Attention Deficit Disorder
Darla Gardner
Attention Deficit/Hyperactivity Disorder, ADHD, is a
non-durable but treatable disorder. It affects between 7% and
12%
of the child population depending on which review is cited (Lubar
et al., 1995). This is around one child per classroom (Lee,
1991). Approximately 20% of children who have one of these
disorders will continue to endure into adulthood (Lubar, 1991).
In a recent version of the Diagnostic and Statistical manual of
the American Psychological Association four main categories were
shown: attention-deficit disorder with or without hyperactivity,
conduct disorders, and learning disorders (DSMIII-R, 1987).
Hooper and Willis (1989) divided the learning disorders category
further. A child may be in one or a combination of these
categories. It h as been shown that depending on which category
the child falls may determine his or her success in EEG
biofeedback treatment.
Currently there are many different approaches to ADHD.
There are essentially four types: treatments involving
medication, nonmedical treatments, biofeedback training, and a
combination of one or all of these (Ingersoll and Goldstein,
1993). Medications include stimulants, tricyclic
antidepressants, and alpha blockers (Lubar et al.,1995).
Nonmedical therapies use behavior therapy, cognitive therapy,
cognitive behavior therapy, attribution therapy, traditional
individual psychotherapy, and family systems approach (Barkley,
1990). Biofeedback training includes electroencephalography
(EEG) training, which will be discussed here, and electromyograph
(EMG) training (Potashkin and Beckles, 1990).
In actuality it is usually a combination of these treatments
that is used at least at one point during the treatment. For
example, Potaskin and Beckles (1990) gave evidence that EMG
treatment is most successful when used in conjunction with
Ritalin, a common stimulant medication.
Lee (1991) reports that in comprehensive literature search
six behavioral characteristics of the hyperactive child are: (a)
restlessness, (b) inattentiveness, (c) distractibility, (d)
excitability/impulsivety, (e) behavior management problems, and
(f) lack of frustration tolerance. These children are found to
be of at least average intelligence (Lee, 1991). Lubar (1991)
states that there are those children who have a pure Attention
Deficit Disorder. These children do not experience the
hyperactivity while still lacking in attention span and have poor
focusing and concentration skills. Rarely though do children
who have are hyperkinetic not have Attention Deficit Disorder.
Lubar (1991) stresses that in order to use EEG analysis to
determine whether EEG biofeedback is appropriate for the child
it must be determined in what categories the child falls. An
example of this is that children with pure ADHD respond
very well to EEG biofeedback treatment (Lubar, 1991). This will
be discussed in length later. A wide variety of professionals
now
believe that the primary symptoms of ADHD, as mentioned above,
are only secondary to an underlying neurological disorder (Lubar
et al., 1995).
It has been in the last fifteen years that EEG biofeedback
became an effective means of treating ADHD and its
sub-components.
It was as early as 1938 that Jaspar, Solomon and Bradley
documented that there were EEG abnormalities in MBD, minimal
brain dysfunction syndrome, children (Lubar, 1991). These EEG
abnormalities manifested themselves primarily in terms of slowing
in the EEG. In 1953 another group of researchers Knott, Platt,
Ashby and Gottleib, also documented slowing of the EEG in
children with behavior disorders and in those children who would
later develop psychopathic personality disorder (Lubar, 1991).
It was during the 1970's that a majority of the foundation for
today's biofeedback techniques were researched. Anchor and
Johnson (1977) wrote "Biofeedback training and behavioral
self-control procedures may prove... to be the treatment of
choice for overactive children and a valuable adjunct to drug
therapy with hyperactive children." It was on this premise that
today's biofeedback is based.
Today, with our knowledge of neurofeedback, or specifically
biofeedback,and the frequency, location, amplitude and duration
of specific EEG activity (Lubar et al., 1995), we are able to use
biofeedback rather effectively to treat ADHD. Lee (1991)
described biofeedback as a technique that uses immediate
information, of feedback, that the individual attains through the
use of instruments attached to a part or parts of the body. The
information is then used in modify or maintain a physiologic
state (Lee, 1991). Electroencephalogram, or EEG, is not the only
type of biofeedback. Other types of biofeedback include
electromyograph, or EMG, galvanic skin response, or GRS, and skin
surface temperature. Both the EEG and EMG biofeedback is used for
ADHD effective also (Potaskin and Beckles, 1990). The purpose of
using EMG biofeedback is that if a patient can learn the skills
to keep muscular tensions at low levels the hyperactivity will be
reduced (Lee, 1991). There is much evidence that EMG is an
effective way of treating ADHD (Denkowski and Denkowski, 1984;
Hughes, Henry and Hughes, 1980; and Omizo, 1980).
