www.quietmindfdn.org

Training Brainwave Activity and Cerebral Perfusion in Autistic Spectrum Disordered Children: an in-school pilot study

Submitted By:

Principal Investigator: Marvin H. Berman, Ph.D., CBT, BCIAC(EEG)

Project Coordinator: Kristine Sudol, Psy.D.

Project Consultants: Eric Miller, Ph.D., Mark Berman, Psy.D.

December 20, 2005

Electroencephalographic Biofeedback Treatment for ADHD & Autism

The following outlines the use of EEG biofeedback, also known as Neurofeedback, as a treatment for ADHD and Autistic Spectrum Disorder (ASD). The range of applications of this technology is far broader than ADHD and ASD and may be more appropriately conceptualized as an intervention for improving the efficient functioning of the central nervous system. In short, the EEG signal is the recording of the difference in electrical potential or voltage between various pairs of sampling electrodes attached to the surface of the scalp (Duffy, 1989). Definitions of neurofeedback vary from Hill and Castro (2002) who describe neurofeedback simply as “a sophisticated form of biofeedback that actually trains the brain to speed up,” to Siegfried Othmer, Ph.D., chief scientist at EEG Institute, who describes neurofeedback in an interview with Psychiatric Times, as “neuroregulation in the time and frequency domains through the use of bioelectrical operant conditioning.” He continues, “By stabilizing the brain and rewarding it for holding particular states, neurofeedback acts as a natural anticonvulsant.” (Oubre, 2002) Essentially, in the most widely used neurofeedback training protocol, individuals are trained to increase the frequency of electrophysiological activity within either the 12-15 Hz (cycles per second) or 16-20 Hz range, while attempting to decrease slower cortical activity in the 4-8 Hz range. These ranges are those which have been associated with varying levels of attention, with slower frequencies associated with sleep and reverie states and faster frequencies associated with more concentrated or focused attention. The rationale for using EEG biofeedback or neurofeedback therapeutically is that it is thought to correct deficits in cerebral regulatory function related to arousal, attention, vigilance and affect (Othmer et al., 1999).

Biological roots of ADHD and Autism

ADHD and autism are characterized by inherited neurological deficits that affect cortical functioning. Some genetic evidence may be found in studies that show a heritability index of .75, with a 30% correlation between siblings and 57% between parents and children. (Farone & Doyle, 2002) Neuroanatomical abnormalities have been identified including small basal ganglia and cerebellum (affects movement and behavior control), small frontal cortex, corpus callosum, caudate, and the anterior cingulate (related to maintaining focused attention), decreased activity of basal ganglia and cerebellum, and PET scan data showing decreased glucose metabolism in frontal regions as well as in the striatum (Raz, 2004). Quantitative EEG (QEEG) findings describe a variety of dynamic interrelationships in the EEG activity all over the head. For instance, hypoarousal would be identified by elevated relative theta power, reduced relative alpha and beta; elevated theta-beta and theta-alpha ratios and hyperarousal is indicated by increased beta; decreased alpha; decreased theta-beta ratio.

Rationale for EEG Biofeedback for ADHD

The prevailing hypothesis concerning the mechanism of action in EEG biofeedback is that recorded cortical activity reflects underlying thalamocortical processes relating to a person’s capacity for attention and behavior control. Subsequent use of these signals as a source of feedback could help people to re-regulate specific brain processes, leading to “normal” behavioral response.

Roots of EEG Biofeedback

Sterman’s discovery of the sensory motor rhythm (SMR) which comprises the 12-14Hz frequency band furthered the development of neurofeedback as a way improve behavioral inhibition. Neurofeedback training was first done with cats and then with jet pilots and with epileptic patients. Lubar and Shouse who the first to publish their use of EEG feedback with ADHD patients. They used SMR training as well as training to increase beta (22-30 Hz) and decrease theta (4-8Hz). (Lubar & Shouse, 1976)

Biofeedback Treatment Protocols

The commonly used protocols include:

SMR enhance/theta suppress and SMR enhance/beta-2 suppress is used for decreasing hyperactivity and impulsivity.
Theta suppress/Beta-1 enhance is used for predominantly inattentive type ADD.
Combined type of ADHD uses SMR enhance/beta-2 suppress with theta suppression/Beta-1 enhance.

