Electrophysiology and intelligence: the electrophysiology of intellectual functions in intellectual disability

Abstract

The electroencephalograms (EEGs) of subjectswith intellectual disability were analysed withregarding to their correlation to intellectual func-tions in order to further understand the relation-ships between EEG and intelligence. The EEGfrequency spectrum was subdivided into , -Hz-wide bands and was recorded from electrodes F,Fpz, Fp, F, F, Fz, F, F, T, C, Cz, C, T,T, P, Pz, P, T, O, Oz and O. Patterns ofacorrelation showed several similarities when com-pared to other analogous studies with normal sub-jects. However, the typical finding in the presentstudy was a high number of correlations involvingthe frontal lobes, mainly the prefrontal portions,which is at variance with the patterns of normalsubjects. Frontal lobes, especially the prefrontalregions, seem to be affected, regardless of its aetiology, in subjects with intellectual disability.

Keywords electrophysiology, intellectual functions,intelligence

Introduction

A major feature of the definition of intellectual dis-ability (ID) is the presence of an abnormally lowIQ. Classically, the IQ threshold for the diagnosis of ID has been defined as (APA ), althoughrecent developments have argued for a higherfigure, such as (Luckasson et al. ). Subjectsclassified as intellectual disabled must also showadaptive deficits in several aspects of daily life; forexample, communication, self-care, social skills andleisure (King et al. ). Although there is alsosome controversy about which of these factors, i.e.IQ or adaptive behaviour, should be emphasized, itnevertheless appears that the presence of a notablybelow-average IQ is imperative in the diagnosis ofID.

Therefore, ID could be considered to be a het-erogeneous condition according to its possible aeti-ologies. Because the diagnosis is mainly based onIQ, many pathogenic processes could exhibit thissign. The American Association on Mental Retarda-tion actually enumerates more than causes ofID (Luckasson et al. ).

As a consequence of this heterogeneity, severalelectroencephalogram (EEG) handbooks haveusually preferred not to define an EEG pattern forID (Nogueira de Melo & Niedermeyer ). In

Journal of Intellectual Disability Research

pp – 63

© Blackwell Science Ltd

Electrophysiology and intelligence: the electrophysiologyof intellectual functions in intellectual disability

M. Martín-Loeches,1,3 J. Muñoz-Ruata,2 L. Martínez-Lebrusant2 & G. Gómez-Jarabo3

1 Brain Mapping Unit, Pluridisciplinary Institute, Complutense University, Madrid, Spain2 Fundación PROMIVA, Madrid, Spain3 Cátedra FORUM de Psicobiología y Discapacidad, Departamento de Psicología Biológica y de la Salud. U.A.M., Madrid,Spain

Correspondence: M. Martín-Loeches, Brain Mapping

Unit, Pluridisciplinary Institute, Complutense University, Po.

Juan XXIII, , -Madrid, Spain

(e-mail: [email protected])

fact, this has been the situation not only for EEGs,but also for neuro-imaging techniques in general(Peterson ). However, ID could be treated as a unitary pathology since there is a major andcommon feature in subjects with ID, regardless ofthe specific aetiology, i.e. low intellectual perfor-mance. Indeed, with the exception of neuro-imaging studies, there has been a large amount of research in which ID is considered as a unitarysyndrome, regardless of its aetiology (e.g. Hughenin; Polloway et al. ; Wyatt & Conners ).On the other hand, low intellectual performance israther unspecific, i.e. it is not just one or two con-crete intellectual areas which are affected; the lowIQ scores found in subjects with ID are usually aconsequence of low mean scores in the majority oreven in all of the intellectual functions measured byintelligence tests. It also appears clear that a gener-alized impairment in intellectual performancecannot be a consequence of damage to any singlepart of the brain. Apart from specialized brainareas, there are also several brain regions or nucleiinvolved in many functions or types of cognitivefunction; for example, the prefrontal lobes (e.g.Fuster ). Accordingly, it appears plausible that certain brain areas could be more frequentlyaffected than others in subjects with ID. However,brain-imaging studies of subjects with ID have beenso scarce and usually so restricted to specific aeti-ologies that no firm conclusion could be drawn(King et al. ).

