perhaps he had something else on his mind or had a different intention for the action. The direction of the waves is also meaningful. It is already clear, for example, that activity waves related to orienting move differently in children – more prominently from back to front – than in adults. With further research, we hope to unravel what these different wave trajectories mean.

David M. Alexander, Peter Jurica, Chris Trengove, Andrey R. Nikolaev, Sergei Gepshtein, Mikhail Zvyagintsev, Klaus Mathiak, Andreas Schulze-Bonhage, Johanna Ruescher, Tonio Ball, Cees van Leeuwen. Traveling waves and trial averaging: The nature of single-trial and averaged brain responses in large-scale cortical signals.NeuroImage, 2013; 73: 95 DOI:10.1016/j.neuroimage.2013.01.016

The Project for a Scientific Psychology (1895): a Freudian anticipation of LTP-memory connection theory http://www.neuro.iastate.edu/Uploads/centonzeEtAl_BrnResRev_04.pdf

Tālākai izpētēi iesakām iepazīties ar MD, PhD (Hon. Multi) Karl H. Pribram pētījumiem sk. šeit!

___________________

We are often asked what research has been conducted with neurofeedback to establish its scientific validity. There are many different modalities within the field of neurofeedback research, yet most neurofeedback or EEG biofeedback share basic similarities.  Below we have provided a list of relevant research papers grouped according to topic. Full references are provided in respect to the dedicated scientist who have carried out the research. 

 

"In my opinion, if any medication had demonstrated such a wide spectrum of efficacy it would be universally accepted and widely used" 

• Frank H. Duffy, M.D., Professor and Pediatric Neurologist at Harvard Medical School

Certain areas of study have been more practical for scientist to address, such areas are: 

Anxiety

 ADD/ADHD & Attention

 Addictions

 Autism & Aspergers

  Depression

 Optimal Performance

 Epilepsy

 Acquired Brain Injury

 Post Traumatic Stress

 Cognitive Performance

 Personality Disorders

 Chronic Fatigue

 Obsessive Compulsive Disorder            

 Seniors Cognitive Improvement               

 Schizophrenia                             

 Pain & Fibromyalgia

"EEG biofeedback meets the AACAP [American Academy of Child and Adolescent Psychiatry] criteria for clinical guideline (CG) for treatment of ADHD, seizure disorders, anxiety (OCD, GAD, PTSD, phobias), depression, reading disabilities, and addictive disorders. This suggests that EEG biofeedback should always be considered as an intervention for these disorders by the clinic." 
 
Hirshberg, L.m., Chiu, S,. Frazier, J, A,. "Emerging brain-based interventions for children and adolescents: overview and clinical perspective." Child and adolescent psychiatric clinics of North America, 2005, Vol.14(1), pp.1-19

Anxiety

“A review of more recent research finds that pharmacologic treatment may not be as effective as previously believed. A more recent neuroscience technology, electroencephalographic (EEG) biofeedback (neurofeedback), seems to hold promise as a methodology for retraining abnormal brain wave patterns. It has been associated with minimal side effects and is less invasive than other methods for addressing biologic brain disorders.”

• Hammond DC.,  Neurofeedback with anxiety and affective disorders.  Child Adolesc Psychiatr Clin N Am. 2005 Jan;14(1):105-23

“The results of (QEEG) guided neurofeedback training for anxiety in nineteen PSTD patients is analysed, along with the change in anxiety in four control patients who did not do neurofeedback. Those who did neurofeedback training experienced clinically significant reductions in anxiety, whereas there was no significant change in anxiety in the control group.”

• Walker, J. E., Anxiety Associated With Post Traumatic Stress Disorder—The Role of Quantitative Electro- encephalograph in Diagnosis and in Guiding Neurofeedback Training to Remediate the Anxiety. Biofeedback, 2009, Vol.37(2), pp.67-70

Further Research:

• D. Corydon H,. Neurofeedback Treatment of Depression and Anxiety. Journal of Adult Development, Vol. 12, Nos. 2/3, August 2005

• Tansey M. A., A simple and a complex tic (Tourette’s syndrome): their response to EEG sensorimotor rhythm biofeedback training. Int J Psychophysiol. 1986 Jul; 4(2): 91-97

ADD/ADHD & Attention

“In conclusion, behavioural improvements induced by NF [Neurofeedback] training in children with ADHD were maintained at a 6-month follow-up… the results confirm the notion that NF is a clinically efficacious module in the treatment of children with ADHD.”

• Gevensleben, H., Holl, B., Albrecht, B., Schlamp, D., Kratz, O., Studer, P,. Rothenberger, A., Moll, G. H., Heinrich, H.,  Neurofeedback training in children with ADHD: 6-month follow-up of a randomised controlled trial. European Child & Adolescent Psychiatry, Sept, 2010, Vol.19(9), p.715(10)

“Neurofeedback treatment has been demonstrated to reduce inattention, impulsivity and hyperactivity in children with attention deficit/hyperactivity disorder (ADHD). However, previous studies did not adequately control confounding variables or did not employ a randomized reinforcer-controlled design. This study addresses those methodological shortcomings… Parents reported significant reductions in primary ADHD symptoms, and inattention improvements in the NF group were higher compared to the control intervention (BF, d (corr) = -.94). NF training also improved attention and reaction times on the psychometric measures. The results indicate that NF effectively reduced inattention symptoms on parent rating scales and reaction time in neuropsychological tests.”

• Bakhshayesh, A, Hänsch, S., Wyschkon, A., Rezai, M,. Esser, G., Neurofeedback in ADHD: a single-blind randomized controlled trial. European child & adolescent psychiatry, 2011, Vol.20(9), pp.481-91

Further Research:

• Levesque J, Beauregard M, Mensour B.:  Effect of neurofeedback training on the neural substrates of selective attention in children with ADD/ADHD: A functional MRI study.   Neuroscience Letters. 2006 Feb 20;394(3):216-21.

• Chabot RJ, diMichele F, Prichep L, John ER:The clinical role of computerized EEG in the evaluation and treatment of learning and attention disorders in children and adolescents. Journal of Neuropsychiatry and Clin Neuroscience, 2001; 13: 171-186

• Egner T, Gruzelier JH Learned self-regulation of EEG frequency components affects attention and event-related brain potentials in humans.  Neuroreport 2001, 12:411-415

• Egner T, Gruzelier JH EEG biofeedback of low bnd beta components: frequency-specific effects of variables of attention and event-related brain potentials.Clinical Neurophysiology, 2003b, in press

• Fisher S, Riding the Waves: Neurofeedback: A breakthrough with learning disabilities? Psychotherapy Networker, Sept/October, page 77-83. 2004 

• Fuchs T, Birbaumer N, Lutzenberger W, Gruzielier JH, Kaiser J, Neurofeedback treatment for ADHD in children: a comparison with methylphenidate, Appl Psychophys Biofeedback 2003 Mar 28 (1):1-12
Hammond DC:  Medical justification for neurofeedback with ADD/ADHD.  Journal of Neurotherapy, 2000; 4(1), 90-93.

• Hirshberg LM, Chiu S, Frazier JA., Emerging brain-based interventions for children and adolescents: overview and clinical perspective.  Child Adolesc Psychiatr Clin N Am. 2005 Jan;14(1):1-19

• Gevensleben, H., Holl, B., Albrecht, B., Vogel, C., Schlamp, D., Kratz, O., Studer, P., Rothenberger, A., Moll, G.H., Heinrich, H. (2009).  Is neurofeedback an efficacious treatment for ADHD?  A randomised controlled clinical trial. Journal of Child Psychology and Psychiatry, 50, 780-789.

• Holtman, M., Grasmann, D., Cionek-Szpak, E., Hager, V., Panzer, N., Beyer, A., Poustka, F., Stadler, C. (2009).  Spezifische wirksamkeit von neurofeedback auf die impulsivitat bei ADHS. Kindheit Und Entwicklung, 18, 95-204.

• Kaiser DA,  Othmer S: Effect of Neurofeedback on variables of attention in a large multi-center trial.  Journal of Neurotherapy, 2000 4(1), 5-15.

• Loo SK, EEG and neurofeedback findings in ADHD The ADHD Report, 2003,. 11:3, 1-4

• Loo SK, Barkley RA: Clinical Utility of EEG in ADHD.  Applied Neuropsychology 2005, Vol. 12, 64-76

• Lubar, J.F., Swartwood, M.O., Swartwood, J.N., & O’Donnell, P.H. (2005).  Evaluation of the effectiveness of EEG neurofeedback training for ADHD in a clinical setting as measured by changes in TOVA scores, behavioral ratings, and WISC-R performance.  Biofeedback and Self Regulation, 20(1), 83-99.

• Lubar JF: Neocortical Dynamics: implications for understanding the role of neurofeedback and related techniques for the enhancement of attention.  Applied Psychophysiology and Biofeedback, 1997 22: 111-25.

• Lubar JF and Lubar JO: Neurofeedback assessment and treatment for attention deficit/hyperactivity disorders.  in Evans JR and Abarbanel A (eds): Introduction to Quantitative EEG and Neurofeedback, Academic Press 1999

• Monastra VJ,  Electroencephalographic biofeedback (neurotherapy) as a treatment for attention deficit hyperactivity disorder: rationale and empirical foundation.  Child Adolesc Psychiatr Clin N Am. 2005 Jan;14(1):55-82, vi.

• Monastra VJ, Lubar JF, Linden M: the development of a quantitative electroencephalographic scanning process for attention deficit hyperactivity disorder: reliability and validation studies. Neuropsychology, 2001 15: 136-144.

• Monastra VJ., Monastra DM., & George,S. . The effects of stimulant therapy, EEG biofeedback, and parenting style on the primary symptoms of ADHD.Applied Psychophysiology & Biofeedback, 2002, 27(4), 231-249.

• Nash JK, Treatment of ADHD with neurotherapy. Clinical Electroencephalography 2000, 31(1), 30-37

• Rossiter, T.R., & La Vaque, T.J. A comparison of EEG biofeedback and psychostimulants in treating attention deficit/hyperactivity disorder.  Journal of  Neurotherapy, . 1995; 1, 48-59

• Sherlin, L., Arns, M., Lubar, J., Sokhadze, E. (2010).  A position paper on neurofeedback for the treatment of ADHD. Journal of Neurofeedback, 14, 66-78.

• Thompson L, Thompson M. Neurofeedback combined with training in metacognitive strategies: effectiveness in students with ADD. Appl Psychophysiol Biofeedback. 1998 Dec;23(4):243-63

• Yucha, C., & Montgomery, D. (2008).  Evidence-based practice in biofeedback and neurofeedback.  Wheat Ridge, CO: Association for Applied Psychophysiology and Biofeedback.

Addictions

“Neurofeedback-training-based neurotherapy is one of the potentially efficacious nonpharmacological treatment options for substance use disorders (SUD) in adults, but it is also a very promising as a treatment modality for adolescents, especially those with stimulant abuse and attention and conduct problems.”

• Sokhadze, E. S., Christopher M. Tasman. A., Daniels, R., Trudeau, David Review of Rationale for Neurofeedback Application in Adolescent Substance Abusers with Comorbid Disruptive Behavioral Disorders. Journal of Neurotherapy, 2011, Vol.15(3), pp.232-261

"Treated alcoholics with depressive syndrome showed sharp reductions in self-assesed depression… Subjects showed significant decreases on the BR scores: schizoid, avoidant, dependant, histrontic… alcohol abuse, drug abuse, psychotic thinking and psychotic depression. Twenty-one-month follow up data indicated sustained prevention of relapse in alcoholics who completed neurofeedback training.”