Hemoencephalographic Biofeedback (HEG)

HEG training uses blood perfusion as indexed by the measurement of infrared energy reflected off the surface of the cortex at the frontal region of the brain. Improvement in blood oxygenation are expressed in terms of changes in behavior and increased concentration, attention, understanding, listening and following instructions, improved reasoning, patience and self control, speech and communication behaviors and the expression of appropriate affect and accurate empathy. (Toomim, 2003)

Evidence for EEG Biofeedback Efficacy with ADHD* (also see appendix 1)

Case Study Research

1. Lubar and Shouse (1976) reported on an 11-year old boy who showed significant decreases in off-task and oppositional behavior, increased cooperativeness and higher completion rate of schoolwork.

2. Thompson and Thompson (1998) reported on 111 patients who showed improvements in attention, impulse control, IQ scores (12 points on average).

3. Kaiser and Othmer (2000) reported on 1089 patients, 186 with ADHD, who showed improved attention and impulse control.

4. Tansey (1993) and Lubar (2003) found that gains made lasted 10 years. They also found that 29-35% of patients do not respond to neurofeedback, which is similar to the response rate of ADHD patients to medication.

Controlled Group Studies

1. Rossiter and La Vague (1995) & Fuchs, et al.(2003) report that comparing EEG to medication, no significant difference was found on TOVA or BASC. There was no randomized assignment of participants in this study.

2. Linden, et al. (1996) randomly assigned participants to EEG biofeedback and a no treatment control group. Significant improvement on Kaufman Brief Intelligence Scale and Iowa-Connors Behavior Rating Scale was reported.

3. Carmody, et al. (2001): randomly assigned participants to EEG biofeedback and no treatment controls. The study showed improved attention and decreased impulsivity and no consistent QEEG patterns were identified for either group.

4. Monastra, et al. (2002: compared Ritalin and EEG to Ritalin alone (participant choice). Findings supported the combination of approaches as preventing relapse after medicine was discontinued.

EEG Biofeedback Training & ADHD In An Elementary School Setting

Subjects for this study included 8 ADHD and 8 non-ADHD middle school students matched for age, gender, and grade, with 4 of each group in experimental and control conditions. The control group was waitlist control (no treatment), the experimental group received EEG feedback. There were 36-48 sessions 3-4 times per week, each session lasting 30 minutes. (Carmody, et. al., 2001)

The experimental group showed greter improvement in attention than the control group, for both ADHD and non-ADHD participants. However, the impulsivity/hyperactivity scales did not show significant improvement for either group, due to placebo effects or corrupted teacher reports. The ADHD group receiving treatment showed significant improvement on various TOVA subtests including commission errors, anticipatory scores, and omission errors. Changes in brainwave activity were generally observable after 20-25 sessions and correlated with changes in teacher reports. There were no consistent or expected changes in brainwaves that would predict specific behaviors.

This study had ecological validity as it occurred in a normal school setting. There was positive feedback from both school, parents, and children. Significant behavior changes were observed in most of the participants. The absence of a randomized control group limits the generalizability of these results and does not account for Hawthorne effects. The non-ADHD group was not screened for other disorders so we can not be sure of the equivalence of the two samples.

A Study of Audio-Visual entrainment program as a treatment for attentional & behavior disorders in a school setting (Carter,1993)

EEG correlates of ADHD indicate that more theta waves (4-7 Hz) and less beta-1 waves (13-21 Hz) lead to lack of focus and attention, and to hyperarousal. In ADHD the brain struggles to sustain an adequate level of arousal and overcompensates, resulting in the restlessness, agitation, fidgeting and distractibility commonly observed in this condition. Neurophysiologically one can observe reduced blood flow and low glucose metabolism in specific brain regions.

Audio-visual entrainment (AVE) involves repetitive and intermittent presentation of light and sound stimulation using preprogrammed light and sound stimulation devices that seek to alter the brainwave activity. This study sample included 14 participants who were students from a Catholic school and 20 from a public school. The Catholic school participated in an ADHD program and the public school in a reading program. Eight members of the reading program and all 20 members of the ADHD program received AVE training.

Independent measures included TOVA for all participants and STAR to those in the reading program. There was significant posttest improvement on TOVA scores for the ADHD group in the areas of inattention, impulsivity, reaction time, and variability measures. The AVE group in the reading program performed better on posttest STAR than did the non-AVE group. Qualitative findings suggest that AVE led to improved relaxation, parent/teacher reports of better concentration, increased extroversion, improved school performance, and “easygoing” mood and reduction in ADHD medication. No statistics were offered for qualitative data.

