Medication Failure: EEG/qEEG Findings
Provide Evidence
ISNR Conference
Workshop 20
September 21, 2013
Ronald J. Swatzyna, Ph.D., L.C.S.W.
The Tarnow Center for Self-Management
drron@tarnowcenter.com
Vijayan K. Pillai, Ph.D.
The University of Texas at Arlington
pillai@uta.edu
Introduction
• Reliability of diagnosis is key to selection and management
of medications and treatment
• Medication failure has been traditionally associated with
misdiagnosis
• Psychological testing provides diagnosis refinement
• Still there are those few challenging cases that defy
countless attempts to help
Study Concept
• We present two diagnostic approaches: psychiatric and
EEG/qEEG technology and attempt to assess the reliability of
traditional psychiatric diagnoses
• Our study (N=400) suggests that EEG/qEEG technology is a
viable alternative approach to medication selection and
treatment planning, especially in refractory cases
• Where one or more medication attempts have failed
• And/or when evidence-based psychological
intervention fails
Goal and Objectives of Study
• Goal: Decrease psychotropic medication failure rate
• Objective 1: To explain what accounts for the small proportion
of medication intervention failure using traditional psychiatric
approaches
• Objective 2: To assess the effectiveness of EEG/qEEG
approach to identify appropriate medical interventions
strategies
What is a Diagnosis
• When enough observed symptoms align themselves with a
named disorder you have a diagnosis
• Psychiatry uses
diagnoses as a guide to
prescribe medications
Common Example of Misdiagnosis
• ADHD
• Symptoms of ADHD and a head injury share many of the
same symptoms
• Difficult to distinguish which happened first
• Can result in an ineffective treatment
• Symptoms can be shared with many diagnosis such as
PTSD, Bipolar Disorder, and anxiety
(http://www.adhd.com.au/qeeg.htm,2013)
ADHD/Head injury
• Shared Symptoms
• Impulsivity
• Lack of attention
• Forgetfulness
• Poor attention
• Distractibility
• Inattentiveness
• Difficulty organizing task
• Poor follow-through
Traditional Psychiatric Practice
• Psychiatry uses DSM criteria to diagnose and to
• Guide medication selection
• Plan treatment
• Diagnosis is based on observation, self-report and
psychological testing
• This method works well the majority of the time
• However, other fields of medicine use objective tests
(scientific data) to confirm diagnosis
• Psychiatry now may have another option with EEG/qEEG
especially in cases where one or more
medications fail to alleviate enough of the
symptoms
Traditional Medication Approach
• ADHD = Stimulants
• ASD = based on symptoms
• Mood dyscontrol = antipsychotics
• Anxiety issues = anxiolytics
• OCD = Selective Serotonin Reuptake Inhibitors (SSRIs)
• MDD = Antidepressants (SSRIs first line)
• ANX = Anxiolytics
qEEG Technology
• The EEG data is recorded and digitized.
• These numbers are then statistically compared to that of a
large normative population data base.
• Such comparisons allow the clinician to determine whether
or not brain functioning is abnormal, to what degree, in what
locations, and in which frequency bands.
History of Pharmaco-EEG Studies
Using qEEG
• Started in the early 1970s when computer technology
became available (E. Roy John & Robert Thatcher)
• Since then, most all psychiatric medications have had an
qEEG study to see its effects on normal EEGs
• 1995 Suffin & Emory published a comprehensive study of
psychiatric medications using Eyes Closed Relative Power
qEEG brainmapping.
• In the early and mid 2000s, Johnstone and Gunkelman
published their work on EEG phenotypes for predicting
medication and treatment response.
“The Basic Application of Pharmaco-EEG
in a Clinical Setting” (Swatzyna, 2008 ISNR Conference)
• Using Suffin & Emory’s report (1995) and Johnstone &
Gunkelman’s (2005) on EEG Phenotypes, Swatzyna
developed simplistic Excel graphs of Eyes Closed Relative
Power data on each patient
• Using the “key/lock” principle medication suggestions were
made based on the deviations identified in the qEEG.