In using the EEG for biofeedback there are many options.
One way it is used is to increase a certain type of EEG activity.
Another way would be to increase one type while decreasing
another (Luber et al., 1995). In Luber et al's (1991) research
in neurofeedback training for ADHD it was found that an increase
in beta activity (14 hertz and above) and the decrease in theta
activity (4-8 hertz) produced results in ADHD patients.
The test of Variables of Attention (T.O.V.A.) is used often
to assess how a patient is performing following neurofeedback
(Lubar et al., 1995). Luber et al. (1995) used T.O.V.A. both
before treatment and after. The T.O.V.A. is a visual continuous
performance test that allows for easy discrimination of visual
stimuli that are presented for 100 milliseconds every 2 seconds
for 22.5 minutes (Lubar et al., 1995). Subjects are told to
watch the screen and to press a button each time they see a
colored square near the top portion of an outer square; when the
square appears at the bottom subjects are told to do nothing
(Basmajian, 1989). They are scored accounting for errors of
omission, errors of commission, mean correct response time, and
variability. Also noteworthy is that Greenberg (1987) showed
that
there was no test-retest practice effects and that subjects did
more poorly due to boredom if the test was repeated.
The goal biofeedback EEG training in relation to ADHD is to
increase to 12 to 15 Hz and then from 14 to 20 Hz, otherwise
referred to as beta 1 and beta 2 waves (Bamajian, 1989). It is
also thought that decreasing 4 to 8 Hz activity along with the
increase in beta is beneficial.
At this point, EEG alone is not always adequate to treat the
ADHD patient completely. Instead, a combination of drugs,
behavior therapy and biofeedback may be the answer. As in most
fields, more research must be done and the theories are always
evolving.
REFERENCES
Anchor, K.N., and Johnson, L.G. (1977). The efficacy of EMG
biofeedback in the treatment of hyperactivity. Behavioral
Engineering, 4, 39-43.
Barkly, R.A. (1990). Attention Deficit Hyperactivity Disorder: A
handbook for diagnosis and treatment. New York: Guilford Press.
Basmajian, J.V. (1989). Biofeedback: Principles and Practice for
Clinicians, Third Edition. Baltimore: Williams and Wilkins.
Denkowski, K.M., and Denkowski, G.C. (1984) is group progressive
relaxation training as effective with hyperactivity children as
individual EMG biofeedback treatment? Biofeedback and Self
Regulation, 9, 353-364.
Greenberg, L. (1987). An objective measure of methylphenidate
response: Clinical use of the MCA. Psychopharmacology Bulletin,
23, 279-282.
Hughes, H., Henry, D. and Hughes, A. (1980) The effect of frontal
EMG biofeedback training on the behavior of children with
activity-level problems. Biofeedback and Self Regulation, 5,
207-219.
Ingersoll, B.D. and Goldstein, S. (1993) Attention Deficit
Disorder and Learning Disabilities: Realities, Myths and
Controversial Treatments. New York: Doubleday.
Lee, S.W. (1991). Biofeedback as a treatment for childhood
hyperactivity: A critical review of the literature. Psychological
Reports, 68, 163-192.
Lubar, J.F. (1991). Discourse on the development of EEG
diagnostics and biofeedback for Attention Deficit/Hyperactivity
Disorders. Biofeedback and Self Regulation, 16:3, 201-225.
Lubar, J.F., Swartwood, M.O., Swarwood, J.N., and O'Donnell, P.H.
(1995). Evaluation of effectiveness of EEG neurofeedback training
in ADHD in clinical setting as measured by changes in T.O.V.A.
scores, behavioral ratings and WISC-R performance. Biofeedback
and Self Regulation, 20:1, 83-99.