Hemoencephalographic Biofeedback as a Treatment for Autism

Dr. Herschel Toomim, Sc.D. suggests that we view the prefrontal cortex as ‘the executive nucleus’ and its modification as the central focus of intervention using biofeedback training. He continues, “As the executive it determines which nuclei will be activated and maintains check on those activities, modifying them as required to best meet the needs of the organism. In the case of ASD the prefrontal cortex fails to develop properly and therefore the other parts of the brain are free to operate in an uncoordinated fashion. Such uncontrolled activity can give rise to any behavior within the range of that nucleus. Such behaviors may well be over excited or even under aroused. If this hypothesis is confirmed then treatment of the prefrontal cortex is clearly indicated.” (Toomim, 2005)

The only published study on the use of HEG biofeedback involves 40 sessions of prefrontal stimulation training with 180 autistic subjects ages 3-18 years (Penkae, 2003). Subjects wore a headband attached to the forehead that permitted the measurement of blood oxygenation using spectrophotometric measurement of reflected infrared light from the cerebral cortex. The study demonstrated a definitive relationship between cortical hypoperfusion, i.e. blood oxygenation, and frontal lobe functioning, with 86% of subjects showing improvement in cognitive functioning. These results were replicated in the current study with both cognitive and fine motor functioning improvements being noted after a total of about 8 hours of HEG training.

These controlled studies, including the current report, are limited in their explanatory power due to small subject pools, patient and clinician variables not measured, 3 studies without random assignment, studies with random assignment had no placebo control. The present study is limited by these same constraints while continuing to demonstrate significant efficacy, these studies only serve to underscore the need for substantive Type 1 clinical studies.

Methodology

Method

Participants

Participants in the current study consisted of sixteen students enrolled at the Philadelphia Academy Charter School. One student did not complete the study. Participants ranged from 6 to fourteen years of age (mean = 10.4, standard deviation = 2.44). There were twelve males (75%) and three females (20%). Fifteen students were Caucasian and one male student was Black.

Measures and Materials

Conners Rating Scales- Revised (CRS-R)

The Conners Ratings Scales- Revised, including the parent and teacher versions, are widely accepted self-report measures for ADHD. The CRS-R utilizes observer ratings as a method by which to measure problem behavior in children and adolescents. The CRS-R are used to screen for hyperactivity, oppositional behavior, and cognitive difficulties such as inattention. The CRS-R also have a large normative database that provides solid reliability and validity, and also enables researchers and clinicians to establish baseline functioning and to monitor behavioral change throughout treatment (Conners, 1997).

Test of Nonverbal Intelligence (TONI-3)

Test of Nonverbal Intelligence-3 (Brown, Sherbenou, & Johnsen, 1997), is an instrument designed to specifically assess individuals who have special difficulties such as motor or language difficulties (where English is not the predominant language or an individual has severe language delays) or where the notion of testing and assessment is unfamiliar to the testee. As such, the TONI-3 is a language-free measure of abstract/figural problem solving, which can be used with individuals who range from approximately 6 to 90 years of age.

NEPSI Tower

The NEPSI (Korkman, Kirk, & Kemp, 1998) is an instrument designed to assess neuropsychological development in preschool and school-age children, specifically for children between the ages of 3 and twelve. A wide range of subtests, including attention/executive functions, language, sensorimotor functions, visuospatial processing, and memory/ learning, enables researchers to examine a comprehensive evaluation or a more specific area of functioning, depending on the reason for evaluation. In particular, the NEPSI tower is a subtest that assesses a child’s ability to plan, monitor, self-regulate, and problem solve through the use of moving three different colored balls to specific positions on three different pegs in numbered moves.

Stroop Color and Word Test

The Stroop Color and Word Test essentially examines an individual’s ability to separate word and color naming stimuli. Three colors (red, green, blue) are used to present different stimuli on three different pages, which each contain one hundred items. The first page contains lists of three different colors (red, green, blue), written in black ink that must be read out loud by the testee. The individual is instructed to read down each column as fast as possible and state each word within forty-five seconds. The second portion of the test also consists of one hundred items, written as “XXXX” in blue, red and green ink respectively. The testee is given forty-five seconds to read each color out loud until time has run out. The last portion of the Stroop Test involves one hundred words written in blue, red, and green ink respectively. The examinee is again given forty-five seconds to read each word out loud.

