R E S E A R C H Open Access
Name recognition in autism: EEG evidence
of altered patterns of brain activity and
, Hanna B. Cygan
Background: Impaired orienting to social stimuli is one of the core early symptoms of autism spectrum disorder
(ASD). However, in contrast to faces, name processing has rarely been studied in individuals with ASD. Here, we
investigated brain activity and functional connectivity associated with recognition of names in the high-functioning
ASD group and in the control group.
Methods: EEG was recorded in 15 young males with ASD and 15 matched one-to-one control individuals. EEG data
were analyzed with the event-related potential (ERP), event-related desynchronization and event-related synchronization
(ERD/S), as well as coherence and direct transfer function (DTF) methods. Four categories of names were presented
visually: one’sown,close-other’s, famous, and unknown.
Results: Differences between the ASD and control groups were found for ERP, coherence, and DTF. In individuals
with ASD, P300 (a positive ERP component) to own-name and to a close-other’s name were similar whereas in control
participants, P300 to own-name was enhanced when compared to all other names. Analysis of coherence and DTF
revealed disruption of fronto-posterior task-related connectivity in individuals with ASD within the beta range
frequencies. Moreover, DTF indicated the directionality of those impaired connections—they were going from
parieto-occipital to frontal regions. DTF also showed inter-group differences in short-range connectivity: weaker
connections within the frontal region and stronger connections within the occipital region in the ASD group in
comparison to the control group.
Conclusions: Our findings suggest a lack of the self-preference effect and impaired functioning of the attentional
network during recognition of visually presented names in individuals with ASD.
Keywords: Autism spectrum disorder, Coherence, ERP, Directed transfer function, Event-related desynchronization and
Abbreviations: ASD, Autism spectrum disorder; DTF, Directed transfer function; ERD/S, Event-related desynchronization
and synchronization; ERP, Event-related potential; FDR, False discovery rate
Autism spectrum disorder (ASD) is a heterogeneous
neurodevelopmental disorder characterized by impaired
social interactions and communication, restricted inter-
ests, and repetitive behaviors. According to the various
data sources the prevalence of ASD is estimated at 1 in
160 children (WHO: www.who.int) up to 1 in 68 chil-
dren (CDC: www.cdc.gov). This high prevalence makes
ASD an important problem, not only for the affected
individuals and their families, but also for the society in
general. Despite extensive research in the field, the
etiology of ASD remains unknown and there is still no
unified theory that would explain all autistic symptoms
or suggest clear guidelines for treatment [1–4].
Many existing studies have tried to understand the
autistic mind by analyzing behavioral and neural re-
sponses during the processing of various information
* Correspondence: email@example.com
Laboratory of Psychophysiology, Department of Neurophysiology, Nencki
Institute of Experimental Biology, Polish Academy of Sciences, 3 Pasteur
Street, 02-093 Warsaw, Poland
Full list of author information is available at the end of the article
© 2016 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Nowicka et al. Molecular Autism (2016) 7:38
that are crucial in social interactions, e.g. gaze direction
and other people’s intentions [5, 6]. In particular, the
topic of impaired face processing in ASD has been ex-
tensively studied (for reviews see [7–10]). Given the fact
that both faces and names are typical cues prompting
interpersonal interactions, investigation of proper names
processing may provide new insights into the nature of
the autistic mind. However, this topic has not gained
enough attention so far and the neural mechanisms of
name processing in ASD remain unknown.
In non-ASD populations, studies on name processing
have consistently shown the preferential status of the
self-name (e.g., [11–15]). This stimulus is easily selected
among other information, i.e., the so-called cocktail
party effect [16, 17] and enters awareness more easily
than other types of emotional and social stimuli [18–20].
Even 5-month-old infants recognize their own name and
use it as a social cue to guide their attention to events
and objects in the external world [21–23]. Preferential
processing of one’s own name has a clear adaptive value.
In every-day life, our name is often called to attract our
attention to the speaker; it may signal some potentially
important information (e.g., a warning) but even more
often it is used to initiate social interaction. Because this
happens countless times throughout the lifespan, people
probably start to respond to this stimulus in a highly
preferential and automatic manner. It has been also pro-
posed that one’s own name is the core element of social
identity and knowledge about oneself; our own name
and surname define us as individuals among other
people [24, 25].
As mentioned earlier, little is known about name pro-
cessing in ASD but the limited existing evidence sug-
gests that it is atypical. Retrospective analyses of home
videos reveal that autistic children often show reduced
reaction or a lack of reaction to their own name; cru-
cially, this symptom is present long before the time of
the actual clinical diagnosis [4, 5, 26, 27]. To the best of
our knowledge, only two studies have investigated the
neural correlates of name processing in ASD. The first
study investigated passive listening to one’s own name
. Such procedure resulted in the right medial and
middle frontal gyri activations. However, those results
are rather difficult to interpret because the experiment
was done on a single sedated individual with ASD and it
lacked crucial control conditions (other names); thus,
this study does not inform on the presence/absence of
the preferential processing of the self-name in ASD. The
second study  was conducted by our group and
investigated event-related potentials (ERPs) in high-
functioning adolescents and adults with ASD during
detection of visually presented names: one’s own, close-
other’s, famous, and unknown. It was found that P300
(the late ERP component with an approximate latency of
300 ms after stimulus onset) in the ASD group did not
differ between the processing of self-name and close-
other’s name, whereas in the control group this differ-
ence was significant . Given that P300 response is
often seen as an electrophysiological index of sustained
attention and allocation of cognitive resources , this
P300 effect suggests that even high-functioning adult in-
dividuals with ASD still show reduced attentional/orient-
ing response to their own name.
