Electroencephalography in children with and without sensory processing disorders during auditory perception.

Abstract

We sought to determine whether children with sensory processing disorder (SPD) differ from typically developing children on a neurophysiological measure, the P300 component of event-related potentials produced in response to brief auditory stimulation.
We used electroencephalographic measures (i.e., N200 and P300 components) to examine auditory processing in 20 children with SPD and 71 typically developing children, ages 5-10 yr.
Children with SPD demonstrated significantly smaller P300 amplitudes and shorter N200 latencies than typically developing children. Brain activity correctly distinguished children with SPD from typically developing children with 77% accuracy. We also found a significant relationship between the neurophysiological measures and functional performance on sensory and motor tasks.
This study presents empirical evidence that children with SPD display unique brain processing mechanisms compared with typical children and, therefore, provide further evidence for the neural deviations associated with SPD.

Full text

Page 1

Electroencephalography in Children With and
Without Sensory Processing Disorders During
Auditory Perception
William J. Gavin, Alycia Dotseth, Kaylea K. Roush, Courtney A. Smith,
Hayley D. Spain, Patricia L. Davies
KEY WORDS
? auditory perception
? electroencephalography
? evoked potentials
? sensation disorders
William J. Gavin, PhD, is Associate Professor,
Department of Human Development and Family Studies,
Colorado State University, Fort Collins.
Alycia Dotseth, MS, OTR/L, was Graduate Student,
Department of Occupational Therapy, Colorado State
University, Fort Collins, at the time of the study.
Kaylea K. Roush, MS, was Graduate Student,
Department of Occupational Therapy, Colorado State
University, Fort Collins, at the time of the study.
Courtney A. Smith, MS, was Graduate Student,
Department of Occupational Therapy, Colorado State
University, Fort Collins, at the time of the study.
Hayley D. Spain, MS, OTR, was Graduate Student,
Department of Occupational Therapy, Colorado State
University, Fort Collins, at the time of the study.
Patricia L. Davies, PhD, OTR, FOTA, is Associate
Professor, Department of Occupational Therapy, Colorado
State University, 219 Occupational Therapy, Fort Collins,
CO 80523; pdavies@lamar.colostate.edu
OBJECTIVE. We sought to determine whether children with sensory processing disorder (SPD) differ
from typically developing children on a neurophysiological measure, the P300 component of event-related
potentials produced in response to brief auditory stimulation.
METHOD. We used electroencephalographic measures (i.e., N200 and P300 components) to examine
auditory processing in 20 children with SPD and 71 typically developing children, ages 5–10 yr.
RESULTS. Children with SPD demonstrated significantly smaller P300 amplitudes and shorter N200 la-
tencies than typically developing children. Brain activity correctly distinguished children with SPD from
typically developing children with 77% accuracy. We also found a significant relationship between the neu-
rophysiological measures and functional performance on sensory and motor tasks.
CONCLUSION. This study presents empirical evidence that children with SPD display unique brain pro-
cessing mechanisms compared with typical children and, therefore, provide further evidence for the neural
deviations associated with SPD.
Gavin, W. J., Dotseth, A., Roush, K. K., Smith, C. A., Spain, H. D., & Davies, P. L. (2011). Electroencephalography in
children with and without sensory processing disorders during auditory perception. American Journal of Occu-
pational Therapy, 65, 370–377. doi: 10.5014/ajot.2011.002055
A
person has difficulty organizing sensory stimuli to make an adaptive response.
Children with SPD often display aversion to movement or touch, unfocused
attention, and poor coordination as a result of their disorganized interpretation
of stimuli (Bundy & Murray, 2002). Several postulates regarding subtypes of
SPD have been developed. For example, Bundy and Murray (2002) categorized
this dysfunction into two subtypes: dyspraxia and poor modulation. Children
with dyspraxia exhibit poor motor planning and coordination, whereas children
identified with modulation disorders fail to appropriately regulate their be-
havior because of inadequacies in processing specific attributes of the sensory
information (Miller, Anzalone, Lane, Cermak, & Osten, 2007). In this study,
we focused on the modulation subtype.
Despite an extensive history, SPD remains a controversial subject and con-
tinues to be a popular area of research in occupational therapy (Bundy & Murray,
2002). Although most research on SPD has used behavior measures, Miller
(2003) advocated that “more objective and direct methods are required to
characterize the population with sensory processing impairments” (p. 6). One
such objective measure is electroencephalography (EEG).
EEG measures voltage changes at the scalp that are related to cortical
neuronal activity (Stern, Ray, & Quigley, 2001). One method for making
ccording to the existing literature, 1 in 20 children has a sensory processing
disorder (SPD; Ahn, Miller, Milberger, & McIntosh, 2004), in which a
370
July/August 2011, Volume 65, Number 4

