abnormal attention in autism

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DOI: 10.1177/1362361300004003004

2000 4: 269AutismMatthew Belmonte

Abnormal Attention in Autism Shown by Steady-State Visual Evoked Potentials

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269

Abnormal attention in autismshown by steady-state visual

evoked potentials

M A T T H E W B E L M O N T E McLean Hospital Brain ImagingCentre,Belmont,MA,USA

A B S T R A C T This study examined brain electrical responses as aphysiological measure of speed and specificity of attentional shifting in

eight adult males with autism. Subjects were required to shift attentionbetween rapidly flashed targets alternating between left and right visualhemifields. When targets were separated by less than 700 ms, steady-state brain electrical response in both hemispheres was augmented andbackground EEG decreased for rightward shifts as compared with left-ward shifts. At longer separations, persons with autism showed nomodulation of background EEG, and high variability in steady-stateresponse. These results contrast with those in normal controls, wherein each hemisphere separately steady-state response increased andbackground EEG descreased for shifts directed contralaterally to thathemisphere. Group differences were significant at p < 0.04 for the

steady-state response andp < 0.0001 for the background EEG. Lack ofhemispherically independent modulation in autism may reflect theoperation of a non-specific mechanism of sensory gating.

A D D R E S S Correspondence should be addressed to: M AT T H E W B E L M O N T E ,MIT 14E-303, 77 Massachusetts Avenue, Cambridge, MA 02139-4307, USA

The ability to attend to relevant stimuli and to filter out irrelevant ones isfundamental to the normal development of a child. Joint social attention,

in particular, rests on an ability to shift the attentive focus rapidly betweena caregiver and some external object (Bakeman and Adamson, 1984).Whena parent shows an infant a toy, for example, the infant must register notonly the image of the toy, the texture of the toy and the sound made by thetoy, but also the parents voice, facial expression and gestures in responseto the infant and the toy. In order to integrate all these stimuli into coher-ent percepts, the developing child must rapidly alter the scope and focus ofattention among many sensory modalities and locations. An accumulationof behavioural evidence indicates that such task-based control over thescope of attention is lacking in autism (Burack et al., 1997). Physiological

autism 2000SAGE Publicationsand The National

Autistic SocietyVol 4(3) 269285; 013642

1362-3613(200009)4:3

K E Y W O R D S

asymmetry;EEG;

spatialattention;

steady-stateevoked

potential;vision

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studies suggest that this deficit in attentional control reflects a lack ofspecificity in perceptual gating, that is, in the process of selecting a few rel-evant stimuli from the large set of sensory inputs and conveying thosestimuli into higher-order perceptual processing. A complete exploration of

this hypothesis requires physiological measures of changes in perceptualgating in response to changing task demands.Poor control over the scope of attention is most evident in tasks that

demand rapid reorganization of perceptual resources in response tochanges in incoming stimuli. In a widely applied task developed by Posneret al. (1984), a cue informs subjects about the likely location of a subse-quently appearing target. The cue may be spatial, as in a highlighting of thetarget area, or symbolic, as in an arrow pointing to the target area. After avariable delay from the onset of the cue, the target may appear in the cued(valid) location or in the uncued (invalid) location. The reaction times of

normal subjects to these targets show a validity effect, that is, a cost ofinvalid cueing and a benefit of valid cueing. In a high-functioning groupof adolescents and young adults with diagnoses of autism or Asperger syn-drome, Wainwright-Sharp and Bryson (1993) found no validity effectwhen the target was presented after a short (100 ms) delay, and a largerthan normal validity effect at a long (800 ms) delay. These results areconsistent with a model of slowed reorienting of attention: the 100 mscue-to-target delay gives persons with autism too little time to apply theinformation given by the cue, and as a result, there is no difference in reac-

tion time between valid and invalid trials. Conversely, at the 800 ms delay,persons with autism, having had sufficient time to shift their attention tothe cued location, must implement another slow shift in order to respondto a target at the uncued location. These abnormal validity effects in autismare overlaid on a pattern of overall slowed responding due to motor apraxia.The two effects, one a general slowing and the other an interaction withcue-to-target delay, can sometimes be difficult to separate. However, theautistic pattern of validity effects is present even when accuracy of dis-crimination is used as a measure instead of speed of response (Townsend

et al., 1996), thus completely removing any motor confound.Although differences in diagnostic criteria, age groups, IQ and controlgroups make individual studies difficult to compare, the finding of dis-ordered control over the scope of attention in autism is in general cor-roborated by other studies. High-functioning adults with diagnoses ofautism or Asperger syndrome show a difficulty distributing attention inorder to detect targets at central and lateral positions (Wainwright-Sharp &Bryson, 1996). In low-functioning children with autism, an attention-directing frame around the relevant region of the visual field improves per-formance, but presentation of distractor stimuli within the frame negates

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this effect (Burack et al., 1997). In 10 adult savants with diagnoses ofautism or PDD-NOS, Casey et al. (1993) found a heightened validity effectnot only at the 800 ms cue-to-target delay but also at 100 ms, illustratingthe potential for variability in results when diagnostic criteria are loose.

