International Journal of Clinical and Health Psychology.docx

International Journal
of Clinical and Health Psychology
www.elsevier.es/ijchp
International Journal of Clinical and Health Psychology (2013)
13, 91−100
1697-2600/$ - see front matter © 2012 Asociación Española de
Psicología Conductual. Published by Elsevier España, S.L. All
rights reserved.
International Journal of
Clinical and Health
Psychology
Publicación cuatrimestral / Four-monthly publication ISSN
1697-2600
Volumen 13, Número 2
Mayo - 2013
Volume 13, Number 2
May - 2013
Director / Editor:
Juan Carlos Sierra
Directores Asociados / Associate Editors:
Stephen N. Haynes
Michael W. Eysenck

Gualberto Buela-Casal
ORIGINAL ARTICLE
Prediction of treatment outcomes and longitudinal analysis in
children with autism undergoing intensive behavioral
intervention
Javier Virues-Ortega*,a, Víctor Rodríguezb, C.T. Yua
aUniversity of Manitoba and St. Amant Research Centre,
Canada
bFundación Planeta Imaginario, Spain
Received September 4, 2012; accepted March 18, 2013
*Corresponding author at: University of Manitoba, Psychology
Department, P518 Duff Roblin Bldg., 190 Dysart Road,
MB R3T Winnipeg, Manitoba, Canada.
E-mail address: [email protected] (J. Virues-Ortega).
Abstract Outcome prediction is an important component of
treatment planning and prognosis.
However, reliable predictors of intensive behavioral
intervention (IBI) have not been clearly
established. IBI is an evidence-based approach to the systematic
teaching of academic, social,
verbal, and daily living skills to individuals with autism
spectrum disorder. Incorporating
longitudinal analysis to IBI outcome studies may help to
identify outcome predictors of clinical
value. Twenty-four children with autism underwent on average
two years of IBI and completed
language, daily living skills, cognitive, and motor assessments
(Early Learning Accomplishment

Profile and the Learning Accomplishment Profile-Diagnostic,
3rd edition) every six months. We
used multilevel analysis to identify potential longitudinal
predictors including gender, age,
intervention intensity, intervention duration, total intervention
time, and pre-intervention
functioning. Results indicated that total intervention time, pre-
intervention functioning, and
age caused the greatest increase in goodness-of-fit of the
longitudinal multilevel models.
Longitudinal analysis is a promising analytical strategy to
identify reliable predictors of the
clinical outcome of IBI.
© 2012 Asociación Española de Psicología Conductual.
Published by Elsevier España, S.L.
All rights reserved.
KEYWORDS
Autism;
Predictors;
Applied behavior
analysis;
Quasi-experiment
(interrupted
time-series with
one group)
Resumen La predicción de resultados de tratamiento es un
componente importante de la
planificación clínica. No obstante, no se han hallado predictores
fiables de los efectos de la in-
tervención conductual intensiva en personas con trastorno del
espectro autista. La incorpora-
ción de análisis longitudinales a la investigación sobre
resultados de tratamiento en este área
puede contribuir a la identificación de predictores con valor

clínico. En el presente estudio se
evaluaron las habilidades verbales, cognitivas y de la vida
diaria (Early Learning Accomplish-
ment Profile y Learning Accomplishment Profile-Diagnostic, 3ª
ed.) de 24 niños con trastorno del
espectro autista en un programa de intervención conductual
intensiva. Las evaluaciones se rea-
lizaron cada seis meses y durante un periodo medio de
intervención de dos años. Mediante
PALABRAS CLAVE
Autismo;
predictor
Análisis aplicado
de la conducta;
Cuasi-experimento
(serie temporal
interrumpida con
un grupo)
92 J. Virues-Ortega et al.
Autism spectrum disorder (ASD) is a pervasive developmental
disorder that affects 1 to 2.5% of children (Baio, 2012). A
number of comprehensive psychosocial interventions for
people with ASD have been developed for which preliminary
evidence exists. These include the Early Start Denver model
(ESDM, Dawson et al., 2010), the Treatment and Education
of Autistic and Related Communication Handicapped
Children (TEACCH, Welterlin, Turner-Brown, Harris, Mesibov,
& Delmolino, 2012), and intensive behavioral intervention
based on the UCLA Young Autism Project model and applied
behavior analysis (IBI, Lovaas, 1987). Although there is no
single approach to treatment for all individuals with ASD,

IBI based on applied behavior analysis is among the few
approaches to treatment that have been tested extensively
using clinical trial methodology (Rogers & Vismara, 2008;
Virués-Ortega, 2010; Wetherby & Woods, 2006).
Applied behavior analysis is devoted to the experimental
study of socially significant behavior as a function of
environmental and social variables, and is the branch of
experimental psychology that supports the conceptual
framework of IBI (Luiselli, Russo, Christian, & Wilczynski,
2008). IBI is a comprehensive and evidence-based approach
to the systematic teaching of behavioral, verbal, cognitive,
and social repertoires to individuals diagnosed with ASD
(Howlin, Magiati, & Charman, 2009). Treatment typically
involves over 20 weekly hours of one-to-one teaching
incorporating multiple learning trails and specific programs
for targeted behavioral goals. Teachers program hundreds
of learning trials per day featuring discrimination training,
prompting, generalization, and other reinforcement-based
procedures known to facilitate the acquisition of new skills
in individuals with and without disabilities (Miltenberger,
2011). The IBI curriculum integrates complex sequences of
programs from basic attending or vocalizing skills, up to
complex verbal, social, and problem-solving skills (Lovaas,
2002).
Over 20 independent trials have been conducted which
jointly suggest that IBI has moderate to large effects on
daily living skills, cognitive functioning, language, and
social behavior (Foxx, 2008; Remington et al., 2007; Virués-
Ortega, 2010). The field of IBI has shown a considerable
growth as suggested by the increasing number of service
providers and certified professionals (Shook & Johnston,
2011).
Parents of children undergoing IBI and other evidence-

based interventions frequently want to know whether their
child will be able to attend school without special support,
what areas of behavioral functioning - whether motor,
social or cognitive - are likely to improve as a consequence
of treatment, and what intervention intensity and duration
may be optimal for their child. Until recently, outcome
research had been of little assistance to respond to these
and other questions pertaining to the longitudinal
progression of children undergoing treatment.
While the evidence available strongly suggests that some
individuals benefit significantly from IBI and other
approaches to treatment, participant and intervention
characteristics associated with greater intervention effects
are not well understood. The wider literature of treatment
outcomes in ASD has examined a range of mediating and
moderating factors that could, potentially, be established
as clinically valuable predictors. These include pre-
intervention IQ, treatment duration and intensity, family
characteristics, age at intervention onset, social initiation
skills, and structural dismorphologies of the central nervous
system. The scant literature available on these factors
have been reviewed by Rogers and Vismara (2008) who
concluded that “The current intervention research focus on
main effects models provides little information about who
does well in which treatments and why” (pp. 28-29).
Age, pre-intervention functioning, and intervention
intensity have been examined in the narrower literature of
IBI outcome predictors. Studies that have examined the
role of age at the onset of IBI have shown that the earlier
the intervention, the greater the intervention effect. For
instance, Granpeesheh, Dixon, Tarbox, Kaplan, and Wilke
(2009) found that children below seven years at treatment
onset mastered more behavioral objectives every month

than children who started IBI intervention above that age.
The studies that have examined pre-intervention
functioning as a predictor of treatment outcome have not
always been consistent in their findings. Perry et al. (2008)
examined progress of children with ASD that received IBI
services by comparing standardized assessments at the
beginning and end of the service. Children were classified
as having either higher, intermediate, or lower functioning
at intake based on their Vineland Adaptive Behavior
Composite score. The higher functioning group made
substantial gains (∼20 IQ increments) relative to the other
two groups. By contrast, Ben-Itzchak, Lahat, Burgin, and
Zachor (2008) reported that pre-intervention IQ (normal,
borderline, low) did not predict the IQ gains after a year of
IBI in a group of 81 young children with ASD and
developmental disabilities.
More evidence has been accrued on the effects of
intervention intensity. However, findings remain
inconsistent. Taking IQ as a prototypical outcome (Table 1),
Makrygianni and Reed (2010) in a correlational study did
not find any effects of intensity – similar results were found
by Sheinkopf and Siegel (1998). Virués-Ortega (2010)
análisis multinivel se examinaron posibles predictores
longitudinales incluyendo sexo, edad,
intensidad y duración de la intervención, tiempo total de
intervención y nivel de funcionamien-
to previo a la intervención. Los resultados indicaron que el
tiempo total de intervención, el
funcionamiento previo y la edad causaban los mayores
incrementos en bondad de ajuste de los
modelos longitudinales. El análisis longitudinal es una
estrategia analítica prometedora en la
identificación de predictores fiables de la efectividad de la

intervención conductual intensiva.
© 2012 Asociación Española de Psicología Conductual.
Publicado por Elsevier España, S.L.
Todos los derechos reservados.
children 93
reported no effects of intensity on IQ in a pooled analysis
of 19 experimental IBI studies. Finally, Reed, Osborne, and
Corness (2007) established a moderate effect of intensity
in a small trial on IBI using the Psychoeducational Profile as
outcome. In summary, treatment intensity has not been
established as a consistent predictor of IBI intervention
effects.
Longitudinal modeling of intervention outcomes may
help to establish intervention predictors more firmly than
traditional pre-post assessments. Longitudinal analyses
are able to fit the mathematical functions followed by
outcome trajectories of individual clients over a period of
time. By doing so longitudinal analysis maximizes the
statistical power of regression models aiming at meaningful
outcome predictors. For instance, if IBI effects were to
follow a non-linear progression, rather than a linear
trajectory, it may be possible to establish the role of a
particular predictor more accurately through longitudinal
multi-level analyses suited to specific non-linear
mathematical functions (Singer & Willett, 2003).
Furthermore, predictors identified based on time-series
spanning the treatment duration, as opposed to pre-post
assessments, may strengthen the clinical utility of the
predictor. For example, pre-intervention functioning could
be a strong predictor of treatment outcomes during the

first year of treatment, but not during the second.
IBI operates through a package of systematic teaching
strategies which are expected to provide the individual
with an increasing set of cognitive and behavioral resources
that will in turn offset, to various extents, the behavioral
excesses and deficits that are characteristic of ASD and
other developmental disabilities. Being a training-based
and goal-directed approach to intervention, IBI may lead to
some degree of behavioral gains for as long as the
intervention is in place. Longitudinal analysis of IBI may
help to identify distinct treatment gain itineraries across
subjects and tie those to specific predictors. For instance,
it may be possible that individuals starting at a higher pre-
intervention level of functioning benefit more from IBI but
reach an asymptote (ceiling) sooner than individuals that
start at a lower level of functioning. The longitudinal
predictors of IBI effects shall be greatly informative, albeit,
they have been rarely explored in the literature. There are
several longitudinal analyses that feature patterns of
change in individuals with ASD (Dietz, Swinkels, Buitelaar,
van Daalen & van Engeland, 2007; Jonsdottir et al., 2007;
Magiati, Moss, Charman, & Howlin, 2011). Nonetheless,
these analyses are constrained by the number of longitudinal
assessments (three or less); the number of treatment
outcomes (e.g., Dietz et al. only reported IQ); and the data
analysis strategy (e.g., no multilevel analyses).
This article describes growth patterns of motor, cognitive,
verbal, daily-living, and social skills in a sample of children
with ASD admitted into a home-based IBI program managed
by trained behavior analysts and delivering 20 to 40 weekly
hours of intervention. We used the children’s performance
in standardized assessments conducted periodically to
longitudinally create curves charting the rates and