Bioexplorer

Pendant

Brainmaster

Mindbowling, Mindskiing, Mindpinball

Procedure

Phase 1

Students enrolled in the Philadelphia Academy Charter School who chose to participate in the present study were all receiving special education services. A list of fifty students was provided to the investigators of this study and thirty-four students were excluded. Inclusion critieria was as follows: students struggling with attentional difficulties and met the criteria for attention deficit hyperactivity disorder (ADHD), as indicated by a diagnosis of ADHD required by the Diagnostic and Statistical Manual of Mental Disorders Fourth Edition-Text Revision (DSM IV-TR, 2000). Students who were diagnosed with a pervasive developmental disorder (DSM IV-TR, 2000) as well as students struggling with learning difficulties were also included in the study.

Current psychological assessments and individualized education plans were obtained from school staff and consultation with staff school psychologists and the special education coordinator provided additional background on each child.

Once students were determined appropriate for the present study, parents were sent letters providing information about the study and asked to sign consent forms as all children were under the age of eighteen. Once consent was obtained, each student was administered psychological assessment measures, which included the TONI-3, the CPT, the NEPSI Tower, and the Stroop Color and Word Test. Parents were also asked to fill out the Conners Parent Rating Scale. All measures at this point functioned as pretests that enabled investigators to establish baseline functioning.

Phase 2

Once students completed all pretesting, the process of conducting neurofeedback began. Students were introduced to the equipment and procedural methodology (such as the hook-up process, the materials involved, and how these would be used and applied) and provided background about the study and the potential benefits that could be obtained with effort, cooperation, and motivation.

Each student then began learning about neurofeedback and the Bioexplorer program through direct testing. Rapport was established and maintained by encouraging all participants to ask questions and help investigators pick a preferred video clip and song. Students were offered numerous video clips (such as star wars), which were accompanied by different music, which ranged from classical to rock and roll. Students were instructed to complete approximately 3 five-minute trials each day they were tested. Students were tested 3 days a week during the first 2 months of testing and then were tested four days per week during the final stages of testing. The goal was for each student to complete one hundred- twenty trials. Students were also given the opportunity to choose prizes after completing trials thirty, sixty, ninety, and one hundred-twenty.

All students began with the same video and music clip for consistency, and then were encouraged to choose which clips they would like for the remaining trials. Staff would document each student’s progress, including theta, alpha, and smr beta levels. Students were also individually coached and monitored regarding muscle tone and relaxation level. Students with pervasive developmental disorders had more difficulty relaxing, but with coaching and individualized attention were able to reduce relaxation levels dramatically.

Results

Table One: TON-3 Scores

Name	Pretest Raw	Pretest Standard	Pretest age at testing (yr/mo)	Posttest Raw	Posttest Standard	Posttest age at testing (yr/mo
01	7	100	6-2	19	127	6-5
02	7	75	9-7	30	125	9-11
03⁺	12	77	14-3	17	83	14-6
04	7	79	9-3	25	113	9-6
05	7	72	11-6	38	135	11-9
06	12	82	10-0	27	115	10-3
07	10	77	12-08	30	115	12-11
08	7	83	7-10		83	8-1
09	11	82	9-10	11	81	10-1
10	15	87	10-3	31	122	10-6

Table Two: Comparison of Pre- and Posttest TONI-3 Scores

Test session	Mean	Standard deviation	Mean difference between groups
Pre-	81.4	7.85	* 28.5
Post	107.69	19.31

* p< .005 ⁺ HEG

Participants were administered the TONI-3 both prior to and following the intervention. Ten participants completed testing at both sessions. Based on the results of an independent sample t-test, the participants showed significant improvement on their TON-3 scores (p<.005).