Omizo, M.M. (1980) The effects of biofeedback induced relaxation
on hyperactive adolescent boys. Journal of Psychology, 105,
131-138.
Potashkin, B.D. and Beckles, N. (1990) Relative efficacy of
ritalin and biofeedback treatments in the management of
hyperactivity. Biofeedback and Self Regulation, 15:4, 305-315.
EEG and Sleep Disorders
by Alicia Jones
It is estimated that approximately 25% of the population
experiences some type of sleep disorder, whether it is primary
insomnia or difficulties sleeping associated with some other
medical condition, such as epilepsy or ADHD.
Electroencephalogram(EEG) biofeedback training has been used
successfully in treating some sleep disorders, like insomnia and
sleep apnea, which is described as the inability to sleep and
breathe at the same time(EEG Spectrum). This type of feedback
involves training the patient to produce a specific brain wave
that can lead the patient towards falling asleep. This brain
wave is called the alpha wave
(8-13cps) and is indicative of a state of relaxed awareness.
The mechanics of an EEG session include the placement of
three electrodes at various points on the patient's head.
For sleep disorders, the placement of the electrodes is usually
done in order to record the alpha waves, which are present in the
occipital region of the brain. Therefore, one active sensor is
placed on the surface of the scalp in the occipital area, and the
other two reference sensors are placed a small distance apart
from each other on the forehead. These electrodes are hooked up
to an Autogen machine which records the brain waves the patient
is producing and, with a tone, feeds that information back to the
patient. Generally, the biofeedback therapist sets the machine
threshold to produce this tone when the patient's brain activity
exceeds the limit on the machine. Thus, the patient learns to
produce more of the alpha waves that can lead towards the theta
waves(4-8cps) necessary for the onset of sleep.
A case study conducted on a 42-year-old woman with chronic
sleep onset insomnia illustrates how EEG biofeedback can be
beneficial. This woman was in good health, with no history of
psychiatric illness. Her sleep difficulties occurred gradually
over a long period of time. She was taking the drug Nitrazepam 5
mg/nocte to facilitate falling asleep.
The patient was instructed to keep a sleep log upon
awakening each morning to record the time of settling down to
sleep, presleep intrusive thoughts, number of awakening, and
total sleep time. The biofeedback therapist instructed the
patient in progressive relaxation techniques, with instructions
to practice the relaxation technique at home as well. The
patient was seen once a week for 10 weeks.
The electrodes were placed at sites T3 and P3, with a
reference electrode over ipsilateral mastoid bone. A Biofeedback
Systems EEG 90 machine was set at approximately 25uV throughout
the treatment sessions. When the fixed threshold was surpassed,
the machine emitted a fixed tone. The patient was told to
produce the tone, thus increasing theta activity.
The results of this study were favorable. The patient was
able to discontinue use of the drug over two 2-week phases. The
first week of each phase showed an increase in the number of
awakenings, which is consistent with well-known drug withdrawal
effects. The second week of each phase showed a return to the
levels on increased sleep time the patient showed at the end of
her treatment(Bell).
Patients have a variety of reasons for seeking out
biofeedback treatment for their sleeping problems rather than
using more conventional methods like prescription medications.
According to J. Touchon in his article Sleep Induction,
non-pharmacological methods provide a better long term efficacy
than prescription drugs. Patients who desire better control over
their bodies afflictions may decide biofeedback is the answer for
them. As Siegfried Othmer, Ph.D., says "Biofeedback, at its
best, is empowerment of the individual"(EEG Biofeedback Training:
A Journey Toward Personal Autonomy). Also, patients like the
woman described earlier who are already taking sleeping
medications, may fear becoming addicted to the drugs, and decide
to try biofeedback as an alternative answer to their problems.
However, biofeedback is still not as widely used for sleep
disorders as medication and other methods. Medications not only
include the usual sleep medications, but also hormones like
melatonin, which regulates a person's sleep cycle, and
anti-depressants like Nortriptylene, which can increase a
person's alpha activity. Other methods used facilitate sleep are
behavioral treatments(Bell). The majority of these are
relaxation-based, on the assumption that heightened physiological
arousal is a feature of insomniacs, so physically relaxing the
muscles of the body would therefore be appropriate. These
techniques include applied relaxation, systematic
desensitization, EMG biofeedback, and electrosleep. Although
some success has been claimed for all of these, firm evidence
that insomniacs are more physiologically aroused than normal is
lacking(Bell 230).