• Saxby E, Peniston EG. Alpha-theta brainwave neurofeedback training: an effective treatment for male and female alcoholics with depressive symptoms. Journal of Clinical Psychology. 1995 September 51(5) 685-93

Further Reasearch:

• Scott, Kaiser, Othmer, Sideroff,   Effects of an EEG Biofeedback Protocol on a Mixed Substance Abusing Population. American  Journal of Alcohol Drug Abuse, Volume 31, Number 3 / 2005   Pages:  455 – 469

• Trudeau DL., Applicability of brain wave biofeedback to substance use disorder in adolescents.  Child Adolesc Psychiatr Clin N Am. 2005 Jan;14(1):125-36, vii

• Burkett, V. S., Cummins, J. M., Dickson, R. M., & Skolnick, M. (2005). An open clinical trial utilizing real-time EEG operant conditioning as an adjunctive therapy in the treatment of crack cocaine dependence. Journal of Neurotherapy, 9(2), 27-48.

• Kelly, M. J. (1997). Native Americans, neurofeedback, and substance abuse theory: Three year outcome of alpha/theta neurofeedback training in the treatment of problem drinking among Dine (Navajo) people. Journal of Neurotherapy, 2(3), 24-60.

• Sokhadze, E. M., Cannon R. L., & Trudeau D. L. (2008) EEG biofeedback as a treatment for Substance Use Disorders: review, rating of efficacy, and recommendations for further research. Journal of Neurotherapy, 12(1), 5- 43.

• Saxby, E., & Peniston, E. G. (1995). Alpha-theta brainwave neurofeedback training: An effective treatment for male and female alcoholics with depressive symptoms. Journal of Clinical Psychology, 51(5), 685-693.

• Trudeau, D. L. (2005). Applicability of brain wave biofeedback to substance use disorder in adolescents. Child & Adolescent Psychiatric Clinics of North America, 14(1), 125-136. 

• Trudeau, D. L. (2008) Branwave biofeedback for additive disorder.  Journal of Neurotherapy, 12(4), 181-183.

Autism & Aspergers

“Previously we demonstrated significant improvement of executive functions and social behavior in children with autism spectrum disorders (ASD) treated with 40 sessions of EEG neurofeedback in a nonrandomized waiting list control group design. In this paper we extend these findings by reporting the long-term results of neurofeedback treatment in the same group of children with ASD after 12 months. The present study indicates maintenance of improvement of executive functions and social behavior after 12 months in comparison with the immediate outcomes. Neurofeedback mediated suppression of theta power is supposed to promote more flexible functioning of the brain by enhancing activation in the medial prefrontal cortex and improving flexibility of activation in the default mode network supporting the improvement of executive functions and theory of mind in ASD."

• Kouijzer, M. E. J., de Moor, J. M. H., Gerrits, B. J. L., Buitelaar, J. K., van Schie, H. T.,  Long-term effects of neurofeedback treatment in autism. Research in Autism Spectrum Disorders, 2009, Vol.3(2), pp.496-501

"Results. Improved ratings of ASD symptoms reflected an 89% success rate. Statistical analyses revealed significant improvement in Autistics who received Neurofeedback compared to a wait list control group. Other major findings included a 40% reduction in core ASD symptomatology (indicated by ATEC Total Scores), and 76% of the experimental group had decreased hyperconnectivity. Reduced cerebral hyperconnectivity was associated with positive clinical outcomes in this population. In all cases of reported improvement in ASD symptomatology, positive treatment outcomes were confirmed by neuropsychological and neurophysiological assessment."

• Coben, R., & Pudolsky, I. (2007).  Assessment-guided neurofeedback for autistic spectrum disorder.  Journal of Neurotherapy, 11(1), 5-23.

Further Research:

• Baruth, J.,  Casanova, M.,  El-Baz, A.,  Horrell, T.,  Mathai, G.,  Sears, L., Sokhadze, E. (2010).  Low-frequency repetitive transcranial magnetic stimulation modulates evoked-gamma frequency oscillations in autism spectrum disorder. Journa1 of Neurotherapy 14(3), 179 – 194.

• Coben, R., & Myers, T. E. (2010).  The relative efficacy of connectivity guided and symptom based EEG biofeedback for autistic disorders.  Applied Psychophysiology & Biofeedback, 35(1), 13-23.

• Coben, R. (2007).  Connectivity-guided neurofeedback for autistic spectrum disorder.  Biofeedback, 35(4), 131-135.

• Jarusiewicz, G. (2007).  Use of neurofeedback with autistic spectrum disorders.  Chapter in J. R. Evans (Ed.), Handbook of Neurofeedback.  Binghampton, NY: Haworth Medical Press, pp. 321-339

• Jarusiewicz, B. (2002). Efficacy of neurofeedback for children in the autistic spectrum: A pilot study. Journal of Neurotherapy, 6(4), 39-49.

• Knezevic, B., Thompson, L., & Thompson, M. (2010).  Pilot project to ascertain the utility of Tower of London Test to assess outcomes of neurofeedback in clients with Asperger’s Syndrome.  Journal of Neurotherapy, 14(3), 3-19.

• Kouijzer, M. E. UJ., de Moor, J. M. H., Gerrits, B. J. L., Buitelaar, J. K., & van Schie, H. T. (2009).  Long-term effects of neurofeedback treatment in autism.  Research in Autism Spectrum Disorders, 3, 496-501.

• Pineda JA, Brang D, Hecht E, Edwards L, Carey S, Bacon M, Futagaki C, Suk D, Tom J, Birnbaum C, Rork A.(2008). Positive behavioral and electrophysiological changes following neurofeedback training in children with autism. Research in Autism Spectrum Disorders 2. 557-581. 

• Pineda, J. A., Brang, D., Futagaki, C., Hecht, E., Grichanik, M., Wood, L., Bacon, M., & Carey, S.  (2007).  Effects of neurofeedback training on action comprehension and imitation learning.  Chapter in Puckhaber, H. L. (Ed.), New research in biofeedback.  Hauppauge, NY: Nova Science Publishers,  pp. 133-152. 

• Scolnick, B. (2005). Effects of electroencephalogram biofeedback with Asperger's syndrome. International Journal of Rehabilitation Research, 28(2), 159-163.

• Sichel, A. G., Fehmi, L. G., & Goldstein, D. M. (1995). Positive outcome with neurofeedback treatment of a case of mild autism. Journal of Neurotherapy, 1(1), 60-64.

• Sokhadze, E., Baruth, J., El-Baz, A., Horrell, T., Sokhadze, G., Carroll, T., Tasman, A., Sears, L., Casanova, M. (2010).Impaired error monitoring and correction function in Autism. Journal of Neurotherapy 14(2), 79-95.

Acquired Brain Injury

"Conclusion. Overall, findings suggest the utility of neurofeedback for the treatment of stroke, with particular gains noted in the areas of cognitive functioning, sleep quality, emotional regulation, and energy."

• Cannon, K. B., Sherlin, L., & Lyle, R. R. (2010).  Neurofeedback efficacy in the treatment of a 43-year-old female stroke victim: a case study.  Journal of Neurotherapy, 14(2), 107-121.

Further Research:

• Doppelmayr, M., Nosko, H., Pecherstorfer, T., & Fink, A.  (2007).  An attempt to increase cognitive performance after stroke with neurofeedback.  Biofeedback, 35(4), 126-130.Byers, A. P. (1995). Neurofeedback therapy for a mild head injury. Journal of Neurotherapy, 1(1), 22-37.

• Duff, J. (2004). The usefulness of quantitative EEG (QEEG) and neurotherapy in the assessment and treatment of post-concussion syndrome. Clinical EEG & Neuroscience, 35(4), 198-209.

• Hoffman, D. A., Stockdale, S., & Van Egren, L. (1996a). Symptom changes in the treatment of mild traumatic brain injury using EEG neurofeedback [Abstract]. Clinical Electroencephalography, 27(3), 164.

• Thornton, K. E., & Carmody, D. P. (2008).  Efficacy of traumatic brain injury rehabilitation: Interventions of QEEG-guided biofeedback, computers, strategies, and medications.  Applied Psychophysiology & Biofeedback, 33(2), 101-124.

• Thornton, K. E., & Carmody, D. P. (2005). Electroencephalogram biofeedback for reading disability and traumatic brain injury. Child & Adolescent Psychiatric Clinics of North America, 14(1), 137-162.

• Tinius, T. P., & Tinius, K. A. (2001). Changes after EEG biofeedback and cognitive retraining in adults with mild traumatic brain injury and attention deficit disorder. Journal of Neurotherapy, 4(2), 27-44.

• Walker, J. E. (2007).  A neurologist's experience with QEEG-guided neurofeedback following brain injury.  Chapter in J. R. Evans (Ed.), Handbook of Neurofeedback.  Binghampton, NY: Haworth Medical Press, pp. 353-361.

• Wing, K. (2001). Effect of neurofeedback on motor recovery of a patient with brain injury: A case study and its implications for stroke rehabilitation. Topics in Stroke Rehabilitation, 8(3), 45-53.

Chronic Fatigue

"In the present case study, the authors applied an EEG neurofeedback biofeedback paradigm as a treatment modality with a CFS patient. Baseline data were acquired using the Wechsler Adult Intelligence Scale-Revised and qualitative and subjective ratings of cognitive improvement. Test results and clinical findings revealed improvements in the patient's cognitive abilities, functional skill level, and quality of life. The patient showed significant differences in pre- and post-test levels on the Wechsler scale."

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

Further research:

• Tansey, M. A. (1993). Neurofeedback and chronic fatigue syndrome: New findings with respect to diagnosis and treatment. CFIDS Chronicle, 9, 30-32.

• Jensen, M.P., Sherlin, L.H., Hakimian, S., Fregni, F. (2009). Neuromodulatory approaches for chronic pain management: Research findings and clinical implications. Journal of Neurotherapy 13(4), 196 – 213.

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

Cognitive Performance

"The enhancement of cognitive performance was significantly larger for the neurofeedback group than for a control group who did not receive feedback."

• Neurofeedback training of the upper alpha frequency band in EEG improves cognitive performance. Zoefel, B., Huster, R. J.v Herrmann, C. S., Neuroimage, 2011, Vol.54(2), pp.1427-1431

“On the first and fifth session, cognitive ability was tested by a mental rotation test. As a result, eleven of the fourteen subjects showed significant training success… The enhancement of cognitive performance was significantly larger for the neurofeedback group than for a control group who did not receive feedback. Thus, enhanced cognitive control went along with an increased upper alpha amplitude that was found in the neurofeedback group only. Research Highlights: Human EEG oscillations in the upper alpha frequency band are enhanced by neurofeedback. EEG-enhancement was accompanied by an improvement of performance in a cognitive task."

• Zoefel, B., Huster, R, J,. Herrmann, C, S, Neurofeedback training of the upper alpha frequency band in EEG improves cognitive performance. Neuroimage, 2011, Vol.54(2), pp.1427-1431

Further Research:

• Budzynski, T.H. (1996). Braining brightening: Can neurofeedback improve cognitive process? Biofeedback, 24(2), 14-17.

• Cerra, J., Fernndez, T., Harmony T., Caballero M.I, Garcia F., Fernandez-Bouzas A., Santiago-Rodriguez E, Prado-Alcala R.A. (2006) "Follow-up study of Learning Disabled children treated with Neurofeedback or placebo." Clinical EEG & Neuroscience, 37(3), 198-203.

• Carter, J. L., & Russell, H. L. (1991). Changes in verbal performance IQ discrepancy scores after left hemisphere frequency control training: A pilot report. American Journal of Clinical Biofeedback, 4(1), 66-67. 

• Cunningham, M., & Murphy, P. (1981). The effects of bilateral EEG biofeedback on verbal, visuospatial and creative skills in LD male adolescents. Journal of Learning Disabilities, 14(4), 204-208.

• Egner, T., & Gruzelier, J. H. (2004). The temporal dynamics of electroencephalographic responses to alpha/theta neurofeedback training in healthy subjects. Journal of Neurotherapy, 8(1), 43-57.

• Fritson, K. K., Wadkins, T. A., Gerdes, P., & Hof, D. (2007).  The impact of neurotherapy on college students’ cognitive abilities and emotions.  Journal of Neurotherapy, 11(4), 1-9.