Reduced attention to self-name in individuals with
ASD fits well with the social motivation theory of
autism, which posits that these individuals generally fail
to “affectively tag”socially relevant stimuli as intrinsic-
ally rewarding, thus, responding to such stimuli does not
become attentionally preferential [4, 31]. What remains
unknown, however, is the type of attention which leads
to atypical name processing; in our previous study we
used a simple detection task which mainly involves
bottom-up attention . Although models of selective
attention propose the existence of two distinct processes,
reflecting distinct operations: a bottom-up, stimulus-
driven (i.e., exogenous) and a top-down, goal-driven (i.e.,
endogenous) attention , it is generally accepted that
they interact with one another to guide selective atten-
tion . Thus, one may wonder whether impaired—on
the neural level—differentiation of one’s own name from
a close-other’s name in individuals with ASD would still
be present in the case of enhanced involvement of
internally-guided, top-down attention. In order to re-
solve this issue we decided to investigate the name rec-
ognition in the ASD and control groups. Crucially, in
comparison to the detection task, the explicit name rec-
ognition involves higher order processing and requires
the involvement of internally guided, top-down attention
. Similarly to our previous study , names were
presented in the visual modality.
In addition, it is also an unknown whether social at-
tention deficits in ASD are indeed specific to self-name
processing only or occur more generally for other types
of names; findings from a single study  are not
enough to answer this question. Finally, it is an unre-
solved issue whether in this clinical group, other—than
P300—markers of attention would reveal any deficits re-
lated to the name recognition task. Investigation of dif-
ferent electrophysiological markers of attention during a
name recognition task could provide important evidence
regarding social motivation/social attention deficits in
autism. The two candidates for such additional electro-
physiological markers of attention were alpha suppres-
sion and beta synchronization.
Briefly, modulation of alpha power reflects processes
that regulate information flow in the cortex via selective
suppression and selection of sensory signals [34–36].
Alpha suppression occurs when attention is directed
Nowicka et al. Molecular Autism (2016) 7:38 Page 2 of 14
towards external stimuli  and—following sensory sti-
mulation—can be observed in the corresponding sensory
areas . Stimuli to-be-ignored are associated with in-
creased alpha synchronization [34, 39]. Moreover, lower
alpha suppression is a likely indicator of weakened top-
down control [40–42]. Beta oscillations, in turn, are usu-
ally associated with alertness and active task engagement
 and are viewed as a “carrier”of visual attention in
humans [44–47]. For instance, only successful visual dis-
crimination is associated with the increase of beta band
power recorded from occipital sites . In addition, it
has been also recently proposed that tasks involving a
strong endogenous, top-down component are associated
with high activity in the beta band . Based on the
role of alpha and beta frequencies in visual processing,
weaker alpha suppression and decreased beta synchroni-
zation—if present in individuals with ASD—would indi-
cate lower attention allocation to visually presented
names and attenuated top-down processes in this clin-
Finally, recognition of visually presented names re-
quires an interaction between distant posterior and an-
terior regions: the former is involved in initial processing
of visual stimuli [49, 50], the latter—in decision-making
whether names are familiar or not (e.g., ). Functional
communication between these two brain regions is def-
initely required to successfully accomplish the recogni-
tion task, by transferring sensory information from the
visual areas to frontal regions involved in goal-directed
attention and planning of the appropriate behavioral re-
sponse [32, 52]. This raises a question whether plausible
attentional impairments in the name recognition would
be accompanied by a long-range disconnectivity of the
autistic brain. Such long-distance under-connectivity
(and short-distance over-connectivity) has been proposed
by recent explanatory models of autism (for reviews see
[53–56]) and a deficit in connections between anterior-
posterior regions of the autistic brain was reported in the
case of resting-EEG (e.g., [57, 58]). Empirical confirmation
of our prediction (on impaired task-related functional
connectivity in individuals with ASD) would provide a
strong evidence for the connectivity models of ASD; evi-
dence based not on general resting-state activity, but on
functional connectivity patterns during the specific behav-
ioral task, i.e., the name recognition.
Therefore, the aim of this EEG study was to investigate
electrophysiological correlates of name processing in
ASD; in particular, we were interested in possible atten-
tion and connectivity deficits during the name recogni-
tion task. In order to achieve these goals, self-, close
other’s, famous, and unknown names were visually pre-
sented to the participants who decided whether each
name was familiar or unfamiliar to them. EEG was con-
tinuously recorded during this task and analyzed using a
range of the following methods. Brain activity was
assessed by ERP and event-related desynchronization and
event-related synchronization (ERD/S)  and functional
connectivity—by coherence as a function of time  and
directed transfer function (DTF) [61, 62]. The latter en-
ables estimation of not only the strength but also the dir-
ection of activity flow from one location to another.
Based on evidence discussed earlier, we hypothesized
that in our ASD group, the following effects would be
present: (i) lack of preferential processing of one’s own
name, reflected in similar P300 responses to the self-
and close-other’s names, (ii) weaker alpha ERD and
weaker beta ERS, and (iii) impaired task-related connect-
ivity between parietal-occipital and frontal brain areas.
Nineteen young males with ASD and 19 control individ-
uals participated in this study. Control participants were
matched one-to-one to individuals with ASD in terms of
age, sex, handedness, and IQ-score. Four individuals with
ASD were excluded from the analyses due to excessive ar-
tifacts in the EEG signal. Consequently, the four control
subjects that matched those individuals with ASD were
also excluded. Thus, the final size of each group was 15.
Subjects’IQs were evaluated using the Wechsler
Intelligence Scale for Adults - Revised (WAIS-R, PL)
. The maximal IQ difference between each individual
with ASD and the matched control subject was ± 15
(see Table 1). The maximal age difference between each
individual with ASD and the matched control subject was
± 5 months. In the ASD group, the age ranged from
16 years and 7 months to 23 years, the mean age was
19 years and 3 months (SD = 2 years and 4 months). In
the control group, the age ranged from 16 years and
11 months to 22 years and 8 months, the mean age was
19 years and 2 months (SD = 2 years and 2 months).
The clinical diagnosis of ASD subjects was confirmed
using standardized tests: the Autism Diagnostic Obser-
vation Schedule –ADOS (module 4) [64, 65] and the
Autism Diagnostic Interview-Revised –ADI-R . Aut-
istic traits in the control participants, in turn, were con-
trolled by the Autism Spectrum Quotient (AQ) ; the
range of AQ scores was 11–19 and the mean AQ score
(± SD) was 14.9 ± 3.2.
Handedness was confirmed with the Edinburgh Inven-
tory . Subjects had normal or corrected-to-normal
vision, and they did not take any medication at the time
of experiment. Subjects were financially compensated
for their participation.