Table Three: Stroop Scores

Name	Pretest Word Score	Posttest Word Score	Word Difference Score	Pretest Color Score	Posttest Color Score	Color Difference Score	Pretest Word Color Score	Posttest Word Color Score	Word Color Difference Score
01	42	36	-6	46	48	2	72	48	-24
02	32	36	4	34	38	4	56	44	-12
03⁺	12	34	22	12	34	22	20	42	22
04	68	42	-26	42	48	6	62	52	-10
05	19	56	37	21	56	35	54	54	0
06	32	46	14	38	52	14	52	50	-2
07	22	46	24	26	70	44	46	56	10
08	34	40	6	50	52	2	60	46	-14
09	28	36	8	38	38	0	50	40	-10
10	44	50	6	42	36	-6	68	40	-28

Table 4: Differences Between Pre- and Post Stroop Scores

Word	8.9
Color	* 12.3
Word Color	-6.8

* p< .05 ⁺ HEG

Continuous Performance Test (CPT)

PRE Test	POST Test
**	10.67
16.3	20.64
10.67	**
20.74	11.79
10.67	6.16
6.16	6.16
**	4.44
15.11	15.11
18.34	18.34
10.6	20.74
**	15.55
9.39	15.02
**	15.11
20.74	15.11
**	20.74

** No Data

All subjects completed the CPT pretesting and postesting but due to data corruption some of the scores were lost. Two subjects’ scores increased from pre to post conditions, these children suffered serious traumatic events in their families (parents with fatal illnesses diagnosed or parental loss) during the period of this research that could account for their increased difficulty with attention and concentration. The other seven children for whom we had complete data showed either improvement or consistency in their scoring on the CPT. Consistent performance is considered an indicator of improvement as the students were able to sustain attention on a task that they already knew was going to be at best tedious and rather boring.

Teacher/Parent Qualitative Feedback

Followup surveys of the faculty and parents were conducted in writing and orally about their impressions of the children’s progress throughout the study. The following quotes are selections from the teacher feedback forms:

“more social”, “more emotional”, “better in math”, “wants to go next year”

“spelling/reading better after”, “really excited about going toward the end of the program” “always did homework”, “more vocal”, “read better”, “more eager at the beginning of the program and less so toward the end which coincided with the end of the school year”, “more quiet but still loses temper”, “wants to go next year”. “showed a great ability to transition from different settings which as an important goal for his treatment plan.” John became more emotional and would say, “I don’t even know why I am so sensitive”.

The general tenor of these comments from faculty as indicated above show that the participants derived both academic and behavioral improvements from the training program. There was a greater awareness among some participants as to their feelings about their disabilities as captured in the last comment by one child’s teacher about his growing self awareness concerning his emotional and sensory sensitivity.

Parent followup was difficult to obtain as the project staff did not have the addresses of the participants. The following are comments from the parent feedback forms we were able to obtain:

“handles his transitions to and from biofeedback very well. He did become more emotionally expressive over the last few months while attending biofeedback.

Academically he remained the same where he was pior to entering the program.”

“Since the end of the school year my husband and I feel he has been able to express himself so much better. He will sit and start a conversation with people. Conversations don’t focus on those limited topics of his liking as it was in the past. He has expressed anger more than he has in the past, however, with expressing what makes him angry he seems less frustrated.”

These comments coincide with the observations made by the project staff (see Appendix 2) regarding positive changes in socialization skills, emotional expressiveness and intelligence, interpersonal skills and self awareness. It is our expectation that with continued biofeedback training, especially when the training is more specifically designed for their needs, these students will continue to grow both academically and socially, thereby reducing the requirement for more intensive special educational, psychiatric, psychological and occupational therapy services.

Discussion

There is a fairly strong suggestion from the data that both EEG-driven biofeedback as well as cerebral blood perfusion biofeedback are effective in helping children with a variety of autistic spectrum, complex behavioral and attention-deficit hyperactivity disorders. The results were achieved in less than ideal circumstances and should be even more robust when applied in more physically and temporally conducive circumstances. The computer equipment used for this study was below standard and had to be repaired regularly by outside contractors who were not always available on short notice leading to frustration among the subjects and staff and reduced quality of responses due to negative expectations as to whether the computers would work that day. There were problems with electrical interference with the EEG equipment that would cause systems to prematurely shut down and again cause frustration and reduced training time and consistency.

Future studies will need to eliminate these problems by obtaining the proper computer technology and on site support services along with proper space and furniture to support the training process. The current study utilized a computer lab setup for a different computer-supported learning program. The room had limited ventiliation so twhen several children and adults were in the room with 3-4 computers running, the temperature became uncomfortably warm within a few minutes. Fans were used to reduce the temperature but added a significant noise factor to the training environment as the door to the room could not be closed.