With regard to EEG, which is one of these brain-imaging techniques, the main studies of ID, regard-less of aetiological variables, were made severalyears ago (Gasser et al. a, b, c) and recentstudies are rare (e.g. Psatta et al. ). This hasnot been the case for the EEG-derived event-related potentials technique (e.g. Zurrón & Díaz; for a review, see Deary & Caryl ).However, event-related potentials are a good index for concrete dysfunctions since these requirethat the task used to elicit event-related potentialsinvolves those dysfunctions. Therefore, event-relatedpotentials are not so optimal in detecting generalpatterns of brain deterioration, the common factorsin ID; EEG is preferable for these purposes.

In the few EEG studies in which ID has beenstudied as a unitary syndrome, an increase in low-frequency bands (i.e. the delta and theta bands) rel-

ative to normal population has been reported forsubjects with ID (Gasser et al. a; Psatta et al.). On the other hand, the high-frequency bands(i.e. the alpha and beta bands) have been seen todisplay a somewhat different pattern. The alphaband has sometimes been seen to decrease in sub-jects with ID (Psatta et al. ), but on other occasions, this band has shown significantly highervalues in subjects with ID compared to normalindividuals, being this the case of absolute powerover frontal leads (especially the lowest frequencyrange of the alpha band) (Gasser et al. a).However, in the above study, the use of relativevalues inverted these differences, mainly at posteriorsites, and affected the highest frequency range ofthis band (Gasser et al. a). At variance with theusual finding of a beta band decrease with cognitiveimpairment (Barlow ), this band can be found to increase in children with ID (Gasser et al.a). However, this finding is not restricted tosubjects with ID since several other pathologieshave also been seen to show this unusual patternfor the beta band (e.g. depression; see John et al.).

Intellectual disability has also been considered asan entity, regardless of aetiology, in an EEG studyby Gasser et al. (c), in order to further under-stand the relationships between intellectual andelectrophysiological variables. This kind of study isof interest because it permits a better understand-ing of both the neurophysiological basis of intellec-tual functions (by relating involved brain areas toconcrete intellectual tasks) and the EEG itself (byrelating particular bands or frequencies to concreteintellectual functions over concrete brain areas).Different results were found depending on whetherthe study population was composed by normal indi-viduals or people with ID.

The most complete and interesting study ofnormal subjects is by Giannitrapani (). Theabove author studied the performance of a largesample of young adolescents on each subtest andscale of the Wechsler Intelligence Scale for Children(WISC), and correlated their attainments to each of the -Hz-wide EEG bands ranging from to Hz. Hence, classical bands were subdivided,yielding a detailed analysis of the differential roleswhich discrete frequencies within the classical di-visions could play. These analyses were made at


M. Martín-Loeches et al. • Electrophysiology and intelligence64

© Blackwell Science Ltd, Journal of Intellectual Disability Research , –

several electrode sites. The results of the Giannitra-pani () study broadly confirmed the above-mentioned general pattern of correlations, but alsofound positive correlations between EEG amplitudeand intellectual functions along bands within thedelta and theta ranges, and around the -Hz frequency band (ranging from to Hz). Ofcourse, the above study also provided an extensiveamount of detailed data, mainly regarding the rela-tionships between specific frequencies and specificintellectual functions at specific brain regions.

On the other hand, when subjects with ID havebeen studied, the relationships between intellectualfunctions and electrophysiological variables haveshown a different but interesting pattern. The maincorrelations have been found to be the inverse ofthose previously described for normal subjects, i.e.a negative correlation was found between EEGamplitudes and intellectual functions, and this wasmainly so for delta band over frontal leads (Gasseret al. c). This inverse pattern has been inter-preted as a result of differences in the maturationaldevelopment of the subjects within the studiedsample. Hence, these findings indicate that childrenwith ID who are more developmentally advanced intheir EEG parameters also show higher IQ scores.

A positive relationship between beta bands andIQ scores, mainly at posterior electrodes, was alsoobserved in the Gasser et al. (c) study in chil-dren with ID, whereas a control group showed the inverse pattern in Beta- band (ranging from. to . Hz). Therefore, considering above-mentioned results relative to a beta band increase insubjects with ID found by the same group (Gasseret al. a), it can be determined that the patternfor this band in relation to intellectual functionsremains unclear.