asymptotes of various behavioral repertoires. Subsequent
analyses were conducted to test the impact of several
personal and intervention-related predictors on the
longitudinal growth of IBI outcomes. The present analysis
may help to enhance the prognostic information available
to families and clinicians by determining the extent to
which specific client- and treatment-related variables more
closely predict treatment outcome over the duration of the
intervention.
Method
Participants
Twenty-four children diagnosed with ASD (Age: Mean =
50.05 months, SD = 28.3; Gender: 21 boys and 3 girls)
admitted to the IBI program of Fundación Planeta
Imaginario (Barcelona, Spain) participated in the study.
An a priori power analysis indicated that a total sample
size of 15 was required to detect large effects (Cohen
effect size = 1). Therefore, our sample would suffice to
identify moderate to large effect sizes. A priori power
analysis assumptions were based on the pooled effect
size of 20 trials on IBI using IQ reported by Virués-Ortega
(2010) (Pooled effect size = 1.19). Participants were
recruited consecutively and were not excluded based on
their age or pre-intervention functioning at the time of
referral. All participants received a diagnosis of ASD from
an external medical consultant based on the diagnostic
criteria of the Diagnostic and Statistical Manual of Mental
Disorders, 4th edition text revised. Diagnosis was
supported by standardized assessments of autism
including either the Autism Diagnostic Interview-Revised
(ADI-R) or the Autism Diagnostic Observation Schedule-
Generic (ADOS-G) (Le Couteur, Haden, Hammal, &
McConachie, 2008). Further personal characteristics are

presented in Table 2.
Table 1 Effect of treatment intensity on IQ in intensive
behavioral intervention outcome studies.
Study Sample Intensity range Analysis Effect
sizea (h/week) size
Makrygianni & Reed (2010) 86 15-30 Correlational (Pearson r)
.22
Sheinkopf & Siegel (1998) 11 21-32 Correlational (Pearson r)
−.06
Virués-Ortega (2010) 340 12-45 Meta-regression .01
Note. Effects reported as Cohen d effect sizes. a Sample size of
the intervention group.
*All effect sizes were non-significant, p > .05.
Instruments
Fine and gross motor, cognitive, language, self-care and
social skills were assessed by means of the Early Learning
Accomplishment Profile (E-LAP; Glover, Priminger, &
Sanford, 1988; Peisner-Feinberg & Hardin, 2001) and the
Learning Accomplishment Profile-Diagnostic, 3rd edition,
(LAP-D; Hardin, Peisner-Feinberg, & Weeks, 2005). The
E-LAP and LAP-D scores are developmental age values
expressed in months. The score range is 0 to 36 for the
E-LAP and 36 to 72 for the LAP-D. If a participant achieved
the upper limit of the score range of E-LAP, the assessment
would be repeated with the LAP-D, which would then
continue to be used as the means of standardized assessment

every 6-month period until treatment was discontinued. In
order to control for potential ceiling effects in our data, if
a participant reached the LAP-D ceiling, assessment could
be repeated one additional time to inform maintenance
(provided that the individual would continue to receive
services through the program for the next six-month
period).
Both the E-LAP and the LAP-D have a high level of
inter-rater reliability, internal consistency, and
convergent validity with IQ (Fleming, 2000; Hardin et al.,
2005; Long, Blackman, Farrell, Smolkin, & Conaway,
2005; Peisner-Feinberg & Hardin, 2001). The test-retest
reliability of both instruments is reportedly excellent,
ranging between .93 and .99 (Peisner-Feinberg & Hardin,
2001, Hardin et al., 2005). Practice effects were unlikely,
as exposure to materials and tasks during the assessment
was minimal (few trials); and prompting, reinforcement,
and correction strategies were not present during the
assessment. The Spanish version of the E-LAP and the
LAP-D materials were used in the present study. The
LAP-D was validated in a representative sample of
Spanish-speaking children (Hardin et al., 2005). No
Spanish validation of the E-LAP is currently available.
Nonetheless, test scoring is performance-based - there
are no standard scores.
Both instruments have been used frequently as
standardized assessments in intervention studies with
individuals with ASD (e.g., Ganz, Simpson & Corbin-
Newsome, 2008). Moreover, the construct validity of E-LAP
and LAP-D is supported by items screening all diagnostic
areas of ASD (e.g., “initiates on play activities,” “responds
correctly when asked to show a toy,” “inflexible and rigid
in behavior”), items informing non-pathognomonic clinical

features of autism (e.g., motor functioning), and items
covering developmentally relevant skills (e.g., matching
skills). In summary, the E-LAP and LAP-D were considered
adequate for the present analysis due to their likely
resilience to practice effects; excellent stability; excellent
convergent validity with intellectual assessment measures;
and relevance to the clinical, adaptive, and behavioral
features of ASD.
Procedure
Participants were admitted consecutively to an IBI program
within the period May 2006 through January 2011. This
program was an official international replication site of the
UCLA Young Autism Project model and affiliated with the
Lovaas Institute (2011). At the onset of intervention,
participants received an average of 31.87 weekly hours (SD
= 10.11, range 15 -47.30) of home-based systematic
teaching following the UCLA young autism model of service
delivery and curriculum (Lovaas, 2002). Average treatment
duration was 21.87 months (SD = 14.38, range 5.33-58.57).
In keeping with all IBI bonafide programs, in addition to the
hours of formal intervention, incidental teaching and
practice goals were operating during most waking hours
(parents and caregivers acted as active co-therapists).
One-to-one teaching was delivered by trained tutors that
were supervised on a weekly basis by licensed psychologists
Table 2 Characteristics of the study sample.
Pre-test Post-test
(N=24) (N= 24)
Age in months, M±SD 51.91±27.31 69.46±27.26
Gender (male:female) 23:1
Ethnicity (% Caucasian) 100%

Social class,a % high 100%
IQ,b M±SD 74.50±13.98 91.50±16.86
Skills mastered in selected areas,c M±SD
Attending (max. 19) 13.04±4.34 19.16±3.05
Imitation (max. 27) 7.84±8.41 19.92±7.24
Matching (max. 13) 6.02±7.48 13.08±6.08
Basic labeling (max. 13) 12.44±5.33 31.21±19.88
Independent play (max. 15) 3.76±4.76 11.72±6.00
Interaction with peers/adults (max. 25) 2.28±3.82 11.60±9.06
Note. aEstimated by parental education and professional
background. bWechsler Preschool and Primary Scale of
Intelligence, 3rd ed.;
Bailey Scales of Infant Development, and Merrill-Palmer Scales
of Mental Tests. cNumber of skills mastered by area (Lovaas
Institute
Midwest, 2010).
M = mean; SD = stardard deviation.
children 95
with a background in behavior analysis. Parents received
weekly or bi-weekly progress updates, and supervision and
specific routines that required their involvement in order
to ensure the consistency of the interventions across
contexts and caregivers. Intervention was individualized
and comprehensive; and targeted motor, behavioral, daily-
living, verbal, cognitive, and social skills. Goals were
informed by a standardized curriculum composed of over
850 skills organized in 45 broad clinical areas (e.g., reading,
self-control skills). These goals are informed by
developmental sequences of typically developing children
(Luiselli et al., 2008) and include skills that are instrumental

for the acquisition of more complex repertoires (e.g.,
matching skills, imitation). Teaching sessions were delivered
via one-to-one teaching with gradual transition to group
activities and natural contexts. Transition to natural social
contexts was emphasized after mastery in one-to-one
teaching format. Decision-making in terms of hour allocation
and treatment discontinuation weighted a number of
factors including availability of school support, progress
achieved, family priorities, and treatment costs. Typically,
individuals that showed a persistent asymptote in their
learning achievements or that became independent at
school were assigned a reduced number of hours in
preparation of service discontinuation (for details on the
IBI curriculum see Lovaas, 2002). The current program was
in line with the guidelines for responsible conduct published
by the Behavior Analyst Certification Board (2010).
All participants underwent standardized assessments
with the E-LAP or the LAP-D prior to the intervention and
approximately every six months into the program (average
data points per participant 3.8, range 2-6). The selection,
administration, and correction of instruments followed the
guidelines by Jurado and Pueyo (2012).The research
assistants conducting the standardized assessments were
not involved in the administration of treatment and were
not familiar with the hypotheses of the study.
Data analysis
Figure 1 shows the individual growth trajectories of
participants for the eight E-LAP and LAP-D outcomes. Visual
inspection of the data plots over time suggests that
trajectories accelerated away from the start point shortly
after the intervention commenced while progression
decelerated as the individual approached a personal or

scale ceiling. Therefore, individual trajectories did not
follow a linear progression but rather an exponential
negative growth. Exponential negative trajectories are
composed formally of a negatively accelerated curve,
Figure 1 Trajectories of Early Learning Accomplishment Profile
and Learning Accomplishment Profile-Diagnostic scores over
time.
Fitted exponential negative curves (solid black line) were
obtained for individuals above (dotted grey lines) and below
(solid grey
lines) the median of pre-intervention functioning at baseline in
each domain.
Gross Motor Fine Motor Pre-writing Cognitive
Receptive
80
80
60
60
40
40
20
20
0

0
0 20 40 60 0 20 40 60 0 20 40 60 0 20 40 60
Expressive Self-care Social
Intervention Time (Months)
Sc
al
e
sc
or
es
Sc
al
e
sc
or
es
ending in an upper asymptote. According to the formal
attributes of the data we selected a multilevel regression
model based on the following exponential negative
function:
Yij = αi – (αi – π0i) e –π TIMEij