EEG biofeedback has been shown to have beneficial effects
for patients with sleep disorders. However, the treatments are
only as effective as the patient desires them to be. Without a
sincere wish for improvement and a commitment from the patient to
practicing the exercises at home, there is little likelihood of
significant improvement in the patient's condition.
Bibliography
1. Bell, J. Stephen. The Use of EEG Theta Biofeedback in the
Treatment of a Patient with Sleep-Onset Insomnia. Biofeedback
and Self-Regulation 4 1979: 229-235.
2.Criswell, Eleanor, Ed.D. Biofeedback and Somatics. Novato:
Freeperson, 1995.
3.EEG Spectrum, Inc. EEG Biofeedback Training for Sleep
Disorders[Online]. Available:
http://members.aol.com/eegspectrm/index.html[no date available].
4.Othmer, Siegfried, Ph.D. EEG Biofeedback Training: A Journey
Toward Personal Autonomy[Online]. EEG Spectrum. Available:
http://members.aol.com/eegspectrm/index.html[April 1994]
5.Padgitt, Steven T., Ph.D. Treating Insomnia with
Neurofeedback[Online]. Library: Neurofeedback. Available:
http://he.tdl.com/bwtc/library/index.html[no date available].
6. Touchon, J. Sleep Induction. Encephale (EFB) 18
Jul-Aug 1992:369-77.
EEG AND EPILEPSY
Debbi Cowan
The technique of EEG biofeedback training was first used
therapeutically for epilepsy dating back to the early 1970's. EEG
biofeedback has been shown to be helpful for all kinds of
epilepsy, including petit mal (small seizures), grand mal (large
seizures), and complex partial seizures (seizures arising form
the temporal lobe, which are partial in nature and are usually
accompanied with strange feelings such as illusions, visions, or
involuntary movements. Usually not remembered by the person).
The EEG form of biofeedback is not considered a substitute
for medication for epileptic patients, but is an additional
therapy to help reduce the frequency of seizures and may aid in
reducing the overall medication needed (Meddleton et al., 1982).
In most cases the electroencephalogram, EEG, will help
establish the authenticity of the seizure disorder and in some
cases it can even suggest the underlying cause. Although an
abnormal electroencephalogram can occur in someone who does not
have seizures, it is good evidence that seizures are occurring in
a patient with poorly understood motor and sensory phenomena.
The EEG displays brain activity as waves, spikes, and
electrical silence. A wave that is sharply contoured and less
than one fifth of a second is called a sharp wave. When a
sharply contoured wave lasts less than one twelfth of a second is
called a spike. Seizure activity usually appears as spikes and
slow waves. Although the EEG alone will not firmly establish the
cause of the seizure disorder, some patterns limit the possible
causes of the brain damage (Lechtenburg, 1984).
To make a diagnosis of epilepsy, it is necessary to
establish a tendency to recurrent spontaneous epileptic seizures.
Many people have a single isolated epileptics seizure in their
lives, but if a person has more than one epileptic seizure, than
a diagnosis of epilepsy may be considered. Most often the person
will have no memory of what has actually happened. It is very
common for someone else such as a parent or spouse to report the
seizure. The physician relies heavily on an accurate eye witness
account of the seizure, which may be the only information on
which the diagnosis rests.
The tests used for diagnosis of epilepsy are blood tests,
which checks the general health of the person and helps to
exclude metabolic cause for the attacks. Brain scans help to
exclude a structural cause. Electroencephalograms measure the
electrical activity of the surface of the brain which can only
give the information about the electrical activity during the
period of recording.. Only if patterns characteristic of
epilepsy are see during the routine recording, is the EEG of
value in the diagnosis of epilepsy. A negative EEG does not
exclude a diagnosis of epilepsy.
The type of biofeedback machine which helps epileptic
patients with seizures has electrodes attached to person's scalp
and to the machine itself which is designed to make a soft noise
when one goes into a particular brain wave state. When the noise
is present the patient is into a particular brain wave.