• Swatzyna presented his findings at the 2008
ISNR conference
Database
• Starting initially in 2009, all findings were included variables
• As the database grew, four findings became very evident
• Encephalopathy (EN)
• Focal slowing (FS)
• Beta spindles (BS)
• Transient discharges (TD)
Four EEG/QEEG Findings
• These findings were hypothesized to account for medication
failure
• The four types of findings were coded and included as
variables in the data base
• Many other variables were dropped from the study because
they responded well to psychiatric/diagnoses based
intervention
• For instance, a vast majority if those diagnosis of
depression responds well to SSRIs: SSRIs
reduce anterior hypercoherent alpha present
Raw EEG Identifies…
qEEG Confirms Significance
• The raw EEG is used to identify encephalopathy (EN)
• The raw EEG is used to identify beta spindles (BS)
• The raw EEG is used to identify the morphology of focal
slowing (FS)
• The raw EEG identifies transient discharges (TD)
• However, qEEG confirms the significance of observed BS,
FS and TDs
• They are quantified in the topographical brain maps
Encephalopathy
Disease, Damage, or Malfunction of the Brain
• Need to identify etiology
• Most common causes identified in the past 4 years:
- Metabolic
- Toxic
- Electrolytic
- Anoxic
- Traumatic
• No medication will work as long as there is insufficient
support for brain function
• Treat by identifying and rectifying the root of the problem
Eyes Open – background EEG rhythms
Scale: 50 mcV/cmNote: muscle artifacts at: Fp1
9 y/o M
Spectra (EEG power vs. EEG frequency) in Eyes Open condition
Spectra differences: patient-norms. Absolute EEG power
Spectra differences: patient-norms. Relative EEG power
Focal Slowing
• Should be evaluated by an electroencephalographer
• May be from:
• Trauma
• Tumor
• Cerebral vascular issues (ischemia, stroke, aneurysm),
• Inflammation
• Other medical condition
• Etiology should be identified prior to any intervention
Focal Slowing
(6 – 9 Hz range)
• Common finding after a grey matter injury heals
• Temporal Mild Sharp & Slow Activity (TMSSA) highly
correlated with cerebral vascular issues (Neidermeyer et.al 1987)
• Also focal slowing in the left anterior temporal lobe highly
correlated with refractory depression
• Does not respond well to medication intervention
• Other neuromodulation techniques are found to be effective
Eyes Open – background EEG rhythms.
Scale: 70 mcV/cm; Note: muscle artifacts at: T3, T4
9 y/o M
Spectra (EEG power vs. EEG frequency) in Eyes Open condition
Spectra differences: patient-norms. Absolute EEG power.
Spectra differences: patient-norms. Relative EEG power.
Eyes Closed – background EEG rhythms.
Scale: 70 mcV/cm Note muscle artifacts T3, T4
12 y/o M
Spectra (EEG power vs. EEG frequency) in Eyes Closed condition
Spectra differences: patient-norms. Absolute EEG power.
Spectra differences: patient-norms. Relative EEG power.
Temporal Mild Sharp & Slow Activity
55 y/o F
Spectra (EEG power vs. EEG frequency) in Eyes Open condition
Spectra differences: patient-norms. Absolute EEG power
Spectra differences: patient-norms. Relative EEG power
Focal Slowing
(1 – 4 Hz range)
• Common to white matter issues or subcortical tumors or
lesions
• MRI recommend to identify structural abnormalities that may
be the cause
• Does not respond well to medication intervention
• Neuromodulation may be of clinical utility. However, white
matter issues have been found more difficult to
neuromodulate.
Eyes Closed – background EEG rhythms.
Scale: 70 mcV/cm
17 y/o M
Spectra (EEG power vs. EEG frequency) in Eyes Closed condition
Spectra differences: patient-norms. Absolute EEG power.
Spectra differences: patient-norms. Relative EEG power.