Page 2

inferences about the meaning of voltage changes is to
examine event-related potentials (ERPs). In this method,
a participant experiences an event, such as listening to
a presented tone, at multiple times throughout the EEG
recording. Segments of the EEG corresponding to the time
of the tone presentations are averaged, producing an av-
eraged ERP. (See Figure 1 for an example of an averaged
ERP to an auditory stimulus and the component labels.)
Then, both the latency and the amplitude of the major
peaks in the averaged ERP (i.e., the components) are mea-
sured and compared between individuals or groups. La-
tency, typically measured in milliseconds, involves the
timing of the component; that is, how much time elapsed
between stimulus presentation and the component. Am-
plitude involves the amount or change in voltage (mea-
sured in microvolts) and can “reflect variations in the
degree to which some process is invoked” (Rugg & Coles,
1995, p. 31). Amplitude and latency values are quantita-
tive and objective measures of neural activity that can help
researchers illustrate the relationship between physiologic
processing and behavioral manifestations (Banaschewski &
Brandeis, 2007).
Examining actual neuronal activity in response to
a stimulus is a technique that has only recently been used
to test the assumption that sensory processing difficulties
are a manifestation of neurological processing deficits
(Davies, Chang, & Gavin, 2009, 2010; Davies & Gavin,
2007). Studies using the sensory registration paradigm
(Davies & Gavin, 2007; Davies et al., 2010) have es-
tablished ERP research as a verifiable measure of the
differences between children with SPD and typically de-
veloping children. The sensory registration paradigm in-
volves two different auditory stimuli, each played at a soft
and a loud volume, presented multiple times while the
participant stares at a fixed mark on a computer screen.
The ERP data from the four stimuli measure an in-
dividual’s ability to discriminate and organize auditory
stimuli. Using only the amplitude and latency of ERP
components generated by the sensory registration para-
digm, Davies et al. (2010) correctly classified children
identified with the modulation subtype of SPD and typ-
ically developing children with 95.6% accuracy. This high
level of accuracy suggests that neurophysiological responses
to simple auditory stimuli may correctly predict sensory
modulation difficulties in children.
In the Davies et al. (2010) study, the highly accurate
group categorization was largely based on the P300 com-
ponent. This late component is indicative of additional
cognitive activity (Polich, 2007). P300 has become the
most studied ERP component (Wu, Liu, & Quinn-Walsh,
2008). Other P300 research has found significant differ-
ences between neurotypical individuals and individuals
with schizophrenia (Klein, Berg, Rockstroh, & Andresen,
1999; Weisbrod, Hill, Niethammer, & Sauer, 1999), at-
tention deficit hyperactivity disorder (Barry, Johnstone,
& Clarke, 2003; Sugawara, Sadeghpour, Traversay, &
Ornitz, 1994; Sunohara et al., 1999), autism (Lincoln,
Courchesne, Harms, & Allen, 1995), and epilepsy
(Naganuma et al., 1997). Although those studies have
demonstrated significant differences between the mean
P300 amplitudes of individuals with and without these
neurological disorders, they have yet to demonstrate
relationships between the P300 and the functional be-
haviors that are used to define the disorder in individuals.
Despite the insights gained from the previous re-
search, additional studies are still needed to investigate the
relationship between the P300 component and SPD. In
this study, we sought to determine whether children with
SPD differ from typically developing children on a neu-
rophysiological measure, the P300 component of ERPs
Figure 1. Averaged event-related potentials to auditory stimuli recorded at the Pz electrode site.
Note. Major components are labeled. SPD 5 sensory processing disorder; SPL 5 sound processing level.
The American Journal of Occupational Therapy
371