Where αi represents the upper asymptote, π0i represents
the lower end of the trajectory, and π1i represents the
slope of the curve. Figure 2 illustrates different exponential
negative patterns of change over time for various parameter
values.
Multilevel models provide two distinct levels of analysis:
level-1 and level-2. The structural parts of the level-1
submodel contain two level-1 parameters and one within-
person variance component (εij). The first parameter, known
as intercept (π0i), represents the initial status of an
individual i in the population. The second parameter, known
as slope (π1i), represents the rate of change for the
individual i in the population by unit of time. Therefore,
level-1 establishes individual change overtime. By contrast,
the parameters at level-2 do not represent individual
variation, but average level of the outcome in the
population. Specifically, the parameters at level-2 represent
the average outcome level in the population corresponding
to the intercept and slope values at level-1. At level-2, the
pattern of change is not examined in terms of time, as is
the case at level-1, but rather, in terms of a predictor. In
summary, there are four parameters at level-2: γ00 is the
population average of level-1 intercept with level-2
predictor value of 0, γ01 is the population average difference
in level-1 intercept for a 1-unit variation in the predictor,
γ10 is the population average of the level-1 slope when the
predictor equals 0, and finally, γ11 is the population average
difference when the predictor equals 1. γ00 and γ10 are
baseline parameters while γ01 and γ11 estimate the association
of the predictor with the initial status and the rate of
change of the longitudinal progression, respectively. The
model also provides a residual variance value for the
intercept (σ02), the slope (σ12), and the covariance among

these two (σ01). For multilevel models incorporating two
predictors we will also report γ12 and γ12, which represents
the population’s average variation in the outcome level for
a one-unit increment in the predictors 1 and 2 (level-1),
respectively (for more details in multilevel analysis refer to
Singer & Willet, 2003). The estimation of the predictor
coefficients at level-2 is presented formally below:
π0i = ϒ00 + ϒ01 (PREDICTORi – PREDICTOR) + ξ0i
π0i = ϒ10 + ϒ11 (PREDICTORi – PREDICTOR) + ξ1i
According to this model, individual growth parameters
(π0i, π1i) across children will be a function of population
average values (γ00, γ10), and population variance components
(ξ0i, ξ1i) represented by residual variances (σ02, σ12) and
covariance (σ01).
We estimated a series of multilevel models using different
sets of predictors in order to select models that would
maximize goodness-of-fit for a given outcome when
compared with an unconditional baseline model (model
with no predictors). This was accomplished in two sequential
multilevel analyses. In the first sets of models we examined
the impact of time-based predictors (intervention duration
in weeks, total hours of intervention - weekly hours of
interventions multiplied by weeks of intervention - and age
in months). We would then select the model incorporating
the single time-based predictor with best goodness-of-fit
for each of the eight outcomes under analysis. Subsequently,
we calculated a new set of two-predictor models
incorporating the predictor previously selected and a
specific personal factor that, when added, resulted in
further increases in goodness-of-fit. The personal factors
examined for each of the eight outcomes were age (if not
selected in the preceding step), gender, and pre-intervention
functioning. Two levels of pre-intervention functioning

were established using the median value at baseline as cut-
off point. The rationale for selecting these predictors is
twofold: a) they are all common individual/treatment
characteristics readily accessible to the clinician, and b)
they have been examined in previous IBI studies although
not in the context of a longitudinal analysis. Longitudinal
predictors that changed overtime (intervention duration,
total intervention duration, age) were re-calculated each
time an individual was assessed.
The Akaike information criterion (AIC) and the Bayesian
information criterion (BIC) were computed as goodness-of-
fit parameters for all one- and two-predictor models. Lower
AIC and BIC values are indicative of better fitting. The best
100
75
50
25
0
E(Y)
Time
0 2 4 6 8 10
α = 100
π1 = .3
π1 = .2

π1 = .1
π0 = 15
Figure 2 Exponential patterns of change based on different
parameter values.
children 97
fitting two-predictor model was selected for each outcome
and was fully reported. All analyses were conducted with
STATA version 11 (STATA Corporation, College Station, TX)
and its GLAMM program for multi-level analysis. A .05 level
of significance was used throughout. Results have been
reported according to the guidelines by Hartley (2012).
By comparing the goodness-of-fit of one- and two-
predictor models with an unconditional model, we aimed
to establish which factors would better explain the
longitudinal variation in our data. This analysis will help to
determine prominent trajectories of intervention outcomes
based on specific predictors. This strategy also serves the
purpose of suggesting causality in the absence of a control
group, similar to the way in which dose-response relations
inform causation (see a discussion relevant to this point in
Arjas and Parner, 2004). Namely, the causation inference
would be supported if intervention intensity (e.g., total
intervention hours at each time of assessment) is indeed
superior in its ability to increase the fit of the model
relative to an arbitrary time-dependent predictor
(individuals’ age).

Results
The examination of the goodness-of-fit parameters of
multilevel regression models showed that one-predictor
and two-predictor models had a superior fit than
unconditional models for every domain of the E-LAP and
the LAP-D. AIC and BIC goodness-of-fit parameters of all
models are reported in Table 3. Total intervention time
(hours per week multiplied by weeks of intervention) was
the single predictor with the highest favorable impact on
goodness-of-fit for all E-LAP and LAP-D outcomes. Other
time-based predictors including individuals’ age and
intervention duration in months had a positive impact in
the model’s fit, but did so to a lesser extent than total
intervention time in all eight outcomes.
Further improvements in goodness-of-fit were achieved
in two-predictor models. Keeping total intervention time as
the first factor, we examined the fit of regression models
incorporating age, gender, or pre-intervention level as a
second predictor. Age was the second most efficient
predictor in terms of improving fit of the regression models
for gross motor function, receptive language, self-care,
and social behavior; while pre-intervention level was the
second most efficient predictor for regression models using
fine motor function, prewriting, cognitive, and expressive
language (Table 3). The regression models of domains
assessing motor, daily living, and social skills (gross motor
function, fine motor function, self-care and social behavior)
achieved better fitting than regression models of language-
related domains (prewriting, receptive language, expressive
language, cognitive).
Table 4 presents the best fitting two-predictor multilevel
model for each E-LAP and LAP-D outcome. Both predictors

were statistically significant (p < .001) for every outcome.
Rate of change attributable to total intervention time in
hours (γ11) ranged from .004 to .009 (outcome average
increase by predictor unit). Coefficient magnitudes for age
in months (γ12) as a predictor ranged from .391 to .514.
Finally, coefficients for the dichotomous variable pre-
Table 3 Goodness-of-fit parameters of all one- and two-
predictor multilevel models of change for Early Learning
Accomplishment Profile and Learning Accomplishment Profile-
Diagnostic scores.
Goodness-of-fit (AIC, BIC)
GMF FMF PWR COG RLG ELG SFC SBH
Unconditional model 761.56 774.54 761.31 774.29 809.53
822.50 782.89 795.87
800.47 813.44 761.91 774.88 755.07 768.05 747.29 760.26
One-predictor models
Intervention duration 715.86 721.17 779.29 746.27 763.16
732.19 706.67 706.42
731.43 736.70 794.86 761.84 778.73 747.77 722.24 721.99
Total intervention time 693.33 691.12 744.67 720.03 737.42
712.48 677.15 688.53
708.59 706.38 759.93 735.29 752.68 727.74 692.41 703.79
Age 716.29 731.43 776.85 761.27 769.47 740.37 708.01 721.45
731.86 747 792.42 776.84 785.03 755.94 723.58 737.02
Two-predictor models
Age 664.01 673.20 727.92 710.50 724.67 702.71 651.35 676.02
681.81 691 745.72 728.30 742.47 720.52 669.16 693.82
Gender 691.93 690.19 745.08 719.15 735.78 711.83 675.18
687.74
709.73 707.99 762.88 736.95 753.59 729.63 692.99 705.55
Pre-intervention level 675.54 661.39 715.56 705.10 726.15
698.89 661.74 676.27

693.35 679.20 733.36 722.90 743.96 716.70 679.55 694.07
Note. AIC = Akaike information criterion; BIC = Bayesian
information criterion; COG = Cognitive; ELG = Expressive
language; FMF = Fine
motor function; GMF = Gross motor function; PWR =
Prewriting; RLG = Receptive language; SBH = Social behavior;
SFC = Self-care.
Intercept constant; slope is established as the intervention
duration in months. Level-2 and level-3 best fitting models by
outcome are
highlighted.
intervention level (γ12) ranged from 22.971 to 35.669. Figure
1 portrays fitted curves based on an exponential negative
growth of subsamples above and below the median value of
pre-intervention level for each of the eight standardized
outcomes.
Discussion
Multilevel regression analyses based on an exponential
negative growth trajectory indicated that total intervention
duration in hours was the single predictor with the highest
contribution to the model fit for all outcomes when
compared with unconditional models. This finding suggests
that a subtle characteristic of the intervention – a
combination of both treatment intensity (weekly hours)
and treatment duration (total weeks of treatment) –
optimizes the fitting of individual trajectories to a specific
mathematical function for the duration of the intervention
and across a range of standardized outcomes. Improvements

in model fitting caused by duration alone did not improve
goodness-of-fit to the extent achieved by total intervention
time as a single predictor (Table 3). Therefore, our data
suggest that both intensity and duration, as represented by
total intervention time, remained important factors of
intervention gains regardless of pre-intervention functioning
or age. Finally, total intervention time remained significant
(p < .001) in all final two-predictor multilevel models (Table
4). When used in one-predictor models, pre-intervention
functioning was inferior to total intervention time in terms
of improving goodness-of-fit for all outcomes.
We tested the impact of pre-intervention functioning in
the goodness-of-fit of multilevel models incorporating two
predictors. Including pre-intervention level as a second
predictor, improved goodness-of-fit for all outcomes in the
two-predictor models (Table 3). For four of the eight
standardized outcomes examined (fine motor, pre-writing,
cognitive, expressive language), pre-intervention level was
the personal characteristic (above age and gender) that
generated the greatest improvement in model fit. Pre-
intervention level was a significant factor (p < .001) in the
final two-predictor models for fine motor, pre-writing,
cognitive and expressive language domains (Table 4).
Interestingly, these outcomes involved more complex
cognitive abilities relative to the remainder of E-LAP and
LAP-D outcomes (e.g., fine vs. gross motor; expressive vs.
receptive language; cognitive vs. self-care).
Our results suggest that individuals starting intervention
at a lower level in a given outcome were more likely to
follow an asymptotical growth as opposed to individuals
that initiated treatment with a higher level of performance
(cf. fitted curves on Fig. 1). The visual inspection of the
individual longitudinal trajectories in our sample suggests

that pre-intervention level is a plausible predictor of
individuals’ performance over the course of the intervention
to the extent that a bimodal pattern seems obvious in most
of the outcomes (e.g., Cognitive, Social). Bimodal
trajectories in our dataset are consistent with the distinction
between most and least positive responders to IBI discussed
by Remington et al. (2007). The visual examination of
individual trajectories on Figure 1 suggests that the pre-
intervention median is an acceptable cut-off point as
attested by the predictors significance and fit gains in
models that incorporated this factor. A more sophisticated
strategy to determine the cut-off point would have required
asymmetrical assignment of participants above and below
the cut-off points, which may have harmed statistical
power and increase the potential for type II error. Therefore,
future analyses would benefit from samples sizes larger
than ours.
Learning processes have been found to accommodate
well to exponential negative or logistic patterns of change
(e.g., Hicklin, 1976). The possibility remains, however, that
non-linear patterns of growth found in the present study
may have been caused by measurement-dependent factors,
like inadequate scaling assumptions or excessive ceiling
Table 4 Multilevel models for Early Learning Accomplishment
Profile and Learning Accomplishment Profile-Diagnostic scores
change over the duration of intensive behavioral intervention.
Goodness-of-fit (AIC, BIC)
GMF FMF PWR COG RLG ELG SFC SBH
Fixed effects
Intercept (γ00) 7.44 13.91** 10.97* 8.87 −3.00 5.46 -4.45 .52
Intervention, hours (γ11) .00** .01** .01** .01** .01** .01**