Stimuli (first and last names, further referred to as
names) were presented visually (white letters against a
Nowicka et al. Molecular Autism (2016) 7:38 Page 3 of 14
black background). The size of the stimuli ranged from
2° × 2° to 2° × 6°. The names belonged to four categories:
subject’s own name (50 presentations), name of a close-
other (50 presentations), name of a famous person (e.g., an
actor, 50 presentations), and unknown names (three names,
each presented 50 times). Three unknown names were used
in order to equalize the probability of behavioral response
for unfamiliar (150) and familiar (150) names.
No restriction was placed on the subjects’choice of the
close-other as we wanted to avoid a situation where the
pre-defined close-other is not really close to a particular
subject [13, 29, 69, 70]. Thus, prior to the experiment, par-
ticipants were asked to choose a person who was currently
(i.e., at the time of our experiment) the most significant to
them and describe their relationship briefly. In the ASD
group, ten participants chose their parent’s name; two –
sibling’s name; two –grandmother’sname;andone–best
friend’s name. In the control group, five participants chose
their parent’s name; three –sibling’s name; one –grand-
mother’s name; two –best friend’s name, and four –girl-
In fact, the whole set of stimuli was individually tailored,
i.e., for each subject, different famous and unknown
names were chosen to match the length of self- and close-
other’s names. Stimuli were presented in a pseudo-
random order with no more than three names of the same
category presented consecutively. Before the experiment,
each participant was asked to confirm that he knew the
famous name (“What is the profession of this person?”)
and did not know the unknown names (“Do you know
anybody whose name is …?”).
Stimuli were displayed in central vision on a 19-inch NEC
MultiSync LCD 1990Fx monitor. Presentation® software
(Neurobehavioral Systems, Albany, CA, USA) was used
for stimuli presentation and response logging. The partici-
pants were seated in an acoustically and electrically
shielded dark room at a distance of 60 cm from the com-
puter monitor. Subjects performed a speeded two-choice
recognition task: unfamiliar vs. familiar (i.e., own-name,
close-other’s, and famous names). They responded by
pressing one of the two buttons on a Cedrus response pad
(RB-830, San Pedro, USA) using the index and the middle
fingers of the right hand to press the keys. Key assignment
was counterbalanced across subjects.
After reading the instructions displayed on the com-
puter screen, the participants started a trial session in
which feedback information was displayed (i.e. “correct”,
“incorrect”,or“reaction too slow!”). The experimental
session followed immediately after. In each trial, presen-
tation of a fixation point (a white “+”against a black
background) for 100 ms was followed by a blank screen
for 500 ms, after which a target item (a name) was dis-
played for 500 ms. Next, the participants were shown a
blank screen for 2000 ms, during which they were to
Table 1 Demographic and cognitive characteristics of the participants
Subject Hand IQ Subject Hand IQ ADI-R ADOS
Verbal Perf. Full Verbal Perf. Full Social (10) Com. (8) RSB (3) Social (4) Com. (2)
C1 R 111 126 118 A1 R 124 93 111 16 21 7 5 2
C2 R 139 122 132 A2 R 114 123 118 24 17 9 12 4
C3 R 107 114 110 A3 R 108 122 114 30 26 12 9 6
C4 R 99 93 97 A4 R 100 69 86 25 23 7 3•3
C5 R 116 126 121 A5 R 119 122 121 25 17 5 9 3
C6 R 111 108 110 A6 R 108 83 97 21 22 8 6 6
C7 R 86 99 91 A7 R 85 95 89 30 20 12 13 4
C8 R 114 112 113 A8 R 101 105 103 21 20 7 6 4
C9 R 130 97 116 A9 R 109 103 106 27 23 12 6 3
C10 R 130 123 128 A10 R 125 107 117 25 22 10 7 3
C11 R 86 93 89 A11 R 96 109 102 25 23 8 8 5
C12 R 120 112 117 A12 R 116 93 106 27 21 11 8 3
C13 L 110 110 110 A13 L 113 112 113 25 20 6 11 4
C14 R 116 117 117 A14 R 104 118 112 26 14 5 11 6
C15 L 122 92 109 A15 L 119 121 122 27 21 8 5 3
Left side: handedness and IQ scores of control participants (C1-C15). Right side: handedness, IQ scores, ADI-R and ADOS scores of individuals with ASD (A1-A15).
In all cases, age differences between an individual with ASD and a matched control participant was ≤5 months. All subjects were males. (•) marks ADOS score that
did not meet the criterion for ASD; nevertheless, this person was included into the ASD group because he had a psychiatric diagnosis of ASD and met all other
criteria. Numbers in parentheses indicate cut-offs for ADI-R and ADOS subscales
Abbreviations:Perf. performance IQ, Com. communication, RSB repetitive and stereotyped behavior
Nowicka et al. Molecular Autism (2016) 7:38 Page 4 of 14
give a response. The inter-trial interval varied between
100, 200, and 300 ms. The whole experiment lasted
about 15 min.
EEG was continuously recorded from 62 scalp sites and
two additional electrodes placed on the left and right
earlobes. We used a 136-channel amplifier (QuickAmp,
Brain Products, Enschede, Netherlands) and the BrainVi-
sionRecorder® software (Brain Products, Munich,
Germany). Electrodes were mounted on an elastic cap
(ActiCAP, Munich, Germany) and positioned according
to the extended 10–20 system. Electrode impedance was
kept below 5 kΩ. EEG signal was recorded against the
average of all channels calculated by the amplifier hard-
ware. The sampling rate was 500 Hz.
Behavioral data analysis
Responses were scored as correct if the appropriate key
was pressed within 150–2000 ms after the stimulus on-
set. Pressing the wrong key or pressing no key at all was
treated as an incorrect response. Reaction times (RTs)
were averaged across correct trials only. For each partici-
pant, percentage accuracy and RTs were analyzed only
for one unfamiliar name, chosen from the set of all three
unknown names. The reason for selecting one unfamiliar
name was to match the number of repetitions between
four categories of stimuli (for details, see the “EEG data
The selection of unknown names was random and bal-
anced in the sense that, for one participant, we selected
the unknown name that was matched to the length of
the self-name, for the next participant, we selected the
unknown name that was matched to the length of the
close-other’s name, etc. This procedure was the same in
both groups. Crucially, different unknown names were
used for different participants thus it is unlikely that our
selection procedure introduced any systematic bias.