Page 3

produced in response to brief auditory stimulation. Such
anoutcomewouldreplicate,inpart,thefindingsofDavies
et al. (2010).
We examined differences in brain processing between
the two groups of children from two viewpoints. First, do
thegroupsdifferineithermeanamplitudeormeanlatency
measures of the P300 ERP component? Second, can the
individual differences in the late ERP component be used
to accurately classify children according to their diagnostic
category? Extending beyond the Davies et al. study, our
third research question focused on whether a relationship
between the P300 and functional behaviors often used to
diagnose children with SPD can be demonstrated. Spe-
cifically, does a significant relationship exist between the
amplitude and latency of the P300 ERP component and
scores on the Short Sensory Profile (SSP; McIntosh,
Miller, Shyu, & Dunn, 1999) and the Clinical Obser-
vation of Motor and Postural Skills (COMPS; Pollock,
Kaplan, & Law, 2000)?
Method
Participants
This study was performed using a subsample of a larger
ongoing study. A total of 91 children ages 5–10 yr were
recruited from two sources, creating two independent
groups. The first group consisted of 20 children with
SPD (mean [M] 5 7.0, standard deviation [SD] 5 1.6)
who were referred to the study by the medical commu-
nity. This group consisted of 14 boys and 6 girls. The
unbalanced male-to-female ratio is representative of the
population with SPD (Ahn et al., 2004). The second
group consisted of 71 typically developing children (M 5
7.5, SD 5 1.5) from the community. The typically de-
veloping children who volunteered for the study had no
known neurological diagnosis and did not have a history
of receiving any special services. Two of the typically
developing children were subsequently excluded because
of missing data on EEG measures or COMPS scores.
Although the typically developing children were as a
group slightly older than the children with SPD, the
difference was not significant (t[87] 5 1.33, p 5 .19).
Each participant’s group membership was independently
confirmed in the laboratory using two behavioral
assessments.
Behavioral Assessments
The SSP is a norm-referenced screening tool appropriate
for children ages 3–10 yr. It includes seven subscales:
Auditory Filtering, Low Energy–Weak, Movement Sen-
sitivity, Tactile Sensitivity, Taste–Smell Sensitivity,
Underresponsive–Seeks Sensation, and Visual–Auditory
Sensitivity. The SSP has acceptable internal consistency
reliability and construct validity (McIntosh et al., 1999);
it is derived from the Sensory Profile (Dunn, 1999) and
formatted as a caregiver questionnaire. The SSP was
completed by the children’s parents before visiting the
lab. Children with SPD scored significantly lower than
the typically developing children on all seven SSP sub-
scales as well as the total score (Table 1).
The COMPS is a short screening test used to identify
motor difficulties with both postural and praxis compo-
nents for children ages 5–15 yr. The COMPS has been
found to have acceptable reliability and validity (Pollock
et al., 2000). This assessment was administered during
the participants’ second visit to the laboratory for this
study. As with the SSP, children with SPD also scored
significantly lower than typically developing children on
the COMPS measure (see Table 1).
Procedure
Informed consent was obtained from the parents of all
participants, and procedures for this research study were
approved by the human research committee at Colorado
Table 1. Results of the Mann–Whitney U Test Comparisons of the Two Groups of Children on Mean Ranks for COMPS and Their Mean Rank
Scores From the Short Sensory Profile
Typically Developing Children; Mean Rank Children With SPD; Mean RankUzp
COMPS
Short Sensory Profile Subscale
Tactile Sensitivity
Taste–Smell Sensitivity
Movement Sensitivity
Underresponsive–Seeks Sensation
Auditory Filtering
Low Energy–Weak
Visual–Auditory Sensitivity
Short Sensory Profile—Total
49.429.7384.523.0.003
52.4
50.6
48.5
52.8
53.2
53.4
49.7
53.7
19.4
25.7
32.8
18.0
16.5
16.1
28.8
15.1
177.5
304.0
447.0
150.5
120.5
111.5
366.0
92.5
25.1
23.9
22.4
25.3
25.6
25.9
23.2
25.9
<.0001
<.0001
.015
<.0001
<.0001
<.0001
.0013
<.0001
Note. COMPS 5 Clinical Observation of Motor and Postural Skills; SPD 5 sensory processing disorder.
372
July/August 2011, Volume 65, Number 4