Beta Spindles
• Spindling Excessive Beta or Beta Spindles are synchronous
activity in the beta range centered around a specific
frequency (CNS hyperarousal)
• Seen in 10% of ADD/ADHD, affective disorders, bipolar
disorder. However, in our experience we see this pattern in
around 20% of our clients, especially clients with ADHD
related symptoms
• Anticonvulsants and valproic acid, gabapentin, pregabalin,
clonidine, guanfacine
Beta Spindles
• Associated with cortical irritability, viral, or toxic
encephalopathies and in epilepsy
• Also associated with pre-epileptic auras
• Need to rule out benzodiazepine toxicity
Eyes Open – background EEG rhythms. 64 y/o F
Scale: 50 mcV/cm Note: muscle artifacts at: T3/T4
Spectra (EEG power vs. EEG frequency) in Eyes Open condition
Spectra differences: patient-norms. Absolute EEG power.
Spectra differences: patient-norms. Relative EEG power.
Transient Discharges
•
•
•
•
•
•
•
•
May be paroxysmal in nature
May or may not be a seizure disorder
Commonly seen in cerebral vascular issues, tumors, and lesions
Discharges affecting the insular cortex often produce psychotic
issues, delusional thinking or paranoia
In need of stabilization
Any medications that lower seizure threshold can make discharges
worse
Research suggests stabilization with anticonvulsants (Lamictal or
Keppra)
However if alpha peak frequency is slow, Trileptal also speeds up
alpha in addition to stabilization
Eyes Closed – background EEG rhythms.
23 y/o M
Scale: 70 mcV/cm; Note: muscle artifacts at: T5, O1
Spectra (EEG power vs. EEG frequency) in Eyes Closed condition
Spectra differences: patient-norms. Absolute EEG power.
Spectra differences: patient-norms. Relative EEG power.
Eyes Closed – background EEG rhythms.
15 y/o F
Scale: 70 mcV/cm; Note: muscle artifacts at: T3, T5, T6
Spectra (EEG power vs. EEG frequency) in Eyes Closed condition
Spectra differences: patient-norms. Absolute EEG power.
Spectra differences: patient-norms. Relative EEG power.
Absence Seizure Case
3 Second Spike and Wave
6 y/o M
Mixed Fast and Slow
• Combination of slow activity and excessive fast activity such
as spindling beta
• Requires a dynamic balance of medication, usually with a
stimulant to speed up the slow or to increase GABA and slow
the fast
• Dynamic balances are hard to maintain
• May want to consider medication for the slow and
neuromodulation for the fast
“This patient’s EEG shows a generalized mixed fast and slow
drowsy-sleep pattern suggestive of a severe generalized
encephalopathy, possibly on a metabolic-toxic or postictal basis.
In addition, there are sharp-slow wave complexes arising in the
left and right temporal regions, raising the possibility of irritative
and potentially epileptogenic lesions in these areas.”
Sample
• Considered all cases referred from June 2009 through
September 2013
• Inclusion criteria: Every single clinical case was considered
• Exclusion criteria: Repeat EEG/qEEGs and all non-clinical
cases
Variables
•
•
•
•
•
•
•
AGE
GEN
EN
FS
BS
TD
RX
Age
Gender
Encephalopathy
Focal Slowing
Beta Spindles
Transient Discharges
Number of medications prescribed
Variables Continued
•
•
•
•
•
•
•
•
•
•
•
ADD
MDD
ANX
ASD
TOU
OCD
SUD
BPD
STK
SEI
PSY
Attention Deficit Hyperactivity Disorder
Major Depressive Disorders
Anxiety Disorders
ASDistic Spectrum Disorders
Tourette’s Disorder
Obsessive Compulsive Disorder
Substance Use Disorder
Bipolar Disorder
Stroke
Seizure
Psychotic Disorders
Most Prevalent Diagnoses Studied
•
•
•
•
Attention Deficit Hyperactivity Disorder (ADD)
ASDism (ASD)
Major Depressive Disorder (MDD)
Anxiety Disorder (ANX)
Variables Lacking Sufficient Number of Cases
•
•
•
•
•
•
Tourette’s Disorder
Substance Use Disorder
Stroke
Seizure Disorder
Psychotic Disorder
Note:
• OCD was recoded to ANX variable
• BPD was recoded to MDD variable
Data Collection
• The first EEG/qEEG was done in June 2009 and as of
September 2013, 454 completed when database closed
• Majority were referred (98%) because of medication and/or
treatment failure
Equipment & Procedure
• Equipment: Deymed TruScan 32
• Standard International 10-20 System
• Linked Ears and Averaging montage assessed
• 10 minutes Eyes Open resting and 10 minutes Eyes Close
Data Collection
(Inter-rater reliability not an issue)
• Artifacting and qEEG topographical brainmapping done by
the Human Brain Institute, Saint Petersburg, Russia
• Each EEG interpreted by neurophysicist /encephalographer
• Each qEEG analyzed by qEEG diplomate
• Note: both have over 40 years of experience in the field
• Note: Each case where TD was identified,
inclusion in this variable was considered only
when there was clinical correlation
Hypotheses
• In a population of patients who have failed on psychiatric
medications:
• 1. The association between psychiatric diagnoses and
qEEG/EEG findings are significant
• 2. The greater the number of diagnoses, the greater the
number of medications prescribed and the greater the
number of EEG/qEEG findings.