• Hanslmayer, S., Sauseng, P., Doppelmayr, M., Schabus, M., & Klimesch, W. (2005).Increasing individual upper alpha by neurofeedback improves cognitive performance in human subjects.Applied Psychophysiology & Biofeedback, 30(1), 1-10.

• Pulvermuller F, Mohr, Schleichert H, Veit, R: Operant conditioning of left hemispheric slow cortical potentials and its effect on word processing.  Biological Psychology, 2000; 53, 177-215.

• Vernon D, Egner T, Cooper N, Compton T, Neilands C, Sheri A, Gruzielier J, The effect of training distinct neurofeedback protocols on aspects of cognitive performance. Intl J Psychophys, 2003, 47: 75-86 

• Vernon, D. J. (2005).Can neurofeedback training enhance performance? An evaluation of the evidence with implications for future research. Applied Psychophysiology & Biofeedback, 30(4), 347-364.

• Vernon, D., Dempster, T., Bazanova, O., Rutterford, N., Pasqualini, M., Andersen, S.  (2009). Alpha neurofeedback training for performance enhancement: Reviewing the methodology. Journal of Neurotherapy 13(4),Pages 214 – 227.

Depression

“Currently there are twenty-one articles in neurofeedback for depression, among which only six present original experimental results. All of them report positive results with the technique.”

• Machado D, A,. Van Deusen, A,. A new neurofeedback protocol for depression. Spanish Journal of Psychology, May, 2011, Vol.14(1), p.374(11)

“Their clinical symptoms, as assessed with the 17-item Hamilton Rating Scale for Depression (HDRS), improved significantly. A control group that underwent a training procedure with the same cognitive strategies but without neurofeedback did not improve clinically.”

• Linden, D. E. J., Real-Time Self-Regulation of Emotion Networks in Patients with Depression (Neurofeedback in Depression) PLoS ONE, 2012, Vol.7(6), p.e38115

Further Research:

• Choi, S. Won., Chi, S. E., Chung, S. Y., Kim, J. W., Ahn, C. Y., Kim, H. T., Is Alpha Wave Neurofeedback Effective with Randomized Clinical Trials in Depression? A Pilot Study.  Neuropsychobiology, 2011, Vol.63(1), p.43-51

• Baehr, E., Rosenfeld, J. P., & Baehr, R.. Clinical use of an alpha asymmetry neurofeedback protocol in the treatment of mood disorders: Follow-up study one to five years post therapy. Journal of Neurotherapy, 2001 4(4), 11-18.

• Hammond, D. C. (2000). Neurofeedback treatment of depression with the Roshi. Journal of Neurotherapy, 4(2), 45-56.

• Hammond, D. C. (2004). Neurofeedback treatment of depression and anxiety. Journal of Adult Development. (in press).

• Baehr, E., & Baehr, R. (1997). The use of neurofeedback as adjunctive therapeutic treatment for depression: Three case studies. Biofeedback, 25, 10-11.

• Baehr, E., Rosenfeld, J., & Baehr, R. (1997). The clinical use of an alpha asymmetry protocol in the neurofeedback treatment of depression. Journal of Neurotherapy, 2(3), 10-23.

• Saxby, E., & Peniston, E. G. (1995). Alpha-theta brainwave neurofeedback training: an effective treatment for male and female alcoholics with depressive symptoms. Journal of Clinical Psychology, 51(5), 685-693.

Epilepsy

“The clinical benefits derived from this neurofeedback training protocol, particularly in patients that are nonresponsive to pharmacotherapy, have been documented in many independent laboratories. Recent advances in computer technology have resulted in the availability of relatively inexpensive high-quality equipment for the application of neurofeedback therapy, thus presenting a viable and promising treatment alternative to the interested clinician.”

• Egner, T., & Sterman, M. B. (2006). Neurofeedback treatment of epilepsy: From basic rationale to practical application. Expert Review of Neurotherapeutics, Abstract. 6(2), 247-257.

“With recent improvements in quantitative EEG measurement and improved neurofeedback protocols, it has become possible in clinical practice to eliminate seizures or reduce the amount of medication required to control them.”

• Walker JE, Kozlowski GP, Neurofeedback treatment of epilepsy. Child and Adolescent Psychiatric Clinics of North America, 2005, Vol.14(1), pp.163-176.

"We conclude that the research literature reviewed in this article justifies the assertion that neurofeedback treatment of epilepsy/seizure disorders constitutes a well-founded and viable alternative to anticonvulsant pharmacotherapy."

• Sterman, M., Egner, T., Foundation and Practice of Neurofeedback for the Treatment of Epilepsy.  Applied Psychophysiology and Biofeedback, 2006, Vol.31(1), pp.21-35

Further Research:

• Uhlmann C, Froscher W. Biofeedback treatment in patients with refractory epilepsy: Changes in depression and control orientation. Seizure 2001, 10(1), 34-38.

• Sterman MB: Basic concepts and clinical findings in the treatment of seizure disorders with EEG operant conditioning.  Clinical Electroencephalography, 2000;31(1), 45-55.

• Meyer-Lindenberg A, Ziemann U, Hajak G, et al:. Transitions between dynamical states of differing stability in the human brain.  Proc Natl Acad Sci U S A. 2002; Aug 20;99(17):10948-53.

• Lantz D, & Sterman MB: Neuropsychological assessment of subjects with uncontrolled epilepsy: Effects of EEG biofeedback training.  Epilepsia, 1988; 29(2), 163-171.

• Tan, G., Thornby, J,. Hammond, D. C., Strehl, U., Canady, B., Arnemann, K., Kaiser, D.A., Meta-analysis of EEG biofeedback in treating epilepsy.  Journal of the EEG and Clinical Neuroscience Society (ENCS), 2009, Vol.40(3), pp.173-9 

Optimal Performance

“Professionally significant enhancement of music and dance performance and mood has followed training with an EEG-neurofeedback protocol… In optimal performance studies we confirmed associations with creativity in musical performance, but effects also included technique and communication. We extended efficacy to dance and social anxiety.”

• Gruzelier, J,. A theory of alpha/theta neurofeedback, creative performance enhancement, long distance functional connectivity and psychological integration. Cognitive Processing, 2009, Vol.10(1), pp.101-109

“Background: By enabling individuals to self-regulate their brainwave activity in the field of optimal performance in healthy individuals, neurofeedback has been found to improve cognitive and artistic performance. Here we assessed whether two distinct EEG neurofeedback protocols could develop surgical skill, given the important role this skill plays in medicine… neurofeedback training provided significant improvement in surgical technique whilst considerably reducing time on task by 26%. There was also evidence that AT training marginally reduced total surgery time, despite suboptimal training efficacies. Overall, the data set provides encouraging evidence of optimised learning of a complex medical specialty via neurofeedback training."

• Ros, T., Moseley, M. J.,  Bloom, P. A., Benjamin, L., Parkinson, L. A., Gruzelier, J. H., Optimizing microsurgical skills with EEG neurofeedback. BMC Neuroscience, July 24, 2009, Vol.10, p.87

“The correct feedback group significantly improved performance… The control group showed no significant pre–post differences in performance… Results support the use of known relationships between EEG and performance to provide biofeedback.”

• Landers, D, M., Petruzzello, S, J., Salazar, W; Crews, D, J.  The influence of electrocortical biofeedback on performance in pre-elite archers. Medicine & Science in Sports & Exercise, Vol 23(1), Jan 1991, 123-129.

Further Research:

• Raymond, J., Sajid, I., Parkinson, L. A., & Gruzelier, J. H. (2005). Biofeedback and dance performance: A preliminary investigation. Applied Psychophysiology & Biofeedback, 30(1), 65-74.

• Thompson, T., Steffert, T., Ros, T., Leach, J., & Gruzelier, J. (2008).  EEG applications for sport and performance.  Methods, 45, 279-288.

• Egner, T., & Gruzelier, J. H. (2003). Ecological validity of neurofeedback: Modulation of slow wave EEG enhances musical performance. NeuroReport, 14(9), 1221-1224.

• Arns, M., Kleinnijenhuis, M., Fallahpour, K., & Bretler, R. (2007).  Golf performance enhancement and real-life neurofeedback training using personalized event-locked EEG profiles.  Journal of Neurotherapy, 11(4), 11-18.

• Mason, L. A., & Brownback, T. S. (2001). Optimal functioning training with EEG biofeedback for clinical populations: A case study. Journal of Neurotherapy, 5(1-2), 33-44.

• Gruzelier, J., Egner, T., & Vernon, D. (2006).Validating the efficacy of neurofeedback for optimising performance. Progress in Brain Research,159, 421-431.

• Gruzelier, J. (2009).  A theory of alpha/theta neurofeedback, creative performance enhancement, long distance functional connectivity and psychological integration.  Cognitive Processing, 10 (Suppl 1), S101-109.

• Boynton, T. (2001). Applied research using alpha/theta training for enhancing creativity and well-being. Journal of Neurotherapy, 5(1-2), 5-18.

Obsessive Compulsive Disorder (OCD)

“Thirty-three out of 36 subjects who received NF [neurofeedback] training showed clinical improvement according to the Yale-Brown obsessive-compulsive scale (Y-BOCS). The Minnesota multiphasic inventory (MMPI) was administered before and after treatment to 17 of the subjects. The MMPI results showed significant improvements not only in OCD measures, but all of the MMPI scores showed a general decrease. Finally, according to the physicians' evaluation of the subjects using the clinical global impression scale (CGI), 33 of the 36 subjects were rated as improved. Thirty-six of the subjects were followed for an average of 26 months after completing the study. According to follow-up interviews conducted with them and/or their family members 19 of the subjects maintained the improvements in their OCD symptoms. This study provides good evidence for the efficacy of NF treatment in OCD.”

• Sürmeli, T,. Ertem, A. Obsessive compulsive disorder and the efficacy of qEEG-guided neurofeedback treatment: a case series. Clinical EEG and neuroscience : official journal of the EEG and Clinical Neuroscience Society (ENCS), 2011, Vol.42(3), pp.195-201

“An MMPI was administered pre-post to one patient, and she showed dramatic improvements not only in OCD symptoms, but also in depression, anxiety, somatic symptoms, and in becoming extroverted rather than introverted and withdrawn. In follow-ups of the two cases at 15 and 13 months after completion of treatment, both patients were maintaining improvements in OCD symptoms as measured by the Padua Inventory and as externally validated through contacts with family members.”

• Hammond, D. C. (2003). QEEG-guided neurofeedback in the treatment of obsessive compulsive disorder. Journal of Neurotherapy, 7(2), 25-52.

Further research:

• Hammond, D. C. (2004). Treatment of the obsessional subtype of obsessive compulsive disorder with neurofeedback. Biofeedback, 32, 9-12.

• Hammond C. QEEG-Guided Neurofeedback in the Treatment of Obsessive Compulsive Disorder, Journal of Neurotherapy, 2003; Vol 7(2)

Post Traumatic Stress

“Collectively, this approach has been found to be much better tolerated than traditional exposure therapies. In addition, it is helpful in shedding of substance dependenceies that are common in treatment-resistant PTSD.”

• Othmer, S,. Susan F,. Post Traumatic Stress Disorder—The Neurofeedback Remedy  Biofeedback, 2009, Vol.37(1), pp.24-31

Further Research:

• Muller, J., Karl, A., Denke C,. Mathier, F., Dittmann, J., Rohleder, N., Knaevelsrud, C., Biofeedback for Pain Management in Traumatised Refugees. Cognitive Behaviour Therapy Vol 38, No 3, pp. 184–190, 2009

• Peniston, E. G., Marrinan, D. A., Deming, W. A., & Kulkosky, P. J. (1993). EEG alpha-theta brainwave synchronization in Vietnam theater veterans with combat-related post-traumatic stress disorder and alcohol abuse. Advances in Medical Psychotherapy, 6, 37-50.

• Sokhadze, T. M., Stewart, C. M., & Hollifield, M. (2007).  Integrating cognitive neuroscience and cognitive behavioral treatment with neurofeedback therapy in drug addiction comorbid with posttraumatic stress disorder: A conceptual review.  Journal of Neurotherapy, 11(2), 13-44.