Interestingly, the different scales of the WISChave not been considered nor has the EEG spec-trum been subdivided into -Hz-wide bands in anystudy of subjects with ID. Therefore, a populationwith ID has not been studied in such a completemanner as Giannitrapani’s () study withnormal subjects. In the present study, the authorsaimed to address this situation by correlating -Hz-wide bands, and each subtest and scale of theWISC in a large sample of subjects with ID. Bydoing so, important conclusions can be drawn not only about which brain areas seem to be more

commonly affected in ID, but also about the elec-trophysiology of intellectual functions. Parallelingthe Giannitrapani () study, these analyses correspond to a first stage in data processing,where valuable and independent conclusions can be obtained. A second data-analysis stage, in whichprincipal components analyses would be performed,should also be undertaken, but this would be thesubject of a subsequent and independent study.

Subjects and methods

A sample of subjects ( males and females)was included in the present study. The mean age ofthese individuals was . years (range = .–.years). All subjects were students in a school forchildren with ID and learning disabilities. Severaldays before or after the EEG recordings, theSpanish version of the WISC-R intelligence test(TEA ) was administered to the subjects.Their mean psychometric measures on this test are summarized in Table . At variance with theusual summation in Digit Span of the digitsrepeated both forwards and backwards, these twopossibilities were differentiated in the present studyand are termed as Direct Digits and InvertedDigits, respectively. All subjects fitted the criteriafor the diagnosis of ID according to the DSM-IV(APA ), and therefore, they all presented withan IQ score under and over , forming asample of subjects with severe (IQ < , n = )and mild ( > IQ < , n = ) ID. All but

(.%) subjects were right-handed. Only .% ofthe subjects were medicated, with the following distributions from the total: .% of the subjectsused neuroleptics, and one case (.%) used bothneuroleptics and antidepressants; .% usedantiepileptic drugs; .%, anxiolytics (benzodi-azepines); .%, thyroid hormone; .%, soma-totropic hormone; .%, antidepressants; and .%used methylphenidate.

Aetiologies for ID were known for a high propor-tion of the subjects. The highest percentage weresubjects who had suffered perinatal or prenatalanoxia, forming % of the total sample. Subjectssuffering kernicterus made up .% of the sample.Meningitis was the aetiology in .% of the sampleand the same was true for prematurity. Subjectssuffering from fragile-X syndrome, Prader–Willi




syndrome, elastopathia and Down’s syndrome werealso present, each .% of the total sample. Onecase (.%) was observed for each of the followingaetiologies: Recklinghausen neurofibromatosis,rubella embryopathia, head trauma, astrocytoma in the IV ventricle, Sotos syndrome, hydrocephalia,Lawrence Moon Bield syndrome, partial agenesia of the corpus callosum and piruvicoquinase enzymedefect. The rest of the subjects (.%) had ID ofunknown origin.

Following the – system, derivations (i.e.F, Fpz, Fp, F, F, Fz, F, F, T, C, Cz, C,T, T, P, Pz, P, T, O, Oz and O) with linkedmastoids as reference were chosen. Tin electrodesattached by means of a cap (Electro-Cap International, Eaton, OH, USA) were used. Imped-ance was always kept below kOhms. The band-pass used was .–. Hz and the sampling ratewas Hz.

The recordings were made during a -min periodat rest while the subjects’ eyes open. The EEG wasvisually inspected and at least , -s EEG epochsfree of artefacts were obtained for each subject. Thefast Fourier transformation (FFT) was performedand frequency spectra were obtained (one foreach electrode) for each subject. All data wereprocessed and analysed with a Brain Atlas III (Bio-Logic Systems Corporation, Illinois, USA).

The frequency spectrum was further subdividedinto , -Hz wide frequency bands. These bands

mainly corresponded to: .–., .–., .–.,.–., .–., .–., .–., .–.,.–., .–., .–., .–.,.–., .–. and .–. Hz. This sub-division of the EEG frequency spectrum largelyfollows the studies of Giannitrapani () andMartín-Loeches et al. (). As in the above workand for the sake of brevity, these frequency bandsare named according to their centroids, i.e. , , ,, , , , , , , , , , and Hz. Theabsolute spectral parameters at each of these bandswere further logarithmically transformed to yield anapproximate normal distribution (Oken & Chiappa).