Analysis
• Strategy: Identifying associations between psychiatric
diagnoses and EEG/qEEG findings
• We used two methods for identifying associations:
• A) Chi-Squared Analysis
• B) Logistic Regression
Analysis
(continued)
• Before we examined associations we looked at univariate
distributions of psychiatric diagnoses and EEG/qEEG findings
across three age groups
• Table 1 showing Gender Proportions
• Table 2 showing Proportions by Age and Gender
• Table 3 showing Proportions with positive EEG/qEEG
Findings, EN, FS, BS, TD
• Table 4 showing Proportion with positive ADD, ASD, MDD
or ANX Diagnoses
Table 1: Proportion by Age and Gender
< 12 years
35.2%
12 – 18 years
27.0%
> 18
37.8%
Female
29.8%
Table 2: N = 400 Gender Proportions
Subscale
Gender
Number in
Study
89
Percentage per
Subscale
58.9%
62
41.1%
Adolescent Males
Adolescent Female
81
27
75%
25%
Child Males
Child Females
111
30
78.7%
21.3%
T0TAL
281 M
119 F
70.3%
29.7%
Adult Males
Adult Females
Table 3: Proportion with Positive EEG/qEEG Findings
Subscale
Gender
Adult Males
EN
FS
BS
TD
14.6%
82.0%
24.7%
30.3%
Adult Females
16.1%
72.6%
35.5%
41.9%
Adolescent Males
13.6%
77.8%
14.8%
37.0%
Adolescent Females
11.1%
70.4%
11.1%
29.6%
Child Males
23.4%
72.9%
19.8%
47.7%
Child Females
20.0%
73.3%
26.7%
30.0%
T0TAL
17.0%
75.8%
22.2%
38.2%
Table 4: Proportion with Diagnoses
Subscale
Gender
Adult Males
ADD
ASD
MDD
ANX
49.4%
6.7%
31.5%
44.9%
Adult Females
35.8%
1.6%
61.3%
50.0%
Adolescent Males
66.7%
16.0%
21.0%
33.3%
Adolescent Females
37.0%
18.5%
18.5
25.9%
Child Males
61.3%
30.6%
9.0%
32.4%
Child Females
50.0%
46.7%
6.7%
26.7%
53.2%
18.2%
23.2%
32.8%
T0TAL
Hypothesis 1
• In a population of patients who have failed on psychiatric
medications:
• The associations between psychiatric diagnoses and
EEG/qEEG findings are significant.