Even more important than diagnostic stringency is the question of the eco-logical validity of the tasks employed: any experiment in which individualstimuli are presented in discrete trials separated by long pauses is a poorreflection of real-world tasks, which demand continuous reorienting ofattention within a constant stream of stimuli.

In a paradigm more reflective of the constant shifting demanded byreal-world situations, Courchesne et al. (1994a) measured the accuracy oftarget detection in two simultaneously presented streams of information,one auditory and the other visual.A target in the currently attended modal-ity served as a cue to shift attention to the other modality. So, for example,

a high tone in a background of low tones signalled subjects to stop attend-ing to tones and to begin watching for a red flash in a background of greenflashes. On detecting the red flash, subjects had to begin ignoring theflashes and listening to the tones again. Adolescents with autism uncom-plicated by severe mental retardation (PIQ > 70) showed a deficit inresponding to targets in different modalities when those targets were sep-arated by less than 2.5 seconds. A like result was obtained for the case ofshifting between separate visual attributes (form and colour) of a singlestimulus (Courchesne et al., 1994b). Reinforcing these results is the

impairment of persons with autism at distributing attention across simul-taneous auditory and visual continuous performance tests (Casey et al.,1993). These complex tasks are an advance over single-trial paradigms inaddressing the problem of ecological validity. However, behaviouralmethods are limited in their ability to relate task performance to the under-lying biology.

Electrophysiological results on autism associate the aforementionedbehavioural pathologies with abnormal modulation of excitability. Inresponse to an attended stimulus, the normal brain produces a series of

electrical potentials (voltages) which can be recorded from electrodes onthe scalp. Over the frontal cortex, salient stimuli that call for responses orthat differ from context during periods of sustained attention evoke nega-tive potentials.At more posterior scalp sites, attention modulates a series ofpotentials evoked by sensory stimuli. One of the earliest of these sensorypotentials, the P1, is a positive voltage appearing over the occipital cortexabout 100 ms after presentation of a visual stimulus. The P1 becomesgradually smaller as stimulation occurs farther away from the spatial focusof attention (Mangun and Hillyard, 1988). A later, negative potential overthe parietal cortex, the N2, is augmented during attentional selection of

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task-relevant stimuli (Eimer, 1996). Finally, at a latency of about 400 ms, apositive potential P3b appears with presentation of a task-relevant stimulusbut not with irrelevant stimuli.

Autism presents a remarkable disruption of all these attention-related

potentials. Despite normal behavioural performance in tasks of static ratherthan shifting attention, frontal negativities are entirely absent (Ciesielski etal., 1990; Courchesne et al., 1989) and the visual P3b is highly variable(Courchesne et al., 1989) with a slightly low average amplitude (Ciesielskiet al., 1990; Novick et al., 1979; Verbaten et al., 1991). The P1, instead ofdeclining gradually with distance from the focus of attention,decreases eitherprecipitously or not at all (Townsend and Courchesne, 1994). In addition tothese failures of normal modulation, neural systems in the autistic brain oftenare inappropriately activated. The visual N2 to novel stimuli is larger whenthe person with autism is performing a task than when (s)he is passively

observing, even when these novel stimuli are not relevant to the task in ques-tion (Kemner et al.,1994). This inappropriate activation occurs across modal-ities also: when a response is required to an auditory stimulus, autisticchildren manifest an enhanced P3 at occipital sites overlying visual process-ing areas (Kemner et al., 1995). Perceptual gating in autism seems to occurin an all-or-none manner, with little specificity for the location of the stim-ulus, for the behavioural relevance of the stimulus, or even for the sensorymodality in which the stimulus appears. Lacking normal mechanisms forgating sensory signals into higher-order processing, the autistic brain seems

to accomplish attentional tasks by some other, substitute mechanism.The aforementioned studies have assessed statically allocated attention,

but by themselves they have little to say about what goes on in the autisticbrain when a demand is made to shift attention. The application of evoked-potential methods to such shifts is not straightforward, since the mostobvious electrophysiological indications of a shift in attention appear onlyafter the shift has occurred. A cue to shift attention evokes a P3 response,but this response cannot be closely associated with the attentional shift itselfsince in normal subjects the shift is already fully implemented when the P3

is only just beginning. The P700 shift-difference (Sd) potential appearsspecifically in circumstances in which an attentional shift has occurred(Akshoomoff and Courchesne, 1994), but again, it can furnish only retro-spective information on the process of attentional shifting.