RTs and accuracy rates were analyzed using mixed-
model ANOVA with group (ASD, control) and category
of name (own, close-other, famous, and unknown) as
factors. All effects with more than one degree of free-
dom in the numerator were adjusted for violations of
sphericity according to the Greenhouse-Geisser formula
. The analyses were conducted in the IBM SPSS
Statistics 21 Advanced Model.
EEG data analyses
The ERP analysis was performed using the BrainVisionA-
nalyzer® software (Brain Products, Gilching, Germany).
Preprocessing steps were analogous to those used in our
previous study on name detection in ASD  since we
aimed to compare the current and the previous P300
findings. First, EEG data were re-referenced to the aver-
aged earlobes and then Butterworth zero phase filters
were implemented: high-pass –0.1 Hz, 12 dB/oct,
low-pass –30 Hz, 12 dB/oct, and notch filter –50 Hz.
Correction of ocular artifacts was then performed using
the Independent Component Analysis –ICA . After
the decomposition of each data set into maximally statisti-
cally independent components, the components repre-
senting eye blinks were rejected based on the visual
inspection of the component map . Ocular-artifact-
free EEG data were obtained by back-projecting the
remaining ICA components after they were multiplied
using the reduced component-mixing matrix. Next,
the EEG was segmented to obtain epochs extending
from 200 ms before to 1000 ms after the stimulus
onset (baseline correction from −200to0ms).Inthe
automatic artifact rejection, the maximum permitted
voltage step per sampling point was 50 μV. In turn,
the maximum permitted absolute difference between
two values in the segment was 200 μV. The minimum
and maximum permitted amplitudes were −200 and
200 μV, respectively, and the lowest permitted activity
difference in the 100 ms interval was 0.5 μV.
ERPs for own, close-other’s, famous, and unknown
names were computed for correct trials only. ERPs for
the unknown category were computed only for one of
the unknown names per subject, randomly chosen from
the set of all three unknown names. This was done to
have similar number of trials for different experimental
conditions, thus to avoid problems with different
signal-to-noise ratios in different experimental condi-
tions (the higher the number of trials/repetitions, the
higher the signal-to-noise ratio). Since we had three
unfamiliar names (each presented 50 times, resulting
in 150 trials in the unfamiliar condition) and one
self-name (50 repetitions), one close-other’sname(50
repetitions), and one famous name (50 repetitions),
for each participant one unfamiliar name (50 repetitions)
was selected for further analyses.
The mean number of segments used to compute ERPs
(in ASD and control groups, respectively) was as follows:
own name - 48, 48, close other’s name - 49, 47, famous
name - 47, 47, and unknown name - 48, 48. We did not
find any significant differences in the number of epochs
used to compute ERPs between name categories or
between groups. Trials were averaged individually for
each electrode site, for each participant, and for each
For each experimental condition, P300 amplitude was
calculated as the mean of values at each time point
within the 400–550 ms time window (i.e., the mean
amplitude method). This method is less affected by the
possibly low signal-to-noise ratio than the peak ampli-
tude method . Mean amplitudes of P300 were
Nowicka et al. Molecular Autism (2016) 7:38 Page 5 of 14
analyzed at CPz that is the typically selected electrode
location for the analysis of P300 (e.g., [12, 29, 75]).
ERD/S, coherence as a function of time, and DTF
ERD/S reflects relative changes of the EEG spectral
power recorded after the stimulus onset in comparison
to a reference period registered before the stimulus pres-
entation . Quantification of ERD/S was performed in
time and frequency domains and was based on a method
similar to the event-related spectral perturbation (ERPS)
proposed by Makeig .
Coherence is a measure of synchronization between two
signals based mainly on phase consistency. Coherence
indicates the level of synchronization in activity between
different neural populations, where high coherence re-
flects greater functional integration due to either cortico-
cortical or cortical-subcortical-cortical connections .
In order to obtain its time course, we estimated coherence
in a way similar to event-related coherence . This is a
method for the analysis of coherence between electrodes
as a function of time (see Additional file 1 for the detailed
description), and it generates coherence values for the en-
tire time-frequency spectrum, allowing the analysis of co-
herence related to particular events in time, such as the
presentation of visual stimuli.
DTF, in turn, measures causal interactions in the fre-
quency domain between two EEG channels, with respect
to connections between all other available channels.
DTF enables estimation of a strength and direction of
activity flow from one location to another [61, 62]. DTF
is defined within the framework of the Mulivariate Auto-
regressive Model –MVAR . In comparison to coher-
ence, a great advantage of the MVAR approach is that it
accounts for the whole multivariate set of signals, so the
analysis is not performed separately for every pair of sig-
nals (which is the case for coherence), thus eliminating
the problem of a presence of common sources in the set
of signals . Detailed description of ERD/S, coherence,
and DTF calculations is provided in Additional file 1.
The EEG data preprocessing was as follows. EEG data
were re-referenced to the averaged earlobes and then
down-sampled to 250 Hz. Next, the signals were seg-
mented into trials with respect to the onset of the fix-
ation point. Trials with amplitudes exceeding ±125 μV
were removed from further analysis. Accepted trials
were passed to a third-order Butterworth bandpass filter
in the frequency range of 3.0–32 Hz.
Then EEG signals were decomposed by means of ICA
, implemented into EEGLab using extended Info-
max. All components identified as a source of eye move-
ments or muscle artifacts were removed, and then the
remaining ICA components were used to reconstruct
the signal in the original electrode space. Next, a 1500-
ms segment was extracted from each trial in each
experimental condition, with respect to the onset of the
blank screen epoch that followed the presentation of the
fixation point. In the case of DTF, the second step of
artifact rejection was omitted because ICA disturbs the
fitting of the MVAR model to EEG data .
The reasons are as follows. The MVAR model assumes
that the amplitude of signal at a given channel and time
sample can be described as a linear combination of pre-
vious samples derived from itself or from other channels
with an added unpredictable random component (noise).