Table 5: Chi-Squared Values
Significance of association assessed through Chi-Squared Values of each of
EEG/qEEG findings and each diagnoses (ADD, ASD, MDD, and ANX by groups)
< 12 years
12 – 18 years
> 18 years
ADD
ASD
MDD ANX
ADD
ASD
MDD ANX
ADD
ASD
MDD ANX
EN
n.s
n.s
n.s
n.s
n.s
n.s
n.s
n.s
n.s
n.s
n.s
n.s
FS
n.s
n.s
n.s
n.s
n.s.
n.s
n.s
*
n.s
n.s
n.s
n.s
BS
n.s.
n.s
n.s
n.s
n.s
n.s
n.s
n.s
n.s
n.s
*
n.s
*
n.s
n.s
n.s
n.s
n.s.
n.s
*
n.s
n.s
n.s
TD n.s
* =p <.05
Of the 48 cell entries in Table 5, only 4 reached significance at
the .05 level pointing in general to a lack of association
between EEG/qEEG findings and psychiatric diagnoses.
Although a significant association was found in
MDD Children with TD
ANX Adolescents BS
ASD adults with TD
MDD adults with BS
It would be a difficult psychiatric decision to stop all medications
that would increase TD or BS and to prescribe anticonvulsants
(TD) and channel blockers (BS) based solely on research
without EEG/qEEG confirmation
Table 6: Logistic Regression
Logistic Regression: Gross Effect (association) between each of EEG/qEEG
finding and each of ADD, ASD, MDD, and ANX by Age Groups
< 12 years
12 – 18 years
> 18 years
ADD
ASD
MDD ANX
ADD
ASD
MDD ANX
ADD
ASD
MDD ANX
EN
n.s
n.s
n.s
n.s
n.s
n.s
n.s
n.s
n.s
n.s
n.s
n.s
FS
n.s
n.s
n.s
n.s
n.s.
n.s
n.s
+/*
n.s
n.s
n.s
n.s
BS
n.s.
n.s
n.s
n.s
n.s
n.s
n.s
n.s
n.s
n.s
+/*
n.s
TD
n.s
n.s
+/*
n.s
n.s
n.s
n.s
n.s.
n.s
n.s
n.s
n.s
• As expected, the odds ratios obtained from the Logistic
regression of each of the psychiatric diagnoses (ADD, ASD,
MDD, ANX) on each of the EEG/qEEG findings are similar to
the findings from the chi-squared analysis of psychiatric
diagnoses and EEG/qEEG findings.
• In general, logistic regression (Gross Effect) results suggest
a lack of association between EEG/qEEG findings and
psychiatric diagnoses Table 6
Table 7: Logistic Regression
Logistic Regression: Net Effect (association) between each of EEG/qEEG
finding and each of ADD, ASD, MDD, and ANX by Age Groups
< 12 years
12 – 18 years
> 18 years
ADD
ASD
MDD ANX
ADD
ASD
MDD ANX
ADD
ASD
MDD ANX
EN
n.s
n.s
n.s
n.s
n.s
n.s
n.s
n.s
n.s
n.s
n.s
n.s
FS
n.s
n.s
n.s
n.s
n.s.
n.s
n.s
+/*
n.s
n.s
n.s
n.s
BS
n.s.
n.s
n.s
n.s
n.s
n.s
n.s
n.s
n.s
n.s
+/*
n.s
TD
n.s
n.s
+/*
n.s
n.s
n.s
n.s
n.s.
n.s
n.s
n.s
n.s
• The results of logistic regression (Net Effect) of
each of the four psychiatric diagnoses on all four of
the EEG/qEEG findings strongly support inferences
drawn from Table 7
Hypothesis 1
Significant Finding
• In general, our analysis suggests poor levels
of association between EEG/qEEG findings
and psychiatric diagnoses
• This finding calls for more coordinated
diagnosis effort between psychiatrists and
EEG professionals.
Hypothesis 2
• In a population of patients who have failed on psychiatric
medications:
• The greater the number of diagnoses, the greater the
number of medications prescribed, and the greater the
number of EEG/qEEG findings.
• We correlated number of medications with
number of psychiatric diagnoses (totDiag) and
number of EEG findings (totEEG). We also
correlated totDiag with totEEG. See Table 7
below.