Psychologists M.B. Sterman and Hoel Lubar's research indicates
that different brain wave states are beneficial for different
people (Richare and Reiter, 1990).
Barry Sterman, Ph.D., originally suggested the use of
biofeedback management of epileptic seizures after his
experiments with cats which indicated that a resistance to
seizure development could be brought about by causing a certain
type of brain wave rhythm in the sensory motor cortex. Another
study by Lubar and Bahler (1976) of severely afflicted patients
who were trained in self control of their EEG rhythms by the sole
use of biofeedback. The patients learned to inhibit specific
seizures. Once they had learned to control their seizures,
usually they no longer needed the biofeedback equipment
(Middleton et al., 1991). TWO CASES OF EPILEPSY WITH SEIZURES
OCCURRING ONLY AT NIGHT
The first case was a 34 year old woman with post traumatic
seizures which began in childhood. Her seizures always occurred
after going to bed at night, either while falling asleep, or
after falling asleep. She would feel exhausted and confused for
three days afterwards. Following sessions of EEG biofeedback she
experienced a marked reduction in seizure frequency and severity,
as well as a disappearance of confusion and tiredness.
The second case was a 22 year old woman with post traumatic
seizures beginning at age 13. Her seizures began with jerking
in both hands which followed by a loss of consciousness. Her
seizures were originally controlled on medications but after she
completed training of biofeedback she was off all medication and
seizure free (Walker, 1995).
The potential for using biofeedback to help epileptics
manage their seizure response is evident, yet in some cases, the
use of biofeedback may encourage seizures. Therefore, it is
considered inappropriate to do biofeedback training with
epileptics unless one is trained in working with that disorder
(Criswell, 1995).
REFERENCES
Criswll, E., Ed.D. (1995) Biofeedback and Somatics, First
Edition. California: Freeperson Press.
Lechtenburg, R. (1984) Epilepsy and the Family. Massachusetts:
Harvard University Press.
Middleton, Attwell and Walsh, (1982) Epilepsy, First Edition.
Boston: Little, Brown and Company.
Neurofeedback Archive Home Page: Epilepsy threads. (1995)
Neurofeedback Archive, Society for the study of Neuronal
Regulations [online]. http//www.primenet.com/~ssnr/walkerj1htm
Richard, A. and Reiter, J. (1990) Epilepsy: A New Approach. New
York: Prentice Hall Press.
AGE-ASSOCIATED MEMORY IMPAIRMENT
Rebecca Allison Deja
By the age of 60 individuals often notice a mild decline in
memory, and after the age of 70 many experience more serious
forms of forgetfulness. A National Institute of Mental Health
work group coined the term "age associated memory impairment"
(AAMI) to describe the memory loss experienced by healthy,
elderly individuals(Crook et al., 1986) and established
diagnostic criteria to operationalize the concept. Based on
pooled normative data from standard clinical memory tests,
Larrabee and Crook (1994) reported that 41% of persons between
the ages of 50 and 59 years and 52% of the those between the ages
of 60 and 69 years had been classified with AAMI. However,
prevalence data have been confounded by self appraisals that
tended to report an inflated incidence of memory dysfunction and
by results from neuropsychological tests that failed to
differentiate early stages of dementia from AAMI (Barker, Jones &
Jennison, 1995; Hanninen et al., 1995; Koivisto et al., 1995).
Based on anatomical and physiological research, Parnetti et
al. (1996) argued that AAMI is an early stage of Alzheimer's
disease (DAT). Using neuroanatomical perfusional and
neurochemical detail data, Parnetti et al. found that AAMI and
DAT individuals had significantly lower N acetylasparate
concentrations compared to controls, and inositol concentrations
were highest for DAT and lowest for controls. From SPECT data,
they also found significant frontal, temporoparietal, and
occipital hypoperfusion only in DAT individuals. However,
longitudinal studies seem to indicate that AAMI appears to be
generally nonprogressive and neuropsychological test predictive
(Hanninen et al., 1995). Soininen,Partanen, et al.(1994) found
that visual memory scores correlated with the magnetic resonance
imaging (MRI) volume of the amygdala and right hippocampus;
normative controls generally had asymmetrical hippocampal
formations, where as those with AAMI were more equal in volume.