.01** .01**
Age, months (γ12) .51** – – – .51** – .57** .39**
Pre-intervention levela (γ12) – 28.43** 35.67** 24.27** –
22.97** – –
Variance components
Level-1: Within-person (σε2) 25.32 23.32 47.64 32.91 47.79
28.39 16.54 18.90
Level-2: Intercept (σ02) 89.91 60.71 158.79 123.89 204.10
122.62 155.74 159.19
Level-2: Slope (σ12) .07 .19 .25 .33 4.37 .40 .10 .32
Level-2: Covariance (σ01) .46 2.37 -4.16 2.76 .11 .98 2.81 3.92
Note. COG = Cognitive; ELG = Expressive language; FMF =
Fine motor function; GMF = Gross motor function; PWR =
Prewriting; RLG =
Receptive language; SBH = Social behavior; SFC = Self-care.
Goodness-of-fit parameters and domain abbreviations in Table
3. *p < .01;
**p < .001. aPre-intervention levels above and below the
median at pre-test.
children 99
effects in the psychometric instrument used to establish
treatment outcomes. These potential shortcomings,
however, may have had little impact on the validity of the
predictors, which is independent from the specific shape of
the longitudinal growth.
The contributions of our study are primarily methodological
and to a lesser extent practical. As discussed in our

introduction, the literature on the effect of intensity and
other predictors on the outcome of IBI have yielded
inconsistent results. This inconsistency may be explained,
at least to some extent, by non-linear variations of the
predictor and the outcome overtime. Therefore, longitudinal
studies may enhance our ability to examine outcome
predictors with sufficient statistical power. Our results
provide evidence in this direction being the first study to
use this methodology in the context of IBI intervention.
In terms of the applied relevance of our findings, future
longitudinal studies expanding the present analysis could
eventually provide the basis for evidence-informed clinical
decision-making. Namely, clinicians could combine various
predictors available at the beginning of the intervention
(e.g., pre-intervention functioning in an specific area, age,
expected treatment intensity and duration) to estimate the
progress of the client over the next years, which could in
turn inform the decision-making of family, caregivers and
health decision-makers in terms of treatment planning and
resource allocation.
In summary, the present analysis helps to identify the
general features of the longitudinal progression of children
with autism undergoing IBI. Our results suggest that
increased intervention time, lower age at intervention
onset, and higher pre-intervention functioning might be
associated with greater IBI outcomes for intervention
programs of up to four years in duration. The present study
provides the methodological basis for predictor identification
in the longitudinal analysis of IBI.
References
Arjas, E., & Parner, J. (2004). Causal reasoning from
longitudinal

data. Scandinavian Journal of Statistics, 31, 171-187.
Baio, J. (2012). Prevalence of autism spectrum disorders:
Autism
and Developmental Disabilities Monitoring Network, 14 sites,
United States, 2008. MMWR Surveillance Summaries,
61(SS03),
1-19.
Behavior Analyst Certification Board (2010). BACB Guidelines
for
responsible conduct for behavior analysts. Available from:
http://www.bacb.com/index.php?page=57 [retrieved 23 Jan
2013].
Ben-Itzchak, E., Lahat, E., Burgin, E., & Zachor, A. D. (2008).
Cognitive, behavior and intervention in young children with
autism. Research in Developmental Disabilities, 29, 447-458.
Dawson, G., Rogers, S., Munson, J., Smith, M., Winter, J.,
Greenson,
J., Donaldson, A., & Varley, J. (2010). Randomized, controlled
trial of an intervention for toddlers with autism: The Early Start
Denver model. Pediatrics, 125, 17-23.
Dietz, C., Swinkels, S. H., Buitelaar, J. K., van Daalen, E., &
van
Engeland, H. (2007). Stability and change of IQ scores in
preschool children diagnosed with autistic spectrum disorder.
European Child & Adolescent Psychiatry, 16, 405-410.
Fleming, J.A. (2000). An examination of inter-rater reliability
of
the E-LAP. Baltimore: Johns Hopkins University.
Foxx, R.M. (2008). Applied behavior analysis treatment of

autism.
Child and Adolescent Psychiatric Clinics of North America, 17,
821-834.
Ganz, J. B., Simpson, R. L., & Corbin-Newsome, J. (2008). The
impact of the picture exchange communication system on
requesting and speech development in preschoolers with autism
spectrum disorders and similar characteristics. Research in
Autism Spectrum Disorders, 2, 157-169.
Glover, M. E., Priminger, J. L. & Sanford, A. R. (1988). Early
learning
accomplishments profile. Winston-Salem, NC: Kaplan.
Granpeesheh, D., Dixon, D. R., Tarbox, J., Kaplan, A. M., &
Wilke,
A. E. (2009). The effects of age and treatment intensity on
behavioral intervention outcomes for children with autism
spectrum disorders. Research in Autism Spectrum Disorders, 3,
1014-1022.
Hardin, B. J., Peisner-Feinberg, E. S., & Weeks, S. W. (2005).
The
Learning Accomplishment Profile-Diagnostic (LAP-D), third
edition. Lewisville, NC: Kaplan Early Learning.
Hartley, J. (2012). New ways of making academic articles easier
to
read. International Journal of Clinical and Health Psychology,
12, 143-160.
Hicklin, W. J. (1976). A model for mastery learning based on
dynamic equilibrium theory. Journal of Mathematical
Psychology, 13, 79-88.
Howlin, P., Magiati, I., & Charman, T. (2009). Systematic

review of
early intensive behavioral interventions for children with
autism. American Journal on Intellectual and Developmental
Disabilities,114, 23-41.
Jónsdóttir, S. L., Saemundsen, E., Asmundsdóttir, G.,
Hjartardóttir,
S., Asgeirsdóttir, B. B., Smáradóttir, H. H., Sigurdardóttir, S.,
&
Smaári, J. (2007). Follow-up of children diagnosed with
pervasive developmental disorders. Journal of Autism and
Developmental Disorders, 37, 1361-1374.
Jurado, M. A., & Pueyo, R. (2012). Doing and reporting a
neuropsychological assessment. International Journal of
Clinical and Health Psychology, 12, 123-141.
Le Couteur, A., Haden, G., Hammal, D., & McConachie, H.
(2008).
Diagnosing autism spectrum disorders in pre-school children
using two standardised assessment instruments: The ADI-R and
the ADOS. Journal of Autism and Developmental Disorders, 38,
362-372.
Long, C. E., Blackman, J. A., Farrell, W. J., Smolkin, M. E. &
Conaway, M. R. (2005). A comparison of developmental versus
functional assessment in the rehabilitation of young children.
Pediatric Rehabilitation, 8, 151-161.
Lovaas, O. I. (1987). Behavioral treatment and normal
educational
and intellectual functioning in young autistic children. Journal
of Consulting and Clinical Psychology, 55, 3-9.
Lovaas, O. I. (2002). Teaching developmentally disable
children:

The ME Book. Austin, TX: Pro-ed.
Lovaas Institute (2011). NIMH Replication Sites. Available
from:
http://www.lovaas.com/contact.php [retrieved 1 Sep 2012].
Lovaas Institute Midwest (2010). Matrix curriculum and clinical
goal areas. Minneapolis, MN: Author.
Luiselli, J. K., Russo, D. C., Christian, W. P., & Wilczynski, S.
M.
(2008). Effective practices for children with autism. New York:
Oxford University Press.
Magiati, I., Moss, J., Charman, T., & Howlin, P. (2011).
Patterns of
change in children with autism spectrum disorders who received
community based comprehensive interventions in their pre-
school years: A seven year follow-up study. Research in Autism
Spectrum Disorders, 5, 1016-1027.
Makrygianni, M. K., & Reed, P. (2010). Factors impacting on
the
outcomes of Greek intervention programmes for children with
autistic spectrum disorders. Research in Autism Spectrum
Disorders, 4, 697-708.
Miltenberger, R. G. (2011). Behavior modification: Principles
and
procedures (5th ed.). Belmont, CA: Cengage.
Peisner-Feinberg, E. S., & Hardin, B. J. (2001). The early

learning
accomplishment profile edition examiner’s manual and
technical report. New York: Kaplan.
Perry, A., Cummings, A., Geier, J. D., Freeman, N. L., Hughes,
S.,
LaRose, L., Managan, T., Reitzel, J. A., & Williams, J. (2008).
Effectiveness of intensive behavioral intervention in a large,
community-based program. Research in Autism Spectrum
Disorders, 2, 621-642.
Reed, P., Osborne, L. A., & Corness, M. (2007). Relative
effectiveness
of different home-based behavioral approaches to early
teaching intervention. Journal of Autism and Developmental
Disorders, 37, 1815-1821.
Remington, B., Hastings, R. P., Kovshoff, H., degli Espinosa,
F.,
Jahr, E., Brown, T., Alsford, P., Lemaic, M., & Ward, N.
(2007).
Early intensive behavioral intervention. American Journal on
Mental Retardation, 112, 418-438.
Rogers, S. J., & Vismara, L. A. (2008). Evidence-based
comprehensive
treatments for early autism. Journal of Clinical Child and
Adolescent Psychology,37, 8-38.
Sheinkopf, S. J., & Siegel, B. (1998). Home-based behavioral
treatment of young children with autism. Journal of Autism
and Developmental Disorders, 28, 15-23.
Shook, G. L., & Johnston, J. M. (2011). Training and
professional
certification. In W. W. Fisher, C. C. Piazza, & H. S. Roane

(Eds.),
Handbook of applied behavior analysis (pp. 498-510). New
York: Guilford Press.
Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data
analysis. New York: Oxford University Press.
Virués-Ortega, J. (2010). Applied behavior analytic intervention
for autism in early childhood. Clinical Psychology Review, 30,
387-399.
Welterlin, A., Turner-Brown, L. M., Harris, S., Mesibov, G., &
Delmolino,
L. (2012). The home TEACCHing program for toddlers with
autism.
Journal of Autism and Developmental Disorders, 42, 1827-1835.
Wetherby, A. M., & Woods, J. J. (2006). Early social
interaction
project for children with autism spectrum disorders beginning
in the second year of life: A preliminary study. Topics in Early
Childhood Special Education, 26, 67-82.
O R I G I N A L P A P E R
Savant Syndrome: Realities, Myths and Misconceptions
Darold A. Treffert
Published online: 6 August 2013
� Springer Science+Business Media New York 2013