Programs like these should begin closer to the beginning of the school year so as to avoid interruptions related to holidays and school trips as well as the declining motivation of the students as the end of year approaches. In addition, teachers should be introduced to the program and their support solicited at the outset to garner ongoing input from their observations concerning student’s reactions to the training and support for the program-as-a-whole to enhance students’ active participation.

Six of the subjects were assigned to the HEG treatment group and unfortunately, we were unable to obtain complete pre/post data on 5 of these subjects leaving only one subject having completed the HEG training. We therefore could not conduct comparative analyses between the two methods. It is worthy of note that the two subjects who reported significant improvement in handwriting legibility were both HEG training group participants.

Note: We wish to acknowledge the invaluable assistance of Jennifer Musico, Psy.D, Dolly Berla, M.S., Keith Youse, M.Ed., Amanda Hansen, B.A. and Megan Woodcock, BA for their outstanding efforts to train the subjects, collect and analyze the outcome data.

References

Carmody, D.P., Radvanski, D.C., Wadhwani, S., Sabo, M.J., & Vergara, L. (2001). EEG

Biofeedback training and attention-deficit/hyperactivity disorder in an elementary

school setting. Journal of Neurotherapy, 4 (3), 5-27.

Carter, J.L. & Russell, H.L. (1993). A pilot investigation of auditory and visual

Entrainment of brain wave activity in learning disabled boys. Journal of the Texas

Center for Educational Research 4, 65-73.

Castro, E., Hill, R.W. (2002) Getting Rid of Ritalin: How Neurofeedback Can Successfully Treat Attention Deficit Disorder, Hampton Roads Publishing Co., Inc., Charlottesville, VA.

Duffy, F.H., Iyer, V.G, Surwillo, W.W., Clinical Electroencephalography and Topographic Brain Mapping: Technology and Practice. Springer-Verlag, New York, 1989, p. 84.

Farone, S.V, Doyle, A.E. The nature and heritability of attention-deficit/hyperactivity disorder. Child Adolescent Psychiatry Clin. N. Am. 10, 299-315, viii-ix, 2001.

Fuchs, T., Birbaumer, N., Lutzenberger, W., Gruzieler, J.H., & Kaiser, J. (2003).

Neurofeeback treatment for attention-deficit/hyperactivity disorder in children: A

comparison with methylphenidate. Applied Psychophysiology and Biofeedback, 28,

(1), 1-12.

Joyce, M. & Siever, D. (2000). Audio-visual entrainment program as a treatment for

behavior disorders in a school setting. Journal of Neurotherapy 4 (2), 9-25.

Lubar, J. Kaiser, D.A., & Othmer, S. (2000). Effect of Neurofeedback on variables of attention in

A large multi-center trial. Journal of Neurotherapy, 4 (1), 5-28.

Linden, M., Habib, T. & Radojevic, V. (1996). A controlled study of the effects of EEG

biofeedback on cognition and behavior of children with attention deficit disorder and

learning disabilities. Biofeedback and Self-regulation, 21 (1), 35-49.

F. (2003). Neurofeedback for the management of attention deficit disorders. In

M.S. Schwartz & F. Andrasik (Eds.), Biofeedback: A Practitioners Guide (34d ed., pp.

409-437). New York: Guilford Press.

Lubar, J.F., & Shouse, M.N. (1976). EEG and behavioral changes in a hyperkinetic child

concurrent with training of the sensorimotor rhythm (SMR): A preliminary report.

Biofeedback and Self-regulation, 1, 293-306.

Monastra, V.L. (2003). Clinical applications of electroencephalographic biofeedback. In

M.S. Schwartz & F. Andrasik (Eds.), Biofeedback: A practitioner’s guide (3^rd ed.,

pp438-63). New York: Guilford Press.

Monastra, V.J., Lynn, S., Linden, M., Lubar, J.F., Gruzelier, J., & LaVaque, T.J. (2005).

Electroencephalographic biofeedback in the treatment of attention-deficit/hyperactivity

Disorder. Applied Psychophysiology and Biofeedback, 30 (20), 95-114.

Monastra, V.J., Monastra, D.M., & George, S. (2002) The effects of stimulant therapy, EEG

Biofeedback and parenting style on the primary symptoms of attention-deficit/

hyperactivity disorder. Applied Psychophysiology and Biofeedback, 27 (4), 231-49.