According to this model, all channels of the multivariate
signal may be more or less correlated but they are
linearly independent. The procedure of the artifact rejec-
tion performed by ICA consists of three major steps: (i)
decomposition of the original signal by ICA; (ii) removal
the ICA components identified as sources of artifacts;
and (iii) reconstruction of EEG signal from the
remaining ICA components. As one can see, the steps
(ii) and (iii) lead to removal from each channel of initial
signal the same activity recognized as undesirable distri-
bution. Thus, steps (ii) and (iii) make the channels of re-
constructed, artifacts-free multivariate signal linearly
dependent. However, the condition of the linear inde-
pendence of the channels must be absolutely satisfied if
he MVAR model has to be fitted to data.
The number of EEG channels that could be used for
estimation of the MVAR model was restricted by the
number of available samples. In our study, the amount
of the measured EEG data allowed the MVAR model to
be fitted for up to 17 electrodes. Thus, 17 electrodes
from the 62 available sensors were selected within our
two regions of interest: frontal (F7, F5, F3, F1, FZ, F2,
F4, F6, and F8) and parieto-occipital (P7, PO7, O1, OZ,
O2, PO8, P8, and Iz). The criteria for selection were as
follows: (i) electrodes had to be evenly distributed within
each region of interest; (ii) in the case of the frontal re-
gion, they had to be at some distance away from the
most anterior electrode sites that are typically strongly
influenced by eye-movements artifacts (in the case of DTF
calculations, ICA-based artifact rejection had to be omit-
ted); and (iii) the number of electrodes located within the
left and right hemisphere had be the same (we had no hy-
pothesis regarding lateralization). ERD/S, coherence, and
DTF were calculated for the same set of electrodes.
ERD/S was analyzed at each of the 17 electrodes in the
following frequency ranges (within the theta, alpha, and
beta bands, respectively): 4–8Hzin100–250 ms time
window, 10–13 Hz in 350–800 ms time window and
13–18 Hz in 100–250 ms time window. Selection of
time windows and frequency ranges was guided by re-
sults of ERD/S collapsed across groups and conditions
(see Additional file 2, Figure A1) .
Coherence was statistically analyzed for selected pairs of
electrodes. In order to avoid the double-dipping problem
Nowicka et al. Molecular Autism (2016) 7:38 Page 6 of 14
, such selection has to be orthogonal to potential dif-
ferences between groups or experimental conditions. To
this end, we (i) collapsed the EEG signal across the two
groups (ASD, controls) and across the four name categor-
ies (self, close-other's, famous, and unknown); (ii) noticed
that local (i.e., within-region) effects were very weak
whereas between-regions effects (i.e., long-range connec-
tions) were clearly visible (see Additional file 2, Figure
A2); (ii) calculated the average coherence values for the
theta, alpha, and beta bands based on all posterior-
anterior pairs of electrodes; and finally, (iii) selected pairs
of electrodes in which the coherence values were higher
than the average calculated for a given frequency band.
In this way, we selected 13, 11, and 21 pairs of electrodes
for the analyses of coherence in the theta, alpha, and beta
bands, respectively, and the following statistical analyses
were run only on these pairs of electrodes. Our selection
procedure is independent from our main analysis; the selec-
tion was based on the collapsed data from both groups and
all conditions, whereas the analysis compared the groups/
conditions between each other. Thus, without introducing
any bias , we could limit the number of electrodes pairs
correction for multiple comparisons.
Selection of time windows and frequency ranges was also
based on coherence results collapsed across groups and
conditions (see Additional file 2, Figure A2) . Coher-
ence was analyzed in the following frequency ranges (within
the theta, alpha, and beta bands, respectively): 4–8Hzin
250–650 ms time window, 10–13 Hz in 500–800 ms time
window, and 21–28 Hz in 250–750 ms time window.
DTFs were calculated for three subsequent time
windows: 0–200, 200–400, and 400–600 ms. DTF calcu-
lations were restricted to frequencies within the beta
band: low (13–18 Hz) and high (18–30 Hz) as moderate
and long-distance cortical connections are based mainly
on beta oscillations [82, 83]. This is also consistent with
the evidence pointing to the role of beta oscillations in
attentional processes in the visual domain [44, 84–87].
Selection of connections for statistical analyses was
based on a procedure that was orthogonal to potential
group differences  and was done on the basis of the
DTF averaged for the control and ASD groups (see
Additional file 2, Figures A3, A4, and A5). Similarly to the
selection procedure used for the coherence, connections
with DTF values higher than the average were further sta-
tistically compared in the two groups. The number of
connections was 56, 64, and 45, for the 0–200, 200–400,
and 400–600 ms time-window, respectively.
Amplitudes of P300, ERD/S (for single electrodes) and
coherence (for single pairs of electrodes) were analyzed
using mixed-model ANOVA with group (ASD, control)
and category of name (own, close-other, famous, and un-
known) as factors. Bonferroni correction for multiple
comparisons was applied to the post-hoc analyses. All ef-
fects with more than one degree of freedom in the numer-
ator were adjusted for violations of sphericity according to
the Greenhouse-Geisser formula . The analyses were
conducted in IBM SPSS Statistics 21 (Advanced Model).
The statistical significance of DTF differences between the
ASD and control groups (for each connection) was veri-
fied by a two-sided Wilcoxon rank sum test using
MATLAB statistical toolbox. Finally, the multiple compar-
isons problem arising in the analyses of ERD/S, coherence,
and DTF was addressed by applying the false discovery
rate (FDR) correction to the obtained pvalues . The
maximum FDR level (qvalue) was set to 5 %.
The mean percent of correct responses and mean RTs are
presented in Table 2. Mixed-model ANOVA revealed that
the main factor of group and category of name as well as
their interaction were non-significant neither for RTs
(group: F(1,28) = 2.010, p= 0.168; name: F(3,26) =
1.167, p= 0.318; group x name: F(3,84) = 0.757, p=0.471)
nor accuracy rates (group: F(1,28) = 1.974, p=0.171;
name: F(3,26) = 2.014, p= 0.096; group x name: F(1,28) =
Grand average ERPs for the ASD and control groups are
presented in Fig. 1. Mixed-model ANOVA revealed a
significant main effect of the category of name (F(3,84) =
31.223, p<0.001, η
= 0.527), and an interaction between
the group and name-category factors (F(3,84) = 3.170, p=
= 0.102). Post-hoc tests indicated larger amplitudes
to one’s own name than to close-other’s(p= 0.030), famous
(p< 0.001), and unknown (p< 0.001) names. Moreover,
P300 amplitudes for close-other’s name were larger than
for famous (p< 0.001) and unknown name (p< 0.001).