Table 8: Correlations
**. Correlation is significant at the 0.01 level (2-tailed)
toEEG=total number of EEG findings
totDiag=total number of psychiatric diagnosis
RX=number of medication
Correlations
totEEg
totEEg
Pearson Correlation
totDiag
1
.040
RX
.197**
.429
.000
Sig. (2-tailed)
totDiag
RX
N
384
384
382
Pearson Correlation
.040
1
.213**
Sig. (2-tailed)
.429
N
384
384
382
.197**
.213**
1
Sig. (2-tailed)
.000
.000
N
382
382
Pearson Correlation
.000
382
Hypothesis 2
Significant Findings
• Our hypotheses that total number of diagnoses and
total number of EEG findings are correlated is not
supported. In fact, the magnitude of the correlation
is low.
• However, as the number of either
TotEEG or totDiag goes up, the total
number of medications goes up. The
correlation is significant.
Conclusions
This Study Suggests
• EEG/qEEG technology analysis can assist in medication and
treatment selection in refractory cases
• Those who do EEG/qEEGs can be of benefit to
prescribing physicians
• EEG/qEEG technology should become an instrument in
psychiatrists tool box to increase the validity of diagnoses
after using self-report, observation and psychological testing
•
• This EEG/qEEG technology is not intended to
take the place of prescribing physicians, just to
provide scientific data to assist them
Study Implications
• Our study provides preliminary empirical evidence
that in the small number of cases where the first
medication fails:
• Psychiatric diagnosis instruments suffer from
poor validity and may not be relied upon for
continued medication management
• EEG/qEEG analysis provides objective
empirical data that can assist in
medication selection and treatment
planning especially in refractory cases
Limitations of Study
• This study only addresses cases that have failed
traditional intervention and cannot be generalized to
the population
• The patients in this study were those that could
afford private pay and cannot be generalized to the
population that cannot
Future Research
• Why was the sample so male skewed (70%)?
• Are females more easily treated?
• What are the gender differences in each age
group?
• Do diagnoses diminish with age?
Titration and Toxicity
• Series EEG/qEEGs are valid ways to titrate medications and
to assess for toxicity
• Beta spindles can easily be seen in raw EEGs, and reduction
in beta spindles can be assessed with reduction of
benzodiazepine type medications or increases in beta
blockers or GABA agonist
• Changes in alpha tuning can also be seen in the raw EEG for
titration of amphetamines (speed up) and beta blockers (slow
down)
• Transient discharges amplitude and duration reduction can
be used in titration of anticonvulsants and the reduction of
medication that lowers seizure threshold
Encephalopathy
Treatment Considerations
• Encephalopathy
• Hold treatment until cause is determined and remediated if
possible
• Studies have found that Low Voltage Slow (LVS) patterns
respond to:
• Interactive Metronome
• We have found that IM is very helpful to establish
synchrony in the brain prior to neurofeedback
• Hyperbaric Oxygen
Focal Slowing
Treatment Considerations
• Focal slowing does not respond well to medication
intervention because medications affect the whole brain and
cannot just address the abnormally slow area
• Neurofeedback (NFB) therapy is a neuromodulation
technique that can target focal slowing very well
• In the absence of TDs, two other neuromodulation
techniques are also indicated
• Transcranial Magnetic Stimulation (TMS)
• Transcranial Direct Current Stimulation (tDCS)
Beta Spindles
Treatment Considerations
• BS that are iatrogenic, titrate down benzodiazapine/sedative
type medications first
• Endogenous BS with normal to fast peak frequency alpha:
• Beta Blockers or medications that increase GABA
• BS with slow peak frequency alpha:
• Buspar (least likely to reduce the alpha peak frequency)
• Note: Mixed fast/slow patterns very difficult to medicate
• Beta spindles are good targets for
neuromodulation: (NFB, TMS & tDCS)
Transient Discharges
Treatment Considerations
• Treatment resistant seizures have been successfully treated
for 4 decades with NFB…transient discharges are much
easier to treat than seizure disorder
• Medication considerations: stop any medication that lowers
seizure threshold…and repeat EEG
• If significant TDs are still seen anticonvulsants may be
considered
• If normal to fast peak alpha Lamictal or Keppra
• If slow peak alpha Trileptal (speeds up alpha as well)
• Once the TD are well controlled, other issues
can be addressed with other medications
Questions?