In order to quantify the relationships betweenparameters of EEG activity, and the scores in testsand subtests of intelligence, Pearson correlationswere preferred rather than a non-parametric testsuch as the Spearman test (used in Giannitrapani) given the large number of subjects and thenormalization of absolute amplitude performed. Forthe sake of clarity, the matrices show only signifi-cances beyond the P < . level of confidence.Pure correlations are also analysed in the presentstudy instead of using a data reduction such ascanonical correlations or principal componentsanalyses. The reason for this is an attempt to makethe present results comparable to those in the first,most sizeable half of the study by Giannitrapani(), who performed the same procedure. In this




Measure Mean Minimum Maximum

Full-Scale IQ 55.15 31 70Verbal IQ 57.78 35 87Performance IQ 63.42 38 87Information 10.61 4 22Comprehension 11.18 3 21Arithmetic 6.36 3 12Similarities 10.86 5 17Vocabulary 29.99 11 54Direct Digits 4.16 2 6Inverted Digits 2.68 2 5Picture Completion 10.73 6 17Picture Arrangement 17.56 6 34Block Design 13.93 1 48Object Assembly 18.06 7 28Coding 2.17 14 61Mazes 12.98 2 21

Table 1 Results for the sample of psy-

chometric measures on the Wechsler

Intelligence Scale for Children – Revised

way, and paralleling the above study, a rich amountof unique information relating to each particularWISC-R test and subtest, and the relationship ofthese to each particular -Hz EEG band at eachelectrode, can be gathered, allowing the presentauthors to focus on either variable. This freedomcannot be obtained with other procedures. Afollow-up principal components analysis is thesubject of an extension of the present study that iscurrently in preparation.

Also, given that independence of many of thevariables employed (i.e. EEG bands, intellectualsubtests and electrodes) cannot be totally war-ranted, a Bonferroni correction of the significancelevel did not appear appropriate. Additionally, thepresent authors could miss truly significant results(type II errors) by using the above method (Berns). Therefore, the P < . level was maintained.Once again, this approach resembles the Giannitra-pani () study which the present authors wantedto make their results comparable with. However,this approach has a limitation: the large number ofcalculations can yield a certain amount of signifi-cant results just by chance (theoretically, % of the comparisons). This problem always has to beconsidered when interpreting the present data.

However, so as to minimize or even remove thistrade-off, only clusters of correlations are discussed.While it is true that randomization may make sig-nificant approximately % of the present compar-isons, what appears implausible is that these %random results should appear polarized aroundEEG bands or around electrodes, or even aroundboth simultaneously. This is basically the sameargument used in current positron emission tomog-raphy (PET) or functional magnetic resonanceimaging (fMRI) neuro-imaging techniques (Frackowiack et al. ). Also, given the age of thesample, maturational factors were not considered to be a relevant factor (Niedermeyer ).Nevertheless, age was controlled by the use ofpartial correlations.

Results

Figure displays the plot of the mean power valuesat each of the , -Hz-wide bands at a representa-tive electrode (Oz). Separate plots were performedfor the subjects with mild ID and those with severeID. As can be observed, there is a peak interruptingthe trend for power decrement with increasing fre-




Figure 1 Plots of the mean power values at each of the 15, 2-Hz-wide bands in the Oz electrode. Separate plots were performed for both the sub-jects with mild (�) and severe (�) intellectual disability.

quency in both groups, occurring at Hz and Hzfor the subjects with mild and severe subjects ID,respectively.

Figures – display the correlation matrices in atopographical layout containing the results in termsof significance obtained when relating the Full-Scale IQ, the Verbal IQ, the Performance IQ andeach of the WISC-R subtests with each of the ,-Hz wide-bands studied. The results are displayedusing a topographical arrangement so that the correlational results are easily interpreted and comprehensive.

According to Fig. a, correlations involving theFull-Scale IQ clearly form two differentiated andindependent groups or clusters. On the one hand,negative correlations corresponding to the lowest

frequency bands (between and Hz, mainly thelatter) were observed, but these were limited to thefrontal regions. On the other, a small group of sig-nificant positive correlations can be identified in the Hz frequency band at electrodes in the mostposterior regions (central and right occipital).Figure b displays correlations involving Verbal IQ.Significant correlations, all of them negative, wereonly obtained at the Hz band and over the frontalregions. On the other hand, the correlationsbetween Performance IQ and EEG variables dis-played in Fig. c were limited to fronto-polar leadsand implicate the lowest frequency band ( Hz).