Pain & Fibromyalgia

"This data supports the efficacy of NFB as a treatment for pain, psychological symptoms and impaired quality of life associated with fibromyalgia."

•  Neurofeedback Intervention in Fibromyalgia Syndrome; a Randomized, Controlled, Rater Blind Clinical Trial. Kayıran, Sadi ; Dursun, Erbil ; Dursun, Nigar ; Ermutlu, Numan ; Karamürsel, Sacit Applied Psychophysiology and Biofeedback, 2010, Vol.35(4), pp.293-302

"Most of the symptoms were decreased after ten sessions. There was also improvement in all of the scales after the treatment. The results of the present study may suggest NFB training as a novel treatment method in FMS."

• Kayrian, S., Dursun, E., Ermutlu, N., Dursun, N., & Karamursel, S.  (2007). Neurofeedback in fibromyalgia syndrome.   The Journal of the Turkish Society of Algology, 19(3), 47-53.Jensen, M. P., Grierson, C., Tracy-Smith, V., Bacigalupi, S. C., Othmer, S. (2007).  Neurofeedback treatment for pain associated with complex regional pain syndrome.  Journal of Neurotherapy, 11(1), 45-53. 

Further Research:

• Neurofeedback and biofeedback with 37 migraineurs: a clinical outcome study Stokes, Deborah A ; Lappin, Martha S Behavioral and Brain Functions : BBF, 2010, Vol.6, p.9-9

• Tansey, M. A. (1991). A neurobiological treatment for migraine: The response of four cases of migraine to EEG biofeedback training. Headache Quarterly: Current Treatment & Research, 90-96.

• Kropp, P., Siniatchkin, M., & Gerber, W. D. (2002). On the pathophysiology of migraine: Links for Aempirically based treatment@ with neurofeedback. Applied Psychophysiology & Biofeedback, 27(3), 203-213.

• Mueller, H. H., Donaldson, C. C. S., Nelson, D. V., & Layman, M. (2001). Treatment of fibromyalgia incorporating EEG-driven stimulation: A clinical outcomes study. Journal of Clinical Psychology, 57(7), 933-952.

• Donaldson, C. C. S., Sella, G. E., & Mueller, H. H. (1998). Fibromyalgia: A retrospective study of 252 consecutive referrals. Canadian Journal of Clinical Medicine, 5 (6), 116-127.

• Mueller, H. H., Donaldson, C. C. S., Nelson, D. V., & Layman, M. (2001). Treatment of fibromyalgia incorporating EEG-driven stimulation: A clinical outcomes study. Journal of Clinical Psychology, 57(7), 933-952.

• Brown, V. W. (1995 ). Neurofeedback and Lyme's Disease: A clinical application of the five phase model of CNS functional transformation and integration. Journal of Neurotherapy, 1(2), 60-73.

• Packard, R. C., & Ham, L. R. (1995). EEG biofeedback in the treatment of Lyme Disease: A case study. Journal of Neurotherapy, 1(3), 22-30.

Personality Disorders

“Neurofeedback can change the view of psychiatrists and psychologists in the future regarding the treatment of personality disorders. This study provides the first evidence for positive effects of neurofeedback treatment in antisocial personality disorders”

• Surmeli, T,. Ertem, A. QEEG guided neurofeedback therapy in personality disorders: 13 case studies. Clinical EEG and neuroscience : official journal of the EEG and Clinical Neuroscience Society (ENCS), 2009, Vol.40(1), pp.5-10

“Neurofeedback training coupled with internal self-exploration served as an effective treatment modality for the unification of patients with Dissociative disorder.”

• Manchester, C., Allen, T., & Tachiki, K. H. (1998). Treatment of dissociative identity disorder with neurotherapy and group self-exploration. Journal of Neurotherapy, 2(4), 40-53.

Further Research:

• Malkowicz, D., Martinez, D. (2009).   Role of quantitative electroencephalography, neurotherapy, and neuroplasticity in recovery from neurological and psychiatric disorders. Journal of Neurotherapy 13(3),176 – 188.

• Peniston, EG, Kulkosky, PJ, VA Hospital:  Alcoholic Personality and Alpha-theta Brainwave Training.  Medical Psychotherapy, 1990, Volume 3, pp.37-55

• Rosenfeld JP: An EEG Biofeedback Protocol for Affective Disorders.  Clin Electroencephalography 2000:7-12 

• Schore A, Affect Regulation and the Origin of the Self,., Laurence Erlbaum, 1994

• Schore A, Affect Dysregulation and Disorders of the Self,.,Norton, 2003

• Schore A, Affect Regulation and the Repair of Self, Norton, 2003

Post Traumatic Stress

“Collectively, this approach has been found to be much better tolerated than traditional exposure therapies. In addition, it is helpful in shedding of substance dependenceies that are common in treatment-resistant PTSD.”

• Post Traumatic Stress Disorder—The Neurofeedback Remedy Othmer, S,. Susan F,. Biofeedback, 2009, Vol.37(1), pp.24-31

Further Research:

• Peniston, E. G., Marrinan, D. A., Deming, W. A., & Kulkosky, P. J. (1993). EEG alpha-theta brainwave synchronization in Vietnam theater veterans with combat-related post-traumatic stress disorder and alcohol abuse. Advances in Medical Psychotherapy, 6, 37-50.

• Sokhadze, T. M., Stewart, C. M., & Hollifield, M. (2007).  Integrating cognitive neuroscience and cognitive behavioral treatment with neurofeedback therapy in drug addiction comorbid with posttraumatic stress disorder: A conceptual review.  Journal of Neurotherapy, 11(2), 13-44.

• Biofeedback for Pain Management in Traumatised Refugees. Julia Muller, Anke Karl, Claudia Denke, Fabienne Mathier, Jennifer Dittmann, Nicolas Rohleder and Christine Knaevelsrud. Cognitive Behaviour Therapy Vol 38, No 3, pp. 184–190, 2009

Schizophrenia

“The author obtained progress using neurofeedback with more than 70 hospitals in patients with chronic schizophrenia. Improvements were seen in the EEG patterns and in cognitive, affective and behavioral patterns that often resulted in successful release from the hospital to live in the community. A 2-year follow up found that positive changes were sustained.”

• Bolea, A, S,. Neurofeedback Treatment of Chronic Inpatient Schizophrenia. Journal of Neurotherapy, 2010, Vol.14(1), pp.47-54

“Of the remaining 48 participants 47 showed clinical improvement after NF treatment, based on changes in their PANSS scores. The participants who were able to take the MMPI [Minnesota Multiphasic Personality Inventory] and the TOVA [Test of Variables of Attention] showed significant improvements in these measures as well. Forty were followed up for more than 22 months, 2 for 1 year, 1 for 9 months, and 3 for between 1 and 3 months after completion of NF. Overall NF was shown to be effective. This study provides the first evidence for positive effects of NF in schizophrenia."

• Surmeli, T,. Ertem, A,. Eralp, E,. Kos, I, H,. Schizophrenia and the efficacy of qEEG-guided neurofeedback treatment: a clinical case series. Clinical EEG and neuroscience : official journal of the EEG and Clinical Neuroscience Society (ENCS), 2012, Vol.43(2), pp.133-44

Further Research:

• Mccarthy J, S,. Taking Back the Brain: Could Neurofeedback Training Be Effective for Relieving Distressing Auditory Verbal Hallucinations in Patients With Schizophrenia? Schizophrenia Bulletin, 2012, Vol. 38(4), pp.678-682

• Bolea, A. S. (2010).  Neurofeedback treatment of chronic inpatient schizophrenia.  Journal of Neurotherapy, 14(1), 47-54.

• Donaldson, M., Moran, D., & Donaldson, S. (2010, Spring).  Schizophrenia in retreat.  NeuroConnections Newsletter, 19-23.

• Gruzelier, J. (2000). Self regulation of electrocortical activity in schizophrenia and schizotypy: A review. Clinical Electroencephalography, 31(1), 23-29.

• Gruzelier, J., Hardman, E., Wild, J., Zaman, R., Nagy, A., & Hirsch, S. (1999). Learned control of interhemispheric slow potential negativity in schizophrenia. International Journal of Psychophysiology, 34, 341-348.

• Schneider, F., Rockstroh, B., Heimann, H. et al. (1992). Self-regulation of slow cortical potentials in psychiatric patients: Schizophrenia. Biofeedback & Self-Regulation, 17, 277-292.

Seniors Cognitive Impairment

• Angelakis, Efthymios ; Stathopoulou, Stamatina ; Frymiare, Jennifer L ; Green, Deborah L ; Lubar, Joel F ; Kounios (2007). EEG neurofeedback: A brief overview and an example of peak alpha frequency training for cognitive enhancement in the elderly. Clinical Neuropsychology, 21(1), 110-129.

• Budzynski, T., Budzynski, H. K., &l Tang, H-Y. (2007).  Brain brightening: restoring the aging mind.  Chapter in J. R. Evans (Ed.), Handbook of Neurofeedback.  Binghampton, NY: Haworth Medical Press, pp. 231-265.

• Albert, A. O., Andrasik, F., Moore, J. L., & Dunn, B. R. (1998). Theta/beta training for attention, concentration and memory improvement in the geriatric population. Applied Psychophysiology & Biofeedback, 23(2), 109.

"Although research in nonlinear dynamic or chaotic systems consistently has revealed the regulating power of feedback in complex systems, individuals accustomed to more traditional, linear-based thinking in western medicine and psychology may find it hard to believe that merely showing the brain to itself has the same strength of effect as a carefully controlled psychoactive medication." 

• Laurence M. Hirshberg, Ph.D., Sufen Chiu, MD, PhD, and Jean Frazier, MD. 

A Randomized Controlled Study of Neurofeedback for Chronic PTSD

• Bessel A. van der Kolk ,
• Hilary Hodgdon,
• Mark Gapen,
• Regina Musicaro,
• Michael K. Suvak,
• Ed Hamlin,
• Joseph Spinazzola

• Published: December 16, 2016
• http://dx.doi.org/10.1371/journal.pone.0166752

Abstract

Introduction

Brain/Computer Interaction (BCI) devices are designed to alter neural signals and, thereby, mental activity. This study was a randomized, waitlist (TAU) controlled trial of a BCI, EEG neurofeedback training (NF), in patients with chronic PTSD to explore the capacity of NF to reduce PTSD symptoms and increase affect regulation capacities.

Study Design

52 individuals with chronic PTSD were randomized to either NF (n = 28) or waitlist (WL) (n = 24). They completed four evaluations, at baseline (T1), after week 6 (T2), at post-treatment (T3), and at one month follow up (T4). Assessment measures were:1. Traumatic Events Screening Inventory (T1); 2. the Clinician Administered PTSD Scale (CAPS; T1, T3, T4); 3. the Davidson Trauma Scale (DTS; T1-T4) and 4. the Inventory of Altered Self-Capacities (IASC; T1-T4). NF training occurred two times per week for 12 weeks and involved a sequential placement with T4 as the active site, P4 as the reference site.

Results

Participants had experienced an average of 9.29 (SD = 2.90) different traumatic events. Post-treatment a significantly smaller proportion of NF (6/22, 27.3%) met criteria for PTSD than the WL condition (15/22, 68.2%), χ2 (n = 44, df = 1) = 7.38, p = .007. There was a significant treatment condition x time interaction (b = -10.45, t = -5.10, p< .001). Measures of tension reduction activities, affect dysregulation, and affect instability exhibited a significant Time x Condition interaction. The effect sizes of NF (d = -2.33 within, d = - 1.71 between groups) are comparable to those reported for the most effective evidence based treatments for PTSD.

Discussion

Compared with the control group NF produced significant PTSD symptom improvement in individuals with chronic PTSD, as well as in affect regulation capacities. NF deserves further investigation for its potential to ameliorate PTSD and to improve affect regulation, and to clarify its mechanisms of action.