Small, Asenath, Komo, Kaplan, and Mandelkern (1995) reported
no significant correlation between cerebral atrophy, age, or
family history and cognitive changes noted after three years in
AAMI persons. Multiple regression analysis indicated that
parietal metabolic asymmetry was a significant predictor of the
change rate for visual/spatial memory, and the level of education
was a consistently significant predictor of change in verbal
memory. Individuals with lower levels of education were more
likely to show decline and less likely to show practice effects.
Hanninen et al.(1995)found that memory and verbal fluency were
the best discriminators between persons with AAMI and early
stages of dementia.
Another body of cognitive aging research investigated event
related brain potentials (ERPs) from EEG data to index CNS
function for specific types of cognitive events. The amplitude of
the P300 component of the cognitive ERP decreases in the adult
brain as age increases for both males and females (Polich, 1996).
Results from the preliminary study reported by Polich indicated a
decline in the EEG power in the alpha band that aids in
determining the amplitude of the P300 wave in healthy young
adults. Soininen, Karhu, et al. (1995) reported that inpaired
memory and frontal lobe functions in AAMI individuals may be
associated with poor habituation of N100; the researchers
proposed that because habituation reflects attending to relevant
features of stimuli, impairment of this mechanism and the
subsequent defective memory trace formation may contribute to the
low performance of AAMI individuals on memory tests.
Central to anatomical and physiological changes in the aging
brain is the inevitable decline of the cardiovascular system. In
a controlled study Saletu, Grunberger, Linzmayer, and Anderer
(1991) investigated the encephalotropic and psychotropic effects
of Actovegin, a protein free metabolically active hemoderivative,
on AAMI patients; Actovegin purportedly improves oxygen and
glucose utilization at the cellular level. Significant positive
changes in brain function were documented for the treatment group
when compared to the placebo controls.
Elevated blood pressure is an established risk factor for
stroke and atherosclerosis; it contributes to silent small vessel
disease and white matter hyperintensities in the brain (Launer,
Masaki, Petrovitch, Foley & Havlik, 1995). In a longitudinal
study, Launer et al. reported that midlife systolic blood
pressure levels were a predictor of cognitive function 25 years
later; for every 10mmHg increase in systolic pressure >160mmHg,
there was an increased 7% risk for some cognitive dysfunction and
an increased 9% risk for even greater impairment. The level of
cognitive dysfunction was not associated with diastolic blood
pressure; this finding may have been attributable to the
increased stroke mortality rate associated with high diastolic
pressure prior to the 1991 to 1993 testing.
Analyses of the MRI and pschometric data collected by
Schmidt et al.(1995) indicated that elderly hypertensives more
commonly had areas of white matter hyperintensities and
moderately severe ventricular enlargement in comparison to
controls. No differences were noted between groups on memory test
and conceptual tasks, but significantly lower results were found
for hypertensives on tasks requiring attentional and
visual/spatial skills than for controls with no brain
abnormalities. However, controls with similar cerebral changes
exhibited a similar pattern of neuropsychological deficits.
Auperin et al. (1995) reported a significant correlation between
moderate stenosis (<40%) of the carotid artery and tests
requiring focused attention for males, but not for females, when
compared to controls.
Other investigators have explored ways to reverse age
related anatomical and physiological changes in the
cardiovascular system. Starr, Whalley, and Deary (1996) treated
an elderly hypertensive group with one of two antihypertensive
medications for 24 weeks. They found no significant difference on
any psychometric test between the captopril and bendrofluazide
conditions; however, the individuals who lowered their diastolic
blood pressure the most (>19mmHg) had improved scores on two
cognitive subtests compared to those with the least responsive
diastolic pressure(<5mmHg). Paulter (1994) has suggested that L
argine might restore the responsiveness of cerebral arterioles to
vasodilators because one of the factors in AAMI may be the
failure of the endothelial cells to respond to vasodilators,
thereby limiting the flow of blood to neuronal cells.
Rozelle and Budzynski (1995) used electoencephalographic
(EEG) entrainment feedback followed by neurofeedback for stroke
rehabilitation of a 55 year old client. The stroke victim
complained of hesitant speech with word finding difficulty and
paraphasia. He also had poor short term memory and poor
concentration. The neurofeedback positioned over sensorimotor and
speech areas helped him to inhibit Hz activity between 4 and 7
and to increase Hz activity between 15 and 21. Improvement was
evident in speech fluency, word finding, attention, and
concentration.