Table 2 Mean reaction times (RTs)± SD and accuracy rates in control participants and individuals with ASD for each category of name
Control group ASD group
Self Close-other Famous Unknown Self Close-other Famous Unknown
RTs (ms) 493 ± 99 524 ± 114 583 ± 105 567 ± 139 595 ± 237 636 ± 254 669 ± 267 659 ± 263
Accuracy (%) 99 ± 01 98 ± 04 94 ± 06 98 ± 03 98 ± 02 95 ± 04 94 ± 04 95 ± 05
Nowicka et al. Molecular Autism (2016) 7:38 Page 7 of 14
Post-hoc comparisons for the interaction showed that in
the control group P300 to one’s own name was higher than
P300s to all other names (close-other’s: p=0.008, famous:
p< 0.001, unknown: p< 0.001), and also that P300 ampli-
tudes to close-other’snamewerehigherthanP300tofam-
ous (p= 0.001) and unknown (p=0.005) names. In the
ASD group, however, P300 response to one’sownname
did not differ from P300 response to close-other’sname
(p> 0.9), but it was significantly higher than P300 ampli-
tudes to famous (p= .015) and unknown (p= 0.003) names.
Results of mixed-model ANOVAs indicated differences
between ASD and control group for the alpha, theta, and
beta frequency ranges. All those results are presented in
Additional files 3 and 4 (Figure A6 and Table A1, respect-
ively). After FDR correction for multiple comparisons, no
effects remained significant.
Mixed-model ANOVAs revealed between-group differ-
ences (i.e., a significant main factor of ‘group’) for alpha,
theta, and beta frequency ranges. All those results are
presented in Additional files 3 and 4 (Figure A7 and
Table A2, respectively). The category of name factor as
well as its interactions with the group factor were non-
significant in all ANOVAs.
After FDR correction for multiple comparisons, de-
creased coherence in the ASD group in reference to the
control group was found within the beta band for the
Fig. 1 Grand average ERPs at CPz in the control group (a-left panel) and in the group of individuals with ASD (a-right panel). Topographical
distribution of P300 in the control group (b–left panel) and in the group of individuals with ASD (b–right panel)
Nowicka et al. Molecular Autism (2016) 7:38 Page 8 of 14
following pairs of electrodes: Fz-O2 (p= 0.009), F1-O2
(p= 0.024), F2-O2 (p= 0.024), and F6-O1 (p= 0.027).
Those results are presented in Fig. 2.
Directed transfer function
In the ASD group, under-connectivity (i.e., lower DTF
values) was found for long-range connections going from
parietal-occipital to frontal sites, accompanied by
under-connectivity within the frontal region and over-
connectivity within parietal-occipital region. Results
that reached the significance level (p< 0.05, uncorrected)
are presented in Additional files 3 and 4 (Figure A8 and
Table A3, respectively).
After FDR correction for multiple comparisons, lower
DTF values in the ASD group, when compared to the
control group, were found for the long-range posterior-to-
anterior connection: O2 →F3 (0–200 ms, 18–30 Hz,
p= 0.03), and for a number of connections within the
frontal region: F6 →F5 (200–400 ms, 13–18 Hz, p=0.02),
F1 →Fz (400–600 ms, 18–30 Hz, p=0.01), F1→F2
(400–600 ms, 13–18 Hz, p<0.001; 18–30 Hz, p<0.001),
F1 →F4 (400–600 ms, 13–18 Hz, p< 0.001; 18–30 Hz,
p< 0.001), F1 →F6 (400–600 ms, 13–18 Hz, p= 0.02;
18–30 Hz, p= 0.01), and F1 →F8 (400–600 ms, 13–
18 Hz, p< 0.001; 18–30 Hz, p<0.001). Moreover,
higher DTF value in the ASD group than in the control
group was observed for the local occipital connection:
Oz →O2 (400–600 ms, 18–30 Hz, p= 0.01). FDR-
corrected DTF results are presented in Fig. 3.
In this EEG study, we investigated patterns of brain ac-
tivity and functional connectivity associated with names
processing in individuals with ASD. In the control
group, ERP results showed enhanced P300 to one’s own
name in comparison to all other names, whereas in the
ASD group P300 to one’s own name and close-other’s
name did not differ. The latter suggests equivalent atten-
tion allocation for one’s own and close-other’s names in
the ASD group. An analogous pattern of P300 findings
in individuals with ASD was previously reported in the
case of name detection (see the Introduction and ).
This is of particular interest since recognition of names
in the present study involves mainly goal-directed atten-
tion, whereas detection of names (no discrimination re-
quired) involves mainly bottom-up attention. Despite the
fact that the two experiments differed with respect to at-
tentional requirements, both detection and recognition of
stimuli did not lead to neural differentiation between one’s
own and the close-other’s names in the ASD group. Appar-
ently, enhanced top-down attention during the name rec-
ognition task did not improve the impaired—on the neural
level—discrimination of self-name from the close-other’s
name in individuals with ASD. In general, this finding may
be also related to the concept of disturbed neural self-
representation in individuals with ASD [89–92].
While the inter-group differences in ERPs were in-
fluenced by the category of name, two measures of
task-related connectivity within the beta band showed
inter-group effects common for all names. Coherence in
the beta band was significantly higher in the control group
than in the ASD group for distant inter- and intra-
hemispheric connections, i.e., between posterior (parietal,
occipital) and anterior (frontal) regions. DTF analysis
revealed weaker connectivity between anterior and
posterior brain regions in the ASD group compared
Fig. 2 Time course of coherence averaged across all categories of names in the control group (a–upper panel) and in the ASD group (b–lower panel).