Figures

  

Citation: van der Kolk BA, Hodgdon H, Gapen M, Musicaro R, Suvak MK, Hamlin E, et al. (2016) A Randomized Controlled Study of Neurofeedback for Chronic PTSD. PLoS ONE 11(12): e0166752. doi:10.1371/journal.pone.0166752

Editor: Yutaka J. Matsuoka, National Cancer Center, JAPAN

Received: March 8, 2016; Accepted: October 27, 2016; Published: December 16, 2016

Copyright: © 2016 van der Kolk et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper.

Funding: Funders did not contribute to the design or execution of the study

Competing interests: No competing interests

Scientific background and rationale

The potential of brain-computer interaction devices (BCI) to alter neural signals and associated mental activity makes them strong candidates to emerge as a new generation of psychiatric interventions [1]. BCIs utilize fMRI or EEG as information about brain activity and provide visual and/or auditory feedback to alter neural activity. Thus far, most BCI research has focused on helping physically disabled users communicate commands, such as stimulation of muscles in paralyzed individuals and in stroke rehabilitation [2].

However, a wide variety of BCIs are currently being explored for performance enhancement, mental focus and tranquility [3]. EEG neurofeedback (NF) training represents one of the earliest applications of BCIs, and even though it has been in use for about three decades with well documented effects in over 2000 peer reviewed scientific publications, serious questions remain about its clinical utility and the validity and scientific rigor of extant research [4].

NF is thought to indirectly modify behavior by changing neuronal activation or connectivity patterns in the CNS via operant conditioning. NF has been shown to be able to reshape neural activity, as measured by electroencephalogram (EEG) frequency components [5–7] and fMRI [8–11]. NF research has focused mainly on performance enhancement (e.g. [12]) and on clinical conditions such as Attention Deficit Hyperactivity Disorder (ADHD)[13–16]. A few studies have investigated its potential in the treatment of depression[17,18], substance abuse[19], and posttraumatic stress disorder (PTSD)[20–22].

In NF training, neural activity is recorded from scalp electrodes and fed back in real time to subjects in a readily understood, visual format (simple computer games). NF associated EEG changes have been correlated with changes in various functional outcomes, including cortico-motor excitability, memory, cognition, sleep, and mood, as well as increases in affect regulation and executive function, sustained attention, and working memory [23–25].

Specific objectives

NF training may help individuals with PTSD acquire self-regulation skills by stabilizing EEG activity, and thereby improve focus and attention. The objective of this study was to investigate whether NF could substantially alter affect regulation capacities, and thereby improve PTSD symptomatology. This is particularly important because recovery from PTSD depends on being able to manage intense arousal [26]. Impaired affect regulation is a major cause of discontinuation of exposure-based PTSD treatments [27–29]. Improving affect regulation has been shown to 1) reduce the severity of PTSD symptoms, 2) decrease risk behaviors (e.g. suicidal and self-injurious behaviors, substance use) and 3) make subsequent exposure therapy more effective [30,31]. EEG markers of PTSD disordered arousal include increased cortical activation (manifested in reduced alpha activity) and increased theta/alpha ratio [32–34]. Brain activity in the alpha-1 band has been linked to attentional processes [35,36], while theta power has been linked to working memory performance [37,38], both of which are impaired in PTSD [26]. A recent study examining potential mechanisms for NF’s effects on PTSD found alterations in arousal (i.e., an increased sense of calm) correlated with changes in intrinsic network connectivity and alpha oscillations [21].

Study Design

This randomized, waitlist-controlled trial evaluated the efficacy of NF to increase affect regulation and reduce PTSD symptoms in adults with multiple trauma exposures and treatment resistant PTSD (i.e., having received six or more months of trauma focused therapy without sustained self-reported clinical improvement).

Study Sample

Following IRB review by the Justice Resource Institute Institutional Review Board and specific approval by that IRB of all aspects of the study, adults 18–58 years old with treatment non-responsive PTSD were recruited via newspaper and radio ads, the Trauma Center website (www.traumacenter.org), and solicitation from mental health professionals. The study was conducted between July 1, 2012 and July 1, 2015. Eligible subjects signed the IRB approved consent form that spelled out procedures, risks and benefits of the study. Trauma history was obtained by self-report ad scored on the Traumatic Events Screening Inventory [39], an 18-item self-report measure assessing lifetime occurrence of both acute (e.g. accident, natural disaster, loss) and interpersonal (e.g. neglect, separation, physical / sexual / emotional abuse, domestic violence) forms of trauma. Individuals were eligible if they met DSM-IV criteria for PTSD per the Clinician Administered PTSD Scale (CAPS) [40], and had received weekly trauma-focused psychotherapy for a minimum of six months. After completion of all initial evaluations subjects were randomly assigned by a computer generated randomization program to either 12 weeks of twice weekly NF or a waitlist (WL) control condition. Both groups were required to continue all ongoing treatments (psychotherapeutic and pharmacological) and to refrain from making changes in their current treatment regimens for the duration of this study. Participants in the WL condition were provided 24 NF sessions free of charge after the time 3 (week 12) follow-up evaluation.

Exclusion criteria included: unstable medical condition; receiving disability benefits; active suicide risk or life-threatening self-mutilation; psychotic or bipolar disorder; traumatic brain injury (TBI); history of seizures; current substance or alcohol abuse; ongoing traumatic exposure (such as domestic violence); changing ongoing treatment during the course of the study; Global Assessment of Functioning (GAF) score <40.

Of the 52 subjects in the ITT sample, 26 (50%) were on psychotropic mediation 21 (40%) were not, and 5 (10%) had missing data. A breakdown of medication rates and types for each group were as follows: waitlist control; 10 subjects on medication (41.7% of waitlist group) with 6 subjects (25%) on SSRIs, 3 subjects (12.5%) on benzodiazepines, 2 subjects (8.3%) on antianxiety, 2 subjects (8.3%) on buproprion, 2 subjects (8.3%) on an SSNRI, and 1 subject (4.2%) on a tricyclic antidepressant. Active NFB; 16 subjects on medication (57.1% of NF group), with 7 subjects (25%) on SSRIs, 4 subjects (14.3%) on stimulants, 3 subjects (10.7%) on antipsychotics, 5 subjects (17.8%) on benzodiazepines, and 3 subjects (10.7%) on buproprion.

Evaluation Procedures

All data were collected and analyzed at the Trauma Center @ JRI. After providing informed consent participants completed four evaluations assessing psychological and behavioral functioning: at study baseline, at week 6 (i.e., session 12 of NF if in active treatment condition), 12 weeks (session 24 -post-treatment) and week 16 (one month follow up). The participants were compensated $25 for baseline and week 6 evaluations, $35 for week 12 evaluations, and $50 for 1-month follow-ups, totaling $135 for NF participants and $245 for WL participants who completed all evaluations. Evaluators were post-doctoral and master’s level clinicians who received training and ongoing supervision in administration of study measures. Inter-rater reliability on the CAPS was established at 80% agreement. Strenuous efforts were made to keep evaluators blind to treatment condition, though in one case the blinding was inadvertently compromised.

NF Intervention Procedures

The NF system utilized EEGer neurofeedback software manufactured by EEG Spectrum International Education and Research, Inc. The system utilized a Procomp2 amplifier manufactured by Thought Technology LTD. The lowpass filtering is provided by the EEGer software. The training sites were fixed for all participants, and involved a sequential placement with T4 as the active site, P4 as the reference site, and the left ear (A1) as the ground (consistent with previous research that demonstrates increased R temporal lobe activation in PTSD)[41,42].

Training was intended to teach subjects to alter the power spectrum of certain filtered frequencies of activity; specifically, we sought to help subjects decrease the power spectrum of slow (2–6 Hz) and fast (22–36 Hz) activity while simultaneously increasing the power spectrum of mid-range (10–13 Hz starting point) activity. By convention, the slow activity we sought to decrease spans the delta and theta range and is generally associated with drowsiness and sleep, while the fast activity is called “high beta” and is associated with high levels of mental activation [43]. In contrast, we sought to help subjects enhance alpha activity, which is generally associated with a calm, relaxed state.

The training protocol employed standard inhibit frequencies of 2–6 HZ for slow activity and 22–36 HZ for fast EEG activity and a beginning reward frequency of 10–13 HZ. These spectral bands were selected based on previous research in studies of NF for PTSD [32,33], including the results of our pilot study [22]. Subjects completed a short checklist after every session, our internally generated “Checklist of Changes Observed After Neurofeedback Training”. Adjustment of the reward band was based on subjects’ responses to the questions on that checklist, and followed a flexible, principle-based manual that provided rules for adjusting the training protocol.

NF training process

NF subjects had 24 training sessions, twice weekly, each lasting up to 30 minutes. Electrodes were applied; impedance was measured for each electrode and maintained below 10 kOhms. After initiating the EEG measurement, subjects were asked to relax and sit quietly while a baseline signal was obtained. Once a stable baseline signal was obtained, thresholds were set such that the 2–6 Hz activity was over the threshold 35% of the time, 10–13 Hz (or adjusted band, based on response) was over the threshold 65% of the time, and 22–36 Hz activity was over the threshold 25% of the time. Subjects received auditory and visual feedback indicating reward; specifically, auditory tones and progress in simple computer games, such as Packman or Space Race. Feedback “rewards” (positive progress in the visual feedback videogame and tones) were given every two seconds that the amplitude or magnitude of EEG activity in both of the inhibit frequency bands fell below the target threshold and the amplitude or magnitude of EEG activity in the reward band exceeded the target threshold. Further adjustment of the thresholds using the same parameters as above was made after approximately three minutes of active training. Any other adjustments to the thresholds were limited but based on clinical judgment. Finally, an artifact filter on the raw EEG was tailored for each subject in an effort to remove and minimize artifact during the training (EMG, EOG, blinking, etc.). No changes were made to the protocol except adjustments to the reward band frequency. These were made based on rated symptoms of over-arousal (including nightmares; sleep difficulties; hyperactivity; aggressive behavior, anger, anxiety; and self-reports of high arousal including self-harm, suicidal and/or homicidal ideation), and symptoms of under-arousal (including inattention, decreased alertness or mental clarity; nausea; depressive symptoms; and decreased energy/fatigue) captured by the Checklist for Changes After Neurofeedback, as well as clinical judgment. If participants reported significant over-arousal symptoms for at least two training sessions, the reward frequency was lowered by 1 Hz. This procedure was continued until the participant reported no change, positive benefit, or symptoms of under-arousal. If the participant reported symptoms of under-arousal, the reward band was raised by ½ Hz until those symptoms remitted. Training time started at 12 minutes; training time was raised in three-minute increments participants reported a positive change. All but one participant achieved an endstate of thirty minutes per NF session.

Clinician Supervision, Fidelity, Assessment and Monitoring

NF sessions were conducted by experienced NF clinicians. Clinicians completed a session fidelity checklist designed to mirror the specific components of each session, requiring the rating of the full, partial or unsuccessful implementation of each component, any factors that impeded protocol adherence, and any modifications to protocol required. Changes to the starting protocol, such as frequency adjustments, were automatically recorded by the NFB software. Clinicians met weekly with the supervisor to review specifics of each NFB session and individual subject logs, session fidelity checklists, and protocol adjustments. Twenty percent of sessions were randomly selected for neurofeedback protocol review to assure that they matched adjustments dictated by supervisory staff.

Measures

1. Clinician Administered PTSD Scale (CAPS) [40], a clinician administered interview that is considered the gold standard for assessing PTSD, was the primary outcome measure of the study. Each of the 17 DSM-IV-TR PTSD symptoms are assessed with regard to their frequency and intensity over the past month using a 5-point scale (0–4). Symptoms endorsed with a frequency equal to or greater than one, and an intensity of equal to or greater than two are considered to meet the minimum threshold to count as a symptom of PTSD. The CAPS can be scored to indicate whether an individual meets the DSM criteria for a PTSD diagnoses, and frequency and intensity items can be summed to produce a symptom severity rating that can range from 0–136, with severity score equal to or greater than 45 necessary for a PTSD diagnosis. The PTSD diagnoses variable was used to evaluate inclusion criteria, while the continuous PTSD severity score was the primary outcome for the study analyses.