One way around this problem is to use perturbations of a steady-statevisual evoked potential (SSVEP) as an index of attentional modulation. TheSSVEP is an electrical resonance of the visual system, produced whenstimuli are flashed periodically at frequencies in the alpha band (Regan,1977). Just as the height attained by a childs swing grows large if the swingis pushed at appropriate intervals, the voltage generated by the visual system

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grows large if a visual stimulus is flashed at a resonant frequency, about 10times per second. The backward and forward swings are synchronized tothe pushes, just as the SSVEP voltage peaks are phase-locked to the flashingstimulus.

In normal subjects, the SSVEP is augmented in the hemisphere con-tralateral to the attended visual hemifield (Belmonte, 1998; Morgan et al.,1996). Conversely, background oscillations occurring in the same fre-quency band as the SSVEP, which are not phase-locked to the stimulus andprobably index the level of task-irrelevant processing, are decreased con-tralateral to the attended hemifield (Belmonte, 1998). When a subject isasked to shift attention across the midline, the pattern of SSVEP modulationinverts beginning at about 300 ms (Belmonte, 1998).

The hypothesis of non-specific sensory gating in autism predicts anabsence of hemispheric specificity in the modulation of the SSVEP and of

background amplitude during rapid attentional shifts. Instead of beingmodulated in opposite directions, amplitude measures in the two hemi-spheres should be similar to each other when the stimulus onset asyn-chrony (SOA) between the cue in the previously attended location and thetarget in the newly attended location is brief.At longer SOAs, when peoplewith autism are not so drastically impaired, a pattern of modulation similarto normal may emerge, but with a prolonged latency reflecting the pro-longed time course of the attentional shift.

Methods

The eight autistic subjects were members of a long-standing subject poolrecruited from a regional centre for developmental disabilities and fromclinical referrals.All the autistic subjects were male,with a mean age of 26.7years and a standard deviation of 5.4 years (range 19.4 to 32.3). Of these,seven were right-handed and one was left-handed. None were medicated,and none had any history of comorbid psychiatric disease. The diagnosisof autism was made by experienced clinicians, including a clinical

psychologist and a paediatric neurologist, according to the structuredobservations of the Autism Diagnostic Observation Schedule (ADOSG)(Lord et al., 1989). In addition, all subjects were administered the Wech-sler Adult Intelligence ScaleRevised and the Childhood Autism Rating Scale(CARS) (Schopler et al., 1988), and each subjects parent was interviewedwith the Autism Diagnostic InterviewRevised (ADIR) (Lord et al., 1994).Table 1 summarizes these behavioural measures. In all eight cases the DSM-IV (American Psychiatric Association, 1994) criteria were met according toboth the ADIR and the ADOS. All subjects with autism were negative forfragile X, as assayed by cytogenetic analysis. The controls were 12 normal,


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right-handed subjects with no history of neurological or psychiatricdisease, five female and seven male, mean age 22.9 years, standard devi-ation 4.1 years, range 17.4 to 30.3, recruited from among local collegestudents and hospital employees. (Constraints on resources prevented

matching the two samples by sex; however, effects in the normal femaleswere highly similar to those in the normal males both for the SSVEP(F(1,10) = 0.02,p = 0.89) and for background activity (F(1,10) = 0.13,p = 0.73).) Informed consent was obtained from each subject. All subjectswere paid for their time.

The paradigm was identical to that used in our previous work on atten-tional shifts in normal subjects (Belmonte, 1998) and consisted essentiallyof two oddball detection tasks running side by side. Stimuli were colouredsquares, 1.8 on each side, centred 3.0 superior and 5.1 lateral to a fix-ation cross. Each square was flashed for 56 ms and was followed by another

56 ms of blank display, producing an SSVEP with a 112 ms cycle (8.9 s1).On detecting a target in the currently attended location, subjects had asrapidly as possible to move a joystick to the opposite side and to shift theirattention to that opposite side. Stimuli were presented in 144 trials, and rancontinuously within each trial. Each trial contained 32 targets and wasabout 50 s in duration. Fixation was monitored by electrooculography andby closed-circuit television. Both detection accuracy and response latencywere recorded as behavioural measures. Although the latter measure issubject to influence by delays in motor implementation, the former

depends solely on detection and not on the speed of motor implemen-tation. Before EEG recording began, subjects were observed during practicetrials until it was clear that they were performing the task as instructed. Inaddition, the behavioural data serve as an ongoing check for appropriateperformance of the task.