Red lines in the scheme of the extended 10–20 system indicate significantly enhanced coherence (i.e. stronger connections) within the beta band in the
control group in comparison to the ASD group (c). F –frontal electrodes; O –occipital electrodes. Odd numbers (e.g., F1) - electrodes located over the
left side of the head; even numbers (e.g., F6) - electrodes located over the right side of the head; “z”(e.g. Fz) - electrodes located at the midline
Nowicka et al. Molecular Autism (2016) 7:38 Page 9 of 14
to the control group and—importantly—this method indi-
cated the directionality of these weaker connections. Dis-
ruption of functional connectivity observed in individuals
with ASD was present mainly in the connections from
parietal-occipital to the frontal electrode sites. In addition,
strong connections within the frontal region were present
in the control group, whereas in the ASD group such
over-connectivity was present in the parietal-occipital
region. In general, the crucial role of beta oscillations
in attentional processes, especially in the visual do-
main, is well-documented (e.g., [44, 84–87]). More-
over, communication within the attentional network
proceeds mainly via long-range synchronization in the
beta band . Therefore, impaired long-range con-
nectivity in the beta frequency range, observed in our
group of participants with ASD, may be linked to
Fig. 3 Directed transfer function (DTF) for the three consecutive time windows: 0–200 ms (a), 200–400 ms (b), and 400–600 ms (c). Please note
that 0 ms corresponds to the stimulus onset. DTF graphs (right panel) present results in the control group (red line) and in the ASD group (blue
line). Gray-colored rectangles indicate significant (FDR-corrected) between-group differences. These differences are illustrated also on the scheme
of the extended 10–20 system (left panel). Red arrows represent connections that are significantly stronger in the control group than in the ASD
group, blue arrow –connections significantly stronger in the ASD group than in the control group. F –frontal electrodes; O –occipital electrodes.
Odd numbers (e.g., F1) - electrodes located over the left side of the head; even numbers (e.g., F6) - electrodes located over the right side of the
head; “z”(e.g. Fz) - electrodes located at the midline
Nowicka et al. Molecular Autism (2016) 7:38 Page 10 of 14
disturbed attentional processes and suggests some
dysfunction of the attentional network involved in
Thus, in the ASD group, we found no the self-
preference effect (i.e., similar P300 to one’s own and the
close-other’s name), disruption of functional long-range
(anterior-posterior) connectivity, decreased short-range
connections in the frontal region, and increased short-
range connections in the parietal-occipital region. In
general, our results are in line with recent EEG studies
investigating connectivity patterns in individuals with
ASD [55, 57, 93–96]. Specifically, attenuated long-range
communication between the frontal lobes and posterior
parts of the autistic brain was reported in numerous
studies (e.g., [57, 58, 93, 96]). Increased coherence, in
turn, was found within frontal, temporal, and occipital sites
in the ASD group . However, some studies also re-
ported lower coherence values for local connections, e.g.,
between frontal electrodes in the right hemisphere .
At this point, it should be noted that the majority of
previous ASD studies investigated connectivity patterns
in the resting states, with eyes-open  or eyes-closed
[57, 58, 93] and even during sleep , while our results
refer to active processing of social stimuli. Thus, it is a
matter of debate whether resting-state connectivity and
task-related connectivity shows similar or rather dissimi-
lar patterns. Nevertheless, the DTF and coherence re-
sults of our study corroborate the notion of a double
dissociation of connectivity patterns in individuals with
ASD, i.e., a lack of long-range connections, with the
most prominent deficit in the fronto-occipital connec-
tions and increased short-range connections. Common
effects found in different cognitive states—active vs.
rest—provide converging evidence of connectivity dis-
ruption in ASD.
Importantly, long-range under-connectivity in ASD
was reported only in few EEG and MEG studies in
which participants were involved in cognitive tasks.
Using wavelet transform coherence, Catarino et al. 
showed a widespread and consistent reduction in inter-
hemispheric connectivity in the ASD group compared to
the control group during visual perception and
categorization of social and inanimate stimuli. These ef-
fects were found across the entire time-frequency
spectrum, though they were most pronounced at fre-
quencies lower than 13 Hz. In a MEG study, disruption
of long-range phase synchronization among frontal, par-
ietal, and occipital areas was found in high-functioning
children with ASD during the performance of executive
function tasks . In addition, a significant prefrontal
synchronization was found in control compared to ASD
participants mostly at the frequency range between 16
and 34 Hz. These effects are in line with our results
showing significantly stronger coherence in high beta
(21–28 Hz) in the frontal region and enhanced DTF at
the frequency range of 13–30 Hz in the control group
compared to the ASD group.
Interestingly, our coherence and DTF results both for
long- and short-range connectivity (described and dis-
cussed above) match also findings of functional connectiv-
ity magnetic resonance imaging (fcMRI) studies in ASD
[94, 101, 102]. Specifically, those studies showed frontal-
posterior under-connectivity  and over-connectivity in
the posterior occipital cortex alongside local hypo-
connectivity in medial prefrontal regions in the autistic
brain [94, 102] (see our DTF findings, Fig. 3).
It is worth to note that present findings revealed a dis-
sociation between behavioral and neural results; while
significant between-groups and/or between-conditions
differences were found in ERP, coherence, and DTF ana-
lyses, no significant effects were present in the behav-
ioral data. That is, recognition rates for all names were
similarly high in individuals with ASD and control par-
ticipants for all names and no between-group difference
in RTs reached statistical significance. Such dissociation
between the neural and behavioral data is not unusual
[29, 99, 103, 104]. In general, similar levels of behavioral
functioning in the ASD group and control participants
may result from compensatory strategies applied by indi-
viduals with ASD (e.g., ). This compensation seems
especially plausible in the present study as only high-
functioning ASD participants were tested.