2.The Davidson Trauma Scale (DTS) [44] is a self-report measure of PTSD assessing the severity and frequency of PTSD symptoms that is structured similarly to CAPS with participants rating the frequency and severity of the 17 DSM-IV-TR PTSD symptoms using a 0–4 scale. PTSD severity scores were computed by adding all of the frequency and intensity items of each symptom, which, like the CAPS, produces a continuous score that can range from 0–136.

3. Inventory of Altered Self-Capacities (IASC) [45] is a 63-item standardized self-report measure consisting of seven subscales that assess the following domains of self-related psychological problems: Interpersonal Conflicts, Idealization-Disillusionment, Abandonment Concerns, Identity Impairment, Susceptibility to Influence, Affect Dysregulation, and Tension Reduction Activities. The number of items per subscale ranges from 5–9 with each item rated using a 1 (never) to 4 (often) scale producing a continuous score for each subscale.

The CAPS, our primary outcome measure, was administered on three occasions (not mid-point, at week 6). We included the DTS as a secondary outcome measure because it is easier to administer; therefore, we could include it as part of the six-week assessment that occurred during NF-training (or the corresponding time on the waitlist). Including an additional assessment increases power to detect significant results in longitudinal studies. The IASC was administered as a secondary measure to evaluate whether NF impacted relevant emotion-regulation and interpersonal processes.

Statistical Power

A post-hoc power analysis using a Monte Carlo simulation method [46] with the Mplus statistical software [47] was conducted to estimate obtained power. The Monte Carlo simulation method provides precise estimates of power for specific, hypothesized or evaluated, models and it can adjust for the missing data patterns in the observed data [48]. We conducted the power analysis using the estimates obtained from the multilevel growth curve models (GCMs, described below) using the CAPS and the DTS. These power analysis indicated power estimates of .56 and .77 to detect a d = .80 (usually considered the cutoff for large effect sizes) difference in change from pre-treatment to the follow-up assessment in the CAPS and DTS severity scores, respectively, between the two treatment conditions.

Participants

After initial phone screens 71 individuals were invited for a baseline assessment. Nineteen did not meet study criteria and were excluded: seven were on disability, six received subclinical scores on the CAPS, two due reported ongoing domestic abuse, one suffered a substance abuse relapse, one started another treatment, and one reported symptoms of psychosis. The remaining 52 individuals were randomized to either the WL (n = 24) or NF (n = 28) conditions and made up the intention-to-treat sample (ITT). Of the 28 individuals randomized to the NF group, six dropped out of treatment: four after having been randomized, but before starting the actual treatment (Fig 1). After starting NF two subjects dropped out—one had a previously undisclosed traumatic brain injury, and one subject reported increased flashbacks. Thus, 22 of the 28 participants assigned to NF completed the protocol. Of the 24 participants randomized to the WL group, one withdrew consent before treatment due to a medical illness; another was excluded after revealing that he received disability, leaving 22 participants assigned to the WL condition who completed the protocol. A chi-squared analysis showed that there was no significant difference between NF (6/28) and WL (2/24) ITT participants who failed to complete the protocol conditions, χ2 (n = 52, df = 1) = 1.70, p = .192.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 1. Flow of participants through the trial.

http://dx.doi.org/10.1371/journal.pone.0166752.g001

Table 1 presents demographic information for the entire sample and as a function of treatment condition. The sample was on average middle-aged and mostly white and female. A series of one-way ANOVAS and chi-squared tests revealed no statistically significant differences in demographic characteristics across conditions. A second series of one-way ANOVAs and chi-squared tests were then conducted to compare the treatment completers to ITT participants who dropped out or were excluded from the study. There was a significant difference for only one demographic variable, marital status, χ2 (n = 48, df = 1) = 15.62, p = .004. Because drop-outs were relatively evenly distributed across conditions, we decided not to include marital status as a co-variate.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 1. Sample Demographic Information

http://dx.doi.org/10.1371/journal.pone.0166752.t001

Data Analyses

Chi-square analyses were first were used to evaluate the impact of NF on PTSD diagnoses (present/absent). Next multilevel Growth Curve Modeling (GCM) using the mixed procedure of the Statistical Package for the Social Sciences (SPSS [48]) examined change in PTSD symptoms and other study variables across the course of treatment through one-month post treatment and whether these changes significantly varied across condition. Multilevel GCMs have become the standard for analyzing psychotherapy outcome data because of several advantages that this approach offers (i.e., efficiency in dealing with missing observations, efficient and powerful estimation techniques, and modeling flexibility [49]). This allowed us to include the entire intention-to-treat sample without using data imputation procedures. Time was modeled by including the number of weeks since baseline assessment (0, 6, 12, and 16, for pre-treatment, mid-treatment, post-treatment, and one-month post-treatment assessments). Prior to examining the impact of treatment condition on change, various unconditional change models (examining change without predictors) were evaluated to determine the most reliable manner to model time (e.g., linear using number of months, quadratic using number of months and number of months squared, or non-linear using a natural-log transformation of number of months). The best fitting change model was determined by examining the difference in the-2 Log Likelihood (i.e., deviance) estimate between competing models, which follows a chi-square distribution. To examine the impact of treatment condition on change in outcomes, a treatment condition dummy-coded variable was added as a predictor of change parameters (to test treatment condition x time interactions). Effect sizes (d) for differences in change between conditions was computed by the procedures described by Fiengold [50] producing effect size estimates comparable to those derived from more traditional repeated measures designs (e.g., repeated measures ANOVA).

Subject Attrition and Missing Data

One of the benefits of using multilevel GCM is that we could use maximum likelihood estimation, so that subject attrition and missing data did not affect the analyses as they would for less nuanced methods. Data were screened for patterns of missingness as MLE does assume that data is missing at random.

Results

On average participants endorsed exposure to 9.29 (SD = 2.90) of the 18 traumatic events assessed by the TESI, which did not significantly vary as a function of condition, F (1,43) = .02, p = .879. The most frequently endorsed events were childhood caregiver emotional abuse (78.8%), sexual abuse (69.2%) and domestic violence, 61.5%. At baseline, all participants met past month criteria for PTSD, and there was not a significant difference between WL (18/24, 75.0%) and NF conditions (24/27, 88.9%) meeting PTSD criteria during the past week, χ2 (n = 51, df = 1) = 1.69, p = .194. At week 12 (post-treatment) assessment, a higher proportion of WL participants (15/22, 68.2%) met criteria for PTSD than participants receiving NF (6/22, 27.3%), χ2 (n = 44, df = 1) = 7.38, p = .007. At the week 16 (one-month post-treatment), a higher proportion of WL participants (17/19, 90%) met criteria for PTSD in the past month than participants receiving NF (8/19, 42%), χ2 (n = 38, df = 1) = 9.47, p = .002.

CAPS severity scores were approximately normally distributed at each time point, with the highest skewness-to-standard-error of skewness ratio being1.91 at the post-assessment. The unconditional change model for the CAPS severity score indicated that modeling time as the natural-log transformation of number of weeks since baseline fit the data best accounting for 79.4% of the within-subjects variance. This pattern of change was characterized by large initial decreases in symptoms (during treatment) that flatten out over time (during the follow-up period). A significant treatment condition x time interaction emerged (b = -10.45, t = -5.10, p< .001). The nature of this effect is depicted in Fig 2 with change over time and associated effect size estimates reported in Table 2. Both the WL (d = -.62) and NF (d = -2.33) conditions exhibited significant decreases from the pre-treatment to the second (1 month) post treatment assessment; however, this decrease was substantially larger for the NF condition (d = -1.71). Both groups exhibited small decreases in CAPS severity score from the first to the second post-treatment to follow-up that did not significantly differ between conditions (d = -.16). The average decrease in CAPS score from the pre-treatment to the 1-month post treatment assessments was 40.35 for the NF condition and 10.78 for the WL group, the former well above and the latter well below the commonly adopted 20-point change in CAPS criteria used to indicate clinically significant change [49]. The bottom portion of Table 2 depicts the results when restricting the analyses to completers only and indicates that the results when using the ITT sample or the completers only sample were virtually identical.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 2. Change in PTSD symptom severity (Total CAPS score) as a function of treatment condition.

WL = waitlist, NF = Neurofeedback. Standard Error bars included at each assessment.

http://dx.doi.org/10.1371/journal.pone.0166752.g002

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 2. Pre-treatment, Post-treatment, and One-Month Post-treatment CAPS levels and Change Estimates

http://dx.doi.org/10.1371/journal.pone.0166752.t002

Estimates for the means of the self-report measures administered at pre-treatment, mid-treatment, post-treatment, and at the follow-up assessments are displayed in Table 3 with the corresponding change parameters depicted in Table 4. DTS scores were also approximately normally distributed at each time point, with the highest skewness-to-standard error of skewness ration being 3.60 at the post-treatment assessment. A linear change model best fit the data for the DTS total score. Mirroring the CAPS severity score findings, a significant treatment condition x time interaction emerged for the DTS (b = -1.52, t = -3.89, p< .001). The WL condition did not exhibit a significant decrease in pre-post change in the DTS (d = -.25) while the NF condition exhibited a significant and large decrease (d = -1.23) with a large effect size difference between conditions (d = -.97). The analyses for the completer only sample, like for the CAPS analysis, was virtually identical. Therefore, we present the results for the self-report measures for only the ITT sample.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 3. Pre-treatment, Mid-Treatment, Post-treatment, and One-Month Post-treatment Means and Confidence Intervals for Self-Report Measures

http://dx.doi.org/10.1371/journal.pone.0166752.t003

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 4. Change Estimates for Self-Report Measures

http://dx.doi.org/10.1371/journal.pone.0166752.t004

As depicted in Table 4, four of the IASC subscales (tension reduction activities, affect dysregulation total, affect skill deficits, affect instability) exhibited significant Time x Condition interactions, with a fifth (affect instability) approaching statistical significance (p = .053). The interpersonal conflict and identity related subscales did not exhibit significant Time x Condition interactions. The IASC subscale with the largest difference between the two conditions was tension reduction, with the NF group exhibiting significant decreases and the WL group exhibiting slight increases. The effect size for the difference in tension reduction activities from pre-treatment to the post-treatment assessment was -.75.

Discussion

Twenty-four sessions of NF produced significant improvements in PTSD symptomatology in multiply traumatized individuals with PTSD who had not responded to at least six months of trauma-focused psychotherapy, compared to a waitlist control group that continued to receive treatment as usual. The effect sizes of NF in this study (d = -2.33 within, d = - 1.71 between groups) is comparable to the results reported for the best evidence based treatments for PTSD: Prolonged Exposure, CBT and EMDR, which, like this study, also generally have employed TAU control groups, and better than any published drug intervention for PTSD [51]. The rate of completion of the NF protocol (79%) was comparable to reported exposure-based PTSD treatments (76%) [52]. In this study 72.7% of the NF sample no longer met criteria for PTSD. This is comparable to the 62% reported in metanalyses of other treatment studies [53]. Only one participant in the active treatment condition (4%) reported significant side effects, an increase in flashbacks.

The NF subjects also had statistically significant improvements in measures of affect regulation, identity impairment, abandonment concerns, and tension reduction activities. In contrast with most evidence based therapies for PTSD, which focus on processing memories of traumatic events, the target of NF is neural regulation and stabilization. Since lack of self-regulation has been identified as a principal cause of failure of exposure-based treatments [27–30], NF may be particularly helpful for traumatized individuals who are too anxious, dissociated or dysregulated to tolerate exposure based treatments. Finding cost-effective treatments for PTSD and other psychiatric conditions is particularly important in light of the limitations of existing treatments. Our results suggest that NF deserves further investigation for its potential to improve affect regulation, executive functioning and attention.