Page 4

State University. Additionally, children were informed of
the study’s procedures in a child-friendly manner, and all
children agreed to participate by signing an assent form.
The participants visited the lab twice. On both visits, each
participant completed the EEG procedures described in
the paragraphs that follow.
The participants sat in a relaxed position in a high-
backed chair, and the EEG sensors, which were contained
in a stretch-fabric cap, were placed on their head. Strat-
egies to reduce artifacts in the EEG recordings caused by
eye blinks, movement, and muscle activity were explained
and demonstrated to the participants. Next, resting EEG
recordings were taken. Earphones (ER-3A; Etymotic
Research, Elk Grove Village, IL) were then inserted, and
an auditory threshold screening was conducted. EEG
recordings were conducted during two ERP paradigms:
(1) a sensory gating paradigm and (2) a sensory regis-
trationparadigm.Eachparadigmrequiredthe participants
to listen to paired clicks or tones varying in frequency and
intensity. In this study, however, we report only the results
of data collected during the sensory registration paradigm
on the first visit. The order of paradigm presentation
was counterbalanced between participants. Participants
watched a silent Wallace and Gromit film (Schelley et al.,
1996) to keep them engaged during each paradigm.
Theauditorysensoryregistrationparadigmwasadapted
from Lincoln et al. (1995) and Davies et al. (2010). For
this paradigm, four different auditory stimuli were pre-
sented in both ears using E-Prime software (Psychological
Software Tools, Pittsburgh, PA). The stimuli were 1 kHz
at 50 dB sound processing level (SPL), 1 kHz at 70 dB
SPL, 3 kHz at 52 dB SPL, and 3 kHz at 73 dB SPL. We
examined only the 3 kHz data in this study. The duration
of each stimulus was 50 ms with 10-ms onset and offset
ramps. The interstimulus interval was 2 s. Stimuli were
presented in 4 blocks of 100 stimuli presentations with
30-s breaks between blocks.
EEG Recording and Analysis
EEG activity was recorded using a 32-channel BioSemi
Active Two EEG system (BioSemi Inc., Amsterdam, the
Netherlands). The electrodes were located in accordance
with the 10–20 system (American Electroencephalographic
Society, 1994). EEG was recorded with the Common Mode
Sense active electrode as the reference and the Driven Right
Leg passive electrode as the ground (BioSemi, Inc., n.d.).
Electrooculograms (EOGs) were recorded from individual
electrodes placed on the left and right outer canthus for
horizontal movements and on the left supraorbital and
infraobital region for vertical movements. Four more
individual electrodes were placed on the left and right
earlobes and mastoids. EEG signals were sampled at an
analog-to-digital rate of 1024 Hz with a bandwidth of
268 Hz.
Weconducted allEEGandERPanalyses offlineusing
the Brain Vision Analyzer2 software (Brain Products
GmbH, Munich, Germany). The left and right earlobe
recordings were averaged and used as the offline reference.
The four individual EOG channels were converted to a
verticalandahorizontalbipolarEOG.TheEEGrecordings
were filtered with a band pass of 0.23–30 Hz (12 dB/
octave). The EEG was segmented about each auditory
stimulus with a duration of 100 ms prestimulus onset
to 800 ms poststimulus onset. Eye-blink artifacts were
removed using a regression procedure. Segments with
deviations greater than ±100 mV on any of the EEG
channels or the bipolar EOG channels were eliminated.
Nonrejected segments for each auditory stimulus were
then baseline corrected using the prestimulus period of
2100 to 0 ms and averaged to create ERP waveforms for
each participant, from which the ERP components were
measured.
We used methods from Lincoln et al. (1995) and
Davies et al. (2010) to measure peak-to-peak amplitude
and latency for N200 and P300 components. The most
negative peak 240–290 ms after stimulus onset was de-
fined as N200. The most positive peak 360–410 ms after
stimulus onset was defined as P300. We calculated the
peak-to-peak amplitude of P300 by subtracting the N200
peak amplitude from the P300 peak amplitude. A com-
puter program, Brainwaves Peak Picker, created by the
Brainwaves Research Laboratory at Colorado State Uni-
versity, was used to provide automatic scoring and visual
inspection and, when necessary, to allow manual marking
of components. Two teams of independent raters com-
pleted the visual inspection of automatic marking of
components. To increase reliability of any manual ad-
justment of component values, all values were checked
and agreed on by the opposite team of raters. Congruent
with Davies et al. (2010), we examined only the ampli-
tude and latency measurements obtained from the Fz and
Pz sensors placed on the scalp over the frontal lobe and
the parietal lobes, respectively, along the sagittal midline.
Statistical Analysis
To answer the first research question, we used a 2 · 2 · 2
analysis of variance (ANOVA) to evaluate whether the
two child groups differed in mean amplitude or mean
latency measures of the P300 ERP component. The first
factor, Group, was a between-subjects factor with two
levels, (1) typically developing children or (2) children
with SPD. The second factor, a within-subject factor, was
The American Journal of Occupational Therapy
373