Abstract It was 126 years ago that Down first described
savant syndrome as a specific condition and 70 years ago
that Kanner first described Early Infantile Autism. While as
many as one in ten autistic persons have savant abilities,
such special skills occur in other CNS conditions as well
such that approximately 50 % of cases of savant syndrome
have autism as the underlying developmental disability and
50 % are associated with other disabilities. This paper sorts
out realities from myths and misconceptions about both
savant syndrome and autism spectrum disorders (ASD) that
have developed through the years. The reality is that low
IQ is not necessarily an accompaniment of savant syn-
drome; in some cases IQ can be superior. Also, savants can
be creative, rather than just duplicative, and the skills
increase over time on a continuum from duplication, to
improvisation to creation, rather than diminishing or sud-
denly disappearing. Genius and prodigy exist separate from
savant syndrome and not all such highly gifted persons

have Asperger’s Disorder. This paper also emphasizes the
critical importance of separating ‘autistic-like’ symptoms
from ASD especially in children when the savant ability
presents as hyperlexia (children who read early) or as
Einstein syndrome (children who speak late), or have
impaired vision (Blindisms) because prognosis and out-
come are very different when that careful distinction is
made. In those cases the term ‘outgrowing autism’ might
be mistakenly applied when in fact the child did not have
ASD in the first place.
Keywords Savant syndrome � Autism � Autism
spectrum disorder � Hyperlexia � Einstein syndrome
Realities
Savant Syndrome Defined
Savant syndrome is a rare but spectacular condition in
which persons with developmental disabilities, including
but not limited to autism, or other CNS disorders or disease
have some spectacular ‘islands of genius’ that stand in
jarring juxtaposition to overall limitations. (Treffert 2010)

The condition can be present from birth and evident in
early childhood (congenital) or develop later in life after
CNS injury or disease (acquired). It affects males 4–6 times
more frequently than females. Typically the skills occur in
five general areas—music, art, calendar calculating,
mathematics or mechanical/visual-spatial skills. Other
skills occur less frequently including language (polyglot),
unusual sensory discrimination, athletics or outstanding
knowledge in specific fields such as neurophysiology, sta-
tistics, navigation or computers, for example. Skills are
usually single skills, but multiple skills can occur as well.
Whatever the skill it is always associated with massive
memory of a habit or procedural type—very narrow but
exceedingly deep within the confines of the special skill. In
some cases massive memory is the special skill.
Savant Syndrome is Not a New Disorder (Nor is
Autism)
It is over 200 years ago since the first case of savant syn-

drome appeared in a scientific journal in Germany (Moritz
1783). And it is 126 years since Dr. J. Langdon Down first
D. A. Treffert (&)
Agnesian HealthCare, 430 East Division Street, Fond du Lac,
WI 54935, USA
e-mail: [email protected]
123
J Autism Dev Disord (2014) 44:564–571
DOI 10.1007/s10803-013-1906-8
described savant syndrome as a distinct condition. (Down
1887). In his 1887 lectures Down described ten cases of
savant syndrome, including a boy who had memorized The
Rise and Fall of the Roman Empire verbatim and could
recite it backward or forward. Interestingly, in those same
lectures Down described a form of mental retardation later
named Down’s syndrome, and he described as well a form
of ‘‘developmental retardation’’ that unmistakably con-
sisted of cases of what we now would term early onset and

late onset autistic disorder. (Treffert 2006) And in Kanner’s
description of early infantile autism cases in 1944 there are
several individuals who would now be considered cases of
savant syndrome.
Down coined the term ‘idiot savant’. He did not intend
that term be degrading or insulting. At the time ‘idiot’ was
an accepted scientific word for persons with an IQ below
25 and ‘savant’ was derived from the French word savoir,
meaning ‘to know’. Because of its pejorative connotation, a
1988 paper suggested it was time to discard that archaic
term and substitute ‘savant syndrome’ instead. (Treffert
1988) Then in 1989 the movie Rain Man made the term
‘‘autistic savant’’ household words.
Not All Savants are Autistic, and Not All Autistic
Persons are Savants
Rain Man was a marvelous movie. It was accurately and
sensitively done. Yet some persons came away from the
movie assuming that, like Raymond Babbitt, all savants are

autistic. Not so. Approximately one in ten persons with
autism has savant skills; so nine out of ten do not.
Approximately 1 out of 1,400 persons with mental retar-
dation or CNS deficits other than autism do have savant
skills so such abilities are not limited to autistic disorder.
(Saloviita 2000) Hence not all autistic persons are savants,
and not all savants are autistic.
Savant Skills Represent a Spectrum of Abilities
While admittedly a subjective scale at this point, savant
skills lie on a spectrum of abilities. Most common are
splinter skill savants who have obsessive preoccupation
with and memorization of music & sports trivia, birthdays,
license plate numbers, historical facts, train or bus sched-
ules, navigation abilities, or maps for example. Talented
savants are those in whom musical, art or other special
abilities are more conspicuous not only in contrast to
individual limitations, but also in contrast to peer group
abilities whether disabled or not. And prodigious savant is

an extremely high threshold term reserved for those
extraordinarily rare individuals in whom the special skill is
so outstanding that were it to be seen in a non-impaired
person such a person would be termed a ‘‘prodigy’’ or
‘‘genius’’.
The Acquired Savant: ‘‘Accidental Genius’’
In 1923 Minogue reported a case in which musical genius
appeared in a three-year old child following meningitis. In
1980 Brink described the case of Mr. Z who demonstrated
savant mechanical skills and traits at age nine after a bullet
wound to the left brain produced muteness, deafness and
left sided paralysis, but precipitated the newly surfaced
savant skills. Dorman in 1991 published a case in which an
8 year old boy began to show exceptional calendar calcu-
lating skills following a left hemispherectomy.
But it was Miller’s reports on 12 individuals with fronto-
temporal dementia who developed exceptional savant art
and musical skills that really brought the ‘acquired savant’

to prominent attention. (Miller et al. 1996, 1998, 2000)
Miller had done SPECT imaging on these 12 patients, and
he also did SPECT imaging on a 9 year old autistic, artistic
savant. In these instances there was left anterior temporal
dysfunction and evidence of what Kapur called ‘‘para-
doxical functional facilitation’’–dysfunction in one area of
the brain which uncovered, or facilitated ‘paradoxical’
function in some other area of still intact brain capacity
(Kapur 1996).
Since that time there have been numerous reports of
what might be called ‘‘acquired savant syndrome’’ fol-
lowing a cerebral insult from stroke, a blow to the head,
dementia or other CNS disease or injury accompanied by
the emergence of savant skills, sometimes at a prodigious
level (Treffert 2010). In most of these cases there was some
sort of ‘trade-off’ of cognitive or other abilities for the new
found savant skills. Yet in other cases, more aptly called
‘accidental genius’ (following being struck by lightning in

one instance) there has been no trade off at all with the
emergence of new found skills. These instances raise many
interesting questions about dormant capacity within us all,
and raise the even more challenging question of how to tap
those buried abilities without enduring some CNS catas-
trophe. These cases of acquired savant syndrome are pre-
sented in much more detail on the savant syndrome web
site at www.savantsyndrome.com.
The Most Important Question of All: How do They
do It?
There have been many theories put forth to try to explain
savant syndrome ranging from early heredity theories to
present day Quantum theory. (Treffert 2010). Some of the
neuropsychological theories such as weak central coher-
ence, mind blindedness are interesting as they apply to the
autistic savant. But 50 % of savants are not autistic. The
J Autism Dev Disord (2014) 44:564–571 565
123

http://www.savantsyndrome.com
role of heredity is no doubt a contributor and the search for
a savant ‘gene’ is underway, one study finding such a gene,
but another not confirming that finding. Compensatory
learning, reinforcement and repetition-compulsion may
also play a role, but then, if those dynamics produce savant
syndrome, why wouldn’t that apply to all persons with
autism or other CNS limitations?
The theory I favor is that what I have come to call the
‘‘three R’s’’ and reflects the process Kapur termed ‘‘para-
doxical functional facilitation’’ in 1996 in which one area
of the brain in released from the inhibiting influence of
some other brain area. In the case of savants, both con-
genital and acquired, there is brain damage in one area,
frequently the left hemisphere, with recruitment of still
intact brain tissue in another area of the brain, rewiring of
circuitry to that new area, and release of dormant capacity,

through a disinhibiting process, of information and skills
already stored in that newly recruited area.
It is genetic memory—the genetic transfer of knowledge
and skills—that accounts for the already stored dormant
capacity tapped by the recruitment, rewiring and release. I
address genetic memory much more fully in Islands of
Genius as well, and on the savant syndrome web site at
www.savantsyndrome.com. Genetic memory is based on
the fact that some savants, particularly those severely
limited in other ways, clearly ‘‘know things they never
learned’’. The only possible way to know things one never
learned—sometimes at complex levels—is for that
knowledge to be factory installed, genetically transmitted.
But there is one other important element that contributes
to the ‘‘how do they do it?’’ question. That is the role of the
family or other caregivers, teachers or mentors in first
discovering the special gift in the savant, then tenderly
nurturing and encouraging that gift, and supporting and

reinforcing it by praise coupled with copious unconditional
love.
Some Mysteries Remain
There are many scientific mysteries still about savant
syndrome. But two are especially intriguing. First is the
conspicuous regularity in which the triad of mental
impairment (often from autism) ? impaired vision ?
musical genius occurs. Savant syndrome is rare but the
frequency of this triad is very conspicuous and dispropor-
tionate throughout the history of savant cases combining
blindness and mental disability with prodigious musical
skills. The first of such persons was Thomas Bethune,
better known as ‘‘Blind Tom’’, who was born in 1850 and
gave his first public piano concert at age 8. From then on he
became an international celebrity and was the most cele-
brated black concert artist of the nineteenth century. His
repertoire was in the thousands of pieces including many of
his own compositions, the first of which he created at age

five.
Since that time a number of such cases with this triad are
glaringly over-represented in savant syndrome, in itself a
rare condition. Leslie Lemke in the United States, Derek,
Paravicini in the United Kingdom and Hikari Oe in Japan
are present day representatives of this amazing combina-
tion of ability and disability. In addition to those examples,
a number of other such cases are documented in detail in
Islands of Genius. The savant syndrome web site provides
further video evidence and documentation of this extraor-
dinary triad of mental impairment ? blindness ? musical
genius.
A second mystery is the why calendar calculating, an
obscure skill in neurotypical persons, is seemingly almost
universally present in persons with savant syndrome. This
ability is a clear example of how savants, sometimes
severely impaired, innately ‘‘know things they never
learned’’. Yes, there are formulas for calendar calculating.