Owing to the small size of the scalp region of interest, a uniformly sizedelectrode array was applied to all subjects regardless of variation in headshape. Within such small regions, it has been our experience that errorsintroduced during the process of electrode placement are comparable to

the small errors introduced by variation in head circumference. Ag-AgClelectrodes, 1 cm in diameter, were placed on each hemisphere along threeparallel lines 1.5 cm, 3.5 cm, and 5.5 cm superior to the line joining theinion and the preauricular point. On the upper and lower lines, electrodeswere placed 4 cm and 8 cm lateral to the midline. On the middle line, elec-trodes were placed 6 cm lateral to the midline. This scheme produced, overeach hemisphere, one central electrode located over occipitoparietal scalpand surrounded by four equally spaced neighbours at a radius of 2.8 cm.

EEG was recorded at a sampling rate of 286 s1 (i.e. 32 samples duringeach 112 ms cycle of the flashing stimulus) using Scan386 digitization

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275

Table1

Subjectsage,WAIS

RperformanceIQ,Childho

odAutismRatingScalescore,andsubscoresfromtheAutism

DiagnosticInterviewRevised

forreciprocalsocialinteraction,communication(verbalandnon-verbalitems),andre

strictedand

repetitivebehavioursandinterests

Subject

Age

PIQ

CARS

ADIR

ADIR

ADIR

AD

IR

social

communication

communication

restricted&

(verbal)

(non-verbal)

rep

etitive

1

32

93

42.5

29

22

12

11

2

31

112

23.5

22

16

8

7

3

19

81

35.5

45

20

14

10

4

31

80

32.5

25

21

14

7

5

29

92

36.5

26

20

14

6

6

31

106

36

21

22

12

10

7

20

108

45

30

20

13

6

8

20

51

36.5

23

27

13

12

Mean(SD)

26.6

(5.8

)

90.4

(19.9

)

36.0

(6.5

)

27.6

(7.7

)

21.0

(3.1)

12.5

(2.0

)

8

.6(2.4

)



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software (Neuroscan, El Paso, Texas) and a Scientific Solutions analogue-to-digital converter with a Grass Model 12 Neurodata Acquisition System.Half-amplitude cutoffs were 0.1 s1 and 100 s1. Bipolar derivations weretransformed off-line into a measure consisting of four times the voltage at

the central electrode minus the voltages at each of the four neighbours. Thisprocedure emphasizes sources that underlie the centrally placed electrodeand diminishes effects of volume conduction (Hjorth, 1975). EEG intervalsin which the range of the median-filtered horizontal electrooculogramexceeded 25 V within 75 ms were rejected. A similar procedure wasapplied in the case of the vertical electrooculogram with a threshold of100 V in 300 ms. These rejection parameters were selected based on theirreliable identification of horizontal saccades and eyeblinks, respectively, inpilot data.

Using the Gnuroscan system (Belmonte, 1997), a set of extensions to

the Neuroscan software, artefact-free intervals consisting of the 900 msepoch following correctly detected targets were averaged into four separatebins depending on the amount of time since the previous correctly detectedtarget: 56728 ms, 8401512 ms, 16242296 ms, and 2408 ms or longer.Since 2296 ms was the longest possible interval between target presen-tations, the 2408 ms bin contained only responses that followed at least onemissed target. For each of the eight successive 112 ms periods within thisepoch, SSVEP and background amplitudes were derived from the Fouriertransform (Mast and Victor, 1991), and the difference between amplitude

in response to left targets and amplitude in response to right targets wascomputed.

Each of the two sets of behavioural measures (latency and accuracy)was subjected to a 2 4 2 analysis of variance (BMDP program 2V)with factors diagnostic group, SOA and target location. Each of the two setsof electrophysiological data (SSVEP and background) was subjected to a2 4 2 8 analysis of variance with factors diagnostic group, SOA,target location and latency.

Results

For the SSVEP data, analysis of variance revealed an effect of SOA group(F(3, 54) = 3.06,p = 0.0359). Group effects on background amplitudeswere more pronounced than those on the SSVEP itself, owing to less vari-ability in the data: for the background amplitudes there were strong effectsof latency group (F(7, 126) = 6.38,p < 0.0001) and of SOA latency group (F(21, 378) = 3.57, p < 0.0001). The complete data set isexpressed graphically in Figure 1; for cells that contribute heavily to thesestatistical effects, means and standard errors are given in the text below.