In a pilot study Budzynski (1996) and Frank Andrasik, a
colleague, successfully used a "brain brightening" program for
training AAMI individuals. Participants received lab
neurofeedback training that focused on decreasing delta/theta
range activity between 2Hz and 8Hz and on increasing beta range
activity between 13Hz and 18Hz; after 20 EEG neurofeedback
sessions, participants were able to reduce the delta/theta band
energy, but they were unable to significantly increase the beta
band energy. Daily at home, participants used an audiovisual
stimulation device to entrain beta frequencies. The flashing
light stimulus used in the device was based on work of
researchers such as Fox and Raichelle who had shown that visual
flashing light stimuli in the 8Hz to 16Hz range could raise blood
flow an average of 30% over baseline levels (Budzynski, 1994);
the audio tones used in the device were based on Budzynski's own
research that had shown a 10% to 30% increase in brain wave
amplitude when individuals were listening to the tonal stimulus.
In addition, the participants listened to specially designed
audio cassettes containing cognitive exercises designed to
increase blood flow particularly in the temporal areas; the
exercises, likened to "mental aerobics" by Budzynski, required a
focusing and manipulation of verbal and visual processes. AAMI
individuals showed improvements in memory and attention test
scores, as well as in their own self appraisal of their ability
to recall recent names or events.
Neurofeedback may hold promise for reversing cognitive aging
associated with AAMI, but much research still needs to be done.
Tansey and Tansey (1994) have questioned the use of wide band EEG
designations because highly specific brain wave signatures appear
to be associated with equally specific cognitive states; for
example, they found that the 10Hz band in alpha (generally
accepted as a marker for a relaxed state) was specific to focused
thought and that increases in peripheral blood flow were
accompanied by increases in the 28Hz band in beta (generally
accepted as a marker for an active, engaged state). As Rozelle
and Budzynski (1995)pointed out, the positive results in
neurofeedback treatment for AAMI individuals may be more
attributable to stimulating the brain than to specific techniques
or protocols employed thus far.
References
Auperin, A., Berr, C., Bonithon Kopp, C., Touboul, P.J.,
Ruelland, I., Ducimetiere, P., & Alperovitch, A. (1996).
Ultrsonic assessment of carotid wall characteristics and
cognitive functions in a community sample of 59 to 71 year olds:
The EVA study group. Stroke, 27, 1290-1295.
Barker, A., Jones, R., & Jennison, C. (1995). A prevalence study
of age associated memory impairment. British Journal of
Psychiatry, 167, 642-648.
Budzynski, T.H. (1996). Brain brightening: Can neurofeedback
improve cognitive process? Biofeedback, 24(2), 14-15,17.
Crook, T., Bartus, R.T., Ferris, S.H., Whitehouse, P., Cohen,
G.D., Gershon, S. (1986). Age associated memory impairment:
Proposed diagnostic criteria and measures of clinical change: A
report of a National Institute of Mental Health work group.
Developmental Neuropsychology, 2, 261-276.
Hanninen, T., Hallikainen, M., Koivisto, K., Helkala, E.L.,
Reinikainen, K.J., Soininen, H., Mykkanen, L., Laakso, M.,
Pyorala, K., & Riekkinen, P.J., Sr. (1995). A follow up study of
age associated memory impairment: Neuropsychological predictors
of dementia. Journal of American Geriatric Society, 43,
1007-1015.
Koivisto, K., Reinikainen, K.J., Hanninen, T., Vanhanen, M.,
Helkala, E.L., Mykkanen, L., Laakso, M., Pyorala, K., &
Riekkinen, P.J., Sr. (1995). Prevalence of age associated memory
impairment in a randomly selected population from eastern
Finland. Neurology, 45, 741-747.
Larrabee, G.J.,& Crook, T.H. (1994). Estimated prevalence of age
associated memory impairment derived from standardized tests of
memory function. International Psychogeriatrics, 6, 95-104.
Launer, L.J., Masaki, K., Petrovitch, H., Foley, D., & Havlik,
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