Correlations involving the Information subtest(Fig. a) show negative relationships, mainly at the and Hz bands, and in both cases, mainly over




(a) (b)

(c)

Figure 2 Topographic display of the correlation matrices for each 2-Hz-wide band at each electrode.The data correspond to the correlations where(a) Full-Scale IQ, (b) Verbal IQ and (c) Performance IQ were the intellectual variables. Correlations with P < 0.05 correspond to a Pearson’s r of0.20–0.30. Correlations with P < 0.01 correspond to a Pearson’s r of 0.30–0.40.

frontal regions. Comprehension (Fig. b) shows ahigh number of correlations, all of them of negativeand mainly involving low frequency bands (from to Hz, mainly Hz) over the frontal regions.Another group of correlations for Comprehensioninvolved the right temporo-occipital regions and thehighest ( and Hz) frequency bands. Correla-tions for Arithmetic (Fig. c) were positive, involvedthe posterior regions and collapsed at the Hzband, although over correlations extended beyondthe Hz band in the T region.

Inverted Digits (Fig. a, top left) also presentedpositive correlations, but over the left temporal (T)region in the – Hz bands. Figure b shows the results corresponding to Vocabulary. For this

subtest, negative correlations were found at and Hz over the fronto-polar electrodes. Similarities(Fig. c) showed a pattern consisting of negativecorrelations in low-frequency bands (from to Hz, mainly the latter) limited to the frontalregions.

Picture Completion (Fig. a) showed positivecorrelations involving the Hz band with a distrib-ution mainly in the central areas, but also implyingthe posterior frontal, temporal and parietal regions.Picture Arrangement (Fig. b) showed a highnumber of negative correlations mainly involvingthe lowest frequency bands (– Hz), especially and Hz. The regions showing correlations withthis test were extensive, but the frontal areas were




Figure 3 Topographic display of the correlation matrices for each 2-Hz-wide band at each electrode.The data correspond to the correlations where(a) Information, (b) Comprehension and (c) Arithmetic were the intellectual variables. Correlations with P < 0.05 correspond to a Pearson’s r of0.20–0.30. Correlations with P < 0.01 correspond to a Pearson’s r of 0.30–0.40.

the most affected. Figure c shows correlations cor-responding to Block Design, which were negativeover the posterior regions (mainly occipital) andinvolved the highest frequency bands (– Hz).Object Assembly and Mazes did not show any sig-nificant correlation, whereas Direct Digits yielded a few positive correlations at O, Oz, O and T inthe Hz band, and Coding only implied two posi-tive correlations at the and Hz bands over lefttemporal electrode (T). For these reasons, no cor-relation matrix is displayed for these subtests.

Discussion

The results of present study have major implica-tions for our knowledge about the differential role

of cortical regions in intellectual functions, andespecially, for their degree of affectation andinvolvement in ID. However, because this kind ofstudy could yield an extensive amount of commentsand discussion, only the most remarkable findingsand implications are discussed here.

Firstly, it should be noted that the frontal poles,measured by the F, Fp and Fpz electrodes,showed the highest number of correlations acrossthe IQ scales and subscales. These correlations werealways negative and involved the lowest frequencybands. This is striking since Giannitrapani ()showed that these regions are among those with the smallest number of significant correlations withintellectual functions in the normal population. Theabove author’s subjects differed slightly from those




Figure 4 Topographic display of the correlation matrices for each 2-Hz-wide band at each electrode.The data correspond to the correlations where(a) Inverted Digits, (b) Vocabulary and (c) Similarities were the intellectual variables. Correlations with P < 0.05 correspond to a Pearson’s r of 0.20–0.30.Correlations with P < 0.01 correspond to a Pearson’s r of 0.30–0.40.