The former Director of the US National Institutes of Mental Health, calling for the development of a next generation of interventions, has noted that four decades of drug development has resulted in over 20 antipsychotics and over 30 antidepressants that have not demonstrably reduced the morbidity or mortality of mental disorders [54]. In an emerging new framework mental disorders are considered to be driven, at least in part, by abnormalities in underlying neural circuits [55]. A concerted effort is currently underway to map these networks, the so-called “human connectome project” [56,57]. The fact that mental disorders frequently are associated with abnormal brain-wave patterns and neural connectivity, including those measured by the EEG, lends support this approach [58].

Neurofeedback is a promising change agent for habitual dysfunctional neuronal patterns. The vast potential of EEG. based brain-computer interface techniques to train neuronal patterns is illustrated by recent research at the University of Minnesota. Using advanced functional neuroimaging including BOLD functional fMRI and EEG source imaging, they trained normal research subjects to control the flight of a virtual helicopter in 3-dimensional space through an obstacle course, based only upon motor imagination [59]. The equipment used in our study was much less sophisticated than that used in the helicopter navigation study, but it cost less than $10,000. If further research confirms the results from our study, neurofeedback has the potential of becoming widely available in community settings since can be economically administered by well-trained technicians in small offices and clinics.

Limitations

There are major limitations in this relatively small, non-placebo controlled study, including: 1) This study employed a waitlist control group that received TAU (psychotherapy + medications). While this is an appropriate control, findings would be more robust with a sham control condition; 2) This study only had a one-month follow up. Further studies are needed to establish the relative permanency of NF generated clinical improvements and investigate the necessity for follow-up booster sessions. 3) In this study we used clinical indications- PTSD symptoms- to guide our approach. It remains to be determined whether it is optimal to target specific abnormalities in brain EEG patterns, or clinical symptomatology in NF research. 4) This study did not examine to what degree clinical changes are correlated with specific alterations in EEG, or other neural activation patterns. 5) The NF protocol targeted R temporal lobe EEG patterns. Future research need to determine the optimal targets and procedures for the treatment of PTSD and other clinical conditions, including whether there are optimal protocols for different clinical conditions, or whether psychiatric patients are best served by individualized targeted interventions that use advanced EEG imaging technologies such as sLORETA [60].

Finally, the power analysis indicated that the study was slightly underpowered. There are two primary problems with underpowered studies: 1) increased risk of Type II errors, and 2) insufficient sample to produce unbiased or stable parameter estimates. Regarding #1, the current study produced hypothesized statistically significant effects ruling out Type II error. Regarding #2, the Monte Carlo procedure used for the power analysis produced estimates of 0.1%, 4.8%, and 92.6% for parameter estimate bias, standard error bias, and coverage, respectively, for the condition x time interaction for the CAPS, and 0.04%, 5.05,% and 93.5% for the DTS power. As per criteria specified by Muthén and Muthén [46] these values suggest that the sample size for the current study produced unbiased and stable estimates. The current study produced statistically significant effects despite being underpowered, which mitigates concerns that have been expressed regarding the use of post-hoc power analysis to argue that the results of negative trials may be meaningful despite the failure to detect significant effects due to being underpowered (e.g., [61]). The current study used post-hoc analysis to investigate the stability of the produced coefficients, and we followed recommendations of those who caution against the use of post-hoc test to report confidence intervals for coefficients.

Future directions

This study suggests that BCIs may have similar clinical potential for psychiatry as they do for rehabilitation medicine. Further clinical trials are needed to further substantiate to what degree BCIs can improve attention and affect regulation, and enhance cognitive performance and executive functioning. Using a combination of fMRI and quantitative EEG technology to define neural circuitry abnormalities, and studying the capacity of targeted neurofeedback interventions to alter these circuits would be a major step in that direction (e.g.11). Clarifying to what degree NF induced psychological changes are correlated with specific changes in neural activity will be a complex scientific challenge akin to correlating the clinical effects of various psychiatric medications with specific neurochemical changes in the brain.

Supporting Information

S1 File.pdf

figshare

 

1 / 3

 

download

(PDF)

S1 File. CONSORT Checklist Part 1

doi:10.1371/journal.pone.0166752.s001

(PDF)

S2 File. CONSORT Checklist Part 2

doi:10.1371/journal.pone.0166752.s002

(PDF)

S3 File. Study Protocol

doi:10.1371/journal.pone.0166752.s003

(PDF)

Acknowledgments

Marla Zucker PhD, Janice Stubblefield-Tave LICSW, Ainat Rogel.

Clinical trials.gov registr. NCT01259921

These data have been previously presented at the annual meeting of the International Society for Traumatic Stress Studies, New Orleans LA, October 20, 2015.

Author Contributions

1. Conceptualization: BvdK, EH.
2. Data curation: HH, MG, BvdK.
3. Formal analysis: MS, MG.
4. Funding acquisition: BvdK.
5. Investigation: MG, RM, MG.
6. Methodology: BvdK, JS, EH, MS.
7. Project administration: MG, HH, BvdK.
8. Resources: BvdK, HH,JS.
9. Software: N/A.
10. Supervision: EH, BvdK.
11. Validation: HH, RM, JS.
12. Visualization: RM.
13. Writing – original draft: BvdK, MS, HH.
14. Writing – review & editing: MG, EH, BvdK.