And yes, if any person puts his or her mind to it, he or she
can learn (laboriously) how to calendar calculate. But
savants seem to have this algorithm or formula ‘uncon-
sciously’ inscribed or inculcated in their brain and in most
such individuals there simply has not been any ‘study’ of
the calendar nor the ‘learning’ of any formula. Why cal-
endar calculating? And why is that so prominent in savant
syndrome but generally not seen in other brain diseases or
disorders? Some imaging studies are underway with cal-
endar calculating savants, comparing them to neurotypical
‘expert’ calendar calculators and control groups.
Myths and Misconceptions
Savants are Not ‘Creative’
Some observers, while extolling the eidetic-like ability and
memory of savants, point out that in contrast to such
astonishing imitative ability, savants, as a group are not
very creative. In fact I was one of those observers who
wrote just that in the original 1988 version of Extraordi-

nary People:Understanding Savant Syndrome. I raised the
question there ‘‘Is the savant creative?’’ I answered it this
way: ‘‘In my experience, not very’’.
I was wrong and have corrected that perception in my
later writings. What changed my mind? Some additional
years of observation. There is always a tremendous
advantage in having a longitudinal view of a patient and his
or her ‘natural history’ of illness or disorder, compared to a
one-time, snapshot consultation. And now, having the
opportunity to observe the ‘natural history’ of how the
savant skills emerge and develop over many years, I have
noted predictable and replicable sequence of steps that
566 J Autism Dev Disord (2014) 44:564–571
123
progresses from imitation, to improvisation, to creation in
savant syndrome. Let me expand on that, using Leslie
Lemke as an example.

Leslie Lemke’s ability to store and replicate music, even
after only a single hearing, was spectacular. Indeed at age
14 he was able to play back Tchaikovsky’s First Piano
concerto flawlessly having heard it for the first time as a
theme song to a television movie.
Over time, however, Leslie began to show improvisa-
tional skills in a addition to replication abilities. For
example at a 1989 concert a young girl came up to the
stage in the challenge portion of the concert and played
‘‘Mississippi Hotdog’’. Leslie listened and then, when
asked, dutifully played back the piece exactly as he had
heard it. But toward the end of the piece he began to look a
bit restless, and seemed more excited and more eager to
play. After the initial playback of Mississippi Hotdog was
completed, flawlessly as usual, Leslie then launched into a
5 min improvisation which could be called ‘‘Variations on
a Theme of Mississippi Hotdog.’’ He changed pitch,
changed tempo and demonstrated convincingly that he

does indeed have innate access to the ‘rules of music’,
confirmed by a number of professional musicians who have
observed him.
Adding to the improvisation skill now is creation and
composing of entirely new pieces. One such song he calls
‘‘Down Home on the Farm in Arpin’’, and another he
names ‘‘Bird Song’’. In that latter piece he duplicates, by
whistling softly as he plays his new tune, the bird songs he
hears as he sits for hours outside his farm home.
That same sequence from replication to improvisation to
creation occurs in other savants whether musicians or
artists. The artists begin their ‘career’ with striking replicas
of what they have seen and stored, usually requiring no
model or constant reference piece. Then some improvisa-
tion begins to appear—a telephone pole deleted here, or a
new tree there—slightly different from the original. Then
comes creation of entirely new pieces, maybe now free-
form or in an entirely different art style.

So the savant can be creative. Some savants prefer to
stay with replication, but many have gone beyond literal
copying, as stunning as that can be, to improvisation and
then creation of something entirely new.
These clinical impressions regarding creativity in the
savant have been bolstered by several formal research
projects. A 1987 study by Hermelin, O’Connor and Lee
looked at musical inventiveness in five musical savants
compared to six non-savant children who had musical
training over a period of 2 years but who had not been
exposed to compositional or improvisational instruction.
Five tasks were used to grade for ‘‘musical inventiveness’’.
On those tests the savant group was superior to the control
group. Similarly, on tests of musical competence—timing,
balance and complexity—the savants (with a mean IQ of
59) were also superior to the control group.
Hermelin and her co-workers indicated this study was
consistent with earlier findings—that a series of separate

intelligences, of which music is but one, exist in each
person rather than a single, consistent intelligence that
permeates all the skills and abilities of each person. With
respect to music, they concluded that savants were able to
show some creativity and improvisation in addition to
mimicry.
Hermelin et al. (1989) conducted a study of improvi-
sations by Leslie Lemke compared to a professional, non-
savant musician after each had heard the same musical
pieces, one lyric (Grieg) and one a-tonal (Bartok). Leslie’s
improvisations were described as ‘‘virtuoso embellish-
ments with a considerable degree of musical inventiveness
and pianistic virtuosity.’’ That study concluded that ‘‘both
subjects’ attempts at improvisation show a high degree of
generative musical ability, and what distinguishes them
from each other is not so much a differential degree of
musicianship but rather their own, different musical pref-
erences as well as their respective personality characteris-

tics.’’ In improvisational style on the Bartok, a-tonal piece,
both musicians resembled each other as well.
In summary, savants can be creative. Most savants travel
along a route of first replication, then improvisation, and
finally creation. As we learn more about the brain from the
study of savants, we may also learn much more about talent
and creativity itself through the unique window into the
brain and special skills savant syndrome provides.
The ‘‘Nadia’’ Effect and the ‘‘Dreaded Trade-Off’’
In 1977 Dr. Lorna Selfe described the case of Nadia, a
prolific childhood artist, whose special abilities disap-
peared after she was sent away to school to increase lan-
guage acquisition, socialization abilities and daily living
skills. With the publication of Dr. Selfe’s 2011 book—
Nadia Revisited—we now have the benefit of long term
follow-up on Nadia. Selfe describes the loss of skills this
way: ‘‘In the years following my first study, and throughout
her school days, Nadia was given intensive help with lan-

guage development and her ability to communicate
improved with the production of two/three word sentences.
She also started to draw like an infant so that, for a period,
two styles coexisted and sometimes on the same piece of
paper. Gradually and inexorably she lost the ability to draw
realistically. Unlike some savant artists such as Stephen
Wiltshire, who has maintained the strength of his drawing
ability, Nadia’s ability appeared to peter out. She is now
middle-aged and lives in a specialist care home but for
many years she has simply refused to draw.’’ But, in spite
of the loss of art skills, Selfe points out, importantly, that
123
even though now Nadia is not interested in art and is totally
dependent on others in that supervised setting there is also
the ‘‘optimistic story of the love and care of the family that
raised her and of the people who now care for Nadia. She is

in the safe and competent hands of dedicated staff who
devote themselves to the care of people who are unable to
look after themselves.’’
We don’t seem to know exactly what did happen with
Nadia and why those special skills disappeared. But what I
do know is that in the many, many savants with whom I
have worked, or know about, such a ‘‘dreaded trade-off’’,
or loss of skills, does not occur as the savant gets older or
when exposed to more formal education and training. To
the contrary, in my experience vigorously ‘‘training the
talent’’, whatever that special skill is, leads, in and of itself,
to increased language, social and daily living skills without
any ‘‘dreaded trade-off’’ of special skills. So Nadia’s
experience is the exception, not the rule.
Putting aside the fear of a ‘‘dreaded trade-off’’ is
important because parents, teachers or therapists are
sometimes reluctant to venture forth with more formal
education or training efforts lest the ‘‘Nadia’’ effect occur.

The good news is that such a fear is, in my experience,
unfounded and should not prevent presenting the savant
with more formal education and training within his or her
area of specialty, as well as in a more general educational
sense. That being the case, parents and teachers can con-
tinue not only to applaud and reinforce the special skills as
they surface, but can confidently add teaching and training
in a more formal sense as well without fear of loss of talent,
ingenuity or enthusiasm on the part of the savant.
Savant Syndrome is Always Associated with Low IQ
Perhaps stemming from Down’s original description
regarding low IQ and the presence of savant skills, a
misconception continues that low IQ is a necessary
accompaniment of savant syndrome. That in fact is not the
case. While it is true that most savants have measured IQ’s
between 50 and 70, in some instances IQ can be as high as
125, or even higher. Thus an IQ level above 70 does not
‘disqualify’ someone from having savant syndrome.

One reason that many savants, or many autistic persons,
have IQ scores below 70 is that IQ measurement depends
so heavily on verbal scales, and many autistic individuals,
including those with savant syndrome, have language
(verbal) deficits as an intrinsic part of the underlying
disorder.
A second reason for low IQ scores among savants is the
fact that IQ tests measure only one facet of ‘‘intelligence’’,
something termed ‘‘IQ’’. Savants tend to do poorly on that
particular measure of ‘‘intelligence’’. But savants point out
forcefully that there are multiple forms of ‘‘intelligence’’
and IQ measures only one such ‘‘intelligence’’. IQ tests do
measure something defined as ‘‘IQ’’. But IQ tests fail to
measure some of the other forms of ‘‘intelligence’’ that
savants possess in greater or lesser measure as well. Some
of the savants are profoundly disabled in capacities as
measured by IQ, but yet they are astoundingly ‘intelligent’
within their ‘‘island of genius’’.

There is much debate among psychologists regarding
single v. multiple intelligence theories. But savant syn-
drome, with sometimes extraordinary ability co-existing
with profound disability in the same individual argues
forcefully for the concept of multiple intelligence. And the
fact of multiple intelligences has profound implications not
just for better understanding and approaching savant syn-
drome, but also for implementing more effective, individ-
ualized and targeted education efforts for all segments of
the population.
Thirdly, in all developmental disabilities, and savant
syndrome, one has to make a distinction between ‘‘actual
retardation’’, as classified by IQ scores, from ‘‘functional
retardation’’—instances in which persons with presumably
normal or high IQ (if it could be accurately measured)
function at levels more consistent with sub-normal IQ. In
such instances, either the language and verbal deficits, or
behavioral traits and symptoms, prevent accurate mea-

surement of ‘‘IQ’’. These individuals, whether savants or
not, ‘‘function’’ as if ‘‘retarded’’, but their abilities in cer-
tain other areas of function belie a below average IQ score.
That is termed ‘‘functional retardation.’’
Leslie Lemke provides an example of how misleading
IQ levels can be as a single measure of intelligence. Leslie
has a measured IQ of 58 on the WAIS-R test, based solely
on verbal scores; performance tests were not done because
such testing relies heavily on vision, and Leslie is blind.
Other tests were carried out as well including the 4th edi-
tion of the Stanford-Binet; the Tactual Performance Test;
the American Association for Mental Deficiency Adaptive
Behavioral Scale; and the Animal List Selective Remind-
ing Test. By looking at the scores on these tests as a whole,
the neuropsychologist concluded Leslie was functioning in
the moderately retarded range of intelligence, defined as an
IQ level between 35 and 55.
Yet a videotape of one of Leslie’s concerts challenges

the accuracy of such a low level of intelligence figures. At
this particular concert Leslie was asked to play a piece he
had never heard before with the other pianist, rather than
waiting for the piece to conclude and then play it back after
hearing it as he usually does. The other pianist began
playing. Leslie waited about 3 seconds and then did indeed
play the piece with the other pianist, separated only by
those 3 seconds. In that three second delay Leslie was
taking in what he heard, processing it, and simultaneously
outputting the music as he played along with the other
123
pianist. Leslie was parallel processing, just as some very
intelligent, but rare, interpreters are able to translate what a
speaker is saying into another language simultaneously,
rather than having the speaker pause from time to time
while the interpreter ‘catches up’.