in the present study in terms of chronological age,although the current sample included subjects ofthe same age as those in Giannitrapani’s ()work. In any case, slight age differences are animplausible explanation for these remarkably quali-tative differences in the patterns of correlationsbetween the two studies. Actually, the frontal poleswere the areas exclusively (or almost exclusively)involved in the correlations with Performance IQ,Vocabulary and Coding. This heterogeneity in the type of task involved would confirm that theseareas, usually termed as prefrontal cortex, have amultipurpose role, i.e. they are involved in manytypes of tasks. This statement would be completelyin line with their classical role as overall controllingareas, necessary for tasks requiring a delay, elabo-

rate anticipations, or the programming of asequence of movements or acts (Kolb & Wishaw). Nevertheless, it remains difficult to explaintheir involvement in a task such as Vocabularyunless their proximity to areas related to verbalworking memory processes is evoked (Paulesu et al.).

On the other hand, the whole frontal region,including the frontal poles, was commonly involvedin correlations in many instances. In fact, thefrontal regions as a whole were the brain areaswhich showed the majority of the correlations inthe present study. As with the frontal poles, the cor-relations over the frontal regions as a whole werealways negative and mainly involved the lowest fre-quency bands. This group of correlations between




Figure 5 Topographic display of the correlation matrices for each 2-Hz-wide band at each electrode.The data correspond to the correlations where(a) Picture Completion, (b) Picture Arrangement and (c) Block Design were the intellectual variables. Correlations with P < 0.05 correspond to aPearson’s r of 0.20–0.30. Correlations with P < 0.01 correspond to a Pearson’s r of 0.30–0.40.

the low-frequency bands and the frontal regionscould be found to a greater or lesser extent forFull-Scale IQ, Verbal IQ, Information, Comprehen-sion, Similarities and Picture Assembly. Accord-ingly, the frontal regions as a whole are againinvolved in tasks involving heterogeneous demands,with a particular incidence of those requiring verbalabilities or planning behaviour. With the exceptionof the involvement of the frontal poles, the presentresults are very much in accordance with Giannitra-pani’s () findings regarding intellectual func-tioning in normal subjects .

Taken together, these results relating to thefrontal regions (frontal poles included) confirm theclassical view of the implication of these areas inmany types of higher cognitive functions (Feuer-stein et al. ; Fuster ), and according to thepresent data, these areas probably play a major andcritical role in ID. This is especially true given thatthe highest number of correlations involved thefrontal poles which have been considered to be themost unspecific overall controlling areas within thefrontal lobes (Niedermeyer ). As mentionedabove, these regions scarcely correlate with intelli-gence in normal subjects (Giannitrapani ).Therefore, according to the present results, some-thing specific for subjects with ID should beascribed to these areas. Thus, it does not seemimplausible to assert that a direct or indirectimpairment of the frontal (mainly prefrontal)regions might be the basis for ID, regardless of aeti-ology. The impairment of a multipurpose area suchas the prefrontal lobes would be likely to accom-pany the intellectual impairment underlying ID.

The lowest frequency bands have been supposedto reflect structural and/or metabolic damage to the brain (Martín-Loeches et al. ; Sharbrough; Saletu ). Nevertheless, the present results do not rule out the possibility that the actualdamage has taken place, not at the frontal cortexitself, but in the underlying white matter orthalamo-cortical connections (Barlow ; Glooret al. ). Therefore, the data would be betterinterpreted as showing that commonly or very fre-quently affected sites in the brains of subjects withID are either the frontal lobes, mainly the pre-frontal portions, or the thalamo-cortical pathwaysto these regions. Interestingly, this latter possibilitywould be, to some extent, in accordance with

Yokochi & Fujimoto’s () report of lesions onthe periventricular white matter and the thalamusin children with ID caused by neonatal asphyxia.The highest proportion of the present subjects suffered perinatal anoxia, but although they repre-sented the largest group, these individuals were notin the majority; therefore, the present populationwas far from potentially being labelled as anoxicsubjects. Therefore, it seems that the data reportedby Yokochi & Fujimoto () can be extrapolatedto other populations of subjects with ID.

An alternative or even complementary possibilitymight be that the areas mainly affected in ID arenot the frontal or prefrontal areas, but that thelatter are subsequently affected by diaschisis tosome degree. In fact, lesions in many cortical orsubcortical areas have been found to affect frontalregions by diaschisis, as in the case of the lenticularnucleus (Giroud et al. ), the cerebellum(Botez-Marquard & Botez ), the pons(Blazquez et al. ) and the thalamus (Casadoet al. ), for example. All in all, the presentresults indicate that further research is needed onfunctional neuro-imaging of the prefrontal lobes insubjects with ID.