Page 5

Stimulus Intensity, which had two levels: 52 dB or 73 dB.
The third factor, also a within-subject factor, was Elec-
trode Site and had two levels, Fz or Pz. The dependent
measure was the peak-to-peak amplitude measures of the
P300 for the first ANOVA and the latency of the N200
for the second ANOVA. The latency of the N200 was
chosen as a measure of P300 timing because it represents
the beginning or onset of the P300 as measured by the
peak-to-peak amplitude. The assumptions of normality
and homogeneity of ANOVA were met for the P300.
However, the assumption of homogeneity between groups
could not be met for the latency of N200 (Box’s M statistic
F [10, 5,565.9] 5 2.72, p 5 .002; Green & Salkind,
1999), so the results of this analysis should be interpreted
with caution.
Discriminant analysis, a form of multiple regression
that allows for the dependent measure to be categorical,
was used to evaluate whether the individual differences in
the P300 ERP component can accurately classify children
into their diagnostic category, answering the second re-
search question. Selected amplitude and latency measures
of P300 served as the variables for predicting diagnostic
category. Along with the classification statistics, discrim-
inant scores were calculated from the prediction equation
and saved for each participant. The discriminant scores are
continuous in nature and represent processing abilities of
the brain as measured by the P300. The scores were then
correlated using the Pearson product–moment procedure
with a second set of discriminant scores derived from the
behavioral measures (SSP and COMPS) to answer the
third research question. All data were managed and an-
alyzed using SPSS Version 18 (SPSS, Inc, Chicago).
Results
The mean amplitude and latency values for children with
SPD were less than those for typically developing children
at each electrode site for each of the two auditory stimulus
intensities (Table 2). ANOVA for the peak-to-peak am-
plitude of the P300 revealed a significant difference be-
tween the two groups (F[1, 87] 5 5.11, p 5 .026, h2
0.06). We also found significant main effects for Intensity
(F[1, 87] 5 14.14, p < .0005, h2
Electrode Site (F[1, 87] 5 36.55, p < .0005, h2
The Intensity · Electrode Site interaction was also sig-
nificant (F[1, 87] 5 4.75, p 5 .032, h2
for the latency of the N200 also revealed a signifi-
cant difference between the two groups (F[1, 87] 5
4.33, p 5 .040, h2
main effect for Electrode Site (F[1, 87] 5 4.96, p 5
.029, h2
p5
p 5 0.14) and for
p5 0.30).
p5 0.05). ANOVA
p5 0.05). We found a significant
p5 0.05) but not for the main effect of Intensity.
However, the Intensity · Electrode Site (F[1, 87] 5
10.33, p 5 .002, h2
Electrode Site · Group (F[1, 87] 5 5.18, p 5 .025,
h2
p5 0.06) interactions were significant.
To determine which P300 amplitude and N200 la-
tency measures in combination might best predict the
groupmembership of eachchild participant,we calculated
zero-order correlations between each measure and group
membership using a point-biserial correlation approach.
We chose the three variables with the highest correlation
coefficients to serve as the predictor variables in the dis-
criminant analysis: (1) P300 peak-to-peak amplitude of
the 3 kHz stimulus at 73 dB SPL measured at Fz (rpb5
2.21, p 5 .046); (2) P300 peak-to-peak amplitude of the
3 kHz stimulus at 73 dB SPL measured at Pz (rpb5
2.20, p 5 .058); and (3) N200 latency of the 3 kHz
stimulus at 53 dB SPL measured at Fz (rpb5 2.31, p 5
.003). Because the distribution of ages and gender was
different in each group, we also entered age and gender
into the discriminant analysis as predictor variables. The
results of this discriminant analysis showed that typically
developing children and children with SPD were signifi-
cantly distinct from each other (Wilks’ L 5 .77, p 5
.001). The discriminant analysis correctly classified 77% of
all child participants: 77% correct classification for typi-
cally developing children and 79% correct classification for
children with SPD. The standardized canonical discrimi-
nant function coefficients were .30 for Fz and .40 for Pz of
the P300 amplitudes for the 3 kHz stimulus presented at
73 dB; .81 for the N200 latency of Fz for the 3 kHz
stimulus presented at 52 dB; .67 for age; and .08 for gender.
The distribution of discriminant scores derived from
the ERP components is depicted in Figure 2 as a function
of the corresponding discriminant scores derived from the
two behavioral measures, the SSP and the COMPS. As
expected, the discriminant analysis using the two behav-
ioral measures showed that typical children and children
with SPD were significantly separated from each other
(Wilks’ L 5 .47, p < .0005). This second discriminant
analysis correctly classified 92% of all child participants,
with 93% correct classification for typically developing
children and 90% correct classification for children with
SPD. Correlation analysis revealed a statistically signifi-
cant relationship between the two discriminant functions
(r 5 .38, p 5 .0003).
p 5 0.11) and the Intensity ·
Discussion
Amajorpurposeofthisstudywastoinvestigatedifferences
in brain processing between children with SPD and
typically developing children. The results of the ANOVA
374
July/August 2011, Volume 65, Number 4