Oubré, A. EEG Neurofeedback for Treating Psychiatric Disorders. Psychiatric Times February 2002 Vol. XIX Issue 2

Penkae, L., Toomim, H., Hemoencephalography (HEG): An additional treatment for autism. Symposium: Autistic child in the new millenium. The 11^th Asian Congress of Pediatrics & the 1^st Asian Congress on Pediatric Nursing, , Bankok, Thailand, November 2-7, 2003, p.71-83.

Raz A. Brain Imaging Data of ADHD Psychiatric Times August 2004 Vol. XXI Issue 9

Rossiter, T.R., & LaVaque, T.J. (1995). A comparison of EEG feedback and psycho-

stimulants in treating attention deficit/hyperactivity disorders. Journal of Neurotherapy,

1, 58-59.

Sterman, M.B. (2000). Basic concepts and clinical findings in the treatment of seizure

Disorders with EEG operant conditioning. Clinical electroencephalography, 31, 45-55.

Sterman, M.B., Wywricka, W., Roth, S.R. (1969). Electrophysiological correlates and

neural substrates of alimentary behavior in the cat. Annals of the New York Academy of

Science, 157, 723-39.

Tansey, M. (1993). Ten-year stability of EEG biofeedback results for a hyperactive boy

who failed the fourth grade perceptually impaired class. Biofeedback and

Self-regulation 18 (1), 33-8.

Thompson, L., & Thompson, M. (1998). Neurofeedback combined with training in

metacognitive strategies: Effectiveness on students with ADD. Applied

Psycho-physiology and Biofeedback, 23 (4), 243-63.

Thornton, K. (2000). Improvement/rehabilitation of memory functioning with neurotherapy/QEEG biofeedback. Journal of Head Trauma Rehabilitation, 15(6), 1285-1296

Toomim, H, What is Hemoencephalography?, Biocomp Research Institute, 2003..

Toomim, H., email thread EEG Biofeedback vs. HEG, correspondence on the Biofeedback Yahoo group listserv, 2005.

Wywricka, W. and Sterman, M.B. (1968). Instrumental conditioning of sensorimotor

Cortex EEG spindles in the waking cat. Physiology and Behavior, 3, 319-324

Appendix 1

Neurofeedback Clinical Efficacy

Biofeedback is the use of bio-monitoring technologies to acquire some self-control over an autonomic or other biological process. For example, one can use a thermometer to increase hand temperature or use an EEG to calm brain waves. The history of biofeedback is one of verifying clinical applications when people acquire self-regulation over processes previously thought to be unconscious. The field grows as technology improves and results are reported.

Neurofeedback is a form of biofeedback. ‘EEG biofeedback’ is no longer synonymous with neurofeedback because of the promising reports about hemoencephalography (HEG) which monitors blood flow dynamics via thermal infrared emissions from the brain. HEG presents that information about to the patient in ways that are amenable to development of conscious control.

Frank H. Duffy, M.D., Professor and Pediatric Neurologist at Harvard Medical School, stated in an editorial in the January 2000 issue of the journal Clinical Electroencephalography that the scholarly literature suggests that neurofeedback 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" (p. v). "It is a field to be taken seriously by all." (p. vii).

Almost 200 conditions reported favorably influenced by neurofeedback

This web site contains a research bibliography www.isnr.org/nfbarch/nbiblio.htm. It contains primarily outcome studies and case reports and is divided by into these problem areas:

Epilepsy; ADD/ADHD, Learning Disabilities, & Academic-Cognitive Enhancement; Anxiety Disorders, PTSD, & Sleep Disorders; Depression, Hemispheric Asymmetry, & Anger; Addictive Disorders; Brain Injury, Stroke, Coma, & Spasticity; Chronic Fatigue Syndrome, Fibromyalgia, & Autoimmune Dysfunction; Pain & Headache; Schizophrenia; Obsessive Compulsive Disorder; Parkinson's Dystonia; Tourette's Syndrome; Autism; Cognitive Decline with Aging; Asthma; Hypertension; Dissociative Disorders; Creativity & Optimal Functioning; Criminals.

Summary of studies in peer-review journals

DISORDERS	# STUDIES	# SUBJECTS	# MEDLINE	# CONTROLLED
ADHD	35	1539	20	7
Anxiety disorders	19	124	15	2
Behavior disorders	27	285	19	6
Epilepsy	45	364	42	12
Other: Tics, Insomnia, etc.	40	466	27	6
TOTAL	166	2778	123	33

Neurofeedback has not yet achieved the gold standard of efficacy through the publication of Type 1 studies in peer-reviewed journals. However given the preponderance of published Type 1 and 2 study evidence, the case for efficacy is now quite strong. Given the lack of contraindications, generally high success rates for many difficult conditions, and durability of results -- neurofeedback is worthy of recommendation.