As mentioned above, most of the correlationsinvolved the lowest frequency bands (– Hz).Classically, the division within this range has beenbetween the delta (.– Hz) and the theta (– Hz)bands, and different types of lesions have even beenascribed to each of these bands (e.g. delta activityseems to be specifically sensitive to brain tumours).Nevertheless, the low-frequency waves in thepresent study neither correlated as a block nor cor-responded with the classical divisions. Indeed, themost important bands seemed to be and Hz,which are only elements of the delta and thetabands, respectively. These data indicate that futureresearch should be undertaken to elucidate whichconcrete lesions or dysfunctions reflect each ofthese -Hz-wide bands.

It should be mentioned that, although the mainresults appeared to be confined to the frontal orprefrontal regions for the majority of the tasks,there were also a few subtests which showed spe-cific or exclusively affected regions. This was thecase for Arithmetic, where positive correlations werefound in the posterior, mainly central and temporalregions of the scalp, partially resembling the data




reported by Giannitrapani () for normal sub-jects, but involving a lower-frequency band (over Hz in the above author’s study and Hz in thepresent work).

Block Design was another subtest that encom-passed specific areas, mainly the occipital and righttemporo-parietal regions, where negative correla-tions involving the highest frequencies were found.This is striking because it would imply that, thehigher the voltage at these bands over these regions,the worse the performance in Block Design, whichwould be at variance with most expected amplitudeincreases for these frequencies during cognitivetasks (e.g. Petsche et al. ). However, it must bementioned that an abnormal increase of the highestfrequencies has been observed in several other psy-chiatric pathologies, such as depression, alcoholismor schizophrenia, and has been interpreted asreflecting a cognitive deficit particularly related toattentional aspects if the distribution is mainly overposterior regions (Ray & Cole ; Gruzelier &Liddiard ; John et al. ), which could beadvanced to explain the present findings.

Digit tasks also implied specific brain areas.Direct Digits involved the occipital and right tem-poral areas, whereas Inverted Digits encompassedthe left temporal regions. The strong differencebetween Direct Digits and Inverse Digits withregard to involved areas is a clear indication thatboth tasks imply notably different operations, i.e.that both are not merely differing in the degree ofdifficulty. However, both are computed together forthe total punctuation of Digit Span in the WISCtest. Perhaps they should be considered separately,as was done in the present study. However, thisdivision was not performed for normal children byGiannitrapani ().

Finally, another set of interesting findings arerelated to what could be named as the dominantfrequency. Giannitrapani () found that themost of the correlations were around the or Hz bands, mainly the later. This was consideredto be related to the dominant alpha frequency andserved to reinforce the importance of these frequen-cies in normal subjects. This was not the case in thepresent study, where the pattern of correlations wasdifferent, mainly involving bands which could notbe considered as pertaining to dominant frequen-cies. However, on a few occasions, correlations

occurred around the alpha range, the most system-atic examples being in the Hz band. This mightindicate that, whereas the dominant frequency surrounds the Hz band in normal subjects, adecrease to about Hz seems to take place in sub-jects with ID. These assumptions are confirmed inFig. , where subjects with mild ID showed theirdominant activity (considered as the slight increaseof power breaking the asymptotic curve of powerdecrease with increasing frequency) at Hz,whereas the subjects with severe ID showed it at Hz. These findings would also be in line with the well-known slowing of the alpha rhythm in the presence of a disease or altered brain function(Barlow ), and would be in agreement with thedata by Osaka & Osaka () in the sense of a dis-appearance of the dominant Hz peak activity insubjects with ID.

Acknowledgements

We would like to thank José A. Torres, General Sec-retary of the PROMIVA Foundation, for encourag-ing and facilitating this study. The diagnostic teamof the Foundation carried out the neuropsychologi-cal testing, María Elena Martino helped with datamanipulation and Pilar Casado assisted us with thefigures.

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Received January ; revised December




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Electrophysiology and intelligence: the electrophysiology of intellectual functions in intellectual disability