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Since the quantity represented is the difference in amplitude between lefttargets (which cue rightward shifts) and right targets (which cue leftwardshifts), positive values indicate greater response to left targets and negativevalues indicate greater response to right targets.

At short SOAs, people with autism showed much greater SSVEPresponse to left targets (rightward shifts) than to right targets (leftwardshifts), beginning at about 500 ms post-target (Figure 1, column 1, bottomtwo panels). This heightened response occurred in both hemispheres, but


277

Figure 1 Difference amplitudes of background EEG and of the phase-locked SSVEP,

computed at 112 ms intervals in each hemisphere following the presentation of a

successfully detected target. Signal in response to right targets (leftward shifts) is

subtracted from signal in response to left targets (rightward shifts). Solid lines and

circles, normals. Dotted lines and squares, autism.Averages are plotted for four

different ranges of SOA. Error bars represent standard error of the mean. At short SOA,

note the similar patterns of activation in left and right hemispheres for autism but

not for controls



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was more consistent in the right hemisphere: for the 56728 ms SOArange, at 600 ms post-target, right-hemisphere mean difference amplitudewas 6.8 6.5 V for people with autism versus 1.4 0.8 V for con-trols, while the left-hemisphere means were 3.3 4.9 V for autism and

0.1 0.4 V for controls. (All ranges are standard error of the mean.) Incontrast to this large, bihemispheric effect in autism, the normal pattern ofSSVEP modulation consisted of a small leftright difference that increased(i.e. became greater for rightward shifts) in the left hemisphere butdecreased (i.e. became greater for leftward shifts) in the right hemisphere.People with autism did not manifest such separate and opposite effects inthe two separate hemispheres; instead, the hemispheres behaved similarlyto each other, each responding more highly during rightward shifts thanduring leftward shifts.

SSVEP differences at long SOAs (lower right quadrant of Figure 1) were

highly variable in the autistic subjects, oscillating between positive andnegative signs. For SOA 2408 ms, right-hemisphere mean differenceamplitude at 600 ms was 14.0 13.2 V for autism versus 2.4 1.1 Vfor controls. The corresponding values for the left hemisphere were 6.1 5.7 V and 0.1 0.5 V. These large and disordered amplitudes in thecase of autism were utterly dissimilar to the normal pattern in which welldefined and oppositely directed modulations in the two hemispheres areevident as early as 300 ms post-target.

In the background (non-phase-locked) EEG, at short SOAs people with

autism showed strongly decreasing response to left targets (rightwardshifts) and increasing response to right targets (leftward shifts) (Figure 1,column 1, top two panels). Again the direction of the effect was the samein both hemispheres. The control subjects, in contrast, manifested a weak,decreasing leftright difference in the left hemisphere but a stronger,increasing trend in the right hemisphere. For the 56728 ms SOA range,by 700 ms post-target the mean leftright difference had fallen to 0.89 0.37 V in autism as compared to 0.32 0.26 V in controls, in the righthemisphere; and to 0.75 0.65 V for autism as compared to 0.03

0.07 V for controls, in the left hemisphere.At longer SOAs, this pattern of bihemispheric decrease in differenceamplitude of the background EEG disappeared and, in the case of the righthemisphere, amplitude became high in response to left targets (rightwardshifts) throughout the epoch (column 2, panel 2). For the 8401512 msSOA range, by 300 ms post-target the amplitude in controls was 0.44 0.17 V, whereas in autism it had increased to 0.54 0.40 V. In addition,right-hemisphere background amplitude modulation that was quitepronounced in control subjects in the two longest SOA ranges was utterlyabsent in autism (columns 3 and 4, panel 2). For SOA 2408 ms,

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difference amplitudes in controls climbed from 0.55 0.22 V at 400 mspost-target to 0.56 0.26 V at 700 ms post-target, whereas for peoplewith autism the amplitudes remained flat, from 0.09 0.22 V at 400 msto 0.0 0.08 V at 700 ms.

The behavioural data are displayed in Figure 2. As expected, there werehighly significant effects of SOA on accuracy (F(6, 66) = 43.27,p < 0.0001)and on response latency (F(6, 66) = 3.49,p = 0.0047). The ratio of correct

target detections to total number of detection opportunities was lower forthe autistic group than for the normal group (0.58 0.04 for autism and0.77 0.06 for controls, F(1, 18) = 6.48, p = 0.0203), and attained itsmaximum at a longer SOA (SOA group effect, F(6, 108) = 9.53,p

abnormal attention in autism

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