Note: This information is for PACS internal Review Only. Not to be distributed with final report and Appendices.

Appendix 2. Qualitative Assessment of PACS Participants:

1. Mike Dealy

*Behavioral: Initially, Mike was well-behaved, sociable, and cooperative. Mike maintained this behavior throughout the course of testing. However, during the last week of school, there were a number of school functions that proved to distract Mike during CPT evaluations; however, he was consistently motivated during the bioexplorer training. He appeared to enjoy coming to the lab and began singing the songs while he trained. He was able to reduce his theta-SMR beta ratio by the completion of his training protocol. Mood- Mike generally demonstrated normal to elevated mood, but was at times, fatigued.

2. John Junod

Behavioral: Initially, John appeared to have discomfort during testing sessions, particularly during electrode hookup. He appeared to dislike being touched. Additionally, he was often fatigued and unmotivated during his testing until his TSS developed a chart and rewards system for him, which delineated how many sessions he finished and how many were left until completion. John slowly adapted to his changing environment and became more adjusted to the noises and distractions of the other children in the room. John was also able to relax significantly near the end of his training as his EMG rating was initially in the 11-12 range and dropped down to approximately 4. John was also able to reduce his theta-SMR beta ratio by the completion of his training protocol.

*Mood- John generally demonstrated agitation and was often fatigued and unmotivated. However, when he discovered that his scores were improving, his motivation to continue and complete testing increased dramatically.

3. Dominic Cianfrani

Behavioral: Initially, Dominic appeared to have discomfort during electrode hookup. He appeared to dislike being touched and having to sit in one place. He was hyperactive and required additional attention by staff to maintain appropriate behavior while in the lab, particularly if there were other children around. However, Dominic’s behavior and scores improved throughout the course of his testing. He was able to reduce his EMG score significantly near the end of his training and appeared to really enjoy coming to the lab to participate.

Mood- Dominic’s mood was often elevated and he was a pleasure to work with. There were times when he appeared more agitated near the end of his training and required additional effort by staff to calm him down.

4. Courtney Ropars

Behavioral: Initially, Courtney was well-behaved, sociable, and cooperative. Courtney maintained this behavior throughout the course of testing. She was able to maintain her attention much more successfully with additional assistance from staff. Courtney appeared to truly enjoy coming to the lab to participate and often asked to stay longer after she completed each day’s training. She was consistently positively engaged with staff and became attached to staff members. Overall, Courtney was engaged, motivated, and inquisitive. She was able to reduce her theta-SMR beta ratio as well as her EMG rating by the completion of her training.

Mood- Courtney consistently demonstrated elevated mood.

5. Chris Ropars

Behavioral: Initially, Chris was well-behaved and cooperative. He was always engaged his in his testing experience and appeared to give 100% effort. He appeared to enjoy the challenge of the protocol and interacting with staff and other students. Chris maintained this behavior throughout the course of testing. He also excelled at mindbowling and was able to obtain the highest score out of all the other students. Overall, Chris was engaged, motivated, and maintained a positive attitude. He was able to reduce his theta-SMR beta ratio as well as his EMG rating by the completion of his training.

Mood- Chris consistently demonstrated normal mood.

6. Samantha Milligan

Behavioral: Initially, Samantha appeared to have discomfort during testing sessions, particularly if she felt that testing would change her normal routine. She had significant anxiety during the last hour of the day and was consistently worried about getting to the bus on time. Additionally, there were times when Samantha was fatigued and unmotivated during her testing. She also was consistently monitoring and comparing her scores to other students in the room. Once staff was able to rearrange her training time as a means by which to alleviate anxiety over missing the bus, her scores and attitude improved dramatically. Samantha slowly adapted to her changing environment and became more adjusted to the noises and distractions of the other children in the room. She, with additional attention from staff, was also able to relax significantly near the end of her training as her EMG rating was reduced. Mood- Samantha generally demonstrated agitation and was often fatigued and unmotivated. However, when she discovered that her scores were improving, her motivation to continue and complete testing increased dramatically.

7. Kellie Wingate