We would like to briefly mention some ERD/S, coher-
ence and DTF results that did not survive FDR correc-
tions. Similarly to FDR-corrected findings of our study,
some of these effects also pointed to impairments in at-
tentional processes involved in the name recognition in
individuals with ASD. In the ASD group in reference to
the control group, we found weaker de-synchronization
and higher coherence within the alpha band in the oc-
cipital region. Alpha suppression typically occurs when
attention is directed towards external stimuli  and
following visual stimulation can be observed in the cor-
responding sensory area, i.e., the occipital region . In
the present study, this was the case in the control group
but not in the ASD group. We observed also decreased
beta synchronization in individuals with ASD. This
finding, in turn, may be related to either attenuated top-
down processes or stronger involvement of bottom-up pro-
cesses . Moreover, in the ASD group, synchronization
within the theta band was weaker and coherence was lower
in comparison to the control group. Although the theta
band is most commonly related to memory processes
, it has also been linked to emotional arousal . Pre-
vious studies showed that theta power increase was ob-
served in response to emotional compared to neutral
stimuli, and the earliest discrimination of the emotional
content of stimuli occurred in the lower theta range during
Nowicka et al. Molecular Autism (2016) 7:38 Page 11 of 14
the first 600 ms after stimulus onset . Similarly, in the
present study, inter-group differences appeared in the
lower theta band, within the similar time range. Therefore,
reported effects in the theta band may indicate that names
are treated as stimuli less emotional by individuals with
ASD. Finally, the only connection from the parieto-
occipital to the frontal region that was stronger in the ASD
group than in the control group was found in the 200–
400 ms time window. This may indicate some delay in the
activity flow from occipital to frontal location in individuals
with ASD because in the control participants such en-
hanced connections were present in the earliest time win-
dow (0–200 ms).
Finally, we would like to comment on the limitations
of the present study. First of all, the study is confined by
a small sample size. Thus, reported results should be
treated with caution and they need to be further investi-
gated in larger groups of individuals with ASD in order
to increase effect sizes and enhance statistical power. In
addition, higher (than 50) number of experimental trials
would also be beneficial; with such an increased number
of repetitions some effects related to the name category
(that were non-significant in our ERD/S, coherence, and
DTF analyses) may be detected. Moreover, in the current
study names were presented in the visual modality.
However, undoubtedly spoken names are more “eco-
logically valid”(spoken versions of one’s own name and
other names are more often encountered in everyday
life) and the auditory version is more adequate in the
context of social communication, i.e. the domain in
which ASD individuals have difficulties. Thus, inter-
group differences observed for visually presented names
may be underestimated/attenuated in comparison to ef-
fects present for aurally presented names, and some ef-
fects may even be missing. This notion is strongly
supported by our earlier fMRI study on name recogni-
tion . Visually presented names generally resulted in
weaker activations than aurally presented names and
some activations associated with self- and close-other’s
name were present only for acoustic presentations. In
addition, we tested high-functioning adolescents and
young adults with ASD. Studies on younger subjects
(children) and severely affected individuals could shed
more light on the issue of name processing in ASD.
However, passive listening to aurally presented names
could be more adequate for these groups and we
theorize that such studies would reveal even more pro-
nounced impairments associated with name processing
than presented here. Future studies are needed to valid-
ate this prediction.
The results of this study demonstrated altered patterns
of brain activity and task-related functional connectivity
during recognition of visually presented names in individ-
uals with ASD. Thus, the current study provides novel evi-
dence on the neural underpinnings of name-processing in
autism; this important aspect of social cognition has been
largely overlooked in the previous neuroscientific litera-
ture on ASD. Using advanced methods of the EEG data
analyses, we could make an effective case further support-
ing that ASD is related to augmented short-range func-
tional connectivity in the sensory brain regions and
attenuated long-range connectivity between the sensory
and higher order associative regions (e.g., ). This pat-
tern of neural architecture is highly consistent with the
behavioral and clinical data showing that people with aut-
ism often have problems with integration of separate per-
ceptual features into a single, coherent mental object
(e.g., ). These converging neuro-cognitive-clinical
observations suggest that the effective therapy of ASD
symptoms could use existing capabilities of the autistic
mind, such as the privileged processing of perceptual parts
and details, in order to improve the limitations of the aut-
istic mind, related to the conceptual integration and
context-dependent cognitive flexibility.
Additional file 1: Description of ERD/S, coherence, and DTF calculations.
(DOCX 133 kb)
Additional file 2: Figures illustrating ERD/S, coherence, and DTF
averaged across groups and experimental conditions. (DOCX 1165 kb)
Additional file 3: Figures illustrating complete results of ERD/S,
coherence, and DTF calculations. (DOCX 3256 kb)
Additional file 4: Tables with ERD/S, coherence, and DTF results that
did not retain the significance after FDR corrections. (DOCX 45 kb)
We thank all participants and families of participants in the ASD group. We
thank also MichałWroniszewski, Joanna Grochowska, and Urszula Wójcik from
the SYNAPSIS Foundation for their help in selecting the groups of participants,
and Izabela Chojnicka for her help in ADI-R and ADOS assessments.
This work was supported by the National Science Centre, Poland (grant
2011/01/B/HS6/00683 to AN) and by the Polish Ministry of Science and
Higher Education (grant IP2010043270 to PT).
Availability of supporting data
The datasets generated and analyzed during the current study are available
from the corresponding author on reasonable request.
AN and PT designed the experiment and paradigm. PT and HBC collected
the data. RK, HBC, and AN analyzed the data. HBC and RK designed and
prepared the figures. AN, HBC, PT, RK, and PO reviewed and discussed the
results. AN, HBC, PT, RK, and PO wrote the manuscript. All authors read and
approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Nowicka et al. Molecular Autism (2016) 7:38 Page 12 of 14
Ethical approval and consent to participate
The experimental procedure was reviewed and approved by the Bioethics
Committee of the Warsaw Medical University. A complete description of the
study was given and written informed consent was obtained from all
participants and their parents or legal caregivers prior to testing.
Laboratory of Psychophysiology, Department of Neurophysiology, Nencki
Institute of Experimental Biology, Polish Academy of Sciences, 3 Pasteur
Street, 02-093 Warsaw, Poland.
Central Institute for Labour Protection -
National Research Institute, Czerniakowska 16, 00-701 Warsaw, Poland.
Body, and Self Laboratory, Department of Neuroscience, Karolinska Institute,
Retzius väg 8, SE-17177 Stockholm, Sweden.
Department of Psychology,
University of Social Sciences and Humanities, 19/31 Chodakowska Street,
03-815 Warsaw, Poland.
Faculty of Physics, University of Warsaw, 5 Pasteur
Street, 02-093 Warsaw, Poland.
Received: 30 June 2016 Accepted: 1 September 2016
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