Leslie was parallel processing. That would not be pos-
sible if the IQ level of 35–55 was an accurate barometer of
his over-all intelligence. He exceeds that level by far with
the parallel processing of music which signals that more
than a single ‘intelligence’ was at work during that com-
plex performance.
In summary, measured IQ levels in savants can range
from sub-normal to exceptional and low IQ is not a pre-
requisite to being classified as a ‘savant’. While many
savants have measured IQ levels below 70, some have
measured IQ’s above normal which can range as high as
125 or above. In assessing IQ scores, one has to differen-
tiate’actual’ retardation from ‘functional’ retardation
All ‘‘Geniuses’’ and ‘‘Prodigies’’, Past and Present are
Really ‘‘Aspies’’
With increased interest in autism and Asperger’s, and
especially with the visibility given to the extraordinary skills
seen in savant syndrome, it seems popular these days to

apply the diagnosis of Asperger’s disorder particularly to
anyone considered to be a ‘genius’ or ‘prodigy’ past or
present. Names such as Einstein, Rembrandt, Mozart,
Jefferson and many others are bandied about in such dis-
cussions. It is difficult enough to make accurate diagnoses of
autism or Asperger’s disorder in real life, with face-to-face
interviews and comprehensive testing, let alone trying to
apply post-mortem diagnoses, sight unseen. Retrospective
medical diagnoses are always problematical and suspect.
And then there are present-day prodigies and geniuses.
Some, outrageously bright, but not autistic children, have
composed multiple symphonies by age seven, or have
mastered instruments, sometimes multiple instruments, by
age three. Others show astonishing artistic, mathematical,
prose or poetry skills well beyond their years. If children,
we call them prodigies. They are neither autistic, nor
Aspergers. If adults, we call them geniuses. They also are
neither Asperger’s nor autistic. Prodigies and geniuses

have special, spectacular abilities in absence of any
underlying disability. Typically, rather than there being
simply one ‘‘island’’of genius as is often the case with
savants, whatever the skills of the prodigy or genius, they
are associated with a high measured IQ in all areas of
functioning.
In short, not every gifted child, nor every ‘absent
minded professor’, has Asperger’s disorder. Instead,
‘‘prodigy’’ and ‘‘genius’’ do exist as independent conditions
separate from any underlying disability or disorder The
temptation to classify all prodigies and geniuses as having
autism or Asperger’s seems to be part of the disease de jour
phenomenon quite rampant these days and needs to be
resisted in favor of careful analysis lest continued ‘diag-
nosis creep’ deletes all meaningful classification, all the
disorders lose their specificity, and the ‘spectrum’ engulfs
us all.
The beginning of wisdom is to call things by their right

name. Asperger’s, autism, and savant syndrome surely do
exist. But so do the categories of ‘‘normal’’, ‘‘gifted’’,
‘‘prodigy’’ and ‘‘genius’’. The important thing is to know
the difference lest every parent of a gifted child for
example, whether mildly gifted or profoundly gifted, fear
their child is autistic.
‘‘Outgrowing’’ Autism: Separating ‘Autistic-Like’
Traits from Autistic Disorder in Children Who Read
Early, Speak Late, or are Blind
I get many ‘‘I’ve got a son or daughter who……..’’ emails
from the savant syndrome web site in which parents describe
various accelerated skills in their children and inquire whe-
ther those might be forms of savant syndrome, and if so, how
should those special skills, and that child, be approached
educationally and otherwise. Among those many inquiries
are children who read early (hyperlexia) or speak late
(Einstein Syndrome). Often children in both those groups are
automatically, and mistakenly, assumed to be autistic when

in fact they only have ‘autistic-like’ behaviors and traits with
very different causes and outcomes than Autistic Disorder.
They tend to ‘outgrow’ their autism (their choice of terms),
which was not autism in the first place.
Based on a number of such cases brought to my atten-
tion separating ‘‘autistic-like’’ behaviors and traits from
‘‘Autistic Disorder’’ in children who read early, or speak
late, or who are blind, is a critical differential diagnosis
with vast causal, treatment and outcome ramifications.
Hyperlexia I, II and III
Some neurotypical children simply read early. They may
be reading, instead of the teacher, to their nursery school
class, or reading at a 7th grade level at age 3 for example.
There are no associated autistic or autistic-like traits or
behaviors. They are entirely ‘normal’ (neurotypical) chil-
dren. Eventually their classmates catch up with reading
skills but in the meantime the advanced, precocious read-
ing ability at such an early age draws considerable atten-

tion. I describe several such examples on the ‘‘Hyperlexia’’
posting on the savant syndrome web site at www.
savantsyndrome.com I refer to this type of early reading
ability as Hyperlexia I.
123
Hyperlexia II is when early reading ability presents as a
‘splinter skill’ as part of an Autistic Spectrum Disorder.
These children read voraciously along with astonishing
memory for what they read. They often have other mem-
orization abilities sometimes linked with fascination with
numbers or calendar calculating skills. These children
show other characteristic language, social and behavioral
symptoms seen in autistic spectrum disorder, including
traits such as withdrawal, poor eye contact, lack of interest
in seeking or giving affection, insistence on sameness, and

obsessive compulsive behavior, for example. They usually
carry a formal diagnosis of Autistic Disorder, Asperger’s
Disorder, or pervasive developmental disorder (PPD/NOS)
with intense fascination with words and numbers present-
ing as a ‘splinter skill’.
Hyperlexia III is a less frequently recognized form of
early reading ability. It is not an autistic spectrum disorder
even though there are some ‘‘autistic-like’’ traits and
behaviors that gradually fade as the child gets older. Some
times this is referred to as ‘‘outgrowing autism’’. These
children read early and have striking memorization abilities
sometimes coupled with precocious abilities in other areas
as well. They may show unusual sensory sensitivity, ech-
olalia, pronoun reversals, intense need for sameness, spe-
cific fears or phobias, have lining/stacking rituals and
demonstrate strong visual and auditory memory. Unlike
children with ASD, however, they are often very outgoing
and affectionate with family, even though reserved and

distant with peers and would be playmates. They do make
eye contact and can be very interactive with persons close
to them, especially adults. These children present as being
very bright, inquisitive and precocious overall. Indeed
these ‘autistic-like’ traits and behaviors do fade as the child
gets older, but in the meantime parents are exposed to
unnecessary fear and dread because the diagnosis of
‘‘autism’’ has been prematurely and inappropriately applied
without ‘‘hyperlexia III’’ being considered in the differen-
tial diagnosis. Space precludes an extensive discussion of
Hyperlexia III, but the savant syndrome website as www.
savantsyndrome.com provides a number of example of
such cases with respect to characteristics and outcome.
Einstein Syndrome: Children Who Speak Late
In his 1997 book Late Talking Children Thomas Sowell
pointed out how often ‘‘autistic-like’’ symptoms, as
opposed to Autistic Disorder itself, appeared in children
with delayed speech based on parental reports in a group of

46 such children. In a follow-up book 4 years later—The
Einstein Syndrome:Bright Children Who Talk Late—
Sowell expanded that group to 239 late talking children
who were exceptionally late in beginning to speak but were
also exceptionally bright. (Sowell 2001). His book is
replete with examples. As with Hyperlexia III, Sowell
found in his correspondence with parents that many of the
children with delayed speech had been given a diagnosis of
ASD along the way but that the ‘‘autistic-like’’ symptoms
in these children were transient, and like with the Hyper-
lexia III children, those traits and behaviors faded over
time. He recommended careful professional evaluation for
children who speak late by clinicians familiar with the
various parameters and conditions involved with such
children, sparing parents unnecessary worry, concern
and pessimism that always accompanies a diagnosis of
‘‘autism’’. This condition is also discussed in more detail
on the savant syndrome website.

Blindisms
Teachers and parents of visually impaired children often
refer to what are called ‘‘blindisms’’ in such children. Ek
and co-workers point out that ‘‘blindisms’’—stereotypical
movements, language problems and certain other behav-
iors—are common in children with congenital or other
types of blindness. (Ek et al. 1998). Hobson described the
similarities in development during pre-school age
(3–4 years) between blind children and those with autism
(Hobson 1993). In both groups impairments in symbolic
play, confusion in the use of language and stereotypes were
frequent. Many of the autistic features in the young, blind
child without cerebral damage disappeared with age. As the
child acquired a better understanding of the surrounding
world, and with the development of language, a basis for
sharing experiences and feelings with other people devel-
oped. Hobson noted ‘‘blindness seems to delay rather than
prevent development in these respects.’’ In 2010 Hobson

and Lee did an 8 year follow-up study on nine congenitally
blind and seven sighted children who met formal diag-
nostic criteria for autism. Follow-up of the nine congeni-
tally blind children with ‘autism’ revealed that, in
adolescence, only one such child satisfied the criteria for
that disorder. In contrast, all of the seven sighted children
still did meet the Autistic Disorder criteria. For the group
with what turned out to be autistic-like symptoms, in the
title of his report Hobson uses the interesting term
‘‘reversible autism’’.
Autism, autistic-like symptoms and blindisms can be
confused with each other in visually impaired children. But
just as with children who read early or speak late, differ-
entiation between Autistic Disorder and ‘‘autistic-like’’
symptoms is critical with these children if parents are to be
spared unnecessary distress from an autism diagnosis
improperly applied and, equally important, if the right
treatment is to be applied to the right patient.

123
Summary
With all the emphasis by some on the autism ‘epidemic’
and with it the need for early identification and a prolif-
eration of programs, it is important to remember that not
every child who reads at 18 months, draws at 2 years,
hums back all the melodies he or she hears, or likes to line
up railroad cars, resists certain foods, insists on routine,
memorizes license plates and birthdays, has certain fear
and phobias or is very late in speaking is on the autistic
spectrum. If one looks up ‘‘hyperlexia’’ on the internet,
though, most often the site links hyperlexia to autism. That
simply is not so in all cases as pointed out above. That
same link to autism is likewise often made for children who
speak late, or are blind.