References

1. 1.Edlinger G, Rizzo C, Guger C: "Brain Computer Interface." Springer Handbook of Medical Technology. Springer, Berlin Heidelberg, 2011. 1003–1017.
2. 2.Shih JJ, Krusienski DJ, Wolpaw JR: Brain-Computer Interfaces in Medicine.Mayo Clinic Proceedings. 2012; 87: 268–279. doi: 10.1016/j.mayocp.2011.12.008. pmid:22325364
   • View Article
   • PubMed/NCBI
   • Google Scholar
3. 3.http://sharpbrains.com/pervasive-neurotechnology/
4. 4.Hurt E., Arnold L. E., & Lofthouse N.. Quantitative EEG neurofeedback for the treatment of pediatric attention-deficit/hyperactivity disorder, autism spectrum disorders, learning disorders, and epilepsy. Child and adolescent psychiatric clinics of North America, 2014; 23, 465–486. doi: 10.1016/j.chc.2014.02.001. pmid:24975622
   • View Article
   • PubMed/NCBI
   • Google Scholar
5. 5.Egner T, Gruzelier JH. Learned self-regulation of EEG frequency components affects attention and event related brain potentials in humans. Neuroreport. 2001; 12: 4155–4159. pmid:11742256
   • View Article
   • PubMed/NCBI
   • Google Scholar
6. 6.Egner T, Gruzelier JH. EEG Biofeedback of low beta band components: frequency-specific effects on variables of attention and event-related brain potentials Clinical Neurophysiology. 2004; 115: 131–139. pmid:14706480
   • View Article
   • PubMed/NCBI
   • Google Scholar
7. 7.Zoefel B, Huster RJ, Herrmann CS. Neurofeedback training of the upper alpha frequency band in EEG improves cognitive performance. Neuroimage. 2011; 54: 1427–1431. doi: 10.1016/j.neuroimage.2010.08.078. pmid:20850552
   • View Article
   • PubMed/NCBI
   • Google Scholar
8. 8.Beauregard M and L´Evesque J. Functional Magnetic Resonance Imaging Investigation of the Effects of Neurofeedback Training on the Neural Bases of Selective Attention and Response Inhibition in Children with Attention-Deficit/Hyperactivity Disorder. Applied Psychophysiology and Biofeedback. 2006; 31:3–20. doi: 10.1007/s10484-006-9001-y. pmid:16552626
   • View Article
   • PubMed/NCBI
   • Google Scholar
9. 9.Paquette V., Beauregard M., & Beaulieu-Prevost D. Effect of psychoneurotherapy on brain electromagnetic tomography in individuals with major depressive disorder. Psychiatry Research: Neuroimaging. 2009; 174: 231–239. doi: 10.1016/j.pscychresns.2009.06.002. pmid:19914046
   • View Article
   • PubMed/NCBI
   • Google Scholar
10. 10.Ros T., Théberge J., Frewen P. A., Kluetsch R., Densmore M., Calhoun V. D., et al. Mind over chatter: plastic up-regulation of the fMRI salience network directly after EEG neurofeedback. Neuroimage. 2013; 65: 324–335. doi: 10.1016/j.neuroimage.2012.09.046. pmid:23022326
    • View Article
    • PubMed/NCBI
    • Google Scholar
11. 11.Nicholson A, Rabellino D, Densmore M; Frewen P, Paret C, Kluetsch R. et al The Neurobiology of Emotion Regulation in Posttraumatic Stress Disorder: Amygdala Downregulation via Real-Time fMRI Neurofeedback. Human Brain Mapping, in Press, 2016
    • View Article
    • PubMed/NCBI
    • Google Scholar
12. 12.Gruzelier J. H. EEG-neurofeedback for optimising performance. I: a review of cognitive and affective outcome in healthy participants. Neuroscience & Biobehavioral Reviews 2014; 44:124–141.
    • View Article
    • PubMed/NCBI
    • Google Scholar
13. 13.Arns M., de R. S., Strehl U., Breteler M., Coenen A.. Efficacy of neurofeedback treatment in ADHD: the effects on inattention, impulsivity and hyperactivity: a meta-analysis. Clin. EEG Neurosci. 2009; 40: 180–189 pmid:19715181
    • View Article
    • PubMed/NCBI
    • Google Scholar
14. 14.Arnold L. E., Lofthouse N., Hersch S., Pan X., Hurt E., Bates B et al. EEG neurofeedback for ADHD: double-blind sham-controlled randomized pilot feasibility trial. J Atten Disord. 2013 Jul;17(5):410–9. Epub 2012 May 22. doi: 10.1177/1087054712446173. pmid:22617866
    • View Article
    • PubMed/NCBI
    • Google Scholar
15. 15.Steiner N. J., Frenette E. C., Rene K. M., Brennan R. T., & Perrin E. C.. In-school neurofeedback training for ADHD: sustained improvements from a randomized control trial. Pediatrics, 2014;133, 483–492. doi: 10.1542/peds.2013-2059. pmid:24534402
    • View Article
    • PubMed/NCBI
    • Google Scholar
16. 16.Wangler S., Gevensleben H., Albrecht B., Studer P., Rothenberger A., Moll G. H et al. Neurofeedback in children with ADHD: specific event-related findings of a randomized controlled trial. Clinical Neurophysiology. 2011; 122: 942–950. doi: 10.1016/j.clinph.2010.06.036. pmid:20843737
    • View Article
    • PubMed/NCBI
    • Google Scholar
17. 17.Linden D. E., Habes I., Johnston S. J., Linden S., Tatineni R., Subramanian L et al Real-time self-regulation of emotion networks in patients with depression. PLoS One, 2012; 7(6), e38115. doi: 10.1371/journal.pone.0038115. pmid:22675513
    • View Article
    • PubMed/NCBI
    • Google Scholar
18. 18.Johnston S., Linden D. E. J., Healy D., Goebel R., Habes I., & Boehm S. G.. Upregulation of emotion areas through neurofeedback with a focus on positive mood. Cognitive, Affective, & Behavioral Neuroscience. 2011; 11: 44–51
    • View Article
    • PubMed/NCBI
    • Google Scholar
19. 19.Sokhadze T. M., Cannon R. L., Trudeau D. L.. EEG biofeedback as a treatment for substance use disorders: review, rating of efficacy and recommendations for further research. Appl. Psychophysiol. Biofeedback. 2008; 33: 1–28 doi: 10.1007/s10484-007-9047-5. pmid:18214670
    • View Article
    • PubMed/NCBI
    • Google Scholar
20. 20.Peniston E.G., &Kulkosky P.J. Alpha-theta brainwave neurofeedback therapy for Vietnam veterans with combat-related post-traumatic stress disorder. Medical Psychotherapy. 1991; 4: 47–60.
    • View Article
    • PubMed/NCBI
    • Google Scholar
21. 21.Kluetsch R. C., Ros T., Theberge J., Frewen P. A., Calhoun V. D., Schmahl C., et al. Plastic modulation of PTSD resting‐state networks and subjective wellbeing by EEG neurofeedback. Acta Psychiatrica Scandinavica. 2014; 130: 123–136. doi: 10.1111/acps.12229. pmid:24266644
    • View Article
    • PubMed/NCBI
    • Google Scholar
22. 22.Gapen M., van der Kolk B. A., Hamlin E., Hirshberg L., Suvak M., & Spinazzola J.. A Pilot Study of Neurofeedback for Chronic PTSD. Applied psychophysiology and biofeedback, 2016; 40, 1–11.
    • View Article
    • PubMed/NCBI
    • Google Scholar
23. 23.Kouijzer M E.J., de Moor J M.H., Gerrits B J.L., Congedo M, van Schie H T. Neurofeedbackimproves executive functioning in children with autism spectrum disorders. Res Autism Spectrum Disorders. 2009; 3: 145–162
    • View Article
    • PubMed/NCBI
    • Google Scholar
24. 24.Ros T., Munneke M. A. M., Parkinson L. A., & Gruzelier J. H.. Neurofeedback facilitation of implicit motor learning. Biological psychology. 2014; 95: 54–58. doi: 10.1016/j.biopsycho.2013.04.013. pmid:23702458
    • View Article
    • PubMed/NCBI
    • Google Scholar
25. 25.Harmon-Jones E, Harmon-Jones C, Fearn M, Sigelman JD and Johnson P: Left Frontal Cortical Activation and Spreading of Alternatives: Tests of the Action-Based Model of Dissonance. J Personality and Social Psychology. 2008; 94: 1–15
    • View Article
    • PubMed/NCBI
    • Google Scholar
26. 26.Frewen P. A., Dozois D. J., Neufeld R. W., & Lanius R. A. (2012). Disturbances of emotional awareness and expression in posttraumatic stress disorder: Meta-mood, emotion regulation, mindfulness, and interference of emotional expressiveness. Psychological Trauma: Theory, Research, Practice, and Policy, 4(2), 152.
    • View Article
    • PubMed/NCBI
    • Google Scholar
27. 27.Jaycox LHF, Foa EB. Obstacles in implementing exposure therapy for PTSD: Case discussions and practical solutions. Clin Psychol Psychotherapy. 1996; 3: 176–184.
    • View Article
    • PubMed/NCBI
    • Google Scholar
28. 28.Burnstein A. Treatment noncompliance in patients with posttraumatic stress disorder. Psychosomatics. 1986; 27: 37–40. doi: 10.1016/S0033-3182(86)72741-2. pmid:3952245
    • View Article
    • PubMed/NCBI
    • Google Scholar
29. 29.Lanius R. A., Vermetten E., Loewenstein R. J., Brand B., Schmahl C., Bremner J. D., et al. (2010). Emotion modulation in PTSD: Clinical and neurobiological evidence for a dissociative subtype. American Journal of Psychiatry, 167(6), 640_647. doi: 10.1176/appi.ajp.2009.09081168. pmid:20360318
    • View Article
    • PubMed/NCBI
    • Google Scholar
30. 30.Pitman R., Altman B., Greenwald E., Longre R. E., Macklin M. L., Pore R. E., et al Psychiatric complications during flooding therapy for posttraumatic stress disorder. J Clin Psychiatry. 1991; 52: 17–20.
    • View Article
    • PubMed/NCBI
    • Google Scholar
31. 31.Cloitre M., Stolbach B., Herman J.L., van der Kolk B., Pynoos R., Wang J. et al.,. A developmental approach to complex PTSD: Childhood and adult cumulative trauma as predictors of symptom complexity. J Trauma Stress. 2009; 22: 399–408. doi: 10.1002/jts.20444. pmid:19795402
    • View Article
    • PubMed/NCBI
    • Google Scholar
32. 32.Keizer AW, Verment RS, Hommel B. Enhancing cognitive control through neurofeedback: A role of gamma-band activity in managing episodic retrieval. NeuroImage. 2010; 49: 3404–3413 doi: 10.1016/j.neuroimage.2009.11.023. pmid:19925870
    • View Article
    • PubMed/NCBI
    • Google Scholar
33. 33.Kropotov JD, Grin-Yatsenko VA, Ponomarev VA, Chutko LS,Yakovenko EA, Nikishena IS: ERPs correlates of EEG relative beta training in ADHD children. Int J Psychophysiology. 2005; 55: 23–34
    • View Article
    • PubMed/NCBI
    • Google Scholar
34. 34.Jokic-Begic N, Begic D. Quantitative electroencephalogram (qEEG) in combat veterans with post-traumatic stress disorder (PTSD). Nordic J Psychiat, 2003; 57: 351–355.
    • View Article
    • PubMed/NCBI
    • Google Scholar
35. 35.Veltmeyer MD, McFarlane AC, Bryant RA, Mayo T, Gordon E, Clark CR. Integrative assessment of brain function in PTSD: Brain stability and working memory, J IntegNeurosci. 2006; 5:123–138.
    • View Article
    • PubMed/NCBI
    • Google Scholar
36. 36.Klimesch W EEG-alpha rhythms and memory processes, Int J Psychophysiol.1997; 26: 319–340. pmid:9203012
    • View Article
    • PubMed/NCBI
    • Google Scholar
37. 37.Klimesch W. EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis. Brain Res Brain Res Rev. 1999; 29:169–195. pmid:10209231
    • View Article
    • PubMed/NCBI
    • Google Scholar
38. 38.Tesche CD, Karhu J, Theta oscillations index human hippocampal activation during a working memory task, P Natl Acad Sci USA. 2000; 97: 919–924.
    • View Article
    • PubMed/NCBI
    • Google Scholar
39. 39.Ford J, Fournier D. Psychological trauma and post-traumatic stress disorder among women in community mental health aftercare following psychiatric intensive care. J Psychiatric Intensive Care. 2007; 3, 27–34.
    • View Article
    • PubMed/NCBI
    • Google Scholar
40. 40.Blake D. D., Weathers F. W., Nagy L. M., Kaloupek D. G., Gusman F. D., Charney D. S.. The development of a clinician-administered PTSD scale. J Trauma Stress. 1995; 8: 75–90. pmid:7712061
    • View Article
    • PubMed/NCBI
    • Google Scholar
41. 41.Rauch S. L., Van der Kolk B., Fisler R., Alpert N. M., Orr S. P., Savage C. R. et al A Symptom Provocation Study of Posttraumatic Stress Disorder Using Positron Emission Tomography and Script-Driven Imagery. Arch General Psychiat. 1996; 53: 380–387.
    • View Article
    • PubMed/NCBI
    • Google Scholar
42. 42.Georgopoulos AP; Tan H-R M; Lewis SM; Leuthold A C; Winskowski A M; Lynch J K et al. The synchronous neural interactions test as a functional neuromarker for posttraumatic stress disorder (PTSD): a robust classification method based on the bootstrap. Journal of Neural Engineering. 2010; 7: 016011,
    • View Article
    • PubMed/NCBI
    • Google Scholar
43. 43.Sanei S., & Chambers J. A.. Introduction to EEG. EEG signal processing, 2007;1–34.
    • View Article
    • PubMed/NCBI
    • Google Scholar
44. 44.Davidson J. R. T., Tharwani H. M., & Connor K. M.. Davidson Trauma Scale (DTS): Normative scores in the general population and effect sizes in placebo-controlled SSRI trials. Depr Anxiety. 2002; 15: 75–78.
    • View Article
    • PubMed/NCBI
    • Google Scholar
45. 45.Briere J. R., M. The Inventory of Altered Self-Capacities: A standardized measure of identity, affect regulation, and relationship disturbance. Assessment.2002; 9(3), 230–239. pmid:12216780
    • View Article
    • PubMed/NCBI
    • Google Scholar
46. 46.Muthén L.K., & Muthén B.O. How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling, 2002; 4, 599–620.
    • View Article
    • PubMed/NCBI
    • Google Scholar
47. 47.Muthén L.K. and Muthén B.O. Mplus User’s Guide. Fifth Edition. Los Angeles, CA: (1998–2009) Muthén & Muthén.
48. 48.Peugh J. L. & Enders C. K. Using the SPSS mixed procedure to fit crosssectional and longitudinal multilevel models. Educational & Psychological Measurement, 2005; 65, 714–741.
    • View Article
    • PubMed/NCBI
    • Google Scholar
49. 49.Singer J., & Willett J.. Applied longitudinal data analysis: Modeling change and event occurrence. NY. Oxford University Press. 2003.
50. 50.Feingold A.. Effect sizes for growth-modeling analysis for controlled clinical trials in the same metric as for classical analysis. Psychological methods, 2009; 14, 43. doi: 10.1037/a0014699. pmid:19271847
    • View Article
    • PubMed/NCBI
    • Google Scholar
51. 51.Jacobson NS, Truax P. Clinical significance: a statistical approach to defining meaningful change in psychotherapy research. J Consult Clinical Psychol. 1991; 59: 12–19.
    • View Article
    • PubMed/NCBI
    • Google Scholar
52. 52.Van Etten ML, Taylor S. Comparative efficacy of treatments for post-traumatic stress disorder: a meta-analysis. Clin. Psychol. Psychother. 1998; 5: 126–144
    • View Article
    • PubMed/NCBI
    • Google Scholar
53. 53.Bradley R., Greene J., Russ E., Dutra L., Westen D. A multidimensional meta-analysis of psychotherapy for PTSD. Am J Psychiat. 2005; 162: 214–227. doi: 10.1176/appi.ajp.162.2.214. pmid:15677582
    • View Article
    • PubMed/NCBI
    • Google Scholar
54. 54.Insel T: The NIMH Experimental Medicine Initiative. World Psychiatry. 2015; 14: 151–153. doi: 10.1002/wps.20227. pmid:26043323
    • View Article
    • PubMed/NCBI
    • Google Scholar
55. 55.Van Essen DC, Barch DM: The human connectome in health and psychopathology World Psychiatry. 2015; 14: 154157.
    • View Article
    • PubMed/NCBI
    • Google Scholar
56. 56.Uhlhaas PJ, Singer W:. Neuronal dynamics and neuropsychiatric disorders: toward a translational paradigm for dysfunctionallarge-scale networks. Neuron 2012; 75:963–80. doi: 10.1016/j.neuron.2012.09.004. pmid:22998866
    • View Article
    • PubMed/NCBI
    • Google Scholar
57. 57.http://www.humanconnectome.org/
58. 58.Duncan N. W., Hayes D. J., Wiebking C., Tiret B., Pietruska K., Chen D. Q., & Northoff G. (2015). Negative childhood experiences alter a prefrontal-insular-motor cortical network in healthy adults: A preliminary multimodal rsfMRI-fMRI-MRS-dMRI study. Human brain mapping, 36(11), 4622–4637. doi: 10.1002/hbm.22941. pmid:26287448
    • View Article
    • PubMed/NCBI
    • Google Scholar
59. 59.Doud AJ; Lucas J P, Pisansky MT, He B. Gribble P L, ed. Continuous Three-Dimensional Control of a Virtual Helicopter Using a Motor Imagery Based Brain-Computer Interface. PLoS ONE 2011 6 (10): e26322. Bibcode:2011PLoSO…626322D. PMC 3202533. doi: 10.1371/journal.pone.0026322. pmid:22046274
    • View Article
    • PubMed/NCBI
    • Google Scholar
60. 60.Pascual-Marqui RD, Biscay RJ, Bosch-Bayard J, Lehmann D, Kochi K, Kinoshita T, et al. Assessing direct paths of intracortical causal information flow of oscillatory activity with the isolated effective coherence (iCoh). Front Hum Neurosci. 2014; 20:448. eCollection 2014.
    • View Article
    • PubMed/NCBI
    • Google Scholar
61. 61.Levine M., & Ensom M. H. Post hoc power analysis: an idea whose time has passed? Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy, 2001; 21: 405–409.
    • View Article
    • PubMed/NCBI
    • Google Scholar

__________________

Terminus un skatījumus Jūs varat meklēt zinātnisko publikāciju datubāzēs.Tās pieejamas šeit!

www.RigaBrain.com