Again, not so in all cases. While early identification of
autism in affected children is important, those efforts need
to be balanced with sensible caution lest parents be
unnecessarily frightened and overwhelmed by premature,
and erroneous, diagnoses. Except in truly ‘classic’ cases,
often some time of watchful observation needs to elapse
until the ‘natural history of the disorder’ reveals the real
diagnosis. Such ‘watchful observation’, diagnostic caution
and separation of ‘autistic-like’ behaviors from Autistic
Disorder can provide example of what some have called
‘‘reversible autism’’.
References
Brink, T. L. (1980). Idiot savant with unusual mechanical
ability: An
organic explanation. American Journal of Psychiatry, 137,
250–251.
Dorman, C. (1991) Exceptional calendar calculating ability after
early
left hemispherectomy. Brain and Cognition 15, 26–36 London:

Churchill.
Down, J.L. (1887). On some of the mental affections of
childhood and
youth. London, Churchill.
Ek, U., Fernello, E., Jacobson, L., & Gillberg, C. (1998).
Relation
between blindness due to retinopathy and autistic spectrum
disorders: A population study. Developmental Medicine and
Child Neurology, 40, 297–301.
Hermelin, B., O’Connor, N., & Lee, S. (1987). Musical
inventiveness
of five idiot-savants. Psychological Medicine, 17, 685–694.
Hermelin, B., O’Connor, N., Lee, S., & Treffert, D. A. (1989).
Intelligence level and musical improvisational ability. Psycho-
logical Medicine, 19, 447–457.
Hobson, R.P. (1993). Autism and the development of the mind.
Hove:
Lawrence Erlbaum.
Hobson, R. P. (2010). Reversible autism in congenitally blind
children? A controlled study Journal of Child Psychology and

Psychiatry, 51(11), 1235–1241.
Kanner, L.:217-25055-58 (1944) Early infantile Autism Journal
of
Pediatrics 25, 200–217.
Kapur, N. (1996). Paradoxical functional facilitation in brain-
behavior research. Brain, 119, 1775–1790.
Miller, B., et al. (1996). Enhanced artistic creativity with
temporal
lobe degeneration. Lancet, 348, 1744–1745.
Miller, B., et al. (1998). Emergence of artistic talent in
frontotemporal
dementia. Neurology, 51, 978–982.
Miller, B., et al. (2000). Functional correlates of musical and
visual
ability in frontotemporal dementia. British Journal of Psychia-
try, 176, 458–463.
Minogue, B. M. (1923). A case of secondary mental deficiency
with
musical talent. Journal of Applied Psychology, 7, 349–357.
Mortiz, K. P. (1783). Gnothi Sauton oder magazin der
erfahrungs-

seelenkunde als ein lesebuch fur gelehrte and ungelehrte.
Berlin,
Germany: Mylius.
Selfe, L. (1977). Nadia: A case of extraordinary drawing ability
in an
Autistic child. London: Academic Press.
Selfe, L. (2011). Nadia revisited: A longitudinal study of an
Autistic
Savant. New York: Psychology Press.
Sowell, T. (1997). Late talking children. New York: Basic
Books.
Sowell, T. (2001). The Einstein syndrome: Bright children who
talk
late. New York: Basic books.
Treffert, D.A. (1988). The idiot savant: A review of the
syndrome.
American Journal of Psychiatry 145563–145572.
Treffert, D.A. (1989). Extraordinary people: Understanding
savant
syndrome. Lincoln, Nebraska: iUniverse.com.
Treffert, D. A. (2006). Dr. Down and ‘‘developmental
disorders’’.

Autism and Developmental Disabilities, 36, 965–966.
Treffert, D. A. (2010). Islands of genius: The bountiful mind of
the
Autistic acquired and sudden savant. London: Jessica Kingsley.
123
Reproduced with permission of the copyright owner. Further
reproduction prohibited without
permission.
c.10803_2013_Article_1906.pdfSavant Syndrome: Realities,
Myths and MisconceptionsAbstractRealitiesSavant Syndrome
DefinedSavant Syndrome is Not a New Disorder (Nor is
Autism)Not All Savants are Autistic, and Not All Autistic
Persons are SavantsSavant Skills Represent a Spectrum of
AbilitiesThe Acquired Savant: ‘‘Accidental Genius’’The Most
Important Question of All: How do They do It?Some Mysteries
RemainMyths and MisconceptionsSavants are Not ‘Creative’The
‘‘Nadia’’ Effect and the ‘‘Dreaded Trade-Off’’Savant Syndrome
is Always Associated with Low IQAll ‘‘Geniuses’’ and
‘‘Prodigies’’, Past and Present are Really
‘‘Aspies’’‘‘Outgrowing’’ Autism: Separating ‘Autistic-Like’
Traits from Autistic Disorder in Children Who Read Early,
Speak Late, or are BlindHyperlexia I, II and IIIEinstein
Syndrome: Children Who Speak
LateBlindismsSummaryReferences
DEVELOPMENTAL MEDICINE & CHILD NEUROLOGY

REVIEW
The genetic landscape of autism spectrum disorders
RASIM O ROSTI1 | ABDELRAHIM A SADEK2 | KEITH K
VAUX1 | JOSEPH G GLEESON1
1 Department of Neurosciences and Pediatrics, Howard Hughes
Medical Institute, University of California, San Diego, CA,
USA. 2 Pediatric Neurology Unit,
Department of Pediatrics, Faculty of Medicine, Sohag
University, Sohag, Egypt.
Correspondence to Joseph G Gleeson at University of California
San Diego, 9500 Gilman Drive M/C 0665, La Jolla, CA, USA.
E-mail: [email protected]
PUBLICATION DATA
Accepted for publication 22nd July 2013.
Published online 1st October 2013.
ABBREVIATIONS
ASD Autism spectrum disorders
CGH Comparative genomic
hybridization
CNV Copy number variants
Autism spectrum disorders (ASDs) are a group of heterogeneous
neurodevelopmental disor-
ders that show impaired communication and socialization,

restricted interests, and stereotypi-
cal behavioral patterns. Recent advances in molecular medicine
and high throughput
screenings, such as array comparative genomic hybridization
(CGH) and exome and whole
genome sequencing, have revealed both novel insights and new
questions about the nature
of this spectrum of disorders. What has emerged is a better
understanding about the genetic
architecture of various genetic subtypes of ASD and
correlations of genetic mutations with
specific autism subtypes. Based on this new information, we
outline a strategy for advancing
diagnosis, prognosis, and counseling for patients and families.
Autism spectrum disorders (ASDs) are a group of com-
plex neurodevelopmental disabilities that affect social inter-
action and communication skills. The prevalence of ASDs
appears to be constantly and gradually increasing, but it is
not clear if this is because of clarification of diagnostic cri-
teria or an actual increase in the number of cases. Most
recent estimates find that the median of prevalence esti-
mates of ASD is 62 out of 10 000.1 Those with molecu-
larly defined causes make up roughly 20% of the cases, but
the heritability has been estimated to be 90%, suggesting
as yet undiscovered causes. However, there are new reports
suggesting that the previous estimate of heritability was
too high and may need to be adjusted downwards.2

The diagnostic criteria have evolved with increasing clin-
ical and molecular understanding of this umbrella term.
This aspect makes the diagnosis more challenging as the
clinical spectrum is highly variable and the etiological sub-
grouping tends to change with the ever-growing molecular
data now fed by high throughput techniques such as array
comparative genomic hybridization (CGH), whole exome
and whole genome sequencing. The judgment of the
physician, critical in achieving accurate prognosis and
genetic counseling, requires a systematic approach. By
incorporating these techniques along with careful clinical
and neuropsychological assessment, a more accurate
diagnosis of an ASD disorder can be achieved.
Distinguishing between essential autism and complex
(syndromic) autism might be considered the starting point
of this systematic approach.3 Of all individuals meeting cri-
teria for autism, essential autism makes up approximately
75% of the cases. Although essentially a diagnosis of exclu-
sion, the main characteristics are the lack of dysmorphic
features, higher male to female ratio (6:1), higher sibling
recurrence risk (up to 35%), and positive family history
(up to 20%). Syndromic autism, on the other hand, is
characterized by accompanying recognizable patterns of
dysmorphology, a reduced male to female ratio (3.5:1),
lower recurrence risk (4%–6%), and family history to a
lesser extent (up to 9%).1
Clinical recognition of well-known phenotypes leading
to a targeted molecular testing approach can strengthen
the hand of the clinician in answering additional questions
about the recurrence risk and prognosis according to the
molecular basis identified by targeted testing. However, for
most forms of essential autism, there is no familiar pheno-
type that points to one particular genetic cause or another.

In the absence of a specific genetic test option after clinical
evaluation, array CGH testing is recommended as it has
been reported to provide the highest diagnostic yield in
cases with ASD – ranging from 10%4 to 18%.5 Once such
a genetic alteration is identified in an affected individual,
more specific information about diagnosis, prognosis,
recurrence risks, and possible targeted therapy options can
be conveyed to the family.
ASD phenotypes caused by genetic alterations can be
divided into three subgroups: cytogenetic alterations that
can be detected by conventional karyotyping (not the
primary focus of this paper). These comprise classic chro-
mosomal syndromes such as Turner and Down syndrome,
and large segmental cytogenetic alterations that have been
associated with autism.6 Other subgroups are single gene
disorders (detected in <5%) and copy number variants
(detected in 10%–35% of the cases1).
NON-SYNDROMIC AND POSSIBLE TREATABLE
FORMS OF ASD: SINGLE GENE DISORDERS
Single gene disorders are mainly composed of metabolic
conditions in which accompanying ASD features are
12 DOI: 10.1111/dmcn.12278 © 2013 Mac Keith Press
reported. Among these, phenylketonuria is common
enough to be included in newborn screening programs in
most countries. Comorbidity of ASD and phenylketonuria
was consistently described in the literature in up to 6% of
patients.7 The fact that none of the 62 patients with phen-
ylketonuria who were treated with the phenylketonuria diet
early in life met ASD diagnostic criteria, whereas 5.7% (2
out of 35) of individuals with late diagnosis met diagnostic

criteria, underlines the importance of early diagnosis in
successful treatment in such metabolic conditions.8 Condi-
tions such as mitochondrial disorders, adenylsuccinate lyase
and creatine deficiencies may also phenocopy ASD.
Accompanying autistic features in these disorders range
from 0.4% to 80%.1,9,10 Although mitochondrial disorders
can present with autistic features, atypical findings of hypo-
tonia, fatigue with activity, failure to thrive, intermittent
episodes of regression, especially those after fever and ele-
vated plasma lactate concentrations, make diagnosis of the
condition more straightforward. Non-specific features such
as epilepsy and intellectual disability that accompany
autism in the setting of mitochondrial disorders on the
other hand, can make the diagnosis more challenging.
A recent addition to this group of disorders was made
by Novarino et al.11 who reported mutations in the
BCKDK gene in the affected individuals from three consan-
guineous families who had epilepsy, autistic features, and
intellectual disability. The encoded protein is responsible
for phosphorylation-mediated inactivation of the E1a sub-
unit of branched-chain ketoacid dehydrogenase enzyme.
Patients with BCKDK mutations displayed reductions in
BCKDK messenger RNA and protein, E1a phosphoryla-
tion, and plasma branched-chain amino acids. Bckdk knock-
out mice showed abnormal brain amino acid profiles and
neurobehavioral deficits that respond to dietary supple-
mentation. By supplementing the diet of humans with
branched-chain amino acids, the authors were able to nor-
malize plasma branched-chain amino acids levels, but the
degree to which neurocognitive changes are treatable or
reversible remains to be determined.
ASD ASSOCIATED WITH RECOGNIZABLE PATTERNS
OF MALFORMATIONS CAUSED BY SINGLE GENE
DISORDERS

International Journal of Clinical and Health Psychology.docx

International Journal of Clinical and Health Psychology.docx

Recommended

Recommended

More Related Content

Similar to International Journal of Clinical and Health Psychology.docx

Similar to International Journal of Clinical and Health Psychology.docx (20)

More from normanibarber20063

More from normanibarber20063 (20)

Recently uploaded

Recently uploaded (20)

International Journal of Clinical and Health Psychology.docx